From f2a8d79d169ad4751bc5081f1400f0b4200f88ed Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 16:01:05 +0200 Subject: [PATCH 01/12] clean code --- .gitignore | 3 + python/adjoint/basis.py | 14 +- python/adjoint/connectivity.py | 10 +- python/adjoint/filters.py | 20 +-- python/adjoint/objective.py | 4 +- python/adjoint/optimization_problem.py | 86 +++------ python/adjoint/utils.py | 30 +--- python/binary_partition_utils.py | 12 +- python/chunk_balancer.py | 4 +- python/examples/3rd-harm-1d.py | 2 +- python/examples/absorbed_power_density.py | 3 +- python/examples/absorber-1d.py | 2 +- python/examples/antenna_pec_ground_plane.py | 15 +- python/examples/binary_grating.py | 6 +- python/examples/binary_grating_phasemap.py | 14 +- python/examples/cavity_arrayslice.py | 7 +- python/examples/cyl-ellipsoid.py | 2 +- python/examples/eps_fit_lorentzian.py | 4 +- python/examples/finite_grating.py | 12 +- python/examples/gaussian-beam.py | 2 +- python/examples/holey-wvg-bands.py | 7 +- python/examples/holey-wvg-cavity.py | 6 +- python/examples/metal-cavity-ldos.py | 4 +- python/examples/metasurface_lens.py | 40 +++-- python/examples/mpb_hole_slab.py | 6 +- python/examples/mpb_tutorial.py | 12 +- python/examples/multilevel-atom.py | 2 +- python/examples/parallel-wvgs-force.py | 9 +- python/examples/perturbation_theory.py | 2 +- python/examples/perturbation_theory_2d.py | 2 +- python/examples/polarization_grating.py | 4 +- python/examples/refl-angular-kz2d.py | 7 +- python/examples/refl-angular.py | 8 +- python/examples/refl-quartz.py | 2 +- python/examples/ring-mode-overlap.py | 2 +- python/examples/ring_gds.py | 6 +- python/examples/stochastic_emitter.py | 9 +- python/examples/stochastic_emitter_line.py | 7 +- python/examples/wvg-src.py | 4 +- python/examples/zone_plate.py | 10 +- python/geom.py | 68 +++---- python/mpb_data.py | 32 +--- python/simulation.py | 27 +-- python/solver.py | 141 ++++++--------- python/source.py | 18 +- python/tests/test_adjoint_cyl.py | 5 +- python/tests/test_adjoint_jax.py | 9 +- python/tests/test_adjoint_solver.py | 31 ++-- python/tests/test_antenna_radiation.py | 12 +- python/tests/test_bend_flux.py | 21 ++- python/tests/test_cavity_arrayslice.py | 7 +- python/tests/test_chunk_balancer.py | 7 +- python/tests/test_conductivity.py | 3 +- python/tests/test_cyl_ellipsoid.py | 2 +- python/tests/test_dft_fields.py | 6 +- python/tests/test_dispersive_eigenmode.py | 4 +- python/tests/test_divide_mpi_processes.py | 6 +- python/tests/test_dump_load.py | 4 +- python/tests/test_fragment_stats.py | 15 +- python/tests/test_gaussianbeam.py | 3 +- python/tests/test_get_epsilon_grid.py | 2 +- python/tests/test_holey_wvg_bands.py | 2 +- python/tests/test_holey_wvg_cavity.py | 4 +- python/tests/test_kdom.py | 2 +- python/tests/test_material_grid.py | 188 ++++++++++---------- python/tests/test_mode_coeffs.py | 14 +- python/tests/test_mpb.py | 19 +- python/tests/test_prism.py | 18 +- python/tests/test_refl_angular.py | 11 +- python/tests/test_ring.py | 2 +- python/tests/test_simulation.py | 12 +- python/tests/test_source.py | 4 +- python/tests/test_special_kz.py | 3 +- python/timing_measurements.py | 5 +- python/verbosity_mgr.py | 4 +- python/visualization.py | 108 +++++------ 76 files changed, 500 insertions(+), 720 deletions(-) diff --git a/.gitignore b/.gitignore index 4ed1ef7e1..a525caf09 100644 --- a/.gitignore +++ b/.gitignore @@ -15,6 +15,9 @@ _build .vscode .idea +python/examples/.ipynb_checkpoints/ +h5utils/ +hdf5/ # autotools stuff Makefile diff --git a/python/adjoint/basis.py b/python/adjoint/basis.py index ac28d512c..937777d61 100644 --- a/python/adjoint/basis.py +++ b/python/adjoint/basis.py @@ -21,7 +21,7 @@ def __init__( size=None, center=mp.Vector3(), ): - self.volume = volume if volume else mp.Volume(center=center, size=size) + self.volume = volume or mp.Volume(center=center, size=size) self.rho_vector = rho_vector def func(self): @@ -59,11 +59,7 @@ def __init__(self, resolution, symmetry=None, **kwargs): super(BilinearInterpolationBasis, self).__init__(**kwargs) # Generate interpolation grid - if symmetry is None or len(symmetry) == 0: - self.symmetry = [] - else: - self.symmetry = symmetry - + self.symmetry = [] if symmetry is None or len(symmetry) == 0 else symmetry if mp.X in set(self.symmetry): self.Nx = int(resolution * self.volume.size.x / 2) + 1 self.rho_x = np.linspace( @@ -243,8 +239,4 @@ def gen_interpolation_matrix( ri += 1 - # From matrix vectors, populate the sparse matrix - A = sparse.coo_matrix((interp_weights, (row_ind, col_ind)), - shape=(output_dimension, input_dimension)) - - return A + return sparse.coo_matrix((interp_weights, (row_ind, col_ind)), shape=(output_dimension, input_dimension)) diff --git a/python/adjoint/connectivity.py b/python/adjoint/connectivity.py index dc2309e94..f3fe9a640 100644 --- a/python/adjoint/connectivity.py +++ b/python/adjoint/connectivity.py @@ -22,14 +22,8 @@ def __init__(self, nx, ny, nz, k0=1000, zeta=0, sp_solver=cg, alpha=None, alpha0 self.p = p #default alpha and alpha0 - if alpha != None: - self.alpha=alpha - else: - self.alpha = 0.1*min(1/nx, 1/ny, 1/nz) - if alpha0 != None: - self.alpha0 = alpha0 - else: - self.alpha0 = -np.log(thresh)/min(nx, nz) + self.alpha = alpha if alpha != None else 0.1*min(1/nx, 1/ny, 1/nz) + self.alpha0 = alpha0 if alpha0 != None else -np.log(thresh)/min(nx, nz) def forward(self, rho_vector): self.rho_vector = rho_vector diff --git a/python/adjoint/filters.py b/python/adjoint/filters.py index c7eddd688..703a1fc81 100644 --- a/python/adjoint/filters.py +++ b/python/adjoint/filters.py @@ -49,13 +49,7 @@ def _edge_pad(arr, pad): bottom_right = npa.tile(arr[-1, -1], (pad[0][1], pad[1][1])) # bottom right - out = npa.concatenate((npa.concatenate( - (top_left, top, top_right)), npa.concatenate((left, arr, right)), - npa.concatenate( - (bottom_left, bottom, bottom_right))), - axis=1) - - return out + return npa.concatenate((npa.concatenate((top_left, top, top_right)), npa.concatenate((left, arr, right)), npa.concatenate((bottom_left, bottom, bottom_right))), axis=1) def simple_2d_filter(x, h): """A simple 2d filter algorithm that is differentiable with autograd. @@ -632,14 +626,13 @@ def get_eta_from_conic(b, R): norm_length = b / R if norm_length < 0: - eta_e = 0 + return 0 elif norm_length < 1: - eta_e = 0.25 * norm_length**2 + 0.5 + return 0.25 * norm_length**2 + 0.5 elif norm_length < 2: - eta_e = -0.25 * norm_length**2 + norm_length + return -0.25 * norm_length**2 + norm_length else: - eta_e = 1 - return eta_e + return 1 def get_conic_radius_from_eta_e(b, eta_e): @@ -714,8 +707,7 @@ def indicator_solid(x, c, filter_f, threshold_f, resolution): raise ValueError( "The gradient fields must be 2 dimensional. Check input array and filter functions." ) - I_s = design_field * npa.exp(-c * grad_mag) - return I_s + return design_field * npa.exp(-c * grad_mag) def constraint_solid(x, c, eta_e, filter_f, threshold_f, resolution): diff --git a/python/adjoint/objective.py b/python/adjoint/objective.py index 7bfa010d4..6194a22d5 100644 --- a/python/adjoint/objective.py +++ b/python/adjoint/objective.py @@ -272,7 +272,7 @@ def place_adjoint_source(self, dJ): for fourier_data in self.all_fouriersrcdata: amp_arr = np.array(fourier_data.amp_arr).reshape(-1, self.num_freq) scale = amp_arr * self._adj_src_scale(include_resolution=False) - + if self.num_freq == 1: sources += [mp.IndexedSource(time_src, fourier_data, scale[:,0], not self.yee_grid)] else: @@ -393,7 +393,7 @@ def place_adjoint_source(self, dJ): if mp.is_electric(src_i.component): src_i.amplitude *= -1 sources += [src_i] - + return sources def __call__(self): diff --git a/python/adjoint/optimization_problem.py b/python/adjoint/optimization_problem.py index a9b0b656a..2d3d7b57d 100644 --- a/python/adjoint/optimization_problem.py +++ b/python/adjoint/optimization_problem.py @@ -58,21 +58,20 @@ def __init__( if frequencies is not None: self.frequencies = frequencies self.nf = np.array(frequencies).size + elif nf == 1: + self.nf = nf + self.frequencies = [fcen] else: - if nf == 1: - self.nf = nf - self.frequencies = [fcen] - else: - fmax = fcen + 0.5 * df - fmin = fcen - 0.5 * df - dfreq = (fmax - fmin) / (nf - 1) - self.frequencies = np.linspace( - fmin, - fmin + dfreq * nf, - num=nf, - endpoint=False, - ) - self.nf = nf + fmax = fcen + 0.5 * df + fmin = fcen - 0.5 * df + dfreq = (fmax - fmin) / (nf - 1) + self.frequencies = np.linspace( + fmin, + fmin + dfreq * nf, + num=nf, + endpoint=False, + ) + self.nf = nf if self.nf == 1: self.fcen_idx = 0 @@ -128,8 +127,7 @@ def __call__(self, rho_vector=None, need_value=True, need_gradient=True, beta=No print("Calculating gradient...") self.calculate_gradient() else: - raise ValueError("Incorrect solver state detected: {}".format( - self.current_state)) + raise ValueError(f"Incorrect solver state detected: {self.current_state}") return self.f0, self.gradient @@ -160,10 +158,7 @@ def prepare_forward_run(self): self.sim.change_sources(self.forward_sources) # register user specified monitors - self.forward_monitors = [ - ] # save reference to monitors so we can track dft convergence - for m in self.objective_arguments: - self.forward_monitors.append(m.register_monitors(self.frequencies)) + self.forward_monitors = [m.register_monitors(self.frequencies) for m in self.objective_arguments] # register design region self.forward_design_region_monitors = utils.install_design_region_monitors( @@ -187,15 +182,12 @@ def forward_run(self): self.maximum_run_time)) # record objective quantities from user specified monitors - self.results_list = [] - for m in self.objective_arguments: - self.results_list.append(m()) - + self.results_list = [m() for m in self.objective_arguments] # evaluate objectives self.f0 = [fi(*self.results_list) for fi in self.objective_functions] if len(self.f0) == 1: self.f0 = self.f0[0] - + # store objective function evaluation in memory self.f_bank.append(self.f0) @@ -204,8 +196,7 @@ def forward_run(self): def prepare_adjoint_run(self): # Compute adjoint sources - self.adjoint_sources = [[] - for i in range(len(self.objective_functions))] + self.adjoint_sources = [[] for _ in range(len(self.objective_functions))] for ar in range(len(self.objective_functions)): for mi, m in enumerate(self.objective_arguments): dJ = jacobian(self.objective_functions[ar], @@ -277,11 +268,10 @@ def calculate_gradient(self): if len(self.gradient) == 1: self.gradient = self.gradient[ 0] # only one objective function and one design region - else: - if len(self.gradient[0]) == 1: - self.gradient = [ - g[0] for g in self.gradient - ] # multiple objective functions bu one design region + elif len(self.gradient[0]) == 1: + self.gradient = [ + g[0] for g in self.gradient + ] # multiple objective functions bu one design region # Return optimizer's state to initialization self.current_state = "INIT" @@ -354,10 +344,7 @@ def calculate_fd_gradient( b0) # initialize design monitors - self.forward_monitors = [] - for m in self.objective_arguments: - self.forward_monitors.append( - m.register_monitors(self.frequencies)) + self.forward_monitors = [m.register_monitors(self.frequencies) for m in self.objective_arguments] if any(isinstance(m, LDOS) for m in self.objective_arguments): self.sim.run(mp.dft_ldos(self.frequencies), until_after_sources=mp.stop_when_energy_decayed(dt=1, decay_by=1e-11)) @@ -368,9 +355,7 @@ def calculate_fd_gradient( self.maximum_run_time)) # record final objective function value - results_list = [] - for m in self.objective_arguments: - results_list.append(m()) + results_list = [m() for m in self.objective_arguments] fm = [fi(*results_list) for fi in self.objective_functions] # -------------------------------------------- # @@ -379,17 +364,12 @@ def calculate_fd_gradient( self.sim.reset_meep() # assign new design vector - if in_interior: b0[k] += 2 * db # central difference rule... - else: b0[k] += db # forward or backward difference... - + b0[k] += 2 * db if in_interior else db self.design_regions[design_variables_idx].update_design_parameters( b0) # initialize design monitors - self.forward_monitors = [] - for m in self.objective_arguments: - self.forward_monitors.append( - m.register_monitors(self.frequencies)) + self.forward_monitors = [m.register_monitors(self.frequencies) for m in self.objective_arguments] # add monitor used to track dft convergence if any(isinstance(m, LDOS) for m in self.objective_arguments): @@ -401,9 +381,7 @@ def calculate_fd_gradient( self.maximum_run_time)) # record final objective function value - results_list = [] - for m in self.objective_arguments: - results_list.append(m()) + results_list = [m() for m in self.objective_arguments] fp = [fi(*results_list) for fi in self.objective_functions] # -------------------------------------------- # @@ -440,10 +418,7 @@ def update_design(self, rho_vector, beta=None): def get_objective_arguments(self): '''Return list of evaluated objective arguments. ''' - objective_args_evaluation = [ - m.get_evaluation() for m in self.objective_arguments - ] - return objective_args_evaluation + return [m.get_evaluation() for m in self.objective_arguments] def plot2D(self, init_opt=False, **kwargs): """Produce a graphical visualization of the geometry and/or fields, @@ -493,7 +468,4 @@ def atleast_3d(*arys): else: result = ary res.append(result) - if len(res) == 1: - return res[0] - else: - return res + return res[0] if len(res) == 1 else res diff --git a/python/adjoint/utils.py b/python/adjoint/utils.py index ef4a94aee..7d38a1bd4 100644 --- a/python/adjoint/utils.py +++ b/python/adjoint/utils.py @@ -29,7 +29,7 @@ def __init__( size=None, center=mp.Vector3() ): - self.volume = volume if volume else mp.Volume(center=center, size=size) + self.volume = volume or mp.Volume(center=center, size=size) self.size = self.volume.size self.center = self.volume.center self.design_parameters = design_parameters @@ -118,17 +118,7 @@ def install_design_region_monitors( decimation_factor: int = 0, ) -> List[mp.DftFields]: """Installs DFT field monitors at the design regions of the simulation.""" - design_region_monitors = [[ - simulation.add_dft_fields( - [comp], - frequencies, - where=design_region.volume, - yee_grid=True, - decimation_factor=decimation_factor, - persist=True - ) for comp in _compute_components(simulation) - ] for design_region in design_regions ] - return design_region_monitors + return [[simulation.add_dft_fields([comp], frequencies, where=design_region.volume, yee_grid=True, decimation_factor=decimation_factor, persist=True) for comp in _compute_components(simulation)] for design_region in design_regions] def gather_monitor_values(monitors: List[ObjectiveQuantity]) -> onp.ndarray: @@ -142,9 +132,7 @@ def gather_monitor_values(monitors: List[ObjectiveQuantity]) -> onp.ndarray: complex128. Note that these values refer to the mode as oriented (i.e. they are unidirectional). """ - monitor_values = [] - for monitor in monitors: - monitor_values.append(monitor()) + monitor_values = [monitor() for monitor in monitors] monitor_values = onp.array(monitor_values) assert monitor_values.ndim in [1, 2] monitor_values = _make_at_least_nd(monitor_values, 2) @@ -206,19 +194,15 @@ def validate_and_update_design( design_variable) in enumerate(zip(design_regions, design_variables)): if design_variable.ndim not in [1, 2, 3]: - raise ValueError( - 'Design variables should be 1D, 2D, or 3D, but the design variable ' - 'at index {} had a shape of {}.'.format( - i, design_variable.shape)) + raise ValueError(f'Design variables should be 1D, 2D, or 3D, but the design variable at index {i} had a shape of {design_variable.shape}.') + design_region_shape = tuple( int(x) for x in design_region.design_parameters.grid_size) design_variable_padded_shape = design_variable.shape + (1, ) * ( 3 - design_variable.ndim) if design_variable_padded_shape != design_region_shape: - raise ValueError( - 'The design variable at index {} with a shape of {} is ' - 'incompatible with the associated design region, which has a shape ' - 'of {}.'.format(i, design_variable.shape, design_region_shape)) + raise ValueError(f'The design variable at index {i} with a shape of {design_variable.shape} is incompatible with the associated design region, which has a shape of {design_region_shape}.') + design_variable = onp.asarray(design_variable, dtype=onp.float64) # Update the design variable in Meep design_region.update_design_parameters(design_variable.flatten()) diff --git a/python/binary_partition_utils.py b/python/binary_partition_utils.py index c37a5d07f..5fe73f492 100644 --- a/python/binary_partition_utils.py +++ b/python/binary_partition_utils.py @@ -30,10 +30,8 @@ def enumerate_leaf_nodes( if is_leaf_node(partition): yield partition else: - for child in enumerate_leaf_nodes(partition.left): - yield child - for child in enumerate_leaf_nodes(partition.right): - yield child + yield from enumerate_leaf_nodes(partition.left) + yield from enumerate_leaf_nodes(partition.right) def partition_has_duplicate_proc_ids(partition: mp.BinaryPartition) -> bool: @@ -219,10 +217,8 @@ def get_total_volume_per_process(partition: mp.BinaryPartition, volumes_per_process = {} leaf_nodes_in_tree = enumerate_leaf_nodes(partition) for leaf in leaf_nodes_in_tree: - total_volume = 0 - for i, owner in enumerate(chunk_owners): - if owner == leaf.proc_id: - total_volume += pixel_volume(chunk_volumes[i]) + total_volume = sum(pixel_volume(chunk_volumes[i]) for i, owner in enumerate(chunk_owners) if owner == leaf.proc_id) + volumes_per_process[leaf.proc_id] = total_volume return volumes_per_process diff --git a/python/chunk_balancer.py b/python/chunk_balancer.py index 956ba979c..d0816fd06 100644 --- a/python/chunk_balancer.py +++ b/python/chunk_balancer.py @@ -130,9 +130,7 @@ def _validate_sim(self, sim: mp.Simulation): node.proc_id for node in bpu.enumerate_leaf_nodes(sim.chunk_layout) ] if set(chunk_owners) != set(proc_ids): - raise ValueError( - 'Processes {} present in chunk_layout but not grid_owners!'.format( - set(proc_ids) - set(chunk_owners))) + raise ValueError(f'Processes {set(proc_ids) - set(chunk_owners)} present in chunk_layout but not grid_owners!') class ChunkBalancer(AbstractChunkBalancer): diff --git a/python/examples/3rd-harm-1d.py b/python/examples/3rd-harm-1d.py index 5ac0f5dd3..b6cbae498 100644 --- a/python/examples/3rd-harm-1d.py +++ b/python/examples/3rd-harm-1d.py @@ -55,7 +55,7 @@ def main(args): 50, mp.Ex, mp.Vector3(0, 0, 0.5*sz - dpml - 0.5), 1e-6)) # sim.display_fluxes(trans) - print("harmonics:, {}, {}, {}, {}".format(k, amp, mp.get_fluxes(trans1)[0], mp.get_fluxes(trans3)[0])) + print(f"harmonics:, {k}, {amp}, {mp.get_fluxes(trans1)[0]}, {mp.get_fluxes(trans3)[0]}") if __name__ == '__main__': parser = argparse.ArgumentParser() diff --git a/python/examples/absorbed_power_density.py b/python/examples/absorbed_power_density.py index 8072a56b9..92947db8b 100644 --- a/python/examples/absorbed_power_density.py +++ b/python/examples/absorbed_power_density.py @@ -64,7 +64,8 @@ absorbed_power = np.sum(absorbed_power_density)*dxy absorbed_flux = mp.get_fluxes(flux_box)[0] err = abs(absorbed_power-absorbed_flux)/absorbed_flux -print("flux:, {} (dft_fields), {} (dft_flux), {} (error)".format(absorbed_power,absorbed_flux,err)) +print(f"flux:, {absorbed_power} (dft_fields), {absorbed_flux} (dft_flux), {err} (error)") + plt.figure() sim.plot2D() diff --git a/python/examples/absorber-1d.py b/python/examples/absorber-1d.py index e6d93633c..b6ec258fa 100644 --- a/python/examples/absorber-1d.py +++ b/python/examples/absorber-1d.py @@ -18,7 +18,7 @@ def main(args): def print_stuff(sim): p = sim.get_field_point(mp.Ex, mp.Vector3()) - print("ex:, {}, {}".format(sim.meep_time(), p.real)) + print(f"ex:, {sim.meep_time()}, {p.real}") sim = mp.Simulation(cell_size=cell_size, resolution=resolution, diff --git a/python/examples/antenna_pec_ground_plane.py b/python/examples/antenna_pec_ground_plane.py index 6b65db7ad..d02af9760 100644 --- a/python/examples/antenna_pec_ground_plane.py +++ b/python/examples/antenna_pec_ground_plane.py @@ -34,9 +34,7 @@ def radial_flux(sim,nearfield_box,r): Px = np.real(E[:,1]*H[:,2]-E[:,2]*H[:,1]) # Ey*Hz-Ez*Hy Py = np.real(E[:,2]*H[:,0]-E[:,0]*H[:,2]) # Ez*Hx-Ex*Hz - Pr = np.sqrt(np.square(Px)+np.square(Py)) - - return Pr + return np.sqrt(np.square(Px)+np.square(Py)) def free_space_radiation(src_cmpt): @@ -82,9 +80,7 @@ def free_space_radiation(src_cmpt): sim.run(until_after_sources=mp.stop_when_dft_decayed()) - Pr = radial_flux(sim,nearfield_box,r) - - return Pr + return radial_flux(sim,nearfield_box,r) def pec_ground_plane_radiation(src_cmpt=mp.Hz): @@ -146,9 +142,7 @@ def pec_ground_plane_radiation(src_cmpt=mp.Hz): sim.run(until_after_sources=mp.stop_when_dft_decayed()) - Pr = radial_flux(sim,nearfield_box,r) - - return Pr + return radial_flux(sim,nearfield_box,r) if __name__ == '__main__': @@ -175,7 +169,8 @@ def pec_ground_plane_radiation(src_cmpt=mp.Hz): plt.plot(np.degrees(angles),Pr_theory_norm,'r-',label='theory') plt.xlabel('angle (degrees)') plt.ylabel('radial flux (normalized by maximum flux)') - plt.title('antenna with {}$_z$ polarization above PEC ground plane'.format('E' if src_cmpt==mp.Ez else r'H')) + plt.title(f"antenna with {'E' if src_cmpt==mp.Ez else r'H'}$_z$ polarization above PEC ground plane") + plt.axis([0,90,0,1.0]) plt.legend() plt.savefig('radiation_pattern.png',bbox_inches='tight') diff --git a/python/examples/binary_grating.py b/python/examples/binary_grating.py index dd63e2770..fcba5dbb7 100644 --- a/python/examples/binary_grating.py +++ b/python/examples/binary_grating.py @@ -101,10 +101,10 @@ plt.axis([min(mode_wvl), max(mode_wvl), min(mode_angle), max(mode_angle)]) plt.xlabel("wavelength (μm)") plt.ylabel("diffraction angle (degrees)") -plt.xticks([t for t in np.arange(0.4,0.7,0.1)]) -plt.yticks([t for t in range(0,35,5)]) +plt.xticks(list(np.arange(0.4,0.7,0.1))) +plt.yticks(list(range(0,35,5))) plt.title("transmittance of diffraction orders") cbar = plt.colorbar() -cbar.set_ticks([t for t in np.arange(0,tran_max+0.1,0.1)]) +cbar.set_ticks(list(np.arange(0,tran_max+0.1,0.1))) cbar.set_ticklabels(["{:.1f}".format(t) for t in np.arange(0,tran_max+0.1,0.1)]) plt.show() diff --git a/python/examples/binary_grating_phasemap.py b/python/examples/binary_grating_phasemap.py index f75fdf3ad..c2d5688dd 100644 --- a/python/examples/binary_grating_phasemap.py +++ b/python/examples/binary_grating_phasemap.py @@ -84,7 +84,7 @@ def grating(gp,gh,gdc,oddz): parser.add_argument('-gh', type=float, default=0.6, help='grating height (default: 0.6 μm)') parser.add_argument('-oddz', action='store_true', default=False, help='oddz? (default: False)') args = parser.parse_args() - + gdc = np.arange(0.1,1.0,0.1) mode_tran = np.empty((gdc.size,nfreq)) mode_phase = np.empty((gdc.size,nfreq)) @@ -97,24 +97,24 @@ def grating(gp,gh,gdc,oddz): plt.pcolormesh(mode_wvl, gdc, mode_tran, cmap='hot_r', shading='gouraud', vmin=0, vmax=mode_tran.max()) plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]]) plt.xlabel("wavelength (μm)") - plt.xticks([t for t in np.arange(wvl_min,wvl_max+0.1,0.1)]) + plt.xticks(list(np.arange(wvl_min,wvl_max+0.1,0.1))) plt.ylabel("grating duty cycle") - plt.yticks([t for t in np.arange(gdc[0],gdc[-1]+0.1,0.1)]) + plt.yticks(list(np.arange(gdc[0],gdc[-1]+0.1,0.1))) plt.title("transmittance") cbar = plt.colorbar() - cbar.set_ticks([t for t in np.arange(0,1.2,0.2)]) + cbar.set_ticks(list(np.arange(0,1.2,0.2))) cbar.set_ticklabels(["{:.1f}".format(t) for t in np.arange(0,1.2,0.2)]) plt.subplot(1,2,2) plt.pcolormesh(mode_wvl, gdc, mode_phase, cmap='RdBu', shading='gouraud', vmin=mode_phase.min(), vmax=mode_phase.max()) plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]]) plt.xlabel("wavelength (μm)") - plt.xticks([t for t in np.arange(wvl_min,wvl_max+0.1,0.1)]) + plt.xticks(list(np.arange(wvl_min,wvl_max+0.1,0.1))) plt.ylabel("grating duty cycle") - plt.yticks([t for t in np.arange(gdc[0],gdc[-1]+0.1,0.1)]) + plt.yticks(list(np.arange(gdc[0],gdc[-1]+0.1,0.1))) plt.title("phase (radians)") cbar = plt.colorbar() - cbar.set_ticks([t for t in range(-3,4)]) + cbar.set_ticks(list(range(-3,4))) cbar.set_ticklabels(["{:.1f}".format(t) for t in range(-3,4)]) plt.tight_layout() diff --git a/python/examples/cavity_arrayslice.py b/python/examples/cavity_arrayslice.py index 6fa9e91aa..863fce44e 100644 --- a/python/examples/cavity_arrayslice.py +++ b/python/examples/cavity_arrayslice.py @@ -23,11 +23,8 @@ geometry = [blk] -for i in range(3): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) - -for i in range(3): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d / -2 - i))) +geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)) for i in range(3)) +geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / -2 - i)) for i in range(3)) sim = mp.Simulation(cell_size=cell, geometry=geometry, diff --git a/python/examples/cyl-ellipsoid.py b/python/examples/cyl-ellipsoid.py index 3e0d50a34..8a727163e 100644 --- a/python/examples/cyl-ellipsoid.py +++ b/python/examples/cyl-ellipsoid.py @@ -27,7 +27,7 @@ def main(): def print_stuff(sim_obj): v = mp.Vector3(4.13, 3.75, 0) p = sim.get_field_point(src_cmpt, v) - print("t, Ez: {} {}+{}i".format(sim.round_time(), p.real, p.imag)) + print(f"t, Ez: {sim.round_time()} {p.real}+{p.imag}i") sim.run(mp.at_beginning(mp.output_epsilon), mp.at_every(0.25, print_stuff), diff --git a/python/examples/eps_fit_lorentzian.py b/python/examples/eps_fit_lorentzian.py index 8b825db69..bbae3076c 100644 --- a/python/examples/eps_fit_lorentzian.py +++ b/python/examples/eps_fit_lorentzian.py @@ -14,7 +14,7 @@ def lorentzfunc(p, x): """Return the complex ε profile given a set of Lorentzian parameters p (σ_0, ω_0, γ_0, σ_1, ω_1, γ_1, ...) for a set of frequencies x. """ - N = int(len(p)/3) + N = len(p) // 3 y = np.zeros(len(x)) for n in range(N): A_n = p[3*n+0] @@ -29,7 +29,7 @@ def lorentzerr(p, x, y, grad): well as the gradient of this error with respect to each Lorentzian polarizability parameter in p and saving the result in grad. """ - N = int(len(p)/3) + N = len(p) // 3 yp = lorentzfunc(p, x) val = np.sum(np.square(abs(y - yp))) for n in range(N): diff --git a/python/examples/finite_grating.py b/python/examples/finite_grating.py index e1cdf1eee..1129410e2 100644 --- a/python/examples/finite_grating.py +++ b/python/examples/finite_grating.py @@ -62,11 +62,13 @@ sim.reset_meep() -for j in range(num_cells): - geometry.append(mp.Block(material=glass, - size=mp.Vector3(gh,gdc*gp,mp.inf), - center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+dpml+dpad+(j+0.5)*gp))) - +geometry.extend( + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + + dpml + dpad + (j + 0.5) * gp)) + for j in range(num_cells)) sim = mp.Simulation(resolution=resolution, cell_size=cell_size, boundary_layers=pml_layers, diff --git a/python/examples/gaussian-beam.py b/python/examples/gaussian-beam.py index bc2747718..0d794a4fa 100644 --- a/python/examples/gaussian-beam.py +++ b/python/examples/gaussian-beam.py @@ -39,4 +39,4 @@ output_plane=mp.Volume(center=mp.Vector3(), size=mp.Vector3(s-2*dpml,s-2*dpml))) -plt.savefig('Ez_angle{}.png'.format(rot_angle),bbox_inches='tight',pad_inches=0) +plt.savefig(f'Ez_angle{rot_angle}.png', bbox_inches='tight', pad_inches=0) diff --git a/python/examples/holey-wvg-bands.py b/python/examples/holey-wvg-bands.py index a04ee390d..d56cc420e 100644 --- a/python/examples/holey-wvg-bands.py +++ b/python/examples/holey-wvg-bands.py @@ -38,10 +38,7 @@ def main(): sim = mp.Simulation(cell_size=cell, geometry=[b, c], sources=[s], symmetries=[sym], boundary_layers=[mp.PML(dpml, direction=mp.Y)], resolution=20) - kx = False # if true, do run at specified kx and get fields - k_interp = 19 # # k-points to interpolate, otherwise - - if kx: + if kx := False: sim.k_point = mp.Vector3(kx) sim.run( @@ -53,6 +50,8 @@ def main(): sim.run(mp.at_every(1 / fcen / 20, mp.output_hfield_z), until=1 / fcen) else: + k_interp = 19 # # k-points to interpolate, otherwise + sim.run_k_points(300, mp.interpolate(k_interp, [mp.Vector3(), mp.Vector3(0.5)])) diff --git a/python/examples/holey-wvg-cavity.py b/python/examples/holey-wvg-cavity.py index 7bedb283d..34f28bd58 100644 --- a/python/examples/holey-wvg-cavity.py +++ b/python/examples/holey-wvg-cavity.py @@ -14,7 +14,7 @@ def main(args): resolution = 20 # pixels/um - + eps = 13 # dielectric constant of waveguide w = 1.2 # width of waveguide r = 0.36 # radius of holes @@ -40,8 +40,6 @@ def main(args): fcen = args.fcen # pulse center frequency df = args.df # pulse frequency width - nfreq = 500 # number of frequencies at which to compute flux - sim = mp.Simulation(cell_size=cell, geometry=geometry, sources=[], @@ -72,6 +70,8 @@ def main(args): freg = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml-0.5), size=mp.Vector3(0,2*w)) + nfreq = 500 # number of frequencies at which to compute flux + # transmitted flux trans = sim.add_flux(fcen, df, nfreq, freg) diff --git a/python/examples/metal-cavity-ldos.py b/python/examples/metal-cavity-ldos.py index b00ed414b..97b6a2c45 100644 --- a/python/examples/metal-cavity-ldos.py +++ b/python/examples/metal-cavity-ldos.py @@ -9,7 +9,7 @@ def metal_cavity(w): resolution = 50 sxy = 2 dpml = 1 - sxy = sxy+2*dpml + sxy += 2*dpml cell = mp.Vector3(sxy,sxy) pml_layers = [mp.PML(dpml)] @@ -60,7 +60,7 @@ def metal_cavity(w): for j in range(len(ws)): ldos_1[j], ldos_2[j] = metal_cavity(ws[j]) - print("ldos:, {}, {}".format(ldos_1[j],ldos_2[2])) + print(f"ldos:, {ldos_1[j]}, {ldos_2[2]}") plt.figure(dpi=150) plt.semilogy(1/ws,ldos_1,'bo-',label="2Q/(πωV)") diff --git a/python/examples/metasurface_lens.py b/python/examples/metasurface_lens.py index 42b56e77d..771d4fb9f 100644 --- a/python/examples/metasurface_lens.py +++ b/python/examples/metasurface_lens.py @@ -56,9 +56,9 @@ def grating(gp,gh,gdc_list): flux_obj = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))) sim.run(until_after_sources=50) - + input_flux = mp.get_fluxes(flux_obj) - + sim.reset_meep() geometry.append(mp.Block(material=glass, size=mp.Vector3(gh,gdc_list[0]*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))) @@ -83,10 +83,10 @@ def grating(gp,gh,gdc_list): mode_phase = np.angle(coeffs[0,0,0]) if mode_phase > 0: mode_phase -= 2*np.pi - + return mode_tran, mode_phase - - else: + + else: sy = num_cells*gp cell_size = mp.Vector3(sx,sy,0) @@ -95,11 +95,12 @@ def grating(gp,gh,gdc_list): center=src_pt, size=mp.Vector3(y=sy))] - for j in range(num_cells): - geometry.append(mp.Block(material=glass, - size=mp.Vector3(gh,gdc_list[j]*gp,mp.inf), - center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+(j+0.5)*gp))) - + geometry.extend( + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc_list[j] * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + + (j + 0.5) * gp)) for j in range(num_cells)) sim = mp.Simulation(resolution=resolution, cell_size=cell_size, boundary_layers=pml_layers, @@ -111,7 +112,7 @@ def grating(gp,gh,gdc_list): n2f_obj = sim.add_near2far(fcen, 0, 1, mp.Near2FarRegion(center=mon_pt, size=mp.Vector3(y=sy))) sim.run(until_after_sources=500) - + return abs(sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(-0.5*sx+dpml+dsub+gh+focal_length), size=mp.Vector3(spot_length))['Ez'])**2 @@ -128,20 +129,20 @@ def grating(gp,gh,gdc_list): plt.subplot(1,2,1) plt.plot(gdc, mode_tran, 'bo-') plt.xlim(gdc[0],gdc[-1]) -plt.xticks([t for t in np.linspace(0.1,0.9,5)]) +plt.xticks(list(np.linspace(0.1,0.9,5))) plt.xlabel("grating duty cycle") plt.ylim(0.96,1.00) -plt.yticks([t for t in np.linspace(0.96,1.00,5)]) +plt.yticks(list(np.linspace(0.96,1.00,5))) plt.title("transmittance") plt.subplot(1,2,2) plt.plot(gdc, mode_phase, 'rs-') plt.grid(True) plt.xlim(gdc[0],gdc[-1]) -plt.xticks([t for t in np.linspace(0.1,0.9,5)]) +plt.xticks(list(np.linspace(0.1,0.9,5))) plt.xlabel("grating duty cycle") plt.ylim(-2*np.pi,0) -plt.yticks([t for t in np.linspace(-6,0,7)]) +plt.yticks(list(np.linspace(-6,0,7))) plt.title("phase (radians)") plt.tight_layout(pad=0.5) @@ -171,9 +172,12 @@ def grating(gp,gh,gdc_list): x = np.linspace(focal_length-0.5*spot_length,focal_length+0.5*spot_length,ff_res*spot_length) plt.figure(dpi=200) -plt.semilogy(x,abs(ff_nc[:,0])**2,'bo-',label='num_cells = {}'.format(2*num_cells[0]+1)) -plt.semilogy(x,abs(ff_nc[:,1])**2,'ro-',label='num_cells = {}'.format(2*num_cells[1]+1)) -plt.semilogy(x,abs(ff_nc[:,2])**2,'go-',label='num_cells = {}'.format(2*num_cells[2]+1)) +plt.semilogy( + x, abs(ff_nc[:, 0])**2, 'bo-', label=f'num_cells = {2*num_cells[0] + 1}') +plt.semilogy( + x, abs(ff_nc[:, 1])**2, 'ro-', label=f'num_cells = {2*num_cells[1] + 1}') +plt.semilogy( + x, abs(ff_nc[:, 2])**2, 'go-', label=f'num_cells = {2*num_cells[2] + 1}') plt.xlabel('x coordinate (μm)') plt.ylabel(r'energy density of far-field electric fields, |E$_z$|$^2$') plt.title('focusing properties of a binary-grating metasurface lens') diff --git a/python/examples/mpb_hole_slab.py b/python/examples/mpb_hole_slab.py index 02442576f..385e8d41a 100644 --- a/python/examples/mpb_hole_slab.py +++ b/python/examples/mpb_hole_slab.py @@ -41,11 +41,7 @@ only_K = False # run with only_K=true to only do this k_point k_interp = 4 # the number of k points to interpolate -if only_K: - k_points = [K] -else: - k_points = mp.interpolate(k_interp, [Gamma, M, K, Gamma]) - +k_points = [K] if only_K else mp.interpolate(k_interp, [Gamma, M, K, Gamma]) resolution = mp.Vector3(32, 32, 16) num_bands = 9 diff --git a/python/examples/mpb_tutorial.py b/python/examples/mpb_tutorial.py index 7b3ded2b4..bc3ceb99f 100644 --- a/python/examples/mpb_tutorial.py +++ b/python/examples/mpb_tutorial.py @@ -74,8 +74,8 @@ def first_tm_gap(r): ms.mesh_size = 7 result = minimize_scalar(first_tm_gap, method='bounded', bounds=[0.1, 0.5], options={'xatol': 0.1}) -print("radius at maximum: {}".format(result.x)) -print("gap size at maximum: {}".format(result.fun * -1)) +print(f"radius at maximum: {result.x}") +print(f"gap size at maximum: {result.fun * -1}") ms.mesh_size = 3 # Reset to default value of 3 @@ -110,7 +110,7 @@ def first_tm_gap(r): ms.get_dfield(25) # compute the D field for band 25 ms.compute_field_energy() # compute the energy density from D c = mp.Cylinder(1.0, material=mp.air) -print("energy in cylinder: {}".format(ms.compute_energy_in_objects([c]))) +print(f"energy in cylinder: {ms.compute_energy_in_objects([c])}") print_heading('5x5 point defect, targeted solver') @@ -130,11 +130,11 @@ def rootfun(eps): # add the cylinder of epsilon = eps to the old geometry: ms.geometry = old_geometry + [mp.Cylinder(0.2, material=mp.Medium(epsilon=eps))] ms.run_tm() # solve for the mode (using the targeted solver) - print("epsilon = {} gives freq. = {}".format(eps, ms.get_freqs()[0])) + print(f"epsilon = {eps} gives freq. = {ms.get_freqs()[0]}") return ms.get_freqs()[0] - 0.314159 # return 1st band freq. - 0.314159 rooteps = ridder(rootfun, 1, 12) -print("root (value of epsilon) is at: {}".format(rooteps)) +print(f"root (value of epsilon) is at: {rooteps}") rootval = rootfun(rooteps) -print("root function at {} = {}".format(rooteps, rootval)) +print(f"root function at {rooteps} = {rootval}") diff --git a/python/examples/multilevel-atom.py b/python/examples/multilevel-atom.py index d3326a3c6..87a6f231d 100644 --- a/python/examples/multilevel-atom.py +++ b/python/examples/multilevel-atom.py @@ -91,6 +91,6 @@ def field_func(p): def print_field(sim): fp = sim.get_field_point(mp.Ex, mp.Vector3(z=(-0.5 * sz) + Lcav + (0.5 * dpad))).real - print("field:, {}, {}".format(sim.meep_time(), fp)) + print(f"field:, {sim.meep_time()}, {fp}") sim.run(mp.after_time(endt - 250, print_field), until=endt) diff --git a/python/examples/parallel-wvgs-force.py b/python/examples/parallel-wvgs-force.py index c7a3e2a42..68bfce7af 100644 --- a/python/examples/parallel-wvgs-force.py +++ b/python/examples/parallel-wvgs-force.py @@ -3,12 +3,12 @@ import matplotlib.pyplot as plt resolution = 40 # pixels/μm - + Si = mp.Medium(index=3.45) dpml = 1.0 pml_layers = [mp.PML(dpml)] - + sx = 5 sy = 3 cell = mp.Vector3(sx+2*dpml,sy+2*dpml,0) @@ -45,7 +45,7 @@ def parallel_waveguide(s,xodd): parity=mp.ODD_Y) fcen = EigenmodeData.freq - print("freq:, {}, {}, {}".format("xodd" if xodd else "xeven", s, fcen)) + print(f'freq:, {"xodd" if xodd else "xeven"}, {s}, {fcen}') sim.reset_meep() @@ -70,7 +70,8 @@ def parallel_waveguide(s,xodd): flux = mp.get_fluxes(wvg_flux)[0] force = mp.get_forces(wvg_force)[0] - print("data:, {}, {}, {}, {}, {}".format("xodd" if xodd else "xeven", s, flux, force, -force/flux)) + print(f'data:, {"xodd" if xodd else "xeven"}, {s}, {flux}, {force}, {-force / flux}') + sim.reset_meep() return flux, force diff --git a/python/examples/perturbation_theory.py b/python/examples/perturbation_theory.py index b6a7d5e4f..a82fe585a 100644 --- a/python/examples/perturbation_theory.py +++ b/python/examples/perturbation_theory.py @@ -114,7 +114,7 @@ def main(args): finite_diff_dw_dR = (frq_perturbed - frq_unperturbed) / dr - print("dwdR:, {} (pert. theory), {} (finite diff.)".format(perturb_theory_dw_dR,finite_diff_dw_dR)) + print(f"dwdR:, {perturb_theory_dw_dR} (pert. theory), {finite_diff_dw_dR} (finite diff.)") if __name__ == '__main__': parser = argparse.ArgumentParser() diff --git a/python/examples/perturbation_theory_2d.py b/python/examples/perturbation_theory_2d.py index 13f2d5d2f..4065a2201 100644 --- a/python/examples/perturbation_theory_2d.py +++ b/python/examples/perturbation_theory_2d.py @@ -134,7 +134,7 @@ def main(args): finite_diff_dw_dR = (frq_perturbed - frq_unperturbed) / dr - print("dwdR:, {} (pert. theory), {} (finite diff.)".format(perturb_theory_dw_dR,finite_diff_dw_dR)) + print(f"dwdR:, {perturb_theory_dw_dR} (pert. theory), {finite_diff_dw_dR} (finite diff.)") if __name__ == '__main__': parser = argparse.ArgumentParser() diff --git a/python/examples/polarization_grating.py b/python/examples/polarization_grating.py index 8cccb1a96..c943b559c 100644 --- a/python/examples/polarization_grating.py +++ b/python/examples/polarization_grating.py @@ -139,7 +139,7 @@ def lc_mat(p): plt.plot(phase,eff_m1,'ro-',clip_on=False,label='±1 orders (meep)') plt.plot(phase,eff_m1_analytic,'r--',clip_on=False,label='±1 orders (analytic)') plt.axis([0, 1.0, 0, 1]) -plt.xticks([t for t in np.arange(0,1.2,0.2)]) +plt.xticks(list(np.arange(0,1.2,0.2))) plt.xlabel("phase delay Δnd/λ") plt.ylabel("diffraction efficiency @ λ = 0.54 μm") plt.legend(loc='center') @@ -154,7 +154,7 @@ def lc_mat(p): plt.plot(phase,eff_m0,'bo-',clip_on=False,label='0th order (meep)') plt.plot(phase,eff_m1,'ro-',clip_on=False,label='±1 orders (meep)') plt.axis([0, 1.0, 0, 1]) -plt.xticks([t for t in np.arange(0,1.2,0.2)]) +plt.xticks(list(np.arange(0,1.2,0.2))) plt.xlabel("phase delay Δnd/λ") plt.ylabel("diffraction efficiency @ λ = 0.54 μm") plt.legend(loc='center') diff --git a/python/examples/refl-angular-kz2d.py b/python/examples/refl-angular-kz2d.py index 89bd49d39..13fea4c88 100644 --- a/python/examples/refl-angular-kz2d.py +++ b/python/examples/refl-angular-kz2d.py @@ -28,7 +28,7 @@ def refl_planar(theta, kz_2d): refl_fr = mp.FluxRegion(center=mp.Vector3(-0.25*sx)) refl = sim.add_flux(fcen, 0, 1, refl_fr) - + sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ey, mp.Vector3(-0.5*sx+dpml), 1e-9)) input_flux = mp.get_fluxes(refl) @@ -56,8 +56,7 @@ def refl_planar(theta, kz_2d): refl_flux = mp.get_fluxes(refl) freqs = mp.get_flux_freqs(refl) - Rmeep = -refl_flux[0]/input_flux[0] - return Rmeep + return -refl_flux[0]/input_flux[0] # rotation angle of source: CCW around Y axis, 0 degrees along +X axis @@ -78,4 +77,4 @@ def refl_planar(theta, kz_2d): # for incident planewave in medium n1 at angle theta_in Rfresnel = lambda theta_in: math.fabs((n2*math.cos(theta_out(theta_in))-n1*math.cos(theta_in))/(n2*math.cos(theta_out(theta_in))+n1*math.cos(theta_in)))**2 -print("refl:, {} (real/imag), {} (complex), {} (3d), {} (analytic)".format(Rmeep_real_imag,Rmeep_complex,Rmeep_3d,Rfresnel(theta_r))) +print(f"refl:, {Rmeep_real_imag} (real/imag), {Rmeep_complex} (complex), {Rmeep_3d} (3d), {Rfresnel(theta_r)} (analytic)") diff --git a/python/examples/refl-angular.py b/python/examples/refl-angular.py index a51791cc9..316beca8a 100644 --- a/python/examples/refl-angular.py +++ b/python/examples/refl-angular.py @@ -26,11 +26,7 @@ def main(args): k = mp.Vector3(math.sin(theta_r),0,math.cos(theta_r)).scale(fmin) # if normal incidence, force number of dimensions to be 1 - if theta_r == 0: - dimensions = 1 - else: - dimensions = 3 - + dimensions = 1 if theta_r == 0 else 3 sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), component=mp.Ex, center=mp.Vector3(0,0,-0.5*sz+dpml))] @@ -73,7 +69,7 @@ def main(args): freqs = mp.get_flux_freqs(refl) for i in range(nfreq): - print("refl:, {}, {}, {}, {}".format(k.x,1/freqs[i],math.degrees(math.asin(k.x/freqs[i])),-refl_flux[i]/empty_flux[i])) + print(f"refl:, {k.x}, {1 / freqs[i]}, {math.degrees(math.asin(k.x/freqs[i]))}, {-refl_flux[i] / empty_flux[i]}") if __name__ == '__main__': parser = argparse.ArgumentParser() diff --git a/python/examples/refl-quartz.py b/python/examples/refl-quartz.py index 55a61a23a..859b9149d 100644 --- a/python/examples/refl-quartz.py +++ b/python/examples/refl-quartz.py @@ -68,6 +68,6 @@ plt.xlabel("wavelength (μm)") plt.ylabel("reflectance") plt.axis([0.4, 0.8, 0.0340, 0.0365]) -plt.xticks([t for t in np.arange(0.4,0.9,0.1)]) +plt.xticks(list(np.arange(0.4,0.9,0.1))) plt.legend(loc='upper right') plt.show() diff --git a/python/examples/ring-mode-overlap.py b/python/examples/ring-mode-overlap.py index 72182fdd7..336cab4e9 100644 --- a/python/examples/ring-mode-overlap.py +++ b/python/examples/ring-mode-overlap.py @@ -57,4 +57,4 @@ def overlap_integral(r, ez1, ez2): return ez1.conjugate() * ez2 res = sim.integrate2_field_function(fields2, [mp.Ez], [mp.Ez], overlap_integral) -print("overlap integral of mode at w and 2w: {}".format(abs(res))) +print(f"overlap integral of mode at w and 2w: {abs(res)}") diff --git a/python/examples/ring_gds.py b/python/examples/ring_gds.py index 64e2b99d5..47e17f0e4 100644 --- a/python/examples/ring_gds.py +++ b/python/examples/ring_gds.py @@ -24,7 +24,7 @@ def create_ring_gds(radius,width): # Reload the library each time to prevent gds library name clashes importlib.reload(gdspy) - ringCell = gdspy.Cell("ring_resonator_r{}_w{}".format(radius,width)) + ringCell = gdspy.Cell(f"ring_resonator_r{radius}_w{width}") # Draw the ring ringCell.add(gdspy.Round((0,0), @@ -53,7 +53,7 @@ def create_ring_gds(radius,width): (radius+width/2+pad,radius+width/2+pad), SIMULATION_LAYER)) - filename = "ring_r{}_w{}.gds".format(radius,width) + filename = f"ring_r{radius}_w{width}.gds" gdspy.write_gds(filename, unit=1.0e-6, precision=1.0e-9) return filename @@ -109,4 +109,4 @@ def find_modes(filename,wvl=1.55,bw=0.05): filename = create_ring_gds(2.0,0.5) wvls, Qs = find_modes(filename,1.55,0.05) for w, Q in zip(wvls,Qs): - print("mode: {}, {}".format(w,Q)) + print(f"mode: {w}, {Q}") diff --git a/python/examples/stochastic_emitter.py b/python/examples/stochastic_emitter.py index c14e51be4..18b497026 100644 --- a/python/examples/stochastic_emitter.py +++ b/python/examples/stochastic_emitter.py @@ -51,11 +51,8 @@ def compute_flux(m=1,n=0): if m == 1: - sources = [] - for n in range(ndipole): - sources.append(mp.Source(mp.CustomSource(src_func=lambda t: np.random.randn()), - component=mp.Ez, - center=mp.Vector3(sx*(-0.5+n/ndipole),-0.5*sy+dAg+0.5*dsub))) + sources = [mp.Source(mp.CustomSource(src_func=lambda t: np.random.randn()), component=mp.Ez, center=mp.Vector3(sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub)) for n in range(ndipole)] + else: sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), component=mp.Ez, @@ -92,5 +89,5 @@ def compute_flux(m=1,n=0): if mp.am_master(): - with open('method{}_{}_res{}_nfreq{}_ndipole{}.npz'.format(args.method,"textured" if args.textured else "flat",resolution,nfreq,ndipole),'wb') as f: + with open(f'method{args.method}_{"textured" if args.textured else "flat"}_res{resolution}_nfreq{nfreq}_ndipole{ndipole}.npz', 'wb') as f: np.savez(f,freqs=freqs,fluxes=fluxes) diff --git a/python/examples/stochastic_emitter_line.py b/python/examples/stochastic_emitter_line.py index eef3bac61..a2e168a6b 100644 --- a/python/examples/stochastic_emitter_line.py +++ b/python/examples/stochastic_emitter_line.py @@ -98,10 +98,5 @@ def compute_flux(m,n): if mp.am_master(): - with open('method{}_{}_res{}_nfreq{}_{}{}.npz'.format(args.method, - "textured" if args.textured else "flat", - resolution, - nfreq, - "ndipole" if args.method == 2 else "nsrc", - ndipole if args.method == 2 else nsrc),'wb') as f: + with open(f'method{args.method}_{"textured" if args.textured else "flat"}_res{resolution}_nfreq{nfreq}_{"ndipole" if args.method == 2 else "nsrc"}{ndipole if args.method == 2 else nsrc}.npz', 'wb') as f: np.savez(f,freqs=freqs,fluxes=fluxes) diff --git a/python/examples/wvg-src.py b/python/examples/wvg-src.py index b5cbbaf09..bcd2edb84 100644 --- a/python/examples/wvg-src.py +++ b/python/examples/wvg-src.py @@ -48,7 +48,7 @@ flux2 = sim.flux_in_box(mp.X, mp.Volume(center=mp.Vector3(6.0), size=mp.Vector3(1.8, 6))) # averaged over y region of width 1.8 -print("left-going flux = {}".format(flux1 / -1.8)) +print(f"left-going flux = {flux1 / -1.8}") # averaged over y region of width 1.8 -print("right-going flux = {}".format(flux2 / 1.8)) +print(f"right-going flux = {flux2 / 1.8}") diff --git a/python/examples/zone_plate.py b/python/examples/zone_plate.py index 9defcac9f..3759035ba 100644 --- a/python/examples/zone_plate.py +++ b/python/examples/zone_plate.py @@ -44,10 +44,7 @@ size=mp.Vector3(sr,0,dpml+dsub), center=mp.Vector3(0.5*sr,0,-0.5*sz+0.5*(dpml+dsub)))] -for n in range(zN-1,-1,-1): - geometry.append(mp.Block(material=glass if n % 2 == 0 else mp.vacuum, - size=mp.Vector3(r[n],0,zh), - center=mp.Vector3(0.5*r[n],0,-0.5*sz+dpml+dsub+0.5*zh))) +geometry.extend(mp.Block(material=glass if n % 2 == 0 else mp.vacuum, size=mp.Vector3(r[n], 0, zh), center=mp.Vector3(0.5 * r[n], 0, -0.5 * sz + dpml + dsub + 0.5 * zh)) for n in range(zN - 1, -1, -1)) sim = mp.Simulation(cell_size=cell_size, boundary_layers=pml_layers, @@ -90,7 +87,7 @@ plt.subplot(1,2,1) plt.semilogy(np.linspace(0,sr-dpml,len(E2_r)),E2_r,'bo-') plt.xlim(-2,20) - plt.xticks([t for t in np.arange(0,25,5)]) + plt.xticks(list(np.arange(0,25,5))) plt.grid(True,axis="y",which="both",ls="-") plt.xlabel(r'$r$ coordinate (μm)') plt.ylabel(r'energy density of far fields, |E|$^2$') @@ -99,6 +96,7 @@ plt.grid(True,axis="y",which="both",ls="-") plt.xlabel(r'$z$ coordinate (μm)') plt.ylabel(r'energy density of far fields, |E|$^2$') - plt.suptitle(r"binary-phase zone plate with focal length $z$ = {} μm".format(focal_length)) + plt.suptitle(f"binary-phase zone plate with focal length $z$ = {focal_length} μm") + plt.tight_layout() plt.savefig("zone_plate_farfields.png") diff --git a/python/geom.py b/python/geom.py index 908337089..a244cc137 100755 --- a/python/geom.py +++ b/python/geom.py @@ -19,7 +19,7 @@ def check_nonnegative(prop, val): if val >= 0: return val else: - raise ValueError("{} cannot be negative. Got {}".format(prop, val)) + raise ValueError(f"{prop} cannot be negative. Got {val}") def init_do_averaging(mat_func): if not hasattr(mat_func, 'do_averaging'): @@ -113,7 +113,7 @@ def __mul__(self, other): elif isinstance(other, Number): return self.scale(other) else: - raise TypeError("No operation known for 'Vector3 * {}'".format(type(other))) + raise TypeError(f"No operation known for 'Vector3 * {type(other)}'") def __truediv__(self, other): if type(other) is Vector3: @@ -121,7 +121,7 @@ def __truediv__(self, other): elif isinstance(other, Number): return Vector3(self.x / other, self.y / other, self.z / other) else: - raise TypeError("No operation known for 'Vector3 / {}'".format(type(other))) + raise TypeError(f"No operation known for 'Vector3 / {type(other)}'") def __rmul__(self, other): """ @@ -135,7 +135,7 @@ def __rmul__(self, other): if isinstance(other, Number): return self.scale(other) else: - raise TypeError("No operation known for '{} * Vector3'".format(type(other))) + raise TypeError(f"No operation known for '{type(other)} * Vector3'") def __getitem__(self, i): if i == 0: @@ -145,10 +145,10 @@ def __getitem__(self, i): elif i == 2: return self.z else: - raise IndexError("No value at index {}".format(i)) + raise IndexError(f"No value at index {i}") def __repr__(self): - return "Vector3<{}, {}, {}>".format(self.x, self.y, self.z) + return f"Vector3<{self.x}, {self.y}, {self.z}>" def __array__(self): return np.array([self.x, self.y, self.z]) @@ -423,8 +423,8 @@ def __init__(self, epsilon_diag=Vector3(1, 1, 1), self.epsilon_offdiag = Vector3(*epsilon_offdiag) self.mu_diag = Vector3(*mu_diag) self.mu_offdiag = Vector3(*mu_offdiag) - self.E_susceptibilities = E_susceptibilities if E_susceptibilities else [] - self.H_susceptibilities = H_susceptibilities if H_susceptibilities else [] + self.E_susceptibilities = E_susceptibilities or [] + self.H_susceptibilities = H_susceptibilities or [] self.E_chi2_diag = Vector3(chi2, chi2, chi2) if chi2 else Vector3(*E_chi2_diag) self.E_chi3_diag = Vector3(chi3, chi3, chi3) if chi3 else Vector3(*E_chi3_diag) self.H_chi2_diag = Vector3(*H_chi2_diag) @@ -501,9 +501,11 @@ def _get_epsmu(self, diag, offdiag, susceptibilities, conductivity_diag, conduct # Check for values outside of allowed ranges if np.min(np.squeeze(freqs)) < self.valid_freq_range.min: - raise ValueError('User specified frequency {} is below the Medium\'s limit, {}.'.format(np.min(np.squeeze(freqs)),self.valid_freq_range.min)) + raise ValueError(f"User specified frequency {np.min(np.squeeze(freqs))} is below the Medium\'s limit, {self.valid_freq_range.min}.") + if np.max(np.squeeze(freqs)) > self.valid_freq_range.max: - raise ValueError('User specified frequency {} is above the Medium\'s limit, {}.'.format(np.max(np.squeeze(freqs)),self.valid_freq_range.max)) + raise ValueError(f"User specified frequency {np.max(np.squeeze(freqs))} is above the Medium\'s limit, {self.valid_freq_range.max}.") + # Initialize with instantaneous dielectric tensor epsmu = np.expand_dims(Matrix(diag=diag,offdiag=offdiag),axis=0) @@ -527,11 +529,10 @@ class MaterialGrid(object): argument of the [`Simulation`](#Simulation) constructor (similar to a [material function](#medium)). """ def check_weights(self, w): - if np.amin(w) < 0. or np.amax(w) > 1.: - warnings.warn('The weights parameter of MaterialGrid must be in the range [0,1].') - return np.clip(w, 0., 1.) - else: + if np.amin(w) >= 0.0 and np.amax(w) <= 1.0: return w + warnings.warn('The weights parameter of MaterialGrid must be in the range [0,1].') + return np.clip(w, 0., 1.) def __init__(self, grid_size, @@ -630,7 +631,8 @@ def update_weights(self, x): Reset the `weights` to `x`. """ if x.size != self.num_params: - raise ValueError("weights of shape {} do not match user specified grid dimension: {}".format(self.weights.size,self.grid_size)) + raise ValueError(f"weights of shape {self.weights.size} do not match user specified grid dimension: {self.grid_size}") + self.weights[:] = self.check_weights(x).flatten().astype(np.float64) class Susceptibility(object): @@ -860,8 +862,8 @@ class MultilevelAtom(Susceptibility): """ def __init__(self, initial_populations=None, transitions=None, **kwargs): super(MultilevelAtom, self).__init__(**kwargs) - self.initial_populations = initial_populations if initial_populations else [] - self.transitions = transitions if transitions else [] + self.initial_populations = initial_populations or [] + self.transitions = transitions or [] class Transition(object): @@ -1263,13 +1265,13 @@ def __mul__(self, m): elif isinstance(m, Number): return self.scale(m) else: - raise TypeError("No operation known for 'Matrix * {}'".format(type(m))) + raise TypeError(f"No operation known for 'Matrix * {type(m)}'") def __rmul__(self, left_arg): if isinstance(left_arg, Number): return self.scale(left_arg) else: - raise TypeError("No operation known for 'Matrix * {}'".format(type(left_arg))) + raise TypeError(f"No operation known for 'Matrix * {type(left_arg)}'") def __truediv__(self, scalar): return Matrix(self.c1 / scalar, self.c2 / scalar, self.c3 / scalar) @@ -1284,9 +1286,7 @@ def __repr__(self): r0 = self.row(0) r1 = self.row(1) r2 = self.row(2) - return "<<{} {} {}>\n <{} {} {}>\n <{} {} {}>>".format(r0[0], r0[1], r0[2], - r1[0], r1[1], r1[2], - r2[0], r2[1], r2[2]) + return f"<<{r0[0]} {r0[1]} {r0[2]}>\n <{r1[0]} {r1[1]} {r1[2]}>\n <{r2[0]} {r2[1]} {r2[2]}>>" def __array__(self): return np.array([self.row(0).__array__(), @@ -1446,10 +1446,7 @@ def reciprocal_to_cartesian(x, lat): m = Matrix(Vector3(s.x), Vector3(y=s.y), Vector3(z=s.z)) Rst = (lat.basis * m).transpose() - if isinstance(x, Vector3): - return Rst.inverse() * x - else: - return (Rst.inverse() * x) * Rst + return Rst.inverse() * x if isinstance(x, Vector3) else (Rst.inverse() * x) * Rst def cartesian_to_reciprocal(x, lat): @@ -1458,10 +1455,7 @@ def cartesian_to_reciprocal(x, lat): m = Matrix(Vector3(s.x), Vector3(y=s.y), Vector3(z=s.z)) Rst = (lat.basis * m).transpose() - if isinstance(x, Vector3): - return Rst * x - else: - return (Rst * x) * Rst.inverse() + return Rst * x if isinstance(x, Vector3) else (Rst * x) * Rst.inverse() def lattice_to_reciprocal(x, lat): @@ -1476,11 +1470,10 @@ def geometric_object_duplicates(shift_vector, min_multiple, max_multiple, go): shift_vector = Vector3(*shift_vector) def _dup(min_multiple, lst): - if min_multiple <= max_multiple: - shifted = go.shift(shift_vector.scale(min_multiple)) - return _dup(min_multiple + 1, [shifted] + lst) - else: + if min_multiple > max_multiple: return lst + shifted = go.shift(shift_vector.scale(min_multiple)) + return _dup(min_multiple + 1, [shifted] + lst) return _dup(min_multiple, []) @@ -1531,8 +1524,7 @@ def memoize(f): f_memo_tab = {} def _mem(y=None): - tab_val = f_memo_tab.get(y, None) - if tab_val: + if tab_val := f_memo_tab.get(y, None): return tab_val fy = f(y) @@ -1550,9 +1542,7 @@ def find_root_deriv(f, tol, x_min, x_max, x_guess=None): f_memo = memoize(f) def lazy(x): - if isinstance(x, numbers.Number): - return x - return x() + return x if isinstance(x, numbers.Number) else x() def pick_bound(which): def _pb(): diff --git a/python/mpb_data.py b/python/mpb_data.py index 87ffadd92..01337de41 100644 --- a/python/mpb_data.py +++ b/python/mpb_data.py @@ -31,11 +31,7 @@ def __init__(self, if periods: self.multiply_size = [periods, periods, periods] else: - self.multiply_size = [ - x if x else 1, - y if y else 1, - z if z else 1 - ] + self.multiply_size = [x or 1, y or 1, z or 1] self.resolution = resolution self.phase_angle = phase_angle @@ -61,7 +57,7 @@ def handle_dataset(self, in_arr): out_dims = [1, 1, 1] rank = len(in_arr.shape) num_ones = 3 - rank - in_dims = [x for x in in_arr.shape] + [1] * num_ones + in_dims = list(in_arr.shape) + [1] * num_ones if np.iscomplexobj(in_arr): in_arr_re = np.real(in_arr) @@ -91,7 +87,7 @@ def handle_dataset(self, in_arr): N *= out_dims[i] if self.verbose: - print("Output data {}x{}x{}".format(out_dims[0], out_dims[1], out_dims[2])) + print(f"Output data {out_dims[0]}x{out_dims[1]}x{out_dims[2]}") out_arr_re = np.zeros(int(N)) @@ -103,11 +99,7 @@ def handle_dataset(self, in_arr): flat_in_arr_re = in_arr_re.ravel() flat_in_arr_im = in_arr_im.ravel() if isinstance(in_arr_im, np.ndarray) else np.array([]) - if self.kpoint: - kvector = [self.kpoint.x, self.kpoint.y, self.kpoint.z] - else: - kvector = [] - + kvector = [self.kpoint.x, self.kpoint.y, self.kpoint.z] if self.kpoint else [] map_data(flat_in_arr_re, flat_in_arr_im, np.array(in_dims, dtype=np.intc), out_arr_re, out_arr_im, np.array(out_dims, dtype=np.intc), self.coord_map, kvector, self.pick_nearest, self.verbose, False) @@ -131,13 +123,13 @@ def handle_cvector_dataset(self, in_arr, multiply_bloch_phase): d_in = [[in_x_re, in_x_im], [in_y_re, in_y_im], [in_z_re, in_z_im]] in_dims = [in_arr.shape[0], in_arr.shape[1], 1] - rank = 2 - if self.verbose: print("Found complex vector dataset...") if self.verbose: fmt = "Input data is rank {}, size {}x{}x{}." + rank = 2 + print(fmt.format(rank, in_dims[0], in_dims[1], in_dims[2])) # rotate vector field according to cart_map @@ -179,11 +171,7 @@ def handle_cvector_dataset(self, in_arr, multiply_bloch_phase): fmt = "Output data {}x{}x{}." print(fmt.format(out_dims[0], out_dims[1], out_dims[2])) - if self.kpoint: - kvector = [self.kpoint.x, self.kpoint.y, self.kpoint.z] - else: - kvector = [] - + kvector = [self.kpoint.x, self.kpoint.y, self.kpoint.z] if self.kpoint else [] converted = [] for dim in range(3): out_arr_re = np.zeros(int(N)) @@ -240,11 +228,7 @@ def init_output_lattice(self): # that our first vector (in the direction of ve) smoothly # interpolates between the original lattice vectors. - if self.have_ve: - ve = self.ve.unit() - else: - ve = Rout.c1.unit() - + ve = self.ve.unit() if self.have_ve else Rout.c1.unit() # First, compute c1 in the direction of ve by smoothly # interpolating the old c1/c2/c3 (formula is slightly tricky) V = Rout.c1.cross(Rout.c2).dot(Rout.c3) diff --git a/python/simulation.py b/python/simulation.py index a3ba603d6..43c7b3c69 100644 --- a/python/simulation.py +++ b/python/simulation.py @@ -66,9 +66,7 @@ def fix_dft_args(args, i): return args def get_num_args(func): - if isinstance(func, Harminv): - return 2 - return func.__code__.co_argcount + return 2 if isinstance(func, Harminv) else func.__code__.co_argcount def vec(*args): @@ -95,14 +93,13 @@ def py_v3_to_vec(dims, iterable, is_cylindrical=False): elif dims == 2: if is_cylindrical: return mp.veccyl(v3.x, v3.z) - else: - v = mp.vec(v3.x, v3.y) - v.set_direction(mp.Z, v3.z) # for special_kz handling - return v + v = mp.vec(v3.x, v3.y) + v.set_direction(mp.Z, v3.z) # for special_kz handling + return v elif dims == 3: return mp.vec(v3.x, v3.y, v3.z) else: - raise ValueError("Invalid dimensions in Volume: {}".format(dims)) + raise ValueError(f"Invalid dimensions in Volume: {dims}") def bands_to_diffractedplanewave(where,bands): if bands.axis is None: @@ -255,7 +252,7 @@ def mean_stretch(self, val): if val >= 1: self._mean_stretch = val else: - raise ValueError("PML.mean_stretch must be >= 1. Got {}".format(val)) + raise ValueError(f"PML.mean_stretch must be >= 1. Got {val}") class Absorber(PML): @@ -404,7 +401,7 @@ def get_vertices(self): zmax = self.center.z + self.size.z/2 # Iterate over and remove duplicates for collapsed dimensions (i.e. min=max)) - return [Vector3(x,y,z) for x in list(set([xmin,xmax])) for y in list(set([ymin,ymax])) for z in list(set([zmin,zmax]))] + return [Vector3(x, y, z) for x in list({xmin, xmax}) for y in list({ymin, ymax}) for z in list({zmin, zmax})] def get_edges(self): @@ -432,10 +429,7 @@ def pt_in_volume(self,pt): zmin = self.center.z - self.size.z/2 zmax = self.center.z + self.size.z/2 - if (pt.x >= xmin and pt.x <= xmax and pt.y >= ymin and pt.y <= ymax and pt.z >= zmin and pt.z <= zmax): - return True - else: - return False + return pt.x >= xmin and pt.x <= xmax and pt.y >= ymin and pt.y <= ymax and pt.z >= zmin and pt.z <= zmax class FluxRegion(object): @@ -917,10 +911,7 @@ def _harminv(self): def _harm(sim): - if self.mxbands is None or self.mxbands == 0: - mb = 100 - else: - mb = self.mxbands + mb = 100 if self.mxbands is None or self.mxbands == 0 else self.mxbands self.modes = self._analyze_harminv(sim, mb) f1 = self._collect_harminv() diff --git a/python/solver.py b/python/solver.py index 17e3ae17e..75f260812 100644 --- a/python/solver.py +++ b/python/solver.py @@ -102,8 +102,8 @@ def __init__(self, self.mode_solver = None self.resolution = resolution self.eigensolver_flags = eigensolver_flags - self.k_points = k_points if k_points else [] - self.geometry = geometry if geometry else [] + self.k_points = k_points or [] + self.geometry = geometry or [] self.geometry_lattice = geometry_lattice self.geometry_center = mp.Vector3(*geometry_center) self.default_material = default_material @@ -170,7 +170,7 @@ def resolution(self, val): self._resolution = [val.x, val.y, val.z] else: t = type(val) - raise TypeError("resolution must be a number or a Vector3: Got {}".format(t)) + raise TypeError(f"resolution must be a number or a Vector3: Got {t}") if self.mode_solver: self.mode_solver.resolution = self._resolution @@ -323,14 +323,10 @@ def allow_negative_epsilon(self): def get_filename_prefix(self): if self.filename_prefix: - return self.filename_prefix + '-' - else: - _, filename = os.path.split(sys.argv[0]) + return f'{self.filename_prefix}-' + _, filename = os.path.split(sys.argv[0]) - if filename == 'ipykernel_launcher.py' or filename == '__main__.py': - return '' - else: - return re.sub(r'\.py$', '', filename) + '-' + return '' if filename in ['ipykernel_launcher.py', '__main__.py'] else re.sub(r'\.py$', '', filename) + '-' def get_freqs(self): return self.mode_solver.get_freqs() @@ -418,9 +414,7 @@ def _get_field(self, f, band, bloch_phase): self.mode_solver.get_curfield_cmplx(arr) arr = np.reshape(arr, dims) - res = MPBArray(arr, self.get_lattice(), self.current_k, bloch_phase=bloch_phase) - - return res + return MPBArray(arr, self.get_lattice(), self.current_k, bloch_phase=bloch_phase) def get_curfield_as_array(self, bloch_phase=True): dims = self.mode_solver.get_dims() @@ -499,20 +493,19 @@ def update_band_range_data(self, brd, freqs, kpoint): def update_brd(brd, freqs, br_start): if not freqs: return br_start + brd - else: - br = ((mp.inf, -1), (-mp.inf, -1)) if not brd else brd[0] - br_rest = [] if not brd else brd[1:] - newmin = (freqs[0], kpoint) if freqs[0] < br[0][0] else br[0] - newmax = (freqs[0], kpoint) if freqs[0] > br[1][0] else br[1] - new_start = br_start + [(newmin, newmax)] - return update_brd(br_rest, freqs[1:], new_start) + br = brd[0] if brd else ((mp.inf, -1), (-mp.inf, -1)) + br_rest = brd[1:] if brd else [] + newmin = (freqs[0], kpoint) if freqs[0] < br[0][0] else br[0] + newmax = (freqs[0], kpoint) if freqs[0] > br[1][0] else br[1] + new_start = br_start + [(newmin, newmax)] + return update_brd(br_rest, freqs[1:], new_start) return update_brd(brd, freqs, []) def output_band_range_data(self, br_data): if verbosity.mpb > 0: + fmt = "Band {} range: {} at {} to {} at {}" for tup, band in zip(br_data, range(1, len(br_data) + 1)): - fmt = "Band {} range: {} at {} to {} at {}" min_band, max_band = tup min_freq, min_kpoint = min_band max_freq, max_kpoint = max_band @@ -524,24 +517,20 @@ def output_gaps(self, br_data): def ogaps(br_cur, br_rest, i, gaps): if not br_rest: - ordered_gaps = [] gaps = list(reversed(gaps)) - for i in range(0, len(gaps), 3): - ordered_gaps.append((gaps[i + 2], gaps[i + 1], gaps[i])) - return ordered_gaps + return [(gaps[i + 2], gaps[i + 1], gaps[i]) for i in range(0, len(gaps), 3)] else: br_rest_min_f = br_rest[0][0][0] br_cur_max_f = br_cur[1][0] if br_cur_max_f >= br_rest_min_f: return ogaps(br_rest[0], br_rest[1:], i + 1, gaps) - else: - gap_size = ((200 * (br_rest_min_f - br_cur_max_f)) / - (br_rest_min_f + br_cur_max_f)) - if verbosity.mpb > 0: - fmt = "Gap from band {} ({}) to band {} ({}), {}%" - print(fmt.format(i, br_cur_max_f, i + 1, br_rest_min_f, gap_size)) - return ogaps(br_rest[0], br_rest[1:], i + 1, - [gap_size, br_cur_max_f, br_rest_min_f] + gaps) + gap_size = ((200 * (br_rest_min_f - br_cur_max_f)) / + (br_rest_min_f + br_cur_max_f)) + if verbosity.mpb > 0: + fmt = "Gap from band {} ({}) to band {} ({}), {}%" + print(fmt.format(i, br_cur_max_f, i + 1, br_rest_min_f, gap_size)) + return ogaps(br_rest[0], br_rest[1:], i + 1, + [gap_size, br_cur_max_f, br_rest_min_f] + gaps) if not br_data: return [] else: @@ -626,7 +615,7 @@ def output_field_to_file(self, component, fname_prefix): elif curfield_type in 'DHBnmR': self._output_scalar_field(curfield_type, fname_prefix) else: - raise ValueError("Unkown field type: {}".format(curfield_type)) + raise ValueError(f"Unkown field type: {curfield_type}") self.mode_solver.curfield_reset() @@ -643,7 +632,7 @@ def _output_complex_scalar_field(self, fname_prefix, output_k): ) fname = self._create_fname(fname, fname_prefix, True) if verbosity.mpb > 0: - print("Outputting complex scalar field to {}...".format(fname)) + print(f"Outputting complex scalar field to {fname}...") with h5py.File(fname, 'w') as f: f['description'] = description.encode() @@ -666,7 +655,7 @@ def _output_vector_field(self, curfield_type, fname_prefix, output_k, component) fname = "{}.k{:02d}.b{:02d}".format(curfield_type, kpoint_index, curfield_band) if component >= 0: - fname += ".{}".format(components[component]) + fname += f".{components[component]}" description = "{} field, kpoint {}, band {}, freq={:.6g}".format( curfield_type, @@ -677,7 +666,7 @@ def _output_vector_field(self, curfield_type, fname_prefix, output_k, component) fname = self._create_fname(fname, fname_prefix, True) if verbosity.mpb > 0: - print("Outputting fields to {}...".format(fname)) + print(f"Outputting fields to {fname}...") with h5py.File(fname, 'w') as f: f['description'] = description.encode() @@ -696,9 +685,9 @@ def _output_vector_field(self, curfield_type, fname_prefix, output_k, component) self.mode_solver.get_curfield_cmplx(field) component_field = field[c_idx::3].reshape(dims) - name = "{}.r".format(c) + name = f"{c}.r" f[name] = np.real(component_field) - name = "{}.i".format(c) + name = f"{c}.i" f[name] = np.imag(component_field) def _output_scalar_field(self, curfield_type, fname_prefix): @@ -719,10 +708,10 @@ def _output_scalar_field(self, curfield_type, fname_prefix): description = descr_fmt.format(curfield_type, kpoint_index, curfield_band, self.freqs[curfield_band - 1]) - parity_suffix = False if curfield_type in 'mn' else True + parity_suffix = curfield_type not in 'mn' fname = self._create_fname(fname, fname_prefix, parity_suffix) if verbosity.mpb > 0: - print("Outputting {}...".format(fname)) + print(f"Outputting {fname}...") with h5py.File(fname, 'w') as f: f['description'] = description.encode() @@ -735,8 +724,7 @@ def _output_scalar_field(self, curfield_type, fname_prefix): for c1 in range(3): for c2 in range(c1, 3): self.mode_solver.get_epsilon_tensor(c1, c2, 0, inv) - dataname = "{}.{}{}".format(inv_str, components[c1], - components[c2]) + dataname = f"{inv_str}.{components[c1]}{components[c2]}" self._create_h5_dataset(f, dataname) if with_hermitian_epsilon() and c1 != c2: @@ -754,11 +742,7 @@ def _create_h5_dataset(self, h5file, key): def _create_fname(self, fname, prefix, parity_suffix): parity_str = self.mode_solver.get_parity_string() - if parity_suffix and parity_str: - suffix = ".{}".format(parity_str) - else: - suffix = '' - + suffix = f".{parity_str}" if parity_suffix and parity_str else '' return prefix + fname + suffix + '.h5' def compute_field_energy(self): @@ -809,12 +793,12 @@ def randomize_fields(self): self.mode_solver.randomize_fields() def display_kpoint_data(self, name, data): - k_index = self.mode_solver.get_kpoint_index() if verbosity.mpb > 0: - print("{}{}:, {}".format(self.parity, name, k_index), end='') + k_index = self.mode_solver.get_kpoint_index() + print(f"{self.parity}{name}:, {k_index}", end='') for d in data: - print(", {}".format(d), end='') + print(f", {d}", end='') print() def display_eigensolver_stats(self): @@ -835,15 +819,15 @@ def display_eigensolver_stats(self): idx2 = ((num_runs + 1) // 2) - 1 median_iters = 0.5 * (sorted_iters[idx1] + sorted_iters[idx2]) if verbosity.mpb > 0: - print(", median = {}".format(median_iters)) + print(f", median = {median_iters}") mean_flops = self.eigensolver_flops / (num_runs * mean_iters) if verbosity.mpb > 0: - print("mean flops per iteration = {}".format(mean_flops)) + print(f"mean flops per iteration = {mean_flops}") mean_time = self.total_run_time / (mean_iters * num_runs) if verbosity.mpb > 0: - print("mean time per iteration = {} s".format(mean_time)) + print(f"mean time per iteration = {mean_time} s") def _get_grid_size(self): grid_size = mp.Vector3(self.resolution[0] * self.geometry_lattice.size.x, @@ -867,9 +851,7 @@ def next_factor2357(self, n): def is_factor2357(n): def divby(n, p): - if n % p == 0: - return divby(n // p, p) - return n + return divby(n // p, p) if n % p == 0 else n return divby(divby(divby(divby(n, 2), 3), 5), 7) == 1 if is_factor2357(n): @@ -899,7 +881,7 @@ def run_parity(self, p, reset_fields, *band_functions): if verbosity.mpb > 0: print("Initializing eigensolver data") - print("Computing {} bands with {} tolerance".format(self.num_bands, self.tolerance)) + print(f"Computing {self.num_bands} bands with {self.tolerance} tolerance") self.init_params(p, reset_fields) @@ -907,12 +889,12 @@ def run_parity(self, p, reset_fields, *band_functions): self.load_eigenvectors(reset_fields) if verbosity.mpb > 0: - print("{} k-points".format(len(self.k_points))) + print(f"{len(self.k_points)} k-points") for kp in self.k_points: - print(" {}".format(kp)) + print(f" {kp}") - print("elapsed time for initialization: {}".format(time.time() - init_time)) + print(f"elapsed time for initialization: {time.time() - init_time}") # TODO: Split over multiple processes # k_split = list_split(self.k_points, self.k_split_num, self.k_split_index) @@ -926,7 +908,7 @@ def run_parity(self, p, reset_fields, *band_functions): self.solve_kpoint(k) self.iterations = self.mode_solver.get_iterations() if verbosity.mpb > 0: - print("elapsed time for k point: {}".format(time.time() - solve_kpoint_time)) + print(f"elapsed time for k point: {time.time() - solve_kpoint_time}") self.all_freqs[i, :] = np.array(self.freqs) self.band_range_data = self.update_band_range_data(self.band_range_data, self.freqs, k) @@ -953,7 +935,7 @@ def run_parity(self, p, reset_fields, *band_functions): end = time.time() - start if verbosity.mpb > 0: - print("total elapsed time for run: {}".format(end)) + print(f"total elapsed time for run: {end}") self.total_run_time += end self.eigensolver_flops = self.mode_solver.get_eigensolver_flops() self.parity = self.mode_solver.get_parity_string() @@ -1021,13 +1003,10 @@ def find_k(self, p, omega, band_min, band_max, korig_and_kdir, tol, def rootfun(b): def _rootfun(k): - # First, look in the cached table - tab_val = bktab.get((b, k), None) - if tab_val: + if tab_val := bktab.get((b, k), None): if verbosity.mpb > 0: - print("find-k {} at {}: {} (cached)".format(b, k, tab_val[0])) + print(f"find-k {b} at {k}: {tab_val[0]} (cached)") return tab_val - # Otherwise, compute bands and cache results else: self.num_bands = b self.k_points = [korig + kdir1.scale(k)] @@ -1035,9 +1014,7 @@ def _rootfun(k): v = self.mode_solver.compute_group_velocity_component(kdir1) # Cache computed values - for _b, _f, _v in zip(range(band_min, b - band_min + 1, 1), - self.freqs[band_min - 1:], - v[band_min - 1:]): + for _b, _f, _v in zip(range(band_min, b - band_min + 1), self.freqs[band_min - 1:], v[band_min - 1:]): tabval = bktab.get((_b, k0s[_b - band_min]), None) if not tabval or abs(_f - omega) < abs(tabval[0]): @@ -1047,7 +1024,7 @@ def _rootfun(k): fun = self.freqs[-1] - omega if verbosity.mpb > 0: - print("find-k {} at {}: {}".format(b, k, fun)) + print(f"find-k {b} at {k}: {fun}") return (fun, v[-1]) return _rootfun @@ -1096,10 +1073,7 @@ def n(k): return mp.reciprocal_to_cartesian(k, self.geometry_lattice).norm() def try_plus(k, v): - if n(k + v) < n(k): - return try_plus(k + v, v) - else: - return k + return try_plus(k + v, v) if n(k + v) < n(k) else k def _try(k, v): return try_plus(try_plus(k, v), mp.Vector3() - v) @@ -1133,10 +1107,7 @@ def compute_symmetry(self, band, W, w): return self.mode_solver.compute_symmetry(band, W, w) def compute_symmetries(self, W, w): - symvals = [] - for band in range(1, self.num_bands+1): - symvals.append(self.mode_solver.compute_symmetry(band, W, w)) - return symvals + return [self.mode_solver.compute_symmetry(band, W, w) for band in range(1, self.num_bands + 1)] # Predefined output functions (functions of the band index), for passing to `run` @@ -1235,7 +1206,7 @@ def output_dpwr(ms, which_band): def output_tot_pwr(ms, which_band): ms.get_tot_pwr(which_band) - ms.output_field_to_file(-1, ms.get_filename_prefix() + 'tot.') + ms.output_field_to_file(-1, f'{ms.get_filename_prefix()}tot.') def output_dpwr_in_objects(output_func, min_energy, objects=[]): @@ -1267,22 +1238,22 @@ def output_charge_density(ms, which_band): def output_poynting(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(-1, ms.get_filename_prefix() + 'flux.') + ms.output_field_to_file(-1, f'{ms.get_filename_prefix()}flux.') def output_poynting_x(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(0, ms.get_filename_prefix() + 'flux.') + ms.output_field_to_file(0, f'{ms.get_filename_prefix()}flux.') def output_poynting_y(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(1, ms.get_filename_prefix() + 'flux.') + ms.output_field_to_file(1, f'{ms.get_filename_prefix()}flux.') def output_poynting_z(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(2, ms.get_filename_prefix() + 'flux.') + ms.output_field_to_file(2, f'{ms.get_filename_prefix()}flux.') def display_yparities(ms): diff --git a/python/source.py b/python/source.py index b50a30acb..765d79d42 100644 --- a/python/source.py +++ b/python/source.py @@ -8,7 +8,7 @@ def check_positive(prop, val): if val > 0: return val else: - raise ValueError("{} must be positive. Got {}".format(prop, val)) + raise ValueError(f"{prop} must be positive. Got {val}") class Source(object): @@ -171,7 +171,8 @@ def __init__(self, frequency=None, start_time=0, end_time=1.0e20, width=0, """ if frequency is None and wavelength is None: - raise ValueError("Must set either frequency or wavelength in {}.".format(self.__class__.__name__)) + raise ValueError(f"Must set either frequency or wavelength in {self.__class__.__name__}.") + super(ContinuousSource, self).__init__(is_integrated=is_integrated, **kwargs) self.frequency = 1 / wavelength if wavelength else float(frequency) @@ -238,7 +239,8 @@ def __init__(self, frequency=None, width=0, fwidth=float('inf'), start_time=0, c `amplitude` or `amp_func` factor that you specified for the source. """ if frequency is None and wavelength is None: - raise ValueError("Must set either frequency or wavelength in {}.".format(self.__class__.__name__)) + raise ValueError(f"Must set either frequency or wavelength in {self.__class__.__name__}.") + super(GaussianSource, self).__init__(is_integrated=is_integrated, **kwargs) self.frequency = 1 / wavelength if wavelength else float(frequency) @@ -478,10 +480,7 @@ def eig_lattice_size(self): @eig_lattice_size.setter def eig_lattice_size(self, val): - if val is None: - self._eig_lattice_size = self.size - else: - self._eig_lattice_size = val + self._eig_lattice_size = self.size if val is None else val @property def eig_lattice_center(self): @@ -489,10 +488,7 @@ def eig_lattice_center(self): @eig_lattice_center.setter def eig_lattice_center(self, val): - if val is None: - self._eig_lattice_center = self.center - else: - self._eig_lattice_center = val + self._eig_lattice_center = self.center if val is None else val @property def component(self): diff --git a/python/tests/test_adjoint_cyl.py b/python/tests/test_adjoint_cyl.py index ab6a3307d..2a59ae128 100644 --- a/python/tests/test_adjoint_cyl.py +++ b/python/tests/test_adjoint_cyl.py @@ -131,7 +131,8 @@ def test_adjoint_solver_cyl_n2f_fields(self): S12_unperturbed = forward_simulation(p) ## compare objective results - print("|Er|^2 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,S12_unperturbed)) + print(f"|Er|^2 -- adjoint solver: {adjsol_obj}, traditional simulation: {S12_unperturbed}") + self.assertClose(adjsol_obj,S12_unperturbed,epsilon=1e-3) ## compute perturbed S12 @@ -142,7 +143,7 @@ def test_adjoint_solver_cyl_n2f_fields(self): adjsol_grad = np.expand_dims(adjsol_grad,axis=1) adj_scale = (dp[None,:]@adjsol_grad).flatten() fd_grad = S12_perturbed-S12_unperturbed - print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) + print(f"Directional derivative -- adjoint solver: {adj_scale}, FD: {fd_grad}") tol = 0.2 if mp.is_single_precision() else 0.1 self.assertClose(adj_scale,fd_grad,epsilon=tol) diff --git a/python/tests/test_adjoint_jax.py b/python/tests/test_adjoint_jax.py index 6513369a1..051209576 100644 --- a/python/tests/test_adjoint_jax.py +++ b/python/tests/test_adjoint_jax.py @@ -205,13 +205,8 @@ def loss_fn(x, excite_port_idx=0): ) monitor_values = wrapped_meep([x]) s1p, s1m, s2m, s2p = monitor_values - if excite_port_idx == 0: - t = s2m / s1p - else: - t = s1m / s2p - # Mean transmission vs wavelength - t_mean = jnp.mean(jnp.square(jnp.abs(t))) - return t_mean + t = s2m / s1p if excite_port_idx == 0 else s1m / s2p + return jnp.mean(jnp.square(jnp.abs(t))) value, adjoint_grad = jax.value_and_grad(loss_fn)( x, excite_port_idx=excite_port_idx) diff --git a/python/tests/test_adjoint_solver.py b/python/tests/test_adjoint_solver.py index f7db4d152..f9d8a9780 100644 --- a/python/tests/test_adjoint_solver.py +++ b/python/tests/test_adjoint_solver.py @@ -272,7 +272,8 @@ def test_DFT_fields(self): unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + tol = 0.07 if mp.is_single_precision() else 0.006 self.assertClose(adj_dd,fnd_dd,epsilon=tol) @@ -298,7 +299,8 @@ def test_eigenmode(self): unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + tol = 0.04 if mp.is_single_precision() else 0.01 self.assertClose(adj_dd,fnd_dd,epsilon=tol) @@ -324,7 +326,8 @@ def test_ldos(self): unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + tol = 0.07 if mp.is_single_precision() else 0.006 self.assertClose(adj_dd,fnd_dd,epsilon=tol) @@ -332,12 +335,12 @@ def test_ldos(self): def test_gradient_backpropagation(self): print("*** TESTING GRADIENT BACKPROPAGATION ***") - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: - # filter/thresholding parameters - filter_radius = 0.21985 - eta = 0.49093 - beta = 4.0698 + # filter/thresholding parameters + filter_radius = 0.21985 + eta = 0.49093 + beta = 4.0698 + for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: mapped_p = self.mapping(self.p,filter_radius,eta,beta) # compute objective value and its gradient for unperturbed design @@ -372,7 +375,8 @@ def test_gradient_backpropagation(self): unperturbed_grad_backprop = np.expand_dims(unperturbed_grad_backprop,axis=1) adj_dd = (self.dp[None,:]@unperturbed_grad_backprop).flatten() fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + tol = 0.025 if mp.is_single_precision() else 0.01 self.assertClose(adj_dd,fnd_dd,epsilon=tol) @@ -395,7 +399,8 @@ def test_complex_fields(self): unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + tol = 0.06 if mp.is_single_precision() else 0.01 self.assertClose(adj_dd,fnd_dd,epsilon=tol) @@ -418,7 +423,8 @@ def test_damping(self): unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + tol = 0.06 if mp.is_single_precision() else 0.04 self.assertClose(adj_dd,fnd_dd,epsilon=tol) @@ -460,7 +466,8 @@ def test_offdiagonal(self): unperturbed_grad_backprop = np.expand_dims(unperturbed_grad_backprop,axis=1) adj_dd = (self.dp[None,:]@unperturbed_grad_backprop).flatten() fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + tol = 0.1 if mp.is_single_precision() else 0.04 self.assertClose(adj_dd,fnd_dd,epsilon=tol) diff --git a/python/tests/test_antenna_radiation.py b/python/tests/test_antenna_radiation.py index 35a8958d8..44dade129 100644 --- a/python/tests/test_antenna_radiation.py +++ b/python/tests/test_antenna_radiation.py @@ -33,9 +33,7 @@ def radial_flux(self,sim,nearfield_box,r): Px = np.real(E[:,1]*H[:,2]-E[:,2]*H[:,1]) # Ey*Hz-Ez*Hy Py = np.real(E[:,2]*H[:,0]-E[:,0]*H[:,2]) # Ez*Hx-Ex*Hz - Pr = np.sqrt(np.square(Px)+np.square(Py)) - - return Pr + return np.sqrt(np.square(Px)+np.square(Py)) def free_space_radiation(self): @@ -83,9 +81,7 @@ def free_space_radiation(self): sim.run(until_after_sources=mp.stop_when_dft_decayed()) - Pr = self.radial_flux(sim,nearfield_box,self.r) - - return Pr + return self.radial_flux(sim,nearfield_box,self.r) def pec_ground_plane_radiation(self): @@ -143,9 +139,7 @@ def pec_ground_plane_radiation(self): sim.run(until_after_sources=mp.stop_when_dft_decayed()) - Pr = self.radial_flux(sim,nearfield_box,self.r) - - return Pr + return self.radial_flux(sim,nearfield_box,self.r) def test_pec_ground_plane(self): diff --git a/python/tests/test_bend_flux.py b/python/tests/test_bend_flux.py index 5f3eb3371..a8d4f4b08 100644 --- a/python/tests/test_bend_flux.py +++ b/python/tests/test_bend_flux.py @@ -29,18 +29,17 @@ def init(self, no_bend=False, gdsii=False): mp.Vector3(-0.5 * sx - 5, wvg_ycen + 0.5 * w)] geometry = [mp.Prism(no_bend_vertices, height, material=mp.Medium(epsilon=12))] + elif gdsii: + geometry = mp.get_GDSII_prisms(mp.Medium(epsilon=12), gdsii_file, 2, 0, height) else: - if gdsii: - geometry = mp.get_GDSII_prisms(mp.Medium(epsilon=12), gdsii_file, 2, 0, height) - else: - bend_vertices = [mp.Vector3(-0.5 * sx, wvg_ycen - 0.5 * w), - mp.Vector3(wvg_xcen + 0.5 * w, wvg_ycen - 0.5 * w), - mp.Vector3(wvg_xcen + 0.5 * w, 0.5 * sy), - mp.Vector3(wvg_xcen - 0.5 * w, 0.5 * sy), - mp.Vector3(wvg_xcen - 0.5 * w, wvg_ycen + 0.5 * w), - mp.Vector3(-0.5 * sx, wvg_ycen + 0.5 * w)] - - geometry = [mp.Prism(bend_vertices, height, material=mp.Medium(epsilon=12))] + bend_vertices = [mp.Vector3(-0.5 * sx, wvg_ycen - 0.5 * w), + mp.Vector3(wvg_xcen + 0.5 * w, wvg_ycen - 0.5 * w), + mp.Vector3(wvg_xcen + 0.5 * w, 0.5 * sy), + mp.Vector3(wvg_xcen - 0.5 * w, 0.5 * sy), + mp.Vector3(wvg_xcen - 0.5 * w, wvg_ycen + 0.5 * w), + mp.Vector3(-0.5 * sx, wvg_ycen + 0.5 * w)] + + geometry = [mp.Prism(bend_vertices, height, material=mp.Medium(epsilon=12))] fcen = 0.15 df = 0.1 diff --git a/python/tests/test_cavity_arrayslice.py b/python/tests/test_cavity_arrayslice.py index 42f758b6f..a46d1db5c 100644 --- a/python/tests/test_cavity_arrayslice.py +++ b/python/tests/test_cavity_arrayslice.py @@ -27,11 +27,8 @@ def setUp(self): geometry = [blk] - for i in range(3): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) - - for i in range(3): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d / -2 - i))) + geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)) for i in range(3)) + geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / -2 - i)) for i in range(3)) sources = [mp.Source(mp.GaussianSource(0.25, fwidth=0.2), mp.Hz, mp.Vector3())] diff --git a/python/tests/test_chunk_balancer.py b/python/tests/test_chunk_balancer.py index 36a85f40c..00b0deebc 100644 --- a/python/tests/test_chunk_balancer.py +++ b/python/tests/test_chunk_balancer.py @@ -30,11 +30,8 @@ def __init__(self, *args, **kwargs): def _structure_get_chunk_owners(self): # Hacky workaround to make this test work on single-core systems - proc_ids = [] - for leaf in bpu.enumerate_leaf_nodes(self.chunk_layout): - # it seems that grid volumes are enumerated - # in the same order as enumerate_leaf_nodes() - proc_ids.append(leaf.proc_id) + proc_ids = [leaf.proc_id for leaf in bpu.enumerate_leaf_nodes(self.chunk_layout)] + return np.array(proc_ids) def init_sim(self): diff --git a/python/tests/test_conductivity.py b/python/tests/test_conductivity.py index 2aa9a7616..9441db99a 100644 --- a/python/tests/test_conductivity.py +++ b/python/tests/test_conductivity.py @@ -75,7 +75,8 @@ def test_conductivity(self): # compute the incident flux for a lossless waveguide incident_flux1, incident_flux2 = self.wvg_flux(res, 0.) self.assertAlmostEqual(incident_flux1/incident_flux2, 1., places=2) - print("incident_flux:, {} (measured), 1.0 (expected)".format(incident_flux2/incident_flux1)) + print(f"incident_flux:, {incident_flux2 / incident_flux1} (measured), 1.0 (expected)") + # compute the flux for a lossy waveguide att_coeff = 37.46 # dB/cm diff --git a/python/tests/test_cyl_ellipsoid.py b/python/tests/test_cyl_ellipsoid.py index 330cfb8da..8ea760d6c 100644 --- a/python/tests/test_cyl_ellipsoid.py +++ b/python/tests/test_cyl_ellipsoid.py @@ -44,7 +44,7 @@ def init(self): def print_stuff(sim_obj): v = mp.Vector3(4.13, 3.75, 0) p = self.sim.get_field_point(self.src_cmpt, v) - print("t, Ez: {} {}+{}i".format(self.sim.round_time(), p.real, p.imag)) + print(f"t, Ez: {self.sim.round_time()} {p.real}+{p.imag}i") self.print_stuff = print_stuff def run_simulation(self): diff --git a/python/tests/test_dft_fields.py b/python/tests/test_dft_fields.py index 3b190adbe..96b557c65 100644 --- a/python/tests/test_dft_fields.py +++ b/python/tests/test_dft_fields.py @@ -26,10 +26,8 @@ def init(self): cell = mp.Vector3(self.sxy, self.sxy) pml_layers = [mp.PML(self.dpml)] - geometry = [ - mp.Cylinder(r + w, material=mp.Medium(epsilon=n * n)), - mp.Cylinder(r, material=mp.vacuum), - ] + geometry = [mp.Cylinder(r + w, material=mp.Medium(epsilon=n**2)), mp.Cylinder(r, material=mp.vacuum)] + self.fcen = 0.118 self.df = 0.1 diff --git a/python/tests/test_dispersive_eigenmode.py b/python/tests/test_dispersive_eigenmode.py index 5fce1d96d..db9820ca0 100644 --- a/python/tests/test_dispersive_eigenmode.py +++ b/python/tests/test_dispersive_eigenmode.py @@ -66,8 +66,8 @@ def verify_output_and_slice(self,material,frequency): self.assertAlmostEqual(n,n_slice, places=4) # Check to make sure h5 output is working with frequency - filename = os.path.join(self.temp_dir, - sim.get_filename_prefix() + '-eps-000000.00.h5') + filename = os.path.join(self.temp_dir, f'{sim.get_filename_prefix()}-eps-000000.00.h5') + mp.output_epsilon(sim,frequency=frequency) n_h5 = 0 mp.all_wait() diff --git a/python/tests/test_divide_mpi_processes.py b/python/tests/test_divide_mpi_processes.py index 574f0e834..7a46ab8e8 100644 --- a/python/tests/test_divide_mpi_processes.py +++ b/python/tests/test_divide_mpi_processes.py @@ -19,7 +19,7 @@ def test_divide_parallel_processes(self): sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), center=mp.Vector3(), component=mp.Ez)] - + symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] @@ -29,7 +29,7 @@ def test_divide_parallel_processes(self): symmetries=symmetries, boundary_layers=pml_layers) - + flux_box = self.sim.add_flux(fcen, 0, 1, mp.FluxRegion(mp.Vector3(y=0.5*sxy), size=mp.Vector3(sxy)), mp.FluxRegion(mp.Vector3(y=-0.5*sxy), size=mp.Vector3(sxy), weight=-1), @@ -40,7 +40,7 @@ def test_divide_parallel_processes(self): self.sim.run(until_after_sources=30) tot_flux = mp.get_fluxes(flux_box)[0] - + tot_fluxes = mp.merge_subgroup_data(tot_flux) fcens = mp.merge_subgroup_data(fcen) diff --git a/python/tests/test_dump_load.py b/python/tests/test_dump_load.py index 744e0c4de..223c98ea6 100644 --- a/python/tests/test_dump_load.py +++ b/python/tests/test_dump_load.py @@ -21,12 +21,12 @@ class TestLoadDump(ApproxComparisonTestCase): fname = fname_base + '-ez-000200.00.h5' def setUp(self): - print("Running {}".format(self._testMethodName)) + print(f"Running {self._testMethodName}") @classmethod def setUpClass(cls): cls.temp_dir = mp.make_output_directory() - print("Saving temp files to dir: {}".format(cls.temp_dir)) + print(f"Saving temp files to dir: {cls.temp_dir}") @classmethod def tearDownClass(cls): diff --git a/python/tests/test_fragment_stats.py b/python/tests/test_fragment_stats.py index 61055ab22..497744820 100644 --- a/python/tests/test_fragment_stats.py +++ b/python/tests/test_fragment_stats.py @@ -3,16 +3,7 @@ def make_dft_vecs(flx_reg=None, n2f_reg=None, frc_reg=None, fldc=None, flds=None, fldw=None, fld_cmp=None): - dft_vecs = { - 'flux_regions': flx_reg, - 'n2f_regions': n2f_reg, - 'force_regions': frc_reg, - 'fields_center': fldc, - 'fields_size': flds, - 'fields_where': fldw, - 'fields_components': fld_cmp - } - return dft_vecs + return {'flux_regions': flx_reg, 'n2f_regions': n2f_reg, 'force_regions': frc_reg, 'fields_center': fldc, 'fields_size': flds, 'fields_where': fldw, 'fields_components': fld_cmp} def make_sim(cell, res, pml, dims, create_gv=True, k_point=False): @@ -62,9 +53,7 @@ def get_fragment_stats(self, block_size, cell_size, dims, box_center=mp.Vector3( center=dft_vecs['fields_center'], size=dft_vecs['fields_size']) gv = sim._create_grid_volume(False) - stats = sim._compute_fragment_stats(gv) - - return stats + return sim._compute_fragment_stats(gv) def _test_1d(self, sym, pml=[]): # A z=30 cell, with a size 10 block in the middle. diff --git a/python/tests/test_gaussianbeam.py b/python/tests/test_gaussianbeam.py index 862543e7e..acad564e1 100644 --- a/python/tests/test_gaussianbeam.py +++ b/python/tests/test_gaussianbeam.py @@ -49,7 +49,8 @@ def gaussian_beam(self, rot_angle): Ez_beam_x0 = Ez_cell[np.squeeze(idx_x)[0],np.squeeze(idx_y)[0]] frac = np.abs(Ez_beam_x0)**2 / np.amax(np.abs(Ez_cell)**2) - print("ratio of the Gaussian beam energy at the focus over the maximum beam energy for the entire cell: {}".format(frac)) + print(f"ratio of the Gaussian beam energy at the focus over the maximum beam energy for the entire cell: {frac}") + self.assertGreater(frac, 0.99) diff --git a/python/tests/test_get_epsilon_grid.py b/python/tests/test_get_epsilon_grid.py index 4e4d80822..b5b98191f 100644 --- a/python/tests/test_get_epsilon_grid.py +++ b/python/tests/test_get_epsilon_grid.py @@ -68,7 +68,7 @@ def test_get_epsilon_grid(self, pt, freq): np.array([0]), freq) eps_pt = self.sim.get_epsilon_point(pt, freq) - print("eps:, ({},{}), {}, {}".format(pt.x,pt.y,eps_grid,eps_pt)) + print(f"eps:, ({pt.x},{pt.y}), {eps_grid}, {eps_pt}") self.assertAlmostEqual(np.real(eps_grid), np.real(eps_pt), places=6) self.assertAlmostEqual(np.imag(eps_grid), np.imag(eps_pt), places=6) diff --git a/python/tests/test_holey_wvg_bands.py b/python/tests/test_holey_wvg_bands.py index 1e51bef6b..648c0c392 100644 --- a/python/tests/test_holey_wvg_bands.py +++ b/python/tests/test_holey_wvg_bands.py @@ -39,7 +39,7 @@ def test_run_k_points(self): (0.19335527231544278, 4.6649450258959025e-4) ] - self.assertTrue(any(l for l in all_freqs)) + self.assertTrue(any(all_freqs)) for (r, i), f in zip(expected, all_freqs[17:21][0]): self.assertAlmostEqual(r, f.real) self.assertAlmostEqual(i, f.imag) diff --git a/python/tests/test_holey_wvg_cavity.py b/python/tests/test_holey_wvg_cavity.py index a66998d28..4227074c6 100644 --- a/python/tests/test_holey_wvg_cavity.py +++ b/python/tests/test_holey_wvg_cavity.py @@ -26,9 +26,7 @@ def setUp(self): geometry = [blk] - for i in range(3): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) - + geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)) for i in range(3)) for i in range(3): geometry.append(mp.Cylinder(r, center=mp.Vector3(d / -2 - i))) diff --git a/python/tests/test_kdom.py b/python/tests/test_kdom.py index 0711831c1..f61618c3f 100644 --- a/python/tests/test_kdom.py +++ b/python/tests/test_kdom.py @@ -40,7 +40,7 @@ def run_kdom(self, theta, num_band): EigenmodeData = sim.get_eigenmode(fcen, mp.X, mp.Volume(center=mp.Vector3(0.3*sx,0,0), size=mp.Vector3(0,sy,0)), num_band, k, parity=eig_parity) kdom = EigenmodeData.kdom - + self.assertAlmostEqual(k.y,kdom.y,places=15) def test_kdom(self): diff --git a/python/tests/test_material_grid.py b/python/tests/test_material_grid.py index c50d0dff2..7ffa3e54c 100644 --- a/python/tests/test_material_grid.py +++ b/python/tests/test_material_grid.py @@ -9,133 +9,129 @@ def compute_transmittance(matgrid_symmetry=False): - resolution = 25 + resolution = 25 - cell_size = mp.Vector3(6,6,0) + cell_size = mp.Vector3(6,6,0) - boundary_layers = [mp.PML(thickness=1.0)] + boundary_layers = [mp.PML(thickness=1.0)] - matgrid_size = mp.Vector3(2,2,0) - matgrid_resolution = 2*resolution + matgrid_size = mp.Vector3(2,2,0) + matgrid_resolution = 2*resolution - Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) + Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) - # ensure reproducible results - rng = np.random.RandomState(2069588) + # ensure reproducible results + rng = np.random.RandomState(2069588) - w = rng.rand(Nx,Ny) - weights = 0.5 * (w + np.fliplr(w)) if not matgrid_symmetry else w + w = rng.rand(Nx,Ny) + weights = w if matgrid_symmetry else 0.5 * (w + np.fliplr(w)) - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mp.Medium(index=3.5), - weights=weights, - do_averaging=False, - grid_type='U_MEAN') + matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), + mp.air, + mp.Medium(index=3.5), + weights=weights, + do_averaging=False, + grid_type='U_MEAN') - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,1.0,mp.inf), - material=mp.Medium(index=3.5)), - mp.Block(center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), - material=matgrid)] + geometry = [mp.Block(center=mp.Vector3(), + size=mp.Vector3(mp.inf,1.0,mp.inf), + material=mp.Medium(index=3.5)), + mp.Block(center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), + material=matgrid)] - if matgrid_symmetry: - geometry.append(mp.Block(center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), - material=matgrid, - e2=mp.Vector3(y=-1))) + if matgrid_symmetry: + geometry.append(mp.Block(center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), + material=matgrid, + e2=mp.Vector3(y=-1))) - eig_parity = mp.ODD_Y + mp.EVEN_Z + eig_parity = mp.ODD_Y + mp.EVEN_Z - fcen = 0.65 - df = 0.2*fcen - sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df), - center=mp.Vector3(-2.0,0), - size=mp.Vector3(0,4.0), - eig_parity=eig_parity)] + fcen = 0.65 + df = 0.2*fcen + sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df), + center=mp.Vector3(-2.0,0), + size=mp.Vector3(0,4.0), + eig_parity=eig_parity)] - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - geometry=geometry) + sim = mp.Simulation(resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + geometry=geometry) - mode_mon = sim.add_flux(fcen, - 0, - 1, - mp.FluxRegion(center=mp.Vector3(2.0,0), - size=mp.Vector3(0,4.0))) + mode_mon = sim.add_flux(fcen, + 0, + 1, + mp.FluxRegion(center=mp.Vector3(2.0,0), + size=mp.Vector3(0,4.0))) - sim.run(until_after_sources=mp.stop_when_dft_decayed()) + sim.run(until_after_sources=mp.stop_when_dft_decayed()) - mode_coeff = sim.get_eigenmode_coefficients(mode_mon,[1],eig_parity).alpha[0,:,0][0] + mode_coeff = sim.get_eigenmode_coefficients(mode_mon,[1],eig_parity).alpha[0,:,0][0] - tran = np.power(np.abs(mode_coeff),2) - print('tran:, {}, {}'.format("sym" if matgrid_symmetry else "nosym", tran)) + tran = np.power(np.abs(mode_coeff),2) + print(f'tran:, {"sym" if matgrid_symmetry else "nosym"}, {tran}') - return tran + return tran def compute_resonant_mode_2d(res,default_mat=False): - cell_size = mp.Vector3(1,1,0) + cell_size = mp.Vector3(1,1,0) - rad = 0.301943 + rad = 0.301943 - fcen = 0.3 - df = 0.2*fcen - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Hz, - center=mp.Vector3(-0.1057,0.2094,0))] + fcen = 0.3 + df = 0.2*fcen + sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), + component=mp.Hz, + center=mp.Vector3(-0.1057,0.2094,0))] - k_point = mp.Vector3(0.3892,0.1597,0) + k_point = mp.Vector3(0.3892,0.1597,0) - matgrid_size = mp.Vector3(1,1,0) - matgrid_resolution = 1200 + matgrid_size = mp.Vector3(1,1,0) + matgrid_resolution = 1200 - # for a fixed resolution, compute the number of grid points - # necessary which are defined on the corners of the voxels - Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) + # for a fixed resolution, compute the number of grid points + # necessary which are defined on the corners of the voxels + Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) - x = np.linspace(-0.5*matgrid_size.x,0.5*matgrid_size.x,Nx) - y = np.linspace(-0.5*matgrid_size.y,0.5*matgrid_size.y,Ny) - xv, yv = np.meshgrid(x,y) - weights = np.sqrt(np.square(xv) + np.square(yv)) < rad - filtered_weights = gaussian_filter(weights, - sigma=3.0, - output=np.double) + x = np.linspace(-0.5*matgrid_size.x,0.5*matgrid_size.x,Nx) + y = np.linspace(-0.5*matgrid_size.y,0.5*matgrid_size.y,Ny) + xv, yv = np.meshgrid(x,y) + weights = np.sqrt(np.square(xv) + np.square(yv)) < rad + filtered_weights = gaussian_filter(weights, + sigma=3.0, + output=np.double) - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mp.Medium(index=3.5), - weights=filtered_weights, - do_averaging=True, - beta=1000, - eta=0.5) + matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), + mp.air, + mp.Medium(index=3.5), + weights=filtered_weights, + do_averaging=True, + beta=1000, + eta=0.5) - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), - material=matgrid)] + geometry = [mp.Block(center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), + material=matgrid)] - sim = mp.Simulation(resolution=res, - cell_size=cell_size, - default_material=matgrid if default_mat else mp.Medium(), - geometry=geometry if not default_mat else [], - sources=sources, - k_point=k_point) + sim = mp.Simulation(resolution=res, cell_size=cell_size, default_material=matgrid if default_mat else mp.Medium(), geometry=[] if default_mat else geometry, sources=sources, k_point=k_point) - h = mp.Harminv(mp.Hz, mp.Vector3(0.3718,-0.2076), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=200) - try: - for m in h.modes: - print("harminv:, {}, {}, {}".format(res,m.freq,m.Q)) - freq = h.modes[0].freq - except: - raise RuntimeError("No resonant modes found.") + h = mp.Harminv(mp.Hz, mp.Vector3(0.3718,-0.2076), fcen, df) + sim.run(mp.after_sources(h), + until_after_sources=200) + + try: + for m in h.modes: + print(f"harminv:, {res}, {m.freq}, {m.Q}") + freq = h.modes[0].freq + except: + raise RuntimeError("No resonant modes found.") - return freq + return freq def compute_resonant_mode_3d(use_matgrid=True): @@ -203,7 +199,7 @@ def compute_resonant_mode_3d(use_matgrid=True): try: for m in h.modes: - print("harminv:, {}, {}, {}".format(resolution,m.freq,m.Q)) + print(f"harminv:, {resolution}, {m.freq}, {m.Q}") freq = h.modes[0].freq except: raise RuntimeError("No resonant modes found.") diff --git a/python/tests/test_mode_coeffs.py b/python/tests/test_mode_coeffs.py index 076b6e530..8e42a73ee 100644 --- a/python/tests/test_mode_coeffs.py +++ b/python/tests/test_mode_coeffs.py @@ -95,7 +95,7 @@ def run_mode_coeffs(self, mode_num, kpoint_func, nf=1, resolution=15): self.sim = sim # test 1: coefficient of excited mode >> coeffs of all other modes - self.assertTrue(TestPassed, msg="cfrel: {}, cbrel: {}".format(cfrel, cbrel)) + self.assertTrue(TestPassed, msg=f"cfrel: {cfrel}, cbrel: {cbrel}") # test 2: |mode coeff|^2 = power self.assertAlmostEqual(mode_power / abs(c0**2), 1.0, places=1) @@ -182,8 +182,8 @@ def test_reciprocity_kpoint(self): direction=mp.NO_DIRECTION, kpoint_func=lambda f,n: mp.Vector3(-1,0,0)) - print("S11:, {}, {}".format(res_fwd.alpha[0,0,1],res_bwd.alpha[0,0,0])) - print("S21:, {}, {}".format(res_fwd.alpha[0,0,0],res_bwd.alpha[0,0,1])) + print(f"S11:, {res_fwd.alpha[0,0,1]}, {res_bwd.alpha[0,0,0]}") + print(f"S21:, {res_fwd.alpha[0,0,0]}, {res_bwd.alpha[0,0,1]}") # |S11|^2 self.assertAlmostEqual(abs(res_fwd.alpha[0,0,1])**2, abs(res_bwd.alpha[0,0,0])**2, places=4) @@ -235,8 +235,8 @@ def test_reciprocity_monitor(self): [1], eig_parity=mp.EVEN_Y+mp.ODD_Z) - print("S11:, {}".format(res_fwd.alpha[0,0,1])) - print("S21:, {}".format(res_fwd.alpha[0,0,0])) + print(f"S11:, {res_fwd.alpha[0,0,1]}") + print(f"S21:, {res_fwd.alpha[0,0,0]}") sim.reset_meep() @@ -267,8 +267,8 @@ def test_reciprocity_monitor(self): [1], eig_parity=mp.EVEN_Y+mp.ODD_Z) - print("S12:, {}".format(res_bwd.alpha[0,0,1])) - print("S22:, {}".format(res_bwd.alpha[0,0,0])) + print(f"S12:, {res_bwd.alpha[0,0,1]}") + print(f"S22:, {res_bwd.alpha[0,0,0]}") # |S21|^2 = |S12|^2 self.assertAlmostEqual(abs(res_fwd.alpha[0,0,0])**2 / abs(res_bwd.alpha[0,0,1])**2, 1.00, places=2) diff --git a/python/tests/test_mpb.py b/python/tests/test_mpb.py index e3242cfff..0ddb79fcf 100644 --- a/python/tests/test_mpb.py +++ b/python/tests/test_mpb.py @@ -167,10 +167,7 @@ def test_list_split(self): def test_first_brillouin_zone(self): ms = self.init_solver() - res = [] - for k in ms.k_points: - res.append(ms.first_brillouin_zone(k)) - + res = [ms.first_brillouin_zone(k) for k in ms.k_points] expected = [ mp.Vector3(0.0, 0.0, 0.0), mp.Vector3(0.10000000000000003, 0.0, 0.0), @@ -219,9 +216,9 @@ def check_fields_against_h5(self, ref_path, field, suffix=''): with h5py.File(ref_path, 'r') as ref: # Reshape the reference data into a component-wise 1d array like # [x1,y1,z1,x2,y2,z2,etc.] - ref_x = mp.complexarray(ref["x.r{}".format(suffix)][()], ref["x.i{}".format(suffix)][()]) - ref_y = mp.complexarray(ref["y.r{}".format(suffix)][()], ref["y.i{}".format(suffix)][()]) - ref_z = mp.complexarray(ref["z.r{}".format(suffix)][()], ref["z.i{}".format(suffix)][()]) + ref_x = mp.complexarray(ref[f"x.r{suffix}"][()], ref[f"x.i{suffix}"][()]) + ref_y = mp.complexarray(ref[f"y.r{suffix}"][()], ref[f"y.i{suffix}"][()]) + ref_z = mp.complexarray(ref[f"z.r{suffix}"][()], ref[f"z.i{suffix}"][()]) ref_arr = np.zeros(np.prod(field.shape), dtype=np.complex128) ref_arr[0::3] = ref_x.ravel() @@ -370,12 +367,12 @@ def test_run_tm(self): def _test_get_field(self, field): ms = self.init_solver() ms.run_te() - get_field_func = getattr(ms, "get_{}field".format(field)) - fix_phase_func = getattr(mpb, "fix_{}field_phase".format(field)) + get_field_func = getattr(ms, f"get_{field}field") + fix_phase_func = getattr(mpb, f"fix_{field}field_phase") fix_phase_func(ms, ms.num_bands) fields = get_field_func(ms.num_bands) - ref_fname = "tutorial-{}.k16.b08.te.h5".format(field) + ref_fname = f"tutorial-{field}.k16.b08.te.h5" ref_path = os.path.join(self.data_dir, ref_fname) self.check_fields_against_h5(ref_path, fields) @@ -400,7 +397,7 @@ def test_output_field_to_file(self): ms.run_te() ms.output_epsilon() - res_path = self.filename_prefix + '-epsilon.h5' + res_path = f'{self.filename_prefix}-epsilon.h5' self.compare_h5_files(data_path, res_path) def test_compute_field_energy(self): diff --git a/python/tests/test_prism.py b/python/tests/test_prism.py index a66f32b5c..534f866a2 100644 --- a/python/tests/test_prism.py +++ b/python/tests/test_prism.py @@ -11,13 +11,13 @@ def nonconvex_marching_squares(self,idx,npts): cell = mp.Vector3(10,10) data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - vertices_file = os.path.join(data_dir, 'nonconvex_prism_vertices{}.npz'.format(idx)) + vertices_file = os.path.join(data_dir, f'nonconvex_prism_vertices{idx}.npz') vertices_obj = np.load(vertices_file) ## prism verticies precomputed from analytic "blob" shape using ## marching squares algorithm of skimage.measure.find_contours ## ref: https://github.com/NanoComp/meep/pull/1142 - vertices_data = vertices_obj["N{}".format(npts)] + vertices_data = vertices_obj[f"N{npts}"] vertices = [mp.Vector3(v[0],v[1],0) for v in vertices_data] geometry = [mp.Prism(vertices, @@ -32,7 +32,7 @@ def nonconvex_marching_squares(self,idx,npts): prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) - print("epsilon-sum:, {} (prism-msq)".format(abs(prism_eps))) + print(f"epsilon-sum:, {abs(prism_eps)} (prism-msq)") return prism_eps @@ -49,7 +49,7 @@ def convex_marching_squares(self,npts): ## prism vertices precomputed for a circle of radius 1.0 using ## marching squares algorithm of skimage.measure.find_contours ## ref: https://github.com/NanoComp/meep/issues/1060 - vertices_data = vertices_obj["N{}".format(npts)] + vertices_data = vertices_obj[f"N{npts}"] vertices = [mp.Vector3(v[0],v[1],0) for v in vertices_data] geometry = [mp.Prism(vertices, @@ -79,7 +79,9 @@ def convex_marching_squares(self,npts): cyl_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) - print("epsilon-sum:, {} (prism-msq), {} (cylinder), {} (relative error)".format(abs(prism_eps),abs(cyl_eps),abs((prism_eps-cyl_eps)/cyl_eps))) + print( + f"epsilon-sum:, {abs(prism_eps)} (prism-msq), {abs(cyl_eps)} (cylinder), {abs((prism_eps-cyl_eps)/cyl_eps)} (relative error)" + ) return abs((prism_eps-cyl_eps)/cyl_eps) @@ -122,7 +124,9 @@ def convex_circle(self,npts,r,sym): cyl_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) - print("epsilon-sum:, {} (prism-cyl), {} (cylinder), {} (relative error)".format(abs(prism_eps),abs(cyl_eps),abs((prism_eps-cyl_eps)/cyl_eps))) + print( + f"epsilon-sum:, {abs(prism_eps)} (prism-cyl), {abs(cyl_eps)} (cylinder), {abs((prism_eps-cyl_eps)/cyl_eps)} (relative error)" + ) return abs((prism_eps-cyl_eps)/cyl_eps) @@ -142,7 +146,7 @@ def spiral_gds(self): prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) - print("epsilon-sum:, {} (prism-gds)".format(abs(prism_eps))) + print(f"epsilon-sum:, {abs(prism_eps)} (prism-gds)") return prism_eps diff --git a/python/tests/test_refl_angular.py b/python/tests/test_refl_angular.py index 0397051b4..1082fc5a4 100644 --- a/python/tests/test_refl_angular.py +++ b/python/tests/test_refl_angular.py @@ -34,11 +34,7 @@ def refl_angular(self, theta): # wavevector (in source medium); plane of incidence is XZ k = mp.Vector3(0,0,1).rotate(mp.Vector3(0,1,0),theta_r).scale(self.n1*self.fmin) - if theta == 0: - dimensions = 1 - else: - dimensions = 3 - + dimensions = 1 if theta == 0 else 3 cell_size = mp.Vector3(z=self.sz) pml_layers = [mp.PML(self.dpml)] @@ -97,10 +93,7 @@ def refl_angular(self, theta): Rs = -np.array(refl_flux)/np.array(empty_flux) - thetas = [] - for i in range(self.nfreq): - thetas.append(math.asin(k.x/(self.n1*freqs[i]))) - + thetas = [math.asin(k.x/(self.n1*freqs[i])) for i in range(self.nfreq)] return freqs, thetas, Rs @parameterized.parameterized.expand([(0,), (20.6,)]) diff --git a/python/tests/test_ring.py b/python/tests/test_ring.py index 7970f23dc..6472f3347 100644 --- a/python/tests/test_ring.py +++ b/python/tests/test_ring.py @@ -21,7 +21,7 @@ def init(self): dpml = 2 sxy = 2 * (r + w + pad + dpml) - dielectric = mp.Medium(epsilon=n * n) + dielectric = mp.Medium(epsilon=n**2) air = mp.Medium() c1 = mp.Cylinder(r + w, material=dielectric) diff --git a/python/tests/test_simulation.py b/python/tests/test_simulation.py index 24d183467..af69f0620 100644 --- a/python/tests/test_simulation.py +++ b/python/tests/test_simulation.py @@ -20,7 +20,7 @@ class TestSimulation(unittest.TestCase): fname = fname_base + '-ez-000200.00.h5' def setUp(self): - print("Running {}".format(self._testMethodName)) + print(f"Running {self._testMethodName}") @classmethod def setUpClass(cls): @@ -141,8 +141,8 @@ def test_at_time(self): sim.use_output_directory(self.temp_dir) sim.run(mp.at_time(100, mp.output_efield_z), until=200) - fname = os.path.join(self.temp_dir, - sim.get_filename_prefix() + '-ez-000100.00.h5') + fname = os.path.join(self.temp_dir, f'{sim.get_filename_prefix()}-ez-000100.00.h5') + self.assertTrue(os.path.exists(fname)) def test_after_sources_and_time(self): @@ -162,8 +162,8 @@ def test_with_prefix(self): sim.use_output_directory(self.temp_dir) sim.run(mp.with_prefix('test_prefix-', mp.at_end(mp.output_efield_z)), until=200) - fname = os.path.join(self.temp_dir, 'test_prefix-' + sim.get_filename_prefix() + - '-ez-000200.00.h5') + fname = os.path.join(self.temp_dir, (f'test_prefix-{sim.get_filename_prefix()}' + '-ez-000200.00.h5')) + self.assertTrue(os.path.exists(fname)) def test_extra_materials(self): @@ -598,7 +598,7 @@ def _check(med, expected, default=mp.Medium()): else: self.assertFalse(result) - print("Estimated memory usage: {}".format(sim.get_estimated_memory_usage())) + print(f"Estimated memory usage: {sim.get_estimated_memory_usage()}") def mat_func(p): return mp.Medium() diff --git a/python/tests/test_source.py b/python/tests/test_source.py index ce397adcc..5006dac8a 100644 --- a/python/tests/test_source.py +++ b/python/tests/test_source.py @@ -98,9 +98,7 @@ def test_custom_source(self): # Bump function def my_src_func(t): - if t > 0 and t < 2: - return math.exp(-1 / (1 - ((t - 1)**2))) - return 0j + return math.exp(-1 / (1 - ((t - 1)**2))) if t > 0 and t < 2 else 0j sources = [mp.Source(src=mp.CustomSource(src_func=my_src_func, end_time=100), component=mp.Ez, center=mp.Vector3(r + 0.1))] diff --git a/python/tests/test_special_kz.py b/python/tests/test_special_kz.py index 8c0e051b2..65f31fd2c 100644 --- a/python/tests/test_special_kz.py +++ b/python/tests/test_special_kz.py @@ -63,8 +63,7 @@ def refl_planar(self, theta, kz_2d): refl_flux = mp.get_fluxes(refl) - Rmeep = -refl_flux[0]/empty_flux[0] - return Rmeep + return -refl_flux[0]/empty_flux[0] def test_special_kz(self): diff --git a/python/timing_measurements.py b/python/timing_measurements.py index 08fc068b6..dd61e61e4 100644 --- a/python/timing_measurements.py +++ b/python/timing_measurements.py @@ -129,9 +129,8 @@ def new_from_simulation( Returns: the resulting `MeepTimingMeasurements` """ - measurements = {} - for name, timing_id in TIMING_MEASUREMENT_IDS.items(): - measurements[name] = sim.time_spent_on(timing_id).tolist() + measurements = {name: sim.time_spent_on(timing_id).tolist() for name, timing_id in TIMING_MEASUREMENT_IDS.items()} + return cls( measurements=measurements, elapsed_time=elapsed_time, diff --git a/python/verbosity_mgr.py b/python/verbosity_mgr.py index 5b9fcc686..86832498f 100644 --- a/python/verbosity_mgr.py +++ b/python/verbosity_mgr.py @@ -63,7 +63,7 @@ def _init(self): instance is created. """ self._master_verbosity = -1 - self._cvars = dict() + self._cvars = {} @classmethod def reset(cls): @@ -152,7 +152,7 @@ def __int__(self): return self.get() def __repr__(self): - return "Verbosity: level={}".format(self.get()) + return f"Verbosity: level={self.get()}" # Some comparison operators def __gt__(self, o): return self.get() > o diff --git a/python/visualization.py b/python/visualization.py index 4d6d1eb10..b1c7e6f64 100644 --- a/python/visualization.py +++ b/python/visualization.py @@ -91,8 +91,7 @@ def filter_dict(dict_to_filter, func_with_kwargs): # Python2 ... filter_keys = inspect.getargspec(func_with_kwargs)[0] - filtered_dict = {filter_key:dict_to_filter[filter_key] for filter_key in filter_keys if filter_key in dict_to_filter} - return filtered_dict + return {filter_key: dict_to_filter[filter_key] for filter_key in filter_keys if filter_key in dict_to_filter} # ------------------------------------------------------- # # Routines to add legends to plot @@ -108,15 +107,8 @@ def place_label(ax, label_text, x, y, centerx, centery, label_parameters=None): alpha = label_parameters['label_alpha'] color = label_parameters['label_color'] - if x > centerx: - xtext = -offset - else: - xtext = offset - if y > centery: - ytext = -offset - else: - ytext = offset - + xtext = -offset if x > centerx else offset + ytext = -offset if y > centery else offset ax.annotate(label_text, xy=(x, y), xytext=(xtext, ytext), textcoords='offset points', ha='center', va='bottom', bbox=dict(boxstyle='round,pad=0.2', fc=color, alpha=alpha), @@ -154,9 +146,8 @@ def intersect_volume_volume(volume1, volume2): L = np.max([L1,L2],axis=0) # For single points we have to check manually - if np.all(U-L == 0): - if (not volume1.pt_in_volume(Vector3(*U))) or (not volume2.pt_in_volume(Vector3(*U))): - return [] + if np.all(U - L == 0) and ((not volume1.pt_in_volume(Vector3(*U))) or (not volume2.pt_in_volume(Vector3(*U)))): + return [] # Check for two volumes that don't intersect if np.any(U-L < 0): @@ -166,9 +157,7 @@ def intersect_volume_volume(volume1, volume2): vertices = [] for x_vals in [L[0],U[0]]: for y_vals in [L[1],U[1]]: - for z_vals in [L[2],U[2]]: - vertices.append(Vector3(x_vals,y_vals,z_vals)) - + vertices.extend(Vector3(x_vals,y_vals,z_vals) for z_vals in [L[2],U[2]]) # Remove any duplicate points caused by coplanar lines vertices = [vertices[i] for i, x in enumerate(vertices) if x not in vertices[i+1:]] @@ -191,11 +180,10 @@ def get_2D_dimensions(sim, output_plane): plane_center, plane_size = (output_plane.center, output_plane.size) elif sim.output_volume: plane_center, plane_size = mp.get_center_and_size(sim.output_volume) + elif (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: + plane_center, plane_size = (sim.geometry_center+mp.Vector3(sim.cell_size.x/2), sim.cell_size) else: - if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: - plane_center, plane_size = (sim.geometry_center+mp.Vector3(sim.cell_size.x/2), sim.cell_size) - else: - plane_center, plane_size = (sim.geometry_center, sim.cell_size) + plane_center, plane_size = (sim.geometry_center, sim.cell_size) plane_volume = Volume(center=plane_center,size=plane_size) if plane_size.x != 0 and plane_size.y != 0 and plane_size.z != 0: @@ -389,11 +377,7 @@ def plot_eps(sim, ax, output_plane=None, eps_parameters=None, frequency=None): sim_size, sim.is_cylindrical) - if eps_parameters['resolution']: - grid_resolution = eps_parameters['resolution'] - else: - grid_resolution = sim.resolution - + grid_resolution = eps_parameters['resolution'] or sim.resolution Nx = int((xmax - xmin) * grid_resolution + 1) Ny = int((ymax - ymin) * grid_resolution + 1) Nz = int((zmax - zmin) * grid_resolution + 1) @@ -409,10 +393,8 @@ def plot_eps(sim, ax, output_plane=None, eps_parameters=None, frequency=None): elif sim_size.y == 0: # Plot x on x axis, z on y axis (XZ plane) extent = [xmin, xmax, zmin, zmax] - if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: - xlabel = 'R' - else: - xlabel = "X" + xlabel = 'R' if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical else "X" + ylabel = 'Z' xtics = np.linspace(xmin, xmax, Nx) ytics = np.array([sim_center.y]) @@ -586,51 +568,44 @@ def plot_fields(sim, ax=None, fields=None, output_plane=None, field_parameters=N else: field_parameters = dict(default_field_parameters, **field_parameters) - # user specifies a field component - if fields in [mp.Ex, mp.Ey, mp.Ez, mp.Er, mp.Ep, mp.Dx, mp.Dy, mp.Dz, mp.Hx, mp.Hy, mp.Hz]: - # Get domain measurements - sim_center, sim_size = get_2D_dimensions(sim, output_plane) + if fields not in [mp.Ex, mp.Ey, mp.Ez, mp.Er, mp.Ep, mp.Dx, mp.Dy, mp.Dz, mp.Hx, mp.Hy, mp.Hz]: + raise ValueError('Please specify a valid field component (mp.Ex, mp.Ey, ...') - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim_center, - sim_size, - sim.is_cylindrical) - if sim_size.x == 0: - # Plot y on x axis, z on y axis (YZ plane) - extent = [ymin, ymax, zmin, zmax] - xlabel = 'Y' - ylabel = 'Z' - elif sim_size.y == 0: - # Plot x on x axis, z on y axis (XZ plane) - extent = [xmin, xmax, zmin, zmax] - if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: - xlabel = 'R' - else: - xlabel = "X" - ylabel = 'Z' - elif sim_size.z == 0: - # Plot x on x axis, y on y axis (XY plane) - extent = [xmin, xmax, ymin, ymax] - xlabel = 'X' - ylabel = 'Y' - fields = sim.get_array(center=sim_center, size=sim_size, component=fields) - else: - raise ValueError('Please specify a valid field component (mp.Ex, mp.Ey, ...') + # Get domain measurements + sim_center, sim_size = get_2D_dimensions(sim, output_plane) + + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim_center, + sim_size, + sim.is_cylindrical) + if sim_size.x == 0: + # Plot y on x axis, z on y axis (YZ plane) + extent = [ymin, ymax, zmin, zmax] + xlabel = 'Y' + ylabel = 'Z' + elif sim_size.y == 0: + # Plot x on x axis, z on y axis (XZ plane) + extent = [xmin, xmax, zmin, zmax] + xlabel = 'R' if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical else "X" + ylabel = 'Z' + elif sim_size.z == 0: + # Plot x on x axis, y on y axis (XY plane) + extent = [xmin, xmax, ymin, ymax] + xlabel = 'X' + ylabel = 'Y' + fields = sim.get_array(center=sim_center, size=sim_size, component=fields) fields = field_parameters['post_process'](fields) if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: fields = np.flipud(fields) else: fields = np.rot90(fields) - # Either plot the field, or return the array - if ax: - if mp.am_master(): - ax.imshow(fields, extent=extent, **filter_dict(field_parameters,ax.imshow)) - return ax - else: + if not ax: return fields + if mp.am_master(): + ax.imshow(fields, extent=extent, **filter_dict(field_parameters,ax.imshow)) return ax def plot2D(sim, ax=None, output_plane=None, fields=None, labels=False, @@ -695,8 +670,7 @@ def plot3D(sim): ztics = np.linspace(zmin, zmax, Nz) eps_data = sim.get_epsilon_grid(xtics, ytics, ztics) - s = mlab.contour3d(eps_data, colormap="YlGnBu") - return s + return mlab.contour3d(eps_data, colormap="YlGnBu") def visualize_chunks(sim): if sim.structure is None: @@ -1025,7 +999,7 @@ def to_jshtml(self,fps): mode_dict = dict(once_checked='', loop_checked='', reflect_checked='') - mode_dict[self.default_mode + '_checked'] = 'checked' + mode_dict[f'{self.default_mode}_checked'] = 'checked' interval = 1000 // fps From 6af8b4cb4d321e0b72b301dc7851ecd7bcb8089a Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 16:10:12 +0200 Subject: [PATCH 02/12] format code with black --- python/adjoint/__init__.py | 2 +- python/adjoint/basis.py | 129 +- python/adjoint/connectivity.py | 206 +- python/adjoint/filter_source.py | 96 +- python/adjoint/filters.py | 241 +- python/adjoint/objective.py | 204 +- python/adjoint/optimization_problem.py | 195 +- python/adjoint/utils.py | 133 +- python/adjoint/wrapper.py | 52 +- python/binary_partition_utils.py | 88 +- python/chunk_balancer.py | 92 +- python/examples/3rd-harm-1d.ipynb | 161 +- python/examples/3rd-harm-1d.py | 65 +- python/examples/absorbed_power_density.ipynb | 121 +- python/examples/absorbed_power_density.py | 130 +- python/examples/absorber-1d.py | 42 +- .../01-Introduction-checkpoint.ipynb | 158 +- .../02-Waveguide_Bend-checkpoint.ipynb | 120 +- ...3-Filtered_Waveguide_Bend-checkpoint.ipynb | 283 ++- .../04-Splitter-checkpoint.ipynb | 289 ++- .../05-Near2Far-checkpoint.ipynb | 273 ++- .../06-Near2Far-Epigraph-checkpoint.ipynb | 307 +-- .../Bend Minimax-checkpoint.ipynb | 299 ++- .../01-Introduction.ipynb | 143 +- .../02-Waveguide_Bend.ipynb | 120 +- .../03-Filtered_Waveguide_Bend.ipynb | 286 ++- .../adjoint_optimization/04-Splitter.ipynb | 294 ++- .../adjoint_optimization/05-Near2Far.ipynb | 201 +- .../06-Near2Far-Epigraph.ipynb | 307 +-- .../adjoint_optimization/Bend Minimax.ipynb | 302 ++- .../ConnectivityConstraint.ipynb | 50 +- .../adjoint_optimization/Fourier-Bend.ipynb | 253 ++- python/examples/antenna-radiation.ipynb | 121 +- python/examples/antenna-radiation.py | 145 +- python/examples/antenna_pec_ground_plane.py | 229 +- python/examples/bend-flux.ipynb | 104 +- python/examples/bend-flux.py | 114 +- python/examples/bent-waveguide.ipynb | 91 +- python/examples/bent-waveguide.py | 52 +- python/examples/binary_grating.ipynb | 314 +-- python/examples/binary_grating.py | 155 +- python/examples/binary_grating_n2f.ipynb | 243 +- python/examples/binary_grating_n2f.py | 249 +- python/examples/binary_grating_oblique.ipynb | 183 +- python/examples/binary_grating_oblique.py | 209 +- python/examples/binary_grating_phasemap.ipynb | 181 +- python/examples/binary_grating_phasemap.py | 267 ++- python/examples/cavity-farfield.ipynb | 94 +- python/examples/cavity-farfield.py | 144 +- python/examples/cavity_arrayslice.py | 18 +- python/examples/cherenkov-radiation.py | 40 +- python/examples/chirped_pulse.py | 56 +- python/examples/coupler.ipynb | 139 +- python/examples/coupler.py | 109 +- python/examples/cyl-ellipsoid.py | 32 +- python/examples/cylinder_cross_section.ipynb | 148 +- python/examples/cylinder_cross_section.py | 148 +- .../examples/differential_cross_section.ipynb | 177 +- python/examples/differential_cross_section.py | 193 +- python/examples/diffracted_planewave.py | 296 +-- python/examples/eps_fit_lorentzian.py | 134 +- python/examples/faraday-rotation.py | 80 +- python/examples/finite_grating.ipynb | 168 +- python/examples/finite_grating.py | 202 +- python/examples/gaussian-beam.py | 54 +- .../examples/grating2d_triangular_lattice.py | 128 +- python/examples/holey-wvg-bands.ipynb | 104 +- python/examples/holey-wvg-bands.py | 21 +- python/examples/holey-wvg-cavity.ipynb | 221 +- python/examples/holey-wvg-cavity.py | 123 +- python/examples/material-dispersion.py | 10 +- python/examples/metal-cavity-ldos.ipynb | 69 +- python/examples/metal-cavity-ldos.py | 60 +- python/examples/metasurface_lens.ipynb | 317 ++- python/examples/metasurface_lens.py | 346 +-- python/examples/mie_scattering.ipynb | 196 +- python/examples/mie_scattering.py | 198 +- python/examples/mode-decomposition.ipynb | 141 +- python/examples/mode-decomposition.py | 162 +- python/examples/mpb_bragg.py | 18 +- python/examples/mpb_bragg_sine.py | 12 +- python/examples/mpb_data_analysis.py | 23 +- python/examples/mpb_diamond.py | 27 +- python/examples/mpb_hole_slab.py | 24 +- python/examples/mpb_honey_rods.py | 37 +- python/examples/mpb_line_defect.py | 24 +- python/examples/mpb_sq_rods.py | 9 +- python/examples/mpb_strip.py | 37 +- python/examples/mpb_tri_holes.py | 19 +- python/examples/mpb_tri_rods.py | 26 +- python/examples/mpb_tutorial.py | 57 +- python/examples/multilevel-atom.py | 75 +- python/examples/oblique-planewave.ipynb | 54 +- python/examples/oblique-planewave.py | 61 +- python/examples/oblique-source.ipynb | 175 +- python/examples/oblique-source.py | 108 +- python/examples/parallel-wvgs-force.ipynb | 190 +- python/examples/parallel-wvgs-force.py | 111 +- python/examples/parallel-wvgs-mpb.py | 78 +- python/examples/perturbation_theory.ipynb | 202 +- python/examples/perturbation_theory.py | 207 +- python/examples/perturbation_theory_2d.py | 251 ++- python/examples/phase_in_material.py | 27 +- python/examples/planar_cavity_ldos.py | 266 ++- python/examples/polarization_grating.ipynb | 201 +- python/examples/polarization_grating.py | 208 +- python/examples/pw-source.py | 10 +- python/examples/refl-angular-kz2d.py | 101 +- python/examples/refl-angular.ipynb | 171 +- python/examples/refl-angular.py | 110 +- python/examples/refl-quartz.ipynb | 85 +- python/examples/refl-quartz.py | 85 +- python/examples/ring-cyl.py | 83 +- python/examples/ring-mode-overlap.py | 28 +- python/examples/ring.ipynb | 77 +- python/examples/ring.py | 48 +- python/examples/ring_gds.py | 128 +- python/examples/solve-cw.ipynb | 119 +- python/examples/solve-cw.py | 125 +- python/examples/stochastic_emitter.ipynb | 171 +- python/examples/stochastic_emitter.py | 135 +- python/examples/stochastic_emitter_line.py | 146 +- python/examples/straight-waveguide.ipynb | 38 +- python/examples/straight-waveguide.py | 46 +- python/examples/wvg-src.py | 36 +- python/examples/zone_plate.ipynb | 176 +- python/examples/zone_plate.py | 191 +- python/geom.py | 443 ++-- python/materials.py | 1578 ++++++++----- python/mpb_data.py | 137 +- python/simulation.py | 2005 +++++++++++------ python/solver.py | 359 +-- python/source.py | 166 +- python/tests/test_3rd_harm_1d.py | 39 +- python/tests/test_absorber_1d.py | 38 +- python/tests/test_adjoint_cyl.py | 150 +- python/tests/test_adjoint_jax.py | 168 +- python/tests/test_adjoint_solver.py | 634 +++--- python/tests/test_adjoint_utils.py | 43 +- python/tests/test_antenna_radiation.py | 363 +-- python/tests/test_array_metadata.py | 106 +- python/tests/test_bend_flux.py | 144 +- python/tests/test_binary_grating.py | 600 ++--- python/tests/test_binary_partition_utils.py | 381 ++-- python/tests/test_cavity_arrayslice.py | 39 +- python/tests/test_cavity_farfield.py | 88 +- python/tests/test_chunk_balancer.py | 231 +- python/tests/test_chunk_layout.py | 71 +- python/tests/test_chunks.py | 95 +- python/tests/test_conductivity.py | 117 +- python/tests/test_cyl_ellipsoid.py | 39 +- python/tests/test_dft_energy.py | 69 +- python/tests/test_dft_fields.py | 59 +- python/tests/test_diffracted_planewave.py | 284 +-- python/tests/test_dispersive_eigenmode.py | 101 +- python/tests/test_divide_mpi_processes.py | 70 +- python/tests/test_dump_load.py | 376 +++- python/tests/test_eigfreq.py | 64 +- python/tests/test_faraday_rotation.py | 98 +- python/tests/test_field_functions.py | 45 +- python/tests/test_force.py | 32 +- python/tests/test_fragment_stats.py | 297 ++- python/tests/test_gaussianbeam.py | 87 +- python/tests/test_geom.py | 253 ++- python/tests/test_get_epsilon_grid.py | 116 +- python/tests/test_get_point.py | 180 +- python/tests/test_holey_wvg_bands.py | 17 +- python/tests/test_holey_wvg_cavity.py | 59 +- python/tests/test_integrated_source.py | 31 +- python/tests/test_kdom.py | 79 +- python/tests/test_ldos.py | 270 ++- python/tests/test_material_dispersion.py | 11 +- python/tests/test_material_grid.py | 289 ++- python/tests/test_materials_library.py | 41 +- python/tests/test_medium_evaluations.py | 39 +- python/tests/test_mode_coeffs.py | 304 ++- python/tests/test_mode_decomposition.py | 740 +++--- python/tests/test_mpb.py | 1734 +++++++++----- python/tests/test_multilevel_atom.py | 40 +- python/tests/test_n2f_periodic.py | 106 +- python/tests/test_oblique_source.py | 82 +- python/tests/test_physical.py | 29 +- python/tests/test_prism.py | 385 ++-- python/tests/test_pw_source.py | 19 +- python/tests/test_refl_angular.py | 137 +- python/tests/test_ring.py | 20 +- python/tests/test_ring_cyl.py | 10 +- python/tests/test_simulation.py | 440 +++- python/tests/test_source.py | 166 +- python/tests/test_special_kz.py | 215 +- python/tests/test_timing_measurements.py | 9 +- python/tests/test_user_defined_material.py | 69 +- python/tests/test_verbosity_mgr.py | 22 +- python/tests/test_visualization.py | 297 ++- python/tests/test_wvg_src.py | 38 +- python/tests/utils.py | 5 +- python/timing_measurements.py | 119 +- python/verbosity_mgr.py | 41 +- python/visualization.py | 867 ++++--- 199 files changed, 21033 insertions(+), 13125 deletions(-) diff --git a/python/adjoint/__init__.py b/python/adjoint/__init__.py index 949d3d689..a787e31e7 100644 --- a/python/adjoint/__init__.py +++ b/python/adjoint/__init__.py @@ -10,7 +10,7 @@ from .basis import BilinearInterpolationBasis -from .optimization_problem import (OptimizationProblem) +from .optimization_problem import OptimizationProblem from .filter_source import FilteredSource diff --git a/python/adjoint/basis.py b/python/adjoint/basis.py index 937777d61..80ea14d64 100644 --- a/python/adjoint/basis.py +++ b/python/adjoint/basis.py @@ -3,23 +3,22 @@ from scipy import sparse from abc import ABCMeta, abstractmethod -ABC = ABCMeta('ABC', (object, ), {'__slots__': - ()}) # compatible with Python 2 and 3 +ABC = ABCMeta("ABC", (object,), {"__slots__": ()}) # compatible with Python 2 and 3 -#---------------------------------------------------------------------- +# ---------------------------------------------------------------------- # Basis is the abstract base class from which classes describing specific # basis sets should inherit. -#---------------------------------------------------------------------- +# ---------------------------------------------------------------------- class Basis(ABC): - """ - """ + """ """ + def __init__( - self, - rho_vector=None, - volume=None, - size=None, - center=mp.Vector3(), + self, + rho_vector=None, + volume=None, + size=None, + center=mp.Vector3(), ): self.volume = volume or mp.Volume(center=center, size=size) self.rho_vector = rho_vector @@ -32,13 +31,11 @@ def _f(p): @abstractmethod def get_basis_vjp(self): - raise NotImplementedError( - "derived class must implement get_basis_vjp method") + raise NotImplementedError("derived class must implement get_basis_vjp method") @abstractmethod def __call__(self, p=[0.0, 0.0]): - raise NotImplementedError( - "derived class must implement __call__() method") + raise NotImplementedError("derived class must implement __call__() method") def set_rho_vector(self, rho_vector): self.rho_vector = rho_vector @@ -50,9 +47,10 @@ def set_rho_vector(self, rho_vector): class BilinearInterpolationBasis(Basis): - ''' + """ Simple bilinear interpolation basis set. - ''' + """ + def __init__(self, resolution, symmetry=None, **kwargs): self.dim = 2 @@ -64,43 +62,60 @@ def __init__(self, resolution, symmetry=None, **kwargs): self.Nx = int(resolution * self.volume.size.x / 2) + 1 self.rho_x = np.linspace( self.volume.center.x, - self.volume.center.x + self.volume.size.x / 2, self.Nx) + self.volume.center.x + self.volume.size.x / 2, + self.Nx, + ) self.mirror_X = True else: self.Nx = int(resolution * self.volume.size.x) + 1 self.rho_x = np.linspace( self.volume.center.x - self.volume.size.x / 2, - self.volume.center.x + self.volume.size.x / 2, self.Nx) + self.volume.center.x + self.volume.size.x / 2, + self.Nx, + ) self.mirror_X = False if mp.Y in set(self.symmetry): self.Ny = int(resolution * self.volume.size.y / 2) + 1 self.rho_y = np.linspace( self.volume.center.y, - self.volume.center.y + self.volume.size.y / 2, self.Ny) + self.volume.center.y + self.volume.size.y / 2, + self.Ny, + ) self.mirror_Y = True else: self.Ny = int(resolution * self.volume.size.y) + 1 self.rho_y = np.linspace( self.volume.center.y - self.volume.size.y / 2, - self.volume.center.y + self.volume.size.y / 2, self.Ny) + self.volume.center.y + self.volume.size.y / 2, + self.Ny, + ) self.mirror_Y = False self.num_design_params = self.Nx * self.Ny if self.rho_vector is None: - self.rho_vector = np.ones((self.num_design_params, )) + self.rho_vector = np.ones((self.num_design_params,)) def __call__(self, p): - x = 2 * self.volume.center.x - p.x if self.mirror_X and p.x < self.volume.center.x else p.x - y = 2 * self.volume.center.y - p.y if self.mirror_Y and p.y < self.volume.center.y else p.y + x = ( + 2 * self.volume.center.x - p.x + if self.mirror_X and p.x < self.volume.center.x + else p.x + ) + y = ( + 2 * self.volume.center.y - p.y + if self.mirror_Y and p.y < self.volume.center.y + else p.y + ) weights, interp_idx = self.get_bilinear_row( - x, y, self.rho_x, self.rho_y) # ignore z coordinate + x, y, self.rho_x, self.rho_y + ) # ignore z coordinate return np.dot(self.rho_vector[interp_idx], weights) def get_basis_vjp(self, dJ_deps, design_grid): - ''' get vector jacobian product of interpolator''' + """get vector jacobian product of interpolator""" dg_Nx, dg_Ny, Nz, Nf = dJ_deps.shape # get important design grid dimensions x_grid = design_grid.x @@ -109,32 +124,37 @@ def get_basis_vjp(self, dJ_deps, design_grid): # take care of symmetries if self.mirror_X: - dJ_deps = dJ_deps[int(dg_Nx / 2):, :, :, :] * 2 - x_grid = x_grid[int(dg_Nx / 2):] + dJ_deps = dJ_deps[int(dg_Nx / 2) :, :, :, :] * 2 + x_grid = x_grid[int(dg_Nx / 2) :] if self.mirror_Y: - dJ_deps = dJ_deps[:, int(dg_Ny / 2):, :, :] * 2 - y_grid = y_grid[int(dg_Ny / 2):] + dJ_deps = dJ_deps[:, int(dg_Ny / 2) :, :, :] * 2 + y_grid = y_grid[int(dg_Ny / 2) :] dg_Nx, dg_Ny, Nz, Nf = dJ_deps.shape # recalculate - Nx, Ny = self.rho_x.size, self.rho_y.size # get important interpolator dimensions + Nx, Ny = ( + self.rho_x.size, + self.rho_y.size, + ) # get important interpolator dimensions # same interpolation matrix for all frequencies and all coordinates in Z direction - A = self.gen_interpolation_matrix(self.rho_x, self.rho_y, x_grid, - y_grid, z_grid) + A = self.gen_interpolation_matrix( + self.rho_x, self.rho_y, x_grid, y_grid, z_grid + ) # TODO ditch the for loops dJ_dp = np.zeros((Nx * Ny, Nf)) for fi in range(Nf): for zi in range(Nz): dJ_dp[:, fi] += np.matmul( - dJ_deps[:, :, zi, fi].reshape(dg_Nx * dg_Ny, order='C'), A) + dJ_deps[:, :, zi, fi].reshape(dg_Nx * dg_Ny, order="C"), A + ) return dJ_dp def get_bilinear_coefficients(self, x, x1, x2, y, y1, y2): - ''' + """ Calculates the bilinear interpolation coefficients for a single point at (x,y). Assumes that the user already knows the four closest points and provides the corresponding (x1,x2) and(y1,y2) coordinates. - ''' + """ b11 = ((x - x2) * (y - y2)) / ((x1 - x2) * (y1 - y2)) b12 = -((x - x2) * (y - y1)) / ((x1 - x2) * (y1 - y2)) b21 = -((x - x1) * (y - y2)) / ((x1 - x2) * (y1 - y2)) @@ -142,17 +162,17 @@ def get_bilinear_coefficients(self, x, x1, x2, y, y1, y2): return [b11, b12, b21, b22] def get_bilinear_row(self, rx, ry, rho_x, rho_y): - ''' + """ Calculates a vector of bilinear interpolation weights that can be used in an inner product with the neighboring function values, or placed inside of an interpolation matrix. - ''' + """ Nx = rho_x.size Ny = rho_y.size # binary search in x direction to get x1 and x2 - xi2 = np.searchsorted(rho_x, rx, side='left') + xi2 = np.searchsorted(rho_x, rx, side="left") if xi2 <= 0: # extrapolation (be careful!) xi1 = 0 xi2 = 1 @@ -166,7 +186,7 @@ def get_bilinear_row(self, rx, ry, rho_x, rho_y): x2 = rho_x[xi2] # binary search in y direction to get y1 and y2 - yi2 = np.searchsorted(rho_y, ry, side='left') + yi2 = np.searchsorted(rho_y, ry, side="left") if yi2 <= 0: # extrapolation (be careful!) yi1 = 0 yi2 = 1 @@ -183,10 +203,10 @@ def get_bilinear_row(self, rx, ry, rho_x, rho_y): weights = self.get_bilinear_coefficients(rx, x1, x2, ry, y1, y2) # get location of nearest neigbor interpolation points - interp_idx = np.array([ - xi1 * Ny + yi1, xi1 * Ny + yi2, (xi2) * Ny + yi1, (xi2) * Ny + yi2 - ], - dtype=np.int64) + interp_idx = np.array( + [xi1 * Ny + yi1, xi1 * Ny + yi2, (xi2) * Ny + yi1, (xi2) * Ny + yi2], + dtype=np.int64, + ) return weights, interp_idx @@ -198,7 +218,7 @@ def gen_interpolation_matrix( rho_y_interp, rho_z_interp, ): - ''' + """ Generates a bilinear interpolation matrix. Arguments: @@ -209,7 +229,7 @@ def gen_interpolation_matrix( Returns: A .................... [N,M] sparse matrix - interpolation matrix - ''' + """ Nx = rho_x.size Ny = rho_y.size @@ -228,15 +248,18 @@ def gen_interpolation_matrix( for rx in rho_x_interp: for ry in rho_y_interp: # get weights - weights, interp_idx = self.get_bilinear_row( - rx, ry, rho_x, rho_y) + weights, interp_idx = self.get_bilinear_row(rx, ry, rho_x, rho_y) # populate sparse matrix vectors - interp_weights[4 * ri:4 * (ri + 1)] = weights - row_ind[4 * ri:4 * (ri + 1)] = np.array([ri, ri, ri, ri], - dtype=np.int64) - col_ind[4 * ri:4 * (ri + 1)] = interp_idx + interp_weights[4 * ri : 4 * (ri + 1)] = weights + row_ind[4 * ri : 4 * (ri + 1)] = np.array( + [ri, ri, ri, ri], dtype=np.int64 + ) + col_ind[4 * ri : 4 * (ri + 1)] = interp_idx ri += 1 - return sparse.coo_matrix((interp_weights, (row_ind, col_ind)), shape=(output_dimension, input_dimension)) + return sparse.coo_matrix( + (interp_weights, (row_ind, col_ind)), + shape=(output_dimension, input_dimension), + ) diff --git a/python/adjoint/connectivity.py b/python/adjoint/connectivity.py index f3fe9a640..e8b631d79 100644 --- a/python/adjoint/connectivity.py +++ b/python/adjoint/connectivity.py @@ -9,77 +9,129 @@ from scipy.sparse.linalg import cg, spsolve from scipy.sparse import kron, diags, csr_matrix, eye, csc_matrix, lil_matrix + class ConnectivityConstraint(object): - def __init__(self, nx, ny, nz, k0=1000, zeta=0, sp_solver=cg, alpha=None, alpha0=None, thresh=0.1, p=2): - #zeta is to prevent singularity when damping is zero; with damping, zeta should be zero - #set ny=1 for 2D - self.nx, self.ny, self.nz= nx, ny, nz - self.n = nx*ny*nz - self.m = nx*ny*(nz-1) + def __init__( + self, + nx, + ny, + nz, + k0=1000, + zeta=0, + sp_solver=cg, + alpha=None, + alpha0=None, + thresh=0.1, + p=2, + ): + # zeta is to prevent singularity when damping is zero; with damping, zeta should be zero + # set ny=1 for 2D + self.nx, self.ny, self.nz = nx, ny, nz + self.n = nx * ny * nz + self.m = nx * ny * (nz - 1) self.solver = sp_solver self.k0, self.zeta = k0, zeta self.thresh = thresh self.p = p - #default alpha and alpha0 - self.alpha = alpha if alpha != None else 0.1*min(1/nx, 1/ny, 1/nz) - self.alpha0 = alpha0 if alpha0 != None else -np.log(thresh)/min(nx, nz) + # default alpha and alpha0 + self.alpha = alpha if alpha != None else 0.1 * min(1 / nx, 1 / ny, 1 / nz) + self.alpha0 = alpha0 if alpha0 != None else -np.log(thresh) / min(nx, nz) def forward(self, rho_vector): self.rho_vector = rho_vector # gradient and -div operator - gx = diags([-1,1], [0,1], shape=(self.nx-1, self.nx), format='csr') + gx = diags([-1, 1], [0, 1], shape=(self.nx - 1, self.nx), format="csr") dx = gx.copy().transpose() - gy = diags([-1,1], [0,1], shape=(self.ny-1, self.ny), format='csr') + gy = diags([-1, 1], [0, 1], shape=(self.ny - 1, self.ny), format="csr") dy = gy.copy().transpose() - gz = diags([1,-1], [0, -1], shape=(self.nz, self.nz), format='csr') - dz = diags([1,-1], [0, 1], shape=(self.nz-1, self.nz), format='csr') + gz = diags([1, -1], [0, -1], shape=(self.nz, self.nz), format="csr") + dz = diags([1, -1], [0, 1], shape=(self.nz - 1, self.nz), format="csr") # kron product for 2D - Ix, Iy, Iz = eye(self.nx), eye(self.ny), eye(self.nz-1) - self.gx, self.gy, self.gz = kron(Iz, kron(Iy, gx)), kron(Iz, kron(gy, Ix)), kron(gz, kron(Iy,Ix)) - self.dx, self.dy, self.dz = kron(Iz, kron(Iy, dx)), kron(Iz, kron(dy, Ix)), kron(dz, kron(Iy, Ix)) - - #conductivity based on rho + Ix, Iy, Iz = eye(self.nx), eye(self.ny), eye(self.nz - 1) + self.gx, self.gy, self.gz = ( + kron(Iz, kron(Iy, gx)), + kron(Iz, kron(gy, Ix)), + kron(gz, kron(Iy, Ix)), + ) + self.dx, self.dy, self.dz = ( + kron(Iz, kron(Iy, dx)), + kron(Iz, kron(dy, Ix)), + kron(dz, kron(Iy, Ix)), + ) + + # conductivity based on rho rho_pad = np.reshape(rho_vector, (self.nz, self.ny, self.nx)) - rhox = np.array([0.5*(rho_pad[k, j, i]+rho_pad[k, j, i+1]) for k in range(self.nz-1) for j in range(self.ny) for i in range(self.nx-1)]) + rhox = np.array( + [ + 0.5 * (rho_pad[k, j, i] + rho_pad[k, j, i + 1]) + for k in range(self.nz - 1) + for j in range(self.ny) + for i in range(self.nx - 1) + ] + ) self.rhox = rhox - rhoy = np.array([0.5*(rho_pad[k, j, i]+rho_pad[k, j+1, i]) for k in range(self.nz-1) for j in range(self.ny-1) for i in range(self.nx)]) + rhoy = np.array( + [ + 0.5 * (rho_pad[k, j, i] + rho_pad[k, j + 1, i]) + for k in range(self.nz - 1) + for j in range(self.ny - 1) + for i in range(self.nx) + ] + ) self.rhoy = rhoy - rhoz = np.array([0.5*(rho_pad[k, j, i]+rho_pad[k+1, j, i]) for k in range(self.nz-1) for j in range(self.ny) for i in range(self.nx)]) - rhoz = np.insert(rhoz, [0]*self.nx*self.ny, 0)#0 outside first row + rhoz = np.array( + [ + 0.5 * (rho_pad[k, j, i] + rho_pad[k + 1, j, i]) + for k in range(self.nz - 1) + for j in range(self.ny) + for i in range(self.nx) + ] + ) + rhoz = np.insert(rhoz, [0] * self.nx * self.ny, 0) # 0 outside first row self.rhoz = rhoz - kx, ky, kz = diags((self.zeta+(1-self.zeta)*rhox)*self.k0), diags((self.zeta+(1-self.zeta)*rhoy)*self.k0), diags((self.zeta+(1-self.zeta)*rhoz)*self.k0) + kx, ky, kz = ( + diags((self.zeta + (1 - self.zeta) * rhox) * self.k0), + diags((self.zeta + (1 - self.zeta) * rhoy) * self.k0), + diags((self.zeta + (1 - self.zeta) * rhoz) * self.k0), + ) self.kx, self.ky, self.kz = kx, ky, kz - #matrices in x, y, z - self.Lx, self.Ly, self.Lz = self.dx * kx * self.gx, self.dy * ky * self.gy, self.dz * kz * self.gz + # matrices in x, y, z + self.Lx, self.Ly, self.Lz = ( + self.dx * kx * self.gx, + self.dy * ky * self.gy, + self.dz * kz * self.gz, + ) # Dirichlet condition on the last row becomes term on the RHS - Bz = csc_matrix(self.Lz)[:,-self.nx*self.ny:] + Bz = csc_matrix(self.Lz)[:, -self.nx * self.ny :] rhs = -Bz.sum(axis=1) - self.rhs=rhs - - #LHS operator after moving the boundary term to the RHS - eq = self.Lz[:, :-self.nx*self.ny]+self.Lx+self.Ly - self.eq=eq - #add damping - damping = self.k0*self.alpha**2*diags(1-rho_vector[:-self.nx*self.ny]) + diags([self.alpha0**2], shape=(self.m, self.m)) + self.rhs = rhs + + # LHS operator after moving the boundary term to the RHS + eq = self.Lz[:, : -self.nx * self.ny] + self.Lx + self.Ly + self.eq = eq + # add damping + damping = self.k0 * self.alpha**2 * diags( + 1 - rho_vector[: -self.nx * self.ny] + ) + diags([self.alpha0**2], shape=(self.m, self.m)) self.A = eq + damping self.damping = damping if self.solver == spsolve: self.T = self.solver(csr_matrix(self.A), rhs) else: self.T, sinfo = self.solver(csr_matrix(self.A), rhs) - #exclude last row of rho and calculate weighted average of temperature - self.rho_vec = rho_vector[:-self.nx*self.ny] + # exclude last row of rho and calculate weighted average of temperature + self.rho_vec = rho_vector[: -self.nx * self.ny] - self.Td_p = (1 - self.T)**self.p - self.Td = (sum(self.Td_p * self.rho_vec)/sum(self.rho_vec))**(1/self.p) + self.Td_p = (1 - self.T) ** self.p + self.Td = (sum(self.Td_p * self.rho_vec) / sum(self.rho_vec)) ** (1 / self.p) return self.Td def adjoint(self): - T_p1 = -(self.T-1) ** (self.p-1) - dg_dT = self.Td**(1-self.p) * (T_p1*self.rho_vec)/sum(self.rho_vec) + T_p1 = -((self.T - 1) ** (self.p - 1)) + dg_dT = self.Td ** (1 - self.p) * (T_p1 * self.rho_vec) / sum(self.rho_vec) if self.solver == spsolve: return self.solver(csr_matrix(self.A.transpose()), dg_dT) aT, _ = self.solver(csr_matrix(self.A.transpose()), dg_dT) @@ -87,55 +139,75 @@ def adjoint(self): def calculate_grad(self): dg_dp = np.zeros(self.n) - dg_dp[:-self.nx*self.ny] = (self.Td_p*sum(self.rho_vec))/sum(self.rho_vec)**2 - dg_dp[:-self.nx*self.ny] = dg_dp[:-self.nx*self.ny] - sum(self.Td_p*self.rho_vec)/sum(self.rho_vec)**2 - dg_dp = self.Td ** (1-self.p) * dg_dp / self.p + dg_dp[: -self.nx * self.ny] = (self.Td_p * sum(self.rho_vec)) / sum( + self.rho_vec + ) ** 2 + dg_dp[: -self.nx * self.ny] = ( + dg_dp[: -self.nx * self.ny] + - sum(self.Td_p * self.rho_vec) / sum(self.rho_vec) ** 2 + ) + dg_dp = self.Td ** (1 - self.p) * dg_dp / self.p dAx = lil_matrix((self.m, self.n)) - gxT = np.reshape(self.gx * self.T, (-1,1)) - drhox = kron(eye(self.nz-1), kron(eye(self.ny), diags([0.5,0.5], [0, 1], shape=[self.nx-1,self.nx]))) - dAx[:, :-self.nx*self.ny] = (1-self.zeta)*self.k0*lil_matrix(self.dx * drhox.multiply(gxT)) #element-wise product + gxT = np.reshape(self.gx * self.T, (-1, 1)) + drhox = kron( + eye(self.nz - 1), + kron(eye(self.ny), diags([0.5, 0.5], [0, 1], shape=[self.nx - 1, self.nx])), + ) + dAx[:, : -self.nx * self.ny] = ( + (1 - self.zeta) * self.k0 * lil_matrix(self.dx * drhox.multiply(gxT)) + ) # element-wise product dAy = lil_matrix((self.m, self.n)) - gyT = np.reshape(self.gy * self.T, (-1,1)) - drhoy = kron(kron(eye(self.nz-1), diags([0.5,0.5], [0, 1], shape=[self.ny-1,self.ny])), eye(self.nx)) - dAy[:, :-self.nx*self.ny] = (1-self.zeta)*self.k0*lil_matrix(self.dy * drhoy.multiply(gyT)) #element-wise product - - Tz = np.pad(self.T, (0, self.nx*self.ny), 'constant', constant_values=1) - gzTz = np.reshape(self.gz * Tz, (-1,1)) - drhoz = diags([0.5,0.5], [0, -1], shape=[self.nz,self.nz], format="lil") - drhoz[0,0]=0 - drhoz = kron(drhoz,eye(self.nx*self.ny)) - dAz = (1-self.zeta)*self.k0*self.dz * drhoz.multiply(gzTz) - d_damping = self.k0*self.alpha**2*diags(-self.T, shape=(self.m, self.n)) - - self.grad = dg_dp + self.adjoint().reshape(1, -1) * csr_matrix(dAz + dAx + dAy + d_damping) + gyT = np.reshape(self.gy * self.T, (-1, 1)) + drhoy = kron( + kron( + eye(self.nz - 1), + diags([0.5, 0.5], [0, 1], shape=[self.ny - 1, self.ny]), + ), + eye(self.nx), + ) + dAy[:, : -self.nx * self.ny] = ( + (1 - self.zeta) * self.k0 * lil_matrix(self.dy * drhoy.multiply(gyT)) + ) # element-wise product + + Tz = np.pad(self.T, (0, self.nx * self.ny), "constant", constant_values=1) + gzTz = np.reshape(self.gz * Tz, (-1, 1)) + drhoz = diags([0.5, 0.5], [0, -1], shape=[self.nz, self.nz], format="lil") + drhoz[0, 0] = 0 + drhoz = kron(drhoz, eye(self.nx * self.ny)) + dAz = (1 - self.zeta) * self.k0 * self.dz * drhoz.multiply(gzTz) + d_damping = self.k0 * self.alpha**2 * diags(-self.T, shape=(self.m, self.n)) + + self.grad = dg_dp + self.adjoint().reshape(1, -1) * csr_matrix( + dAz + dAx + dAy + d_damping + ) return self.grad[0] def __call__(self, rho_vector): Td = self.forward(rho_vector) grad = self.calculate_grad() - return Td-self.thresh, grad + return Td - self.thresh, grad def calculate_fd_grad(self, rho_vector, num, db=1e-4): fdidx = np.random.choice(self.n, num) fdgrad = [] for k in fdidx: - rho_vector[k]+=db + rho_vector[k] += db fp = self.forward(rho_vector) - rho_vector[k]-=2*db + rho_vector[k] -= 2 * db fm = self.forward(rho_vector) - fdgrad.append((fp-fm)/(2*db)) - rho_vector[k]+=db + fdgrad.append((fp - fm) / (2 * db)) + rho_vector[k] += db return fdidx, fdgrad def calculate_all_fd_grad(self, rho_vector, db=1e-4): fdgrad = [] for k in range(self.n): - rho_vector[k]+=db + rho_vector[k] += db fp = self.forward(rho_vector) - rho_vector[k]-=2*db + rho_vector[k] -= 2 * db fm = self.forward(rho_vector) - fdgrad.append((fp-fm)/(2*db)) - rho_vector[k]+=db + fdgrad.append((fp - fm) / (2 * db)) + rho_vector[k] += db return range(self.n), np.array(fdgrad) diff --git a/python/adjoint/filter_source.py b/python/adjoint/filter_source.py index 6c82cff27..15d0928bd 100644 --- a/python/adjoint/filter_source.py +++ b/python/adjoint/filter_source.py @@ -32,23 +32,29 @@ def __init__( self.bf = [ lambda t, i=i: 0 - if t > self.T else (self.nuttall(t, self.center_frequencies) / - (self.dt / np.sqrt(2 * np.pi)))[i] + if t > self.T + else ( + self.nuttall(t, self.center_frequencies) + / (self.dt / np.sqrt(2 * np.pi)) + )[i] for i in range(len(self.center_frequencies)) ] self.time_src_bf = [ - CustomSource(src_func=bfi, - center_frequency=center_frequency, - is_integrated=False, - end_time=self.T, - fwidth=fwidth) for bfi in self.bf + CustomSource( + src_func=bfi, + center_frequency=center_frequency, + is_integrated=False, + end_time=self.T, + fwidth=fwidth, + ) + for bfi in self.bf ] if time_src: # get the cutoff of the input signal - signal_t = np.array([ - time_src.swigobj.current(ti, dt) for ti in self.t - ]) # time domain signal + signal_t = np.array( + [time_src.swigobj.current(ti, dt) for ti in self.t] + ) # time domain signal signal_dtft = self.dtft(signal_t, self.frequencies) else: signal_dtft = 1 @@ -60,38 +66,47 @@ def __init__( self.nodes, self.err = self.estimate_impulse_response(H) # initialize super - super(FilteredSource, self).__init__(src_func=f, - center_frequency=center_frequency, - is_integrated=False, - end_time=self.T, - fwidth=fwidth) + super(FilteredSource, self).__init__( + src_func=f, + center_frequency=center_frequency, + is_integrated=False, + end_time=self.T, + fwidth=fwidth, + ) def cos_window_td(self, a, t, f0): - cos_sum = np.sum([(-1)**k * a[k] * np.cos(2 * np.pi * t * k / self.T) - for k in range(len(a))], - axis=0) + cos_sum = np.sum( + [ + (-1) ** k * a[k] * np.cos(2 * np.pi * t * k / self.T) + for k in range(len(a)) + ], + axis=0, + ) return np.exp(-1j * 2 * np.pi * f0 * t) * (cos_sum) def cos_window_fd(self, a, f, f0): df = 1 / (self.N * self.dt) cos_sum = a[0] * self.sinc(f, f0) for k in range(1, len(a)): - cos_sum += (-1)**k * a[k] / 2 * self.sinc(f, f0 - k * df) + ( - -1)**k * a[k] / 2 * self.sinc(f, f0 + k * df) + cos_sum += (-1) ** k * a[k] / 2 * self.sinc(f, f0 - k * df) + (-1) ** k * a[ + k + ] / 2 * self.sinc(f, f0 + k * df) return cos_sum def sinc(self, f, f0): - num = np.where(f == f0, self.N + 1, - (1 - np.exp(1j * (self.N + 1) * (2 * np.pi) * - (f - f0) * self.dt))) - den = np.where(f == f0, 1, - (1 - np.exp(1j * (2 * np.pi) * (f - f0) * self.dt))) + num = np.where( + f == f0, + self.N + 1, + (1 - np.exp(1j * (self.N + 1) * (2 * np.pi) * (f - f0) * self.dt)), + ) + den = np.where(f == f0, 1, (1 - np.exp(1j * (2 * np.pi) * (f - f0) * self.dt))) return num / den def rect(self, t, f0): n = np.rint((t) / self.dt) - return np.where(n.any() < 0.0 or n.any() > self.N, 0, - np.exp(-1j * 2 * np.pi * f0 * t)) + return np.where( + n.any() < 0.0 or n.any() > self.N, 0, np.exp(-1j * 2 * np.pi * f0 * t) + ) def hann(self, t, f0): a = [0.5, 0.5] @@ -115,24 +130,30 @@ def nuttall_dtft(self, f, f0): ## C / f^3 where C is a constant and f is the frequency def nuttall_bandwidth(self): tol = 1e-7 - fwidth = 1/(self.N * self.dt) - frq_inf = 10000*fwidth + fwidth = 1 / (self.N * self.dt) + frq_inf = 10000 * fwidth na_dtft = self.nuttall_dtft(frq_inf, 0) coeff = frq_inf**3 * np.abs(na_dtft) na_dtft_max = self.nuttall_dtft(0, 0) - bw = 2 * np.power(coeff / (tol * na_dtft_max), 1/3) + bw = 2 * np.power(coeff / (tol * na_dtft_max), 1 / 3) return bw.real def dtft(self, y, f): - return np.matmul( - np.exp(1j * 2 * np.pi * f[:, np.newaxis] * np.arange(y.size) * - self.dt), y) * self.dt / np.sqrt(2 * np.pi) + return ( + np.matmul( + np.exp(1j * 2 * np.pi * f[:, np.newaxis] * np.arange(y.size) * self.dt), + y, + ) + * self.dt + / np.sqrt(2 * np.pi) + ) def __call__(self, t): if t > self.T: return 0 vec = self.nuttall(t, self.center_frequencies) / ( - self.dt / np.sqrt(2 * np.pi)) # compensate for meep dtft + self.dt / np.sqrt(2 * np.pi) + ) # compensate for meep dtft return np.inner(vec, self.nodes) def func(self): @@ -145,9 +166,10 @@ def estimate_impulse_response(self, H): # Use vandermonde matrix to calculate weights of each gaussian. Each window is centered at each frequency point. # TODO: come up with a more sophisticated way to choose temporal window size and basis locations # that will minimize l2 estimation error and the node weights (since matrix is ill-conditioned) - vandermonde = self.nuttall_dtft(self.frequencies[:, np.newaxis], - self.center_frequencies[np.newaxis, :]) + vandermonde = self.nuttall_dtft( + self.frequencies[:, np.newaxis], self.center_frequencies[np.newaxis, :] + ) nodes = np.matmul(linalg.pinv(vandermonde), H.T) H_hat = np.matmul(vandermonde, nodes) - l2_err = np.sum(np.abs(H - H_hat.T)**2 / np.abs(H)**2) + l2_err = np.sum(np.abs(H - H_hat.T) ** 2 / np.abs(H) ** 2) return nodes, l2_err diff --git a/python/adjoint/filters.py b/python/adjoint/filters.py index 703a1fc81..233269e81 100644 --- a/python/adjoint/filters.py +++ b/python/adjoint/filters.py @@ -8,24 +8,26 @@ from scipy import special from scipy import signal -def _proper_pad(x,n): - ''' + +def _proper_pad(x, n): + """ Parameters ---------- x : array_like (2D) Input array. Must be 2D. n : int Total size to be padded to. - ''' + """ N = x.size - k = n - (2*N-1) - return np.concatenate((x,np.zeros((k,)),np.flipud(x[1:]))) + k = n - (2 * N - 1) + return np.concatenate((x, np.zeros((k,)), np.flipud(x[1:]))) + def _centered(arr, newshape): - '''Helper function that reformats the padded array of the fft filter operation. + """Helper function that reformats the padded array of the fft filter operation. Borrowed from scipy: https://github.com/scipy/scipy/blob/v1.4.1/scipy/signal/signaltools.py#L263-L270 - ''' + """ # Return the center newshape portion of the array. newshape = np.asarray(newshape) currshape = np.array(arr.shape) @@ -34,6 +36,7 @@ def _centered(arr, newshape): myslice = [slice(startind[k], endind[k]) for k in range(len(endind))] return arr[tuple(myslice)] + def _edge_pad(arr, pad): # fill sides @@ -46,10 +49,17 @@ def _edge_pad(arr, pad): top_left = npa.tile(arr[0, 0], (pad[0][0], pad[1][0])) # top left top_right = npa.tile(arr[-1, 0], (pad[0][1], pad[1][0])) # top right bottom_left = npa.tile(arr[0, -1], (pad[0][0], pad[1][1])) # bottom left - bottom_right = npa.tile(arr[-1, -1], - (pad[0][1], pad[1][1])) # bottom right + bottom_right = npa.tile(arr[-1, -1], (pad[0][1], pad[1][1])) # bottom right + + return npa.concatenate( + ( + npa.concatenate((top_left, top, top_right)), + npa.concatenate((left, arr, right)), + npa.concatenate((bottom_left, bottom, bottom_right)), + ), + axis=1, + ) - return npa.concatenate((npa.concatenate((top_left, top, top_right)), npa.concatenate((left, arr, right)), npa.concatenate((bottom_left, bottom, bottom_right))), axis=1) def simple_2d_filter(x, h): """A simple 2d filter algorithm that is differentiable with autograd. @@ -71,11 +81,14 @@ def simple_2d_filter(x, h): The output of the 2d convolution. """ (kx, ky) = x.shape - x = _edge_pad(x,((kx, kx), (ky, ky))) - return _centered(npa.real(npa.fft.ifft2(npa.fft.fft2(x)*npa.fft.fft2(h))),(kx, ky)) + x = _edge_pad(x, ((kx, kx), (ky, ky))) + return _centered( + npa.real(npa.fft.ifft2(npa.fft.fft2(x) * npa.fft.fft2(h))), (kx, ky) + ) + def cylindrical_filter(x, radius, Lx, Ly, resolution): - '''A uniform cylindrical filter [1]. Typically allows for sharper transitions. + """A uniform cylindrical filter [1]. Typically allows for sharper transitions. Parameters ---------- @@ -99,19 +112,19 @@ def cylindrical_filter(x, radius, Lx, Ly, resolution): ---------- [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. - ''' - Nx = int(Lx*resolution) - Ny = int(Ly*resolution) - x = x.reshape(Nx, Ny) # Ensure the input is 2D + """ + Nx = int(Lx * resolution) + Ny = int(Ly * resolution) + x = x.reshape(Nx, Ny) # Ensure the input is 2D - xv = np.arange(0,Lx/2,1/resolution) - yv = np.arange(0,Ly/2,1/resolution) + xv = np.arange(0, Lx / 2, 1 / resolution) + yv = np.arange(0, Ly / 2, 1 / resolution) cylindrical = lambda a: np.where(a <= radius, 1, 0) hx = cylindrical(xv) hy = cylindrical(yv) - h = np.outer(_proper_pad(hx,3*Nx),_proper_pad(hy,3*Ny)) + h = np.outer(_proper_pad(hx, 3 * Nx), _proper_pad(hy, 3 * Ny)) # Normalize kernel h = h / np.sum(h.flatten()) # Normalize the filter @@ -121,7 +134,7 @@ def cylindrical_filter(x, radius, Lx, Ly, resolution): def conic_filter(x, radius, Lx, Ly, resolution): - '''A linear conic filter, also known as a "Hat" filter in the literature [1]. + """A linear conic filter, also known as a "Hat" filter in the literature [1]. Parameters ---------- @@ -145,19 +158,19 @@ def conic_filter(x, radius, Lx, Ly, resolution): ---------- [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. - ''' - Nx = int(Lx*resolution) - Ny = int(Ly*resolution) - x = x.reshape(Nx, Ny) # Ensure the input is 2D + """ + Nx = int(Lx * resolution) + Ny = int(Ly * resolution) + x = x.reshape(Nx, Ny) # Ensure the input is 2D - xv = np.arange(0,Lx/2,1/resolution) - yv = np.arange(0,Ly/2,1/resolution) + xv = np.arange(0, Lx / 2, 1 / resolution) + yv = np.arange(0, Ly / 2, 1 / resolution) conic = lambda a: np.where(np.abs(a**2) <= radius**2, (1 - a / radius), 0) hx = conic(xv) hy = conic(yv) - h = np.outer(_proper_pad(hx,3*Nx),_proper_pad(hy,3*Ny)) + h = np.outer(_proper_pad(hx, 3 * Nx), _proper_pad(hy, 3 * Ny)) # Normalize kernel h = h / np.sum(h.flatten()) # Normalize the filter @@ -167,7 +180,7 @@ def conic_filter(x, radius, Lx, Ly, resolution): def gaussian_filter(x, sigma, Lx, Ly, resolution): - '''A simple gaussian filter of the form exp(-x **2 / sigma ** 2) [1]. + """A simple gaussian filter of the form exp(-x **2 / sigma ** 2) [1]. Parameters ---------- @@ -191,19 +204,19 @@ def gaussian_filter(x, sigma, Lx, Ly, resolution): ---------- [1] Wang, E. W., Sell, D., Phan, T., & Fan, J. A. (2019). Robust design of topology-optimized metasurfaces. Optical Materials Express, 9(2), 469-482. - ''' - Nx = int(Lx*resolution) - Ny = int(Ly*resolution) - x = x.reshape(Nx, Ny) # Ensure the input is 2D + """ + Nx = int(Lx * resolution) + Ny = int(Ly * resolution) + x = x.reshape(Nx, Ny) # Ensure the input is 2D - xv = np.arange(0,Lx/2,1/resolution) - yv = np.arange(0,Ly/2,1/resolution) + xv = np.arange(0, Lx / 2, 1 / resolution) + yv = np.arange(0, Ly / 2, 1 / resolution) - gaussian = lambda a: np.exp(-a**2 / sigma**2) + gaussian = lambda a: np.exp(-(a**2) / sigma**2) hx = gaussian(xv) hy = gaussian(yv) - h = np.outer(_proper_pad(hx,3*Nx),_proper_pad(hy,3*Ny)) + h = np.outer(_proper_pad(hx, 3 * Nx), _proper_pad(hy, 3 * Ny)) # Normalize kernel h = h / np.sum(h.flatten()) # Normalize the filter @@ -211,14 +224,15 @@ def gaussian_filter(x, sigma, Lx, Ly, resolution): # Filter the response return simple_2d_filter(x, h) -''' + +""" # ------------------------------------------------------------------------------------ # Erosion and dilation operators -''' +""" def exponential_erosion(x, radius, beta, Lx, Ly, resolution): - ''' Performs and exponential erosion operation. + """Performs and exponential erosion operation. Parameters ---------- @@ -247,15 +261,18 @@ def exponential_erosion(x, radius, beta, Lx, Ly, resolution): [2] Schevenels, M., & Sigmund, O. (2016). On the implementation and effectiveness of morphological close-open and open-close filters for topology optimization. Structural and Multidisciplinary Optimization, 54(1), 15-21. - ''' + """ x_hat = npa.exp(beta * (1 - x)) - return 1 - npa.log( - cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten()) / beta + return ( + 1 + - npa.log(cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten()) + / beta + ) def exponential_dilation(x, radius, beta, Lx, Ly, resolution): - ''' Performs a exponential dilation operation. + """Performs a exponential dilation operation. Parameters ---------- @@ -284,15 +301,16 @@ def exponential_dilation(x, radius, beta, Lx, Ly, resolution): [2] Schevenels, M., & Sigmund, O. (2016). On the implementation and effectiveness of morphological close-open and open-close filters for topology optimization. Structural and Multidisciplinary Optimization, 54(1), 15-21. - ''' + """ x_hat = npa.exp(beta * x) - return npa.log( - cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten()) / beta + return ( + npa.log(cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten()) / beta + ) def heaviside_erosion(x, radius, beta, Lx, Ly, resolution): - ''' Performs a heaviside erosion operation. + """Performs a heaviside erosion operation. Parameters ---------- @@ -319,14 +337,14 @@ def heaviside_erosion(x, radius, beta, Lx, Ly, resolution): [1] Guest, J. K., Prévost, J. H., & Belytschko, T. (2004). Achieving minimum length scale in topology optimization using nodal design variables and projection functions. International journal for numerical methods in engineering, 61(2), 238-254. - ''' + """ x_hat = cylindrical_filter(x, radius, Lx, Ly, resolution).flatten() return npa.exp(-beta * (1 - x_hat)) + npa.exp(-beta) * (1 - x_hat) def heaviside_dilation(x, radius, beta, Lx, Ly, resolution): - ''' Performs a heaviside dilation operation. + """Performs a heaviside dilation operation. Parameters ---------- @@ -353,14 +371,14 @@ def heaviside_dilation(x, radius, beta, Lx, Ly, resolution): [1] Guest, J. K., Prévost, J. H., & Belytschko, T. (2004). Achieving minimum length scale in topology optimization using nodal design variables and projection functions. International journal for numerical methods in engineering, 61(2), 238-254. - ''' + """ x_hat = cylindrical_filter(x, radius, Lx, Ly, resolution).flatten() return 1 - npa.exp(-beta * x_hat) + npa.exp(-beta) * x_hat def geometric_erosion(x, radius, alpha, Lx, Ly, resolution): - ''' Performs a geometric erosion operation. + """Performs a geometric erosion operation. Parameters ---------- @@ -386,14 +404,15 @@ def geometric_erosion(x, radius, alpha, Lx, Ly, resolution): ---------- [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. - ''' + """ x_hat = npa.log(x + alpha) - return npa.exp(cylindrical_filter(x_hat, radius, Lx, Ly, - resolution)).flatten() - alpha + return ( + npa.exp(cylindrical_filter(x_hat, radius, Lx, Ly, resolution)).flatten() - alpha + ) def geometric_dilation(x, radius, alpha, Lx, Ly, resolution): - ''' Performs a geometric dilation operation. + """Performs a geometric dilation operation. Parameters ---------- @@ -419,15 +438,18 @@ def geometric_dilation(x, radius, alpha, Lx, Ly, resolution): ---------- [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. - ''' + """ x_hat = npa.log(1 - x + alpha) - return -npa.exp(cylindrical_filter(x_hat, radius, Lx, Ly, - resolution)).flatten() + alpha + 1 + return ( + -npa.exp(cylindrical_filter(x_hat, radius, Lx, Ly, resolution)).flatten() + + alpha + + 1 + ) def harmonic_erosion(x, radius, alpha, Lx, Ly, resolution): - ''' Performs a harmonic erosion operation. + """Performs a harmonic erosion operation. Parameters ---------- @@ -453,15 +475,14 @@ def harmonic_erosion(x, radius, alpha, Lx, Ly, resolution): ---------- [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. - ''' + """ x_hat = 1 / (x + alpha) - return 1 / cylindrical_filter(x_hat, radius, Lx, Ly, - resolution).flatten() - alpha + return 1 / cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten() - alpha def harmonic_dilation(x, radius, alpha, Lx, Ly, resolution): - ''' Performs a harmonic dilation operation. + """Performs a harmonic dilation operation. Parameters ---------- @@ -487,21 +508,22 @@ def harmonic_dilation(x, radius, alpha, Lx, Ly, resolution): ---------- [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. - ''' + """ x_hat = 1 / (1 - x + alpha) - return 1 - 1 / cylindrical_filter(x_hat, radius, Lx, Ly, - resolution).flatten() + alpha + return ( + 1 - 1 / cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten() + alpha + ) -''' +""" # ------------------------------------------------------------------------------------ # Projection filters -''' +""" def tanh_projection(x, beta, eta): - '''Projection filter that thresholds the input parameters between 0 and 1. Typically + """Projection filter that thresholds the input parameters between 0 and 1. Typically the "strongest" projection. Parameters @@ -521,16 +543,15 @@ def tanh_projection(x, beta, eta): ---------- [1] Wang, F., Lazarov, B. S., & Sigmund, O. (2011). On projection methods, convergence and robust formulations in topology optimization. Structural and Multidisciplinary Optimization, 43(6), 767-784. - ''' + """ - return (npa.tanh(beta * eta) + - npa.tanh(beta * - (x - eta))) / (npa.tanh(beta * eta) + npa.tanh(beta * - (1 - eta))) + return (npa.tanh(beta * eta) + npa.tanh(beta * (x - eta))) / ( + npa.tanh(beta * eta) + npa.tanh(beta * (1 - eta)) + ) def heaviside_projection(x, beta, eta): - '''Projection filter that thresholds the input parameters between 0 and 1. + """Projection filter that thresholds the input parameters between 0 and 1. Parameters ---------- @@ -550,22 +571,25 @@ def heaviside_projection(x, beta, eta): ---------- [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. - ''' + """ case1 = eta * npa.exp(-beta * (eta - x) / eta) - (eta - x) * npa.exp(-beta) - case2 = 1 - (1 - eta) * npa.exp(-beta * (x - eta) / - (1 - eta)) - (eta - x) * npa.exp(-beta) + case2 = ( + 1 + - (1 - eta) * npa.exp(-beta * (x - eta) / (1 - eta)) + - (eta - x) * npa.exp(-beta) + ) return npa.where(x < eta, case1, case2) -''' +""" # ------------------------------------------------------------------------------------ # Length scale operations -''' +""" def get_threshold_wang(delta, sigma): - '''Calculates the threshold point according to the gaussian filter radius (`sigma`) and + """Calculates the threshold point according to the gaussian filter radius (`sigma`) and the perturbation parameter (`sigma`) needed to ensure the proper length scale and morphological transformation according to Wang et. al. [2]. @@ -587,13 +611,13 @@ def get_threshold_wang(delta, sigma): photonic crystal waveguides with tailored dispersion properties. JOSA B, 28(3), 387-397. [2] Wang, E. W., Sell, D., Phan, T., & Fan, J. A. (2019). Robust design of topology-optimized metasurfaces. Optical Materials Express, 9(2), 469-482. - ''' + """ return 0.5 - special.erf(delta / sigma) def get_eta_from_conic(b, R): - ''' Extracts the eroded threshold point (`eta_e`) for a conic filter given the desired + """Extracts the eroded threshold point (`eta_e`) for a conic filter given the desired minimum length (`b`) and the filter radius (`R`). This only works for conic filters. Note that the units for `b` and `R` can be arbitrary so long as they are consistent. @@ -622,7 +646,7 @@ def get_eta_from_conic(b, R): Optimization, 43(6), 767-784. [3] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. - ''' + """ norm_length = b / R if norm_length < 0: @@ -668,11 +692,12 @@ def get_conic_radius_from_eta_e(b, eta_e): return b / (2 - 2 * np.sqrt(1 - eta_e)) else: raise ValueError( - "The erosion threshold point (eta_e) must be between 0.5 and 1.") + "The erosion threshold point (eta_e) must be between 0.5 and 1." + ) def indicator_solid(x, c, filter_f, threshold_f, resolution): - '''Calculates the indicator function for the void phase needed for minimum length optimization [1]. + """Calculates the indicator function for the void phase needed for minimum length optimization [1]. Parameters ---------- @@ -696,13 +721,14 @@ def indicator_solid(x, c, filter_f, threshold_f, resolution): ---------- [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. - ''' + """ filtered_field = filter_f(x) design_field = threshold_f(filtered_field) gradient_filtered_field = npa.gradient(filtered_field) - grad_mag = (gradient_filtered_field[0] * - resolution)**2 + (gradient_filtered_field[1] * resolution)**2 + grad_mag = (gradient_filtered_field[0] * resolution) ** 2 + ( + gradient_filtered_field[1] * resolution + ) ** 2 if grad_mag.ndim != 2: raise ValueError( "The gradient fields must be 2 dimensional. Check input array and filter functions." @@ -711,7 +737,7 @@ def indicator_solid(x, c, filter_f, threshold_f, resolution): def constraint_solid(x, c, eta_e, filter_f, threshold_f, resolution): - '''Calculates the constraint function of the solid phase needed for minimum length optimization [1]. + """Calculates the constraint function of the solid phase needed for minimum length optimization [1]. Parameters ---------- @@ -740,16 +766,17 @@ def constraint_solid(x, c, eta_e, filter_f, threshold_f, resolution): ---------- [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. - ''' + """ filtered_field = filter_f(x) - I_s = indicator_solid(x.reshape(filtered_field.shape), c, filter_f, - threshold_f, resolution).flatten() - return npa.mean(I_s * npa.minimum(filtered_field.flatten() - eta_e, 0)**2) + I_s = indicator_solid( + x.reshape(filtered_field.shape), c, filter_f, threshold_f, resolution + ).flatten() + return npa.mean(I_s * npa.minimum(filtered_field.flatten() - eta_e, 0) ** 2) def indicator_void(x, c, filter_f, threshold_f, resolution): - '''Calculates the indicator function for the void phase needed for minimum length optimization [1]. + """Calculates the indicator function for the void phase needed for minimum length optimization [1]. Parameters ---------- @@ -773,13 +800,14 @@ def indicator_void(x, c, filter_f, threshold_f, resolution): ---------- [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. - ''' + """ filtered_field = filter_f(x).reshape(x.shape) design_field = threshold_f(filtered_field) gradient_filtered_field = npa.gradient(filtered_field) - grad_mag = (gradient_filtered_field[0] * - resolution)**2 + (gradient_filtered_field[1] * resolution)**2 + grad_mag = (gradient_filtered_field[0] * resolution) ** 2 + ( + gradient_filtered_field[1] * resolution + ) ** 2 if grad_mag.ndim != 2: raise ValueError( "The gradient fields must be 2 dimensional. Check input array and filter functions." @@ -788,7 +816,7 @@ def indicator_void(x, c, filter_f, threshold_f, resolution): def constraint_void(x, c, eta_d, filter_f, threshold_f, resolution): - '''Calculates the constraint function of the void phase needed for minimum length optimization [1]. + """Calculates the constraint function of the void phase needed for minimum length optimization [1]. Parameters ---------- @@ -817,16 +845,17 @@ def constraint_void(x, c, eta_d, filter_f, threshold_f, resolution): ---------- [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. - ''' + """ filtered_field = filter_f(x) - I_v = indicator_void(x.reshape(filtered_field.shape), c, filter_f, - threshold_f, resolution).flatten() - return npa.mean(I_v * npa.minimum(eta_d - filtered_field.flatten(), 0)**2) + I_v = indicator_void( + x.reshape(filtered_field.shape), c, filter_f, threshold_f, resolution + ).flatten() + return npa.mean(I_v * npa.minimum(eta_d - filtered_field.flatten(), 0) ** 2) def gray_indicator(x): - '''Calculates a measure of "grayness" according to [1]. + """Calculates a measure of "grayness" according to [1]. Lower numbers ( < 2%) indicate a good amount of binarization [1]. @@ -844,5 +873,5 @@ def gray_indicator(x): ---------- [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. - ''' + """ return npa.mean(4 * x.flatten() * (1 - x.flatten())) * 100 diff --git a/python/adjoint/objective.py b/python/adjoint/objective.py index 6194a22d5..64d940b42 100644 --- a/python/adjoint/objective.py +++ b/python/adjoint/objective.py @@ -7,7 +7,8 @@ from meep.simulation import py_v3_to_vec from collections import namedtuple -Grid = namedtuple('Grid', ['x', 'y', 'z', 'w']) +Grid = namedtuple("Grid", ["x", "y", "z", "w"]) + class ObjectiveQuantity(abc.ABC): """A differentiable objective quantity. @@ -17,6 +18,7 @@ class ObjectiveQuantity(abc.ABC): frequencies: the frequencies at which the objective quantity is evaluated. num_freq: the number of frequencies at which the objective quantity is evaluated. """ + def __init__(self, sim): self.sim = sim self._eval = None @@ -48,7 +50,7 @@ def get_evaluation(self): return self._eval else: raise RuntimeError( - 'You must first run a forward simulation before requesting the evaluation of an objective quantity.' + "You must first run a forward simulation before requesting the evaluation of an objective quantity." ) def _adj_src_scale(self, include_resolution=True): @@ -68,23 +70,47 @@ def _adj_src_scale(self, include_resolution=True): ) # scaled frequency factor with discrete time derivative fix # an ugly way to calcuate the scaled dtft of the forward source - y = np.array([src.swigobj.current(t, dt) - for t in np.arange(0, T, dt)]) # time domain signal - fwd_dtft = np.matmul( - np.exp(1j * 2 * np.pi * self._frequencies[:, np.newaxis] * - np.arange(y.size) * dt), y) * dt / np.sqrt( - 2 * np.pi) # dtft + y = np.array( + [src.swigobj.current(t, dt) for t in np.arange(0, T, dt)] + ) # time domain signal + fwd_dtft = ( + np.matmul( + np.exp( + 1j + * 2 + * np.pi + * self._frequencies[:, np.newaxis] + * np.arange(y.size) + * dt + ), + y, + ) + * dt + / np.sqrt(2 * np.pi) + ) # dtft # Interestingly, the real parts of the DTFT and fourier transform match, but the imaginary parts are very different... - #fwd_dtft = src.fourier_transform(src.frequency) - ''' + # fwd_dtft = src.fourier_transform(src.frequency) + """ For some reason, there seems to be an additional phase factor at the center frequency that needs to be applied to *all* frequencies... - ''' - src_center_dtft = np.matmul( - np.exp(1j * 2 * np.pi * np.array([src.frequency])[:, np.newaxis] * - np.arange(y.size) * dt), y) * dt / np.sqrt(2 * np.pi) + """ + src_center_dtft = ( + np.matmul( + np.exp( + 1j + * 2 + * np.pi + * np.array([src.frequency])[:, np.newaxis] + * np.arange(y.size) + * dt + ), + y, + ) + * dt + / np.sqrt(2 * np.pi) + ) adj_src_phase = np.exp(1j * np.angle(src_center_dtft)) * self.fwidth_scale if self._frequencies.size == 1: @@ -109,10 +135,11 @@ def _create_time_profile(self, fwidth_frac=0.1, adj_cutoff=5): The user may specify a scalar valued objective function across multiple frequencies (e.g. MSE) in which case we should check that all the frequencies fit in the specified bandwidth. """ - self.fwidth_scale = np.exp(-2j*np.pi*adj_cutoff/fwidth_frac) + self.fwidth_scale = np.exp(-2j * np.pi * adj_cutoff / fwidth_frac) return mp.GaussianSource( np.mean(self._frequencies), - fwidth=fwidth_frac * np.mean(self._frequencies),cutoff=adj_cutoff + fwidth=fwidth_frac * np.mean(self._frequencies), + cutoff=adj_cutoff, ) @@ -129,20 +156,24 @@ class EigenmodeCoefficient(ObjectiveQuantity): specifying `kpoint_func`. When specified, this overrides the effect of `forward` and should have a value of either 0 or 1. """ - def __init__(self, - sim, - volume, - mode, - forward=True, - kpoint_func=None, - kpoint_func_overlap_idx=0, - decimation_factor=0, - **kwargs): + + def __init__( + self, + sim, + volume, + mode, + forward=True, + kpoint_func=None, + kpoint_func_overlap_idx=0, + decimation_factor=0, + **kwargs + ): super().__init__(sim) if kpoint_func_overlap_idx not in [0, 1]: raise ValueError( - '`kpoint_func_overlap_idx` should be either 0 or 1, but got %d' - % (kpoint_func_overlap_idx, )) + "`kpoint_func_overlap_idx` should be either 0 or 1, but got %d" + % (kpoint_func_overlap_idx,) + ) self.volume = volume self.mode = mode self.forward = forward @@ -174,13 +205,14 @@ def place_adjoint_source(self, dJ): if self.kpoint_func: eig_kpoint = -1 * self.kpoint_func(time_src.frequency, self.mode) else: - center_frequency = 0.5 * (np.min(self.frequencies) + np.max( - self.frequencies)) + center_frequency = 0.5 * ( + np.min(self.frequencies) + np.max(self.frequencies) + ) direction = mp.Vector3( - *(np.eye(3)[self._monitor.normal_direction] * - np.abs(center_frequency))) + *(np.eye(3)[self._monitor.normal_direction] * np.abs(center_frequency)) + ) eig_kpoint = -1 * direction if self.forward else direction - + if self._frequencies.size == 1: amp = da_dE * dJ * scale src = time_src @@ -211,10 +243,12 @@ def __call__(self): kpoint_func = self.kpoint_func overlap_idx = self.kpoint_func_overlap_idx else: - center_frequency = 0.5 * (np.min(self.frequencies) + np.max( - self.frequencies)) - kpoint = mp.Vector3(*(np.eye(3)[self._monitor.normal_direction] * - np.abs(center_frequency))) + center_frequency = 0.5 * ( + np.min(self.frequencies) + np.max(self.frequencies) + ) + kpoint = mp.Vector3( + *(np.eye(3)[self._monitor.normal_direction] * np.abs(center_frequency)) + ) kpoint_func = lambda *not_used: kpoint if self.forward else -1 * kpoint overlap_idx = 0 ob = self.sim.get_eigenmode_coefficients( @@ -242,22 +276,32 @@ def __init__(self, sim, volume, component, yee_grid=False, decimation_factor=0): def register_monitors(self, frequencies): self._frequencies = np.asarray(frequencies) self._monitor = self.sim.add_dft_fields( - [self.component], self._frequencies, where=self.volume, yee_grid=self.yee_grid, decimation_factor=self.decimation_factor) + [self.component], + self._frequencies, + where=self.volume, + yee_grid=self.yee_grid, + decimation_factor=self.decimation_factor, + ) return self._monitor def place_adjoint_source(self, dJ): time_src = self._create_time_profile() sources = [] - mon_size = self.sim.fields.dft_monitor_size(self._monitor.swigobj, self.volume.swigobj, self.component) + mon_size = self.sim.fields.dft_monitor_size( + self._monitor.swigobj, self.volume.swigobj, self.component + ) dJ = dJ.astype(np.complex128) - if np.prod(mon_size)*self.num_freq != dJ.size and np.prod(mon_size)*self.num_freq**2 != dJ.size: - raise ValueError('The format of J is incorrect!') + if ( + np.prod(mon_size) * self.num_freq != dJ.size + and np.prod(mon_size) * self.num_freq**2 != dJ.size + ): + raise ValueError("The format of J is incorrect!") # The objective function J is a vector. Each component corresponds to a frequency. - if np.prod(mon_size)*self.num_freq**2 == dJ.size and self.num_freq > 1: - dJ = np.sum(dJ,axis=1) - '''The adjoint solver requires the objective function + if np.prod(mon_size) * self.num_freq**2 == dJ.size and self.num_freq > 1: + dJ = np.sum(dJ, axis=1) + """The adjoint solver requires the objective function to be scalar valued with regard to objective arguments and position, but the function may be vector valued with regard to frequency. In this case, the Jacobian @@ -265,28 +309,45 @@ def place_adjoint_source(self, dJ): frequencies. Because of linearity, we can sum across the second frequency dimension to calculate a frequency scale factor for each point (rather than a scale vector). - ''' + """ - self.all_fouriersrcdata = self._monitor.swigobj.fourier_sourcedata(self.volume.swigobj, self.component, self.sim.fields, dJ) + self.all_fouriersrcdata = self._monitor.swigobj.fourier_sourcedata( + self.volume.swigobj, self.component, self.sim.fields, dJ + ) for fourier_data in self.all_fouriersrcdata: amp_arr = np.array(fourier_data.amp_arr).reshape(-1, self.num_freq) scale = amp_arr * self._adj_src_scale(include_resolution=False) if self.num_freq == 1: - sources += [mp.IndexedSource(time_src, fourier_data, scale[:,0], not self.yee_grid)] + sources += [ + mp.IndexedSource( + time_src, fourier_data, scale[:, 0], not self.yee_grid + ) + ] else: - src = FilteredSource(time_src.frequency,self._frequencies,scale,self.sim.fields.dt) + src = FilteredSource( + time_src.frequency, self._frequencies, scale, self.sim.fields.dt + ) (num_basis, num_pts) = src.nodes.shape for basis_i in range(num_basis): - sources += [mp.IndexedSource(src.time_src_bf[basis_i], fourier_data, src.nodes[basis_i], not self.yee_grid)] + sources += [ + mp.IndexedSource( + src.time_src_bf[basis_i], + fourier_data, + src.nodes[basis_i], + not self.yee_grid, + ) + ] return sources def __call__(self): - self._eval = np.array([ - self.sim.get_dft_array(self._monitor, self.component, i) - for i in range(self.num_freq) - ]) + self._eval = np.array( + [ + self.sim.get_dft_array(self._monitor, self.component, i) + for i in range(self.num_freq) + ] + ) return self._eval @@ -294,7 +355,7 @@ class Near2FarFields(ObjectiveQuantity): def __init__(self, sim, Near2FarRegions, far_pts, decimation_factor=0): super().__init__(sim) self.Near2FarRegions = Near2FarRegions - self.far_pts = far_pts #list of far pts + self.far_pts = far_pts # list of far pts self._nfar_pts = len(far_pts) self.decimation_factor = decimation_factor @@ -322,7 +383,8 @@ def place_adjoint_source(self, dJ): ) all_nearsrcdata = self._monitor.swigobj.near_sourcedata( - far_pt_vec, farpt_list, self._nfar_pts, dJ) + far_pt_vec, farpt_list, self._nfar_pts, dJ + ) for near_data in all_nearsrcdata: cur_comp = near_data.near_fd_comp amp_arr = np.array(near_data.amp_arr).reshape(-1, self.num_freq) @@ -349,12 +411,12 @@ def place_adjoint_source(self, dJ): return sources def __call__(self): - self._eval = np.array([ - self.sim.get_farfield(self._monitor, far_pt) - for far_pt in self.far_pts - ]).reshape((self._nfar_pts, self.num_freq, 6)) + self._eval = np.array( + [self.sim.get_farfield(self._monitor, far_pt) for far_pt in self.far_pts] + ).reshape((self._nfar_pts, self.num_freq, 6)) return self._eval - + + class LDOS(ObjectiveQuantity): def __init__(self, sim, decimation_factor=0, **kwargs): super().__init__(sim) @@ -372,7 +434,9 @@ def place_adjoint_source(self, dJ): dJ = np.sum(dJ, axis=1) dJ = dJ.flatten() sources = [] - forward_f_scale = np.array([self.ldos_scale/self.ldos_Jdata[k] for k in range(self.num_freq)]) + forward_f_scale = np.array( + [self.ldos_scale / self.ldos_Jdata[k] for k in range(self.num_freq)] + ) if self._frequencies.size == 1: amp = (dJ * self._adj_src_scale(False) * forward_f_scale)[0] src = time_src @@ -387,9 +451,25 @@ def place_adjoint_source(self, dJ): amp = 1 for forward_src_i in self.forward_src: if isinstance(forward_src_i, mp.EigenModeSource): - src_i = mp.EigenModeSource(src, component=forward_src_i.component, eig_kpoint = forward_src_i.eig_kpoint, amplitude=amp, eig_band=forward_src_i.eig_band, size = forward_src_i.size,center=forward_src_i.center, **self.srckwarg) + src_i = mp.EigenModeSource( + src, + component=forward_src_i.component, + eig_kpoint=forward_src_i.eig_kpoint, + amplitude=amp, + eig_band=forward_src_i.eig_band, + size=forward_src_i.size, + center=forward_src_i.center, + **self.srckwarg, + ) else: - src_i = mp.Source(src, component=forward_src_i.component, amplitude=amp, size = forward_src_i.size,center=forward_src_i.center, **self.srckwarg) + src_i = mp.Source( + src, + component=forward_src_i.component, + amplitude=amp, + size=forward_src_i.size, + center=forward_src_i.center, + **self.srckwarg, + ) if mp.is_electric(src_i.component): src_i.amplitude *= -1 sources += [src_i] diff --git a/python/adjoint/optimization_problem.py b/python/adjoint/optimization_problem.py index 2d3d7b57d..7b026171c 100644 --- a/python/adjoint/optimization_problem.py +++ b/python/adjoint/optimization_problem.py @@ -5,6 +5,7 @@ from . import utils, DesignRegion, LDOS + class OptimizationProblem(object): """Top-level class in the MEEP adjoint module. @@ -18,6 +19,7 @@ class OptimizationProblem(object): This is done by the __call__ method. """ + def __init__( self, simulation, @@ -32,7 +34,7 @@ def __init__( decimation_factor=0, minimum_run_time=0, maximum_run_time=None, - finite_difference_step=utils.FD_DEFAULT + finite_difference_step=utils.FD_DEFAULT, ): self.sim = simulation @@ -49,9 +51,7 @@ def __init__( else: self.design_regions = [design_regions] - self.num_design_params = [ - ni.num_design_params for ni in self.design_regions - ] + self.num_design_params = [ni.num_design_params for ni in self.design_regions] self.num_design_regions = len(self.design_regions) # TODO typecheck frequency choices @@ -79,15 +79,20 @@ def __init__( self.fcen_idx = int( np.argmin( np.abs( - np.asarray(self.frequencies) - - np.mean(np.asarray(self.frequencies)))** - 2)) # index of center frequency + np.asarray(self.frequencies) + - np.mean(np.asarray(self.frequencies)) + ) + ** 2 + ) + ) # index of center frequency self.decay_by = decay_by self.decimation_factor = decimation_factor self.minimum_run_time = minimum_run_time self.maximum_run_time = maximum_run_time - self.finite_difference_step = finite_difference_step # step size used in Aᵤ computation + self.finite_difference_step = ( + finite_difference_step # step size used in Aᵤ computation + ) # store sources for finite difference estimations self.forward_sources = self.sim.sources @@ -101,8 +106,7 @@ def __init__( self.gradient = [] def __call__(self, rho_vector=None, need_value=True, need_gradient=True, beta=None): - """Evaluate value and/or gradient of objective function. - """ + """Evaluate value and/or gradient of objective function.""" if rho_vector: self.update_design(rho_vector=rho_vector, beta=beta) @@ -127,7 +131,9 @@ def __call__(self, rho_vector=None, need_value=True, need_gradient=True, beta=No print("Calculating gradient...") self.calculate_gradient() else: - raise ValueError(f"Incorrect solver state detected: {self.current_state}") + raise ValueError( + f"Incorrect solver state detected: {self.current_state}" + ) return self.f0, self.gradient @@ -140,6 +146,7 @@ def get_fdf_funcs(self): f_func(beta) = objective function value for design variables beta df_func(beta) = objective function gradient for design variables beta """ + def _f(x=None): (fq, _) = self.__call__(rho_vector=x, need_gradient=False) return fq @@ -158,7 +165,9 @@ def prepare_forward_run(self): self.sim.change_sources(self.forward_sources) # register user specified monitors - self.forward_monitors = [m.register_monitors(self.frequencies) for m in self.objective_arguments] + self.forward_monitors = [ + m.register_monitors(self.frequencies) for m in self.objective_arguments + ] # register design region self.forward_design_region_monitors = utils.install_design_region_monitors( @@ -171,15 +180,18 @@ def forward_run(self): # Forward run if any(isinstance(m, LDOS) for m in self.objective_arguments): - self.sim.run(mp.dft_ldos(self.frequencies), until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time)) + self.sim.run( + mp.dft_ldos(self.frequencies), + until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, self.minimum_run_time, self.maximum_run_time + ), + ) else: - self.sim.run(until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time)) + self.sim.run( + until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, self.minimum_run_time, self.maximum_run_time + ) + ) # record objective quantities from user specified monitors self.results_list = [m() for m in self.objective_arguments] @@ -199,12 +211,12 @@ def prepare_adjoint_run(self): self.adjoint_sources = [[] for _ in range(len(self.objective_functions))] for ar in range(len(self.objective_functions)): for mi, m in enumerate(self.objective_arguments): - dJ = jacobian(self.objective_functions[ar], - mi)(*self.results_list) + dJ = jacobian(self.objective_functions[ar], mi)(*self.results_list) # get gradient of objective w.r.t. monitor if np.any(dJ): self.adjoint_sources[ar] += m.place_adjoint_source( - dJ) # place the appropriate adjoint sources + dJ + ) # place the appropriate adjoint sources def adjoint_run(self): # set up adjoint sources and monitors @@ -216,7 +228,7 @@ def adjoint_run(self): # flip the k point if self.sim.k_point: - self.sim.change_k_point(-1*self.sim.k_point) + self.sim.change_k_point(-1 * self.sim.k_point) self.adjoint_design_region_monitors = [] for ar in range(len(self.objective_functions)): @@ -228,46 +240,57 @@ def adjoint_run(self): self.sim.change_sources(self.adjoint_sources[ar]) # register design dft fields - self.adjoint_design_region_monitors.append(utils.install_design_region_monitors( - self.sim, self.design_regions, self.frequencies, self.decimation_factor - )) + self.adjoint_design_region_monitors.append( + utils.install_design_region_monitors( + self.sim, + self.design_regions, + self.frequencies, + self.decimation_factor, + ) + ) self.sim._evaluate_dft_objects() # Adjoint run - self.sim.run(until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time - )) + self.sim.run( + until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, self.minimum_run_time, self.maximum_run_time + ) + ) # reset the m number if utils._check_if_cylindrical(self.sim): self.sim.m = -self.sim.m # reset the k point - if self.sim.k_point: self.sim.k_point *= -1 + if self.sim.k_point: + self.sim.k_point *= -1 # update optimizer's state self.current_state = "ADJ" def calculate_gradient(self): # Iterate through all design regions and calculate gradient - self.gradient = [[ - dr.get_gradient( - self.sim, - self.adjoint_design_region_monitors[ar][dri], - self.forward_design_region_monitors[dri], - self.frequencies, - self.finite_difference_step - ) for dri, dr in enumerate(self.design_regions) - ] for ar in range(len(self.objective_functions))] + self.gradient = [ + [ + dr.get_gradient( + self.sim, + self.adjoint_design_region_monitors[ar][dri], + self.forward_design_region_monitors[dri], + self.frequencies, + self.finite_difference_step, + ) + for dri, dr in enumerate(self.design_regions) + ] + for ar in range(len(self.objective_functions)) + ] # Cleanup list of lists if len(self.gradient) == 1: self.gradient = self.gradient[0] # only one objective function if len(self.gradient) == 1: self.gradient = self.gradient[ - 0] # only one objective function and one design region + 0 + ] # only one objective function and one design region elif len(self.gradient[0]) == 1: self.gradient = [ g[0] for g in self.gradient @@ -282,7 +305,7 @@ def calculate_fd_gradient( design_variables_idx=0, filter=None, ): - ''' + """ Estimate central difference gradients. Parameters @@ -299,7 +322,7 @@ def calculate_fd_gradient( fd_gradient ... : lists [number of objective functions][number of gradients] - ''' + """ if filter is None: filter = lambda x: x if num_gradients < self.num_design_params[design_variables_idx]: @@ -316,7 +339,7 @@ def calculate_fd_gradient( "The requested number of gradients must be less than or equal to the total number of design parameters." ) - assert db < 0.2, "The step size of finite difference is too large." + assert db < 0.2, "The step size of finite difference is too large." # cleanup simulation object self.sim.reset_meep() @@ -327,9 +350,8 @@ def calculate_fd_gradient( for k in fd_gradient_idx: - b0 = np.ones((self.num_design_params[design_variables_idx], )) - b0[:] = (self.design_regions[design_variables_idx]. - design_parameters.weights) + b0 = np.ones((self.num_design_params[design_variables_idx],)) + b0[:] = self.design_regions[design_variables_idx].design_parameters.weights # -------------------------------------------- # # left function evaluation # -------------------------------------------- # @@ -337,22 +359,31 @@ def calculate_fd_gradient( # assign new design vector in_interior = True # b0[k] is not too close to the boundaries 0 and 1 - if b0[k] < db or b0[k]+db > 1: in_interior = False # b0[k] is too close to 0 or 1 + if b0[k] < db or b0[k] + db > 1: + in_interior = False # b0[k] is too close to 0 or 1 - if b0[k] >= db: b0[k] -= db - self.design_regions[design_variables_idx].update_design_parameters( - b0) + if b0[k] >= db: + b0[k] -= db + self.design_regions[design_variables_idx].update_design_parameters(b0) # initialize design monitors - self.forward_monitors = [m.register_monitors(self.frequencies) for m in self.objective_arguments] + self.forward_monitors = [ + m.register_monitors(self.frequencies) for m in self.objective_arguments + ] if any(isinstance(m, LDOS) for m in self.objective_arguments): - self.sim.run(mp.dft_ldos(self.frequencies), until_after_sources=mp.stop_when_energy_decayed(dt=1, decay_by=1e-11)) + self.sim.run( + mp.dft_ldos(self.frequencies), + until_after_sources=mp.stop_when_energy_decayed( + dt=1, decay_by=1e-11 + ), + ) else: - self.sim.run(until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time)) + self.sim.run( + until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, self.minimum_run_time, self.maximum_run_time + ) + ) # record final objective function value results_list = [m() for m in self.objective_arguments] @@ -365,20 +396,27 @@ def calculate_fd_gradient( # assign new design vector b0[k] += 2 * db if in_interior else db - self.design_regions[design_variables_idx].update_design_parameters( - b0) + self.design_regions[design_variables_idx].update_design_parameters(b0) # initialize design monitors - self.forward_monitors = [m.register_monitors(self.frequencies) for m in self.objective_arguments] + self.forward_monitors = [ + m.register_monitors(self.frequencies) for m in self.objective_arguments + ] # add monitor used to track dft convergence if any(isinstance(m, LDOS) for m in self.objective_arguments): - self.sim.run(mp.dft_ldos(self.frequencies), until_after_sources=mp.stop_when_energy_decayed(dt=1, decay_by=1e-11)) + self.sim.run( + mp.dft_ldos(self.frequencies), + until_after_sources=mp.stop_when_energy_decayed( + dt=1, decay_by=1e-11 + ), + ) else: - self.sim.run(until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time)) + self.sim.run( + until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, self.minimum_run_time, self.maximum_run_time + ) + ) # record final objective function value results_list = [m() for m in self.objective_arguments] @@ -387,10 +425,12 @@ def calculate_fd_gradient( # -------------------------------------------- # # estimate derivative # -------------------------------------------- # - fd_gradient.append([ - np.squeeze((fp[fi] - fm[fi]) / db / (2 if in_interior else 1)) - for fi in range(len(self.objective_functions)) - ]) + fd_gradient.append( + [ + np.squeeze((fp[fi] - fm[fi]) / db / (2 if in_interior else 1)) + for fi in range(len(self.objective_functions)) + ] + ) # Cleanup singleton dimensions if len(fd_gradient) == 1: @@ -416,14 +456,13 @@ def update_design(self, rho_vector, beta=None): self.current_state = "INIT" def get_objective_arguments(self): - '''Return list of evaluated objective arguments. - ''' + """Return list of evaluated objective arguments.""" return [m.get_evaluation() for m in self.objective_arguments] def plot2D(self, init_opt=False, **kwargs): """Produce a graphical visualization of the geometry and/or fields, - as appropriately autodetermined based on the current state of - progress. + as appropriately autodetermined based on the current state of + progress. """ if init_opt: @@ -431,9 +470,11 @@ def plot2D(self, init_opt=False, **kwargs): self.sim.plot2D(**kwargs) + def atleast_3d(*arys): from numpy import array, asanyarray, newaxis - ''' + + """ Modified version of numpy's `atleast_3d` Keeps one dimensional array data in first dimension, as @@ -455,7 +496,7 @@ def atleast_3d(*arys): returned. For example, a 1-D array of shape ``(N,)`` becomes a view of shape ``(N, 1, 1)``, and a 2-D array of shape ``(M, N)`` becomes a view of shape ``(M, N, 1)``. - ''' + """ res = [] for ary in arys: ary = asanyarray(ary) diff --git a/python/adjoint/utils.py b/python/adjoint/utils.py index 7d38a1bd4..0c6a461bc 100644 --- a/python/adjoint/utils.py +++ b/python/adjoint/utils.py @@ -21,14 +21,9 @@ # default finite difference step size when calculating Aᵤ FD_DEFAULT = 1e-3 + class DesignRegion(object): - def __init__( - self, - design_parameters, - volume=None, - size=None, - center=mp.Vector3() - ): + def __init__(self, design_parameters, volume=None, size=None, center=mp.Vector3()): self.volume = volume or mp.Volume(center=center, size=size) self.size = self.volume.size self.center = self.volume.center @@ -38,35 +33,54 @@ def __init__( def update_design_parameters(self, design_parameters): self.design_parameters.update_weights(design_parameters) - def update_beta(self,beta): - self.design_parameters.beta=beta + def update_beta(self, beta): + self.design_parameters.beta = beta - def get_gradient(self, sim, fields_a, fields_f, frequencies, finite_difference_step): + def get_gradient( + self, sim, fields_a, fields_f, frequencies, finite_difference_step + ): num_freqs = onp.array(frequencies).size - '''We have the option to linearly scale the gradients up front + """We have the option to linearly scale the gradients up front using the scalegrad parameter (leftover from MPB API). Not currently needed for any existing feature (but available for - future use)''' + future use)""" scalegrad = 1 grad = onp.zeros((num_freqs, self.num_design_params)) # preallocate vol = sim._fit_volume_to_simulation(self.volume) # compute the gradient - mp._get_gradient(grad,scalegrad, - fields_a[0].swigobj,fields_a[1].swigobj,fields_a[2].swigobj, - fields_f[0].swigobj,fields_f[1].swigobj,fields_f[2].swigobj, - sim.gv,vol.swigobj,onp.array(frequencies), - sim.geps,finite_difference_step) + mp._get_gradient( + grad, + scalegrad, + fields_a[0].swigobj, + fields_a[1].swigobj, + fields_a[2].swigobj, + fields_f[0].swigobj, + fields_f[1].swigobj, + fields_f[2].swigobj, + sim.gv, + vol.swigobj, + onp.array(frequencies), + sim.geps, + finite_difference_step, + ) return onp.squeeze(grad).T + def _check_if_cylindrical(sim): return sim.is_cylindrical or (sim.dimensions == mp.CYLINDRICAL) + def _compute_components(sim): - return _ADJOINT_FIELD_COMPONENTS_CYL if _check_if_cylindrical(sim) else _ADJOINT_FIELD_COMPONENTS + return ( + _ADJOINT_FIELD_COMPONENTS_CYL + if _check_if_cylindrical(sim) + else _ADJOINT_FIELD_COMPONENTS + ) + def _make_at_least_nd(x: onp.ndarray, dims: int = 3) -> onp.ndarray: """Makes an array have at least the specified number of dimensions.""" - return onp.reshape(x, x.shape + onp.maximum(dims - x.ndim, 0) * (1, )) + return onp.reshape(x, x.shape + onp.maximum(dims - x.ndim, 0) * (1,)) def calculate_vjps( @@ -77,7 +91,7 @@ def calculate_vjps( adj_fields: List[List[onp.ndarray]], design_variable_shapes: List[Tuple[int, ...]], sum_freq_partials: bool = True, - finite_difference_step: float = FD_DEFAULT + finite_difference_step: float = FD_DEFAULT, ) -> List[onp.ndarray]: """Calculates the VJP for a given set of forward and adjoint fields.""" vjps = [ @@ -87,7 +101,8 @@ def calculate_vjps( fwd_fields[i], frequencies, finite_difference_step, - ) for i, design_region in enumerate(design_regions) + ) + for i, design_region in enumerate(design_regions) ] if sum_freq_partials: vjps = [ @@ -96,7 +111,7 @@ def calculate_vjps( ] else: vjps = [ - vjp.reshape(shape + (-1, )) + vjp.reshape(shape + (-1,)) for vjp, shape in zip(vjps, design_variable_shapes) ] return vjps @@ -118,7 +133,20 @@ def install_design_region_monitors( decimation_factor: int = 0, ) -> List[mp.DftFields]: """Installs DFT field monitors at the design regions of the simulation.""" - return [[simulation.add_dft_fields([comp], frequencies, where=design_region.volume, yee_grid=True, decimation_factor=decimation_factor, persist=True) for comp in _compute_components(simulation)] for design_region in design_regions] + return [ + [ + simulation.add_dft_fields( + [comp], + frequencies, + where=design_region.volume, + yee_grid=True, + decimation_factor=decimation_factor, + persist=True, + ) + for comp in _compute_components(simulation) + ] + for design_region in design_regions + ] def gather_monitor_values(monitors: List[ObjectiveQuantity]) -> onp.ndarray: @@ -175,8 +203,8 @@ def gather_design_region_fields( def validate_and_update_design( - design_regions: List[DesignRegion], - design_variables: Iterable[onp.ndarray]) -> None: + design_regions: List[DesignRegion], design_variables: Iterable[onp.ndarray] +) -> None: """Validate the design regions and variables. In particular the design variable should be 1,2,3-D and the design region @@ -190,18 +218,24 @@ def validate_and_update_design( Raises: ValueError if the validation of dimensions fails. """ - for i, (design_region, - design_variable) in enumerate(zip(design_regions, - design_variables)): + for i, (design_region, design_variable) in enumerate( + zip(design_regions, design_variables) + ): if design_variable.ndim not in [1, 2, 3]: - raise ValueError(f'Design variables should be 1D, 2D, or 3D, but the design variable at index {i} had a shape of {design_variable.shape}.') + raise ValueError( + f"Design variables should be 1D, 2D, or 3D, but the design variable at index {i} had a shape of {design_variable.shape}." + ) design_region_shape = tuple( - int(x) for x in design_region.design_parameters.grid_size) - design_variable_padded_shape = design_variable.shape + (1, ) * ( - 3 - design_variable.ndim) + int(x) for x in design_region.design_parameters.grid_size + ) + design_variable_padded_shape = design_variable.shape + (1,) * ( + 3 - design_variable.ndim + ) if design_variable_padded_shape != design_region_shape: - raise ValueError(f'The design variable at index {i} with a shape of {design_variable.shape} is incompatible with the associated design region, which has a shape of {design_region_shape}.') + raise ValueError( + f"The design variable at index {i} with a shape of {design_variable.shape} is incompatible with the associated design region, which has a shape of {design_region_shape}." + ) design_variable = onp.asarray(design_variable, dtype=onp.float64) # Update the design variable in Meep @@ -209,25 +243,30 @@ def validate_and_update_design( def create_adjoint_sources( - monitors: Iterable[ObjectiveQuantity], - monitor_values_grad: onp.ndarray) -> List[mp.Source]: - monitor_values_grad = onp.asarray(monitor_values_grad, - dtype=onp.complex64 if mp.is_single_precision() else onp.complex128) + monitors: Iterable[ObjectiveQuantity], monitor_values_grad: onp.ndarray +) -> List[mp.Source]: + monitor_values_grad = onp.asarray( + monitor_values_grad, + dtype=onp.complex64 if mp.is_single_precision() else onp.complex128, + ) if not onp.any(monitor_values_grad): raise RuntimeError( - 'The gradient of all monitor values is zero, which ' - 'means that no adjoint sources can be placed to set ' - 'up an adjoint simulation in Meep. Possible causes ' - 'could be:\n\n' - ' * the forward simulation was not run for long enough ' - 'to allow the input pulse(s) to reach the monitors' - ' * the monitor values are disconnected from the ' - 'objective function output.') + "The gradient of all monitor values is zero, which " + "means that no adjoint sources can be placed to set " + "up an adjoint simulation in Meep. Possible causes " + "could be:\n\n" + " * the forward simulation was not run for long enough " + "to allow the input pulse(s) to reach the monitors" + " * the monitor values are disconnected from the " + "objective function output." + ) adjoint_sources = [] for monitor_idx, monitor in enumerate(monitors): # `dj` for each monitor will have a shape of (num frequencies,) - dj = onp.asarray(monitor_values_grad[monitor_idx], - dtype=onp.complex64 if mp.is_single_precision() else onp.complex128) + dj = onp.asarray( + monitor_values_grad[monitor_idx], + dtype=onp.complex64 if mp.is_single_precision() else onp.complex128, + ) if onp.any(dj): adjoint_sources += monitor.place_adjoint_source(dj) assert adjoint_sources diff --git a/python/adjoint/wrapper.py b/python/adjoint/wrapper.py index 8ecd38a3f..f3e5144bc 100644 --- a/python/adjoint/wrapper.py +++ b/python/adjoint/wrapper.py @@ -90,6 +90,7 @@ class MeepJaxWrapper: https://meep.readthedocs.io/en/latest/Python_User_Interface/#Simulation for more information. The default is true. """ + _log_fn = print def __init__( @@ -103,7 +104,7 @@ def __init__( minimum_run_time: float = 0, maximum_run_time: float = onp.inf, until_after_sources: bool = True, - finite_difference_step: float = utils.FD_DEFAULT + finite_difference_step: float = utils.FD_DEFAULT, ): self.simulation = simulation self.sources = sources @@ -150,23 +151,27 @@ def _run_fwd_simulation(self, design_variables): ) self.simulation.init_sim() sim_run_args = { - 'until_after_sources' if self.until_after_sources else 'until': - mp.stop_when_dft_decayed(self.dft_threshold,self.minimum_run_time,self.maximum_run_time) + "until_after_sources" + if self.until_after_sources + else "until": mp.stop_when_dft_decayed( + self.dft_threshold, self.minimum_run_time, self.maximum_run_time + ) } self.simulation.run(**sim_run_args) monitor_values = utils.gather_monitor_values(self.monitors) - return (jnp.asarray(monitor_values), - fwd_design_region_monitors) + return (jnp.asarray(monitor_values), fwd_design_region_monitors) def _run_adjoint_simulation(self, monitor_values_grad): """Runs adjoint simulation, returning design region fields.""" if not self.design_regions: raise RuntimeError( - 'An adjoint simulation was attempted when no design ' - 'regions are present.') - adjoint_sources = utils.create_adjoint_sources(self.monitors, - monitor_values_grad) + "An adjoint simulation was attempted when no design " + "regions are present." + ) + adjoint_sources = utils.create_adjoint_sources( + self.monitors, monitor_values_grad + ) # TODO refactor with optimization_problem.py # self.simulation.restart_fields() self.simulation.clear_dft_monitors() @@ -179,8 +184,11 @@ def _run_adjoint_simulation(self, monitor_values_grad): ) self.simulation.init_sim() sim_run_args = { - 'until_after_sources' if self.until_after_sources else 'until': - mp.stop_when_dft_decayed(self.dft_threshold,self.minimum_run_time,self.maximum_run_time) + "until_after_sources" + if self.until_after_sources + else "until": mp.stop_when_dft_decayed( + self.dft_threshold, self.minimum_run_time, self.maximum_run_time + ) } self.simulation.run(**sim_run_args) @@ -202,12 +210,12 @@ def _calculate_vjps( adj_fields, design_variable_shapes, sum_freq_partials=sum_freq_partials, - finite_difference_step=self.finite_difference_step + finite_difference_step=self.finite_difference_step, ) - def _initialize_callable( - self) -> Callable[[List[jnp.ndarray]], jnp.ndarray]: + def _initialize_callable(self) -> Callable[[List[jnp.ndarray]], jnp.ndarray]: """Initializes the callable JAX function and registers its VJP.""" + @jax.custom_vjp def simulate(design_variables: List[jnp.ndarray]) -> jnp.ndarray: monitor_values, _ = self._run_fwd_simulation(design_variables) @@ -216,17 +224,23 @@ def simulate(design_variables: List[jnp.ndarray]) -> jnp.ndarray: def _simulate_fwd(design_variables): """Runs forward simulation, returning monitor values and fields.""" monitor_values, self.fwd_design_region_monitors = self._run_fwd_simulation( - design_variables) + design_variables + ) design_variable_shapes = [x.shape for x in design_variables] return monitor_values, (design_variable_shapes) def _simulate_rev(res, monitor_values_grad): """Runs adjoint simulation, returning VJP of design wrt monitor values.""" design_variable_shapes = res - self.adj_design_region_monitors = self._run_adjoint_simulation(monitor_values_grad) - vjps = self._calculate_vjps(self.fwd_design_region_monitors, self.adj_design_region_monitors, - design_variable_shapes) - return ([jnp.asarray(vjp) for vjp in vjps], ) + self.adj_design_region_monitors = self._run_adjoint_simulation( + monitor_values_grad + ) + vjps = self._calculate_vjps( + self.fwd_design_region_monitors, + self.adj_design_region_monitors, + design_variable_shapes, + ) + return ([jnp.asarray(vjp) for vjp in vjps],) simulate.defvjp(_simulate_fwd, _simulate_rev) diff --git a/python/binary_partition_utils.py b/python/binary_partition_utils.py index 5fe73f492..fb3558a79 100644 --- a/python/binary_partition_utils.py +++ b/python/binary_partition_utils.py @@ -18,7 +18,8 @@ def is_leaf_node(partition: mp.BinaryPartition) -> bool: def enumerate_leaf_nodes( - partition: mp.BinaryPartition) -> Generator[mp.BinaryPartition, None, None]: + partition: mp.BinaryPartition, +) -> Generator[mp.BinaryPartition, None, None]: """Enumerates all leaf nodes of a partition. Args: @@ -47,8 +48,9 @@ def partition_has_duplicate_proc_ids(partition: mp.BinaryPartition) -> bool: return len(set(proc_ids)) != len(proc_ids) -def get_total_weight(partition: mp.BinaryPartition, - weights_by_proc_id: List[float]) -> float: +def get_total_weight( + partition: mp.BinaryPartition, weights_by_proc_id: List[float] +) -> float: """Computes the total weights contained within a BinaryPartition subtree. Args: @@ -62,7 +64,7 @@ def get_total_weight(partition: mp.BinaryPartition, ValueError: if sim.chunk_layout includes nodes with duplicate proc_ids """ if partition_has_duplicate_proc_ids(partition): - raise ValueError('Duplicate proc_ids found in chunk_layout!') + raise ValueError("Duplicate proc_ids found in chunk_layout!") if partition.proc_id is not None: return weights_by_proc_id[partition.proc_id] elif partition.left is not None and partition.right is not None: @@ -70,12 +72,12 @@ def get_total_weight(partition: mp.BinaryPartition, right_weight = get_total_weight(partition.right, weights_by_proc_id) return left_weight + right_weight else: - raise ValueError('Partition missing proc_id or left, right attributes!') + raise ValueError("Partition missing proc_id or left, right attributes!") def get_left_right_total_weights( - partition: mp.BinaryPartition, - weights_by_proc_id: List[float]) -> Tuple[float, float]: + partition: mp.BinaryPartition, weights_by_proc_id: List[float] +) -> Tuple[float, float]: """Computes the total weights contained in left and right subtrees. Args: @@ -94,7 +96,7 @@ def get_left_right_total_weights( right_weight = get_total_weight(partition.right, weights_by_proc_id) return (left_weight, right_weight) else: - raise ValueError('Partition missing left, right attributes!') + raise ValueError("Partition missing left, right attributes!") def pixel_volume(grid_volume: mp.grid_volume) -> int: @@ -114,9 +116,11 @@ def pixel_volume(grid_volume: mp.grid_volume) -> int: return grid_volume.nx() * grid_volume.ny() -def get_total_volume(partition: mp.BinaryPartition, - chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: onp.ndarray) -> float: +def get_total_volume( + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: onp.ndarray, +) -> float: """Computes the total pixel volume in a subtree from associated chunk volumes. NOTE: If multiple chunks are owned by the same process, this function may @@ -133,14 +137,15 @@ def get_total_volume(partition: mp.BinaryPartition, Returns: The total pixel volume occupied by all chunks owned by the partition. """ - my_grid_volumes = get_grid_volumes_in_tree(partition, chunk_volumes, - chunk_owners) + my_grid_volumes = get_grid_volumes_in_tree(partition, chunk_volumes, chunk_owners) return sum(pixel_volume(vol) for vol in my_grid_volumes) def get_left_right_total_volumes( - partition: mp.BinaryPartition, chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: onp.ndarray) -> Tuple[float, float]: + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: onp.ndarray, +) -> Tuple[float, float]: """Computes the total pixel volume in left and right subtrees. Args: @@ -159,16 +164,17 @@ def get_left_right_total_volumes( """ if partition.left is not None and partition.right is not None: left_volume = get_total_volume(partition.left, chunk_volumes, chunk_owners) - right_volume = get_total_volume(partition.right, chunk_volumes, - chunk_owners) + right_volume = get_total_volume(partition.right, chunk_volumes, chunk_owners) return (left_volume, right_volume) else: - raise ValueError('Partition missing left, right attributes!') + raise ValueError("Partition missing left, right attributes!") -def get_grid_volumes_in_tree(partition: mp.BinaryPartition, - chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: onp.ndarray) -> List[mp.grid_volume]: +def get_grid_volumes_in_tree( + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: onp.ndarray, +) -> List[mp.grid_volume]: """Fetches a list of grid_volumes contained in a BinaryPartition subtree. NOTE: If multiple chunks are owned by the same process, this function may @@ -187,7 +193,7 @@ def get_grid_volumes_in_tree(partition: mp.BinaryPartition, the partition. The list is not necessarily ordered by proc_id values. """ if partition_has_duplicate_proc_ids(partition): - warnings.warn('Partition has duplicate proc_ids, overcounting possible!') + warnings.warn("Partition has duplicate proc_ids, overcounting possible!") my_proc_ids = [node.proc_id for node in enumerate_leaf_nodes(partition)] @@ -198,9 +204,11 @@ def get_grid_volumes_in_tree(partition: mp.BinaryPartition, ] -def get_total_volume_per_process(partition: mp.BinaryPartition, - chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: onp.ndarray) -> Dict[int, float]: +def get_total_volume_per_process( + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: onp.ndarray, +) -> Dict[int, float]: """Computes the total pixel volume per process contained in a BinaryPartition. Args: @@ -217,15 +225,20 @@ def get_total_volume_per_process(partition: mp.BinaryPartition, volumes_per_process = {} leaf_nodes_in_tree = enumerate_leaf_nodes(partition) for leaf in leaf_nodes_in_tree: - total_volume = sum(pixel_volume(chunk_volumes[i]) for i, owner in enumerate(chunk_owners) if owner == leaf.proc_id) + total_volume = sum( + pixel_volume(chunk_volumes[i]) + for i, owner in enumerate(chunk_owners) + if owner == leaf.proc_id + ) volumes_per_process[leaf.proc_id] = total_volume return volumes_per_process def get_box_ranges( - partition: mp.BinaryPartition, chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: onp.ndarray + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: onp.ndarray, ) -> Tuple[float, float, float, float, float, float]: """Gets the max and min x, y, z dimensions spanned by a partition. @@ -268,12 +281,10 @@ def get_box_ranges( xmaxs.append(vol.surroundings().get_max_corner().x()) ymaxs.append(vol.surroundings().get_max_corner().y()) zmaxs.append(vol.surroundings().get_max_corner().z()) - return (min(xmins), max(xmaxs), min(ymins), max(ymaxs), min(zmins), - max(zmaxs)) + return (min(xmins), max(xmaxs), min(ymins), max(ymaxs), min(zmins), max(zmaxs)) -def partitions_are_equal(bp1: mp.BinaryPartition, - bp2: mp.BinaryPartition) -> bool: +def partitions_are_equal(bp1: mp.BinaryPartition, bp2: mp.BinaryPartition) -> bool: """Determines if two partitions have all nodes with identical attributes. Args: @@ -286,10 +297,13 @@ def partitions_are_equal(bp1: mp.BinaryPartition, if is_leaf_node(bp1) and is_leaf_node(bp2): return bp1.proc_id == bp2.proc_id elif (not is_leaf_node(bp1)) and (not is_leaf_node(bp2)): - return all([ - bp1.split_dir == bp2.split_dir, bp1.split_pos == bp2.split_pos, - partitions_are_equal(bp1.left, bp2.left), - partitions_are_equal(bp1.right, bp2.right) - ]) + return all( + [ + bp1.split_dir == bp2.split_dir, + bp1.split_pos == bp2.split_pos, + partitions_are_equal(bp1.left, bp2.left), + partitions_are_equal(bp1.right, bp2.right), + ] + ) else: return False diff --git a/python/chunk_balancer.py b/python/chunk_balancer.py index d0816fd06..fe92ab4b1 100644 --- a/python/chunk_balancer.py +++ b/python/chunk_balancer.py @@ -25,7 +25,8 @@ class AbstractChunkBalancer(abc.ABC): """ def make_initial_chunk_layout( - self, sim: mp.Simulation) -> Union[mp.BinaryPartition, None]: + self, sim: mp.Simulation + ) -> Union[mp.BinaryPartition, None]: """Generates an initial chunk layout based on expected compute costs. Args: @@ -66,21 +67,26 @@ def adjust_chunk_layout(self, sim: mp.Simulation, **kwargs) -> None: timing_measurements = MeepTimingMeasurements.new_from_simulation(sim, -1) - new_chunk_layout = self.compute_new_chunk_layout(timing_measurements, - old_chunk_layout, - chunk_volumes, - chunk_owners, **kwargs) + new_chunk_layout = self.compute_new_chunk_layout( + timing_measurements, + old_chunk_layout, + chunk_volumes, + chunk_owners, + **kwargs, + ) sim.reset_meep() sim.chunk_layout = new_chunk_layout sim.init_sim() @abc.abstractmethod - def compute_new_chunk_layout(self, - timing_measurements: MeepTimingMeasurements, - old_partition: mp.BinaryPartition, - chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: np.ndarray) -> mp.BinaryPartition: + def compute_new_chunk_layout( + self, + timing_measurements: MeepTimingMeasurements, + old_partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: np.ndarray, + ) -> mp.BinaryPartition: """Rebalances the partition to equalize simulation time for node's children. Args: @@ -123,14 +129,14 @@ def _validate_sim(self, sim: mp.Simulation): """ # Check that chunk_layout does not contain duplicate nodes for same process if bpu.partition_has_duplicate_proc_ids(sim.chunk_layout): - raise ValueError('Duplicate proc_ids found in chunk_layout!') + raise ValueError("Duplicate proc_ids found in chunk_layout!") # Check that proc_ids in chunk_layout are assigned properly to grid owners chunk_owners = sim.structure.get_chunk_owners() - proc_ids = [ - node.proc_id for node in bpu.enumerate_leaf_nodes(sim.chunk_layout) - ] + proc_ids = [node.proc_id for node in bpu.enumerate_leaf_nodes(sim.chunk_layout)] if set(chunk_owners) != set(proc_ids): - raise ValueError(f'Processes {set(proc_ids) - set(chunk_owners)} present in chunk_layout but not grid_owners!') + raise ValueError( + f"Processes {set(proc_ids) - set(chunk_owners)} present in chunk_layout but not grid_owners!" + ) class ChunkBalancer(AbstractChunkBalancer): @@ -154,16 +160,16 @@ class ChunkBalancer(AbstractChunkBalancer): """ def compute_new_chunk_layout( - self, - timing_measurements: MeepTimingMeasurements, - partition: mp.BinaryPartition, - chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: np.ndarray, - new_partition: Optional[mp.BinaryPartition] = None, - new_partition_root: Optional[mp.BinaryPartition] = None, - xyz_bounds: Optional[Tuple[float, float, float, float, float, - float]] = None, - sensitivity: float = 0.5) -> mp.BinaryPartition: + self, + timing_measurements: MeepTimingMeasurements, + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: np.ndarray, + new_partition: Optional[mp.BinaryPartition] = None, + new_partition_root: Optional[mp.BinaryPartition] = None, + xyz_bounds: Optional[Tuple[float, float, float, float, float, float]] = None, + sensitivity: float = 0.5, + ) -> mp.BinaryPartition: """Rebalances the partition to equalize simulation time for node's children. Args: @@ -202,17 +208,17 @@ def compute_new_chunk_layout( elif partition.split_dir == mp.Z: _, _, _, _, d_min, d_max = xyz_bounds else: - raise ValueError('Split direction must be mp.X, mp.Y, or mp.Z!') + raise ValueError("Split direction must be mp.X, mp.Y, or mp.Z!") - sim_times = list( - self._compute_working_times_per_process(timing_measurements)) + sim_times = list(self._compute_working_times_per_process(timing_measurements)) n_left = partition.left.numchunks() n_right = partition.right.numchunks() t_left, t_right = bpu.get_left_right_total_weights(partition, sim_times) - v_left, v_right = bpu.get_left_right_total_volumes(partition, chunk_volumes, - chunk_owners) + v_left, v_right = bpu.get_left_right_total_volumes( + partition, chunk_volumes, chunk_owners + ) v_left_new = v_left * t_right * n_left v_right_new = v_right * t_left * n_right @@ -221,8 +227,7 @@ def compute_new_chunk_layout( old_split_pos = partition.split_pos new_split_pos = (d_max - d_min) * split_frac + d_min - new_split_pos = sensitivity * new_split_pos + (1 - - sensitivity) * old_split_pos + new_split_pos = sensitivity * new_split_pos + (1 - sensitivity) * old_split_pos # Adjust the split pos on new_partition. We can't modify the old partition # because it could affect left/right volume ratios for subsequent calls @@ -248,7 +253,8 @@ def compute_new_chunk_layout( new_partition=new_partition.left, new_partition_root=new_partition_root, xyz_bounds=left_xyz_bounds, - sensitivity=sensitivity) + sensitivity=sensitivity, + ) self.compute_new_chunk_layout( timing_measurements, partition.right, @@ -257,24 +263,26 @@ def compute_new_chunk_layout( new_partition=new_partition.right, new_partition_root=new_partition_root, xyz_bounds=right_xyz_bounds, - sensitivity=sensitivity) + sensitivity=sensitivity, + ) return new_partition def _compute_working_times_per_process( - self, timing_measurements: MeepTimingMeasurements) -> np.ndarray: + self, timing_measurements: MeepTimingMeasurements + ) -> np.ndarray: """Computes the time spent by each MPI process actively working.""" time_sinks_to_include = [ - 'time_stepping', - 'boundaries_copying', - 'field_output', - 'fourier_transform', - 'mpb', - 'near_to_farfield_transform', + "time_stepping", + "boundaries_copying", + "field_output", + "fourier_transform", + "mpb", + "near_to_farfield_transform", ] - num_processes = len(timing_measurements.measurements['time_stepping']) + num_processes = len(timing_measurements.measurements["time_stepping"]) working_times = np.zeros(num_processes) for category in time_sinks_to_include: working_times += np.array(timing_measurements.measurements[category]) diff --git a/python/examples/3rd-harm-1d.ipynb b/python/examples/3rd-harm-1d.ipynb index 715a97e31..c4b7fd017 100644 --- a/python/examples/3rd-harm-1d.ipynb +++ b/python/examples/3rd-harm-1d.ipynb @@ -35,6 +35,7 @@ "import meep as mp\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", + "\n", "%matplotlib notebook" ] }, @@ -51,12 +52,12 @@ "metadata": {}, "outputs": [], "source": [ - "sz = 100 # size of cell in z direction\n", - "fcen = 1 / 3.0 # center frequency of source\n", - "df = fcen / 20.0 # frequency width of source\n", - "amp = 1 # amplitude of source\n", - "k = 10**-5 # Kerr susceptibility\n", - "dpml = 1.0 # PML thickness" + "sz = 100 # size of cell in z direction\n", + "fcen = 1 / 3.0 # center frequency of source\n", + "df = fcen / 20.0 # frequency width of source\n", + "amp = 1 # amplitude of source\n", + "k = 10**-5 # Kerr susceptibility\n", + "dpml = 1.0 # PML thickness" ] }, { @@ -109,8 +110,12 @@ "metadata": {}, "outputs": [], "source": [ - "sources = mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ex,\n", - " center=mp.Vector3(0, 0, -0.5*sz + dpml), amplitude=amp)" + "sources = mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ex,\n", + " center=mp.Vector3(0, 0, -0.5 * sz + dpml),\n", + " amplitude=amp,\n", + ")" ] }, { @@ -130,16 +135,22 @@ "fmin = fcen / 2.0\n", "fmax = fcen * 4\n", "\n", - "sim = mp.Simulation(cell_size=cell,\n", - " geometry=[],\n", - " sources=[sources],\n", - " boundary_layers=[pml_layers],\n", - " default_material=default_material,\n", - " resolution=resolution,\n", - " dimensions=dimensions)\n", + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=[],\n", + " sources=[sources],\n", + " boundary_layers=[pml_layers],\n", + " default_material=default_material,\n", + " resolution=resolution,\n", + " dimensions=dimensions,\n", + ")\n", "\n", - "trans = sim.add_flux(0.5 * (fmin + fmax), fmax - fmin, nfreq,\n", - " mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))" + "trans = sim.add_flux(\n", + " 0.5 * (fmin + fmax),\n", + " fmax - fmin,\n", + " nfreq,\n", + " mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5)),\n", + ")" ] }, { @@ -178,8 +189,11 @@ } ], "source": [ - "sim.run(until_after_sources=mp.stop_when_fields_decayed(\n", - " 50, mp.Ex, mp.Vector3(0, 0, 0.5*sz - dpml - 0.5), 1e-6))" + "sim.run(\n", + " until_after_sources=mp.stop_when_fields_decayed(\n", + " 50, mp.Ex, mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5), 1e-6\n", + " )\n", + ")" ] }, { @@ -212,10 +226,10 @@ "spectra = mp.get_fluxes(trans)\n", "\n", "plt.figure(dpi=150)\n", - "plt.semilogy(freqs,spectra)\n", + "plt.semilogy(freqs, spectra)\n", "plt.grid(True)\n", - "plt.xlabel('Frequency')\n", - "plt.ylabel('Transmitted Power (a.u.)')\n", + "plt.xlabel(\"Frequency\")\n", + "plt.ylabel(\"Transmitted Power (a.u.)\")\n", "plt.show()" ] }, @@ -234,40 +248,55 @@ "metadata": {}, "outputs": [], "source": [ - "def run_chi3(k_pow,amp=1):\n", - " k = 10**k_pow \n", + "def run_chi3(k_pow, amp=1):\n", + " k = 10**k_pow\n", " default_material = mp.Medium(index=1, chi3=k)\n", - " \n", - " sources = mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ex,\n", - " center=mp.Vector3(0, 0, -0.5*sz + dpml), amplitude=amp)\n", - " \n", - " sim = mp.Simulation(cell_size=cell,\n", - " geometry=[],\n", - " sources=[sources],\n", - " boundary_layers=[pml_layers],\n", - " default_material=default_material,\n", - " resolution=resolution,\n", - " dimensions=dimensions)\n", "\n", - " trans = sim.add_flux(0.5 * (fmin + fmax), fmax - fmin, nfreq,\n", - " mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))\n", - " \n", + " sources = mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ex,\n", + " center=mp.Vector3(0, 0, -0.5 * sz + dpml),\n", + " amplitude=amp,\n", + " )\n", + "\n", + " sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=[],\n", + " sources=[sources],\n", + " boundary_layers=[pml_layers],\n", + " default_material=default_material,\n", + " resolution=resolution,\n", + " dimensions=dimensions,\n", + " )\n", + "\n", + " trans = sim.add_flux(\n", + " 0.5 * (fmin + fmax),\n", + " fmax - fmin,\n", + " nfreq,\n", + " mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5)),\n", + " )\n", + "\n", " # Single frequency point at omega\n", - " trans1 = sim.add_flux(fcen, 0, 1,\n", - " mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))\n", + " trans1 = sim.add_flux(\n", + " fcen, 0, 1, mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5))\n", + " )\n", "\n", " # Singel frequency point at 3omega\n", - " trans3 = sim.add_flux(3 * fcen, 0, 1,\n", - " mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))\n", - " \n", - " sim.run(until_after_sources=mp.stop_when_fields_decayed(\n", - " 50, mp.Ex, mp.Vector3(0, 0, 0.5*sz - dpml - 0.5), 1e-6))\n", - " \n", + " trans3 = sim.add_flux(\n", + " 3 * fcen, 0, 1, mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5))\n", + " )\n", + "\n", + " sim.run(\n", + " until_after_sources=mp.stop_when_fields_decayed(\n", + " 50, mp.Ex, mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5), 1e-6\n", + " )\n", + " )\n", + "\n", " omega_flux = mp.get_fluxes(trans1)\n", " omega3_flux = mp.get_fluxes(trans3)\n", " freqs = mp.get_flux_freqs(trans)\n", " spectra = mp.get_fluxes(trans)\n", - " \n", + "\n", " return freqs, spectra, omega_flux, omega3_flux" ] }, @@ -355,13 +384,13 @@ } ], "source": [ - "k_pow = [-3,-2,-1,0]\n", + "k_pow = [-3, -2, -1, 0]\n", "freqs = []\n", "spectra = []\n", "for k_iter in k_pow:\n", " freqs_iter, spectra_iter, omega_flux, omega3_flux = run_chi3(k_iter)\n", " spectra.append(spectra_iter)\n", - " freqs = freqs_iter # Each iteration will simulate over the same frequencies, so just remember the last set." + " freqs = freqs_iter # Each iteration will simulate over the same frequencies, so just remember the last set." ] }, { @@ -384,10 +413,10 @@ ], "source": [ "plt.figure(dpi=150)\n", - "plt.semilogy(freqs,np.array(spectra).T)\n", + "plt.semilogy(freqs, np.array(spectra).T)\n", "plt.grid(True)\n", - "plt.xlabel('Frequency')\n", - "plt.ylabel('Transmitted Power (a.u.)')\n", + "plt.xlabel(\"Frequency\")\n", + "plt.ylabel(\"Transmitted Power (a.u.)\")\n", "plt.legend([\"$\\chi^{{(3)}} = 10^{{{}}}$\".format(i) for i in k_pow])\n", "plt.show()" ] @@ -432,7 +461,7 @@ ], "source": [ "_, _, omega_flux_cal, omega3_flux_cal = run_chi3(-16)\n", - "print(\"Omega: {}, 3Omega: {}\".format(omega_flux_cal[0],omega3_flux_cal[0]))" + "print(\"Omega: {}, 3Omega: {}\".format(omega_flux_cal[0], omega3_flux_cal[0]))" ] }, { @@ -787,7 +816,7 @@ } ], "source": [ - "pts = np.linspace(-6,0,20)\n", + "pts = np.linspace(-6, 0, 20)\n", "_, _, omega_psd, omega3_psd = zip(*[run_chi3(k_iter) for k_iter in pts])" ] }, @@ -810,15 +839,15 @@ } ], "source": [ - "quad = (10 ** pts) ** 2\n", + "quad = (10**pts) ** 2\n", "\n", "plt.figure(dpi=150)\n", - "plt.loglog(10 ** pts,np.array(omega_psd)/omega_flux_cal[0],'o-',label='$\\omega$')\n", - "plt.loglog(10 ** pts,np.array(omega3_psd)/omega_flux_cal[0],'o-',label='$3\\omega$')\n", - "plt.loglog(10**pts,quad,'k',label='quadratic line')\n", + "plt.loglog(10**pts, np.array(omega_psd) / omega_flux_cal[0], \"o-\", label=\"$\\omega$\")\n", + "plt.loglog(10**pts, np.array(omega3_psd) / omega_flux_cal[0], \"o-\", label=\"$3\\omega$\")\n", + "plt.loglog(10**pts, quad, \"k\", label=\"quadratic line\")\n", "plt.grid(True)\n", - "plt.xlabel('$\\chi^{(3)}$')\n", - "plt.ylabel('Transmission/ Incident Power')\n", + "plt.xlabel(\"$\\chi^{(3)}$\")\n", + "plt.ylabel(\"Transmission/ Incident Power\")\n", "plt.legend()\n", "plt.show()" ] @@ -879,11 +908,15 @@ } ], "source": [ - "_, _, omega_flux_1, omega3_flux_1 = run_chi3(-3,1)\n", - "_, _, omega_flux_2, omega3_flux_2 = run_chi3(-6,10)\n", + "_, _, omega_flux_1, omega3_flux_1 = run_chi3(-3, 1)\n", + "_, _, omega_flux_2, omega3_flux_2 = run_chi3(-6, 10)\n", "\n", - "print('-------------------------------')\n", - "print(\"Difference between powers: {}%\".format(abs(omega3_flux_1[0]-omega3_flux_2[0])/omega3_flux_1[0] * 100))" + "print(\"-------------------------------\")\n", + "print(\n", + " \"Difference between powers: {}%\".format(\n", + " abs(omega3_flux_1[0] - omega3_flux_2[0]) / omega3_flux_1[0] * 100\n", + " )\n", + ")" ] }, { diff --git a/python/examples/3rd-harm-1d.py b/python/examples/3rd-harm-1d.py index b6cbae498..9082e8bdf 100644 --- a/python/examples/3rd-harm-1d.py +++ b/python/examples/3rd-harm-1d.py @@ -6,14 +6,15 @@ import meep as mp import argparse + def main(args): - sz = 100 # size of cell in z direction - fcen = 1 / 3.0 # center frequency of source + sz = 100 # size of cell in z direction + fcen = 1 / 3.0 # center frequency of source df = fcen / 20.0 # frequency width of source - amp = args.amp # amplitude of source - k = 10**args.logk # Kerr susceptibility - dpml = 1.0 # PML thickness + amp = args.amp # amplitude of source + k = 10**args.logk # Kerr susceptibility + dpml = 1.0 # PML thickness # We'll use an explicitly 1d simulation. Setting dimensions=1 will actually # result in faster execution than just using two no-size dimensions. However, @@ -28,38 +29,54 @@ def main(args): # and pass it to the Simulation constructor default_material = mp.Medium(index=1, chi3=k) - sources = mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ex, - center=mp.Vector3(0, 0, -0.5*sz + dpml), amplitude=amp) + sources = mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ex, + center=mp.Vector3(0, 0, -0.5 * sz + dpml), + amplitude=amp, + ) # frequency range for flux calculation nfreq = 400 fmin = fcen / 2.0 fmax = fcen * 4 - sim = mp.Simulation(cell_size=cell, - geometry=[], - sources=[sources], - boundary_layers=[pml_layers], - default_material=default_material, - resolution=resolution, - dimensions=dimensions) + sim = mp.Simulation( + cell_size=cell, + geometry=[], + sources=[sources], + boundary_layers=[pml_layers], + default_material=default_material, + resolution=resolution, + dimensions=dimensions, + ) # trans = sim.add_flux(0.5 * (fmin + fmax), fmax - fmin, nfreq, # mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5))) - trans1 = sim.add_flux(fcen, 0, 1, - mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5))) - trans3 = sim.add_flux(3 * fcen, 0, 1, - mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5))) + trans1 = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5)) + ) + trans3 = sim.add_flux( + 3 * fcen, 0, 1, mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5)) + ) - sim.run(until_after_sources=mp.stop_when_fields_decayed( - 50, mp.Ex, mp.Vector3(0, 0, 0.5*sz - dpml - 0.5), 1e-6)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ex, mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5), 1e-6 + ) + ) # sim.display_fluxes(trans) - print(f"harmonics:, {k}, {amp}, {mp.get_fluxes(trans1)[0]}, {mp.get_fluxes(trans3)[0]}") + print( + f"harmonics:, {k}, {amp}, {mp.get_fluxes(trans1)[0]}, {mp.get_fluxes(trans3)[0]}" + ) + -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-amp', type=float, default=1.0, help='amplitude of source') - parser.add_argument('-logk', type=float, default=0, help='logarithm of Kerr susceptibility') + parser.add_argument("-amp", type=float, default=1.0, help="amplitude of source") + parser.add_argument( + "-logk", type=float, default=0, help="logarithm of Kerr susceptibility" + ) args = parser.parse_args() main(args) diff --git a/python/examples/absorbed_power_density.ipynb b/python/examples/absorbed_power_density.ipynb index d6188fb16..fbce592f8 100644 --- a/python/examples/absorbed_power_density.ipynb +++ b/python/examples/absorbed_power_density.ipynb @@ -187,60 +187,75 @@ "dpml = 1.0\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "r = 1.0 # radius of cylinder\n", + "r = 1.0 # radius of cylinder\n", "dair = 2.0 # air padding thickness\n", "\n", - "s = 2*(dpml+dair+r)\n", - "cell_size = mp.Vector3(s,s)\n", + "s = 2 * (dpml + dair + r)\n", + "cell_size = mp.Vector3(s, s)\n", "\n", "wvl = 1.0\n", - "fcen = 1/wvl\n", + "fcen = 1 / wvl\n", "\n", "# is_integrated=True necessary for any planewave source extending into PML\n", - "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.1*fcen,is_integrated=True),\n", - " center=mp.Vector3(-0.5*s+dpml),\n", - " size=mp.Vector3(0,s),\n", - " component=mp.Ez)]\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=0.1 * fcen, is_integrated=True),\n", + " center=mp.Vector3(-0.5 * s + dpml),\n", + " size=mp.Vector3(0, s),\n", + " component=mp.Ez,\n", + " )\n", + "]\n", "\n", "symmetries = [mp.Mirror(mp.Y)]\n", "\n", - "geometry = [mp.Cylinder(material=SiO2,\n", - " center=mp.Vector3(),\n", - " radius=r,\n", - " height=mp.inf)]\n", + "geometry = [mp.Cylinder(material=SiO2, center=mp.Vector3(), radius=r, height=mp.inf)]\n", "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=mp.Vector3(),\n", - " symmetries=symmetries,\n", - " geometry=geometry)\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=mp.Vector3(),\n", + " symmetries=symmetries,\n", + " geometry=geometry,\n", + ")\n", "\n", - "dft_fields = sim.add_dft_fields([mp.Dz,mp.Ez],\n", - " fcen,0,1,\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(2*r,2*r),\n", - " yee_grid=True)\n", + "dft_fields = sim.add_dft_fields(\n", + " [mp.Dz, mp.Ez],\n", + " fcen,\n", + " 0,\n", + " 1,\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(2 * r, 2 * r),\n", + " yee_grid=True,\n", + ")\n", "\n", "# closed box surrounding cylinder for computing total incoming flux\n", - "flux_box = sim.add_flux(fcen, 0, 1,\n", - " mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r),weight=+1),\n", - " mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r),weight=-1),\n", - " mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0),weight=-1),\n", - " mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0),weight=+1))\n", + "flux_box = sim.add_flux(\n", + " fcen,\n", + " 0,\n", + " 1,\n", + " mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r), weight=+1),\n", + " mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r), weight=-1),\n", + " mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0), weight=-1),\n", + " mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0), weight=+1),\n", + ")\n", "\n", "sim.run(until_after_sources=100)\n", "\n", - "Dz = sim.get_dft_array(dft_fields,mp.Dz,0)\n", - "Ez = sim.get_dft_array(dft_fields,mp.Ez,0)\n", - "absorbed_power_density = 2*np.pi*fcen * np.imag(np.conj(Ez)*Dz)\n", + "Dz = sim.get_dft_array(dft_fields, mp.Dz, 0)\n", + "Ez = sim.get_dft_array(dft_fields, mp.Ez, 0)\n", + "absorbed_power_density = 2 * np.pi * fcen * np.imag(np.conj(Ez) * Dz)\n", "\n", - "dxy = 1/resolution**2\n", - "absorbed_power = np.sum(absorbed_power_density)*dxy\n", + "dxy = 1 / resolution**2\n", + "absorbed_power = np.sum(absorbed_power_density) * dxy\n", "absorbed_flux = mp.get_fluxes(flux_box)[0]\n", - "err = abs(absorbed_power-absorbed_flux)/absorbed_flux\n", - "print(\"flux:, {} (dft_fields), {} (dft_flux), {} (error)\".format(absorbed_power,absorbed_flux,err))" + "err = abs(absorbed_power - absorbed_flux) / absorbed_flux\n", + "print(\n", + " \"flux:, {} (dft_fields), {} (dft_flux), {} (error)\".format(\n", + " absorbed_power, absorbed_flux, err\n", + " )\n", + ")" ] }, { @@ -324,21 +339,29 @@ ], "source": [ "plt.figure()\n", - "x = np.linspace(-r,r,Dz.shape[0])\n", - "y = np.linspace(-r,r,Dz.shape[1])\n", - "plt.pcolormesh(x,\n", - " y,\n", - " np.transpose(absorbed_power_density),\n", - " cmap='inferno_r',\n", - " shading='gouraud',\n", - " vmin=0,\n", - " vmax=np.amax(absorbed_power_density))\n", + "x = np.linspace(-r, r, Dz.shape[0])\n", + "y = np.linspace(-r, r, Dz.shape[1])\n", + "plt.pcolormesh(\n", + " x,\n", + " y,\n", + " np.transpose(absorbed_power_density),\n", + " cmap=\"inferno_r\",\n", + " shading=\"gouraud\",\n", + " vmin=0,\n", + " vmax=np.amax(absorbed_power_density),\n", + ")\n", "plt.xlabel(\"x (μm)\")\n", - "plt.xticks(np.linspace(-r,r,5))\n", + "plt.xticks(np.linspace(-r, r, 5))\n", "plt.ylabel(\"y (μm)\")\n", - "plt.yticks(np.linspace(-r,r,5))\n", - "plt.gca().set_aspect('equal')\n", - "plt.title(\"absorbed power density\" + \"\\n\" +\"SiO2 Labs(λ={} μm) = {:.2f} μm\".format(wvl,wvl/np.imag(np.sqrt(SiO2.epsilon(fcen)[0][0]))))\n", + "plt.yticks(np.linspace(-r, r, 5))\n", + "plt.gca().set_aspect(\"equal\")\n", + "plt.title(\n", + " \"absorbed power density\"\n", + " + \"\\n\"\n", + " + \"SiO2 Labs(λ={} μm) = {:.2f} μm\".format(\n", + " wvl, wvl / np.imag(np.sqrt(SiO2.epsilon(fcen)[0][0]))\n", + " )\n", + ")\n", "plt.colorbar()" ] } diff --git a/python/examples/absorbed_power_density.py b/python/examples/absorbed_power_density.py index 92947db8b..3bf34183d 100644 --- a/python/examples/absorbed_power_density.py +++ b/python/examples/absorbed_power_density.py @@ -1,6 +1,7 @@ import numpy as np import matplotlib -matplotlib.use('agg') + +matplotlib.use("agg") import matplotlib.pyplot as plt import meep as mp @@ -11,81 +12,102 @@ dpml = 1.0 pml_layers = [mp.PML(thickness=dpml)] -r = 1.0 # radius of cylinder +r = 1.0 # radius of cylinder dair = 2.0 # air padding thickness -s = 2*(dpml+dair+r) -cell_size = mp.Vector3(s,s) +s = 2 * (dpml + dair + r) +cell_size = mp.Vector3(s, s) wvl = 1.0 -fcen = 1/wvl +fcen = 1 / wvl # is_integrated=True necessary for any planewave source extending into PML -sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.1*fcen,is_integrated=True), - center=mp.Vector3(-0.5*s+dpml), - size=mp.Vector3(0,s), - component=mp.Ez)] +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=0.1 * fcen, is_integrated=True), + center=mp.Vector3(-0.5 * s + dpml), + size=mp.Vector3(0, s), + component=mp.Ez, + ) +] symmetries = [mp.Mirror(mp.Y)] -geometry = [mp.Cylinder(material=SiO2, - center=mp.Vector3(), - radius=r, - height=mp.inf)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=mp.Vector3(), - symmetries=symmetries, - geometry=geometry) - -dft_fields = sim.add_dft_fields([mp.Dz,mp.Ez], - fcen,0,1, - center=mp.Vector3(), - size=mp.Vector3(2*r,2*r), - yee_grid=True) +geometry = [mp.Cylinder(material=SiO2, center=mp.Vector3(), radius=r, height=mp.inf)] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=mp.Vector3(), + symmetries=symmetries, + geometry=geometry, +) + +dft_fields = sim.add_dft_fields( + [mp.Dz, mp.Ez], + fcen, + 0, + 1, + center=mp.Vector3(), + size=mp.Vector3(2 * r, 2 * r), + yee_grid=True, +) # closed box surrounding cylinder for computing total incoming flux -flux_box = sim.add_flux(fcen, 0, 1, - mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r),weight=+1), - mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r),weight=-1), - mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0),weight=-1), - mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0),weight=+1)) +flux_box = sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r), weight=+1), + mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r), weight=-1), + mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0), weight=-1), + mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0), weight=+1), +) sim.run(until_after_sources=100) -Dz = sim.get_dft_array(dft_fields,mp.Dz,0) -Ez = sim.get_dft_array(dft_fields,mp.Ez,0) -absorbed_power_density = 2*np.pi*fcen * np.imag(np.conj(Ez)*Dz) +Dz = sim.get_dft_array(dft_fields, mp.Dz, 0) +Ez = sim.get_dft_array(dft_fields, mp.Ez, 0) +absorbed_power_density = 2 * np.pi * fcen * np.imag(np.conj(Ez) * Dz) -dxy = 1/resolution**2 -absorbed_power = np.sum(absorbed_power_density)*dxy +dxy = 1 / resolution**2 +absorbed_power = np.sum(absorbed_power_density) * dxy absorbed_flux = mp.get_fluxes(flux_box)[0] -err = abs(absorbed_power-absorbed_flux)/absorbed_flux -print(f"flux:, {absorbed_power} (dft_fields), {absorbed_flux} (dft_flux), {err} (error)") +err = abs(absorbed_power - absorbed_flux) / absorbed_flux +print( + f"flux:, {absorbed_power} (dft_fields), {absorbed_flux} (dft_flux), {err} (error)" +) plt.figure() sim.plot2D() -plt.savefig('power_density_cell.png',dpi=150,bbox_inches='tight') +plt.savefig("power_density_cell.png", dpi=150, bbox_inches="tight") plt.figure() -x = np.linspace(-r,r,Dz.shape[0]) -y = np.linspace(-r,r,Dz.shape[1]) -plt.pcolormesh(x, - y, - np.transpose(absorbed_power_density), - cmap='inferno_r', - shading='gouraud', - vmin=0, - vmax=np.amax(absorbed_power_density)) +x = np.linspace(-r, r, Dz.shape[0]) +y = np.linspace(-r, r, Dz.shape[1]) +plt.pcolormesh( + x, + y, + np.transpose(absorbed_power_density), + cmap="inferno_r", + shading="gouraud", + vmin=0, + vmax=np.amax(absorbed_power_density), +) plt.xlabel("x (μm)") -plt.xticks(np.linspace(-r,r,5)) +plt.xticks(np.linspace(-r, r, 5)) plt.ylabel("y (μm)") -plt.yticks(np.linspace(-r,r,5)) -plt.gca().set_aspect('equal') -plt.title("absorbed power density" + "\n" +"SiO2 Labs(λ={} μm) = {:.2f} μm".format(wvl,wvl/np.imag(np.sqrt(SiO2.epsilon(fcen)[0][0])))) +plt.yticks(np.linspace(-r, r, 5)) +plt.gca().set_aspect("equal") +plt.title( + "absorbed power density" + + "\n" + + "SiO2 Labs(λ={} μm) = {:.2f} μm".format( + wvl, wvl / np.imag(np.sqrt(SiO2.epsilon(fcen)[0][0])) + ) +) plt.colorbar() -plt.savefig('power_density_map.png',dpi=150,bbox_inches='tight') +plt.savefig("power_density_map.png", dpi=150, bbox_inches="tight") diff --git a/python/examples/absorber-1d.py b/python/examples/absorber-1d.py index b6ec258fa..606e01b80 100644 --- a/python/examples/absorber-1d.py +++ b/python/examples/absorber-1d.py @@ -10,29 +10,41 @@ def main(args): resolution = 40 cell_size = mp.Vector3(z=10) - boundary_layers = [mp.PML(1, direction=mp.Z) if args.pml else mp.Absorber(1, direction=mp.Z)] - - sources = [mp.Source(src=mp.GaussianSource(1 / 0.803, fwidth=0.1), - center=mp.Vector3(), - component=mp.Ex)] + boundary_layers = [ + mp.PML(1, direction=mp.Z) if args.pml else mp.Absorber(1, direction=mp.Z) + ] + + sources = [ + mp.Source( + src=mp.GaussianSource(1 / 0.803, fwidth=0.1), + center=mp.Vector3(), + component=mp.Ex, + ) + ] def print_stuff(sim): p = sim.get_field_point(mp.Ex, mp.Vector3()) print(f"ex:, {sim.meep_time()}, {p.real}") - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - dimensions=1, - default_material=Al, - boundary_layers=boundary_layers, - sources=sources) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + dimensions=1, + default_material=Al, + boundary_layers=boundary_layers, + sources=sources, + ) - sim.run(mp.at_every(10, print_stuff), - until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-6)) + sim.run( + mp.at_every(10, print_stuff), + until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-6), + ) -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-pml', action='store_true', default=False, help='Use PML as boundary layer') + parser.add_argument( + "-pml", action="store_true", default=False, help="Use PML as boundary layer" + ) args = parser.parse_args() main(args) diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb index 321cf70de..dfc36d0cd 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb @@ -38,6 +38,7 @@ "import numpy as np\n", "from autograd import numpy as npa\n", "from matplotlib import pyplot as plt\n", + "\n", "mp.quiet(quietval=True)\n", "seed = 240\n", "np.random.seed(seed)\n", @@ -62,7 +63,7 @@ "\n", "Sx = 6\n", "Sy = 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(1.0)]" ] @@ -80,19 +81,23 @@ "metadata": {}, "outputs": [], "source": [ - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-1,0,0]\n", - "source_size = mp.Vector3(0,2,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]" + "source_center = [-1, 0, 0]\n", + "source_size = mp.Vector3(0, 2, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]" ] }, { @@ -114,16 +119,29 @@ "Nx = design_region_resolution\n", "Ny = design_region_resolution\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0)))\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables, volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0))\n", + ")\n", "\n", "\n", "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", - " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " mp.Block(\n", + " center=design_region.center,\n", + " size=design_region.size,\n", + " material=design_variables,\n", + " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " ),\n", "]" ] }, @@ -141,10 +159,10 @@ "outputs": [], "source": [ "def mapping(x):\n", - " x = x.reshape(Nx,Ny)\n", - " x = (npa.flipud(x) + x)/2 # left-right symmetry\n", - " x = (npa.fliplr(x) + x)/2 # up-down symmetry\n", - " x = (npa.rot90(x) + x) # 90-degree symmetry\n", + " x = x.reshape(Nx, Ny)\n", + " x = (npa.flipud(x) + x) / 2 # left-right symmetry\n", + " x = (npa.fliplr(x) + x) / 2 # up-down symmetry\n", + " x = npa.rot90(x) + x # 90-degree symmetry\n", " return x" ] }, @@ -161,12 +179,14 @@ "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " eps_averaging=False,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " eps_averaging=False,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -184,7 +204,9 @@ "metadata": {}, "outputs": [], "source": [ - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,1,0),size=mp.Vector3(x=2)),mode=1)\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim, mp.Volume(center=mp.Vector3(0, 1, 0), size=mp.Vector3(x=2)), mode=1\n", + ")\n", "ob_list = [TE0]" ] }, @@ -224,9 +246,9 @@ " objective_arguments=ob_list,\n", " design_regions=[design_region],\n", " fcen=fcen,\n", - " df = 0,\n", - " nf = 1,\n", - " decay_fields=[mp.Ez]\n", + " df=0,\n", + " nf=1,\n", + " decay_fields=[mp.Ez],\n", ")" ] }, @@ -243,7 +265,7 @@ "metadata": {}, "outputs": [], "source": [ - "x0 = np.random.rand(Nx*Ny)\n", + "x0 = np.random.rand(Nx * Ny)\n", "opt.update_design([x0])" ] }, @@ -273,7 +295,7 @@ } ], "source": [ - "opt.plot2D(True,frequency=1/1.55)\n", + "opt.plot2D(True, frequency=1 / 1.55)\n", "plt.show()" ] }, @@ -342,7 +364,7 @@ ], "source": [ "plt.figure()\n", - "plt.imshow(np.rot90(dJ_du.reshape(Nx,Ny)))" + "plt.imshow(np.rot90(dJ_du.reshape(Nx, Ny)))" ] }, { @@ -362,7 +384,7 @@ "source": [ "db = 1e-3\n", "choose = 10\n", - "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose,db=db)" + "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose, db=db)" ] }, { @@ -410,27 +432,31 @@ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", "\n", - "fig = plt.figure(figsize=(12,6))\n", + "fig = plt.figure(figsize=(12, 6))\n", "\n", - "plt.subplot(1,2,1)\n", - "plt.plot([min_g, max_g],[min_g, max_g],label='y=x comparison')\n", - "plt.plot([min_g, max_g],[m*min_g+b, m*max_g+b],'--',label='Best fit')\n", - "plt.plot(g_discrete,dJ_du[idx],'o',label='Adjoint comparison')\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Adjoint Gradient')\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot([min_g, max_g], [min_g, max_g], label=\"y=x comparison\")\n", + "plt.plot([min_g, max_g], [m * min_g + b, m * max_g + b], \"--\", label=\"Best fit\")\n", + "plt.plot(g_discrete, dJ_du[idx], \"o\", label=\"Adjoint comparison\")\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Adjoint Gradient\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.axis(\"square\")\n", "\n", - "plt.subplot(1,2,2)\n", - "rel_err = np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx])) / np.abs(np.squeeze(g_discrete)) * 100\n", - "plt.semilogy(g_discrete,rel_err,'o')\n", + "plt.subplot(1, 2, 2)\n", + "rel_err = (\n", + " np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx]))\n", + " / np.abs(np.squeeze(g_discrete))\n", + " * 100\n", + ")\n", + "plt.semilogy(g_discrete, rel_err, \"o\")\n", "plt.grid(True)\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Relative Error (%)')\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Relative Error (%)\")\n", "\n", "fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - "fig.suptitle('Resolution: {} Seed: {} Nx: {} Ny: {}'.format(resolution,seed,Nx,Ny))\n", + "fig.suptitle(\"Resolution: {} Seed: {} Nx: {} Ny: {}\".format(resolution, seed, Nx, Ny))\n", "plt.show()" ] }, @@ -460,7 +486,7 @@ "resolution = 20\n", "opt.sim.resolution = resolution\n", "f0, dJ_du = opt()\n", - "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose,db=db)" + "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose, db=db)" ] }, { @@ -485,27 +511,31 @@ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", "\n", - "fig = plt.figure(figsize=(12,6))\n", + "fig = plt.figure(figsize=(12, 6))\n", "\n", - "plt.subplot(1,2,1)\n", - "plt.plot([min_g, max_g],[min_g, max_g],label='y=x comparison')\n", - "plt.plot([min_g, max_g],[m*min_g+b, m*max_g+b],'--',label='Best fit')\n", - "plt.plot(g_discrete,dJ_du[idx],'o',label='Adjoint comparison')\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Adjoint Gradient')\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot([min_g, max_g], [min_g, max_g], label=\"y=x comparison\")\n", + "plt.plot([min_g, max_g], [m * min_g + b, m * max_g + b], \"--\", label=\"Best fit\")\n", + "plt.plot(g_discrete, dJ_du[idx], \"o\", label=\"Adjoint comparison\")\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Adjoint Gradient\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.axis(\"square\")\n", "\n", - "plt.subplot(1,2,2)\n", - "rel_err = np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx])) / np.abs(np.squeeze(g_discrete)) * 100\n", - "plt.semilogy(g_discrete,rel_err,'o')\n", + "plt.subplot(1, 2, 2)\n", + "rel_err = (\n", + " np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx]))\n", + " / np.abs(np.squeeze(g_discrete))\n", + " * 100\n", + ")\n", + "plt.semilogy(g_discrete, rel_err, \"o\")\n", "plt.grid(True)\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Relative Error (%)')\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Relative Error (%)\")\n", "\n", "fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - "fig.suptitle('Resolution: {} Seed: {} Nx: {} Ny: {}'.format(resolution,seed,Nx,Ny))\n", + "fig.suptitle(\"Resolution: {} Seed: {} Nx: {} Ny: {}\".format(resolution, seed, Nx, Ny))\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb index 5fed22162..4df3e0677 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb @@ -31,6 +31,7 @@ "from autograd import numpy as npa\n", "import nlopt\n", "from matplotlib import pyplot as plt\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -53,66 +54,83 @@ "\n", "Sx = 6\n", "Sy = 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(1.0)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-1.5,0,0]\n", - "source_size = mp.Vector3(0,2,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]\n", + "source_center = [-1.5, 0, 0]\n", + "source_size = mp.Vector3(0, 2, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]\n", "\n", "design_region_resolution = 10\n", "Nx = design_region_resolution\n", "Ny = design_region_resolution\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0)))\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables, volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0))\n", + ")\n", "\n", "\n", "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", - " # \n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; We give an alternative approach to impose symmetry in later tutorials.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", - "\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " eps_averaging=False,\n", - " resolution=resolution)\n", + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " eps_averaging=False,\n", + " resolution=resolution,\n", + ")\n", "\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,1,0),size=mp.Vector3(x=2)),mode=1)\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim, mp.Volume(center=mp.Vector3(0, 1, 0), size=mp.Vector3(x=2)), mode=1\n", + ")\n", "ob_list = [TE_top]\n", "\n", + "\n", "def J(alpha):\n", " return npa.abs(alpha) ** 2\n", "\n", + "\n", "opt = mpa.OptimizationProblem(\n", " simulation=sim,\n", " objective_functions=J,\n", " objective_arguments=ob_list,\n", " design_regions=[design_region],\n", " fcen=fcen,\n", - " df = 0,\n", - " nf = 1\n", + " df=0,\n", + " nf=1,\n", ")" ] }, @@ -142,7 +160,7 @@ } ], "source": [ - "x0 = 0.5*np.ones((Nx*Ny,))\n", + "x0 = 0.5 * np.ones((Nx * Ny,))\n", "opt.update_design([x0])\n", "\n", "opt.plot2D(True)\n", @@ -166,9 +184,11 @@ "source": [ "evaluation_history = []\n", "sensitivity = [0]\n", + "\n", + "\n", "def f(x, grad):\n", " f0, dJ_du = opt([x])\n", - " f0 = f0[0] # f0 is an array of length 1 \n", + " f0 = f0[0] # f0 is an array of length 1\n", " if grad.size > 0:\n", " grad[:] = np.squeeze(dJ_du)\n", " evaluation_history.append(np.real(f0))\n", @@ -236,10 +256,10 @@ "outputs": [], "source": [ "plt.figure()\n", - "plt.plot(evaluation_history,'o-')\n", + "plt.plot(evaluation_history, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('FOM')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"FOM\")\n", "plt.show()" ] }, @@ -257,7 +277,11 @@ "outputs": [], "source": [ "opt.update_design([x])\n", - "opt.plot2D(True,plot_monitors_flag=False,output_plane=mp.Volume(center=(0,0,0),size=(2,2,0)))\n", + "opt.plot2D(\n", + " True,\n", + " plot_monitors_flag=False,\n", + " output_plane=mp.Volume(center=(0, 0, 0), size=(2, 2, 0)),\n", + ")\n", "plt.axis(\"off\");" ] }, @@ -276,11 +300,15 @@ "metadata": {}, "outputs": [], "source": [ - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(-1,0,0),size=mp.Vector3(y=2)),mode=1)\n", - "ob_list = [TE0,TE_top]\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim, mp.Volume(center=mp.Vector3(-1, 0, 0), size=mp.Vector3(y=2)), mode=1\n", + ")\n", + "ob_list = [TE0, TE_top]\n", + "\n", + "\n", + "def J(source, top):\n", + " return npa.abs(top / source) ** 2\n", "\n", - "def J(source,top):\n", - " return npa.abs(top/source) ** 2\n", "\n", "opt.objective_functions = [J]\n", "opt.objective_arguments = ob_list\n", @@ -323,10 +351,10 @@ "outputs": [], "source": [ "plt.figure()\n", - "plt.plot(np.array(evaluation_history)*100,'o-')\n", + "plt.plot(np.array(evaluation_history) * 100, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Transmission (%)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Transmission (%)\")\n", "plt.show()" ] }, @@ -361,7 +389,11 @@ "outputs": [], "source": [ "opt.update_design([x])\n", - "opt.plot2D(True,plot_monitors_flag=False,output_plane=mp.Volume(center=(0,0,0),size=(2,2,0)))\n", + "opt.plot2D(\n", + " True,\n", + " plot_monitors_flag=False,\n", + " output_plane=mp.Volume(center=(0, 0, 0), size=(2, 2, 0)),\n", + ")\n", "plt.axis(\"off\");" ] }, @@ -378,7 +410,7 @@ "metadata": {}, "outputs": [], "source": [ - "plt.imshow(np.rot90(np.squeeze(np.abs(sensitivity[0].reshape(Nx,Ny)))));" + "plt.imshow(np.rot90(np.squeeze(np.abs(sensitivity[0].reshape(Nx, Ny)))));" ] } ], diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb index 049ea8d2e..18cf5784d 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb @@ -38,6 +38,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -66,7 +67,7 @@ "\n", "resolution = 30\n", "\n", - "frequencies = 1/np.linspace(1.5,1.6,10)" + "frequencies = 1 / np.linspace(1.5, 1.6, 10)" ] }, { @@ -92,13 +93,15 @@ } ], "source": [ - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "print(filter_radius)\n", - "design_region_resolution = int(1*resolution)" + "design_region_resolution = int(1 * resolution)" ] }, { @@ -114,25 +117,29 @@ "metadata": {}, "outputs": [], "source": [ - "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", - "source_size = mp.Vector3(0,Sy,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]" + "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", + "source_size = mp.Vector3(0, Sy, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]" ] }, { @@ -148,11 +155,17 @@ "metadata": {}, "outputs": [], "source": [ - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")" ] }, { @@ -168,18 +181,20 @@ "metadata": {}, "outputs": [], "source": [ - "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", - "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", - "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", + "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", + "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", "\n", - "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", - "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", + "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", + "top_wg_mask = (Y_g == design_region_width / 2) & (np.abs(X_g) <= waveguide_width / 2)\n", "Si_mask = left_wg_mask | top_wg_mask\n", "\n", - "border_mask = ((X_g == -design_region_width/2) | \n", - " (X_g == design_region_width/2) | \n", - " (Y_g == -design_region_height/2) | \n", - " (Y_g == design_region_height/2))\n", + "border_mask = (\n", + " (X_g == -design_region_width / 2)\n", + " | (X_g == design_region_width / 2)\n", + " | (Y_g == -design_region_height / 2)\n", + " | (Y_g == design_region_height / 2)\n", + ")\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False" ] @@ -197,14 +212,20 @@ "metadata": {}, "outputs": [], "source": [ - "def mapping(x,eta,beta):\n", - " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", + "def mapping(x, eta, beta):\n", + " x = npa.where(Si_mask.flatten(), 1, npa.where(SiO2_mask.flatten(), 0, x))\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -225,19 +246,32 @@ "outputs": [], "source": [ "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", - " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " mp.Block(\n", + " center=design_region.center,\n", + " size=design_region.size,\n", + " material=design_variables,\n", + " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " ),\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -255,11 +289,27 @@ "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=Sy)),mode)\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,Sx/2 - pml_size - 2*waveguide_length/3,0),size=mp.Vector3(x=Sx)),mode)\n", - "ob_list = [TE0,TE_top]\n", - "def J(source,top):\n", - " power = npa.abs(top/source)\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", + " size=mp.Vector3(y=Sy),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(0, Sx / 2 - pml_size - 2 * waveguide_length / 3, 0),\n", + " size=mp.Vector3(x=Sx),\n", + " ),\n", + " mode,\n", + ")\n", + "ob_list = [TE0, TE_top]\n", + "\n", + "\n", + "def J(source, top):\n", + " power = npa.abs(top / source)\n", " return npa.mean(power)" ] }, @@ -270,10 +320,10 @@ "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = J,\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", ")" ] @@ -304,9 +354,9 @@ } ], "source": [ - "rho_vector = np.random.rand(Nx*Ny)\n", - "opt.update_design([mapping(rho_vector,eta_i,1e3)])\n", - "opt.plot2D(True,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "rho_vector = np.random.rand(Nx * Ny)\n", + "opt.update_design([mapping(rho_vector, eta_i, 1e3)])\n", + "opt.plot2D(True, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.show()" ] }, @@ -339,19 +389,19 @@ "beta = 2048\n", "\n", "plt.figure()\n", - "ax1 = plt.subplot(1,3,1)\n", - "opt.update_design([mapping(rho_vector,0.498,beta)])\n", - "opt.plot2D(True,ax=ax1,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "ax1 = plt.subplot(1, 3, 1)\n", + "opt.update_design([mapping(rho_vector, 0.498, beta)])\n", + "opt.plot2D(True, ax=ax1, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.title(\"Dilation\")\n", "\n", - "ax2 = plt.subplot(1,3,2)\n", - "opt.update_design([mapping(rho_vector,0.5,beta)])\n", - "opt.plot2D(True,ax=ax2,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "ax2 = plt.subplot(1, 3, 2)\n", + "opt.update_design([mapping(rho_vector, 0.5, beta)])\n", + "opt.plot2D(True, ax=ax2, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.title(\"Intermediate\")\n", "\n", - "ax3 = plt.subplot(1,3,3)\n", - "opt.update_design([mapping(rho_vector,0.502,beta)])\n", - "opt.plot2D(True,ax=ax3,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "ax3 = plt.subplot(1, 3, 3)\n", + "opt.update_design([mapping(rho_vector, 0.502, beta)])\n", + "opt.plot2D(True, ax=ax3, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.title(\"Erosion\")\n", "\n", "plt.tight_layout()\n", @@ -373,27 +423,36 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", - " \n", - " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)]) # compute objective and gradient\n", - " \n", + " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", + "\n", + " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)]) # compute objective and gradient\n", + "\n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1)) # backprop\n", - " \n", - " \n", + " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", + " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", + " ) # backprop\n", + "\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", + "\n", " cur_iter[0] = cur_iter[0] + 1\n", - " \n", + "\n", " return np.real(f0)" ] }, @@ -1996,17 +2055,17 @@ ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx*Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "cur_beta = 4\n", @@ -2017,10 +2076,10 @@ " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", + " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", " solver.set_maxeval(update_factor)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -2050,10 +2109,10 @@ ], "source": [ "plt.figure()\n", - "plt.plot(10*np.log10(evaluation_history),'o-')\n", + "plt.plot(10 * np.log10(evaluation_history), \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Average Insertion Loss (dB)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Average Insertion Loss (dB)\")\n", "plt.show()" ] }, @@ -2083,13 +2142,19 @@ } ], "source": [ - "opt.update_design([mapping(x,eta_i,cur_beta)])\n", + "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -2114,11 +2179,11 @@ } ], "source": [ - "f0, dJ_du = opt([mapping(x,eta_i,cur_beta)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef/source_coef) ** 2" + "top_profile = np.abs(top_coef / source_coef) ** 2" ] }, { @@ -2153,19 +2218,19 @@ ], "source": [ "plt.figure()\n", - "plt.plot(1/frequencies,top_profile*100,'-o')\n", + "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Transmission (%)')\n", - "plt.ylim(98,100)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Transmission (%)\")\n", + "plt.ylim(98, 100)\n", "plt.show()\n", "\n", "plt.figure()\n", - "plt.plot(1/frequencies,10*np.log10(top_profile),'-o')\n", + "plt.plot(1 / frequencies, 10 * np.log10(top_profile), \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Insertion Loss (dB)')\n", - "plt.ylim(-0.1,0)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Insertion Loss (dB)\")\n", + "plt.ylim(-0.1, 0)\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb index f67d5f19a..3e68cbcca 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb @@ -33,6 +33,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "import nlopt\n", + "\n", "seed = 240\n", "np.random.seed(seed)\n", "mp.quiet(quietval=True)\n", @@ -63,13 +64,13 @@ "\n", "minimum_length = 0.09\n", "eta_e = 0.55\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "eta_i = 0.5\n", - "eta_d = 1-eta_e\n", - "design_region_resolution = int(5*resolution)\n", + "eta_d = 1 - eta_e\n", + "design_region_resolution = int(5 * resolution)\n", "\n", - "frequencies = 1/np.linspace(1.5,1.6,10)\n", - "#print(1/frequencies)" + "frequencies = 1 / np.linspace(1.5, 1.6, 10)\n", + "# print(1/frequencies)" ] }, { @@ -85,25 +86,29 @@ "metadata": {}, "outputs": [], "source": [ - "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.56\n", + "fcen = 1 / 1.56\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", - "source_size = mp.Vector3(0,2,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]" + "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", + "source_size = mp.Vector3(0, 2, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]" ] }, { @@ -119,11 +124,17 @@ "metadata": {}, "outputs": [], "source": [ - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")" ] }, { @@ -139,13 +150,19 @@ "metadata": {}, "outputs": [], "source": [ - "def mapping(x,eta,beta): \n", + "def mapping(x, eta, beta):\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -164,21 +181,27 @@ "outputs": [], "source": [ "# Define spatial arrays used to generate bit masks\n", - "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", - "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", - "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", + "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", + "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", "\n", "# Define the core mask\n", - "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", - "top_right_wg_mask = (X_g == design_region_width/2) & (np.abs(Y_g + arm_separation/2) <= waveguide_width/2)\n", - "bottom_right_wg_mask = (X_g == design_region_width/2) & (np.abs(Y_g - arm_separation/2) <= waveguide_width/2)\n", + "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", + "top_right_wg_mask = (X_g == design_region_width / 2) & (\n", + " np.abs(Y_g + arm_separation / 2) <= waveguide_width / 2\n", + ")\n", + "bottom_right_wg_mask = (X_g == design_region_width / 2) & (\n", + " np.abs(Y_g - arm_separation / 2) <= waveguide_width / 2\n", + ")\n", "Si_mask = left_wg_mask | top_right_wg_mask | bottom_right_wg_mask\n", "\n", "# Define the cladding mask\n", - "border_mask = ((X_g == -design_region_width/2) | \n", - " (X_g == design_region_width/2) | \n", - " (Y_g == -design_region_height/2) | \n", - " (Y_g == design_region_height/2))\n", + "border_mask = (\n", + " (X_g == -design_region_width / 2)\n", + " | (X_g == design_region_width / 2)\n", + " | (Y_g == -design_region_height / 2)\n", + " | (Y_g == design_region_height / 2)\n", + ")\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False" ] @@ -197,20 +220,41 @@ "outputs": [], "source": [ "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # left waveguide\n", - " mp.Block(center=mp.Vector3(x=Sx/4, y=arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # top right waveguide\n", - " mp.Block(center=mp.Vector3(x=Sx/4, y=-arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # bottom right waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e2=mp.Vector3(y=-1))\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4),\n", + " material=Si,\n", + " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", + " ), # left waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(x=Sx / 4, y=arm_separation / 2),\n", + " material=Si,\n", + " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", + " ), # top right waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(x=Sx / 4, y=-arm_separation / 2),\n", + " material=Si,\n", + " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", + " ), # bottom right waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ),\n", + " mp.Block(\n", + " center=design_region.center,\n", + " size=design_region.size,\n", + " material=design_variables,\n", + " e2=mp.Vector3(y=-1),\n", + " ),\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " symmetries=[mp.Mirror(direction=mp.Y)],\n", - " default_material=SiO2,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " symmetries=[mp.Mirror(direction=mp.Y)],\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -228,20 +272,47 @@ "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=1.5)),mode)\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,arm_separation/2,0),size=mp.Vector3(y=arm_separation)),mode)\n", - "TE_bottom = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,-arm_separation/2,0),size=mp.Vector3(y=arm_separation)),mode)\n", - "ob_list = [TE0,TE_top,TE_bottom]\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", + " size=mp.Vector3(y=1.5),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(\n", + " Sx / 2 - pml_size - 2 * waveguide_length / 3, arm_separation / 2, 0\n", + " ),\n", + " size=mp.Vector3(y=arm_separation),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_bottom = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(\n", + " Sx / 2 - pml_size - 2 * waveguide_length / 3, -arm_separation / 2, 0\n", + " ),\n", + " size=mp.Vector3(y=arm_separation),\n", + " ),\n", + " mode,\n", + ")\n", + "ob_list = [TE0, TE_top, TE_bottom]\n", + "\n", "\n", - "def J(source,top,bottom):\n", - " power = npa.abs(top/source) ** 2 + npa.abs(bottom/source) ** 2\n", + "def J(source, top, bottom):\n", + " power = npa.abs(top / source) ** 2 + npa.abs(bottom / source) ** 2\n", " return npa.mean(power)\n", "\n", + "\n", "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = J,\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", ")" ] @@ -273,7 +344,13 @@ ], "source": [ "plt.figure()\n", - "x0 = mapping(np.random.rand(Nx*Ny,),eta_i,128)\n", + "x0 = mapping(\n", + " np.random.rand(\n", + " Nx * Ny,\n", + " ),\n", + " eta_i,\n", + " 128,\n", + ")\n", "opt.update_design([x0])\n", "opt.plot2D(True)\n", "plt.show()" @@ -294,27 +371,37 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", - " \n", - " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)])\n", - " \n", + " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", + "\n", + " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)])\n", + "\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", - " plt.savefig('media/splitter_{:03d}.png'.format(cur_iter[0]),dpi=300)\n", + " ax.axis(\"off\")\n", + " plt.savefig(\"media/splitter_{:03d}.png\".format(cur_iter[0]), dpi=300)\n", " plt.show()\n", - " \n", + "\n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1))\n", - " \n", + " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", + " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", + " )\n", + "\n", " evaluation_history.append(np.max(np.real(f0)))\n", - " \n", + "\n", " cur_iter[0] = cur_iter[0] + 1\n", - " \n", + "\n", " return np.real(f0)" ] }, @@ -1703,17 +1790,17 @@ ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx*Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "cur_beta = 4\n", @@ -1721,16 +1808,16 @@ "num_betas = 6\n", "update_factor = 12\n", "for iters in range(num_betas):\n", - " print(\"current beta: \",cur_beta)\n", - " \n", + " print(\"current beta: \", cur_beta)\n", + "\n", " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", + " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", " solver.set_maxeval(update_factor)\n", " solver.set_xtol_rel(1e-4)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -1760,10 +1847,10 @@ ], "source": [ "plt.figure()\n", - "plt.plot(10*np.log10(0.5*np.array(evaluation_history)),'o-')\n", + "plt.plot(10 * np.log10(0.5 * np.array(evaluation_history)), \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Mean Splitting Ratio (dB)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Mean Splitting Ratio (dB)\")\n", "plt.show()" ] }, @@ -1788,12 +1875,12 @@ } ], "source": [ - "f0, dJ_du = opt([mapping(x,eta_i,cur_beta)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef, bottom_ceof = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef/source_coef) ** 2\n", - "bottom_profile = np.abs(bottom_ceof/source_coef) ** 2" + "top_profile = np.abs(top_coef / source_coef) ** 2\n", + "bottom_profile = np.abs(bottom_ceof / source_coef) ** 2" ] }, { @@ -1816,13 +1903,13 @@ ], "source": [ "plt.figure()\n", - "plt.plot(1/frequencies,top_profile*100,'-o' ,label = 'Top Arm')\n", - "plt.plot(1/frequencies,bottom_profile*100,'--o',label = 'Bottom Arm')\n", + "plt.plot(1 / frequencies, top_profile * 100, \"-o\", label=\"Top Arm\")\n", + "plt.plot(1 / frequencies, bottom_profile * 100, \"--o\", label=\"Bottom Arm\")\n", "plt.legend()\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Splitting Ratio (%)')\n", - "plt.ylim(48.5,50)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Splitting Ratio (%)\")\n", + "plt.ylim(48.5, 50)\n", "plt.show()" ] }, @@ -1852,13 +1939,19 @@ } ], "source": [ - "opt.update_design([mapping(x,eta_i,cur_beta)])\n", + "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb index 3d6528ff8..bab3fce98 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb @@ -24,6 +24,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -49,69 +50,88 @@ "\n", "resolution = 20\n", "\n", - "Sx = 2*pml_size + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "nf = 2\n", - "frequencies = np.array([1/1.5, 1/1.6])\n", + "frequencies = np.array([1 / 1.5, 1 / 1.6])\n", "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "design_region_resolution = int(resolution)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [0,-(design_region_height/2 + 1.5),0]\n", - "source_size = mp.Vector3(design_region_width,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.Source(src,\n", - " component=mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "source_center = [0, -(design_region_height / 2 + 1.5), 0]\n", + "source_size = mp.Vector3(design_region_width, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", + "\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", "\n", - "def mapping(x,eta,beta):\n", + "def mapping(x, eta, beta):\n", "\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", - " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", + " projected_field = (\n", + " npa.flipud(projected_field) + projected_field\n", + " ) / 2 # left-right symmetry\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", + "\n", "geometry = [\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", - " # \n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ),\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "kpoint = mp.Vector3()\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " symmetries=[mp.Mirror(direction=mp.X)],\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution,\n", + ")" ] }, { @@ -129,12 +149,20 @@ "metadata": {}, "outputs": [], "source": [ - "far_x = [mp.Vector3(0,40,0)]\n", - "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", - "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", + "far_x = [mp.Vector3(0, 40, 0)]\n", + "NearRegions = [\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(0, design_region_height / 2 + 1.5),\n", + " size=mp.Vector3(design_region_width, 0),\n", + " weight=+1,\n", + " )\n", + "]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions, far_x)\n", "ob_list = [FarFields]\n", + "\n", + "\n", "def J1(alpha):\n", - " return -npa.abs(alpha[0,:,2])**2" + " return -npa.abs(alpha[0, :, 2]) ** 2" ] }, { @@ -144,10 +172,10 @@ "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = [J1],\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=[J1],\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", ")\n", "opt.plot2D(True)" @@ -168,9 +196,11 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(x, grad):\n", - " t = x[0] # \"dummy\" parameter\n", - " v = x[1:] # design parameters\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", " if grad.size > 0:\n", " grad[0] = 1\n", " grad[1:] = 0\n", @@ -200,39 +230,49 @@ "metadata": {}, "outputs": [], "source": [ - "def c(result,x,gradient,eta,beta):\n", - " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", - " \n", - " t = x[0] # dummy parameter\n", - " v = x[1:] # design parameters\n", + "def c(result, x, gradient, eta, beta):\n", + " print(\n", + " \"Current iteration: {}; current eta: {}, current beta: {}\".format(\n", + " cur_iter[0], eta, beta\n", + " )\n", + " )\n", + "\n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", "\n", - " f0, dJ_du = opt([mapping(v,eta,beta)])\n", + " f0, dJ_du = opt([mapping(v, eta, beta)])\n", "\n", " # Backprop the gradients through our mapping function\n", " my_grad = np.zeros(dJ_du.shape)\n", - " for k in range(opt.nf): \n", - " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k])\n", + " for k in range(opt.nf):\n", + " my_grad[:, k] = tensor_jacobian_product(mapping, 0)(v, eta, beta, dJ_du[:, k])\n", "\n", " # Assign gradients\n", " if gradient.size > 0:\n", - " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", - " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", - " \n", + " gradient[:, 0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:, 1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + "\n", " result[:] = np.real(f0) - t\n", - " \n", + "\n", " # store results\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " # visualize\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", - " cur_iter[0] = cur_iter[0] + 1\n" + "\n", + " cur_iter[0] = cur_iter[0] + 1" ] }, { @@ -249,19 +289,19 @@ "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", - "ub = np.ones((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "\n", "# insert dummy parameter bounds and variable\n", - "x = np.insert(x,0,0) # our initial guess for the worst error\n", - "lb = np.insert(lb,0,-np.inf)\n", - "ub = np.insert(ub,0,0)\n", + "x = np.insert(x, 0, 0) # our initial guess for the worst error\n", + "lb = np.insert(lb, 0, -np.inf)\n", + "ub = np.insert(ub, 0, 0)\n", "\n", "cur_beta = 4\n", "beta_scale = 2\n", @@ -269,15 +309,17 @@ "update_factor = 12\n", "ftol = 1e-5\n", "for iters in range(num_betas):\n", - " solver = nlopt.opt(algorithm, n+1)\n", + " solver = nlopt.opt(algorithm, n + 1)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", " solver.set_min_objective(f)\n", " solver.set_maxeval(update_factor)\n", " solver.set_ftol_rel(ftol)\n", - " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", + " solver.add_inequality_mconstraint(\n", + " lambda r, x, g: c(r, x, g, eta_i, cur_beta), np.array([1e-3] * nf)\n", + " )\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -288,18 +330,18 @@ }, "outputs": [], "source": [ - "lb = -np.min(evaluation_history,axis=1)\n", - "ub = -np.max(evaluation_history,axis=1)\n", - "mean = -np.mean(evaluation_history,axis=1)\n", + "lb = -np.min(evaluation_history, axis=1)\n", + "ub = -np.max(evaluation_history, axis=1)\n", + "mean = -np.mean(evaluation_history, axis=1)\n", "\n", "num_iters = lb.size\n", "\n", "plt.figure()\n", - "plt.fill_between(np.arange(num_iters),ub,lb,alpha=0.3)\n", - "plt.plot(mean,'o-')\n", + "plt.fill_between(np.arange(num_iters), ub, lb, alpha=0.3)\n", + "plt.plot(mean, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('FOM')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"FOM\")\n", "plt.show()" ] }, @@ -316,13 +358,19 @@ "metadata": {}, "outputs": [], "source": [ - "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -339,23 +387,22 @@ "metadata": {}, "outputs": [], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,90),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.5,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 90),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.5, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", - "opt.sim.plot2D(fields=mp.Ez)\n", - "\n" + "plt.figure(figsize=(10, 20))\n", + "opt.sim.plot2D(fields=mp.Ez)" ] }, { @@ -364,21 +411,21 @@ "metadata": {}, "outputs": [], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,90),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.6,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 90),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.6, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", + "plt.figure(figsize=(10, 20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb index 3e552be49..f30f97f89 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb @@ -32,6 +32,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "Air = mp.Medium(index=1.0)" @@ -57,68 +58,87 @@ "\n", "resolution = 30\n", "\n", - "Sx = 2*pml_size + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "nf = 3\n", - "frequencies = np.array([1/1.5, 1/1.55, 1/1.6])\n", + "frequencies = np.array([1 / 1.5, 1 / 1.55, 1 / 1.6])\n", "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "design_region_resolution = int(resolution)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [0,-(design_region_height/2 + 1.5),0]\n", - "source_size = mp.Vector3(design_region_width,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.Source(src,\n", - " component=mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "source_center = [0, -(design_region_height / 2 + 1.5), 0]\n", + "source_size = mp.Vector3(design_region_width, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", + "\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), Air, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),Air,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", "\n", - "def mapping(x,eta,beta):\n", + "def mapping(x, eta, beta):\n", "\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", - " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", + " projected_field = (\n", + " npa.flipud(projected_field) + projected_field\n", + " ) / 2 # left-right symmetry\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", + "\n", "geometry = [\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", - " # \n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ),\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "kpoint = mp.Vector3()\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " symmetries=[mp.Mirror(direction=mp.X)],\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution,\n", + ")" ] }, { @@ -138,12 +158,20 @@ "metadata": {}, "outputs": [], "source": [ - "far_x = [mp.Vector3(0,15,0)]\n", - "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", - "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", + "far_x = [mp.Vector3(0, 15, 0)]\n", + "NearRegions = [\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(0, design_region_height / 2 + 1.5),\n", + " size=mp.Vector3(design_region_width, 0),\n", + " weight=+1,\n", + " )\n", + "]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions, far_x)\n", "ob_list = [FarFields]\n", + "\n", + "\n", "def J1(FF):\n", - " return -npa.abs(FF[0,:,2])**2" + " return -npa.abs(FF[0, :, 2]) ** 2" ] }, { @@ -166,12 +194,12 @@ ], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = [J1],\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=[J1],\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", - " maximum_run_time = 2000\n", + " maximum_run_time=2000,\n", ")\n", "opt.plot2D(True)" ] @@ -191,9 +219,11 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(x, grad):\n", - " t = x[0] # \"dummy\" parameter\n", - " v = x[1:] # design parameters\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", " if grad.size > 0:\n", " grad[0] = 1\n", " grad[1:] = 0\n", @@ -223,40 +253,50 @@ "metadata": {}, "outputs": [], "source": [ - "def c(result,x,gradient,eta,beta):\n", - " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", - " \n", - " t = x[0] # dummy parameter\n", - " v = x[1:] # design parameters\n", + "def c(result, x, gradient, eta, beta):\n", + " print(\n", + " \"Current iteration: {}; current eta: {}, current beta: {}\".format(\n", + " cur_iter[0], eta, beta\n", + " )\n", + " )\n", + "\n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", "\n", - " f0, dJ_du = opt([mapping(v,eta,beta)])\n", + " f0, dJ_du = opt([mapping(v, eta, beta)])\n", "\n", " # Backprop the gradients through our mapping function\n", " my_grad = np.zeros(dJ_du.shape)\n", - " for k in range(opt.nf): \n", - " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k]) \n", - " #Note that we now backpropogate the gradients at individual frequencies\n", + " for k in range(opt.nf):\n", + " my_grad[:, k] = tensor_jacobian_product(mapping, 0)(v, eta, beta, dJ_du[:, k])\n", + " # Note that we now backpropogate the gradients at individual frequencies\n", "\n", " # Assign gradients\n", " if gradient.size > 0:\n", - " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", - " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", - " \n", + " gradient[:, 0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:, 1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + "\n", " result[:] = np.real(f0) - t\n", - " \n", + "\n", " # store results\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " # visualize\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", - " cur_iter[0] = cur_iter[0] + 1\n" + "\n", + " cur_iter[0] = cur_iter[0] + 1" ] }, { @@ -1704,19 +1744,19 @@ ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", - "ub = np.ones((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "\n", "# insert dummy parameter bounds and variable\n", - "x = np.insert(x,0,-1) # our initial guess for the worst error\n", - "lb = np.insert(lb,0,-np.inf)\n", - "ub = np.insert(ub,0,0)\n", + "x = np.insert(x, 0, -1) # our initial guess for the worst error\n", + "lb = np.insert(lb, 0, -np.inf)\n", + "ub = np.insert(ub, 0, 0)\n", "\n", "cur_beta = 4\n", "beta_scale = 2\n", @@ -1724,15 +1764,17 @@ "update_factor = 12\n", "ftol = 1e-5\n", "for iters in range(num_betas):\n", - " solver = nlopt.opt(algorithm, n+1)\n", + " solver = nlopt.opt(algorithm, n + 1)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", " solver.set_min_objective(f)\n", " solver.set_maxeval(update_factor)\n", " solver.set_ftol_rel(ftol)\n", - " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", + " solver.add_inequality_mconstraint(\n", + " lambda r, x, g: c(r, x, g, eta_i, cur_beta), np.array([1e-3] * nf)\n", + " )\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -1756,18 +1798,18 @@ } ], "source": [ - "lb = -np.min(evaluation_history,axis=1)\n", - "ub = -np.max(evaluation_history,axis=1)\n", - "mean = -np.mean(evaluation_history,axis=1)\n", + "lb = -np.min(evaluation_history, axis=1)\n", + "ub = -np.max(evaluation_history, axis=1)\n", + "mean = -np.mean(evaluation_history, axis=1)\n", "\n", "num_iters = lb.size\n", "\n", "plt.figure()\n", - "plt.fill_between(np.arange(num_iters),ub,lb,alpha=0.3)\n", - "plt.plot(mean,'o-')\n", + "plt.fill_between(np.arange(num_iters), ub, lb, alpha=0.3)\n", + "plt.plot(mean, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('FOM')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"FOM\")\n", "plt.show()" ] }, @@ -1797,13 +1839,19 @@ } ], "source": [ - "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -1828,7 +1876,7 @@ } ], "source": [ - "f0, dJ_du = opt([mapping(x[1:],eta_i,cur_beta//2)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta // 2)], need_gradient=False)\n", "frequencies = opt.frequencies" ] }, @@ -1851,14 +1899,13 @@ } ], "source": [ - "\n", - "intensities = np.abs(opt.get_objective_arguments()[0][0,:,2])**2\n", + "intensities = np.abs(opt.get_objective_arguments()[0][0, :, 2]) ** 2\n", "\n", "plt.figure()\n", - "plt.plot(1/frequencies,intensities,'-o')\n", + "plt.plot(1 / frequencies, intensities, \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('|Ez|^2 Intensities')\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"|Ez|^2 Intensities\")\n", "plt.show()" ] }, @@ -1898,21 +1945,21 @@ } ], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.5,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.5, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", + "plt.figure(figsize=(10, 20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, @@ -1945,21 +1992,21 @@ } ], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.55,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.55, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", + "plt.figure(figsize=(10, 20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, @@ -1992,21 +2039,21 @@ } ], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.6,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.6, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", + "plt.figure(figsize=(10, 20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb index 15f9cbf0a..f84a6b0ec 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb @@ -38,6 +38,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.quiet(quietval=True)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -75,82 +76,117 @@ "resolution = 30\n", "\n", "nf = 10\n", - "frequencies = 1/np.linspace(1.5,1.6,nf)\n", + "frequencies = 1 / np.linspace(1.5, 1.6, nf)\n", "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "print(filter_radius)\n", - "design_region_resolution = int(1*resolution)\n", + "design_region_resolution = int(1 * resolution)\n", "\n", - "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", - "source_size = mp.Vector3(0,Sy,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]\n", + "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", + "source_size = mp.Vector3(0, Sy, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]\n", "\n", - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")\n", "\n", - "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", - "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", - "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", + "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", + "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", "\n", - "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", - "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", + "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", + "top_wg_mask = (Y_g == design_region_width / 2) & (np.abs(X_g) <= waveguide_width / 2)\n", "Si_mask = left_wg_mask | top_wg_mask\n", "\n", - "border_mask = ((X_g == -design_region_width/2) | \n", - " (X_g == design_region_width/2) | \n", - " (Y_g == -design_region_height/2) | \n", - " (Y_g == design_region_height/2))\n", + "border_mask = (\n", + " (X_g == -design_region_width / 2)\n", + " | (X_g == design_region_width / 2)\n", + " | (Y_g == -design_region_height / 2)\n", + " | (Y_g == design_region_height / 2)\n", + ")\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False\n", "\n", - "def mapping(x,eta,beta):\n", - " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", + "\n", + "def mapping(x, eta, beta):\n", + " x = npa.where(Si_mask.flatten(), 1, npa.where(SiO2_mask.flatten(), 0, x))\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", + "\n", "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", - " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " mp.Block(\n", + " center=design_region.center,\n", + " size=design_region.size,\n", + " material=design_variables,\n", + " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " ),\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -168,13 +204,28 @@ "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=Sy)),mode)\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,Sx/2 - pml_size - 2*waveguide_length/3,0),size=mp.Vector3(x=Sx)),mode)\n", - "ob_list = [TE0,TE_top]\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", + " size=mp.Vector3(y=Sy),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(0, Sx / 2 - pml_size - 2 * waveguide_length / 3, 0),\n", + " size=mp.Vector3(x=Sx),\n", + " ),\n", + " mode,\n", + ")\n", + "ob_list = [TE0, TE_top]\n", + "\n", "\n", - "def J(source,top):\n", - " power = npa.abs(top/source)\n", - " return npa.abs(1-power)" + "def J(source, top):\n", + " power = npa.abs(top / source)\n", + " return npa.abs(1 - power)" ] }, { @@ -184,10 +235,10 @@ "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = J,\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", ")" ] @@ -207,9 +258,11 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(x, grad):\n", - " t = x[0] # \"dummy\" parameter\n", - " v = x[1:] # design parameters\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", " if grad.size > 0:\n", " grad[0] = 1\n", " grad[1:] = 0\n", @@ -239,38 +292,48 @@ "metadata": {}, "outputs": [], "source": [ - "def c(result,x,gradient,eta,beta):\n", - " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", - " \n", - " t = x[0] # dummy parameter\n", - " v = x[1:] # design parameters\n", + "def c(result, x, gradient, eta, beta):\n", + " print(\n", + " \"Current iteration: {}; current eta: {}, current beta: {}\".format(\n", + " cur_iter[0], eta, beta\n", + " )\n", + " )\n", + "\n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", + "\n", + " f0, dJ_du = opt([mapping(v, eta, beta)])\n", "\n", - " f0, dJ_du = opt([mapping(v,eta,beta)])\n", - " \n", " # Backprop the gradients through our mapping function\n", " my_grad = np.zeros(dJ_du.shape)\n", - " for k in range(opt.nf): \n", - " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k])\n", + " for k in range(opt.nf):\n", + " my_grad[:, k] = tensor_jacobian_product(mapping, 0)(v, eta, beta, dJ_du[:, k])\n", "\n", " # Assign gradients\n", " if gradient.size > 0:\n", - " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", - " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", - " \n", + " gradient[:, 0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:, 1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + "\n", " result[:] = np.real(f0) - t\n", - " \n", + "\n", " # store results\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " # visualize\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", + "\n", " cur_iter[0] = cur_iter[0] + 1" ] }, @@ -1873,37 +1936,39 @@ ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx*Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "# insert dummy parameter bounds and variable\n", - "x = np.insert(x,0,0.5) # our initial guess for the worst error\n", - "lb = np.insert(lb,0,0) # we can't get less than 0 error!\n", - "ub = np.insert(ub,0,1) # we can't get more than 1 error!\n", + "x = np.insert(x, 0, 0.5) # our initial guess for the worst error\n", + "lb = np.insert(lb, 0, 0) # we can't get less than 0 error!\n", + "ub = np.insert(ub, 0, 1) # we can't get more than 1 error!\n", "\n", "cur_beta = 4\n", "beta_scale = 2\n", "num_betas = 6\n", "update_factor = 12\n", "for iters in range(num_betas):\n", - " solver = nlopt.opt(algorithm, n+1)\n", + " solver = nlopt.opt(algorithm, n + 1)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", " solver.set_min_objective(f)\n", " solver.set_maxeval(update_factor)\n", - " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", + " solver.add_inequality_mconstraint(\n", + " lambda r, x, g: c(r, x, g, eta_i, cur_beta), np.array([1e-3] * nf)\n", + " )\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -1932,18 +1997,20 @@ } ], "source": [ - "lb = 1-np.min(evaluation_history,axis=1)\n", - "ub = 1-np.max(evaluation_history,axis=1)\n", - "mean = 1-np.mean(evaluation_history,axis=1)\n", + "lb = 1 - np.min(evaluation_history, axis=1)\n", + "ub = 1 - np.max(evaluation_history, axis=1)\n", + "mean = 1 - np.mean(evaluation_history, axis=1)\n", "\n", "num_iters = lb.size\n", "\n", "plt.figure()\n", - "plt.fill_between(np.arange(1,num_iters+1),10*np.log10(lb),10*np.log10(ub),alpha=0.3)\n", - "plt.plot(10*np.log10(mean),'o-')\n", + "plt.fill_between(\n", + " np.arange(1, num_iters + 1), 10 * np.log10(lb), 10 * np.log10(ub), alpha=0.3\n", + ")\n", + "plt.plot(10 * np.log10(mean), \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Average Insertion Loss (dB)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Average Insertion Loss (dB)\")\n", "plt.show()" ] }, @@ -1973,13 +2040,19 @@ } ], "source": [ - "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -2004,11 +2077,11 @@ } ], "source": [ - "f0, dJ_du = opt([mapping(x[1:],eta_i,cur_beta)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta)], need_gradient=False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef/source_coef) ** 2" + "top_profile = np.abs(top_coef / source_coef) ** 2" ] }, { @@ -2043,19 +2116,19 @@ ], "source": [ "plt.figure()\n", - "plt.plot(1/frequencies,top_profile*100,'-o')\n", + "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Transmission (%)')\n", - "plt.ylim(98,100)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Transmission (%)\")\n", + "plt.ylim(98, 100)\n", "plt.show()\n", "\n", "plt.figure()\n", - "plt.plot(1/frequencies,10*np.log10(top_profile),'-o')\n", + "plt.plot(1 / frequencies, 10 * np.log10(top_profile), \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Insertion Loss (dB)')\n", - "plt.ylim(-0.1,0)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Insertion Loss (dB)\")\n", + "plt.ylim(-0.1, 0)\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/01-Introduction.ipynb b/python/examples/adjoint_optimization/01-Introduction.ipynb index d40973f54..22771aa8f 100644 --- a/python/examples/adjoint_optimization/01-Introduction.ipynb +++ b/python/examples/adjoint_optimization/01-Introduction.ipynb @@ -34,6 +34,7 @@ "import numpy as np\n", "from autograd import numpy as npa\n", "from matplotlib import pyplot as plt\n", + "\n", "mp.verbosity(0)\n", "seed = 240\n", "np.random.seed(seed)\n", @@ -58,7 +59,7 @@ "\n", "Sx = 6\n", "Sy = 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(1.0)]" ] @@ -76,19 +77,23 @@ "metadata": {}, "outputs": [], "source": [ - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.1\n", "fwidth = width * fcen\n", - "source_center = [-1,0,0]\n", - "source_size = mp.Vector3(0,2,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]" + "source_center = [-1, 0, 0]\n", + "source_size = mp.Vector3(0, 2, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]" ] }, { @@ -110,17 +115,25 @@ "Nx = design_region_resolution\n", "Ny = design_region_resolution\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0)))\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables, volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0))\n", + ")\n", "\n", "\n", "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", - " # \n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; We give an alternative approach to impose symmetry in later tutorials.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", @@ -140,12 +153,14 @@ "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " eps_averaging=False,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " eps_averaging=False,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -163,7 +178,9 @@ "metadata": {}, "outputs": [], "source": [ - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,1,0),size=mp.Vector3(x=2)),mode=1)\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim, mp.Volume(center=mp.Vector3(0, 1, 0), size=mp.Vector3(x=2)), mode=1\n", + ")\n", "ob_list = [TE0]" ] }, @@ -203,8 +220,8 @@ " objective_arguments=ob_list,\n", " design_regions=[design_region],\n", " fcen=fcen,\n", - " df = 0,\n", - " nf = 1,\n", + " df=0,\n", + " nf=1,\n", ")" ] }, @@ -221,7 +238,7 @@ "metadata": {}, "outputs": [], "source": [ - "x0 = np.random.rand(Nx*Ny)\n", + "x0 = np.random.rand(Nx * Ny)\n", "opt.update_design([x0])" ] }, @@ -259,7 +276,7 @@ } ], "source": [ - "opt.plot2D(True,frequency=1/1.55)\n", + "opt.plot2D(True, frequency=1 / 1.55)\n", "plt.show()" ] }, @@ -328,7 +345,7 @@ ], "source": [ "plt.figure()\n", - "plt.imshow(np.rot90(dJ_du.reshape(Nx,Ny)))" + "plt.imshow(np.rot90(dJ_du.reshape(Nx, Ny)))" ] }, { @@ -348,7 +365,7 @@ "source": [ "db = 1e-3\n", "choose = 10\n", - "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose,db=db)" + "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose, db=db)" ] }, { @@ -396,27 +413,31 @@ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", "\n", - "fig = plt.figure(figsize=(12,6))\n", + "fig = plt.figure(figsize=(12, 6))\n", "\n", - "plt.subplot(1,2,1)\n", - "plt.plot([min_g, max_g],[min_g, max_g],label='y=x comparison')\n", - "plt.plot([min_g, max_g],[m*min_g+b, m*max_g+b],'--',label='Best fit')\n", - "plt.plot(g_discrete,dJ_du[idx],'o',label='Adjoint comparison')\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Adjoint Gradient')\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot([min_g, max_g], [min_g, max_g], label=\"y=x comparison\")\n", + "plt.plot([min_g, max_g], [m * min_g + b, m * max_g + b], \"--\", label=\"Best fit\")\n", + "plt.plot(g_discrete, dJ_du[idx], \"o\", label=\"Adjoint comparison\")\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Adjoint Gradient\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.axis(\"square\")\n", "\n", - "plt.subplot(1,2,2)\n", - "rel_err = np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx])) / np.abs(np.squeeze(g_discrete)) * 100\n", - "plt.semilogy(g_discrete,rel_err,'o')\n", + "plt.subplot(1, 2, 2)\n", + "rel_err = (\n", + " np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx]))\n", + " / np.abs(np.squeeze(g_discrete))\n", + " * 100\n", + ")\n", + "plt.semilogy(g_discrete, rel_err, \"o\")\n", "plt.grid(True)\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Relative Error (%)')\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Relative Error (%)\")\n", "\n", "fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - "fig.suptitle('Resolution: {} Seed: {} Nx: {} Ny: {}'.format(resolution,seed,Nx,Ny))\n", + "fig.suptitle(\"Resolution: {} Seed: {} Nx: {} Ny: {}\".format(resolution, seed, Nx, Ny))\n", "plt.show()" ] }, @@ -446,7 +467,7 @@ "resolution = 30\n", "opt.sim.resolution = resolution\n", "f0, dJ_du = opt()\n", - "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose,db=db)" + "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose, db=db)" ] }, { @@ -471,27 +492,31 @@ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", "\n", - "fig = plt.figure(figsize=(12,6))\n", + "fig = plt.figure(figsize=(12, 6))\n", "\n", - "plt.subplot(1,2,1)\n", - "plt.plot([min_g, max_g],[min_g, max_g],label='y=x comparison')\n", - "plt.plot([min_g, max_g],[m*min_g+b, m*max_g+b],'--',label='Best fit')\n", - "plt.plot(g_discrete,dJ_du[idx],'o',label='Adjoint comparison')\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Adjoint Gradient')\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot([min_g, max_g], [min_g, max_g], label=\"y=x comparison\")\n", + "plt.plot([min_g, max_g], [m * min_g + b, m * max_g + b], \"--\", label=\"Best fit\")\n", + "plt.plot(g_discrete, dJ_du[idx], \"o\", label=\"Adjoint comparison\")\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Adjoint Gradient\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.axis(\"square\")\n", "\n", - "plt.subplot(1,2,2)\n", - "rel_err = np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx])) / np.abs(np.squeeze(g_discrete)) * 100\n", - "plt.semilogy(g_discrete,rel_err,'o')\n", + "plt.subplot(1, 2, 2)\n", + "rel_err = (\n", + " np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx]))\n", + " / np.abs(np.squeeze(g_discrete))\n", + " * 100\n", + ")\n", + "plt.semilogy(g_discrete, rel_err, \"o\")\n", "plt.grid(True)\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Relative Error (%)')\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Relative Error (%)\")\n", "\n", "fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - "fig.suptitle('Resolution: {} Seed: {} Nx: {} Ny: {}'.format(resolution,seed,Nx,Ny))\n", + "fig.suptitle(\"Resolution: {} Seed: {} Nx: {} Ny: {}\".format(resolution, seed, Nx, Ny))\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb b/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb index 6ffc6cacd..750031271 100644 --- a/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb +++ b/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb @@ -31,6 +31,7 @@ "from autograd import numpy as npa\n", "import nlopt\n", "from matplotlib import pyplot as plt\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -53,66 +54,83 @@ "\n", "Sx = 6\n", "Sy = 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(1.0)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-1.5,0,0]\n", - "source_size = mp.Vector3(0,2,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]\n", + "source_center = [-1.5, 0, 0]\n", + "source_size = mp.Vector3(0, 2, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]\n", "\n", "design_region_resolution = 10\n", "Nx = design_region_resolution\n", "Ny = design_region_resolution\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0)))\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables, volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0))\n", + ")\n", "\n", "\n", "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", - " # \n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; We give an alternative approach to impose symmetry in later tutorials.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", - "\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " eps_averaging=False,\n", - " resolution=resolution)\n", + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " eps_averaging=False,\n", + " resolution=resolution,\n", + ")\n", "\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,1,0),size=mp.Vector3(x=2)),mode=1)\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim, mp.Volume(center=mp.Vector3(0, 1, 0), size=mp.Vector3(x=2)), mode=1\n", + ")\n", "ob_list = [TE_top]\n", "\n", + "\n", "def J(alpha):\n", " return npa.abs(alpha) ** 2\n", "\n", + "\n", "opt = mpa.OptimizationProblem(\n", " simulation=sim,\n", " objective_functions=J,\n", " objective_arguments=ob_list,\n", " design_regions=[design_region],\n", " fcen=fcen,\n", - " df = 0,\n", - " nf = 1\n", + " df=0,\n", + " nf=1,\n", ")" ] }, @@ -142,7 +160,7 @@ } ], "source": [ - "x0 = 0.5*np.ones((Nx*Ny,))\n", + "x0 = 0.5 * np.ones((Nx * Ny,))\n", "opt.update_design([x0])\n", "\n", "opt.plot2D(True)\n", @@ -166,9 +184,11 @@ "source": [ "evaluation_history = []\n", "sensitivity = [0]\n", + "\n", + "\n", "def f(x, grad):\n", " f0, dJ_du = opt([x])\n", - " f0 = f0[0] # f0 is an array of length 1 \n", + " f0 = f0[0] # f0 is an array of length 1\n", " if grad.size > 0:\n", " grad[:] = np.squeeze(dJ_du)\n", " evaluation_history.append(np.real(f0))\n", @@ -277,10 +297,10 @@ ], "source": [ "plt.figure()\n", - "plt.plot(evaluation_history,'o-')\n", + "plt.plot(evaluation_history, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('FOM')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"FOM\")\n", "plt.show()" ] }, @@ -311,7 +331,11 @@ ], "source": [ "opt.update_design([x])\n", - "opt.plot2D(True,plot_monitors_flag=False,output_plane=mp.Volume(center=(0,0,0),size=(2,2,0)))\n", + "opt.plot2D(\n", + " True,\n", + " plot_monitors_flag=False,\n", + " output_plane=mp.Volume(center=(0, 0, 0), size=(2, 2, 0)),\n", + ")\n", "plt.axis(\"off\");" ] }, @@ -330,11 +354,15 @@ "metadata": {}, "outputs": [], "source": [ - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(-1,0,0),size=mp.Vector3(y=2)),mode=1)\n", - "ob_list = [TE0,TE_top]\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim, mp.Volume(center=mp.Vector3(-1, 0, 0), size=mp.Vector3(y=2)), mode=1\n", + ")\n", + "ob_list = [TE0, TE_top]\n", + "\n", + "\n", + "def J(source, top):\n", + " return npa.abs(top / source) ** 2\n", "\n", - "def J(source,top):\n", - " return npa.abs(top/source) ** 2\n", "\n", "opt.objective_functions = [J]\n", "opt.objective_arguments = ob_list\n", @@ -427,10 +455,10 @@ ], "source": [ "plt.figure()\n", - "plt.plot(np.array(evaluation_history)*100,'o-')\n", + "plt.plot(np.array(evaluation_history) * 100, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Transmission (%)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Transmission (%)\")\n", "plt.show()" ] }, @@ -486,7 +514,11 @@ ], "source": [ "opt.update_design([x])\n", - "opt.plot2D(True,plot_monitors_flag=False,output_plane=mp.Volume(center=(0,0,0),size=(2,2,0)))\n", + "opt.plot2D(\n", + " True,\n", + " plot_monitors_flag=False,\n", + " output_plane=mp.Volume(center=(0, 0, 0), size=(2, 2, 0)),\n", + ")\n", "plt.axis(\"off\");" ] }, @@ -516,7 +548,7 @@ } ], "source": [ - "plt.imshow(np.rot90(np.squeeze(np.abs(sensitivity[0].reshape(Nx,Ny)))));" + "plt.imshow(np.rot90(np.squeeze(np.abs(sensitivity[0].reshape(Nx, Ny)))));" ] } ], diff --git a/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb b/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb index b7f61e4f7..ba833ce4c 100644 --- a/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb +++ b/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb @@ -38,6 +38,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -66,7 +67,7 @@ "\n", "resolution = 20\n", "\n", - "frequencies = 1/np.linspace(1.5,1.6,10)" + "frequencies = 1 / np.linspace(1.5, 1.6, 10)" ] }, { @@ -92,13 +93,15 @@ } ], "source": [ - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "print(filter_radius)\n", - "design_region_resolution = int(1*resolution)" + "design_region_resolution = int(1 * resolution)" ] }, { @@ -114,25 +117,29 @@ "metadata": {}, "outputs": [], "source": [ - "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.1\n", "fwidth = width * fcen\n", - "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", - "source_size = mp.Vector3(0,Sy,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]" + "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", + "source_size = mp.Vector3(0, Sy, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]" ] }, { @@ -148,11 +155,17 @@ "metadata": {}, "outputs": [], "source": [ - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")" ] }, { @@ -168,18 +181,20 @@ "metadata": {}, "outputs": [], "source": [ - "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", - "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", - "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", + "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", + "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", "\n", - "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", - "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", + "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", + "top_wg_mask = (Y_g == design_region_width / 2) & (np.abs(X_g) <= waveguide_width / 2)\n", "Si_mask = left_wg_mask | top_wg_mask\n", "\n", - "border_mask = ((X_g == -design_region_width/2) | \n", - " (X_g == design_region_width/2) | \n", - " (Y_g == -design_region_height/2) | \n", - " (Y_g == design_region_height/2))\n", + "border_mask = (\n", + " (X_g == -design_region_width / 2)\n", + " | (X_g == design_region_width / 2)\n", + " | (Y_g == -design_region_height / 2)\n", + " | (Y_g == design_region_height / 2)\n", + ")\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False" ] @@ -197,17 +212,25 @@ "metadata": {}, "outputs": [], "source": [ - "def mapping(x,eta,beta):\n", - " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", - " \n", + "def mapping(x, eta, beta):\n", + " x = npa.where(Si_mask.flatten(), 1, npa.where(SiO2_mask.flatten(), 0, x))\n", + "\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", - " projected_field = (npa.rot90(projected_field.T, 2) + projected_field)/2 # 90-degree symmetry\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", + " projected_field = (\n", + " npa.rot90(projected_field.T, 2) + projected_field\n", + " ) / 2 # 90-degree symmetry\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -228,22 +251,30 @@ "outputs": [], "source": [ "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", - " # \n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -261,11 +292,27 @@ "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=Sy)),mode)\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,Sx/2 - pml_size - 2*waveguide_length/3,0),size=mp.Vector3(x=Sx)),mode)\n", - "ob_list = [TE0,TE_top]\n", - "def J(source,top):\n", - " power = npa.abs(top/source)**2\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", + " size=mp.Vector3(y=Sy),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(0, Sx / 2 - pml_size - 2 * waveguide_length / 3, 0),\n", + " size=mp.Vector3(x=Sx),\n", + " ),\n", + " mode,\n", + ")\n", + "ob_list = [TE0, TE_top]\n", + "\n", + "\n", + "def J(source, top):\n", + " power = npa.abs(top / source) ** 2\n", " return npa.mean(power)" ] }, @@ -276,10 +323,10 @@ "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = J,\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", ")" ] @@ -310,9 +357,9 @@ } ], "source": [ - "rho_vector = np.random.rand(Nx*Ny)\n", - "opt.update_design([mapping(rho_vector,eta_i,1e3)])\n", - "opt.plot2D(True,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "rho_vector = np.random.rand(Nx * Ny)\n", + "opt.update_design([mapping(rho_vector, eta_i, 1e3)])\n", + "opt.plot2D(True, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.show()" ] }, @@ -345,19 +392,19 @@ "beta = 2048\n", "\n", "plt.figure()\n", - "ax1 = plt.subplot(1,3,1)\n", - "opt.update_design([mapping(rho_vector,0.498,beta)])\n", - "opt.plot2D(True,ax=ax1,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "ax1 = plt.subplot(1, 3, 1)\n", + "opt.update_design([mapping(rho_vector, 0.498, beta)])\n", + "opt.plot2D(True, ax=ax1, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.title(\"Dilation\")\n", "\n", - "ax2 = plt.subplot(1,3,2)\n", - "opt.update_design([mapping(rho_vector,0.5,beta)])\n", - "opt.plot2D(True,ax=ax2,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "ax2 = plt.subplot(1, 3, 2)\n", + "opt.update_design([mapping(rho_vector, 0.5, beta)])\n", + "opt.plot2D(True, ax=ax2, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.title(\"Intermediate\")\n", "\n", - "ax3 = plt.subplot(1,3,3)\n", - "opt.update_design([mapping(rho_vector,0.502,beta)])\n", - "opt.plot2D(True,ax=ax3,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "ax3 = plt.subplot(1, 3, 3)\n", + "opt.update_design([mapping(rho_vector, 0.502, beta)])\n", + "opt.plot2D(True, ax=ax3, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.title(\"Erosion\")\n", "\n", "plt.tight_layout()\n", @@ -379,27 +426,36 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", - " \n", - " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)]) # compute objective and gradient\n", - " \n", + " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", + "\n", + " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)]) # compute objective and gradient\n", + "\n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1)) # backprop\n", - " \n", - " \n", + " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", + " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", + " ) # backprop\n", + "\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", + "\n", " cur_iter[0] = cur_iter[0] + 1\n", - " \n", + "\n", " return np.real(f0)" ] }, @@ -2002,17 +2058,17 @@ ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx*Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "cur_beta = 4\n", @@ -2023,10 +2079,10 @@ " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", + " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", " solver.set_maxeval(update_factor)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -2056,10 +2112,10 @@ ], "source": [ "plt.figure()\n", - "plt.plot(10*np.log10(evaluation_history),'o-')\n", + "plt.plot(10 * np.log10(evaluation_history), \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Average Insertion Loss (dB)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Average Insertion Loss (dB)\")\n", "plt.show()" ] }, @@ -2089,13 +2145,19 @@ } ], "source": [ - "opt.update_design([mapping(x,eta_i,cur_beta)])\n", + "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -2120,11 +2182,11 @@ } ], "source": [ - "f0, dJ_du = opt([mapping(x,eta_i,cur_beta//2)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x, eta_i, cur_beta // 2)], need_gradient=False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef/source_coef) ** 2" + "top_profile = np.abs(top_coef / source_coef) ** 2" ] }, { @@ -2159,19 +2221,19 @@ ], "source": [ "plt.figure()\n", - "plt.plot(1/frequencies,top_profile*100,'-o')\n", + "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Transmission (%)')\n", - "#plt.ylim(98,100)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Transmission (%)\")\n", + "# plt.ylim(98,100)\n", "plt.show()\n", "\n", "plt.figure()\n", - "plt.plot(1/frequencies,10*np.log10(top_profile),'-o')\n", + "plt.plot(1 / frequencies, 10 * np.log10(top_profile), \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Insertion Loss (dB)')\n", - "#plt.ylim(-0.1,0)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Insertion Loss (dB)\")\n", + "# plt.ylim(-0.1,0)\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/04-Splitter.ipynb b/python/examples/adjoint_optimization/04-Splitter.ipynb index 4518401a7..ff4b04f57 100644 --- a/python/examples/adjoint_optimization/04-Splitter.ipynb +++ b/python/examples/adjoint_optimization/04-Splitter.ipynb @@ -41,6 +41,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "import nlopt\n", + "\n", "seed = 240\n", "np.random.seed(seed)\n", "mp.quiet(quietval=True)\n", @@ -71,13 +72,13 @@ "\n", "minimum_length = 0.09\n", "eta_e = 0.55\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "eta_i = 0.5\n", - "eta_d = 1-eta_e\n", - "design_region_resolution = int(5*resolution)\n", + "eta_d = 1 - eta_e\n", + "design_region_resolution = int(5 * resolution)\n", "\n", - "frequencies = 1/np.linspace(1.5,1.6,10)\n", - "#print(1/frequencies)" + "frequencies = 1 / np.linspace(1.5, 1.6, 10)\n", + "# print(1/frequencies)" ] }, { @@ -93,25 +94,29 @@ "metadata": {}, "outputs": [], "source": [ - "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.56\n", + "fcen = 1 / 1.56\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", - "source_size = mp.Vector3(0,2,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]" + "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", + "source_size = mp.Vector3(0, 2, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]" ] }, { @@ -127,11 +132,17 @@ "metadata": {}, "outputs": [], "source": [ - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")" ] }, { @@ -147,16 +158,24 @@ "metadata": {}, "outputs": [], "source": [ - "def mapping(x,eta,beta):\n", - " \n", + "def mapping(x, eta, beta):\n", + "\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", - " projected_field = (npa.fliplr(projected_field) + projected_field)/2 # up-down symmetry\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", + " projected_field = (\n", + " npa.fliplr(projected_field) + projected_field\n", + " ) / 2 # up-down symmetry\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -175,21 +194,27 @@ "outputs": [], "source": [ "# Define spatial arrays used to generate bit masks\n", - "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", - "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", - "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", + "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", + "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", "\n", "# Define the core mask\n", - "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", - "top_right_wg_mask = (X_g == design_region_width/2) & (np.abs(Y_g + arm_separation/2) <= waveguide_width/2)\n", - "bottom_right_wg_mask = (X_g == design_region_width/2) & (np.abs(Y_g - arm_separation/2) <= waveguide_width/2)\n", + "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", + "top_right_wg_mask = (X_g == design_region_width / 2) & (\n", + " np.abs(Y_g + arm_separation / 2) <= waveguide_width / 2\n", + ")\n", + "bottom_right_wg_mask = (X_g == design_region_width / 2) & (\n", + " np.abs(Y_g - arm_separation / 2) <= waveguide_width / 2\n", + ")\n", "Si_mask = left_wg_mask | top_right_wg_mask | bottom_right_wg_mask\n", "\n", "# Define the cladding mask\n", - "border_mask = ((X_g == -design_region_width/2) | \n", - " (X_g == design_region_width/2) | \n", - " (Y_g == -design_region_height/2) | \n", - " (Y_g == design_region_height/2))\n", + "border_mask = (\n", + " (X_g == -design_region_width / 2)\n", + " | (X_g == design_region_width / 2)\n", + " | (Y_g == -design_region_height / 2)\n", + " | (Y_g == design_region_height / 2)\n", + ")\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False" ] @@ -208,24 +233,40 @@ "outputs": [], "source": [ "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # left waveguide\n", - " mp.Block(center=mp.Vector3(x=Sx/4, y=arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # top right waveguide\n", - " mp.Block(center=mp.Vector3(x=Sx/4, y=-arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # bottom right waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e2=mp.Vector3(y=-1))\n", - " # \n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4),\n", + " material=Si,\n", + " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", + " ), # left waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(x=Sx / 4, y=arm_separation / 2),\n", + " material=Si,\n", + " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", + " ), # top right waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(x=Sx / 4, y=-arm_separation / 2),\n", + " material=Si,\n", + " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", + " ), # bottom right waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ),\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e2=mp.Vector3(y=-1))\n", + " #\n", " # The commented line above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " symmetries=[mp.Mirror(direction=mp.Y)],\n", - " default_material=SiO2,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " symmetries=[mp.Mirror(direction=mp.Y)],\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -243,22 +284,49 @@ "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=1.5)),mode)\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,arm_separation/2,0),size=mp.Vector3(y=arm_separation)),mode)\n", - "TE_bottom = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,-arm_separation/2,0),size=mp.Vector3(y=arm_separation)),mode)\n", - "ob_list = [TE0,TE_top,TE_bottom]\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", + " size=mp.Vector3(y=1.5),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(\n", + " Sx / 2 - pml_size - 2 * waveguide_length / 3, arm_separation / 2, 0\n", + " ),\n", + " size=mp.Vector3(y=arm_separation),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_bottom = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(\n", + " Sx / 2 - pml_size - 2 * waveguide_length / 3, -arm_separation / 2, 0\n", + " ),\n", + " size=mp.Vector3(y=arm_separation),\n", + " ),\n", + " mode,\n", + ")\n", + "ob_list = [TE0, TE_top, TE_bottom]\n", "\n", - "def J(source,top,bottom):\n", - " power = npa.abs(top/source) ** 2 + npa.abs(bottom/source) ** 2\n", + "\n", + "def J(source, top, bottom):\n", + " power = npa.abs(top / source) ** 2 + npa.abs(bottom / source) ** 2\n", " return npa.mean(power)\n", "\n", + "\n", "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = J,\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", - " decay_by = 1e-5,\n", + " decay_by=1e-5,\n", ")" ] }, @@ -289,7 +357,13 @@ ], "source": [ "plt.figure()\n", - "x0 = mapping(np.random.rand(Nx*Ny,),eta_i,128)\n", + "x0 = mapping(\n", + " np.random.rand(\n", + " Nx * Ny,\n", + " ),\n", + " eta_i,\n", + " 128,\n", + ")\n", "opt.update_design([x0])\n", "opt.plot2D(True)\n", "plt.show()" @@ -310,26 +384,36 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", - " \n", - " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)])\n", - " \n", + " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", + "\n", + " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)])\n", + "\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", + "\n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1))\n", - " \n", + " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", + " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", + " )\n", + "\n", " evaluation_history.append(np.max(np.real(f0)))\n", - " \n", + "\n", " cur_iter[0] = cur_iter[0] + 1\n", - " \n", + "\n", " return np.real(f0)" ] }, @@ -1938,17 +2022,17 @@ ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx*Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "cur_beta = 4\n", @@ -1956,15 +2040,15 @@ "num_betas = 6\n", "update_factor = 12\n", "for iters in range(num_betas):\n", - " print(\"current beta: \",cur_beta)\n", - " \n", + " print(\"current beta: \", cur_beta)\n", + "\n", " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", + " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", " solver.set_maxeval(update_factor)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -1994,10 +2078,10 @@ ], "source": [ "plt.figure()\n", - "plt.plot(10*np.log10(0.5*np.array(evaluation_history)),'o-')\n", + "plt.plot(10 * np.log10(0.5 * np.array(evaluation_history)), \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Mean Splitting Ratio (dB)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Mean Splitting Ratio (dB)\")\n", "plt.show()" ] }, @@ -2022,12 +2106,12 @@ } ], "source": [ - "f0, dJ_du = opt([mapping(x,eta_i,cur_beta)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef, bottom_ceof = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef/source_coef) ** 2\n", - "bottom_profile = np.abs(bottom_ceof/source_coef) ** 2" + "top_profile = np.abs(top_coef / source_coef) ** 2\n", + "bottom_profile = np.abs(bottom_ceof / source_coef) ** 2" ] }, { @@ -2050,13 +2134,13 @@ ], "source": [ "plt.figure()\n", - "plt.plot(1/frequencies,top_profile*100,'-o' ,label = 'Top Arm')\n", - "plt.plot(1/frequencies,bottom_profile*100,'--o',label = 'Bottom Arm')\n", + "plt.plot(1 / frequencies, top_profile * 100, \"-o\", label=\"Top Arm\")\n", + "plt.plot(1 / frequencies, bottom_profile * 100, \"--o\", label=\"Bottom Arm\")\n", "plt.legend()\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Splitting Ratio (%)')\n", - "#plt.ylim(46.5,50)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Splitting Ratio (%)\")\n", + "# plt.ylim(46.5,50)\n", "plt.show()" ] }, @@ -2086,13 +2170,19 @@ } ], "source": [ - "opt.update_design([mapping(x,eta_i,cur_beta)])\n", + "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] } diff --git a/python/examples/adjoint_optimization/05-Near2Far.ipynb b/python/examples/adjoint_optimization/05-Near2Far.ipynb index b1396e9b6..676d68bb5 100644 --- a/python/examples/adjoint_optimization/05-Near2Far.ipynb +++ b/python/examples/adjoint_optimization/05-Near2Far.ipynb @@ -32,6 +32,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "Air = mp.Medium(index=1.0)" @@ -57,68 +58,87 @@ "\n", "resolution = 30\n", "\n", - "Sx = 2*pml_size + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "nf = 3\n", - "frequencies = np.array([1/1.5, 1/1.55, 1/1.6])\n", + "frequencies = np.array([1 / 1.5, 1 / 1.55, 1 / 1.6])\n", "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "design_region_resolution = int(resolution)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [0,-(design_region_height/2 + 1.5),0]\n", - "source_size = mp.Vector3(design_region_width,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.Source(src,\n", - " component=mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "source_center = [0, -(design_region_height / 2 + 1.5), 0]\n", + "source_size = mp.Vector3(design_region_width, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", + "\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), Air, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),Air,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", "\n", - "def mapping(x,eta,beta):\n", + "def mapping(x, eta, beta):\n", "\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", - " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", + " projected_field = (\n", + " npa.flipud(projected_field) + projected_field\n", + " ) / 2 # left-right symmetry\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", + "\n", "geometry = [\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", - " # \n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ),\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "kpoint = mp.Vector3()\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " symmetries=[mp.Mirror(direction=mp.X)],\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution,\n", + ")" ] }, { @@ -138,12 +158,20 @@ "metadata": {}, "outputs": [], "source": [ - "far_x = [mp.Vector3(0,15,0)]\n", - "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", - "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", + "far_x = [mp.Vector3(0, 15, 0)]\n", + "NearRegions = [\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(0, design_region_height / 2 + 1.5),\n", + " size=mp.Vector3(design_region_width, 0),\n", + " weight=+1,\n", + " )\n", + "]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions, far_x)\n", "ob_list = [FarFields]\n", + "\n", + "\n", "def J1(FF):\n", - " return npa.mean(npa.abs(FF[0,:,2])**2)" + " return npa.mean(npa.abs(FF[0, :, 2]) ** 2)" ] }, { @@ -166,12 +194,12 @@ ], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = [J1],\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=[J1],\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", - " maximum_run_time = 2000\n", + " maximum_run_time=2000,\n", ")\n", "opt.plot2D(True)" ] @@ -184,27 +212,36 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", - " \n", - " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)]) # compute objective and gradient\n", - " \n", + " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", + "\n", + " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)]) # compute objective and gradient\n", + "\n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1)) # backprop\n", - " \n", - " \n", + " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", + " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", + " ) # backprop\n", + "\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", + "\n", " cur_iter[0] = cur_iter[0] + 1\n", - " \n", + "\n", " return np.real(f0)" ] }, @@ -1807,14 +1844,14 @@ ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", - "ub = np.ones((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "\n", "\n", "cur_beta = 4\n", @@ -1826,11 +1863,11 @@ " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", + " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", " solver.set_maxeval(update_factor)\n", " solver.set_ftol_rel(ftol)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -1854,14 +1891,12 @@ } ], "source": [ - "\n", - "\n", "plt.figure()\n", "\n", - "plt.plot(evaluation_history,'o-')\n", + "plt.plot(evaluation_history, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('FOM')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"FOM\")\n", "plt.show()" ] }, @@ -1891,13 +1926,19 @@ } ], "source": [ - "opt.update_design([mapping(x,eta_i,cur_beta)])\n", + "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -1922,11 +1963,11 @@ } ], "source": [ - "f0, dJ_du = opt([mapping(x,eta_i,cur_beta//2)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x, eta_i, cur_beta // 2)], need_gradient=False)\n", "frequencies = opt.frequencies\n", "\n", "\n", - "intensities = np.abs(opt.get_objective_arguments()[0][0,:,2])**2" + "intensities = np.abs(opt.get_objective_arguments()[0][0, :, 2]) ** 2" ] }, { @@ -1948,13 +1989,11 @@ } ], "source": [ - "\n", - "\n", "plt.figure()\n", - "plt.plot(1/frequencies,intensities,'-o')\n", + "plt.plot(1 / frequencies, intensities, \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('|Ez|^2 Intensities')\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"|Ez|^2 Intensities\")\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb b/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb index 3e552be49..f30f97f89 100644 --- a/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb +++ b/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb @@ -32,6 +32,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "Air = mp.Medium(index=1.0)" @@ -57,68 +58,87 @@ "\n", "resolution = 30\n", "\n", - "Sx = 2*pml_size + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "nf = 3\n", - "frequencies = np.array([1/1.5, 1/1.55, 1/1.6])\n", + "frequencies = np.array([1 / 1.5, 1 / 1.55, 1 / 1.6])\n", "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "design_region_resolution = int(resolution)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [0,-(design_region_height/2 + 1.5),0]\n", - "source_size = mp.Vector3(design_region_width,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.Source(src,\n", - " component=mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "source_center = [0, -(design_region_height / 2 + 1.5), 0]\n", + "source_size = mp.Vector3(design_region_width, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", + "\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), Air, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),Air,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", "\n", - "def mapping(x,eta,beta):\n", + "def mapping(x, eta, beta):\n", "\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", - " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", + " projected_field = (\n", + " npa.flipud(projected_field) + projected_field\n", + " ) / 2 # left-right symmetry\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", + "\n", "geometry = [\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", - " # \n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ),\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "kpoint = mp.Vector3()\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " symmetries=[mp.Mirror(direction=mp.X)],\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution,\n", + ")" ] }, { @@ -138,12 +158,20 @@ "metadata": {}, "outputs": [], "source": [ - "far_x = [mp.Vector3(0,15,0)]\n", - "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", - "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", + "far_x = [mp.Vector3(0, 15, 0)]\n", + "NearRegions = [\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(0, design_region_height / 2 + 1.5),\n", + " size=mp.Vector3(design_region_width, 0),\n", + " weight=+1,\n", + " )\n", + "]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions, far_x)\n", "ob_list = [FarFields]\n", + "\n", + "\n", "def J1(FF):\n", - " return -npa.abs(FF[0,:,2])**2" + " return -npa.abs(FF[0, :, 2]) ** 2" ] }, { @@ -166,12 +194,12 @@ ], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = [J1],\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=[J1],\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", - " maximum_run_time = 2000\n", + " maximum_run_time=2000,\n", ")\n", "opt.plot2D(True)" ] @@ -191,9 +219,11 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(x, grad):\n", - " t = x[0] # \"dummy\" parameter\n", - " v = x[1:] # design parameters\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", " if grad.size > 0:\n", " grad[0] = 1\n", " grad[1:] = 0\n", @@ -223,40 +253,50 @@ "metadata": {}, "outputs": [], "source": [ - "def c(result,x,gradient,eta,beta):\n", - " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", - " \n", - " t = x[0] # dummy parameter\n", - " v = x[1:] # design parameters\n", + "def c(result, x, gradient, eta, beta):\n", + " print(\n", + " \"Current iteration: {}; current eta: {}, current beta: {}\".format(\n", + " cur_iter[0], eta, beta\n", + " )\n", + " )\n", + "\n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", "\n", - " f0, dJ_du = opt([mapping(v,eta,beta)])\n", + " f0, dJ_du = opt([mapping(v, eta, beta)])\n", "\n", " # Backprop the gradients through our mapping function\n", " my_grad = np.zeros(dJ_du.shape)\n", - " for k in range(opt.nf): \n", - " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k]) \n", - " #Note that we now backpropogate the gradients at individual frequencies\n", + " for k in range(opt.nf):\n", + " my_grad[:, k] = tensor_jacobian_product(mapping, 0)(v, eta, beta, dJ_du[:, k])\n", + " # Note that we now backpropogate the gradients at individual frequencies\n", "\n", " # Assign gradients\n", " if gradient.size > 0:\n", - " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", - " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", - " \n", + " gradient[:, 0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:, 1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + "\n", " result[:] = np.real(f0) - t\n", - " \n", + "\n", " # store results\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " # visualize\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", - " cur_iter[0] = cur_iter[0] + 1\n" + "\n", + " cur_iter[0] = cur_iter[0] + 1" ] }, { @@ -1704,19 +1744,19 @@ ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", - "ub = np.ones((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "\n", "# insert dummy parameter bounds and variable\n", - "x = np.insert(x,0,-1) # our initial guess for the worst error\n", - "lb = np.insert(lb,0,-np.inf)\n", - "ub = np.insert(ub,0,0)\n", + "x = np.insert(x, 0, -1) # our initial guess for the worst error\n", + "lb = np.insert(lb, 0, -np.inf)\n", + "ub = np.insert(ub, 0, 0)\n", "\n", "cur_beta = 4\n", "beta_scale = 2\n", @@ -1724,15 +1764,17 @@ "update_factor = 12\n", "ftol = 1e-5\n", "for iters in range(num_betas):\n", - " solver = nlopt.opt(algorithm, n+1)\n", + " solver = nlopt.opt(algorithm, n + 1)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", " solver.set_min_objective(f)\n", " solver.set_maxeval(update_factor)\n", " solver.set_ftol_rel(ftol)\n", - " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", + " solver.add_inequality_mconstraint(\n", + " lambda r, x, g: c(r, x, g, eta_i, cur_beta), np.array([1e-3] * nf)\n", + " )\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -1756,18 +1798,18 @@ } ], "source": [ - "lb = -np.min(evaluation_history,axis=1)\n", - "ub = -np.max(evaluation_history,axis=1)\n", - "mean = -np.mean(evaluation_history,axis=1)\n", + "lb = -np.min(evaluation_history, axis=1)\n", + "ub = -np.max(evaluation_history, axis=1)\n", + "mean = -np.mean(evaluation_history, axis=1)\n", "\n", "num_iters = lb.size\n", "\n", "plt.figure()\n", - "plt.fill_between(np.arange(num_iters),ub,lb,alpha=0.3)\n", - "plt.plot(mean,'o-')\n", + "plt.fill_between(np.arange(num_iters), ub, lb, alpha=0.3)\n", + "plt.plot(mean, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('FOM')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"FOM\")\n", "plt.show()" ] }, @@ -1797,13 +1839,19 @@ } ], "source": [ - "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -1828,7 +1876,7 @@ } ], "source": [ - "f0, dJ_du = opt([mapping(x[1:],eta_i,cur_beta//2)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta // 2)], need_gradient=False)\n", "frequencies = opt.frequencies" ] }, @@ -1851,14 +1899,13 @@ } ], "source": [ - "\n", - "intensities = np.abs(opt.get_objective_arguments()[0][0,:,2])**2\n", + "intensities = np.abs(opt.get_objective_arguments()[0][0, :, 2]) ** 2\n", "\n", "plt.figure()\n", - "plt.plot(1/frequencies,intensities,'-o')\n", + "plt.plot(1 / frequencies, intensities, \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('|Ez|^2 Intensities')\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"|Ez|^2 Intensities\")\n", "plt.show()" ] }, @@ -1898,21 +1945,21 @@ } ], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.5,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.5, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", + "plt.figure(figsize=(10, 20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, @@ -1945,21 +1992,21 @@ } ], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.55,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.55, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", + "plt.figure(figsize=(10, 20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, @@ -1992,21 +2039,21 @@ } ], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.6,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.6, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", + "plt.figure(figsize=(10, 20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, diff --git a/python/examples/adjoint_optimization/Bend Minimax.ipynb b/python/examples/adjoint_optimization/Bend Minimax.ipynb index 1253d83df..ea883612e 100644 --- a/python/examples/adjoint_optimization/Bend Minimax.ipynb +++ b/python/examples/adjoint_optimization/Bend Minimax.ipynb @@ -38,6 +38,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -75,89 +76,121 @@ "resolution = 20\n", "\n", "nf = 10\n", - "frequencies = 1/np.linspace(1.5,1.6,nf)\n", + "frequencies = 1 / np.linspace(1.5, 1.6, nf)\n", "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "print(filter_radius)\n", - "design_region_resolution = int(1*resolution)\n", + "design_region_resolution = int(1 * resolution)\n", "\n", - "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", - "source_size = mp.Vector3(0,Sy,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]\n", + "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", + "source_size = mp.Vector3(0, Sy, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]\n", "\n", - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")\n", "\n", - "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", - "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", - "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", + "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", + "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", "\n", - "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", - "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", + "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", + "top_wg_mask = (Y_g == design_region_width / 2) & (np.abs(X_g) <= waveguide_width / 2)\n", "Si_mask = left_wg_mask | top_wg_mask\n", "\n", - "border_mask = ((X_g == -design_region_width/2) | \n", - " (X_g == design_region_width/2) | \n", - " (Y_g == -design_region_height/2) | \n", - " (Y_g == design_region_height/2))\n", + "border_mask = (\n", + " (X_g == -design_region_width / 2)\n", + " | (X_g == design_region_width / 2)\n", + " | (Y_g == -design_region_height / 2)\n", + " | (Y_g == design_region_height / 2)\n", + ")\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False\n", "\n", - "def mapping(x,eta,beta):\n", - " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", - " \n", + "\n", + "def mapping(x, eta, beta):\n", + " x = npa.where(Si_mask.flatten(), 1, npa.where(SiO2_mask.flatten(), 0, x))\n", + "\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", - " projected_field = (npa.rot90(projected_field.T, 2) + projected_field)/2 # 90-degree symmetry\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", + " projected_field = (\n", + " npa.rot90(projected_field.T, 2) + projected_field\n", + " ) / 2 # 90-degree symmetry\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", + "\n", "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", - " # \n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -175,13 +208,28 @@ "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=Sy)),mode)\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,Sx/2 - pml_size - 2*waveguide_length/3,0),size=mp.Vector3(x=Sx)),mode)\n", - "ob_list = [TE0,TE_top]\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", + " size=mp.Vector3(y=Sy),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(0, Sx / 2 - pml_size - 2 * waveguide_length / 3, 0),\n", + " size=mp.Vector3(x=Sx),\n", + " ),\n", + " mode,\n", + ")\n", + "ob_list = [TE0, TE_top]\n", "\n", - "def J(source,top):\n", - " power = npa.abs(top/source)**2\n", - " return 1-power" + "\n", + "def J(source, top):\n", + " power = npa.abs(top / source) ** 2\n", + " return 1 - power" ] }, { @@ -191,10 +239,10 @@ "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = J,\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", ")" ] @@ -214,9 +262,11 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(x, grad):\n", - " t = x[0] # \"dummy\" parameter\n", - " v = x[1:] # design parameters\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", " if grad.size > 0:\n", " grad[0] = 1\n", " grad[1:] = 0\n", @@ -246,38 +296,48 @@ "metadata": {}, "outputs": [], "source": [ - "def c(result,x,gradient,eta,beta):\n", - " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", - " \n", - " t = x[0] # dummy parameter\n", - " v = x[1:] # design parameters\n", + "def c(result, x, gradient, eta, beta):\n", + " print(\n", + " \"Current iteration: {}; current eta: {}, current beta: {}\".format(\n", + " cur_iter[0], eta, beta\n", + " )\n", + " )\n", + "\n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", + "\n", + " f0, dJ_du = opt([mapping(v, eta, beta)])\n", "\n", - " f0, dJ_du = opt([mapping(v,eta,beta)])\n", - " \n", " # Backprop the gradients through our mapping function\n", " my_grad = np.zeros(dJ_du.shape)\n", - " for k in range(opt.nf): \n", - " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k])\n", + " for k in range(opt.nf):\n", + " my_grad[:, k] = tensor_jacobian_product(mapping, 0)(v, eta, beta, dJ_du[:, k])\n", "\n", " # Assign gradients\n", " if gradient.size > 0:\n", - " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", - " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", - " \n", + " gradient[:, 0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:, 1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + "\n", " result[:] = np.real(f0) - t\n", - " \n", + "\n", " # store results\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " # visualize\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", + "\n", " cur_iter[0] = cur_iter[0] + 1" ] }, @@ -1880,37 +1940,39 @@ ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx*Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "# insert dummy parameter bounds and variable\n", - "x = np.insert(x,0,0.5) # our initial guess for the worst error\n", - "lb = np.insert(lb,0,0) # we can't get less than 0 error!\n", - "ub = np.insert(ub,0,1) # we can't get more than 1 error!\n", + "x = np.insert(x, 0, 0.5) # our initial guess for the worst error\n", + "lb = np.insert(lb, 0, 0) # we can't get less than 0 error!\n", + "ub = np.insert(ub, 0, 1) # we can't get more than 1 error!\n", "\n", "cur_beta = 4\n", "beta_scale = 2\n", "num_betas = 6\n", "update_factor = 12\n", "for iters in range(num_betas):\n", - " solver = nlopt.opt(algorithm, n+1)\n", + " solver = nlopt.opt(algorithm, n + 1)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", " solver.set_min_objective(f)\n", " solver.set_maxeval(update_factor)\n", - " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-6]*nf))\n", + " solver.add_inequality_mconstraint(\n", + " lambda r, x, g: c(r, x, g, eta_i, cur_beta), np.array([1e-6] * nf)\n", + " )\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -1941,18 +2003,20 @@ } ], "source": [ - "lb = 1-np.min(evaluation_history,axis=1)\n", - "ub = 1-np.max(evaluation_history,axis=1)\n", - "mean = 1-np.mean(evaluation_history,axis=1)\n", + "lb = 1 - np.min(evaluation_history, axis=1)\n", + "ub = 1 - np.max(evaluation_history, axis=1)\n", + "mean = 1 - np.mean(evaluation_history, axis=1)\n", "\n", "num_iters = lb.size\n", "\n", "plt.figure()\n", - "plt.fill_between(np.arange(1,num_iters+1),10*np.log10(lb),10*np.log10(ub),alpha=0.3)\n", - "plt.plot(10*np.log10(mean),'o-')\n", + "plt.fill_between(\n", + " np.arange(1, num_iters + 1), 10 * np.log10(lb), 10 * np.log10(ub), alpha=0.3\n", + ")\n", + "plt.plot(10 * np.log10(mean), \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Average Insertion Loss (dB)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Average Insertion Loss (dB)\")\n", "plt.show()" ] }, @@ -1982,13 +2046,19 @@ } ], "source": [ - "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -2013,11 +2083,11 @@ } ], "source": [ - "f0, dJ_du = opt([mapping(x[1:],eta_i,cur_beta//2)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta // 2)], need_gradient=False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef/source_coef) ** 2" + "top_profile = np.abs(top_coef / source_coef) ** 2" ] }, { @@ -2052,19 +2122,19 @@ ], "source": [ "plt.figure()\n", - "plt.plot(1/frequencies,top_profile*100,'-o')\n", + "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Transmission (%)')\n", - "#plt.ylim(70,100)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Transmission (%)\")\n", + "# plt.ylim(70,100)\n", "plt.show()\n", "\n", "plt.figure()\n", - "plt.plot(1/frequencies,10*np.log10(top_profile),'-o')\n", + "plt.plot(1 / frequencies, 10 * np.log10(top_profile), \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Insertion Loss (dB)')\n", - "#plt.ylim(-0.1,0)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Insertion Loss (dB)\")\n", + "# plt.ylim(-0.1,0)\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb b/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb index 56279b124..9978da26d 100644 --- a/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb +++ b/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb @@ -69,25 +69,25 @@ } ], "source": [ - "nz, ny, nx =25, 1, 25\n", - "c = mpa.ConnectivityConstraint(nx, ny, nz,sp_solver=cg)\n", + "nz, ny, nx = 25, 1, 25\n", + "c = mpa.ConnectivityConstraint(nx, ny, nz, sp_solver=cg)\n", "\n", - "r = np.zeros((nz, ny,nx))+0.0001\n", - "r[:10,0, 8]=0.999\n", - "r[7,0,5:20]=0.999\n", - "r[5:,0, 18]=0.999\n", - "r[7,0,11:16]=0.0001\n", - "r[17:18,0,9:10]=0.999\n", + "r = np.zeros((nz, ny, nx)) + 0.0001\n", + "r[:10, 0, 8] = 0.999\n", + "r[7, 0, 5:20] = 0.999\n", + "r[5:, 0, 18] = 0.999\n", + "r[7, 0, 11:16] = 0.0001\n", + "r[17:18, 0, 9:10] = 0.999\n", "\n", - "r=r.flatten()#flatten the structure before pass in\n", + "r = r.flatten() # flatten the structure before pass in\n", "f = c.forward(r)\n", "ag = c.calculate_grad()\n", "\n", - "print(\"foward value\", f) \n", + "print(\"foward value\", f)\n", "\n", "plt.figure()\n", "plt.pcolormesh(r.reshape((nz, nx)))\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -116,10 +116,12 @@ } ], "source": [ - "#padding to show the side with Dirichlet BC\n", - "fullT = np.pad(c.T, (0, c.nx*c.ny), 'constant', constant_values=1).reshape((nz, ny, nx)) \n", + "# padding to show the side with Dirichlet BC\n", + "fullT = np.pad(c.T, (0, c.nx * c.ny), \"constant\", constant_values=1).reshape(\n", + " (nz, ny, nx)\n", + ")\n", "plt.figure()\n", - "plt.contourf(fullT[:,0,:], 100, cmap=\"RdBu\")\n", + "plt.contourf(fullT[:, 0, :], 100, cmap=\"RdBu\")\n", "plt.colorbar()\n", "plt.show()" ] @@ -213,29 +215,31 @@ } ], "source": [ - "r = np.zeros((nz, ny,nx))+0.0001\n", - "r[:10,0, 8]=0.999\n", - "r[7,0,5:20]=0.999\n", - "r[5:,0, 18]=0.999\n", + "r = np.zeros((nz, ny, nx)) + 0.0001\n", + "r[:10, 0, 8] = 0.999\n", + "r[7, 0, 5:20] = 0.999\n", + "r[5:, 0, 18] = 0.999\n", "\n", - "r=r.flatten()#flatten the structure before pass in\n", + "r = r.flatten() # flatten the structure before pass in\n", "f = c.forward(r)\n", "ag = c.calculate_grad()\n", "\n", - "print(\"foward value\", f) \n", + "print(\"foward value\", f)\n", "\n", "plt.figure()\n", "plt.pcolormesh(r.reshape((nz, nx)))\n", "plt.show()\n", "\n", - "fullT = np.pad(c.T, (0, c.nx*c.ny), 'constant', constant_values=1).reshape((nz, ny, nx)) \n", + "fullT = np.pad(c.T, (0, c.nx * c.ny), \"constant\", constant_values=1).reshape(\n", + " (nz, ny, nx)\n", + ")\n", "plt.figure()\n", - "plt.contourf(fullT[:,0,:], 100,cmap=\"RdBu\")\n", + "plt.contourf(fullT[:, 0, :], 100, cmap=\"RdBu\")\n", "plt.colorbar()\n", "plt.show()\n", "\n", "plt.figure()\n", - "plt.contourf(ag.reshape((nz, nx)), 100,cmap=\"RdBu\")\n", + "plt.contourf(ag.reshape((nz, nx)), 100, cmap=\"RdBu\")\n", "plt.colorbar()\n", "plt.show()" ] diff --git a/python/examples/adjoint_optimization/Fourier-Bend.ipynb b/python/examples/adjoint_optimization/Fourier-Bend.ipynb index c1b1cc45d..d0ec785b0 100644 --- a/python/examples/adjoint_optimization/Fourier-Bend.ipynb +++ b/python/examples/adjoint_optimization/Fourier-Bend.ipynb @@ -25,6 +25,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.quiet(quietval=True)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -53,7 +54,7 @@ "\n", "resolution = 40\n", "\n", - "frequencies = 1/np.linspace(1.5,1.6,3)" + "frequencies = 1 / np.linspace(1.5, 1.6, 3)" ] }, { @@ -70,13 +71,15 @@ } ], "source": [ - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "print(filter_radius)\n", - "design_region_resolution = int(1*resolution)" + "design_region_resolution = int(1 * resolution)" ] }, { @@ -85,25 +88,29 @@ "metadata": {}, "outputs": [], "source": [ - "Sx = 2*pml_size + 2*waveguide_length + design_region_width + 2\n", - "Sy = 2*pml_size + design_region_height + 2\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width + 2\n", + "Sy = 2 * pml_size + design_region_height + 2\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", - "source_size = mp.Vector3(0,Sy,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]" + "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", + "source_size = mp.Vector3(0, Sy, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]" ] }, { @@ -112,11 +119,17 @@ "metadata": {}, "outputs": [], "source": [ - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si)\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si)\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")" ] }, { @@ -125,18 +138,20 @@ "metadata": {}, "outputs": [], "source": [ - "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", - "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", - "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", + "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", + "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", "\n", - "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", - "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", + "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", + "top_wg_mask = (Y_g == design_region_width / 2) & (np.abs(X_g) <= waveguide_width / 2)\n", "Si_mask = left_wg_mask | top_wg_mask\n", "\n", - "border_mask = ((X_g == -design_region_width/2) | \n", - " (X_g == design_region_width/2) | \n", - " (Y_g == -design_region_height/2) | \n", - " (Y_g == design_region_height/2))\n", + "border_mask = (\n", + " (X_g == -design_region_width / 2)\n", + " | (X_g == design_region_width / 2)\n", + " | (Y_g == -design_region_height / 2)\n", + " | (Y_g == design_region_height / 2)\n", + ")\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False" ] @@ -147,14 +162,20 @@ "metadata": {}, "outputs": [], "source": [ - "def mapping(x,eta,beta):\n", - " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", + "def mapping(x, eta, beta):\n", + " x = npa.where(Si_mask.flatten(), 1, npa.where(SiO2_mask.flatten(), 0, x))\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -173,17 +194,25 @@ "outputs": [], "source": [ "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables) # design region\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -205,11 +234,20 @@ "metadata": {}, "outputs": [], "source": [ - "Ez_top = mpa.FourierFields(sim,mp.Volume(center=mp.Vector3(0,Sy/2 - pml_size - 0.1 ,0),size=mp.Vector3(x=waveguide_width)),mp.Ez)\n", + "Ez_top = mpa.FourierFields(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(0, Sy / 2 - pml_size - 0.1, 0),\n", + " size=mp.Vector3(x=waveguide_width),\n", + " ),\n", + " mp.Ez,\n", + ")\n", "ob_list = [Ez_top]\n", + "\n", + "\n", "def J(top):\n", - " power = npa.abs(top[1,7])**2\n", - " return power\n" + " power = npa.abs(top[1, 7]) ** 2\n", + " return power" ] }, { @@ -226,13 +264,13 @@ "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = J,\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", - " decay_by = 1e-6,\n", - " decay_fields=[mp.Ez]\n", + " decay_by=1e-6,\n", + " decay_fields=[mp.Ez],\n", ")" ] }, @@ -255,8 +293,8 @@ } ], "source": [ - "rho_vector = np.random.rand(Nx*Ny)\n", - "opt.update_design([mapping(rho_vector,eta_i,1e3)])\n", + "rho_vector = np.random.rand(Nx * Ny)\n", + "opt.update_design([mapping(rho_vector, eta_i, 1e3)])\n", "opt.plot2D(True)\n", "plt.show()" ] @@ -269,28 +307,37 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", - " \n", - " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)]) # compute objective and gradient\n", - " \n", + " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", + "\n", + " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)]) # compute objective and gradient\n", + "\n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1)) # backprop\n", - " \n", - " \n", + " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", + " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", + " ) # backprop\n", + "\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", - " #plt.savefig('media/bend_{:03d}.png'.format(cur_iter[0]),dpi=300)\n", + " ax.axis(\"off\")\n", + " # plt.savefig('media/bend_{:03d}.png'.format(cur_iter[0]),dpi=300)\n", " plt.show()\n", - " \n", + "\n", " cur_iter[0] = cur_iter[0] + 1\n", - " \n", + "\n", " return np.real(f0)" ] }, @@ -2298,17 +2345,17 @@ ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx*Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "cur_beta = 4\n", @@ -2319,11 +2366,11 @@ " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", + " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", " solver.set_maxeval(update_factor)\n", " solver.set_xtol_rel(1e-4)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -2353,10 +2400,10 @@ ], "source": [ "plt.figure()\n", - "plt.plot((evaluation_history),'o-')\n", + "plt.plot((evaluation_history), \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('FOM')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"FOM\")\n", "plt.show()" ] }, @@ -2379,13 +2426,19 @@ } ], "source": [ - "opt.update_design([mapping(x,eta_i,cur_beta)])\n", + "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -2415,10 +2468,10 @@ } ], "source": [ - "f0, dJ_du = opt([mapping(x,eta_i,cur_beta)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", "Ez_coef = opt.get_objective_arguments()[0]\n", "plt.figure()\n", - "plt.plot(np.abs(Ez_coef[1,:])**2,'-o')\n", + "plt.plot(np.abs(Ez_coef[1, :]) ** 2, \"-o\")\n", "plt.show()" ] }, @@ -2451,16 +2504,20 @@ } ], "source": [ - "src = mp.ContinuousSource(frequency=1/1.55,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]\n", + "src = mp.ContinuousSource(frequency=1 / 1.55, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]\n", "opt.sim.change_sources(source)\n", "opt.sim.run(until=500)\n", - "opt.sim.plot2D(fields=mp.Ez)\n" + "opt.sim.plot2D(fields=mp.Ez)" ] }, { diff --git a/python/examples/antenna-radiation.ipynb b/python/examples/antenna-radiation.ipynb index bf2498020..177d2fa83 100644 --- a/python/examples/antenna-radiation.ipynb +++ b/python/examples/antenna-radiation.ipynb @@ -53,44 +53,53 @@ "\n", "sxy = 4\n", "dpml = 1\n", - "cell = mp.Vector3(sxy+2*dpml,sxy+2*dpml)\n", + "cell = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml)\n", "\n", "pml_layers = [mp.PML(dpml)]\n", "\n", "fcen = 1.0\n", "df = 0.4\n", "src_cmpt = mp.Ey\n", - "sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df),\n", - " center=mp.Vector3(),\n", - " component=src_cmpt)]\n", + "sources = [\n", + " mp.Source(\n", + " src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), component=src_cmpt\n", + " )\n", + "]\n", "\n", "if src_cmpt == mp.Ex:\n", - " symmetries = [mp.Mirror(mp.X,phase=-1),\n", - " mp.Mirror(mp.Y,phase=+1)]\n", + " symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=+1)]\n", "elif src_cmpt == mp.Ey:\n", - " symmetries = [mp.Mirror(mp.X,phase=+1),\n", - " mp.Mirror(mp.Y,phase=-1)]\n", + " symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=-1)]\n", "elif src_cmpt == mp.Ez:\n", - " symmetries = [mp.Mirror(mp.X,phase=+1),\n", - " mp.Mirror(mp.Y,phase=+1)]\n", + " symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=+1)]\n", "\n", - "sim = mp.Simulation(cell_size=cell,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - " boundary_layers=pml_layers)\n", + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + " boundary_layers=pml_layers,\n", + ")\n", "\n", - "nearfield_box = sim.add_near2far(fcen, 0, 1,\n", - " mp.Near2FarRegion(mp.Vector3(y=0.5*sxy), size=mp.Vector3(sxy)),\n", - " mp.Near2FarRegion(mp.Vector3(y=-0.5*sxy), size=mp.Vector3(sxy), weight=-1),\n", - " mp.Near2FarRegion(mp.Vector3(0.5*sxy), size=mp.Vector3(y=sxy)),\n", - " mp.Near2FarRegion(mp.Vector3(-0.5*sxy), size=mp.Vector3(y=sxy), weight=-1))\n", + "nearfield_box = sim.add_near2far(\n", + " fcen,\n", + " 0,\n", + " 1,\n", + " mp.Near2FarRegion(mp.Vector3(y=0.5 * sxy), size=mp.Vector3(sxy)),\n", + " mp.Near2FarRegion(mp.Vector3(y=-0.5 * sxy), size=mp.Vector3(sxy), weight=-1),\n", + " mp.Near2FarRegion(mp.Vector3(0.5 * sxy), size=mp.Vector3(y=sxy)),\n", + " mp.Near2FarRegion(mp.Vector3(-0.5 * sxy), size=mp.Vector3(y=sxy), weight=-1),\n", + ")\n", "\n", - "flux_box = sim.add_flux(fcen, 0, 1,\n", - " mp.FluxRegion(mp.Vector3(y=0.5*sxy), size=mp.Vector3(sxy)),\n", - " mp.FluxRegion(mp.Vector3(y=-0.5*sxy), size=mp.Vector3(sxy), weight=-1),\n", - " mp.FluxRegion(mp.Vector3(0.5*sxy), size=mp.Vector3(y=sxy)),\n", - " mp.FluxRegion(mp.Vector3(-0.5*sxy), size=mp.Vector3(y=sxy), weight=-1))" + "flux_box = sim.add_flux(\n", + " fcen,\n", + " 0,\n", + " 1,\n", + " mp.FluxRegion(mp.Vector3(y=0.5 * sxy), size=mp.Vector3(sxy)),\n", + " mp.FluxRegion(mp.Vector3(y=-0.5 * sxy), size=mp.Vector3(sxy), weight=-1),\n", + " mp.FluxRegion(mp.Vector3(0.5 * sxy), size=mp.Vector3(y=sxy)),\n", + " mp.FluxRegion(mp.Vector3(-0.5 * sxy), size=mp.Vector3(y=sxy), weight=-1),\n", + ")" ] }, { @@ -155,7 +164,9 @@ } ], "source": [ - "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, src_cmpt, mp.Vector3(), 1e-8))" + "sim.run(\n", + " until_after_sources=mp.stop_when_fields_decayed(50, src_cmpt, mp.Vector3(), 1e-8)\n", + ")" ] }, { @@ -187,12 +198,24 @@ "metadata": {}, "outputs": [], "source": [ - "r = 1000/fcen # half side length of far-field square box OR radius of far-field circle\n", - "res_ff = 1 # resolution of far fields (points/μm)\n", - "far_flux_box = (nearfield_box.flux(mp.Y, mp.Volume(center=mp.Vector3(y=r), size=mp.Vector3(2*r)), res_ff)[0]\n", - " - nearfield_box.flux(mp.Y, mp.Volume(center=mp.Vector3(y=-r), size=mp.Vector3(2*r)), res_ff)[0]\n", - " + nearfield_box.flux(mp.X, mp.Volume(center=mp.Vector3(r), size=mp.Vector3(y=2*r)), res_ff)[0]\n", - " - nearfield_box.flux(mp.X, mp.Volume(center=mp.Vector3(-r), size=mp.Vector3(y=2*r)), res_ff)[0])" + "r = (\n", + " 1000 / fcen\n", + ") # half side length of far-field square box OR radius of far-field circle\n", + "res_ff = 1 # resolution of far fields (points/μm)\n", + "far_flux_box = (\n", + " nearfield_box.flux(\n", + " mp.Y, mp.Volume(center=mp.Vector3(y=r), size=mp.Vector3(2 * r)), res_ff\n", + " )[0]\n", + " - nearfield_box.flux(\n", + " mp.Y, mp.Volume(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r)), res_ff\n", + " )[0]\n", + " + nearfield_box.flux(\n", + " mp.X, mp.Volume(center=mp.Vector3(r), size=mp.Vector3(y=2 * r)), res_ff\n", + " )[0]\n", + " - nearfield_box.flux(\n", + " mp.X, mp.Volume(center=mp.Vector3(-r), size=mp.Vector3(y=2 * r)), res_ff\n", + " )[0]\n", + ")" ] }, { @@ -216,25 +239,25 @@ } ], "source": [ - "npts = 100 # number of points in [0,2*pi) range of angles\n", - "angles = 2*math.pi/npts*np.arange(npts)\n", + "npts = 100 # number of points in [0,2*pi) range of angles\n", + "angles = 2 * math.pi / npts * np.arange(npts)\n", "\n", - "E = np.zeros((npts,3),dtype=np.complex128)\n", - "H = np.zeros((npts,3),dtype=np.complex128)\n", + "E = np.zeros((npts, 3), dtype=np.complex128)\n", + "H = np.zeros((npts, 3), dtype=np.complex128)\n", "for n in range(npts):\n", - " ff = sim.get_farfield(nearfield_box,\n", - " mp.Vector3(r*math.cos(angles[n]),\n", - " r*math.sin(angles[n])))\n", - " E[n,:] = [np.conj(ff[j]) for j in range(3)]\n", - " H[n,:] = [ff[j+3] for j in range(3)]\n", + " ff = sim.get_farfield(\n", + " nearfield_box, mp.Vector3(r * math.cos(angles[n]), r * math.sin(angles[n]))\n", + " )\n", + " E[n, :] = [np.conj(ff[j]) for j in range(3)]\n", + " H[n, :] = [ff[j + 3] for j in range(3)]\n", "\n", - "Px = np.real(np.multiply(E[:,1],H[:,2])-np.multiply(E[:,2],H[:,1]))\n", - "Py = np.real(np.multiply(E[:,2],H[:,0])-np.multiply(E[:,0],H[:,2]))\n", - "Pr = np.sqrt(np.square(Px)+np.square(Py))\n", + "Px = np.real(np.multiply(E[:, 1], H[:, 2]) - np.multiply(E[:, 2], H[:, 1]))\n", + "Py = np.real(np.multiply(E[:, 2], H[:, 0]) - np.multiply(E[:, 0], H[:, 2]))\n", + "Pr = np.sqrt(np.square(Px) + np.square(Py))\n", "\n", - "far_flux_circle = np.sum(Pr)*2*np.pi*r/len(Pr)\n", + "far_flux_circle = np.sum(Pr) * 2 * np.pi * r / len(Pr)\n", "\n", - "print(\"flux:, {:.6f}, {:.6f}, {:.6f}\".format(near_flux,far_flux_box,far_flux_circle))" + "print(\"flux:, {:.6f}, {:.6f}, {:.6f}\".format(near_flux, far_flux_box, far_flux_circle))" ] }, { @@ -266,10 +289,10 @@ ], "source": [ "plt.figure(dpi=200)\n", - "ax = plt.subplot(111, projection='polar')\n", - "ax.plot(angles,Pr/max(Pr),'b-')\n", + "ax = plt.subplot(111, projection=\"polar\")\n", + "ax.plot(angles, Pr / max(Pr), \"b-\")\n", "ax.set_rmax(1)\n", - "ax.set_rticks([0,0.5,1])\n", + "ax.set_rticks([0, 0.5, 1])\n", "ax.grid(True)\n", "ax.set_rlabel_position(22)" ] diff --git a/python/examples/antenna-radiation.py b/python/examples/antenna-radiation.py index 9d6ea4cb1..83b9b09ac 100644 --- a/python/examples/antenna-radiation.py +++ b/python/examples/antenna-radiation.py @@ -8,109 +8,108 @@ sxy = 4 dpml = 1 -cell = mp.Vector3(sxy+2*dpml,sxy+2*dpml) +cell = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml) pml_layers = [mp.PML(dpml)] fcen = 1.0 df = 0.4 src_cmpt = mp.Ez -sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df), - center=mp.Vector3(), - component=src_cmpt)] +sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), component=src_cmpt + ) +] if src_cmpt == mp.Ex: - symmetries = [mp.Mirror(mp.X,phase=-1), - mp.Mirror(mp.Y,phase=+1)] + symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=+1)] elif src_cmpt == mp.Ey: - symmetries = [mp.Mirror(mp.X,phase=+1), - mp.Mirror(mp.Y,phase=-1)] + symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=-1)] elif src_cmpt == mp.Ez: - symmetries = [mp.Mirror(mp.X,phase=+1), - mp.Mirror(mp.Y,phase=+1)] - -sim = mp.Simulation(cell_size=cell, - resolution=resolution, - sources=sources, - symmetries=symmetries, - boundary_layers=pml_layers) - -nearfield_box = sim.add_near2far(fcen, 0, 1, - mp.Near2FarRegion(center=mp.Vector3(0,+0.5*sxy), - size=mp.Vector3(sxy,0)), - mp.Near2FarRegion(center=mp.Vector3(0,-0.5*sxy), - size=mp.Vector3(sxy,0), - weight=-1), - mp.Near2FarRegion(center=mp.Vector3(+0.5*sxy,0), - size=mp.Vector3(0,sxy)), - mp.Near2FarRegion(center=mp.Vector3(-0.5*sxy,0), - size=mp.Vector3(0,sxy), - weight=-1)) - -flux_box = sim.add_flux(fcen, 0, 1, - mp.FluxRegion(center=mp.Vector3(0,+0.5*sxy), - size=mp.Vector3(sxy,0)), - mp.FluxRegion(center=mp.Vector3(0,-0.5*sxy), - size=mp.Vector3(sxy,0), - weight=-1), - mp.FluxRegion(center=mp.Vector3(+0.5*sxy,0), - size=mp.Vector3(0,sxy)), - mp.FluxRegion(center=mp.Vector3(-0.5*sxy,0), - size=mp.Vector3(0,sxy), - weight=-1)) + symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=+1)] + +sim = mp.Simulation( + cell_size=cell, + resolution=resolution, + sources=sources, + symmetries=symmetries, + boundary_layers=pml_layers, +) + +nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion(center=mp.Vector3(0, +0.5 * sxy), size=mp.Vector3(sxy, 0)), + mp.Near2FarRegion( + center=mp.Vector3(0, -0.5 * sxy), size=mp.Vector3(sxy, 0), weight=-1 + ), + mp.Near2FarRegion(center=mp.Vector3(+0.5 * sxy, 0), size=mp.Vector3(0, sxy)), + mp.Near2FarRegion( + center=mp.Vector3(-0.5 * sxy, 0), size=mp.Vector3(0, sxy), weight=-1 + ), +) + +flux_box = sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(center=mp.Vector3(0, +0.5 * sxy), size=mp.Vector3(sxy, 0)), + mp.FluxRegion(center=mp.Vector3(0, -0.5 * sxy), size=mp.Vector3(sxy, 0), weight=-1), + mp.FluxRegion(center=mp.Vector3(+0.5 * sxy, 0), size=mp.Vector3(0, sxy)), + mp.FluxRegion(center=mp.Vector3(-0.5 * sxy, 0), size=mp.Vector3(0, sxy), weight=-1), +) sim.run(until_after_sources=mp.stop_when_dft_decayed()) near_flux = mp.get_fluxes(flux_box)[0] # half side length of far-field square box OR radius of far-field circle -r = 1000/fcen +r = 1000 / fcen # resolution of far fields (points/μm) res_ff = 1 -far_flux_box = (nearfield_box.flux(mp.Y, - mp.Volume(center=mp.Vector3(y=r), - size=mp.Vector3(2*r)), - res_ff)[0] - - nearfield_box.flux(mp.Y, - mp.Volume(center=mp.Vector3(y=-r), - size=mp.Vector3(2*r)), - res_ff)[0] + - nearfield_box.flux(mp.X, - mp.Volume(center=mp.Vector3(r), - size=mp.Vector3(y=2*r)), - res_ff)[0] - - nearfield_box.flux(mp.X, - mp.Volume(center=mp.Vector3(-r), - size=mp.Vector3(y=2*r)), - res_ff)[0]) +far_flux_box = ( + nearfield_box.flux( + mp.Y, mp.Volume(center=mp.Vector3(y=r), size=mp.Vector3(2 * r)), res_ff + )[0] + - nearfield_box.flux( + mp.Y, mp.Volume(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r)), res_ff + )[0] + + nearfield_box.flux( + mp.X, mp.Volume(center=mp.Vector3(r), size=mp.Vector3(y=2 * r)), res_ff + )[0] + - nearfield_box.flux( + mp.X, mp.Volume(center=mp.Vector3(-r), size=mp.Vector3(y=2 * r)), res_ff + )[0] +) npts = 100 # number of points in [0,2*pi) range of angles -angles = 2*math.pi/npts*np.arange(npts) +angles = 2 * math.pi / npts * np.arange(npts) -E = np.zeros((npts,3),dtype=np.complex128) -H = np.zeros((npts,3),dtype=np.complex128) +E = np.zeros((npts, 3), dtype=np.complex128) +H = np.zeros((npts, 3), dtype=np.complex128) for n in range(npts): - ff = sim.get_farfield(nearfield_box, - mp.Vector3(r*math.cos(angles[n]), - r*math.sin(angles[n]))) - E[n,:] = [np.conj(ff[j]) for j in range(3)] - H[n,:] = [ff[j+3] for j in range(3)] + ff = sim.get_farfield( + nearfield_box, mp.Vector3(r * math.cos(angles[n]), r * math.sin(angles[n])) + ) + E[n, :] = [np.conj(ff[j]) for j in range(3)] + H[n, :] = [ff[j + 3] for j in range(3)] -Px = np.real(E[:,1]*H[:,2]-E[:,2]*H[:,1]) -Py = np.real(E[:,2]*H[:,0]-E[:,0]*H[:,2]) -Pr = np.sqrt(np.square(Px)+np.square(Py)) +Px = np.real(E[:, 1] * H[:, 2] - E[:, 2] * H[:, 1]) +Py = np.real(E[:, 2] * H[:, 0] - E[:, 0] * H[:, 2]) +Pr = np.sqrt(np.square(Px) + np.square(Py)) # integrate the radial flux over the circle circumference -far_flux_circle = np.sum(Pr)*2*np.pi*r/len(Pr) +far_flux_circle = np.sum(Pr) * 2 * np.pi * r / len(Pr) -print("flux:, {:.6f}, {:.6f}, {:.6f}".format(near_flux,far_flux_box,far_flux_circle)) +print("flux:, {:.6f}, {:.6f}, {:.6f}".format(near_flux, far_flux_box, far_flux_circle)) -ax = plt.subplot(111, projection='polar') -ax.plot(angles,Pr/max(Pr),'b-') +ax = plt.subplot(111, projection="polar") +ax.plot(angles, Pr / max(Pr), "b-") ax.set_rmax(1) -ax.set_rticks([0,0.5,1]) +ax.set_rticks([0, 0.5, 1]) ax.grid(True) ax.set_rlabel_position(22) plt.show() diff --git a/python/examples/antenna_pec_ground_plane.py b/python/examples/antenna_pec_ground_plane.py index d02af9760..cdd26c4ff 100644 --- a/python/examples/antenna_pec_ground_plane.py +++ b/python/examples/antenna_pec_ground_plane.py @@ -7,90 +7,95 @@ import math import numpy as np import matplotlib -matplotlib.use('agg') + +matplotlib.use("agg") import matplotlib.pyplot as plt resolution = 200 # pixels/um -n = 1.2 # refractive index of surrounding medium -h = 1.25 # height of antenna (point dipole source) above ground plane -wvl = 0.65 # vacuum wavelength -r = 1000*wvl # radius of far-field circle -npts = 50 # number of points in [0,pi/2) range of angles +n = 1.2 # refractive index of surrounding medium +h = 1.25 # height of antenna (point dipole source) above ground plane +wvl = 0.65 # vacuum wavelength +r = 1000 * wvl # radius of far-field circle +npts = 50 # number of points in [0,pi/2) range of angles -angles = 0.5*math.pi/npts*np.arange(npts) +angles = 0.5 * math.pi / npts * np.arange(npts) -def radial_flux(sim,nearfield_box,r): - E = np.zeros((npts,3),dtype=np.complex128) - H = np.zeros((npts,3),dtype=np.complex128) +def radial_flux(sim, nearfield_box, r): + E = np.zeros((npts, 3), dtype=np.complex128) + H = np.zeros((npts, 3), dtype=np.complex128) for n in range(npts): - ff = sim.get_farfield(nearfield_box, - mp.Vector3(r*math.sin(angles[n]), - r*math.cos(angles[n]))) - E[n,:] = [np.conj(ff[j]) for j in range(3)] - H[n,:] = [ff[j+3] for j in range(3)] + ff = sim.get_farfield( + nearfield_box, mp.Vector3(r * math.sin(angles[n]), r * math.cos(angles[n])) + ) + E[n, :] = [np.conj(ff[j]) for j in range(3)] + H[n, :] = [ff[j + 3] for j in range(3)] - Px = np.real(E[:,1]*H[:,2]-E[:,2]*H[:,1]) # Ey*Hz-Ez*Hy - Py = np.real(E[:,2]*H[:,0]-E[:,0]*H[:,2]) # Ez*Hx-Ex*Hz - return np.sqrt(np.square(Px)+np.square(Py)) + Px = np.real(E[:, 1] * H[:, 2] - E[:, 2] * H[:, 1]) # Ey*Hz-Ez*Hy + Py = np.real(E[:, 2] * H[:, 0] - E[:, 0] * H[:, 2]) # Ez*Hx-Ex*Hz + return np.sqrt(np.square(Px) + np.square(Py)) def free_space_radiation(src_cmpt): sxy = 4 dpml = 1 - cell_size = mp.Vector3(sxy+2*dpml,sxy+2*dpml) + cell_size = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml) pml_layers = [mp.PML(dpml)] - fcen = 1/wvl - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - center=mp.Vector3(), - component=src_cmpt)] + fcen = 1 / wvl + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=mp.Vector3(), + component=src_cmpt, + ) + ] if src_cmpt == mp.Hz: - symmetries = [mp.Mirror(mp.X,phase=-1), - mp.Mirror(mp.Y,phase=-1)] + symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)] elif src_cmpt == mp.Ez: - symmetries = [mp.Mirror(mp.X,phase=+1), - mp.Mirror(mp.Y,phase=+1)] + symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=+1)] else: symmetries = [] - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - sources=sources, - symmetries=symmetries, - boundary_layers=pml_layers, - default_material=mp.Medium(index=n)) - - nearfield_box = sim.add_near2far(fcen, - 0, - 1, - mp.Near2FarRegion(center=mp.Vector3(0,+0.5*sxy), - size=mp.Vector3(sxy,0)), - mp.Near2FarRegion(center=mp.Vector3(0,-0.5*sxy), - size=mp.Vector3(sxy,0), - weight=-1), - mp.Near2FarRegion(center=mp.Vector3(+0.5*sxy,0), - size=mp.Vector3(0,sxy)), - mp.Near2FarRegion(center=mp.Vector3(-0.5*sxy,0), - size=mp.Vector3(0,sxy), - weight=-1)) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + sources=sources, + symmetries=symmetries, + boundary_layers=pml_layers, + default_material=mp.Medium(index=n), + ) + + nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion(center=mp.Vector3(0, +0.5 * sxy), size=mp.Vector3(sxy, 0)), + mp.Near2FarRegion( + center=mp.Vector3(0, -0.5 * sxy), size=mp.Vector3(sxy, 0), weight=-1 + ), + mp.Near2FarRegion(center=mp.Vector3(+0.5 * sxy, 0), size=mp.Vector3(0, sxy)), + mp.Near2FarRegion( + center=mp.Vector3(-0.5 * sxy, 0), size=mp.Vector3(0, sxy), weight=-1 + ), + ) sim.run(until_after_sources=mp.stop_when_dft_decayed()) - return radial_flux(sim,nearfield_box,r) + return radial_flux(sim, nearfield_box, r) def pec_ground_plane_radiation(src_cmpt=mp.Hz): - L = 8.0 # length of non-PML region + L = 8.0 # length of non-PML region dpml = 1.0 # thickness of PML - sxy = dpml+L+dpml - cell_size = mp.Vector3(sxy,sxy,0) + sxy = dpml + L + dpml + cell_size = mp.Vector3(sxy, sxy, 0) boundary_layers = [mp.PML(dpml)] - fcen = 1/wvl + fcen = 1 / wvl # The near-to-far field transformation feature only supports # homogeneous media which means it cannot explicitly take into @@ -101,51 +106,61 @@ def pec_ground_plane_radiation(src_cmpt=mp.Hz): # equivalent to a PEC boundary condition on one side. # Note: This setup means that the radiation pattern can only # be measured in the top half above the dipole. - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=src_cmpt, - center=mp.Vector3(0,+h)), - mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=src_cmpt, - center=mp.Vector3(0,-h), - amplitude=-1 if src_cmpt==mp.Ez else +1)] - - symmetries = [mp.Mirror(direction=mp.X, - phase=+1 if src_cmpt==mp.Ez else -1), - mp.Mirror(direction=mp.Y, - phase=-1 if src_cmpt==mp.Ez else +1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=n)) - - nearfield_box = sim.add_near2far(fcen, - 0, - 1, - mp.Near2FarRegion(center=mp.Vector3(0,2*h), - size=mp.Vector3(2*h,0), - weight=+1), - mp.Near2FarRegion(center=mp.Vector3(0,-2*h), - size=mp.Vector3(2*h,0), - weight=-1), - mp.Near2FarRegion(center=mp.Vector3(h,0), - size=mp.Vector3(0,4*h), - weight=+1), - mp.Near2FarRegion(center=mp.Vector3(-h,0), - size=mp.Vector3(0,4*h), - weight=-1)) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=src_cmpt, + center=mp.Vector3(0, +h), + ), + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=src_cmpt, + center=mp.Vector3(0, -h), + amplitude=-1 if src_cmpt == mp.Ez else +1, + ), + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=+1 if src_cmpt == mp.Ez else -1), + mp.Mirror(direction=mp.Y, phase=-1 if src_cmpt == mp.Ez else +1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=n), + ) + + nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(0, 2 * h), size=mp.Vector3(2 * h, 0), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(0, -2 * h), size=mp.Vector3(2 * h, 0), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(h, 0), size=mp.Vector3(0, 4 * h), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(-h, 0), size=mp.Vector3(0, 4 * h), weight=-1 + ), + ) sim.plot2D() - plt.savefig('antenna_pec_ground_plane.png',bbox_inches='tight') + plt.savefig("antenna_pec_ground_plane.png", bbox_inches="tight") sim.run(until_after_sources=mp.stop_when_dft_decayed()) - return radial_flux(sim,nearfield_box,r) + return radial_flux(sim, nearfield_box, r) -if __name__ == '__main__': +if __name__ == "__main__": src_cmpt = mp.Ez # TM/P: Hz or TE/S: Ez Pr_fsp = free_space_radiation(src_cmpt) Pr_pec = pec_ground_plane_radiation(src_cmpt) @@ -156,23 +171,27 @@ def pec_ground_plane_radiation(src_cmpt=mp.Hz): # as derived in Section 6.2 "Two-Element Array" of # Antenna Theory: Analysis and Design, Fourth Edition # (2016) by C.A. Balanis. - k = 2*np.pi/(wvl/n) # wavevector in free space - Pr_theory = np.zeros(npts,) - for i,ang in enumerate(angles): - Pr_theory[i] = Pr_fsp[i] * 2*np.sin(k*h*np.cos(ang)) + k = 2 * np.pi / (wvl / n) # wavevector in free space + Pr_theory = np.zeros( + npts, + ) + for i, ang in enumerate(angles): + Pr_theory[i] = Pr_fsp[i] * 2 * np.sin(k * h * np.cos(ang)) - Pr_pec_norm = Pr_pec/np.max(Pr_pec) - Pr_theory_norm = (Pr_theory/max(Pr_theory))**2 + Pr_pec_norm = Pr_pec / np.max(Pr_pec) + Pr_theory_norm = (Pr_theory / max(Pr_theory)) ** 2 plt.figure() - plt.plot(np.degrees(angles),Pr_pec_norm,'b-',label='Meep') - plt.plot(np.degrees(angles),Pr_theory_norm,'r-',label='theory') - plt.xlabel('angle (degrees)') - plt.ylabel('radial flux (normalized by maximum flux)') - plt.title(f"antenna with {'E' if src_cmpt==mp.Ez else r'H'}$_z$ polarization above PEC ground plane") - - plt.axis([0,90,0,1.0]) + plt.plot(np.degrees(angles), Pr_pec_norm, "b-", label="Meep") + plt.plot(np.degrees(angles), Pr_theory_norm, "r-", label="theory") + plt.xlabel("angle (degrees)") + plt.ylabel("radial flux (normalized by maximum flux)") + plt.title( + f"antenna with {'E' if src_cmpt==mp.Ez else r'H'}$_z$ polarization above PEC ground plane" + ) + + plt.axis([0, 90, 0, 1.0]) plt.legend() - plt.savefig('radiation_pattern.png',bbox_inches='tight') + plt.savefig("radiation_pattern.png", bbox_inches="tight") - print("norm:, {:.6f}".format(np.linalg.norm(Pr_pec_norm-Pr_theory_norm))) + print("norm:, {:.6f}".format(np.linalg.norm(Pr_pec_norm - Pr_theory_norm))) diff --git a/python/examples/bend-flux.ipynb b/python/examples/bend-flux.ipynb index df16d007f..bf9a54c7e 100644 --- a/python/examples/bend-flux.ipynb +++ b/python/examples/bend-flux.ipynb @@ -34,11 +34,11 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 10 # pixels/um\n", + "resolution = 10 # pixels/um\n", "\n", "sx = 16 # size of cell in X direction\n", "sy = 32 # size of cell in Y direction\n", - "cell = mp.Vector3(sx,sy,0)\n", + "cell = mp.Vector3(sx, sy, 0)\n", "\n", "dpml = 1.0\n", "pml_layers = [mp.PML(dpml)]" @@ -58,7 +58,7 @@ "outputs": [], "source": [ "pad = 4 # padding distance between waveguide and cell edge\n", - "w = 1 # width of waveguide" + "w = 1 # width of waveguide" ] }, { @@ -74,8 +74,8 @@ "metadata": {}, "outputs": [], "source": [ - "wvg_xcen = 0.5*(sx-w-2*pad) # x center of horiz. wvg\n", - "wvg_ycen = -0.5*(sy-w-2*pad) # y center of vert. wvg" + "wvg_xcen = 0.5 * (sx - w - 2 * pad) # x center of horiz. wvg\n", + "wvg_ycen = -0.5 * (sy - w - 2 * pad) # y center of vert. wvg" ] }, { @@ -91,9 +91,13 @@ "metadata": {}, "outputs": [], "source": [ - "geometry = [mp.Block(size=mp.Vector3(mp.inf,w,mp.inf),\n", - " center=mp.Vector3(0,wvg_ycen,0),\n", - " material=mp.Medium(epsilon=12))]" + "geometry = [\n", + " mp.Block(\n", + " size=mp.Vector3(mp.inf, w, mp.inf),\n", + " center=mp.Vector3(0, wvg_ycen, 0),\n", + " material=mp.Medium(epsilon=12),\n", + " )\n", + "]" ] }, { @@ -110,11 +114,15 @@ "outputs": [], "source": [ "fcen = 0.15 # pulse center frequency\n", - "df = 0.1 # pulse width (in frequency)\n", - "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(-0.5*sx+dpml,wvg_ycen,0),\n", - " size=mp.Vector3(0,w,0))]" + "df = 0.1 # pulse width (in frequency)\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(-0.5 * sx + dpml, wvg_ycen, 0),\n", + " size=mp.Vector3(0, w, 0),\n", + " )\n", + "]" ] }, { @@ -156,25 +164,31 @@ } ], "source": [ - "sim = mp.Simulation(cell_size=cell,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources,\n", - " resolution=resolution)\n", + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources,\n", + " resolution=resolution,\n", + ")\n", "\n", "nfreq = 100 # number of frequencies at which to compute flux\n", "\n", "# reflected flux\n", - "refl_fr = mp.FluxRegion(center=mp.Vector3(-0.5*sx+dpml+0.5,wvg_ycen,0), size=mp.Vector3(0,2*w,0)) \n", + "refl_fr = mp.FluxRegion(\n", + " center=mp.Vector3(-0.5 * sx + dpml + 0.5, wvg_ycen, 0), size=mp.Vector3(0, 2 * w, 0)\n", + ")\n", "refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", "\n", "# transmitted flux\n", - "tran_fr = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml,wvg_ycen,0), size=mp.Vector3(0,2*w,0))\n", + "tran_fr = mp.FluxRegion(\n", + " center=mp.Vector3(0.5 * sx - dpml, wvg_ycen, 0), size=mp.Vector3(0, 2 * w, 0)\n", + ")\n", "tran = sim.add_flux(fcen, df, nfreq, tran_fr)\n", "\n", "plt.figure(dpi=150)\n", "sim.plot2D()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -206,9 +220,9 @@ } ], "source": [ - "pt = mp.Vector3(0.5*sx-dpml-0.5,wvg_ycen)\n", + "pt = mp.Vector3(0.5 * sx - dpml - 0.5, wvg_ycen)\n", "\n", - "sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,pt,1e-3))\n", + "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, pt, 1e-3))\n", "\n", "# for normalization run, save flux fields data for reflection plane\n", "straight_refl_data = sim.get_flux_data(refl)" @@ -284,25 +298,39 @@ "source": [ "sim.reset_meep()\n", "\n", - "geometry = [mp.Block(mp.Vector3(sx-pad,w,mp.inf), center=mp.Vector3(-0.5*pad,wvg_ycen), material=mp.Medium(epsilon=12)),\n", - " mp.Block(mp.Vector3(w,sy-pad,mp.inf), center=mp.Vector3(wvg_xcen,0.5*pad), material=mp.Medium(epsilon=12))]\n", + "geometry = [\n", + " mp.Block(\n", + " mp.Vector3(sx - pad, w, mp.inf),\n", + " center=mp.Vector3(-0.5 * pad, wvg_ycen),\n", + " material=mp.Medium(epsilon=12),\n", + " ),\n", + " mp.Block(\n", + " mp.Vector3(w, sy - pad, mp.inf),\n", + " center=mp.Vector3(wvg_xcen, 0.5 * pad),\n", + " material=mp.Medium(epsilon=12),\n", + " ),\n", + "]\n", "\n", - "sim = mp.Simulation(cell_size=cell,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources,\n", - " resolution=resolution)\n", + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources,\n", + " resolution=resolution,\n", + ")\n", "\n", "# reflected flux\n", "refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", "\n", - "tran_fr = mp.FluxRegion(center=mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5,0), size=mp.Vector3(2*w,0,0))\n", + "tran_fr = mp.FluxRegion(\n", + " center=mp.Vector3(wvg_xcen, 0.5 * sy - dpml - 0.5, 0), size=mp.Vector3(2 * w, 0, 0)\n", + ")\n", "tran = sim.add_flux(fcen, df, nfreq, tran_fr)\n", "\n", "# for normal run, load negated fields to subtract incident from refl. fields\n", "sim.load_minus_flux_data(refl, straight_refl_data)\n", "\n", - "pt = mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5)\n", + "pt = mp.Vector3(wvg_xcen, 0.5 * sy - dpml - 0.5)\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, pt, 1e-3))\n", "\n", @@ -346,15 +374,15 @@ "Rs = []\n", "Ts = []\n", "for i in range(nfreq):\n", - " wl = np.append(wl, 1/flux_freqs[i])\n", - " Rs = np.append(Rs,-bend_refl_flux[i]/straight_tran_flux[i])\n", - " Ts = np.append(Ts,bend_tran_flux[i]/straight_tran_flux[i]) \n", + " wl = np.append(wl, 1 / flux_freqs[i])\n", + " Rs = np.append(Rs, -bend_refl_flux[i] / straight_tran_flux[i])\n", + " Ts = np.append(Ts, bend_tran_flux[i] / straight_tran_flux[i])\n", "\n", "if mp.am_master():\n", " plt.figure(dpi=150)\n", - " plt.plot(wl,Rs,'bo-',label='reflectance')\n", - " plt.plot(wl,Ts,'ro-',label='transmittance')\n", - " plt.plot(wl,1-Rs-Ts,'go-',label='loss')\n", + " plt.plot(wl, Rs, \"bo-\", label=\"reflectance\")\n", + " plt.plot(wl, Ts, \"ro-\", label=\"transmittance\")\n", + " plt.plot(wl, 1 - Rs - Ts, \"go-\", label=\"loss\")\n", " plt.axis([5.0, 10.0, 0, 1])\n", " plt.xlabel(\"wavelength (μm)\")\n", " plt.legend(loc=\"upper right\")\n", diff --git a/python/examples/bend-flux.py b/python/examples/bend-flux.py index 0b788fd3b..66bebed80 100644 --- a/python/examples/bend-flux.py +++ b/python/examples/bend-flux.py @@ -7,51 +7,65 @@ import numpy as np import matplotlib.pyplot as plt -resolution = 10 # pixels/um +resolution = 10 # pixels/um sx = 16 # size of cell in X direction sy = 32 # size of cell in Y direction -cell = mp.Vector3(sx,sy,0) +cell = mp.Vector3(sx, sy, 0) dpml = 1.0 pml_layers = [mp.PML(dpml)] pad = 4 # padding distance between waveguide and cell edge -w = 1 # width of waveguide +w = 1 # width of waveguide -wvg_xcen = 0.5*(sx-w-2*pad) # x center of vert. wvg -wvg_ycen = -0.5*(sy-w-2*pad) # y center of horiz. wvg +wvg_xcen = 0.5 * (sx - w - 2 * pad) # x center of vert. wvg +wvg_ycen = -0.5 * (sy - w - 2 * pad) # y center of horiz. wvg -geometry = [mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), - center=mp.Vector3(0,wvg_ycen,0), - material=mp.Medium(epsilon=12))] +geometry = [ + mp.Block( + size=mp.Vector3(mp.inf, w, mp.inf), + center=mp.Vector3(0, wvg_ycen, 0), + material=mp.Medium(epsilon=12), + ) +] fcen = 0.15 # pulse center frequency -df = 0.1 # pulse width (in frequency) -sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(-0.5*sx+dpml,wvg_ycen,0), - size=mp.Vector3(0,w,0))] - -sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) +df = 0.1 # pulse width (in frequency) +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(-0.5 * sx + dpml, wvg_ycen, 0), + size=mp.Vector3(0, w, 0), + ) +] + +sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, +) nfreq = 100 # number of frequencies at which to compute flux # reflected flux -refl_fr = mp.FluxRegion(center=mp.Vector3(-0.5*sx+dpml+0.5,wvg_ycen,0),size=mp.Vector3(0,2*w,0)) -refl = sim.add_flux(fcen,df,nfreq,refl_fr) +refl_fr = mp.FluxRegion( + center=mp.Vector3(-0.5 * sx + dpml + 0.5, wvg_ycen, 0), size=mp.Vector3(0, 2 * w, 0) +) +refl = sim.add_flux(fcen, df, nfreq, refl_fr) # transmitted flux -tran_fr = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml,wvg_ycen,0),size=mp.Vector3(0,2*w,0)) -tran = sim.add_flux(fcen,df,nfreq,tran_fr) +tran_fr = mp.FluxRegion( + center=mp.Vector3(0.5 * sx - dpml, wvg_ycen, 0), size=mp.Vector3(0, 2 * w, 0) +) +tran = sim.add_flux(fcen, df, nfreq, tran_fr) -pt = mp.Vector3(0.5*sx-dpml-0.5,wvg_ycen) +pt = mp.Vector3(0.5 * sx - dpml - 0.5, wvg_ycen) -sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,pt,1e-3)) +sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, pt, 1e-3)) # for normalization run, save flux fields data for reflection plane straight_refl_data = sim.get_flux_data(refl) @@ -61,25 +75,39 @@ sim.reset_meep() -geometry = [mp.Block(mp.Vector3(sx-pad,w,mp.inf),center=mp.Vector3(-0.5*pad,wvg_ycen),material=mp.Medium(epsilon=12)), - mp.Block(mp.Vector3(w,sy-pad,mp.inf),center=mp.Vector3(wvg_xcen,0.5*pad),material=mp.Medium(epsilon=12))] - -sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) +geometry = [ + mp.Block( + mp.Vector3(sx - pad, w, mp.inf), + center=mp.Vector3(-0.5 * pad, wvg_ycen), + material=mp.Medium(epsilon=12), + ), + mp.Block( + mp.Vector3(w, sy - pad, mp.inf), + center=mp.Vector3(wvg_xcen, 0.5 * pad), + material=mp.Medium(epsilon=12), + ), +] + +sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, +) # reflected flux refl = sim.add_flux(fcen, df, nfreq, refl_fr) -tran_fr = mp.FluxRegion(center=mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5,0),size=mp.Vector3(2*w,0,0)) -tran = sim.add_flux(fcen,df,nfreq,tran_fr) +tran_fr = mp.FluxRegion( + center=mp.Vector3(wvg_xcen, 0.5 * sy - dpml - 0.5, 0), size=mp.Vector3(2 * w, 0, 0) +) +tran = sim.add_flux(fcen, df, nfreq, tran_fr) # for normal run, load negated fields to subtract incident from refl. fields -sim.load_minus_flux_data(refl,straight_refl_data) +sim.load_minus_flux_data(refl, straight_refl_data) -pt = mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5) +pt = mp.Vector3(wvg_xcen, 0.5 * sy - dpml - 0.5) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, pt, 1e-3)) @@ -92,15 +120,15 @@ Rs = [] Ts = [] for i in range(nfreq): - wl = np.append(wl, 1/flux_freqs[i]) - Rs = np.append(Rs,-bend_refl_flux[i]/straight_tran_flux[i]) - Ts = np.append(Ts,bend_tran_flux[i]/straight_tran_flux[i]) + wl = np.append(wl, 1 / flux_freqs[i]) + Rs = np.append(Rs, -bend_refl_flux[i] / straight_tran_flux[i]) + Ts = np.append(Ts, bend_tran_flux[i] / straight_tran_flux[i]) if mp.am_master(): plt.figure() - plt.plot(wl,Rs,'bo-',label='reflectance') - plt.plot(wl,Ts,'ro-',label='transmittance') - plt.plot(wl,1-Rs-Ts,'go-',label='loss') + plt.plot(wl, Rs, "bo-", label="reflectance") + plt.plot(wl, Ts, "ro-", label="transmittance") + plt.plot(wl, 1 - Rs - Ts, "go-", label="loss") plt.axis([5.0, 10.0, 0, 1]) plt.xlabel("wavelength (μm)") plt.legend(loc="upper right") diff --git a/python/examples/bent-waveguide.ipynb b/python/examples/bent-waveguide.ipynb index ca9d4e5d0..f03241857 100644 --- a/python/examples/bent-waveguide.ipynb +++ b/python/examples/bent-waveguide.ipynb @@ -34,6 +34,7 @@ "from matplotlib import pyplot as plt\n", "import numpy as np\n", "from IPython.display import Video\n", + "\n", "%matplotlib inline" ] }, @@ -50,13 +51,19 @@ "metadata": {}, "outputs": [], "source": [ - "cell = mp.Vector3(16,16,0)\n", - "geometry = [mp.Block(mp.Vector3(12,1,mp.inf),\n", - " center=mp.Vector3(-2.5,-3.5),\n", - " material=mp.Medium(epsilon=12)),\n", - " mp.Block(mp.Vector3(1,12,mp.inf),\n", - " center=mp.Vector3(3.5,2),\n", - " material=mp.Medium(epsilon=12))]\n", + "cell = mp.Vector3(16, 16, 0)\n", + "geometry = [\n", + " mp.Block(\n", + " mp.Vector3(12, 1, mp.inf),\n", + " center=mp.Vector3(-2.5, -3.5),\n", + " material=mp.Medium(epsilon=12),\n", + " ),\n", + " mp.Block(\n", + " mp.Vector3(1, 12, mp.inf),\n", + " center=mp.Vector3(3.5, 2),\n", + " material=mp.Medium(epsilon=12),\n", + " ),\n", + "]\n", "pml_layers = [mp.PML(1.0)]\n", "resolution = 10" ] @@ -81,10 +88,14 @@ "metadata": {}, "outputs": [], "source": [ - "sources = [mp.Source(mp.ContinuousSource(wavelength=2*(11**0.5), width=20),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(-7,-3.5),\n", - " size=mp.Vector3(0,1))]" + "sources = [\n", + " mp.Source(\n", + " mp.ContinuousSource(wavelength=2 * (11**0.5), width=20),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(-7, -3.5),\n", + " size=mp.Vector3(0, 1),\n", + " )\n", + "]" ] }, { @@ -100,11 +111,13 @@ "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(cell_size=cell,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -141,7 +154,7 @@ ], "source": [ "f = plt.figure(dpi=150)\n", - "sim.plot2D(ax = f.gca())\n", + "sim.plot2D(ax=f.gca())\n", "plt.show()" ] }, @@ -169,7 +182,7 @@ "source": [ "f = plt.figure(dpi=150)\n", "Animate = mp.Animate2D(sim, fields=mp.Ez, f=f, realtime=False, normalize=True)\n", - "sim.run(mp.at_every(0.5,Animate),until=100)\n", + "sim.run(mp.at_every(0.5, Animate), until=100)\n", "plt.close()" ] }, @@ -204,7 +217,7 @@ "source": [ "filename = \"media/bent_waveguide.mp4\"\n", "fps = 10\n", - "Animate.to_mp4(fps,filename)\n", + "Animate.to_mp4(fps, filename)\n", "Video(filename)" ] }, @@ -253,16 +266,22 @@ ], "source": [ "sim.reset_meep()\n", - "cell = mp.Vector3(16,40,0)\n", - "geometry = [mp.Block(mp.Vector3(12,1,mp.inf),\n", - " center=mp.Vector3(-2.5,-3.5),\n", - " material=mp.Medium(epsilon=12)),\n", - " mp.Block(mp.Vector3(1,42,mp.inf),\n", - " center=mp.Vector3(3.5,17),\n", - " material=mp.Medium(epsilon=12))]\n", + "cell = mp.Vector3(16, 40, 0)\n", + "geometry = [\n", + " mp.Block(\n", + " mp.Vector3(12, 1, mp.inf),\n", + " center=mp.Vector3(-2.5, -3.5),\n", + " material=mp.Medium(epsilon=12),\n", + " ),\n", + " mp.Block(\n", + " mp.Vector3(1, 42, mp.inf),\n", + " center=mp.Vector3(3.5, 17),\n", + " material=mp.Medium(epsilon=12),\n", + " ),\n", + "]\n", "sim.cell_size = cell\n", "sim.geometry = geometry\n", - "sim.geometry_center = mp.Vector3(0,12,0)\n", + "sim.geometry_center = mp.Vector3(0, 12, 0)\n", "\n", "sim.run(until=400)\n", "\n", @@ -314,18 +333,22 @@ "source": [ "vals = []\n", "\n", + "\n", "def get_slice(sim):\n", - " vals.append(sim.get_array(center=mp.Vector3(0,-3.5), size=mp.Vector3(16,0), component=mp.Ez))\n", + " vals.append(\n", + " sim.get_array(\n", + " center=mp.Vector3(0, -3.5), size=mp.Vector3(16, 0), component=mp.Ez\n", + " )\n", + " )\n", + "\n", "\n", "sim.reset_meep()\n", - "sim.run(mp.at_beginning(mp.output_epsilon),\n", - " mp.at_every(0.6, get_slice),\n", - " until=200)\n", + "sim.run(mp.at_beginning(mp.output_epsilon), mp.at_every(0.6, get_slice), until=200)\n", "\n", "plt.figure(dpi=150)\n", - "plt.imshow(vals, interpolation='spline36', cmap='RdBu')\n", - "plt.axis('off')\n", - "plt.show()\n" + "plt.imshow(vals, interpolation=\"spline36\", cmap=\"RdBu\")\n", + "plt.axis(\"off\")\n", + "plt.show()" ] } ], diff --git a/python/examples/bent-waveguide.py b/python/examples/bent-waveguide.py index 17d465c83..1faaad84e 100644 --- a/python/examples/bent-waveguide.py +++ b/python/examples/bent-waveguide.py @@ -5,27 +5,41 @@ import meep as mp -cell = mp.Vector3(16,16,0) -geometry = [mp.Block(mp.Vector3(12,1,mp.inf), - center=mp.Vector3(-2.5,-3.5), - material=mp.Medium(epsilon=12)), - mp.Block(mp.Vector3(1,12,mp.inf), - center=mp.Vector3(3.5,2), - material=mp.Medium(epsilon=12))] +cell = mp.Vector3(16, 16, 0) +geometry = [ + mp.Block( + mp.Vector3(12, 1, mp.inf), + center=mp.Vector3(-2.5, -3.5), + material=mp.Medium(epsilon=12), + ), + mp.Block( + mp.Vector3(1, 12, mp.inf), + center=mp.Vector3(3.5, 2), + material=mp.Medium(epsilon=12), + ), +] pml_layers = [mp.PML(1.0)] resolution = 10 -sources = [mp.Source(mp.ContinuousSource(wavelength=2*(11**0.5), width=20), - component=mp.Ez, - center=mp.Vector3(-7,-3.5), - size=mp.Vector3(0,1))] +sources = [ + mp.Source( + mp.ContinuousSource(wavelength=2 * (11**0.5), width=20), + component=mp.Ez, + center=mp.Vector3(-7, -3.5), + size=mp.Vector3(0, 1), + ) +] -sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) +sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, +) -sim.run(mp.at_beginning(mp.output_epsilon), - mp.to_appended("ez", mp.at_every(0.6, mp.output_efield_z)), - until=200) +sim.run( + mp.at_beginning(mp.output_epsilon), + mp.to_appended("ez", mp.at_every(0.6, mp.output_efield_z)), + until=200, +) diff --git a/python/examples/binary_grating.ipynb b/python/examples/binary_grating.ipynb index 670528df6..a49b08fe1 100644 --- a/python/examples/binary_grating.ipynb +++ b/python/examples/binary_grating.ipynb @@ -80,46 +80,57 @@ "source": [ "fused_quartz = mp.Medium(index=1.5)\n", "\n", - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 3.0 # substrate thickness\n", - "dpad = 3.0 # padding between grating and PML\n", - "gp = 10.0 # grating period\n", - "gh = 0.5 # grating height\n", - "gdc = 0.5 # grating duty cycle\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 3.0 # substrate thickness\n", + "dpad = 3.0 # padding between grating and PML\n", + "gp = 10.0 # grating period\n", + "gh = 0.5 # grating height\n", + "gdc = 0.5 # grating duty cycle\n", "\n", - "sx = dpml+dsub+gh+dpad+dpml\n", + "sx = dpml + dsub + gh + dpad + dpml\n", "sy = gp\n", "\n", - "cell_size = mp.Vector3(sx,sy,0)\n", - "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", - "\n", - "wvl_min = 0.4 # min wavelength\n", - "wvl_max = 0.6 # max wavelength\n", - "fmin = 1/wvl_max # min frequency\n", - "fmax = 1/wvl_min # max frequency\n", - "fcen = 0.5*(fmin+fmax) # center frequency\n", - "df = fmax-fmin # frequency width\n", - "\n", - "src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub)\n", - "sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(y=sy))]\n", - "\n", - "k_point = mp.Vector3(0,0,0)\n", - "\n", - "symmetries=[mp.Mirror(mp.Y)]\n", - "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " default_material=fused_quartz,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", + "cell_size = mp.Vector3(sx, sy, 0)\n", + "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", + "\n", + "wvl_min = 0.4 # min wavelength\n", + "wvl_max = 0.6 # max wavelength\n", + "fmin = 1 / wvl_max # min frequency\n", + "fmax = 1 / wvl_min # max frequency\n", + "fcen = 0.5 * (fmin + fmax) # center frequency\n", + "df = fmax - fmin # frequency width\n", + "\n", + "src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub)\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy),\n", + " )\n", + "]\n", + "\n", + "k_point = mp.Vector3(0, 0, 0)\n", + "\n", + "symmetries = [mp.Mirror(mp.Y)]\n", + "\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " default_material=fused_quartz,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + ")\n", "\n", "nfreq = 21\n", - "mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad)\n", - "flux_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", + "mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad)\n", + "flux_mon = sim.add_flux(\n", + " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", + ")\n", "\n", "f = plt.figure(dpi=120)\n", "sim.plot2D(ax=f.gca())\n", @@ -203,18 +214,32 @@ "source": [ "sim.reset_meep()\n", "\n", - "geometry = [mp.Block(material=fused_quartz, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub))),\n", - " mp.Block(material=fused_quartz, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))]\n", - "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", - "\n", - "mode_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", + "geometry = [\n", + " mp.Block(\n", + " material=fused_quartz,\n", + " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", + " ),\n", + " mp.Block(\n", + " material=fused_quartz,\n", + " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh),\n", + " ),\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + ")\n", + "\n", + "mode_mon = sim.add_flux(\n", + " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", + ")\n", "\n", "f2 = plt.figure(dpi=120)\n", "sim.plot2D(ax=f2.gca())\n", @@ -270,7 +295,9 @@ "freqs = mp.get_eigenmode_freqs(mode_mon)\n", "\n", "nmode = 10\n", - "res = sim.get_eigenmode_coefficients(mode_mon, range(1,nmode+1), eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", + "res = sim.get_eigenmode_coefficients(\n", + " mode_mon, range(1, nmode + 1), eig_parity=mp.ODD_Z + mp.EVEN_Y\n", + ")\n", "coeffs = res.alpha\n", "kdom = res.kdom\n", "\n", @@ -279,13 +306,13 @@ "mode_tran = []\n", "\n", "for nm in range(nmode):\n", - " for nf in range(nfreq):\n", - " mode_wvl.append(1/freqs[nf])\n", - " mode_angle.append(math.degrees(math.acos(kdom[nm*nfreq+nf].x/freqs[nf])))\n", - " tran = abs(coeffs[nm,nf,0])**2/input_flux[nf]\n", - " mode_tran.append(0.5*tran if nm != 0 else tran)\n", + " for nf in range(nfreq):\n", + " mode_wvl.append(1 / freqs[nf])\n", + " mode_angle.append(math.degrees(math.acos(kdom[nm * nfreq + nf].x / freqs[nf])))\n", + " tran = abs(coeffs[nm, nf, 0]) ** 2 / input_flux[nf]\n", + " mode_tran.append(0.5 * tran if nm != 0 else tran)\n", "\n", - "tran_max = round(max(mode_tran),1)" + "tran_max = round(max(mode_tran), 1)" ] }, { @@ -317,22 +344,24 @@ ], "source": [ "plt.figure(dpi=200)\n", - "plt.pcolormesh(np.reshape(mode_wvl,(nmode,nfreq)),\n", - " np.reshape(mode_angle,(nmode,nfreq)),\n", - " np.reshape(mode_tran,(nmode,nfreq)),\n", - " cmap='Blues',\n", - " shading='flat',\n", - " vmin=0,\n", - " vmax=tran_max)\n", + "plt.pcolormesh(\n", + " np.reshape(mode_wvl, (nmode, nfreq)),\n", + " np.reshape(mode_angle, (nmode, nfreq)),\n", + " np.reshape(mode_tran, (nmode, nfreq)),\n", + " cmap=\"Blues\",\n", + " shading=\"flat\",\n", + " vmin=0,\n", + " vmax=tran_max,\n", + ")\n", "plt.axis([min(mode_wvl), max(mode_wvl), min(mode_angle), max(mode_angle)])\n", "plt.xlabel(\"wavelength (μm)\")\n", "plt.ylabel(\"diffraction angle (degrees)\")\n", - "plt.xticks([t for t in np.linspace(wvl_min,wvl_max,3)])\n", - "plt.yticks([t for t in range(0,35,5)])\n", + "plt.xticks([t for t in np.linspace(wvl_min, wvl_max, 3)])\n", + "plt.yticks([t for t in range(0, 35, 5)])\n", "plt.title(\"transmittance of diffraction orders\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.arange(0,tran_max+0.1,0.1)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.arange(0,tran_max+0.1,0.1)])" + "cbar.set_ticks([t for t in np.arange(0, tran_max + 0.1, 0.1)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.arange(0, tran_max + 0.1, 0.1)])" ] }, { @@ -413,46 +442,57 @@ "source": [ "from meep.materials import fused_quartz\n", "\n", - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 3.0 # substrate thickness\n", - "dpad = 3.0 # padding between grating and PML\n", - "gp = 10.0 # grating period\n", - "gh = 0.5 # grating height\n", - "gdc = 0.5 # grating duty cycle\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 3.0 # substrate thickness\n", + "dpad = 3.0 # padding between grating and PML\n", + "gp = 10.0 # grating period\n", + "gh = 0.5 # grating height\n", + "gdc = 0.5 # grating duty cycle\n", "\n", - "sx = dpml+dsub+gh+dpad+dpml\n", + "sx = dpml + dsub + gh + dpad + dpml\n", "sy = gp\n", "\n", - "cell_size = mp.Vector3(sx,sy,0)\n", - "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", - "\n", - "wvl_min = 0.4 # min wavelength\n", - "wvl_max = 0.6 # max wavelength\n", - "fmin = 1/wvl_max # min frequency\n", - "fmax = 1/wvl_min # max frequency\n", - "fcen = 0.5*(fmin+fmax) # center frequency\n", - "df = fmax-fmin # frequency width\n", - "\n", - "src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub)\n", - "sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(y=sy))]\n", - "\n", - "k_point = mp.Vector3(0,0,0)\n", - "\n", - "symmetries=[mp.Mirror(mp.Y)]\n", - "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " default_material=fused_quartz,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", + "cell_size = mp.Vector3(sx, sy, 0)\n", + "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", + "\n", + "wvl_min = 0.4 # min wavelength\n", + "wvl_max = 0.6 # max wavelength\n", + "fmin = 1 / wvl_max # min frequency\n", + "fmax = 1 / wvl_min # max frequency\n", + "fcen = 0.5 * (fmin + fmax) # center frequency\n", + "df = fmax - fmin # frequency width\n", + "\n", + "src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub)\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy),\n", + " )\n", + "]\n", + "\n", + "k_point = mp.Vector3(0, 0, 0)\n", + "\n", + "symmetries = [mp.Mirror(mp.Y)]\n", + "\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " default_material=fused_quartz,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + ")\n", "\n", "nfreq = 21\n", - "mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad)\n", - "flux_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", + "mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad)\n", + "flux_mon = sim.add_flux(\n", + " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", + ")\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))\n", "\n", @@ -460,25 +500,41 @@ "\n", "sim.reset_meep()\n", "\n", - "geometry = [mp.Block(material=fused_quartz, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub))),\n", - " mp.Block(material=fused_quartz, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))]\n", - "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", - "\n", - "mode_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", + "geometry = [\n", + " mp.Block(\n", + " material=fused_quartz,\n", + " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", + " ),\n", + " mp.Block(\n", + " material=fused_quartz,\n", + " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh),\n", + " ),\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + ")\n", + "\n", + "mode_mon = sim.add_flux(\n", + " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", + ")\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))\n", "\n", "freqs = mp.get_eigenmode_freqs(mode_mon)\n", "\n", "nmode = 10\n", - "res = sim.get_eigenmode_coefficients(mode_mon, range(1,nmode+1), eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", + "res = sim.get_eigenmode_coefficients(\n", + " mode_mon, range(1, nmode + 1), eig_parity=mp.ODD_Z + mp.EVEN_Y\n", + ")\n", "coeffs = res.alpha\n", "kdom = res.kdom\n", "\n", @@ -487,31 +543,33 @@ "mode_tran = []\n", "\n", "for nm in range(nmode):\n", - " for nf in range(nfreq):\n", - " mode_wvl.append(1/freqs[nf])\n", - " mode_angle.append(math.degrees(math.acos(kdom[nm*nfreq+nf].x/freqs[nf])))\n", - " tran = abs(coeffs[nm,nf,0])**2/input_flux[nf]\n", - " mode_tran.append(0.5*tran if nm != 0 else tran)\n", + " for nf in range(nfreq):\n", + " mode_wvl.append(1 / freqs[nf])\n", + " mode_angle.append(math.degrees(math.acos(kdom[nm * nfreq + nf].x / freqs[nf])))\n", + " tran = abs(coeffs[nm, nf, 0]) ** 2 / input_flux[nf]\n", + " mode_tran.append(0.5 * tran if nm != 0 else tran)\n", "\n", - "tran_max = round(max(mode_tran),1)\n", + "tran_max = round(max(mode_tran), 1)\n", "\n", "plt.figure(dpi=200)\n", - "plt.pcolormesh(np.reshape(mode_wvl,(nmode,nfreq)),\n", - " np.reshape(mode_angle,(nmode,nfreq)),\n", - " np.reshape(mode_tran,(nmode,nfreq)),\n", - " cmap='Blues',\n", - " shading='flat',\n", - " vmin=0,\n", - " vmax=tran_max)\n", + "plt.pcolormesh(\n", + " np.reshape(mode_wvl, (nmode, nfreq)),\n", + " np.reshape(mode_angle, (nmode, nfreq)),\n", + " np.reshape(mode_tran, (nmode, nfreq)),\n", + " cmap=\"Blues\",\n", + " shading=\"flat\",\n", + " vmin=0,\n", + " vmax=tran_max,\n", + ")\n", "plt.axis([min(mode_wvl), max(mode_wvl), min(mode_angle), max(mode_angle)])\n", "plt.xlabel(\"wavelength (μm)\")\n", "plt.ylabel(\"diffraction angle (degrees)\")\n", - "plt.xticks([t for t in np.linspace(wvl_min,wvl_max,3)])\n", - "plt.yticks([t for t in range(0,35,5)])\n", + "plt.xticks([t for t in np.linspace(wvl_min, wvl_max, 3)])\n", + "plt.yticks([t for t in range(0, 35, 5)])\n", "plt.title(\"transmittance of diffraction orders\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.arange(0,tran_max+0.1,0.1)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.arange(0,tran_max+0.1,0.1)])" + "cbar.set_ticks([t for t in np.arange(0, tran_max + 0.1, 0.1)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.arange(0, tran_max + 0.1, 0.1)])" ] }, { diff --git a/python/examples/binary_grating.py b/python/examples/binary_grating.py index fcba5dbb7..74fb36992 100644 --- a/python/examples/binary_grating.py +++ b/python/examples/binary_grating.py @@ -5,48 +5,59 @@ import numpy as np import matplotlib.pyplot as plt -resolution = 60 # pixels/μm +resolution = 60 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 3.0 # substrate thickness -dpad = 3.0 # padding between grating and PML -gp = 10.0 # grating period -gh = 0.5 # grating height -gdc = 0.5 # grating duty cycle +dpml = 1.0 # PML thickness +dsub = 3.0 # substrate thickness +dpad = 3.0 # padding between grating and PML +gp = 10.0 # grating period +gh = 0.5 # grating height +gdc = 0.5 # grating duty cycle -sx = dpml+dsub+gh+dpad+dpml +sx = dpml + dsub + gh + dpad + dpml sy = gp -cell_size = mp.Vector3(sx,sy,0) -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +cell_size = mp.Vector3(sx, sy, 0) +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] -wvl_min = 0.4 # min wavelength -wvl_max = 0.6 # max wavelength -fmin = 1/wvl_max # min frequency -fmax = 1/wvl_min # max frequency -fcen = 0.5*(fmin+fmax) # center frequency -df = fmax-fmin # frequency width +wvl_min = 0.4 # min wavelength +wvl_max = 0.6 # max wavelength +fmin = 1 / wvl_max # min frequency +fmax = 1 / wvl_min # max frequency +fcen = 0.5 * (fmin + fmax) # center frequency +df = fmax - fmin # frequency width -src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub,0,0) -sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(0,sy,0))] +src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub, 0, 0) +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(0, sy, 0), + ) +] -k_point = mp.Vector3(0,0,0) +k_point = mp.Vector3(0, 0, 0) glass = mp.Medium(index=1.5) -symmetries=[mp.Mirror(mp.Y)] +symmetries = [mp.Mirror(mp.Y)] -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - default_material=glass, - sources=sources, - symmetries=symmetries) +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + default_material=glass, + sources=sources, + symmetries=symmetries, +) nfreq = 21 -mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0) -flux_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0))) +mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0) +flux_mon = sim.add_flux( + fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0)) +) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9)) @@ -54,25 +65,41 @@ sim.reset_meep() -geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)), - mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - -mode_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0))) +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0), + ), +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, +) + +mode_mon = sim.add_flux( + fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0)) +) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9)) freqs = mp.get_eigenmode_freqs(mode_mon) nmode = 10 -res = sim.get_eigenmode_coefficients(mode_mon, range(1,nmode+1), eig_parity=mp.ODD_Z+mp.EVEN_Y) +res = sim.get_eigenmode_coefficients( + mode_mon, range(1, nmode + 1), eig_parity=mp.ODD_Z + mp.EVEN_Y +) coeffs = res.alpha kdom = res.kdom @@ -81,30 +108,36 @@ mode_tran = [] for nm in range(nmode): - for nf in range(nfreq): - mode_wvl.append(1/freqs[nf]) - mode_angle.append(math.degrees(math.acos(kdom[nm*nfreq+nf].x/freqs[nf]))) - tran = abs(coeffs[nm,nf,0])**2/input_flux[nf] - mode_tran.append(0.5*tran if nm != 0 else tran) - print("grating{}:, {:.5f}, {:.2f}, {:.8f}".format(nm,mode_wvl[-1],mode_angle[-1],mode_tran[-1])) - -tran_max = round(max(mode_tran),1) + for nf in range(nfreq): + mode_wvl.append(1 / freqs[nf]) + mode_angle.append(math.degrees(math.acos(kdom[nm * nfreq + nf].x / freqs[nf]))) + tran = abs(coeffs[nm, nf, 0]) ** 2 / input_flux[nf] + mode_tran.append(0.5 * tran if nm != 0 else tran) + print( + "grating{}:, {:.5f}, {:.2f}, {:.8f}".format( + nm, mode_wvl[-1], mode_angle[-1], mode_tran[-1] + ) + ) + +tran_max = round(max(mode_tran), 1) plt.figure() -plt.pcolormesh(np.reshape(mode_wvl,(nmode,nfreq)), - np.reshape(mode_angle,(nmode,nfreq)), - np.reshape(mode_tran,(nmode,nfreq)), - cmap='Blues', - shading='nearest', - vmin=0, - vmax=tran_max) +plt.pcolormesh( + np.reshape(mode_wvl, (nmode, nfreq)), + np.reshape(mode_angle, (nmode, nfreq)), + np.reshape(mode_tran, (nmode, nfreq)), + cmap="Blues", + shading="nearest", + vmin=0, + vmax=tran_max, +) plt.axis([min(mode_wvl), max(mode_wvl), min(mode_angle), max(mode_angle)]) plt.xlabel("wavelength (μm)") plt.ylabel("diffraction angle (degrees)") -plt.xticks(list(np.arange(0.4,0.7,0.1))) -plt.yticks(list(range(0,35,5))) +plt.xticks(list(np.arange(0.4, 0.7, 0.1))) +plt.yticks(list(range(0, 35, 5))) plt.title("transmittance of diffraction orders") cbar = plt.colorbar() -cbar.set_ticks(list(np.arange(0,tran_max+0.1,0.1))) -cbar.set_ticklabels(["{:.1f}".format(t) for t in np.arange(0,tran_max+0.1,0.1)]) +cbar.set_ticks(list(np.arange(0, tran_max + 0.1, 0.1))) +cbar.set_ticklabels(["{:.1f}".format(t) for t in np.arange(0, tran_max + 0.1, 0.1)]) plt.show() diff --git a/python/examples/binary_grating_n2f.ipynb b/python/examples/binary_grating_n2f.ipynb index 771b5c682..892af4622 100644 --- a/python/examples/binary_grating_n2f.ipynb +++ b/python/examples/binary_grating_n2f.ipynb @@ -229,123 +229,190 @@ "from numpy import linalg as LA\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 25 # pixels/μm\n", + "resolution = 25 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 3.0 # substrate thickness\n", - "dpad = 3.0 # padding between grating and PML\n", - "gp = 10.0 # grating period\n", - "gh = 0.5 # grating height\n", - "gdc = 0.5 # grating duty cycle\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 3.0 # substrate thickness\n", + "dpad = 3.0 # padding between grating and PML\n", + "gp = 10.0 # grating period\n", + "gh = 0.5 # grating height\n", + "gdc = 0.5 # grating duty cycle\n", "\n", - "nperiods = 10 # number of unit cells in finite periodic grating\n", + "nperiods = 10 # number of unit cells in finite periodic grating\n", "\n", - "ff_distance = 1e8 # far-field distance from near-field monitor\n", - "ff_angle = 20 # far-field cone angle\n", - "ff_npts = 500 # number of far-field points\n", + "ff_distance = 1e8 # far-field distance from near-field monitor\n", + "ff_angle = 20 # far-field cone angle\n", + "ff_npts = 500 # number of far-field points\n", "\n", - "ff_length = ff_distance*math.tan(math.radians(ff_angle))\n", - "ff_res = ff_npts/ff_length\n", + "ff_length = ff_distance * math.tan(math.radians(ff_angle))\n", + "ff_res = ff_npts / ff_length\n", "\n", - "sx = dpml+dsub+gh+dpad+dpml\n", + "sx = dpml + dsub + gh + dpad + dpml\n", "cell_size = mp.Vector3(sx)\n", "\n", - "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", + "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", "\n", "symmetries = [mp.Mirror(mp.Y)]\n", "\n", - "wvl_min = 0.4 # min wavelength\n", - "wvl_max = 0.6 # max wavelength\n", - "fmin = 1/wvl_max # min frequency\n", - "fmax = 1/wvl_min # max frequency\n", - "fcen = 0.5*(fmin+fmax) # center frequency\n", - "df = fmax-fmin # frequency width\n", + "wvl_min = 0.4 # min wavelength\n", + "wvl_max = 0.6 # max wavelength\n", + "fmin = 1 / wvl_max # min frequency\n", + "fmax = 1 / wvl_min # max frequency\n", + "fcen = 0.5 * (fmin + fmax) # center frequency\n", + "df = fmax - fmin # frequency width\n", "\n", - "src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub)\n", - "sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt)]\n", + "src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub)\n", + "sources = [\n", + " mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt)\n", + "]\n", "\n", "k_point = mp.Vector3()\n", "\n", "glass = mp.Medium(index=1.5)\n", "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " default_material=glass,\n", - " sources=sources)\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " default_material=glass,\n", + " sources=sources,\n", + ")\n", "\n", "nfreq = 21\n", - "n2f_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad)\n", + "n2f_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad)\n", "n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt))\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9))\n", "\n", - "ff_source = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length))\n", + "ff_source = sim.get_farfields(\n", + " n2f_obj,\n", + " ff_res,\n", + " center=mp.Vector3(ff_distance, 0.5 * ff_length),\n", + " size=mp.Vector3(y=ff_length),\n", + ")\n", "\n", "sim.reset_meep()\n", "\n", "### unit cell with periodic boundaries\n", "\n", "sy = gp\n", - "cell_size = mp.Vector3(sx,sy)\n", - "\n", - "sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(y=sy))]\n", - "\n", - "geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub))),\n", - " mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))]\n", - "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " split_chunks_evenly=True,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", - "\n", - "n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)), nperiods=nperiods)\n", + "cell_size = mp.Vector3(sx, sy)\n", + "\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy),\n", + " )\n", + "]\n", + "\n", + "geometry = [\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", + " ),\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh),\n", + " ),\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " split_chunks_evenly=True,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + ")\n", + "\n", + "n2f_obj = sim.add_near2far(\n", + " fcen,\n", + " df,\n", + " nfreq,\n", + " mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)),\n", + " nperiods=nperiods,\n", + ")\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9))\n", "\n", - "ff_unitcell = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length))\n", + "ff_unitcell = sim.get_farfields(\n", + " n2f_obj,\n", + " ff_res,\n", + " center=mp.Vector3(ff_distance, 0.5 * ff_length),\n", + " size=mp.Vector3(y=ff_length),\n", + ")\n", "\n", "sim.reset_meep()\n", "\n", "### finite periodic grating with flat surface termination extending into PML\n", "\n", - "num_cells = 2*nperiods+1\n", - "sy = dpml+num_cells*gp+dpml\n", - "cell_size = mp.Vector3(sx,sy)\n", + "num_cells = 2 * nperiods + 1\n", + "sy = dpml + num_cells * gp + dpml\n", + "cell_size = mp.Vector3(sx, sy)\n", "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(y=sy-2*dpml))]\n", - "\n", - "geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))]\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy - 2 * dpml),\n", + " )\n", + "]\n", + "\n", + "geometry = [\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", + " )\n", + "]\n", "\n", "for j in range(num_cells):\n", - " geometry.append(mp.Block(material=glass,\n", - " size=mp.Vector3(gh,gdc*gp,mp.inf),\n", - " center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+dpml+(j+0.5)*gp)))\n", - "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " split_chunks_evenly=True, \n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", - "\n", - "n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy-2*dpml)))\n", + " geometry.append(\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", + " center=mp.Vector3(\n", + " -0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + dpml + (j + 0.5) * gp\n", + " ),\n", + " )\n", + " )\n", + "\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " split_chunks_evenly=True,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + ")\n", + "\n", + "n2f_obj = sim.add_near2far(\n", + " fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy - 2 * dpml))\n", + ")\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9))\n", "\n", - "ff_supercell = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length))\n", + "ff_supercell = sim.get_farfields(\n", + " n2f_obj,\n", + " ff_res,\n", + " center=mp.Vector3(ff_distance, 0.5 * ff_length),\n", + " size=mp.Vector3(y=ff_length),\n", + ")\n", "\n", - "norm_err = LA.norm(ff_unitcell['Ez']-ff_supercell['Ez'])/nperiods\n", - "print(\"error:, {}, {}\".format(nperiods,norm_err))" + "norm_err = LA.norm(ff_unitcell[\"Ez\"] - ff_supercell[\"Ez\"]) / nperiods\n", + "print(\"error:, {}, {}\".format(nperiods, norm_err))" ] }, { @@ -375,35 +442,35 @@ ], "source": [ "freqs = mp.get_near2far_freqs(n2f_obj)\n", - "wvl = np.divide(1,freqs)\n", - "ff_lengths = np.linspace(0,ff_length,ff_npts)\n", - "angles = [math.degrees(math.atan(f)) for f in ff_lengths/ff_distance]\n", + "wvl = np.divide(1, freqs)\n", + "ff_lengths = np.linspace(0, ff_length, ff_npts)\n", + "angles = [math.degrees(math.atan(f)) for f in ff_lengths / ff_distance]\n", "\n", "wvl_slice = 0.5\n", - "idx_slice = np.where(np.asarray(freqs) == 1/wvl_slice)[0][0]\n", + "idx_slice = np.where(np.asarray(freqs) == 1 / wvl_slice)[0][0]\n", "\n", - "rel_enh = np.absolute(ff_unitcell['Ez'])**2/np.absolute(ff_source['Ez'])**2\n", + "rel_enh = np.absolute(ff_unitcell[\"Ez\"]) ** 2 / np.absolute(ff_source[\"Ez\"]) ** 2\n", "\n", "plt.figure(dpi=150)\n", "\n", - "plt.subplot(1,2,1)\n", - "plt.pcolormesh(wvl,angles,rel_enh,cmap='Blues',shading='flat')\n", - "plt.axis([wvl_min,wvl_max,0,ff_angle])\n", + "plt.subplot(1, 2, 1)\n", + "plt.pcolormesh(wvl, angles, rel_enh, cmap=\"Blues\", shading=\"flat\")\n", + "plt.axis([wvl_min, wvl_max, 0, ff_angle])\n", "plt.xlabel(\"wavelength (μm)\")\n", "plt.ylabel(\"angle (degrees)\")\n", - "plt.grid(linewidth=0.5,linestyle='--')\n", - "plt.xticks([t for t in np.arange(wvl_min,wvl_max+0.1,0.1)])\n", - "plt.yticks([t for t in range(0,ff_angle+1,10)])\n", + "plt.grid(linewidth=0.5, linestyle=\"--\")\n", + "plt.xticks([t for t in np.arange(wvl_min, wvl_max + 0.1, 0.1)])\n", + "plt.yticks([t for t in range(0, ff_angle + 1, 10)])\n", "plt.title(\"far-field spectra\")\n", "\n", - "plt.subplot(1,2,2)\n", - "plt.plot(angles,rel_enh[:,idx_slice],'bo-')\n", - "plt.xlim(0,ff_angle)\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(angles, rel_enh[:, idx_slice], \"bo-\")\n", + "plt.xlim(0, ff_angle)\n", "plt.ylim(0)\n", - "plt.xticks([t for t in range(0,ff_angle+1,10)])\n", + "plt.xticks([t for t in range(0, ff_angle + 1, 10)])\n", "plt.xlabel(\"angle (degrees)\")\n", "plt.ylabel(\"relative enhancement\")\n", - "plt.grid(axis='x',linewidth=0.5,linestyle='--')\n", + "plt.grid(axis=\"x\", linewidth=0.5, linestyle=\"--\")\n", "plt.title(\"f.-f. spectra @ λ = {:.1} μm\".format(wvl_slice))\n", "\n", "plt.tight_layout(pad=0.5)\n", diff --git a/python/examples/binary_grating_n2f.py b/python/examples/binary_grating_n2f.py index 8ad39fdbb..f1b41450f 100644 --- a/python/examples/binary_grating_n2f.py +++ b/python/examples/binary_grating_n2f.py @@ -6,160 +6,221 @@ from numpy import linalg as LA import matplotlib.pyplot as plt -resolution = 25 # pixels/μm +resolution = 25 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 3.0 # substrate thickness -dpad = 3.0 # padding between grating and PML -gp = 10.0 # grating period -gh = 0.5 # grating height -gdc = 0.5 # grating duty cycle +dpml = 1.0 # PML thickness +dsub = 3.0 # substrate thickness +dpad = 3.0 # padding between grating and PML +gp = 10.0 # grating period +gh = 0.5 # grating height +gdc = 0.5 # grating duty cycle -nperiods = 10 # number of unit cells in finite periodic grating +nperiods = 10 # number of unit cells in finite periodic grating -ff_distance = 1e8 # far-field distance from near-field monitor -ff_angle = 20 # far-field cone angle -ff_npts = 500 # number of far-field points +ff_distance = 1e8 # far-field distance from near-field monitor +ff_angle = 20 # far-field cone angle +ff_npts = 500 # number of far-field points -ff_length = ff_distance*math.tan(math.radians(ff_angle)) -ff_res = ff_npts/ff_length +ff_length = ff_distance * math.tan(math.radians(ff_angle)) +ff_res = ff_npts / ff_length -sx = dpml+dsub+gh+dpad+dpml +sx = dpml + dsub + gh + dpad + dpml cell_size = mp.Vector3(sx) -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] symmetries = [mp.Mirror(mp.Y)] -wvl_min = 0.4 # min wavelength -wvl_max = 0.6 # max wavelength -fmin = 1/wvl_max # min frequency -fmax = 1/wvl_min # max frequency -fcen = 0.5*(fmin+fmax) # center frequency -df = fmax-fmin # frequency width +wvl_min = 0.4 # min wavelength +wvl_max = 0.6 # max wavelength +fmin = 1 / wvl_max # min frequency +fmax = 1 / wvl_min # max frequency +fcen = 0.5 * (fmin + fmax) # center frequency +df = fmax - fmin # frequency width -src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub) -sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt)] +src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub) +sources = [ + mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt) +] k_point = mp.Vector3() glass = mp.Medium(index=1.5) -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - default_material=glass, - sources=sources) +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + default_material=glass, + sources=sources, +) nfreq = 21 -n2f_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad) +n2f_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad) n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt)) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9)) -ff_source = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length)) +ff_source = sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3(ff_distance, 0.5 * ff_length), + size=mp.Vector3(y=ff_length), +) sim.reset_meep() ### unit cell with periodic boundaries sy = gp -cell_size = mp.Vector3(sx,sy) - -sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df, is_integrated=True), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(y=sy))] - -geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub))), - mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))] - -sim = mp.Simulation(resolution=resolution, - split_chunks_evenly=True, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - -n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)), nperiods=nperiods) +cell_size = mp.Vector3(sx, sy) + +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df, is_integrated=True), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) +] + +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh), + ), +] + +sim = mp.Simulation( + resolution=resolution, + split_chunks_evenly=True, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, +) + +n2f_obj = sim.add_near2far( + fcen, + df, + nfreq, + mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)), + nperiods=nperiods, +) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9)) -ff_unitcell = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length)) +ff_unitcell = sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3(ff_distance, 0.5 * ff_length), + size=mp.Vector3(y=ff_length), +) sim.reset_meep() ### finite periodic grating with flat surface termination extending into PML -num_cells = 2*nperiods+1 -sy = dpml+num_cells*gp+dpml -cell_size = mp.Vector3(sx,sy) +num_cells = 2 * nperiods + 1 +sy = dpml + num_cells * gp + dpml +cell_size = mp.Vector3(sx, sy) pml_layers = [mp.PML(thickness=dpml)] -sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df, is_integrated=True), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(y=sy))] - -geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))] +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df, is_integrated=True), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) +] + +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + ) +] for j in range(num_cells): - geometry.append(mp.Block(material=glass, - size=mp.Vector3(gh,gdc*gp,mp.inf), - center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+dpml+(j+0.5)*gp))) - -sim = mp.Simulation(resolution=resolution, - split_chunks_evenly=True, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - -n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy-2*dpml))) + geometry.append( + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3( + -0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + dpml + (j + 0.5) * gp + ), + ) + ) + +sim = mp.Simulation( + resolution=resolution, + split_chunks_evenly=True, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, +) + +n2f_obj = sim.add_near2far( + fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy - 2 * dpml)) +) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9)) -ff_supercell = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length)) +ff_supercell = sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3(ff_distance, 0.5 * ff_length), + size=mp.Vector3(y=ff_length), +) -norm_err = LA.norm(ff_unitcell['Ez']-ff_supercell['Ez'])/nperiods -print("error:, {}, {}".format(nperiods,norm_err)) +norm_err = LA.norm(ff_unitcell["Ez"] - ff_supercell["Ez"]) / nperiods +print("error:, {}, {}".format(nperiods, norm_err)) freqs = mp.get_near2far_freqs(n2f_obj) -wvl = np.divide(1,freqs) -ff_lengths = np.linspace(0,ff_length,ff_npts) -angles = [math.degrees(math.atan(f)) for f in ff_lengths/ff_distance] +wvl = np.divide(1, freqs) +ff_lengths = np.linspace(0, ff_length, ff_npts) +angles = [math.degrees(math.atan(f)) for f in ff_lengths / ff_distance] wvl_slice = 0.5 -idx_slice = np.where(np.asarray(freqs) == 1/wvl_slice)[0][0] +idx_slice = np.where(np.asarray(freqs) == 1 / wvl_slice)[0][0] -rel_enh = np.absolute(ff_unitcell['Ez'])**2/np.absolute(ff_source['Ez'])**2 +rel_enh = np.absolute(ff_unitcell["Ez"]) ** 2 / np.absolute(ff_source["Ez"]) ** 2 plt.figure(dpi=150) -plt.subplot(1,2,1) -plt.pcolormesh(wvl,angles,rel_enh,cmap='Blues',shading='flat') -plt.axis([wvl_min,wvl_max,0,ff_angle]) +plt.subplot(1, 2, 1) +plt.pcolormesh(wvl, angles, rel_enh, cmap="Blues", shading="flat") +plt.axis([wvl_min, wvl_max, 0, ff_angle]) plt.xlabel("wavelength (μm)") plt.ylabel("angle (degrees)") -plt.grid(linewidth=0.5,linestyle='--') -plt.xticks([t for t in np.arange(wvl_min,wvl_max+0.1,0.1)]) -plt.yticks([t for t in range(0,ff_angle+1,10)]) +plt.grid(linewidth=0.5, linestyle="--") +plt.xticks([t for t in np.arange(wvl_min, wvl_max + 0.1, 0.1)]) +plt.yticks([t for t in range(0, ff_angle + 1, 10)]) plt.title("far-field spectra") -plt.subplot(1,2,2) -plt.plot(angles,rel_enh[:,idx_slice],'bo-') -plt.xlim(0,ff_angle) +plt.subplot(1, 2, 2) +plt.plot(angles, rel_enh[:, idx_slice], "bo-") +plt.xlim(0, ff_angle) plt.ylim(0) -plt.xticks([t for t in range(0,ff_angle+1,10)]) +plt.xticks([t for t in range(0, ff_angle + 1, 10)]) plt.xlabel("angle (degrees)") plt.ylabel("relative enhancement") -plt.grid(axis='x',linewidth=0.5,linestyle='--') +plt.grid(axis="x", linewidth=0.5, linestyle="--") plt.title("f.-f. spectra @ λ = {:.1} μm".format(wvl_slice)) plt.tight_layout(pad=0.5) diff --git a/python/examples/binary_grating_oblique.ipynb b/python/examples/binary_grating_oblique.ipynb index 494b44cb5..8454e37f7 100644 --- a/python/examples/binary_grating_oblique.ipynb +++ b/python/examples/binary_grating_oblique.ipynb @@ -61,20 +61,20 @@ "metadata": {}, "outputs": [], "source": [ - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 3.0 # substrate thickness\n", - "dpad = 3.0 # length of padding between grating and PML\n", - "gp = 10.0 # grating period\n", - "gh = 0.5 # grating height\n", - "gdc = 0.5 # grating duty cycle\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 3.0 # substrate thickness\n", + "dpad = 3.0 # length of padding between grating and PML\n", + "gp = 10.0 # grating period\n", + "gh = 0.5 # grating height\n", + "gdc = 0.5 # grating duty cycle\n", "\n", - "sx = dpml+dsub+gh+dpad+dpml\n", + "sx = dpml + dsub + gh + dpad + dpml\n", "sy = gp\n", "\n", - "cell_size = mp.Vector3(sx,sy,0)\n", - "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] " + "cell_size = mp.Vector3(sx, sy, 0)\n", + "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]" ] }, { @@ -93,34 +93,41 @@ "ng = 1.5\n", "glass = mp.Medium(index=ng)\n", "\n", - "wvl = 0.5 # center wavelength\n", - "fcen = 1/wvl # center frequency\n", - "df = 0.05*fcen # frequency width\n", + "wvl = 0.5 # center wavelength\n", + "fcen = 1 / wvl # center frequency\n", + "df = 0.05 * fcen # frequency width\n", "\n", "# rotation angle of incident planewave; counter clockwise (CCW) about Z axis, 0 degrees along +X axis\n", "theta_in = math.radians(10.7)\n", "\n", "# k (in source medium) with correct length (plane of incidence: XY)\n", - "k = mp.Vector3(fcen*ng).rotate(mp.Vector3(z=1), theta_in)\n", + "k = mp.Vector3(fcen * ng).rotate(mp.Vector3(z=1), theta_in)\n", "\n", "symmetries = []\n", "eig_parity = mp.ODD_Z\n", "if theta_in == 0:\n", - " k = mp.Vector3(0,0,0)\n", - " symmetries = [mp.Mirror(mp.Y)]\n", - " eig_parity += mp.EVEN_Y\n", + " k = mp.Vector3(0, 0, 0)\n", + " symmetries = [mp.Mirror(mp.Y)]\n", + " eig_parity += mp.EVEN_Y\n", "\n", - "def pw_amp(k,x0):\n", - " def _pw_amp(x):\n", - " return cmath.exp(1j*2*math.pi*k.dot(x+x0))\n", - " return _pw_amp\n", "\n", - "src_pt = mp.Vector3(-0.5*sx+dpml+0.3*dsub,0,0)\n", - "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(0,sy,0),\n", - " amp_func=pw_amp(k,src_pt))]" + "def pw_amp(k, x0):\n", + " def _pw_amp(x):\n", + " return cmath.exp(1j * 2 * math.pi * k.dot(x + x0))\n", + "\n", + " return _pw_amp\n", + "\n", + "\n", + "src_pt = mp.Vector3(-0.5 * sx + dpml + 0.3 * dsub, 0, 0)\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(0, sy, 0),\n", + " amp_func=pw_amp(k, src_pt),\n", + " )\n", + "]" ] }, { @@ -136,16 +143,20 @@ "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k,\n", - " default_material=glass,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k,\n", + " default_material=glass,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + ")\n", "\n", - "refl_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub,0,0)\n", - "refl_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0,sy,0)))" + "refl_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub, 0, 0)\n", + "refl_flux = sim.add_flux(\n", + " fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, sy, 0))\n", + ")" ] }, { @@ -229,22 +240,38 @@ "source": [ "sim.reset_meep()\n", "\n", - "geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)),\n", - " mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))]\n", + "geometry = [\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0),\n", + " ),\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0),\n", + " ),\n", + "]\n", "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + ")\n", "\n", - "refl_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0,sy,0)))\n", - "sim.load_minus_flux_data(refl_flux,input_flux_data)\n", + "refl_flux = sim.add_flux(\n", + " fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, sy, 0))\n", + ")\n", + "sim.load_minus_flux_data(refl_flux, input_flux_data)\n", "\n", - "tran_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0)\n", - "tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0)))" + "tran_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0)\n", + "tran_flux = sim.add_flux(\n", + " fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0))\n", + ")" ] }, { @@ -345,23 +372,31 @@ ], "source": [ "# Calculate the number of reflected orders\n", - "nm_r = np.floor((fcen*ng-k.y)*gp)-np.ceil((-fcen*ng-k.y)*gp) # number of reflected orders\n", + "nm_r = np.floor((fcen * ng - k.y) * gp) - np.ceil(\n", + " (-fcen * ng - k.y) * gp\n", + ") # number of reflected orders\n", "if theta_in == 0:\n", - " nm_r = nm_r/2 # since eig_parity removes degeneracy in y-direction\n", + " nm_r = nm_r / 2 # since eig_parity removes degeneracy in y-direction\n", "nm_r = int(nm_r)\n", "\n", "# Extract the coefficients for the reflected orders\n", - "res = sim.get_eigenmode_coefficients(refl_flux, range(1,nm_r+1), eig_parity=eig_parity)\n", + "res = sim.get_eigenmode_coefficients(\n", + " refl_flux, range(1, nm_r + 1), eig_parity=eig_parity\n", + ")\n", "r_coeffs = res.alpha\n", "\n", "# Calculate the number of transmitted orders\n", - "nm_t = np.floor((fcen-k.y)*gp)-np.ceil((-fcen-k.y)*gp) # number of transmitted orders\n", + "nm_t = np.floor((fcen - k.y) * gp) - np.ceil(\n", + " (-fcen - k.y) * gp\n", + ") # number of transmitted orders\n", "if theta_in == 0:\n", - " nm_t = nm_t/2 # since eig_parity removes degeneracy in y-direction\n", + " nm_t = nm_t / 2 # since eig_parity removes degeneracy in y-direction\n", "nm_t = int(nm_t)\n", "\n", "# Extract the coefficients for the transmitted orders\n", - "res = sim.get_eigenmode_coefficients(tran_flux, range(1,nm_t+1), eig_parity=eig_parity)\n", + "res = sim.get_eigenmode_coefficients(\n", + " tran_flux, range(1, nm_t + 1), eig_parity=eig_parity\n", + ")\n", "t_coeffs = res.alpha" ] }, @@ -391,20 +426,30 @@ } ], "source": [ - "r_angle = np.squeeze([math.degrees(np.sign(r_kdom.y)*math.acos(r_kdom.x/(ng*fcen))) for r_kdom in res.kdom])\n", - "Rmode = abs(r_coeffs[:,0,1])**2/input_flux[0]\n", + "r_angle = np.squeeze(\n", + " [\n", + " math.degrees(np.sign(r_kdom.y) * math.acos(r_kdom.x / (ng * fcen)))\n", + " for r_kdom in res.kdom\n", + " ]\n", + ")\n", + "Rmode = abs(r_coeffs[:, 0, 1]) ** 2 / input_flux[0]\n", "idx_r = np.argsort(r_angle)\n", "\n", - "t_angle = np.squeeze([math.degrees(np.sign(t_kdom.y)*math.acos(t_kdom.x/fcen)) for t_kdom in res.kdom])\n", - "Tmode = abs(t_coeffs[:,0,0])**2/input_flux[0]\n", + "t_angle = np.squeeze(\n", + " [\n", + " math.degrees(np.sign(t_kdom.y) * math.acos(t_kdom.x / fcen))\n", + " for t_kdom in res.kdom\n", + " ]\n", + ")\n", + "Tmode = abs(t_coeffs[:, 0, 0]) ** 2 / input_flux[0]\n", "idx_t = np.argsort(t_angle)\n", "\n", "plt.figure(dpi=150)\n", - "plt.plot(r_angle[idx_r],Rmode[idx_r], 'o-',color='blue',label='Reflection')\n", - "plt.plot(t_angle[idx_t],Tmode[idx_t], 'o-',color='red',label='Transmission')\n", + "plt.plot(r_angle[idx_r], Rmode[idx_r], \"o-\", color=\"blue\", label=\"Reflection\")\n", + "plt.plot(t_angle[idx_t], Tmode[idx_t], \"o-\", color=\"red\", label=\"Transmission\")\n", "plt.grid(True)\n", - "plt.xlabel('Diffraction Angle (degrees)')\n", - "plt.ylabel('Relative Power (au)')\n", + "plt.xlabel(\"Diffraction Angle (degrees)\")\n", + "plt.ylabel(\"Relative Power (au)\")\n", "plt.legend()\n", "plt.show()" ] @@ -431,12 +476,16 @@ } ], "source": [ - "print(\"mode-coeff:, {:.6f}, {:.6f}, {:.6f}\".format(np.sum(Rmode),np.sum(Tmode),np.sum(Rmode)+np.sum(Tmode)))\n", + "print(\n", + " \"mode-coeff:, {:.6f}, {:.6f}, {:.6f}\".format(\n", + " np.sum(Rmode), np.sum(Tmode), np.sum(Rmode) + np.sum(Tmode)\n", + " )\n", + ")\n", "r_flux = mp.get_fluxes(refl_flux)\n", "t_flux = mp.get_fluxes(tran_flux)\n", - "Rflux = -r_flux[0]/input_flux[0]\n", - "Tflux = t_flux[0]/input_flux[0]\n", - "print(\"poynting-flux:, {:.6f}, {:.6f}, {:.6f}\".format(Rflux,Tflux,Rflux+Tflux))" + "Rflux = -r_flux[0] / input_flux[0]\n", + "Tflux = t_flux[0] / input_flux[0]\n", + "print(\"poynting-flux:, {:.6f}, {:.6f}, {:.6f}\".format(Rflux, Tflux, Rflux + Tflux))" ] } ], diff --git a/python/examples/binary_grating_oblique.py b/python/examples/binary_grating_oblique.py index 0214b6318..e4644c8fe 100644 --- a/python/examples/binary_grating_oblique.py +++ b/python/examples/binary_grating_oblique.py @@ -5,139 +5,176 @@ import cmath import numpy as np -resolution = 50 # pixels/μm +resolution = 50 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 3.0 # substrate thickness -dpad = 3.0 # length of padding between grating and PML -gp = 10.0 # grating period -gh = 0.5 # grating height -gdc = 0.5 # grating duty cycle +dpml = 1.0 # PML thickness +dsub = 3.0 # substrate thickness +dpad = 3.0 # length of padding between grating and PML +gp = 10.0 # grating period +gh = 0.5 # grating height +gdc = 0.5 # grating duty cycle -sx = dpml+dsub+gh+dpad+dpml +sx = dpml + dsub + gh + dpad + dpml sy = gp -cell_size = mp.Vector3(sx,sy,0) -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +cell_size = mp.Vector3(sx, sy, 0) +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] -wvl = 0.5 # center wavelength -fcen = 1/wvl # center frequency -df = 0.05*fcen # frequency width +wvl = 0.5 # center wavelength +fcen = 1 / wvl # center frequency +df = 0.05 * fcen # frequency width ng = 1.5 glass = mp.Medium(index=ng) use_cw_solver = False # CW solver or time stepping? -tol = 1e-6 # CW solver tolerance -max_iters = 2000 # CW solver max iterations -L = 10 # CW solver L +tol = 1e-6 # CW solver tolerance +max_iters = 2000 # CW solver max iterations +L = 10 # CW solver L # rotation angle of incident planewave; counter clockwise (CCW) about Z axis, 0 degrees along +X axis theta_in = math.radians(10.7) # k (in source medium) with correct length (plane of incidence: XY) -k = mp.Vector3(fcen*ng).rotate(mp.Vector3(z=1), theta_in) +k = mp.Vector3(fcen * ng).rotate(mp.Vector3(z=1), theta_in) symmetries = [] eig_parity = mp.ODD_Z if theta_in == 0: - k = mp.Vector3(0,0,0) - symmetries = [mp.Mirror(mp.Y)] - eig_parity += mp.EVEN_Y - -def pw_amp(k,x0): - def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) - return _pw_amp - -src_pt = mp.Vector3(-0.5*sx+dpml+0.3*dsub,0,0) -sources = [mp.Source(mp.ContinuousSource(fcen,fwidth=df) if use_cw_solver else mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(0,sy,0), - amp_func=pw_amp(k,src_pt))] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k, - default_material=glass, - sources=sources, - symmetries=symmetries) - -refl_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub,0,0) -refl_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0,sy,0))) + k = mp.Vector3(0, 0, 0) + symmetries = [mp.Mirror(mp.Y)] + eig_parity += mp.EVEN_Y + + +def pw_amp(k, x0): + def _pw_amp(x): + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + + return _pw_amp + + +src_pt = mp.Vector3(-0.5 * sx + dpml + 0.3 * dsub, 0, 0) +sources = [ + mp.Source( + mp.ContinuousSource(fcen, fwidth=df) + if use_cw_solver + else mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(0, sy, 0), + amp_func=pw_amp(k, src_pt), + ) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k, + default_material=glass, + sources=sources, + symmetries=symmetries, +) + +refl_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub, 0, 0) +refl_flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, sy, 0)) +) if use_cw_solver: - sim.init_sim() - sim.solve_cw(tol, max_iters, L) + sim.init_sim() + sim.solve_cw(tol, max_iters, L) else: - sim.run(until_after_sources=100) + sim.run(until_after_sources=100) input_flux = mp.get_fluxes(refl_flux) input_flux_data = sim.get_flux_data(refl_flux) sim.reset_meep() -geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)), - mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k, - sources=sources, - symmetries=symmetries) - -refl_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0,sy,0))) -sim.load_minus_flux_data(refl_flux,input_flux_data) - -tran_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0) -tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0))) +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0), + ), +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k, + sources=sources, + symmetries=symmetries, +) + +refl_flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, sy, 0)) +) +sim.load_minus_flux_data(refl_flux, input_flux_data) + +tran_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0) +tran_flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) +) if use_cw_solver: - sim.init_sim() - sim.solve_cw(tol, max_iters, L) + sim.init_sim() + sim.solve_cw(tol, max_iters, L) else: - sim.run(until_after_sources=200) + sim.run(until_after_sources=200) -nm_r = np.floor((fcen*ng-k.y)*gp)-np.ceil((-fcen*ng-k.y)*gp) # number of reflected orders +nm_r = np.floor((fcen * ng - k.y) * gp) - np.ceil( + (-fcen * ng - k.y) * gp +) # number of reflected orders if theta_in == 0: - nm_r = nm_r/2 # since eig_parity removes degeneracy in y-direction + nm_r = nm_r / 2 # since eig_parity removes degeneracy in y-direction nm_r = int(nm_r) -res = sim.get_eigenmode_coefficients(refl_flux, range(1,nm_r+1), eig_parity=eig_parity) +res = sim.get_eigenmode_coefficients( + refl_flux, range(1, nm_r + 1), eig_parity=eig_parity +) r_coeffs = res.alpha Rsum = 0 for nm in range(nm_r): - r_kdom = res.kdom[nm] - Rmode = abs(r_coeffs[nm,0,1])**2/input_flux[0] - r_angle = np.sign(r_kdom.y)*math.acos(r_kdom.x/(ng*fcen)) - print("refl:, {:2d}, {:6.2f}, {:.8f}".format(nm,math.degrees(r_angle),Rmode)) - Rsum += Rmode - -nm_t = np.floor((fcen-k.y)*gp)-np.ceil((-fcen-k.y)*gp) # number of transmitted orders + r_kdom = res.kdom[nm] + Rmode = abs(r_coeffs[nm, 0, 1]) ** 2 / input_flux[0] + r_angle = np.sign(r_kdom.y) * math.acos(r_kdom.x / (ng * fcen)) + print("refl:, {:2d}, {:6.2f}, {:.8f}".format(nm, math.degrees(r_angle), Rmode)) + Rsum += Rmode + +nm_t = np.floor((fcen - k.y) * gp) - np.ceil( + (-fcen - k.y) * gp +) # number of transmitted orders if theta_in == 0: - nm_t = nm_t/2 # since eig_parity removes degeneracy in y-direction + nm_t = nm_t / 2 # since eig_parity removes degeneracy in y-direction nm_t = int(nm_t) -res = sim.get_eigenmode_coefficients(tran_flux, range(1,nm_t+1), eig_parity=eig_parity) +res = sim.get_eigenmode_coefficients( + tran_flux, range(1, nm_t + 1), eig_parity=eig_parity +) t_coeffs = res.alpha Tsum = 0 for nm in range(nm_t): - t_kdom = res.kdom[nm] - Tmode = abs(t_coeffs[nm,0,0])**2/input_flux[0] - t_angle = np.sign(t_kdom.y)*math.acos(t_kdom.x/fcen) - print("tran:, {:2d}, {:6.2f}, {:.8f}".format(nm,math.degrees(t_angle),Tmode)) - Tsum += Tmode + t_kdom = res.kdom[nm] + Tmode = abs(t_coeffs[nm, 0, 0]) ** 2 / input_flux[0] + t_angle = np.sign(t_kdom.y) * math.acos(t_kdom.x / fcen) + print("tran:, {:2d}, {:6.2f}, {:.8f}".format(nm, math.degrees(t_angle), Tmode)) + Tsum += Tmode -print("mode-coeff:, {:11.6f}, {:.6f}, {:.6f}".format(Rsum,Tsum,Rsum+Tsum)) +print("mode-coeff:, {:11.6f}, {:.6f}, {:.6f}".format(Rsum, Tsum, Rsum + Tsum)) r_flux = mp.get_fluxes(refl_flux) t_flux = mp.get_fluxes(tran_flux) -Rflux = -r_flux[0]/input_flux[0] -Tflux = t_flux[0]/input_flux[0] -print("poynting-flux:, {:.6f}, {:.6f}, {:.6f}".format(Rflux,Tflux,Rflux+Tflux)) +Rflux = -r_flux[0] / input_flux[0] +Tflux = t_flux[0] / input_flux[0] +print("poynting-flux:, {:.6f}, {:.6f}, {:.6f}".format(Rflux, Tflux, Rflux + Tflux)) diff --git a/python/examples/binary_grating_phasemap.ipynb b/python/examples/binary_grating_phasemap.ipynb index f160cb571..295e97635 100644 --- a/python/examples/binary_grating_phasemap.ipynb +++ b/python/examples/binary_grating_phasemap.ipynb @@ -756,85 +756,114 @@ "import numpy.matlib\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 3.0 # substrate thickness\n", - "dpad = 3.0 # padding between grating and PML\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 3.0 # substrate thickness\n", + "dpad = 3.0 # padding between grating and PML\n", "\n", - "wvl_min = 0.4 # min wavelength\n", - "wvl_max = 0.6 # max wavelength\n", - "fmin = 1/wvl_max # min frequency\n", - "fmax = 1/wvl_min # max frequency\n", - "fcen = 0.5*(fmin+fmax) # center frequency\n", - "df = fmax-fmin # frequency width\n", - "nfreq = 21 # number of frequency bins\n", + "wvl_min = 0.4 # min wavelength\n", + "wvl_max = 0.6 # max wavelength\n", + "fmin = 1 / wvl_max # min frequency\n", + "fmax = 1 / wvl_min # max frequency\n", + "fcen = 0.5 * (fmin + fmax) # center frequency\n", + "df = fmax - fmin # frequency width\n", + "nfreq = 21 # number of frequency bins\n", "\n", - "k_point = mp.Vector3(0,0,0)\n", + "k_point = mp.Vector3(0, 0, 0)\n", "\n", "glass = mp.Medium(index=1.5)\n", "\n", - "def grating(gp,gh,gdc,oddz):\n", - " sx = dpml+dsub+gh+dpad+dpml\n", - " sy = gp\n", "\n", - " cell_size = mp.Vector3(sx,sy,0)\n", - " pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", + "def grating(gp, gh, gdc, oddz):\n", + " sx = dpml + dsub + gh + dpad + dpml\n", + " sy = gp\n", "\n", - " src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub,0,0)\n", - " sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez if oddz else mp.Hz, center=src_pt, size=mp.Vector3(0,sy,0))]\n", + " cell_size = mp.Vector3(sx, sy, 0)\n", + " pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", "\n", - " symmetries=[mp.Mirror(mp.Y, phase=+1 if oddz else -1)]\n", - " \n", - " sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " default_material=glass,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", + " src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub, 0, 0)\n", + " sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez if oddz else mp.Hz,\n", + " center=src_pt,\n", + " size=mp.Vector3(0, sy, 0),\n", + " )\n", + " ]\n", "\n", - " mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0)\n", - " flux_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0)))\n", + " symmetries = [mp.Mirror(mp.Y, phase=+1 if oddz else -1)]\n", "\n", - " sim.run(until_after_sources=100)\n", + " sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " default_material=glass,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + " )\n", "\n", - " input_flux = mp.get_fluxes(flux_mon)\n", + " mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0)\n", + " flux_mon = sim.add_flux(\n", + " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0))\n", + " )\n", "\n", - " sim.reset_meep()\n", + " sim.run(until_after_sources=100)\n", "\n", - " geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)),\n", - " mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))]\n", + " input_flux = mp.get_fluxes(flux_mon)\n", "\n", - " sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", + " sim.reset_meep()\n", "\n", - " mode_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0)))\n", + " geometry = [\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0),\n", + " ),\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0),\n", + " ),\n", + " ]\n", "\n", - " sim.run(until_after_sources=300)\n", + " sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + " )\n", "\n", - " freqs = mp.get_eigenmode_freqs(mode_mon)\n", - " res = sim.get_eigenmode_coefficients(mode_mon, [1], eig_parity=mp.ODD_Z+mp.EVEN_Y if oddz else mp.EVEN_Z+mp.ODD_Y)\n", - " coeffs = res.alpha\n", + " mode_mon = sim.add_flux(\n", + " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0))\n", + " )\n", "\n", - " mode_wvl = [1/freqs[nf] for nf in range(nfreq)]\n", - " mode_tran = [abs(coeffs[0,nf,0])**2/input_flux[nf] for nf in range(nfreq)]\n", - " mode_phase = [np.angle(coeffs[0,nf,0]) for nf in range(nfreq)]\n", + " sim.run(until_after_sources=300)\n", + "\n", + " freqs = mp.get_eigenmode_freqs(mode_mon)\n", + " res = sim.get_eigenmode_coefficients(\n", + " mode_mon, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y if oddz else mp.EVEN_Z + mp.ODD_Y\n", + " )\n", + " coeffs = res.alpha\n", + "\n", + " mode_wvl = [1 / freqs[nf] for nf in range(nfreq)]\n", + " mode_tran = [abs(coeffs[0, nf, 0]) ** 2 / input_flux[nf] for nf in range(nfreq)]\n", + " mode_phase = [np.angle(coeffs[0, nf, 0]) for nf in range(nfreq)]\n", + "\n", + " return mode_wvl, mode_tran, mode_phase\n", "\n", - " return mode_wvl, mode_tran, mode_phase\n", "\n", "gp = 0.35\n", - "gh = 0.6 \n", - "gdc = np.linspace(0.1,1.0,10)\n", - "mode_tran = np.empty((gdc.size,nfreq))\n", - "mode_phase = np.empty((gdc.size,nfreq))\n", + "gh = 0.6\n", + "gdc = np.linspace(0.1, 1.0, 10)\n", + "mode_tran = np.empty((gdc.size, nfreq))\n", + "mode_phase = np.empty((gdc.size, nfreq))\n", "for n in range(gdc.size):\n", - " mode_wvl, mode_tran[n,:], mode_phase[n,:] = grating(gp,gh,gdc[n],True)" + " mode_wvl, mode_tran[n, :], mode_phase[n, :] = grating(gp, gh, gdc[n], True)" ] }, { @@ -866,29 +895,45 @@ ], "source": [ "plt.figure(dpi=150)\n", - "plt.subplot(1,2,1)\n", - "plt.pcolormesh(mode_wvl, gdc, mode_tran, cmap='hot_r', shading='gouraud', vmin=0, vmax=mode_tran.max())\n", + "plt.subplot(1, 2, 1)\n", + "plt.pcolormesh(\n", + " mode_wvl,\n", + " gdc,\n", + " mode_tran,\n", + " cmap=\"hot_r\",\n", + " shading=\"gouraud\",\n", + " vmin=0,\n", + " vmax=mode_tran.max(),\n", + ")\n", "plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]])\n", "plt.xlabel(\"wavelength (μm)\")\n", - "plt.xticks([t for t in np.linspace(wvl_min,wvl_max,3)])\n", + "plt.xticks([t for t in np.linspace(wvl_min, wvl_max, 3)])\n", "plt.ylabel(\"grating duty cycle\")\n", - "plt.yticks([t for t in np.arange(gdc[0],gdc[-1]+0.1,0.1)])\n", + "plt.yticks([t for t in np.arange(gdc[0], gdc[-1] + 0.1, 0.1)])\n", "plt.title(\"transmittance\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.arange(0,1.2,0.2)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0,1,6)])\n", + "cbar.set_ticks([t for t in np.arange(0, 1.2, 0.2)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0, 1, 6)])\n", "\n", - "plt.subplot(1,2,2)\n", - "plt.pcolormesh(mode_wvl, gdc, mode_phase, cmap='RdBu', shading='gouraud', vmin=mode_phase.min(), vmax=mode_phase.max())\n", + "plt.subplot(1, 2, 2)\n", + "plt.pcolormesh(\n", + " mode_wvl,\n", + " gdc,\n", + " mode_phase,\n", + " cmap=\"RdBu\",\n", + " shading=\"gouraud\",\n", + " vmin=mode_phase.min(),\n", + " vmax=mode_phase.max(),\n", + ")\n", "plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]])\n", "plt.xlabel(\"wavelength (μm)\")\n", - "plt.xticks([t for t in np.linspace(wvl_min,wvl_max,3)])\n", + "plt.xticks([t for t in np.linspace(wvl_min, wvl_max, 3)])\n", "plt.ylabel(\"grating duty cycle\")\n", - "plt.yticks([t for t in np.arange(gdc[0],gdc[-1]+0.1,0.1)])\n", + "plt.yticks([t for t in np.arange(gdc[0], gdc[-1] + 0.1, 0.1)])\n", "plt.title(\"phase (radians)\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in range(-3,4)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in range(-3,4)])\n", + "cbar.set_ticks([t for t in range(-3, 4)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in range(-3, 4)])\n", "\n", "plt.subplots_adjust(wspace=0.5)" ] diff --git a/python/examples/binary_grating_phasemap.py b/python/examples/binary_grating_phasemap.py index c2d5688dd..6f23df160 100644 --- a/python/examples/binary_grating_phasemap.py +++ b/python/examples/binary_grating_phasemap.py @@ -6,116 +6,169 @@ import numpy.matlib import argparse -resolution = 60 # pixels/μm +resolution = 60 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 3.0 # substrate thickness -dpad = 3.0 # padding between grating and PML +dpml = 1.0 # PML thickness +dsub = 3.0 # substrate thickness +dpad = 3.0 # padding between grating and PML -wvl_min = 0.4 # min wavelength -wvl_max = 0.6 # max wavelength -fmin = 1/wvl_max # min frequency -fmax = 1/wvl_min # max frequency -fcen = 0.5*(fmin+fmax) # center frequency -df = fmax-fmin # frequency width -nfreq = 21 # number of frequency bins +wvl_min = 0.4 # min wavelength +wvl_max = 0.6 # max wavelength +fmin = 1 / wvl_max # min frequency +fmax = 1 / wvl_min # max frequency +fcen = 0.5 * (fmin + fmax) # center frequency +df = fmax - fmin # frequency width +nfreq = 21 # number of frequency bins -k_point = mp.Vector3(0,0,0) +k_point = mp.Vector3(0, 0, 0) glass = mp.Medium(index=1.5) -def grating(gp,gh,gdc,oddz): - sx = dpml+dsub+gh+dpad+dpml - sy = gp - - cell_size = mp.Vector3(sx,sy,0) - pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] - - src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub,0,0) - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez if oddz else mp.Hz, center=src_pt, size=mp.Vector3(0,sy,0))] - - symmetries=[mp.Mirror(mp.Y, phase=+1 if oddz else -1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - default_material=glass, - sources=sources, - symmetries=symmetries) - - mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0) - flux_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0))) - - sim.run(until_after_sources=100) - - input_flux = mp.get_fluxes(flux_mon) - - sim.reset_meep() - - geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)), - mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - - mode_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0))) - - sim.run(until_after_sources=300) - - freqs = mp.get_eigenmode_freqs(mode_mon) - res = sim.get_eigenmode_coefficients(mode_mon, [1], eig_parity=mp.ODD_Z+mp.EVEN_Y if oddz else mp.EVEN_Z+mp.ODD_Y) - coeffs = res.alpha - - mode_wvl = [1/freqs[nf] for nf in range(nfreq)] - mode_tran = [abs(coeffs[0,nf,0])**2/input_flux[nf] for nf in range(nfreq)] - mode_phase = [np.angle(coeffs[0,nf,0]) for nf in range(nfreq)] - - return mode_wvl, mode_tran, mode_phase - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('-gp', type=float, default=0.35, help='grating periodicity (default: 0.35 μm)') - parser.add_argument('-gh', type=float, default=0.6, help='grating height (default: 0.6 μm)') - parser.add_argument('-oddz', action='store_true', default=False, help='oddz? (default: False)') - args = parser.parse_args() - - gdc = np.arange(0.1,1.0,0.1) - mode_tran = np.empty((gdc.size,nfreq)) - mode_phase = np.empty((gdc.size,nfreq)) - for n in range(gdc.size): - mode_wvl, mode_tran[n,:], mode_phase[n,:] = grating(args.gp,args.gh,gdc[n],args.oddz) - - plt.figure(dpi=150) - - plt.subplot(1,2,1) - plt.pcolormesh(mode_wvl, gdc, mode_tran, cmap='hot_r', shading='gouraud', vmin=0, vmax=mode_tran.max()) - plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]]) - plt.xlabel("wavelength (μm)") - plt.xticks(list(np.arange(wvl_min,wvl_max+0.1,0.1))) - plt.ylabel("grating duty cycle") - plt.yticks(list(np.arange(gdc[0],gdc[-1]+0.1,0.1))) - plt.title("transmittance") - cbar = plt.colorbar() - cbar.set_ticks(list(np.arange(0,1.2,0.2))) - cbar.set_ticklabels(["{:.1f}".format(t) for t in np.arange(0,1.2,0.2)]) - - plt.subplot(1,2,2) - plt.pcolormesh(mode_wvl, gdc, mode_phase, cmap='RdBu', shading='gouraud', vmin=mode_phase.min(), vmax=mode_phase.max()) - plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]]) - plt.xlabel("wavelength (μm)") - plt.xticks(list(np.arange(wvl_min,wvl_max+0.1,0.1))) - plt.ylabel("grating duty cycle") - plt.yticks(list(np.arange(gdc[0],gdc[-1]+0.1,0.1))) - plt.title("phase (radians)") - cbar = plt.colorbar() - cbar.set_ticks(list(range(-3,4))) - cbar.set_ticklabels(["{:.1f}".format(t) for t in range(-3,4)]) - - plt.tight_layout() - plt.show() + +def grating(gp, gh, gdc, oddz): + sx = dpml + dsub + gh + dpad + dpml + sy = gp + + cell_size = mp.Vector3(sx, sy, 0) + pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] + + src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez if oddz else mp.Hz, + center=src_pt, + size=mp.Vector3(0, sy, 0), + ) + ] + + symmetries = [mp.Mirror(mp.Y, phase=+1 if oddz else -1)] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + default_material=glass, + sources=sources, + symmetries=symmetries, + ) + + mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0) + flux_mon = sim.add_flux( + fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0)) + ) + + sim.run(until_after_sources=100) + + input_flux = mp.get_fluxes(flux_mon) + + sim.reset_meep() + + geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0), + ), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, + ) + + mode_mon = sim.add_flux( + fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0)) + ) + + sim.run(until_after_sources=300) + + freqs = mp.get_eigenmode_freqs(mode_mon) + res = sim.get_eigenmode_coefficients( + mode_mon, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y if oddz else mp.EVEN_Z + mp.ODD_Y + ) + coeffs = res.alpha + + mode_wvl = [1 / freqs[nf] for nf in range(nfreq)] + mode_tran = [abs(coeffs[0, nf, 0]) ** 2 / input_flux[nf] for nf in range(nfreq)] + mode_phase = [np.angle(coeffs[0, nf, 0]) for nf in range(nfreq)] + + return mode_wvl, mode_tran, mode_phase + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "-gp", type=float, default=0.35, help="grating periodicity (default: 0.35 μm)" + ) + parser.add_argument( + "-gh", type=float, default=0.6, help="grating height (default: 0.6 μm)" + ) + parser.add_argument( + "-oddz", action="store_true", default=False, help="oddz? (default: False)" + ) + args = parser.parse_args() + + gdc = np.arange(0.1, 1.0, 0.1) + mode_tran = np.empty((gdc.size, nfreq)) + mode_phase = np.empty((gdc.size, nfreq)) + for n in range(gdc.size): + mode_wvl, mode_tran[n, :], mode_phase[n, :] = grating( + args.gp, args.gh, gdc[n], args.oddz + ) + + plt.figure(dpi=150) + + plt.subplot(1, 2, 1) + plt.pcolormesh( + mode_wvl, + gdc, + mode_tran, + cmap="hot_r", + shading="gouraud", + vmin=0, + vmax=mode_tran.max(), + ) + plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]]) + plt.xlabel("wavelength (μm)") + plt.xticks(list(np.arange(wvl_min, wvl_max + 0.1, 0.1))) + plt.ylabel("grating duty cycle") + plt.yticks(list(np.arange(gdc[0], gdc[-1] + 0.1, 0.1))) + plt.title("transmittance") + cbar = plt.colorbar() + cbar.set_ticks(list(np.arange(0, 1.2, 0.2))) + cbar.set_ticklabels(["{:.1f}".format(t) for t in np.arange(0, 1.2, 0.2)]) + + plt.subplot(1, 2, 2) + plt.pcolormesh( + mode_wvl, + gdc, + mode_phase, + cmap="RdBu", + shading="gouraud", + vmin=mode_phase.min(), + vmax=mode_phase.max(), + ) + plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]]) + plt.xlabel("wavelength (μm)") + plt.xticks(list(np.arange(wvl_min, wvl_max + 0.1, 0.1))) + plt.ylabel("grating duty cycle") + plt.yticks(list(np.arange(gdc[0], gdc[-1] + 0.1, 0.1))) + plt.title("phase (radians)") + cbar = plt.colorbar() + cbar.set_ticks(list(range(-3, 4))) + cbar.set_ticklabels(["{:.1f}".format(t) for t in range(-3, 4)]) + + plt.tight_layout() + plt.show() diff --git a/python/examples/cavity-farfield.ipynb b/python/examples/cavity-farfield.ipynb index 08ea67ee1..2314b9090 100644 --- a/python/examples/cavity-farfield.ipynb +++ b/python/examples/cavity-farfield.ipynb @@ -178,53 +178,74 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 20 # pixels/μm\n", + "resolution = 20 # pixels/μm\n", "\n", - "eps = 13 # dielectric constant of waveguide\n", - "w = 1.2 # width of waveguide\n", - "r = 0.36 # radius of holes\n", - "d = 1.4 # defect spacing (ordinary spacing = 1)\n", - "N = 3 # number of holes on either side of defect\n", + "eps = 13 # dielectric constant of waveguide\n", + "w = 1.2 # width of waveguide\n", + "r = 0.36 # radius of holes\n", + "d = 1.4 # defect spacing (ordinary spacing = 1)\n", + "N = 3 # number of holes on either side of defect\n", "\n", - "sy = 6 # size of cell in y direction (perpendicular to wvg.)\n", - "pad = 2 # padding between last hole and PML edge\n", - "dpml = 1 # PML thickness\n", - "sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction\n", + "sy = 6 # size of cell in y direction (perpendicular to wvg.)\n", + "pad = 2 # padding between last hole and PML edge\n", + "dpml = 1 # PML thickness\n", + "sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction\n", "\n", "cell = mp.Vector3(sx, sy, 0)\n", "pml_layers = mp.PML(dpml)\n", "\n", - "geometry = [mp.Block(center=mp.Vector3(),\n", - " size=mp.Vector3(mp.inf, w, mp.inf),\n", - " material=mp.Medium(epsilon=eps))]\n", + "geometry = [\n", + " mp.Block(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(mp.inf, w, mp.inf),\n", + " material=mp.Medium(epsilon=eps),\n", + " )\n", + "]\n", "\n", "for i in range(N):\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(0.5*d+i)))\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(-0.5*d-i)))\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(0.5 * d + i)))\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(-0.5 * d - i)))\n", "\n", - "fcen = 0.25 # pulse center frequency\n", - "df = 0.2 # pulse width (in frequency)\n", + "fcen = 0.25 # pulse center frequency\n", + "df = 0.2 # pulse width (in frequency)\n", "\n", - "sources = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3())\n", + "sources = mp.Source(\n", + " src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3()\n", + ")\n", "\n", - "symmetries = [mp.Mirror(mp.X, phase=-1),\n", - " mp.Mirror(mp.Y, phase=-1)]\n", + "symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)]\n", "\n", - "sim = mp.Simulation(cell_size=cell,\n", - " geometry=geometry,\n", - " sources=[sources],\n", - " symmetries=symmetries,\n", - " boundary_layers=[pml_layers],\n", - " resolution=resolution)\n", + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " sources=[sources],\n", + " symmetries=symmetries,\n", + " boundary_layers=[pml_layers],\n", + " resolution=resolution,\n", + ")\n", "\n", "d1 = 0.2\n", "\n", - "nearfield = sim.add_near2far(fcen, 0, 1,\n", - " mp.Near2FarRegion(mp.Vector3(y=0.5*w+d1), size=mp.Vector3(sx-2*dpml)),\n", - " mp.Near2FarRegion(mp.Vector3(-0.5*sx+dpml,0.5*w+0.5*d1), size=mp.Vector3(y=d1), weight=-1.0),\n", - " mp.Near2FarRegion(mp.Vector3(0.5*sx-dpml,0.5*w+0.5*d1), size=mp.Vector3(y=d1)))\n", + "nearfield = sim.add_near2far(\n", + " fcen,\n", + " 0,\n", + " 1,\n", + " mp.Near2FarRegion(mp.Vector3(y=0.5 * w + d1), size=mp.Vector3(sx - 2 * dpml)),\n", + " mp.Near2FarRegion(\n", + " mp.Vector3(-0.5 * sx + dpml, 0.5 * w + 0.5 * d1),\n", + " size=mp.Vector3(y=d1),\n", + " weight=-1.0,\n", + " ),\n", + " mp.Near2FarRegion(\n", + " mp.Vector3(0.5 * sx - dpml, 0.5 * w + 0.5 * d1), size=mp.Vector3(y=d1)\n", + " ),\n", + ")\n", "\n", - "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Hz, mp.Vector3(0.12,-0.37), 1e-8))" + "sim.run(\n", + " until_after_sources=mp.stop_when_fields_decayed(\n", + " 50, mp.Hz, mp.Vector3(0.12, -0.37), 1e-8\n", + " )\n", + ")" ] }, { @@ -259,11 +280,16 @@ "d2 = 20\n", "h = 4\n", "\n", - "ff = sim.get_farfields(nearfield, resolution, center=mp.Vector3(y=0.5*w+d2+0.5*h), size=mp.Vector3(sx-2*dpml,h))\n", + "ff = sim.get_farfields(\n", + " nearfield,\n", + " resolution,\n", + " center=mp.Vector3(y=0.5 * w + d2 + 0.5 * h),\n", + " size=mp.Vector3(sx - 2 * dpml, h),\n", + ")\n", "\n", "plt.figure(dpi=200)\n", - "plt.imshow(np.rot90(np.real(ff['Hz']),1),cmap='RdBu')\n", - "plt.axis('off')" + "plt.imshow(np.rot90(np.real(ff[\"Hz\"]), 1), cmap=\"RdBu\")\n", + "plt.axis(\"off\")" ] } ], diff --git a/python/examples/cavity-farfield.py b/python/examples/cavity-farfield.py index 46ee7624e..f7a061355 100644 --- a/python/examples/cavity-farfield.py +++ b/python/examples/cavity-farfield.py @@ -1,34 +1,39 @@ import meep as mp import numpy as np import matplotlib -matplotlib.use('agg') + +matplotlib.use("agg") import matplotlib.pyplot as plt -resolution = 20 # pixels/μm +resolution = 20 # pixels/μm -fcen = 0.25 # pulse center frequency -df = 0.2 # pulse width (in frequency) +fcen = 0.25 # pulse center frequency +df = 0.2 # pulse width (in frequency) -eps = 13 # dielectric constant of waveguide -w = 1.2 # width of waveguide -r = 0.36 # radius of holes -d = 1.4 # defect spacing (ordinary spacing = 1) -N = 3 # number of holes on either side of defect +eps = 13 # dielectric constant of waveguide +w = 1.2 # width of waveguide +r = 0.36 # radius of holes +d = 1.4 # defect spacing (ordinary spacing = 1) +N = 3 # number of holes on either side of defect -dpad = 32 # padding between last hole and PML edge -dpml = 0.5/(fcen-0.5*df) # PML thickness (> half the largest wavelength) -sx = 2*(dpad+dpml+N) + d - 1 # size of cell in x direction +dpad = 32 # padding between last hole and PML edge +dpml = 0.5 / (fcen - 0.5 * df) # PML thickness (> half the largest wavelength) +sx = 2 * (dpad + dpml + N) + d - 1 # size of cell in x direction -d1 = 0.2 # y-distance from waveguide edge to near2far surface -d2 = 2.0 # y-distance from near2far surface to far-field line -sy = w + 2*(d1+d2+dpml) # size of cell in y direction (perpendicular to wvg.) +d1 = 0.2 # y-distance from waveguide edge to near2far surface +d2 = 2.0 # y-distance from near2far surface to far-field line +sy = w + 2 * (d1 + d2 + dpml) # size of cell in y direction (perpendicular to wvg.) -cell = mp.Vector3(sx,sy,0) +cell = mp.Vector3(sx, sy, 0) -geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf, w, mp.inf), - material=mp.Medium(epsilon=eps))] +geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + material=mp.Medium(epsilon=eps), + ) +] for i in range(N): geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) @@ -36,29 +41,36 @@ pml_layers = [mp.PML(dpml)] -sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), - component=mp.Hz, - center=mp.Vector3())] - -symmetries = [mp.Mirror(mp.X, phase=-1), - mp.Mirror(mp.Y, phase=-1)] - -sim = mp.Simulation(cell_size=cell, - geometry=geometry, - sources=sources, - symmetries=symmetries, - boundary_layers=pml_layers, - resolution=resolution) +sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3() + ) +] + +symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)] + +sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + sources=sources, + symmetries=symmetries, + boundary_layers=pml_layers, + resolution=resolution, +) nearfield = sim.add_near2far( - fcen, 0, 1, - mp.Near2FarRegion(mp.Vector3(0, 0.5*w + d1), - size=mp.Vector3(sx - 2*dpml)), - mp.Near2FarRegion(mp.Vector3(-0.5*sx + dpml, 0.5*w + 0.5*d1), - size=mp.Vector3(0, d1), - weight=-1.0), - mp.Near2FarRegion(mp.Vector3(0.5*sx - dpml, 0.5*w + 0.5*d1), - size=mp.Vector3(0, d1)), + fcen, + 0, + 1, + mp.Near2FarRegion(mp.Vector3(0, 0.5 * w + d1), size=mp.Vector3(sx - 2 * dpml)), + mp.Near2FarRegion( + mp.Vector3(-0.5 * sx + dpml, 0.5 * w + 0.5 * d1), + size=mp.Vector3(0, d1), + weight=-1.0, + ), + mp.Near2FarRegion( + mp.Vector3(0.5 * sx - dpml, 0.5 * w + 0.5 * d1), size=mp.Vector3(0, d1) + ), ) mon = sim.add_dft_fields( @@ -66,49 +78,57 @@ fcen, 0, 1, - center=mp.Vector3(0, 0.5*w + d1 + d2), - size=mp.Vector3(sx - 2*(dpad+dpml), 0) + center=mp.Vector3(0, 0.5 * w + d1 + d2), + size=mp.Vector3(sx - 2 * (dpad + dpml), 0), ) sim.run(until_after_sources=mp.stop_when_dft_decayed()) sim.plot2D() if mp.am_master(): - plt.savefig(f'cavity_farfield_plot2D_dpad{dpad}_{d1}_{d2}.png',bbox_inches='tight',dpi=150) + plt.savefig( + f"cavity_farfield_plot2D_dpad{dpad}_{d1}_{d2}.png", bbox_inches="tight", dpi=150 + ) Hz_mon = sim.get_dft_array(mon, mp.Hz, 0) -(x,y,z,w) = sim.get_array_metadata(dft_cell=mon) +(x, y, z, w) = sim.get_array_metadata(dft_cell=mon) ff = [] for xc in x: - ff_pt = sim.get_farfield(nearfield, mp.Vector3(xc,y[0])) + ff_pt = sim.get_farfield(nearfield, mp.Vector3(xc, y[0])) ff.append(ff_pt[5]) ff = np.array(ff) if mp.am_master(): plt.figure() - plt.subplot(1,3,1) - plt.plot(np.real(Hz_mon),'bo-',label='DFT') - plt.plot(np.real(ff),'ro-',label='N2F') + plt.subplot(1, 3, 1) + plt.plot(np.real(Hz_mon), "bo-", label="DFT") + plt.plot(np.real(ff), "ro-", label="N2F") plt.legend() - plt.xlabel('$x$ (μm)') - plt.ylabel('real(Hz)') + plt.xlabel("$x$ (μm)") + plt.ylabel("real(Hz)") - plt.subplot(1,3,2) - plt.plot(np.imag(Hz_mon),'bo-',label='DFT') - plt.plot(np.imag(ff),'ro-',label='N2F') + plt.subplot(1, 3, 2) + plt.plot(np.imag(Hz_mon), "bo-", label="DFT") + plt.plot(np.imag(ff), "ro-", label="N2F") plt.legend() - plt.xlabel('$x$ (μm)') - plt.ylabel('imag(Hz)') + plt.xlabel("$x$ (μm)") + plt.ylabel("imag(Hz)") - plt.subplot(1,3,3) - plt.plot(np.abs(Hz_mon),'bo-',label='DFT') - plt.plot(np.abs(ff),'ro-',label='N2F') + plt.subplot(1, 3, 3) + plt.plot(np.abs(Hz_mon), "bo-", label="DFT") + plt.plot(np.abs(ff), "ro-", label="N2F") plt.legend() - plt.xlabel('$x$ (μm)') - plt.ylabel('|Hz|') + plt.xlabel("$x$ (μm)") + plt.ylabel("|Hz|") - plt.suptitle(f'comparison of near2far and actual DFT fields\n dpad={dpad}, d1={d1}, d2={d2}') + plt.suptitle( + f"comparison of near2far and actual DFT fields\n dpad={dpad}, d1={d1}, d2={d2}" + ) plt.subplots_adjust(wspace=0.6) - plt.savefig(f'test_Hz_dft_vs_n2f_res{resolution}_dpad{dpad}_d1{d1}_d2{d2}.png',bbox_inches='tight',dpi=150) + plt.savefig( + f"test_Hz_dft_vs_n2f_res{resolution}_dpad{dpad}_d1{d1}_d2{d2}.png", + bbox_inches="tight", + dpi=150, + ) diff --git a/python/examples/cavity_arrayslice.py b/python/examples/cavity_arrayslice.py index 863fce44e..7e7627531 100644 --- a/python/examples/cavity_arrayslice.py +++ b/python/examples/cavity_arrayslice.py @@ -18,23 +18,23 @@ cell = mp.Vector3(sx, sy, 0) -blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), - material=mp.Medium(epsilon=eps)) +blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=eps)) geometry = [blk] geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)) for i in range(3)) geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / -2 - i)) for i in range(3)) -sim = mp.Simulation(cell_size=cell, - geometry=geometry, - sources=[], - boundary_layers=[mp.PML(dpml)], - resolution=20) +sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + sources=[], + boundary_layers=[mp.PML(dpml)], + resolution=20, +) # add sources -sim.sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - mp.Hz, mp.Vector3())] +sim.sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Hz, mp.Vector3())] # run until sources are finished (and no later) sim._run_sources_until(0, []) diff --git a/python/examples/cherenkov-radiation.py b/python/examples/cherenkov-radiation.py index f6f165376..f2f17b5db 100644 --- a/python/examples/cherenkov-radiation.py +++ b/python/examples/cherenkov-radiation.py @@ -4,26 +4,38 @@ sx = 60 sy = 60 -cell_size = mp.Vector3(sx,sy,0) +cell_size = mp.Vector3(sx, sy, 0) dpml = 1.0 pml_layers = [mp.PML(thickness=dpml)] -v = 0.7 # velocity of point charge +v = 0.7 # velocity of point charge symmetries = [mp.Mirror(direction=mp.Y)] -sim = mp.Simulation(resolution=10, - cell_size=cell_size, - default_material=mp.Medium(index=1.5), - symmetries=symmetries, - boundary_layers=pml_layers) +sim = mp.Simulation( + resolution=10, + cell_size=cell_size, + default_material=mp.Medium(index=1.5), + symmetries=symmetries, + boundary_layers=pml_layers, +) -def move_source(sim): - sim.change_sources([mp.Source(mp.ContinuousSource(frequency=1e-10), - component=mp.Ex, - center=mp.Vector3(-0.5*sx+dpml+v*sim.meep_time()))]) -sim.run(move_source, - mp.at_every(2, mp.output_png(mp.Hz, "-vZc dkbluered -M 1")), - until=sx/v) +def move_source(sim): + sim.change_sources( + [ + mp.Source( + mp.ContinuousSource(frequency=1e-10), + component=mp.Ex, + center=mp.Vector3(-0.5 * sx + dpml + v * sim.meep_time()), + ) + ] + ) + + +sim.run( + move_source, + mp.at_every(2, mp.output_png(mp.Hz, "-vZc dkbluered -M 1")), + until=sx / v, +) diff --git a/python/examples/chirped_pulse.py b/python/examples/chirped_pulse.py index b768b6ab1..57064bf18 100644 --- a/python/examples/chirped_pulse.py +++ b/python/examples/chirped_pulse.py @@ -6,31 +6,43 @@ resolution = 40 dpml = 2 -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] sx = 40 sy = 6 -cell_size = mp.Vector3(sx+2*dpml,sy) +cell_size = mp.Vector3(sx + 2 * dpml, sy) v0 = 1.0 # pulse center frequency -a = 0.2 # Gaussian envelope half-width +a = 0.2 # Gaussian envelope half-width b = -0.5 # linear chirp rate (positive: up-chirp, negative: down-chirp) -t0 = 15 # peak time - -chirp = lambda t: np.exp(1j*2*np.pi*v0*(t-t0)) * np.exp(-a*(t-t0)**2+1j*b*(t-t0)**2) - -sources = [mp.Source(src=mp.CustomSource(src_func=chirp), - center=mp.Vector3(-0.5*sx), - size=mp.Vector3(y=sy), - component=mp.Ez)] - -sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - resolution=resolution, - k_point=mp.Vector3(), - sources=sources, - symmetries=[mp.Mirror(mp.Y)]) - -sim.run(mp.in_volume(mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx,sy)), - mp.at_every(2.7, mp.output_efield_z)), - until=t0+50) +t0 = 15 # peak time + +chirp = lambda t: np.exp(1j * 2 * np.pi * v0 * (t - t0)) * np.exp( + -a * (t - t0) ** 2 + 1j * b * (t - t0) ** 2 +) + +sources = [ + mp.Source( + src=mp.CustomSource(src_func=chirp), + center=mp.Vector3(-0.5 * sx), + size=mp.Vector3(y=sy), + component=mp.Ez, + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + resolution=resolution, + k_point=mp.Vector3(), + sources=sources, + symmetries=[mp.Mirror(mp.Y)], +) + +sim.run( + mp.in_volume( + mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx, sy)), + mp.at_every(2.7, mp.output_efield_z), + ), + until=t0 + 50, +) diff --git a/python/examples/coupler.ipynb b/python/examples/coupler.ipynb index 8e4df35df..47de7645e 100644 --- a/python/examples/coupler.ipynb +++ b/python/examples/coupler.ipynb @@ -878,11 +878,11 @@ "import numpy\n", "import matplotlib.pyplot as plt\n", "\n", - "res = 25 # pixels/μm\n", - "three_d = False # 3d calculation?\n", - "d = 0.12 # branch separation\n", + "res = 25 # pixels/μm\n", + "three_d = False # 3d calculation?\n", + "d = 0.12 # branch separation\n", "\n", - "gdsII_file = 'coupler.gds'\n", + "gdsII_file = \"coupler.gds\"\n", "CELL_LAYER = 0\n", "PORT1_LAYER = 1\n", "PORT2_LAYER = 2\n", @@ -898,24 +898,28 @@ "t_air = 0.78\n", "\n", "dpml = 1\n", - "cell_thickness = dpml+t_oxide+t_Si+t_air+dpml\n", + "cell_thickness = dpml + t_oxide + t_Si + t_air + dpml\n", "\n", "oxide = mp.Medium(epsilon=2.25)\n", - "silicon=mp.Medium(epsilon=12)\n", + "silicon = mp.Medium(epsilon=12)\n", "\n", "lcen = 1.55\n", - "fcen = 1/lcen\n", - "df = 0.2*fcen\n", + "fcen = 1 / lcen\n", + "df = 0.2 * fcen\n", "\n", - "cell_zmax = 0.5*cell_thickness if three_d else 0\n", - "cell_zmin = -0.5*cell_thickness if three_d else 0\n", - "si_zmax = 0.5*t_Si if three_d else 10\n", - "si_zmin = -0.5*t_Si if three_d else -10\n", + "cell_zmax = 0.5 * cell_thickness if three_d else 0\n", + "cell_zmin = -0.5 * cell_thickness if three_d else 0\n", + "si_zmax = 0.5 * t_Si if three_d else 10\n", + "si_zmin = -0.5 * t_Si if three_d else -10\n", "\n", "# read cell size, volumes for source region and flux monitors,\n", "# and coupler geometry from GDSII file\n", - "upper_branch = mp.get_GDSII_prisms(silicon, gdsII_file, UPPER_BRANCH_LAYER, si_zmin, si_zmax)\n", - "lower_branch = mp.get_GDSII_prisms(silicon, gdsII_file, LOWER_BRANCH_LAYER, si_zmin, si_zmax)\n", + "upper_branch = mp.get_GDSII_prisms(\n", + " silicon, gdsII_file, UPPER_BRANCH_LAYER, si_zmin, si_zmax\n", + ")\n", + "lower_branch = mp.get_GDSII_prisms(\n", + " silicon, gdsII_file, LOWER_BRANCH_LAYER, si_zmin, si_zmax\n", + ")\n", "\n", "cell = mp.GDSII_vol(gdsII_file, CELL_LAYER, cell_zmin, cell_zmax)\n", "p1 = mp.GDSII_vol(gdsII_file, PORT1_LAYER, si_zmin, si_zmax)\n", @@ -926,14 +930,14 @@ "\n", "# displace upper and lower branches of coupler (as well as source and flux regions)\n", "if d != default_d:\n", - " delta_y = 0.5*(d-default_d)\n", + " delta_y = 0.5 * (d - default_d)\n", " delta = mp.Vector3(y=delta_y)\n", " p1.center += delta\n", " p2.center -= delta\n", " p3.center += delta\n", " p4.center -= delta\n", " src_vol.center += delta\n", - " cell.size += 2*delta\n", + " cell.size += 2 * delta\n", " for np in range(len(lower_branch)):\n", " lower_branch[np].center -= delta\n", " for nv in range(len(lower_branch[np].vertices)):\n", @@ -943,26 +947,32 @@ " for nv in range(len(upper_branch[np].vertices)):\n", " upper_branch[np].vertices[nv] += delta\n", "\n", - "geometry = upper_branch+lower_branch\n", + "geometry = upper_branch + lower_branch\n", "\n", "if three_d:\n", - " oxide_center = mp.Vector3(z=-0.5*t_oxide)\n", - " oxide_size = mp.Vector3(cell.size.x,cell.size.y,t_oxide)\n", + " oxide_center = mp.Vector3(z=-0.5 * t_oxide)\n", + " oxide_size = mp.Vector3(cell.size.x, cell.size.y, t_oxide)\n", " oxide_layer = [mp.Block(material=oxide, center=oxide_center, size=oxide_size)]\n", - " geometry = geometry+oxide_layer\n", + " geometry = geometry + oxide_layer\n", "\n", - "sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df),\n", - " size=src_vol.size,\n", - " center=src_vol.center,\n", - " eig_band=1,\n", - " eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z,\n", - " eig_match_freq=True)]\n", + "sources = [\n", + " mp.EigenModeSource(\n", + " src=mp.GaussianSource(fcen, fwidth=df),\n", + " size=src_vol.size,\n", + " center=src_vol.center,\n", + " eig_band=1,\n", + " eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z,\n", + " eig_match_freq=True,\n", + " )\n", + "]\n", "\n", - "sim = mp.Simulation(resolution=res,\n", - " cell_size=cell.size,\n", - " boundary_layers=[mp.PML(dpml)],\n", - " sources=sources,\n", - " geometry=geometry)\n", + "sim = mp.Simulation(\n", + " resolution=res,\n", + " cell_size=cell.size,\n", + " boundary_layers=[mp.PML(dpml)],\n", + " sources=sources,\n", + " geometry=geometry,\n", + ")\n", "\n", "mode1 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p1))\n", "mode2 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p2))\n", @@ -1007,17 +1017,25 @@ ], "source": [ "# S parameters\n", - "p1_coeff = sim.get_eigenmode_coefficients(mode1, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0]\n", - "p2_coeff = sim.get_eigenmode_coefficients(mode2, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,1]\n", - "p3_coeff = sim.get_eigenmode_coefficients(mode3, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0]\n", - "p4_coeff = sim.get_eigenmode_coefficients(mode4, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0]\n", + "p1_coeff = sim.get_eigenmode_coefficients(\n", + " mode1, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", + ").alpha[0, 0, 0]\n", + "p2_coeff = sim.get_eigenmode_coefficients(\n", + " mode2, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", + ").alpha[0, 0, 1]\n", + "p3_coeff = sim.get_eigenmode_coefficients(\n", + " mode3, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", + ").alpha[0, 0, 0]\n", + "p4_coeff = sim.get_eigenmode_coefficients(\n", + " mode4, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", + ").alpha[0, 0, 0]\n", "\n", "# transmittance\n", - "p2_trans = abs(p2_coeff)**2/abs(p1_coeff)**2\n", - "p3_trans = abs(p3_coeff)**2/abs(p1_coeff)**2\n", - "p4_trans = abs(p4_coeff)**2/abs(p1_coeff)**2\n", + "p2_trans = abs(p2_coeff) ** 2 / abs(p1_coeff) ** 2\n", + "p3_trans = abs(p3_coeff) ** 2 / abs(p1_coeff) ** 2\n", + "p4_trans = abs(p4_coeff) ** 2 / abs(p1_coeff) ** 2\n", "\n", - "print(\"trans:, {:.2f}, {:.6f}, {:.6f}, {:.6f}\".format(d,p2_trans,p3_trans,p4_trans))" + "print(\"trans:, {:.2f}, {:.6f}, {:.6f}, {:.6f}\".format(d, p2_trans, p3_trans, p4_trans))" ] }, { @@ -1913,28 +1931,41 @@ "source": [ "sim.reset_meep()\n", "\n", - "sources = [mp.EigenModeSource(src=mp.ContinuousSource(fcen,fwidth=df),\n", - " size=src_vol.size,\n", - " center=src_vol.center,\n", - " eig_band=1,\n", - " eig_parity=mp.EVEN_Y+mp.ODD_Z,\n", - " eig_match_freq=True)]\n", + "sources = [\n", + " mp.EigenModeSource(\n", + " src=mp.ContinuousSource(fcen, fwidth=df),\n", + " size=src_vol.size,\n", + " center=src_vol.center,\n", + " eig_band=1,\n", + " eig_parity=mp.EVEN_Y + mp.ODD_Z,\n", + " eig_match_freq=True,\n", + " )\n", + "]\n", "\n", - "sim = mp.Simulation(resolution=res,\n", - " cell_size=cell.size,\n", - " boundary_layers=[mp.PML(dpml)],\n", - " sources=sources,\n", - " geometry=geometry)\n", + "sim = mp.Simulation(\n", + " resolution=res,\n", + " cell_size=cell.size,\n", + " boundary_layers=[mp.PML(dpml)],\n", + " sources=sources,\n", + " geometry=geometry,\n", + ")\n", "\n", - "sim.run(until=400) # arbitrary long run time to ensure that fields have reached steady state\n", + "sim.run(\n", + " until=400\n", + ") # arbitrary long run time to ensure that fields have reached steady state\n", "\n", "eps_data = sim.get_epsilon()\n", "ez_data = numpy.real(sim.get_efield_z())\n", "\n", "plt.figure(dpi=200)\n", - "plt.imshow(numpy.transpose(eps_data), interpolation='spline36', cmap='binary')\n", - "plt.imshow(numpy.flipud(numpy.transpose(ez_data)), interpolation='spline36', cmap='RdBu', alpha=0.9)\n", - "plt.axis('off')\n", + "plt.imshow(numpy.transpose(eps_data), interpolation=\"spline36\", cmap=\"binary\")\n", + "plt.imshow(\n", + " numpy.flipud(numpy.transpose(ez_data)),\n", + " interpolation=\"spline36\",\n", + " cmap=\"RdBu\",\n", + " alpha=0.9,\n", + ")\n", + "plt.axis(\"off\")\n", "plt.show()" ] }, diff --git a/python/examples/coupler.py b/python/examples/coupler.py index b141681f9..3e2c4dfd3 100644 --- a/python/examples/coupler.py +++ b/python/examples/coupler.py @@ -1,7 +1,7 @@ import meep as mp import argparse -gdsII_file = 'coupler.gds' +gdsII_file = "coupler.gds" CELL_LAYER = 0 PORT1_LAYER = 1 PORT2_LAYER = 2 @@ -17,24 +17,29 @@ t_air = 0.78 dpml = 1 -cell_thickness = dpml+t_oxide+t_Si+t_air+dpml +cell_thickness = dpml + t_oxide + t_Si + t_air + dpml oxide = mp.Medium(epsilon=2.25) silicon = mp.Medium(epsilon=12) -fcen = 1/1.55 -df = 0.2*fcen +fcen = 1 / 1.55 +df = 0.2 * fcen + def main(args): - cell_zmax = 0.5*cell_thickness if args.three_d else 0 - cell_zmin = -0.5*cell_thickness if args.three_d else 0 - si_zmax = 0.5*t_Si if args.three_d else 10 - si_zmin = -0.5*t_Si if args.three_d else -10 + cell_zmax = 0.5 * cell_thickness if args.three_d else 0 + cell_zmin = -0.5 * cell_thickness if args.three_d else 0 + si_zmax = 0.5 * t_Si if args.three_d else 10 + si_zmin = -0.5 * t_Si if args.three_d else -10 # read cell size, volumes for source region and flux monitors, # and coupler geometry from GDSII file - upper_branch = mp.get_GDSII_prisms(silicon, gdsII_file, UPPER_BRANCH_LAYER, si_zmin, si_zmax) - lower_branch = mp.get_GDSII_prisms(silicon, gdsII_file, LOWER_BRANCH_LAYER, si_zmin, si_zmax) + upper_branch = mp.get_GDSII_prisms( + silicon, gdsII_file, UPPER_BRANCH_LAYER, si_zmin, si_zmax + ) + lower_branch = mp.get_GDSII_prisms( + silicon, gdsII_file, LOWER_BRANCH_LAYER, si_zmin, si_zmax + ) cell = mp.GDSII_vol(gdsII_file, CELL_LAYER, cell_zmin, cell_zmax) p1 = mp.GDSII_vol(gdsII_file, PORT1_LAYER, si_zmin, si_zmax) @@ -45,14 +50,14 @@ def main(args): # displace upper and lower branches of coupler (as well as source and flux regions) if args.d != default_d: - delta_y = 0.5*(args.d-default_d) + delta_y = 0.5 * (args.d - default_d) delta = mp.Vector3(y=delta_y) p1.center += delta p2.center -= delta p3.center += delta p4.center -= delta src_vol.center += delta - cell.size += 2*delta + cell.size += 2 * delta for np in range(len(lower_branch)): lower_branch[np].center -= delta for nv in range(len(lower_branch[np].vertices)): @@ -62,23 +67,29 @@ def main(args): for nv in range(len(upper_branch[np].vertices)): upper_branch[np].vertices[nv] += delta - geometry = upper_branch+lower_branch + geometry = upper_branch + lower_branch if args.three_d: - oxide_center = mp.Vector3(z=-0.5*t_oxide) - oxide_size = mp.Vector3(cell.size.x,cell.size.y,t_oxide) + oxide_center = mp.Vector3(z=-0.5 * t_oxide) + oxide_size = mp.Vector3(cell.size.x, cell.size.y, t_oxide) oxide_layer = [mp.Block(material=oxide, center=oxide_center, size=oxide_size)] - geometry = geometry+oxide_layer - - sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df), - volume=src_vol, - eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y+mp.ODD_Z)] - - sim = mp.Simulation(resolution=args.res, - cell_size=cell.size, - boundary_layers=[mp.PML(dpml)], - sources=sources, - geometry=geometry) + geometry = geometry + oxide_layer + + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=df), + volume=src_vol, + eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y + mp.ODD_Z, + ) + ] + + sim = mp.Simulation( + resolution=args.res, + cell_size=cell.size, + boundary_layers=[mp.PML(dpml)], + sources=sources, + geometry=geometry, + ) mode1 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p1)) mode2 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p2)) @@ -88,22 +99,44 @@ def main(args): sim.run(until_after_sources=100) # S parameters - p1_coeff = sim.get_eigenmode_coefficients(mode1, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0] - p2_coeff = sim.get_eigenmode_coefficients(mode2, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,1] - p3_coeff = sim.get_eigenmode_coefficients(mode3, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0] - p4_coeff = sim.get_eigenmode_coefficients(mode4, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0] + p1_coeff = sim.get_eigenmode_coefficients( + mode1, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y + mp.ODD_Z + ).alpha[0, 0, 0] + p2_coeff = sim.get_eigenmode_coefficients( + mode2, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y + mp.ODD_Z + ).alpha[0, 0, 1] + p3_coeff = sim.get_eigenmode_coefficients( + mode3, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y + mp.ODD_Z + ).alpha[0, 0, 0] + p4_coeff = sim.get_eigenmode_coefficients( + mode4, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y + mp.ODD_Z + ).alpha[0, 0, 0] # transmittance - p2_trans = abs(p2_coeff)**2/abs(p1_coeff)**2 - p3_trans = abs(p3_coeff)**2/abs(p1_coeff)**2 - p4_trans = abs(p4_coeff)**2/abs(p1_coeff)**2 + p2_trans = abs(p2_coeff) ** 2 / abs(p1_coeff) ** 2 + p3_trans = abs(p3_coeff) ** 2 / abs(p1_coeff) ** 2 + p4_trans = abs(p4_coeff) ** 2 / abs(p1_coeff) ** 2 + + print( + "trans:, {:.2f}, {:.6f}, {:.6f}, {:.6f}".format( + args.d, p2_trans, p3_trans, p4_trans + ) + ) - print("trans:, {:.2f}, {:.6f}, {:.6f}, {:.6f}".format(args.d,p2_trans,p3_trans,p4_trans)) -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-res', type=int, default=50, help='resolution (default: 50 pixels/um)') - parser.add_argument('-d', type=float, default=0.1, help='branch separation (default: 0.1 um)') - parser.add_argument('--three_d', action='store_true', default=False, help='3d calculation? (default: False)') + parser.add_argument( + "-res", type=int, default=50, help="resolution (default: 50 pixels/um)" + ) + parser.add_argument( + "-d", type=float, default=0.1, help="branch separation (default: 0.1 um)" + ) + parser.add_argument( + "--three_d", + action="store_true", + default=False, + help="3d calculation? (default: False)", + ) args = parser.parse_args() main(args) diff --git a/python/examples/cyl-ellipsoid.py b/python/examples/cyl-ellipsoid.py index 8a727163e..cf4be178f 100644 --- a/python/examples/cyl-ellipsoid.py +++ b/python/examples/cyl-ellipsoid.py @@ -9,7 +9,9 @@ def main(): e = mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf)) src_cmpt = mp.Hz - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.1), component=src_cmpt, center=mp.Vector3()) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.1), component=src_cmpt, center=mp.Vector3() + ) if src_cmpt == mp.Ez: symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] @@ -17,26 +19,30 @@ def main(): if src_cmpt == mp.Hz: symmetries = [mp.Mirror(mp.X, -1), mp.Mirror(mp.Y, -1)] - sim = mp.Simulation(cell_size=mp.Vector3(10, 10), - geometry=[c, e], - boundary_layers=[mp.PML(1.0)], - sources=[sources], - symmetries=symmetries, - resolution=100) + sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), + geometry=[c, e], + boundary_layers=[mp.PML(1.0)], + sources=[sources], + symmetries=symmetries, + resolution=100, + ) def print_stuff(sim_obj): v = mp.Vector3(4.13, 3.75, 0) p = sim.get_field_point(src_cmpt, v) print(f"t, Ez: {sim.round_time()} {p.real}+{p.imag}i") - sim.run(mp.at_beginning(mp.output_epsilon), - mp.at_every(0.25, print_stuff), - mp.at_end(print_stuff), - mp.at_end(mp.output_efield_z), - until=23) + sim.run( + mp.at_beginning(mp.output_epsilon), + mp.at_every(0.25, print_stuff), + mp.at_end(print_stuff), + mp.at_end(mp.output_efield_z), + until=23, + ) print("stopped at meep time = {}".format(sim.round_time())) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/cylinder_cross_section.ipynb b/python/examples/cylinder_cross_section.ipynb index e0dad84e6..991295a92 100644 --- a/python/examples/cylinder_cross_section.ipynb +++ b/python/examples/cylinder_cross_section.ipynb @@ -115,47 +115,67 @@ "r = 0.7 # radius of cylinder\n", "h = 2.3 # height of cylinder\n", "\n", - "wvl_min = 2*np.pi*r/10\n", - "wvl_max = 2*np.pi*r/2\n", + "wvl_min = 2 * np.pi * r / 10\n", + "wvl_max = 2 * np.pi * r / 2\n", "\n", - "frq_min = 1/wvl_max\n", - "frq_max = 1/wvl_min\n", - "frq_cen = 0.5*(frq_min+frq_max)\n", - "dfrq = frq_max-frq_min\n", + "frq_min = 1 / wvl_max\n", + "frq_max = 1 / wvl_min\n", + "frq_cen = 0.5 * (frq_min + frq_max)\n", + "dfrq = frq_max - frq_min\n", "nfrq = 100\n", "\n", "## at least 8 pixels per smallest wavelength, i.e. np.floor(8/wvl_min)\n", "resolution = 25\n", "\n", - "dpml = 0.5*wvl_max\n", - "dair = 1.0*wvl_max\n", + "dpml = 0.5 * wvl_max\n", + "dair = 1.0 * wvl_max\n", "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "sr = r+dair+dpml\n", - "sz = dpml+dair+h+dair+dpml\n", - "cell_size = mp.Vector3(sr,0,sz)\n", - "\n", - "sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n", - " component=mp.Er,\n", - " center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml),\n", - " size=mp.Vector3(sr)),\n", - " mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n", - " component=mp.Ep,\n", - " center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml),\n", - " size=mp.Vector3(sr),\n", - " amplitude=-1j)]\n", - "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " dimensions=mp.CYLINDRICAL,\n", - " m=-1)\n", - "\n", - "box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,-0.5*h),size=mp.Vector3(r)))\n", - "box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,+0.5*h),size=mp.Vector3(r)))\n", - "box_r = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r),size=mp.Vector3(z=h)))\n", + "sr = r + dair + dpml\n", + "sz = dpml + dair + h + dair + dpml\n", + "cell_size = mp.Vector3(sr, 0, sz)\n", + "\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True),\n", + " component=mp.Er,\n", + " center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml),\n", + " size=mp.Vector3(sr),\n", + " ),\n", + " mp.Source(\n", + " mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True),\n", + " component=mp.Ep,\n", + " center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml),\n", + " size=mp.Vector3(sr),\n", + " amplitude=-1j,\n", + " ),\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " dimensions=mp.CYLINDRICAL,\n", + " m=-1,\n", + ")\n", + "\n", + "box_z1 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, -0.5 * h), size=mp.Vector3(r)),\n", + ")\n", + "box_z2 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, +0.5 * h), size=mp.Vector3(r)),\n", + ")\n", + "box_r = sim.add_flux(\n", + " frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r), size=mp.Vector3(z=h))\n", + ")\n", "\n", "sim.run(until_after_sources=10)\n", "\n", @@ -169,21 +189,39 @@ "sim.reset_meep()\n", "\n", "n_cyl = 2.0\n", - "geometry = [mp.Block(material=mp.Medium(index=n_cyl),\n", - " center=mp.Vector3(0.5*r),\n", - " size=mp.Vector3(r,0,h))]\n", - "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " dimensions=mp.CYLINDRICAL,\n", - " m=-1)\n", - "\n", - "box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,-0.5*h),size=mp.Vector3(r)))\n", - "box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,+0.5*h),size=mp.Vector3(r)))\n", - "box_r = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r),size=mp.Vector3(z=h)))\n", + "geometry = [\n", + " mp.Block(\n", + " material=mp.Medium(index=n_cyl),\n", + " center=mp.Vector3(0.5 * r),\n", + " size=mp.Vector3(r, 0, h),\n", + " )\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " dimensions=mp.CYLINDRICAL,\n", + " m=-1,\n", + ")\n", + "\n", + "box_z1 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, -0.5 * h), size=mp.Vector3(r)),\n", + ")\n", + "box_z2 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, +0.5 * h), size=mp.Vector3(r)),\n", + ")\n", + "box_r = sim.add_flux(\n", + " frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r), size=mp.Vector3(z=h))\n", + ")\n", "\n", "sim.load_minus_flux_data(box_z1, box_z1_data)\n", "sim.load_minus_flux_data(box_z2, box_z2_data)\n", @@ -195,16 +233,16 @@ "box_z2_flux = mp.get_fluxes(box_z2)\n", "box_r_flux = mp.get_fluxes(box_r)\n", "\n", - "scatt_flux = np.asarray(box_z1_flux)-np.asarray(box_z2_flux)-np.asarray(box_r_flux)\n", - "intensity = np.asarray(box_z1_flux0)/(np.pi*r**2)\n", - "scatt_cross_section = np.divide(-scatt_flux,intensity)\n", + "scatt_flux = np.asarray(box_z1_flux) - np.asarray(box_z2_flux) - np.asarray(box_r_flux)\n", + "intensity = np.asarray(box_z1_flux0) / (np.pi * r**2)\n", + "scatt_cross_section = np.divide(-scatt_flux, intensity)\n", "\n", "plt.figure(dpi=150)\n", - "plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_cross_section,'bo-')\n", - "plt.grid(True,which=\"both\",ls=\"-\")\n", - "plt.xlabel('(cylinder circumference)/wavelength, 2πr/λ')\n", - "plt.ylabel('scattering cross section, σ')\n", - "plt.title('Scattering Cross Section of a Lossless Dielectric Cylinder')\n", + "plt.loglog(2 * np.pi * r * np.asarray(freqs), scatt_cross_section, \"bo-\")\n", + "plt.grid(True, which=\"both\", ls=\"-\")\n", + "plt.xlabel(\"(cylinder circumference)/wavelength, 2πr/λ\")\n", + "plt.ylabel(\"scattering cross section, σ\")\n", + "plt.title(\"Scattering Cross Section of a Lossless Dielectric Cylinder\")\n", "plt.tight_layout()\n", "plt.show()" ] diff --git a/python/examples/cylinder_cross_section.py b/python/examples/cylinder_cross_section.py index 73960e545..489ce3ff1 100644 --- a/python/examples/cylinder_cross_section.py +++ b/python/examples/cylinder_cross_section.py @@ -5,47 +5,67 @@ r = 0.7 # radius of cylinder h = 2.3 # height of cylinder -wvl_min = 2*np.pi*r/10 -wvl_max = 2*np.pi*r/2 +wvl_min = 2 * np.pi * r / 10 +wvl_max = 2 * np.pi * r / 2 -frq_min = 1/wvl_max -frq_max = 1/wvl_min -frq_cen = 0.5*(frq_min+frq_max) -dfrq = frq_max-frq_min +frq_min = 1 / wvl_max +frq_max = 1 / wvl_min +frq_cen = 0.5 * (frq_min + frq_max) +dfrq = frq_max - frq_min nfrq = 100 ## at least 8 pixels per smallest wavelength, i.e. np.floor(8/wvl_min) resolution = 25 -dpml = 0.5*wvl_max -dair = 1.0*wvl_max +dpml = 0.5 * wvl_max +dair = 1.0 * wvl_max pml_layers = [mp.PML(thickness=dpml)] -sr = r+dair+dpml -sz = dpml+dair+h+dair+dpml -cell_size = mp.Vector3(sr,0,sz) - -sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True), - component=mp.Er, - center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml), - size=mp.Vector3(sr)), - mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True), - component=mp.Ep, - center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml), - size=mp.Vector3(sr), - amplitude=-1j)] - -sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - resolution=resolution, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1) - -box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,-0.5*h),size=mp.Vector3(r))) -box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,+0.5*h),size=mp.Vector3(r))) -box_r = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r),size=mp.Vector3(z=h))) +sr = r + dair + dpml +sz = dpml + dair + h + dair + dpml +cell_size = mp.Vector3(sr, 0, sz) + +sources = [ + mp.Source( + mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True), + component=mp.Er, + center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml), + size=mp.Vector3(sr), + ), + mp.Source( + mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True), + component=mp.Ep, + center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml), + size=mp.Vector3(sr), + amplitude=-1j, + ), +] + +sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + resolution=resolution, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, +) + +box_z1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, -0.5 * h), size=mp.Vector3(r)), +) +box_z2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, +0.5 * h), size=mp.Vector3(r)), +) +box_r = sim.add_flux( + frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r), size=mp.Vector3(z=h)) +) sim.run(until_after_sources=10) @@ -59,21 +79,39 @@ sim.reset_meep() n_cyl = 2.0 -geometry = [mp.Block(material=mp.Medium(index=n_cyl), - center=mp.Vector3(0.5*r), - size=mp.Vector3(r,0,h))] - -sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - resolution=resolution, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1) - -box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,-0.5*h),size=mp.Vector3(r))) -box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,+0.5*h),size=mp.Vector3(r))) -box_r = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r),size=mp.Vector3(z=h))) +geometry = [ + mp.Block( + material=mp.Medium(index=n_cyl), + center=mp.Vector3(0.5 * r), + size=mp.Vector3(r, 0, h), + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + resolution=resolution, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, +) + +box_z1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, -0.5 * h), size=mp.Vector3(r)), +) +box_z2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, +0.5 * h), size=mp.Vector3(r)), +) +box_r = sim.add_flux( + frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r), size=mp.Vector3(z=h)) +) sim.load_minus_flux_data(box_z1, box_z1_data) sim.load_minus_flux_data(box_z2, box_z2_data) @@ -85,16 +123,16 @@ box_z2_flux = mp.get_fluxes(box_z2) box_r_flux = mp.get_fluxes(box_r) -scatt_flux = np.asarray(box_z1_flux)-np.asarray(box_z2_flux)-np.asarray(box_r_flux) -intensity = np.asarray(box_z1_flux0)/(np.pi*r**2) -scatt_cross_section = np.divide(-scatt_flux,intensity) +scatt_flux = np.asarray(box_z1_flux) - np.asarray(box_z2_flux) - np.asarray(box_r_flux) +intensity = np.asarray(box_z1_flux0) / (np.pi * r**2) +scatt_cross_section = np.divide(-scatt_flux, intensity) if mp.am_master(): plt.figure(dpi=150) - plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_cross_section,'bo-') - plt.grid(True,which="both",ls="-") - plt.xlabel('(cylinder circumference)/wavelength, 2πr/λ') - plt.ylabel('scattering cross section, σ') - plt.title('Scattering Cross Section of a Lossless Dielectric Cylinder') + plt.loglog(2 * np.pi * r * np.asarray(freqs), scatt_cross_section, "bo-") + plt.grid(True, which="both", ls="-") + plt.xlabel("(cylinder circumference)/wavelength, 2πr/λ") + plt.ylabel("scattering cross section, σ") + plt.title("Scattering Cross Section of a Lossless Dielectric Cylinder") plt.tight_layout() plt.savefig("cylinder_cross_section.png") diff --git a/python/examples/differential_cross_section.ipynb b/python/examples/differential_cross_section.ipynb index 12f091350..e26b07def 100644 --- a/python/examples/differential_cross_section.ipynb +++ b/python/examples/differential_cross_section.ipynb @@ -312,44 +312,72 @@ "\n", "frq_cen = 1.0\n", "\n", - "resolution = 20 # pixels/um\n", + "resolution = 20 # pixels/um\n", "\n", "dpml = 0.5\n", - "dair = 1.5 # at least 0.5/frq_cen padding between source and near-field monitor\n", + "dair = 1.5 # at least 0.5/frq_cen padding between source and near-field monitor\n", "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "s = 2*(dpml+dair+r)\n", - "cell_size = mp.Vector3(s,s,s)\n", + "s = 2 * (dpml + dair + r)\n", + "cell_size = mp.Vector3(s, s, s)\n", "\n", "# circularly-polarized source with propagation axis along x\n", "# is_integrated=True necessary for any planewave source extending into PML\n", - "sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=0.2*frq_cen,is_integrated=True),\n", - " center=mp.Vector3(-0.5*s+dpml),\n", - " size=mp.Vector3(0,s,s),\n", - " component=mp.Ez),\n", - " mp.Source(mp.GaussianSource(frq_cen,fwidth=0.2*frq_cen,is_integrated=True),\n", - " center=mp.Vector3(-0.5*s+dpml),\n", - " size=mp.Vector3(0,s,s),\n", - " component=mp.Ey,\n", - " amplitude=1j)]\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(frq_cen, fwidth=0.2 * frq_cen, is_integrated=True),\n", + " center=mp.Vector3(-0.5 * s + dpml),\n", + " size=mp.Vector3(0, s, s),\n", + " component=mp.Ez,\n", + " ),\n", + " mp.Source(\n", + " mp.GaussianSource(frq_cen, fwidth=0.2 * frq_cen, is_integrated=True),\n", + " center=mp.Vector3(-0.5 * s + dpml),\n", + " size=mp.Vector3(0, s, s),\n", + " component=mp.Ey,\n", + " amplitude=1j,\n", + " ),\n", + "]\n", "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=mp.Vector3())\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=mp.Vector3(),\n", + ")\n", "\n", - "box_flux = sim.add_flux(frq_cen, 0, 1,\n", - " mp.FluxRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r)))\n", + "box_flux = sim.add_flux(\n", + " frq_cen,\n", + " 0,\n", + " 1,\n", + " mp.FluxRegion(center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r)),\n", + ")\n", "\n", - "nearfield_box = sim.add_near2far(frq_cen, 0, 1,\n", - " mp.Near2FarRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r),weight=+1),\n", - " mp.Near2FarRegion(center=mp.Vector3(x=+2*r),size=mp.Vector3(0,4*r,4*r),weight=-1),\n", - " mp.Near2FarRegion(center=mp.Vector3(y=-2*r),size=mp.Vector3(4*r,0,4*r),weight=+1),\n", - " mp.Near2FarRegion(center=mp.Vector3(y=+2*r),size=mp.Vector3(4*r,0,4*r),weight=-1),\n", - " mp.Near2FarRegion(center=mp.Vector3(z=-2*r),size=mp.Vector3(4*r,4*r,0),weight=+1),\n", - " mp.Near2FarRegion(center=mp.Vector3(z=+2*r),size=mp.Vector3(4*r,4*r,0),weight=-1))\n", + "nearfield_box = sim.add_near2far(\n", + " frq_cen,\n", + " 0,\n", + " 1,\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=+1\n", + " ),\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(x=+2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=-1\n", + " ),\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(y=-2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=+1\n", + " ),\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(y=+2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=-1\n", + " ),\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(z=-2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=+1\n", + " ),\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(z=+2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=-1\n", + " ),\n", + ")\n", "\n", "sim.run(until_after_sources=10)\n", "\n", @@ -359,54 +387,85 @@ "sim.reset_meep()\n", "\n", "n_sphere = 2.0\n", - "geometry = [mp.Sphere(material=mp.Medium(index=n_sphere),\n", - " center=mp.Vector3(),\n", - " radius=r)]\n", + "geometry = [\n", + " mp.Sphere(material=mp.Medium(index=n_sphere), center=mp.Vector3(), radius=r)\n", + "]\n", "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=mp.Vector3(),\n", - " geometry=geometry)\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=mp.Vector3(),\n", + " geometry=geometry,\n", + ")\n", "\n", - "nearfield_box = sim.add_near2far(frq_cen, 0, 1,\n", - " mp.Near2FarRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r),weight=+1),\n", - " mp.Near2FarRegion(center=mp.Vector3(x=+2*r),size=mp.Vector3(0,4*r,4*r),weight=-1),\n", - " mp.Near2FarRegion(center=mp.Vector3(y=-2*r),size=mp.Vector3(4*r,0,4*r),weight=+1),\n", - " mp.Near2FarRegion(center=mp.Vector3(y=+2*r),size=mp.Vector3(4*r,0,4*r),weight=-1),\n", - " mp.Near2FarRegion(center=mp.Vector3(z=-2*r),size=mp.Vector3(4*r,4*r,0),weight=+1),\n", - " mp.Near2FarRegion(center=mp.Vector3(z=+2*r),size=mp.Vector3(4*r,4*r,0),weight=-1))\n", + "nearfield_box = sim.add_near2far(\n", + " frq_cen,\n", + " 0,\n", + " 1,\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=+1\n", + " ),\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(x=+2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=-1\n", + " ),\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(y=-2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=+1\n", + " ),\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(y=+2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=-1\n", + " ),\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(z=-2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=+1\n", + " ),\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(z=+2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=-1\n", + " ),\n", + ")\n", "\n", "sim.load_minus_near2far_data(nearfield_box, nearfield_box_data)\n", "\n", "sim.run(until_after_sources=100)\n", "\n", - "npts = 100 # number of points in [0,pi) range of polar angles to sample far fields along semi-circle\n", - "angles = np.pi/npts*np.arange(npts)\n", + "npts = 100 # number of points in [0,pi) range of polar angles to sample far fields along semi-circle\n", + "angles = np.pi / npts * np.arange(npts)\n", "\n", - "ff_r = 10000*r # radius of far-field semi-circle\n", + "ff_r = 10000 * r # radius of far-field semi-circle\n", "\n", - "E = np.zeros((npts,3),dtype=np.complex128)\n", - "H = np.zeros((npts,3),dtype=np.complex128)\n", + "E = np.zeros((npts, 3), dtype=np.complex128)\n", + "H = np.zeros((npts, 3), dtype=np.complex128)\n", "for n in range(npts):\n", - " ff = sim.get_farfield(nearfield_box, ff_r*mp.Vector3(np.cos(angles[n]),0,np.sin(angles[n])))\n", - " E[n,:] = [np.conj(ff[j]) for j in range(3)]\n", - " H[n,:] = [ff[j+3] for j in range(3)]\n", + " ff = sim.get_farfield(\n", + " nearfield_box, ff_r * mp.Vector3(np.cos(angles[n]), 0, np.sin(angles[n]))\n", + " )\n", + " E[n, :] = [np.conj(ff[j]) for j in range(3)]\n", + " H[n, :] = [ff[j + 3] for j in range(3)]\n", "\n", "# compute Poynting flux Pr in the radial direction. At large r,\n", "# all of the flux is radial so we can simply compute the magnitude of the Poynting vector.\n", - "Px = np.real(np.multiply(E[:,1],H[:,2])-np.multiply(E[:,2],H[:,1]))\n", - "Py = np.real(np.multiply(E[:,2],H[:,0])-np.multiply(E[:,0],H[:,2]))\n", - "Pz = np.real(np.multiply(E[:,0],H[:,1])-np.multiply(E[:,1],H[:,0]))\n", - "Pr = np.sqrt(np.square(Px)+np.square(Py)+np.square(Pz))\n", + "Px = np.real(np.multiply(E[:, 1], H[:, 2]) - np.multiply(E[:, 2], H[:, 1]))\n", + "Py = np.real(np.multiply(E[:, 2], H[:, 0]) - np.multiply(E[:, 0], H[:, 2]))\n", + "Pz = np.real(np.multiply(E[:, 0], H[:, 1]) - np.multiply(E[:, 1], H[:, 0]))\n", + "Pr = np.sqrt(np.square(Px) + np.square(Py) + np.square(Pz))\n", "\n", - "intensity = input_flux/(4*r)**2\n", + "intensity = input_flux / (4 * r) ** 2\n", "diff_cross_section = ff_r**2 * Pr / intensity\n", - "scatt_cross_section_meep = 2*np.pi * np.sum(diff_cross_section * np.sin(angles)) * np.pi/npts # trapezoidal rule integration\n", - "scatt_cross_section_theory = ps.MieQ(n_sphere,1000/frq_cen,2*r*1000,asDict=True,asCrossSection=True)['Csca']*1e-6 # units of um^2\n", + "scatt_cross_section_meep = (\n", + " 2 * np.pi * np.sum(diff_cross_section * np.sin(angles)) * np.pi / npts\n", + ") # trapezoidal rule integration\n", + "scatt_cross_section_theory = (\n", + " ps.MieQ(n_sphere, 1000 / frq_cen, 2 * r * 1000, asDict=True, asCrossSection=True)[\n", + " \"Csca\"\n", + " ]\n", + " * 1e-6\n", + ") # units of um^2\n", "\n", - "print(\"scatt:, {:.16f}, {:.16f}\".format(scatt_cross_section_meep,scatt_cross_section_theory))" + "print(\n", + " \"scatt:, {:.16f}, {:.16f}\".format(\n", + " scatt_cross_section_meep, scatt_cross_section_theory\n", + " )\n", + ")" ] }, { diff --git a/python/examples/differential_cross_section.py b/python/examples/differential_cross_section.py index 74c5e14b7..33f597ada 100644 --- a/python/examples/differential_cross_section.py +++ b/python/examples/differential_cross_section.py @@ -7,44 +7,72 @@ frq_cen = 1.0 -resolution = 20 # pixels/um +resolution = 20 # pixels/um dpml = 0.5 -dair = 1.5 # at least 0.5/frq_cen padding between source and near-field monitor +dair = 1.5 # at least 0.5/frq_cen padding between source and near-field monitor pml_layers = [mp.PML(thickness=dpml)] -s = 2*(dpml+dair+r) -cell_size = mp.Vector3(s,s,s) +s = 2 * (dpml + dair + r) +cell_size = mp.Vector3(s, s, s) # circularly-polarized source with propagation axis along x # is_integrated=True necessary for any planewave source extending into PML -sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=0.2*frq_cen,is_integrated=True), - center=mp.Vector3(-0.5*s+dpml), - size=mp.Vector3(0,s,s), - component=mp.Ez), - mp.Source(mp.GaussianSource(frq_cen,fwidth=0.2*frq_cen,is_integrated=True), - center=mp.Vector3(-0.5*s+dpml), - size=mp.Vector3(0,s,s), - component=mp.Ey, - amplitude=1j)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=mp.Vector3()) - -box_flux = sim.add_flux(frq_cen, 0, 1, - mp.FluxRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r))) - -nearfield_box = sim.add_near2far(frq_cen, 0, 1, - mp.Near2FarRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(x=+2*r),size=mp.Vector3(0,4*r,4*r),weight=-1), - mp.Near2FarRegion(center=mp.Vector3(y=-2*r),size=mp.Vector3(4*r,0,4*r),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(y=+2*r),size=mp.Vector3(4*r,0,4*r),weight=-1), - mp.Near2FarRegion(center=mp.Vector3(z=-2*r),size=mp.Vector3(4*r,4*r,0),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(z=+2*r),size=mp.Vector3(4*r,4*r,0),weight=-1)) +sources = [ + mp.Source( + mp.GaussianSource(frq_cen, fwidth=0.2 * frq_cen, is_integrated=True), + center=mp.Vector3(-0.5 * s + dpml), + size=mp.Vector3(0, s, s), + component=mp.Ez, + ), + mp.Source( + mp.GaussianSource(frq_cen, fwidth=0.2 * frq_cen, is_integrated=True), + center=mp.Vector3(-0.5 * s + dpml), + size=mp.Vector3(0, s, s), + component=mp.Ey, + amplitude=1j, + ), +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=mp.Vector3(), +) + +box_flux = sim.add_flux( + frq_cen, + 0, + 1, + mp.FluxRegion(center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r)), +) + +nearfield_box = sim.add_near2far( + frq_cen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(x=+2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(y=-2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(y=+2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(z=-2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(z=+2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=-1 + ), +) sim.run(until_after_sources=10) @@ -54,49 +82,80 @@ sim.reset_meep() n_sphere = 2.0 -geometry = [mp.Sphere(material=mp.Medium(index=n_sphere), - center=mp.Vector3(), - radius=r)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=mp.Vector3(), - geometry=geometry) - -nearfield_box = sim.add_near2far(frq_cen, 0, 1, - mp.Near2FarRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(x=+2*r),size=mp.Vector3(0,4*r,4*r),weight=-1), - mp.Near2FarRegion(center=mp.Vector3(y=-2*r),size=mp.Vector3(4*r,0,4*r),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(y=+2*r),size=mp.Vector3(4*r,0,4*r),weight=-1), - mp.Near2FarRegion(center=mp.Vector3(z=-2*r),size=mp.Vector3(4*r,4*r,0),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(z=+2*r),size=mp.Vector3(4*r,4*r,0),weight=-1)) +geometry = [ + mp.Sphere(material=mp.Medium(index=n_sphere), center=mp.Vector3(), radius=r) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=mp.Vector3(), + geometry=geometry, +) + +nearfield_box = sim.add_near2far( + frq_cen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(x=+2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(y=-2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(y=+2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(z=-2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(z=+2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=-1 + ), +) sim.load_minus_near2far_data(nearfield_box, nearfield_box_data) sim.run(until_after_sources=100) -npts = 100 # number of points in [0,pi) range of polar angles to sample far fields along semi-circle -angles = np.pi/npts*np.arange(npts) +npts = 100 # number of points in [0,pi) range of polar angles to sample far fields along semi-circle +angles = np.pi / npts * np.arange(npts) -ff_r = 10000*r # radius of far-field semi-circle +ff_r = 10000 * r # radius of far-field semi-circle -E = np.zeros((npts,3),dtype=np.complex128) -H = np.zeros((npts,3),dtype=np.complex128) +E = np.zeros((npts, 3), dtype=np.complex128) +H = np.zeros((npts, 3), dtype=np.complex128) for n in range(npts): - ff = sim.get_farfield(nearfield_box, ff_r*mp.Vector3(np.cos(angles[n]),0,np.sin(angles[n]))) - E[n,:] = [np.conj(ff[j]) for j in range(3)] - H[n,:] = [ff[j+3] for j in range(3)] - -Px = np.real(np.multiply(E[:,1],H[:,2])-np.multiply(E[:,2],H[:,1])) -Py = np.real(np.multiply(E[:,2],H[:,0])-np.multiply(E[:,0],H[:,2])) -Pz = np.real(np.multiply(E[:,0],H[:,1])-np.multiply(E[:,1],H[:,0])) -Pr = np.sqrt(np.square(Px)+np.square(Py)+np.square(Pz)) - -intensity = input_flux/(4*r)**2 + ff = sim.get_farfield( + nearfield_box, ff_r * mp.Vector3(np.cos(angles[n]), 0, np.sin(angles[n])) + ) + E[n, :] = [np.conj(ff[j]) for j in range(3)] + H[n, :] = [ff[j + 3] for j in range(3)] + +Px = np.real(np.multiply(E[:, 1], H[:, 2]) - np.multiply(E[:, 2], H[:, 1])) +Py = np.real(np.multiply(E[:, 2], H[:, 0]) - np.multiply(E[:, 0], H[:, 2])) +Pz = np.real(np.multiply(E[:, 0], H[:, 1]) - np.multiply(E[:, 1], H[:, 0])) +Pr = np.sqrt(np.square(Px) + np.square(Py) + np.square(Pz)) + +intensity = input_flux / (4 * r) ** 2 diff_cross_section = ff_r**2 * Pr / intensity -scatt_cross_section_meep = 2*np.pi * np.sum(np.multiply(diff_cross_section,np.sin(angles))) * np.pi/npts -scatt_cross_section_theory = ps.MieQ(n_sphere,1000/frq_cen,2*r*1000,asDict=True,asCrossSection=True)['Csca']*1e-6 # units of um^2 - -print("scatt:, {:.16f} (meep), {:.16f} (theory)".format(scatt_cross_section_meep,scatt_cross_section_theory)) +scatt_cross_section_meep = ( + 2 * np.pi * np.sum(np.multiply(diff_cross_section, np.sin(angles))) * np.pi / npts +) +scatt_cross_section_theory = ( + ps.MieQ(n_sphere, 1000 / frq_cen, 2 * r * 1000, asDict=True, asCrossSection=True)[ + "Csca" + ] + * 1e-6 +) # units of um^2 + +print( + "scatt:, {:.16f} (meep), {:.16f} (theory)".format( + scatt_cross_section_meep, scatt_cross_section_theory + ) +) diff --git a/python/examples/diffracted_planewave.py b/python/examples/diffracted_planewave.py index c9a059db9..ce5409e47 100644 --- a/python/examples/diffracted_planewave.py +++ b/python/examples/diffracted_planewave.py @@ -10,143 +10,171 @@ import cmath import numpy as np + def binary_grating_diffraction(gp, gh, gdc, theta): - resolution = 50 # pixels/μm + resolution = 50 # pixels/μm - dpml = 1.0 # PML thickness - dsub = 3.0 # substrate thickness - dpad = 3.0 # length of padding between grating and PML - - sx = dpml+dsub+gh+dpad+dpml - sy = gp - - cell_size = mp.Vector3(sx,sy,0) - pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] - - wvl = 0.5 # center wavelength - fcen = 1/wvl # center frequency - df = 0.05*fcen # frequency width - - ng = 1.5 - glass = mp.Medium(index=ng) - - # rotation angle of incident planewave; counter clockwise (CCW) about Z axis, 0 degrees along +X axis - theta_in = math.radians(theta) - - eig_parity = mp.EVEN_Z - - # k (in source medium) with correct length (plane of incidence: XY) - k = mp.Vector3(fcen*ng).rotate(mp.Vector3(z=1), theta_in) - - symmetries = [] - if theta_in == 0: - k = mp.Vector3() - eig_parity += mp.ODD_Y - symmetries = [mp.Mirror(direction=mp.Y,phase=-1)] - - def pw_amp(k,x0): - def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) - return _pw_amp - - src_pt = mp.Vector3(-0.5*sx+dpml,0,0) - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Hz, - center=src_pt, - size=mp.Vector3(0,sy,0), - amp_func=pw_amp(k,src_pt))] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k, - default_material=glass, - sources=sources, - symmetries=symmetries) - - tran_pt = mp.Vector3(0.5*sx-dpml,0,0) - tran_mon = sim.add_flux(fcen, 0, 1, - mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0))) - - sim.run(until_after_sources=50) - - input_flux = mp.get_fluxes(tran_mon) - - sim.reset_meep() - - geometry = [mp.Block(material=glass, - size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), - center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)), - mp.Block(material=glass, - size=mp.Vector3(gh,gdc*gp,mp.inf), - center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k, - sources=sources, - symmetries=symmetries) - - tran_mon = sim.add_mode_monitor(fcen, 0, 1, - mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0))) - - sim.run(until_after_sources=100) - - # number of (non-evanescent) transmitted orders - nm_t = np.floor((fcen-k.y)*gp)-np.ceil((-fcen-k.y)*gp) - if theta_in == 0: - nm_t = nm_t/2 - nm_t = int(nm_t)+1 - - bands = range(1,nm_t+1) - - if theta_in == 0: - orders = range(0,nm_t) - else: - orders = range(int(np.ceil((-fcen-k.y)*gp)),int(np.floor((fcen-k.y)*gp))+1) - - eig_sum = 0 - dp_sum = 0 - - for band,order in zip(bands,orders): - res = sim.get_eigenmode_coefficients(tran_mon, [band], eig_parity=eig_parity) - if res is not None: - tran_eig = abs(res.alpha[0,0,0])**2/input_flux[0] - if theta_in == 0: - tran_eig = 0.5*tran_eig - else: - tran_eig = 0 - eig_sum += tran_eig - - res = sim.get_eigenmode_coefficients(tran_mon, mp.DiffractedPlanewave((0,order,0),mp.Vector3(0,1,0),0,1)) - if res is not None: - tran_dp = abs(res.alpha[0,0,0])**2/input_flux[0] - if (theta_in == 0) and (order == 0): - tran_dp = 0.5*tran_dp - else: - tran_dp = 0 - dp_sum += tran_dp + dpml = 1.0 # PML thickness + dsub = 3.0 # substrate thickness + dpad = 3.0 # length of padding between grating and PML + + sx = dpml + dsub + gh + dpad + dpml + sy = gp + + cell_size = mp.Vector3(sx, sy, 0) + pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] + + wvl = 0.5 # center wavelength + fcen = 1 / wvl # center frequency + df = 0.05 * fcen # frequency width + + ng = 1.5 + glass = mp.Medium(index=ng) + + # rotation angle of incident planewave; counter clockwise (CCW) about Z axis, 0 degrees along +X axis + theta_in = math.radians(theta) + + eig_parity = mp.EVEN_Z + + # k (in source medium) with correct length (plane of incidence: XY) + k = mp.Vector3(fcen * ng).rotate(mp.Vector3(z=1), theta_in) + + symmetries = [] + if theta_in == 0: + k = mp.Vector3() + eig_parity += mp.ODD_Y + symmetries = [mp.Mirror(direction=mp.Y, phase=-1)] + + def pw_amp(k, x0): + def _pw_amp(x): + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + + return _pw_amp + + src_pt = mp.Vector3(-0.5 * sx + dpml, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Hz, + center=src_pt, + size=mp.Vector3(0, sy, 0), + amp_func=pw_amp(k, src_pt), + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k, + default_material=glass, + sources=sources, + symmetries=symmetries, + ) + + tran_pt = mp.Vector3(0.5 * sx - dpml, 0, 0) + tran_mon = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) + ) + + sim.run(until_after_sources=50) + + input_flux = mp.get_fluxes(tran_mon) + + sim.reset_meep() + + geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0), + ), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k, + sources=sources, + symmetries=symmetries, + ) + + tran_mon = sim.add_mode_monitor( + fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) + ) + + sim.run(until_after_sources=100) + + # number of (non-evanescent) transmitted orders + nm_t = np.floor((fcen - k.y) * gp) - np.ceil((-fcen - k.y) * gp) + if theta_in == 0: + nm_t = nm_t / 2 + nm_t = int(nm_t) + 1 + + bands = range(1, nm_t + 1) if theta_in == 0: - err = abs(tran_eig-tran_dp)/tran_eig - print("tran:, {:2d}, {:.8f}, {:2d}, {:.8f}, {:.8f}".format(band,tran_eig,order,tran_dp,err)) + orders = range(0, nm_t) else: - print("tran:, {:2d}, {:.8f}, {:2d}, {:.8f}".format(band,tran_eig,order,tran_dp)) - - flux = mp.get_fluxes(tran_mon) - t_flux = flux[0]/input_flux[0] - if (theta_in == 0): - t_flux = 0.5*t_flux - - err = abs(dp_sum-t_flux)/t_flux - print("flux:, {:.8f}, {:.8f}, {:.8f}, {:.8f}".format(eig_sum, - dp_sum, - t_flux, - err)) - -if __name__ == '__main__': - binary_grating_diffraction(2.6,0.4,0.3,0) - binary_grating_diffraction(3.7,0.6,0.4,13.5) + orders = range( + int(np.ceil((-fcen - k.y) * gp)), int(np.floor((fcen - k.y) * gp)) + 1 + ) + + eig_sum = 0 + dp_sum = 0 + + for band, order in zip(bands, orders): + res = sim.get_eigenmode_coefficients(tran_mon, [band], eig_parity=eig_parity) + if res is not None: + tran_eig = abs(res.alpha[0, 0, 0]) ** 2 / input_flux[0] + if theta_in == 0: + tran_eig = 0.5 * tran_eig + else: + tran_eig = 0 + eig_sum += tran_eig + + res = sim.get_eigenmode_coefficients( + tran_mon, mp.DiffractedPlanewave((0, order, 0), mp.Vector3(0, 1, 0), 0, 1) + ) + if res is not None: + tran_dp = abs(res.alpha[0, 0, 0]) ** 2 / input_flux[0] + if (theta_in == 0) and (order == 0): + tran_dp = 0.5 * tran_dp + else: + tran_dp = 0 + dp_sum += tran_dp + + if theta_in == 0: + err = abs(tran_eig - tran_dp) / tran_eig + print( + "tran:, {:2d}, {:.8f}, {:2d}, {:.8f}, {:.8f}".format( + band, tran_eig, order, tran_dp, err + ) + ) + else: + print( + "tran:, {:2d}, {:.8f}, {:2d}, {:.8f}".format( + band, tran_eig, order, tran_dp + ) + ) + + flux = mp.get_fluxes(tran_mon) + t_flux = flux[0] / input_flux[0] + if theta_in == 0: + t_flux = 0.5 * t_flux + + err = abs(dp_sum - t_flux) / t_flux + print("flux:, {:.8f}, {:.8f}, {:.8f}, {:.8f}".format(eig_sum, dp_sum, t_flux, err)) + + +if __name__ == "__main__": + binary_grating_diffraction(2.6, 0.4, 0.3, 0) + binary_grating_diffraction(3.7, 0.6, 0.4, 13.5) diff --git a/python/examples/eps_fit_lorentzian.py b/python/examples/eps_fit_lorentzian.py index bbae3076c..4d3de9833 100644 --- a/python/examples/eps_fit_lorentzian.py +++ b/python/examples/eps_fit_lorentzian.py @@ -5,94 +5,105 @@ import nlopt import numpy as np import matplotlib -matplotlib.use('agg') + +matplotlib.use("agg") import matplotlib.pyplot as plt import meep as mp def lorentzfunc(p, x): """Return the complex ε profile given a set of Lorentzian parameters p - (σ_0, ω_0, γ_0, σ_1, ω_1, γ_1, ...) for a set of frequencies x. + (σ_0, ω_0, γ_0, σ_1, ω_1, γ_1, ...) for a set of frequencies x. """ N = len(p) // 3 y = np.zeros(len(x)) for n in range(N): - A_n = p[3*n+0] - x_n = p[3*n+1] - g_n = p[3*n+2] - y = y + A_n/(np.square(x_n) - np.square(x) - 1j*x*g_n) + A_n = p[3 * n + 0] + x_n = p[3 * n + 1] + g_n = p[3 * n + 2] + y = y + A_n / (np.square(x_n) - np.square(x) - 1j * x * g_n) return y + def lorentzerr(p, x, y, grad): """Return the error (or residual or loss funnction) as the L2 norm - of the difference of ε(p,x) and y over a set of frequencies x as - well as the gradient of this error with respect to each Lorentzian - polarizability parameter in p and saving the result in grad. + of the difference of ε(p,x) and y over a set of frequencies x as + well as the gradient of this error with respect to each Lorentzian + polarizability parameter in p and saving the result in grad. """ N = len(p) // 3 yp = lorentzfunc(p, x) val = np.sum(np.square(abs(y - yp))) for n in range(N): - A_n = p[3*n+0] - x_n = p[3*n+1] - g_n = p[3*n+2] - d = 1 / (np.square(x_n) - np.square(x) - 1j*x*g_n) + A_n = p[3 * n + 0] + x_n = p[3 * n + 1] + g_n = p[3 * n + 2] + d = 1 / (np.square(x_n) - np.square(x) - 1j * x * g_n) if grad.size > 0: - grad[3*n+0] = 2 * np.real(np.dot(np.conj(yp - y), d)) - grad[3*n+1] = -4 * x_n * A_n * np.real(np.dot(np.conj(yp - y), np.square(d))) - grad[3*n+2] = -2 * A_n * np.imag(np.dot(np.conj(yp - y), x * np.square(d))) + grad[3 * n + 0] = 2 * np.real(np.dot(np.conj(yp - y), d)) + grad[3 * n + 1] = ( + -4 * x_n * A_n * np.real(np.dot(np.conj(yp - y), np.square(d))) + ) + grad[3 * n + 2] = ( + -2 * A_n * np.imag(np.dot(np.conj(yp - y), x * np.square(d))) + ) return val + def lorentzfit(p0, x, y, alg=nlopt.LD_LBFGS, tol=1e-25, maxeval=10000): """Return the optimal Lorentzian polarizability parameters and error - which minimize the error in ε(p0,x) relative to y for an initial - set of Lorentzian polarizability parameters p0 over a set of - frequencies x using the NLopt algorithm alg for a relative - tolerance tol and a maximum number of iterations maxeval. + which minimize the error in ε(p0,x) relative to y for an initial + set of Lorentzian polarizability parameters p0 over a set of + frequencies x using the NLopt algorithm alg for a relative + tolerance tol and a maximum number of iterations maxeval. """ opt = nlopt.opt(alg, len(p0)) opt.set_ftol_rel(tol) opt.set_maxeval(maxeval) opt.set_lower_bounds(np.zeros(len(p0))) - opt.set_upper_bounds(float('inf')*np.ones(len(p0))) + opt.set_upper_bounds(float("inf") * np.ones(len(p0))) opt.set_min_objective(lambda p, grad: lorentzerr(p, x, y, grad)) local_opt = nlopt.opt(nlopt.LD_LBFGS, len(p0)) local_opt.set_ftol_rel(1e-10) local_opt.set_xtol_rel(1e-8) opt.set_local_optimizer(local_opt) - popt = opt.optimize(p0); + popt = opt.optimize(p0) minf = opt.last_optimum_value() return popt, minf # format of input data file is three comma-separated columns: # wavelength (nm), real(n), complex(n) -mydata = np.genfromtxt('mymaterial.csv',delimiter=',') -n = mydata[:,1] + 1j*mydata[:,2] -eps_inf = 1.1 # should be >= 1.0 for stability and chosen such that np.amin(np.real(eps)) is ~1.0 +mydata = np.genfromtxt("mymaterial.csv", delimiter=",") +n = mydata[:, 1] + 1j * mydata[:, 2] +eps_inf = 1.1 # should be >= 1.0 for stability and chosen such that np.amin(np.real(eps)) is ~1.0 eps = np.square(n) - eps_inf -wl = mydata[:,0] -wl_min = 399 # minimum wavelength (units of nm) -wl_max = 701 # maximum wavelength (units of nm) +wl = mydata[:, 0] +wl_min = 399 # minimum wavelength (units of nm) +wl_max = 701 # maximum wavelength (units of nm) start_idx = np.where(wl > wl_min) idx_start = start_idx[0][0] end_idx = np.where(wl < wl_max) -idx_end = end_idx[0][-1]+1 -freqs = 1000/wl # units of 1/μm +idx_end = end_idx[0][-1] + 1 +freqs = 1000 / wl # units of 1/μm freqs_reduced = freqs[idx_start:idx_end] wl_reduced = wl[idx_start:idx_end] eps_reduced = eps[idx_start:idx_end] num_lorentzians = 2 -num_repeat = 30 # number of times to repeat local optimization with random initial values -ps = np.zeros((num_repeat,3*num_lorentzians)) +num_repeat = ( + 30 # number of times to repeat local optimization with random initial values +) +ps = np.zeros((num_repeat, 3 * num_lorentzians)) mins = np.zeros(num_repeat) for m in range(num_repeat): # specify Lorentzian polarizability terms each consisting of three parameters (σ_0, ω_0, γ_0) # with random initial values # note: for the case of no absorption, set γ (every third parameter) to zero - p_rand = [10**(np.random.random()) for _ in range(3*num_lorentzians)] - ps[m,:], mins[m] = lorentzfit(p_rand, freqs_reduced, eps_reduced, nlopt.LD_MMA, 1e-25, 50000) + p_rand = [10 ** (np.random.random()) for _ in range(3 * num_lorentzians)] + ps[m, :], mins[m] = lorentzfit( + p_rand, freqs_reduced, eps_reduced, nlopt.LD_MMA, 1e-25, 50000 + ) print(f"iteration{m}:, {ps[m,:]}, {mins[m]}") # find the best performing set of parameters @@ -104,42 +115,49 @@ def lorentzfit(p0, x, y, alg=nlopt.LD_LBFGS, tol=1e-25, maxeval=10000): E_susceptibilities = [] for n in range(num_lorentzians): - mymaterial_freq = ps[idx_opt][0][3*n+1] - mymaterial_gamma = ps[idx_opt][0][3*n+2] + mymaterial_freq = ps[idx_opt][0][3 * n + 1] + mymaterial_gamma = ps[idx_opt][0][3 * n + 2] if mymaterial_freq == 0: - mymaterial_sigma = ps[idx_opt][0][3*n+0] - E_susceptibilities.append(mp.DrudeSusceptibility(frequency=1.0, - gamma=mymaterial_gamma, - sigma=mymaterial_sigma)) + mymaterial_sigma = ps[idx_opt][0][3 * n + 0] + E_susceptibilities.append( + mp.DrudeSusceptibility( + frequency=1.0, gamma=mymaterial_gamma, sigma=mymaterial_sigma + ) + ) else: - mymaterial_sigma = ps[idx_opt][0][3*n+0] / mymaterial_freq**2 - E_susceptibilities.append(mp.LorentzianSusceptibility(frequency=mymaterial_freq, - gamma=mymaterial_gamma, - sigma=mymaterial_sigma)) + mymaterial_sigma = ps[idx_opt][0][3 * n + 0] / mymaterial_freq**2 + E_susceptibilities.append( + mp.LorentzianSusceptibility( + frequency=mymaterial_freq, + gamma=mymaterial_gamma, + sigma=mymaterial_sigma, + ) + ) -mymaterial = mp.Medium(epsilon=eps_inf, - E_susceptibilities=E_susceptibilities) +mymaterial = mp.Medium(epsilon=eps_inf, E_susceptibilities=E_susceptibilities) # plot the fit and the actual data for comparison mymaterial_eps = [mymaterial.epsilon(f)[0][0] for f in freqs_reduced] -plt.subplot(1,2,1) -plt.plot(wl_reduced,np.real(eps_reduced),'bo-',label='actual') -plt.plot(wl_reduced,np.real(mymaterial_eps),'ro-',label='fit') -plt.xlabel('wavelength (nm)') -plt.ylabel(r'real($\epsilon$)') +plt.subplot(1, 2, 1) +plt.plot(wl_reduced, np.real(eps_reduced), "bo-", label="actual") +plt.plot(wl_reduced, np.real(mymaterial_eps), "ro-", label="fit") +plt.xlabel("wavelength (nm)") +plt.ylabel(r"real($\epsilon$)") plt.legend() -plt.subplot(1,2,2) -plt.plot(wl_reduced,np.imag(eps_reduced),'bo-',label='actual') -plt.plot(wl_reduced,np.imag(mymaterial_eps),'ro-',label='fit') -plt.xlabel('wavelength (nm)') -plt.ylabel(r'imag($\epsilon$)') +plt.subplot(1, 2, 2) +plt.plot(wl_reduced, np.imag(eps_reduced), "bo-", label="actual") +plt.plot(wl_reduced, np.imag(mymaterial_eps), "ro-", label="fit") +plt.xlabel("wavelength (nm)") +plt.ylabel(r"imag($\epsilon$)") plt.legend() -plt.suptitle('Comparison of Actual Material Data and Fit using Drude-Lorentzian Susceptibility') +plt.suptitle( + "Comparison of Actual Material Data and Fit using Drude-Lorentzian Susceptibility" +) plt.subplots_adjust(wspace=0.3) -plt.savefig('eps_fit_sample.png',dpi=150,bbox_inches='tight') +plt.savefig("eps_fit_sample.png", dpi=150, bbox_inches="tight") diff --git a/python/examples/faraday-rotation.py b/python/examples/faraday-rotation.py index 449c18b10..426744939 100644 --- a/python/examples/faraday-rotation.py +++ b/python/examples/faraday-rotation.py @@ -3,14 +3,17 @@ import meep as mp ## Parameters for a gyrotropic Lorentzian medium -epsn = 1.5 # background permittivity -f0 = 1.0 # natural frequency -gamma = 1e-6 # damping rate -sn = 0.1 # sigma parameter -b0 = 0.15 # magnitude of bias vector +epsn = 1.5 # background permittivity +f0 = 1.0 # natural frequency +gamma = 1e-6 # damping rate +sn = 0.1 # sigma parameter +b0 = 0.15 # magnitude of bias vector -susc = [mp.GyrotropicLorentzianSusceptibility(frequency=f0, gamma=gamma, sigma=sn, - bias=mp.Vector3(0, 0, b0))] +susc = [ + mp.GyrotropicLorentzianSusceptibility( + frequency=f0, gamma=gamma, sigma=sn, bias=mp.Vector3(0, 0, b0) + ) +] mat = mp.Medium(epsilon=epsn, mu=1, E_susceptibilities=susc) ## Set up and run the Meep simulation: @@ -20,12 +23,22 @@ fsrc, src_z = 0.8, -8.5 pml_layers = [mp.PML(thickness=1.0, direction=mp.Z)] -sources = [mp.Source(mp.ContinuousSource(frequency=fsrc), - component=mp.Ex, center=mp.Vector3(0, 0, src_z))] +sources = [ + mp.Source( + mp.ContinuousSource(frequency=fsrc), + component=mp.Ex, + center=mp.Vector3(0, 0, src_z), + ) +] -sim = mp.Simulation(cell_size=cell, geometry=[], sources=sources, - boundary_layers=pml_layers, - default_material=mat, resolution=50) +sim = mp.Simulation( + cell_size=cell, + geometry=[], + sources=sources, + boundary_layers=pml_layers, + default_material=mat, + resolution=50, +) sim.run(until=tmax) ## Plot results: @@ -35,35 +48,38 @@ ex_data = sim.get_efield_x().real ey_data = sim.get_efield_y().real -z = np.linspace(-L/2, L/2, len(ex_data)) +z = np.linspace(-L / 2, L / 2, len(ex_data)) plt.figure(1) -plt.plot(z, ex_data, label='Ex') -plt.plot(z, ey_data, label='Ey') -plt.xlim(-L/2, L/2); plt.xlabel('z') +plt.plot(z, ex_data, label="Ex") +plt.plot(z, ey_data, label="Ey") +plt.xlim(-L / 2, L / 2) +plt.xlabel("z") plt.legend() ## Comparison with analytic result: -dfsq = (f0**2 - 1j*fsrc*gamma - fsrc**2) -eperp = epsn + sn * f0**2 * dfsq / (dfsq**2 - (fsrc*b0)**2) -eta = sn * f0**2 * fsrc * b0 / (dfsq**2 - (fsrc*b0)**2) +dfsq = f0**2 - 1j * fsrc * gamma - fsrc**2 +eperp = epsn + sn * f0**2 * dfsq / (dfsq**2 - (fsrc * b0) ** 2) +eta = sn * f0**2 * fsrc * b0 / (dfsq**2 - (fsrc * b0) ** 2) -k_gyro = 2*np.pi*fsrc * np.sqrt(0.5*(eperp - np.sqrt(eperp**2 - eta**2))) +k_gyro = 2 * np.pi * fsrc * np.sqrt(0.5 * (eperp - np.sqrt(eperp**2 - eta**2))) Ex_theory = 0.37 * np.cos(k_gyro * (z - src_z)).real Ey_theory = 0.37 * np.sin(k_gyro * (z - src_z)).real plt.figure(2) -plt.subplot(2,1,1) -plt.plot(z, ex_data, label='Ex (MEEP)') -plt.plot(z, Ex_theory, 'k--') -plt.plot(z, -Ex_theory, 'k--', label='Ex envelope (theory)') -plt.xlim(-L/2, L/2); plt.xlabel('z') -plt.legend(loc='lower right') +plt.subplot(2, 1, 1) +plt.plot(z, ex_data, label="Ex (MEEP)") +plt.plot(z, Ex_theory, "k--") +plt.plot(z, -Ex_theory, "k--", label="Ex envelope (theory)") +plt.xlim(-L / 2, L / 2) +plt.xlabel("z") +plt.legend(loc="lower right") -plt.subplot(2,1,2) -plt.plot(z, ey_data, label='Ey (MEEP)') -plt.plot(z, Ey_theory, 'k--') -plt.plot(z, -Ey_theory, 'k--', label='Ey envelope (theory)') -plt.xlim(-L/2, L/2); plt.xlabel('z') -plt.legend(loc='lower right') +plt.subplot(2, 1, 2) +plt.plot(z, ey_data, label="Ey (MEEP)") +plt.plot(z, Ey_theory, "k--") +plt.plot(z, -Ey_theory, "k--", label="Ey envelope (theory)") +plt.xlim(-L / 2, L / 2) +plt.xlabel("z") +plt.legend(loc="lower right") plt.tight_layout() plt.show() diff --git a/python/examples/finite_grating.ipynb b/python/examples/finite_grating.ipynb index e60714631..2f6600f25 100644 --- a/python/examples/finite_grating.ipynb +++ b/python/examples/finite_grating.ipynb @@ -138,21 +138,21 @@ "# False: plot the diffraction spectra based on a 1d cross section of the scattered fields\n", "field_profile = True\n", "\n", - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 2.0 # substrate thickness\n", - "dpad = 1.0 # flat-surface padding\n", - "gp = 1.0 # grating periodicity\n", - "gh = 0.5 # grating height\n", - "gdc = 0.5 # grating duty cycle\n", - "num_cells = 5 # number of grating unit cells\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 2.0 # substrate thickness\n", + "dpad = 1.0 # flat-surface padding\n", + "gp = 1.0 # grating periodicity\n", + "gh = 0.5 # grating height\n", + "gdc = 0.5 # grating duty cycle\n", + "num_cells = 5 # number of grating unit cells\n", "\n", "# air region thickness adjacent to grating\n", "dair = 10 if field_profile else dpad\n", "\n", - "wvl = 0.5 # center wavelength\n", - "fcen = 1/wvl # center frequency\n", + "wvl = 0.5 # center wavelength\n", + "fcen = 1 / wvl # center frequency\n", "\n", "k_point = mp.Vector3()\n", "\n", @@ -160,32 +160,49 @@ "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "symmetries=[mp.Mirror(mp.Y)]\n", - "\n", - "sx = dpml+dsub+gh+dair+dpml\n", - "sy = dpml+dpad+num_cells*gp+dpad+dpml\n", - "cell_size = mp.Vector3(sx,sy)\n", - "\n", - "src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub)\n", - "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.2*fcen),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy))]\n", - "\n", - "geometry = [mp.Block(material=glass,\n", - " size=mp.Vector3(dpml+dsub,mp.inf,mp.inf),\n", - " center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))]\n", - "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", - "\n", - "mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dair)\n", - "near_fields = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=mon_pt, size=mp.Vector3(dair if field_profile else 0,sy-2*dpml))\n", + "symmetries = [mp.Mirror(mp.Y)]\n", + "\n", + "sx = dpml + dsub + gh + dair + dpml\n", + "sy = dpml + dpad + num_cells * gp + dpad + dpml\n", + "cell_size = mp.Vector3(sx, sy)\n", + "\n", + "src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub)\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=0.2 * fcen),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy),\n", + " )\n", + "]\n", + "\n", + "geometry = [\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", + " )\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + ")\n", + "\n", + "mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dair)\n", + "near_fields = sim.add_dft_fields(\n", + " [mp.Ez],\n", + " fcen,\n", + " 0,\n", + " 1,\n", + " center=mon_pt,\n", + " size=mp.Vector3(dair if field_profile else 0, sy - 2 * dpml),\n", + ")\n", "\n", "sim.run(until_after_sources=100)\n", "\n", @@ -194,19 +211,35 @@ "sim.reset_meep()\n", "\n", "for j in range(num_cells):\n", - " geometry.append(mp.Block(material=glass,\n", - " size=mp.Vector3(gh,gdc*gp,mp.inf),\n", - " center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+dpml+dpad+(j+0.5)*gp)))\n", - "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", - "\n", - "near_fields = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=mon_pt, size=mp.Vector3(dair if field_profile else 0,sy-2*dpml))\n", + " geometry.append(\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", + " center=mp.Vector3(\n", + " -0.5 * sx + dpml + dsub + 0.5 * gh,\n", + " -0.5 * sy + dpml + dpad + (j + 0.5) * gp,\n", + " ),\n", + " )\n", + " )\n", + "\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + ")\n", + "\n", + "near_fields = sim.add_dft_fields(\n", + " [mp.Ez],\n", + " fcen,\n", + " 0,\n", + " 1,\n", + " center=mon_pt,\n", + " size=mp.Vector3(dair if field_profile else 0, sy - 2 * dpml),\n", + ")\n", "\n", "sim.run(until_after_sources=100)" ] @@ -232,39 +265,48 @@ "source": [ "grating_dft = sim.get_dft_array(near_fields, mp.Ez, 0)\n", "\n", - "scattered_field = grating_dft-flat_dft\n", - "scattered_amplitude = np.abs(scattered_field)**2\n", + "scattered_field = grating_dft - flat_dft\n", + "scattered_amplitude = np.abs(scattered_field) ** 2\n", "\n", - "[x,y,z,w] = sim.get_array_metadata(dft_cell=near_fields)\n", + "[x, y, z, w] = sim.get_array_metadata(dft_cell=near_fields)\n", "\n", "if field_profile:\n", " plt.figure(dpi=150)\n", - " plt.pcolormesh(x,y,np.rot90(scattered_amplitude),cmap='inferno',shading='gouraud',vmin=0,vmax=scattered_amplitude.max())\n", - " plt.gca().set_aspect('equal')\n", - " plt.xlabel('x (μm)')\n", - " plt.ylabel('y (μm)')\n", + " plt.pcolormesh(\n", + " x,\n", + " y,\n", + " np.rot90(scattered_amplitude),\n", + " cmap=\"inferno\",\n", + " shading=\"gouraud\",\n", + " vmin=0,\n", + " vmax=scattered_amplitude.max(),\n", + " )\n", + " plt.gca().set_aspect(\"equal\")\n", + " plt.xlabel(\"x (μm)\")\n", + " plt.ylabel(\"y (μm)\")\n", " # ensure that the height of the colobar matches that of the plot\n", " from mpl_toolkits.axes_grid1 import make_axes_locatable\n", + "\n", " divider = make_axes_locatable(plt.gca())\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " plt.colorbar(cax=cax)\n", " plt.tight_layout()\n", " plt.show()\n", "else:\n", - " ky = np.fft.fftshift(np.fft.fftfreq(len(scattered_field), 1/resolution))\n", + " ky = np.fft.fftshift(np.fft.fftfreq(len(scattered_field), 1 / resolution))\n", " FT_scattered_field = np.fft.fftshift(np.fft.fft(scattered_field))\n", " plt.figure(dpi=150)\n", " plt.subplots_adjust(hspace=0.3)\n", "\n", - " plt.subplot(2,1,1)\n", - " plt.plot(y,scattered_amplitude,'bo-')\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(y, scattered_amplitude, \"bo-\")\n", " plt.xlabel(\"y (μm)\")\n", " plt.ylabel(\"field amplitude\")\n", "\n", - " plt.subplot(2,1,2)\n", - " plt.plot(ky,np.abs(FT_scattered_field)**2,'ro-')\n", - " plt.gca().ticklabel_format(axis='y',style='sci',scilimits=(0,0))\n", - " plt.xlabel(r'wavevector k$_y$, 2π (μm)$^{-1}$')\n", + " plt.subplot(2, 1, 2)\n", + " plt.plot(ky, np.abs(FT_scattered_field) ** 2, \"ro-\")\n", + " plt.gca().ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0, 0))\n", + " plt.xlabel(r\"wavevector k$_y$, 2π (μm)$^{-1}$\")\n", " plt.ylabel(\"Fourier transform\")\n", " plt.gca().set_xlim([-3, 3])\n", "\n", diff --git a/python/examples/finite_grating.py b/python/examples/finite_grating.py index 1129410e2..36be29976 100644 --- a/python/examples/finite_grating.py +++ b/python/examples/finite_grating.py @@ -7,21 +7,21 @@ # False: plot the diffraction spectra based on a 1d cross section of the scattered fields field_profile = True -resolution = 50 # pixels/μm +resolution = 50 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 2.0 # substrate thickness -dpad = 1.0 # flat-surface padding -gp = 1.0 # grating periodicity -gh = 0.5 # grating height -gdc = 0.5 # grating duty cycle -num_cells = 5 # number of grating unit cells +dpml = 1.0 # PML thickness +dsub = 2.0 # substrate thickness +dpad = 1.0 # flat-surface padding +gp = 1.0 # grating periodicity +gh = 0.5 # grating height +gdc = 0.5 # grating duty cycle +num_cells = 5 # number of grating unit cells # air region thickness adjacent to grating dair = 10 if field_profile else dpad -wvl = 0.5 # center wavelength -fcen = 1/wvl # center frequency +wvl = 0.5 # center wavelength +fcen = 1 / wvl # center frequency k_point = mp.Vector3() @@ -29,32 +29,49 @@ pml_layers = [mp.PML(thickness=dpml)] -symmetries=[mp.Mirror(mp.Y)] +symmetries = [mp.Mirror(mp.Y)] -sx = dpml+dsub+gh+dair+dpml -sy = dpml+dpad+num_cells*gp+dpad+dpml -cell_size = mp.Vector3(sx,sy) +sx = dpml + dsub + gh + dair + dpml +sy = dpml + dpad + num_cells * gp + dpad + dpml +cell_size = mp.Vector3(sx, sy) -src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub) -sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.2*fcen,is_integrated=True), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(y=sy))] +src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub) +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=0.2 * fcen, is_integrated=True), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) +] -geometry = [mp.Block(material=glass, - size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), - center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - -mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dair) -near_fields = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=mon_pt, size=mp.Vector3(dair if field_profile else 0,sy-2*dpml)) +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + ) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, +) + +mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dair) +near_fields = sim.add_dft_fields( + [mp.Ez], + fcen, + 0, + 1, + center=mon_pt, + size=mp.Vector3(dair if field_profile else 0, sy - 2 * dpml), +) sim.run(until_after_sources=100) @@ -66,61 +83,82 @@ mp.Block( material=glass, size=mp.Vector3(gh, gdc * gp, mp.inf), - center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + - dpml + dpad + (j + 0.5) * gp)) - for j in range(num_cells)) -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - -near_fields = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=mon_pt, size=mp.Vector3(dair if field_profile else 0,sy-2*dpml)) + center=mp.Vector3( + -0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + dpml + dpad + (j + 0.5) * gp + ), + ) + for j in range(num_cells) +) +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, +) + +near_fields = sim.add_dft_fields( + [mp.Ez], + fcen, + 0, + 1, + center=mon_pt, + size=mp.Vector3(dair if field_profile else 0, sy - 2 * dpml), +) sim.run(until_after_sources=100) grating_dft = sim.get_dft_array(near_fields, mp.Ez, 0) -scattered_field = grating_dft-flat_dft -scattered_amplitude = np.abs(scattered_field)**2 +scattered_field = grating_dft - flat_dft +scattered_amplitude = np.abs(scattered_field) ** 2 -[x,y,z,w] = sim.get_array_metadata(dft_cell=near_fields) +[x, y, z, w] = sim.get_array_metadata(dft_cell=near_fields) if field_profile: - if mp.am_master(): - plt.figure(dpi=150) - plt.pcolormesh(x,y,np.rot90(scattered_amplitude),cmap='inferno',shading='gouraud',vmin=0,vmax=scattered_amplitude.max()) - plt.gca().set_aspect('equal') - plt.xlabel('x (μm)') - plt.ylabel('y (μm)') - - # ensure that the height of the colobar matches that of the plot - from mpl_toolkits.axes_grid1 import make_axes_locatable - divider = make_axes_locatable(plt.gca()) - cax = divider.append_axes("right", size="5%", pad=0.05) - plt.colorbar(cax=cax) - plt.tight_layout() - plt.show() + if mp.am_master(): + plt.figure(dpi=150) + plt.pcolormesh( + x, + y, + np.rot90(scattered_amplitude), + cmap="inferno", + shading="gouraud", + vmin=0, + vmax=scattered_amplitude.max(), + ) + plt.gca().set_aspect("equal") + plt.xlabel("x (μm)") + plt.ylabel("y (μm)") + + # ensure that the height of the colobar matches that of the plot + from mpl_toolkits.axes_grid1 import make_axes_locatable + + divider = make_axes_locatable(plt.gca()) + cax = divider.append_axes("right", size="5%", pad=0.05) + plt.colorbar(cax=cax) + plt.tight_layout() + plt.show() else: - ky = np.fft.fftshift(np.fft.fftfreq(len(scattered_field), 1/resolution)) - FT_scattered_field = np.fft.fftshift(np.fft.fft(scattered_field)) - if mp.am_master(): - plt.figure(dpi=150) - plt.subplots_adjust(hspace=0.3) - - plt.subplot(2,1,1) - plt.plot(y,scattered_amplitude,'bo-') - plt.xlabel("y (μm)") - plt.ylabel("field amplitude") - - plt.subplot(2,1,2) - plt.plot(ky,np.abs(FT_scattered_field)**2,'ro-') - plt.gca().ticklabel_format(axis='y',style='sci',scilimits=(0,0)) - plt.xlabel(r'wavevector k$_y$, 2π (μm)$^{-1}$') - plt.ylabel("Fourier transform") - plt.gca().set_xlim([-3, 3]) - - plt.tight_layout(pad=1.0) - plt.show() + ky = np.fft.fftshift(np.fft.fftfreq(len(scattered_field), 1 / resolution)) + FT_scattered_field = np.fft.fftshift(np.fft.fft(scattered_field)) + if mp.am_master(): + plt.figure(dpi=150) + plt.subplots_adjust(hspace=0.3) + + plt.subplot(2, 1, 1) + plt.plot(y, scattered_amplitude, "bo-") + plt.xlabel("y (μm)") + plt.ylabel("field amplitude") + + plt.subplot(2, 1, 2) + plt.plot(ky, np.abs(FT_scattered_field) ** 2, "ro-") + plt.gca().ticklabel_format(axis="y", style="sci", scilimits=(0, 0)) + plt.xlabel(r"wavevector k$_y$, 2π (μm)$^{-1}$") + plt.ylabel("Fourier transform") + plt.gca().set_xlim([-3, 3]) + + plt.tight_layout(pad=1.0) + plt.show() diff --git a/python/examples/gaussian-beam.py b/python/examples/gaussian-beam.py index 0d794a4fa..eeb6a13ec 100644 --- a/python/examples/gaussian-beam.py +++ b/python/examples/gaussian-beam.py @@ -3,40 +3,52 @@ import meep as mp import math import matplotlib -matplotlib.use('agg') + +matplotlib.use("agg") import matplotlib.pyplot as plt s = 14 resolution = 50 dpml = 2 -cell_size = mp.Vector3(s,s) +cell_size = mp.Vector3(s, s) boundary_layers = [mp.PML(thickness=dpml)] -beam_x0 = mp.Vector3(0,3.0) # beam focus (relative to source center) +beam_x0 = mp.Vector3(0, 3.0) # beam focus (relative to source center) rot_angle = 0 # CCW rotation angle about z axis (0: +y axis) -beam_kdir = mp.Vector3(0,1,0).rotate(mp.Vector3(0,0,1),math.radians(rot_angle)) # beam propagation direction +beam_kdir = mp.Vector3(0, 1, 0).rotate( + mp.Vector3(0, 0, 1), math.radians(rot_angle) +) # beam propagation direction beam_w0 = 0.8 # beam waist radius -beam_E0 = mp.Vector3(0,0,1) +beam_E0 = mp.Vector3(0, 0, 1) fcen = 1 -sources = [mp.GaussianBeamSource(src=mp.ContinuousSource(fcen), - center=mp.Vector3(0,-0.5*s+dpml+1.0), - size=mp.Vector3(s), - beam_x0=beam_x0, - beam_kdir=beam_kdir, - beam_w0=beam_w0, - beam_E0=beam_E0)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources) +sources = [ + mp.GaussianBeamSource( + src=mp.ContinuousSource(fcen), + center=mp.Vector3(0, -0.5 * s + dpml + 1.0), + size=mp.Vector3(s), + beam_x0=beam_x0, + beam_kdir=beam_kdir, + beam_w0=beam_w0, + beam_E0=beam_E0, + ) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, +) sim.run(until=20) -sim.plot2D(fields=mp.Ez, - output_plane=mp.Volume(center=mp.Vector3(), - size=mp.Vector3(s-2*dpml,s-2*dpml))) +sim.plot2D( + fields=mp.Ez, + output_plane=mp.Volume( + center=mp.Vector3(), size=mp.Vector3(s - 2 * dpml, s - 2 * dpml) + ), +) -plt.savefig(f'Ez_angle{rot_angle}.png', bbox_inches='tight', pad_inches=0) +plt.savefig(f"Ez_angle{rot_angle}.png", bbox_inches="tight", pad_inches=0) diff --git a/python/examples/grating2d_triangular_lattice.py b/python/examples/grating2d_triangular_lattice.py index 6ea00dc7a..61ce7ed8b 100644 --- a/python/examples/grating2d_triangular_lattice.py +++ b/python/examples/grating2d_triangular_lattice.py @@ -13,7 +13,7 @@ glass = mp.Medium(index=ng) wvl = 0.5 # wavelength -fcen = 1/wvl +fcen = 1 / wvl # rectangular supercell sx = 1.0 @@ -25,63 +25,83 @@ hcyl = 0.5 # cylinder height rcyl = 0.1 # cylinder radius -sz = dpml+dsub+hcyl+dair+dpml +sz = dpml + dsub + hcyl + dair + dpml -cell_size = mp.Vector3(sx,sy,sz) +cell_size = mp.Vector3(sx, sy, sz) -boundary_layers = [mp.PML(thickness=dpml,direction=mp.Z)] +boundary_layers = [mp.PML(thickness=dpml, direction=mp.Z)] # periodic boundary conditions k_point = mp.Vector3() -src_pt = mp.Vector3(0,0,-0.5*sz+dpml) -sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.1*fcen), - size=mp.Vector3(sx,sy,0), - center=src_pt, - component=mp.Ex)] - -substrate = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,dpml+dsub), - center=mp.Vector3(0,0,-0.5*sz+0.5*(dpml+dsub)), - material=glass)] - -cyl_grating = [mp.Cylinder(center=mp.Vector3(0,0,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(-0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(-0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass)] +src_pt = mp.Vector3(0, 0, -0.5 * sz + dpml) +sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.1 * fcen), + size=mp.Vector3(sx, sy, 0), + center=src_pt, + component=mp.Ex, + ) +] + +substrate = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, dpml + dsub), + center=mp.Vector3(0, 0, -0.5 * sz + 0.5 * (dpml + dsub)), + material=glass, + ) +] + +cyl_grating = [ + mp.Cylinder( + center=mp.Vector3(0, 0, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3(0.5 * sx, 0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3(-0.5 * sx, 0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3(-0.5 * sx, -0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3(0.5 * sx, -0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), +] geometry = substrate + cyl_grating -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - geometry=geometry, - boundary_layers=boundary_layers, - k_point=k_point) +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + geometry=geometry, + boundary_layers=boundary_layers, + k_point=k_point, +) -tran_pt = mp.Vector3(0,0,0.5*sz-dpml) -tran_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=tran_pt, - size=mp.Vector3(sx,sy,0))) +tran_pt = mp.Vector3(0, 0, 0.5 * sz - dpml) +tran_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=tran_pt, size=mp.Vector3(sx, sy, 0)) +) -sim.run(until_after_sources=mp.stop_when_fields_decayed(20,mp.Ex,src_pt,1e-6)) +sim.run(until_after_sources=mp.stop_when_fields_decayed(20, mp.Ex, src_pt, 1e-6)) # diffraction order of unit cell (triangular lattice) mx = 0 @@ -92,14 +112,12 @@ # only even orders should produce nonzero power nx = mx for ny in range(4): - kz2 = fcen**2-(nx/sx)**2-(ny/sy)**2 + kz2 = fcen**2 - (nx / sx) ** 2 - (ny / sy) ** 2 if kz2 > 0: - res = sim.get_eigenmode_coefficients(tran_flux, - mp.DiffractedPlanewave((nx,ny,0), - mp.Vector3(0,1,0), - 1, - 0)) + res = sim.get_eigenmode_coefficients( + tran_flux, mp.DiffractedPlanewave((nx, ny, 0), mp.Vector3(0, 1, 0), 1, 0) + ) t_coeffs = res.alpha - tran = abs(t_coeffs[0,0,0])**2 + tran = abs(t_coeffs[0, 0, 0]) ** 2 - print("order:, {}, {}, {:.5f}".format(nx,ny,tran)) + print("order:, {}, {}, {:.5f}".format(nx, ny, tran)) diff --git a/python/examples/holey-wvg-bands.ipynb b/python/examples/holey-wvg-bands.ipynb index 8af8e0a94..9ed14d556 100644 --- a/python/examples/holey-wvg-bands.ipynb +++ b/python/examples/holey-wvg-bands.ipynb @@ -33,6 +33,7 @@ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from IPython.display import Video\n", + "\n", "%matplotlib notebook" ] }, @@ -50,21 +51,21 @@ "outputs": [], "source": [ "# Some parameters to describe the geometry:\n", - "eps = 13 # dielectric constant of waveguide\n", - "w = 1.2 # width of waveguide\n", - "r = 0.36 # radius of holes\n", + "eps = 13 # dielectric constant of waveguide\n", + "w = 1.2 # width of waveguide\n", + "r = 0.36 # radius of holes\n", "\n", "# The cell dimensions\n", - "sy = 12 # size of cell in y direction (perpendicular to wvg.)\n", - "dpml = 1 # PML thickness (y direction only!)\n", + "sy = 12 # size of cell in y direction (perpendicular to wvg.)\n", + "dpml = 1 # PML thickness (y direction only!)\n", "cell = mp.Vector3(1, sy)\n", "\n", "b = mp.Block(size=mp.Vector3(1e20, w, 1e20), material=mp.Medium(epsilon=eps))\n", "c = mp.Cylinder(radius=r)\n", "\n", - "geometry = [b,c]\n", + "geometry = [b, c]\n", "\n", - "resolution=20" + "resolution = 20" ] }, { @@ -99,10 +100,15 @@ "outputs": [], "source": [ "fcen = 0.25 # pulse center frequency\n", - "df = 1.5 # pulse freq. width: large df = short impulse\n", + "df = 1.5 # pulse freq. width: large df = short impulse\n", "\n", - "s = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz,\n", - " center=mp.Vector3(0.1234,0))]" + "s = [\n", + " mp.Source(\n", + " src=mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Hz,\n", + " center=mp.Vector3(0.1234, 0),\n", + " )\n", + "]" ] }, { @@ -162,12 +168,14 @@ } ], "source": [ - "sim = mp.Simulation(cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=s,\n", - " symmetries=sym,\n", - " resolution=resolution)\n", + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=s,\n", + " symmetries=sym,\n", + " resolution=resolution,\n", + ")\n", "f = plt.figure(dpi=150)\n", "sim.plot2D(ax=f.gca())\n", "plt.show()" @@ -210,8 +218,10 @@ "kx = 0.4\n", "sim.k_point = mp.Vector3(kx)\n", "\n", - "sim.run(mp.after_sources(mp.Harminv(mp.Hz, mp.Vector3(0.1234), fcen, df)),\n", - " until_after_sources=300)" + "sim.run(\n", + " mp.after_sources(mp.Harminv(mp.Hz, mp.Vector3(0.1234), fcen, df)),\n", + " until_after_sources=300,\n", + ")" ] }, { @@ -263,18 +273,18 @@ ], "source": [ "kx = [k.x for k in kpts]\n", - "fig = plt.figure(dpi=100,figsize=(5,5))\n", + "fig = plt.figure(dpi=100, figsize=(5, 5))\n", "ax = plt.subplot(111)\n", "for i in range(len(all_freqs)):\n", " for ii in range(len(all_freqs[i])):\n", - " plt.scatter(kx[i],np.real(all_freqs[i][ii]),color='b')\n", + " plt.scatter(kx[i], np.real(all_freqs[i][ii]), color=\"b\")\n", "\n", - "ax.fill_between(kx, kx, 1.0, interpolate=True, color='gray', alpha = 0.3)\n", - "plt.xlim(0,0.5)\n", - "plt.ylim(0,1)\n", + "ax.fill_between(kx, kx, 1.0, interpolate=True, color=\"gray\", alpha=0.3)\n", + "plt.xlim(0, 0.5)\n", + "plt.ylim(0, 1)\n", "plt.grid(True)\n", - "plt.xlabel('$k_x(2\\pi)$')\n", - "plt.ylabel('$\\omega(2\\pi c)$')\n", + "plt.xlabel(\"$k_x(2\\pi)$\")\n", + "plt.ylabel(\"$\\omega(2\\pi c)$\")\n", "plt.tight_layout()\n", "plt.show()" ] @@ -314,19 +324,26 @@ "outputs": [], "source": [ "def run_sim(kx, omega, filename):\n", - " s = [mp.Source(src=mp.GaussianSource(omega, fwidth=0.01), component=mp.Hz,\n", - " center=mp.Vector3(0.1234,0))]\n", - " sim = mp.Simulation(cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=s,\n", - " symmetries=sym,\n", - " k_point = mp.Vector3(kx),\n", - " resolution=resolution)\n", + " s = [\n", + " mp.Source(\n", + " src=mp.GaussianSource(omega, fwidth=0.01),\n", + " component=mp.Hz,\n", + " center=mp.Vector3(0.1234, 0),\n", + " )\n", + " ]\n", + " sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=s,\n", + " symmetries=sym,\n", + " k_point=mp.Vector3(kx),\n", + " resolution=resolution,\n", + " )\n", " f = plt.figure(dpi=100)\n", - " animate = mp.Animate2D(sim,fields=mp.Hz,f=f,normalize=True,realtime=False)\n", - " sim.run(mp.at_every(5,animate),until_after_sources=1)\n", - " animate.to_mp4(10,filename)\n", + " animate = mp.Animate2D(sim, fields=mp.Hz, f=f, normalize=True, realtime=False)\n", + " sim.run(mp.at_every(5, animate), until_after_sources=1)\n", + " animate.to_mp4(10, filename)\n", " plt.close()" ] }, @@ -459,11 +476,13 @@ } ], "source": [ - "kx = [0.4,0.4,0.1,0.3,0.25]\n", - "omega = [0.1896,0.3175,0.4811,0.8838,0.2506]\n", - "filename = ['media/holey-wvg-bands-{}-{}.mp4'.format(k,om) for (k,om) in zip (kx,omega)]\n", - "for (k,om,fn) in zip(kx,omega,filename):\n", - " run_sim(*[k,om,fn])" + "kx = [0.4, 0.4, 0.1, 0.3, 0.25]\n", + "omega = [0.1896, 0.3175, 0.4811, 0.8838, 0.2506]\n", + "filename = [\n", + " \"media/holey-wvg-bands-{}-{}.mp4\".format(k, om) for (k, om) in zip(kx, omega)\n", + "]\n", + "for (k, om, fn) in zip(kx, omega, filename):\n", + " run_sim(*[k, om, fn])" ] }, { @@ -544,6 +563,7 @@ ], "source": [ "import IPython\n", + "\n", "for i in range(len(filename)):\n", " IPython.display.display(IPython.display.Video(filename[i]))" ] diff --git a/python/examples/holey-wvg-bands.py b/python/examples/holey-wvg-bands.py index d56cc420e..456793bdf 100644 --- a/python/examples/holey-wvg-bands.py +++ b/python/examples/holey-wvg-bands.py @@ -30,13 +30,22 @@ def main(): fcen = 0.25 # pulse center frequency df = 1.5 # pulse freq. width: large df = short impulse - s = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, - center=mp.Vector3(0.1234)) + s = mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Hz, + center=mp.Vector3(0.1234), + ) sym = mp.Mirror(direction=mp.Y, phase=-1) - sim = mp.Simulation(cell_size=cell, geometry=[b, c], sources=[s], symmetries=[sym], - boundary_layers=[mp.PML(dpml, direction=mp.Y)], resolution=20) + sim = mp.Simulation( + cell_size=cell, + geometry=[b, c], + sources=[s], + symmetries=[sym], + boundary_layers=[mp.PML(dpml, direction=mp.Y)], + resolution=20, + ) if kx := False: sim.k_point = mp.Vector3(kx) @@ -44,7 +53,7 @@ def main(): sim.run( mp.at_beginning(mp.output_epsilon), mp.after_sources(mp.Harminv(mp.Hz, mp.Vector3(0.1234), fcen, df)), - until_after_sources=300 + until_after_sources=300, ) sim.run(mp.at_every(1 / fcen / 20, mp.output_hfield_z), until=1 / fcen) @@ -55,5 +64,5 @@ def main(): sim.run_k_points(300, mp.interpolate(k_interp, [mp.Vector3(), mp.Vector3(0.5)])) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/holey-wvg-cavity.ipynb b/python/examples/holey-wvg-cavity.ipynb index 1e58e242b..393bb3187 100644 --- a/python/examples/holey-wvg-cavity.ipynb +++ b/python/examples/holey-wvg-cavity.ipynb @@ -34,6 +34,7 @@ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from IPython.display import Video\n", + "\n", "%matplotlib notebook" ] }, @@ -50,17 +51,17 @@ "metadata": {}, "outputs": [], "source": [ - "resolution = 20 # pixels/um\n", + "resolution = 20 # pixels/um\n", "\n", - "eps = 13 # dielectric constant of waveguide\n", - "w = 1.2 # width of waveguide\n", - "r = 0.36 # radius of holes\n", - "d = 1.4 # defect spacing (ordinary spacing = 1)\n", - "N = 3 # number of holes on either side of defect\n", + "eps = 13 # dielectric constant of waveguide\n", + "w = 1.2 # width of waveguide\n", + "r = 0.36 # radius of holes\n", + "d = 1.4 # defect spacing (ordinary spacing = 1)\n", + "N = 3 # number of holes on either side of defect\n", "\n", - "sy = 6 # size of cell in y direction (perpendicular to wvg.)\n", - "pad = 2 # padding between last hole and PML edge\n", - "dpml = 1 # PML thickness" + "sy = 6 # size of cell in y direction (perpendicular to wvg.)\n", + "pad = 2 # padding between last hole and PML edge\n", + "dpml = 1 # PML thickness" ] }, { @@ -76,7 +77,7 @@ "metadata": {}, "outputs": [], "source": [ - "sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction" + "sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction" ] }, { @@ -92,7 +93,7 @@ "metadata": {}, "outputs": [], "source": [ - "cell = mp.Vector3(sx,sy,0)" + "cell = mp.Vector3(sx, sy, 0)" ] }, { @@ -108,12 +109,12 @@ "metadata": {}, "outputs": [], "source": [ - "blk = mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), material=mp.Medium(epsilon=eps))\n", + "blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=eps))\n", "geometry = [blk]\n", "\n", "for i in range(N):\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(d/2+i)))\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d/2+i))))" + " geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)))\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d / 2 + i))))" ] }, { @@ -147,8 +148,8 @@ "metadata": {}, "outputs": [], "source": [ - "fcen = 0.25 # pulse center frequency\n", - "df = 0.2 # pulse frequency width" + "fcen = 0.25 # pulse center frequency\n", + "df = 0.2 # pulse frequency width" ] }, { @@ -164,10 +165,14 @@ "metadata": {}, "outputs": [], "source": [ - "src = [mp.Source(mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ey,\n", - " center=mp.Vector3(-0.5*sx+dpml),\n", - " size=mp.Vector3(0,w))]" + "src = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ey,\n", + " center=mp.Vector3(-0.5 * sx + dpml),\n", + " size=mp.Vector3(0, w),\n", + " )\n", + "]" ] }, { @@ -199,12 +204,14 @@ "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=src,\n", - " symmetries=sym,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=src,\n", + " symmetries=sym,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -220,10 +227,11 @@ "metadata": {}, "outputs": [], "source": [ - "freg = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml-0.5),\n", - " size=mp.Vector3(0,2*w))\n", + "freg = mp.FluxRegion(\n", + " center=mp.Vector3(0.5 * sx - dpml - 0.5), size=mp.Vector3(0, 2 * w)\n", + ")\n", "\n", - "nfreq = 500 # number of frequencies at which to compute flux\n", + "nfreq = 500 # number of frequencies at which to compute flux\n", "\n", "# transmitted flux\n", "trans = sim.add_flux(fcen, df, nfreq, freg)" @@ -336,10 +344,14 @@ ], "source": [ "f = plt.figure(dpi=150)\n", - "animate = mp.Animate2D(sim,f=f,fields=mp.Hz,realtime=False,normalize=True)\n", + "animate = mp.Animate2D(sim, f=f, fields=mp.Hz, realtime=False, normalize=True)\n", "\n", - "sim.run(mp.during_sources(mp.at_every(0.4, animate)),\n", - " until_after_sources=mp.stop_when_fields_decayed(50, mp.Ey, mp.Vector3(0.5*sx-dpml-0.5), 1e-3))\n", + "sim.run(\n", + " mp.during_sources(mp.at_every(0.4, animate)),\n", + " until_after_sources=mp.stop_when_fields_decayed(\n", + " 50, mp.Ey, mp.Vector3(0.5 * sx - dpml - 0.5), 1e-3\n", + " ),\n", + ")\n", "plt.close()" ] }, @@ -372,8 +384,8 @@ } ], "source": [ - "filename = 'media/hole-wvg-cavity.mp4'\n", - "animate.to_mp4(10,filename)\n", + "filename = \"media/hole-wvg-cavity.mp4\"\n", + "animate.to_mp4(10, filename)\n", "Video(filename)" ] }, @@ -392,35 +404,46 @@ "metadata": {}, "outputs": [], "source": [ - "def sim_cavity(N=3,sy=6):\n", - " sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction\n", - " cell = mp.Vector3(sx,sy,0)\n", - " blk = mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), material=mp.Medium(epsilon=eps))\n", + "def sim_cavity(N=3, sy=6):\n", + " sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction\n", + " cell = mp.Vector3(sx, sy, 0)\n", + " blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=eps))\n", " geometry = [blk]\n", "\n", " for i in range(N):\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(d/2+i)))\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d/2+i))))\n", - " \n", - " src = [mp.Source(mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ey,\n", - " center=mp.Vector3(-0.5*sx+dpml),\n", - " size=mp.Vector3(0,w))]\n", - " \n", - " sim = mp.Simulation(cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=src,\n", - " symmetries=sym,\n", - " resolution=resolution)\n", - " \n", - " freg = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml-0.5),\n", - " size=mp.Vector3(0,2*w))\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)))\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d / 2 + i))))\n", + "\n", + " src = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ey,\n", + " center=mp.Vector3(-0.5 * sx + dpml),\n", + " size=mp.Vector3(0, w),\n", + " )\n", + " ]\n", + "\n", + " sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=src,\n", + " symmetries=sym,\n", + " resolution=resolution,\n", + " )\n", + "\n", + " freg = mp.FluxRegion(\n", + " center=mp.Vector3(0.5 * sx - dpml - 0.5), size=mp.Vector3(0, 2 * w)\n", + " )\n", " nfreq = 500\n", " trans = sim.add_flux(fcen, df, nfreq, freg)\n", - " \n", - " sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ey, mp.Vector3(0.5*sx-dpml-0.5), 1e-3))\n", - " \n", + "\n", + " sim.run(\n", + " until_after_sources=mp.stop_when_fields_decayed(\n", + " 50, mp.Ey, mp.Vector3(0.5 * sx - dpml - 0.5), 1e-3\n", + " )\n", + " )\n", + "\n", " freqs = mp.get_flux_freqs(trans)\n", " psd = mp.get_fluxes(trans)\n", "\n", @@ -502,8 +525,8 @@ } ], "source": [ - "freqs_wg, psd_wg = sim_cavity(N=0) # simple waveguide\n", - "freqs_cav, psd_cav = sim_cavity() # cavity" + "freqs_wg, psd_wg = sim_cavity(N=0) # simple waveguide\n", + "freqs_cav, psd_cav = sim_cavity() # cavity" ] }, { @@ -525,17 +548,17 @@ } ], "source": [ - "fig = plt.figure(figsize=(11,6),dpi=100)\n", + "fig = plt.figure(figsize=(11, 6), dpi=100)\n", "ax = fig.add_subplot(111)\n", - "plt.plot(freqs_cav,np.array(psd_cav)/np.array(psd_wg),'o-')\n", + "plt.plot(freqs_cav, np.array(psd_cav) / np.array(psd_wg), \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Frequency')\n", - "plt.ylabel('Transmission')\n", + "plt.xlabel(\"Frequency\")\n", + "plt.ylabel(\"Transmission\")\n", "\n", "ax2 = fig.add_axes([0.52, 0.6, 0.2, 0.25])\n", - "plt.plot(freqs_cav,np.array(psd_cav)/np.array(psd_wg),'o-')\n", - "plt.xlim(0.23,0.24)\n", - "plt.ylim(0,0.8)\n", + "plt.plot(freqs_cav, np.array(psd_cav) / np.array(psd_wg), \"o-\")\n", + "plt.xlim(0.23, 0.24)\n", + "plt.ylim(0, 0.8)\n", "plt.grid(True)" ] }, @@ -567,27 +590,29 @@ "metadata": {}, "outputs": [], "source": [ - "def sim_cavity(N=3,sy=6,fcen=0.25,df=0.2):\n", - " sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction\n", - " cell = mp.Vector3(sx,sy,0)\n", - " blk = mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), material=mp.Medium(epsilon=eps))\n", + "def sim_cavity(N=3, sy=6, fcen=0.25, df=0.2):\n", + " sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction\n", + " cell = mp.Vector3(sx, sy, 0)\n", + " blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=eps))\n", " geometry = [blk]\n", "\n", " for i in range(N):\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(d/2+i)))\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d/2+i))))\n", - " \n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)))\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d / 2 + i))))\n", + "\n", " src = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Hz, mp.Vector3(0))]\n", - " \n", + "\n", " sym = [mp.Mirror(mp.Y, phase=-1), mp.Mirror(mp.X, phase=-1)]\n", - " \n", - " sim = mp.Simulation(cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=src,\n", - " symmetries=sym,\n", - " resolution=resolution)\n", - " \n", + "\n", + " sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=src,\n", + " symmetries=sym,\n", + " resolution=resolution,\n", + " )\n", + "\n", " return sim" ] }, @@ -676,13 +701,12 @@ ], "source": [ "h = mp.Harminv(mp.Hz, mp.Vector3(), fcen, df)\n", - "sim.run(mp.after_sources(h),\n", - " until_after_sources=400)\n", + "sim.run(mp.after_sources(h), until_after_sources=400)\n", "\n", "f = plt.figure(dpi=150)\n", - "animate = mp.Animate2D(sim,f=f,fields=mp.Hz,realtime=False,normalize=True)\n", + "animate = mp.Animate2D(sim, f=f, fields=mp.Hz, realtime=False, normalize=True)\n", "\n", - "sim.run(mp.at_every(1/fcen/20, animate), until=1/fcen)\n", + "sim.run(mp.at_every(1 / fcen / 20, animate), until=1 / fcen)\n", "plt.close()" ] }, @@ -715,8 +739,8 @@ } ], "source": [ - "filename = 'media/hole-wvg-cavity-res.mp4'\n", - "animate.to_mp4(10,filename)\n", + "filename = \"media/hole-wvg-cavity-res.mp4\"\n", + "animate.to_mp4(10, filename)\n", "Video(filename)" ] }, @@ -745,7 +769,7 @@ "Q = [m.Q for m in h.modes]\n", "\n", "for fiter, qiter in zip(f, Q):\n", - " print(f'Resonant frequency: {fiter}, Q: {qiter}') " + " print(f\"Resonant frequency: {fiter}, Q: {qiter}\")" ] }, { @@ -1072,14 +1096,13 @@ } ], "source": [ - "N_vec = np.arange(2,16,2)\n", + "N_vec = np.arange(2, 16, 2)\n", "f = []\n", "Q = []\n", "for N in N_vec:\n", " sim = sim_cavity(N=N)\n", " h = mp.Harminv(mp.Hz, mp.Vector3(), fcen, df)\n", - " sim.run(mp.after_sources(h),\n", - " until_after_sources=400)\n", + " sim.run(mp.after_sources(h), until_after_sources=400)\n", " f.append([m.freq for m in h.modes])\n", " Q.append([m.Q for m in h.modes])" ] @@ -1105,7 +1128,7 @@ ], "source": [ "for fiter, qiter in zip(f, Q):\n", - " print(f'Resonant frequency: {fiter}, Q: {qiter}') " + " print(f\"Resonant frequency: {fiter}, Q: {qiter}\")" ] }, { @@ -1141,10 +1164,10 @@ " Q_fund[i] = Q[i][int(idx[i])]\n", "\n", "plt.figure(dpi=150)\n", - "plt.semilogy(N_vec,Q_fund,'o-')\n", + "plt.semilogy(N_vec, Q_fund, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('N (# of periods)')\n", - "plt.ylabel('Q')\n", + "plt.xlabel(\"N (# of periods)\")\n", + "plt.ylabel(\"Q\")\n", "plt.show()" ] }, @@ -1245,7 +1268,7 @@ } ], "source": [ - "sim = sim_cavity(N=16,sy=12,fcen=0.328227374843021,df=0.1)\n", + "sim = sim_cavity(N=16, sy=12, fcen=0.328227374843021, df=0.1)\n", "sim.run(until_after_sources=600)" ] }, @@ -1269,7 +1292,7 @@ ], "source": [ "f = plt.figure(dpi=150)\n", - "sim.plot2D(ax=f.gca(),fields=mp.Hz)\n", + "sim.plot2D(ax=f.gca(), fields=mp.Hz)\n", "plt.show()" ] }, diff --git a/python/examples/holey-wvg-cavity.py b/python/examples/holey-wvg-cavity.py index 34f28bd58..0868401e4 100644 --- a/python/examples/holey-wvg-cavity.py +++ b/python/examples/holey-wvg-cavity.py @@ -12,84 +12,119 @@ import argparse import meep as mp + def main(args): - resolution = 20 # pixels/um + resolution = 20 # pixels/um - eps = 13 # dielectric constant of waveguide - w = 1.2 # width of waveguide - r = 0.36 # radius of holes - d = 1.4 # defect spacing (ordinary spacing = 1) - N = args.N # number of holes on either side of defect + eps = 13 # dielectric constant of waveguide + w = 1.2 # width of waveguide + r = 0.36 # radius of holes + d = 1.4 # defect spacing (ordinary spacing = 1) + N = args.N # number of holes on either side of defect sy = args.sy # size of cell in y direction (perpendicular to wvg.) - pad = 2 # padding between last hole and PML edge - dpml = 1 # PML thickness + pad = 2 # padding between last hole and PML edge + dpml = 1 # PML thickness - sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction + sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction - cell = mp.Vector3(sx,sy,0) + cell = mp.Vector3(sx, sy, 0) - blk = mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), - material=mp.Medium(epsilon=eps)) + blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=eps)) geometry = [blk] for i in range(N): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d/2+i))) - geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d/2+i)))) + geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) + geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d / 2 + i)))) fcen = args.fcen # pulse center frequency - df = args.df # pulse frequency width - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - sources=[], - boundary_layers=[mp.PML(dpml)], - resolution=20) + df = args.df # pulse frequency width + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + sources=[], + boundary_layers=[mp.PML(dpml)], + resolution=20, + ) if args.resonant_modes: - sim.sources.append(mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Hz, - center=mp.Vector3())) + sim.sources.append( + mp.Source( + mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3() + ) + ) sim.symmetries.append(mp.Mirror(mp.Y, phase=-1)) sim.symmetries.append(mp.Mirror(mp.X, phase=-1)) - sim.run(mp.at_beginning(mp.output_epsilon), - mp.after_sources(mp.Harminv(mp.Hz, mp.Vector3(), fcen, df)), - until_after_sources=400) + sim.run( + mp.at_beginning(mp.output_epsilon), + mp.after_sources(mp.Harminv(mp.Hz, mp.Vector3(), fcen, df)), + until_after_sources=400, + ) - sim.run(mp.at_every(1/fcen/20, mp.output_hfield_z), until=1/fcen) + sim.run(mp.at_every(1 / fcen / 20, mp.output_hfield_z), until=1 / fcen) else: - sim.sources.append(mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Ey, - center=mp.Vector3(-0.5*sx+dpml), - size=mp.Vector3(0,w))) + sim.sources.append( + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ey, + center=mp.Vector3(-0.5 * sx + dpml), + size=mp.Vector3(0, w), + ) + ) sim.symmetries.append(mp.Mirror(mp.Y, phase=-1)) - freg = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml-0.5), - size=mp.Vector3(0,2*w)) + freg = mp.FluxRegion( + center=mp.Vector3(0.5 * sx - dpml - 0.5), size=mp.Vector3(0, 2 * w) + ) - nfreq = 500 # number of frequencies at which to compute flux + nfreq = 500 # number of frequencies at which to compute flux # transmitted flux trans = sim.add_flux(fcen, df, nfreq, freg) vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sx)) - sim.run(mp.at_beginning(mp.output_epsilon), - mp.during_sources(mp.in_volume(vol, mp.to_appended("hz-slice", mp.at_every(0.4, mp.output_hfield_z)))), - until_after_sources=mp.stop_when_fields_decayed(50, mp.Ey, mp.Vector3(0.5*sx-dpml-0.5), 1e-3)) + sim.run( + mp.at_beginning(mp.output_epsilon), + mp.during_sources( + mp.in_volume( + vol, + mp.to_appended("hz-slice", mp.at_every(0.4, mp.output_hfield_z)), + ) + ), + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ey, mp.Vector3(0.5 * sx - dpml - 0.5), 1e-3 + ), + ) sim.display_fluxes(trans) # print out the flux spectrum -if __name__ == '__main__': + +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-r', '--resonant_modes', action='store_true', default=False, - help="Compute resonant modes. Default is transmission spectrum.") - parser.add_argument('-N', type=int, default=3, help='number of holes on either side of defect') - parser.add_argument('-sy', type=int, default=6, help='size of cell in y direction (perpendicular to wvg.)') - parser.add_argument('-fcen', type=float, default=0.25, help='pulse center frequency') - parser.add_argument('-df', type=float, default=0.2, help='pulse frequency width') + parser.add_argument( + "-r", + "--resonant_modes", + action="store_true", + default=False, + help="Compute resonant modes. Default is transmission spectrum.", + ) + parser.add_argument( + "-N", type=int, default=3, help="number of holes on either side of defect" + ) + parser.add_argument( + "-sy", + type=int, + default=6, + help="size of cell in y direction (perpendicular to wvg.)", + ) + parser.add_argument( + "-fcen", type=float, default=0.25, help="pulse center frequency" + ) + parser.add_argument("-df", type=float, default=0.2, help="pulse frequency width") args = parser.parse_args() main(args) diff --git a/python/examples/material-dispersion.py b/python/examples/material-dispersion.py index 10acd16cc..860a81886 100644 --- a/python/examples/material-dispersion.py +++ b/python/examples/material-dispersion.py @@ -18,7 +18,7 @@ susceptibilities = [ mp.LorentzianSusceptibility(frequency=1.1, gamma=1e-5, sigma=0.5), - mp.LorentzianSusceptibility(frequency=0.5, gamma=0.1, sigma=2e-5) + mp.LorentzianSusceptibility(frequency=0.5, gamma=0.1, sigma=2e-5), ] default_material = mp.Medium(epsilon=2.25, E_susceptibilities=susceptibilities) @@ -26,7 +26,9 @@ fcen = 1.0 df = 2.0 -sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3())] +sources = [ + mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3()) +] kmin = 0.3 kmax = 2.2 @@ -39,11 +41,11 @@ geometry=[], sources=sources, default_material=default_material, - resolution=resolution + resolution=resolution, ) all_freqs = sim.run_k_points(200, kpts) # a list of lists of frequencies for fs, kx in zip(all_freqs, [v.x for v in kpts]): for f in fs: - print("eps:, {:.6g}, {:.6g}, {:.6g}".format(f.real, f.imag, (kx / f)**2)) + print("eps:, {:.6g}, {:.6g}, {:.6g}".format(f.real, f.imag, (kx / f) ** 2)) diff --git a/python/examples/metal-cavity-ldos.ipynb b/python/examples/metal-cavity-ldos.ipynb index 6de91e4da..9f488cc65 100644 --- a/python/examples/metal-cavity-ldos.ipynb +++ b/python/examples/metal-cavity-ldos.ipynb @@ -34,37 +34,46 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", + "\n", "def metal_cavity(w):\n", " resolution = 50\n", " sxy = 2\n", " dpml = 1\n", - " sxy = sxy+2*dpml\n", - " cell = mp.Vector3(sxy,sxy)\n", + " sxy = sxy + 2 * dpml\n", + " cell = mp.Vector3(sxy, sxy)\n", "\n", " pml_layers = [mp.PML(dpml)]\n", " a = 1\n", " t = 0.1\n", - " geometry = [mp.Block(mp.Vector3(a+2*t,a+2*t,mp.inf), material=mp.metal),\n", - " mp.Block(mp.Vector3(a,a,mp.inf), material=mp.air)]\n", - "\n", - " geometry.append(mp.Block(center=mp.Vector3(a/2),\n", - " size=mp.Vector3(2*t,w,mp.inf),\n", - " material=mp.air))\n", - "\n", - " fcen = math.sqrt(0.5)/a\n", + " geometry = [\n", + " mp.Block(mp.Vector3(a + 2 * t, a + 2 * t, mp.inf), material=mp.metal),\n", + " mp.Block(mp.Vector3(a, a, mp.inf), material=mp.air),\n", + " ]\n", + "\n", + " geometry.append(\n", + " mp.Block(\n", + " center=mp.Vector3(a / 2), size=mp.Vector3(2 * t, w, mp.inf), material=mp.air\n", + " )\n", + " )\n", + "\n", + " fcen = math.sqrt(0.5) / a\n", " df = 0.2\n", - " sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df),\n", - " component=mp.Ez,\n", - " center=mp.Vector3())]\n", + " sources = [\n", + " mp.Source(\n", + " src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3()\n", + " )\n", + " ]\n", "\n", " symmetries = [mp.Mirror(mp.Y)]\n", "\n", - " sim = mp.Simulation(cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - " resolution=resolution)\n", + " sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + " resolution=resolution,\n", + " )\n", "\n", " h = mp.Harminv(mp.Ez, mp.Vector3(), fcen, df)\n", " sim.run(mp.after_sources(h), until_after_sources=500)\n", @@ -72,13 +81,13 @@ " m = h.modes[0]\n", " f = m.freq\n", " Q = m.Q\n", - " Vmode = 0.25*a*a\n", + " Vmode = 0.25 * a * a\n", " ldos_1 = Q / Vmode / (2 * math.pi * f * math.pi * 0.5)\n", - " \n", + "\n", " sim.reset_meep()\n", - " \n", - " T = 2*Q*(1/f)\n", - " sim.run(mp.dft_ldos(f,0,1), until_after_sources=T)\n", + "\n", + " T = 2 * Q * (1 / f)\n", + " sim.run(mp.dft_ldos(f, 0, 1), until_after_sources=T)\n", " ldos_2 = sim.ldos_data[0]\n", "\n", " return ldos_1, ldos_2" @@ -99,13 +108,13 @@ "metadata": {}, "outputs": [], "source": [ - "ws = np.arange(0.2,0.5,0.1)\n", + "ws = np.arange(0.2, 0.5, 0.1)\n", "ldos_1 = np.zeros(len(ws))\n", "ldos_2 = np.zeros(len(ws))\n", "\n", "for j in range(len(ws)):\n", " ldos_1[j], ldos_2[j] = metal_cavity(ws[j])\n", - " print(\"ldos:, {}, {}\".format(ldos_1[j],ldos_2[2]))" + " print(\"ldos:, {}, {}\".format(ldos_1[j], ldos_2[2]))" ] }, { @@ -115,10 +124,10 @@ "outputs": [], "source": [ "plt.figure(dpi=150)\n", - "plt.semilogy(1/ws,ldos_1,'bo-',label=\"2Q/(πωV)\")\n", - "plt.semilogy(1/ws,ldos_2,'rs-',label=\"LDOS\")\n", - "plt.xlabel('a/w')\n", - "plt.ylabel('2Q/(πωW) or LDOS')\n", + "plt.semilogy(1 / ws, ldos_1, \"bo-\", label=\"2Q/(πωV)\")\n", + "plt.semilogy(1 / ws, ldos_2, \"rs-\", label=\"LDOS\")\n", + "plt.xlabel(\"a/w\")\n", + "plt.ylabel(\"2Q/(πωW) or LDOS\")\n", "plt.show()" ] }, diff --git a/python/examples/metal-cavity-ldos.py b/python/examples/metal-cavity-ldos.py index 97b6a2c45..3f67eff78 100644 --- a/python/examples/metal-cavity-ldos.py +++ b/python/examples/metal-cavity-ldos.py @@ -5,37 +5,46 @@ import numpy as np import matplotlib.pyplot as plt + def metal_cavity(w): resolution = 50 sxy = 2 dpml = 1 - sxy += 2*dpml - cell = mp.Vector3(sxy,sxy) + sxy += 2 * dpml + cell = mp.Vector3(sxy, sxy) pml_layers = [mp.PML(dpml)] a = 1 t = 0.1 - geometry = [mp.Block(mp.Vector3(a+2*t,a+2*t,mp.inf), material=mp.metal), - mp.Block(mp.Vector3(a,a,mp.inf), material=mp.air)] + geometry = [ + mp.Block(mp.Vector3(a + 2 * t, a + 2 * t, mp.inf), material=mp.metal), + mp.Block(mp.Vector3(a, a, mp.inf), material=mp.air), + ] - geometry.append(mp.Block(center=mp.Vector3(a/2), - size=mp.Vector3(2*t,w,mp.inf), - material=mp.air)) + geometry.append( + mp.Block( + center=mp.Vector3(a / 2), size=mp.Vector3(2 * t, w, mp.inf), material=mp.air + ) + ) - fcen = math.sqrt(0.5)/a + fcen = math.sqrt(0.5) / a df = 0.2 - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3())] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3() + ) + ] symmetries = [mp.Mirror(mp.Y)] - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - symmetries=symmetries, - resolution=resolution) + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + symmetries=symmetries, + resolution=resolution, + ) h = mp.Harminv(mp.Ez, mp.Vector3(), fcen, df) sim.run(mp.after_sources(h), until_after_sources=500) @@ -43,18 +52,19 @@ def metal_cavity(w): m = h.modes[0] f = m.freq Q = m.Q - Vmode = 0.25*a*a + Vmode = 0.25 * a * a ldos_1 = Q / Vmode / (2 * math.pi * f * math.pi * 0.5) sim.reset_meep() - T = 2*Q*(1/f) - sim.run(mp.dft_ldos(f,0,1), until_after_sources=T) + T = 2 * Q * (1 / f) + sim.run(mp.dft_ldos(f, 0, 1), until_after_sources=T) ldos_2 = sim.ldos_data[0] return ldos_1, ldos_2 -ws = np.arange(0.2,0.5,0.1) + +ws = np.arange(0.2, 0.5, 0.1) ldos_1 = np.zeros(len(ws)) ldos_2 = np.zeros(len(ws)) @@ -63,8 +73,8 @@ def metal_cavity(w): print(f"ldos:, {ldos_1[j]}, {ldos_2[2]}") plt.figure(dpi=150) -plt.semilogy(1/ws,ldos_1,'bo-',label="2Q/(πωV)") -plt.semilogy(1/ws,ldos_2,'rs-',label="LDOS") -plt.xlabel('a/w') -plt.ylabel('2Q/(πωW) or LDOS') +plt.semilogy(1 / ws, ldos_1, "bo-", label="2Q/(πωV)") +plt.semilogy(1 / ws, ldos_2, "rs-", label="LDOS") +plt.xlabel("a/w") +plt.ylabel("2Q/(πωW) or LDOS") plt.show() diff --git a/python/examples/metasurface_lens.ipynb b/python/examples/metasurface_lens.ipynb index a855f3b82..7fb38949a 100644 --- a/python/examples/metasurface_lens.ipynb +++ b/python/examples/metasurface_lens.ipynb @@ -26,114 +26,163 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 2.0 # substrate thickness\n", - "dpad = 2.0 # padding between grating and PML\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 2.0 # substrate thickness\n", + "dpad = 2.0 # padding between grating and PML\n", "\n", - "lcen = 0.5 # center wavelength\n", - "fcen = 1/lcen # center frequency\n", - "df = 0.2*fcen # frequency width\n", + "lcen = 0.5 # center wavelength\n", + "fcen = 1 / lcen # center frequency\n", + "df = 0.2 * fcen # frequency width\n", "\n", - "focal_length = 200 # focal length of metalens\n", - "spot_length = 100 # far field line length\n", - "ff_res = 10 # far field resolution (points/μm)\n", + "focal_length = 200 # focal length of metalens\n", + "spot_length = 100 # far field line length\n", + "ff_res = 10 # far field resolution (points/μm)\n", "\n", - "k_point = mp.Vector3(0,0,0)\n", + "k_point = mp.Vector3(0, 0, 0)\n", "\n", "glass = mp.Medium(index=1.5)\n", "\n", - "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", + "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", "\n", - "symmetries=[mp.Mirror(mp.Y)]\n", + "symmetries = [mp.Mirror(mp.Y)]\n", "\n", - "def grating(gp,gh,gdc_list):\n", - " sx = dpml+dsub+gh+dpad+dpml\n", - " src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub)\n", - " mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad)\n", - " geometry = [mp.Block(material=glass,\n", - " size=mp.Vector3(dpml+dsub,mp.inf,mp.inf),\n", - " center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))]\n", "\n", - " num_cells = len(gdc_list)\n", - " if num_cells == 1:\n", - " sy = gp\n", - " cell_size = mp.Vector3(sx,sy)\n", + "def grating(gp, gh, gdc_list):\n", + " sx = dpml + dsub + gh + dpad + dpml\n", + " src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub)\n", + " mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad)\n", + " geometry = [\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", + " )\n", + " ]\n", "\n", - " sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy))]\n", + " num_cells = len(gdc_list)\n", + " if num_cells == 1:\n", + " sy = gp\n", + " cell_size = mp.Vector3(sx, sy)\n", "\n", - " sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " default_material=glass,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", + " sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy),\n", + " )\n", + " ]\n", "\n", - " flux_obj = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", + " sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " default_material=glass,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + " )\n", "\n", - " sim.run(until_after_sources=50)\n", - " \n", - " input_flux = mp.get_fluxes(flux_obj)\n", - " \n", - " sim.reset_meep()\n", + " flux_obj = sim.add_flux(\n", + " fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", + " )\n", "\n", - " geometry.append(mp.Block(material=glass, size=mp.Vector3(gh,gdc_list[0]*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh)))\n", + " sim.run(until_after_sources=50)\n", "\n", - " sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", + " input_flux = mp.get_fluxes(flux_obj)\n", "\n", - " flux_obj = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", + " sim.reset_meep()\n", "\n", - " sim.run(until_after_sources=200)\n", - " \n", - " freqs = mp.get_eigenmode_freqs(flux_obj)\n", - " res = sim.get_eigenmode_coefficients(flux_obj, [1], eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", - " coeffs = res.alpha\n", + " geometry.append(\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(gh, gdc_list[0] * gp, mp.inf),\n", + " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh),\n", + " )\n", + " )\n", "\n", - " mode_tran = abs(coeffs[0,0,0])**2/input_flux[0]\n", - " mode_phase = np.angle(coeffs[0,0,0])\n", - " if mode_phase > 0:\n", - " mode_phase -= 2*np.pi\n", - " \n", - " return mode_tran, mode_phase\n", - " \n", - " else: \n", - " sy = num_cells*gp\n", - " cell_size = mp.Vector3(sx,sy)\n", + " sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + " )\n", "\n", - " sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy))]\n", + " flux_obj = sim.add_flux(\n", + " fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", + " )\n", "\n", - " for j in range(num_cells):\n", - " geometry.append(mp.Block(material=glass,\n", - " size=mp.Vector3(gh,gdc_list[j]*gp,mp.inf),\n", - " center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+(j+0.5)*gp)))\n", + " sim.run(until_after_sources=200)\n", "\n", - " sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries)\n", + " freqs = mp.get_eigenmode_freqs(flux_obj)\n", + " res = sim.get_eigenmode_coefficients(\n", + " flux_obj, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y\n", + " )\n", + " coeffs = res.alpha\n", "\n", - " n2f_obj = sim.add_near2far(fcen, 0, 1, mp.Near2FarRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", + " mode_tran = abs(coeffs[0, 0, 0]) ** 2 / input_flux[0]\n", + " mode_phase = np.angle(coeffs[0, 0, 0])\n", + " if mode_phase > 0:\n", + " mode_phase -= 2 * np.pi\n", "\n", - " sim.run(until_after_sources=100)\n", - " \n", - " return abs(sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(-0.5*sx+dpml+dsub+gh+focal_length), size=mp.Vector3(spot_length))['Ez'])**2" + " return mode_tran, mode_phase\n", + "\n", + " else:\n", + " sy = num_cells * gp\n", + " cell_size = mp.Vector3(sx, sy)\n", + "\n", + " sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy),\n", + " )\n", + " ]\n", + "\n", + " for j in range(num_cells):\n", + " geometry.append(\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(gh, gdc_list[j] * gp, mp.inf),\n", + " center=mp.Vector3(\n", + " -0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + (j + 0.5) * gp\n", + " ),\n", + " )\n", + " )\n", + "\n", + " sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + " )\n", + "\n", + " n2f_obj = sim.add_near2far(\n", + " fcen, 0, 1, mp.Near2FarRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", + " )\n", + "\n", + " sim.run(until_after_sources=100)\n", + "\n", + " return (\n", + " abs(\n", + " sim.get_farfields(\n", + " n2f_obj,\n", + " ff_res,\n", + " center=mp.Vector3(-0.5 * sx + dpml + dsub + gh + focal_length),\n", + " size=mp.Vector3(spot_length),\n", + " )[\"Ez\"]\n", + " )\n", + " ** 2\n", + " )" ] }, { @@ -1080,14 +1129,14 @@ } ], "source": [ - "gp = 0.3 # grating periodicity\n", - "gh = 1.8 # grating height\n", - "gdc = np.linspace(0.1,0.9,30) # grating duty cycle\n", + "gp = 0.3 # grating periodicity\n", + "gh = 1.8 # grating height\n", + "gdc = np.linspace(0.1, 0.9, 30) # grating duty cycle\n", "\n", "mode_tran = np.empty((gdc.size))\n", "mode_phase = np.empty((gdc.size))\n", "for n in range(gdc.size):\n", - " mode_tran[n], mode_phase[n] = grating(gp,gh,[gdc[n]])" + " mode_tran[n], mode_phase[n] = grating(gp, gh, [gdc[n]])" ] }, { @@ -1110,23 +1159,23 @@ ], "source": [ "plt.figure(dpi=200)\n", - "plt.subplot(1,2,1)\n", - "plt.plot(gdc, mode_tran, 'bo-')\n", - "plt.xlim(gdc[0],gdc[-1])\n", - "plt.xticks([t for t in np.linspace(0.1,0.9,5)])\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(gdc, mode_tran, \"bo-\")\n", + "plt.xlim(gdc[0], gdc[-1])\n", + "plt.xticks([t for t in np.linspace(0.1, 0.9, 5)])\n", "plt.xlabel(\"grating duty cycle\")\n", - "plt.ylim(0.96,1.00)\n", - "plt.yticks([t for t in np.linspace(0.96,1.00,5)])\n", + "plt.ylim(0.96, 1.00)\n", + "plt.yticks([t for t in np.linspace(0.96, 1.00, 5)])\n", "plt.title(\"transmittance\")\n", "\n", - "plt.subplot(1,2,2)\n", - "plt.plot(gdc, mode_phase, 'rs-')\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(gdc, mode_phase, \"rs-\")\n", "plt.grid(True)\n", - "plt.xlim(gdc[0],gdc[-1])\n", - "plt.xticks([t for t in np.linspace(0.1,0.9,5)])\n", + "plt.xlim(gdc[0], gdc[-1])\n", + "plt.xticks([t for t in np.linspace(0.1, 0.9, 5)])\n", "plt.xlabel(\"grating duty cycle\")\n", - "plt.ylim(-2*np.pi,0)\n", - "plt.yticks([t for t in np.linspace(-6,0,7)])\n", + "plt.ylim(-2 * np.pi, 0)\n", + "plt.yticks([t for t in np.linspace(-6, 0, 7)])\n", "plt.title(\"phase (radians)\")\n", "\n", "plt.tight_layout(pad=0.5)\n", @@ -1478,27 +1527,39 @@ } ], "source": [ - "gdc_new = np.linspace(0.16,0.65,500)\n", + "gdc_new = np.linspace(0.16, 0.65, 500)\n", "mode_phase_interp = np.interp(gdc_new, gdc, mode_phase)\n", - "print(\"phase-range:, {:.6f}\".format(mode_phase_interp.max()-mode_phase_interp.min()))\n", + "print(\"phase-range:, {:.6f}\".format(mode_phase_interp.max() - mode_phase_interp.min()))\n", "\n", "phase_tol = 1e-2\n", - "num_cells = [100,200,400]\n", - "ff_nc = np.empty((spot_length*ff_res,len(num_cells)))\n", + "num_cells = [100, 200, 400]\n", + "ff_nc = np.empty((spot_length * ff_res, len(num_cells)))\n", "\n", "for k in range(len(num_cells)):\n", - " gdc_list = []\n", - " for j in range(-num_cells[k],num_cells[k]+1):\n", - " phase_local = 2*np.pi/lcen * (focal_length-((j*gp)**2 + focal_length**2)**0.5) # local phase at the center of the j'th unit cell\n", - " phase_mod = phase_local % (-2*np.pi) # restrict phase to [-2*pi,0]\n", - " if phase_mod > mode_phase_interp.max():\n", - " phase_mod = mode_phase_interp.max()\n", - " if phase_mod < mode_phase_interp.min():\n", - " phase_mod = mode_phase_interp.min()\n", - " idx = np.transpose(np.nonzero(np.logical_and(mode_phase_interp > phase_mod-phase_tol, mode_phase_interp < phase_mod+phase_tol))).ravel()\n", - " gdc_list.append(gdc_new[idx[0]])\n", + " gdc_list = []\n", + " for j in range(-num_cells[k], num_cells[k] + 1):\n", + " phase_local = (\n", + " 2\n", + " * np.pi\n", + " / lcen\n", + " * (focal_length - ((j * gp) ** 2 + focal_length**2) ** 0.5)\n", + " ) # local phase at the center of the j'th unit cell\n", + " phase_mod = phase_local % (-2 * np.pi) # restrict phase to [-2*pi,0]\n", + " if phase_mod > mode_phase_interp.max():\n", + " phase_mod = mode_phase_interp.max()\n", + " if phase_mod < mode_phase_interp.min():\n", + " phase_mod = mode_phase_interp.min()\n", + " idx = np.transpose(\n", + " np.nonzero(\n", + " np.logical_and(\n", + " mode_phase_interp > phase_mod - phase_tol,\n", + " mode_phase_interp < phase_mod + phase_tol,\n", + " )\n", + " )\n", + " ).ravel()\n", + " gdc_list.append(gdc_new[idx[0]])\n", "\n", - " ff_nc[:,k] = grating(gp,gh,gdc_list)" + " ff_nc[:, k] = grating(gp, gh, gdc_list)" ] }, { @@ -1531,15 +1592,25 @@ } ], "source": [ - "x = np.linspace(focal_length-0.5*spot_length,focal_length+0.5*spot_length,ff_res*spot_length)\n", + "x = np.linspace(\n", + " focal_length - 0.5 * spot_length,\n", + " focal_length + 0.5 * spot_length,\n", + " ff_res * spot_length,\n", + ")\n", "plt.figure(dpi=200)\n", - "plt.semilogy(x,abs(ff_nc[:,0])**2,'bo-',label='num_cells = {}'.format(2*num_cells[0]+1))\n", - "plt.semilogy(x,abs(ff_nc[:,1])**2,'ro-',label='num_cells = {}'.format(2*num_cells[1]+1))\n", - "plt.semilogy(x,abs(ff_nc[:,2])**2,'go-',label='num_cells = {}'.format(2*num_cells[2]+1))\n", - "plt.xlabel('x coordinate (μm)')\n", - "plt.ylabel(r'energy density of far-field electric fields, |E$_z$|$^2$')\n", - "plt.title('focusing properties of a binary-grating metasurface lens')\n", - "plt.legend(loc='upper right')\n", + "plt.semilogy(\n", + " x, abs(ff_nc[:, 0]) ** 2, \"bo-\", label=\"num_cells = {}\".format(2 * num_cells[0] + 1)\n", + ")\n", + "plt.semilogy(\n", + " x, abs(ff_nc[:, 1]) ** 2, \"ro-\", label=\"num_cells = {}\".format(2 * num_cells[1] + 1)\n", + ")\n", + "plt.semilogy(\n", + " x, abs(ff_nc[:, 2]) ** 2, \"go-\", label=\"num_cells = {}\".format(2 * num_cells[2] + 1)\n", + ")\n", + "plt.xlabel(\"x coordinate (μm)\")\n", + "plt.ylabel(r\"energy density of far-field electric fields, |E$_z$|$^2$\")\n", + "plt.title(\"focusing properties of a binary-grating metasurface lens\")\n", + "plt.legend(loc=\"upper right\")\n", "plt.tight_layout()\n", "plt.show()" ] diff --git a/python/examples/metasurface_lens.py b/python/examples/metasurface_lens.py index 771d4fb9f..71451d6f3 100644 --- a/python/examples/metasurface_lens.py +++ b/python/examples/metasurface_lens.py @@ -5,182 +5,242 @@ import matplotlib.pyplot as plt -resolution = 50 # pixels/μm +resolution = 50 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 2.0 # substrate thickness -dpad = 2.0 # padding between grating and PML +dpml = 1.0 # PML thickness +dsub = 2.0 # substrate thickness +dpad = 2.0 # padding between grating and PML -lcen = 0.5 # center wavelength -fcen = 1/lcen # center frequency -df = 0.2*fcen # frequency width +lcen = 0.5 # center wavelength +fcen = 1 / lcen # center frequency +df = 0.2 * fcen # frequency width -focal_length = 200 # focal length of metalens -spot_length = 100 # far field line length -ff_res = 10 # far field resolution (points/μm) +focal_length = 200 # focal length of metalens +spot_length = 100 # far field line length +ff_res = 10 # far field resolution (points/μm) -k_point = mp.Vector3(0,0,0) +k_point = mp.Vector3(0, 0, 0) glass = mp.Medium(index=1.5) -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] -symmetries=[mp.Mirror(mp.Y)] +symmetries = [mp.Mirror(mp.Y)] -def grating(gp,gh,gdc_list): - sx = dpml+dsub+gh+dpad+dpml - src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub) - mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad) - geometry = [mp.Block(material=glass, - size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), - center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))] - num_cells = len(gdc_list) - if num_cells == 1: - sy = gp - cell_size = mp.Vector3(sx,sy,0) - - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(y=sy))] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - default_material=glass, - sources=sources, - symmetries=symmetries) - - flux_obj = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))) - - sim.run(until_after_sources=50) - - input_flux = mp.get_fluxes(flux_obj) - - sim.reset_meep() - - geometry.append(mp.Block(material=glass, size=mp.Vector3(gh,gdc_list[0]*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))) - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - - flux_obj = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))) - - sim.run(until_after_sources=200) - - freqs = mp.get_eigenmode_freqs(flux_obj) - res = sim.get_eigenmode_coefficients(flux_obj, [1], eig_parity=mp.ODD_Z+mp.EVEN_Y) - coeffs = res.alpha - - mode_tran = abs(coeffs[0,0,0])**2/input_flux[0] - mode_phase = np.angle(coeffs[0,0,0]) - if mode_phase > 0: - mode_phase -= 2*np.pi - - return mode_tran, mode_phase - - else: - sy = num_cells*gp - cell_size = mp.Vector3(sx,sy,0) - - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(y=sy))] - - geometry.extend( +def grating(gp, gh, gdc_list): + sx = dpml + dsub + gh + dpad + dpml + src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub) + mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad) + geometry = [ mp.Block( material=glass, - size=mp.Vector3(gh, gdc_list[j] * gp, mp.inf), - center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + - (j + 0.5) * gp)) for j in range(num_cells)) - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - - n2f_obj = sim.add_near2far(fcen, 0, 1, mp.Near2FarRegion(center=mon_pt, size=mp.Vector3(y=sy))) - - sim.run(until_after_sources=500) - - return abs(sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(-0.5*sx+dpml+dsub+gh+focal_length), size=mp.Vector3(spot_length))['Ez'])**2 - - -gp = 0.3 # grating periodicity -gh = 1.8 # grating height -gdc = np.linspace(0.1,0.9,30) # grating duty cycle + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + ) + ] + + num_cells = len(gdc_list) + if num_cells == 1: + sy = gp + cell_size = mp.Vector3(sx, sy, 0) + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + default_material=glass, + sources=sources, + symmetries=symmetries, + ) + + flux_obj = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)) + ) + + sim.run(until_after_sources=50) + + input_flux = mp.get_fluxes(flux_obj) + + sim.reset_meep() + + geometry.append( + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc_list[0] * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh), + ) + ) + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, + ) + + flux_obj = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)) + ) + + sim.run(until_after_sources=200) + + freqs = mp.get_eigenmode_freqs(flux_obj) + res = sim.get_eigenmode_coefficients( + flux_obj, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y + ) + coeffs = res.alpha + + mode_tran = abs(coeffs[0, 0, 0]) ** 2 / input_flux[0] + mode_phase = np.angle(coeffs[0, 0, 0]) + if mode_phase > 0: + mode_phase -= 2 * np.pi + + return mode_tran, mode_phase + + else: + sy = num_cells * gp + cell_size = mp.Vector3(sx, sy, 0) + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) + ] + + geometry.extend( + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc_list[j] * gp, mp.inf), + center=mp.Vector3( + -0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + (j + 0.5) * gp + ), + ) + for j in range(num_cells) + ) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, + ) + + n2f_obj = sim.add_near2far( + fcen, 0, 1, mp.Near2FarRegion(center=mon_pt, size=mp.Vector3(y=sy)) + ) + + sim.run(until_after_sources=500) + + return ( + abs( + sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3(-0.5 * sx + dpml + dsub + gh + focal_length), + size=mp.Vector3(spot_length), + )["Ez"] + ) + ** 2 + ) + + +gp = 0.3 # grating periodicity +gh = 1.8 # grating height +gdc = np.linspace(0.1, 0.9, 30) # grating duty cycle mode_tran = np.empty((gdc.size)) mode_phase = np.empty((gdc.size)) for n in range(gdc.size): - mode_tran[n], mode_phase[n] = grating(gp,gh,[gdc[n]]) + mode_tran[n], mode_phase[n] = grating(gp, gh, [gdc[n]]) plt.figure(dpi=200) -plt.subplot(1,2,1) -plt.plot(gdc, mode_tran, 'bo-') -plt.xlim(gdc[0],gdc[-1]) -plt.xticks(list(np.linspace(0.1,0.9,5))) +plt.subplot(1, 2, 1) +plt.plot(gdc, mode_tran, "bo-") +plt.xlim(gdc[0], gdc[-1]) +plt.xticks(list(np.linspace(0.1, 0.9, 5))) plt.xlabel("grating duty cycle") -plt.ylim(0.96,1.00) -plt.yticks(list(np.linspace(0.96,1.00,5))) +plt.ylim(0.96, 1.00) +plt.yticks(list(np.linspace(0.96, 1.00, 5))) plt.title("transmittance") -plt.subplot(1,2,2) -plt.plot(gdc, mode_phase, 'rs-') +plt.subplot(1, 2, 2) +plt.plot(gdc, mode_phase, "rs-") plt.grid(True) -plt.xlim(gdc[0],gdc[-1]) -plt.xticks(list(np.linspace(0.1,0.9,5))) +plt.xlim(gdc[0], gdc[-1]) +plt.xticks(list(np.linspace(0.1, 0.9, 5))) plt.xlabel("grating duty cycle") -plt.ylim(-2*np.pi,0) -plt.yticks(list(np.linspace(-6,0,7))) +plt.ylim(-2 * np.pi, 0) +plt.yticks(list(np.linspace(-6, 0, 7))) plt.title("phase (radians)") plt.tight_layout(pad=0.5) plt.show() -gdc_new = np.linspace(0.16,0.65,500) +gdc_new = np.linspace(0.16, 0.65, 500) mode_phase_interp = np.interp(gdc_new, gdc, mode_phase) -print("phase-range:, {:.6f}".format(mode_phase_interp.max()-mode_phase_interp.min())) +print("phase-range:, {:.6f}".format(mode_phase_interp.max() - mode_phase_interp.min())) phase_tol = 1e-2 -num_cells = [100,200,400] -ff_nc = np.empty((spot_length*ff_res,len(num_cells))) +num_cells = [100, 200, 400] +ff_nc = np.empty((spot_length * ff_res, len(num_cells))) for k in range(len(num_cells)): - gdc_list = [] - for j in range(-num_cells[k],num_cells[k]+1): - phase_local = 2*np.pi/lcen * (focal_length-((j*gp)**2 + focal_length**2)**0.5) # local phase at the center of the j'th unit cell - phase_mod = phase_local % (-2*np.pi) # restrict phase to [-2*pi,0] - if phase_mod > mode_phase_interp.max(): - phase_mod = mode_phase_interp.max() - if phase_mod < mode_phase_interp.min(): - phase_mod = mode_phase_interp.min() - idx = np.transpose(np.nonzero(np.logical_and(mode_phase_interp > phase_mod-phase_tol, mode_phase_interp < phase_mod+phase_tol))) - gdc_list.append(gdc_new[idx[0][0]]) - - ff_nc[:,k] = grating(gp,gh,gdc_list) - -x = np.linspace(focal_length-0.5*spot_length,focal_length+0.5*spot_length,ff_res*spot_length) + gdc_list = [] + for j in range(-num_cells[k], num_cells[k] + 1): + phase_local = ( + 2 + * np.pi + / lcen + * (focal_length - ((j * gp) ** 2 + focal_length**2) ** 0.5) + ) # local phase at the center of the j'th unit cell + phase_mod = phase_local % (-2 * np.pi) # restrict phase to [-2*pi,0] + if phase_mod > mode_phase_interp.max(): + phase_mod = mode_phase_interp.max() + if phase_mod < mode_phase_interp.min(): + phase_mod = mode_phase_interp.min() + idx = np.transpose( + np.nonzero( + np.logical_and( + mode_phase_interp > phase_mod - phase_tol, + mode_phase_interp < phase_mod + phase_tol, + ) + ) + ) + gdc_list.append(gdc_new[idx[0][0]]) + + ff_nc[:, k] = grating(gp, gh, gdc_list) + +x = np.linspace( + focal_length - 0.5 * spot_length, + focal_length + 0.5 * spot_length, + ff_res * spot_length, +) plt.figure(dpi=200) -plt.semilogy( - x, abs(ff_nc[:, 0])**2, 'bo-', label=f'num_cells = {2*num_cells[0] + 1}') -plt.semilogy( - x, abs(ff_nc[:, 1])**2, 'ro-', label=f'num_cells = {2*num_cells[1] + 1}') -plt.semilogy( - x, abs(ff_nc[:, 2])**2, 'go-', label=f'num_cells = {2*num_cells[2] + 1}') -plt.xlabel('x coordinate (μm)') -plt.ylabel(r'energy density of far-field electric fields, |E$_z$|$^2$') -plt.title('focusing properties of a binary-grating metasurface lens') -plt.legend(loc='upper right') +plt.semilogy(x, abs(ff_nc[:, 0]) ** 2, "bo-", label=f"num_cells = {2*num_cells[0] + 1}") +plt.semilogy(x, abs(ff_nc[:, 1]) ** 2, "ro-", label=f"num_cells = {2*num_cells[1] + 1}") +plt.semilogy(x, abs(ff_nc[:, 2]) ** 2, "go-", label=f"num_cells = {2*num_cells[2] + 1}") +plt.xlabel("x coordinate (μm)") +plt.ylabel(r"energy density of far-field electric fields, |E$_z$|$^2$") +plt.title("focusing properties of a binary-grating metasurface lens") +plt.legend(loc="upper right") plt.tight_layout() plt.show() diff --git a/python/examples/mie_scattering.ipynb b/python/examples/mie_scattering.ipynb index d6b2790a2..65050ed7a 100644 --- a/python/examples/mie_scattering.ipynb +++ b/python/examples/mie_scattering.ipynb @@ -24,48 +24,83 @@ "\n", "r = 1.0 # radius of sphere\n", "\n", - "wvl_min = 2*np.pi*r/10\n", - "wvl_max = 2*np.pi*r/2\n", + "wvl_min = 2 * np.pi * r / 10\n", + "wvl_max = 2 * np.pi * r / 2\n", "\n", - "frq_min = 1/wvl_max\n", - "frq_max = 1/wvl_min\n", - "frq_cen = 0.5*(frq_min+frq_max)\n", - "dfrq = frq_max-frq_min\n", + "frq_min = 1 / wvl_max\n", + "frq_max = 1 / wvl_min\n", + "frq_cen = 0.5 * (frq_min + frq_max)\n", + "dfrq = frq_max - frq_min\n", "nfrq = 100\n", "\n", "## at least 8 pixels per smallest wavelength, i.e. np.floor(8/wvl_min)\n", "resolution = 25\n", "\n", - "dpml = 0.5*wvl_max\n", - "dair = 0.5*wvl_max\n", + "dpml = 0.5 * wvl_max\n", + "dair = 0.5 * wvl_max\n", "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "symmetries = [mp.Mirror(mp.Y),\n", - " mp.Mirror(mp.Z,phase=-1)]\n", + "symmetries = [mp.Mirror(mp.Y), mp.Mirror(mp.Z, phase=-1)]\n", "\n", - "s = 2*(dpml+dair+r)\n", - "cell_size = mp.Vector3(s,s,s)\n", + "s = 2 * (dpml + dair + r)\n", + "cell_size = mp.Vector3(s, s, s)\n", "\n", "# is_integrated=True necessary for any planewave source extending into PML\n", - "sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n", - " center=mp.Vector3(-0.5*s+dpml),\n", - " size=mp.Vector3(0,s,s),\n", - " component=mp.Ez)]\n", - "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=mp.Vector3(),\n", - " symmetries=symmetries)\n", - "\n", - "box_x1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r,2*r)))\n", - "box_x2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r,2*r)))\n", - "box_y1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0,2*r)))\n", - "box_y2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0,2*r)))\n", - "box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=-r),size=mp.Vector3(2*r,2*r,0)))\n", - "box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=+r),size=mp.Vector3(2*r,2*r,0)))\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True),\n", + " center=mp.Vector3(-0.5 * s + dpml),\n", + " size=mp.Vector3(0, s, s),\n", + " component=mp.Ez,\n", + " )\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=mp.Vector3(),\n", + " symmetries=symmetries,\n", + ")\n", + "\n", + "box_x1 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r, 2 * r)),\n", + ")\n", + "box_x2 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r, 2 * r)),\n", + ")\n", + "box_y1 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0, 2 * r)),\n", + ")\n", + "box_y2 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0, 2 * r)),\n", + ")\n", + "box_z1 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(z=-r), size=mp.Vector3(2 * r, 2 * r, 0)),\n", + ")\n", + "box_z2 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(z=+r), size=mp.Vector3(2 * r, 2 * r, 0)),\n", + ")\n", "\n", "sim.run(until_after_sources=10)\n", "\n", @@ -82,24 +117,56 @@ "sim.reset_meep()\n", "\n", "n_sphere = 2.0\n", - "geometry = [mp.Sphere(material=mp.Medium(index=n_sphere),\n", - " center=mp.Vector3(),\n", - " radius=r)]\n", - "\n", - "sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=mp.Vector3(),\n", - " symmetries=symmetries,\n", - " geometry=geometry)\n", - "\n", - "box_x1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r,2*r)))\n", - "box_x2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r,2*r)))\n", - "box_y1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0,2*r)))\n", - "box_y2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0,2*r)))\n", - "box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=-r),size=mp.Vector3(2*r,2*r,0)))\n", - "box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=+r),size=mp.Vector3(2*r,2*r,0)))\n", + "geometry = [\n", + " mp.Sphere(material=mp.Medium(index=n_sphere), center=mp.Vector3(), radius=r)\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=mp.Vector3(),\n", + " symmetries=symmetries,\n", + " geometry=geometry,\n", + ")\n", + "\n", + "box_x1 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r, 2 * r)),\n", + ")\n", + "box_x2 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r, 2 * r)),\n", + ")\n", + "box_y1 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0, 2 * r)),\n", + ")\n", + "box_y2 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0, 2 * r)),\n", + ")\n", + "box_z1 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(z=-r), size=mp.Vector3(2 * r, 2 * r, 0)),\n", + ")\n", + "box_z2 = sim.add_flux(\n", + " frq_cen,\n", + " dfrq,\n", + " nfrq,\n", + " mp.FluxRegion(center=mp.Vector3(z=+r), size=mp.Vector3(2 * r, 2 * r, 0)),\n", + ")\n", "\n", "sim.load_minus_flux_data(box_x1, box_x1_data)\n", "sim.load_minus_flux_data(box_x2, box_x2_data)\n", @@ -117,11 +184,20 @@ "box_z1_flux = mp.get_fluxes(box_z1)\n", "box_z2_flux = mp.get_fluxes(box_z2)\n", "\n", - "scatt_flux = np.asarray(box_x1_flux)-np.asarray(box_x2_flux)+np.asarray(box_y1_flux)-np.asarray(box_y2_flux)+np.asarray(box_z1_flux)-np.asarray(box_z2_flux)\n", - "intensity = np.asarray(box_x1_flux0)/(2*r)**2\n", - "scatt_cross_section = np.divide(scatt_flux,intensity)\n", - "scatt_eff_meep = scatt_cross_section*-1/(np.pi*r**2)\n", - "scatt_eff_theory = [ps.MieQ(n_sphere,1000/f,2*r*1000,asDict=True)['Qsca'] for f in freqs]" + "scatt_flux = (\n", + " np.asarray(box_x1_flux)\n", + " - np.asarray(box_x2_flux)\n", + " + np.asarray(box_y1_flux)\n", + " - np.asarray(box_y2_flux)\n", + " + np.asarray(box_z1_flux)\n", + " - np.asarray(box_z2_flux)\n", + ")\n", + "intensity = np.asarray(box_x1_flux0) / (2 * r) ** 2\n", + "scatt_cross_section = np.divide(scatt_flux, intensity)\n", + "scatt_eff_meep = scatt_cross_section * -1 / (np.pi * r**2)\n", + "scatt_eff_theory = [\n", + " ps.MieQ(n_sphere, 1000 / f, 2 * r * 1000, asDict=True)[\"Qsca\"] for f in freqs\n", + "]" ] }, { @@ -140,13 +216,13 @@ "outputs": [], "source": [ "plt.figure(dpi=150)\n", - "plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_eff_meep,'bo-',label='Meep')\n", - "plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_eff_theory,'ro-',label='theory')\n", - "plt.grid(True,which=\"both\",ls=\"-\")\n", - "plt.xlabel('(sphere circumference)/wavelength, 2πr/λ')\n", - "plt.ylabel('scattering efficiency, σ/πr$^{2}$')\n", - "plt.legend(loc='upper right')\n", - "plt.title('Mie Scattering of a Lossless Dielectric Sphere')\n", + "plt.loglog(2 * np.pi * r * np.asarray(freqs), scatt_eff_meep, \"bo-\", label=\"Meep\")\n", + "plt.loglog(2 * np.pi * r * np.asarray(freqs), scatt_eff_theory, \"ro-\", label=\"theory\")\n", + "plt.grid(True, which=\"both\", ls=\"-\")\n", + "plt.xlabel(\"(sphere circumference)/wavelength, 2πr/λ\")\n", + "plt.ylabel(\"scattering efficiency, σ/πr$^{2}$\")\n", + "plt.legend(loc=\"upper right\")\n", + "plt.title(\"Mie Scattering of a Lossless Dielectric Sphere\")\n", "plt.tight_layout()\n", "plt.show()" ] diff --git a/python/examples/mie_scattering.py b/python/examples/mie_scattering.py index 33a674a1e..3dc54cdca 100644 --- a/python/examples/mie_scattering.py +++ b/python/examples/mie_scattering.py @@ -5,48 +5,83 @@ r = 1.0 # radius of sphere -wvl_min = 2*np.pi*r/10 -wvl_max = 2*np.pi*r/2 +wvl_min = 2 * np.pi * r / 10 +wvl_max = 2 * np.pi * r / 2 -frq_min = 1/wvl_max -frq_max = 1/wvl_min -frq_cen = 0.5*(frq_min+frq_max) -dfrq = frq_max-frq_min +frq_min = 1 / wvl_max +frq_max = 1 / wvl_min +frq_cen = 0.5 * (frq_min + frq_max) +dfrq = frq_max - frq_min nfrq = 100 ## at least 8 pixels per smallest wavelength, i.e. np.floor(8/wvl_min) resolution = 25 -dpml = 0.5*wvl_max -dair = 0.5*wvl_max +dpml = 0.5 * wvl_max +dair = 0.5 * wvl_max pml_layers = [mp.PML(thickness=dpml)] -symmetries = [mp.Mirror(mp.Y), - mp.Mirror(mp.Z,phase=-1)] +symmetries = [mp.Mirror(mp.Y), mp.Mirror(mp.Z, phase=-1)] -s = 2*(dpml+dair+r) -cell_size = mp.Vector3(s,s,s) +s = 2 * (dpml + dair + r) +cell_size = mp.Vector3(s, s, s) # is_integrated=True necessary for any planewave source extending into PML -sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True), - center=mp.Vector3(-0.5*s+dpml), - size=mp.Vector3(0,s,s), - component=mp.Ez)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=mp.Vector3(), - symmetries=symmetries) - -box_x1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r,2*r))) -box_x2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r,2*r))) -box_y1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0,2*r))) -box_y2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0,2*r))) -box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=-r),size=mp.Vector3(2*r,2*r,0))) -box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=+r),size=mp.Vector3(2*r,2*r,0))) +sources = [ + mp.Source( + mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True), + center=mp.Vector3(-0.5 * s + dpml), + size=mp.Vector3(0, s, s), + component=mp.Ez, + ) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=mp.Vector3(), + symmetries=symmetries, +) + +box_x1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r, 2 * r)), +) +box_x2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r, 2 * r)), +) +box_y1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0, 2 * r)), +) +box_y2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0, 2 * r)), +) +box_z1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(z=-r), size=mp.Vector3(2 * r, 2 * r, 0)), +) +box_z2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(z=+r), size=mp.Vector3(2 * r, 2 * r, 0)), +) sim.run(until_after_sources=10) @@ -63,24 +98,56 @@ sim.reset_meep() n_sphere = 2.0 -geometry = [mp.Sphere(material=mp.Medium(index=n_sphere), - center=mp.Vector3(), - radius=r)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=mp.Vector3(), - symmetries=symmetries, - geometry=geometry) - -box_x1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r,2*r))) -box_x2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r,2*r))) -box_y1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0,2*r))) -box_y2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0,2*r))) -box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=-r),size=mp.Vector3(2*r,2*r,0))) -box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=+r),size=mp.Vector3(2*r,2*r,0))) +geometry = [ + mp.Sphere(material=mp.Medium(index=n_sphere), center=mp.Vector3(), radius=r) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=mp.Vector3(), + symmetries=symmetries, + geometry=geometry, +) + +box_x1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r, 2 * r)), +) +box_x2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r, 2 * r)), +) +box_y1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0, 2 * r)), +) +box_y2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0, 2 * r)), +) +box_z1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(z=-r), size=mp.Vector3(2 * r, 2 * r, 0)), +) +box_z2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(z=+r), size=mp.Vector3(2 * r, 2 * r, 0)), +) sim.load_minus_flux_data(box_x1, box_x1_data) sim.load_minus_flux_data(box_x2, box_x2_data) @@ -98,20 +165,31 @@ box_z1_flux = mp.get_fluxes(box_z1) box_z2_flux = mp.get_fluxes(box_z2) -scatt_flux = np.asarray(box_x1_flux)-np.asarray(box_x2_flux)+np.asarray(box_y1_flux)-np.asarray(box_y2_flux)+np.asarray(box_z1_flux)-np.asarray(box_z2_flux) -intensity = np.asarray(box_x1_flux0)/(2*r)**2 -scatt_cross_section = np.divide(scatt_flux,intensity) -scatt_eff_meep = scatt_cross_section*-1/(np.pi*r**2) -scatt_eff_theory = [ps.MieQ(n_sphere,1000/f,2*r*1000,asDict=True)['Qsca'] for f in freqs] +scatt_flux = ( + np.asarray(box_x1_flux) + - np.asarray(box_x2_flux) + + np.asarray(box_y1_flux) + - np.asarray(box_y2_flux) + + np.asarray(box_z1_flux) + - np.asarray(box_z2_flux) +) +intensity = np.asarray(box_x1_flux0) / (2 * r) ** 2 +scatt_cross_section = np.divide(scatt_flux, intensity) +scatt_eff_meep = scatt_cross_section * -1 / (np.pi * r**2) +scatt_eff_theory = [ + ps.MieQ(n_sphere, 1000 / f, 2 * r * 1000, asDict=True)["Qsca"] for f in freqs +] if mp.am_master(): plt.figure(dpi=150) - plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_eff_meep,'bo-',label='Meep') - plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_eff_theory,'ro-',label='theory') - plt.grid(True,which="both",ls="-") - plt.xlabel('(sphere circumference)/wavelength, 2πr/λ') - plt.ylabel('scattering efficiency, σ/πr$^{2}$') - plt.legend(loc='upper right') - plt.title('Mie Scattering of a Lossless Dielectric Sphere') + plt.loglog(2 * np.pi * r * np.asarray(freqs), scatt_eff_meep, "bo-", label="Meep") + plt.loglog( + 2 * np.pi * r * np.asarray(freqs), scatt_eff_theory, "ro-", label="theory" + ) + plt.grid(True, which="both", ls="-") + plt.xlabel("(sphere circumference)/wavelength, 2πr/λ") + plt.ylabel("scattering efficiency, σ/πr$^{2}$") + plt.legend(loc="upper right") + plt.title("Mie Scattering of a Lossless Dielectric Sphere") plt.tight_layout() plt.savefig("mie_scattering.png") diff --git a/python/examples/mode-decomposition.ipynb b/python/examples/mode-decomposition.ipynb index 3c97872ea..ee0a2f252 100644 --- a/python/examples/mode-decomposition.ipynb +++ b/python/examples/mode-decomposition.ipynb @@ -396,28 +396,27 @@ "import meep as mp\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 25 # pixels/μm\n", + "resolution = 25 # pixels/μm\n", "\n", - "w1 = 1.0 # width of waveguide 1\n", - "w2 = 2.0 # width of waveguide 2\n", - "Lw = 10.0 # length of waveguides 1 and 2\n", + "w1 = 1.0 # width of waveguide 1\n", + "w2 = 2.0 # width of waveguide 2\n", + "Lw = 10.0 # length of waveguides 1 and 2\n", "\n", "# lengths of waveguide taper\n", "Lts = [2**m for m in range(4)]\n", "\n", - "dair = 3.0 # length of air region\n", - "dpml_x = 6.0 # length of PML in x direction\n", - "dpml_y = 2.0 # length of PML in y direction\n", + "dair = 3.0 # length of air region\n", + "dpml_x = 6.0 # length of PML in x direction\n", + "dpml_y = 2.0 # length of PML in y direction\n", "\n", - "sy = dpml_y+dair+w2+dair+dpml_y\n", + "sy = dpml_y + dair + w2 + dair + dpml_y\n", "\n", "Si = mp.Medium(epsilon=12.0)\n", "\n", - "boundary_layers = [mp.PML(dpml_x,direction=mp.X),\n", - " mp.PML(dpml_y,direction=mp.Y)]\n", + "boundary_layers = [mp.PML(dpml_x, direction=mp.X), mp.PML(dpml_y, direction=mp.Y)]\n", "\n", - "lcen = 6.67 # mode wavelength\n", - "fcen = 1/lcen # mode frequency\n", + "lcen = 6.67 # mode wavelength\n", + "fcen = 1 / lcen # mode frequency\n", "\n", "symmetries = [mp.Mirror(mp.Y)]\n", "\n", @@ -425,35 +424,45 @@ "R_flux = []\n", "\n", "for Lt in Lts:\n", - " sx = dpml_x+Lw+Lt+Lw+dpml_x\n", - " cell_size = mp.Vector3(sx,sy,0)\n", + " sx = dpml_x + Lw + Lt + Lw + dpml_x\n", + " cell_size = mp.Vector3(sx, sy, 0)\n", "\n", - " src_pt = mp.Vector3(-0.5*sx+dpml_x+0.2*Lw)\n", - " sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=0.2*fcen),\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy-2*dpml_y),\n", - " eig_match_freq=True,\n", - " eig_parity=mp.ODD_Z+mp.EVEN_Y)]\n", + " src_pt = mp.Vector3(-0.5 * sx + dpml_x + 0.2 * Lw)\n", + " sources = [\n", + " mp.EigenModeSource(\n", + " src=mp.GaussianSource(fcen, fwidth=0.2 * fcen),\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy - 2 * dpml_y),\n", + " eig_match_freq=True,\n", + " eig_parity=mp.ODD_Z + mp.EVEN_Y,\n", + " )\n", + " ]\n", "\n", " # straight waveguide\n", - " vertices = [mp.Vector3(-0.5*sx-1,0.5*w1),\n", - " mp.Vector3(0.5*sx+1,0.5*w1),\n", - " mp.Vector3(0.5*sx+1,-0.5*w1),\n", - " mp.Vector3(-0.5*sx-1,-0.5*w1)]\n", + " vertices = [\n", + " mp.Vector3(-0.5 * sx - 1, 0.5 * w1),\n", + " mp.Vector3(0.5 * sx + 1, 0.5 * w1),\n", + " mp.Vector3(0.5 * sx + 1, -0.5 * w1),\n", + " mp.Vector3(-0.5 * sx - 1, -0.5 * w1),\n", + " ]\n", "\n", - " sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=boundary_layers,\n", - " geometry=[mp.Prism(vertices,height=mp.inf,material=Si)],\n", - " sources=sources,\n", - " symmetries=symmetries)\n", + " sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=boundary_layers,\n", + " geometry=[mp.Prism(vertices, height=mp.inf, material=Si)],\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + " )\n", "\n", - " mon_pt = mp.Vector3(-0.5*sx+dpml_x+0.7*Lw)\n", - " flux = sim.add_flux(fcen,0,1,mp.FluxRegion(center=mon_pt,size=mp.Vector3(y=sy-2*dpml_y)))\n", + " mon_pt = mp.Vector3(-0.5 * sx + dpml_x + 0.7 * Lw)\n", + " flux = sim.add_flux(\n", + " fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy - 2 * dpml_y))\n", + " )\n", "\n", - " sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mon_pt,1e-9))\n", + " sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))\n", "\n", - " res = sim.get_eigenmode_coefficients(flux,[1],eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", + " res = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y)\n", " incident_coeffs = res.alpha\n", " incident_flux = mp.get_fluxes(flux)\n", " incident_flux_data = sim.get_flux_data(flux)\n", @@ -461,34 +470,42 @@ " sim.reset_meep()\n", "\n", " # linear taper\n", - " vertices = [mp.Vector3(-0.5*sx-1,0.5*w1),\n", - " mp.Vector3(-0.5*Lt,0.5*w1),\n", - " mp.Vector3(0.5*Lt,0.5*w2),\n", - " mp.Vector3(0.5*sx+1,0.5*w2),\n", - " mp.Vector3(0.5*sx+1,-0.5*w2),\n", - " mp.Vector3(0.5*Lt,-0.5*w2),\n", - " mp.Vector3(-0.5*Lt,-0.5*w1),\n", - " mp.Vector3(-0.5*sx-1,-0.5*w1)]\n", + " vertices = [\n", + " mp.Vector3(-0.5 * sx - 1, 0.5 * w1),\n", + " mp.Vector3(-0.5 * Lt, 0.5 * w1),\n", + " mp.Vector3(0.5 * Lt, 0.5 * w2),\n", + " mp.Vector3(0.5 * sx + 1, 0.5 * w2),\n", + " mp.Vector3(0.5 * sx + 1, -0.5 * w2),\n", + " mp.Vector3(0.5 * Lt, -0.5 * w2),\n", + " mp.Vector3(-0.5 * Lt, -0.5 * w1),\n", + " mp.Vector3(-0.5 * sx - 1, -0.5 * w1),\n", + " ]\n", "\n", - " sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=boundary_layers,\n", - " geometry=[mp.Prism(vertices,height=mp.inf,material=Si)],\n", - " sources=sources,\n", - " symmetries=symmetries)\n", + " sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=boundary_layers,\n", + " geometry=[mp.Prism(vertices, height=mp.inf, material=Si)],\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + " )\n", "\n", - " flux = sim.add_flux(fcen,0,1,mp.FluxRegion(center=mon_pt,size=mp.Vector3(y=sy-2*dpml_y)))\n", - " sim.load_minus_flux_data(flux,incident_flux_data)\n", + " flux = sim.add_flux(\n", + " fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy - 2 * dpml_y))\n", + " )\n", + " sim.load_minus_flux_data(flux, incident_flux_data)\n", "\n", - " sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mon_pt,1e-9))\n", + " sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))\n", "\n", - " res2 = sim.get_eigenmode_coefficients(flux,[1],eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", + " res2 = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y)\n", " taper_coeffs = res2.alpha\n", " taper_flux = mp.get_fluxes(flux)\n", "\n", - " R_coeffs.append(abs(taper_coeffs[0,0,1])**2/abs(incident_coeffs[0,0,0])**2)\n", - " R_flux.append(-taper_flux[0]/incident_flux[0])\n", - " print(\"refl:, {}, {:.8f}, {:.8f}\".format(Lt,R_coeffs[-1],R_flux[-1]))" + " R_coeffs.append(\n", + " abs(taper_coeffs[0, 0, 1]) ** 2 / abs(incident_coeffs[0, 0, 0]) ** 2\n", + " )\n", + " R_flux.append(-taper_flux[0] / incident_flux[0])\n", + " print(\"refl:, {}, {:.8f}, {:.8f}\".format(Lt, R_coeffs[-1], R_flux[-1]))" ] }, { @@ -518,12 +535,14 @@ ], "source": [ "plt.figure(dpi=200)\n", - "plt.loglog(Lts,R_coeffs,'bo-',label='mode decomposition')\n", - "plt.loglog(Lts,R_flux,'ro-',label='Poynting flux')\n", - "plt.loglog(Lts,[0.005/Lt**2 for Lt in Lts],'k-',label=r'quadratic reference (1/Lt$^2$)')\n", - "plt.legend(loc='upper right')\n", - "plt.xlabel('taper length Lt (μm)')\n", - "plt.ylabel('reflectance')\n", + "plt.loglog(Lts, R_coeffs, \"bo-\", label=\"mode decomposition\")\n", + "plt.loglog(Lts, R_flux, \"ro-\", label=\"Poynting flux\")\n", + "plt.loglog(\n", + " Lts, [0.005 / Lt**2 for Lt in Lts], \"k-\", label=r\"quadratic reference (1/Lt$^2$)\"\n", + ")\n", + "plt.legend(loc=\"upper right\")\n", + "plt.xlabel(\"taper length Lt (μm)\")\n", + "plt.ylabel(\"reflectance\")\n", "plt.show()" ] }, diff --git a/python/examples/mode-decomposition.py b/python/examples/mode-decomposition.py index 6663c68fd..60492ec58 100644 --- a/python/examples/mode-decomposition.py +++ b/python/examples/mode-decomposition.py @@ -3,28 +3,27 @@ import meep as mp import matplotlib.pyplot as plt -resolution = 25 # pixels/μm +resolution = 25 # pixels/μm -w1 = 1.0 # width of waveguide 1 -w2 = 2.0 # width of waveguide 2 -Lw = 10.0 # length of waveguides 1 and 2 +w1 = 1.0 # width of waveguide 1 +w2 = 2.0 # width of waveguide 2 +Lw = 10.0 # length of waveguides 1 and 2 # lengths of waveguide taper Lts = [2**m for m in range(4)] -dair = 3.0 # length of air region -dpml_x = 6.0 # length of PML in x direction -dpml_y = 2.0 # length of PML in y direction +dair = 3.0 # length of air region +dpml_x = 6.0 # length of PML in x direction +dpml_y = 2.0 # length of PML in y direction -sy = dpml_y+dair+w2+dair+dpml_y +sy = dpml_y + dair + w2 + dair + dpml_y Si = mp.Medium(epsilon=12.0) -boundary_layers = [mp.PML(dpml_x,direction=mp.X), - mp.PML(dpml_y,direction=mp.Y)] +boundary_layers = [mp.PML(dpml_x, direction=mp.X), mp.PML(dpml_y, direction=mp.Y)] -lcen = 6.67 # mode wavelength -fcen = 1/lcen # mode frequency +lcen = 6.67 # mode wavelength +fcen = 1 / lcen # mode frequency symmetries = [mp.Mirror(mp.Y)] @@ -32,35 +31,45 @@ R_flux = [] for Lt in Lts: - sx = dpml_x+Lw+Lt+Lw+dpml_x - cell_size = mp.Vector3(sx,sy,0) - - src_pt = mp.Vector3(-0.5*sx+dpml_x+0.2*Lw) - sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - center=src_pt, - size=mp.Vector3(y=sy-2*dpml_y), - eig_match_freq=True, - eig_parity=mp.ODD_Z+mp.EVEN_Y)] + sx = dpml_x + Lw + Lt + Lw + dpml_x + cell_size = mp.Vector3(sx, sy, 0) + + src_pt = mp.Vector3(-0.5 * sx + dpml_x + 0.2 * Lw) + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=src_pt, + size=mp.Vector3(y=sy - 2 * dpml_y), + eig_match_freq=True, + eig_parity=mp.ODD_Z + mp.EVEN_Y, + ) + ] # straight waveguide - vertices = [mp.Vector3(-0.5*sx-1,0.5*w1), - mp.Vector3(0.5*sx+1,0.5*w1), - mp.Vector3(0.5*sx+1,-0.5*w1), - mp.Vector3(-0.5*sx-1,-0.5*w1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - geometry=[mp.Prism(vertices,height=mp.inf,material=Si)], - sources=sources, - symmetries=symmetries) - - mon_pt = mp.Vector3(-0.5*sx+dpml_x+0.7*Lw) - flux = sim.add_flux(fcen,0,1,mp.FluxRegion(center=mon_pt,size=mp.Vector3(y=sy-2*dpml_y))) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mon_pt,1e-9)) - - res = sim.get_eigenmode_coefficients(flux,[1],eig_parity=mp.ODD_Z+mp.EVEN_Y) + vertices = [ + mp.Vector3(-0.5 * sx - 1, 0.5 * w1), + mp.Vector3(0.5 * sx + 1, 0.5 * w1), + mp.Vector3(0.5 * sx + 1, -0.5 * w1), + mp.Vector3(-0.5 * sx - 1, -0.5 * w1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], + sources=sources, + symmetries=symmetries, + ) + + mon_pt = mp.Vector3(-0.5 * sx + dpml_x + 0.7 * Lw) + flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy - 2 * dpml_y)) + ) + + sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9)) + + res = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y) incident_coeffs = res.alpha incident_flux = mp.get_fluxes(flux) incident_flux_data = sim.get_flux_data(flux) @@ -68,42 +77,55 @@ sim.reset_meep() # linear taper - vertices = [mp.Vector3(-0.5*sx-1,0.5*w1), - mp.Vector3(-0.5*Lt,0.5*w1), - mp.Vector3(0.5*Lt,0.5*w2), - mp.Vector3(0.5*sx+1,0.5*w2), - mp.Vector3(0.5*sx+1,-0.5*w2), - mp.Vector3(0.5*Lt,-0.5*w2), - mp.Vector3(-0.5*Lt,-0.5*w1), - mp.Vector3(-0.5*sx-1,-0.5*w1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - geometry=[mp.Prism(vertices,height=mp.inf,material=Si)], - sources=sources, - symmetries=symmetries) - - flux = sim.add_flux(fcen,0,1,mp.FluxRegion(center=mon_pt,size=mp.Vector3(y=sy-2*dpml_y))) - sim.load_minus_flux_data(flux,incident_flux_data) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mon_pt,1e-9)) - - res2 = sim.get_eigenmode_coefficients(flux,[1],eig_parity=mp.ODD_Z+mp.EVEN_Y) + vertices = [ + mp.Vector3(-0.5 * sx - 1, 0.5 * w1), + mp.Vector3(-0.5 * Lt, 0.5 * w1), + mp.Vector3(0.5 * Lt, 0.5 * w2), + mp.Vector3(0.5 * sx + 1, 0.5 * w2), + mp.Vector3(0.5 * sx + 1, -0.5 * w2), + mp.Vector3(0.5 * Lt, -0.5 * w2), + mp.Vector3(-0.5 * Lt, -0.5 * w1), + mp.Vector3(-0.5 * sx - 1, -0.5 * w1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], + sources=sources, + symmetries=symmetries, + ) + + flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy - 2 * dpml_y)) + ) + sim.load_minus_flux_data(flux, incident_flux_data) + + sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9)) + + res2 = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y) taper_coeffs = res2.alpha taper_flux = mp.get_fluxes(flux) - R_coeffs.append(abs(taper_coeffs[0,0,1])**2/abs(incident_coeffs[0,0,0])**2) - R_flux.append(-taper_flux[0]/incident_flux[0]) - print("refl:, {}, {:.8f}, {:.8f}".format(Lt,R_coeffs[-1],R_flux[-1])) + R_coeffs.append( + abs(taper_coeffs[0, 0, 1]) ** 2 / abs(incident_coeffs[0, 0, 0]) ** 2 + ) + R_flux.append(-taper_flux[0] / incident_flux[0]) + print("refl:, {}, {:.8f}, {:.8f}".format(Lt, R_coeffs[-1], R_flux[-1])) if mp.am_master(): plt.figure() - plt.loglog(Lts,R_coeffs,'bo-',label='mode decomposition') - plt.loglog(Lts,R_flux,'ro-',label='Poynting flux') - plt.loglog(Lts,[0.005/Lt**2 for Lt in Lts],'k-',label=r'quadratic reference (1/Lt$^2$)') - plt.legend(loc='upper right') - plt.xlabel('taper length Lt (μm)') - plt.ylabel('reflectance') + plt.loglog(Lts, R_coeffs, "bo-", label="mode decomposition") + plt.loglog(Lts, R_flux, "ro-", label="Poynting flux") + plt.loglog( + Lts, + [0.005 / Lt**2 for Lt in Lts], + "k-", + label=r"quadratic reference (1/Lt$^2$)", + ) + plt.legend(loc="upper right") + plt.xlabel("taper length Lt (μm)") + plt.ylabel("reflectance") plt.show() diff --git a/python/examples/mpb_bragg.py b/python/examples/mpb_bragg.py index 8cfd8bf76..d8c503950 100644 --- a/python/examples/mpb_bragg.py +++ b/python/examples/mpb_bragg.py @@ -12,8 +12,13 @@ geometry_lattice = mp.Lattice(size=mp.Vector3(1)) # 1d cell default_material = mp.Medium(index=n_lo) -geometry = mp.Cylinder(material=mp.Medium(index=n_hi), center=mp.Vector3(), axis=mp.Vector3(1), - radius=mp.inf, height=w_hi) +geometry = mp.Cylinder( + material=mp.Medium(index=n_hi), + center=mp.Vector3(), + axis=mp.Vector3(1), + radius=mp.inf, + height=w_hi, +) kx = 0.5 k_points = [mp.Vector3(kx)] @@ -27,12 +32,15 @@ geometry_lattice=geometry_lattice, geometry=[geometry], resolution=resolution, - default_material=default_material + default_material=default_material, ) def main(): - ms.run_tm(mpb.output_hfield_y) # note that TM and TE bands are degenerate, so we only need TM + ms.run_tm( + mpb.output_hfield_y + ) # note that TM and TE bands are degenerate, so we only need TM + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/mpb_bragg_sine.py b/python/examples/mpb_bragg_sine.py index e0a52098b..606b7a7a4 100644 --- a/python/examples/mpb_bragg_sine.py +++ b/python/examples/mpb_bragg_sine.py @@ -16,8 +16,11 @@ # * (1 + cos(2*pi*x)) # This is periodic, and also has inversion symmetry. def eps_func(p): - return mp.Medium(index=index_min + 0.5 * (index_max - index_min) * - (1 + math.cos(2 * math.pi * p.x))) + return mp.Medium( + index=index_min + + 0.5 * (index_max - index_min) * (1 + math.cos(2 * math.pi * p.x)) + ) + geometry_lattice = mp.Lattice(size=mp.Vector3(1)) # 1d cell @@ -34,7 +37,7 @@ def eps_func(p): k_points=k_points, geometry_lattice=geometry_lattice, resolution=resolution, - default_material=default_material + default_material=default_material, ) @@ -42,5 +45,6 @@ def main(): # the TM and TE bands are degenerate, so we only need TM: ms.run_tm() -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_data_analysis.py b/python/examples/mpb_data_analysis.py index 4a338aab5..8893f6133 100644 --- a/python/examples/mpb_data_analysis.py +++ b/python/examples/mpb_data_analysis.py @@ -21,8 +21,11 @@ def tri_rods(): def get_efields(tr_ms, band): efields.append(tr_ms.get_efield(band)) - tr_ms.run_tm(mpb.output_at_kpoint(mp.Vector3(1 / -3, 1 / 3), mpb.fix_efield_phase, - get_efields)) + tr_ms.run_tm( + mpb.output_at_kpoint( + mp.Vector3(1 / -3, 1 / 3), mpb.fix_efield_phase, get_efields + ) + ) # Create an MPBData instance to transform the efields md = mpb.MPBData(rectify=True, resolution=32, periods=3) @@ -36,21 +39,21 @@ def get_efields(tr_ms, band): tr_ms.run_te() eps = tr_ms.get_epsilon() - plt.imshow(eps.T, interpolation='spline36', cmap='binary') - plt.axis('off') + plt.imshow(eps.T, interpolation="spline36", cmap="binary") + plt.axis("off") plt.show() md = mpb.MPBData(rectify=True, resolution=32, periods=3) rectangular_data = md.convert(eps) - plt.imshow(rectangular_data.T, interpolation='spline36', cmap='binary') - plt.axis('off') + plt.imshow(rectangular_data.T, interpolation="spline36", cmap="binary") + plt.axis("off") plt.show() for i, f in enumerate(converted): plt.subplot(331 + i) - plt.contour(rectangular_data.T, cmap='binary') - plt.imshow(np.real(f).T, interpolation='spline36', cmap='RdBu', alpha=0.9) - plt.axis('off') + plt.contour(rectangular_data.T, cmap="binary") + plt.imshow(np.real(f).T, interpolation="spline36", cmap="RdBu", alpha=0.9) + plt.axis("off") plt.show() @@ -72,6 +75,6 @@ def get_dpwr(ms, band): # TODO: Plot -if __name__ == '__main__': +if __name__ == "__main__": tri_rods() diamond() diff --git a/python/examples/mpb_diamond.py b/python/examples/mpb_diamond.py index b97fbdf50..818f6141f 100644 --- a/python/examples/mpb_diamond.py +++ b/python/examples/mpb_diamond.py @@ -10,19 +10,19 @@ basis_size=mp.Vector3(sqrt_half, sqrt_half, sqrt_half), basis1=mp.Vector3(0, 1, 1), basis2=mp.Vector3(1, 0, 1), - basis3=mp.Vector3(1, 1) + basis3=mp.Vector3(1, 1), ) # Corners of the irreducible Brillouin zone for the fcc lattice, # in a canonical order: vlist = [ - mp.Vector3(0, 0.5, 0.5), # X - mp.Vector3(0, 0.625, 0.375), # U - mp.Vector3(0, 0.5, 0), # L - mp.Vector3(0, 0, 0), # Gamma - mp.Vector3(0, 0.5, 0.5), # X - mp.Vector3(0.25, 0.75, 0.5), # W - mp.Vector3(0.375, 0.75, 0.375) # K + mp.Vector3(0, 0.5, 0.5), # X + mp.Vector3(0, 0.625, 0.375), # U + mp.Vector3(0, 0.5, 0), # L + mp.Vector3(0, 0, 0), # Gamma + mp.Vector3(0, 0.5, 0.5), # X + mp.Vector3(0.25, 0.75, 0.5), # W + mp.Vector3(0.375, 0.75, 0.375), # K ] k_points = mp.interpolate(4, vlist) @@ -34,8 +34,10 @@ diel = mp.Medium(epsilon=eps) # A diamond lattice has two "atoms" per unit cell: -geometry = [mp.Sphere(r, center=mp.Vector3(0.125, 0.125, 0.125), material=diel), - mp.Sphere(r, center=mp.Vector3(-0.125, -0.125, -0.125), material=diel)] +geometry = [ + mp.Sphere(r, center=mp.Vector3(0.125, 0.125, 0.125), material=diel), + mp.Sphere(r, center=mp.Vector3(-0.125, -0.125, -0.125), material=diel), +] # (A simple fcc lattice would have only one sphere/object at the origin.) @@ -49,7 +51,7 @@ geometry=geometry, resolution=resolution, num_bands=num_bands, - mesh_size=mesh_size + mesh_size=mesh_size, ) @@ -57,5 +59,6 @@ def main(): # run calculation, outputting electric_field energy density at the U point: ms.run(mpb.output_at_kpoint(mp.Vector3(0, 0.625, 0.375), mpb.output_dpwr)) -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_hole_slab.py b/python/examples/mpb_hole_slab.py index 385e8d41a..88ba31528 100644 --- a/python/examples/mpb_hole_slab.py +++ b/python/examples/mpb_hole_slab.py @@ -23,15 +23,20 @@ supercell_h = 4 # height of the supercell # triangular lattice with vertical supercell: -geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1, supercell_h), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 1, supercell_h), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) geometry = [ - mp.Block(material=mp.Medium(epsilon=loweps), center=mp.Vector3(z=0.25 * supercell_h), - size=mp.Vector3(mp.inf, mp.inf, 0.5 * supercell_h)), + mp.Block( + material=mp.Medium(epsilon=loweps), + center=mp.Vector3(z=0.25 * supercell_h), + size=mp.Vector3(mp.inf, mp.inf, 0.5 * supercell_h), + ), mp.Block(material=mp.Medium(epsilon=eps), size=mp.Vector3(mp.inf, mp.inf, h)), - mp.Cylinder(r, material=mp.air, height=supercell_h) + mp.Cylinder(r, material=mp.air, height=supercell_h), ] # 1st Brillouin zone of a triangular lattice: @@ -40,7 +45,7 @@ K = mp.Vector3(1 / -3, 1 / 3) only_K = False # run with only_K=true to only do this k_point -k_interp = 4 # the number of k points to interpolate +k_interp = 4 # the number of k points to interpolate k_points = [K] if only_K else mp.interpolate(k_interp, [Gamma, M, K, Gamma]) resolution = mp.Vector3(32, 32, 16) num_bands = 9 @@ -50,7 +55,7 @@ geometry=geometry, resolution=resolution, num_bands=num_bands, - k_points=k_points + k_points=k_points, ) @@ -65,5 +70,6 @@ def main(): ms.display_eigensolver_stats() -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_honey_rods.py b/python/examples/mpb_honey_rods.py index b24c6cc28..445642d6e 100644 --- a/python/examples/mpb_honey_rods.py +++ b/python/examples/mpb_honey_rods.py @@ -13,23 +13,35 @@ eps = 12 # the rod dielectric constant # triangular lattice: -geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 1), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) # Two rods per unit cell, at the correct positions to form a honeycomb # lattice, and arranged to have inversion symmetry: -geometry = [mp.Cylinder(r, center=mp.Vector3(1 / 6, 1 / 6), height=mp.inf, - material=mp.Medium(epsilon=eps)), - mp.Cylinder(r, center=mp.Vector3(1 / -6, 1 / -6), height=mp.inf, - material=mp.Medium(epsilon=eps))] +geometry = [ + mp.Cylinder( + r, + center=mp.Vector3(1 / 6, 1 / 6), + height=mp.inf, + material=mp.Medium(epsilon=eps), + ), + mp.Cylinder( + r, + center=mp.Vector3(1 / -6, 1 / -6), + height=mp.inf, + material=mp.Medium(epsilon=eps), + ), +] # The k_points list, for the Brillouin zone of a triangular lattice: k_points = [ - mp.Vector3(), # Gamma - mp.Vector3(y=0.5), # M + mp.Vector3(), # Gamma + mp.Vector3(y=0.5), # M mp.Vector3(1 / -3, 1 / 3), # K - mp.Vector3() # Gamma + mp.Vector3(), # Gamma ] k_interp = 4 # number of k_points to interpolate @@ -43,7 +55,7 @@ geometry=geometry, k_points=k_points, resolution=resolution, - num_bands=num_bands + num_bands=num_bands, ) @@ -51,11 +63,12 @@ def main(): ms.run_tm() ms.run_te() + # Since there is a complete gap, we could instead see it just by using: # run() # The gap is between bands 12 and 13 in this case. (Note that there is # a false gap between bands 2 and 3, which disappears as you increase the # k_point resolution.) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/mpb_line_defect.py b/python/examples/mpb_line_defect.py index fcbb69752..8799cb4c1 100644 --- a/python/examples/mpb_line_defect.py +++ b/python/examples/mpb_line_defect.py @@ -13,9 +13,11 @@ supercell_y = 7 # the (odd) number of lateral supercell periods -geometry_lattice = mp.Lattice(size=mp.Vector3(1, supercell_y), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +geometry_lattice = mp.Lattice( + size=mp.Vector3(1, supercell_y), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) eps = 12 # the dielectric constant of the rods r = 0.2 # the rod radius in the bulk crystal @@ -29,7 +31,9 @@ geometry += [mp.Cylinder(r, material=mp.air)] Gamma = mp.Vector3() -K_prime = mp.lattice_to_reciprocal(mp.Vector3(0.5), geometry_lattice) # edge of Brillouin zone. +K_prime = mp.lattice_to_reciprocal( + mp.Vector3(0.5), geometry_lattice +) # edge of Brillouin zone. k_points = mp.interpolate(4, [Gamma, K_prime]) # the bigger the supercell, the more bands you need to compute to get @@ -44,7 +48,7 @@ geometry=geometry, k_points=k_points, num_bands=num_bands, - resolution=resolution + resolution=resolution, ) @@ -53,8 +57,12 @@ def main(): # band. (In general, the guided mode in such an air defect may have # exited the gap by the time it reaches the edge of the Brillouin # zone at K_prime.) - ms.run_tm(mpb.output_at_kpoint(k_points[len(k_points) // 2]), ms.fix_efield_phase, - mpb.output_efield_z) + ms.run_tm( + mpb.output_at_kpoint(k_points[len(k_points) // 2]), + ms.fix_efield_phase, + mpb.output_efield_z, + ) + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/mpb_sq_rods.py b/python/examples/mpb_sq_rods.py index d78c2fd8e..78ee0b3e0 100644 --- a/python/examples/mpb_sq_rods.py +++ b/python/examples/mpb_sq_rods.py @@ -32,7 +32,7 @@ geometry=geometry, k_points=k_points, resolution=resolution, - num_bands=num_bands + num_bands=num_bands, ) @@ -41,9 +41,12 @@ def main(): t0 = time.time() ms.run_te() ms.run_tm() - print("total time for both TE and TM bands: {:.2f} seconds".format(time.time() - t0)) + print( + "total time for both TE and TM bands: {:.2f} seconds".format(time.time() - t0) + ) ms.display_eigensolver_stats() -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_strip.py b/python/examples/mpb_strip.py index fd918fdcd..f14ead8b4 100644 --- a/python/examples/mpb_strip.py +++ b/python/examples/mpb_strip.py @@ -28,9 +28,14 @@ geometry_lattice = mp.Lattice(size=mp.Vector3(0, sc_y, sc_z)) # define the 2d blocks for the strip and substrate -geometry = [mp.Block(size=mp.Vector3(mp.inf, mp.inf, 0.5 * (sc_z - h)), - center=mp.Vector3(z=0.25 * (sc_z + h)), material=SiO2), - mp.Block(size=mp.Vector3(mp.inf, w, h), material=Si)] +geometry = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, 0.5 * (sc_z - h)), + center=mp.Vector3(z=0.25 * (sc_z + h)), + material=SiO2, + ), + mp.Block(size=mp.Vector3(mp.inf, w, h), material=Si), +] # The k (i.e. beta, i.e. propagation constant) points to look at, in # units of 2*pi/um. We'll look at num_k points from k_min to k_max. @@ -46,7 +51,7 @@ # is the corresponding column in the output if you grep for "freqs:".) num_bands = 4 -filename_prefix = 'strip-' # use this prefix for output files +filename_prefix = "strip-" # use this prefix for output files ms = mpb.ModeSolver( geometry_lattice=geometry_lattice, @@ -54,7 +59,7 @@ k_points=k_points, resolution=resolution, num_bands=num_bands, - filename_prefix=filename_prefix + filename_prefix=filename_prefix, ) @@ -75,9 +80,21 @@ def main(): omega = 1 / 1.55 # frequency corresponding to 1.55um # Output the x component of the Poynting vector for num_bands bands at omega - ms.find_k(mp.NO_PARITY, omega, 1, num_bands, mp.Vector3(1), 1e-3, omega * 3.45, - omega * 0.1, omega * 4, mpb.output_poynting_x, mpb.display_yparities, - mpb.display_group_velocities) - -if __name__ == '__main__': + ms.find_k( + mp.NO_PARITY, + omega, + 1, + num_bands, + mp.Vector3(1), + 1e-3, + omega * 3.45, + omega * 0.1, + omega * 4, + mpb.output_poynting_x, + mpb.display_yparities, + mpb.display_group_velocities, + ) + + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_tri_holes.py b/python/examples/mpb_tri_holes.py index 9c89b8bb8..113d0c7dd 100644 --- a/python/examples/mpb_tri_holes.py +++ b/python/examples/mpb_tri_holes.py @@ -11,17 +11,19 @@ # first, define the lattice vectors and k-points for a triangular lattice: -geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 1), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) kz = 0 # use non-zero kz to consider vertical propagation k_points = [ - mp.Vector3(z=kz), # Gamma - mp.Vector3(0, 0.5, kz), # M + mp.Vector3(z=kz), # Gamma + mp.Vector3(0, 0.5, kz), # M mp.Vector3(1 / -3, 1 / 3, kz), # K - mp.Vector3(z=kz) # Gamma + mp.Vector3(z=kz), # Gamma ] k_interp = 4 @@ -44,7 +46,7 @@ k_points=k_points, default_material=default_material, resolution=resolution, - num_bands=num_bands + num_bands=num_bands, ) @@ -55,5 +57,6 @@ def main(): else: ms.run() # if kz != 0 there are no purely te and tm bands -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_tri_rods.py b/python/examples/mpb_tri_rods.py index 0052cae88..f7d4a79db 100644 --- a/python/examples/mpb_tri_rods.py +++ b/python/examples/mpb_tri_rods.py @@ -10,17 +10,19 @@ num_bands = 8 -geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 1), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) geometry = [mp.Cylinder(0.2, material=mp.Medium(epsilon=12))] k_points = [ - mp.Vector3(), # Gamma - mp.Vector3(y=0.5), # M + mp.Vector3(), # Gamma + mp.Vector3(y=0.5), # M mp.Vector3(1 / -3, 1 / 3), # K - mp.Vector3(), # Gamma + mp.Vector3(), # Gamma ] k_points = mp.interpolate(4, k_points) @@ -32,14 +34,18 @@ geometry_lattice=geometry_lattice, k_points=k_points, resolution=resolution, - num_bands=num_bands + num_bands=num_bands, ) def main(): - ms.run_tm(mpb.output_at_kpoint(mp.Vector3(1 / -3, 1 / 3), mpb.fix_efield_phase, - mpb.output_efield_z)) + ms.run_tm( + mpb.output_at_kpoint( + mp.Vector3(1 / -3, 1 / 3), mpb.fix_efield_phase, mpb.output_efield_z + ) + ) ms.run_te() -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_tutorial.py b/python/examples/mpb_tutorial.py index bc3ceb99f..33734e400 100644 --- a/python/examples/mpb_tutorial.py +++ b/python/examples/mpb_tutorial.py @@ -11,26 +11,31 @@ def print_heading(h): stars = "*" * 10 print("{0} {1} {0}".format(stars, h)) + # Our First Band Structure print_heading("Square lattice of rods in air") num_bands = 8 -k_points = [mp.Vector3(), # Gamma - mp.Vector3(0.5), # X - mp.Vector3(0.5, 0.5), # M - mp.Vector3()] # Gamma +k_points = [ + mp.Vector3(), # Gamma + mp.Vector3(0.5), # X + mp.Vector3(0.5, 0.5), # M + mp.Vector3(), +] # Gamma k_points = mp.interpolate(4, k_points) geometry = [mp.Cylinder(0.2, material=mp.Medium(epsilon=12))] geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1)) resolution = 32 -ms = mpb.ModeSolver(num_bands=num_bands, - k_points=k_points, - geometry=geometry, - geometry_lattice=geometry_lattice, - resolution=resolution) +ms = mpb.ModeSolver( + num_bands=num_bands, + k_points=k_points, + geometry=geometry, + geometry_lattice=geometry_lattice, + resolution=resolution, +) print_heading("Square lattice of rods: TE bands") ms.run_te() @@ -48,21 +53,25 @@ def print_heading(h): print_heading("Triangular lattice of rods in air") -ms.geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +ms.geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 1), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) -ms.k_points = [mp.Vector3(), # Gamma - mp.Vector3(y=0.5), # M - mp.Vector3(-1 / 3, 1 / 3), # K - mp.Vector3()] # Gamma +ms.k_points = [ + mp.Vector3(), # Gamma + mp.Vector3(y=0.5), # M + mp.Vector3(-1 / 3, 1 / 3), # K + mp.Vector3(), +] # Gamma ms.k_points = mp.interpolate(4, k_points) ms.run_tm() # Maximizing the First TM Gap -print_heading('Maximizing the first TM gap') +print_heading("Maximizing the first TM gap") def first_tm_gap(r): @@ -70,10 +79,13 @@ def first_tm_gap(r): ms.run_tm() return -1 * ms.retrieve_gap(1) + ms.num_bands = 2 ms.mesh_size = 7 -result = minimize_scalar(first_tm_gap, method='bounded', bounds=[0.1, 0.5], options={'xatol': 0.1}) +result = minimize_scalar( + first_tm_gap, method="bounded", bounds=[0.1, 0.5], options={"xatol": 0.1} +) print(f"radius at maximum: {result.x}") print(f"gap size at maximum: {result.fun * -1}") @@ -81,7 +93,7 @@ def first_tm_gap(r): # A Complete 2D Gap with an Anisotropic Dielectric -print_heading('Anisotropic complete 2d gap') +print_heading("Anisotropic complete 2d gap") ms.geometry = [mp.Cylinder(0.3, material=mp.Medium(epsilon_diag=mp.Vector3(1, 1, 12)))] @@ -91,7 +103,7 @@ def first_tm_gap(r): # Finding a Point-defect State -print_heading('5x5 point defect') +print_heading("5x5 point defect") ms.geometry_lattice = mp.Lattice(size=mp.Vector3(5, 5)) ms.geometry = [mp.Cylinder(0.2, material=mp.Medium(epsilon=12))] @@ -112,7 +124,7 @@ def first_tm_gap(r): c = mp.Cylinder(1.0, material=mp.air) print(f"energy in cylinder: {ms.compute_energy_in_objects([c])}") -print_heading('5x5 point defect, targeted solver') +print_heading("5x5 point defect, targeted solver") ms.num_bands = 1 # only need to compute a single band, now! ms.target_freq = (0.2812 + 0.4174) / 2 @@ -121,7 +133,7 @@ def first_tm_gap(r): # Tuning the Point-defect Mode -print_heading('Tuning the 5x5 point defect') +print_heading("Tuning the 5x5 point defect") old_geometry = ms.geometry # save the 5x5 grid with a missing rod @@ -133,6 +145,7 @@ def rootfun(eps): print(f"epsilon = {eps} gives freq. = {ms.get_freqs()[0]}") return ms.get_freqs()[0] - 0.314159 # return 1st band freq. - 0.314159 + rooteps = ridder(rootfun, 1, 12) print(f"root (value of epsilon) is at: {rooteps}") diff --git a/python/examples/multilevel-atom.py b/python/examples/multilevel-atom.py index 87a6f231d..1a9aea6e2 100644 --- a/python/examples/multilevel-atom.py +++ b/python/examples/multilevel-atom.py @@ -7,10 +7,10 @@ # Cavity definitions resolution = 400 -ncav = 1.5 # cavity refractive index -Lcav = 1 # cavity length -dpad = 1 # padding thickness -dpml = 1 # PML thickness +ncav = 1.5 # cavity refractive index +Lcav = 1 # cavity length +dpad = 1 # padding thickness +dpml = 1 # PML thickness sz = Lcav + dpad + dpml cell_size = mp.Vector3(z=sz) dimensions = 1 @@ -24,15 +24,17 @@ # These different conventions can cause a bit of confusion when comparing against SALT, so here we perform # this transformation explicitly. -omega_a = 40 # omega_a in SALT -freq_21 = omega_a/(2*math.pi) # emission frequency (units of 2πc/a) +omega_a = 40 # omega_a in SALT +freq_21 = omega_a / (2 * math.pi) # emission frequency (units of 2πc/a) -gamma_perp = 4 # HWHM in angular frequency, SALT -gamma_21 = (2*gamma_perp)/(2*math.pi) # FWHM emission linewidth in sec^-1 (units of 2πc/a) +gamma_perp = 4 # HWHM in angular frequency, SALT +gamma_21 = (2 * gamma_perp) / ( + 2 * math.pi +) # FWHM emission linewidth in sec^-1 (units of 2πc/a) # Note that 2*pi*gamma_21 = 2*gamma_perp in SALT. -theta = 1 # theta, the off-diagonal dipole matrix element, in SALT -sigma_21 = 2*theta*theta*omega_a # dipole coupling strength (hbar = 1) +theta = 1 # theta, the off-diagonal dipole matrix element, in SALT +sigma_21 = 2 * theta * theta * omega_a # dipole coupling strength (hbar = 1) # The gain medium in MEEP is allowed to have an arbitrary number of levels, and is not # restricted to a two-level gain medium, as it simulates the populations of every individual @@ -55,32 +57,49 @@ # Gain medium pump and decay rates are specified in units of c/a. -rate_21 = 0.005 # non-radiative rate (units of c/a) -N0 = 37 # initial population density of ground state -Rp = 0.0051 # pumping rate of ground to excited state +rate_21 = 0.005 # non-radiative rate (units of c/a) +N0 = 37 # initial population density of ground state +Rp = 0.0051 # pumping rate of ground to excited state # so for example, these parameters have D_0 (SALT) = 0.0693. # Make the actual medium in MEEP: -transitions = [mp.Transition(1, 2, pumping_rate=Rp, frequency=freq_21, gamma=gamma_21, - sigma_diag=mp.Vector3(sigma_21,0,0)), - mp.Transition(2, 1, transition_rate=rate_21)] +transitions = [ + mp.Transition( + 1, + 2, + pumping_rate=Rp, + frequency=freq_21, + gamma=gamma_21, + sigma_diag=mp.Vector3(sigma_21, 0, 0), + ), + mp.Transition(2, 1, transition_rate=rate_21), +] ml_atom = mp.MultilevelAtom(sigma=1, transitions=transitions, initial_populations=[N0]) two_level = mp.Medium(index=ncav, E_susceptibilities=[ml_atom]) # Specify the cavity geometry: -geometry = [mp.Block(center=mp.Vector3(z=-0.5*sz+0.5*Lcav), - size=mp.Vector3(mp.inf,mp.inf,Lcav), material=two_level)] - -sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - geometry=geometry, - dimensions=dimensions) +geometry = [ + mp.Block( + center=mp.Vector3(z=-0.5 * sz + 0.5 * Lcav), + size=mp.Vector3(mp.inf, mp.inf, Lcav), + material=two_level, + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + geometry=geometry, + dimensions=dimensions, +) sim.init_sim() + def field_func(p): - return 1 if p.z==-0.5*sz + 0.5*Lcav else 0 + return 1 if p.z == -0.5 * sz + 0.5 * Lcav else 0 + sim.fields.initialize_field(mp.Ex, field_func) @@ -89,8 +108,12 @@ def field_func(p): # Note that the total number of time steps run is endt*resolution*2. This is the origin of the extra # factor of 2 in the definition of dt in fieldfft_meep.m. + def print_field(sim): - fp = sim.get_field_point(mp.Ex, mp.Vector3(z=(-0.5 * sz) + Lcav + (0.5 * dpad))).real + fp = sim.get_field_point( + mp.Ex, mp.Vector3(z=(-0.5 * sz) + Lcav + (0.5 * dpad)) + ).real print(f"field:, {sim.meep_time()}, {fp}") + sim.run(mp.after_time(endt - 250, print_field), until=endt) diff --git a/python/examples/oblique-planewave.ipynb b/python/examples/oblique-planewave.ipynb index 75efcdad6..4083cccc3 100644 --- a/python/examples/oblique-planewave.ipynb +++ b/python/examples/oblique-planewave.ipynb @@ -53,40 +53,46 @@ "metadata": {}, "outputs": [], "source": [ - "def run_sim(rot_angle = 0):\n", + "def run_sim(rot_angle=0):\n", "\n", - " resolution = 50 # pixels/μm\n", + " resolution = 50 # pixels/μm\n", "\n", - " cell_size = mp.Vector3(14,10,0)\n", + " cell_size = mp.Vector3(14, 10, 0)\n", "\n", - " pml_layers = [mp.PML(thickness=2,direction=mp.X)]\n", + " pml_layers = [mp.PML(thickness=2, direction=mp.X)]\n", "\n", - " fsrc = 1.0 # frequency of planewave (wavelength = 1/fsrc)\n", + " fsrc = 1.0 # frequency of planewave (wavelength = 1/fsrc)\n", "\n", - " n = 1.5 # refractive index of homogeneous material\n", + " n = 1.5 # refractive index of homogeneous material\n", " default_material = mp.Medium(index=n)\n", "\n", - " k_point = mp.Vector3(fsrc*n).rotate(mp.Vector3(z=1), rot_angle)\n", + " k_point = mp.Vector3(fsrc * n).rotate(mp.Vector3(z=1), rot_angle)\n", "\n", - " sources = [mp.EigenModeSource(src=mp.ContinuousSource(fsrc),\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(y=10),\n", - " direction=mp.AUTOMATIC if rot_angle == 0 else mp.NO_DIRECTION,\n", - " eig_kpoint=k_point,\n", - " eig_band=1,\n", - " eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", - " eig_match_freq=True)]\n", + " sources = [\n", + " mp.EigenModeSource(\n", + " src=mp.ContinuousSource(fsrc),\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(y=10),\n", + " direction=mp.AUTOMATIC if rot_angle == 0 else mp.NO_DIRECTION,\n", + " eig_kpoint=k_point,\n", + " eig_band=1,\n", + " eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", + " eig_match_freq=True,\n", + " )\n", + " ]\n", "\n", - " sim = mp.Simulation(cell_size=cell_size,\n", - " resolution=resolution,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=k_point,\n", - " default_material=default_material,\n", - " symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [])\n", + " sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " resolution=resolution,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=k_point,\n", + " default_material=default_material,\n", + " symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [],\n", + " )\n", "\n", " sim.run(until=100)\n", - " \n", + "\n", " plt.figure(dpi=100)\n", " sim.plot2D(fields=mp.Ez)\n", " plt.show()" @@ -219,7 +225,7 @@ } ], "source": [ - "for rot_angle in np.radians([0,20,40]):\n", + "for rot_angle in np.radians([0, 20, 40]):\n", " run_sim(rot_angle)" ] }, diff --git a/python/examples/oblique-planewave.py b/python/examples/oblique-planewave.py index 4b3b971d4..2742fb9e1 100644 --- a/python/examples/oblique-planewave.py +++ b/python/examples/oblique-planewave.py @@ -2,46 +2,51 @@ import numpy as np import matplotlib.pyplot as plt -resolution = 50 # pixels/μm +resolution = 50 # pixels/μm -cell_size = mp.Vector3(14,10,0) +cell_size = mp.Vector3(14, 10, 0) -pml_layers = [mp.PML(thickness=2,direction=mp.X)] +pml_layers = [mp.PML(thickness=2, direction=mp.X)] # rotation angle (in degrees) of planewave, counter clockwise (CCW) around z-axis rot_angle = np.radians(0) -fsrc = 1.0 # frequency of planewave (wavelength = 1/fsrc) +fsrc = 1.0 # frequency of planewave (wavelength = 1/fsrc) -n = 1.5 # refractive index of homogeneous material +n = 1.5 # refractive index of homogeneous material default_material = mp.Medium(index=n) -k_point = mp.Vector3(fsrc*n).rotate(mp.Vector3(z=1), rot_angle) - -sources = [mp.EigenModeSource(src=mp.ContinuousSource(fsrc), - center=mp.Vector3(), - size=mp.Vector3(y=10), - direction=mp.AUTOMATIC if rot_angle == 0 else mp.NO_DIRECTION, - eig_kpoint=k_point, - eig_band=1, - eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, - eig_match_freq=True)] - -sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - k_point=k_point, - default_material=default_material, - symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else []) +k_point = mp.Vector3(fsrc * n).rotate(mp.Vector3(z=1), rot_angle) + +sources = [ + mp.EigenModeSource( + src=mp.ContinuousSource(fsrc), + center=mp.Vector3(), + size=mp.Vector3(y=10), + direction=mp.AUTOMATIC if rot_angle == 0 else mp.NO_DIRECTION, + eig_kpoint=k_point, + eig_band=1, + eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, + eig_match_freq=True, + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + k_point=k_point, + default_material=default_material, + symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [], +) sim.run(until=100) -nonpml_vol = mp.Volume(center=mp.Vector3(), size=mp.Vector3(10,10,0)) +nonpml_vol = mp.Volume(center=mp.Vector3(), size=mp.Vector3(10, 10, 0)) -sim.plot2D(fields=mp.Ez, - output_plane=nonpml_vol) +sim.plot2D(fields=mp.Ez, output_plane=nonpml_vol) if mp.am_master(): - plt.axis('off') - plt.savefig('pw.png',bbox_inches='tight') + plt.axis("off") + plt.savefig("pw.png", bbox_inches="tight") diff --git a/python/examples/oblique-source.ipynb b/python/examples/oblique-source.ipynb index 96dec7d27..af62389a5 100644 --- a/python/examples/oblique-source.ipynb +++ b/python/examples/oblique-source.ipynb @@ -62,33 +62,43 @@ "metadata": {}, "outputs": [], "source": [ - "resolution = 20 # pixels/μm\n", + "resolution = 20 # pixels/μm\n", "\n", - "cell_size = mp.Vector3(14,14)\n", + "cell_size = mp.Vector3(14, 14)\n", "\n", "pml_layers = [mp.PML(thickness=2)]\n", "\n", "# rotation angle (in degrees) of waveguide, counter clockwise (CCW) around z-axis\n", "rot_angle = np.radians(20)\n", "\n", - "geometry = [mp.Block(center=mp.Vector3(),\n", - " size=mp.Vector3(mp.inf,1,mp.inf),\n", - " e1 = mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle),\n", - " e2 = mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle),\n", - " material=mp.Medium(epsilon=12))]\n", + "geometry = [\n", + " mp.Block(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(mp.inf, 1, mp.inf),\n", + " e1=mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle),\n", + " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle),\n", + " material=mp.Medium(epsilon=12),\n", + " )\n", + "]\n", "\n", - "fsrc = 0.15 # frequency of eigenmode or constant-amplitude source\n", + "fsrc = 0.15 # frequency of eigenmode or constant-amplitude source\n", "\n", - "sources = [mp.Source(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc),\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(y=2),\n", - " component=mp.Ez)]\n", + "sources = [\n", + " mp.Source(\n", + " src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc),\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(y=2),\n", + " component=mp.Ez,\n", + " )\n", + "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " resolution=resolution,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " geometry=geometry)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " resolution=resolution,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " geometry=geometry,\n", + ")" ] }, { @@ -160,8 +170,8 @@ ], "source": [ "f = plt.figure(dpi=100)\n", - "animate = mp.Animate2D(sim,mp.Ez,f=f,normalize=True)\n", - "sim.run(mp.at_every(1,animate),until_after_sources=50)\n", + "animate = mp.Animate2D(sim, mp.Ez, f=f, normalize=True)\n", + "sim.run(mp.at_every(1, animate), until_after_sources=50)\n", "plt.close()" ] }, @@ -201,8 +211,8 @@ } ], "source": [ - "filename = 'media/oblique-source-normal.mp4'\n", - "animate.to_mp4(10,filename)\n", + "filename = \"media/oblique-source-normal.mp4\"\n", + "animate.to_mp4(10, filename)\n", "Video(filename)" ] }, @@ -223,26 +233,34 @@ "metadata": {}, "outputs": [], "source": [ - "kx = 0.4 # initial guess for wavevector in x-direction of eigenmode\n", - "kpoint = mp.Vector3(kx).rotate(mp.Vector3(z=1), rot_angle) # Rotate the vector by the specified amount\n", + "kx = 0.4 # initial guess for wavevector in x-direction of eigenmode\n", + "kpoint = mp.Vector3(kx).rotate(\n", + " mp.Vector3(z=1), rot_angle\n", + ") # Rotate the vector by the specified amount\n", "\n", - "bnum = 1 # band number of eigenmode\n", + "bnum = 1 # band number of eigenmode\n", "\n", - "sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc),\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(y=14),\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " eig_band=bnum,\n", - " eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", - " eig_match_freq=True)]\n", + "sources = [\n", + " mp.EigenModeSource(\n", + " src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc),\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(y=14),\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " eig_band=bnum,\n", + " eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", + " eig_match_freq=True,\n", + " )\n", + "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " resolution=resolution,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " geometry=geometry,\n", - " symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [])" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " resolution=resolution,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " geometry=geometry,\n", + " symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [],\n", + ")" ] }, { @@ -280,8 +298,8 @@ ], "source": [ "f = plt.figure(dpi=100)\n", - "animate = mp.Animate2D(sim,mp.Ez,f=f,normalize=True)\n", - "sim.run(mp.at_every(1,animate),until_after_sources=50)\n", + "animate = mp.Animate2D(sim, mp.Ez, f=f, normalize=True)\n", + "sim.run(mp.at_every(1, animate), until_after_sources=50)\n", "plt.close()" ] }, @@ -314,8 +332,8 @@ } ], "source": [ - "filename = 'media/oblique-source-eig.mp4'\n", - "animate.to_mp4(10,filename)\n", + "filename = \"media/oblique-source-eig.mp4\"\n", + "animate.to_mp4(10, filename)\n", "Video(filename)" ] }, @@ -427,40 +445,57 @@ ], "source": [ "for rot_angle in np.radians([0, 20, 40]):\n", - " \n", - " resolution = 20\n", "\n", - " geometry = [mp.Block(center=mp.Vector3(),\n", - " size=mp.Vector3(mp.inf,1,mp.inf),\n", - " e1=mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle),\n", - " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle),\n", - " material=mp.Medium(epsilon=12))]\n", + " resolution = 20\n", "\n", - " sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc),\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(y=14),\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " eig_band=bnum,\n", - " eig_parity=mp.ODD_Z,\n", - " eig_match_freq=True)]\n", + " geometry = [\n", + " mp.Block(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(mp.inf, 1, mp.inf),\n", + " e1=mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle),\n", + " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle),\n", + " material=mp.Medium(epsilon=12),\n", + " )\n", + " ]\n", "\n", - " sim = mp.Simulation(cell_size=cell_size,\n", - " resolution=resolution,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " geometry=geometry)\n", + " sources = [\n", + " mp.EigenModeSource(\n", + " src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc),\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(y=14),\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " eig_band=bnum,\n", + " eig_parity=mp.ODD_Z,\n", + " eig_match_freq=True,\n", + " )\n", + " ]\n", "\n", + " sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " resolution=resolution,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " geometry=geometry,\n", + " )\n", "\n", - " tran = sim.add_flux(fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5), size=mp.Vector3(y=14)))\n", + " tran = sim.add_flux(\n", + " fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5), size=mp.Vector3(y=14))\n", + " )\n", " sim.run(until_after_sources=50)\n", - " res = sim.get_eigenmode_coefficients(tran,\n", - " [1],\n", - " eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", - " direction=mp.NO_DIRECTION,\n", - " kpoint_func=lambda f,n: kpoint)\n", - " print(\"flux:, {:.6f}, {:.6f}\".format(mp.get_fluxes(tran)[0],abs(res.alpha[0,0,0])**2))\n", - " \n", + " res = sim.get_eigenmode_coefficients(\n", + " tran,\n", + " [1],\n", + " eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", + " direction=mp.NO_DIRECTION,\n", + " kpoint_func=lambda f, n: kpoint,\n", + " )\n", + " print(\n", + " \"flux:, {:.6f}, {:.6f}\".format(\n", + " mp.get_fluxes(tran)[0], abs(res.alpha[0, 0, 0]) ** 2\n", + " )\n", + " )\n", + "\n", " sim.plot2D(fields=mp.Ez)\n", " plt.show()" ] diff --git a/python/examples/oblique-source.py b/python/examples/oblique-source.py index 243825dc0..25458e449 100644 --- a/python/examples/oblique-source.py +++ b/python/examples/oblique-source.py @@ -2,66 +2,94 @@ import numpy as np import matplotlib.pyplot as plt -resolution = 50 # pixels/μm +resolution = 50 # pixels/μm -cell_size = mp.Vector3(14,14) +cell_size = mp.Vector3(14, 14) pml_layers = [mp.PML(thickness=2)] # rotation angle (in degrees) of waveguide, counter clockwise (CCW) around z-axis rot_angle = np.radians(20) -w = 1.0 # width of waveguide +w = 1.0 # width of waveguide -geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,w,mp.inf), - e1=mp.Vector3(x=1).rotate(mp.Vector3(z=1), rot_angle), - e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle), - material=mp.Medium(epsilon=12))] +geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + e1=mp.Vector3(x=1).rotate(mp.Vector3(z=1), rot_angle), + e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle), + material=mp.Medium(epsilon=12), + ) +] -fsrc = 0.15 # frequency of eigenmode or constant-amplitude source -bnum = 1 # band number of eigenmode +fsrc = 0.15 # frequency of eigenmode or constant-amplitude source +bnum = 1 # band number of eigenmode kpoint = mp.Vector3(x=1).rotate(mp.Vector3(z=1), rot_angle) -compute_flux = True # compute flux (True) or plot the field profile (False) +compute_flux = True # compute flux (True) or plot the field profile (False) -eig_src = True # eigenmode (True) or constant-amplitude (False) source +eig_src = True # eigenmode (True) or constant-amplitude (False) source if eig_src: - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc) if compute_flux else mp.ContinuousSource(fsrc), - center=mp.Vector3(), - size=mp.Vector3(y=3*w), - direction=mp.NO_DIRECTION, - eig_kpoint=kpoint, - eig_band=bnum, - eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, - eig_match_freq=True)] + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc) + if compute_flux + else mp.ContinuousSource(fsrc), + center=mp.Vector3(), + size=mp.Vector3(y=3 * w), + direction=mp.NO_DIRECTION, + eig_kpoint=kpoint, + eig_band=bnum, + eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, + eig_match_freq=True, + ) + ] else: - sources = [mp.Source(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc) if compute_flux else mp.ContinuousSource(fsrc), - center=mp.Vector3(), - size=mp.Vector3(y=3*w), - component=mp.Ez)] + sources = [ + mp.Source( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc) + if compute_flux + else mp.ContinuousSource(fsrc), + center=mp.Vector3(), + size=mp.Vector3(y=3 * w), + component=mp.Ez, + ) + ] -sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else []) +sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [], +) if compute_flux: - tran = sim.add_flux(fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5), size=mp.Vector3(y=14))) + tran = sim.add_flux( + fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5), size=mp.Vector3(y=14)) + ) sim.run(until_after_sources=50) - res = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: kpoint) - print("flux:, {:.6f}, {:.6f}".format(mp.get_fluxes(tran)[0],abs(res.alpha[0,0,0])**2)) + res = sim.get_eigenmode_coefficients( + tran, + [1], + eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, + direction=mp.NO_DIRECTION, + kpoint_func=lambda f, n: kpoint, + ) + print( + "flux:, {:.6f}, {:.6f}".format( + mp.get_fluxes(tran)[0], abs(res.alpha[0, 0, 0]) ** 2 + ) + ) else: sim.run(until=100) - sim.plot2D(output_plane=mp.Volume(center=mp.Vector3(), size=mp.Vector3(10,10)), - fields=mp.Ez, - field_parameters={'alpha':0.9}) + sim.plot2D( + output_plane=mp.Volume(center=mp.Vector3(), size=mp.Vector3(10, 10)), + fields=mp.Ez, + field_parameters={"alpha": 0.9}, + ) plt.show() diff --git a/python/examples/parallel-wvgs-force.ipynb b/python/examples/parallel-wvgs-force.ipynb index 32e9eb0d8..db2954e22 100644 --- a/python/examples/parallel-wvgs-force.ipynb +++ b/python/examples/parallel-wvgs-force.ipynb @@ -33,7 +33,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 40 # pixels/μm\n", + "resolution = 40 # pixels/μm\n", "\n", "Si = mp.Medium(index=3.45)\n", "\n", @@ -42,67 +42,89 @@ "\n", "sx = 5\n", "sy = 3\n", - "cell = mp.Vector3(sx+2*dpml,sy+2*dpml,0)\n", + "cell = mp.Vector3(sx + 2 * dpml, sy + 2 * dpml, 0)\n", "\n", - "a = 1.0 # waveguide width/height\n", + "a = 1.0 # waveguide width/height\n", "\n", "k_point = mp.Vector3(z=0.5)\n", "\n", - "def parallel_waveguide(s,xodd):\n", - " geometry = [mp.Block(center=mp.Vector3(-0.5*(s+a)),\n", - " size=mp.Vector3(a,a,mp.inf),\n", - " material=Si),\n", - " mp.Block(center=mp.Vector3(0.5*(s+a)),\n", - " size=mp.Vector3(a,a,mp.inf),\n", - " material=Si)]\n", "\n", - " symmetries = [mp.Mirror(mp.X, phase=-1 if xodd else 1),\n", - " mp.Mirror(mp.Y, phase=-1)]\n", + "def parallel_waveguide(s, xodd):\n", + " geometry = [\n", + " mp.Block(\n", + " center=mp.Vector3(-0.5 * (s + a)),\n", + " size=mp.Vector3(a, a, mp.inf),\n", + " material=Si,\n", + " ),\n", + " mp.Block(\n", + " center=mp.Vector3(0.5 * (s + a)), size=mp.Vector3(a, a, mp.inf), material=Si\n", + " ),\n", + " ]\n", + "\n", + " symmetries = [mp.Mirror(mp.X, phase=-1 if xodd else 1), mp.Mirror(mp.Y, phase=-1)]\n", + "\n", + " sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " symmetries=symmetries,\n", + " k_point=k_point,\n", + " )\n", "\n", - " sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " symmetries=symmetries,\n", - " k_point=k_point)\n", - " \n", " sim.init_sim()\n", - " EigenmodeData = sim.get_eigenmode(0.22,\n", - " mp.Z,\n", - " mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx,sy)),\n", - " 2 if xodd else 1,\n", - " k_point,\n", - " match_frequency=False,\n", - " parity=mp.ODD_Y)\n", + " EigenmodeData = sim.get_eigenmode(\n", + " 0.22,\n", + " mp.Z,\n", + " mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx, sy)),\n", + " 2 if xodd else 1,\n", + " k_point,\n", + " match_frequency=False,\n", + " parity=mp.ODD_Y,\n", + " )\n", "\n", " fcen = EigenmodeData.freq\n", " print(\"freq:, {}, {}, {}\".format(\"xodd\" if xodd else \"xeven\", s, fcen))\n", "\n", " sim.reset_meep()\n", "\n", - " eig_sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen, fwidth=0.1*fcen),\n", - " size=mp.Vector3(sx,sy),\n", - " center=mp.Vector3(),\n", - " eig_band=2 if xodd else 1,\n", - " eig_kpoint=k_point,\n", - " eig_match_freq=False,\n", - " eig_parity=mp.ODD_Y)]\n", + " eig_sources = [\n", + " mp.EigenModeSource(\n", + " src=mp.GaussianSource(fcen, fwidth=0.1 * fcen),\n", + " size=mp.Vector3(sx, sy),\n", + " center=mp.Vector3(),\n", + " eig_band=2 if xodd else 1,\n", + " eig_kpoint=k_point,\n", + " eig_match_freq=False,\n", + " eig_parity=mp.ODD_Y,\n", + " )\n", + " ]\n", "\n", " sim.change_sources(eig_sources)\n", "\n", - " flux_reg = mp.FluxRegion(direction=mp.Z, center=mp.Vector3(), size=mp.Vector3(sx,sy))\n", + " flux_reg = mp.FluxRegion(\n", + " direction=mp.Z, center=mp.Vector3(), size=mp.Vector3(sx, sy)\n", + " )\n", " wvg_flux = sim.add_flux(fcen, 0, 1, flux_reg)\n", "\n", - " force_reg1 = mp.ForceRegion(mp.Vector3(0.49*s), direction=mp.X, weight=1, size=mp.Vector3(y=sy))\n", - " force_reg2 = mp.ForceRegion(mp.Vector3(0.5*s+1.01*a), direction=mp.X, weight=-1, size=mp.Vector3(y=sy))\n", + " force_reg1 = mp.ForceRegion(\n", + " mp.Vector3(0.49 * s), direction=mp.X, weight=1, size=mp.Vector3(y=sy)\n", + " )\n", + " force_reg2 = mp.ForceRegion(\n", + " mp.Vector3(0.5 * s + 1.01 * a), direction=mp.X, weight=-1, size=mp.Vector3(y=sy)\n", + " )\n", " wvg_force = sim.add_force(fcen, 0, 1, force_reg1, force_reg2)\n", "\n", " sim.run(until_after_sources=1500)\n", "\n", " flux = mp.get_fluxes(wvg_flux)[0]\n", " force = mp.get_forces(wvg_force)[0]\n", - " print(\"data:, {}, {}, {}, {}, {}\".format(\"xodd\" if xodd else \"xeven\", s, flux, force, -force/flux))\n", - " \n", + " print(\n", + " \"data:, {}, {}, {}, {}, {}\".format(\n", + " \"xodd\" if xodd else \"xeven\", s, flux, force, -force / flux\n", + " )\n", + " )\n", + "\n", " sim.reset_meep()\n", " return flux, force" ] @@ -2319,15 +2341,15 @@ } ], "source": [ - "s = np.arange(0.1,1.1,0.1)\n", + "s = np.arange(0.1, 1.1, 0.1)\n", "fluxes_odd = np.zeros(s.size)\n", "forces_odd = np.zeros(s.size)\n", "fluxes_even = np.zeros(s.size)\n", "forces_even = np.zeros(s.size)\n", "\n", "for k in range(len(s)):\n", - " fluxes_odd[k], forces_odd[k] = parallel_waveguide(s[k],True)\n", - " fluxes_even[k], forces_even[k] = parallel_waveguide(s[k],False)" + " fluxes_odd[k], forces_odd[k] = parallel_waveguide(s[k], True)\n", + " fluxes_even[k], forces_even[k] = parallel_waveguide(s[k], False)" ] }, { @@ -2350,12 +2372,12 @@ ], "source": [ "plt.figure(dpi=150)\n", - "plt.plot(s,-forces_odd/fluxes_odd,'rs',label='anti-symmetric')\n", - "plt.plot(s,-forces_even/fluxes_even,'bo',label='symmetric')\n", + "plt.plot(s, -forces_odd / fluxes_odd, \"rs\", label=\"anti-symmetric\")\n", + "plt.plot(s, -forces_even / fluxes_even, \"bo\", label=\"symmetric\")\n", "plt.grid(True)\n", - "plt.xlabel('waveguide separation s/a')\n", - "plt.ylabel('optical force (F/L)(ac/P)')\n", - "plt.legend(loc='upper right')\n", + "plt.xlabel(\"waveguide separation s/a\")\n", + "plt.ylabel(\"optical force (F/L)(ac/P)\")\n", + "plt.legend(loc=\"upper right\")\n", "plt.show()" ] }, @@ -3185,7 +3207,7 @@ "Si = mp.Medium(index=3.45)\n", "\n", "syz = 10\n", - "geometry_lattice = mp.Lattice(size=mp.Vector3(0,syz,syz))\n", + "geometry_lattice = mp.Lattice(size=mp.Vector3(0, syz, syz))\n", "\n", "k_points = [mp.Vector3(0.5)]\n", "\n", @@ -3194,20 +3216,29 @@ "\n", "a = 1.0 # waveguide width\n", "\n", - "def parallel_waveguide(s,yodd):\n", - " geometry = [mp.Block(center=mp.Vector3(0,-0.5*(s+a),0),\n", - " size=mp.Vector3(mp.inf,a,a),\n", - " material=Si),\n", - " mp.Block(center=mp.Vector3(0,0.5*(s+a),0),\n", - " size=mp.Vector3(mp.inf,a,a),\n", - " material=Si)]\n", "\n", - " ms = mpb.ModeSolver(resolution=resolution,\n", - " k_points=k_points,\n", - " geometry_lattice=geometry_lattice,\n", - " geometry=geometry,\n", - " num_bands=num_bands,\n", - " tolerance=tolerance)\n", + "def parallel_waveguide(s, yodd):\n", + " geometry = [\n", + " mp.Block(\n", + " center=mp.Vector3(0, -0.5 * (s + a), 0),\n", + " size=mp.Vector3(mp.inf, a, a),\n", + " material=Si,\n", + " ),\n", + " mp.Block(\n", + " center=mp.Vector3(0, 0.5 * (s + a), 0),\n", + " size=mp.Vector3(mp.inf, a, a),\n", + " material=Si,\n", + " ),\n", + " ]\n", + "\n", + " ms = mpb.ModeSolver(\n", + " resolution=resolution,\n", + " k_points=k_points,\n", + " geometry_lattice=geometry_lattice,\n", + " geometry=geometry,\n", + " num_bands=num_bands,\n", + " tolerance=tolerance,\n", + " )\n", "\n", " if yodd:\n", " ms.run_yodd_zodd()\n", @@ -3215,11 +3246,12 @@ " ms.run_yeven_zodd()\n", "\n", " f = ms.get_freqs()[0]\n", - " vg = ms.compute_group_velocity_component(mp.Vector3(1,0,0))[0]\n", + " vg = ms.compute_group_velocity_component(mp.Vector3(1, 0, 0))[0]\n", + "\n", + " return f, vg\n", "\n", - " return f,vg\n", "\n", - "ss = np.arange(0.05,1.15,0.1)\n", + "ss = np.arange(0.05, 1.15, 0.1)\n", "\n", "f_odd = np.zeros(len(ss))\n", "vg_odd = np.zeros(len(ss))\n", @@ -3227,19 +3259,21 @@ "vg_even = np.zeros(len(ss))\n", "\n", "for j in range(len(ss)):\n", - " f_odd[j], vg_odd[j] = parallel_waveguide(ss[j],True)\n", - " f_even[j], vg_even[j] = parallel_waveguide(ss[j],False)\n", + " f_odd[j], vg_odd[j] = parallel_waveguide(ss[j], True)\n", + " f_even[j], vg_even[j] = parallel_waveguide(ss[j], False)\n", + "\n", + "ds = ss[1] - ss[0]\n", + "\n", "\n", - "ds = ss[1]-ss[0]\n", + "def compute_force(f, vg):\n", + " f_avg = 0.5 * (f[:-1] + f[1:])\n", + " df = f[1:] - f[:-1]\n", + " vg_avg = 0.5 * (vg[:-1] + vg[1:])\n", + " return -1 / f_avg * df / ds * 1 / vg_avg\n", "\n", - "def compute_force(f,vg):\n", - " f_avg = 0.5*(f[:-1]+f[1:])\n", - " df = f[1:]-f[:-1]\n", - " vg_avg = 0.5*(vg[:-1]+vg[1:])\n", - " return -1/f_avg * df/ds * 1/vg_avg\n", "\n", - "force_odd = compute_force(f_odd,vg_odd)\n", - "force_even = compute_force(f_even,vg_even)" + "force_odd = compute_force(f_odd, vg_odd)\n", + "force_even = compute_force(f_even, vg_even)" ] }, { @@ -3262,13 +3296,13 @@ ], "source": [ "plt.figure(dpi=200)\n", - "plt.plot(ss[:-1],force_odd,'b-',label='anti-symmetric')\n", - "plt.plot(ss[:-1],force_even,'r-',label='symmetric')\n", + "plt.plot(ss[:-1], force_odd, \"b-\", label=\"anti-symmetric\")\n", + "plt.plot(ss[:-1], force_even, \"r-\", label=\"symmetric\")\n", "plt.xlabel(\"waveguide separation s/a\")\n", "plt.ylabel(\"optical force (F/L)(ac/P)\")\n", - "plt.legend(loc='upper right')\n", - "plt.xticks(np.arange(0,1.2,0.2))\n", - "plt.yticks(np.arange(-1.5,1.0,0.5))\n", + "plt.legend(loc=\"upper right\")\n", + "plt.xticks(np.arange(0, 1.2, 0.2))\n", + "plt.yticks(np.arange(-1.5, 1.0, 0.5))\n", "plt.show()" ] } diff --git a/python/examples/parallel-wvgs-force.py b/python/examples/parallel-wvgs-force.py index 68bfce7af..d45500365 100644 --- a/python/examples/parallel-wvgs-force.py +++ b/python/examples/parallel-wvgs-force.py @@ -2,7 +2,7 @@ import numpy as np import matplotlib.pyplot as plt -resolution = 40 # pixels/μm +resolution = 40 # pixels/μm Si = mp.Medium(index=3.45) @@ -11,87 +11,106 @@ sx = 5 sy = 3 -cell = mp.Vector3(sx+2*dpml,sy+2*dpml,0) +cell = mp.Vector3(sx + 2 * dpml, sy + 2 * dpml, 0) -a = 1.0 # waveguide width/height +a = 1.0 # waveguide width/height k_point = mp.Vector3(z=0.5) -def parallel_waveguide(s,xodd): - geometry = [mp.Block(center=mp.Vector3(-0.5*(s+a)), - size=mp.Vector3(a,a,mp.inf), - material=Si), - mp.Block(center=mp.Vector3(0.5*(s+a)), - size=mp.Vector3(a,a,mp.inf), - material=Si)] - symmetries = [mp.Mirror(mp.X, phase=-1 if xodd else 1), - mp.Mirror(mp.Y, phase=-1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - symmetries=symmetries, - k_point=k_point) +def parallel_waveguide(s, xodd): + geometry = [ + mp.Block( + center=mp.Vector3(-0.5 * (s + a)), + size=mp.Vector3(a, a, mp.inf), + material=Si, + ), + mp.Block( + center=mp.Vector3(0.5 * (s + a)), size=mp.Vector3(a, a, mp.inf), material=Si + ), + ] + + symmetries = [mp.Mirror(mp.X, phase=-1 if xodd else 1), mp.Mirror(mp.Y, phase=-1)] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + symmetries=symmetries, + k_point=k_point, + ) sim.init_sim() - EigenmodeData = sim.get_eigenmode(0.22, - mp.Z, - mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx,sy)), - 2 if xodd else 1, - k_point, - match_frequency=False, - parity=mp.ODD_Y) + EigenmodeData = sim.get_eigenmode( + 0.22, + mp.Z, + mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx, sy)), + 2 if xodd else 1, + k_point, + match_frequency=False, + parity=mp.ODD_Y, + ) fcen = EigenmodeData.freq print(f'freq:, {"xodd" if xodd else "xeven"}, {s}, {fcen}') sim.reset_meep() - eig_sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen, fwidth=0.1*fcen), - size=mp.Vector3(sx,sy), - center=mp.Vector3(), - eig_band=2 if xodd else 1, - eig_kpoint=k_point, - eig_match_freq=False, - eig_parity=mp.ODD_Y)] + eig_sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=0.1 * fcen), + size=mp.Vector3(sx, sy), + center=mp.Vector3(), + eig_band=2 if xodd else 1, + eig_kpoint=k_point, + eig_match_freq=False, + eig_parity=mp.ODD_Y, + ) + ] sim.change_sources(eig_sources) - flux_reg = mp.FluxRegion(direction=mp.Z, center=mp.Vector3(), size=mp.Vector3(sx,sy)) + flux_reg = mp.FluxRegion( + direction=mp.Z, center=mp.Vector3(), size=mp.Vector3(sx, sy) + ) wvg_flux = sim.add_flux(fcen, 0, 1, flux_reg) - force_reg1 = mp.ForceRegion(mp.Vector3(0.49*s), direction=mp.X, weight=1, size=mp.Vector3(y=sy)) - force_reg2 = mp.ForceRegion(mp.Vector3(0.5*s+1.01*a), direction=mp.X, weight=-1, size=mp.Vector3(y=sy)) + force_reg1 = mp.ForceRegion( + mp.Vector3(0.49 * s), direction=mp.X, weight=1, size=mp.Vector3(y=sy) + ) + force_reg2 = mp.ForceRegion( + mp.Vector3(0.5 * s + 1.01 * a), direction=mp.X, weight=-1, size=mp.Vector3(y=sy) + ) wvg_force = sim.add_force(fcen, 0, 1, force_reg1, force_reg2) sim.run(until_after_sources=1500) flux = mp.get_fluxes(wvg_flux)[0] force = mp.get_forces(wvg_force)[0] - print(f'data:, {"xodd" if xodd else "xeven"}, {s}, {flux}, {force}, {-force / flux}') - + print( + f'data:, {"xodd" if xodd else "xeven"}, {s}, {flux}, {force}, {-force / flux}' + ) sim.reset_meep() return flux, force -s = np.arange(0.05,1.05,0.05) +s = np.arange(0.05, 1.05, 0.05) fluxes_odd = np.zeros(s.size) forces_odd = np.zeros(s.size) fluxes_even = np.zeros(s.size) forces_even = np.zeros(s.size) for k in range(len(s)): - fluxes_odd[k], forces_odd[k] = parallel_waveguide(s[k],True) - fluxes_even[k], forces_even[k] = parallel_waveguide(s[k],False) + fluxes_odd[k], forces_odd[k] = parallel_waveguide(s[k], True) + fluxes_even[k], forces_even[k] = parallel_waveguide(s[k], False) plt.figure(dpi=150) -plt.plot(s,-forces_odd/fluxes_odd,'rs',label='anti-symmetric') -plt.plot(s,-forces_even/fluxes_even,'bo',label='symmetric') +plt.plot(s, -forces_odd / fluxes_odd, "rs", label="anti-symmetric") +plt.plot(s, -forces_even / fluxes_even, "bo", label="symmetric") plt.grid(True) -plt.xlabel('waveguide separation s/a') -plt.ylabel('optical force (F/L)(ac/P)') -plt.legend(loc='upper right') +plt.xlabel("waveguide separation s/a") +plt.ylabel("optical force (F/L)(ac/P)") +plt.legend(loc="upper right") plt.show() diff --git a/python/examples/parallel-wvgs-mpb.py b/python/examples/parallel-wvgs-mpb.py index 1436b908c..dbd421137 100644 --- a/python/examples/parallel-wvgs-mpb.py +++ b/python/examples/parallel-wvgs-mpb.py @@ -10,26 +10,35 @@ Si = mp.Medium(index=3.45) syz = 10 -geometry_lattice = mp.Lattice(size=mp.Vector3(0,syz,syz)) +geometry_lattice = mp.Lattice(size=mp.Vector3(0, syz, syz)) k_points = [mp.Vector3(0.5)] a = 1.0 # waveguide width -def parallel_waveguide(s,yodd): - geometry = [mp.Block(center=mp.Vector3(0,-0.5*(s+a),0), - size=mp.Vector3(mp.inf,a,a), - material=Si), - mp.Block(center=mp.Vector3(0,0.5*(s+a),0), - size=mp.Vector3(mp.inf,a,a), - material=Si)] - - ms = mpb.ModeSolver(resolution=resolution, - k_points=k_points, - geometry_lattice=geometry_lattice, - geometry=geometry, - num_bands=1, - tolerance=1e-9) + +def parallel_waveguide(s, yodd): + geometry = [ + mp.Block( + center=mp.Vector3(0, -0.5 * (s + a), 0), + size=mp.Vector3(mp.inf, a, a), + material=Si, + ), + mp.Block( + center=mp.Vector3(0, 0.5 * (s + a), 0), + size=mp.Vector3(mp.inf, a, a), + material=Si, + ), + ] + + ms = mpb.ModeSolver( + resolution=resolution, + k_points=k_points, + geometry_lattice=geometry_lattice, + geometry=geometry, + num_bands=1, + tolerance=1e-9, + ) if yodd: ms.run_yodd_zodd() @@ -37,11 +46,12 @@ def parallel_waveguide(s,yodd): ms.run_yeven_zodd() f = ms.get_freqs()[0] - vg = ms.compute_group_velocity_component(mp.Vector3(1,0,0))[0] + vg = ms.compute_group_velocity_component(mp.Vector3(1, 0, 0))[0] + + return f, vg - return f,vg -ss = np.arange(0.025,1.075,0.05) +ss = np.arange(0.025, 1.075, 0.05) f_odd = np.zeros(len(ss)) vg_odd = np.zeros(len(ss)) @@ -49,26 +59,28 @@ def parallel_waveguide(s,yodd): vg_even = np.zeros(len(ss)) for j in range(len(ss)): - f_odd[j], vg_odd[j] = parallel_waveguide(ss[j],True) - f_even[j], vg_even[j] = parallel_waveguide(ss[j],False) + f_odd[j], vg_odd[j] = parallel_waveguide(ss[j], True) + f_even[j], vg_even[j] = parallel_waveguide(ss[j], False) + +ds = ss[1] - ss[0] + -ds = ss[1]-ss[0] +def compute_force(f, vg): + f_avg = 0.5 * (f[:-1] + f[1:]) + df = f[1:] - f[:-1] + vg_avg = 0.5 * (vg[:-1] + vg[1:]) + return -1 / f_avg * df / ds * 1 / vg_avg -def compute_force(f,vg): - f_avg = 0.5*(f[:-1]+f[1:]) - df = f[1:]-f[:-1] - vg_avg = 0.5*(vg[:-1]+vg[1:]) - return -1/f_avg * df/ds * 1/vg_avg -force_odd = compute_force(f_odd,vg_odd) -force_even = compute_force(f_even,vg_even) +force_odd = compute_force(f_odd, vg_odd) +force_even = compute_force(f_even, vg_even) plt.figure(dpi=200) -plt.plot(ss[:-1],force_odd,'b-',label='anti-symmetric') -plt.plot(ss[:-1],force_even,'r-',label='symmetric') +plt.plot(ss[:-1], force_odd, "b-", label="anti-symmetric") +plt.plot(ss[:-1], force_even, "r-", label="symmetric") plt.xlabel("waveguide separation s/a") plt.ylabel("optical force (F/L)(ac/P)") -plt.legend(loc='upper right') -plt.xticks(np.arange(0,1.2,0.2)) -plt.yticks(np.arange(-1.5,1.0,0.5)) +plt.legend(loc="upper right") +plt.xticks(np.arange(0, 1.2, 0.2)) +plt.yticks(np.arange(-1.5, 1.0, 0.5)) plt.show() diff --git a/python/examples/perturbation_theory.ipynb b/python/examples/perturbation_theory.ipynb index e4cfa98b4..c78cb2cb9 100644 --- a/python/examples/perturbation_theory.ipynb +++ b/python/examples/perturbation_theory.ipynb @@ -169,53 +169,62 @@ "import meep as mp\n", "import numpy as np\n", "\n", - "resolution = 100 # pixels/um\n", + "resolution = 100 # pixels/um\n", "\n", - "perpendicular = False # perpendicular (Hz) or parallel (Ez) source?\n", + "perpendicular = False # perpendicular (Hz) or parallel (Ez) source?\n", "\n", "if perpendicular:\n", " src_cmpt = mp.Hz\n", - " fcen = 0.21 # pulse center frequency\n", + " fcen = 0.21 # pulse center frequency\n", "else:\n", " src_cmpt = mp.Ez\n", - " fcen = 0.17 # pulse center frequency\n", + " fcen = 0.17 # pulse center frequency\n", "\n", - "n = 3.4 # index of waveguide\n", - "w = 1 # ring width\n", - "r = 1 # inner radius of ring\n", - "pad = 4 # padding between waveguide and edge of PML\n", - "dpml = 2 # thickness of PML\n", - "m = 5 # angular dependence\n", + "n = 3.4 # index of waveguide\n", + "w = 1 # ring width\n", + "r = 1 # inner radius of ring\n", + "pad = 4 # padding between waveguide and edge of PML\n", + "dpml = 2 # thickness of PML\n", + "m = 5 # angular dependence\n", "\n", "pml_layers = [mp.PML(dpml)]\n", "\n", - "sr = r + w + pad + dpml # radial size (cell is from 0 to sr)\n", - "dimensions = mp.CYLINDRICAL # coordinate system is (r,phi,z) instead of (x,y,z)\n", + "sr = r + w + pad + dpml # radial size (cell is from 0 to sr)\n", + "dimensions = mp.CYLINDRICAL # coordinate system is (r,phi,z) instead of (x,y,z)\n", "cell = mp.Vector3(sr)\n", "\n", - "geometry = [mp.Block(center=mp.Vector3(r + (w / 2)),\n", - " size=mp.Vector3(w, mp.inf, mp.inf),\n", - " material=mp.Medium(index=n))]\n", + "geometry = [\n", + " mp.Block(\n", + " center=mp.Vector3(r + (w / 2)),\n", + " size=mp.Vector3(w, mp.inf, mp.inf),\n", + " material=mp.Medium(index=n),\n", + " )\n", + "]\n", "\n", "# find resonant frequency of unperturbed geometry using broadband source\n", "\n", - "df = 0.2*fcen # pulse width (in frequency)\n", - "\n", - "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", - " component=src_cmpt,\n", - " center=mp.Vector3(r+0.1))]\n", - "\n", - "sim = mp.Simulation(cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " dimensions=dimensions,\n", - " m=m)\n", - "\n", - "h = mp.Harminv(src_cmpt, mp.Vector3(r+0.1), fcen, df)\n", - "sim.run(mp.after_sources(h),\n", - " until_after_sources=100)\n", + "df = 0.2 * fcen # pulse width (in frequency)\n", + "\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=src_cmpt,\n", + " center=mp.Vector3(r + 0.1),\n", + " )\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " dimensions=dimensions,\n", + " m=m,\n", + ")\n", + "\n", + "h = mp.Harminv(src_cmpt, mp.Vector3(r + 0.1), fcen, df)\n", + "sim.run(mp.after_sources(h), until_after_sources=100)\n", "\n", "frq_unperturbed = h.modes[0].freq\n", "\n", @@ -224,35 +233,69 @@ "# unperturbed geometry with narrowband source centered at resonant frequency\n", "\n", "fcen = frq_unperturbed\n", - "df = 0.05*fcen\n", - "\n", - "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", - " component=src_cmpt,\n", - " center=mp.Vector3(r+0.1))]\n", - "\n", - "sim = mp.Simulation(cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " dimensions=dimensions,\n", - " m=m)\n", + "df = 0.05 * fcen\n", + "\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=src_cmpt,\n", + " center=mp.Vector3(r + 0.1),\n", + " )\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " dimensions=dimensions,\n", + " m=m,\n", + ")\n", "\n", "sim.run(until_after_sources=100)\n", "\n", "deps = 1 - n**2\n", - "deps_inv = 1 - 1/n**2\n", + "deps_inv = 1 - 1 / n**2\n", "\n", "if perpendicular:\n", - " para_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ep, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ep, mp.Vector3(r+w)))**2)\n", - " perp_integral = deps_inv*2*np.pi*(-r*abs(sim.get_field_point(mp.Dr, mp.Vector3(r)))**2 + (r+w)*abs(sim.get_field_point(mp.Dr, mp.Vector3(r+w)))**2)\n", + " para_integral = (\n", + " deps\n", + " * 2\n", + " * np.pi\n", + " * (\n", + " r * abs(sim.get_field_point(mp.Ep, mp.Vector3(r))) ** 2\n", + " - (r + w) * abs(sim.get_field_point(mp.Ep, mp.Vector3(r + w))) ** 2\n", + " )\n", + " )\n", + " perp_integral = (\n", + " deps_inv\n", + " * 2\n", + " * np.pi\n", + " * (\n", + " -r * abs(sim.get_field_point(mp.Dr, mp.Vector3(r))) ** 2\n", + " + (r + w) * abs(sim.get_field_point(mp.Dr, mp.Vector3(r + w))) ** 2\n", + " )\n", + " )\n", " numerator_integral = para_integral + perp_integral\n", "else:\n", - " numerator_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ez, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ez, mp.Vector3(r+w)))**2)\n", - "\n", - "denominator_integral = sim.electric_energy_in_box(center=mp.Vector3(0.5*sr), size=mp.Vector3(sr))\n", - "\n", - "perturb_theory_dw_dR = -frq_unperturbed * numerator_integral / (4 * denominator_integral)\n", + " numerator_integral = (\n", + " deps\n", + " * 2\n", + " * np.pi\n", + " * (\n", + " r * abs(sim.get_field_point(mp.Ez, mp.Vector3(r))) ** 2\n", + " - (r + w) * abs(sim.get_field_point(mp.Ez, mp.Vector3(r + w))) ** 2\n", + " )\n", + " )\n", + "\n", + "denominator_integral = sim.electric_energy_in_box(\n", + " center=mp.Vector3(0.5 * sr), size=mp.Vector3(sr)\n", + ")\n", + "\n", + "perturb_theory_dw_dR = (\n", + " -frq_unperturbed * numerator_integral / (4 * denominator_integral)\n", + ")\n", "\n", "sim.reset_meep()\n", "\n", @@ -260,31 +303,44 @@ "\n", "dr = 0.01\n", "\n", - "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", - " component=src_cmpt,\n", - " center=mp.Vector3(r + dr + 0.1))]\n", - "\n", - "geometry = [mp.Block(center=mp.Vector3(r + dr + (w / 2)),\n", - " size=mp.Vector3(w, mp.inf, mp.inf),\n", - " material=mp.Medium(index=n))]\n", - "\n", - "sim = mp.Simulation(cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " dimensions=dimensions,\n", - " m=m)\n", - "\n", - "h = mp.Harminv(src_cmpt, mp.Vector3(r+dr+0.1), fcen, df)\n", - "sim.run(mp.after_sources(h),\n", - " until_after_sources=100)\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=src_cmpt,\n", + " center=mp.Vector3(r + dr + 0.1),\n", + " )\n", + "]\n", + "\n", + "geometry = [\n", + " mp.Block(\n", + " center=mp.Vector3(r + dr + (w / 2)),\n", + " size=mp.Vector3(w, mp.inf, mp.inf),\n", + " material=mp.Medium(index=n),\n", + " )\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " dimensions=dimensions,\n", + " m=m,\n", + ")\n", + "\n", + "h = mp.Harminv(src_cmpt, mp.Vector3(r + dr + 0.1), fcen, df)\n", + "sim.run(mp.after_sources(h), until_after_sources=100)\n", "\n", "frq_perturbed = h.modes[0].freq\n", "\n", "finite_diff_dw_dR = (frq_perturbed - frq_unperturbed) / dr\n", "\n", - "print(\"dwdR:, {} (pert. theory), {} (finite diff.)\".format(perturb_theory_dw_dR,finite_diff_dw_dR))" + "print(\n", + " \"dwdR:, {} (pert. theory), {} (finite diff.)\".format(\n", + " perturb_theory_dw_dR, finite_diff_dw_dR\n", + " )\n", + ")" ] }, { diff --git a/python/examples/perturbation_theory.py b/python/examples/perturbation_theory.py index a82fe585a..df9f35d42 100644 --- a/python/examples/perturbation_theory.py +++ b/python/examples/perturbation_theory.py @@ -6,47 +6,56 @@ def main(args): if args.perpendicular: src_cmpt = mp.Hz - fcen = 0.21 # pulse center frequency + fcen = 0.21 # pulse center frequency else: src_cmpt = mp.Ez - fcen = 0.17 # pulse center frequency + fcen = 0.17 # pulse center frequency - n = 3.4 # index of waveguide - w = 1 # ring width - r = 1 # inner radius of ring - pad = 4 # padding between waveguide and edge of PML - dpml = 2 # thickness of PML - m = 5 # angular dependence + n = 3.4 # index of waveguide + w = 1 # ring width + r = 1 # inner radius of ring + pad = 4 # padding between waveguide and edge of PML + dpml = 2 # thickness of PML + m = 5 # angular dependence pml_layers = [mp.PML(dpml)] - sr = r + w + pad + dpml # radial size (cell is from 0 to sr) - dimensions = mp.CYLINDRICAL # coordinate system is (r,phi,z) instead of (x,y,z) + sr = r + w + pad + dpml # radial size (cell is from 0 to sr) + dimensions = mp.CYLINDRICAL # coordinate system is (r,phi,z) instead of (x,y,z) cell = mp.Vector3(sr) - geometry = [mp.Block(center=mp.Vector3(r + (w / 2)), - size=mp.Vector3(w, mp.inf, mp.inf), - material=mp.Medium(index=n))] + geometry = [ + mp.Block( + center=mp.Vector3(r + (w / 2)), + size=mp.Vector3(w, mp.inf, mp.inf), + material=mp.Medium(index=n), + ) + ] # find resonant frequency of unperturbed geometry using broadband source - df = 0.2*fcen # pulse width (in frequency) - - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=src_cmpt, - center=mp.Vector3(r+0.1))] - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - dimensions=dimensions, - m=m) - - h = mp.Harminv(src_cmpt, mp.Vector3(r+0.1), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=100) + df = 0.2 * fcen # pulse width (in frequency) + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + 0.1), + ) + ] + + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + dimensions=dimensions, + m=m, + ) + + h = mp.Harminv(src_cmpt, mp.Vector3(r + 0.1), fcen, df) + sim.run(mp.after_sources(h), until_after_sources=100) frq_unperturbed = h.modes[0].freq @@ -55,34 +64,68 @@ def main(args): # unperturbed geometry with narrowband source centered at resonant frequency fcen = frq_unperturbed - df = 0.05*fcen - - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=src_cmpt, - center=mp.Vector3(r+0.1))] - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - dimensions=dimensions, - m=m) + df = 0.05 * fcen + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + 0.1), + ) + ] + + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + dimensions=dimensions, + m=m, + ) sim.run(until_after_sources=100) deps = 1 - n**2 - deps_inv = 1 - 1/n**2 + deps_inv = 1 - 1 / n**2 if args.perpendicular: - para_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ep, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ep, mp.Vector3(r+w)))**2) - perp_integral = deps_inv*2*np.pi*(-r*abs(sim.get_field_point(mp.Dr, mp.Vector3(r)))**2 + (r+w)*abs(sim.get_field_point(mp.Dr, mp.Vector3(r+w)))**2) + para_integral = ( + deps + * 2 + * np.pi + * ( + r * abs(sim.get_field_point(mp.Ep, mp.Vector3(r))) ** 2 + - (r + w) * abs(sim.get_field_point(mp.Ep, mp.Vector3(r + w))) ** 2 + ) + ) + perp_integral = ( + deps_inv + * 2 + * np.pi + * ( + -r * abs(sim.get_field_point(mp.Dr, mp.Vector3(r))) ** 2 + + (r + w) * abs(sim.get_field_point(mp.Dr, mp.Vector3(r + w))) ** 2 + ) + ) numerator_integral = para_integral + perp_integral else: - numerator_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ez, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ez, mp.Vector3(r+w)))**2) - - denominator_integral = sim.electric_energy_in_box(center=mp.Vector3(0.5*sr), size=mp.Vector3(sr)) - perturb_theory_dw_dR = -frq_unperturbed * numerator_integral / (4 * denominator_integral) + numerator_integral = ( + deps + * 2 + * np.pi + * ( + r * abs(sim.get_field_point(mp.Ez, mp.Vector3(r))) ** 2 + - (r + w) * abs(sim.get_field_point(mp.Ez, mp.Vector3(r + w))) ** 2 + ) + ) + + denominator_integral = sim.electric_energy_in_box( + center=mp.Vector3(0.5 * sr), size=mp.Vector3(sr) + ) + perturb_theory_dw_dR = ( + -frq_unperturbed * numerator_integral / (4 * denominator_integral) + ) sim.reset_meep() @@ -90,35 +133,53 @@ def main(args): dr = 0.01 - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=src_cmpt, - center=mp.Vector3(r + dr + 0.1))] - - geometry = [mp.Block(center=mp.Vector3(r + dr + (w / 2)), - size=mp.Vector3(w, mp.inf, mp.inf), - material=mp.Medium(index=n))] - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - dimensions=dimensions, - m=m) - - h = mp.Harminv(src_cmpt, mp.Vector3(r+dr+0.1), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=100) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + dr + 0.1), + ) + ] + + geometry = [ + mp.Block( + center=mp.Vector3(r + dr + (w / 2)), + size=mp.Vector3(w, mp.inf, mp.inf), + material=mp.Medium(index=n), + ) + ] + + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + dimensions=dimensions, + m=m, + ) + + h = mp.Harminv(src_cmpt, mp.Vector3(r + dr + 0.1), fcen, df) + sim.run(mp.after_sources(h), until_after_sources=100) frq_perturbed = h.modes[0].freq finite_diff_dw_dR = (frq_perturbed - frq_unperturbed) / dr - print(f"dwdR:, {perturb_theory_dw_dR} (pert. theory), {finite_diff_dw_dR} (finite diff.)") + print( + f"dwdR:, {perturb_theory_dw_dR} (pert. theory), {finite_diff_dw_dR} (finite diff.)" + ) + -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-perpendicular', action='store_true', help='use perpendicular field source (default: parallel field source)') - parser.add_argument('-res', type=int, default=100, help='resolution (default: 100 pixels/um)') + parser.add_argument( + "-perpendicular", + action="store_true", + help="use perpendicular field source (default: parallel field source)", + ) + parser.add_argument( + "-res", type=int, default=100, help="resolution (default: 100 pixels/um)" + ) args = parser.parse_args() main(args) diff --git a/python/examples/perturbation_theory_2d.py b/python/examples/perturbation_theory_2d.py index 4065a2201..920efe9fb 100644 --- a/python/examples/perturbation_theory_2d.py +++ b/python/examples/perturbation_theory_2d.py @@ -6,56 +6,66 @@ def main(args): if args.perpendicular: src_cmpt = mp.Hz - fcen = 0.21 # pulse center frequency + fcen = 0.21 # pulse center frequency else: src_cmpt = mp.Ez - fcen = 0.17 # pulse center frequency + fcen = 0.17 # pulse center frequency - n = 3.4 # index of waveguide - w = 1 # ring width - r = 1 # inner radius of ring - pad = 4 # padding between waveguide and edge of PML - dpml = 2 # thickness of PML + n = 3.4 # index of waveguide + w = 1 # ring width + r = 1 # inner radius of ring + pad = 4 # padding between waveguide and edge of PML + dpml = 2 # thickness of PML pml_layers = [mp.PML(dpml)] - sxy = 2*(r+w+pad+dpml) - cell_size = mp.Vector3(sxy,sxy) + sxy = 2 * (r + w + pad + dpml) + cell_size = mp.Vector3(sxy, sxy) - symmetries = [mp.Mirror(mp.X,phase=+1 if args.perpendicular else -1), - mp.Mirror(mp.Y,phase=-1 if args.perpendicular else +1)] + symmetries = [ + mp.Mirror(mp.X, phase=+1 if args.perpendicular else -1), + mp.Mirror(mp.Y, phase=-1 if args.perpendicular else +1), + ] - geometry = [mp.Cylinder(material=mp.Medium(index=n), - radius=r+w, - height=mp.inf, - center=mp.Vector3()), - mp.Cylinder(material=mp.vacuum, - radius=r, - height=mp.inf, - center=mp.Vector3())] + geometry = [ + mp.Cylinder( + material=mp.Medium(index=n), + radius=r + w, + height=mp.inf, + center=mp.Vector3(), + ), + mp.Cylinder(material=mp.vacuum, radius=r, height=mp.inf, center=mp.Vector3()), + ] # find resonant frequency of unperturbed geometry using broadband source - df = 0.2*fcen # pulse width (in frequency) - - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(r+0.1)), - mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(-(r+0.1)), - amplitude=-1)] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - symmetries=symmetries) - - h = mp.Harminv(src_cmpt, mp.Vector3(r+0.1), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=100) + df = 0.2 * fcen # pulse width (in frequency) + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + 0.1), + ), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + symmetries=symmetries, + ) + + h = mp.Harminv(src_cmpt, mp.Vector3(r + 0.1), fcen, df) + sim.run(mp.after_sources(h), until_after_sources=100) frq_unperturbed = h.modes[0].freq @@ -64,37 +74,73 @@ def main(args): # unperturbed geometry with narrowband source centered at resonant frequency fcen = frq_unperturbed - df = 0.05*fcen - - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(r+0.1)), - mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(-(r+0.1)), - amplitude=-1)] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - symmetries=symmetries) + df = 0.05 * fcen + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + 0.1), + ), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + symmetries=symmetries, + ) sim.run(until_after_sources=100) deps = 1 - n**2 - deps_inv = 1 - 1/n**2 + deps_inv = 1 - 1 / n**2 if args.perpendicular: - para_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ey, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ey, mp.Vector3(r+w)))**2) - perp_integral = deps_inv*2*np.pi*(-r*abs(sim.get_field_point(mp.Dy, mp.Vector3(y=r)))**2 + (r+w)*abs(sim.get_field_point(mp.Dy, mp.Vector3(y=r+w)))**2) + para_integral = ( + deps + * 2 + * np.pi + * ( + r * abs(sim.get_field_point(mp.Ey, mp.Vector3(r))) ** 2 + - (r + w) * abs(sim.get_field_point(mp.Ey, mp.Vector3(r + w))) ** 2 + ) + ) + perp_integral = ( + deps_inv + * 2 + * np.pi + * ( + -r * abs(sim.get_field_point(mp.Dy, mp.Vector3(y=r))) ** 2 + + (r + w) * abs(sim.get_field_point(mp.Dy, mp.Vector3(y=r + w))) ** 2 + ) + ) numerator_integral = para_integral + perp_integral else: - numerator_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ez, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ez, mp.Vector3(r+w)))**2) - - denominator_integral = sim.electric_energy_in_box(center=mp.Vector3(), size=mp.Vector3(sxy-2*dpml,sxy-2*dpml)) - perturb_theory_dw_dR = -frq_unperturbed * numerator_integral / (8 * denominator_integral) + numerator_integral = ( + deps + * 2 + * np.pi + * ( + r * abs(sim.get_field_point(mp.Ez, mp.Vector3(r))) ** 2 + - (r + w) * abs(sim.get_field_point(mp.Ez, mp.Vector3(r + w))) ** 2 + ) + ) + + denominator_integral = sim.electric_energy_in_box( + center=mp.Vector3(), size=mp.Vector3(sxy - 2 * dpml, sxy - 2 * dpml) + ) + perturb_theory_dw_dR = ( + -frq_unperturbed * numerator_integral / (8 * denominator_integral) + ) # perturbed geometry with narrowband source @@ -102,43 +148,62 @@ def main(args): sim.reset_meep() - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(r+dr+0.1)), - mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(-(r+dr+0.1)), - amplitude=-1)] - - geometry = [mp.Cylinder(material=mp.Medium(index=n), - radius=r+dr+w, - height=mp.inf, - center=mp.Vector3()), - mp.Cylinder(material=mp.vacuum, - radius=r+dr, - height=mp.inf, - center=mp.Vector3())] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - symmetries=symmetries) - - h = mp.Harminv(src_cmpt, mp.Vector3(r+dr+0.1), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=100) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + dr + 0.1), + ), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(-(r + dr + 0.1)), + amplitude=-1, + ), + ] + + geometry = [ + mp.Cylinder( + material=mp.Medium(index=n), + radius=r + dr + w, + height=mp.inf, + center=mp.Vector3(), + ), + mp.Cylinder( + material=mp.vacuum, radius=r + dr, height=mp.inf, center=mp.Vector3() + ), + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + symmetries=symmetries, + ) + + h = mp.Harminv(src_cmpt, mp.Vector3(r + dr + 0.1), fcen, df) + sim.run(mp.after_sources(h), until_after_sources=100) frq_perturbed = h.modes[0].freq finite_diff_dw_dR = (frq_perturbed - frq_unperturbed) / dr - print(f"dwdR:, {perturb_theory_dw_dR} (pert. theory), {finite_diff_dw_dR} (finite diff.)") + print( + f"dwdR:, {perturb_theory_dw_dR} (pert. theory), {finite_diff_dw_dR} (finite diff.)" + ) + -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-perpendicular', action='store_true', help='use perpendicular field source (default: parallel field source)') - parser.add_argument('-res', type=int, default=30, help='resolution (default: 30 pixels/um)') + parser.add_argument( + "-perpendicular", + action="store_true", + help="use perpendicular field source (default: parallel field source)", + ) + parser.add_argument( + "-res", type=int, default=30, help="resolution (default: 30 pixels/um)" + ) args = parser.parse_args() main(args) diff --git a/python/examples/phase_in_material.py b/python/examples/phase_in_material.py index 5f629579a..6ccdc0eb0 100644 --- a/python/examples/phase_in_material.py +++ b/python/examples/phase_in_material.py @@ -1,26 +1,25 @@ import meep as mp -cell_size = mp.Vector3(6,6,0) +cell_size = mp.Vector3(6, 6, 0) -geometry1 = [mp.Cylinder(center=mp.Vector3(),radius=1.0,material=mp.Medium(index=3.5))] +geometry1 = [ + mp.Cylinder(center=mp.Vector3(), radius=1.0, material=mp.Medium(index=3.5)) +] -sim1 = mp.Simulation(cell_size=cell_size, - geometry=geometry1, - resolution=20) +sim1 = mp.Simulation(cell_size=cell_size, geometry=geometry1, resolution=20) sim1.init_sim() -geometry2 = [mp.Cylinder(center=mp.Vector3(1,1),radius=1.0,material=mp.Medium(index=3.5))] +geometry2 = [ + mp.Cylinder(center=mp.Vector3(1, 1), radius=1.0, material=mp.Medium(index=3.5)) +] -sim2 = mp.Simulation(cell_size=cell_size, - geometry=geometry2, - resolution=20) +sim2 = mp.Simulation(cell_size=cell_size, geometry=geometry2, resolution=20) sim2.init_sim() -sim1.fields.phase_in_material(sim2.structure,10.0) - -sim1.run(mp.at_beginning(mp.output_epsilon), - mp.at_every(0.5,mp.output_epsilon), - until=10) +sim1.fields.phase_in_material(sim2.structure, 10.0) +sim1.run( + mp.at_beginning(mp.output_epsilon), mp.at_every(0.5, mp.output_epsilon), until=10 +) diff --git a/python/examples/planar_cavity_ldos.py b/python/examples/planar_cavity_ldos.py index ac9fd5cc5..e6b2e516a 100644 --- a/python/examples/planar_cavity_ldos.py +++ b/python/examples/planar_cavity_ldos.py @@ -7,7 +7,8 @@ import meep as mp import numpy as np import matplotlib -matplotlib.use('agg') + +matplotlib.use("agg") import matplotlib.pyplot as plt @@ -17,169 +18,196 @@ resolution = 70 # pixels/μm -dpml = 0.5 # thickness of PML -L = 6.0 # length of non-PML region -n = 2.4 # refractive index of surrounding medium -wvl = 1.0 # wavelength (in vacuum) +dpml = 0.5 # thickness of PML +L = 6.0 # length of non-PML region +n = 2.4 # refractive index of surrounding medium +wvl = 1.0 # wavelength (in vacuum) -fcen = 1/wvl +fcen = 1 / wvl def bulk_ldos_cyl(): - sr = L+dpml - sz = L+2*dpml - cell_size = mp.Vector3(sr,0,sz) + sr = L + dpml + sz = L + 2 * dpml + cell_size = mp.Vector3(sr, 0, sz) pml_layers = [mp.PML(dpml)] - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Er, - center=mp.Vector3())] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1, - default_material=mp.Medium(index=n)) - - sim.run(mp.dft_ldos(fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Er, - mp.Vector3(), - 1e-6)) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Er, + center=mp.Vector3(), + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=n), + ) + + sim.run( + mp.dft_ldos(fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed(20, mp.Er, mp.Vector3(), 1e-6), + ) return sim.ldos_data[0] def cavity_ldos_cyl(sz): - sr = L+dpml - cell_size = mp.Vector3(sr,0,sz) - - pml_layers = [mp.PML(dpml,direction=mp.R)] - - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Er, - center=mp.Vector3())] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1, - default_material=mp.Medium(index=n)) - - sim.run(mp.dft_ldos(fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Er, - mp.Vector3(), - 1e-6)) + sr = L + dpml + cell_size = mp.Vector3(sr, 0, sz) + + pml_layers = [mp.PML(dpml, direction=mp.R)] + + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Er, + center=mp.Vector3(), + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=n), + ) + + sim.run( + mp.dft_ldos(fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed(20, mp.Er, mp.Vector3(), 1e-6), + ) return sim.ldos_data[0] def bulk_ldos_3D(): - s = L+2*dpml - cell_size = mp.Vector3(s,s,s) + s = L + 2 * dpml + cell_size = mp.Vector3(s, s, s) pml_layers = [mp.PML(dpml)] - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Ex, - center=mp.Vector3())] - - symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y), - mp.Mirror(direction=mp.Z)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=n)) - - sim.run(mp.dft_ldos(fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Ex, - mp.Vector3(), - 1e-6)) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Ex, + center=mp.Vector3(), + ) + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=n), + ) + + sim.run( + mp.dft_ldos(fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed(20, mp.Ex, mp.Vector3(), 1e-6), + ) return sim.ldos_data[0] def cavity_ldos_3D(sz): - sxy = L+2*dpml - cell_size = mp.Vector3(sxy,sxy,sz) - - boundary_layers = [mp.PML(dpml,direction=mp.X), - mp.PML(dpml,direction=mp.Y)] - - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Ex, - center=mp.Vector3())] - - symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y), - mp.Mirror(direction=mp.Z)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=n)) - - sim.run(mp.dft_ldos(fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Ex, - mp.Vector3(), - 1e-6)) + sxy = L + 2 * dpml + cell_size = mp.Vector3(sxy, sxy, sz) + + boundary_layers = [mp.PML(dpml, direction=mp.X), mp.PML(dpml, direction=mp.Y)] + + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Ex, + center=mp.Vector3(), + ) + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=n), + ) + + sim.run( + mp.dft_ldos(fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed(20, mp.Ex, mp.Vector3(), 1e-6), + ) return sim.ldos_data[0] -if __name__ == '__main__': +if __name__ == "__main__": ldos_bulk_cyl = bulk_ldos_cyl() ldos_bulk_3D = bulk_ldos_3D() # units of wavelength in medium - cavity_thickness = np.arange(0.50,2.55,0.05) + cavity_thickness = np.arange(0.50, 2.55, 0.05) - gap = cavity_thickness*wvl/n + gap = cavity_thickness * wvl / n ldos_cavity_cyl = np.zeros(len(cavity_thickness)) ldos_cavity_3D = np.zeros(len(cavity_thickness)) - for idx,g in enumerate(gap): + for idx, g in enumerate(gap): ldos_cavity_cyl[idx] = cavity_ldos_cyl(g) ldos_cavity_3D[idx] = cavity_ldos_3D(g) - print("purcell-enh:, {:.3f}, " - "{:.6f} (cyl.), {:.6f} (3D)".format(cavity_thickness[idx], - ldos_cavity_cyl[idx]/ldos_bulk_cyl, - ldos_cavity_3D[idx]/ldos_bulk_3D)) + print( + "purcell-enh:, {:.3f}, " + "{:.6f} (cyl.), {:.6f} (3D)".format( + cavity_thickness[idx], + ldos_cavity_cyl[idx] / ldos_bulk_cyl, + ldos_cavity_3D[idx] / ldos_bulk_3D, + ) + ) # Purcell enhancement factor (relative to bulk medium) pe_meep_cyl = ldos_cavity_cyl / ldos_bulk_cyl pe_meep_3D = ldos_cavity_3D / ldos_bulk_3D # equation 7 of reference - pe_theory = (3*np.fix(cavity_thickness+0.5)/(4*cavity_thickness) + - (4*np.power(np.fix(cavity_thickness+0.5),3) - - np.fix(cavity_thickness+0.5)) / - (16*np.power(cavity_thickness,3))) + pe_theory = 3 * np.fix(cavity_thickness + 0.5) / (4 * cavity_thickness) + ( + 4 * np.power(np.fix(cavity_thickness + 0.5), 3) - np.fix(cavity_thickness + 0.5) + ) / (16 * np.power(cavity_thickness, 3)) if mp.am_master(): - plt.plot(cavity_thickness,pe_meep_3D,'b-',label='Meep (3D)') - plt.plot(cavity_thickness,pe_meep_cyl,'r-',label='Meep (cylin.)') - plt.plot(cavity_thickness,pe_theory,'g-',label='theory') - plt.plot(cavity_thickness,np.ones(len(cavity_thickness)),'k--') - plt.xlabel('cavity thickness, $nL/\lambda$') - plt.ylabel('Purcell enhancement factor') - plt.title("planar point dipole at λ=1.0 μm in a planar cavity\n" - "with n=2.4 and lossless metallic walls") - plt.axis([0.5,2.5,0.4,3.1]) + plt.plot(cavity_thickness, pe_meep_3D, "b-", label="Meep (3D)") + plt.plot(cavity_thickness, pe_meep_cyl, "r-", label="Meep (cylin.)") + plt.plot(cavity_thickness, pe_theory, "g-", label="theory") + plt.plot(cavity_thickness, np.ones(len(cavity_thickness)), "k--") + plt.xlabel("cavity thickness, $nL/\lambda$") + plt.ylabel("Purcell enhancement factor") + plt.title( + "planar point dipole at λ=1.0 μm in a planar cavity\n" + "with n=2.4 and lossless metallic walls" + ) + plt.axis([0.5, 2.5, 0.4, 3.1]) plt.legend() - plt.savefig('cavity_purcell_factor_vs_thickness.png', - bbox_inches='tight') + plt.savefig("cavity_purcell_factor_vs_thickness.png", bbox_inches="tight") diff --git a/python/examples/polarization_grating.ipynb b/python/examples/polarization_grating.ipynb index c7e49ba03..65165d5c6 100644 --- a/python/examples/polarization_grating.ipynb +++ b/python/examples/polarization_grating.ipynb @@ -28,63 +28,102 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 30 # pixels/μm\n", + "resolution = 30 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 1.0 # substrate thickness\n", - "dpad = 1.0 # padding thickness\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 1.0 # substrate thickness\n", + "dpad = 1.0 # padding thickness\n", "\n", - "k_point = mp.Vector3(0,0,0)\n", + "k_point = mp.Vector3(0, 0, 0)\n", "\n", - "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", + "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", "\n", "n_0 = 1.55\n", "delta_n = 0.159\n", - "epsilon_diag = mp.Matrix(mp.Vector3(n_0**2,0,0),mp.Vector3(0,n_0**2,0),mp.Vector3(0,0,(n_0+delta_n)**2))\n", + "epsilon_diag = mp.Matrix(\n", + " mp.Vector3(n_0**2, 0, 0),\n", + " mp.Vector3(0, n_0**2, 0),\n", + " mp.Vector3(0, 0, (n_0 + delta_n) ** 2),\n", + ")\n", "\n", - "wvl = 0.54 # center wavelength\n", - "fcen = 1/wvl # center frequency\n", + "wvl = 0.54 # center wavelength\n", + "fcen = 1 / wvl # center frequency\n", "\n", - "def pol_grating(d,ph,gp,nmode):\n", - " sx = dpml+dsub+d+d+dpad+dpml\n", + "\n", + "def pol_grating(d, ph, gp, nmode):\n", + " sx = dpml + dsub + d + d + dpad + dpml\n", " sy = gp\n", "\n", - " cell_size = mp.Vector3(sx,sy,0)\n", + " cell_size = mp.Vector3(sx, sy, 0)\n", "\n", " # twist angle of nematic director; from equation 1b\n", " def phi(p):\n", - " xx = p.x-(-0.5*sx+dpml+dsub)\n", + " xx = p.x - (-0.5 * sx + dpml + dsub)\n", " if (xx >= 0) and (xx <= d):\n", - " return math.pi*p.y/gp + ph*xx/d\n", + " return math.pi * p.y / gp + ph * xx / d\n", " else:\n", - " return math.pi*p.y/gp - ph*xx/d + 2*ph\n", + " return math.pi * p.y / gp - ph * xx / d + 2 * ph\n", "\n", " # return the anisotropic permittivity tensor for a uniaxial, twisted nematic liquid crystal\n", " def lc_mat(p):\n", " # rotation matrix for rotation around x axis\n", - " Rx = mp.Matrix(mp.Vector3(1,0,0),mp.Vector3(0,math.cos(phi(p)),math.sin(phi(p))),mp.Vector3(0,-math.sin(phi(p)),math.cos(phi(p))))\n", + " Rx = mp.Matrix(\n", + " mp.Vector3(1, 0, 0),\n", + " mp.Vector3(0, math.cos(phi(p)), math.sin(phi(p))),\n", + " mp.Vector3(0, -math.sin(phi(p)), math.cos(phi(p))),\n", + " )\n", " lc_epsilon = Rx * epsilon_diag * Rx.transpose()\n", - " lc_epsilon_diag = mp.Vector3(lc_epsilon[0].x,lc_epsilon[1].y,lc_epsilon[2].z)\n", - " lc_epsilon_offdiag = mp.Vector3(lc_epsilon[1].x,lc_epsilon[2].x,lc_epsilon[2].y)\n", - " return mp.Medium(epsilon_diag=lc_epsilon_diag,epsilon_offdiag=lc_epsilon_offdiag)\n", + " lc_epsilon_diag = mp.Vector3(lc_epsilon[0].x, lc_epsilon[1].y, lc_epsilon[2].z)\n", + " lc_epsilon_offdiag = mp.Vector3(\n", + " lc_epsilon[1].x, lc_epsilon[2].x, lc_epsilon[2].y\n", + " )\n", + " return mp.Medium(\n", + " epsilon_diag=lc_epsilon_diag, epsilon_offdiag=lc_epsilon_offdiag\n", + " )\n", "\n", - " geometry = [mp.Block(center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)),size=mp.Vector3(dpml+dsub,mp.inf,mp.inf),material=mp.Medium(index=n_0)),\n", - " mp.Block(center=mp.Vector3(-0.5*sx+dpml+dsub+d),size=mp.Vector3(2*d,mp.inf,mp.inf),material=lc_mat)]\n", + " geometry = [\n", + " mp.Block(\n", + " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", + " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", + " material=mp.Medium(index=n_0),\n", + " ),\n", + " mp.Block(\n", + " center=mp.Vector3(-0.5 * sx + dpml + dsub + d),\n", + " size=mp.Vector3(2 * d, mp.inf, mp.inf),\n", + " material=lc_mat,\n", + " ),\n", + " ]\n", "\n", " # linear-polarized planewave pulse source\n", - " src_pt = mp.Vector3(-0.5*sx+dpml+0.3*dsub,0,0)\n", - " sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.05*fcen), component=mp.Ez, center=src_pt, size=mp.Vector3(0,sy,0)),\n", - " mp.Source(mp.GaussianSource(fcen,fwidth=0.05*fcen), component=mp.Ey, center=src_pt, size=mp.Vector3(0,sy,0))]\n", + " src_pt = mp.Vector3(-0.5 * sx + dpml + 0.3 * dsub, 0, 0)\n", + " sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=0.05 * fcen),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(0, sy, 0),\n", + " ),\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=0.05 * fcen),\n", + " component=mp.Ey,\n", + " center=src_pt,\n", + " size=mp.Vector3(0, sy, 0),\n", + " ),\n", + " ]\n", "\n", - " sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " default_material=mp.Medium(index=n_0))\n", + " sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " default_material=mp.Medium(index=n_0),\n", + " )\n", "\n", - " tran_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0)\n", - " tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0)))\n", + " tran_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0)\n", + " tran_flux = sim.add_flux(\n", + " fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0))\n", + " )\n", "\n", " sim.run(until_after_sources=100)\n", "\n", @@ -92,22 +131,30 @@ "\n", " sim.reset_meep()\n", "\n", - " sim = mp.Simulation(resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " geometry=geometry)\n", + " sim = mp.Simulation(\n", + " resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " geometry=geometry,\n", + " )\n", "\n", - " tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0)))\n", + " tran_flux = sim.add_flux(\n", + " fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0))\n", + " )\n", "\n", " sim.run(until_after_sources=300)\n", "\n", - " res1 = sim.get_eigenmode_coefficients(tran_flux, range(1,nmode+1), eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", - " res2 = sim.get_eigenmode_coefficients(tran_flux, range(1,nmode+1), eig_parity=mp.EVEN_Z+mp.ODD_Y)\n", - " angles = [math.degrees(math.acos(kdom.x/fcen)) for kdom in res1.kdom]\n", + " res1 = sim.get_eigenmode_coefficients(\n", + " tran_flux, range(1, nmode + 1), eig_parity=mp.ODD_Z + mp.EVEN_Y\n", + " )\n", + " res2 = sim.get_eigenmode_coefficients(\n", + " tran_flux, range(1, nmode + 1), eig_parity=mp.EVEN_Z + mp.ODD_Y\n", + " )\n", + " angles = [math.degrees(math.acos(kdom.x / fcen)) for kdom in res1.kdom]\n", "\n", - " return input_flux[0], angles, res1.alpha[:,0,0], res2.alpha[:,0,0];" + " return input_flux[0], angles, res1.alpha[:, 0, 0], res2.alpha[:, 0, 0];" ] }, { @@ -3144,11 +3191,11 @@ } ], "source": [ - "ph_uniaxial = 0 # chiral layer twist angle for uniaxial grating\n", - "ph_twisted = 70 # chiral layer twist angle for bilayer grating\n", - "gp = 6.5 # grating period\n", - "nmode = 5 # number of mode coefficients to compute\n", - "dd = np.arange(0.2,3.5,0.2) # chiral layer thickness\n", + "ph_uniaxial = 0 # chiral layer twist angle for uniaxial grating\n", + "ph_twisted = 70 # chiral layer twist angle for bilayer grating\n", + "gp = 6.5 # grating period\n", + "nmode = 5 # number of mode coefficients to compute\n", + "dd = np.arange(0.2, 3.5, 0.2) # chiral layer thickness\n", "\n", "m0_uniaxial = np.zeros(dd.size)\n", "m1_uniaxial = np.zeros(dd.size)\n", @@ -3159,18 +3206,22 @@ "ang_twisted = np.zeros(dd.size)\n", "\n", "for k in range(len(dd)):\n", - " input_flux, angles, coeffs1, coeffs2 = pol_grating(0.5*dd[k],math.radians(ph_uniaxial),gp,nmode)\n", - " tran = (abs(coeffs1)**2+abs(coeffs2)**2)/input_flux\n", + " input_flux, angles, coeffs1, coeffs2 = pol_grating(\n", + " 0.5 * dd[k], math.radians(ph_uniaxial), gp, nmode\n", + " )\n", + " tran = (abs(coeffs1) ** 2 + abs(coeffs2) ** 2) / input_flux\n", " for m in range(nmode):\n", - " print(\"tran (uniaxial):, {}, {:.2f}, {:.5f}\".format(m,angles[m],tran[m]))\n", + " print(\"tran (uniaxial):, {}, {:.2f}, {:.5f}\".format(m, angles[m], tran[m]))\n", " m0_uniaxial[k] = tran[0]\n", " m1_uniaxial[k] = tran[1]\n", " ang_uniaxial[k] = angles[1]\n", "\n", - " input_flux, angles, coeffs1, coeffs2 = pol_grating(dd[k],math.radians(ph_twisted),gp,nmode)\n", - " tran = (abs(coeffs1)**2+abs(coeffs2)**2)/input_flux\n", + " input_flux, angles, coeffs1, coeffs2 = pol_grating(\n", + " dd[k], math.radians(ph_twisted), gp, nmode\n", + " )\n", + " tran = (abs(coeffs1) ** 2 + abs(coeffs2) ** 2) / input_flux\n", " for m in range(nmode):\n", - " print(\"tran (twisted):, {}, {:.2f}, {:.5f}\".format(m,angles[m],tran[m]))\n", + " print(\"tran (twisted):, {}, {:.2f}, {:.5f}\".format(m, angles[m], tran[m]))\n", " m0_twisted[k] = tran[0]\n", " m1_twisted[k] = tran[1]\n", " ang_twisted[k] = angles[1]" @@ -3190,13 +3241,13 @@ "outputs": [], "source": [ "cos_angles = [math.cos(math.radians(t)) for t in ang_uniaxial]\n", - "tran = m0_uniaxial+2*m1_uniaxial\n", - "eff_m0 = m0_uniaxial/tran\n", - "eff_m1 = (2*m1_uniaxial/tran)/cos_angles\n", + "tran = m0_uniaxial + 2 * m1_uniaxial\n", + "eff_m0 = m0_uniaxial / tran\n", + "eff_m1 = (2 * m1_uniaxial / tran) / cos_angles\n", "\n", - "phase = delta_n*dd/wvl\n", - "eff_m0_analytic = [math.cos(math.pi*p)**2 for p in phase]\n", - "eff_m1_analytic = [math.sin(math.pi*p)**2 for p in phase]" + "phase = delta_n * dd / wvl\n", + "eff_m0_analytic = [math.cos(math.pi * p) ** 2 for p in phase]\n", + "eff_m1_analytic = [math.sin(math.pi * p) ** 2 for p in phase]" ] }, { @@ -3219,31 +3270,31 @@ ], "source": [ "plt.figure(dpi=150)\n", - "plt.subplot(1,2,1)\n", - "plt.plot(phase,eff_m0,'bo-',clip_on=False,label='0th order (meep)')\n", - "plt.plot(phase,eff_m0_analytic,'b--',clip_on=False,label='0th order (analytic)')\n", - "plt.plot(phase,eff_m1,'ro-',clip_on=False,label='±1 orders (meep)')\n", - "plt.plot(phase,eff_m1_analytic,'r--',clip_on=False,label='±1 orders (analytic)')\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(phase, eff_m0, \"bo-\", clip_on=False, label=\"0th order (meep)\")\n", + "plt.plot(phase, eff_m0_analytic, \"b--\", clip_on=False, label=\"0th order (analytic)\")\n", + "plt.plot(phase, eff_m1, \"ro-\", clip_on=False, label=\"±1 orders (meep)\")\n", + "plt.plot(phase, eff_m1_analytic, \"r--\", clip_on=False, label=\"±1 orders (analytic)\")\n", "plt.axis([0, 1.0, 0, 1])\n", - "plt.xticks([t for t in np.arange(0,1.2,0.2)])\n", + "plt.xticks([t for t in np.arange(0, 1.2, 0.2)])\n", "plt.xlabel(\"phase delay Δnd/λ\")\n", "plt.ylabel(\"diffraction efficiency @ λ = 0.54 μm\")\n", - "plt.legend(loc='center')\n", + "plt.legend(loc=\"center\")\n", "plt.title(\"homogeneous uniaxial grating\")\n", "\n", "cos_angles = [math.cos(math.radians(t)) for t in ang_twisted]\n", - "tran = m0_twisted+2*m1_twisted\n", - "eff_m0 = m0_twisted/tran\n", - "eff_m1 = (2*m1_twisted/tran)/cos_angles\n", + "tran = m0_twisted + 2 * m1_twisted\n", + "eff_m0 = m0_twisted / tran\n", + "eff_m1 = (2 * m1_twisted / tran) / cos_angles\n", "\n", - "plt.subplot(1,2,2)\n", - "plt.plot(phase,eff_m0,'bo-',clip_on=False,label='0th order (meep)')\n", - "plt.plot(phase,eff_m1,'ro-',clip_on=False,label='±1 orders (meep)')\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(phase, eff_m0, \"bo-\", clip_on=False, label=\"0th order (meep)\")\n", + "plt.plot(phase, eff_m1, \"ro-\", clip_on=False, label=\"±1 orders (meep)\")\n", "plt.axis([0, 1.0, 0, 1])\n", - "plt.xticks([t for t in np.arange(0,1.2,0.2)])\n", + "plt.xticks([t for t in np.arange(0, 1.2, 0.2)])\n", "plt.xlabel(\"phase delay Δnd/λ\")\n", "plt.ylabel(\"diffraction efficiency @ λ = 0.54 μm\")\n", - "plt.legend(loc='center')\n", + "plt.legend(loc=\"center\")\n", "plt.title(\"bilayer twisted-nematic grating\")\n", "\n", "plt.tight_layout()\n", diff --git a/python/examples/polarization_grating.py b/python/examples/polarization_grating.py index c943b559c..25cb227e5 100644 --- a/python/examples/polarization_grating.py +++ b/python/examples/polarization_grating.py @@ -8,63 +8,102 @@ import math import matplotlib.pyplot as plt -resolution = 50 # pixels/μm +resolution = 50 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 1.0 # substrate thickness -dpad = 1.0 # padding thickness +dpml = 1.0 # PML thickness +dsub = 1.0 # substrate thickness +dpad = 1.0 # padding thickness -k_point = mp.Vector3(0,0,0) +k_point = mp.Vector3(0, 0, 0) -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] n_0 = 1.55 delta_n = 0.159 -epsilon_diag = mp.Matrix(mp.Vector3(n_0**2,0,0),mp.Vector3(0,n_0**2,0),mp.Vector3(0,0,(n_0+delta_n)**2)) +epsilon_diag = mp.Matrix( + mp.Vector3(n_0**2, 0, 0), + mp.Vector3(0, n_0**2, 0), + mp.Vector3(0, 0, (n_0 + delta_n) ** 2), +) -wvl = 0.54 # center wavelength -fcen = 1/wvl # center frequency +wvl = 0.54 # center wavelength +fcen = 1 / wvl # center frequency -def pol_grating(d,ph,gp,nmode): - sx = dpml+dsub+d+d+dpad+dpml + +def pol_grating(d, ph, gp, nmode): + sx = dpml + dsub + d + d + dpad + dpml sy = gp - cell_size = mp.Vector3(sx,sy,0) + cell_size = mp.Vector3(sx, sy, 0) # twist angle of nematic director; from equation 1b def phi(p): - xx = p.x-(-0.5*sx+dpml+dsub) + xx = p.x - (-0.5 * sx + dpml + dsub) if (xx >= 0) and (xx <= d): - return math.pi*p.y/gp + ph*xx/d + return math.pi * p.y / gp + ph * xx / d else: - return math.pi*p.y/gp - ph*xx/d + 2*ph + return math.pi * p.y / gp - ph * xx / d + 2 * ph # return the anisotropic permittivity tensor for a uniaxial, twisted nematic liquid crystal def lc_mat(p): # rotation matrix for rotation around x axis - Rx = mp.Matrix(mp.Vector3(1,0,0),mp.Vector3(0,math.cos(phi(p)),math.sin(phi(p))),mp.Vector3(0,-math.sin(phi(p)),math.cos(phi(p)))) + Rx = mp.Matrix( + mp.Vector3(1, 0, 0), + mp.Vector3(0, math.cos(phi(p)), math.sin(phi(p))), + mp.Vector3(0, -math.sin(phi(p)), math.cos(phi(p))), + ) lc_epsilon = Rx * epsilon_diag * Rx.transpose() - lc_epsilon_diag = mp.Vector3(lc_epsilon[0].x,lc_epsilon[1].y,lc_epsilon[2].z) - lc_epsilon_offdiag = mp.Vector3(lc_epsilon[1].x,lc_epsilon[2].x,lc_epsilon[2].y) - return mp.Medium(epsilon_diag=lc_epsilon_diag,epsilon_offdiag=lc_epsilon_offdiag) - - geometry = [mp.Block(center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)),size=mp.Vector3(dpml+dsub,mp.inf,mp.inf),material=mp.Medium(index=n_0)), - mp.Block(center=mp.Vector3(-0.5*sx+dpml+dsub+d),size=mp.Vector3(2*d,mp.inf,mp.inf),material=lc_mat)] + lc_epsilon_diag = mp.Vector3(lc_epsilon[0].x, lc_epsilon[1].y, lc_epsilon[2].z) + lc_epsilon_offdiag = mp.Vector3( + lc_epsilon[1].x, lc_epsilon[2].x, lc_epsilon[2].y + ) + return mp.Medium( + epsilon_diag=lc_epsilon_diag, epsilon_offdiag=lc_epsilon_offdiag + ) + + geometry = [ + mp.Block( + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + material=mp.Medium(index=n_0), + ), + mp.Block( + center=mp.Vector3(-0.5 * sx + dpml + dsub + d), + size=mp.Vector3(2 * d, mp.inf, mp.inf), + material=lc_mat, + ), + ] # linear-polarized planewave pulse source - src_pt = mp.Vector3(-0.5*sx+dpml+0.3*dsub,0,0) - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.05*fcen), component=mp.Ez, center=src_pt, size=mp.Vector3(0,sy,0)), - mp.Source(mp.GaussianSource(fcen,fwidth=0.05*fcen), component=mp.Ey, center=src_pt, size=mp.Vector3(0,sy,0))] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - sources=sources, - default_material=mp.Medium(index=n_0)) - - tran_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0) - tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0))) + src_pt = mp.Vector3(-0.5 * sx + dpml + 0.3 * dsub, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=0.05 * fcen), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(0, sy, 0), + ), + mp.Source( + mp.GaussianSource(fcen, fwidth=0.05 * fcen), + component=mp.Ey, + center=src_pt, + size=mp.Vector3(0, sy, 0), + ), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + sources=sources, + default_material=mp.Medium(index=n_0), + ) + + tran_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0) + tran_flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) + ) sim.run(until_after_sources=100) @@ -72,30 +111,37 @@ def lc_mat(p): sim.reset_meep() - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - sources=sources, - geometry=geometry) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + sources=sources, + geometry=geometry, + ) - tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0))) + tran_flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) + ) sim.run(until_after_sources=300) - res1 = sim.get_eigenmode_coefficients(tran_flux, range(1,nmode+1), eig_parity=mp.ODD_Z+mp.EVEN_Y) - res2 = sim.get_eigenmode_coefficients(tran_flux, range(1,nmode+1), eig_parity=mp.EVEN_Z+mp.ODD_Y) - angles = [math.degrees(math.acos(kdom.x/fcen)) for kdom in res1.kdom] - - return input_flux[0], angles, res1.alpha[:,0,0], res2.alpha[:,0,0]; + res1 = sim.get_eigenmode_coefficients( + tran_flux, range(1, nmode + 1), eig_parity=mp.ODD_Z + mp.EVEN_Y + ) + res2 = sim.get_eigenmode_coefficients( + tran_flux, range(1, nmode + 1), eig_parity=mp.EVEN_Z + mp.ODD_Y + ) + angles = [math.degrees(math.acos(kdom.x / fcen)) for kdom in res1.kdom] + return input_flux[0], angles, res1.alpha[:, 0, 0], res2.alpha[:, 0, 0] -ph_uniaxial = 0 # chiral layer twist angle for uniaxial grating -ph_twisted = 70 # chiral layer twist angle for bilayer grating -gp = 6.5 # grating period -nmode = 5 # number of mode coefficients to compute -dd = np.arange(0.1,3.5,0.1) # chiral layer thickness +ph_uniaxial = 0 # chiral layer twist angle for uniaxial grating +ph_twisted = 70 # chiral layer twist angle for bilayer grating +gp = 6.5 # grating period +nmode = 5 # number of mode coefficients to compute +dd = np.arange(0.1, 3.5, 0.1) # chiral layer thickness m0_uniaxial = np.zeros(dd.size) m1_uniaxial = np.zeros(dd.size) @@ -106,58 +152,62 @@ def lc_mat(p): ang_twisted = np.zeros(dd.size) for k in range(len(dd)): - input_flux, angles, coeffs1, coeffs2 = pol_grating(0.5*dd[k],math.radians(ph_uniaxial),gp,nmode) - tran = (abs(coeffs1)**2+abs(coeffs2)**2)/input_flux + input_flux, angles, coeffs1, coeffs2 = pol_grating( + 0.5 * dd[k], math.radians(ph_uniaxial), gp, nmode + ) + tran = (abs(coeffs1) ** 2 + abs(coeffs2) ** 2) / input_flux for m in range(nmode): - print("tran (uniaxial):, {}, {:.2f}, {:.5f}".format(m,angles[m],tran[m])) + print("tran (uniaxial):, {}, {:.2f}, {:.5f}".format(m, angles[m], tran[m])) m0_uniaxial[k] = tran[0] m1_uniaxial[k] = tran[1] ang_uniaxial[k] = angles[1] - input_flux, angles, coeffs1, coeffs2 = pol_grating(dd[k],math.radians(ph_twisted),gp,nmode) - tran = (abs(coeffs1)**2+abs(coeffs2)**2)/input_flux + input_flux, angles, coeffs1, coeffs2 = pol_grating( + dd[k], math.radians(ph_twisted), gp, nmode + ) + tran = (abs(coeffs1) ** 2 + abs(coeffs2) ** 2) / input_flux for m in range(nmode): - print("tran (twisted):, {}, {:.2f}, {:.5f}".format(m,angles[m],tran[m])) + print("tran (twisted):, {}, {:.2f}, {:.5f}".format(m, angles[m], tran[m])) m0_twisted[k] = tran[0] m1_twisted[k] = tran[1] ang_twisted[k] = angles[1] cos_angles = [math.cos(math.radians(t)) for t in ang_uniaxial] -tran = m0_uniaxial+2*m1_uniaxial -eff_m0 = m0_uniaxial/tran -eff_m1 = (2*m1_uniaxial/tran)/cos_angles +tran = m0_uniaxial + 2 * m1_uniaxial +eff_m0 = m0_uniaxial / tran +eff_m1 = (2 * m1_uniaxial / tran) / cos_angles -phase = delta_n*dd/wvl -eff_m0_analytic = [math.cos(math.pi*p)**2 for p in phase] -eff_m1_analytic = [math.sin(math.pi*p)**2 for p in phase] +phase = delta_n * dd / wvl +eff_m0_analytic = [math.cos(math.pi * p) ** 2 for p in phase] +eff_m1_analytic = [math.sin(math.pi * p) ** 2 for p in phase] plt.figure(dpi=150) -plt.subplot(1,2,1) -plt.plot(phase,eff_m0,'bo-',clip_on=False,label='0th order (meep)') -plt.plot(phase,eff_m0_analytic,'b--',clip_on=False,label='0th order (analytic)') -plt.plot(phase,eff_m1,'ro-',clip_on=False,label='±1 orders (meep)') -plt.plot(phase,eff_m1_analytic,'r--',clip_on=False,label='±1 orders (analytic)') +plt.subplot(1, 2, 1) +plt.plot(phase, eff_m0, "bo-", clip_on=False, label="0th order (meep)") +plt.plot(phase, eff_m0_analytic, "b--", clip_on=False, label="0th order (analytic)") +plt.plot(phase, eff_m1, "ro-", clip_on=False, label="±1 orders (meep)") +plt.plot(phase, eff_m1_analytic, "r--", clip_on=False, label="±1 orders (analytic)") plt.axis([0, 1.0, 0, 1]) -plt.xticks(list(np.arange(0,1.2,0.2))) +plt.xticks(list(np.arange(0, 1.2, 0.2))) plt.xlabel("phase delay Δnd/λ") plt.ylabel("diffraction efficiency @ λ = 0.54 μm") -plt.legend(loc='center') +plt.legend(loc="center") plt.title("homogeneous uniaxial grating") cos_angles = [math.cos(math.radians(t)) for t in ang_twisted] -tran = m0_twisted+2*m1_twisted -eff_m0 = m0_twisted/tran -eff_m1 = (2*m1_twisted/tran)/cos_angles +tran = m0_twisted + 2 * m1_twisted +eff_m0 = m0_twisted / tran +eff_m1 = (2 * m1_twisted / tran) / cos_angles -plt.subplot(1,2,2) -plt.plot(phase,eff_m0,'bo-',clip_on=False,label='0th order (meep)') -plt.plot(phase,eff_m1,'ro-',clip_on=False,label='±1 orders (meep)') +plt.subplot(1, 2, 2) +plt.plot(phase, eff_m0, "bo-", clip_on=False, label="0th order (meep)") +plt.plot(phase, eff_m1, "ro-", clip_on=False, label="±1 orders (meep)") plt.axis([0, 1.0, 0, 1]) -plt.xticks(list(np.arange(0,1.2,0.2))) +plt.xticks(list(np.arange(0, 1.2, 0.2))) plt.xlabel("phase delay Δnd/λ") plt.ylabel("diffraction efficiency @ λ = 0.54 μm") -plt.legend(loc='center') +plt.legend(loc="center") plt.title("bilayer twisted-nematic grating") plt.show() diff --git a/python/examples/pw-source.py b/python/examples/pw-source.py index c22d9b227..f9932bc8f 100644 --- a/python/examples/pw-source.py +++ b/python/examples/pw-source.py @@ -26,12 +26,14 @@ def pw_amp(k, x0): def _pw_amp(x): return cmath.exp(1j * k.dot(x + x0)) + return _pw_amp + fcen = 0.8 # pulse center frequency df = 0.02 # turn-on bandwidth kdir = mp.Vector3(1, 1) # direction of k (length is irrelevant) -n = 1 # refractive index of material containing the source +n = 1 # refractive index of material containing the source k = kdir.unit().scale(2 * math.pi * fcen * n) # k with correct length sources = [ @@ -40,15 +42,15 @@ def _pw_amp(x): component=mp.Ez, center=mp.Vector3(-0.5 * s, 0), size=mp.Vector3(0, s), - amp_func=pw_amp(k, mp.Vector3(x=-0.5 * s)) + amp_func=pw_amp(k, mp.Vector3(x=-0.5 * s)), ), mp.Source( mp.ContinuousSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3(0, -0.5 * s), size=mp.Vector3(s, 0), - amp_func=pw_amp(k, mp.Vector3(y=-0.5 * s)) - ) + amp_func=pw_amp(k, mp.Vector3(y=-0.5 * s)), + ), ] sim = mp.Simulation( diff --git a/python/examples/refl-angular-kz2d.py b/python/examples/refl-angular-kz2d.py index 13fea4c88..bf67ac337 100644 --- a/python/examples/refl-angular-kz2d.py +++ b/python/examples/refl-angular-kz2d.py @@ -1,12 +1,13 @@ import meep as mp import math + def refl_planar(theta, kz_2d): resolution = 100 dpml = 1.0 sx = 10 - sx = 10 + 2*dpml + sx = 10 + 2 * dpml cell_size = mp.Vector3(sx) pml_layers = [mp.PML(dpml)] @@ -15,66 +16,94 @@ def refl_planar(theta, kz_2d): # plane of incidence is XZ k = mp.Vector3(z=math.sin(theta)).scale(fcen) - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Ey, - center=mp.Vector3(-0.5*sx+dpml))] - - sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=k, - kz_2d=kz_2d, - resolution=resolution) - - refl_fr = mp.FluxRegion(center=mp.Vector3(-0.25*sx)) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Ey, + center=mp.Vector3(-0.5 * sx + dpml), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=k, + kz_2d=kz_2d, + resolution=resolution, + ) + + refl_fr = mp.FluxRegion(center=mp.Vector3(-0.25 * sx)) refl = sim.add_flux(fcen, 0, 1, refl_fr) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ey, mp.Vector3(-0.5*sx+dpml), 1e-9)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ey, mp.Vector3(-0.5 * sx + dpml), 1e-9 + ) + ) input_flux = mp.get_fluxes(refl) input_data = sim.get_flux_data(refl) sim.reset_meep() # add a block with n=3.5 for the air-dielectric interface - geometry = [mp.Block(size=mp.Vector3(0.5*sx,mp.inf,mp.inf), - center=mp.Vector3(0.25*sx), - material=mp.Medium(index=3.5))] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - k_point=k, - kz_2d=kz_2d, - resolution=resolution) + geometry = [ + mp.Block( + size=mp.Vector3(0.5 * sx, mp.inf, mp.inf), + center=mp.Vector3(0.25 * sx), + material=mp.Medium(index=3.5), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + k_point=k, + kz_2d=kz_2d, + resolution=resolution, + ) refl = sim.add_flux(fcen, 0, 1, refl_fr) sim.load_minus_flux_data(refl, input_data) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ey, mp.Vector3(-0.5*sx+dpml), 1e-9)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ey, mp.Vector3(-0.5 * sx + dpml), 1e-9 + ) + ) refl_flux = mp.get_fluxes(refl) freqs = mp.get_flux_freqs(refl) - return -refl_flux[0]/input_flux[0] + return -refl_flux[0] / input_flux[0] # rotation angle of source: CCW around Y axis, 0 degrees along +X axis theta_r = math.radians(19.4) -Rmeep_real_imag = refl_planar(theta_r,"real/imag") -Rmeep_complex = refl_planar(theta_r,"complex") -Rmeep_3d = refl_planar(theta_r,"3d") +Rmeep_real_imag = refl_planar(theta_r, "real/imag") +Rmeep_complex = refl_planar(theta_r, "complex") +Rmeep_3d = refl_planar(theta_r, "3d") -n1=1 -n2=3.5 +n1 = 1 +n2 = 3.5 # compute angle of refracted planewave in medium n2 # for incident planewave in medium n1 at angle theta_in -theta_out = lambda theta_in: math.asin(n1*math.sin(theta_in)/n2) +theta_out = lambda theta_in: math.asin(n1 * math.sin(theta_in) / n2) # compute Fresnel reflectance for S-polarization in medium n2 # for incident planewave in medium n1 at angle theta_in -Rfresnel = lambda theta_in: math.fabs((n2*math.cos(theta_out(theta_in))-n1*math.cos(theta_in))/(n2*math.cos(theta_out(theta_in))+n1*math.cos(theta_in)))**2 - -print(f"refl:, {Rmeep_real_imag} (real/imag), {Rmeep_complex} (complex), {Rmeep_3d} (3d), {Rfresnel(theta_r)} (analytic)") +Rfresnel = ( + lambda theta_in: math.fabs( + (n2 * math.cos(theta_out(theta_in)) - n1 * math.cos(theta_in)) + / (n2 * math.cos(theta_out(theta_in)) + n1 * math.cos(theta_in)) + ) + ** 2 +) + +print( + f"refl:, {Rmeep_real_imag} (real/imag), {Rmeep_complex} (complex), {Rmeep_3d} (3d), {Rfresnel(theta_r)} (analytic)" +) diff --git a/python/examples/refl-angular.ipynb b/python/examples/refl-angular.ipynb index b0dd4e27a..ae5c0b948 100644 --- a/python/examples/refl-angular.ipynb +++ b/python/examples/refl-angular.ipynb @@ -32,47 +32,60 @@ "import numpy.matlib\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 50 # pixels/um\n", + "resolution = 50 # pixels/um\n", "\n", - "dpml = 1.0 # PML thickness\n", - "sz = 10 + 2*dpml\n", + "dpml = 1.0 # PML thickness\n", + "sz = 10 + 2 * dpml\n", "cell_size = mp.Vector3(z=sz)\n", "pml_layers = [mp.PML(dpml)]\n", "\n", - "wvl_min = 0.4 # min wavelength\n", - "wvl_max = 0.8 # max wavelength\n", - "fmin = 1/wvl_max # min frequency\n", - "fmax = 1/wvl_min # max frequency\n", - "fcen = 0.5*(fmin+fmax) # center frequency\n", - "df = fmax-fmin # frequency width\n", - "nfreq = 50 # number of frequency bins\n", + "wvl_min = 0.4 # min wavelength\n", + "wvl_max = 0.8 # max wavelength\n", + "fmin = 1 / wvl_max # min frequency\n", + "fmax = 1 / wvl_min # max frequency\n", + "fcen = 0.5 * (fmin + fmax) # center frequency\n", + "df = fmax - fmin # frequency width\n", + "nfreq = 50 # number of frequency bins\n", "\n", - "def planar_reflectance(theta): \n", + "\n", + "def planar_reflectance(theta):\n", " # rotation angle (in degrees) of source: CCW around Y axis, 0 degrees along +Z axis\n", " theta_r = math.radians(theta)\n", "\n", " # plane of incidence is XZ; rotate counter clockwise (CCW) about y-axis\n", " k = mp.Vector3(z=fmin).rotate(mp.Vector3(y=1), theta_r)\n", - " \n", + "\n", " # if normal incidence, force number of dimensions to be 1\n", " if theta_r == 0:\n", " dimensions = 1\n", " else:\n", " dimensions = 3\n", - " \n", - " sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), component=mp.Ex, center=mp.Vector3(z=-0.5*sz+dpml))]\n", "\n", - " sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=k,\n", - " dimensions=dimensions,\n", - " resolution=resolution)\n", + " sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ex,\n", + " center=mp.Vector3(z=-0.5 * sz + dpml),\n", + " )\n", + " ]\n", + "\n", + " sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=k,\n", + " dimensions=dimensions,\n", + " resolution=resolution,\n", + " )\n", "\n", - " refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25*sz))\n", + " refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25 * sz))\n", " refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", - " \n", - " sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(z=-0.5*sz+dpml), 1e-9))\n", + "\n", + " sim.run(\n", + " until_after_sources=mp.stop_when_fields_decayed(\n", + " 50, mp.Ex, mp.Vector3(z=-0.5 * sz + dpml), 1e-9\n", + " )\n", + " )\n", "\n", " empty_flux = mp.get_fluxes(refl)\n", " empty_data = sim.get_flux_data(refl)\n", @@ -80,20 +93,32 @@ " sim.reset_meep()\n", "\n", " # add a block with n=3.5 for the air-dielectric interface\n", - " geometry = [mp.Block(mp.Vector3(mp.inf,mp.inf,0.5*sz), center=mp.Vector3(z=0.25*sz), material=mp.Medium(index=3.5))]\n", + " geometry = [\n", + " mp.Block(\n", + " mp.Vector3(mp.inf, mp.inf, 0.5 * sz),\n", + " center=mp.Vector3(z=0.25 * sz),\n", + " material=mp.Medium(index=3.5),\n", + " )\n", + " ]\n", "\n", - " sim = mp.Simulation(cell_size=cell_size,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=k,\n", - " dimensions=dimensions,\n", - " resolution=resolution)\n", + " sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=k,\n", + " dimensions=dimensions,\n", + " resolution=resolution,\n", + " )\n", "\n", " refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", " sim.load_minus_flux_data(refl, empty_data)\n", "\n", - " sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(z=-0.5*sz+dpml), 1e-9))\n", + " sim.run(\n", + " until_after_sources=mp.stop_when_fields_decayed(\n", + " 50, mp.Ex, mp.Vector3(z=-0.5 * sz + dpml), 1e-9\n", + " )\n", + " )\n", "\n", " refl_flux = mp.get_fluxes(refl)\n", " freqs = mp.get_flux_freqs(refl)\n", @@ -102,12 +127,12 @@ " theta_out = np.empty(nfreq)\n", " R = np.empty(nfreq)\n", " for i in range(nfreq):\n", - " wvls[i] = 1/freqs[i]\n", - " theta_out[i] = math.degrees(math.asin(k.x/freqs[i]))\n", - " R[i] = -refl_flux[i]/empty_flux[i]\n", - " print(\"refl:, {}, {}, {}, {}\".format(k.x,wvls[i],theta_out[i],R[i]))\n", - " \n", - " return k.x*np.ones(nfreq), wvls, theta_out, R " + " wvls[i] = 1 / freqs[i]\n", + " theta_out[i] = math.degrees(math.asin(k.x / freqs[i]))\n", + " R[i] = -refl_flux[i] / empty_flux[i]\n", + " print(\"refl:, {}, {}, {}, {}\".format(k.x, wvls[i], theta_out[i], R[i]))\n", + "\n", + " return k.x * np.ones(nfreq), wvls, theta_out, R" ] }, { @@ -1472,17 +1497,17 @@ } ], "source": [ - "theta_in = np.arange(0,85,5)\n", + "theta_in = np.arange(0, 85, 5)\n", "wvl = np.empty(nfreq)\n", - "kxs = np.empty((nfreq,theta_in.size))\n", - "thetas = np.empty((nfreq,theta_in.size))\n", - "Rmeep = np.empty((nfreq,theta_in.size))\n", + "kxs = np.empty((nfreq, theta_in.size))\n", + "thetas = np.empty((nfreq, theta_in.size))\n", + "Rmeep = np.empty((nfreq, theta_in.size))\n", "\n", "for j in range(theta_in.size):\n", - " kxs[:,j], wvl, thetas[:,j], Rmeep[:,j] = planar_reflectance(theta_in[j])\n", + " kxs[:, j], wvl, thetas[:, j], Rmeep[:, j] = planar_reflectance(theta_in[j])\n", "\n", "# create a 2d matrix for the wavelength by repeating the column vector for each angle\n", - "wvls = np.transpose(np.matlib.repmat(wvl,theta_in.size,1))" + "wvls = np.transpose(np.matlib.repmat(wvl, theta_in.size, 1))" ] }, { @@ -1516,15 +1541,17 @@ ], "source": [ "plt.figure(dpi=200)\n", - "plt.pcolormesh(kxs, wvls, Rmeep, cmap='inferno', shading='gouraud', vmin=0, vmax=Rmeep.max())\n", - "plt.axis([kxs[0,0], kxs[0,-1], wvl_min, wvl_max])\n", - "plt.yticks([t for t in np.linspace(0.4,0.8,5)])\n", + "plt.pcolormesh(\n", + " kxs, wvls, Rmeep, cmap=\"inferno\", shading=\"gouraud\", vmin=0, vmax=Rmeep.max()\n", + ")\n", + "plt.axis([kxs[0, 0], kxs[0, -1], wvl_min, wvl_max])\n", + "plt.yticks([t for t in np.linspace(0.4, 0.8, 5)])\n", "plt.xlabel(r\"Bloch-periodic wavevector (k$_x$/2π)\")\n", "plt.ylabel(\"wavelength (μm)\")\n", "plt.title(\"reflectance (meep)\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.linspace(0,0.4,5)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0,0.4,5)])" + "cbar.set_ticks([t for t in np.linspace(0, 0.4, 5)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0, 0.4, 5)])" ] }, { @@ -1547,16 +1574,18 @@ ], "source": [ "plt.figure(dpi=200)\n", - "plt.pcolormesh(thetas, wvls, Rmeep, cmap='inferno', shading='gouraud', vmin=0, vmax=Rmeep.max())\n", + "plt.pcolormesh(\n", + " thetas, wvls, Rmeep, cmap=\"inferno\", shading=\"gouraud\", vmin=0, vmax=Rmeep.max()\n", + ")\n", "plt.axis([thetas.min(), thetas.max(), wvl_min, wvl_max])\n", - "plt.xticks([t for t in range(0,100,20)])\n", - "plt.yticks([t for t in np.linspace(0.4,0.8,5)])\n", + "plt.xticks([t for t in range(0, 100, 20)])\n", + "plt.yticks([t for t in np.linspace(0.4, 0.8, 5)])\n", "plt.xlabel(\"angle of incident planewave (degrees)\")\n", "plt.ylabel(\"wavelength (μm)\")\n", "plt.title(\"reflectance (meep)\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.linspace(0,0.4,5)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0,0.4,5)])" + "cbar.set_ticks([t for t in np.linspace(0, 0.4, 5)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0, 0.4, 5)])" ] }, { @@ -1578,33 +1607,47 @@ } ], "source": [ - "n1=1\n", - "n2=3.5\n", + "n1 = 1\n", + "n2 = 3.5\n", "\n", "# compute angle of refracted planewave in medium n2\n", "# for incident planewave in medium n1 at angle theta_in\n", - "theta_out = lambda theta_in: math.asin(n1*math.sin(theta_in)/n2)\n", + "theta_out = lambda theta_in: math.asin(n1 * math.sin(theta_in) / n2)\n", "\n", "# compute Fresnel reflectance for P-polarization in medium n2\n", "# for incident planewave in medium n1 at angle theta_in\n", - "Rfresnel = lambda theta_in: math.fabs((n1*math.cos(theta_out(theta_in))-n2*math.cos(theta_in))/(n1*math.cos(theta_out(theta_in))+n2*math.cos(theta_in)))**2\n", + "Rfresnel = (\n", + " lambda theta_in: math.fabs(\n", + " (n1 * math.cos(theta_out(theta_in)) - n2 * math.cos(theta_in))\n", + " / (n1 * math.cos(theta_out(theta_in)) + n2 * math.cos(theta_in))\n", + " )\n", + " ** 2\n", + ")\n", "\n", "Ranalytic = np.empty((nfreq, theta_in.size))\n", "for m in range(wvl.size):\n", " for n in range(theta_in.size):\n", - " Ranalytic[m,n] = Rfresnel(math.radians(thetas[m,n]))\n", + " Ranalytic[m, n] = Rfresnel(math.radians(thetas[m, n]))\n", "\n", "plt.figure(dpi=200)\n", - "plt.pcolormesh(thetas, wvls, Ranalytic, cmap='inferno', shading='gouraud', vmin=0, vmax=Ranalytic.max())\n", + "plt.pcolormesh(\n", + " thetas,\n", + " wvls,\n", + " Ranalytic,\n", + " cmap=\"inferno\",\n", + " shading=\"gouraud\",\n", + " vmin=0,\n", + " vmax=Ranalytic.max(),\n", + ")\n", "plt.axis([thetas.min(), thetas.max(), wvl_min, wvl_max])\n", - "plt.xticks([t for t in range(0,100,20)])\n", - "plt.yticks([t for t in np.linspace(0.4,0.8,5)])\n", + "plt.xticks([t for t in range(0, 100, 20)])\n", + "plt.yticks([t for t in np.linspace(0.4, 0.8, 5)])\n", "plt.xlabel(\"angle of incident planewave (degrees)\")\n", "plt.ylabel(\"wavelength (μm)\")\n", "plt.title(\"reflectance (analytic)\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.linspace(0,0.4,5)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0,0.4,5)])" + "cbar.set_ticks([t for t in np.linspace(0, 0.4, 5)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0, 0.4, 5)])" ] } ], diff --git a/python/examples/refl-angular.py b/python/examples/refl-angular.py index 316beca8a..55230fb4c 100644 --- a/python/examples/refl-angular.py +++ b/python/examples/refl-angular.py @@ -6,74 +6,104 @@ def main(args): resolution = args.res - dpml = 1.0 # PML thickness - sz = 10 + 2*dpml # size of computational cell (without PMLs) - cell_size = mp.Vector3(0,0,sz) + dpml = 1.0 # PML thickness + sz = 10 + 2 * dpml # size of computational cell (without PMLs) + cell_size = mp.Vector3(0, 0, sz) pml_layers = [mp.PML(dpml)] - wvl_min = 0.4 # min wavelength - wvl_max = 0.8 # max wavelength - fmin = 1/wvl_max # min frequency - fmax = 1/wvl_min # max frequency - fcen = 0.5*(fmin+fmax) # center frequency - df = fmax-fmin # frequency width - nfreq = 50 # number of frequency bins + wvl_min = 0.4 # min wavelength + wvl_max = 0.8 # max wavelength + fmin = 1 / wvl_max # min frequency + fmax = 1 / wvl_min # max frequency + fcen = 0.5 * (fmin + fmax) # center frequency + df = fmax - fmin # frequency width + nfreq = 50 # number of frequency bins # rotation angle (in degrees) of source: CCW around Y axis, 0 degrees along +Z axis theta_r = math.radians(args.theta) # plane of incidence is XZ - k = mp.Vector3(math.sin(theta_r),0,math.cos(theta_r)).scale(fmin) + k = mp.Vector3(math.sin(theta_r), 0, math.cos(theta_r)).scale(fmin) # if normal incidence, force number of dimensions to be 1 dimensions = 1 if theta_r == 0 else 3 - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ex, - center=mp.Vector3(0,0,-0.5*sz+dpml))] - - sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=k, - dimensions=dimensions, - resolution=resolution) - - refl_fr = mp.FluxRegion(center=mp.Vector3(0,0,-0.25*sz)) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ex, + center=mp.Vector3(0, 0, -0.5 * sz + dpml), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=k, + dimensions=dimensions, + resolution=resolution, + ) + + refl_fr = mp.FluxRegion(center=mp.Vector3(0, 0, -0.25 * sz)) refl = sim.add_flux(fcen, df, nfreq, refl_fr) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(0,0,-0.5*sz+dpml), 1e-9)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ex, mp.Vector3(0, 0, -0.5 * sz + dpml), 1e-9 + ) + ) empty_flux = mp.get_fluxes(refl) empty_data = sim.get_flux_data(refl) sim.reset_meep() # add a block with n=3.5 for the air-dielectric interface - geometry = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,0.5*sz), - center=mp.Vector3(0,0,0.25*sz), - material=mp.Medium(index=3.5))] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - k_point=k, - dimensions=dimensions, - resolution=resolution) + geometry = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, 0.5 * sz), + center=mp.Vector3(0, 0, 0.25 * sz), + material=mp.Medium(index=3.5), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + k_point=k, + dimensions=dimensions, + resolution=resolution, + ) refl = sim.add_flux(fcen, df, nfreq, refl_fr) sim.load_minus_flux_data(refl, empty_data) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(0,0,-0.5*sz+dpml), 1e-9)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ex, mp.Vector3(0, 0, -0.5 * sz + dpml), 1e-9 + ) + ) refl_flux = mp.get_fluxes(refl) freqs = mp.get_flux_freqs(refl) for i in range(nfreq): - print(f"refl:, {k.x}, {1 / freqs[i]}, {math.degrees(math.asin(k.x/freqs[i]))}, {-refl_flux[i] / empty_flux[i]}") + print( + f"refl:, {k.x}, {1 / freqs[i]}, {math.degrees(math.asin(k.x/freqs[i]))}, {-refl_flux[i] / empty_flux[i]}" + ) -if __name__ == '__main__': + +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-res', type=int, default=200, help='resolution (default: 200 pixels/um)') - parser.add_argument('-theta', type=float, default=0, help='angle of incident planewave (default: 0 degrees)') + parser.add_argument( + "-res", type=int, default=200, help="resolution (default: 200 pixels/um)" + ) + parser.add_argument( + "-theta", + type=float, + default=0, + help="angle of incident planewave (default: 0 degrees)", + ) args = parser.parse_args() main(args) diff --git a/python/examples/refl-quartz.ipynb b/python/examples/refl-quartz.ipynb index fcf26bf51..e11604456 100644 --- a/python/examples/refl-quartz.ipynb +++ b/python/examples/refl-quartz.ipynb @@ -68,27 +68,35 @@ "resolution = 200 # pixels/μm\n", "\n", "dpml = 1.0\n", - "sz = 10+2*dpml\n", + "sz = 10 + 2 * dpml\n", "cell_size = mp.Vector3(z=sz)\n", "pml_layers = [mp.PML(dpml)]\n", "\n", "wvl_min = 0.4\n", "wvl_max = 0.8\n", - "fmin = 1/wvl_max\n", - "fmax = 1/wvl_min\n", - "fcen = 0.5*(fmax+fmin)\n", - "df = fmax-fmin\n", + "fmin = 1 / wvl_max\n", + "fmax = 1 / wvl_min\n", + "fcen = 0.5 * (fmax + fmin)\n", + "df = fmax - fmin\n", "nfreq = 50\n", "\n", - "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), component=mp.Ex, center=mp.Vector3(z=-0.5*sz+dpml))]\n", - "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " dimensions=1,\n", - " resolution=resolution)\n", - "\n", - "refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25*sz))\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ex,\n", + " center=mp.Vector3(z=-0.5 * sz + dpml),\n", + " )\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " dimensions=1,\n", + " resolution=resolution,\n", + ")\n", + "\n", + "refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25 * sz))\n", "refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-9))\n", @@ -97,14 +105,22 @@ "empty_data = sim.get_flux_data(refl)\n", "sim.reset_meep()\n", "\n", - "geometry = [mp.Block(mp.Vector3(mp.inf,mp.inf,0.5*sz), center=mp.Vector3(z=0.25*sz), material=fused_quartz)]\n", - "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources,\n", - " dimensions=1,\n", - " resolution=resolution)\n", + "geometry = [\n", + " mp.Block(\n", + " mp.Vector3(mp.inf, mp.inf, 0.5 * sz),\n", + " center=mp.Vector3(z=0.25 * sz),\n", + " material=fused_quartz,\n", + " )\n", + "]\n", + "\n", + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources,\n", + " dimensions=1,\n", + " resolution=resolution,\n", + ")\n", "\n", "refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", "sim.load_minus_flux_data(refl, empty_data)\n", @@ -112,7 +128,7 @@ "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-9))\n", "\n", "refl_flux = mp.get_fluxes(refl)\n", - "R_meep = -1*np.divide(refl_flux,empty_flux)" + "R_meep = -1 * np.divide(refl_flux, empty_flux)" ] }, { @@ -142,20 +158,27 @@ ], "source": [ "freqs = mp.get_flux_freqs(refl)\n", - "wvls = np.divide(1,freqs)\n", - "\n", - "eps_quartz = lambda l: 1+0.6961663*math.pow(l,2)/(pow(l,2)-pow(0.0684043,2))+0.4079426*pow(l,2)/(pow(l,2)-pow(0.1162414,2))+0.8974794*pow(l,2)/(pow(l,2)-pow(9.896161,2))\n", - "R_fresnel = lambda l: math.pow(math.fabs(1-math.sqrt(eps_quartz(l)))/(1+math.sqrt(eps_quartz(l))),2)\n", + "wvls = np.divide(1, freqs)\n", + "\n", + "eps_quartz = (\n", + " lambda l: 1\n", + " + 0.6961663 * math.pow(l, 2) / (pow(l, 2) - pow(0.0684043, 2))\n", + " + 0.4079426 * pow(l, 2) / (pow(l, 2) - pow(0.1162414, 2))\n", + " + 0.8974794 * pow(l, 2) / (pow(l, 2) - pow(9.896161, 2))\n", + ")\n", + "R_fresnel = lambda l: math.pow(\n", + " math.fabs(1 - math.sqrt(eps_quartz(l))) / (1 + math.sqrt(eps_quartz(l))), 2\n", + ")\n", "R_analytic = [R_fresnel(i) for i in wvls]\n", "\n", "plt.figure()\n", - "plt.plot(wvls,R_meep,'bo-',label='meep')\n", - "plt.plot(wvls,R_analytic,'rs-',label='analytic')\n", + "plt.plot(wvls, R_meep, \"bo-\", label=\"meep\")\n", + "plt.plot(wvls, R_analytic, \"rs-\", label=\"analytic\")\n", "plt.xlabel(\"wavelength (μm)\")\n", "plt.ylabel(\"reflectance\")\n", "plt.axis([0.4, 0.8, 0.0340, 0.0365])\n", - "plt.xticks([t for t in np.arange(0.4,0.9,0.1)])\n", - "plt.legend(loc='upper right')\n", + "plt.xticks([t for t in np.arange(0.4, 0.9, 0.1)])\n", + "plt.legend(loc=\"upper right\")\n", "plt.show()" ] } diff --git a/python/examples/refl-quartz.py b/python/examples/refl-quartz.py index 859b9149d..76ec061af 100644 --- a/python/examples/refl-quartz.py +++ b/python/examples/refl-quartz.py @@ -9,27 +9,35 @@ resolution = 200 # pixels/μm dpml = 1.0 -sz = 10+2*dpml +sz = 10 + 2 * dpml cell_size = mp.Vector3(z=sz) pml_layers = [mp.PML(dpml)] wvl_min = 0.4 wvl_max = 0.8 -fmin = 1/wvl_max -fmax = 1/wvl_min -fcen = 0.5*(fmax+fmin) -df = fmax-fmin +fmin = 1 / wvl_max +fmax = 1 / wvl_min +fcen = 0.5 * (fmax + fmin) +df = fmax - fmin nfreq = 50 -sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), component=mp.Ex, center=mp.Vector3(z=-0.5*sz+dpml))] - -sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - dimensions=1, - resolution=resolution) - -refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25*sz)) +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ex, + center=mp.Vector3(z=-0.5 * sz + dpml), + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=1, + resolution=resolution, +) + +refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25 * sz)) refl = sim.add_flux(fcen, df, nfreq, refl_fr) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-9)) @@ -38,14 +46,22 @@ empty_data = sim.get_flux_data(refl) sim.reset_meep() -geometry = [mp.Block(mp.Vector3(mp.inf,mp.inf,0.5*sz), center=mp.Vector3(z=0.25*sz), material=fused_quartz)] - -sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - dimensions=1, - resolution=resolution) +geometry = [ + mp.Block( + mp.Vector3(mp.inf, mp.inf, 0.5 * sz), + center=mp.Vector3(z=0.25 * sz), + material=fused_quartz, + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + dimensions=1, + resolution=resolution, +) refl = sim.add_flux(fcen, df, nfreq, refl_fr) sim.load_minus_flux_data(refl, empty_data) @@ -53,21 +69,28 @@ sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-9)) refl_flux = mp.get_fluxes(refl) -R_meep = -1*np.divide(refl_flux,empty_flux) +R_meep = -1 * np.divide(refl_flux, empty_flux) freqs = mp.get_flux_freqs(refl) -wvls = np.divide(1,freqs) - -eps_quartz = lambda l: 1+0.6961663*math.pow(l,2)/(pow(l,2)-pow(0.0684043,2))+0.4079426*pow(l,2)/(pow(l,2)-pow(0.1162414,2))+0.8974794*pow(l,2)/(pow(l,2)-pow(9.896161,2)) -R_fresnel = lambda l: math.pow(math.fabs(1-math.sqrt(eps_quartz(l)))/(1+math.sqrt(eps_quartz(l))),2) +wvls = np.divide(1, freqs) + +eps_quartz = ( + lambda l: 1 + + 0.6961663 * math.pow(l, 2) / (pow(l, 2) - pow(0.0684043, 2)) + + 0.4079426 * pow(l, 2) / (pow(l, 2) - pow(0.1162414, 2)) + + 0.8974794 * pow(l, 2) / (pow(l, 2) - pow(9.896161, 2)) +) +R_fresnel = lambda l: math.pow( + math.fabs(1 - math.sqrt(eps_quartz(l))) / (1 + math.sqrt(eps_quartz(l))), 2 +) R_analytic = [R_fresnel(i) for i in wvls] plt.figure() -plt.plot(wvls,R_meep,'bo-',label='meep') -plt.plot(wvls,R_analytic,'rs-',label='analytic') +plt.plot(wvls, R_meep, "bo-", label="meep") +plt.plot(wvls, R_analytic, "rs-", label="analytic") plt.xlabel("wavelength (μm)") plt.ylabel("reflectance") plt.axis([0.4, 0.8, 0.0340, 0.0365]) -plt.xticks(list(np.arange(0.4,0.9,0.1))) -plt.legend(loc='upper right') +plt.xticks(list(np.arange(0.4, 0.9, 0.1))) +plt.legend(loc="upper right") plt.show() diff --git a/python/examples/ring-cyl.py b/python/examples/ring-cyl.py index d4eafd4dd..843535a2a 100644 --- a/python/examples/ring-cyl.py +++ b/python/examples/ring-cyl.py @@ -5,13 +5,14 @@ import meep as mp import argparse + def main(args): - n = 3.4 # index of waveguide - w = 1 # width of waveguide - r = 1 # inner radius of ring - pad = 4 # padding between waveguide and edge of PML - dpml = 32 # thickness of PML + n = 3.4 # index of waveguide + w = 1 # width of waveguide + r = 1 # inner radius of ring + pad = 4 # padding between waveguide and edge of PML + dpml = 32 # thickness of PML sr = r + w + pad + dpml # radial size (cell is from 0 to sr) dimensions = mp.CYLINDRICAL @@ -21,9 +22,13 @@ def main(args): # is given by exp(i m phi), where m is given by: m = args.m - geometry = [mp.Block(center=mp.Vector3(r + (w / 2)), - size=mp.Vector3(w, mp.inf, mp.inf), - material=mp.Medium(index=n))] + geometry = [ + mp.Block( + center=mp.Vector3(r + (w / 2)), + size=mp.Vector3(w, mp.inf, mp.inf), + material=mp.Medium(index=n), + ) + ] pml_layers = [mp.PML(dpml)] resolution = 20 @@ -33,38 +38,58 @@ def main(args): # arbitrary direction. We will only look for Ez-polarized modes. fcen = args.fcen # pulse center frequency - df = args.df # pulse frequency width - sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), - component=mp.Ez, - center=mp.Vector3(r + 0.1))] + df = args.df # pulse frequency width + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(r + 0.1), + ) + ] # note that the r -> -r mirror symmetry is exploited automatically - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - resolution=resolution, - sources=sources, - dimensions=dimensions, - m=m) + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + resolution=resolution, + sources=sources, + dimensions=dimensions, + m=m, + ) - sim.run(mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df)), - until_after_sources=200) + sim.run( + mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df)), + until_after_sources=200, + ) # Output fields for one period at the end. (If we output # at a single time, we might accidentally catch the Ez field when it is # almost zero and get a distorted view.) We'll append the fields # to a file to get an r-by-t picture. We'll also output from -sr to -sr # instead of from 0 to sr. - sim.run(mp.in_volume(mp.Volume(center=mp.Vector3(), size=mp.Vector3(2 * sr)), - mp.at_beginning(mp.output_epsilon), - mp.to_appended("ez", mp.at_every(1 / fcen / 20, mp.output_efield_z))), - until=1 / fcen) + sim.run( + mp.in_volume( + mp.Volume(center=mp.Vector3(), size=mp.Vector3(2 * sr)), + mp.at_beginning(mp.output_epsilon), + mp.to_appended("ez", mp.at_every(1 / fcen / 20, mp.output_efield_z)), + ), + until=1 / fcen, + ) + -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-fcen', type=float, default=0.15, help='pulse center frequency') - parser.add_argument('-df', type=float, default=0.1, help='pulse frequency width') - parser.add_argument('-m', type=int, default=3, help='phi (angular) dependence of the fields given by exp(i m phi)') + parser.add_argument( + "-fcen", type=float, default=0.15, help="pulse center frequency" + ) + parser.add_argument("-df", type=float, default=0.1, help="pulse frequency width") + parser.add_argument( + "-m", + type=int, + default=3, + help="phi (angular) dependence of the fields given by exp(i m phi)", + ) args = parser.parse_args() main(args) diff --git a/python/examples/ring-mode-overlap.py b/python/examples/ring-mode-overlap.py index 336cab4e9..4617e81b9 100644 --- a/python/examples/ring-mode-overlap.py +++ b/python/examples/ring-mode-overlap.py @@ -17,7 +17,7 @@ # and the inner (air) cylinder second. geometry = [ mp.Cylinder(radius=r + w, height=mp.inf, material=mp.Medium(index=n)), - mp.Cylinder(radius=r, height=mp.inf, material=mp.air) + mp.Cylinder(radius=r, height=mp.inf, material=mp.air), ] pml_layers = [mp.PML(dpml)] @@ -28,19 +28,26 @@ # arbitrary direction. We will only look for Ez-polarized modes. fcen = 0.118 # pulse center frequency -df = 0.010 # pulse width (in frequency) -sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, - center=mp.Vector3(r + 0.1))] +df = 0.010 # pulse width (in frequency) +sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(r + 0.1), + ) +] # exploit the mirror symmetry in structure+source: symmetries = [mp.Mirror(mp.Y)] -sim = mp.Simulation(cell_size=cell, - resolution=resolution, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - symmetries=symmetries) +sim = mp.Simulation( + cell_size=cell, + resolution=resolution, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + symmetries=symmetries, +) h1 = mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df) sim.run(mp.after_sources(h1), until_after_sources=300) @@ -56,5 +63,6 @@ def overlap_integral(r, ez1, ez2): return ez1.conjugate() * ez2 + res = sim.integrate2_field_function(fields2, [mp.Ez], [mp.Ez], overlap_integral) print(f"overlap integral of mode at w and 2w: {abs(res)}") diff --git a/python/examples/ring.ipynb b/python/examples/ring.ipynb index 18b1fd4e7..e4a8090bf 100644 --- a/python/examples/ring.ipynb +++ b/python/examples/ring.ipynb @@ -32,16 +32,17 @@ "source": [ "import meep as mp\n", "import matplotlib.pyplot as plt\n", + "\n", "%matplotlib inline\n", "import numpy as np\n", "from IPython.display import Video\n", "\n", - "n = 3.4 # index of waveguide\n", - "w = 1 # width of waveguide\n", - "r = 1 # inner radius of ring\n", - "pad = 4 # padding between waveguide and edge of PML\n", - "dpml = 2 # thickness of PML\n", - "sxy = 2*(r+w+pad+dpml) # cell size" + "n = 3.4 # index of waveguide\n", + "w = 1 # width of waveguide\n", + "r = 1 # inner radius of ring\n", + "pad = 4 # padding between waveguide and edge of PML\n", + "dpml = 2 # thickness of PML\n", + "sxy = 2 * (r + w + pad + dpml) # cell size" ] }, { @@ -57,7 +58,7 @@ "metadata": {}, "outputs": [], "source": [ - "c1 = mp.Cylinder(radius=r+w, material=mp.Medium(index=n))\n", + "c1 = mp.Cylinder(radius=r + w, material=mp.Medium(index=n))\n", "c2 = mp.Cylinder(radius=r)" ] }, @@ -76,9 +77,9 @@ "metadata": {}, "outputs": [], "source": [ - "fcen = 0.15 # pulse center frequency\n", - "df = 0.1 # pulse frequency width\n", - "src = mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r+0.1))" + "fcen = 0.15 # pulse center frequency\n", + "df = 0.1 # pulse frequency width\n", + "src = mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1))" ] }, { @@ -119,11 +120,13 @@ } ], "source": [ - "sim = mp.Simulation(cell_size=mp.Vector3(sxy, sxy),\n", - " geometry=[c1, c2],\n", - " sources=[src],\n", - " resolution=10, \n", - " boundary_layers=[mp.PML(dpml)])\n", + "sim = mp.Simulation(\n", + " cell_size=mp.Vector3(sxy, sxy),\n", + " geometry=[c1, c2],\n", + " sources=[src],\n", + " resolution=10,\n", + " boundary_layers=[mp.PML(dpml)],\n", + ")\n", "plt.figure(dpi=150)\n", "sim.plot2D()\n", "plt.show()" @@ -155,9 +158,11 @@ } ], "source": [ - "sim.run(mp.at_beginning(mp.output_epsilon),\n", - " mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r+0.1), fcen, df)),\n", - " until_after_sources=300)" + "sim.run(\n", + " mp.at_beginning(mp.output_epsilon),\n", + " mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df)),\n", + " until_after_sources=300,\n", + ")" ] }, { @@ -228,24 +233,26 @@ ], "source": [ "sim.reset_meep()\n", - "fcen=0.118\n", + "fcen = 0.118\n", "df = 0.1\n", - "sim.sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r+0.1))]\n", + "sim.sources = [\n", + " mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1))\n", + "]\n", "\n", "# Start the simulation and get into steady state\n", - "sim.run(until=600) \n", + "sim.run(until=600)\n", "\n", "# Prepare the animator and record the steady state response\n", "f = plt.figure(dpi=150)\n", "Animate = mp.Animate2D(sim, fields=mp.Ez, f=f, realtime=False, normalize=True)\n", - "sim.run(mp.at_every(0.5,Animate),until=25)\n", + "sim.run(mp.at_every(0.5, Animate), until=25)\n", "\n", "# Close the animator's working frame\n", "plt.close()\n", "\n", "# Process the animation and view it\n", "filename = \"media/ring_simple.mp4\"\n", - "Animate.to_mp4(5,filename)\n", + "Animate.to_mp4(5, filename)\n", "Video(filename)" ] }, @@ -295,25 +302,27 @@ ], "source": [ "sim.reset_meep()\n", - "fcen=0.147\n", + "fcen = 0.147\n", "df = 0.1\n", - "sim.sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r+0.1))]\n", + "sim.sources = [\n", + " mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1))\n", + "]\n", "sim.init_sim()\n", "\n", "# Start the simulation and get into steady state\n", - "sim.run(until=500) \n", + "sim.run(until=500)\n", "\n", "# Prepare the animator and record the steady state response\n", "f = plt.figure(dpi=150)\n", "Animate = mp.Animate2D(sim, fields=mp.Ez, f=f, realtime=False, normalize=True)\n", - "sim.run(mp.at_every(0.5,Animate),until=25)\n", + "sim.run(mp.at_every(0.5, Animate), until=25)\n", "\n", "# Close the animator's working frame\n", "plt.close()\n", "\n", "# Process the animation and view it\n", "filename = \"media/ring_mid.mp4\"\n", - "Animate.to_mp4(5,filename)\n", + "Animate.to_mp4(5, filename)\n", "Video(filename)" ] }, @@ -363,24 +372,26 @@ ], "source": [ "sim.reset_meep()\n", - "fcen=0.175\n", + "fcen = 0.175\n", "df = 0.1\n", - "sim.sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r+0.1))]\n", + "sim.sources = [\n", + " mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1))\n", + "]\n", "\n", "# Start the simulation and get into steady state\n", - "sim.run(until=500) \n", + "sim.run(until=500)\n", "\n", "# Prepare the animator and record the steady state response\n", "f = plt.figure(dpi=150)\n", "Animate = mp.Animate2D(sim, fields=mp.Ez, f=f, realtime=False, normalize=True)\n", - "sim.run(mp.at_every(0.5,Animate),until=25)\n", + "sim.run(mp.at_every(0.5, Animate), until=25)\n", "\n", "# Close the animator's working frame\n", "plt.close()\n", "\n", "# Process the animation and view it\n", "filename = \"media/ring_large.mp4\"\n", - "Animate.to_mp4(5,filename)\n", + "Animate.to_mp4(5, filename)\n", "Video(filename)" ] } diff --git a/python/examples/ring.py b/python/examples/ring.py index 1c2b2dbd4..ad3a589ef 100644 --- a/python/examples/ring.py +++ b/python/examples/ring.py @@ -3,45 +3,51 @@ import meep as mp + def main(): - n = 3.4 # index of waveguide - w = 1 # width of waveguide - r = 1 # inner radius of ring - pad = 4 # padding between waveguide and edge of PML - dpml = 2 # thickness of PML - sxy = 2*(r+w+pad+dpml) # cell size + n = 3.4 # index of waveguide + w = 1 # width of waveguide + r = 1 # inner radius of ring + pad = 4 # padding between waveguide and edge of PML + dpml = 2 # thickness of PML + sxy = 2 * (r + w + pad + dpml) # cell size # Create a ring waveguide by two overlapping cylinders - later objects # take precedence over earlier objects, so we put the outer cylinder first. # and the inner (air) cylinder second. - c1 = mp.Cylinder(radius=r+w, material=mp.Medium(index=n)) + c1 = mp.Cylinder(radius=r + w, material=mp.Medium(index=n)) c2 = mp.Cylinder(radius=r) # If we don't want to excite a specific mode symmetry, we can just # put a single point source at some arbitrary place, pointing in some # arbitrary direction. We will only look for Ez-polarized modes. - fcen = 0.15 # pulse center frequency - df = 0.1 # pulse width (in frequency) + fcen = 0.15 # pulse center frequency + df = 0.1 # pulse width (in frequency) - src = mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r+0.1)) + src = mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1)) - sim = mp.Simulation(cell_size=mp.Vector3(sxy, sxy), - geometry=[c1, c2], - sources=[src], - resolution=10, - symmetries=[mp.Mirror(mp.Y)], - boundary_layers=[mp.PML(dpml)]) + sim = mp.Simulation( + cell_size=mp.Vector3(sxy, sxy), + geometry=[c1, c2], + sources=[src], + resolution=10, + symmetries=[mp.Mirror(mp.Y)], + boundary_layers=[mp.PML(dpml)], + ) - sim.run(mp.at_beginning(mp.output_epsilon), - mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r+0.1), fcen, df)), - until_after_sources=300) + sim.run( + mp.at_beginning(mp.output_epsilon), + mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df)), + until_after_sources=300, + ) # Output fields for one period at the end. (If we output # at a single time, we might accidentally catch the Ez field when it is # almost zero and get a distorted view.) - sim.run(mp.at_every(1/fcen/20, mp.output_efield_z), until=1/fcen) + sim.run(mp.at_every(1 / fcen / 20, mp.output_efield_z), until=1 / fcen) + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/ring_gds.py b/python/examples/ring_gds.py index 47e17f0e4..7f44c98b9 100644 --- a/python/examples/ring_gds.py +++ b/python/examples/ring_gds.py @@ -5,99 +5,111 @@ import meep as mp # core and cladding materials -Si = mp.Medium(index=3.4) +Si = mp.Medium(index=3.4) SiO2 = mp.Medium(index=1.4) # layer numbers for GDS file -RING_LAYER = 0 -SOURCE0_LAYER = 1 -SOURCE1_LAYER = 2 -MONITOR_LAYER = 3 +RING_LAYER = 0 +SOURCE0_LAYER = 1 +SOURCE1_LAYER = 2 +MONITOR_LAYER = 3 SIMULATION_LAYER = 4 -resolution = 50 # pixels/μm -dpml = 1 # thickness of PML -zmin = 0 # minimum z value of simulation domain (0 for 2D) -zmax = 0 # maximum z value of simulation domain (0 for 2D) +resolution = 50 # pixels/μm +dpml = 1 # thickness of PML +zmin = 0 # minimum z value of simulation domain (0 for 2D) +zmax = 0 # maximum z value of simulation domain (0 for 2D) -def create_ring_gds(radius,width): + +def create_ring_gds(radius, width): # Reload the library each time to prevent gds library name clashes importlib.reload(gdspy) ringCell = gdspy.Cell(f"ring_resonator_r{radius}_w{width}") # Draw the ring - ringCell.add(gdspy.Round((0,0), - inner_radius=radius-width/2, - radius=radius+width/2, - layer=RING_LAYER)) + ringCell.add( + gdspy.Round( + (0, 0), + inner_radius=radius - width / 2, + radius=radius + width / 2, + layer=RING_LAYER, + ) + ) # Draw the first source - ringCell.add(gdspy.Rectangle((radius-width,0), - (radius+width,0), - SOURCE0_LAYER)) + ringCell.add( + gdspy.Rectangle((radius - width, 0), (radius + width, 0), SOURCE0_LAYER) + ) # Draw the second source - ringCell.add(gdspy.Rectangle((-radius-width,0), - (-radius+width,0), - SOURCE1_LAYER)) + ringCell.add( + gdspy.Rectangle((-radius - width, 0), (-radius + width, 0), SOURCE1_LAYER) + ) # Draw the monitor location - ringCell.add(gdspy.Rectangle((radius-width/2,0), - (radius+width/2,0), - MONITOR_LAYER)) + ringCell.add( + gdspy.Rectangle((radius - width / 2, 0), (radius + width / 2, 0), MONITOR_LAYER) + ) # Draw the simulation domain pad = 2 # padding between waveguide and edge of PML - ringCell.add(gdspy.Rectangle((-radius-width/2-pad,-radius-width/2-pad), - (radius+width/2+pad,radius+width/2+pad), - SIMULATION_LAYER)) + ringCell.add( + gdspy.Rectangle( + (-radius - width / 2 - pad, -radius - width / 2 - pad), + (radius + width / 2 + pad, radius + width / 2 + pad), + SIMULATION_LAYER, + ) + ) filename = f"ring_r{radius}_w{width}.gds" gdspy.write_gds(filename, unit=1.0e-6, precision=1.0e-9) return filename -def find_modes(filename,wvl=1.55,bw=0.05): + +def find_modes(filename, wvl=1.55, bw=0.05): # Read in the ring structure - geometry = mp.get_GDSII_prisms(Si,filename,RING_LAYER,-100,100) + geometry = mp.get_GDSII_prisms(Si, filename, RING_LAYER, -100, 100) - cell = mp.GDSII_vol(filename,SIMULATION_LAYER,zmin,zmax) + cell = mp.GDSII_vol(filename, SIMULATION_LAYER, zmin, zmax) - src_vol0 = mp.GDSII_vol(filename,SOURCE0_LAYER,zmin,zmax) - src_vol1 = mp.GDSII_vol(filename,SOURCE1_LAYER,zmin,zmax) + src_vol0 = mp.GDSII_vol(filename, SOURCE0_LAYER, zmin, zmax) + src_vol1 = mp.GDSII_vol(filename, SOURCE1_LAYER, zmin, zmax) - mon_vol = mp.GDSII_vol(filename,MONITOR_LAYER,zmin,zmax) + mon_vol = mp.GDSII_vol(filename, MONITOR_LAYER, zmin, zmax) - fcen = 1/wvl - df = bw*fcen + fcen = 1 / wvl + df = bw * fcen - src = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Hz, - volume=src_vol0), - mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Hz, - volume=src_vol1, - amplitude=-1)] + src = [ + mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, volume=src_vol0), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Hz, + volume=src_vol1, + amplitude=-1, + ), + ] - sim = mp.Simulation(cell_size=cell.size, - geometry=geometry, - sources=src, - resolution=resolution, - boundary_layers=[mp.PML(dpml)], - default_material=SiO2) + sim = mp.Simulation( + cell_size=cell.size, + geometry=geometry, + sources=src, + resolution=resolution, + boundary_layers=[mp.PML(dpml)], + default_material=SiO2, + ) - h = mp.Harminv(mp.Hz,mon_vol.center,fcen,df) + h = mp.Harminv(mp.Hz, mon_vol.center, fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=100) + sim.run(mp.after_sources(h), until_after_sources=100) plt.figure() - sim.plot2D(fields=mp.Hz, - eps_parameters={'contour':True}) - plt.savefig('ring_fields.png',bbox_inches='tight',dpi=150) + sim.plot2D(fields=mp.Hz, eps_parameters={"contour": True}) + plt.savefig("ring_fields.png", bbox_inches="tight", dpi=150) - wvl = np.array([1/m.freq for m in h.modes]) + wvl = np.array([1 / m.freq for m in h.modes]) Q = np.array([m.Q for m in h.modes]) sim.reset_meep() @@ -105,8 +117,8 @@ def find_modes(filename,wvl=1.55,bw=0.05): return wvl, Q -if __name__ == '__main__': - filename = create_ring_gds(2.0,0.5) - wvls, Qs = find_modes(filename,1.55,0.05) - for w, Q in zip(wvls,Qs): +if __name__ == "__main__": + filename = create_ring_gds(2.0, 0.5) + wvls, Qs = find_modes(filename, 1.55, 0.05) + for w, Q in zip(wvls, Qs): print(f"mode: {w}, {Q}") diff --git a/python/examples/solve-cw.ipynb b/python/examples/solve-cw.ipynb index dc1799b6b..a6823a4ab 100644 --- a/python/examples/solve-cw.ipynb +++ b/python/examples/solve-cw.ipynb @@ -89,36 +89,41 @@ "pad = 4\n", "dpml = 2\n", "\n", - "sxy = 2*(r+w+pad+dpml)\n", - "cell_size = mp.Vector3(sxy,sxy)\n", + "sxy = 2 * (r + w + pad + dpml)\n", + "cell_size = mp.Vector3(sxy, sxy)\n", "\n", "pml_layers = [mp.PML(dpml)]\n", "\n", - "nonpml_vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sxy-2*dpml,sxy-2*dpml))\n", + "nonpml_vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sxy - 2 * dpml, sxy - 2 * dpml))\n", "\n", - "geometry = [mp.Cylinder(radius=r+w, material=mp.Medium(index=n)),\n", - " mp.Cylinder(radius=r)]\n", + "geometry = [\n", + " mp.Cylinder(radius=r + w, material=mp.Medium(index=n)),\n", + " mp.Cylinder(radius=r),\n", + "]\n", "\n", "fcen = 0.118\n", "\n", - "src = [mp.Source(mp.ContinuousSource(fcen),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(r+0.1)),\n", - " mp.Source(mp.ContinuousSource(fcen),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(-(r+0.1)),\n", - " amplitude=-1)]\n", + "src = [\n", + " mp.Source(mp.ContinuousSource(fcen), component=mp.Ez, center=mp.Vector3(r + 0.1)),\n", + " mp.Source(\n", + " mp.ContinuousSource(fcen),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(-(r + 0.1)),\n", + " amplitude=-1,\n", + " ),\n", + "]\n", "\n", - "symmetries = [mp.Mirror(mp.X,phase=-1),\n", - " mp.Mirror(mp.Y,phase=+1)]\n", + "symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=+1)]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " geometry=geometry,\n", - " sources=src,\n", - " resolution=10,\n", - " force_complex_fields=True,\n", - " symmetries=symmetries,\n", - " boundary_layers=pml_layers)\n", + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " geometry=geometry,\n", + " sources=src,\n", + " resolution=10,\n", + " force_complex_fields=True,\n", + " symmetries=symmetries,\n", + " boundary_layers=pml_layers,\n", + ")\n", "f = plt.figure(dpi=120)\n", "sim.plot2D(ax=f.gca())\n", "plt.show()" @@ -138,13 +143,13 @@ "outputs": [], "source": [ "num_tols = 5\n", - "tols = np.power(10, np.arange(-8.0,-8.0-num_tols,-1.0))\n", - "ez_dat = np.zeros((122,122,num_tols), dtype=np.complex_)\n", + "tols = np.power(10, np.arange(-8.0, -8.0 - num_tols, -1.0))\n", + "ez_dat = np.zeros((122, 122, num_tols), dtype=np.complex_)\n", "\n", "for i in range(num_tols):\n", " sim.init_sim()\n", " sim.solve_cw(tols[i], 10000, 10)\n", - " ez_dat[:,:,i] = sim.get_array(vol=nonpml_vol, component=mp.Ez)" + " ez_dat[:, :, i] = sim.get_array(vol=nonpml_vol, component=mp.Ez)" ] }, { @@ -173,14 +178,14 @@ } ], "source": [ - "err_dat = np.zeros(num_tols-1)\n", - "for i in range(num_tols-1):\n", - " err_dat[i] = LA.norm(ez_dat[:,:,i]-ez_dat[:,:,num_tols-1])\n", + "err_dat = np.zeros(num_tols - 1)\n", + "for i in range(num_tols - 1):\n", + " err_dat[i] = LA.norm(ez_dat[:, :, i] - ez_dat[:, :, num_tols - 1])\n", "\n", "plt.figure(dpi=150)\n", - "plt.loglog(tols[:num_tols-1], err_dat, 'bo-');\n", - "plt.xlabel(\"frequency-domain solver tolerance\");\n", - "plt.ylabel(\"L2 norm of error in fields\");\n", + "plt.loglog(tols[: num_tols - 1], err_dat, \"bo-\")\n", + "plt.xlabel(\"frequency-domain solver tolerance\")\n", + "plt.ylabel(\"L2 norm of error in fields\")\n", "plt.show()" ] }, @@ -211,18 +216,22 @@ ], "source": [ "eps_data = sim.get_array(vol=nonpml_vol, component=mp.Dielectric)\n", - "ez_data = np.real(ez_dat[:,:,num_tols-1])\n", + "ez_data = np.real(ez_dat[:, :, num_tols - 1])\n", "\n", "plt.figure()\n", - "plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary')\n", - "plt.imshow(ez_data.transpose(), interpolation='spline36', cmap='RdBu', alpha=0.9)\n", - "plt.axis('off')\n", + "plt.imshow(eps_data.transpose(), interpolation=\"spline36\", cmap=\"binary\")\n", + "plt.imshow(ez_data.transpose(), interpolation=\"spline36\", cmap=\"RdBu\", alpha=0.9)\n", + "plt.axis(\"off\")\n", "plt.show()\n", "\n", "if np.all(np.diff(err_dat) < 0):\n", - " print(\"PASSED solve_cw test: error in the fields is decreasing with increasing resolution\")\n", + " print(\n", + " \"PASSED solve_cw test: error in the fields is decreasing with increasing resolution\"\n", + " )\n", "else:\n", - " print(\"FAILED solve_cw test: error in the fields is NOT decreasing with increasing resolution\")" + " print(\n", + " \"FAILED solve_cw test: error in the fields is NOT decreasing with increasing resolution\"\n", + " )" ] }, { @@ -244,20 +253,26 @@ "sim.reset_meep()\n", "\n", "df = 0.08\n", - "src = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(r+0.1)),\n", - " mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(-(r+0.1)),\n", - " amplitude=-1)]\n", + "src = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3(r + 0.1)\n", + " ),\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(-(r + 0.1)),\n", + " amplitude=-1,\n", + " ),\n", + "]\n", "\n", - "sim = mp.Simulation(cell_size=mp.Vector3(sxy,sxy),\n", - " geometry=geometry,\n", - " sources=src,\n", - " resolution=10,\n", - " symmetries=symmetries,\n", - " boundary_layers=pml_layers)\n", + "sim = mp.Simulation(\n", + " cell_size=mp.Vector3(sxy, sxy),\n", + " geometry=geometry,\n", + " sources=src,\n", + " resolution=10,\n", + " symmetries=symmetries,\n", + " boundary_layers=pml_layers,\n", + ")\n", "\n", "dft_obj = sim.add_dft_fields([mp.Ez], fcen, 0, 1, where=nonpml_vol)\n", "\n", @@ -287,9 +302,9 @@ "ez_data = np.real(sim.get_dft_array(dft_obj, mp.Ez, 0))\n", "\n", "plt.figure()\n", - "plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary')\n", - "plt.imshow(ez_data.transpose(), interpolation='spline36', cmap='RdBu', alpha=0.9)\n", - "plt.axis('off')\n", + "plt.imshow(eps_data.transpose(), interpolation=\"spline36\", cmap=\"binary\")\n", + "plt.imshow(ez_data.transpose(), interpolation=\"spline36\", cmap=\"RdBu\", alpha=0.9)\n", + "plt.axis(\"off\")\n", "plt.show()" ] } diff --git a/python/examples/solve-cw.py b/python/examples/solve-cw.py index b97bbb5c1..95b1f0498 100644 --- a/python/examples/solve-cw.py +++ b/python/examples/solve-cw.py @@ -9,87 +9,102 @@ pad = 4 dpml = 2 -sxy = 2*(r+w+pad+dpml) -cell_size = mp.Vector3(sxy,sxy) +sxy = 2 * (r + w + pad + dpml) +cell_size = mp.Vector3(sxy, sxy) pml_layers = [mp.PML(dpml)] -nonpml_vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sxy-2*dpml,sxy-2*dpml)) +nonpml_vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sxy - 2 * dpml, sxy - 2 * dpml)) -geometry = [mp.Cylinder(radius=r+w, material=mp.Medium(index=n)), - mp.Cylinder(radius=r)] +geometry = [ + mp.Cylinder(radius=r + w, material=mp.Medium(index=n)), + mp.Cylinder(radius=r), +] fcen = 0.118 -src = [mp.Source(mp.ContinuousSource(fcen), - component=mp.Ez, - center=mp.Vector3(r+0.1)), - mp.Source(mp.ContinuousSource(fcen), - component=mp.Ez, - center=mp.Vector3(-(r+0.1)), - amplitude=-1)] - -symmetries = [mp.Mirror(mp.X,phase=-1), - mp.Mirror(mp.Y,phase=+1)] - -sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - sources=src, - resolution=10, - force_complex_fields=True, - symmetries=symmetries, - boundary_layers=pml_layers) +src = [ + mp.Source(mp.ContinuousSource(fcen), component=mp.Ez, center=mp.Vector3(r + 0.1)), + mp.Source( + mp.ContinuousSource(fcen), + component=mp.Ez, + center=mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), +] + +symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=+1)] + +sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + sources=src, + resolution=10, + force_complex_fields=True, + symmetries=symmetries, + boundary_layers=pml_layers, +) num_tols = 5 -tols = np.power(10, np.arange(-8.0,-8.0-num_tols,-1.0)) -ez_dat = np.zeros((122,122,num_tols), dtype=np.complex_) +tols = np.power(10, np.arange(-8.0, -8.0 - num_tols, -1.0)) +ez_dat = np.zeros((122, 122, num_tols), dtype=np.complex_) for i in range(num_tols): sim.init_sim() sim.solve_cw(tols[i], 10000, 10) - ez_dat[:,:,i] = sim.get_array(vol=nonpml_vol, component=mp.Ez) + ez_dat[:, :, i] = sim.get_array(vol=nonpml_vol, component=mp.Ez) -err_dat = np.zeros(num_tols-1) -for i in range(num_tols-1): - err_dat[i] = LA.norm(ez_dat[:,:,i]-ez_dat[:,:,num_tols-1]) +err_dat = np.zeros(num_tols - 1) +for i in range(num_tols - 1): + err_dat[i] = LA.norm(ez_dat[:, :, i] - ez_dat[:, :, num_tols - 1]) plt.figure(dpi=150) -plt.loglog(tols[:num_tols-1], err_dat, 'bo-'); -plt.xlabel("frequency-domain solver tolerance"); -plt.ylabel("L2 norm of error in fields"); +plt.loglog(tols[: num_tols - 1], err_dat, "bo-") +plt.xlabel("frequency-domain solver tolerance") +plt.ylabel("L2 norm of error in fields") plt.show() eps_data = sim.get_array(vol=nonpml_vol, component=mp.Dielectric) -ez_data = np.real(ez_dat[:,:,num_tols-1]) +ez_data = np.real(ez_dat[:, :, num_tols - 1]) plt.figure() -plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary') -plt.imshow(ez_data.transpose(), interpolation='spline36', cmap='RdBu', alpha=0.9) -plt.axis('off') +plt.imshow(eps_data.transpose(), interpolation="spline36", cmap="binary") +plt.imshow(ez_data.transpose(), interpolation="spline36", cmap="RdBu", alpha=0.9) +plt.axis("off") plt.show() if np.all(np.diff(err_dat) < 0): - print("PASSED solve_cw test: error in the fields is decreasing with increasing resolution") + print( + "PASSED solve_cw test: error in the fields is decreasing with increasing resolution" + ) else: - print("FAILED solve_cw test: error in the fields is NOT decreasing with increasing resolution") + print( + "FAILED solve_cw test: error in the fields is NOT decreasing with increasing resolution" + ) sim.reset_meep() df = 0.08 -src = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(r+0.1)), - mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(-(r+0.1)), - amplitude=-1)] - -sim = mp.Simulation(cell_size=mp.Vector3(sxy,sxy), - geometry=geometry, - sources=src, - resolution=10, - symmetries=symmetries, - boundary_layers=pml_layers) +src = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3(r + 0.1) + ), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), +] + +sim = mp.Simulation( + cell_size=mp.Vector3(sxy, sxy), + geometry=geometry, + sources=src, + resolution=10, + symmetries=symmetries, + boundary_layers=pml_layers, +) dft_obj = sim.add_dft_fields([mp.Ez], fcen, 0, 1, where=nonpml_vol) @@ -99,7 +114,7 @@ ez_data = np.real(sim.get_dft_array(dft_obj, mp.Ez, 0)) plt.figure() -plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary') -plt.imshow(ez_data.transpose(), interpolation='spline36', cmap='RdBu', alpha=0.9) -plt.axis('off') +plt.imshow(eps_data.transpose(), interpolation="spline36", cmap="binary") +plt.imshow(ez_data.transpose(), interpolation="spline36", cmap="RdBu", alpha=0.9) +plt.axis("off") plt.show() diff --git a/python/examples/stochastic_emitter.ipynb b/python/examples/stochastic_emitter.ipynb index 59f33cec8..a18bf3022 100644 --- a/python/examples/stochastic_emitter.ipynb +++ b/python/examples/stochastic_emitter.ipynb @@ -28,13 +28,27 @@ "import numpy as np\n", "\n", "import argparse\n", + "\n", "parser = argparse.ArgumentParser()\n", - "parser.add_argument('-res', type=int, default=50, help='resolution (pixels/um)')\n", - "parser.add_argument('-nr', type=int, default=20, help='number of random trials (method 1)')\n", - "parser.add_argument('-nd', type=int, default=10, help='number of dipoles')\n", - "parser.add_argument('-nf', type=int, default=500, help='number of frequencies')\n", - "parser.add_argument('-textured', action='store_true', default=False, help='flat (default) or textured surface')\n", - "parser.add_argument('-method', type=int, choices=[1,2], default=1, help='type of method: (1) random dipoles with nr trials or (2) single dipole with 1 run per dipole')\n", + "parser.add_argument(\"-res\", type=int, default=50, help=\"resolution (pixels/um)\")\n", + "parser.add_argument(\n", + " \"-nr\", type=int, default=20, help=\"number of random trials (method 1)\"\n", + ")\n", + "parser.add_argument(\"-nd\", type=int, default=10, help=\"number of dipoles\")\n", + "parser.add_argument(\"-nf\", type=int, default=500, help=\"number of frequencies\")\n", + "parser.add_argument(\n", + " \"-textured\",\n", + " action=\"store_true\",\n", + " default=False,\n", + " help=\"flat (default) or textured surface\",\n", + ")\n", + "parser.add_argument(\n", + " \"-method\",\n", + " type=int,\n", + " choices=[1, 2],\n", + " default=1,\n", + " help=\"type of method: (1) random dipoles with nr trials or (2) single dipole with 1 run per dipole\",\n", + ")\n", "args = parser.parse_args()\n", "\n", "resolution = args.res\n", @@ -47,56 +61,81 @@ "dAg = 0.5\n", "\n", "sx = 1.1\n", - "sy = dpml+dair+hrod+dsub+dAg\n", + "sy = dpml + dair + hrod + dsub + dAg\n", "\n", - "cell_size = mp.Vector3(sx,sy)\n", + "cell_size = mp.Vector3(sx, sy)\n", "\n", - "pml_layers = [mp.PML(direction=mp.Y,\n", - " thickness=dpml,\n", - " side=mp.High)]\n", + "pml_layers = [mp.PML(direction=mp.Y, thickness=dpml, side=mp.High)]\n", "\n", "fcen = 1.0\n", "df = 0.2\n", "nfreq = args.nf\n", "ndipole = args.nd\n", "ntrial = args.nr\n", - "run_time = 2*nfreq/df\n", - "\n", - "geometry = [mp.Block(material=mp.Medium(index=3.45),\n", - " center=mp.Vector3(0,0.5*sy-dpml-dair-hrod-0.5*dsub),\n", - " size=mp.Vector3(mp.inf,dsub,mp.inf)),\n", - " mp.Block(material=Ag,\n", - " center=mp.Vector3(0,-0.5*sy+0.5*dAg),\n", - " size=mp.Vector3(mp.inf,dAg,mp.inf))]\n", + "run_time = 2 * nfreq / df\n", + "\n", + "geometry = [\n", + " mp.Block(\n", + " material=mp.Medium(index=3.45),\n", + " center=mp.Vector3(0, 0.5 * sy - dpml - dair - hrod - 0.5 * dsub),\n", + " size=mp.Vector3(mp.inf, dsub, mp.inf),\n", + " ),\n", + " mp.Block(\n", + " material=Ag,\n", + " center=mp.Vector3(0, -0.5 * sy + 0.5 * dAg),\n", + " size=mp.Vector3(mp.inf, dAg, mp.inf),\n", + " ),\n", + "]\n", "\n", "if args.textured:\n", - " geometry.append(mp.Block(material=mp.Medium(index=3.45),\n", - " center=mp.Vector3(0,0.5*sy-dpml-dair-0.5*hrod),\n", - " size=mp.Vector3(wrod,hrod,mp.inf)))\n", + " geometry.append(\n", + " mp.Block(\n", + " material=mp.Medium(index=3.45),\n", + " center=mp.Vector3(0, 0.5 * sy - dpml - dair - 0.5 * hrod),\n", + " size=mp.Vector3(wrod, hrod, mp.inf),\n", + " )\n", + " )\n", "\n", - "def compute_flux(m=1,n=0):\n", + "\n", + "def compute_flux(m=1, n=0):\n", " if m == 1:\n", " sources = []\n", " for n in range(ndipole):\n", - " sources.append(mp.Source(mp.CustomSource(src_func=lambda t: np.random.randn()),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(sx*(-0.5+n/ndipole),-0.5*sy+dAg+0.5*dsub)))\n", + " sources.append(\n", + " mp.Source(\n", + " mp.CustomSource(src_func=lambda t: np.random.randn()),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(\n", + " sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub\n", + " ),\n", + " )\n", + " )\n", " else:\n", - " sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(sx*(-0.5+n/ndipole),-0.5*sy+dAg+0.5*dsub))]\n", - "\n", - " sim = mp.Simulation(cell_size=cell_size,\n", - " resolution=resolution,\n", - " k_point=mp.Vector3(),\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources)\n", - "\n", - " flux_mon = sim.add_flux(fcen,\n", - " df,\n", - " nfreq,\n", - " mp.FluxRegion(center=mp.Vector3(0,0.5*sy-dpml),size=mp.Vector3(sx)))\n", + " sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(\n", + " sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub\n", + " ),\n", + " )\n", + " ]\n", + "\n", + " sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " resolution=resolution,\n", + " k_point=mp.Vector3(),\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources,\n", + " )\n", + "\n", + " flux_mon = sim.add_flux(\n", + " fcen,\n", + " df,\n", + " nfreq,\n", + " mp.FluxRegion(center=mp.Vector3(0, 0.5 * sy - dpml), size=mp.Vector3(sx)),\n", + " )\n", "\n", " sim.run(until=run_time)\n", "\n", @@ -105,19 +144,29 @@ "\n", " return freqs, flux\n", "\n", + "\n", "if args.method == 1:\n", - " fluxes = np.zeros((nfreq,ntrial))\n", + " fluxes = np.zeros((nfreq, ntrial))\n", " for t in range(ntrial):\n", - " freqs, fluxes[:,t] = compute_flux(m=1)\n", + " freqs, fluxes[:, t] = compute_flux(m=1)\n", "else:\n", - " fluxes = np.zeros((nfreq,ndipole))\n", + " fluxes = np.zeros((nfreq, ndipole))\n", " for d in range(ndipole):\n", - " freqs, fluxes[:,d] = compute_flux(m=2,n=d)\n", + " freqs, fluxes[:, d] = compute_flux(m=2, n=d)\n", "\n", "\n", "if mp.am_master():\n", - " with open('method{}_{}_res{}_nfreq{}_ndipole{}.npz'.format(args.method,\"textured\" if args.textured else \"flat\",resolution,nfreq,ndipole),'wb') as f:\n", - " np.savez(f,freqs=freqs,fluxes=fluxes)" + " with open(\n", + " \"method{}_{}_res{}_nfreq{}_ndipole{}.npz\".format(\n", + " args.method,\n", + " \"textured\" if args.textured else \"flat\",\n", + " resolution,\n", + " nfreq,\n", + " ndipole,\n", + " ),\n", + " \"wb\",\n", + " ) as f:\n", + " np.savez(f, freqs=freqs, fluxes=fluxes)" ] }, { @@ -155,24 +204,24 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "method1_f0 = np.load('method1_flat_res50_nfreq500_ndipole10.npz')\n", - "method1_f1 = np.load('method1_textured_res50_nfreq500_ndipole10.npz')\n", + "method1_f0 = np.load(\"method1_flat_res50_nfreq500_ndipole10.npz\")\n", + "method1_f1 = np.load(\"method1_textured_res50_nfreq500_ndipole10.npz\")\n", "\n", - "method1_freqs = method1_f0['freqs']\n", - "method1_f0_mean = np.mean(method1_f0['fluxes'],axis=1)\n", - "method1_f1_mean = np.mean(method1_f1['fluxes'],axis=1)\n", + "method1_freqs = method1_f0[\"freqs\"]\n", + "method1_f0_mean = np.mean(method1_f0[\"fluxes\"], axis=1)\n", + "method1_f1_mean = np.mean(method1_f1[\"fluxes\"], axis=1)\n", "\n", - "method2_f0 = np.load('method2_flat_res50_nfreq500_ndipole10.npz')\n", - "method2_f1 = np.load('method2_textured_res50_nfreq500_ndipole10.npz')\n", + "method2_f0 = np.load(\"method2_flat_res50_nfreq500_ndipole10.npz\")\n", + "method2_f1 = np.load(\"method2_textured_res50_nfreq500_ndipole10.npz\")\n", "\n", - "method2_freqs = method2_f0['freqs']\n", - "method2_f0_mean = np.mean(method2_f0['fluxes'],axis=1)\n", - "method2_f1_mean = np.mean(method2_f1['fluxes'],axis=1)\n", + "method2_freqs = method2_f0[\"freqs\"]\n", + "method2_f0_mean = np.mean(method2_f0[\"fluxes\"], axis=1)\n", + "method2_f1_mean = np.mean(method2_f1[\"fluxes\"], axis=1)\n", "\n", - "plt.semilogy(method1_freqs,method1_f1_mean/method1_f0_mean,'b-',label='Method 1')\n", - "plt.semilogy(method2_freqs,method2_f1_mean/method2_f0_mean,'r-',label='Method 2')\n", - "plt.xlabel('frequency')\n", - "plt.ylabel('normalized flux')\n", + "plt.semilogy(method1_freqs, method1_f1_mean / method1_f0_mean, \"b-\", label=\"Method 1\")\n", + "plt.semilogy(method2_freqs, method2_f1_mean / method2_f0_mean, \"r-\", label=\"Method 2\")\n", + "plt.xlabel(\"frequency\")\n", + "plt.ylabel(\"normalized flux\")\n", "plt.legend()\n", "plt.show()" ] diff --git a/python/examples/stochastic_emitter.py b/python/examples/stochastic_emitter.py index 18b497026..fece7d6ca 100644 --- a/python/examples/stochastic_emitter.py +++ b/python/examples/stochastic_emitter.py @@ -3,13 +3,27 @@ import numpy as np import argparse + parser = argparse.ArgumentParser() -parser.add_argument('-res', type=int, default=50, help='resolution (pixels/um)') -parser.add_argument('-nr', type=int, default=20, help='number of random trials (method 1)') -parser.add_argument('-nd', type=int, default=10, help='number of dipoles') -parser.add_argument('-nf', type=int, default=500, help='number of frequencies') -parser.add_argument('-textured', action='store_true', default=False, help='flat (default) or textured surface') -parser.add_argument('-method', type=int, choices=[1,2], default=1, help='type of method: (1) random dipoles with nr trials or (2) single dipole with 1 run per dipole') +parser.add_argument("-res", type=int, default=50, help="resolution (pixels/um)") +parser.add_argument( + "-nr", type=int, default=20, help="number of random trials (method 1)" +) +parser.add_argument("-nd", type=int, default=10, help="number of dipoles") +parser.add_argument("-nf", type=int, default=500, help="number of frequencies") +parser.add_argument( + "-textured", + action="store_true", + default=False, + help="flat (default) or textured surface", +) +parser.add_argument( + "-method", + type=int, + choices=[1, 2], + default=1, + help="type of method: (1) random dipoles with nr trials or (2) single dipole with 1 run per dipole", +) args = parser.parse_args() resolution = args.res @@ -22,53 +36,81 @@ dAg = 0.5 sx = 1.1 -sy = dpml+dair+hrod+dsub+dAg +sy = dpml + dair + hrod + dsub + dAg -cell_size = mp.Vector3(sx,sy) +cell_size = mp.Vector3(sx, sy) -pml_layers = [mp.PML(direction=mp.Y, - thickness=dpml, - side=mp.High)] +pml_layers = [mp.PML(direction=mp.Y, thickness=dpml, side=mp.High)] fcen = 1.0 df = 0.2 nfreq = args.nf ndipole = args.nd ntrial = args.nr -run_time = 2*nfreq/df - -geometry = [mp.Block(material=mp.Medium(index=3.45), - center=mp.Vector3(0,0.5*sy-dpml-dair-hrod-0.5*dsub), - size=mp.Vector3(mp.inf,dsub,mp.inf)), - mp.Block(material=Ag, - center=mp.Vector3(0,-0.5*sy+0.5*dAg), - size=mp.Vector3(mp.inf,dAg,mp.inf))] +run_time = 2 * nfreq / df + +geometry = [ + mp.Block( + material=mp.Medium(index=3.45), + center=mp.Vector3(0, 0.5 * sy - dpml - dair - hrod - 0.5 * dsub), + size=mp.Vector3(mp.inf, dsub, mp.inf), + ), + mp.Block( + material=Ag, + center=mp.Vector3(0, -0.5 * sy + 0.5 * dAg), + size=mp.Vector3(mp.inf, dAg, mp.inf), + ), +] if args.textured: - geometry.append(mp.Block(material=mp.Medium(index=3.45), - center=mp.Vector3(0,0.5*sy-dpml-dair-0.5*hrod), - size=mp.Vector3(wrod,hrod,mp.inf))) + geometry.append( + mp.Block( + material=mp.Medium(index=3.45), + center=mp.Vector3(0, 0.5 * sy - dpml - dair - 0.5 * hrod), + size=mp.Vector3(wrod, hrod, mp.inf), + ) + ) + -def compute_flux(m=1,n=0): +def compute_flux(m=1, n=0): if m == 1: - sources = [mp.Source(mp.CustomSource(src_func=lambda t: np.random.randn()), component=mp.Ez, center=mp.Vector3(sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub)) for n in range(ndipole)] + sources = [ + mp.Source( + mp.CustomSource(src_func=lambda t: np.random.randn()), + component=mp.Ez, + center=mp.Vector3( + sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub + ), + ) + for n in range(ndipole) + ] else: - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(sx*(-0.5+n/ndipole),-0.5*sy+dAg+0.5*dsub))] - - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - k_point=mp.Vector3(), - boundary_layers=pml_layers, - geometry=geometry, - sources=sources) - - flux_mon = sim.add_flux(fcen, - df, - nfreq, - mp.FluxRegion(center=mp.Vector3(0,0.5*sy-dpml),size=mp.Vector3(sx))) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3( + sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub + ), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + k_point=mp.Vector3(), + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + ) + + flux_mon = sim.add_flux( + fcen, + df, + nfreq, + mp.FluxRegion(center=mp.Vector3(0, 0.5 * sy - dpml), size=mp.Vector3(sx)), + ) sim.run(until=run_time) @@ -79,15 +121,18 @@ def compute_flux(m=1,n=0): if args.method == 1: - fluxes = np.zeros((nfreq,ntrial)) + fluxes = np.zeros((nfreq, ntrial)) for t in range(ntrial): - freqs, fluxes[:,t] = compute_flux(m=1) + freqs, fluxes[:, t] = compute_flux(m=1) else: - fluxes = np.zeros((nfreq,ndipole)) + fluxes = np.zeros((nfreq, ndipole)) for d in range(ndipole): - freqs, fluxes[:,d] = compute_flux(m=2,n=d) + freqs, fluxes[:, d] = compute_flux(m=2, n=d) if mp.am_master(): - with open(f'method{args.method}_{"textured" if args.textured else "flat"}_res{resolution}_nfreq{nfreq}_ndipole{ndipole}.npz', 'wb') as f: - np.savez(f,freqs=freqs,fluxes=fluxes) + with open( + f'method{args.method}_{"textured" if args.textured else "flat"}_res{resolution}_nfreq{nfreq}_ndipole{ndipole}.npz', + "wb", + ) as f: + np.savez(f, freqs=freqs, fluxes=fluxes) diff --git a/python/examples/stochastic_emitter_line.py b/python/examples/stochastic_emitter_line.py index a2e168a6b..54a44ffe0 100644 --- a/python/examples/stochastic_emitter_line.py +++ b/python/examples/stochastic_emitter_line.py @@ -3,13 +3,29 @@ import numpy as np import argparse + parser = argparse.ArgumentParser() -parser.add_argument('-res', type=int, default=50, help='resolution (pixels/um)') -parser.add_argument('-nf', type=int, default=500, help='number of frequencies') -parser.add_argument('-nsrc', type=int, default=15, help='number of line sources with cosine Fourier series amplitude function (method 3)') -parser.add_argument('-textured', action='store_true', default=False, help='flat (default) or textured surface') -parser.add_argument('-method', type=int, choices=[2,3], default=2, - help='type of method: (2) single dipole with 1 run per dipole or (3) line source with cosine Fourier series amplitude function') +parser.add_argument("-res", type=int, default=50, help="resolution (pixels/um)") +parser.add_argument("-nf", type=int, default=500, help="number of frequencies") +parser.add_argument( + "-nsrc", + type=int, + default=15, + help="number of line sources with cosine Fourier series amplitude function (method 3)", +) +parser.add_argument( + "-textured", + action="store_true", + default=False, + help="flat (default) or textured surface", +) +parser.add_argument( + "-method", + type=int, + choices=[2, 3], + default=2, + help="type of method: (2) single dipole with 1 run per dipole or (3) line source with cosine Fourier series amplitude function", +) args = parser.parse_args() resolution = args.res @@ -22,62 +38,89 @@ dAg = 0.4 sx = 1.5 -sy = dpml+dair+hrod+dsub+dAg +sy = dpml + dair + hrod + dsub + dAg -cell_size = mp.Vector3(sx,sy) +cell_size = mp.Vector3(sx, sy) -pml_layers = [mp.PML(direction=mp.Y, - thickness=dpml, - side=mp.High)] +pml_layers = [mp.PML(direction=mp.Y, thickness=dpml, side=mp.High)] fcen = 1.0 df = 0.2 nfreq = args.nf nsrc = args.nsrc -ndipole = int(sx*resolution) -run_time = 2*nfreq/df - -geometry = [mp.Block(material=mp.Medium(index=3.45), - center=mp.Vector3(0,0.5*sy-dpml-dair-hrod-0.5*dsub), - size=mp.Vector3(mp.inf,dsub,mp.inf)), - mp.Block(material=Ag, - center=mp.Vector3(0,-0.5*sy+0.5*dAg), - size=mp.Vector3(mp.inf,dAg,mp.inf))] +ndipole = int(sx * resolution) +run_time = 2 * nfreq / df + +geometry = [ + mp.Block( + material=mp.Medium(index=3.45), + center=mp.Vector3(0, 0.5 * sy - dpml - dair - hrod - 0.5 * dsub), + size=mp.Vector3(mp.inf, dsub, mp.inf), + ), + mp.Block( + material=Ag, + center=mp.Vector3(0, -0.5 * sy + 0.5 * dAg), + size=mp.Vector3(mp.inf, dAg, mp.inf), + ), +] if args.textured: - geometry.append(mp.Block(material=mp.Medium(index=3.45), - center=mp.Vector3(0,0.5*sy-dpml-dair-0.5*hrod), - size=mp.Vector3(wrod,hrod,mp.inf))) + geometry.append( + mp.Block( + material=mp.Medium(index=3.45), + center=mp.Vector3(0, 0.5 * sy - dpml - dair - 0.5 * hrod), + size=mp.Vector3(wrod, hrod, mp.inf), + ) + ) + def src_amp_func(n): def _src_amp_func(p): if n == 0: - return 1/np.sqrt(sx) + return 1 / np.sqrt(sx) else: - return np.sqrt(2/sx) * np.cos(n*np.pi*(p.x+0.5*sx)/sx) + return np.sqrt(2 / sx) * np.cos(n * np.pi * (p.x + 0.5 * sx) / sx) + return _src_amp_func -def compute_flux(m,n): + +def compute_flux(m, n): if m == 2: - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(sx*(-0.5+n/ndipole),-0.5*sy+dAg+0.5*dsub))] + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3( + sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub + ), + ) + ] else: - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(0,-0.5*sy+dAg+0.5*dsub), - size=mp.Vector3(sx,0), - amp_func=src_amp_func(n))] - - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - k_point=mp.Vector3(), - boundary_layers=pml_layers, - geometry=geometry, - sources=sources) - - flux_mon = sim.add_flux(fcen, df, nfreq, - mp.FluxRegion(center=mp.Vector3(0,0.5*sy-dpml),size=mp.Vector3(sx))) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(0, -0.5 * sy + dAg + 0.5 * dsub), + size=mp.Vector3(sx, 0), + amp_func=src_amp_func(n), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + k_point=mp.Vector3(), + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + ) + + flux_mon = sim.add_flux( + fcen, + df, + nfreq, + mp.FluxRegion(center=mp.Vector3(0, 0.5 * sy - dpml), size=mp.Vector3(sx)), + ) sim.run(until=run_time) @@ -88,15 +131,18 @@ def compute_flux(m,n): if args.method == 2: - fluxes = np.zeros((nfreq,ndipole)) + fluxes = np.zeros((nfreq, ndipole)) for d in range(ndipole): - freqs, fluxes[:,d] = compute_flux(2,d) + freqs, fluxes[:, d] = compute_flux(2, d) else: - fluxes = np.zeros((nfreq,nsrc)) + fluxes = np.zeros((nfreq, nsrc)) for d in range(nsrc): - freqs, fluxes[:,d] = compute_flux(3,d) + freqs, fluxes[:, d] = compute_flux(3, d) if mp.am_master(): - with open(f'method{args.method}_{"textured" if args.textured else "flat"}_res{resolution}_nfreq{nfreq}_{"ndipole" if args.method == 2 else "nsrc"}{ndipole if args.method == 2 else nsrc}.npz', 'wb') as f: - np.savez(f,freqs=freqs,fluxes=fluxes) + with open( + f'method{args.method}_{"textured" if args.textured else "flat"}_res{resolution}_nfreq{nfreq}_{"ndipole" if args.method == 2 else "nsrc"}{ndipole if args.method == 2 else nsrc}.npz', + "wb", + ) as f: + np.savez(f, freqs=freqs, fluxes=fluxes) diff --git a/python/examples/straight-waveguide.ipynb b/python/examples/straight-waveguide.ipynb index 91d703bf3..53de3462b 100644 --- a/python/examples/straight-waveguide.ipynb +++ b/python/examples/straight-waveguide.ipynb @@ -44,7 +44,7 @@ "metadata": {}, "outputs": [], "source": [ - "cell = mp.Vector3(16,8,0)" + "cell = mp.Vector3(16, 8, 0)" ] }, { @@ -68,9 +68,13 @@ "metadata": {}, "outputs": [], "source": [ - "geometry = [mp.Block(mp.Vector3(mp.inf,1,mp.inf),\n", - " center=mp.Vector3(),\n", - " material=mp.Medium(epsilon=12))]" + "geometry = [\n", + " mp.Block(\n", + " mp.Vector3(mp.inf, 1, mp.inf),\n", + " center=mp.Vector3(),\n", + " material=mp.Medium(epsilon=12),\n", + " )\n", + "]" ] }, { @@ -94,9 +98,11 @@ "metadata": {}, "outputs": [], "source": [ - "sources = [mp.Source(mp.ContinuousSource(frequency=0.15),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(-7,0))]" + "sources = [\n", + " mp.Source(\n", + " mp.ContinuousSource(frequency=0.15), component=mp.Ez, center=mp.Vector3(-7, 0)\n", + " )\n", + "]" ] }, { @@ -176,11 +182,13 @@ "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(cell_size=cell,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -224,6 +232,7 @@ ], "source": [ "from matplotlib import pyplot as plt\n", + "\n", "%matplotlib inline\n", "plt.figure(dpi=100)\n", "sim.plot2D()\n", @@ -357,7 +366,7 @@ } ], "source": [ - "sim.run(mp.at_every(1,Animate),until=100)\n", + "sim.run(mp.at_every(1, Animate), until=100)\n", "plt.close()" ] }, @@ -383,7 +392,7 @@ ], "source": [ "filename = \"media/straight_waveguide.mp4\"\n", - "Animate.to_mp4(10,filename)" + "Animate.to_mp4(10, filename)" ] }, { @@ -416,6 +425,7 @@ ], "source": [ "from IPython.display import Video\n", + "\n", "Video(filename)" ] }, diff --git a/python/examples/straight-waveguide.py b/python/examples/straight-waveguide.py index b17bd37d4..c845e6497 100644 --- a/python/examples/straight-waveguide.py +++ b/python/examples/straight-waveguide.py @@ -5,25 +5,33 @@ import meep as mp -cell = mp.Vector3(16,8,0) - -geometry = [mp.Block(mp.Vector3(mp.inf,1,mp.inf), - center=mp.Vector3(), - material=mp.Medium(epsilon=12))] - -sources = [mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(-7,0))] +cell = mp.Vector3(16, 8, 0) + +geometry = [ + mp.Block( + mp.Vector3(mp.inf, 1, mp.inf), + center=mp.Vector3(), + material=mp.Medium(epsilon=12), + ) +] + +sources = [ + mp.Source( + mp.ContinuousSource(frequency=0.15), component=mp.Ez, center=mp.Vector3(-7, 0) + ) +] pml_layers = [mp.PML(1.0)] resolution = 10 -sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) +sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, +) sim.run(until=200) @@ -32,13 +40,13 @@ eps_data = sim.get_array(center=mp.Vector3(), size=cell, component=mp.Dielectric) plt.figure() -plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary') -plt.axis('off') +plt.imshow(eps_data.transpose(), interpolation="spline36", cmap="binary") +plt.axis("off") plt.show() ez_data = sim.get_array(center=mp.Vector3(), size=cell, component=mp.Ez) plt.figure() -plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary') -plt.imshow(ez_data.transpose(), interpolation='spline36', cmap='RdBu', alpha=0.9) -plt.axis('off') +plt.imshow(eps_data.transpose(), interpolation="spline36", cmap="binary") +plt.imshow(ez_data.transpose(), interpolation="spline36", cmap="RdBu", alpha=0.9) +plt.axis("off") plt.show() diff --git a/python/examples/wvg-src.py b/python/examples/wvg-src.py index bcd2edb84..421022523 100644 --- a/python/examples/wvg-src.py +++ b/python/examples/wvg-src.py @@ -10,17 +10,27 @@ # an asymmetrical dielectric waveguide: geometry = [ - mp.Block(center=mp.Vector3(), size=mp.Vector3(mp.inf, 1, mp.inf), - material=mp.Medium(epsilon=12)), - mp.Block(center=mp.Vector3(y=0.3), size=mp.Vector3(mp.inf, 0.1, mp.inf), - material=mp.Medium()) + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1, mp.inf), + material=mp.Medium(epsilon=12), + ), + mp.Block( + center=mp.Vector3(y=0.3), + size=mp.Vector3(mp.inf, 0.1, mp.inf), + material=mp.Medium(), + ), ] # create a transparent source that excites a right-going waveguide mode sources = [ - mp.EigenModeSource(src=mp.ContinuousSource(0.15), size=mp.Vector3(y=6), - center=mp.Vector3(x=-5), component=mp.Dielectric, - eig_parity=mp.ODD_Z) + mp.EigenModeSource( + src=mp.ContinuousSource(0.15), + size=mp.Vector3(y=6), + center=mp.Vector3(x=-5), + component=mp.Dielectric, + eig_parity=mp.ODD_Z, + ) ] pml_layers = [mp.PML(1.0)] @@ -35,17 +45,21 @@ sources=sources, boundary_layers=pml_layers, force_complex_fields=force_complex_fields, - resolution=resolution + resolution=resolution, ) sim.run( mp.at_beginning(mp.output_epsilon), mp.at_end(mp.output_png(mp.Ez, "-a yarg -A $EPS -S3 -Zc dkbluered", rm_h5=False)), - until=200 + until=200, ) -flux1 = sim.flux_in_box(mp.X, mp.Volume(center=mp.Vector3(-6.0), size=mp.Vector3(1.8, 6))) -flux2 = sim.flux_in_box(mp.X, mp.Volume(center=mp.Vector3(6.0), size=mp.Vector3(1.8, 6))) +flux1 = sim.flux_in_box( + mp.X, mp.Volume(center=mp.Vector3(-6.0), size=mp.Vector3(1.8, 6)) +) +flux2 = sim.flux_in_box( + mp.X, mp.Volume(center=mp.Vector3(6.0), size=mp.Vector3(1.8, 6)) +) # averaged over y region of width 1.8 print(f"left-going flux = {flux1 / -1.8}") diff --git a/python/examples/zone_plate.ipynb b/python/examples/zone_plate.ipynb index e97e055e6..5796367a2 100644 --- a/python/examples/zone_plate.ipynb +++ b/python/examples/zone_plate.ipynb @@ -30,85 +30,139 @@ "import math\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 25 # pixels/μm\n", + "resolution = 25 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 2.0 # substrate thickness\n", - "dpad = 2.0 # padding betweeen zone plate and PML\n", - "zh = 0.5 # zone-plate height\n", - "zN = 25 # number of zones (odd zones: π phase shift, even zones: none)\n", - "focal_length = 200 # focal length of zone plate\n", - "spot_length = 100 # far-field line length\n", - "ff_res = 10 # far-field resolution\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 2.0 # substrate thickness\n", + "dpad = 2.0 # padding betweeen zone plate and PML\n", + "zh = 0.5 # zone-plate height\n", + "zN = 25 # number of zones (odd zones: π phase shift, even zones: none)\n", + "focal_length = 200 # focal length of zone plate\n", + "spot_length = 100 # far-field line length\n", + "ff_res = 10 # far-field resolution\n", "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", "wvl_cen = 0.5\n", - "frq_cen = 1/wvl_cen\n", - "dfrq = 0.2*frq_cen\n", + "frq_cen = 1 / wvl_cen\n", + "dfrq = 0.2 * frq_cen\n", "\n", "## radii of zones\n", "## ref: eq. 7 of http://zoneplate.lbl.gov/theory\n", - "r = [math.sqrt(n*wvl_cen*(focal_length+n*wvl_cen/4)) for n in range(1,zN+1)]\n", - "\n", - "sr = r[-1]+dpad+dpml\n", - "sz = dpml+dsub+zh+dpad+dpml\n", - "cell_size = mp.Vector3(sr,0,sz)\n", - "\n", - "sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n", - " component=mp.Er,\n", - " center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml),\n", - " size=mp.Vector3(sr)),\n", - " mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n", - " component=mp.Ep,\n", - " center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml),\n", - " size=mp.Vector3(sr),\n", - " amplitude=-1j)]\n", + "r = [\n", + " math.sqrt(n * wvl_cen * (focal_length + n * wvl_cen / 4)) for n in range(1, zN + 1)\n", + "]\n", + "\n", + "sr = r[-1] + dpad + dpml\n", + "sz = dpml + dsub + zh + dpad + dpml\n", + "cell_size = mp.Vector3(sr, 0, sz)\n", + "\n", + "sources = [\n", + " mp.Source(\n", + " mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True),\n", + " component=mp.Er,\n", + " center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml),\n", + " size=mp.Vector3(sr),\n", + " ),\n", + " mp.Source(\n", + " mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True),\n", + " component=mp.Ep,\n", + " center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml),\n", + " size=mp.Vector3(sr),\n", + " amplitude=-1j,\n", + " ),\n", + "]\n", "\n", "glass = mp.Medium(index=1.5)\n", "\n", - "geometry = [mp.Block(material=glass,\n", - " size=mp.Vector3(sr,0,dpml+dsub),\n", - " center=mp.Vector3(0.5*sr,0,-0.5*sz+0.5*(dpml+dsub)))]\n", - "\n", - "for n in range(zN-1,-1,-1):\n", - " geometry.append(mp.Block(material=glass if n % 2 == 0 else mp.vacuum,\n", - " size=mp.Vector3(r[n],0,zh),\n", - " center=mp.Vector3(0.5*r[n],0,-0.5*sz+dpml+dsub+0.5*zh)))\n", - " \n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " geometry=geometry,\n", - " dimensions=mp.CYLINDRICAL,\n", - " m=-1)\n", + "geometry = [\n", + " mp.Block(\n", + " material=glass,\n", + " size=mp.Vector3(sr, 0, dpml + dsub),\n", + " center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + 0.5 * (dpml + dsub)),\n", + " )\n", + "]\n", + "\n", + "for n in range(zN - 1, -1, -1):\n", + " geometry.append(\n", + " mp.Block(\n", + " material=glass if n % 2 == 0 else mp.vacuum,\n", + " size=mp.Vector3(r[n], 0, zh),\n", + " center=mp.Vector3(0.5 * r[n], 0, -0.5 * sz + dpml + dsub + 0.5 * zh),\n", + " )\n", + " )\n", + "\n", + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " geometry=geometry,\n", + " dimensions=mp.CYLINDRICAL,\n", + " m=-1,\n", + ")\n", "\n", "## near-field monitor\n", - "n2f_obj = sim.add_near2far(frq_cen, 0, 1, mp.Near2FarRegion(center=mp.Vector3(0.5*(sr-dpml),0,0.5*sz-dpml),size=mp.Vector3(sr-dpml)))\n", + "n2f_obj = sim.add_near2far(\n", + " frq_cen,\n", + " 0,\n", + " 1,\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(0.5 * (sr - dpml), 0, 0.5 * sz - dpml),\n", + " size=mp.Vector3(sr - dpml),\n", + " ),\n", + ")\n", "\n", "sim.run(until_after_sources=100)\n", "\n", - "ff_r = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(0.5*(sr-dpml),0,-0.5*sz+dpml+dsub+zh+focal_length),size=mp.Vector3(sr-dpml))\n", - "ff_z = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(z=-0.5*sz+dpml+dsub+zh+focal_length),size=mp.Vector3(z=spot_length))\n", - "\n", - "E2_r = np.absolute(ff_r['Ex'])**2+np.absolute(ff_r['Ey'])**2+np.absolute(ff_r['Ez'])**2\n", - "E2_z = np.absolute(ff_z['Ex'])**2+np.absolute(ff_z['Ey'])**2+np.absolute(ff_z['Ez'])**2\n", + "ff_r = sim.get_farfields(\n", + " n2f_obj,\n", + " ff_res,\n", + " center=mp.Vector3(\n", + " 0.5 * (sr - dpml), 0, -0.5 * sz + dpml + dsub + zh + focal_length\n", + " ),\n", + " size=mp.Vector3(sr - dpml),\n", + ")\n", + "ff_z = sim.get_farfields(\n", + " n2f_obj,\n", + " ff_res,\n", + " center=mp.Vector3(z=-0.5 * sz + dpml + dsub + zh + focal_length),\n", + " size=mp.Vector3(z=spot_length),\n", + ")\n", + "\n", + "E2_r = (\n", + " np.absolute(ff_r[\"Ex\"]) ** 2\n", + " + np.absolute(ff_r[\"Ey\"]) ** 2\n", + " + np.absolute(ff_r[\"Ez\"]) ** 2\n", + ")\n", + "E2_z = (\n", + " np.absolute(ff_z[\"Ex\"]) ** 2\n", + " + np.absolute(ff_z[\"Ey\"]) ** 2\n", + " + np.absolute(ff_z[\"Ez\"]) ** 2\n", + ")\n", "\n", "plt.figure(dpi=200)\n", - "plt.subplot(1,2,1)\n", - "plt.semilogy(np.linspace(0,sr-dpml,len(E2_r)),E2_r,'bo-')\n", - "plt.xlim(-2,20)\n", - "plt.xticks([t for t in np.arange(0,25,5)])\n", - "plt.grid(True,axis=\"y\",which=\"both\",ls=\"-\")\n", - "plt.xlabel(r'$r$ coordinate (μm)')\n", - "plt.ylabel(r'energy density of far fields, |E|$^2$')\n", - "plt.subplot(1,2,2)\n", - "plt.semilogy(np.linspace(focal_length-0.5*spot_length,focal_length+0.5*spot_length,len(E2_z)),E2_z,'bo-')\n", - "plt.grid(True,axis=\"y\",which=\"both\",ls=\"-\")\n", - "plt.xlabel(r'$z$ coordinate (μm)')\n", - "plt.ylabel(r'energy density of far fields, |E|$^2$')\n", - "plt.suptitle(r\"binary-phase zone plate with focal length $z$ = {} μm\".format(focal_length))\n", + "plt.subplot(1, 2, 1)\n", + "plt.semilogy(np.linspace(0, sr - dpml, len(E2_r)), E2_r, \"bo-\")\n", + "plt.xlim(-2, 20)\n", + "plt.xticks([t for t in np.arange(0, 25, 5)])\n", + "plt.grid(True, axis=\"y\", which=\"both\", ls=\"-\")\n", + "plt.xlabel(r\"$r$ coordinate (μm)\")\n", + "plt.ylabel(r\"energy density of far fields, |E|$^2$\")\n", + "plt.subplot(1, 2, 2)\n", + "plt.semilogy(\n", + " np.linspace(\n", + " focal_length - 0.5 * spot_length, focal_length + 0.5 * spot_length, len(E2_z)\n", + " ),\n", + " E2_z,\n", + " \"bo-\",\n", + ")\n", + "plt.grid(True, axis=\"y\", which=\"both\", ls=\"-\")\n", + "plt.xlabel(r\"$z$ coordinate (μm)\")\n", + "plt.ylabel(r\"energy density of far fields, |E|$^2$\")\n", + "plt.suptitle(\n", + " r\"binary-phase zone plate with focal length $z$ = {} μm\".format(focal_length)\n", + ")\n", "plt.tight_layout()\n", "plt.savefig(\"zone_plate_farfields.png\")" ] diff --git a/python/examples/zone_plate.py b/python/examples/zone_plate.py index 3759035ba..aec9b4991 100644 --- a/python/examples/zone_plate.py +++ b/python/examples/zone_plate.py @@ -3,99 +3,148 @@ import math import matplotlib.pyplot as plt -resolution = 25 # pixels/μm +resolution = 25 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 2.0 # substrate thickness -dpad = 2.0 # padding betweeen zone plate and PML -zh = 0.5 # zone-plate height -zN = 25 # number of zones (odd zones: π phase shift, even zones: none) -focal_length = 200 # focal length of zone plate -spot_length = 100 # far-field line length -ff_res = 10 # far-field resolution +dpml = 1.0 # PML thickness +dsub = 2.0 # substrate thickness +dpad = 2.0 # padding betweeen zone plate and PML +zh = 0.5 # zone-plate height +zN = 25 # number of zones (odd zones: π phase shift, even zones: none) +focal_length = 200 # focal length of zone plate +spot_length = 100 # far-field line length +ff_res = 10 # far-field resolution pml_layers = [mp.PML(thickness=dpml)] wvl_cen = 0.5 -frq_cen = 1/wvl_cen -dfrq = 0.2*frq_cen +frq_cen = 1 / wvl_cen +dfrq = 0.2 * frq_cen ## radii of zones ## ref: eq. 7 of http://zoneplate.lbl.gov/theory -r = [math.sqrt(n*wvl_cen*(focal_length+n*wvl_cen/4)) for n in range(1,zN+1)] - -sr = r[-1]+dpad+dpml -sz = dpml+dsub+zh+dpad+dpml -cell_size = mp.Vector3(sr,0,sz) - -sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True), - component=mp.Er, - center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml), - size=mp.Vector3(sr)), - mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True), - component=mp.Ep, - center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml), - size=mp.Vector3(sr), - amplitude=-1j)] +r = [ + math.sqrt(n * wvl_cen * (focal_length + n * wvl_cen / 4)) for n in range(1, zN + 1) +] + +sr = r[-1] + dpad + dpml +sz = dpml + dsub + zh + dpad + dpml +cell_size = mp.Vector3(sr, 0, sz) + +sources = [ + mp.Source( + mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True), + component=mp.Er, + center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml), + size=mp.Vector3(sr), + ), + mp.Source( + mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True), + component=mp.Ep, + center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml), + size=mp.Vector3(sr), + amplitude=-1j, + ), +] glass = mp.Medium(index=1.5) -geometry = [mp.Block(material=glass, - size=mp.Vector3(sr,0,dpml+dsub), - center=mp.Vector3(0.5*sr,0,-0.5*sz+0.5*(dpml+dsub)))] - -geometry.extend(mp.Block(material=glass if n % 2 == 0 else mp.vacuum, size=mp.Vector3(r[n], 0, zh), center=mp.Vector3(0.5 * r[n], 0, -0.5 * sz + dpml + dsub + 0.5 * zh)) for n in range(zN - 1, -1, -1)) - -sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - resolution=resolution, - sources=sources, - geometry=geometry, - dimensions=mp.CYLINDRICAL, - m=-1) +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(sr, 0, dpml + dsub), + center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + 0.5 * (dpml + dsub)), + ) +] + +geometry.extend( + mp.Block( + material=glass if n % 2 == 0 else mp.vacuum, + size=mp.Vector3(r[n], 0, zh), + center=mp.Vector3(0.5 * r[n], 0, -0.5 * sz + dpml + dsub + 0.5 * zh), + ) + for n in range(zN - 1, -1, -1) +) + +sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + resolution=resolution, + sources=sources, + geometry=geometry, + dimensions=mp.CYLINDRICAL, + m=-1, +) ## near-field monitor -n2f_obj = sim.add_near2far(frq_cen, - 0, - 1, - mp.Near2FarRegion(center=mp.Vector3(0.5*(sr-dpml),0,0.5*sz-dpml), - size=mp.Vector3(sr-dpml)), - mp.Near2FarRegion(center=mp.Vector3(sr-dpml,0,0.5*sz-dpml-0.5*(dsub+zh+dpad)), - size=mp.Vector3(z=dsub+zh+dpad))) +n2f_obj = sim.add_near2far( + frq_cen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(0.5 * (sr - dpml), 0, 0.5 * sz - dpml), + size=mp.Vector3(sr - dpml), + ), + mp.Near2FarRegion( + center=mp.Vector3(sr - dpml, 0, 0.5 * sz - dpml - 0.5 * (dsub + zh + dpad)), + size=mp.Vector3(z=dsub + zh + dpad), + ), +) sim.plot2D() if mp.am_master(): - plt.savefig("zone_plate_epsilon.png",bbox_inches='tight',dpi=150) + plt.savefig("zone_plate_epsilon.png", bbox_inches="tight", dpi=150) sim.run(until_after_sources=100) -ff_r = sim.get_farfields(n2f_obj, - ff_res, - center=mp.Vector3(0.5*(sr-dpml),0,-0.5*sz+dpml+dsub+zh+focal_length), - size=mp.Vector3(sr-dpml)) - -ff_z = sim.get_farfields(n2f_obj, - ff_res, - center=mp.Vector3(z=-0.5*sz+dpml+dsub+zh+focal_length), - size=mp.Vector3(z=spot_length)) - -E2_r = np.absolute(ff_r['Ex'])**2+np.absolute(ff_r['Ey'])**2+np.absolute(ff_r['Ez'])**2 -E2_z = np.absolute(ff_z['Ex'])**2+np.absolute(ff_z['Ey'])**2+np.absolute(ff_z['Ez'])**2 +ff_r = sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3( + 0.5 * (sr - dpml), 0, -0.5 * sz + dpml + dsub + zh + focal_length + ), + size=mp.Vector3(sr - dpml), +) + +ff_z = sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3(z=-0.5 * sz + dpml + dsub + zh + focal_length), + size=mp.Vector3(z=spot_length), +) + +E2_r = ( + np.absolute(ff_r["Ex"]) ** 2 + + np.absolute(ff_r["Ey"]) ** 2 + + np.absolute(ff_r["Ez"]) ** 2 +) +E2_z = ( + np.absolute(ff_z["Ex"]) ** 2 + + np.absolute(ff_z["Ey"]) ** 2 + + np.absolute(ff_z["Ez"]) ** 2 +) if mp.am_master(): plt.figure(dpi=200) - plt.subplot(1,2,1) - plt.semilogy(np.linspace(0,sr-dpml,len(E2_r)),E2_r,'bo-') - plt.xlim(-2,20) - plt.xticks(list(np.arange(0,25,5))) - plt.grid(True,axis="y",which="both",ls="-") - plt.xlabel(r'$r$ coordinate (μm)') - plt.ylabel(r'energy density of far fields, |E|$^2$') - plt.subplot(1,2,2) - plt.semilogy(np.linspace(focal_length-0.5*spot_length,focal_length+0.5*spot_length,len(E2_z)),E2_z,'bo-') - plt.grid(True,axis="y",which="both",ls="-") - plt.xlabel(r'$z$ coordinate (μm)') - plt.ylabel(r'energy density of far fields, |E|$^2$') + plt.subplot(1, 2, 1) + plt.semilogy(np.linspace(0, sr - dpml, len(E2_r)), E2_r, "bo-") + plt.xlim(-2, 20) + plt.xticks(list(np.arange(0, 25, 5))) + plt.grid(True, axis="y", which="both", ls="-") + plt.xlabel(r"$r$ coordinate (μm)") + plt.ylabel(r"energy density of far fields, |E|$^2$") + plt.subplot(1, 2, 2) + plt.semilogy( + np.linspace( + focal_length - 0.5 * spot_length, + focal_length + 0.5 * spot_length, + len(E2_z), + ), + E2_z, + "bo-", + ) + plt.grid(True, axis="y", which="both", ls="-") + plt.xlabel(r"$z$ coordinate (μm)") + plt.ylabel(r"energy density of far fields, |E|$^2$") plt.suptitle(f"binary-phase zone plate with focal length $z$ = {focal_length} μm") plt.tight_layout() diff --git a/python/geom.py b/python/geom.py index a244cc137..323bb59ad 100755 --- a/python/geom.py +++ b/python/geom.py @@ -12,7 +12,7 @@ import meep as mp -FreqRange = namedtuple('FreqRange', ['min', 'max']) +FreqRange = namedtuple("FreqRange", ["min", "max"]) def check_nonnegative(prop, val): @@ -21,8 +21,9 @@ def check_nonnegative(prop, val): else: raise ValueError(f"{prop} cannot be negative. Got {val}") + def init_do_averaging(mat_func): - if not hasattr(mat_func, 'do_averaging'): + if not hasattr(mat_func, "do_averaging"): mat_func.do_averaging = False @@ -220,9 +221,11 @@ def close(self, v, tol=1.0e-7): v1.close(v2, [tol]) ``` """ - return (abs(self.x - v.x) <= tol and - abs(self.y - v.y) <= tol and - abs(self.z - v.z) <= tol) + return ( + abs(self.x - v.x) <= tol + and abs(self.y - v.y) <= tol + and abs(self.z - v.z) <= tol + ) def rotate(self, axis, theta): """ @@ -307,32 +310,36 @@ class Medium(object): Dispersive dielectric and magnetic materials, above, are specified via a list of objects that are subclasses of type `Susceptibility`. """ - def __init__(self, epsilon_diag=Vector3(1, 1, 1), - epsilon_offdiag=Vector3(), - mu_diag=Vector3(1, 1, 1), - mu_offdiag=Vector3(), - E_susceptibilities=None, - H_susceptibilities=None, - E_chi2_diag=Vector3(), - E_chi3_diag=Vector3(), - H_chi2_diag=Vector3(), - H_chi3_diag=Vector3(), - D_conductivity_diag=Vector3(), - D_conductivity_offdiag=Vector3(), - B_conductivity_diag=Vector3(), - B_conductivity_offdiag=Vector3(), - epsilon=None, - index=None, - mu=None, - chi2=None, - chi3=None, - D_conductivity=None, - B_conductivity=None, - E_chi2=None, - E_chi3=None, - H_chi2=None, - H_chi3=None, - valid_freq_range=FreqRange(min=-mp.inf, max=mp.inf)): + + def __init__( + self, + epsilon_diag=Vector3(1, 1, 1), + epsilon_offdiag=Vector3(), + mu_diag=Vector3(1, 1, 1), + mu_offdiag=Vector3(), + E_susceptibilities=None, + H_susceptibilities=None, + E_chi2_diag=Vector3(), + E_chi3_diag=Vector3(), + H_chi2_diag=Vector3(), + H_chi3_diag=Vector3(), + D_conductivity_diag=Vector3(), + D_conductivity_offdiag=Vector3(), + B_conductivity_diag=Vector3(), + B_conductivity_offdiag=Vector3(), + epsilon=None, + index=None, + mu=None, + chi2=None, + chi3=None, + D_conductivity=None, + B_conductivity=None, + E_chi2=None, + E_chi3=None, + H_chi2=None, + H_chi3=None, + valid_freq_range=FreqRange(min=-mp.inf, max=mp.inf), + ): """ Creates a `Medium` object. @@ -406,9 +413,13 @@ def __init__(self, epsilon_diag=Vector3(1, 1, 1), mu_diag = Vector3(mu, mu, mu) if D_conductivity: - D_conductivity_diag = Vector3(D_conductivity, D_conductivity, D_conductivity) + D_conductivity_diag = Vector3( + D_conductivity, D_conductivity, D_conductivity + ) if B_conductivity: - B_conductivity_diag = Vector3(B_conductivity, B_conductivity, B_conductivity) + B_conductivity_diag = Vector3( + B_conductivity, B_conductivity, B_conductivity + ) if E_chi2: E_chi2_diag = Vector3(E_chi2, E_chi2, E_chi2) @@ -436,7 +447,7 @@ def __init__(self, epsilon_diag=Vector3(1, 1, 1), self.valid_freq_range = valid_freq_range def __repr__(self): - return 'Medium()' + return "Medium()" def transform(self, m): """ @@ -451,12 +462,22 @@ def transform(self, m): left-handed materials, which are [problematic in nondispersive media](FAQ.md#why-does-my-simulation-diverge-if-0). """ - eps = Matrix(mp.Vector3(self.epsilon_diag.x, self.epsilon_offdiag.x, self.epsilon_offdiag.y), - mp.Vector3(self.epsilon_offdiag.x, self.epsilon_diag.y, self.epsilon_offdiag.z), - mp.Vector3(self.epsilon_offdiag.y, self.epsilon_offdiag.z, self.epsilon_diag.z)) - mu = Matrix(mp.Vector3(self.mu_diag.x, self.mu_offdiag.x, self.mu_offdiag.y), - mp.Vector3(self.mu_offdiag.x, self.mu_diag.y, self.mu_offdiag.z), - mp.Vector3(self.mu_offdiag.y, self.mu_offdiag.z, self.mu_diag.z)) + eps = Matrix( + mp.Vector3( + self.epsilon_diag.x, self.epsilon_offdiag.x, self.epsilon_offdiag.y + ), + mp.Vector3( + self.epsilon_offdiag.x, self.epsilon_diag.y, self.epsilon_offdiag.z + ), + mp.Vector3( + self.epsilon_offdiag.y, self.epsilon_offdiag.z, self.epsilon_diag.z + ), + ) + mu = Matrix( + mp.Vector3(self.mu_diag.x, self.mu_offdiag.x, self.mu_offdiag.y), + mp.Vector3(self.mu_offdiag.x, self.mu_diag.y, self.mu_offdiag.z), + mp.Vector3(self.mu_offdiag.y, self.mu_offdiag.z, self.mu_diag.z), + ) new_eps = (m * eps * m.transpose()) / abs(m.determinant()) new_mu = (m * mu * m.transpose()) / abs(m.determinant()) @@ -472,26 +493,48 @@ def transform(self, m): s.transform(m) def rotate(self, axis, theta): - T = get_rotation_matrix(axis,theta) + T = get_rotation_matrix(axis, theta) self.transform(T) - def epsilon(self,freq): + def epsilon(self, freq): """ Returns the medium's permittivity tensor as a 3x3 Numpy array at the specified frequency `freq` which can be either a scalar, list, or Numpy array. In the case of a list/array of N frequency points, a Numpy array of size Nx3x3 is returned. """ - return self._get_epsmu(self.epsilon_diag, self.epsilon_offdiag, self.E_susceptibilities, self.D_conductivity_diag, self.D_conductivity_offdiag, freq) + return self._get_epsmu( + self.epsilon_diag, + self.epsilon_offdiag, + self.E_susceptibilities, + self.D_conductivity_diag, + self.D_conductivity_offdiag, + freq, + ) - def mu(self,freq): + def mu(self, freq): """ Returns the medium's permeability tensor as a 3x3 Numpy array at the specified frequency `freq` which can be either a scalar, list, or Numpy array. In the case of a list/array of N frequency points, a Numpy array of size Nx3x3 is returned. """ - return self._get_epsmu(self.mu_diag, self.mu_offdiag, self.H_susceptibilities, self.B_conductivity_diag, self.B_conductivity_offdiag, freq) - - def _get_epsmu(self, diag, offdiag, susceptibilities, conductivity_diag, conductivity_offdiag, freq): + return self._get_epsmu( + self.mu_diag, + self.mu_offdiag, + self.H_susceptibilities, + self.B_conductivity_diag, + self.B_conductivity_offdiag, + freq, + ) + + def _get_epsmu( + self, + diag, + offdiag, + susceptibilities, + conductivity_diag, + conductivity_offdiag, + freq, + ): # Clean the input if np.isscalar(freq): freqs = np.array(freq)[np.newaxis, np.newaxis, np.newaxis] @@ -501,49 +544,60 @@ def _get_epsmu(self, diag, offdiag, susceptibilities, conductivity_diag, conduct # Check for values outside of allowed ranges if np.min(np.squeeze(freqs)) < self.valid_freq_range.min: - raise ValueError(f"User specified frequency {np.min(np.squeeze(freqs))} is below the Medium\'s limit, {self.valid_freq_range.min}.") + raise ValueError( + f"User specified frequency {np.min(np.squeeze(freqs))} is below the Medium's limit, {self.valid_freq_range.min}." + ) if np.max(np.squeeze(freqs)) > self.valid_freq_range.max: - raise ValueError(f"User specified frequency {np.max(np.squeeze(freqs))} is above the Medium\'s limit, {self.valid_freq_range.max}.") - + raise ValueError( + f"User specified frequency {np.max(np.squeeze(freqs))} is above the Medium's limit, {self.valid_freq_range.max}." + ) # Initialize with instantaneous dielectric tensor - epsmu = np.expand_dims(Matrix(diag=diag,offdiag=offdiag),axis=0) + epsmu = np.expand_dims(Matrix(diag=diag, offdiag=offdiag), axis=0) # Iterate through susceptibilities for i_sus in range(len(susceptibilities)): epsmu = epsmu + susceptibilities[i_sus].eval_susceptibility(freqs) # Account for conductivity term (only multiply if nonzero to avoid unnecessary complex numbers) - conductivity = np.expand_dims(Matrix(diag=conductivity_diag,offdiag=conductivity_offdiag),axis=0) + conductivity = np.expand_dims( + Matrix(diag=conductivity_diag, offdiag=conductivity_offdiag), axis=0 + ) if np.count_nonzero(conductivity) > 0: - epsmu = (1 + 1j/(2*np.pi*freqs) * conductivity) * epsmu + epsmu = (1 + 1j / (2 * np.pi * freqs) * conductivity) * epsmu # Convert list matrix to 3D numpy array size [freqs,3,3] return np.squeeze(epsmu) + class MaterialGrid(object): """ This class is used to specify materials on a rectilinear grid. A class object is passed as the `material` argument of a [`Block`](#block) geometric object or the `default_material` argument of the [`Simulation`](#Simulation) constructor (similar to a [material function](#medium)). """ + def check_weights(self, w): if np.amin(w) >= 0.0 and np.amax(w) <= 1.0: return w - warnings.warn('The weights parameter of MaterialGrid must be in the range [0,1].') - return np.clip(w, 0., 1.) - - def __init__(self, - grid_size, - medium1, - medium2, - weights=None, - grid_type="U_DEFAULT", - do_averaging=True, - beta=0, - eta=0.5, - damping=0): + warnings.warn( + "The weights parameter of MaterialGrid must be in the range [0,1]." + ) + return np.clip(w, 0.0, 1.0) + + def __init__( + self, + grid_size, + medium1, + medium2, + weights=None, + grid_type="U_DEFAULT", + do_averaging=True, + beta=0, + eta=0.5, + damping=0, + ): """ Creates a `MaterialGrid` object. @@ -594,15 +648,17 @@ def __init__(self, self.grid_size = mp.Vector3(*grid_size) self.medium1 = medium1 self.medium2 = medium2 + def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): - return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) - if isclose(self.grid_size.x,0): + return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) + + if isclose(self.grid_size.x, 0): self.grid_size.x = 1 - if isclose(self.grid_size.y,0): + if isclose(self.grid_size.y, 0): self.grid_size.y = 1 - if isclose(self.grid_size.z,0): + if isclose(self.grid_size.z, 0): self.grid_size.z = 1 - self.num_params=int(self.grid_size.x*self.grid_size.y*self.grid_size.z) + self.num_params = int(self.grid_size.x * self.grid_size.y * self.grid_size.z) self.do_averaging = do_averaging self.beta = beta self.eta = eta @@ -610,18 +666,21 @@ def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): if weights is None: self.weights = np.zeros((self.num_params,)) elif weights.size != self.num_params: - raise ValueError("weights of shape {} do not match user specified grid dimension: {}".format(weights.size,self.grid_size)) + raise ValueError( + "weights of shape {} do not match user specified grid dimension: {}".format( + weights.size, self.grid_size + ) + ) else: self.weights = self.check_weights(weights).flatten().astype(np.float64) - grid_type_dict = { - "U_MIN":0, - "U_PROD":1, - "U_MEAN":2, - "U_DEFAULT":3 - } + grid_type_dict = {"U_MIN": 0, "U_PROD": 1, "U_MEAN": 2, "U_DEFAULT": 3} if grid_type not in grid_type_dict: - raise ValueError("Invalid grid_type: {}. Must be either U_MIN, U_PROD, U_MEAN, or U_DEFAULT".format(grid_type_dict)) + raise ValueError( + "Invalid grid_type: {}. Must be either U_MIN, U_PROD, U_MEAN, or U_DEFAULT".format( + grid_type_dict + ) + ) self.grid_type = grid_type_dict[grid_type] self.swigobj = None @@ -631,15 +690,19 @@ def update_weights(self, x): Reset the `weights` to `x`. """ if x.size != self.num_params: - raise ValueError(f"weights of shape {self.weights.size} do not match user specified grid dimension: {self.grid_size}") + raise ValueError( + f"weights of shape {self.weights.size} do not match user specified grid dimension: {self.grid_size}" + ) self.weights[:] = self.check_weights(x).flatten().astype(np.float64) + class Susceptibility(object): """ Parent class for various dispersive susceptibility terms, parameterized by an anisotropic amplitude $\\sigma$. See [Material Dispersion](Materials.md#material-dispersion). """ + def __init__(self, sigma_diag=Vector3(), sigma_offdiag=Vector3(), sigma=None): """ + **`sigma` [`number`]** — The scale factor $\\sigma$. @@ -652,11 +715,13 @@ def __init__(self, sigma_diag=Vector3(), sigma_offdiag=Vector3(), sigma=None): \\begin{pmatrix} a & u & v \\\\ u & b & w \\\\ v & w & c \\end{pmatrix} """ - self.sigma_diag = Vector3(sigma, sigma, sigma) if sigma else Vector3(*sigma_diag) + self.sigma_diag = ( + Vector3(sigma, sigma, sigma) if sigma else Vector3(*sigma_diag) + ) self.sigma_offdiag = Vector3(*sigma_offdiag) def transform(self, m): - sigma = Matrix(diag=self.sigma_diag,offdiag=self.sigma_offdiag) + sigma = Matrix(diag=self.sigma_diag, offdiag=self.sigma_offdiag) new_sigma = (m * sigma * m.transpose()) / abs(m.determinant()) self.sigma_diag = mp.Vector3(new_sigma.c1.x, new_sigma.c2.y, new_sigma.c3.z) self.sigma_offdiag = mp.Vector3(new_sigma.c2.x, new_sigma.c3.x, new_sigma.c3.y) @@ -668,6 +733,7 @@ class LorentzianSusceptibility(Susceptibility): oscillator) form. See [Material Dispersion](Materials.md#material-dispersion), with the parameters (in addition to $\\sigma$): """ + def __init__(self, frequency=0.0, gamma=0.0, **kwargs): """ + **`frequency` [`number`]** — The resonance frequency $f_n = \\omega_n / 2\\pi$. @@ -682,12 +748,28 @@ def __init__(self, frequency=0.0, gamma=0.0, **kwargs): self.frequency = frequency self.gamma = gamma - def eval_susceptibility(self,freq): - sigma = np.expand_dims(Matrix(diag=self.sigma_diag,offdiag=self.sigma_offdiag),axis=0) + def eval_susceptibility(self, freq): + sigma = np.expand_dims( + Matrix(diag=self.sigma_diag, offdiag=self.sigma_offdiag), axis=0 + ) if self.gamma == 0: - return self.frequency*self.frequency / (self.frequency*self.frequency - freq*freq) * sigma + return ( + self.frequency + * self.frequency + / (self.frequency * self.frequency - freq * freq) + * sigma + ) else: - return self.frequency*self.frequency / (self.frequency*self.frequency - freq*freq - 1j*self.gamma*freq) * sigma + return ( + self.frequency + * self.frequency + / ( + self.frequency * self.frequency + - freq * freq + - 1j * self.gamma * freq + ) + * sigma + ) class DrudeSusceptibility(Susceptibility): @@ -695,6 +777,7 @@ class DrudeSusceptibility(Susceptibility): Specifies a single dispersive susceptibility of Drude form. See [Material Dispersion](Materials.md#material-dispersion), with the parameters (in addition to $\\sigma$): """ + def __init__(self, frequency=0.0, gamma=0.0, **kwargs): """ + **`frequency` [`number`]** — The frequency scale factor $f_n = \\omega_n / 2\\pi$ @@ -706,12 +789,19 @@ def __init__(self, frequency=0.0, gamma=0.0, **kwargs): self.frequency = frequency self.gamma = gamma - def eval_susceptibility(self,freq): - sigma = np.expand_dims(Matrix(diag=self.sigma_diag,offdiag=self.sigma_offdiag),axis=0) + def eval_susceptibility(self, freq): + sigma = np.expand_dims( + Matrix(diag=self.sigma_diag, offdiag=self.sigma_offdiag), axis=0 + ) if self.gamma == 0: - return -self.frequency*self.frequency / (freq*(freq)) * sigma + return -self.frequency * self.frequency / (freq * (freq)) * sigma else: - return -self.frequency*self.frequency / (freq*(freq + 1j*self.gamma)) * sigma + return ( + -self.frequency + * self.frequency + / (freq * (freq + 1j * self.gamma)) + * sigma + ) class NoisyLorentzianSusceptibility(LorentzianSusceptibility): @@ -722,6 +812,7 @@ class NoisyLorentzianSusceptibility(LorentzianSusceptibility): `gamma` parameters, but with an additional Gaussian random noise term (uncorrelated in space and time, zero mean) added to the **P** damped-oscillator equation. """ + def __init__(self, noise_amp=0.0, **kwargs): """ + **`noise_amp` [`number`]** — The noise has root-mean square amplitude σ $\\times$ @@ -752,6 +843,7 @@ class NoisyDrudeSusceptibility(DrudeSusceptibility): `gamma` parameters, but with an additional Gaussian random noise term (uncorrelated in space and time, zero mean) added to the **P** damped-oscillator equation. """ + def __init__(self, noise_amp=0.0, **kwargs): """ + **`noise_amp` [`number`]** — The noise has root-mean square amplitude σ $\\times$ @@ -782,6 +874,7 @@ class GyrotropicLorentzianSusceptibility(LorentzianSusceptibility): parameters are `sigma`, `frequency`, and `gamma`, which have the [usual meanings](#susceptibility), and an additional 3-vector `bias`: """ + def __init__(self, bias=Vector3(), **kwargs): """ + **`bias` [`Vector3`]** — The gyrotropy vector. Its direction determines the @@ -800,6 +893,7 @@ class GyrotropicDrudeSusceptibility(DrudeSusceptibility): parameters are `sigma`, `frequency`, and `gamma`, which have the [usual meanings](#susceptibility), and an additional 3-vector `bias`: """ + def __init__(self, bias=Vector3(), **kwargs): """ + **`bias` [`Vector3`]** — The gyrotropy vector. Its direction determines the @@ -820,6 +914,7 @@ class GyrotropicSaturatedSusceptibility(Susceptibility): different from the Lorentzian and Drude case. It also takes a 3-vector `bias` parameter and an `alpha` parameter: """ + def __init__(self, bias=Vector3(), frequency=0.0, gamma=0.0, alpha=0.0, **kwargs): """ + **`sigma` [`number`]** — The coupling factor $\\sigma_n / 2\\pi$ between the @@ -860,6 +955,7 @@ class MultilevelAtom(Susceptibility): atomic level. See [Materials/Saturable Gain and Absorption](Materials.md#saturable-gain-and-absorption). """ + def __init__(self, initial_populations=None, transitions=None, **kwargs): super(MultilevelAtom, self).__init__(**kwargs) self.initial_populations = initial_populations or [] @@ -867,17 +963,18 @@ def __init__(self, initial_populations=None, transitions=None, **kwargs): class Transition(object): - """ - """ - - def __init__(self, - from_level, - to_level, - transition_rate=0, - frequency=0, - sigma_diag=Vector3(1, 1, 1), - gamma=0, - pumping_rate=0): + """ """ + + def __init__( + self, + from_level, + to_level, + transition_rate=0, + frequency=0, + sigma_diag=Vector3(1, 1, 1), + gamma=0, + pumping_rate=0, + ): """ Construct a `Transition`. @@ -896,8 +993,8 @@ def __init__(self, + **`pumping_rate` [`number`]** — The pumping rate $f = \\omega / 2\\pi$. Default is 0. """ - self.from_level = check_nonnegative('from_level', from_level) - self.to_level = check_nonnegative('to_level', to_level) + self.from_level = check_nonnegative("from_level", from_level) + self.to_level = check_nonnegative("to_level", to_level) self.transition_rate = transition_rate self.frequency = frequency self.sigma_diag = sigma_diag @@ -958,6 +1055,7 @@ class GeometricObject(object): geometry = [mp.Prism(vertices, height=1.5, center=mp.Vector3(), material=cSi)] ``` """ + def __init__(self, material=Medium(), center=Vector3(), epsilon_func=None): """ Construct a `GeometricObject`. @@ -1092,7 +1190,10 @@ class Wedge(Cylinder): """ Represents a cylindrical wedge. """ - def __init__(self, radius, wedge_angle=2 * math.pi, wedge_start=Vector3(1, 0, 0), **kwargs): + + def __init__( + self, radius, wedge_angle=2 * math.pi, wedge_start=Vector3(1, 0, 0), **kwargs + ): """ Constructs a `Wedge`. """ @@ -1109,6 +1210,7 @@ class Cone(Cylinder): the radius of the tip is given by the new property, `radius2`. The `center` of a cone is halfway between the two circular ends. """ + def __init__(self, radius, radius2=0, **kwargs): """ Construct a `Cone`. @@ -1125,7 +1227,15 @@ class Block(GeometricObject): """ A parallelepiped (i.e., a brick, possibly with non-orthogonal axes). """ - def __init__(self, size, e1=Vector3(1, 0, 0), e2=Vector3(0, 1, 0), e3=Vector3(0, 0, 1), **kwargs): + + def __init__( + self, + size, + e1=Vector3(1, 0, 0), + e2=Vector3(0, 1, 0), + e3=Vector3(0, 0, 1), + **kwargs, + ): """ Construct a `Block`. @@ -1150,6 +1260,7 @@ class Ellipsoid(Block): An ellipsoid. This is actually a subclass of `Block`, and inherits all the same properties, but defines an ellipsoid inscribed inside the block. """ + def __init__(self, **kwargs): """ Construct an `Ellipsoid`. @@ -1161,7 +1272,16 @@ class Prism(GeometricObject): """ Polygonal prism type. """ - def __init__(self, vertices, height, axis=Vector3(z=1), center=None, sidewall_angle=0, **kwargs): + + def __init__( + self, + vertices, + height, + axis=Vector3(z=1), + center=None, + sidewall_angle=0, + **kwargs, + ): """ Construct a `Prism`. @@ -1188,8 +1308,12 @@ def __init__(self, vertices, height, axis=Vector3(z=1), center=None, sidewall_an radians. Default is 0. """ - centroid = sum(vertices, Vector3(0)) * (1.0 / len(vertices)) # centroid of floor polygon - original_center = centroid + (0.5*height)*axis # center as computed from vertices, height, axis + centroid = sum(vertices, Vector3(0)) * ( + 1.0 / len(vertices) + ) # centroid of floor polygon + original_center = ( + centroid + (0.5 * height) * axis + ) # center as computed from vertices, height, axis if center is not None and len(vertices): center = Vector3(*center) # translate vertices to center prism at requested center @@ -1242,7 +1366,15 @@ class Matrix(object): Scales the matrix `m` by the number `s`. """ - def __init__(self, c1=Vector3(), c2=Vector3(), c3=Vector3(), diag=Vector3(), offdiag=Vector3()): + + def __init__( + self, + c1=Vector3(), + c2=Vector3(), + c3=Vector3(), + diag=Vector3(), + offdiag=Vector3(), + ): """ Constructs a `Matrix`. """ @@ -1289,23 +1421,23 @@ def __repr__(self): return f"<<{r0[0]} {r0[1]} {r0[2]}>\n <{r1[0]} {r1[1]} {r1[2]}>\n <{r2[0]} {r2[1]} {r2[2]}>>" def __array__(self): - return np.array([self.row(0).__array__(), - self.row(1).__array__(), - self.row(2).__array__()]) + return np.array( + [self.row(0).__array__(), self.row(1).__array__(), self.row(2).__array__()] + ) def row(self, i): return Vector3(self.c1[i], self.c2[i], self.c3[i]) def mm_mult(self, m): - c1 = Vector3(self.row(0).dot(m.c1), - self.row(1).dot(m.c1), - self.row(2).dot(m.c1)) - c2 = Vector3(self.row(0).dot(m.c2), - self.row(1).dot(m.c2), - self.row(2).dot(m.c2)) - c3 = Vector3(self.row(0).dot(m.c3), - self.row(1).dot(m.c3), - self.row(2).dot(m.c3)) + c1 = Vector3( + self.row(0).dot(m.c1), self.row(1).dot(m.c1), self.row(2).dot(m.c1) + ) + c2 = Vector3( + self.row(0).dot(m.c2), self.row(1).dot(m.c2), self.row(2).dot(m.c2) + ) + c3 = Vector3( + self.row(0).dot(m.c3), self.row(1).dot(m.c3), self.row(2).dot(m.c3) + ) return Matrix(c1, c2, c3) @@ -1316,16 +1448,20 @@ def scale(self, s): return Matrix(self.c1.scale(s), self.c2.scale(s), self.c3.scale(s)) def determinant(self): - sum1 = sum([ - functools.reduce(operator.mul, [self[x][x] for x in range(3)]), - functools.reduce(operator.mul, [self[0][1], self[1][2], self[2][0]]), - functools.reduce(operator.mul, [self[1][0], self[2][1], self[0][2]]) - ]) - sum2 = sum([ - functools.reduce(operator.mul, [self[0][2], self[1][1], self[2][0]]), - functools.reduce(operator.mul, [self[0][1], self[1][0], self[2][2]]), - functools.reduce(operator.mul, [self[1][2], self[2][1], self[0][0]]) - ]) + sum1 = sum( + [ + functools.reduce(operator.mul, [self[x][x] for x in range(3)]), + functools.reduce(operator.mul, [self[0][1], self[1][2], self[2][0]]), + functools.reduce(operator.mul, [self[1][0], self[2][1], self[0][2]]), + ] + ) + sum2 = sum( + [ + functools.reduce(operator.mul, [self[0][2], self[1][1], self[2][0]]), + functools.reduce(operator.mul, [self[0][1], self[1][0], self[2][2]]), + functools.reduce(operator.mul, [self[1][2], self[2][1], self[0][0]]), + ] + ) return sum1 - sum2 def conj(self): @@ -1361,13 +1497,14 @@ def inverse(self): class Lattice(object): - - def __init__(self, - size=Vector3(1, 1, 1), - basis_size=Vector3(1, 1, 1), - basis1=Vector3(1, 0, 0), - basis2=Vector3(0, 1, 0), - basis3=Vector3(0, 0, 1)): + def __init__( + self, + size=Vector3(1, 1, 1), + basis_size=Vector3(1, 1, 1), + basis1=Vector3(1, 0, 0), + basis2=Vector3(0, 1, 0), + basis3=Vector3(0, 0, 1), + ): self.size = Vector3(*size) self.basis_size = Vector3(*basis_size) @@ -1469,6 +1606,7 @@ def reciprocal_to_lattice(x, lat): def geometric_object_duplicates(shift_vector, min_multiple, max_multiple, go): shift_vector = Vector3(*shift_vector) + def _dup(min_multiple, lst): if min_multiple > max_multiple: return lst @@ -1482,12 +1620,13 @@ def geometric_objects_duplicates(shift_vector, min_multiple, max_multiple, go_li dups = [] shift_vector = Vector3(*shift_vector) for go in go_list: - dups += geometric_object_duplicates(shift_vector, min_multiple, max_multiple, go) + dups += geometric_object_duplicates( + shift_vector, min_multiple, max_multiple, go + ) return dups def geometric_objects_lattice_duplicates(lat, go_list, *usize): - def lat_to_lattice(v): return cartesian_to_lattice(lat.basis * v, lat) @@ -1530,6 +1669,7 @@ def _mem(y=None): fy = f(y) f_memo_tab[y] = fy return fy + return _mem @@ -1557,6 +1697,7 @@ def _pb(): return x_max else: raise ValueError("failed to bracket the root in find_root_deriv") + return _pb def in_bounds(x, f, df, a, b): @@ -1576,7 +1717,11 @@ def newton(x, a, b, dx): a_prime = x if f < 0 else a b_prime = x if f > 0 else b - if dx != x_max - x_min and dx * (f / df) < 0 and f_memo(lazy(a_prime))[0] * f_memo(lazy(b_prime))[0] > 0: + if ( + dx != x_max - x_min + and dx * (f / df) < 0 + and f_memo(lazy(a_prime))[0] * f_memo(lazy(b_prime))[0] > 0 + ): raise ValueError("failed to bracket the root in find_root_deriv") if isinstance(a, numbers.Number) and isinstance(b, numbers.Number): @@ -1598,8 +1743,12 @@ def newton(x, a, b, dx): if x_guess is None: x_guess = (x_min + x_max) * 0.5 - return newton(x_guess, pick_bound(lambda aa: aa < 0), - pick_bound(lambda aa: aa > 0), x_max - x_min) + return newton( + x_guess, + pick_bound(lambda aa: aa < 0), + pick_bound(lambda aa: aa > 0), + x_max - x_min, + ) def get_rotation_matrix(axis, theta): @@ -1612,6 +1761,8 @@ def get_rotation_matrix(axis, theta): + `theta` [`number`] — The rotation angle (in radians). """ - return Matrix(Vector3(x=1).rotate(axis, theta), - Vector3(y=1).rotate(axis, theta), - Vector3(z=1).rotate(axis, theta)) + return Matrix( + Vector3(x=1).rotate(axis, theta), + Vector3(y=1).rotate(axis, theta), + Vector3(z=1).rotate(axis, theta), + ) diff --git a/python/materials.py b/python/materials.py index 6da4e66e0..31056b6e6 100644 --- a/python/materials.py +++ b/python/materials.py @@ -8,944 +8,1110 @@ um_scale = 1.0 # conversion factor for eV to 1/μm [=1/hc] -eV_um_scale = um_scale/1.23984193 +eV_um_scale = um_scale / 1.23984193 -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # crystalline silicon (c-Si) from A. Deinega et al., J. Optical Society of America A, Vol. 28, No. 5, pp. 770-77 (2011) # based on experimental data for intrinsic silicon at T=300K from M.A. Green and M. Keevers, Progress in Photovoltaics, Vol. 3, pp. 189-92 (1995) # wavelength range: 0.4 - 1.0 μm -cSi_range = mp.FreqRange(min=um_scale, max=um_scale/0.4) +cSi_range = mp.FreqRange(min=um_scale, max=um_scale / 0.4) -cSi_frq1 = 3.64/um_scale +cSi_frq1 = 3.64 / um_scale cSi_gam1 = 0 cSi_sig1 = 8 -cSi_frq2 = 2.76/um_scale -cSi_gam2 = 2*0.063/um_scale +cSi_frq2 = 2.76 / um_scale +cSi_gam2 = 2 * 0.063 / um_scale cSi_sig2 = 2.85 -cSi_frq3 = 1.73/um_scale -cSi_gam3 = 2*2.5/um_scale +cSi_frq3 = 1.73 / um_scale +cSi_gam3 = 2 * 2.5 / um_scale cSi_sig3 = -0.107 -cSi_susc = [mp.LorentzianSusceptibility(frequency=cSi_frq1, gamma=cSi_gam1, sigma=cSi_sig1), - mp.LorentzianSusceptibility(frequency=cSi_frq2, gamma=cSi_gam2, sigma=cSi_sig2), - mp.LorentzianSusceptibility(frequency=cSi_frq3, gamma=cSi_gam3, sigma=cSi_sig3)] +cSi_susc = [ + mp.LorentzianSusceptibility(frequency=cSi_frq1, gamma=cSi_gam1, sigma=cSi_sig1), + mp.LorentzianSusceptibility(frequency=cSi_frq2, gamma=cSi_gam2, sigma=cSi_sig2), + mp.LorentzianSusceptibility(frequency=cSi_frq3, gamma=cSi_gam3, sigma=cSi_sig3), +] cSi = mp.Medium(epsilon=1.0, E_susceptibilities=cSi_susc, valid_freq_range=cSi_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # amorphous silicon (a-Si) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.21 - 0.83 μm -aSi_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.21) +aSi_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.21) -aSi_frq1 = 1/(0.315481407124682*um_scale) -aSi_gam1 = 1/(0.645751005208333*um_scale) +aSi_frq1 = 1 / (0.315481407124682 * um_scale) +aSi_gam1 = 1 / (0.645751005208333 * um_scale) aSi_sig1 = 14.571 -aSi_susc = [mp.LorentzianSusceptibility(frequency=aSi_frq1, gamma=aSi_gam1, sigma=aSi_sig1)] +aSi_susc = [ + mp.LorentzianSusceptibility(frequency=aSi_frq1, gamma=aSi_gam1, sigma=aSi_sig1) +] aSi = mp.Medium(epsilon=3.109, E_susceptibilities=aSi_susc, valid_freq_range=aSi_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # hydrogenated amorphous silicon (a-Si:H) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.21 - 0.83 μm -aSi_H_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.21) +aSi_H_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.21) -aSi_H_frq1 = 1/(0.334189199460916*um_scale) -aSi_H_gam1 = 1/(0.579365387850467*um_scale) +aSi_H_frq1 = 1 / (0.334189199460916 * um_scale) +aSi_H_gam1 = 1 / (0.579365387850467 * um_scale) aSi_H_sig1 = 12.31 -aSi_H_susc = [mp.LorentzianSusceptibility(frequency=aSi_H_frq1, gamma=aSi_H_gam1, sigma=aSi_H_sig1)] +aSi_H_susc = [ + mp.LorentzianSusceptibility( + frequency=aSi_H_frq1, gamma=aSi_H_gam1, sigma=aSi_H_sig1 + ) +] -aSi_H = mp.Medium(epsilon=3.22, E_susceptibilities=aSi_H_susc, valid_freq_range=aSi_H_range) +aSi_H = mp.Medium( + epsilon=3.22, E_susceptibilities=aSi_H_susc, valid_freq_range=aSi_H_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # indium tin oxide (ITO) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.21 - 0.83 μm -ITO_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.21) +ITO_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.21) -ITO_frq1 = 1/(0.182329695588235*um_scale) -ITO_gam1 = 1/(1.94637665620094*um_scale) +ITO_frq1 = 1 / (0.182329695588235 * um_scale) +ITO_gam1 = 1 / (1.94637665620094 * um_scale) ITO_sig1 = 2.5 -ITO_susc = [mp.LorentzianSusceptibility(frequency=ITO_frq1, gamma=ITO_gam1, sigma=ITO_sig1)] +ITO_susc = [ + mp.LorentzianSusceptibility(frequency=ITO_frq1, gamma=ITO_gam1, sigma=ITO_sig1) +] ITO = mp.Medium(epsilon=1.0, E_susceptibilities=ITO_susc, valid_freq_range=ITO_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # alumina (Al2O3) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.21 - 2.07 μm -Al2O3_range = mp.FreqRange(min=um_scale/2.07, max=um_scale/0.21) +Al2O3_range = mp.FreqRange(min=um_scale / 2.07, max=um_scale / 0.21) -Al2O3_frq1 = 1/(0.101476668030774*um_scale) +Al2O3_frq1 = 1 / (0.101476668030774 * um_scale) Al2O3_gam1 = 0 Al2O3_sig1 = 1.52 -Al2O3_susc = [mp.LorentzianSusceptibility(frequency=Al2O3_frq1, gamma=Al2O3_gam1, sigma=Al2O3_sig1)] +Al2O3_susc = [ + mp.LorentzianSusceptibility( + frequency=Al2O3_frq1, gamma=Al2O3_gam1, sigma=Al2O3_sig1 + ) +] -Al2O3 = mp.Medium(epsilon=1.0, E_susceptibilities=Al2O3_susc, valid_freq_range=Al2O3_range) +Al2O3 = mp.Medium( + epsilon=1.0, E_susceptibilities=Al2O3_susc, valid_freq_range=Al2O3_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # aluminum nitride (AlN) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.26 - 1.65 μm -AlN_range = mp.FreqRange(min=um_scale/1.65, max=um_scale/0.26) +AlN_range = mp.FreqRange(min=um_scale / 1.65, max=um_scale / 0.26) -AlN_frq1 = 1/(0.139058089950651*um_scale) +AlN_frq1 = 1 / (0.139058089950651 * um_scale) AlN_gam1 = 0 AlN_sig1 = 3.306 -AlN_susc = [mp.LorentzianSusceptibility(frequency=AlN_frq1, gamma=AlN_gam1, sigma=AlN_sig1)] +AlN_susc = [ + mp.LorentzianSusceptibility(frequency=AlN_frq1, gamma=AlN_gam1, sigma=AlN_sig1) +] AlN = mp.Medium(epsilon=1.0, E_susceptibilities=AlN_susc, valid_freq_range=AlN_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # aluminum arsenide (AlAs) from R.E. Fern and A. Onton, J. Applied Physics, Vol. 42, pp. 3499-500 (1971) # ref: https://refractiveindex.info/?shelf=main&book=AlAs&page=Fern # wavelength range: 0.56 - 2.2 μm -AlAs_range = mp.FreqRange(min=um_scale/2.2, max=um_scale/0.56) +AlAs_range = mp.FreqRange(min=um_scale / 2.2, max=um_scale / 0.56) -AlAs_frq1 = 1/(0.2822*um_scale) +AlAs_frq1 = 1 / (0.2822 * um_scale) AlAs_gam1 = 0 AlAs_sig1 = 6.0840 -AlAs_frq2 = 1/(27.62*um_scale) +AlAs_frq2 = 1 / (27.62 * um_scale) AlAs_gam2 = 0 AlAs_sig2 = 1.900 -AlAs_susc = [mp.LorentzianSusceptibility(frequency=AlAs_frq1, gamma=AlAs_gam1, sigma=AlAs_sig1), - mp.LorentzianSusceptibility(frequency=AlAs_frq2, gamma=AlAs_gam2, sigma=AlAs_sig2)] +AlAs_susc = [ + mp.LorentzianSusceptibility(frequency=AlAs_frq1, gamma=AlAs_gam1, sigma=AlAs_sig1), + mp.LorentzianSusceptibility(frequency=AlAs_frq2, gamma=AlAs_gam2, sigma=AlAs_sig2), +] -AlAs = mp.Medium(epsilon=2.0792, E_susceptibilities=AlAs_susc, valid_freq_range=AlAs_range) +AlAs = mp.Medium( + epsilon=2.0792, E_susceptibilities=AlAs_susc, valid_freq_range=AlAs_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # borosilicate glass (BK7) from SCHOTT Zemax catalog 2017-01-20b # ref: https://refractiveindex.info/?shelf=glass&book=BK7&page=SCHOTT # wavelength range: 0.3 - 2.5 μm -BK7_range = mp.FreqRange(min=um_scale/2.5, max=um_scale/0.3) +BK7_range = mp.FreqRange(min=um_scale / 2.5, max=um_scale / 0.3) -BK7_frq1 = 1/(0.07746417668832478*um_scale) +BK7_frq1 = 1 / (0.07746417668832478 * um_scale) BK7_gam1 = 0 BK7_sig1 = 1.03961212 -BK7_frq2 = 1/(0.14148467902921502*um_scale) +BK7_frq2 = 1 / (0.14148467902921502 * um_scale) BK7_gam2 = 0 BK7_sig2 = 0.231792344 -BK7_frq3 = 1/(10.176475470417055*um_scale) +BK7_frq3 = 1 / (10.176475470417055 * um_scale) BK7_gam3 = 0 BK7_sig3 = 1.01046945 -BK7_susc = [mp.LorentzianSusceptibility(frequency=BK7_frq1, gamma=BK7_gam1, sigma=BK7_sig1), - mp.LorentzianSusceptibility(frequency=BK7_frq2, gamma=BK7_gam2, sigma=BK7_sig2), - mp.LorentzianSusceptibility(frequency=BK7_frq3, gamma=BK7_gam3, sigma=BK7_sig3)] +BK7_susc = [ + mp.LorentzianSusceptibility(frequency=BK7_frq1, gamma=BK7_gam1, sigma=BK7_sig1), + mp.LorentzianSusceptibility(frequency=BK7_frq2, gamma=BK7_gam2, sigma=BK7_sig2), + mp.LorentzianSusceptibility(frequency=BK7_frq3, gamma=BK7_gam3, sigma=BK7_sig3), +] BK7 = mp.Medium(epsilon=1.0, E_susceptibilities=BK7_susc, valid_freq_range=BK7_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # fused quartz (silica) from I.H. Malitson, J. Optical Society of America, Vol. 55, pp. 1205-9 (1965) # ref: https://refractiveindex.info/?shelf=glass&book=fused_silica&page=Malitson # wavelength range: 0.21 - 6.7 μm -fused_quartz_range = mp.FreqRange(min=um_scale/6.7, max=um_scale/0.21) +fused_quartz_range = mp.FreqRange(min=um_scale / 6.7, max=um_scale / 0.21) -fused_quartz_frq1 = 1/(0.0684043*um_scale) +fused_quartz_frq1 = 1 / (0.0684043 * um_scale) fused_quartz_gam1 = 0 fused_quartz_sig1 = 0.696166300 -fused_quartz_frq2 = 1/(0.1162414*um_scale) +fused_quartz_frq2 = 1 / (0.1162414 * um_scale) fused_quartz_gam2 = 0 fused_quartz_sig2 = 0.407942600 -fused_quartz_frq3 = 1/(9.896161*um_scale) +fused_quartz_frq3 = 1 / (9.896161 * um_scale) fused_quartz_gam3 = 0 fused_quartz_sig3 = 0.897479400 -fused_quartz_susc = [mp.LorentzianSusceptibility(frequency=fused_quartz_frq1, gamma=fused_quartz_gam1, sigma=fused_quartz_sig1), - mp.LorentzianSusceptibility(frequency=fused_quartz_frq2, gamma=fused_quartz_gam2, sigma=fused_quartz_sig2), - mp.LorentzianSusceptibility(frequency=fused_quartz_frq3, gamma=fused_quartz_gam3, sigma=fused_quartz_sig3)] - -fused_quartz = mp.Medium(epsilon=1.0, E_susceptibilities=fused_quartz_susc, valid_freq_range=fused_quartz_range) - -#------------------------------------------------------------------ +fused_quartz_susc = [ + mp.LorentzianSusceptibility( + frequency=fused_quartz_frq1, gamma=fused_quartz_gam1, sigma=fused_quartz_sig1 + ), + mp.LorentzianSusceptibility( + frequency=fused_quartz_frq2, gamma=fused_quartz_gam2, sigma=fused_quartz_sig2 + ), + mp.LorentzianSusceptibility( + frequency=fused_quartz_frq3, gamma=fused_quartz_gam3, sigma=fused_quartz_sig3 + ), +] + +fused_quartz = mp.Medium( + epsilon=1.0, + E_susceptibilities=fused_quartz_susc, + valid_freq_range=fused_quartz_range, +) + +# ------------------------------------------------------------------ # gallium arsenide (GaAs) from T. Skauli et al., J. Applied Physics, Vol. 94, pp. 6447-55 (2003) # ref: https://refractiveindex.info/?shelf=main&book=GaAs&page=Skauli # wavelength range: 0.97 - 17 μm -GaAs_range = mp.FreqRange(min=um_scale/17, max=um_scale/0.97) +GaAs_range = mp.FreqRange(min=um_scale / 17, max=um_scale / 0.97) -GaAs_frq1 = 1/(0.4431307*um_scale) +GaAs_frq1 = 1 / (0.4431307 * um_scale) GaAs_gam1 = 0 GaAs_sig1 = 5.466742 -GaAs_frq2 = 1/(0.8746453*um_scale) +GaAs_frq2 = 1 / (0.8746453 * um_scale) GaAs_gam2 = 0 GaAs_sig2 = 0.02429960 -GaAs_frq3 = 1/(36.9166*um_scale) +GaAs_frq3 = 1 / (36.9166 * um_scale) GaAs_gam3 = 0 GaAs_sig3 = 1.957522 -GaAs_susc = [mp.LorentzianSusceptibility(frequency=GaAs_frq1, gamma=GaAs_gam1, sigma=GaAs_sig1), - mp.LorentzianSusceptibility(frequency=GaAs_frq2, gamma=GaAs_gam2, sigma=GaAs_sig2), - mp.LorentzianSusceptibility(frequency=GaAs_frq3, gamma=GaAs_gam3, sigma=GaAs_sig3)] +GaAs_susc = [ + mp.LorentzianSusceptibility(frequency=GaAs_frq1, gamma=GaAs_gam1, sigma=GaAs_sig1), + mp.LorentzianSusceptibility(frequency=GaAs_frq2, gamma=GaAs_gam2, sigma=GaAs_sig2), + mp.LorentzianSusceptibility(frequency=GaAs_frq3, gamma=GaAs_gam3, sigma=GaAs_sig3), +] -GaAs = mp.Medium(epsilon=5.372514, E_susceptibilities=GaAs_susc, valid_freq_range=GaAs_range) +GaAs = mp.Medium( + epsilon=5.372514, E_susceptibilities=GaAs_susc, valid_freq_range=GaAs_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon nitride (Si3N4) from H. R. Philipp, J. Electrochemical Society 120, 295-300 (1973) # ref: https://refractiveindex.info/?shelf=main&book=Si3N4&page=Philipp # wavelength range: 0.207 - 1.24 μm -Si3N4_VISNIR_range = mp.FreqRange(min=um_scale/1.24, max=um_scale/0.207) +Si3N4_VISNIR_range = mp.FreqRange(min=um_scale / 1.24, max=um_scale / 0.207) -Si3N4_VISNIR_frq1 = 1/(0.13967*um_scale) +Si3N4_VISNIR_frq1 = 1 / (0.13967 * um_scale) Si3N4_VISNIR_gam1 = 0 Si3N4_VISNIR_sig1 = 2.8939 -Si3N4_VISNIR_susc = [mp.LorentzianSusceptibility(frequency=Si3N4_VISNIR_frq1, gamma=Si3N4_VISNIR_gam1, sigma=Si3N4_VISNIR_sig1)] +Si3N4_VISNIR_susc = [ + mp.LorentzianSusceptibility( + frequency=Si3N4_VISNIR_frq1, gamma=Si3N4_VISNIR_gam1, sigma=Si3N4_VISNIR_sig1 + ) +] -Si3N4_VISNIR = mp.Medium(epsilon=1.0, E_susceptibilities=Si3N4_VISNIR_susc, valid_freq_range=Si3N4_VISNIR_range) +Si3N4_VISNIR = mp.Medium( + epsilon=1.0, + E_susceptibilities=Si3N4_VISNIR_susc, + valid_freq_range=Si3N4_VISNIR_range, +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon nitride (Si3N4) from K. Luke, et. al., Optics Letters, Vol. 40, pp. 4823-26 (2015) # ref: https://refractiveindex.info/?shelf=main&book=Si3N4&page=Luke # wavelength range: 0.310 - 5.504 μm -Si3N4_NIR_range = mp.FreqRange(min=um_scale/5.504, max=um_scale/0.310) +Si3N4_NIR_range = mp.FreqRange(min=um_scale / 5.504, max=um_scale / 0.310) -Si3N4_NIR_frq1 = 1/(0.1353406*um_scale) +Si3N4_NIR_frq1 = 1 / (0.1353406 * um_scale) Si3N4_NIR_gam1 = 0 Si3N4_NIR_sig1 = 3.0249 -Si3N4_NIR_frq2 = 1/(1239.842*um_scale) +Si3N4_NIR_frq2 = 1 / (1239.842 * um_scale) Si3N4_NIR_gam2 = 0 Si3N4_NIR_sig2 = 40314 -Si3N4_NIR_susc = [mp.LorentzianSusceptibility(frequency=Si3N4_NIR_frq1, gamma=Si3N4_NIR_gam1, sigma=Si3N4_NIR_sig1), - mp.LorentzianSusceptibility(frequency=Si3N4_NIR_frq2, gamma=Si3N4_NIR_gam2, sigma=Si3N4_NIR_sig2)] +Si3N4_NIR_susc = [ + mp.LorentzianSusceptibility( + frequency=Si3N4_NIR_frq1, gamma=Si3N4_NIR_gam1, sigma=Si3N4_NIR_sig1 + ), + mp.LorentzianSusceptibility( + frequency=Si3N4_NIR_frq2, gamma=Si3N4_NIR_gam2, sigma=Si3N4_NIR_sig2 + ), +] -Si3N4_NIR = mp.Medium(epsilon=1.0, E_susceptibilities=Si3N4_NIR_susc, valid_freq_range=Si3N4_NIR_range) +Si3N4_NIR = mp.Medium( + epsilon=1.0, E_susceptibilities=Si3N4_NIR_susc, valid_freq_range=Si3N4_NIR_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # elemental metals from A.D. Rakic et al., Applied Optics, Vol. 37, No. 22, pp. 5271-83 (1998) # wavelength range: 0.2 - 12.4 μm -metal_range = mp.FreqRange(min=um_scale/12.398, max=um_scale/.24797) +metal_range = mp.FreqRange(min=um_scale / 12.398, max=um_scale / 0.24797) # silver (Ag) -Ag_plasma_frq = 9.01*eV_um_scale +Ag_plasma_frq = 9.01 * eV_um_scale Ag_f0 = 0.845 Ag_frq0 = 1e-10 -Ag_gam0 = 0.048*eV_um_scale -Ag_sig0 = Ag_f0*Ag_plasma_frq**2/Ag_frq0**2 +Ag_gam0 = 0.048 * eV_um_scale +Ag_sig0 = Ag_f0 * Ag_plasma_frq**2 / Ag_frq0**2 Ag_f1 = 0.065 -Ag_frq1 = 0.816*eV_um_scale # 1.519 μm -Ag_gam1 = 3.886*eV_um_scale -Ag_sig1 = Ag_f1*Ag_plasma_frq**2/Ag_frq1**2 +Ag_frq1 = 0.816 * eV_um_scale # 1.519 μm +Ag_gam1 = 3.886 * eV_um_scale +Ag_sig1 = Ag_f1 * Ag_plasma_frq**2 / Ag_frq1**2 Ag_f2 = 0.124 -Ag_frq2 = 4.481*eV_um_scale # 0.273 μm -Ag_gam2 = 0.452*eV_um_scale -Ag_sig2 = Ag_f2*Ag_plasma_frq**2/Ag_frq2**2 +Ag_frq2 = 4.481 * eV_um_scale # 0.273 μm +Ag_gam2 = 0.452 * eV_um_scale +Ag_sig2 = Ag_f2 * Ag_plasma_frq**2 / Ag_frq2**2 Ag_f3 = 0.011 -Ag_frq3 = 8.185*eV_um_scale # 0.152 μm -Ag_gam3 = 0.065*eV_um_scale -Ag_sig3 = Ag_f3*Ag_plasma_frq**2/Ag_frq3**2 +Ag_frq3 = 8.185 * eV_um_scale # 0.152 μm +Ag_gam3 = 0.065 * eV_um_scale +Ag_sig3 = Ag_f3 * Ag_plasma_frq**2 / Ag_frq3**2 Ag_f4 = 0.840 -Ag_frq4 = 9.083*eV_um_scale # 0.137 μm -Ag_gam4 = 0.916*eV_um_scale -Ag_sig4 = Ag_f4*Ag_plasma_frq**2/Ag_frq4**2 +Ag_frq4 = 9.083 * eV_um_scale # 0.137 μm +Ag_gam4 = 0.916 * eV_um_scale +Ag_sig4 = Ag_f4 * Ag_plasma_frq**2 / Ag_frq4**2 Ag_f5 = 5.646 -Ag_frq5 = 20.29*eV_um_scale # 0.061 μm -Ag_gam5 = 2.419*eV_um_scale -Ag_sig5 = Ag_f5*Ag_plasma_frq**2/Ag_frq5**2 - -Ag_susc = [mp.DrudeSusceptibility(frequency=Ag_frq0, gamma=Ag_gam0, sigma=Ag_sig0), - mp.LorentzianSusceptibility(frequency=Ag_frq1, gamma=Ag_gam1, sigma=Ag_sig1), - mp.LorentzianSusceptibility(frequency=Ag_frq2, gamma=Ag_gam2, sigma=Ag_sig2), - mp.LorentzianSusceptibility(frequency=Ag_frq3, gamma=Ag_gam3, sigma=Ag_sig3), - mp.LorentzianSusceptibility(frequency=Ag_frq4, gamma=Ag_gam4, sigma=Ag_sig4), - mp.LorentzianSusceptibility(frequency=Ag_frq5, gamma=Ag_gam5, sigma=Ag_sig5)] +Ag_frq5 = 20.29 * eV_um_scale # 0.061 μm +Ag_gam5 = 2.419 * eV_um_scale +Ag_sig5 = Ag_f5 * Ag_plasma_frq**2 / Ag_frq5**2 + +Ag_susc = [ + mp.DrudeSusceptibility(frequency=Ag_frq0, gamma=Ag_gam0, sigma=Ag_sig0), + mp.LorentzianSusceptibility(frequency=Ag_frq1, gamma=Ag_gam1, sigma=Ag_sig1), + mp.LorentzianSusceptibility(frequency=Ag_frq2, gamma=Ag_gam2, sigma=Ag_sig2), + mp.LorentzianSusceptibility(frequency=Ag_frq3, gamma=Ag_gam3, sigma=Ag_sig3), + mp.LorentzianSusceptibility(frequency=Ag_frq4, gamma=Ag_gam4, sigma=Ag_sig4), + mp.LorentzianSusceptibility(frequency=Ag_frq5, gamma=Ag_gam5, sigma=Ag_sig5), +] Ag = mp.Medium(epsilon=1.0, E_susceptibilities=Ag_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # gold (Au) -metal_range = mp.FreqRange(min=um_scale/6.1992, max=um_scale/.24797) +metal_range = mp.FreqRange(min=um_scale / 6.1992, max=um_scale / 0.24797) -Au_plasma_frq = 9.03*eV_um_scale +Au_plasma_frq = 9.03 * eV_um_scale Au_f0 = 0.760 Au_frq0 = 1e-10 -Au_gam0 = 0.053*eV_um_scale -Au_sig0 = Au_f0*Au_plasma_frq**2/Au_frq0**2 +Au_gam0 = 0.053 * eV_um_scale +Au_sig0 = Au_f0 * Au_plasma_frq**2 / Au_frq0**2 Au_f1 = 0.024 -Au_frq1 = 0.415*eV_um_scale # 2.988 μm -Au_gam1 = 0.241*eV_um_scale -Au_sig1 = Au_f1*Au_plasma_frq**2/Au_frq1**2 +Au_frq1 = 0.415 * eV_um_scale # 2.988 μm +Au_gam1 = 0.241 * eV_um_scale +Au_sig1 = Au_f1 * Au_plasma_frq**2 / Au_frq1**2 Au_f2 = 0.010 -Au_frq2 = 0.830*eV_um_scale # 1.494 μm -Au_gam2 = 0.345*eV_um_scale -Au_sig2 = Au_f2*Au_plasma_frq**2/Au_frq2**2 +Au_frq2 = 0.830 * eV_um_scale # 1.494 μm +Au_gam2 = 0.345 * eV_um_scale +Au_sig2 = Au_f2 * Au_plasma_frq**2 / Au_frq2**2 Au_f3 = 0.071 -Au_frq3 = 2.969*eV_um_scale # 0.418 μm -Au_gam3 = 0.870*eV_um_scale -Au_sig3 = Au_f3*Au_plasma_frq**2/Au_frq3**2 +Au_frq3 = 2.969 * eV_um_scale # 0.418 μm +Au_gam3 = 0.870 * eV_um_scale +Au_sig3 = Au_f3 * Au_plasma_frq**2 / Au_frq3**2 Au_f4 = 0.601 -Au_frq4 = 4.304*eV_um_scale # 0.288 μm -Au_gam4 = 2.494*eV_um_scale -Au_sig4 = Au_f4*Au_plasma_frq**2/Au_frq4**2 +Au_frq4 = 4.304 * eV_um_scale # 0.288 μm +Au_gam4 = 2.494 * eV_um_scale +Au_sig4 = Au_f4 * Au_plasma_frq**2 / Au_frq4**2 Au_f5 = 4.384 -Au_frq5 = 13.32*eV_um_scale # 0.093 μm -Au_gam5 = 2.214*eV_um_scale -Au_sig5 = Au_f5*Au_plasma_frq**2/Au_frq5**2 - -Au_susc = [mp.DrudeSusceptibility(frequency=Au_frq0, gamma=Au_gam0, sigma=Au_sig0), - mp.LorentzianSusceptibility(frequency=Au_frq1, gamma=Au_gam1, sigma=Au_sig1), - mp.LorentzianSusceptibility(frequency=Au_frq2, gamma=Au_gam2, sigma=Au_sig2), - mp.LorentzianSusceptibility(frequency=Au_frq3, gamma=Au_gam3, sigma=Au_sig3), - mp.LorentzianSusceptibility(frequency=Au_frq4, gamma=Au_gam4, sigma=Au_sig4), - mp.LorentzianSusceptibility(frequency=Au_frq5, gamma=Au_gam5, sigma=Au_sig5)] +Au_frq5 = 13.32 * eV_um_scale # 0.093 μm +Au_gam5 = 2.214 * eV_um_scale +Au_sig5 = Au_f5 * Au_plasma_frq**2 / Au_frq5**2 + +Au_susc = [ + mp.DrudeSusceptibility(frequency=Au_frq0, gamma=Au_gam0, sigma=Au_sig0), + mp.LorentzianSusceptibility(frequency=Au_frq1, gamma=Au_gam1, sigma=Au_sig1), + mp.LorentzianSusceptibility(frequency=Au_frq2, gamma=Au_gam2, sigma=Au_sig2), + mp.LorentzianSusceptibility(frequency=Au_frq3, gamma=Au_gam3, sigma=Au_sig3), + mp.LorentzianSusceptibility(frequency=Au_frq4, gamma=Au_gam4, sigma=Au_sig4), + mp.LorentzianSusceptibility(frequency=Au_frq5, gamma=Au_gam5, sigma=Au_sig5), +] Au = mp.Medium(epsilon=1.0, E_susceptibilities=Au_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # copper (Cu) -metal_range = mp.FreqRange(min=um_scale/12.398, max=um_scale/.20664) +metal_range = mp.FreqRange(min=um_scale / 12.398, max=um_scale / 0.20664) -Cu_plasma_frq = 10.83*eV_um_scale +Cu_plasma_frq = 10.83 * eV_um_scale Cu_f0 = 0.575 Cu_frq0 = 1e-10 -Cu_gam0 = 0.030*eV_um_scale -Cu_sig0 = Cu_f0*Cu_plasma_frq**2/Cu_frq0**2 +Cu_gam0 = 0.030 * eV_um_scale +Cu_sig0 = Cu_f0 * Cu_plasma_frq**2 / Cu_frq0**2 Cu_f1 = 0.061 -Cu_frq1 = 0.291*eV_um_scale # 4.261 μm -Cu_gam1 = 0.378*eV_um_scale -Cu_sig1 = Cu_f1*Cu_plasma_frq**2/Cu_frq1**2 +Cu_frq1 = 0.291 * eV_um_scale # 4.261 μm +Cu_gam1 = 0.378 * eV_um_scale +Cu_sig1 = Cu_f1 * Cu_plasma_frq**2 / Cu_frq1**2 Cu_f2 = 0.104 -Cu_frq2 = 2.957*eV_um_scale # 0.419 μm -Cu_gam2 = 1.056*eV_um_scale -Cu_sig2 = Cu_f2*Cu_plasma_frq**2/Cu_frq2**2 +Cu_frq2 = 2.957 * eV_um_scale # 0.419 μm +Cu_gam2 = 1.056 * eV_um_scale +Cu_sig2 = Cu_f2 * Cu_plasma_frq**2 / Cu_frq2**2 Cu_f3 = 0.723 -Cu_frq3 = 5.300*eV_um_scale # 0.234 μm -Cu_gam3 = 3.213*eV_um_scale -Cu_sig3 = Cu_f3*Cu_plasma_frq**2/Cu_frq3**2 +Cu_frq3 = 5.300 * eV_um_scale # 0.234 μm +Cu_gam3 = 3.213 * eV_um_scale +Cu_sig3 = Cu_f3 * Cu_plasma_frq**2 / Cu_frq3**2 Cu_f4 = 0.638 -Cu_frq4 = 11.18*eV_um_scale # 0.111 μm -Cu_gam4 = 4.305*eV_um_scale -Cu_sig4 = Cu_f4*Cu_plasma_frq**2/Cu_frq4**2 - -Cu_susc = [mp.DrudeSusceptibility(frequency=Cu_frq0, gamma=Cu_gam0, sigma=Cu_sig0), - mp.LorentzianSusceptibility(frequency=Cu_frq1, gamma=Cu_gam1, sigma=Cu_sig1), - mp.LorentzianSusceptibility(frequency=Cu_frq2, gamma=Cu_gam2, sigma=Cu_sig2), - mp.LorentzianSusceptibility(frequency=Cu_frq3, gamma=Cu_gam3, sigma=Cu_sig3), - mp.LorentzianSusceptibility(frequency=Cu_frq4, gamma=Cu_gam4, sigma=Cu_sig4)] +Cu_frq4 = 11.18 * eV_um_scale # 0.111 μm +Cu_gam4 = 4.305 * eV_um_scale +Cu_sig4 = Cu_f4 * Cu_plasma_frq**2 / Cu_frq4**2 + +Cu_susc = [ + mp.DrudeSusceptibility(frequency=Cu_frq0, gamma=Cu_gam0, sigma=Cu_sig0), + mp.LorentzianSusceptibility(frequency=Cu_frq1, gamma=Cu_gam1, sigma=Cu_sig1), + mp.LorentzianSusceptibility(frequency=Cu_frq2, gamma=Cu_gam2, sigma=Cu_sig2), + mp.LorentzianSusceptibility(frequency=Cu_frq3, gamma=Cu_gam3, sigma=Cu_sig3), + mp.LorentzianSusceptibility(frequency=Cu_frq4, gamma=Cu_gam4, sigma=Cu_sig4), +] Cu = mp.Medium(epsilon=1.0, E_susceptibilities=Cu_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # aluminum (Al) -Al_plasma_frq = 14.98*eV_um_scale +Al_plasma_frq = 14.98 * eV_um_scale Al_f0 = 0.523 Al_frq0 = 1e-10 -Al_gam0 = 0.047*eV_um_scale -Al_sig0 = Al_f0*Al_plasma_frq**2/Al_frq0**2 +Al_gam0 = 0.047 * eV_um_scale +Al_sig0 = Al_f0 * Al_plasma_frq**2 / Al_frq0**2 Al_f1 = 0.227 -Al_frq1 = 0.162*eV_um_scale # 7.654 μm -Al_gam1 = 0.333*eV_um_scale -Al_sig1 = Al_f1*Al_plasma_frq**2/Al_frq1**2 +Al_frq1 = 0.162 * eV_um_scale # 7.654 μm +Al_gam1 = 0.333 * eV_um_scale +Al_sig1 = Al_f1 * Al_plasma_frq**2 / Al_frq1**2 Al_f2 = 0.050 -Al_frq2 = 1.544*eV_um_scale # 0.803 μm -Al_gam2 = 0.312*eV_um_scale -Al_sig2 = Al_f2*Al_plasma_frq**2/Al_frq2**2 +Al_frq2 = 1.544 * eV_um_scale # 0.803 μm +Al_gam2 = 0.312 * eV_um_scale +Al_sig2 = Al_f2 * Al_plasma_frq**2 / Al_frq2**2 Al_f3 = 0.166 -Al_frq3 = 1.808*eV_um_scale # 0.686 μm -Al_gam3 = 1.351*eV_um_scale -Al_sig3 = Al_f3*Al_plasma_frq**2/Al_frq3**2 +Al_frq3 = 1.808 * eV_um_scale # 0.686 μm +Al_gam3 = 1.351 * eV_um_scale +Al_sig3 = Al_f3 * Al_plasma_frq**2 / Al_frq3**2 Al_f4 = 0.030 -Al_frq4 = 3.473*eV_um_scale # 0.357 μm -Al_gam4 = 3.382*eV_um_scale -Al_sig4 = Al_f4*Al_plasma_frq**2/Al_frq4**2 - -Al_susc = [mp.DrudeSusceptibility(frequency=Al_frq0, gamma=Al_gam0, sigma=Al_sig0), - mp.LorentzianSusceptibility(frequency=Al_frq1, gamma=Al_gam1, sigma=Al_sig1), - mp.LorentzianSusceptibility(frequency=Al_frq2, gamma=Al_gam2, sigma=Al_sig2), - mp.LorentzianSusceptibility(frequency=Al_frq3, gamma=Al_gam3, sigma=Al_sig3), - mp.LorentzianSusceptibility(frequency=Al_frq4, gamma=Al_gam4, sigma=Al_sig4)] +Al_frq4 = 3.473 * eV_um_scale # 0.357 μm +Al_gam4 = 3.382 * eV_um_scale +Al_sig4 = Al_f4 * Al_plasma_frq**2 / Al_frq4**2 + +Al_susc = [ + mp.DrudeSusceptibility(frequency=Al_frq0, gamma=Al_gam0, sigma=Al_sig0), + mp.LorentzianSusceptibility(frequency=Al_frq1, gamma=Al_gam1, sigma=Al_sig1), + mp.LorentzianSusceptibility(frequency=Al_frq2, gamma=Al_gam2, sigma=Al_sig2), + mp.LorentzianSusceptibility(frequency=Al_frq3, gamma=Al_gam3, sigma=Al_sig3), + mp.LorentzianSusceptibility(frequency=Al_frq4, gamma=Al_gam4, sigma=Al_sig4), +] Al = mp.Medium(epsilon=1.0, E_susceptibilities=Al_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # beryllium (Be) -Be_plasma_frq = 18.51*eV_um_scale +Be_plasma_frq = 18.51 * eV_um_scale Be_f0 = 0.084 Be_frq0 = 1e-10 -Be_gam0 = 0.035*eV_um_scale -Be_sig0 = Be_f0*Be_plasma_frq**2/Be_frq0**2 +Be_gam0 = 0.035 * eV_um_scale +Be_sig0 = Be_f0 * Be_plasma_frq**2 / Be_frq0**2 Be_f1 = 0.031 -Be_frq1 = 0.100*eV_um_scale # 12.398 μm -Be_gam1 = 1.664*eV_um_scale -Be_sig1 = Be_f1*Be_plasma_frq**2/Be_frq1**2 +Be_frq1 = 0.100 * eV_um_scale # 12.398 μm +Be_gam1 = 1.664 * eV_um_scale +Be_sig1 = Be_f1 * Be_plasma_frq**2 / Be_frq1**2 Be_f2 = 0.140 -Be_frq2 = 1.032*eV_um_scale # 1.201 μm -Be_gam2 = 3.395*eV_um_scale -Be_sig2 = Be_f2*Be_plasma_frq**2/Be_frq2**2 +Be_frq2 = 1.032 * eV_um_scale # 1.201 μm +Be_gam2 = 3.395 * eV_um_scale +Be_sig2 = Be_f2 * Be_plasma_frq**2 / Be_frq2**2 Be_f3 = 0.530 -Be_frq3 = 3.183*eV_um_scale # 0.390 μm -Be_gam3 = 4.454*eV_um_scale -Be_sig3 = Be_f3*Be_plasma_frq**2/Be_frq3**2 +Be_frq3 = 3.183 * eV_um_scale # 0.390 μm +Be_gam3 = 4.454 * eV_um_scale +Be_sig3 = Be_f3 * Be_plasma_frq**2 / Be_frq3**2 Be_f4 = 0.130 -Be_frq4 = 4.604*eV_um_scale # 0.269 μm -Be_gam4 = 1.802*eV_um_scale -Be_sig4 = Be_f4*Be_plasma_frq**2/Be_frq4**2 - -Be_susc = [mp.DrudeSusceptibility(frequency=Be_frq0, gamma=Be_gam0, sigma=Be_sig0), - mp.LorentzianSusceptibility(frequency=Be_frq1, gamma=Be_gam1, sigma=Be_sig1), - mp.LorentzianSusceptibility(frequency=Be_frq2, gamma=Be_gam2, sigma=Be_sig2), - mp.LorentzianSusceptibility(frequency=Be_frq3, gamma=Be_gam3, sigma=Be_sig3), - mp.LorentzianSusceptibility(frequency=Be_frq4, gamma=Be_gam4, sigma=Be_sig4)] +Be_frq4 = 4.604 * eV_um_scale # 0.269 μm +Be_gam4 = 1.802 * eV_um_scale +Be_sig4 = Be_f4 * Be_plasma_frq**2 / Be_frq4**2 + +Be_susc = [ + mp.DrudeSusceptibility(frequency=Be_frq0, gamma=Be_gam0, sigma=Be_sig0), + mp.LorentzianSusceptibility(frequency=Be_frq1, gamma=Be_gam1, sigma=Be_sig1), + mp.LorentzianSusceptibility(frequency=Be_frq2, gamma=Be_gam2, sigma=Be_sig2), + mp.LorentzianSusceptibility(frequency=Be_frq3, gamma=Be_gam3, sigma=Be_sig3), + mp.LorentzianSusceptibility(frequency=Be_frq4, gamma=Be_gam4, sigma=Be_sig4), +] Be = mp.Medium(epsilon=1.0, E_susceptibilities=Be_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # chromium (Cr) -Cr_plasma_frq = 10.75*eV_um_scale +Cr_plasma_frq = 10.75 * eV_um_scale Cr_f0 = 0.168 Cr_frq0 = 1e-10 -Cr_gam0 = 0.047*eV_um_scale -Cr_sig0 = Cr_f0*Cr_plasma_frq**2/Cr_frq0**2 +Cr_gam0 = 0.047 * eV_um_scale +Cr_sig0 = Cr_f0 * Cr_plasma_frq**2 / Cr_frq0**2 Cr_f1 = 0.151 -Cr_frq1 = 0.121*eV_um_scale # 10.247 μm -Cr_gam1 = 3.175*eV_um_scale -Cr_sig1 = Cr_f1*Cr_plasma_frq**2/Cr_frq1**2 +Cr_frq1 = 0.121 * eV_um_scale # 10.247 μm +Cr_gam1 = 3.175 * eV_um_scale +Cr_sig1 = Cr_f1 * Cr_plasma_frq**2 / Cr_frq1**2 Cr_f2 = 0.150 -Cr_frq2 = 0.543*eV_um_scale # 2.283 μm -Cr_gam2 = 1.305*eV_um_scale -Cr_sig2 = Cr_f2*Cr_plasma_frq**2/Cr_frq2**2 +Cr_frq2 = 0.543 * eV_um_scale # 2.283 μm +Cr_gam2 = 1.305 * eV_um_scale +Cr_sig2 = Cr_f2 * Cr_plasma_frq**2 / Cr_frq2**2 Cr_f3 = 1.149 -Cr_frq3 = 1.970*eV_um_scale # 0.629 μm -Cr_gam3 = 2.676*eV_um_scale -Cr_sig3 = Cr_f3*Cr_plasma_frq**2/Cr_frq3**2 +Cr_frq3 = 1.970 * eV_um_scale # 0.629 μm +Cr_gam3 = 2.676 * eV_um_scale +Cr_sig3 = Cr_f3 * Cr_plasma_frq**2 / Cr_frq3**2 Cr_f4 = 0.825 -Cr_frq4 = 8.775*eV_um_scale # 0.141 μm -Cr_gam4 = 1.335*eV_um_scale -Cr_sig4 = Cr_f4*Cr_plasma_frq**2/Cr_frq4**2 - -Cr_susc = [mp.DrudeSusceptibility(frequency=Cr_frq0, gamma=Cr_gam0, sigma=Cr_sig0), - mp.LorentzianSusceptibility(frequency=Cr_frq1, gamma=Cr_gam1, sigma=Cr_sig1), - mp.LorentzianSusceptibility(frequency=Cr_frq2, gamma=Cr_gam2, sigma=Cr_sig2), - mp.LorentzianSusceptibility(frequency=Cr_frq3, gamma=Cr_gam3, sigma=Cr_sig3), - mp.LorentzianSusceptibility(frequency=Cr_frq4, gamma=Cr_gam4, sigma=Cr_sig4)] +Cr_frq4 = 8.775 * eV_um_scale # 0.141 μm +Cr_gam4 = 1.335 * eV_um_scale +Cr_sig4 = Cr_f4 * Cr_plasma_frq**2 / Cr_frq4**2 + +Cr_susc = [ + mp.DrudeSusceptibility(frequency=Cr_frq0, gamma=Cr_gam0, sigma=Cr_sig0), + mp.LorentzianSusceptibility(frequency=Cr_frq1, gamma=Cr_gam1, sigma=Cr_sig1), + mp.LorentzianSusceptibility(frequency=Cr_frq2, gamma=Cr_gam2, sigma=Cr_sig2), + mp.LorentzianSusceptibility(frequency=Cr_frq3, gamma=Cr_gam3, sigma=Cr_sig3), + mp.LorentzianSusceptibility(frequency=Cr_frq4, gamma=Cr_gam4, sigma=Cr_sig4), +] Cr = mp.Medium(epsilon=1.0, E_susceptibilities=Cr_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # nickel (Ni) -Ni_plasma_frq = 15.92*eV_um_scale +Ni_plasma_frq = 15.92 * eV_um_scale Ni_f0 = 0.096 Ni_frq0 = 1e-10 -Ni_gam0 = 0.048*eV_um_scale -Ni_sig0 = Ni_f0*Ni_plasma_frq**2/Ni_frq0**2 +Ni_gam0 = 0.048 * eV_um_scale +Ni_sig0 = Ni_f0 * Ni_plasma_frq**2 / Ni_frq0**2 Ni_f1 = 0.100 -Ni_frq1 = 0.174*eV_um_scale # 7.126 μm -Ni_gam1 = 4.511*eV_um_scale -Ni_sig1 = Ni_f1*Ni_plasma_frq**2/Ni_frq1**2 +Ni_frq1 = 0.174 * eV_um_scale # 7.126 μm +Ni_gam1 = 4.511 * eV_um_scale +Ni_sig1 = Ni_f1 * Ni_plasma_frq**2 / Ni_frq1**2 Ni_f2 = 0.135 -Ni_frq2 = 0.582*eV_um_scale # 2.130 μm -Ni_gam2 = 1.334*eV_um_scale -Ni_sig2 = Ni_f2*Ni_plasma_frq**2/Ni_frq2**2 +Ni_frq2 = 0.582 * eV_um_scale # 2.130 μm +Ni_gam2 = 1.334 * eV_um_scale +Ni_sig2 = Ni_f2 * Ni_plasma_frq**2 / Ni_frq2**2 Ni_f3 = 0.106 -Ni_frq3 = 1.597*eV_um_scale # 0.776 μm -Ni_gam3 = 2.178*eV_um_scale -Ni_sig3 = Ni_f3*Ni_plasma_frq**2/Ni_frq3**2 +Ni_frq3 = 1.597 * eV_um_scale # 0.776 μm +Ni_gam3 = 2.178 * eV_um_scale +Ni_sig3 = Ni_f3 * Ni_plasma_frq**2 / Ni_frq3**2 Ni_f4 = 0.729 -Ni_frq4 = 6.089*eV_um_scale # 0.204 μm -Ni_gam4 = 6.292*eV_um_scale -Ni_sig4 = Ni_f4*Ni_plasma_frq**2/Ni_frq4**2 - -Ni_susc = [mp.DrudeSusceptibility(frequency=Ni_frq0, gamma=Ni_gam0, sigma=Ni_sig0), - mp.LorentzianSusceptibility(frequency=Ni_frq1, gamma=Ni_gam1, sigma=Ni_sig1), - mp.LorentzianSusceptibility(frequency=Ni_frq2, gamma=Ni_gam2, sigma=Ni_sig2), - mp.LorentzianSusceptibility(frequency=Ni_frq3, gamma=Ni_gam3, sigma=Ni_sig3), - mp.LorentzianSusceptibility(frequency=Ni_frq4, gamma=Ni_gam4, sigma=Ni_sig4)] +Ni_frq4 = 6.089 * eV_um_scale # 0.204 μm +Ni_gam4 = 6.292 * eV_um_scale +Ni_sig4 = Ni_f4 * Ni_plasma_frq**2 / Ni_frq4**2 + +Ni_susc = [ + mp.DrudeSusceptibility(frequency=Ni_frq0, gamma=Ni_gam0, sigma=Ni_sig0), + mp.LorentzianSusceptibility(frequency=Ni_frq1, gamma=Ni_gam1, sigma=Ni_sig1), + mp.LorentzianSusceptibility(frequency=Ni_frq2, gamma=Ni_gam2, sigma=Ni_sig2), + mp.LorentzianSusceptibility(frequency=Ni_frq3, gamma=Ni_gam3, sigma=Ni_sig3), + mp.LorentzianSusceptibility(frequency=Ni_frq4, gamma=Ni_gam4, sigma=Ni_sig4), +] Ni = mp.Medium(epsilon=1.0, E_susceptibilities=Ni_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # palladium (Pd) -Pd_plasma_frq = 9.72*eV_um_scale +Pd_plasma_frq = 9.72 * eV_um_scale Pd_f0 = 0.330 Pd_frq0 = 1e-10 -Pd_gam0 = 0.008*eV_um_scale -Pd_sig0 = Pd_f0*Pd_plasma_frq**2/Pd_frq0**2 +Pd_gam0 = 0.008 * eV_um_scale +Pd_sig0 = Pd_f0 * Pd_plasma_frq**2 / Pd_frq0**2 Pd_f1 = 0.649 -Pd_frq1 = 0.336*eV_um_scale # 3.690 μm -Pd_gam1 = 2.950*eV_um_scale -Pd_sig1 = Pd_f1*Pd_plasma_frq**2/Pd_frq1**2 +Pd_frq1 = 0.336 * eV_um_scale # 3.690 μm +Pd_gam1 = 2.950 * eV_um_scale +Pd_sig1 = Pd_f1 * Pd_plasma_frq**2 / Pd_frq1**2 Pd_f2 = 0.121 -Pd_frq2 = 0.501*eV_um_scale # 2.475 μm -Pd_gam2 = 0.555*eV_um_scale -Pd_sig2 = Pd_f2*Pd_plasma_frq**2/Pd_frq2**2 +Pd_frq2 = 0.501 * eV_um_scale # 2.475 μm +Pd_gam2 = 0.555 * eV_um_scale +Pd_sig2 = Pd_f2 * Pd_plasma_frq**2 / Pd_frq2**2 Pd_f3 = 0.638 -Pd_frq3 = 1.659*eV_um_scale # 0.747 μm -Pd_gam3 = 4.621*eV_um_scale -Pd_sig3 = Pd_f3*Pd_plasma_frq**2/Pd_frq3**2 +Pd_frq3 = 1.659 * eV_um_scale # 0.747 μm +Pd_gam3 = 4.621 * eV_um_scale +Pd_sig3 = Pd_f3 * Pd_plasma_frq**2 / Pd_frq3**2 Pd_f4 = 0.453 -Pd_frq4 = 5.715*eV_um_scale # 0.217 μm -Pd_gam4 = 3.236*eV_um_scale -Pd_sig4 = Pd_f4*Pd_plasma_frq**2/Pd_frq4**2 - -Pd_susc = [mp.DrudeSusceptibility(frequency=Pd_frq0, gamma=Pd_gam0, sigma=Pd_sig0), - mp.LorentzianSusceptibility(frequency=Pd_frq1, gamma=Pd_gam1, sigma=Pd_sig1), - mp.LorentzianSusceptibility(frequency=Pd_frq2, gamma=Pd_gam2, sigma=Pd_sig2), - mp.LorentzianSusceptibility(frequency=Pd_frq3, gamma=Pd_gam3, sigma=Pd_sig3), - mp.LorentzianSusceptibility(frequency=Pd_frq4, gamma=Pd_gam4, sigma=Pd_sig4)] +Pd_frq4 = 5.715 * eV_um_scale # 0.217 μm +Pd_gam4 = 3.236 * eV_um_scale +Pd_sig4 = Pd_f4 * Pd_plasma_frq**2 / Pd_frq4**2 + +Pd_susc = [ + mp.DrudeSusceptibility(frequency=Pd_frq0, gamma=Pd_gam0, sigma=Pd_sig0), + mp.LorentzianSusceptibility(frequency=Pd_frq1, gamma=Pd_gam1, sigma=Pd_sig1), + mp.LorentzianSusceptibility(frequency=Pd_frq2, gamma=Pd_gam2, sigma=Pd_sig2), + mp.LorentzianSusceptibility(frequency=Pd_frq3, gamma=Pd_gam3, sigma=Pd_sig3), + mp.LorentzianSusceptibility(frequency=Pd_frq4, gamma=Pd_gam4, sigma=Pd_sig4), +] Pd = mp.Medium(epsilon=1.0, E_susceptibilities=Pd_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # platinum (Pt) -Pt_plasma_frq = 9.59*eV_um_scale +Pt_plasma_frq = 9.59 * eV_um_scale Pt_f0 = 0.333 Pt_frq0 = 1e-10 -Pt_gam0 = 0.080*eV_um_scale -Pt_sig0 = Pt_f0*Pt_plasma_frq**2/Pt_frq0**2 +Pt_gam0 = 0.080 * eV_um_scale +Pt_sig0 = Pt_f0 * Pt_plasma_frq**2 / Pt_frq0**2 Pt_f1 = 0.191 -Pt_frq1 = 0.780*eV_um_scale # 1.590 μm -Pt_gam1 = 0.517*eV_um_scale -Pt_sig1 = Pt_f1*Pt_plasma_frq**2/Pt_frq1**2 +Pt_frq1 = 0.780 * eV_um_scale # 1.590 μm +Pt_gam1 = 0.517 * eV_um_scale +Pt_sig1 = Pt_f1 * Pt_plasma_frq**2 / Pt_frq1**2 Pt_f2 = 0.659 -Pt_frq2 = 1.314*eV_um_scale # 0.944 μm -Pt_gam2 = 1.838*eV_um_scale -Pt_sig2 = Pt_f2*Pt_plasma_frq**2/Pt_frq2**2 +Pt_frq2 = 1.314 * eV_um_scale # 0.944 μm +Pt_gam2 = 1.838 * eV_um_scale +Pt_sig2 = Pt_f2 * Pt_plasma_frq**2 / Pt_frq2**2 Pt_f3 = 0.547 -Pt_frq3 = 3.141*eV_um_scale # 0.395 μm -Pt_gam3 = 3.668*eV_um_scale -Pt_sig3 = Pt_f3*Pt_plasma_frq**2/Pt_frq3**2 +Pt_frq3 = 3.141 * eV_um_scale # 0.395 μm +Pt_gam3 = 3.668 * eV_um_scale +Pt_sig3 = Pt_f3 * Pt_plasma_frq**2 / Pt_frq3**2 Pt_f4 = 3.576 -Pt_frq4 = 9.249*eV_um_scale # 0.134 μm -Pt_gam4 = 8.517*eV_um_scale -Pt_sig4 = Pt_f4*Pt_plasma_frq**2/Pt_frq4**2 - -Pt_susc = [mp.DrudeSusceptibility(frequency=Pt_frq0, gamma=Pt_gam0, sigma=Pt_sig0), - mp.LorentzianSusceptibility(frequency=Pt_frq1, gamma=Pt_gam1, sigma=Pt_sig1), - mp.LorentzianSusceptibility(frequency=Pt_frq2, gamma=Pt_gam2, sigma=Pt_sig2), - mp.LorentzianSusceptibility(frequency=Pt_frq3, gamma=Pt_gam3, sigma=Pt_sig3), - mp.LorentzianSusceptibility(frequency=Pt_frq4, gamma=Pt_gam4, sigma=Pt_sig4)] +Pt_frq4 = 9.249 * eV_um_scale # 0.134 μm +Pt_gam4 = 8.517 * eV_um_scale +Pt_sig4 = Pt_f4 * Pt_plasma_frq**2 / Pt_frq4**2 + +Pt_susc = [ + mp.DrudeSusceptibility(frequency=Pt_frq0, gamma=Pt_gam0, sigma=Pt_sig0), + mp.LorentzianSusceptibility(frequency=Pt_frq1, gamma=Pt_gam1, sigma=Pt_sig1), + mp.LorentzianSusceptibility(frequency=Pt_frq2, gamma=Pt_gam2, sigma=Pt_sig2), + mp.LorentzianSusceptibility(frequency=Pt_frq3, gamma=Pt_gam3, sigma=Pt_sig3), + mp.LorentzianSusceptibility(frequency=Pt_frq4, gamma=Pt_gam4, sigma=Pt_sig4), +] Pt = mp.Medium(epsilon=1.0, E_susceptibilities=Pt_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # titanium (Ti) -Ti_plasma_frq = 7.29*eV_um_scale +Ti_plasma_frq = 7.29 * eV_um_scale Ti_f0 = 0.148 Ti_frq0 = 1e-10 -Ti_gam0 = 0.082*eV_um_scale -Ti_sig0 = Ti_f0*Ti_plasma_frq**2/Ti_frq0**2 +Ti_gam0 = 0.082 * eV_um_scale +Ti_sig0 = Ti_f0 * Ti_plasma_frq**2 / Ti_frq0**2 Ti_f1 = 0.899 -Ti_frq1 = 0.777*eV_um_scale # 1.596 μm -Ti_gam1 = 2.276*eV_um_scale -Ti_sig1 = Ti_f1*Ti_plasma_frq**2/Ti_frq1**2 +Ti_frq1 = 0.777 * eV_um_scale # 1.596 μm +Ti_gam1 = 2.276 * eV_um_scale +Ti_sig1 = Ti_f1 * Ti_plasma_frq**2 / Ti_frq1**2 Ti_f2 = 0.393 -Ti_frq2 = 1.545*eV_um_scale # 0.802 μm -Ti_gam2 = 2.518*eV_um_scale -Ti_sig2 = Ti_f2*Ti_plasma_frq**2/Ti_frq2**2 +Ti_frq2 = 1.545 * eV_um_scale # 0.802 μm +Ti_gam2 = 2.518 * eV_um_scale +Ti_sig2 = Ti_f2 * Ti_plasma_frq**2 / Ti_frq2**2 Ti_f3 = 0.187 -Ti_frq3 = 2.509*eV_um_scale # 0.494 μm -Ti_gam3 = 1.663*eV_um_scale -Ti_sig3 = Ti_f3*Ti_plasma_frq**2/Ti_frq3**2 +Ti_frq3 = 2.509 * eV_um_scale # 0.494 μm +Ti_gam3 = 1.663 * eV_um_scale +Ti_sig3 = Ti_f3 * Ti_plasma_frq**2 / Ti_frq3**2 Ti_f4 = 0.001 -Ti_frq4 = 19.43*eV_um_scale # 0.064 μm -Ti_gam4 = 1.762*eV_um_scale -Ti_sig4 = Ti_f4*Ti_plasma_frq**2/Ti_frq4**2 - -Ti_susc = [mp.DrudeSusceptibility(frequency=Ti_frq0, gamma=Ti_gam0, sigma=Ti_sig0), - mp.LorentzianSusceptibility(frequency=Ti_frq1, gamma=Ti_gam1, sigma=Ti_sig1), - mp.LorentzianSusceptibility(frequency=Ti_frq2, gamma=Ti_gam2, sigma=Ti_sig2), - mp.LorentzianSusceptibility(frequency=Ti_frq3, gamma=Ti_gam3, sigma=Ti_sig3), - mp.LorentzianSusceptibility(frequency=Ti_frq4, gamma=Ti_gam4, sigma=Ti_sig4)] +Ti_frq4 = 19.43 * eV_um_scale # 0.064 μm +Ti_gam4 = 1.762 * eV_um_scale +Ti_sig4 = Ti_f4 * Ti_plasma_frq**2 / Ti_frq4**2 + +Ti_susc = [ + mp.DrudeSusceptibility(frequency=Ti_frq0, gamma=Ti_gam0, sigma=Ti_sig0), + mp.LorentzianSusceptibility(frequency=Ti_frq1, gamma=Ti_gam1, sigma=Ti_sig1), + mp.LorentzianSusceptibility(frequency=Ti_frq2, gamma=Ti_gam2, sigma=Ti_sig2), + mp.LorentzianSusceptibility(frequency=Ti_frq3, gamma=Ti_gam3, sigma=Ti_sig3), + mp.LorentzianSusceptibility(frequency=Ti_frq4, gamma=Ti_gam4, sigma=Ti_sig4), +] Ti = mp.Medium(epsilon=1.0, E_susceptibilities=Ti_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # tungsten (W) -W_plasma_frq = 13.22*eV_um_scale +W_plasma_frq = 13.22 * eV_um_scale W_f0 = 0.206 W_frq0 = 1e-10 -W_gam0 = 0.064*eV_um_scale -W_sig0 = W_f0*W_plasma_frq**2/W_frq0**2 +W_gam0 = 0.064 * eV_um_scale +W_sig0 = W_f0 * W_plasma_frq**2 / W_frq0**2 W_f1 = 0.054 -W_frq1 = 1.004*eV_um_scale # 1.235 μm -W_gam1 = 0.530*eV_um_scale -W_sig1 = W_f1*W_plasma_frq**2/W_frq1**2 +W_frq1 = 1.004 * eV_um_scale # 1.235 μm +W_gam1 = 0.530 * eV_um_scale +W_sig1 = W_f1 * W_plasma_frq**2 / W_frq1**2 W_f2 = 0.166 -W_frq2 = 1.917*eV_um_scale # 0.647 μm -W_gam2 = 1.281*eV_um_scale -W_sig2 = W_f2*W_plasma_frq**2/W_frq2**2 +W_frq2 = 1.917 * eV_um_scale # 0.647 μm +W_gam2 = 1.281 * eV_um_scale +W_sig2 = W_f2 * W_plasma_frq**2 / W_frq2**2 W_f3 = 0.706 -W_frq3 = 3.580*eV_um_scale # 0.346 μm -W_gam3 = 3.332*eV_um_scale -W_sig3 = W_f3*W_plasma_frq**2/W_frq3**2 +W_frq3 = 3.580 * eV_um_scale # 0.346 μm +W_gam3 = 3.332 * eV_um_scale +W_sig3 = W_f3 * W_plasma_frq**2 / W_frq3**2 W_f4 = 2.590 -W_frq4 = 7.498*eV_um_scale # 0.165 μm -W_gam4 = 5.836*eV_um_scale -W_sig4 = W_f4*W_plasma_frq**2/W_frq4**2 - -W_susc = [mp.DrudeSusceptibility(frequency=W_frq0, gamma=W_gam0, sigma=W_sig0), - mp.LorentzianSusceptibility(frequency=W_frq1, gamma=W_gam1, sigma=W_sig1), - mp.LorentzianSusceptibility(frequency=W_frq2, gamma=W_gam2, sigma=W_sig2), - mp.LorentzianSusceptibility(frequency=W_frq3, gamma=W_gam3, sigma=W_sig3), - mp.LorentzianSusceptibility(frequency=W_frq4, gamma=W_gam4, sigma=W_sig4)] +W_frq4 = 7.498 * eV_um_scale # 0.165 μm +W_gam4 = 5.836 * eV_um_scale +W_sig4 = W_f4 * W_plasma_frq**2 / W_frq4**2 + +W_susc = [ + mp.DrudeSusceptibility(frequency=W_frq0, gamma=W_gam0, sigma=W_sig0), + mp.LorentzianSusceptibility(frequency=W_frq1, gamma=W_gam1, sigma=W_sig1), + mp.LorentzianSusceptibility(frequency=W_frq2, gamma=W_gam2, sigma=W_sig2), + mp.LorentzianSusceptibility(frequency=W_frq3, gamma=W_gam3, sigma=W_sig3), + mp.LorentzianSusceptibility(frequency=W_frq4, gamma=W_gam4, sigma=W_sig4), +] W = mp.Medium(epsilon=1.0, E_susceptibilities=W_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # metals from D. Barchiesi and T. Grosges, J. Nanophotonics, Vol. 8, 08996 (2015) # wavelength range: 0.4 - 0.8 μm -metal_visible_range = mp.FreqRange(min=um_scale/0.8, max=um_scale/0.4) +metal_visible_range = mp.FreqRange(min=um_scale / 0.8, max=um_scale / 0.4) # gold (Au) # fit to P.B. Johnson and R.W. Christy, Physical Review B, Vol. 6, pp. 4370-9 (1972) -Au_JC_visible_frq0 = 1/(0.139779231751333*um_scale) -Au_JC_visible_gam0 = 1/(1.12834046202759*um_scale) +Au_JC_visible_frq0 = 1 / (0.139779231751333 * um_scale) +Au_JC_visible_gam0 = 1 / (1.12834046202759 * um_scale) Au_JC_visible_sig0 = 1 -Au_JC_visible_frq1 = 1/(0.404064525036786*um_scale) -Au_JC_visible_gam1 = 1/(26.1269913352870*um_scale) +Au_JC_visible_frq1 = 1 / (0.404064525036786 * um_scale) +Au_JC_visible_gam1 = 1 / (26.1269913352870 * um_scale) Au_JC_visible_sig1 = 2.07118534879440 -Au_JC_visible_susc = [mp.DrudeSusceptibility(frequency=Au_JC_visible_frq0, gamma=Au_JC_visible_gam0, sigma=Au_JC_visible_sig0), - mp.LorentzianSusceptibility(frequency=Au_JC_visible_frq1, gamma=Au_JC_visible_gam1, sigma=Au_JC_visible_sig1)] +Au_JC_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Au_JC_visible_frq0, gamma=Au_JC_visible_gam0, sigma=Au_JC_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Au_JC_visible_frq1, gamma=Au_JC_visible_gam1, sigma=Au_JC_visible_sig1 + ), +] Au_JC_visible = mp.Medium(epsilon=6.1599, E_susceptibilities=Au_JC_visible_susc) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # gold (Au) # fit to E.D. Palik, Handbook of Optical Constants, Academic Press (1985) -Au_visible_frq0 = 1/(0.0473629248511456*um_scale) -Au_visible_gam0 = 1/(0.255476199605166*um_scale) +Au_visible_frq0 = 1 / (0.0473629248511456 * um_scale) +Au_visible_gam0 = 1 / (0.255476199605166 * um_scale) Au_visible_sig0 = 1 -Au_visible_frq1 = 1/(0.800619321082804*um_scale) -Au_visible_gam1 = 1/(0.381870287531951*um_scale) +Au_visible_frq1 = 1 / (0.800619321082804 * um_scale) +Au_visible_gam1 = 1 / (0.381870287531951 * um_scale) Au_visible_sig1 = -169.060953137985 -Au_visible_susc = [mp.DrudeSusceptibility(frequency=Au_visible_frq0, gamma=Au_visible_gam0, sigma=Au_visible_sig0), - mp.LorentzianSusceptibility(frequency=Au_visible_frq1, gamma=Au_visible_gam1, sigma=Au_visible_sig1)] - -Au_visible = mp.Medium(epsilon=0.6888, E_susceptibilities=Au_visible_susc, valid_freq_range=metal_visible_range) - -#------------------------------------------------------------------ +Au_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Au_visible_frq0, gamma=Au_visible_gam0, sigma=Au_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Au_visible_frq1, gamma=Au_visible_gam1, sigma=Au_visible_sig1 + ), +] + +Au_visible = mp.Medium( + epsilon=0.6888, + E_susceptibilities=Au_visible_susc, + valid_freq_range=metal_visible_range, +) + +# ------------------------------------------------------------------ ## WARNING: unstable; field divergence may occur # silver (Au) # fit to E.D. Palik, Handbook of Optical Constants, Academic Press (1985) -Ag_visible_frq0 = 1/(0.142050162130618*um_scale) -Ag_visible_gam0 = 1/(18.0357292925015*um_scale) +Ag_visible_frq0 = 1 / (0.142050162130618 * um_scale) +Ag_visible_gam0 = 1 / (18.0357292925015 * um_scale) Ag_visible_sig0 = 1 -Ag_visible_frq1 = 1/(0.115692151792108*um_scale) -Ag_visible_gam1 = 1/(0.257794324096575*um_scale) +Ag_visible_frq1 = 1 / (0.115692151792108 * um_scale) +Ag_visible_gam1 = 1 / (0.257794324096575 * um_scale) Ag_visible_sig1 = 3.74465275944019 -Ag_visible_susc = [mp.DrudeSusceptibility(frequency=Ag_visible_frq0, gamma=Ag_visible_gam0, sigma=Ag_visible_sig0), - mp.LorentzianSusceptibility(frequency=Ag_visible_frq1, gamma=Ag_visible_gam1, sigma=Ag_visible_sig1)] - -Ag_visible = mp.Medium(epsilon=0.0067526, E_susceptibilities=Ag_visible_susc, valid_freq_range=metal_visible_range) - -#------------------------------------------------------------------ +Ag_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Ag_visible_frq0, gamma=Ag_visible_gam0, sigma=Ag_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Ag_visible_frq1, gamma=Ag_visible_gam1, sigma=Ag_visible_sig1 + ), +] + +Ag_visible = mp.Medium( + epsilon=0.0067526, + E_susceptibilities=Ag_visible_susc, + valid_freq_range=metal_visible_range, +) + +# ------------------------------------------------------------------ ## WARNING: unstable; field divergence may occur # aluminum (Al) # fit to E.D. Palik, Handbook of Optical Constants, Academic Press (1985) -Al_visible_frq0 = 1/(0.0625841659042985*um_scale) -Al_visible_gam0 = 1/(0.606007002962666*um_scale) +Al_visible_frq0 = 1 / (0.0625841659042985 * um_scale) +Al_visible_gam0 = 1 / (0.606007002962666 * um_scale) Al_visible_sig0 = 1 -Al_visible_frq1 = 1/(0.528191199577075*um_scale) -Al_visible_gam1 = 1/(0.291862527666814*um_scale) +Al_visible_frq1 = 1 / (0.528191199577075 * um_scale) +Al_visible_gam1 = 1 / (0.291862527666814 * um_scale) Al_visible_sig1 = -44.4456675577921 -Al_visible_susc = [mp.DrudeSusceptibility(frequency=Al_visible_frq0, gamma=Al_visible_gam0, sigma=Al_visible_sig0), - mp.LorentzianSusceptibility(frequency=Al_visible_frq1, gamma=Al_visible_gam1, sigma=Al_visible_sig1)] - -Al_visible = mp.Medium(epsilon=0.13313, E_susceptibilities=Al_visible_susc, valid_freq_range=metal_visible_range) - -#------------------------------------------------------------------ +Al_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Al_visible_frq0, gamma=Al_visible_gam0, sigma=Al_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Al_visible_frq1, gamma=Al_visible_gam1, sigma=Al_visible_sig1 + ), +] + +Al_visible = mp.Medium( + epsilon=0.13313, + E_susceptibilities=Al_visible_susc, + valid_freq_range=metal_visible_range, +) + +# ------------------------------------------------------------------ # chroimium (Cr) # fit to E.D. Palik, Handbook of Optical Constants, Academic Press (1985) -Cr_visible_frq0 = 1/(0.118410119507342*um_scale) -Cr_visible_gam0 = 1/(0.628596264869804*um_scale) +Cr_visible_frq0 = 1 / (0.118410119507342 * um_scale) +Cr_visible_gam0 = 1 / (0.628596264869804 * um_scale) Cr_visible_sig0 = 1 -Cr_visible_frq1 = 1/(0.565709598452496*um_scale) -Cr_visible_gam1 = 1/(0.731117670900812*um_scale) +Cr_visible_frq1 = 1 / (0.565709598452496 * um_scale) +Cr_visible_gam1 = 1 / (0.731117670900812 * um_scale) Cr_visible_sig1 = 13.2912419951294 -Cr_visible_susc = [mp.DrudeSusceptibility(frequency=Cr_visible_frq0, gamma=Cr_visible_gam0, sigma=Cr_visible_sig0), - mp.LorentzianSusceptibility(frequency=Cr_visible_frq1, gamma=Cr_visible_gam1, sigma=Cr_visible_sig1)] - -Cr_visible = mp.Medium(epsilon=2.7767, E_susceptibilities=Cr_visible_susc, valid_freq_range=metal_visible_range) - -#------------------------------------------------------------------ +Cr_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Cr_visible_frq0, gamma=Cr_visible_gam0, sigma=Cr_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Cr_visible_frq1, gamma=Cr_visible_gam1, sigma=Cr_visible_sig1 + ), +] + +Cr_visible = mp.Medium( + epsilon=2.7767, + E_susceptibilities=Cr_visible_susc, + valid_freq_range=metal_visible_range, +) + +# ------------------------------------------------------------------ ## WARNING: unstable; field divergence may occur # titanium (Ti) # fit to E.D. Palik, Handbook of Optical Constants, Academic Press (1985) -Ti_visible_frq0 = 1/(0.101331651921602*um_scale) -Ti_visible_gam0 = 1/(0.365743382258719*um_scale) +Ti_visible_frq0 = 1 / (0.101331651921602 * um_scale) +Ti_visible_gam0 = 1 / (0.365743382258719 * um_scale) Ti_visible_sig0 = 1 -Ti_visible_frq1 = 1/(4.56839173979216e-09*um_scale) -Ti_visible_gam1 = 1/(5.86441957443603e-10*um_scale) +Ti_visible_frq1 = 1 / (4.56839173979216e-09 * um_scale) +Ti_visible_gam1 = 1 / (5.86441957443603e-10 * um_scale) Ti_visible_sig1 = 54742662.1963414 -Ti_visible_susc = [mp.DrudeSusceptibility(frequency=Ti_visible_frq0, gamma=Ti_visible_gam0, sigma=Ti_visible_sig0), - mp.LorentzianSusceptibility(frequency=Ti_visible_frq1, gamma=Ti_visible_gam1, sigma=Ti_visible_sig1)] - -Ti_visible = mp.Medium(epsilon=-5.4742e7, E_susceptibilities=Ti_visible_susc, valid_freq_range=metal_visible_range) - -#------------------------------------------------------------------ +Ti_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Ti_visible_frq0, gamma=Ti_visible_gam0, sigma=Ti_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Ti_visible_frq1, gamma=Ti_visible_gam1, sigma=Ti_visible_sig1 + ), +] + +Ti_visible = mp.Medium( + epsilon=-5.4742e7, + E_susceptibilities=Ti_visible_susc, + valid_freq_range=metal_visible_range, +) + +# ------------------------------------------------------------------ # aluminum (Al) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.19 - 0.83 μm -Al_drude_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.19) +Al_drude_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.19) -Al_drude_frq = 1/(0.0789607648707171*um_scale) -Al_drude_gam = 1/(1.78138208333333*um_scale) +Al_drude_frq = 1 / (0.0789607648707171 * um_scale) +Al_drude_gam = 1 / (1.78138208333333 * um_scale) Al_drude_sig = 1 -Al_drude_susc = [mp.DrudeSusceptibility(frequency=Al_drude_frq, gamma=Al_drude_gam, sigma=Al_drude_sig)] +Al_drude_susc = [ + mp.DrudeSusceptibility( + frequency=Al_drude_frq, gamma=Al_drude_gam, sigma=Al_drude_sig + ) +] -Al_drude = mp.Medium(epsilon=1.0, E_susceptibilities=Al_drude_susc, valid_freq_range=Al_drude_range) +Al_drude = mp.Medium( + epsilon=1.0, E_susceptibilities=Al_drude_susc, valid_freq_range=Al_drude_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # cobalt (Co) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.26 - 1.65 μm -Co_range = mp.FreqRange(min=um_scale/1.65, max=um_scale/0.26) +Co_range = mp.FreqRange(min=um_scale / 1.65, max=um_scale / 0.26) -Co_frq = 1/(0.0789607648707171*um_scale) -Co_gam = 1/(0.213802712536644*um_scale) +Co_frq = 1 / (0.0789607648707171 * um_scale) +Co_gam = 1 / (0.213802712536644 * um_scale) Co_sig = 1 Co_susc = [mp.DrudeSusceptibility(frequency=Co_frq, gamma=Co_gam, sigma=Co_sig)] Co = mp.Medium(epsilon=3.694, E_susceptibilities=Co_susc, valid_freq_range=Co_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ ## WARNING: unstable; field divergence may occur # molybdenum (Mo) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.25 - 0.83 μm -Mo_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.25) +Mo_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.25) -Mo_frq = 1/(0.0620790071099539*um_scale) -Mo_gam = 1/(0.148359690080172*um_scale) +Mo_frq = 1 / (0.0620790071099539 * um_scale) +Mo_gam = 1 / (0.148359690080172 * um_scale) Mo_sig = 1 Mo_susc = [mp.DrudeSusceptibility(frequency=Mo_frq, gamma=Mo_gam, sigma=Mo_sig)] Mo = mp.Medium(epsilon=-1.366, E_susceptibilities=Mo_susc, valid_freq_range=Mo_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # nickel chrome (NiCr) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.25 - 0.83 μm -NiCr_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.25) +NiCr_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.25) -NiCr_frq = 1/(0.0868845080588648*um_scale) -NiCr_gam = 1/(0.308418390547264*um_scale) +NiCr_frq = 1 / (0.0868845080588648 * um_scale) +NiCr_gam = 1 / (0.308418390547264 * um_scale) NiCr_sig = 1 NiCr_susc = [mp.DrudeSusceptibility(frequency=NiCr_frq, gamma=NiCr_gam, sigma=NiCr_sig)] NiCr = mp.Medium(epsilon=1.0, E_susceptibilities=NiCr_susc, valid_freq_range=NiCr_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # nickel iron (NiFe) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.25 - 0.83 μm -NiFe_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.25) +NiFe_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.25) -NiFe_frq = 1/(0.0838297450980392*um_scale) -NiFe_gam = 1/(0.259381156903766*um_scale) +NiFe_frq = 1 / (0.0838297450980392 * um_scale) +NiFe_gam = 1 / (0.259381156903766 * um_scale) NiFe_sig = 1 NiFe_susc = [mp.DrudeSusceptibility(frequency=NiFe_frq, gamma=NiFe_gam, sigma=NiFe_sig)] NiFe = mp.Medium(epsilon=1.0, E_susceptibilities=NiFe_susc, valid_freq_range=NiFe_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # titanium (Ti) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.21 - 1.24 μm -Ti_drude_range = mp.FreqRange(min=um_scale/1.24, max=um_scale/0.21) +Ti_drude_range = mp.FreqRange(min=um_scale / 1.24, max=um_scale / 0.21) -Ti_drude_frq = 1/(0.113746966055046*um_scale) -Ti_drude_gam = 1/(0.490056098814229*um_scale) +Ti_drude_frq = 1 / (0.113746966055046 * um_scale) +Ti_drude_gam = 1 / (0.490056098814229 * um_scale) Ti_drude_sig = 1 -Ti_drude_susc = [mp.DrudeSusceptibility(frequency=Ti_drude_frq, gamma=Ti_drude_gam, sigma=Ti_drude_sig)] +Ti_drude_susc = [ + mp.DrudeSusceptibility( + frequency=Ti_drude_frq, gamma=Ti_drude_gam, sigma=Ti_drude_sig + ) +] -Ti_drude = mp.Medium(epsilon=1.0, E_susceptibilities=Ti_drude_susc, valid_freq_range=Ti_drude_range) +Ti_drude = mp.Medium( + epsilon=1.0, E_susceptibilities=Ti_drude_susc, valid_freq_range=Ti_drude_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon nitride (SiN), non-stoichiometric, from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.21 - 2.07 μm -SiN_range = mp.FreqRange(min=um_scale/2.07, max=um_scale/0.21) +SiN_range = mp.FreqRange(min=um_scale / 2.07, max=um_scale / 0.21) -SiN_frq1 = 1/(0.190891752117013*um_scale) -SiN_gam1 = 1/(3.11518072864322*um_scale) +SiN_frq1 = 1 / (0.190891752117013 * um_scale) +SiN_gam1 = 1 / (3.11518072864322 * um_scale) SiN_sig1 = 1.2650 -SiN_susc = [mp.LorentzianSusceptibility(frequency=SiN_frq1, gamma=SiN_gam1, sigma=SiN_sig1)] +SiN_susc = [ + mp.LorentzianSusceptibility(frequency=SiN_frq1, gamma=SiN_gam1, sigma=SiN_sig1) +] SiN = mp.Medium(epsilon=2.320, E_susceptibilities=SiN_susc, valid_freq_range=SiN_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon nitride (Si3N4), stoichiometric, from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.23 - 0.83 μm -Si3N4_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.23) +Si3N4_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.23) -Si3N4_frq1 = 1/(0.389153148148148*um_scale) -Si3N4_gam1 = 1/(0.693811936205932*um_scale) +Si3N4_frq1 = 1 / (0.389153148148148 * um_scale) +Si3N4_gam1 = 1 / (0.693811936205932 * um_scale) Si3N4_sig1 = 4.377 -Si3N4_susc = [mp.LorentzianSusceptibility(frequency=Si3N4_frq1, gamma=Si3N4_gam1, sigma=Si3N4_sig1)] +Si3N4_susc = [ + mp.LorentzianSusceptibility( + frequency=Si3N4_frq1, gamma=Si3N4_gam1, sigma=Si3N4_sig1 + ) +] -Si3N4 = mp.Medium(epsilon=1.0, E_susceptibilities=Si3N4_susc, valid_freq_range=Si3N4_range) +Si3N4 = mp.Medium( + epsilon=1.0, E_susceptibilities=Si3N4_susc, valid_freq_range=Si3N4_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon dioxide (SiO2) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.25 - 1.77 μm -SiO2_range = mp.FreqRange(min=um_scale/1.77, max=um_scale/0.25) +SiO2_range = mp.FreqRange(min=um_scale / 1.77, max=um_scale / 0.25) -SiO2_frq1 = 1/(0.103320160833333*um_scale) -SiO2_gam1 = 1/(12.3984193000000*um_scale) +SiO2_frq1 = 1 / (0.103320160833333 * um_scale) +SiO2_gam1 = 1 / (12.3984193000000 * um_scale) SiO2_sig1 = 1.12 -SiO2_susc = [mp.LorentzianSusceptibility(frequency=SiO2_frq1, gamma=SiO2_gam1, sigma=SiO2_sig1)] +SiO2_susc = [ + mp.LorentzianSusceptibility(frequency=SiO2_frq1, gamma=SiO2_gam1, sigma=SiO2_sig1) +] SiO2 = mp.Medium(epsilon=1.0, E_susceptibilities=SiO2_susc, valid_freq_range=SiO2_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # indium phosphide (InP) from Handbook of Optics, 2nd edition, Vol. 2, McGraw-Hill (1994) # ref: https://refractiveindex.info/?shelf=main&book=InP&page=Pettit # wavelength range: 0.95 - 10 μm -InP_range = mp.FreqRange(min=um_scale/10, max=um_scale/0.95) +InP_range = mp.FreqRange(min=um_scale / 10, max=um_scale / 0.95) -InP_frq1 = 1/(0.6263*um_scale) +InP_frq1 = 1 / (0.6263 * um_scale) InP_gam1 = 0 InP_sig1 = 2.316 -InP_frq2 = 1/(32.935*um_scale) +InP_frq2 = 1 / (32.935 * um_scale) InP_gam2 = 0 InP_sig2 = 2.765 -InP_susc = [mp.LorentzianSusceptibility(frequency=InP_frq1, gamma=InP_gam1, sigma=InP_sig1), - mp.LorentzianSusceptibility(frequency=InP_frq2, gamma=InP_gam2, sigma=InP_sig2)] +InP_susc = [ + mp.LorentzianSusceptibility(frequency=InP_frq1, gamma=InP_gam1, sigma=InP_sig1), + mp.LorentzianSusceptibility(frequency=InP_frq2, gamma=InP_gam2, sigma=InP_sig2), +] InP = mp.Medium(epsilon=7.255, E_susceptibilities=InP_susc, valid_freq_range=InP_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # germanium (Ge) from N. P. Barnes and M. S. Piltch, J. Optical Society America, Vol. 69, pp. 178-180 (1979) # ref: https://refractiveindex.info/?shelf=main&book=Ge&page=Icenogle # wavelength range: 2.5 - 12 μm -Ge_range = mp.FreqRange(min=um_scale/12, max=um_scale/2.5) +Ge_range = mp.FreqRange(min=um_scale / 12, max=um_scale / 2.5) -Ge_frq1 = 1/(0.6641159*um_scale) +Ge_frq1 = 1 / (0.6641159 * um_scale) Ge_gam1 = 0 Ge_sig1 = 6.7288 -Ge_frq2 = 1/(62.210127*um_scale) +Ge_frq2 = 1 / (62.210127 * um_scale) Ge_gam2 = 0 Ge_sig2 = 0.21307 -Ge_susc = [mp.LorentzianSusceptibility(frequency=Ge_frq1, gamma=Ge_gam1, sigma=Ge_sig1), - mp.LorentzianSusceptibility(frequency=Ge_frq2, gamma=Ge_gam2, sigma=Ge_sig2)] +Ge_susc = [ + mp.LorentzianSusceptibility(frequency=Ge_frq1, gamma=Ge_gam1, sigma=Ge_sig1), + mp.LorentzianSusceptibility(frequency=Ge_frq2, gamma=Ge_gam2, sigma=Ge_sig2), +] Ge = mp.Medium(epsilon=9.28156, E_susceptibilities=Ge_susc, valid_freq_range=Ge_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon (Si) from C. D. Salzberg and J. J. Villa, , J. Optical Society America, Vol. 47, pp. 244-246 (1957) # ref: https://refractiveindex.info/?shelf=main&book=Si&page=Salzberg # wavelength range: 1.36 - 11 μm -Si_range = mp.FreqRange(min=um_scale/11, max=um_scale/1.36) +Si_range = mp.FreqRange(min=um_scale / 11, max=um_scale / 1.36) -Si_frq1 = 1/(0.301516485*um_scale) +Si_frq1 = 1 / (0.301516485 * um_scale) Si_gam1 = 0 Si_sig1 = 10.6684293 -Si_frq2 = 1/(1.13475115*um_scale) +Si_frq2 = 1 / (1.13475115 * um_scale) Si_gam2 = 0 Si_sig2 = 0.0030434748 -Si_frq3 = 1/(1104*um_scale) +Si_frq3 = 1 / (1104 * um_scale) Si_gam3 = 0 Si_sig3 = 1.54133408 -Si_susc = [mp.LorentzianSusceptibility(frequency=Si_frq1, gamma=Si_gam1, sigma=Si_sig1), - mp.LorentzianSusceptibility(frequency=Si_frq2, gamma=Si_gam2, sigma=Si_sig2), - mp.LorentzianSusceptibility(frequency=Si_frq3, gamma=Si_gam3, sigma=Si_sig3)] +Si_susc = [ + mp.LorentzianSusceptibility(frequency=Si_frq1, gamma=Si_gam1, sigma=Si_sig1), + mp.LorentzianSusceptibility(frequency=Si_frq2, gamma=Si_gam2, sigma=Si_sig2), + mp.LorentzianSusceptibility(frequency=Si_frq3, gamma=Si_gam3, sigma=Si_sig3), +] Si = mp.Medium(epsilon=1.0, E_susceptibilities=Si_susc, valid_freq_range=Si_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # poly(methyl methacrylate) (PMMA) from N. Sultanova et al., Acta Physica Polonica A, Vol. 116, pp. 585-7 (2009) # ref: https://refractiveindex.info/?shelf=organic&book=poly%28methyl_methacrylate%29&page=Sultanova # wavelength range: 0.437 - 1.052 μm -PMMA_range = mp.FreqRange(min=um_scale/1.052, max=um_scale/0.437) +PMMA_range = mp.FreqRange(min=um_scale / 1.052, max=um_scale / 0.437) -PMMA_frq1 = 1/(0.106362587407415*um_scale) +PMMA_frq1 = 1 / (0.106362587407415 * um_scale) PMMA_gam1 = 0 PMMA_sig1 = 1.1819 -PMMA_susc = [mp.LorentzianSusceptibility(frequency=PMMA_frq1, gamma=PMMA_gam1, sigma=PMMA_sig1)] +PMMA_susc = [ + mp.LorentzianSusceptibility(frequency=PMMA_frq1, gamma=PMMA_gam1, sigma=PMMA_sig1) +] PMMA = mp.Medium(epsilon=1.0, E_susceptibilities=PMMA_susc, valid_freq_range=PMMA_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # polycarbonate (PC) from N. Sultanova et al., Acta Physica Polonica A, Vol. 116, pp. 585-7 (2009) # ref: https://refractiveindex.info/?shelf=organic&book=polycarbonate&page=Sultanova # wavelength range: 0.437 - 1.052 μm -PC_range = mp.FreqRange(min=um_scale/1.052, max=um_scale/0.437) +PC_range = mp.FreqRange(min=um_scale / 1.052, max=um_scale / 0.437) -PC_frq1 = 1/(0.145958898324152*um_scale) +PC_frq1 = 1 / (0.145958898324152 * um_scale) PC_gam1 = 0 PC_sig1 = 1.4182 @@ -953,14 +1119,14 @@ PC = mp.Medium(epsilon=1.0, E_susceptibilities=PC_susc, valid_freq_range=PC_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # polystyrene (PS) from N. Sultanova et al., Acta Physica Polonica A, Vol. 116, pp. 585-7 (2009) # ref: https://refractiveindex.info/?shelf=organic&book=polystyren&page=Sultanova # wavelength range: 0.437 - 1.052 μm -PS_range = mp.FreqRange(min=um_scale/1.052, max=um_scale/0.437) +PS_range = mp.FreqRange(min=um_scale / 1.052, max=um_scale / 0.437) -PS_frq1 = 1/(0.142182980697410*um_scale) +PS_frq1 = 1 / (0.142182980697410 * um_scale) PS_gam1 = 0 PS_sig1 = 1.4435 @@ -968,22 +1134,24 @@ PS = mp.Medium(epsilon=1.0, E_susceptibilities=PS_susc, valid_freq_range=PS_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # cellulose (CLS) from N. Sultanova et al., Acta Physica Polonica A, Vol. 116, pp. 585-7 (2009) # ref: https://refractiveindex.info/?shelf=organic&book=cellulose&page=Sultanova # wavelength range: 0.437 - 1.052 μm -CLS_range = mp.FreqRange(min=um_scale/1.052, max=um_scale/0.437) +CLS_range = mp.FreqRange(min=um_scale / 1.052, max=um_scale / 0.437) -CLS_frq1 = 1/(0.105294824184287*um_scale) +CLS_frq1 = 1 / (0.105294824184287 * um_scale) CLS_gam1 = 0 CLS_sig1 = 1.124 -CLS_susc = [mp.LorentzianSusceptibility(frequency=CLS_frq1, gamma=CLS_gam1, sigma=CLS_sig1)] +CLS_susc = [ + mp.LorentzianSusceptibility(frequency=CLS_frq1, gamma=CLS_gam1, sigma=CLS_sig1) +] CLS = mp.Medium(epsilon=1.0, E_susceptibilities=CLS_susc, valid_freq_range=CLS_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # barium borate (BaB2O4), beta phase, from G. Tamosauskas et al., Optical Materials Express, Vol. 8, pp. 1410-18 (2018) # ref: https://refractiveindex.info/?shelf=main&book=BaB2O4&page=Tamosauskas-o # ref: https://refractiveindex.info/?shelf=main&book=BaB2O4&page=Tamosauskas-e @@ -991,39 +1159,71 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -BaB2O4_range = mp.FreqRange(min=um_scale/5.2, max=um_scale/0.188) +BaB2O4_range = mp.FreqRange(min=um_scale / 5.2, max=um_scale / 0.188) -BaB2O4_frq1 = 1/(0.06265780079128216*um_scale) +BaB2O4_frq1 = 1 / (0.06265780079128216 * um_scale) BaB2O4_gam1 = 0 BaB2O4_sig1 = 0.90291 -BaB2O4_frq2 = 1/(0.13706202975295528*um_scale) +BaB2O4_frq2 = 1 / (0.13706202975295528 * um_scale) BaB2O4_gam2 = 0 BaB2O4_sig2 = 0.83155 -BaB2O4_frq3 = 1/(7.746612162745725*um_scale) +BaB2O4_frq3 = 1 / (7.746612162745725 * um_scale) BaB2O4_gam3 = 0 BaB2O4_sig3 = 0.76536 -BaB2O4_susc_o = [mp.LorentzianSusceptibility(frequency=BaB2O4_frq1, gamma=BaB2O4_gam1, sigma_diag=BaB2O4_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=BaB2O4_frq2, gamma=BaB2O4_gam2, sigma_diag=BaB2O4_sig2*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=BaB2O4_frq3, gamma=BaB2O4_gam3, sigma_diag=BaB2O4_sig3*mp.Vector3(1,1,0))] - -BaB2O4_frq1 = 1/(0.0845103543951864*um_scale) +BaB2O4_susc_o = [ + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq1, + gamma=BaB2O4_gam1, + sigma_diag=BaB2O4_sig1 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq2, + gamma=BaB2O4_gam2, + sigma_diag=BaB2O4_sig2 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq3, + gamma=BaB2O4_gam3, + sigma_diag=BaB2O4_sig3 * mp.Vector3(1, 1, 0), + ), +] + +BaB2O4_frq1 = 1 / (0.0845103543951864 * um_scale) BaB2O4_gam1 = 0 BaB2O4_sig1 = 1.151075 -BaB2O4_frq2 = 1/(0.15029970059850417*um_scale) +BaB2O4_frq2 = 1 / (0.15029970059850417 * um_scale) BaB2O4_gam2 = 0 BaB2O4_sig2 = 0.21803 -BaB2O4_frq3 = 1/(16.217274740226856*um_scale) +BaB2O4_frq3 = 1 / (16.217274740226856 * um_scale) BaB2O4_gam3 = 0 BaB2O4_sig3 = 0.656 -BaB2O4_susc_e = [mp.LorentzianSusceptibility(frequency=BaB2O4_frq1, gamma=BaB2O4_gam1, sigma_diag=BaB2O4_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=BaB2O4_frq2, gamma=BaB2O4_gam2, sigma_diag=BaB2O4_sig2*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=BaB2O4_frq3, gamma=BaB2O4_gam3, sigma_diag=BaB2O4_sig3*mp.Vector3(0,0,1))] - -BaB2O4 = mp.Medium(epsilon=1.0, E_susceptibilities=BaB2O4_susc_o+BaB2O4_susc_e, valid_freq_range=BaB2O4_range) - -#------------------------------------------------------------------ +BaB2O4_susc_e = [ + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq1, + gamma=BaB2O4_gam1, + sigma_diag=BaB2O4_sig1 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq2, + gamma=BaB2O4_gam2, + sigma_diag=BaB2O4_sig2 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq3, + gamma=BaB2O4_gam3, + sigma_diag=BaB2O4_sig3 * mp.Vector3(0, 0, 1), + ), +] + +BaB2O4 = mp.Medium( + epsilon=1.0, + E_susceptibilities=BaB2O4_susc_o + BaB2O4_susc_e, + valid_freq_range=BaB2O4_range, +) + +# ------------------------------------------------------------------ # lithium niobate (LiNbO3) from D.E. Zelmon et al., J. Optical Society of America B, Vol. 14, pp. 3319-22 (1997) # ref: https://refractiveindex.info/?shelf=main&book=LiNbO3&page=Zelmon-o # ref: https://refractiveindex.info/?shelf=main&book=LiNbO3&page=Zelmon-e @@ -1031,39 +1231,71 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -LiNbO3_range = mp.FreqRange(min=um_scale/5.0, max=um_scale/0.4) +LiNbO3_range = mp.FreqRange(min=um_scale / 5.0, max=um_scale / 0.4) -LiNbO3_frq1 = 1/(0.13281566172707193*um_scale) +LiNbO3_frq1 = 1 / (0.13281566172707193 * um_scale) LiNbO3_gam1 = 0 LiNbO3_sig1 = 2.6734 -LiNbO3_frq2 = 1/(0.24318717071424636*um_scale) +LiNbO3_frq2 = 1 / (0.24318717071424636 * um_scale) LiNbO3_gam2 = 0 LiNbO3_sig2 = 1.2290 -LiNbO3_frq3 = 1/(21.78531615561271*um_scale) +LiNbO3_frq3 = 1 / (21.78531615561271 * um_scale) LiNbO3_gam3 = 0 LiNbO3_sig3 = 12.614 -LiNbO3_susc_o = [mp.LorentzianSusceptibility(frequency=LiNbO3_frq1, gamma=LiNbO3_gam1, sigma_diag=LiNbO3_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=LiNbO3_frq2, gamma=LiNbO3_gam2, sigma_diag=LiNbO3_sig2*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=LiNbO3_frq3, gamma=LiNbO3_gam3, sigma_diag=LiNbO3_sig3*mp.Vector3(1,1,0))] - -LiNbO3_frq1 = 1/(0.14307340773183533*um_scale) +LiNbO3_susc_o = [ + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq1, + gamma=LiNbO3_gam1, + sigma_diag=LiNbO3_sig1 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq2, + gamma=LiNbO3_gam2, + sigma_diag=LiNbO3_sig2 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq3, + gamma=LiNbO3_gam3, + sigma_diag=LiNbO3_sig3 * mp.Vector3(1, 1, 0), + ), +] + +LiNbO3_frq1 = 1 / (0.14307340773183533 * um_scale) LiNbO3_gam1 = 0 LiNbO3_sig1 = 2.9804 -LiNbO3_frq2 = 1/(0.2580697580112788*um_scale) +LiNbO3_frq2 = 1 / (0.2580697580112788 * um_scale) LiNbO3_gam2 = 0 LiNbO3_sig2 = 0.5981 -LiNbO3_frq3 = 1/(20.39803912144498*um_scale) +LiNbO3_frq3 = 1 / (20.39803912144498 * um_scale) LiNbO3_gam3 = 0 LiNbO3_sig3 = 8.9543 -LiNbO3_susc_e = [mp.LorentzianSusceptibility(frequency=LiNbO3_frq1, gamma=LiNbO3_gam1, sigma_diag=LiNbO3_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=LiNbO3_frq2, gamma=LiNbO3_gam2, sigma_diag=LiNbO3_sig2*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=LiNbO3_frq3, gamma=LiNbO3_gam3, sigma_diag=LiNbO3_sig3*mp.Vector3(0,0,1))] - -LiNbO3 = mp.Medium(epsilon=1.0, E_susceptibilities=LiNbO3_susc_o+LiNbO3_susc_e, valid_freq_range=LiNbO3_range) - -#------------------------------------------------------------------ +LiNbO3_susc_e = [ + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq1, + gamma=LiNbO3_gam1, + sigma_diag=LiNbO3_sig1 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq2, + gamma=LiNbO3_gam2, + sigma_diag=LiNbO3_sig2 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq3, + gamma=LiNbO3_gam3, + sigma_diag=LiNbO3_sig3 * mp.Vector3(0, 0, 1), + ), +] + +LiNbO3 = mp.Medium( + epsilon=1.0, + E_susceptibilities=LiNbO3_susc_o + LiNbO3_susc_e, + valid_freq_range=LiNbO3_range, +) + +# ------------------------------------------------------------------ # calcium tungstate (CaWO4) from W.L. Bond, J. Applied Physics, Vol. 36, pp. 1674-77 (1965) # ref: https://refractiveindex.info/?shelf=main&book=CaWO4&page=Bond-o # ref: https://refractiveindex.info/?shelf=main&book=CaWO4&page=Bond-e @@ -1071,31 +1303,55 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -CaWO4_range = mp.FreqRange(min=um_scale/4.0, max=um_scale/0.45) +CaWO4_range = mp.FreqRange(min=um_scale / 4.0, max=um_scale / 0.45) -CaWO4_frq1 = 1/(0.1347*um_scale) +CaWO4_frq1 = 1 / (0.1347 * um_scale) CaWO4_gam1 = 0 CaWO4_sig1 = 2.5493 -CaWO4_frq2 = 1/(10.815*um_scale) +CaWO4_frq2 = 1 / (10.815 * um_scale) CaWO4_gam2 = 0 CaWO4_sig2 = 0.9200 -CaWO4_susc_o = [mp.LorentzianSusceptibility(frequency=CaWO4_frq1, gamma=CaWO4_gam1, sigma_diag=CaWO4_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=CaWO4_frq2, gamma=CaWO4_gam2, sigma_diag=CaWO4_sig2*mp.Vector3(1,1,0))] - -CaWO4_frq1 = 1/(0.1379*um_scale) +CaWO4_susc_o = [ + mp.LorentzianSusceptibility( + frequency=CaWO4_frq1, + gamma=CaWO4_gam1, + sigma_diag=CaWO4_sig1 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=CaWO4_frq2, + gamma=CaWO4_gam2, + sigma_diag=CaWO4_sig2 * mp.Vector3(1, 1, 0), + ), +] + +CaWO4_frq1 = 1 / (0.1379 * um_scale) CaWO4_gam1 = 0 CaWO4_sig1 = 2.6041 -CaWO4_frq2 = 1/(21.371*um_scale) +CaWO4_frq2 = 1 / (21.371 * um_scale) CaWO4_gam2 = 0 CaWO4_sig2 = 4.1237 -CaWO4_susc_e = [mp.LorentzianSusceptibility(frequency=CaWO4_frq1, gamma=CaWO4_gam1, sigma_diag=CaWO4_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=CaWO4_frq2, gamma=CaWO4_gam2, sigma_diag=CaWO4_sig2*mp.Vector3(0,0,1))] - -CaWO4 = mp.Medium(epsilon=1.0, E_susceptibilities=CaWO4_susc_o+CaWO4_susc_e, valid_freq_range=CaWO4_range) - -#------------------------------------------------------------------ +CaWO4_susc_e = [ + mp.LorentzianSusceptibility( + frequency=CaWO4_frq1, + gamma=CaWO4_gam1, + sigma_diag=CaWO4_sig1 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=CaWO4_frq2, + gamma=CaWO4_gam2, + sigma_diag=CaWO4_sig2 * mp.Vector3(0, 0, 1), + ), +] + +CaWO4 = mp.Medium( + epsilon=1.0, + E_susceptibilities=CaWO4_susc_o + CaWO4_susc_e, + valid_freq_range=CaWO4_range, +) + +# ------------------------------------------------------------------ # calcium carbonate (CaCO3) from G. Ghosh, Optics Communication, Vol. 163, pp. 95-102 (1999) # ref: https://refractiveindex.info/?shelf=main&book=CaCO3&page=Ghosh-o # ref: https://refractiveindex.info/?shelf=main&book=CaCO3&page=Ghosh-e @@ -1103,31 +1359,55 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -CaCO3_range = mp.FreqRange(min=um_scale/2.172, max=um_scale/0.204) +CaCO3_range = mp.FreqRange(min=um_scale / 2.172, max=um_scale / 0.204) -CaCO3_frq1 = 1/(0.13940057496294625*um_scale) +CaCO3_frq1 = 1 / (0.13940057496294625 * um_scale) CaCO3_gam1 = 0 CaCO3_sig1 = 0.96464345 -CaCO3_frq2 = 1/(10.954451150103322*um_scale) +CaCO3_frq2 = 1 / (10.954451150103322 * um_scale) CaCO3_gam2 = 0 CaCO3_sig2 = 1.82831454 -CaCO3_susc_o = [mp.LorentzianSusceptibility(frequency=CaCO3_frq1, gamma=CaCO3_gam1, sigma_diag=CaCO3_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=CaCO3_frq2, gamma=CaCO3_gam2, sigma_diag=CaCO3_sig2*mp.Vector3(1,1,0))] - -CaCO3_frq1 = 1/(0.1032906302623815*um_scale) +CaCO3_susc_o = [ + mp.LorentzianSusceptibility( + frequency=CaCO3_frq1, + gamma=CaCO3_gam1, + sigma_diag=CaCO3_sig1 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=CaCO3_frq2, + gamma=CaCO3_gam2, + sigma_diag=CaCO3_sig2 * mp.Vector3(1, 1, 0), + ), +] + +CaCO3_frq1 = 1 / (0.1032906302623815 * um_scale) CaCO3_gam1 = 0 CaCO3_sig1 = 0.82427830 -CaCO3_frq2 = 1/(10.954451150103322*um_scale) +CaCO3_frq2 = 1 / (10.954451150103322 * um_scale) CaCO3_gam2 = 0 CaCO3_sig2 = 0.14429128 -CaCO3_susc_e = [mp.LorentzianSusceptibility(frequency=CaCO3_frq1, gamma=CaCO3_gam1, sigma_diag=CaCO3_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=CaCO3_frq2, gamma=CaCO3_gam2, sigma_diag=CaCO3_sig2*mp.Vector3(0,0,1))] - -CaCO3 = mp.Medium(epsilon_diag=mp.Vector3(1.73358749,1.73358749,1.35859695), E_susceptibilities=CaCO3_susc_o+CaCO3_susc_e, valid_freq_range=CaCO3_range) - -#------------------------------------------------------------------ +CaCO3_susc_e = [ + mp.LorentzianSusceptibility( + frequency=CaCO3_frq1, + gamma=CaCO3_gam1, + sigma_diag=CaCO3_sig1 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=CaCO3_frq2, + gamma=CaCO3_gam2, + sigma_diag=CaCO3_sig2 * mp.Vector3(0, 0, 1), + ), +] + +CaCO3 = mp.Medium( + epsilon_diag=mp.Vector3(1.73358749, 1.73358749, 1.35859695), + E_susceptibilities=CaCO3_susc_o + CaCO3_susc_e, + valid_freq_range=CaCO3_range, +) + +# ------------------------------------------------------------------ # silicon dioxide (SiO2) from G. Ghosh, Optics Communication, Vol. 163, pp. 95-102 (1999) # ref: https://refractiveindex.info/?shelf=main&book=SiO2&page=Ghosh-o # ref: https://refractiveindex.info/?shelf=main&book=SiO2&page=Ghosh-e @@ -1135,31 +1415,47 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -SiO2_range = mp.FreqRange(min=um_scale/2.0531, max=um_scale/0.198) +SiO2_range = mp.FreqRange(min=um_scale / 2.0531, max=um_scale / 0.198) -SiO2_frq1 = 1/(0.10029257051247614*um_scale) +SiO2_frq1 = 1 / (0.10029257051247614 * um_scale) SiO2_gam1 = 0 SiO2_sig1 = 1.07044083 -SiO2_frq2 = 1/(10*um_scale) +SiO2_frq2 = 1 / (10 * um_scale) SiO2_gam2 = 0 SiO2_sig2 = 1.10202242 -SiO2_susc_o = [mp.LorentzianSusceptibility(frequency=SiO2_frq1, gamma=SiO2_gam1, sigma_diag=SiO2_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=SiO2_frq2, gamma=SiO2_gam2, sigma_diag=SiO2_sig2*mp.Vector3(1,1,0))] +SiO2_susc_o = [ + mp.LorentzianSusceptibility( + frequency=SiO2_frq1, gamma=SiO2_gam1, sigma_diag=SiO2_sig1 * mp.Vector3(1, 1, 0) + ), + mp.LorentzianSusceptibility( + frequency=SiO2_frq2, gamma=SiO2_gam2, sigma_diag=SiO2_sig2 * mp.Vector3(1, 1, 0) + ), +] -SiO2_frq1 = 1/(0.10104546699382412*um_scale) +SiO2_frq1 = 1 / (0.10104546699382412 * um_scale) SiO2_gam1 = 0 SiO2_sig1 = 1.09509924 -SiO2_frq2 = 1/(10*um_scale) +SiO2_frq2 = 1 / (10 * um_scale) SiO2_gam2 = 0 SiO2_sig2 = 1.15662475 -SiO2_susc_e = [mp.LorentzianSusceptibility(frequency=SiO2_frq1, gamma=SiO2_gam1, sigma_diag=SiO2_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=SiO2_frq2, gamma=SiO2_gam2, sigma_diag=SiO2_sig2*mp.Vector3(0,0,1))] - -SiO2_aniso = mp.Medium(epsilon_diag=mp.Vector3(1.28604141,1.28604141,1.28851804), E_susceptibilities=SiO2_susc_o+SiO2_susc_e, valid_freq_range=SiO2_range) - -#------------------------------------------------------------------ +SiO2_susc_e = [ + mp.LorentzianSusceptibility( + frequency=SiO2_frq1, gamma=SiO2_gam1, sigma_diag=SiO2_sig1 * mp.Vector3(0, 0, 1) + ), + mp.LorentzianSusceptibility( + frequency=SiO2_frq2, gamma=SiO2_gam2, sigma_diag=SiO2_sig2 * mp.Vector3(0, 0, 1) + ), +] + +SiO2_aniso = mp.Medium( + epsilon_diag=mp.Vector3(1.28604141, 1.28604141, 1.28851804), + E_susceptibilities=SiO2_susc_o + SiO2_susc_e, + valid_freq_range=SiO2_range, +) + +# ------------------------------------------------------------------ # gallium nitride (GaN), alpha phase (wurtzite), from A.S. Barker Jr. and M. Ilegems, Physical Review B, Vol. 7, pp. 743-50 (1973) # ref: https://refractiveindex.info/?shelf=main&book=GaN&page=Barker-o # ref: https://refractiveindex.info/?shelf=main&book=GaN&page=Barker-e @@ -1167,27 +1463,41 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -GaN_range = mp.FreqRange(min=um_scale/10.0, max=um_scale/0.35) +GaN_range = mp.FreqRange(min=um_scale / 10.0, max=um_scale / 0.35) -GaN_frq1 = 1/(0.256*um_scale) +GaN_frq1 = 1 / (0.256 * um_scale) GaN_gam1 = 0 GaN_sig1 = 1.75 -GaN_frq2 = 1/(17.86*um_scale) +GaN_frq2 = 1 / (17.86 * um_scale) GaN_gam2 = 0 GaN_sig2 = 4.1 -GaN_susc_o = [mp.LorentzianSusceptibility(frequency=GaN_frq1, gamma=GaN_gam1, sigma_diag=GaN_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=GaN_frq2, gamma=GaN_gam2, sigma_diag=GaN_sig2*mp.Vector3(1,1,0))] +GaN_susc_o = [ + mp.LorentzianSusceptibility( + frequency=GaN_frq1, gamma=GaN_gam1, sigma_diag=GaN_sig1 * mp.Vector3(1, 1, 0) + ), + mp.LorentzianSusceptibility( + frequency=GaN_frq2, gamma=GaN_gam2, sigma_diag=GaN_sig2 * mp.Vector3(1, 1, 0) + ), +] -GaN_frq1 = 1/(18.76*um_scale) +GaN_frq1 = 1 / (18.76 * um_scale) GaN_gam1 = 0 GaN_sig1 = 5.08 -GaN_susc_e = [mp.LorentzianSusceptibility(frequency=GaN_frq1, gamma=GaN_gam1, sigma_diag=GaN_sig1*mp.Vector3(0,0,1))] +GaN_susc_e = [ + mp.LorentzianSusceptibility( + frequency=GaN_frq1, gamma=GaN_gam1, sigma_diag=GaN_sig1 * mp.Vector3(0, 0, 1) + ) +] -GaN = mp.Medium(epsilon_diag=mp.Vector3(3.6,3.6,5.35), E_susceptibilities=GaN_susc_o+GaN_susc_e, valid_freq_range=GaN_range) +GaN = mp.Medium( + epsilon_diag=mp.Vector3(3.6, 3.6, 5.35), + E_susceptibilities=GaN_susc_o + GaN_susc_e, + valid_freq_range=GaN_range, +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # aluminum nitride (AlN) from J. Pastrnak and L. Roskovcova, Physica Status Solidi, Vol. 14, K5-8 (1966) # ref: https://refractiveindex.info/?shelf=main&book=AlN&page=Pastrnak-o # ref: https://refractiveindex.info/?shelf=main&book=AlN&page=Pastrnak-e @@ -1195,31 +1505,47 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -AlN_range = mp.FreqRange(min=um_scale/5.0, max=um_scale/0.22) +AlN_range = mp.FreqRange(min=um_scale / 5.0, max=um_scale / 0.22) -AlN_frq1 = 1/(0.1715*um_scale) +AlN_frq1 = 1 / (0.1715 * um_scale) AlN_gam1 = 0 AlN_sig1 = 1.3786 -AlN_frq2 = 1/(15.03*um_scale) +AlN_frq2 = 1 / (15.03 * um_scale) AlN_gam2 = 0 AlN_sig2 = 3.861 -AlN_susc_o = [mp.LorentzianSusceptibility(frequency=AlN_frq1, gamma=AlN_gam1, sigma_diag=AlN_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=AlN_frq2, gamma=AlN_gam2, sigma_diag=AlN_sig2*mp.Vector3(1,1,0))] +AlN_susc_o = [ + mp.LorentzianSusceptibility( + frequency=AlN_frq1, gamma=AlN_gam1, sigma_diag=AlN_sig1 * mp.Vector3(1, 1, 0) + ), + mp.LorentzianSusceptibility( + frequency=AlN_frq2, gamma=AlN_gam2, sigma_diag=AlN_sig2 * mp.Vector3(1, 1, 0) + ), +] -AlN_frq1 = 1/(0.1746*um_scale) +AlN_frq1 = 1 / (0.1746 * um_scale) AlN_gam1 = 0 AlN_sig1 = 1.6173 -AlN_frq2 = 1/(15.03*um_scale) +AlN_frq2 = 1 / (15.03 * um_scale) AlN_gam2 = 0 AlN_sig2 = 4.139 -AlN_susc_e = [mp.LorentzianSusceptibility(frequency=AlN_frq1, gamma=AlN_gam1, sigma_diag=AlN_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=AlN_frq2, gamma=AlN_gam2, sigma_diag=AlN_sig2*mp.Vector3(0,0,1))] - -AlN_aniso = mp.Medium(epsilon_diag=mp.Vector3(3.1399,3.1399,3.0729), E_susceptibilities=AlN_susc_o+AlN_susc_e, valid_freq_range=AlN_range) - -#------------------------------------------------------------------ +AlN_susc_e = [ + mp.LorentzianSusceptibility( + frequency=AlN_frq1, gamma=AlN_gam1, sigma_diag=AlN_sig1 * mp.Vector3(0, 0, 1) + ), + mp.LorentzianSusceptibility( + frequency=AlN_frq2, gamma=AlN_gam2, sigma_diag=AlN_sig2 * mp.Vector3(0, 0, 1) + ), +] + +AlN_aniso = mp.Medium( + epsilon_diag=mp.Vector3(3.1399, 3.1399, 3.0729), + E_susceptibilities=AlN_susc_o + AlN_susc_e, + valid_freq_range=AlN_range, +) + +# ------------------------------------------------------------------ # alumina/sapphire (Al2O3) from I.H. Malitson and M.J. Dodge, J. Optical Society of America, Vol. 62, pp. 1405 (1972) # ref: https://refractiveindex.info/?shelf=main&book=Al2O3&page=Malitson-o # ref: https://refractiveindex.info/?shelf=main&book=Al2O3&page=Malitson-e @@ -1227,87 +1553,127 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -Al2O3_range = mp.FreqRange(min=um_scale/5.0, max=um_scale/0.2) +Al2O3_range = mp.FreqRange(min=um_scale / 5.0, max=um_scale / 0.2) -Al2O3_frq1 = 1/(0.0726631*um_scale) +Al2O3_frq1 = 1 / (0.0726631 * um_scale) Al2O3_gam1 = 0 Al2O3_sig1 = 1.4313493 -Al2O3_frq2 = 1/(0.1193242*um_scale) +Al2O3_frq2 = 1 / (0.1193242 * um_scale) Al2O3_gam2 = 0 Al2O3_sig2 = 0.65054713 -Al2O3_frq3 = 1/(18.02825*um_scale) +Al2O3_frq3 = 1 / (18.02825 * um_scale) Al2O3_gam3 = 0 Al2O3_sig3 = 5.3414021 -Al2O3_susc_o = [mp.LorentzianSusceptibility(frequency=Al2O3_frq1, gamma=Al2O3_gam1, sigma_diag=Al2O3_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=Al2O3_frq2, gamma=Al2O3_gam2, sigma_diag=Al2O3_sig2*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=Al2O3_frq3, gamma=Al2O3_gam3, sigma_diag=Al2O3_sig3*mp.Vector3(1,1,0))] - -Al2O3_frq1 = 1/(0.0740288*um_scale) +Al2O3_susc_o = [ + mp.LorentzianSusceptibility( + frequency=Al2O3_frq1, + gamma=Al2O3_gam1, + sigma_diag=Al2O3_sig1 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=Al2O3_frq2, + gamma=Al2O3_gam2, + sigma_diag=Al2O3_sig2 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=Al2O3_frq3, + gamma=Al2O3_gam3, + sigma_diag=Al2O3_sig3 * mp.Vector3(1, 1, 0), + ), +] + +Al2O3_frq1 = 1 / (0.0740288 * um_scale) Al2O3_gam1 = 0 Al2O3_sig1 = 1.5039759 -Al2O3_frq2 = 1/(0.1216529*um_scale) +Al2O3_frq2 = 1 / (0.1216529 * um_scale) Al2O3_gam2 = 0 Al2O3_sig2 = 0.55069141 -Al2O3_frq3 = 1/(20.072248*um_scale) +Al2O3_frq3 = 1 / (20.072248 * um_scale) Al2O3_gam3 = 0 Al2O3_sig3 = 6.5927379 -Al2O3_susc_e = [mp.LorentzianSusceptibility(frequency=Al2O3_frq1, gamma=Al2O3_gam1, sigma_diag=Al2O3_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=Al2O3_frq2, gamma=Al2O3_gam2, sigma_diag=Al2O3_sig2*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=Al2O3_frq3, gamma=Al2O3_gam3, sigma_diag=Al2O3_sig3*mp.Vector3(0,0,1))] - -Al2O3_aniso = mp.Medium(epsilon=1, E_susceptibilities=Al2O3_susc_o+Al2O3_susc_e, valid_freq_range=Al2O3_range) - -#------------------------------------------------------------------ +Al2O3_susc_e = [ + mp.LorentzianSusceptibility( + frequency=Al2O3_frq1, + gamma=Al2O3_gam1, + sigma_diag=Al2O3_sig1 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=Al2O3_frq2, + gamma=Al2O3_gam2, + sigma_diag=Al2O3_sig2 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=Al2O3_frq3, + gamma=Al2O3_gam3, + sigma_diag=Al2O3_sig3 * mp.Vector3(0, 0, 1), + ), +] + +Al2O3_aniso = mp.Medium( + epsilon=1, + E_susceptibilities=Al2O3_susc_o + Al2O3_susc_e, + valid_freq_range=Al2O3_range, +) + +# ------------------------------------------------------------------ # yttrium oxide (Y2O3) from Y. Nigara, Japanese J. of Applied Physics, Vol. 7, pp. 404-8 (1968) # ref: https://refractiveindex.info/?shelf=main&book=Y2O3&page=Nigara # wavelength range: 0.25 - 9.6 μm -Y2O3_range = mp.FreqRange(min=um_scale/9.6, max=um_scale/0.25) +Y2O3_range = mp.FreqRange(min=um_scale / 9.6, max=um_scale / 0.25) -Y2O3_frq1 = 1/(0.1387*um_scale) +Y2O3_frq1 = 1 / (0.1387 * um_scale) Y2O3_gam1 = 0 Y2O3_sig1 = 2.578 -Y2O3_frq2 = 1/(22.936*um_scale) +Y2O3_frq2 = 1 / (22.936 * um_scale) Y2O3_gam2 = 0 Y2O3_sig2 = 3.935 -Y2O3_susc = [mp.LorentzianSusceptibility(frequency=Y2O3_frq1, gamma=Y2O3_gam1, sigma=Y2O3_sig1), - mp.LorentzianSusceptibility(frequency=Y2O3_frq2, gamma=Y2O3_gam2, sigma=Y2O3_sig2)] +Y2O3_susc = [ + mp.LorentzianSusceptibility(frequency=Y2O3_frq1, gamma=Y2O3_gam1, sigma=Y2O3_sig1), + mp.LorentzianSusceptibility(frequency=Y2O3_frq2, gamma=Y2O3_gam2, sigma=Y2O3_sig2), +] Y2O3 = mp.Medium(epsilon=1.0, E_susceptibilities=Y2O3_susc, valid_freq_range=Y2O3_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # undoped yttrium aluminum garnet (YAG) from D.E. Zelmon et al., Applied Optics, Vol. 37, 4933-5 (1998) # ref: https://refractiveindex.info/?shelf=main&book=Y3Al5O12&page=Zelmon # wavelength range: 0.4 - 5.0 μm -YAG_range = mp.FreqRange(min=um_scale/5.0, max=um_scale/0.4) +YAG_range = mp.FreqRange(min=um_scale / 5.0, max=um_scale / 0.4) -YAG_frq1 = 1/(0.1088577052853862*um_scale) +YAG_frq1 = 1 / (0.1088577052853862 * um_scale) YAG_gam1 = 0 YAG_sig1 = 2.28200 -YAG_frq2 = 1/(16.814695953242804*um_scale) +YAG_frq2 = 1 / (16.814695953242804 * um_scale) YAG_gam2 = 0 YAG_sig2 = 3.27644 -YAG_susc = [mp.LorentzianSusceptibility(frequency=YAG_frq1, gamma=YAG_gam1, sigma=YAG_sig1), - mp.LorentzianSusceptibility(frequency=YAG_frq2, gamma=YAG_gam2, sigma=YAG_sig2)] +YAG_susc = [ + mp.LorentzianSusceptibility(frequency=YAG_frq1, gamma=YAG_gam1, sigma=YAG_sig1), + mp.LorentzianSusceptibility(frequency=YAG_frq2, gamma=YAG_gam2, sigma=YAG_sig2), +] YAG = mp.Medium(epsilon=1.0, E_susceptibilities=YAG_susc, valid_freq_range=YAG_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # cadmium telluride (CdTe) from D.T.F. Marple, J. Applied Physics, Vol. 35, pp. 539-42 (1964) # ref: https://refractiveindex.info/?shelf=main&book=CdTe&page=Marple # wavelength range: 0.86 - 2.5 μm -CdTe_range = mp.FreqRange(min=um_scale/2.5, max=um_scale/0.86) +CdTe_range = mp.FreqRange(min=um_scale / 2.5, max=um_scale / 0.86) -CdTe_frq1 = 1/(0.6049793384901669*um_scale) +CdTe_frq1 = 1 / (0.6049793384901669 * um_scale) CdTe_gam1 = 0 CdTe_sig1 = 1.53 -CdTe_susc = [mp.LorentzianSusceptibility(frequency=CdTe_frq1, gamma=CdTe_gam1, sigma=CdTe_sig1)] +CdTe_susc = [ + mp.LorentzianSusceptibility(frequency=CdTe_frq1, gamma=CdTe_gam1, sigma=CdTe_sig1) +] -CdTe = mp.Medium(epsilon=5.68, E_susceptibilities=CdTe_susc, valid_freq_range=CdTe_range) +CdTe = mp.Medium( + epsilon=5.68, E_susceptibilities=CdTe_susc, valid_freq_range=CdTe_range +) diff --git a/python/mpb_data.py b/python/mpb_data.py index 01337de41..2b7d987ee 100644 --- a/python/mpb_data.py +++ b/python/mpb_data.py @@ -10,19 +10,21 @@ class MPBData(object): TWOPI = 6.2831853071795864769252867665590057683943388 - def __init__(self, - lattice=None, - kpoint=None, - rectify=False, - x=0, - y=0, - z=0, - periods=0, - resolution=0, - phase_angle=0, - pick_nearest=False, - ve=None, - verbose=False): + def __init__( + self, + lattice=None, + kpoint=None, + rectify=False, + x=0, + y=0, + z=0, + periods=0, + resolution=0, + phase_angle=0, + pick_nearest=False, + ve=None, + verbose=False, + ): self.lattice = lattice self.kpoint = kpoint @@ -48,8 +50,10 @@ def __init__(self, self.verbose = verbose self.scaleby = complex(1, 0) - self.phase = complex(math.cos(self.TWOPI * self.phase_angle / 360.0), - math.sin(self.TWOPI * self.phase_angle / 360.0)) + self.phase = complex( + math.cos(self.TWOPI * self.phase_angle / 360.0), + math.sin(self.TWOPI * self.phase_angle / 360.0), + ) self.scaleby *= self.phase def handle_dataset(self, in_arr): @@ -97,12 +101,24 @@ def handle_dataset(self, in_arr): out_arr_im = np.array([]) flat_in_arr_re = in_arr_re.ravel() - flat_in_arr_im = in_arr_im.ravel() if isinstance(in_arr_im, np.ndarray) else np.array([]) + flat_in_arr_im = ( + in_arr_im.ravel() if isinstance(in_arr_im, np.ndarray) else np.array([]) + ) kvector = [self.kpoint.x, self.kpoint.y, self.kpoint.z] if self.kpoint else [] - map_data(flat_in_arr_re, flat_in_arr_im, np.array(in_dims, dtype=np.intc), - out_arr_re, out_arr_im, np.array(out_dims, dtype=np.intc), self.coord_map, - kvector, self.pick_nearest, self.verbose, False) + map_data( + flat_in_arr_re, + flat_in_arr_im, + np.array(in_dims, dtype=np.intc), + out_arr_re, + out_arr_im, + np.array(out_dims, dtype=np.intc), + self.coord_map, + kvector, + self.pick_nearest, + self.verbose, + False, + ) if np.iscomplexobj(in_arr): # multiply * scaleby for complex data @@ -137,9 +153,15 @@ def handle_cvector_dataset(self, in_arr, multiply_bloch_phase): fmt1 = "Rotating vectors by matrix [ {:.10g}, {:.10g}, {:.10g}" fmt2 = " {:.10g}, {:.10g}, {:.10g}" fmt3 = " {:.10g}, {:.10g}, {:.10g} ]" - print(fmt1.format(self.cart_map.c1.x, self.cart_map.c2.x, self.cart_map.c3.x)) - print(fmt2.format(self.cart_map.c1.y, self.cart_map.c2.y, self.cart_map.c3.y)) - print(fmt3.format(self.cart_map.c1.z, self.cart_map.c2.z, self.cart_map.c3.z)) + print( + fmt1.format(self.cart_map.c1.x, self.cart_map.c2.x, self.cart_map.c3.x) + ) + print( + fmt2.format(self.cart_map.c1.y, self.cart_map.c2.y, self.cart_map.c3.y) + ) + print( + fmt3.format(self.cart_map.c1.z, self.cart_map.c2.z, self.cart_map.c3.z) + ) N = in_dims[0] * in_dims[1] for ri in range(2): @@ -177,9 +199,19 @@ def handle_cvector_dataset(self, in_arr, multiply_bloch_phase): out_arr_re = np.zeros(int(N)) out_arr_im = np.zeros(int(N)) - map_data(d_in[dim][0].ravel(), d_in[dim][1].ravel(), np.array(in_dims, dtype=np.intc), - out_arr_re, out_arr_im, np.array(out_dims, dtype=np.intc), self.coord_map, - kvector, self.pick_nearest, self.verbose, multiply_bloch_phase) + map_data( + d_in[dim][0].ravel(), + d_in[dim][1].ravel(), + np.array(in_dims, dtype=np.intc), + out_arr_re, + out_arr_im, + np.array(out_dims, dtype=np.intc), + self.coord_map, + kvector, + self.pick_nearest, + self.verbose, + multiply_bloch_phase, + ) # multiply * scaleby complex_out = np.vectorize(complex)(out_arr_re, out_arr_im) @@ -196,15 +228,13 @@ def handle_cvector_dataset(self, in_arr, multiply_bloch_phase): def init_output_lattice(self): cart_map = mp.Matrix( - mp.Vector3(1, 0, 0), - mp.Vector3(0, 1, 0), - mp.Vector3(0, 0, 1) + mp.Vector3(1, 0, 0), mp.Vector3(0, 1, 0), mp.Vector3(0, 0, 1) ) Rin = mp.Matrix( mp.Vector3(*self.lattice[0]), mp.Vector3(*self.lattice[1]), - mp.Vector3(*self.lattice[2]) + mp.Vector3(*self.lattice[2]), ) if self.verbose: @@ -213,9 +243,19 @@ def init_output_lattice(self): fmt = "Read Bloch wavevector ({:.6g}, {:.6g}, {:.6g})" print(fmt.format(self.kpoint.x, self.kpoint.y, self.kpoint.z)) fmt = "Input lattice = ({:.6g}, {:.6g}, {:.6g}), ({:.6g}, {:.6g}, {:.6g}), ({:.6g}, {:.6g}, {:.6g})" - print(fmt.format(Rin.c1.x, Rin.c1.y, Rin.c1.z, - Rin.c2.x, Rin.c2.y, Rin.c2.z, - Rin.c3.x, Rin.c3.y, Rin.c3.z)) + print( + fmt.format( + Rin.c1.x, + Rin.c1.y, + Rin.c1.z, + Rin.c2.x, + Rin.c2.y, + Rin.c2.z, + Rin.c3.x, + Rin.c3.y, + Rin.c3.z, + ) + ) Rout = mp.Matrix(Rin.c1, Rin.c2, Rin.c3) @@ -239,8 +279,9 @@ def init_output_lattice(self): # Now, orthogonalize c2 and c3 Rout.c2 = Rout.c2 - ve.scale(ve.dot(Rout.c2)) Rout.c3 = Rout.c3 - ve.scale(ve.dot(Rout.c3)) - Rout.c3 = Rout.c3 - Rout.c2.scale(Rout.c2.dot(Rout.c3) / - Rout.c2.dot(Rout.c2)) + Rout.c3 = Rout.c3 - Rout.c2.scale( + Rout.c2.dot(Rout.c3) / Rout.c2.dot(Rout.c2) + ) cart_map.c1 = Rout.c1.unit() cart_map.c2 = Rout.c2.unit() @@ -253,9 +294,19 @@ def init_output_lattice(self): if self.verbose: fmt = "Output lattice = ({:.6g}, {:.6g}, {:.6g}), ({:.6g}, {:.6g}, {:.6g}), ({:.6g}, {:.6g}, {:.6g})" - print(fmt.format(Rout.c1.x, Rout.c1.y, Rout.c1.z, - Rout.c2.x, Rout.c2.y, Rout.c2.z, - Rout.c3.x, Rout.c3.y, Rout.c3.z)) + print( + fmt.format( + Rout.c1.x, + Rout.c1.y, + Rout.c1.z, + Rout.c2.x, + Rout.c2.y, + Rout.c2.z, + Rout.c3.x, + Rout.c3.y, + Rout.c3.z, + ) + ) self.coord_map = Rin.inverse() * Rout self.Rout = Rout @@ -267,11 +318,13 @@ def convert(self, arr, kpoint=None): self.kpoint = arr.kpoint if self.lattice is None: - err = ("Couldn't find 'lattice.' You must do one of the following:\n" + - " 1. Pass the ModeSolver lattice to the MPBData constructor\n" + - " i.e., MPBData(lattice=ms.get_lattice())\n" + - " 2. Create an MPBArray to pass to MPBData.convert()\n" + - " i.e., mpb_arr = MPBArray(arr, ms.get_lattice(), ... ); mpb_data.convert(mpb_arr))") + err = ( + "Couldn't find 'lattice.' You must do one of the following:\n" + + " 1. Pass the ModeSolver lattice to the MPBData constructor\n" + + " i.e., MPBData(lattice=ms.get_lattice())\n" + + " 2. Create an MPBArray to pass to MPBData.convert()\n" + + " i.e., mpb_arr = MPBArray(arr, ms.get_lattice(), ... ); mpb_data.convert(mpb_arr))" + ) raise ValueError(err) if kpoint: diff --git a/python/simulation.py b/python/simulation.py index 43c7b3c69..ee3162745 100644 --- a/python/simulation.py +++ b/python/simulation.py @@ -22,7 +22,13 @@ import meep as mp from meep.geom import Vector3, init_do_averaging, GeometricObject, Medium -from meep.source import Source, EigenModeSource, GaussianBeamSource, IndexedSource, check_positive +from meep.source import ( + Source, + EigenModeSource, + GaussianBeamSource, + IndexedSource, + check_positive, +) import meep.visualization as vis from meep.verbosity_mgr import Verbosity @@ -34,11 +40,12 @@ try: from ipywidgets import FloatProgress from IPython.display import display + do_progress = True except ImportError: do_progress = False -verbosity = Verbosity(mp.cvar, 'meep', 1) +verbosity = Verbosity(mp.cvar, "meep", 1) mp.setup() @@ -48,23 +55,38 @@ mp.set_ctl_printf_callback(mp.py_master_printf_wrap) mp.set_mpb_printf_callback(mp.py_master_printf_wrap) -EigCoeffsResult = namedtuple('EigCoeffsResult', ['alpha', 'vgrp', 'kpoints', 'kdom', 'cscale']) -FluxData = namedtuple('FluxData', ['E', 'H']) -ForceData = namedtuple('ForceData', ['offdiag1', 'offdiag2', 'diag']) -NearToFarData = namedtuple('NearToFarData', ['F']) +EigCoeffsResult = namedtuple( + "EigCoeffsResult", ["alpha", "vgrp", "kpoints", "kdom", "cscale"] +) +FluxData = namedtuple("FluxData", ["E", "H"]) +ForceData = namedtuple("ForceData", ["offdiag1", "offdiag2", "diag"]) +NearToFarData = namedtuple("NearToFarData", ["F"]) + def fix_dft_args(args, i): - if len(args) > i+2 and isinstance(args[i],(int,float)) and isinstance(args[i+1],(int,float)) and isinstance(args[i+2],int): + if ( + len(args) > i + 2 + and isinstance(args[i], (int, float)) + and isinstance(args[i + 1], (int, float)) + and isinstance(args[i + 2], int) + ): fcen = args[i] - df = args[i+1] - nfreq = args[i+2] - freq = [fcen] if nfreq == 1 else np.linspace(fcen-0.5*df,fcen+0.5*df,nfreq) - return args[:i] + (freq,) + args[i+3:] + df = args[i + 1] + nfreq = args[i + 2] + freq = ( + [fcen] + if nfreq == 1 + else np.linspace(fcen - 0.5 * df, fcen + 0.5 * df, nfreq) + ) + return args[:i] + (freq,) + args[i + 3 :] elif not isinstance(args[i], (np.ndarray, list)): - raise TypeError("add_dft functions only accept fcen,df,nfreq (3 numbers) or freq (array/list)") + raise TypeError( + "add_dft functions only accept fcen,df,nfreq (3 numbers) or freq (array/list)" + ) else: return args + def get_num_args(func): return 2 if isinstance(func, Harminv) else func.__code__.co_argcount @@ -94,14 +116,15 @@ def py_v3_to_vec(dims, iterable, is_cylindrical=False): if is_cylindrical: return mp.veccyl(v3.x, v3.z) v = mp.vec(v3.x, v3.y) - v.set_direction(mp.Z, v3.z) # for special_kz handling + v.set_direction(mp.Z, v3.z) # for special_kz handling return v elif dims == 3: return mp.vec(v3.x, v3.y, v3.z) else: raise ValueError(f"Invalid dimensions in Volume: {dims}") -def bands_to_diffractedplanewave(where,bands): + +def bands_to_diffractedplanewave(where, bands): if bands.axis is None: if where.in_direction(mp.X) != 0: axis = np.array([1, 0, 0], dtype=np.float64) @@ -110,8 +133,10 @@ def bands_to_diffractedplanewave(where,bands): elif where.in_direction(mp.Z) != 0: axis = np.array([0, 0, 1], dtype=np.float64) else: - raise ValueError("axis parameter of DiffractedPlanewave must be a non-zero Vector3") - elif isinstance(bands.axis,mp.Vector3): + raise ValueError( + "axis parameter of DiffractedPlanewave must be a non-zero Vector3" + ) + elif isinstance(bands.axis, mp.Vector3): axis = np.array([bands.axis.x, bands.axis.y, bands.axis.z], dtype=np.float64) else: raise TypeError("axis parameter of DiffractedPlanewave must be a Vector3") @@ -119,19 +144,17 @@ def bands_to_diffractedplanewave(where,bands): np.array(bands.g, dtype=np.intc), axis, bands.s * 1.0, - bands.p * 1.0 + bands.p * 1.0, ] return mp.diffractedplanewave(*diffractedplanewave_args) + class DiffractedPlanewave(object): """ For mode decomposition or eigenmode source, specify a diffracted planewave in homogeneous media. Should be passed as the `bands` argument of `get_eigenmode_coefficients`, `band_num` of `get_eigenmode`, or `eig_band` of `EigenModeSource`. """ - def __init__(self, - g=None, - axis=None, - s=None, - p=None): + + def __init__(self, g=None, axis=None, s=None, p=None): """ Construct a `DiffractedPlanewave`. @@ -164,9 +187,11 @@ def s(self): def p(self): return self._p + DefaultPMLProfile = lambda u: u * u Vector3Type = Union[Vector3, Tuple[float, ...]] + class PML(object): """ This class is used for specifying the PML absorbing boundary layers around the cell, @@ -177,12 +202,16 @@ class PML(object): sets up one or more PML layers around the boundaries according to the following properties. """ - def __init__(self, thickness, - direction=mp.ALL, - side=mp.ALL, - R_asymptotic=1e-15, - mean_stretch=1.0, - pml_profile=DefaultPMLProfile): + + def __init__( + self, + thickness, + direction=mp.ALL, + side=mp.ALL, + R_asymptotic=1e-15, + mean_stretch=1.0, + pml_profile=DefaultPMLProfile, + ): """ + **`thickness` [`number`]** — The spatial thickness of the PML layer which extends from the boundary towards the *inside* of the cell. The thinner it is, @@ -233,7 +262,9 @@ def __init__(self, thickness, elif direction == mp.ALL: self.swigobj = mp.pml(thickness, side, R_asymptotic, mean_stretch) else: - self.swigobj = mp.pml(thickness, direction, side, R_asymptotic, mean_stretch) + self.swigobj = mp.pml( + thickness, direction, side, R_asymptotic, mean_stretch + ) @property def R_asymptotic(self): @@ -241,7 +272,7 @@ def R_asymptotic(self): @R_asymptotic.setter def R_asymptotic(self, val): - self._R_asymptotic = check_positive('PML.R_asymptotic', val) + self._R_asymptotic = check_positive("PML.R_asymptotic", val) @property def mean_stretch(self): @@ -284,7 +315,6 @@ class Absorber(PML): """ - class Symmetry(object): """ This class is used for the `symmetries` input variable to specify symmetries which @@ -306,6 +336,7 @@ class Symmetry(object): **`Rotate4`** — A 90° (fourfold) rotational symmetry (a.k.a. $C_4$). `direction` is the axis of the rotation. """ + def __init__(self, direction, phase=1): """ Construct a `Symmetry`. @@ -346,8 +377,7 @@ class Mirror(Symmetry): class Identity(Symmetry): - """ - """ + """ """ class Volume(object): @@ -362,7 +392,14 @@ class Volume(object): `size` will automatically be computed from this list. """ - def __init__(self, center=Vector3(), size=Vector3(), dims=2, is_cylindrical=False, vertices=[]): + def __init__( + self, + center=Vector3(), + size=Vector3(), + dims=2, + is_cylindrical=False, + vertices=[], + ): """ Construct a Volume. """ @@ -371,16 +408,16 @@ def __init__(self, center=Vector3(), size=Vector3(), dims=2, is_cylindrical=Fals self.size = Vector3(*size) else: vertices = np.array([np.array(i) for i in vertices]) - self.center = Vector3(*np.mean(vertices,axis=0)) - x_list = np.unique(vertices[:,0]) - y_list = np.unique(vertices[:,1]) - z_list = np.unique(vertices[:,2]) + self.center = Vector3(*np.mean(vertices, axis=0)) + x_list = np.unique(vertices[:, 0]) + y_list = np.unique(vertices[:, 1]) + z_list = np.unique(vertices[:, 2]) x_size = 0 if x_list.size == 1 else np.abs(np.diff(x_list)[0]) y_size = 0 if y_list.size == 1 else np.abs(np.diff(y_list)[0]) z_size = 0 if z_list.size == 1 else np.abs(np.diff(z_list)[0]) - self.size = Vector3(x_size,y_size,z_size) + self.size = Vector3(x_size, y_size, z_size) self.dims = dims @@ -393,43 +430,62 @@ def __init__(self, center=Vector3(), size=Vector3(), dims=2, is_cylindrical=Fals self.swigobj = mp.volume(vec1, vec2) def get_vertices(self): - xmin = self.center.x - self.size.x/2 - xmax = self.center.x + self.size.x/2 - ymin = self.center.y - self.size.y/2 - ymax = self.center.y + self.size.y/2 - zmin = self.center.z - self.size.z/2 - zmax = self.center.z + self.size.z/2 + xmin = self.center.x - self.size.x / 2 + xmax = self.center.x + self.size.x / 2 + ymin = self.center.y - self.size.y / 2 + ymax = self.center.y + self.size.y / 2 + zmin = self.center.z - self.size.z / 2 + zmax = self.center.z + self.size.z / 2 # Iterate over and remove duplicates for collapsed dimensions (i.e. min=max)) - return [Vector3(x, y, z) for x in list({xmin, xmax}) for y in list({ymin, ymax}) for z in list({zmin, zmax})] - + return [ + Vector3(x, y, z) + for x in list({xmin, xmax}) + for y in list({ymin, ymax}) + for z in list({zmin, zmax}) + ] def get_edges(self): vertices = self.get_vertices() edges = [] # Useful for importing weird geometries and the sizes are slightly off - def nearly_equal(a,b,sig_fig=10): - return a==b or (abs(a-b) < 10**(-sig_fig)) + def nearly_equal(a, b, sig_fig=10): + return a == b or (abs(a - b) < 10 ** (-sig_fig)) for iter1 in range(len(vertices)): - for iter2 in range(iter1+1,len(vertices)): - if ((iter1 != iter2) and - nearly_equal((vertices[iter1]-vertices[iter2]).norm(),self.size.x) or - nearly_equal((vertices[iter1]-vertices[iter2]).norm(),self.size.y) or - nearly_equal((vertices[iter1]-vertices[iter2]).norm(),self.size.z)): - edges.append([vertices[iter1],vertices[iter2]]) + for iter2 in range(iter1 + 1, len(vertices)): + if ( + (iter1 != iter2) + and nearly_equal( + (vertices[iter1] - vertices[iter2]).norm(), self.size.x + ) + or nearly_equal( + (vertices[iter1] - vertices[iter2]).norm(), self.size.y + ) + or nearly_equal( + (vertices[iter1] - vertices[iter2]).norm(), self.size.z + ) + ): + edges.append([vertices[iter1], vertices[iter2]]) return edges - def pt_in_volume(self,pt): - xmin = self.center.x - self.size.x/2 - xmax = self.center.x + self.size.x/2 - ymin = self.center.y - self.size.y/2 - ymax = self.center.y + self.size.y/2 - zmin = self.center.z - self.size.z/2 - zmax = self.center.z + self.size.z/2 - - return pt.x >= xmin and pt.x <= xmax and pt.y >= ymin and pt.y <= ymax and pt.z >= zmin and pt.z <= zmax + def pt_in_volume(self, pt): + xmin = self.center.x - self.size.x / 2 + xmax = self.center.x + self.size.x / 2 + ymin = self.center.y - self.size.y / 2 + ymax = self.center.y + self.size.y / 2 + zmin = self.center.z - self.size.z / 2 + zmax = self.center.z + self.size.z / 2 + + return ( + pt.x >= xmin + and pt.x <= xmax + and pt.y >= ymin + and pt.y <= ymax + and pt.z >= zmin + and pt.z <= zmax + ) class FluxRegion(object): @@ -445,7 +501,15 @@ class FluxRegion(object): example, if you want to compute the outward flux through a box, so that the sides of the box add instead of subtract. """ - def __init__(self, center=None, size=Vector3(), direction=mp.AUTOMATIC, weight=1.0, volume=None): + + def __init__( + self, + center=None, + size=Vector3(), + direction=mp.AUTOMATIC, + weight=1.0, + volume=None, + ): """ Construct a `FluxRegion` object. @@ -528,7 +592,6 @@ class EnergyRegion(FluxRegion): class FieldsRegion(object): - def __init__(self, where=None, center=None, size=None): if where: self.center = where.center @@ -563,6 +626,7 @@ class DftObj(object): `Simulation._evaluate_dft_objects` is called, then we initialize the C++ object through swigobj_attr and return the property they requested. """ + def __init__(self, func, args): """Construct a `DftObj`.""" self.func = func @@ -576,32 +640,31 @@ def swigobj_attr(self, attr): @property def save_hdf5(self): - return self.swigobj_attr('save_hdf5') + return self.swigobj_attr("save_hdf5") @property def load_hdf5(self): - return self.swigobj_attr('load_hdf5') + return self.swigobj_attr("load_hdf5") @property def scale_dfts(self): - return self.swigobj_attr('scale_dfts') + return self.swigobj_attr("scale_dfts") @property def remove(self): - return self.swigobj_attr('remove') + return self.swigobj_attr("remove") @property def freq(self): - return self.swigobj_attr('freq') + return self.swigobj_attr("freq") @property def where(self): - return self.swigobj_attr('where') + return self.swigobj_attr("where") class DftFlux(DftObj): - """ - """ + """ """ def __init__(self, func, args): """Construct a `DftFlux`.""" @@ -612,35 +675,35 @@ def __init__(self, func, args): @property def flux(self): - return self.swigobj_attr('flux') + return self.swigobj_attr("flux") @property def E(self): - return self.swigobj_attr('E') + return self.swigobj_attr("E") @property def H(self): - return self.swigobj_attr('H') + return self.swigobj_attr("H") @property def cE(self): - return self.swigobj_attr('cE') + return self.swigobj_attr("cE") @property def cH(self): - return self.swigobj_attr('cH') + return self.swigobj_attr("cH") @property def normal_direction(self): - return self.swigobj_attr('normal_direction') + return self.swigobj_attr("normal_direction") @property def freq(self): - return self.swigobj_attr('freq') + return self.swigobj_attr("freq") + class DftForce(DftObj): - """ - """ + """ """ def __init__(self, func, args): """Construct a `DftForce`.""" @@ -651,27 +714,27 @@ def __init__(self, func, args): @property def force(self): - return self.swigobj_attr('force') + return self.swigobj_attr("force") @property def offdiag1(self): - return self.swigobj_attr('offdiag1') + return self.swigobj_attr("offdiag1") @property def offdiag2(self): - return self.swigobj_attr('offdiag2') + return self.swigobj_attr("offdiag2") @property def diag(self): - return self.swigobj_attr('diag') + return self.swigobj_attr("diag") @property def freq(self): - return self.swigobj_attr('freq') + return self.swigobj_attr("freq") + class DftNear2Far(DftObj): - """ - """ + """ """ def __init__(self, func, args): """Construct a `DftNear2Far`.""" @@ -683,23 +746,23 @@ def __init__(self, func, args): @property def farfield(self): - return self.swigobj_attr('farfield') + return self.swigobj_attr("farfield") @property def save_farfields(self): - return self.swigobj_attr('save_farfields') + return self.swigobj_attr("save_farfields") @property def F(self): - return self.swigobj_attr('F') + return self.swigobj_attr("F") @property def eps(self): - return self.swigobj_attr('eps') + return self.swigobj_attr("eps") @property def mu(self): - return self.swigobj_attr('mu') + return self.swigobj_attr("mu") def flux(self, direction, where, resolution): """ @@ -709,15 +772,15 @@ def flux(self, direction, where, resolution): FDTD solution) and return its Poynting flux in `direction` as a list. The dataset is a 1d array of `nfreq` dimensions. """ - return self.swigobj_attr('flux')(direction, where.swigobj, resolution) + return self.swigobj_attr("flux")(direction, where.swigobj, resolution) @property def freq(self): - return self.swigobj_attr('freq') + return self.swigobj_attr("freq") + class DftEnergy(DftObj): - """ - """ + """ """ def __init__(self, func, args): """Construct a `DftEnergy`.""" @@ -728,23 +791,23 @@ def __init__(self, func, args): @property def electric(self): - return self.swigobj_attr('electric') + return self.swigobj_attr("electric") @property def magnetic(self): - return self.swigobj_attr('magnetic') + return self.swigobj_attr("magnetic") @property def total(self): - return self.swigobj_attr('total') + return self.swigobj_attr("total") @property def freq(self): - return self.swigobj_attr('freq') + return self.swigobj_attr("freq") + class DftFields(DftObj): - """ - """ + """ """ def __init__(self, func, args): """Construct a `DftFields`.""" @@ -755,14 +818,13 @@ def __init__(self, func, args): @property def chunks(self): - return self.swigobj_attr('chunks') + return self.swigobj_attr("chunks") -Mode = namedtuple('Mode', ['freq', 'decay', 'Q', 'amp', 'err']) +Mode = namedtuple("Mode", ["freq", "decay", "Q", "amp", "err"]) class EigenmodeData(object): - def __init__(self, band_num, freq, group_velocity, k, swigobj, kdom): """Construct an `EigenmodeData`.""" self.band_num = band_num @@ -824,6 +886,7 @@ class Harminv(object): # do something with h.modes ``` """ + def __init__(self, c, pt, fcen, df, mxbands=None): """ Construct a Harminv object. @@ -859,7 +922,6 @@ def __call__(self, sim, todo): self.step_func(sim, todo) def _collect_harminv(self): - def _collect1(c, pt): self.t0 = 0 @@ -867,13 +929,17 @@ def _collect2(sim): self.data_dt = sim.meep_time() - self.t0 self.t0 = sim.meep_time() self.data.append(sim.get_field_point(c, pt)) + return _collect2 + return _collect1 def _check_freqs(self, sim): - source_freqs = [(s.src.frequency, 0 if s.src.width == 0 else 1 / s.src.width) - for s in sim.sources - if hasattr(s.src, 'frequency')] + source_freqs = [ + (s.src.frequency, 0 if s.src.width == 0 else 1 / s.src.width) + for s in sim.sources + if hasattr(s.src, "frequency") + ] harminv_max = self.fcen + 0.5 * self.df harminv_min = self.fcen - 0.5 * self.df @@ -889,26 +955,37 @@ def _check_freqs(self, sim): warnings.warn(warn_fmt.format(harminv_min, sf_min), RuntimeWarning) def _analyze_harminv(self, sim, maxbands): - harminv_cols = ['frequency', 'imag. freq.', 'Q', '|amp|', 'amplitude', 'error'] - display_run_data(sim, 'harminv', harminv_cols) + harminv_cols = ["frequency", "imag. freq.", "Q", "|amp|", "amplitude", "error"] + display_run_data(sim, "harminv", harminv_cols) self._check_freqs(sim) dt = self.data_dt if self.data_dt is not None else sim.fields.dt - bands = mp.py_do_harminv(self.data, dt, self.fcen - self.df / 2, self.fcen + self.df / 2, maxbands, - self.spectral_density, self.Q_thresh, self.rel_err_thresh, self.err_thresh, - self.rel_amp_thresh, self.amp_thresh) + bands = mp.py_do_harminv( + self.data, + dt, + self.fcen - self.df / 2, + self.fcen + self.df / 2, + maxbands, + self.spectral_density, + self.Q_thresh, + self.rel_err_thresh, + self.err_thresh, + self.rel_amp_thresh, + self.amp_thresh, + ) modes = [] for freq, amp, err in bands: - Q = freq.real / (-2 * freq.imag) if freq.imag != 0 else float('inf') + Q = freq.real / (-2 * freq.imag) if freq.imag != 0 else float("inf") modes.append(Mode(freq.real, freq.imag, Q, amp, err)) - display_run_data(sim, 'harminv', [freq.real, freq.imag, Q, abs(amp), amp, err]) + display_run_data( + sim, "harminv", [freq.real, freq.imag, Q, abs(amp), amp, err] + ) return modes def _harminv(self): - def _harm(sim): mb = 100 if self.mxbands is None or self.mxbands == 0 else self.mxbands @@ -924,41 +1001,44 @@ class Simulation(object): The `Simulation` [class](#classes) contains all the attributes that you can set to control various parameters of the Meep computation. """ - def __init__(self, - cell_size: Optional[Vector3Type] = None, - resolution: float = None, - geometry: Optional[List[GeometricObject]] = None, - sources: Optional[List[Source]] = None, - eps_averaging: bool = True, - dimensions: int = 3, - boundary_layers: Optional[List[PML]] = None, - symmetries: Optional[List[Symmetry]] = None, - force_complex_fields: bool = False, - default_material: Medium = mp.Medium(), - m: float = 0, - k_point: Union[Vector3Type, bool] = False, - kz_2d: str = "complex", - extra_materials: Optional[List[Medium]] = None, - material_function: Optional[Callable[[Vector3Type], Medium]] = None, - epsilon_func: Optional[Callable[[Vector3Type], float]] = None, - epsilon_input_file: str = '', - progress_interval: float = 4, - subpixel_tol: float = 1e-4, - subpixel_maxeval: int = 100000, - loop_tile_base_db: int = 0, - loop_tile_base_eh: int = 0, - ensure_periodicity: bool = True, - num_chunks: int = 0, - Courant: float = 0.5, - accurate_fields_near_cylorigin: bool = False, - filename_prefix: Optional[str] = None, - output_volume: Optional[Volume] = None, - output_single_precision: bool = False, - geometry_center: Vector3Type = Vector3(), - force_all_components: bool = False, - split_chunks_evenly: bool = True, - chunk_layout = None, - collect_stats: bool = False): + + def __init__( + self, + cell_size: Optional[Vector3Type] = None, + resolution: float = None, + geometry: Optional[List[GeometricObject]] = None, + sources: Optional[List[Source]] = None, + eps_averaging: bool = True, + dimensions: int = 3, + boundary_layers: Optional[List[PML]] = None, + symmetries: Optional[List[Symmetry]] = None, + force_complex_fields: bool = False, + default_material: Medium = mp.Medium(), + m: float = 0, + k_point: Union[Vector3Type, bool] = False, + kz_2d: str = "complex", + extra_materials: Optional[List[Medium]] = None, + material_function: Optional[Callable[[Vector3Type], Medium]] = None, + epsilon_func: Optional[Callable[[Vector3Type], float]] = None, + epsilon_input_file: str = "", + progress_interval: float = 4, + subpixel_tol: float = 1e-4, + subpixel_maxeval: int = 100000, + loop_tile_base_db: int = 0, + loop_tile_base_eh: int = 0, + ensure_periodicity: bool = True, + num_chunks: int = 0, + Courant: float = 0.5, + accurate_fields_near_cylorigin: bool = False, + filename_prefix: Optional[str] = None, + output_volume: Optional[Volume] = None, + output_single_precision: bool = False, + geometry_center: Vector3Type = Vector3(), + force_all_components: bool = False, + split_chunks_evenly: bool = True, + chunk_layout=None, + collect_stats: bool = False, + ): """ All `Simulation` attributes are described in further detail below. In brackets after each variable is the type of value that it should hold. The classes, complex @@ -1206,7 +1286,11 @@ def __init__(self, self.extra_materials = extra_materials if extra_materials else [] self.default_material = default_material self.epsilon_input_file = epsilon_input_file - self.num_chunks = chunk_layout.numchunks() if isinstance(chunk_layout,mp.BinaryPartition) else num_chunks + self.num_chunks = ( + chunk_layout.numchunks() + if isinstance(chunk_layout, mp.BinaryPartition) + else num_chunks + ) self._num_chunks_original = self.num_chunks self.Courant = Courant self.global_d_conductivity = 0 @@ -1225,7 +1309,7 @@ def __init__(self, self.output_append_h5 = None self.output_single_precision = output_single_precision self.output_volume = output_volume - self.last_eps_filename = '' + self.last_eps_filename = "" self.output_h5_hook = lambda fname: False self.interactive = False self.is_cylindrical = False @@ -1239,7 +1323,7 @@ def __init__(self, self._chunk_layout_original = self.chunk_layout self.collect_stats = collect_stats self.fragment_stats = None - self._output_stats = os.environ.get('MEEP_STATS', None) + self._output_stats = os.environ.get("MEEP_STATS", None) self.load_single_parallel_file = True self.load_structure_file = None @@ -1256,14 +1340,23 @@ def __init__(self, elif kz_2d == "3d": self.special_kz = False else: - raise ValueError("Invalid kz_2d option: {} not in [complex, real/imag, 3d]".format(kz_2d)) + raise ValueError( + "Invalid kz_2d option: {} not in [complex, real/imag, 3d]".format( + kz_2d + ) + ) # To prevent the user from having to specify `dims` and `is_cylindrical` # to Volumes they create, the library will adjust them appropriately based # on the settings in the Simulation instance. This method must be called on # any user-defined Volume before passing it to meep via its `swigobj`. def _fit_volume_to_simulation(self, vol): - return Volume(vol.center, vol.size, dims=self.dimensions, is_cylindrical=self.is_cylindrical) + return Volume( + vol.center, + vol.size, + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ) # Every function that takes a user volume can be specified either by a volume # (a Python Volume or a SWIG-wrapped meep::volume), or a center and a size @@ -1276,8 +1369,12 @@ def _volume_from_kwargs(self, vol=None, center=None, size=None): # A SWIG-wrapped meep::volume return vol elif size is not None and center is not None: - return Volume(center=Vector3(*center), size=Vector3(*size), dims=self.dimensions, - is_cylindrical=self.is_cylindrical).swigobj + return Volume( + center=Vector3(*center), + size=Vector3(*size), + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ).swigobj else: raise ValueError("Need either a Volume, or a size and center") @@ -1297,8 +1394,8 @@ def use_2d(self, k): return self.dimensions def _get_valid_material_frequencies(self): - fmin = float('-inf') - fmax = float('inf') + fmin = float("-inf") + fmax = float("inf") all_materials = [go.material for go in self.geometry] + self.extra_materials all_materials.append(self.default_material) @@ -1315,18 +1412,22 @@ def _get_valid_material_frequencies(self): def _check_material_frequencies(self): min_freq, max_freq = self._get_valid_material_frequencies() - source_freqs = [(s.src.frequency, 0 if s.src.width == 0 else 1 / s.src.width) - for s in self.sources - if hasattr(s.src, 'frequency')] + source_freqs = [ + (s.src.frequency, 0 if s.src.width == 0 else 1 / s.src.width) + for s in self.sources + if hasattr(s.src, "frequency") + ] dft_freqs = [] for dftf in self.dft_objects: dft_freqs.append(dftf.freq[0]) dft_freqs.append(dftf.freq[-1]) - warn_src = ('Note: your sources include frequencies outside the range of validity of the ' + - 'material models. This is fine as long as you eventually only look at outputs ' + - '(fluxes, resonant modes, etc.) at valid frequencies.') + warn_src = ( + "Note: your sources include frequencies outside the range of validity of the " + + "material models. This is fine as long as you eventually only look at outputs " + + "(fluxes, resonant modes, etc.) at valid frequencies." + ) warn_dft_fmt = "DFT frequency {} is out of material's range of {}-{}" @@ -1336,7 +1437,9 @@ def _check_material_frequencies(self): for dftf in dft_freqs: if dftf > max_freq or dftf < min_freq: - warnings.warn(warn_dft_fmt.format(dftf, min_freq, max_freq), RuntimeWarning) + warnings.warn( + warn_dft_fmt.format(dftf, min_freq, max_freq), RuntimeWarning + ) def _create_grid_volume(self, k): dims = self._infer_dimensions(k) @@ -1347,7 +1450,9 @@ def _create_grid_volume(self, k): self.dimensions = 2 gv = mp.vol2d(self.cell_size.x, self.cell_size.y, self.resolution) elif dims == 3: - gv = mp.vol3d(self.cell_size.x, self.cell_size.y, self.cell_size.z, self.resolution) + gv = mp.vol3d( + self.cell_size.x, self.cell_size.y, self.cell_size.z, self.resolution + ) elif dims == mp.CYLINDRICAL: gv = mp.volcyl(self.cell_size.x, self.cell_size.z, self.resolution) self.dimensions = 2 @@ -1356,7 +1461,9 @@ def _create_grid_volume(self, k): raise ValueError("Unsupported dimentionality: {}".format(dims)) gv.center_origin() - gv.shift_origin(py_v3_to_vec(self.dimensions, self.geometry_center, self.is_cylindrical)) + gv.shift_origin( + py_v3_to_vec(self.dimensions, self.geometry_center, self.is_cylindrical) + ) return gv def _create_symmetries(self, gv): @@ -1381,10 +1488,15 @@ def _create_symmetries(self, gv): return sym def _get_dft_volumes(self): - volumes = [self._volume_from_kwargs(vol=r.where if hasattr(r, 'where') else None, - center=r.center, size=r.size) - for dft in self.dft_objects - for r in dft.regions] + volumes = [ + self._volume_from_kwargs( + vol=r.where if hasattr(r, "where") else None, + center=r.center, + size=r.size, + ) + for dft in self.dft_objects + for r in dft.regions + ] return volumes @@ -1403,32 +1515,32 @@ def _boundaries_to_vols_1d(self, boundaries): def _boundaries_to_vols_2d_3d(self, boundaries, cyl=False): side_thickness = OrderedDict() - side_thickness['top'] = 0 - side_thickness['bottom'] = 0 - side_thickness['left'] = 0 - side_thickness['right'] = 0 - side_thickness['near'] = 0 - side_thickness['far'] = 0 + side_thickness["top"] = 0 + side_thickness["bottom"] = 0 + side_thickness["left"] = 0 + side_thickness["right"] = 0 + side_thickness["near"] = 0 + side_thickness["far"] = 0 for bl in boundaries: d = bl.direction s = bl.side if d == mp.X or d == mp.ALL: if s == mp.High or s == mp.ALL: - side_thickness['right'] = bl.thickness + side_thickness["right"] = bl.thickness if s == mp.Low or s == mp.ALL: - side_thickness['left'] = bl.thickness + side_thickness["left"] = bl.thickness if d == mp.Y or d == mp.ALL: if s == mp.High or s == mp.ALL: - side_thickness['top'] = bl.thickness + side_thickness["top"] = bl.thickness if s == mp.Low or s == mp.ALL: - side_thickness['bottom'] = bl.thickness + side_thickness["bottom"] = bl.thickness if self.dimensions == 3: if d == mp.Z or d == mp.ALL: if s == mp.High or s == mp.ALL: - side_thickness['far'] = bl.thickness + side_thickness["far"] = bl.thickness if s == mp.Low or s == mp.ALL: - side_thickness['near'] = bl.thickness + side_thickness["near"] = bl.thickness xmax = self.cell_size.x / 2 ymax = self.cell_size.z / 2 if cyl else self.cell_size.y / 2 @@ -1436,30 +1548,38 @@ def _boundaries_to_vols_2d_3d(self, boundaries, cyl=False): ytot = self.cell_size.z if cyl else self.cell_size.y def get_overlap_0(side, d): - if side == 'top' or side == 'bottom': - ydir = 1 if side == 'top' else -1 - xsz = self.cell_size.x - (side_thickness['left'] + side_thickness['right']) + if side == "top" or side == "bottom": + ydir = 1 if side == "top" else -1 + xsz = self.cell_size.x - ( + side_thickness["left"] + side_thickness["right"] + ) ysz = d - zsz = self.cell_size.z - (side_thickness['near'] + side_thickness['far']) - xcen = xmax - side_thickness['right'] - (xsz / 2) - ycen = ydir*ymax + (-ydir*0.5*d) - zcen = zmax - side_thickness['far'] - (zsz / 2) - elif side == 'left' or side == 'right': - xdir = 1 if side == 'right' else -1 + zsz = self.cell_size.z - ( + side_thickness["near"] + side_thickness["far"] + ) + xcen = xmax - side_thickness["right"] - (xsz / 2) + ycen = ydir * ymax + (-ydir * 0.5 * d) + zcen = zmax - side_thickness["far"] - (zsz / 2) + elif side == "left" or side == "right": + xdir = 1 if side == "right" else -1 xsz = d - ysz = ytot - (side_thickness['top'] + side_thickness['bottom']) - zsz = self.cell_size.z - (side_thickness['near'] + side_thickness['far']) - xcen = xdir*xmax + (-xdir*0.5*d) - ycen = ymax - side_thickness['top'] - (ysz / 2) - zcen = zmax - side_thickness['far'] - (zsz / 2) - elif side == 'near' or side == 'far': - zdir = 1 if side == 'far' else -1 - xsz = self.cell_size.x - (side_thickness['left'] + side_thickness['right']) - ysz = ytot - (side_thickness['top'] + side_thickness['bottom']) + ysz = ytot - (side_thickness["top"] + side_thickness["bottom"]) + zsz = self.cell_size.z - ( + side_thickness["near"] + side_thickness["far"] + ) + xcen = xdir * xmax + (-xdir * 0.5 * d) + ycen = ymax - side_thickness["top"] - (ysz / 2) + zcen = zmax - side_thickness["far"] - (zsz / 2) + elif side == "near" or side == "far": + zdir = 1 if side == "far" else -1 + xsz = self.cell_size.x - ( + side_thickness["left"] + side_thickness["right"] + ) + ysz = ytot - (side_thickness["top"] + side_thickness["bottom"]) zsz = d - xcen = xmax - side_thickness['right'] - (xsz / 2) - ycen = ymax - side_thickness['top'] - (ysz / 2) - zcen = zdir*zmax + (-zdir*0.5*d) + xcen = xmax - side_thickness["right"] - (xsz / 2) + ycen = ymax - side_thickness["top"] - (ysz / 2) + zcen = zdir * zmax + (-zdir * 0.5 * d) if cyl: cen = mp.Vector3(xcen, 0, ycen) @@ -1474,31 +1594,37 @@ def get_overlap_1(side1, side2, d): if side_thickness[side2] == 0: return [] - if side1 == 'top' or side1 == 'bottom': - ydir = 1 if side1 == 'top' else -1 + if side1 == "top" or side1 == "bottom": + ydir = 1 if side1 == "top" else -1 ysz = d - ycen = ydir*ymax + (-ydir*0.5*d) - if side2 == 'left' or side2 == 'right': - xdir = 1 if side2 == 'right' else -1 + ycen = ydir * ymax + (-ydir * 0.5 * d) + if side2 == "left" or side2 == "right": + xdir = 1 if side2 == "right" else -1 xsz = side_thickness[side2] - zsz = self.cell_size.z - (side_thickness['near'] + side_thickness['far']) - xcen = xdir*xmax + (-xdir*0.5*side_thickness[side2]) - zcen = zmax - side_thickness['far'] - (zsz / 2) - elif side2 == 'near' or side2 == 'far': - zdir = 1 if side2 == 'far' else -1 - xsz = self.cell_size.x - (side_thickness['left'] + side_thickness['right']) + zsz = self.cell_size.z - ( + side_thickness["near"] + side_thickness["far"] + ) + xcen = xdir * xmax + (-xdir * 0.5 * side_thickness[side2]) + zcen = zmax - side_thickness["far"] - (zsz / 2) + elif side2 == "near" or side2 == "far": + zdir = 1 if side2 == "far" else -1 + xsz = self.cell_size.x - ( + side_thickness["left"] + side_thickness["right"] + ) zsz = side_thickness[side2] - xcen = xmax - side_thickness['right'] - (xsz / 2) - zcen = zdir*zmax + (-zdir*0.5*side_thickness[side2]) - elif side1 == 'near' or side1 == 'far': - xdir = 1 if side2 == 'right' else -1 - zdir = 1 if side1 == 'far' else -1 + xcen = xmax - side_thickness["right"] - (xsz / 2) + zcen = zdir * zmax + (-zdir * 0.5 * side_thickness[side2]) + elif side1 == "near" or side1 == "far": + xdir = 1 if side2 == "right" else -1 + zdir = 1 if side1 == "far" else -1 xsz = side_thickness[side2] - ysz = self.cell_size.y - (side_thickness['top'] + side_thickness['bottom']) + ysz = self.cell_size.y - ( + side_thickness["top"] + side_thickness["bottom"] + ) zsz = d - xcen = xdir*xmax + (-xdir*0.5*side_thickness[side2]) - ycen = ymax - side_thickness['top'] - (ysz / 2) - zcen = zdir*zmax + (-zdir*0.5*d) + xcen = xdir * xmax + (-xdir * 0.5 * side_thickness[side2]) + ycen = ymax - side_thickness["top"] - (ysz / 2) + zcen = zdir * zmax + (-zdir * 0.5 * d) if cyl: cen = mp.Vector3(xcen, 0, ycen) @@ -1511,15 +1637,15 @@ def get_overlap_1(side1, side2, d): def get_overlap_2(side1, side2, side3, d): if side_thickness[side2] == 0 or side_thickness[side3] == 0: return [] - xdir = 1 if side2 == 'right' else -1 - ydir = 1 if side1 == 'top' else -1 - zdir = 1 if side3 == 'far' else -1 + xdir = 1 if side2 == "right" else -1 + ydir = 1 if side1 == "top" else -1 + zdir = 1 if side3 == "far" else -1 xsz = side_thickness[side2] ysz = d zsz = side_thickness[side3] - xcen = xdir*xmax + (-xdir*0.5*xsz) - ycen = ydir*ymax + (-ydir*0.5*d) - zcen = zdir*zmax + (-zdir*0.5*zsz) + xcen = xdir * xmax + (-xdir * 0.5 * xsz) + ycen = ydir * ymax + (-ydir * 0.5 * d) + zcen = zdir * zmax + (-zdir * 0.5 * zsz) cen = mp.Vector3(xcen, ycen, zcen) sz = mp.Vector3(xsz, ysz, zsz) @@ -1534,19 +1660,19 @@ def get_overlap_2(side1, side2, side3, d): continue v1.append(get_overlap_0(side, thickness)) - if side == 'top' or side == 'bottom': - v2.append(get_overlap_1(side, 'left', thickness)) - v2.append(get_overlap_1(side, 'right', thickness)) + if side == "top" or side == "bottom": + v2.append(get_overlap_1(side, "left", thickness)) + v2.append(get_overlap_1(side, "right", thickness)) if self.dimensions == 3: - v2.append(get_overlap_1(side, 'near', thickness)) - v2.append(get_overlap_1(side, 'far', thickness)) - v3.append(get_overlap_2(side, 'left', 'near', thickness)) - v3.append(get_overlap_2(side, 'right', 'near', thickness)) - v3.append(get_overlap_2(side, 'left', 'far', thickness)) - v3.append(get_overlap_2(side, 'right', 'far', thickness)) - if side == 'near' or side == 'far': - v2.append(get_overlap_1(side, 'left', thickness)) - v2.append(get_overlap_1(side, 'right', thickness)) + v2.append(get_overlap_1(side, "near", thickness)) + v2.append(get_overlap_1(side, "far", thickness)) + v3.append(get_overlap_2(side, "left", "near", thickness)) + v3.append(get_overlap_2(side, "right", "near", thickness)) + v3.append(get_overlap_2(side, "left", "far", thickness)) + v3.append(get_overlap_2(side, "right", "far", thickness)) + if side == "near" or side == "far": + v2.append(get_overlap_1(side, "left", thickness)) + v2.append(get_overlap_1(side, "right", thickness)) return [v for v in v1 if v], [v for v in v2 if v], [v for v in v3 if v] @@ -1564,21 +1690,29 @@ def _boundary_layers_to_vol_list(self, boundaries): if self.dimensions == 1: vols1 = self._boundaries_to_vols_1d(boundaries) else: - vols1, vols2, vols3 = self._boundaries_to_vols_2d_3d(boundaries, self.is_cylindrical) + vols1, vols2, vols3 = self._boundaries_to_vols_2d_3d( + boundaries, self.is_cylindrical + ) return vols1, vols2, vols3 def _make_fragment_lists(self, gv): - def convert_volumes(dft_obj): volumes = [] for r in dft_obj.regions: - volumes.append(self._volume_from_kwargs(vol=r.where if hasattr(r, 'where') else None, - center=r.center, size=r.size)) + volumes.append( + self._volume_from_kwargs( + vol=r.where if hasattr(r, "where") else None, + center=r.center, + size=r.size, + ) + ) return volumes - dft_data_list = [mp.dft_data(o.nfreqs, o.num_components, convert_volumes(o)) - for o in self.dft_objects] + dft_data_list = [ + mp.dft_data(o.nfreqs, o.num_components, convert_volumes(o)) + for o in self.dft_objects + ] pmls = [] absorbers = [] @@ -1589,14 +1723,24 @@ def convert_volumes(dft_obj): absorbers.append(bl) pml_vols1, pml_vols2, pml_vols3 = self._boundary_layers_to_vol_list(pmls) - absorber_vols1, absorber_vols2, absorber_vols3 = self._boundary_layers_to_vol_list(absorbers) + ( + absorber_vols1, + absorber_vols2, + absorber_vols3, + ) = self._boundary_layers_to_vol_list(absorbers) absorber_vols = absorber_vols1 + absorber_vols2 + absorber_vols3 return (dft_data_list, pml_vols1, pml_vols2, pml_vols3, absorber_vols) def _compute_fragment_stats(self, gv): - dft_data_list, pml_vols1, pml_vols2, pml_vols3, absorber_vols = self._make_fragment_lists(gv) + ( + dft_data_list, + pml_vols1, + pml_vols2, + pml_vols3, + absorber_vols, + ) = self._make_fragment_lists(gv) stats = mp.compute_fragment_stats( self.geometry, @@ -1613,7 +1757,7 @@ def _compute_fragment_stats(self, gv): self.subpixel_tol, self.subpixel_maxeval, self.ensure_periodicity, - self.eps_averaging + self.eps_averaging, ) mirror_symmetries = [sym for sym in self.symmetries if isinstance(sym, Mirror)] @@ -1632,8 +1776,8 @@ def _compute_fragment_stats(self, gv): def _init_structure(self, k=False): if verbosity.meep > 0: - print('-' * 11) - print('Initializing structure...') + print("-" * 11) + print("Initializing structure...") gv = self._create_grid_volume(k) sym = self._create_symmetries(gv) @@ -1654,13 +1798,23 @@ def _init_structure(self, k=False): if self.collect_stats and isinstance(self.default_material, mp.Medium): self.fragment_stats = self._compute_fragment_stats(gv) - if self._output_stats and isinstance(self.default_material, mp.Medium) and verbosity.meep > 0: + if ( + self._output_stats + and isinstance(self.default_material, mp.Medium) + and verbosity.meep > 0 + ): stats = self._compute_fragment_stats(gv) print("FRAGMENT:, aniso_eps:, {}".format(stats.num_anisotropic_eps_pixels)) print("FRAGMENT:, aniso_mu:, {}".format(stats.num_anisotropic_mu_pixels)) print("FRAGMENT:, nonlinear:, {}".format(stats.num_nonlinear_pixels)) - print("FRAGMENT:, susceptibility:, {}".format(stats.num_susceptibility_pixels)) - print("FRAGMENT:, conductivity:, {}".format(stats.num_nonzero_conductivity_pixels)) + print( + "FRAGMENT:, susceptibility:, {}".format(stats.num_susceptibility_pixels) + ) + print( + "FRAGMENT:, conductivity:, {}".format( + stats.num_nonzero_conductivity_pixels + ) + ) print("FRAGMENT:, pml_1d:, {}".format(stats.num_1d_pml_pixels)) print("FRAGMENT:, pml_2d:, {}".format(stats.num_2d_pml_pixels)) print("FRAGMENT:, pml_3d:, {}".format(stats.num_3d_pml_pixels)) @@ -1697,10 +1851,15 @@ def _init_structure(self, k=False): absorbers, self.extra_materials, self.split_chunks_evenly, - False if self.chunk_layout and not isinstance(self.chunk_layout,mp.BinaryPartition) else True, + False + if self.chunk_layout + and not isinstance(self.chunk_layout, mp.BinaryPartition) + else True, None, True if self._output_stats is not None else False, - self.chunk_layout if self.chunk_layout and isinstance(self.chunk_layout,mp.BinaryPartition) else None + self.chunk_layout + if self.chunk_layout and isinstance(self.chunk_layout, mp.BinaryPartition) + else None, ) self.geps = mp._set_materials( self.structure, @@ -1719,13 +1878,13 @@ def _init_structure(self, k=False): True, None, False, - None + None, ) if self._output_stats is not None: sys.exit(0) - if self.chunk_layout and not isinstance(self.chunk_layout,mp.BinaryPartition): + if self.chunk_layout and not isinstance(self.chunk_layout, mp.BinaryPartition): self.load_chunk_layout(br, self.chunk_layout) self.set_materials() @@ -1737,7 +1896,8 @@ def _init_structure(self, k=False): if self.load_structure_file: self.load_structure( - self.load_structure_file, self.load_single_parallel_file) + self.load_structure_file, self.load_single_parallel_file + ) def _is_outer_boundary(self, vol, direction, side): @@ -1752,11 +1912,15 @@ def _is_outer_boundary(self, vol, direction, side): # TODO: Support shifted origins def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): - return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) + return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) - if side == mp.Low and isclose(vol.get_min_corner().in_direction(direction), -half_cell_size): + if side == mp.Low and isclose( + vol.get_min_corner().in_direction(direction), -half_cell_size + ): return True - if side == mp.High and isclose(vol.get_max_corner().in_direction(direction), half_cell_size): + if side == mp.High and isclose( + vol.get_max_corner().in_direction(direction), half_cell_size + ): return True return False @@ -1801,8 +1965,10 @@ def get_num_pixels(vol, direction, side, target_direction, mult_direction=None): def _get_chunk_communication_areas(self): if self.dimensions == 1 or self.is_cylindrical: - warnings.warn("Can currently only get chunk communication area from 2d or 3d simulations", - RuntimeWarning) + warnings.warn( + "Can currently only get chunk communication area from 2d or 3d simulations", + RuntimeWarning, + ) return if self.structure is None: @@ -1878,7 +2044,7 @@ def set_materials(self, geometry=None, default_material=None): True, self.structure, False, - None + None, ) self.geps = mp._set_materials( self.structure, @@ -1897,7 +2063,7 @@ def set_materials(self, geometry=None, default_material=None): True, None, False, - None + None, ) def dump_structure(self, fname, single_parallel_file=True): @@ -1905,20 +2071,30 @@ def dump_structure(self, fname, single_parallel_file=True): Dumps the structure to the file `fname`. """ if self.structure is None: - raise ValueError("Structure must be initialized before calling dump_structure") + raise ValueError( + "Structure must be initialized before calling dump_structure" + ) self.structure.dump(fname, single_parallel_file) if verbosity.meep > 0: - print("Dumped structure to file: %s (%s)" % (fname, str(single_parallel_file))) + print( + "Dumped structure to file: %s (%s)" % (fname, str(single_parallel_file)) + ) def load_structure(self, fname, single_parallel_file=True): """ Loads a structure from the file `fname`. """ if self.structure is None: - raise ValueError("Structure must be initialized before loading structure from file '%s'" % fname) + raise ValueError( + "Structure must be initialized before loading structure from file '%s'" + % fname + ) self.structure.load(fname, single_parallel_file) if verbosity.meep > 0: - print("Loaded structure from file: %s (%s)" % (fname, str(single_parallel_file))) + print( + "Loaded structure from file: %s (%s)" + % (fname, str(single_parallel_file)) + ) def dump_fields(self, fname, single_parallel_file=True): """ @@ -1935,23 +2111,33 @@ def load_fields(self, fname, single_parallel_file=True): Loads a fields from the file `fname`. """ if self.fields is None: - raise ValueError("Fields must be initialized before loading fields from file '%s'" % fname) + raise ValueError( + "Fields must be initialized before loading fields from file '%s'" + % fname + ) self._evaluate_dft_objects() self.fields.load(fname, single_parallel_file) if verbosity.meep > 0: - print("Loaded fields from file: %s (%s)" % (fname, str(single_parallel_file))) + print( + "Loaded fields from file: %s (%s)" % (fname, str(single_parallel_file)) + ) def dump_chunk_layout(self, fname): """ Dumps the chunk layout to file `fname`. """ if self.structure is None: - raise ValueError("Structure must be initialized before calling dump_chunk_layout") + raise ValueError( + "Structure must be initialized before calling dump_chunk_layout" + ) self.structure.dump_chunk_layout(fname) def load_chunk_layout(self, br, source): if self.structure is None: - raise ValueError("Structure must be initialized before loading chunk layout from file '%s'" % fname) + raise ValueError( + "Structure must be initialized before loading chunk layout from file '%s'" + % fname + ) if isinstance(source, Simulation): vols = source.structure.get_chunk_volumes() @@ -1970,10 +2156,12 @@ def get_load_dump_dirname(self, dirname, single_parallel_file): else: # When doing a sharded dump (each process to its own file), use # the process rank to get a unique name. - dump_dirname = os.path.join(dirname, 'rank%02d' % mp.my_rank()) + dump_dirname = os.path.join(dirname, "rank%02d" % mp.my_rank()) return dump_dirname - def dump(self, dirname, dump_structure=True, dump_fields=True, single_parallel_file=True): + def dump( + self, dirname, dump_structure=True, dump_fields=True, single_parallel_file=True + ): """ Dumps simulation state. """ @@ -1981,14 +2169,16 @@ def dump(self, dirname, dump_structure=True, dump_fields=True, single_parallel_f os.makedirs(dump_dirname, exist_ok=True) if dump_structure: - structure_dump_filename = os.path.join(dump_dirname, 'structure.h5') + structure_dump_filename = os.path.join(dump_dirname, "structure.h5") self.dump_structure(structure_dump_filename, single_parallel_file) if dump_fields: - fields_dump_filename = os.path.join(dump_dirname, 'fields.h5') + fields_dump_filename = os.path.join(dump_dirname, "fields.h5") self.dump_fields(fields_dump_filename, single_parallel_file) - def load(self, dirname, load_structure=True, load_fields=True, single_parallel_file=True): + def load( + self, dirname, load_structure=True, load_fields=True, single_parallel_file=True + ): """ Loads simulation state. @@ -1999,36 +2189,42 @@ def load(self, dirname, load_structure=True, load_fields=True, single_parallel_f self.load_single_parallel_file = single_parallel_file if load_structure: - load_structure_file = os.path.join(dump_dirname, 'structure.h5') - # If structure is already initialized, load it straight away. - # Otherwise, do a delayed load. - if self.structure: - self.load_structure(load_structure_file, self.load_single_parallel_file) - else: - self.load_structure_file = load_structure_file + load_structure_file = os.path.join(dump_dirname, "structure.h5") + # If structure is already initialized, load it straight away. + # Otherwise, do a delayed load. + if self.structure: + self.load_structure(load_structure_file, self.load_single_parallel_file) + else: + self.load_structure_file = load_structure_file if load_fields: - load_fields_file = os.path.join(dump_dirname, 'fields.h5') - if self.fields: - self.load_fields(load_fields_file, self.load_single_parallel_file) - else: - self.load_fields_file = load_fields_file + load_fields_file = os.path.join(dump_dirname, "fields.h5") + if self.fields: + self.load_fields(load_fields_file, self.load_single_parallel_file) + else: + self.load_fields_file = load_fields_file def init_sim(self): if self._is_initialized: return - materials = [g.material for g in self.geometry if isinstance(g.material, mp.Medium)] + materials = [ + g.material for g in self.geometry if isinstance(g.material, mp.Medium) + ] if isinstance(self.default_material, mp.Medium): materials.append(self.default_material) for med in materials: - if ((med.epsilon_diag.x < 1 and med.epsilon_diag.x > -mp.inf) or - (med.epsilon_diag.y < 1 and med.epsilon_diag.y > -mp.inf) or - (med.epsilon_diag.z < 1 and med.epsilon_diag.z > -mp.inf)): - - eps_warning = ("Epsilon < 1 may require adjusting the Courant parameter. " + - "See the 'Numerical Stability' entry under the 'Materials' " + - "section of the documentation") + if ( + (med.epsilon_diag.x < 1 and med.epsilon_diag.x > -mp.inf) + or (med.epsilon_diag.y < 1 and med.epsilon_diag.y > -mp.inf) + or (med.epsilon_diag.z < 1 and med.epsilon_diag.z > -mp.inf) + ): + + eps_warning = ( + "Epsilon < 1 may require adjusting the Courant parameter. " + + "See the 'Numerical Stability' entry under the 'Materials' " + + "section of the documentation" + ) warnings.warn(eps_warning, RuntimeWarning) if self.structure is None: @@ -2039,7 +2235,8 @@ def init_sim(self): self.m if self.is_cylindrical else 0, self.k_point.z if self.special_kz and self.k_point else 0, not self.accurate_fields_near_cylorigin, - self.loop_tile_base_db, self.loop_tile_base_eh + self.loop_tile_base_db, + self.loop_tile_base_eh, ) if self.force_all_components and self.dimensions != 1: @@ -2052,7 +2249,11 @@ def init_sim(self): print("Meep: using complex fields.") if self.k_point: - v = Vector3(self.k_point.x, self.k_point.y) if self.special_kz else self.k_point + v = ( + Vector3(self.k_point.x, self.k_point.y) + if self.special_kz + else self.k_point + ) self.fields.use_bloch(py_v3_to_vec(self.dimensions, v, self.is_cylindrical)) self.add_sources() @@ -2063,8 +2264,7 @@ def init_sim(self): self._is_initialized = True if self.load_fields_file: - self.load_fields( - self.load_fields_file, self.load_single_parallel_file) + self.load_fields(self.load_fields_file, self.load_single_parallel_file) def using_real_fields(self): cond1 = self.is_cylindrical and self.m != 0 @@ -2089,11 +2289,12 @@ def require_dimensions(self): mp.set_dimensions(self._infer_dimensions(self.k_point)) def has_mu(self): - def _has_mu(medium): if not isinstance(medium, mp.Medium): return False - return medium.mu_diag != mp.Vector3(1, 1, 1) or medium.mu_offdiag != mp.Vector3(0j, 0j, 0j) + return medium.mu_diag != mp.Vector3( + 1, 1, 1 + ) or medium.mu_offdiag != mp.Vector3(0j, 0j, 0j) for go in self.geometry: if _has_mu(go.material): @@ -2111,13 +2312,29 @@ def get_estimated_memory_usage(self): self.init_sim() if self.fragment_stats is None: - self.fragment_stats = self._compute_fragment_stats(self.structure.user_volume) + self.fragment_stats = self._compute_fragment_stats( + self.structure.user_volume + ) - is_complex = (self.k_point and self.k_point != mp.Vector3(0, 0, 0)) or self.force_complex_fields + is_complex = ( + self.k_point and self.k_point != mp.Vector3(0, 0, 0) + ) or self.force_complex_fields realnums_per_grid_point = 1 if self.dimensions == 1 else 3 - E_realnums = self.fragment_stats.num_pixels_in_box * (2 if is_complex else 1) * realnums_per_grid_point - H_realnums = self.fragment_stats.num_pixels_in_box * (2 if is_complex else 1) * realnums_per_grid_point - D_realnums = self.fragment_stats.num_pixels_in_box * (2 if is_complex else 1) * realnums_per_grid_point + E_realnums = ( + self.fragment_stats.num_pixels_in_box + * (2 if is_complex else 1) + * realnums_per_grid_point + ) + H_realnums = ( + self.fragment_stats.num_pixels_in_box + * (2 if is_complex else 1) + * realnums_per_grid_point + ) + D_realnums = ( + self.fragment_stats.num_pixels_in_box + * (2 if is_complex else 1) + * realnums_per_grid_point + ) chi1inv_realnums = self.fragment_stats.num_pixels_in_box * 9 Mu_realnums = 0 @@ -2125,10 +2342,18 @@ def get_estimated_memory_usage(self): Mu_realnums = chi1inv_realnums + H_realnums dft_realnums = self.fragment_stats.num_dft_pixels * 2 - dispersive_realnums = self.fragment_stats.num_susceptibility_pixels * 6 * (2 if is_complex else 1) + dispersive_realnums = ( + self.fragment_stats.num_susceptibility_pixels * 6 * (2 if is_complex else 1) + ) - total_realnums = (E_realnums + H_realnums + D_realnums + Mu_realnums + - dft_realnums + dispersive_realnums) + total_realnums = ( + E_realnums + + H_realnums + + D_realnums + + Mu_realnums + + dft_realnums + + dispersive_realnums + ) total_bytes = total_realnums * mp.get_realnum_size() @@ -2205,7 +2430,7 @@ def get_epsilon_point(self, pt, frequency=0): frequency-independent part of $\\varepsilon$ (the $\\omega\\to\\infty$ limit). """ v3 = py_v3_to_vec(self.dimensions, pt, self.is_cylindrical) - return self.fields.get_eps(v3,frequency) + return self.fields.get_eps(v3, frequency) def get_mu_point(self, pt, frequency=0): """ @@ -2215,7 +2440,7 @@ def get_mu_point(self, pt, frequency=0): frequency-independent part of $\\mu$ (the $\\omega\\to\\infty$ limit). """ v3 = py_v3_to_vec(self.dimensions, pt, self.is_cylindrical) - return self.fields.get_mu(v3,frequency) + return self.fields.get_mu(v3, frequency) def get_epsilon_grid(self, xtics=None, ytics=None, ztics=None, frequency=0): """ @@ -2229,19 +2454,27 @@ def get_epsilon_grid(self, xtics=None, ytics=None, ztics=None, frequency=0): sampling the material geometry to any arbitrary resolution. The return value is a NumPy array with shape equivalent to `numpy.meshgrid(xtics,ytics,ztics)`. Empty dimensions are collapsed. """ - grid_vals = np.squeeze(np.empty((len(xtics), len(ytics), len(ztics)), dtype=np.complex128)) + grid_vals = np.squeeze( + np.empty((len(xtics), len(ytics), len(ztics)), dtype=np.complex128) + ) gv = self._create_grid_volume(False) - mp._get_epsilon_grid(self.geometry, - self.extra_materials, - self.default_material, - self.ensure_periodicity and not not self.k_point, - gv, - self.cell_size, self.geometry_center, - len(xtics), xtics, - len(ytics), ytics, - len(ztics), ztics, - grid_vals, - frequency) + mp._get_epsilon_grid( + self.geometry, + self.extra_materials, + self.default_material, + self.ensure_periodicity and not not self.k_point, + gv, + self.cell_size, + self.geometry_center, + len(xtics), + xtics, + len(ytics), + ytics, + len(ztics), + ztics, + grid_vals, + frequency, + ) return grid_vals def get_filename_prefix(self): @@ -2260,14 +2493,16 @@ def get_filename_prefix(self): elif self.filename_prefix is None: _, filename = os.path.split(sys.argv[0]) - if filename == 'ipykernel_launcher.py' or filename == '__main__.py': - return '' + if filename == "ipykernel_launcher.py" or filename == "__main__.py": + return "" else: - return re.sub(r'\.py$', '', filename) + return re.sub(r"\.py$", "", filename) else: - raise TypeError("Expected a string for filename_prefix, or None for the default.") + raise TypeError( + "Expected a string for filename_prefix, or None for the default." + ) - def use_output_directory(self, dname=''): + def use_output_directory(self, dname=""): """ Output all files into a subdirectory, which is created if necessary. If the optional argument `dname` is specified, that is the name of the directory. If `dname` @@ -2277,17 +2512,17 @@ def use_output_directory(self, dname=''): name is set to `filename_prefix` + "-out" and `filename_prefix` is then reset to `None`. """ if not dname: - dname = self.get_filename_prefix() + '-out' + dname = self.get_filename_prefix() + "-out" - closure = {'trashed': False} + closure = {"trashed": False} def hook(): if verbosity.meep > 0: print("Meep: using output directory '{}'".format(dname)) self.fields.set_output_directory(dname) - if not closure['trashed']: + if not closure["trashed"]: mp.trash_output_directory(dname) - closure['trashed'] = True + closure["trashed"] = True self.init_sim_hooks.append(hook) @@ -2317,34 +2552,46 @@ def stop_cond(sim): cond[i] = stop_cond step_funcs = list(step_funcs) - step_funcs.append(display_progress(t0, t0 + stop_time, self.progress_interval)) + step_funcs.append( + display_progress(t0, t0 + stop_time, self.progress_interval) + ) if do_progress: - self.progress = FloatProgress(value=t0, min=t0, max=t0 + stop_time, description="0% done ") + self.progress = FloatProgress( + value=t0, min=t0, max=t0 + stop_time, description="0% done " + ) display(self.progress) else: - assert callable(cond[i]), "Stopping condition {} is not an integer or a function".format(cond[i]) + assert callable( + cond[i] + ), "Stopping condition {} is not an integer or a function".format( + cond[i] + ) while not any([x(self) for x in cond]): for func in step_funcs: - _eval_step_func(self, func, 'step') + _eval_step_func(self, func, "step") self.fields.step() # Translating the recursive scheme version of run-until into an iterative version # (because python isn't tail-call-optimized) means we need one extra iteration to # be the same as scheme. for func in step_funcs: - _eval_step_func(self, func, 'step') + _eval_step_func(self, func, "step") for func in step_funcs: - _eval_step_func(self, func, 'finish') + _eval_step_func(self, func, "finish") if do_progress and self.progress: self.progress.value = t0 + stop_time self.progress.description = "100% done " if verbosity.meep > 0: - print("run {} finished at t = {} ({} timesteps)".format(self.run_index, self.meep_time(), self.fields.t)) + print( + "run {} finished at t = {} ({} timesteps)".format( + self.run_index, self.meep_time(), self.fields.t + ) + ) self.run_index += 1 def _run_sources_until(self, cond, step_funcs): @@ -2360,8 +2607,10 @@ def _run_sources_until(self, cond, step_funcs): if isinstance(cond[i], numbers.Number): new_conds.append((ts - self.round_time()) + cond[i]) else: + def f(sim): return cond[i](sim) and sim.round_time() >= ts + new_conds.append(f) self._run_until(new_conds, step_funcs) @@ -2383,12 +2632,24 @@ def run_k_point(self, t, k): components = [s.component for s in self.sources] pts = [s.center for s in self.sources] - src_freqs_min = min([s.src.frequency - 1 / s.src.width / 2 if isinstance(s.src, mp.GaussianSource) else mp.inf - for s in self.sources]) + src_freqs_min = min( + [ + s.src.frequency - 1 / s.src.width / 2 + if isinstance(s.src, mp.GaussianSource) + else mp.inf + for s in self.sources + ] + ) fmin = max(0, src_freqs_min) - fmax = max([s.src.frequency + 1 / s.src.width / 2 if isinstance(s.src, mp.GaussianSource) else 0 - for s in self.sources]) + fmax = max( + [ + s.src.frequency + 1 / s.src.width / 2 + if isinstance(s.src, mp.GaussianSource) + else 0 + for s in self.sources + ] + ) if not components or fmin > fmax: raise ValueError("Running with k_points requires a 'GaussianSource' source") @@ -2425,10 +2686,12 @@ def run_k_points(self, t, k_points): freqs = [complex(m.freq, m.decay) for m in harminv.modes] if verbosity.meep > 0: - print("freqs:, {}, {}, {}, {}, ".format(k_index, k.x, k.y, k.z), end='') - print(', '.join([str(f.real) for f in freqs])) - print("freqs-im:, {}, {}, {}, {}, ".format(k_index, k.x, k.y, k.z), end='') - print(', '.join([str(f.imag) for f in freqs])) + print("freqs:, {}, {}, {}, {}, ".format(k_index, k.x, k.y, k.z), end="") + print(", ".join([str(f.real) for f in freqs])) + print( + "freqs-im:, {}, {}, {}, {}, ".format(k_index, k.x, k.y, k.z), end="" + ) + print(", ".join([str(f.imag) for f in freqs])) all_freqs.append(freqs) @@ -2438,7 +2701,9 @@ def set_epsilon(self, eps): if self.fields is None: self.init_sim() - self.structure.set_epsilon(eps, self.eps_averaging, self.subpixel_tol, self.subpixel_maxeval) + self.structure.set_epsilon( + eps, self.eps_averaging, self.subpixel_tol, self.subpixel_maxeval + ) def add_source(self, src): if self.fields is None: @@ -2446,11 +2711,21 @@ def add_source(self, src): if isinstance(src, IndexedSource): self.fields.register_src_time(src.src.swigobj) - self.fields.add_srcdata(src.srcdata, src.src.swigobj, src.num_pts, src.amp_arr, src.needs_boundary_fix) + self.fields.add_srcdata( + src.srcdata, + src.src.swigobj, + src.num_pts, + src.amp_arr, + src.needs_boundary_fix, + ) return - where = Volume(src.center, src.size, dims=self.dimensions, - is_cylindrical=self.is_cylindrical).swigobj + where = Volume( + src.center, + src.size, + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ).swigobj if isinstance(src, EigenModeSource): if src.direction < 0: @@ -2458,8 +2733,12 @@ def add_source(self, src): else: direction = src.direction - eig_vol = Volume(src.eig_lattice_center, src.eig_lattice_size, self.dimensions, - is_cylindrical=self.is_cylindrical).swigobj + eig_vol = Volume( + src.eig_lattice_center, + src.eig_lattice_size, + self.dimensions, + is_cylindrical=self.is_cylindrical, + ).swigobj if isinstance(src.eig_band, DiffractedPlanewave): eig_band = 1 @@ -2474,46 +2753,66 @@ def add_source(self, src): where, eig_vol, eig_band, - py_v3_to_vec(self.dimensions, src.eig_kpoint, is_cylindrical=self.is_cylindrical), + py_v3_to_vec( + self.dimensions, src.eig_kpoint, is_cylindrical=self.is_cylindrical + ), src.eig_match_freq, src.eig_parity, src.eig_resolution, src.eig_tolerance, - src.amplitude + src.amplitude, ] - add_eig_src = functools.partial(self.fields.add_eigenmode_source, *add_eig_src_args) + add_eig_src = functools.partial( + self.fields.add_eigenmode_source, *add_eig_src_args + ) if isinstance(src.eig_band, DiffractedPlanewave): add_eig_src(src.amp_func, diffractedplanewave) else: add_eig_src(src.amp_func) - elif isinstance (src, GaussianBeamSource): + elif isinstance(src, GaussianBeamSource): gaussianbeam_args = [ - py_v3_to_vec(self.dimensions, src.beam_x0, is_cylindrical=self.is_cylindrical), - py_v3_to_vec(self.dimensions, src.beam_kdir, is_cylindrical=self.is_cylindrical), + py_v3_to_vec( + self.dimensions, src.beam_x0, is_cylindrical=self.is_cylindrical + ), + py_v3_to_vec( + self.dimensions, src.beam_kdir, is_cylindrical=self.is_cylindrical + ), src.beam_w0, src.src.swigobj.frequency().real, - self.fields.get_eps(py_v3_to_vec(self.dimensions, src.center, self.is_cylindrical)).real, - self.fields.get_mu(py_v3_to_vec(self.dimensions, src.center, self.is_cylindrical)).real, - np.array([src.beam_E0.x, src.beam_E0.y, src.beam_E0.z], dtype=np.complex128) + self.fields.get_eps( + py_v3_to_vec(self.dimensions, src.center, self.is_cylindrical) + ).real, + self.fields.get_mu( + py_v3_to_vec(self.dimensions, src.center, self.is_cylindrical) + ).real, + np.array( + [src.beam_E0.x, src.beam_E0.y, src.beam_E0.z], dtype=np.complex128 + ), ] gaussianbeam = mp.gaussianbeam(*gaussianbeam_args) add_vol_src_args = [src.src.swigobj, where, gaussianbeam] - add_vol_src = functools.partial(self.fields.add_volume_source, *add_vol_src_args) + add_vol_src = functools.partial( + self.fields.add_volume_source, *add_vol_src_args + ) add_vol_src() else: add_vol_src_args = [src.component, src.src.swigobj, where] - add_vol_src = functools.partial(self.fields.add_volume_source, *add_vol_src_args) + add_vol_src = functools.partial( + self.fields.add_volume_source, *add_vol_src_args + ) if src.amp_func_file: - fname_dset = src.amp_func_file.rsplit(':', 1) + fname_dset = src.amp_func_file.rsplit(":", 1) if len(fname_dset) != 2: - err_msg = "Expected a string of the form 'h5filename:dataset'. Got '{}'" + err_msg = ( + "Expected a string of the form 'h5filename:dataset'. Got '{}'" + ) raise ValueError(err_msg.format(src.amp_func_file)) fname, dset = fname_dset - if not fname.endswith('.h5'): - fname += '.h5' + if not fname.endswith(".h5"): + fname += ".h5" add_vol_src(fname, dset, src.amplitude * 1.0) elif src.amp_func: @@ -2525,17 +2824,16 @@ def add_source(self, src): def add_sources(self): if self.fields is None: - self.init_sim() # in case only some processes have IndexedSources + self.init_sim() # in case only some processes have IndexedSources for s in self.sources: self.add_source(s) - self.fields.require_source_components() # needed by IndexedSource objects + self.fields.require_source_components() # needed by IndexedSource objects def _evaluate_dft_objects(self): for dft in self.dft_objects: if dft.swigobj is None: dft.swigobj = dft.func(*dft.args) - def add_dft_fields(self, *args, **kwargs): """ `add_dft_fields(cs, fcen, df, nfreq, freq, where=None, center=None, size=None, yee_grid=False, decimation_factor=0, persist=False)` ##sig @@ -2560,32 +2858,51 @@ def add_dft_fields(self, *args, **kwargs): components = args[0] args = fix_dft_args(args, 1) freq = args[1] - where = kwargs.get('where', None) - center = kwargs.get('center', None) - size = kwargs.get('size', None) - yee_grid = kwargs.get('yee_grid', False) - decimation_factor = kwargs.get('decimation_factor', 0) - persist = kwargs.get('persist', False) + where = kwargs.get("where", None) + center = kwargs.get("center", None) + size = kwargs.get("size", None) + yee_grid = kwargs.get("yee_grid", False) + decimation_factor = kwargs.get("decimation_factor", 0) + persist = kwargs.get("persist", False) center_v3 = Vector3(*center) if center is not None else None size_v3 = Vector3(*size) if size is not None else None use_centered_grid = not yee_grid - dftf = DftFields(self._add_dft_fields, [ - components, where, center_v3, size_v3, freq, use_centered_grid, - decimation_factor,persist - ]) + dftf = DftFields( + self._add_dft_fields, + [ + components, + where, + center_v3, + size_v3, + freq, + use_centered_grid, + decimation_factor, + persist, + ], + ) self.dft_objects.append(dftf) return dftf - def _add_dft_fields(self, components, where, center, size, freq, - use_centered_grid, decimation_factor, persist): + def _add_dft_fields( + self, + components, + where, + center, + size, + freq, + use_centered_grid, + decimation_factor, + persist, + ): if self.fields is None: self.init_sim() try: where = self._volume_from_kwargs(where, center, size) except ValueError: where = self.fields.total_volume() - return self.fields.add_dft_fields(components, where, freq, - use_centered_grid, decimation_factor, persist) + return self.fields.add_dft_fields( + components, where, freq, use_centered_grid, decimation_factor, persist + ) def output_dft(self, dft_fields, fname): """ @@ -2597,9 +2914,11 @@ def output_dft(self, dft_fields, fname): self.init_sim() if not self.dft_objects: - raise RuntimeError('DFT monitor dft_fields must be initialized before calling output_dft') + raise RuntimeError( + "DFT monitor dft_fields must be initialized before calling output_dft" + ) - if hasattr(dft_fields, 'swigobj'): + if hasattr(dft_fields, "swigobj"): dft_fields_swigobj = dft_fields.swigobj else: dft_fields_swigobj = dft_fields @@ -2633,17 +2952,20 @@ def add_near2far(self, *args, **kwargs): args = fix_dft_args(args, 0) freq = args[0] near2fars = args[1:] - nperiods = kwargs.get('nperiods', 1) - decimation_factor = kwargs.get('decimation_factor', 0) - n2f = DftNear2Far(self._add_near2far, [freq, nperiods, near2fars, decimation_factor]) + nperiods = kwargs.get("nperiods", 1) + decimation_factor = kwargs.get("decimation_factor", 0) + n2f = DftNear2Far( + self._add_near2far, [freq, nperiods, near2fars, decimation_factor] + ) self.dft_objects.append(n2f) return n2f def _add_near2far(self, freq, nperiods, near2fars, decimation_factor): if self.fields is None: self.init_sim() - return self._add_fluxish_stuff(self.fields.add_dft_near2far, freq, near2fars, - decimation_factor, nperiods) + return self._add_fluxish_stuff( + self.fields.add_dft_near2far, freq, near2fars, decimation_factor, nperiods + ) def add_energy(self, *args, **kwargs): """ @@ -2666,7 +2988,7 @@ def add_energy(self, *args, **kwargs): args = fix_dft_args(args, 0) freq = args[0] energys = args[1:] - decimation_factor = kwargs.get('decimation_factor', 0) + decimation_factor = kwargs.get("decimation_factor", 0) en = DftEnergy(self._add_energy, [freq, energys, decimation_factor]) self.dft_objects.append(en) return en @@ -2674,14 +2996,19 @@ def add_energy(self, *args, **kwargs): def _add_energy(self, freq, energys, decimation_factor): if self.fields is None: self.init_sim() - return self._add_fluxish_stuff(self.fields.add_dft_energy, freq, energys, - decimation_factor) + return self._add_fluxish_stuff( + self.fields.add_dft_energy, freq, energys, decimation_factor + ) def _display_energy(self, name, func, energys): if energys: freqs = get_energy_freqs(energys[0]) if verbosity.meep > 0: - display_csv(self, "{}-energy".format(name), zip(freqs, *[func(f) for f in energys])) + display_csv( + self, + "{}-energy".format(name), + zip(freqs, *[func(f) for f in energys]), + ) def display_electric_energy(self, *energys): """ @@ -2691,7 +3018,7 @@ def display_electric_energy(self, *energys): All of the energy should be for the same `fcen`/`df`/`nfreq` or `freq`. The first column are the frequencies, and subsequent columns are the energy density spectra. """ - self._display_energy('electric', get_electric_energy, energys) + self._display_energy("electric", get_electric_energy, energys) def display_magnetic_energy(self, *energys): """ @@ -2701,7 +3028,7 @@ def display_magnetic_energy(self, *energys): All of the energy should be for the same `fcen`/`df`/`nfreq` or `freq`. The first column are the frequencies, and subsequent columns are the energy density spectra. """ - self._display_energy('magnetic', get_magnetic_energy, energys) + self._display_energy("magnetic", get_magnetic_energy, energys) def display_total_energy(self, *energys): """ @@ -2711,7 +3038,7 @@ def display_total_energy(self, *energys): be for the same `fcen`/`df`/`nfreq` or `freq`. The first column are the frequencies, and subsequent columns are the energy density spectra. """ - self._display_energy('total', get_total_energy, energys) + self._display_energy("total", get_total_energy, energys) def load_energy(self, fname, energy): """ @@ -2724,7 +3051,7 @@ def load_energy(self, fname, energy): """ if self.fields is None: self.init_sim() - energy.load_hdf5(self.fields, fname, '', self.get_filename_prefix()) + energy.load_hdf5(self.fields, fname, "", self.get_filename_prefix()) def save_energy(self, fname, energy): """ @@ -2734,7 +3061,7 @@ def save_energy(self, fname, energy): """ if self.fields is None: self.init_sim() - energy.save_hdf5(self.fields, fname, '', self.get_filename_prefix()) + energy.save_hdf5(self.fields, fname, "", self.get_filename_prefix()) def load_minus_energy(self, fname, energy): """ @@ -2755,7 +3082,10 @@ def get_farfield(self, near2far, x): $(E_r^1,E_\\phi^1,E_z^1,H_r^1,H_\\phi^1,H_z^1,E_r^2,E_\\phi^2,E_z^2,H_r^2,H_\\phi^2,H_z^2,...)$ in cylindrical coordinates for the frequencies 1,2,...,`nfreq`. """ - return mp._get_farfield(near2far.swigobj, py_v3_to_vec(self.dimensions, x, is_cylindrical=self.is_cylindrical)) + return mp._get_farfield( + near2far.swigobj, + py_v3_to_vec(self.dimensions, x, is_cylindrical=self.is_cylindrical), + ) def get_farfields(self, near2far, resolution, where=None, center=None, size=None): """ @@ -2782,15 +3112,17 @@ def get_farfields(self, near2far, resolution, where=None, center=None, size=None res_hy = complexarray(result[8], result[9]) res_hz = complexarray(result[10], result[11]) return { - 'Ex': res_ex, - 'Ey': res_ey, - 'Ez': res_ez, - 'Hx': res_hx, - 'Hy': res_hy, - 'Hz': res_hz, + "Ex": res_ex, + "Ey": res_ey, + "Ez": res_ez, + "Hx": res_hx, + "Hy": res_hy, + "Hz": res_hz, } - def output_farfields(self, near2far, fname, resolution, where=None, center=None, size=None): + def output_farfields( + self, near2far, fname, resolution, where=None, center=None, size=None + ): """ Given an HDF5 file name `fname` (does *not* include the `.h5` suffix), a `Volume` given by `where` (may be 0d, 1d, 2d, or 3d), and a `resolution` (in grid points / @@ -2821,7 +3153,7 @@ def load_near2far(self, fname, near2far): """ if self.fields is None: self.init_sim() - near2far.load_hdf5(self.fields, fname, '', self.get_filename_prefix()) + near2far.load_hdf5(self.fields, fname, "", self.get_filename_prefix()) def save_near2far(self, fname, near2far): """ @@ -2831,7 +3163,7 @@ def save_near2far(self, fname, near2far): """ if self.fields is None: self.init_sim() - near2far.save_hdf5(self.fields, fname, '', self.get_filename_prefix()) + near2far.save_hdf5(self.fields, fname, "", self.get_filename_prefix()) def load_minus_near2far(self, fname, near2far): """ @@ -2891,7 +3223,7 @@ def add_force(self, *args, **kwargs): args = fix_dft_args(args, 0) freq = args[0] forces = args[1:] - decimation_factor = kwargs.get('decimation_factor', 0) + decimation_factor = kwargs.get("decimation_factor", 0) force = DftForce(self._add_force, [freq, forces, decimation_factor]) self.dft_objects.append(force) return force @@ -2899,8 +3231,9 @@ def add_force(self, *args, **kwargs): def _add_force(self, freq, forces, decimation_factor): if self.fields is None: self.init_sim() - return self._add_fluxish_stuff(self.fields.add_dft_force, freq, forces, - decimation_factor) + return self._add_fluxish_stuff( + self.fields.add_dft_force, freq, forces, decimation_factor + ) def display_forces(self, *forces): """ @@ -2912,7 +3245,9 @@ def display_forces(self, *forces): """ force_freqs = get_force_freqs(forces[0]) if verbosity.meep > 0: - display_csv(self, 'force', zip(force_freqs, *[get_forces(f) for f in forces])) + display_csv( + self, "force", zip(force_freqs, *[get_forces(f) for f in forces]) + ) def load_force(self, fname, force): """ @@ -2925,7 +3260,7 @@ def load_force(self, fname, force): """ if self.fields is None: self.init_sim() - force.load_hdf5(self.fields, fname, '', self.get_filename_prefix()) + force.load_hdf5(self.fields, fname, "", self.get_filename_prefix()) def save_force(self, fname, force): """ @@ -2935,7 +3270,7 @@ def save_force(self, fname, force): """ if self.fields is None: self.init_sim() - force.save_hdf5(self.fields, fname, '', self.get_filename_prefix()) + force.save_hdf5(self.fields, fname, "", self.get_filename_prefix()) def load_minus_force(self, fname, force): """ @@ -2953,9 +3288,11 @@ def get_force_data(self, force): only useful for passing to `load_force_data` below and should be considered opaque. """ - return ForceData(offdiag1=self.get_dft_data(force.offdiag1), - offdiag2=self.get_dft_data(force.offdiag2), - diag=self.get_dft_data(force.diag)) + return ForceData( + offdiag1=self.get_dft_data(force.offdiag1), + offdiag2=self.get_dft_data(force.offdiag2), + diag=self.get_dft_data(force.diag), + ) def load_force_data(self, force, fdata): """ @@ -3001,7 +3338,7 @@ def add_flux(self, *args, **kwargs): args = fix_dft_args(args, 0) freq = args[0] fluxes = args[1:] - decimation_factor = kwargs.get('decimation_factor', 0) + decimation_factor = kwargs.get("decimation_factor", 0) flux = DftFlux(self._add_flux, [freq, fluxes, decimation_factor]) self.dft_objects.append(flux) return flux @@ -3009,8 +3346,9 @@ def add_flux(self, *args, **kwargs): def _add_flux(self, freq, fluxes, decimation_factor): if self.fields is None: self.init_sim() - return self._add_fluxish_stuff(self.fields.add_dft_flux, - freq, fluxes, decimation_factor) + return self._add_fluxish_stuff( + self.fields.add_dft_flux, freq, fluxes, decimation_factor + ) def add_mode_monitor(self, *args, **kwargs): """ @@ -3021,9 +3359,11 @@ def add_mode_monitor(self, *args, **kwargs): args = fix_dft_args(args, 0) freq = args[0] fluxes = args[1:] - decimation_factor = kwargs.get('decimation_factor', 0) + decimation_factor = kwargs.get("decimation_factor", 0) yee_grid = kwargs.get("yee_grid", False) - flux = DftFlux(self._add_mode_monitor, [freq, fluxes, yee_grid, decimation_factor]) + flux = DftFlux( + self._add_mode_monitor, [freq, fluxes, yee_grid, decimation_factor] + ) self.dft_objects.append(flux) return flux @@ -3032,15 +3372,26 @@ def _add_mode_monitor(self, freq, fluxes, yee_grid, decimation_factor): self.init_sim() if len(fluxes) != 1: - raise ValueError("add_mode_monitor expected just one ModeRegion. Got {}".format(len(fluxes))) + raise ValueError( + "add_mode_monitor expected just one ModeRegion. Got {}".format( + len(fluxes) + ) + ) region = fluxes[0] centered_grid = not yee_grid - v = mp.Volume(region.center, region.size, dims=self.dimensions, is_cylindrical=self.is_cylindrical) + v = mp.Volume( + region.center, + region.size, + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ) d0 = region.direction d = self.fields.normal_direction(v.swigobj) if d0 < 0 else d0 - return self.fields.add_mode_monitor(d, v.swigobj, freq, centered_grid, decimation_factor) + return self.fields.add_mode_monitor( + d, v.swigobj, freq, centered_grid, decimation_factor + ) def display_fluxes(self, *fluxes): """ @@ -3051,7 +3402,11 @@ def display_fluxes(self, *fluxes): subsequent columns are the flux spectra. """ if verbosity.meep > 0: - display_csv(self, 'flux', zip(get_flux_freqs(fluxes[0]), *[get_fluxes(f) for f in fluxes])) + display_csv( + self, + "flux", + zip(get_flux_freqs(fluxes[0]), *[get_fluxes(f) for f in fluxes]), + ) def load_flux(self, fname, flux): """ @@ -3065,7 +3420,7 @@ def load_flux(self, fname, flux): if self.fields is None: self.init_sim() - flux.load_hdf5(self.fields, fname, '', self.get_filename_prefix()) + flux.load_hdf5(self.fields, fname, "", self.get_filename_prefix()) load_mode = load_flux @@ -3078,7 +3433,7 @@ def save_flux(self, fname, flux): if self.fields is None: self.init_sim() - flux.save_hdf5(self.fields, fname, '', self.get_filename_prefix()) + flux.save_hdf5(self.fields, fname, "", self.get_filename_prefix()) save_mode = save_flux @@ -3139,7 +3494,7 @@ def flux_in_box(self, d, box=None, center=None, size=None): construct the appropriate volume for you. """ if self.fields is None: - raise RuntimeError('Fields must be initialized before using flux_in_box') + raise RuntimeError("Fields must be initialized before using flux_in_box") box = self._volume_from_kwargs(box, center, size) @@ -3158,7 +3513,9 @@ def electric_energy_in_box(self, box=None, center=None, size=None): volume. """ if self.fields is None: - raise RuntimeError('Fields must be initialized before using electric_energy_in_box') + raise RuntimeError( + "Fields must be initialized before using electric_energy_in_box" + ) box = self._volume_from_kwargs(box, center, size) @@ -3177,7 +3534,9 @@ def magnetic_energy_in_box(self, box=None, center=None, size=None): volume. """ if self.fields is None: - raise RuntimeError('Fields must be initialized before using magnetic_energy_in_box') + raise RuntimeError( + "Fields must be initialized before using magnetic_energy_in_box" + ) box = self._volume_from_kwargs(box, center, size) @@ -3196,7 +3555,9 @@ def field_energy_in_box(self, box=None, center=None, size=None): volume. """ if self.fields is None: - raise RuntimeError('Fields must be initialized before using field_energy_in_box') + raise RuntimeError( + "Fields must be initialized before using field_energy_in_box" + ) box = self._volume_from_kwargs(box, center, size) @@ -3228,7 +3589,9 @@ def modal_volume_in_box(self, box=None, center=None, size=None): via the functions: """ if self.fields is None: - raise RuntimeError('Fields must be initialized before using modal_volume_in_box') + raise RuntimeError( + "Fields must be initialized before using modal_volume_in_box" + ) try: box = self._volume_from_kwargs(box, center, size) @@ -3239,35 +3602,50 @@ def modal_volume_in_box(self, box=None, center=None, size=None): def solve_cw(self, tol=1e-8, maxiters=10000, L=2): if self.fields is None: - raise RuntimeError('Fields must be initialized before using solve_cw') + raise RuntimeError("Fields must be initialized before using solve_cw") self._evaluate_dft_objects() return self.fields.solve_cw(tol, maxiters, L) - def solve_eigfreq(self, tol=1e-7, maxiters=100, - guessfreq=None, cwtol=None, cwmaxiters=10000, L=10): + def solve_eigfreq( + self, tol=1e-7, maxiters=100, guessfreq=None, cwtol=None, cwmaxiters=10000, L=10 + ): if self.fields is None: - raise RuntimeError('Fields must be initialized before using solve_cw') + raise RuntimeError("Fields must be initialized before using solve_cw") if cwtol is None: - cwtol = tol * 1e-3 # solve CW problems much more accurately than eigenvalue tolerance + cwtol = ( + tol * 1e-3 + ) # solve CW problems much more accurately than eigenvalue tolerance self._evaluate_dft_objects() eigfreq = np.array(0, dtype=np.complex128) if guessfreq is None: self.fields.solve_cw(cwtol, cwmaxiters, L, eigfreq, tol, maxiters) else: - self.fields.solve_cw(cwtol, cwmaxiters, guessfreq, L, eigfreq, tol, maxiters) + self.fields.solve_cw( + cwtol, cwmaxiters, guessfreq, L, eigfreq, tol, maxiters + ) return eigfreq.item() - def _add_fluxish_stuff(self, add_dft_stuff, freq, stufflist, decimation_factor, *args): + def _add_fluxish_stuff( + self, add_dft_stuff, freq, stufflist, decimation_factor, *args + ): vol_list = None for s in stufflist: - v = Volume(center=s.center, size=s.size, dims=self.dimensions, - is_cylindrical=self.is_cylindrical) + v = Volume( + center=s.center, + size=s.size, + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ) d0 = s.direction d = self.fields.normal_direction(v.swigobj) if d0 < 0 else d0 c = mp.direction_component(mp.Sx, d) - v2 = Volume(center=s.center, size=s.size, dims=self.dimensions, - is_cylindrical=self.is_cylindrical).swigobj + v2 = Volume( + center=s.center, + size=s.size, + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ).swigobj vol_list = mp.make_volume_list(v2, c, s.weight, vol_list) stuff = add_dft_stuff(vol_list, freq, decimation_factor, *args) vol_list.__swig_destroy__(vol_list) @@ -3276,26 +3654,46 @@ def _add_fluxish_stuff(self, add_dft_stuff, freq, stufflist, decimation_factor, def output_component(self, c, h5file=None, frequency=0): if self.fields is None: - raise RuntimeError("Fields must be initialized before calling output_component") + raise RuntimeError( + "Fields must be initialized before calling output_component" + ) - vol = self.fields.total_volume() if self.output_volume is None else self.output_volume + vol = ( + self.fields.total_volume() + if self.output_volume is None + else self.output_volume + ) h5 = self.output_append_h5 if h5file is None else h5file append = h5file is None and self.output_append_h5 is not None - self.fields.output_hdf5(c, vol, h5, append, self.output_single_precision,self.get_filename_prefix(), frequency) + self.fields.output_hdf5( + c, + vol, + h5, + append, + self.output_single_precision, + self.get_filename_prefix(), + frequency, + ) if h5file is None: - nm = self.fields.h5file_name(mp.component_name(c), self.get_filename_prefix(), True) + nm = self.fields.h5file_name( + mp.component_name(c), self.get_filename_prefix(), True + ) if c == mp.Dielectric: self.last_eps_filename = nm self.output_h5_hook(nm) def output_components(self, fname, *components): if self.fields is None: - raise RuntimeError("Fields must be initialized before calling output_component") + raise RuntimeError( + "Fields must be initialized before calling output_component" + ) if self.output_append_h5 is None: - f = self.fields.open_h5file(fname, mp.h5file.WRITE, self.get_filename_prefix(), True) + f = self.fields.open_h5file( + fname, mp.h5file.WRITE, self.get_filename_prefix(), True + ) else: f = None @@ -3305,14 +3703,26 @@ def output_components(self, fname, *components): f.prevent_deadlock() if self.output_append_h5 is None: - self.output_h5_hook(self.fields.h5file_name(fname, self.get_filename_prefix(), True)) + self.output_h5_hook( + self.fields.h5file_name(fname, self.get_filename_prefix(), True) + ) def h5topng(self, rm_h5, option, *step_funcs): opts = "h5topng {}".format(option) - cmd = re.sub(r'\$EPS', self.last_eps_filename, opts) + cmd = re.sub(r"\$EPS", self.last_eps_filename, opts) return convert_h5(rm_h5, cmd, *step_funcs) - def get_array(self, component=None, vol=None, center=None, size=None, cmplx=None, arr=None, frequency=0, snap=False): + def get_array( + self, + component=None, + vol=None, + center=None, + size=None, + cmplx=None, + arr=None, + frequency=0, + snap=False, + ): """ Takes as input a subregion of the cell and the field/material component. The method returns a NumPy array containing values of the field/material at the @@ -3387,23 +3797,31 @@ def get_array(self, component=None, vol=None, center=None, size=None, cmplx=None else: v = self._volume_from_kwargs(vol, center, size) - _, dirs = mp._get_array_slice_dimensions(self.fields, v, dim_sizes, not snap, snap) + _, dirs = mp._get_array_slice_dimensions( + self.fields, v, dim_sizes, not snap, snap + ) dims = [s for s in dim_sizes if s != 0] if cmplx is None: - cmplx = frequency != 0 or (component < mp.Dielectric and not self.fields.is_real) + cmplx = frequency != 0 or ( + component < mp.Dielectric and not self.fields.is_real + ) if arr is not None: if cmplx and not np.iscomplexobj(arr): - raise ValueError("Requested a complex slice, but provided array of type {}.".format(arr.dtype)) + raise ValueError( + "Requested a complex slice, but provided array of type {}.".format( + arr.dtype + ) + ) for a, b in zip(arr.shape, dims): if a != b: fmt = "Expected dimensions {}, but got {}" raise ValueError(fmt.format(dims, arr.shape)) - arr = np.require(arr, requirements=['C', 'W']) + arr = np.require(arr, requirements=["C", "W"]) else: if mp.is_single_precision(): @@ -3435,21 +3853,27 @@ def get_dft_array(self, dft_obj, component, num_freq): `nfreq` parameter to `add_dft_fields`, `add_flux`, etc. """ if not self.dft_objects: - raise RuntimeError('DFT monitor dft_obj must be initialized before calling get_dft_array') + raise RuntimeError( + "DFT monitor dft_obj must be initialized before calling get_dft_array" + ) - if hasattr(dft_obj, 'swigobj'): + if hasattr(dft_obj, "swigobj"): dft_swigobj = dft_obj.swigobj else: dft_swigobj = dft_obj if type(dft_swigobj) is mp.dft_fields: - return mp.get_dft_fields_array(self.fields, dft_swigobj, component, num_freq) + return mp.get_dft_fields_array( + self.fields, dft_swigobj, component, num_freq + ) elif type(dft_swigobj) is mp.dft_flux: return mp.get_dft_flux_array(self.fields, dft_swigobj, component, num_freq) elif type(dft_swigobj) is mp.dft_force: return mp.get_dft_force_array(self.fields, dft_swigobj, component, num_freq) elif type(dft_swigobj) is mp.dft_near2far: - return mp.get_dft_near2far_array(self.fields, dft_swigobj, component, num_freq) + return mp.get_dft_near2far_array( + self.fields, dft_swigobj, component, num_freq + ) else: raise ValueError("Invalid type of dft object: {}".format(dft_swigobj)) @@ -3467,12 +3891,15 @@ def get_source(self, component, vol=None, center=None, size=None): dim_sizes = np.zeros(3, dtype=np.uintp) mp._get_array_slice_dimensions(self.fields, v, dim_sizes, True, False) dims = [s for s in dim_sizes if s != 0] - arr = np.zeros(dims, dtype=np.complex64 if mp.is_single_precision() else np.complex128) + arr = np.zeros( + dims, dtype=np.complex64 if mp.is_single_precision() else np.complex128 + ) self.fields.get_source_slice(v, component, arr) return arr - def get_array_metadata(self, vol=None, center=None, size=None, dft_cell=None, - return_pw=False): + def get_array_metadata( + self, vol=None, center=None, size=None, dft_cell=None, return_pw=False + ): """ This routine provides geometric information useful for interpreting the arrays returned by `get_array` or `get_dft_array` for the spatial region defined by `vol` @@ -3518,13 +3945,15 @@ def get_array_metadata(self, vol=None, center=None, size=None, dft_cell=None, offset, tics = 0, [] for n in range(3): N = int(xyzw_vector[offset]) - tics.append( xyzw_vector[offset+1:offset+1+N] ) - offset += 1+N - wshape = [len(t) for t in tics if len(t)>1] + tics.append(xyzw_vector[offset + 1 : offset + 1 + N]) + offset += 1 + N + wshape = [len(t) for t in tics if len(t) > 1] weights = np.reshape(xyzw_vector[offset:], wshape) if return_pw: - points=[ mp.Vector3(x,y,z) for x in tics[0] for y in tics[1] for z in tics[2] ] - return points,weights + points = [ + mp.Vector3(x, y, z) for x in tics[0] for y in tics[1] for z in tics[2] + ] + return points, weights return tuple(tics) + (weights,) def get_array_slice_dimensions(self, component, vol=None, center=None, size=None): @@ -3543,14 +3972,25 @@ def get_array_slice_dimensions(self, component, vol=None, center=None, size=None v = self._volume_from_kwargs(vol, center, size) dim_sizes = np.zeros(3, dtype=np.uintp) corners = [] - _,_ = mp._get_array_slice_dimensions(self.fields, v, dim_sizes, False, False, component, corners) - dim_sizes[dim_sizes==0] = 1 + _, _ = mp._get_array_slice_dimensions( + self.fields, v, dim_sizes, False, False, component, corners + ) + dim_sizes[dim_sizes == 0] = 1 min_corner = corners[0] max_corner = corners[1] return dim_sizes, min_corner, max_corner - def get_eigenmode_coefficients(self, flux, bands, eig_parity=mp.NO_PARITY, eig_vol=None, - eig_resolution=0, eig_tolerance=1e-12, kpoint_func=None, direction=mp.AUTOMATIC): + def get_eigenmode_coefficients( + self, + flux, + bands, + eig_parity=mp.NO_PARITY, + eig_vol=None, + eig_resolution=0, + eig_tolerance=1e-12, + kpoint_func=None, + direction=mp.AUTOMATIC, + ): """ Given a flux object and list of band indices `bands` or `DiffractedPlanewave`, return a `namedtuple` with the following fields: @@ -3566,7 +4006,9 @@ def get_eigenmode_coefficients(self, flux, bands, eig_parity=mp.NO_PARITY, eig_v calculations. """ if self.fields is None: - raise ValueError("Fields must be initialized before calling get_eigenmode_coefficients") + raise ValueError( + "Fields must be initialized before calling get_eigenmode_coefficients" + ) if eig_vol is None: eig_vol = flux.where else: @@ -3575,7 +4017,7 @@ def get_eigenmode_coefficients(self, flux, bands, eig_parity=mp.NO_PARITY, eig_v direction = flux.normal_direction try: - bands_list_range = isinstance(bands, (list,range)) + bands_list_range = isinstance(bands, (list, range)) except TypeError: bands_list_range = isinstance(bands, list) @@ -3597,7 +4039,7 @@ def get_eigenmode_coefficients(self, flux, bands, eig_parity=mp.NO_PARITY, eig_v vgrp, kpoint_func, cscale, - direction + direction, ) elif isinstance(bands, DiffractedPlanewave): num_bands = 1 @@ -3618,15 +4060,34 @@ def get_eigenmode_coefficients(self, flux, bands, eig_parity=mp.NO_PARITY, eig_v vgrp, kpoint_func, cscale, - direction + direction, ) else: - raise TypeError("get_eigenmode_coefficients: bands must be either a list or DiffractedPlanewave object") + raise TypeError( + "get_eigenmode_coefficients: bands must be either a list or DiffractedPlanewave object" + ) - return EigCoeffsResult(np.reshape(coeffs, (num_bands, flux.freq.size(), 2)), vgrp, kpoints, kdom, cscale) + return EigCoeffsResult( + np.reshape(coeffs, (num_bands, flux.freq.size(), 2)), + vgrp, + kpoints, + kdom, + cscale, + ) - def get_eigenmode(self, frequency, direction, where, band_num, kpoint, eig_vol=None, match_frequency=True, - parity=mp.NO_PARITY, resolution=0, eigensolver_tol=1e-12): + def get_eigenmode( + self, + frequency, + direction, + where, + band_num, + kpoint, + eig_vol=None, + match_frequency=True, + parity=mp.NO_PARITY, + resolution=0, + eigensolver_tol=1e-12, + ): """ The parameters of this routine are the same as that of `get_eigenmode_coefficients` or `EigenModeSource`, but this function returns an @@ -3659,12 +4120,30 @@ def get_eigenmode(self, frequency, direction, where, band_num, kpoint, eig_vol=N swig_kpoint = mp.vec(kpoint.x, kpoint.y, kpoint.z) kdom = np.zeros(3) - emdata = mp._get_eigenmode(self.fields, frequency, direction, where, eig_vol, band_num, swig_kpoint, - match_frequency, parity, resolution, eigensolver_tol, kdom) + emdata = mp._get_eigenmode( + self.fields, + frequency, + direction, + where, + eig_vol, + band_num, + swig_kpoint, + match_frequency, + parity, + resolution, + eigensolver_tol, + kdom, + ) Gk = mp._get_eigenmode_Gk(emdata) - return EigenmodeData(emdata.band_num, emdata.frequency, emdata.group_velocity, Gk, - emdata, mp.Vector3(kdom[0], kdom[1], kdom[2])) + return EigenmodeData( + emdata.band_num, + emdata.frequency, + emdata.group_velocity, + Gk, + emdata, + mp.Vector3(kdom[0], kdom[1], kdom[2]), + ) def output_field_function(self, name, cs, func, real_only=False, h5file=None): """ @@ -3675,16 +4154,28 @@ def output_field_function(self, name, cs, func, real_only=False, h5file=None): `real_only` is True, only outputs the real part of `func`. """ if self.fields is None: - raise RuntimeError("Fields must be initialized before calling output_field_function") + raise RuntimeError( + "Fields must be initialized before calling output_field_function" + ) ov = self.output_volume if self.output_volume else self.fields.total_volume() h5 = self.output_append_h5 if h5file is None else h5file append = h5file is None and self.output_append_h5 is not None - self.fields.output_hdf5(name, [cs, func], ov, h5, append, self.output_single_precision, - self.get_filename_prefix(), real_only) + self.fields.output_hdf5( + name, + [cs, func], + ov, + h5, + append, + self.output_single_precision, + self.get_filename_prefix(), + real_only, + ) if h5file is None: - self.output_h5_hook(self.fields.h5file_name(name, self.get_filename_prefix(), True)) + self.output_h5_hook( + self.fields.h5file_name(name, self.get_filename_prefix(), True) + ) def _get_field_function_volume(self, where=None, center=None, size=None): try: @@ -3714,7 +4205,9 @@ def integrate_field_function(self, cs, func, where=None, center=None, size=None) where = self._get_field_function_volume(where, center, size) return self.fields.integrate([cs, func], where) - def integrate2_field_function(self, fields2, cs1, cs2, func, where=None, center=None, size=None): + def integrate2_field_function( + self, fields2, cs1, cs2, func, where=None, center=None, size=None + ): """ Similar to `integrate_field_function`, but takes additional parameters `fields2` and `cs2`. `fields2` is a `meep::fields*` object similar to the global `fields` @@ -3776,7 +4269,9 @@ def change_k_point(self, k): self.k_point = k if self.fields: - needs_complex_fields = not (not self.k_point or self.k_point == mp.Vector3()) + needs_complex_fields = not ( + not self.k_point or self.k_point == mp.Vector3() + ) if needs_complex_fields and self.fields.is_real: self.fields = None @@ -3784,7 +4279,9 @@ def change_k_point(self, k): self.init_sim() else: if self.k_point: - self.fields.use_bloch(py_v3_to_vec(self.dimensions, self.k_point, self.is_cylindrical)) + self.fields.use_bloch( + py_v3_to_vec(self.dimensions, self.k_point, self.is_cylindrical) + ) def change_sources(self, new_sources): """ @@ -3822,13 +4319,13 @@ def restart_fields(self): else: self._is_initialized = False self.init_sim() - + def clear_dft_monitors(self): """ Remove all of the dft monitors from the simulation. """ for m in self.dft_objects: - if not (isinstance(m,DftFields) and (m.chunks) and (m.chunks.persist)): + if not (isinstance(m, DftFields) and (m.chunks) and (m.chunks.persist)): m.remove() self.fields.clear_dft_monitors() @@ -3855,8 +4352,8 @@ def run(self, *step_funcs, **kwargs): returns `True`. Like `until` above, `until_after_sources` can take a list of stopping conditions. """ - until = kwargs.pop('until', None) - until_after_sources = kwargs.pop('until_after_sources', None) + until = kwargs.pop("until", None) + until_after_sources = kwargs.pop("until_after_sources", None) if self.fields is None: self.init_sim() @@ -3931,26 +4428,26 @@ def output_times(self, fname): for each column and one row per process. """ if self.fields: - if not fname.endswith('.csv'): - fname += '.csv' + if not fname.endswith(".csv"): + fname += ".csv" self.fields.output_times(fname) - def get_epsilon(self,frequency=0,snap=False): - return self.get_array(component=mp.Dielectric,frequency=frequency,snap=snap) + def get_epsilon(self, frequency=0, snap=False): + return self.get_array(component=mp.Dielectric, frequency=frequency, snap=snap) - def get_mu(self,frequency=0,snap=False): - return self.get_array(component=mp.Permeability,frequency=frequency,snap=snap) + def get_mu(self, frequency=0, snap=False): + return self.get_array(component=mp.Permeability, frequency=frequency, snap=snap) - def get_hpwr(self,snap=False): - return self.get_array(component=mp.H_EnergyDensity,snap=snap) + def get_hpwr(self, snap=False): + return self.get_array(component=mp.H_EnergyDensity, snap=snap) - def get_dpwr(self,snap=False): - return self.get_array(component=mp.D_EnergyDensity,snap=snap) + def get_dpwr(self, snap=False): + return self.get_array(component=mp.D_EnergyDensity, snap=snap) - def get_tot_pwr(self,snap=False): - return self.get_array(component=mp.EnergyDensity,snap=snap) + def get_tot_pwr(self, snap=False): + return self.get_array(component=mp.EnergyDensity, snap=snap) - def get_hfield(self,snap=False): + def get_hfield(self, snap=False): if self.is_cylindrical: r = self.get_array(mp.Hr, cmplx=not self.fields.is_real, snap=snap) p = self.get_array(mp.Hp, cmplx=not self.fields.is_real, snap=snap) @@ -3961,22 +4458,22 @@ def get_hfield(self,snap=False): z = self.get_array(mp.Hz, cmplx=not self.fields.is_real, snap=snap) return np.stack([x, y, z], axis=-1) - def get_hfield_x(self,snap=False): + def get_hfield_x(self, snap=False): return self.get_array(mp.Hx, cmplx=not self.fields.is_real, snap=snap) - def get_hfield_y(self,snap=False): + def get_hfield_y(self, snap=False): return self.get_array(mp.Hy, cmplx=not self.fields.is_real, snap=snap) - def get_hfield_z(self,snap=False): + def get_hfield_z(self, snap=False): return self.get_array(mp.Hz, cmplx=not self.fields.is_real, snap=snap) - def get_hfield_r(self,snap=False): + def get_hfield_r(self, snap=False): return self.get_array(mp.Hr, cmplx=not self.fields.is_real, snap=snap) - def get_hfield_p(self,snap=False): + def get_hfield_p(self, snap=False): return self.get_array(mp.Hp, cmplx=not self.fields.is_real, snap=snap) - def get_bfield(self,snap=False): + def get_bfield(self, snap=False): if self.is_cylindrical: r = self.get_array(mp.Br, cmplx=not self.fields.is_real, snap=snap) p = self.get_array(mp.Bp, cmplx=not self.fields.is_real, snap=snap) @@ -3987,22 +4484,22 @@ def get_bfield(self,snap=False): z = self.get_array(mp.Bz, cmplx=not self.fields.is_real, snap=snap) return np.stack([x, y, z], axis=-1) - def get_bfield_x(self,snap=False): + def get_bfield_x(self, snap=False): return self.get_array(mp.Bx, cmplx=not self.fields.is_real, snap=snap) - def get_bfield_y(self,snap=False): + def get_bfield_y(self, snap=False): return self.get_array(mp.By, cmplx=not self.fields.is_real, snap=snap) - def get_bfield_z(self,snap=False): + def get_bfield_z(self, snap=False): return self.get_array(mp.Bz, cmplx=not self.fields.is_real, snap=snap) - def get_bfield_r(self,snap=False): + def get_bfield_r(self, snap=False): return self.get_array(mp.Br, cmplx=not self.fields.is_real, snap=snap) - def get_bfield_p(self,snap=False): + def get_bfield_p(self, snap=False): return self.get_array(mp.Bp, cmplx=not self.fields.is_real, snap=snap) - def get_efield(self,snap=False): + def get_efield(self, snap=False): if self.is_cylindrical: r = self.get_array(mp.Er, cmplx=not self.fields.is_real, snap=snap) p = self.get_array(mp.Ep, cmplx=not self.fields.is_real, snap=snap) @@ -4013,22 +4510,22 @@ def get_efield(self,snap=False): z = self.get_array(mp.Ez, cmplx=not self.fields.is_real, snap=snap) return np.stack([x, y, z], axis=-1) - def get_efield_x(self,snap=False): + def get_efield_x(self, snap=False): return self.get_array(mp.Ex, cmplx=not self.fields.is_real, snap=snap) - def get_efield_y(self,snap=False): + def get_efield_y(self, snap=False): return self.get_array(mp.Ey, cmplx=not self.fields.is_real, snap=snap) - def get_efield_z(self,snap=False): + def get_efield_z(self, snap=False): return self.get_array(mp.Ez, cmplx=not self.fields.is_real, snap=snap) - def get_efield_r(self,snap=False): + def get_efield_r(self, snap=False): return self.get_array(mp.Er, cmplx=not self.fields.is_real, snap=snap) - def get_efield_p(self,snap=False): + def get_efield_p(self, snap=False): return self.get_array(mp.Ep, cmplx=not self.fields.is_real, snap=snap) - def get_dfield(self,snap=False): + def get_dfield(self, snap=False): if self.is_cylindrical: r = self.get_array(mp.Dr, cmplx=not self.fields.is_real, snap=snap) p = self.get_array(mp.Dp, cmplx=not self.fields.is_real, snap=snap) @@ -4039,22 +4536,22 @@ def get_dfield(self,snap=False): z = self.get_array(mp.Dz, cmplx=not self.fields.is_real, snap=snap) return np.stack([x, y, z], axis=-1) - def get_dfield_x(self,snap=False): + def get_dfield_x(self, snap=False): return self.get_array(mp.Dx, cmplx=not self.fields.is_real, snap=snap) - def get_dfield_y(self,snap=False): + def get_dfield_y(self, snap=False): return self.get_array(mp.Dy, cmplx=not self.fields.is_real, snap=snap) - def get_dfield_z(self,snap=False): + def get_dfield_z(self, snap=False): return self.get_array(mp.Dz, cmplx=not self.fields.is_real, snap=snap) - def get_dfield_r(self,snap=False): + def get_dfield_r(self, snap=False): return self.get_array(mp.Dr, cmplx=not self.fields.is_real, snap=snap) - def get_dfield_p(self,snap=False): + def get_dfield_p(self, snap=False): return self.get_array(mp.Dp, cmplx=not self.fields.is_real, snap=snap) - def get_sfield(self,snap=False): + def get_sfield(self, snap=False): if self.is_cylindrical: r = self.get_array(mp.Sr, cmplx=not self.fields.is_real, snap=snap) p = self.get_array(mp.Sp, cmplx=not self.fields.is_real, snap=snap) @@ -4065,29 +4562,39 @@ def get_sfield(self,snap=False): z = self.get_array(mp.Sz, cmplx=not self.fields.is_real, snap=snap) return np.stack([x, y, z], axis=-1) - def get_sfield_x(self,snap=False): + def get_sfield_x(self, snap=False): return self.get_array(mp.Sx, cmplx=not self.fields.is_real, snap=snap) - def get_sfield_y(self,snap=False): + def get_sfield_y(self, snap=False): return self.get_array(mp.Sy, cmplx=not self.fields.is_real, snap=snap) - def get_sfield_z(self,snap=False): + def get_sfield_z(self, snap=False): return self.get_array(mp.Sz, cmplx=not self.fields.is_real, snap=snap) - def get_sfield_r(self,snap=False): + def get_sfield_r(self, snap=False): return self.get_array(mp.Sr, cmplx=not self.fields.is_real, snap=snap) - def get_sfield_p(self,snap=False): + def get_sfield_p(self, snap=False): return self.get_array(mp.Sp, cmplx=not self.fields.is_real, snap=snap) - - def plot2D(self, ax=None, output_plane=None, fields=None, labels=False, - eps_parameters=None, boundary_parameters=None, - source_parameters=None, monitor_parameters=None, - field_parameters=None, frequency=None, - plot_eps_flag=True, plot_sources_flag=True, - plot_monitors_flag=True, plot_boundaries_flag=True, - **kwargs): + def plot2D( + self, + ax=None, + output_plane=None, + fields=None, + labels=False, + eps_parameters=None, + boundary_parameters=None, + source_parameters=None, + monitor_parameters=None, + field_parameters=None, + frequency=None, + plot_eps_flag=True, + plot_sources_flag=True, + plot_monitors_flag=True, + plot_boundaries_flag=True, + **kwargs, + ): """ Plots a 2D cross section of the simulation domain using `matplotlib`. The plot includes the geometry, boundary layers, sources, and monitors. Fields can also be @@ -4176,22 +4683,24 @@ def plot2D(self, ax=None, output_plane=None, fields=None, labels=False, - `post_process=np.real`: post processing function to apply to fields (must be a function object) """ - return vis.plot2D(self, - ax=ax, - output_plane=output_plane, - fields=fields, - labels=labels, - eps_parameters=eps_parameters, - boundary_parameters=boundary_parameters, - source_parameters=source_parameters, - monitor_parameters=monitor_parameters, - field_parameters=field_parameters, - frequency=frequency, - plot_eps_flag=plot_eps_flag, - plot_sources_flag=plot_sources_flag, - plot_monitors_flag=plot_monitors_flag, - plot_boundaries_flag=plot_boundaries_flag, - **kwargs) + return vis.plot2D( + self, + ax=ax, + output_plane=output_plane, + fields=fields, + labels=labels, + eps_parameters=eps_parameters, + boundary_parameters=boundary_parameters, + source_parameters=source_parameters, + monitor_parameters=monitor_parameters, + field_parameters=field_parameters, + frequency=frequency, + plot_eps_flag=plot_eps_flag, + plot_sources_flag=plot_sources_flag, + plot_monitors_flag=plot_monitors_flag, + plot_boundaries_flag=plot_boundaries_flag, + **kwargs, + ) def plot_fields(self, **kwargs): return vis.plot_fields(self, **kwargs) @@ -4253,20 +4762,26 @@ def _create_boundary_region_from_boundary_layers(boundary_layers, gv): loop_stop_bi = mp.Low while b != loop_stop_bi: - br += mp.boundary_region(*(boundary_region_args + [layer.direction, b])) + br += mp.boundary_region( + *(boundary_region_args + [layer.direction, b]) + ) b = (b + 1) % 2 loop_stop_bi = mp.High else: - br += mp.boundary_region(*(boundary_region_args + [layer.direction, layer.side])) + br += mp.boundary_region( + *(boundary_region_args + [layer.direction, layer.side]) + ) return br # Private step functions + def _combine_step_funcs(*step_funcs): def _combine(sim, todo): for func in step_funcs: _eval_step_func(sim, func, todo) + return _combine @@ -4274,9 +4789,11 @@ def _eval_step_func(sim, func, todo): num_args = get_num_args(func) if num_args != 1 and num_args != 2: - raise ValueError("Step function '{}'' requires 1 or 2 arguments".format(func.__name__)) + raise ValueError( + "Step function '{}'' requires 1 or 2 arguments".format(func.__name__) + ) elif num_args == 1: - if todo == 'step': + if todo == "step": func(sim) elif num_args == 2: func(sim, todo) @@ -4284,24 +4801,28 @@ def _eval_step_func(sim, func, todo): def _when_true_funcs(cond, *step_funcs): def _true(sim, todo): - if todo == 'finish' or cond(sim): + if todo == "finish" or cond(sim): for f in step_funcs: _eval_step_func(sim, f, todo) + return _true # Public step functions + def after_sources(*step_funcs): """ Given zero or more step functions, evaluates them only for times after all of the sources have turned off. """ + def _after_sources(sim, todo): time = sim.fields.last_source_time() if sim.round_time() >= time: for func in step_funcs: _eval_step_func(sim, func, todo) + return _after_sources @@ -4310,11 +4831,13 @@ def after_sources_and_time(t, *step_funcs): Given zero or more step functions, evaluates them only for times after all of the sources have turned off, plus an additional $T$ time units have elapsed. """ + def _after_s_and_t(sim, todo): time = sim.fields.last_source_time() + t - sim.round_time() if sim.round_time() >= time: for func in step_funcs: _eval_step_func(sim, func, todo) + return _after_s_and_t @@ -4323,8 +4846,10 @@ def after_time(t, *step_funcs): Given zero or more step functions, evaluates them only for times after a $T$ time units have elapsed from the start of the run. """ + def _after_t(sim): return sim.round_time() >= t + return _when_true_funcs(_after_t, *step_funcs) @@ -4333,13 +4858,14 @@ def at_beginning(*step_funcs): Given zero or more step functions, evaluates them only once, at the beginning of the run. """ - closure = {'done': False} + closure = {"done": False} def _beg(sim, todo): - if not closure['done']: + if not closure["done"]: for f in step_funcs: _eval_step_func(sim, f, todo) - closure['done'] = True + closure["done"] = True + return _beg @@ -4347,12 +4873,14 @@ def at_end(*step_funcs): """ Given zero or more step functions, evaluates them only once, at the end of the run. """ + def _end(sim, todo): - if todo == 'finish': + if todo == "finish": for func in step_funcs: - _eval_step_func(sim, func, 'step') + _eval_step_func(sim, func, "step") for func in step_funcs: - _eval_step_func(sim, func, 'finish') + _eval_step_func(sim, func, "finish") + return _end @@ -4361,14 +4889,15 @@ def at_every(dt, *step_funcs): Given zero or more step functions, evaluates them at every time interval of $dT$ units (rounded up to the next time step). """ - closure = {'tlast': 0.0} + closure = {"tlast": 0.0} def _every(sim, todo): t = sim.round_time() - if todo == 'finish' or t >= closure['tlast'] + dt + (-0.5 * sim.fields.dt): + if todo == "finish" or t >= closure["tlast"] + dt + (-0.5 * sim.fields.dt): for func in step_funcs: _eval_step_func(sim, func, todo) - closure['tlast'] = t + closure["tlast"] = t + return _every @@ -4377,13 +4906,14 @@ def at_time(t, *step_funcs): Given zero or more step functions, evaluates them only once, after a $T$ time units have elapsed from the start of the run. """ - closure = {'done': False} + closure = {"done": False} def _at_time(sim, todo): - if not closure['done'] or todo == 'finish': + if not closure["done"] or todo == "finish": for f in step_funcs: _eval_step_func(sim, f, todo) - closure['done'] = closure['done'] or todo == 'step' + closure["done"] = closure["done"] or todo == "step" + return after_time(t, _at_time) @@ -4392,8 +4922,10 @@ def before_time(t, *step_funcs): Given zero or more step functions, evaluates them only for times before a $T$ time units have elapsed from the start of the run. """ + def _before_t(sim): return sim.round_time() < t + return _when_true_funcs(_before_t, *step_funcs) @@ -4402,17 +4934,18 @@ def during_sources(*step_funcs): Given zero or more step functions, evaluates them only for times *before* all of the sources have turned off. """ - closure = {'finished': False} + closure = {"finished": False} def _during_sources(sim, todo): time = sim.fields.last_source_time() if sim.round_time() < time: for func in step_funcs: - _eval_step_func(sim, func, 'step') - elif closure['finished'] is False: + _eval_step_func(sim, func, "step") + elif closure["finished"] is False: for func in step_funcs: - _eval_step_func(sim, func, 'finish') - closure['finished'] = True + _eval_step_func(sim, func, "finish") + closure["finished"] = True + return _during_sources @@ -4422,7 +4955,7 @@ def in_volume(v, *step_funcs): output a subset (or a superset) of the cell, corresponding to the `meep::volume* v` (created by the `Volume` function). """ - closure = {'cur_eps': ''} + closure = {"cur_eps": ""} def _in_volume(sim, todo): v_save = sim.output_volume @@ -4430,15 +4963,16 @@ def _in_volume(sim, todo): sim.output_volume = sim._fit_volume_to_simulation(v).swigobj - if closure['cur_eps']: - sim.last_eps_filename = closure['cur_eps'] + if closure["cur_eps"]: + sim.last_eps_filename = closure["cur_eps"] for func in step_funcs: _eval_step_func(sim, func, todo) - closure['cur_eps'] = sim.last_eps_filename + closure["cur_eps"] = sim.last_eps_filename sim.output_volume = v_save if eps_save: sim.last_eps_filename = eps_save + return _in_volume @@ -4458,21 +4992,24 @@ def to_appended(fname, *step_funcs): an `.h5` suffix and the current filename prefix). They append by adding an *extra dimension* to their datasets, corresponding to time. """ - closure = {'h5': None} + closure = {"h5": None} def _to_appended(sim, todo): - if closure['h5'] is None: - closure['h5'] = sim.fields.open_h5file(fname, mp.h5file.WRITE, sim.get_filename_prefix()) + if closure["h5"] is None: + closure["h5"] = sim.fields.open_h5file( + fname, mp.h5file.WRITE, sim.get_filename_prefix() + ) h5save = sim.output_append_h5 - sim.output_append_h5 = closure['h5'] + sim.output_append_h5 = closure["h5"] for func in step_funcs: _eval_step_func(sim, func, todo) - if todo == 'finish': - closure['h5'] = None + if todo == "finish": + closure["h5"] = None sim.output_h5_hook(sim.fields.h5file_name(fname, sim.get_filename_prefix())) sim.output_append_h5 = h5save + return _to_appended @@ -4496,26 +5033,34 @@ def stop_when_fields_decayed(dt=None, c=None, pt=None, decay_by=None): raise ValueError("dt, c, pt, and decay_by are all required.") closure = { - 'max_abs': 0, - 'cur_max': 0, - 't0': 0, + "max_abs": 0, + "cur_max": 0, + "t0": 0, } def _stop(sim): fabs = abs(sim.get_field_point(c, pt)) * abs(sim.get_field_point(c, pt)) - closure['cur_max'] = max(closure['cur_max'], fabs) + closure["cur_max"] = max(closure["cur_max"], fabs) - if sim.round_time() <= dt + closure['t0']: + if sim.round_time() <= dt + closure["t0"]: return False else: - old_cur = closure['cur_max'] - closure['cur_max'] = 0 - closure['t0'] = sim.round_time() - closure['max_abs'] = max(closure['max_abs'], old_cur) - if closure['max_abs'] != 0 and verbosity.meep > 0: + old_cur = closure["cur_max"] + closure["cur_max"] = 0 + closure["t0"] = sim.round_time() + closure["max_abs"] = max(closure["max_abs"], old_cur) + if closure["max_abs"] != 0 and verbosity.meep > 0: fmt = "field decay(t = {}): {} / {} = {}" - print(fmt.format(sim.meep_time(), old_cur, closure['max_abs'], old_cur / closure['max_abs'])) - return old_cur <= closure['max_abs'] * decay_by + print( + fmt.format( + sim.meep_time(), + old_cur, + closure["max_abs"], + old_cur / closure["max_abs"], + ) + ) + return old_cur <= closure["max_abs"] * decay_by + return _stop @@ -4536,22 +5081,30 @@ def stop_when_energy_decayed(dt=None, decay_by=None): raise ValueError("dt and decay_by are all required.") closure = { - 'max_abs': 0, - 't0': 0, + "max_abs": 0, + "t0": 0, } def _stop(sim): - if sim.round_time() <= dt + closure['t0']: + if sim.round_time() <= dt + closure["t0"]: return False else: cell_volume = mp.Volume(center=sim.geometry_center, size=sim.cell_size) cur_abs = abs(sim.field_energy_in_box(box=cell_volume)) - closure['max_abs'] = max(closure['max_abs'], cur_abs) - closure['t0'] = sim.round_time() - if closure['max_abs'] != 0 and verbosity.meep > 0: + closure["max_abs"] = max(closure["max_abs"], cur_abs) + closure["t0"] = sim.round_time() + if closure["max_abs"] != 0 and verbosity.meep > 0: fmt = "energy decay(t = {}): {} / {} = {}" - print(fmt.format(sim.meep_time(), cur_abs, closure['max_abs'], cur_abs / closure['max_abs'])) - return cur_abs <= closure['max_abs'] * decay_by + print( + fmt.format( + sim.meep_time(), + cur_abs, + closure["max_abs"], + cur_abs / closure["max_abs"], + ) + ) + return cur_abs <= closure["max_abs"] * decay_by + return _stop @@ -4561,10 +5114,12 @@ def stop_after_walltime(t): parameter. Stops the simulation after `t` seconds of wall time have passed. """ start = mp.wall_time() + def _stop_after_walltime(sim): if mp.wall_time() - start > t: return True return False + return _stop_after_walltime @@ -4589,6 +5144,7 @@ def _stop(sim): return _stop + def stop_when_dft_decayed(tol=1e-11, minimum_run_time=0, maximum_run_time=None): """ Return a `condition` function, suitable for passing to `Simulation.run` as the `until` @@ -4601,33 +5157,42 @@ def stop_when_dft_decayed(tol=1e-11, minimum_run_time=0, maximum_run_time=None): """ # Record data in closure so that we can persistently edit - closure = {'previous_fields':0, 't0':0, 'dt':0, 'maxchange':0} + closure = {"previous_fields": 0, "t0": 0, "dt": 0, "maxchange": 0} + def _stop(_sim): if _sim.fields.t == 0: - closure['dt'] = max(1/_sim.fields.dft_maxfreq()/_sim.fields.dt,_sim.fields.max_decimation()) + closure["dt"] = max( + 1 / _sim.fields.dft_maxfreq() / _sim.fields.dt, + _sim.fields.max_decimation(), + ) if maximum_run_time and _sim.round_time() > maximum_run_time: return True - elif _sim.fields.t <= closure['dt'] + closure['t0']: + elif _sim.fields.t <= closure["dt"] + closure["t0"]: return False else: - previous_fields = closure['previous_fields'] - current_fields = _sim.fields.dft_norm() - change = np.abs(previous_fields-current_fields) - closure['maxchange'] = max(closure['maxchange'],change) + previous_fields = closure["previous_fields"] + current_fields = _sim.fields.dft_norm() + change = np.abs(previous_fields - current_fields) + closure["maxchange"] = max(closure["maxchange"], change) if previous_fields == 0: - closure['previous_fields'] = current_fields + closure["previous_fields"] = current_fields return False - closure['previous_fields'] = current_fields - closure['t0'] = _sim.fields.t + closure["previous_fields"] = current_fields + closure["t0"] = _sim.fields.t if verbosity.meep > 1: fmt = "DFT fields decay(t = {0:0.2f}): {1:0.4e}" - print(fmt.format(_sim.meep_time(), np.real(change/closure['maxchange']))) - return (change/closure['maxchange']) <= tol and _sim.round_time() >= minimum_run_time + print( + fmt.format(_sim.meep_time(), np.real(change / closure["maxchange"])) + ) + return ( + change / closure["maxchange"] + ) <= tol and _sim.round_time() >= minimum_run_time return _stop + def combine_step_funcs(*step_funcs): """ Given zero or more step functions, return a new step function that on each step calls @@ -4643,11 +5208,13 @@ def synchronized_magnetic(*step_funcs): electric field. See [Synchronizing the Magnetic and Electric Fields](Synchronizing_the_Magnetic_and_Electric_Fields.md). """ + def _sync(sim, todo): sim.fields.synchronize_magnetic_fields() for f in step_funcs: _eval_step_func(sim, f, todo) sim.fields.restore_magnetic_fields() + return _sync @@ -4672,6 +5239,7 @@ def with_prefix(pre, *step_funcs): Given zero or more step functions, modifies any output functions among them to prepend the string `prefix` to the file names (much like `filename_prefix`, above). """ + def _with_prefix(sim, todo): saved_pre = sim.filename_prefix sim.filename_prefix = pre + sim.get_filename_prefix() @@ -4679,6 +5247,7 @@ def _with_prefix(sim, todo): for f in step_funcs: _eval_step_func(sim, f, todo) sim.filename_prefix = saved_pre + return _with_prefix @@ -4689,11 +5258,11 @@ def display_csv(sim, name, data): def display_progress(t0, t, dt): t_0 = mp.wall_time() - closure = {'tlast': mp.wall_time()} + closure = {"tlast": mp.wall_time()} def _disp(sim): t1 = mp.wall_time() - if t1 - closure['tlast'] >= dt: + if t1 - closure["tlast"] >= dt: msg_fmt = "Meep progress: {}/{} = {:.1f}% done in {:.1f}s, {:.1f}s to go" val1 = sim.meep_time() - t0 val2 = val1 / (0.01 * t) @@ -4706,14 +5275,14 @@ def _disp(sim): if verbosity.meep > 0: print(msg_fmt.format(val1, t, val2, val3, val4)) - closure['tlast'] = t1 + closure["tlast"] = t1 return _disp def data_to_str(d): if type(d) is complex: - sign = '+' if d.imag >= 0 else '' + sign = "+" if d.imag >= 0 else "" return "{}{}{}i".format(d.real, sign, d.imag) else: return str(d) @@ -4725,11 +5294,10 @@ def display_run_data(sim, data_name, data): else: data_str = [data_to_str(data)] if verbosity.meep > 0: - print("{}{}:, {}".format(data_name, sim.run_index, ', '.join(data_str))) + print("{}{}:, {}".format(data_name, sim.run_index, ", ".join(data_str))) def convert_h5(rm_h5, convert_cmd, *step_funcs): - def convert(fname): if mp.my_rank() == 0: cmd = convert_cmd.split() @@ -4771,24 +5339,27 @@ def output_png(compnt, options, rm_h5=True): By default, `output_png` deletes the `.h5` file when it is done. To preserve the `.h5` file requires `output_png(component, h5topng_options, rm_h5=False)`. """ - closure = {'maxabs': 0.0} + closure = {"maxabs": 0.0} def _output_png(sim, todo): - if todo == 'step': + if todo == "step": if sim.output_volume is None: ov = sim.fields.total_volume() else: ov = sim.output_volume - closure['maxabs'] = max(closure['maxabs'], - sim.fields.max_abs(compnt, ov)) - convert = sim.h5topng(rm_h5, "-M {} {}".format(closure['maxabs'], options), - lambda sim: sim.output_component(compnt)) + closure["maxabs"] = max(closure["maxabs"], sim.fields.max_abs(compnt, ov)) + convert = sim.h5topng( + rm_h5, + "-M {} {}".format(closure["maxabs"], options), + lambda sim: sim.output_component(compnt), + ) convert(sim, todo) + return _output_png -def output_epsilon(sim=None,*step_func_args,**kwargs): +def output_epsilon(sim=None, *step_func_args, **kwargs): """ Given a frequency `frequency`, (provided as a keyword argument) output $\\varepsilon$ (relative permittivity); for an anisotropic $\\varepsilon$ tensor the output is the [harmonic @@ -4802,11 +5373,11 @@ def output_epsilon(sim=None,*step_func_args,**kwargs): if sim is None: return lambda sim: mp.output_epsilon(sim, *step_func_args, **kwargs) - frequency = kwargs.pop('frequency', 0.0) - sim.output_component(mp.Dielectric,frequency=frequency) + frequency = kwargs.pop("frequency", 0.0) + sim.output_component(mp.Dielectric, frequency=frequency) -def output_mu(sim=None,*step_func_args,**kwargs): +def output_mu(sim=None, *step_func_args, **kwargs): """ Given a frequency `frequency`, (provided as a keyword argument) output $\\mu$ (relative permeability); for an anisotropic $\\mu$ tensor the output is the [harmonic @@ -4820,8 +5391,8 @@ def output_mu(sim=None,*step_func_args,**kwargs): if sim is None: return lambda sim: mp.output_mu(sim, *step_func_args, **kwargs) - frequency = kwargs.pop('frequency', 0.0) - sim.output_component(mp.Permeability,frequency=frequency) + frequency = kwargs.pop("frequency", 0.0) + sim.output_component(mp.Permeability, frequency=frequency) def output_hpwr(sim): @@ -4853,7 +5424,7 @@ def output_hfield(sim): Outputs *all* the components of the field *h*, (magnetic) to an HDF5 file. That is, the different components are stored as different datasets within the *same* file. """ - sim.output_components('h', mp.Hx, mp.Hy, mp.Hz, mp.Hr, mp.Hp) + sim.output_components("h", mp.Hx, mp.Hy, mp.Hz, mp.Hr, mp.Hp) def output_hfield_x(sim): @@ -4906,7 +5477,7 @@ def output_bfield(sim): Outputs *all* the components of the field *b*, (magnetic) to an HDF5 file. That is, the different components are stored as different datasets within the *same* file. """ - sim.output_components('b', mp.Bx, mp.By, mp.Bz, mp.Br, mp.Bp) + sim.output_components("b", mp.Bx, mp.By, mp.Bz, mp.Br, mp.Bp) def output_bfield_x(sim): @@ -4962,7 +5533,7 @@ def output_efield(sim): Outputs *all* the components of the field *e*, (electric) to an HDF5 file. That is, the different components are stored as different datasets within the *same* file. """ - sim.output_components('e', mp.Ex, mp.Ey, mp.Ez, mp.Er, mp.Ep) + sim.output_components("e", mp.Ex, mp.Ey, mp.Ez, mp.Er, mp.Ep) def output_efield_x(sim): @@ -5018,7 +5589,7 @@ def output_dfield(sim): Outputs *all* the components of the field *d*, (displacement) to an HDF5 file. That is, the different components are stored as different datasets within the *same* file. """ - sim.output_components('d', mp.Dx, mp.Dy, mp.Dz, mp.Dr, mp.Dp) + sim.output_components("d", mp.Dx, mp.Dy, mp.Dz, mp.Dr, mp.Dp) def output_dfield_x(sim): @@ -5077,7 +5648,7 @@ def output_poynting(sim): accurately. See [Synchronizing the Magnetic and Electric Fields](Synchronizing_the_Magnetic_and_Electric_Fields.md). """ - sim.output_components('s', mp.Sx, mp.Sy, mp.Sz, mp.Sr, mp.Sp) + sim.output_components("s", mp.Sx, mp.Sy, mp.Sz, mp.Sr, mp.Sp) def output_poynting_x(sim): @@ -5108,7 +5679,7 @@ def output_sfield(sim): compute it more accurately. See [Synchronizing the Magnetic and Electric Fields](Synchronizing_the_Magnetic_and_Electric_Fields.md). """ - sim.output_components('s', mp.Sx, mp.Sy, mp.Sz, mp.Sr, mp.Sp) + sim.output_components("s", mp.Sx, mp.Sy, mp.Sz, mp.Sr, mp.Sp) def output_sfield_x(sim): @@ -5188,17 +5759,19 @@ def dft_ldos(*args, **kwargs): is also stored in the `ldos_data` variable of the `Simulation` object after the `run` is complete. """ - ldos = kwargs.get('ldos', None) + ldos = kwargs.get("ldos", None) if ldos is None: args = fix_dft_args(args, 0) freq = args[0] - if isinstance(freq, (np.ndarray,list)): + if isinstance(freq, (np.ndarray, list)): ldos = mp._dft_ldos(freq) else: - raise TypeError("dft_ldos only accepts freq_min,freq_max,nfreq (3 numbers) or freq (array/list) or ldos (keyword argument)") + raise TypeError( + "dft_ldos only accepts freq_min,freq_max,nfreq (3 numbers) or freq (array/list) or ldos (keyword argument)" + ) def _ldos(sim, todo): - if todo == 'step': + if todo == "step": ldos.update(sim.fields) else: sim.ldos_data = mp._dft_ldos_ldos(ldos) @@ -5206,7 +5779,8 @@ def _ldos(sim, todo): sim.ldos_Jdata = mp._dft_ldos_J(ldos) sim.ldos_scale = ldos.overall_scale() if verbosity.meep > 0: - display_csv(sim, 'ldos', zip(mp.get_ldos_freqs(ldos), sim.ldos_data)) + display_csv(sim, "ldos", zip(mp.get_ldos_freqs(ldos), sim.ldos_data)) + return _ldos @@ -5370,6 +5944,7 @@ def get_center_and_size(vol): size = v3rmax - v3rmin return center, size + def GDSII_layers(fname): """ Returns a list of integer-valued layer indices for the layers present in @@ -5382,6 +5957,7 @@ def GDSII_layers(fname): """ return list(mp.get_GDSII_layers(fname)) + def GDSII_vol(fname, layer, zmin, zmax): """ Returns a `mp.Volume` read from a GDSII file `fname` on layer number `layer` with @@ -5419,6 +5995,7 @@ def complexarray(re, im): z += re return z + def quiet(quietval=True): """ Meep ordinarily prints various diagnostic and progress information to standard output. @@ -5429,52 +6006,61 @@ def quiet(quietval=True): This function is deprecated, please use the [Verbosity](#verbosity) class instead. """ verbosity(int(not quietval)) - warnings.warn("quiet has been deprecated; use the Verbosity class instead", RuntimeWarning) + warnings.warn( + "quiet has been deprecated; use the Verbosity class instead", RuntimeWarning + ) def get_num_groups(): # Lazy import from mpi4py import MPI + comm = MPI.COMM_WORLD return comm.allreduce(int(mp.my_rank() == 0), op=MPI.SUM) + def get_group_masters(): # Lazy import from mpi4py import MPI + comm = MPI.COMM_WORLD num_workers = comm.Get_size() num_groups = mp.get_num_groups # Check if current worker is a group master is_group_master = True if mp.my_rank() == 0 else False - group_master_idx = np.zeros((num_workers,),dtype=np.bool) + group_master_idx = np.zeros((num_workers,), dtype=np.bool) # Formulate send and receive packets - smsg = [np.array([is_group_master]),([1]*num_workers, [0]*num_workers)] - rmsg = [group_master_idx,([1]*num_workers, list(range(num_workers)))] + smsg = [np.array([is_group_master]), ([1] * num_workers, [0] * num_workers)] + rmsg = [group_master_idx, ([1] * num_workers, list(range(num_workers)))] # Send and receive comm.Alltoallv(smsg, rmsg) # get rank of each group master - group_masters = np.arange(num_workers)[group_master_idx] # rank index of each group leader + group_masters = np.arange(num_workers)[ + group_master_idx + ] # rank index of each group leader return group_masters + def merge_subgroup_data(data): # Lazy import from mpi4py import MPI + comm = MPI.COMM_WORLD num_workers = comm.Get_size() num_groups = get_num_groups() # Initialize new input and output datasets - input=np.array(data,copy=True,order='F') - shape=input.shape - size=input.size - out_shape=shape + (num_groups,) - output=np.zeros(out_shape,input.dtype,order='F') + input = np.array(data, copy=True, order="F") + shape = input.shape + size = input.size + out_shape = shape + (num_groups,) + output = np.zeros(out_shape, input.dtype, order="F") # Get group masters group_masters = get_group_masters() @@ -5492,7 +6078,7 @@ def merge_subgroup_data(data): rdsp = [0] * num_workers buf_idx = 0 for grpidx in group_masters: - rdsp[grpidx] = buf_idx # offset group leader worker by size of each count + rdsp[grpidx] = buf_idx # offset group leader worker by size of each count buf_idx += size # Formulate send and receive packets @@ -5504,12 +6090,22 @@ def merge_subgroup_data(data): return output + class BinaryPartition(object): """ Binary tree class used for specifying a cell partition of arbitrary sized chunks for use as the `chunk_layout` parameter of the `Simulation` class object. """ - def __init__(self, data=None, split_dir=None, split_pos=None, left=None, right=None, proc_id=None): + + def __init__( + self, + data=None, + split_dir=None, + split_pos=None, + left=None, + right=None, + proc_id=None, + ): """ The constructor accepts three separate groups of arguments: (1) `data`: a list of lists where each list entry is either (a) a node defined as `[ (split_dir,split_pos), left, right ]` for which `split_dir` @@ -5527,18 +6123,26 @@ def __init__(self, data=None, split_dir=None, split_pos=None, left=None, right=N self.left = None self.right = None if data is not None: - if isinstance(data,list) and len(data) == 3: - if isinstance(data[0],tuple) and len(data[0]) == 2: + if isinstance(data, list) and len(data) == 3: + if isinstance(data[0], tuple) and len(data[0]) == 2: self.split_dir = data[0][0] self.split_pos = data[0][1] else: - raise ValueError("expecting 2-tuple (split_dir,split_pos) but got {}".format(data[0])) + raise ValueError( + "expecting 2-tuple (split_dir,split_pos) but got {}".format( + data[0] + ) + ) self.left = BinaryPartition(data=data[1]) self.right = BinaryPartition(data=data[2]) - elif isinstance(data,int): + elif isinstance(data, int): self.proc_id = data else: - raise ValueError("expecting list [(split_dir,split_pos), left, right] or int (proc_id) but got {}".format(data)) + raise ValueError( + "expecting list [(split_dir,split_pos), left, right] or int (proc_id) but got {}".format( + data + ) + ) elif split_dir is not None: self.split_dir = split_dir self.split_pos = split_pos @@ -5555,10 +6159,10 @@ def print(self): def _print(self, prefix="", is_root=True): # pointers - ptr_l = ' ├L─ ' - ext_l = ' │ ' - ptr_r = ' └R─ ' - ext_r = ' ' + ptr_l = " ├L─ " + ext_l = " │ " + ptr_r = " └R─ " + ext_r = " " if is_root: yield prefix + self._node_info() @@ -5566,28 +6170,23 @@ def _print(self, prefix="", is_root=True): if self.left is not None and self.right is not None: yield prefix + ptr_l + self.left._node_info() if self.left.left is not None and self.left.right is not None: - yield from self.left._print(prefix=prefix+ext_l, is_root=False) + yield from self.left._print(prefix=prefix + ext_l, is_root=False) yield prefix + ptr_r + self.right._node_info() if self.right.left is not None and self.right.right is not None: - yield from self.right._print(prefix=prefix+ext_r, is_root=False) + yield from self.right._print(prefix=prefix + ext_r, is_root=False) def _node_info(self) -> str: if self.proc_id is not None: return "".format(self.proc_id) else: - split_dir_str = { - mp.X: "X", - mp.Y: "Y", - mp.Z: "Z" - }[self.split_dir] - return "".format( - split_dir_str, self.split_pos) - - def _numchunks(self,bp): + split_dir_str = {mp.X: "X", mp.Y: "Y", mp.Z: "Z"}[self.split_dir] + return "".format(split_dir_str, self.split_pos) + + def _numchunks(self, bp): if bp is None: return 0 - return max(self._numchunks(bp.left)+self._numchunks(bp.right), 1) + return max(self._numchunks(bp.left) + self._numchunks(bp.right), 1) def numchunks(self): return self._numchunks(self) diff --git a/python/solver.py b/python/solver.py index 75f260812..62777c316 100644 --- a/python/solver.py +++ b/python/solver.py @@ -14,6 +14,7 @@ from meep.geom import init_do_averaging from meep.simulation import get_num_args from meep.verbosity_mgr import Verbosity + try: basestring except NameError: @@ -23,10 +24,10 @@ U_PROD = 1 U_MEAN = 2 -verbosity = Verbosity(mp.cvar, 'meep', 1) +verbosity = Verbosity(mp.cvar, "meep", 1) -class MPBArray(np.ndarray): +class MPBArray(np.ndarray): def __new__(cls, input_array, lattice, kpoint=None, bloch_phase=False): # Input array is an already formed ndarray instance # We first cast to be our class type @@ -64,40 +65,41 @@ def __array_finalize__(self, obj): # method sees all creation of default objects - with the # MPBArray.__new__ constructor, but also with # arr.view(MPBArray). - self.lattice = getattr(obj, 'lattice', None) - self.kpoint = getattr(obj, 'kpoint', None) - self.bloch_phase = getattr(obj, 'bloch_phase', False) + self.lattice = getattr(obj, "lattice", None) + self.kpoint = getattr(obj, "kpoint", None) + self.bloch_phase = getattr(obj, "bloch_phase", False) class ModeSolver(object): - - def __init__(self, - resolution=10, - is_negative_epsilon_ok=False, - eigensolver_flops=0, - eigensolver_flags=68, - use_simple_preconditioner=False, - force_mu=False, - mu_input_file='', - epsilon_input_file='', - mesh_size=3, - target_freq=0.0, - tolerance=1.0e-7, - num_bands=1, - k_points=None, - ensure_periodicity=True, - geometry=None, - geometry_lattice=mp.Lattice(), - geometry_center=mp.Vector3(0, 0, 0), - default_material=mp.Medium(epsilon=1), - dimensions=3, - random_fields=False, - filename_prefix='', - deterministic=False, - verbose=False, - optimize_grid_size=True, - eigensolver_nwork=3, - eigensolver_block_size=-11): + def __init__( + self, + resolution=10, + is_negative_epsilon_ok=False, + eigensolver_flops=0, + eigensolver_flags=68, + use_simple_preconditioner=False, + force_mu=False, + mu_input_file="", + epsilon_input_file="", + mesh_size=3, + target_freq=0.0, + tolerance=1.0e-7, + num_bands=1, + k_points=None, + ensure_periodicity=True, + geometry=None, + geometry_lattice=mp.Lattice(), + geometry_center=mp.Vector3(0, 0, 0), + default_material=mp.Medium(epsilon=1), + dimensions=3, + random_fields=False, + filename_prefix="", + deterministic=False, + verbose=False, + optimize_grid_size=True, + eigensolver_nwork=3, + eigensolver_block_size=-11, + ): self.mode_solver = None self.resolution = resolution @@ -110,7 +112,7 @@ def __init__(self, self.random_fields = random_fields self.filename_prefix = filename_prefix self.optimize_grid_size = optimize_grid_size - self.parity = '' + self.parity = "" self.iterations = 0 self.all_freqs = None self.freqs = [] @@ -123,7 +125,9 @@ def __init__(self, grid_size = self._adjust_grid_size() - if type(self.default_material) is not mp.Medium and callable(self.default_material): + if type(self.default_material) is not mp.Medium and callable( + self.default_material + ): init_do_averaging(self.default_material) self.default_material.eps = False @@ -323,10 +327,14 @@ def allow_negative_epsilon(self): def get_filename_prefix(self): if self.filename_prefix: - return f'{self.filename_prefix}-' + return f"{self.filename_prefix}-" _, filename = os.path.split(sys.argv[0]) - return '' if filename in ['ipykernel_launcher.py', '__main__.py'] else re.sub(r'\.py$', '', filename) + '-' + return ( + "" + if filename in ["ipykernel_launcher.py", "__main__.py"] + else re.sub(r"\.py$", "", filename) + "-" + ) def get_freqs(self): return self.mode_solver.get_freqs() @@ -358,7 +366,7 @@ def ExH(e, h): flat_res = res.ravel() self.mode_solver.set_curfield_cmplx(flat_res) - self.mode_solver.set_curfield_type('v') + self.mode_solver.set_curfield_type("v") arr = np.reshape(res, dims) return MPBArray(arr, self.get_lattice(), self.current_k) @@ -372,16 +380,16 @@ def get_mu(self): return self.get_curfield_as_array(False) def get_bfield(self, which_band, bloch_phase=True): - return self._get_field('b', which_band, bloch_phase) + return self._get_field("b", which_band, bloch_phase) def get_efield(self, which_band, bloch_phase=True): - return self._get_field('e', which_band, bloch_phase) + return self._get_field("e", which_band, bloch_phase) def get_dfield(self, which_band, bloch_phase=True): - return self._get_field('d', which_band, bloch_phase) + return self._get_field("d", which_band, bloch_phase) def get_hfield(self, which_band, bloch_phase=True): - return self._get_field('h', which_band, bloch_phase) + return self._get_field("h", which_band, bloch_phase) def get_charge_density(self, which_band, bloch_phase=True): self.get_efield(which_band, bloch_phase) @@ -389,15 +397,17 @@ def get_charge_density(self, which_band, bloch_phase=True): def _get_field(self, f, band, bloch_phase): if self.mode_solver is None: - raise ValueError("Must call a run function before attempting to get a field") + raise ValueError( + "Must call a run function before attempting to get a field" + ) - if f == 'b': + if f == "b": self.mode_solver.get_bfield(band) - elif f == 'd': + elif f == "d": self.mode_solver.get_dfield(band) - elif f == 'e': + elif f == "e": self.mode_solver.get_efield(band) - elif f == 'h': + elif f == "h": self.mode_solver.get_hfield(band) dims = self.mode_solver.get_dims() @@ -414,7 +424,9 @@ def _get_field(self, f, band, bloch_phase): self.mode_solver.get_curfield_cmplx(arr) arr = np.reshape(arr, dims) - return MPBArray(arr, self.get_lattice(), self.current_k, bloch_phase=bloch_phase) + return MPBArray( + arr, self.get_lattice(), self.current_k, bloch_phase=bloch_phase + ) def get_curfield_as_array(self, bloch_phase=True): dims = self.mode_solver.get_dims() @@ -422,7 +434,9 @@ def get_curfield_as_array(self, bloch_phase=True): self.mode_solver.get_curfield(arr) arr = np.reshape(arr, dims) - return MPBArray(arr, self.get_lattice(), self.current_k, bloch_phase=bloch_phase) + return MPBArray( + arr, self.get_lattice(), self.current_k, bloch_phase=bloch_phase + ) def get_dpwr(self, band): self.get_dfield(band, False) @@ -459,7 +473,7 @@ def get_tot_pwr(self, which_band): tot_pwr = epwr + hpwr self.mode_solver.set_curfield(tot_pwr.ravel()) - self.mode_solver.set_curfield_type('R') + self.mode_solver.set_curfield_type("R") return MPBArray(tot_pwr, self.get_lattice(), self.current_k, bloch_phase=False) @@ -473,13 +487,13 @@ def set_eigenvectors(self, ev, first_band): self.mode_solver.set_eigenvectors(first_band - 1, ev.flatten()) def save_eigenvectors(self, filename): - with h5py.File(filename, 'w') as f: + with h5py.File(filename, "w") as f: ev = self.get_eigenvectors(1, self.num_bands) - f['rawdata'] = ev + f["rawdata"] = ev def load_eigenvectors(self, filename): - with h5py.File(filename, 'r') as f: - ev = f['rawdata'][()] + with h5py.File(filename, "r") as f: + ev = f["rawdata"][()] self.set_eigenvectors(ev, 1) self.mode_solver.curfield_reset() @@ -489,7 +503,6 @@ def load_eigenvectors(self, filename): # Here, we update this data with a new list of band frequencies, and return the new # data. If band-range-data is null or too short, the needed entries will be created. def update_band_range_data(self, brd, freqs, kpoint): - def update_brd(brd, freqs, br_start): if not freqs: return br_start + brd @@ -514,23 +527,30 @@ def output_band_range_data(self, br_data): # Output any gaps in the given band ranges, and return a list of the gaps as # a list of (percent, freq-min, freq-max) tuples. def output_gaps(self, br_data): - def ogaps(br_cur, br_rest, i, gaps): if not br_rest: gaps = list(reversed(gaps)) - return [(gaps[i + 2], gaps[i + 1], gaps[i]) for i in range(0, len(gaps), 3)] + return [ + (gaps[i + 2], gaps[i + 1], gaps[i]) for i in range(0, len(gaps), 3) + ] else: br_rest_min_f = br_rest[0][0][0] br_cur_max_f = br_cur[1][0] if br_cur_max_f >= br_rest_min_f: return ogaps(br_rest[0], br_rest[1:], i + 1, gaps) - gap_size = ((200 * (br_rest_min_f - br_cur_max_f)) / - (br_rest_min_f + br_cur_max_f)) + gap_size = (200 * (br_rest_min_f - br_cur_max_f)) / ( + br_rest_min_f + br_cur_max_f + ) if verbosity.mpb > 0: fmt = "Gap from band {} ({}) to band {} ({}), {}%" print(fmt.format(i, br_cur_max_f, i + 1, br_rest_min_f, gap_size)) - return ogaps(br_rest[0], br_rest[1:], i + 1, - [gap_size, br_cur_max_f, br_rest_min_f] + gaps) + return ogaps( + br_rest[0], + br_rest[1:], + i + 1, + [gap_size, br_cur_max_f, br_rest_min_f] + gaps, + ) + if not br_data: return [] else: @@ -554,7 +574,6 @@ def retrieve_gap(self, lower_band): # given by index (in 0..num-1), along with the index in L of the first # element of the piece, as a list: [first-index, piece-of-L] def list_split(self, l, num, index): - def list_sub(l, start, length, index, rest): if not l: return list(reversed(rest)) @@ -604,15 +623,15 @@ def output_field_to_file(self, component, fname_prefix): curfield_type = self.mode_solver.get_curfield_type() output_k = self.mode_solver.get_output_k() - if curfield_type in 'Rv': + if curfield_type in "Rv": # Generic scalar/vector field. Don't know k output_k = [0, 0, 0] - if curfield_type in 'dhbecv': + if curfield_type in "dhbecv": self._output_vector_field(curfield_type, fname_prefix, output_k, component) - elif curfield_type == 'C': + elif curfield_type == "C": self._output_complex_scalar_field(fname_prefix, output_k) - elif curfield_type in 'DHBnmR': + elif curfield_type in "DHBnmR": self._output_scalar_field(curfield_type, fname_prefix) else: raise ValueError(f"Unkown field type: {curfield_type}") @@ -620,23 +639,20 @@ def output_field_to_file(self, component, fname_prefix): self.mode_solver.curfield_reset() def _output_complex_scalar_field(self, fname_prefix, output_k): - curfield_type = 'C' + curfield_type = "C" kpoint_index = self.mode_solver.get_kpoint_index() curfield_band = self.mode_solver.curfield_band fname = "{}.k{:02d}.b{:02d}".format(curfield_type, kpoint_index, curfield_band) description = "{} field, kpoint {}, band {}, freq={:.6g}".format( - curfield_type, - kpoint_index, - curfield_band, - self.freqs[curfield_band - 1] + curfield_type, kpoint_index, curfield_band, self.freqs[curfield_band - 1] ) fname = self._create_fname(fname, fname_prefix, True) if verbosity.mpb > 0: print(f"Outputting complex scalar field to {fname}...") - with h5py.File(fname, 'w') as f: - f['description'] = description.encode() - f['Bloch wavevector'] = np.array(output_k) + with h5py.File(fname, "w") as f: + f["description"] = description.encode() + f["Bloch wavevector"] = np.array(output_k) self._write_lattice_vectors(f) dims = self.mode_solver.get_dims() @@ -645,11 +661,11 @@ def _output_complex_scalar_field(self, fname_prefix, output_k): reshaped_field = field.reshape(dims) - f['c.r'] = np.real(reshaped_field) - f['c.i'] = np.imag(reshaped_field) + f["c.r"] = np.real(reshaped_field) + f["c.i"] = np.imag(reshaped_field) def _output_vector_field(self, curfield_type, fname_prefix, output_k, component): - components = ['x', 'y', 'z'] + components = ["x", "y", "z"] kpoint_index = self.mode_solver.get_kpoint_index() curfield_band = self.mode_solver.curfield_band fname = "{}.k{:02d}.b{:02d}".format(curfield_type, kpoint_index, curfield_band) @@ -658,22 +674,19 @@ def _output_vector_field(self, curfield_type, fname_prefix, output_k, component) fname += f".{components[component]}" description = "{} field, kpoint {}, band {}, freq={:.6g}".format( - curfield_type, - kpoint_index, - curfield_band, - self.freqs[curfield_band - 1] + curfield_type, kpoint_index, curfield_band, self.freqs[curfield_band - 1] ) fname = self._create_fname(fname, fname_prefix, True) if verbosity.mpb > 0: print(f"Outputting fields to {fname}...") - with h5py.File(fname, 'w') as f: - f['description'] = description.encode() - f['Bloch wavevector'] = np.array(output_k) + with h5py.File(fname, "w") as f: + f["description"] = description.encode() + f["Bloch wavevector"] = np.array(output_k) self._write_lattice_vectors(f) - if curfield_type != 'v': + if curfield_type != "v": self.mode_solver.multiply_bloch_phase() for c_idx, c in enumerate(components): @@ -691,36 +704,41 @@ def _output_vector_field(self, curfield_type, fname_prefix, output_k, component) f[name] = np.imag(component_field) def _output_scalar_field(self, curfield_type, fname_prefix): - components = ['x', 'y', 'z'] - - if curfield_type == 'n': - fname = 'epsilon' - description = 'dielectric function, epsilon' - elif curfield_type == 'm': - fname = 'mu' - description = 'permeability mu' + components = ["x", "y", "z"] + + if curfield_type == "n": + fname = "epsilon" + description = "dielectric function, epsilon" + elif curfield_type == "m": + fname = "mu" + description = "permeability mu" else: kpoint_index = self.mode_solver.get_kpoint_index() curfield_band = self.mode_solver.curfield_band - fname = "{}pwr.k{:02d}.b{:02d}".format(curfield_type.lower(), - kpoint_index, curfield_band) + fname = "{}pwr.k{:02d}.b{:02d}".format( + curfield_type.lower(), kpoint_index, curfield_band + ) descr_fmt = "{} field energy density, kpoint {}, band {}, freq={:.6g}" - description = descr_fmt.format(curfield_type, kpoint_index, curfield_band, - self.freqs[curfield_band - 1]) - - parity_suffix = curfield_type not in 'mn' + description = descr_fmt.format( + curfield_type, + kpoint_index, + curfield_band, + self.freqs[curfield_band - 1], + ) + + parity_suffix = curfield_type not in "mn" fname = self._create_fname(fname, fname_prefix, parity_suffix) if verbosity.mpb > 0: print(f"Outputting {fname}...") - with h5py.File(fname, 'w') as f: - f['description'] = description.encode() - self._create_h5_dataset(f, 'data') + with h5py.File(fname, "w") as f: + f["description"] = description.encode() + self._create_h5_dataset(f, "data") self._write_lattice_vectors(f) - if curfield_type == 'n': + if curfield_type == "n": for inv in [False, True]: - inv_str = 'epsilon_inverse' if inv else 'epsilon' + inv_str = "epsilon_inverse" if inv else "epsilon" for c1 in range(3): for c2 in range(c1, 3): self.mode_solver.get_epsilon_tensor(c1, c2, 0, inv) @@ -729,21 +747,21 @@ def _output_scalar_field(self, curfield_type, fname_prefix): if with_hermitian_epsilon() and c1 != c2: self.mode_solver.get_epsilon_tensor(c1, c2, 1, inv) - dataname += '.i' + dataname += ".i" self._create_h5_dataset(f, dataname) def _write_lattice_vectors(self, h5file): lattice = np.zeros((3, 3)) self.mode_solver.get_lattice(lattice) - h5file['lattice vectors'] = lattice + h5file["lattice vectors"] = lattice def _create_h5_dataset(self, h5file, key): h5file[key] = self.get_curfield_as_array(False) def _create_fname(self, fname, prefix, parity_suffix): parity_str = self.mode_solver.get_parity_string() - suffix = f".{parity_str}" if parity_suffix and parity_str else '' - return prefix + fname + suffix + '.h5' + suffix = f".{parity_str}" if parity_suffix and parity_str else "" + return prefix + fname + suffix + ".h5" def compute_field_energy(self): return self.mode_solver.compute_field_energy() @@ -780,8 +798,9 @@ def compute_one_group_velocity(self, which_band): return self.mode_solver.compute_1_group_velocity(which_band) def compute_one_group_velocity_component(self, direction, which_band): - return self.mode_solver.compute_1_group_velocity_component(direction, - which_band) + return self.mode_solver.compute_1_group_velocity_component( + direction, which_band + ) def compute_zparities(self): return self.mode_solver.compute_zparities() @@ -795,10 +814,10 @@ def randomize_fields(self): def display_kpoint_data(self, name, data): if verbosity.mpb > 0: k_index = self.mode_solver.get_kpoint_index() - print(f"{self.parity}{name}:, {k_index}", end='') + print(f"{self.parity}{name}:, {k_index}", end="") for d in data: - print(f", {d}", end='') + print(f", {d}", end="") print() def display_eigensolver_stats(self): @@ -812,7 +831,7 @@ def display_eigensolver_stats(self): mean_iters = np.mean(self.eigensolver_iters) if verbosity.mpb > 0: fmt = "eigensolver iterations for {} kpoints: {}-{}, mean = {}" - print(fmt.format(num_runs, min_iters, max_iters, mean_iters), end='') + print(fmt.format(num_runs, min_iters, max_iters, mean_iters), end="") sorted_iters = sorted(self.eigensolver_iters) idx1 = num_runs // 2 @@ -830,9 +849,11 @@ def display_eigensolver_stats(self): print(f"mean time per iteration = {mean_time} s") def _get_grid_size(self): - grid_size = mp.Vector3(self.resolution[0] * self.geometry_lattice.size.x, - self.resolution[1] * self.geometry_lattice.size.y, - self.resolution[2] * self.geometry_lattice.size.z) + grid_size = mp.Vector3( + self.resolution[0] * self.geometry_lattice.size.x, + self.resolution[1] * self.geometry_lattice.size.y, + self.resolution[2] * self.geometry_lattice.size.z, + ) grid_size.x = max(math.ceil(grid_size.x), 1) grid_size.y = max(math.ceil(grid_size.y), 1) @@ -847,11 +868,10 @@ def _optimize_grid_size(self, grid_size): return grid_size def next_factor2357(self, n): - def is_factor2357(n): - def divby(n, p): return divby(n // p, p) if n % p == 0 else n + return divby(divby(divby(divby(n, 2), 3), 5), 7) == 1 if is_factor2357(n): @@ -908,10 +928,13 @@ def run_parity(self, p, reset_fields, *band_functions): self.solve_kpoint(k) self.iterations = self.mode_solver.get_iterations() if verbosity.mpb > 0: - print(f"elapsed time for k point: {time.time() - solve_kpoint_time}") + print( + f"elapsed time for k point: {time.time() - solve_kpoint_time}" + ) self.all_freqs[i, :] = np.array(self.freqs) - self.band_range_data = self.update_band_range_data(self.band_range_data, - self.freqs, k) + self.band_range_data = self.update_band_range_data( + self.band_range_data, self.freqs, k + ) self.eigensolver_iters += [self.iterations / self.num_bands] for f in band_functions: @@ -924,8 +947,10 @@ def run_parity(self, p, reset_fields, *band_functions): f(self, band) band += 1 else: - raise ValueError("Band function should take 1 or 2 arguments. " - "The first must be a ModeSolver instance") + raise ValueError( + "Band function should take 1 or 2 arguments. " + "The first must be a ModeSolver instance" + ) if len(k_split[1]) > 1: self.output_band_range_data(self.band_range_data) @@ -976,15 +1001,28 @@ def run_yodd_zodd(self, *band_functions): run_tm_yeven = run_yeven_zodd run_tm_yodd = run_yodd_zodd - def find_k(self, p, omega, band_min, band_max, korig_and_kdir, tol, - kmag_guess, kmag_min, kmag_max, *band_funcs): + def find_k( + self, + p, + omega, + band_min, + band_max, + korig_and_kdir, + tol, + kmag_guess, + kmag_min, + kmag_max, + *band_funcs, + ): num_bands_save = self.num_bands kpoints_save = self.k_points nb = band_max - band_min + 1 kdir = korig_and_kdir[1] if type(korig_and_kdir) is list else korig_and_kdir lat = self.geometry_lattice - kdir1 = mp.cartesian_to_reciprocal(mp.reciprocal_to_cartesian(kdir, lat).unit(), lat) + kdir1 = mp.cartesian_to_reciprocal( + mp.reciprocal_to_cartesian(kdir, lat).unit(), lat + ) if type(korig_and_kdir) is list: korig = korig_and_kdir[0] @@ -1001,7 +1039,6 @@ def find_k(self, p, omega, band_min, band_max, korig_and_kdir, tol, bktab = {} def rootfun(b): - def _rootfun(k): if tab_val := bktab.get((b, k), None): if verbosity.mpb > 0: @@ -1014,7 +1051,11 @@ def _rootfun(k): v = self.mode_solver.compute_group_velocity_component(kdir1) # Cache computed values - for _b, _f, _v in zip(range(band_min, b - band_min + 1), self.freqs[band_min - 1:], v[band_min - 1:]): + for _b, _f, _v in zip( + range(band_min, b - band_min + 1), + self.freqs[band_min - 1 :], + v[band_min - 1 :], + ): tabval = bktab.get((_b, k0s[_b - band_min]), None) if not tabval or abs(_f - omega) < abs(tabval[0]): @@ -1035,10 +1076,14 @@ def _rootfun(k): ks = [] for b in range(band_max, band_max - nb, -1): - ks.append(mp.find_root_deriv(rootfun(b), tol, kmag_min, kmag_max, k0s[b - band_min])) + ks.append( + mp.find_root_deriv( + rootfun(b), tol, kmag_min, kmag_max, k0s[b - band_min] + ) + ) if band_funcs: - for b, k in zip(range(1, band_max +1), reversed(ks)): + for b, k in zip(range(1, band_max + 1), reversed(ks)): self.num_bands = b self.k_points = [korig + kdir1.scale(k)] @@ -1053,13 +1098,16 @@ def bfunc(ms, b_prime): self.k_points = kpoints_save ks = list(reversed(ks)) if verbosity.mpb > 0: - print("{}kvals:, {}, {}, {}".format(self.parity, omega, band_min, band_max), end='') + print( + "{}kvals:, {}, {}, {}".format(self.parity, omega, band_min, band_max), + end="", + ) for k in korig: - print(", {}".format(k), end='') + print(", {}".format(k), end="") for k in kdir1: - print(", {}".format(k), end='') + print(", {}".format(k), end="") for k in ks: - print(", {}".format(k), end='') + print(", {}".format(k), end="") print() return ks @@ -1069,6 +1117,7 @@ def first_brillouin_zone(self, k): Function to convert a k-point k into an equivalent point in the first Brillouin zone (not necessarily the irreducible Brillouin zone) """ + def n(k): return mp.reciprocal_to_cartesian(k, self.geometry_lattice).norm() @@ -1079,10 +1128,18 @@ def _try(k, v): return try_plus(try_plus(k, v), mp.Vector3() - v) try_list = [ - mp.Vector3(1, 0, 0), mp.Vector3(0, 1, 0), mp.Vector3(0, 0, 1), - mp.Vector3(0, 1, 1), mp.Vector3(1, 0, 1), mp.Vector3(1, 1, 0), - mp.Vector3(0, 1, -1), mp.Vector3(1, 0, -1), mp.Vector3(1, -1, 0), - mp.Vector3(1, 1, 1), mp.Vector3(-1, 1, 1), mp.Vector3(1, -1, 1), + mp.Vector3(1, 0, 0), + mp.Vector3(0, 1, 0), + mp.Vector3(0, 0, 1), + mp.Vector3(0, 1, 1), + mp.Vector3(1, 0, 1), + mp.Vector3(1, 1, 0), + mp.Vector3(0, 1, -1), + mp.Vector3(1, 0, -1), + mp.Vector3(1, -1, 0), + mp.Vector3(1, 1, 1), + mp.Vector3(-1, 1, 1), + mp.Vector3(1, -1, 1), mp.Vector3(1, 1, -1), ] @@ -1105,13 +1162,17 @@ def transformed_overlap(self, W, w): def compute_symmetry(self, band, W, w): return self.mode_solver.compute_symmetry(band, W, w) - + def compute_symmetries(self, W, w): - return [self.mode_solver.compute_symmetry(band, W, w) for band in range(1, self.num_bands + 1)] + return [ + self.mode_solver.compute_symmetry(band, W, w) + for band in range(1, self.num_bands + 1) + ] # Predefined output functions (functions of the band index), for passing to `run` + def output_hfield(ms, which_band): ms.get_hfield(which_band, False) ms.output_field() @@ -1206,7 +1267,7 @@ def output_dpwr(ms, which_band): def output_tot_pwr(ms, which_band): ms.get_tot_pwr(which_band) - ms.output_field_to_file(-1, f'{ms.get_filename_prefix()}tot.') + ms.output_field_to_file(-1, f"{ms.get_filename_prefix()}tot.") def output_dpwr_in_objects(output_func, min_energy, objects=[]): @@ -1238,39 +1299,40 @@ def output_charge_density(ms, which_band): def output_poynting(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(-1, f'{ms.get_filename_prefix()}flux.') + ms.output_field_to_file(-1, f"{ms.get_filename_prefix()}flux.") def output_poynting_x(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(0, f'{ms.get_filename_prefix()}flux.') + ms.output_field_to_file(0, f"{ms.get_filename_prefix()}flux.") def output_poynting_y(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(1, f'{ms.get_filename_prefix()}flux.') + ms.output_field_to_file(1, f"{ms.get_filename_prefix()}flux.") def output_poynting_z(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(2, f'{ms.get_filename_prefix()}flux.') + ms.output_field_to_file(2, f"{ms.get_filename_prefix()}flux.") def display_yparities(ms): - ms.display_kpoint_data('yparity', ms.mode_solver.compute_yparities()) + ms.display_kpoint_data("yparity", ms.mode_solver.compute_yparities()) def display_zparities(ms): - ms.display_kpoint_data('zparity', ms.mode_solver.compute_zparities()) + ms.display_kpoint_data("zparity", ms.mode_solver.compute_zparities()) def display_group_velocities(ms): - ms.display_kpoint_data('velocity', ms.compute_group_velocities()) + ms.display_kpoint_data("velocity", ms.compute_group_velocities()) # Band functions to pick a canonical phase for the eigenstate of the # given band based upon the spatial representation of the given field + def fix_hfield_phase(ms, which_band): ms.get_hfield(which_band, False) ms.mode_solver.fix_field_phase() @@ -1314,6 +1376,7 @@ def combine_band_functions(*band_funcs): def _combine(ms, which_band): for f in band_funcs: apply_band_func(ms, f, which_band) + return _combine diff --git a/python/source.py b/python/source.py index 765d79d42..007530399 100644 --- a/python/source.py +++ b/python/source.py @@ -33,8 +33,19 @@ class Source(object): the book [Advances in FDTD Computational Electrodynamics: Photonics and Nanotechnology](https://www.amazon.com/Advances-FDTD-Computational-Electrodynamics-Nanotechnology/dp/1608071707). """ - def __init__(self, src, component, center=None, volume=None, size=Vector3(), amplitude=1.0, amp_func=None, - amp_func_file='', amp_data=None): + + def __init__( + self, + src, + component, + center=None, + volume=None, + size=Vector3(), + amplitude=1.0, + amp_func=None, + amp_func_file="", + amp_data=None, + ): """ Construct a `Source`. @@ -120,6 +131,7 @@ class SourceTime(object): This is the parent for classes describing the time dependence of sources; it should not be instantiated directly. """ + def __init__(self, is_integrated=False): self.is_integrated = is_integrated @@ -132,9 +144,18 @@ class ContinuousSource(SourceTime): response](FAQ.md#why-doesnt-the-continuous-wave-cw-source-produce-an-exact-single-frequency-response). """ - def __init__(self, frequency=None, start_time=0, end_time=1.0e20, width=0, - fwidth=float('inf'), cutoff=3.0, wavelength=None, - is_integrated=False, **kwargs): + def __init__( + self, + frequency=None, + start_time=0, + end_time=1.0e20, + width=0, + fwidth=float("inf"), + cutoff=3.0, + wavelength=None, + is_integrated=False, + **kwargs, + ): """ Construct a `ContinuousSource`. @@ -171,8 +192,9 @@ def __init__(self, frequency=None, start_time=0, end_time=1.0e20, width=0, """ if frequency is None and wavelength is None: - raise ValueError(f"Must set either frequency or wavelength in {self.__class__.__name__}.") - + raise ValueError( + f"Must set either frequency or wavelength in {self.__class__.__name__}." + ) super(ContinuousSource, self).__init__(is_integrated=is_integrated, **kwargs) self.frequency = 1 / wavelength if wavelength else float(frequency) @@ -180,8 +202,9 @@ def __init__(self, frequency=None, start_time=0, end_time=1.0e20, width=0, self.end_time = end_time self.width = max(width, 1 / fwidth) self.cutoff = cutoff - self.swigobj = mp.continuous_src_time(self.frequency, self.width, self.start_time, - self.end_time, self.cutoff) + self.swigobj = mp.continuous_src_time( + self.frequency, self.width, self.start_time, self.end_time, self.cutoff + ) self.swigobj.is_integrated = self.is_integrated @@ -193,8 +216,18 @@ class GaussianSource(SourceTime): (t-t_0)^2/2w^2)$, but the difference between this and a true Gaussian is usually irrelevant. """ - def __init__(self, frequency=None, width=0, fwidth=float('inf'), start_time=0, cutoff=5.0, - is_integrated=False, wavelength=None, **kwargs): + + def __init__( + self, + frequency=None, + width=0, + fwidth=float("inf"), + start_time=0, + cutoff=5.0, + is_integrated=False, + wavelength=None, + **kwargs, + ): """ Construct a `GaussianSource`. @@ -239,8 +272,9 @@ def __init__(self, frequency=None, width=0, fwidth=float('inf'), start_time=0, c `amplitude` or `amp_func` factor that you specified for the source. """ if frequency is None and wavelength is None: - raise ValueError(f"Must set either frequency or wavelength in {self.__class__.__name__}.") - + raise ValueError( + f"Must set either frequency or wavelength in {self.__class__.__name__}." + ) super(GaussianSource, self).__init__(is_integrated=is_integrated, **kwargs) self.frequency = 1 / wavelength if wavelength else float(frequency) @@ -248,8 +282,12 @@ def __init__(self, frequency=None, width=0, fwidth=float('inf'), start_time=0, c self.start_time = start_time self.cutoff = cutoff - self.swigobj = mp.gaussian_src_time(self.frequency, self.width, self.start_time, - self.start_time + 2 * self.width * self.cutoff) + self.swigobj = mp.gaussian_src_time( + self.frequency, + self.width, + self.start_time, + self.start_time + 2 * self.width * self.cutoff, + ) self.swigobj.is_integrated = self.is_integrated def fourier_transform(self, freq): @@ -269,8 +307,16 @@ class CustomSource(SourceTime): [`examples/chirped_pulse.py`](https://github.com/NanoComp/meep/blob/master/python/examples/chirped_pulse.py). """ - def __init__(self, src_func, start_time=-1.0e20, end_time=1.0e20, is_integrated=False, - center_frequency=0, fwidth=0, **kwargs): + def __init__( + self, + src_func, + start_time=-1.0e20, + end_time=1.0e20, + is_integrated=False, + center_frequency=0, + fwidth=0, + **kwargs, + ): """ Construct a `CustomSource`. @@ -309,8 +355,9 @@ def __init__(self, src_func, start_time=-1.0e20, end_time=1.0e20, is_integrated= self.end_time = end_time self.fwidth = fwidth self.center_frequency = center_frequency - self.swigobj = mp.custom_py_src_time(src_func, start_time, end_time, - center_frequency, fwidth) + self.swigobj = mp.custom_py_src_time( + src_func, start_time, end_time, center_frequency, fwidth + ) self.swigobj.is_integrated = self.is_integrated @@ -366,21 +413,24 @@ class EigenModeSource(Source): The `SourceTime` object (`Source.src`), which specifies the time dependence of the source, can be one of `ContinuousSource`, `GaussianSource` or `CustomSource`. """ - def __init__(self, - src, - center=None, - volume=None, - eig_lattice_size=None, - eig_lattice_center=None, - component=mp.ALL_COMPONENTS, - direction=mp.AUTOMATIC, - eig_band=1, - eig_kpoint=Vector3(), - eig_match_freq=True, - eig_parity=mp.NO_PARITY, - eig_resolution=0, - eig_tolerance=1e-12, - **kwargs): + + def __init__( + self, + src, + center=None, + volume=None, + eig_lattice_size=None, + eig_lattice_center=None, + component=mp.ALL_COMPONENTS, + direction=mp.AUTOMATIC, + eig_band=1, + eig_kpoint=Vector3(), + eig_match_freq=True, + eig_parity=mp.NO_PARITY, + eig_resolution=0, + eig_tolerance=1e-12, + **kwargs, + ): """ Construct an `EigenModeSource`. @@ -497,7 +547,10 @@ def component(self): @component.setter def component(self, val): if val != mp.ALL_COMPONENTS: - warnings.warn("EigenModeSource component is not ALL_COMPONENTS (the default), which makes it non-unidirectional.",RuntimeWarning) + warnings.warn( + "EigenModeSource component is not ALL_COMPONENTS (the default), which makes it non-unidirectional.", + RuntimeWarning, + ) self._component = val @property @@ -507,7 +560,7 @@ def eig_band(self): @eig_band.setter def eig_band(self, val): if isinstance(val, int): - self._eig_band = check_positive('EigenModeSource.eig_band', val) + self._eig_band = check_positive("EigenModeSource.eig_band", val) else: self._eig_band = val @@ -517,7 +570,7 @@ def eig_resolution(self): @eig_resolution.setter def eig_resolution(self, val): - self._eig_resolution = check_nonnegative('EigenModeSource.eig_resolution', val) + self._eig_resolution = check_nonnegative("EigenModeSource.eig_resolution", val) @property def eig_tolerance(self): @@ -525,16 +578,17 @@ def eig_tolerance(self): @eig_tolerance.setter def eig_tolerance(self, val): - self._eig_tolerance = check_positive('EigenModeSource.eig_tolerance', val) + self._eig_tolerance = check_positive("EigenModeSource.eig_tolerance", val) - def eig_power(self,freq): + def eig_power(self, freq): """ Returns the total power of the fields from the eigenmode source at frequency `freq`. """ amp = self.amplitude if callable(getattr(self.src, "fourier_transform", None)): - amp *= self.src.fourier_transform(freq) - return abs(amp)**2 + amp *= self.src.fourier_transform(freq) + return abs(amp) ** 2 + class GaussianBeamSource(Source): """ @@ -545,16 +599,18 @@ class GaussianBeamSource(Source): The `SourceTime` object (`Source.src`), which specifies the time dependence of the source, should normally be a narrow-band `ContinuousSource` or `GaussianSource`. (For a `CustomSource`, the beam frequency is determined by the source's `center_frequency` parameter.) """ - def __init__(self, - src, - center=None, - volume=None, - component=mp.ALL_COMPONENTS, - beam_x0=Vector3(), - beam_kdir=Vector3(), - beam_w0=None, - beam_E0=Vector3(), - **kwargs): + def __init__( + self, + src, + center=None, + volume=None, + component=mp.ALL_COMPONENTS, + beam_x0=Vector3(), + beam_kdir=Vector3(), + beam_w0=None, + beam_E0=Vector3(), + **kwargs, + ): """ Construct a `GaussianBeamSource`. @@ -567,7 +623,9 @@ def __init__(self, + **`beam_E0` [`Vector3`]** — The polarization vector of the beam. Elements can be complex valued (i.e., for circular polarization). The polarization vector must be *parallel* to the source region in order to generate a transverse mode. """ - super(GaussianBeamSource, self).__init__(src, component, center, volume, **kwargs) + super(GaussianBeamSource, self).__init__( + src, component, center, volume, **kwargs + ) self._beam_x0 = beam_x0 self._beam_kdir = beam_kdir self._beam_w0 = beam_w0 @@ -589,13 +647,15 @@ def beam_w0(self): def beam_E0(self): return self._beam_E0 + class IndexedSource(Source): """ created a source object using (SWIG-wrapped mp::srcdata*) srcdata. """ + def __init__(self, src, srcdata, amp_arr, needs_boundary_fix=False): self.src = src self.num_pts = len(amp_arr) self.srcdata = srcdata self.amp_arr = amp_arr - self.needs_boundary_fix = needs_boundary_fix \ No newline at end of file + self.needs_boundary_fix = needs_boundary_fix diff --git a/python/tests/test_3rd_harm_1d.py b/python/tests/test_3rd_harm_1d.py index 87e9ee4e7..9e9d645d4 100644 --- a/python/tests/test_3rd_harm_1d.py +++ b/python/tests/test_3rd_harm_1d.py @@ -2,8 +2,8 @@ import meep as mp from utils import ApproxComparisonTestCase -class Test3rdHarm1d(ApproxComparisonTestCase): +class Test3rdHarm1d(ApproxComparisonTestCase): def setUp(self): self.sz = 100 fcen = 1 / 3.0 @@ -18,23 +18,31 @@ def setUp(self): pml_layers = mp.PML(self.dpml) - sources = mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ex, - center=mp.Vector3(0, 0, (-0.5 * self.sz) + self.dpml), amplitude=self.amp) + sources = mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ex, + center=mp.Vector3(0, 0, (-0.5 * self.sz) + self.dpml), + amplitude=self.amp, + ) nfreq = 400 fmin = fcen / 2.0 fmax = fcen * 4 - self.sim = mp.Simulation(cell_size=cell, - geometry=[], - sources=[sources], - boundary_layers=[pml_layers], - default_material=default_material, - resolution=20, - dimensions=dimensions) + self.sim = mp.Simulation( + cell_size=cell, + geometry=[], + sources=[sources], + boundary_layers=[pml_layers], + default_material=default_material, + resolution=20, + dimensions=dimensions, + ) fr = mp.FluxRegion(mp.Vector3(0, 0, (0.5 * self.sz) - self.dpml - 0.5)) - self.trans = self.sim.add_flux(0.5 * (fmin + fmax), fmax - fmin, nfreq, fr, decimation_factor=1) + self.trans = self.sim.add_flux( + 0.5 * (fmin + fmax), fmax - fmin, nfreq, fr, decimation_factor=1 + ) self.trans1 = self.sim.add_flux(fcen, 0, 1, fr, decimation_factor=1) self.trans3 = self.sim.add_flux(3 * fcen, 0, 1, fr, decimation_factor=1) @@ -48,11 +56,16 @@ def test_3rd_harm_1d(self): ) ) - harmonics = [self.k, self.amp, mp.get_fluxes(self.trans1)[0], mp.get_fluxes(self.trans3)[0]] + harmonics = [ + self.k, + self.amp, + mp.get_fluxes(self.trans1)[0], + mp.get_fluxes(self.trans3)[0], + ] tol = 3e-5 if mp.is_single_precision() else 1e-7 self.assertClose(expected_harmonics, harmonics, epsilon=tol) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_absorber_1d.py b/python/tests/test_absorber_1d.py index b8c9690ce..04fdc8c7e 100644 --- a/python/tests/test_absorber_1d.py +++ b/python/tests/test_absorber_1d.py @@ -4,7 +4,6 @@ class TestAbsorber(unittest.TestCase): - def setUp(self): resolution = 40 @@ -12,19 +11,30 @@ def setUp(self): absorber_layers = [mp.Absorber(1, direction=mp.Z)] - sources = [mp.Source(src=mp.GaussianSource(1 / 0.803, fwidth=0.1), center=mp.Vector3(), - component=mp.Ex)] - - self.sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - dimensions=1, - default_material=Al, - boundary_layers=absorber_layers, - sources=sources) + sources = [ + mp.Source( + src=mp.GaussianSource(1 / 0.803, fwidth=0.1), + center=mp.Vector3(), + component=mp.Ex, + ) + ] + + self.sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + dimensions=1, + default_material=Al, + boundary_layers=absorber_layers, + sources=sources, + ) def test_absorber(self): - self.sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-6)) + self.sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ex, mp.Vector3(), 1e-6 + ) + ) f = self.sim.get_field_point(mp.Ex, mp.Vector3()) self.assertAlmostEqual(f.real, 3.218846961494622e-13, places=6) @@ -33,14 +43,14 @@ def test_absorber_2d(self): source = mp.Source( src=mp.GaussianSource(frequency=0.1, fwidth=0.1), component=mp.Hz, - center=mp.Vector3() + center=mp.Vector3(), ) sim = mp.Simulation( cell_size=mp.Vector3(20, 20, 0), resolution=10, sources=[source], - boundary_layers=[mp.Absorber(5)] + boundary_layers=[mp.Absorber(5)], ) sim.run(until_after_sources=1000) @@ -50,5 +60,5 @@ def test_absorber_2d(self): self.assertAlmostEqual(-4.058476603571745e-11, p.real) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_adjoint_cyl.py b/python/tests/test_adjoint_cyl.py index 2a59ae128..67acf54f5 100644 --- a/python/tests/test_adjoint_cyl.py +++ b/python/tests/test_adjoint_cyl.py @@ -1,4 +1,5 @@ import meep as mp + try: import meep.adjoint as mpa except: @@ -13,7 +14,7 @@ rng = np.random.RandomState(2) resolution = 20 dimensions = mp.CYLINDRICAL -m=0 +m = 0 Si = mp.Medium(index=3.4) SiO2 = mp.Medium(index=1.44) @@ -23,87 +24,110 @@ dpml = 1.0 boundary_layers = [mp.PML(thickness=dpml)] -design_region_resolution = int(2*resolution) +design_region_resolution = int(2 * resolution) design_r = 5 design_z = 2 -Nr, Nz = int(design_r*design_region_resolution), int(design_z*design_region_resolution) +Nr, Nz = int(design_r * design_region_resolution), int( + design_z * design_region_resolution +) -fcen = 1/1.55 +fcen = 1 / 1.55 width = 0.2 fwidth = width * fcen -source_center = [design_r/2,0,-(sz/2-dpml+design_z/2)/2] -source_size = mp.Vector3(design_r,0,0) -src = mp.GaussianSource(frequency=fcen,fwidth=fwidth) -source = [mp.Source(src,component=mp.Er, - center=source_center, - size=source_size)] +source_center = [design_r / 2, 0, -(sz / 2 - dpml + design_z / 2) / 2] +source_size = mp.Vector3(design_r, 0, 0) +src = mp.GaussianSource(frequency=fcen, fwidth=fwidth) +source = [mp.Source(src, component=mp.Er, center=source_center, size=source_size)] ## random design region -p = 0.5*rng.rand(Nr*Nz) +p = 0.5 * rng.rand(Nr * Nz) ## random epsilon perturbation for design region deps = 1e-5 -dp = deps*rng.rand(Nr*Nz) +dp = deps * rng.rand(Nr * Nz) def forward_simulation(design_params): - matgrid = mp.MaterialGrid(mp.Vector3(Nr,0,Nz), - SiO2, - Si, - weights=design_params.reshape(Nr,1,Nz)) - - geometry = [mp.Block(center=mp.Vector3(design_r/2,0,0), - size=mp.Vector3(design_r,0,design_z), - material=matgrid)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=source, - geometry=geometry, - dimensions=dimensions, - m=m) + matgrid = mp.MaterialGrid( + mp.Vector3(Nr, 0, Nz), SiO2, Si, weights=design_params.reshape(Nr, 1, Nz) + ) + + geometry = [ + mp.Block( + center=mp.Vector3(design_r / 2, 0, 0), + size=mp.Vector3(design_r, 0, design_z), + material=matgrid, + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=source, + geometry=geometry, + dimensions=dimensions, + m=m, + ) frequencies = [fcen] - far_x = [mp.Vector3(5,0,20)] + far_x = [mp.Vector3(5, 0, 20)] mode = sim.add_near2far( frequencies, - mp.Near2FarRegion(center=mp.Vector3(design_r/2,0,(sz/2-dpml+design_z/2)/2), - size=mp.Vector3(design_r,0,0),weight=+1)) + mp.Near2FarRegion( + center=mp.Vector3(design_r / 2, 0, (sz / 2 - dpml + design_z / 2) / 2), + size=mp.Vector3(design_r, 0, 0), + weight=+1, + ), + ) sim.run(until_after_sources=1200) Er = sim.get_farfield(mode, far_x[0]) sim.reset_meep() - return abs(Er[0])**2 + return abs(Er[0]) ** 2 def adjoint_solver(design_params): - design_variables = mp.MaterialGrid(mp.Vector3(Nr,0,Nz),SiO2,Si) - design_region = mpa.DesignRegion(design_variables, - volume=mp.Volume(center=mp.Vector3(design_r/2,0,0), - size=mp.Vector3(design_r,0,design_z))) - geometry = [mp.Block(center=design_region.center, - size=design_region.size, - material=design_variables)] - - sim = mp.Simulation(cell_size=cell_size, + design_variables = mp.MaterialGrid(mp.Vector3(Nr, 0, Nz), SiO2, Si) + design_region = mpa.DesignRegion( + design_variables, + volume=mp.Volume( + center=mp.Vector3(design_r / 2, 0, 0), + size=mp.Vector3(design_r, 0, design_z), + ), + ) + geometry = [ + mp.Block( + center=design_region.center, + size=design_region.size, + material=design_variables, + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, boundary_layers=boundary_layers, geometry=geometry, sources=source, resolution=resolution, dimensions=dimensions, - m=m) - - far_x = [mp.Vector3(5,0,20)] - NearRegions = [mp.Near2FarRegion(center=mp.Vector3(design_r/2,0,(sz/2-dpml+design_z/2)/2), - size=mp.Vector3(design_r,0,0), - weight=+1)] - FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x) + m=m, + ) + + far_x = [mp.Vector3(5, 0, 20)] + NearRegions = [ + mp.Near2FarRegion( + center=mp.Vector3(design_r / 2, 0, (sz / 2 - dpml + design_z / 2) / 2), + size=mp.Vector3(design_r, 0, 0), + weight=+1, + ) + ] + FarFields = mpa.Near2FarFields(sim, NearRegions, far_x) ob_list = [FarFields] def J(alpha): - return npa.abs(alpha[0,0,0])**2 + return npa.abs(alpha[0, 0, 0]) ** 2 opt = mpa.OptimizationProblem( simulation=sim, @@ -111,9 +135,10 @@ def J(alpha): objective_arguments=ob_list, design_regions=[design_region], fcen=fcen, - df = 0, - nf = 1, - maximum_run_time=1200) + df=0, + nf=1, + maximum_run_time=1200, + ) f, dJ_du = opt([design_params]) sim.reset_meep() @@ -122,7 +147,6 @@ def J(alpha): class TestAdjointSolver(ApproxComparisonTestCase): - def test_adjoint_solver_cyl_n2f_fields(self): print("*** TESTING CYLINDRICAL Near2Far ADJOINT FEATURES ***") adjsol_obj, adjsol_grad = adjoint_solver(p) @@ -131,24 +155,24 @@ def test_adjoint_solver_cyl_n2f_fields(self): S12_unperturbed = forward_simulation(p) ## compare objective results - print(f"|Er|^2 -- adjoint solver: {adjsol_obj}, traditional simulation: {S12_unperturbed}") + print( + f"|Er|^2 -- adjoint solver: {adjsol_obj}, traditional simulation: {S12_unperturbed}" + ) - self.assertClose(adjsol_obj,S12_unperturbed,epsilon=1e-3) + self.assertClose(adjsol_obj, S12_unperturbed, epsilon=1e-3) ## compute perturbed S12 - S12_perturbed = forward_simulation(p+dp) + S12_perturbed = forward_simulation(p + dp) ## compare gradients if adjsol_grad.ndim < 2: - adjsol_grad = np.expand_dims(adjsol_grad,axis=1) - adj_scale = (dp[None,:]@adjsol_grad).flatten() - fd_grad = S12_perturbed-S12_unperturbed + adjsol_grad = np.expand_dims(adjsol_grad, axis=1) + adj_scale = (dp[None, :] @ adjsol_grad).flatten() + fd_grad = S12_perturbed - S12_unperturbed print(f"Directional derivative -- adjoint solver: {adj_scale}, FD: {fd_grad}") tol = 0.2 if mp.is_single_precision() else 0.1 - self.assertClose(adj_scale,fd_grad,epsilon=tol) - - + self.assertClose(adj_scale, fd_grad, epsilon=tol) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_adjoint_jax.py b/python/tests/test_adjoint_jax.py index 051209576..782c691e5 100644 --- a/python/tests/test_adjoint_jax.py +++ b/python/tests/test_adjoint_jax.py @@ -10,7 +10,7 @@ import numpy as onp # The calculation of finite difference gradients requires that JAX be operated with double precision -jax.config.update('jax_enable_x64', True) +jax.config.update("jax_enable_x64", True) # The step size for the finite difference gradient calculation _FD_STEP = 1e-4 @@ -41,9 +41,13 @@ def build_straight_wg_simulation( # Simulation domain size sx = 2 * pml_width + 2 * wg_length + design_region_shape[0] - sy = 2 * pml_width + 2 * wg_padding + max( - wg_width, - design_region_shape[1], + sy = ( + 2 * pml_width + + 2 * wg_padding + + max( + wg_width, + design_region_shape[1], + ) ) # Mean / center frequency @@ -54,8 +58,7 @@ def build_straight_wg_simulation( sources = [ mp.EigenModeSource( - mp.GaussianSource(frequency=fmean, - fwidth=fmean * gaussian_rel_width), + mp.GaussianSource(frequency=fmean, fwidth=fmean * gaussian_rel_width), eig_band=1, direction=mp.NO_DIRECTION, eig_kpoint=mp.Vector3(1, 0, 0), @@ -63,8 +66,7 @@ def build_straight_wg_simulation( center=[-sx / 2 + pml_width + source_to_pml, 0, 0], ), mp.EigenModeSource( - mp.GaussianSource(frequency=fmean, - fwidth=fmean * gaussian_rel_width), + mp.GaussianSource(frequency=fmean, fwidth=fmean * gaussian_rel_width), eig_band=1, direction=mp.NO_DIRECTION, eig_kpoint=mp.Vector3(-1, 0, 0), @@ -72,12 +74,14 @@ def build_straight_wg_simulation( center=[sx / 2 - pml_width - source_to_pml, 0, 0], ), ] - nx, ny = int(design_region_shape[0]*design_region_resolution), int(design_region_shape[1]*design_region_resolution) + nx, ny = int(design_region_shape[0] * design_region_resolution), int( + design_region_shape[1] * design_region_resolution + ) mat_grid = mp.MaterialGrid( mp.Vector3(nx, ny), sio2, si, - grid_type='U_DEFAULT', + grid_type="U_DEFAULT", ) design_regions = [ @@ -95,19 +99,25 @@ def build_straight_wg_simulation( ] geometry = [ - mp.Block(center=mp.Vector3(x=-design_region_shape[0] / 2 - - wg_length / 2 - pml_width / 2), - material=si, - size=mp.Vector3(wg_length + pml_width, wg_width, - 0)), # left wg - mp.Block(center=mp.Vector3(x=+design_region_shape[0] / 2 + - wg_length / 2 + pml_width / 2), - material=si, - size=mp.Vector3(wg_length + pml_width, wg_width, - 0)), # right wg - mp.Block(center=design_regions[0].center, - size=design_regions[0].size, - material=mat_grid), # design region + mp.Block( + center=mp.Vector3( + x=-design_region_shape[0] / 2 - wg_length / 2 - pml_width / 2 + ), + material=si, + size=mp.Vector3(wg_length + pml_width, wg_width, 0), + ), # left wg + mp.Block( + center=mp.Vector3( + x=+design_region_shape[0] / 2 + wg_length / 2 + pml_width / 2 + ), + material=si, + size=mp.Vector3(wg_length + pml_width, wg_width, 0), + ), # right wg + mp.Block( + center=design_regions[0].center, + size=design_regions[0].size, + material=mat_grid, + ), # design region ] simulation = mp.Simulation( @@ -125,11 +135,14 @@ def build_straight_wg_simulation( monitor_size = mp.Vector3(y=wg_width + 2 * wg_padding) monitors = [ - mpa.EigenmodeCoefficient(simulation, - mp.Volume(center=center, size=monitor_size), - mode=1, - forward=forward) - for center in monitor_centers for forward in [True, False] + mpa.EigenmodeCoefficient( + simulation, + mp.Volume(center=center, size=monitor_size), + mode=1, + forward=forward, + ) + for center in monitor_centers + for forward in [True, False] ] return simulation, sources, monitors, design_regions, frequencies @@ -150,36 +163,74 @@ def test_mode_monitor_helpers(self): self.simulation.run(until=100) monitor_values = mpa.utils.gather_monitor_values(self.monitors) self.assertEqual(monitor_values.dtype, onp.complex128) - self.assertEqual(monitor_values.shape, - (len(self.monitors), len(self.frequencies))) - + self.assertEqual( + monitor_values.shape, (len(self.monitors), len(self.frequencies)) + ) + def test_dist_dft_pointers(self): fwd_design_region_monitors = mpa.utils.install_design_region_monitors( self.simulation, self.design_regions, self.frequencies, ) - self.assertEqual(len(fwd_design_region_monitors[0]),_NUM_DES_REG_MON) - + self.assertEqual(len(fwd_design_region_monitors[0]), _NUM_DES_REG_MON) class WrapperTest(ApproxComparisonTestCase): - @parameterized.parameterized.expand([ - ('1500_1550bw_01relative_gaussian_port1', - onp.linspace(1 / 1.50, 1 / 1.55, 3).tolist(), 0.1, 0.5, 0), - ('1550_1600bw_02relative_gaussian_port1', - onp.linspace(1 / 1.55, 1 / 1.60, 3).tolist(), 0.2, 0.5, 0), - ('1500_1600bw_03relative_gaussian_port1', - onp.linspace(1 / 1.50, 1 / 1.60, 4).tolist(), 0.3, 0.5, 0), - ('1500_1550bw_01relative_gaussian_port2', - onp.linspace(1 / 1.50, 1 / 1.55, 3).tolist(), 0.1, 0.5, 1), - ('1550_1600bw_02relative_gaussian_port2', - onp.linspace(1 / 1.55, 1 / 1.60, 3).tolist(), 0.2, 0.5, 1), - ('1500_1600bw_03relative_gaussian_port2', - onp.linspace(1 / 1.50, 1 / 1.60, 4).tolist(), 0.3, 0.5, 1), - ]) - def test_wrapper_gradients(self, _, frequencies, gaussian_rel_width, - design_variable_fill_value, excite_port_idx): + @parameterized.parameterized.expand( + [ + ( + "1500_1550bw_01relative_gaussian_port1", + onp.linspace(1 / 1.50, 1 / 1.55, 3).tolist(), + 0.1, + 0.5, + 0, + ), + ( + "1550_1600bw_02relative_gaussian_port1", + onp.linspace(1 / 1.55, 1 / 1.60, 3).tolist(), + 0.2, + 0.5, + 0, + ), + ( + "1500_1600bw_03relative_gaussian_port1", + onp.linspace(1 / 1.50, 1 / 1.60, 4).tolist(), + 0.3, + 0.5, + 0, + ), + ( + "1500_1550bw_01relative_gaussian_port2", + onp.linspace(1 / 1.50, 1 / 1.55, 3).tolist(), + 0.1, + 0.5, + 1, + ), + ( + "1550_1600bw_02relative_gaussian_port2", + onp.linspace(1 / 1.55, 1 / 1.60, 3).tolist(), + 0.2, + 0.5, + 1, + ), + ( + "1500_1600bw_03relative_gaussian_port2", + onp.linspace(1 / 1.50, 1 / 1.60, 4).tolist(), + 0.3, + 0.5, + 1, + ), + ] + ) + def test_wrapper_gradients( + self, + _, + frequencies, + gaussian_rel_width, + design_variable_fill_value, + excite_port_idx, + ): """Tests gradient from the JAX-Meep wrapper against finite differences.""" ( simulation, @@ -187,11 +238,13 @@ def test_wrapper_gradients(self, _, frequencies, gaussian_rel_width, monitors, design_regions, frequencies, - ) = build_straight_wg_simulation(frequencies=frequencies, - gaussian_rel_width=gaussian_rel_width) + ) = build_straight_wg_simulation( + frequencies=frequencies, gaussian_rel_width=gaussian_rel_width + ) design_shape = tuple( - int(i) for i in design_regions[0].design_parameters.grid_size)[:2] + int(i) for i in design_regions[0].design_parameters.grid_size + )[:2] x = onp.ones(design_shape) * design_variable_fill_value # Define a loss function @@ -209,7 +262,8 @@ def loss_fn(x, excite_port_idx=0): return jnp.mean(jnp.square(jnp.abs(t))) value, adjoint_grad = jax.value_and_grad(loss_fn)( - x, excite_port_idx=excite_port_idx) + x, excite_port_idx=excite_port_idx + ) projection = [] fd_projection = [] @@ -226,14 +280,14 @@ def loss_fn(x, excite_port_idx=0): x_perturbed = x + random_perturbation_vector # Calculate T(p + dp) - value_perturbed = loss_fn(x_perturbed, - excite_port_idx=excite_port_idx) + value_perturbed = loss_fn(x_perturbed, excite_port_idx=excite_port_idx) projection.append( onp.dot( random_perturbation_vector.ravel(), adjoint_grad.ravel(), - )) + ) + ) fd_projection.append(value_perturbed - value) projection = onp.stack(projection) @@ -247,5 +301,5 @@ def loss_fn(x, excite_port_idx=0): ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_adjoint_solver.py b/python/tests/test_adjoint_solver.py index f9d8a9780..dd23d1448 100644 --- a/python/tests/test_adjoint_solver.py +++ b/python/tests/test_adjoint_solver.py @@ -1,4 +1,5 @@ import meep as mp + try: import meep.adjoint as mpa except: @@ -11,7 +12,7 @@ from utils import ApproxComparisonTestCase -MonitorObject = Enum('MonitorObject', 'EIGENMODE DFT LDOS') +MonitorObject = Enum("MonitorObject", "EIGENMODE DFT LDOS") class TestAdjointSolver(ApproxComparisonTestCase): @@ -20,115 +21,161 @@ def setUpClass(cls): cls.resolution = 30 # pixels/μm cls.silicon = mp.Medium(epsilon=12) - cls.sapphire = mp.Medium(epsilon_diag=(10.225,10.225,9.95), - epsilon_offdiag=(-0.825,-0.55*np.sqrt(3/2),0.55*np.sqrt(3/2))) + cls.sapphire = mp.Medium( + epsilon_diag=(10.225, 10.225, 9.95), + epsilon_offdiag=(-0.825, -0.55 * np.sqrt(3 / 2), 0.55 * np.sqrt(3 / 2)), + ) cls.sxy = 5.0 - cls.cell_size = mp.Vector3(cls.sxy,cls.sxy,0) + cls.cell_size = mp.Vector3(cls.sxy, cls.sxy, 0) cls.dpml = 1.0 cls.pml_xy = [mp.PML(thickness=cls.dpml)] - cls.pml_x = [mp.PML(thickness=cls.dpml,direction=mp.X)] + cls.pml_x = [mp.PML(thickness=cls.dpml, direction=mp.X)] - cls.eig_parity = mp.EVEN_Y+mp.ODD_Z + cls.eig_parity = mp.EVEN_Y + mp.ODD_Z - cls.design_region_size = mp.Vector3(1.5,1.5) - cls.design_region_resolution = int(2*cls.resolution) - cls.Nx = int(cls.design_region_size.x*cls.design_region_resolution) - cls.Ny = int(cls.design_region_size.y*cls.design_region_resolution) + cls.design_region_size = mp.Vector3(1.5, 1.5) + cls.design_region_resolution = int(2 * cls.resolution) + cls.Nx = int(cls.design_region_size.x * cls.design_region_resolution) + cls.Ny = int(cls.design_region_size.y * cls.design_region_resolution) # ensure reproducible results rng = np.random.RandomState(9861548) # random design region - cls.p = 0.5*rng.rand(cls.Nx*cls.Ny) + cls.p = 0.5 * rng.rand(cls.Nx * cls.Ny) # random perturbation for design region deps = 1e-5 - cls.dp = deps*rng.rand(cls.Nx*cls.Ny) + cls.dp = deps * rng.rand(cls.Nx * cls.Ny) cls.w = 1.0 - cls.waveguide_geometry = [mp.Block(material=cls.silicon, - center=mp.Vector3(), - size=mp.Vector3(mp.inf,cls.w,mp.inf))] - - cls.fcen = 1/1.55 - cls.df = 0.2*cls.fcen - cls.mode_source = [mp.EigenModeSource(src=mp.GaussianSource(cls.fcen,fwidth=cls.df), - center=mp.Vector3(-0.5*cls.sxy+cls.dpml,0), - size=mp.Vector3(0,cls.sxy-2*cls.dpml), - eig_parity=cls.eig_parity)] - - cls.pt_source = [mp.Source(src=mp.GaussianSource(cls.fcen,fwidth=cls.df), - center=mp.Vector3(-0.5*cls.sxy+cls.dpml,0), - size=mp.Vector3(), - component=mp.Ez)] - - cls.line_source = [mp.Source(src=mp.GaussianSource(cls.fcen,fwidth=cls.df), - center=mp.Vector3(-0.85,0), - size=mp.Vector3(0,cls.sxy-2*cls.dpml), - component=mp.Ez)] - - cls.k_point = mp.Vector3(0.23,-0.38) - - - def adjoint_solver(self, design_params, mon_type: MonitorObject, frequencies=None, - mat2=None, need_gradient=True): - matgrid = mp.MaterialGrid(mp.Vector3(self.Nx,self.Ny), - mp.air, - self.silicon if mat2 is None else mat2, - weights=np.ones((self.Nx,self.Ny))) - - matgrid_region = mpa.DesignRegion(matgrid, - volume=mp.Volume(center=mp.Vector3(), - size=mp.Vector3(self.design_region_size.x, - self.design_region_size.y, - 0))) - - matgrid_geometry = [mp.Block(center=matgrid_region.center, - size=matgrid_region.size, - material=matgrid)] + cls.waveguide_geometry = [ + mp.Block( + material=cls.silicon, + center=mp.Vector3(), + size=mp.Vector3(mp.inf, cls.w, mp.inf), + ) + ] + + cls.fcen = 1 / 1.55 + cls.df = 0.2 * cls.fcen + cls.mode_source = [ + mp.EigenModeSource( + src=mp.GaussianSource(cls.fcen, fwidth=cls.df), + center=mp.Vector3(-0.5 * cls.sxy + cls.dpml, 0), + size=mp.Vector3(0, cls.sxy - 2 * cls.dpml), + eig_parity=cls.eig_parity, + ) + ] + + cls.pt_source = [ + mp.Source( + src=mp.GaussianSource(cls.fcen, fwidth=cls.df), + center=mp.Vector3(-0.5 * cls.sxy + cls.dpml, 0), + size=mp.Vector3(), + component=mp.Ez, + ) + ] + + cls.line_source = [ + mp.Source( + src=mp.GaussianSource(cls.fcen, fwidth=cls.df), + center=mp.Vector3(-0.85, 0), + size=mp.Vector3(0, cls.sxy - 2 * cls.dpml), + component=mp.Ez, + ) + ] + + cls.k_point = mp.Vector3(0.23, -0.38) + + def adjoint_solver( + self, + design_params, + mon_type: MonitorObject, + frequencies=None, + mat2=None, + need_gradient=True, + ): + matgrid = mp.MaterialGrid( + mp.Vector3(self.Nx, self.Ny), + mp.air, + self.silicon if mat2 is None else mat2, + weights=np.ones((self.Nx, self.Ny)), + ) + + matgrid_region = mpa.DesignRegion( + matgrid, + volume=mp.Volume( + center=mp.Vector3(), + size=mp.Vector3( + self.design_region_size.x, self.design_region_size.y, 0 + ), + ), + ) + + matgrid_geometry = [ + mp.Block( + center=matgrid_region.center, size=matgrid_region.size, material=matgrid + ) + ] geometry = self.waveguide_geometry + matgrid_geometry - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.pml_xy, - sources=self.mode_source, - geometry=geometry) + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.pml_xy, + sources=self.mode_source, + geometry=geometry, + ) if not frequencies: frequencies = [self.fcen] - if mon_type.name == 'EIGENMODE': - obj_list = [mpa.EigenmodeCoefficient(sim, - mp.Volume(center=mp.Vector3(0.5*self.sxy-self.dpml), - size=mp.Vector3(0,self.sxy-2*self.dpml,0)), - 1, - eig_parity=self.eig_parity)] + if mon_type.name == "EIGENMODE": + obj_list = [ + mpa.EigenmodeCoefficient( + sim, + mp.Volume( + center=mp.Vector3(0.5 * self.sxy - self.dpml), + size=mp.Vector3(0, self.sxy - 2 * self.dpml, 0), + ), + 1, + eig_parity=self.eig_parity, + ) + ] def J(mode_mon): - return npa.power(npa.abs(mode_mon),2) - elif mon_type.name == 'DFT': - obj_list = [mpa.FourierFields(sim, - mp.Volume(center=mp.Vector3(1.25), - size=mp.Vector3(0.25,1,0)), - mp.Ez)] + return npa.power(npa.abs(mode_mon), 2) + + elif mon_type.name == "DFT": + obj_list = [ + mpa.FourierFields( + sim, + mp.Volume(center=mp.Vector3(1.25), size=mp.Vector3(0.25, 1, 0)), + mp.Ez, + ) + ] def J(mode_mon): - return npa.power(npa.abs(mode_mon[:,4,10]),2) - elif mon_type.name == 'LDOS': + return npa.power(npa.abs(mode_mon[:, 4, 10]), 2) + + elif mon_type.name == "LDOS": sim.change_sources(self.line_source) obj_list = [mpa.LDOS(sim)] def J(mode_mon): return npa.array(mode_mon) - opt = mpa.OptimizationProblem(simulation=sim, - objective_functions=J, - objective_arguments=obj_list, - design_regions=[matgrid_region], - frequencies=frequencies) + opt = mpa.OptimizationProblem( + simulation=sim, + objective_functions=J, + objective_arguments=obj_list, + design_regions=[matgrid_region], + frequencies=frequencies, + ) if need_gradient: f, dJ_du = opt([design_params]) @@ -137,47 +184,61 @@ def J(mode_mon): f = opt([design_params], need_gradient=False) return f[0] - - def adjoint_solver_complex_fields(self, design_params, frequencies=None, need_gradient=True): - matgrid = mp.MaterialGrid(mp.Vector3(self.Nx,self.Ny), - mp.air, - self.silicon, - weights=np.ones((self.Nx,self.Ny))) - - matgrid_region = mpa.DesignRegion(matgrid, - volume=mp.Volume(center=mp.Vector3(), - size=mp.Vector3(self.design_region_size.x, - self.design_region_size.y, - 0))) - - geometry = [mp.Block(center=matgrid_region.center, - size=matgrid_region.size, - material=matgrid)] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - default_material=self.silicon, - k_point=self.k_point, - boundary_layers=self.pml_x, - sources=self.pt_source, - geometry=geometry) + def adjoint_solver_complex_fields( + self, design_params, frequencies=None, need_gradient=True + ): + matgrid = mp.MaterialGrid( + mp.Vector3(self.Nx, self.Ny), + mp.air, + self.silicon, + weights=np.ones((self.Nx, self.Ny)), + ) + + matgrid_region = mpa.DesignRegion( + matgrid, + volume=mp.Volume( + center=mp.Vector3(), + size=mp.Vector3( + self.design_region_size.x, self.design_region_size.y, 0 + ), + ), + ) + + geometry = [ + mp.Block( + center=matgrid_region.center, size=matgrid_region.size, material=matgrid + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + default_material=self.silicon, + k_point=self.k_point, + boundary_layers=self.pml_x, + sources=self.pt_source, + geometry=geometry, + ) if not frequencies: frequencies = [self.fcen] - obj_list = [mpa.FourierFields(sim, - mp.Volume(center=mp.Vector3(0.9), - size=mp.Vector3(0.2,0.5)), - mp.Dz)] + obj_list = [ + mpa.FourierFields( + sim, mp.Volume(center=mp.Vector3(0.9), size=mp.Vector3(0.2, 0.5)), mp.Dz + ) + ] def J(dft_mon): - return npa.power(npa.abs(dft_mon[:,3,9]),2) + return npa.power(npa.abs(dft_mon[:, 3, 9]), 2) - opt = mpa.OptimizationProblem(simulation=sim, - objective_functions=J, - objective_arguments=obj_list, - design_regions=[matgrid_region], - frequencies=frequencies) + opt = mpa.OptimizationProblem( + simulation=sim, + objective_functions=J, + objective_arguments=obj_list, + design_regions=[matgrid_region], + frequencies=frequencies, + ) if need_gradient: f, dJ_du = opt([design_params]) @@ -186,50 +247,69 @@ def J(dft_mon): f = opt([design_params], need_gradient=False) return f[0] - - def adjoint_solver_damping(self, design_params, frequencies=None, mat2=None, need_gradient=True): - matgrid = mp.MaterialGrid(mp.Vector3(self.Nx,self.Ny), - mp.air, - self.silicon if mat2 is None else mat2, - weights=np.ones((self.Nx,self.Ny)), - damping=np.pi*self.fcen) - - matgrid_region = mpa.DesignRegion(matgrid, - volume=mp.Volume(center=mp.Vector3(), - size=mp.Vector3(self.design_region_size.x, - self.design_region_size.y, - 0))) - - matgrid_geometry = [mp.Block(center=matgrid_region.center, - size=matgrid_region.size, - material=matgrid)] + def adjoint_solver_damping( + self, design_params, frequencies=None, mat2=None, need_gradient=True + ): + matgrid = mp.MaterialGrid( + mp.Vector3(self.Nx, self.Ny), + mp.air, + self.silicon if mat2 is None else mat2, + weights=np.ones((self.Nx, self.Ny)), + damping=np.pi * self.fcen, + ) + + matgrid_region = mpa.DesignRegion( + matgrid, + volume=mp.Volume( + center=mp.Vector3(), + size=mp.Vector3( + self.design_region_size.x, self.design_region_size.y, 0 + ), + ), + ) + + matgrid_geometry = [ + mp.Block( + center=matgrid_region.center, size=matgrid_region.size, material=matgrid + ) + ] geometry = self.waveguide_geometry + matgrid_geometry - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.pml_xy, - sources=self.mode_source, - geometry=geometry) + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.pml_xy, + sources=self.mode_source, + geometry=geometry, + ) if not frequencies: frequencies = [self.fcen] - obj_list = [mpa.EigenmodeCoefficient(sim, - mp.Volume(center=mp.Vector3(0.5*self.sxy-self.dpml), - size=mp.Vector3(0,self.sxy-2*self.dpml,0)), - 1, - eig_parity=self.eig_parity)] + obj_list = [ + mpa.EigenmodeCoefficient( + sim, + mp.Volume( + center=mp.Vector3(0.5 * self.sxy - self.dpml), + size=mp.Vector3(0, self.sxy - 2 * self.dpml, 0), + ), + 1, + eig_parity=self.eig_parity, + ) + ] def J(mode_mon): - return npa.power(npa.abs(mode_mon),2) + return npa.power(npa.abs(mode_mon), 2) - opt = mpa.OptimizationProblem(simulation=sim, - objective_functions=J, - objective_arguments=obj_list, - design_regions=[matgrid_region], - frequencies=frequencies, - minimum_run_time=150) + opt = mpa.OptimizationProblem( + simulation=sim, + objective_functions=J, + objective_arguments=obj_list, + design_regions=[matgrid_region], + frequencies=frequencies, + minimum_run_time=150, + ) if need_gradient: f, dJ_du = opt([design_params]) @@ -238,99 +318,102 @@ def J(mode_mon): f = opt([design_params], need_gradient=False) return f[0] + def mapping(self, x, filter_radius, eta, beta): + filtered_field = mpa.conic_filter( + x, + filter_radius, + self.design_region_size.x, + self.design_region_size.y, + self.design_region_resolution, + ) - def mapping(self,x,filter_radius,eta,beta): - filtered_field = mpa.conic_filter(x, - filter_radius, - self.design_region_size.x, - self.design_region_size.y, - self.design_region_resolution) - - projected_field = mpa.tanh_projection(filtered_field,beta,eta) + projected_field = mpa.tanh_projection(filtered_field, beta, eta) return projected_field.flatten() - def test_DFT_fields(self): print("*** TESTING DFT OBJECTIVE ***") # test the single frequency and multi frequency cases - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver(self.p, - MonitorObject.DFT, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver( + self.p, MonitorObject.DFT, frequencies + ) # compute objective value for perturbed design - perturbed_val = self.adjoint_solver(self.p+self.dp, - MonitorObject.DFT, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver( + self.p + self.dp, MonitorObject.DFT, frequencies, need_gradient=False + ) # compare directional derivative if unperturbed_grad.ndim < 2: - unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() - fnd_dd = perturbed_val-unperturbed_val - print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + unperturbed_grad = np.expand_dims(unperturbed_grad, axis=1) + adj_dd = (self.dp[None, :] @ unperturbed_grad).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) tol = 0.07 if mp.is_single_precision() else 0.006 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) - + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_eigenmode(self): print("*** TESTING EIGENMODE OBJECTIVE ***") # test the single frequency and multi frequency case - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver(self.p, - MonitorObject.EIGENMODE, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver( + self.p, MonitorObject.EIGENMODE, frequencies + ) # compute objective for perturbed design - perturbed_val = self.adjoint_solver(self.p+self.dp, - MonitorObject.EIGENMODE, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver( + self.p + self.dp, + MonitorObject.EIGENMODE, + frequencies, + need_gradient=False, + ) # compare directional derivative if unperturbed_grad.ndim < 2: - unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() - fnd_dd = perturbed_val-unperturbed_val - print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + unperturbed_grad = np.expand_dims(unperturbed_grad, axis=1) + adj_dd = (self.dp[None, :] @ unperturbed_grad).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) tol = 0.04 if mp.is_single_precision() else 0.01 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) - + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_ldos(self): print("*** TESTING LDOS OBJECTIVE ***") # test the single frequency and multi frequency cases - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver(self.p, - MonitorObject.LDOS, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver( + self.p, MonitorObject.LDOS, frequencies + ) # compute objective for perturbed design - perturbed_val = self.adjoint_solver(self.p+self.dp, - MonitorObject.LDOS, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver( + self.p + self.dp, MonitorObject.LDOS, frequencies, need_gradient=False + ) # compare directional derivative if unperturbed_grad.ndim < 2: - unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() - fnd_dd = perturbed_val-unperturbed_val - print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + unperturbed_grad = np.expand_dims(unperturbed_grad, axis=1) + adj_dd = (self.dp[None, :] @ unperturbed_grad).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) tol = 0.07 if mp.is_single_precision() else 0.006 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) - + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_gradient_backpropagation(self): print("*** TESTING GRADIENT BACKPROPAGATION ***") @@ -340,137 +423,152 @@ def test_gradient_backpropagation(self): eta = 0.49093 beta = 4.0698 - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: - mapped_p = self.mapping(self.p,filter_radius,eta,beta) + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: + mapped_p = self.mapping(self.p, filter_radius, eta, beta) # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver(mapped_p, - MonitorObject.EIGENMODE, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver( + mapped_p, MonitorObject.EIGENMODE, frequencies + ) # backpropagate the gradient using vector-Jacobian product if len(frequencies) > 1: unperturbed_grad_backprop = np.zeros(unperturbed_grad.shape) for i in range(len(frequencies)): - unperturbed_grad_backprop[:,i] = tensor_jacobian_product(self.mapping,0)(self.p, - filter_radius, - eta, - beta, - unperturbed_grad[:,i]) + unperturbed_grad_backprop[:, i] = tensor_jacobian_product( + self.mapping, 0 + )(self.p, filter_radius, eta, beta, unperturbed_grad[:, i]) else: - unperturbed_grad_backprop = tensor_jacobian_product(self.mapping,0)(self.p, - filter_radius, - eta, - beta, - unperturbed_grad) + unperturbed_grad_backprop = tensor_jacobian_product(self.mapping, 0)( + self.p, filter_radius, eta, beta, unperturbed_grad + ) # compute objective for perturbed design - perturbed_val = self.adjoint_solver(self.mapping(self.p+self.dp,filter_radius,eta,beta), - MonitorObject.EIGENMODE, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver( + self.mapping(self.p + self.dp, filter_radius, eta, beta), + MonitorObject.EIGENMODE, + frequencies, + need_gradient=False, + ) # compare directional derivative if unperturbed_grad_backprop.ndim < 2: - unperturbed_grad_backprop = np.expand_dims(unperturbed_grad_backprop,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad_backprop).flatten() - fnd_dd = perturbed_val-unperturbed_val - print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + unperturbed_grad_backprop = np.expand_dims( + unperturbed_grad_backprop, axis=1 + ) + adj_dd = (self.dp[None, :] @ unperturbed_grad_backprop).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) tol = 0.025 if mp.is_single_precision() else 0.01 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) - + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_complex_fields(self): print("*** TESTING COMPLEX FIELDS ***") - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver_complex_fields(self.p, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver_complex_fields( + self.p, frequencies + ) # compute objective value perturbed design - perturbed_val = self.adjoint_solver_complex_fields(self.p+self.dp, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver_complex_fields( + self.p + self.dp, frequencies, need_gradient=False + ) # compare directional derivative if unperturbed_grad.ndim < 2: - unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() - fnd_dd = perturbed_val-unperturbed_val - print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + unperturbed_grad = np.expand_dims(unperturbed_grad, axis=1) + adj_dd = (self.dp[None, :] @ unperturbed_grad).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) tol = 0.06 if mp.is_single_precision() else 0.01 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) - + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_damping(self): print("*** TESTING CONDUCTIVITY ***") - for frequencies in [[1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver_damping(self.p, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver_damping( + self.p, frequencies + ) # compute objective value perturbed design - perturbed_val = self.adjoint_solver_damping(self.p+self.dp, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver_damping( + self.p + self.dp, frequencies, need_gradient=False + ) # compare directional derivative if unperturbed_grad.ndim < 2: - unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() - fnd_dd = perturbed_val-unperturbed_val - print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + unperturbed_grad = np.expand_dims(unperturbed_grad, axis=1) + adj_dd = (self.dp[None, :] @ unperturbed_grad).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) tol = 0.06 if mp.is_single_precision() else 0.04 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) - + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_offdiagonal(self): print("*** TESTING ANISOTROPIC ε ***") - filt = lambda x: mpa.conic_filter(x.reshape((self.Nx,self.Ny)), - 0.25, - self.design_region_size.x, - self.design_region_size.y, - self.design_region_resolution).flatten() + filt = lambda x: mpa.conic_filter( + x.reshape((self.Nx, self.Ny)), + 0.25, + self.design_region_size.x, + self.design_region_size.y, + self.design_region_resolution, + ).flatten() # test the single frequency and multi frequency case - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver(filt(self.p), - MonitorObject.EIGENMODE, - frequencies, - self.sapphire) + unperturbed_val, unperturbed_grad = self.adjoint_solver( + filt(self.p), MonitorObject.EIGENMODE, frequencies, self.sapphire + ) # backpropagate the gradient using vector-Jacobian product if len(frequencies) > 1: unperturbed_grad_backprop = np.zeros(unperturbed_grad.shape) for i in range(len(frequencies)): - unperturbed_grad_backprop[:,i] = tensor_jacobian_product(filt,0)(self.p, - unperturbed_grad[:,i]) + unperturbed_grad_backprop[:, i] = tensor_jacobian_product(filt, 0)( + self.p, unperturbed_grad[:, i] + ) else: - unperturbed_grad_backprop = tensor_jacobian_product(filt,0)(self.p,unperturbed_grad) + unperturbed_grad_backprop = tensor_jacobian_product(filt, 0)( + self.p, unperturbed_grad + ) # compute objective value perturbed design - perturbed_val = self.adjoint_solver(filt(self.p+self.dp), - MonitorObject.EIGENMODE, - frequencies, - self.sapphire, - need_gradient=False) + perturbed_val = self.adjoint_solver( + filt(self.p + self.dp), + MonitorObject.EIGENMODE, + frequencies, + self.sapphire, + need_gradient=False, + ) # compare directional derivative if unperturbed_grad_backprop.ndim < 2: - unperturbed_grad_backprop = np.expand_dims(unperturbed_grad_backprop,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad_backprop).flatten() - fnd_dd = perturbed_val-unperturbed_val - print(f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)") + unperturbed_grad_backprop = np.expand_dims( + unperturbed_grad_backprop, axis=1 + ) + adj_dd = (self.dp[None, :] @ unperturbed_grad_backprop).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) tol = 0.1 if mp.is_single_precision() else 0.04 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) + self.assertClose(adj_dd, fnd_dd, epsilon=tol) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_adjoint_utils.py b/python/tests/test_adjoint_utils.py index 6ed5c684f..267d32813 100644 --- a/python/tests/test_adjoint_utils.py +++ b/python/tests/test_adjoint_utils.py @@ -1,12 +1,13 @@ -'''test_adjoint_utils.py +"""test_adjoint_utils.py Check various components of the adjoint solver codebase, like filters, which may not need explicit gradient computation (i.e. forward and adjoint runs). -''' +""" import meep as mp + try: import meep.adjoint as mpa except: @@ -24,25 +25,29 @@ ## ensure reproducible results rng = np.random.RandomState(9861548) + class TestAdjointUtils(ApproxComparisonTestCase): - @parameterized.parameterized.expand([ - ('1.0_1.0_20_conic',1.0, 1.0, 20, 0.24, mpa.conic_filter), - ('1.0_1.0_23_conic',1.0, 1.0, 23, 0.24, mpa.conic_filter), - ('0.887_1.56_conic',0.887, 1.56, 20, 0.24, mpa.conic_filter), - ('0.887_1.56_conic',0.887, 1.56, 31, 0.24, mpa.conic_filter), - ('0.887_1.56_gaussian',0.887, 1.56, 20, 0.24, mpa.gaussian_filter), - ('0.887_1.56_cylindrical',0.887, 1.56, 20, 0.24, mpa.cylindrical_filter) - ]) - def test_filter_offset(self,test_name,Lx,Ly,resolution,radius,filter_func): - '''ensure that the filters are indeed zero-phase''' - print("Testing ",test_name) - Nx, Ny = int(resolution*Lx), int(resolution*Ly) - x = np.random.rand(Nx,Ny) + @parameterized.parameterized.expand( + [ + ("1.0_1.0_20_conic", 1.0, 1.0, 20, 0.24, mpa.conic_filter), + ("1.0_1.0_23_conic", 1.0, 1.0, 23, 0.24, mpa.conic_filter), + ("0.887_1.56_conic", 0.887, 1.56, 20, 0.24, mpa.conic_filter), + ("0.887_1.56_conic", 0.887, 1.56, 31, 0.24, mpa.conic_filter), + ("0.887_1.56_gaussian", 0.887, 1.56, 20, 0.24, mpa.gaussian_filter), + ("0.887_1.56_cylindrical", 0.887, 1.56, 20, 0.24, mpa.cylindrical_filter), + ] + ) + def test_filter_offset(self, test_name, Lx, Ly, resolution, radius, filter_func): + """ensure that the filters are indeed zero-phase""" + print("Testing ", test_name) + Nx, Ny = int(resolution * Lx), int(resolution * Ly) + x = np.random.rand(Nx, Ny) x = x + np.fliplr(x) x = x + np.flipud(x) y = filter_func(x, radius, Lx, Ly, resolution) - self.assertClose(y,np.fliplr(y),epsilon=_TOL) - self.assertClose(y,np.flipud(y),epsilon=_TOL) + self.assertClose(y, np.fliplr(y), epsilon=_TOL) + self.assertClose(y, np.flipud(y), epsilon=_TOL) + -if __name__ == '__main__': - unittest.main() \ No newline at end of file +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_antenna_radiation.py b/python/tests/test_antenna_radiation.py index 44dade129..89e3ee504 100644 --- a/python/tests/test_antenna_radiation.py +++ b/python/tests/test_antenna_radiation.py @@ -4,95 +4,101 @@ import unittest from utils import ApproxComparisonTestCase -class TestAntennaRadiation(ApproxComparisonTestCase): +class TestAntennaRadiation(ApproxComparisonTestCase): @classmethod def setUp(cls): cls.resolution = 100 # pixels/μm - cls.h = 1.125 # height of point source above ground plane - cls.n = 1.2 # refractive index of surrounding medium + cls.h = 1.125 # height of point source above ground plane + cls.n = 1.2 # refractive index of surrounding medium cls.src_cmpt = mp.Ez cls.wvl = 0.65 - cls.npts = 50 # number of points in [0,pi/2) range of angles - cls.angles = 0.5*math.pi/cls.npts*np.arange(cls.npts) - cls.r = 1000*cls.wvl # radius of far-field hemicircle + cls.npts = 50 # number of points in [0,pi/2) range of angles + cls.angles = 0.5 * math.pi / cls.npts * np.arange(cls.npts) + cls.r = 1000 * cls.wvl # radius of far-field hemicircle - - def radial_flux(self,sim,nearfield_box,r): - E = np.zeros((self.npts,3),dtype=np.complex128) - H = np.zeros((self.npts,3),dtype=np.complex128) + def radial_flux(self, sim, nearfield_box, r): + E = np.zeros((self.npts, 3), dtype=np.complex128) + H = np.zeros((self.npts, 3), dtype=np.complex128) for n in range(self.npts): - ff = sim.get_farfield(nearfield_box, - mp.Vector3(r*math.sin(self.angles[n]), - r*math.cos(self.angles[n]))) - E[n,:] = [np.conj(ff[j]) for j in range(3)] - H[n,:] = [ff[j+3] for j in range(3)] - - Px = np.real(E[:,1]*H[:,2]-E[:,2]*H[:,1]) # Ey*Hz-Ez*Hy - Py = np.real(E[:,2]*H[:,0]-E[:,0]*H[:,2]) # Ez*Hx-Ex*Hz - return np.sqrt(np.square(Px)+np.square(Py)) + ff = sim.get_farfield( + nearfield_box, + mp.Vector3(r * math.sin(self.angles[n]), r * math.cos(self.angles[n])), + ) + E[n, :] = [np.conj(ff[j]) for j in range(3)] + H[n, :] = [ff[j + 3] for j in range(3)] + Px = np.real(E[:, 1] * H[:, 2] - E[:, 2] * H[:, 1]) # Ey*Hz-Ez*Hy + Py = np.real(E[:, 2] * H[:, 0] - E[:, 0] * H[:, 2]) # Ez*Hx-Ex*Hz + return np.sqrt(np.square(Px) + np.square(Py)) def free_space_radiation(self): sxy = 4 dpml = 1 - cell_size = mp.Vector3(sxy+2*dpml,sxy+2*dpml) + cell_size = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml) pml_layers = [mp.PML(dpml)] - fcen = 1/self.wvl + fcen = 1 / self.wvl - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - center=mp.Vector3(), - component=self.src_cmpt)] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=mp.Vector3(), + component=self.src_cmpt, + ) + ] if self.src_cmpt == mp.Hz: - symmetries = [mp.Mirror(mp.X,phase=-1), - mp.Mirror(mp.Y,phase=-1)] + symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)] elif self.src_cmpt == mp.Ez: - symmetries = [mp.Mirror(mp.X,phase=+1), - mp.Mirror(mp.Y,phase=+1)] + symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=+1)] else: symmetries = [] - sim = mp.Simulation(cell_size=cell_size, - resolution=self.resolution, - sources=sources, - symmetries=symmetries, - boundary_layers=pml_layers, - default_material=mp.Medium(index=self.n)) - - nearfield_box = sim.add_near2far(fcen, - 0, - 1, - mp.Near2FarRegion(center=mp.Vector3(0,+0.5*sxy), - size=mp.Vector3(sxy,0)), - mp.Near2FarRegion(center=mp.Vector3(0,-0.5*sxy), - size=mp.Vector3(sxy,0), - weight=-1), - mp.Near2FarRegion(center=mp.Vector3(+0.5*sxy,0), - size=mp.Vector3(0,sxy)), - mp.Near2FarRegion(center=mp.Vector3(-0.5*sxy,0), - size=mp.Vector3(0,sxy), - weight=-1)) + sim = mp.Simulation( + cell_size=cell_size, + resolution=self.resolution, + sources=sources, + symmetries=symmetries, + boundary_layers=pml_layers, + default_material=mp.Medium(index=self.n), + ) + + nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(0, +0.5 * sxy), size=mp.Vector3(sxy, 0) + ), + mp.Near2FarRegion( + center=mp.Vector3(0, -0.5 * sxy), size=mp.Vector3(sxy, 0), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(+0.5 * sxy, 0), size=mp.Vector3(0, sxy) + ), + mp.Near2FarRegion( + center=mp.Vector3(-0.5 * sxy, 0), size=mp.Vector3(0, sxy), weight=-1 + ), + ) sim.run(until_after_sources=mp.stop_when_dft_decayed()) - return self.radial_flux(sim,nearfield_box,self.r) - + return self.radial_flux(sim, nearfield_box, self.r) def pec_ground_plane_radiation(self): - L = 8.0 # length of non-PML region + L = 8.0 # length of non-PML region dpml = 1.0 # thickness of PML - sxy = dpml+L+dpml - cell_size = mp.Vector3(sxy,sxy,0) + sxy = dpml + L + dpml + cell_size = mp.Vector3(sxy, sxy, 0) boundary_layers = [mp.PML(dpml)] - fcen = 1/self.wvl + fcen = 1 / self.wvl # The near-to-far field transformation feature only supports # homogeneous media which means it cannot explicitly take into @@ -103,44 +109,57 @@ def pec_ground_plane_radiation(self): # equivalent to a PEC boundary condition on one side. # Note: This setup means that the radiation pattern can only # be measured in the top half above the dipole. - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=self.src_cmpt, - center=mp.Vector3(0,+self.h)), - mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=self.src_cmpt, - center=mp.Vector3(0,-self.h), - amplitude=-1 if self.src_cmpt==mp.Ez else +1)] - - symmetries = [mp.Mirror(direction=mp.X, - phase=+1 if self.src_cmpt==mp.Ez else -1), - mp.Mirror(direction=mp.Y, - phase=-1 if self.src_cmpt==mp.Ez else +1)] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=self.n)) - - nearfield_box = sim.add_near2far(fcen, - 0, - 1, - mp.Near2FarRegion(center=mp.Vector3(0,2*self.h), - size=mp.Vector3(2*self.h,0)), - mp.Near2FarRegion(center=mp.Vector3(0,-2*self.h), - size=mp.Vector3(2*self.h,0), - weight=-1), - mp.Near2FarRegion(center=mp.Vector3(self.h,0), - size=mp.Vector3(0,4*self.h)), - mp.Near2FarRegion(center=mp.Vector3(-self.h,0), - size=mp.Vector3(0,4*self.h), - weight=-1)) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=self.src_cmpt, + center=mp.Vector3(0, +self.h), + ), + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=self.src_cmpt, + center=mp.Vector3(0, -self.h), + amplitude=-1 if self.src_cmpt == mp.Ez else +1, + ), + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=+1 if self.src_cmpt == mp.Ez else -1), + mp.Mirror(direction=mp.Y, phase=-1 if self.src_cmpt == mp.Ez else +1), + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=self.n), + ) + + nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(0, 2 * self.h), size=mp.Vector3(2 * self.h, 0) + ), + mp.Near2FarRegion( + center=mp.Vector3(0, -2 * self.h), + size=mp.Vector3(2 * self.h, 0), + weight=-1, + ), + mp.Near2FarRegion( + center=mp.Vector3(self.h, 0), size=mp.Vector3(0, 4 * self.h) + ), + mp.Near2FarRegion( + center=mp.Vector3(-self.h, 0), size=mp.Vector3(0, 4 * self.h), weight=-1 + ), + ) sim.run(until_after_sources=mp.stop_when_dft_decayed()) - return self.radial_flux(sim,nearfield_box,self.r) - + return self.radial_flux(sim, nearfield_box, self.r) def test_pec_ground_plane(self): """Unit test for near-to-far field transformation and symmetries. @@ -157,18 +176,19 @@ def test_pec_ground_plane(self): Pr_fsp = self.free_space_radiation() Pr_pec = self.pec_ground_plane_radiation() - k = 2*np.pi/(self.wvl/self.n) # wavevector in medium - Pr_theory = np.zeros(self.npts,) - for i,ang in enumerate(self.angles): - Pr_theory[i] = Pr_fsp[i] * 2*np.sin(k*self.h*np.cos(ang)) + k = 2 * np.pi / (self.wvl / self.n) # wavevector in medium + Pr_theory = np.zeros( + self.npts, + ) + for i, ang in enumerate(self.angles): + Pr_theory[i] = Pr_fsp[i] * 2 * np.sin(k * self.h * np.cos(ang)) - Pr_pec_norm = Pr_pec/np.max(Pr_pec) - Pr_theory_norm = (Pr_theory/max(Pr_theory))**2 + Pr_pec_norm = Pr_pec / np.max(Pr_pec) + Pr_theory_norm = (Pr_theory / max(Pr_theory)) ** 2 tol = 0.02 self.assertClose(Pr_pec_norm, Pr_theory_norm, epsilon=tol) - def test_poynting_theorem(self): """Unit test for near-to-far field transformation in 2d. @@ -183,87 +203,102 @@ def test_poynting_theorem(self): resolution = 50 sxy = 4 dpml = 1 - cell = mp.Vector3(sxy+2*dpml,sxy+2*dpml) + cell = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml) pml_layers = mp.PML(dpml) fcen = 1.0 df = 0.4 - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df), - center=mp.Vector3(), - component=mp.Ez)] - - symmetries = [mp.Mirror(mp.X), - mp.Mirror(mp.Y)] - - sim = mp.Simulation(cell_size=cell, - resolution=resolution, - sources=sources, - symmetries=symmetries, - boundary_layers=[pml_layers]) - - nearfield_box = sim.add_near2far(fcen, - 0, - 1, - mp.Near2FarRegion(mp.Vector3(y=0.5*sxy), - size=mp.Vector3(sxy)), - mp.Near2FarRegion(mp.Vector3(y=-0.5*sxy), - size=mp.Vector3(sxy), - weight=-1), - mp.Near2FarRegion(mp.Vector3(0.5*sxy), - size=mp.Vector3(y=sxy)), - mp.Near2FarRegion(mp.Vector3(-0.5*sxy), - size=mp.Vector3(y=sxy), - weight=-1)) - - flux_box = sim.add_flux(fcen, - 0, - 1, - mp.FluxRegion(mp.Vector3(y=0.5*sxy), - size=mp.Vector3(sxy)), - mp.FluxRegion(mp.Vector3(y=-0.5*sxy), - size=mp.Vector3(sxy), - weight=-1), - mp.FluxRegion(mp.Vector3(0.5*sxy), - size=mp.Vector3(y=sxy)), - mp.FluxRegion(mp.Vector3(-0.5*sxy), - size=mp.Vector3(y=sxy), - weight=-1)) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(), 1e-8)) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + center=mp.Vector3(), + component=mp.Ez, + ) + ] + + symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] + + sim = mp.Simulation( + cell_size=cell, + resolution=resolution, + sources=sources, + symmetries=symmetries, + boundary_layers=[pml_layers], + ) + + nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion(mp.Vector3(y=0.5 * sxy), size=mp.Vector3(sxy)), + mp.Near2FarRegion( + mp.Vector3(y=-0.5 * sxy), size=mp.Vector3(sxy), weight=-1 + ), + mp.Near2FarRegion(mp.Vector3(0.5 * sxy), size=mp.Vector3(y=sxy)), + mp.Near2FarRegion( + mp.Vector3(-0.5 * sxy), size=mp.Vector3(y=sxy), weight=-1 + ), + ) + + flux_box = sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(mp.Vector3(y=0.5 * sxy), size=mp.Vector3(sxy)), + mp.FluxRegion(mp.Vector3(y=-0.5 * sxy), size=mp.Vector3(sxy), weight=-1), + mp.FluxRegion(mp.Vector3(0.5 * sxy), size=mp.Vector3(y=sxy)), + mp.FluxRegion(mp.Vector3(-0.5 * sxy), size=mp.Vector3(y=sxy), weight=-1), + ) + + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-8 + ) + ) near_flux = mp.get_fluxes(flux_box)[0] - r = 1000/fcen # radius of far field circle - Pr = self.radial_flux(sim,nearfield_box,r) - far_flux_circle = 4*np.sum(Pr)*0.5*np.pi*r/len(Pr) - - rr = 20/fcen # length of far-field square box - res_far = 20 # resolution of far-field square box - far_flux_square = (nearfield_box.flux(mp.Y, - mp.Volume(center=mp.Vector3(y=0.5*rr), - size=mp.Vector3(rr)), - res_far)[0] - - nearfield_box.flux(mp.Y, - mp.Volume(center=mp.Vector3(y=-0.5*rr), - size=mp.Vector3(rr)), - res_far)[0] + - nearfield_box.flux(mp.X, - mp.Volume(center=mp.Vector3(0.5*rr), - size=mp.Vector3(y=rr)), - res_far)[0] - - nearfield_box.flux(mp.X, - mp.Volume(center=mp.Vector3(-0.5*rr), - size=mp.Vector3(y=rr)), - res_far)[0]) - - print("flux:, {:.6f}, {:.6f}, {:.6f}".format(near_flux,far_flux_circle,far_flux_square)) + r = 1000 / fcen # radius of far field circle + Pr = self.radial_flux(sim, nearfield_box, r) + far_flux_circle = 4 * np.sum(Pr) * 0.5 * np.pi * r / len(Pr) + + rr = 20 / fcen # length of far-field square box + res_far = 20 # resolution of far-field square box + far_flux_square = ( + nearfield_box.flux( + mp.Y, + mp.Volume(center=mp.Vector3(y=0.5 * rr), size=mp.Vector3(rr)), + res_far, + )[0] + - nearfield_box.flux( + mp.Y, + mp.Volume(center=mp.Vector3(y=-0.5 * rr), size=mp.Vector3(rr)), + res_far, + )[0] + + nearfield_box.flux( + mp.X, + mp.Volume(center=mp.Vector3(0.5 * rr), size=mp.Vector3(y=rr)), + res_far, + )[0] + - nearfield_box.flux( + mp.X, + mp.Volume(center=mp.Vector3(-0.5 * rr), size=mp.Vector3(y=rr)), + res_far, + )[0] + ) + + print( + "flux:, {:.6f}, {:.6f}, {:.6f}".format( + near_flux, far_flux_circle, far_flux_square + ) + ) self.assertAlmostEqual(near_flux, far_flux_circle, places=2) self.assertAlmostEqual(far_flux_circle, far_flux_square, places=2) self.assertAlmostEqual(far_flux_square, near_flux, places=2) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_array_metadata.py b/python/tests/test_array_metadata.py index 05f1d8e02..b79b2b150 100644 --- a/python/tests/test_array_metadata.py +++ b/python/tests/test_array_metadata.py @@ -3,8 +3,8 @@ import numpy as np from utils import ApproxComparisonTestCase -class TestArrayMetadata(ApproxComparisonTestCase): +class TestArrayMetadata(ApproxComparisonTestCase): def test_array_metadata(self): resolution = 25 @@ -14,75 +14,101 @@ def test_array_metadata(self): pad = 4 dpml = 2 - sxy = 2*(r+w+pad+dpml) - cell_size = mp.Vector3(sxy,sxy) + sxy = 2 * (r + w + pad + dpml) + cell_size = mp.Vector3(sxy, sxy) - nonpml_vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sxy-2*dpml,sxy-2*dpml)) + nonpml_vol = mp.Volume( + mp.Vector3(), size=mp.Vector3(sxy - 2 * dpml, sxy - 2 * dpml) + ) - geometry = [mp.Cylinder(radius=r+w, material=mp.Medium(index=n)), - mp.Cylinder(radius=r)] + geometry = [ + mp.Cylinder(radius=r + w, material=mp.Medium(index=n)), + mp.Cylinder(radius=r), + ] fcen = 0.118 df = 0.08 - symmetries = [mp.Mirror(mp.X,phase=-1), - mp.Mirror(mp.Y,phase=+1)] + symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=+1)] pml_layers = [mp.PML(dpml)] # CW source - src = [mp.Source(mp.ContinuousSource(fcen,fwidth=df), mp.Ez, mp.Vector3(r+0.1)), - mp.Source(mp.ContinuousSource(fcen,fwidth=df), mp.Ez, mp.Vector3(-(r+0.1)), amplitude=-1)] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - sources=src, - resolution=resolution, - force_complex_fields=True, - symmetries=symmetries, - boundary_layers=pml_layers) + src = [ + mp.Source(mp.ContinuousSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1)), + mp.Source( + mp.ContinuousSource(fcen, fwidth=df), + mp.Ez, + mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + sources=src, + resolution=resolution, + force_complex_fields=True, + symmetries=symmetries, + boundary_layers=pml_layers, + ) sim.init_sim() sim.solve_cw(1e-5 if mp.is_single_precision() else 1e-6, 1000, 10) def electric_energy(r, ez, eps): - return np.real(eps * np.conj(ez)*ez) + return np.real(eps * np.conj(ez) * ez) def vec_func(r): - return r.x**2 + 2*r.y**2 - - electric_energy_total = sim.integrate_field_function([mp.Ez,mp.Dielectric],electric_energy,nonpml_vol) - electric_energy_max = sim.max_abs_field_function([mp.Ez,mp.Dielectric],electric_energy,nonpml_vol) - vec_func_total = sim.integrate_field_function([],vec_func,nonpml_vol) + return r.x**2 + 2 * r.y**2 + + electric_energy_total = sim.integrate_field_function( + [mp.Ez, mp.Dielectric], electric_energy, nonpml_vol + ) + electric_energy_max = sim.max_abs_field_function( + [mp.Ez, mp.Dielectric], electric_energy, nonpml_vol + ) + vec_func_total = sim.integrate_field_function([], vec_func, nonpml_vol) cw_modal_volume = (electric_energy_total / electric_energy_max) * vec_func_total sim.reset_meep() # pulsed source - src = [mp.Source(mp.GaussianSource(fcen,fwidth=df), mp.Ez, mp.Vector3(r+0.1)), - mp.Source(mp.GaussianSource(fcen,fwidth=df), mp.Ez, mp.Vector3(-(r+0.1)), amplitude=-1)] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - k_point=mp.Vector3(), - sources=src, - resolution=resolution, - symmetries=symmetries, - boundary_layers=pml_layers) + src = [ + mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1)), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + mp.Ez, + mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + k_point=mp.Vector3(), + sources=src, + resolution=resolution, + symmetries=symmetries, + boundary_layers=pml_layers, + ) dft_obj = sim.add_dft_fields([mp.Ez], fcen, 0, 1, where=nonpml_vol) sim.run(until_after_sources=100) Ez = sim.get_dft_array(dft_obj, mp.Ez, 0) - (X,Y,Z,W) = sim.get_array_metadata(dft_cell=dft_obj) + (X, Y, Z, W) = sim.get_array_metadata(dft_cell=dft_obj) Eps = sim.get_array(vol=nonpml_vol, component=mp.Dielectric) - EpsE2 = np.real(Eps*np.conj(Ez)*Ez) + EpsE2 = np.real(Eps * np.conj(Ez) * Ez) xm, ym = np.meshgrid(X, Y) - vec_func_sum = np.sum(W*(xm**2 + 2*ym**2)) - pulse_modal_volume = np.sum(W*EpsE2)/np.max(EpsE2) * vec_func_sum + vec_func_sum = np.sum(W * (xm**2 + 2 * ym**2)) + pulse_modal_volume = np.sum(W * EpsE2) / np.max(EpsE2) * vec_func_sum tol = 5e-2 if mp.is_single_precision() else 1e-2 - self.assertClose(cw_modal_volume/pulse_modal_volume, 1.0, epsilon=tol) + self.assertClose(cw_modal_volume / pulse_modal_volume, 1.0, epsilon=tol) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_bend_flux.py b/python/tests/test_bend_flux.py index a8d4f4b08..37d298dcd 100644 --- a/python/tests/test_bend_flux.py +++ b/python/tests/test_bend_flux.py @@ -6,7 +6,6 @@ class TestBendFlux(ApproxComparisonTestCase): - def init(self, no_bend=False, gdsii=False): sx = 16 sy = 32 @@ -16,60 +15,91 @@ def init(self, no_bend=False, gdsii=False): wvg_ycen = -0.5 * (sy - w - (2 * pad)) wvg_xcen = 0.5 * (sx - w - (2 * pad)) height = mp.inf - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - gdsii_file = os.path.join(data_dir, 'bend-flux.gds') + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + gdsii_file = os.path.join(data_dir, "bend-flux.gds") if no_bend: if gdsii: - geometry = mp.get_GDSII_prisms(mp.Medium(epsilon=12), gdsii_file, 1, 0, height) + geometry = mp.get_GDSII_prisms( + mp.Medium(epsilon=12), gdsii_file, 1, 0, height + ) else: - no_bend_vertices = [mp.Vector3(-0.5 * sx - 5, wvg_ycen - 0.5 * w), - mp.Vector3(+0.5 * sx + 5, wvg_ycen - 0.5 * w), - mp.Vector3(+0.5 * sx + 5, wvg_ycen + 0.5 * w), - mp.Vector3(-0.5 * sx - 5, wvg_ycen + 0.5 * w)] - - geometry = [mp.Prism(no_bend_vertices, height, material=mp.Medium(epsilon=12))] + no_bend_vertices = [ + mp.Vector3(-0.5 * sx - 5, wvg_ycen - 0.5 * w), + mp.Vector3(+0.5 * sx + 5, wvg_ycen - 0.5 * w), + mp.Vector3(+0.5 * sx + 5, wvg_ycen + 0.5 * w), + mp.Vector3(-0.5 * sx - 5, wvg_ycen + 0.5 * w), + ] + + geometry = [ + mp.Prism(no_bend_vertices, height, material=mp.Medium(epsilon=12)) + ] elif gdsii: - geometry = mp.get_GDSII_prisms(mp.Medium(epsilon=12), gdsii_file, 2, 0, height) + geometry = mp.get_GDSII_prisms( + mp.Medium(epsilon=12), gdsii_file, 2, 0, height + ) else: - bend_vertices = [mp.Vector3(-0.5 * sx, wvg_ycen - 0.5 * w), - mp.Vector3(wvg_xcen + 0.5 * w, wvg_ycen - 0.5 * w), - mp.Vector3(wvg_xcen + 0.5 * w, 0.5 * sy), - mp.Vector3(wvg_xcen - 0.5 * w, 0.5 * sy), - mp.Vector3(wvg_xcen - 0.5 * w, wvg_ycen + 0.5 * w), - mp.Vector3(-0.5 * sx, wvg_ycen + 0.5 * w)] + bend_vertices = [ + mp.Vector3(-0.5 * sx, wvg_ycen - 0.5 * w), + mp.Vector3(wvg_xcen + 0.5 * w, wvg_ycen - 0.5 * w), + mp.Vector3(wvg_xcen + 0.5 * w, 0.5 * sy), + mp.Vector3(wvg_xcen - 0.5 * w, 0.5 * sy), + mp.Vector3(wvg_xcen - 0.5 * w, wvg_ycen + 0.5 * w), + mp.Vector3(-0.5 * sx, wvg_ycen + 0.5 * w), + ] geometry = [mp.Prism(bend_vertices, height, material=mp.Medium(epsilon=12))] fcen = 0.15 df = 0.1 - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, - center=mp.Vector3(1 + (-0.5 * sx), wvg_ycen), size=mp.Vector3(0, w))] + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(1 + (-0.5 * sx), wvg_ycen), + size=mp.Vector3(0, w), + ) + ] pml_layers = [mp.PML(1.0)] resolution = 10 nfreq = 100 - self.sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) + self.sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, + ) if no_bend: - fr = mp.FluxRegion(center=mp.Vector3((sx / 2) - 1.5, wvg_ycen), size=mp.Vector3(0, w * 2)) + fr = mp.FluxRegion( + center=mp.Vector3((sx / 2) - 1.5, wvg_ycen), size=mp.Vector3(0, w * 2) + ) else: - fr = mp.FluxRegion(center=mp.Vector3(wvg_xcen, (sy / 2) - 1.5), size=mp.Vector3(w * 2, 0)) + fr = mp.FluxRegion( + center=mp.Vector3(wvg_xcen, (sy / 2) - 1.5), size=mp.Vector3(w * 2, 0) + ) self.trans = self.sim.add_flux(fcen, df, nfreq, fr, decimation_factor=1) - self.trans_decimated = self.sim.add_flux(fcen, df, nfreq, fr, decimation_factor=5) - - refl_fr = mp.FluxRegion(center=mp.Vector3((-0.5 * sx) + 1.5, wvg_ycen), - size=mp.Vector3(0, w * 2)) - self.refl = self.sim.add_flux(np.linspace(fcen-0.5*df,fcen+0.5*df,nfreq), refl_fr, - decimation_factor=1) - self.refl_decimated = self.sim.add_flux(np.linspace(fcen-0.5*df,fcen+0.5*df,nfreq), refl_fr, - decimation_factor=10) + self.trans_decimated = self.sim.add_flux( + fcen, df, nfreq, fr, decimation_factor=5 + ) + + refl_fr = mp.FluxRegion( + center=mp.Vector3((-0.5 * sx) + 1.5, wvg_ycen), size=mp.Vector3(0, w * 2) + ) + self.refl = self.sim.add_flux( + np.linspace(fcen - 0.5 * df, fcen + 0.5 * df, nfreq), + refl_fr, + decimation_factor=1, + ) + self.refl_decimated = self.sim.add_flux( + np.linspace(fcen - 0.5 * df, fcen + 0.5 * df, nfreq), + refl_fr, + decimation_factor=10, + ) if no_bend: self.pt = mp.Vector3((sx / 2) - 1.5, wvg_ycen) @@ -107,13 +137,21 @@ def run_bend_flux(self, from_gdsii_file): (0.119191919192, 0.0254987474079, 0.0252348211592), ] - res = list(zip(mp.get_flux_freqs(self.trans), - mp.get_fluxes(self.trans), - mp.get_fluxes(self.refl))) - - res_decimated = list(zip(mp.get_flux_freqs(self.trans_decimated), - mp.get_fluxes(self.trans_decimated), - mp.get_fluxes(self.refl_decimated))) + res = list( + zip( + mp.get_flux_freqs(self.trans), + mp.get_fluxes(self.trans), + mp.get_fluxes(self.refl), + ) + ) + + res_decimated = list( + zip( + mp.get_flux_freqs(self.trans_decimated), + mp.get_fluxes(self.trans_decimated), + mp.get_fluxes(self.refl_decimated), + ) + ) tol = 1e-6 if mp.is_single_precision() else 1e-8 self.assertClose(np.array(expected), np.array(res[:20]), epsilon=tol) @@ -150,13 +188,21 @@ def run_bend_flux(self, from_gdsii_file): (0.11919191919191931, 0.008646855439680507, -0.005614491919262783), ] - res = list(zip(mp.get_flux_freqs(self.trans), - mp.get_fluxes(self.trans), - mp.get_fluxes(self.refl))) - - res_decimated = list(zip(mp.get_flux_freqs(self.trans_decimated), - mp.get_fluxes(self.trans_decimated), - mp.get_fluxes(self.refl_decimated))) + res = list( + zip( + mp.get_flux_freqs(self.trans), + mp.get_fluxes(self.trans), + mp.get_fluxes(self.refl), + ) + ) + + res_decimated = list( + zip( + mp.get_flux_freqs(self.trans_decimated), + mp.get_fluxes(self.trans_decimated), + mp.get_fluxes(self.refl_decimated), + ) + ) tol = 1e-3 self.assertClose(np.array(expected), np.array(res[:20]), epsilon=tol) @@ -168,5 +214,5 @@ def test_bend_flux(self): self.run_bend_flux(True) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_binary_grating.py b/python/tests/test_binary_grating.py index 51b5a0e69..eff359a9b 100644 --- a/python/tests/test_binary_grating.py +++ b/python/tests/test_binary_grating.py @@ -9,282 +9,330 @@ class TestEigCoeffs(unittest.TestCase): - @classmethod - def setUpClass(cls): - cls.resolution = 30 # pixels/μm - - cls.dpml = 1.0 # PML thickness - cls.dsub = 1.0 # substrate thickness - cls.dpad = 1.0 # padding thickness between grating and PML - cls.gp = 6.0 # grating period - cls.gh = 0.5 # grating height - cls.gdc = 0.5 # grating duty cycle - - cls.sx = cls.dpml+cls.dsub+cls.gh+cls.dpad+cls.dpml - cls.sy = cls.gp - - cls.cell_size = mp.Vector3(cls.sx,cls.sy,0) - - # replace anisotropic PML with isotropic Absorber to - # attenuate parallel-directed fields of oblique source - cls.abs_layers = [mp.Absorber(thickness=cls.dpml,direction=mp.X)] - - wvl = 0.5 # center wavelength - cls.fcen = 1/wvl # center frequency - cls.df = 0.05*cls.fcen # frequency width - - cls.ng = 1.5 - cls.glass = mp.Medium(index=cls.ng) - - cls.geometry = [mp.Block(material=cls.glass, - size=mp.Vector3(cls.dpml+cls.dsub,mp.inf,mp.inf), - center=mp.Vector3(-0.5*cls.sx+0.5*(cls.dpml+cls.dsub),0,0)), - mp.Block(material=cls.glass, - size=mp.Vector3(cls.gh,cls.gdc*cls.gp,mp.inf), - center=mp.Vector3(-0.5*cls.sx+cls.dpml+cls.dsub+0.5*cls.gh,0,0))] - - - @parameterized.parameterized.expand([(0,), (10.7,)]) - def test_binary_grating_oblique(self, theta): - # rotation angle of incident planewave - # counterclockwise (CCW) about Z axis, 0 degrees along +X axis - theta_in = math.radians(theta) - - # k (in source medium) with correct length (plane of incidence: XY) - k = mp.Vector3(self.fcen*self.ng).rotate(mp.Vector3(0,0,1), theta_in) - - symmetries = [] - eig_parity = mp.ODD_Z - if theta_in == 0: - symmetries = [mp.Mirror(mp.Y)] - eig_parity += mp.EVEN_Y - - def pw_amp(k,x0): - def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) - return _pw_amp - - src_pt = mp.Vector3(-0.5*self.sx+self.dpml,0,0) - sources = [mp.Source(mp.GaussianSource(self.fcen,fwidth=self.df), - component=mp.Ez, # S polarization - center=src_pt, - size=mp.Vector3(0,self.sy,0), - amp_func=pw_amp(k,src_pt))] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.abs_layers, - k_point=k, - default_material=self.glass, - sources=sources, - symmetries=symmetries) - - refl_pt = mp.Vector3(-0.5*self.sx+self.dpml+0.5*self.dsub,0,0) - refl_flux = sim.add_flux(self.fcen, - 0, - 1, - mp.FluxRegion(center=refl_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - input_flux = mp.get_fluxes(refl_flux) - input_flux_data = sim.get_flux_data(refl_flux) - - sim.reset_meep() - - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.abs_layers, - geometry=self.geometry, - k_point=k, - sources=sources, - symmetries=symmetries) - - refl_flux = sim.add_flux(self.fcen, - 0, - 1, - mp.FluxRegion(center=refl_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.load_minus_flux_data(refl_flux,input_flux_data) - - tran_pt = mp.Vector3(0.5*self.sx-self.dpml-0.5*self.dpad,0,0) - tran_flux = sim.add_flux(self.fcen, - 0, - 1, - mp.FluxRegion(center=tran_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - # number of reflected orders - nm_r = np.floor((self.fcen*self.ng-k.y)*self.gp)-np.ceil((-self.fcen*self.ng-k.y)*self.gp) - if theta_in == 0: - nm_r = nm_r/2 # since eig_parity removes degeneracy in y-direction - nm_r = int(nm_r) - - res = sim.get_eigenmode_coefficients(refl_flux, - range(1,nm_r+1), - eig_parity=eig_parity) - r_coeffs = res.alpha - - Rsum = 0 - for nm in range(nm_r): - Rsum += abs(r_coeffs[nm,0,1])**2/input_flux[0] - - # number of transmitted orders - nm_t = np.floor((self.fcen-k.y)*self.gp)-np.ceil((-self.fcen-k.y)*self.gp) - if theta_in == 0: - nm_t = nm_t/2 # since eig_parity removes degeneracy in y-direction - nm_t = int(nm_t) - - res = sim.get_eigenmode_coefficients(tran_flux, - range(1,nm_t+1), - eig_parity=eig_parity) - t_coeffs = res.alpha - - Tsum = 0 - for nm in range(nm_t): - Tsum += abs(t_coeffs[nm,0,0])**2/input_flux[0] - - r_flux = mp.get_fluxes(refl_flux) - t_flux = mp.get_fluxes(tran_flux) - Rflux = -r_flux[0]/input_flux[0] - Tflux = t_flux[0]/input_flux[0] - - print("refl:, {}, {}".format(Rsum,Rflux)) - print("tran:, {}, {}".format(Tsum,Tflux)) - print("sum:, {}, {}".format(Rsum+Tsum,Rflux+Tflux)) - - self.assertAlmostEqual(Rsum,Rflux,places=2) - self.assertAlmostEqual(Tsum,Tflux,places=2) - self.assertAlmostEqual(Rsum+Tsum,1.00,places=2) - - - @parameterized.parameterized.expand([(13.2,"real/imag"), - (17.7,"complex"), - (21.2, "3d")]) - def test_binary_grating_special_kz(self, theta, kz_2d): - # rotation angle of incident planewave - # counterclockwise (CCW) about Y axis, 0 degrees along +X axis - theta_in = math.radians(theta) - - # k (in source medium) with correct length (plane of incidence: XZ) - k = mp.Vector3(self.fcen*self.ng).rotate(mp.Vector3(0,1,0), theta_in) - - symmetries = [mp.Mirror(mp.Y)] - - def pw_amp(k,x0): - def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) - return _pw_amp - - src_pt = mp.Vector3(-0.5*self.sx+self.dpml,0,0) - sources = [mp.Source(mp.GaussianSource(self.fcen,fwidth=self.df), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(0,self.sy,0), - amp_func=pw_amp(k,src_pt))] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.abs_layers, - k_point=k, - default_material=self.glass, - sources=sources, - symmetries=symmetries, - kz_2d=kz_2d) - - refl_pt = mp.Vector3(-0.5*self.sx+self.dpml+0.5*self.dsub,0,0) - refl_flux = sim.add_mode_monitor(self.fcen, - 0, - 1, - mp.FluxRegion(center=refl_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - input_flux = mp.get_fluxes(refl_flux) - input_flux_data = sim.get_flux_data(refl_flux) - - sim.reset_meep() - - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.abs_layers, - geometry=self.geometry, - k_point=k, - sources=sources, - symmetries=symmetries, - kz_2d=kz_2d) - - refl_flux = sim.add_mode_monitor(self.fcen, - 0, - 1, - mp.FluxRegion(center=refl_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.load_minus_flux_data(refl_flux,input_flux_data) - - tran_pt = mp.Vector3(0.5*self.sx-self.dpml-0.5*self.dpad,0,0) - tran_flux = sim.add_mode_monitor(self.fcen, - 0, - 1, - mp.FluxRegion(center=tran_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - # number of reflected orders - nm_r = (np.ceil((np.sqrt((self.fcen*self.ng)**2-k.z**2)-k.y)*self.gp) - - np.floor((-np.sqrt((self.fcen*self.ng)**2-k.z**2)-k.y)*self.gp)) - nm_r = int(nm_r/2) - - Rsum = 0 - for nm in range(nm_r): - for S_pol in [False, True]: - res = sim.get_eigenmode_coefficients(refl_flux, - mp.DiffractedPlanewave([0,nm,0], - mp.Vector3(1,0,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) + @classmethod + def setUpClass(cls): + cls.resolution = 30 # pixels/μm + + cls.dpml = 1.0 # PML thickness + cls.dsub = 1.0 # substrate thickness + cls.dpad = 1.0 # padding thickness between grating and PML + cls.gp = 6.0 # grating period + cls.gh = 0.5 # grating height + cls.gdc = 0.5 # grating duty cycle + + cls.sx = cls.dpml + cls.dsub + cls.gh + cls.dpad + cls.dpml + cls.sy = cls.gp + + cls.cell_size = mp.Vector3(cls.sx, cls.sy, 0) + + # replace anisotropic PML with isotropic Absorber to + # attenuate parallel-directed fields of oblique source + cls.abs_layers = [mp.Absorber(thickness=cls.dpml, direction=mp.X)] + + wvl = 0.5 # center wavelength + cls.fcen = 1 / wvl # center frequency + cls.df = 0.05 * cls.fcen # frequency width + + cls.ng = 1.5 + cls.glass = mp.Medium(index=cls.ng) + + cls.geometry = [ + mp.Block( + material=cls.glass, + size=mp.Vector3(cls.dpml + cls.dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * cls.sx + 0.5 * (cls.dpml + cls.dsub), 0, 0), + ), + mp.Block( + material=cls.glass, + size=mp.Vector3(cls.gh, cls.gdc * cls.gp, mp.inf), + center=mp.Vector3( + -0.5 * cls.sx + cls.dpml + cls.dsub + 0.5 * cls.gh, 0, 0 + ), + ), + ] + + @parameterized.parameterized.expand([(0,), (10.7,)]) + def test_binary_grating_oblique(self, theta): + # rotation angle of incident planewave + # counterclockwise (CCW) about Z axis, 0 degrees along +X axis + theta_in = math.radians(theta) + + # k (in source medium) with correct length (plane of incidence: XY) + k = mp.Vector3(self.fcen * self.ng).rotate(mp.Vector3(0, 0, 1), theta_in) + + symmetries = [] + eig_parity = mp.ODD_Z + if theta_in == 0: + symmetries = [mp.Mirror(mp.Y)] + eig_parity += mp.EVEN_Y + + def pw_amp(k, x0): + def _pw_amp(x): + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + + return _pw_amp + + src_pt = mp.Vector3(-0.5 * self.sx + self.dpml, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(self.fcen, fwidth=self.df), + component=mp.Ez, # S polarization + center=src_pt, + size=mp.Vector3(0, self.sy, 0), + amp_func=pw_amp(k, src_pt), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.abs_layers, + k_point=k, + default_material=self.glass, + sources=sources, + symmetries=symmetries, + ) + + refl_pt = mp.Vector3(-0.5 * self.sx + self.dpml + 0.5 * self.dsub, 0, 0) + refl_flux = sim.add_flux( + self.fcen, + 0, + 1, + mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.run(until_after_sources=mp.stop_when_dft_decayed()) + + input_flux = mp.get_fluxes(refl_flux) + input_flux_data = sim.get_flux_data(refl_flux) + + sim.reset_meep() + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.abs_layers, + geometry=self.geometry, + k_point=k, + sources=sources, + symmetries=symmetries, + ) + + refl_flux = sim.add_flux( + self.fcen, + 0, + 1, + mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.load_minus_flux_data(refl_flux, input_flux_data) + + tran_pt = mp.Vector3(0.5 * self.sx - self.dpml - 0.5 * self.dpad, 0, 0) + tran_flux = sim.add_flux( + self.fcen, + 0, + 1, + mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.run(until_after_sources=mp.stop_when_dft_decayed()) + + # number of reflected orders + nm_r = np.floor((self.fcen * self.ng - k.y) * self.gp) - np.ceil( + (-self.fcen * self.ng - k.y) * self.gp + ) + if theta_in == 0: + nm_r = nm_r / 2 # since eig_parity removes degeneracy in y-direction + nm_r = int(nm_r) + + res = sim.get_eigenmode_coefficients( + refl_flux, range(1, nm_r + 1), eig_parity=eig_parity + ) r_coeffs = res.alpha - Rmode = abs(r_coeffs[0,0,1])**2/input_flux[0] - print("refl-order:, {}, {}, {}".format("s" if S_pol else "p",nm,Rmode)) - Rsum += Rmode if nm == 0 else 2*Rmode - - # number of transmitted orders - nm_t = (np.ceil((np.sqrt(self.fcen**2-k.z**2)-k.y)*self.gp) - - np.floor((-np.sqrt(self.fcen**2-k.z**2)-k.y)*self.gp)) - nm_t = int(nm_t/2) - - Tsum = 0 - for nm in range(nm_t): - for S_pol in [False, True]: - res = sim.get_eigenmode_coefficients(tran_flux, - mp.DiffractedPlanewave([0,nm,0], - mp.Vector3(1,0,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) - t_coeffs = res.alpha - Tmode = abs(t_coeffs[0,0,0])**2/input_flux[0] - print("tran-order:, {}, {}, {}".format("s" if S_pol else "p",nm,Tmode)) - Tsum += Tmode if nm == 0 else 2*Tmode - - r_flux = mp.get_fluxes(refl_flux) - t_flux = mp.get_fluxes(tran_flux) - Rflux = -r_flux[0]/input_flux[0] - Tflux = t_flux[0]/input_flux[0] - - print("refl:, {}, {}".format(Rsum,Rflux)) - print("tran:, {}, {}".format(Tsum,Tflux)) - print("sum:, {}, {}".format(Rsum+Tsum,Rflux+Tflux)) - - self.assertAlmostEqual(Rsum,Rflux,places=2) - self.assertAlmostEqual(Tsum,Tflux,places=2) - self.assertAlmostEqual(Rsum+Tsum,1.00,places=2) + Rsum = 0 + for nm in range(nm_r): + Rsum += abs(r_coeffs[nm, 0, 1]) ** 2 / input_flux[0] + + # number of transmitted orders + nm_t = np.floor((self.fcen - k.y) * self.gp) - np.ceil( + (-self.fcen - k.y) * self.gp + ) + if theta_in == 0: + nm_t = nm_t / 2 # since eig_parity removes degeneracy in y-direction + nm_t = int(nm_t) + + res = sim.get_eigenmode_coefficients( + tran_flux, range(1, nm_t + 1), eig_parity=eig_parity + ) + t_coeffs = res.alpha -if __name__ == '__main__': - unittest.main() + Tsum = 0 + for nm in range(nm_t): + Tsum += abs(t_coeffs[nm, 0, 0]) ** 2 / input_flux[0] + + r_flux = mp.get_fluxes(refl_flux) + t_flux = mp.get_fluxes(tran_flux) + Rflux = -r_flux[0] / input_flux[0] + Tflux = t_flux[0] / input_flux[0] + + print("refl:, {}, {}".format(Rsum, Rflux)) + print("tran:, {}, {}".format(Tsum, Tflux)) + print("sum:, {}, {}".format(Rsum + Tsum, Rflux + Tflux)) + + self.assertAlmostEqual(Rsum, Rflux, places=2) + self.assertAlmostEqual(Tsum, Tflux, places=2) + self.assertAlmostEqual(Rsum + Tsum, 1.00, places=2) + + @parameterized.parameterized.expand( + [(13.2, "real/imag"), (17.7, "complex"), (21.2, "3d")] + ) + def test_binary_grating_special_kz(self, theta, kz_2d): + # rotation angle of incident planewave + # counterclockwise (CCW) about Y axis, 0 degrees along +X axis + theta_in = math.radians(theta) + + # k (in source medium) with correct length (plane of incidence: XZ) + k = mp.Vector3(self.fcen * self.ng).rotate(mp.Vector3(0, 1, 0), theta_in) + + symmetries = [mp.Mirror(mp.Y)] + + def pw_amp(k, x0): + def _pw_amp(x): + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + + return _pw_amp + + src_pt = mp.Vector3(-0.5 * self.sx + self.dpml, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(self.fcen, fwidth=self.df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(0, self.sy, 0), + amp_func=pw_amp(k, src_pt), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.abs_layers, + k_point=k, + default_material=self.glass, + sources=sources, + symmetries=symmetries, + kz_2d=kz_2d, + ) + + refl_pt = mp.Vector3(-0.5 * self.sx + self.dpml + 0.5 * self.dsub, 0, 0) + refl_flux = sim.add_mode_monitor( + self.fcen, + 0, + 1, + mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.run(until_after_sources=mp.stop_when_dft_decayed()) + + input_flux = mp.get_fluxes(refl_flux) + input_flux_data = sim.get_flux_data(refl_flux) + + sim.reset_meep() + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.abs_layers, + geometry=self.geometry, + k_point=k, + sources=sources, + symmetries=symmetries, + kz_2d=kz_2d, + ) + + refl_flux = sim.add_mode_monitor( + self.fcen, + 0, + 1, + mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.load_minus_flux_data(refl_flux, input_flux_data) + + tran_pt = mp.Vector3(0.5 * self.sx - self.dpml - 0.5 * self.dpad, 0, 0) + tran_flux = sim.add_mode_monitor( + self.fcen, + 0, + 1, + mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.run(until_after_sources=mp.stop_when_dft_decayed()) + + # number of reflected orders + nm_r = np.ceil( + (np.sqrt((self.fcen * self.ng) ** 2 - k.z**2) - k.y) * self.gp + ) - np.floor((-np.sqrt((self.fcen * self.ng) ** 2 - k.z**2) - k.y) * self.gp) + nm_r = int(nm_r / 2) + + Rsum = 0 + for nm in range(nm_r): + for S_pol in [False, True]: + res = sim.get_eigenmode_coefficients( + refl_flux, + mp.DiffractedPlanewave( + [0, nm, 0], + mp.Vector3(1, 0, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) + r_coeffs = res.alpha + Rmode = abs(r_coeffs[0, 0, 1]) ** 2 / input_flux[0] + print( + "refl-order:, {}, {}, {}".format("s" if S_pol else "p", nm, Rmode) + ) + Rsum += Rmode if nm == 0 else 2 * Rmode + + # number of transmitted orders + nm_t = np.ceil((np.sqrt(self.fcen**2 - k.z**2) - k.y) * self.gp) - np.floor( + (-np.sqrt(self.fcen**2 - k.z**2) - k.y) * self.gp + ) + nm_t = int(nm_t / 2) + + Tsum = 0 + for nm in range(nm_t): + for S_pol in [False, True]: + res = sim.get_eigenmode_coefficients( + tran_flux, + mp.DiffractedPlanewave( + [0, nm, 0], + mp.Vector3(1, 0, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) + t_coeffs = res.alpha + Tmode = abs(t_coeffs[0, 0, 0]) ** 2 / input_flux[0] + print( + "tran-order:, {}, {}, {}".format("s" if S_pol else "p", nm, Tmode) + ) + Tsum += Tmode if nm == 0 else 2 * Tmode + + r_flux = mp.get_fluxes(refl_flux) + t_flux = mp.get_fluxes(tran_flux) + Rflux = -r_flux[0] / input_flux[0] + Tflux = t_flux[0] / input_flux[0] + + print("refl:, {}, {}".format(Rsum, Rflux)) + print("tran:, {}, {}".format(Tsum, Tflux)) + print("sum:, {}, {}".format(Rsum + Tsum, Rflux + Tflux)) + + self.assertAlmostEqual(Rsum, Rflux, places=2) + self.assertAlmostEqual(Tsum, Tflux, places=2) + self.assertAlmostEqual(Rsum + Tsum, 1.00, places=2) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_binary_partition_utils.py b/python/tests/test_binary_partition_utils.py index 5b6f72866..1267fa233 100644 --- a/python/tests/test_binary_partition_utils.py +++ b/python/tests/test_binary_partition_utils.py @@ -8,11 +8,11 @@ import numpy as np PARTITION_NO_DUPLICATE_PROC_ID = mp.BinaryPartition( - data=[(mp.X, -2.), 0, [(mp.Y, 1.5), [(mp.X, 4.), 1, [(mp.Y, - 0.5), 4, 3]], 2]]) + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] +) PARTITION_DUPLICATE_PROC_ID = mp.BinaryPartition( - data=[(mp.X, -2.), 0, [(mp.Y, 1.5), [(mp.X, 4.), 1, [(mp.Y, - 0.5), 1, 3]], 0]]) + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 1, 3]], 0]] +) # Mocked chunk IDs since we are in a single-core environment CHUNK_OWNERS_NO_DUPLICATE_PROC_ID = np.array([0, 1, 4, 3, 2]) @@ -20,22 +20,25 @@ class BinaryPartitionUtilsTest(unittest.TestCase): - - @parameterized.parameterized.expand([ - (PARTITION_NO_DUPLICATE_PROC_ID, False), - (PARTITION_NO_DUPLICATE_PROC_ID.right, False), - (PARTITION_NO_DUPLICATE_PROC_ID.left, True), - (PARTITION_DUPLICATE_PROC_ID, False), - (PARTITION_DUPLICATE_PROC_ID.right, False), - (PARTITION_DUPLICATE_PROC_ID.left, True), - ]) + @parameterized.parameterized.expand( + [ + (PARTITION_NO_DUPLICATE_PROC_ID, False), + (PARTITION_NO_DUPLICATE_PROC_ID.right, False), + (PARTITION_NO_DUPLICATE_PROC_ID.left, True), + (PARTITION_DUPLICATE_PROC_ID, False), + (PARTITION_DUPLICATE_PROC_ID.right, False), + (PARTITION_DUPLICATE_PROC_ID.left, True), + ] + ) def test_is_leaf_node(self, partition, expected_leaf_status): self.assertEqual(bpu.is_leaf_node(partition), expected_leaf_status) - @parameterized.parameterized.expand([ - (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID), - (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID), - ]) + @parameterized.parameterized.expand( + [ + (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID), + (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID), + ] + ) def test_enumerate_leaf_nodes(self, partition, chunk_owners): leaf_nodes = list(bpu.enumerate_leaf_nodes(partition)) self.assertEqual(len(leaf_nodes), partition.numchunks()) @@ -44,216 +47,237 @@ def test_enumerate_leaf_nodes(self, partition, chunk_owners): def test_partition_has_duplicate_proc_ids(self): self.assertFalse( - bpu.partition_has_duplicate_proc_ids(PARTITION_NO_DUPLICATE_PROC_ID)) + bpu.partition_has_duplicate_proc_ids(PARTITION_NO_DUPLICATE_PROC_ID) + ) self.assertTrue( - bpu.partition_has_duplicate_proc_ids(PARTITION_DUPLICATE_PROC_ID)) + bpu.partition_has_duplicate_proc_ids(PARTITION_DUPLICATE_PROC_ID) + ) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, [0, 1, 2, 3, 4], - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, [0, 1, 2, 3, 4], - ), - ]) + ), + ] + ) def test_get_total_weight(self, partition, weights_by_proc_id): if not bpu.partition_has_duplicate_proc_ids(partition): self.assertEqual( bpu.get_total_weight(partition, weights_by_proc_id), - sum(weights_by_proc_id)) + sum(weights_by_proc_id), + ) self.assertEqual( bpu.get_total_weight(partition.right.left, weights_by_proc_id), - weights_by_proc_id[1] + weights_by_proc_id[4] + weights_by_proc_id[3]) + weights_by_proc_id[1] + weights_by_proc_id[4] + weights_by_proc_id[3], + ) else: with self.assertRaises(ValueError): bpu.get_total_weight(partition, weights_by_proc_id) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, [0, 1, 2, 3, 4], - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, [0, 1, 2, 3, 4], - ), - ]) + ), + ] + ) def test_get_left_right_total_weights(self, partition, weights_by_proc_id): proc_ids = [node.proc_id for node in bpu.enumerate_leaf_nodes(partition)] no_duplicates = len(set(proc_ids)) == len(proc_ids) if no_duplicates: self.assertEqual( bpu.get_left_right_total_weights(partition, weights_by_proc_id), - (bpu.get_total_weight(partition.left, weights_by_proc_id), - bpu.get_total_weight(partition.right, weights_by_proc_id))) + ( + bpu.get_total_weight(partition.left, weights_by_proc_id), + bpu.get_total_weight(partition.right, weights_by_proc_id), + ), + ) else: with self.assertRaises(ValueError): bpu.get_left_right_total_weights(partition, weights_by_proc_id) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, 2, [1500, 2400, 300, 100, 700], - ), - ( + ), + ( PARTITION_NO_DUPLICATE_PROC_ID, 3, [15000, 24000, 3000, 1000, 7000], - ), - ]) + ), + ] + ) def test_pixel_volume(self, partition, dims, expected_pixel_volumes): - cell_size = mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3( - 10.0, 5.0, 0.0) - sim = mp.Simulation( - cell_size=cell_size, resolution=10, chunk_layout=partition) + cell_size = ( + mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3(10.0, 5.0, 0.0) + ) + sim = mp.Simulation(cell_size=cell_size, resolution=10, chunk_layout=partition) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() - self.assertEqual([bpu.pixel_volume(vol) for vol in chunk_volumes], - expected_pixel_volumes) + self.assertEqual( + [bpu.pixel_volume(vol) for vol in chunk_volumes], expected_pixel_volumes + ) - @parameterized.parameterized.expand([ - (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3500), - (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3500), - ]) - def test_get_total_volume_2d(self, partition, chunk_owners, - expected_total_volume): + @parameterized.parameterized.expand( + [ + (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3500), + (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3500), + ] + ) + def test_get_total_volume_2d(self, partition, chunk_owners, expected_total_volume): sim = mp.Simulation( - cell_size=mp.Vector3(10.0, 5.0, 0.0), - resolution=10, - chunk_layout=partition) + cell_size=mp.Vector3(10.0, 5.0, 0.0), resolution=10, chunk_layout=partition + ) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() total_volume = sim.cell_size[0] * sim.cell_size[1] * sim.resolution**2 self.assertEqual( - bpu.get_total_volume(partition, chunk_volumes, chunk_owners), - total_volume) + bpu.get_total_volume(partition, chunk_volumes, chunk_owners), total_volume + ) if not bpu.partition_has_duplicate_proc_ids(partition): self.assertEqual( bpu.get_total_volume(partition.right, chunk_volumes, chunk_owners), - expected_total_volume) + expected_total_volume, + ) - @parameterized.parameterized.expand([ - (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 35000), - (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 35000), - ]) - def test_get_total_volume_3d(self, partition, chunk_owners, - expected_total_volume): + @parameterized.parameterized.expand( + [ + (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 35000), + (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 35000), + ] + ) + def test_get_total_volume_3d(self, partition, chunk_owners, expected_total_volume): sim = mp.Simulation( - cell_size=mp.Vector3(10.0, 5.0, 1.0), - resolution=10, - chunk_layout=partition) + cell_size=mp.Vector3(10.0, 5.0, 1.0), resolution=10, chunk_layout=partition + ) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() - total_volume = sim.cell_size[0] * sim.cell_size[1] * sim.cell_size[ - 2] * sim.resolution**3 + total_volume = ( + sim.cell_size[0] * sim.cell_size[1] * sim.cell_size[2] * sim.resolution**3 + ) self.assertEqual( - bpu.get_total_volume(partition, chunk_volumes, chunk_owners), - total_volume) + bpu.get_total_volume(partition, chunk_volumes, chunk_owners), total_volume + ) if not bpu.partition_has_duplicate_proc_ids(partition): self.assertEqual( bpu.get_total_volume(partition.right, chunk_volumes, chunk_owners), - expected_total_volume) + expected_total_volume, + ) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 2, - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 2, - ), - ( + ), + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3, - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3, - ), - ]) + ), + ] + ) def test_get_left_right_total_volumes(self, partition, chunk_owners, dims): - cell_size = mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3( - 10.0, 5.0, 0.0) - sim = mp.Simulation( - cell_size=cell_size, resolution=10, chunk_layout=partition) + cell_size = ( + mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3(10.0, 5.0, 0.0) + ) + sim = mp.Simulation(cell_size=cell_size, resolution=10, chunk_layout=partition) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() self.assertEqual( - bpu.get_left_right_total_volumes(partition, chunk_volumes, - chunk_owners), - (bpu.get_total_volume(partition.left, chunk_volumes, chunk_owners), - bpu.get_total_volume(partition.right, chunk_volumes, chunk_owners))) + bpu.get_left_right_total_volumes(partition, chunk_volumes, chunk_owners), + ( + bpu.get_total_volume(partition.left, chunk_volumes, chunk_owners), + bpu.get_total_volume(partition.right, chunk_volumes, chunk_owners), + ), + ) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 2, - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 2, - ), - ( + ), + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3, - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3, - ), - ]) + ), + ] + ) def test_get_grid_volumes_in_tree(self, partition, chunk_owners, dims): - cell_size = mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3( - 10.0, 5.0, 0.0) - sim = mp.Simulation( - cell_size=cell_size, resolution=10, chunk_layout=partition) + cell_size = ( + mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3(10.0, 5.0, 0.0) + ) + sim = mp.Simulation(cell_size=cell_size, resolution=10, chunk_layout=partition) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() - grid_volumes_in_tree = bpu.get_grid_volumes_in_tree(partition, - chunk_volumes, - chunk_owners) + grid_volumes_in_tree = bpu.get_grid_volumes_in_tree( + partition, chunk_volumes, chunk_owners + ) self.assertEqual(set(grid_volumes_in_tree), set(chunk_volumes)) no_duplicates = len(set(chunk_owners)) == len(chunk_owners) - grid_volumes_in_right_expected = chunk_volumes[ - 1:] if no_duplicates else chunk_volumes + grid_volumes_in_right_expected = ( + chunk_volumes[1:] if no_duplicates else chunk_volumes + ) grid_volumes_in_right = bpu.get_grid_volumes_in_tree( - partition.right, chunk_volumes, chunk_owners) + partition.right, chunk_volumes, chunk_owners + ) self.assertEqual( - set(grid_volumes_in_right), set(grid_volumes_in_right_expected)) + set(grid_volumes_in_right), set(grid_volumes_in_right_expected) + ) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 2, - { - 0: 1500, - 1: 2400, - 4: 300, - 3: 100, - 2: 700 - }, - ), - ( + {0: 1500, 1: 2400, 4: 300, 3: 100, 2: 700}, + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 2, @@ -262,20 +286,14 @@ def test_get_grid_volumes_in_tree(self, partition, chunk_owners, dims): 1: 2700, 3: 100, }, - ), - ( + ), + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3, - { - 0: 15000, - 1: 24000, - 4: 3000, - 3: 1000, - 2: 7000 - }, - ), - ( + {0: 15000, 1: 24000, 4: 3000, 3: 1000, 2: 7000}, + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3, @@ -284,95 +302,110 @@ def test_get_grid_volumes_in_tree(self, partition, chunk_owners, dims): 1: 27000, 3: 1000, }, - ), - ]) - def test_get_total_volume_per_process(self, partition, chunk_owners, dims, - expected_volumes_per_process): - cell_size = mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3( - 10.0, 5.0, 0.0) - sim = mp.Simulation( - cell_size=cell_size, resolution=10, chunk_layout=partition) + ), + ] + ) + def test_get_total_volume_per_process( + self, partition, chunk_owners, dims, expected_volumes_per_process + ): + cell_size = ( + mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3(10.0, 5.0, 0.0) + ) + sim = mp.Simulation(cell_size=cell_size, resolution=10, chunk_layout=partition) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() volumes_per_process = bpu.get_total_volume_per_process( - partition, chunk_volumes, chunk_owners) + partition, chunk_volumes, chunk_owners + ) self.assertEqual(volumes_per_process, expected_volumes_per_process) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 2, (-5.0, 5.0, -2.5, 2.5, 0.0, 0.0), (-2.0, 5.0, -2.5, 2.5, 0.0, 0.0), - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 2, (-5.0, 5.0, -2.5, 2.5, 0.0, 0.0), (-5.0, 5.0, -2.5, 2.5, 0.0, 0.0), - ), - ( + ), + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3, (-5.0, 5.0, -2.5, 2.5, -0.5, 0.5), (-2.0, 5.0, -2.5, 2.5, -0.5, 0.5), - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3, (-5.0, 5.0, -2.5, 2.5, -0.5, 0.5), (-5.0, 5.0, -2.5, 2.5, -0.5, 0.5), - ), - ]) - def test_get_box_ranges(self, partition, chunk_owners, dims, - expected_box_ranges, expected_right_box_ranges): - cell_size = mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3( - 10.0, 5.0, 0.0) - sim = mp.Simulation( - cell_size=cell_size, resolution=10, chunk_layout=partition) + ), + ] + ) + def test_get_box_ranges( + self, + partition, + chunk_owners, + dims, + expected_box_ranges, + expected_right_box_ranges, + ): + cell_size = ( + mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3(10.0, 5.0, 0.0) + ) + sim = mp.Simulation(cell_size=cell_size, resolution=10, chunk_layout=partition) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() self.assertEqual( bpu.get_box_ranges(partition, chunk_volumes, chunk_owners), - expected_box_ranges) + expected_box_ranges, + ) self.assertEqual( bpu.get_box_ranges(partition.right, chunk_volumes, chunk_owners), - expected_right_box_ranges) + expected_right_box_ranges, + ) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( copy.deepcopy(PARTITION_NO_DUPLICATE_PROC_ID), copy.deepcopy(PARTITION_NO_DUPLICATE_PROC_ID), True, - ), - ( + ), + ( copy.deepcopy(PARTITION_DUPLICATE_PROC_ID), copy.deepcopy(PARTITION_DUPLICATE_PROC_ID), True, - ), - ( + ), + ( copy.deepcopy(PARTITION_NO_DUPLICATE_PROC_ID), copy.deepcopy(PARTITION_DUPLICATE_PROC_ID), False, - ), - ( + ), + ( mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), True, - ), - ( + ), + ( mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.4), 1, 2]]), mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), False, - ), - ]) + ), + ] + ) def test_partitions_are_equal(self, bp1, bp2, is_equal): self.assertEqual(bpu.partitions_are_equal(bp1, bp2), is_equal) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_cavity_arrayslice.py b/python/tests/test_cavity_arrayslice.py index a46d1db5c..f4945bf5a 100644 --- a/python/tests/test_cavity_arrayslice.py +++ b/python/tests/test_cavity_arrayslice.py @@ -7,9 +7,9 @@ class TestCavityArraySlice(ApproxComparisonTestCase): - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - expected_1d = np.load(os.path.join(data_dir, 'cavity_arrayslice_1d.npy')) - expected_2d = np.load(os.path.join(data_dir, 'cavity_arrayslice_2d.npy')) + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + expected_1d = np.load(os.path.join(data_dir, "cavity_arrayslice_1d.npy")) + expected_2d = np.load(os.path.join(data_dir, "cavity_arrayslice_2d.npy")) def setUp(self): @@ -22,8 +22,9 @@ def setUp(self): cell = mp.Vector3(sx, sy, 0) - blk = mp.Block(size=mp.Vector3(mp.inf, 1.2, mp.inf), - material=mp.Medium(epsilon=13)) + blk = mp.Block( + size=mp.Vector3(mp.inf, 1.2, mp.inf), material=mp.Medium(epsilon=13) + ) geometry = [blk] @@ -37,7 +38,7 @@ def setUp(self): geometry=geometry, sources=sources, boundary_layers=[mp.PML(dpml)], - resolution=20 + resolution=20, ) self.x_min = -0.25 * sx @@ -49,7 +50,9 @@ def setUp(self): self.center_1d = mp.Vector3((self.x_min + self.x_max) / 2) self.size_2d = mp.Vector3(self.x_max - self.x_min, self.y_max - self.y_min) - self.center_2d = mp.Vector3((self.x_min + self.x_max) / 2, (self.y_min + self.y_max) / 2) + self.center_2d = mp.Vector3( + (self.x_min + self.x_max) / 2, (self.y_min + self.y_max) / 2 + ) def test_1d_slice(self): self.sim.run(until_after_sources=0) @@ -67,7 +70,9 @@ def test_2d_slice(self): def test_1d_slice_user_array(self): self.sim.run(until_after_sources=0) - arr = np.zeros(126, dtype=np.float32 if mp.is_single_precision() else np.float64) + arr = np.zeros( + 126, dtype=np.float32 if mp.is_single_precision() else np.float64 + ) vol = mp.Volume(center=self.center_1d, size=self.size_1d) self.sim.get_array(mp.Hz, vol, arr=arr) tol = 1e-5 if mp.is_single_precision() else 1e-8 @@ -75,7 +80,9 @@ def test_1d_slice_user_array(self): def test_2d_slice_user_array(self): self.sim.run(until_after_sources=0) - arr = np.zeros((126, 38), dtype=np.float32 if mp.is_single_precision() else np.float64) + arr = np.zeros( + (126, 38), dtype=np.float32 if mp.is_single_precision() else np.float64 + ) vol = mp.Volume(center=self.center_2d, size=self.size_2d) self.sim.get_array(mp.Hz, vol, arr=arr) tol = 1e-5 if mp.is_single_precision() else 1e-8 @@ -103,16 +110,24 @@ def test_1d_complex_slice(self): self.sim.run(until_after_sources=0) vol = mp.Volume(center=self.center_1d, size=self.size_1d) hl_slice1d = self.sim.get_array(mp.Hz, vol, cmplx=True) - self.assertTrue(hl_slice1d.dtype == np.complex64 if mp.is_single_precision() else np.complex128) + self.assertTrue( + hl_slice1d.dtype == np.complex64 + if mp.is_single_precision() + else np.complex128 + ) self.assertTrue(hl_slice1d.shape[0] == 126) def test_2d_complex_slice(self): self.sim.run(until_after_sources=0) vol = mp.Volume(center=self.center_2d, size=self.size_2d) hl_slice2d = self.sim.get_array(mp.Hz, vol, cmplx=True) - self.assertTrue(hl_slice2d.dtype == np.complex64 if mp.is_single_precision() else np.complex128) + self.assertTrue( + hl_slice2d.dtype == np.complex64 + if mp.is_single_precision() + else np.complex128 + ) self.assertTrue(hl_slice2d.shape[0] == 126 and hl_slice2d.shape[1] == 38) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_cavity_farfield.py b/python/tests/test_cavity_farfield.py index 93de61f01..c9b0284a5 100644 --- a/python/tests/test_cavity_farfield.py +++ b/python/tests/test_cavity_farfield.py @@ -7,7 +7,7 @@ class TestCavityFarfield(ApproxComparisonTestCase): - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) def run_test(self, nfreqs): eps = 13 @@ -22,8 +22,13 @@ def run_test(self, nfreqs): cell = mp.Vector3(sx, sy, 0) - geometry = [mp.Block(center=mp.Vector3(), size=mp.Vector3(mp.inf, w, mp.inf), - material=mp.Medium(epsilon=eps))] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + material=mp.Medium(epsilon=eps), + ) + ] for i in range(N): geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) @@ -35,55 +40,66 @@ def run_test(self, nfreqs): fcen = 0.25 df = 0.2 - sources = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3()) + sources = mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3() + ) symmetries = [mp.Mirror(mp.Y, phase=-1), mp.Mirror(mp.X, phase=-1)] d1 = 0.2 - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - sources=[sources], - symmetries=symmetries, - boundary_layers=[pml_layers], - resolution=resolution) + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + sources=[sources], + symmetries=symmetries, + boundary_layers=[pml_layers], + resolution=resolution, + ) nearfield = sim.add_near2far( - fcen, 0.1, nfreqs, - mp.Near2FarRegion(mp.Vector3(0, 0.5 * w + d1), - size=mp.Vector3(2 * dpml - sx)), - mp.Near2FarRegion(mp.Vector3(-0.5 * sx + dpml, 0.5 * w + 0.5 * d1), - size=mp.Vector3(0, d1), - weight=-1.0), - mp.Near2FarRegion(mp.Vector3(0.5 * sx - dpml, 0.5 * w + 0.5 * d1), - size=mp.Vector3(0, d1)), - decimation_factor=1 + fcen, + 0.1, + nfreqs, + mp.Near2FarRegion( + mp.Vector3(0, 0.5 * w + d1), size=mp.Vector3(2 * dpml - sx) + ), + mp.Near2FarRegion( + mp.Vector3(-0.5 * sx + dpml, 0.5 * w + 0.5 * d1), + size=mp.Vector3(0, d1), + weight=-1.0, + ), + mp.Near2FarRegion( + mp.Vector3(0.5 * sx - dpml, 0.5 * w + 0.5 * d1), size=mp.Vector3(0, d1) + ), + decimation_factor=1, ) sim.run(until=200) d2 = 20 h = 4 - vol = mp.Volume(mp.Vector3(0, (0.5 * w) + d2 + (0.5 * h)), size=mp.Vector3(sx - 2 * dpml, h)) + vol = mp.Volume( + mp.Vector3(0, (0.5 * w) + d2 + (0.5 * h)), size=mp.Vector3(sx - 2 * dpml, h) + ) result = sim.get_farfields(nearfield, resolution, where=vol) - fname = 'cavity-farfield.h5' if nfreqs == 1 else 'cavity-farfield-4-freqs.h5' + fname = "cavity-farfield.h5" if nfreqs == 1 else "cavity-farfield-4-freqs.h5" ref_file = os.path.join(self.data_dir, fname) - with h5py.File(ref_file, 'r') as f: + with h5py.File(ref_file, "r") as f: # Get reference data into memory - ref_ex = mp.complexarray(f['ex.r'][()], f['ex.i'][()]) - ref_ey = mp.complexarray(f['ey.r'][()], f['ey.i'][()]) - ref_ez = mp.complexarray(f['ez.r'][()], f['ez.i'][()]) - ref_hx = mp.complexarray(f['hx.r'][()], f['hx.i'][()]) - ref_hy = mp.complexarray(f['hy.r'][()], f['hy.i'][()]) - ref_hz = mp.complexarray(f['hz.r'][()], f['hz.i'][()]) + ref_ex = mp.complexarray(f["ex.r"][()], f["ex.i"][()]) + ref_ey = mp.complexarray(f["ey.r"][()], f["ey.i"][()]) + ref_ez = mp.complexarray(f["ez.r"][()], f["ez.i"][()]) + ref_hx = mp.complexarray(f["hx.r"][()], f["hx.i"][()]) + ref_hy = mp.complexarray(f["hy.r"][()], f["hy.i"][()]) + ref_hz = mp.complexarray(f["hz.r"][()], f["hz.i"][()]) tol = 1e-5 if mp.is_single_precision() else 1e-7 - self.assertClose(ref_ex, result['Ex'], epsilon=tol) - self.assertClose(ref_ey, result['Ey'], epsilon=tol) - self.assertClose(ref_ez, result['Ez'], epsilon=tol) - self.assertClose(ref_hx, result['Hx'], epsilon=tol) - self.assertClose(ref_hy, result['Hy'], epsilon=tol) - self.assertClose(ref_hz, result['Hz'], epsilon=tol) - + self.assertClose(ref_ex, result["Ex"], epsilon=tol) + self.assertClose(ref_ey, result["Ey"], epsilon=tol) + self.assertClose(ref_ez, result["Ez"], epsilon=tol) + self.assertClose(ref_hx, result["Hx"], epsilon=tol) + self.assertClose(ref_hy, result["Hy"], epsilon=tol) + self.assertClose(ref_hz, result["Hz"], epsilon=tol) def test_cavity_farfield(self): self.run_test(nfreqs=1) @@ -92,5 +108,5 @@ def test_cavity_farfield_four_freqs(self): self.run_test(nfreqs=4) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_chunk_balancer.py b/python/tests/test_chunk_balancer.py index 00b0deebc..dd9b4770e 100644 --- a/python/tests/test_chunk_balancer.py +++ b/python/tests/test_chunk_balancer.py @@ -30,14 +30,15 @@ def __init__(self, *args, **kwargs): def _structure_get_chunk_owners(self): # Hacky workaround to make this test work on single-core systems - proc_ids = [leaf.proc_id for leaf in bpu.enumerate_leaf_nodes(self.chunk_layout)] + proc_ids = [ + leaf.proc_id for leaf in bpu.enumerate_leaf_nodes(self.chunk_layout) + ] return np.array(proc_ids) def init_sim(self): super().init_sim() - setattr(self.structure, "get_chunk_owners", - self._structure_get_chunk_owners) + setattr(self.structure, "get_chunk_owners", self._structure_get_chunk_owners) def time_spent_on(self, time_sink: int): # We're going to pretend the amount of time spent is ~volume so that @@ -72,51 +73,49 @@ def time_spent_on(self, time_sink: int): TEST_SIM_1 = lambda: MockSimulation( cell_size=mp.Vector3(10.0, 5.0, 0), resolution=20, - chunk_layout=mp.BinaryPartition(data=[ - (mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2] - ])) + chunk_layout=mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] + ), +) TEST_SIM_2 = lambda: MockSimulation( cell_size=mp.Vector3(10.0, 5.0, 3.0), resolution=10, - chunk_layout=mp.BinaryPartition(data=[ - (mp.X, -2.0), 0, [(mp.Y, 1.0), [(mp.X, 3.0), 1, [(mp.Y, 0.5), 4, 3]], 2] - ])) + chunk_layout=mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.0), [(mp.X, 3.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] + ), +) TEST_SIM_3 = lambda: MockSimulation( cell_size=mp.Vector3(6.0, 4.0, 0), resolution=10, - chunk_layout=mp.BinaryPartition(data=[(mp.X, -2.0), 0, [(mp.X, 2.0), 1, 2]]) + chunk_layout=mp.BinaryPartition(data=[(mp.X, -2.0), 0, [(mp.X, 2.0), 1, 2]]), ) TEST_SIM_4 = lambda: MockSimulation( cell_size=mp.Vector3(6.0, 4.0, 0), resolution=10, chunk_layout=mp.BinaryPartition( - data=[(mp.X, -2.0), 0, - [(mp.X, -0.5), 1, [(mp.X, 1.0), 2, [(mp.X, 2.0), 3, 4]]]])) + data=[(mp.X, -2.0), 0, [(mp.X, -0.5), 1, [(mp.X, 1.0), 2, [(mp.X, 2.0), 3, 4]]]] + ), +) TEST_SIM_DUPLICATE_PROC_ID = lambda: MockSimulation( cell_size=mp.Vector3(10.0, 5.0, 0), resolution=10, - chunk_layout=mp.BinaryPartition(data=[ - (mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 2, 1]], 2] - ])) + chunk_layout=mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 2, 1]], 2]] + ), +) TEST_CHUNK_DATA_1 = { - "chunk_layout": - mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), - "cell_size": - mp.Vector3(6.0, 4.0, 0), + "chunk_layout": mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), + "cell_size": mp.Vector3(6.0, 4.0, 0), "time_stepping": [1.0, 1.0, 1.0], - "new_chunk_layout": - mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), + "new_chunk_layout": mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), } TEST_CHUNK_DATA_2 = { - "chunk_layout": - mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), - "cell_size": - mp.Vector3(6.0, 4.0, 0), + "chunk_layout": mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), + "cell_size": mp.Vector3(6.0, 4.0, 0), "time_stepping": [3.0 - 2.5, 2.5 + 2.5, 3.0 - 2.5], - "new_chunk_layout": - mp.BinaryPartition(data=[(mp.X, -1.0), 0, [(mp.X, 1.0), 1, 2]]), + "new_chunk_layout": mp.BinaryPartition(data=[(mp.X, -1.0), 0, [(mp.X, 1.0), 1, 2]]), } TEST_CHUNK_DATA_3 = { "chunk_layout": mp.BinaryPartition(data=[(mp.X, 2.0), 0, 1]), @@ -131,43 +130,40 @@ def time_spent_on(self, time_sink: int): "new_chunk_layout": mp.BinaryPartition(data=[(mp.X, 0.0), 0, 1]), } TEST_CHUNK_DATA_5 = { - "chunk_layout": - mp.BinaryPartition(data=[( - mp.X, - -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]]), - "cell_size": - mp.Vector3(10.0, 5.0, 0), + "chunk_layout": mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] + ), + "cell_size": mp.Vector3(10.0, 5.0, 0), "time_stepping": [1.0, 1.0, 1.0, 1.0, 1.0], - "new_chunk_layout": - mp.BinaryPartition(data=[( - mp.X, - -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]]), + "new_chunk_layout": mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] + ), } TEST_CHUNK_DATA_6 = { - "chunk_layout": - mp.BinaryPartition(data=[( - mp.X, - -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]]), - "cell_size": - mp.Vector3(10.0, 5.0, 0), + "chunk_layout": mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] + ), + "cell_size": mp.Vector3(10.0, 5.0, 0), "time_stepping": [1500.0, 2400.0, 700.0, 100.0, 300.0], - "new_chunk_layout": - mp.BinaryPartition( - data=[(mp.X, -3.0), 0, - [(mp.Y, 1.25 - ), [(mp.X, -0.3333333333333335), 1, [(mp.Y, - -0.625), 4, 3]], 2]]), + "new_chunk_layout": mp.BinaryPartition( + data=[ + (mp.X, -3.0), + 0, + [(mp.Y, 1.25), [(mp.X, -0.3333333333333335), 1, [(mp.Y, -0.625), 4, 3]], 2], + ] + ), } class MockSimulationTest(unittest.TestCase): - - @parameterized.parameterized.expand([ - (TEST_SIM_1, [6000.0, 9600.0, 2800.0, 400.0, 1200.0]), - (TEST_SIM_2, [45000.0, 52500.0, 31500.0, 3000.0, 18000.0]), - (TEST_SIM_3, [400.0, 1600.0, 400.0]), - (TEST_SIM_4, [400.0, 600.0, 600.0, 400.0, 400.0]), - ]) + @parameterized.parameterized.expand( + [ + (TEST_SIM_1, [6000.0, 9600.0, 2800.0, 400.0, 1200.0]), + (TEST_SIM_2, [45000.0, 52500.0, 31500.0, 3000.0, 18000.0]), + (TEST_SIM_3, [400.0, 1600.0, 400.0]), + (TEST_SIM_4, [400.0, 600.0, 600.0, 400.0, 400.0]), + ] + ) def test_time_spent_on(self, test_sim_constructor, expected_stepping_times): test_sim = test_sim_constructor() test_sim.init_sim() @@ -175,20 +171,25 @@ def test_time_spent_on(self, test_sim_constructor, expected_stepping_times): for time_sink in TIMING_MEASUREMENT_IDS.values(): if time_sink == TIMING_MEASUREMENT_IDS["time_stepping"]: self.assertListEqual( - list(test_sim.time_spent_on(time_sink)), expected_stepping_times) + list(test_sim.time_spent_on(time_sink)), expected_stepping_times + ) else: self.assertListEqual( list(test_sim.time_spent_on(time_sink)), - [0.0] * len(expected_stepping_times)) - - @parameterized.parameterized.expand([ - (TEST_SIM_1, [0, 1, 4, 3, 2]), - (TEST_SIM_2, [0, 1, 4, 3, 2]), - (TEST_SIM_3, [0, 1, 2]), - (TEST_SIM_4, [0, 1, 2, 3, 4]), - ]) - def test_structure_get_chunk_owners(self, test_sim_constructor, - expected_chunk_owners): + [0.0] * len(expected_stepping_times), + ) + + @parameterized.parameterized.expand( + [ + (TEST_SIM_1, [0, 1, 4, 3, 2]), + (TEST_SIM_2, [0, 1, 4, 3, 2]), + (TEST_SIM_3, [0, 1, 2]), + (TEST_SIM_4, [0, 1, 2, 3, 4]), + ] + ) + def test_structure_get_chunk_owners( + self, test_sim_constructor, expected_chunk_owners + ): test_sim = test_sim_constructor() test_sim.init_sim() @@ -198,11 +199,12 @@ def test_structure_get_chunk_owners(self, test_sim_constructor, class ChunkBalancerTest(unittest.TestCase): - - @parameterized.parameterized.expand([ - (TEST_SIM_1, False), - (TEST_SIM_DUPLICATE_PROC_ID, True), - ]) + @parameterized.parameterized.expand( + [ + (TEST_SIM_1, False), + (TEST_SIM_DUPLICATE_PROC_ID, True), + ] + ) def test_validate_sim(self, test_sim_constructor, should_raise_exception): test_sim = test_sim_constructor() test_sim.init_sim() @@ -215,31 +217,33 @@ def test_validate_sim(self, test_sim_constructor, should_raise_exception): else: chunk_balancer._validate_sim(test_sim) - @parameterized.parameterized.expand([ - (TEST_SIM_1,), - (TEST_SIM_2,), - (TEST_SIM_3,), - (TEST_SIM_4,), - ]) + @parameterized.parameterized.expand( + [ + (TEST_SIM_1,), + (TEST_SIM_2,), + (TEST_SIM_3,), + (TEST_SIM_4,), + ] + ) def test_chunk_layout_improvement(self, test_sim_constructor): """Tests that chunk_balancer improves balance after 1 iteration.""" test_sim = test_sim_constructor() test_sim.init_sim() old_timing_measurements = MeepTimingMeasurements.new_from_simulation( - test_sim, -1) + test_sim, -1 + ) chunk_balancer = ChunkBalancer() chunk_balancer.adjust_chunk_layout(test_sim, sensitivity=1.0) new_timing_measurements = MeepTimingMeasurements.new_from_simulation( - test_sim, -1) + test_sim, -1 + ) - old_step_times = np.array( - old_timing_measurements.measurements["time_stepping"]) - new_step_times = np.array( - new_timing_measurements.measurements["time_stepping"]) + old_step_times = np.array(old_timing_measurements.measurements["time_stepping"]) + new_step_times = np.array(new_timing_measurements.measurements["time_stepping"]) old_max_time = np.max(old_step_times) new_max_time = np.max(new_step_times) @@ -250,12 +254,14 @@ def test_chunk_layout_improvement(self, test_sim_constructor): self.assertLess(new_max_time, old_max_time) self.assertGreater(new_min_time, old_min_time) - @parameterized.parameterized.expand([ - (TEST_SIM_1,), - (TEST_SIM_2,), - (TEST_SIM_3,), - (TEST_SIM_4,), - ]) + @parameterized.parameterized.expand( + [ + (TEST_SIM_1,), + (TEST_SIM_2,), + (TEST_SIM_3,), + (TEST_SIM_4,), + ] + ) def test_chunk_layout_convergence(self, test_sim_constructor): """Tests that chunk_balancer converges to load balanced state.""" test_sim = test_sim_constructor() @@ -269,31 +275,35 @@ def test_chunk_layout_convergence(self, test_sim_constructor): chunk_balancer.adjust_chunk_layout(test_sim, sensitivity=0.5) new_timing_measurements = MeepTimingMeasurements.new_from_simulation( - test_sim, -1) + test_sim, -1 + ) new_step_times = np.array( - new_timing_measurements.measurements["time_stepping"]) + new_timing_measurements.measurements["time_stepping"] + ) # Check that new stepping times have converged to close to the mean value tolerance = 0.05 mean_step_time = np.mean(new_step_times) - self.assertTrue( - np.allclose(mean_step_time, new_step_times, rtol=tolerance)) - - @parameterized.parameterized.expand([ - (TEST_CHUNK_DATA_1,), - (TEST_CHUNK_DATA_2,), - (TEST_CHUNK_DATA_3,), - (TEST_CHUNK_DATA_4,), - (TEST_CHUNK_DATA_5,), - (TEST_CHUNK_DATA_6,), - ]) + self.assertTrue(np.allclose(mean_step_time, new_step_times, rtol=tolerance)) + + @parameterized.parameterized.expand( + [ + (TEST_CHUNK_DATA_1,), + (TEST_CHUNK_DATA_2,), + (TEST_CHUNK_DATA_3,), + (TEST_CHUNK_DATA_4,), + (TEST_CHUNK_DATA_5,), + (TEST_CHUNK_DATA_6,), + ] + ) def test_split_pos_adjustment(self, test_chunk_data): chunk_layout = test_chunk_data["chunk_layout"] sim = MockSimulation( cell_size=test_chunk_data["cell_size"], resolution=10, - chunk_layout=chunk_layout) + chunk_layout=chunk_layout, + ) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() chunk_owners = sim.structure.get_chunk_owners() @@ -307,20 +317,23 @@ def test_split_pos_adjustment(self, test_chunk_data): measurements[name] = test_chunk_data["time_stepping"] else: measurements[name] = [0] * num_procs - timing_measurements = MeepTimingMeasurements(measurements, -1, None, None, - None, None, None) + timing_measurements = MeepTimingMeasurements( + measurements, -1, None, None, None, None, None + ) new_chunk_layout = chunk_balancer.compute_new_chunk_layout( timing_measurements, chunk_layout, chunk_volumes, chunk_owners, - sensitivity=1.0) + sensitivity=1.0, + ) expected_chunk_layout = test_chunk_data["new_chunk_layout"] self.assertTrue( - bpu.partitions_are_equal(new_chunk_layout, expected_chunk_layout)) + bpu.partitions_are_equal(new_chunk_layout, expected_chunk_layout) + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_chunk_layout.py b/python/tests/test_chunk_layout.py index 91a8082f1..ea0227a6f 100644 --- a/python/tests/test_chunk_layout.py +++ b/python/tests/test_chunk_layout.py @@ -3,38 +3,38 @@ import unittest -def traverse_tree(bp=None,min_corner=None,max_corner=None): +def traverse_tree(bp=None, min_corner=None, max_corner=None): process_ids = [] chunk_areas = [] - def _traverse_tree(bp=None,min_corner=None,max_corner=None): - if ((min_corner.x > max_corner.x) or (min_corner.y > max_corner.y)): + def _traverse_tree(bp=None, min_corner=None, max_corner=None): + if (min_corner.x > max_corner.x) or (min_corner.y > max_corner.y): raise RuntimeError("min_corner/max_corner have been incorrectly defined.") ## reached a leaf - if (bp.left is None and bp.right is None): + if bp.left is None and bp.right is None: process_ids.append(bp.proc_id) - chunk_area = (max_corner.x-min_corner.x)*(max_corner.y-min_corner.y) + chunk_area = (max_corner.x - min_corner.x) * (max_corner.y - min_corner.y) chunk_areas.append(chunk_area) ## traverse the left branch - if (bp.left is not None): + if bp.left is not None: new_max_corner = copy.deepcopy(max_corner) if bp.split_dir == mp.X: new_max_corner.x = bp.split_pos else: new_max_corner.y = bp.split_pos - _traverse_tree(bp.left,min_corner,new_max_corner) + _traverse_tree(bp.left, min_corner, new_max_corner) ## traverse the right branch - if (bp.right is not None): + if bp.right is not None: new_min_corner = copy.deepcopy(min_corner) if bp.split_dir == mp.X: new_min_corner.x = bp.split_pos else: new_min_corner.y = bp.split_pos - _traverse_tree(bp.right,new_min_corner,max_corner) + _traverse_tree(bp.right, new_min_corner, max_corner) _traverse_tree(bp=bp, min_corner=min_corner, max_corner=max_corner) @@ -42,40 +42,57 @@ def _traverse_tree(bp=None,min_corner=None,max_corner=None): class TestChunkLayoutBinaryPartition(unittest.TestCase): - def test_chunk_layout_binary_partition(self): - chunk_layout = mp.BinaryPartition(data=[ (mp.X,-2.0), 0, [ (mp.Y,1.5), [ (mp.X,3.0), 1, [ (mp.Y,-0.5), 4, 3 ] ], 2 ] ]) + chunk_layout = mp.BinaryPartition( + data=[ + (mp.X, -2.0), + 0, + [(mp.Y, 1.5), [(mp.X, 3.0), 1, [(mp.Y, -0.5), 4, 3]], 2], + ] + ) - cell_size = mp.Vector3(10.0,5.0,0) + cell_size = mp.Vector3(10.0, 5.0, 0) - sim = mp.Simulation(cell_size=cell_size, - resolution=10, - chunk_layout=chunk_layout) + sim = mp.Simulation( + cell_size=cell_size, resolution=10, chunk_layout=chunk_layout + ) sim.init_sim() owners = sim.structure.get_chunk_owners() - areas = [ v.surroundings().full_volume() for v in sim.structure.get_chunk_volumes() ] + areas = [ + v.surroundings().full_volume() for v in sim.structure.get_chunk_volumes() + ] - process_ids, chunk_areas = traverse_tree(chunk_layout,-0.5*cell_size,0.5*cell_size) + process_ids, chunk_areas = traverse_tree( + chunk_layout, -0.5 * cell_size, 0.5 * cell_size + ) - self.assertListEqual([int(f) for f in owners],[f % mp.count_processors() for f in process_ids]) - self.assertListEqual(areas,chunk_areas) + self.assertListEqual( + [int(f) for f in owners], [f % mp.count_processors() for f in process_ids] + ) + self.assertListEqual(areas, chunk_areas) def test_meep_default_chunk_layout(self): - cell_size = mp.Vector3(10.0,5.0,0) - sim = mp.Simulation(cell_size=cell_size, - resolution=10) + cell_size = mp.Vector3(10.0, 5.0, 0) + sim = mp.Simulation(cell_size=cell_size, resolution=10) sim.init_sim() owners = sim.structure.get_chunk_owners() - areas = [ v.surroundings().full_volume() for v in sim.structure.get_chunk_volumes() ] + areas = [ + v.surroundings().full_volume() for v in sim.structure.get_chunk_volumes() + ] chunk_layout = sim.chunk_layout - process_ids, chunk_areas = traverse_tree(chunk_layout,-0.5*cell_size,0.5*cell_size) + process_ids, chunk_areas = traverse_tree( + chunk_layout, -0.5 * cell_size, 0.5 * cell_size + ) + + self.assertListEqual( + [int(f) for f in owners], [f % mp.count_processors() for f in process_ids] + ) + self.assertListEqual(areas, chunk_areas) - self.assertListEqual([int(f) for f in owners],[f % mp.count_processors() for f in process_ids]) - self.assertListEqual(areas,chunk_areas) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_chunks.py b/python/tests/test_chunks.py index 2af803b07..97db1a712 100644 --- a/python/tests/test_chunks.py +++ b/python/tests/test_chunks.py @@ -3,7 +3,6 @@ class TestChunks(unittest.TestCase): - @classmethod def setUpClass(cls): cls.temp_dir = mp.make_output_directory() @@ -17,7 +16,7 @@ def test_chunks(self): cell = mp.Vector3(sxy, sxy, 0) fcen = 1.0 # pulse center frequency - df = 0.1 # pulse width (in frequency) + df = 0.1 # pulse width (in frequency) sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3())] @@ -25,51 +24,87 @@ def test_chunks(self): pml_layers = [mp.PML(dpml)] resolution = 10 - sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - sources=sources, - resolution=resolution, - split_chunks_evenly=False) + sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + sources=sources, + resolution=resolution, + split_chunks_evenly=False, + ) sim.use_output_directory(self.temp_dir) - top = mp.FluxRegion(center=mp.Vector3(0,+0.5*sxy-dpml), size=mp.Vector3(sxy-2*dpml,0), weight=+1.0) - bot = mp.FluxRegion(center=mp.Vector3(0,-0.5*sxy+dpml), size=mp.Vector3(sxy-2*dpml,0), weight=-1.0) - rgt = mp.FluxRegion(center=mp.Vector3(+0.5*sxy-dpml,0), size=mp.Vector3(0,sxy-2*dpml), weight=+1.0) - lft = mp.FluxRegion(center=mp.Vector3(-0.5*sxy+dpml,0), size=mp.Vector3(0,sxy-2*dpml), weight=-1.0) + top = mp.FluxRegion( + center=mp.Vector3(0, +0.5 * sxy - dpml), + size=mp.Vector3(sxy - 2 * dpml, 0), + weight=+1.0, + ) + bot = mp.FluxRegion( + center=mp.Vector3(0, -0.5 * sxy + dpml), + size=mp.Vector3(sxy - 2 * dpml, 0), + weight=-1.0, + ) + rgt = mp.FluxRegion( + center=mp.Vector3(+0.5 * sxy - dpml, 0), + size=mp.Vector3(0, sxy - 2 * dpml), + weight=+1.0, + ) + lft = mp.FluxRegion( + center=mp.Vector3(-0.5 * sxy + dpml, 0), + size=mp.Vector3(0, sxy - 2 * dpml), + weight=-1.0, + ) tot_flux = sim.add_flux(fcen, 0, 1, top, bot, rgt, lft, decimation_factor=1) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(), 1e-5)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-5 + ) + ) - sim.save_flux('tot_flux', tot_flux) + sim.save_flux("tot_flux", tot_flux) sim1 = sim - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(sxy, sxy, mp.inf), - material=mp.Medium(index=3.5)), - mp.Block(center=mp.Vector3(), - size=mp.Vector3(sxy-2*dpml, sxy-2*dpml, mp.inf), - material=mp.air)] - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - resolution=resolution, - chunk_layout=sim1) + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(sxy, sxy, mp.inf), + material=mp.Medium(index=3.5), + ), + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(sxy - 2 * dpml, sxy - 2 * dpml, mp.inf), + material=mp.air, + ), + ] + + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + resolution=resolution, + chunk_layout=sim1, + ) sim.use_output_directory(self.temp_dir) tot_flux = sim.add_flux(fcen, 0, 1, top, bot, rgt, lft, decimation_factor=1) - sim.load_minus_flux('tot_flux', tot_flux) + sim.load_minus_flux("tot_flux", tot_flux) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(), 1e-5)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-5 + ) + ) places = 3 if mp.is_single_precision() else 7 - self.assertAlmostEqual(86.90826609300862, mp.get_fluxes(tot_flux)[0], places=places) + self.assertAlmostEqual( + 86.90826609300862, mp.get_fluxes(tot_flux)[0], places=places + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_conductivity.py b/python/tests/test_conductivity.py index 9441db99a..8f9176ad2 100644 --- a/python/tests/test_conductivity.py +++ b/python/tests/test_conductivity.py @@ -4,8 +4,8 @@ dB_cm_to_dB_um = 1e-4 -class TestConductivity(unittest.TestCase): +class TestConductivity(unittest.TestCase): def wvg_flux(self, res, att_coeff): """ Computes the Poynting flux in a single-mode waveguide at two @@ -14,88 +14,105 @@ def wvg_flux(self, res, att_coeff): coefficient att_coeff (dB/cm). """ - cell_size = mp.Vector3(14.,14.) + cell_size = mp.Vector3(14.0, 14.0) - pml_layers = [mp.PML(thickness=2.)] + pml_layers = [mp.PML(thickness=2.0)] - w = 1. # width of waveguide + w = 1.0 # width of waveguide - fsrc = 0.15 # frequency (in vacuum) + fsrc = 0.15 # frequency (in vacuum) # note: MPB can only compute modes of lossless material systems. # The imaginary part of ε is ignored and the launched # waveguide mode is therefore inaccurate. For small values # of imag(ε) (which is proportional to att_coeff), this # inaccuracy tends to be insignificant. - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc), - center=mp.Vector3(-5.), - size=mp.Vector3(y=10.), - eig_parity=mp.EVEN_Y+mp.ODD_Z)] + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(-5.0), + size=mp.Vector3(y=10.0), + eig_parity=mp.EVEN_Y + mp.ODD_Z, + ) + ] # ref: https://en.wikipedia.org/wiki/Mathematical_descriptions_of_opacity # Note that this is the loss of a planewave, which is only approximately # the loss of a waveguide mode. In principle, we could compute the latter # semi-analytically if we wanted to run this unit test to greater accuracy # (e.g. to test convergence with resolution). - n_eff = np.sqrt(12.) + 1j * (1/fsrc) * (dB_cm_to_dB_um * att_coeff) / (4 * np.pi) + n_eff = np.sqrt(12.0) + 1j * (1 / fsrc) * (dB_cm_to_dB_um * att_coeff) / ( + 4 * np.pi + ) eps_eff = n_eff * n_eff # ref: https://meep.readthedocs.io/en/latest/Materials/#conductivity-and-complex sigma_D = 2 * np.pi * fsrc * np.imag(eps_eff) / np.real(eps_eff) - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,w,mp.inf), - material=mp.Medium(epsilon=np.real(eps_eff), - D_conductivity=sigma_D))] - - sim = mp.Simulation(cell_size=cell_size, - resolution=res, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=[mp.Mirror(mp.Y)]) - - tran1 = sim.add_flux(fsrc, - 0, - 1, - mp.FluxRegion(center=mp.Vector3(x=0.), size=mp.Vector3(y=10.))) - - tran2 = sim.add_flux(fsrc, - 0, - 1, - mp.FluxRegion(center=mp.Vector3(x=5.), size=mp.Vector3(y=10.))) + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + material=mp.Medium(epsilon=np.real(eps_eff), D_conductivity=sigma_D), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=res, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=[mp.Mirror(mp.Y)], + ) + + tran1 = sim.add_flux( + fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=0.0), size=mp.Vector3(y=10.0)) + ) + + tran2 = sim.add_flux( + fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5.0), size=mp.Vector3(y=10.0)) + ) sim.run(until_after_sources=20) return mp.get_fluxes(tran1)[0], mp.get_fluxes(tran2)[0] - def test_conductivity(self): - res = 25. # pixels/μm + res = 25.0 # pixels/μm # compute the incident flux for a lossless waveguide - incident_flux1, incident_flux2 = self.wvg_flux(res, 0.) - self.assertAlmostEqual(incident_flux1/incident_flux2, 1., places=2) - print(f"incident_flux:, {incident_flux2 / incident_flux1} (measured), 1.0 (expected)") - + incident_flux1, incident_flux2 = self.wvg_flux(res, 0.0) + self.assertAlmostEqual(incident_flux1 / incident_flux2, 1.0, places=2) + print( + f"incident_flux:, {incident_flux2 / incident_flux1} (measured), 1.0 (expected)" + ) # compute the flux for a lossy waveguide - att_coeff = 37.46 # dB/cm + att_coeff = 37.46 # dB/cm attenuated_flux1, attenuated_flux2 = self.wvg_flux(res, att_coeff) - L1 = 5. + L1 = 5.0 expected_att1 = np.exp(-att_coeff * dB_cm_to_dB_um * L1) - self.assertAlmostEqual(attenuated_flux1/incident_flux2, expected_att1, places=2) - print("flux:, {}, {:.6f} (measured), {:.6f} (expected)".format(L1, - attenuated_flux1/incident_flux2, - expected_att1)) - - L2 = 10. + self.assertAlmostEqual( + attenuated_flux1 / incident_flux2, expected_att1, places=2 + ) + print( + "flux:, {}, {:.6f} (measured), {:.6f} (expected)".format( + L1, attenuated_flux1 / incident_flux2, expected_att1 + ) + ) + + L2 = 10.0 expected_att2 = np.exp(-att_coeff * dB_cm_to_dB_um * L2) - self.assertAlmostEqual(attenuated_flux2/incident_flux2, expected_att2, places=2) - print("flux:, {}, {:.6f} (measured), {:.6f} (expected)".format(L2, - attenuated_flux2/incident_flux2, - expected_att2)) + self.assertAlmostEqual( + attenuated_flux2 / incident_flux2, expected_att2, places=2 + ) + print( + "flux:, {}, {:.6f} (measured), {:.6f} (expected)".format( + L2, attenuated_flux2 / incident_flux2, expected_att2 + ) + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_cyl_ellipsoid.py b/python/tests/test_cyl_ellipsoid.py index 8ea760d6c..82e717f4b 100644 --- a/python/tests/test_cyl_ellipsoid.py +++ b/python/tests/test_cyl_ellipsoid.py @@ -1,9 +1,11 @@ import unittest import meep as mp + def dummy_eps(vec): return 1.0 + class TestCylEllipsoid(unittest.TestCase): ref_Ez = -8.29555720049629e-5 @@ -22,9 +24,11 @@ def init(self): c = mp.Cylinder(radius=3, material=mp.Medium(index=3.5)) e = mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf)) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.1), - component=self.src_cmpt, - center=mp.Vector3()) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.1), + component=self.src_cmpt, + center=mp.Vector3(), + ) if self.src_cmpt == mp.Ez: symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] @@ -32,12 +36,14 @@ def init(self): if self.src_cmpt == mp.Hz: symmetries = [mp.Mirror(mp.X, -1), mp.Mirror(mp.Y, -1)] - self.sim = mp.Simulation(cell_size=mp.Vector3(10, 10), - geometry=[c, e], - boundary_layers=[mp.PML(1.0)], - sources=[sources], - symmetries=symmetries, - resolution=100) + self.sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), + geometry=[c, e], + boundary_layers=[mp.PML(1.0)], + sources=[sources], + symmetries=symmetries, + resolution=100, + ) self.sim.use_output_directory(self.temp_dir) @@ -45,15 +51,18 @@ def print_stuff(sim_obj): v = mp.Vector3(4.13, 3.75, 0) p = self.sim.get_field_point(self.src_cmpt, v) print(f"t, Ez: {self.sim.round_time()} {p.real}+{p.imag}i") + self.print_stuff = print_stuff def run_simulation(self): - self.sim.run(mp.at_beginning(mp.output_epsilon), - mp.at_every(0.25, self.print_stuff), - mp.at_end(self.print_stuff), - mp.at_end(mp.output_efield_z), - until=23) + self.sim.run( + mp.at_beginning(mp.output_epsilon), + mp.at_every(0.25, self.print_stuff), + mp.at_end(self.print_stuff), + mp.at_end(mp.output_efield_z), + until=23, + ) ref_out_field = self.ref_Ez if self.src_cmpt == mp.Ez else self.ref_Hz out_field = self.sim.fields.get_field(self.src_cmpt, mp.vec(4.13, 3.75)).real @@ -74,5 +83,5 @@ def test_hz_field(self): self.run_simulation() -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_dft_energy.py b/python/tests/test_dft_energy.py index db43946ea..bf88c8633 100644 --- a/python/tests/test_dft_energy.py +++ b/python/tests/test_dft_energy.py @@ -7,33 +7,60 @@ class TestDftEnergy(unittest.TestCase): - def test_dft_energy(self): resolution = 20 cell = mp.Vector3(10, 5) - geom = [mp.Block(size=mp.Vector3(mp.inf, 1, mp.inf), material=mp.Medium(epsilon=12))] + geom = [ + mp.Block(size=mp.Vector3(mp.inf, 1, mp.inf), material=mp.Medium(epsilon=12)) + ] pml = [mp.PML(1)] - fsrc = 0.15 - sources = [mp.EigenModeSource(src=mp.GaussianSource(frequency=fsrc, fwidth=0.2*fsrc), - center=mp.Vector3(-3), size=mp.Vector3(y=5), - eig_band=1, eig_parity=mp.ODD_Z+mp.EVEN_Y, - eig_match_freq=True)] - - sim = mp.Simulation(resolution=resolution, cell_size=cell, geometry=geom, - boundary_layers=pml, sources=sources, symmetries=[mp.Mirror(direction=mp.Y)]) - - flux = sim.add_flux(fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), - decimation_factor=1) - energy = sim.add_energy(fsrc, 0, 1, mp.EnergyRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), - decimation_factor=1) - energy_decimated = sim.add_energy(fsrc, 0, 1, - mp.EnergyRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), - decimation_factor=10) + fsrc = 0.15 + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(frequency=fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(-3), + size=mp.Vector3(y=5), + eig_band=1, + eig_parity=mp.ODD_Z + mp.EVEN_Y, + eig_match_freq=True, + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + geometry=geom, + boundary_layers=pml, + sources=sources, + symmetries=[mp.Mirror(direction=mp.Y)], + ) + + flux = sim.add_flux( + fsrc, + 0, + 1, + mp.FluxRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), + decimation_factor=1, + ) + energy = sim.add_energy( + fsrc, + 0, + 1, + mp.EnergyRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), + decimation_factor=1, + ) + energy_decimated = sim.add_energy( + fsrc, + 0, + 1, + mp.EnergyRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), + decimation_factor=10, + ) sim.run(until_after_sources=100) - res = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z+mp.EVEN_Y) + res = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y) mode_vg = res.vgrp[0] - poynting_flux = mp.get_fluxes(flux)[0] + poynting_flux = mp.get_fluxes(flux)[0] e_energy = mp.get_electric_energy(energy)[0] ratio_vg = (0.5 * poynting_flux) / e_energy m_energy = mp.get_magnetic_energy(energy)[0] @@ -48,5 +75,5 @@ def test_dft_energy(self): self.assertAlmostEqual(m_energy, m_energy_decimated, places=1) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_dft_fields.py b/python/tests/test_dft_fields.py index 96b557c65..c90af5ab0 100644 --- a/python/tests/test_dft_fields.py +++ b/python/tests/test_dft_fields.py @@ -5,8 +5,8 @@ from utils import ApproxComparisonTestCase import os -class TestDFTFields(ApproxComparisonTestCase): +class TestDFTFields(ApproxComparisonTestCase): @classmethod def setUpClass(cls): cls.temp_dir = mp.make_output_directory() @@ -26,7 +26,10 @@ def init(self): cell = mp.Vector3(self.sxy, self.sxy) pml_layers = [mp.PML(self.dpml)] - geometry = [mp.Cylinder(r + w, material=mp.Medium(epsilon=n**2)), mp.Cylinder(r, material=mp.vacuum)] + geometry = [ + mp.Cylinder(r + w, material=mp.Medium(epsilon=n**2)), + mp.Cylinder(r, material=mp.vacuum), + ] self.fcen = 0.118 self.df = 0.1 @@ -41,6 +44,7 @@ def init(self): sources=sources, boundary_layers=pml_layers, ) + def test_use_centered_grid(self): sim = self.init() sim.init_sim() @@ -51,14 +55,20 @@ def test_get_dft_array(self): sim = self.init() sim.init_sim() dft_fields = sim.add_dft_fields([mp.Ez], self.fcen, 0, 1) - fr = mp.FluxRegion(mp.Vector3(), size=mp.Vector3(self.sxy, self.sxy), direction=mp.X) + fr = mp.FluxRegion( + mp.Vector3(), size=mp.Vector3(self.sxy, self.sxy), direction=mp.X + ) dft_flux = sim.add_flux(self.fcen, 0, 1, fr) # volumes with zero thickness in x and y directions to test collapsing # of empty dimensions in DFT array and HDF5 output routines - thin_x_volume = mp.Volume(center=mp.Vector3(0.35*self.sxy), size=mp.Vector3(y=0.8*self.sxy)) + thin_x_volume = mp.Volume( + center=mp.Vector3(0.35 * self.sxy), size=mp.Vector3(y=0.8 * self.sxy) + ) thin_x_flux = sim.add_dft_fields([mp.Ez], self.fcen, 0, 1, where=thin_x_volume) - thin_y_volume = mp.Volume(center=mp.Vector3(y=0.25*self.sxy), size=mp.Vector3(x=self.sxy)) + thin_y_volume = mp.Volume( + center=mp.Vector3(y=0.25 * self.sxy), size=mp.Vector3(x=self.sxy) + ) thin_y_flux = sim.add_flux(self.fcen, 0, 1, mp.FluxRegion(volume=thin_y_volume)) sim.run(until_after_sources=100) @@ -69,14 +79,14 @@ def test_get_dft_array(self): np.testing.assert_equal(thin_x_array.ndim, 1) np.testing.assert_equal(thin_y_array.ndim, 1) - sim.output_dft(thin_x_flux, os.path.join(self.temp_dir, 'thin-x-flux')) - sim.output_dft(thin_y_flux, os.path.join(self.temp_dir, 'thin-y-flux')) + sim.output_dft(thin_x_flux, os.path.join(self.temp_dir, "thin-x-flux")) + sim.output_dft(thin_y_flux, os.path.join(self.temp_dir, "thin-y-flux")) - with h5py.File(os.path.join(self.temp_dir, 'thin-x-flux.h5'), 'r') as thin_x: - thin_x_h5 = mp.complexarray(thin_x['ez_0.r'][()], thin_x['ez_0.i'][()]) + with h5py.File(os.path.join(self.temp_dir, "thin-x-flux.h5"), "r") as thin_x: + thin_x_h5 = mp.complexarray(thin_x["ez_0.r"][()], thin_x["ez_0.i"][()]) - with h5py.File(os.path.join(self.temp_dir, 'thin-y-flux.h5'), 'r') as thin_y: - thin_y_h5 = mp.complexarray(thin_y['ez_0.r'][()], thin_y['ez_0.i'][()]) + with h5py.File(os.path.join(self.temp_dir, "thin-y-flux.h5"), "r") as thin_y: + thin_y_h5 = mp.complexarray(thin_y["ez_0.r"][()], thin_y["ez_0.i"][()]) tol = 1e-6 self.assertClose(thin_x_array, thin_x_h5, epsilon=tol) @@ -86,12 +96,14 @@ def test_get_dft_array(self): fields_arr = sim.get_dft_array(dft_fields, mp.Ez, 0) flux_arr = sim.get_dft_array(dft_flux, mp.Ez, 0) - sim.output_dft(dft_fields, os.path.join(self.temp_dir, 'dft-fields')) - sim.output_dft(dft_flux, os.path.join(self.temp_dir, 'dft-flux')) + sim.output_dft(dft_fields, os.path.join(self.temp_dir, "dft-fields")) + sim.output_dft(dft_flux, os.path.join(self.temp_dir, "dft-flux")) - with h5py.File(os.path.join(self.temp_dir, 'dft-fields.h5'), 'r') as fields, h5py.File(os.path.join(self.temp_dir, 'dft-flux.h5'), 'r') as flux: - exp_fields = mp.complexarray(fields['ez_0.r'][()], fields['ez_0.i'][()]) - exp_flux = mp.complexarray(flux['ez_0.r'][()], flux['ez_0.i'][()]) + with h5py.File( + os.path.join(self.temp_dir, "dft-fields.h5"), "r" + ) as fields, h5py.File(os.path.join(self.temp_dir, "dft-flux.h5"), "r") as flux: + exp_fields = mp.complexarray(fields["ez_0.r"][()], fields["ez_0.i"][()]) + exp_flux = mp.complexarray(flux["ez_0.r"][()], flux["ez_0.i"][()]) tol = 1e-6 self.assertClose(exp_fields, fields_arr, epsilon=tol) @@ -100,13 +112,12 @@ def test_get_dft_array(self): def test_decimated_dft_fields_are_almost_equal_to_undecimated_fields(self): sim = self.init() sim.init_sim() - undecimated_field = sim.add_dft_fields([mp.Ez], self.fcen, 0, 1, - decimation_factor=1) - decimated_field = sim.add_dft_fields([mp.Ez], - self.fcen, - 0, - 1, - decimation_factor=4) + undecimated_field = sim.add_dft_fields( + [mp.Ez], self.fcen, 0, 1, decimation_factor=1 + ) + decimated_field = sim.add_dft_fields( + [mp.Ez], self.fcen, 0, 1, decimation_factor=4 + ) sim.run(until_after_sources=100) @@ -115,5 +126,5 @@ def test_decimated_dft_fields_are_almost_equal_to_undecimated_fields(self): self.assertClose(expected_dft, actual_dft, epsilon=1e-3) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_diffracted_planewave.py b/python/tests/test_diffracted_planewave.py index 7c0d8b3c0..0096eae22 100644 --- a/python/tests/test_diffracted_planewave.py +++ b/python/tests/test_diffracted_planewave.py @@ -10,139 +10,153 @@ # that the results are the same when using either a band number # or `DiffractedPlanewave` object in `get_eigenmode_coefficients`. -class TestDiffractedPlanewave(unittest.TestCase): - @classmethod - def setUp(cls): - cls.resolution = 50 # pixels/um - cls.dpml = 1.0 # PML thickness - cls.dsub = 3.0 # substrate thickness - cls.dpad = 3.0 # length of padding between grating and PML - - cls.wvl = 0.5 # center wavelength - cls.fcen = 1/cls.wvl # center frequency - - cls.ng = 1.5 - cls.glass = mp.Medium(index=cls.ng) - - cls.pml_layers = [mp.PML(thickness=cls.dpml,direction=mp.X)] - - - def run_binary_grating_diffraction(self,gp,gh,gdc,theta): - sx = self.dpml+self.dsub+gh+self.dpad+self.dpml - sy = gp - cell_size = mp.Vector3(sx,sy,0) - - # rotation angle of incident planewave - # counter clockwise (CCW) about Z axis, 0 degrees along +X axis - theta_in = math.radians(theta) - - # k (in source medium) with correct length (plane of incidence: XY) - k = mp.Vector3(self.fcen*self.ng).rotate(mp.Vector3(z=1), theta_in) - - eig_parity = mp.ODD_Z - if theta == 0: - k = mp.Vector3() - eig_parity += mp.EVEN_Y - symmetries = [mp.Mirror(direction=mp.Y)] - else: - symmetries = [] - - def pw_amp(k,x0): - def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) - return _pw_amp - - src_pt = mp.Vector3(-0.5*sx+self.dpml,0,0) - sources = [mp.Source(mp.GaussianSource(self.fcen,fwidth=0.1*self.fcen), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(0,sy,0), - amp_func=pw_amp(k,src_pt))] - - geometry = [mp.Block(material=self.glass, - size=mp.Vector3(self.dpml+self.dsub,mp.inf,mp.inf), - center=mp.Vector3(-0.5*sx+0.5*(self.dpml+self.dsub),0,0)), - mp.Block(material=self.glass, - size=mp.Vector3(gh,gdc*gp,mp.inf), - center=mp.Vector3(-0.5*sx+self.dpml+self.dsub+0.5*gh,0,0))] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=self.pml_layers, - geometry=geometry, - k_point=k, - sources=sources, - symmetries=symmetries) - - tran_pt = mp.Vector3(0.5*sx-self.dpml,0,0) - tran_flux = sim.add_mode_monitor(self.fcen, - 0, - 1, - mp.FluxRegion(center=tran_pt, - size=mp.Vector3(0,sy,0))) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(20,mp.Ez,src_pt,1e-6)) - - m_plus = int(np.floor((self.fcen-k.y)*gp)) - m_minus = int(np.ceil((-self.fcen-k.y)*gp)) - - if theta == 0: - orders = range(m_plus) - else: - # ordering of the modes computed by MPB is according to *decreasing* - # values of kx (i.e., closest to propagation direction of 0° or +x) - ms = range(m_minus,m_plus+1) - kx = lambda m: np.power(self.fcen,2) - np.power(k.y+m/gp,2) - kxs = [kx(m) for m in ms] - ids = np.flip(np.argsort(kxs)) - orders = [ms[d] for d in ids] - - for band,order in enumerate(orders): - res = sim.get_eigenmode_coefficients(tran_flux, - [band+1], - eig_parity=eig_parity) - tran_ref = abs(res.alpha[0,0,0])**2 - if theta == 0: - tran_ref = 0.5*tran_ref - vg_ref = res.vgrp[0] - kdom_ref = res.kdom[0] - - res = sim.get_eigenmode_coefficients(tran_flux, - mp.DiffractedPlanewave((0,order,0), - mp.Vector3(0,1,0), - 1, - 0)) - tran_dp = abs(res.alpha[0,0,0])**2 - if (theta == 0) and (order == 0): - tran_dp = 0.5*tran_dp - vg_dp = res.vgrp[0] - kdom_dp = res.kdom[0] - - err = abs(tran_ref-tran_dp)/tran_ref - print("tran:, {:2d} (band), {:2d} (order), " - "{:10.8f} (band num.), {:10.8f} (diff. pw.), " - "{:10.8f} (error)".format(band,order,tran_ref,tran_dp,err)) - - self.assertAlmostEqual(vg_ref,vg_dp,places=4) - self.assertAlmostEqual(tran_ref,tran_dp,places=4) - if theta == 0: - self.assertAlmostEqual(abs(kdom_ref.x),kdom_dp.x,places=5) - self.assertAlmostEqual(abs(kdom_ref.y),kdom_dp.y,places=5) - self.assertAlmostEqual(abs(kdom_ref.z),kdom_dp.z,places=5) - else: - self.assertAlmostEqual(kdom_ref.x,kdom_dp.x,places=5) - self.assertAlmostEqual(kdom_ref.y,kdom_dp.y,places=5) - self.assertAlmostEqual(kdom_ref.z,kdom_dp.z,places=5) - - print("PASSED.") - - def test_diffracted_planewave(self): - self.run_binary_grating_diffraction(2.6,0.4,0.6,0) - self.run_binary_grating_diffraction(2.6,0.4,0.6,13.4) - - # self.run_binary_grating_diffraction(10.0,0.5,0.5,0) - # self.run_binary_grating_diffraction(10.0,0.5,0.5,10.7) - -if __name__ == '__main__': - unittest.main() +class TestDiffractedPlanewave(unittest.TestCase): + @classmethod + def setUp(cls): + cls.resolution = 50 # pixels/um + cls.dpml = 1.0 # PML thickness + cls.dsub = 3.0 # substrate thickness + cls.dpad = 3.0 # length of padding between grating and PML + + cls.wvl = 0.5 # center wavelength + cls.fcen = 1 / cls.wvl # center frequency + + cls.ng = 1.5 + cls.glass = mp.Medium(index=cls.ng) + + cls.pml_layers = [mp.PML(thickness=cls.dpml, direction=mp.X)] + + def run_binary_grating_diffraction(self, gp, gh, gdc, theta): + sx = self.dpml + self.dsub + gh + self.dpad + self.dpml + sy = gp + cell_size = mp.Vector3(sx, sy, 0) + + # rotation angle of incident planewave + # counter clockwise (CCW) about Z axis, 0 degrees along +X axis + theta_in = math.radians(theta) + + # k (in source medium) with correct length (plane of incidence: XY) + k = mp.Vector3(self.fcen * self.ng).rotate(mp.Vector3(z=1), theta_in) + + eig_parity = mp.ODD_Z + if theta == 0: + k = mp.Vector3() + eig_parity += mp.EVEN_Y + symmetries = [mp.Mirror(direction=mp.Y)] + else: + symmetries = [] + + def pw_amp(k, x0): + def _pw_amp(x): + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + + return _pw_amp + + src_pt = mp.Vector3(-0.5 * sx + self.dpml, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(self.fcen, fwidth=0.1 * self.fcen), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(0, sy, 0), + amp_func=pw_amp(k, src_pt), + ) + ] + + geometry = [ + mp.Block( + material=self.glass, + size=mp.Vector3(self.dpml + self.dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (self.dpml + self.dsub), 0, 0), + ), + mp.Block( + material=self.glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + self.dpml + self.dsub + 0.5 * gh, 0, 0), + ), + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=self.pml_layers, + geometry=geometry, + k_point=k, + sources=sources, + symmetries=symmetries, + ) + + tran_pt = mp.Vector3(0.5 * sx - self.dpml, 0, 0) + tran_flux = sim.add_mode_monitor( + self.fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) + ) + + sim.run( + until_after_sources=mp.stop_when_fields_decayed(20, mp.Ez, src_pt, 1e-6) + ) + + m_plus = int(np.floor((self.fcen - k.y) * gp)) + m_minus = int(np.ceil((-self.fcen - k.y) * gp)) + + if theta == 0: + orders = range(m_plus) + else: + # ordering of the modes computed by MPB is according to *decreasing* + # values of kx (i.e., closest to propagation direction of 0° or +x) + ms = range(m_minus, m_plus + 1) + kx = lambda m: np.power(self.fcen, 2) - np.power(k.y + m / gp, 2) + kxs = [kx(m) for m in ms] + ids = np.flip(np.argsort(kxs)) + orders = [ms[d] for d in ids] + + for band, order in enumerate(orders): + res = sim.get_eigenmode_coefficients( + tran_flux, [band + 1], eig_parity=eig_parity + ) + tran_ref = abs(res.alpha[0, 0, 0]) ** 2 + if theta == 0: + tran_ref = 0.5 * tran_ref + vg_ref = res.vgrp[0] + kdom_ref = res.kdom[0] + + res = sim.get_eigenmode_coefficients( + tran_flux, + mp.DiffractedPlanewave((0, order, 0), mp.Vector3(0, 1, 0), 1, 0), + ) + tran_dp = abs(res.alpha[0, 0, 0]) ** 2 + if (theta == 0) and (order == 0): + tran_dp = 0.5 * tran_dp + vg_dp = res.vgrp[0] + kdom_dp = res.kdom[0] + + err = abs(tran_ref - tran_dp) / tran_ref + print( + "tran:, {:2d} (band), {:2d} (order), " + "{:10.8f} (band num.), {:10.8f} (diff. pw.), " + "{:10.8f} (error)".format(band, order, tran_ref, tran_dp, err) + ) + + self.assertAlmostEqual(vg_ref, vg_dp, places=4) + self.assertAlmostEqual(tran_ref, tran_dp, places=4) + if theta == 0: + self.assertAlmostEqual(abs(kdom_ref.x), kdom_dp.x, places=5) + self.assertAlmostEqual(abs(kdom_ref.y), kdom_dp.y, places=5) + self.assertAlmostEqual(abs(kdom_ref.z), kdom_dp.z, places=5) + else: + self.assertAlmostEqual(kdom_ref.x, kdom_dp.x, places=5) + self.assertAlmostEqual(kdom_ref.y, kdom_dp.y, places=5) + self.assertAlmostEqual(kdom_ref.z, kdom_dp.z, places=5) + + print("PASSED.") + + def test_diffracted_planewave(self): + self.run_binary_grating_diffraction(2.6, 0.4, 0.6, 0) + self.run_binary_grating_diffraction(2.6, 0.4, 0.6, 13.4) + + # self.run_binary_grating_diffraction(10.0,0.5,0.5,0) + # self.run_binary_grating_diffraction(10.0,0.5,0.5,10.7) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_dispersive_eigenmode.py b/python/tests/test_dispersive_eigenmode.py index db9820ca0..ea5294f21 100644 --- a/python/tests/test_dispersive_eigenmode.py +++ b/python/tests/test_dispersive_eigenmode.py @@ -1,4 +1,3 @@ - # dispersive_eigenmode.py - Tests the meep eigenmode features (eigenmode source, # eigenmode decomposition, and get_eigenmode) with dispersive materials. # TODO: @@ -13,23 +12,24 @@ import h5py import os + class TestDispersiveEigenmode(ApproxComparisonTestCase): # ----------------------------------------- # # ----------- Helper Functions ------------ # # ----------------------------------------- # # Directly cals the C++ chi1 routine - def call_chi1(self,material,frequency): + def call_chi1(self, material, frequency): - sim = mp.Simulation(cell_size=mp.Vector3(1,1,1), - default_material=material, - resolution=20) + sim = mp.Simulation( + cell_size=mp.Vector3(1, 1, 1), default_material=material, resolution=20 + ) sim.init_sim() - v3 = mp.py_v3_to_vec(sim.dimensions, mp.Vector3(0,0,0), sim.is_cylindrical) - chi1inv = np.zeros((3,3),dtype=np.complex128) - for i, com in enumerate([mp.Ex,mp.Ey,mp.Ez]): - for k, dir in enumerate([mp.X,mp.Y,mp.Z]): - chi1inv[i,k] = sim.structure.get_chi1inv(com,dir,v3,frequency) + v3 = mp.py_v3_to_vec(sim.dimensions, mp.Vector3(0, 0, 0), sim.is_cylindrical) + chi1inv = np.zeros((3, 3), dtype=np.complex128) + for i, com in enumerate([mp.Ex, mp.Ey, mp.Ez]): + for k, dir in enumerate([mp.X, mp.Y, mp.Z]): + chi1inv[i, k] = sim.structure.get_chi1inv(com, dir, v3, frequency) n = np.real(np.sqrt(np.linalg.inv(chi1inv.astype(np.complex128)))) n_actual = np.real(np.sqrt(material.epsilon(frequency).astype(np.complex128))) @@ -45,35 +45,38 @@ def setUpClass(cls): def tearDownClass(cls): mp.delete_directory(cls.temp_dir) - def verify_output_and_slice(self,material,frequency): + def verify_output_and_slice(self, material, frequency): # Since the slice routines average the diagonals, we need to do that too: chi1 = material.epsilon(frequency).astype(np.complex128) chi1inv = np.linalg.inv(chi1) chi1inv = np.diag(chi1inv) N = chi1inv.size - n = np.sqrt(N/np.sum(chi1inv)) - - sim = mp.Simulation(cell_size=mp.Vector3(2,2,2), - default_material=material, - resolution=20, - eps_averaging=False - ) + n = np.sqrt(N / np.sum(chi1inv)) + + sim = mp.Simulation( + cell_size=mp.Vector3(2, 2, 2), + default_material=material, + resolution=20, + eps_averaging=False, + ) sim.use_output_directory(self.temp_dir) sim.init_sim() # Check to make sure the get_slice routine is working with frequency - n_slice = np.sqrt(np.max(sim.get_epsilon(frequency=frequency,snap=True))) - self.assertAlmostEqual(n,n_slice, places=4) + n_slice = np.sqrt(np.max(sim.get_epsilon(frequency=frequency, snap=True))) + self.assertAlmostEqual(n, n_slice, places=4) # Check to make sure h5 output is working with frequency - filename = os.path.join(self.temp_dir, f'{sim.get_filename_prefix()}-eps-000000.00.h5') + filename = os.path.join( + self.temp_dir, f"{sim.get_filename_prefix()}-eps-000000.00.h5" + ) - mp.output_epsilon(sim,frequency=frequency) + mp.output_epsilon(sim, frequency=frequency) n_h5 = 0 mp.all_wait() - with h5py.File(filename, 'r') as f: - n_h5 = np.sqrt(np.max(mp.complexarray(f['eps.r'][()],f['eps.i'][()]))) - self.assertAlmostEqual(n,n_h5, places=4) + with h5py.File(filename, "r") as f: + n_h5 = np.sqrt(np.max(mp.complexarray(f["eps.r"][()], f["eps.i"][()]))) + self.assertAlmostEqual(n, n_h5, places=4) # ----------------------------------------- # # ----------- Test Routines --------------- # @@ -88,33 +91,34 @@ def test_chi1_routine(self): # Check Silicon w0 = Si.valid_freq_range.min w1 = Si.valid_freq_range.max - self.call_chi1(Si,w0) - self.call_chi1(Si,w1) + self.call_chi1(Si, w0) + self.call_chi1(Si, w1) # Check Silver w0 = Ag.valid_freq_range.min w1 = Ag.valid_freq_range.max - self.call_chi1(Ag,w0) - self.call_chi1(Ag,w1) + self.call_chi1(Ag, w0) + self.call_chi1(Ag, w1) # Check Gold w0 = Au.valid_freq_range.min w1 = Au.valid_freq_range.max - self.call_chi1(Au,w0) - self.call_chi1(Au,w1) + self.call_chi1(Au, w0) + self.call_chi1(Au, w1) # Check Lithium Niobate (X,X) w0 = LiNbO3.valid_freq_range.min w1 = LiNbO3.valid_freq_range.max - self.call_chi1(LiNbO3,w0) - self.call_chi1(LiNbO3,w1) + self.call_chi1(LiNbO3, w0) + self.call_chi1(LiNbO3, w1) # Now let's rotate LN import copy + rotLiNbO3 = copy.deepcopy(LiNbO3) - rotLiNbO3.rotate(mp.Vector3(1,1,1),np.radians(34)) - self.call_chi1(rotLiNbO3,w0) - self.call_chi1(rotLiNbO3,w1) + rotLiNbO3.rotate(mp.Vector3(1, 1, 1), np.radians(34)) + self.call_chi1(rotLiNbO3, w0) + self.call_chi1(rotLiNbO3, w1) def test_get_with_dispersion(self): # Checks the get_array_slice and output_fields method @@ -125,34 +129,35 @@ def test_get_with_dispersion(self): # Check Silicon w0 = Si.valid_freq_range.min w1 = Si.valid_freq_range.max - self.verify_output_and_slice(Si,w0) - self.verify_output_and_slice(Si,w1) + self.verify_output_and_slice(Si, w0) + self.verify_output_and_slice(Si, w1) # Check Silver w0 = Ag.valid_freq_range.min w1 = Ag.valid_freq_range.max - self.verify_output_and_slice(Ag,w0) - self.verify_output_and_slice(Ag,w1) + self.verify_output_and_slice(Ag, w0) + self.verify_output_and_slice(Ag, w1) # Check Gold w0 = Au.valid_freq_range.min w1 = Au.valid_freq_range.max - self.verify_output_and_slice(Au,w0) - self.verify_output_and_slice(Au,w1) + self.verify_output_and_slice(Au, w0) + self.verify_output_and_slice(Au, w1) # Check Lithium Niobate w0 = LiNbO3.valid_freq_range.min w1 = LiNbO3.valid_freq_range.max - self.verify_output_and_slice(LiNbO3,w0) - self.verify_output_and_slice(LiNbO3,w1) + self.verify_output_and_slice(LiNbO3, w0) + self.verify_output_and_slice(LiNbO3, w1) # Now let's rotate LN import copy + rotLiNbO3 = copy.deepcopy(LiNbO3) - rotLiNbO3.rotate(mp.Vector3(1,1,1),np.radians(34)) - self.verify_output_and_slice(rotLiNbO3,w0) - self.verify_output_and_slice(rotLiNbO3,w1) + rotLiNbO3.rotate(mp.Vector3(1, 1, 1), np.radians(34)) + self.verify_output_and_slice(rotLiNbO3, w0) + self.verify_output_and_slice(rotLiNbO3, w1) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_divide_mpi_processes.py b/python/tests/test_divide_mpi_processes.py index 7a46ab8e8..e0312fffa 100644 --- a/python/tests/test_divide_mpi_processes.py +++ b/python/tests/test_divide_mpi_processes.py @@ -1,55 +1,63 @@ import unittest import meep as mp + @unittest.skipIf(mp.count_processors() < 2, "MPI specific test") class TestDivideParallelProcesses(unittest.TestCase): - - def test_divide_parallel_processes(self): + def test_divide_parallel_processes(self): resolution = 20 sxy = 4 dpml = 1 - cell = mp.Vector3(sxy+2*dpml,sxy+2*dpml) + cell = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml) pml_layers = [mp.PML(dpml)] n = mp.divide_parallel_processes(2) - fcen = 1.0/(n+1) - - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - center=mp.Vector3(), - component=mp.Ez)] - - symmetries = [mp.Mirror(mp.X), - mp.Mirror(mp.Y)] - - self.sim = mp.Simulation(cell_size=cell, - resolution=resolution, - sources=sources, - symmetries=symmetries, - boundary_layers=pml_layers) - - - flux_box = self.sim.add_flux(fcen, 0, 1, - mp.FluxRegion(mp.Vector3(y=0.5*sxy), size=mp.Vector3(sxy)), - mp.FluxRegion(mp.Vector3(y=-0.5*sxy), size=mp.Vector3(sxy), weight=-1), - mp.FluxRegion(mp.Vector3(0.5*sxy), size=mp.Vector3(y=sxy)), - mp.FluxRegion(mp.Vector3(-0.5*sxy), size=mp.Vector3(y=sxy), weight=-1), - decimation_factor=1) + fcen = 1.0 / (n + 1) + + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=mp.Vector3(), + component=mp.Ez, + ) + ] + + symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] + + self.sim = mp.Simulation( + cell_size=cell, + resolution=resolution, + sources=sources, + symmetries=symmetries, + boundary_layers=pml_layers, + ) + + flux_box = self.sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(mp.Vector3(y=0.5 * sxy), size=mp.Vector3(sxy)), + mp.FluxRegion(mp.Vector3(y=-0.5 * sxy), size=mp.Vector3(sxy), weight=-1), + mp.FluxRegion(mp.Vector3(0.5 * sxy), size=mp.Vector3(y=sxy)), + mp.FluxRegion(mp.Vector3(-0.5 * sxy), size=mp.Vector3(y=sxy), weight=-1), + decimation_factor=1, + ) self.sim.run(until_after_sources=30) tot_flux = mp.get_fluxes(flux_box)[0] tot_fluxes = mp.merge_subgroup_data(tot_flux) - fcens = mp.merge_subgroup_data(fcen) + fcens = mp.merge_subgroup_data(fcen) - self.assertEqual(fcens[0],1) - self.assertEqual(fcens[1],0.5) + self.assertEqual(fcens[0], 1) + self.assertEqual(fcens[1], 0.5) places = 4 if mp.is_single_precision() else 7 self.assertAlmostEqual(tot_fluxes[0], 9.8628728533, places=places) self.assertAlmostEqual(tot_fluxes[1], 19.6537275387, places=places) - -if __name__ == '__main__': - unittest.main() + +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_dump_load.py b/python/tests/test_dump_load.py index 223c98ea6..3fd66c7f1 100644 --- a/python/tests/test_dump_load.py +++ b/python/tests/test_dump_load.py @@ -17,8 +17,8 @@ class TestLoadDump(ApproxComparisonTestCase): - fname_base = re.sub(r'\.py$', '', os.path.split(sys.argv[0])[1]) - fname = fname_base + '-ez-000200.00.h5' + fname_base = re.sub(r"\.py$", "", os.path.split(sys.argv[0])[1]) + fname = fname_base + "-ez-000200.00.h5" def setUp(self): print(f"Running {self._testMethodName}") @@ -33,24 +33,35 @@ def tearDownClass(cls): mp.delete_directory(cls.temp_dir) # Tests various combinations of dumping/loading structure & chunk layout in 2d. - def _load_dump_structure_2d(self, chunk_file=False, chunk_sim=False, single_parallel_file=True): + def _load_dump_structure_2d( + self, chunk_file=False, chunk_sim=False, single_parallel_file=True + ): from meep.materials import Al + resolution = 50 cell = mp.Vector3(5, 5) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.2), center=mp.Vector3(), component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.2), center=mp.Vector3(), component=mp.Ez + ) one_by_one = mp.Vector3(1, 1, mp.inf) - geometry = [mp.Block(material=Al, center=mp.Vector3(), size=one_by_one), - mp.Block(material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one)] + geometry = [ + mp.Block(material=Al, center=mp.Vector3(), size=one_by_one), + mp.Block( + material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one + ), + ] pml_layers = [mp.PML(0.5)] symmetries = [mp.Mirror(mp.Y)] - sim1 = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - symmetries=symmetries, - sources=[sources]) + sim1 = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + symmetries=symmetries, + sources=[sources], + ) sample_point = mp.Vector3(0.12, -0.29) ref_field_points = [] @@ -61,25 +72,37 @@ def get_ref_field_point(sim): sim1.run(mp.at_every(5, get_ref_field_point), until=50) - dump_dirname = os.path.join(self.temp_dir, 'test_load_dump_structure') - sim1.dump(dump_dirname, dump_structure=True, dump_fields=False, single_parallel_file=single_parallel_file) + dump_dirname = os.path.join(self.temp_dir, "test_load_dump_structure") + sim1.dump( + dump_dirname, + dump_structure=True, + dump_fields=False, + single_parallel_file=single_parallel_file, + ) dump_chunk_fname = None chunk_layout = None if chunk_file: - dump_chunk_fname = os.path.join(dump_dirname, 'chunk_layout.h5') + dump_chunk_fname = os.path.join(dump_dirname, "chunk_layout.h5") sim1.dump_chunk_layout(dump_chunk_fname) chunk_layout = dump_chunk_fname if chunk_sim: chunk_layout = sim1 - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - sources=[sources], - symmetries=symmetries, - chunk_layout=chunk_layout) - sim.load(dump_dirname, load_structure=True, load_fields=False, single_parallel_file=single_parallel_file) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + sources=[sources], + symmetries=symmetries, + chunk_layout=chunk_layout, + ) + sim.load( + dump_dirname, + load_structure=True, + load_fields=False, + single_parallel_file=single_parallel_file, + ) field_points = [] def get_field_point(sim): @@ -105,25 +128,38 @@ def test_load_dump_chunk_layout_sim_2d(self): self._load_dump_structure_2d(chunk_sim=True) # Tests various combinations of dumping/loading structure & chunk layout in 3d. - def _load_dump_structure_3d(self, chunk_file=False, chunk_sim=False, single_parallel_file=True): + def _load_dump_structure_3d( + self, chunk_file=False, chunk_sim=False, single_parallel_file=True + ): from meep.materials import aSi + resolution = 15 cell = mp.Vector3(2.3, 2.1, 2.7) - sources = mp.Source(src=mp.GaussianSource(2.5, fwidth=0.1), center=mp.Vector3(), component=mp.Hy) + sources = mp.Source( + src=mp.GaussianSource(2.5, fwidth=0.1), center=mp.Vector3(), component=mp.Hy + ) one_by_one_by_one = mp.Vector3(1, 1, 1) - geometry = [mp.Block(material=mp.Medium(index=2.3), center=mp.Vector3(), size=one_by_one_by_one), - mp.Block(material=aSi, center=mp.Vector3(1), size=one_by_one_by_one)] + geometry = [ + mp.Block( + material=mp.Medium(index=2.3), + center=mp.Vector3(), + size=one_by_one_by_one, + ), + mp.Block(material=aSi, center=mp.Vector3(1), size=one_by_one_by_one), + ] default_material = mp.Medium(index=1.4) boundary_layers = [mp.Absorber(0.2)] k_point = mp.Vector3(0.4, -1.3, 0.7) - sim1 = mp.Simulation(resolution=resolution, - cell_size=cell, - k_point=k_point, - geometry=geometry, - boundary_layers=boundary_layers, - default_material=default_material, - sources=[sources]) + sim1 = mp.Simulation( + resolution=resolution, + cell_size=cell, + k_point=k_point, + geometry=geometry, + boundary_layers=boundary_layers, + default_material=default_material, + sources=[sources], + ) sample_point = mp.Vector3(0.73, -0.33, 0.61) ref_field_points = [] @@ -134,25 +170,37 @@ def get_ref_field_point(sim): sim1.run(mp.at_every(5, get_ref_field_point), until=50) - dump_dirname = os.path.join(self.temp_dir, 'test_load_dump_structure') - sim1.dump(dump_dirname, dump_structure=True, dump_fields=False, single_parallel_file=single_parallel_file) + dump_dirname = os.path.join(self.temp_dir, "test_load_dump_structure") + sim1.dump( + dump_dirname, + dump_structure=True, + dump_fields=False, + single_parallel_file=single_parallel_file, + ) dump_chunk_fname = None chunk_layout = None if chunk_file: - dump_chunk_fname = os.path.join(dump_dirname, 'chunk_layout.h5') + dump_chunk_fname = os.path.join(dump_dirname, "chunk_layout.h5") sim1.dump_chunk_layout(dump_chunk_fname) chunk_layout = dump_chunk_fname if chunk_sim: chunk_layout = sim1 - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - sources=[sources], - k_point=k_point, - boundary_layers=boundary_layers, - chunk_layout=chunk_layout) - sim.load(dump_dirname, load_structure=True, load_fields=False, single_parallel_file=single_parallel_file) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + sources=[sources], + k_point=k_point, + boundary_layers=boundary_layers, + chunk_layout=chunk_layout, + ) + sim.load( + dump_dirname, + load_structure=True, + load_fields=False, + single_parallel_file=single_parallel_file, + ) field_points = [] def get_field_point(sim): @@ -181,68 +229,103 @@ def test_load_dump_chunk_layout_sim_3d(self): def _load_dump_fields_2d(self, single_parallel_file=True): resolution = 50 cell = mp.Vector3(5, 5) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Ez + ) one_by_one = mp.Vector3(1, 1, mp.inf) - geometry = [mp.Block(material=mp.Medium(index=3.2), center=mp.Vector3(), size=one_by_one), - mp.Block(material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one)] + geometry = [ + mp.Block( + material=mp.Medium(index=3.2), center=mp.Vector3(), size=one_by_one + ), + mp.Block( + material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one + ), + ] pml_layers = [mp.PML(0.5)] symmetries = [mp.Mirror(mp.Y)] - sim1 = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - symmetries=symmetries, - sources=[sources]) - - mon_dft = sim1.add_dft_fields([mp.Ez], - 1.1, 0.1, 3, - center=mp.Vector3(1.2,0.4), - size=mp.Vector3(1.5,0.3), - yee_grid=True) + sim1 = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + symmetries=symmetries, + sources=[sources], + ) + + mon_dft = sim1.add_dft_fields( + [mp.Ez], + 1.1, + 0.1, + 3, + center=mp.Vector3(1.2, 0.4), + size=mp.Vector3(1.5, 0.3), + yee_grid=True, + ) sample_point = mp.Vector3(0.12, -0.29) - dump_dirname = os.path.join(self.temp_dir, 'test_load_dump_fields') + dump_dirname = os.path.join(self.temp_dir, "test_load_dump_fields") os.makedirs(dump_dirname, exist_ok=True) ref_field_points = {} + def get_ref_field_point(sim): p = sim.get_field_point(mp.Ez, sample_point) ref_field_points[sim.meep_time()] = p.real # First run until t=15 and save structure/fields sim1.run(mp.at_every(1, get_ref_field_point), until=15) - sim1.dump(dump_dirname, dump_structure=True, dump_fields=True, single_parallel_file=single_parallel_file) - + sim1.dump( + dump_dirname, + dump_structure=True, + dump_fields=True, + single_parallel_file=single_parallel_file, + ) # Then continue running another 5 until t=20 sim1.run(mp.at_every(1, get_ref_field_point), until=5) sim1_mon_dft = sim1.get_dft_array(mon_dft, mp.Ez, 2) # Now create a new simulation and try restoring state. - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - sources=[sources], - symmetries=symmetries, - chunk_layout=sim1) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + sources=[sources], + symmetries=symmetries, + chunk_layout=sim1, + ) # Just restore structure first. - sim.load(dump_dirname, load_structure=True, load_fields=False, single_parallel_file=single_parallel_file) + sim.load( + dump_dirname, + load_structure=True, + load_fields=False, + single_parallel_file=single_parallel_file, + ) field_points = {} - mon_dft = sim.add_dft_fields([mp.Ez], - 1.1, 0.1, 3, - center=mp.Vector3(1.2,0.4), - size=mp.Vector3(1.5,0.3), - yee_grid=True) + mon_dft = sim.add_dft_fields( + [mp.Ez], + 1.1, + 0.1, + 3, + center=mp.Vector3(1.2, 0.4), + size=mp.Vector3(1.5, 0.3), + yee_grid=True, + ) def get_field_point(sim): p = sim.get_field_point(mp.Ez, sample_point) field_points[sim.meep_time()] = p.real # Now load the fields (at t=15) and then continue to t=20 - sim.load(dump_dirname, load_structure=False, load_fields=True, single_parallel_file=single_parallel_file) + sim.load( + dump_dirname, + load_structure=False, + load_fields=True, + single_parallel_file=single_parallel_file, + ) sim.run(mp.at_every(1, get_field_point), until=5) for t, v in field_points.items(): @@ -262,68 +345,107 @@ def test_load_dump_fields_sharded_2d(self): def _load_dump_fields_3d(self, single_parallel_file=True): resolution = 15 cell = mp.Vector3(2.6, 2.2, 2.3) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Hx) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Hx + ) one_by_one_by_one = mp.Vector3(1, 1, 1) - geometry = [mp.Block(material=mp.Medium(index=2.4), center=mp.Vector3(), size=one_by_one_by_one), - mp.Block(material=mp.Medium(epsilon=7.9), center=mp.Vector3(1), size=one_by_one_by_one)] + geometry = [ + mp.Block( + material=mp.Medium(index=2.4), + center=mp.Vector3(), + size=one_by_one_by_one, + ), + mp.Block( + material=mp.Medium(epsilon=7.9), + center=mp.Vector3(1), + size=one_by_one_by_one, + ), + ] pml_layers = [mp.PML(0.5)] - symmetries = [mp.Mirror(mp.Y,phase=-1),mp.Mirror(mp.Z,phase=-1)] - - sim1 = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - symmetries=symmetries, - sources=[sources]) - - mon_dft = sim1.add_dft_fields([mp.Hx], - 0.9, 0.05, 4, - center=mp.Vector3(0.5,0.4,0.2), - size=mp.Vector3(1.5,0.3,0.2), - yee_grid=True) + symmetries = [mp.Mirror(mp.Y, phase=-1), mp.Mirror(mp.Z, phase=-1)] + + sim1 = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + symmetries=symmetries, + sources=[sources], + ) + + mon_dft = sim1.add_dft_fields( + [mp.Hx], + 0.9, + 0.05, + 4, + center=mp.Vector3(0.5, 0.4, 0.2), + size=mp.Vector3(1.5, 0.3, 0.2), + yee_grid=True, + ) sample_point = mp.Vector3(0.37, -0.42, 0.55) - dump_dirname = os.path.join(self.temp_dir, 'test_load_dump_fields') + dump_dirname = os.path.join(self.temp_dir, "test_load_dump_fields") os.makedirs(dump_dirname, exist_ok=True) ref_field_points = {} + def get_ref_field_point(sim): p = sim.get_field_point(mp.Hx, sample_point) ref_field_points[sim.meep_time()] = p.real # First run until t=15 and save structure/fields sim1.run(mp.at_every(1, get_ref_field_point), until=15) - sim1.dump(dump_dirname, dump_structure=True, dump_fields=True, single_parallel_file=single_parallel_file) - + sim1.dump( + dump_dirname, + dump_structure=True, + dump_fields=True, + single_parallel_file=single_parallel_file, + ) # Then continue running another 5 until t=20 sim1.run(mp.at_every(1, get_ref_field_point), until=5) sim1_mon_dft = sim1.get_dft_array(mon_dft, mp.Hx, 3) # Now create a new simulation and try restoring state. - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - sources=[sources], - symmetries=symmetries, - chunk_layout=sim1) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + sources=[sources], + symmetries=symmetries, + chunk_layout=sim1, + ) # Just restore structure first. - sim.load(dump_dirname, load_structure=True, load_fields=False, single_parallel_file=single_parallel_file) + sim.load( + dump_dirname, + load_structure=True, + load_fields=False, + single_parallel_file=single_parallel_file, + ) field_points = {} - mon_dft = sim.add_dft_fields([mp.Hx], - 0.9, 0.05, 4, - center=mp.Vector3(0.5,0.4,0.2), - size=mp.Vector3(1.5,0.3,0.2), - yee_grid=True) + mon_dft = sim.add_dft_fields( + [mp.Hx], + 0.9, + 0.05, + 4, + center=mp.Vector3(0.5, 0.4, 0.2), + size=mp.Vector3(1.5, 0.3, 0.2), + yee_grid=True, + ) def get_field_point(sim): p = sim.get_field_point(mp.Hx, sample_point) field_points[sim.meep_time()] = p.real # Now load the fields (at t=15) and then continue to t=20 - sim.load(dump_dirname, load_structure=False, load_fields=True, single_parallel_file=single_parallel_file) + sim.load( + dump_dirname, + load_structure=False, + load_fields=True, + single_parallel_file=single_parallel_file, + ) sim.run(mp.at_every(1, get_field_point), until=5) for t, v in field_points.items(): @@ -345,27 +467,39 @@ def test_load_dump_fields_sharded_3d(self): def test_dump_fails_for_non_null_polarization_state(self): resolution = 50 cell = mp.Vector3(5, 5) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Ez + ) one_by_one = mp.Vector3(1, 1, mp.inf) from meep.materials import Al - geometry = [mp.Block(material=Al, center=mp.Vector3(), size=one_by_one), - mp.Block(material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one)] - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=[], - geometry=geometry, - symmetries=[], - sources=[sources]) - - dump_dirname = os.path.join(self.temp_dir, 'test_load_dump_fields') + geometry = [ + mp.Block(material=Al, center=mp.Vector3(), size=one_by_one), + mp.Block( + material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one + ), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=[], + geometry=geometry, + symmetries=[], + sources=[sources], + ) + + dump_dirname = os.path.join(self.temp_dir, "test_load_dump_fields") os.makedirs(dump_dirname, exist_ok=True) sim.run(until=1) # NOTE: We do not yet support checkpoint/restore when there is a # non-null polarization_state - with self.assertRaisesRegex(RuntimeError, 'meep: non-null polarization_state in fields::dump'): - sim.dump(dump_dirname, dump_structure=True, dump_fields=True) + with self.assertRaisesRegex( + RuntimeError, "meep: non-null polarization_state in fields::dump" + ): + sim.dump(dump_dirname, dump_structure=True, dump_fields=True) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_eigfreq.py b/python/tests/test_eigfreq.py index f73fe73ba..b0f3a7041 100644 --- a/python/tests/test_eigfreq.py +++ b/python/tests/test_eigfreq.py @@ -1,42 +1,54 @@ import meep as mp import unittest -class TestEigfreq(unittest.TestCase): - @unittest.skipIf(mp.is_single_precision(), "double-precision floating point specific test") +class TestEigfreq(unittest.TestCase): + @unittest.skipIf( + mp.is_single_precision(), "double-precision floating point specific test" + ) def test_eigfreq(self): - w = 1.2 # width of waveguide - r = 0.36 # radius of holes - d = 1.4 # defect spacing (ordinary spacing = 1) - N = 3 # number of holes on either side of defect - sy = 6 # size of cell in y direction (perpendicular to wvg.) - pad = 2 # padding between last hole and PML edge - dpml = 1 # PML thickness - sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction - - geometry = [mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), material=mp.Medium(epsilon=13))] + w = 1.2 # width of waveguide + r = 0.36 # radius of holes + d = 1.4 # defect spacing (ordinary spacing = 1) + N = 3 # number of holes on either side of defect + sy = 6 # size of cell in y direction (perpendicular to wvg.) + pad = 2 # padding between last hole and PML edge + dpml = 1 # PML thickness + sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction + + geometry = [ + mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=13)) + ] for i in range(N): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d/2+i))) - geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d/2+i)))) + geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) + geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d / 2 + i)))) fcen = 0.25 df = 0.2 - src = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Hz, - center=mp.Vector3(0), - size=mp.Vector3(0,0))] - - sim = mp.Simulation(cell_size=mp.Vector3(sx,sy), force_complex_fields=True, - geometry=geometry, - boundary_layers=[mp.PML(1.0)], - sources=src, - symmetries=[mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)], - resolution=20) + src = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Hz, + center=mp.Vector3(0), + size=mp.Vector3(0, 0), + ) + ] + + sim = mp.Simulation( + cell_size=mp.Vector3(sx, sy), + force_complex_fields=True, + geometry=geometry, + boundary_layers=[mp.PML(1.0)], + sources=src, + symmetries=[mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)], + resolution=20, + ) sim.init_sim() eigfreq = sim.solve_eigfreq(tol=1e-6) self.assertAlmostEqual(eigfreq.real, 0.23445413142440263, places=5) self.assertAlmostEqual(eigfreq.imag, -0.0003147775697388, places=5) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_faraday_rotation.py b/python/tests/test_faraday_rotation.py index 7c0f337a2..d1c610a8a 100644 --- a/python/tests/test_faraday_rotation.py +++ b/python/tests/test_faraday_rotation.py @@ -4,40 +4,55 @@ ## Farady rotation rate for gyrotropic Lorentzian medium def kgyro_lorentzian(freq, epsn, f0, gamma, sigma, b0): - dfsq = (f0**2 - 1j*freq*gamma - freq**2) - eperp = epsn + sigma * f0**2 * dfsq / (dfsq**2 - (freq*b0)**2) - eta = sigma * f0**2 * freq * b0 / (dfsq**2 - (freq*b0)**2) - return 2*np.pi*freq * np.sqrt(0.5*(eperp - np.sqrt(eperp**2 - eta**2))) + dfsq = f0**2 - 1j * freq * gamma - freq**2 + eperp = epsn + sigma * f0**2 * dfsq / (dfsq**2 - (freq * b0) ** 2) + eta = sigma * f0**2 * freq * b0 / (dfsq**2 - (freq * b0) ** 2) + return 2 * np.pi * freq * np.sqrt(0.5 * (eperp - np.sqrt(eperp**2 - eta**2))) + ## Farady rotation rate for gyrotropic Drude medium def kgyro_drude(freq, epsn, f0, gamma, sigma, b0): - dfsq = - 1j*freq*gamma - freq**2 - eperp = epsn + sigma * f0**2 * dfsq / (dfsq**2 - (freq*b0)**2) - eta = sigma * f0**2 * freq * b0 / (dfsq**2 - (freq*b0)**2) - return 2*np.pi*freq * np.sqrt(0.5*(eperp - np.sqrt(eperp**2 - eta**2))) + dfsq = -1j * freq * gamma - freq**2 + eperp = epsn + sigma * f0**2 * dfsq / (dfsq**2 - (freq * b0) ** 2) + eta = sigma * f0**2 * freq * b0 / (dfsq**2 - (freq * b0) ** 2) + return 2 * np.pi * freq * np.sqrt(0.5 * (eperp - np.sqrt(eperp**2 - eta**2))) + ## Farady rotation rate for Landau-Lifshitz-Gilbert medium def kgyro_llg(freq, epsn, f0, gamma, sigma, alpha): - df1 = f0 - 1j*freq*alpha - df2 = freq + 1j*gamma - eperp = epsn + sigma * df1/(df1**2 - df2**2) + df1 = f0 - 1j * freq * alpha + df2 = freq + 1j * gamma + eperp = epsn + sigma * df1 / (df1**2 - df2**2) eta = sigma * df2 / (df1**2 - df2**2) - return 2*np.pi*freq * np.sqrt(0.5*(eperp - np.sqrt(eperp**2 - eta**2))) + return 2 * np.pi * freq * np.sqrt(0.5 * (eperp - np.sqrt(eperp**2 - eta**2))) + class TestFaradayRotation(unittest.TestCase): ## Simulate a linearly polarized plane wave traveling along the gyrotropy axis. ## Extract Faraday rotation angle by comparing the Ex and Ey amplitudes, and ## compare to a theoretical result corresponding to rotation rate KPRED. ## The default acceptable tolerance TOL is 1.5 degrees. - def check_rotation(self, mat, L, fsrc, zsrc, resolution, tmax, zout, kpred, tol=1.5): + def check_rotation( + self, mat, L, fsrc, zsrc, resolution, tmax, zout, kpred, tol=1.5 + ): cell = mp.Vector3(0, 0, L) pml_layers = [mp.PML(thickness=1.0, direction=mp.Z)] - sources = [mp.Source(mp.ContinuousSource(frequency=fsrc), - component=mp.Ex, center=mp.Vector3(0, 0, zsrc))] - - self.sim = mp.Simulation(cell_size=cell, geometry=[], sources=sources, - boundary_layers=pml_layers, - default_material=mat, resolution=resolution) + sources = [ + mp.Source( + mp.ContinuousSource(frequency=fsrc), + component=mp.Ex, + center=mp.Vector3(0, 0, zsrc), + ) + ] + + self.sim = mp.Simulation( + cell_size=cell, + geometry=[], + sources=sources, + boundary_layers=pml_layers, + default_material=mat, + resolution=resolution, + ) record_Ex, record_Ey, record_t = [], [], [] @@ -46,15 +61,17 @@ def record_ex_ey(sim): record_Ey.append(sim.get_field_point(mp.Ey, mp.Vector3(0, 0, zout))) record_t.append(sim.meep_time()) - self.sim.run(mp.after_time(0.5*tmax, mp.at_every(1e-6, record_ex_ey)), until=tmax) + self.sim.run( + mp.after_time(0.5 * tmax, mp.at_every(1e-6, record_ex_ey)), until=tmax + ) ex_rel = np.amax(abs(np.fft.fft(record_Ex))) ey_rel = np.amax(abs(np.fft.fft(record_Ey))) - result = np.arctan2(ey_rel, ex_rel) * 180/np.pi + result = np.arctan2(ey_rel, ex_rel) * 180 / np.pi Ex_theory = np.abs(np.cos(kpred * (zout - zsrc)).real) Ey_theory = np.abs(np.sin(kpred * (zout - zsrc)).real) - expected = np.arctan2(Ey_theory, Ex_theory) * 180/np.pi + expected = np.arctan2(Ey_theory, Ex_theory) * 180 / np.pi print("Rotation angle (in degrees): {}, expected {}\n".format(result, expected)) np.testing.assert_allclose(expected, result, atol=tol) @@ -65,34 +82,47 @@ def test_faraday_rotation(self): resolution = 24 ## Test gyrotropic Lorentzian medium - epsn, f0, gamma, sn, b0 = 1.5, 1.0, 1e-3, 0.1, 0.15 - susc = [mp.GyrotropicLorentzianSusceptibility(frequency=f0, gamma=gamma, sigma=sn, - bias=mp.Vector3(0, 0, b0))] + epsn, f0, gamma, sn, b0 = 1.5, 1.0, 1e-3, 0.1, 0.15 + susc = [ + mp.GyrotropicLorentzianSusceptibility( + frequency=f0, gamma=gamma, sigma=sn, bias=mp.Vector3(0, 0, b0) + ) + ] mat = mp.Medium(epsilon=epsn, mu=1, E_susceptibilities=susc) k = kgyro_lorentzian(freq, epsn, f0, gamma, sn, b0) - print('=' * 24) + print("=" * 24) print("Testing Faraday rotation for gyrotropic Lorentzian model...") self.check_rotation(mat, L, freq, zsrc, resolution, tmax, zout, k) ## Test gyrotropic Drude medium - susc = [mp.GyrotropicDrudeSusceptibility(frequency=f0, gamma=gamma, sigma=sn, - bias=mp.Vector3(0, 0, b0))] + susc = [ + mp.GyrotropicDrudeSusceptibility( + frequency=f0, gamma=gamma, sigma=sn, bias=mp.Vector3(0, 0, b0) + ) + ] mat = mp.Medium(epsilon=epsn, mu=1, E_susceptibilities=susc) k = kgyro_drude(freq, epsn, f0, gamma, sn, b0) - print('=' * 24) + print("=" * 24) print("Testing Faraday rotation for gyrotropic Drude model...") self.check_rotation(mat, L, freq, zsrc, resolution, tmax, zout, k) ## Test Landau-Lifshitz-Gilbert medium alpha = 1e-5 - susc = [mp.GyrotropicSaturatedSusceptibility(frequency=f0, gamma=gamma, sigma=sn, - alpha=alpha, - bias=mp.Vector3(0, 0, 1.0))] + susc = [ + mp.GyrotropicSaturatedSusceptibility( + frequency=f0, + gamma=gamma, + sigma=sn, + alpha=alpha, + bias=mp.Vector3(0, 0, 1.0), + ) + ] mat = mp.Medium(epsilon=epsn, mu=1, E_susceptibilities=susc) k = kgyro_llg(freq, epsn, f0, gamma, sn, alpha) - print('=' * 24) + print("=" * 24) print("Testing Faraday rotation for Landau-Lifshitz-Gilbert model...") self.check_rotation(mat, L, freq, zsrc, resolution, tmax, zout, k) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_field_functions.py b/python/tests/test_field_functions.py index 33cdc41af..ce35758e1 100644 --- a/python/tests/test_field_functions.py +++ b/python/tests/test_field_functions.py @@ -33,16 +33,19 @@ def init(self): fcen = 1.0 df = 1.0 - sources = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), - component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), component=mp.Ez + ) symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] - return mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=[pml_layers], - sources=[sources], - symmetries=symmetries) + return mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=[pml_layers], + sources=[sources], + symmetries=symmetries, + ) def init2(self): n = 3.4 @@ -55,7 +58,7 @@ def init2(self): geometry = [ mp.Cylinder(radius=r + w, height=mp.inf, material=mp.Medium(index=n)), - mp.Cylinder(radius=r, height=mp.inf, material=mp.air) + mp.Cylinder(radius=r, height=mp.inf, material=mp.air), ] pml_layers = [mp.PML(dpml)] @@ -63,17 +66,24 @@ def init2(self): fcen = 0.118 df = 0.010 - sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, - center=mp.Vector3(r + 0.1))] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(r + 0.1), + ) + ] symmetries = [mp.Mirror(mp.Y)] - return mp.Simulation(cell_size=cell, - resolution=resolution, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - symmetries=symmetries) + return mp.Simulation( + cell_size=cell, + resolution=resolution, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + symmetries=symmetries, + ) def test_integrate_field_function(self): sim = self.init() @@ -107,5 +117,6 @@ def test_max_abs_field_function(self): res = sim.max_abs_field_function(self.cs, f, self.vol) self.assertAlmostEqual(res, 0.27593732304637586) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_force.py b/python/tests/test_force.py index 5a3078bb2..7a728db5c 100644 --- a/python/tests/test_force.py +++ b/python/tests/test_force.py @@ -3,7 +3,6 @@ class TestForce(unittest.TestCase): - def setUp(self): resolution = 20 @@ -11,21 +10,30 @@ def setUp(self): pml_layers = mp.PML(1.0) fcen = 1.0 df = 1.0 - sources = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), - component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), component=mp.Ez + ) - self.sim = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=[pml_layers], - sources=[sources]) + self.sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=[pml_layers], + sources=[sources], + ) fr = mp.ForceRegion(mp.Vector3(y=1.27), direction=mp.Y, size=mp.Vector3(4.38)) self.myforce = self.sim.add_force(fcen, 0, 1, fr, decimation_factor=1) - self.myforce_decimated = self.sim.add_force(fcen, 0, 1, fr, decimation_factor=10) + self.myforce_decimated = self.sim.add_force( + fcen, 0, 1, fr, decimation_factor=10 + ) def test_force(self): - self.sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(), 1e-6)) + self.sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-6 + ) + ) # Test store and load of force as numpy array fdata = self.sim.get_force_data(self.myforce) @@ -37,8 +45,10 @@ def test_force(self): self.assertAlmostEqual(f[0], -0.11039089113393187) places = 6 if mp.is_single_precision() else 7 - self.assertAlmostEqual(f[0], mp.get_forces(self.myforce_decimated)[0], places=places) + self.assertAlmostEqual( + f[0], mp.get_forces(self.myforce_decimated)[0], places=places + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_fragment_stats.py b/python/tests/test_fragment_stats.py index 497744820..56c4c47fe 100644 --- a/python/tests/test_fragment_stats.py +++ b/python/tests/test_fragment_stats.py @@ -2,19 +2,40 @@ import meep as mp -def make_dft_vecs(flx_reg=None, n2f_reg=None, frc_reg=None, fldc=None, flds=None, fldw=None, fld_cmp=None): - return {'flux_regions': flx_reg, 'n2f_regions': n2f_reg, 'force_regions': frc_reg, 'fields_center': fldc, 'fields_size': flds, 'fields_where': fldw, 'fields_components': fld_cmp} +def make_dft_vecs( + flx_reg=None, + n2f_reg=None, + frc_reg=None, + fldc=None, + flds=None, + fldw=None, + fld_cmp=None, +): + return { + "flux_regions": flx_reg, + "n2f_regions": n2f_reg, + "force_regions": frc_reg, + "fields_center": fldc, + "fields_size": flds, + "fields_where": fldw, + "fields_components": fld_cmp, + } def make_sim(cell, res, pml, dims, create_gv=True, k_point=False): - sim = mp.Simulation(cell_size=cell, resolution=res, boundary_layers=pml, dimensions=dims, k_point=k_point) + sim = mp.Simulation( + cell_size=cell, + resolution=res, + boundary_layers=pml, + dimensions=dims, + k_point=k_point, + ) if create_gv: sim._create_grid_volume(False) return sim class TestFragmentStats(unittest.TestCase): - def check_stats(self, fragment, a_eps, a_mu, nonlin, susc, cond): self.assertEqual(fragment.num_anisotropic_eps_pixels, a_eps) self.assertEqual(fragment.num_anisotropic_mu_pixels, a_mu) @@ -22,35 +43,62 @@ def check_stats(self, fragment, a_eps, a_mu, nonlin, susc, cond): self.assertEqual(fragment.num_susceptibility_pixels, susc) self.assertEqual(fragment.num_nonzero_conductivity_pixels, cond) - def get_fragment_stats(self, block_size, cell_size, dims, box_center=mp.Vector3(), dft_vecs=None, - def_mat=mp.air, sym=[], geom=None, pml=[]): + def get_fragment_stats( + self, + block_size, + cell_size, + dims, + box_center=mp.Vector3(), + dft_vecs=None, + def_mat=mp.air, + sym=[], + geom=None, + pml=[], + ): mat = mp.Medium( epsilon=12, epsilon_offdiag=mp.Vector3(z=1), mu_offdiag=mp.Vector3(x=20), E_chi2_diag=mp.Vector3(1, 1), H_chi3_diag=mp.Vector3(z=1), - E_susceptibilities=[mp.LorentzianSusceptibility(), mp.NoisyLorentzianSusceptibility()], + E_susceptibilities=[ + mp.LorentzianSusceptibility(), + mp.NoisyLorentzianSusceptibility(), + ], H_susceptibilities=[mp.DrudeSusceptibility()], D_conductivity_diag=mp.Vector3(y=1), - B_conductivity_diag=mp.Vector3(x=1, z=1) + B_conductivity_diag=mp.Vector3(x=1, z=1), ) if geom is None: geom = [mp.Block(size=block_size, center=box_center, material=mat)] - sim = mp.Simulation(cell_size=cell_size, resolution=10, geometry=geom, dimensions=dims, - default_material=def_mat, symmetries=sym, boundary_layers=pml) + sim = mp.Simulation( + cell_size=cell_size, + resolution=10, + geometry=geom, + dimensions=dims, + default_material=def_mat, + symmetries=sym, + boundary_layers=pml, + ) if dft_vecs: - if dft_vecs['flux_regions']: - sim.add_flux(1, 0.5, 5, *dft_vecs['flux_regions']) - if dft_vecs['n2f_regions']: - sim.add_near2far(1, 0.5, 7, *dft_vecs['n2f_regions']) - if dft_vecs['force_regions']: - sim.add_force(1, 0.5, 9, *dft_vecs['force_regions']) - if dft_vecs['fields_components']: - sim.add_dft_fields(dft_vecs['fields_components'], 0, 1, 5, where=dft_vecs['fields_where'], - center=dft_vecs['fields_center'], size=dft_vecs['fields_size']) + if dft_vecs["flux_regions"]: + sim.add_flux(1, 0.5, 5, *dft_vecs["flux_regions"]) + if dft_vecs["n2f_regions"]: + sim.add_near2far(1, 0.5, 7, *dft_vecs["n2f_regions"]) + if dft_vecs["force_regions"]: + sim.add_force(1, 0.5, 9, *dft_vecs["force_regions"]) + if dft_vecs["fields_components"]: + sim.add_dft_fields( + dft_vecs["fields_components"], + 0, + 1, + 5, + where=dft_vecs["fields_where"], + center=dft_vecs["fields_center"], + size=dft_vecs["fields_size"], + ) gv = sim._create_grid_volume(False) return sim._compute_fragment_stats(gv) @@ -62,18 +110,22 @@ def _test_1d(self, sym, pml=[]): dft_vecs = make_dft_vecs( [mp.FluxRegion(mp.Vector3(z=-10), size=mp.Vector3(z=10))], [mp.Near2FarRegion(mp.Vector3(), size=mp.Vector3(z=10))], - [mp.ForceRegion(mp.Vector3(z=10), direction=mp.X, size=mp.Vector3(z=10))] + [mp.ForceRegion(mp.Vector3(z=10), direction=mp.X, size=mp.Vector3(z=10))], ) - fs = self.get_fragment_stats(mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs, sym=sym, pml=pml) + fs = self.get_fragment_stats( + mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs, sym=sym, pml=pml + ) sym_factor = 2 if sym else 1 - self.check_stats(fs, - a_eps=300 / sym_factor, - a_mu=300 / sym_factor, - nonlin=300 / sym_factor, - susc=300 / sym_factor, - cond=300 / sym_factor) + self.check_stats( + fs, + a_eps=300 / sym_factor, + a_mu=300 / sym_factor, + nonlin=300 / sym_factor, + susc=300 / sym_factor, + cond=300 / sym_factor, + ) # Check DFT regions self.assertEqual(fs.num_dft_pixels, 40800) @@ -102,11 +154,15 @@ def test_1d_with_partial_fragment(self): # dft_flux with 2 volumes, 1 covering the first 10 units and one covering # half of the second 10 - dft_vecs = make_dft_vecs(flx_reg=[ - mp.FluxRegion(mp.Vector3(z=-9), mp.Vector3(z=8)), - mp.FluxRegion(mp.Vector3(z=-2.5), mp.Vector3(z=5)) - ]) - fs = self.get_fragment_stats(mp.Vector3(z=16), mp.Vector3(z=26), 1, dft_vecs=dft_vecs) + dft_vecs = make_dft_vecs( + flx_reg=[ + mp.FluxRegion(mp.Vector3(z=-9), mp.Vector3(z=8)), + mp.FluxRegion(mp.Vector3(z=-2.5), mp.Vector3(z=5)), + ] + ) + fs = self.get_fragment_stats( + mp.Vector3(z=16), mp.Vector3(z=26), 1, dft_vecs=dft_vecs + ) self.check_stats(fs, a_eps=260, a_mu=260, nonlin=480, susc=480, cond=480) # Check dft stats @@ -116,13 +172,21 @@ def test_1d_dft_fields(self): # A z=30 cell with a block covering the middle 10 units. # dft_fields covering first 10 units - dft_vecs = make_dft_vecs(fldc=mp.Vector3(z=-10), flds=mp.Vector3(z=10), fld_cmp=[mp.X, mp.Y]) - fs = self.get_fragment_stats(mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs) + dft_vecs = make_dft_vecs( + fldc=mp.Vector3(z=-10), flds=mp.Vector3(z=10), fld_cmp=[mp.X, mp.Y] + ) + fs = self.get_fragment_stats( + mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs + ) self.assertEqual(fs.num_dft_pixels, 4000) # Same test with volume instead of center and size - dft_vecs = make_dft_vecs(fldw=mp.Volume(mp.Vector3(z=-10), mp.Vector3(z=10)), fld_cmp=[mp.X, mp.Y]) - fs = self.get_fragment_stats(mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs) + dft_vecs = make_dft_vecs( + fldw=mp.Volume(mp.Vector3(z=-10), mp.Vector3(z=10)), fld_cmp=[mp.X, mp.Y] + ) + fs = self.get_fragment_stats( + mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs + ) self.assertEqual(fs.num_dft_pixels, 4000) def _test_2d(self, sym, pml=[]): @@ -132,10 +196,20 @@ def _test_2d(self, sym, pml=[]): dft_vecs = make_dft_vecs( [mp.FluxRegion(mp.Vector3(-10, 10), size=mp.Vector3(10, 10))], [mp.Near2FarRegion(mp.Vector3(0, 10), size=mp.Vector3(10, 10))], - [mp.ForceRegion(mp.Vector3(10, 10), direction=mp.X, size=mp.Vector3(10, 10))] + [ + mp.ForceRegion( + mp.Vector3(10, 10), direction=mp.X, size=mp.Vector3(10, 10) + ) + ], + ) + fs = self.get_fragment_stats( + mp.Vector3(10, 10), + mp.Vector3(30, 30), + 2, + dft_vecs=dft_vecs, + sym=sym, + pml=pml, ) - fs = self.get_fragment_stats(mp.Vector3(10, 10), mp.Vector3(30, 30), 2, - dft_vecs=dft_vecs, sym=sym, pml=pml) # Check fragment boxes self.assertEqual(fs.box.low.x, -15) @@ -145,12 +219,14 @@ def _test_2d(self, sym, pml=[]): # Middle fragment contains entire block sym_factor = 4 if sym else 1 - self.check_stats(fs, - a_eps=90000 / sym_factor, - a_mu=90000 / sym_factor, - nonlin=30000 / sym_factor, - susc=30000 / sym_factor, - cond=30000 / sym_factor) + self.check_stats( + fs, + a_eps=90000 / sym_factor, + a_mu=90000 / sym_factor, + nonlin=30000 / sym_factor, + susc=30000 / sym_factor, + cond=30000 / sym_factor, + ) # Check DFT regions self.assertEqual(fs.num_dft_pixels, 2040000) @@ -163,14 +239,17 @@ def test_2d_with_symmetry(self): self._test_2d([mp.Mirror(mp.X), mp.Mirror(mp.Y)]) def test_2d_with_pml_all_sides(self): - self._test_2d([], pml=[mp.PML(1, mp.Y), mp.PML(2, mp.X, mp.Low), mp.PML(3, mp.X, mp.High)]) + self._test_2d( + [], pml=[mp.PML(1, mp.Y), mp.PML(2, mp.X, mp.Low), mp.PML(3, mp.X, mp.High)] + ) self.assertEqual(self.fs.num_1d_pml_pixels, 19000) self.assertEqual(self.fs.num_2d_pml_pixels, 1000) self.assertEqual(self.fs.num_3d_pml_pixels, 0) def test_2d_with_absorbers(self): - fs = self.get_fragment_stats(mp.Vector3(10, 10), mp.Vector3(30, 30), 2, - geom=[], pml=[mp.Absorber(1)]) + fs = self.get_fragment_stats( + mp.Vector3(10, 10), mp.Vector3(30, 30), 2, geom=[], pml=[mp.Absorber(1)] + ) self.assertEqual(fs.num_1d_pml_pixels, 0) self.assertEqual(fs.num_2d_pml_pixels, 0) self.assertEqual(fs.num_3d_pml_pixels, 0) @@ -181,17 +260,29 @@ def test_2d_dft_fields(self): # dft_fields covering 20 by 20 area in center of cell. Test with volume, and center/size cmpts = [mp.Ex, mp.Ey, mp.Ez] - dft_fields_size_center = make_dft_vecs(fldc=mp.Vector3(), flds=mp.Vector3(20, 20), fld_cmp=cmpts) - dft_fields_where = make_dft_vecs(fldw=mp.Volume(mp.Vector3(), mp.Vector3(20, 20)), fld_cmp=cmpts) + dft_fields_size_center = make_dft_vecs( + fldc=mp.Vector3(), flds=mp.Vector3(20, 20), fld_cmp=cmpts + ) + dft_fields_where = make_dft_vecs( + fldw=mp.Volume(mp.Vector3(), mp.Vector3(20, 20)), fld_cmp=cmpts + ) for dft_vec in [dft_fields_size_center, dft_fields_where]: - fs = self.get_fragment_stats(mp.Vector3(10, 10), mp.Vector3(30, 30), 2, dft_vecs=dft_vec) - self.assertEqual(fs.num_dft_pixels, 300000 + 4*75000 + 4*150000) + fs = self.get_fragment_stats( + mp.Vector3(10, 10), mp.Vector3(30, 30), 2, dft_vecs=dft_vec + ) + self.assertEqual(fs.num_dft_pixels, 300000 + 4 * 75000 + 4 * 150000) def test_2d_pml_and_absorber(self): - blayers = [mp.PML(1, mp.Y, mp.High), mp.PML(2, mp.Y, mp.Low), - mp.Absorber(1, mp.X, mp.High), mp.Absorber(3, mp.X, mp.Low)] - fs = self.get_fragment_stats(mp.Vector3(), mp.Vector3(30, 30), 2, pml=blayers, geom=[]) + blayers = [ + mp.PML(1, mp.Y, mp.High), + mp.PML(2, mp.Y, mp.Low), + mp.Absorber(1, mp.X, mp.High), + mp.Absorber(3, mp.X, mp.Low), + ] + fs = self.get_fragment_stats( + mp.Vector3(), mp.Vector3(30, 30), 2, pml=blayers, geom=[] + ) self.assertEqual(fs.num_nonzero_conductivity_pixels, 12000) self.assertEqual(fs.num_1d_pml_pixels, 9000) self.assertEqual(fs.num_2d_pml_pixels, 0) @@ -205,18 +296,32 @@ def _test_3d(self, sym, pml=[]): dft_vecs = make_dft_vecs( [mp.FluxRegion(mp.Vector3(-10, -10, -10), size=mp.Vector3(10, 10, 10))], [mp.Near2FarRegion(mp.Vector3(-10, -10, 0), size=mp.Vector3(10, 10, 10))], - [mp.ForceRegion(mp.Vector3(-10, -10, 10), direction=mp.X, size=mp.Vector3(10, 10, 10))] + [ + mp.ForceRegion( + mp.Vector3(-10, -10, 10), + direction=mp.X, + size=mp.Vector3(10, 10, 10), + ) + ], + ) + fs = self.get_fragment_stats( + mp.Vector3(10, 10, 10), + mp.Vector3(30, 30, 30), + 3, + dft_vecs=dft_vecs, + sym=sym, + pml=pml, ) - fs = self.get_fragment_stats(mp.Vector3(10, 10, 10), mp.Vector3(30, 30, 30), 3, - dft_vecs=dft_vecs, sym=sym, pml=pml) sym_factor = 8 if sym else 1 - self.check_stats(fs, - a_eps=27000000 / sym_factor, - a_mu=27000000 / sym_factor, - nonlin=3000000 / sym_factor, - susc=3000000 / sym_factor, - cond=3000000 / sym_factor) + self.check_stats( + fs, + a_eps=27000000 / sym_factor, + a_mu=27000000 / sym_factor, + nonlin=3000000 / sym_factor, + susc=3000000 / sym_factor, + cond=3000000 / sym_factor, + ) # Check DFT regions self.assertEqual(fs.num_dft_pixels, 102000000) @@ -229,16 +334,25 @@ def test_3d_with_symmetry(self): self._test_3d([mp.Mirror(mp.X), mp.Mirror(mp.Y), mp.Mirror(mp.Z)]) def test_3d_with_pml(self): - self._test_3d([], pml=[mp.PML(1, mp.Y, mp.High), mp.PML(2, mp.Y, mp.Low), mp.PML(3, mp.X), - mp.PML(1, mp.Z, mp.High), mp.PML(2, mp.Z, mp.Low)]) + self._test_3d( + [], + pml=[ + mp.PML(1, mp.Y, mp.High), + mp.PML(2, mp.Y, mp.Low), + mp.PML(3, mp.X), + mp.PML(1, mp.Z, mp.High), + mp.PML(2, mp.Z, mp.Low), + ], + ) self.assertEqual(self.fs.num_1d_pml_pixels, 8262000) self.assertEqual(self.fs.num_2d_pml_pixels, 1188000) self.assertEqual(self.fs.num_3d_pml_pixels, 54000) def test_3d_with_absorbers(self): - fs = self.get_fragment_stats(mp.Vector3(), mp.Vector3(30, 30, 30), 3, - geom=[], pml=[mp.Absorber(1)]) + fs = self.get_fragment_stats( + mp.Vector3(), mp.Vector3(30, 30, 30), 3, geom=[], pml=[mp.Absorber(1)] + ) self.assertEqual(fs.num_1d_pml_pixels, 0) self.assertEqual(fs.num_2d_pml_pixels, 0) self.assertEqual(fs.num_3d_pml_pixels, 0) @@ -251,15 +365,25 @@ def test_cyl(self): dft_vecs = make_dft_vecs( [mp.FluxRegion(mp.Vector3(-10, z=10), size=mp.Vector3(10, z=10))], [mp.Near2FarRegion(mp.Vector3(0, z=10), size=mp.Vector3(10, z=10))], - [mp.ForceRegion(mp.Vector3(10, z=10), direction=mp.X, size=mp.Vector3(10, z=10))] + [ + mp.ForceRegion( + mp.Vector3(10, z=10), direction=mp.X, size=mp.Vector3(10, z=10) + ) + ], + ) + fs = self.get_fragment_stats( + mp.Vector3(10, 0, 10), + mp.Vector3(30, 0, 30), + mp.CYLINDRICAL, + dft_vecs=dft_vecs, ) - fs = self.get_fragment_stats(mp.Vector3(10, 0, 10), mp.Vector3(30, 0, 30), - mp.CYLINDRICAL, dft_vecs=dft_vecs) self.assertEqual(fs.box.low.x, -15) self.assertEqual(fs.box.low.z, -15) self.assertEqual(fs.box.high.x, 15) self.assertEqual(fs.box.high.z, 15) - self.check_stats(fs, a_eps=90000, a_mu=90000, nonlin=30000, susc=30000, cond=30000) + self.check_stats( + fs, a_eps=90000, a_mu=90000, nonlin=30000, susc=30000, cond=30000 + ) self.assertEqual(fs.num_dft_pixels, 2040000) def test_no_geometry(self): @@ -269,13 +393,20 @@ def test_no_geometry(self): mu_offdiag=mp.Vector3(x=20), E_chi2_diag=mp.Vector3(1, 1), H_chi3_diag=mp.Vector3(x=1), - E_susceptibilities=[mp.LorentzianSusceptibility(), mp.NoisyLorentzianSusceptibility()], + E_susceptibilities=[ + mp.LorentzianSusceptibility(), + mp.NoisyLorentzianSusceptibility(), + ], H_susceptibilities=[mp.DrudeSusceptibility()], D_conductivity_diag=mp.Vector3(y=1), - B_conductivity_diag=mp.Vector3(x=1, z=1) + B_conductivity_diag=mp.Vector3(x=1, z=1), + ) + fs = self.get_fragment_stats( + mp.Vector3(), mp.Vector3(10, 10), 2, def_mat=mat, geom=[] + ) + self.check_stats( + fs, a_eps=10000, a_mu=10000, nonlin=30000, susc=30000, cond=30000 ) - fs = self.get_fragment_stats(mp.Vector3(), mp.Vector3(10, 10), 2, def_mat=mat, geom=[]) - self.check_stats(fs, a_eps=10000, a_mu=10000, nonlin=30000, susc=30000, cond=30000) def test_1d_cell_smaller_than_minimum_fragment_size(self): fs = self.get_fragment_stats(mp.Vector3(z=1), mp.Vector3(z=1), 1) @@ -303,7 +434,6 @@ def test_3d_cell_smaller_than_minimum_fragment_size(self): class TestPMLToVolList(unittest.TestCase): - def check1d(self, vol, expected_min, expected_max): min_vec = vol.get_min_corner() max_vec = vol.get_max_corner() @@ -356,7 +486,9 @@ def test_1d_high_side(self): self.check1d(v1[0], 4, 5) def test_1d_two_sides_different_thickness(self): - sim = make_sim(mp.Vector3(z=10), 10, [mp.PML(1, side=mp.High), mp.PML(2, side=mp.Low)], 1) + sim = make_sim( + mp.Vector3(z=10), 10, [mp.PML(1, side=mp.High), mp.PML(2, side=mp.Low)], 1 + ) v1, v2, v3 = sim._boundary_layers_to_vol_list(sim.boundary_layers) self.assertFalse(v2) @@ -390,7 +522,7 @@ def test_2d_all_sides_different_thickness_in_X(self): pmls = [ mp.PML(thickness=1, direction=mp.Y), mp.PML(thickness=3, direction=mp.X, side=mp.High), - mp.PML(thickness=2, direction=mp.X, side=mp.Low) + mp.PML(thickness=2, direction=mp.X, side=mp.Low), ] sim = make_sim(mp.Vector3(10, 10), 10, pmls, 2) v1, v2, v3 = sim._boundary_layers_to_vol_list(sim.boundary_layers) @@ -546,7 +678,6 @@ def test_cylindrical_all_directions_all_sides(self): @unittest.skipIf(mp.count_processors() != 2, "MPI specific test") class TestChunkCommunicationArea(unittest.TestCase): - def test_2d_periodic(self): sim = make_sim(mp.Vector3(10, 6), 10, [mp.PML(1)], 2, k_point=mp.Vector3(1, 1)) max_comm = sim.get_max_chunk_communication_area() @@ -555,7 +686,9 @@ def test_2d_periodic(self): self.assertEqual(avg_comm, 220) def test_3d_periodic(self): - sim = make_sim(mp.Vector3(10, 8, 6), 10, [mp.PML(1)], 3, k_point=mp.Vector3(1, 1, 1)) + sim = make_sim( + mp.Vector3(10, 8, 6), 10, [mp.PML(1)], 3, k_point=mp.Vector3(1, 1, 1) + ) max_comm = sim.get_max_chunk_communication_area() avg_comm = sim.get_avg_chunk_communication_area() self.assertEqual(max_comm, 2360) @@ -576,5 +709,5 @@ def test_3d(self): self.assertEqual(avg_comm, 480) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_gaussianbeam.py b/python/tests/test_gaussianbeam.py index acad564e1..32104cebe 100644 --- a/python/tests/test_gaussianbeam.py +++ b/python/tests/test_gaussianbeam.py @@ -3,59 +3,82 @@ import math import numpy as np -class TestGaussianBeamSource(unittest.TestCase): +class TestGaussianBeamSource(unittest.TestCase): def gaussian_beam(self, rot_angle): s = 14 resolution = 25 dpml = 2 - cell_size = mp.Vector3(s,s) + cell_size = mp.Vector3(s, s) boundary_layers = [mp.PML(thickness=dpml)] - beam_x0 = mp.Vector3(0,7.0) # beam focus (relative to source center) - beam_kdir = mp.Vector3(0,1,0).rotate(mp.Vector3(0,0,1),math.radians(rot_angle)) # beam propagation direction + beam_x0 = mp.Vector3(0, 7.0) # beam focus (relative to source center) + beam_kdir = mp.Vector3(0, 1, 0).rotate( + mp.Vector3(0, 0, 1), math.radians(rot_angle) + ) # beam propagation direction beam_w0 = 0.8 # beam waist radius - beam_E0 = mp.Vector3(0,0,1) - beam_x0 = beam_x0.rotate(mp.Vector3(0,0,1),math.radians(rot_angle)) + beam_E0 = mp.Vector3(0, 0, 1) + beam_x0 = beam_x0.rotate(mp.Vector3(0, 0, 1), math.radians(rot_angle)) fcen = 1 src_x = 0 - src_y = -0.5*s+dpml+1.0 - sources = [mp.GaussianBeamSource(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - center=mp.Vector3(src_x,src_y), - size=mp.Vector3(s), - beam_x0=beam_x0, - beam_kdir=beam_kdir, - beam_w0=beam_w0, - beam_E0=beam_E0)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources) - - cell_dft_fields = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=mp.Vector3(), size=mp.Vector3(s-2*dpml,s-2*dpml)) + src_y = -0.5 * s + dpml + 1.0 + sources = [ + mp.GaussianBeamSource( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=mp.Vector3(src_x, src_y), + size=mp.Vector3(s), + beam_x0=beam_x0, + beam_kdir=beam_kdir, + beam_w0=beam_w0, + beam_E0=beam_E0, + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + ) + + cell_dft_fields = sim.add_dft_fields( + [mp.Ez], + fcen, + 0, + 1, + center=mp.Vector3(), + size=mp.Vector3(s - 2 * dpml, s - 2 * dpml), + ) sim.run(until_after_sources=50) Ez_cell = sim.get_dft_array(cell_dft_fields, mp.Ez, 0) - [x,y,z,w] = sim.get_array_metadata(dft_cell=cell_dft_fields) + [x, y, z, w] = sim.get_array_metadata(dft_cell=cell_dft_fields) tol = 0.05 - idx_x = np.nonzero((np.squeeze(x) > (src_x+beam_x0.x-tol)) & (np.squeeze(x) < (src_x+beam_x0.x+tol))) - idx_y = np.nonzero((np.squeeze(y) > (src_y+beam_x0.y-tol)) & (np.squeeze(y) < (src_y+beam_x0.y+tol))) - - Ez_beam_x0 = Ez_cell[np.squeeze(idx_x)[0],np.squeeze(idx_y)[0]] - - frac = np.abs(Ez_beam_x0)**2 / np.amax(np.abs(Ez_cell)**2) - print(f"ratio of the Gaussian beam energy at the focus over the maximum beam energy for the entire cell: {frac}") - + idx_x = np.nonzero( + (np.squeeze(x) > (src_x + beam_x0.x - tol)) + & (np.squeeze(x) < (src_x + beam_x0.x + tol)) + ) + idx_y = np.nonzero( + (np.squeeze(y) > (src_y + beam_x0.y - tol)) + & (np.squeeze(y) < (src_y + beam_x0.y + tol)) + ) + + Ez_beam_x0 = Ez_cell[np.squeeze(idx_x)[0], np.squeeze(idx_y)[0]] + + frac = np.abs(Ez_beam_x0) ** 2 / np.amax(np.abs(Ez_cell) ** 2) + print( + f"ratio of the Gaussian beam energy at the focus over the maximum beam energy for the entire cell: {frac}" + ) self.assertGreater(frac, 0.99) def test_gaussian_beam(self): self.gaussian_beam(-40) -if __name__ == '__main__': - unittest.main() + +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_geom.py b/python/tests/test_geom.py index 4b2f3a974..2fe9a9bdc 100644 --- a/python/tests/test_geom.py +++ b/python/tests/test_geom.py @@ -15,7 +15,6 @@ def ones(): class TestGeom(unittest.TestCase): - def test_geometric_object_duplicates_x(self): rad = 1 s = mp.Sphere(rad) @@ -26,7 +25,7 @@ def test_geometric_object_duplicates_x(self): mp.Sphere(rad, center=mp.Vector3(x=4)), mp.Sphere(rad, center=mp.Vector3(x=3)), mp.Sphere(rad, center=mp.Vector3(x=2)), - mp.Sphere(rad, center=mp.Vector3(x=1)) + mp.Sphere(rad, center=mp.Vector3(x=1)), ] for r, e in zip(res, expected): @@ -71,9 +70,11 @@ def test_geometric_objects_duplicates(self): self.assertEqual(r.center, e.center) def test_geometric_objects_lattice_duplicates(self): - geometry_lattice = mp.Lattice(size=mp.Vector3(1, 7), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) + geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 7), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), + ) eps = 12 r = 0.2 @@ -97,15 +98,16 @@ def test_geometric_objects_lattice_duplicates(self): self.assertEqual(exp.radius, res.radius) def test_cartesian_to_lattice(self): - lattice = mp.Lattice(size=mp.Vector3(1, 7), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) + lattice = mp.Lattice( + size=mp.Vector3(1, 7), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), + ) res = mp.cartesian_to_lattice(lattice.basis * mp.Vector3(1), lattice) self.assertEqual(res, mp.Vector3(1)) class TestSphere(unittest.TestCase): - def test_kwargs_passed_to_parent(self): s = gm.Sphere(1.0) self.assertEqual(s.material.epsilon_diag, ones()) @@ -124,9 +126,9 @@ def test_kwargs_passed_to_parent(self): def test_invalid_kwarg_raises_exception(self): with self.assertRaises(TypeError): - gm.Sphere(invalid='This is not allowed') + gm.Sphere(invalid="This is not allowed") with self.assertRaises(TypeError): - gm.Sphere(radius=1.0, oops='Nope') + gm.Sphere(radius=1.0, oops="Nope") def test_non_neg_radius_constructor(self): gm.Sphere(radius=0.0) @@ -186,7 +188,6 @@ def test_info(self): class TestCylinder(unittest.TestCase): - def test_non_neg_height_constructor(self): gm.Cylinder(radius=1.0, height=0.0) gm.Cylinder(radius=1.0, height=1.0) @@ -220,9 +221,9 @@ def test_wrong_type_exception(self): class TestWedge(unittest.TestCase): - def test_default_properties(self): import math + w = gm.Wedge(center=zeros(), radius=2.0, height=4.0, axis=gm.Vector3(0, 0, 1)) self.assertEqual(w.wedge_angle, 8 * math.atan(1)) @@ -232,37 +233,37 @@ def test_contains_point(self): class TestCone(unittest.TestCase): - def test_contains_point(self): c = gm.Cone(center=zeros(), radius=2.0, height=3.0, axis=gm.Vector3(0, 0, 1)) self.assertIn(gm.Vector3(0, 0, 1), c) class TestBlock(unittest.TestCase): - def test_contains_point(self): b = gm.Block(size=ones(), center=zeros()) self.assertIn(zeros(), b) class TestEllipsoid(unittest.TestCase): - def test_contains_point(self): e = gm.Ellipsoid(size=ones(), center=zeros()) self.assertIn(zeros(), e) class TestPrism(unittest.TestCase): - def test_contains_point(self): - vertices = [gm.Vector3(-1, 1), gm.Vector3(1, 1), gm.Vector3(1, -1), gm.Vector3(-1, -1)] + vertices = [ + gm.Vector3(-1, 1), + gm.Vector3(1, 1), + gm.Vector3(1, -1), + gm.Vector3(-1, -1), + ] p = gm.Prism(vertices, height=1) self.assertIn(zeros(), p) self.assertNotIn(gm.Vector3(2, 2), p) class TestMedium(unittest.TestCase): - def test_D_conductivity(self): m = gm.Medium(D_conductivity=2) self.assertEqual(m.D_conductivity_diag.x, 2) @@ -326,50 +327,82 @@ def check_warnings(sim, should_warn=True): for s in invalid_sources: # Check for invalid extra_materials - sim = mp.Simulation(cell_size=cell_size, resolution=resolution, sources=s, extra_materials=[mat]) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + sources=s, + extra_materials=[mat], + ) check_warnings(sim) # Check for invalid geometry materials - sim = mp.Simulation(cell_size=cell_size, resolution=resolution, sources=s, geometry=geom) + sim = mp.Simulation( + cell_size=cell_size, resolution=resolution, sources=s, geometry=geom + ) check_warnings(sim) valid_sources = [ [mp.Source(mp.GaussianSource(15, fwidth=1), mp.Ez, mp.Vector3())], - [mp.Source(mp.ContinuousSource(15, width=5), mp.Ez, mp.Vector3())] + [mp.Source(mp.ContinuousSource(15, width=5), mp.Ez, mp.Vector3())], ] for s in valid_sources: - sim = mp.Simulation(cell_size=cell_size, resolution=resolution, sources=s, extra_materials=[mat]) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + sources=s, + extra_materials=[mat], + ) check_warnings(sim, False) # Check DFT frequencies # Invalid extra_materials - sim = mp.Simulation(cell_size=cell_size, resolution=resolution, sources=valid_sources[0], - extra_materials=[mat]) - fregion = mp.FluxRegion(center=mp.Vector3(0, 1), size=mp.Vector3(2, 2), direction=mp.X) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + sources=valid_sources[0], + extra_materials=[mat], + ) + fregion = mp.FluxRegion( + center=mp.Vector3(0, 1), size=mp.Vector3(2, 2), direction=mp.X + ) sim.add_flux(18, 6, 2, fregion, decimation_factor=1) check_warnings(sim) # Invalid geometry material - sim = mp.Simulation(cell_size=cell_size, resolution=resolution, sources=valid_sources[0], geometry=geom) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + sources=valid_sources[0], + geometry=geom, + ) sim.add_flux(18, 6, 2, fregion, decimation_factor=1) check_warnings(sim) def test_transform(self): - e_sus = [mp.LorentzianSusceptibility(sigma_diag=mp.Vector3(1, 2, 3), - sigma_offdiag=mp.Vector3(12, 13, 14)), - mp.DrudeSusceptibility(sigma_diag=mp.Vector3(1, 2, 3), - sigma_offdiag=mp.Vector3(12, 13, 14))] + e_sus = [ + mp.LorentzianSusceptibility( + sigma_diag=mp.Vector3(1, 2, 3), sigma_offdiag=mp.Vector3(12, 13, 14) + ), + mp.DrudeSusceptibility( + sigma_diag=mp.Vector3(1, 2, 3), sigma_offdiag=mp.Vector3(12, 13, 14) + ), + ] - mat = mp.Medium(epsilon_diag=mp.Vector3(1, 2, 3), epsilon_offdiag=mp.Vector3(12, 13, 14), - E_susceptibilities=e_sus) + mat = mp.Medium( + epsilon_diag=mp.Vector3(1, 2, 3), + epsilon_offdiag=mp.Vector3(12, 13, 14), + E_susceptibilities=e_sus, + ) rot_angle = math.radians(23.9) - rot_matrix = mp.Matrix(mp.Vector3(math.cos(rot_angle), math.sin(rot_angle), 0), - mp.Vector3(-math.sin(rot_angle), math.cos(rot_angle), 0), - mp.Vector3(0, 0, 1)) + rot_matrix = mp.Matrix( + mp.Vector3(math.cos(rot_angle), math.sin(rot_angle), 0), + mp.Vector3(-math.sin(rot_angle), math.cos(rot_angle), 0), + mp.Vector3(0, 0, 1), + ) mat.transform(rot_matrix) expected_diag = mp.Vector3(-7.72552, 10.72552, 3) @@ -380,14 +413,21 @@ def test_transform(self): self.assertEqual(mat.mu_diag, mp.Vector3(1, 1, 1)) self.assertEqual(mat.mu_offdiag, mp.Vector3()) self.assertEqual(len(mat.E_susceptibilities), 2) - self.assertTrue(mat.E_susceptibilities[0].sigma_diag.close(expected_diag, tol=4)) - self.assertTrue(mat.E_susceptibilities[0].sigma_offdiag.close(expected_offdiag, tol=4)) - self.assertTrue(mat.E_susceptibilities[1].sigma_diag.close(expected_diag, tol=4)) - self.assertTrue(mat.E_susceptibilities[1].sigma_offdiag.close(expected_offdiag, tol=4)) + self.assertTrue( + mat.E_susceptibilities[0].sigma_diag.close(expected_diag, tol=4) + ) + self.assertTrue( + mat.E_susceptibilities[0].sigma_offdiag.close(expected_offdiag, tol=4) + ) + self.assertTrue( + mat.E_susceptibilities[1].sigma_diag.close(expected_diag, tol=4) + ) + self.assertTrue( + mat.E_susceptibilities[1].sigma_offdiag.close(expected_offdiag, tol=4) + ) class TestVector3(unittest.TestCase): - def test_use_as_numpy_array(self): v = gm.Vector3(10, 10, 10) res = np.add(v, np.array([10, 10, 10])) @@ -439,14 +479,18 @@ def test_rotate_lattice(self): v = mp.Vector3(2, 2, 2) lattice = mp.Lattice(size=mp.Vector3(1, 1)) res = v.rotate_lattice(axis, 3, lattice) - self.assertTrue(res.close(mp.Vector3(2.0, -2.262225009320625, -1.6977449770811563))) + self.assertTrue( + res.close(mp.Vector3(2.0, -2.262225009320625, -1.6977449770811563)) + ) def test_rotate_reciprocal(self): axis = mp.Vector3(1) v = mp.Vector3(2, 2, 2) lattice = mp.Lattice(size=mp.Vector3(1, 1)) res = v.rotate_reciprocal(axis, 3, lattice) - self.assertTrue(res.close(mp.Vector3(2.0, -2.262225009320625, -1.6977449770811563))) + self.assertTrue( + res.close(mp.Vector3(2.0, -2.262225009320625, -1.6977449770811563)) + ) def test_complex_norm(self): # issue #722 @@ -455,15 +499,18 @@ def test_complex_norm(self): class TestLattice(unittest.TestCase): - def test_basis(self): - lattice = mp.Lattice(size=mp.Vector3(1, 7), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) + lattice = mp.Lattice( + size=mp.Vector3(1, 7), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), + ) b = lattice.basis - exp = mp.Matrix(mp.Vector3(0.8660254037844388, 0.5000000000000001), - mp.Vector3(0.8660254037844388, -0.5000000000000001), - mp.Vector3(z=1.0)) + exp = mp.Matrix( + mp.Vector3(0.8660254037844388, 0.5000000000000001), + mp.Vector3(0.8660254037844388, -0.5000000000000001), + mp.Vector3(z=1.0), + ) for e, r in zip([exp.c1, exp.c2, exp.c3], [b.c1, b.c2, b.c3]): self.assertTrue(e.close(r)) @@ -493,16 +540,14 @@ def test_row(self): self.assertEqual(self.identity.row(2), self.identity.c3) def test_mm_mult(self): - m1 = mp.Matrix(mp.Vector3(1, 2, 3), - mp.Vector3(4, 5, 6), - mp.Vector3(7, 8, 9)) - m2 = mp.Matrix(mp.Vector3(9, 8, 7), - mp.Vector3(6, 5, 4), - mp.Vector3(3, 2, 1)) + m1 = mp.Matrix(mp.Vector3(1, 2, 3), mp.Vector3(4, 5, 6), mp.Vector3(7, 8, 9)) + m2 = mp.Matrix(mp.Vector3(9, 8, 7), mp.Vector3(6, 5, 4), mp.Vector3(3, 2, 1)) res = m1 * m2 - exp = mp.Matrix(mp.Vector3(90.0, 114.0, 138.0), - mp.Vector3(54.0, 69.0, 84.0), - mp.Vector3(18.0, 24.0, 30.0)) + exp = mp.Matrix( + mp.Vector3(90.0, 114.0, 138.0), + mp.Vector3(54.0, 69.0, 84.0), + mp.Vector3(18.0, 24.0, 30.0), + ) self.matrix_eq(exp, res) @@ -520,91 +565,105 @@ def test_sub(self): self.assertEqual(result.row(2), zeros()) def test_mv_mult(self): - lattice = mp.Lattice(size=mp.Vector3(1, 7), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) + lattice = mp.Lattice( + size=mp.Vector3(1, 7), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), + ) res = lattice.basis * mp.Vector3(1) exp = mp.Vector3(0.8660254037844388, 0.5000000000000001) self.assertTrue(res.close(exp)) def test_scale(self): - m = mp.Matrix(mp.Vector3(90.0, 114.0, 138.0), - mp.Vector3(54.0, 69.0, 84.0), - mp.Vector3(18.0, 24.0, 30.0)) + m = mp.Matrix( + mp.Vector3(90.0, 114.0, 138.0), + mp.Vector3(54.0, 69.0, 84.0), + mp.Vector3(18.0, 24.0, 30.0), + ) res = m.scale(0.5) - exp = mp.Matrix(mp.Vector3(45.0, 57.0, 69.0), - mp.Vector3(27.0, 34.5, 42.0), - mp.Vector3(9.0, 12.0, 15.0)) + exp = mp.Matrix( + mp.Vector3(45.0, 57.0, 69.0), + mp.Vector3(27.0, 34.5, 42.0), + mp.Vector3(9.0, 12.0, 15.0), + ) self.matrix_eq(exp, res) self.matrix_eq(exp, m * 0.5) self.matrix_eq(exp, 0.5 * m) def test_determinant(self): - m = mp.Matrix(mp.Vector3(2), - mp.Vector3(y=2), - mp.Vector3(z=2)) + m = mp.Matrix(mp.Vector3(2), mp.Vector3(y=2), mp.Vector3(z=2)) - m1 = mp.Matrix(mp.Vector3(1, 2, 3), - mp.Vector3(4, 5, 6), - mp.Vector3(7, 8, 9)) + m1 = mp.Matrix(mp.Vector3(1, 2, 3), mp.Vector3(4, 5, 6), mp.Vector3(7, 8, 9)) self.assertEqual(8, m.determinant()) self.assertEqual(0, m1.determinant()) def test_transpose(self): - m = mp.Matrix(mp.Vector3(1, 2, 3), - mp.Vector3(4, 5, 6), - mp.Vector3(7, 8, 9)) - exp = mp.Matrix(mp.Vector3(1, 4, 7), - mp.Vector3(2, 5, 8), - mp.Vector3(3, 6, 9)) + m = mp.Matrix(mp.Vector3(1, 2, 3), mp.Vector3(4, 5, 6), mp.Vector3(7, 8, 9)) + exp = mp.Matrix(mp.Vector3(1, 4, 7), mp.Vector3(2, 5, 8), mp.Vector3(3, 6, 9)) self.matrix_eq(exp, m.transpose()) def test_inverse(self): self.matrix_eq(self.identity, self.identity.inverse()) - lattice = mp.Lattice(size=mp.Vector3(1, 7), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) + lattice = mp.Lattice( + size=mp.Vector3(1, 7), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), + ) res = lattice.basis.inverse() - exp = mp.Matrix(mp.Vector3(0.5773502691896256, 0.5773502691896256, -0.0), - mp.Vector3(0.9999999999999998, -0.9999999999999998, -0.0), - mp.Vector3(-0.0, -0.0, 1.0)) + exp = mp.Matrix( + mp.Vector3(0.5773502691896256, 0.5773502691896256, -0.0), + mp.Vector3(0.9999999999999998, -0.9999999999999998, -0.0), + mp.Vector3(-0.0, -0.0, 1.0), + ) self.matrix_close(exp, res) def test_get_rotation_matrix(self): result = mp.get_rotation_matrix(ones(), 5) - self.assertTrue(result.c1.close(mp.Vector3(0.5224414569754843, -0.3148559165969717, 0.7924144596214877))) - self.assertTrue(result.c2.close(mp.Vector3(0.7924144596214877, 0.5224414569754843, -0.3148559165969717))) - self.assertTrue(result.c3.close(mp.Vector3(-0.3148559165969717, 0.7924144596214877, 0.5224414569754843))) + self.assertTrue( + result.c1.close( + mp.Vector3(0.5224414569754843, -0.3148559165969717, 0.7924144596214877) + ) + ) + self.assertTrue( + result.c2.close( + mp.Vector3(0.7924144596214877, 0.5224414569754843, -0.3148559165969717) + ) + ) + self.assertTrue( + result.c3.close( + mp.Vector3(-0.3148559165969717, 0.7924144596214877, 0.5224414569754843) + ) + ) def test_conj(self): - m = mp.Matrix(mp.Vector3(x=1+1j), mp.Vector3(y=1+1j), mp.Vector3(z=1+1j)) + m = mp.Matrix(mp.Vector3(x=1 + 1j), mp.Vector3(y=1 + 1j), mp.Vector3(z=1 + 1j)) result = m.conj() - self.assertEqual(result.c1, mp.Vector3(x=1-1j)) - self.assertEqual(result.c2, mp.Vector3(y=1-1j)) - self.assertEqual(result.c3, mp.Vector3(z=1-1j)) + self.assertEqual(result.c1, mp.Vector3(x=1 - 1j)) + self.assertEqual(result.c2, mp.Vector3(y=1 - 1j)) + self.assertEqual(result.c3, mp.Vector3(z=1 - 1j)) def test_adjoint(self): - m = mp.Matrix(mp.Vector3(1+1j), mp.Vector3(1+1j), mp.Vector3(1+1j)) + m = mp.Matrix(mp.Vector3(1 + 1j), mp.Vector3(1 + 1j), mp.Vector3(1 + 1j)) getH_result = m.getH() H_result = m.H - self.assertEqual(getH_result.c1, mp.Vector3(1-1j, 1-1j, 1-1j)) + self.assertEqual(getH_result.c1, mp.Vector3(1 - 1j, 1 - 1j, 1 - 1j)) self.assertEqual(getH_result.c2, mp.Vector3()) self.assertEqual(getH_result.c3, mp.Vector3()) np.testing.assert_allclose(getH_result, H_result) def test_to_numpy_array(self): - m = mp.Matrix(mp.Vector3(1+1j), mp.Vector3(1+1j), mp.Vector3(1+1j)) + m = mp.Matrix(mp.Vector3(1 + 1j), mp.Vector3(1 + 1j), mp.Vector3(1 + 1j)) adjoint = m.H m_arr = np.array(m) np_adjoint = m_arr.conj().transpose() np.testing.assert_allclose(adjoint, np_adjoint) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_get_epsilon_grid.py b/python/tests/test_get_epsilon_grid.py index b5b98191f..07876836a 100644 --- a/python/tests/test_get_epsilon_grid.py +++ b/python/tests/test_get_epsilon_grid.py @@ -2,76 +2,98 @@ import parameterized import numpy as np import meep as mp + try: import meep.adjoint as mpa except: import adjoint as mpa from meep.materials import SiN, Co -class TestGetEpsilonGrid(unittest.TestCase): +class TestGetEpsilonGrid(unittest.TestCase): def setUp(self): resolution = 60 - self.cell_size = mp.Vector3(1.0,1.0,0) + self.cell_size = mp.Vector3(1.0, 1.0, 0) matgrid_resolution = 200 - matgrid_size = mp.Vector3(1.0,1.0,mp.inf) - Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) - x = np.linspace(-0.5*matgrid_size.x,0.5*matgrid_size.x,Nx) - y = np.linspace(-0.5*matgrid_size.y,0.5*matgrid_size.y,Ny) - xv, yv = np.meshgrid(x,y) + matgrid_size = mp.Vector3(1.0, 1.0, mp.inf) + Nx, Ny = int(matgrid_size.x * matgrid_resolution), int( + matgrid_size.y * matgrid_resolution + ) + x = np.linspace(-0.5 * matgrid_size.x, 0.5 * matgrid_size.x, Nx) + y = np.linspace(-0.5 * matgrid_size.y, 0.5 * matgrid_size.y, Ny) + xv, yv = np.meshgrid(x, y) rad = 0.201943 w = 0.104283 - weights = np.logical_and(np.sqrt(np.square(xv) + np.square(yv)) > rad, - np.sqrt(np.square(xv) + np.square(yv)) < rad+w, - dtype=np.double) + weights = np.logical_and( + np.sqrt(np.square(xv) + np.square(yv)) > rad, + np.sqrt(np.square(xv) + np.square(yv)) < rad + w, + dtype=np.double, + ) - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mp.Medium(index=3.5), - weights=weights, - do_averaging=False, - beta=0, - eta=0.5) + matgrid = mp.MaterialGrid( + mp.Vector3(Nx, Ny), + mp.air, + mp.Medium(index=3.5), + weights=weights, + do_averaging=False, + beta=0, + eta=0.5, + ) - geometry = [mp.Cylinder(center=mp.Vector3(0.35,0.1), - radius=0.1, - height=mp.inf, - material=mp.Medium(index=1.5)), - mp.Block(center=mp.Vector3(-0.15,-0.2), - size=mp.Vector3(0.2,0.24,mp.inf), - material=SiN), - mp.Block(center=mp.Vector3(-0.2,0.2), - size=mp.Vector3(0.4,0.4,mp.inf), - material=matgrid), - mp.Prism(vertices=[mp.Vector3(0.05,0.45), - mp.Vector3(0.32,0.22), - mp.Vector3(0.15,0.10)], - height=0.5, - material=Co)] + geometry = [ + mp.Cylinder( + center=mp.Vector3(0.35, 0.1), + radius=0.1, + height=mp.inf, + material=mp.Medium(index=1.5), + ), + mp.Block( + center=mp.Vector3(-0.15, -0.2), + size=mp.Vector3(0.2, 0.24, mp.inf), + material=SiN, + ), + mp.Block( + center=mp.Vector3(-0.2, 0.2), + size=mp.Vector3(0.4, 0.4, mp.inf), + material=matgrid, + ), + mp.Prism( + vertices=[ + mp.Vector3(0.05, 0.45), + mp.Vector3(0.32, 0.22), + mp.Vector3(0.15, 0.10), + ], + height=0.5, + material=Co, + ), + ] - self.sim = mp.Simulation(resolution=resolution, - cell_size=self.cell_size, - geometry=geometry, - eps_averaging=False) + self.sim = mp.Simulation( + resolution=resolution, + cell_size=self.cell_size, + geometry=geometry, + eps_averaging=False, + ) self.sim.init_sim() - @parameterized.parameterized.expand([ - (mp.Vector3(0.2,0.2), 1.1), - (mp.Vector3(-0.2,0.1), 0.7), - (mp.Vector3(-0.2,-0.25), 0.55), - (mp.Vector3(0.4,0.1), 0) - ]) + @parameterized.parameterized.expand( + [ + (mp.Vector3(0.2, 0.2), 1.1), + (mp.Vector3(-0.2, 0.1), 0.7), + (mp.Vector3(-0.2, -0.25), 0.55), + (mp.Vector3(0.4, 0.1), 0), + ] + ) def test_get_epsilon_grid(self, pt, freq): - eps_grid = self.sim.get_epsilon_grid(np.array([pt.x]), - np.array([pt.y]), - np.array([0]), - freq) + eps_grid = self.sim.get_epsilon_grid( + np.array([pt.x]), np.array([pt.y]), np.array([0]), freq + ) eps_pt = self.sim.get_epsilon_point(pt, freq) print(f"eps:, ({pt.x},{pt.y}), {eps_grid}, {eps_pt}") self.assertAlmostEqual(np.real(eps_grid), np.real(eps_pt), places=6) self.assertAlmostEqual(np.imag(eps_grid), np.imag(eps_pt), places=6) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_get_point.py b/python/tests/test_get_point.py index 154336d92..146309b85 100644 --- a/python/tests/test_get_point.py +++ b/python/tests/test_get_point.py @@ -5,103 +5,115 @@ class TestGetPoint(unittest.TestCase): - def test_get_point(self): - sxy = 6 # cell size - dpml = 1 # thickness of PML + sxy = 6 # cell size + dpml = 1 # thickness of PML def sinusoid(p): - r = (p.x**2+p.y**2)**0.5 - return mp.Medium(index=1.0+math.sin(2*math.pi*r)**2) + r = (p.x**2 + p.y**2) ** 0.5 + return mp.Medium(index=1.0 + math.sin(2 * math.pi * r) ** 2) - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(sxy,sxy), - material=sinusoid)] + geometry = [ + mp.Block(center=mp.Vector3(), size=mp.Vector3(sxy, sxy), material=sinusoid) + ] - src = [mp.Source(mp.GaussianSource(1.0, fwidth=0.1), - component=mp.Ez, - center=mp.Vector3())] + src = [ + mp.Source( + mp.GaussianSource(1.0, fwidth=0.1), component=mp.Ez, center=mp.Vector3() + ) + ] - sim = mp.Simulation(cell_size=mp.Vector3(sxy,sxy), - geometry=geometry, - sources=src, - k_point=mp.Vector3(), - resolution=20, - symmetries=[mp.Mirror(mp.X),mp.Mirror(mp.Y)], - boundary_layers=[mp.PML(dpml)]) + sim = mp.Simulation( + cell_size=mp.Vector3(sxy, sxy), + geometry=geometry, + sources=src, + k_point=mp.Vector3(), + resolution=20, + symmetries=[mp.Mirror(mp.X), mp.Mirror(mp.Y)], + boundary_layers=[mp.PML(dpml)], + ) sim.run(until_after_sources=100) ## reference values for Ez and epsilon from serial run - ez_ref = [ -0.0002065983, - -0.0001954795, - -0.0000453570, - 0.0000311267, - -0.0000121473, - -0.0000410032, - -0.0000341301, - -0.0000275021, - -0.0000397990, - -0.0000351730, - 0.0000079602, - 0.0000227437, - -0.0001092821, - -0.0002202751, - -0.0001408186, - 0.0006325076, - 0.0024890489, - 0.0027476069, - 0.0014815873, - 0.0004714913, - -0.0004332029, - -0.0007101315, - -0.0003818581, - -0.0000748507, - 0.0001408819, - 0.0001119776, - 0.0000395008, - 0.0000078844, - -0.0000010431 ] + ez_ref = [ + -0.0002065983, + -0.0001954795, + -0.0000453570, + 0.0000311267, + -0.0000121473, + -0.0000410032, + -0.0000341301, + -0.0000275021, + -0.0000397990, + -0.0000351730, + 0.0000079602, + 0.0000227437, + -0.0001092821, + -0.0002202751, + -0.0001408186, + 0.0006325076, + 0.0024890489, + 0.0027476069, + 0.0014815873, + 0.0004714913, + -0.0004332029, + -0.0007101315, + -0.0003818581, + -0.0000748507, + 0.0001408819, + 0.0001119776, + 0.0000395008, + 0.0000078844, + -0.0000010431, + ] - eps_ref = [ 1.6458346134, - 1.2752837068, - 1.0974010956, - 1.0398089537, - 1.0465784716, - 1.0779924737, - 1.1059439286, - 1.1135579291, - 1.0971979186, - 1.0653178566, - 1.0391657283, - 1.0513779677, - 1.1466009312, - 1.3882154483, - 1.8496939317, - 2.5617731415, - 3.3788212533, - 3.9019494270, - 3.6743431894, - 2.7285622651, - 1.6635165033, - 1.0891237010, - 1.1485969863, - 1.9498398061, - 3.3100416367, - 3.9038800599, - 2.8471862395, - 1.4742605488, - 1.0370162714 ] + eps_ref = [ + 1.6458346134, + 1.2752837068, + 1.0974010956, + 1.0398089537, + 1.0465784716, + 1.0779924737, + 1.1059439286, + 1.1135579291, + 1.0971979186, + 1.0653178566, + 1.0391657283, + 1.0513779677, + 1.1466009312, + 1.3882154483, + 1.8496939317, + 2.5617731415, + 3.3788212533, + 3.9019494270, + 3.6743431894, + 2.7285622651, + 1.6635165033, + 1.0891237010, + 1.1485969863, + 1.9498398061, + 3.3100416367, + 3.9038800599, + 2.8471862395, + 1.4742605488, + 1.0370162714, + ] - x = np.linspace(-0.865692,2.692867,29) + x = np.linspace(-0.865692, 2.692867, 29) places = 5 if mp.is_single_precision() else 10 for j in range(x.size): - self.assertAlmostEqual(np.real(sim.get_field_point(mp.Ez, mp.Vector3(x[j],-0.394862))), - ez_ref[j], - places=places) - self.assertAlmostEqual(sim.get_epsilon_point(mp.Vector3(x[j],2.967158)), - eps_ref[j], - places=places) + self.assertAlmostEqual( + np.real(sim.get_field_point(mp.Ez, mp.Vector3(x[j], -0.394862))), + ez_ref[j], + places=places, + ) + self.assertAlmostEqual( + sim.get_epsilon_point(mp.Vector3(x[j], 2.967158)), + eps_ref[j], + places=places, + ) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_holey_wvg_bands.py b/python/tests/test_holey_wvg_bands.py index 648c0c392..4f15f44d2 100644 --- a/python/tests/test_holey_wvg_bands.py +++ b/python/tests/test_holey_wvg_bands.py @@ -3,10 +3,11 @@ class TestHoleyWvgBands(unittest.TestCase): - def setUp(self): cell = mp.Vector3(1, 12) - b = mp.Block(size=mp.Vector3(mp.inf, 1.2, mp.inf), material=mp.Medium(epsilon=13)) + b = mp.Block( + size=mp.Vector3(mp.inf, 1.2, mp.inf), material=mp.Medium(epsilon=13) + ) c = mp.Cylinder(0.36) self.fcen = 0.25 @@ -15,7 +16,7 @@ def setUp(self): s = mp.Source( src=mp.GaussianSource(self.fcen, fwidth=self.df), component=mp.Hz, - center=mp.Vector3(0.1234) + center=mp.Vector3(0.1234), ) sym = mp.Mirror(direction=mp.Y, phase=-1) @@ -26,17 +27,19 @@ def setUp(self): sources=[s], symmetries=[sym], boundary_layers=[mp.PML(1, direction=mp.Y)], - resolution=20 + resolution=20, ) def test_run_k_points(self): - all_freqs = self.sim.run_k_points(5, mp.interpolate(19, [mp.Vector3(), mp.Vector3(0.5)])) + all_freqs = self.sim.run_k_points( + 5, mp.interpolate(19, [mp.Vector3(), mp.Vector3(0.5)]) + ) expected = [ (0.1942497850393511, 0.001381460274205755), (0.19782709203322993, -0.0013233828667934015), (0.1927618763491877, 0.001034260690735336), - (0.19335527231544278, 4.6649450258959025e-4) + (0.19335527231544278, 4.6649450258959025e-4), ] self.assertTrue(any(all_freqs)) @@ -71,5 +74,5 @@ def test_fields_at_kx(self): self.assertAlmostEqual(m.decay, i, places=places) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_holey_wvg_cavity.py b/python/tests/test_holey_wvg_cavity.py index 4227074c6..44e7f5386 100644 --- a/python/tests/test_holey_wvg_cavity.py +++ b/python/tests/test_holey_wvg_cavity.py @@ -4,7 +4,6 @@ class TestHoleyWvgCavity(ApproxComparisonTestCase): - def setUp(self): eps = 13 self.w = 1.2 @@ -21,8 +20,9 @@ def setUp(self): cell = mp.Vector3(self.sx, sy, 0) - blk = mp.Block(size=mp.Vector3(mp.inf, self.w, mp.inf), - material=mp.Medium(epsilon=eps)) + blk = mp.Block( + size=mp.Vector3(mp.inf, self.w, mp.inf), material=mp.Medium(epsilon=eps) + ) geometry = [blk] @@ -30,11 +30,13 @@ def setUp(self): for i in range(3): geometry.append(mp.Cylinder(r, center=mp.Vector3(d / -2 - i))) - self.sim = mp.Simulation(cell_size=cell, - geometry=geometry, - sources=[], - boundary_layers=[mp.PML(self.dpml)], - resolution=20) + self.sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + sources=[], + boundary_layers=[mp.PML(self.dpml)], + resolution=20, + ) @classmethod def setUpClass(cls): @@ -45,17 +47,19 @@ def tearDownClass(cls): mp.delete_directory(cls.temp_dir) def test_resonant_modes(self): - self.sim.sources = [mp.Source(mp.GaussianSource(self.fcen, fwidth=self.df), - mp.Hz, mp.Vector3())] + self.sim.sources = [ + mp.Source(mp.GaussianSource(self.fcen, fwidth=self.df), mp.Hz, mp.Vector3()) + ] - self.sim.symmetries = [mp.Mirror(mp.Y, phase=-1), - mp.Mirror(mp.X, phase=-1)] + self.sim.symmetries = [mp.Mirror(mp.Y, phase=-1), mp.Mirror(mp.X, phase=-1)] self.sim.use_output_directory(self.temp_dir) h = mp.Harminv(mp.Hz, mp.Vector3(), self.fcen, self.df) - self.sim.run(mp.at_beginning(mp.output_epsilon), - mp.after_sources(h), - until_after_sources=400) + self.sim.run( + mp.at_beginning(mp.output_epsilon), + mp.after_sources(h), + until_after_sources=400, + ) expected = [ 0.23445415346009466, @@ -123,21 +127,29 @@ def test_transmission_spectrum(self): ] self.sim.sources = [ - mp.Source(mp.GaussianSource(self.fcen, fwidth=self.df), mp.Ey, - mp.Vector3(self.dpml + (-0.5 * self.sx)), size=mp.Vector3(0, self.w)) + mp.Source( + mp.GaussianSource(self.fcen, fwidth=self.df), + mp.Ey, + mp.Vector3(self.dpml + (-0.5 * self.sx)), + size=mp.Vector3(0, self.w), + ) ] self.sim.symmetries = [mp.Mirror(mp.Y, phase=-1)] - freg = mp.FluxRegion(center=mp.Vector3((0.5 * self.sx) - self.dpml - 0.5), - size=mp.Vector3(0, 2 * self.w)) + freg = mp.FluxRegion( + center=mp.Vector3((0.5 * self.sx) - self.dpml - 0.5), + size=mp.Vector3(0, 2 * self.w), + ) - trans = self.sim.add_flux(self.fcen, self.df, self.nfreq, freg, - decimation_factor=1) + trans = self.sim.add_flux( + self.fcen, self.df, self.nfreq, freg, decimation_factor=1 + ) self.sim.run( until_after_sources=mp.stop_when_fields_decayed( - 50, mp.Ey, mp.Vector3((0.5 * self.sx) - self.dpml - 0.5, 0), 1e-1) + 50, mp.Ey, mp.Vector3((0.5 * self.sx) - self.dpml - 0.5, 0), 1e-1 + ) ) res = zip(mp.get_flux_freqs(trans), mp.get_fluxes(trans)) @@ -147,6 +159,5 @@ def test_transmission_spectrum(self): self.assertClose(e, r, epsilon=tol) - -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_integrated_source.py b/python/tests/test_integrated_source.py index 61717090b..f380ceff4 100644 --- a/python/tests/test_integrated_source.py +++ b/python/tests/test_integrated_source.py @@ -7,18 +7,24 @@ # https://meep.readthedocs.io/en/latest/Perfectly_Matched_Layer/#planewave-sources-extending-into-pml # Regression test for issue #2043. -class TestIntegratedSource(unittest.TestCase): +class TestIntegratedSource(unittest.TestCase): def test_integrated_source(self): - sources = [mp.Source(mp.ContinuousSource(1,is_integrated=True), - center=mp.Vector3(-2), - size=mp.Vector3(y=6), - component=mp.Ez)] - sim = mp.Simulation(resolution=20, - cell_size=(6,6), - boundary_layers=[mp.PML(thickness=1)], - sources=sources, - k_point=mp.Vector3()) + sources = [ + mp.Source( + mp.ContinuousSource(1, is_integrated=True), + center=mp.Vector3(-2), + size=mp.Vector3(y=6), + component=mp.Ez, + ) + ] + sim = mp.Simulation( + resolution=20, + cell_size=(6, 6), + boundary_layers=[mp.PML(thickness=1)], + sources=sources, + k_point=mp.Vector3(), + ) sim.run(until=30) # field in mid-plane should be nearly constant, @@ -26,7 +32,8 @@ def test_integrated_source(self): ez = sim.get_array(mp.Ez, center=mp.Vector3(2), size=mp.Vector3(y=6)) std = np.std(ez) / np.sqrt(np.mean(ez**2)) print("std = ", std) - self.assertAlmostEqual(std, 0.0, places = 4 if mp.is_single_precision() else 8) + self.assertAlmostEqual(std, 0.0, places=4 if mp.is_single_precision() else 8) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_kdom.py b/python/tests/test_kdom.py index f61618c3f..87f430a9a 100644 --- a/python/tests/test_kdom.py +++ b/python/tests/test_kdom.py @@ -2,50 +2,59 @@ import meep as mp import math + class TestKdom(unittest.TestCase): + def run_kdom(self, theta, num_band): + + resolution = 20 # pixels/um + + sx = 5 + sy = 10 + cell_size = mp.Vector3(sx, sy, 0) - def run_kdom(self, theta, num_band): + fcen = 1 # center frequency (wavelength = 1 um) + ng = 1.5 + glass = mp.Medium(index=ng) - resolution = 20 # pixels/um + # angle of incident planewave; CCW about Y axis, 0 degrees along +X axis + theta_in = math.radians(theta) - sx = 5 - sy = 10 - cell_size = mp.Vector3(sx,sy,0) + # k (in source medium) with correct length (plane of incidence: XY) + k = mp.Vector3(math.cos(theta_in), math.sin(theta_in), 0).scale(fcen * ng) - fcen = 1 # center frequency (wavelength = 1 um) - ng = 1.5 - glass = mp.Medium(index=ng) + symmetries = [] + eig_parity = mp.ODD_Z + if theta_in == 0: + k = mp.Vector3(0, 0, 0) + eig_parity += mp.EVEN_Y + symmetries = [mp.Mirror(mp.Y)] - # angle of incident planewave; CCW about Y axis, 0 degrees along +X axis - theta_in = math.radians(theta) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + k_point=k, + symmetries=symmetries, + default_material=glass, + ) - # k (in source medium) with correct length (plane of incidence: XY) - k = mp.Vector3(math.cos(theta_in),math.sin(theta_in),0).scale(fcen*ng) + sim.init_sim() - symmetries = [] - eig_parity = mp.ODD_Z - if theta_in == 0: - k = mp.Vector3(0,0,0) - eig_parity += mp.EVEN_Y - symmetries = [mp.Mirror(mp.Y)] + EigenmodeData = sim.get_eigenmode( + fcen, + mp.X, + mp.Volume(center=mp.Vector3(0.3 * sx, 0, 0), size=mp.Vector3(0, sy, 0)), + num_band, + k, + parity=eig_parity, + ) + kdom = EigenmodeData.kdom - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - k_point=k, - symmetries=symmetries, - default_material=glass) + self.assertAlmostEqual(k.y, kdom.y, places=15) - sim.init_sim() + def test_kdom(self): + self.run_kdom(10.7, 6) + self.run_kdom(22.9, 12) - EigenmodeData = sim.get_eigenmode(fcen, mp.X, mp.Volume(center=mp.Vector3(0.3*sx,0,0), size=mp.Vector3(0,sy,0)), - num_band, k, parity=eig_parity) - kdom = EigenmodeData.kdom - self.assertAlmostEqual(k.y,kdom.y,places=15) - - def test_kdom(self): - self.run_kdom(10.7, 6) - self.run_kdom(22.9, 12) - -if __name__ == '__main__': - unittest.main() +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_ldos.py b/python/tests/test_ldos.py index 9807ad6f8..2536c1a74 100644 --- a/python/tests/test_ldos.py +++ b/python/tests/test_ldos.py @@ -9,184 +9,214 @@ class TestLDOS(unittest.TestCase): - @classmethod def setUp(cls): cls.resolution = 25 # pixels/μm - cls.dpml = 0.5 # thickness of PML - cls.L = 6.0 # length of non-PML region - cls.n = 2.4 # refractive index of surrounding medium - cls.wvl = 1.0 # wavelength (in vacuum) - - cls.fcen = 1/cls.wvl + cls.dpml = 0.5 # thickness of PML + cls.L = 6.0 # length of non-PML region + cls.n = 2.4 # refractive index of surrounding medium + cls.wvl = 1.0 # wavelength (in vacuum) + cls.fcen = 1 / cls.wvl def bulk_ldos_cyl(self): - sr = self.L+self.dpml - sz = self.L+2*self.dpml - cell_size = mp.Vector3(sr,0,sz) + sr = self.L + self.dpml + sz = self.L + 2 * self.dpml + cell_size = mp.Vector3(sr, 0, sz) pml_layers = [mp.PML(self.dpml)] - sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), - component=mp.Er, - center=mp.Vector3())] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1, - default_material=mp.Medium(index=self.n)) - - sim.run(mp.dft_ldos(self.fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Er, - mp.Vector3(), - 1e-6)) + sources = [ + mp.Source( + src=mp.GaussianSource(self.fcen, fwidth=0.2 * self.fcen), + component=mp.Er, + center=mp.Vector3(), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=self.n), + ) + + sim.run( + mp.dft_ldos(self.fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed( + 20, mp.Er, mp.Vector3(), 1e-6 + ), + ) return sim.ldos_data[0] - - def cavity_ldos_cyl(self,sz): - sr = self.L+self.dpml - cell_size = mp.Vector3(sr,0,sz) - - pml_layers = [mp.PML(self.dpml,direction=mp.R)] - - sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), - component=mp.Er, - center=mp.Vector3())] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1, - default_material=mp.Medium(index=self.n)) - - sim.run(mp.dft_ldos(self.fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Er, - mp.Vector3(), - 1e-6)) + def cavity_ldos_cyl(self, sz): + sr = self.L + self.dpml + cell_size = mp.Vector3(sr, 0, sz) + + pml_layers = [mp.PML(self.dpml, direction=mp.R)] + + sources = [ + mp.Source( + src=mp.GaussianSource(self.fcen, fwidth=0.2 * self.fcen), + component=mp.Er, + center=mp.Vector3(), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=self.n), + ) + + sim.run( + mp.dft_ldos(self.fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed( + 20, mp.Er, mp.Vector3(), 1e-6 + ), + ) return sim.ldos_data[0] - def bulk_ldos_3D(self): - s = self.L+2*self.dpml - cell_size = mp.Vector3(s,s,s) + s = self.L + 2 * self.dpml + cell_size = mp.Vector3(s, s, s) pml_layers = [mp.PML(self.dpml)] - sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), - component=mp.Ex, - center=mp.Vector3())] - - symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y), - mp.Mirror(direction=mp.Z)] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=self.n)) - - sim.run(mp.dft_ldos(self.fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Ex, - mp.Vector3(), - 1e-6)) + sources = [ + mp.Source( + src=mp.GaussianSource(self.fcen, fwidth=0.2 * self.fcen), + component=mp.Ex, + center=mp.Vector3(), + ) + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z), + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=self.n), + ) + + sim.run( + mp.dft_ldos(self.fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed( + 20, mp.Ex, mp.Vector3(), 1e-6 + ), + ) return sim.ldos_data[0] - - def cavity_ldos_3D(self,sz): - sxy = self.L+2*self.dpml - cell_size = mp.Vector3(sxy,sxy,sz) - - boundary_layers = [mp.PML(self.dpml,direction=mp.X), - mp.PML(self.dpml,direction=mp.Y)] - - sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), - component=mp.Ex, - center=mp.Vector3())] - - symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y), - mp.Mirror(direction=mp.Z)] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=self.n)) - - sim.run(mp.dft_ldos(ldos=mp.Ldos(self.fcen,0,1)), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Ex, - mp.Vector3(), - 1e-6)) + def cavity_ldos_3D(self, sz): + sxy = self.L + 2 * self.dpml + cell_size = mp.Vector3(sxy, sxy, sz) + + boundary_layers = [ + mp.PML(self.dpml, direction=mp.X), + mp.PML(self.dpml, direction=mp.Y), + ] + + sources = [ + mp.Source( + src=mp.GaussianSource(self.fcen, fwidth=0.2 * self.fcen), + component=mp.Ex, + center=mp.Vector3(), + ) + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z), + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=self.n), + ) + + sim.run( + mp.dft_ldos(ldos=mp.Ldos(self.fcen, 0, 1)), + until_after_sources=mp.stop_when_fields_decayed( + 20, mp.Ex, mp.Vector3(), 1e-6 + ), + ) return sim.ldos_data[0] - - def purcell_enh_theory(self,c): + def purcell_enh_theory(self, c): # equation 7 of reference - return (3*np.fix(c+0.5)/(4*c) + - (4*np.power(np.fix(c+0.5),3) - - np.fix(c+0.5))/(16*np.power(c,3))) - + return 3 * np.fix(c + 0.5) / (4 * c) + ( + 4 * np.power(np.fix(c + 0.5), 3) - np.fix(c + 0.5) + ) / (16 * np.power(c, 3)) def test_ldos_cyl(self): ldos_bulk = self.bulk_ldos_cyl() # not a Van Hove singularity cavity_thickness = 1.63 - gap = cavity_thickness*self.wvl/self.n + gap = cavity_thickness * self.wvl / self.n ldos_cavity = self.cavity_ldos_cyl(gap) # Purcell enhancement factor (relative to bulk medium) - pe_meep = ldos_cavity/ldos_bulk + pe_meep = ldos_cavity / ldos_bulk pe_theory = self.purcell_enh_theory(cavity_thickness) - rel_err = abs(pe_meep-pe_theory)/pe_theory + rel_err = abs(pe_meep - pe_theory) / pe_theory - print("ldos-cyl:, {:.6f} (Meep), {:.6f} (theory), " - "{:.6f} (error)".format(pe_meep,pe_theory,rel_err)) + print( + "ldos-cyl:, {:.6f} (Meep), {:.6f} (theory), " + "{:.6f} (error)".format(pe_meep, pe_theory, rel_err) + ) self.assertAlmostEqual(pe_meep, pe_theory, delta=0.1) - def test_ldos_3D(self): ldos_bulk = self.bulk_ldos_3D() # not a Van Hove singularity cavity_thickness = 0.75 - gap = cavity_thickness*self.wvl/self.n + gap = cavity_thickness * self.wvl / self.n ldos_cavity = self.cavity_ldos_3D(gap) # Purcell enhancement factor (relative to bulk medium) - pe_meep = ldos_cavity/ldos_bulk + pe_meep = ldos_cavity / ldos_bulk pe_theory = self.purcell_enh_theory(cavity_thickness) - rel_err = abs(pe_meep-pe_theory)/pe_theory + rel_err = abs(pe_meep - pe_theory) / pe_theory - print("ldos-3D:, {:.6f} (Meep), {:.6f} (theory), " - "{:.6f} (error)".format(pe_meep,pe_theory,rel_err)) + print( + "ldos-3D:, {:.6f} (Meep), {:.6f} (theory), " + "{:.6f} (error)".format(pe_meep, pe_theory, rel_err) + ) self.assertAlmostEqual(pe_meep, pe_theory, delta=0.1) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_material_dispersion.py b/python/tests/test_material_dispersion.py index 220df72c1..1ffcd5186 100644 --- a/python/tests/test_material_dispersion.py +++ b/python/tests/test_material_dispersion.py @@ -4,11 +4,10 @@ class TestMaterialDispersion(unittest.TestCase): - def test_material_dispersion_with_user_material(self): susceptibilities = [ mp.LorentzianSusceptibility(frequency=1.1, gamma=1e-5, sigma=0.5), - mp.LorentzianSusceptibility(frequency=0.5, gamma=0.1, sigma=2e-5) + mp.LorentzianSusceptibility(frequency=0.5, gamma=0.1, sigma=2e-5), ] def mat_func(p): @@ -18,9 +17,7 @@ def mat_func(p): df = 2.0 sources = mp.Source( - mp.GaussianSource(fcen, fwidth=df), - component=mp.Ez, - center=mp.Vector3() + mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3() ) kmin = 0.3 @@ -35,7 +32,7 @@ def mat_func(p): sources=[sources], material_function=mat_func, default_material=mp.air, - resolution=20 + resolution=20, ) all_freqs = self.sim.run_k_points(200, kpts) @@ -54,5 +51,5 @@ def mat_func(p): np.testing.assert_allclose(expected, res) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_material_grid.py b/python/tests/test_material_grid.py index 7ffa3e54c..a5d7acf30 100644 --- a/python/tests/test_material_grid.py +++ b/python/tests/test_material_grid.py @@ -1,4 +1,5 @@ import meep as mp + try: import meep.adjoint as mpa except: @@ -11,118 +12,153 @@ def compute_transmittance(matgrid_symmetry=False): resolution = 25 - cell_size = mp.Vector3(6,6,0) + cell_size = mp.Vector3(6, 6, 0) boundary_layers = [mp.PML(thickness=1.0)] - matgrid_size = mp.Vector3(2,2,0) - matgrid_resolution = 2*resolution + matgrid_size = mp.Vector3(2, 2, 0) + matgrid_resolution = 2 * resolution - Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) + Nx, Ny = int(matgrid_size.x * matgrid_resolution), int( + matgrid_size.y * matgrid_resolution + ) # ensure reproducible results rng = np.random.RandomState(2069588) - w = rng.rand(Nx,Ny) + w = rng.rand(Nx, Ny) weights = w if matgrid_symmetry else 0.5 * (w + np.fliplr(w)) - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mp.Medium(index=3.5), - weights=weights, - do_averaging=False, - grid_type='U_MEAN') - - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,1.0,mp.inf), - material=mp.Medium(index=3.5)), - mp.Block(center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), - material=matgrid)] + matgrid = mp.MaterialGrid( + mp.Vector3(Nx, Ny), + mp.air, + mp.Medium(index=3.5), + weights=weights, + do_averaging=False, + grid_type="U_MEAN", + ) + + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1.0, mp.inf), + material=mp.Medium(index=3.5), + ), + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x, matgrid_size.y, 0), + material=matgrid, + ), + ] if matgrid_symmetry: - geometry.append(mp.Block(center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), - material=matgrid, - e2=mp.Vector3(y=-1))) + geometry.append( + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x, matgrid_size.y, 0), + material=matgrid, + e2=mp.Vector3(y=-1), + ) + ) eig_parity = mp.ODD_Y + mp.EVEN_Z fcen = 0.65 - df = 0.2*fcen - sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df), - center=mp.Vector3(-2.0,0), - size=mp.Vector3(0,4.0), - eig_parity=eig_parity)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - geometry=geometry) - - mode_mon = sim.add_flux(fcen, - 0, - 1, - mp.FluxRegion(center=mp.Vector3(2.0,0), - size=mp.Vector3(0,4.0))) + df = 0.2 * fcen + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=df), + center=mp.Vector3(-2.0, 0), + size=mp.Vector3(0, 4.0), + eig_parity=eig_parity, + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + geometry=geometry, + ) + + mode_mon = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=mp.Vector3(2.0, 0), size=mp.Vector3(0, 4.0)) + ) sim.run(until_after_sources=mp.stop_when_dft_decayed()) - mode_coeff = sim.get_eigenmode_coefficients(mode_mon,[1],eig_parity).alpha[0,:,0][0] + mode_coeff = sim.get_eigenmode_coefficients(mode_mon, [1], eig_parity).alpha[ + 0, :, 0 + ][0] - tran = np.power(np.abs(mode_coeff),2) + tran = np.power(np.abs(mode_coeff), 2) print(f'tran:, {"sym" if matgrid_symmetry else "nosym"}, {tran}') return tran -def compute_resonant_mode_2d(res,default_mat=False): - cell_size = mp.Vector3(1,1,0) +def compute_resonant_mode_2d(res, default_mat=False): + cell_size = mp.Vector3(1, 1, 0) rad = 0.301943 fcen = 0.3 - df = 0.2*fcen - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Hz, - center=mp.Vector3(-0.1057,0.2094,0))] - - k_point = mp.Vector3(0.3892,0.1597,0) - - matgrid_size = mp.Vector3(1,1,0) + df = 0.2 * fcen + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Hz, + center=mp.Vector3(-0.1057, 0.2094, 0), + ) + ] + + k_point = mp.Vector3(0.3892, 0.1597, 0) + + matgrid_size = mp.Vector3(1, 1, 0) matgrid_resolution = 1200 # for a fixed resolution, compute the number of grid points # necessary which are defined on the corners of the voxels - Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) + Nx, Ny = int(matgrid_size.x * matgrid_resolution), int( + matgrid_size.y * matgrid_resolution + ) - x = np.linspace(-0.5*matgrid_size.x,0.5*matgrid_size.x,Nx) - y = np.linspace(-0.5*matgrid_size.y,0.5*matgrid_size.y,Ny) - xv, yv = np.meshgrid(x,y) + x = np.linspace(-0.5 * matgrid_size.x, 0.5 * matgrid_size.x, Nx) + y = np.linspace(-0.5 * matgrid_size.y, 0.5 * matgrid_size.y, Ny) + xv, yv = np.meshgrid(x, y) weights = np.sqrt(np.square(xv) + np.square(yv)) < rad - filtered_weights = gaussian_filter(weights, - sigma=3.0, - output=np.double) - - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mp.Medium(index=3.5), - weights=filtered_weights, - do_averaging=True, - beta=1000, - eta=0.5) - - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), - material=matgrid)] - - sim = mp.Simulation(resolution=res, cell_size=cell_size, default_material=matgrid if default_mat else mp.Medium(), geometry=[] if default_mat else geometry, sources=sources, k_point=k_point) - - - h = mp.Harminv(mp.Hz, mp.Vector3(0.3718,-0.2076), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=200) + filtered_weights = gaussian_filter(weights, sigma=3.0, output=np.double) + + matgrid = mp.MaterialGrid( + mp.Vector3(Nx, Ny), + mp.air, + mp.Medium(index=3.5), + weights=filtered_weights, + do_averaging=True, + beta=1000, + eta=0.5, + ) + + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x, matgrid_size.y, 0), + material=matgrid, + ) + ] + + sim = mp.Simulation( + resolution=res, + cell_size=cell_size, + default_material=matgrid if default_mat else mp.Medium(), + geometry=[] if default_mat else geometry, + sources=sources, + k_point=k_point, + ) + + h = mp.Harminv(mp.Hz, mp.Vector3(0.3718, -0.2076), fcen, df) + sim.run(mp.after_sources(h), until_after_sources=200) try: for m in h.modes: @@ -138,8 +174,8 @@ def compute_resonant_mode_3d(use_matgrid=True): resolution = 25 wvl = 1.27 - fcen = 1/wvl - df = 0.02*fcen + fcen = 1 / wvl + df = 0.02 * fcen nSi = 3.45 Si = mp.Medium(index=nSi) @@ -147,55 +183,58 @@ def compute_resonant_mode_3d(use_matgrid=True): SiO2 = mp.Medium(index=nSiO2) s = 1.0 - cell_size = mp.Vector3(s,s,s) + cell_size = mp.Vector3(s, s, s) rad = 0.34 # radius of sphere if use_matgrid: - matgrid_resolution = 2*resolution - N = int(s*matgrid_resolution) - coord = np.linspace(-0.5*s,0.5*s,N) - xv, yv, zv = np.meshgrid(coord,coord,coord) + matgrid_resolution = 2 * resolution + N = int(s * matgrid_resolution) + coord = np.linspace(-0.5 * s, 0.5 * s, N) + xv, yv, zv = np.meshgrid(coord, coord, coord) weights = np.sqrt(np.square(xv) + np.square(yv) + np.square(zv)) < rad - filtered_weights = gaussian_filter(weights, - sigma=4/resolution, - output=np.double) - - matgrid = mp.MaterialGrid(mp.Vector3(N,N,N), - SiO2, - Si, - weights=filtered_weights, - do_averaging=True, - beta=1000, - eta=0.5) - - geometry = [mp.Block(center=mp.Vector3(), - size=cell_size, - material=matgrid)] + filtered_weights = gaussian_filter( + weights, sigma=4 / resolution, output=np.double + ) + + matgrid = mp.MaterialGrid( + mp.Vector3(N, N, N), + SiO2, + Si, + weights=filtered_weights, + do_averaging=True, + beta=1000, + eta=0.5, + ) + + geometry = [mp.Block(center=mp.Vector3(), size=cell_size, material=matgrid)] else: - geometry = [mp.Sphere(center=mp.Vector3(), - radius=rad, - material=Si)] + geometry = [mp.Sphere(center=mp.Vector3(), radius=rad, material=Si)] - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df), - size=mp.Vector3(), - center=mp.Vector3(0.13,0.25,0.06), - component=mp.Ez)] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + size=mp.Vector3(), + center=mp.Vector3(0.13, 0.25, 0.06), + component=mp.Ez, + ) + ] - k_point = mp.Vector3(0.23,-0.17,0.35) + k_point = mp.Vector3(0.23, -0.17, 0.35) - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - default_material=SiO2, - k_point=k_point, - geometry=geometry) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + default_material=SiO2, + k_point=k_point, + geometry=geometry, + ) - h = mp.Harminv(mp.Ez, mp.Vector3(-0.2684,0.1185,0.0187), fcen, df) + h = mp.Harminv(mp.Ez, mp.Vector3(-0.2684, 0.1185, 0.0187), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=200) + sim.run(mp.after_sources(h), until_after_sources=200) try: for m in h.modes: @@ -208,7 +247,6 @@ def compute_resonant_mode_3d(use_matgrid=True): class TestMaterialGrid(unittest.TestCase): - def test_subpixel_smoothing(self): # "exact" frequency computed using MaterialGrid at resolution = 300 freq_ref = 0.29826813873225283 @@ -223,23 +261,24 @@ def test_subpixel_smoothing(self): # verify that the relative error is decreasing with increasing resolution # and is better than linear convergence because of subpixel smoothing - self.assertLess(abs(freq_matgrid[1]-freq_ref)*(res[1]/res[0]), - abs(freq_matgrid[0]-freq_ref)) + self.assertLess( + abs(freq_matgrid[1] - freq_ref) * (res[1] / res[0]), + abs(freq_matgrid[0] - freq_ref), + ) freq_matgrid_default_mat = compute_resonant_mode_2d(res[0], True) self.assertAlmostEqual(freq_matgrid[0], freq_matgrid_default_mat) - def test_matgrid_3d(self): freq_matgrid = compute_resonant_mode_3d(True) freq_geomobj = compute_resonant_mode_3d(False) self.assertAlmostEqual(freq_matgrid, freq_geomobj, places=2) - def test_symmetry(self): - tran_nosym = compute_transmittance(False) - tran_sym = compute_transmittance(True) - self.assertAlmostEqual(tran_nosym, tran_sym, places=5) + tran_nosym = compute_transmittance(False) + tran_sym = compute_transmittance(True) + self.assertAlmostEqual(tran_nosym, tran_sym, places=5) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_materials_library.py b/python/tests/test_materials_library.py index 1e61af952..99d79d544 100644 --- a/python/tests/test_materials_library.py +++ b/python/tests/test_materials_library.py @@ -3,34 +3,43 @@ class TestMaterialsLibrary(unittest.TestCase): - def test_materials_library(self): - self.assertAlmostEqual(InP.epsilon(1/3.3)[0][0], (3.1031)**2, places=2) + self.assertAlmostEqual(InP.epsilon(1 / 3.3)[0][0], (3.1031) ** 2, places=2) - self.assertAlmostEqual(Ge.epsilon(1/6.8)[0][0], (4.0091)**2, places=2) + self.assertAlmostEqual(Ge.epsilon(1 / 6.8)[0][0], (4.0091) ** 2, places=2) - self.assertAlmostEqual(Si.epsilon(1/1.55)[0][0], (3.4777)**2, places=2) + self.assertAlmostEqual(Si.epsilon(1 / 1.55)[0][0], (3.4777) ** 2, places=2) - self.assertAlmostEqual(LiNbO3.epsilon(1/1.55)[0][0], (2.2111)**2, places=2) - self.assertAlmostEqual(LiNbO3.epsilon(1/1.55)[1][1], (2.2111)**2, places=2) - self.assertAlmostEqual(LiNbO3.epsilon(1/1.55)[2][2], (2.1376)**2, places=2) + self.assertAlmostEqual(LiNbO3.epsilon(1 / 1.55)[0][0], (2.2111) ** 2, places=2) + self.assertAlmostEqual(LiNbO3.epsilon(1 / 1.55)[1][1], (2.2111) ** 2, places=2) + self.assertAlmostEqual(LiNbO3.epsilon(1 / 1.55)[2][2], (2.1376) ** 2, places=2) - self.assertAlmostEqual(SiO2_aniso.epsilon(1/1.55)[0][0], (1.5277)**2, places=2) - self.assertEqual(SiO2_aniso.epsilon(1/1.55)[1][0], 0) - self.assertAlmostEqual(SiO2_aniso.epsilon(1/1.55)[1][1], (1.5277)**2, places=2) - self.assertAlmostEqual(SiO2_aniso.epsilon(1/1.55)[2][2], (1.5362)**2, places=2) + self.assertAlmostEqual( + SiO2_aniso.epsilon(1 / 1.55)[0][0], (1.5277) ** 2, places=2 + ) + self.assertEqual(SiO2_aniso.epsilon(1 / 1.55)[1][0], 0) + self.assertAlmostEqual( + SiO2_aniso.epsilon(1 / 1.55)[1][1], (1.5277) ** 2, places=2 + ) + self.assertAlmostEqual( + SiO2_aniso.epsilon(1 / 1.55)[2][2], (1.5362) ** 2, places=2 + ) - self.assertAlmostEqual(Ag.epsilon(1/0.65)[0][0], (0.14623 + 1j*3.9367)**2, places=2) + self.assertAlmostEqual( + Ag.epsilon(1 / 0.65)[0][0], (0.14623 + 1j * 3.9367) ** 2, places=2 + ) - self.assertAlmostEqual(Cr.epsilon(1/0.71)[0][0], (3.8275 + 1j*4.3457)**2, places=2) + self.assertAlmostEqual( + Cr.epsilon(1 / 0.71)[0][0], (3.8275 + 1j * 4.3457) ** 2, places=2 + ) try: - Ag.epsilon(1/0.2)[0][0] + Ag.epsilon(1 / 0.2)[0][0] except ValueError: pass else: raise AssertionError("Ag is not defined at a wavelength of 0.2 μm") -if __name__ == '__main__': - unittest.main() +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_medium_evaluations.py b/python/tests/test_medium_evaluations.py index 0d74bd93c..ef680563a 100644 --- a/python/tests/test_medium_evaluations.py +++ b/python/tests/test_medium_evaluations.py @@ -1,4 +1,3 @@ - # medium_evaluations.py - Tests the evaluation of material permitivity profiles. # Checks materials with lorentizian, drude, and non uniform diagonals. # The extracted values are compared against actual datapoints pulled from @@ -13,53 +12,53 @@ class TestMediumEvaluations(unittest.TestCase): - def test_medium_evaluations(self): from meep.materials import Si, Ag, LiNbO3, fused_quartz # Check that scalars work w0 = LiNbO3.valid_freq_range.min eps = LiNbO3.epsilon(w0) - self.assertAlmostEqual(np.real(np.sqrt(eps[0,0])), 2.0508, places=4) + self.assertAlmostEqual(np.real(np.sqrt(eps[0, 0])), 2.0508, places=4) # Check numpy arrays try: w0 = Si.valid_freq_range.min w1 = Si.valid_freq_range.max - eps = Si.epsilon(np.linspace(w0,w1,100)) + eps = Si.epsilon(np.linspace(w0, w1, 100)) except ExceptionType: self.fail("myFunc() raised ExceptionType unexpectedly!") # Check that regions outside of domain don't work - self.assertRaises(ValueError,LiNbO3.epsilon,-1.0) - self.assertRaises(ValueError,LiNbO3.epsilon,10000.0) + self.assertRaises(ValueError, LiNbO3.epsilon, -1.0) + self.assertRaises(ValueError, LiNbO3.epsilon, 10000.0) # Check complex vs non complex numbers - self.assertTrue(np.iscomplex(Ag.epsilon(1.0)[0,0])) - self.assertFalse(np.iscomplex(fused_quartz.epsilon(1.0)[0,0])) + self.assertTrue(np.iscomplex(Ag.epsilon(1.0)[0, 0])) + self.assertFalse(np.iscomplex(fused_quartz.epsilon(1.0)[0, 0])) # Check Silicon w0 = Si.valid_freq_range.min w1 = Si.valid_freq_range.max - eps = Si.epsilon([w0,w1]) - self.assertAlmostEqual(np.real(np.sqrt(eps[0,0,0])), 3.4175, places=4) - self.assertAlmostEqual(np.real(np.sqrt(eps[1,0,0])), 3.4971, places=4) + eps = Si.epsilon([w0, w1]) + self.assertAlmostEqual(np.real(np.sqrt(eps[0, 0, 0])), 3.4175, places=4) + self.assertAlmostEqual(np.real(np.sqrt(eps[1, 0, 0])), 3.4971, places=4) # Check Silver w0 = Ag.valid_freq_range.min w1 = Ag.valid_freq_range.max - eps = Ag.epsilon([w0,w1]) - self.assertAlmostEqual(np.real(np.sqrt(eps[0,0,0])), 17.485, places=2) - self.assertAlmostEqual(np.real(np.sqrt(eps[1,0,0])), 0.44265, places=4) + eps = Ag.epsilon([w0, w1]) + self.assertAlmostEqual(np.real(np.sqrt(eps[0, 0, 0])), 17.485, places=2) + self.assertAlmostEqual(np.real(np.sqrt(eps[1, 0, 0])), 0.44265, places=4) # Check Lithium Niobate w0 = LiNbO3.valid_freq_range.min w1 = LiNbO3.valid_freq_range.max - eps = LiNbO3.epsilon([w0,w1]) - self.assertAlmostEqual(np.real(np.sqrt(eps[0,0,0])), 2.0508, places=4) - self.assertAlmostEqual(np.real(np.sqrt(eps[1,0,0])), 2.4393, places=4) - self.assertAlmostEqual(np.real(np.sqrt(eps[0,2,2])), 2.0025, places=4) - self.assertAlmostEqual(np.real(np.sqrt(eps[1,2,2])), 2.3321, places=4) + eps = LiNbO3.epsilon([w0, w1]) + self.assertAlmostEqual(np.real(np.sqrt(eps[0, 0, 0])), 2.0508, places=4) + self.assertAlmostEqual(np.real(np.sqrt(eps[1, 0, 0])), 2.4393, places=4) + self.assertAlmostEqual(np.real(np.sqrt(eps[0, 2, 2])), 2.0025, places=4) + self.assertAlmostEqual(np.real(np.sqrt(eps[1, 2, 2])), 2.3321, places=4) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_mode_coeffs.py b/python/tests/test_mode_coeffs.py index 8e42a73ee..d826b7f80 100644 --- a/python/tests/test_mode_coeffs.py +++ b/python/tests/test_mode_coeffs.py @@ -4,10 +4,9 @@ class TestModeCoeffs(unittest.TestCase): - def run_mode_coeffs(self, mode_num, kpoint_func, nf=1, resolution=15): - w = 1 # width of waveguide + w = 1 # width of waveguide L = 10 # length of waveguide Si = mp.Medium(epsilon=12.0) @@ -21,10 +20,12 @@ def run_mode_coeffs(self, mode_num, kpoint_func, nf=1, resolution=15): prism_x = sx + 1 prism_y = w / 2 - vertices = [mp.Vector3(-prism_x, prism_y), - mp.Vector3(prism_x, prism_y), - mp.Vector3(prism_x, -prism_y), - mp.Vector3(-prism_x, -prism_y)] + vertices = [ + mp.Vector3(-prism_x, prism_y), + mp.Vector3(prism_x, prism_y), + mp.Vector3(prism_x, -prism_y), + mp.Vector3(-prism_x, -prism_y), + ] geometry = [mp.Prism(vertices, height=mp.inf, material=Si)] @@ -32,25 +33,39 @@ def run_mode_coeffs(self, mode_num, kpoint_func, nf=1, resolution=15): # mode frequency fcen = 0.20 # > 0.5/sqrt(11) to have at least 2 modes - df = 0.5*fcen + df = 0.5 * fcen - source = mp.EigenModeSource(src=mp.GaussianSource(fcen, fwidth=df), - eig_band=mode_num, - size=mp.Vector3(0,sy-2*dpml,0), - center=mp.Vector3(-0.5*sx+dpml,0,0)) + source = mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=df), + eig_band=mode_num, + size=mp.Vector3(0, sy - 2 * dpml, 0), + center=mp.Vector3(-0.5 * sx + dpml, 0, 0), + ) symmetries = [mp.Mirror(mp.Y, phase=1 if mode_num % 2 == 1 else -1)] - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - geometry=geometry, - sources=[source], - symmetries=symmetries) - - xm = 0.5*sx - dpml # x-coordinate of monitor - mflux = sim.add_mode_monitor(fcen, df, nf, mp.ModeRegion(center=mp.Vector3(xm,0), size=mp.Vector3(0,sy-2*dpml))) - mode_flux = sim.add_flux(fcen, df, nf, mp.FluxRegion(center=mp.Vector3(xm,0), size=mp.Vector3(0,sy-2*dpml))) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + geometry=geometry, + sources=[source], + symmetries=symmetries, + ) + + xm = 0.5 * sx - dpml # x-coordinate of monitor + mflux = sim.add_mode_monitor( + fcen, + df, + nf, + mp.ModeRegion(center=mp.Vector3(xm, 0), size=mp.Vector3(0, sy - 2 * dpml)), + ) + mode_flux = sim.add_flux( + fcen, + df, + nf, + mp.FluxRegion(center=mp.Vector3(xm, 0), size=mp.Vector3(0, sy - 2 * dpml)), + ) # sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(-0.5*sx+dpml,0), 1e-10)) sim.run(until_after_sources=100) @@ -65,27 +80,34 @@ def run_mode_coeffs(self, mode_num, kpoint_func, nf=1, resolution=15): # and check that it agrees with the prediction # of the eig_power() class method in EigenmodeSource. ################################################## - if nf>1: - power_observed=mp.get_fluxes(mode_flux) - freqs=mp.get_flux_freqs(mode_flux) - power_expected=[source.eig_power(f) for f in freqs] + if nf > 1: + power_observed = mp.get_fluxes(mode_flux) + freqs = mp.get_flux_freqs(mode_flux) + power_expected = [source.eig_power(f) for f in freqs] return freqs, power_expected, power_observed - modes_to_check = [1, 2] # indices of modes for which to compute expansion coefficients - res = sim.get_eigenmode_coefficients(mflux, modes_to_check, kpoint_func=kpoint_func) + modes_to_check = [ + 1, + 2, + ] # indices of modes for which to compute expansion coefficients + res = sim.get_eigenmode_coefficients( + mflux, modes_to_check, kpoint_func=kpoint_func + ) self.assertTrue(res.kpoints[0].close(mp.Vector3(0.604301, 0, 0))) self.assertTrue(res.kpoints[1].close(mp.Vector3(0.494353, 0, 0), tol=1e-2)) self.assertTrue(res.kdom[0].close(mp.Vector3(0.604301, 0, 0))) self.assertTrue(res.kdom[1].close(mp.Vector3(0.494353, 0, 0), tol=1e-2)) - self.assertAlmostEqual(res.cscale[0],0.50000977,places=5) - self.assertAlmostEqual(res.cscale[1],0.50096888,places=5) + self.assertAlmostEqual(res.cscale[0], 0.50000977, places=5) + self.assertAlmostEqual(res.cscale[1], 0.50096888, places=5) mode_power = mp.get_fluxes(mode_flux)[0] TestPassed = True TOLERANCE = 5.0e-3 - c0 = res.alpha[mode_num - 1, 0, 0] # coefficient of forward-traveling wave for mode #mode_num - for nm in range(1, len(modes_to_check)+1): + c0 = res.alpha[ + mode_num - 1, 0, 0 + ] # coefficient of forward-traveling wave for mode #mode_num + for nm in range(1, len(modes_to_check) + 1): if nm != mode_num: cfrel = np.abs(res.alpha[nm - 1, 0, 0]) / np.abs(c0) cbrel = np.abs(res.alpha[nm - 1, 0, 1]) / np.abs(c0) @@ -117,123 +139,162 @@ def test_modes(self): hz_at_eval_point = emdata.amplitude(eval_point, mp.Hz) places = 5 if mp.is_single_precision() else 7 - self.assertAlmostEqual(ex_at_eval_point, 0.4887779638178009+0.484240145324284j, places=places) - self.assertAlmostEqual(hz_at_eval_point, 3.4249236584603495-3.455974863884166j, places=places) + self.assertAlmostEqual( + ex_at_eval_point, 0.4887779638178009 + 0.484240145324284j, places=places + ) + self.assertAlmostEqual( + hz_at_eval_point, 3.4249236584603495 - 3.455974863884166j, places=places + ) def test_kpoint_func(self): - def kpoint_func(freq, mode): return mp.Vector3() self.run_mode_coeffs(1, kpoint_func) def test_eigensource_normalization(self): - f, p_exp, p_obs=self.run_mode_coeffs(1, None, nf=51, resolution=15) - #self.assertAlmostEqual(max(p_exp),max(p_obs),places=1) - max_exp, max_obs=max(p_exp), max(p_obs) - self.assertLess(abs(max_exp-max_obs), 0.5*max(abs(max_exp),abs(max_obs))) + f, p_exp, p_obs = self.run_mode_coeffs(1, None, nf=51, resolution=15) + # self.assertAlmostEqual(max(p_exp),max(p_obs),places=1) + max_exp, max_obs = max(p_exp), max(p_obs) + self.assertLess(abs(max_exp - max_obs), 0.5 * max(abs(max_exp), abs(max_obs))) def test_reciprocity_kpoint(self): resolution = 40 sx = 7.0 sy = 5.0 - cell_size = mp.Vector3(sx,sy) + cell_size = mp.Vector3(sx, sy) dpml = 1.0 pml_layers = [mp.PML(thickness=dpml)] w = 1.0 - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,w,mp.inf), - material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] fsrc = 0.15 - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc), - center=mp.Vector3(x=-0.5*sx+dpml), - size=mp.Vector3(y=sy), - eig_parity=mp.EVEN_Y+mp.ODD_Z)] + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(x=-0.5 * sx + dpml), + size=mp.Vector3(y=sy), + eig_parity=mp.EVEN_Y + mp.ODD_Z, + ) + ] symmetries = [mp.Mirror(mp.Y)] - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=symmetries) - - tran = sim.add_mode_monitor(fsrc, 0, 1, - mp.ModeRegion(center=mp.Vector3(x=0.5*sx-dpml), - size=mp.Vector3(y=sy)), - yee_grid=False) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=symmetries, + ) + + tran = sim.add_mode_monitor( + fsrc, + 0, + 1, + mp.ModeRegion(center=mp.Vector3(x=0.5 * sx - dpml), size=mp.Vector3(y=sy)), + yee_grid=False, + ) sim.run(until_after_sources=50) - res_fwd = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z, - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: mp.Vector3(+1,0,0)) - - res_bwd = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z, - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: mp.Vector3(-1,0,0)) + res_fwd = sim.get_eigenmode_coefficients( + tran, + [1], + eig_parity=mp.EVEN_Y + mp.ODD_Z, + direction=mp.NO_DIRECTION, + kpoint_func=lambda f, n: mp.Vector3(+1, 0, 0), + ) + + res_bwd = sim.get_eigenmode_coefficients( + tran, + [1], + eig_parity=mp.EVEN_Y + mp.ODD_Z, + direction=mp.NO_DIRECTION, + kpoint_func=lambda f, n: mp.Vector3(-1, 0, 0), + ) print(f"S11:, {res_fwd.alpha[0,0,1]}, {res_bwd.alpha[0,0,0]}") print(f"S21:, {res_fwd.alpha[0,0,0]}, {res_bwd.alpha[0,0,1]}") # |S11|^2 - self.assertAlmostEqual(abs(res_fwd.alpha[0,0,1])**2, abs(res_bwd.alpha[0,0,0])**2, places=4) + self.assertAlmostEqual( + abs(res_fwd.alpha[0, 0, 1]) ** 2, abs(res_bwd.alpha[0, 0, 0]) ** 2, places=4 + ) # |S21|^2 - self.assertAlmostEqual(abs(res_fwd.alpha[0,0,0])**2 / abs(res_bwd.alpha[0,0,1])**2, 1.00, places=2) + self.assertAlmostEqual( + abs(res_fwd.alpha[0, 0, 0]) ** 2 / abs(res_bwd.alpha[0, 0, 1]) ** 2, + 1.00, + places=2, + ) def test_reciprocity_monitor(self): resolution = 25 sx = 7.0 sy = 5.0 - cell_size = mp.Vector3(sx,sy) + cell_size = mp.Vector3(sx, sy) dpml = 1.0 pml_layers = [mp.PML(thickness=dpml)] w = 1.0 - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,w,mp.inf), - material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] fsrc = 0.15 # source is at the left edge of the waveguide - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc), - center=mp.Vector3(x=-0.5*sx+dpml), - size=mp.Vector3(y=sy), - eig_parity=mp.EVEN_Y+mp.ODD_Z)] + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(x=-0.5 * sx + dpml), + size=mp.Vector3(y=sy), + eig_parity=mp.EVEN_Y + mp.ODD_Z, + ) + ] symmetries = [mp.Mirror(mp.Y)] - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=symmetries) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=symmetries, + ) # monitor is at the right edge of the waveguide - tran = sim.add_mode_monitor(fsrc, 0, 1, - mp.ModeRegion(center=mp.Vector3(x=0.5*sx-dpml), - size=mp.Vector3(y=sy)), - yee_grid=False) + tran = sim.add_mode_monitor( + fsrc, + 0, + 1, + mp.ModeRegion(center=mp.Vector3(x=0.5 * sx - dpml), size=mp.Vector3(y=sy)), + yee_grid=False, + ) sim.run(until_after_sources=50) - res_fwd = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z) + res_fwd = sim.get_eigenmode_coefficients( + tran, [1], eig_parity=mp.EVEN_Y + mp.ODD_Z + ) print(f"S11:, {res_fwd.alpha[0,0,1]}") print(f"S21:, {res_fwd.alpha[0,0,0]}") @@ -241,41 +302,56 @@ def test_reciprocity_monitor(self): sim.reset_meep() # source is at the right edge of the waveguide - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc), - center=mp.Vector3(x=0.5*sx-dpml), - size=mp.Vector3(y=sy), - direction=mp.NO_DIRECTION, - eig_kpoint=mp.Vector3(-1,0,0), - eig_parity=mp.EVEN_Y+mp.ODD_Z)] - - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=symmetries) + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(x=0.5 * sx - dpml), + size=mp.Vector3(y=sy), + direction=mp.NO_DIRECTION, + eig_kpoint=mp.Vector3(-1, 0, 0), + eig_parity=mp.EVEN_Y + mp.ODD_Z, + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=symmetries, + ) # monitor is at the left edge of the waveguide - tran = sim.add_mode_monitor(fsrc, 0, 1, - mp.ModeRegion(center=mp.Vector3(x=-0.5*sx+dpml), - size=mp.Vector3(y=sy)), - yee_grid=False) + tran = sim.add_mode_monitor( + fsrc, + 0, + 1, + mp.ModeRegion(center=mp.Vector3(x=-0.5 * sx + dpml), size=mp.Vector3(y=sy)), + yee_grid=False, + ) sim.run(until_after_sources=50) - res_bwd = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z) + res_bwd = sim.get_eigenmode_coefficients( + tran, [1], eig_parity=mp.EVEN_Y + mp.ODD_Z + ) print(f"S12:, {res_bwd.alpha[0,0,1]}") print(f"S22:, {res_bwd.alpha[0,0,0]}") # |S21|^2 = |S12|^2 - self.assertAlmostEqual(abs(res_fwd.alpha[0,0,0])**2 / abs(res_bwd.alpha[0,0,1])**2, 1.00, places=2) + self.assertAlmostEqual( + abs(res_fwd.alpha[0, 0, 0]) ** 2 / abs(res_bwd.alpha[0, 0, 1]) ** 2, + 1.00, + places=2, + ) # |S11|^2 = |S22|^2 - self.assertAlmostEqual(abs(res_fwd.alpha[0,0,1])**2, abs(res_bwd.alpha[0,0,0])**2, places=2) + self.assertAlmostEqual( + abs(res_fwd.alpha[0, 0, 1]) ** 2, abs(res_bwd.alpha[0, 0, 0]) ** 2, places=2 + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_mode_decomposition.py b/python/tests/test_mode_decomposition.py index 6d0262475..127f6772c 100644 --- a/python/tests/test_mode_decomposition.py +++ b/python/tests/test_mode_decomposition.py @@ -6,7 +6,6 @@ class TestModeDecomposition(unittest.TestCase): - def test_linear_taper_2d(self): resolution = 10 w1 = 1 @@ -24,142 +23,183 @@ def test_linear_taper_2d(self): symmetries = [mp.Mirror(mp.Y)] Lt = 2 sx = dpml + Lw + Lt + Lw + dpml - cell_size = mp.Vector3(sx,sy,0) + cell_size = mp.Vector3(sx, sy, 0) prism_x = sx + 1 half_Lt = 0.5 * Lt - src_pt = mp.Vector3(-0.5*sx+dpml+0.2*Lw,0,0) - sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen, fwidth=0.2*fcen), - center=src_pt, - size=mp.Vector3(0,sy-2*dpml,0), - eig_match_freq=True, - eig_parity=mp.ODD_Z+mp.EVEN_Y)] - - vertices = [mp.Vector3(-prism_x, half_w1), - mp.Vector3(prism_x, half_w1), - mp.Vector3(prism_x, -half_w1), - mp.Vector3(-prism_x, -half_w1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], - sources=sources, - symmetries=symmetries) - - mon_pt = mp.Vector3(-0.5*sx+dpml+0.5*Lw,0,0) - flux = sim.add_flux(fcen, - 0, - 1, - mp.FluxRegion(center=mon_pt, - size=mp.Vector3(0,sy-2*dpml,0))) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, src_pt, 1e-9)) - - res = sim.get_eigenmode_coefficients(flux, - [1], - eig_parity=mp.ODD_Z+mp.EVEN_Y) + src_pt = mp.Vector3(-0.5 * sx + dpml + 0.2 * Lw, 0, 0) + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=src_pt, + size=mp.Vector3(0, sy - 2 * dpml, 0), + eig_match_freq=True, + eig_parity=mp.ODD_Z + mp.EVEN_Y, + ) + ] + + vertices = [ + mp.Vector3(-prism_x, half_w1), + mp.Vector3(prism_x, half_w1), + mp.Vector3(prism_x, -half_w1), + mp.Vector3(-prism_x, -half_w1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], + sources=sources, + symmetries=symmetries, + ) + + mon_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * Lw, 0, 0) + flux = sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy - 2 * dpml, 0)), + ) + + sim.run( + until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, src_pt, 1e-9) + ) + + res = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y) incident_coeffs = res.alpha incident_flux = mp.get_fluxes(flux) incident_flux_data = sim.get_flux_data(flux) sim.reset_meep() - vertices = [mp.Vector3(-prism_x, half_w1), - mp.Vector3(-half_Lt, half_w1), - mp.Vector3(half_Lt, half_w2), - mp.Vector3(prism_x, half_w2), - mp.Vector3(prism_x, -half_w2), - mp.Vector3(half_Lt, -half_w2), - mp.Vector3(-half_Lt, -half_w1), - mp.Vector3(-prism_x, -half_w1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], - sources=sources, - symmetries=symmetries) - - refl_flux = sim.add_flux(fcen, - 0, - 1, - mp.FluxRegion(center=mon_pt, - size=mp.Vector3(0,sy-2*dpml,0))) + vertices = [ + mp.Vector3(-prism_x, half_w1), + mp.Vector3(-half_Lt, half_w1), + mp.Vector3(half_Lt, half_w2), + mp.Vector3(prism_x, half_w2), + mp.Vector3(prism_x, -half_w2), + mp.Vector3(half_Lt, -half_w2), + mp.Vector3(-half_Lt, -half_w1), + mp.Vector3(-prism_x, -half_w1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], + sources=sources, + symmetries=symmetries, + ) + + refl_flux = sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy - 2 * dpml, 0)), + ) sim.load_minus_flux_data(refl_flux, incident_flux_data) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, src_pt, 1e-9)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, src_pt, 1e-9) + ) - res = sim.get_eigenmode_coefficients(refl_flux, - [1], - eig_parity=mp.ODD_Z+mp.EVEN_Y) + res = sim.get_eigenmode_coefficients( + refl_flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y + ) coeffs = res.alpha taper_flux = mp.get_fluxes(refl_flux) - self.assertAlmostEqual(abs(coeffs[0,0,1])**2 / abs(incident_coeffs[0,0,0])**2, - -taper_flux[0] / incident_flux[0], - places=4) + self.assertAlmostEqual( + abs(coeffs[0, 0, 1]) ** 2 / abs(incident_coeffs[0, 0, 0]) ** 2, + -taper_flux[0] / incident_flux[0], + places=4, + ) def test_oblique_waveguide_backward_mode(self): sxy = 12.0 - cell_size = mp.Vector3(sxy,sxy,0) + cell_size = mp.Vector3(sxy, sxy, 0) dpml = 0.6 pml_layers = [mp.PML(thickness=dpml)] - fcen = 1/1.55 + fcen = 1 / 1.55 rot_angle = np.radians(35.0) - kpoint = mp.Vector3(1,0,0).rotate(mp.Vector3(0,0,1), rot_angle) * -1.0 - sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=0.1), - center=mp.Vector3(0.5*sxy-3.4,0,0), - size=mp.Vector3(0,sxy,0), - direction=mp.NO_DIRECTION, - eig_kpoint=kpoint, - eig_band=1, - eig_parity=mp.ODD_Z, - eig_match_freq=True)] - - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,1,mp.inf), - e1 = mp.Vector3(1,0,0).rotate(mp.Vector3(0,0,1), rot_angle), - e2 = mp.Vector3(0,1,0).rotate(mp.Vector3(0,0,1), rot_angle), - material=mp.Medium(index=3.5))] - - sim = mp.Simulation(cell_size=cell_size, - resolution=20, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry) - - mode = sim.add_mode_monitor(fcen, 0, 1, - mp.FluxRegion(center=mp.Vector3(-0.5*sxy+dpml,0,0), - size=mp.Vector3(0,sxy,0)), - decimation_factor=1) - mode_decimated = sim.add_mode_monitor(fcen, - 0, - 1, - mp.FluxRegion(center=mp.Vector3(-0.5*sxy+dpml,0,0), - size=mp.Vector3(0,sxy,0)), - decimation_factor=10) + kpoint = mp.Vector3(1, 0, 0).rotate(mp.Vector3(0, 0, 1), rot_angle) * -1.0 + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=0.1), + center=mp.Vector3(0.5 * sxy - 3.4, 0, 0), + size=mp.Vector3(0, sxy, 0), + direction=mp.NO_DIRECTION, + eig_kpoint=kpoint, + eig_band=1, + eig_parity=mp.ODD_Z, + eig_match_freq=True, + ) + ] + + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1, mp.inf), + e1=mp.Vector3(1, 0, 0).rotate(mp.Vector3(0, 0, 1), rot_angle), + e2=mp.Vector3(0, 1, 0).rotate(mp.Vector3(0, 0, 1), rot_angle), + material=mp.Medium(index=3.5), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=20, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + ) + + mode = sim.add_mode_monitor( + fcen, + 0, + 1, + mp.FluxRegion( + center=mp.Vector3(-0.5 * sxy + dpml, 0, 0), size=mp.Vector3(0, sxy, 0) + ), + decimation_factor=1, + ) + mode_decimated = sim.add_mode_monitor( + fcen, + 0, + 1, + mp.FluxRegion( + center=mp.Vector3(-0.5 * sxy + dpml, 0, 0), size=mp.Vector3(0, sxy, 0) + ), + decimation_factor=10, + ) sim.run(until_after_sources=30) flux = mp.get_fluxes(mode)[0] - coeff = sim.get_eigenmode_coefficients(mode,[1], - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: kpoint).alpha[0,0,0] + coeff = sim.get_eigenmode_coefficients( + mode, [1], direction=mp.NO_DIRECTION, kpoint_func=lambda f, n: kpoint + ).alpha[0, 0, 0] flux_decimated = mp.get_fluxes(mode_decimated)[0] - coeff_decimated = sim.get_eigenmode_coefficients(mode_decimated,[1], - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: kpoint).alpha[0,0,0] - - print("oblique-waveguide-flux:, {:.6f}, {:.6f}".format(-flux, abs(coeff)**2)) - print("oblique-waveguide-flux (decimated):, {:.6f}, {:.6f}".format(-flux_decimated, - abs(coeff_decimated)**2)) + coeff_decimated = sim.get_eigenmode_coefficients( + mode_decimated, + [1], + direction=mp.NO_DIRECTION, + kpoint_func=lambda f, n: kpoint, + ).alpha[0, 0, 0] + + print("oblique-waveguide-flux:, {:.6f}, {:.6f}".format(-flux, abs(coeff) ** 2)) + print( + "oblique-waveguide-flux (decimated):, {:.6f}, {:.6f}".format( + -flux_decimated, abs(coeff_decimated) ** 2 + ) + ) ## the magnitude of |flux| is 100.008731 and so we check two significant digits of accuracy - self.assertAlmostEqual(-1,abs(coeff)**2/flux,places=2) - self.assertAlmostEqual(flux,flux_decimated,places=3) - self.assertAlmostEqual(coeff,coeff_decimated,places=3) - + self.assertAlmostEqual(-1, abs(coeff) ** 2 / flux, places=2) + self.assertAlmostEqual(flux, flux_decimated, places=3) + self.assertAlmostEqual(coeff, coeff_decimated, places=3) def test_grating_3d(self): """Unit test for mode decomposition in 3d with zero k_point. @@ -179,7 +219,7 @@ def test_grating_3d(self): SiO2 = mp.Medium(index=nSiO2) wvl = 0.5 # wavelength - fcen = 1/wvl + fcen = 1 / wvl dpml = 1.0 # PML thickness dsub = 3.0 # substrate thickness @@ -189,40 +229,46 @@ def test_grating_3d(self): sx = 1.1 sy = 0.8 - sz = dpml+dsub+hcyl+dair+dpml + sz = dpml + dsub + hcyl + dair + dpml - cell_size = mp.Vector3(sx,sy,sz) + cell_size = mp.Vector3(sx, sy, sz) - boundary_layers = [mp.PML(thickness=dpml,direction=mp.Z)] + boundary_layers = [mp.PML(thickness=dpml, direction=mp.Z)] # periodic boundary conditions k_point = mp.Vector3() src_cmpt = mp.Ex - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - size=mp.Vector3(sx,sy,0), - center=mp.Vector3(0,0,-0.5*sz+dpml), - component=src_cmpt)] - - symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y,phase=+1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - default_material=SiO2, - boundary_layers=boundary_layers, - k_point=k_point, - symmetries=symmetries) - - refl_pt = mp.Vector3(0,0,-0.5*sz+dpml+0.5*dsub) - refl_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=refl_pt, - size=mp.Vector3(sx,sy,0))) - - stop_cond = mp.stop_when_energy_decayed(20,1e-6) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + size=mp.Vector3(sx, sy, 0), + center=mp.Vector3(0, 0, -0.5 * sz + dpml), + component=src_cmpt, + ) + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=-1), + mp.Mirror(direction=mp.Y, phase=+1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + default_material=SiO2, + boundary_layers=boundary_layers, + k_point=k_point, + symmetries=symmetries, + ) + + refl_pt = mp.Vector3(0, 0, -0.5 * sz + dpml + 0.5 * dsub) + refl_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=refl_pt, size=mp.Vector3(sx, sy, 0)) + ) + + stop_cond = mp.stop_when_energy_decayed(20, 1e-6) sim.run(until_after_sources=stop_cond) input_flux = mp.get_fluxes(refl_flux) @@ -230,34 +276,43 @@ def test_grating_3d(self): sim.reset_meep() - geometry = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,dpml+dsub), - center=mp.Vector3(0,0,-0.5*sz+0.5*(dpml+dsub)), - material=SiO2), - mp.Cylinder(height=hcyl, - radius=rcyl, - center=mp.Vector3(0,0,-0.5*sz+dpml+dsub+0.5*hcyl), - material=Si)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - geometry=geometry, - boundary_layers=boundary_layers, - k_point=k_point, - symmetries=symmetries) - - refl_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=refl_pt, - size=mp.Vector3(sx,sy,0))) - sim.load_minus_flux_data(refl_flux,input_flux_data) - - tran_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=mp.Vector3(0,0,0.5*sz-dpml), - size=mp.Vector3(sx,sy,0))) + geometry = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, dpml + dsub), + center=mp.Vector3(0, 0, -0.5 * sz + 0.5 * (dpml + dsub)), + material=SiO2, + ), + mp.Cylinder( + height=hcyl, + radius=rcyl, + center=mp.Vector3(0, 0, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + material=Si, + ), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + geometry=geometry, + boundary_layers=boundary_layers, + k_point=k_point, + symmetries=symmetries, + ) + + refl_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=refl_pt, size=mp.Vector3(sx, sy, 0)) + ) + sim.load_minus_flux_data(refl_flux, input_flux_data) + + tran_flux = sim.add_mode_monitor( + fcen, + 0, + 1, + mp.ModeRegion( + center=mp.Vector3(0, 0, 0.5 * sz - dpml), size=mp.Vector3(sx, sy, 0) + ), + ) sim.run(until_after_sources=stop_cond) @@ -265,67 +320,80 @@ def test_grating_3d(self): Rsum = 0 # number of reflected modes/orders in SiO2 in x and y directions (upper bound) - nm_x = int(fcen*nSiO2*sx) + 1 - nm_y = int(fcen*nSiO2*sy) + 1 + nm_x = int(fcen * nSiO2 * sx) + 1 + nm_y = int(fcen * nSiO2 * sy) + 1 for m_x in range(nm_x): for m_y in range(nm_y): - for S_pol in [False,True]: - res = sim.get_eigenmode_coefficients(refl_flux, - mp.DiffractedPlanewave([m_x,m_y,0], - mp.Vector3(1,0,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) + for S_pol in [False, True]: + res = sim.get_eigenmode_coefficients( + refl_flux, + mp.DiffractedPlanewave( + [m_x, m_y, 0], + mp.Vector3(1, 0, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) r_coeffs = res.alpha - Rmode = abs(r_coeffs[0,0,1])**2/input_flux[0] - print("refl-order:, {}, {}, {}, {:.6f}".format("s" if S_pol else "p",m_x,m_y,Rmode)) + Rmode = abs(r_coeffs[0, 0, 1]) ** 2 / input_flux[0] + print( + "refl-order:, {}, {}, {}, {:.6f}".format( + "s" if S_pol else "p", m_x, m_y, Rmode + ) + ) if m_x == 0 and m_y == 0: Rsum += Rmode elif (m_x != 0 and m_y == 0) or (m_x == 0 and m_y != 0): - Rsum += 2*Rmode + Rsum += 2 * Rmode else: - Rsum += 4*Rmode - + Rsum += 4 * Rmode # sum the Poynting flux in z direction for all transmitted orders Tsum = 0 # number of transmitted modes/orders in air in x and y directions (upper bound) - nm_x = int(fcen*sx) + 1 - nm_y = int(fcen*sy) + 1 + nm_x = int(fcen * sx) + 1 + nm_y = int(fcen * sy) + 1 for m_x in range(nm_x): for m_y in range(nm_y): - for S_pol in [False,True]: - res = sim.get_eigenmode_coefficients(tran_flux, - mp.DiffractedPlanewave([m_x,m_y,0], - mp.Vector3(1,0,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) + for S_pol in [False, True]: + res = sim.get_eigenmode_coefficients( + tran_flux, + mp.DiffractedPlanewave( + [m_x, m_y, 0], + mp.Vector3(1, 0, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) t_coeffs = res.alpha - Tmode = abs(t_coeffs[0,0,0])**2/input_flux[0] - print("tran-order:, {}, {}, {}, {:.6f}".format("s" if S_pol else "p",m_x,m_y,Tmode)) + Tmode = abs(t_coeffs[0, 0, 0]) ** 2 / input_flux[0] + print( + "tran-order:, {}, {}, {}, {:.6f}".format( + "s" if S_pol else "p", m_x, m_y, Tmode + ) + ) if m_x == 0 and m_y == 0: Tsum += Tmode elif (m_x != 0 and m_y == 0) or (m_x == 0 and m_y != 0): - Tsum += 2*Tmode + Tsum += 2 * Tmode else: - Tsum += 4*Tmode - + Tsum += 4 * Tmode r_flux = mp.get_fluxes(refl_flux) t_flux = mp.get_fluxes(tran_flux) - Rflux = -r_flux[0]/input_flux[0] - Tflux = t_flux[0]/input_flux[0] + Rflux = -r_flux[0] / input_flux[0] + Tflux = t_flux[0] / input_flux[0] - print("refl:, {}, {}".format(Rsum,Rflux)) - print("tran:, {}, {}".format(Tsum,Tflux)) - print("sum:, {}, {}".format(Rsum+Tsum,Rflux+Tflux)) + print("refl:, {}, {}".format(Rsum, Rflux)) + print("tran:, {}, {}".format(Tsum, Tflux)) + print("sum:, {}, {}".format(Rsum + Tsum, Rflux + Tflux)) ## to obtain agreement for two decimal digits, ## the resolution must be increased to 200 - self.assertAlmostEqual(Rsum,Rflux,places=1) - self.assertAlmostEqual(Tsum,Tflux,places=2) - self.assertAlmostEqual(Rsum+Tsum,1.00,places=1) - + self.assertAlmostEqual(Rsum, Rflux, places=1) + self.assertAlmostEqual(Tsum, Tflux, places=2) + self.assertAlmostEqual(Rsum + Tsum, 1.00, places=1) def test_triangular_lattice_oblique(self): """Unit test for mode decomposition in 3d with nonzero k_point. @@ -342,7 +410,7 @@ def test_triangular_lattice_oblique(self): glass = mp.Medium(index=ng) wvl = 0.5 - fcen = 1/wvl + fcen = 1 / wvl dpml = 1.0 dsub = 2.0 @@ -353,13 +421,13 @@ def test_triangular_lattice_oblique(self): a = 0.6 sx = a - sy = a*np.sqrt(3) + sy = a * np.sqrt(3) - sz = dpml+dsub+hcyl+dair+dpml + sz = dpml + dsub + hcyl + dair + dpml - cell_size = mp.Vector3(sx,sy,sz) + cell_size = mp.Vector3(sx, sy, sz) - boundary_layers = [mp.PML(thickness=dpml,direction=mp.Z)] + boundary_layers = [mp.PML(thickness=dpml, direction=mp.Z)] # plane of incidence is yz # 0° is +z with CCW rotation about x @@ -371,39 +439,44 @@ def test_triangular_lattice_oblique(self): # The planewave source is incident from within the high-index # medium which means ω = c|k|/n where n is the index of medium. # In Meep units (c=1), this implies |k| = nω. - k = mp.Vector3(0,0,ng*fcen).rotate(mp.Vector3(1,0,0),theta) + k = mp.Vector3(0, 0, ng * fcen).rotate(mp.Vector3(1, 0, 0), theta) - def pw_amp(k,x0): + def pw_amp(k, x0): def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + return _pw_amp - src_pt = mp.Vector3(0,0,-0.5*sz+dpml) - src_cmpt = mp.Ex # S-pol: Ex / P-pol: Ey - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.1*fcen), - size=mp.Vector3(sx,sy,0), - center=src_pt, - component=src_cmpt, - amp_func=pw_amp(k,src_pt))] - - symmetries = [mp.Mirror(direction=mp.X, phase=-1 if src_cmpt==mp.Ex else +1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - default_material=glass, - boundary_layers=boundary_layers, - k_point=k, - symmetries=symmetries) - - refl_pt = mp.Vector3(0,0,-0.5*sz+dpml+0.5*dsub) - refl_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=refl_pt, - size=mp.Vector3(sx,sy,0))) - - stop_cond = mp.stop_when_fields_decayed(25,src_cmpt,src_pt,1e-6) + src_pt = mp.Vector3(0, 0, -0.5 * sz + dpml) + src_cmpt = mp.Ex # S-pol: Ex / P-pol: Ey + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.1 * fcen), + size=mp.Vector3(sx, sy, 0), + center=src_pt, + component=src_cmpt, + amp_func=pw_amp(k, src_pt), + ) + ] + + symmetries = [mp.Mirror(direction=mp.X, phase=-1 if src_cmpt == mp.Ex else +1)] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + default_material=glass, + boundary_layers=boundary_layers, + k_point=k, + symmetries=symmetries, + ) + + refl_pt = mp.Vector3(0, 0, -0.5 * sz + dpml + 0.5 * dsub) + refl_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=refl_pt, size=mp.Vector3(sx, sy, 0)) + ) + + stop_cond = mp.stop_when_fields_decayed(25, src_cmpt, src_pt, 1e-6) sim.run(until_after_sources=stop_cond) input_flux = mp.get_fluxes(refl_flux)[0] @@ -411,55 +484,77 @@ def _pw_amp(x): sim.reset_meep() - substrate = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,dpml+dsub), - center=mp.Vector3(0,0,-0.5*sz+0.5*(dpml+dsub)), - material=glass)] - - grating = [mp.Cylinder(center=mp.Vector3(0,0,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(-0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(-0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass)] + substrate = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, dpml + dsub), + center=mp.Vector3(0, 0, -0.5 * sz + 0.5 * (dpml + dsub)), + material=glass, + ) + ] + + grating = [ + mp.Cylinder( + center=mp.Vector3(0, 0, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3( + 0.5 * sx, 0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl + ), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3( + -0.5 * sx, 0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl + ), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3( + 0.5 * sx, -0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl + ), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3( + -0.5 * sx, -0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl + ), + radius=rcyl, + height=hcyl, + material=glass, + ), + ] geometry = substrate + grating - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - geometry=geometry, - boundary_layers=boundary_layers, - k_point=k, - symmetries=symmetries) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + geometry=geometry, + boundary_layers=boundary_layers, + k_point=k, + symmetries=symmetries, + ) - refl_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=refl_pt, - size=mp.Vector3(sx,sy,0))) + refl_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=refl_pt, size=mp.Vector3(sx, sy, 0)) + ) sim.load_minus_flux_data(refl_flux, input_flux_data) - tran_pt = mp.Vector3(0,0,0.5*sz-dpml) - tran_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=tran_pt, - size=mp.Vector3(sx,sy,0))) + tran_pt = mp.Vector3(0, 0, 0.5 * sz - dpml) + tran_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=tran_pt, size=mp.Vector3(sx, sy, 0)) + ) sim.run(until_after_sources=stop_cond) @@ -467,76 +562,107 @@ def _pw_amp(x): Tsum = 0 m = 5 tol = 1e-6 - for nx in range(-m,m+1): - for ny in range(-m,m+1): + for nx in range(-m, m + 1): + for ny in range(-m, m + 1): # convert supercell order to unit cell order mx = nx my = (nx + ny) // 2 # consider only propagating modes in high-index medium - kz2 = (ng*fcen)**2-(k.x+nx/sx)**2-(k.y+ny/sy)**2 + kz2 = (ng * fcen) ** 2 - (k.x + nx / sx) ** 2 - (k.y + ny / sy) ** 2 if kz2 > 0: Rpol = 0 for S_pol in [True, False]: - res = sim.get_eigenmode_coefficients(refl_flux, - mp.DiffractedPlanewave((nx,ny,0), - mp.Vector3(0,1,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) + res = sim.get_eigenmode_coefficients( + refl_flux, + mp.DiffractedPlanewave( + (nx, ny, 0), + mp.Vector3(0, 1, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) coeffs = res.alpha - refl = abs(coeffs[0,0,1])**2 / input_flux + refl = abs(coeffs[0, 0, 1]) ** 2 / input_flux - pol_str = 'S' if S_pol else 'P' + pol_str = "S" if S_pol else "P" if refl > tol: # determine whether diffracted order is for the unit cell or super cell if (nx + ny) % 2 == 0: Rpol += refl - print("refl:, {}, {:2d}, {:2d}, {:.5f}, (unit cell)".format(pol_str,mx,my,refl)) + print( + "refl:, {}, {:2d}, {:2d}, {:.5f}, (unit cell)".format( + pol_str, mx, my, refl + ) + ) else: - print("refl:, {}, {:2d}, {:2d}, {:.7f}, (super cell)".format(pol_str,nx,ny,refl)) + print( + "refl:, {}, {:2d}, {:2d}, {:.7f}, (super cell)".format( + pol_str, nx, ny, refl + ) + ) Rsum += Rpol # consider only propagating modes in air - kz2 = fcen**2-(k.x+nx/sx)**2-(k.y+ny/sy)**2 + kz2 = fcen**2 - (k.x + nx / sx) ** 2 - (k.y + ny / sy) ** 2 if kz2 > 0: Tpol = 0 for S_pol in [True, False]: - res = sim.get_eigenmode_coefficients(tran_flux, - mp.DiffractedPlanewave((nx,ny,0), - mp.Vector3(0,1,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) + res = sim.get_eigenmode_coefficients( + tran_flux, + mp.DiffractedPlanewave( + (nx, ny, 0), + mp.Vector3(0, 1, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) coeffs = res.alpha - tran = abs(coeffs[0,0,0])**2 / input_flux + tran = abs(coeffs[0, 0, 0]) ** 2 / input_flux - pol_str = 'S' if S_pol else 'P' + pol_str = "S" if S_pol else "P" if tran > tol: # determine whether diffracted order is for the unit cell or super cell if (nx + ny) % 2 == 0: Tpol += tran - print("tran:, {}, {:2d}, {:2d}, {:.5f}, (unit cell)".format(pol_str,mx,my,tran)) + print( + "tran:, {}, {:2d}, {:2d}, {:.5f}, (unit cell)".format( + pol_str, mx, my, tran + ) + ) else: - print("tran:, {}, {:2d}, {:2d}, {:.7f}, (super cell)".format(pol_str,nx,ny,tran)) + print( + "tran:, {}, {:2d}, {:2d}, {:.7f}, (super cell)".format( + pol_str, nx, ny, tran + ) + ) Tsum += Tpol - Rflux = -mp.get_fluxes(refl_flux)[0] / input_flux - err = abs(Rflux-Rsum)/Rflux - print("refl:, {:.6f} (flux), {:.6f} (orders), {:.6f} (error)".format(Rflux,Rsum,err)) + err = abs(Rflux - Rsum) / Rflux + print( + "refl:, {:.6f} (flux), {:.6f} (orders), {:.6f} (error)".format( + Rflux, Rsum, err + ) + ) Tflux = mp.get_fluxes(tran_flux)[0] / input_flux - err = abs(Tflux-Tsum)/Tflux - print("tran:, {:.6f} (flux), {:.6f} (orders), {:.6f} (error)".format(Tflux,Tsum,err)) + err = abs(Tflux - Tsum) / Tflux + print( + "tran:, {:.6f} (flux), {:.6f} (orders), {:.6f} (error)".format( + Tflux, Tsum, err + ) + ) - self.assertAlmostEqual(Rsum,Rflux,places=3) - self.assertAlmostEqual(Tsum,Tflux,places=3) - self.assertAlmostEqual(Rsum+Tsum,1.00,places=2) + self.assertAlmostEqual(Rsum, Rflux, places=3) + self.assertAlmostEqual(Tsum, Tflux, places=3) + self.assertAlmostEqual(Rsum + Tsum, 1.00, places=2) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_mpb.py b/python/tests/test_mpb.py index 0ddb79fcf..b50c87608 100644 --- a/python/tests/test_mpb.py +++ b/python/tests/test_mpb.py @@ -14,20 +14,25 @@ from meep import mpb from utils import compare_arrays -@unittest.skipIf(os.getenv('MEEP_SKIP_LARGE_TESTS', False), 'skipping large tests') + +@unittest.skipIf(os.getenv("MEEP_SKIP_LARGE_TESTS", False), "skipping large tests") class TestModeSolver(unittest.TestCase): - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - examples_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'examples')) + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + examples_dir = os.path.abspath( + os.path.join(os.path.dirname(__file__), "..", "examples") + ) sys.path.insert(0, examples_dir) def setUp(self): self.start = time.time() - self.filename_prefix = os.path.join(self.temp_dir, self.id().split('.')[-1] + '-' + str(mp.my_rank())) + self.filename_prefix = os.path.join( + self.temp_dir, self.id().split(".")[-1] + "-" + str(mp.my_rank()) + ) print() print(self.filename_prefix) - print('=' * 24) + print("=" * 24) @classmethod def setUpClass(cls): @@ -43,12 +48,7 @@ def tearDownClass(cls): def init_solver(self, geom=True): num_bands = 8 - k_points = [ - mp.Vector3(), - mp.Vector3(0.5), - mp.Vector3(0.5, 0.5), - mp.Vector3() - ] + k_points = [mp.Vector3(), mp.Vector3(0.5), mp.Vector3(0.5, 0.5), mp.Vector3()] geometry = [mp.Cylinder(0.2, material=mp.Medium(epsilon=12))] if geom else [] k_points = mp.interpolate(4, k_points) @@ -63,7 +63,7 @@ def init_solver(self, geom=True): resolution=resolution, filename_prefix=self.filename_prefix, deterministic=True, - tolerance=1e-12 + tolerance=1e-12, ) def test_attribute_accessors(self): @@ -78,8 +78,8 @@ def test_attribute_accessors(self): self.assertEqual(ms.mode_solver.get_libctl_ensure_periodicity(), True) self.assertEqual(ms.mode_solver.eigensolver_flops, 0) self.assertEqual(ms.mode_solver.negative_epsilon_ok, False) - self.assertEqual(ms.mode_solver.epsilon_input_file, '') - self.assertEqual(ms.mode_solver.mu_input_file, '') + self.assertEqual(ms.mode_solver.epsilon_input_file, "") + self.assertEqual(ms.mode_solver.mu_input_file, "") self.assertEqual(ms.mode_solver.force_mu, False) self.assertEqual(ms.mode_solver.use_simple_preconditioner, False) self.assertEqual(ms.mode_solver.eigensolver_nwork, 3) @@ -95,8 +95,8 @@ def test_attribute_accessors(self): ms.ensure_periodicity = False ms.eigensolver_flops = 20 ms.is_negative_epsilon_ok = True - ms.epsilon_input_file = 'test' - ms.mu_input_file = 'test' + ms.epsilon_input_file = "test" + ms.mu_input_file = "test" ms.force_mu = True ms.use_simple_preconditioner = True ms.eigensolver_nwork = 4 @@ -111,8 +111,8 @@ def test_attribute_accessors(self): self.assertEqual(ms.mode_solver.get_libctl_ensure_periodicity(), False) self.assertEqual(ms.mode_solver.eigensolver_flops, 20) self.assertEqual(ms.mode_solver.negative_epsilon_ok, True) - self.assertEqual(ms.mode_solver.epsilon_input_file, 'test') - self.assertEqual(ms.mode_solver.mu_input_file, 'test') + self.assertEqual(ms.mode_solver.epsilon_input_file, "test") + self.assertEqual(ms.mode_solver.mu_input_file, "test") self.assertEqual(ms.mode_solver.force_mu, True) self.assertEqual(ms.mode_solver.use_simple_preconditioner, True) self.assertEqual(ms.mode_solver.eigensolver_nwork, 4) @@ -129,12 +129,7 @@ def test_resolution(self): ms.resolution = [32, 32, 32] def test_list_split(self): - k_points = [ - mp.Vector3(), - mp.Vector3(0.5), - mp.Vector3(0.5, 0.5), - mp.Vector3() - ] + k_points = [mp.Vector3(), mp.Vector3(0.5), mp.Vector3(0.5, 0.5), mp.Vector3()] k_points = mp.interpolate(4, k_points) ms = mpb.ModeSolver() @@ -196,13 +191,17 @@ def check_band_range_data(self, expected_brd, result, places=3, tol=1e-3): self.assertAlmostEqual(exp[0][0], res[0][0], places=places) # Compare min k msg = "expected {}, got {}" - self.assertTrue(exp[0][1].close(res[0][1], tol=tol), - msg=msg.format(exp[0][1], res[0][1])) + self.assertTrue( + exp[0][1].close(res[0][1], tol=tol), + msg=msg.format(exp[0][1], res[0][1]), + ) # Compare max freqs self.assertAlmostEqual(exp[1][0], res[1][0], places=places) # Compare max k - self.assertTrue(exp[1][1].close(res[1][1], tol=tol), - msg=msg.format(exp[1][1], res[1][1])) + self.assertTrue( + exp[1][1].close(res[1][1], tol=tol), + msg=msg.format(exp[1][1], res[1][1]), + ) def check_freqs(self, expected_freqs, result): for exp, res in zip(expected_freqs, result): @@ -212,8 +211,8 @@ def check_freqs(self, expected_freqs, result): def check_gap_list(self, expected_gap_list, result): self.check_freqs(expected_gap_list, result) - def check_fields_against_h5(self, ref_path, field, suffix=''): - with h5py.File(ref_path, 'r') as ref: + def check_fields_against_h5(self, ref_path, field, suffix=""): + with h5py.File(ref_path, "r") as ref: # Reshape the reference data into a component-wise 1d array like # [x1,y1,z1,x2,y2,z2,etc.] ref_x = mp.complexarray(ref[f"x.r{suffix}"][()], ref[f"x.i{suffix}"][()]) @@ -229,18 +228,25 @@ def check_fields_against_h5(self, ref_path, field, suffix=''): def compare_h5_files(self, ref_path, res_path, tol=1e-3): with h5py.File(ref_path) as ref: - with h5py.File(res_path, 'r') as res: + with h5py.File(res_path, "r") as res: for k in ref.keys(): - if k == 'description': + if k == "description": self.assertEqual(ref[k][()], res[k][()]) else: compare_arrays(self, ref[k][()], res[k][()], tol=tol) def test_update_band_range_data(self): brd = [] - freqs = [0.0, 1.0000000001231053, 1.0000000001577114, 1.000000000183077, - 1.0000000003647922, 1.4142135627385737, 1.4142135630373556, - 1.4142135634172286] + freqs = [ + 0.0, + 1.0000000001231053, + 1.0000000001577114, + 1.000000000183077, + 1.0000000003647922, + 1.4142135627385737, + 1.4142135630373556, + 1.4142135634172286, + ] kpoint = mp.Vector3() expected = [ @@ -260,27 +266,50 @@ def test_update_band_range_data(self): def test_run_te_no_geometry(self): - expected_freqs = [0.0, 1.0, 1.0000000000000004, 1.0000000000000013, - 1.0000000000000016, 1.4142135623730958, 1.4142135623730965, - 1.414213562373097] + expected_freqs = [ + 0.0, + 1.0, + 1.0000000000000004, + 1.0000000000000013, + 1.0000000000000016, + 1.4142135623730958, + 1.4142135623730965, + 1.414213562373097, + ] expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.7071067811865476, mp.Vector3(0.5, 0.5, 0.0))), - ((0.5000000000350678, mp.Vector3(0.5, 0.0, 0.0)), - (1.0000000000464613, mp.Vector3(0.0, 0.0, 0.0))), - ((0.7071067811884221, mp.Vector3(0.5, 0.5, 0.0)), - (1.1180339887555244, mp.Vector3(0.5, 0.0, 0.0))), - ((0.7071067811901163, mp.Vector3(0.5, 0.5, 0.0)), - (1.1313708499266775, mp.Vector3(0.2, 0.2, 0.0))), - ((1.000000000001028, mp.Vector3(0.0, 0.0, 0.0)), - (1.5811388300846416, mp.Vector3(0.5, 0.5, 0.0))), - ((1.1180339887687103, mp.Vector3(0.5, 0.0, 0.0)), - (1.5811388300858549, mp.Vector3(0.5, 0.5, 0.0))), - ((1.2806248475163993, mp.Vector3(0.20000000000000004, 0.0, 0.0)), - (1.5811388300892486, mp.Vector3(0.5, 0.5, 0.0))), - ((1.4142135623752818, mp.Vector3(0.0, 0.0, 0.0)), - (1.8027756376524435, mp.Vector3(0.5, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.7071067811865476, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.5000000000350678, mp.Vector3(0.5, 0.0, 0.0)), + (1.0000000000464613, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.7071067811884221, mp.Vector3(0.5, 0.5, 0.0)), + (1.1180339887555244, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.7071067811901163, mp.Vector3(0.5, 0.5, 0.0)), + (1.1313708499266775, mp.Vector3(0.2, 0.2, 0.0)), + ), + ( + (1.000000000001028, mp.Vector3(0.0, 0.0, 0.0)), + (1.5811388300846416, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (1.1180339887687103, mp.Vector3(0.5, 0.0, 0.0)), + (1.5811388300858549, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (1.2806248475163993, mp.Vector3(0.20000000000000004, 0.0, 0.0)), + (1.5811388300892486, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (1.4142135623752818, mp.Vector3(0.0, 0.0, 0.0)), + (1.8027756376524435, mp.Vector3(0.5, 0.0, 0.0)), + ), ] ms = self.init_solver(geom=False) @@ -296,27 +325,53 @@ def test_run_te_no_geometry(self): def test_run_te(self): - expected_freqs = [0.0, 0.5527092320101986, 0.7732265593069759, 0.773229883948054, - 0.9229965195855876, 1.0001711176882833, 1.0001720032257042, - 1.092820931747826] + expected_freqs = [ + 0.0, + 0.5527092320101986, + 0.7732265593069759, + 0.773229883948054, + 0.9229965195855876, + 1.0001711176882833, + 1.0001720032257042, + 1.092820931747826, + ] expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.49683586474489877, mp.Vector3(0.5, 0.5, 0.0))), - ((0.4415884497225449, mp.Vector3(0.5, 0.0, 0.0)), - (0.5931405141160885, mp.Vector3(0.5, 0.5, 0.0))), - ((0.5931535863117832, mp.Vector3(0.5, 0.5, 0.0)), - (0.7732265593069759, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6791690130757013, mp.Vector3(0.5, 0.5, 0.0)), - (0.80968915516771, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0))), - ((0.8241814443502151, mp.Vector3(0.5, 0.30000000000000004, 0.0)), - (0.9229965195855876, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8819770916660669, mp.Vector3(0.5, 0.5, 0.0)), - (1.0291597050650205, mp.Vector3(0.5, 0.0, 0.0))), - ((0.8819818134421844, mp.Vector3(0.5, 0.5, 0.0)), - (1.086072932359415, mp.Vector3(0.5, 0.0, 0.0))), - ((1.0878689635052692, mp.Vector3(0.5, 0.0, 0.0)), - (1.1119173707556929, mp.Vector3(0.5, 0.5, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.49683586474489877, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.4415884497225449, mp.Vector3(0.5, 0.0, 0.0)), + (0.5931405141160885, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.5931535863117832, mp.Vector3(0.5, 0.5, 0.0)), + (0.7732265593069759, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6791690130757013, mp.Vector3(0.5, 0.5, 0.0)), + ( + 0.80968915516771, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + ), + ( + (0.8241814443502151, mp.Vector3(0.5, 0.30000000000000004, 0.0)), + (0.9229965195855876, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8819770916660669, mp.Vector3(0.5, 0.5, 0.0)), + (1.0291597050650205, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.8819818134421844, mp.Vector3(0.5, 0.5, 0.0)), + (1.086072932359415, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.0878689635052692, mp.Vector3(0.5, 0.0, 0.0)), + (1.1119173707556929, mp.Vector3(0.5, 0.5, 0.0)), + ), ] expected_gap_list = [ @@ -341,22 +396,41 @@ def test_run_te(self): def test_run_tm(self): expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.28094795352537366, mp.Vector3(0.5, 0.5, 0.0))), - ((0.4171142493246634, mp.Vector3(0.5, 0.0, 0.0)), - (0.5460267793370319, mp.Vector3(0.0, 0.0, 0.0))), - ((0.49619745276546123, mp.Vector3(0.5, 0.5, 0.0)), - (0.5576576362977246, mp.Vector3(0.5, 0.0, 0.0))), - ((0.5520955864542503, mp.Vector3(0.0, 0.0, 0.0)), - (0.7133951516423513, mp.Vector3(0.5, 0.0, 0.0))), - ((0.7413109657068678, mp.Vector3(0.5, 0.0, 0.0)), - (0.8594741069571248, mp.Vector3(0.5, 0.5, 0.0))), - ((0.8295176150251525, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0)), - (0.8783155473463833, mp.Vector3(0.5, 0.5, 0.0))), - ((0.8625159053811312, mp.Vector3(0.5, 0.0, 0.0)), - (0.9511074539064021, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8793510958294801, mp.Vector3(0.5, 0.5, 0.0)), - (1.0825923841450287, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.28094795352537366, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.4171142493246634, mp.Vector3(0.5, 0.0, 0.0)), + (0.5460267793370319, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.49619745276546123, mp.Vector3(0.5, 0.5, 0.0)), + (0.5576576362977246, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.5520955864542503, mp.Vector3(0.0, 0.0, 0.0)), + (0.7133951516423513, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.7413109657068678, mp.Vector3(0.5, 0.0, 0.0)), + (0.8594741069571248, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + ( + 0.8295176150251525, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + (0.8783155473463833, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.8625159053811312, mp.Vector3(0.5, 0.0, 0.0)), + (0.9511074539064021, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8793510958294801, mp.Vector3(0.5, 0.5, 0.0)), + (1.0825923841450287, mp.Vector3(0.0, 0.0, 0.0)), + ), ] ms = self.init_solver() @@ -378,26 +452,26 @@ def _test_get_field(self, field): self.check_fields_against_h5(ref_path, fields) def test_get_bfield(self): - self._test_get_field('b') + self._test_get_field("b") def test_get_efield(self): - self._test_get_field('e') + self._test_get_field("e") def test_get_dfield(self): - self._test_get_field('d') + self._test_get_field("d") def test_get_hfield(self): - self._test_get_field('h') + self._test_get_field("h") def test_output_field_to_file(self): - fname = 'tutorial-epsilon.h5' + fname = "tutorial-epsilon.h5" data_path = os.path.join(self.data_dir, fname) ms = self.init_solver() ms.run_te() ms.output_epsilon() - res_path = f'{self.filename_prefix}-epsilon.h5' + res_path = f"{self.filename_prefix}-epsilon.h5" self.compare_h5_files(data_path, res_path) def test_compute_field_energy(self): @@ -415,21 +489,21 @@ def test_compute_field_energy(self): self.assertAlmostEqual(pt, 1.330368347216153e-9) expected_fp = mp.Vector3( - 2.582338850993523e-05+6.674311589555886e-12j, - -2.5759589225968237e-05-6.657825513845581e-12j, - 0.0 - 0.0j + 2.582338850993523e-05 + 6.674311589555886e-12j, + -2.5759589225968237e-05 - 6.657825513845581e-12j, + 0.0 - 0.0j, ) expected_bloch_fp = mp.Vector3( 2.5823356723958247e-5 + 6.713243287584132e-12j, -2.575955745071957e-5 - 6.696552990958943e-12j, - -0.0 - 0.0j + -0.0 - 0.0j, ) expected_eps_inv_tensor = mp.Matrix( mp.Vector3(1.0 + 0.0j, 0.0 - 0.0j, 0.0 - 0.0j), mp.Vector3(0.0 + 0.0j, 1.0 + 0.0j, 0.0 - 0.0j), - mp.Vector3(0.0 + 0.0j, 0.0 + 0.0j, 1.0 + 0.0j) + mp.Vector3(0.0 + 0.0j, 0.0 + 0.0j, 1.0 + 0.0j), ) self.assertTrue(expected_fp.close(field_pt)) @@ -440,13 +514,22 @@ def test_compute_field_energy(self): energy_in_dielectric = ms.compute_energy_in_dielectric(0, 1) - expected_energy = [1.0000000000000002, 1.726755206037815e-5, 0.4999827324479414, - 1.7267552060375955e-5, 0.4999827324479377, 0.0, 0.0] + expected_energy = [ + 1.0000000000000002, + 1.726755206037815e-5, + 0.4999827324479414, + 1.7267552060375955e-5, + 0.4999827324479377, + 0.0, + 0.0, + ] expected_energy_in_dielectric = 0.6990769686037558 compare_arrays(self, np.array(expected_energy), np.array(energy)) - self.assertAlmostEqual(expected_energy_in_dielectric, energy_in_dielectric, places=3) + self.assertAlmostEqual( + expected_energy_in_dielectric, energy_in_dielectric, places=3 + ) def test_compute_group_velocity(self): ms = self.init_solver() @@ -456,8 +539,13 @@ def test_compute_group_velocity(self): res3 = ms.compute_one_group_velocity_component(mp.Vector3(0.5, 0.5), 8) expected1 = [ - 0.0, 1.470762578355642e-4, -1.4378185933055663e-4, 1.1897503996483383e-4, - -4.892687048681629e-4, 1.1240346140784176e-4, 1.5842474585356007e-4, + 0.0, + 1.470762578355642e-4, + -1.4378185933055663e-4, + 1.1897503996483383e-4, + -4.892687048681629e-4, + 1.1240346140784176e-4, + 1.5842474585356007e-4, 4.496945573323881e-5, ] @@ -476,23 +564,23 @@ def test_output_efield_z(self): mpb.fix_efield_phase(ms, 8) mpb.output_efield_z(ms, 8) - ref_fname = 'tutorial-e.k16.b08.z.tm.h5' + ref_fname = "tutorial-e.k16.b08.z.tm.h5" ref_path = os.path.join(self.data_dir, ref_fname) - res_path = re.sub('tutorial', ms.filename_prefix, ref_fname) + res_path = re.sub("tutorial", ms.filename_prefix, ref_fname) self.compare_h5_files(ref_path, res_path) def test_output_dpwr_in_objects(self): ms = self.init_solver() ms.run_te(mpb.output_dpwr_in_objects(mpb.output_dfield, 0.85, ms.geometry)) - ref_fname1 = 'tutorial-d.k01.b02.te.h5' - ref_fname2 = 'tutorial-d.k16.b02.te.h5' + ref_fname1 = "tutorial-d.k01.b02.te.h5" + ref_fname2 = "tutorial-d.k16.b02.te.h5" ref_path1 = os.path.join(self.data_dir, ref_fname1) ref_path2 = os.path.join(self.data_dir, ref_fname2) - res_path1 = re.sub('tutorial', ms.filename_prefix, ref_fname1) - res_path2 = re.sub('tutorial', ms.filename_prefix, ref_fname2) + res_path1 = re.sub("tutorial", ms.filename_prefix, ref_fname1) + res_path2 = re.sub("tutorial", ms.filename_prefix, ref_fname2) self.compare_h5_files(ref_path1, res_path1) self.compare_h5_files(ref_path2, res_path2) @@ -500,36 +588,70 @@ def test_output_dpwr_in_objects(self): def test_triangular_lattice(self): expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.2746902258623623, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.44533108084715683, mp.Vector3(0.0, 0.5, 0.0)), - (0.5605181423162835, mp.Vector3(0.0, 0.0, 0.0))), - ((0.4902389149027666, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.5605607947797747, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5932960873585144, mp.Vector3(0.0, 0.0, 0.0)), - (0.7907195974443698, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.790832076332758, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.8374511167537562, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8375948528443267, mp.Vector3(0.0, 0.0, 0.0)), - (0.867200926490345, mp.Vector3(-0.2, 0.39999999999999997, 0.0))), - ((0.8691349955739203, mp.Vector3(-0.13333333333333336, 0.4333333333333333, 0.0)), - (0.9941291022664892, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8992499095547049, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (1.098318352915696, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.2746902258623623, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.44533108084715683, mp.Vector3(0.0, 0.5, 0.0)), + (0.5605181423162835, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.4902389149027666, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.5605607947797747, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.5932960873585144, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.7907195974443698, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.790832076332758, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.8374511167537562, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8375948528443267, mp.Vector3(0.0, 0.0, 0.0)), + (0.867200926490345, mp.Vector3(-0.2, 0.39999999999999997, 0.0)), + ), + ( + ( + 0.8691349955739203, + mp.Vector3(-0.13333333333333336, 0.4333333333333333, 0.0), + ), + (0.9941291022664892, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.8992499095547049, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (1.098318352915696, mp.Vector3(0.0, 0.0, 0.0)), + ), ] ms = self.init_solver() ms.geometry_lattice = mp.Lattice( size=mp.Vector3(1, 1), basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5) + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), ) k_points = [ mp.Vector3(), mp.Vector3(y=0.5), mp.Vector3(-1 / 3, 1 / 3), - mp.Vector3() + mp.Vector3(), ] ms.k_points = mp.interpolate(4, k_points) @@ -538,7 +660,6 @@ def test_triangular_lattice(self): self.check_band_range_data(expected_brd, ms.band_range_data) def test_maximize_first_tm_gap(self): - def first_tm_gap(r): ms.geometry = [mp.Cylinder(r, material=mp.Medium(epsilon=12))] ms.run_tm() @@ -548,34 +669,53 @@ def first_tm_gap(r): ms.num_bands = 2 ms.mesh_size = 7 - result = minimize_scalar(first_tm_gap, method='bounded', bounds=[0.1, 0.5], options={'xatol': 0.1}) + result = minimize_scalar( + first_tm_gap, method="bounded", bounds=[0.1, 0.5], options={"xatol": 0.1} + ) expected = 39.10325687542367 self.assertAlmostEqual(expected, result.fun * -1, places=2) def test_anisotropic_2d_gap(self): expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.2213165540404889, mp.Vector3(0.5, 0.5, 0.0))), - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.23068427462181276, mp.Vector3(0.5, 0.5, 0.0))), - ((0.21691192680454566, mp.Vector3(0.5, 0.0, 0.0)), - (0.319020283148369, mp.Vector3(0.0, 0.0, 0.0))), - ((0.30110868065428525, mp.Vector3(0.5, 0.0, 0.0)), - (0.3648353129125716, mp.Vector3(0.0, 0.0, 0.0))), - ((0.30701621910773247, mp.Vector3(0.5, 0.5, 0.0)), - (0.3852513546698513, mp.Vector3(0.1, 0.1, 0.0))), - ((0.341835260571013, mp.Vector3(0.5, 0.30000000000000004, 0.0)), - (0.391421048600237, mp.Vector3(0.5, 0.10000000000000003, 0.0))), - ((0.34982139739904294, mp.Vector3(0.5, 0.5, 0.0)), - (0.4075642914057991, mp.Vector3(0.4, 0.0, 0.0))), - ((0.3963465468598276, mp.Vector3(0.1, 0.1, 0.0)), - (0.4772237204606825, mp.Vector3(0.5, 0.5, 0.0))), - + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.2213165540404889, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.23068427462181276, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.21691192680454566, mp.Vector3(0.5, 0.0, 0.0)), + (0.319020283148369, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.30110868065428525, mp.Vector3(0.5, 0.0, 0.0)), + (0.3648353129125716, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.30701621910773247, mp.Vector3(0.5, 0.5, 0.0)), + (0.3852513546698513, mp.Vector3(0.1, 0.1, 0.0)), + ), + ( + (0.341835260571013, mp.Vector3(0.5, 0.30000000000000004, 0.0)), + (0.391421048600237, mp.Vector3(0.5, 0.10000000000000003, 0.0)), + ), + ( + (0.34982139739904294, mp.Vector3(0.5, 0.5, 0.0)), + (0.4075642914057991, mp.Vector3(0.4, 0.0, 0.0)), + ), + ( + (0.3963465468598276, mp.Vector3(0.1, 0.1, 0.0)), + (0.4772237204606825, mp.Vector3(0.5, 0.5, 0.0)), + ), ] ms = self.init_solver() - ms.geometry = [mp.Cylinder(0.3, material=mp.Medium(epsilon_diag=mp.Vector3(1, 1, 12)))] + ms.geometry = [ + mp.Cylinder(0.3, material=mp.Medium(epsilon_diag=mp.Vector3(1, 1, 12))) + ] ms.default_material = mp.Medium(epsilon_diag=mp.Vector3(12, 12, 1)) ms.num_bands = 8 ms.run() @@ -588,7 +728,9 @@ def test_point_defect_state(self): ms.geometry_lattice = mp.Lattice(size=mp.Vector3(5, 5)) ms.geometry = [mp.Cylinder(0.2, material=mp.Medium(epsilon=12))] - ms.geometry = mp.geometric_objects_lattice_duplicates(ms.geometry_lattice, ms.geometry) + ms.geometry = mp.geometric_objects_lattice_duplicates( + ms.geometry_lattice, ms.geometry + ) ms.geometry.append(mp.Cylinder(0.2, material=mp.air)) ms.resolution = 16 @@ -613,8 +755,10 @@ def test_point_defect_state(self): ms.run_tm() expected_brd = [ - ((0.37730041222979477, mp.Vector3(0.5, 0.5, 0.0)), - (0.37730041222979477, mp.Vector3(0.5, 0.5, 0.0))), + ( + (0.37730041222979477, mp.Vector3(0.5, 0.5, 0.0)), + (0.37730041222979477, mp.Vector3(0.5, 0.5, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) @@ -622,7 +766,9 @@ def test_point_defect_state(self): old_geometry = ms.geometry # save the 5x5 grid with a missing rod def rootfun(eps): - ms.geometry = old_geometry + [mp.Cylinder(0.2, material=mp.Medium(epsilon=eps))] + ms.geometry = old_geometry + [ + mp.Cylinder(0.2, material=mp.Medium(epsilon=eps)) + ] ms.run_tm() return ms.get_freqs()[0] - 0.314159 @@ -638,42 +784,60 @@ def test_output_charge_density(self): mpb.fix_efield_phase(ms, 8) mpb.output_charge_density(ms, 8) - ref_fn = 'tutorial-C.k16.b08.te.h5' + ref_fn = "tutorial-C.k16.b08.te.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('tutorial', ms.filename_prefix, ref_fn) + res_path = re.sub("tutorial", ms.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) def test_bragg_sine(self): from mpb_bragg_sine import ms + ms.deterministic = True ms.tolerance = 1e-12 ms.filename_prefix = self.filename_prefix ms.run_tm() expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.19477466366820298, mp.Vector3(0.5, 0.0, 0.0))), - ((0.306403026230844, mp.Vector3(0.5, 0.0, 0.0)), - (0.4687748525867193, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5466257501317459, mp.Vector3(0.0, 0.0, 0.0)), - (0.7316504426541637, mp.Vector3(0.5, 0.0, 0.0))), - ((0.7842615905093812, mp.Vector3(0.5, 0.0, 0.0)), - (0.9893486155437277, mp.Vector3(0.0, 0.0, 0.0))), - ((1.0240548648147831, mp.Vector3(0.0, 0.0, 0.0)), - (1.244098004202588, mp.Vector3(0.5, 0.0, 0.0))), - ((1.266656686185507, mp.Vector3(0.5, 0.0, 0.0)), - (1.4970379696966822, mp.Vector3(0.0, 0.0, 0.0))), - ((1.5115800994652884, mp.Vector3(0.0, 0.0, 0.0)), - (1.7488359039910502, mp.Vector3(0.5, 0.0, 0.0))), - ((1.7581683208483643, mp.Vector3(0.5, 0.0, 0.0)), - (1.9999072007119787, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.19477466366820298, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.306403026230844, mp.Vector3(0.5, 0.0, 0.0)), + (0.4687748525867193, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.5466257501317459, mp.Vector3(0.0, 0.0, 0.0)), + (0.7316504426541637, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.7842615905093812, mp.Vector3(0.5, 0.0, 0.0)), + (0.9893486155437277, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (1.0240548648147831, mp.Vector3(0.0, 0.0, 0.0)), + (1.244098004202588, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.266656686185507, mp.Vector3(0.5, 0.0, 0.0)), + (1.4970379696966822, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (1.5115800994652884, mp.Vector3(0.0, 0.0, 0.0)), + (1.7488359039910502, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.7581683208483643, mp.Vector3(0.5, 0.0, 0.0)), + (1.9999072007119787, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) def test_bragg(self): from mpb_bragg import ms + ms.deterministic = True ms.tolerance = 1e-12 ms.filename_prefix = self.filename_prefix @@ -681,14 +845,15 @@ def test_bragg(self): mpb.fix_hfield_phase(ms, 8) mpb.output_hfield_y(ms, 8) - ref_fn = 'bragg-h.k01.b08.y.tm.h5' + ref_fn = "bragg-h.k01.b08.y.tm.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('bragg', self.filename_prefix, ref_fn) + res_path = re.sub("bragg", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) def test_diamond(self): from mpb_diamond import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 @@ -697,46 +862,62 @@ def test_diamond(self): def get_dpwr(ms, band): dpwr.append(ms.get_dpwr(band)) - ms.run(mpb.output_at_kpoint(mp.Vector3(0, 0.625, 0.375), - mpb.fix_dfield_phase, - mpb.output_dpwr, - get_dpwr)) + ms.run( + mpb.output_at_kpoint( + mp.Vector3(0, 0.625, 0.375), + mpb.fix_dfield_phase, + mpb.output_dpwr, + get_dpwr, + ) + ) expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.39455107895905156, mp.Vector3(0.25, 0.75, 0.5))), - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.3967658014080592, mp.Vector3(0.29999999999999993, 0.75, 0.45000000000000007))), - ((0.4423707668172989, mp.Vector3(0.0, 0.5, 0.0)), - (0.5955899630254676, mp.Vector3(0.0, 0.0, 0.0))), - ((0.44355516512198145, mp.Vector3(0.0, 0.5, 0.0)), - (0.5958191312898851, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5030135895148902, mp.Vector3(0.0, 0.6, 0.4)), - (0.5958386856926985, mp.Vector3(0.0, 0.0, 0.0))), - - + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.39455107895905156, mp.Vector3(0.25, 0.75, 0.5)), + ), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.3967658014080592, + mp.Vector3(0.29999999999999993, 0.75, 0.45000000000000007), + ), + ), + ( + (0.4423707668172989, mp.Vector3(0.0, 0.5, 0.0)), + (0.5955899630254676, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.44355516512198145, mp.Vector3(0.0, 0.5, 0.0)), + (0.5958191312898851, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.5030135895148902, mp.Vector3(0.0, 0.6, 0.4)), + (0.5958386856926985, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) - ref_fn = 'diamond-dpwr.k06.b05.h5' + ref_fn = "diamond-dpwr.k06.b05.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('diamond', self.filename_prefix, ref_fn) + res_path = re.sub("diamond", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) # Test MPBData.convert() md = mpb.MPBData(rectify=True, periods=2, resolution=32) converted_dpwr = [md.convert(d) for d in dpwr] - ref_fn = 'converted-diamond-dpwr.k06.b05.h5' + ref_fn = "converted-diamond-dpwr.k06.b05.h5" ref_path = os.path.join(self.data_dir, ref_fn) - with h5py.File(ref_path, 'r') as f: - expected = f['data-new'][()] + with h5py.File(ref_path, "r") as f: + expected = f["data-new"][()] compare_arrays(self, expected, converted_dpwr[-1]) def test_hole_slab(self): from mpb_hole_slab import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.k_points = [mp.Vector3(1 / -3, 1 / 3)] @@ -746,58 +927,123 @@ def test_hole_slab(self): mpb.fix_hfield_phase(ms, 9) mpb.output_hfield_z(ms, 9) - ref_fn = 'hole-slab-h.k01.b09.z.zeven.h5' + ref_fn = "hole-slab-h.k01.b09.z.zeven.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('hole-slab', self.filename_prefix, ref_fn) + res_path = re.sub("hole-slab", self.filename_prefix, ref_fn) ms.display_eigensolver_stats() self.compare_h5_files(ref_path, res_path) def test_honey_rods(self): from mpb_honey_rods import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 expected_tm_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.3351167660354989, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.3351850759916969, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.42984811237816406, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5751709345431462, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.7255897672261712, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6918627724774271, mp.Vector3(0.0, 0.5, 0.0)), - (0.747622077830657, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.7443374497087805, mp.Vector3(-0.06666666666666667, 0.06666666666666667, 0.0)), - (0.7793792212092525, mp.Vector3(0.0, 0.5, 0.0))), - ((0.7852786984418492, mp.Vector3(0.0, 0.0, 0.0)), - (0.8193652861712535, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.7856577771856611, mp.Vector3(0.0, 0.0, 0.0)), - (0.9122560439014182, mp.Vector3(0.0, 0.5, 0.0))), - ((1.0540350508135123, mp.Vector3(0.0, 0.5, 0.0)), - (1.1492769389234725, mp.Vector3(0.0, 0.0, 0.0))), - + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.3351167660354989, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.3351850759916969, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.42984811237816406, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.5751709345431462, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.7255897672261712, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6918627724774271, mp.Vector3(0.0, 0.5, 0.0)), + ( + 0.747622077830657, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.7443374497087805, + mp.Vector3(-0.06666666666666667, 0.06666666666666667, 0.0), + ), + (0.7793792212092525, mp.Vector3(0.0, 0.5, 0.0)), + ), + ( + (0.7852786984418492, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.8193652861712535, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.7856577771856611, mp.Vector3(0.0, 0.0, 0.0)), + (0.9122560439014182, mp.Vector3(0.0, 0.5, 0.0)), + ), + ( + (1.0540350508135123, mp.Vector3(0.0, 0.5, 0.0)), + (1.1492769389234725, mp.Vector3(0.0, 0.0, 0.0)), + ), ] ms.run_tm() self.check_band_range_data(expected_tm_brd, ms.band_range_data) expected_te_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.5535093489972593, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.5203183590840945, mp.Vector3(0.0, 0.5, 0.0)), - (0.7278447515454929, mp.Vector3(0.0, 0.0, 0.0))), - ((0.576335859651312, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.7880878930618354, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8161730293674944, mp.Vector3(0.0, 0.5, 0.0)), - (0.9209611432140968, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8385562359606971, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.9220849425898038, mp.Vector3(0.0, 0.0, 0.0))), - ((1.0168656683915511, mp.Vector3(0.0, 0.0, 0.0)), - (1.1083536673418435, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((1.0184507253059425, mp.Vector3(0.0, 0.0, 0.0)), - (1.159370227370719, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((1.1636719050364361, mp.Vector3(-0.2, 0.2, 0.0)), - (1.2433411839870618, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.5535093489972593, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.5203183590840945, mp.Vector3(0.0, 0.5, 0.0)), + (0.7278447515454929, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.576335859651312, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.7880878930618354, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8161730293674944, mp.Vector3(0.0, 0.5, 0.0)), + (0.9209611432140968, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.8385562359606971, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.9220849425898038, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (1.0168656683915511, mp.Vector3(0.0, 0.0, 0.0)), + ( + 1.1083536673418435, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (1.0184507253059425, mp.Vector3(0.0, 0.0, 0.0)), + ( + 1.159370227370719, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (1.1636719050364361, mp.Vector3(-0.2, 0.2, 0.0)), + (1.2433411839870618, mp.Vector3(0.0, 0.0, 0.0)), + ), ] ms.run_te() @@ -805,21 +1051,25 @@ def test_honey_rods(self): def test_line_defect(self): from mpb_line_defect import ms, k_points + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 - ms.run_tm(mpb.output_at_kpoint(k_points[len(k_points) // 2]), - mpb.fix_efield_phase, - mpb.output_efield_z) + ms.run_tm( + mpb.output_at_kpoint(k_points[len(k_points) // 2]), + mpb.fix_efield_phase, + mpb.output_efield_z, + ) - ref_fn = 'line-defect-e.k04.b12.z.tm.h5' + ref_fn = "line-defect-e.k04.b12.z.tm.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('line-defect', self.filename_prefix, ref_fn) + res_path = re.sub("line-defect", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) def test_sq_rods(self): from mpb_sq_rods import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 @@ -827,22 +1077,44 @@ def test_sq_rods(self): ms.run_te() expected_te_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.5036058015219026, mp.Vector3(0.5, 0.5, 0.0))), - ((0.4446229134706744, mp.Vector3(0.5, 0.0, 0.0)), - (0.5943440245519593, mp.Vector3(0.5, 0.5, 0.0))), - ((0.5943566394470321, mp.Vector3(0.5, 0.5, 0.0)), - (0.7808428121911926, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6793887413076383, mp.Vector3(0.5, 0.5, 0.0)), - (0.8173893719542897, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0))), - ((0.83045738223392, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0)), - (0.9243716830585584, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8957817684117546, mp.Vector3(0.5, 0.5, 0.0)), - (1.0331104139200438, mp.Vector3(0.5, 0.0, 0.0))), - ((0.8957868745330811, mp.Vector3(0.5, 0.5, 0.0)), - (1.0958021492221048, mp.Vector3(0.5, 0.0, 0.0))), - ((1.097416809585406, mp.Vector3(0.5, 0.0, 0.0)), - (1.1280127648119964, mp.Vector3(0.5, 0.5, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.5036058015219026, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.4446229134706744, mp.Vector3(0.5, 0.0, 0.0)), + (0.5943440245519593, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.5943566394470321, mp.Vector3(0.5, 0.5, 0.0)), + (0.7808428121911926, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6793887413076383, mp.Vector3(0.5, 0.5, 0.0)), + ( + 0.8173893719542897, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + ), + ( + ( + 0.83045738223392, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + (0.9243716830585584, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8957817684117546, mp.Vector3(0.5, 0.5, 0.0)), + (1.0331104139200438, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.8957868745330811, mp.Vector3(0.5, 0.5, 0.0)), + (1.0958021492221048, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.097416809585406, mp.Vector3(0.5, 0.0, 0.0)), + (1.1280127648119964, mp.Vector3(0.5, 0.5, 0.0)), + ), ] self.check_band_range_data(expected_te_brd, ms.band_range_data) @@ -850,28 +1122,48 @@ def test_sq_rods(self): ms.run_tm() expected_tm_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.285905779119655, mp.Vector3(0.5, 0.5, 0.0))), - ((0.42065733839975095, mp.Vector3(0.5, 0.0, 0.0)), - (0.5503360754831277, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5029830978365978, mp.Vector3(0.5, 0.5, 0.0)), - (0.5671632878128118, mp.Vector3(0.5, 0.0, 0.0))), - ((0.5613397939889757, mp.Vector3(0.0, 0.0, 0.0)), - (0.7200918204563993, mp.Vector3(0.5, 0.0, 0.0))), - ((0.7472029910480836, mp.Vector3(0.5, 0.0, 0.0)), - (0.874359380500508, mp.Vector3(0.5, 0.5, 0.0))), - ((0.8404509697526803, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0)), - (0.8833173725822788, mp.Vector3(0.5, 0.5, 0.0))), - ((0.8770118718330763, mp.Vector3(0.5, 0.0, 0.0)), - (0.9653253808981632, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8929933495598104, mp.Vector3(0.5, 0.5, 0.0)), - (1.089377682009333, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.285905779119655, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.42065733839975095, mp.Vector3(0.5, 0.0, 0.0)), + (0.5503360754831277, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.5029830978365978, mp.Vector3(0.5, 0.5, 0.0)), + (0.5671632878128118, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.5613397939889757, mp.Vector3(0.0, 0.0, 0.0)), + (0.7200918204563993, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.7472029910480836, mp.Vector3(0.5, 0.0, 0.0)), + (0.874359380500508, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + ( + 0.8404509697526803, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + (0.8833173725822788, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.8770118718330763, mp.Vector3(0.5, 0.0, 0.0)), + (0.9653253808981632, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8929933495598104, mp.Vector3(0.5, 0.5, 0.0)), + (1.089377682009333, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_tm_brd, ms.band_range_data) def test_strip(self): from mpb_strip import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 @@ -881,8 +1173,18 @@ def test_strip(self): y_parities = ms.mode_solver.compute_yparities() z_parities = ms.mode_solver.compute_zparities() - expected_y_parities = [-0.9997979443175137, 1.0000061871738222, -1.000010781704281, -0.9997880312884855] - expected_z_parities = [0.9992335747085693, -0.9955122771195629, -0.9970929846091117, -0.9945407488966614] + expected_y_parities = [ + -0.9997979443175137, + 1.0000061871738222, + -1.000010781704281, + -0.9997880312884855, + ] + expected_z_parities = [ + 0.9992335747085693, + -0.9955122771195629, + -0.9970929846091117, + -0.9945407488966614, + ] for e, r in zip(expected_y_parities, y_parities): self.assertAlmostEqual(e, r, places=3) @@ -892,28 +1194,40 @@ def test_strip(self): frequency = 1 / 1.55 - kvals = ms.find_k(mp.NO_PARITY, frequency, 1, ms.num_bands, mp.Vector3(1), 1e-3, - frequency * 3.45, frequency * 0.1, frequency * 4, mpb.output_poynting_x, - mpb.display_yparities, mpb.display_group_velocities) + kvals = ms.find_k( + mp.NO_PARITY, + frequency, + 1, + ms.num_bands, + mp.Vector3(1), + 1e-3, + frequency * 3.45, + frequency * 0.1, + frequency * 4, + mpb.output_poynting_x, + mpb.display_yparities, + mpb.display_group_velocities, + ) expected_kvals = [ 1.0395768316060294, 0.9776221778906993, 0.8358057689930384, - 0.788801145849691 + 0.788801145849691, ] for e, r in zip(expected_kvals, kvals): self.assertAlmostEqual(e, r, places=3) - ref_fn = 'strip-flux.v.k01.b04.x.h5' + ref_fn = "strip-flux.v.k01.b04.x.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('strip', self.filename_prefix, ref_fn) + res_path = re.sub("strip", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) def test_tri_holes(self): from mpb_tri_holes import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 @@ -921,23 +1235,56 @@ def test_tri_holes(self): ms.run_te() expected_te_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.2993049473117476, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.4924342823622065, mp.Vector3(0.0, 0.5, 0.0)), - (0.6568362683499375, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5269710506448809, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.7156232200212518, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6568031427446027, mp.Vector3(0.0, 0.5, 0.0)), - (0.7578382217502109, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.7383774303752574, mp.Vector3(0.0, 0.0, 0.0)), - (0.7988168792802597, mp.Vector3(0.0, 0.5, 0.0))), - ((0.8259787164701536, mp.Vector3(0.0, 0.0, 0.0)), - (0.9629215441012396, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.8271634538840886, mp.Vector3(0.0, 0.0, 0.0)), - (0.9834563303529568, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.9984200611839882, mp.Vector3(-0.26666666666666666, 0.26666666666666666, 0.0)), - (1.0411551252079034, mp.Vector3(0.0, 0.0, 0.0))), - + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.2993049473117476, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.4924342823622065, mp.Vector3(0.0, 0.5, 0.0)), + (0.6568362683499375, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.5269710506448809, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.7156232200212518, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6568031427446027, mp.Vector3(0.0, 0.5, 0.0)), + ( + 0.7578382217502109, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.7383774303752574, mp.Vector3(0.0, 0.0, 0.0)), + (0.7988168792802597, mp.Vector3(0.0, 0.5, 0.0)), + ), + ( + (0.8259787164701536, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.9629215441012396, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.8271634538840886, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.9834563303529568, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.9984200611839882, + mp.Vector3(-0.26666666666666666, 0.26666666666666666, 0.0), + ), + (1.0411551252079034, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_te_brd, ms.band_range_data) @@ -945,82 +1292,161 @@ def test_tri_holes(self): ms.run_tm() expected_tm_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.28009156817399916, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.28015523913784407, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.3985126081046686, mp.Vector3(0.0, 0.0, 0.0))), - ((0.4390817228448606, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.49288810189980625, mp.Vector3(0.0, 0.0, 0.0))), - ((0.49336847788268695, mp.Vector3(0.0, 0.0, 0.0)), - (0.5808701365268192, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.581035246804371, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.6824860801372987, mp.Vector3(0.0, 0.0, 0.0))), - ((0.682531744671499, mp.Vector3(0.0, 0.0, 0.0)), - (0.7011061593213783, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.6920145742134771, mp.Vector3(0.0, 0.0, 0.0)), - (0.7841042622393081, mp.Vector3(0.0, 0.4, 0.0))), - ((0.7980077872594108, mp.Vector3(0.0, 0.4, 0.0)), - (0.8982239424823442, mp.Vector3(0.0, 0.0, 0.0))), - + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.28009156817399916, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.28015523913784407, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.3985126081046686, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.4390817228448606, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.49288810189980625, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.49336847788268695, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.5808701365268192, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.581035246804371, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.6824860801372987, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.682531744671499, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.7011061593213783, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.6920145742134771, mp.Vector3(0.0, 0.0, 0.0)), + (0.7841042622393081, mp.Vector3(0.0, 0.4, 0.0)), + ), + ( + (0.7980077872594108, mp.Vector3(0.0, 0.4, 0.0)), + (0.8982239424823442, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_tm_brd, ms.band_range_data) def test_tri_rods(self): from mpb_tri_rods import ms + ms.deterministic = True ms.tolerance = 1e-12 ms.filename_prefix = self.filename_prefix - ms.run_tm(mpb.output_at_kpoint(mp.Vector3(1 / -3, 1 / 3), - mpb.fix_efield_phase, - mpb.output_efield_z)) + ms.run_tm( + mpb.output_at_kpoint( + mp.Vector3(1 / -3, 1 / 3), mpb.fix_efield_phase, mpb.output_efield_z + ) + ) - ref_fn = 'tri-rods-e.k11.b08.z.tm.h5' + ref_fn = "tri-rods-e.k11.b08.z.tm.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('tri-rods', self.filename_prefix, ref_fn) + res_path = re.sub("tri-rods", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) # Test MPBData - with h5py.File(ref_path, 'r') as f: - efield_re = f['z.r'][()] - efield_im = f['z.i'][()] + with h5py.File(ref_path, "r") as f: + efield_re = f["z.r"][()] + efield_im = f["z.i"][()] efield = np.vectorize(complex)(efield_re, efield_im) # rectangularize - md = mpb.MPBData(lattice=ms.get_lattice(), rectify=True, resolution=32, periods=3, verbose=True) + md = mpb.MPBData( + lattice=ms.get_lattice(), + rectify=True, + resolution=32, + periods=3, + verbose=True, + ) new_efield = md.convert(efield, ms.k_points[10]) # check with ref file - ref_fn = 'tri-rods-e.k11.b08.z.tm-r-m3-n32.h5' + ref_fn = "tri-rods-e.k11.b08.z.tm-r-m3-n32.h5" ref_path = os.path.join(self.data_dir, ref_fn) - with h5py.File(ref_path, 'r') as f: - expected_re = f['z.r-new'][()] - expected_im = f['z.i-new'][()] + with h5py.File(ref_path, "r") as f: + expected_re = f["z.r-new"][()] + expected_im = f["z.i-new"][()] expected = np.vectorize(complex)(expected_re, expected_im) compare_arrays(self, expected, new_efield) ms.run_te() expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.49123581186757737, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.4730722390280754, mp.Vector3(0.0, 0.5, 0.0)), - (0.5631059378714038, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.5631505198559038, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.7939289395839766, mp.Vector3(0.0, 0.0, 0.0))), - ((0.7676614799039024, mp.Vector3(0.0, 0.5, 0.0)), - (0.8214230044191525, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.865194814441525, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (1.0334130018594276, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8652307994936862, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (1.0334230910419813, mp.Vector3(0.0, 0.0, 0.0))), - ((1.021367669109368, mp.Vector3(0.0, 0.5, 0.0)), - (1.115966990757518, mp.Vector3(0.0, 0.0, 0.0))), - ((1.108662658537423, mp.Vector3(-0.26666666666666666, 0.26666666666666666, 0.0)), - (1.1168107191255379, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.49123581186757737, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.4730722390280754, mp.Vector3(0.0, 0.5, 0.0)), + ( + 0.5631059378714038, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.5631505198559038, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.7939289395839766, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.7676614799039024, mp.Vector3(0.0, 0.5, 0.0)), + ( + 0.8214230044191525, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.865194814441525, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (1.0334130018594276, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.8652307994936862, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (1.0334230910419813, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (1.021367669109368, mp.Vector3(0.0, 0.5, 0.0)), + (1.115966990757518, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 1.108662658537423, + mp.Vector3(-0.26666666666666666, 0.26666666666666666, 0.0), + ), + (1.1168107191255379, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) @@ -1029,11 +1455,11 @@ def test_tri_rods(self): eps = ms.get_epsilon() md = mpb.MPBData(rectify=True, resolution=32, periods=3, verbose=True) new_eps = md.convert(eps) - ref_fn = 'tri-rods-epsilon-r-m3-n32.h5' + ref_fn = "tri-rods-epsilon-r-m3-n32.h5" ref_path = os.path.join(self.data_dir, ref_fn) - with h5py.File(ref_path, 'r') as f: - ref = f['data-new'][()] + with h5py.File(ref_path, "r") as f: + ref = f["data-new"][()] compare_arrays(self, ref, new_eps, tol=1e-3) def test_subpixel_averaging(self): @@ -1042,33 +1468,52 @@ def test_subpixel_averaging(self): ms.output_epsilon() expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.49683586474489877, mp.Vector3(0.5, 0.5, 0.0))), - ((0.4415884497225449, mp.Vector3(0.5, 0.0, 0.0)), - (0.5931405141160885, mp.Vector3(0.5, 0.5, 0.0))), - ((0.5931535863117832, mp.Vector3(0.5, 0.5, 0.0)), - (0.7732265593069759, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6791690130757013, mp.Vector3(0.5, 0.5, 0.0)), - (0.80968915516771, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0))), - ((0.8241814443502151, mp.Vector3(0.5, 0.30000000000000004, 0.0)), - (0.9229965195855876, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8819770916660669, mp.Vector3(0.5, 0.5, 0.0)), - (1.0291597050650205, mp.Vector3(0.5, 0.0, 0.0))), - ((0.8819818134421844, mp.Vector3(0.5, 0.5, 0.0)), - (1.086072932359415, mp.Vector3(0.5, 0.0, 0.0))), - ((1.0878689635052692, mp.Vector3(0.5, 0.0, 0.0)), - (1.1119173707556929, mp.Vector3(0.5, 0.5, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.49683586474489877, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.4415884497225449, mp.Vector3(0.5, 0.0, 0.0)), + (0.5931405141160885, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.5931535863117832, mp.Vector3(0.5, 0.5, 0.0)), + (0.7732265593069759, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6791690130757013, mp.Vector3(0.5, 0.5, 0.0)), + ( + 0.80968915516771, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + ), + ( + (0.8241814443502151, mp.Vector3(0.5, 0.30000000000000004, 0.0)), + (0.9229965195855876, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8819770916660669, mp.Vector3(0.5, 0.5, 0.0)), + (1.0291597050650205, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.8819818134421844, mp.Vector3(0.5, 0.5, 0.0)), + (1.086072932359415, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.0878689635052692, mp.Vector3(0.5, 0.0, 0.0)), + (1.1119173707556929, mp.Vector3(0.5, 0.5, 0.0)), + ), ] expected_gap_list = [ (0.0022038709776893727, 0.5931405141160885, 0.5931535863117832), (1.7739824912427062, 0.80968915516771, 0.8241814443502151), - (0.1652326724344101, 1.086072932359415, 1.0878689635052692) + (0.1652326724344101, 1.086072932359415, 1.0878689635052692), ] - ref_fn = 'subpixel_avg-epsilon.h5' + ref_fn = "subpixel_avg-epsilon.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('subpixel_avg', self.filename_prefix, ref_fn) + res_path = re.sub("subpixel_avg", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) self.check_band_range_data(expected_brd, ms.band_range_data) @@ -1079,36 +1524,55 @@ def test_run_te_with_mu_material(self): ms.geometry = [mp.Cylinder(0.2, material=mp.Medium(mu=5))] expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.4165291233037574, mp.Vector3(0.5, 0.5, 0.0))), - ((0.47328232348733695, mp.Vector3(0.5, 0.0, 0.0)), - (0.6699867281290507, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6301802646818523, mp.Vector3(0.5, 0.5, 0.0)), - (0.8037365323032135, mp.Vector3(0.5, 0.0, 0.0))), - ((0.7017932556977557, mp.Vector3(0.5, 0.5, 0.0)), - (0.8863448167711359, mp.Vector3(0.5, 0.10000000000000003, 0.0))), - ((0.9047498485809726, mp.Vector3(0.5, 0.10000000000000003, 0.0)), - (1.0557468193007016, mp.Vector3(0.5, 0.5, 0.0))), - ((1.0077925606103986, mp.Vector3(0.2, 0.2, 0.0)), - (1.1815403744341757, mp.Vector3(0.0, 0.0, 0.0))), - ((1.122424251973878, mp.Vector3(0.20000000000000004, 0.0, 0.0)), - (1.2351567679231688, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0))), - ((1.2059728636717586, mp.Vector3(0.0, 0.0, 0.0)), - (1.3135062523646421, mp.Vector3(0.30000000000000004, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.4165291233037574, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.47328232348733695, mp.Vector3(0.5, 0.0, 0.0)), + (0.6699867281290507, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6301802646818523, mp.Vector3(0.5, 0.5, 0.0)), + (0.8037365323032135, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.7017932556977557, mp.Vector3(0.5, 0.5, 0.0)), + (0.8863448167711359, mp.Vector3(0.5, 0.10000000000000003, 0.0)), + ), + ( + (0.9047498485809726, mp.Vector3(0.5, 0.10000000000000003, 0.0)), + (1.0557468193007016, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (1.0077925606103986, mp.Vector3(0.2, 0.2, 0.0)), + (1.1815403744341757, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (1.122424251973878, mp.Vector3(0.20000000000000004, 0.0, 0.0)), + ( + 1.2351567679231688, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + ), + ( + (1.2059728636717586, mp.Vector3(0.0, 0.0, 0.0)), + (1.3135062523646421, mp.Vector3(0.30000000000000004, 0.0, 0.0)), + ), ] ms.run_te() ms.output_mu() self.check_band_range_data(expected_brd, ms.band_range_data) - fname = 'tutorial-mu.h5' + fname = "tutorial-mu.h5" data_path = os.path.join(self.data_dir, fname) - res_path = re.sub('tutorial', self.filename_prefix, fname) + res_path = re.sub("tutorial", self.filename_prefix, fname) self.compare_h5_files(data_path, res_path) mu = ms.get_mu() - with h5py.File(data_path, 'r') as f: - data = f['data'][()] + with h5py.File(data_path, "r") as f: + data = f["data"][()] compare_arrays(self, data, mu) def test_output_tot_pwr(self): @@ -1116,16 +1580,16 @@ def test_output_tot_pwr(self): ms.run_te() mpb.output_tot_pwr(ms, 8) - ref_fname = 'tutorial-tot.rpwr.k16.b08.te.h5' + ref_fname = "tutorial-tot.rpwr.k16.b08.te.h5" ref_path = os.path.join(self.data_dir, ref_fname) - res_path = re.sub('tutorial', self.filename_prefix, ref_fname) + res_path = re.sub("tutorial", self.filename_prefix, ref_fname) self.compare_h5_files(ref_path, res_path) # Test get_tot_pwr arr = ms.get_tot_pwr(8) - with h5py.File(ref_path, 'r') as f: - expected = f['data'][()] + with h5py.File(ref_path, "r") as f: + expected = f["data"][()] compare_arrays(self, expected, arr) @@ -1134,19 +1598,19 @@ def test_get_eigenvectors(self): ms.run_te(mpb.fix_hfield_phase) def compare_eigenvectors(ref_fn, start, cols): - with h5py.File(os.path.join(self.data_dir, ref_fn), 'r') as f: - expected = f['rawdata'][()] + with h5py.File(os.path.join(self.data_dir, ref_fn), "r") as f: + expected = f["rawdata"][()] # Reshape the last dimension of 2 reals into one complex expected = np.vectorize(complex)(expected[..., 0], expected[..., 1]) ev = ms.get_eigenvectors(start, cols) np.testing.assert_allclose(expected, ev, rtol=1e-3) # Get all columns - compare_eigenvectors('tutorial-te-eigenvectors.h5', 1, 8) + compare_eigenvectors("tutorial-te-eigenvectors.h5", 1, 8) # Get last column - compare_eigenvectors('tutorial-te-eigenvectors-8-1.h5', 8, 1) + compare_eigenvectors("tutorial-te-eigenvectors-8-1.h5", 8, 1) # Get columns 3,4, and 5 - compare_eigenvectors('tutorial-te-eigenvectors-3-3.h5', 3, 3) + compare_eigenvectors("tutorial-te-eigenvectors-3-3.h5", 3, 3) def test_set_eigenvectors(self): ms = self.init_solver() @@ -1169,7 +1633,7 @@ def set_H_to_zero_and_check(start, num_bands): def test_load_and_save_eigenvectors(self): ms = self.init_solver() ms.run_te() - fn = self.filename_prefix + '.h5' + fn = self.filename_prefix + ".h5" ev = ms.get_eigenvectors(8, 1) zeros = np.zeros(ev.shape, dtype=np.complex128) @@ -1183,6 +1647,7 @@ def test_load_and_save_eigenvectors(self): def test_handle_cvector(self): from mpb_tri_rods import ms + ms.deterministic = True ms.tolerance = 1e-12 ms.filename_prefix = self.filename_prefix @@ -1197,22 +1662,30 @@ def get_efields(ms, band): md = mpb.MPBData(rectify=True, periods=3, resolution=32, verbose=True) result = md.convert(efields[-1]) - ref_fn = 'converted-tri-rods-e.k11.b08.tm.h5' + ref_fn = "converted-tri-rods-e.k11.b08.tm.h5" ref_path = os.path.join(self.data_dir, ref_fn) - self.check_fields_against_h5(ref_path, result.ravel(), suffix='-new') + self.check_fields_against_h5(ref_path, result.ravel(), suffix="-new") def test_epsilon_input_file(self): ms = self.init_solver(geom=False) - eps_fn = 'eps_input_file_test.h5' + eps_fn = "eps_input_file_test.h5" ms.epsilon_input_file = os.path.join(self.data_dir, eps_fn) ms.run_te() - expected_freqs = np.array([ - 0.0, 0.5543986136451342, 0.7613327775255415, 0.7613339178956054, - 0.8940893915924257, 0.998342969572652, 0.9983441882455961, 1.0747466061007138 - ]) + expected_freqs = np.array( + [ + 0.0, + 0.5543986136451342, + 0.7613327775255415, + 0.7613339178956054, + 0.8940893915924257, + 0.998342969572652, + 0.9983441882455961, + 1.0747466061007138, + ] + ) expected_gap_list = [ (3.848610367089048e-5, 0.5781812856814899, 0.5781815082009817), @@ -1221,22 +1694,47 @@ def test_epsilon_input_file(self): ] expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.4970977843772992, mp.Vector3(0.5, 0.5, 0.0))), - ((0.4402896410505961, mp.Vector3(0.5, 0.0, 0.0)), - (0.5781812856814899, mp.Vector3(0.5, 0.5, 0.0))), - ((0.5781815082009817, mp.Vector3(0.5, 0.5, 0.0)), - (0.761332777525562, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6689126424359774, mp.Vector3(0.5, 0.5, 0.0)), - (0.8051999839699242, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0))), - ((0.8170847453549156, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0)), - (0.8940893915924548, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8826671164993868, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0)), - (1.0014926328155058, mp.Vector3(0.5, 0.0, 0.0))), - ((0.8832199143682116, mp.Vector3(0.5, 0.5, 0.0)), - (1.0580309832489785, mp.Vector3(0.5, 0.0, 0.0))), - ((1.0660233597945266, mp.Vector3(0.2, 0.2, 0.0)), - (1.087345829555555, mp.Vector3(0.5, 0.5, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.4970977843772992, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.4402896410505961, mp.Vector3(0.5, 0.0, 0.0)), + (0.5781812856814899, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.5781815082009817, mp.Vector3(0.5, 0.5, 0.0)), + (0.761332777525562, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6689126424359774, mp.Vector3(0.5, 0.5, 0.0)), + ( + 0.8051999839699242, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + ), + ( + ( + 0.8170847453549156, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + (0.8940893915924548, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.8826671164993868, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + (1.0014926328155058, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.8832199143682116, mp.Vector3(0.5, 0.5, 0.0)), + (1.0580309832489785, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.0660233597945266, mp.Vector3(0.2, 0.2, 0.0)), + (1.087345829555555, mp.Vector3(0.5, 0.5, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) @@ -1251,78 +1749,265 @@ def test_hermitian_eps(self): ms = self.init_solver() ms.num_bands = 10 ms.geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1)) - ms.k_points = mp.interpolate(2, [mp.Vector3(), mp.Vector3(0.5), - mp.Vector3(0.5, 0.5), mp.Vector3()]) + ms.k_points = mp.interpolate( + 2, [mp.Vector3(), mp.Vector3(0.5), mp.Vector3(0.5, 0.5), mp.Vector3()] + ) if mpb.with_hermitian_epsilon(): mu_offdiag = mp.Vector3(0 + 12.4j, 0j, 0j) else: mu_offdiag = mp.Vector3(1.1 + 0j, 0j, 0j) - mat = mp.Medium(epsilon=15, mu_diag=mp.Vector3(14, 14, 1), mu_offdiag=mu_offdiag) + mat = mp.Medium( + epsilon=15, mu_diag=mp.Vector3(14, 14, 1), mu_offdiag=mu_offdiag + ) ms.geometry = [mp.Cylinder(0.11, material=mat)] ms.run_tm() expected_freqs_with_herm_eps = [ - (0.0, 0.4632939699892961, 0.5786056046494645, 0.6510415824942094, 0.895078332795855, - 0.9065629078750282, 0.9718615669186841, 1.0031527446201098, 1.0142458802909737, 1.0248230445372033), - (0.12937769848279101, 0.4619527556321284, 0.5815596420443466, 0.6509684019890601, - 0.8515085512592131, 0.8991327667388375, 0.9483381392427291, 0.9868373331614826, - 1.0201294704318276, 1.1113539456722876), - (0.240618523732184, 0.45558173972666255, 0.5943681967388351, 0.650900939169996, - 0.7483572681471874, 0.9008690943027022, 0.9516677787271349, 0.9850364677965752, - 1.0516821906747753, 1.1214943845916658), - (0.2922690373273036, 0.4479821144386504, 0.613801526149148, 0.6509434790185421, - 0.6876982556529582, 0.9015193133195613, 0.9570671227792547, 0.9847711283698155, - 1.0828576982954996, 1.1106109238677777), - (0.30027099141005154, 0.46956697245972573, 0.5973756776701357, 0.6743055424064167, - 0.6920274562199666, 0.8979504826615435, 0.9268408824618528, 0.9691495966240021, - 0.9983260085044127, 1.1065083471050117), - (0.31666208802132145, 0.5137036663942733, 0.5882926042080675, 0.6899229118026092, - 0.730249913793744, 0.8458381750994521, 0.8595877992328249, 0.9137388415537298, - 0.9866008233455089, 1.137383764975547), - (0.3251247277636798, 0.5292011796591106, 0.6018330031246529, 0.7028040151334913, - 0.7794097325510528, 0.7819161956650196, 0.8016335408886606, 0.910192351683647, - 0.98598162196522, 1.1535093885340242), - (0.29904860785910004, 0.49821749875617755, 0.5818628214691952, 0.6702162529015839, - 0.792305404698029, 0.8010082951265327, 0.9011331789530838, 0.9347832593312477, - 0.9878915728570912, 1.1274287845362319), - (0.17916974535365005, 0.46423758343427207, 0.5818626159151191, 0.6522567432851836, - 0.8578543711269236, 0.8654932847250656, 0.914866301019591, 0.9836433091978996, - 1.0332068416637614, 1.1280615056475125), - (0.0, 0.46329396998948535, 0.5786056046501502, 0.6510415824943165, 0.8950783327952294, - 0.9065629078748172, 0.9718615669185836, 1.0031527446209287, 1.01424588029229, 1.0248230445379556) + ( + 0.0, + 0.4632939699892961, + 0.5786056046494645, + 0.6510415824942094, + 0.895078332795855, + 0.9065629078750282, + 0.9718615669186841, + 1.0031527446201098, + 1.0142458802909737, + 1.0248230445372033, + ), + ( + 0.12937769848279101, + 0.4619527556321284, + 0.5815596420443466, + 0.6509684019890601, + 0.8515085512592131, + 0.8991327667388375, + 0.9483381392427291, + 0.9868373331614826, + 1.0201294704318276, + 1.1113539456722876, + ), + ( + 0.240618523732184, + 0.45558173972666255, + 0.5943681967388351, + 0.650900939169996, + 0.7483572681471874, + 0.9008690943027022, + 0.9516677787271349, + 0.9850364677965752, + 1.0516821906747753, + 1.1214943845916658, + ), + ( + 0.2922690373273036, + 0.4479821144386504, + 0.613801526149148, + 0.6509434790185421, + 0.6876982556529582, + 0.9015193133195613, + 0.9570671227792547, + 0.9847711283698155, + 1.0828576982954996, + 1.1106109238677777, + ), + ( + 0.30027099141005154, + 0.46956697245972573, + 0.5973756776701357, + 0.6743055424064167, + 0.6920274562199666, + 0.8979504826615435, + 0.9268408824618528, + 0.9691495966240021, + 0.9983260085044127, + 1.1065083471050117, + ), + ( + 0.31666208802132145, + 0.5137036663942733, + 0.5882926042080675, + 0.6899229118026092, + 0.730249913793744, + 0.8458381750994521, + 0.8595877992328249, + 0.9137388415537298, + 0.9866008233455089, + 1.137383764975547, + ), + ( + 0.3251247277636798, + 0.5292011796591106, + 0.6018330031246529, + 0.7028040151334913, + 0.7794097325510528, + 0.7819161956650196, + 0.8016335408886606, + 0.910192351683647, + 0.98598162196522, + 1.1535093885340242, + ), + ( + 0.29904860785910004, + 0.49821749875617755, + 0.5818628214691952, + 0.6702162529015839, + 0.792305404698029, + 0.8010082951265327, + 0.9011331789530838, + 0.9347832593312477, + 0.9878915728570912, + 1.1274287845362319, + ), + ( + 0.17916974535365005, + 0.46423758343427207, + 0.5818626159151191, + 0.6522567432851836, + 0.8578543711269236, + 0.8654932847250656, + 0.914866301019591, + 0.9836433091978996, + 1.0332068416637614, + 1.1280615056475125, + ), + ( + 0.0, + 0.46329396998948535, + 0.5786056046501502, + 0.6510415824943165, + 0.8950783327952294, + 0.9065629078748172, + 0.9718615669185836, + 1.0031527446209287, + 1.01424588029229, + 1.0248230445379556, + ), ] expected_freqs = [ - (0.0, 0.2658849785488965, 0.35685238626885807, 0.3689901366690736, 0.5038968919475888, - 0.5065518587501845, 0.5399110558762593, 0.6356807845993113, 0.6458520209139631, 0.6600163331973673), - (0.1233059084828522, 0.2782923927646106, 0.3573659215347579, 0.36946091907607376, - 0.5037974922871195, 0.5066416041828202, 0.538216765672376, 0.6355701950896693, 0.6455953849366743, - 0.6594309541221774), - (0.1946550854369502, 0.3314575373594674, 0.3616788807546181, 0.3779471264000764, - 0.5026931348465893, 0.5067552606455699, 0.5272272884443048, 0.6333347872001693, - 0.6442148265479308, 0.6532866817588999), - (0.20879926720698877, 0.34897792345617423, 0.3652783351764913, 0.443855894582484, - 0.47790148240242897, 0.506844624322928, 0.5090758743980286, 0.6175265666609957, - 0.6397097609325341, 0.6468898906944409), - (0.21067290783587236, 0.35110317640925215, 0.3649145592788066, 0.4533551114677223, - 0.5005024186371949, 0.507017761099229, 0.5101250893240828, 0.6168213478492276, - 0.6424780509991848, 0.6468935605826212), - (0.214080585764866, 0.353216867533926, 0.3650014330955567, 0.47197667085082984, - 0.5071647195850403, 0.5107899381039265, 0.563894415993932, 0.6150518764874886, - 0.6461781982138443, 0.6483568369087811), - (0.21564852631365375, 0.3536024256611885, 0.3652692866946287, 0.4807484678430083, - 0.5073352894281589, 0.5125343692391049, 0.6079200818245155, 0.6197842747116765, - 0.648907835949042, 0.6503229392874306), - (0.21087876573006833, 0.35463024054783404, 0.3612449317676656, 0.43948603208371123, - 0.5070218413172007, 0.508251630347181, 0.5438146806988851, 0.6205991881007313, - 0.6465891122723096, 0.6511544851425487), - (0.16235265342991828, 0.2958744870280601, 0.3562142355919711, 0.37216060317897104, - 0.5042580244528116, 0.5067350381508423, 0.5373546859406302, 0.6332312603115383, - 0.6459156623195174, 0.6571698237808499), - (0.0, 0.2658849786929068, 0.3568523862640937, 0.36899013667229497, 0.5038968919421172, - 0.506551858753244, 0.5399110559018708, 0.6356807846017583, 0.6458520213315968, 0.6600163321775416), + ( + 0.0, + 0.2658849785488965, + 0.35685238626885807, + 0.3689901366690736, + 0.5038968919475888, + 0.5065518587501845, + 0.5399110558762593, + 0.6356807845993113, + 0.6458520209139631, + 0.6600163331973673, + ), + ( + 0.1233059084828522, + 0.2782923927646106, + 0.3573659215347579, + 0.36946091907607376, + 0.5037974922871195, + 0.5066416041828202, + 0.538216765672376, + 0.6355701950896693, + 0.6455953849366743, + 0.6594309541221774, + ), + ( + 0.1946550854369502, + 0.3314575373594674, + 0.3616788807546181, + 0.3779471264000764, + 0.5026931348465893, + 0.5067552606455699, + 0.5272272884443048, + 0.6333347872001693, + 0.6442148265479308, + 0.6532866817588999, + ), + ( + 0.20879926720698877, + 0.34897792345617423, + 0.3652783351764913, + 0.443855894582484, + 0.47790148240242897, + 0.506844624322928, + 0.5090758743980286, + 0.6175265666609957, + 0.6397097609325341, + 0.6468898906944409, + ), + ( + 0.21067290783587236, + 0.35110317640925215, + 0.3649145592788066, + 0.4533551114677223, + 0.5005024186371949, + 0.507017761099229, + 0.5101250893240828, + 0.6168213478492276, + 0.6424780509991848, + 0.6468935605826212, + ), + ( + 0.214080585764866, + 0.353216867533926, + 0.3650014330955567, + 0.47197667085082984, + 0.5071647195850403, + 0.5107899381039265, + 0.563894415993932, + 0.6150518764874886, + 0.6461781982138443, + 0.6483568369087811, + ), + ( + 0.21564852631365375, + 0.3536024256611885, + 0.3652692866946287, + 0.4807484678430083, + 0.5073352894281589, + 0.5125343692391049, + 0.6079200818245155, + 0.6197842747116765, + 0.648907835949042, + 0.6503229392874306, + ), + ( + 0.21087876573006833, + 0.35463024054783404, + 0.3612449317676656, + 0.43948603208371123, + 0.5070218413172007, + 0.508251630347181, + 0.5438146806988851, + 0.6205991881007313, + 0.6465891122723096, + 0.6511544851425487, + ), + ( + 0.16235265342991828, + 0.2958744870280601, + 0.3562142355919711, + 0.37216060317897104, + 0.5042580244528116, + 0.5067350381508423, + 0.5373546859406302, + 0.6332312603115383, + 0.6459156623195174, + 0.6571698237808499, + ), + ( + 0.0, + 0.2658849786929068, + 0.3568523862640937, + 0.36899013667229497, + 0.5038968919421172, + 0.506551858753244, + 0.5399110559018708, + 0.6356807846017583, + 0.6458520213315968, + 0.6600163321775416, + ), ] if mpb.with_hermitian_epsilon(): @@ -1351,7 +2036,9 @@ def f2(F, eps, r): ms.get_efield(8) field_integral = ms.compute_field_integral(f2) - self.assertAlmostEqual(field_integral, 02.0735366548785272e-18 - 3.0259168624899837e-6j) + self.assertAlmostEqual( + field_integral, 02.0735366548785272e-18 - 3.0259168624899837e-6j + ) def test_multiply_bloch_phase(self): ms = self.init_solver() @@ -1361,7 +2048,7 @@ def test_multiply_bloch_phase(self): efield = ms.get_efield(8, False) bloch_efield = ms.multiply_bloch_phase(efield) - ref_fn = 'tutorial-e.k16.b08.te.h5' + ref_fn = "tutorial-e.k16.b08.te.h5" ref_path = os.path.join(self.data_dir, ref_fn) self.check_fields_against_h5(ref_path, bloch_efield) @@ -1391,9 +2078,9 @@ def test_poynting(self): ms.run_te(mpb.output_at_kpoint(mp.Vector3(0.5, 0.5), mpb.output_poynting)) - ref_fn = 'tutorial-flux.v.k11.b08.te.h5' + ref_fn = "tutorial-flux.v.k11.b08.te.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('tutorial', self.filename_prefix, ref_fn) + res_path = re.sub("tutorial", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) @@ -1404,14 +2091,38 @@ def test_fractional_lattice(self): ms.run_te() expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), (2.041241452319313, mp.Vector3(0.5, 0.5, 0.0))), - ((1.4433756729740665, mp.Vector3(0.5, 0.0, 0.0)), (2.886751345948131, mp.Vector3(0.0, 0.0, 0.0))), - ((2.041241452319316, mp.Vector3(0.5, 0.5, 0.0)), (3.2274861218395094, mp.Vector3(0.5, 0.0, 0.0))), - ((2.0412414523193187, mp.Vector3(0.5, 0.5, 0.0)), (3.2659863237109055, mp.Vector3(0.2, 0.2, 0.0))), - ((2.88675134594813, mp.Vector3(0.0, 0.0, 0.0)), (4.564354645876381, mp.Vector3(0.5, 0.5, 0.0))), - ((3.227486121839514, mp.Vector3(0.5, 0.0, 0.0)), (4.564354645876382, mp.Vector3(0.5, 0.5, 0.0))), - ((3.6968455021364752, mp.Vector3(0.20000000000000004, 0.0, 0.0)), (4.564354645876384, mp.Vector3(0.5, 0.5, 0.0))), - ((4.0824829046386295, mp.Vector3(0.0, 0.0, 0.0)), (5.204164998665332, mp.Vector3(0.5, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (2.041241452319313, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (1.4433756729740665, mp.Vector3(0.5, 0.0, 0.0)), + (2.886751345948131, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (2.041241452319316, mp.Vector3(0.5, 0.5, 0.0)), + (3.2274861218395094, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (2.0412414523193187, mp.Vector3(0.5, 0.5, 0.0)), + (3.2659863237109055, mp.Vector3(0.2, 0.2, 0.0)), + ), + ( + (2.88675134594813, mp.Vector3(0.0, 0.0, 0.0)), + (4.564354645876381, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (3.227486121839514, mp.Vector3(0.5, 0.0, 0.0)), + (4.564354645876382, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (3.6968455021364752, mp.Vector3(0.20000000000000004, 0.0, 0.0)), + (4.564354645876384, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (4.0824829046386295, mp.Vector3(0.0, 0.0, 0.0)), + (5.204164998665332, mp.Vector3(0.5, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) @@ -1419,59 +2130,64 @@ def test_fractional_lattice(self): def test_symmetry_overlap(self): ms = mpb.ModeSolver( num_bands=6, - k_points = [mp.Vector3(0.5, 0.5, 0.5),], - geometry = [mp.Sphere(0.25, material=mp.Medium(epsilon=13))], - geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1, 1)), - resolution = 32, - filename_prefix = self.filename_prefix, - deterministic = True, - tolerance = 1e-12 + k_points=[ + mp.Vector3(0.5, 0.5, 0.5), + ], + geometry=[mp.Sphere(0.25, material=mp.Medium(epsilon=13))], + geometry_lattice=mp.Lattice(size=mp.Vector3(1, 1, 1)), + resolution=32, + filename_prefix=self.filename_prefix, + deterministic=True, + tolerance=1e-12, ) ms.run() # inversion symmetry - Wi = mp.Matrix([-1,0,0], [0,-1,0], [0,0,-1]) - w = mp.Vector3(0,0,0) - symeigs = ms.compute_symmetries(Wi, w) - symeigs_expected = [1,1,1,-1,-1,-1] - for i in range(0,5): + Wi = mp.Matrix([-1, 0, 0], [0, -1, 0], [0, 0, -1]) + w = mp.Vector3(0, 0, 0) + symeigs = ms.compute_symmetries(Wi, w) + symeigs_expected = [1, 1, 1, -1, -1, -1] + for i in range(0, 5): self.assertAlmostEqual(symeigs[i].real, symeigs_expected[i], places=3) self.assertAlmostEqual(symeigs[i].imag, 0, places=3) - + # check that transformed_overlap agrees when called manually, with a # "preloaded" bfield - for i in range(0,5): - ms.get_bfield(i+1, bloch_phase=False) + for i in range(0, 5): + ms.get_bfield(i + 1, bloch_phase=False) symeig_bfield = ms.transformed_overlap(Wi, w) self.assertAlmostEqual(symeigs[i].real, symeig_bfield.real, places=3) self.assertAlmostEqual(symeigs[i].imag, symeig_bfield.imag, places=3) - ms.get_dfield(i+1, bloch_phase=False) + ms.get_dfield(i + 1, bloch_phase=False) symeig_dfield = ms.transformed_overlap(Wi, w) self.assertAlmostEqual(symeigs[i].real, symeig_dfield.real, places=3) self.assertAlmostEqual(symeigs[i].imag, symeig_dfield.imag, places=3) # 2-fold symmetry (across MPI xy-transposition dimensions) - W2 = mp.Matrix([-1,0,0], [0,1,0], [0,0,-1]) + W2 = mp.Matrix([-1, 0, 0], [0, 1, 0], [0, 0, -1]) symeigs = ms.compute_symmetries(W2, w) - self.assertAlmostEqual(sum(symeigs[0:3]), -1+0j, places=3) - self.assertAlmostEqual(sum(symeigs[3:6]), -1+0j, places=3) + self.assertAlmostEqual(sum(symeigs[0:3]), -1 + 0j, places=3) + self.assertAlmostEqual(sum(symeigs[3:6]), -1 + 0j, places=3) - # mirror symmetry: compare with run_zeven & run_zodd - ms.k_points = [mp.Vector3(0,0,0)] # must be at Γ cf. https://github.com/NanoComp/mpb/issues/114 - Wz = mp.Matrix([1,0,0], [0,1,0], [0,0,-1]) + # mirror symmetry: compare with run_zeven & run_zodd + ms.k_points = [ + mp.Vector3(0, 0, 0) + ] # must be at Γ cf. https://github.com/NanoComp/mpb/issues/114 + Wz = mp.Matrix([1, 0, 0], [0, 1, 0], [0, 0, -1]) ms.run_zeven() symeigs_zeven = ms.compute_symmetries(Wz, w) - for i in range(0,5): + for i in range(0, 5): self.assertAlmostEqual(symeigs_zeven[i].real, 1, places=3) self.assertAlmostEqual(symeigs_zeven[i].imag, 0, places=3) ms.run_zodd() symeigs_zodd = ms.compute_symmetries(Wz, w) - for i in range(0,5): + for i in range(0, 5): self.assertAlmostEqual(symeigs_zodd[i].real, -1, places=3) - self.assertAlmostEqual(symeigs_zodd[i].imag, 0, places=3) + self.assertAlmostEqual(symeigs_zodd[i].imag, 0, places=3) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_multilevel_atom.py b/python/tests/test_multilevel_atom.py index 33aa17b39..17885d8e8 100644 --- a/python/tests/test_multilevel_atom.py +++ b/python/tests/test_multilevel_atom.py @@ -4,8 +4,9 @@ class TestMultiLevelAtom(unittest.TestCase): - - @unittest.skipIf(mp.is_single_precision(), "double-precision floating point specific test") + @unittest.skipIf( + mp.is_single_precision(), "double-precision floating point specific test" + ) def test_multilevel_atom(self): resolution = 40 ncav = 1.5 @@ -37,26 +38,37 @@ def test_multilevel_atom(self): pumping_rate=Rp, frequency=freq_21, gamma=gamma_21, - sigma_diag=mp.Vector3(sigma_21, sigma_21, sigma_21) + sigma_diag=mp.Vector3(sigma_21, sigma_21, sigma_21), ) t2 = mp.Transition(2, 1, transition_rate=rate_21) - ml_atom = mp.MultilevelAtom(sigma=1, transitions=[t1, t2], initial_populations=[N0]) + ml_atom = mp.MultilevelAtom( + sigma=1, transitions=[t1, t2], initial_populations=[N0] + ) two_level = mp.Medium(index=ncav, E_susceptibilities=[ml_atom]) - geometry = [mp.Block(center=mp.Vector3(z=(-0.5 * sz) + (0.5 * Lcav)), - size=mp.Vector3(mp.inf, mp.inf, Lcav), material=two_level)] - - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - geometry=geometry, - dimensions=dimensions) + geometry = [ + mp.Block( + center=mp.Vector3(z=(-0.5 * sz) + (0.5 * Lcav)), + size=mp.Vector3(mp.inf, mp.inf, Lcav), + material=two_level, + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + geometry=geometry, + dimensions=dimensions, + ) def field_func(p): return 1 if p.z == (-0.5 * sz) + (0.5 * Lcav) else 0 def check_field(sim): - fp = sim.get_field_point(mp.Ex, mp.Vector3(z=(-0.5 * sz) + Lcav + (0.5 * dpad))).real + fp = sim.get_field_point( + mp.Ex, mp.Vector3(z=(-0.5 * sz) + Lcav + (0.5 * dpad)) + ).real self.assertAlmostEqual(fp, -2.7110969214986387) sim.init_sim() @@ -64,5 +76,5 @@ def check_field(sim): sim.run(mp.at_end(check_field), until=7000) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_n2f_periodic.py b/python/tests/test_n2f_periodic.py index 5cac78d5c..922415b4c 100644 --- a/python/tests/test_n2f_periodic.py +++ b/python/tests/test_n2f_periodic.py @@ -4,65 +4,95 @@ import numpy as np from numpy import linalg as LA -class TestNear2FarPeriodicBoundaries(unittest.TestCase): +class TestNear2FarPeriodicBoundaries(unittest.TestCase): def test_nea2far_periodic(self): - dpml = 1.0 # PML thickness - dsub = 3.0 # substrate thickness - dpad = 20.0 # padding between grating and PML - gp = 10.0 # grating period - gh = 0.5 # grating height - gdc = 0.5 # grating duty cycle - - sx = dpml+dsub+gh+dpad+dpml + dpml = 1.0 # PML thickness + dsub = 3.0 # substrate thickness + dpad = 20.0 # padding between grating and PML + gp = 10.0 # grating period + gh = 0.5 # grating height + gdc = 0.5 # grating duty cycle + + sx = dpml + dsub + gh + dpad + dpml sy = gp - pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] + pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] - wvl = 0.5 # center wavelength - fcen = 1/wvl # center frequency - df = 0.05*fcen # frequency width + wvl = 0.5 # center wavelength + fcen = 1 / wvl # center frequency + df = 0.05 * fcen # frequency width - src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub) - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(y=sy))] + src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) + ] glass = mp.Medium(index=1.5) - geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub))), - mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))] - - k_point = mp.Vector3(0,0,0) + geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh), + ), + ] + + k_point = mp.Vector3(0, 0, 0) symmetries = [mp.Mirror(mp.Y)] - n2f_pt = mp.Vector3(-0.5*sx+dpml+dsub+gh+1.0) - dft_pt = mp.Vector3(0.5*sx-dpml) + n2f_pt = mp.Vector3(-0.5 * sx + dpml + dsub + gh + 1.0) + dft_pt = mp.Vector3(0.5 * sx - dpml) - res = [20,25,30] + res = [20, 25, 30] norm = np.empty(3) for j in range(3): - sim = mp.Simulation(resolution=res[j], - cell_size=mp.Vector3(sx,sy), - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - - n2f_obj = sim.add_near2far(fcen, 0, 1, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)), nperiods=10) - dft_obj = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=dft_pt, size=mp.Vector3(y=sy)) + sim = mp.Simulation( + resolution=res[j], + cell_size=mp.Vector3(sx, sy), + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, + ) + + n2f_obj = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)), + nperiods=10, + ) + dft_obj = sim.add_dft_fields( + [mp.Ez], fcen, 0, 1, center=dft_pt, size=mp.Vector3(y=sy) + ) sim.run(until_after_sources=300) - n2f_Ez = sim.get_farfields(n2f_obj, res[j], center=dft_pt, size=mp.Vector3(y=sy)) + n2f_Ez = sim.get_farfields( + n2f_obj, res[j], center=dft_pt, size=mp.Vector3(y=sy) + ) dft_Ez = sim.get_dft_array(dft_obj, mp.Ez, 0) - norm[j] = LA.norm(n2f_Ez['Ez']-dft_Ez[1:-1]) - print("norm:, {}, {:.5f}".format(res[j],norm[j])) + norm[j] = LA.norm(n2f_Ez["Ez"] - dft_Ez[1:-1]) + print("norm:, {}, {:.5f}".format(res[j], norm[j])) sim.reset_meep() - self.assertGreater(norm[0],norm[1]) - self.assertGreater(norm[1],norm[2]) + self.assertGreater(norm[0], norm[1]) + self.assertGreater(norm[1], norm[2]) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_oblique_source.py b/python/tests/test_oblique_source.py index edf0042a0..d5a9c463d 100644 --- a/python/tests/test_oblique_source.py +++ b/python/tests/test_oblique_source.py @@ -2,55 +2,69 @@ import math import unittest -class TestEigenmodeSource(unittest.TestCase): +class TestEigenmodeSource(unittest.TestCase): def test_waveguide_flux(self): - cell_size = mp.Vector3(10,10) + cell_size = mp.Vector3(10, 10) pml_layers = [mp.PML(thickness=2.0)] - rot_angles = range(0,60,20) # rotation angle of waveguide, CCW around z-axis + rot_angles = range(0, 60, 20) # rotation angle of waveguide, CCW around z-axis fluxes = [] coeff_fluxes = [] for t in rot_angles: rot_angle = math.radians(t) - kpoint = mp.Vector3(math.cos(rot_angle),math.sin(rot_angle),0) - sources = [mp.EigenModeSource(src=mp.GaussianSource(1.0,fwidth=0.1), - size=mp.Vector3(y=10), - center=mp.Vector3(x=-3), - direction=mp.NO_DIRECTION, - eig_kpoint=kpoint, - eig_band=1, - eig_parity=mp.ODD_Z, - eig_match_freq=True)] + kpoint = mp.Vector3(math.cos(rot_angle), math.sin(rot_angle), 0) + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(1.0, fwidth=0.1), + size=mp.Vector3(y=10), + center=mp.Vector3(x=-3), + direction=mp.NO_DIRECTION, + eig_kpoint=kpoint, + eig_band=1, + eig_parity=mp.ODD_Z, + eig_match_freq=True, + ) + ] - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,1,mp.inf), - e1 = mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle), - e2 = mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle), - material=mp.Medium(index=1.5))] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1, mp.inf), + e1=mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle), + e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle), + material=mp.Medium(index=1.5), + ) + ] - sim = mp.Simulation(cell_size=cell_size, - resolution=50, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry) + sim = mp.Simulation( + cell_size=cell_size, + resolution=50, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + ) - tran = sim.add_flux(1.0, 0, 1, mp.FluxRegion(center=mp.Vector3(x=3), size=mp.Vector3(y=10))) + tran = sim.add_flux( + 1.0, 0, 1, mp.FluxRegion(center=mp.Vector3(x=3), size=mp.Vector3(y=10)) + ) sim.run(until_after_sources=100) - res = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z if t == 0 else mp.ODD_Z, - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: kpoint) + res = sim.get_eigenmode_coefficients( + tran, + [1], + eig_parity=mp.EVEN_Y + mp.ODD_Z if t == 0 else mp.ODD_Z, + direction=mp.NO_DIRECTION, + kpoint_func=lambda f, n: kpoint, + ) fluxes.append(mp.get_fluxes(tran)[0]) - coeff_fluxes.append(abs(res.alpha[0,0,0])**2) - print("flux:, {:.2f}, {:.6f}".format(t,fluxes[-1])) - print("coef_flux:, {:.2f}, {:.6f}".format(t,coeff_fluxes[-1])) + coeff_fluxes.append(abs(res.alpha[0, 0, 0]) ** 2) + print("flux:, {:.2f}, {:.6f}".format(t, fluxes[-1])) + print("coef_flux:, {:.2f}, {:.6f}".format(t, coeff_fluxes[-1])) self.assertAlmostEqual(fluxes[0], fluxes[1], places=0) self.assertAlmostEqual(fluxes[1], fluxes[2], places=0) @@ -61,9 +75,9 @@ def test_waveguide_flux(self): # sadly the above line requires a workaround due to the # following annoying numerical accident: # AssertionError: 100.33815231783535 != 99.81145343586365 within 0 places - f0,f2=fluxes[0],fluxes[2] - self.assertLess( abs(f0-f2), 0.5*max(abs(f0),abs(f2)) ) + f0, f2 = fluxes[0], fluxes[2] + self.assertLess(abs(f0 - f2), 0.5 * max(abs(f0), abs(f2))) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_physical.py b/python/tests/test_physical.py index 08b745a89..ef75ea112 100644 --- a/python/tests/test_physical.py +++ b/python/tests/test_physical.py @@ -3,7 +3,6 @@ class TestPhysical(unittest.TestCase): - def test_physical(self): a = 10.0 @@ -15,14 +14,22 @@ def test_physical(self): cell_size = mp.Vector3(xmax, ymax) pml_layers = [mp.PML(ymax / 3.0)] - sources = [mp.Source(src=mp.ContinuousSource(w), component=mp.Ez, - center=mp.Vector3(-dx), size=mp.Vector3())] - - sim = mp.Simulation(cell_size=cell_size, - resolution=a, - boundary_layers=pml_layers, - sources=sources, - force_complex_fields=True) + sources = [ + mp.Source( + src=mp.ContinuousSource(w), + component=mp.Ez, + center=mp.Vector3(-dx), + size=mp.Vector3(), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=a, + boundary_layers=pml_layers, + sources=sources, + force_complex_fields=True, + ) sim.init_sim() sim.solve_cw(tol=1e-5 if mp.is_single_precision() else 1e-6) @@ -33,7 +40,7 @@ def test_physical(self): amp2 = sim.get_field_point(mp.Ez, p2) ratio = abs(amp1) / abs(amp2) - ratio = ratio ** 2 # in 2d, decay is ~1/sqrt(r), so square to get 1/r + ratio = ratio**2 # in 2d, decay is ~1/sqrt(r), so square to get 1/r fail_fmt = "Failed: amp1 = ({}, {}), amp2 = ({}, {})\nabs(amp1/amp2){} = {}, too far from 2.0" fail_msg = fail_fmt.format(amp1.real, amp1, amp2.real, amp2, "^2", ratio) @@ -41,5 +48,5 @@ def test_physical(self): self.assertTrue(ratio <= 2.12 and ratio >= 1.88, fail_msg) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_prism.py b/python/tests/test_prism.py index 534f866a2..1dbb0d48f 100644 --- a/python/tests/test_prism.py +++ b/python/tests/test_prism.py @@ -3,239 +3,252 @@ import numpy as np import meep as mp -class TestPrism(unittest.TestCase): - - def nonconvex_marching_squares(self,idx,npts): - resolution = 25 - - cell = mp.Vector3(10,10) - - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - vertices_file = os.path.join(data_dir, f'nonconvex_prism_vertices{idx}.npz') - vertices_obj = np.load(vertices_file) - - ## prism verticies precomputed from analytic "blob" shape using - ## marching squares algorithm of skimage.measure.find_contours - ## ref: https://github.com/NanoComp/meep/pull/1142 - vertices_data = vertices_obj[f"N{npts}"] - vertices = [mp.Vector3(v[0],v[1],0) for v in vertices_data] - - geometry = [mp.Prism(vertices, - height=mp.inf, - material=mp.Medium(epsilon=12))] - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - resolution=resolution) - - sim.init_sim() - - prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) - - print(f"epsilon-sum:, {abs(prism_eps)} (prism-msq)") - return prism_eps - - - def convex_marching_squares(self,npts): - resolution = 50 - - cell = mp.Vector3(3,3) - - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - vertices_file = os.path.join(data_dir, 'convex_prism_vertices.npz') - vertices_obj = np.load(vertices_file) - - ## prism vertices precomputed for a circle of radius 1.0 using - ## marching squares algorithm of skimage.measure.find_contours - ## ref: https://github.com/NanoComp/meep/issues/1060 - vertices_data = vertices_obj[f"N{npts}"] - vertices = [mp.Vector3(v[0],v[1],0) for v in vertices_data] - - geometry = [mp.Prism(vertices, - height=mp.inf, - material=mp.Medium(epsilon=12))] - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - resolution=resolution) - - sim.init_sim() - - prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) +class TestPrism(unittest.TestCase): + def nonconvex_marching_squares(self, idx, npts): + resolution = 25 - sim.reset_meep() + cell = mp.Vector3(10, 10) - geometry = [mp.Cylinder(radius=1.0, - center=mp.Vector3(), - height=mp.inf, - material=mp.Medium(epsilon=12))] + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + vertices_file = os.path.join(data_dir, f"nonconvex_prism_vertices{idx}.npz") + vertices_obj = np.load(vertices_file) - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - resolution=resolution) + ## prism verticies precomputed from analytic "blob" shape using + ## marching squares algorithm of skimage.measure.find_contours + ## ref: https://github.com/NanoComp/meep/pull/1142 + vertices_data = vertices_obj[f"N{npts}"] + vertices = [mp.Vector3(v[0], v[1], 0) for v in vertices_data] - sim.init_sim() + geometry = [mp.Prism(vertices, height=mp.inf, material=mp.Medium(epsilon=12))] - cyl_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) + sim = mp.Simulation(cell_size=cell, geometry=geometry, resolution=resolution) - print( - f"epsilon-sum:, {abs(prism_eps)} (prism-msq), {abs(cyl_eps)} (cylinder), {abs((prism_eps-cyl_eps)/cyl_eps)} (relative error)" - ) + sim.init_sim() - return abs((prism_eps-cyl_eps)/cyl_eps) + prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) + print(f"epsilon-sum:, {abs(prism_eps)} (prism-msq)") - def convex_circle(self,npts,r,sym): - resolution = 50 + return prism_eps - cell = mp.Vector3(3,3) + def convex_marching_squares(self, npts): + resolution = 50 - ### prism vertices computed as equally-spaced points - ### along the circumference of a circle with radius r - angles = 2*np.pi/npts * np.arange(npts) - vertices = [mp.Vector3(r*np.cos(ang),r*np.sin(ang)) for ang in angles] - geometry = [mp.Prism(vertices, - height=mp.inf, - material=mp.Medium(epsilon=12))] + cell = mp.Vector3(3, 3) - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - symmetries=[mp.Mirror(direction=mp.X),mp.Mirror(direction=mp.Y)] if sym else [], - resolution=resolution) + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + vertices_file = os.path.join(data_dir, "convex_prism_vertices.npz") + vertices_obj = np.load(vertices_file) - sim.init_sim() + ## prism vertices precomputed for a circle of radius 1.0 using + ## marching squares algorithm of skimage.measure.find_contours + ## ref: https://github.com/NanoComp/meep/issues/1060 + vertices_data = vertices_obj[f"N{npts}"] + vertices = [mp.Vector3(v[0], v[1], 0) for v in vertices_data] - prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) + geometry = [mp.Prism(vertices, height=mp.inf, material=mp.Medium(epsilon=12))] - sim.reset_meep() + sim = mp.Simulation(cell_size=cell, geometry=geometry, resolution=resolution) - geometry = [mp.Cylinder(radius=r, - center=mp.Vector3(), - height=mp.inf, - material=mp.Medium(epsilon=12))] + sim.init_sim() - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - symmetries=[mp.Mirror(direction=mp.X),mp.Mirror(direction=mp.Y)] if sym else [], - resolution=resolution) + prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) - sim.init_sim() + sim.reset_meep() - cyl_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) + geometry = [ + mp.Cylinder( + radius=1.0, + center=mp.Vector3(), + height=mp.inf, + material=mp.Medium(epsilon=12), + ) + ] - print( - f"epsilon-sum:, {abs(prism_eps)} (prism-cyl), {abs(cyl_eps)} (cylinder), {abs((prism_eps-cyl_eps)/cyl_eps)} (relative error)" - ) + sim = mp.Simulation(cell_size=cell, geometry=geometry, resolution=resolution) - return abs((prism_eps-cyl_eps)/cyl_eps) + sim.init_sim() - def spiral_gds(self): - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - gdsii_file = os.path.join(data_dir, 'spiral.gds') + cyl_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) - resolution = 25 - cell_size = mp.Vector3(12,16) - geometry = mp.get_GDSII_prisms(mp.Medium(index=3.5), gdsii_file, 0, 0, mp.inf) + print( + f"epsilon-sum:, {abs(prism_eps)} (prism-msq), {abs(cyl_eps)} (cylinder), {abs((prism_eps-cyl_eps)/cyl_eps)} (relative error)" + ) - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - resolution=resolution) + return abs((prism_eps - cyl_eps) / cyl_eps) - sim.init_sim() + def convex_circle(self, npts, r, sym): + resolution = 50 - prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) + cell = mp.Vector3(3, 3) - print(f"epsilon-sum:, {abs(prism_eps)} (prism-gds)") + ### prism vertices computed as equally-spaced points + ### along the circumference of a circle with radius r + angles = 2 * np.pi / npts * np.arange(npts) + vertices = [mp.Vector3(r * np.cos(ang), r * np.sin(ang)) for ang in angles] + geometry = [mp.Prism(vertices, height=mp.inf, material=mp.Medium(epsilon=12))] - return prism_eps + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + symmetries=[mp.Mirror(direction=mp.X), mp.Mirror(direction=mp.Y)] + if sym + else [], + resolution=resolution, + ) - # lots of tests, turned off by default since they run too long; - # rename to test_something to run these tests. - def bigtest_prism(self): - print("Testing Non-Convex Prism #1 using marching squares algorithm...") - d1_a = self.nonconvex_marching_squares(1,208) - d1_b = self.nonconvex_marching_squares(1,448) - d1_ref = 458.27922623563074 ## self.nonconvex_marching_squares(1,904) + sim.init_sim() - self.assertLess(abs((d1_b-d1_ref)/d1_ref),abs((d1_a-d1_ref)/d1_ref)) + prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) - print("Testing Non-Convex Prism #2 using marching squares algorithm...") - d2_a = self.nonconvex_marching_squares(2,128) - d2_b = self.nonconvex_marching_squares(2,256) - d2_ref = 506.79940504342534 ## self.nonconvex_marching_squares(2,516) + sim.reset_meep() - self.assertLess(abs((d2_b-d2_ref)/d2_ref),abs((d2_a-d2_ref)/d2_ref)) + geometry = [ + mp.Cylinder( + radius=r, + center=mp.Vector3(), + height=mp.inf, + material=mp.Medium(epsilon=12), + ) + ] - print("Testing Non-Convex Prism #3 using marching squares algorithm...") - d3_a = self.nonconvex_marching_squares(3,164) - d3_b = self.nonconvex_marching_squares(3,336) - d3_ref = 640.0738356076143 ## self.nonconvex_marching_squares(3,672) + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + symmetries=[mp.Mirror(direction=mp.X), mp.Mirror(direction=mp.Y)] + if sym + else [], + resolution=resolution, + ) - self.assertLess(abs((d3_b-d3_ref)/d3_ref),abs((d3_a-d3_ref)/d3_ref)) + sim.init_sim() - print("Testing Convex Prism using marching squares algorithm...") - d = [self.convex_marching_squares(92), - self.convex_marching_squares(192), - self.convex_marching_squares(392)] + cyl_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) - self.assertLess(d[1],d[0]) - self.assertLess(d[2],d[1]) + print( + f"epsilon-sum:, {abs(prism_eps)} (prism-cyl), {abs(cyl_eps)} (cylinder), {abs((prism_eps-cyl_eps)/cyl_eps)} (relative error)" + ) - print("Testing Convex Prism #1 using circle formula (without symmetry)...") - r = 1.0458710786934182 - d_nosym = [self.convex_circle(51,r,False), - self.convex_circle(101,r,False), - self.convex_circle(201,r,False)] + return abs((prism_eps - cyl_eps) / cyl_eps) - self.assertLess(d_nosym[1],d_nosym[0]) - self.assertLess(d_nosym[2],d_nosym[1]) + def spiral_gds(self): + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + gdsii_file = os.path.join(data_dir, "spiral.gds") - print("Testing Convex Prism #1 using circle formula (with symmetry)...") - d_sym = [self.convex_circle(51,r,True), - self.convex_circle(101,r,True), - self.convex_circle(201,r,True)] + resolution = 25 + cell_size = mp.Vector3(12, 16) + geometry = mp.get_GDSII_prisms(mp.Medium(index=3.5), gdsii_file, 0, 0, mp.inf) - self.assertLess(d_sym[1],d_sym[0]) - self.assertLess(d_sym[2],d_sym[1]) + sim = mp.Simulation( + cell_size=cell_size, geometry=geometry, resolution=resolution + ) - self.assertAlmostEqual(d_nosym[0],d_sym[0],places=3) - self.assertAlmostEqual(d_nosym[1],d_sym[1],places=3) - self.assertAlmostEqual(d_nosym[2],d_sym[2],places=3) + sim.init_sim() - print("Testing Convex Prism #2 using circle formula (without symmetry)...") - r = 1.2896871096581341 - d_nosym = [self.convex_circle(31,r,False), - self.convex_circle(61,r,False), - self.convex_circle(121,r,False)] + prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) - self.assertLess(d_nosym[1],d_nosym[0]) - self.assertLess(d_nosym[2],d_nosym[1]) + print(f"epsilon-sum:, {abs(prism_eps)} (prism-gds)") - print("Testing Convex Prism #2 using circle formula (with symmetry)...") - d_sym = [self.convex_circle(31,r,True), - self.convex_circle(61,r,True), - self.convex_circle(121,r,True)] + return prism_eps - self.assertLess(d_sym[1],d_sym[0]) - self.assertLess(d_sym[2],d_sym[1]) + # lots of tests, turned off by default since they run too long; + # rename to test_something to run these tests. + def bigtest_prism(self): + print("Testing Non-Convex Prism #1 using marching squares algorithm...") + d1_a = self.nonconvex_marching_squares(1, 208) + d1_b = self.nonconvex_marching_squares(1, 448) + d1_ref = 458.27922623563074 ## self.nonconvex_marching_squares(1,904) + + self.assertLess(abs((d1_b - d1_ref) / d1_ref), abs((d1_a - d1_ref) / d1_ref)) - self.assertAlmostEqual(d_nosym[0],d_sym[0],places=3) - self.assertAlmostEqual(d_nosym[1],d_sym[1],places=3) - self.assertAlmostEqual(d_nosym[2],d_sym[2],places=3) + print("Testing Non-Convex Prism #2 using marching squares algorithm...") + d2_a = self.nonconvex_marching_squares(2, 128) + d2_b = self.nonconvex_marching_squares(2, 256) + d2_ref = 506.79940504342534 ## self.nonconvex_marching_squares(2,516) - print("Testing Non-Convex Prism from GDSII file...") - d = self.spiral_gds() - d_ref = 455.01744881372224 - self.assertAlmostEqual(d,d_ref,places=5) + self.assertLess(abs((d2_b - d2_ref) / d2_ref), abs((d2_a - d2_ref) / d2_ref)) + + print("Testing Non-Convex Prism #3 using marching squares algorithm...") + d3_a = self.nonconvex_marching_squares(3, 164) + d3_b = self.nonconvex_marching_squares(3, 336) + d3_ref = 640.0738356076143 ## self.nonconvex_marching_squares(3,672) + + self.assertLess(abs((d3_b - d3_ref) / d3_ref), abs((d3_a - d3_ref) / d3_ref)) + + print("Testing Convex Prism using marching squares algorithm...") + d = [ + self.convex_marching_squares(92), + self.convex_marching_squares(192), + self.convex_marching_squares(392), + ] + + self.assertLess(d[1], d[0]) + self.assertLess(d[2], d[1]) + + print("Testing Convex Prism #1 using circle formula (without symmetry)...") + r = 1.0458710786934182 + d_nosym = [ + self.convex_circle(51, r, False), + self.convex_circle(101, r, False), + self.convex_circle(201, r, False), + ] + + self.assertLess(d_nosym[1], d_nosym[0]) + self.assertLess(d_nosym[2], d_nosym[1]) + + print("Testing Convex Prism #1 using circle formula (with symmetry)...") + d_sym = [ + self.convex_circle(51, r, True), + self.convex_circle(101, r, True), + self.convex_circle(201, r, True), + ] + + self.assertLess(d_sym[1], d_sym[0]) + self.assertLess(d_sym[2], d_sym[1]) + + self.assertAlmostEqual(d_nosym[0], d_sym[0], places=3) + self.assertAlmostEqual(d_nosym[1], d_sym[1], places=3) + self.assertAlmostEqual(d_nosym[2], d_sym[2], places=3) + + print("Testing Convex Prism #2 using circle formula (without symmetry)...") + r = 1.2896871096581341 + d_nosym = [ + self.convex_circle(31, r, False), + self.convex_circle(61, r, False), + self.convex_circle(121, r, False), + ] - def test_prism(self): - print("Testing Non-Convex Prism #3 using marching squares algorithm...") - d3_a = self.nonconvex_marching_squares(3,164) - d3_b = self.nonconvex_marching_squares(3,336) - d3_ref = 640.0738356076143 ## self.nonconvex_marching_squares(3,672) - self.assertLess(abs((d3_b-d3_ref)/d3_ref),abs((d3_a-d3_ref)/d3_ref)) - self.assertLess(abs((d3_b-d3_ref)/d3_ref), 0.02) + self.assertLess(d_nosym[1], d_nosym[0]) + self.assertLess(d_nosym[2], d_nosym[1]) -if __name__ == '__main__': - unittest.main() + print("Testing Convex Prism #2 using circle formula (with symmetry)...") + d_sym = [ + self.convex_circle(31, r, True), + self.convex_circle(61, r, True), + self.convex_circle(121, r, True), + ] + + self.assertLess(d_sym[1], d_sym[0]) + self.assertLess(d_sym[2], d_sym[1]) + + self.assertAlmostEqual(d_nosym[0], d_sym[0], places=3) + self.assertAlmostEqual(d_nosym[1], d_sym[1], places=3) + self.assertAlmostEqual(d_nosym[2], d_sym[2], places=3) + + print("Testing Non-Convex Prism from GDSII file...") + d = self.spiral_gds() + d_ref = 455.01744881372224 + self.assertAlmostEqual(d, d_ref, places=5) + + def test_prism(self): + print("Testing Non-Convex Prism #3 using marching squares algorithm...") + d3_a = self.nonconvex_marching_squares(3, 164) + d3_b = self.nonconvex_marching_squares(3, 336) + d3_ref = 640.0738356076143 ## self.nonconvex_marching_squares(3,672) + self.assertLess(abs((d3_b - d3_ref) / d3_ref), abs((d3_a - d3_ref) / d3_ref)) + self.assertLess(abs((d3_b - d3_ref) / d3_ref), 0.02) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_pw_source.py b/python/tests/test_pw_source.py index 797e3c894..4e16c9952 100644 --- a/python/tests/test_pw_source.py +++ b/python/tests/test_pw_source.py @@ -4,8 +4,8 @@ import unittest from utils import ApproxComparisonTestCase -class TestPwSource(ApproxComparisonTestCase): +class TestPwSource(ApproxComparisonTestCase): def setUp(self): s = 11 dpml = 1 @@ -19,6 +19,7 @@ def setUp(self): def pw_amp(k, x0): def _pw_amp(x): return cmath.exp(1j * k.dot(x + x0)) + return _pw_amp fcen = 0.8 @@ -32,22 +33,22 @@ def _pw_amp(x): component=mp.Ez, center=mp.Vector3(-0.5 * s, 0), size=mp.Vector3(0, s), - amp_func=pw_amp(self.k, mp.Vector3(x=-0.5 * s)) + amp_func=pw_amp(self.k, mp.Vector3(x=-0.5 * s)), ), mp.Source( mp.ContinuousSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3(0, -0.5 * s), size=mp.Vector3(s, 0), - amp_func=pw_amp(self.k, mp.Vector3(y=-0.5 * s)) - ) + amp_func=pw_amp(self.k, mp.Vector3(y=-0.5 * s)), + ), ] self.sim = mp.Simulation( cell_size=cell, sources=sources, boundary_layers=pml_layers, - resolution=resolution + resolution=resolution, ) self.sim.use_output_directory(self.temp_dir) self.s = s @@ -72,7 +73,11 @@ def test_pw_source(self): tol = 1e-4 if mp.is_single_precision() else 1e-9 self.assertClose(pt1 / pt2, 27.557668029008262, epsilon=tol) - self.assertAlmostEqual(cmath.exp(1j * self.k.dot(v1 - v2)), 0.7654030066070924 - 0.6435512702783076j) + self.assertAlmostEqual( + cmath.exp(1j * self.k.dot(v1 - v2)), + 0.7654030066070924 - 0.6435512702783076j, + ) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_refl_angular.py b/python/tests/test_refl_angular.py index 1082fc5a4..fb365238e 100644 --- a/python/tests/test_refl_angular.py +++ b/python/tests/test_refl_angular.py @@ -18,72 +18,81 @@ def setUpClass(cls): cls.dpml = 1.0 cls.dz = 7.0 - cls.sz = cls.dz+2*cls.dpml + cls.sz = cls.dz + 2 * cls.dpml cls.wvl_min = 0.4 cls.wvl_max = 0.8 - cls.fmin = 1/cls.wvl_max - cls.fmax = 1/cls.wvl_min - cls.fcen = 0.5*(cls.fmin+cls.fmax) - cls.df = cls.fmax-cls.fmin + cls.fmin = 1 / cls.wvl_max + cls.fmax = 1 / cls.wvl_min + cls.fcen = 0.5 * (cls.fmin + cls.fmax) + cls.df = cls.fmax - cls.fmin cls.nfreq = 11 def refl_angular(self, theta): theta_r = math.radians(theta) # wavevector (in source medium); plane of incidence is XZ - k = mp.Vector3(0,0,1).rotate(mp.Vector3(0,1,0),theta_r).scale(self.n1*self.fmin) + k = ( + mp.Vector3(0, 0, 1) + .rotate(mp.Vector3(0, 1, 0), theta_r) + .scale(self.n1 * self.fmin) + ) dimensions = 1 if theta == 0 else 3 cell_size = mp.Vector3(z=self.sz) pml_layers = [mp.PML(self.dpml)] - sources = [mp.Source(mp.GaussianSource(self.fcen, fwidth=self.df), - component=mp.Ex, # P polarization - center=mp.Vector3(z=-0.5*self.sz+self.dpml))] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - dimensions=dimensions, - default_material=mp.Medium(index=self.n1), - sources=sources, - boundary_layers=pml_layers, - k_point=k) - - mon_pt = -0.5*self.sz+self.dpml+0.25*self.dz + sources = [ + mp.Source( + mp.GaussianSource(self.fcen, fwidth=self.df), + component=mp.Ex, # P polarization + center=mp.Vector3(z=-0.5 * self.sz + self.dpml), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + dimensions=dimensions, + default_material=mp.Medium(index=self.n1), + sources=sources, + boundary_layers=pml_layers, + k_point=k, + ) + + mon_pt = -0.5 * self.sz + self.dpml + 0.25 * self.dz refl_fr = mp.FluxRegion(center=mp.Vector3(z=mon_pt)) - refl = sim.add_flux(self.fcen, - self.df, - self.nfreq, - refl_fr) - - termination_cond = mp.stop_when_fields_decayed(50, - mp.Ex, - mp.Vector3(z=mon_pt), - 1e-9) + refl = sim.add_flux(self.fcen, self.df, self.nfreq, refl_fr) + + termination_cond = mp.stop_when_fields_decayed( + 50, mp.Ex, mp.Vector3(z=mon_pt), 1e-9 + ) sim.run(until_after_sources=termination_cond) empty_data = sim.get_flux_data(refl) empty_flux = mp.get_fluxes(refl) sim.reset_meep() - geometry = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,0.5*self.sz), - center=mp.Vector3(z=0.25*self.sz), - material=mp.Medium(index=self.n2))] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - dimensions=dimensions, - default_material=mp.Medium(index=self.n1), - sources=sources, - boundary_layers=pml_layers, - k_point=k, - geometry=geometry) - - refl = sim.add_flux(self.fcen, - self.df, - self.nfreq, - refl_fr) + geometry = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, 0.5 * self.sz), + center=mp.Vector3(z=0.25 * self.sz), + material=mp.Medium(index=self.n2), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + dimensions=dimensions, + default_material=mp.Medium(index=self.n1), + sources=sources, + boundary_layers=pml_layers, + k_point=k, + geometry=geometry, + ) + + refl = sim.add_flux(self.fcen, self.df, self.nfreq, refl_fr) sim.load_minus_flux_data(refl, empty_data) sim.run(until_after_sources=termination_cond) @@ -91,38 +100,48 @@ def refl_angular(self, theta): refl_flux = mp.get_fluxes(refl) freqs = mp.get_flux_freqs(refl) - Rs = -np.array(refl_flux)/np.array(empty_flux) + Rs = -np.array(refl_flux) / np.array(empty_flux) - thetas = [math.asin(k.x/(self.n1*freqs[i])) for i in range(self.nfreq)] + thetas = [math.asin(k.x / (self.n1 * freqs[i])) for i in range(self.nfreq)] return freqs, thetas, Rs @parameterized.parameterized.expand([(0,), (20.6,)]) - def test_refl_angular(self,theta): + def test_refl_angular(self, theta): fmeep, tmeep, Rmeep = self.refl_angular(theta) # angle of refracted planewave in medium n2 for an # incident planewave in medium n1 at angle theta_in - theta_out = lambda theta_in: math.asin(self.n1*math.sin(theta_in)/self.n2) + theta_out = lambda theta_in: math.asin(self.n1 * math.sin(theta_in) / self.n2) # Fresnel reflectance for P polarization in medium n2 for # an incident planewave in medium n1 at angle theta_in - Rfresnel = lambda theta_in: (math.fabs((self.n1*math.cos(theta_out(theta_in))-self.n2*math.cos(theta_in)) / - (self.n1*math.cos(theta_out(theta_in))+self.n2*math.cos(theta_in)))**2) + Rfresnel = lambda theta_in: ( + math.fabs( + (self.n1 * math.cos(theta_out(theta_in)) - self.n2 * math.cos(theta_in)) + / ( + self.n1 * math.cos(theta_out(theta_in)) + + self.n2 * math.cos(theta_in) + ) + ) + ** 2 + ) Ranalytic = np.empty((self.nfreq,)) - print("refl:, wavelength (μm), incident angle (°), reflectance (Meep), reflectance (analytic), error") + print( + "refl:, wavelength (μm), incident angle (°), reflectance (Meep), reflectance (analytic), error" + ) for i in range(self.nfreq): Ranalytic[i] = Rfresnel(tmeep[i]) - err = abs(Rmeep[i]-Ranalytic[i])/Ranalytic[i] - print("refl:, {:4.2f}, {:4.2f}, {:8.6f}, {:8.6f}, {:6.4f}".format(1/fmeep[i], - math.degrees(tmeep[i]), - Rmeep[i], - Ranalytic[i], - err)) + err = abs(Rmeep[i] - Ranalytic[i]) / Ranalytic[i] + print( + "refl:, {:4.2f}, {:4.2f}, {:8.6f}, {:8.6f}, {:6.4f}".format( + 1 / fmeep[i], math.degrees(tmeep[i]), Rmeep[i], Ranalytic[i], err + ) + ) tol = 0.005 if mp.is_single_precision() else 0.004 self.assertClose(Rmeep, Ranalytic, epsilon=tol) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_ring.py b/python/tests/test_ring.py index 6472f3347..46ed58c24 100644 --- a/python/tests/test_ring.py +++ b/python/tests/test_ring.py @@ -3,8 +3,8 @@ import unittest import meep as mp -class TestRing(unittest.TestCase): +class TestRing(unittest.TestCase): @classmethod def setUpClass(cls): cls.temp_dir = mp.make_output_directory() @@ -32,12 +32,14 @@ def init(self): src = mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1)) - self.sim = mp.Simulation(cell_size=mp.Vector3(sxy, sxy), - geometry=[c1, c2], - sources=[src], - resolution=10, - symmetries=[mp.Mirror(mp.Y)], - boundary_layers=[mp.PML(dpml)]) + self.sim = mp.Simulation( + cell_size=mp.Vector3(sxy, sxy), + geometry=[c1, c2], + sources=[src], + resolution=10, + symmetries=[mp.Mirror(mp.Y)], + boundary_layers=[mp.PML(dpml)], + ) self.sim.use_output_directory(self.temp_dir) self.h = mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df) @@ -48,7 +50,7 @@ def test_harminv(self): self.sim.run( mp.at_beginning(mp.output_epsilon), mp.after_sources(self.h), - until_after_sources=300 + until_after_sources=300, ) m1 = self.h.modes[0] @@ -68,5 +70,5 @@ def test_harminv(self): self.assertAlmostEqual(fp, -0.08185972142450348, places=places) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_ring_cyl.py b/python/tests/test_ring_cyl.py index a6ec6c27d..6762b448b 100644 --- a/python/tests/test_ring_cyl.py +++ b/python/tests/test_ring_cyl.py @@ -2,8 +2,8 @@ import meep as mp from utils import ApproxComparisonTestCase -class TestRingCyl(ApproxComparisonTestCase): +class TestRingCyl(ApproxComparisonTestCase): def setUp(self): n = 3.4 w = 1 @@ -19,7 +19,7 @@ def setUp(self): mp.Block( center=mp.Vector3(self.r + (w / 2)), size=mp.Vector3(w, mp.inf, mp.inf), - material=mp.Medium(index=n) + material=mp.Medium(index=n), ) ] @@ -32,7 +32,7 @@ def setUp(self): mp.Source( src=mp.GaussianSource(self.fcen, fwidth=self.df), component=mp.Ez, - center=mp.Vector3(self.r + 0.1) + center=mp.Vector3(self.r + 0.1), ) ] @@ -44,7 +44,7 @@ def setUp(self): sources=sources, dimensions=dimensions, m=m, - split_chunks_evenly=False + split_chunks_evenly=False, ) def test_ring_cyl(self): @@ -67,5 +67,5 @@ def test_ring_cyl(self): self.assertClose(expected, res, epsilon=tol) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_simulation.py b/python/tests/test_simulation.py index af69f0620..2a72f9f1b 100644 --- a/python/tests/test_simulation.py +++ b/python/tests/test_simulation.py @@ -16,8 +16,8 @@ class TestSimulation(unittest.TestCase): - fname_base = re.sub(r'\.py$', '', os.path.split(sys.argv[0])[1]) - fname = fname_base + '-ez-000200.00.h5' + fname_base = re.sub(r"\.py$", "", os.path.split(sys.argv[0])[1]) + fname = fname_base + "-ez-000200.00.h5" def setUp(self): print(f"Running {self._testMethodName}") @@ -33,16 +33,106 @@ def tearDownClass(cls): def test_interpolate_numbers(self): expected = [ - 1.0, 1.0909090909090908, 1.1818181818181819, 1.2727272727272727, 1.3636363636363635, 1.4545454545454546, 1.5454545454545454, 1.6363636363636365, 1.7272727272727273, 1.8181818181818181, 1.9090909090909092, - 2.0, 2.090909090909091, 2.1818181818181817, 2.272727272727273, 2.3636363636363638, 2.4545454545454546, 2.5454545454545454, 2.6363636363636362, 2.727272727272727, 2.8181818181818183, 2.909090909090909, - 3.0, 3.090909090909091, 3.1818181818181817, 3.272727272727273, 3.3636363636363638, 3.4545454545454546, 3.5454545454545454, 3.6363636363636362, 3.727272727272727, 3.8181818181818183, 3.909090909090909, - 4.0, 4.090909090909091, 4.181818181818182, 4.2727272727272725, 4.363636363636363, 4.454545454545454, 4.545454545454546, 4.636363636363637, 4.7272727272727275, 4.818181818181818, 4.909090909090909, - 5.0, 5.090909090909091, 5.181818181818182, 5.2727272727272725, 5.363636363636363, 5.454545454545454, 5.545454545454546, 5.636363636363637, 5.7272727272727275, 5.818181818181818, 5.909090909090909, - 6.0, 6.090909090909091, 6.181818181818182, 6.2727272727272725, 6.363636363636363, 6.454545454545454, 6.545454545454546, 6.636363636363637, 6.7272727272727275, 6.818181818181818, 6.909090909090909, - 7.0, 7.090909090909091, 7.181818181818182, 7.2727272727272725, 7.363636363636363, 7.454545454545454, 7.545454545454546, 7.636363636363637, 7.7272727272727275, 7.818181818181818, 7.909090909090909, - 8.0, 8.090909090909092, 8.181818181818182, 8.272727272727273, 8.363636363636363, 8.454545454545455, 8.545454545454545, 8.636363636363637, 8.727272727272727, 8.818181818181818, 8.909090909090908, - 9.0, 9.090909090909092, 9.181818181818182, 9.272727272727273, 9.363636363636363, 9.454545454545455, 9.545454545454545, 9.636363636363637, 9.727272727272727, 9.818181818181818, 9.909090909090908, - 10.0 + 1.0, + 1.0909090909090908, + 1.1818181818181819, + 1.2727272727272727, + 1.3636363636363635, + 1.4545454545454546, + 1.5454545454545454, + 1.6363636363636365, + 1.7272727272727273, + 1.8181818181818181, + 1.9090909090909092, + 2.0, + 2.090909090909091, + 2.1818181818181817, + 2.272727272727273, + 2.3636363636363638, + 2.4545454545454546, + 2.5454545454545454, + 2.6363636363636362, + 2.727272727272727, + 2.8181818181818183, + 2.909090909090909, + 3.0, + 3.090909090909091, + 3.1818181818181817, + 3.272727272727273, + 3.3636363636363638, + 3.4545454545454546, + 3.5454545454545454, + 3.6363636363636362, + 3.727272727272727, + 3.8181818181818183, + 3.909090909090909, + 4.0, + 4.090909090909091, + 4.181818181818182, + 4.2727272727272725, + 4.363636363636363, + 4.454545454545454, + 4.545454545454546, + 4.636363636363637, + 4.7272727272727275, + 4.818181818181818, + 4.909090909090909, + 5.0, + 5.090909090909091, + 5.181818181818182, + 5.2727272727272725, + 5.363636363636363, + 5.454545454545454, + 5.545454545454546, + 5.636363636363637, + 5.7272727272727275, + 5.818181818181818, + 5.909090909090909, + 6.0, + 6.090909090909091, + 6.181818181818182, + 6.2727272727272725, + 6.363636363636363, + 6.454545454545454, + 6.545454545454546, + 6.636363636363637, + 6.7272727272727275, + 6.818181818181818, + 6.909090909090909, + 7.0, + 7.090909090909091, + 7.181818181818182, + 7.2727272727272725, + 7.363636363636363, + 7.454545454545454, + 7.545454545454546, + 7.636363636363637, + 7.7272727272727275, + 7.818181818181818, + 7.909090909090909, + 8.0, + 8.090909090909092, + 8.181818181818182, + 8.272727272727273, + 8.363636363636363, + 8.454545454545455, + 8.545454545454545, + 8.636363636363637, + 8.727272727272727, + 8.818181818181818, + 8.909090909090908, + 9.0, + 9.090909090909092, + 9.181818181818182, + 9.272727272727273, + 9.363636363636363, + 9.454545454545455, + 9.545454545454545, + 9.636363636363637, + 9.727272727272727, + 9.818181818181818, + 9.909090909090908, + 10.0, ] nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] @@ -73,7 +163,7 @@ def test_interpolate_vectors(self): mp.Vector3(0.42499999999999993), mp.Vector3(0.44999999999999996), mp.Vector3(0.475), - mp.Vector3(0.5) + mp.Vector3(0.5), ] res = mp.interpolate(19, [mp.Vector3(), mp.Vector3(0.5)]) @@ -94,7 +184,7 @@ def test_arith_sequence(self): 0.15240480961923594, 0.15280561122244193, 0.15320641282564793, - 0.15360721442885392 + 0.15360721442885392, ] res = np.linspace(0.15, 0.15 + 0.000400801603206 * 10, num=10, endpoint=False) @@ -112,17 +202,20 @@ def init_simple_simulation(self, **kwargs): fcen = 1.0 df = 1.0 - sources = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), - component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), component=mp.Ez + ) symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] - return mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=[pml_layers], - sources=[sources], - symmetries=symmetries, - **kwargs) + return mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=[pml_layers], + sources=[sources], + symmetries=symmetries, + **kwargs, + ) @unittest.skipIf(not mp.with_mpi(), "MPI specific test") def test_mpi(self): @@ -130,7 +223,7 @@ def test_mpi(self): def test_use_output_directory_default(self): sim = self.init_simple_simulation() - output_dir = os.path.join(self.temp_dir, 'simulation-out') + output_dir = os.path.join(self.temp_dir, "simulation-out") sim.use_output_directory(output_dir) sim.run(mp.at_end(mp.output_efield_z), until=200) @@ -141,7 +234,9 @@ def test_at_time(self): sim.use_output_directory(self.temp_dir) sim.run(mp.at_time(100, mp.output_efield_z), until=200) - fname = os.path.join(self.temp_dir, f'{sim.get_filename_prefix()}-ez-000100.00.h5') + fname = os.path.join( + self.temp_dir, f"{sim.get_filename_prefix()}-ez-000100.00.h5" + ) self.assertTrue(os.path.exists(fname)) @@ -160,9 +255,14 @@ def _done(sim, todo): def test_with_prefix(self): sim = self.init_simple_simulation() sim.use_output_directory(self.temp_dir) - sim.run(mp.with_prefix('test_prefix-', mp.at_end(mp.output_efield_z)), until=200) + sim.run( + mp.with_prefix("test_prefix-", mp.at_end(mp.output_efield_z)), until=200 + ) - fname = os.path.join(self.temp_dir, (f'test_prefix-{sim.get_filename_prefix()}' + '-ez-000200.00.h5')) + fname = os.path.join( + self.temp_dir, + (f"test_prefix-{sim.get_filename_prefix()}" + "-ez-000200.00.h5"), + ) self.assertTrue(os.path.exists(fname)) @@ -189,25 +289,29 @@ def test_infer_dimensions(self): def test_in_volume(self): sim = self.init_simple_simulation() sim.use_output_directory(self.temp_dir) - sim.filename_prefix = 'test_in_volume' + sim.filename_prefix = "test_in_volume" vol = mp.Volume(mp.Vector3(), size=mp.Vector3(x=2)) sim.run(mp.at_end(mp.in_volume(vol, mp.output_efield_z)), until=200) - fn = os.path.join(self.temp_dir, 'test_in_volume-ez-000200.00.h5') + fn = os.path.join(self.temp_dir, "test_in_volume-ez-000200.00.h5") self.assertTrue(os.path.exists(fn)) def test_in_point(self): sim = self.init_simple_simulation() sim.use_output_directory(self.temp_dir) - sim.filename_prefix = 'test_in_point' + sim.filename_prefix = "test_in_point" pt = mp.Vector3() sim.run(mp.at_end(mp.in_point(pt, mp.output_efield_z)), until=200) - fn = os.path.join(self.temp_dir, 'test_in_point-ez-000200.00.h5') + fn = os.path.join(self.temp_dir, "test_in_point-ez-000200.00.h5") self.assertTrue(os.path.exists(fn)) def test_epsilon_input_file(self): sim = self.init_simple_simulation() - eps_input_fname = 'cyl-ellipsoid-eps-ref.h5' - eps_input_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'tests')) + eps_input_fname = "cyl-ellipsoid-eps-ref.h5" + eps_input_dir = os.path.abspath( + os.path.join( + os.path.dirname(os.path.abspath(__file__)), "..", "..", "tests" + ) + ) eps_input_path = os.path.join(eps_input_dir, eps_input_fname) sim.epsilon_input_file = eps_input_path @@ -219,19 +323,25 @@ def test_epsilon_input_file(self): # Test unicode file name for Python 2 if sys.version_info[0] == 2: - sim = self.init_simple_simulation(epsilon_input_file=unicode(eps_input_path)) + sim = self.init_simple_simulation( + epsilon_input_file=unicode(eps_input_path) + ) sim.run(until=200) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) self.assertAlmostEqual(fp, -0.002989654055823199) def test_numpy_epsilon(self): sim = self.init_simple_simulation() - eps_input_fname = 'cyl-ellipsoid-eps-ref.h5' - eps_input_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'tests')) + eps_input_fname = "cyl-ellipsoid-eps-ref.h5" + eps_input_dir = os.path.abspath( + os.path.join( + os.path.dirname(os.path.abspath(__file__)), "..", "..", "tests" + ) + ) eps_input_path = os.path.join(eps_input_dir, eps_input_fname) - with h5py.File(eps_input_path, 'r') as f: - sim.default_material = f['eps'][()] + with h5py.File(eps_input_path, "r") as f: + sim.default_material = f["eps"][()] sim.run(until=200) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) @@ -240,51 +350,68 @@ def test_numpy_epsilon(self): self.assertAlmostEqual(fp, -0.002989654055823199, places=places) def test_set_materials(self): - def change_geom(sim): t = sim.meep_time() fn = t * 0.02 - geom = [mp.Cylinder(radius=3, material=mp.Medium(index=3.5), center=mp.Vector3(fn, fn)), - mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf), center=mp.Vector3(fn, fn))] + geom = [ + mp.Cylinder( + radius=3, material=mp.Medium(index=3.5), center=mp.Vector3(fn, fn) + ), + mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf), center=mp.Vector3(fn, fn)), + ] sim.set_materials(geometry=geom) c = mp.Cylinder(radius=3, material=mp.Medium(index=3.5)) e = mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf)) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.1), component=mp.Hz, center=mp.Vector3()) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.1), component=mp.Hz, center=mp.Vector3() + ) symmetries = [mp.Mirror(mp.X, -1), mp.Mirror(mp.Y, -1)] - sim = mp.Simulation(cell_size=mp.Vector3(10, 10), - geometry=[c, e], - boundary_layers=[mp.PML(1.0)], - sources=[sources], - symmetries=symmetries, - resolution=16) + sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), + geometry=[c, e], + boundary_layers=[mp.PML(1.0)], + sources=[sources], + symmetries=symmetries, + resolution=16, + ) - eps = {'arr1': None, 'arr2': None} + eps = {"arr1": None, "arr2": None} def get_arr1(sim): - eps['arr1'] = sim.get_array(mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10))) + eps["arr1"] = sim.get_array( + mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10)) + ) def get_arr2(sim): - eps['arr2'] = sim.get_array(mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10))) - - sim.run(mp.at_time(50, get_arr1), mp.at_time(100, change_geom), - mp.at_end(get_arr2), until=200) + eps["arr2"] = sim.get_array( + mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10)) + ) + + sim.run( + mp.at_time(50, get_arr1), + mp.at_time(100, change_geom), + mp.at_end(get_arr2), + until=200, + ) - self.assertFalse(np.array_equal(eps['arr1'], eps['arr2'])) + self.assertFalse(np.array_equal(eps["arr1"], eps["arr2"])) def test_modal_volume_in_box(self): sim = self.init_simple_simulation() sim.run(until=200) vol = sim.fields.total_volume() - self.assertAlmostEqual(sim.fields.modal_volume_in_box(vol), - sim.modal_volume_in_box()) + self.assertAlmostEqual( + sim.fields.modal_volume_in_box(vol), sim.modal_volume_in_box() + ) vol = mp.Volume(mp.Vector3(), size=mp.Vector3(1, 1, 1)) - self.assertAlmostEqual(sim.fields.modal_volume_in_box(vol.swigobj), - sim.modal_volume_in_box(vol)) + self.assertAlmostEqual( + sim.fields.modal_volume_in_box(vol.swigobj), sim.modal_volume_in_box(vol) + ) def test_in_box_volumes(self): sim = self.init_simple_simulation() @@ -307,7 +434,7 @@ def test_get_array_output(self): sim.use_output_directory(self.temp_dir) sim.symmetries = [] sim.geometry = [mp.Cylinder(0.2, material=mp.Medium(index=3))] - sim.filename_prefix = 'test_get_array_output' + sim.filename_prefix = "test_get_array_output" sim.run(until=20) mp.output_epsilon(sim) @@ -320,21 +447,21 @@ def test_get_array_output(self): energy_arr = sim.get_tot_pwr(snap=True) efield_arr = sim.get_efield(snap=True) - fname_fmt = os.path.join(self.temp_dir, 'test_get_array_output-{}-000020.00.h5') + fname_fmt = os.path.join(self.temp_dir, "test_get_array_output-{}-000020.00.h5") - with h5py.File(fname_fmt.format('eps'), 'r') as f: - eps = f['eps'][()] + with h5py.File(fname_fmt.format("eps"), "r") as f: + eps = f["eps"][()] - with h5py.File(fname_fmt.format('ez'), 'r') as f: - efield_z = f['ez'][()] + with h5py.File(fname_fmt.format("ez"), "r") as f: + efield_z = f["ez"][()] - with h5py.File(fname_fmt.format('energy'), 'r') as f: - energy = f['energy'][()] + with h5py.File(fname_fmt.format("energy"), "r") as f: + energy = f["energy"][()] - with h5py.File(fname_fmt.format('e'), 'r') as f: - ex = f['ex'][()] - ey = f['ey'][()] - ez = f['ez'][()] + with h5py.File(fname_fmt.format("e"), "r") as f: + ex = f["ex"][()] + ey = f["ey"][()] + ez = f["ez"][()] efield = np.stack([ex, ey, ez], axis=-1) np.testing.assert_allclose(eps, eps_arr) @@ -346,13 +473,21 @@ def test_synchronized_magnetic(self): # Issue 309 cell = mp.Vector3(16, 8, 0) - geometry = [mp.Block(mp.Vector3(1e20, 1, 1e20), - center=mp.Vector3(0, 0), - material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + mp.Vector3(1e20, 1, 1e20), + center=mp.Vector3(0, 0), + material=mp.Medium(epsilon=12), + ) + ] - sources = [mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(-7, 0))] + sources = [ + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + center=mp.Vector3(-7, 0), + ) + ] pml_layers = [mp.PML(1.0)] resolution = 10 @@ -362,14 +497,13 @@ def test_synchronized_magnetic(self): boundary_layers=pml_layers, geometry=geometry, sources=sources, - resolution=resolution + resolution=resolution, ) sim.use_output_directory(self.temp_dir) sim.run(mp.synchronized_magnetic(mp.output_bfield_y), until=10) def test_harminv_warnings(self): - def check_warnings(sim, h, should_warn=True): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") @@ -381,21 +515,30 @@ def check_warnings(sim, h, should_warn=True): else: self.assertEqual(len(w), 0) - sources = [mp.Source(src=mp.GaussianSource(1, fwidth=1), center=mp.Vector3(), component=mp.Ez)] - sim = mp.Simulation(cell_size=mp.Vector3(10, 10), resolution=10, sources=sources) + sources = [ + mp.Source( + src=mp.GaussianSource(1, fwidth=1), center=mp.Vector3(), component=mp.Ez + ) + ] + sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), resolution=10, sources=sources + ) h = mp.Harminv(mp.Ez, mp.Vector3(), 1.4, 0.5) check_warnings(sim, h) - sim = mp.Simulation(cell_size=mp.Vector3(10, 10), resolution=10, sources=sources) + sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), resolution=10, sources=sources + ) h = mp.Harminv(mp.Ez, mp.Vector3(), 0.5, 0.5) check_warnings(sim, h) - sim = mp.Simulation(cell_size=mp.Vector3(10, 10), resolution=10, sources=sources) + sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), resolution=10, sources=sources + ) h = mp.Harminv(mp.Ez, mp.Vector3(), 1, 1) check_warnings(sim, h, should_warn=False) def test_vec_constructor(self): - def assert_one(v): self.assertEqual(v.z(), 1) @@ -429,14 +572,16 @@ def check_iterable(one, two, three, four): assert_two(v2) v3 = mp.vec(three) assert_three(v3) - assert_raises(four, (NotImplementedError,TypeError)) + assert_raises(four, (NotImplementedError, TypeError)) check_iterable([1], [1, 2], [1, 2, 3], [1, 2, 3, 4]) check_iterable((1,), (1, 2), (1, 2, 3), (1, 2, 3, 4)) - check_iterable(np.array([1.]), - np.array([1., 2.]), - np.array([1., 2., 3.]), - np.array([1., 2., 3., 4.])) + check_iterable( + np.array([1.0]), + np.array([1.0, 2.0]), + np.array([1.0, 2.0, 3.0]), + np.array([1.0, 2.0, 3.0, 4.0]), + ) with self.assertRaises(TypeError): mp.vec([1, 2], 3) @@ -444,7 +589,9 @@ def check_iterable(one, two, three, four): with self.assertRaises(TypeError): mp.vec(1, [2, 3]) - @unittest.skipIf(mp.is_single_precision(), "double-precision floating point specific test") + @unittest.skipIf( + mp.is_single_precision(), "double-precision floating point specific test" + ) def test_epsilon_warning(self): ## fields blow up using dispersive material ## when compiled using single precision @@ -452,9 +599,11 @@ def test_epsilon_warning(self): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") from meep.materials import Si + self.assertEqual(len(w), 0) from meep.materials import Mo + geom = [mp.Sphere(radius=0.2, material=Mo)] sim = self.init_simple_simulation(geometry=geom) with warnings.catch_warnings(record=True) as w: @@ -464,6 +613,7 @@ def test_epsilon_warning(self): self.assertIn("Epsilon", str(w[0].message)) from meep.materials import SiO2 + geom = [mp.Sphere(radius=0.2, material=SiO2)] sim = self.init_simple_simulation(geometry=geom) with warnings.catch_warnings(record=True) as w: @@ -475,7 +625,7 @@ def test_epsilon_warning(self): def test_get_filename_prefix(self): sim = self.init_simple_simulation() self.assertEqual(sim.get_filename_prefix(), self.fname_base) - sim.filename_prefix = '' + sim.filename_prefix = "" self.assertEqual(sim.get_filename_prefix(), "") sim.filename_prefix = False with self.assertRaises(TypeError): @@ -506,16 +656,32 @@ def test_geometry_center(self): fcen = 0.15 df = 0.1 - sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, - center=mp.Vector3())] - geometry = [mp.Block(center=mp.Vector3(), size=mp.Vector3(mp.inf, 3, mp.inf), - material=mp.Medium(epsilon=12))] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(), + ) + ] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 3, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] def print_field(sim): result.append(sim.get_field_point(mp.Ez, mp.Vector3(2, -1))) - sim = mp.Simulation(resolution=resolution, cell_size=cell_size, boundary_layers=pml, - sources=sources, geometry=geometry, geometry_center=center) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml, + sources=sources, + geometry=geometry, + geometry_center=center, + ) sim.run(mp.at_end(print_field), until=50) self.assertAlmostEqual(result[0], -0.0599602798684155) @@ -529,29 +695,50 @@ def test_timing_data(self): fcen = 0.15 df = 0.1 - sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, - center=mp.Vector3())] - geometry = [mp.Block(center=mp.Vector3(), size=mp.Vector3(mp.inf, 3, mp.inf), - material=mp.Medium(epsilon=12))] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(), + ) + ] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 3, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] - sim = mp.Simulation(resolution=resolution, cell_size=cell_size, boundary_layers=pml, - sources=sources, geometry=geometry, geometry_center=center) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml, + sources=sources, + geometry=geometry, + geometry_center=center, + ) sim.run(until=50) timing_data = sim.get_timing_data() # Non-exhaustive collection of steps where some time should be spent: - EXPECTED_NONZERO_TIMESINKS = (mp.Stepping, mp.Boundaries, - mp.FieldUpdateB, mp.FieldUpdateH, - mp.FieldUpdateD, mp.FieldUpdateE) + EXPECTED_NONZERO_TIMESINKS = ( + mp.Stepping, + mp.Boundaries, + mp.FieldUpdateB, + mp.FieldUpdateH, + mp.FieldUpdateD, + mp.FieldUpdateE, + ) # Due to the problem setup, no time should be spent on these steps: EXPECTED_ZERO_TIMESINKS = (mp.MPBTime, mp.GetFarfieldsTime) - for sink in itertools.chain(EXPECTED_NONZERO_TIMESINKS, - EXPECTED_ZERO_TIMESINKS): + for sink in itertools.chain( + EXPECTED_NONZERO_TIMESINKS, EXPECTED_ZERO_TIMESINKS + ): self.assertIn(sink, timing_data.keys()) self.assertEqual(len(timing_data[sink]), mp.count_processors()) - np.testing.assert_array_equal(sim.time_spent_on(sink), - timing_data[sink]) + np.testing.assert_array_equal(sim.time_spent_on(sink), timing_data[sink]) for sink in EXPECTED_NONZERO_TIMESINKS: for t in timing_data[sink]: @@ -563,34 +750,40 @@ def test_timing_data(self): self.assertGreaterEqual( sum(timing_data[mp.Stepping]), - sum(timing_data[mp.FieldUpdateB]) + - sum(timing_data[mp.FieldUpdateH]) + - sum(timing_data[mp.FieldUpdateD]) + - sum(timing_data[mp.FieldUpdateE]) + - sum(timing_data[mp.FourierTransforming])) + sum(timing_data[mp.FieldUpdateB]) + + sum(timing_data[mp.FieldUpdateH]) + + sum(timing_data[mp.FieldUpdateD]) + + sum(timing_data[mp.FieldUpdateE]) + + sum(timing_data[mp.FourierTransforming]), + ) def test_source_slice(self): sim = self.init_simple_simulation() sim.run(until=1) - vol1d = mp.Volume(center=mp.Vector3(0.1234,0), size=mp.Vector3(0,5.07)) + vol1d = mp.Volume(center=mp.Vector3(0.1234, 0), size=mp.Vector3(0, 5.07)) source_slice = sim.get_source(mp.Ez, vol=vol1d) - x,y,z,w = sim.get_array_metadata(vol=vol1d) + x, y, z, w = sim.get_array_metadata(vol=vol1d) self.assertEqual(source_slice.shape, w.shape) self.assertEqual(np.sum(source_slice), 0) - vol2d = mp.Volume(center=mp.Vector3(-0.541,0.791), size=mp.Vector3(3.5,2.8)) + vol2d = mp.Volume(center=mp.Vector3(-0.541, 0.791), size=mp.Vector3(3.5, 2.8)) source_slice = sim.get_source(mp.Ez, vol=vol2d) - x,y,z,w = sim.get_array_metadata(vol=vol2d) + x, y, z, w = sim.get_array_metadata(vol=vol2d) self.assertEqual(source_slice.shape, w.shape) self.assertNotEqual(np.sum(source_slice), 0) def test_has_mu(self): - def _check(med, expected, default=mp.Medium()): - geometry = [mp.Block(center=mp.Vector3(), size=mp.Vector3(1, 1), material=med)] - sim = mp.Simulation(cell_size=mp.Vector3(5, 5), resolution=10, geometry=geometry, - default_material=default) + geometry = [ + mp.Block(center=mp.Vector3(), size=mp.Vector3(1, 1), material=med) + ] + sim = mp.Simulation( + cell_size=mp.Vector3(5, 5), + resolution=10, + geometry=geometry, + default_material=default, + ) result = sim.has_mu() if expected: @@ -633,5 +826,6 @@ def test_iterable_as_v3(self): self.assertAlmostEqual(pt1, expected) self.assertAlmostEqual(pt2, expected) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_source.py b/python/tests/test_source.py index 5006dac8a..b009a24b6 100644 --- a/python/tests/test_source.py +++ b/python/tests/test_source.py @@ -8,11 +8,10 @@ from meep.source import EigenModeSource, ContinuousSource, GaussianSource -data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) +data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) class TestEigenModeSource(unittest.TestCase): - def test_eig_lattice_defaults(self): src = ContinuousSource(5.0) center = Vector3() @@ -23,19 +22,20 @@ def test_eig_lattice_defaults(self): elc = Vector3(1, 1, 1) els = Vector3(1, 1, 1) - custom_lattice = EigenModeSource(src, center, eig_lattice_center=elc, eig_lattice_size=els) + custom_lattice = EigenModeSource( + src, center, eig_lattice_center=elc, eig_lattice_size=els + ) self.assertEqual(custom_lattice.eig_lattice_size, els) self.assertEqual(custom_lattice.eig_lattice_center, elc) class TestSourceTime(unittest.TestCase): - def test_source_wavelength(self): g_src = GaussianSource(wavelength=10) c_src = ContinuousSource(wavelength=10) - self.assertAlmostEqual(1. / 10., g_src.frequency) - self.assertAlmostEqual(1. / 10., c_src.frequency) + self.assertAlmostEqual(1.0 / 10.0, g_src.frequency) + self.assertAlmostEqual(1.0 / 10.0, c_src.frequency) def test_source_frequency(self): g_src = GaussianSource(10) @@ -52,9 +52,7 @@ def test_source_frequency(self): class TestSourceTypemaps(unittest.TestCase): - def setUp(self): - def dummy_eps(v): return 1.0 @@ -88,7 +86,7 @@ def test_custom_source(self): geometry = [ mp.Cylinder(r + w, material=mp.Medium(index=n)), - mp.Cylinder(r, material=mp.air) + mp.Cylinder(r, material=mp.air), ] boundary_layers = [mp.PML(dpml)] @@ -98,25 +96,34 @@ def test_custom_source(self): # Bump function def my_src_func(t): - return math.exp(-1 / (1 - ((t - 1)**2))) if t > 0 and t < 2 else 0j - - sources = [mp.Source(src=mp.CustomSource(src_func=my_src_func, end_time=100), - component=mp.Ez, center=mp.Vector3(r + 0.1))] + return math.exp(-1 / (1 - ((t - 1) ** 2))) if t > 0 and t < 2 else 0j + + sources = [ + mp.Source( + src=mp.CustomSource(src_func=my_src_func, end_time=100), + component=mp.Ez, + center=mp.Vector3(r + 0.1), + ) + ] symmetries = [mp.Mirror(mp.Y)] - sim = mp.Simulation(cell_size=cell, - resolution=resolution, - geometry=geometry, - boundary_layers=boundary_layers, - sources=sources, - symmetries=symmetries) + sim = mp.Simulation( + cell_size=cell, + resolution=resolution, + geometry=geometry, + boundary_layers=boundary_layers, + sources=sources, + symmetries=symmetries, + ) h = mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df) sim.run(mp.after_sources(h), until_after_sources=200) fp = sim.get_field_point(mp.Ez, mp.Vector3(1)) - self.assertAlmostEqual(fp, -0.021997617628500023 + 0j, 5 if mp.is_single_precision() else 7) + self.assertAlmostEqual( + fp, -0.021997617628500023 + 0j, 5 if mp.is_single_precision() else 7 + ) def amp_fun(p): @@ -124,7 +131,6 @@ def amp_fun(p): class TestAmpFileFunc(unittest.TestCase): - def create_h5data(self): N = 100 M = 200 @@ -145,18 +151,39 @@ def init_and_run(self, test_type): cen = mp.Vector3(0.1, 0.2) sz = mp.Vector3(0.3, 0.2) - amp_file = os.path.join(data_dir, 'amp_func_file') - amp_file += ':amp_data' - - if test_type == 'file': - sources = [mp.Source(mp.ContinuousSource(fcen, fwidth=df), component=mp.Ez, center=cen, - size=sz, amp_func_file=amp_file)] - elif test_type == 'func': - sources = [mp.Source(mp.ContinuousSource(fcen, fwidth=df), component=mp.Ez, center=cen, - size=sz, amp_func=amp_fun)] - elif test_type == 'arr': - sources = [mp.Source(mp.ContinuousSource(fcen, fwidth=df), component=mp.Ez, center=cen, - size=sz, amp_data=self.amp_data)] + amp_file = os.path.join(data_dir, "amp_func_file") + amp_file += ":amp_data" + + if test_type == "file": + sources = [ + mp.Source( + mp.ContinuousSource(fcen, fwidth=df), + component=mp.Ez, + center=cen, + size=sz, + amp_func_file=amp_file, + ) + ] + elif test_type == "func": + sources = [ + mp.Source( + mp.ContinuousSource(fcen, fwidth=df), + component=mp.Ez, + center=cen, + size=sz, + amp_func=amp_fun, + ) + ] + elif test_type == "arr": + sources = [ + mp.Source( + mp.ContinuousSource(fcen, fwidth=df), + component=mp.Ez, + center=cen, + size=sz, + amp_data=self.amp_data, + ) + ] sim = mp.Simulation(cell_size=cell, resolution=resolution, sources=sources) sim.run(until=200) @@ -164,15 +191,15 @@ def init_and_run(self, test_type): def test_amp_file_func(self): self.create_h5data() - field_point_amp_file = self.init_and_run(test_type='file') - field_point_amp_func = self.init_and_run(test_type='func') - field_point_amp_arr = self.init_and_run(test_type='arr') + field_point_amp_file = self.init_and_run(test_type="file") + field_point_amp_func = self.init_and_run(test_type="func") + field_point_amp_arr = self.init_and_run(test_type="arr") self.assertAlmostEqual(field_point_amp_file, field_point_amp_func, places=4) self.assertAlmostEqual(field_point_amp_arr, field_point_amp_func, places=4) -class TestCustomEigenModeSource(unittest.TestCase): +class TestCustomEigenModeSource(unittest.TestCase): def test_custom_em_source(self): resolution = 20 @@ -181,43 +208,56 @@ def test_custom_em_source(self): sx = 40 sy = 12 - cell_size = mp.Vector3(sx+2*dpml,sy) + cell_size = mp.Vector3(sx + 2 * dpml, sy) v0 = 0.15 # pulse center frequency - a = 0.2*v0 # Gaussian envelope half-width + a = 0.2 * v0 # Gaussian envelope half-width b = -0.1 # linear chirp rate (positive: up-chirp, negative: down-chirp) - t0 = 15 # peak time + t0 = 15 # peak time - chirp = lambda t: np.exp(1j*2*np.pi*v0*(t-t0)) * np.exp(-a*(t-t0)**2+1j*b*(t-t0)**2) + chirp = lambda t: np.exp(1j * 2 * np.pi * v0 * (t - t0)) * np.exp( + -a * (t - t0) ** 2 + 1j * b * (t - t0) ** 2 + ) - geometry = [mp.Block(center=mp.Vector3(0,0,0),size=mp.Vector3(mp.inf,1,mp.inf),material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + center=mp.Vector3(0, 0, 0), + size=mp.Vector3(mp.inf, 1, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] - kx = 0.4 # initial guess for wavevector in x-direction of eigenmode + kx = 0.4 # initial guess for wavevector in x-direction of eigenmode kpoint = mp.Vector3(kx) bnum = 1 - sources = [mp.EigenModeSource(src=mp.CustomSource(src_func=chirp,center_frequency=v0), - center=mp.Vector3(-0.5*sx + dpml + 1), - size=mp.Vector3(y=sy), - eig_kpoint=kpoint, - eig_band=bnum, - eig_parity=mp.EVEN_Y+mp.ODD_Z, - eig_match_freq=True - )] - - sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - resolution=resolution, - k_point=mp.Vector3(), - sources=sources, - geometry=geometry, - symmetries=[mp.Mirror(mp.Y)]) - - t = np.linspace(0,50,1000) - sim.run(until=t0+50) + sources = [ + mp.EigenModeSource( + src=mp.CustomSource(src_func=chirp, center_frequency=v0), + center=mp.Vector3(-0.5 * sx + dpml + 1), + size=mp.Vector3(y=sy), + eig_kpoint=kpoint, + eig_band=bnum, + eig_parity=mp.EVEN_Y + mp.ODD_Z, + eig_match_freq=True, + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + resolution=resolution, + k_point=mp.Vector3(), + sources=sources, + geometry=geometry, + symmetries=[mp.Mirror(mp.Y)], + ) + + t = np.linspace(0, 50, 1000) + sim.run(until=t0 + 50) # For now, just check to make sure the simulation can run and the fields don't blow up. -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_special_kz.py b/python/tests/test_special_kz.py index 65f31fd2c..067381d6b 100644 --- a/python/tests/test_special_kz.py +++ b/python/tests/test_special_kz.py @@ -7,64 +7,81 @@ class TestSpecialKz(unittest.TestCase): - def refl_planar(self, theta, kz_2d): resolution = 100 # pixels/um dpml = 1.0 - sx = 3.0 + 2*dpml - sy = 1/resolution - cell_size = mp.Vector3(sx,sy) - pml_layers = [mp.PML(dpml,direction=mp.X)] + sx = 3.0 + 2 * dpml + sy = 1 / resolution + cell_size = mp.Vector3(sx, sy) + pml_layers = [mp.PML(dpml, direction=mp.X)] fcen = 1.0 # plane of incidence is XZ - k_point = mp.Vector3(1,0,0).rotate(mp.Vector3(0,1,0),theta).scale(fcen) - - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Ez, # P-polarization - center=mp.Vector3(-0.5*sx+dpml), - size=mp.Vector3(y=sy))] - - sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=k_point, - kz_2d=kz_2d, - resolution=resolution) - - refl_fr = mp.FluxRegion(center=mp.Vector3(-0.25*sx), - size=mp.Vector3(y=sy)) - refl = sim.add_flux(fcen,0,1,refl_fr) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mp.Vector3(),1e-9)) + k_point = mp.Vector3(1, 0, 0).rotate(mp.Vector3(0, 1, 0), theta).scale(fcen) + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Ez, # P-polarization + center=mp.Vector3(-0.5 * sx + dpml), + size=mp.Vector3(y=sy), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=k_point, + kz_2d=kz_2d, + resolution=resolution, + ) + + refl_fr = mp.FluxRegion(center=mp.Vector3(-0.25 * sx), size=mp.Vector3(y=sy)) + refl = sim.add_flux(fcen, 0, 1, refl_fr) + + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-9 + ) + ) empty_flux = mp.get_fluxes(refl) empty_data = sim.get_flux_data(refl) sim.reset_meep() - geometry = [mp.Block(material=mp.Medium(index=3.5), - size=mp.Vector3(0.5*sx,mp.inf,mp.inf), - center=mp.Vector3(0.25*sx))] - - sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - k_point=k_point, - kz_2d=kz_2d, - resolution=resolution) - - refl = sim.add_flux(fcen,0,1,refl_fr) - sim.load_minus_flux_data(refl,empty_data) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mp.Vector3(),1e-9)) + geometry = [ + mp.Block( + material=mp.Medium(index=3.5), + size=mp.Vector3(0.5 * sx, mp.inf, mp.inf), + center=mp.Vector3(0.25 * sx), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + k_point=k_point, + kz_2d=kz_2d, + resolution=resolution, + ) + + refl = sim.add_flux(fcen, 0, 1, refl_fr) + sim.load_minus_flux_data(refl, empty_data) + + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-9 + ) + ) refl_flux = mp.get_fluxes(refl) - return -refl_flux[0]/empty_flux[0] - + return -refl_flux[0] / empty_flux[0] def test_special_kz(self): n1 = 1 @@ -72,12 +89,17 @@ def test_special_kz(self): # compute angle of refracted planewave in medium n2 # for incident planewave in medium n1 at angle theta_in - theta_out = lambda theta_in: math.asin(n1*math.sin(theta_in)/n2) + theta_out = lambda theta_in: math.asin(n1 * math.sin(theta_in) / n2) # compute Fresnel reflectance for P-polarization in medium n2 # for incident planewave in medium n1 at angle theta_in - Rfresnel = lambda theta_in: math.fabs((n1*math.cos(theta_out(theta_in))-n2*math.cos(theta_in)) - / (n1*math.cos(theta_out(theta_in))+n2*math.cos(theta_in)))**2 + Rfresnel = ( + lambda theta_in: math.fabs( + (n1 * math.cos(theta_out(theta_in)) - n2 * math.cos(theta_in)) + / (n1 * math.cos(theta_out(theta_in)) + n2 * math.cos(theta_in)) + ) + ** 2 + ) # angle of incident planewave; clockwise (CW) about Y axis, 0 degrees along +X axis theta = math.radians(23) @@ -92,74 +114,79 @@ def test_special_kz(self): Rfres = Rfresnel(theta) - self.assertAlmostEqual(Rmeep_complex,Rfres,places=2) - self.assertAlmostEqual(Rmeep_real_imag,Rfres,places=2) - + self.assertAlmostEqual(Rmeep_complex, Rfres, places=2) + self.assertAlmostEqual(Rmeep_real_imag, Rfres, places=2) + # the real/imag algorithm should be faster, but on CI machines performance is too variable # for this to reliably hold - # self.assertLess(t_real_imag,t_complex) - + # self.assertLess(t_real_imag,t_complex) - @parameterized.parameterized.expand([("complex",),("real/imag",)]) + @parameterized.parameterized.expand([("complex",), ("real/imag",)]) def test_eigsrc_kz(self, kz_2d): - resolution = 30 # pixels/um + resolution = 30 # pixels/um - cell_size = mp.Vector3(14,14) + cell_size = mp.Vector3(14, 14) pml_layers = [mp.PML(thickness=2)] - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,1,mp.inf), - material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] fsrc = 0.3 # frequency of eigenmode or constant-amplitude source - bnum = 1 # band number of eigenmode - kz = 0.2 # fixed out-of-plane wavevector component - - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc), - center=mp.Vector3(), - size=mp.Vector3(y=14), - eig_band=bnum, - eig_parity=mp.EVEN_Y, - eig_match_freq=True)] - - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=[mp.Mirror(mp.Y)], - k_point=mp.Vector3(z=kz), - kz_2d=kz_2d) - - tran = sim.add_flux(fsrc, - 0, - 1, - mp.FluxRegion(center=mp.Vector3(x=5), - size=mp.Vector3(y=14))) + bnum = 1 # band number of eigenmode + kz = 0.2 # fixed out-of-plane wavevector component + + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(), + size=mp.Vector3(y=14), + eig_band=bnum, + eig_parity=mp.EVEN_Y, + eig_match_freq=True, + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=[mp.Mirror(mp.Y)], + k_point=mp.Vector3(z=kz), + kz_2d=kz_2d, + ) + + tran = sim.add_flux( + fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5), size=mp.Vector3(y=14)) + ) sim.run(until_after_sources=50) - res = sim.get_eigenmode_coefficients(tran, - [1,2], - eig_parity=mp.EVEN_Y) + res = sim.get_eigenmode_coefficients(tran, [1, 2], eig_parity=mp.EVEN_Y) total_flux = mp.get_fluxes(tran)[0] - mode1_flux = abs(res.alpha[0,0,0])**2 - mode2_flux = abs(res.alpha[1,0,0])**2 + mode1_flux = abs(res.alpha[0, 0, 0]) ** 2 + mode2_flux = abs(res.alpha[1, 0, 0]) ** 2 mode1_frac = 0.99 - self.assertGreater(mode1_flux/total_flux, mode1_frac) - self.assertLess(mode2_flux/total_flux, 1-mode1_frac) + self.assertGreater(mode1_flux / total_flux, mode1_frac) + self.assertLess(mode2_flux / total_flux, 1 - mode1_frac) d = 3.5 - ez1 = sim.get_field_point(mp.Ez, mp.Vector3(2.3,-5.7,4.8)) - ez2 = sim.get_field_point(mp.Ez, mp.Vector3(2.3,-5.7,4.8+d)) - ratio_ez = ez2/ez1 - phase_diff = cmath.exp(1j*2*cmath.pi*kz*d) - self.assertAlmostEqual(ratio_ez.real,phase_diff.real,places=10) - self.assertAlmostEqual(ratio_ez.imag,phase_diff.imag,places=10) + ez1 = sim.get_field_point(mp.Ez, mp.Vector3(2.3, -5.7, 4.8)) + ez2 = sim.get_field_point(mp.Ez, mp.Vector3(2.3, -5.7, 4.8 + d)) + ratio_ez = ez2 / ez1 + phase_diff = cmath.exp(1j * 2 * cmath.pi * kz * d) + self.assertAlmostEqual(ratio_ez.real, phase_diff.real, places=10) + self.assertAlmostEqual(ratio_ez.imag, phase_diff.imag, places=10) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_timing_measurements.py b/python/tests/test_timing_measurements.py index 93ec0ca02..e748cbbab 100644 --- a/python/tests/test_timing_measurements.py +++ b/python/tests/test_timing_measurements.py @@ -6,7 +6,6 @@ class TimingTest(unittest.TestCase): - def test_timing_measurements(self): """Tests that timing measurements have expected names and can be updated.""" sim = mp.Simulation( @@ -22,11 +21,15 @@ def test_timing_measurements(self): set(timing_measurements.measurement_names), set(timing.TIMING_MEASUREMENT_IDS.keys()), ) - self.assertTrue(timing_measurements.elapsed_time > 0 or timing_measurements.elapsed_time == -1) + self.assertTrue( + timing_measurements.elapsed_time > 0 + or timing_measurements.elapsed_time == -1 + ) self.assertGreater(timing_measurements.num_time_steps, 0) self.assertGreaterEqual(timing_measurements.comm_efficiency, 0) self.assertGreaterEqual(timing_measurements.comm_efficiency_one_to_one, 0) self.assertGreaterEqual(timing_measurements.comm_efficiency_all_to_all, 0) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_user_defined_material.py b/python/tests/test_user_defined_material.py index 5a363e4dc..bed170591 100644 --- a/python/tests/test_user_defined_material.py +++ b/python/tests/test_user_defined_material.py @@ -39,22 +39,28 @@ def my_epsilon_func(p): class TestUserMaterials(unittest.TestCase): - def setUp(self): self.resolution = 10 self.cell = mp.Vector3(10, 10) self.symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] self.boundary_layers = [mp.PML(1.0)] - self.sources = [mp.Source(src=mp.GaussianSource(0.2, fwidth=0.1), - component=mp.Ez, center=mp.Vector3())] + self.sources = [ + mp.Source( + src=mp.GaussianSource(0.2, fwidth=0.1), + component=mp.Ez, + center=mp.Vector3(), + ) + ] def test_user_material_func(self): - sim = mp.Simulation(cell_size=self.cell, - resolution=self.resolution, - symmetries=self.symmetries, - boundary_layers=self.boundary_layers, - sources=self.sources, - material_function=my_material_func) + sim = mp.Simulation( + cell_size=self.cell, + resolution=self.resolution, + symmetries=self.symmetries, + boundary_layers=self.boundary_layers, + sources=self.sources, + material_function=my_material_func, + ) sim.run(until=200) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) @@ -63,12 +69,14 @@ def test_user_material_func(self): self.assertAlmostEqual(fp, 4.816403627871773e-4, places=places) def test_epsilon_func(self): - sim = mp.Simulation(cell_size=self.cell, - resolution=self.resolution, - symmetries=self.symmetries, - boundary_layers=self.boundary_layers, - sources=self.sources, - epsilon_func=my_epsilon_func) + sim = mp.Simulation( + cell_size=self.cell, + resolution=self.resolution, + symmetries=self.symmetries, + boundary_layers=self.boundary_layers, + sources=self.sources, + epsilon_func=my_epsilon_func, + ) sim.run(until=100) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) @@ -78,12 +86,14 @@ def test_epsilon_func(self): def test_geometric_obj_with_user_material(self): geometry = [mp.Cylinder(5, material=my_material_func)] - sim = mp.Simulation(cell_size=self.cell, - resolution=self.resolution, - symmetries=self.symmetries, - geometry=geometry, - boundary_layers=self.boundary_layers, - sources=self.sources) + sim = mp.Simulation( + cell_size=self.cell, + resolution=self.resolution, + symmetries=self.symmetries, + geometry=geometry, + boundary_layers=self.boundary_layers, + sources=self.sources, + ) sim.run(until=200) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) @@ -93,16 +103,19 @@ def test_geometric_obj_with_user_material(self): def test_geometric_obj_with_epsilon_func(self): geometry = [mp.Cylinder(5, epsilon_func=my_epsilon_func)] - sim = mp.Simulation(cell_size=self.cell, - resolution=self.resolution, - symmetries=self.symmetries, - geometry=geometry, - boundary_layers=self.boundary_layers, - sources=self.sources) + sim = mp.Simulation( + cell_size=self.cell, + resolution=self.resolution, + symmetries=self.symmetries, + geometry=geometry, + boundary_layers=self.boundary_layers, + sources=self.sources, + ) sim.run(until=100) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) self.assertAlmostEqual(fp, -7.895783750440999e-4) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_verbosity_mgr.py b/python/tests/test_verbosity_mgr.py index f36688f98..097e71718 100644 --- a/python/tests/test_verbosity_mgr.py +++ b/python/tests/test_verbosity_mgr.py @@ -3,29 +3,28 @@ class VerbosityForTest(Verbosity): - """Allows for testing of Verbosity without interfering with the singleton.""" - _instance = None + """Allows for testing of Verbosity without interfering with the singleton.""" + + _instance = None class MyCvar: - def __init__(self): self.verbosity = 1 + def __init__(self): + self.verbosity = 1 class TestVerbosity(unittest.TestCase): - def setUp(self): VerbosityForTest.reset() - self.v1 = VerbosityForTest(name='foo') - self.v2 = VerbosityForTest(MyCvar(), 'bar') - + self.v1 = VerbosityForTest(name="foo") + self.v2 = VerbosityForTest(MyCvar(), "bar") def test_identity(self): # Ensure each verbosity is really the same singleton instance v1, v2 = self.v1, self.v2 self.assertTrue(v1 is v2) self.assertEqual(id(v1), id(v2)) - self.assertEqual(v1.get_all(), [1,1]) - + self.assertEqual(v1.get_all(), [1, 1]) def test_initial_value(self): v1, v2 = self.v1, self.v2 @@ -33,7 +32,6 @@ def test_initial_value(self): v2.set(2) self.assertEqual(v1.get(), 2) - def test_properties(self): v1, v2 = self.v1, self.v2 self.assertEqual(v1.foo, 1) @@ -43,7 +41,6 @@ def test_properties(self): self.assertEqual(v2.foo, 2) self.assertEqual(v2.bar, 3) - def test_operators(self): v1, v2 = self.v1, self.v2 @@ -72,6 +69,5 @@ def test_out_of_range(self): v2.bar = -5 - -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_visualization.py b/python/tests/test_visualization.py index 064e50635..4a7a5c46b 100644 --- a/python/tests/test_visualization.py +++ b/python/tests/test_visualization.py @@ -13,106 +13,168 @@ # Make sure we have matplotlib installed import matplotlib -matplotlib.use('agg') # Set backend for consistency and to pull pixels quickly + +matplotlib.use("agg") # Set backend for consistency and to pull pixels quickly from matplotlib import pyplot as plt import io + def hash_figure(fig): buf = io.BytesIO() - fig.savefig(buf, format='raw') + fig.savefig(buf, format="raw") buf.seek(0) data = np.frombuffer(buf.getvalue(), dtype=np.uint8) return np.sum((data > np.mean(data)) + data) + def setup_sim(zDim=0): - cell = mp.Vector3(16,8,zDim) + cell = mp.Vector3(16, 8, zDim) # A simple waveguide - geometry = [mp.Block(mp.Vector3(mp.inf,1,1), - center=mp.Vector3(), - material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + mp.Vector3(mp.inf, 1, 1), + center=mp.Vector3(), + material=mp.Medium(epsilon=12), + ) + ] # Add point sources - sources = [mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(-5,0), - size=mp.Vector3(0,0,2)), - mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(0,2), - size=mp.Vector3(0,0,2)), - mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(-1,1), - size=mp.Vector3(0,0,2)), - mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(-2,-2,1), - size=mp.Vector3(0,0,0)), - ] + sources = [ + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + center=mp.Vector3(-5, 0), + size=mp.Vector3(0, 0, 2), + ), + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + center=mp.Vector3(0, 2), + size=mp.Vector3(0, 0, 2), + ), + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + center=mp.Vector3(-1, 1), + size=mp.Vector3(0, 0, 2), + ), + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + center=mp.Vector3(-2, -2, 1), + size=mp.Vector3(0, 0, 0), + ), + ] # Add line sources - sources += [mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - size=mp.Vector3(0,2,2), - center=mp.Vector3(-6,0)), - mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - size=mp.Vector3(0,2,2), - center=mp.Vector3(0,1))] + sources += [ + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + size=mp.Vector3(0, 2, 2), + center=mp.Vector3(-6, 0), + ), + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + size=mp.Vector3(0, 2, 2), + center=mp.Vector3(0, 1), + ), + ] # Add plane sources - sources += [mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - size=mp.Vector3(2,2,2), - center=mp.Vector3(-3,0)), - mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - size=mp.Vector3(2,2,2), - center=mp.Vector3(0,-2))] + sources += [ + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + size=mp.Vector3(2, 2, 2), + center=mp.Vector3(-3, 0), + ), + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + size=mp.Vector3(2, 2, 2), + center=mp.Vector3(0, -2), + ), + ] # Different pml layers - pml_layers = [mp.PML(2.0,mp.X),mp.PML(1.0,mp.Y,mp.Low),mp.PML(1.5,mp.Y,mp.High)] + pml_layers = [ + mp.PML(2.0, mp.X), + mp.PML(1.0, mp.Y, mp.Low), + mp.PML(1.5, mp.Y, mp.High), + ] if zDim > 0: - pml_layers += [mp.PML(1.5,mp.Z)] + pml_layers += [mp.PML(1.5, mp.Z)] resolution = 10 - sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) + sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, + ) # Line monitor - sim.add_flux(1,0,1,mp.FluxRegion(center=mp.Vector3(5,0,0),size=mp.Vector3(0,4,4), direction=mp.X)) + sim.add_flux( + 1, + 0, + 1, + mp.FluxRegion( + center=mp.Vector3(5, 0, 0), size=mp.Vector3(0, 4, 4), direction=mp.X + ), + ) # Plane monitor - sim.add_flux(1,0,1,mp.FluxRegion(center=mp.Vector3(2,0,0),size=mp.Vector3(4,4,4), direction=mp.X)) + sim.add_flux( + 1, + 0, + 1, + mp.FluxRegion( + center=mp.Vector3(2, 0, 0), size=mp.Vector3(4, 4, 4), direction=mp.X + ), + ) return sim + def view_sim(): sim = setup_sim(8) - xy0 = mp.Volume(center=mp.Vector3(0,0,0), size=mp.Vector3(sim.cell_size.x,sim.cell_size.y,0)) - xy1 = mp.Volume(center=mp.Vector3(0,0,1), size=mp.Vector3(sim.cell_size.x,sim.cell_size.y,0)) - yz0 = mp.Volume(center=mp.Vector3(0,0,0), size=mp.Vector3(0,sim.cell_size.y,sim.cell_size.z)) - yz1 = mp.Volume(center=mp.Vector3(1,0,0), size=mp.Vector3(0,sim.cell_size.y,sim.cell_size.z)) - xz0 = mp.Volume(center=mp.Vector3(0,0,0), size=mp.Vector3(sim.cell_size.x,0,sim.cell_size.z)) - xz1 = mp.Volume(center=mp.Vector3(0,1,0), size=mp.Vector3(sim.cell_size.x,0,sim.cell_size.z)) - vols = [xy0,xy1,yz0,yz1,xz0,xz1] - titles = ['xy0','xy1','yz0','yz1','xz0','xz1'] - xlabel = ['x','x','y','y','x','x'] - ylabel = ['y','y','z','z','z','z'] + xy0 = mp.Volume( + center=mp.Vector3(0, 0, 0), size=mp.Vector3(sim.cell_size.x, sim.cell_size.y, 0) + ) + xy1 = mp.Volume( + center=mp.Vector3(0, 0, 1), size=mp.Vector3(sim.cell_size.x, sim.cell_size.y, 0) + ) + yz0 = mp.Volume( + center=mp.Vector3(0, 0, 0), size=mp.Vector3(0, sim.cell_size.y, sim.cell_size.z) + ) + yz1 = mp.Volume( + center=mp.Vector3(1, 0, 0), size=mp.Vector3(0, sim.cell_size.y, sim.cell_size.z) + ) + xz0 = mp.Volume( + center=mp.Vector3(0, 0, 0), size=mp.Vector3(sim.cell_size.x, 0, sim.cell_size.z) + ) + xz1 = mp.Volume( + center=mp.Vector3(0, 1, 0), size=mp.Vector3(sim.cell_size.x, 0, sim.cell_size.z) + ) + vols = [xy0, xy1, yz0, yz1, xz0, xz1] + titles = ["xy0", "xy1", "yz0", "yz1", "xz0", "xz1"] + xlabel = ["x", "x", "y", "y", "x", "x"] + ylabel = ["y", "y", "z", "z", "z", "z"] for k in range(len(vols)): - ax = plt.subplot(2,3,k+1) - sim.plot2D(ax=ax,output_plane=vols[k]) + ax = plt.subplot(2, 3, k + 1) + sim.plot2D(ax=ax, output_plane=vols[k]) ax.set_xlabel(xlabel[k]) ax.set_ylabel(ylabel[k]) ax.set_title(titles[k]) plt.tight_layout() plt.show() -class TestVisualization(unittest.TestCase): + +class TestVisualization(unittest.TestCase): @classmethod def setUpClass(cls): cls.temp_dir = mp.make_output_directory() @@ -129,79 +191,120 @@ def test_plot2D(self): ax = sim.plot2D(ax=ax) if mp.am_master(): hash_figure(f) - #self.assertAlmostEqual(hash_figure(f),10231488) + # self.assertAlmostEqual(hash_figure(f),10231488) # Check plotting of fields after timestepping f = plt.figure() ax = f.gca() sim.run(until=200) - ax = sim.plot2D(ax=ax,fields=mp.Ez) + ax = sim.plot2D(ax=ax, fields=mp.Ez) if mp.am_master(): hash_figure(f) - #self.assertAlmostEqual(hash_figure(f),79786722) + # self.assertAlmostEqual(hash_figure(f),79786722) # Check output_plane feature f = plt.figure() ax = f.gca() - vol = mp.Volume(center=mp.Vector3(),size=mp.Vector3(2,2)) - ax = sim.plot2D(ax=ax,fields=mp.Ez,output_plane=vol) + vol = mp.Volume(center=mp.Vector3(), size=mp.Vector3(2, 2)) + ax = sim.plot2D(ax=ax, fields=mp.Ez, output_plane=vol) if mp.am_master(): hash_figure(f) - #self.assertAlmostEqual(hash_figure(f),68926258) + # self.assertAlmostEqual(hash_figure(f),68926258) - @unittest.skipIf(call(['which', 'ffmpeg']) != 0, "ffmpeg is not installed") + @unittest.skipIf(call(["which", "ffmpeg"]) != 0, "ffmpeg is not installed") def test_animation_output(self): # ------------------------- # # Check over 2D domain # ------------------------- # - sim = setup_sim() # generate 2D simulation + sim = setup_sim() # generate 2D simulation - Animate = mp.Animate2D(sim,fields=mp.Ez, realtime=False, normalize=False) # Check without normalization - Animate_norm = mp.Animate2D(sim, mp.Ez, realtime=False, normalize=True) # Check with normalization + Animate = mp.Animate2D( + sim, fields=mp.Ez, realtime=False, normalize=False + ) # Check without normalization + Animate_norm = mp.Animate2D( + sim, mp.Ez, realtime=False, normalize=True + ) # Check with normalization # test both animation objects during same run - sim.run( - mp.at_every(1,Animate), - mp.at_every(1,Animate_norm), - until=5) + sim.run(mp.at_every(1, Animate), mp.at_every(1, Animate_norm), until=5) # Test outputs - Animate.to_mp4(5,os.path.join(self.temp_dir, 'test_2D.mp4')) # Check mp4 output - Animate.to_gif(150,os.path.join(self.temp_dir, 'test_2D.gif')) # Check gif output - Animate.to_jshtml(10) # Check jshtml output - Animate_norm.to_mp4(5,os.path.join(self.temp_dir, 'test_2D_norm.mp4')) # Check mp4 output - Animate_norm.to_gif(150,os.path.join(self.temp_dir, 'test_2D_norm.gif')) # Check gif output - Animate_norm.to_jshtml(10) # Check jshtml output + Animate.to_mp4( + 5, os.path.join(self.temp_dir, "test_2D.mp4") + ) # Check mp4 output + Animate.to_gif( + 150, os.path.join(self.temp_dir, "test_2D.gif") + ) # Check gif output + Animate.to_jshtml(10) # Check jshtml output + Animate_norm.to_mp4( + 5, os.path.join(self.temp_dir, "test_2D_norm.mp4") + ) # Check mp4 output + Animate_norm.to_gif( + 150, os.path.join(self.temp_dir, "test_2D_norm.gif") + ) # Check gif output + Animate_norm.to_jshtml(10) # Check jshtml output # ------------------------- # # Check over 3D domain # ------------------------- # - sim = setup_sim(5) # generate 2D simulation + sim = setup_sim(5) # generate 2D simulation - Animate_xy = mp.Animate2D(sim,fields=mp.Ey, realtime=False, normalize=True) # Check without normalization - Animate_xz = mp.Animate2D(sim,mp.Ey,realtime=False,normalize=True) # Check with normalization + Animate_xy = mp.Animate2D( + sim, fields=mp.Ey, realtime=False, normalize=True + ) # Check without normalization + Animate_xz = mp.Animate2D( + sim, mp.Ey, realtime=False, normalize=True + ) # Check with normalization # test both animation objects during same run sim.run( - mp.at_every(1,mp.in_volume(mp.Volume(center=mp.Vector3(),size=mp.Vector3(sim.cell_size.x,sim.cell_size.y)),Animate_xy)), - mp.at_every(1,mp.in_volume(mp.Volume(center=mp.Vector3(),size=mp.Vector3(sim.cell_size.x,0,sim.cell_size.z)),Animate_xz)), - until=5) + mp.at_every( + 1, + mp.in_volume( + mp.Volume( + center=mp.Vector3(), + size=mp.Vector3(sim.cell_size.x, sim.cell_size.y), + ), + Animate_xy, + ), + ), + mp.at_every( + 1, + mp.in_volume( + mp.Volume( + center=mp.Vector3(), + size=mp.Vector3(sim.cell_size.x, 0, sim.cell_size.z), + ), + Animate_xz, + ), + ), + until=5, + ) # Test outputs - Animate_xy.to_mp4(5,os.path.join(self.temp_dir, 'test_3D_xy.mp4')) # Check mp4 output - Animate_xy.to_gif(150,os.path.join(self.temp_dir, 'test_3D_xy.gif')) # Check gif output - Animate_xy.to_jshtml(10) # Check jshtml output - Animate_xz.to_mp4(5,os.path.join(self.temp_dir, 'test_3D_xz.mp4')) # Check mp4 output - Animate_xz.to_gif(150,os.path.join(self.temp_dir, 'test_3D_xz.gif')) # Check gif output - Animate_xz.to_jshtml(10) # Check jshtml output - ''' + Animate_xy.to_mp4( + 5, os.path.join(self.temp_dir, "test_3D_xy.mp4") + ) # Check mp4 output + Animate_xy.to_gif( + 150, os.path.join(self.temp_dir, "test_3D_xy.gif") + ) # Check gif output + Animate_xy.to_jshtml(10) # Check jshtml output + Animate_xz.to_mp4( + 5, os.path.join(self.temp_dir, "test_3D_xz.mp4") + ) # Check mp4 output + Animate_xz.to_gif( + 150, os.path.join(self.temp_dir, "test_3D_xz.gif") + ) # Check gif output + Animate_xz.to_jshtml(10) # Check jshtml output + + """ Travis does not play well with Mayavi def test_3D_mayavi(self): sim = setup_sim(4) sim.plot3D() - ''' + """ + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() - diff --git a/python/tests/test_wvg_src.py b/python/tests/test_wvg_src.py index e4b636398..608ae2515 100644 --- a/python/tests/test_wvg_src.py +++ b/python/tests/test_wvg_src.py @@ -5,22 +5,31 @@ class TestWvgSrc(unittest.TestCase): - def setUp(self): cell = mp.Vector3(16, 8) geometry = [ - mp.Block(center=mp.Vector3(), size=mp.Vector3(mp.inf, 1, mp.inf), - material=mp.Medium(epsilon=12)), - mp.Block(center=mp.Vector3(y=0.3), size=mp.Vector3(mp.inf, 0.1, mp.inf), - material=mp.Medium()) + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1, mp.inf), + material=mp.Medium(epsilon=12), + ), + mp.Block( + center=mp.Vector3(y=0.3), + size=mp.Vector3(mp.inf, 0.1, mp.inf), + material=mp.Medium(), + ), ] sources = [ - mp.EigenModeSource(src=mp.ContinuousSource(0.15), size=mp.Vector3(y=6), - center=mp.Vector3(x=-5), component=mp.Dielectric, - eig_parity=mp.ODD_Z) + mp.EigenModeSource( + src=mp.ContinuousSource(0.15), + size=mp.Vector3(y=6), + center=mp.Vector3(x=-5), + component=mp.Dielectric, + eig_parity=mp.ODD_Z, + ) ] pml_layers = [mp.PML(1.0)] @@ -31,18 +40,23 @@ def setUp(self): sources=sources, boundary_layers=pml_layers, force_complex_fields=True, - resolution=10 + resolution=10, ) def test_wvg_src(self): self.sim.run(until=200) - flux1 = self.sim.flux_in_box(mp.X, mp.Volume(center=mp.Vector3(-6.0), size=mp.Vector3(1.8, 6))) - flux2 = self.sim.flux_in_box(mp.X, mp.Volume(center=mp.Vector3(6.0), size=mp.Vector3(1.8, 6))) + flux1 = self.sim.flux_in_box( + mp.X, mp.Volume(center=mp.Vector3(-6.0), size=mp.Vector3(1.8, 6)) + ) + flux2 = self.sim.flux_in_box( + mp.X, mp.Volume(center=mp.Vector3(6.0), size=mp.Vector3(1.8, 6)) + ) self.assertAlmostEqual(flux1, -1.775216564842667e-03) places = 5 if mp.is_single_precision() else 7 self.assertAlmostEqual(flux2, 7.215785537102116, places=places) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/utils.py b/python/tests/utils.py index a249e8e26..771aff7b0 100644 --- a/python/tests/utils.py +++ b/python/tests/utils.py @@ -1,6 +1,7 @@ import unittest import numpy as np + def compare_arrays(test_instance, exp, res, tol=1e-3): exp_1d = exp.ravel() res_1d = res.ravel() @@ -18,11 +19,11 @@ def compare_arrays(test_instance, exp, res, tol=1e-3): class ApproxComparisonTestCase(unittest.TestCase): """A mixin for adding proper floating point value and vector comparison.""" - def assertClose(self, x, y, epsilon = 1e-2, msg = ''): + def assertClose(self, x, y, epsilon=1e-2, msg=""): """Asserts that two values or vectors satisfy ‖x-y‖ ≤ ε * max(‖x‖, ‖y‖).""" x = np.atleast_1d(x).ravel() y = np.atleast_1d(y).ravel() x_norm = np.linalg.norm(x, ord=np.inf) y_norm = np.linalg.norm(y, ord=np.inf) diff_norm = np.linalg.norm(x - y, ord=np.inf) - self.assertLessEqual(diff_norm, epsilon * np.maximum(x_norm, y_norm), msg) \ No newline at end of file + self.assertLessEqual(diff_norm, epsilon * np.maximum(x_norm, y_norm), msg) diff --git a/python/timing_measurements.py b/python/timing_measurements.py index dd61e61e4..de19a2e67 100644 --- a/python/timing_measurements.py +++ b/python/timing_measurements.py @@ -7,28 +7,28 @@ # https://meep.readthedocs.io/en/latest/Python_User_Interface/#simulation-time # for more information. TIMING_MEASUREMENT_IDS = { - 'connecting_chunks': mp.Connecting, - 'time_stepping': mp.Stepping, - 'boundaries_copying': mp.Boundaries, - 'mpi_all_to_all': mp.MpiAllTime, - 'mpi_one_to_one': mp.MpiOneTime, - 'field_output': mp.FieldOutput, - 'fourier_transform': mp.FourierTransforming, - 'mpb': mp.MPBTime, - 'near_to_farfield_transform': mp.GetFarfieldsTime, - 'other': mp.Other, - 'field_update_b': mp.FieldUpdateB, - 'field_update_h': mp.FieldUpdateH, - 'field_update_d': mp.FieldUpdateD, - 'field_update_e': mp.FieldUpdateE, - 'boundary_stepping_b': mp.BoundarySteppingB, - 'boundary_stepping_wh': mp.BoundarySteppingWH, - 'boundary_stepping_ph': mp.BoundarySteppingPH, - 'boundary_stepping_h': mp.BoundarySteppingH, - 'boundary_stepping_d': mp.BoundarySteppingD, - 'boundary_stepping_we': mp.BoundarySteppingWE, - 'boundary_stepping_pe': mp.BoundarySteppingPE, - 'boundary_stepping_e': mp.BoundarySteppingE, + "connecting_chunks": mp.Connecting, + "time_stepping": mp.Stepping, + "boundaries_copying": mp.Boundaries, + "mpi_all_to_all": mp.MpiAllTime, + "mpi_one_to_one": mp.MpiOneTime, + "field_output": mp.FieldOutput, + "fourier_transform": mp.FourierTransforming, + "mpb": mp.MPBTime, + "near_to_farfield_transform": mp.GetFarfieldsTime, + "other": mp.Other, + "field_update_b": mp.FieldUpdateB, + "field_update_h": mp.FieldUpdateH, + "field_update_d": mp.FieldUpdateD, + "field_update_e": mp.FieldUpdateE, + "boundary_stepping_b": mp.BoundarySteppingB, + "boundary_stepping_wh": mp.BoundarySteppingWH, + "boundary_stepping_ph": mp.BoundarySteppingPH, + "boundary_stepping_h": mp.BoundarySteppingH, + "boundary_stepping_d": mp.BoundarySteppingD, + "boundary_stepping_we": mp.BoundarySteppingWE, + "boundary_stepping_pe": mp.BoundarySteppingPE, + "boundary_stepping_e": mp.BoundarySteppingE, } @@ -73,14 +73,16 @@ class MeepTimingMeasurements: transforming. This is zero if no simulation work has been performed. """ - def __init__(self, - measurements: Dict[str, List[float]], - elapsed_time: float, - num_time_steps: int, - time_per_step: List[float], - dft_relative_change: List[float], - overlap_relative_change: List[float], - relative_energy: List[float]): + def __init__( + self, + measurements: Dict[str, List[float]], + elapsed_time: float, + num_time_steps: int, + time_per_step: List[float], + dft_relative_change: List[float], + overlap_relative_change: List[float], + relative_energy: List[float], + ): self.measurements = measurements self.elapsed_time = elapsed_time self.num_time_steps = num_time_steps @@ -91,14 +93,14 @@ def __init__(self, @classmethod def new_from_simulation( - cls, - sim: mp.Simulation, - elapsed_time: Optional[float] = -1, - time_per_step: Optional[List[float]] = None, - dft_relative_change: Optional[List[float]] = None, - overlap_relative_change: Optional[List[float]] = None, - relative_energy: Optional[List[float]] = None, - ) -> 'MeepTimingMeasurements': + cls, + sim: mp.Simulation, + elapsed_time: Optional[float] = -1, + time_per_step: Optional[List[float]] = None, + dft_relative_change: Optional[List[float]] = None, + overlap_relative_change: Optional[List[float]] = None, + relative_energy: Optional[List[float]] = None, + ) -> "MeepTimingMeasurements": """Creates a new `MeepTimingMeasurements` from a Meep simulation object. Usage example: @@ -129,15 +131,22 @@ def new_from_simulation( Returns: the resulting `MeepTimingMeasurements` """ - measurements = {name: sim.time_spent_on(timing_id).tolist() for name, timing_id in TIMING_MEASUREMENT_IDS.items()} + measurements = { + name: sim.time_spent_on(timing_id).tolist() + for name, timing_id in TIMING_MEASUREMENT_IDS.items() + } return cls( measurements=measurements, elapsed_time=elapsed_time, num_time_steps=get_current_time_step(sim), time_per_step=time_per_step if time_per_step is not None else [], - dft_relative_change=dft_relative_change if dft_relative_change is not None else [], - overlap_relative_change=overlap_relative_change if overlap_relative_change is not None else [], + dft_relative_change=dft_relative_change + if dft_relative_change is not None + else [], + overlap_relative_change=overlap_relative_change + if overlap_relative_change is not None + else [], relative_energy=relative_energy if relative_energy is not None else [], ) @@ -147,28 +156,36 @@ def measurement_names(self) -> List[str]: @property def comm_efficiency(self) -> float: - computation_time = np.mean(self.measurements['time_stepping']) + np.mean( - self.measurements['fourier_transform']) + computation_time = np.mean(self.measurements["time_stepping"]) + np.mean( + self.measurements["fourier_transform"] + ) if computation_time == 0: return 0 else: - return np.mean(self.measurements['mpi_all_to_all'] + - self.measurements['mpi_one_to_one']) / computation_time + return ( + np.mean( + self.measurements["mpi_all_to_all"] + + self.measurements["mpi_one_to_one"] + ) + / computation_time + ) @property def comm_efficiency_one_to_one(self) -> float: - computation_time = np.mean(self.measurements['time_stepping']) + np.mean( - self.measurements['fourier_transform']) + computation_time = np.mean(self.measurements["time_stepping"]) + np.mean( + self.measurements["fourier_transform"] + ) if computation_time == 0: return 0 else: - return np.mean(self.measurements['mpi_one_to_one']) / computation_time + return np.mean(self.measurements["mpi_one_to_one"]) / computation_time @property def comm_efficiency_all_to_all(self) -> float: - computation_time = np.mean(self.measurements['time_stepping']) + np.mean( - self.measurements['fourier_transform']) + computation_time = np.mean(self.measurements["time_stepping"]) + np.mean( + self.measurements["fourier_transform"] + ) if computation_time == 0: return 0 else: - return np.mean(self.measurements['mpi_all_to_all']) / computation_time + return np.mean(self.measurements["mpi_all_to_all"]) / computation_time diff --git a/python/verbosity_mgr.py b/python/verbosity_mgr.py index 86832498f..e7bb3e57e 100644 --- a/python/verbosity_mgr.py +++ b/python/verbosity_mgr.py @@ -83,18 +83,20 @@ def add_verbosity_var(self, cvar=None, name=None, initial_level=1): Add a new verbosity flag to be managed. `cvar` should be some object that has a `verbosity` attribute, such as `meep.cvar` or `mpb.cvar`. """ - if cvar is None or not hasattr(cvar, 'verbosity'): + if cvar is None or not hasattr(cvar, "verbosity"): # If we're not given a module.cvar (e.g., while testing) or if the # cvar does not have a verbosity member (e.g. the lib hasn't been # updated to have a verbosity flag yet) then use a dummy object so # things can still run without it. - class _dummy(): - def __init__(self): self.verbosity = 1 + class _dummy: + def __init__(self): + self.verbosity = 1 + cvar = _dummy() # If a name is not given then manufacture one if name is None: - name = 'cvar_{}'.format(len(self._cvars)) + name = "cvar_{}".format(len(self._cvars)) self._cvars[name] = cvar # And create a property for so it can be accessed like `verbosity.mpb` @@ -116,7 +118,7 @@ def get_all(self): is mostly intended for debugging this class and won't likely be useful otherwise. """ - return [value.verbosity for value in self._cvars.values() ] + return [value.verbosity for value in self._cvars.values()] def set(self, level): """ @@ -124,7 +126,7 @@ def set(self, level): former value. """ if level < 0 or level > 3: - raise ValueError('Only verbosity levels 0-3 are supported') + raise ValueError("Only verbosity levels 0-3 are supported") old = self._master_verbosity for cvar in self._cvars.values(): cvar.verbosity = level @@ -155,23 +157,36 @@ def __repr__(self): return f"Verbosity: level={self.get()}" # Some comparison operators - def __gt__(self, o): return self.get() > o - def __lt__(self, o): return self.get() < o - def __eq__(self, o): return self.get() == o - def __ne__(self, o): return self.get() != o - def __le__(self, o): return self.get() <= o - def __ge__(self, o): return self.get() >= o + def __gt__(self, o): + return self.get() > o + + def __lt__(self, o): + return self.get() < o + + def __eq__(self, o): + return self.get() == o + + def __ne__(self, o): + return self.get() != o + + def __le__(self, o): + return self.get() <= o + + def __ge__(self, o): + return self.get() >= o def make_property(self, name): """ Add a property to the class with the given name that gets or sets the verbosity in the cvar with that name in self._cvars. """ + def _getter(self, name=name): return self._cvars[name].verbosity + def _setter(self, level, name=name): if level < 0 or level > 3: - raise ValueError('Only verbosity levels 0-3 are supported') + raise ValueError("Only verbosity levels 0-3 are supported") self._cvars[name].verbosity = level setattr(Verbosity, name, property(_getter, _setter)) diff --git a/python/visualization.py b/python/visualization.py index b1c7e6f64..b7f7620de 100644 --- a/python/visualization.py +++ b/python/visualization.py @@ -20,68 +20,65 @@ # for the different plotting routines. default_source_parameters = { - 'color':'r', - 'edgecolor':'r', - 'facecolor':'none', - 'hatch':'/', - 'linewidth':2 - } + "color": "r", + "edgecolor": "r", + "facecolor": "none", + "hatch": "/", + "linewidth": 2, +} default_monitor_parameters = { - 'color':'b', - 'edgecolor':'b', - 'facecolor':'none', - 'hatch':'/', - 'linewidth':2 - } + "color": "b", + "edgecolor": "b", + "facecolor": "none", + "hatch": "/", + "linewidth": 2, +} default_field_parameters = { - 'interpolation':'spline36', - 'cmap':'RdBu', - 'alpha':0.8, - 'post_process':np.real - } + "interpolation": "spline36", + "cmap": "RdBu", + "alpha": 0.8, + "post_process": np.real, +} default_eps_parameters = { - 'interpolation':'spline36', - 'cmap':'binary', - 'alpha':1.0, - 'contour':False, - 'contour_linewidth':1, - 'frequency':None, - 'resolution':None - } + "interpolation": "spline36", + "cmap": "binary", + "alpha": 1.0, + "contour": False, + "contour_linewidth": 1, + "frequency": None, + "resolution": None, +} default_boundary_parameters = { - 'color':'g', - 'edgecolor':'g', - 'facecolor':'none', - 'hatch':'/' - } + "color": "g", + "edgecolor": "g", + "facecolor": "none", + "hatch": "/", +} default_volume_parameters = { - 'alpha':1.0, - 'color':'k', - 'linestyle':'-', - 'linewidth':1, - 'marker':'.', - 'edgecolor':'k', - 'facecolor':'none', - 'hatch':'/' - } - -default_label_parameters = { - 'label_color':'r', - 'offset':20, - 'label_alpha':0.3 + "alpha": 1.0, + "color": "k", + "linestyle": "-", + "linewidth": 1, + "marker": ".", + "edgecolor": "k", + "facecolor": "none", + "hatch": "/", } +default_label_parameters = {"label_color": "r", "offset": 20, "label_alpha": 0.3} + # Used to remove the elements of a dictionary (dict_to_filter) that # don't correspond to the keyword arguments of a particular # function (func_with_kwargs.) # Adapted from https://stackoverflow.com/questions/26515595/how-does-one-ignore-unexpected-keyword-arguments-passed-to-a-function/44052550 def filter_dict(dict_to_filter, func_with_kwargs): import inspect + filter_keys = [] try: # Python3 ... @@ -91,11 +88,17 @@ def filter_dict(dict_to_filter, func_with_kwargs): # Python2 ... filter_keys = inspect.getargspec(func_with_kwargs)[0] - return {filter_key: dict_to_filter[filter_key] for filter_key in filter_keys if filter_key in dict_to_filter} + return { + filter_key: dict_to_filter[filter_key] + for filter_key in filter_keys + if filter_key in dict_to_filter + } + # ------------------------------------------------------- # # Routines to add legends to plot + def place_label(ax, label_text, x, y, centerx, centery, label_parameters=None): if label_parameters is None: @@ -103,19 +106,25 @@ def place_label(ax, label_text, x, y, centerx, centery, label_parameters=None): else: label_parameters = dict(default_label_parameters, **label_parameters) - offset = label_parameters['offset'] - alpha = label_parameters['label_alpha'] - color = label_parameters['label_color'] + offset = label_parameters["offset"] + alpha = label_parameters["label_alpha"] + color = label_parameters["label_color"] xtext = -offset if x > centerx else offset ytext = -offset if y > centery else offset - ax.annotate(label_text, xy=(x, y), xytext=(xtext, ytext), - textcoords='offset points', ha='center', va='bottom', - bbox=dict(boxstyle='round,pad=0.2', fc=color, alpha=alpha), - arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.5', - color=color)) + ax.annotate( + label_text, + xy=(x, y), + xytext=(xtext, ytext), + textcoords="offset points", + ha="center", + va="bottom", + bbox=dict(boxstyle="round,pad=0.2", fc=color, alpha=alpha), + arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=0.5", color=color), + ) return ax + # ------------------------------------------------------- # # Helper functions used to plot volumes on a 2D plane @@ -127,42 +136,56 @@ def intersect_volume_volume(volume1, volume2): # volume2 ............... [volume] # Represent the volumes by an "upper" and "lower" coordinate - U1 = [volume1.center.x+volume1.size.x/2, - volume1.center.y+volume1.size.y/2, - volume1.center.z+volume1.size.z/2] - L1 = [volume1.center.x-volume1.size.x/2, - volume1.center.y-volume1.size.y/2, - volume1.center.z-volume1.size.z/2] - - U2 = [volume2.center.x+volume2.size.x/2, - volume2.center.y+volume2.size.y/2, - volume2.center.z+volume2.size.z/2] - L2 = [volume2.center.x-volume2.size.x/2, - volume2.center.y-volume2.size.y/2, - volume2.center.z-volume2.size.z/2] + U1 = [ + volume1.center.x + volume1.size.x / 2, + volume1.center.y + volume1.size.y / 2, + volume1.center.z + volume1.size.z / 2, + ] + L1 = [ + volume1.center.x - volume1.size.x / 2, + volume1.center.y - volume1.size.y / 2, + volume1.center.z - volume1.size.z / 2, + ] + + U2 = [ + volume2.center.x + volume2.size.x / 2, + volume2.center.y + volume2.size.y / 2, + volume2.center.z + volume2.size.z / 2, + ] + L2 = [ + volume2.center.x - volume2.size.x / 2, + volume2.center.y - volume2.size.y / 2, + volume2.center.z - volume2.size.z / 2, + ] # Evaluate intersection - U = np.min([U1,U2],axis=0) - L = np.max([L1,L2],axis=0) + U = np.min([U1, U2], axis=0) + L = np.max([L1, L2], axis=0) # For single points we have to check manually - if np.all(U - L == 0) and ((not volume1.pt_in_volume(Vector3(*U))) or (not volume2.pt_in_volume(Vector3(*U)))): + if np.all(U - L == 0) and ( + (not volume1.pt_in_volume(Vector3(*U))) + or (not volume2.pt_in_volume(Vector3(*U))) + ): return [] # Check for two volumes that don't intersect - if np.any(U-L < 0): + if np.any(U - L < 0): return [] # Pull all possible vertices vertices = [] - for x_vals in [L[0],U[0]]: - for y_vals in [L[1],U[1]]: - vertices.extend(Vector3(x_vals,y_vals,z_vals) for z_vals in [L[2],U[2]]) + for x_vals in [L[0], U[0]]: + for y_vals in [L[1], U[1]]: + vertices.extend(Vector3(x_vals, y_vals, z_vals) for z_vals in [L[2], U[2]]) # Remove any duplicate points caused by coplanar lines - vertices = [vertices[i] for i, x in enumerate(vertices) if x not in vertices[i+1:]] + vertices = [ + vertices[i] for i, x in enumerate(vertices) if x not in vertices[i + 1 :] + ] return vertices + # All of the 2D plotting routines need an output plane over which to plot. # The user has many options to specify this output plane. They can pass # the output_plane parameter, which is a 2D volume object. They can specify @@ -181,27 +204,36 @@ def get_2D_dimensions(sim, output_plane): elif sim.output_volume: plane_center, plane_size = mp.get_center_and_size(sim.output_volume) elif (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: - plane_center, plane_size = (sim.geometry_center+mp.Vector3(sim.cell_size.x/2), sim.cell_size) + plane_center, plane_size = ( + sim.geometry_center + mp.Vector3(sim.cell_size.x / 2), + sim.cell_size, + ) else: plane_center, plane_size = (sim.geometry_center, sim.cell_size) - plane_volume = Volume(center=plane_center,size=plane_size) + plane_volume = Volume(center=plane_center, size=plane_size) if plane_size.x != 0 and plane_size.y != 0 and plane_size.z != 0: raise ValueError("Plane volume must be 2D (a plane).") if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: - center = sim.geometry_center+mp.Vector3(sim.cell_size.x/2) - check_volume = mp.Volume(center=center, size = sim.cell_size) + center = sim.geometry_center + mp.Vector3(sim.cell_size.x / 2) + check_volume = mp.Volume(center=center, size=sim.cell_size) else: check_volume = Volume(center=sim.geometry_center, size=sim.cell_size) vertices = intersect_volume_volume(check_volume, plane_volume) if len(vertices) == 0: - raise ValueError("The specified user volume is completely outside of the simulation domain.") + raise ValueError( + "The specified user volume is completely outside of the simulation domain." + ) intersection_vol = Volume(vertices=vertices) - if (intersection_vol.size != plane_volume.size) or (intersection_vol.center != plane_volume.center): - warnings.warn('The specified user volume is larger than the simulation domain and has been truncated.') + if (intersection_vol.size != plane_volume.size) or ( + intersection_vol.center != plane_volume.center + ): + warnings.warn( + "The specified user volume is larger than the simulation domain and has been truncated." + ) sim_center, sim_size = (intersection_vol.center, intersection_vol.size) return sim_center, sim_size @@ -215,19 +247,23 @@ def box_vertices(box_center, box_size, is_cylindrical=False): xmin = 0 xmax = box_size.x else: - xmin = box_center.x - 0.5*box_size.x - xmax = box_center.x + 0.5*box_size.x - ymin = box_center.y - 0.5*box_size.y - ymax = box_center.y + 0.5*box_size.y - zmin = box_center.z - 0.5*box_size.z - zmax = box_center.z + 0.5*box_size.z + xmin = box_center.x - 0.5 * box_size.x + xmax = box_center.x + 0.5 * box_size.x + ymin = box_center.y - 0.5 * box_size.y + ymax = box_center.y + 0.5 * box_size.y + zmin = box_center.z - 0.5 * box_size.z + zmax = box_center.z + 0.5 * box_size.z return xmin, xmax, ymin, ymax, zmin, zmax + # ------------------------------------------------------- # # actual plotting routines -def plot_volume(sim, ax, volume, output_plane=None, plotting_parameters=None, label=None): + +def plot_volume( + sim, ax, volume, output_plane=None, plotting_parameters=None, label=None +): import matplotlib.patches as patches from matplotlib import pyplot as plt from meep.simulation import Volume @@ -246,36 +282,40 @@ def plot_volume(sim, ax, volume, output_plane=None, plotting_parameters=None, la size = volume.size center = volume.center - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(center, - size, - sim.is_cylindrical) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(center, size, sim.is_cylindrical) # Add labels if requested if label is not None and mp.am_master(): if sim_size.x == 0: - ax = place_label(ax, - label, - center.y, - center.z, - sim_center.y, - sim_center.z, - label_parameters=plotting_parameters) + ax = place_label( + ax, + label, + center.y, + center.z, + sim_center.y, + sim_center.z, + label_parameters=plotting_parameters, + ) elif sim_size.y == 0: - ax = place_label(ax, - label, - center.x, - center.z, - sim_center.x, - sim_center.z, - label_parameters=plotting_parameters) + ax = place_label( + ax, + label, + center.x, + center.z, + sim_center.x, + sim_center.z, + label_parameters=plotting_parameters, + ) elif sim_size.z == 0: - ax = place_label(ax, - label, - center.x, - center.y, - sim_center.x, - sim_center.y, - label_parameters=plotting_parameters) + ax = place_label( + ax, + label, + center.x, + center.y, + sim_center.x, + sim_center.y, + label_parameters=plotting_parameters, + ) # Intersect plane with volume intersection = intersect_volume_volume(volume, plane) @@ -284,57 +324,93 @@ def plot_volume(sim, ax, volume, output_plane=None, plotting_parameters=None, la def sort_points(xy): xy = np.squeeze(xy) xy_mean = np.mean(xy, axis=0) - theta = np.arctan2(xy[:,1] - xy_mean[1], xy[:,0] - xy_mean[0]) + theta = np.arctan2(xy[:, 1] - xy_mean[1], xy[:, 0] - xy_mean[0]) return xy[np.argsort(theta, axis=0), :] if mp.am_master(): # Point volume if len(intersection) == 1: - point_args = {key:value for key, value in plotting_parameters.items() if key in ['color','marker','alpha','linewidth']} - if sim_size.y==0: - ax.scatter(center.x,center.z, **point_args) + point_args = { + key: value + for key, value in plotting_parameters.items() + if key in ["color", "marker", "alpha", "linewidth"] + } + if sim_size.y == 0: + ax.scatter(center.x, center.z, **point_args) return ax - elif sim_size.x==0: - ax.scatter(center.y,center.z, **point_args) + elif sim_size.x == 0: + ax.scatter(center.y, center.z, **point_args) return ax - elif sim_size.z==0: - ax.scatter(center.x,center.y, **point_args) + elif sim_size.z == 0: + ax.scatter(center.x, center.y, **point_args) return ax else: return ax # Line volume elif len(intersection) == 2: - line_args = {key:value for key, value in plotting_parameters.items() if key in ['color','linestyle','linewidth','alpha']} + line_args = { + key: value + for key, value in plotting_parameters.items() + if key in ["color", "linestyle", "linewidth", "alpha"] + } # Plot YZ if sim_size.x == 0: - ax.plot([a.y for a in intersection], [a.z for a in intersection], **line_args) + ax.plot( + [a.y for a in intersection], + [a.z for a in intersection], + **line_args, + ) return ax # Plot XZ elif sim_size.y == 0: - ax.plot([a.x for a in intersection], [a.z for a in intersection], **line_args) + ax.plot( + [a.x for a in intersection], + [a.z for a in intersection], + **line_args, + ) return ax # Plot XY elif sim_size.z == 0: - ax.plot([a.x for a in intersection], [a.y for a in intersection], **line_args) + ax.plot( + [a.x for a in intersection], + [a.y for a in intersection], + **line_args, + ) return ax else: return ax # Planar volume elif len(intersection) > 2: - planar_args = {key:value for key, value in plotting_parameters.items() if key in ['edgecolor','linewidth','facecolor','hatch','alpha']} + planar_args = { + key: value + for key, value in plotting_parameters.items() + if key in ["edgecolor", "linewidth", "facecolor", "hatch", "alpha"] + } # Plot YZ if sim_size.x == 0: - ax.add_patch(patches.Polygon(sort_points([[a.y,a.z] for a in intersection]), **planar_args)) + ax.add_patch( + patches.Polygon( + sort_points([[a.y, a.z] for a in intersection]), **planar_args + ) + ) return ax # Plot XZ - elif sim_size.y==0: - ax.add_patch(patches.Polygon(sort_points([[a.x,a.z] for a in intersection]), **planar_args)) + elif sim_size.y == 0: + ax.add_patch( + patches.Polygon( + sort_points([[a.x, a.z] for a in intersection]), **planar_args + ) + ) return ax # Plot XY elif sim_size.z == 0: - ax.add_patch(patches.Polygon(sort_points([[a.x,a.y] for a in intersection]), **planar_args)) + ax.add_patch( + patches.Polygon( + sort_points([[a.x, a.y] for a in intersection]), **planar_args + ) + ) return ax else: return ax @@ -342,6 +418,7 @@ def sort_points(xy): return ax return ax + def plot_eps(sim, ax, output_plane=None, eps_parameters=None, frequency=None): # consolidate plotting parameters if eps_parameters is None: @@ -351,33 +428,35 @@ def plot_eps(sim, ax, output_plane=None, eps_parameters=None, frequency=None): # Determine a frequency to plot all epsilon if frequency is not None: - warnings.warn("The frequency parameter of plot2D has been deprecated. " - "Use the frequency key of the eps_parameters dictionary instead.") - eps_parameters['frequency'] = frequency - if eps_parameters['frequency'] is None: + warnings.warn( + "The frequency parameter of plot2D has been deprecated. " + "Use the frequency key of the eps_parameters dictionary instead." + ) + eps_parameters["frequency"] = frequency + if eps_parameters["frequency"] is None: try: # Continuous sources - eps_parameters['frequency'] = sim.sources[0].frequency + eps_parameters["frequency"] = sim.sources[0].frequency except: try: # Gaussian sources - eps_parameters['frequency'] = sim.sources[0].src.frequency + eps_parameters["frequency"] = sim.sources[0].src.frequency except: try: # Custom sources - eps_parameters['frequency'] = sim.sources[0].src.center_frequency + eps_parameters["frequency"] = sim.sources[0].src.center_frequency except: # No sources - eps_parameters['frequency'] = 0 + eps_parameters["frequency"] = 0 # Get domain measurements sim_center, sim_size = get_2D_dimensions(sim, output_plane) - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim_center, - sim_size, - sim.is_cylindrical) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices( + sim_center, sim_size, sim.is_cylindrical + ) - grid_resolution = eps_parameters['resolution'] or sim.resolution + grid_resolution = eps_parameters["resolution"] or sim.resolution Nx = int((xmax - xmin) * grid_resolution + 1) Ny = int((ymax - ymin) * grid_resolution + 1) Nz = int((zmax - zmin) * grid_resolution + 1) @@ -385,36 +464,48 @@ def plot_eps(sim, ax, output_plane=None, eps_parameters=None, frequency=None): if sim_size.x == 0: # Plot y on x axis, z on y axis (YZ plane) extent = [ymin, ymax, zmin, zmax] - xlabel = 'Y' - ylabel = 'Z' + xlabel = "Y" + ylabel = "Z" xtics = np.array([sim_center.x]) ytics = np.linspace(ymin, ymax, Ny) ztics = np.linspace(zmin, zmax, Nz) elif sim_size.y == 0: # Plot x on x axis, z on y axis (XZ plane) extent = [xmin, xmax, zmin, zmax] - xlabel = 'R' if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical else "X" + xlabel = ( + "R" if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical else "X" + ) - ylabel = 'Z' + ylabel = "Z" xtics = np.linspace(xmin, xmax, Nx) ytics = np.array([sim_center.y]) ztics = np.linspace(zmin, zmax, Nz) elif sim_size.z == 0: # Plot x on x axis, y on y axis (XY plane) extent = [xmin, xmax, ymin, ymax] - xlabel = 'X' - ylabel = 'Y' + xlabel = "X" + ylabel = "Y" xtics = np.linspace(xmin, xmax, Nx) ytics = np.linspace(ymin, ymax, Ny) ztics = np.array([sim_center.z]) else: raise ValueError("A 2D plane has not been specified...") - eps_data = np.rot90(np.real(sim.get_epsilon_grid(xtics, ytics, ztics, eps_parameters['frequency']))) + eps_data = np.rot90( + np.real(sim.get_epsilon_grid(xtics, ytics, ztics, eps_parameters["frequency"])) + ) if mp.am_master(): - if eps_parameters['contour']: - ax.contour(eps_data, 0, levels=np.unique(eps_data), colors='black', origin='upper', extent=extent, linewidths=eps_parameters['contour_linewidth']) + if eps_parameters["contour"]: + ax.contour( + eps_data, + 0, + levels=np.unique(eps_data), + colors="black", + origin="upper", + extent=extent, + linewidths=eps_parameters["contour_linewidth"], + ) else: ax.imshow(eps_data, extent=extent, **filter_dict(eps_parameters, ax.imshow)) ax.set_xlabel(xlabel) @@ -422,6 +513,7 @@ def plot_eps(sim, ax, output_plane=None, eps_parameters=None, frequency=None): return ax + def plot_boundaries(sim, ax, output_plane=None, boundary_parameters=None): # consolidate plotting parameters if boundary_parameters is None: @@ -434,62 +526,59 @@ def get_boundary_volumes(thickness, direction, side): thickness = boundary.thickness - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim.geometry_center, - sim.cell_size, - sim.is_cylindrical) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices( + sim.geometry_center, sim.cell_size, sim.is_cylindrical + ) if direction == mp.X and side == mp.Low: - return Volume(center=Vector3(xmin+0.5*thickness, - sim.geometry_center.y, - sim.geometry_center.z), - size=Vector3(thickness, - sim.cell_size.y, - sim.cell_size.z)) + return Volume( + center=Vector3( + xmin + 0.5 * thickness, sim.geometry_center.y, sim.geometry_center.z + ), + size=Vector3(thickness, sim.cell_size.y, sim.cell_size.z), + ) elif (direction == mp.X and side == mp.High) or direction == mp.R: - return Volume(center=Vector3(xmax-0.5*thickness, - sim.geometry_center.y, - sim.geometry_center.z), - size=Vector3(thickness, - sim.cell_size.y, - sim.cell_size.z)) + return Volume( + center=Vector3( + xmax - 0.5 * thickness, sim.geometry_center.y, sim.geometry_center.z + ), + size=Vector3(thickness, sim.cell_size.y, sim.cell_size.z), + ) elif direction == mp.Y and side == mp.Low: - return Volume(center=Vector3(sim.geometry_center.x, - ymin+0.5*thickness, - sim.geometry_center.z), - size=Vector3(sim.cell_size.x, - thickness, - sim.cell_size.z)) + return Volume( + center=Vector3( + sim.geometry_center.x, ymin + 0.5 * thickness, sim.geometry_center.z + ), + size=Vector3(sim.cell_size.x, thickness, sim.cell_size.z), + ) elif direction == mp.Y and side == mp.High: - return Volume(center=Vector3(sim.geometry_center.x, - ymax-0.5*thickness, - sim.geometry_center.z), - size=Vector3(sim.cell_size.x, - thickness, - sim.cell_size.z)) + return Volume( + center=Vector3( + sim.geometry_center.x, ymax - 0.5 * thickness, sim.geometry_center.z + ), + size=Vector3(sim.cell_size.x, thickness, sim.cell_size.z), + ) elif direction == mp.Z and side == mp.Low: xcen = sim.geometry_center.x if sim.is_cylindrical: - xcen += 0.5*sim.cell_size.x - return Volume(center=Vector3(xcen, - sim.geometry_center.y, - zmin+0.5*thickness), - size=Vector3(sim.cell_size.x, - sim.cell_size.y, - thickness)) + xcen += 0.5 * sim.cell_size.x + return Volume( + center=Vector3(xcen, sim.geometry_center.y, zmin + 0.5 * thickness), + size=Vector3(sim.cell_size.x, sim.cell_size.y, thickness), + ) elif direction == mp.Z and side == mp.High: xcen = sim.geometry_center.x if sim.is_cylindrical: - xcen += 0.5*sim.cell_size.x - return Volume(center=Vector3(xcen, - sim.geometry_center.y, - zmax-0.5*thickness), - size=Vector3(sim.cell_size.x, - sim.cell_size.y, - thickness)) + xcen += 0.5 * sim.cell_size.x + return Volume( + center=Vector3(xcen, sim.geometry_center.y, zmax - 0.5 * thickness), + size=Vector3(sim.cell_size.x, sim.cell_size.y, thickness), + ) else: raise ValueError("Invalid boundary type") import itertools + for boundary in sim.boundary_layers: # boundary on all four sides if boundary.direction == mp.ALL and boundary.side == mp.ALL: @@ -504,25 +593,40 @@ def get_boundary_volumes(thickness, direction, side): else: raise ValueError("Invalid simulation dimensions") for permutation in itertools.product(dims, [mp.Low, mp.High]): - if ((permutation[0] == mp.X) and (permutation[1] == mp.Low)) and (sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical): + if ((permutation[0] == mp.X) and (permutation[1] == mp.Low)) and ( + sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical + ): continue - vol = get_boundary_volumes(boundary.thickness,*permutation) - ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=boundary_parameters) + vol = get_boundary_volumes(boundary.thickness, *permutation) + ax = plot_volume( + sim, ax, vol, output_plane, plotting_parameters=boundary_parameters + ) # boundary on only two of four sides elif boundary.side == mp.ALL: for side in [mp.Low, mp.High]: - if ((boundary.direction == mp.X) and (side == mp.Low)) and (sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical): + if ((boundary.direction == mp.X) and (side == mp.Low)) and ( + sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical + ): continue - vol = get_boundary_volumes(boundary.thickness,boundary.direction,side) - ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=boundary_parameters) + vol = get_boundary_volumes(boundary.thickness, boundary.direction, side) + ax = plot_volume( + sim, ax, vol, output_plane, plotting_parameters=boundary_parameters + ) # boundary on just one side else: - if ((boundary.direction == mp.X) and (boundary.side == mp.Low)) and (sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical): + if ((boundary.direction == mp.X) and (boundary.side == mp.Low)) and ( + sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical + ): continue - vol = get_boundary_volumes(boundary.thickness,boundary.direction,boundary.side) - ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=boundary_parameters) + vol = get_boundary_volumes( + boundary.thickness, boundary.direction, boundary.side + ) + ax = plot_volume( + sim, ax, vol, output_plane, plotting_parameters=boundary_parameters + ) return ax + def plot_sources(sim, ax, output_plane=None, labels=False, source_parameters=None): from meep.simulation import Volume @@ -532,13 +636,21 @@ def plot_sources(sim, ax, output_plane=None, labels=False, source_parameters=Non else: source_parameters = dict(default_source_parameters, **source_parameters) - label = 'source' if labels else None + label = "source" if labels else None for src in sim.sources: - vol = Volume(center=src.center,size=src.size) - ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=source_parameters,label=label) + vol = Volume(center=src.center, size=src.size) + ax = plot_volume( + sim, + ax, + vol, + output_plane, + plotting_parameters=source_parameters, + label=label, + ) return ax + def plot_monitors(sim, ax, output_plane=None, labels=False, monitor_parameters=None): from meep.simulation import Volume @@ -548,14 +660,22 @@ def plot_monitors(sim, ax, output_plane=None, labels=False, monitor_parameters=N else: monitor_parametesr = dict(default_monitor_parameters, **monitor_parameters) - label = 'monitor' if labels else None + label = "monitor" if labels else None for mon in sim.dft_objects: for reg in mon.regions: - vol = Volume(center=reg.center,size=reg.size) - ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=monitor_parameters,label=label) + vol = Volume(center=reg.center, size=reg.size) + ax = plot_volume( + sim, + ax, + vol, + output_plane, + plotting_parameters=monitor_parameters, + label=label, + ) return ax + def plot_fields(sim, ax=None, fields=None, output_plane=None, field_parameters=None): if not sim._is_initialized: sim.init_sim() @@ -568,35 +688,48 @@ def plot_fields(sim, ax=None, fields=None, output_plane=None, field_parameters=N else: field_parameters = dict(default_field_parameters, **field_parameters) - if fields not in [mp.Ex, mp.Ey, mp.Ez, mp.Er, mp.Ep, mp.Dx, mp.Dy, mp.Dz, mp.Hx, mp.Hy, mp.Hz]: - raise ValueError('Please specify a valid field component (mp.Ex, mp.Ey, ...') - + if fields not in [ + mp.Ex, + mp.Ey, + mp.Ez, + mp.Er, + mp.Ep, + mp.Dx, + mp.Dy, + mp.Dz, + mp.Hx, + mp.Hy, + mp.Hz, + ]: + raise ValueError("Please specify a valid field component (mp.Ex, mp.Ey, ...") # Get domain measurements sim_center, sim_size = get_2D_dimensions(sim, output_plane) - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim_center, - sim_size, - sim.is_cylindrical) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices( + sim_center, sim_size, sim.is_cylindrical + ) if sim_size.x == 0: # Plot y on x axis, z on y axis (YZ plane) extent = [ymin, ymax, zmin, zmax] - xlabel = 'Y' - ylabel = 'Z' + xlabel = "Y" + ylabel = "Z" elif sim_size.y == 0: # Plot x on x axis, z on y axis (XZ plane) extent = [xmin, xmax, zmin, zmax] - xlabel = 'R' if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical else "X" + xlabel = ( + "R" if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical else "X" + ) - ylabel = 'Z' + ylabel = "Z" elif sim_size.z == 0: # Plot x on x axis, y on y axis (XY plane) extent = [xmin, xmax, ymin, ymax] - xlabel = 'X' - ylabel = 'Y' + xlabel = "X" + ylabel = "Y" fields = sim.get_array(center=sim_center, size=sim_size, component=fields) - fields = field_parameters['post_process'](fields) + fields = field_parameters["post_process"](fields) if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: fields = np.flipud(fields) else: @@ -605,61 +738,98 @@ def plot_fields(sim, ax=None, fields=None, output_plane=None, field_parameters=N if not ax: return fields if mp.am_master(): - ax.imshow(fields, extent=extent, **filter_dict(field_parameters,ax.imshow)) + ax.imshow(fields, extent=extent, **filter_dict(field_parameters, ax.imshow)) return ax -def plot2D(sim, ax=None, output_plane=None, fields=None, labels=False, - eps_parameters=None, boundary_parameters=None, - source_parameters=None, monitor_parameters=None, - field_parameters=None, frequency=None, - plot_eps_flag=True, plot_sources_flag=True, - plot_monitors_flag=True, plot_boundaries_flag=True): + +def plot2D( + sim, + ax=None, + output_plane=None, + fields=None, + labels=False, + eps_parameters=None, + boundary_parameters=None, + source_parameters=None, + monitor_parameters=None, + field_parameters=None, + frequency=None, + plot_eps_flag=True, + plot_sources_flag=True, + plot_monitors_flag=True, + plot_boundaries_flag=True, +): # Ensure a figure axis exists if ax is None and mp.am_master(): from matplotlib import pyplot as plt + ax = plt.gca() # validate the output plane to ensure proper 2D coordinates from meep.simulation import Volume + sim_center, sim_size = get_2D_dimensions(sim, output_plane) output_plane = Volume(center=sim_center, size=sim_size) # Plot geometry if plot_eps_flag: - ax = plot_eps(sim, ax, output_plane=output_plane, - eps_parameters=eps_parameters, frequency=frequency) + ax = plot_eps( + sim, + ax, + output_plane=output_plane, + eps_parameters=eps_parameters, + frequency=frequency, + ) # Plot boundaries if plot_boundaries_flag: - ax = plot_boundaries(sim, ax, output_plane=output_plane, - boundary_parameters=boundary_parameters) + ax = plot_boundaries( + sim, ax, output_plane=output_plane, boundary_parameters=boundary_parameters + ) # Plot sources if plot_sources_flag: - ax = plot_sources(sim, ax, output_plane=output_plane, - labels=labels, source_parameters=source_parameters) + ax = plot_sources( + sim, + ax, + output_plane=output_plane, + labels=labels, + source_parameters=source_parameters, + ) # Plot monitors if plot_monitors_flag: - ax = plot_monitors(sim, ax, output_plane=output_plane, - labels=labels, monitor_parameters=monitor_parameters) + ax = plot_monitors( + sim, + ax, + output_plane=output_plane, + labels=labels, + monitor_parameters=monitor_parameters, + ) # Plot fields if fields is not None: - ax = plot_fields(sim, ax, fields, output_plane=output_plane, - field_parameters=field_parameters) + ax = plot_fields( + sim, + ax, + fields, + output_plane=output_plane, + field_parameters=field_parameters, + ) return ax + def plot3D(sim): from mayavi import mlab if sim.dimensions < 3: raise ValueError("Simulation must have 3 dimensions to visualize 3D") - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim.geometry_center, - sim.cell_size) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices( + sim.geometry_center, sim.cell_size + ) Nx = int(sim.cell_size.x * sim.resolution) + 1 Ny = int(sim.cell_size.y * sim.resolution) + 1 @@ -672,6 +842,7 @@ def plot3D(sim): eps_data = sim.get_epsilon_grid(xtics, ytics, ztics) return mlab.contour3d(eps_data, colormap="YlGnBu") + def visualize_chunks(sim): if sim.structure is None: sim.init_sim() @@ -713,10 +884,10 @@ def plot_box(box, proc, fig, ax): [points[0], points[3], points[5], points[7]], [points[1], points[4], points[2], points[6]], [points[3], points[4], points[1], points[5]], - [points[0], points[7], points[6], points[2]] + [points[0], points[7], points[6], points[2]], ] - faces = Poly3DCollection(edges, linewidths=1, edgecolors='k') + faces = Poly3DCollection(edges, linewidths=1, edgecolors="k") color_with_alpha = matplotlib.colors.to_rgba(chunk_colors[proc], alpha=0.2) faces.set_facecolor(color_with_alpha) ax.add_collection3d(faces) @@ -735,11 +906,9 @@ def plot_box(box, proc, fig, ax): points += [low + y_len] points = np.array([np.array(v)[:-1] for v in points]) - edges = [ - [points[0], points[2], points[1], points[3]] - ] + edges = [[points[0], points[2], points[1], points[3]]] - faces = PolyCollection(edges, linewidths=1, edgecolors='k') + faces = PolyCollection(edges, linewidths=1, edgecolors="k") color_with_alpha = matplotlib.colors.to_rgba(chunk_colors[proc]) faces.set_facecolor(color_with_alpha) ax.add_collection(faces) @@ -749,29 +918,32 @@ def plot_box(box, proc, fig, ax): if mp.am_master(): fig = plt.figure() - ax = fig.add_subplot(111, projection='3d' if sim.structure.gv.dim == 2 else None) + ax = fig.add_subplot( + 111, projection="3d" if sim.structure.gv.dim == 2 else None + ) chunk_colors = matplotlib.cm.rainbow(np.linspace(0, 1, mp.count_processors())) for i, v in enumerate(vols): plot_box(mp.gv2box(v.surroundings()), owners[i], fig, ax) - ax.set_xlabel('x') - ax.set_ylabel('y') - ax.set_aspect('equal') + ax.set_xlabel("x") + ax.set_ylabel("y") + ax.set_aspect("equal") cell_box = mp.gv2box(sim.structure.gv.surroundings()) if sim.structure.gv.dim == 2: - ax.set_xlim3d(left=cell_box.low.x,right=cell_box.high.x) - ax.set_ylim3d(bottom=cell_box.low.y,top=cell_box.high.y) - ax.set_zlim3d(bottom=cell_box.low.z,top=cell_box.high.z) - ax.set_zlabel('z') + ax.set_xlim3d(left=cell_box.low.x, right=cell_box.high.x) + ax.set_ylim3d(bottom=cell_box.low.y, top=cell_box.high.y) + ax.set_zlim3d(bottom=cell_box.low.z, top=cell_box.high.z) + ax.set_zlabel("z") else: - ax.set_xlim(left=cell_box.low.x,right=cell_box.high.x) - ax.set_ylim(bottom=cell_box.low.y,top=cell_box.high.y) + ax.set_xlim(left=cell_box.low.x, right=cell_box.high.x) + ax.set_ylim(bottom=cell_box.low.y, top=cell_box.high.y) plt.tight_layout() plt.show() + # ------------------------------------------------------- # # Animate2D # ------------------------------------------------------- # @@ -792,6 +964,7 @@ def plot_box(box, proc, fig, ax): # customization_args .. [dict] other customization args # to pass to plot2D() + class Animate2D(object): """ A class used to record the fields during timestepping (i.e., a [`run`](#run-functions) @@ -823,8 +996,17 @@ class Animate2D(object): Multiple `Animate2D` objects can be initialized and passed to the run function to track different volume locations (using `mp.in_volume`) or field components. """ - def __init__(self, sim, fields, f=None, realtime=False, normalize=False, - plot_modifiers=None, **customization_args): + + def __init__( + self, + sim, + fields, + f=None, + realtime=False, + normalize=False, + plot_modifiers=None, + **customization_args, + ): """ Construct an `Animate2D` object. @@ -868,6 +1050,7 @@ def mod1(ax): self.ax = self.f.gca() elif mp.am_master(): from matplotlib import pyplot as plt + self.f = plt.figure() self.ax = self.f.gca() else: @@ -882,20 +1065,20 @@ def mod1(ax): self.cumulative_fields = [] self._saved_frames = [] - self.frame_format = 'png' # format in which each frame is saved in memory - self.codec = 'h264' # encoding of mp4 video - self.default_mode = 'loop' # html5 video control mode + self.frame_format = "png" # format in which each frame is saved in memory + self.codec = "h264" # encoding of mp4 video + self.default_mode = "loop" # html5 video control mode self.init = False # Needed for step functions - self.__code__ = namedtuple('gna_hack',['co_argcount']) - self.__code__.co_argcount=2 + self.__code__ = namedtuple("gna_hack", ["co_argcount"]) + self.__code__.co_argcount = 2 - def __call__(self,sim,todo): + def __call__(self, sim, todo): from matplotlib import pyplot as plt - if todo == 'step': + if todo == "step": # Initialize the plot if not self.init: filtered_plot2D = filter_dict(self.customization_args, plot2D) @@ -912,11 +1095,13 @@ def __call__(self,sim,todo): self.init = True else: # Update the plot - filtered_plot_fields= filter_dict(self.customization_args, plot_fields) + filtered_plot_fields = filter_dict(self.customization_args, plot_fields) fields = sim.plot_fields(fields=self.fields, **filtered_plot_fields) if mp.am_master(): self.ax.images[-1].set_data(fields) - self.ax.images[-1].set_clim(vmin=0.8*np.min(fields), vmax=0.8*np.max(fields)) + self.ax.images[-1].set_clim( + vmin=0.8 * np.min(fields), vmax=0.8 * np.max(fields) + ) if self.realtime and mp.am_master(): # Redraw the current figure if requested @@ -931,15 +1116,17 @@ def __call__(self,sim,todo): # to avoid writing to disk. self.grab_frame() return - elif todo == 'finish': + elif todo == "finish": # Normalize the frames, if requested, and export if self.normalize and mp.am_master(): if mp.verbosity.meep > 0: print("Normalizing field data...") - fields = np.array(self.cumulative_fields) / np.max(np.abs(self.cumulative_fields), axis=(0,1,2)) + fields = np.array(self.cumulative_fields) / np.max( + np.abs(self.cumulative_fields), axis=(0, 1, 2) + ) for k in range(len(self.cumulative_fields)): - self.ax.images[-1].set_data(fields[k,:,:]) + self.ax.images[-1].set_data(fields[k, :, :]) self.ax.images[-1].set_clim(vmin=-0.8, vmax=0.8) self.grab_frame() @@ -956,9 +1143,10 @@ def grab_frame(self): # Saves the figures frame to memory. # modified from matplotlib library from io import BytesIO + bin_data = BytesIO() self.f.savefig(bin_data, format=self.frame_format) - #imgdata64 = base64.encodebytes(bin_data.getvalue()).decode('ascii') + # imgdata64 = base64.encodebytes(bin_data.getvalue()).decode('ascii') self._saved_frames.append(bin_data.getvalue()) def _embedded_frames(self, frame_list, frame_format): @@ -966,12 +1154,18 @@ def _embedded_frames(self, frame_list, frame_format): # frame_list should be a list of base64-encoded png files # modified from matplotlib import base64 + template = ' frames[{0}] = "data:image/{1};base64,{2}"\n' return "\n" + "".join( - template.format(i, frame_format, base64.encodebytes(frame_data).decode('ascii').replace('\n', '\\\n')) - for i, frame_data in enumerate(frame_list)) - - def to_jshtml(self,fps): + template.format( + i, + frame_format, + base64.encodebytes(frame_data).decode("ascii").replace("\n", "\\\n"), + ) + for i, frame_data in enumerate(frame_list) + ) + + def to_jshtml(self, fps): """ Outputs an interactable JSHTML animation object that is embeddable in Jupyter notebooks. The object is packaged with controls to manipulate the video's @@ -984,36 +1178,44 @@ def to_jshtml(self,fps): # Only works with Python3 and matplotlib > 3.1.0 from distutils.version import LooseVersion import matplotlib + if LooseVersion(matplotlib.__version__) < LooseVersion("3.1.0"): - print('-------------------------------') - print('Warning: JSHTML output is not supported with your current matplotlib build. Consider upgrading to 3.1.0+') - print('-------------------------------') + print("-------------------------------") + print( + "Warning: JSHTML output is not supported with your current matplotlib build. Consider upgrading to 3.1.0+" + ) + print("-------------------------------") return if mp.am_master(): from uuid import uuid4 - from matplotlib._animation_data import (DISPLAY_TEMPLATE, INCLUDED_FRAMES, JS_INCLUDE, STYLE_INCLUDE) + from matplotlib._animation_data import ( + DISPLAY_TEMPLATE, + INCLUDED_FRAMES, + JS_INCLUDE, + STYLE_INCLUDE, + ) # save the frames to an html file fill_frames = self._embedded_frames(self._saved_frames, self.frame_format) Nframes = len(self._saved_frames) - mode_dict = dict(once_checked='', - loop_checked='', - reflect_checked='') - mode_dict[f'{self.default_mode}_checked'] = 'checked' + mode_dict = dict(once_checked="", loop_checked="", reflect_checked="") + mode_dict[f"{self.default_mode}_checked"] = "checked" interval = 1000 // fps html_string = "" html_string += JS_INCLUDE html_string += STYLE_INCLUDE - html_string += DISPLAY_TEMPLATE.format(id=uuid4().hex, - Nframes=Nframes, - fill_frames=fill_frames, - interval=interval, - **mode_dict) + html_string += DISPLAY_TEMPLATE.format( + id=uuid4().hex, + Nframes=Nframes, + fill_frames=fill_frames, + interval=interval, + **mode_dict, + ) return JS_Animation(html_string) - def to_gif(self,fps,filename): + def to_gif(self, fps, filename): """ Generates and outputs a GIF file of the animation with the filename, `filename`, and the frame rate, `fps`. Note that GIFs are significantly larger than mp4 videos @@ -1027,30 +1229,41 @@ def to_gif(self,fps,filename): if mp.am_master(): from subprocess import Popen, PIPE from io import TextIOWrapper, BytesIO - FFMPEG_BIN = 'ffmpeg' - command = [FFMPEG_BIN, - '-f', 'image2pipe', # force piping of rawvideo - '-vcodec', self.frame_format, # raw input codec - '-s', '%dx%d' % (self.frame_size), - '-r', str(fps), # frame rate in frames per second - '-i', 'pipe:', # The input comes from a pipe - '-vcodec', 'gif', # output gif format - '-r', str(fps), # frame rate in frames per second - '-y', - '-vf', 'pad=width=ceil(iw/2)*2:height=ceil(ih/2)*2', - '-an', filename # output filename + + FFMPEG_BIN = "ffmpeg" + command = [ + FFMPEG_BIN, + "-f", + "image2pipe", # force piping of rawvideo + "-vcodec", + self.frame_format, # raw input codec + "-s", + "%dx%d" % (self.frame_size), + "-r", + str(fps), # frame rate in frames per second + "-i", + "pipe:", # The input comes from a pipe + "-vcodec", + "gif", # output gif format + "-r", + str(fps), # frame rate in frames per second + "-y", + "-vf", + "pad=width=ceil(iw/2)*2:height=ceil(ih/2)*2", + "-an", + filename, # output filename ] if mp.verbosity.meep > 0: print("Generating GIF...") proc = Popen(command, stdin=PIPE, stdout=PIPE, stderr=PIPE) for i in range(len(self._saved_frames)): proc.stdin.write(self._saved_frames[i]) - out, err = proc.communicate() # pipe in images + out, err = proc.communicate() # pipe in images proc.stdin.close() proc.wait() return - def to_mp4(self,fps,filename): + def to_mp4(self, fps, filename): """ Generates and outputs an mp4 video file of the animation with the filename, `filename`, and the frame rate, `fps`. Default encoding is h264 with yuv420p @@ -1062,27 +1275,39 @@ def to_mp4(self,fps,filename): if mp.am_master(): from subprocess import Popen, PIPE from io import TextIOWrapper, BytesIO - FFMPEG_BIN = 'ffmpeg' - command = [FFMPEG_BIN, - '-f', 'image2pipe', # force piping of rawvideo - '-vcodec', self.frame_format, # raw input codec - '-s', '%dx%d' % (self.frame_size), - #'-pix_fmt', self.frame_format, - '-r', str(fps), # frame rate in frames per second - '-i', 'pipe:', # The input comes from a pipe - '-vcodec', self.codec, # output mp4 format - '-pix_fmt','yuv420p', - '-r', str(fps), # frame rate in frames per second - '-y', - '-vf', 'pad=width=ceil(iw/2)*2:height=ceil(ih/2)*2', - '-an', filename # output filename + + FFMPEG_BIN = "ffmpeg" + command = [ + FFMPEG_BIN, + "-f", + "image2pipe", # force piping of rawvideo + "-vcodec", + self.frame_format, # raw input codec + "-s", + "%dx%d" % (self.frame_size), + #'-pix_fmt', self.frame_format, + "-r", + str(fps), # frame rate in frames per second + "-i", + "pipe:", # The input comes from a pipe + "-vcodec", + self.codec, # output mp4 format + "-pix_fmt", + "yuv420p", + "-r", + str(fps), # frame rate in frames per second + "-y", + "-vf", + "pad=width=ceil(iw/2)*2:height=ceil(ih/2)*2", + "-an", + filename, # output filename ] if mp.verbosity.meep > 0: print("Generating MP4...") proc = Popen(command, stdin=PIPE, stdout=PIPE, stderr=PIPE) for i in range(len(self._saved_frames)): proc.stdin.write(self._saved_frames[i]) - out, err = proc.communicate() # pipe in images + out, err = proc.communicate() # pipe in images proc.stdin.close() proc.wait() return @@ -1092,19 +1317,23 @@ def reset(self): self.ax = None self.f = None - def set_figure(self,f): + def set_figure(self, f): self.f = f + # ------------------------------------------------------- # # JS_Animation # ------------------------------------------------------- # # A helper class used to make jshtml animations embed # seamlessly within Jupyter notebooks. -class JS_Animation(): - def __init__(self,jshtml): + +class JS_Animation: + def __init__(self, jshtml): self.jshtml = jshtml + def _repr_html_(self): return self.jshtml + def get_jshtml(self): return self.jshtml From 6b47646259f7573637f6fc5edbbdc4b677556ab0 Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 16:30:09 +0200 Subject: [PATCH 03/12] run black and add pre-commit --- .gitattributes | 2 +- .github/dependabot.yml | 11 + .github/workflows/pages.yml | 40 + .github/workflows/pre-commit.yml | 16 + .github/workflows/pythonpublish.yml | 30 + .github/workflows/test_code.yml | 35 + .../test_code_updated_requirements.yml | 35 + .github/workflows/test_docs.yml | 30 + .pre-commit-config.yaml | 108 + AUTHORS | 2 +- doc/_api_snippets/class_template.md | 1 - doc/_api_snippets/function_template.md | 2 +- doc/_api_snippets/method_template.md | 2 +- doc/docs/2d_Cell_Special_kz.md | 2 +- doc/docs/C++_Tutorial.md | 2 +- doc/docs/Developer_Codes/Makefile.manual | 2 +- doc/docs/Download.md | 2 +- doc/docs/FAQ.md | 2 +- doc/docs/Installation.md | 2 +- doc/docs/Materials.md | 2 +- doc/docs/Python_Tutorials/Basics.md | 30 +- .../Local_Density_of_States.md | 2 +- .../Python_Tutorials/Mode_Decomposition.md | 2 +- .../Multilevel_Atomic_Susceptibility.md | 3 +- doc/docs/Python_Tutorials/Optical_Forces.md | 4 +- ..._and_Transmission_in_a_Waveguide_Cavity.md | 8 +- doc/docs/Python_User_Interface.md | 4 +- doc/docs/Scheme_Tutorials/Basics.md | 4 +- doc/docs/Scheme_Tutorials/Casimir_Forces.md | 1 - doc/docs/Scheme_Tutorials/Custom_Source.md | 2 +- .../Cylindrical_Coordinates.md | 2 +- .../Local_Density_of_States.md | 2 +- .../Multilevel_Atomic_Susceptibility.md | 6 +- doc/docs/Scheme_Tutorials/Optical_Forces.md | 2 +- ..._and_Transmission_in_a_Waveguide_Cavity.md | 4 +- doc/docs/Subpixel_Smoothing.md | 2 +- ...nizing_the_Magnetic_and_Electric_Fields.md | 2 +- doc/docs/The_Run_Function_Is_Not_A_Loop.md | 4 +- doc/docs/Yee_Lattice.md | 2 +- doc/docs/mdx_math.py | 45 +- doc/docs/setup.py | 19 +- doc/generate_py_api.py | 156 +- m4/ax_check_compiler_flags.m4 | 4 +- m4/ax_compiler_vendor.m4 | 5 +- m4/ax_gcc_x86_cpuid.m4 | 2 +- m4/pkg.m4 | 4 +- python/adjoint/__init__.py | 15 +- python/adjoint/basis.py | 8 +- python/adjoint/connectivity.py | 5 +- python/adjoint/filter_source.py | 5 +- python/adjoint/filters.py | 5 +- python/adjoint/objective.py | 8 +- python/adjoint/optimization_problem.py | 10 +- python/adjoint/utils.py | 7 +- python/adjoint/wrapper.py | 7 +- python/binary_partition_utils.py | 5 +- python/chunk_balancer.py | 6 +- python/examples/3rd-harm-1d.ipynb | 593 +--- python/examples/3rd-harm-1d.py | 4 +- python/examples/README.md | 14 +- python/examples/absorbed_power_density.ipynb | 217 +- python/examples/absorbed_power_density.py | 4 +- python/examples/absorber-1d.py | 6 +- .../01-Introduction-checkpoint.ipynb | 156 +- .../02-Waveguide_Bend-checkpoint.ipynb | 46 +- ...3-Filtered_Waveguide_Bend-checkpoint.ipynb | 1740 +--------- .../04-Splitter-checkpoint.ipynb | 1483 +------- .../05-Near2Far-checkpoint.ipynb | 4 +- .../06-Near2Far-Epigraph-checkpoint.ipynb | 1620 +-------- .../Bend Minimax-checkpoint.ipynb | 1696 +-------- .../01-Introduction.ipynb | 160 +- .../02-Waveguide_Bend.ipynb | 216 +- .../03-Filtered_Waveguide_Bend.ipynb | 1740 +--------- .../adjoint_optimization/04-Splitter.ipynb | 1711 +-------- .../adjoint_optimization/05-Near2Far.ipynb | 1691 +-------- .../06-Near2Far-Epigraph.ipynb | 1620 +-------- .../adjoint_optimization/Bend Minimax.ipynb | 1700 +-------- .../ConnectivityConstraint.ipynb | 118 +- .../adjoint_optimization/Fourier-Bend.ipynb | 2128 +----------- python/examples/antenna-radiation.ipynb | 84 +- python/examples/antenna-radiation.py | 7 +- python/examples/antenna_pec_ground_plane.py | 10 +- python/examples/bend-flux.ipynb | 121 +- python/examples/bend-flux.py | 7 +- python/examples/bent-waveguide.ipynb | 154 +- python/examples/bent-waveguide.py | 4 - python/examples/binary_grating.ipynb | 184 +- python/examples/binary_grating.py | 10 +- python/examples/binary_grating_n2f.ipynb | 220 +- python/examples/binary_grating_n2f.py | 12 +- python/examples/binary_grating_oblique.ipynb | 179 +- python/examples/binary_grating_oblique.py | 16 +- python/examples/binary_grating_phasemap.ipynb | 752 +--- python/examples/binary_grating_phasemap.py | 12 +- python/examples/cavity-farfield.ipynb | 141 +- python/examples/cavity-farfield.py | 6 +- python/examples/cavity_arrayslice.py | 5 +- python/examples/cherenkov-radiation.py | 1 - python/examples/chirped_pulse.py | 2 +- python/examples/coupler.ipynb | 1752 +--------- python/examples/coupler.py | 3 +- python/examples/cyl-ellipsoid.py | 4 +- python/examples/cylinder_cross_section.ipynb | 89 +- python/examples/cylinder_cross_section.py | 5 +- .../examples/differential_cross_section.ipynb | 285 +- python/examples/differential_cross_section.py | 5 +- python/examples/diffracted_planewave.py | 10 +- python/examples/eps_fit_lorentzian.py | 4 +- python/examples/faraday-rotation.py | 4 +- python/examples/finite_grating.ipynb | 126 +- python/examples/finite_grating.py | 6 +- python/examples/gaussian-beam.py | 5 +- .../examples/grating2d_triangular_lattice.py | 7 +- python/examples/holey-wvg-bands.ipynb | 298 +- python/examples/holey-wvg-bands.py | 4 - python/examples/holey-wvg-cavity.ipynb | 769 +---- python/examples/holey-wvg-cavity.py | 5 +- python/examples/material-dispersion.py | 5 +- python/examples/metal-cavity-ldos.py | 8 +- python/examples/metasurface_lens.ipynb | 1305 +------ python/examples/metasurface_lens.py | 12 +- python/examples/mie_scattering.py | 5 +- python/examples/mode-decomposition.ipynb | 393 +-- python/examples/mode-decomposition.py | 5 +- python/examples/mpb_bragg_sine.py | 1 + python/examples/mpb_data_analysis.py | 6 +- python/examples/mpb_diamond.py | 1 + python/examples/mpb_hole_slab.py | 3 +- python/examples/mpb_honey_rods.py | 3 +- python/examples/mpb_line_defect.py | 3 +- python/examples/mpb_sq_rods.py | 7 +- python/examples/mpb_strip.py | 2 - python/examples/mpb_tri_holes.py | 3 +- python/examples/mpb_tri_rods.py | 3 +- python/examples/mpb_tutorial.py | 7 +- python/examples/multilevel-atom.py | 3 +- python/examples/oblique-planewave.ipynb | 132 +- python/examples/oblique-planewave.py | 5 +- python/examples/oblique-source.ipynb | 227 +- python/examples/oblique-source.py | 5 +- python/examples/parallel-wvgs-force.ipynb | 3038 +--------------- python/examples/parallel-wvgs-force.py | 5 +- python/examples/parallel-wvgs-mpb.py | 5 +- python/examples/perturbation_theory.ipynb | 142 +- python/examples/perturbation_theory.py | 6 +- python/examples/perturbation_theory_2d.py | 6 +- python/examples/planar_cavity_ldos.py | 8 +- python/examples/polarization_grating.ipynb | 3046 +---------------- python/examples/polarization_grating.py | 13 +- python/examples/pw-source.py | 4 +- python/examples/refl-angular-kz2d.py | 3 +- python/examples/refl-angular.ipynb | 1404 +------- python/examples/refl-angular.py | 3 +- python/examples/refl-quartz.ipynb | 57 +- python/examples/refl-quartz.py | 10 +- python/examples/ring-cyl.py | 3 +- python/examples/ring-mode-overlap.py | 1 - python/examples/ring.ipynb | 171 +- python/examples/ring.py | 2 - python/examples/ring_gds.py | 6 +- python/examples/solve-cw.ipynb | 127 +- python/examples/solve-cw.py | 5 +- python/examples/stochastic_emitter.py | 7 +- python/examples/stochastic_emitter_line.py | 7 +- python/examples/straight-waveguide.ipynb | 147 +- python/examples/straight-waveguide.py | 6 +- python/examples/wvg-src.py | 3 - python/examples/zone_plate.py | 6 +- python/geom.py | 49 +- python/materials.py | 3 +- python/meep.i | 6 +- python/mpb_data.py | 9 +- python/simulation.py | 141 +- python/solver.py | 23 +- python/source.py | 21 +- python/tests/test_3rd_harm_1d.py | 4 +- python/tests/test_absorber_1d.py | 4 +- python/tests/test_adjoint_cyl.py | 6 +- python/tests/test_adjoint_jax.py | 8 +- python/tests/test_adjoint_solver.py | 7 +- python/tests/test_adjoint_utils.py | 9 +- python/tests/test_antenna_radiation.py | 6 +- python/tests/test_array_metadata.py | 4 +- python/tests/test_bend_flux.py | 4 +- python/tests/test_binary_grating.py | 22 +- python/tests/test_binary_partition_utils.py | 4 +- python/tests/test_cavity_arrayslice.py | 6 +- python/tests/test_cavity_farfield.py | 6 +- python/tests/test_chunk_balancer.py | 11 +- python/tests/test_chunk_layout.py | 3 +- python/tests/test_chunks.py | 3 +- python/tests/test_conductivity.py | 2 + python/tests/test_cyl_ellipsoid.py | 1 + python/tests/test_dft_energy.py | 1 + python/tests/test_dft_fields.py | 6 +- python/tests/test_diffracted_planewave.py | 7 +- python/tests/test_dispersive_eigenmode.py | 15 +- python/tests/test_divide_mpi_processes.py | 1 + python/tests/test_dump_load.py | 4 +- python/tests/test_eigfreq.py | 3 +- python/tests/test_faraday_rotation.py | 5 +- python/tests/test_field_functions.py | 3 +- python/tests/test_force.py | 1 + python/tests/test_fragment_stats.py | 1 + python/tests/test_gaussianbeam.py | 6 +- python/tests/test_geom.py | 4 +- python/tests/test_get_epsilon_grid.py | 7 +- python/tests/test_get_point.py | 6 +- python/tests/test_holey_wvg_bands.py | 3 +- python/tests/test_holey_wvg_cavity.py | 6 +- python/tests/test_integrated_source.py | 4 +- python/tests/test_kdom.py | 3 +- python/tests/test_ldos.py | 2 + python/tests/test_material_dispersion.py | 2 + python/tests/test_material_grid.py | 4 +- python/tests/test_materials_library.py | 3 +- python/tests/test_medium_evaluations.py | 7 +- python/tests/test_mode_coeffs.py | 4 +- python/tests/test_mode_decomposition.py | 14 +- python/tests/test_mpb.py | 10 +- python/tests/test_multilevel_atom.py | 3 +- python/tests/test_n2f_periodic.py | 8 +- python/tests/test_oblique_source.py | 7 +- python/tests/test_physical.py | 1 + python/tests/test_prism.py | 2 + python/tests/test_pw_source.py | 4 +- python/tests/test_refl_angular.py | 10 +- python/tests/test_ring.py | 1 + python/tests/test_ring_cyl.py | 4 +- python/tests/test_simulation.py | 2 + python/tests/test_source.py | 6 +- python/tests/test_special_kz.py | 8 +- python/tests/test_user_defined_material.py | 1 + python/tests/test_verbosity_mgr.py | 1 + python/tests/test_visualization.py | 11 +- python/tests/test_wvg_src.py | 2 +- python/tests/utils.py | 1 + python/timing_measurements.py | 4 +- python/verbosity_mgr.py | 9 +- python/visualization.py | 20 +- scheme/casimir.scm | 34 +- scheme/examples/3rd-harm-1d.ctl | 6 +- scheme/examples/bend-flux.ctl | 14 +- scheme/examples/holey-wvg-bands.ctl | 4 +- scheme/examples/holey-wvg-cavity.ctl | 12 +- scheme/examples/material-dispersion.ctl | 6 +- scheme/examples/ring-cyl.ctl | 10 +- scheme/materials.scm | 46 +- scheme/meep_op_renames.i | 1 - src/dft.cpp | 10 +- src/fix_boundary_sources.cpp | 2 +- src/loop_in_chunks.cpp | 14 +- src/meep.hpp | 2 +- src/meepgeom.cpp | 16 +- src/structure.cpp | 2 +- tests/cyl-ellipsoid.ctl | 2 +- 256 files changed, 1768 insertions(+), 34102 deletions(-) create mode 100644 .github/dependabot.yml create mode 100644 .github/workflows/pages.yml create mode 100644 .github/workflows/pre-commit.yml create mode 100644 .github/workflows/pythonpublish.yml create mode 100644 .github/workflows/test_code.yml create mode 100644 .github/workflows/test_code_updated_requirements.yml create mode 100644 .github/workflows/test_docs.yml create mode 100644 .pre-commit-config.yaml diff --git a/.gitattributes b/.gitattributes index 579877f02..17cd3619d 100644 --- a/.gitattributes +++ b/.gitattributes @@ -5,4 +5,4 @@ tests/* linguist-documentation scheme/examples/* linguist-documentation python/numpy.i linguist-vendored src/support/mt19937ar.c linguist-vendored -src/support/meep_mt.h linguist-language=C++ \ No newline at end of file +src/support/meep_mt.h linguist-language=C++ diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 000000000..0aada4dc0 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,11 @@ +version: 2 +updates: + - package-ecosystem: "pip" + directory: "/" # Location of package manifests + schedule: + interval: "daily" + + - package-ecosystem: github-actions + directory: / + schedule: + interval: monthly diff --git a/.github/workflows/pages.yml b/.github/workflows/pages.yml new file mode 100644 index 000000000..3e775a690 --- /dev/null +++ b/.github/workflows/pages.yml @@ -0,0 +1,40 @@ +name: Sphinx docs to gh-pages + +on: + push: + branches: + - master + workflow_dispatch: + +jobs: + sphinx_docs_to_gh-pages: + runs-on: ubuntu-latest + name: Sphinx docs to gh-pages + steps: + - name: Cancel Workflow Action + uses: styfle/cancel-workflow-action@0.10.0 + - uses: actions/checkout@v3 + - uses: conda-incubator/setup-miniconda@v2 + with: + python-version: 3.9 + mamba-version: "*" + channels: conda-forge,defaults + channel-priority: true + activate-environment: anaconda-client-env + - name: Add conda to system path + run: | + echo $CONDA/bin >> $GITHUB_PATH + - name: Installing the library + shell: bash -l {0} + run: | + pip install -e . + pip install -r requirements_dev.txt + sudo wget https://github.com/jgm/pandoc/releases/download/1.16.0.2/pandoc-1.16.0.2-1-amd64.deb + sudo dpkg -i pandoc-1.16.0.2-1-amd64.deb + #sudo apt install pandoc + - name: Running the Sphinx to gh-pages Action + uses: uibcdf/action-sphinx-docs-to-gh-pages@v1.0-beta.2 + with: + branch: master + dir_docs: docs + sphinxopts: "" diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 000000000..088c7b916 --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,16 @@ +--- +name: pre-commit + +on: + pull_request: + push: + +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: 3.9 + - uses: pre-commit/action@v3.0.0 diff --git a/.github/workflows/pythonpublish.yml b/.github/workflows/pythonpublish.yml new file mode 100644 index 000000000..c96bda539 --- /dev/null +++ b/.github/workflows/pythonpublish.yml @@ -0,0 +1,30 @@ +--- +name: Upload Python Package + +on: + release: + types: [created, published] + push: + branches: [master] + tags: [v*] + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: 3.x + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine + - name: Build and publish + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: | + python setup.py sdist bdist_wheel + twine upload dist/* diff --git a/.github/workflows/test_code.yml b/.github/workflows/test_code.yml new file mode 100644 index 000000000..2a589bacb --- /dev/null +++ b/.github/workflows/test_code.yml @@ -0,0 +1,35 @@ +name: Test code and lint + +on: + pull_request: + push: + schedule: + - cron: 0 2 * * * # run at 2 AM UTC + +jobs: + build: + runs-on: ${{ matrix.os }} + strategy: + max-parallel: 12 + matrix: + python-version: [3.8, 3.9] + os: [ubuntu-latest] + + steps: + - name: Cancel Workflow Action + uses: styfle/cancel-workflow-action@0.10.0 + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' + - name: Install dependencies + run: | + pip install -e . + pip install flake8 pytest + - name: Lint with flake8 + run: | + flake8 . + - name: Test with pytest + run: pytest diff --git a/.github/workflows/test_code_updated_requirements.yml b/.github/workflows/test_code_updated_requirements.yml new file mode 100644 index 000000000..4afe7afc8 --- /dev/null +++ b/.github/workflows/test_code_updated_requirements.yml @@ -0,0 +1,35 @@ +--- +name: test with updated requirements (bleeding edge versions) +# https://docs.github.com/en/free-pro-team@latest/actions/guides/building-and-testing-python + +on: + pull_request: + push: + schedule: + - cron: 0 2 * * * # run at 2 AM UTC + +jobs: + build: + runs-on: ${{ matrix.os }} + strategy: + max-parallel: 12 + matrix: + python-version: [3.9] + os: [ubuntu-latest] + + steps: + - name: Cancel Workflow Action + uses: styfle/cancel-workflow-action@0.9.1 + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pur pytest + pur -r requirements.txt + pip install . + - name: Test with pytest + run: pytest diff --git a/.github/workflows/test_docs.yml b/.github/workflows/test_docs.yml new file mode 100644 index 000000000..a1e85f3b4 --- /dev/null +++ b/.github/workflows/test_docs.yml @@ -0,0 +1,30 @@ +name: Test documentation + +on: + pull_request: + push: + schedule: + - cron: "0 2 * * *" # run at 2 AM UTC + - + +jobs: + build-linux: + runs-on: ubuntu-latest + + steps: + - name: Cancel Workflow Action + uses: styfle/cancel-workflow-action@0.10.0 + - uses: actions/checkout@v3 + - name: Set up Python 3.9 + uses: actions/setup-python@v4 + with: + python-version: 3.9 + - name: Install dependencies + run: | + pip install -r requirements_dev.txt + pip install . + sudo apt install pandoc + - name: Test documentation + run: | + cd docs + make html diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..6e666bc53 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,108 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: "b0f06dc9f2260909f3423243de180edfc823ec5a" + hooks: + - id: check-yaml + - id: end-of-file-fixer + - id: trailing-whitespace + + # - repo: https://github.com/hakancelik96/unimport + # rev: df8eb1a4c91acb84da197828af8157708968b596 + # hooks: + # - id: unimport + # args: [--remove, --include-star-import] + - repo: https://github.com/pycqa/isort + rev: "12cc5fbd67eebf92eb2213b03c07b138ae1fb448" + hooks: + - id: isort + files: "python/.*" + args: ["--profile", "black", "--filter-files"] + + - repo: https://github.com/psf/black + rev: "6debce63bc2429b1680f8838592f2e56e3df6b27" + hooks: + - id: black + + # - repo: https://gitlab.com/pycqa/flake8 + # rev: "21d3c70d676007470908d39b73f0521d39b3b997" + # hooks: + # - id: flake8 + + - repo: https://github.com/kynan/nbstripout + rev: 8cafdcc393232045208137698dbeb42d6e0dd9e8 + hooks: + - id: nbstripout + files: ".ipynb" + + - repo: https://github.com/asottile/pyupgrade + rev: 85f02f45f0d2889f3826f16b60f27188ea84c1ae + hooks: + - id: pyupgrade + args: [--py37-plus, --keep-runtime-typing] + + # - repo: https://github.com/codespell-project/codespell + # rev: 4d782511b3999c243feb3858cd7062270eb13291 + # hooks: + # - id: codespell + # args: ["-L TE,TE/TM,te,ba,FPR,fpr_spacing,ro"] + + - repo: https://github.com/shellcheck-py/shellcheck-py + rev: f87a493c6596a5338d69395905a4e13ed65584f6 + hooks: + - id: shellcheck + + - repo: https://github.com/pre-commit/pygrep-hooks + rev: 8e68d79b05355c9e107f81fc267f2bfc3e9e5a04 # Use the ref you want to point at + hooks: + - id: python-use-type-annotations + + - repo: https://github.com/PyCQA/bandit + rev: 44c05fcac53de3a39589173177c931b38de5bb0f + hooks: + - id: bandit + args: [--exit-zero] + # ignore all tests, not just tests data + exclude: ^tests/ + # - repo: https://github.com/pre-commit/mirrors-mypy + # rev: "214c33306afe17f1cc7d2d55e4da705b6ebe0627" + # hooks: + # - id: mypy + # exclude: ^(docs/|example-plugin/|tests/fixtures) + # additional_dependencies: + # - "pydantic" + # - repo: https://github.com/terrencepreilly/darglint + # rev: master + # hooks: + # - id: darglint + + # - repo: https://github.com/pycqa/pydocstyle + # rev: "" + # hooks: + # - id: pydocstyle + # - repo: https://github.com/asottile/reorder_python_imports + # rev: 2b2f0c74acdb3de316e23ceb7dd0d7945c354050 + # hooks: + # - id: reorder-python-imports + # - repo: https://github.com/PyCQA/pylint + # rev: v2.14.1 + # hooks: + # - id: pylint + # args: [--exit-zero] + # - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks + # rev: 6565d773ca281682d7062d4c0be74538cc474cc9 + # hooks: + # - id: pretty-format-java + # args: [--autofix] + # - id: pretty-format-kotlin + # args: [--autofix] + # - id: pretty-format-yaml + # args: [--autofix, --indent, "2"] + # - repo: https://github.com/adrienverge/yamllint.git + # rev: v1.21.0 # or higher tag + # hooks: + # - id: yamllint + # args: [--format, parsable, --strict] + # - repo: https://github.com/jumanjihouse/pre-commit-hook-yamlfmt + # rev: 0.1.0 # or specific tag + # hooks: + # - id: yamlfmt diff --git a/AUTHORS b/AUTHORS index 67c49fe38..f26a2989f 100644 --- a/AUTHORS +++ b/AUTHORS @@ -17,4 +17,4 @@ Ian Williamson Andreas Hoenselaar Ben Bartlett Krishna Gadepalli -Mo Chen \ No newline at end of file +Mo Chen diff --git a/doc/_api_snippets/class_template.md b/doc/_api_snippets/class_template.md index 35de0fb34..4de4206ec 100644 --- a/doc/_api_snippets/class_template.md +++ b/doc/_api_snippets/class_template.md @@ -12,4 +12,3 @@ class {class_name}({base_classes}): {docstring} - diff --git a/doc/_api_snippets/function_template.md b/doc/_api_snippets/function_template.md index 89c85b739..adcba9c4c 100644 --- a/doc/_api_snippets/function_template.md +++ b/doc/_api_snippets/function_template.md @@ -9,4 +9,4 @@ def {function_name}{parameters}:{other_signatures} {docstring} - \ No newline at end of file + diff --git a/doc/_api_snippets/method_template.md b/doc/_api_snippets/method_template.md index 71cc0ac3d..560b31be2 100644 --- a/doc/_api_snippets/method_template.md +++ b/doc/_api_snippets/method_template.md @@ -13,4 +13,4 @@ def {method_name}{parameters}:{other_signatures} - \ No newline at end of file + diff --git a/doc/docs/2d_Cell_Special_kz.md b/doc/docs/2d_Cell_Special_kz.md index 11cd1fdcc..655881cf0 100644 --- a/doc/docs/2d_Cell_Special_kz.md +++ b/doc/docs/2d_Cell_Special_kz.md @@ -100,4 +100,4 @@ The Meep results are identical to within six decimal digits and agree well with ``` refl:, 0.3272338236967464 (real/imag), 0.3272338244372344 (complex), 0.3272330216564413 (3d), 0.3293821216165117 (analytic) -``` \ No newline at end of file +``` diff --git a/doc/docs/C++_Tutorial.md b/doc/docs/C++_Tutorial.md index 5e158b86b..2e152877a 100644 --- a/doc/docs/C++_Tutorial.md +++ b/doc/docs/C++_Tutorial.md @@ -86,7 +86,7 @@ const double half_cavity_width = d2; const int N = 5; ``` -Meep supports perfectly matching layers (PML) as absorbing boundary conditions. The PML begins at the edge of the computational volume and works inwards. Hence, we specify the size of the cell as follows: +Meep supports perfectly matching layers (PML) as absorbing boundary conditions. The PML begins at the edge of the computational volume and works inwards. Hence, we specify the size of the cell as follows: ```c++ const double pml_thickness = 1.0; diff --git a/doc/docs/Developer_Codes/Makefile.manual b/doc/docs/Developer_Codes/Makefile.manual index 5abb9d261..b0501ad8a 100644 --- a/doc/docs/Developer_Codes/Makefile.manual +++ b/doc/docs/Developer_Codes/Makefile.manual @@ -15,5 +15,5 @@ LIBS = -lmeepgeom -lmeep -lctlgeom WriteChunkInfo: WriteChunkInfo.o $(CXX) $(LDFLAGS) -o $@ $^ $(LIBS) -clean: +clean: rm *.o *.a diff --git a/doc/docs/Download.md b/doc/docs/Download.md index 730c067c0..deb875b33 100644 --- a/doc/docs/Download.md +++ b/doc/docs/Download.md @@ -28,4 +28,4 @@ You will also be able to install the [parallel version of Meep](https://packages sudo apt-get install python3-meep-openmpi ``` -These upcoming Meep packages for Ubuntu 21.04 are derived from the Debian 11 ("Bullseye") packages ([serial](https://packages.debian.org/bullseye/python3-meep) and [parallel](https://packages.debian.org/bullseye/python3-meep-openmpi)). Debian 11 is currently the testing distribution. \ No newline at end of file +These upcoming Meep packages for Ubuntu 21.04 are derived from the Debian 11 ("Bullseye") packages ([serial](https://packages.debian.org/bullseye/python3-meep) and [parallel](https://packages.debian.org/bullseye/python3-meep-openmpi)). Debian 11 is currently the testing distribution. diff --git a/doc/docs/FAQ.md b/doc/docs/FAQ.md index 6754daa2d..619e9f4b2 100644 --- a/doc/docs/FAQ.md +++ b/doc/docs/FAQ.md @@ -320,7 +320,7 @@ Only the [harmonic mean](https://en.wikipedia.org/wiki/Harmonic_mean) of eigenva ### How do I model graphene or other 2d materials with single-atom thickness? -Typically, graphene and similar "2d" materials are mathematically represented as a [delta function](https://en.wikipedia.org/wiki/Dirac_delta_function) conductivity in Maxwell's equations because their thickness is negligible compared to the wavelength, where the conductivity is furthermore usually anisotropic (producing surface-parallel currents in response to the surface-parallel components of the electric field). In a discretized computer model like Meep, this is approximated by an anisotropic volume conductivity (or other polarizable dispersive material) whose thickness is proportional to (`1/resolution`) *and* whose amplitude is proportional `resolution`. For example, this could be represented by a one-pixel-thick [conductor](Materials.md#conductivity-and-complex) can be represented by e.g. a [`Block`](Python_User_Interface.md#block) with `size=meep.Vector3(x,y,1/resolution)` in a 3d cell, with the value of the conductivity explicitly multiplied by `resolution`. +Typically, graphene and similar "2d" materials are mathematically represented as a [delta function](https://en.wikipedia.org/wiki/Dirac_delta_function) conductivity in Maxwell's equations because their thickness is negligible compared to the wavelength, where the conductivity is furthermore usually anisotropic (producing surface-parallel currents in response to the surface-parallel components of the electric field). In a discretized computer model like Meep, this is approximated by an anisotropic volume conductivity (or other polarizable dispersive material) whose thickness is proportional to (`1/resolution`) *and* whose amplitude is proportional `resolution`. For example, this could be represented by a one-pixel-thick [conductor](Materials.md#conductivity-and-complex) can be represented by e.g. a [`Block`](Python_User_Interface.md#block) with `size=meep.Vector3(x,y,1/resolution)` in a 3d cell, with the value of the conductivity explicitly multiplied by `resolution`. ### How do I model a continuously varying permittivity? You can use a [material function](Python_User_Interface.md#medium) to model any arbitrary, position-dependent permittivity/permeability function $\varepsilon(\vec{r})$/$\mu(\vec{r})$ including anisotropic, [dispersive](Materials.md#material-dispersion), and [nonlinear](Materials.md#nonlinearity) media. For an example involving a non-dispersive, anisotropic material, see [Tutorial/Mode Decomposition/Diffraction Spectrum of Liquid-Crystal Polarization Gratings](Python_Tutorials/Mode_Decomposition.md#diffraction-spectrum-of-liquid-crystal-polarization-gratings). The material function construct can also be used to specify arbitrary *shapes* (e.g., curves such as parabolas, sinusoids, etc.) within: (1) the interior boundary of a [`GeometricObject`](Python_User_Interface.md#geometricobject) (e.g., `Block`, `Sphere`, `Cylinder`, etc.), (2) the entire cell via the [`material_function`](Python_User_Interface.md#material-function) parameter of the `Simulation` constructor, or (3) a combination of the two. diff --git a/doc/docs/Installation.md b/doc/docs/Installation.md index ead7b677b..c99eb1029 100644 --- a/doc/docs/Installation.md +++ b/doc/docs/Installation.md @@ -123,7 +123,7 @@ There are upcoming packages for [Meep version 1.17.1](https://github.com/NanoCom In the meantime, the following dependencies are already available as precompiled packages: BLAS and LAPACK possibly as part of a package for [Atlas BLAS](https://en.wikipedia.org/wiki/Automatically_Tuned_Linear_Algebra_Software), Guile, MPI, and HDF5. One thing to be careful of is that many distributions split packages into two parts: one main package for the libraries and programs, and a **devel** package for [header files](https://en.wikipedia.org/wiki/Header_file) and other things needed to compile software using those libraries. You will need to install **both**. So, for example, you will probably need both a `guile` package (probably installed by default) and a `guile-dev` or `guile-devel` package (probably *not* installed by default), and similarly for HDF5 etcetera. You will probably also want to install a `libpng-dev` or `libpng-devel` package in order to compile the `h5topng` utility in [h5utils](https://github.com/NanoComp/h5utils/blob/master/README.md). -Installation from source on macOS +Installation from source on macOS --------------------------------- Most macOS users will probably want to install via the Conda packages as described above. It is also possible to compile Meep from source, however. diff --git a/doc/docs/Materials.md b/doc/docs/Materials.md index 2c5c131cb..25e6ceda4 100644 --- a/doc/docs/Materials.md +++ b/doc/docs/Materials.md @@ -188,7 +188,7 @@ Similar relationships can be found for systems with more than two atomic levels ### Natural Units for Saturable Media A conversion chart for different choices of parameter nomenclature for the saturable medium. - + There is no standard convention in the literature on lasers and saturable gain media for defining the various constants in the equations above. The following are the relationships among these constants for the three different groups of work discussed in this section: $$ \omega_n \; (\textrm{Meep}) = \omega_{ba} \; (\textrm{Boyd}) = \omega_a \; (\textrm{SALT}) $$ diff --git a/doc/docs/Python_Tutorials/Basics.md b/doc/docs/Python_Tutorials/Basics.md index d2a9c28d7..4ab0fde4c 100644 --- a/doc/docs/Python_Tutorials/Basics.md +++ b/doc/docs/Python_Tutorials/Basics.md @@ -242,7 +242,7 @@ Above, we outputted the full 2d data slice at every 0.6 time units, resulting in To create the movie above, all we really need are the *images* corresponding to each time. Images can be stored much more efficiently than raw arrays of numbers — to exploit this fact, Meep allows you to output PNG images instead of HDF5 files. In particular, instead of `output_efield_z` as above, we can use `mp.output_png(mp.Ez, "-Zc dkbluered")`, where Ez is the component to output and the `"-Zc` `dkbluered"` are options for `h5topng` of [h5utils](https://github.com/NanoComp/h5utils/blob/master/README.md) which is the program that is actually used to create the image files. That is: ```py -sim.run(mp.at_every(0.6 , mp.output_png(mp.Ez, "-Zc dkbluered")), until=200) +sim.run(mp.at_every(0.6 , mp.output_png(mp.Ez, "-Zc dkbluered")), until=200) ``` will output a PNG file file every 0.6 time units, which can then be combined with `convert` as above to create a movie. The movie will be similar to the one before, but not identical because of how the color scale is determined. Before, we used the `-R` option to make h5topng use a uniform color scale for all images, based on the minimum/maximum field values over all time steps. That is not possible because we output an image before knowing the field values at future time steps. Thus, what `output_png` does is to set its color scale based on the minimum/maximum field values from all *past* times — therefore, the color scale will slowly "ramp up" as the source turns on. @@ -258,7 +258,7 @@ This will put *all* of the output files (.h5, .png, etcetera) into a newly-creat What if we want to output an $x \times t$ slice, as above? To do this, we only really wanted the values at $y=-3.5$, and therefore we can exploit another powerful output feature — Meep allows us to output only **a subset of the computational cell**. This is done using the `in_volume` function, which like `at_every` and `to_appended` is another function that modifies the behavior of other output functions. In particular, we can do: ``` -sim.run(mp.in_volume(mp.Volume(mp.Vector3(0,-3.5), size=mp.Vector3(16,0)), mp.to_appended("ez-slice", mp.output_efield_z)), until=200) +sim.run(mp.in_volume(mp.Volume(mp.Vector3(0,-3.5), size=mp.Vector3(16,0)), mp.to_appended("ez-slice", mp.output_efield_z)), until=200) ``` The first argument to `in_volume` is a volume which applies to all of the nested output functions. Note that `to_appended`, `at_every`, and `in_volume` are cumulative regardless of what order you put them in. This creates the output file `ez-slice.h5` which contains a dataset of size 162x330 corresponding to the desired $x \times t$ slice. @@ -334,7 +334,7 @@ sim = mp.Simulation(cell_size=cell, nfreq = 100 # number of frequencies at which to compute flux # reflected flux -refl_fr = mp.FluxRegion(center=mp.Vector3(-0.5*sx+dpml+0.5,wvg_ycen,0), size=mp.Vector3(0,2*w,0)) +refl_fr = mp.FluxRegion(center=mp.Vector3(-0.5*sx+dpml+0.5,wvg_ycen,0), size=mp.Vector3(0,2*w,0)) refl = sim.add_flux(fcen, df, nfreq, refl_fr) # transmitted flux @@ -348,7 +348,7 @@ The fluxes will be computed for `nfreq=100` frequencies centered on `fcen`, from As described in [Introduction/Transmittance/Reflectance Spectra](../Introduction.md#transmittancereflectance-spectra), computing the reflection spectra requires some care because we need to separate the incident and reflected fields. We do this by first saving the Fourier-transformed fields from the normalization run. And then, before we start the second run, we load these fields, *negated*. The latter subtracts the Fourier-transformed incident fields from the Fourier transforms of the scattered fields. Logically, we might subtract these after the run, but it turns out to be more convenient to subtract the incident fields first and then accumulate the Fourier transform. All of this is accomplished with two commands which use the raw simulation data: `get_flux_data` and `load_minus_flux_data`. We run the first simulation as follows: -```py +```py pt = mp.Vector3(0.5*sx-dpml-0.5,wvg_ycen) sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,pt,1e-3)) @@ -372,7 +372,7 @@ We need to run the second simulation which involves the waveguide bend. We reset ```py sim.reset_meep() - + geometry = [mp.Block(mp.Vector3(sx-pad,w,mp.inf), center=mp.Vector3(-0.5*pad,wvg_ycen), material=mp.Medium(epsilon=12)), mp.Block(mp.Vector3(w,sy-pad,mp.inf), center=mp.Vector3(wvg_xcen,0.5*pad), material=mp.Medium(epsilon=12))] @@ -387,7 +387,7 @@ refl = sim.add_flux(fcen, df, nfreq, refl_fr) tran_fr = mp.FluxRegion(center=mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5,0), size=mp.Vector3(2*w,0,0)) tran = sim.add_flux(fcen, df, nfreq, tran_fr) - + # for normal run, load negated fields to subtract incident from refl. fields sim.load_minus_flux_data(refl, straight_refl_data) @@ -402,7 +402,7 @@ flux_freqs = mp.get_flux_freqs(refl) ``` With the flux data, we are ready to compute and plot the reflectance and transmittance. The reflectance is the reflected flux divided by the incident flux. We also have to multiply by -1 because all fluxes in Meep are computed in the positive-coordinate direction by default, and we want the flux in the $-x$ direction. The transmittance is the transmitted flux divided by the incident flux. Finally, the scattered loss is simply $1-transmittance-reflectance$. The results are plotted in the accompanying figure. - + ```py wl = [] Rs = [] @@ -410,7 +410,7 @@ Ts = [] for i in range(nfreq): wl = np.append(wl, 1/flux_freqs[i]) Rs = np.append(Rs,-bend_refl_flux[i]/straight_tran_flux[i]) - Ts = np.append(Ts,bend_tran_flux[i]/straight_tran_flux[i]) + Ts = np.append(Ts,bend_tran_flux[i]/straight_tran_flux[i]) if mp.am_master(): plt.figure() @@ -469,7 +469,7 @@ def main(args): fcen = 0.5*(fmin+fmax) # center frequency df = fmax-fmin # frequency width nfreq = 50 # number of frequency bins - + # rotation angle (in degrees) of source: CCW around Y axis, 0 degrees along +Z axis theta_r = math.radians(args.theta) @@ -481,7 +481,7 @@ def main(args): dimensions = 1 else: dimensions = 3 - + sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), component=mp.Ex, center=mp.Vector3(0,0,-0.5*sz+dpml))] @@ -495,7 +495,7 @@ def main(args): refl_fr = mp.FluxRegion(center=mp.Vector3(0,0,-0.25*sz)) refl = sim.add_flux(fcen, df, nfreq, refl_fr) - + sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(0,0,-0.5*sz+dpml), 1e-9)) empty_flux = mp.get_fluxes(refl) @@ -522,10 +522,10 @@ def main(args): refl_flux = mp.get_fluxes(refl) freqs = mp.get_flux_freqs(refl) - + for i in range(nfreq): print("refl:, {}, {}, {}, {}".format(k.x,1/freqs[i],math.degrees(math.asin(k.x/freqs[i])),-refl_flux[i]/empty_flux[i])) - + if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-res', type=int, default=200, help='resolution (default: 200 pixels/um)') @@ -882,7 +882,7 @@ for n in range(npts): E[n,:] = [np.conj(ff[j]) for j in range(3)] H[n,:] = [ff[j+3] for j in range(3)] -# compute Poynting flux Pr in the radial direction. At large r, +# compute Poynting flux Pr in the radial direction. At large r, # all of the flux is radial so we can simply compute the magnitude of the Poynting vector. Px = np.real(np.multiply(E[:,1],H[:,2])-np.multiply(E[:,2],H[:,1])) Py = np.real(np.multiply(E[:,2],H[:,0])-np.multiply(E[:,0],H[:,2])) @@ -1078,7 +1078,7 @@ Finally, we are ready to run the simulation. The basic idea is to run until the sim = mp.Simulation(cell_size=mp.Vector3(sxy, sxy), geometry=[c1, c2], sources=[src], - resolution=10, + resolution=10, boundary_layers=[mp.PML(dpml)]) sim.run(mp.at_beginning(mp.output_epsilon), diff --git a/doc/docs/Python_Tutorials/Local_Density_of_States.md b/doc/docs/Python_Tutorials/Local_Density_of_States.md index 5ceaae47e..fb66cb29b 100644 --- a/doc/docs/Python_Tutorials/Local_Density_of_States.md +++ b/doc/docs/Python_Tutorials/Local_Density_of_States.md @@ -25,7 +25,7 @@ In 3D, each simulation uses three [mirror symmetries](../Exploiting_Symmetry.md# In cylindrical coordinates, the dipole is polarized in the $r$ direction. Setting up a linearly polarized source in cylindrical coordiantes is demonstrated in [Tutorial/Cylindrical Coordinates/Scattering Cross Section of a Finite Dielectric Cylinder](Cylindrical_Coordinates.md#scattering-cross-section-of-a-finite-dielectric-cylinder). However, all that is necessary in this example which involves a single point dipole rather than a planewave is one simulation involving an $E_r$ source at $r=0$ with $m=-1$. This is actually a circularly polarized source but this is sufficient because the $m=+1$ simulation produces an identical result to the $m=-1$ simulation. It is therefore not necessary to perform two separate simulations for $m=\pm 1$ in order to average the results from the left- and right-circularly polarized sources. -One important parameter when setting up this calculation is the grid resolution. +One important parameter when setting up this calculation is the grid resolution. A key feature of the LDOS in this geometry is that it experiences discontinuities, called [Van Hove singularities](https://en.wikipedia.org/wiki/Van_Hove_singularity), any time the cavity thickness/λ passes through the cutoff for a waveguide mode, which occurs for cavity-thickness/λ values of 0.5, 1.5, 2.5, etc. (Mathematically, Van Hove singularities depend strongly on the dimensionality — it is a discontinuity in this case because the waves are propagating along two dimensions, i.e. each cutoff is a minimum in the 2d dispersion relation $\omega(k_x,k_y)$.) This discontinuity also means that the LDOS *exactly at* the cutoff thickness/λ is ill-defined and convergence with discretization can be problematic at this point. (In consequence, the LDOS *exactly* at the Van Hove discontinuity can behave erratically with resolution, and should be viewed with caution.) diff --git a/doc/docs/Python_Tutorials/Mode_Decomposition.md b/doc/docs/Python_Tutorials/Mode_Decomposition.md index d3e44ebe6..4565f3cb5 100644 --- a/doc/docs/Python_Tutorials/Mode_Decomposition.md +++ b/doc/docs/Python_Tutorials/Mode_Decomposition.md @@ -340,7 +340,7 @@ sx = dpml+dsub+gh+dpad+dpml sy = gp cell_size = mp.Vector3(sx,sy,0) -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] wvl = 0.5 # center wavelength fcen = 1/wvl # center frequency diff --git a/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md b/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md index 64fca8bf6..65825035a 100644 --- a/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md +++ b/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md @@ -101,7 +101,7 @@ $$ \mathbf{E} \; (\textrm{SALT}) = \frac{2 |\theta|}{\hbar \sqrt{\gamma_\perp \g For two-level gain media, $\gamma_\parallel = \gamma_{12} + \gamma_{21}$. We can also verify that the system is not exhibiting relaxation oscillations by directly plotting the electric field as a function of time and looking for very long time-scale oscillations. In the continuum limit, these modes would appear as Dirac delta functions in the spectra. The discretized model, however, produces peaks with finite width. Thus, we need to integrate a fixed number of points around each peak to calculate the correct modal intensity. -By varying $N_0$ or the pumping rate $R_p$, we can change the total gain available in the cavity. This is used to find the laser's modal intensities as a function of the strength of the gain. We can compare the simulated modal intensity with SALT as well as an independent FDTD solver based on the Maxwell-Bloch equations. All three methods produce results with good agreement close to the first lasing threshold. +By varying $N_0$ or the pumping rate $R_p$, we can change the total gain available in the cavity. This is used to find the laser's modal intensities as a function of the strength of the gain. We can compare the simulated modal intensity with SALT as well as an independent FDTD solver based on the Maxwell-Bloch equations. All three methods produce results with good agreement close to the first lasing threshold.

Near threshold comparison @@ -113,4 +113,3 @@ Further increasing the gain continues to yield good agreement.

Near threshold comparison

- diff --git a/doc/docs/Python_Tutorials/Optical_Forces.md b/doc/docs/Python_Tutorials/Optical_Forces.md index 39433bd3f..28131a30c 100644 --- a/doc/docs/Python_Tutorials/Optical_Forces.md +++ b/doc/docs/Python_Tutorials/Optical_Forces.md @@ -29,12 +29,12 @@ import numpy as np import matplotlib.pyplot as plt resolution = 40 # pixels/μm - + Si = mp.Medium(index=3.45) dpml = 1.0 pml_layers = [mp.PML(dpml)] - + sx = 5 sy = 3 cell = mp.Vector3(sx+2*dpml,sy+2*dpml,0) diff --git a/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md b/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md index e1d291ce6..3fac28958 100644 --- a/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md +++ b/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md @@ -37,7 +37,7 @@ Next, we'll define some parameters of our structure as in the figure above. All ```py resolution = 20 # pixels/um - + eps = 13 # dielectric constant of waveguide w = 1.2 # width of waveguide r = 0.36 # radius of holes @@ -206,7 +206,7 @@ The structure is exactly the same as above, and only the sources and analysis ar ```py if args.resonant_modes: ...new sources and run command... -else: +else: ...sources and run from above, to get spectrum... ``` @@ -260,7 +260,7 @@ unix% convert holey-wvg-cavity-hz-*.png holey-wvg-cavity-hz.gif

-The mode has a frequency of 0.235, just as we saw in the transmission spectrum, and a $Q$ of 373 which we could have also found by fitting the transmission spectrum. This lifetime $Q$ includes two independent decay channels: light can decay from the cavity into the waveguide with lifetime $Q_w$, or it can radiate from the cavity into the surrounding air with lifetime $Q_r$, where +The mode has a frequency of 0.235, just as we saw in the transmission spectrum, and a $Q$ of 373 which we could have also found by fitting the transmission spectrum. This lifetime $Q$ includes two independent decay channels: light can decay from the cavity into the waveguide with lifetime $Q_w$, or it can radiate from the cavity into the surrounding air with lifetime $Q_r$, where $$\frac{1}{Q} = \frac{1}{Q_w} + \frac{1}{Q_r}$$ @@ -387,7 +387,7 @@ unix% grep freqs: holey-wvg-bands.out > fre.dat unix% grep freqs-im: holey-wvg-bands.out > fim.dat ``` -Plotting the real parts of ω, where the light cone ω > *ck* is shaded gray, we find: +Plotting the real parts of ω, where the light cone ω > *ck* is shaded gray, we find:

diff --git a/doc/docs/Python_User_Interface.md b/doc/docs/Python_User_Interface.md index 3df3c2f2d..a6959c85f 100644 --- a/doc/docs/Python_User_Interface.md +++ b/doc/docs/Python_User_Interface.md @@ -80,7 +80,7 @@ control various parameters of the Meep computation. - +

@@ -356,7 +356,7 @@ use. See also [SWIG Wrappers](#swig-wrappers).
- +
diff --git a/doc/docs/Scheme_Tutorials/Basics.md b/doc/docs/Scheme_Tutorials/Basics.md index e45c00837..7fd12258f 100644 --- a/doc/docs/Scheme_Tutorials/Basics.md +++ b/doc/docs/Scheme_Tutorials/Basics.md @@ -108,7 +108,7 @@ This will create `eps-000000.00.png`, where the `-S3` increases the image scale unix% h5topng -S3 -Zc dkbluered -a yarg -A eps-000000.00.h5 ez-000200.00.h5 ``` -Briefly, the `-Zc dkbluered` makes the color scale go from dark blue (negative) to white (zero) to dark red (positive), and the `-a/-A` options overlay the dielectric function as light gray contours. This results in the image: +Briefly, the `-Zc dkbluered` makes the color scale go from dark blue (negative) to white (zero) to dark red (positive), and the `-a/-A` options overlay the dielectric function as light gray contours. This results in the image:
![](../images/Tutorial-wvg-straight-ez-000200.00.png)
@@ -433,7 +433,7 @@ The simulation script is [examples/refl-angular.ctl](https://github.com/NanoComp (define df (- fmax fmin)) ; frequency width of source (define-param nfreq 50) ; number of frequency bins -; rotation angle (in degrees) of source: CCW around Y axis, 0 degrees along +Z axis +; rotation angle (in degrees) of source: CCW around Y axis, 0 degrees along +Z axis (define-param theta 0) (define theta-r (deg->rad theta)) diff --git a/doc/docs/Scheme_Tutorials/Casimir_Forces.md b/doc/docs/Scheme_Tutorials/Casimir_Forces.md index c0e6b2cea..109d0b486 100644 --- a/doc/docs/Scheme_Tutorials/Casimir_Forces.md +++ b/doc/docs/Scheme_Tutorials/Casimir_Forces.md @@ -526,4 +526,3 @@ If you examine periodic-sphere-plate.ctl in the example above, you will notice t As discussed in Part I, the temporal convergence of the force $F(t)$ can be accelerated by picking the right value of the global conductivity $\sigma$. $\sigma$ should be high enough to dampen out oscillations in $F(t)$, but on the other hand a high conductivity reduces the velocity of waves propagating in the medium (see Part I), slowing convergence. We've found that if the characteristic separation between two objects (e.g. the distance between parallel plates) in vacuum is $d$, then picking $\sigma ~\sim~ 0.5/d$. This is illustrated in the function `scale-sigma-T` in periodic-sphere-plate.ctl above. Both the value of $\sigma$ and the total runtime $T$ of the simulation are adjusted depending on the separation between the objects. If the dielectric of the medium is non-zero, then for non-dispersive media the optimal value of $\sigma$ follows from group velocity considerations. For dispersive media, the convergence should be experimented with to determine the best value. Generally, as the dielectric $\epsilon$ of the medium increases, $\sigma$ should decrease. - diff --git a/doc/docs/Scheme_Tutorials/Custom_Source.md b/doc/docs/Scheme_Tutorials/Custom_Source.md index 88c7422f9..7efb6fe62 100644 --- a/doc/docs/Scheme_Tutorials/Custom_Source.md +++ b/doc/docs/Scheme_Tutorials/Custom_Source.md @@ -172,4 +172,4 @@ One situation in which you may need to perform brute-force Monte Carlo simulatio *Note regarding convergence properties of Method 2:* In this demonstration, the number of point dipoles used in Method 2 is one per pixel. However, because this example is a unit-cell calculation involving *periodic* boundaries, the number of point dipoles (equivalent to the number of simulations) that are actually necessary to obtain results with sufficient accuracy can be significantly reduced. For smooth periodic functions, it is well known that a [trapezoidal rule](https://en.wikipedia.org/wiki/Trapezoidal_rule) converges quite fast — generally even faster than a cosine-series expansion and comparable to a cosine+sine Fourier series. See these [tutorial notes](http://math.mit.edu/~stevenj/trap-iap-2011.pdf) for the mathematical details. In this example, placing one dipole at every fifth pixel for a total of 15 rather than 75 simulations produces nearly equivalent results for the flux spectrum. More generally, an alternative approach for Method 2 would be to sample a set of dipole points and repeatedly double the sampling density until it converges. Sampling every grid point is usually not necessary. -*Note regarding polarization:* The previous demonstration involved a single-polarization source. For random polarizations, three separate simulations (for $\mathcal{J}_x$, $\mathcal{J}_y$, and $\mathcal{J}_z$) are required. Since the different polarizations are uncorrelated, the results (i.e., the flux spectrum) from each set of single-polarization simulations (which, in general, will have different convergence properties) are then summed in post processing. If the emitters involve *anisotropic* polarizability then the polarizations are correlated. However, in this case choosing the polarizations to correspond to the three principal axes will again make them uncorrelated. \ No newline at end of file +*Note regarding polarization:* The previous demonstration involved a single-polarization source. For random polarizations, three separate simulations (for $\mathcal{J}_x$, $\mathcal{J}_y$, and $\mathcal{J}_z$) are required. Since the different polarizations are uncorrelated, the results (i.e., the flux spectrum) from each set of single-polarization simulations (which, in general, will have different convergence properties) are then summed in post processing. If the emitters involve *anisotropic* polarizability then the polarizations are correlated. However, in this case choosing the polarizations to correspond to the three principal axes will again make them uncorrelated. diff --git a/doc/docs/Scheme_Tutorials/Cylindrical_Coordinates.md b/doc/docs/Scheme_Tutorials/Cylindrical_Coordinates.md index a15d15f1c..4b6fcc95d 100644 --- a/doc/docs/Scheme_Tutorials/Cylindrical_Coordinates.md +++ b/doc/docs/Scheme_Tutorials/Cylindrical_Coordinates.md @@ -551,4 +551,4 @@ Shown below is the far-field energy-density profile around the focal length for
![](../images/zone_plate_farfield.png) -
\ No newline at end of file + diff --git a/doc/docs/Scheme_Tutorials/Local_Density_of_States.md b/doc/docs/Scheme_Tutorials/Local_Density_of_States.md index ec91d66ca..f900cae5b 100644 --- a/doc/docs/Scheme_Tutorials/Local_Density_of_States.md +++ b/doc/docs/Scheme_Tutorials/Local_Density_of_States.md @@ -85,4 +85,4 @@ We run several simulations spanning a number of different notch sizes and plot t
![](../images/Metalcavity_ldos.png) -
\ No newline at end of file + diff --git a/doc/docs/Scheme_Tutorials/Multilevel_Atomic_Susceptibility.md b/doc/docs/Scheme_Tutorials/Multilevel_Atomic_Susceptibility.md index fc6a08009..490369823 100644 --- a/doc/docs/Scheme_Tutorials/Multilevel_Atomic_Susceptibility.md +++ b/doc/docs/Scheme_Tutorials/Multilevel_Atomic_Susceptibility.md @@ -57,7 +57,7 @@ The term in parenthesis on the right-hand side is the definition of $D_0$ in nor           (initial-populations N0))))) (set! geometry (list (make block (center 0 0 (+ (* -0.5 sz) (* 0.5 Lcav))) -                           (size infinity infinity Lcav) (material two-level)))) +                           (size infinity infinity Lcav) (material two-level)))) ``` Definition of the two-level medium involves the `multilevel-atom` sub-class of the `E-susceptibilities` material type. Each radiative and non-radiative `transition` is specified separately. Note that internally, Meep treats `pumping-rate` and `transition-rate` identically, and you can use them interchangeably, but it is important to specify the `from-level` and `to-level` parameters correctly, otherwise the results will be undefined. The choice of these parameters requires some care. For example, choosing a pumping rate that lies far beyond the first lasing threshold will yield large inversion, and thus large gain, which is not realistic, as most physical devices will overheat before reaching such a regime. Meep will still produce accurate results in this regime though. Additionally, choosing the total simulation time is especially important when operating near the threshold of a lasing mode, as the fields contain relaxation oscillations and require sufficient time to reach steady state. @@ -90,7 +90,7 @@ $$ \mathbf{E} \; (\textrm{SALT}) = \frac{2 |\theta|}{\hbar \sqrt{\gamma_\perp \g For two-level gain media, $\gamma_\parallel = \gamma_{12} + \gamma_{21}$. We can also verify that the system is not exhibiting relaxation oscillations by directly plotting the electric field as a function of time and looking for very long time-scale oscillations. In the continuum limit, these modes would appear as Dirac delta functions in the spectra. The discretized model, however, produces peaks with finite width. Thus, we need to integrate a fixed number of points around each peak to calculate the correct modal intensity. -By varying $N_0$ or the pumping rate $R_p$, we can change the total gain available in the cavity. This is used to find the laser's modal intensities as a function of the strength of the gain. We can compare the simulated modal intensity with SALT as well as an independent FDTD solver based on the Maxwell-Bloch equations. All three methods produce results with good agreement close to the first lasing threshold. +By varying $N_0$ or the pumping rate $R_p$, we can change the total gain available in the cavity. This is used to find the laser's modal intensities as a function of the strength of the gain. We can compare the simulated modal intensity with SALT as well as an independent FDTD solver based on the Maxwell-Bloch equations. All three methods produce results with good agreement close to the first lasing threshold.
![Near threshold comparison](../images/meep_salt_comparison_thresh.png) @@ -100,4 +100,4 @@ Further increasing the gain continues to yield good agreement.
![Near threshold comparison](../images/meep_salt_comparison_full.png) -
\ No newline at end of file +
diff --git a/doc/docs/Scheme_Tutorials/Optical_Forces.md b/doc/docs/Scheme_Tutorials/Optical_Forces.md index 2dd1529e7..d28fc2323 100644 --- a/doc/docs/Scheme_Tutorials/Optical_Forces.md +++ b/doc/docs/Scheme_Tutorials/Optical_Forces.md @@ -188,4 +188,4 @@ function force = compute_force(data) force = -1./f_avg .* df/ds .* 1./vg_avg; return endfunction -``` \ No newline at end of file +``` diff --git a/doc/docs/Scheme_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md b/doc/docs/Scheme_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md index b82a4c24c..9f17e834c 100644 --- a/doc/docs/Scheme_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md +++ b/doc/docs/Scheme_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md @@ -242,7 +242,7 @@ unix% convert holey-wvg-cavity-hz-*.png holey-wvg-cavity-hz.gif ![](../images/Holey-wvg-cavity-hz.gif) -The mode has a frequency of 0.235, just as we saw in the transmission spectrum, and a $Q$ of 373 which we could have also found by fitting the transmission spectrum. This lifetime $Q$ includes two independent decay channels: light can decay from the cavity into the waveguide with lifetime $Q_w$, or it can radiate from the cavity into the surrounding air with lifetime $Q_r$, where +The mode has a frequency of 0.235, just as we saw in the transmission spectrum, and a $Q$ of 373 which we could have also found by fitting the transmission spectrum. This lifetime $Q$ includes two independent decay channels: light can decay from the cavity into the waveguide with lifetime $Q_w$, or it can radiate from the cavity into the surrounding air with lifetime $Q_r$, where $$\frac{1}{Q} = \frac{1}{Q_w} + \frac{1}{Q_r}$$ @@ -363,7 +363,7 @@ unix% grep freqs: holey-wvg-bands.out > fre.dat unix% grep freqs-im: holey-wvg-bands.out > fim.dat ``` -Plotting the real parts of ω, where the light cone ω > *ck* is shaded gray, we find: +Plotting the real parts of ω, where the light cone ω > *ck* is shaded gray, we find:
![](../images/Holey-wvg-bands.png) diff --git a/doc/docs/Subpixel_Smoothing.md b/doc/docs/Subpixel_Smoothing.md index e9fab5122..c03f28e67 100644 --- a/doc/docs/Subpixel_Smoothing.md +++ b/doc/docs/Subpixel_Smoothing.md @@ -85,7 +85,7 @@ for rad in np.arange(1.800,2.001,0.005): radius=rad, height=mp.inf, center=mp.Vector3())] - + sim = mp.Simulation(cell_size=mp.Vector3(sxy,sxy), geometry=geometry, eps_averaging=True, diff --git a/doc/docs/Synchronizing_the_Magnetic_and_Electric_Fields.md b/doc/docs/Synchronizing_the_Magnetic_and_Electric_Fields.md index d1a4cde8b..a230a2f7d 100644 --- a/doc/docs/Synchronizing_the_Magnetic_and_Electric_Fields.md +++ b/doc/docs/Synchronizing_the_Magnetic_and_Electric_Fields.md @@ -41,4 +41,4 @@ it will output the same quantities, but more accurately because the fields will Alternatively, if you want to synchronize the magnetic and electric fields in some context other than that of a step function, e.g. you are doing some computation like `integrate_field_function` (Python) or `integrate-field-function` (Scheme) outside of the timestepping, you can instead call two lower-level functions. Before doing your computations, you should call `meep.Simulation.fields.synchronize_magnetic_fields()` (Python) or `(meep-fields-synchronize-magnetic-fields fields)` (Scheme) to synchronize the magnetic fields with the electric fields, and after your computation you should call `meep.Simulation.fields.restore_magnetic_fields()` (Python) or `(meep-fields-restore-magnetic-fields fields)` (Scheme) to restore the fields to their unsynchronized state for timestepping. In the C++ interface, these correspond to `fields::synchronize_magnetic_fields` and `fields::restore_magnetic_fields`. If you *don't* call `meep.Simulation.fields.restore_magnetic_fields` or `meep-fields-restore-magnetic-fields` before timestepping, then the fields will be re-synchronized after *every* timestep, which will greatly increase the cost of timestepping. -In future versions, the fields may be synchronized automatically whenever you output something like the Poynting vector or do another field computation that involves both magnetic and electric fields, but currently you must do this manually (Issue [#719](https://github.com/NanoComp/meep/issues/719)). In any case, Meep does no additional work when you nest synchronization calls, so it is harmless to insert redundant field synchronizations. The `flux_in_box` (Python) or `flux-in-box` (Scheme) and `field_energy_in_box` (Python) or `field-energy-in-box` (Scheme) routines are already automatically synchronized, however. \ No newline at end of file +In future versions, the fields may be synchronized automatically whenever you output something like the Poynting vector or do another field computation that involves both magnetic and electric fields, but currently you must do this manually (Issue [#719](https://github.com/NanoComp/meep/issues/719)). In any case, Meep does no additional work when you nest synchronization calls, so it is harmless to insert redundant field synchronizations. The `flux_in_box` (Python) or `flux-in-box` (Scheme) and `field_energy_in_box` (Python) or `field-energy-in-box` (Scheme) routines are already automatically synchronized, however. diff --git a/doc/docs/The_Run_Function_Is_Not_A_Loop.md b/doc/docs/The_Run_Function_Is_Not_A_Loop.md index 258bd6681..e7fb49c04 100644 --- a/doc/docs/The_Run_Function_Is_Not_A_Loop.md +++ b/doc/docs/The_Run_Function_Is_Not_A_Loop.md @@ -36,7 +36,7 @@ sim.run(meep.output_efield_z, print("Hello World!"), until=200) **Scheme** ```scm -(run-until 200 output-efield-z (print "Hello World!\n")) +(run-until 200 output-efield-z (print "Hello World!\n")) ``` **This is wrong.** It will output "Hello World!" *once*, then give an error. What is going on? The problem is that you are thinking of `run` (Python) or `run-until` (Scheme) in the wrong way, as if it were a loop: @@ -104,4 +104,4 @@ sim.run(meep.output_efield_z, lambda sim: print("Hello World!"), until=200) (run-until 200 output-efield-z (lambda () (print "Hello World!\n"))) ``` -In Python, `lambda sim: ...` defines a function of one argument `sim`. In Scheme, `(lambda () ...)` defines a function of no arguments `()`. Both functions, when called, execute the `...` statements. \ No newline at end of file +In Python, `lambda sim: ...` defines a function of one argument `sim`. In Scheme, `(lambda () ...)` defines a function of no arguments `()`. Both functions, when called, execute the `...` statements. diff --git a/doc/docs/Yee_Lattice.md b/doc/docs/Yee_Lattice.md index 7464961c9..34864e30d 100644 --- a/doc/docs/Yee_Lattice.md +++ b/doc/docs/Yee_Lattice.md @@ -30,4 +30,4 @@ In two dimensions, the arrangement is similar except that we set $\hat{\mathbf{e The consequence of the Yee lattice is that, whenever you need to access field components, e.g. to find the energy density $(\mathbf{E}^* \cdot \mathbf{D} + |\mathbf{H}|^2)/2$ or the flux $\textrm{Re}\, \mathbf{E}^* \times \mathbf{H}$, then the components need to be **interpolated** to some common point in order to remain second-order accurate. Meep automatically does this [interpolation](Introduction.md#the-illusion-of-continuity) for you wherever necessary — in particular, whenever you compute [energy density](Python_User_Interface.md#energy-density-spectra) or [Poynting flux](Python_User_Interface.md#flux-spectra), or whenever you [output a field to a file](Python_User_Interface.md#output-functions), it is stored at the centers of each grid voxel: $(i+0.5,j+0.5,k+0.5)$. -In a Meep simulation, the coordinates of the Yee lattice can be obtained using a [field function](Field_Functions.md#coordinates-of-the-yee-grid). \ No newline at end of file +In a Meep simulation, the coordinates of the Yee lattice can be obtained using a [field function](Field_Functions.md#coordinates-of-the-yee-grid). diff --git a/doc/docs/mdx_math.py b/doc/docs/mdx_math.py index 75ecd3238..ef2cfc0fd 100644 --- a/doc/docs/mdx_math.py +++ b/doc/docs/mdx_math.py @@ -1,57 +1,60 @@ -# -*- coding: utf-8 -*- - -''' +""" Math extension for Python-Markdown ================================== Adds support for displaying math formulas using [MathJax](http://www.mathjax.org/). Author: 2015, Dmitry Shachnev . -''' - +""" import markdown + class MathExtension(markdown.extensions.Extension): def __init__(self, *args, **kwargs): self.config = { - 'enable_dollar_delimiter': [False, 'Enable single-dollar delimiter'], + "enable_dollar_delimiter": [False, "Enable single-dollar delimiter"], } - super(MathExtension, self).__init__(*args, **kwargs) + super().__init__(*args, **kwargs) def extendMarkdown(self, md, md_globals): def handle_match_inline(m): - node = markdown.util.etree.Element('script') - node.set('type', 'math/tex') + node = markdown.util.etree.Element("script") + node.set("type", "math/tex") node.text = markdown.util.AtomicString(m.group(3)) return node def handle_match(m): - node = markdown.util.etree.Element('script') - node.set('type', 'math/tex; mode=display') - if '\\begin' in m.group(2): - node.text = markdown.util.AtomicString(m.group(2) + m.group(4) + m.group(5)) + node = markdown.util.etree.Element("script") + node.set("type", "math/tex; mode=display") + if "\\begin" in m.group(2): + node.text = markdown.util.AtomicString( + m.group(2) + m.group(4) + m.group(5) + ) else: node.text = markdown.util.AtomicString(m.group(3)) return node configs = self.getConfigs() inlinemathpatterns = ( - markdown.inlinepatterns.Pattern(r'(? 50: params = [] for idx, param in enumerate(parameters): - params.append('('+str(param) if idx==0 else ' '*indent+param) - param_str = ',\n'.join(params) - param_str += ')' + params.append( + "(" + str(param) if idx == 0 else " " * indent + param + ) + param_str = ",\n".join(params) + param_str += ")" return param_str except ValueError as ex: - print("Warning: falling back to old parameter extraction method for {}\n{}".format(self.name, ex)) + print( + "Warning: falling back to old parameter extraction method for {}\n{}".format( + self.name, ex + ) + ) except OSError: # This happens when there is no source for some function/class (like NamedTuples) pass @@ -164,12 +169,13 @@ def get_parameters(self, indent): parameters = list(self.sig.parameters.values()) params = [] for idx, param in enumerate(parameters): - params.append('('+str(param) if idx==0 else ' '*indent+str(param)) - param_str = ',\n'.join(params) - param_str += ')' + params.append( + "(" + str(param) if idx == 0 else " " * indent + str(param) + ) + param_str = ",\n".join(params) + param_str += ")" return param_str - def get_parameters_from_ast(self): # Use Python's ast module to parse the function's code and pull out parameter names and # the text for default values. @@ -185,13 +191,15 @@ def parse_name(node): func = module.body[0] if not isinstance(func, ast.FunctionDef): - raise ValueError('Unsupported node type: {}'.format(type(func))) + raise ValueError(f"Unsupported node type: {type(func)}") # Get the param name and defaults together into a list of tuples args = reversed(func.args.args) defaults = reversed(func.args.defaults) iter = itertools.zip_longest(args, defaults, fillvalue=None) - parameters = [(parse_name(name), default) for name, default in reversed(list(iter))] + parameters = [ + (parse_name(name), default) for name, default in reversed(list(iter)) + ] # Convert each of the parameters (name, ast.node) to valid Python parameter # code, like what would have been in the actual source code. @@ -201,13 +209,13 @@ def parse_name(node): transformed.append(name) else: default = self.transform_node(name, node) - transformed.append("{}={}".format(name, default)) + transformed.append(f"{name}={default}") # Check for vararg (like *foo) and kwarg (like **bar) parameters if func.args.vararg is not None: - transformed.append('*{}'.format(func.args.vararg.arg)) + transformed.append(f"*{func.args.vararg.arg}") if func.args.kwarg is not None: - transformed.append('**{}'.format(func.args.kwarg.arg)) + transformed.append(f"**{func.args.kwarg.arg}") return transformed @@ -239,42 +247,41 @@ def transform_node(self, name, node): # print('Using inspect module for {}={} in {}'.format(name, default, self.name)) return default - def check_other_signatures(self, docstring): - """ Search for alternate function signatures in the docstring """ - SIG = '##sig' - SIG_KEEP = '##sig-keep' - other_signatures = '' + """Search for alternate function signatures in the docstring""" + SIG = "##sig" + SIG_KEEP = "##sig-keep" + other_signatures = "" if SIG in docstring or SIG_KEEP in docstring: other_sigs = [] docstring_lines = [] - for line in docstring.split('\n'): + for line in docstring.split("\n"): if line.endswith(SIG): - line = line.replace(SIG, '') + line = line.replace(SIG, "") line = line.strip() - line = line.replace('`', '') + line = line.replace("`", "") other_sigs.append(line) elif line.endswith(SIG_KEEP): - line = line.replace(SIG_KEEP, '') + line = line.replace(SIG_KEEP, "") line = line.strip() docstring_lines.append(line) - line = line.replace('`', '') + line = line.replace("`", "") other_sigs.append(line) else: docstring_lines.append(line) - docstring = '\n'.join(docstring_lines) - other_signatures = ''.join(['\ndef {}:'.format(item) for item in other_sigs]) + docstring = "\n".join(docstring_lines) + other_signatures = "".join([f"\ndef {item}:" for item in other_sigs]) return docstring, other_signatures def create_markdown(self): # pull relevant attributes into local variables function_name = self.name - function_name_escaped = function_name.replace('_', '\\_') - docstring = self.docstring if self.docstring else '' + function_name_escaped = function_name.replace("_", "\\_") + docstring = self.docstring if self.docstring else "" parameters = self.get_parameters(len(function_name) + 1) docstring, other_signatures = self.check_other_signatures(docstring) @@ -287,20 +294,21 @@ class MethodItem(FunctionItem): An introspected item that is a method of a class. Mostly the same as a FunctionItem, but can do extra stuff for methods if needed. """ - template_name = 'method_template.md' + + template_name = "method_template.md" def __init__(self, name, obj, klass): - super(MethodItem, self).__init__(name, obj) + super().__init__(name, obj) self.klass = klass self.method_name = name - self.name = '{}.{}'.format(klass.name, name) + self.name = f"{klass.name}.{name}" def create_markdown(self): # pull relevant attributes into local variables class_name = self.klass.name method_name = self.method_name - method_name_escaped = method_name.replace('_', '\\_') - docstring = self.docstring if self.docstring else '' + method_name_escaped = method_name.replace("_", "\\_") + docstring = self.docstring if self.docstring else "" parameters = self.get_parameters(4 + len(method_name) + 1) docstring, other_signatures = self.check_other_signatures(docstring) @@ -312,10 +320,11 @@ class ClassItem(Item): """ An introspected item that is a Class. """ - template_name = 'class_template.md' + + template_name = "class_template.md" def __init__(self, name, obj): - super(ClassItem, self).__init__(name, obj) + super().__init__(name, obj) self.add_methods() def add_methods(self): @@ -323,40 +332,45 @@ def add_methods(self): # inherited members def _predicate(value): if inspect.isfunction(value): - class_name = value.__qualname__.split('.')[0] + class_name = value.__qualname__.split(".")[0] return class_name == self.name else: return False self.methods = [] for name, member in inspect.getmembers(self.obj, _predicate): - if inspect.isfunction(member): # unbound methods are just functions at this point + if inspect.isfunction( + member + ): # unbound methods are just functions at this point self.methods.append(MethodItem(name, member, self)) def create_markdown(self): # pull relevant attributes into local variables class_name = self.name - docstring = self.docstring if self.docstring else '' + docstring = self.docstring if self.docstring else "" base_classes = [base.__name__ for base in self.obj.__bases__] - base_classes = ', '.join(base_classes) + base_classes = ", ".join(base_classes) # Substitute values into the template docs = dict() class_doc = self.template.format(**locals()) docs[class_name] = class_doc - docs[class_name+'[all-methods]'] = class_doc + '\n' + self.create_method_markdown(False) - docs[class_name+'[methods-with-docstrings]'] = class_doc + '\n' + self.create_method_markdown(True) + docs[class_name + "[all-methods]"] = ( + class_doc + "\n" + self.create_method_markdown(False) + ) + docs[class_name + "[methods-with-docstrings]"] = ( + class_doc + "\n" + self.create_method_markdown(True) + ) return docs - def create_method_markdown(self, only_with_docstrings=True): method_docs = [] if self.methods: # reorder self.methods so __init__ comes first, if it isn't already methods = self.methods[:] for idx, meth in enumerate(self.methods): - if meth.name == '__init__': + if meth.name == "__init__": if idx != 0: methods.remove(meth) methods.insert(0, meth) @@ -365,22 +379,24 @@ def create_method_markdown(self, only_with_docstrings=True): for item in methods: if only_with_docstrings and not item.docstring: continue - if not check_excluded(item.name) and \ - not check_excluded('{}.{}'.format(self.name, item.name)): - doc = item.create_markdown() - method_docs.append(doc) + if not check_excluded(item.name) and not check_excluded( + f"{self.name}.{item.name}" + ): + doc = item.create_markdown() + method_docs.append(doc) # join the methods into a single string - method_docs = '\n'.join(method_docs) + method_docs = "\n".join(method_docs) return method_docs -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def check_excluded(name): if name in EXCLUDES: return True - if name.startswith('_') and not (name.startswith('__') and name.endswith('__')): + if name.startswith("_") and not (name.startswith("__") and name.endswith("__")): # It's probably meant to be private return True return False @@ -428,27 +444,25 @@ def update_api_document(doc_items): to DESTDOC. """ # read - with open(SRCDOC, 'r') as f: + with open(SRCDOC) as f: srcdoc = f.read() # manipulate for name, doc in doc_items.items(): - tag = '@@ {} @@'.format(name) + tag = f"@@ {name} @@" if tag in srcdoc: srcdoc = srcdoc.replace(tag, doc) # write results - with open(DESTDOC, 'w') as f: + with open(DESTDOC, "w") as f: f.write(srcdoc) - def main(args): items = load_module(meep) doc_items = generate_docs(items) update_api_document(doc_items) - -if __name__ == '__main__': - main(sys.argv[1:]) \ No newline at end of file +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/m4/ax_check_compiler_flags.m4 b/m4/ax_check_compiler_flags.m4 index 86eaf15ef..ad166dc8f 100644 --- a/m4/ax_check_compiler_flags.m4 +++ b/m4/ax_check_compiler_flags.m4 @@ -20,13 +20,13 @@ AS_LITERAL_IF([$1], [AC_CACHE_VAL(AS_TR_SH(ax_cv_[]_AC_LANG_ABBREV[]_flags_$1), [ ax_save_FLAGS=$[]_AC_LANG_PREFIX[]FLAGS _AC_LANG_PREFIX[]FLAGS="$1" - AC_COMPILE_IFELSE([AC_LANG_PROGRAM()], + AC_COMPILE_IFELSE([AC_LANG_PROGRAM()], AS_TR_SH(ax_cv_[]_AC_LANG_ABBREV[]_flags_$1)=yes, AS_TR_SH(ax_cv_[]_AC_LANG_ABBREV[]_flags_$1)=no) _AC_LANG_PREFIX[]FLAGS=$ax_save_FLAGS])], [ax_save_FLAGS=$[]_AC_LANG_PREFIX[]FLAGS _AC_LANG_PREFIX[]FLAGS="$1" - AC_COMPILE_IFELSE([AC_LANG_PROGRAM()], + AC_COMPILE_IFELSE([AC_LANG_PROGRAM()], eval AS_TR_SH(ax_cv_[]_AC_LANG_ABBREV[]_flags_$1)=yes, eval AS_TR_SH(ax_cv_[]_AC_LANG_ABBREV[]_flags_$1)=no) _AC_LANG_PREFIX[]FLAGS=$ax_save_FLAGS]) diff --git a/m4/ax_compiler_vendor.m4 b/m4/ax_compiler_vendor.m4 index 1b8ed401c..6c3db2625 100644 --- a/m4/ax_compiler_vendor.m4 +++ b/m4/ax_compiler_vendor.m4 @@ -4,7 +4,7 @@ dnl @category C dnl @category C++ dnl dnl Determine the vendor of the C/C++ compiler, e.g., gnu, intel, ibm, -dnl sun, hp, borland, comeau, dec, cray, kai, lcc, metrowerks, sgi, +dnl sun, hp, borland, comeau, dec, cray, kai, lcc, metrowerks, sgi, dnl microsoft, watcom, etc. The vendor is returned in the cache variable dnl $ax_cv_c_compiler_vendor for C and $ax_cv_cxx_compiler_vendor for C++. dnl @@ -17,7 +17,7 @@ AC_DEFUN([AX_COMPILER_VENDOR], AC_CACHE_CHECK([for _AC_LANG compiler vendor], ax_cv_[]_AC_LANG_ABBREV[]_compiler_vendor, [ax_cv_[]_AC_LANG_ABBREV[]_compiler_vendor=unknown # note: don't check for gcc first since some other compilers define __GNUC__ - for ventest in intel:__ICC,__ECC,__INTEL_COMPILER ibm:__xlc__,__xlC__,__IBMC__,__IBMCPP__ gnu:__GNUC__ sun:__SUNPRO_C,__SUNPRO_CC hp:__HP_cc,__HP_aCC dec:__DECC,__DECCXX,__DECC_VER,__DECCXX_VER borland:__BORLANDC__,__TURBOC__ comeau:__COMO__ cray:_CRAYC kai:__KCC lcc:__LCC__ metrowerks:__MWERKS__ sgi:__sgi,sgi microsoft:_MSC_VER watcom:__WATCOMC__ portland:__PGI; do + for ventest in intel:__ICC,__ECC,__INTEL_COMPILER ibm:__xlc__,__xlC__,__IBMC__,__IBMCPP__ gnu:__GNUC__ sun:__SUNPRO_C,__SUNPRO_CC hp:__HP_cc,__HP_aCC dec:__DECC,__DECCXX,__DECC_VER,__DECCXX_VER borland:__BORLANDC__,__TURBOC__ comeau:__COMO__ cray:_CRAYC kai:__KCC lcc:__LCC__ metrowerks:__MWERKS__ sgi:__sgi,sgi microsoft:_MSC_VER watcom:__WATCOMC__ portland:__PGI; do vencpp="defined("`echo $ventest | cut -d: -f2 | sed 's/,/) || defined(/g'`")" AC_COMPILE_IFELSE([AC_LANG_PROGRAM(,[ #if !($vencpp) @@ -27,4 +27,3 @@ AC_CACHE_CHECK([for _AC_LANG compiler vendor], ax_cv_[]_AC_LANG_ABBREV[]_compile done ]) ]) - diff --git a/m4/ax_gcc_x86_cpuid.m4 b/m4/ax_gcc_x86_cpuid.m4 index 50fe009e9..d91509c2c 100644 --- a/m4/ax_gcc_x86_cpuid.m4 +++ b/m4/ax_gcc_x86_cpuid.m4 @@ -57,7 +57,7 @@ AC_CACHE_CHECK(for x86 cpuid $1 output, ax_cv_gcc_x86_cpuid_$1, fprintf(f, "%x:%x:%x:%x\n", eax, ebx, ecx, edx); fclose(f); return 0; -])], +])], [ax_cv_gcc_x86_cpuid_$1=`cat conftest_cpuid`; rm -f conftest_cpuid], [ax_cv_gcc_x86_cpuid_$1=unknown; rm -f conftest_cpuid], [ax_cv_gcc_x86_cpuid_$1=unknown])]) diff --git a/m4/pkg.m4 b/m4/pkg.m4 index 1b49cd6b6..72139eadf 100644 --- a/m4/pkg.m4 +++ b/m4/pkg.m4 @@ -34,7 +34,7 @@ AC_DEFUN([PKG_CHECK_MODULES], [ else $1_CFLAGS="" $1_LIBS="" - ## If we have a custom action on failure, don't print errors, but + ## If we have a custom action on failure, don't print errors, but ## do set a variable so people can do so. $1_PKG_ERRORS=`$PKG_CONFIG --errors-to-stdout --print-errors "$2"` ifelse([$4], ,echo $$1_PKG_ERRORS,) @@ -54,5 +54,3 @@ AC_DEFUN([PKG_CHECK_MODULES], [ ifelse([$4], , AC_MSG_ERROR([Library requirements ($2) not met; consider adjusting the PKG_CONFIG_PATH environment variable if your libraries are in a nonstandard prefix so pkg-config can find them.]), [$4]) fi ]) - - diff --git a/python/adjoint/__init__.py b/python/adjoint/__init__.py index a787e31e7..16de93442 100644 --- a/python/adjoint/__init__.py +++ b/python/adjoint/__init__.py @@ -2,21 +2,14 @@ Adjoint-based sensitivity-analysis module for pymeep. Authors: Homer Reid , Alec Hammond , Ian Williamson """ - -from .objective import * - from . import utils -from .utils import DesignRegion - from .basis import BilinearInterpolationBasis - -from .optimization_problem import OptimizationProblem - +from .connectivity import * from .filter_source import FilteredSource - from .filters import * - -from .connectivity import * +from .objective import * +from .optimization_problem import OptimizationProblem +from .utils import DesignRegion try: from .wrapper import MeepJaxWrapper diff --git a/python/adjoint/basis.py b/python/adjoint/basis.py index 80ea14d64..af54f6877 100644 --- a/python/adjoint/basis.py +++ b/python/adjoint/basis.py @@ -1,7 +1,9 @@ -import meep as mp +from abc import ABCMeta, abstractmethod + import numpy as np from scipy import sparse -from abc import ABCMeta, abstractmethod + +import meep as mp ABC = ABCMeta("ABC", (object,), {"__slots__": ()}) # compatible with Python 2 and 3 @@ -54,7 +56,7 @@ class BilinearInterpolationBasis(Basis): def __init__(self, resolution, symmetry=None, **kwargs): self.dim = 2 - super(BilinearInterpolationBasis, self).__init__(**kwargs) + super().__init__(**kwargs) # Generate interpolation grid self.symmetry = [] if symmetry is None or len(symmetry) == 0 else symmetry diff --git a/python/adjoint/connectivity.py b/python/adjoint/connectivity.py index e8b631d79..450e6f4eb 100644 --- a/python/adjoint/connectivity.py +++ b/python/adjoint/connectivity.py @@ -4,13 +4,12 @@ BC: Dirichlet on last slice rho[-Nx*Ny:], 0 outside the first slice, and Neumann on sides. Mo Chen """ - import numpy as np +from scipy.sparse import csc_matrix, csr_matrix, diags, eye, kron, lil_matrix from scipy.sparse.linalg import cg, spsolve -from scipy.sparse import kron, diags, csr_matrix, eye, csc_matrix, lil_matrix -class ConnectivityConstraint(object): +class ConnectivityConstraint: def __init__( self, nx, diff --git a/python/adjoint/filter_source.py b/python/adjoint/filter_source.py index 15d0928bd..9c23b3b17 100644 --- a/python/adjoint/filter_source.py +++ b/python/adjoint/filter_source.py @@ -1,5 +1,6 @@ import numpy as np -from scipy import signal, linalg +from scipy import linalg, signal + from meep import CustomSource @@ -66,7 +67,7 @@ def __init__( self.nodes, self.err = self.estimate_impulse_response(H) # initialize super - super(FilteredSource, self).__init__( + super().__init__( src_func=f, center_frequency=center_frequency, is_integrated=False, diff --git a/python/adjoint/filters.py b/python/adjoint/filters.py index 233269e81..262f37937 100644 --- a/python/adjoint/filters.py +++ b/python/adjoint/filters.py @@ -1,12 +1,11 @@ """ General filter functions to be used in other projection and morphological transform routines. """ - import numpy as np from autograd import numpy as npa +from scipy import signal, special + import meep as mp -from scipy import special -from scipy import signal def _proper_pad(x, n): diff --git a/python/adjoint/objective.py b/python/adjoint/objective.py index 64d940b42..07aeea618 100644 --- a/python/adjoint/objective.py +++ b/python/adjoint/objective.py @@ -1,11 +1,13 @@ """Handling of objective functions and objective quantities.""" - import abc +from collections import namedtuple + import numpy as np +from meep.simulation import py_v3_to_vec + import meep as mp + from .filter_source import FilteredSource -from meep.simulation import py_v3_to_vec -from collections import namedtuple Grid = namedtuple("Grid", ["x", "y", "z", "w"]) diff --git a/python/adjoint/optimization_problem.py b/python/adjoint/optimization_problem.py index 7b026171c..707eb8beb 100644 --- a/python/adjoint/optimization_problem.py +++ b/python/adjoint/optimization_problem.py @@ -1,12 +1,14 @@ -import meep as mp +from collections import namedtuple + import numpy as np from autograd import grad, jacobian -from collections import namedtuple -from . import utils, DesignRegion, LDOS +import meep as mp + +from . import LDOS, DesignRegion, utils -class OptimizationProblem(object): +class OptimizationProblem: """Top-level class in the MEEP adjoint module. Intended to be instantiated from user scripts with mandatory constructor diff --git a/python/adjoint/utils.py b/python/adjoint/utils.py index 0c6a461bc..fb37d2b0b 100644 --- a/python/adjoint/utils.py +++ b/python/adjoint/utils.py @@ -1,8 +1,9 @@ -from typing import List, Iterable, Tuple +from typing import Iterable, List, Tuple -import meep as mp import numpy as onp +import meep as mp + from . import ObjectiveQuantity # Meep field components used to compute adjoint sensitivities @@ -22,7 +23,7 @@ FD_DEFAULT = 1e-3 -class DesignRegion(object): +class DesignRegion: def __init__(self, design_parameters, volume=None, size=None, center=mp.Vector3()): self.volume = volume or mp.Volume(center=center, size=size) self.size = self.volume.size diff --git a/python/adjoint/wrapper.py b/python/adjoint/wrapper.py index f3e5144bc..06d9ad96e 100644 --- a/python/adjoint/wrapper.py +++ b/python/adjoint/wrapper.py @@ -46,16 +46,15 @@ def loss(x): value, grad = jax.value_and_grad(loss)(x) ``` """ - from typing import Callable, List, Tuple import jax import jax.numpy as jnp -import meep as mp import numpy as onp -from . import utils -from . import DesignRegion, EigenmodeCoefficient +import meep as mp + +from . import DesignRegion, EigenmodeCoefficient, utils _norm_fn = onp.linalg.norm _reduce_fn = onp.max diff --git a/python/binary_partition_utils.py b/python/binary_partition_utils.py index fb3558a79..53f1d65b5 100644 --- a/python/binary_partition_utils.py +++ b/python/binary_partition_utils.py @@ -1,9 +1,10 @@ -from typing import Dict, Generator, List, Tuple import warnings +from typing import Dict, Generator, List, Tuple -import meep as mp import numpy as onp +import meep as mp + def is_leaf_node(partition: mp.BinaryPartition) -> bool: """Returns True if the partition has no children. diff --git a/python/chunk_balancer.py b/python/chunk_balancer.py index fe92ab4b1..8162ca7bc 100644 --- a/python/chunk_balancer.py +++ b/python/chunk_balancer.py @@ -2,11 +2,11 @@ import copy from typing import Optional, Tuple, Union -import meep as mp -from meep import binary_partition_utils as bpu +import numpy as np from meep.timing_measurements import MeepTimingMeasurements -import numpy as np +import meep as mp +from meep import binary_partition_utils as bpu class AbstractChunkBalancer(abc.ABC): diff --git a/python/examples/3rd-harm-1d.ipynb b/python/examples/3rd-harm-1d.ipynb index c4b7fd017..c4c4df04c 100644 --- a/python/examples/3rd-harm-1d.ipynb +++ b/python/examples/3rd-harm-1d.ipynb @@ -20,17 +20,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -48,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -127,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -162,32 +154,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.1691008357827005e-12 / 4.1691008357827005e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420168812386e-08 / 1.0165420168812386e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650785262405232e-06 / 4.650785262405232e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454866366283557 / 0.0005454866366283557 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017815669509667658 / 0.017815669509667658 = 1.0\n", - "field decay(t = 350.175): 0.13192124155368243 / 0.13192124155368243 = 1.0\n", - "field decay(t = 400.20000000000005): 0.2505510561658855 / 0.2505510561658855 = 1.0\n", - "field decay(t = 450.225): 0.2499503102652878 / 0.2505510561658855 = 0.9976023014638582\n", - "field decay(t = 500.25): 0.11167289094888443 / 0.2505510561658855 = 0.4457091207587955\n", - "field decay(t = 550.275): 0.012841517437730344 / 0.2505510561658855 = 0.05125309641172774\n", - "field decay(t = 600.3000000000001): 0.0003786349795018242 / 0.2505510561658855 = 0.0015112088741351644\n", - "field decay(t = 650.325): 2.4161065872412755e-06 / 0.2505510561658855 = 9.643170634417973e-06\n", - "field decay(t = 700.35): 4.490921803004586e-09 / 0.2505510561658855 = 1.7924178296144254e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "sim.run(\n", " until_after_sources=mp.stop_when_fields_decayed(\n", @@ -205,22 +174,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "freqs = mp.get_flux_freqs(trans)\n", "spectra = mp.get_fluxes(trans)\n", @@ -244,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -309,80 +265,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.1691008357817126e-12 / 4.1691008357817126e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420158581846e-08 / 1.0165420158581846e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650783221205379e-06 / 4.650783221205379e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454598673598967 / 0.0005454598673598967 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017781404956060017 / 0.017781404956060017 = 1.0\n", - "field decay(t = 350.175): 0.13014055244440104 / 0.13014055244440104 = 1.0\n", - "field decay(t = 400.20000000000005): 0.2443206506136912 / 0.2443206506136912 = 1.0\n", - "field decay(t = 450.225): 0.2437445632391615 / 0.2443206506136912 = 0.9976420848050188\n", - "field decay(t = 500.25): 0.11044620451678894 / 0.2443206506136912 = 0.4520543156682302\n", - "field decay(t = 550.275): 0.012822890989720101 / 0.2443206506136912 = 0.05248386068681144\n", - "field decay(t = 600.3000000000001): 0.00037862010232358545 / 0.2443206506136912 = 0.0015496852246118257\n", - "field decay(t = 650.325): 2.41614906850782e-06 / 0.2443206506136912 = 9.8892543976077e-06\n", - "field decay(t = 700.35): 4.492194624953141e-09 / 0.2443206506136912 = 1.838647127727241e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835772216e-12 / 4.169100835772216e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420065576282e-08 / 1.0165420065576282e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650764664560235e-06 / 4.650764664560235e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005452160206480376 / 0.0005452160206480376 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017507545378757882 / 0.017507545378757882 = 1.0\n", - "field decay(t = 350.175): 0.11744036969268859 / 0.11744036969268859 = 1.0\n", - "field decay(t = 400.20000000000005): 0.20769602060422127 / 0.20769602060422127 = 1.0\n", - "field decay(t = 450.225): 0.2072148912173212 / 0.20769602060422127 = 0.9976834925122764\n", - "field decay(t = 500.25): 0.10101160503184105 / 0.20769602060422127 = 0.48634347802130234\n", - "field decay(t = 550.275): 0.012670991144119082 / 0.20769602060422127 = 0.06100738525108532\n", - "field decay(t = 600.3000000000001): 0.00037847776427704205 / 0.20769602060422127 = 0.001822267769868624\n", - "field decay(t = 650.325): 2.416057119618937e-06 / 0.20769602060422127 = 1.1632659656117805e-05\n", - "field decay(t = 700.35): 4.491338910111841e-09 / 0.20769602060422127 = 2.162457854053155e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835677209e-12 / 4.169100835677209e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165419135520952e-08 / 1.0165419135520952e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650579070094068e-06 / 4.650579070094068e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005427300142038431 / 0.0005427300142038431 = 1.0\n", - "field decay(t = 300.15000000000003): 0.015051531229532927 / 0.015051531229532927 = 1.0\n", - "field decay(t = 350.175): 0.10911319432715134 / 0.10911319432715134 = 1.0\n", - "field decay(t = 400.20000000000005): 0.22726682483704244 / 0.22726682483704244 = 1.0\n", - "field decay(t = 450.225): 0.22744269278874502 / 0.22744269278874502 = 1.0\n", - "field decay(t = 500.25): 0.10405323422921754 / 0.22744269278874502 = 0.45749209593585416\n", - "field decay(t = 550.275): 0.011388920462070745 / 0.22744269278874502 = 0.05007380242657031\n", - "field decay(t = 600.3000000000001): 0.00037704599306494387 / 0.22744269278874502 = 0.001657762614581575\n", - "field decay(t = 650.325): 2.4160426967236526e-06 / 0.22744269278874502 = 1.062264373983533e-05\n", - "field decay(t = 700.35): 4.491856927586553e-09 / 0.22744269278874502 = 1.9749400926055307e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.16910083472712e-12 / 4.16910083472712e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165409834939769e-08 / 1.0165409834939769e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.648720329217878e-06 / 4.648720329217878e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005182735319569587 / 0.0005182735319569587 = 1.0\n", - "field decay(t = 300.15000000000003): 0.013974697322011669 / 0.013974697322011669 = 1.0\n", - "field decay(t = 350.175): 0.07995289002032732 / 0.07995289002032732 = 1.0\n", - "field decay(t = 400.20000000000005): 0.17867229467477655 / 0.17867229467477655 = 1.0\n", - "field decay(t = 450.225): 0.23241654769547887 / 0.23241654769547887 = 1.0\n", - "field decay(t = 500.25): 0.18817882350661239 / 0.23241654769547887 = 0.8096618996043756\n", - "field decay(t = 550.275): 0.014319414613175686 / 0.23241654769547887 = 0.06161099437694745\n", - "field decay(t = 600.3000000000001): 0.0003754598154438489 / 0.23241654769547887 = 0.0016154607714756648\n", - "field decay(t = 650.325): 2.9312708100424914e-06 / 0.23241654769547887 = 1.261214332244172e-05\n", - "field decay(t = 700.35): 1.8599873539004834e-07 / 0.23241654769547887 = 8.002818096831515e-07\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "k_pow = [-3, -2, -1, 0]\n", "freqs = []\n", @@ -395,22 +280,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(dpi=150)\n", "plt.semilogy(freqs, np.array(spectra).T)\n", @@ -432,33 +304,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.016542016891584e-08 / 1.016542016891584e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650785283023354e-06 / 4.650785283023354e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454869069704206 / 0.0005454869069704206 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017816014066636514 / 0.017816014066636514 = 1.0\n", - "field decay(t = 350.175): 0.13193941695549694 / 0.13193941695549694 = 1.0\n", - "field decay(t = 400.20000000000005): 0.2506140638300714 / 0.2506140638300714 = 1.0\n", - "field decay(t = 450.225): 0.2500128531691806 / 0.2506140638300714 = 0.9976010497906516\n", - "field decay(t = 500.25): 0.11168480654371428 / 0.2506140638300714 = 0.4456446092324733\n", - "field decay(t = 550.275): 0.012841704978003306 / 0.2506140638300714 = 0.05124095903376999\n", - "field decay(t = 600.3000000000001): 0.0003786351315011641 / 0.2506140638300714 = 0.001510829542901859\n", - "field decay(t = 650.325): 2.4161060812423408e-06 / 0.2506140638300714 = 9.640744195747047e-06\n", - "field decay(t = 700.35): 4.490904760943549e-09 / 0.2506140638300714 = 1.7919603921305078e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "Omega: 225.25726603587026, 3Omega: 5.026979907074879e-16\n" - ] - } - ], + "outputs": [], "source": [ "_, _, omega_flux_cal, omega3_flux_cal = run_chi3(-16)\n", "print(\"Omega: {}, 3Omega: {}\".format(omega_flux_cal[0], omega3_flux_cal[0]))" @@ -473,348 +321,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420168905492e-08 / 1.0165420168905492e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650785280961551e-06 / 4.650785280961551e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454868799362667 / 0.0005454868799362667 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017815979612345514 / 0.017815979612345514 = 1.0\n", - "field decay(t = 350.175): 0.13193760003745314 / 0.13193760003745314 = 1.0\n", - "field decay(t = 400.20000000000005): 0.2506077676780644 / 0.2506077676780644 = 1.0\n", - "field decay(t = 450.225): 0.250006603482365 / 0.2506077676780644 = 0.9976011749305723\n", - "field decay(t = 500.25): 0.1116836154292776 / 0.2506077676780644 = 0.4456510524955018\n", - "field decay(t = 550.275): 0.012841686224530456 / 0.2506077676780644 = 0.051242171555620476\n", - "field decay(t = 600.3000000000001): 0.00037863511629851986 / 0.2506077676780644 = 0.0015108674396115361\n", - "field decay(t = 650.325): 2.416106131823971e-06 / 0.2506077676780644 = 9.640986607118051e-06\n", - "field decay(t = 700.35): 4.4909064662334595e-09 / 0.2506077676780644 = 1.7920060929645904e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420168894454e-08 / 1.0165420168894454e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650785278757183e-06 / 4.650785278757183e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454868510330065 / 0.0005454868510330065 = 1.0\n", - "field decay(t = 300.15000000000003): 0.0178159427756053 / 0.0178159427756053 = 1.0\n", - "field decay(t = 350.175): 0.13193565734819723 / 0.13193565734819723 = 1.0\n", - "field decay(t = 400.20000000000005): 0.2506010350871025 / 0.2506010350871025 = 1.0\n", - "field decay(t = 450.225): 0.24999992057177803 / 0.2506010350871025 = 0.9976013087291696\n", - "field decay(t = 500.25): 0.11168234185404839 / 0.2506010350871025 = 0.4456579431734209\n", - "field decay(t = 550.275): 0.012841666174342072 / 0.2506010350871025 = 0.05124346820785731\n", - "field decay(t = 600.3000000000001): 0.00037863510004548367 / 0.2506010350871025 = 0.0015109079653796314\n", - "field decay(t = 650.325): 2.416106185906883e-06 / 0.2506010350871025 = 9.641245835505452e-06\n", - "field decay(t = 700.35): 4.490908289112879e-09 / 0.2506010350871025 = 1.7920549639996314e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.016542016887149e-08 / 1.016542016887149e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650785274196042e-06 / 4.650785274196042e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454867912281307 / 0.0005454867912281307 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017815866554169706 / 0.017815866554169706 = 1.0\n", - "field decay(t = 350.175): 0.1319316371539862 / 0.1319316371539862 = 1.0\n", - "field decay(t = 400.20000000000005): 0.250587100703692 / 0.250587100703692 = 1.0\n", - "field decay(t = 450.225): 0.24998608899268374 / 0.250587100703692 = 0.997601585599097\n", - "field decay(t = 500.25): 0.11167970629203668 / 0.250587100703692 = 0.4456722073020547\n", - "field decay(t = 550.275): 0.012841624687286904 / 0.250587100703692 = 0.05124615214121316\n", - "field decay(t = 600.3000000000001): 0.00037863506641780544 / 0.250587100703692 = 0.0015109918481619068\n", - "field decay(t = 650.325): 2.416106297824614e-06 / 0.250587100703692 = 9.64178240236536e-06\n", - "field decay(t = 700.35): 4.49091206005567e-09 / 0.250587100703692 = 1.7921561195466212e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835782728e-12 / 4.169100835782728e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.016542016882427e-08 / 1.016542016882427e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650785264758402e-06 / 4.650785264758402e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454866674834187 / 0.0005454866674834187 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017815708836639688 / 0.017815708836639688 = 1.0\n", - "field decay(t = 350.175): 0.13192331666924295 / 0.13192331666924295 = 1.0\n", - "field decay(t = 400.20000000000005): 0.2505582526127686 / 0.2505582526127686 = 1.0\n", - "field decay(t = 450.225): 0.24995745365544636 / 0.2505582526127686 = 0.9976021585756716\n", - "field decay(t = 500.25): 0.11167425141378004 / 0.2505582526127686 = 0.4457017489915599\n", - "field decay(t = 550.275): 0.012841538842926424 / 0.2505582526127686 = 0.05125170976815797\n", - "field decay(t = 600.3000000000001): 0.000378634996846925 / 0.2505582526127686 = 0.0015111655389459305\n", - "field decay(t = 650.325): 2.4161065294750964e-06 / 0.2505582526127686 = 9.642893436079026e-06\n", - "field decay(t = 700.35): 4.49091985908172e-09 / 0.2505582526127686 = 1.7923655725769776e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.1691008357828475e-12 / 4.1691008357828475e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420168726412e-08 / 1.0165420168726412e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650785245230697e-06 / 4.650785245230697e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454864114377906 / 0.0005454864114377906 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017815382476511977 / 0.017815382476511977 = 1.0\n", - "field decay(t = 350.175): 0.13190609125009525 / 0.13190609125009525 = 1.0\n", - "field decay(t = 400.20000000000005): 0.2504984938450849 / 0.2504984938450849 = 1.0\n", - "field decay(t = 450.225): 0.24989813526348434 / 0.2504984938450849 = 0.9976033445455691\n", - "field decay(t = 500.25): 0.11166295794325522 / 0.2504984938450849 = 0.4457629913428168\n", - "field decay(t = 550.275): 0.012841361210895873 / 0.2504984938450849 = 0.05126322723056899\n", - "field decay(t = 600.3000000000001): 0.00037863485293490557 / 0.2504984938450849 = 0.0015115254671713265\n", - "field decay(t = 650.325): 2.4161070090188778e-06 / 0.2504984938450849 = 9.645195753205065e-06\n", - "field decay(t = 700.35): 4.490935979334019e-09 / 0.2504984938450849 = 1.792799593482321e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835782678e-12 / 4.169100835782678e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420168523849e-08 / 1.0165420168523849e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650785204825125e-06 / 4.650785204825125e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454858816409209 / 0.0005454858816409209 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017814707103397807 / 0.017814707103397807 = 1.0\n", - "field decay(t = 350.175): 0.13187041021795567 / 0.13187041021795567 = 1.0\n", - "field decay(t = 400.20000000000005): 0.250374554159776 / 0.250374554159776 = 1.0\n", - "field decay(t = 450.225): 0.24977510744804346 / 0.250374554159776 = 0.9976058001830729\n", - "field decay(t = 500.25): 0.11163956208436386 / 0.250374554159776 = 0.4458902082083042\n", - "field decay(t = 550.275): 0.012840993630635223 / 0.250374554159776 = 0.051287135283087794\n", - "field decay(t = 600.3000000000001): 0.00037863455532979455 / 0.250374554159776 = 0.0015122725094826118\n", - "field decay(t = 650.325): 2.4161080020963407e-06 / 0.250374554159776 = 9.649974256387518e-06\n", - "field decay(t = 700.35): 4.490969255239367e-09 / 0.250374554159776 = 1.7937003503852332e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835782697e-12 / 4.169100835782697e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420168104837e-08 / 1.0165420168104837e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650785121220347e-06 / 4.650785121220347e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454847854047326 / 0.0005454847854047326 = 1.0\n", - "field decay(t = 300.15000000000003): 0.01781330928257498 / 0.01781330928257498 = 1.0\n", - "field decay(t = 350.175): 0.1317964137861031 / 0.1317964137861031 = 1.0\n", - "field decay(t = 400.20000000000005): 0.25011687030360397 / 0.25011687030360397 = 1.0\n", - "field decay(t = 450.225): 0.24951931317382292 / 0.25011687030360397 = 0.9976108883456933\n", - "field decay(t = 500.25): 0.11159103280876947 / 0.25011687030360397 = 0.4461555618911866\n", - "field decay(t = 550.275): 0.012840232906414287 / 0.25011687030360397 = 0.051336932574073034\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "field decay(t = 600.3000000000001): 0.0003786339402465762 / 0.25011687030360397 = 0.0015138280747994807\n", - "field decay(t = 650.325): 2.416110058754147e-06 / 0.25011687030360397 = 9.659924401826058e-06\n", - "field decay(t = 700.35): 4.491037714235971e-09 / 0.25011687030360397 = 1.795575687791284e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835782566e-12 / 4.169100835782566e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.016542016723775e-08 / 1.016542016723775e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650784948230582e-06 / 4.650784948230582e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454825170836912 / 0.0005454825170836912 = 1.0\n", - "field decay(t = 300.15000000000003): 0.01781041537070784 / 0.01781041537070784 = 1.0\n", - "field decay(t = 350.175): 0.1316425937154995 / 0.1316425937154995 = 1.0\n", - "field decay(t = 400.20000000000005): 0.24957847104378408 / 0.24957847104378408 = 1.0\n", - "field decay(t = 450.225): 0.2489848350078133 / 0.24957847104378408 = 0.99762144533746\n", - "field decay(t = 500.25): 0.11149010814744109 / 0.24957847104378408 = 0.4467136435333084\n", - "field decay(t = 550.275): 0.012838658222915229 / 0.24957847104378408 = 0.05144136899798106\n", - "field decay(t = 600.3000000000001): 0.0003786326703923277 / 0.24957847104378408 = 0.0015170886687814647\n", - "field decay(t = 650.325): 2.4161143069600103e-06 / 0.24957847104378408 = 9.680780144438605e-06\n", - "field decay(t = 700.35): 4.4911771950517e-09 / 0.24957847104378408 = 1.7995050519657217e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.1691008357823855e-12 / 4.1691008357823855e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420165443837e-08 / 1.0165420165443837e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650784590290659e-06 / 4.650784590290659e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454778233702294 / 0.0005454778233702294 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017804420515894783 / 0.017804420515894783 = 1.0\n", - "field decay(t = 350.175): 0.13132132371111363 / 0.13132132371111363 = 1.0\n", - "field decay(t = 400.20000000000005): 0.24844276804284895 / 0.24844276804284895 = 1.0\n", - "field decay(t = 450.225): 0.24785728763909465 / 0.24844276804284895 = 0.9976433992892346\n", - "field decay(t = 500.25): 0.11127911965592859 / 0.24844276804284895 = 0.4479064556096729\n", - "field decay(t = 550.275): 0.01283539726004091 / 0.24844276804284895 = 0.05166339660902179\n", - "field decay(t = 600.3000000000001): 0.0003786300533999004 / 0.24844276804284895 = 0.0015240131817183668\n", - "field decay(t = 650.325): 2.416122931610749e-06 / 0.24844276804284895 = 9.725068476108912e-06\n", - "field decay(t = 700.35): 4.491452301556477e-09 / 0.24844276804284895 = 1.8078418369504868e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.1691008357820155e-12 / 4.1691008357820155e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420161731733e-08 / 1.0165420161731733e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650783849662967e-06 / 4.650783849662967e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454681103843687 / 0.0005454681103843687 = 1.0\n", - "field decay(t = 300.15000000000003): 0.0177919867184528 / 0.0177919867184528 = 1.0\n", - "field decay(t = 350.175): 0.13064418737686334 / 0.13064418737686334 = 1.0\n", - "field decay(t = 400.20000000000005): 0.24608078239905923 / 0.24608078239905923 = 1.0\n", - "field decay(t = 450.225): 0.2454910539147474 / 0.24608078239905923 = 0.9976035167047076\n", - "field decay(t = 500.25): 0.11083352926803994 / 0.24608078239905923 = 0.45039489954280826\n", - "field decay(t = 550.275): 0.012828638329285625 / 0.24608078239905923 = 0.052131817057058695\n", - "field decay(t = 600.3000000000001): 0.00037862466733590196 / 0.24608078239905923 = 0.0015386194063781123\n", - "field decay(t = 650.325): 2.4161389249309716e-06 / 0.24608078239905923 = 9.818478718150437e-06\n", - "field decay(t = 700.35): 4.491929211746334e-09 / 0.24608078239905923 = 1.8253880566999964e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835781244e-12 / 4.169100835781244e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420154050911e-08 / 1.0165420154050911e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650782317199479e-06 / 4.650782317199479e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454480084043649 / 0.0005454480084043649 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017766133980041062 / 0.017766133980041062 = 1.0\n", - "field decay(t = 350.175): 0.12943453172485406 / 0.12943453172485406 = 1.0\n", - "field decay(t = 400.20000000000005): 0.24162814253773343 / 0.24162814253773343 = 1.0\n", - "field decay(t = 450.225): 0.2410719556831018 / 0.24162814253773343 = 0.9976981702181286\n", - "field decay(t = 500.25): 0.10987489072588702 / 0.24162814253773343 = 0.45472720839514214\n", - "field decay(t = 550.275): 0.012814604477285866 / 0.24162814253773343 = 0.05303440378549736\n", - "field decay(t = 600.3000000000001): 0.00037861349073161515 / 0.24162814253773343 = 0.0015669262973889296\n", - "field decay(t = 650.325): 2.4161558408009105e-06 / 0.24162814253773343 = 9.999480256831406e-06\n", - "field decay(t = 700.35): 4.492861541918341e-09 / 0.24162814253773343 = 1.8594115299366343e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835779622e-12 / 4.169100835779622e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420138158315e-08 / 1.0165420138158315e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650779146309852e-06 / 4.650779146309852e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454063955002492 / 0.0005454063955002492 = 1.0\n", - "field decay(t = 300.15000000000003): 0.01771662173687749 / 0.01771662173687749 = 1.0\n", - "field decay(t = 350.175): 0.12689706943678725 / 0.12689706943678725 = 1.0\n", - "field decay(t = 400.20000000000005): 0.23320693569541642 / 0.23320693569541642 = 1.0\n", - "field decay(t = 450.225): 0.23267453504759023 / 0.23320693569541642 = 0.9977170462523398\n", - "field decay(t = 500.25): 0.10799807195922452 / 0.23320693569541642 = 0.46309974288362116\n", - "field decay(t = 550.275): 0.012785358594898285 / 0.23320693569541642 = 0.05482409241720325\n", - "field decay(t = 600.3000000000001): 0.000378589249816587 / 0.23320693569541642 = 0.0016234047614735157\n", - "field decay(t = 650.325): 2.416107138602139e-06 / 0.23320693569541642 = 1.0360357128304853e-05\n", - "field decay(t = 700.35): 4.493125804229867e-09 / 0.23320693569541642 = 1.9266690293028782e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835776254e-12 / 4.169100835776254e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420105274407e-08 / 1.0165420105274407e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.6507725852541425e-06 / 4.6507725852541425e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005453202108380395 / 0.0005453202108380395 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017624098227291815 / 0.017624098227291815 = 1.0\n", - "field decay(t = 350.175): 0.12218039718656669 / 0.12218039718656669 = 1.0\n", - "field decay(t = 400.20000000000005): 0.21924792050420605 / 0.21924792050420605 = 1.0\n", - "field decay(t = 450.225): 0.21875381922070578 / 0.21924792050420605 = 0.9977463809811103\n", - "field decay(t = 500.25): 0.10436138938509092 / 0.21924792050420605 = 0.47599716861665214\n", - "field decay(t = 550.275): 0.012733297749038119 / 0.21924792050420605 = 0.05807716542877698\n", - "field decay(t = 600.3000000000001): 0.00037853854019597556 / 0.21924792050420605 = 0.0017265319521637774\n", - "field decay(t = 650.325): 2.4161208970651143e-06 / 0.21924792050420605 = 1.102004019700047e-05\n", - "field decay(t = 700.35): 4.491390315511021e-09 / 0.21924792050420605 = 2.0485440888935858e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835769312e-12 / 4.169100835769312e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420037232922e-08 / 1.0165420037232922e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650759009321587e-06 / 4.650759009321587e-06 = 1.0\n", - "field decay(t = 250.125): 0.000545141533396581 / 0.000545141533396581 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017421291008439022 / 0.017421291008439022 = 1.0\n", - "field decay(t = 350.175): 0.11463570073897522 / 0.11463570073897522 = 1.0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "field decay(t = 400.20000000000005): 0.20163815113463796 / 0.20163815113463796 = 1.0\n", - "field decay(t = 450.225): 0.20120253259282825 / 0.20163815113463796 = 0.9978396025783889\n", - "field decay(t = 500.25): 0.10109030457587416 / 0.20163815113463796 = 0.5013451274326259\n", - "field decay(t = 550.275): 0.01262521101057978 / 0.20163815113463796 = 0.06261320558404479\n", - "field decay(t = 600.3000000000001): 0.0003784354270167391 / 0.20163815113463796 = 0.0018768046864506805\n", - "field decay(t = 650.325): 2.4161436765026146e-06 / 0.20163815113463796 = 1.1982572062413455e-05\n", - "field decay(t = 700.35): 4.492170330890932e-09 / 0.20163815113463796 = 2.2278374928618628e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835754938e-12 / 4.169100835754938e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.01654198964457e-08 / 1.01654198964457e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650730917976917e-06 / 4.650730917976917e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005447703360772811 / 0.0005447703360772811 = 1.0\n", - "field decay(t = 300.15000000000003): 0.01702522296927303 / 0.01702522296927303 = 1.0\n", - "field decay(t = 350.175): 0.10518227880576582 / 0.10518227880576582 = 1.0\n", - "field decay(t = 400.20000000000005): 0.1772727477817735 / 0.1772727477817735 = 1.0\n", - "field decay(t = 450.225): 0.17689224598144007 / 0.1772727477817735 = 0.9978535798361865\n", - "field decay(t = 500.25): 0.0941896635654982 / 0.1772727477817735 = 0.5313262458223283\n", - "field decay(t = 550.275): 0.01239882951385902 / 0.1772727477817735 = 0.06994210711463807\n", - "field decay(t = 600.3000000000001): 0.00037822000681681363 / 0.1772727477817735 = 0.0021335485095679255\n", - "field decay(t = 650.325): 2.4160847007280536e-06 / 0.1772727477817735 = 1.3629194170907224e-05\n", - "field decay(t = 700.35): 4.493256393452313e-09 / 0.1772727477817735 = 2.5346571594769923e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835725167e-12 / 4.169100835725167e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.016541960513757e-08 / 1.016541960513757e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650672789399802e-06 / 4.650672789399802e-06 = 1.0\n", - "field decay(t = 250.125): 0.000543995966140328 / 0.000543995966140328 = 1.0\n", - "field decay(t = 300.15000000000003): 0.016307143716002143 / 0.016307143716002143 = 1.0\n", - "field decay(t = 350.175): 0.09153211905255858 / 0.09153211905255858 = 1.0\n", - "field decay(t = 400.20000000000005): 0.22855260392168325 / 0.22855260392168325 = 1.0\n", - "field decay(t = 450.225): 0.22862376766772263 / 0.22862376766772263 = 1.0\n", - "field decay(t = 500.25): 0.0829714083179759 / 0.22862376766772263 = 0.36291680941311816\n", - "field decay(t = 550.275): 0.011966639931831521 / 0.22862376766772263 = 0.0523420642302755\n", - "field decay(t = 600.3000000000001): 0.0003777734295152949 / 0.22862376766772263 = 0.0016523803862087667\n", - "field decay(t = 650.325): 2.416110201923171e-06 / 0.22862376766772263 = 1.0568062221049122e-05\n", - "field decay(t = 700.35): 4.492316812744696e-09 / 0.22862376766772263 = 1.9649386669516103e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835663596e-12 / 4.169100835663596e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165419002380455e-08 / 1.0165419002380455e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650552497483136e-06 / 4.650552497483136e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005423672260522596 / 0.0005423672260522596 = 1.0\n", - "field decay(t = 300.15000000000003): 0.01462324952255367 / 0.01462324952255367 = 1.0\n", - "field decay(t = 350.175): 0.11286948557899079 / 0.11286948557899079 = 1.0\n", - "field decay(t = 400.20000000000005): 0.22340193165372965 / 0.22340193165372965 = 1.0\n", - "field decay(t = 450.225): 0.22423295751360775 / 0.22423295751360775 = 1.0\n", - "field decay(t = 500.25): 0.10875776760662008 / 0.22423295751360775 = 0.4850213314428591\n", - "field decay(t = 550.275): 0.011253173596244885 / 0.22423295751360775 = 0.050185190085458235\n", - "field decay(t = 600.3000000000001): 0.0003768391018113586 / 0.22423295751360775 = 0.0016805696450241478\n", - "field decay(t = 650.325): 2.416028682322177e-06 / 0.22423295751360775 = 1.0774636829091274e-05\n", - "field decay(t = 700.35): 4.491221029097323e-09 / 0.22423295751360775 = 2.002926366800817e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835536189e-12 / 4.169100835536189e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.016541775519228e-08 / 1.016541775519228e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650303529004109e-06 / 4.650303529004109e-06 = 1.0\n", - "field decay(t = 250.125): 0.000538888737006022 / 0.000538888737006022 = 1.0\n", - "field decay(t = 300.15000000000003): 0.012940856099568963 / 0.012940856099568963 = 1.0\n", - "field decay(t = 350.175): 0.10610514124453076 / 0.10610514124453076 = 1.0\n", - "field decay(t = 400.20000000000005): 0.21497310015816543 / 0.21497310015816543 = 1.0\n", - "field decay(t = 450.225): 0.22343131384615714 / 0.22343131384615714 = 1.0\n", - "field decay(t = 500.25): 0.12060305444368463 / 0.22343131384615714 = 0.5397768664007654\n", - "field decay(t = 550.275): 0.010356579521407651 / 0.22343131384615714 = 0.046352408456670664\n", - "field decay(t = 600.3000000000001): 0.0003748549630386882 / 0.22343131384615714 = 0.0016777190116547107\n", - "field decay(t = 650.325): 2.4161257892828187e-06 / 0.22343131384615714 = 1.0813729497855586e-05\n", - "field decay(t = 700.35): 4.502116718465644e-09 / 0.22343131384615714 = 2.0149891440756423e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835272573e-12 / 4.169100835272573e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165415174584912e-08 / 1.0165415174584912e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.649788088402476e-06 / 4.649788088402476e-06 = 1.0\n", - "field decay(t = 250.125): 0.00053174759709517 / 0.00053174759709517 = 1.0\n", - "field decay(t = 300.15000000000003): 0.01247454839135486 / 0.01247454839135486 = 1.0\n", - "field decay(t = 350.175): 0.08976065034162566 / 0.08976065034162566 = 1.0\n", - "field decay(t = 400.20000000000005): 0.18870963593996914 / 0.18870963593996914 = 1.0\n", - "field decay(t = 450.225): 0.22826836798863853 / 0.22826836798863853 = 1.0\n", - "field decay(t = 500.25): 0.12497876557323505 / 0.22826836798863853 = 0.5475080348384299\n", - "field decay(t = 550.275): 0.009665581444509555 / 0.22826836798863853 = 0.042343061063066935\n", - "field decay(t = 600.3000000000001): 0.0003923542204648252 / 0.22826836798863853 = 0.0017188286923940053\n", - "field decay(t = 650.325): 2.7457665901575746e-06 / 0.22826836798863853 = 1.2028677535795227e-05\n", - "field decay(t = 700.35): 5.739133736098904e-09 / 0.22826836798863853 = 2.5142045683633898e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.16910083472712e-12 / 4.16910083472712e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165409834939769e-08 / 1.0165409834939769e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.648720329217878e-06 / 4.648720329217878e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005182735319569587 / 0.0005182735319569587 = 1.0\n", - "field decay(t = 300.15000000000003): 0.013974697322011669 / 0.013974697322011669 = 1.0\n", - "field decay(t = 350.175): 0.07995289002032732 / 0.07995289002032732 = 1.0\n", - "field decay(t = 400.20000000000005): 0.17867229467477655 / 0.17867229467477655 = 1.0\n", - "field decay(t = 450.225): 0.23241654769547887 / 0.23241654769547887 = 1.0\n", - "field decay(t = 500.25): 0.18817882350661239 / 0.23241654769547887 = 0.8096618996043756\n", - "field decay(t = 550.275): 0.014319414613175686 / 0.23241654769547887 = 0.06161099437694745\n", - "field decay(t = 600.3000000000001): 0.0003754598154438489 / 0.23241654769547887 = 0.0016154607714756648\n", - "field decay(t = 650.325): 2.9312708100424914e-06 / 0.23241654769547887 = 1.261214332244172e-05\n", - "field decay(t = 700.35): 1.8599873539004834e-07 / 0.23241654769547887 = 8.002818096831515e-07\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "pts = np.linspace(-6, 0, 20)\n", "_, _, omega_psd, omega3_psd = zip(*[run_chi3(k_iter) for k_iter in pts])" @@ -822,22 +331,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "quad = (10**pts) ** 2\n", "\n", @@ -863,50 +359,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.1691008357817126e-12 / 4.1691008357817126e-12 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420158581846e-08 / 1.0165420158581846e-08 = 1.0\n", - "field decay(t = 200.10000000000002): 4.650783221205379e-06 / 4.650783221205379e-06 = 1.0\n", - "field decay(t = 250.125): 0.0005454598673598967 / 0.0005454598673598967 = 1.0\n", - "field decay(t = 300.15000000000003): 0.017781404956060017 / 0.017781404956060017 = 1.0\n", - "field decay(t = 350.175): 0.13014055244440104 / 0.13014055244440104 = 1.0\n", - "field decay(t = 400.20000000000005): 0.2443206506136912 / 0.2443206506136912 = 1.0\n", - "field decay(t = 450.225): 0.2437445632391615 / 0.2443206506136912 = 0.9976420848050188\n", - "field decay(t = 500.25): 0.11044620451678894 / 0.2443206506136912 = 0.4520543156682302\n", - "field decay(t = 550.275): 0.012822890989720101 / 0.2443206506136912 = 0.05248386068681144\n", - "field decay(t = 600.3000000000001): 0.00037862010232358545 / 0.2443206506136912 = 0.0015496852246118257\n", - "field decay(t = 650.325): 2.41614906850782e-06 / 0.2443206506136912 = 9.8892543976077e-06\n", - "field decay(t = 700.35): 4.492194624953141e-09 / 0.2443206506136912 = 1.838647127727241e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "field decay(t = 100.05000000000001): 4.169100835782652e-10 / 4.169100835782652e-10 = 1.0\n", - "field decay(t = 150.07500000000002): 1.0165420167882405e-06 / 1.0165420167882405e-06 = 1.0\n", - "field decay(t = 200.10000000000002): 0.0004650785076841832 / 0.0004650785076841832 = 1.0\n", - "field decay(t = 250.125): 0.05454842035005856 / 0.05454842035005856 = 1.0\n", - "field decay(t = 300.15000000000003): 1.7812567092193932 / 1.7812567092193932 = 1.0\n", - "field decay(t = 350.175): 13.175704416847648 / 13.175704416847648 = 1.0\n", - "field decay(t = 400.20000000000005): 24.99794167845129 / 24.99794167845129 = 1.0\n", - "field decay(t = 450.225): 24.9382864152439 / 24.99794167845129 = 0.9976135929919857\n", - "field decay(t = 500.25): 11.156520728608672 / 24.99794167845129 = 0.44629757410090326\n", - "field decay(t = 550.275): 1.2839829021813205 / 24.99794167845129 = 0.051363544994912066\n", - "field decay(t = 600.3000000000001): 0.03786336141250103 / 24.99794167845129 = 0.0015146591627237845\n", - "field decay(t = 650.325): 0.0002416111150361613 / 24.99794167845129 = 9.665240368347396e-06\n", - "field decay(t = 700.35): 4.491073802270905e-07 / 24.99794167845129 = 1.7965774382705666e-08\n", - "run 0 finished at t = 700.35 (28014 timesteps)\n", - "-------------------------------\n", - "Difference between powers: 1.3663690082658835%\n" - ] - } - ], + "outputs": [], "source": [ "_, _, omega_flux_1, omega3_flux_1 = run_chi3(-3, 1)\n", "_, _, omega_flux_2, omega3_flux_2 = run_chi3(-6, 10)\n", diff --git a/python/examples/3rd-harm-1d.py b/python/examples/3rd-harm-1d.py index 9082e8bdf..022efaac5 100644 --- a/python/examples/3rd-harm-1d.py +++ b/python/examples/3rd-harm-1d.py @@ -1,10 +1,8 @@ # 1d simulation of a plane wave propagating through a Kerr medium # and generating the third-harmonic frequency component. - -from __future__ import division +import argparse import meep as mp -import argparse def main(args): diff --git a/python/examples/README.md b/python/examples/README.md index 13e1e2d4f..e7aac224f 100644 --- a/python/examples/README.md +++ b/python/examples/README.md @@ -4,7 +4,7 @@ Meep simulations are Python scripts which involve specifying the device geometry Python libraries such as NumPy, SciPy, and Matplotlib can be used to augment the simulation functionality and will also be demonstrated. Much of the functionality of the low-level C++ interface has been abstracted in Python which means that you don't need to be an experienced programmer to set up simulations. Reasonable defaults are available where necessary. -Several tutorials and examples are found here. These tutorials are meant to illustrate Meep's various features in an interactive and application-oriented manner. +Several tutorials and examples are found here. These tutorials are meant to illustrate Meep's various features in an interactive and application-oriented manner. ## iPython/Jupyter Notebooks @@ -16,7 +16,7 @@ The recommended method to run the tutorial notebooks is by 1.) installing `meep` ### nbviewer -`nbviewer` is a web platform that can render interactive features found inside Jupyter notebooks openly stored on web-servers. While `nbviewer` can't run python code, it can execute stored javascript code used to animate the simulations. +`nbviewer` is a web platform that can render interactive features found inside Jupyter notebooks openly stored on web-servers. While `nbviewer` can't run python code, it can execute stored javascript code used to animate the simulations. ### GitHub @@ -26,7 +26,7 @@ GitHub is able to render some of the smaller notebooks as plain text. However, t Below are summaries for each tutorial, along with the features the tutorials highlight. While there is no particular order to the tutorials, they progressively incorporate more complicated features. -#### Basics +#### Basics * __`straight-waveguide.ipynb`__ - A simple 2D straight waveguide tutorial that explores basic meep features like `geometry`, `sources`, and `PML` layers. The tutorial also explores basic visualization and animation features. @@ -47,12 +47,12 @@ Computes the resonant mode frequencies of a 2D ring resonator using `harminv`. * __`holey-wg-cavity.ipynb`__ - Calculates the transmission and resonant modes of a waveguide photonic crystal cavity. Demonstrates the `harminv` routines and how to estimate the $Q$ of cavities. -* __`holey-wg-bands.ipynb`__ - +* __`holey-wg-bands.ipynb`__ - Computes the band diagram of the infinite periodic waveguide by itself with no defects in the time domain. Explores the `k_point`, `run_k_point`, and periodic boundary conditions features. #### MPB and Band diagrams -* __`mpb_strip.ipynb`__ - +* __`mpb_strip.ipynb`__ - #### Eigenmode Source @@ -79,12 +79,12 @@ Computes the band diagram of the infinite periodic waveguide by itself with no d #### Nonlinear Optics -* __`3rd-harm-1d.ipynb`__ - +* __`3rd-harm-1d.ipynb`__ - Examines 3rd harmonic generation in a $\chi^{(3)}$ material. Explores the proper way to do 1d simulations, how to include nonlinearities in materials, and compares experimental results to theory. #### Near to Far Field Spectra -* __`antenna-radiation.ipynb`__ - +* __`antenna-radiation.ipynb`__ - Computes the radiation pattern of a simple point source "antenna". Explores `add_near2far`, `add_flux`, `get_fluxes`, and `get_farfield` features. * __`metasurface_lens.ipynb`__ - diff --git a/python/examples/absorbed_power_density.ipynb b/python/examples/absorbed_power_density.ipynb index fbce592f8..191be8d9d 100644 --- a/python/examples/absorbed_power_density.ipynb +++ b/python/examples/absorbed_power_density.ipynb @@ -13,168 +13,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00278592 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8 x 8 x 0 with resolution 100\n", - " cylinder, center = (0,0,0)\n", - " radius 1, height 1e+20, axis (0, 0, 1)\n", - " dielectric constant epsilon diagonal = (1,1,1)\n", - "time for set_epsilon = 0.94641 s\n", - "lorentzian susceptibility: frequency=9.67865, gamma=0.0806554\n", - "-----------\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d636f14778924d5ca3612af32c90cddd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=200.0)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 3.24/200.0 = 1.6% done in 4.0s, 243.3s to go\n", - "on time step 650 (time=3.25), 0.00616112 s/step\n", - "Meep progress: 6.595/200.0 = 3.3% done in 8.0s, 234.8s to go\n", - "on time step 1322 (time=6.61), 0.00595972 s/step\n", - "Meep progress: 10.0/200.0 = 5.0% done in 12.0s, 228.2s to go\n", - "on time step 2003 (time=10.015), 0.00587584 s/step\n", - "Meep progress: 13.16/200.0 = 6.6% done in 16.0s, 227.3s to go\n", - "on time step 2635 (time=13.175), 0.00632952 s/step\n", - "Meep progress: 16.52/200.0 = 8.3% done in 20.0s, 222.3s to go\n", - "on time step 3308 (time=16.54), 0.00595308 s/step\n", - "Meep progress: 19.39/200.0 = 9.7% done in 24.0s, 223.7s to go\n", - "on time step 3882 (time=19.41), 0.00697273 s/step\n", - "Meep progress: 22.77/200.0 = 11.4% done in 28.0s, 218.1s to go\n", - "on time step 4559 (time=22.795), 0.00591308 s/step\n", - "Meep progress: 26.085/200.0 = 13.0% done in 32.0s, 213.5s to go\n", - "on time step 5223 (time=26.115), 0.00603051 s/step\n", - "Meep progress: 29.465/200.0 = 14.7% done in 36.0s, 208.5s to go\n", - "on time step 5898 (time=29.49), 0.00592621 s/step\n", - "Meep progress: 32.87/200.0 = 16.4% done in 40.0s, 203.5s to go\n", - "on time step 6579 (time=32.895), 0.00587512 s/step\n", - "Meep progress: 36.265/200.0 = 18.1% done in 44.0s, 198.8s to go\n", - "on time step 7258 (time=36.29), 0.00589753 s/step\n", - "Meep progress: 39.63/200.0 = 19.8% done in 48.0s, 194.4s to go\n", - "on time step 7931 (time=39.655), 0.00595055 s/step\n", - "Meep progress: 43.075/200.0 = 21.5% done in 52.0s, 189.6s to go\n", - "on time step 8621 (time=43.105), 0.00580339 s/step\n", - "Meep progress: 46.385/200.0 = 23.2% done in 56.0s, 185.6s to go\n", - "on time step 9282 (time=46.41), 0.00605481 s/step\n", - "Meep progress: 49.795/200.0 = 24.9% done in 60.0s, 181.1s to go\n", - "on time step 9965 (time=49.825), 0.00586147 s/step\n", - "Meep progress: 53.120000000000005/200.0 = 26.6% done in 64.0s, 177.1s to go\n", - "on time step 10629 (time=53.145), 0.0060261 s/step\n", - "Meep progress: 56.52/200.0 = 28.3% done in 68.1s, 172.8s to go\n", - "on time step 11309 (time=56.545), 0.00588784 s/step\n", - "Meep progress: 59.885/200.0 = 29.9% done in 72.1s, 168.6s to go\n", - "on time step 11982 (time=59.91), 0.00594881 s/step\n", - "Meep progress: 63.22/200.0 = 31.6% done in 76.1s, 164.6s to go\n", - "on time step 12649 (time=63.245), 0.0059984 s/step\n", - "Meep progress: 66.57000000000001/200.0 = 33.3% done in 80.1s, 160.5s to go\n", - "on time step 13319 (time=66.595), 0.0059716 s/step\n", - "Meep progress: 69.935/200.0 = 35.0% done in 84.1s, 156.3s to go\n", - "on time step 13993 (time=69.965), 0.00593998 s/step\n", - "Meep progress: 73.265/200.0 = 36.6% done in 88.1s, 152.3s to go\n", - "on time step 14659 (time=73.295), 0.00600958 s/step\n", - "Meep progress: 76.64/200.0 = 38.3% done in 92.1s, 148.2s to go\n", - "on time step 15335 (time=76.675), 0.00592433 s/step\n", - "Meep progress: 80.035/200.0 = 40.0% done in 96.1s, 144.0s to go\n", - "on time step 16013 (time=80.065), 0.00590122 s/step\n", - "Meep progress: 83.44/200.0 = 41.7% done in 100.1s, 139.8s to go\n", - "on time step 16694 (time=83.47), 0.00587495 s/step\n", - "Meep progress: 86.82000000000001/200.0 = 43.4% done in 104.1s, 135.7s to go\n", - "on time step 17371 (time=86.855), 0.00591555 s/step\n", - "Meep progress: 90.22500000000001/200.0 = 45.1% done in 108.1s, 131.5s to go\n", - "on time step 18052 (time=90.26), 0.00587912 s/step\n", - "Meep progress: 93.58500000000001/200.0 = 46.8% done in 112.1s, 127.5s to go\n", - "on time step 18723 (time=93.615), 0.00596502 s/step\n", - "Meep progress: 96.95/200.0 = 48.5% done in 116.1s, 123.4s to go\n", - "on time step 19397 (time=96.985), 0.00593889 s/step\n", - "Meep progress: 100.32000000000001/200.0 = 50.2% done in 120.1s, 119.3s to go\n", - "on time step 20071 (time=100.355), 0.00594026 s/step\n", - "Meep progress: 103.68/200.0 = 51.8% done in 124.1s, 115.3s to go\n", - "on time step 20743 (time=103.715), 0.00596124 s/step\n", - "Meep progress: 107.11500000000001/200.0 = 53.6% done in 128.1s, 111.1s to go\n", - "on time step 21430 (time=107.15), 0.00582342 s/step\n", - "Meep progress: 110.525/200.0 = 55.3% done in 132.1s, 106.9s to go\n", - "on time step 22113 (time=110.565), 0.00586115 s/step\n", - "Meep progress: 113.855/200.0 = 56.9% done in 136.1s, 103.0s to go\n", - "on time step 22779 (time=113.895), 0.00601218 s/step\n", - "Meep progress: 117.17/200.0 = 58.6% done in 140.1s, 99.0s to go\n", - "on time step 23442 (time=117.21), 0.00604035 s/step\n", - "Meep progress: 120.55/200.0 = 60.3% done in 144.1s, 95.0s to go\n", - "on time step 24119 (time=120.595), 0.00591443 s/step\n", - "Meep progress: 123.93/200.0 = 62.0% done in 148.1s, 90.9s to go\n", - "on time step 24795 (time=123.975), 0.00591771 s/step\n", - "Meep progress: 127.345/200.0 = 63.7% done in 152.1s, 86.8s to go\n", - "on time step 25478 (time=127.39), 0.00586339 s/step\n", - "Meep progress: 130.695/200.0 = 65.3% done in 156.1s, 82.8s to go\n", - "on time step 26148 (time=130.74), 0.00597442 s/step\n", - "Meep progress: 134.075/200.0 = 67.0% done in 160.1s, 78.7s to go\n", - "on time step 26824 (time=134.12), 0.005918 s/step\n", - "Meep progress: 137.465/200.0 = 68.7% done in 164.1s, 74.7s to go\n", - "on time step 27502 (time=137.51), 0.005903 s/step\n", - "Meep progress: 140.85/200.0 = 70.4% done in 168.1s, 70.6s to go\n", - "on time step 28179 (time=140.895), 0.00591219 s/step\n", - "Meep progress: 144.24/200.0 = 72.1% done in 172.1s, 66.5s to go\n", - "on time step 28858 (time=144.29), 0.00590082 s/step\n", - "Meep progress: 147.625/200.0 = 73.8% done in 176.1s, 62.5s to go\n", - "on time step 29535 (time=147.675), 0.0059085 s/step\n", - "Meep progress: 151.025/200.0 = 75.5% done in 180.1s, 58.4s to go\n", - "on time step 30216 (time=151.08), 0.00587643 s/step\n", - "Meep progress: 154.425/200.0 = 77.2% done in 184.1s, 54.3s to go\n", - "on time step 30896 (time=154.48), 0.00588855 s/step\n", - "Meep progress: 157.82500000000002/200.0 = 78.9% done in 188.1s, 50.3s to go\n", - "on time step 31577 (time=157.885), 0.00588064 s/step\n", - "Meep progress: 161.245/200.0 = 80.6% done in 192.1s, 46.2s to go\n", - "on time step 32261 (time=161.305), 0.00585296 s/step\n", - "Meep progress: 164.605/200.0 = 82.3% done in 196.1s, 42.2s to go\n", - "on time step 32933 (time=164.665), 0.00595906 s/step\n", - "Meep progress: 167.955/200.0 = 84.0% done in 200.1s, 38.2s to go\n", - "on time step 33603 (time=168.015), 0.00597094 s/step\n", - "Meep progress: 171.31/200.0 = 85.7% done in 204.2s, 34.2s to go\n", - "on time step 34275 (time=171.375), 0.00596032 s/step\n", - "Meep progress: 174.695/200.0 = 87.3% done in 208.2s, 30.2s to go\n", - "on time step 34953 (time=174.765), 0.00590648 s/step\n", - "Meep progress: 178.01500000000001/200.0 = 89.0% done in 212.2s, 26.2s to go\n", - "on time step 35617 (time=178.085), 0.00602658 s/step\n", - "Meep progress: 181.37/200.0 = 90.7% done in 216.2s, 22.2s to go\n", - "on time step 36288 (time=181.44), 0.00597098 s/step\n", - "Meep progress: 184.735/200.0 = 92.4% done in 220.2s, 18.2s to go\n", - "on time step 36961 (time=184.805), 0.0059467 s/step\n", - "Meep progress: 188.035/200.0 = 94.0% done in 224.2s, 14.3s to go\n", - "on time step 37621 (time=188.105), 0.00606458 s/step\n", - "Meep progress: 191.455/200.0 = 95.7% done in 228.2s, 10.2s to go\n", - "on time step 38307 (time=191.535), 0.00583756 s/step\n", - "Meep progress: 194.79/200.0 = 97.4% done in 232.2s, 6.2s to go\n", - "on time step 38973 (time=194.865), 0.00600972 s/step\n", - "Meep progress: 198.16/200.0 = 99.1% done in 236.2s, 2.2s to go\n", - "on time step 39649 (time=198.245), 0.00592657 s/step\n", - "run 0 finished at t = 200.0 (40000 timesteps)\n", - "flux:, 0.13120421825956843 (dft_fields), 0.13249534167200247 (dft_flux), 0.0097446702362583 (error)\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -271,32 +112,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "x = np.linspace(-r, r, Dz.shape[0])\n", diff --git a/python/examples/absorbed_power_density.py b/python/examples/absorbed_power_density.py index 3bf34183d..7e82f8489 100644 --- a/python/examples/absorbed_power_density.py +++ b/python/examples/absorbed_power_density.py @@ -1,11 +1,11 @@ -import numpy as np import matplotlib +import numpy as np matplotlib.use("agg") import matplotlib.pyplot as plt +from meep.materials import SiO2 import meep as mp -from meep.materials import SiO2 resolution = 100 # pixels/um diff --git a/python/examples/absorber-1d.py b/python/examples/absorber-1d.py index 606e01b80..49fb6a862 100644 --- a/python/examples/absorber-1d.py +++ b/python/examples/absorber-1d.py @@ -1,9 +1,9 @@ -from __future__ import division - import argparse -import meep as mp + from meep.materials import Al +import meep as mp + def main(args): diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb index dfc36d0cd..311f3b285 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb @@ -17,21 +17,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'meep'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmeep\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmp\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmeep\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01madjoint\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpa\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'meep'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -55,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -111,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -175,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -200,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -219,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -236,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -261,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -278,22 +266,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "opt.plot2D(True, frequency=1 / 1.55)\n", "plt.show()" @@ -310,19 +285,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "f0, dJ_du = opt()" ] @@ -336,32 +301,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.figure()\n", "plt.imshow(np.rot90(dJ_du.reshape(Nx, Ny)))" @@ -378,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -412,22 +354,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", @@ -469,19 +398,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "resolution = 20\n", "opt.sim.resolution = resolution\n", @@ -491,22 +410,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb index 4df3e0677..c2e360d25 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb @@ -13,17 +13,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -46,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -143,22 +135,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "x0 = 0.5 * np.ones((Nx * Ny,))\n", "opt.update_design([x0])\n", @@ -178,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -209,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -228,16 +207,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n", - "Starting adjoint run...\n" - ] - } - ], + "outputs": [], "source": [ "x = solver.optimize(x0);" ] diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb index 18cf5784d..7a759ed66 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb @@ -17,17 +17,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -53,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -81,17 +73,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.20124611797498096\n" - ] - } - ], + "outputs": [], "source": [ "minimum_length = 0.09 # minimum length scale (microns)\n", "eta_i = (\n", @@ -113,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -151,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -208,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -241,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -283,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -315,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -337,22 +321,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "rho_vector = np.random.rand(Nx * Ny)\n", "opt.update_design([mapping(rho_vector, eta_i, 1e3)])\n", @@ -369,22 +340,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "beta = 2048\n", "\n", @@ -417,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -465,1594 +423,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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BA8fADTtt/Q2ZsEthgWnGom+hVGLLEnB8E2uJirvnOCvPcbsuhqEeBCe9tK8eVv7s7KwSPs+k41ZbDjMg+MViUT2UE0/j5fny6taz4GezWeXW60DIiUicA3roQSZoLL2lgtbyns7ms7Vvx6LvgPbC6ytLtKk3wFac++Y5S8+DArYBuCauzTfcWw/BZ7E8tsmJQxb85eVlXF5e1kQPK6/PrMeggooAXHI+dj5vDk26ltyaGng4218SuIWfY9G3wAkpnc+u/e+44fG9Uv0d1r205FVm3flG1wUxEMfzO+J5nviiLjdmz0HsEDy79rDy8Cw4bOBBJSJq4uZz53wEvI0umXmthmh5T4VvkXfDom8BQkH/Oa8Pp0+N5dp7Fstn1p0FzxnuiMOsNmfpYd0zsfNceX5aDVtePHL76uoqLi4u4uLiohL9crmsjglWngXPScLJZFI1+ERENfBp2AOPQc+P3/ma6wSdUiNP6WXKWPQt7Pf7mhvMS0bp7LOmWrx22LHgdXos4EkrWbcdsvRNgteFKTabTXXsi8UiLi8vK9FfXV1Vi2TwAhmcKMT+OJbnnAUn6jQforX5bEHLUqUky/A3NfWYMp1E38cmCdxEm80mnj17Fufn53F+fh6Xl5dVsosFog0oHMtmotfwgF3giDi4uSF4dNt1ETwsqzb8YACD0NXKr9fr6hiQqce+zs7Oqhl7k8kkIm7uD3yv1L9Qisd5oIg4fFSX3n+28M9HJ9H3+WIOBoNYrVbx6NGjePz4cSUWiD6z1NqJpk022kvPA0VE1LLTLPbSghgQYfaMed4/XHpN3GEQg+A5jodnkdX/OZbnfEbEoZUuzbnne0vzAer5ZGGP3pt9vle7Yve+hf1+H5eXl/Hpp5/Go0ePYrvdVgLhcpvG8Npgk2X69caGCCBabq2FteW4mhN2WWmOu+2QtINLD9FziQ7Hxd4FBM/7hJVHxl7d+tIcAVQAsodrAE1+gqYFMc1xNIp+sVgcjLZ9AJYuIuJ3v/td/PKXv4w//vGP1fXgJaPUwpdce510wteVrZaubYeSHLvzcLHbXHp255G040w9x/C8si2OAYMNJ+8wyGhHHYC15oEMn+GZePywTHy/1PeARCEGmVL3Y/Zvc0ij6J88eRK/+MUv4le/+lVMp9NajbUP7Pf7+POf/xyfffZZzYXPmnBwI8LqZ/X8JrHzyjPcZafxNHfclQTPJTnE7yx2vNBjzy2y/Kw7rhKw4NXKZw1L2nKL8+IHbWj/gF43Fv1oNKoJnq+l5g76dI/ehlZL/8EHH8QHH3wQERGz2axK1txn0A223+/j7bffjnfeeSdOTk6q57ZrR1hE1G5A7Zcvubslt5enx7Jrre21KngcB9fguSSHhB0673gGHQSPd+7y0/3hOmRlSZ1Nh20iP8Cih3vPsTznILLSHg+8WcJPB2HH+Ic0in6/38fV1VX17+Vy+aUf0MsAW/LHjx/H48ePY7PZ1B72yL3mgBtyWAwq9oioLS2F7SI25xZXCA4uPjfF4Hts4RG/I1l3cXFRVR6yLD2OjcWZ9dbzxB228pyr4PwFzjeb+suix/55kGSvKuKLQXiz2VSDsYZWmZdhyjSKfjAYxOnpafXv2WyWTiq5j+DmmUwmcX19HcPhsBJbRNSsPMNZeHyGb96snRauPFt4nhOvj6IqLV2dNd08ffo0nj59WpUbLy8vqzIjVwtwfCx4XVev9OgrfvAGQgWcP5+nLteF7ahrn7Xujsfjg8FFl/FSj8rktGbv2erx0sd9AZZTy2gR9aw0blrE5viZrZ2KANZPrSqLngcDfIcfKKFZeo7hIXiIHmXG1Wp1EC/juHXlHRY8t/Ny3R8vzl1EROUNQfDqLUTEgRFRC86/x7GW+h6aavvmBpfsWtCpoRE3lgdxKsQHNxQWPqtTl54rpxadBZc9cw7gRkeLMHrp4dZD9NxMBGHx4IHwQo+F5+JHRC2M0Nbk0qw8CJ4HEGxL23PhsXAijz2H9Xod4/H4oA1aXX5TxqJvgUtxEVFzzTmTjc8hy8zJLE3WQdA8YYYXscQceF2qmgXPTUC48Vnw7NJjYQyN4/lc9NFXLHidoaeC18dWRxxm7NlbwfbW63UtRNJEHIuXk3gQPoteVxiypS9j0R+Bdshxkwq72XzDwsVnC6+CZ7GzOLiJBYMHx79ciy912l1dXdUeLc3HpZY4K82VXHp9Vr3W+rnshwGMV9fh89B+fc3Ewxvg+fWj0ejgQRzZFGdziEXfEbZcEAqsIZft+JUteJE9gCJL0vHyViwKCJ0z9Vktnjvt1MLz4KWz9rQ0h3PDYIa8gT7UUtfA0yW1NPnIAm5DS4MIo7LZjhZ9OxZ9C5yI46SUrhrDSbVS9jpbopoTdRAix+5Ze2s2eabUS89TfiNuBi9eSJO7/XTKLJ8TN/xA8Cx6WGS+Vhg8eCBrqqFrDwNbe2Y4HDZaelPGou9ASby8PhzHvfgOd9mp1ePEFru+7M5r40pTLz033iBDnz1DPpsqy11+GndzXZyThZwYRDVArTyHNOy5lESps/BwPbnNWfdj9/54LPoWWIDcF8/ZbnZbufWUE2Wc+OOlo9kS6nPg2MqxdVfBc/zO0301fkeTTNuaerrenbb16poC8ECypKU+446tOwtTW5I5ZOIOSfYSOK636Ltj0XeAb0TcwBwPw8XnBS+0l55dXP2ZRQG4u4+tO8fvaK09Pz9PBc89BVyHRz8/BI8JPAgxtN0VgmfRc5mOk3d6zjqpRvvmNUOP6gcGnoibWr5+HqLnVYi40mJyLPoOsHXiaaecDNPSGj7DbbZs9Vjo7MpmglPBI37nttqrq6uDZa508CnN2lO3nrvjNFuvKwZpH0J2bpwbiKiHKpyl53n2DH+PF94cjUYHS5ZpDsMcYtG3wJM9uHNNs/mcnebpo5nIIRItwwEVPEpycOkhdl7MkpNpHL9ngofoEdfDY8H3dOENLc/po664f4Cvk8blGNB0urHmQcbjcW22YslLUMG7Rt8Ni74DmlFm4av4s1iWxZ5l5PFz5tJzDI+4HZY+W7KL+wJ0oguvuqMNOBzHc+4gEzxPqtG+/azMyGLE9ksTdPj6wppz1QIvfEb7792G245F3wJno1nQ7IryJBq2rviuTs7hG5NvVM2Uq1sPN14fO4WmGxWgtvtqe202S48Tdlyayx5ZzQtksIvP03zZkmvYkHXQIbzQGj636HIJsbQKkSlj0beAmJzLaiXLrTd+1mLKQtBFNnh1XGSlIT402nDDDT6LEhYSdzonP3uCrZYZebCB2LEvrsWzhedKRRa/c1afB7hs7j27+Jngdcoy78PW/ThaRc837mQy6c3UWtyw8/k8vva1r8XZ2VklHG5Pjaiv+jIej2sCj4gDIXCCDLEzrCgsPPe1Q4T4nT5JlnMMJbGrdeeSGDrZtBwI0XMLLwaYLIGHc80EryLtKlQu07FFR1cfPoN9m3a8iEYLu90uXnvttfj6178eDx8+rCbDQGh6M2riSa2SWnKeuKJTVfWlYo+IdMIMt9SW5uBH1Kex8tTczK1nEUccLuKpMwt5dVxtT86ac7Q3gb+bLZzBg67mW7q29/aVoxbRmM/nB0sd30eGw2HlNr/55pvx1ltvxRtvvFG1qKILj63aer2uxbJcd9fmGlhPnqzCZbCsnxzHw7Evd9fxSju82o6uVKNeB0IKzh3owzwgOIQPEJc2FGlcnVl3nn3IYQEPBJql5/PmQQHXl70NbQAyhzSK/uTkJH784x/HD37wg+o/uE9x03b7xXLX6/U6rbVHHNaOISTux9duOrjQyLzro7I0s80TeDhpyFn57MEXHIrwSjWaU+AwQstynF3naoUmLkF2LdSd5849ru1rxl8HCu3g48pJU+efqdMo+tdffz1+9KMfxXvvvfdVHc9LA26aP/zhD/Hb3/42Pv/882qBDLZmOglGa9TsCcDCI1bmzjZefYabVjRe5aYglN9Ki2Yig68JO7jfqA6w4HEcnBnPuu04N8AeTuZFqEuvDTiw9FnYkSX6tEqiE5Us+mZaLX3f+e53vxv7/T5+//vfV+ves9vN4U7WUaZWXmeosVuvNztvk5eemkwmB111+vALTdixGLXpRgcgnqnG3kWpEUm3j32wpeewBH0E3FefWXnN8EfcxO+65BjP6NOOPlPHJbsWBoNBzGazeO2116q2T4hfa834PFCRsVXluL60HW1WgUXjx1l1FXyT2Nmt5zo8u+BZAxJbVHgn8CC0aQbbQ8ON9tFrHM9i1+OB6HnuA583hwrmED/AsgBbnvF4HKenp7Hf72OxWFQ3Nouab27tPlPR6+IPmTWLqCfrOGGn69HzQy/4pldXm0tyPD0WD7zgQSdrPFJrqtULCJ3DFE3c8fXlXEBEPTzIMv1s5VXwsPbaIGQO8QMsW8DMtAcPHtQaWDj+hHghsIh6xl7r74iZM0vISa5s1VxdbUdjWey7JHi28DpbTl16TZZpNyL2VRJ85rnwRCSt8UfUG3H0+9g3L/HFC5FolcLk2L1vgZteVqtVWuuGNecknIoeLi+3srJVxbtaVlgwrsHrohtaKWAXmWv/7NarS8/iKrUW89ThiMP59hB6dn46eHBVgRt49Bz0GvF1wTXRPgSLvhmLvgW2upod1k4ztvZZj7laLraYHLuzlVdLz4LHd1lo6oFkokfWPmuthbC1pKbuOCfbshheS3O8kg4/y44z/9ycUyrR8fXAAJiFNhZ9GYu+A1pLzmbLsZXKWk3VTY24WRWGBwDcuCx6xLAcs+L7qB6w4Nndzrr/OJeAtlqg8weyQQ7nx7PlWPS8HVw37vnXQUvDCkY9D55ExIuTaqOQKWPRt8CxaNb1lU0QiThMfnLmmS2mbpdFn/2MmjbEvt1u08YWFr0uMqElORyfCl3Fw8m1JsFHRG3g4nUAWfDcxYdz4evInkIpz8E5Dbv23bDoO6DWnS2YzjDj/vOIekuuuqtZV5r+mwcErgigPMbC1Ey6xtqw7Foz1/2ocDiplk0YKnXt8Vx+Dk30fBAW8TXka43tariTJRh1MDaHWPQd4ZtQhc+JN/48LBe7+FyOYyvP9efs5mdB840dUZ6Jlgm9LVGn1p2/j8EDIQPnEgAnPrMn5uja91ztQMcjXxdsO1trMHs+gMXejkV/BJmrD5dTS2+wXFzGAyx4teoRN6vRAHaBWeDa0JL9nY8dwsgWrswWsGTrjmPgmX5ah+e5/Drbr/TwSkwk2m631fe32+1BRQPHyFl/FbvphkXfAY0zM8HypBaIHr34KmJ17dny8r5YeGy9OUmIASUTuR6rWkudRMSJQK40NIUM3HgzGo1q/QRoINLJP/BcOC+hk2awTl5EHAyMWexeyvqbQyz6jmRxvVp7CADCbLL0Kki1VlkPevbKbnANExB6wApz2Sxr7OHtZj31WofH/rSfgEWvD9HgEh0GErb0uJZ45wFB4WPl33E4ZW6w6G+Jilaz3Rx3a3KvLeOvll0tLIsum72G7XK/vr64Vo7vsAehVp9Lkdqui4GjaSEPTeBx6JBZeL4WWT6FrxO/29K3Y9E/J2r1+fcqpNINycKNqC+/xdlyFjy7/RE3eQC2ippBz553zzV4bY7RpCBb94gbtxseBC/CmT2Uk117nHeWSMyWHNPwo+QB6TGaQyz6DnSxHmzR+Xts8Zvcz0xwWaMP/g60msDzy/W5eaWn4kZEbfs6042PAfuEYHXVXX0WveYNsP0s58BJUV2IhK9LKb+gA6zJseiPoHQzsaueufUQCW8n+5n/zaUy9QS0rAbhw5qyG69iz8pcWu7TBKKeK4uU5wfwwyp1+m3WJZc14nCSU69T23wGW/puWPRHkpXMOFaPqK8EowOCNu6U9gG0g49r9tgXZ+e1gSXL0LObjP1lsbtaTQgRAtZKQDbXnkuAeg3bBMphDuAmoMlkks5YtPCbseg7wjd/ZvFZ+G0Ju4go3phZeZAbdErJO1juUmY+S37xAJJ17/Hn1PLidzrbT2v97I5rhYBr/TrIZKEFBhzuC0CjkC19dyz6I2iy8gzEqrEr/zuDs+4Myn8luH6tQs9q79zV16UkF3GTtGNvhhOGWZ0fQt/v98W/c3zOlQrt7+ccCKbjsqXXgcrCL5zzh5oAAAd+SURBVGPRd6AUg6vl43fAFhbvbDmziS9ZjqCElrJ4AMD+VOzaD1By7/lYsS/2YrRiwRUHtNRGRG0QYkueTQjivn6N1wHOQ79nwXfDou9IFpMq6obr9/hzWg/XRFZEfappNgg0fVfd+CYPQ4XPg1EpCRdxWEZjsfP2tOMPf1PR8+q8+Dcv1MnXazgcpnF8KU9ibrDo7wjO1nPdm8WDd7W0vI2IqCXcMkG3Wf+IqFnGJsuXHYsOanw8mbA42aY5Ap32yp/hJbYysWexOs8f4HyArXt3LPpb0EWQHIPq3zOPINuuZsC13xz7iTjMxPM7C1LPg7erVQb8De40fsY7XtxurGLP5rrjvDPR6xN/sD0cD+J5HtSya2bKWPQdKbnXJdc5E7V+r6nk1yR+hYXP2XKOsbMBJiIOkn5dRKOxM3szsMIq+OzBGEgcwsJnj/jS2YuYnMOz8JrmMJhDLPoOZILOLLd+vmTtEee2JZ4yq15KwmWtqTxtlTvqIm7EAkvNffHZftkl531mPQOYGsulPO6wi2gWvT6sE8ekAtfBxcLvhkV/BJn4S3/L/s5JMQhDO/VU1JwX0DZf/L4keIiUBwWg7a+Il7OFNDjBp5NustIetoPP4RHnKnpdGhyCXy6XB7MI+fo1Pd1GG5DMIRb9kTRl09u+py671sv5HT/zfrIEGltdrm83NbhkoYWWFNv2kZXIcG7ayBMR1SQaoO20vGgnD14RNyGIij2bT6CzHc0hFv0tKGXfNTYv1fSVkvh4W9kgw8ItfV/3g3cdQLS8x5/T8IEtfGbl4cXgXT0CrdOr2PmzWZ9/NpPPlr47Fv0dUarNqzBVjKXPMqVyX+k76kVoxQDf44w/PAn+DGf/S3E89qcZf37Htji0ydp+tROQLTyvuYcFOviRXjxf3xn8Ziz6W5AJuSkp1/T50t/1+2x9S4lFFhn/jIYZCFt73EvHm/XEawUA20QpjRNu3JSjU3g1D6HXj7cBweNJvXhwJ1bl0YlFx4ZefcOif05UyPo3fS9Z8myw0J57jfE1UZjlAPC57XZbs/hcxtOBKHPnFa67c+uvzrTjwUA9CH3HceusQQiel+BiS589m96CL2PR3wElMeNv/M61eHWRVYTc8473trq9WlHE1uxO43PstmtdXy287n+329Vq8DzFFuLXSoDW87O+BO64G41GtSf1np2dpY/mzkRvylj0tyDLxOvEGv6suud4Z6FzfJ0NHiwSdplV/Cp6nsSC7+ijsLC/bKorD0ycJOMVekqLaPB1wfHwMbKHwF7NYDCoJe1g4SF4du0z0Vv4zVj0HclEDisHYWTxcUaT258lA0uDjMbxvH0WPbLkPP8c38fPcP8V7XzTlXkmk8nBWnjZyjzwJkajUazX69q+dJVbeApw69mlV/d+NpvV1sO34Nux6DvAYsMNCsvGteTSxI+2m5DFXpolpm2y2n3GnkRJ9CiNjcfjWK1WleWGWDD/Hd9jeD08XupaH1mVJdS4NZiPAQMId97xwKKr6kLovMouknyO6btj0XeABc8NIrCO+oQb/S6/t1HKAZRc+8yyaWacZ67pU2yn02lt9Rl08mW1cogaj4lm4Zdiax2AeP/z+bzWX4/z4YU9IXyty/N+M+/ClLHoW1ArDyvHgs9WmuHvN70z2fd1O5mFb7L0WRNM6dHV+uQa5CK0G64kPLjYOneeOwVLT9HlfbE3wUtr6xLe2ZNrHdO3Y9F3gAXPVmkymdSe865xuP7M4my6KUvbwbGUBA8wAPEa+Sw4FjpPbsnWmYP7z8+p46W1IU6eXKODULb8VfbYbOxL19LXdf/052yNPou+jEXfAls6blXFIFAqbfH3m96POQ7AWe6Sx8DlOC3ZQeQ8X11XoeF9aR4jE2JpuWuuxXNPfTYRiCcBqZjZi8iee8dz9t1734xF3wKEhRsv4uZRTm3rsmUCL/3c9VhK22W04SYTHItdJ+VgG1mlInvh71k1ITsObvrRwVK7+VTM/PfsZ1v6diz6DvCssWMF3/XfXei6jVLbb/bi82DB8/YzgZWEpsekXXjcgccvbdTJqhRZE0/p3aIvM2hKHkVE7xceY8GUbthjuIubsUsJkH8u9QN06Q/Ae5dXdmylngTdV9M++d+ln7scSw9JL4BF3xEV0qtI6bi7nE8moNuK6rb7a9unRX6ARW9Mz0hF7zSnMT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnjFu+fvgKzkKY8xXhi29MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjesb/A4OocJPms6K1AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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zGery3W4XJycnODk5CXF8lpVXQY6q8DSWtzMAtVuPX20sr403DEcUMfKT3Hr/bN5Df+7YD076ZyJG5Ly/A7JHXenAS510qxtpqKVVQtCqkoAcctntdkPyrtvtBitv953Ta9BY3jbW2N177FhtWw3g56n3opuB6KKjtX0V+dh77CW5DwMn/Z6wVkf/zUYSYLv9M5adt73wJLfW5En82NZZmjiMEd6W6PLKaNo6q+O9Gc/TynORIelZFtTGHV63fqadkW+z+ta1V+ji5Pj6cNLviVhMbltJAURjUx18oZbdEt6693StY7p6Ep472FCE0+v1cH5+Hlx7tfIKXYSYvOMmHiS93bmnWq1ujc4m8VWMw+uipafiTif5AGkprU7f5c9jDTt8n1ZNHMXgpN8DtqwV+wpsz3ZTC5+VmbcNNPr72IhtWlQSkCKc8/NzXFxcoNfr4ezsDN1uN3N3Wqvv1117+v0+hsNhUN8BT1Zey4Fs1aV7zxKdFRfpHD0NAXg9mojkvY6VPFUIFBtJ5tgNJ31BWMuuGWkrg4259XaQZExHb3fB1Vq8Lio6oabRaIRM/dnZGS4vL3F5eYmLiwucnp4GQtohGboQab98v9/H/f19autrlfdS2sv23JOTk7AZJifhaOJOFy5Kb7ko6iIZI3hMA2FDgayQwJENJ30B2IRSLBGnlhhIT7ONWT2bnVdprWa6bSONlspo4Q8PD3F6eoper4fLy0u8evUK5+fnoS5vx1rromU76bgDbr/fx3g8Duo7na3HTTBZGaBrDwDL5TJk3+123kmShHO3UloluF6r/b1aeduP7+QvBid9QWiyzopo1P22C4QuDLFynCW9Tri1dXgSngMxDg4OQqvs+fn5lpU/OjraiuVjmXpa+Pfv3+P29jZsez2dTkOrq80bsDLAJCHdeg2BNHPPRCDwNFarCGKuvRKfYU5M1eeIw0lfANbCx7TwusEjkI6X1cpbwttxV6ql17hW949nPM0sfa/Xw8XFBS4uLnB+fh7IGNubTs+LMlsl/O3tbcrKA0/lwE6nEyS9Z2dnIUnYaDSQJElw46mxt1tvAU+luLymGZssZTLP9upbV98JXwxO+h2wFltd88lkkqql0xVWoYuNbbO65XS7KbWCfMhp5Smv7Xa76PV6gfCarbdyWxXKWMIPBgPc3t7i+voa19fXuLu7C3vdq5WnhecxqeVvtVqoVCqB1LPZLNw3uvi27Tfmvut7bDlUcwF2Uw4n/P5w0u9AlmhFX5PJJDWUksTabDapibVWUqtddCpxJelVzkryaZaeMTwtPBtqtHymijsuXOyeGwwGeP/+Pa6vr/Hu3Tvc3NyEQRm08jwmCa/hQ6fTCVaeWX7VKVgC27jclttipVANF9SVdwv/fBQifRlFEXyQVqtVyGwPBgM8PDyE7DZr2SSxJvhIeGbHbXZe59plNc/wPFQEc3R0hNPT0xDDk/DMorfb7VQcn6W2I+Fvbm7w7t07XF9f4/7+HsPhMJy3ehZnZ2e4uLjA5eUler1eKAVWq0+710yn0615eDYvERunpQo+IN3ppy4+PyPLtXfyF0Mh0pf9Zk4mE7x79w7v378PRLm/v0e/30+JZ5TwmrxTxZ2q8FSXbsmhiSrV0zOmVpeepTm69NbC85iz2Qyj0QjD4TBcx/X1NW5vb3F/fx9m39Eq063XReby8nLLynMRIzG14qDyWd1Gy/bcA9vlTSs5zsr2l/353Bfu3u/AcrnE3d0d/vKXv+D6+hrD4RAPDw9BuMKJsEr4mMyWC4CW47TpBkhvTMF2Vc6qV6Udra0m05TwmiyzSjsm7ZTwdOm5KHGYZrvd3nLrKfhptVohYafjrK2GQVV2JLptw42VN5X0GjKU0ev80Mgl/WQyKeWNVnfys88+w+9//3t8/vnn6Pf7QQ+v02vUNbdqNLsAqGzX1vXV9VXCsyTHDD2tfB7heVy688PhEPf397i9vQ2Ef//+Pfr9fmqcNb2Ler0ePAvmD5i8Y1UAQIjjrX4h5rVQussQhFtfa6KROQddRJn8i5Uz7cuxG7mk7/f7+MMf/oA//vGPaDabKenkSwcFJldXV/jyyy8xHo9TmzXamjyA8DOSWy2WJvlicSoJTwuo7bEk/KtXr/Dq1auQSNMsvSW8tsdSdMP4/ebmBnd3d2GcNRenSqUSPouNO5bwnJXPMppOxNW+AhX0aKmR+gIlPu818x9WdsyvumiqJ+BDNfbDTkv/6aef4tNPPwUAtFqtkNV9yaDbmiQJLi8v8f3vfx+tVis8YOqGazxp3Xv93j6QVkbKQRQqq2XSzlp4JTyTdmotVWX38PCA29tbvHv3DldXV1tlOYYcKnRh8k479ZTw9frfHhur7LMiJV4XgFQikk06XDzUiquKT8t8/Gqn/1riO3Yjl/RJkmA8Hod/z+fzb/yEvgvQ5NHd3V2wUloXpqpME0n68Kqrq1Ci6+RYkr3T6eDo6Cio3s7OznB+fh6+6sx627/OujgJT+v+9u1bvH37Nkp4LQ8y3maJzopw1CUnyazSUNV3KpeloIik5/3kZ2jCkZ+nvQb8PFsBsR2Ibu13I5f0lUoFh4eH4d+tVitaVnqJ4APXbDYxm82CbpwkzWr2sLVmQjPNTGAxbqcrz/hdZa6cb6dbUdneeE3azedzjEYj3N/f4/r6Gl999RXevHmDq6sr3N/fYzAYBHEQyaEuOJtpeA5q4Ul43h+dsqMiJSWr3ThTQxKKetRa8xrUvefCVq/XU3oHO/dfrb1WDRxp7Mze64MbG3f00kFLliRJcEeBp24v61pr3diqz/geS3ZtYLFE59QbxsHMfNuOufV6nZLV3t7e4u3bt/jyyy9xdXWF9+/fYzAYpLaj4nXw3Or1eojlaeEp9tFSoNbRWcHgWC2SnvkB6vU5pJNdf7w3ttSnKkaeoy5Oedt62dKnIw4v2e0AH3AlsNbPaW2ZZWaXmarrtEFEh0+ofp6xM+P1brebin11tLTNeGt77GAwwN3dHa6urvD27duQuONmFbSMQHobKrr1OmaLmXomcXk/tO6vY7U4YYfXzL57Ep7XZK28zd6r9Qaw5ZG0Wq0t70IrKU76fDjpd8DGifow083mA0ypaGw2vIpsNEmnO89QN697xzObb/vhVaqqcTzdehKeHXNKeHV9GW602210u90Qy9OtZ+yttfSYJFnHaqmKkC69SoQZyzMmtzkRvTaiUqlgPp+jVquFUWKTyST0P6hAyq19Ppz0BUHykridTidYLcLW5oGnDjVaUsbtdOXpRisp1I23E2KA9IBN3ZSCbj1lte/fv8dwONzqDeD16DBNrclrdSBWGeDxqO7j1tU6HYfXS9IfHR2FZCA9I3pQqh4EtrvsAITQiQ1PnMdP0uugDid8Ppz0BUDC03rRNWe/uq2Pq5WPjaXWDShIdsa6at3tsEglgSa9LOEpEyYZ1cLr9fDcVM9vBTi2pLZYLFKEJ+lpbYGnhY6k56vT6YTrWi6XuV1y+m9d4HSxUuLHZgk64nDSF4S6wVpW44MMIJWFBp4efpu95otkp3Vn6S5m3W1VgFp+7Za7ubkJQzAGg0GwvjrAQpOJ6tKfn5+nevHZpaeS2uVyubV1NY+jWXudzEuyq6ZAcx12AAbPj+eq8wkYEvD/Qi29ag6c8Plw0u+AltlIFIpMaKU1q8+H08bxOkiSL+vKs5RnS4Hq9uoQDxKe9fibm5sgrWU/vG1S0ck7TNrpAI7j4+MQxwNPsmJt2CHh+/1+yrVnWZOk55RczU8okWP3WcdyUQhm9fzA3xaEdrvtlv4ZcNIXgD6IasWstbftn4zlVXPOF8tZuiuMbgBhLbzKVO2IK/bDa1nOKtp4DVyIWJbjTD3bI6/ZdduD3+/38fDwkGrUYXxuF8fYrjokrk246Y63tNj0UvTf9BKYzNO9AZz0u+GkLwASUON6uux29xgSXoU8LLnRomtG3sbuSnYgPpfeEp5Ju4eHB4zH41StXKfNaHhCC089PwdpsjynMlcSXq38cDgM03KZsef1cJGzyUhelzYcqWek5xnTh/A99AhIdoYXuouOIxtO+oLQeDPm6vMh1y41K7PlA5014w1Iu/JakosRnnr6m5ubMLKaSjslO7PpSnidq9fr9VJuPWNnWniW5QaDAQaDQaj5W8IzV6ALnFUNap1fS2yaXGSfPu+Dberh7+zIMa/VF4OTviC0Tk+Lr/E6y3dKbh0UkTfI0bq56s6TeCT8w8MD7u7ugoXXTL1aeDa6qCCIZTnOuWO2XqWxAELjDLv0dAMMCnGm02kqfFAvxvbKax+CbapRlaf2NGiyj4uGHbKp8wZjE4kdcTjp94Aqw9SCM27XhFyM7Fnxuq1PqzUk4XUuPafW0qUfDochrqa1paVnd5uOytbOOd0MA3iSHVN4MxwOgzvPpB2HbQBIJR91sYtJa7VBhzJaJSrfv16v0Wg0wn3Q+6WzB+2mIC7OKQYn/Q5ock6zy7GxT1wIsghPaEeYWjVr3XWrKd2I4u7uLszoe3x8DKIYDR9UGxBr4LEjspMkCRbYJuysAIdZdb1OvXYlvHbykZSxrjzNQdC9V7mx9RYYgliyO3bDSb8DdDlt8s2W17Trzn4PxPem14dYZ+rREo7H49RATr5IQt0ggyGHVhdo3S3ZbeJR++E5NFMHgLIVV7esZo+7JuA0YUdyM/GmTTo6CViHbgBPI7KtAlGHdPB7O6/AUQw7Sc//ROBvoosyttZqNxzdYbshZJ6rzn/rQ6oEt1tdMZbmdtHD4TC1i6wSkHr/2K43at1V089GISA93suGEfQmWBGghWfoYCsV+pkxwtuZgerx0KJb6H1Ur0g/l/BW2mLwIRoF8L3vfS9kuzl5VseHKXHUJbXuqd3pRptG7Gs8HgfiWwEKR0PTFWb7qqrrbBOPFcgAT+4223LpVXDSL8U33MRSu+fsEBB6Daw2aIecZuvthh5aFdHhHAC2PCINgazIh++PiZscaew1RKPdbqcknS8V1IYnSYLXr1/jhz/8IS4vL3F8fBxeurPLcrlMbR1FoYqdaGO700ajUXixLEZJKWvQSnSNfUk+6ts5Gluz8lQM6jbSdJ/5/6i73TBLz7Ic6/A6Q4/XpUlMG8eT8Ey4xRqRAGx5CTGyaz3fluI0v6JfnfD5yCV9p9PBr371K/z0pz9NJXzKgvV6HeSsbIZRoQ2AkHTTkpI2kvDnzMKPx+PgrtOaWmtOommJyrb22s0kdXsrluFioYi2r/K8dF96Jgc5CFRLZJqw01o8rbyeqybslPSqIYjV5HlPYwNFbautaiL4/2JbkB3byCX96ekpfvGLX+DnP//53+t8vjMgQd68eYMvvvgCj4+PwWXXh5QP82KxCA+xnUxLYU1Mxqpxuibm7IOuyTISXkdj63ZTSviserltkSXhNUuvvfcqsSXBNFywjTnMUdgYnteiGXq669pGq6U+9R70XqhWwklfHDstfdnxgx/8APV6HVdXV0H8ohloFZdovRp4cm8pY1ULbxNzKlRRN9Z2nVnCc9eZy8vLrTHVmlxjQszKeXlOqrSjddb2YN0AgwlD9SC4oDDhp6Os2IwDPG3TxYWEhCbptXSo3o7eD81lsFKhXXxO+nx4yW4HqtUqDg8PcX5+HrLmVKPpgEZNdOkkHVXT0aIyhrfdYbbWrDV+ZudZd9e95bjzTNYQS/U4mFOw3XI6IVfHadGi8vjaMGSnBmkFgqS3O9WQlNZd12SnLWUq4YEnBaBWVZz0xeEbWGZAe7xbrRaOj4+DxWO2m8S37rC2wNoSHGN3KtvoLWi5zzbKUEbLrj6O2GL/uyW7Wnfbncfdbkh2luW4ANGNplehOQSq+xjPazuxJbzmJZh85DmpyCnWTRgr6wFPnlS9Xg89D3YqjyYVHXH4BpY7QNLx4VutVsHCURVGEuumlLpppRI/lpFXsgNPgzfVheUDrvPwY9l5ID1OK2bhSXpV9SnhrbSWJOeiUoTwqrTjteWpGTURaMt0+v5KpZJqdNIhojqxt8zP7C64e78D2lWnCTu6qEpstW5Ket2XXvdp4+frsUg2Eo0POMtyOjxTE3UAUu6x1seV8LGynApvVHSjMwRspp7H22Xh7T20cwXsYA29BqvUAxDCDOT+RagAAA3QSURBVHo92j+g04kd2XDSF4Btl9UHiw+pzVZrok+3adKkHz8bQCphZmNoxq0xRSCQ1rjbqoEOkbS6AB1bzXMi6VXDrxUJjcf5+cxrMIbXJKB6LbwWHa6hugGeh1UyqgIwNnpMF0An/G446XdAFWNWY67xqO0VVzefvyOZqNxj+2uM8NQE6ORdK6HlgsLvVSCj+QSKgajys5Nm2G+vLjjPRa9Xr1MtvCU88JRlp8fCScD8qloHCn9UuGPzG3YmAF37mBbB3ft8OOkLQhtpYi/9GwUtGfvESXD+vcbO6kqr+MXuMMMEIktlauFsndwq/BhqMO6nu06LbNtjbVurehA2S8+EJICtWXn0UmjpbV6EXYL2/vEcGPIo6bNKdU74fDjpC0CtR1ZCqtlsbk1usdZKqyBq3dXC87PoWusxAITYm8S3oYZ2sjHc0K2fNBseawm203i1HJcVw1uX3iYgbcJNp+JSwszFRK21dtxpmU6Hi8asvCMfTvo9ECO8WmXtHddk2Hq9RrPZ3BLc0MVX0jOxZS2Xyltp5fUB13IXyanhhXaz6fnb+X0qLtLefhXNxJJ2hN0bQDfC1B5+KvF4LQxHeC9iE3VsIjC2IYhjN5z0e8Baeq1ft1qtEKuT7NpVFhPdKOHzGkZUpaeTYfRn+ndW1WcXK80XaD9B1gCM2MQblQzHOv60tKhTejhPH0Cw8upBNJtNzOfzEA7pvVL5LxcqJ/z+cNIXhIp1bEmLxGFCipZ6F+ltDM33AulZ7ypHtc0oSv7YMXQxIdF1Hj2Jn9UPr//W8qOdeMNr0E0w2fGn22RpiY4bV9Cb4H3UcVz8fM112AoKUE4B2XPhpH8GlLRMvlHAQ6IpQa3k1FYDstRp2n9vS4EqZNEH3opqeH7MmttdZ2LlP22ltYlBnkusDs8Ynn0B2tevijkeR8k/m81SljxGeivmsR6PE78YnPR7Qi0+SUvy0zJqXZkxvRJfM9IqdNEaOMmnNXDW+2mFSXh1g5MkScXmuj+8SlaZBKPlVIIr4enW68Jj9fCxHWqta2/3xuNnrFarVF4kZukBpLwhIF0m1XDGyb8bTvpnIiuTr8knAKEObttHgacHl5+lDzBdXjtTjoo+1evr52umWzeQpJJPBT6avNOSHJCuBKhXYTsAueDpVl86scc2w9BC85zVW1LyayWEx9IF0o7esufGYzi24aTfE3bgA5DWzDMxZQeOWDeU1lnfr9ZLJ83oQx07tsbUuoUWCX98fJxSr+noLHXr1WW2pLLZdPUkGDpoec56E3os9Ux0odLxW/ScWKLk+3gudr6ghlFu6fPhpC8IS2D7M32ISXgtuVliayZeQQsbe4A1iah1fw0xdLNM69LrsAk7TYfnowuNWng9B8bljOMZPqj4JraHnbW8McGTyn15/7SCUK1WQx8DZcS6u42797vhpN8DGjNmJY80VreKPf2crPfbn+siwn9rfoCLgB0qoQS0JS4SXZtctEFIwwk7qosuOo/JaoDdhVfLf3rPeLyseDx2v9XL0cRep9MJMw5sctGRDSf9nthFfKsmi1l7/Zysh9S6/iSZNqXwd9a111KcjdvpSfD9WhbUkhylulqSY6+A6gvoWWi4oGVHHo8xPH9ue+btEExtT9b4nkq+Wq0WSK/DOz2TvxtO+j2ghLcvIO2uqvpNH1ZaaivLBdLJKj0m58nF5L0qXtHNLrRtVYmtbjI/X0lvZ+oDT+EDycaf8VjqxgNPgy11SrAmMrUMaF/W21Arvl6vQzm0VquFPfX4t+7eF4OTviBsTJ+V0IsJRixhLWlj789aABQ2Jqal1SEXSnaSkJ9tY3m19iypkfD8TM2m6whsIJ1om06nqbIf/4bXQdLbcd+M0a3yT2f9U9bMBSrWV+DIhpP+ayBm4fXf/N6Cf0s3FsBWHkA/M+vz9Ge29k9i0arGtAGEuuGawON5kbBacqQXo9abIhsNGxaLRTTGV9Kz5Zcv3fRDSc1FKEkStFqt4B3omDK39LvhpN8TsfhdXXrCZqAtqWld1QJaia8Sy+YEYuEB/62LifVI7OIR8yZoMTU3wWuymn4lN4+tSkLV9fOaVPSj8wM5PJTf09Kr5JiLh04ijuVVHNlw0u8JSzwlMpAefxWrRzMbT1c75u4zcaby3BjxNRmoZUAtvWn2OxaKaJ1cvQBr0QGkVHJacuSxVEBkZbV2PwBLes7u4wKg/f9arSDxdWHKuj+OOJz0z4C18EpOzYpbV91a/Dx5rq1bx+r9mv1X8ulWUlb/r0RRGTG/8pix6yViOgI9byur1UYZfg7bajnZhxaem23YEdp07dXzsLMIvOOuGJz0BaHucOxla/EAUgSzUDc/VgEoYrXUimvvu2bGVVGnll4Jz3IcqwyqFtRsO+N2OwZM4/9arYbpdJoiu5JeE4z0CNS9Z0aesbzeG9Xha0uz9STc4ufDSb8H8lz7WLJNSaaxu0UsLrWWPObak/C05rTwKlHVOrcq60h4df1JYrX2OllXvQf22tvPpcVVGS9fqr1nHkBn+DGTrxuIxKy6dg2q0tAHYxaDk/4ZKGqJ7d9qjd6+NOGmIQITXzHSa3MOXfhYz71tSNFynT2u5iE0dufCoSGDehm8VpbndCHhVw2B6JWonJblOZ0crHkQCpBU489Zeb6PXXE46fdE1gNlCWSJpVbbfo7+PlaL51f9nu+xijZ7TEvqrEy3Wnn1VLhwaHKQoYKGJlrB0JwFrX6l8tQJaMVAmrSzQ0f4WTpC++joCKenp1u7+/hwzGJw0n8N2Oy5WtLY72MiHX1pmS2rxGarAwqboLPNK/oenosq3ZbLZerc1X3XxSXWKMRwIMu95oKioYjNO2jFgZ+lE3+63S5OTk7CRB5uye272+wHJ/0zESN8jAxqEfMaTbShJCYw0TDAPtQs8SVJEvTxMcku21TtudMtt4uS1cPHMvZcXIAnktrpvrGJvSS+1eDrtVar1dQ+fpzI0+v10Ov1cHp6Grb1ypov6NiGk35PxCy0WkBrfe3fxVzsvGMB2wnEmAvLmJm6dCrp+D2teGwghl24NFcQCzv0HEh4VgBs1l7JyMYbvS+xxYmfy9ZdWvjz83NcXFzg4uIC5+fnYSKPjeed+Plw0n9NxEitv9O/IWIlPibsrAeg7r3tN7efo1aUI7lpcXXghMbk6uJrQlDdeD13Hld3v9Hputpxx8m3KsjhrH5ek1p1Lbnx/ZzGQ8Lr1tzq2rMy4ITfDSf9M2FLdnxo7cAJIpYl3+Xy22SWHseSX0thTJTZkdW0ntPpNJyLhhVKfrX6QLoBSPe5Yxsve/e1p55dfsDTRF0tBdpjaCzPuQAkfK/XC1a+1+vh5ORka0srPUcnfzac9HvCWiZ2mgFIJaHykJfEy9KSqzw3a4ouP5sWmx1zzI5T+ELiq5fAPoAkSYJlVhJZtZ3uNKPDNpWI1CaolefxddHi5hb0bLiYMEtPK9/r9XB2dhbm72Vl7Z3w+XDS7wEbb/LhBxBmusXKYfp9nvWPlfYIJUksO0/QW9BJOCyLcfda7WJrNpuYzWZoNBopUYwq+Ljo2M0oOXePM/HsEEy18nou4/E4tQU3+/eTJAm1+E6ng5OTkzBVV8nO4+jiYu+DIxtO+oKw1p2z2WmZYqOabI09z/W0NXX7OTEXX917/Rzb6aYad5Le7mCrs+Z0IAXwlJXXgZskO4du0soznud50crbBUjVdzwegLCQ6jhtHkPn79nuPU/iFYeTfgdssokWnqUuJXzMylvlnf4shlh4YN+/q/NOE3okG4mtVl571nVYBcMDHlsz87TotLh07e2EXRX9aIlOFxgrt2UCT0MH3ZhDx39ZCx/zqhxxOOkLQuNPPqDcuipWdrJfY5Z+n4czaxGxn2HLiLGWV93CWgkYs/I2lNFtp3Wved0aKxZuqCjH7tSjyUI9lm2o0bp/LJHplr4YnPQFoBaWoJsfK6/Z9xUhu9avs35X5LNsbkAJp1tjqaXVCTRav1fNuxJRd43V7bTzPA9dhPSrCoT0eLp9trb8WqJzgXHCF4eTvgA0g81/67BIIH9HlV2u53Me1rz3xBKDVlJru+asYEevU7vb7D72MSJmnUusNKl5EDvQw75iIZKX6PZHZUeJyYeN/X9kZdr190UevA/1cBbJCRTRBtiBGDYvYYUzMXHQLuJl3btYwhLAlsuuv4sdywmfieiNcUtfEPYhK1KP/yaOXRRZ5OfXGPliXkssh5CXkIyVJGPnlXX/ipLaif58uKUvIfL+z7N0Bgon3EeD6H+Uk97heLmIkt4lTA5HyeCkdzhKBie9w1EyOOkdjpLBSe9wlAxOeoejZHDSOxwlg5Pe4SgZnPQOR8ngpHc4SgYnvcNRMjjpHY6SwUnvcJQMTnqHo2Rw0jscJYOT3uEoGZz0DkfJ4KR3OEoGJ73DUTI46R2OksFJ73CUDE56h6NkcNI7HCWDk97hKBmc9A5HyeCkdzhKBie9w1EyOOkdjpLBSe9wlAxOeoejZHDSOxwlg5Pe4SgZnPQOR8ngpHc4SgYnvcNRMjjpHY6SwUnvcJQMTnqHo2Rw0jscJYOT3uEoGZz0DkfJ4KR3OEoGJ73DUTI46R2OksFJ73CUDE56h6NkcNI7HCWDk97hKBmc9A5HyeCkdzhKBie9w1EyOOkdjpKhvuP3lb/LWTgcjr8b3NI7HCWDk97hKBmc9A5HyeCkdzhKBie9w1EyOOkdjpLh/wHd6U0bQcI2KwAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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lf/bsme3v7y9Ibs3ywz95ThGe5/KE96o+3Zfv+mPCUtecy41Qr8Csv/4u8DQE6Z+IZQ9dztLw/XRxy5J2LM3pdxKe2nqd07v1tPLerffqNybMqPrTeG+eT+cR0Tlii5UAhikkvs+8+zCB31fOogfhPwxB+jWRewj976s+7z9H6+rHUHHSbq48x+PJMnLA5d7enj179syeP3+eknftdju76SSvQ4RnAo9hBCsDmoOnKgDLf/QaOMSTSkGz4tARWvyyEl0Q/sMRpH8iPHF9E4l/H39nnMr98Ki607RZ1cc5gJIuvdljHN9oNAqEVxzPEt0yt55lOp1/OBwWSnQipSy8dsTRgqKBmswNKFyhft7M0nu9fkHnWQYfRq3zmcDXCNJvAE9yn1k2s9SAkkvU0frRnfdJO4pwZO1ZMqPVlWWt1+sFEc7R0ZEdHx/b8fFxQWOv2jnviSOw1D5L9R1lsYrZ2Y+vTj3JefU98P5k6efzx2m2ywQ2PsbX9xok/3AE6TcECe+HRZgVp8fqxcWBVp2iG8ps5VYzpmcbq1kxAcbEnVz6Fy9e2PPnz1PyrtVqZd169uerfbbX61mv10sbYkp9J0LLpVd7rsi/vb1dqKWzCqEQQeelR+SlyXqPJ7vuW58pa00OLEeQfk14K89Z7BxLlasdc5YdrbqsN7P0vjynFy2l2SNRFMdrl9nj42N78eKFnZyc2PHx8UIsz6y6L89dX19bt9u1q6sr6/V6qZOOoYtkvdoKi9163O+O3oNe9Hx8Qs+79bnqB2N/vT/XpRhYjiD9GvDyUCbe+DJ7fDDZOOOtHoktVz5HdIlvOBRDx5XkVTH1s2fP7OjoyF6+fGkvX760Fy9epFheCbZcqEHC93o9e//+/QLpzR5Vd9r1VpUBeRLaZkoLmq9G+DZcX2oUqBnQn+kF5BJ//hiB5QjSrwlPeMaqssJmRbfbrFin9so6vyecyM5ZdyKPiMDM+fb2dqEWf3JyYp999pm9fPkyWXmW6FhGkyaAhL+8vLTLy0t7//693dzcFDaxUClQ1p2S3larlVpiqUPw8/p0D2rDJbw4J/fvXOxo8ddN/gW+RpB+DfiYnCOrOJDSrKglp5XPxewivEjPGjzbZJVEEzFUKhMBj46O7Pnz5/by5Us7OTlJsbym4vjhGBQBifAXFxd2fn5uFxcXqW9ecbh0/Kr9P3/+PJF+b2/Ptre3bT6fF4Zk+p10KMrxZTcukNQN6OVdfk/4nAgqUI4g/Qowjqd77pNsTLCx1uwFNyrHMVEn667knt+uiRaOFldlORFeyTtfopMl5MKl0ly327WLiwt79+6dvXv3zt6/f58SeEqiacxWp9Ox58+fp9ezZ8/SdtG6/9FotODlsBlI98Of/vtmZSSnAixrWw6shyD9CjDZJdHKzc2NXV9fpwGRzEzTyqu8Jm+A6jrv0usY3IpKx+PGjsrSy8U+OTlJr+Pj4wWpLSWt8/k8iW9Go1Fy6d++fWtnZ2fJyt/e3iZXXZWBg4OD5FGcnJwkHb9ieVl5Zt9zpNVPuuhejqwQxFv7XHzP4wbWw1qkr6IKSg/SdDpN1rnX69nV1VVKdvX7/aRYoxtqVtwgIqejJ9k59SZHErr0GmxJAnqXXnPsvYWn+Kbf79vl5aW9e/fOzs7OkpW/vr5O4Yoad9rtdrYUqIy9CMqsepmVZhce43N9Z2b50MDPzfc6fX5fgeVYi/RV/zJvb2/t7du3dnFxYZeXl9m4V2CyzO8K41/c8jrXLWdmiRx0sVWW8zG8LLz2lleIQXmthly+f/8+ufTn5+eJ8KPRqFAh4ACOFy9e2IsXLxasvPIQ3qvIueUkvTyRXBlR18yFIEd8oorG6SkI934F7u/v7fLy0l6/fm1nZ2c2GAys1+slWSzdev1U3My+dJXGWKendc81z6iBxbv0qsPnXHpm6im8GY/HhSz9+fm5vXv3zi4uLqzb7dr19fVCHK/zHh4epjieQzhUl5eQpsylF1m5nRY3zMxVOvxgEJ4rp4wMwq+PpaS/vb2t5JQSPcRmZq9fv7bf/e539vr1a+t2u8lNl0vP99K1ZVlPJOc+c7TuJDxFNxxbLff65OQk1eJFeM6u9zLYyWSSdPRXV1cFwl9eXiaPhRUICXnoWbBEp4y91HfUEVC0pPsRsRWiSNHHYRu6ZnpIXqvP/fl8j71/BcqxlPS9Xs9+//vf2x/+8IfkMlbhCxWRZ7OZnZ6e2l/+8hcbDAYFkYngS0dmjzPmZOmpQeeDrXMxSaV+eLnziqclvFEd3mfpSXid37vz5+fnKWH3/v37lLSjx+JFOGrNVd1fC0yj0VgYbMHFzOwxCcm++93d3bRppogv70iJRv9d+W2z9f35xGcVns2PgZWW/quvvrKvvvrKzL7WXHPW+acKlaDm87kdHR3ZyclJqkXL/WX2WS+zIgFk7fWTllDwZGfCjt1yuaSdXHqftFO1gDX4d+/e2Zs3bxLh+/1+ml/PvgEuOBymScKrO44xOCW3Cln4/XDTTBF+Z2enoB/w8l16Qloct7e3FzbwDOJvhqWkn8/nNhwO05+1JfGnDpKy2+0muSszxkxIieQCO+n81BjBy0mlsFMc7a3sixcv0jAMZunlgem6Kbq5urqy8/Nze/v2rb1588bevXtnV1dXabItrbKuhdp6eRhs2lFrrgjGCgU33BDpdX0iut87T9adFQZKmynoqdVqC6IoEt/H/FVPQJdhKem3trZsb28v/XlnZ6fg2n7KEEG3t7eT1VEmXXVzrxnX5xhz5spwnBNPsrfb7SS60YgrxdKytmpukWtcFsN3u107Pz+309NTOz09tbOzsyS8kfqPOQlaeTbv5Cbu8D79VlusZtCtl6BIll770tPzIfG9tFk/6/V6YafenKgpsBwrs/f8Ev2Ipirg7u7OzL4e+FAmI2VDDl1SDp6gPpzjoqWf52YUIrkGYIjsIoyfYiv39u7uLtXgLy4u7M2bN3Z6eprcem08SUJR3dZsNm13d7fQTMNNMbx+P6dOFOnliishqPZb3YPyQxy4mcve+++8VqsVGpb8ll5VTDxviijZrQAJzBFPjMPpXtMVNXvM6Ot3kavZbCYSaJspZsnZwSbScaAlPYvZbJb64QeDgV1eXtrZ2Zmdnp4mfUGv10slQ4Yiuj4v79Urp9/3hOd+9RQqqdwoD0auvb4zWWZf9WB8zwVV332z2UznzW3eGbH9cgTp14RPdvmauIhOQQwJRVe31WoVEnWM3RVDy8L6XWVZJWC3nNx6xfFnZ2dJdDMYDApJOz/kgw08iuO9fp8JNxKec/wkOJrP58mT0cIm0qsjT1bey3B9Dd6X5cxsoS3Z7/RTNW90UwTp1wSttOrMKl2ZFfdfp4XSZziLfn9/PyXqOK1WrjxjaC0sJAcz2j6Op7T28vIyxfBe9cduNd+xp4VHwzd8GCHCq/7PHgQtKHLrJRve29srTNfR9+W7Ej2oyKMQSNaeTUu5fQACiwjSr4mc/l0W2MxS0o7WKWfdPeHlxtOVl3WlK0/C08KrCUhKO0lrr66uFnaY9c0rytSrZZZhBd163Z/cell4zdGTlVfOh8k7WXrN0BPpFWb4xplc1xxJrOuv1+sL8wgirl8PQfo14MUlIu7u7m6B9LTyejDlFShBdnBwkGJ4ufF05Rm3k/De5eWushTfiPCsw+fGbLE8KLWf+uQPDg5SdcCsOORSQzcGg0HavpqSZHkQFOJQjMNdbEl2TsVhOdO32er7rdVqBdJz+Ei498sRpF8BNr5QNHNwcJDkqMpEi/RmxXlyys5rzJTmy8my0wpSmpqzdqssvIQ3StqxgpCz8EogivDqnqMAR4sMFX7dbtf6/X6aDcBWXC0oFOGw4kDC83tm6dDX3c0sJSD1eb/zT1j69RCkXwNUy6n8JALLeskqCczQi/R6sXQlQiju98k6kp3NM2r1JeGlpaflpTvPIRyS96p5R91zcuuZXddMgOFwmCx8r9ezfr9fmMfvN8HILWS6Jy0my4aFsC9B3wM1AL5rMUi/HoL0K0DVnB5kEllSWL9jC+N/WXO9RIZVZDcrClbUqaek3cXFhZ2dndnbt2/t/Pzcut1uavdl0kvHlRWmvJftuVzE2D+gMOL6+jq59HLrlbHnfXO7K92f7knEVS2eElsurrTuLE9SsMPNPVWr91OHAosI0q8B3wijeFWuutxhas39lk/MxosInuzMYNMakvAaUy09vUpzao+lIo7KQW5QobKcLLy0/O12uyCNJeGVtGPyjp15VCv6EqPuh9p6qu584lPSZoULdOtJarYtcy5BxPTLEaRfA3SPFQ+LQBTP6N/95o5+G2dOjPFZef1OdZrm66mXX+2x7JjjxBszK5yH2135GN7vc6d2WcXw2mVH1t3vtqNz6Sf75JXrYEeh3+rK75OnmF7374d6MimphUM1+twwksAigvRrgEkw1tspL5W195Nh6L4vI7r+zIdbDzR3nlGWXlN8er1eYfspM1u4Bt9AQ4mvVHJK3MkSKzMusnvCM2GphYXeCwVEWkjotXBnH79t1nw+X9AmMIsvYpfNJwjCL0eQfgVYVtJD7fvCZSUl1qGrW0Z23xHGkh+toVxrleX06na7hbhaohh6GrkmHgmBqKmn+IZyXiXspLpTCdD33tMtp0uvRYjtr37XGz90g8k8hga+XMlBJUH4zRCkXwEm5WjhRXj9Pfdn9wTwlp3xLZNadFclM1WJrNfrWbfbTXvMyZ0nCeVZ+H58tshKeMM8hKymhoAOBoN0Lk3WGQ6HibxKEFJezPulSy8tvu5R1lnlRN8GmxPnaGFgu63vZozk3fpYSXo2jDSbzUq11s7n88J8OpGGVlIyWXareQvlrbmsE8doSVFGXTlHbYvoOfUZE2iM3b3qT4IiNb2oDq+FQ4lC1eG1wPh5gLxHhRC+60/kZqhCd9x3IOaESF5uzL9jSZElP79gBBYRQzRWoFar2Q9+8AN79eqVff755/b8+fPsdlFmj257mWVnnZ3kZuMIZ+NzU4zcfHyzR9Ipv0Cyy7pr6g3zDorfdTzV4VUd6Pf7qQ7PnWtJUp+vMHsc5MGJQZ7wuWQjv0uGOszY07L7WQY+jAril2OjIRqtVqvQlvmpolarpR7vL774wn70ox/Zq1evUjws0rP+7DPvfqgG3XZtmKHkmLrgcnva5Sbn6hqpb2eiLtetR3mvWXG2vK/DqxbPRhbW0n1pjptqsDeengwHXZg9ElXVDYUFsuA+7GECz/9fcZKRn1cYWMRS0rfbbfvlL39pP/nJTwpjkqoAxt2NRqPQGsrdY+R6MrlFS8/Z92xUySXJOA1GCwVdXJYO2QcgsrMMR3eeElhWB6jfZ1mOfeoinK9ecIw1LbRq/CJ7Tjhj9ph/YKnOzArXxlDAJ+r0PVAQ5BegQB5LSX94eGg///nP7Wc/+9k3dT3fGSi21Hy5u7u7QuypB5Cxph5gKs9877nIThmrJtIqTs+N2BLhKKNlK6xUday7MwTx/QG+HNjv99MCxP35SEpZZSkT/eLH4yp84QafUtdJ8Wf2taWWhFdgToBTb33CjwuQ1/eHpS/HSktfdajR5urqKoU2PjFFi8SykrekcptJMJXBmNEm5LrqJ/eHV2ecVHWcWsshll7cQjmvrDsJ74dt+DkCenEnHd4vcxMUzoi06kzUPYn0lN7mtgH33wmvicNGgvTLESW7FajX69ZutwvWR3pzDZSQC8stphnDq+RFcjELT5LRjWc1gAM4tHustpnyhPfxe87rEOEVw+c241TOgiIfEl4ehO6ZW3FzUxC69fV6PYVMbKjhT6+yY4aefQS+X195Fj+NJ1BEbGBZAj406pbTw0TRiTre6KJzS2uRQC9l6pnc8oQ3KyaoRDQm69j/TrGNj999XkFJRJblVArk/vIU3UhSrHyGREjqLJSF1/3StacHJGtOF92s2GfABB619EryzefzhTZnSqFVmQjSlyM2sFwBJc0Ezb6T9aQ+nckvkiBXdqPL6627XGA+3Bq+wWm5Xlnna+WcdkMLL+uucEPbWjHOlutON5oCJBLe6wtIeLr1ZsWSH60yh2Tou/FhAQeAsPdBpPdTggN5hHu/AnIl1fHlVWeK2f0EF85m9/3eLLl9UfIAAA2QSURBVD0pscVzcdyUxDZy6zlxR226sm5M1PH6dI0S+/g4XgSVFTWzQljBdllOtMkRPrf1tpkVyny+29DrGryWXrkFZutp5f1c/rD0yxGkXwE/gMKPgvauPElPd5+z8/gA+1IcLat636mfF9ll1cwey1ysHnhCcoilcgy5fex8dx6biHxZLbfRhY/hfdXBZ/6pDKR3QtIrb8DchsqVmmCkxGUk8VYjSL8GvOstMFPPBJ7faolus37mynEkGZNmIr7fqFKLjpml/eGZtGMi0Q+RVFceO/PY2uobhrjQ+Rieix0rEF5xx3uih0JpN/MbzCVxTgG9H2/lg/SrEaRfA9R0lz1QOaUYSeQ1+TqOJwabZnzyjO2v2nmHY6r055wVJjFpjX3PgLL+PgvOMiRlxD5PwQWOtf3cbrVK7GkRoZtP0uu7JOk1vUjHzE0ODuQRpN8QvpTGaTnMuCsHoIfVNyr5DjXff0+Fmd6rcpaIwvP6TL1cbYYZLA3yGnh+XQ8XKJFZFQAmJ30VQu54rVYr1NDlrSgPIW9FhFffvdfU08uit8BFJDdMNIhfjiD9E+BjcPbZ+7i9rBOMSSn24Csu9d6BjnN/f18ghxYDMyvEwww1fOtpLknnJbU6J8uT7B2QhfeiIi1kIrxccG5rpXukx3J/f18IMZjgNHvUCvhJuyHI2RxB+jXhrY5vPNnZ2Skk08qmuXjvQET33oJAPXtu+AY7+fjvtOg8L8mj6/YTa5kXUNlMhPd1eCbtSHiO/FbVQXkJzuHTAJC7u7t0DZTqcpH0QzdzMwsCqxGkfwJ8rVkPoqyi9OQUpvCzsqZMlDEuNyuWr5SwY/eZb0LxOQWv1y+T0vraOzP/Zo86+LI6PAdYyntgI5Df/VZlv9lsZuPx2La2vt7thiW8ZrO5IFjivwXZPwxB+g1Bi0/33pNeRKYeX//G9k8fi9JKsxIg99or1Zgr8KVFJf4ooVXffc495sQbxvFea+CFNyIgCS8hkTbQUJZdZUbq7SeTSWH+f7PZtOl0WujCy1UTBHo4/LtYEPII0j8RJD8TYX4ijC/b8fNmjw+sQAtPDbqfK0cRjo4nt5mLCbPdeim2ZpecmaUFhBNvfE+8F97kGnIkF+amnNxEQ+fQT7bp6sX5BGZWSGh6MQ/zJjmvJ1BEkH5N6EHyDxVLc7KuZaA1EtG9dVKsyzZVkt43oTBbzryB16crxi4rc3Gx4ogrn/3nqCuzYv2c+/zJ2vOcdO3NLFlzPy6cTT/K6pPsXsTjNfqB5QjSbwBPUMGLd+i++9ozrT4VdAIfZpGMYp+c5+CTXVStecKzz57y3VzTC/v7fY+/73bz55NSjgNE5dozDPFJTVZD/D3q+1HijwshxVBB/OUI0m8Inzn3D1mZiEfv9a6oPy6Jl1OnUbqr85Hssu6sjbP11CfueG3MI3gysTFIsbjieJKeOQO/000uxvY5Esbs8gr43XDuHj0QhjtB+uUI0m8AT0xabB+bE+wgY9bdu/h0/X25zVtJxtM58onoftdYP1SD2fqyMELnlWWX602vwvfae0Uf77ds8eNC5mN3LjoUHVENyPJhEL8cQfo14WN6Xw/PPWRK5PnjrPqcPmtmBZGOXF49/HqxDZeluJxVZ2uw/sxpOn5MlVlxiOX9/X0iM70LLix0xXV8EVmLjA9bvFfDhVVWXItVvV4vaAV892IQfjmC9BuAljjn2guM8RWzUmji3+vddX/OsvMxJhYBla2X1FUustxi/U5rzxZc3zRDQY/OyQpBbiIuvQfKbfVZJil9XM4FRwsDLbj6BfxnqRkI0i9HkH4NlCXwCFoyWSOzxa4x/VsuvmXyzy8QZeIbCnCo2adll5pPVlrnYthBEopAXFAUw9PTYOJNiwibfVQJuL+/T5oB3QtlvWwE8uVJH2boO4oE3tMRpP9IEOn1e655RD9FQlk+/xmq9ngcv1DkqgXsTxdZ1HbrY2WzYoKRyj8mCEV4NtXwnkV4yWrNimGDj/HpXfj2XM4jYKzOuX1+Hn4QfjME6T8APussF9534ZGUdLn9w+rfR1LliE/ZLv9ex+YUWb8A5cIIhhAU3fDaciVDLSw6Bj0Hr+2nR8CuPU4e4kAObbSh79bMFuL3VYtjoIgg/RPh43bFviSPuuwY2yu2zcloly0SOaKaFTPbXEhYIciVFXm+XLZc5/b3zJ9U8Mmqs1tQyUVfutNnRXrN76OuP5dQ9CIkX+/3zUqBPIL0a8BbSbrxbLwxK2b5RfycCEXurxYKHY/eA8m+TOxDC8umHLq/ObecixWv05fOFIqwHEahUG7hkmWfTCaFOQH67spcfE96WnOB3Y1aUGI+3voI0m8AT4bcixApRSSWrhRne9KY2QLx+FPH9Vl9KueYBZelzA3xEHkeHh5S34AWJ2b3GfPLlef5FEaQnLLszO6zuYjNPOziY1aebj0Xv7KNN3j8IH45gvRrguTzDyHdd8JbKA8OxPDCHpYHt7a2FkirfysjvHePGd/7UqIWDy0CtOjU4XulHpNsvttPXgz18RQZ+VKhb+xh3kDfoRYRjsvKjb8Owi9HkP6J8OTXa526vSy8H6OVi719RUBg3E5ilb1y7r1XCoqkDCG8Dp+y15ywRqEJcw1aPKjmk6XnWC8/6YfXSomxmnnUsuvn/gfplyNIvwGWZb35ErxM1y8InhS59+Vmx/HzlLTmlG1M6HGBoZWXdaeYRucmubkJBdtrfRZd35WSmPpJb4M6/5zGX8dQUlCE1xQebfahiTychqvPBvII0n8E5EiVy6r7pNqyTLu3+Ktie76Xn2ErKz/nE3HeW2HoQOKL6DmC+ox/Tj+v37mQUCmo98gjEuG1S+/h4WFhS+4Ygb05gvQfgDJCC16f74nmk3H+GCSUWT7Bp7+n2EcVgYeHh2TR/aKic5QtVLpOjubiwsZ8BXUFLKOxakERjw8rfHVB7+UAkIODgzSJR5t2yr2Xax/Z+/UQpN8A3lp5NZuXg3pS5x5yWvSy3z08+efzeaEkyBIfKwq5azZb7AJkLO/vQdel87BGrkSb3xnHT9il9Rd8clQlORFem3aenJykXXr9bj9B+PUQpH8Ccm51GZm9ZTUrJuhycT4FKHq/r9V7q0YyN5vNVBLMjZiiJ8HEGRevshFUug6dR9ci4lGBx/3reW9mlrL5LHXyPrlxp1x6bs0tK88JQCHMWQ9B+ifCW1MvXiFJSGS53H5oZs6a5s7l1We5jD43vJCWnaTQ+fRZJdVEfGb7/WKlz/j96dTZx+EZnEevY/umH1+y3Np63JV2b28vS/ijo6OUwPNZe74CeQTpN4B3QdnSKmGLt/TeZc+FB/53s8cWUp3Xi4BIfkGf50BLylw5PlrbSNGSSzegc9Iy8xo4M5/TckRCbmjhZbe6Ls3CY1efFjXNze90OnZ8fJwSd5qsq0GbfmBHJPHWQ5B+Q+jBlCurqa7q/iqL03OlPv8zt1AsEwJ50mvh4FBL39BCjftoNLLt7e2FwZcc1qGEHRc5jtPmIExuW5WT3LKdlrV5fW+K5blL79HRUaE858+zahxXYBFB+jXhH3ztaGNmaea9z97zs/y5KXxTjG+UERjXc5KtJ/1wOCxsTaWRU17FxwWLW2iL7LLGJL1iekp5FTJQcuu38TZ7VNyJ9J1OJ+2Qo+P7ef0+ax/EX40g/QrwYSLh9aCK8LlRTZ7s/sFc5wHN1d2XPeCMy721z1l6r3f3QymoiNNmFtwmmlN2mcQjWI+n3Jbn0sKivIBktn6iLnX8vknoQxfXqiBIvyZYStrZ2UkP6bLZbJ6wud/5c9m5lx1P8CU5ymZz3Wzeyvv99+jdKIbPbZrBshmTd6z5szeAG2ZocfFeFOf8eaueS2YG4ddHkH4NeDeaVr/Mped7l/30v686Tu79IphZvhbvt8byL9+R52Ww3KSzbPhmrjVX18OSILv+WCGgoCe3bx2P7X/qWoPw62FrmQjEzGIGkeUHYuYUeGVYRdhNUfYZT/yceIgW1+vofeOMSE8ievFNzsX24pucGtBXOZYlKsvKcWHhVyL7pQTp10SOUDmX3mPZw/j3elDLrpULlVcHeiHOsooBCblObTxXqvSVCn+cMpLnvrsgfCmyX0y492vCP2SryP5NXYtQdj1lJcGyxausXLiKiKuIt67EeN3wJ4j+dISlrxCW/V+vQ8RVfxf4ziHc+0CgYsiSPjoUAoGKIUgfCFQMQfpAoGII0gcCFUOQPhCoGIL0gUDFEKQPBCqGIH0gUDEE6QOBiiFIHwhUDEH6QKBiCNIHAhVDkD4QqBiC9IFAxRCkDwQqhiB9IFAxBOkDgYohSB8IVAxB+kCgYgjSBwIVQ5A+EKgYgvSBQMUQpA8EKoYgfSBQMQTpA4GKIUgfCFQMQfpAoGII0gcCFUOQPhCoGIL0gUDFEKQPBCqGIH0gUDEE6QOBiiFIHwhUDEH6QKBiCNIHAhVDkD4QqBiC9IFAxRCkDwQqhiB9IFAxBOkDgYohSB8IVAxB+kCgYgjSBwIVQ5A+EKgYgvSBQMUQpA8EKoYgfSBQMQTpA4GKIUgfCFQMQfpAoGII0gcCFUNjxb9vfSNXEQgEvjGEpQ8EKoYgfSBQMQTpA4GKIUgfCFQMQfpAoGII0gcCFcO/AYg6/cz/2kifAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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G+k/AsOyxPthPUd+F1eX5sF9dXaHdbqNer+P4+BiHh4fOte92u4HpM3y/npNu/crKCr7++musr6+jXC4jnU4HynT+52I8f35+jsPDQ5ydnaHdbrt4HLgrJ7IMyJ782dlZp99n1n5YuAMgcE94XH7l+7kQTExMBCy9YXQY6ceE75r6GEZ8n+x+HK39+hSykPD7+/uuPs4SnTbWhCXvZmdnUavV8OLFC2xsbGB+fh6ZTCbQUBMWVlBye35+7oQ/1AAQurBw6s7s7CzS6bQrA3KQh/+Z1QvyB4vovdCZeirptSTe+DDSjwG/C4zfa3nsKS89nlp4xvBhhD86OkK9Xg9k7MNcXcby+Xwey8vL2NjYwNLSEnK53D19vQ8t1VHiy00sdYHjOUj4bDaLubk5Z+XVhdf7xnvEmjyz/Vo69DX3/IwPEd7i+qfBSD8mwlo8/YeaX5XkYa60T3ha+Eaj4XTuOzs72Nvbw8nJiSP8sIefVnRmZgalUgmrq6tYXl5GsVgMDMYIIwmvhYlDtfJKeLrrOoiDM/NTqZSbgAPcuenqkjOrzxKfTv3xr4fH0K9h99bwNBjpPxFKfv/h84nuW3d+DbPwTNrt7u5ie3sb29vbOD4+dgMrfLeeIJnZL7+wsIDl5WWUy2UnxPEbavzr6fV66Ha7aDQaqNfraDaboUMsma3PZrMoFAqBEVtablMxjj+rT8U8WuLTz6LQcGmY52R4GEb6MRDW6vrU9/k/D3PpSXiVvfpCHN+t13iZ+vr5+XnUajVXk3+M8Npcc3p66lx7X17LyTu5XA6lUgnFYhHZbNZN3AHgjuV31+ksPc7qY1eeqvDCPCQlfVgS0vA4jPQjwnfnh8X0/nvC3veQS7+7u4uPHz862Sv19Q+VqzSxNjs7i0ql8qjyTq+JibNut+s8jfPzc6eTV7IlEglkMhmUSiVUKpUA6dlxp5+r3W67hiBtnGHfAuN6xvP0OBhGaFjgjxQ3Sz8ajPRjYlgsH/Z3JJRaVi1HXV5e3htOsbW1ha2trcCgCn8MVpiVpwXO5/OBiThq5R8KM66urtBsNp0eoNFoONeepCfhi8UiarUaarWaa97hQAyW/LgZCJWDXEBYt/ddfJKe7bp8cdEJm0RkhB8NRvoxEJa993+v/07Ch2npKbxhlp5Ju52dnYCFpyjGF+HwgddhHnNzc6hWq1haWkK1WnWxfJgHwq90xdvttruOw8NDtFqtgPqOW0kxdFhcXES1WkU+n3dJQsbwbA7i8EwO+OCx6OLTOyHpKbdluU+n5TIcoLU30o8OI/2Y0Lg+LJGnROdXX3TT7XYDSruDgwPs7u660hxjeCbuwmJ4PvR0uUn4sIx9mBus0tZutxvomT8+Pka73XafRZt2arUalpeX3c62c3NzbhgGe9/ZK9BqtXBxcXFvrz1+BrXevEa69mxZpsyZi4OO2Db3fjQY6UeAT3LVu/s/h6ns2FBCd56jrkj4/f19HB4eunZZJu1o4X0hkApa6HJXq1Wsra1hY2PDNdWovp7XQ+hgjnq9jp2dHXz48AHb29uo1+vOtfcXlZWVFSwvL6NWq7mcQSwWc3JZJgObzSaazaZbvGjFNUPvu+skvDYu0SvQ8p659+PBSD8GtO6s9We1OtSmcxHQqbUcZMn2WEpr2SrLAZesxT9EeE2qVatVrK+v46uvvsL6+rprqqHbDAQ3zGCyjLPv9vf38eHDB7x//94NyaBlZt2/WCxieXkZz549w8rKirPyOhaLuYF2u+0sPD8PcLfDDy2+DhJl1x4/G/9O1XtKerPyo8NIPwLUmjMm13HOSnISS5N1zM5zzBVHSOuMO81w+51zhFpHTsIh4b/55hu8fPkyoL4jeQjNjjNxt7+/j3fv3uH169fY2trC+fk5rq6uHAFZAlxYWMDq6ipWV1dRq9XcOWjlueDRm2m3265H4ObmZijhfZLrZ+VCEDaByAg/Ooz0T4QqwzQJRwum1okPr06tZULr9PTUEf7k5CRAdo67orVkbVxFMVonTyQSyGazWFhYwPr6Ol6+fImvvvoKq6urKBaLgTKdWnclfKvVwsHBATY3N/Hdd9/h3bt3ODg4QKfTCUyqoVv/7NkzrK+vY2FhIVAK1MWO94fjuPmZtALABYtJOXojwyohWi60WP7TYKR/Imi1dTBls9kMbFGtTSQ6hILKtpOTE9chR7JfXFwELKGfJyCU8HTps9ksFhcX8fz5c3zzzTd48eIFlpeXA+UzLkD+ohVG+Ddv3mBvb8+VB2ldU6kUCoUCVlZWsLa2hqWlpcA5aN0Z/+s8PBKen4HJuUQigWQyeS8Tr6GMvzsvj2Fk/zQ8ifRR7mZSl94fZHF6enov7tURTxw1xTHVJycnrg+ebq/vyvvWXaF18rm5OSwuLuLFixf41a9+ha+++gpLS0soFotuOIYq1vzcQrPZxMHBAd69e4fvvvsOP/zwg3PrtZauiwvj+HK5HNjGWkuX6gkp4dW6s++egzZ0Rx0/V6ITdsLuybDfG4bjSaSP8qo6GAwc2VutFk5OTlyWnRJVWkS1VsxeK+mpY1eyqzV7qHuMC0oymcTc3BxqtZqL4TkYo1AoOMLr9tI8Nl3u8/NzR/gffvgBr1+/xs7Ojvs8dNM5SLNSqbg4vlqtYm5uLrBPHXMOfpWCCxjr7GzBnZ2dxezsLDKZjBu2oYuHDs7gwsHzhN0vI/5oMPf+EVxfX+Po6AhbW1tOpXZwcIDT01MX96qiTBNZFN1wyykdU61lOJLFf3DD6vBalnv58iVevHjxqIWn5WXX3M7ODjY3N/Hq1Stsbm5if38fjUbDzaJn7TyRSCCfz2NxcdEl7vL5fGBOPnA/dCBZ2UVHF55ju3K5HLLZLDKZjNs7D4DzeHgM7aUHEGjiCfMEDE/Dg6TvdDqRHFbApBwAbG5u4l//9V/x/v1718vO8c8qINHJrzrxhvLTMFdey3AaswLhe8mT8BsbG86lpwAnbKdZWl2W5I6OjvDx40e8efMGb968wY8//uhKc9o6y4z67OwsyuUyVlZWsLS0hHK5fG9jDL+PQHfNBeCm6rDkx468fD4fqtXX5iPdiJPVAf9lxB8dD5K+Xq/jX/7lX/DDDz84gUcUbixJf3t7i+3tbbx//95tycykmyrEaBW5UHC+nO7mQos1rAwXRnYeP5lMIpPJoFKpYGNjAy9fvsTLly+xsrLiLLxq61VWS03/3t4e3r17h1evXuHt27fY2trC6elpoPoAwB2DVn5paQnLy8uoVCrIZDKB6bm8V7rAkKi8P7TyXLTy+bxrw+UC4k/GZWceFxCdpcd/8/9dF88oh6NPwaOW/re//S1++9vfAoAbPfylQ7PIHP/EUpQ2n7BvnS+fcJrQ8q37MMvODD3LWdxSularYXV11Y290iy9T3ha3Ha7jdPTU2xvb+P169f4/vvv8fbtW+zt7QWm2vrXQitfrVaxvLyM+fn5exN3CJ/w3JX29vbPM+rT6bSz8rlcDsViEfl8PtCcoyU9DQ98Sw/ALaLdbvee9xQFg/Q58CDpB4MB2u22+5nNEl861ApzaIUfv9LtpjuqO7MwLtUylk94n+zaMJNMJh1JyuUy5ufnsby8HJC+5vP5oTH8zc0N2u02Tk5O8OHDB3z//ff405/+hLdv3+Lg4MCV5HyisNxIff3CwgLm5+dRKpWcVQ4jvPYSUFw0GAzcwsUNM+nWMxHI+8f7oElAWnHdzmowGKDb7bpWXZY7tRnJFyIZ7uNB0sdiMaTTafdzIpEIDEaMAvhQa61ZxS5KZiWexpp+/K6E17o7p9AUi0VUKhXUajXMz8+7QRjlchn5fD50l1m1uJ1OB6enp/jw4QP++Mc/4ttvv8Xbt29xdHTkSozavOMnDJmxX1paQq1Wc269P7RC43jKi7kRRiwWc3X4TCaDQqGAQqGAbDaLdDrt8h+6MaWfFyD51XWfnJx0lRQSX3fqNff+cTyavVerp7uKRBVhHXVM6GliKyy5pFZShSoke6VSweLiIpaWlrCysuL61En0mZkZRyRVpWk8zE65jx8/4rvvvsO3336LV69e4ejoyHW5hQ3hYLiiclsdwEEr7y9sOg/g4uLCiZUmJiaQSqUwOzvrdsilW59MJl0LrfYscNHyy3W8Xi4O09PTrpFHW3Z7vd69GXuG+7CS3SdAa8S6+cOwJB2/p3XnhJtSqYSVlRVsbGxgfX3dzbTjzDkSfVhnGQlIMdDu7i5ev36NP/7xj87CP4Xw09PTLpZfXFx0yTsmcfUz+zvvkIQs+zF5l8vlnJXnpFxa+Zubm4D8VvsatCSns/VYBmw2m2g0Go743W4XmUzG4vonwEj/GRFGdt/VZE2fce78/LzrjHv+/LmrudOlDusb1xZZFd5cXFzg6OjIyWo3NzdxeHj4KOEZZqRSKRSLRSwsLGBhYcENxlC33o/j2URE4dHl5aWL5+fm5jA3N4dcLuc2wWCVg4uH3/LrC260c0/1/TMzM26hYeuuufhPg5F+TIQ1fPgWOCwzz4x8oVDA4uKiU9Wtr69jfn7eEc134cOGXwB3OQeN43/44QeXtOOuNA/t567S3lqt5gZj+Fbe785TwnMWv+/ak/AMTVTA5BP+IZktCU0Xv9VqBUhPrQHlw0b64TDSjwGto2uvN38H3DXf6CBHbgpRLBYd4dfX111fejabDZTF/BwASeETvtvtol6vu9IcVXZPIbyGGeVyGcvLy87KM/bmufxyYKPRwNnZmZMYU8KbSCQCO95QZ+83AIURUwVJfs5CLbnO3mNcTz0EFX6GcBjpR4SSmXJVPpwacycSCZeRT6VSzs0tlUrOmjI7Ts28Np6EWXclPuN4Ns/s7e1hc3MTr1+/xvb2NhqNRmDctA8tE3I4BoU4pVIpkGHXZiBWB7Rr8PT0FK1Wy5UuWXLkRpaak/AXL7+MOWzarcb7JD0n7PJ7kt7i+odhpB8Bfj3dH+pAkQ4f+Lm5OadAK5VKbj58pVJBqVQK7Ajjx+48H3C/484n/MHBAd6+fYtXr17h48ePbgDGY4TXybnU11OIk0gkAhl1LQc2m01H+OPjYydLZo2fm1mGVRt4/bqQaK2eEmDW+Fn+A+52sGW1hPLmTqfjvrd6/eMw0o8A1cH7STaKUFRfXqvVUK1WUS6XXclqbm7ONZr4WXm17mGur2rcSfjDw0Nn4d+9e4eTkxO3OcWwKoISnqHGs2fPAlZexTLaoVev13F2duaGgLAzD4CL2endKOH9ZJ0SmJZeh4NQbut3C3IhY4hBcQ6VgLT0lswbDiP9CNB+cGaiVXvPxBWFNZw7XywWA8ksuvGPbdigVloz2Wyg8QdgHBwcoNVqBbT0QDAHQc8knU47bT2lvQsLC8hkMq4VlsIYzvbTuX5hcTxbf4cRngsRRTeU2ZL4unjqGG2GGVwoeG3UCFCL78uKDeEw0j8RJAuFNCQxrf7MzAzy+TxKpZJT0ZXLZRQKBaeg82vtYWU4Iizu1cEcdOn/9Kc/uTje33MuTAzE6gFjeE7OXVpacsk7AE4/ry3CJycn7kXtvtbk+TWRSAQIrxZaR2npVldhpKel96W/TCbSwqt6z99Z13AfRvonQstt+Xzeqcs4BSabzbotniqVyj1xTZhV92vufgzvC2HoXtPCf//993j16hW2trZQr9cD+8f756Cmn9td0aVfXV0NzLubmJhwajhuYkl3XqcFsR6vn4uk15HbbJZhFYFkZfKNajrdwYaNXZ1O514jk9bqeSxV7xnhH4eR/onQue9UmbEkxRi+XC6jVCoFNOZK+DCr7rvw/KrWnfHr+fk5dnd3sbm5iTdv3mBzcxO7u7uBEVdqFXVEFd15blKxvLyMxcVFlMtlVypkA4xuU310dOSGh3DzDTbUMEwA4MiqwzW4WGkikBaemXe29uqgDB1Kou3KmvwDEOhitL76p8NI/wQwHqalzGazTnTCBaBYLAZ08lqC08x1WEZeFWiasGLMymz57u4u3r9/j83NTfz44484OjoKqOB4nXSJGXYwz8BuPW5Flcvl3Fz8WCzmBm5wE839/f3AaDAKYGjhU6kUALiwwSepuu46BtzfzFJblXk8v5GIHg8Jr4lA1SIY4R+Hkf4J4EOdTqeRy+WQy+Wci6naa6MAABRrSURBVE9dOQdDsDHGz8oTYQTX0pWO1/a3rf7xxx/dLrZqdUkEneKjIcfi4qJ7VSoVFAoFF3ZwNh0tZqvVwtHREXZ2drC9ve1Gg3G3HcbeyWTSZdx115mJiQmXe/ATgTpJiDV1AENnEqg2gJZeOxpVoguEy54N9/Eo6dVd5JSTKIHDIDKZDMrlMqrVqkvS5fN5N++N2nK/5ZVQ111LVprJ1m41xtLcxfbg4ACHh4dudDbdeY2rdehGuVzGwsICFhcXsbCwgGq16ppeuLedDvHs9Xpot9s4OzvD3t4ePn78iL29PZycnKDVajkLz8YcIGjh2d2mhNeda1Ujz3o6j6UZf3bfaU8975Pq8gHcs+6aHDUMhw3ReATJZBLPnz/H8vIyXrx4gbW1Nbf9M6fqUGYaRnh9KNVysQedhGg0GgEd+9nZmdsMg0RX1ZnOswsj+9LSEhYXF1Gr1VzJUKWwTLLRitKtPzw8xN7eHvb393F0dOSGiPR6PRc6MOE2PT3t5teziUY3t9Bda+v1uht6QQvPECGdTjtCx+Nxt3CwHKcjscJyIDxW2GJruI+Rhmgkk0n3H/YlgxnswWCAly9f4u///u8DLa+cWKMbNah7C9xZdP1eG1WazWYgK65iF52eG7ZLDK/Rn583Pz+PlZUVrKysuIk32WzWlRb1+tRVZofe6empm/bLGJ4DKvgezbBzZj2tvA621E0+OBGYAzZIbopw1AuKx+Po9/su9tfJONpe6+cAWDkYtjuv4Q4Pkn5mZgb/+I//iL/5m79xq2gUEiW0gr1ez02g9cc/+9102myjFom1aV/Cur+/j93dXezu7uLw8BCnp6cBa65Zbw2p6Mpz0GShUMDCwgJWVlawurrqOuQ4TsuvHvD6tCWXbj030tSknSbaSCyWKenh8LlgCy+bcU5PT92GnOzA8916HXrJz3dzc+MGcuggUt5PQnX69Dj8ur7hPh4kfS6Xwz/8wz/g17/+9V/qen5WuL29ddaJSjqdzKLk0Zo64W+DxU0mtre3sbW1hZ2dHWdVmc2m5VMpKV/qynPTyuXlZbcRxeLiIkql0lA9vx8PM8QgQelpMEFIy+5bd25WwYQlAGeNmXz0Cc/jMfnHEIWLCWfaT05OuoVIN7/UhVQ78TQnwM9sVv5hPGrpo45MJuMeOrXmvhac32vWWUl1dnaG3d1dbG1t4ePHj9jd3cXx8bGLmeka81j+3Ht15Wndnz17hrW1NaysrKBarTrrzsXJT2qR9GGex/HxMc7OzgIyXu0c5GKTyWRc/wCtvCbsmI/gsbiYaVjIezg5OekSmDwOS4cXFxdusfDHtKngKJFIIJ1OO6GUP7zTcB9WsnsEjJ2H1dX5swpRWO+mm8uSG637/v6+2yFH41U9B6sGOjST9Xb24j979sy589w4wh++ocdTsQ8Jr3vsKeGpQCTxU6mUIzwlyKzmMCfA4zEvQZktzw3c6RR0DNb19bXL2rN8yO45nSbM9yvhuWOOViXM0j8M28DyCfDlsv5Xqs04j/3y8hKtVgvn5+c4OjrC3t4ednZ2sLu763bJ8QmvIGnpUnOTCKrp1tbWnDvPzLxWDnxLpyVCnXjD5hlu5EEXnMIeXgu7B7VDULeiooWv1+s4Pz93jTj+gkavxdcp8LqoVaByTzez4P8Dv3IoSTabdbvlUHdgeBi2geUI4IPrE54PLWvszWbTEX5/f9+VwI6Pj92ecTramfdXu+DYzUd3nmq61dVVNylXRTbDtAG0slyU/AEYek2DwSDg1bAqwfkATAwq4Xm8MMLrJpa8Hn5evrggUZ/P0GPYJha08lyEOKtgbm4uoPk3DIe59yNACQ8E3fqwUhwFNUosrbHruC2SgKVADuHQSTvcbUYltFqm0uvSBUmvjxZeB2Bwz0KSKZVK3Zv+Q7LTiwgjPF36xzwYHUZCy68ts/7OQLpY6Hgvyp+5+Ok8P8NwGOnHhCbv1Iq2Wq1AfZqJOlp2JgOnp6fvPcwsO7Evv1gsOgXg/Px8oDdf41clOXA/fmf1gAsShT+0yoPBIDC/TuW8TAr6O/L6Lr1uwa2bgxBKcnbi+Zt1aIw/TG2n7c2lUsktgBbPPx1G+jExzJpqn7gKaugqsztNH2aSS2fpsYHH79xTnYASXYUrqvrrdruO8OqCk6QAAu2wHO6hHXN0w2l9wwivevowC8/OOZbYVCykhKcnpBN1/BJdOp1GoVBAtVp1JUr1QgwPw0g/JtTSa8eXtnhqfZttr7TwWnenFDWTybjElK/rZzztu8O6z5vvedDC0/vgqGiWwnRGAAmlKkNq8ymp1Tq879KzF8DfLkvbZDk/jzkITujRxVEz/bxPwF2bcDKZdGEPRUjcQMPi+afBSD8GhnXK8WHng87WVpbfVN3GrDjdebbsMkPOLDmlrvF43CXkdGa8tp7qNs8seXG/Nw6P5PvVmuu0G3Xzddw1a+ckOwnPKoSOsuI90AoEPycXr6mpKbegAHBSX788x68MOZjAYyyv++wZngYj/YgIq9cTfMg5HJIkJ+H9JhnKWWnlSQoSne25AFz5iudVMZDu/aYde9qwonPoeGztbtMRV8BdtxyP6yvthpXlgKA7Ty9GFzM26DD8YPutWnXmP/gzFyc2FjHc0TDBrPzTYKQfA76lB+4edLqxPslICm0O8TXslLVqJ5zGuLTk/ktFQRpmaExM990/r+48o4RnnVwJrxZ+WNKO90EHiHLegG6vfXt7i2636+b6dTodJ0ZiCQ+4K9FxkWIIxGSmWfnRYaQfE757DwS7vZTkbCwBglaLlotuvlp2utbUALA6oBs7UJeu46J4Dp3DT0s+Ozsb8Ch8wnORUe+Ak3tIeJXW+nV4jeH9bbfZjsz8wfX1NVqtFvr9PrrdrlvotIznfxYuVpQAa13erPzTYaQfA0p2Xzii5S5tRVVVGd171Yn7GWtVpmkpULvP/H3ZNZfAWjt16f5Gkv5GFLwGnTnnE77RaAS2hR6WpWfczcnAnP8/NzfnhD2dTgexWMzpB7gwqQKP94sLJDfR8OcC+GGW4WEY6UdAWPLOH+7AOrxaLa2f8zjMuvP3HFLBn0l4jpjSbZlp6bX2T3KwCUZn+nGsl47z8i08s+gkPIdjhglv1KsgdMGhS0+NAeXC3CqLiUT2zWtfPhdLbcrRYR1aWQhbeA2Pw0g/BjSR5g9n1EXAr+Xz37TuTLJQfKJZeJX1+hs1aleeuvIkP+f56cBONsr4bbc8J3Bn7dmQw8WGhPcrFcAd4Vn+Y3adm35wmEcqlXJddGxbpoVXEZDG9LrFFQmv8l3/nhseh5F+RKiVV624n0gLy6jrgEdaM53rxqw143laei27MY5necxX0VGvzww3E2gcyU3Cq3pNPREV9HCCj5bltMUVCIY0Oi2Y3kU2mw303nMh5EKhC18ikXChg+7+q9l5vzSplQmdQWAYDiP9iOCDpZNr9cUHUX9m/K1jr8ISYAT/nQk8bWDRQRQq3yXZlXCc3MvEl+8e8/P4kl16GBxVzTDCV8cBwaoFE23MIegio6TV+8hj8JqmpqbcggkgMP7K72bUvIbfmmwYDiP9COBDpbVxEpsvJbtm3XULJ3oACiUSFxUm82jVdTAlcN+65/N5J1pR6z5sI0lfsusTntdLsqtHwmvWxhy24OoIcN4vLnL0JvxNKvh5NCcyrH2Z10jv56EtuQ33YaQfEb7cNaxmrsTWjRb58qe7KqH8WXYAHAFoCYG7KgDjd5Ke8l1/koyv3uPx6VGogs+fS6/NLjoGXXfQUYEP8wTaj8DrpxCHCyHd+bA2XJXkUrwTj8ddfoOk1+5Fc+8fh5F+RGhyTq2xEl0TTPqzxv5U66mrTsvIBJvGu37HmcbRJL1OtVHRCq2knstvC2ZZUON3tvrSijMW1wSizr6nB6BzAScnJ9Hr9VxDjA7jZGJS8xR+QpTXz4UE+PNUZn0v43qz9E+DkX5EqAXy5+KFPXTqCvsiEnVf9feqW6eV5bE0608yqn5fu/CYEATgkmPqReg0HWrzaTWphKPLrkMrdailxuMAXCKw0WhgMLjbR57hBX+n7b4aSqjH5M/Foyegrr2vVTA8DiP9mPCluCQ0SUoNPmvPNzc3rm2VD7O6z9qNprP0/Sy3tr+y64zkp3Wna81zXF5ehs7Mo0XVhKMm0LiYaFcgFwWV3tJzuL6+DtT8SWy9NiA4iZcWm8RnLkQXU94jLjSaxPMz94bHYaT/RPjxuC4AGkP7rjndZLquvqusmzdwEdAFQInvD7nQstdD3gWvTcuPtPLMFWjOgdfhVyFIePUcWG5kkk8bY/wRY3TT/Y09/IoBQw6//Kn/F4bHYaQfE75l54AMTnWlVdY6vN9fTtL4iTIm6HQBGGb1eWzGyjqOCwjuZKOLj16/Lgwq9kmlUgEVoS9K4u993T9fKqoZto01k5skvDbxhHUwaijlj90y0j8NRvoRobG5uvJMtlF2yxhYyamJOS1ZqTWltadV5LGV8OqqU6yiJTgd7KG75KirzOPwXP4kXW0e6vV6gYYYXzkYNjxjYmICV1dXLrzRphoujHTz1a3nNfsLlMbsep/8KTyGx2GkHwM+6f0MOy2iWj19kci0zP6IaD/21pc/Jsu3vlod8Hd85ft5zep60xKrzDUsYcnFSsMBFe/oAkZyc9GgB6TVA1Ur+tdKqDdCXYDOINDtrMzaPw4j/ZjwM+m08Bozq+XRBcGHkoxkUCixSZqwEVm+bkB3fPUXFm275YvE13CBsTmz+5pEC4vt9f6wBMmf+Vl8Oa0uIH4lRAdpsE2ZmgROzgnbX9AwHEb6EaHxrx8P+w+dxqGaeFM3XV1dEp7H9N16jV2VOKzDK+F16o2WtPhextuqKqSrH4vFnNUl4TXLThUcrb56HrwPfg5BF7Swz6zTb3XxoHXnUA5ux12r1VAul5HL5czSjwgj/Zh4KCNO+Jbffx//JkyQAiAQQ2vMrVZT3W5tRKEVDrPE7HSjTNgfhqlWWK17WLLNr49rzoNeheYQtMfAD4f8e6u9BclkMrBpJ7fzYo++JfKeDiP9J8KPqf1Y21eZDXuFkVNJ3+v1AsShpSfxtReAC4Em9DTjTqj4x++6Y0mNi4e68sNq40p2LlJM5Gm+QK/bhxKe8TstfK1Ww8rKCp49e4bFxUUUCgWk02nbtHJEGOnHRJgUV7Pxj8XbDyXcSCTG8Kz/syLg5wXCYmC9TlpuXQyGwW8bHhZv+4k2v+yoWgPV5PNzqSSYFp3JT34OWvhcLof5+Xk8e/YMGxsbWF1dDUzi0U48w+Mw0o8JJbZvZVWqS7L5rrY//EHfp6Up/3faYadlO7+Mp/EzodfqE9j/nmVAjbvDoN6HrxLkRBy63xQkUfXH70l4ah2YzyDha7Uanj17hufPn2NtbQ3z8/NuV5uwLbkND8NIPyL8OFRLWnShlXRh+nwtm9G1VqITdJNVeecnAnlNTOaxuUWlt7T0WiXwG4N84pPk6sLz2nltaqX9SbvsA9ABlpTndrtdd+2qHWASk1l6xvCrq6vY2NjA+vo6lpaW3OgtbRc20j8dRvox4YtH4vF4YOQUCcLauBJEVXnT09MBCau67n7GX2W5fmyvCT1tlb24uHCx+8XFhTuP/z7f9X+ogUXjdp2Lx6k5/q48bAdmHwA77Fgt0EEYbNWdm5tDpVLB0tIS1tbWXBxfLBbd7HzL2I8HI/2YoLXmQ894228UUY/Ab7fVsVphghTVu2sjjm/pgaDrTlkrt7TiYE22pOpQDwCuUYZ4iOx+Wy/HaufzeZRKJRSLRTd1l1aeix8XI14Te+KZJAT+HC7Mzs6iWCy67bmXlpZQrVaRz+cxOzvrFj+z8uPBSD8i1GIzUQXADZcIK9Np4stXuvkvIDi0QmNlrdX7pTslvTaykPDZbNbNvONkHDa6+OU9DVEI1dPTjedornK5jHK5jEqlgkKh4Hr6tZ2W16Uz/9gey646Zus5WLNaraJSqaBcLgc2tzDCfxqM9CNArTsnv8ZiMbeZhR//PgT/bzVUCJP5as3bd2n9siGVdDpN1x+hTZEN6+9aWQhrnaW3wW24stksCoWCI3u5XEY+n3fz+LhQAcESoE7o0fbYwWAQ2KAyl8u5Cb607qolMMKPDyP9iNDMMpVtPuH9vw/7/qHj86vfBacvRViJ0J9512q1HPnVyut+d0p49Vi0645DL4vFohuvzd11OU+flpjQ69J5gtoiq/eV+9/5G3KodTfCjw8j/QhQS0/LO4zw/kP5FPKH/c2w92l5jl+1aqAkY/KM5Ndx2jqxRsuJhJbhmJXnlN1sNuu079o6G1Y+HFb/D+sJ0JbiMO/GCP9piD3ihtookhDog/yYG6947GF96N8fe+9D5Fd5ro6PDmucITk1YcdQJmzjS2bgfbIPW5TCtAFaxvRfZtk/CaE3zUj/CRiF8KNinIdcr8dPIoapB1UNqITn+elSk/h8+RUEn5Rh4UfYdelXf7Ewon8WGOmjijAvYJgiT/GY9eXfGH62MNIb7hBmfcMwaiLS8LOCkd5giBhCSW/9iAZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAzxR/499he5CoPB8BeDWXqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDH8f/aq/EM/063/AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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On4RkXkHDAT88MEwHI/0nQtgCMI7gYeU5IGj9dEIOY/nT01OcnZ2h2Wy6HvZJbj2TXoVCAevr63jx4gX29vZQrVaRy+Wc8i6sVMh4nlWCy8tLp/bzVX4kfCKRcKO3SHouHnp//J9+h6CqD1XQw+/0Fx49nuFxGOk/AmFWZhLJfVc+zMLTzdXkXbPZdIRnHK9ufVh8Sz1AOp1GtVrF8+fP8fz5czfd1q/L++cxHA7R6XRcSKE1+bDSHAnPF+W8wH1ZTqECnLDWYJXsatJwbm4uIOU1TA8j/Qx4ysPmkzxMQKLHUfWaWngm7xqNxgOLG6ZK0+RdIpFAqVTC1tYWnj9/js3NTTfd1ie8npPq7DmDj7V137oyZ5BKpZDNZt3wjWQy6Qga1iTjlzGZ9VdxkC6CdPn9zkHD9DDSzwh9iMd1so1Ti6lFBR4SnmO4lPB06zudjsueh5XngGDyrlarYWdnB9vb26hWq8hms2PdekJlt/Qs6FUQvC6WArPZrHtxUVEprp9x5+c57YcSYE7Z8RuM+N/j+gkMT4eR/iMRlkzyy1/+e/Wh9gmv9Xjq3E9OTtxYql6v98DNJjQ+ppXf2NjA9vY2VldX3aYV45JqWibsdrtO199utx9k7LmwMFuvI7Q5V49uOaW3OvkGuB9brcTXUEBdfX+hnBQuGSbDSD8Dwqy8/1CGfcb/qXGrEr7ZbKLRaODw8BD1eh3Hx8c4Pz9Hp9N5UvIuHo8jl8theXkZm5ub2NjYeJCtV49Er4GJt1arhdPTU1xcXKDX64Vaebr1hUIBxWLRkV5FNX6vvzbbqKVXUQ8z9ATDBH63fx5G9ulgpJ8SSlr/pZZJLZa68equ0hLqHPtms4nz83McHR050p+dnaHdbjsr75MduI+RWaIrl8vY3NzE9vY2VlZWAtn6sOvh77Ty5+fnrpvO19jHYj/1wbNbTyfvcCtrbf/l6/r62oUJTPLR0vN42rhDj8TPjYQl/oz4T4eRfgaogMWvF/sk0r+Fkb3X67n5+exV52aUx8fHribPDrqwTL0mxUhEKu/W1tZQLpcdGcflGOjW93o9NJtN9/1XV1eBchkAR1ISfmlpyXXrpVKpQMlPNwPRRhsuPtqRSGuvpPeJH9bBaISfDkb6KeBbed9yq6SW/62JJ39nGE3YaR2emXq/T94f+azEYU0+n89jZWUF29vbzso/lrzzM/Zsrjk+PnaCHP2eeDyObDaLcrmM5eVlrKysONKzeebm5sZVIHRTDC4e6gnxmCT97e1t6KQhAO49/lxCw9NhpJ8SSnjNTCvJ/TKaEp4jrtrttsuOUwCjtXjumkMLz0SaXyngQpNMJl0cv729HSjRqZUPux56H91uF6enp9jf38ePP/6I09NTR1QgWBUolUpYWVnBxsYGarUalpaWXPcb43ffynNDDLbGUp6rCT0dr+UrGpkD8IeOWlw/HYz0U8J37f3xT5qg8i08iaCuPMtxOgyDE2k04x0mfQ0j/O7uLr766ivs7e09iOWVGBpy0BU/OzvD/v4+vv/+e7x//z7g2pN4qVQKxWIRtVoNGxsb2NjYcN8Ti8XQ7XYxGo1c6ZGxPD0VnndYJYDf4S8G9Az8ur4f1xueBiP9lFCZrD/+aW5u7sHUGrrNJLw/AIM98bTu3J8ubLgloUSJx+PI5/OoVqvY3t7Gr371K3z55ZfY2NhAqVRyc+98jbvG8dfX12g2mzg8PMTbt2/x3XffodFouJ55AAHCV6tVbG1tYWdnx3XrJRIJdy/YrMP99jRrr8TmcfXF69Nr1f565hNUzGOYDkb6J8K38CQyH2rtJ+fDzdKT7v/GRB3jdibqOp2OI5kKb/jdQNCd151pGMN/9dVX+PLLL/Hs2TMsLS0FYnm9DgCBxN3l5SUODg7w5s0bvHnzBh8+fHAbWPB7E4mE8yaePXuG3d1dPHv2DMvLy8hkMgDgwhYudFqmC5PN+u3F/DluKo4m+7T0aJZ+Ohjpp4AmvDirjmOj6JL6GzYOBoMA4RuNhnuFJep85Zo/504Jn8/nUavVsLu7iy+//NIRfnl5Gfl8/oEQxyc8W2dJ+G+//Rbff/89Go0Grq+v3XdzRv7S0hI2Njawu7uLnZ0dp+PnoAz1dijIoa4gTLOvsbyWOzVX4st2NXuv7r0R/+kw0j8RdOtZU2fGXfdx09gUgEuOXV1d4ezszFn58/Nz1ykXZt3HTXlVTX0ul0OtVsPz58/x1Vdf4YsvvsDW1pYjvA6w4PkDDy384eEh3rx5g2+++QavX7/G0dFRoGde3fq1tTXXuLOxseGShADcfYnF7rfB0iGdzNr7iTu66trPr3kSf2deP9Y3TI8nkT7KjQ3q0lOPfnp66uSx1KUD9+UkPvi6w+vZ2ZmL3Vl31045zQWExe9aLqOF39vbcy49CZ/L5UIJr1l6n/Bff/01Xr58iYODA1xeXgbCFS4wDCH29vYCo7b8ujqAwKQbLWdq9j2ZTCKZTIbuYc8Fww8L/DDHMBueRPoo3+DRaBTYyun4+Bjv37938+LoBvtWnq2plNU2m02XmaclnTTXDsCDWJd1eCbt1KWvVCoBwod1q9HzOD8/d4T/9ttv8erVK0d4Sm75nRyXzU499SY4W48DKwEEJuDojjQAAnvXc5yWnq/2IPDFe+RrHvz/R1E2TNPC3PtH0O/3cXx8jD/+8Y+o1+uo1+v48OFDoMdcLRgfXpXWtttttFotl/SjJR1n3YEg4am0y2azqFQqLmn3xRdfYHNzM5TwwMM5e+12G2dnZ3j//r0j/Lt371Cv113iTmf3JxIJN1dvd3fXEV7ltsB93kG/S+fhqzvPyTrahktJrk4L4othBu+xttqOWywNkzGR9N1uN5I3lbEsALx79w6/+93v8PbtWxwdHbl4vNPpuIdd1WR07ZnsoxSVD3BY7O6780Bwg0ytw+/t7bmyHJN2kwhPd55jrP/4xz/i1atXeP36tUvatVqtQAcck5GZTAaVSgVbW1uuU49lQL8FVgnrD70g8blwFQoF5PP5B224et+Y+dfQSafo8OX36xsex0TSN5tN/O53v8PLly+dKxeFG0vS393dYX9/H999952L3+maM1OtmzTw/lCcoi9NTo2L3YGgjl7LcrVazbn0X331Fba3tx8k7ZTwvjtfr9fx7t07vHz5Eq9fv8b+/v7YQZcs0RWLRWxsbLhMPTv1/Hn2auF9IQ7vC+fnlUol9+IEHwDuHrEUStLTY2CYoF6AL14yPA2PWvrf/va3+O1vfwsASCaTD0Yff45QkU0ul0M2m0UsFgtk2bVmzgebRNBklJ/FDpv64sfujH1V7rq7u4sXL144eW2lUplIeJYKqbJ7/fo1vvnmG7x588ZtWNHv90MHW8zPz7vk3dbWFjY3N13dX8uAmi/QHXQp3eW9SaVSrsS4uLiIxcVFFItFpNNpzM3NuX4ELhx8aYMOF1l6Ab7wx68SGMZjIulHoxE6nY77b5amPneo1eDgirA5b4xVdRFQIqguf5zYRo/DZF0qlUKpVMLy8jLW19exvb2NnZ0dbG1tYXV1FeVyGdlsNnRnGtUGNBoN/PDDD/jXf/1XfP311y5EYV++Wkj1MPj9a2tr2NzcDOyE49f9tYzJ/AVzFlyMmIAsl8tYWlpy58/96HnPuKiqpef58Xupj+CLcmXd0dYwGRNJH4vFkM1m3X+zoSJKoOtKd9UXg2jjCBAs8WmpTOHH7er+FotFLC8vY21tzU292dzcdE0t+Xw+4GKHxfBsnPnhhx/wL//yL/jDH/6A169fo9FouOTjpGGamUwGy8vL2NraCrj1fhmQ30fCX15eOtIDcF6BduTpttgAnJtO0vuZe80zAPeqPy4wrIbk83n3/8Is/WQ8mr3XB5ZuVFRBEtOakDS+gk718mE5EN+V55jqSqXiiL6zs+M62MrlsiM7xSy+xSVpuA3V/v4+vvnmG/z+97/H69evcXx87AZqjtsSKhaLIZlMYnFx0W19tby8HNqaqyIfLUtyNDePxTh+eXkZ1WrVWXlm4gEEqgDq6vNc9b6yHZmLjFZFMplMJHJOHwsr2X0ihGXiCXXjNRdA6760tIT19XXs7Ozg+fPn2N7eRq1Wc3E7yR7WSqqEp+jmw4cPePnyJf7whz/gzZs3OD4+DiTsxpUHE4kEstksVlZWsLm5iZWVFRSLxYBbr9+nPQWsarDiQ7VdsVhEpVJBtVrF8vIyisViQMU3GAxcAlRLcVqao/WmR8UZBHy1Wi2Uy2Ubi/1EGOlnhN/eCtwPzlAX0+8YY0abWXkKbZ4/f44XL17g2bNnqFarLtGl206Ftcdqpp4DMN6+fYtvvvkG7969cxb+McJTiFOpVAIbXHLmnbbD6tRe3V+Po7JHo5Gb01cul1GpVLC8vIzFxUXkcjmn4gMQaAhiyDBOw6DxfavVwtXVlXt1Oh2USiVz8Z8AI/2UUJJojMu/6QPqZ+WZ1KLLS9HLF198gd3dXadn9+vuYS2nfnssE3fff/89Xr58ibdv3+Lk5CSwpfQ4L4TnRX29Ju+46PA7/f6D09NTN62XU3NjsZhb1BYXF13yrlAouIw93Xc/ZAiDnnu/38fc3FyA9LT2rEZQ7GMIh5F+Cmh3mK8ZV0KppWHZKh6PI51OO5EN3fmdnR08e/YMtVrtgXVXN95vnAmL49+/f++EN/V6He12e+IOr7weWuVqteqm5y4tLSGdTgfi7dFo5BJ3fptws9l0kmReq1+TZ8mP8L0Xv5NOt7imy897wrieL53BZ5Z+Moz0U0AlsVou0/o7QXENLTtLVszMb25uYnNzE6urq1haWnLWXRNm4x5cP45vNpv48OEDXr9+jVevXgX64R8jvCrvNjc3sbW15fa64/WR8Pw+Nh3V63UcHh6i0Wi472P4ks/nUSwWUSwWkc/nA/PwlZRavtQOOp4bqybq7nO+IGcQcCSX7rVnGA8j/RRQEjOjDQSn3qq7zBIc49pqtYparYZareZiXLq82mI6yUqpS+93y718+RI//vhjYFb9JMJrnzzHZVNq629LxS7DVquFs7MzHBwc4ODgwI3JppXX+5PP5x3heX2UKWt4olqBeDyORCIRmC2ghCepOUVYXzoi3Or142GkfyJofbi5A8Uq6t6zzk2N+eLioktiVatVLC0tYXFxEaVSCblczpGB1t0nvOYIgHDC1+v1QPOMDsAYpw8g4Vk52NzcdA011WoVmUzGzZ9nrZwbYJydneHo6AgHBweu07DdbmM4HLqMPQmfy+UehCv0inRjSs1/8P7xb7T0wH1plP0EtO5U6PV6vYCk2BAOI/0TwZJWJpNBsVhEqVRyMa82x1BuqllrJrLYZJJKpQL19jDCA+Ejt9WlPzg4CMhrqbbzR2X7yj8KZkj458+fY2dnB6urq8jn8y67zvCALj03wDg8PMTR0ZEj/GAwcGFPKpUKbFetiUBabR0Dzpo88FMmXwlPD4NNNyp00oYmle6GbaVtCMJI/0ToQAmSnokp7s5Ct7ZYLLqsNZNYun3zY2RXaFlOh1hyxNXXX3+Nb7/9FgcHBy577hNeJba0xBQC7e3tYW9vD5ubm86tB+Ay4dfX127yz9HREer1upv+QwtPD4elyEwm4wjv1+DVSuuIb4qeEomE29eeHoZf0iOhSXy/sSnKArKnwEj/RKilz+VyyOVyLl7NZDIufuW+btzbjT3jGrM/Ztn1v9XacVNJEp4jrnTijY6WokutjS9spNnc3HSVg9XVVSwuLiKTybgGGNb9ua/e0dERjo6OcHJy4uriJDzJrc012nqrZOR1sOTGtmOd1KMLhF/H1+y89t5r27K595NhpH8CfFGNEr9QKLiyFDPVdOOZsVadvJJdlWaEJgV9qSs75t6+fYtXr17h+++/R71eD4y4Au6HaPo5hnK5jNXVVWxtbWFra8vJbKn6Y+KOWnpad7rz5+fnbroOy2c8vk94HSaisTg78Uh4JuB4PFr7m5ubsaO7tTmHC4om/QyTYaR/ArSezXIdy3ClUgmLi4sol8soFosuQUfrroT3y1T83X+pa0vV28nJCd6/f493797hu+++w/7+Pk5OTgKWEriX+pI8mUwGpVIJ1WoV6+vrrg5PTT+TiQAcKTkW7PDwEAcHByX0ZzoAABRJSURBVKjX6zg/Pw8IYLQCQNdeS47MBTBe57FZYuMmGNpUQ5mtyo3576rS054HJf04AZIhCCP9E6BZZQpsGNuXy2WUy+VARj6ZTI4twWlyTiWnuluObn3FHWz39/fxww8/uD3mKIbhYE3gPlGn5ULq+jc2NrC1tRXo1qPWgBZ5MBi4CTv7+/vY3993GXouLkp4kj6M8Hwvj61xvI78JtH9JiK9X/50HJUD++IjS949jkdJr+4V+5+jBDaPUGHGMly1WnUWnmozTdSFufJab9aJr/5oLQ7h5A44nM3HWflM2ClpGFuHkX1jYwOrq6uoVCooFovIZDIPXPDhcIh2u43T01N8+PABP/74I96/f4+zs7OA0Ae4H6elhOciNxqNAr36KqShhdfcg25TzXun2gB6Akpuf+FkmDRuvz5DEDZE4xEkEgk8e/bMzXzf29tzPeYkO627b600vtQYXTPY2jHG9lRuask97i4uLlwLKfvMCU2gFQoFLC0tBRR/6+vrWF5edtUG7ZhTwnc6HVxcXLjBn7TwV1dXoVtM061XwgL37dd077mIsfddFw8NQbRnQfvqdcyYIqxxyA8LDOGYaohGKpVyMdrnDGawR6MR9vb28Dd/8zfY2dlxwyzYHkr1mJJda8nqmurQR2bFuZ8dX7qnHefxaUlKM9OTknQcvLGysoJyuRyYOkuvgFZTlXbHx8eo1+uuBk/C043mNZLsmrijmIfvZ3jCwRoMD9Q74TF4PcBPCwHPideuswV5fzVZyYXPH/9tCMdE0mcyGfz93/89/uqv/urBMMTPHcwQx+NxlwjTUdMa9hBKJNanNQNPa3pycuKSZMfHx65hRQdC0CPQRQQI9r7TlWejzM7ODra3t7G2tuZGVfvtuXo85g9arZbbwENFN7rVlk+udDrtXkwE8v06SUe37VINAbfC0uQdAKe3p8JOcwPjrDsXPy0VGsZjIulLpRL+7u/+Dr/+9a//VOfzZwO6vxx3TZIlEokHctKwcpG2oXJrKzapfPjwAQcHB64Mpq67ztRTaDMKy4aVSgWrq6vY2dlxG0pyhl7YxhfAfbecEp6luaOjIzfPPyxBqITn7HougIy9WYPnIsbuN52Cw3vLYzNUoGZ+OBw6L4e5C03WqdiI3hYXIH/DTsNDPGrpo45MJuMeWh2YwQdYM+76gKp1Z1JOS2DsTFOyj5tbpx1o3FeOXXG7u7vY29vD1taWm3SjDTz8vFYLeG4kPN36cTE8s+upVMptUkHVXTwedzV4zVGwDk/iagKYx2QtniIbVdqplj6s9u6fExOp/owDw0NYye4RkGhKSM0iszylQhNm4lutFi4uLtBoNHB8fOz2v6P3oEMhNQ/g95czS86a+/LyMra3tx3hNzc3AyIb7fUn/AEYvvim0WgEhDf8fi1XUpRE8ZHW92nhSfh2ux2YyecLh25vb51190MZJjrHqey4kDEJqN185t4/DtvA8gkg+bRE5MtKm82mm+DSbrdxdXXlNq7kOCmW23QvO7/27H8nXepMJoNyuYyVlRW3P/zu7i7W19dRqVTchNmwoZlMJLL+f3V15TyPDx8+4OjoyG18oaU0Fd5Qn6BuPaWw9Bp8woftS69JOJ4f76fW9TUHoGEB7w3PibP0mVy17P3jsA0snwBtn9UHWN3k8/NznJ6eOqlqs9l02XhOiWVyTLd98kUltGIkHPXyJLwm7NbX190ADk3W8bhqOXWm3dnZmcstaBzPUVe04LFYzCXIGMdrI41KhFlx0CaasH3pdUCIhh68H/RGdPPKMCvPhZDaCbY6W+b+cZh7/0T4pKRbqttRq0Vnzd0ngh+3q9XTbDQtGfvyq9UqNjY2XP2dM+w4j16HcwLBra10aq12yzGOJ+HpyvN4WpbjLrN0n2nhSXiN4Znn8AlP0tNSj9t7T8t0fuWC3g/FUpxRkMvlLIn3RBjpp4RafR0DzYw1X6xP+zuw8Bg6NsonA62YDuKo1WpYXV11HXGMqbWSoAKXMF0AZ9oxt8BwQ8dcUbyjZTBtGqLizie8ZtvD5urTe2HfPfsSuIjwfmoiVEmvdXxu+cWZBaVSKTC11zAZRvoZoTJR3WmVtWUdBKEyUSBo3X0NO8txbNEl6fmiZp4CG+YF6B6rB8KEHUONRqPhPBESVV16kpIqOxKTu8uomlCn0bJ5RptfCF+nT+9BvQZm/8N09gpdjFjB4I45/pZbhvEw0s8IjZn9xhmtJ/vCE7/2rVLWdDrtCK+DJbkApFIpF0uzxMXNIoAg4TWbrrkFhhskKEnOerevg2co49fhmbSkS69lR0LDFb1GdiCq5+BPvg3LBaiVZ38Brby59k+HkX5K+BZI3XM+3CSQlr7U4mvsrDEzs+P8yQEdzMrf3d25Mli32w2o7OgSa4deWIJNm12oYFM3nuQkgWjZdfhFWB0+zMLzGrU7kclA1epzIfNbj1UXoeEPd/PlCDKz8tPBSP8R8Ic50nVl7Z2WTjf+pKtLoulMOZJck2Z0rVnTBxCI3bUtl++hToDyV82E+9p5Ktl09Jd232ndnBtLhNXhlfDqzvPa6LWwD0CVd6PRyO0MrFl93mOdD8BqBgeW+D0FRvzHYaT/CKgOno0vJIouBsxkq9CGwhK69KpjZ9IMuO8ZZ12fOQT+zo40fY8mwwh/Dr96FGp96c5TpKNlScbwk+rwJLwKZ/xdbhKJBEajEa6vr50ij/vN6/bfYVbeXyTNyk8PI/2M0Iw0H8Z0Ou2sMeNPrcmrXpxWXl86OZaZbN9y86e/nbPflELyUVFIt5jqNeYMtPauhOfCocKby8vL0Dq83hN/ajArD5w/kM1mMT8/j5ubG7RaLQBwcmUuduy0U9JzMdS8gMXxs8FIPyM0WaeuJ7PQXBC0XKeyVn+UlpasALj6uu7gwherBFoPB4J1bK2xU6/POJjz/HQuvU684TEHg0EgL6AbSoSNmqY3wd5+TtxdW1tzfQFspe31es7Kt9ttR2LtvvObcvyqgi9EMjwNRvoZMK6UxJjYH9/kJ/J4DJbLGDeT/HTTOUmHSTlNxjGMUE07XWC6u6rXZ8kvbLMNfq8OsSTh/am1Wof33XpVEHKYx/r6ekA5GI/HcXd357a07nQ6gTzG9fW1ew9JzWPrT+C+bOqP0jJXfzKM9FNCRzX50lwd6sjMtcbWSnyWy/r9fkA+q11w3LpJZ8T7vfZAcNw1PQzddKNarboddnT3WN1bTo/pLzj+QuNn6TU5qEq5arWKlZUVJ6DRibt3d3cuJPLDHJ1LAATbb3mf/Gm4Nvr66TDSzwDNmvt7rfkZZFXvjct0qwZdZ8vpdk0U/Sg5/Y48jXkLhYLbMLNarWJ5edlN7PUTYCyXKeG5G6xWAMbp4DWvwQnB9CzY20+vghJeVeTRQ1IxEAd38Ht0UaQnxHtC4hvpnwYj/RRQy+4P0KB10lHNfA8tko698jXl+h08LrvN/BHPfjlLB0loDXtpaSmwfx5bb3W4hk7RYdstN4TkgsMSZNj5hnXica8/5gxY9w9Luqnwhi+695of0XvjnyfvkR9CGcJhpJ8B2q5KUvuWXBcFPqjcjUXn5hFKCG3f9dV9GtNqtpyuMjfe4KRexu9+dYCLkmr06dKHjalWj0LPWUuWWn70t/LW/gDeD1+nT9Kz5s5QgPdb1YY6XVd3qzU8DiP9lFDCh1l73/L7iwAXCd/N91tOffUe3Wj9jE6kZe26UCgELLuWtng+1O378/s4C0Bn3AMIWODhcBgopYUp+jTuZpjC42jDDhdBlfD6AzB97wpAIM+hjT4W1z8NRvopoQ9kGMnDYl5+Th9if6YeEIzvddS0Jg/12BpLM2vOyTYqtqE1Z/zux8c6b58lQX+YBmWz7B/wQwtVD1IuzNr7aDRyCUvmDSjp9ROU/j31KxQAAnvT87PjxmoZHsJIPwPU+vjxOS0zraJmnjWbr262dqLpy8/KA0FZqqr72I6rWXlKaUl2Kt7oSajElsk7dt5xSCWtOK/Nb3fVMhqP1+12nXfB//Zn46voR910FR35o7Z43iS9djSO68wzPISRfkZoUk276WiZ/aSeP7PeHxbpJ7L0J+NcnQKrE2opDtJhmNwQg8MnNVZW0uugDZLo7u7Olf3owVBoFDZ/H0CA8MC9a99utwMKOuoSdA4Bpb3asUeXX0VAtPQMCfx97AxPg5F+Rmj8rSo7n5RqpTVLrcozv/TFn3ypNeXx/UVAs+O0fnquYecPBMMV5hsAONKrJ8Jz8BV5jNN5DGbXO51OYOqOCoHoeaj4SAeLhk3fAYI76Khiz5ptng4j/YzQUhNJShUZyeqryJTIOmSDx9NmFX5ef9fZ9/4DrtUEP9/gVwr879MwArhP0Ol7/fwEiaszBAaDAebn553Ett1uP5i+w7hfJ/MyMecTflyZUisaGjoZ8Z8GI/0MUAtPMnOYJPveOeLZnyrLB58CEz7YGiYo4fk5380H7kt7WhnQmFhjY+24U49DwwLNvOu1admRpOKCwmtgKOOHIDqYg9ei0mOdOOQP4/CrGz7ZdacdliON9I/DSD8lfMKr7HY0GrkMNeNiWnn/vX7N3q/H+2OiSSgtXdGd1jFW1ANobKyusmbd/Tl4d3d3gUYWJbPvQegCo7kLgvdH36d9AbwO/3z9aoh/73UPgHw+j0KhEOghMDwOI/0M8IlPcmtnmHbVTTpG2LZNSmz+Tg/CLwFqT73Kdce5ykBwH3u/RMZrAe6Tcex1V5Weyl/9ciXPUe8JF4ww0utxwgiv9zuRSLihmOwl0BHgZukfh5F+RoSV0fhgMtOswzSGw2Egxr+5uXGWkPDdWX2FZd/VxabnoFl4JX2YN6GWm+EAcweM2XlMTbJp6BBm5VVNp3kCldX6giU/P6DwO/gWFxdRq9VQq9Vcj75OzzFMhpF+BoxLGKmlV6s9rpzkZ87DxChhWXpCS26+vp8kUimwWmKGIfx8PB4P1PF9ia6fJ1CC6nWP63Hn774Ho55CmKKOIRF1CIuLi9jc3MTW1pbbrJOz/43wT4ORfkqoe0342XJV3fnWzM+oMx7XbL4v9lHS+1JdJXeYhJXnqi+eM9V52rLqC3dIfL8yEEZSvysuzEvh+WjZzb9mQkd8ZbNZLC8vY2trCzs7O3j27BlWVlZsMOYMMNJPCZ84Wt/WraafklXXJJsuCtotpgo8HSzhLz4kGpNdem7893HWtN/vP/i7n8ALc721lAbgAcm1WsF/BxAIaXz410ELX6lUsL6+7jbtXF9fR7lcdptcmGv/dBjpZ4Rac3+gg98Z5pM+rJ6ulk+tshLWT8SpGo6k8mvuqnunNfUttf+daoknhSlKMl8l6GsMSHh17zVUUe+Av9PCc+zWixcv8OLFC2xtbaFSqVgCb0YY6WeAL7MNa75RS+mX5fQB14Ya1q99955k8sU+Wh0IS+p1u11HIjbRaHVBZ9Gpd6LXOC4noXkNPSfdwcavzY9G96PANBmpPwG4z3CS7tbWFvb29vDixQvs7OygVqu5eXsamhieBiP9jPAJTEunGXJmr9V6qQw3kUgExlf71lWTd74eX4dqMvHGBhtuTMFNL7QNlXPm6Zor2X2ij0tA6jX59X6O1OYmHboHnpYAuSDNzc0FxEO8Ns7Z29jYwN7eHvb29vDs2TPUajU3esvm3c8GI/1HgKSksIUlOD8uVm/A9wp8LbnfhONrAUgytfbAw11qSXg2tHD8FTXu/hhtkjIMmktQD4VtvWzpLZVKbmoO97DnOWpvvS5C3AOPHghnA5TLZdRqNWxvb2N7exsbGxtup16KccZJkg2TYaSfEtpgw9oxANdAM849DouZ/dh5nCBFlXz8qTE8v8+39Oxi48x63ayCAy+19s7XuNo7SaY7zeTzeTeSi3vLcfCmSm5Z+tNR3jphl4umjs9eW1vD2tqaq8f7E4CM8LPBSD8FtBuOSaa5uTnXgur3dE9KfIXVsNWt9wU/YTV7vwlGu9zYc05LT9LrdBwOsPAHb/pz/Ph92rfPAZi6q64OwaRbz0VNSa8NNjqhh/Ja7ta7tLSESqUSsO5+m7BhehjppwStPC28bls1LgYGgjVs/Tnuffx90ovvUQ9Cqwkkvu4j729oqeOttY+dL3XnGbMzwcYXx2rncrnAXvbAw7BDJ/zSyjOTz+Ge9CDy+byb3OvnMIzws8NIPwXUtQd+irk1Iw48HGk1ze/j/i1sofDfE1ZuYxmRFpYxPi29P31GZ/epToD1du5/VygUArvk0O3WmryeV5gQSZV9wH07r2b/wyy7Ef7jEZtknQDYOJIQ+HH5Uyz8Y/82zd/99/ihgk9+ko0JPnoAfvOM3+qrijhuHKk762qf/CQrrPcqTJPA69GypD8o5Kn3xRBA6A0z0n8EHrl3nwTTPOj++YxbAFRM5O90q+U8zdLrphQquHmqBQ4T+eh38VrDwhfDzDDSRxXjNPh+SVHfNymR+Cms7zjBj+GTwkhvuEdY9WAczOr+YmGkNxgihlDSm2jZYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIYeGRv8f+JGdhMBj+ZDBLbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgihv8P2IHQIJzwWOwAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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Kygo2NjZMFVy7br3X68Xk5CQ++OADLC0tIRqNmlLX82AxTTqdxtbWlhnFdd5rZx3/wMCAac31+XxmrBeLbuQ8AXmmt07K4Znf2qBjt9uNq6/u/dVQ0b8B6I5flnu3/lurx5BufaVSQS6XM279q1evkE6nm6rg2iEej+PDDz/EgwcPMDIyAp/Pd2kLLkdrs8aegbhWsNEmGo1iaGgIAwMDCIfDpvZeNu/IlCb/lqJnoxKfX47XkqPIZGPRRe+p0hoV/TWwNrZY++T5/bK0nHwseY7P5/PY3d3FysoKVlZWsL29jXw+35FbHwwGMTMzg4cPH2JmZubCFJ2kXq+jXC5jd3cXOzs7OD4+bvl7dO2DwSAGBwdNa67f7wcAE8izzgWQzUmsxHM6nSbg12oGn/y3VlOFlPZQ0V+TNzHIQVbcUfBHR0fY29vDy5cvTbT+8PCwqW31Mux2O0ZGRrC0tISFhQXEYrELo/Xyek5OTlAoFLC7u3thAI+FOJFIBMPDw4jH4wgGg01Vc7TWshmIwudcPI/HA6/Xa8pw5VTfVqLnY6jYO0dFf0UusvLt/r18HCn4fD5vBP/kyROsrKwYt76TzbE+nw/z8/NYWlrC0NDQpdF6eW3cmpNOp18rxpE4HA5j5UdHRxGNRs02HLrlpVIJxWLRXD97FHjGZyWez+czR4KLxol1mvpUmlHRXxN5Fm/nd61/J4N20sKvrKzgu+++w7Nnz64UrXe5XBgeHsb8/DxmZ2cvjdZLzs7OkM/nkUgksLe3h0ql0vL3WH0Xj8cxNjaGwcFBBINBuFwuE4QrFArIZrPI5XIoFotm6i7wUzEPBc/NOjKIJ8//FDi7GtvxWpTXUdFfEWuRCS39ZaJsFbDjYIlcLmcE/+TJEzx9+hSbm5vI5/MdufUA0Nvbi8XFRSwuLmJgYMBU3rXzuk5OTnBwcGC66VpZeQo2HA5jfHwck5OTGBgYQG9vL+x2O46Pj1EsFnFwcIBUKoVsNotCoWDm3csAntvtRm9vr5nYw9daqVSaGpdo/TmtSA4BVdpHRX8NpHj53/K7/D35+zIfzZRWNpvF9vY2Xr58iWfPnuHFixfY3t426blO3Hqn04mRkRHcv38fc3Nz8Pv9bS+f5KCM7e1tLC8vI5PJtLyRORwOBAIB0603MTGBvr4+Uy/PgqJkMtk0MZdReAqVZ3paeqfTac7/DO7JXgZ24smfKZ2hor8CVtec+Wbp5rcSfivrzkEYm5ubWFlZwYsXL7C2toZUKmUi9Z0I3m63Ix6P4/bt27h7966pvGvHGjI3n0wmsbKygvX1deTz+dc8GI62YuPO7OwshoeHEQwG0dPTY0ppM5kMdnd3sbe3h3w+b6rqbDZbU0suz/ScvtvT04Narda0MIQRe3bZccKOWvrOUdFfkVaiB5q77vh7/C7HOdP13dnZwdramimv3draQjabNQ0mnbj0drsdoVAIt27dwkcffYSpqSkjxHYj9vl8Hi9evMB3332HZDKJ4+Pj1+oNHA4HotEoZmZmcO/ePUxNTZkWXQYk8/k8kskk9vb2kM1mUSqVmnLtco4/z/Ws2uPRgV4Ah2oCeO13rcNFlctR0V8Ba624HNdss9leKxrh77KhRFr3ly9f4sWLF1hfX0cymUSxWDTufCepQLrbi4uL+M1vfoN3330Xg4ODZihFO6+nVCphc3MT3333HX744QdTdisDb06nE6FQCDMzM1haWsLi4iKGh4cRCARgt9vNthy69pyJLysIObueNxA5H49WnWd23iBYwkuPQIpe6QwVfYdYz+SyPFRuTpXTZOnOl0olM0n21atXePHihaml57y5TrrmgJ+KY8LhMObn5/Hpp5/iN7/5DSYmJkwhzmVWkGfwvb09PHr0CI8ePcL29rax8ryZsb5+YmICS0tLeOedd8xZniOsbDabOc9nMhkTk5DNMXLwJi39eePCeWOgN2VdmaXufeeo6K+AdQ9co9Ew1kgub+DNgTPtMpkMEomEse6vXr1CMplEoVC4knUHfgyE9ff3Y2FhAX/3d3+Hjz/+GHNzc6YM9rL6+kbjx7HW6XQajx8/xpdffonnz5+bNVXy2MIA4YMHD/Duu+9idnYWkUgEbre7qW6eos/lcmaMV6uSWT4m3Xh5g5J19XTfZSHPRUM8lYtR0XeIFHKpVEKhUDCjohhZptvJrbBHR0dIp9NN7nwikcD+/v6VrDvwU8osGo3izp07+OSTT8w5nlH0ds7xtVoN+/v7ePz4Mf7whz/g22+/RSaTaartp+AYIPzggw8wPz9vCnF4tKE1Z4ERb2ZyeYW08hS8zLnzceQoLVm6y6MAg3wq+s5R0XcIhzoUCgXs7++b0ljmm5lOAtA0pnpjYwOrq6vY2NhoimZfxbpTMNFoFO+88w4+/fRTfPDBB5icnGyy8Bc197D4ZX9/H99//z0+//xzfPnll9jd3W0qoAFg1lfPz8/jgw8+wK1btzA0NGQ218g4xtnZGcrlsplp16qgSKbeeD7nzYA3VKbt5ABMWcWnAbyr05botX0R5oNNIe/u7mJzc9OsdJIbWhiMKhaLZi791tYWdnZ2zHz6q1h34nA40N/fj7t37+LTTz/Fhx9+aCL1l6WxaEmlS//ZZ5/hT3/602vddHwMr9eLGzdu4N1338XS0hJGR0fh9/ubKuL4/ezszIy6ZrSe8JwuBe/z+Zom4LJ0l7PuWcxj7WSUr0fpjLZE3+13UwbtyuUyMpmMmRfHJhgWktDS22w/bnOhlU+n09jf379yZF7icDgQDodx+/Zt49JPTk62JXhmEcrlMvb29vD999/jj3/8I7744gusr6+b+IT1+ehRPHjwAJOTk2ZktnXXHb0H1trL/ntZWedyudDb2wu/3w+/3296Angc4oz7UqlkhM+KPNlhp/30V0Pd+0tg+onNJ2tra3j8+DFWVlawv7+PWq3WdD6Vc90KhQIKhULTh/c6Y5vtdjuCwSDm5+eN4OV2mvPcXRmwKxQK2NnZwaNHj/D555/jP//zP7Gzs4NqtfqagLgUY3Z2Fg8fPjQBQpkGlM/H183Z9jJ4xzM5S265zjoUCpniIc70LxaL5njAgh6W9sqdduy+U+F3xoWi5/9x3famMhoPAK9evcL333+P9fV17OzsIJFIYHNzE7lczsxyk+kmVptxzBM/qNed52az2RAMBrG4uIh/+Id/MEG7cDhsct7nCZ5ucy6Xw8uXL/HVV1/hiy++wJMnT5BKpc7tlXc4HBgfHzf5+IGBgZYz8ml16Q1RsHIyDgOPfr8f/f39GBgYQDweNxNzWbTE4CiXWzAQyLM85+XJkVnS9Vcu50LRHx4e4ptvvsHz589NcKgbbgAUfaPRwObmJpaXl7G3t4f9/X0cHR01WR/rQAgA5oPIKPR13zOXy2XSch9//DE++eQTzM7OXhq04zWUy2WkUik8ffoUf/7zn/Hll19ieXkZR0dH516bw+FAKBTCwsIClpaWMDY2Zmr4Wz2XjHcwSMnHAX70Gvx+P6LRKEZHRzE2NoaBgQEEg0FTDchKxXw+j3K53BQI5NGAG3qLxWLTgEztuGufSy397373O/zud78D8ONa4fPmpL1N8PzYaDRMcI4fahnZblV+y39/E8M1uP11cHAQd+7cwccff4z33nsPk5OTJi13keBpebe3t/Htt9/is88+w1/+8hckEomW7rzE4/FgYmICd+/exezsLPr6+loKi6+T23dyuRyOjo5MYQ9r530+HyKRCMbHx3Hz5k2Mjo4iHA7D4XCYRRdy7VWlUkGtVjOfN1p6uv9HR0coFAomA8KMiXI5F4q+0WiYpYEAzEritx157qYreR6tOujeBBT73Nwc7t27h3v37mFhYQEjIyOXBu0Y8CqVStjY2MCf//xnfPbZZ/j222+xt7fX1o2bNfyLi4tmweVFz0crn06ncXh4aKoUWScfjUYxPj6OmZkZ3LhxA7FYzEzXYWCTm2y4zUbOtpc/Pzo6QjabxeHhIQqFgokLtNtJ2O1cKHqbzYbe3l7z3263u+vGDnfinr8Jwfv9fsRiMUxOTmJxcRF3797FwsICxsfHzflXRs5bXUO9XkexWMT6+jq++OIL/P73v8ejR4+QyWTaugav14vR0VHcu3cPN2/efC09J5+LHkWpVML+/r7pDqSXxKPJxMQEZmdnMT09jaGhIfj9fthsNmO5WQpMCy9jIfK5yuUyDg8PcXh4iIODA2SzWfT396O3t1dd/Da5NHovrZ4uDfyvgUGuvr4+TE1NYWlpCQ8ePMDMzAyGhoYQCoXg8/nazsGXy2UkEgn8+7//O/7lX/4FX3/99bkz61sRjUbNxJ14PG4yA61gMU02m8Xe3h7S6TTK5TLsdju8Xq8ZsjE/P4+5uTmMjY2Z4CNFzdp6Oe7aGg+hN8CGpVwuh4ODA+zv7yMejyMUCrVVhahoyu4XQW9vL27evGlq2m/duoWxsTH09/fD6/W21VhCCy8bZ37/+9/ju+++Qy6Xa/ta3G43JiYmcO/ePYyOjqK3t7dl8E5W9XEZx9bWFvb3902xUm9vL4aGhjAzM4O5uTlMTk4iGo3C6/UCgCnCYQyFo61lKk5G5nmDOTo6ahJ+LpdDJBJpa7S3oqL/WeEG2fn5eTx8+BAPHz7E/Pw8YrFYkxvf7gCM4+NjZLNZU1b7zTffIJ1Ot309drsdfX19mJmZwcLCAqLRaMv2VWvwjn0FiUQCR0dHqNfr8Hq9iEQiuHHjBqanp3Hjxg3E43H4/X5Ty9BoNMxrlOOuZRrOKvzj42OTFiwUCuZ8n8/nEQwG1cVvAxX9zwDXLY+OjuL+/fv4+OOPcf/+fYyNjZnBkp20jFKAR0dHePnyJb788kt89dVXODg46Oi6fD4fpqamsLi4aEptrcExKfhqtYpsNoutrS0zD4CuPUdpTU5OYnx8HLFYrOm10a3n419WYSfFzxFj/KK1j8Vi8Hq9KvpLUNH/DZBpPZ/Ph1gshsXFRbz33ntYWlrC3NycGSrZaeeYtLiJRMII/qIFFa2w2+2IRCK4c+cO5ufn0d/f33SWt04AqtVqyOVySCQSZrRWLpdDvV43c/CZj4/H4wgEAqYHno/TqoZe1j3IEWRyDiGHbrLaMZfLmWKpi0ZnKz+ior8CsqwUaJ2Xl0J3uVwIBoOIxWIYHx/H7Ows7t69i9u3b2N8fNyU0XY6EIJiqNVq2N7exqNHj/CnP/0Jq6urHadXQ6EQpqamcOvWLUxMTLQ8H1Pw7JdPJBJYXl7GysoKkskkqtUqbDYbAoEAYrEYRkZGTAGOPK5IwUtBW7vo5HttvQaKnq6+dTqPcj4q+g6RdfZcv2Qts2X1mMfjQSAQQDQaNSmrhYUFTE9Pm+IUtqdexToxd51Op/HXv/4Vf/jDH/Ds2TMcHh529DherxcTExN45513zGAMWnlp4VlazE22T58+xZMnT8xG27OzM/j9foTD4aaddhx4KacJUbzyvWMHHt1+ORuP1wD81L6bz+dNfT6/tFDnclT0HeJ0OuHz+RAIBExAih84upasMR8aGsLNmzdNfnp8fBwDAwPo6+trKwV3Eayn5wCMP/7xj/j666+RzWY7ehyPx4ORkRHcu3cP9+/fx/DwsJmRL8/RPMPTpX/y5Al++OEHvHz50kTsXS6XCeDF4/Gm1ynddetySlY5yik67KPnCC4JvRtZxMNMAMt29Vx/Pir6DmDuORqNmtQTrQ43yXo8HoTDYYyNjWF6etqkqrj9xTrFtVOsAzAeP36Mf/3Xf8VXX31lpte2i8fjwejoKB48eID3338fMzMz6O/vbxr1xVQai282Njbw/PlzPH36FGtrazg4OEC1WjVNR6FQCNFoFJFIBIFAoGlkF49BnBlYrVaNcBnY8/l8ZqaeXF7J107497zZMqrPG4iK/nxU9B3As/ng4CAGBwfR29vbNOmFI6hHR0cxNTWF6elpjIyMGBdXpuCuI/hqtYpMJoPHjx/j888/xxdffIG1tbW2z/Fs0R0dHcWdO3fw0Ucf4c6dO4jH46bAhW43x30lk0msra3h+fPnWF5eRiKRMEMvOSrM5/MhFAqhv7/fLLGkxyAn67Cbjvl2dnPSU+DMAebtW839Z0CP7z33CLCATIN557SqLp4AABfMSURBVKOi7wC3242+vj7EYjGTSwd+WrbIANbExATGx8fNBlc5xPGqFojlrpVKBclkEt9//z3+7d/+DV988QW2trYubaABYKbJhsNhzMzM4P79+1haWsLs7CwGBgbg9/tht9uNJWY6bHNzEy9evMCzZ8+wvr6OdDptBl7KPfM+nw/hcBjBYBBer/e1mIfst8/lckin08hkMmapB/fcU+xsoz0Pip43CDbodEMn6HVQ0XcAy0r7+vrg9/tNsIsz5xmxHh4eNjvarxKVl1gr3zY3N/HNN9/gyy+/xDfffINEIoFyuXzpB93pdKKvrw8TExNYWFjA7du3MT8/j4mJCfT395uGFRmd39nZwcrKCp49e4aVlRXs7Ow0pcYYbWcnot/vRyAQMLly3jyk4IvFInK5HDKZjJkoRI+BmQ65zOKiJhpeK70BVvOp6C9GRd8mMhrP7ap0a3t7e5sEzwaQ657dGd0ulUpIpVJ49eoVvvvuO/zlL3/BkydPkE6nL+2YY4xhcHAQMzMzuH37Nm7dumVKYmWg7fT0FJVKBfv7+1hfX8eTJ0/w/fff49WrV0ilUsa6W4dUsgXY7/cbC8/aAbrbnJB7cHCATCaD/f19MyJbThTq6elpOe22lZB5VJAjyFTwl6OibxN+sDnIkYErn8+Hvr4+xONxxGIxk4brRPDWwhe5HINDOJ8/f45Hjx7hr3/9KxKJhJlBZ/2Q8wjBDALXVd+7d89Y9mg0Cr/fD7fbbYJ2TMflcjm8evUK3377Lb755husrq4im82aYJtMS1LwHHLJuMXJyYnJmzPQyeUX6XQaBwcHZhhJvV43Y61bNfZcJGTOLuR16XTc9rhU9DIgwkUO3QSDSDabrWmlks/ng9frRSgUMmf8VgG785CVZvzw8szOSHkikcDq6qpZjMGGFp7frZNogZ/WW42Pj2NhYQGLi4smg8BCGRljkDeaQqGAra0tPH78GI8ePcLKyoqZ9iuDabJwhjdDuWaa8+5PTk5QKBRwcHBgXHnrXjun0wmv14ve3l5TsyADeBdZcF77ycmJuRbtqb8cHaLRBkNDQ01TZJiu83q9CAQCCIfD5wreWn0GvL7bjm5vKpUy1nBvbw+JRAIbGxvY3d1FLpdrSsfJwhkATV1ts7OzuHPnDu7evYvp6WkTpKNltw6zpPudTqexsrKCx48f4+XLlzg4OHhtJRXh4gneAFkQQ5de7gbgfnpad+bg6SXIYRlOp9NsD+JzX1Rlx+vq6enR/XZt0tEQDY/H09Ha5F8rdrvduM5zc3P46KOPcP/+fczPzzf1l9O6MPjEszHQXEsurTqLXAqFArLZLJLJJLa3t7G5uYmNjQ1sbW0hmUwil8s1zcdvJTwZSOvv78fU1JSJyM/Pz79WAmt1fyl4dudtbGzgyZMnWF1dRS6Xe010/Hv5vNKt5+MwzXdwcICDgwMcHh6aeID8/NCqyxsjq/A4FPOyGQ78O3oMF/X+Kz9yoeh9Ph/+6Z/+CUtLSyaw0g2BEjnRNhAIYHR01FTTMTJNwci2T1lxxseRFW2MXnOUNnPeW1tbyGQyODo6Mq7vRQE6ecPp7e01/e8PHjzA7du3TUReeh78O6C5Y+309BTFYhF7e3tmoWY6nb7QwjPYRrfc7Xaj0WgYt57trrlcrmkvvVxvJYesVqvVptSfFP1FnXcMpHJ5BgOsaukv5kLRh8Nh/P3f/z0++eSTv9X1/KJgNRrwY7qO7jHw03nSWjlG0cvzMtNt6XQaGxsbWF5exvLyMl6+fGnSYO1W0lHwbrcbsVgM09PTePDgAd577z0sLCyYoiGZ7rpoUm6lUkEmk8Hq6iqWl5exu7uLcrn8muBltN7tdsPn88Hv98Pn88Fms5koOodb0LpbN9TwsRgbYo88bwKsE5BezkWi54IRxlukt6W05lJLr+A195ZQVLRgPKtKt7lYLOLw8BC7u7tYWVnB06dP8fz5cyQSiaYUWDuw2cfn82FwcNAsk+Tmmb6+vnNdeSKbXdgPzxvR2toa8vn8axt4rIJnLIN1CPV63Qy14ECLUqnUFIizXoN833hj5L/LnPtFgWOWRTMIKFt3lfPRd6gNWrmL1g40jptmAIpTW5PJJDY2NvDixQusra0Zy85lEO0g03DBYNDsh3///fdx586dpuEbF/XjS8GzWy6RSJjiGwbuWvW206X3+XwIBoMIBAKmIpE5+MPDQ1NW286CD3k9rbrvLlv/5XA44PV60dfX99rmHeV8dIFlG1yWeqM7ynMsK8645HJzcxNbW1vIZrNGEJ1Yd0amI5GIGZz53nvvYXFxEUNDQ20VAlktvGyPffbsGXZ2dpp20rM9mLEDFiH5/X4Eg0HTb398fIx8Pt9UR9/JRh95XTwanRe4tELRs8GH67GUi9EFlldERuSZnkomk9jZ2cH29jYSiYTZVJvNZlEsFpvWMLUD23R9Ph/i8Tjm5ubw4MED3L9/HzMzM4jFYm1PyZWZA1r4p0+f4ocffsD6+jqOjo5M/EJG6GXxDRdOMpjJPDwXT1QqlY4EL48gsquvHdEzXRgIBBCPxxGNRnUabpuoe38NrH3mu7u7Zgf99vY2UqmUCWjJppOLkJF5j8eDUCiEkZERU1V369Yt3Lhxw0x/PS8vba3yY01ANps1Lj3Tc9ls1mykYaWhXCnt8/ng8/maouOMV+TzeRSLxSsJXl63vIm2sz+R5cWRSKRp/p6K/nJU9FdEWs9KpYLDw0Ozi353d9d0j7HU1Jq3t0KxU2hs4GFf/uLiImZmZjAyMmICdjKTYH1ceX30RDKZDLa2trCysoLl5WWsr68jk8mYZhe32w0Axp1npR2rENk1xzP80dFR0065do8t9CKk8KXo2+kWpPfDXgfW/CuXo6K/BnIgBMc30fJd1OYpy2dlDbvL5UIgEEB/fz8GBwfN3jfufqNFk9ZdWnR+p9hZv8821s3NTbx69Qpra2umyo/tsYz6u1wuk/OWYgdgauqtLv1VBN8q4NhqWEYrHA4HgsEghoeHm5ZnaBCvPVT010AGoeR5VFp1eT6WH0qKnRbV7/cjEolgYGAAIyMjGB0dxfDwMAYHB80edznGiulBeS1S7BwYub+/j93dXWxsbCCRSGB3dxcHBwcmF89KNnlup+CZ85Z74xml5yTadqLsfC/k0Es+tozW83VchsPhMHP4BgcHEQgENFXXAfpOXRH54WQduuwFZ005fy4r0RigY767v78f8XgcQ0NDGB4exsDAgGngYWqM8/hktZ686XDeHD2ObDaLTCaDZDKJZDKJdDptml1OT0+Ni8w8N4dfsB/e4XCY4h2W5ObzebOVllNq2jnDWzvyZD6ddfqtZuFZ4Q3U6/Wa96yvr8/cDJX2UNFfEwa/KOBQKIRCoWAsFxtIKAzpPnMKz+DgoBF7NBo1u+tk6ytLWeWUGDkQslwuo1QqGdebCyBkKSyvgcG5UCiEvr4+RKNR9PX1IRAIwOfzoaenx0Tm+VystOPY6XaDdrTq1uh/T0+PqVSk59JOB6fT6UQgEEAkEkF/f7/pzNMAXvuo6K+JrJJjNJkjnhwOhznz8r/dbjf8fn+T4AcHB41l9/v95hzdaDSMuMvlsjlPM3aQz+dNJVypVDJfHAtN15vPzZgBn3tgYADxeByRSMQ05rDZiLvfZS29NRPRztmbIu3v78fAwIAR6unpKfL5PFKplKnIowdyUaCTr4Fe0HUnE3UjKvprQteevfWRSMTUkrtcLhSLRSM8zpELBoPo7+83ffhSdHKuHANxXMucSqWQSqVM55qc+04XWZ6vmX5j+2skEjFeBT2L/v5+s32GzS5s+qEwM5mM6frrRPButxvBYBAjIyO4ceOGWcrpcrlQLpeRSqXQaDSMp1Kr1ZpGbxMZF3E6nWYsFyvwVPCdoaJ/A1Dgvb29CIVCZu4AK+lY2sr/5sgtnm0bjYa5UbCVlGm2g4MDU/Szt7dnxkwVi8Uml58wWMYvl8tlgl4TExOYmJjA6OioWUQhc+9Mx9HqMhAoi4vaCdrRpeco8IWFBczNzWFkZATBYBCNRgP5fB4ul8ukO2U3XqtZBNZYiCy57faK0U5R0V8BmR6TQxxku6mcNkMLyhSdnArLYZSVSsUEt1j4wn77vb09JJNJ7O/vG7Hz5tCqtZeBM/bZj4+PY2pqCjMzM2a3HCfWMtXFczWzApzew5ZfHhfaETx7BEZGRrC4uIh79+7h5s2biEQicLvdODs7Q29vL05OTnBwcGCuhUcHa3syBc+bidPpBICm65U3COViVPRXpFUFmXRB5XomAE2LI7iogTPwPB5Pk7WtVCrGyu/v7zcNopB97nx+2S9PC+/z+cw6rfn5eczOzjaN5Xa73U2Cl6uiGbHnPDsu8mgnLcfjRCwWw9TUlFnjNTQ0ZLbgMrAZCoWa4hgMelLwfG3WtCcbnKxDMZX2UNFfEYqE1oZf1vpxjn7iuVtaMN4cKD6ucObmF1a80brLoRLSGvLx+JjM+U9MTGBubg7z8/O48f/vh5fbY6V7zOtl4I5ju+SAy8uExRteKBTC0NCQOcfHYrGmrjwG5Fgb4PP5zI2Pz8Xfk6+N7ztLiovFYtM0Xa3Iaw8V/RWw5sdpcVp9VSoVM+zSmtu2drPxsRnNptCla22tD7COr5KCn56exszMDMbHx5sm4Mo5fvK1yAUXmUwGh4eHJtXXbi7e7Xab1VbMSFg31raqcaBXdNEGIFp4zhU8OjoywUwW6KiLfzkq+itAy0hXneKW21P5xci0TIHJKTsAXvugyyo/GaTjTUJW/AEw3gJTY2NjY5icnMTNmzcxNDR07lhu+Trk5plMJoNMJtN2G7A81jCmQY+Co8V4g2TQUq6jomXntbW6Ocgbk7U6kNfJ3gHlYlT0V4SCtApefpj5Rat/XmrNavGt0WtaQ+burf/OYh8ZpR8fH2/qPrPulOPj06WXm2cYMJTLIOU1SeRZm3l5h8NhMgEsGALQVGh0eHjYlHa0lhXLa+R3Zjd45GGtAifwajCvPVT0V0DWucuVSvzf0pLLzjHrWC0pegat5KgrOZse+Kkhha60HF/FwqDBwUEMDQ2ZzjPOo2OBkJxSQ+vL+EE6nUY6nTarqyg0Xkurijl5wyLHx8fm8bh2ulgswul0otFomIEjzEjIsl56BfL9I3wdrD6U8Q6+p1qOezkq+g7hB9HqgvNDKuvwZRRfnqNlLT6xWkz5JX8mU1cyRchKu/7+fvj9fjNKulKpAPhRiDJ4J9tu6danUins7++jVCqh0WiY56DYZAxAXre0rqzRT6VSsNvtJg9Pdx9AUysyx33LgiD5/vI953PxOWRRUqfDSbodFf0VkeKnkNkLz0i+dPGt3WhyIiyFzFy//GKDCm8e/C5/zoo7NtAAPwqLVpWTcWXwToqegTHW1nNXvNfrbWoaOm/jDMXIzEM+nzfrrLLZLHZ3d5ui97T8uVzOpCPpotPKy7SkhDcgfsl6BaU9VPTXQJ6raXWltZf5c9k7z2IdfsApep7P2alHK84KNIrcegPged9ms5kyWrmZCGi+Sck8uCz5ZarQZrOZdl++Hrvdbspk6eFYi5Ske85ag2w223IQB4eHsoGHBUfysa1Cls8nG3SsGRDlYlT0V0QKnj3xjUajaaactNh0lVmPL62U9BSky84RVVbhy7w+8JN4mU2wBhHlXjgJbxZyqAWvx+PxtBzvZY1TWMUpbyLHx8dG/LxuHlcYB2HZL130dkaK8flZlyBvesrlqOg7xDoYgyLnh5CiPTk5Ma65dNP5c7m2iUjR0+LLZQ608tblk7TSrAfgV7lcbpps06pykI/P5+RwCwYJecPgTYpeirUi0Qo9AqYDKUyKXpb90mq3I3j+f8AbLfsYdMlF+6jor4gcDMEBF0Czy0wRM6BnFZrc78aGHJlLp7AoOlp2ur8UvKxOY6sth1VKSy/rAuTUHrrycmEEn5evRVYfWrMRF7nidOelyOUNS1Yvtit4lvqGw+GmEWIq+vZQ0V8BeUbnmZofWApSVpgBrfes22w2k6qi6003XebFKZZqtWpuCrKohqKn2KXg6TZLKy+PEzwS0CrL1V0ck0WPQfboW8/1VvjvsmvO2ivPa2pX8MBP23kjkYgZJxYIBFT0HaCivyJSPPILQJPVl5adwblqtWpabtmAIoNXnEvHgh6rt0DRy+g7c9cUKAV/Xv+77NungGu1WlMTjrwGazVhpykya60B/63TPQAejwfxeBxTU1OYmprCwMAAent7te6+A1T010BaFusZV25sucgaSrdZdphJa0xXXObtgZ/SVxQmrbE1MNYq4MbnppfCY4L0TGj9WzUTXRd5Te2Ini27kUgEi4uLuHv3Lm7evKnrrK6Aiv6ayJy3dKVlEItW0lqiK2v3ZY88g4LWI0SrKLWcmSfz3Ly288QlPQrZ60+sN63rFr+0aqJp9+YhBb+wsICHDx/izp07GBoaMuu1lPZR0V8Dq7jl+VyeuTnfTkbV5bJLeTNoNUJbRr5p6eVR4rwafmtzjfwuS3pbvS7+7lWFbj3+WOv323lcORcvHA5jYWEBH330EZaWljA+Pt40I09pHxX9FbHW31P0LCWl+Ol2U/RWF5wWv9VZmaKhJT47OzORd2sMQRb+yHJVptgkUtTWUtc3gbUYSf639fgiewFaQQs/NzeH3/72t/jwww+NW6+Cvxoq+isgz++09LIwRjbhyPO1tK602tzvDsAUrcjUGq28tTzX5XK9dr6Xk3NlxJ3ehLXS7b+idNXabWetK5B5fwAtYx6yTiAajWJ+ft4IfnZ2Fv39/XqOvwYq+isig3C0lrS0PC9LZG5cLoWUvfeytp1uvVwQwVJWa6EOABOMq1arTaOyDw8PX0vjAWhrxnwn0A1nhoJFM9YJvywgkscbGT+Q477i8Thu376Nhw8f4t1338XU1BT6+/vNqG5N0V0NFf01oTg5MIKtpK3GaUmPwDphR7blAmgq/LFWzbHE1zqDj1NluPAil8uZefXcsSdr3XnMuGo0Xk6n5d56dvrJSbsAmoqIeB2lUum1a5A756enp3H//n3cu3cPN27caHLpVfBXR0V/BWQ6jTX3dNOtX9ZUnhxAKXPk1gIa6xFA1u5bI/l8fNayl0ols9oqm82aVVQUvlyWwXJg6WVYq+xk6bH0QDjrPx6PY3Bw0CzPkLP0eWSpVqtNizoY45CiZx4+HA5jZGQEs7OzmJmZwfDwsM65f4Oo6K8Aa799Ph9sth93q7VqQLmoS4zfz8tVy4IfufjROkdOluvKTAIn7XLeXTabNdZVFvLI44U1pShFL/sH+BUMBhGNRjE2NobR0VEMDg6ir6+vaZY+z/EU/dHRkfE8ZA89nyMYDGJgYKBpeSdr61XwbwbbJcEcbVI+B55RrVNwzns/O/mwyrJV65f15/I5KXxZk89zvdXqy4YfayDSmj6UXo3f70c4HG7a0MMtPaFQyIyyblXkw8pBq5Xnc8iVXxyNzbJgDdpdiZYfOhX9NeikoqxdWt0cOrlhyOME4wg853NoBSfVsCDovHFfQPNGXp7duTE2FouZFdo8gsgUnfV65AJOmark8/AII2cGqHW/Fir6bsPq9svpvPzf8kuOq5LReGYazhvuIef6tXM9Mm5gzdvrQIw3ioq+m5E3AKvFlZti5Ex+Wl6mCBlEtMYWlF8sKnqlmVYNQrLTT1pfPVf/KlHRK0qX0VL0eutWlC5DRa8oXYaKXlG6DBW9onQZKnpF6TJU9IrSZajoFaXLUNErSpeholeULkNFryhdhopeUboMFb2idBkqekXpMlT0itJlqOgVpctQ0StKl6GiV5QuQ0WvKF2Gil5RugwVvaJ0GSp6RekyVPSK0mWo6BWly1DRK0qXoaJXlC5DRa8oXYaKXlG6DBW9onQZKnpF6TJU9IrSZajoFaXLUNErSpeholeULkNFryhdhopeUboMFb2idBkqekXpMlT0itJlqOgVpctQ0StKl6GiV5QuQ0WvKF2Gil5RugwVvaJ0GSp6RekyVPSK0mWo6BWly1DRK0qXoaJXlC5DRa8oXYaKXlG6DBW9onQZKnpF6TJU9IrSZTgu+bntb3IViqL8zVBLryhdhopeUboMFb2idBkqekXpMlT0itJlqOgVpcv4/wDCVUZN6c73cgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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PcXBwMNAubzabceXKFfz617/GnTt34Pf7+zrv+DXU63UkEgns7e2hVCqpn3N0Op0q3JmcnITT6eyosuPvg2ri6ThCSU3knKNMPuqGQyKnRiXk4afv5Uw/HCL6C9CthPUsT/x5BU/NL7e2tvDkyRN8/fXX2NraQrFYPNNjPzY2hnA4jLt37+Lu3buIRCIDD62g83wsFlPHiG6Mj4/DarXC7/erslzuHCSznsz0arWqHIEUc7fb7XA6nXA6nao9Fzn46O95px1aOCRkNxwi+kugW+nroBl42ufhTrtarYZcLoft7W08ffoUX331FV6/fq0q28664U0mE5aWlnD//n3Mzc0N3NmWhFYulxGPx5FIJHqm91LhDjXfoEw6/jwk+GKxiFKppHZso9EIq9UKp9MJj8cDj8cDq9UKAKhWqzg6Ouqotmu1Wsqjz1uFifDPh4h+COhG4zecVvBn0S3xRiv4bDarBP/ll1/i5cuXAwter9djYmIC165dw8rKykDneH4tdKRIJpMq+UfL2NgYLBYLQqEQpqen4fF4OiIC5K2noqBMJoNCoaBCjAaDARaLBW63Gz6fD16vF2azWaX21mq1U1YD/6zocxw0m1H4ARH9BdHuNL282/3+m9AKnkz6r776Ci9fvkQymRzoHA+8P8svLS0hFArBYrEMLI7j42OUy2Xs7+8jkUigVqt1PcYYjUZ4vV7Mzs4iEol0xP15Bl4+n0cikUAikUAul1PPZzAYYLfbleipESctMJVKBUajUYX/eFETFToJ50dEfwG0uz1wuuFFv7/l33dz2mkFT/Xyg2C323Ht2rWBwnPa62q1Wshms9jY2FD18lrGx8dht9sxPT2N+fl5hMNh1SMfgAq3HR4eIh6PY29vDwcHB8jn8+q8Tv4Al8sFj8cDr9cLo9GoQnHlchlWq1V59qlLEe/TL9Ntzo+Ifki44HnTykH/ln+vFTyZ9I8fP8arV6/OLXiDwYBgMIjl5WVMTU0N5K0njo+PUalUEIvF8ObNGySTya7ZdwaDAYFAAMvLy1hYWOiIClBxDSUS7ezsYGtrC/F4HIeHh2i32yrn32KxwOl0wuVywel0qgq6VqsFm80Gq9UKs9kMo9GoFguTyaQWAtntz4+I/gKQYEn83Sru6Pd65dJTmSwX/JMnT4YWvE6ng9frxeLiIubn5zExMTHw2Ccem9/Y2MDm5iYKhULHcwM/LCperxcLCwtYXV3F7OysCtXpdLqOWv/9/X1sbm4iGo0inU4r55zJZFKpuzabTQlcr9erjD2r1QqLxaJEbzKZYDabVUNQmWwzHCL6IdDu8t3KaruZ/Bw+3om89GTSP378GOvr60in0yoJZVBMJhPm5uZw+/ZtzMzMqB11kPdEJbTv3r3Ds2fPEI/HlaedoN15bm4Ot27dwrVr1xAIBJRgKd22UqkgmUxie3sb29vbSCaTKBaLKhmH99unIh3avennWpEfHR2pmD7t9rLTnx8R/ZCQ4Hn3WZ6Rx6vu6Oc8y45M4Eqlopx2z549w1dffYVXr16pDLhBd3jgB+ddKBTC7du3sba2hkAgoHbfszg5OUGlUkE0GsXTp0/x/PlzlXbL35PZbEYwGMTNmzfVwuJyudTrUCy9WCwiFothZ2cHBwcHqqIOeN8+i87o5Kwjhx01v6RKPfo37ULAy3dltx8cEf0Q8HM4H7XUrWU0vxlpF6QsM+p8s7GxoYS2ubmJTCaDWq12rmGURqMRk5OTePDgAX75y19icXERLpdroIk31JQjlUrhyZMn+Oabb7C7u4tGo9ERhtTr9fB6vbh+/TrW1tawtLSkwmx8gWs0Gsjn84hGozg4OFAee0q95WOsuJOu25BP3mCUaulplx/kvQmnEdGfE14bTmdxMr8pcYTXkhM8FbVcLiOXyyEWi2FzcxOvXr3CmzdvsLe3h0KhcK5CEkqDDQaDWFtbw2effabSbencfNZ7aTabqknG559/jpcvX6JSqXQcK6i7DVXq3bhxA8FgEDabTUUGKGmG/BPUIpuKY/jr6nS6U4M+yRrq1vaazxjgC4WI/vyI6IeAn8Vp2AMAlVZKpifFq7nYqVVUNBrF5uYm3r17h93dXdVFplutei+4uX3v3j385je/wYMHDxCJRFRm3CBFNdlsFuvr63j48CG+/vprJBIJlddOu67RaMTMzAw++ugjfPTRR8pfwJ2E9H7JR5FOp1W6MD/a0GdFiyPvjc+r8ShXn1fk8YVCGA4R/TnhHWCo7VOtVgOAU+Gkbn3hDg4OsLe3h2g0qpJVSqXSuctEaYcPhUL4xS9+gb//+7/H/fv3MT09fWrwZLf3QNeWy+Xw4sUL/Pu//zsePXqEWCx2qmaevPWrq6u4f/8+5ufn4Xa7Oxxp3AKi5pf02fAFhHZsPoOPdnjqhENpu6VSCZVKpaNhhtb8F87PQKIf9dxmbZ13pVJRJaeJRAKVSgXA+3ZR1O+dBlHkcjmkUikkEgkkk0mk02kUCoWha8JJ8NPT07h//z5++9vf4t69e4hEIipB5qwWWEdHR8hkMnjx4gX+8Ic/4PPPP8f29jYqlYoSMInLZrNhcXERd+/exfXr1+Hz+dTRQeuzINHncjlUKpWOxB4y6Y1GIywWi0q8oaMBld6Wy2UcHh6iUCjg8PAQ5XJZlfWS+Ef9nrwIA4l+1FdVPo6pUCggFoupODalxup0ug5zleq+C4UCstksstksCoWCagZBZvwwNy8/w//mN7/B/fv3lUnfL/OORkvVajUkk0l89913ePToER49eoTNzU1UKpWOBYjM+nA4jHv37uHWrVsIhUIdRwdtVOLo6AjFYlEtamSW0+/q9XqVkENttUjM9JlRi2zqjV8ul1Gr1VS/ez7tludJjPp9Oihi3p/B0dGRMuNJ8K9fv8b333+PaDSKcrmMVqulUkNJBNS2mcpJ+YTXYbu9cC89neHv3buH6elp5VDrtsNzh125XMbBwQGePHmCP/3pT/jyyy8RjUZRrVZPXdf4+Dg8Hg+uXbuGtbU1zM/Pd5zju0Um6vU6CoWCckjyazcYDB1pt263W4meFsLDw0Ok02kkk0mkUinkcjkUi0U0m02VlUfdd6S8djj6ip5uhFH7UHl23fb2Nl68eKEyymKxGPb29joGTJDZyqvBSOC8NdRFP0e+6/Idnkz6bjsdP78fHh5ia2sLjx8/xqNHj/Ds2TMkEomutfK0K8/NzeHu3btYWlqCz+dT5ni3DENaVMgsb7Va6rMZHx9XjTMnJycxOTmpuuwAUGZ9NptFMplEPB5HOp1GPp9XFgNZKTSsUzriDkdf0RcKBXz77bd4/fq1ctqMwgJAoj85OVEJJul0Ws2MI8cbL+/kX3nizmWI3WAwwOFwIBKJ4M6dO/jkk0+wtramPOgk+G5CJMcYTaf54osv8OjRI7x8+RLZbLanYKg0d3V1Fbdv31btsrtFBGiXbzQaODw8RD6fVyE/7rhzuVwIBoOYmppCKBRSpbgAUKvVUCwWkU6nkUgkkEql1BAPSuqhyAD1zKNmmfQZi3k/GGfu9L/73e/wu9/9DsAPZ8lBOq/+3OEJJE6nEw6HQ+0y2t5sPImEuIzmDpSMYrFY4Pf7lSONdl2Kkffy0pMQa7UaEokEXrx4gX/7t39TDrtSqdT3+qxWKxYWFnDz5k3Mz8/D5XKpFNler1WpVJDJZFT4EYDKsnM4HAgEApidncWVK1cQDoeVec/9Jel0WgmeT+Klz7VcLqNcLquGHNSJh2L5wtn0FX273VaeaQCn8rA/VPjud3h4qIY0ktg5vLED/3oRqE59ZmYG8/PzuHr1KhYXF7G4uKiaVfDmk1pIhNVqFfF4HF9//TX+8Ic/4Msvv8Tu7m7P1leETqeDx+PBzZs3ce3atTPNeupnVywW1Tm82Wwqs54WritXruDq1auYnZ1V+frc/0GDLbPZLIrFIsrlckfGI+303LNfKpVQr9eVE1M4m76fEoVrCJPJdK7ijw8B8hSfxWWI3Ww2Y3JyEjMzM1hcXMTy8jKWl5cxOzur+s9RSLBXHTkX/MHBAR4/fozf//73+OKLLxCLxQZ6LzabDZFIBDdu3MDs7GzfRB8K09EuH4/HUSgUVP68yWSC1+vF3Nwcrl69qkZjO51OjI2NodFoqCMIiblYLCqznhZaEn21WkWxWESxWEQ+n0c2m4Xf7++o8hP6c+bSyHc2MqOEy0Wv18Nut2N2dhZra2u4f/8+rl27hmAwCLfbrUJbvMCkG/wMn0gk8M033+Bf//Vf8fnnnyOVSg1cvENtthYWFuDz+XqW5nInYaFQUN1xyDKyWCxwuVyIRCK4evUqrl69ikgkooZmks+DrpnGXfEe99y6ohZaNBKrUCggn8+jWCyqGgDJ1DsbsYd+ZGgQxa1bt/Dxxx9jbW1NNaUgk3WQSjLyIdBI6efPn+P3v/89/uM//gPJZHJgwZtMJkQiEayuriIcDsNisfR03pHHnpKVotEocrmcanZhtVoRDAbVEWVmZgYTExOwWq0YGxtTrcF4Fh4552iD4ZYlL1Qi4efzeZX/QJ10hf6I6H9ELBYLZmdnce/ePXzyyScqq47aSJ+nFRSdjQuFgkqrPY9JD/zgPHS73VhYWFAVdP12eYrLU/FQNBpFsVhU783n82F2dhYLCwuYmZnpaJHNOw7R8/B+9lzw3ClK5ch0ni8Wi8jlcjg8PFSpwWLi90dE/zeGklQotfXjjz/Gp59+ips3b3bs7udtv3V8fKxmyP/5z3/G559/joODg3PV45tMJszMzGBpaQlTU1Ow2Ww9HYXceRePx7G/v696AFCIMRwO48qVK6d8EjTBhkLA5DfpNn5a24eQt9Sm3PxsNot8Pq8iGiL6/ojoL4A2I+2s3yFPdjAYxOrqKv7u7/4O9+7dw9WrV+Hz+c7Vy46/Lh80+Ze//AV/+ctfsL29faaXXovL5cLy8jIWFxfhdrtP7fI83ZYP09zZ2UE0GlX972w2mwrPzczMIBAIwOVywWw2Kw87ndOpCpEEr3UUU4YjvTbVDZRKJSV68uRTp2AJ3fVHRD8ElGXGK8y0Nytv/mA2m2G32zExMaGcWnfu3MHq6uopc/488B03Ho/j22+/xcOHD/Hq1auOUOsgWCwWhMNhLC8vY2ZmRp27+WvR65HgU6kUdnZ2sL29jUQiodpZeTwehEIhzMzMIBQKwe12q5n1fL4db0RCJj33X1ANA0H5D5QTQOFUepC3X0J3/ZFP55yQeU6NGQF0eKF5swfKM5+YmMD09DQWFhZUzJ0y0iwWy7nNeYIcd+l0Gs+fP8fDhw+xvr6OfD5/rufRdrblcXmef8BbfKVSKWxtbeHt27fY399Xs+6sVqsKO05NTcHr9XZMseVZiyR48tQDUE0yaBHoZmmQZUOFODTrjvoRUJaf0B0R/TnhRSMWiwUAOqrmqIqMUk5nZ2cxPz+P2dlZNfpJu/MNI3htPfzDhw/x+PFjpNPpc+UMGAwGuN1uzM/P49atW5ienobValWC57srmdXpdBpbW1t4/fo13r17h1QqpXZ5n8+HcDiMqakpTExMwG63qxRu/pxk0pMDj6ba0ARbXpzULSGq0WioWnvK6ONNSORc3xsR/TmgMU5+vx+Tk5Ow2+3Q6XTK40yxaa/Xi6mpKczOzmJubg6RSAQTExNwOBwDxdv7QWY2dbx58eIF/vjHP+KLL77A/v7+ubIm9Xo9XC4XFhcXcefOHSwvL6u4PG9qQem8NLhia2sL7969w/b2NlKpFGq1mhpE6ff7EQ6H1QRb3qqaC77RaHTs1mSWU7dbqpun39fCFw36nubf0QIidEdEfw6MRiPcbjfC4TBCoRDsdjsAqPM8/TudZ2dmZtTNT04sOidfRPBUQEOhuYcPH2Jzc3OgWfWE2WzGxMQEFhYWsLa2ho8++kjt8rSokL+gVCohk8kgFotha2sLm5ubiMViyOVyODo66hhPFQwGOyro6HzNBVyv1zsaZVBxDllJlJRDRTz8/XOopRYdEWinF9H3R0R/Dij2TGa62WxWLaBMJhNcLhf8fr+qIvP5fLDZbJfSuZW36UqlUlhfX8ef/vQnPHz4ENvb213r4bXwcOHU1BSWlpZw+/ZtrKysYGZmBm63G+Pj4/EScpQAABgeSURBVOq4QoMnY7EYtre31dCKVCqlCl2oPbXVaoXP54Pf7+/wVQDvW35TI1FaRJLJpCqsaTabSvQkeG7+d4PMerpeWgBGoRL0IojoB0Sn06lacI/Ho0pax8fHVX55IBC41HM7gFMe8/39fTx9+hR//vOf8fXXX2N3d/dUx5tu6PV6OJ1O5VBcWlrC1atXMTc3B7/fr94PH75BO/vGxobqIUChsXa7rQpq9Ho9HA4HvF4vPB6Pqv4DoARMgqfy2Xg8rmrmDw8PcXR0pNqA0QJXrVb7DqokS6TZbHZEAET0/RHRD4jJZILD4YDL5VJdaijuTiEqcl5pzflhTXkyh6vVKtLpdMeMO2qAoW1iyaFuueRUvHLlCq5du4Zr166phBlytAHvs90SiQQ2Nzfx+vVrvH37Fnt7e6pclnelpT55NG6aFkMSPCXd8OYa1Bz04OAA2WwWpVKpo9kGDbfg0266OfOA9/36eVKPCP5sRPQDQA46t9utnFPUzdXr9SIcDiMcDisR8bLXQQSvDYtxxxRlvL19+xbffPMNnj59ip2dHdVCSisGEo/BYIDL5UI4HMbKygpWV1dVpp3P51NORcqKoxZU2WwW7969w9OnT/HixQvEYjEUi0W1u1ODEXoto9EIp9MJt9t9agGhR7FYRCaTwcHBgRqAkc1mUS6XVTWe2Ww+VUF4VrkyWRBk0ssE28E4U/TctDIYDCNRWsuTROiGcjgcaoY6id/tdmNiYgKTk5Pw+Xyw2+3KW33WDcjTS/nNSymmZF5vbGzg7du3ePfuHXZ2dpDNZlXHWqDTIUgidLvdmJubw8rKCq5fv47FxcWO3HfuY6CjA6Xx7u/v4+XLl1hfX8fOzo46b2sbZnL/AH0eFMKkfvXUL49GVe/t7SEej6vOOnREsFgsKhLCZ/zxHni9hE/mPDUdGTbnYZSQJho9oJvcZrNhdXUV165dw8rKCqampuB2u5Wp73K5YLfb1QDGXo0tCK3QybGVzWZV1xhqGRWNRrG9vY1YLIZMJoNGo9Hz5idH4tTUFJaXl3Hr1i2srq6qMzu1m9b28qOvFBHY2NjA999/rxJuuOlMTkuyJqxWK9xuN9xut8rgI6datVpFNptV72F/fx+JRELNtKPce6PRqLox0XGEmmVwx1w/s53nRwyT2ThqnKuJhtlsPlcBx8+V8fFxtcD96le/wj/+4z+qsc/k8CKvNR+82K+xBX3l026y2SxSqRRisRh2d3eVOOLxuNrReU46v/l5S2nKglteXsbdu3dx+/ZtzM/Pq+MGtz44fKJMsVhENBrtyLDrVgDD21i7XC54vV6VV99qtXB4eIhqtarmAtB7ymQyqnMwP8OT4MlhR/9eLpc7Ku56QZ+50WiE1WpV3XWF3vQVvdVqxT/90z9hbW1N3dij4CihPuzNZhOLi4u4efMmfD6fqi3nv6f9qk0b5Tu7dkrtmzdv8OrVK+zs7CCRSKBQKHS0y9Z+1ny6y9jYGPR6PTweD65evarGTS0vL2N6eloJsdsMd74IUUprMpnE1tYW9vf3VfxdK3hKMSbn4MTEhApLAkC5XFYpuru7u2piLR9gSQImUx4A6vW6+lxNJpPqxFOtVjtGWmnhE4Vozr2Mrz6bvqJ3u9347LPP8Omnn/6trucnATmRuAna62bq1yOPm/G1Wg2ZTAa7u7t4/fo11tfX8f3332N3dxeZTKZnG2p6Hi5avV6vYu0rKyu4e/cu7ty5g8XFRbU4nZUIxIt1CoUC9vf3VYYdpcXyv6VFplsba6PRiHq9rhaPvb09tZCROc/n2fHPmHre04ZCr02JNv26CdMCZLFY1KNX/b/wnjN3eqE//OblOzulr1Lvt3g8rrziT548webmJrLZbN+dTHuzU5iMHHUfffSRmiAbDoc7PPJn7XbkwCsWizg4OFBmOIXQAHQsGiQwt9uNQCCgohU2mw3Hx8col8tIp9PY3d3F7u4uEomEOiJoBc/fG31O1CuPau3pPN/PcUzhQupYLO2yBkNCdpcEFzsfXJnL5bCzs4P19XU1QyAWi3VMu+2HdhxUMBhUuzu11pqcnFSVbIO01dKWx25tbWF7exvZbFb5MrjTjsxop9OJYDCISCSCYDAIp9MJnU6HSqWCdDqNvb09JXgKKfbbqfnRh4foaHTVWTMDqEX4xMQEvF7vmaO5hR+QAZZncB5TkXYtOrtTNdrz58+V4JPJpDrf8hu93+vzMNzq6irW1tZw8+ZNzM3Nqd7x3c7unF718Nvb23j79i0ODg5UGI0m7pLg6QxPxTRUFmwwGJTXf39/X2XtkeAH7SJMzkTa1QcZFELZe9RP3+/3w2KxiOgHQAZYXhLagpJCoYBoNIoXL17g2bNnePv2LTKZTMeU2n6CJ18CVfUtLCzg9u3bylkXDAaVZ/6sxplaZyIJfnNzE2/evMHu7i7y+Tza7bayFugMT156v9+PYDCoUowNBgMajYYKy9EEX4rrnyfKw/0h/Fr7fT5keVD6M03SlXv1bMS8vyT4Llqr1ZDP51UGGpm73c62BN2sfO6b2+3G9PQ0lpaWcOvWLaysrGB2dhYTExMd0216CZ7v7rQYlUoltcO/efMG7969QyaTQavVUrF34AdRURze5/NhYmJCpdmOj4+jXq8rwVMRDoXZBkng4pEIulbg/S7fT/A6nQ52ux2Tk5OYmppSZc6U/iv0R0R/CXTLrqPMOqrx5u2zyFPN488U+6cMt0AggJmZGdVtZ2FhAaFQqKPXXL8efdyZSMeNfD6PeDyO3d1dbG1tYXd3F8lkUrWsdjqdanenUll6UCYfJWxRqW23pJtBjizcZ0Bwwfd7DrrWSCSCubk5tcuLaT8YIvq/EmSe22w2uFwuVTM+Pj7eEQ6j1ls2mw1OpxMTExOYmprCzMwMZmdnMT09rSr7epXpasOEPI21Wq2q8c8HBwfY3d1VCUBUMUcOMRK7y+WC2+1W2YZ8nDQ9F5n0qVQKxWKxa1iuG7z/Hc+x15r4/TCZTPD7/ZiZmUEkEuk7Z084jXxSlwA3Vanlk81mg8/nw9TUFGq1GvR6PQqFggpN8Zi3x+OBz+dDIBBAIBDA5OSkMqcdDofKNOOxd94/jkcNtMU6uVwO8XhcmeHJZBKFQgG1Wg3tdluZ8TQz3uv1qp2dkpF4I8p0Ot3htCuVSgMLnqwcWrTovQw62Zcci3a7HcFgENPT0yrFWEJ1gyOivyS484vy4EOhEE5OTmA2mxEIBFQMnM9qp5JUEhuV7losFiV02hXp+EACo/8mE576xhWLRZXiG4/HkUgkkE6nldhbrZZKW3W73fD7/aoFGFXLUXNJqpKjxBuKw5Pge8XhtZ8NiZ2KYrSL1yDTkMlj7/P5MD09jVAopI4d4sAbHBH9JUI7GYWSyBvucrnUdFUASnB2ux12ux1WqxVWq1WVlnarw6ccdDLbeQfYSqWixjcXCgXkcjmk02k1AZZe+/j4uKOx5+TkJEKhEEKhUMcRghxilCBTrVaRSCSws7Nzrjg88H53JsuGH1HI90F97gb5bKnOIBwOq/l1UlJ7PkT0lwQ370k0FGO32+0dI5fpHE8PEro21k6OON7tlcTN57jlcjnk83nk83k17oly+HmXXhIdxdsjkQimpqZUGI4SfID3/eeoPRcl3pwnDs9Td8lBSY5IcgjShFv++XSrOSDR0/VTmbCU0p4fEf0lwm9OgnZ+2hF5X3xuumvDV1zsJA7eZiqRSCCTyajpLrVaDbVa7VQpLM9Pp4YfkUgEMzMzKpWWauHJGUaNJuv1ukqt3dnZUe2yBhU8OQipYSYNvqBc/Ww2q7IXyS+gFT5fTM1ms4rN85RjEf35ENFfkG6mLZ2/aRcijz0JUeu9JnipK3V3JbFHo1Hs7Oxgb28PsVis44xOCT/8+bk1Qe2yKMQ1OzuLYDDY0UGHrAw6PtBgSirEicViAwuepw37/X5MT0/jypUrqvoPAIrFosroo+MJfS48zs/baNGkIC74Xv8PhN6I6C8BHqcntGWwADri8lyk9By8JzyV4CYSCezu7mJjYwPb29sdnWdIgHxXJIHw/n3T09OYn59X02Np7j1vrMGvjxfi0Jw6ijyclXjDBe/xeDA7O6sm5wQCAVgsFjSbTeTzeTVlN5PJqGYaPKGIN+zgPfHJcTdIIo9wGhH9BegWX+bhM62HXXuTauvaeU94CrVRcw06T5MpT/X29By0sFB+AM3Om5ubU6O0eB9+KkMlwfOMQl5AQw0wyBF4lsAoZOlwODA1NdUxm97tdkOv16sOQIVCQXUdMplMqNfrp5J2eJiPfB/A+/bXvAOumPmDIaIfEi7ybmInofN5bbQz82EO2hbR/PxOabzRaBTZbFY1qOThLd7Aw2AwqOKYQCCAK1euYGlp6VSPvG4NKHkKMfXno4WmWq0OJHier09TfiKRCEKhkJppR9fLHZncmUltuPliRsKn4ZdkCfGhlSL6wRHRXwASC0955RVi3CFWq9VQrVaVw41PZuHeeUqCyWazqm8eZc5xZyA5vLj5y2fo8R2ej9XqJ3iq/U8kEqqAhnrSDxKao93YZrN15B5QVIB3XuK7M9/NtX4O/t+81djh4SGKxSIqlYo64wuDIaIfEr6zk9i1OzdlxvEwG92s5XIZ9Xr91IQWEn+1WlUDGkno5BiknZDHwCmNNxAIIBKJYH5+HnNzcyqezePjdP1awVOr6lgspnwHgzRD1SbfWCwWlXdATkwSLLXnogWQMhTpeWgR0PoOyLlZKpVQKBRUiNLn86nBIpKVNxgi+iHQVq+RGc+/J+873aTZbBaZTAaZTAa5XE4NbiTBa48G9N80TJJ2cu0OSc4talIZDAYxNTWF6elpBAIBuFyuDk839ylwMdKoqXg8rvrS12o1JUiKSHSLofPdmu/oVOhzeHioEpWoy04ul1Nz7Pr5C7j4qUowl8up3ATycTidTjHxB0REPyS0s5PY6ezOM+YqlQpKpRLy+TwymQwSiQRSqRTy+bza6fnsNdrN+bgsbdksfc+z3CiM5fF4VLNKyq4jAVKYTZv4w/MASPC5XE6d43kUopuguOONFhZqbJnNZpVX3uFwKNHTv9FnoV38tAsAd4TS50mCJz/HKMxjuCxE9EPAd3rurOMz1XhGG5n41C2W4tLctKVdmyfTUKFNN2cXfU+Tduj36UFnXD7FlQuerpmskXw+j2QyiXQ6jXK5rKbIGo3GDkdZt92e+wd4PwGdTqcailB6b7vdVr4D2q2paIfXFXCLhBYcem4SO2UeijPvfIjoh4SLnj+0Dj2td197I9ODt3F2Op1qkIbT6YTNZjuVm0/5+dyzTQ8699MCow0davP3aUEqFAoolUoqR99sNnf0pSeLhr8nLjJ6brISGo2GGlhptVrVNfPFplwuK+FSkhFvlc1fY2xsTB1FuE9k0Co94QdE9BegW9gO6EzHpd2St2qm+DJln/FyXCpx9fl8yvvtdDo7RM8HV/QSMz3I4uCz5bRHEfoZ/Y1Op1PHBnovJDgSrfY9U7Ucefq5T4NbKuQb4MU29LrdFkt6j5SMw4dWyljq4RDRXwDtWZtuaC52i8UCm80Gh8Ohqsnod8gzz0Xmdrvh9Xrh8/mUZ5pq6mmSK3fK8bRZSqwpFovK/KWJM7wAh0+N0VoK5Dy02Wwd6cK8Kw2Jlr7nX3U6nZoXQItMvV5X1gn3zvNFh1tJ3YRMP6PfIx8Ft3rEtB8MEf0F0abb8huWx+t5yiw56XhX3G7lthaLBWazWU1x0Q6epN2QzGWqo6dIQTabRaFQUJEC3uyCknkoR5/SW8nTT62xyAnILQISHD+20H/TZ0LCJ4HyODxBwh+k+y09P09GosxD6Xd/PkT0F4Cbvlzw2sQX3sed/p1yybWlr9qdnP89iYhExQdg8ghBMplEKpVCLpdTIS1uPmsbfthsNrRaLTWeihxuQPcdlickdetpR+Kk99stn56OJfx4cpapzq+bMg8pSiEltoMjoh8SrSNOG9bicWvaxWm35+Lj4SbaVavVqjq7c4Hz0Vp0tuV5AIlEQome0nZpCKZ23DRPeeVibjabHU00eJIQWQp8d+7mzaf3QtdOYUMSOI8C0PMMCiUiUUtu6gwsO/3giOgvkV6JK3TGpwWAHvV6XTnGtI44coRVKhXlvecZdXyXz+VySKVS6sEFTzs830npeMAXIDqCUHSAt80mnwA37wfZmXuF0LgVoXUI9oI+S7PZjGAwiPn5eczOzqruOSL6wRHRXwJ8x9JW12l3Q15Ky73Y5Myi1NpCodARd6dKNLq5SZC8RVY2m0Uul0OpVOo4w5PotfAUWVpkyEzmP6ev9LNBRErvVWsB8fc/KDyvPxAI4Pr167hx4wZmZ2fhdDplaOU5EdFfEN6FlveM07a64iEyXohD5jNNaSVTl8J4JpMJZrNZOfSotJQ6zlDX21KppBpYklD5QgScbi/Nze1ms9khHDpq8PcyqNiBTitH68Tjrzvo8xgMBkxOTmJlZQX379/HysqKjLIaEhH9BdAKnnvHtT+n6joylfmD78w8s4yPySYPPsW6gfdVZ1SSyzvo9LIseuXP82442kSi8+7KPE+B2mbxsJ828YbO+N2ei45GPp8P169fx8cff4zbt29jenpaHHhDIqIfEm7Ck/lLcXgec+bpuLVarSMhhb7nD56NRqa+1hfAb3R6PW12Hq8/11430Fl4o/23YeFOTJ4iTIsVAPWZ0Pfc7O+2GNEOf/36dXzyySd48OAB5ubmlFkvu/z5EdEPiTYcx4tuupn8XGRcoHRW5V5zgreL4s5A3iab/AJ0xufWBC0ylE1HCwCPFlwWvMyX0ol5vgH5Dyjtt1KpqAw+AB3HEPpszGYz/H4/rl+/jl/96lf4xS9+gcXFRdVcUwQ/HCL6IdHGqUmc/Oe9cuNJvDRdhrespvM4gI6GkNqzvba3Ha/fp3M+1fGTJ5+q2QB0dewNC2/RRSnE1FabD5akXHxKIqLEIer3RxYLTbEJBAK4ceMGHjx4gDt37uDKlSvweDzSBfeCiOiHoFuVGZmafCHQJtdwBx4PhfGGEnTzA+9j6ZS7zgXPTVtyunXrjc8fVFjDX48WgWGgRcjhcKiR0VNTUwgGg5icnOzoaNNsNlXHG6qHp/bdvFKOGnr6/X4sLi7i9u3buHHjhuqkSzu8CH54RPRDoG0aAXTPYtNW2Gk765DjjxfC8Pg3twrowRtVaGvjaUHhoi8UCkpc5XJZJfNQpRrt/ryCTnve5845OmdTRhxNm5menkYkEkE4HFatuUigx8fH6roovMiviU+4MRqNcLvdCIfDqvtPMBhUFoMI/uKI6IeATHlqxTxooop2EdCW4XLPOxea9pigjX1rfQiU1MPj99Slhmr5ec8+ciryyjyeNUi1AvyIYbVa4ff7EYlEcOXKFUQikY7BGdwS4VV3JHxaeMi0b7d/6KzjcDjg8/nUEE/qscfTk4WLoTvjZpW6xR5woWo95P12Im2cvJt1wJ+Df+3VvYYvJtyKoLM9jcCiHZbH83neAEUaeLow8H5Kj9VqVRNmJicnEQgEEAwG4ff7O/rw8YWp23XRosTDlGRBUEUitcam5xPBD0XXG1FEfwHOimMPKv5BGMSk1Yqfj63mveUKhYI6R/OOP/TguQJ0nCDRu91uTExMIBQKwefzqSo3iiictTBpox3a1+JdgbRVecK5EdGPEjwez8/7vA03dybyyEG73VbHF2rDReOztf89zDm7V6JQt7Rd4UKI6EcZ7kegXZY3udBW4lFEgp/jeX6AdsKu8JNERC90ovVLdOvhp80zEH5WiOgFYcToKnpZugVhxBDRC8KIIaIXhBFDRC8II4aIXhBGDBG9IIwYInpBGDFE9IIwYojoBWHEENELwoghoheEEUNELwgjhoheEEYMEb0gjBgiekEYMUT0gjBiiOgFYcQQ0QvCiCGiF4QRQ0QvCCOGiF4QRgwRvSCMGCJ6QRgxRPSCMGKI6AVhxBDRC8KIIaIXhBFDRC8II4aIXhBGDBG9IIwYInpBGDFE9IIwYojoBWHEENELwoghoheEEUNELwgjhoheEEYMEb0gjBgiekEYMUT0gjBiiOgFYcQQ0QvCiCGiF4QRQ0QvCCOGiF4QRgwRvSCMGCJ6QRgxRPSCMGKI6AVhxBDRC8KIIaIXhBFDRC8II4aIXhBGDBG9IIwY+jP+Xfc3uQpBEP5myE4vCCOGiF4QRgwRvSCMGCJ6QRgxRPSCMGKI6AVhxPj/lntgLPlRnC4AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 63\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 65\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 66\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 67\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 68\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 69\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 70\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 71\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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4sLCwAJvNlvI8rMkl2/fH4/Ekz32qxwsbbhCZQaJ/w2Dtrefn5/HTTz/hf/7nfzA0NJSRKRuPxyGVStHc3IzDhw/zBJlMYF1w/X5/yt8LbwDd3d04duxYyvNEo1GsrKxgYGAAy8vLKY8lkUigVCp5Wy5WsMNaXad6vFwu5334SfiZQaJ/g2CpqNPT07hx4wa++uorDA4OZtTPnolRIpHg4sWLOHLkSMaTaVh57MjICFZXV5N+zmCCLywsxIkTJ9DZ2cnbXrH9OMumGxsbw/3797GysrLhXCKRCHK5HAaDAVVVVdBqtfB6vRCLxTwsJzwv2wawWXt7PXwjGyDRvyEwgczMzODnn3/GN998g6GhoYwHWDCB6PV6tLW1oba2dtsQ3Xqi0Sjm5ubwyy+/YHx8fMOxGWq1GsePH0d3dzfKysp4R1628gYCAUxNTWFwcBBjY2MIh8MbziUSiZCXl4fS0lI0NTWhoKAAdrsdoVAIdrs9pR9ALpdDpVLxuD+RGfSOvQHEYjG4XC48efIEP/74I3766Sc8ffo0I8Ezp1oikYDBYMDFixdRV1eX8SrP6tenp6dx9+7dDdECYdJOeXk5Lly4gMbGxg3nYTUC/f396O3t3bQUVyKRQKvVoqqqCg0NDdDr9bBYLHA6nVhYWIDT6eQJOMCLPAGFQoG8vDzew4/M+8wg0b9mYrEYVlZW8PDhQ/z888/47bffMDExkbFnWvj4o0eP4uOPP0ZFRUXGgojH45iZmUF/fz83x1OF0PR6PU6ePIkjR47AZDJtOE8wGMTKygoGBwcxMjKyqRNSLBZDr9ejoqIClZWV0Gg0kMlkWFpagkqlglQqTbIQWM//3NzcLXv5EZtDon9NsCSTxcVF3L17F//+97/x+++/b+rhTgexWIzW1lacPn2ap8JmIvpEIgGHw4G7d+/i999/52b1+lJaqVSK8+fP48qVK6isrNywfYjH47Db7RgdHcXz58956W+quLtEIoHBYEBxcTEMBgNUKhUikQh0Oh3y8/OTeuWx1yiVSiGTyXiIj8gMEv0uWF/Wme5Ip3A4jGAwyB12zEPPymR3SnV1NT766COcOnUKGo0m41Xe5XLh/v37+PnnnzE0NJTyMQqFAmfOnMGnn36KQ4cOQaPR8Ncl7G+/tLSE3t5ezMzM8Oemsl6kUin0ej1vu83GaOXm5vJJOULEYjFvpCGRSEj0O4BEv0NYZlgsFkNOTg7v2b4Z7AbBOsQMDQ3hzp07+OOPP/D48eNdpZNKJBI0Njbi4sWLuHDhAmpqajIeNMkE/89//hO9vb2bPq6lpQWffPIJjh49ioKCgpSPYS2uBgcHeZedzVAqldDpdHw0l3CPnioBh8XomeBpP585JPodwuq+fT4fnysnHA4hNEljsRjW1tZgsVi4N/vmzZtJveYymUojRCaToaWlBR999BEuX76M6upq5OXlZbTX9Xg86Ovrw5dffokff/wRPp8v5fVUVlbi/fffx7Fjx2A0GpN+JxQfe73rfQLrkUgkyM/Ph06n49NwWCaiz+eD3+/fkJXHeuOTA2/nkOh3AMuYM5vNWFxchFwu5/3gcnNz+SrLcsS9Xi8WFhZw79493Lx5E6Ojo3A6nUne+Z0IXi6Xo6OjA5999hnOnDmDqqoqqFSqjExej8eDgYEBfPXVV/j+++959p1wD82skI6ODpw5cwYmk2nTUBnzVfj9fu4T2Ez0CoUCRqMRBoMBSqUSIpGIJwVZrVbe414IS9PNZEoukUxaos/mHOdUH6pwOIylpSU8evSIp8dqNBoYDAYYjUae8mq32zE1NYXJyUnMzs5ienoas7OzST3tdrrCSyQStLe347PPPsO5c+dQW1ubcg+8FW63G/39/fjyyy9x7do1Hp4TXhMTfHt7O86fP48DBw5s2QwjGo3C5XLBZrNtcASuR6VSoaKiAkVFRVAoFIjH41hbW8Py8jLm5+fhcrn4kMv1sO0S1dJnTlqipzf1T0cVc8D99ttvuHPnDubm5hAMBiGTyZCXlweTyQS9Xg8AsFgsGBsbw8TEBILBID+WUFQ7vaG2tLTggw8+wIULF3hlWyaCdzqduH//Pr766itcvXo1ZeYdu86qqip8/vnnOHXqFAoLC7dMiAmFQpifn8fk5OS2eQYmkwn19fUwGo28w63VasX09DSmp6fh9XpTds5lxThUdLMzyLzfhkgkwvfu8XgcFosFv//+O77++msMDw8jHo/zDyarBWeTWVgefaqa8N2wb98+fPDBB7h06RLKy8uTGlakg9PpxJ07d/DPf/4TP/zwAzfp15NIJFBYWIienh6cPn0aNTU122bAsVLgkZERbtGker0mkwmNjY1obGxEYWEhxGIx1tbWMDc3h7GxMSwtLSXdKBmsRJeNumZDNYn02fIv6Pf7s/Jumkgk+Ko5OTmJkZERmM1mOBwOzM7OYnh4GNPT0xlNldmpGS+VSpNWzLa2Nly6dAkXL15EbW0tHw6RzmsCAKvVijt37uAf//gHfv31Vy749bFwdiOrqanBuXPntm2xxWDFQuPj45uu9Gq1Gl1dXTh16hRqa2uhVqsRDodht9sxPj6O6elpuFyulEVGsVgM0WgUa2tr8Hg8SbF8Ij22FL3L5cLAwACePXvGzcdsuAEw0ScSCUxNTeHZs2ewWq2w2WxYXV3lH+bN+sWtP5bw33QQHpedS6vVor29HRcuXMCFCxe4lz4dk55Nml1eXsbdu3fxzTff4JdffkEwGExKqxU+HgByc3Nx+PBhdHZ2btkfX5itx+brpfIPAC964nd0dODcuXM4fPgwiouLIZFI4PP5YLVa+Sq/2Q2Vdcx1OBywWq0oKCjgTkAiPbZd6b/44gt88cUXAF54izMtAHnbYaISmvGMzRxMu2X9MbRaLT7++GN8+OGHaG9vh16v5wMetyMcDsPlcnE/xPfff48HDx5sezNSKBS4ePEiTp8+jeLiYl5MkwomuGg0yptlCH/HzqFSqdDR0YG//e1vOHHiBCoqKiCXy3myksViwfz8/LYTe1jm4OrqKioqKqDVailJJwO2FH0ikUj6A+5kSOLbzqvqwZZqxT148CAOHjyIlpYWdHZ2oqmpCQaDIW2PdTQaxerqKu7fv4/vv/8e9+7dw9zc3AZHnRBhae6JEyfQ0dGRVuvqRCIBt9uNycnJlAk5eXl5OHr0KD766CO89957qKys5EU6kUgEPp8Pq6urfJrtVudhORJsAEYkEqFquwzY8p1iZY8MNkU0m0i1wu81QvHpdDoYDAbU1NTg/PnzOHXqFGpqaiCXy/meOh3BRyIRmM1m3Lp1C19//TVu3bq1IREo1SrPXmtzczOamppgNBrTWkVZDf6DBw8wOTmZdDyVSoWuri5cuXIFZ8+eRVlZGXc+shbXHo8HFosFXq93y/MwwXu9XjidTqyurkKv10Mmk5FDL022vT0KP/CRSIS6j74EmPgUCgXOnTuHS5cuobW1FUajETqdLq2pskLi8TisVivu3buHL774Ajdu3EhKctluC9LQ0IArV66gqqoqbbM5HA5zn4Ew314ul6O5uRmffPIJzpw5w6MNwJ83r3g8DrfbDavVmrLmfv15LBYLCgsLYbVaMTc3h8LCQqjV6ozLiLMVsoleE+v7zZWVleHSpUv46KOP0NnZuaMWV8CfTrvnz5/j66+/xr1797YV0vrrqaiowHvvvYfS0tK0riEajfJx0rOzs0nmeUNDAz744AOcOnUKVVVVXJjrHYcsC287n1EkEoHVauXOQrPZDLfbnXUW6G4g0b9iWFSACaysrAy1tbU4ceIEzp8/j7a2tg2e8kyyzuLxOGZnZ3H79m309vbC5XKl/TzghbOtubmZp/SmQzgcxtjYWFLTDbFYjNLSUpw+fZpHGzZbiePxOHw+H1wuV1qOYpaXzybduFwueDyepNoHYnNI9K8Y4eq+b98+fPTRRzh37hyampqg1+tTCi1dwbN9dW9vL3766Sc+lWY7hGOpWEHNdsMmGdFoFDabDQ8fPsTvv/+OtbU1AC9i8e+99x5Onz6N+vr6DccTOi5ZLcPa2lra28dYLMaPYbFYsLi4CJVKhfz8fArfbQOJ/hWQykt+6NAhfPLJJzh37hxqamqgUql2vUo5HA7cuXMH3333Hfr6+tJ+Hhs1JRaLcejQIXR2dqa9yns8HgwNDaGvrw8LCwuIRqOQSCSoqqpCT08P2tratk0gYpmLqTLwNiMQCMDv9yMYDGJhYQEqlSplC25iIyT6VwATfFlZGWpqalBZWYmTJ0/ixIkTqK2t3ZNwExP8P/7xD9y+fZv/PJN5dwcPHsSBAwdgMpnSqsf3er148uQJrl69iocPH/LjlJaW4sSJE2hra0NhYeG2fQbYpNpM2nw7nU5YLBbodDqeH8Dm1RNbQ6J/ycjlcuTm5sJkMuHMmTO4cOECGhoaeI/3vRC82+3G3bt38V//9V/4+eefEQqFuGMuXRE0NDTg448/Rn19fVqCX1tbw+joKK5du4Zff/2Vz7sTi8VoampCT08PysrKIJPJ0lrlM50173A4sLy8DJPJxBOVaIVPDxL9S0IikUCv16O9vR1HjhxBU1MT6urqUFFRAb1ev2cZZG63G319ffjXv/6F7777jidQbbc3FloACoUC3d3dOH36NCoqKra8ESUSCXg8Hjx58gTff/89vv32WywuLvLXXFpaivb2djQ3N6f1OiORCDfTM1mlmcc/EAikPdCTeAGJfgfk5+dDq9UiNzeX97xjYTGWQKNWq9HW1ob3338fhw4dQnl5Oe9os5sVSejJ93g8ePjwIb744gtcv36d74nXhwOFCNOKgRfe+p6eHvzlL39BVVXVpjkB8XgcsVgMdrsdjx49wvfff49ff/0VMzMz3CyXSqU4ePAgOjo6UFhYuGWBDksOYv3t3W53xu+LsDU22yZQ1d32kOgzRKFQoKysDNXV1dDpdIjFYgiFQnxWukKhgFQq5fvazs5OlJSUZNyzbjOYMFwuF/r7+/Hf//3fuHbtGlZXV5My3DaD/U4sFqOkpARdXV349NNPcezYMahUqpThwWg0Crvdjrm5OQwPD+P27du4ceMGzGYzv6ZEIgG9Xo+Ojg40NDRsWwQjEokQiUTgdDoxPT0Ns9mc1AknnVVb2JA0Go3y8dV79V6/q5DoM4QNZqipqYFWq03ak7LqvKKiIjQ2NqKtrW1PBc9wuVy4d+8e/vWvf+GHH37gsfFMzNuOjg58+OGHOHnyJGpra6HX6zfE0ZnpbbFYMDIyguvXr+P27duYnZ1N8rQnEgnI5XJUVFSgrq5u2wIdZh3ZbDaMj49jaGgIS0tLAF5sEYQreDqwNlsulwuBQIDGXW0DiT4DNBoNysrKYDKZoFarIZVKeXwbADctKyoq0NDQwDvCZIqwIGb9z1h67X/+53/i119/3TT5hnWNFXrEtVotDh48iKamJnR1daGrqwvV1dUbmnAws9vhcGB4eBi3bt3CH3/8gfHx8aTYv3AbYTAYcODAAZSVlSE3NzdpxRZ+z1Zki8WCx48f486dO+jv7+ede5hpnk68ntXWs+tlAzep6m5rSPQZoNFoUFpaioKCAkgkkqTGj/F4HGKxGBqNBiaTCQUFBWk1nUjFerOYjZpaWFhAX18frl69il9++SVlAwxhMU00GoVUKkVBQQF0Oh0OHz6Mc+fOobu7G0VFRby3vJBEIoG1tTVMT0/j/v37XPDMOy88h1CYxcXFaGlpQVFRUVJTC2Gdvc/ng9lsxsTEBEZHR/Ho0SM8efIEKysr3CcibHi53Wofj8cRDAZ5F51QKMRn1xObQ6LPAKVSCa1WC6VSCbFYzLPCmOjlcjnKy8tRVlaWdoOLrRB2hp2ensYff/zBB1EIBbHZ98CLXnoffvghdyYaDAYUFBRsaoEEAgFMTk7i2rVr+PLLL/Hs2bMNqbHrzyGXy1FaWorq6mpoNJokC4B1A7bZbFhcXMTjx4/xxx9/YGhoCBaLhZvyrNVYJsRiMQQCAe7BZwMwKHS3NduKXviHkEqlWVfYIPRMMzOY9WYD/jRHxWIxb4xpMBi23NNuRiQSQTAY5Gar3W7H4OAgbt26hUePHmFhYQEOh2PDqr5+VdRqtTh69ChaWzp/86QAABZzSURBVFvR2tqKtrY2VFdXc8/8ZqKIRCKw2Wy4efMmvvzyS4yOjqYV+isuLsa+ffv4zYRVY7LGGCMjI7h37x4eP36MhYUF3t5aCOstyG6g6cDSdwOBAB+EyW7IxOZQE400KCwsREtLC9ra2lBVVQWFQoFwOMzjy/F4HEqlckMP9+0Qmshs8s3o6Cjm5+fh8Xjgdrvx7Nkz9Pf3J3WTWV8Pz/7VarUoKirC8ePHcfnyZXR1dcFgMPDZb9vhdrsxPDyMmzdv4vHjxwC2Dv8BL6yf2tpa7huIRCJ8JsD4+DhGRkYwNDSEoaGhlPPpGSyRaKta/1SwrYNMJuOip5Dd1mTUREOhUGSUKvm2IhaL+d6woqICx48f5xVwWq0WoVAINpsNKysrsFgsCIVCKCgoQFlZGdRqdVofumAwCLvdjtXVVfh8PjgcDjx9+hTXr1/H4ODgltVxqQRRXFyMnp4enD17Fp2dnSgvL4dGo0lbAJFIBIuLi/jll1+S5thtJXiJRAKj0Yj9+/ejpKQE0WgUi4uLWF5exqNHj3Dr1i0MDw9vGHe91evKxGsvFoshl8shlUohl8uhVCp37EfJJrYUvVKpxN///nd0dHRAKpUiJycnK7KeRCIRN7Hz8/NRVVWFuro6mEwm3ieQDbZgYSK5XL7tKs9i+n6/HysrK+jr68ONGzcwNjYGr9eLQCAAm822beHJenO+o6MDV65cwalTp1BfX4+CgoKMthesHp556lmG3XYYDAa0traitrYWSqUSFosFk5OTuH//PgYHBzE/P7/roZxbkZOTA5VKhby8PN45h/bz27Ol6LVaLXp6enDq1KlXdT1vBEKPM/BiRWMrCgD+vVqtRnFxMfcas8GKDKH5Ho/HYbPZ8Pz5cwwODmJ8fBxPnjzB48ePU7aIkkgk3ORN5ZkXiUQwGo1oaWnBX//6V16tt5MQYSQSwdjYGG7evImpqamkc22GXq9HU1MTOjo6YDKZ4PV6MTk5ibt372JgYIAn7qRzrJ0ikUh4MtTLOse7yLYrPZEasVgMmUwGmUwGpVKJUCiUVCnGvPs+nw8ejwerq6sYHx/H77//jt9++w1zc3P8WKmy0FJto9jvpVIp9u/fjzNnzuDs2bNob2/fcU4AS63t6+vD7du3uZWxlYC0Wi1aW1tx9OhR1NXVQSKRYGJiAnfv3k2Kua+/7r1GJBIl3ZBplU8PCtntAczzHAwG+eBG1g3GbDbzMNWTJ0+wvLy8YWXfThTCm4JcLsfZs2dx+fJlHDlyhLeA3qnzyuPx4NGjR7h37x5mZ2f5+Ta7Jib4EydO4MCBA5DL5Zifn8ejR48wNDQEm822o+vYCcziKSgo4HkHJPztoQGWaZCuJ56FqBwOBxwOBxYXFzE2NobHjx/jyZMnSWJnbbO2em/Xe7J1Oh3OnDmDzz77DMeOHYPJZNrVPtbr9WJkZATffvstHjx4kPRaUqHT6dDa2oqTJ0/ytl5skOfw8DAsFssrTYxhI8NKSkpQUVGR9rSfbIcGWO4RQtFPTk7ycVjj4+Mpp+KkIw4mPrlcDqPRiPPnz+PTTz/FoUOHoNPp+PF2cq2sHv7q1av49ddfk/bg62Flws3NzTh27BgXvMViwfDwMAYGBjA/P//KIztsVr3BYEBhYSGt9GlC5v0ekUgkeNXY/Pw8xsbG8Pz58w2rXyamPPBidT1+/DjOnDmDI0eOoK6uDjqdbscfblaH/vTpU3z33Xf497//jeXl5U0fL5PJUFxcjNbWVnR2dmL//v1QKBQwm80YHBxEb28vpqenM2p1tRfk5+ejtLQUhYWFyMvLg1QqpaScNCHR7xEsk4xNalleXobT6Uwro02YhSZMtGlpacGhQ4dw/PhxtLe3o6SkhPeM34xUxTrAC7FHIhGe5ffjjz/it99+w/T0NKLR6IZ9vEwmQ1FRESorK1FfX4+GhgZUVFRAKpVibm4Ojx8/Rn9/PyYmJjadevsy0el02L9/P9ra2mA0GmnCTQbQO7VHiMViSCQSiMViBINBeDyetMxd4Z5doVDwgp0DBw7g7Nmz6Orq4pVr6XywU1kArFEFm7jb29uL3t7epHh8IpGAVCqFSqVCQUEBSkpKUFNTg/3796O8vBwqlQp+vx+Tk5MYHh7Go0ePNpTYvkoUCgXKy8tx4MABFBcXUw19BpDo9wixWAyFQsHbWGdqfuv1enR1deHEiRNoaWlBWVkZSktLodfrt13dU8HGOXu9XlitVjx//hw3btzA3bt3sbCwAL/fn3TtarUaVVVVaG1tRWNjIyorK1FYWMgba9hsNkxOTuLBgwfcaZfOEI2XhUgkQn5+PoxG466iF9kIiX6PEIvFUKlUqKioQH19PSYmJmCz2Xgf+FSo1WrU1tZi//792L9/PxobG9HU1MSr9NZXjG029IJlD0ajUd4yyu12Y2JiAv39/RgaGsLc3Bzm5uawurqa1D2ntLQUTU1NaGxsRE1NDWpqavjNRiqVIhQKYX5+nsfhBwcHYbVaM/bS72XyDCuu0el0UCgUJPgMIdHvESKRCLm5uSgpKUF7ezsX/MzMDI/dsy2ASqWCwWBAU1MTjh8/jq6uLlRVVUGj0SA3N3fTRBNhCI8N1mQdY1ZWVrC4uAibzcanxUxPT2NgYABjY2NJIpXJZNDr9aipqeGNO5ubm3lnWZZyzYpnFhcXueBTTaRNByZ4mUzGqzVDodCObgRqtRr79u1DeXk5pFIp72VApAeJfo8QiUQ8NXf//v2IRCIQiUR4+PAhZmdnEQgEeEOL+vp6dHR0oL29HdXV1SgqKuJi2wwWEvR4PHA6nVhbW0MwGMTa2hqWl5cxOjqKoaEhTExMwG6385bS60WlUChQVVWFU6dO4b333kNjYyOMRiPUajUUCgXEYjF3LAaDQaysrGBoaAgPHz7cseAB8DZiFRUV0Gg0cLvdWFhYgM1mS2uUFYMV+dTX16OysjLtikbiT0j0ewir+mKruFgshk6nw8TEBFwuF+RyOcrKytDc3Izm5mZUVlZCo9FsCDcJzXg2CMLv93NxP3jwAGNjY3A6nQiFQnyem8vl2tKxVllZiUOHDuHYsWM4ePAgamtreemtsCklw+fzYXh4GH/88ceWcfztMJlMfHJOdXU1ZDIZLBYLHjx4wB2K6W4XWFzeZDJBq9VS+u0OINHvMTk5OZDL5SgoKEBDQwPkcjkKCwtht9shkUhQXFyM6upqlJSUQKVSbdrphbWcdrvdmJ2dxcjICEZHRzE6OoqRkZEta9PFYjHP/U8kElAoFKipqcH777+Pc+fO8ckzMplsQ0SAWQbBYJDH4oeHh3fstKusrERPTw8uX76MgwcPwmAwIJFIwG63Q6vVwuv1wu12b2iqsRkSiSQpkiEsQCLSg0T/EmDFOGzvzspLWUcZn88Hp9PJu+3IZDJuVrNOsT6fD1arFVNTU7h//z5u3LiB0dHRtEpV2X4feGHOHzhwAB9++CHOnDmDuro6qFSqLf0GsVgMDoeD32hSVQGmA5vq8/nnn6O9vR16vZ473RQKBdra2njMP13RM4skEAjA6/UiHA7TXPoMIdG/ROLxOJ+zZrPZuNCXlpZgNBp5A02VSsWFH4vF4PF4MDMzg4cPH6Kvrw9jY2OwWq0Zr7YqlQqHDh3ClStX0NPTg5qamrTy02OxGJaWlnD79m1eapspKpUK7733Hi5fvoyOjg4UFBQkbWFYajFrJZ4uwr7/VqsVxcXFyM/PJw9+BpDo9xjmWQ+Hw/B4PFhcXMTTp0/x7NkzrK6uIhQK8SQYrVYLrVablEYqfN74+DhmZ2eTHF3phr40Gg0OHz7MV/jKysq04v3xeJxHHQYGBjaUyaZLSUkJTp48ic7OTj7eKpVTUaPRpD0WG/jz9btcLjgcjozHYREk+j1FKHjmnR4ZGeHpqjabjU/CAf6sB2eZfMCLLQBr6bzZObZDo9Ggs7MTn376KXp6elBeXp52J51wOMxvVKztdaYxdoVCgX379qGurg4GgyFphU/Vyz+TVZr1uPd6vXC5XFhbW0M4HKY03Aygd2qPYJ12wuEw1tbWYDab8fTpUwwODuLp06ew2WxJwxZfFvn5+Th8+DA+/vhjnDx5EuXl5WnveZm/YXR0FP39/TxrL9OVVKVS8UKY9fFzdqxYLAa/3w+3251RKm84HIbdbkdhYSEcDgfMZjOfMUDCTw/KaNglbHVnWXHBYBAulwsLCwsYGxvD9PQ07HZ7xoLfiTfaZDLhxIkT+Otf/4oTJ06goqICcrmce7i3E28oFOJx+eHh4R13P2Ydg5xOJ3w+34bXzXIAlpaW8OzZs7QbZ7JrtFgssNvtsNlsmJ+fh81me60pwW8bdGvcBcLsOBZPZwk0NpsNq6urcLvdfM5dpsfeDrFYDKlUitzc3KQincOHD6O8vJwn26RzrkgkAqvVipGREYyMjOwqEcfv92NmZgaDg4PQaDSoqalBfn4+JBIJb6u+vLyMBw8e4P79+xl120kkEnA6ndy8Z+9xNnRp3itI9LuEhcfYF4uNs5TbvZq4wkJVOTk5yMnJ4U4wVv66f/9+tLS0oLGxkZfgCodhrIfdrKLRKMLhMJxOJ548eYLe3l5MTk7uyjkWCoUwMzODa9euwe12o7u7G2VlZVAqlbzz7uPHj9Hb24uhoaG0w3UMYZ0Bm2VHzrz0IdHvgvVmM0vFValUMJlMqK6uxtLSEhwOB/+gpgsTOFvJ1Wo1NBoNtFotNBoNNBoN7w1XWlqKsrIyFBcXQ6fTQSqVclGsv06hZcIGdlitVkxOTmJgYAADAwOw2WzbDrnYDrfbjdHRUayuruLhw4e8PXg8HofX68Xi4iLm5+fhdDp3JFgS+c4h0e8C4bBFoamt1+uTetyHw2FMTk7C5XKlJXyW3JObm4uCggIUFxejrKwM5eXlfGyWVquFWq1GXl4elEolbwUdjUbhcrl4sYzf74fP54PP50MwGEQkEuFfzOlotVoxNzeHyclJrKysIBQK8RkHuxGXz+fDzMwMZmZm+OsSZgruBmGHIboBZAaJfpcIBy+yDzUzwZl5z1o0j4+Pw+Vy8YaOqRDePIxGI/bt24eGhgbU19ejrKwMBoMBKpWKp6Lm5OTwrjgulwtutxtOpxN2ux1WqxVmsxmrq6uw2WxwuVzw+Xy8XTfbkrDuvaxIR/g69nKvLMwU3C1s25KTk8OzGYn0INHvAuFKz2CCkUgkkEqlfOSSQqGASqXCs2fP+BQb4YrHjsOel5eXB61WC5PJxCfhFhcXQ6PRcAcdc8Ctra3BZrNhYWEBk5OTmJqa4tsKj8cDn8+HQCDAxZ4KdrNhN7BMRka/DuLxOC9l1mq1FK7LAHqndsn6FUZo7rNyUolEwuvIJRIJnj17BqvVyoUvbGohtBxYaIsNzGCZayz/XlhLPzMzg+fPn2NychLz8/Ow2+1pr9LCir6dDJF8HbCc+6qqKhiNRpphlwEk+l2ymVnJfq5QKKDT6VBVVcXHKvt8Pt45NxQKJa2mbPVmDja2t/Z6vbBYLLyclKXLms1mTE9Pc7G7XK6krL90EDr4hJbLmyp8VsKs1+tppd8B9E69RIQOvry8PBiNRpSWlsJoNMJiscDj8XCBCWfeJRIJ3m2HDbWcnp5OytOPRCJ82q3VauWNNXa7Z35ThS7EYDDg4MGDaG9vh06nI8FnCL1be8x6waz/v9B8ZwJjZjV7rDB+7vf74XK5YDab+Zz5nJwcHh1gHvl3PTmFWUMqlQpHjhzho8Pz8/PJiZchJPo9QChsoZnOxMvaWrFV2eFwwOfzca95qtVVaHJHo1G+DRB6qoW98t512PvT3NyMK1eu4NixYyguLqZVfgfQO7ZL1q/QQiEywbvdbiwtLWFiYgJTU1NYWVnhok/XlE43f/5dQ5gk1N7ejr/97W84efIkSktLSfA7hN61XbK+O62wHTXLw19dXcXMzAympqYwNzcHu90Or9e7o0ER2SZ65lxsbW3F559/jkuXLqGyspKGW+wCEv0uEJrfsViMC57VxLNKM6vVCqvVCo/Hg3g8zpNvWGsqoYmeygcA/BnOE5r4LDGFPU+Yhy70F7wO83/9tTKEPox0kMlkaGlpwd///ndcvHgRdXV1JPhdQqLfBUxooVCIC5+lt7L4eiAQQCwW4xl2wIs5bB6PhzfLCIfD/Bhsn88EI5PJeK0460fPMv2Y6Nl1CAt+hI01mR/hVaHRaKDX66HRaCCTyZLCgMFgkPs2tuv3ZzAYcOTIEVy+fBk9PT3Yt28fCX4PINHvkvUrK/BnsYxcLodarYZEIkF+fj5MJhN8Ph/8fj8CgQCCwSCCwSCP3TNPfCKR4JV0SqVyQ349S/IRNtNkN5+1tTW43W54PB7epMJgMMDhcCAUCiEWi/FtRzpNNrdDpVJBqVTytF29Xo99+/ahpqaGN8IMBAJYW1vjY7bsdjsKCgqwtraWFHUQdh7SaDTo6OjApUuXcOLECZSWlpLg9wgS/S5g4mZhtPUltuxr/f+FBS9M+MFgkK/2ACCVSjcVPVvlhTF+JmbWTcZqtcLtdvNech6Ph5/f7XZjeXkZFosFa2trSVl4QOrqQWF6LjPR8/LyUF5ejurqaj5zjzW7rKqqglqt5gVAq6urcDgcvD+/3W6H3+9P2nqIRCLeINRoNOLYsWPo6OggL/0eI9pmb5VdXqMdwFan9YLZ7PtUTj/hDYE9XliPz4QuNOnX75NZJh9bVb1eL7xeL5aXlzE/Pw+r1crF7fP5YLfbeWNJ4XGYAzIQCCAcDvNxXbm5uZDL5dybHovFIJPJUFFRwefW6/V65ObmIi8vD/n5+TxzkF0TszyWl5exsLAAh8OR9P7I5XKUlpaivLwcxcXFMBqN0Ov1aff3IzaQMoGBRP8KEX7AU/27HiZs4Z54q0SUVOFCm80Gi8UCl8vFO/qYzWZe8itczYHkfH9m/svlcj6Yg5UOFxQUQK1W8xTj8vJyaDQabgkItx7sJhGPx+H3+/k1eTwefu3xeJy3xWaptVRBt2tI9NlGIvGicyz7Yum8i4uLCIfDSa20hJEHluHHqv7kcjkvaMnLy0NpaSmqqqr4ZFuZTAaFQpGWCc6GbjL/hfBa2bhv4QQbYleQ6LMdltLr8XiSHGjRaJSb3h6PB5FIBLm5ubxRB2u9BYA7JbVaLXJzc2la7JsNiZ7ABt8Bc8qxVGHWvVYulyM/P5+P3RL222OeejK733hI9MTmMCceS/AROhKJtxYSPUFkGSlFTxsygsgySPQEkWWQ6AkiyyDRE0SWQaIniCyDRE8QWQaJniCyDBI9QWQZJHqCyDJI9ASRZZDoCSLLINETRJZBoieILINETxBZBomeILIMEj1BZBkkeoLIMkj0BJFlkOgJIssg0RNElkGiJ4gsg0RPEFkGiZ4gsgwSPUFkGSR6gsgySPQEkWWQ6AkiyyDRE0SWQaIniCyDRE8QWQaJniCyDBI9QWQZJHqCyDJI9ASRZZDoCSLLINETRJZBoieILINETxBZBomeILIMEj1BZBkkeoLIMkj0BJFlkOgJIssg0RNElkGiJ4gsg0RPEFkGiZ4gsgwSPUFkGSR6gsgySPQEkWWQ6AkiyyDRE0SWQaIniCyDRE8QWYZkm9+LXslVEATxyqCVniCyDBI9QWQZJHqCyDJI9ASRZZDoCSLLINETRJbx/wO8eIB2W2Ah3gAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 72\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", "n = Nx * Ny # number of parameters\n", @@ -2091,22 +464,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(10 * np.log10(evaluation_history), \"o-\")\n", @@ -2125,22 +485,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", @@ -2167,17 +514,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", "frequencies = opt.frequencies\n", @@ -2188,34 +527,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb index 3e68cbcca..ddf535c4b 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb @@ -13,17 +13,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -50,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -176,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -215,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -266,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -326,22 +318,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "x0 = mapping(\n", @@ -365,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -414,1380 +393,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 4\n", - "Current iteration: 1\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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QesAYQg8YQ+gBYwg9YAyhB4wh9IAxhB4whtADxhB6wBhCDxhD6AFjCD1gDKEHjCH0gDGEHjCG0APGEHrAGEIPGEPoAWMIPWAMoQeMIfSAMYQeMIbQA8YQesAYQg8YQ+gBYwg9YAyhB4wh9IAxhB4whtADxhB6wBhCDxhD6AFjCD1gDKEHjCH0gDGEHjCG0APGEHrAGEIPGEPoAWMIPWAMoQeMIfSAMYQeMCba8udBJ+8CQGdo6QFjCD1gDKEHjCH0gDGEHjCG0APG/B0HFjsW0aWLiAAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 16\n", - "Current iteration: 15\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 32\n", - "Current iteration: 27\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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w2e32ATXekw+bUPhde9K26PLzdP7M4uaQ6HMA975ardr+/r7duXPHzD646ZjLY/krAnO+uSR+9Ii2Q3Rp1W+x4hQIzjen5GW1ZnZpro0bhBgTmw/MxaLqPv/Ou/twMxG/RRcXNcVWJErwnweJPieI3rfbbbt161ZwpzG/54g9UnpsQX3ALNYK2wvB5785Lcb15Tw92GwuduHBTjw8n4YgeVDBX57/x3bFxaDDUwdYcr4uPx3wsYE08YubRaLPARfkNBoN6/V6wYo3Go1EJxtU5bEIORrvg2k+cMYuus9TsxXmijaO3MMrQMMP3gqbq+G84Dn1BivOYuZr8OL383RfYx8T/OdAgcAkEn1OOILfarXCjxc5+9iOLvyj8+68j1bj5kXvS1FjUXxfd8/ijd0gcP8+sNzsuvP7sbvO1+UtO9hmvn6TwpTgk2SKXl/WB3wtOkfx8WOPNYH0ZKW+WOg+GMdW3QfDOJjnpw0QtZ+Ps7XmAKF/DQ8OHIjzr+WU27aCx3QE1+49IXFzyNLnxAsftfcx153TdLGoOFttTuHx+SBEHyDjuTO/lq/Dr2bzeXD+PLHP6efdsRQeBB5z2fncaaW9u7u7W7n6VxUMie3JFP22i/KLAgsxVkwCEbP4fKEMB764/BTn8J4Cz525Lx/m2rw7Da9F98E/vs5YX/tSqZTIKqB+AI9hIMLr0gpnYL3Zcqd9jzyVybLysQVBscpCvgYEUv1SXSFLvxVs1bj6DH8xL/apKbj8OAdHxHnDiDTR+2g6XoPBAtV1EI6PIbB1Z5FyKTBECs/Eey1cFmtmoUEFwGCB+2luetp0hwN9/JrY0l/vycRKdmu1mnW7Xbt9+3aolYA3Ia/gA5mif/r06ee6jh8EseAa18LD1fbVZ4ig+2W1bGk4ms7WGqL3LjqLnvPyfnEKrjNW0Yf3ZFFzPh+wq8zTFV/ei/fgaD28Ag7ixVx1XwDE7+uFz6+BlfbrA9CYBIuWEERtNpuhz2C325XoI2SK/g9/+MONuvhZLuDnPL+3LhDLzs5OSMWhsg4iRCoMbrbZRbUer6NHnt5XsMHaw1oDTrux6DmqznN0XDdbbnbx/XyZu/zE3F54JOVy2ebzechGlMvlRFSfV8L5NF1sKuFLffmzxeoTcKwfzDDtqNfr1ul07O7du/bw4cOwdTUvVUYT0aI2xkwjU/S/+93vPtd1/GCA2DA3x04zvDwWP0AWpNnFCjzeSw4iZtGze455KbvcLI5Yasy76wgq+uW1ZsnGGzh+b28vYZH9e0L4GNxqtVqidp9r5326zs/T/VQCsBfC22PFPheLHhWRvV7PHjx4YL/4xS/sm2++scePH9vh4WHie/fxEQn/A6WsL+LBgweF+5Z4Pmxml/LvbMnL5XLCXcVUAC4+ltOaffiRs1vuXXpuuRULyLFVBNwxh/eTjy3Nhbj8PDrmJfh4Qqzyjj8H1/H7c/rNOHBNaef0MQoInQeRRqNh9+/ft2+++ca+/fZbe/Tokd25c8darZaCdkmibm6mpX/58uXNXMqPFIi+0WgE95G3gPY7zvJzMYuIHzRvXMlr4s0sMQAxLHQMFhAYIu4snJjr7L2EtKIitsZe7LGpBncW4upELknmoKZfV88DiG8ksru7a81m0x4+fGhff/21PXr0yI6OjqxWq93cf/xPDEXvc8BLVKfTaUL0LDAI0buV7FLz/JRFwaW7wAsWIvU7xC6Xy8Q5fI89Poc/FwYpv700xxfgacQaXuB8vkeA30U3TfQ+sMkDCTwOFPLs7+8nGpU0Go3E8mXxgbR4lkR/DVarVWjo6OfDsSBZrHCF22RvI3o/x+UOu3j9arWycrkcKgTxnnyN/jrZW4BIvVBjGYWYB4LPw4JPW4cAISMjwTUIXLQE74IDpugtyIG6WC5fxFFxTk58ftgL1Be9mF0spkmbR6OzDmIA7CXwedmLQISd5/s+bbZer61cLptZMuXF/8Z9bt+FOAa38PJWnz8Prs9beXbvY228ObiJ+gdOS/r6hNlsZuv1OqRE8X3pd5oP1d5fA44y+3Qfovp8LP/lY1DAg7kqLDQXyOB43GD5KpVKYu6blef2YuVz8hzez8W98NPWFfC58Hr2FDgCz98L1/GnLQrirrrlcjl8dj+QSPjbI/f+I8APl62nrw1gocVy1xzFx/HeU+DUl09rIa2GY7iwBoUzfD/t/D7iDuGzq++nLbjP1+fjDH4hEn9O9lIwUEDovMqPC4nm83li2iHyI9F/BN66Zi0aiYkflWIYODAI+OAbu8QgJnAUFHEREd4jdr0+io/z+owCbj6z4F/vYwRpvf9wLajk8+fxc3SuG8D35mMpYnsk+k9IWgWgFxuO40KWtMo5Ph4i4bJXPO9vsJ6x9zfLrm1ny+1LcX2FHV+rP45vXqCY4rD3wgMWBg/c9+eU4K+P/CPx0Sj28+NClv4TkmZ5YkE0/OX7vhSXj/eur7d4MVc767pi3geX0fpKQC6S4bm82UUsw18vXHcc49/Pvw+vUOTSY18XwF6SyI9E/xH43LAXg9nluTwf4+fCXELr38dH2HEs7nMZr693T8th+ykBrtE3+cDr+PqBH4SQjeAb4gpZgTzuDIRgHlJ3voEIahIk/Osh0X8EnJP3VhtwEA3WlwXvt7hOS43545Hb52o6X5bLgwm/d8zK+9w5RIfnY4VI/to4ZYdo/DYpOxZ77Ia+BdPpNKzo42XFIh8qzskJV8fFUlE++swBOk61sUXm4hwfmWYLCdFzWgzFL/4+R9tjYuWAH4JnbN0heIgyJtrY9+Fr7rG8mAezWHFOzKLzYIBlzPAOuCkoB0TF1ag45xpwasoLykeY2c31Zbg+NZZWhgsgel+GC4vK6TVv8b3oMTdGJoCLZDhNlrWZJg9mOBeLnmvv+bvCYMNNRbjqLtZjH4OA2YcORdPpNLG1l9geuffXYHd3N6zbxvJaM0vMa3mVGzeW2GbBDQfhzC5X/aUtuOFzpBXF8Dl9YRGAJfWr/XywjZf7Yr7PpbgsfHwuFr2vu/c9A/l7g1tfKpVssVhYp9MJAwSvWtQAcEGa5yPR5wBLa7Hgo16v297exVfIP3heVWaWDJDlXVrrrZnPifultRzci52Hz4UGIPP5PBwzn8/DIMCLa9j95+h6bDDjeT1X9LEXwU1FYoL3HobZh8FpNptZs9m09+/fW7fbtXK5bM1mU1V6W5Ip+gcPHnyu6/hBwVaQm2hgL3k00YDYUCnmV6pVq9VE8YnvXc8uNnsIbOlZdFlNNNj6c+AOnydWzGOWrPbj1JyZJYTNPfC5/XaW8LkyL+bec09934wjlqLEYDcej21v78MuQ/C0dnd3rdVq3dyP4idEpuh/9atffa7r+EGAHzwEViqVQs813y7LLNlWarP50FQSgwIfb3bROcevImOrltbAgteTe/HHsgDeQnJLLr8TjdmHwcN3wWWxcz8/L352vf2gxJ/Fr7Lj9/B9+dPSmWYXLck4rYdbr9ezRqNxaYFQUfHTRJAp+l//+tc3cjE/ZFBUAhFwY0z88PxyTyyaQTdceALoxIpzxppGcE25mSV+4GaX95i/TmNM3De7EDQ/x++P52GBuV23F73v28e589jiIs5IcPbA5/W96H0hUrlcttFoZIPBwE5OTuz7779PNMbc39+3g4ODwjfG/OUvfxl9PLNH3j/+8Y/CfVNsYc0u+t7hcQh3PB7bcDi81AIbAT5Yeoy2GEhiLbDZ1efcP7+OG074zSTNsltg+6aW3KDC7HJnHwxqHGjjVJpvlRXrPx/7Xj2x4iCfovQDAH9HmE41m80w3Wq1WnZ0dGR37tyxTqdj9Xo9FAcVTfi//e1vo25OpujNrFjfUgo+rzybzWwwGNhgMLDhcBiCYZVKJVh5BK/8VMBvdgEBe3cdP3yIylvdbTe74C2xkObiTrwcIedrjA1O3tPA69lzSFvjH6sGNLvc8z7tNTyvZw/FDza1Ws06nY42uzCz3//+9/kbY4oP+LnlZrOxer2e2M0VRTaw9LH5P1a+cXDrqm2t2EOIldlmbWuFaQWfz6feODXm5/EceIwJ3lv5mPCz8Bae//rvn//i88W8itlsZm/fvrV+vx+mZEibig+oOGcLYmkjH1n2JaicOsNr/ZwW5/LufUz0sQIZvD42YHCunWsGcE4WPw8ALGKOzPsiozSRZj0HYkJnUW4zPcj6ba7X61C2Ky4jS58TH+GGleYa/NgKOP6B81pxs8sddzi/vl6vE/+OrTiLDRgcuPNRfY4ZpEW3+Vz8Opxvs9kkvA0+FrEC/7lBTOxpLn7WY+J6qPY+B17wWAiyXq9D1Rm7uWnnYIF4AcaCcTxHx3ZT6KXHx3hvAOLHa7igB8Ll13Jgb71eW6VSuZTTR5wA9Qn4LLgW4IXPAwC/B+7zX3GzyNLnBOKbzWY2HA7t7OwsiMGveGPrz+60FxKO47p9tv5srTGFQDdcs4t8rF+og3OsViurVquX+slDuOzuo97eL/f1GQOui/d/eR7t59JXufzi5pHoc8BFJZPJxE5PT+39+/e2s7MTFqr4DSY4+Ofr1v3cPG0NPIuey3ZhpXGcj97HqvJw7N7e3iXBm12squNMQ6wPPQf9YttTceERPrdHAbYvg0SfE1TWDQYDOz4+tjdv3tju7q6dn58H68rBOg7mcf09V9al5aXxGA8IbO3NLqw83tfvIuNr8bECjlN2gL0RPM871PquNpwO5JQgUop4LU8HeIEOrjs2rxc3h0SfA7jK5+fnNhwO7c2bN/by5Uvb29sLa735uMViEXZQ5ce94NmdN0s25IAofH2+3zyT3XH2LBaLRWITC18CDHxmwAvcR/i5ag+R8ul0apPJxCaTiY3H45Dj9yk/jkUwft4vbgaJPidYy93v9+3du3f2+vXr4N6zUObzedh6CZaWU14cQIN19ivEOAjGlr5SqZiZhdZU/JxfWbdcLq1er1/aQCJWv+9Tir7wxT/GpboQPKoU8Rc3DArwCnZ2dhJFSTwISvg3i0SfA47ao+77+Pg4RL55BdpsNrN2ux0WgMQWP3DqDgVAsOr84/elpxA6Dxy+PRaul6vmcG0cT/BFPb4EGOfxNwgfxUXIZIzH41AXf3Z2FioX+/2+DQYDm0wmQfxI96EOwccXrkrjiesh0ecAop9OpzYajazf74fova9zH4/HdnBwYM1mM1TocaANc20s3WU3n6P+AIMCfvxYvcfVgrEuObEAog+qxeoK/Ofm+zwF4IpBb/Eh/NPTUzs9PbWzs7MwELD4Y6W93sMQnw6JPieY0/PcFRYTlm82m9l4PLazs7Owjz03iUTVXq1Ws0ajkbDYPCjEcvj8eJqV96lCrqLjG8cNYgFED+fTvcVHDAPix3eDtObp6amdnJyE22AwsNFolHD5OWDIwU6UGuMzpC3qEdsh0eeAg2O8Ag3uNkezR6OR7e/vhxVgKN5BMw6suW+324lCFhYbW20uvuGy2pjo06y1L4KJZQ348azvAedgl9wH+LDx5OHhYfCKvOgnk0kI9rHYs7r0IHiI12sAyIdEnxP+cXP3GMxJ8W/8KFnw6KYDC7+/vx9cW7PL4kOuPTbvhvhZ9H5NQAwWiBf5NqL3A4e3+mz5G42GNZtN29/ft3a7bd1u146OjmwwGNh4PA6C51V63oPw9QGLxcLG47G9fv3anj9/bm/fvrXRaBSuj+MZIo5Efw34x+kbSeCHOZvNLnWr5b3fm81m+NFzkYw/Pxfm+Hk/i52f95Z+m3LqbY+PBddiQT6U/6LFWKPRsHa7bdPpNCzZja0w9HUO3s1fLBY2HA7tX//6l3W7Xfv73/9uL168sLOzs0QQUKQj0V8DtsRmFyvh8COFm8/luNypFt11JpNJWFMf6yCDmn68J1v82Dx+27l52ufJesxPCTgm4MXPC3O4JTYGOy7p9RWJ/Bn8IALrP51O7dGjR/bw4UO7c+eO/eUvf7EnT57Y6elpvv/IgiLR54QFxzX1ELyZhVVn3voikAfr562dv/lcOgfovNX3K+hi83qwzUCQ5zg+ngcAFv96vQ7pSe/Z+M+SFZPA93zv3j27f/++HR4ehlqIp0+f2snJSdioQzn/OBJ9DjiCjoKamNXHsbG5+t7eXqJMNT/YrKMAAAidSURBVNZ6Ki215tN2sevj9/OPb/P5sp7z8QAWrb/P1ppFzeLla/VeStqghde2Wi1rNpshE3L79m17/vy5vXr1yt68eWP9fj/sw+f/j4qORJ8TFj1vdGF2OajFrzG7GAi4mMcvYOEBwNfH+4j9Nj/ibVz3bdnWcvrzs/jNLgcTY0K/Kq5QKpWsXq/bvXv3rNls2s9+9jN7//69vXr1yv75z3/ay5cvbTKZXCpxFhJ9LmBpEZDjfu6A3X3AFputta/D5241vkV2zBX2IvE/bC82vsZtP2/s+FgEf9tzpd3Psu6xc3GcpNFo2OHhoT148MAePXpkjx8/tuPjY5vNZpmZjKIi0eeERc8bXvj5LP6mCXG1Wl3a1pmj1LxklS1+rMjGz6N9DT+/tyd2fWnPpUXr094nZvFjx+ZJGaZdJ6obMQBgnzsJ/jISfU6wwg197Wu1WmLFGne6Af4+LBUCf35aEOuay1s/cX0AerujrZZP77G138Yix/L4fjCLDTz8mm2FHMsEXAfOaJTLZWs0Gtc+VxGQ6HOAHxcq6prNpjWbzcSOL9ythvGCw+DAK83wOq7sQ0kvylpR/ttut63Valmj0UgUAPGUY5tpQJal9tfPHglnGdKi8FkpxKzMQiw9mHasLHl+JPqcwL1HRV2r1Qolp2aXo/ixH6x/jPvL8QKWxWIRVq6Nx2MbDAZ2enpqh4eHdnBwkBA++u1jcQ/X+rMA+Rpi1jptbs1pRO6Ow0tiYWnTds5Nu6VNR2LfF3sFHxuz+KmTNiBK9DngAFKj0bBOp2Pdbjfs8oofMbrocHkuiFnZUqmUWFTC0XvUr2PFWrvdtuPj47B1E+r7cavX6wnrH+vZxwUvbLXN0otksObA7x+HGgOOdaD3P6YePPBwPUHsFht0YtOI2FJg/n8S6Uj0OUGuvdFoWLfbtVu3biWaSfrINFz+rPJQuPrebeadZkajkQ2HQ2s0GuGGXHWz2Uw8jpsXPrvLPlXoK+N8ARIGISyf5b34EEhDxR1fl9/th5tu+h1tY0uD8f3wVALnqVQqcvGvgUSfEy/627dv22KxSLi0+BGfn58HK26W7XbycxAi+sqh5TXWqUNEWMDD22Oj2o9vvNuOF7yvBWBh8kAR64kHj6ZUKoXgZrPZDFMPTH9g+XlZMa845ClJWiMQvD8q/PBeaRV8Ih2JPgf4ce3u7lq9Xg+WfrVaJX685XL5UiAN0fqsHDdcWFhX7/6XSiWbzWbhGmAVfY0/iwr3OZXnVwn6cl+/173Z5VgDd+DBQFGtVoOlh/Bh8TEoYWDkwQnLjOGZcO0DpklYfrvZbKxSqVi73Q6DTawrkUhHos8JBFetVq3dbtvR0VH4IeJHzIU74/E4CJ77zEMwafl9/muWjPbztcTq8f1AwJYT54p1qDG7WMOfthWWb+GN98bnxXeA6Qdv6AlLz/N/TEU4JoFmoqXSxfZcKF3ebDbWaDTsq6++CtYebr7YDon+GiBt12q1rNvt2maT3JseFo3dfb/TrO+Iu02VW1auO/bYVUKIvVfe1+JYDBRYUozpB0fz4W1gQOJlt/AQeJDA98Jttc3M2u22LZdL6/V6dnh4qGh9TiT6a8Bz2FarFVaQwXrhB+7Xu+M+XHUE+HhhTp4SVx/RjgULP0YQMeH7aDkPEpgCYEccDAA87/b9ADjiD8FD9IhB8LTCzKzT6Vij0bCf//znYRBVue32SPQ5YauGOSyi17BccGU5QIVuMWgggcg83HYWfyxFlUfQN2n5YjvV8DRgZ2cnxAp2d3cTK918ZoOnIfie2EPgeT1/P+fn53b37l0bj8fql3cNJPprgB8r5qSlUinhqsLNb7Va4YZW0OgEiwYas9ks4e5zsO9Ls+11QJQeFnpaPp1LaHkA8MuXuQCoUqmEAfOH8l39mJDorwHmpbVaLbj29Xo9uKEonOl0Otbr9ezo6Cg0hkRraDSGREdY3w6a20j9GNm22s4sWSyEKQ8HCTmoiGrIRqORCPiJ7ZHoc8JVefV63XZ2dqxer1/aVIK7wU4mkyBwf+MdYNAzD+LnJhu44T18E0oIh/+a5Xf1fRYAj6X99bvbssWOlfL6VCReyzl7biQKNx9xlGq1akdHR/b1119br9cLc38Jf3sk+pyw6OHW84/Zb/vEQSi0zebFNLj5/eBQc8/H8Otx3+8Th1w2p9W88GPRejNLpPdYkH5OzoFJnotzEC5tTs7XxZV1PDXy1YRcvVer1azdbtudO3fs7t27Vq1WJficSPTXgINWXlRsYXkg8AOCXzvPffR5EID1500hWPS8Px0X28QabfL1MWzZEa/IqonnvDxvjslCjYmee9fjfVn0vkgH5+HrwfvgWOXo81O6wv1TlOQKrnKfY1aWV+KxVfauPK9mu6q1lt8SKubiX7VElW++vDWrKaevoec0HYhNPXBe3onXn8tXNca6AEv0qUS/GIn+C5DlbntPwT+W9jfr+LT392LxKbXYMVnHxR5P+5yxc6bFDvJcm0gg0QtRMKKivzqfIoT4SaFA3o+A6xSgXMf1/dSFLv4a/Pnlnn8Z5N4L8dNF7r0QQqIXonBI9EIUDIleiIIh0QtRMCR6IQqGRC9EwZDohSgYEr0QBUOiF6JgSPRCFAyJXoiCIdELUTAkeiEKhkQvRMGQ6IUoGBK9EAVDoheiYEj0QhQMiV6IgiHRC1EwJHohCoZEL0TBkOiFKBgSvRAFQ6IXomBI9EIUDIleiIIh0QtRMCR6IQqGRC9EwZDohSgYEr0QBUOiF6JgSPRCFAyJXoiCIdELUTAkeiEKhkQvRMGQ6IUoGBK9EAVDoheiYEj0QhQMiV6IgiHRC1EwJHohCoZEL0TBkOiFKBgSvRAFQ6IXomBI9EIUDIleiIIh0QtRMPaueL70Wa5CCPHZkKUXomBI9EIUDIleiIIh0QtRMCR6IQqGRC9Ewfj/+NPwx9UfnUcAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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sqc2SDSFi8+9YCgwC5tf7ZbVmyX33eA88L3w/wHB+nHPrnD6DiGHhIXzM0/1mHjE3XoJ//0j0GYA1hujv378f/oZe7EdHR2Euj+AdC5Aj3H7rKh+hx+9YHH4ra25ggb/7Y8Y63njhezc+JnLcEHhjt55312Whp22qycFACf79ItFngKP33W7Xjo+Pg9vearUS3WyQa+f5tW83DVgAgIUQ25OeRY/z2mw2VqlUEgE6CJhdcS9MduNjgwRH13l7bF5tyGk3Pjcvdp/T959XvHsk+oxgDt5sNu3o6CiUvqL9M69z58AcXrvZbIJV5mg5t51i0fKUIDa3xvuwaPh9UKvO22KjF79v7+WnAfwaL2xfOJM2T/eW3E9f3tf/TIPKayT6jMCqQuQQJH7HYudCGQjZB+rYpcbfrq+vb4kewvJuMw8QOD+u0uPXsmXmJp4s/Jjg2dJzTICLadhjSJunv2kJ8F2R4JNsFb2+rFfEVqshoIcLHhF0TpXxa7blu3ldvl9eysKNCR+v4UGFRc9CZvFzWSveizeygHXHlIBfz2LHdGCbdU/7Tnna470b8e6Qpc+Ij7iXy+UwN+VyVLTQMnttxThy7a2qz03HVpt5a8uVeV40fExvkdNueA4PBt4L8HUCnAHA+WwTOp8vYPHz8/b5P2iAyM5W0e+7KD8vpOXPfWksp958us6vOOP+8Rg80paYsvAwd8bzfYoPx/R5dS9yX+rK0XWfXoylE/GZvcfBf9vnu9z23Njf8H3GRF8sFq3RaNxasCReIUu/Bxxt9ls7wf3FvB6C4vk9ix5WGsE0uM5cleZr31mULFRQLpcTgw8GGryGo+0sdrwPz8M9fgEPsgQYWHgNf2yhD5NW059WJITXsMfEWQk/QG02G6vVatbv9+309DQUR+Gc5RW8Yqvo//rXv76v8/gg8Na1UCiE+nhceBA99qWfTqc2n8/DvJ73lEM9Pc+v04JkaZ14fHGOnzMjluC3iGLR+7QaH4+tOI4LcVcqlYTFjzX/RFUgewQxy4r39fP4WLDPrwfA+fDP+N9gCTMqH1utVqiM7Pf7En2EraL/9a9//U5dfG/Vvqn390LjIhdsIYXNEiFerCJDBRpaNPGec74EF652LAXml4jyecUKWcxuL7ZhzwIW2Hfv9fN1FgM2g+Rtovl4vIoPZb9s+WN1CBBr2nLfWPAP/wvOgPhbo9Gwg4MDu3//vn300Ud2//790IAEW341Go3EoCtesVX0v/rVr97XeXww8Fy4XC6H7jbtdjvRpw5Ra5Sh8kaObOlZ9ByFj+1o68/B7PayWOAF75fksqXnQhue17O19YL0XYEqlUqilJY9Bb+FF1tj3vbat/ziBTzsxfjOwVymXCwWgwv/8OFD+8EPfmCfffaZffTRR3Z0dJQofY6tZRBmhW1fxMOHD3P3LfH8G9V3KLjhhpawqlzxZmZBeLyclveFj9Xes+sbW3DDc20WAa/Qw7myQFiUPt3mj8/H5hhGrCjIi95beH9usWIlnu7g/HxZsu8ChJ+bzaZ973vfs88++8w+++wz+/jjj+3k5MTa7baCdkmibu5W0RcKhdyJfhsYADqdjrVaLavVagkrbvb6woQAuR02P8/strvsu916wfvXoKUWC9833+DIP4uWpz6+AQcXFHGgjOf3PtoPisViok+An+pA9Hgtphu81Ja/Iz8AFgoF63Q69vHHH9uPf/xj+/TTT+34+NiazWa0TiLnREWv6H0GeMHJZDIJQSKej+7Tsgpuc8xa+wuXc9+wyAiyXV5eJtbi4z0Yn3rjKD3OAe8N8bM4eQDg6QDPw+GSY5DjPoDYjNOLnmsPuL6fpx78/vAECoWCtdvtxGYgmrvHSYtnSfR3AIE8ROx9GSxHnxme7/rW2H4jC+AtPSypb77BnoWv5vMixfv4nDo8FO7mG2vKEZt2YMCChcd+e9z3D/ENn06MLdxhr4Q9ATMLXYv8e8a+c3EbFedkhPPmvhedFz+e72HRX15eWrVatevr60SHHQQL/etLpVKImler1ZBlQOAxNj3wwUCf+4Z3gMeIX6APIAYnP8Dx58FgxFbedwOOTT38Ah+/qAcpUqzdv76+jg4iYn9Ue38HfDSdBcT3/HyGg4Wc++asgXep8TqeFvB8G8L3x/d5cPwdguHpAHsPPBdP67/PATbu+ddoNKzRaCTm8rENOzi7sGv9PmIe6/U6eCOxCL3Yjdz7N4Dz6CBtXulLZlHEgnlqLE/Px4ArHiuoweNYF1y+N3s9cHCOHb9ni+0DcWypfYUcRF8qlRKv8ZttpmUIOCXIgUnceGpxeXl5qz24yIZE/wZsE3fa8yByjvoXi8VQ2eatdZpXwe+D56cVI3lh++lDrLoPwmc33/ffw3t6S4/nQ/Cx1+G78kHP2I3TjhjcZOHvjkT/FmCxeeGlTZG4ica2Qpy0n33knFOGu47Nx4lNPbgwBhacC2zMXlfv+SKeWCvutGwGBh4chwOOseNK7G8H+UfirXOXWJDiR+8PWfq3QCxqzz/HLuhYQMy7tWnH4vw5W9pY+tAs7t6nHccX5PCNpxA+E+DrCHzDTZ6KAN94wx/PryrkDkMaJO6ORP8GxKL1acElDuThNSxWv70142vavdudtskFv5bPzw8YOP/Yuv3VapXINsTm3zgfFNX47wfnsavUd1sEnwt3fCpSZEOifwM4ZcU/x6wnC9lbaK7Mw2O8B3fl8c/nlXWxlBhex/cQXSynz7lz9PM3e91Rd1fKrlwuB4E2Go0gZo7g+0AeDzIxkSNlhzbbWNzj6/TF/qg4JyO+qm5XcQ5ew7B4uaIuVpzjrT5beK6931a7n1ac40XI4uPsAvLk/vkc9ecSXJTe7irOYQvvW2uzZeeNNZCuxGo/v1JQ7EbFOXfAR5SZXXNyPGffMlzvwvJSVV+Gi/f01tuLg88hJkT030O5cSz/78twIXpfhoutuvnz4T1Y8L7JB/fi4zJcHBc/b+u6I+LIvb8DWDqLBTdmyTl7LPccK6fdZ8FNbE6OwYJvd11ww54BBA9BrlarW+sAfB2/XzXoq/Ji1h7vxQtuuMEHrwRkj4ALmQ4ODsL6B24zpgHgNWmGR6LPAFxXdNKp1+vhQsOFx6Jkd5tdbG4UAeHFylzNbi+v5YBdbGltpVIJAw3EuWtpLQfqzJIFRhzJjw0gLE4cF1MV1OFzrXya6LlBh+++w+dSKBRstVpZu922wWBg/X7fqtVqIk4htrNV9A8fPnxf5/FBgQsd82asnUc/NmxZhXk3d6eFxeXFKlxFF7O4viCGz8OX+nLRjHfxUbLK7a64tp2baPDx+TPz4LStiQYvf0VwDbAXw9V5nJlg99430eBKPR4E8fN8Prdi8XXH20KhYKenp9Zut9/VJfGdYqvof/rTn76v8/ggYHHiwjs4OAjtsuC6woqi3x1HlTmYldYuy7ei9nNtkFY5xwMEd83Z1S6LI+y+XZbPz/Ng5ne1YXebRe9LemF9fVoSn43bZfHN7HZfAv65Vqslmm9gPf7h4WFol+WDmnnET/XAVtH//Oc/fycn8yHDYimVStbpdKzX61mj0Qilouv1OuzJzt1wzbY3xuQmlWiO6XeP9cTKboG3+BA9R/8heljf5XIZXs+BMZ6jw6Jz+gw9/Xy/fE6h+Tk+vs+08tlYjCD2OhY+7sfjsY1GIzs/P7cnT56Expjtdjs0xvTff974yU9+Ev39VtH/7Gc/eycn8yHDc/RCIdkCGxc1WmCPRqMg+vV6HeazsZVpPlrNWztzS2oPi4KtsK+A29UCO5bGM3slfA7kwfrG1rmniZ7PLa1ghgUfW+wT83Y4EMoZhEKhEAT/1Vdf2R/+8Icg8GazacfHx3bv3j07ODhIDNZ5E/6dRP/pp5++k5P5tsHWFmksbi5Rr9dDtVqsAQUHyniN+GKxsMVicas7rXdJISg/rwa7+t5zkY0PykEQsTSfF7jfKAOC55797I34zxLL8ftByMMDRayaj2MPm83G6vW69ft9Ozk5yf1mF7/85S+jv1f0fg840IaLDiktdMFFEI9F77u/ojvOarVKVNTFLL23zLu2tYqJHueJ8/MDh5/T82o9wK53LOfvH8dc9W14a7/tf+BfF5sSLZdLe/78uV1cXITGpfAsxCtUnLMHLED87CPuSKFB8NwAAgKMBZZ4/h17f55nw0tAmy2O/qeJnoOT1Wo1DBqVSiU0sOBsBUfOufoP05dYBaIXLV83addQTOw+m8DHiL0+jZubm+BFidvI0mcEFyFbTs67s2A4COXdWfTMhwvs5+ksKA7GsZuLIBpX13G0nN+b03sQfKzVNI7JkXzf6hreDX8efAe8Gs+TNjh4z2CfQUPcHdXeZ4Dnk5jbz+fzMDeGpfSv8YEp9g64os9PI8xei57z2ywSFj2XB7Poed6LZpoQsV/4E+vwa2aJgQwDDn82ZC+8kLcNADz353vxbpGlzwhH8CeTiY3HY7u+vg4Bo2094WL173C/09bF43jsDcQWmsQsPd43ZrXNkjvRcLAOxTRcWYc0I6+GQ0Qfx/NFO74ACJ8n7XsV7weJPiObzcaurq5sOp3aYDCw8/Nz22w21mq1bLPZ3GrrxPNqFp53aXk64BfNsIuOeTiXzPKc3i9hjS2fxfFY7Jyu4/gB1xPwvX/s98nzRTdppbXi/SPRZ4Dn19Pp1F6+fGnPnj2zQqFgvV4vMbeHdeVurix6X5DDOXcux4VIEQNAi2yIHcfkRThs6fmccAyshuNzwCDEaThv1f1j3m4bxUoYJCB+jkdwbACBQ3gx/FnFu0WizwAu1uVyaaPRyJ4/f25PnjyxQqGQKMVld5pz9Wz1ODDGTTh8DT6n0yBYno/j3ufqWfS+2UatVkvUurMHwNbZ18ZzYwtYd4h9sVgktu7GYMADANbps5fC6bS0+b94u0j0GUEAD6L/+uuvzczCxQ0rvlqtQnGIFyEHsXiBjK86A36RDaw82mbH8vTscXAQj5eupuXTOSLPXkmsyw0LH9WJ0+k0DAD4HSoQMVjg/MGuwJ94e0j0GYD7O5/PQxnoixcvwgXsS1cXi4W1Wq2EtQfsavu0nc9Rs+t/fX0dpgxYYbarV573MLhclqvm0tJ2fgDwpbr4rLD20+nUJpOJTSYTu7i4CAHP8Xhs8/nc5vN5Yu08PCQeEJW2e3dI9BlAEG+xWNhkMrHRaGSDwcDMLGHhV6uVzedzOzg4CJ1jeK7Nc/BarZZw3zndxvCqOk71+dr7WH2AFzAPLCCt6Cb2ej8IcLsrWPXpdBoWxVxcXNhwOLSLiwu7uLiw8Xhss9kstMDidfSxlXvi7SLRZwTpOnZnIVr8bbVahYu+3W6H9fc8r8bCnEajEdx1nwtna82LTjgGwKL3mYNYxVyakGKiT3utd/9x814OrP5oNLLhcGjn5+c2GAxsMBjYeDy2yWQS3H6/3JjTfz4GIt4MiT4DmB/7CDZXo2GuO5/PbTKZWKvVSrSLQt4braQ6nc6twhyI2rvpfolprFyWrXxMvP6xF/qusteY+Ll2gFN+ED+Ef3JyYoPBIFj9yWQSgn7c4IN3rPXbVt/c3CSmFFwdKPZDos9IbOnpZrMJ4sfFC0+A19WjQSQaR3Y6neDa+nk8RMXC52o9Px1gwXNuPrZQBaStgIsJP1ZCy495Pu4tP3oSHB4e2vHxcZjvQ/B+bs8BQ5//x7Lm8/Nze/r0qQ2Hw1ANaPY6EyKvIB2JPiPeteW57WazCZYJEW3uDYfFOLDy8/k8dS09W1PucutLeL3oOfq/S8jMviXX3mOIeQD4fvx+9Z1OJzS05KIfH7jjlKHvhY9KyCdPntg//vEP++KLL+z8/Nwmk0l4rdiORJ8RFge7ttytFe4++rVzo0xY/GazGQJZmMPG3Gb2ALjKLubSbxN97Px3fT4PR/p9eo3vEYhEepFjGM1m89Y6feCrB30xE6z/fD63s7Mz++STT+zzzz+33/3ud/a3v/3NZrPZzv+fkOgzw/XxuLh9gIxz8GyBEWGv1WpB8Khf58o49iSazWbi/fg8fBzAB/m8gPe15mnufdog4gcD3PusBMSPz+YHNH/+/D48fbi6urLZbGaPHj2ye/fuWa1Ws1KpZF9++aWNx+Mwz1fOP45En3MWU8sAAAiTSURBVBEuhPGWySy5BTUsFF/U5XL5Vq06ewr+5sUTS+cBnsfvCsylvXbX31moaY/5NRgQvPX3783xil1ZhJubG+v1etbtdkN//YcPH9qXX35pL1++tJcvX9poNLq1ElADwCsk+gzAynONe8zCxRaX4IJGgIuFza6ud2V9qsqn9u7yGd42aSlAfuzdf/6bH6x2TTF4AKxUKnZwcGA//OEP7fz83J49e2ZffPGFPX361BaLxc6BMo9I9BkpFouJZadpy2c9GAgwcHCUml1636Per8rzc3YvEC8o75ZnufjTLGRsHr/ve6U93tc7wWeCmKvVqnU6Hbt3757N53O7uLiwTz75xM7OzsIOPe9ioPs2I9FnAO45cu0QPtzIWC48VvkGbwAluGl5bq5x5wGC3X4EyhDlhyDSBoVt6bht6T3/mXYV+qQdK3Y++9YK8GvweQEyBe12205PT0NWQIK/jUSfETTKaDQaIe++Wq3M7LXrDaseEwXPcbHgxE8NuLgFy1b5vtfr2dXVVdhaC/X4aVV5+85rYwOUPz8zS2QW/GeMHXOX+NOChPueHx7zjkQiHYk+AxArRN9qtazZbAargkId3wbKPzZ7HfDDhhMo7onV8POKNSxd7Xa71m63E7vCcqlvrEIvxq75OD+Pc/B8Y0+B59u76gZ4UIgFBff9n4hsSPQZgTVBsUmn00lsowx4E4k0Nxn4JaZclIIadizwGQ6H1u/3rdfrWafTCcJHFDttAIjV48esddq0gNOI3B0HUw0f5Iwt843dYiXDIM3T4L/5x7HvN6+kDYgSfQZwkWInFexzh7wxLl7e7pnZJn5eXsr96lj0g8HAut1u2Lap0+lYq9UK97hhb3jsssM76OK4XPzCvexiVprz4yiFRb08cuIYDLHJpz+2X0gUu+Fv+K75e/LTCGQw9okbiCQSfUYg+larZf1+346OjkIHWXanl8ulFQqF1O2qGFzMECC66vKqNdTxDwaDxNQCu+iy6GPC9zvG+rZdOAdf4suBR2zYifgCCovMXrXzqtfr1mq1gvcBD4SbhfLNN/6ITUf8tAJeBQKqu1J84jYSfUZwwTWbTTs8PLTT09MQifcWFR1ieGfatKi+t/rI6cPVLxaLtlgsEktz4UrDnUedO5bsYnUf755rllw0xNWAEJDfNAPnhFiDr5svFF7t+YfBB4Uz8EZ4B1+/vJi3AGOvgEXvewqWSiWr1+tmZlunByKORJ8RzF0bjYb1+307PT1NLC7h7axKpVIQvq+wiwWs/NyaL/hisRjiBLzENra8lgcFXvCD9+QaAW/pIXpYYj9Q8EaWXHuAY7bbbWu320H4HGz0AxUWH3E8As/hqQiOi8GzWq1at9tNnKvYH4k+A7AosDS9Xs9OTk5urShjq1Uul8P8t1h8vX20F76P9PvcOTePjJ2Xmd2aI3u3GfA5+PNJm2P7xUX+HPFZ4Wk0m01rt9vR+T2Ej+fydAC/wyCFaQ7Wzm82G2s2m/bgwYMQP4CbL/ZDor8DxeKr3Wkxr99sNokls7CubC1hqeG6c/BsW1HPvik1/7d9Cl1idQT7vs6/BoPhcrlMdN1lofNAhDl5LA6A/gOA+++bmXW7XVuv1yGmomh9NiT6O4CLlzvfcKUeW1iIHoJApV1arnub+w9iv08LFr6JIGLC9/X+PEhgClAul4NQeWESBgbc8+CAeADcfHbbeVphZtbr9azZbNqjR4/Cd6ly2/2R6DPCwS5Uf202m4ToMb9lV7Zardp0Og2dYrgXHOb8aS5+TNBZBoS3SWwxkZ9eIPaAvfG2FedgWsCtxPB94f18u+7lcmn3799P7DUg9keivwM8r0fJJ6r0kEJDygwRbHSBnU6nQfxwWX132Q+FfcWUNjix0GMrArlAhy0/b8/Fr+Og5XK5/KC+q28TEv0dgHtfr9dDEI9Xx6En3MHBgR0eHoZKOrSEZvHzRhAoduGVdd9WWNC7QEARgULUKfhqPsRSECBE8wy59dmQ6DPi6+9LpVIih8wltLzdEzaA4HvU0c9ms0TRCzeC5EHAbzbJ9yyctN72+34+XxmXFhyExwNx+lQfCz6t5BeW2++Syz9D2Ij4Hx0d2aeffmqHh4chQCjh749EnxEuxS0UCqH9E1/QLFJeHut3efUbQC6XyzAAoFMsbwfFe8Nx5x1uH+3X53vx71oK66vi/FZbPBfnWnukK5F/h/hxTD9QwZJzFB9TIzzGAMDVe/V63brdrt27dy+0ypLgsyHR3wFYMIg/FoDzN9/7jjvkxHaCxdZPED3vA8fttjC39f3h2frzecXwi192Cd/PwSF0rlPA3xHV9zvYbDavN97kjT92LRrCMfBc5eizU9jhAiosuoNdLnRaDj626IUX2nDLLF895++9oGJWPlb9F4um+5VvPhiXJn4MgH5rLf68PP3AMfk9OJAX26LLFx3tWjYsLPrFSPTvmdj3nZaqy3K/7XXbju2Fv+ux/x3fb3vNrsEx9j67zkdi34lEL0TOiIo+eztVIcS3GgXyvgXcNfX2Po6T5RzS6vzF+0XuvRDfXeTeCyEkeiFyh0QvRM6Q6IXIGRK9EDlDohciZ0j0QuQMiV6InCHRC5EzJHohcoZEL0TOkOiFyBkSvRA5Q6IXImdI9ELkDIleiJwh0QuRMyR6IXKGRC9EzpDohcgZEr0QOUOiFyJnSPRC5AyJXoicIdELkTMkeiFyhkQvRM6Q6IXIGRK9EDlDohciZ0j0QuQMiV6InCHRC5EzJHohcoZEL0TOkOiFyBkSvRA5Q6IXImdI9ELkDIleiJwh0QuRMyR6IXKGRC9EzpDohcgZEr0QOUOiFyJnSPRC5AyJXoicIdELkTMkeiFyhkQvRM6Q6IXIGRK9EDmjvOPvhfdyFkKI94YsvRA5Q6IXImdI9ELkDIleiJwh0QuRMyR6IXLGvwOWptwW2SLNGwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 64\n", - "Current iteration: 39\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 128\n", - "Current iteration: 51\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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49JhWq4VarYZyuYx8Po90Oo1sNotCoYBaraZZNICPBUU0Ajufz4ubfuIuNcikxprMbNFP9OzeTwh1l9VPYL2O1Fl9NptsiakdNfWoo1MB4KOAi8UiMpkMzs/PxdFZLpdDpVLRJLKQV0CuO4mcrDq93ijNQ2bJqnNsQAuLfkLkJB3693VUw+kDaSTkWq2GUqkkrDX1gifhAx86/FSrVWSzWZyenuL4+BjHx8c4PT1FLpcTPe/lbYDcFYgseaPR0KTLjnPds8AsXcssMFD0/Me6TL+AnGxNxo3GD/t9r7257GrLotcPqKC010QigaOjIyQSCaRSKeHeU1xAznmXtw6DWnsPK1W9CfQnDrzNGA5b+iH0E3Cvn1/Fyvc7g9YLnvbahUIB+Xwe1WpVuPfyVBoaTiFnxJ2fnyOdTiOfz6NcLmui+PQaNHee3P5BrvGsGIVZuY5PhYGin5Vsr1llWmmz+mSbXnt42mfn83lks1lks1kUi0UxQosGQlIykNyxN5VKaQJ41WpVFLPoj/9kF15+f7MqrH7X5XA4YLfbDV+i3Au29FdATnYh5H39MPeXHqsvLZWz3eToey6XQzabRSaTEVa+3W5rBkLS4EfKfaejNEp9lUdZ0T5dDuTJyTvybVrcRIDP7/cjGo3C5/NBURSx3ZnVheumGSj6N2/e3NR1zASyVet2u7BYLFBVFaqqCgsq30ce8dRqtUQxDllc/eAJ+p7ERokylPBCYpRryeksvFgsIpfLIZ/Po1AoiG6v9JpUD0CibzabIlsul8uJPbx8xCYL/qYEMa3XMZlMcLlcYowW1ULQ6OtoNAq/38+i78FA0X///ffX6uJfd8HIpM9PFk9OuqHxyRSxp8ISyjWvVquYm5uDx+OB1+sVNfQ0kIKuR46Qk6ArlYrm7JuOx8rlMkqlEorFoiZwR9F0svCUI6AoCux2u7D0pVIJhUJBuPTUwVcveBl9PcC47v11WXJ9bMHlcuHRo0f4/PPPRYtwqlqkhdrhcIiiJhb8RwaK/q9//etNXcfMQB+udrsNh8OBaDSKlZUVeL3eS5Nc6/W62GNXKhXRFcfv92N+fh6qqsJut4tkGblAhcQti14v+EqlIoRPopWTcUjwZPHkfSzVsFM1G0Xqh4mdkDP1xuko2+u5zWazpsGn3OhC3sqM87zUOfiPf/wjNjc3EY1G+9Y6MFoGiv7HH3+8qeuYGUwmkxAGWQ2v1yvERJbeZPowwoosdrPZhMlkgqIoolZeLqcFcOlYTD8Ikv5NkXraOtB95Ow5CtypqiruRzPgSVT1el2c5VN8oF+hjfwzen80D17+m4xrMVVVxfz8vJioS+42xSoo+YfiDZRs1O//xm63IxQK4cmTJ/jmm2/w5ZdfYnl5mWfVj8FA0ScSiZu6jjsHNcagWAAhu9fAx8CfbGnlAB/wMcZA96GUW4rAU1Cu2WxqPAs5XiALnp5D/nev826K7uu72ciBP/k9UKScgork9YTDYYRCIfh8PjGSq91ui+AkVeTl83nN9oUWmmaziVqthm63C5/PhwcPHohxX0tLS5dqHa572/ipw9H7a4Ks8zTn3cmi75UdR9l6JAL6t3w/uSJPv5Doq/T05/ey+OXH2mw2OJ1OTRuwYDAIn88Hn88ntjsul0t4IpRHQEFKillQMZAc58jlcjg9PUWlUsHCwgIePnyIBw8eIBwOQ1EUca0crNPSb+Fj0X9C9DpG05fUkichV8zRfel+FotFY6XpZ3rvRO+hyHtweqzVaoWqquKYbHV1VbQI8/v9UFVVnCrIWyM5rVjOAiTBy8U+JycnePPmDc7PzxEMBvHZZ5+Jyb3EXWo1dt1wcs4IDPo7jJJGO230giGBykLtNejSarVeyrKjRYOOGhVF0dwoJiEP3SDPYG5uDg6HAx6PB4FAAOFwGEtLS4hEIqJN2Dh7bXo/1ImH9vo0vPPo6AiqqmJhYQEul0u8R/37ZAbDufcjMIt/B9niy653q9USwpe/0h6eKgPpPVF8gM76VVUVQTePxyOCknQOTqW7+vvPz8+Lo0q6vyzKUaCFhZ6ftgxOpxMAhGV3u92XJv+w2EeH3ftPFNnV1u+7O52OEDlZdbnGX7+fp5MAh8MBt9uN+fl5zV7c7XaL40c6tiTRO51OcWQou/GTpu+SR0JbB8o7oMKier0Oh8Nx5+b73SQs+k8YOT+fvspz9sh1ByAWAnnfT9sBqsGXhe/z+RAIBODz+YS1JWtPorfb7WJLQAvCtHoKyCcCdDRJAz31CwszHiz6O4K+aAf46PLKwpfP4el7cvFJUCRkp9Mp3HvZklPDDhI/WWb9dcjXMM77kL/Sc9CiRJmILPjJ4fIjZizGcddZmLMJW/o7gnxkpd9P66vnZPcegCYISEdnVMorJ/u0222NhbfZbGIboW/KeZX3QchWn6L6jUZDnEIwk8Gi/4SRE2tk4clBLlnw+uQacvcBaPruFYtFUSFIhUXlclkTyKMtABW3UO4/HdFd1crLmYv0+oVCQYi+Xw0BMxwW/SeKnJQjF7To+/aRMEjw+hp6suLyXp/uX6vVUCwWNWO85CM+Er3X6xU3OuajI7txxS8fP5LXkclkcHJygmQyCeDDsBGeTDs5nJwzArOWnANoRaoPxtH3AEQ0X54Iq8+f1zfSaLfbIv1VriyUF5NByTnRaHRqyTmFQgEnJyfY29vD8fGxiOLrLT0n54wOJ+eMwKz9HWQrT6423WivLae6Ah9HPOu72tLz0H2p065+aIZ8PNhut8WCY7Va4XK54PP5EIlEEIvFRBquz+cTwzupS698nCc3E+mXhkt593Iart/vR6lU0pTjsthHh937Twh5D0+WVj4rdzgconEEAM35PaDdJ+v39fRzs9ksioT0cQB9TAD4WObrdDrx/v17vH79GqFQCH6/X3Oj/gKKoojXkwtuCoWCaBJCRTe0AOTzeZydnYn7zs/PY2lpCaurq+Jvc9WjwrsIF9zcIGQBae+rP8PWu6a9utTI5bf6+5L7Tsk0lFBD9fRyaa38Vb4G2cUnEfZ6ffIKhpXWkkWmPH66pl6ltVRPTy3AqNEn9f2r1+ua16b4QrfbRa1Wg8PhQDgcxmeffYZQKCQWEnovXFo7mIGiv3fv3k1dx0xBH27queZ2u+FwODQpogA0TTQajYZmvJUczZbHTMmNM/TNNHr1zaOBFBSEA6ARPUXOXS6XRvQU9ZaHWMiLT6+Fh34nW/Ve3oEefYlvtVpFPp/H6ekpVFXF4eEhvF6vaKIhl9ZSOe2oTTSo+y9NDfb7/aKJRq+mIMxlBor+22+/vanrmAnkc+tOpyPaZa2url5ql0WCp+GN1C7L6/XC5/OJ+nG9paeoNJ2DD2uXRaKoVCrCAlLhDBWlkPCpR5zcLovej7xPJ3oFwvS5/IP66Y0CuerpdPpSrT69njyIcxDd7ocWZVRtR33xGo3GpXZZbPGhqbeQGSj6P//5z9dyMbOMvL+lxpjRaBQej0cEo+RWWYVCAdlsFrVarWdjTLk3nFwOK/fIo73rqI0xyYLL1W5yey5qdWW32wFA07W3X268vmJP/ve4fz/5OQGIAOFVkI8fAeD4+Bg//fQTKpUKXr16hcXFRbHouVwu0SHXyI0xv/jii54/Hyj6P/3pT9dyMbOMHK22WCzCkuobS5CFmp+fRygUEu2o5Qh6v3PqdrsNp9MprL6+/TW1t5Kj2dR1l9pZV6tVUUbbrwU2iZ760ZH7LLfL0otBH6kftxvNdYlL/7ylUgkvXrzA4eGhWPD0LbB9Pp+YImzErjr9RM+jqq9Ar4BYr3RYuq/eWsndaHoNvJDPrUulkhA9DbugclNabGjBIUtPwy56TaulrYLcCUcfvLuqa9+LftuLaeL3+8WRoZH73v/tb3/jUdXThizmqPeVvwIQH0ZKrgH6j7WiYJ3H44HL5UI2m0WpVEK9XgfwoRGnnDXXarVE0EweiyUPrZTLbOm4Tj7G058oTIObEB7FWOj9UwyG+QAn54xALxe9Xwlov/uP+vyyNyAvCBSxp7gCufTVahWdTufSAEsKRFqtVs0pg2zhySMgYVCyS6/xVrOK/m9N10rbGeYybOlHoF/ShyyISaPFvR6nfw19/zs5MYby0OVR1VSFRvtcAJpjQhK8POJK3k4A0Lj9g66939/pJrmJLcNdgnPvr4DeCk7awmnYY+j3JGx53z03N6eJ5Mt5AXIffPlaKYOOhnSQS0/lteQVUOCP2mjrj9ZmQWCz7onMImzpJ0TOQwegccOnuVjKngBV05Hwu92uxtJTHoFc/077fHlL4PV6RUBPHllFwqb9Px0X0lGhfAIwTGhsfWcXFv2E1Go1kTZKmXiUhTdqcG9UZAHpG0aS+y5X2cl5AfIRIqXGBoNBkQHXbrc1vfRoQAZlyVHRC+XHl8tlkVtACwB5CDIs9NmFRT8B3W4XmUwGL1++RCqVEkk89+7d01jaaWaEyf3uqNhGL3p9PX23273UvZZET+f8shdBXoXs4pPI5cxButHPKSsxnU4jk8mITMB+74GujbkdWPQT0Ol0cHZ2hmfPnmFvbw9utxsPHz5Es9mExWKB3++/1MxiWug75cjHa/qxVABEyq488JLy/Gl/rj+eAz6WvZIVlwdrymnEhUIBqVQKh4eHePfuHfb393F8fIxcLtcz8Ye5fVj0E9But5FOp/Hrr7/ip59+gqIoOD8/F+fma2trWFhY6Ds6Wf/hH3VRkK09iZwsdb+kILq/XIrbqwx10HGjfHSnTxqqVCrIZrM4OTnB+vo6Dg8PkUwmcXFxgWw2K5KDcrncSO+t1zX0Wix4AZkcFv0ENBoN5HI5HB8fY29vDwBEPXi5XEatVsPDhw8RDAY1M9wIWWDjnu33E8eg55HP/uVptb2eZ9C1yPkDdKRHQyuj0Sg2NjZEuWw6nRYdb3Z3d/Hq1SukUqmR3iNzvbDoJ4BKQvP5vPjZ8fExut2ucHnPzs6wsrKCQCAgSkptNpuIpMtBt3G3AXJHm6suFuMgLyx0zVTpNz8/j8XFRVHSWywWcX5+juXlZYRCISwtLWk8AFogeyU5jWPF9anNzHBY9EPQC4s6vlCdO9FqtXB2doZKpYJUKoW9vT3cv38fsVhMFH/IJbB0ow6zctR9VGYpj0KOK9A2gmbRLS8v46uvvsLZ2Znwjl69eoVEIoFMJnMp8j/u6wLs7o8Di35M6EhLXypKbaZqtRrS6TQSiQQODw8Ri8VE8QflzXs8HgSDQYRCIdFRhpp0ALMl5nGhRdJqtcLtdsPlciESiWBjYwP5fB5HR0dYWVlBNBrFwcEB0uk0isWiJltQLvTRpwPT7yiPgHPqx4dFPyaUlKNPU9VXz2UyGTQaDVxcXIjOO9THzufzIRqNYn19HY8fP8b6+roIyl1Hgs9N0iteQVWAlMsQCASwvr6OTCYjgp9ye246MZBPDej3zWYT+Xwee3t7ePbsmSa/Xi6uYcvfHxb9BOhLagmr1apxVUulEkqlEk5OTgB8EILdbofb7UYkEhHNHpvNJuLxOBYWFqCq6rUc9d02lDNAHk8sFtP0wOtVUky9BWThN5tNpNNp7OzswGQyYXd3F/l8XhNcZAbDop+QSQTZ7XbFFqBarYpgYDqdRqlUwtbWFqLRKICPyTb68/NPFb3lH4ac5iwvDK1WC6VSCeFwGCsrK3j27Bn++c9/4sWLF1xVNyIs+gnoJ8RxrAy5tYVCAfl8XtSzdzodBINB0V9v2im9nwoUF6B23kSn0xHdcR48eIB4PA6Xy4Vut4v9/X0UCgVN0hG7+Zdh0Y8JZcKNE2nvl2zSbDZxcXEhil5qtRoKhQK2trawsrICj8cDQFu9dxesfi8hjvq+5CNOm82Gzc1N1Go1BAIBJBIJXFxcIJVKIZ1Oi14D+vJno8OiHxMqeKHxUaMw7IOWz+fx22+/iYIWcmWpIeekc+FmlWm+D7fbje3tbcTjcZRKJaRSKbx9+xbv3r1DNpu90nHgXYVFPwT9B1SuW+/VVXYSOp0OisUiDg4OAHzwALLZLJ48eYJ4PI5wOAyXy6UppJGt1l1ZDPSMYpVtNhuCwSCCwSDa7TaKxSIikQju378vXH2jt8LWw6KfAGpCOS3RE7VaDfv7+8jn87hc3uoAAAc/SURBVEilUigUCqKJRSQSgaqql+bB3eUP87jvzWKxwOv1wmazIRKJiCpCRguLfgKoRl0fZJoG9XodJycn4lyaprZubGxgdXUVoVAILpdrJEFcxwd+1hcZk8kksh2Z3rDoR0R2EanzrKIo1/Z66XQav/zyi5jYurW1he3tbXz++ecIh8Oi/508XfYmGMVV7tdTkJkNWPQTQEMw/H4/FEURwxWnaVk7nY7oc5/NZsX3JycnWFpagt/v14zPouk2csDvJgTXK65wm0Jnd/4j/f4fWPQT4na7sbq6itXVVRwcHIj+88D0j4YuLi7QaDRwenqKnZ0dhMNhzSz4paUlBAIBeDweMQ76Os/4KTOOmnFQow6qIpzWqUY/Bi0q7FkMh0U/IX6/HxsbGzg6OkKpVEIikRC/m7a1ka3+/v4+VFVFKBRCLBZDPB7H0tISgsGgxuo7HA5N7EGu6ZfTXel4ULbYcsttEhFlw1GxEc3GoyMx2vLQUA66ydsQfYYhC/R2YNGPiN599fv92NzcxMXFBU5PT5FKpa48pHFUyuUyEokEisUi3r9/L87yFUURJwtU2kqz+Ox2u+iHTwM0Sbxya2vKQ9B31qVOOfqGmPLjFEURxUSrq6uIxWJYXFwU10cLkHybhLt+VHndsOgnxO12IxaLYXNzE0dHR8jlcjg9PRViuu7CD6rgu7i46Pl7s9ksBE/in5ubQ7f7Ydwz5f/T9cqddaiRJm0Rut2u6Is3aHKMyWSCz+fD8vIy4vE4Hjx4gJWVFfh8PtE7gIRPiwAtUPRvOS5B2yS5AIeagU47V8JI8ADLCWm1WiKhZmdnBzs7O3j9+jUODw9xcnKi6Qmnn0xz1b3suI+nSTj0eBLRdQW9HA4HFhYWxJZDVVUhVLLwNptNbAeot8Di4iL8fj/cbreYuNvpdMR8+0KhAEVRRB8C8iCYvvR0hVj0E0IfRuoGe3BwgJcvX2J3dxe//vorDg4ONMUfRkbfrVce2uHxeLC4uIhYLIbV1VVEo1H4/X7RVKTT6aBQKCCZTCKdTkNVVcTjcTx69AjxeBzBYJDd/P7w1NppQm6mx+MRfeLcbje8Xq+4vXnzBslkcqBFHfWMfVCRSi8XV+44M+x9jPN6cn+8Xo+VW2vLz0X17noymQxyuRwuLi5wcHAAn88nKgzJva9UKsjlciiVSrBYLHjz5g0KhQJcLheCwaDmdXgBGA6LfkKo9JNEYLFYoCiK6A7r9/tFC+xkMtnX4l/lfJ8edxVvYpzXHiTeq5DP51EqlXB0dKSpYgQgrD2l1Ha7Xbx//x4OhwNfffUV98ibABb9FaAPKH2lllAOhwMulwterxeRSATJZFJ8sPP5vGiXfVMVYIOs3yyIhabqjPr3yGazqFQqmgk9zOiw6K+APGRCHillsVigqioikQi2trZEjXcymcTh4SEODg6QTCZxfn7edwTUNJkFYU+TWCyGpaUlqKoqfsa18qPDop8CcrIJnW07nU4Eg0HEYjGUy2VkMhmcnp7i+PgYR0dHODk5wcXFBYrFIqrVKhqNhrB2cr83SqCh7Df5a6fT0fz7Jj70tMhRA08anElHffohmgTFGOQ+dvLz6JOIZA8K+Diey+/3Ix6P4+uvv0YgEOj5/8AMhqP31wy5rvL4Zxr9XK1WRWabPAaazqTlhBh6HD1WP0RSTpqR+8qNOziCREcRdjniTr+j5B1KCHK5XCIngL632+1CuJQQRI0uaWEzm80iicjlcokkIn0SDz2PfrBGOByG2+2+xv+9Tx4+sptFKEhF4qf6eRIIzYsrFoviRhNky+WymBRDwteLHhieH6BvWkkCp5t+60JilNNu5fRbGtlNgc5OpyMWM3pv1O6bhmrSYxVFEdmAJHhZ/LQQjduyzKCw6D8F5M6v1AOeLD4tCCQcWURkQcl9lgdEEKMcHepvFEWX8+Zl8cujsOWb7OLTNVGPALo+eh7KzKPnkt17zri7Eiz6WWGSqbXjivim0RfR9HpPo3gazFRh0d82wywtw0wZzsi7bVjYzCzAop8xruKyX7e7fxuLFi+U04fde4a5u/RcMTk0yjAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAbDOuT3phu5CoZhbgy29AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzD+P4ZntIb7VXAPAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", "n = Nx * Ny # number of parameters\n", @@ -1829,22 +437,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(10 * np.log10(0.5 * np.array(evaluation_history)), \"o-\")\n", @@ -1863,17 +458,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", "frequencies = opt.frequencies\n", @@ -1885,22 +472,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb index bab3fce98..8b9ffc01e 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb @@ -325,9 +325,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "lb = -np.min(evaluation_history, axis=1)\n", diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb index f30f97f89..407781c03 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb @@ -11,17 +11,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -47,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -176,22 +168,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", " simulation=sim,\n", @@ -213,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -249,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -308,1440 +287,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 0; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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d1sAZGTRHfJZE4xQRcN12o7Fv+Sh91TsQZud479ion/q1EVsStoyV2BYrSSthIzMEK0Eix85ocAtLeNdgzSLlDHpGh1hbRmKJyZYzXfm3Fpy19meUkT7NygmrjlnnL21av/Xflh9L7HuMDpB7zXJL2UF0R4RxhNbloLY/2rlnNtDsBu9dXs183q1Xb4SZl5yvUOfM+4He7F7WFcnvJfXOzu2RYyJXITPOe4b4zyR9Ia0m0JKT9W66W/dTre8wq3+3sL5JVtuJ+O4dV/3UW1j0sVZ5fXxrpVr+v+TTaFast1j0WHNZn2VbD2y9OFS8NmoRab8a517+aORr+gljrWM9WoNB9e9+v5fT6TRkR/aR+prse9H4j/ifyUs88Gb0Cyl1oKqA1a+qqV/NIRtbX1bU37oh9Vfe6EHCavQZl+6t2wPegKHft1h6G0MeGxXXnn1tU8ayNSiuyQ3LdmSGpev37PZuW41OLnpfFdWz7bVba2FX+9waPL04yH7izdy9vmdR+3SNxel0MgV8ZAK09Gu4ZpMmuvVk397eyv1+/9DYo9+dVBv+druV6/X6/LpnKbr153w+PxvMu59V65cNfb1evxgJD4dDOZ1Oz8aLiJmsS38oQiZAjYN1302P+NK2fji1FDEZW+vp/L3vjJL+WjGuPkpR8wYC3am1XemntCtj0YqzvHqQdmWcawzqa5bYWD7LuFp2a05of6NxlnZlfbV+7atud+mrzBkZZ53LOod7/soYWTmh+8nhcCjn8/lZR7Vvnbe0qfu0jnHt09ErV+m39l+2YaYYp4luHaG++eYbs7F1x2rN3mTSv7+/Pxtei+7pdHqKbv2/NYLX5KkNr4Wx+ldtWmLgzarqe7Ie2RGkLe2nNxvUoqA/YVTfr/YsUezN2mWnut1uzzrkANTy2bIrfa22W3ZHtrtJf+XfVny9tvN8btmtNqu/2i/Lrm4/ebWmBzK98NjLCctnbVPGwss3HXfddtF+Eul7Na7X6/X5vxTdGtvL5fJhcPaQsZLnL/vJ8Xgsv/32W+ouhkNL3Eop0254vL+/lz/++MMN/OhlmHXJ27PXq6MTC5NRvyP1LLHZsrvUXs/umjqisd4ivkttbxGHaB2zc2KN3S36iXfbomVjVvs9Ho/y7bfflsvlMmSvg+tcmugCAHwiXNFNXUhbMkICAGzN2iuUEVJFN/PEAABekd2fMgYA8JlAdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEkF0AQASQXQBABJBdAEAEjl33j+keAEA8ElgpgsAkAiiCwCQCKILAJAIogsAkAiiCwCQCKILAJDIfwHEImdFHrOVsAAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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qHUYOTpp0RPyNv1NGTu4tp+ARMkmQtXgnK9YzRYAcmxOLznNIPr6tkOmpj5UGVFWjrYecC/Wj6mU2QOP0dJa7MHSup6jMStgWI1T1j7LSfFA/nHR9rNRbT9Eld4/s2Q/WqhXduh5Kn4+OjpqpeGunC3d+eEROB+w6wfr6fD4fOVsfr3Sac8HtpBoH9/q7bek8jV0LzL3QNdLV5m+fDE6IhFFVoxV6RaKMWn0LjqdAjM4YZTDiOpR+y5PzBgdFc1RcKTsXJDxSlmK0Ih5PjfTXywm8LlNWzw7ULqNir4HqO5YkvP3fA0YPHo14diFo7CJ5/dudBfVA11FbjBAlWxov645OrF7GYBuMUumk/EacPyIf/WVUz+iPJR31ndFuK7NhmYVRMx15y+ExA+SxvBPMAwvKiWUx2iXvptSHx7ayR11Du0TUF22P86ieJSPaPOeTMtDuIQZl7DODCe0P74VupKvBaaM3hUjS2u/3Q/rNNIBG7RGqR2/6q3NZC5NRSWm5VUnnMdrS7gOlR57usD/ugRnhMAKgwtMJMB3j3TRqQ/1jH5gWtby6rqXtTso0eF1FGL5jg9eXktKxMEOZTCaju58OEYCgcXiqrf6qbs5SBbcrMdWnPrRKDF6GOjSHnnGRfLhTwZ2Y+nVIdhqv5pfH0vlRTz0C90DFMyAe42OnvBkAaK6enp4GfeOCs+vIoTl8enoa7WRhmYHnkXCrarRgJ9m0FvA4F60o3yNmlrtU8nKnwXa1zbEXuj17Ybfbje7a0YTSe2nCRYqt1J9opVBMhVvGdQh+Ha7Sy2v6temNfcytyI1Gxaid3ptRQVWNlFR9O9R/EovkdnR0NHpmgObCo1oSBxdiRIxOcG746hcXZ9QPly9/Y/rvKTWdreTjDybR9yR6liL0YVrJlF7Haz4YfWpsvPGCzkH/lsNSnZ+lJc6zris5i8jpXCUHjkf9bjkKn/eql7dstxy3lyuoo+pTi2hdl5m9+QJrq/78OXjZhuTNOXFoTpid+LM6mBGRgFleaI35S6P7QppIl89MoHHRk3HriY7xCVfZgZPPqFJgWq0N7vq/K5+UmWkvlcAjVvW7NVk0DBKUR0la6VdbJEX3/gQNiukv61qMqkiydGaUNY9V20znWJ/0dK+qRvPCqI/RufrKMorLntdhGsxIV1Ei64gtudJxMEU+VMvldbw+yKjfIzEuojKLOFRS07FMq+lwvIRCuD6wD34jB7drsmxAx/c5HRaYwTEQYX+kf3IokiF3Lsxms2F/N8HskHatOfTAxzNEOpuWDbPvVfXtLqSRdJmuyGC4gZqRgddv5FF1eySvoevoI0VgykQPSONR37jI4ymMlJ8RLslTykQS91JIy0i1quv3o9PoOH6P4qXgnvpK2RnlVb1M/1vj5TiUPvIONC0aqV0tcLRKQR5ZMbpjzZwLSnQ67qDcebGvrTogHbfGzznlmOkovITFIIEGLzJR6ey1UhjtwcnGiYrykF5wnJITnbpkpP74/nSv6ZJ0Sbyt0kWrfNaKROm41YZ0nH3abrc1n89Hay6MRHmDBuvAfkMOuaTFF7vdbnQdXne/3w+3yvfCn066s9ls2PdY9bwI06rlcAGL0YdWPFulBSo9t6z4xKsd7dmj1/SaJPvOVFGkSlLzEoNHA06+Vc8GQs9MD827v7zW1SpPUN5e9mgZGBeatBfz7u6ubm5u6ubmZri1k7LR2EVMug5lwEiU80My4jg5Fxwb9+bKWTP7YTTPCMYzJo+kNSfSO/0lYcpR8/5/XVvn8nkTnGePXOk0SGIC+8LonJ+WY5dOMqqULrRqoYd0k3LXvPLGBncualt9lmzVHz7CUf3RjibuD26VGKQL2+32xYKd5O+Ey4yUOytI6Pv9fnRbdQ90X0jj3Tyz2WyINFV28KiMaT7J9OHhYRCyJqkVZVSNo1GRNbftOFHKGKjMJDMquNfP1L6uJ5L0rTQe3flYGT2zj7xFU8fRACg/RraMYFoyIXEzOtA+TD1F7O7ubkj15PQ89WR05qmd+shokZEcoxzphMZJ2XukK1mTtNRHfsc2WzV5ljZE5pSr+sNdKYyOn56eXjz/mZE2AwMRBrOAQyUrOkGSmY7TXFBv1D7LOu64dY7koTqzxqTrSt56MpfXit1WqE++SMuH0Kg/dA6Hsi3qpJ4NQR2gvHSsrsFIlzLY7XajhzT1QHfS1cKU9lJS4VRvqnq52KLvJUyVBDiBvpCi8+jF2J5PLo1RXp2G6crkH7Ylg6RXZlrH24n5CEiWIFyJpCh8zq3f9eU1LC910PEoGmIqSNLTg09ub2+Hu46YlrZKA94u59LngMRTVaPIhM6oVarxf/O6kpdkxnozHV+rX2yP9U+OWaSjNvS0tVZpRyRG2fLmEu8P58kdpXSfT/CqqhdBhBO12lVZToSl66ivepzmdDred91yeE6ulDkJT98f0gGfBw82qPtV1SwteL9cFyhfykRRrx5/2gvdH2KuO3la4bzX8VoRqs6T8oq8GdHwHAqTwpZycjuXFJMRr67ndSNP1Z1kpOh6U4Ue2q7r6FF2Xv9STY8ERM+suqqiA/VDhsM0lhGZ2tIY+GYBlUaUhslwuPmdT0PjQg1Xhz2CdNLgvJMQJRMndIFprs5lyk1nomN4VxKNzmuavD4N1tN5tSNHzLRUkZT2daumOpvNRrsUlKlozL6Hm2N34lX9kWUNX6ug7LnQ6c5fOqQy0WQyGd2Gr6iU+uxlCAY2OkY6Koei8ez3+6F220r/1Q4Jl2skuha3cfJmKS4Ccy5ZVnO4TX2zpNsqAVS9vCmANcb9fl+LxWJIa2gwWuBhOitjlxEyzdU5ntLyAS4kXR0jhWYkorYYrZCI1ScqigyHkZAmnfVpGQrrT775XJG4toFR8Uh6HjUq7WXdb7FYDKTAVW4+nFvjE9GyZshxkkAoK2YwJAX932u2zBooF9b9dK5+5zyTqFv1QdZUmUnpGnScLMHMZrPBYU4mk8EZUV56PnPV8yKS5kURtMhXsvbFQ7WpeaFD4UKQxq5siesjmnfVTuX4/QliLGnp5p9DkSHLcJxf/d569KTmQzIkJ/A4jtvLavqwju7lC2Vt3LVBvnHy9aCpF7qRLqOeVvrj6QzTGnl43hbMNEaKrOiHk+EezO9ocuXy1EjK1Er5XSFIHK0oT/92T6w22X8pWOtuH0XMTPs8G6ARMzrxyM3nQrLmmwPciBiFuHORs/AygMbFc9Tebvd8y25rU79nEHRw+p4pqY9J+sKMhkbG0kUrwt1sNqOtZ1XPURnnn6/44bOSX6s78uNlK8qCUSWjY5Eu66U8TqQr2/GynUeCDHjYT47TZSdZe71afWuVEWijCkSqajQG2YHbpuZF+q4FOr0WiesrzKipcz7vrWj4a6Eb6U6n09FL/XxClS6zSK/jdrvfXinSelBM1Th9ZE1M31HhPQ1l2YDKdkhRnEgJti2F4CKCiNXruK20VuTlq62M9Kk0rVonDYvRO+u2TNMUqfB1Lf4ga0bqgiJx7x/looiNkfd0Oh1tFaRT8kjHI1ufG68xOrFz3F7+4PmC14T1fAK1yflhBqO5FfHqjkY6NGYEjB4ZnfvOgUP6yUzNnZ5AolU0K71X29zT7NHmIdAevBThx9CmlZVwrMxQvG1f9KR81G9/8afmsCVnZsMs//RCN9Kdz+fDa280YKaZVeNaXmvVVMSlqFKgIZL42HbVswLo2oyyZFSMkGmIHg28Rng0QCmQJpaGyZsW2JandZ7Seb1boAd34nXyUT2LCq/omsTAv1U1qh1q65YrdcthsV8k5UNRBmWpvvlx/n8vKRy6Jgm31f6h1JrRIktGlE1rWxIXfUiQrcjLyZX90Ri5XtHKHjgHHKf0jiSscxQccHGUutMifsqNgY3acj0lodNeefOI/t/Kdljz14dbNhnU7ff7Qe6aKwVtkqNs7+zs7Nsl3Tdv3oxepOcpi3throwr0pXiM0VsGTmVwJWPHtSVkukOo1YqktDy4q3+kBy9FEAlf4185Ay0z3Eyea59Vo1r5mpH12d5RHBioXNxA2N6x4hD7TjBtWpl7gwV2VEGbsjMQrx/QquNVqRF5yRHwYiaBu6kJTDV5VO1RErs7yFH2nJGLFlwd4T3R8eIOGUzDFBacP32BTHNhcte56qM4s7Ax9rSZf/O54NZgGedrk8+z74uoSBG8ytHpPIc12smk8mwHnJxcTFaEP7a6Eq6esc9PZAUVn+1baXqWRnpzVjoJ5l4cV3nu4KQcBn5sU0RL8sAXgsW3Ot7hEqnwmNbBOtORH1W3Ur901i9TENDUt9Zl9zv9yOSY/mD0fB2u63lcjl6K7DKBoyoWC+TLA6BWQznR237qjOvRwdKA6asKTs6WrUh/aIj1zW5BawFjutQfVPj000A+ogMKF/Kn/tiPfvhvLK/fL6DFvb0hl3KhjJpjYkfz4L0bzql15wiF/MkI9oi5UYHouu486S8Xdc0ZywrtLY+KjhTiYyvP9Kxx8fHdXZ2Ntot87XRtaarIrcmVmmYvK3SMxbLmRaxrssFLu5c0KKce2VfiGCqx2uqTRpB69kFHllWtZ+f6gsPPJak4ZE5CVTGKdJhXZtKxlSRxKsxyeC56ELSZb1S28lIum7IrfodIz7PBKqet8RpTjwSkow98ud3rWiL8tJ49VfynU6nw6KYO+lWBuP9aJEYSUeEu1wuh1KaP2iIqbyyOo1Z33udX3MrmTEb9Pl0Z+SEyqiypZN0KOqvR52t+iptRbbFgEjlLDp9zpOiU2+HNXiNn22KF3zPuM4Vx3ABmmWUo6Oj4e3D6sfXxlcn3VakqWhKtRYdo4WH5XI5bH1ppeVMFUm+mgxGE6wptaJRvyVQbdGTynhI5q+hFZ2QMNQv9YPbtJzUdby/g4vRucbCdp3wlJK25MF5opz5nAquQnukKWMlmbIdEiCdDTMc9eFQeslx6VgnQSdjyaYV+bFmz/5pnK16KaNCEZ6Oa8nL02rK2ufBSd6dstf1dS7b5MIzFzSpZ5ovlsPoKKWHXkZqgZmEtnUyYKKjlq3pZZh8hgbni1mU3zziDtWdts5XW1wYVolBYCDze2z6S6LrO9KcHDy6lHC09UYTWvV8owLrjSJvXYcRT9XL+mSrT616kQzo5ORkiFg82m1FXLqm0CI8T6FJWF6mEOhMPNJQCigldYKjoWpsMkwpZ1X7gShOZrqmKzjJlxEroxovS+h4b6dVJngtSmPfSGIekdMQfQGLtV0/x/9N43Rn4XPq29PUB15LNXeBEaqiNY6fEXqrJk55+Xg0Zl+McwffshXqOT+Mck9OTkYykz5p7HTcretSR3zft+rOVc/bPmnXlI2XFPx9fuQg14Ue6PY8XRGkGxqVmne00FiVejiBCC7Eqpfvu9J5nvo6GFmyXsQUhhEco58WkTBddlJoGRP7wUjAZUrlZS3OoxbCIwLV1n2jPh9G3VJK/dtJkOP0RcJWVO5zKtDAqBu+7UdzzAVQj9jomGXIkjWPdVlPJpNRlOUZBIld16Ij492SrM8yfa96JnwSAvvHcXvt2onCydV//z166FkMo+WWg2Ntmo6tlQGxHx6w+LyRD3i+70SiXNVPcQgXOzU+ZhH6cG3okO18SXS9I41eXmhFB4+Pj0O6wgiL51Aw7sVbRMG0vAUaLNMm7iumd2152qrx/k1G59ySwyjmUPrIqIP1vUPRrsizarx1itG2G5v6yXngHPAmFG+fcmebTPm4Ct/KCngNOk3Jh3pBQySBt8oQrQhPn0NRFmXu37eIsmWYuj6342n/ufpDcKeM6s3sp+Qh5+g64vqgPug8zhWJ0uv4jCqpA/rwJiPqlNuEghUvG34uiqRT5jxqDCztcO7cUes8jYU67JmkzpGj+Fwp5Uuia3nBF5ZceZle6LZf1p6qXqaT3obXU1sTruPUH38uZ1U1o1onHfZZ3lIP0OBNBeqrp7FVzxu4GYE5WUpOVEQfjxRTixW+OEUHUlUvSMwNmGQgGdCQKWMaYKueyXnwHQxe96Xx8DxmDh6R8jzdGuv6RVlSzoycWrVlRn1qh1E2yZpbtxjlqU/6jbqludDKumc/rSjX5913ATh5S256vgLth/PtWZfk4duwvA/SFY9+XTe5AH4opWcWxTHQZlq1cgYgrC2zz549uu32QlfS5aRSEBSqp+mMTJ2saRjeFv+2+iIvzpsBql6ujrb2TPJDwtzv96OHfevhPjIyL6uw7se77Rgxqk8e7Ut27IenTdzFoJqb6ssau6f5uq4vpvE2ZUZ+nAs6SWYHMrrWivUh+boxHHJa1JMWmToJUFYcq66peaIxz+fzUdSlkgGjecrr0OZ+jUEOUPMj3RM5q4+tu6mkH4Tbi5diXB7b7Xa011eyY7RLJ7NYLEZO322AdskdGMyoBO01f43oDtkv7b1FnlXjW33ZN8+yDvFRD/TbnPZ/4VGV0EpbpUBVzykIo45WiucT1iJnRrqqXSpCrHreF8xN14w2nRgU9eh6inKpbE5UMgrftqY+M6VrjYsyk7GKMHyzvIyBTwVTlMMSA1MwncPN+ky1WXpgfZOk66k/iY3Xp3FyTCQNnceIkucwGuS4PQrlmNl/L+cwclL/6QioX3xuhMurNXZuc9I1WI9n2usr+Jo3ypUEKWfqTkvHSA9I8E7WAvsvfWTkTfnRyWhdgI5O7Wt3kstQMvfMqEWGmi/ygc5vLZQe4hrKrie6k26rLqOPk62MTApJz+qC9Ot7DZFKQW9Oj0iy0c6F09PTkUfVX68zynB8IeEQ4XvE6Ds5qEgt8lX/aZAic0UmijIlK8pBBtyKRjx9J4FLfhqjfmf/q9plAMqCJEWHSpKjQek76ouu686a35F0fUFPMmuVrORgql7WOEnsLJewX2qDc0cnwMU/LjCTcFUb9UUg3zbppQ/PyDgnTphOfJSj9JmlNZ7PcyUH/dXWMBIxnYpk6LtbqCs+v4Jnw84PHum73jgH8W8PdH3KGGtZJAJPP6k0Il8K2Cedi01eM2P0wTTY00M+8lB7c/WhQ+AEqq8kCCc4XZNpJyMfKgLH4DskiFZ0omtK0TVOT0/VJq/riwkaLx2Ip5P6d6vWzUhW4DEcv+sIo05dv1VOUB9pkByXHIobGY+l7nDxr6peLIC1oiMnN0aaGqNnAN7PlgNmBsRtZ7oe5eO65LXalmxd5/b7/Yvn8VK+JG/PIFlGop3wxhCV2Q7ZJvvrv7dsvJUJ8zt3KOQbb5dZUC90vSNN9Z6q58ey+UR7iuULLCQansNFHxcq00YR02KxqOVyOSg2I2s3WBKlJo7XqxobIBVFhMt9vlIA/qYxkjC5H1G/e+rJGzukPNzg75EJDUW7QxRtyTjUltJEJ2Q6CI9KNAd82hOf80pCotw8MvW02EmKUatnMjTu19qRg9LcMDJjtiFw7K2FHEWmVTXahldVtVgshnH77oFDc6V5YTblDp26yDnQR9eZTqcv7kRkICLb0jgOBRK8Pu2C5SfPHLSdTBnkcrkcbjZiBuILccw0XAdos/quFYnLBmlLuj7Xbb5J0q2qplchkdKjanFht3t+mhWjL0ZuXAn2upquQU8tg9Pikj8/9+7ubvQ8gpOTk2FyVRNjvVfKttvthvqsVpUZCdDp0CBFRiQYGrZHVIyG/Nmwbnzqr5RdCi8l18o59zX6vlb9VV+1C0IGRqeiMfpj9rho6NEzjUVj4OIjdUXHc0eA+kAZ0DHoupoLyUPzwyykqkZ1Ue5BdcdLveNznXm7q86fz+e1XC7r/Py8Li4uarlc1nQ6HXa46O3WXg/2uji3UdLxvKYvGjuDDZGe+u6LeNJfOgmWjOjAGKGvVqu6vb2tT58+1f39/aAjWkDjjUaH+iqinEyeH+ZPMMqlIyI/SF8l/5Yjdu7ohe41XU0+CUnele8S0yQq7WHdR2QjY2Jq6FGoiNC9okeT+/3zpmreNnh5eVkXFxfDw3o0icvlclAgRp96kaNSJUW0MlSWMfTxO2bcKTHFlKy4B1HnSAZ0Cqenp/XmzZt69+5dvX37ts7Pz4d+LZfLQeHUfz5Ht/V8XJaH2C7f9irjknFT7iK01kPSW9E4ozBGfYwamdl42YFptvrJW7tFiNIB9Y8LMzyfK92MokUOdK4nJyd1dnZWx8fHdX5+Xu/evav379/X5eXlULZiDZ5O1Z0sMzuRjfSo6pmoWkEN7Y4Ph+EYRVir1aru7++HsTBC1TnMRnW+Xkt1c3NT19fX9enTp7q7uxsifxKf+sj1Bo6Z5SmOnztIVGsm6dKx08ZVJmR24QuNPdH1xZR66IRWbvV3t9uNXtyoF1hWjXcSVD2/ttmfGMQISxPAqLiqXhC9DJ7vMZNXFWG9ffu2vvvuu3r//n1Np9PhnWJ8Sr0mebVaDQq33f62qfzi4mJ0PdaPRVAkXa8d0iCU7pMU5ZAUGdC4Tk9P6+Liot6/f18fPnyoy8vLOj09HSJIvbVW8tILFrfbbd3d3dX9/f2LCJfbgVi6YbTL9nUbtdqRw1A0pHYVIVY91z8Z8dE5891eetUQo96q8Z1vVTWUcDQnl5eXAyHKKZJ0pD9y+kpVmWmQAPgamaOjozo9PR0cwfn5eV1dXdWHDx/q/fv3dX5+PpCQSJcZARdXFdnyN+o0o2lG7S4T6gUJV/O7Xq/r5uamPn78WDc3N4P+bjabwWl4WYQPkrm+vq6ff/65fvzxx/r111+HF5mqj3qojAKVyWQyetUWbZT2rGBIts8nE8rRSXa8A01zrieIiXh1nnYXfdPvSKtqP9dATyU6Ojqq7XZb9/f3VTV+4Isis1b9kDsNZNwiq6rxLcIyGE0mlZJRy36/H14VrxsdRG6Kcs/Ozmq5XNZ8/tt7zh4eHqpq7BSk5IpSZ7Pnx/ApWmQaz75KEaVwLFf44gWvK6I7OzurN2/e1NXVVV1dXdW7d+9Gxr7f70cPfGaU/unTp6FNRlVME5kliFzkRCSfs7OzIUvge9hWq9VgeBq7UnLW9vhEfz5HWREnF0G5HU7ZjSASXywWdX5+XpeXl/Xu3bvh+c4ss9zd3Y2iTi6+MOLUvPEJbCTnqucH93MeLi8vB71ROx510llQJ11nWddluUzzyqyPZMZIU28xlg7IqW82m2E/rcaigIO2pTvvNK9+YxDtlR9mbazTq398VKXIXbLw0pK+57xwDk5OToaSjshe41f5oycmnwmvv1js/fT02yvYWwswAov27JcLt4XW9fw6LbA9P5YT64sn3p763lp4IGm1+nloXEyRf29fW/0+1GdPwUk0rNmxjdbf1u/enrd76OPXek0er/WlddyhT+v63ieXRWtuXBasLfvC62vyaNnB5/Tzc3PzmowOtes65PVlP/eQ/R7q6yH8UVv2gKnVJvvuxyrr+8K13YOD7Ea6QRAE/0E4SLpdywvynr/nOEcrImn99nuu97mouYXeKchfAZ+LPIMxfIHtPw3/r/b0R/TsNdv/HG+8ds2ec5ZINwiC4MvjIIv3ewVmEARBENINgiDoiZBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdMf/M75MuvQiCIPgPQSLdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFH/B/K/Xhd93b7mQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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3ttr03DAvrM2wAye3m7GA/CkXUPPF/5iTPE4ez6xg6RslO18nZ44QLyVDK2z6cnNz88ckXXYvsLGZ4vuhtM0r/3k/L46XN6hb6ZgscRD+5sK7a29WaPn8yWQSq9UqxuNxt+XKE+zyhQMFpMU1++puTn2BFXBWuhH7ajcbrZ2OFI1+9xGvyQBj944PatN532bEs9Jl94Tb6B0AfaBfEINJzKTbV4bJStzlDvf/UJ36EDjOdUVS3BycHIQOpdfMlcfXitABgywiZwoO9ATPPhLq+322C5c4cjuyzVgxo26xCfulf983B7b1bIO5LS6FuP9W7h7XLJg4py9YEqzwacp37CJqhWa7F1iNpSbqdNxPmHkwkP1OeZgA73u0wVhlZhKPeN4cznlOi028nOM9sCbRvH3tY+lkriPiFKRMLnu4fOLj6acXCE2+XtBiPJxW2vAYo/yzL+jw8UKcMxXmLtfI8/X77sm1/DO3xUTAh3t6j3HftT92/b5jcVQ/fQbZu8zC+d7+ZNJxOYd7mkQtODyGflDHpOUF32wjzFfEczbnwOQtl3kusa9cl3bt1ec4IPt67Czo2wrK+DmoejtnRHRPVTrouFxD3xyorHbxZdrd9wSr1yhov/e0H8rMPieaPpFGVIz4RSGt1+tuQLw4FRFdvczEZXVppWuD5xwbvKOpyQ+SzbU9jIJygvcQ2jHG4/ELNWYiz6rbzsjfOR+jsjO5ZpUJA4JDufUZCuPlUowX/zjP6tFlGMZ5uVx282UlaAKMeA4GecEnL/TwexOQF5CcDaCeIcDj4+MuZdztdt1iJdeivOC6KuTmVDqPF32wImPrYN7Mj23meiTnORhxbas1p+9W3pnYDNuBxYfTZcbP6s79tC9YMGQbs1r2WoRFh1Vt3q3j7WYGx2K/HOd2OEDltQavl1DaM5Ez/9gA88fcEcwcgOjLaxnZ50Yz0nVnI57T0ly74W85RcrIaQfXcRpl5denvPK9mcBMrMvlMqbTaae6bZC8C6EvbcyGm9vgaO6++O+0ycrWx0PYEGg2VEiDfY9WVDhFLrPQJu7nMeXRV0iXPcykn54DjwOLNfn3OEcme/rmmjQPp/DeBpzLffBKuctX9In5c2rvshPnmXB5XwN1RGcWg8Hzk1M88suY9AVd2mvSdWnJNtFnH/7pD+cy7wRaBwsflzONfF2TsVN4P7nIv1GM/DvvZsjtNLHmn1mg2J4ZkxxMjOx32FPmhb4tj63Q9C1jdDgi9mpAJoAMD67TXKdyub6XU6mc0qHQvCILSFfcXl+TPZoQD5HaKtbKh75S6Hdkd+pEGuS0EyfPZQOPTd5twDi67k1/vdsi18lcL7XDT6fTvVILJMF5fgwV1bZcLjsCdPDIi2tWlE6T1+t1p6w5ls33ZDe0w47p2j3j6gVEqyy3hfHyODi7YQy5D4/gMga0j2M5lyDomr/Hjnm2CHBfPJcmxj7C8d/9II9LVl7oNIF5rBz0+tqWiZf/m8BccsNG/JIgZz9sW3MbDy2k8unjgdfgEl8uD+ZyRgv8JuUFpyv8Lte0Mpnxk0myikHp5ZQh1x8hXL94BNLl735xi9O3XMvinn3qNuLlK+UcGFzbc/2QOrNTU+DySF6swPFdjnhNCUXsb4uyuuG4bIh2OsbGBOfAlpHVi9votJz78JNSiFPpfH2PI/egPznNj3gOgG4XY5cdzyUXSBnbwBYIfC6dMH4OiK595/vkOm0m1zy2Hk/6a4WLfVtYRMSeYMhz4LJI3vJmG8cWc50/q2yXjbxVzI8BM6d+UIg5ykoU8UTZj4eM2Dlh/zK5eq2mrwbNcX9I0t1sNjGfz+P8/LwzSKclEc9PLFEXckSyao2IzihQSI5kVsERz0oBYuOVdjg6uxNynZXrZ0PtBu+AQoAwcp03p6UmbUgi1/5yyuVFCCtJty2rftfHnaJa3dlo+3Yu2NCZKztcDgg2bjso52aF6TFyX13L5/8mL+5nIvACVSa0TFwusdBGZyVc38HANUyQXzxDyQUbon+MmdubFZiDam4v/XIdNAcvK27GigBhG2Rs8gJu/uTMwWUwxi5nd/Yfv87TPueMzguh2datZtfr5+1q2CPjzLyhvrFV5oQaMoSOwGIvfSv8Ji+8IQKzSOO65Waz2SvGewK8Gd0LPVlJ9aXhrh96IS7i+ckyr+haLeU6l9NQf/pqYTioX4Ho+itjwL19381m06XpHO93HthQnJ6bjOzQlBj806k9bfWiiF8MwvsicELPTUTsbSXqSzndvtxOSMBkb3Vv0nXmYPLySraPwwZymuqAxXkOXtTzyWyYUwczl0+y2nLZIY9xrol6vCh3WaEzNsAq2oHUtXnXUE1E2CJk5aDibC2TOp8sRNwG+okdW3V7USviF2LmiTGXrRyA7Ufb7bbrg99sFxFd+cc+Rz9cRmScLSZub2//uPt07+7u4uHhIbbbbZdSewXSUfy1NMxFe9fXrFowhByV+9KIvgK+Jz2nq05Zs8KL2CdbXmWJkbhWl9/GhJF6PEz42ehoH6+lxLjoAxGd+uh6vY6Tk5Mu6Dnl43hKOKRxbID3i8Xzgov77P3VrmGDPqXp+qpLS/weJeY5wkmz0vYOC/7Nfa0srYAB5wPqnw6InhPOWa1+eWuVS0mM0dHR0YuFOJcpeCoqr/j7XrSd+afdkD37sD0nDmh+2fjt7W33QEPE82soeW9vXgCjj3ncvEhFdmCfOORTfb4HXMY5FFi9WMcY9l3T5TCCd65HE4Du7u5e7Bb5kmhaXiCyOA2gRhvx8qkgdjf4w+AwARiP03UTZFZZvhbRLafQfhAjlwG8wOD6EmkTbYYcHx4e9l5SzbVJqXJac3p6upemRuzXU3lJB6mTH8G1GsNYvWC52+06dY/S5xswSL82m+e3W5k8vajj9JWgZvXH/ewUrn0SQGjboVq4V/r7yid2OB9vWzJRMYZ5m1IuO0DWuf/OlqbTaefAuf/YgkmXej3qGLt3JoRtezEvExmKnt+NRqPuxfS2c6+X8I7l6+vruL297b5ZBbvhwR//zvV+goq/TcT+gK0yBrxXmcBopQyyuPJag4UN1/W49b1C1Jmbx82lFDJECwP818LgS6P57gWTpz9MPhNEKtJX+Lcqoqbj37sGxeR4Yp0Sb7fbF4V5HAnDI8UHJtxc+wJ2wpxyQxZe7OMau91u79sNIAinRA5e3BcSpVbOuRgtY5QdJi8W2olMjB7XvELvskBWtzmg5mv7a3K8m8KEblI2WXIdzsnXMGGZEBh3lwX84T6uba/X+w+xMN4eZxO6yQtbxKYcuE2O1EFzYMP2KLnYbmjHarXqvj3E9Wm+YQSVywvTHdysXp2hsABFoKc8aOLN9p5fgJNLZsylxRBjmmvHtM3jyvU9vpPJLy8u51WsfjUAbehbH3otq/6SaEa6EINrTRi0V18xaKIk76PNaaKdPWK/psm1nHYdWhjLqYi3sXGs01/uYaNz7Yo0MpOxDS+n03x9j4nMCw75nbUYnrciQYYYotMpjCqnZnmRwmmcFyT4f67vcQ7OlgnNc++xdtAxoTKPzJ/H2yTla1spZTtxOSoHBdd9vaADaCvjz15iKzLbMtfqW3XPY+z2WE1TSsF2eVG+20ow4HqQLtmWM6TVatX7VUgm2b6FsxywmF9KZflRZWyZdtF/BIDr/y6b4Y/MK/bs7x60vTBunm8EGt+piB/CH66Tu26OjeSMogWake54PN5bwcQ47Mx+Os3pOmmpV8CZCC9+mOBcsHeaA6mbOLLxgb40NuI5ejtacx+TumtQRGaTGqrNRuv0NOL5a4qslA+RmsfQyoxxoj2uC3u7EyRgMjDxcl2XhnAmSh5++UmGA5dVBg7H391uHJI+MPaei1yScJ/5u+uSORXvUzkcz2PgLhn0BS6uCdH4468x4uXZLBYztoyvlXRW+4eCAjbiBTvm04RuZW9btbL297qRKebx9gJnnt9sm/Yrq34WiWkTY+fFQGc33NdlBfpAmYxvdWEMHPzyWEREV/I5PT19sfbzJdGMdCeTSVxeXsbZ2VlEPK8oQqJE9IjYU20YO9GW+iOEFbH/yGAuxLs27NVXK+O8A4HJtlLNiySOkn57EuqEyZ7NZi9I1/t8GRuThReRrL6djnnF2rAxslhpY7V6g3RNqL5XLgOhPKxcGVsTrhcl+oIY89S344M2ejysWl+D5ygfb3vA9vKikZ0cmxsOh90LjiD8QzVOqz1IwtkG9eVc3/d45O9R87jYvj0+Lp94oS0vLDuYcx1sz0/gYc/YKOsx1EutvFGdHt/sP3mXj7ee4SsuQ+QSTC4B+D7+1gpKDChrC7O8+IY4mU6ncXl5uTc2XxrNSff09HSv1uj6l9Vfn2KkdpUVb1YrTDqOkgv1dkwbtSfTNU+XRfKEO7XhvQDc33uJMQTuY2WTVQ3HWVVwPRbCrNxdJ3V5ZTab7a3cmqSz+utbyMhqw2qJtvcFBztHrpV6brKic0rr+cxqr0/p+vhsE7me6Loi17WTc42IfYflWASAFw3dP/qVHzDweDvF9+PH1CRzbZ1xxwaY//yV6/iGywxebKOPDnp5D64DtoMF6pz/cz3XVjmfMclB1fbgfjNGeX69JtM3p34YxA+E5BKks0SXMtmh8ockXcoLx8fHXbqMkmIS7LQmt81m0w2qlUWuRXpCvAjmCY94nkCngHmHAmmu9/XaOMfj8R7hnpycvHhc1IrHjpaVQCYdp8QYCYsFkD0kAJk4bcaQnREQpKykvNfZCz2k1fm73HIwoBSCYbuvXMuOx7jyO7/e046WA6Ftoa8eb2Vux8z1V39c6841Z/roOeyzUy/iMY+2CeyCtQVUsMfdL9rGpvzgjuc1160JWF5LcKDO/kA6bbLEH3K5h/MQCycnJy++8dl1aD82bXFh4vMWRtprn3Rb8G/XwA0HbROux41reKsY7aWtCDmXZb40mt1pOBx29VwG0KvXEfHiRdkoLYrrs9msS2H7aqM5VfNuCICzuLbJx6v0Lup7td6Oy3YgHMWvi6R9ufbn8eBnTm1Rpe4PioaHKbxCbGejPoqD4tRecMQ4+dhYTbpeOCFI9SmRHFicQVgJ0b+srnItnfP6CNclICt0kGvsfdfMbee4vEhHX+hf7jv9IF3tI1zSXY6nlsm4c3/mK+9dZ07plzMD5i1nJShd1649hnnM8iKgRQwB3/Ver1Nst9s98ss7F7Cbx8fHF/PH4uQh/yTYeeHO2Z3r0PkVji63+AtUQc4uGLs8Nl8CTbeM5Yiat1BNp9N4eHjo3iSFsTLxDDJ7HCEoJtkLY16Uc4oX8ayMmQzUM0ZCFM0LC16Ay8rXqoEJ79vE3VfDdF+4f06FMTZIdTgc7i0MmPi8+OHUOuK55JFr1SY+v7eWD8qaazr1pb+5rJDVKX9jnPwx+fWVJSL2305lxzbRZqfxwl1OUV1W8PEOePwOpetjfK73ifolOS4t5X55axR26+Br5en2WKX5nbceV2eDFiaeZ4+9VWHe+sZ5VrTOpEy43i7IvyFPdiI5E3N5AdvC1kya9nFnTla4Fh/5yTVKI5zn8W2Npi+88SRgSFaXTmlRdqjHXIehLup9pC4p5JpZxP77XiFrbyPJBpaVrLd0WaHiNDZelK2Vs6O8Dd7lBHYdeNGBfwMbXsTLp7tMVjnVsxJn7LxNy87GeT6f++fruDZsp8j3dVutlHLpIRNh3rVi5PKRzzPZuq1cLwcfOyXtccoN4TIOuT0ed+YzYr82nPcJOxA4Y6HfuTzl7MH27VQ8Z4K+n/ucicfjlGvDfXaP0qeveUeHg0DeSufSTt51YZXrR+dR+g5atkfOgUdYtMTPOc8lKQs/Z2lfCk1Jl8cNs+pwJOSRRVQlZJqVQsT+t0NkB7EayQsvdvg+9QPhQrZWuThlVgx2ZNf8uF5edc6pk0sJqFm//xbSzXVf/m3SpR+0zcbN71H6DkKMs999YSehPx6/XHPMY5SzAI+5yYc+ZicHVm65Ty6nAB9rIsvB0Om9gwSlAI8x13V5w4RussLxOZ4A3/dEVQ7avlfekuZxtD3YDmxXLmuR+Tk7o+32rxzAsRcHGGzZapWHM3Kb7V/OxnIGiP273GPbZl4sJJg7Cyk4hMfXvY0xCx54x6W/L42mpJs3VfcRKBu62ScYEXupA+cZ2ZlBVlm0w5OeDcMqIhton+rjmhjobrfbq7nidDmd9DhYrXsPY0TsOTj3clR2ZHba7Taa/EyCEC/jDumypcz1PRu+x9uEaxXkbT+eDwjIY8d1PL5ZcZg4c9qL43uxJQf1TGggl3tweko0Oc3PKbnbYuJlfy/XsdKl/8wFdujU3GRum7aN53Ez4eZFYlQiAsCvffRDB/ZDavmDweAFeXvMPPeUHnKphDGgjbapPn/N/bXw8PG2DfzI7zs5tKPG/uKtey3Q9Jsj3HlPUsSzo2Gw7CGNiK7UYMPIpJJTxDyBTm/tAH7bU0TsfYkj6b3TMP5tw0fF8Pv8tSWQgknJJOqUk/JCVjKQtxXPawaag4frf+4P98cxImLP+fvm0UHKCt0LZFa7fcGQn31ZQw6uDpqMSR8pZ0LyGDGmWcHmBSbGhrZBSB4v21tOvd0Hp605A8tK0yo4C4E+IZGvyf38Fq389jrbt8soOXtaLpd7dgLpZrvPgZd+YXMofQdD/mYy9Fwe8mEHcI89PhPxLCIg3b73ROSFWwuVVmi6kGaQ6ngymSDUro2LTfg5apGOc81cs8rH5FKGN7hbNVkN+DynpYB28dPXZlGAhb2+koQfSrCB+0O7MillAsiEPBw+v3TFK+koIm/1skMxbtTQXDJhLK2qvXBn4+Z6Hi8HXNqf0/QcXDmnbz9lVrM+zzU/7mPFHLH/RKIDgY8xyVD+cV+8hSnXvLERE25+3SMEldW8lTT3zMoWle2FYT/thu1k8nQ5Ahvkuh6vbHe2+VzPx2/YreDyBf1gnCg1glxGPOTLWanSBnZJ8BrSvkVsL7JzjRzYvjSavnvBq/I2Vjs9g8cLXTA0jNcqrC8d4ff+d1YhvodfhEwZA4NgK4pVuJWBr49R+s1FpOjc1313vcoGzLHZGNw+Kw47txWHicY7MVA6fvOW1TNGbtXKJ6egrh9zH67hNvTVA/1gBP1D5THfzHleUMxbjHLa6nNol3cJ5BV6+uA6JW3KNuPfuX6dHyxwOSqXYLyvlLnAdphf9yvPN/e3EEDl+vHivE3RtuZg4H2s/M4BHR/I93dq7gDPXCKcuAf2gn+5tp5LFsah7NUql3nN7yfxPfJidrbTVmhKul6ltjLoS9Xt+BALkdqRk2t/yv05DqM79HjtdDrtnuOmLofR0A9Uiw3GysGOTbshLlLdvjpTROwtyEXsf/26jT6TLWBsI14qLD8swlj7Og5Oed78Ny9IZVXoNBP0lStyuYV6ttvD/fICm1P7TEDAO01ofw6atg8TurOZTIKMX1b3Hts+lesdKWwto+0uNZCJ9e3tdmbgrMAlBdfi3b8sEOxPnk/Gzqo4B3Lsh0CU97Gv1798tXlfJpPbZ/vOc5Lb7/n2/SH6rPat9L3lzQvbzspaoOnDEfmFHLkOxyTYmUh9GPRsILnUkNOPrLCseq08I57fFZvfo+Aone/n18jllLCPVHOq5H2c+foeJ8ofXgTr26JDP1CQvHshK1lvO7PxZofqK2XQN6eMXDvXdjmX0onn3QowB6G+WnCfAvVP4Dnm776P7+/jc5vX6/WLYGNVl1Ua7eXhFtuM59PjRNtNIn6vs4nJwbTPPjwOzjzI3kyKWTWakHO5xn30tslsry6vrFarTtEvFou9AGV7yYLjkE97rh1k+Zu5ItuFg2J+oIQA+IcmXdSAU8XRaLQX2fMjeU4jnEa6psgAMrB5Aczt8OZypyevvWjERJUfPXRtri9dt8p0lN3tdnvPxFsBW6H1Ob5XyvteNL7dbjvF0XfNiNirbbn+5YyDazMGVlsOVh5TSjP+u8fRJQSTq9tox87EZgXnc/L+bJ9jEvE4cS/mk7Yypzizx6FvLJh/nrCEECAV5p92YgsEo6ze8tayTLgmMSvRLAA8597+aFWeSw78znbsRe18Hav/7C/YBfOSHxzJ829f4Pp5DcGCA1unzQbjRTv8zdjezePySQs0LS/k+gkEuVr98v1droG6CO/olbczcR2/3s1fygd5RDyTIhGbrxePeJ6w1eqXN+2TXvEY7W73/C6G09PTODs7694lYXKjfWzFct3Izmb1g5HZqSNefmULzuTtMV4E8SIE53vPMV8QyMIiaoxaGIsxJhmngu4HYzYYDLqFOh59hXRd0+WJJQcICC1iv47t4GbbAVnZEpisljnPpRDGJddg7Yi2u7yQMx6Pu3IM7fOiG3OwXC73Fsv8HWHYzsnJSTcPu92ue4uX/cNrHQ7i9C3bCXPj44GDot/V4Jow/TDR+1Flxoe+ur85vb+/v4/5fL73jpDhcPjiW4G9GBjxHIh5cRTky24EBybzCOe6nMB9LZLydrYcOFuQb/PdC7nGhlMul8/fJ8ZkuubIIKG+cF6M6ezsLN68edORbsTzS4/9ch3vJoB0IRlvt5nP551zQK7Hx8dxfn4el5eXcXl5GW/evInZbLYXHBaLRff1PNS6ci3Pj0AyFhg3ZNyX7rl+68WjnOZzr9lsFmdnZ3F5eRlv376Nq6urPWeHGBkr9jj6nQt5UYVghorgPtTAedNazhpoqxca84JHtg9v77ECtyLL6bLr6nkRClLKj3dn1cX5VmWc56/V8b232+3et5gQiM7OzmI8Hnd28+7du85umAcToGuSBHMrYcpMDoLZn2w3OXtwgOH3ZEUQKIRG1nlychKDwaATILnuf3d3170sfT6fd98ywXexuaaaX0xDwKdfzNHx8XFERLcF7ObmpgtOeUHTmZMDOoSLGPPeZHyopcIFTb8jbbFY7Ckgr16TaqG0MGCOMzn4K3VQWOfn5/Hu3bu4uLjoyhWQHxHXiwZZJfL0iu95cnISb968iXfv3sVXX30V5+fnHYldXV3F2dlZ9wIaXuJ9c3MTP/74Y/dyjdlsFoPBoDMiO6qVrBdpsqrLJQTvx+x7+s2E+/bt23j//n28f/8+zs/Pu/Zst9vu/RY42NPTL9+Mulgs4ubmJhaLRWy3z8/W57eTESwcmFAxfqkLhEYm8PDwELe3t3F9fd3Npb+2hxqba4RWxXlF2grRi56e54jolM5sNovLy8u4uLjonJEx8UMFrgE6S3Haj03SBx4yOT4+jsFg0AXuy8vL+Oqrr+Lt27dxdnbW9ZESE4HYL2FyP+j/oXplXl8AeSHML8xxqQWfu7m5eWG7p6enHekyJp4TbP/Dhw/x4cOHuLm5iYeHh711Aq7ljJD7LhaLbq75ooOzs7NOFN3e3sZ2u+2+Kp3v8Ht6etp7iCqvTfiJUr/tzePK9bye9KWJuKnSJQq7LgoJo6r8NiCIxKmEV1gxotPT086o37x5E8PhMObzeWw2m3h4eNgrsnth4ujoKJbL5Z5C8AIdx5B6QcJXV1dxeXnZqUa+gDLi+W397FOkngUhop5wcn6XlZ5Xs3lcNy8goESscAkWVlZv376Ny8vLvW+lhUy5D4Hj5uamUz3e0eD2O2gS9PytICZcv6Ut4hcnm8/nXWkDgnG6R/0RNUo5xlsJaVdfukzwpA+uLU+n0y4YXV1dxenpaee0Lg2ZfK3OxuPxntpi3vwNENybdPri4iKurq7i6uoqLi4uuv3SjD0BjLY7lcb+cx06Z0+MF3Vpq377CzbihUB2C9Fu7seCmOearV6ek/l8Hj/99FP3UBPb1lCS2KgXjpkTBxLGgSB1enoa2+2280FUtHcleF3Aj5XTV+b7/Py88zO+iDLvRmmFQa6PJXy2HcNEFNcc/W+nQ17B/BhM5K7NOC3P6sB47Z5O87l+rvPle/Xtqsh1WU/yp/Qzr9x+7Ly+Nuf75v574STvvvA1+fdrv89/90+umXd6HOpD37m5XYeOOXTdvBD42vXz7/J1D/3+U+zG6JuHj/lBnx99CoFk+8u+km03ly4Otdfnv+ZLfW10+/vu9alj8to9c9vBdrvt1mY+Iw5ORDPSLRQKhf8gHCTd5u/T/ZRI/KmRDPya9IA2fMl7+D7/P8OKp/Bp+CPM+7+LL+1Tre7xpVFKt1AoFD4/DrJ9u8cwCoVCoVCkWygUCi1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg0x/sjfB01aUSgUCv8hKKVbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoN8X8BTGk/N2cNQcsAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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+vGyUgPE5kTL/zGO73U75S9I47jFst9uU78MjZD55HtbQr+ueD+93JXbj4tGHe0BOIt764xGCp2e4v7c0uUy6sfHx+trkhS8q4ni/brjwBOlagHQhOdbfPVL3jrkf9+J10mTuhbuTAnkfDodCUctz0J6CY77wwPH43dN3Eve18GiH93Md1xsvpLpu5a2XGErvpODv/sOa5O2Srn+eXsnnwO+BrhHJvK/w97FRKumSMHel6Pf76YHJB6K8npfKPRnCfboLvOUkt4Asgi80Y8I79TDRPVlJKZyfTCYpZKbwh7dEiEvuE2+VsB4F9gq//x0v0O+JdyW9zWHjeXqqQTrlgjEsbulzw5YXTSAAFAXh5vmYE/JvXjkmH4ZQt9vtQmqCnHSuINzDQ/Lco+EZCHe9es6zE4ZCbBj1RqOR8pUonxcq3QPKizx4a6wBz8L1IBpkkw4awmEvAHmawb1aHA6u7c6AF7MgQn4z/xhVj5xIVbk8IPvMrTshyL57rNvtNkVn0qkYRdrOyR/HYjKZpCKiy1sewaG3kHKui3zOPVjmwmWIdJKnwtwxcyPPs5Evvri4SG2cueH5SZKuh5TSqeCFd9Fut7VYLAp5V1cUhAhl9qS+L5aH+nkYL6mg8CgVi8vrLiyQGIUYvNZut5sWDiWlqu+hMeEZhgEF8YV2b+1c3ssFEUGCeBg7+UBJhXlzYsrblXLFpfeUe/o48pwnIS5CTe6aa+Z5U+Yyjz748TDZCR7PEKNC4dTXyucaw+5E6BVzJ7Fc0bgW88B73QMnCkHm/HkwoDwrxIHX6oYQuUc2mDfkgufPi7Csm8+nk5TXO3IHwlMnGDPWinFiSOv1eiIo5sULejgio9Eo5ZhxJs7pkV8jJz03gG6g3NlCjphjL5LywzOyXuShqc/w4wVEntsj4E+N0kgXZcoXkSR8v99PRRJXbrdo7lH1+/1U5JKUBFp614tzrxchzPOy3m7i18Crm8/nyatBWai28ndXQq6HIckX2XuOscw+Rrx5iBcPgXujZBCAew4eRruX4A3tnoZwoSMPyBi9c8BDTz7roSHpITesTho8P88onaKQvFBH6sK9bfLs7hE74UDWkCtjeCwP6UYPeP4TY+Vr5IUaJxUvAFKPwJBS1POahLeP8czkbfNNPS6TyIynQ3gexgOpedTkMs3as64um+gRGy1YK34gfzoc+CG3z5qdSyvkEWROusyt662kxBWe7nHv3g0V60a/MvxCbQanwuXe02dloFTS9aq5E4sT53K5TG1O3ua12+0S4XU6HQ0GA11eXibv0vNK0slqerEJged3nj8CCBbkP5vNUk8wBRNCZ35c+L1ggvJ73tHTEiiqdzgQzmGpJaXQzAWT3B4Gye8hqWDR8f6oEqO8CN45r8/nhLHxQ/RAOoXXz+Xf83ExT/66e46e73QC95SSh8gon6eIPI3A9T2sd88Gbx2F9Ly5G2iQ5zh9nB+aOz6P7NdqtUIB0sN3npv583FSOHUCR9aYI+6LvDm585z8cA9/xnzHmn+eYhQOiW92Yvx+PZ8PL2Yiv36Pc+lESBRvl3TSfD6XpMKmnEqlktr86CByDxlPnWeZz+c/rUIaQr/bvW0Ed2uTKxK5UkmpZ5RrcB2E1XNnLJ4XbDzX4wUWz/14XtRBGM+i+v281QvvUzoVhTyfhgC5cnpYj2B7iMrfPIdLCJiHiJ6qyHNoKBJhVq/XS5siXOiZX99xh9edeyR4vZ6zc+/D00Lu2fr6ea7R83xemGw2m4W0knsvvrnBFa9arRZ6UP15kDkUk2dg7b0w4wSbe2juJfM37sUc9/v9Qn+2FyD5nMsTXjlOxmKxKBgpXvMIinll/vlNOsSf2wt5eLa+cxMSd2/ee3hzPcEbzvP70+k0ebx4jm5kXbd4HtaYeclTDegI43EPnqjZnR1+SHddXFxoOBwm54UaCw4LqZTZbFbQhdxgfmyU2jI2nU6Tt8XkQFpe+GDLsIfGHmbl+SomKs8Du1cmvf+AFvfIpJNwodQoCUTvHRc8D3Byz/PS7tXkVX1vC2PcnlqRTkLr13MvyIm72+1qMBhoMBgkMmB+mRueOT9TQHrrPbgXjvdPSkM6eeB5t4GTrXsv3nlAyEg+GMLkGTAQPA9r4DlX7t1oNAp9m0QlECDpj8VikQx8no/1HDSylacVfOs4Y+x2u2nn4mAwSHlD5J455jrMM+kpFJ6Qn+vzPD6fPJ+TlXusODL5+Qq8H0Pt0RDz7vPvRI9Mu4fLpiB+fMvwua6Fc0TmROnefJ5icCPnzoXni50n3ED3+/203hQy0Wmi48lk8tPydAFEigVGmTwn6rlBFBJlQACxVmwI8Ov7xgNffM+7ocDeGO/wsMY9PN/QgRfF4uYChmB4fhDS4X7uuUOQGCDfzlqpVFKezXOeLtjuMTJv7KYbDAbJO/T97BAM5JB7hswNnra3vkE8kJKHb54XZLzc058pz725IfYw0cNFvHU8Hrxir1ZDbm4k2SrOnHlO1hU6b//yrgVkDBn08TEH/X4/7Vhk3pw4fa28Na1arabncyPLOvMb2XFv0zc3oC9s6un1eoUWPo/wPAeMt+tjcjkG7oigC2xw8kKhGwOe2fPxzI1HJ7zP70FUyfN7Nwsc4Jt5PGpCDzA4ni6cz+ep35l+6J8k6e73e00mk3RugLeXIHjsg/YCjRdZaIlhUiuVSuonZZFYkPz0KxYAwfTWI4jac7Dcx6vqhGcIA3lR9/ak4nZirDWLz7OiAHglEGOlUknhr1t+lMyf08NpruXFPQoJ7j1Lp84FSQUD4T8YKMZOrhHPZr/fFzxIukhIdbgX7pEHqRvW3XPjrHse4rNe7un6fHraxncWOakwB61WS6vVqpCG8tRBnidlfIfDIbX/4S2y8cVb2pBNH5//YNTx5vIiHs/p+Vv3AjG6rI/n1Vn3Xq+XjC3pK8boss4aeNTB/53AkBOMEdehsJrvYnRSRY/IyyIzFLBIr3gaxvPFLu/cezKZ6M2bN+kEPi9q+pxxLUnpYCG2NvtuWM7mKAulphfG43HyajynhEIgTH6IDGCh3cvw0JjQLN/Hn3syXgzzvCDbk907dkHjXk5YnuAnRKYIh0IwXpQGa41AejjEmBB0FAyF8YoxoRH3gSj4wVhBRF4V9+4JJxnvNPCcMT3KfiIXY5eUPGufI/fsWY+8w8SRGxYMJ/B0FK2GeNOQGV6Qv5/58Otwf6+Qk3ZxefTPc33mj1ZASep0OhoOh7q4uEipBWTBr+P1BQq7+Zq4w+FElHvZLuPIoqcWaKdE1yS904IG8TI290gxdB4RIoNe9PVCWJ4Sgnjfp3d5SiI3Lt4+SOTAsaOQ7vF4atvk2bwlDoMJ2c5mM02n09RZNB6PC8b6U6P0sxf6/X6h+OKtLYSzKF+e6/GwlQUmtMuLZ56Tcs+T3CBeoAsIn8fqQbjcx/+NUDrp4nVxPRSDDgisK72kCHOem5NUCPUhGDo7xuNxOnsh332UzyeCh1fm+TAIxVM5/lnGzRGRo9EohXQetuZhqFfLPTeHtysVe3/5LK1inDjmvce9Xq9QSGVOeA+eOM/rnjzzyT3y4iaevBdkub7LEPMrnRSbXtX7+/u0C817fUllsUOLUJnxet4bckEm3JtF/pn/SqWibrebjA5FOZcbyB9S5D6crUBOHEeFYys9RYVMMFfL5fKsV+wFOXTY015EKf1+X7VaLa271zlYa6+JsJaQ72Kx0P39vR4eHjSdTpMR8E1GyDu6ttvtCqRLDpozm39yni5Cy4N783Wz2dRqtSrshsIye87TyYCwxPfBS6ctvdIpRERJvCXFCceb07m2KxoKCNwrdKFz4XNi2W636ZhFmsgJ7di9RZWVUJBx5Dm13W6XTkh6eHhI/ZW0suHZeUoEcoHkvTcaQ4Hx8fyXRw0cfIKAenXcc8VScTsqwp4XQTzFwUlw/M2r6543Zt187vNCGoAcSNMsl8tC7t6JzeUhj2Y8UvF1J3xnLsgVc/gPcwLZeqohzyv6UZW+8cGjQMbsJM+ZHKTbms2mLi4ukoz6s3ir2GKxSKfI3d/fJ8KiBXM4HOrm5qZgUCFSl38vILu8Se9uNvAcr5Mznq7rpY/fHRDk6nA4aDqdJi8V79e7HDwdg3Ej0vM8MemL7Xb702sZA5AlVto9IPeEpFOPYLvdLrSneBMzEzmbzdKC5nmlvCeVcfiYWFB+vGCBwJHqYFyeZ85TGX5dzlq9u7vT3d1d4fxgioAosR8OzfMhsDzrdDpNHieEhULjmXj45l7EdrtN4aJX3PMtq57WwbjhMRBVVCoV9Xo9XV5eJg+L53XD6hEJxogfD/8gJu85xgjjvXm04d66p45oNdvv92luGBfj8RSUh7++WcELYJJSIQ+PkuIgp9iRmvDijqTk4eU9oqRiiF78tDmXX19H5pCDhjBayNBwOCzoFGNkTTGefBsEx0MSml9cXKRNHX6GM/Ls8uRGAWONnLrBdR1C95lH10F0Hq/d11s6OWy0d+UbSDx9QQrDtxp7Kokfxog8sNbOD58KpZIuv8+lAKTi1tDj8Viwsp5/4zeHwkhKOR0+572ufn0vXHiIS2Eqz8FWKpWCQqD0fA4Lyv3znNlisUjHH04mk5SHpfI+mUw0mUwSeXml3Y+x83NuOW4PkqlWqymX5wUtz7lRcJNUKLTlbUU8M2GoH2rD2jSbzXfacShUOOk4UXonA2uC8HvnCnPg3Qz0ckNUeFJuUHl+8ruQAnONkkG4rCcGx3c8cQ+vDxyPx6TMg8EgGXqe1XOU+dfIeBThpM65GhCVF8+8gHc4HNLzsy44I6QZhsNhyo96gYoiGnLmec3FYpEMNve4vLwsGF7k2L1DIgX3xj0F4LsVkafVaqV2u13oiHGd9IIeP7wP44ITkMsXXSPD4VDD4bDwNTwYCuehnIvKRmmk6zkxPEXp3ZCecAevwvdpY40oLLEgTC65TUJg9264NoSDJwVciZ2oUVAvWkinwh4VXG97ci/MvQQPHbmft8BAYJAmvZB51RUP0Qs+3sfKXOfeiefEfYw8vxshwmVIHkXyYh1KtdlskkLT2eB5TUkpd+jPjXd7OBwKxRvW3PPcGDXmC+8ahYIwvLeT1yFP93IhGydoT+VgaCAvgJfKHDC/5Em9e4DjHV3WfM7dA+Me3l3D1nI6RVwPINxqtZrWaTweazAYFAgHY5CfHOZ5U+6fOxbIqUdbkK7rbN494g4SEaiTG8aQLxvwrd152yKRChEpMoSDxs4zvrliOBwWoi8KnjnP5J1TZeKTk67navKNBdKpaov1JIRHcNzD8bDQvSXCA/I5LAhnF/B3Qicax/m89zo63OM9l5bwQpGnRjzcxWJ7vsktOn9zgsTT9X5ED6uYJ2+RcTL0YlieF/NqMMU1z7+RxhiNRnr9+nVqzcHQeRGU9YWk2QbqJEHONVdynzMUHJkgTeJdBx5q4snlZOXrx/whL3m/K0Yr93ScRLkPhhCP+ng8JgIi103+lzw7PdJEEZ6+8XSWnxrmuWPfpelr6zlLN9p4rnxHHwYFXcHQEqlwRoRHl+fkyItoLvN594nrib/m+XIcHuowXBPj65ENkU/eBQOZMxekyfgqLb4DD4cMXvGoRSq2nbJT0/nqU6I0T7fRaKQdO5AFFnu5XKper6vX66WwxZXWiwh4IBCxdLK0rrh8zsM6Fonwg+t6n60XIdziAw9NpHd7Qd37hBAxIhgHr+Z6IQtvy0md0DMvSDjhOtl4SItykTLxnKb3PjKPnpeERPFgKd4QUnMdqvK+PRrl83SEF9QgTOndrxtH0b0IBbxAynuZA5QaefDqv6+T388LlXyGZ4GwuQZFJ9Yj/+YTSLfRaBSq5H5eAHOcN/6fI0Q3VsC7Z5gH77zxfCrRhsuD9zqzTjwjnvW5rgTSHnlk5L/zOSa9kOfl6fjx4irRhOupG9w80uQzbCriK7UuLy/TF7fiZHga0LdAU58gH+73+9QojXRbrZaePHmii4uLRCCQAArC1zKjoN7ywqliFHWcUD009jYb7zPEgjLRFD88xHBScNJ15XSL7gKNgObvg1xQKgpHWHzGg1AisO4xeLGA1AvFOIxMLpikaSBblJPf3jKF0jNOVy4iCXLeEA9zwI4894Y85+7z4gTsf8sLWXmBzBU09/Y8Z+zE6ddA2V2hj8djIiIUDgV1j5gClofOpBKm02nypDAQbhxzY53PuxfyKNb1er2UZ+eZPPT3CA5Z8u4TqfgtKW7AXN4oUvuaMz6X7zya8pqF38f1xA2dr6evuef7ST0xd65HPL+PiXH4eRecOAih835y57SJ8cw4JRgV7/P91CiNdNvttp49e6ZOp1OwOlQQpbdN5v7dR1Jxl06/3y984yy5HhdISYWQjgX2g3VckfA4uIZ0OkXKw2MXOCfTvFWF10mZ4BW6p+edBt5+ds6jprB0cXGRwldyuvl2U99C6uQkKXVyuCflfbp8RnrbonR5eanBYJAIgL5gCI1WP1dCnj3PJZOz9WM9MQS+RrkBc0/IX/eIgPd55d4/4wf45OkgUllOmKzdYx0puRHyIg15dU7A4xwGDlxBpr19DweAdabXF6fAC1REGv5tzvlpWsxH7oEyd566QCYwCGxM8WKj59bdKHp6KNcTJ2Lg80Z7pPcyM5+kEzwy87QAz+dnavf7/cLuS/8shEvkwRx6IRluKAul5XTr9bouLy/TuQukCahMSlK3200WCcFCIbxQ5LuVCCPI+yBEnp7IDxghJ/fYHm73kNwrzfOGbrXdG/YCjoePbqFZbN/J5YU8J4V+v18oGPlXlaOcfrao9O6XStbrp75WPGsnXffuD4eDrq+vdXt7q/v7e41Go5RPRtE8BYKS++dzT5MKuYf8Hi572Jynerife/8+ZznBePjJvHuxyCMCP7uB8eXePvdHmZ1weA66J4bDoW5vb3V9fV3ov4ao3MOmV5sUF14oxo/19Nykh8WNRiMV7a6vrwu93hgP5g2j7x4vr3ungRcbXR88febz4lEY3rPLiOsoukYvs5M/1/ZiX77DFH3AaNEmhi4hh14P8UN5fBchc0EHDnP+k8rpuoKxwBQVEAh2I3FGg+e0PDzNK+dYSG+T8dDVPQhybxA/1VgnXZQWReKULgqBecHBhTHvVmBxOTPY81AYFrwMhJW5cc8GgYMwvXWGZ3HvCzJx78NzZk4sHhLiHTJGiAOid2JCMSim+fzlHhWki3eap12cbL1f1r3PD4H59+o245He/XJLD28hXW815J7uHDAml5Fq9XRgzcXFRaH/2ftN864Rbw3LvXi8aj9vw4tIyBFeLl41UcS5bcbk2Xu9Xvp3u91OcuTGIQ/pvT7h6SCXFd9Nh1wyXxCoH3Tu9Q5PkyHDnlvn/T5O5sE/6+mfyWSSjpxEz72e5M9VBtmC0s7T9aS9ewxY20qlkrbq+Vc8nyNTgHCwSAiMpCR8hG0oAULA531bqJM1nkev10v9fx4u+jGP3uKE0kunr6d2hcgPQSeshfg9TEcxyN26wULAXKC9QIMSAw/Xc6MHwXkujDnMQ38Ih/mjhceb5vPowtudiEgIFyFB8pRe2HMjxGfytcqr757X9Ny4p3wgIubXc/7Mp4/hcDikwtNjMpanRrg+Y+e5kVVX+jxK8kjJC5ReBEKuKCDRscA8I9fIIWOBpNgJ6d91x3XzYhnzDEEi+56Sy/WZ5+PZz3UgeDRCioguGSdd9CKXD38299rn83naREQR2DnC8+WM2Y3xp0bpX8Ge54NYEBSYra5e3KD44t6g59hYyEqlkoo93krklVkWzL2ec/kvckZ4Lu5NkLfz4/NypSEf5gUcJ2qExwkzT294bhJjRV+jd1nwLCgqW4QhSPesuPZ+v087kkg78Hf3mN1zYWxS8Us9PXzF+8Bg5a1fCLp76W5o8qZ795S8Ou9eu2+59fDXPS3vsPDKuhcr8VjdwCNzHrm4zLgT4M9GVw6eva8Pa42X7R69e/b+1fYYGjc2nvpCpnhuL4hJp7yrn+eBPBF6o2u85jly7udeLdclWvPWT9/A4imOPEWEwZJU8Hi5BgaQ5/CIiXVgrqXT1ns2EnkNyD/jcuXbgHPH7lOgdNJ14mPSPddGz6Enxfm3E4ArTZ638jDXi0p4Jm7lcgH1HHR+RgEC7ukBzyUhJK4seLuEnnzmcDgkq+4nLaGkjFcq7keHgD1fm1d6OS/gcDj1eFK0yL0w7ushvXsYXmnnc9JpV6ArtbceeVcG3pcbEy/2uDeYk5q/7gbW1xrvj0gK+LU8j5uPw6vseHMuDyite47g3DgheP+GXH8GxuthsROWb+TIx80PZOVFR7+WFw29nkB6AY92s9mo1Wql3YSeE10ulwWHAuLt9XoFIoP4vFbh+VOPNvIoDWeAXDfyCWfwmXyuc5lnrjkvhLQCtSP3rr0egNFx7/lTo9TzdJfLZdq7Lr3bo4lgs93PvS8snHspXpSRzvfQOkFJ7279JWzzMMhzPd4FkBeGnKggA3aQPTw8pCPjKG54F4UrMcYGEnavBOJEUJg3V9pzYTXhItV5iid8xr1G/s018t1JKDjetisb7/H8nKdP+Kzn8KRTH7Xnk72I6NGQkyNRD3DZ8TCRcXstgfnwNJI34rOWzLsrO2uTe2l5VMI1iAQ8RcLn8lwkpOFydG7TDU5D3i2DUXFvk8gATxUDKJ3CesZPrtM3kHjqA0fAHYK8xY/nz9snPbXgZ6hA2rkhdHlwvc6Ld+ijpwgxFhTjMVru5Pn13UlCF8tAqWcvuCD4RDg8rPCzO/OcY14IOodcMTykoLLpe8n5DAIOcXjeyDskPE/Fgk8mE71+/VqvX7/WZDLRfr9PKQUPE3kmL5YsFouCZ8IBIXl7jxO/KwfXzAsgrVYrFQJ9swnejJ83wVxCShAEBtC9hDx9QVThbWn+uiu7dPI63BC64nvuNA+N3QDkVW6uzWeYg7xAx/sYjz9DnjP358YwSqedcHkrHvBIjGt79wFkh2Hygl1u2HJS8wiLNc3bviBK6W1LZh5meyqD07uIGjwSYReZyz7GwQuszPm5uZZU2F3p3nJuuB7TZ/9h/O6s+bdVe5rDr8FzYKi9llIGSiNdqbhhwAXKX0MIsNhukfF6nbgcThjnQkgPKfLDO/AMpbeK5N8v5luR3VqTu5OU0gQcm3d/f58OWN7v3x7hiDB4KOhGxi1urVZLgoOy8Ax5esEVAMKiaOielW8EwHPxHW/SqUeZe+QHkKBgXgzi+Zl7b6THaLLGeKve+UEe+Xg8psKcF9uk4rcP5Adh89s7Do7HU5GM4o8ffs5zuyx5qJu3RznBIKueinJjg8xxbeaG+SAaYC0oIuW1AQjBDb0bMCdd3pufj+Bz5w4Pc8tn/BzjSuV0lonXE+j8cW+V+5BaGgwGBafGdVtSYXeYk26ur7k+v49LWEffKZmvb566Qme8Q6oslHrgzbk+1zwn6eTRarVSfpJFOecJ+TU833WOdN26n7OEKPbl5aVubm50fX1dCGnzTgFe2+/3hUZswhtJaZ87P3wmz0czrnMeZ54H5fW8AODhn9/H54b3M69esGCuvIDhoR9KjrAzDrxyX2uMlXtMkJV7h4zP0ywuEzkpOllTeOOZIAdkwjeicAKbP5sTooe3FJkkFUgar6jRaKTCkxOGRy/ujfn1PRfra8izuhF2ecKg5KkFL775ebEuE7l3ixHPU0w4EdVqVdPptPBtDzgC7rQwJp9nSaltC7L3yNC943OpQE8J5brt6+Ebj5x0mT9P0TwWGeOdPxYtfwqURrok8FkUqVj19oKDh3aegHeyfF/OL28hyifcQxkPZ1CGdrutwWCQDtCA7BEw93r833jnPjYvivm9PI2B5wcBQX6unLkAupUntPJ7cj0PuxFynxPIZTabJZKBmPwrTc4ZiJxM3ZPFm8OL83xtnibysJn58Bx73g6GrOR5Z7xUyNllId895zLFZ323Il63E6fPgf+bOdhut6nt8Xg8FgwKBdecAM5Fd3iDnPTGOpDPpb7hcpSTubez5T3lvN91x2XUOyk85UPaJe9q8XswL5PJ5J3irZPpuSKmE6+vmx8VwPq53rlOnOMPnx9kE/mkv9ojsk+N0g+86ff7Wi6X6cFbrVahUp6Tiltx6fQ11VhPLBWLSVsLC+5KI50qsLR+0SKEMrjwOTFKpwKdkzrwUD0P7VjgPOdHrqzb7SYPw3duuWDU6/WCYULgSHPwdz6Xz60Xmdzw4OniUTEXhIjj8Th1OLhS0B3BnHoenC3bHLFHzrJarRaI3eeUuWdjAmTLHPrhN57H5Fp41cyttwrm68dnfFss8gCx4NXlhTYP792IUgwbjUY6Ho+az+dpzPV6PW1V9dSAy7oXd5fLZWE3lR/niQy5zDIWrttoNJIRpojm3wrtO7B2u12h+EkXgJOr53dZj7zDBh30lj+e3cnVD91hftyYs4aQIXKB7GO43NN9jDN8rHCD9497cZkiYxkobRtwu93WZ599lk7b8p02hE8UdaTTLiBv+Mb78MZvr0C78FB0IV8onU6np2d2MBikEIn3bLdvvzPpxYsXyePjiD4Wjv3eHE4CqRNSjUaj9K0CkD7j8zMIPOyr1WqFbZQIKMLM9cn75t0XkIH/DY/Zuya8FY80Qr/f12g00na7Ta1DeaHRQ1tyoygUpDIYDHRxcaGrq6u0LRUjAol7p4jno30e8jFCJB7C54TbaDQKHrl7VVKxtY0vbXSP2EnAU0CeI5RORtF3ZUlvnQEM1HQ6Tffg6EHmyHc2UqPY7/eFzTLMF/rBmrrn77u/IBOiCowxcsTzea+59wd7Wx99q6wNW2UHg0HBg3ZvF2eDvPDd3Z1evHih6XSa5s1z6/nuOcZK/3Kz2UxfZe+pPC/AezcEsu/5Zq+N+BECfk4Duoh+O199SpR64M3nn3+eDrfwgtZsNtPDw0MqPiFwHrqxQ43X8Lw4qpHtkAifh/7sSnHPlIVot9sp4U9bG/m00WikJ0+e6Pnz53r27Jmurq7U7XZ1dXWlq6srXVxcpPCH3sAXL17o+++/13w+T6RNIQyCIIxFabHsubDgVTJXeDcIlnuxHsZDVigM4/XzXclL4iGzDpy1wFqg7E7eGDPCV+7DeQ23t7e6ublJ6+FFOycTyJ3+UOkUiTBOD0XzkF46FbF4HW/HvXDplMOGCImG3PMjZbFardI2dM8JUmPw9A/zR/jP+5gPCO/29lZPnz5NxogojDnEoZjP5xqNRoUIDTnmmfBk6Trx3ts8PcAc4dVhXPBK8er3+31h/Q+Ht191fjgcNBgMdHNz805XCmvBAeqvXr3SixcvdHd3lxwPDKoXQjE6kgo74jCakDMGjq4gNjt4t5BHXMyfb5F32STS9l2SbIPmfO0yUBrpNptNPXnyJIWbXnEkJONAaCa2Xq9rPp8XPBPaWwhZe72enjx5op///Od6+vRpUgy+pnk0GqXCjCuz5xkhMG8mJ5/28PCQSBkreXl5qeFwmM7uZNx3d3f65ptv9O2332qxWKharWo2m6nRaOjZs2dJwfwbFfCqSDG4d+cFHfKE5F+pNBMOItSuaE6El5eXSdCAh7nb7dudPK9evUreysPDQypkssOMU8c89UL4SuERT5evTnFPimLZdDpNa8Tc+/Zb7wZw0vbip6eDnDQ9BcX/8cr7/b6ePHmi6+vr9M0OECDvnc/nBY/Ox8RYMQB4lb4WrVYrHabdaDR0e3urzz//XE+ePEnfhu0FZW+Bw9iTn/Z2KOYIOeKAHeltzhjv0A0QBpO59A4Wn/PpdKqXL1/qm2++0cPDgw6Hg7rdrrbbrYbDob744ou0yWe32xW6R16/fq2XL1/qb3/7m7777juNx+NCZ4KnIrwIy6lfXhDjfNunT5/q6uoqOWl3d3f6+9//nvLm3vmD1050xhohE81mM11zOBwmDvA89E8yvdBsNnV9fZ0U3AtBtVotfeWNV2a9Fca3J3LdZrOpy8tLffbZZ/r1r3+t58+fq9FopF5DtgP6Vk6fYBcG9xK4PiSHp1yr1QoHJmMIEELIhB1m1Wo15eTc8nr/L8rg4RqExhzU6/VEHJLe8eQgP/KTHPDDt7uijD63PGOlUlG/39fNzY1Go5EGg0FSDk8FYIwwEF7oYFs0hDscDtMcuQfmhTuOO8SIQIrk3/B2GDfK6WdUeNHOjSlzD5GiYHSl4I33+/3CjkIiCgjPi6WsnR9Jutvt0lpDAswTMjEYDPTkyRPd3NykE608n+s6cnFxoZubG93f3xdO4oIc/DO5J+tGETnxYiXy4ek6DD6hOYaDNjxJKZ9cqVSSlwrp73ZvvyiyVqsl2Sdn76kd17O8a8MjV/LPnU4nGSqIv9/vp9qDnzLo0Q2G1nvu6Ytmbjmw3PPfpFF8LT4lKh/oUftoDWwIsf/f/+1hYT6mvM3Diy/nihp5ddqT9HxOKn4dTz4mf9+5YoV7Kt7u5YTIZ89VbfO5+dDc+TN5n3M+Fz4nHp5/CJCOh7L5c+QtN/477w7J38v7vHUpXyefi/w+ubz4Nd93fX8Pc+/je998510Kfs38Pnnaw41h3vP7vjVgHc6t87m1zjsBPnSP/HWXKffg/TnoTsiNxblugXx+8vt6US2fR5cffybu5ffI19bnL7+v68E5YyfpHYfkI+DRi5VGuoFAIPAfhEdJt9Qdaf8MPuT15fhnrNNjntGnvte/+tl/BZ/6Pu+bmzJCsP8qKGOe/ivI1P+PLj2mn5/iXj8GhKcbCAQCHx+PMn25X/geCAQC/+EI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQImof+D1SimjCAQCgf8QhKcbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRPw/r0ioF84a7+EAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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HY0kKXlkikdB4PFa32w3639bWVvAouT/a3dHRUZASJpNJIELu1263dX19rXq9Hp7VGgoWgiUApAPGkzGlr+nvbDarbDarra0tzWazjfrzpn/J2KPvcU1L7Da3AOlavTOa+bbGqNfrqV6v6/r6WltbW9rZ2QlJvt3d3aB37+3t6ejoKHhRGFkkJPT4yWQSPH1JwXBKCnkPchx2TpAksvPHSlXM13w+HxyTdDodSIl+sJIbzwt593q94FHSB/QxfRuNzjKZTDBmP1TbtcDw2utZuTCah4BobRQRjW6tpNftdtVoNIIMN5vN9Pd///d/dnt/KGIj3dFopKurq7CIbWhgM6p4LDYUi5JrVMsjKyu9DZui+CE6LmDC4m21Wi3t7++H9lljYds6nU5DQi2Xy621x052Jhf3YnEul0sNBgPd39+r3+8rnU6rWCwqn88HOQMykd5mm23YDqnk8/k1Dc9qfITKhHPoacViMSz2nZ0d9fv9d5Kg9Xpd9Xo9kLgdG/57U4lY1Ku1P9YDxbuLEnlUh4yWLvHc9CsEAPHaMiOMV9SIRrVXe32iNKKGYrGo7e3tkGDF60P35DlsmG+9b6IKKiWQFxaLhYbDYVgPRBmdTkfdbjdEhxiR4XCofr+v+XweEl61Wm3N87ZaO4QVdWJsAs1GltY40WZrCPr9fljPf46zs8m7tEk21jhkjFYb/V50PK0MRduI1CSFyhMScnEgNnmBLKe1rplMRqVSKWi3kIWtrbSWlM7E8ysWiyoWi6H0A53MhhfSW0J7rH2bYAdzMpno4eFBFxcXKpVKQRflfniHEAREZ7U0JiJWmMm+tbW1ZqEJ9/E2ms2mJKnf76+VKlnrv1q9yWjbMJSSOSYshMZEZPFPp9OQrIB4IWvb70xUkhgPDw9qt9vh+SxJ8rxECY/VWtv6WbxTFrfV7DHQkLNNePA7u5CIDuhfwmmy/ptKBmkz84ixgvQg8dlspna7vebBElqXSiVVKhXt7OwE3dwSLnqxNVLor8wJW/mBpINUMRgM1Ov1QvIKiQYyHgwGYT7s7e1Jeuv9W8/UylTWGbESxWq1CgnEZDIZvHaMNo7F/f29Li4u9PDwsObdvs/J2USwm9YInqpNQkoKz00ewsI+L2OZSqWUy+VUqVSCtMa6nk6najQaoWror0peYLJSb8nCZsHmcrk162qTO9EBsB5FpVIJITSLkw0XUUGfzrSDK71/s8Rq9aYOEwGecqNqtRoWIvWgpVIplKEQGhM6QhS0z5azWc/K/jd/y8Kn72wihnB1NBqFRW5DTbTHqCGjj6Q3EsRsNgteW3Qhkogi01+v19VutzUcDrVYLELUYY2bvV90QwnPjDEgYkkkEmtEG63sQGe2hEl/2aqW8XgcJAp0QeYOBp6IwoIFa4mJe2AUSH5R/8zf2KiCZ7UVKf1+P5Alfc46wOPmGZbLpYbDoYbD4VqEguZsdVtkCoiQsWMubaqY4Hs2CSlpjVC3tra0t7cXauTz+bwKhYIkhTIySu4uLi5CbfyHtFw7z+0atLBRARo0kUA+nw8lgmwg4l/GCSnMJp7z+bwqlUqoL0aSmE6nQcaLC7GWjPV6Pe3s7Ly58f8tfqYzDg8PQ4g0GAzU7XaD3oKHwmBRvnJ8fKxqtapUKhW8HGokrUYXTaRZr8lqRVEQug0GAzWbTd3c3KhUKgUvirIwvG3uKymQLsk1q/1SAhf16q1nRZkVYX0+n1+TFyDbfr+/VmdImzFeVt4gIceiY9Lxt3YRspjJ7Hc6HbVaLbXbbfV6veBJ49FawrIhqfVQouNgvXDazsIfDAYaj8driwEPhmvaEJlnYa7ZcaB90tvdU9L6LjCuab1j+xxWAoMArVFBiybRiuePBz4YDEJNOobGaulo7q1WS6PRKJCFNWbMGRwVHBDmHfIIpVaMp62Dx8sn42/rW9E80fx3d3clvdVxbZTU7XZ1e3u7lkhF2/8Q8VrCja7L6A/Sy87OTlhjlGyS9LTXwTkpFAoql8sqFApBmqOayEZ6o9EotDsuxOrpdjodVSoVSW8WAxIBpT5kt+/u7vT69evgSTFZmKT5fF77+/t6+vSpqtWqVqs3xfaE2CzI6I/FhyaGTbr0ej1ls9lQQkPSBC+KsFzSWnshV7LSeBl2E4RNHFmJIZ/Pq1qthkmCgUKjRFvFu4A0CBGXy2XQ9SSt1T1CSnwXz9fKHhb2mSBriMKWdrFQbAJjUxJR0hrJQXR49daLARgn+sqGuiw8rk17o56qva/1Um3+gKiJttuEHL+zyUD6wxrc6PNaI4dRQyKDuEkyUnserYigvZBKuVwOkQ99Alkjb0CS3NeW5rEGMUjMz6jht5EKpMycbrfbajabajQaofoiWom0CTbCjP5dNDlHBFGtVrWzs6NE4s3usul0GpLxNtGHI7Ozs6OTk5O1umyiFJy54XAYpJn3OV8/NmIj3cViEbaNEuox6ahpTCQS6vf72traUq/X0/39fbDA0lvPiEHY3d1VpVIJE77dbn8U4UYztu9rMwuh2+2q3W6HRMZgMAgGg3ZBIjZ8Z0EyMSBMPJNisShJa94qWne1Wg0kQDhqdb/hcBgSWdZbgxDxvCqVSihsR0qQ1jeNsHDJGGNIbLvRFKNVBdbQ8bx2AdsxBCQ28OhZFFFdkTGg/rpUKoVEEZUaGB++w3V5/mKxGIw6hpT727635AvR4sHzfPQdv7dS1/7+fuhnIgzkkWw2G8apWCyuPQdGEg0frRyihIRYN+VyOZAqxjqbzQZ5oVAoBO3Stns8HofNO8w/K/dhZHgu2y+0gUQbc6HT6bwT/byPwN639jatW8a+WCyGDR/JZFLtdjvwRzSSIgF8cnKi58+fh91ydrcj5Nvv99Xr9T66tvjHQKzyAjtcGHDE7XQ6rUKhEBYJGykoDEdLk9YPlkG/kbQWSlnv4zF8jGBudeXhcBgWxcPDQ/Am8HZtQmxTthevDF1usVgEgrPJGlvcTyjKLjXCftqElod1x2uBcHZ3d8OmjlqtFsrKIFsIbblchnMV0HUlhZCMUBnvxoab1FNiEGySA2ODVGGB7lYsFkNfzufztS3gaId4NxgiPJfZbBZK6Vj0LDprmEkCUQ1gk1m2LXYceT4ICQIlGSYpRCS7u7t68uSJnj59Gs5cYByt5ME8hXD5O6QArg952g0j/EvoTx9bOQTDXCqVQtLVRlCE1ezkQmLI5XJrO7IwRlYLxqDaLfzU8drkno1yPrS2HoMtrWMNYwB5Vms0Ab/j3Jb9/X3t7++HHavUkSNfknzECMWFWEl3MBiEB8ejKxaL2t3d1Ww2CwNPKM1ihljspGm1Wmq1WqE4HqvFITR4CptkhGg50PukBq6BfspkI0uNvGE9TbwFQkMMAe0HlrQgOElrybLV6s3ec5swY6LYjRUkI/kplUo6ODjQ0dFROEDF1gxTeoP3h3drTzDDs1qtVur1ekE/JMmB91mr1YLHbpN46PG03xo5xrlcLgeDsFgs1rRQ+i6ZTAZPZ3d3N2w6mc/nYc5sbW2tlS1RScDfsxjRWAmTmQMQLGclEHrb7eicZcHzY8C2t7fXogk8TOYVfcszEbZbTXi1Wqlara6ddYAhwZjYsj3WEm1DeiGyIVNvd6BRqYLXb+cgxtU6M3aLOZ4nem6j0VC9Xg9nbnzsKWNWpnlM9uM+RHKsbeqOqVXGeLD+NmnrGBLmIZ653dWGExQXYikZ41+2fUI8TBR2uJDFtZ/bQScsaDQaOj8/DxY/nU4HfYcssc1O25Db6nTWG33MQtvQBeIlRMNbiJZjWZ3S6rsseO6Np4fXg5fFv9bis9A6nU6Y7HZXErIL12L/PSRiJznhPx62DSNtPSOf42FzsEur1QqTNJVKBeLEO+UsCpvNtslN9FmkhWKxGMJjW9eLzggZViqVULGCp4vRRZJAOrDjzPgkEolAVFaHtrXAjCfGCy+cdnc6nSC1UCWBHEDfRWUKjALerg3VLQnZU8x4tmhVh63KsTo+kQrGBgkCo0k/QWREEBhfu/sLY2BlB1vxgJNDfe6mtbZpHdm+sX1gpQS71pAAOp1OOKNlPp/r5uZG5+fnYYtyNAqmz7jGarVSq9XSw8NDqL5hAxPOoC2//KkRq6YrSYPBIEx4W+KBdppOp9XpdNaOZQOE3IS4bAYoFArB8tuJaS2vzUJjxRl8FozdIcV3bLbc6qlWM8VLpZTFhqM8OyEiyQxbUWB3h9EvVoezRfG3t7e6vb1Vq9UKyZdyubx2LbwW+owzA+zvqFiAbO22ULsg2H12dXWls7OzsHWbsbOhP/1pJRRbD4zna2uMrU5IJplFhofH37FJhDMNkJHa7baWy2XoW0jCygbRBJ8tN5LeeOhIOfQT5FssFoPBhADZDp1IJHR2dqZnz57p8PAwaK22KgEDg8FjzkEMksJYM7doO+MnKXiZRD94vowBkSO5huiZy1Y+sREOBkbSmkxhk2zM4WKxGLxrm7C0a0V6d7eorfLYlIgEELDNfzD/kSgvLi50fX2tVqsV5jGcwjolIYmxaDab4XwLCJfKIuZXXIiNdBmEaPmX1TFHo5EymUzYEWMz6ehZ/LfNnNdqtWAdNxGnDf9tFpzFn0gk1o6/i35f0poXQyhfKBRCRYAte2LRSwrbDuv1uu7v78NJXHgVh4eHa144Pyw6ZIHFYhFOZuMcUevlkmRAE4X8OAMVUrF6HZsXWGB2I4Ml3NevX+v777/X9fV1COnoexYsbWTBULZG2RceP5/b0jBIjv3+9Xo9JD2sYYRcWPjsxIIIiaSsjENfsMuObbcQhjUwEB8kyed2zDFOzLder6fr62u9evVKBwcHIQ8RTUQxnrZckM0M9DXJKYwN64N5NRwOg1dtNWqqeawcEt1swr92Czfbx7vdrg4ODrS3txfKIK3Hy9zHo7YbklgT1mO3sB4uDgGyjM1H2GS5jY6t18uBSZSoWVmA+3Ot4XCoZrMZjFO9Xtfd3V04twIHzRr1uBA76UKcqVRKk8lE/X5/LbQhoRKtYbWCPpMUbYm/Q8+z3ukmWJmB/4+2c9P/sxgt0VipwhI7meeHhwedn5+HQ5cpxGYHW7Va1d7eXlisfNeGSqlUKhSj397e6u7uLmxbRFJAKyQpgiGhNAxNLPpD9touUAiJk6Zev369dk+2CqMd2mP0ohGHfRbK3axmZwvTMRIcIoNxZkFEQ2GkAysV2LIsrsd/Q7pW85e0Nl+IMIbDYUjIMd4kaCqVSgh9pTebBahZ5QQu+jQqj0HstCP6Q5KPSErS2njaLatETBz/Ga3CsIRCmdf9/b3+9Kc/6dWrV6ESpVqt6smTJ3rx4oWeP38eDITtF+Z2VH6xUs2H1o8lYJwd+5mNSrkX44+MwNbz6BGfeNMYCYjXzjMOy0HPj3NDhEXspBvV+Gw9q/UsqCstFotrkxHLBplQJsYp9rbzrScTbYslF9oS9XKtxZXeeodMSJupj35vPn9zVuz5+bl+//vf6/vvv9f9/X3YxFEul0PyZH9/P4SlTGBbdkWNM+fORjVE6Q351mq1cDYESTe7oEko2aJxq9vZ8cFotFqtoJ2lUqmgPddqtZCt39raCl4Iu9Vs/TBesO1vjCahIN4PlS02k24PPokaU0sEtgaZcBb9Ea8KA0RkA1HZjQgsWBsh7e7uant7W/v7+8FDarVaYePLYDAIWuH+/n7oazxV6Y1cQYUG3jmeJ0QivT0PFvlstVqFIzbx7pGrIF10WlsrTT+hWz48POjs7Ezff/+9zs7Owkl1uVxO19fX6nQ6khQkiuicRi5iUwx9Zh0iWy8NrDdrde0oQUejUUh3Op0GY8y/1jO2hqBYLIaqDbxzCNaWcFod367xOPCzvQ14U1LHhry2hAUPActkdTG8DbQ8FoH1GkHUo2US2ckahd0dA6GjiVEjysEm3IPJx5sILi4u9Pr1a7Xb7TABl8ul7u/vdXV1pWfPnqlUKgWCIbxH0+p0OkHLhdBYvOhy1WpVh4eH2t/fVyKRCKSBzjmZTJRKpYIMYQ8Litb3cn/CfciF8JC3SBwdHalQKGg6na6d5k/YbPVqScEQWPJFioBUIXZK0BKJRJBx0Mnxehg7Sg5JsEVDc1uKReUCpISWHo0Q8Azta5843vPo6EjSG3Lk2Mn5fB4MFImZqGRkq0HwhCFEDBD10iTwGBPeaIE3j0TBUYU3Nzf65JNPQoQB4RCBWGmKczOo9mBHVjab1fHxsT755JO1GmvuieaOxAQ54o2+bw0h30S9cNaMlQeslLdcLkMfR8+flt4mPjnc/+DgIGyIwPng+zZpGifJRhEb6UJ49l8rmmO5OL82lUqFInNIDiHdWlZ0P3as4V3hJdj6UCvoRysYPqZszHq+1Ct2u13t7OwEy8p9rMxgEw4QidWqCH3IkrMY+ezm5kbX19dqNptBI7UZX7zc3d1dVatVSW/P+mVB0T8Qls0WR5+VfuaNCDc3N2F7NecZ4PGyY+/6+lpXV1eBoJEgMJ52rz+arh1XrmtrgxkfSHW1WoX3rdlt1qvVKpwLQAkf/Usoz5ZRklUQoK1xpsKAhUr/4RUvl8uwoQHZgWvypo6Li4tw0Let190ES2RsCsJTK5fLoQxvuVyqVquFtWCfj4QRB84fHh4G48Hz2QSlrezhOrY6AYNl1waSC5uEbJ9sWh+bYKsbIF4iGuv12rGwdek2T2ATlCRXibxOT0/DiwYWi0V475xdg7TjMfnxp0bsnq7Vh6S3Jxuh4yEVlMtlLZfLcEI9GXG8XTrPZuLx2iSFbD2kJ72tlrC1lzZkeox4rdW3bcaDtMk7iIX78PYHFiiEBHGRzcVzsyVabEagCN0SJkCK4R72ZYqj0SjUaUKOJBEmk0mQSRgXawju7u706tUrvXz5UtfX14Hc7A48Jj+vQOE9YcvlMmh9hP3Su68GYtLb0js7/rauGIOGrkm4ilHlXAruQ0iKV2vP8LC6r22TjS7oI1srnEgkgjbMzjHOtuUzpBc2ZdBuWxXCnKWuHBKjvzhcBq2cA1vsePGckCovEW21WuFVOkRCRAREklwbA2M3qjBf7SaVaILUbtqIjutjiMpA/M7OPcY5ukmIPouWyyWTydDu4+NjnZ6ehsPIk8mkut2uut3uWqTDfaIyVZwEHBvp0sH2xHlJofyGrXuU7BCy53K5EAJ1u92gFVoR3FY2EO7g9WLlbG0o5ESoQUIiGvZsAveyIj4TgGswwSjhQUOFAGwxPSGrTQbhWeBB8WZc+tESEYkTvosHwXMWi8W15BQbK3gFi9WFISveBMxhJhA+UgaZ5MFgoO3t7ZB1txlhjKCtppDWo4Do80S1c7bv8nsqLYhWbCmTLRnCU7ekau9tDb+9t918Yk/tSiQS4QxhFm+z2Vx7GWcymQx1u69fv9ann36q4+PjtYNnrKRlD3ciCiCPQUUMmjSAlPEUeSY7brSdbci279ilV6vVQskbBo+tw+j8NhKMzu9oPuRD4BpUDbEzkkoRHBeiPJs8Z71t8lIxFuwIfPbsmY6OjpTP58NYspmK3We2qgUpz5ZwxoGfnHSZHHafuj3YBJJJpVKhY5bLZZgknBXApgC7Txriw0uR3h5eQqfaMxHQywh5IY4PwS5OJputp4zu249aTSYcmhyvsCYTjsdrydNOarxZvGWuT4JJUiAJ6lO3t9+8oJCQjLZDYlaDixoc+wxs4WVh2DpotuHiSdpr2ppkyunQhCFkmwTZlChjztgaTwwmhLTpCEj7HCxia2DYes2GEjwvDJ3tS4iLRUwiy775hGuhIeJUWC3XeoNW+0Q6SaVS2tvbC7v7IAS73RstmJpfScEzpvrFOgXWCNvzCyqVSki8DYfDEAFG562dzxh4W+ttSZc19pi3y7XtPODHHloEF1hjaJ+Ha7AmyuWy9vf3dXx8rP39/XDqIE4ChGvPhpDenheytbUVIhTbzp8SsXm6+Xxex8fHKpVK4XUyVq+RFMI1wmiIkn3UR0dHYVOADXmspoSXhGUmVCEEJ7FDEo42PObh2klnE1xWF7SkK62fwI/nYa041p5QLnqSFKTLItvZ2dHR0VHYzcbpaywCtEe8rui5u5JC4T+elPUYraGSpFKppMPDQz158kSVSiW83JJrQTzcw3pUVs4hS8z96R+qGWxEgHdDKZ4lWjwaxtCSLuNpN52gX0NIeL+ExyzgVCoVdGSuYStZSIZZXXO5XIayLa4FqJd++vRpOF/ZVofYCMAmf6gCQJawBwDZg4Z4Lvt6+Hw+r8PDQx0dHYVzTBgDazTQSpEuCoVCeNMEf490Y9cXa8B6oNLbg8QtsVsjE4VNpiGx8c5B5hNJVutMsdbsBgraksvlQgL58PBQlUolbISxCUqShpvWIedyxPVSSilG0t3e3tbR0VFIqtgDxwF1t41GI4jhLMhyuRxOFbPhsl3k1nO2p2Vx0hSLwGpTXOcx4rVW3tYkWjHfTjjagRdhD/qGQEqlUkiUQEaQOWTCs2WzWe3v74cQNp/Ph3CfBcxOJfQrvAUbMmFoMplMOBbQhuk2XGUhf/bZZ/r+++91dXW1pifbRI7dGhrtPwjZVgdAOFQdWG+Wz2zdtjUG9K3NQFvPiueAyCFcSskYd/4bMkenttqtBXMM0rWJKBvqlstlPXv2TJ9++qkODg7WtFGuYxd8uVzWYrEI85K3T+DlEmUwtryrjpdvctjO8fGxnj9/vvamajRc5pM1fuVyWeVyOXiDSAwQlU222bkdrTuW3kYjOB6b5oHtPxKaq9Vq7ahSaobR0nn2aL6FaJAoJXqoUbSGv9FohB2uRHyML1UilJjFhdhIF8skvdWhbMiAlb2/v9fl5WXI3LLFVVKQJ2w9pfWUJa0tBjzBYrEYTsFPpVIhE2/DyE2Ea3UoSJvsMh6S3T4rrW9jxFtncTDRqM1lUwS6crSyAf23VqutEUkmk9HDw0MgXrLKJMlYxBgF+h1PyRIasKTLhCa6qFQqodyNa0BSbFG1Xh/EQk0zhfSMFwvaelB262n0WEmSRd1u9x1t0Rpa2//0F16ePe0LI2gTOxAGUYKteolWweABUwtOn3IYP3XX9NMmzZowu1QqBc/VHtCCl0cFC8RB9MP4nJyc6OTkRAcHB+HoTuqMbcUIxjiXy4VX+eRyObVarbCrj7I8623avIWNGJn/xWJxrSoELzMqM1g9m7nO8xOxSOtyFuRr1zfzy2rgGBqS7d1uV1dXV2ubejjnl/GLcsRflaYLKI8ho2o9FkCB/eXlZQibeeEfHoqtDWRRWqJFa+X3lKDVarXwfjMmr51cUbCo0MLYOYbuxr8kwaL6naTgoT958iQQLIeREEKS8CO8AlzDbkhAsyMZRp9g2dGuOIXLJqGiHjkEuikpY714IgYroTBBMQyMJWRkDQQH8EBkkK49T8B+z2aqMUZ41Hg+3JO5ED2/who9G9ZG626lt4X7EFmxWAwasbR+NgQkZr0u681D4OiFVsu1uQfaFY2UaA99wHGi/DDeNrQ+Pj4Ob1BBpqK/rFGlDSTpdnd3dXJyEsJvtuBT3xpNQEJQhUIhrAWiL9a3lQGI1CxsH6xWq+AQsc7smRY2dxJd3zYfwPMSCUwmk3Ag1uXlZZAjrY5OH9PPzOO4EOubI5rNpsrl8lqdqbT+6ufBYKC7u7sQ7nQ6nbDNlXBoU/hnr2cFfgaWU5c4tITvbYINpThpimMS9/b2QrZ0b29vjXSjyZJk8s3ZqScnJ8ET4uQvwkdOr8czwTOzXhietD2Ry8ocEBIaVrlcDkTAgoYIIAm8DklrYSSEhfyCtxGdlPy/1dPpbxYnEgpJLOvF43nYqgNbToX3hZdGWGoPK0e/t1GHLRtj4fJ9fjDekAvjTYmf3YrLs+Nx8RzR/rDjQMIND8xGduyMguzxxvmxhila4meJzEZgeHvo2TYstx4dei6yx2q1Cp50u90OZzFgWGypJAaiUChof38/bDiwzgBJP2uINxEvPxg75J1+vx8Sw9Y4PZaco52cPEjirF6v6+rqSnd3d6FiwRK31ej/ql/XQ90kmxYeI08SXI1GI3yPzQeEpbbkCNhyEjuoTE57Nm80w2tJA1hPD9Lm/FykgUqlsvbeJelt3S8JiUQiEQ7fZnsoVQwkDvBQOUyZxWhfZwRJ2MSdLemxk4jXtvPMkBHkbSUQSNBKJEQDkMemUFHSmh5vQzX7pmZ7ji91r3h3drwgC64ZNWCQlSUfPB/63NaWRjX2aC01fYlxhYRoO54ixjC6azGaMOIzSJfyMZtvsHOO8NgaIp4PI9Hv99dyF9GSRCIl+pJokpJAxi6VSoX8AZGffbMFkh0v22Tsow4F83JnZyfkGWh7v98PXjRSQ3TO2AiXvoB4kT6sM2HngPRWz2UM6TuIFWmBIxyju9fsXGWtcpRAnOcwxHq0o81QSu8WKEc9XmtFi8Xi2uBbkngMEEH0XFDrxT1GuNGJTVttssceU8di4ryCu7u7sKmAkMyGwSzCROJNnSDvm6I2lMzqdDoNRM1Epl14BbQR4uUoRe5n27spHMSbs14cYwTJRyUU2282zCczjpeLXiopSDX8t/WgWEDWS+N58USl9TI5PpMUFp31BBkrEmYYMu6LPmk1PYysLYWyNa2WdO0Pz0/pkdWuLeEwJ20lAHOe58A7twaG69sT4aJki2ODEbca8Hw+D2uI6zDetJmoi2iQ61vHwnqn1pBHjUC0DNGub+QlvGJbUmmf6zFYw010QE0751hw2I2dpzbSg/w5n+SvSl7ggZlY0VrKKKymxxZOSBOysp33PkQng6SglWEd36fpWpLEC7eF3NZqS29fM89RiK9fv9Z0Og3a7+7ubij1om9IlrRarXAgznK5DJtF8FhKpVLoH6u18pw8G4RrS9qoi7SlaVSR0G76wS4uiB/ytLt6rCcDaUUPHcHLRbcmQUe7refNorEhujUyRCuQJwvaVqHYN2lYT5/df9aztJtpaAdt4aQ762VDfva5Wfg21LdyiiVbq+Hao0BpD5IPRGmJ1+7CsslG+pVEFqF1s9kML2olkZtMJlWr1d7x/HBC2G7ebDbDWINKpRKMpC3nslIGUgNbuNHsLfg+UpjdYWo3+nzMuo5GMbZaiHm6KaFn20JOw+5UiwOxbgO2HqENG+znTFDIw2YypXWCeF9HMXjWekKSLNTHdtTwPYrJqXqgtIU3VIxGo1AZsFqtwklOFxcXOjs7083NTQh9qSektIXJQigffSNFOp0OhEu/ZTKZ4BnYGl0WHoXghPVoxzaUZkLzrw31CEX53EoFuVxOvV5PktYICQNoQ0CyypZguUcikQjVDBDUfP7mRDa8fqt/spjx2HgNDZUDvK/LvtLIeuRo+gcHByHBSH/bFylaY4a3T/JNencnIvop8gSkaz1nnsMaAhs5MAbRhFp0TuC1Wk/ZlmghA3GoTbPZDDW4zBHyGTbCSSaTYXt4vV7X7e2t6vV6MMj25DXK1Kz0wRZmSh1ZL3YzTXSN2kRq1EjyXFYa2gT4g2ulUql3Kh4g0k2RrOWfx5yunxKx7UjD67RWDDc/GoIwUdkzbj0NwpL3WScb8tmibjwGFuemAZHeVgwcHR2Ft4lKbyUSJttgMAjhKtoSJ01xOr2ktZO+bNhsDZDVNdE+pTfhLvfhO8AmpZAqrPTAcZfRfrKRAgTDoibUtx6WlVdoq61WIOHEfZAZ7Ft4bZTD2FCGB9mjyW1KTPJseN9ku1l0EL8NJSHRcrkcXvOTTCYDibFoiYJY9GjulnDtRp7Van3XlDVszC+cBMY1mUyGhJHVnGmvldbQGR8eHsL5ya1WK1QLbPJU7VnGbC+GdEjoRT1vO3+Yo+xQa7fbajQa2t3dDU4HJG3fZMKJahhSyBlD8Ji2a5OzyAXWKL1PYrBJSzxq1rSda5sINRohw0vvI/kfG7F5uhAZE8AiWjpmPWJb2gHp2pOigPXg8D6imqz9Hd5IVHfCk+AVzs+ePVvb8WR1UwYaQreHgeBlEeZyPqoNjahQwJvkmEQmhpUFmPQ2tLNHFaZSb7egoo/Z17dQN2qf04boZK+5Nwewt1qtoJVGCcL+Nz9kuHlxJ2SK7mhLgaIlZjwrkg5zgGgluvPPGhmrceOl2w0ytvbXViMg99BXSAtouUQlUa/JGkv6YDQaBT0fsuPvoh5jNJeB4ePlp+QEOIqRJCvPYj1oW+2C/ELf2XOpKd3bNAftoeQ22rQVLBhQS3DL5XJtWy+/s+dSRNcX42MTmVYK5HfkKTZ5qna92Q0gVvbbdF/rlNijSu3a+KkR250ymYz29/e1s7OzVr4DSUUHxy5mGxJtOtnLem50qtX++C6EZEnQep72WpagyfTyud0pZ5MhNmxCT6M2F5nCasQUh+/t7YX3NfEKesirWq2Gt/muVqtAYFbLxKuCLEgQQX7VavVRq0//4NmQ3KAM5/Lyco2Q6Wc0SPs72lyr1cJbiCFdtL5erxfGgTZjTPBkIT7rgSJx2Oy29Fa7JUGHvJDJZNYOs7bziWsiv5RKJe3u7objExeLRTgT4OHhYc3Tt8lc+7vpdKp2u62zszMtFgvd39+Hg+kzmUx4Fc6m0NmSbrfbVbPZ1MPDg+7u7sKh8Hh0EKl1POwGD3Rz5lM2m1WtVtPx8XHYuWXPJKGfmKNEcHb3mI1imPOQLCeh2TMs7M61qFNj1xTjaeUC2pTNZt85PtL2l11rzInHqpEYJ/Rwu2EHHqDtcSA20i0UCvr8889VKBSC2E0oxEE3LHgbgrChAtKzQjlZaKtZsmiZXJPJRM1mMyQrsIzs4Y8m00hu3d/f65tvvtF4PA4vHCS0PTo60uHhYXg1i6RwVN/NzY0uLy+DtwMYaJsAs0Xo6XRaOzs74YBoaxxIYrCTztb2YrQkhbDO6pVRr8IaDwgaYzAcDnV1daVmsxl+0JlZ9BgMwnkiA3baIcmcnp6GjS2Mo9Un0aajiSrOJECDZ7HYcxdYyLSfnVhUe9hEIH1nvVrOHWa+sC2WBTiZTMLRouz0s1EO17fVCqPRKNSF3t/fh9O8qOnGk6bGepM0gZ5JVECyyW5hZQ3wGd5vsVjU7u6uDg8P1+qR2Xa+t7eng4ODUCdujRcJSunNi2ObzWYwhBxYX6vVwvNWKpXgBa9Wq1CixnvIzs7OdHd3t9HLtWsUfZ0XSHIUJ2VpyWTynXfa2TVq5RDGwCZ7MeQYPt60zIYd2mHluzgQm6ZbKpX0t3/7t4EIyVDbk+/Zkoh3azOghFLRAzmwvLx6mnd2QQoQSaPRCAtkPp+v1a5a/YcBpS0PDw86OTnRp59+qs8++0yHh4c6PT3V06dPw1tp2cRxd3en//iP/9DXX38d3s01HA61u7ur6XT6TrKAUAoN9ODgIFh36y3M5/NApHYvvj1dC23WJrRYiPRJVLvCq6jVauFM1pcvX+rs7Cy8z83WepJgtN4J5FatVvXixQt9/vnn+sUvfqHT09N36pjR4SCNTqejRqOher2+ltyh7MyWSEFULDAqPGwZmK3eICtOwgvtFyJkz709BIk5Mx6Pw4Yc+ny1Wq3tbrIeP/Os1+up0Wjo9vZWlUpFJycnwRg9efIkHG4Tzc6zMaNSqWhvb09XV1fveOf0N+uGHYhIS0RShORWc4dw2L5uSx2RhGazmR4eHnR5eamHhwctl0vV63WtVisdHR2FDT42EWhlldvbW7169Sq8IcU6UMw1jAtrbz6fhxeR4rnOZrNAhhzy1G63g1yBd08Ckf6HG3DebMWH3b3HtnYcIPiI6qA4tN1YPd3PPvss6J8kyrrdrgqFQqhVtVqbzUqyQ8lmKCUFz+jFixf65S9/qcPDQyUSifBCyHq9vvZCRSw62e6ovCC9TX7Y8rJEIhFOpT86OlKtVguhK+159eqVfv/73+v8/Dx41J1OR9lsVr/+9a/DM9lSKVtyhHeFFweR2tOXWq1WeIWOzWpDinj0eORHR0ch278JkObh4aG63a6+/vprtVotXV1drR2jmclk1t7swP22tt4c9HJ8fKwvvvhCv/jFL/Ts2TPt7++HhNemEh/eeJBMJgN5UMZHmZPd0Satl8QBS8qSgi5qazUlhaqCg4MDPX/+PEgJtorAJrPw6CEC6U20wpGILP5NMk86nQ7eUz6f1+npqQ4ODt45hByQ9GPM7u7u9PLly+Dt22w8ZJlKpVQqlcLr5yEWjCsOhNVQo8dn2oqMm5sbffPNN/r+++9DRPXw8KDpdKoXL17oyy+/DDKGNbz9fl/fffed/vSnP+nbb79Vp9PZmJxmTbHTdDgc6v7+PowZfZzP57W3t6fT01Pt7+9rtVrp9vZW3377rV69eqVOp7N2jgZRVDTfY9daIpFQtVrVZ599pk8++eSdNx4j68WF2DzdTCajw8PDIHpLb7xXwmneARbVkMhiMpB2YwTh5d7enn71q1/pH/7hH3R4eKjJZKI//vGPqtfrurm5CW89IPtKssCWCNl72HZLCpnsRCKxtpUXzxmi5G0CDDhGgndSsVjwkHgensmek0p78FYIq+0WWrxl+gkJg9eXHB4eBm/8fYDkDg8Ptbe3t1araz1M2giZ2r34JycnOj091fHx8dqZsJs2sdiwnNcU2XA7qmeTfMX44kXZZ2YckaNs9QvXta81whBFPRtLbLPZTE+ePAm70tLptFqtVtA8KSFEv4eobf9hpKmVfR9I4B4cHISqAUJ/5gLPsly+3UgiKXiH6MjRiMZWD9nPIGjeUILhgPA7nY6azeZaYpgxZYdmMpkM1RJ2G7YF97SEjWyC42BLNZ8/f64vvvhCmUxGd3d3SiaTweDZ/uW/rcbPurBlj3t7ezo5OdHz58/X3ugBH2HY4/B0E5usksGPVsCGpYsK6zYbb0P8tUZ+oCNsyUm0PvKxBFI0G/8+WG8hmkEHlO7YcyX4G1vU/qGFF4XNlkdrah9LjNkyuQ/t7oneC1Jngdvn4FnsvfgdPzaB8qFxY+xt5tneJ3qdTeNlyZrPbEWBvZ7NlH9M26LhvU3EReeP/W9bGWCli49FtMLjsTXBM23S638IrIGyz0ffI+NE5xLrzFYyfMxa4t8oF/B7W31kPfL3bXawbYpez66J6NjThk0G+D+JRy8WG+k6HA7HfyE8Sro/2yvYo/iQhfwQHrNS/9nrfsw9PuZ+P6YV/Vhv4qe6/o99v/+XEHff/NRj/TH3+s/M+z8HP9Va/kuZp+7pOhwOx4+PRxk+vuPSHQ6Hw+Gk63A4HHHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGJH+wOeJWFrhcDgc/0Xgnq7D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y8f8DK7QtpWdmlUMAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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JbGrFgl4sFkomk2HQowudSuebChjvgmQyGRYy0YZNnVOplFKpl0M4nU5DpN1sNtXtdkM09FBFH8LA8CGTwWCg+/t73d3dBW03mhLP5/OQkrfb7ZDukUnU63UNh8MQBUBA7XZbGxsbms1mIRWFSGwBo1QqaX9/P8hB3DdySLfbVb1eDxEHRM3CsYscfc4ugOi8Walga2tLhUIhyETMubUR/st983sQPP/GfNlI2i5Wa1/WsVsSgDzJAphT5KzFYhEic1JwClhWS8Sp4XCpbQyHwxXnMRwOVa/XQ8E2nU4HOYsot9/vK5lMKp/PK5PJBKmG7KhSqSifz6tUKq1kF9zPuo4PG4F3u13d398HTZcoEJu3dstayOVyymQyGgwG7026FpAoa9lmJDgz/hsNxpgv5no+n4dnZp2SmUoK0tLf//3f/+j7fV/ESroXFxcqFosrUSzkyMJ8lzYOFijRE3+XtBKNWLyPjsvvUwGXFBYJRBadbJs+skBJy9fpp9wnCyaRSGgymWhjYyPodtVqVYPBIBAhY0Q6zoIg7cxms5rP5+p2u2q1WppMJsFx0ElBqxGLcLFYhEjaPnOxWNTe3l6QeCBbUvx2u612ux3SbcbMtvfZCMS2GPFzUnwWGNGN1fNJXy2BQuh8J1oeujfp9vb2doj2kDHWpb72/iC+6LNICpkVVXwcF5pyLpfT/v6+9vb2VCgUgoYOwU2nU43H41AsHY/HgRQymYwymUyYz2azqeVyGa41HA7VaDR0e3sbMsRUKqV2ux3uBakul8spnU5rOp0GmQdHxLhLq44M27S1BgIi7NvaPYXT6LrgmX/MegO27c2ubenl2kdyelNEzT1gf6xPG4BQBO90OkEbjgOxyQvj8Vi1Wk2SwuJPp9PK5XLa3t4Ok0iBhMFlgQEWKC1KpPtEx1EvaK+3jsjfZBj8PlHG9fW1stlsaL2iaprP57W/v6/Nzc1QRYZEJ5PJygK2ER7PaIGBjcdjdbtddTqdIJvgZGykwj32+/1AVIPBIEQbm5ubgYQwPAiKe5nP5+F3iMJJHcka6DpBW4XwiWRZbLZajI5rC30AkuN37PNJCrILRVTG0S5AxouxRlOGtLEVSIMiD1IB48cfJBzmHruzfb5o+DxrPp8PdlgoFALBMc7WHpG8cACMM98NGaBbtlqt4LSRr8gskECQ5yB07ptuDubG2hk2FI0C+RxjXSwWdXR0pPF4rFKppHw+H+6R9dZut3V9fa16vR7Ww9uyy4d+bmUu1q/NKCkG2yyK5+B7bfaCA6VGsL29HRwSGcVisVCtVgvBxa9KXsB7kw6xqLe3t1eiKTxXtIpsiSqTyahYLKpSqahQKASpgVTBpiIg6kklhZ+/iXin06m63a5ubm4C2dLfh1Hv7e0pnU6H6IOKNYuC++aa6Hx20XOP9j6JKPg8BGKjCKrM6IG2IyKZTIZCTKVSCc6M52KhjsfjoJ9JWonGaBtqtVpBR6YwRWRuI1bIbF2TviU06ZUUgLPid+hSQI+FuIhabJXZygek6miW/GHRQc48f3Tc0RJtOm4JmevzHWRsFG7IELBDInTmyUa3gGstFgv1ej01Gg11u93QHZHL5cK9Q66MnR1TG/VBRva5bBEP3RNitmTHmG9tbenk5CTo1khOBBvIXNVqVWdnZ7q9vQ1tc29C9H7WtW+y5umEoZMBssxkMmEO7SYim11ZaQrpw9ZkmBMKoW+7758SsbaMsWhJadGkJCmbzQYPbr04RkBUsLGxEXoFKSAlEgn1+/3QDWF10+gOFUkr32UXQBREhHQlMPGLxUKVSiUQ1d7ennZ2dlaq3eirvV4vECDRka3WQ1aMEQsH57K1tSXpZTRdKBTC36n8TyaT4KwgXDYs0OdLcclKMnyeKJdeYIqUg8EgaI5sPCANo5jFfXJPUWdnJQTG0zpVS8zSq+qyJXb7+5ubmyt9r5Y8cGLj8Tik1twj94c8E3UCtpgICbEwuUer6TP+NiBIJF5t4qDQQ7+sbXu0ko/dHYXToKtnOp2GjRgQr41a+bwkjUajlfqHzYiwKUus6Md2p5ct8EK6R0dH2tvbW3GiaNmj0Uh3d3eh1e3m5ibIHm/rg7eEG12XNvviWba3t7WzsxMCHavHRovlzAOFQsYrm80ql8uFYjL2T1H6V026pID0Y0IMlUoleFo8PuRkm9Np3zk4ONDp6amKxaKWy6VarVYwXLuIo8UC8DbDwAghXRY8CwBjhRiJ6DB0IlpSNfudlnBtOmqf1fYaEj1DoMvlcsXJoKuhrWGIaLUU4/L5fKiKk8ay0Kw2jvHaYqQlGH5O5B1dRDbds4SL88Q5WeJlIVinZFNMpBjSeYgKucNKSmiZVsPFJqwObMkk2utsCTkamVlHbgtvfL8dM+abqIr2NqJxHAUE3ev1wg6rRCKhwWCwIqVhF5AQv2PnOpvNhnm0zhX5ABuzuqm1UWuXdi1ZuafT6YTulevra9VqtUC6b2sZsxlm9PeiY5/JZEK7XrlcDrLHfD4Pm2W4N5w8Gd7e3t5Ke6SdM9rqeJb/SeHvfREr6RI5QUo0aReLxVDtnc1mqtfrQaTvdruvTQRV4kqlolKpFBZrq9UKEcmbCNf+/U3GASENBgM1m01ls1kVCoXQhI8HJQqzFWKiUDS3aB8oTgQSsQU1FhaLamtrS7u7u9rb21M+nw/RKbqvTXOj6evW1laoqO/u7gYnxaKETKItOkSTEG00AuF3ohpmlICkV1ohUovVjEkdIVFI07ZYUUgsFAra2dkJmwbYdYdzIMqiMEVHgd2uy6LDidjftdu2bbRui3fMH4uc+9rf39fp6akeP36sR48ehfYp5AgrkUGidoNCq9VSuVwOqTPXp+iJgyT63dnZCZ0J/X4/3Dc71cjKsGOyNpxUdAchAY3dbMK/k6Hxhy3wtVpN1Wo1bFQg43pT9vimtRddqzhb1h0tmKlUKqxH6yitze/v7+vp06fa29sLkojVxGk9I2B50z3/1Ij17AWiMZv6YfDsdkokEsrn82GnDosKT2QnxkZS1oNF25d+LGzK1ev1dH9/Hwya+2ZbbjQasvcVTf2plGI09BnjjcvlsnZ2dsJWY1IszjbgOSF+2wFim/GLxaIODw/15MkTPXr0KOzGsqTK7xPFI/vY60GKtuuEPlBIje+xhEthajweh4iEzxLp4cTY6IEGy04kIrLt7W2Vy+Ug5UC61AjQtW2qn8/nVS6XV/qTraxAexW72dhOm0gkVoifuaQrhefg+ff39/Xs2TN9/PHHevz4sSqVysqYcV0iSoo6aLXJZFLHx8d6/Pixrq+vgz7PONk2LRw9ztqm/LQoko5T/GJsKR7RrokNWgnDyihWVsGh8vnb21tdXV3p9vZW9/f3QUZ7W5T7LrDZQbTf1hbLotchY2ETx9OnT7W/vx8yYeofts8dR/SrJV1b5GExkS4T9SGWUyRjfz7RIr2mtVpNxWIxeNZms7lS7KGIsY54o8WrddEwsERBxIvBQwI2TeUzRCxWs4WwiHTR5ejgoHCCk2HRMT424rCVdQhGerXHfHNzU/v7+3r8+LGOj49VLpeDLMIzseiXy2XQwaxWaH+/0+no5uZm5TkgA2QLPke0jmTCgqQRnc+xAaJcLod2N6tdEhlz+FGlUlk5uGcymYR+3lwuFw454d75bkiXrgM7B8wbxVlS+GgHDIRrnQ5zkMvldHh4qNPTUx0dHa0Ua4iIraQSjSKXy6XK5bJOTk705MkT9Xq9oK1D0rSmYUs268B5ollWKhWdnJzo6OhoZdv2fD4PW635bubbOl3+2NZLiA+5rVarqVarBcKNkuM6RAvFrBULe51+vx/OwygUCuF+iLLt5irWAnJEpVIJmTBSgv1eGwC+iw79UyKWljH+C+mShpM6sO3Vaos25WOi5vOXWzqr1Wogg8PDw7AhodFohIjHRtMYNpO+Tqtct23QfhZDxRC63W4odlgjtbqSLXwQjdgWOIinUCiEvk6KjJBnOp0O6SOtRqPRSK1WK9yDpOC0kAey2az29vZUqVRC25EtMEYLRjaisuQJQUgKxbVerxdIBMJGHrJjbQtj9to8ezTCRB6BsCFoiOT4+Fj7+/thAaLhY08QKYUjmy6jT0Ke/L8dD57XOiP+a3VR6g5IJBx4hB1Et1gjB9ksgCyK/6dAvLu7q0qlEuYZQrUV+263G75va2sryCSFQkH5fD5Ezfv7+yGbwlnQ90ugwTNYmYtaiy2yIcPQKURhtd/vrxDuuggUW4p2stgaAZ9lvChCc84K9rFYLELHRK1WC0Vj1ja1F9YQ69baLjInn7W95v/TKP1dEGukK+k10t3c3NTd3V2IMLa3t0MKbieCxWQ1I3pS6XygZcjuvY5KEaTgFE4gXDoZommGLRRxDxCd7f0jQuNZcR60+kAKTHgymQwRm9XQGBdbHKjX60GaYFdUs9kMW2MtkdkKNsUktlCycHgWSWFhRU+eghQ4fYuDbZrNpnq9XvgMUbXdGWX1azQzNovQZmaNnIWBzsbJZZPJZIVwcrmcSqVSGDfmja3OtA1azdWeemajGVJYCIuxhaSYD+tI2ZZttfNEIqHb21vd3Nzo2bNn4aAjyAXyx/ESlXGP0quebDRRHLmtY0D2tl+60+mEIyTt5oyTkxPt7e2t9LATMdteblukRXJCZ7dnZUivduVBzPYZos5kXdpve51Zd0iBtojF51nrrGsO3hkOh7q5udHV1VWwf2v72D+BCeNVq9V0d3cXghX0XMslcSE20rVpmk0HbDS4WCxUKBRCp4Ptc7RecbFYrBTNyuVyWLhvSnEgUDZXcE0Inu+OErUtptiFTDpnuxn4nPSqBazf76tWq+nFixc6Pz9Xu90OC4V02JI7uvbu7u5KPy0pPruS+KxNVy1pjkajcKoXhSa0RBZW9DAagJTTbDZ1cXGhH374IZA/ToVxtLqvbWOLRhUUoWzLDttZJYWODLIVNEqiUgpqyFCMCz/nWnQDcJ/L5TLIL0hP1oFDumyRhlwowOHAeE5boBoMBqrX63rx4oUeP34cimHYmSUc64z4Q9TMqXmclmeLnMvlcqVQKkn1el3lcllHR0d69OhRkBL29/dDZ4MtDNpC2/39va6vr8MJb9ZBobVHN7VA2ui/9oxeuzZsZwifw/EgkxB5M4YPtW6ShdidkK1WK5wHgSzANbgmOzRxmpwr3Wg0QnROL7XNxONC7KRr+y5Ho5HS6bQ6nU4wVAbCHtkWjcDwkr1eL+hctkEfAiESsdIC18aoiJbWpUXRSJmFZA3SErJNFVkozWZTZ2dn+vLLL/WXv/wlHJjNWZ4nJyc6PT1diUokhWiLot1sNlOj0dD5+blubm5CVwdFNkuceG1IE5IbjUY6OjoKGQXRaZSsiQAHg4FqtZouLi5Uq9VCuktkS0dFuVwOWzP7/X4gTVs4tBERBTl20WETEJA92Cba02wzFenVDj4iV2QlvoPjACErqve215hnRnOGzKO7ukqlkvb29kJAYO2Yw9oPDw/D5hjIJto2ZzcDkUVUq9WQSaBlMy92vBifRCIRIr10Oq39/f0Q7a47ujPq/L///vuwfiAjq/MiFVm7sC1+0Yxx3Zqx/xb9rC02M45RCdAGAjhKe36zlTQYa6JoHEoikQhnOkO4ZAy2N/dXTboQQrR/kQVqq/G01thGebtwqTxSRIDwmCyrY9p7wDCQE5Aj1nU8rGuDIk3knu3GAr6fKOjq6kqff/65Pv/8c52fn4dKKRoupGsLBVb/xNM3m82QVnHodiKRWIm2IT+iSBYa35dMvjyLd39/f+XoRNunaTU2Fmm1Wl15awIbQp48eRJ6pefzeTgEp9lsBmKzvcd27CEeKuq0sEkKXQH0cnOftuhp/xClEYkRFfOd7KBizohybHEL+YdImA0ayCG7u7sqlUo6PT0NZH13dxfqEMPhUNVqVbVaLRQu1wUM2KWNsOv1uq6ursLxkMwr0aTN4IbDYeigICLmMPOTk5MVu7F9qYPBQI1GQxcXF3rx4oWur6+DzAXpzmazlXOJLYHiMOzmmygpR9eKXXPWIT5UvLYBDBkCa9gWwGxbJARN5klhlSIuzw6HWCdu1/qvknQt7ETaPlBJYZca2pEtkthtjnye6Mh6TnpY7WKXVrcC24gZ8l038NaAuT/plWZ0f38fejKjTfFEiufn52FRWTmB1ptGo6FKpRKiB4zMRkLn5+e6vLwMr8+hs2E+f7ktkop1LpcL99bpdIKei2Tx6NEjSVqJjG2kwhhB9HZfPcUvyOfk5EQ7Ozva2NhQp9MJb2Lg3FMb/VttngjSptpoiUT3LF5a32jLs9tXsQlSf7ZjW0LAabEt12r+RO3cn+0nXiwW4XwJsg5a+awWzOuaRqOR6vW6rq+v9cEHH2h/f/+1MeW/tlhJRwwn00GidF5sbW2FQt3d3d1KRM96oI+bXmxkEZ6LA5Tq9XqQpnAqkoLkUyqV9PjxYz158mTF+VqHdH9/vxKN244du06i693et5UCGBcrg1gdnLW2XC5DXcC28kG429vb2t/fD7votre3Q+Bgi3b2Hq1dxolfhHSj5GdJl902nMtJ9MYi5JU8fBb9kAox34+mB8lJr3pHrf4ZjZ7WwU4URmF3D3EiU1TX5f5IZ6xB2kWO9ml1ZhsBfffddyvVWpwJ0Ws+nw99ibwuJpfLSVIoPtCOxRsO1u3AYQzoZ/zhhx/0/fffq1qthgidqNq+oaDX66larYa+zcFgEFrKbCcGRUsrAfD/9CqzWQbtlJY4WzyLki6EIUk7OztBlrC7jYhw2VZuo04bEbKJgCzBpvrcC2n81tZWKN7O53NVq1V9//33evTokQ4ODkL2sm5x45Rpf8JR8fzHx8c6ODgI7wCcz+e6vr4O8pGNPOv1us7OzsI5t/bwIfRTbIyxs/fDXNhAxf7cXqvdbofNFawfvuOhYlRUpiOzYo1yDdYXNR7shvVjJR++g0Lg0dGRnj17psePH4dNFPaQc1sTsmvwl0BspGu1SkkraZZN8dnmu7Ozo2QyqXK5HFIs+2JLIlsMQlJIRyFXJtQW7mwBzOo/b5sAKy/YrZPRdMpqVzSy28IVP7cFKNtFIWklMjk/P9f5+XkoYkHethtje3s79LAWCoXwPBSmbMRWqVR0enoaFrSNRK0TuLi40JdffhlIFz0XMsVBkPLd3NyEg+nRBu0JXzY6sxqtfRbbq10qlYKUwfVsXysLiMVr28hwxOh+9ihFZAWIgBYoxh7tG/KxZyVICnJXo9FQs9kM51FADqlUKrS3sfgtgdgxJjKu1Wrh1UtoxwcHBzo8PFQ2mw0HDvGMViNG68VWKpVK6Pm2Y4+Dtg6D8cE5Wju1bZV2DTDmrAFbfH3b+uf6+Xw+kCzzz1plDGxfOsVk7tfKEBxk8/jxY3344YehPjKfz8PmKmwCrmF92uJdnAQcO+mSzkG+RBa24AKRoNEtly/fLMtWQyJYqxcxERjEcrkMUZYdaJrmOZSZXUkPFdOiz4BR2F1B9uwF2+0A8UY3HhCdlstllUqlsEi4PkWdVqulRqMRtD5bhMSIaaniD0RFIzzvbKNftlar6ebmRo8ePQr9uxSlmItOpxN2HEEIVJuJNnCUhUIhdElA7sxHIvFqCywLdl13CVEIvak4Ufs8dvcf4w15W/KA8O0hQA/tULQZDj/HKRBRLRaLsBtxY2MjtMxVq1U1Go2gXUsKRS1e6PnBBx+EMcbeiRjpKqnVamFzATv0ePGj3QTC/NrTzrAFnhN7abVa4RBzG1WyU61cLocMgA4P+zLMdbYMMbMRhkwnSswPrRvbwUAnCffGEYs4Bit9cY/Mi/0+7oc2Ofs6rdFoFI4cQAe3b/fA7imE/ipJFw2LnUfS62k7/XO2hSWVevliyIODA+3v74dIyhbCrD6LBmz7K6VXxwgS5XKyVtTAHiJdK+wTebGpIUq6PBuLFxLhXjAWdDv0a5wFxGs/y/1DUHZ8pFcFMJzCcrnU0dFRaNciQiBTIGpm0UWNjrFgXKPGz32SfpOF8Nx2oREN2QKJjW6tLCOtvsXVtqXRcWHPUuDZeT7riG3hlcXM2OL4LSHyM9t/SnrO3n1JgdxwRnYMbUXd3o8ldyJxCJsC5e7urh49eqTDw8NwuBHfZ7M1um6I1O3BNOjk0WfE5nh3Wz6fDwXFdZ+1uqm1+WKxGGzedhi8ibQseVPwtJsYbDuhtRNrf3ZcsSFkJ95JyJjRrYJExFvErUNHV08mk2HDSFz42UmXychkMjo4OFCpVFqpJlvi7XQ6oemfToZou87d3V3YQUIVE33PVsUhKYzUbqWkx9O2Br2pvxey5b/RvkPbAWALD7Sq8Ooe68ltz6NtwrfR4MbGq1fnUPSwkZU9WQqjWiwWaw87wcmg+dlnsQUvCmXHx8c6OTnR2dlZSNFsQYQCJsYb3d1ldTxLvCwcHAQRFPNle575w+9FOwGwLwqQNuXl33hNOPdoe3QTicRKlmAzKJv+2gKbHWucg5U50GNPTk7CLj1LyLY6jywym81CJ8uHH36ok5MTlctlbWxsBF0ZHZUxspkjZAoZWjtijKy0YFsFWYOTyeorelhPluisXGaLsIy3JWsLK6tAfFa75+Qym5XY7qZ1Ua6N3NkajlMlsGALMXxCpIuNIKlg95avfk7EFulmMhkdHh6Gh7TFEOnlxHQ6HTUaDVWrVRUKBS2XyxAZUz3e3d0NxSsb8doKpV3sRElskcQ4aDejmf6hIoBdJFGtyy72aGHCdjfYwgPFIg6UsSd0YWz8SSaTKhQKOjo6UjqdVqFQWNmCzL0RhVHsIgqASDGqfr8fFmiU3Gy6WigUdHJyok8++UTff/+9Go3Ga4VAu4ggNDv2NrKJbtyAFHA+9g9nWuCM0PWoYI9Go5UxswUWMgAymnK5LOmlA0bXtWc0Q7r2rcN0XVgHaqUPiMBmaNhCLpfT8fGxPv3006At2g0KkBd2WS6XdXh4GCJ4TiljVxsRtz3ukXoApFkoFMKxn/ZQf2zIZn+QFVkDdsTvIaOw8QZb4t6tvfPc0XXBvESBdNXtdsNZI2SLNiKlMMl42+3XtgjHwTa08iGpcbxpvV4PEpB9rRXPwtzjGDlzOQ7ERrromHY/t9XZogUBeh8ZULy8feWGtPp2BUkhRYd4bGGOQ5AxYKKIh1rGoh6eE8aorkImNl22lWWiKjRq6eVW1kqlokePHml/fz+kkPYgFYwEAkwkEiqVSmq327q/vw8FK56bQ4D4HN6chc3C63a7wViZA+nVwd08MxXh4+NjHR0d6YcffgjFIqI1nhV5wb6Chu+A0OyLPSESG3Gw+CADWsQoctkDj6LarI20bMrIuEovSdduFYaUbGsSTsgWdblPdFgri+FUuAcOzGGHmC16WcnJ3vf29rYODw81nU5VKBTCG6cpcFL440hCewAQ16NNjMLj9vZ20Cn7/X6YC+Yml8vp4OAgEHgulwtZFAEJBGUd6DonGl0XzE20E8gGRNwX1+d+kcSo71gnt67Tht58HNt0Og3So22zrNfrITtk/FnrcMT7FAR/CsS6OYL2HogpuoA4wejy8lLL5cu+PAoCNP2zSCzB2VSKyY72YmIYkBIG/VBKhLESlbDnn6MI+S+TFk1LmFT6R0ulkrLZrCqVivb29sJC2d7e1mKxCP2Etr2HNq9MJhMKIBQA2Spt2+ZsBsCOIqKhxWIRsgyMdDQarWzqsNGdpBX9zUY3kAmka3U3SStEhpOKaq38Lm1+kDTaNpsmSBU7nU54PjITq1UWCoWgVUKeSEAQifQqQqe/lwVtsxAciPRKnsE5E8kxDkTXUfK2Y2lb0ngVEpJCqVQKfcb2dDK7C41ah22XsucsHBwchG4cMgN24tEDzXxtb2+HzThHR0dqNBq6u7sLrznie6xEZAu3drwLhUKYDyJS2wWyjnjJMu2B7tgHTsLKCtaubHsfc4+jQF/v9/u6u7sLhWCiditjWufAPT2U6f4ciI100Th555Jd3FYwp7K7sfHqsJadnZ0gN9h2Fen1HWMMYrQYYrslEO2jhgGsWI8OxkHrNMhDmtEKLs+CQe3s7ATi39vbC9tE7bmtGAvaFk3gNgUn4saj284M22dJF4gtriDNEI3wOU7KosfZbkSxr5chwrPkzJzyzJaorWxAem2jVPuH+7cLElKj8tzr9cJbAljk2EJ0fkhbyXhsZG2vZTVbG/lAnDhs2xNNZwRgPAAOEAnD9sRaErXdCmi+OBzplWOix9q+5YPrQn47Ozva2dkJbxlhAwgRJcEG88GhQcfHx+EwI9LwyWQSWjSjnSasCXY+VioVtdvt4KAgUp7Zyn7RMWJ9QqzYCGRv1290bVvSJdjh2r1eT81mU3d3d6HrB2nPFuK4D7Io5iMuxEq67XZ7pX92XXRIVMLhIxYI5UyQ/bz1hnbSrXTAol73Ass3SQv2DQRojjs7O6+1etnqNFE0u8V4p9ve3l7YuEAqxIEnnFlgCbdYLIboSdKKxMF4EIUitbDLiwiF4gnjxKJGypjNZit9sFZPwzHZNDnqYBh3WxiDUCAcSNcW7uxhO5KCtmfHkMgQIrEaX3SusZvoEYs2MrWnjnHf3J/V+qyGGI3OrfOxY2JTaO7XFnohUQiXk/VscdA6UYIDpBh+F2kLm8Q+rG6KZm11cnZqIdlJL3fEUYRrNBoheofo7XkgBA+29YyzLCB2AgPbA2tthedjXjmcaN16tZmC9Gr3mw2KkCSm02kgXNsmuY74LWfgeKxE+XMj1qMdiVLsorHEGU1BePdTNptdeTWH9XIPIVpBt99rI5GHuhZskYAiENe1PZMQLpNLUYu0rd/vh2ewkoSNIIbDYUiJiB5IIZED7IKzp/0jf3A/PKtN37lfxsyOA1EKxTZIis/aFi2rA1vtLjouVpJgoSEt2egOJ2Z7i3mpoz1vw0Z46woepNFIMxA/329PJrPnNyMNQKx8F2OKI6EjJhoxoXPaz3BmAs7GygoUciBK2ybI79jU27bN2Uq7lcuIEiFKerPtC2BLpZLm83l4/RM2xP2Px+MgW5Ge21PWGBeem/uJdsHY9r91azPaxUDww7bvdet2Hey4S68K19gO3xtd33bOojwTdRI/J2IjXeSDN6X1GB/pBVEb5GCN/l3AAGMY0qsJikZy6z5ndUbICE8cvV8msdPphBOnLi8vQyRhU2Jb4SW6azabqlaroRhgiwzRoxjR/3gGFoDtgrBFJNsqR0SG1oeEwIKAXLPZrHZ3d3V4eBg6RojAbIYQHSsOJI+OF06Wxnp6lIvFYuiFZfccGi4RP2Ng/0ACzKU9qo/0G0eCLESfM62JtlLO/BFBMdZEhNgkRGp7jon+KpVK6LG1b3mwKTEpPpkF0TrdKERfVoKxzpZno+OB+5tOp6Hliw0Xi8UinENCxhXdYMM4EiGTbRHxJ5MvD0myDtRGoMwtTjeq/0d13WjgY7MHu07ftq6B5RWI1nLFQ5GulTGQkOJCrGcvWL3Tpg38LFp8gHRZ7PTX8m/v4g3tji0M33YLrPsOjLxYLIajCzF4q3cyyRjKaPTytdRXV1e6vLzU7e1tKNhEI7eog8EQ0WQhRzS06XQaIgkiOIpvRDr2wHCIVnp1PCZGzWfseEejN/qDDw8PdXh4qEajsaLxWtLlXm0PMmNl5xpiZpvs0dFReOUSrx2/v79f0fvm83mI4Le2tkJRElvgzQK2oEekRYqdz+dDZ0AqlQo77jjMJyovIENgq0T4du6wGwpBxWJRBwcH4VxbSNeOke0Xj/bvoilLr07Q40xizhDALm2GwNjY3lTaB5fLZbCD6BnCVje3hytxBCIOktoBtmQ1a4Ihfjeqy64rUNlol3sgGFi3424dIFOygnQ6HeyFjMdmYpZf+HyUi+JEbKRrdVge3lYjpVedB2iTVt/i7ADOYHiINK0Ht+1e9qhArvXQgNNm9vjx45W93ExctFiCoSHk2+MNIUQbFUQNIkqK3F9U42Lx21Y12zLH85L2EbFaDd2OPxqsHXNr8LSX0c7F+aTRiB+CI4qCiGzqzP2R9tumdj7XbDZXNEQ7N7ZyXiwWlclkApHwJgtg00mcFLsaIX46ZYgEcURkBmy/jRZm7VjbcaSYh4xh5xBbjra1RQuT2DqvqaHXtFarBY3SpvKMsd29FW1/tFq7tS0IL2qXBAC8/orDp7AbtHbb92rbAiuVijKZzIrjtIg6eeukrVwStVX7eUibojPbfi1n2KzE2hFBhV0PUZnz50bsfbpWzwQ2xY+mLjYapKpvT/8H0YFk4dueQjQuoox1KRCGsLu7q6dPn+rDDz8MaXi/35f0ajuvvUc8L5oSZEwquK69DI2zUChod3c3vA6GqJxWNbtN2EobNuq2vYjWqO1OPO6b57SSy2AwCF0G7FSyXRRRIrTGzBwRBUN00qtuBvoy+TvRJM7DHgzEvLFo7Bzabac4K/tzbIHvsOk8URskxPXt5/ld+4x2a2w0QLAEj9zR7/fDvVJAWy6XQVNdV8uggMwuxtvbW11eXoYD5G3B0xY3GQMcUqlUWmkZ3N7eDi2KFGVtXzk2iGRlOxLQSMfjcficLTyjY9vDm9hWzNGi9owUuz5tDQCitZKO7XGOSg5E57aAZ7eqRyNYS+DMO9fl2X+Vfbqbm5vhNetbW1uvVaTX6SqWdPFk9kBiq5FKq8Uv264EQWGgVPO73e5KbyHfgb4EedjCx3K5DBEN5M29kt7bk6c4YIeGfxsBos/u7e2FrarlcjnIBrY9B+0TZ2PJluZ2279qn5WeSmuE0qsoaLlcruzDTyaTmkwmajQaur6+VrvdDmNkK8gWtsLMBhA6VTh4GrmEnmUbeVm9s1AohDMJcJQcTh2tphOZFgqFMAY4u0KhEMbcFgEhed4Uy1s8cJBsyrApfzRNto5TUpA6rq+vwzv7cBx0sRAN2kgOYsd+2u12OJfYvuKcN6ksFotwZjJRN10uksLGFjbX8IaPo6OjlZ53SA1HUyqVVCwWg1RD3ze6N+MSzThYg4zzYDB4bedmNGPB7skoGD9sl6JhVEdn3NGgkV9ssdZmYPy+lXdsYZ7/52dxITbSzWaz4R1S6C5MLOK9jV5J64gcAAPNoogWNPBgEOVsNtP9/X3YWMErWYhorE4nvXq32M3Njb744gvNZjOdnJwE0uQtu5wjQVTE2a0XFxc6OztTvV4P90ZBg3tjgm03BhsgcCiSQnM/bWmcxsQY2BfssXBTqZdvz7XE/1BKS6RSLBbDASzMBe/Ruri4UL1eD8UVHBodFsyBbWPideI7OzshwyA6s5EVaaVdbIVCQeVyOaSy0qvTxtirb/u0s9lsOAY0m82Gz7DxgCzBLjwcf6VSCW1FjIct6s1mL1+RxLPZjhvsDSfAcZicuHZ8fBxIjpYte//RQIE5wgGyLmgphETJgNDusWvm8eDgIGy+YOwgSMYjmvVgl4vFy4Pba7VaKOhh77u7u+EekSHIasjsGo2GLi8v9e233+rm5iZkSYAxxrlubGxoOByq0WiEIjTtdLS1Rbd546SIxHGQkkKvu9WSuSYOlXnASWxubgaHHhdiO/Aml8vpo48+CvvhIbdms6nLy8vwUj6KBtEiGwOLjkPF3e5jjx6HR18ke7GpFkMYmUwm9E3aCZ1MJuE4xdvbW3388cf6zW9+o48++kj7+/vhgOlCoRAKOrPZTDc3N/r888/15ZdfqtPphD3/+/v74ZqkNxgD6THRYdRgbBREWtVqtcJbTa2jslIHY767uxsKgdECBWNQLpfDGN/d3ens7Ezffvutzs/Pw+Ha3A9OwBa6IF228B4eHurp06fh0Ber09nIjhTRknGhUNB8PlculwvdBJA51+Y5lstlKCoVi8WVQh9RPu8rI8qx74azabbVCtvttq6urjQcDlWr1bS9vR3mz/bM2kyKHXMc5v7kyRM9f/5cn3zySThy0L600gKHQf830oytBTCndBm02+0gBRDp2jmOaro2lYf0mYuNjZfnd9ze3urs7EzX19eaTqeqVqtaLBY6PDzU6enpazIP339xcaHLy0t9/fXX+stf/hJ2uEUjVCuFJJNJjUajcA26CLDbSqUSdqG22+1wZCWBBxtNWK+WG+yOQtuJ8+jRI52enobuEtZCq9UKYx6HthtbpLu1taXT09PQusP2vXa7ra2trXB+LNogKQSiPbt1EPopBGCsv/3tb/U3f/M3IXo5OzvTV199FQ68sG0t9pQxWwACEBDelPT9+Pg4nHZGtMQkTSYTfffdd/rzn/+sq6ursDmBQ2Z+85vf6Pnz5ys9xkToGGK0I8OSE3/vdrthxw1H1llJgSo8e+wPDw/DOQDrYKPs6XQaUtovvvhCV1dXK/v9Sf1tIz5jBzEWi8XQnfD48eNAupYMWDS8Th4JgqhDUtA/bbRvI1xLLFZTJtJZLpdhWytkweKzB+pYZ0Ahi7S5Xq+HKIjo0OqRdGhwfCbzRGZQKBT0t3/7tzo6OtLOzs5awuVZKN4eHR3p9vZ25fVEFKUYN66xs7Oz0qUQPdXMfj+2Fi3WUuhlzr/++mu1Wi0tl0vd3d1pNpvp6dOn+uyzz17rz10sXr4A4Ntvv9XXX3+t//7v/1aj0XhNiuFadDQgE9pWL5x3oVDQ6empPv30Uz19+lRbWy/fdvL555/rT3/600qLIxkzmbBtGUP2IEssl8t6/vy5Pv744xCYcX2i/7gQ+yljGAb6aLlc1mg0Ci1Wo9FopUGfaq1N7diJRXHu8ePH+t3vfqd/+Id/UKFQCC/cu7q6CuRtq7X8Pwt6XbXcGi3R5Xw+D/ooaaL9To7Fs1ogUXaz2QzEzzPxfDxvlHAhAhudE+3axnkiftI2tocigbxNryJS5VB1JIzoLh2rEUtaWTCpVCpo13abNEUKSzYQIoWZ4XAYTp8iXbYtZhQRHyp8QuR2Xz+teraQRkRsz4O180x3y3K5DDuueCYIwzpuSUHztMChoP/baP9NSKfT4a0RRLy5XC5Ed8gg0qu2MrvpJNqKFiXeaPcQ9jifz9VsNlWr1YImzjxR2GPOGJ9EIhHWwnw+D2cc2N1+69aT3RBiN4PY7pxCoaCPPvpIn332mQqFQnhbOIEG820LkvzdSjf8HQnq9PRUH3zwQThEimvbLdhxIBEtXkXwkzWwsShsEURa3WCwrsXD/u462OIZJGi9alSqiFaN7X/fdA2ICYkiek9surDnEdiCFfrRuzR/R2E7OmyxYN04ca9Wx30fEDGuK2zaokSU/Pi5vfa6lp91zxZto4sW/N4n5VvXVWDH5V3ag2z3TLRrwT6rvd91P4+m4u8Krmuvve77baYUJdT3ARH0Qzu4iHCjtsQ42Uj8XdaS/S/fA2wROLqeo0W1dXjIZm1bZjTIImj5MWvzDXhwImIjXYfD4fhfhAdJ9xd5G/A6vM1Dvg0Pefj/6fe+yzXe5Xo/tUD/puf6Ka71Y8ctjkJE3Pglx+Lnnud3udb/xO5/DH6utfzXYpse6TocDsdPjwcZ/icVMRwOh8PxZjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRqTe8vNELHfhcDgc/0vgka7D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y8f8DSoGedywXN+EAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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qyZMnKpfL3+mavy9+dNKFcAaDgV6+fBmSUHiGdkFaq4cnyaJgsUqvF24qlQoL2grymUwmDO6fmpmEdBHZbRjEd7Lxp9OpRqNRILhutxtCUEIfYBeDTSKwUNBU7+7ugiXO5XJKJBIhJJ7NZmo0Gup0Omq329rZ2QlhZaPRUL1eD5qv9Xh6vV7wNCFzex8s+HK5rN3dXQ2HwxXPtNfrBfmiXq8HsoAM7X1C4nZD2Hm2JMX85XK5sLG3trbCz61htgQW9ZbQLDHSksJagOC5zmgFBdfL79m1CEni9RFhIIGhN1YqFe3s7ITog7lnbiF2vHHmEi13Op1qMpno7u5OqVQqhPbj8ThEFtfX1+r3+8G7J4JBekGn3NraCnuFqhtraKIZfsYBD7zZbIZIc3NzM4zZuuQg3jlRxHclXWCrDFifGFDmwY5l1BljrPkda5xthNlut5VMJoO09Otf/zqMxY9dzRCbpzsYDHR2dqZisRgWna0uiJaivK3sBi8FzQ+PjQGDiO17v4uOy++zwCSp3++r2+2qUqmskCTfab0n9DS8n+jmth4UG8Yu6Pl8Hqoj0L1ZzHh88/k8aORUPFjPq9PpaDqdBp0Pz1a6NxA2urDJHvTRnZ2dUJ2A9kmysNPphBAPHTc63g8lSRgzvhNjmc1mlcvltLm5GWQDxtJ60DZM53PRowlJM5mMCoVCKPXDS35bSRrXQsjK99j5Ho1G6nQ6YZ63traCUdza2tLe3p52dnZULBaDps+cE/qy/q3Wnc/nAxFPp1M1m81QgQI5kMtoNpsaj8dKpVLhWohGqKBAlmEsrFcLiUVJKZprsNVEEK0lNUkhKTUYDELegO/5rvtt3dqxpWJ8JoZrHblHuWMdt+DYsH4xLHEhNk+XMg/rmSB4b25uajwehwGxWXBCIxvu8r6tra2g+0n3mijfy6SzKNcR+NsWBotxNBrp9vZWFxcXyuVyIUMMcbHZIAuIGauMdiZp5V4gdP6f/+N6sciLxWIlqWTHkM8dDAZBax6PxxqNRlosFsrn8yoUCiHMxzjZigm8aUidz7VZ7ul0qn6/HyQNstrMI4TOfWBA1iX6rFfNd0C41shBchCANYLAXh9yFZ+DsbL1q9Fw3xoESxQ2JLfXSWIXz3k6nQYtGeNh1y0elw3/bU0rpAhZET632221Wq1gREigtdvtMB7sK5s8hFgw9oy/1fAZQ7sW+beNHqvVqo6OjjSZTELlCh61pFBZcH5+rpubm/D/76oCeMiTtGvERoK2ssDyBONq9wJzxffw/9lsNnANjs1wOAx6seWNvxhPdzqdqtVqBWKSXmcUt7a2VC6XtVwuNRwOV7wMO7jRSSgWi6pWqyHEs3XAVh8C0VBIuieIh4iXRUjyAt10f38/hL4kTfCmMpmMSqWSNjc3AwFy3VYmsRvaXpd92d+HbFl81pghZ+AF42VnMplQbbCzs7Oi36Ipo2tRgyspfAYv5CCkhOFwGMiDkJ4NzoaxiREb0tr5gAyszIGxYUPYcBL5wRIFn428QQIJj5n7Yk0xH6yTqGfEBrdaPNfKHJJLsNo8FQTlcnll7Lhnfs9WXvDC2A0GA9XrdTWbzTB/VGbgWTPmtqrB1nk/tJas4Wdt8/usLZyW+Xyuzc1NHR4eBo0cnR1ng/rlq6srPX/+XJeXl6Fk085PFNbrtHvQIqrvWokPaUu6LzMl4rT6LuTLntnc3FS5XA5VClRoUIXzXUvd/hTETrrlclnz+TxshM3NzdBIwCbDK2AjWcE8mXxdYlatVnV4eBiSVZQeTSaTFbKOkraklc+yCzUKvJpWqxXqO0la7OzshJCdUirpnkyoLCDDanVNdMN1iRdbzI33t1gstLm5GRoPIBnIFWMzHA5D1n42m4UIgiJ2FmI0y0vyyn42lRXICN1uN3i3NmlkpR3r2UmrHV7253bjQXCE9aPRKGidEDufZZNy/L5NfiFX4Rky75Rska2OygY2qQa5Rz12fm49WO7JGgTGiUYCPDEMmdVvrTHAuF9eXr5R/pjP50PCDEOVy+VCcpXkKyVVeMesYRttsQ8gK7su8dwh3f39fdVqtRXvX1KQQer1ul69eqUXL16EKgdbjfQQLOFG96V9JRKvG1FKpVIo9UPSkbRSSmrfYxO4SBS2ocUaMKQ51lkciLVOl6SPFdwJxyuVSgiT+v2+Wq1WqK+02heWb3d3V48ePVK1WlUikQjWr9frrbTq2pfFuxYGixDSxSNDzoBs+H+8Ed6LF7SubtV6u7Zt2NYV2yx+IpFYWXiLxeuaVTpxCPnR1iAgogr0P7w/rsNqnLZbKapT23vi9y0Z8TvcE5vJhu6Mh90cjAsvfsY4RDVzogjkkslkEgyD/W7IxMoI0QiC77PaYTS8tdf30D3aVlS+xxIZY0aCjLnFMONxUerW6XRCwT+GhqQiFR35fF7ValVbW1tKJpNBjuh0OsHI4Knb7+V6iZxs2RhzZDV+25xBFMWr3W7r5uZG5+fnoYQN0n1XyZiNMKO/Z/+PPVCpVLS3t6dSqaTlchmciGazGeQ3u89oYKlUKkFzt5IWDgfaNc0xcSH25giy37PZLJBVuVwOiYf5fB5KngaDQShTssmmzc1NlUol1Wq1YIkTiYR6vd7KQnmIcO2/37Y4kDoo7SGzvrW1FTyNzc3N4PGy0O0ms2cn8HebPcdrwbuCACF46V7CoBlkPp8H/RLCIXQlOgAQVbVaVblcVqFQeENXlRS8asrhMFy2bdp65xhBm6Th3i1hoS/imVu9kXvHOGBcbSWE9SKpqKjVaspmsyGjD3kQVfDZVsejVE66Jxc+m3uP1rtawuf+mFfCfJt0PDk50cnJiQ4PD0PdJ3qzXYfkI2yTQrVaVaFQCKGv1VtHo1HohrONGuVyWalUKlQyJBKvKy1wDJhDO3+2uYSowCaB+Tvvt8YGB6jX6+nu7k7X19ehrKzZbAYd/m3R49v2nk1wsjYpA6QNnL1NN6o1lCCbzWp7e1uPHz9WrVZTKpUKCTMiKAiX/3/bNf/QiPXsBbwxW2sJcdF4kEwmQ7kTlox+emuJJa14LPwc4iFM+lMAieBR0aqJp86CgBzXZW0hYDYr5Ig3QbswYTNWulwuhxpbivMhfIwWZGI3DhstnU6rVCppb29Px8fH2t/fD91RfLfVvSzxJJPJ8F1kwtFb2bCWsGxlRFTPlRTCX0lhs9PvXiwWVSgUlMvlQkUGnieaKA0iOzs7Ojg4CF11k8kk1IzmcrmgKRKWFotFlUolFYvFsPEwjJJC8wkhLNdhJSDuh/8bDAYr3j41qh9++KE++eST0CQAQbImuSeujXFlrGgwODs7U6vVCt6bnVs+A9KmRjyZTIY1P51OVSgUgiODQcOYQTKMk9XUmUOI1nrBkC5dl1dXVzo/Pw812uzr79OEFN1zjHW04sRGHvxuNMlOE8fBwYGePn2qnZ0dLRavuxLhICpwaJrp9Xp/maRLUb8tak4kEtra2goLsVwuh01/d3eni4sL1ev1oPWygGmuQEeVpGazGUja9u+vkxHWZawfWijWc+31esHC5vP54HlFPTtJgcyk+2QPG5lrYyOxifg7IRCbFeKxIasNwTBceOaQ6P7+vh4/fqyjoyOVy+Ugi3C/hNd4rVFvzxan93o9XV5eBs/NhuaUpHGNNtsPkbJh2dCZTCbUtVYqldDQYj0sMtXISYeHh0HHh3Tp+8/n87q9vQ1toVYLhJxGo1FIxlhZxYaxlJnZXIIlXJJmaOSSwiY/OTkJZyEwFnje0n0y1ZIc81Uul3V0dKSTkxO1Wi1J95l6Gw1Y42ZlECp6aNemXZmuOKIfSrvIfeDhWr3clmFauQmJgRwH7d9Er1GN9m37zu4vCzxydGNkRjrzlsvX3Zh0tFouIQ9SLpe1t7envb09VavVlSjLdoLCRUgicSGWkjH+hHRJglD8bS2aXUB4cjYR0+/3lUqlQp86kkKn09Ht7W0Ic6JlQVZjtIkTq7s9RNBcP8TbarXCASd4H3YT2VBtY2MjhKI2vJW0QrhbW1vBC0MiIcwnfMR7R4fiGiSFWk82JZtve3s7EDr3YY0Bv095l/VQIQg8Mmoye73eyrxymhe97YyBdH++QXSMkQzw2CBRiNGSAR77o0ePggEhKcYBRLZOdTKZrCTpMCbpdDrkDAaDQZgL60mRUJS0Uo5EEmy5XIZ7wrOixpOxskeAMrb28KN1ITGyAZ1dJDOZW1tZQk22pJBopTNsa2tLh4eHOj4+1vb29orEISm00EJUfDdSjG3ZtrW5koJDROTXarVC6/e7SMtqyNF9Z40Hc8Je48iAjY2NkK+4vLzUixcvwnkfGDbyDLY0zMpbSArIlpCtPY3sT/HS3xexyguSAunivWWzWTUajdDBUywWQwYeT8Rm90nIMUGj0UjFYjEUO1POFLW8EC4bgMVlWzWjZTeSVrxXmwSDHPP5fNB4bcbYJm34fTYSITREi5eBV2lLeCSFvn7IotVq6e7uLtRssqEwUFR4EFZSEYDnKa02KLBYbWealWs6nY6ur69DKElXG0RgZRabrMGr4IWeyBhFv8c2XnS73TAneNGUCVKxQla+2+0GQo2WsxE+2+/k77YygrGFpOyZxRgRSJBKBJwEtM1OpxOIzkY/GFOkMNYE18O6oM4Vrdtq/NJ9S7MdJ0774mhNvNxarRbKo1j7yClU5XD9zBv3HT0rQ1KIaEhMMc/sCWtso3vISiN40Oy5aMsw64FIj5b3bDYbGpQuLy91dXWlTqcTSgBtow1rHq2WIx/p4MTLtUm/dyXWf0jERrqQH2GNdF+Og4ewXC5Dx5rVcu0Lr4P3s5jwRKPZZAsICV2MxB2eSjR0txl2673xGSxCW80g3SdqrCzB4SFXV1fq9XohdN/f31+5NkDozaKE/C4vL1fOEIX8CcshQrwb2nUJ39k06zwa64XQmNBqtXR2dqYXL16E8J0ohTFAl4X42CwYmHa7HUI5tES64ui4k143RDQajRUPCq3Z6q+QMNeIFAERjcfjkBBkTigzsqeKseFsayjXRplWPp8PHt5oNAr/lu7PiL69vdXLly/1+PHjUBdtJRY8bus42LJI7rter4fyJfYG69LW+eKR12o1HR0d6fDwUAcHB0H3rlQqK2drsB7Yf81mU+fn5+GoUE4Gs1UiUdLlO235mk3Gsk+sBs77GAtkEuaLMUwm3yzdjBpvGnM4pa7dbocGIL6XFxIIMk6329XV1VVwVCBde/ZyHB4uiJ10sWqEG/1+X+12O4SwHPqBuG1LfexnYfG73W4YXOm+ZRGLG5UW+G6sriXTdddsQx88Q15WF7Yva2BarZZevXqlZ8+eBeIaj8fhcA17IpkNv2x2n8x+vV7XixcvdHZ2FkJEsueUIHEvkBL6G5oy+uHm5mYgfts0YWWA4XCoer2us7Mz3d3drWTsOXGKWs7Nzc3gseENQ3Bo7Na7s5odRGJ/10YetpSOa4XE7HVHoxXC4FQqFXR0vG57EhUE2O/3Jb2OnpA9JAUjXavVVpLBSAyclXB2dhbID4953dhicGg8ob338vIylC9ZOYLrY71zmh5jR6042j5GwkZohNH1el0vX77Us2fPwr7b3d3Vhx9+qOFwqMePHwcvP0qk9sU+iza/vC0/Yt+DPmz3T1QCtNUTJKKRBVgPwPLEfP76pL27u7sQCUG49nhQW5v7F026bDosPjper9cLVpBJJDNOkb/NjNqNQsbSemDRonx7DSwMNijVFOu6aSB+m5XHi8T6TiaTlUNx+Ox+v6/z83N9/vnn+uKLL3R6ehr0UI7G29/f1/7+ftik9pp5jcdjNRoNnZ6ehtPD6CZbLBaBIGxJmdV/OVEplUqpUCiEZhQ8dGvQbIUI78XDRRKxTyl49OiRKpVKuN/b21vd3d2taH1EInbs0SYhAxuC2/AVrZGf2/InK/VYL5hw1nqoSE6E5tFTw5B/uG/mlgoCEm1cVyqVCk/zQHrgSSKHh4ehnMt6i4yvlRkgwqurq5VxRqvms5fLZaiDxTHp9/taLpcriUYb8tu/c+g/T8t4/vx58OwvLi6ChMUcI5HYfct+Y1xYU5Zwo9ULzBlaMFUq9rOjXjFG1SZj7ZxYg2kJmsgEvZtKG9rWIVsiDbvX/yJJ18JOZLR+1WZ37YJdLBbh6EQGHKmBRA1kjTW1m1168xlfthD/oVIXK/jzGZBGs9lUo9FYOTvX6rGEnq9evdLp6WkgS6w5m61er4cWY8gFT7Ddbuvq6kovXrwIn0HyAI8hn89rb29PT548CYXjNqzn0BR+D4/RGjg7RnjFPHmCEj48vkqlopOTEz1+/Fg7OztKJBJB90X6sFqbBZtXUtD0xuNxOMgeY2CTNxS5Q4zoqbbwn/MvSMjZuUKG4BwHe5wmei/rinUoKRyIQtKPWmmAt4nzQO3qkydPtLu7uzKewNbCSgolWPV6PRjkYrEYSr44lY+KHrtmZ7OZbm5uVq6NBhrWEXOJrsl6s80UVHwUi0UdHx/r0aNHK8TId+I9tlqtEJpbb/whWS/q4Nj5ZYysVIGHa5OHttvSkrRtmtnd3dX+/n5IHmPU+G5rhLiuaDVFHPhJSNfeKDoXpEb/Om20lBNJrweJWszoYiCDa70lMtrAJkiwou8TYkR1XkIYMsHolUgcdqMRStqCf0sGPBKFR+KQied0/FevXumPf/yjvv3223CkH6TCBi4Wizo5OdHTp0/DM6Tq9bokBaPEiWPUuq7LGtsx5XDt58+fh6TFYrFYqRO1mrjVrO182PNcbYE90o8lkGQyGdqd0YgJmenMg3RtpJRIJFQsFnV0dBRKhGaz+8c7USJEMbwt2WNNcN8YBc5xsN189iwEntnXbreDdn5xcaFKpaKjoyPt7e298Tw3xhgSYo4hQg7P4TO2t7eVzWbD4d3n5+ehicbKKNfX1/rmm29CrTHSBB45ewRvL5q4YhwxRlE5zureNBgwX9G1sw7Wo2W8SXJyLbwX0kXegBvYK8wRHjb16AcHB3ry5ImOjo5CvT/RjLTa5LOOh+JEbKQbLZfBK2QBofkxiNVqValUSrVaLSSBWBQUqNtyEOm+tAYryaJkYinmZ2Pb0rX3mQBb/WC9FXt/NvlGWIp+iuZKJphifMaBBc8mvr6+1unpqV68eKHr6+sQshOaJhKvmwxKpZK2t7fDOa5ID/1+P4TAPEKmVqtpf38/SAzrtPLBYKCrqys9e/ZM3377ra6urkKykVpazoEgu3x5eRlaQe188Gc0Wx2VfKT72l28W7oUIXo8eyIYPofKBowoB66j29Gjj8xgKzggJRsdkWiTFIwVskWpVAreabPZDIfJS68NXC6X0/7+vo6Pj4OEZPVZ6yhwCPzl5aWazWaYS+q/d3Z2glbfarVCk4cN50nUXl9f6+XLl6E9mLllHxAJ0lXJk0DYc0Qa9oS2qJfOXFm91eY23rV32H9IgRA1+9h6ruwxxst2eLK/2Eechvb06VMdHR2Fz6dpw/KLJXe79uMk4NhJF73OZvoJH8iMUzqGhwOB4FXiwVqZwh6kwmIgi8zEUheLRyApeEQUT79L22HhFAqF0PG0bqFai03dIA0MtjWX0NkWykMUNIHwVAYrcUS/gwoQPLL5fB5aS+/u7oIccnFxEZ6wbI89tN/NYSYXFxfhqQZEDHhKLGTK9XjGGRl5YEvQoi++0xbE42WSLcejtg0CNsFiG0M46S2RSISnOfCKhr82dLYePkRt74EDvbPZbChnY2xsrfR0Og1HgN7e3urk5GSldtnKTiQ4SZ5x0psti6MhYDKZrHSORSUhPrPRaIT1QogNKWI4aKfFe55Op2Ff2AaM6Fq2HYTsn06n82ASOrr3iT6pdokmRSFhW/WBpxvNt/B5ELhtwd7b2wvdjDRtwC14vqx5Pou9HxdiI120PTaItHqeJ1lhG7ow+JVKJVh+zhyQ7nvKreZqJxLPlEVHwo2NjIdjvdSHSNeSeXQBrnvUNp6YDaWx3iyWQqEQ3k/Jl000Um9qQ3XCShYx3qT9HozW7u5u8MZshQByBokNS4zcazTjzlhHFz/Gz2pnNrKIbhQ8feup29Im3s98MtY2SUL9LERqz7Kw4WR0Xdix4vejura9X95Hso/68EajEbx6W7Zka06ja4E/bQK42+2uPNa9Wq1qf39/5ckTXAuSCOuY+7MlXtybPX/Depi0mBeLRW1tbWkwGIR5wdvEq4yuBRJVvD/agv420rJRLl61dRZsxQpJUWuQbSmo3Yf2PA6e20ZpIIYWSaTX662cWmebgd7XePxQ+NFJlwGnLbVSqbzRwMBG4QGP1NIxqYRdOzs7ajQaK089sFl+ElCSVs4zsDWC1C8yIRxV+K5SF7s5mXjrfdmFh8fISWCQHosmetRc9Gmx1hNE4yaDTYcbY0rNI1EAdaI8SBHvEY0Lzc+W2NgEIOc82BCZBKAlDfRsFq+dy2hW2m5MPBzCWiISe0ANkkJUf08kVo/AtOEpn8X7CMs5DpADb1h3s9ksELGNDrjuaLKX6IPmHLqjbPSxsbGhSqWi4+NjHRwchLG2DT42Q7+x8fo8aebs8ePHevz4sfb29kIiiO/Fc2fdsdb4DOQYtFC7hizxWj2eMUHHxqMnB0AuxYb969arNV7rdF1bsYKmzudQ6x49PxljY+dLus9jEBWQQLSdiUSvHARPmRmeLvuZ8ZC0cq8/NmLzdHO5nA4ODsKBIhAdBLJcLlcyrDxanRIoyKdWq4VaOwbRbkBbhYB1RgqAcKlfJYNrN08UtoTFhrXSqrZlCRPJpNPphIO/rXeKnov2Zt9ni+fREPf397WxsaFisRgeWGkTGTxsjy4uK1ewiMmCc0QghiJaB51IvO5Q2t/f10cffaQvv/xSl5eXK0kJSStElE6nV5IwfA4hIPXAtnEDgkdHZBMiu9jH3aAz02Vkw/VoaRTfSZi+WCxCq66tXojWHGPM0IQtcbH57UMuLQlICvNzfHysp0+fam9vL6w3qx3acjYy7kQ9ts2ZSIykJuViiURiJTKi0qFarapWq4UqGMiHkjc8e+63WCyGdWS9QtrLx+NxqBixxG2jEns/tjZ+nfOCseZoV8iWpKn1SMldWA3elvfhjHDSIGe22JzP7e1tSFASUdiqFqILqj0g3TgQq7xA8sqWVbGRCbnq9bpOT0+VSLxulGABLpfLMNBoVdJqOyffgzeFReZoONodmYR+v7+SEY0uFrvQ7CPBrQdmPRkAeUJUbEiuvVar6fDwULu7u6EeEo8DD5mHXBYKhVD3ub29HXQ7NsZyuVzZLDbczWazqlQqQSOl/ZjyNunNig3uOZ/Pa3d3VwcHByqVSqFZxYZ/6GXSvdbLJrWeJmc/oE/axA6SD2fFckgNiSs6zWwyzB64Y6MYZB6bYGGDdrvd8HQNdEy+Hz2cbDob3SbsKKuSFLxk1iDkXSwWtbe3t9LsEvWcIHJK+HZ3dzWfz1UsFrW/v69SqbSSi8Cg0vLNPuC8C466LJVKwZhjiGm5x9FYLu9remmw4PwC5hVtfV2m38pr0WiC+bdyj71na6TJoUhaiT6pJ8YZs52D9jrY56wbDBTaOKWOtsSS/SXdn6lBYjHqTP3YiI10GfR1HgqgfS+TyYTi7+3t7ZUjCW3LazQRIq22G+ON2BCS/2cDs1AeIh/IG28ZDxULTYi/bnPhiZC4yufz4QxgezrWcnlfK2lLyGgOgDwpn4MIWNxUJxBG0T7LvRcKhVB1gBFgUbPBozpaNKnBy5IupGTLrKT7k8+QUOyTjG3VAaV3kBaNMCRbkX46nY7u7u7CKXI8LYASMzw9HgjJ+wmH8ebsaVhECnhQ0eMPLXHgPfEejJhNxlh5I5o85O94yXja6fTrIysxrrbci/2CB0gnGg5FPp/Xzs5OKE+zdeI0qlCNwbm9y+Xreubj42OVSiUdHR2p0Wio0WiEszkI06Owkhrrnz+5H7sm1zVJMO72uXeMPeuS8V7XsGTlCzRd3osj1W63dXd3p9vb29AujPMT1fu5ziix/9iIjXRpiaV4P7ogrSXkaDsW6Hg8DrV3tnia90Y1OFueZAfXholY0XWhEOEHeiMeRKlUCuGvNQZRPZfJzWQy4flpHKy8vb0dit6l+8dK25ZZakvxqCB2jEar1XpD44RECZ+tMSCMovSOpEm/3w8L2ZaPQTxck02GWeNiN4UlXBsZ8LlWp4vq8HjHksJ1oSvSIMLj3u01MTbodr1eL0Q0jG+0qYXrtglL/g2pIGlhWBgTiN6uk2i3GR7mcDgMBpWf2Y6u+XwewlpbssXc4HESYttWaPYAejBzy3swvMwhuQObTDs4OAjSxd3dXahwwWjZvRXNY1QqFW1vb4dEoq1Ft4aYz7DAi+U6KW+0ElG0tDBacYLhIVqxjUTcCyV90UjWykasL4xxXIj1GWntdnsl67zOO6T8JZq0SqfTK09S4GdRTxfpIkq+LFj7/C3bpRJdHBCWTfBQ7lKtVsNDMa0mK61mv5fLZajPJTNtmz1IXpBws+cBo8XZQ8SjNb+2DpbNj+W23hiht702/qSRgc+DaNBQWbDSm00tdtwwUhChfeaaJVsIA8+G32Vd2KcAQ2BsDNvOG/1uCK/VaoUqBwjNEiqRA2sFowO54nWjwbNObDLHkm10THAcuH48Pnvewmw2Cx4jYxYlb6sd26gDuYBxs/cJ6UbXD7kQTh6zxDoajYLs1Ww2V8aDXADr21bG1Gq10O7N6Ww2OZdKrT4e3Ua3VINgEOwawcO1Bjm6/qxTJN1HIq1Wa6V+2o6/hTXww+EwNLjEhViPdmSQ8T5sQgRYS4fnhkUkBLQel12sFlHpgcHn4BD72JKHvF1bAA7R26ynJTs+B6+Vp+cOh8NwdCPkR5jP9w6HQ93d3eny8jKEeuhvLHy+y9as5vP5ED7aoyWjWX1b20qYazP59skckB96Z/RxN1xztNDcemu2+oAxt6EkLwgB3Z41wnPPbIUA8853ROcKTY8DkChvQutEK4XsbGeflUrQo/GWM5lMIEtrgCzh8x5bGcAY8aetXyY3wLxESYfvsaWGzDn1yNb4QpQQCBU+JEV5pFU+nw+fZ+t92VtWQ6YszDZ3QP6sC6Ipm2jmtS5ct1IUY2q/m7G1hPsQbDWMjWBtFGvny64V25SFkfqLkhesBxjVfaIWzBIlv0fdoT156l2hAN/JorK1oVZXsq25FkwMi53FhScOCVjZgr/bx5lcXl6q3++rWCyGQ2Hsd9j3NJtN3dzcqF6vB9KlgsN6jiRsyuVyWGxsYruYogktu8GZC6ut2VASr7BWq4XH41BMbytGovXHtgwuWkfL79INhURTqVSUzWaDbEIVBlUftsoA4rH3auUijIdtkuD3q9Vq+P9WqxXkCnvkJHo1Ug5lbdJq/Ws0vI2eusYZt9HuMWuwqeggMoNwmRvr5ZHwIdLLZlefKwhxIhXc3t6GGtx8Pq/Z7PWjnba3t1eSncA2bHCusJW1SApG175da9YhYK9GHSp7n9GEqNXI3weQNPdjOSLKMdZARq+F+uooOf+YiPXsBQZhnXdp9RYbBlvSJVywi3/dYFlZAh0Lfc0unofKW7DmZIdZ3ExQVGvDspP0oVzl+vo6hKfb29thoZGQQgqwGiqeHpuPJBp1k5Z0qXAghI7qiTaMtqVtUQ2aTPI67e7w8FD7+/vhFKqo3oaBim48wkv0V37OEYmUR/GwQXvEI+NrtVvpddkhWjr6Nnpv9GATpAHp9Vm6PLpmuXx9WhdP0mXjsi4heOYUD9IaGuuFEUmUy2Xt7+/r4OAgGBLmhTGCPK0hZw4krejtGBM0bAwAkUeUdImwbKUD87K5ublSKmcNJsbXPhGl3++H+l/kM7xRq8eynqnEsA0o0SSY3edW1iNCouKCiC2aPwA2R8MaSaVSb5BuVDa0hsZGwA9Fuj8mYmuOkFa7dPj3OqGcjWC9XWoU6TW3PfTR72ORW++rWCxqPr9/CkE0AWdBFvfx48c6Pj4Op/izgWwSAP2KRdPpdII+S5mVJSq7cS0ZEZbZzHfUo7TlUHhLeOycro8HZfXVdYkwW1sp3T/5ONotxkMO2VT8LvfC9TIWeJqcAcw9YGz4TOvpcq+cf2rHhu9g49MIYCtR6BTj963WD9GhxXOPRBc0eHB99shL7ie6oTEEjKVtULAVCHYtQ5qMe3Qv2NDbJg+vr691e3sbWqy5H+QlSJd7jhJJdG3ZdZVIrLbZYiRns1lYw7YaZLlcrhhEjCT7K5l8fYymrRiJRqXRZJgtKeS0uYfOfgBWFmC94xDYe1knU0STn/x73ff8WIi1ThcLb0l2XZaTBQrhYF2ZdBJhNlSyk2S1TBuWYvWjExudFKoOPvzwQ33wwQdKp9MhAbdcrj7DzJIonglJHwgo+rw3q0dDJNVqNRzOQV0lXi6kZ0u+rHeLl2zHhAXHONiuOftivHmCAwkYkhN4m3ax2o3D5rXJMgwDGwuvE08I8uE1n89XjID9eVSXtvNmq1lsKywG136O1eTt/yG/2EoVm9yxpMnLjoGFlTuoHbXefvSp0VZ6gEh4+ClPCHn58qWurq7C2sAB4NrRZtGtqcumDpdOzuhBONY5YY3m8/lwmpxtF4cUrTGT7o0NxIuUsbGxEaQie0YK986c2SYJ9PqoFMac2UiYMeX5iMhT1otnTKPjbdeL5Ye/yDpdWiRJaEQTWtHyrahmiidlT89nIqyHZpsZkASk+8OjWShbW1vBE7XWOCpNoFFC1ovFQuVyeaVGV7q3vu12e6X1l0SO7YhjEbHYq9VqKNgvl8uhU4jTttg0dN3gobDBkSNsHSeLKyqv2PuEfDAYeHt4LDyRmeyuJTpbM2nJAwLgevHcaKIgNEb6sITGOrHJMsaJe1iXTc/n8+FAIK6Tz4iGu4wBffuz2Sw0FNgyN3uildW7rcHkxXy0Wi1dXFwol8tpMBiE9cJcovNG67pZ5/aQo8vLy3ACGTo/nmq0pdyuQxyGyWQS6sRrtVp4Mi7G31bncI4I997pdNTpdNRoNFStVsOTMGz0VCgUgjSUTCZDAhGDY8fGrjv0ckja5hkYV4iQKNfKgHweBgrytsbRGkRLuBgpmxugIQaDHQdi+6ZsNhuaBNgAECgPi7OHSzOIEAuLlMOMoycGWc/WltPMZjM1Go0wsJyOT1lRdFKZ0MvLS33++eeaTCY6Pj4OjREkJCj/ond7NBqp2WzqxYsX+uqrr3RzcxMW09HR0RtdbJLCRrHdY/apBsgjbFabjCQJZh+whwdEVcD29nb4HMg0WupE4welWVRP3N3d6fz8XKenp+FYSen+ABrG3cojECaF95VKJXQaMcYYApogpPskFVFEoVAImh3XiI7N5mezkmwk7Lbv4SnDy+UyjFsqlQr/X6vVwprDCKHbN5vNcEYA3rmVOrgP5rLb7erly5fBWPE0Xgwu6wevys6DNZSLxSJIH5QQEjlB2MwViSBCdB49jgZsk3xcA80qVk+2Gnar1dL19bWWy2U4PJ1rZ97IMSyXr0siMdjNZlNnZ2f66quvdHV1tdbLJeJFruKgf2QiyukoraROP5rHIeK1pWnRM13sd2Io7IE9rCO83rgQ69kLjx8/1u7u7krIyeHPFxcXajQagUCiNX5McjTjjqWrVqva29sLTRTWwzo/P9fNzU2QBfACWDBRCzkej3V3d6fBYKDr62t98skn+qu/+it99NFHOjg40MHBQUiw2Z798/Nz/e53v9MXX3wRWm57vZ6q1ap+9rOfBS+cjYrngCdXrVbfWDBgNpuFUioMEcbHtqvaUhkSPEQXUQ8Lr6VcLgdCv7291bfffquvvvpKL1++VKPRCAmpVOr+cT32e0g+Ed7u7++HA6WpZbbfzfxyFi/6PN5muVwOEos9HAjCsLXRtoffPnSSum7bcCAphOHWc2ON2WQoD26kyoFrkVafsYfMQ4ng1dWVTk9P9eTJE3366af65JNPdHR0pJ2dnZAAjJY5YlwpcbPdcdHEkE2WUoKGgbHeGmQHUdtSPdt9iNEcj8fhDOWLiwvN53Odn59rPB6HM5ht+68tDzs/P9erV6/05Zdf6tmzZyHpaiMLjBr7Dgfo6upq5V6l+8QnyTsMASWYrB+qfIj+bHkp/GErcY6OjsKaRKojz4P0EwdiP2WMlkXCRxJAWHc2M+Ecwj0HRfNzNimE++tf/1q/+tWvQpXA8+fP9cUXX4QkBB4fIYVNbq0T+/k5xd/JZFKPHz8OoRoZalvr+OzZM/3ud7/T6elp0DB59tunn36qjz/+OHiI0YRWtHaX6+Ba7AE/1AFTy8qiQx+VXm/EnZ2dcKDKQ+GT9bJns5nOzs50cXERnumGNo23idfDYmcu7EE+29vbOjo60qNHj94g/Ci5SQp6pdWDc7ncSqkUHj+6OF4hxgKpyurQjDW/R9stT3WwxsDqqu12W8vlUvV6PXiHaKS2OYHkKW261MYSGZRKJf3yl78Mxw7ynnV7JJ1Oh5PhdnZ2Quuurd9lPXQ6neABo/GTXF2X+bdevPWybSXB9fW1vvjiC3399ddqNpuaz+e6ubnRaDTSo0eP9Nd//dcrZ/qynsbjsV68eKGvv/5a//Vf/6Wbm5u1FUU4BTgGlOpRL45nXiwW9ejRI/385z/X06dPlc1mdXd3p9///vf6z//8z5VTAUlwwg382+Z6bJLzo48+0kcffaRSqSRJYb2QW7F89WMiNk83k8mEpxvQZ55MJkM4fXl5GUqs8B6k+6QI4O+Ey4VCQScnJ/rNb36jzz77TMViMTwB9vT0NJADHhmb24YdIBoKATqdEonEG+GJdP9sLZ49ZbPbo9EoHCxtw0gWviWGaGKGUIrvQc+0GihejM1qs8h2d3cf7KW3QNvj3mazWciWR/U4tDyyzWS7U6lUSPrVarXQsQfhW+8OnZaxbTQaYTPb1mF7HoM9wHxd9xZ6f1R+InxEFuFQHasn2s9irHu9XrgfSs1I8JIvIBwfDAYr98Y5D9PpNMgieOdvw8bG/RGmnA1LuReGw+YQbJ7Cdqaty8ZHk83cL8aLOnHqe1mTtNbaBK70et3O5/NAYM1mU6PRaCVMX7efbCmdrYLA46Za5pNPPtHf/M3faGtrS91uV5lMRjc3N8EgRpO7rE97CppN2NVqNZ2cnOjJkyfa2trSYnH/wEqchriQWFcyZfCDFbBRDhPNnEurJUjrssLrFms0ibbOa8ELs5Y3msCwfz4EJpfNu25R4yXZwvZolcK60PJ9YK8TwrUlMfY6bYLMhs/vC9sVtW4e1pU62Z9HqwXeRjSMk9V8190Pf7f/v+777Tg9NC5vK0WKXle0xO+h719nMG3i5rsmaex+eGhcbO21jZy+j6fG3rQncdn9FU3WRe8dj/V9al6j1xn9fZwQm+yz0ZEdj4d4Ifp9Vtqw92CrcL7v3nzbrT74g7hI1+FwOP4H4UHS/UmeBrwO77KQ78JDFv5P/dz3+Y73+b4fQyv6Mb/r+4xbHHrYT4V1HtT7enU/5Pf+kJ/9Q35XHPvsx+KIuOGersPhcPzweJDhf1ARw+FwOBxvh5Ouw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhiRPodP0/EchUOh8PxPwTu6TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgR/z/6UejDAZSB1QAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 63; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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fj0qlgo2NDbx69QqNRgP9/f0YHh5GKBSSSDSfq1eUHgBcLhf6+/sxNTWFzc1N7O7uivDb2dlBMplEOp3G1tYWisWiZEgchhk8Nf+OY0qorTALhELosEXUfQ0GVLgIu327vF73IjXv8SgajYa4RAAgFouh3W6jUqmIeTowMICpqSn09/d3aJjm8/LHzIAJhUIYGRkRzX1hYQEOhwPj4+PweDwoFotYXl7GkydPsLS0hHw+D4/Hg2AwCIfDgUKhgHQ6jWKxCK/Xi6tXryIUCmFgYACBQECUEjPj46i5mc1msbS0JEEpasimhkv4Dim4jpsPJ4EZShS8vC9zzXdv2pzH5rvvHv9arYZCoYBsNotcLge73Q6Hw4FEInGoO+e0sUzoUsuh3wV4vbhpPvVyB/SaFKbZwZfC3btbazoNaK5Sk6GLgRPX7XYjEAiIiwN4rSXk83kRAkd9t7kwGTTL5/NYW1vDysqKmLf021HAM7CRTCaxsrIiPjUG9JgNEovFEI1GJdBRq9VECNH3RUFFGDA5c+YM1tbWUKvVJECZSqVQrVZxcHCAfD5/It+5aR6bUAByLMxUQGo4TA06idBl2iB/n5oLLQnODV6313w76jqmtp5MJsWvzncRCAQwMTGBmZkZxGKxjhQnXpfPxP/SQvD5fIhGo6KtrqysIJVKScZJPp/H5uamxC4ajUaH+V0oFFAulwEAXq8XoVBI1hvXCDkuaNRsNlEoFFAsFjtStuiqMDcoauvZbFbu6bTgWje1csZsellJ5u/1erd0hXAs6JLkBmgVlgrdQqEgWqPH40E4HEYkEsHe3p4sLmLm53YPICeSz+cTTRNAh3/0tPy5NpsNuVwOCwsLOHfuHJxOp1yv2WzC7/fj3Llz2N3dRTqdlqAbd/yjghac0Ob9MqLKQBrNWk40n88n7gFqvZVKBVtbW28EkXw+HxqNBhKJBEqlkvhXmXpHy8D0h5ouHF4LACqVCmq1mmj01ND4HIeNd3fgxvwc/56fodnL69Fi6Q6e9YJaPDcTLn5+nyl4uwWDGWQ6SuPl5xqNRodLgEG8vr4+uN1ueL1eSa0zhQM1W9PfzIVfKpWQzWaxt7eHra0tCZRxbLp/l/fKlD76pDmepnba7cc/yq3AuUYXxYULF5DL5RCLxXD27FlJ3eS7r9Vq2N7exsLCAnK53KkFo/geuBFzzTHwZbpout8R36/5HrlxhMNhhMNhmddUKP5XCV2aMczdpJkLvBYKsVgMkUhETOvuQTTNCOBbE9Tv96O/v19SZRio4XecltBttVpIp9N4+PAhRkZG4HA4JH+z0WggHA7jvffeQyKREJ/p9PQ0AoFAx30cZsqaC4Eak9frfaPIgjmL/f39iEajsNlsolGXy2XJHOBiZhpNJpNBq9VCIBCA3++XSUxBRpORWjTwWuDl83kJ7GSzWWSzWRlb/vA9muaf+c7NjbM7t5bPZArc7kCIqdEc9z7N7Bbz+7iZ8Jn52V7jz2cxr2n+O5/T9MWXy2URugCQTqexv7+PfD7f4dOnNlosFkUgOhwOycZZWVnBs2fPMDc3h/39fdls+SzMRvB6vYjFYvD7/Wi328hkMkgmkx1ZAw6HQ4p56Aro3mh6uRhMQRYMBvH+++8jFouhVCohFAphamoK4XBYLAqu6UePHuHRo0cy104D043o8/kQDAbh9XplvGh1UPnge+V7N5+X7ycSiSAej8Pn88k7MTdQXvd3ncVgmaaby+Xw7NkzDA8Pw+l0iqYUDAYlWFAqlVCr1WQw6Czvjoh7PB4kEgmcP38eY2NjsNvtEtGlhnBaQrfRaCCTyeDp06fiP7tw4QLi8bi4Fs6dO4fJyUnRQDweD/r6+mRicLFS4HT7oru1QCbg+/1+SYmJRqO4ePEirly5gsHBQdTrdayuruLFixdYXV1FsViUyiHTx1UsFgFAfLuM1jJQ1mq1EA6H0d/fj1AoBLvdjkqlgnQ6je3tbSSTSWSzWUlF6qVZUIvqDnZS2HYvbPN3TcFMgXtUyt9hwT/zzxTYzPgwBU+34De/s3tj6H4OcyGb2rfpI04mk5KJEI1GZeM4ODjA3t6e+BG5CTYaDezv72NhYQHz8/NYW1uTbB1+t8vlkoBxf38/Lly4gMnJSbhcLiSTSTx79gzz8/NIp9NifZnanKkRmm6B7kwfU8Hx+XwyrynMzJQ8bsrz8/P44osv8OTJE2QymVPTGPke3G434vE4xsfHkUgk0Gq1sL6+LkG17jgBNyq6VNxutygW0WgUwWAQzWZT/N/MaHj//fdP5b5PgmWabiaTwZdffokzZ85IGV61WoXT6cTIyAiGh4el4GFvbw+7u7tvpH5w8jMv8Pr165iengYAbGxsSJAunU6f6v2XSiWsrq7C4/GIO4Mar9frhc/ne2Pymi6BXlFyU0ukNkZh43K5EIlEEI1GxVd49epVfPzxxxIgKZVKmJubk8AOc3u7CwsAyE7OABX/nqlSzWZTAldutxvtdlsWGQNEvSwOU1CZaX+mn5b3YQpG8974OTNg1isQws+ZkXd+tlfWAf/NjLCbWlG3xt0dV+i1cXRvLr00Z7prGGOgVkwhVSgU4PV6xT/KuES1WkUulxM3gXlvfB9OpxPj4+O4c+cOLl26BJ/Ph4ODA5w5cwbBYBDPnj1DoVBAMBhEJBJ5I2fd3NT4Dk2XDseNsQoGMs33X6/XxbJaWlrCb37zG8zOzmJtbU0yRk4Lu92OYDCIkZERXLp0CWNjYwBexykYOKQf23xvtBYHBgYwMDAAv98v89jhcCCVSknMpVwuY2lpCX/6p38q3/O/RtMtFAp4+vSpTDr++Hw+DA4OYmRkBIFAQJL679+/LxOwG5fLhWg0ipGREYyMjAB4PdjJZFJMqtOEaUarq6sIhUIIhULwer0yMWm+mpOTwoC+auYdM/2Jpn0gEOgIUNB1Mjg4iKGhIZl0H374Ib73ve9Jwj3918vLy3j+/DmAbxeMuWBZrXThwgVMT09jYGBAFhXwWiDzXfT19cnPxMQE3n//feTzedTrdWxsbCCfz3fkSnen0PXKteUE5vOb5rpZ0EC/JZ/DzJfld9Evxwg6swnok+tOhmdQzsz35fs079Nms70RJTcFvxnMMTVCUxgHg0GMjY3he9/7Ht577z1MTEwgHA7Lc3q9XskFZVaBmd1y4cIFfPPNN9jd3ZV7IPxzIBDA9PQ0zp8/j4mJCbjdbimi4HzY2tpCMBgUYWNuZlRqCoWCuDj4vvn8HDP+mEKXSlChUMDm5iaePn2Kx48fi6V1mn5RjiuFp7nWq9WqKCS9YCD49u3buHTpErxeLwqFAra2tpBKpZBMJkXhYJFPdxuC3yWWaLrAtylj29vbEgSLRCIYGRlBLBbD+fPnMTw8LOZuKpVCKpWSgAq/i5OHjXLo1zH9jL8Lpzjv/9WrVx2bBjMXuk1p00xtNpvic6aPlM85PDwsmjKDV0NDQzh79qxEssfHxzE+Po5oNCpVcczRHBoaQjQalQR5U6i5XC4kEgl88MEH+OijjzA5OQmPx4N2uy0+wkKhAKfTiXA4LMKf1oTf70coFEIkEsHnn38uwRIGpWj+daf0AN8m4dtsNnlfpjDl73IcWdpcKpXEX2gKPQZBBgYGEI/H4fF4UK1Wsb+/j93dXeRyuY65ctj3m4UQvA8KZn6u3W53BGXNQCA1V/4/x+7cuXP4wz/8Q/zwhz/E+fPnRcDSvUFtlQn53Lg51h999BFWVlawu7srlVfm5tbX14doNIrBwUGZBxSMsVgMExMTmJychMPhQDQaxdmzZzE0NASfz9eRt86ColwuB4fDIRYVfaZmWWy3S6Xdbkva4OPHj3H//n28evVK/P2nCecKlRfTLUfFpbuQhe/K6/Vienoad+/exZUrV2C327G9vS0B5729PWSzWXFnmvd/mpr6YVim6XIxMA2FEzcSiYiPKxqNwuFwoFQqYWpqCvPz81JbzcFgovTi4iISiQQajQb6+vqwvb2NjY0NCUIcF+3+LtCJ//LlS4TDYYyMjGBsbEzSeswoONAZUOIEKRaL2NvbE23T6XSKAKQvanh4GFevXkWlUsHy8rL0O6DWzBp7jiEFS6lUEq3G4XBIkO+TTz7BzMyMCHdTE+aGQc3dDEA5nU5cvHgR9XodyWRSigC4qTF9ztx4+N2MmDNQYS5KCjpmsHDT4AJgcUGr1ZKk/LGxMZw9exbT09NSN8/+EMvLy1hcXMTGxoZUPXIDCwQCiEQicLvdqFarEuzpThNisCoUCokW3e2KYIDRzAOntnjhwgV8//vfx8WLFzsa+lAjZhCTGx61S2p0MzMz+OSTT7CxsYEvv/wSuVxO/MR0C9Etx54cNptNLAhuwk6nE1NTU7h69SqGhobkurRCnE4nSqUSUqmUvGe6IuheMkurTSWCY7C2tobHjx/j1atXSKfTvxMlhz7bTCaDzc1NxGIxVKtVlMtlPHnyBIuLiygUCh3rnJtEIBDA1NQUzpw5I/EPps+Vy2XkcjmJU3S7vazA0t4LAGQHp88sk8lIyWulUpG0m3A4LCWUZlCl1WqhVCpJtVU2m0UikUA2m8Xa2lrPctTTghOBQaZCodARKe8OxpgmNjXyUqkk/SaGh4fFrDebhwSDQUxNTYmvlVHvvb098VMWCgUsLy9jfX1dIuhmYrrH4xH/3/nz52XBmhuDWdbMVBzzMxQoTD2jFmp2kmq32+IO8Xq9HRoK/ZRmZoFplns8HgQCAckGoauA+dBMkRsaGsLNmzdx584dTE1NiXBh/vTy8jKCwSAajYYULnA8mF/KqkG6isyOZGbQln5E+mX5XkqlEpLJpGwgpg+bJalm1okZfDPdOdwQuwN0Pp8P58+fx507d7C+vi5pecC3hSzlcln6BjSbTSmWSafTKJVKCIfDGB0dxeXLlyWDhlYIFQEGeNnEhkoPNwKzaMacz+Y8ZxwhnU7/zhQcvpdsNov19XW43W7s7OwglUrhxYsXMu9NXz7XDzdaBiCZV55KpZDJZJDL5WRzNuexVVgudKlhcLLu7e1hYWEB0WgUwOs+B/v7+6IR9youMAVvs9nE0NBQR6XWSdNWuqPYJ8FMaTLb+HXn43YHihilXlpawqtXrxAIBCTzorvFHCcOI8f0Oc3Pz8Nms6FQKCCZTGJ1dRUbGxvSHYtaEXf7/v5+9Pf3S6FBd7DP1Gi6CyT4rMViEa9evcLc3Jx0UDPNfpvNhkAggJGREUQiEWmXyIofaq18L92ZBsC3ObbcXMzerAxIhcNhjI2NSVkrXUqBQACNRgMvXrxAq9WS7Bdi9jg2r2n6v7kpsNiBlV6RSES0VpqjOzs7IhA4Rul0Gs+fP8fCwgKmp6ffqGwyff6mq4yaLP/sdDrlne3s7Mj300WztbWFr776Cuvr65icnMTg4CACgYD8fiKRwOTkpHQp6y715fsuFApYX1+XwFd/fz8SiYRsAGbKlSl4WYHJlKvv2lTqpOvO3Li3t7exu7uL7e1tUXh6rXM+M9cIs4+ePXuGpaUl7O3tiVuBmUF8H1bxzvrpcgdiFNRme91UZGhoCOVyWbIXDoskUvPa398X0+htCiPMYoC3mTw0jbkoTT+Y+Rlqu6bg+vTTT3Hv3j3s7u4iEokgkUjg9u3bh14jFAphdHQUpVIJ8/PzePXqFVZWVrC1tYVMJiPBCzNrgAUPXq8XHo9HzCm/3y/32p0b271hABABtrKygi+++AIvX758w2dKbTUej2NoaAiBQECyR/L5vLTupEbCH24C7FdB87dYLEozE5rv7XZbTEH6aOnX5ve53W6USiXkcjlZUHwG3mu1WoXD4ZA8WWqq3IzMnFu6XUKhEHw+H0KhEPr6+qRHcLd2VKlU8PLlS3z++ec4e/asuFu6NV6zYMLMQ6bPn1WPfE5q5sz0SSaT2N3dxdzcnKRAjYyMYGpqCufPn8fU1BRGR0fFn9xr7dA19/jxY2SzWWxsbMhzX758GaFQqCPF0ZyTDGxFo1HRot8m0s95zXVxkvJxADI+tDLpejrsGvV6HXt7e5ifn4fX68XOzg5evnwpjanMwwesFLbEcqFr7vTUzvb392Vi7e/vy25spgj1GmTuhJVK5Q1/1FEvtNv0OylmxN3n8x3bgZ75jI8ePcLPfvYz/OpXv8LW1hYajYaUfd69exdTU1M9NXqHwyF+SaaIPX/+vEPjpJvAbOXncrmkWffu7i6WlpbQbDYRj8clgGOmXnUvHE7GfD6Px48f49GjR+LL5DjQ9cD8X5r32WwWu7u7HcGt7k2N2iW1GApJaqmmRtztmuiliXEBd+d1c+OgKQlAFm93/jc3Xzb3oWvB7/dLP92hoSH09/dLMNEM8BQKBTx69AiPHz/G1NSU5Mh2Cy7OIQaI2PBmf38fq6urSCaTYj1wjVBBMRu92Gw2bG1tYXt7G+VyWYQtNdxeMLf766+/xsuXL1EqlbCxsYGdnR1kMhn82Z/9GW7evCkxhF4wqMfUSdN6Ognm54/7Pc5Rs2iHmT9Hfd5u/7ZXMhtAbW5uSr9t0/14WsUcb8M7Pa6Hmgb761JwhEIhqSChy6CXNtput6U9IHMKORFOyttWr5l5tYyC9+pRywWzvLyMf//3f8cvfvELbG9vd5RPzs/P4/Hjx7h+/bq4Abph0HFjYwOLi4tIJpMdlXs0Xf1+v7QTZPpPX18fdnd3JX+TrQJ9Pl9Pd4J5zXa7jf39fTx//lxa+5kCz+fzYXR0FDMzM0gkEh3loJubm2/01uX3UkPlc1HYcQPgxslx6uvrkwosAB2+VDNbgr5h+p2Bb4N5zebrdpXcpLs7VJmaKzXe/f39jkyLoaEhJBIJzMzMoFwuY3Nzs0PjajQaSCaTmJubw/e//33E4/GeWqC5ydlsNlQqFWxubmJhYQFra2vY29uD1+uVYhsGF9nW0Hz3DKh5PB5cvHjxyOb9rVYLuVwOT548wYsXLzoComyTarfbEQqFcPXq1Z7WD7M6OL5v24OW76u7gvEwzAyYSqUiVkqv3zOtw2g0Crfb3bGhdfvi3yXvXOhyolNzYBqL3++XXrGtVgt7e3tvTCoK7UKhIIMO4MS773cxL6jp0ldUKBTEx9g9AavVKhYWFjA7Oyv9Jcw0pFwuJ4KUFUT8Dmp4BwcHePXqFZ48eSJ9dLvv2eVyYWpqCnfu3MHY2Jj4GXd3d5HJZJDJZJBKpdBqtdDf349YLHbsYmk0GtKtjO0YKXD7+vowNDSEmZkZjIyMoNlsigm3trYmC7o7ssxFZwbbqKUykEPN0uPxwOVyIRgMymkNbGpPk5tjxAKba9euIRaLSW4xexrQp8ygT3dgtjufmJ9hkx/6/6jVs6CEgpBCMJ/PY2FhARsbGzhz5oxoaL3g9fL5PF6+fImHDx8ilUoBeN00/9y5c4jFYrDb7djY2MC9e/ekvzKhtZBMJvH06VPcvHlTupKZ6V4Uljs7O1hcXJTgpvl+9vb2MDs7i7t37+LcuXOiqRNzrTEDhNk3b3NSi7nmjlqj5ubEakhmK/T6HVauzczMiIuHZfK0Ft6lS8HknR/BzgnLCRAIBCQvtd1uY3R0FB6PBw8fPsT+/v4bLRu5OOjopxZ1lIvBrMR5G/OC7g6md5XLZdGgejUsZ7CQvtfuSCuAjn6qpp+Z/VEfP36MX/7yl5ibm0OpVOo5YcLhMG7evIkf//jHGB4eRqVSwerqKmZnZ7G6uor9/X1sbGygXq9jaGgIg4ODHZVpve47k8ngwYMHWFhY6DDp6Mf1+XzSp3d/fx/Ly8sd/ZHNYJv5vd3BLPOzzN+enJwUDToYDCIUCiGRSMDhcIhvlhose9gyeMTuZ3t7e1haWsLy8nKHtXSYxWT+mQKJi5ULvVarwe/3S0YHc4UpkMrlMl69eoUHDx7g+vXrR54ewvSrxcVF/OY3v8Hz589Rr9eRSCQk5WtyclLSIXm6RC6Xe+N72Mf5v//7vxGLxXD79m1Eo1HRRCmc6V/vdilx42MQ8zChRIuBPmBmeHADPAlUjjimR61RWkN8b2aTJRMWS127dg13797FxMQEgNdVqiy7NqsH3zXvXOgCnUn1Pp8PY2NjmJmZkXJfh8Mh0eNeJZ8UxDyqA+iMuppwstBcNXMuTwLzRiORiJjph8GAG4sOzIopl8slOaosvTW1wGw2iydPnuAXv/gFvvrqKzF3u7Hb7QiHwzh79qykU1E7CwaDqFQqSKVSEniMxWIYGxuDz+freYYXr//q1Svcu3dP3Bm8b5vNhnK5LEElHk65vb3d0XSH99b9HnoJPEbx6dNlRRyPBjJLVVn2yYXOgpBoNCrNlKhxsjy6l4ZrXp//NbVxzrFWqyVpS43G6763PAuPGpgZlNvZ2cGvf/1r/OAHP0B/f3/PgBY3G+bkPnjwAHt7ezK3gsEghoeHMTQ0JLGKmZkZhMPhngKOmTH37t1DMBiEy+XCtWvXJLWOG53b7UYsFkMoFJLovzkfg8HgsScoMM5Aa/S401G6oSuMCok51sTcFEyLqNc6ZTHE1NQUPvzwQ9y5cwfRaFQUsaWlJbnGu9ZwieVCt3vScJI3Gg0pTQyFQtINKBQKoVqtSmqUGWzh7wOdvj5eh9oQ0Bn84UkM1D5PmmZm+o0GBwclZecwX6wZdGBFFP2Ifr8fIyMjOHPmTEfvVQoSLkgKvsM0AvpBzSAaE+Xj8Tj6+vrQaDSkA9u9e/dw9uxZDA4OSoc2UyhQA1tZWcHq6uobFYFmp7F6vQ6/3y9ukG6fGZ+VHDbGnANmjwK6KPhM/JxZlEBttN3+Nv2N2mkul5Ma+17d60x4n6bANedVrVaTIh12EGNLTfO9MMawurqK5eVl3Lp1S0pxzc80m02xJO7du4etrS1pSs9sEPb1YDYK3S1HZfPs7Ozgq6++Qjwel6o1np7MyrUzZ85gZGRExpfPTiuDmSG9rsNURs59Zjqc1K/LeT8wMACn0ykVl7SkOObUnqkAmO/EFJx09SUSCdy8eRM3b96UqstSqYTd3V3JZjis++DbaOmnheVCt9euz8VD0wX4tsG5y+XCxMQEbt26JTXeuVzujQII0z/FSWNqWhS4PNI9Eokgk8lI5sRJhS6/h1pqr5QxPheFkekWYODL6/ViZGQEExMTUgVFU5BBqZcvX0rn/l73x2fkycpMkGc12uTkJKampqT7Gv16L168wPe+9z1EIpGeGwYXx2EuCLp0+GebzfZGgIv3xwwLjl33wuH74Wd55Pzy8jJisZgsdFbI0fTnO6PpyZ98Po/19XUsLy9jb29P7tPUnrqv3yuo2O1yoPugWCx2FIn0ghvtYYHKZrMpJzVvb293KBtTU1OYmpoSDZ8ph3TbHBav4LzZ29vDy5cvpf8DM2yoLExMTGB4eBgrKytS1MFnZNqdWYLcPU5er1fyl82DL08ChTs3hUgkIkemM63QtKj4XKb/2QxCulwuhEIhXLhwAbdu3cLExIRk7TAjxmzq1Msf/DaBwNPCMqHLCciF160R0ae0u7srxRF8qcFgEBcvXsTdu3elobhZeWZqJcQUusx7pMBlaSDrsU9aUWMuWr70wyZdq9XqiP6bE9nhcEg/YFZjmdoFyxaZvE4T0exOxvux2V731V1ZWUEymUQ8Hpdjw8+cOYMPPvhAWv4Vi0UJAh72zDbb6xzVa9eu4Q/+4A+wsrLSM2LMjZK+6MPMN7MwwPxdcyz47PxcuVyWoE+r1RL/JK2Bbs0RgKSMsTft1taW+C/NACtNVfP61CY5l7rHxrTGeJ1e42ez2aQP7dWrV4/06fK72HbQ7/fjypUr+PDDD+XoJZvNJoGy1dVV5PP5nil+fAbOk0KhIOuD90W3WiwWk2Y4bMVIBWFnZwfz8/P48MMPEQgE3tg0OJZm57nu93EU9MHzJOVQKCQZS6lU6o1z5Uz/unktU+CeO3cOd+/excWLFxEMBmGz2cSVxv4th8VUTOvobTKeflssE7qc+H19feKbNaFZvb6+jrm5OQwODgKA7PgDAwN47733pFyYuafd5h3LbbmAmSLV39+P0dFRDA0NweVyYX9/X45cOamvh5OXC69bG+i+j/X1dczPz3f0GeWEiUQiMvnNKLOZl8ouZBS2/Hsuel67UChgZWUF6+vrmJ6eFn9tIpHArVu3pKfu5uam9C8+LMeYk3FiYgJ/9Ed/hNnZ2UNLqymI+N9eGiQLNRwOh+RZ8lmZDcE0MZfLJcK1WCxic3MT1WpVUvKYkcDgFYN67GMAQNK9yuWyWBQMqtJVQO3JtFz6+vo6gi3dAT9q9L18kOZ7PXv2LD755BNMTEwcmZLn9XoxNDSE0dFRhEIhjI2N4eOPP8bNmzeRSCTg8XikQc/6+rpopgA6GuiY7SFpFbjdblkb5mZomvehUAgul6sjbS+TyeD58+dYX1+Xhv3d85vClnOx1yZwGNSmd3Z2EI/HMTo6ipGREZkHrBbj++0WlPwOlnePjY3hzp07uH37NgYGBiS4zRzd+fl5rK+vd7jITOj+q1QqR8ZmTpvfudDlC+Hkps+xF7VaDclkEvfv34fNZsONGzcwNTWFWCwmg3zr1i05K4qTsBszB5NaJc0Zp9OJg4MDbG5uIpPJnLg7Ehu0mG3wTPdFN/V6Hfv7+xJt5iZAX+61a9fk+BNTu6pWqyiVSrDZbEgkEpienhZtkicp7+3tdTSRoVm5ubmJYrGIeDwuE2pwcBDvv/8+7HY7FhYWYLfbJa3oqM3G4/Hg/PnzuHjxIubm5g4NQpnlsGbxBLUiBob4me62itTQWE7NptOsKmLGCk175kbz/dKKYcWdeTApU7ZYg8/N3jRhmQsaCAQAfKsNm0pBt7bVa9xoIVy4cAEXLlx4I+Wq1/iOj4/j+vXraLVaOHfuHG7fvo2hoaGO5kHsc7C3tyfvm88dDoeRSCQ6+gyw0TnwbSGIaaH5/X6cPXsWN27cQLValcNF+Uz0qTP/t/sZTeuERxR5PB7R3I+DrjC2oGRlJ1P7uCkftrkRr9eL8+fP49atW3I8Frv4LS8v49GjR/jNb36DnZ2dQ9c43R3mScBWuBss7TLWyxne/ZlisSglp6lUCjdv3sTly5cxOjoqR1IPDw/3LEgwYc4vd0b6x3K5HJLJpFSnnETLNaO7sVhMAh1HZS+0220JisTjcdTrdcm/vHHjBq5du4apqSk5c4oR9nw+L0GO/v5+OUonFAqh1XrdNf/hw4dYWFgQDZP5k2yCbWYP9PX1YXh4GLdu3cLAwACq1SoGBgZkjI7yyTE17Ki0IFPomoEQapnM8qCv3oz0m/9varx8Hm4s1JDpCup2T5jaKhsmmX5kan68LoWoGWjl/drtdtHqqQWaFtRh0LqilXEYZrbG0NCQvCNaYUxHpNVTLpclH9y8V6bWMTeXed/FYlEaFKXTaXi9XslFbrVe9wthA/SJiQk8evQIL1++RC6Xk+yGo+6f2QvmOuBGeJLCA/rtU6mUHKRqbr6cS8e5/DiveeoMi0yePXuGR48e4fnz59JY/agAbnccwgos66dLR/9xCcr1eh25XA5LS0sd5igr1LjAaGIddV0KArOyh30d2JD7JFATSiQSGBsbw/T0NMbGxhAMBg9diA6HA/39/bh+/boIzgsXLuDq1asYHx+X4BCzJxjESKVSctwLE75HRkaQSCTQbrcRj8eRyWSws7MjGjHplRpDk3N8fByxWAy1Wk0sAAZwzGbVHLtG43VLRvb+NTGDHPw8/94sn41Go3JOWKFQEGFm9sjgjxkkogvFPLbJTPkyNU5TkHIcTY2ZG69papsxAApZ5lpHIhHZBOgLNAV2d3CpO5h7cHAgZcLd1W68D477yMiICFqWlZubeC+BQBcX2xcyYm+z2bC3tyenBfMIoFKphEQiIX0kPB4PRkZGMDAwgEuXLuGDDz7A06dP8eLFC6lapKneC7vdLg3bp6enJQeaGUAnKZSgcrGzs4NWqyV9Jsz3e5wQNF1H7OHy/Plz3Lt3D8+fP8f29vaxjdX5Psw0RyuEr2WaLh/quOYyXID0/Tidr88H6+/vl/4MJ03xMDMjOLDUCE/ako4mCLt2MeNgYGDgjXaJ5nUBIBQK4fLlyxgfH5fcY1bZcUxKpRIODg6kJyyb2TDPlidVMJLfaDTkrLP9/X3RYszAhBnhNTVBahZmrjMFoanxUsvLZrMdm1O3kOkWBhR2gUAA8Xi8w53D8aZAo4ZjXtPsM0BzlRvJYVaSGUgzMxtqtZr4/ih4zUwJvlvOEZYH09fJbAmesnCUssDvM4UuK+W6NzIKAVovtCJ6ZU/QZ93dL8PpfH2E0/DwMMbGxjAwMCDPsLOzIw2HlpeXEY1GMTY2hjNnzmBqagr9/f1ivbAp+tTUFC5evCg+f7qDumMWFPherxeDg4MYHx9HKpWSg0vL5XKH++gweK+5XE5SKc2q1JMqQ2bRx+bmpjSFYqzmqBav5ibIe7IKy1s7Hid0+Vm+mGw2i62tLezs7CAQCKBWq3WY0EfBhU3NjkEwU3s5DgYr2ESaPkrTtWBOzu5gmM/nQzgcxsTEhJx4YC6wZrOJZDKJr7/+Go8fP8bOzo5U/LAMdGhoSIR8OBzG4OAgYrGYRGbZPIe5zQyAmD7n7og/BZQ5yfl7HBfmbprCo5crggKXHajGxsakVSAb2pgNenhfPN2WPkwWdZinGndHnI/yuZm+fD6fzWaTFpxerxeVSkV8nabGzI5eDsfrs+/6+/uRSqWwsbEh6Xi9tCZzXGj2m5kQ/H5znM1Kru73wnfDDdPn84nfls3vad4zV5a9iHO5nBxWmk6nRYscGhpCNpuVk4TNYhNmNExOTgL4NquCPt3ujZHzhJs8Wz3S+jxJlSefkdfi+nyboBxT+Khps+UjO7Wd1NVhpsxZxTtp7XiSl0INlV2f9vf3kUgk5Jynk2qpzH9MJBKiKbMPwXF0R7c5Ubs1L1NQUeDmcjk5FoSNok2Bw89zwrx48UIyHegCyGQyaLfbSCQSInQZHBsZGUEymQQAKSLh2WGmsD0s2Gfej+kmMINgg4ODmJmZQSQSeWMi9zJ5Q6EQzpw5g0uXLiESiciJq9yAAEiGQigUkiYyNpsN6XRagjpmNkR30Mt0h3DRmH7ebsFF3/rw8DBisRja7TZSqRR2dnY60qpo9rtcLgwNDSEWiyGbzcLtduPly5cdG4H53OY8iUQiclpBt5ZrjnkvgcvPmeNr+onpHgAgp5YMDAygr69PYhXLy8uYm5uTUxVardf9EXK5HPr6+jAzM4OZmZk33hsFHuctg3RmD4duN4m5jimEuamdRLHi7/r9fsTjcfFfZ7PZY/Pmee18Po9cLgen0ykFNd3n5R13D1YKW/JOyoBP8qB8sbVaTU5b4JlpJ806oO9rfHwcFy9elEW4ubl5ovtkIGx4eBiJREICFEyQN7UB3jMbc2xtbWFxcVHaMLIxjKkVm/nJhUJBmnfz2dh0Znl5GWfPnpXKNaab8VRgm+11FyYedcO81qMCP1xM3X/He3M6nUgkErh69SqmpqbEt3tYGg/Ha2RkBGfPnhUNbHNzU34PgGyC58+fx40bNzA6OopyuYz5+XkUCgVks9k3vpfv0twAaZbS79/tKzYFYigUwtmzZ3Hx4kV4vV5sbGzg8ePHePnyJbLZbIe/2OPxYGhoCGNjY+IqWFtbe+N++P9mscvU1BSuXbuGeDz+hnXQHfk/SqOjUGGFHsurmQLJDYuHM9ZqNaRSKSwvL8uRSmZP4VKpJHnfNP9NK41uLp7Pls/nRYjS3dKtVLAzWb1el8Nl+WwMUh9Fu/26LHl4eBgXL16U7+dGfRw8yieZTKKvrw/ZbLajC9lJZcy74Pei90IvOPG4A1IToq/3sNw7QqESiURw9uxZXL16FQBkge/t7R15fafTiYGBAVy7dg0TExOSYmW322USM8mbC4oLJZPJYHV1FUtLS1LkwfvtnhDcGBjlLxQKHf7JarUqC4ZlsVwMPHqlXC5ja2sLjx8/lmY21Iy50A8bo14aDP+eaXoTExPS+Oaod8X7Y4N2lraatfz83oGBAZw5cwaTk5PSvHxhYeHYfElq4aaf9rj3yJMtmKLn8Xiwvb0twpQ4HA45SZfBJB4LdJRWxHc4MTGB0dHRjkq+XuN6nIuk0XjdXjKVSmFubg5PnjyRYg9mO/AgSQYeC4WCzEkze4VBzUgk8kbJuhmIpDLDtpzBYFDKyKmhm26/fD6PUqkEu92ORCKB/v5+acrD+z0uhczv92NqagpXrlwB8G2/3729vTfiB91jVKlUkEwmsbm5Ca/XK0cH9Qom/75heUUaNZaT+lzoLM9ms3A4HBLh79Xi0IRpXvSBDgwMSJoOCy6OmhR9fX04f/48PvroIwwPD8s9MEADvJlEz8m7u7uLtbU1Mf8BvGH2U4D7/X5J/Wk0GqIdV6tVmdCJROKN5tQU8CwWqFQqmJ2dFdPs+vXr4vM7SvBynM2iEnOB2Ww2yX89Ci6ESqUiVYQUXvQLc7yoLZmVXQxcmVpgL+FErYxzqNvN1CvwY1ZR0W1lXptzhX0F+vv7EQwGJTh03AYPQIpAmOdrmuAU2kc1kuEYsnQ8mUxKh7nZ2VmpamTRRHeKHluhJhIJETwej0dyvW/evImJiQk5nJTPbWactNttOaJ9fX0dExMT0pCJc9Z0KzCNMpFIiDtiYmJClKSjjjWnxcO0r3a7LfPVLNroRbvdFjfh6uoqwuGwWAMnFbim1QHgSKvwtLFM6HLR+/1+8bucxPfCnZVVRoyUHvd7nEgs86tWq9LPIRgMyumwh8EFy+gxJ3Gj0ZATGMyG6RRS2WwWCwsLmJ+fx9bWFmKxmHQ148I3o+fMl3Q4HBgcHMTq6iq2t7elwcrAwADOnz+PkZERqZriqbQ8YJFC1yyhbDQaoqWzO9VhQTCOM4UDN6RcLie+T47pYf42aubUdplJce3aNemFnE6nAUCshkKhIO+VATczv7bbIgDwRgku74X/zkCaqVVyw2SD8Hw+j3a7LcFRAIjH47h48SKuXr0q7URZkNLtyzWhEtFuvz7scWdnB5OTkyLsOSbU0A/Tcjl/KPCePHmCL774Avfv38fm5mZHJR9zmPl3Ho8Ho6OjeO+99xAIBCTgRqHGk3HZXc4MWnHDoQVQqVTkPZmbpimc6OJgRhGtCbvdjnQ6faJ+DMxyYfc9+v3NdXtUfm2z+bpp0Pr6umjdJynpNzcZZrfQgrAKyyrS6Cuj2cksBGoSvZzvfBlmE236kg6LJPOFsfMWu+UXCgU5L4kVXkeZMMViEd988w2q1Spu3ryJ6elpxONxSRsbHBzsOPyP6UIPHjzAz3/+czx9+hSVSgXj4+NynFB3Pizvkx2lRkZGpGyX2hUFNk3JfD4vJ5syEMSJRi17bm5OTPyhoaGO99BrzOiz48SnqfnkyRN8+umnePHihUxMbp7d9fAMcvF+w+GwBGT6+/tx7do1ER4+nw/xeBzBYFDaQnLxUOibJrmJWR1mCg4z7Yr/xoVbKBSwsbEhJdChUAjXr1+X8+e8Xi9GR0dx6dIlaSfJ3rnMNOhu1mM+M8tPX7x4gc8++wxOpxNXr14VnyvTDo/qEsa/Z7OjJ0+eYG5uTpr2mCmXPA2BmwczM2KxGK5evSpWEhvv8DRk87RfXpNrhictbGxsYH19XXo9uN1ufPzxxx0d6Xj6c7PZhNvtRjqdxsbGBpaWlvDw4UPMzc0d6ZflNavVKhYXF6XScHl5GQcHB3LeIa2ZXrKBLpVkMinzhj7mw/piUMjSX21252Ox1UmzJ34bLNN0GWgYHh7uaGyyu7uLjY0N7O/vv9FNixob+5YC3x5R0h1pj8VimJqaQjQaRb1eRyqVkkMS79+/j2fPnknfWk4IajK9aLVen1bx5ZdfYmlpCe+//z4++eQTXLx4UY7F6TbF5+fn8W//9m/4/PPPJcBVr9fl1OJepjMXrZkL3B0hNschnU5jf38fu7u7HWWtHC9qdcx88Hq9x5q11OjZH6FQKGB2dhY/+9nP8PXXX8vR7wDkJA9qf3wP1H5isZiUXLPJ0MTEBD744AOpq6dg39/fx4sXLySpnxVoplbIMeMGbJq3dNFw46BgNO+LR0HR6hgdHcX58+fFX8nybqY98TtKpRKi0agUFvB0En6vmYUAvD7yZnl5GYVCAel0GvV6XXq78jon8T/zGPtMJiMmsynsWc6cTCaRSqXE5eD1etHf3/9GxkqvLJZeLphWq4W1tTUsLCzI3GFL0eHhYdy5c6fD/ROJRAC83nxXV1fx2Wef4euvvxY3yHHPCQCpVAr37t2T98TMFaa5JRIJuFwuZDIZrKysIJ1Od2S1sOUmqx2pNHQLaGrn8Xhc8po5zo1GQw4StQrLhK7b7cbAwID4iZhGlUwm8etf/xoPHjzoGaihT/coV8D09DR+8pOf4Ic//CFCoRDS6TR++ctf4j/+4z+wsrIiE5cLhebzSZqXU2g2Gg1cvnwZP/7xj6VRtClAK5UKfvWrX+Gzzz7r6O6/tbWFJ0+eSCpMN90+XmIKUk4ORuuZt8xorSmE6LZxuVxy8sJJTCcKOpqYX331FT799FOpw+fGwFp7+lN5bZb89vf3i8A1y3GZGgR8649OpVLY3d1FOp2WHEuzYTozMCh0KXi7YbGJWT7LxccAp91ux8jICKLRKC5cuCBFKr2KEvhMfJ5wOCwmN69jdqiia4faVj6fx9DQEG7cuPFW7Q9Z7cXm5+apCWYSf7FYxM7OjnTJo6ZmarF8p93v+LDr0rLhEfPA6wyBzz77DDdv3sSNGzdEMHEusEH/+vo67t27J8fGHwd7aWxvb3fMI5vtde721NQU/uRP/gQ/+tGPEIvFcHBwgF/96lf413/9V7x69eoNJeO4KrhWq4W+vj5cunQJd+7cweDgIFqtlign1NitwtIuY+FwGLFYTMofadblcjmsrKx0mAamGXcUHo8HH374IX7605/izJkzsgNWq1Xcu3cP6+vrPYNIpklxmIvBvHa5XEahUOio7Tcpl8tYXFxEpVLp2DXb7bY00z7KnXHUAqHgpblVLBbFReLz+USLZkqV1+sVV8jbdPY3XQ0U6nTXmMEms4UetU9eMx6PS4nzYX5kmr6xWAyxWAzAtyW5pgDlWHNTY6WZOYbUsplKxvdvBr8Y3AIg1zxMszGDbyxlTiQSyOfz0iqSFWvtdltMfAp8m80mQrHdbr/14Y0ulwv9/f2Ix+NSeMD7p+uFApn56nxHvbqCnQS6rdLp9BuniZjzultD5rjTHdY97w+7Vndg13QNsXz+ww8/xEcffST3Mz4+LsHp44Rst+xwOBxIJBK4dOkSbt++jXA4LO1DbbbXjemPswZPE9sxkb5Ty7tgAKi7Oxe1s+O6+x8Gd0ezMxMFAivXup/xKOF31HU4yXppjkxt65U/zMqi3/bF0o3B6qnu9DMzQMfTBr5LgIDP0l1G2cs8NSe46bs86XXpajgsMMprmdrNYZ/p/mz3Z8y+HSfBzLIwo+K9NsTucTKzGd4WWndmSW2v98DA8HcVtiZmF7Zu2MntsHnP3PLTSNPie+p2i7Xb7Y62nm8LN3QzlkOrptlsdhQ/nRKHvhDLhK6iKMr/IQ4Vur83xRG/zS55VBrOaXISbeKk7oPfluOe7TSu913G77te97TfVS+svDcrxt+qOfXbzPvvyu9iTVuRmXASVNNVFEU5fQ6V8NZlBCuKoigqdBVFUaxEha6iKIqFqNBVFEWxEBW6iqIoFqJCV1EUxUJU6CqKoliICl1FURQLUaGrKIpiISp0FUVRLESFrqIoioWo0FUURbEQFbqKoigWokJXURTFQlToKoqiWIgKXUVRFAtRoasoimIhKnQVRVEsRIWuoiiKhajQVRRFsRAVuoqiKBaiQldRFMVCVOgqiqJYiApdRVEUC1GhqyiKYiEqdBVFUSxEha6iKIqFqNBVFEWxEBW6iqIoFqJCV1EUxUJU6CqKoliICl1FURQLUaGrKIpiISp0FUVRLESFrqIoioWo0FUURbEQFbqKoigWokJXURTFQlToKoqiWIgKXUVRFAtRoasoimIhKnQVRVEsRIWuoiiKhajQVRRFsRAVuoqiKBaiQldRFMVCVOgqiqJYiApdRVEUC1GhqyiKYiEqdBVFUSxEha6iKIqFqNBVFEWxEBW6iqIoFqJCV1EUxUJU6CqKoliICl1FURQLUaGrKIpiISp0FUVRLESFrqIoioWo0FUURbEQFbqKoigWokJXURTFQlToKoqiWIgKXUVRFAtRoasoimIhzmP+3WbJXSiKovwfQTVdRVEUC1GhqyiKYiEqdBVFUSxEha6iKIqFqNBVFEWxEBW6iqIoFvL/AxG6tMuyjayXAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "lb = -np.min(evaluation_history, axis=1)\n", "ub = -np.max(evaluation_history, axis=1)\n", @@ -1822,22 +355,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", @@ -1864,17 +384,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta // 2)], need_gradient=False)\n", "frequencies = opt.frequencies" @@ -1882,22 +394,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "intensities = np.abs(opt.get_objective_arguments()[0][0, :, 2]) ** 2\n", "\n", @@ -1918,32 +417,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.sim = mp.Simulation(\n", " cell_size=mp.Vector3(Sx, 40),\n", @@ -1965,32 +441,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.sim = mp.Simulation(\n", " cell_size=mp.Vector3(Sx, 40),\n", @@ -2012,32 +465,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Dgm8ujda+BFtmRtXsVmfaL38a7zBSfFDV42u+A50XBKgFIdxnv2RqK0m3duslICBO2eY/U39h6eXwyep8XlZ79vMvv1g0KVcf0ZONrY0qcZn/tUDgYcC+v6Dq/e970zN43WuvYXTmAw8RPrerRULNc3/pzi38hz/8JL77TWI+kOdzGdDzbwoEkH6POiGeAUGapD+nCce3T/D4D70HH3nT9+L6l341aLzbgMA0gwCMYwGAaRSVrv6GkwAB6/Hjf/K/4PEf/w7c/Io/iEde8+VIP/vT+VqpFCcwnLaA2/et55YfAk5+5m/hif/tv8ezv+E/wqO7Bwt0BIO+UD6HKAIvRqdJiFnIUxzACg6sx4//8f8imojr7xdNx/gKTMsg1wQQlXvI36FoE8LkNAkBFCIiSAQwiU+KOZI+cvVRaV8CAJqZDhLqvrWlX33m5Vv4lh95Et/5e57B7/jSRzGq2sHeANMyWW9KrLDQAYUWEgjFp8bb5GtTjYN8Cs04uSYEJR2/fEuFnk56mGuA0HwatPhjRB1IsNrS4yfN6pLzzIP9tS0UmPbgOGtSbpbx54Dmc14fy0B1/PJLeMcPfzM+8rXfg+tf+rq+HMmg4HDQJo55EtXUDwBQwAc/+8G5+fse0oUFg25aUQMRMz7w49+NP3Dyx/Cnr307vvUrviEPsnK+ruDKRtuZ6bN/qSgAzDj+/EvlpbnqtrzsqMfZ1KIHgKCrKXjrTTxytTgqtWrEqhzN3zwz1bJ5FVq2+aumg2Yzxg1mBABAmTFoDqV+Xn6paFLspbTbVfVBB4Fgcp/tmpfunOAbP/UEvudNMhMbmcHMHfuxe+5KKfIMRLvAZz5/C9/yw0/iu75WoMCukYHbmQUUCqL7HFog4KkI9zSBmHF85wSP/9A3iiPgr/2twNkrct5dS5zA0ySCnpN4+DODx30FAGCWmf24z4L/5Kf/Bm78zT+Dm//O78WjRw8i/ew/E6CYdNaSuJgTUgI79/sWDLjjmv/C3f8f3n3nQ3j66tfjdfylOPvnP1PqMlLWFlAIIO20ZFAQZJbPZmJQLQIRAcNONBE//dfx+I9/Bz7y2/8orv2yXw9+5V9Kwww71SoMqlkIDhQiiPb5fkj218ODHHvhzot458d1SatzVCVmTAoHYM7COTBVcADUvgptP/vM52/h9/3Ik/jg73kGv+O1j2Ky4cP9jrIQle8BAgtmavKg0ELCp2+L+ePDuqTSVjfYbymbaRRlO0JlTQgev/xSmQm/dj4ThoMBYqf9M2HHEEjws2HWdx/JrS4RTYcJ87V3tLfMtLcMGOgt+S1lzPUxG9fbOmqgwJ2vHHlf+zpkTWu74kaX6hYNqUeYkMeHUmdy3Qd+7DvF/P3GP9lZ8ni+9EsDDGamg9Jo1tEzFDz67fjWr/imPMhW1/tUkV4zA9aO7c0ZQorfmDUFgFJzox7PAhDozoyBulNn7/rbJ3jy4wIFjzaagsR9OKihwHVkpyGw72YzzPnPM8LlF2E1KSxJWVOlSbmWNQVNnTRQMG0EAjDwwsvH+OZPPYnv/tpn8NU6E6tM7nVp6mQjkEuhOWwzve/62mfwhqvXJLuooSA4LUFwQBDz4FxrB0TYixA/uX1LBpXf+Wdx/dd+pfgU2HnTCEyjAICBwDRmLUELAaI5SCLQU8LxT/8NPPF3vxfPfPk34PXxtdj/zL/Iwp4nlmu1zZMDBMAawuqy3/4v3v17+Iaffg7f/6sex+umB7H/Wd1zPpaKpRAQDAiCwoCeJwoZHgwcaNiJxoACjn/2f8ONv/VB3Pwtv1/MEz//L0S7EAe5VxyAYSfv3LATSKAADANAsdYmGCSEAUgjQBHHn/80bvzg12ebMwNgkqInJhCL+EjqQApwBQdQcAgORH33+sznb+H3OihIC/0OXFaskOYBzEX7RAQmFlnC2vfAeOm2aMqeeuxZPPzgo7Jk1j3DmmEGB9LiDcWsQMHXfk8Rem3K8i0AmBwodGbEZnJQTcfzt18Un6a3PpOX5JqpZwkOlmJPtMfNvNLGiWg1KUxR6trXCQUwktOKBPRGkZM7L+LtP/JefPT3fJfWjx9v23qy8dfqyGSJ1psHBEhP+8CPfw/+4PF/NtcUrPqpLadfGmDg00Eo+IYCBVuF3aopINRQoEIvd34v/IBKAAJ9IGg97H1wlUevXqs7GpXfeDhYg4LWvljU16YedJqCJaellZS1KZUm5ZsECh56ZF4vDRQkFIE/eQgA8sqCokVgvPjyCd77QyK0f8drH9XfdGBgberhob35+Jc+P4eC2AEC0xBEUhM5bJBvtAPJtACq3r/zEt7+F74FH/0//hm88dd+hSzpG0eZ/ZsGYBrBBgjjPmsMeBxrELDj0wROjDSOOPmZv4133XkKT3/pu/H68BDGn7uLpJ1MwIIdJBgcdMwJCyPxS/u/j2/6F/8jvveX/wf4an4t9j//StYIAMimgxCCDHtRNQcUAKdJCIEqYCA6A0XCC6/8Xbzz730fnv3178WjVx4C3/15IARwHESrMAxFY6DaAwyDwILBQxwWIeH4J/8yHv+R9+Ijb34K1770dwiUqcYhUERS+ZYSSd8CYyISnwRmEdAMTCTiJjBnkwCz9B/TFHz1ax+t+2FvTuI6nzkeEhsHiDOiB4SX7tzCN35KVse84bWPYgIQmLOJIULETdFYLcABVAhaxvWvh4I3LkGBTybo8jtlUADMZsQIOL5zkpf0Xbv6qBtvl+HgPI6fx58rcSK8T5bdV+oj6hGpE3MQl7K6egEADgAVcDi+80KtKXD1sFQ/Jem7kL1V9BglfQ5l8/efuv5f4Fu/6r3rhd2YLgwYtM6GlZDqqnwkVVDwlU5T0IOCLR5o5imtzz3+fA0FW4CghYHe2mXLlS25+/BbbuLhh67NrfjNrMNPQFahQIW+rZ74yJu+v3aUaU0sG+umTa15Jd+lAwXJ1YepcVstQdEiCCC8dEccub5TocDkmIGEr4L1vMuf4hMg3z+jUPDdb34GDz94LWsIIjkIWNMOqPAXhyOnKdB/x59/CW////zH+OjX/Dd446/+LeC7P4dsIrgPGGAV9rd+9m/j3T/5ND70a5/E63dfjvFMgwvp+SUQ8BBg1/hk518a/z7e+8r/jO9+zf8Zrw8P5Wuz/1kMWfuWgj2jgAJGuYYoYIoEYMqaBQoBL7zyd/Hun3waH776Hrzh6MuRzs5AYZ9NEBQH8F40BrwACTQMAhHO5GCQ8Pw/+CtS/7/rv8f1f+u3gsdTdYQs5owQBv2rQpYFClj7qcGrTO9F9R+0z75w5wS/V30Kvvq13mfhQL/Uc35lC+ydcIDwmc/fwnt/WBxhX/9aXTKbWHwOCIjMmMrcGKg0XQ0cdByu8/v7td9TrT44NB6QczBk+880CW5G/PydE+dzJKuf2GblDRxo9utq64yZPvnx89GrLmKl1Sv5SRSpfwqXfGRAUB8TNnAS4X388ovZp+Ca1c+hsdKbF9wEiuC00MwAJXzgx78Pf+Dkj+FPXfvjoimoKnnumNhcsJiFCwMG9bJAriqhsg0R5YapfQoa8wEwh4lWsDVOcR4IQKTep9+Ej7zpz+HaQ49Ugq43EwZQ2831gM+F71M+ot4j6h1t2ZwBgs92e2ABCk5uv1Ai3j34+kpD0AWCHnydQ5OSoSnEuq4cLKVUTAdJ1bL58woUvO61jxbWQzNItKOJn8y6Q9nCowO7QcH3vVm80z0MEGSGG6DyAw4GWlNBAwNgueb5z38ab/+Lvx8fvfYduP4rf5NCwQEzwbiHmRXMUdCAgNUc0IOCNzzw60qhnWmAYgBPCSEEgQPzA3A1SLpiwMMCRdEUvPeV/xnf+8v/A7xh9+VN83cGpRDcx5CfX+VLdd4pMV76+b+Nr/+Hz+DpL303HvnXfx14YiSMYmpILM+wd38aBRLGvQCAghSFCJ6KuYGcueH4H/9VvP3k2wTKfu1XgPevqBah/kc0CnRovw1BVkiIBktgISUZbE1bNbGsXvmWHxFH1dep+YC5QAObts/X0UI/lXmrdmW95kX1eTFH2ARGYAlGHVTcTyTag2AtytIMCSYIHRyoPLbZ8fGdTzvv+jf033/pGPNjZkpE6UvtjPj4zqfzkr5r6sgIfXYNB6U6eiJ36/hZxRlp1Qbw/hiiQSC1xTATwHNAOLmtmtA3f58G11J/E7Lx0/XtaiLbvBtOI02smgMK+MBPfA/+wMm3409f++P4fQYFjbP6DAY6k7NeukBg4Cug7og9x5EPVKsPvjH3mLxUx9+z1T7YXyNedWaS46r+elk6xXNvfkpWBxywl2cYsJkAnNLC9Wb75CPqPXL10cYjmg62f2b1FSh4x6fU+91BwWYgmD2wrqsuFKgKt9RR7PoTZHOB/3wACto0w5WW+YAZCJijIEF8Ct77w+/EU29+Fo88dH0dBjhlwT+DAbXxe9MBccKPfv4v4e3P/wFd5//vAqevVNoBMIOnPTBNFRB4v4ElKACAWz/3d/Dun3xahOprfp02Y5I2GKReRaQh+xNUdXYg7u+Ld/8uvvlf/r/w5/7NG3jYQ8daivNOSxUslPMv3v17Dgp+fT4u5fNYPIkWOAGMUQRdVmXrzCxGIO6yFgYUcPJTn8Xb/9K346MP/5e4/qt+C3B2F8EcF0MEh6H4I1BwkBAzJFCIiEHANgVCSsiQ8Jk7J/imTz2Jp978LF734LWq75r2awYKwNokr0p5dYOHYkKGAxPFxFBvPPdZqECbQ2fJmAocMOH5zx2Xdfg2PlAQOdpMwLqpcuCbA8Lxy5/R8eH78uoGzvgzhwOvNVh8pGlugCo4lUUktUTuRrMln5LRChJMi+AB4fh2WZJ+7eojWkbO9WLm1DIRXZ5AVWUwR8Of+F78gVsCBd/6Ve+ty+6hYHUyu1xXFwcMKn252YOQtQS2/IWg3pumfvmq98LUMuIdnzQEqxOAldDXw3ldtVV+ueb5l7VTPPa0eM+eEwZmgVKakr5w5wTv8cFVmlrorZE/mLpQMNcU5Drxv+ulRpsCOCj4/Et4xw+vaFJChK+nJSiwwXIJCr7ra2X1gWXZxqpetXgIAOYg4J0IX9JB/enHnsW1h653VhUswABzFv4VDDDDOxH+6E/+Jbz91h/GR1//x3HtV/xG8NlpvZrAA0G+l4OCWVNoCUIAh4Rb/+Jv490vP40f+PL34NFf9m/X3UAFfuy36qZ061/+Hbznpz+CDz/0Hjz6y3794R+cM73wyt/Bu3/qB6r8e4AoqxvmLc2c8quOcS+rHJiBaRJAYK5XN/yK3wic3QUTgdVp0cwP0GWPFSSY0yLRIiQ8//ItfL2tDrh6LWu+UvL9mbqgAPgJQ79+zOflO7/2Gbw+938lgwYOlAmyeSOpM1JgUepnp0QTfkmWDJp6v8zk7a3swEFvgpUbxM369SXNS/re9H0l+BsgsTccHJzHsc6Pq7fcpOoNV6/NxlefQnYEhFvhgQ4kSHsTAc/fPi5huM38kSdWjjCIwGyabBRICLFuXD9+AvjAX/3+GgrWzNOAq6f2ffglYEqYvyPagM5pBMz4Mz/2XWWd51e9VwYKANlhRAU8l9s0t9VKNjsjIA2sjXKs6/BvvuUZXPsyhYIFGPAvujnQAZgNAPJZvryo6/Cf+rpn8fCD1/JLDcw95X2aR9BrtAWaju90zAdW9EWVYEejovUCoNKorDoa2tIwHHYy9C+6TxZcyNSzVp+t4M/14ouyAALkzn1a6/+Zt9zEo1927fCKAnfMYCBDA8syxOwfkBKO/8Fn8fYX/yg+8tX/D1z7N34DeH9WgMA0DC0U5MIEC5cBWQ2m9nIgB005+Zn/De/8+9+Pm//2f4Rr/8ZvqH7bS9QbfBeuBQWc/PO/iXd//kN47jf/J7j2K/7d/nXAMlB24h942Dn5mb+Fd33uQ7j5G78F1375v7N8/04+c1n8cfOZCBE0TXj+p/8GbvzVP43nvurbBMr2Z6JFiANoGrNDI0bnp6ArF7orGxR0DRJOXn4JT37yXXjOrcOfsnMiNT4z66BgL7qvsU+/XENBFgkrswSDBg8HS06Jxy83YY5VWLdjaJ4R9yZYS+MFNM7Lj7zX+SzopI3ns19q/A2cp4QoPjrg5COevsGZX0s9uPtT2QM0Txi0UTwkJNXqgGTS9uTHnsDNtz2nSyrrcZbdBCxDgqq0mP1Mv9NQFOaagnM4sbdpTcNyYcDAulq3PtWR5gOfdRtK5CUdodOxIY3ljfY5hXy+bZTn77wopPjWZ3D9y95Y28ZXYKA9ZqltuBfvnOAbf+hJfN+bn8EbHvTmA8p+BdsZukncWX2wxNKtmrADAwBmJhZZkrhuXmnrzDsZtlDQagtechHjXv/aazkfS7PfDAtYBgF7tUIgvHhbBpWbb7uJNz50TQU/OxMAnw8GmrgCz//UZ/GOv/Sf4yO//T/DtV/x7xaTQQ5WBJnZWgq+ZFbPKZepFYwn/+x/xRN/57tw83//n+Lar/xNRVhEp/3KTeoiDbpn+Zl51e4h4Pgf/1Xc+FsfxEde95/j+v/uKxZq3SUPAU5YLEVWfP4f/1U88f/9Ltz8yvfh+q/6TXLOYiz4/tgLGtMAVJW0bMc//Tdw46//t7j5FX9AoGAapa0Sye9DEK1OCOKLMO4FHHRJZDD/hAVIOP78Z/D4j3yzaMr+rd8GjHfBYUBQM5pMYSj70WQNAtZBwUwNn375pPIpaJMfxlqtARo4AM+dEm/dPsETGvzn2tVHwNkE6Rzv8ox4ARCkwuftA+D485/RJX3fLZqIpWT2+fNoRFHMr0899mz2yVobb/05My9kINA6C8pnTIQX1JHxmbfoLpvwmoRiapD7dSDByuPfayeDPvAT33MYCiotwXzvmF45e+nigIErbc+v6YO2ocTXfAe+9bd9i3Rq+SUqOIAHAv1xnvk6oacqRTkecHznFm584p24+bbncE1XB7RAsCTggNJYM8rV71Xs9AeL+cByZKryzanxLSirD1yY5vYnlf9F/2GVA2a+znwudEObh67NHAy9VqUXn2ARCjR95uVb+GYNQ/yGB6/NOn+NdoUIlkCgjUT4wu0TvPMTN3DzrT8gS7LGu+iuJnA+A4swYKsKdNbPKeH4H/443vHZP4Hnfusflpm2bm1cQYEvTxbq7hWObkAxZ6Us9P46bvzND+C53/af4Y2/5it1JmvX2OzWQYDfq8CFHZY2zi9GftbzP/mX8Y7P/gl89Hd+AG/80t8xy2+VuqYoU9nq++DDNQM4/gefxY2/9l/jo4/8F7j+a76qqjsAxafCQ4Xdq7fh2VTDQ4aC3/J/x/Vf9Zvz9aLpjurkKQKcOKg/hoZknkYxNah/Qg8Sjn/qs3jHX/hW3UXyq/LqBoKaM0JEVK1ZDOJbkwI1GrNlUHjp5RO894ffie95s/T/UqMldd8B/f0SHEBFukVMrLY2buzq5pgIokVAQL5jnY5f/nSBAh/8zScds3P/49bXYNkAJo6GdRjrfFtXD/XzOhUHNZYoEBgovHjnGO/RXTbf4Lduz2NMMTUATb0hFkgAxDSVMyh/vaPhKhQ0WoIWDLYu47wwYOALaXHpAWmUD372g/i2v/g+vP9r3q/7UUuHna3RJSCvXPBppp5BjqkOChJG8+PvlIhoD8mGPFthYL7r37y1Xnr5BN/8qSfxfW96Nr/0Pi6B33xnLRF8lykpb3jy5qey9+z8x8X/YvkBrS+GdNbsc/F1H5LgJAsOhucBqbZ+vrGpn27hgXr5kYMBH3zIHK/MRFA2LHoa17/kd4D2p9gMAxqFsAcDJsye/0c/gcd/7E/iua/6Q7j+K38TspagrVcAGDqzeqtzP6tXwU8h4Pif/DU8/hPvx0cf+S9x/Ut+WxZeJuzz5kQhOi1PGXg4Dz4GyB1N0I/+fnzkzX8O164+smqzlRv4NiwD4sy5VQfL45c/jbe/8Efw0d/1Z3H9S3570caAEbje84GsLQwMmrrugcPxP/1rqin4g7j+q3/LPLu2gZoHBKAGqBBA06h16CAhDjj5Rz8uG1J9zX8nezeMp13HRbglkEQajlnr3ZZB+ncj6edP3zZHxmeKUAK2TQ1nTTOHA9kbwm1NDns3+o6JZl6YAYJqYauosNDgb2o+yJqCc/gPuNxnHYcvell98GzOfymrpNVxeAYMTtUI4NN3jvGNuovq6x68hlE1CQYNyY0rLSQUU4PXvsWSN07ZUf5P6eqD1lxwnhVuWxOdZxvZX6yJvpQY3/zFzsVlukyX6TJdpsv0r0b6io9/FX7ix3+sO6O8MBqDf/6fvpI/24T1xdslotX1L5OZJLl/MFoD+rNkfzNHY695/2vwc+975eDsVm47t4Xrk1cf282Cfvf28Fwm6sTf15O142GZic12UGMX2jNn7OD8D94JE8BMzSXH+maDQ1oCn5Xetsjd+sr1Rjlb3kSQtQRhXi/t/gR9nwHRDizuT+A3KzowWwWA3m6EPRt/qwkomwC50L8UcnAe85i3z7LMTh3iKGYVdnH8jJXTp/l3+HgRPogUgMohzrfRavu4dhFDU+nIFgtipsmxf9a+C9Ei/WoQaPv1Vn74DaUWNQuNj8NqO+WO1tfaWBvlXSEX9m3IJp5QlkZmf6aqrZbjeyyNQyjNttw2nTYC+u/PfGzRJ7ROzZUt/VUeX6SyV/2U2rGlDZsOfOHGZ6szu+5QPf5r738N7r7vFcBpKi0M9/KD52OqLxMwH0sB4I0fXzDZ4AKBQbuO39RHOaKVqt7rq0SVDTMYAaiWz+hVAOaVvvIitjAAlAEUOP8gav4DjJJNn7uqkzVp/uJ2Utd0QHr8gEqvfVnt2IKH7CEggJ1zWVtzEEJTrq0voRcwYM7Cg1qB4gSHBR4yNbYJmWTBhCw08T0IGKAvZBZhoLcxkK6vh3N4q477v7AAPBrvf2IkMFJijf+vfVi/M+vWVyxtMCXWQVaWsE1WNtdAvpTe+pvdI4hAQcJHExFisJVEBrjQ8NFyPBDl7wEBgSJiAMJg7cmoo0la+zmoG+Q4Kex5oCNn6kGnDX37WBu2bZl9JLrmhj0skBJPo+zz4Jc/mjknCbj5nSRl34ZUvhMBYZD+TjEHJSqOiZDoizru2B4OhPX3ycaYRcdmlj1GbLfG2o6OYl7I/gcs5fNOilJBWlfN0ryl1DOVLjhW+SO91QkByDtiwsGBrwO7PYCD47RkQ0MzkxhW8l4VAGxDq7zLJaEKSe3rEZC6vqXmaVnd4KBgq3xyY2uvLC/cOcG89Uu6OGCgfwnArdvHePcnn8DTj0nsa3tJrJKybR5Wrd77Nlb3q4SYO7G0fK4WeIe3XV1K1vTef2BRsLuyt0LwvImJCtWv+C14R8w1erec3QsQyN+68/ZytGV2c1A7cA9OhH7GabsXHnSAW0hZCABzIBh2MxiAEySphYG4Q/aM1+8GAmMC0igCPSXGlFA+c9EUjIkxcsI4iSAYE2Nilh0p9dqJOe+pkBQmgL6fjCXTDgSi/B6GQIgq9CkQBpLvQ5D2GyJhoIAhUNYcGCBEkMCBhiMOYcAwDBkUaNqD3fJR+y4xHyZQHHWXUC77TzhI4HEPInE09JBHIXbb1B9rnRZl7V+YrWyQc6LxyW1NEcyqQUgJpNtBc4ggNn+ESSFB/kYKCERg7fMpOSBgBlOxe7MfmFZSjn8ANOGXtwOCXE4o0QGTlo/hfbroQF6AZtzZmkjHfwcDBy5343a5dnXc1uvy3hUke1UAmG1otQoIAJ7/nGyI5zd0snx5B0vu/O1FzG3HUluyeRVXF4tzYcDAkhX66ceelUpVqDQ4gBJku3JhiRpbIXbr9jEA6Ja9DQzArp2rV32nWuuX1u+N0FunwrVldeUaNC+p48pO7II2cedFnYGAz+yKuUCfet9A0EutdgCY18m5geCACjprB5ZMBYe84Zs6rEwGDghoKBv8IJQ184uaAQcDHHcCDJBNfaZJhPbYCPQxiawapwIBIwP7cRIwSMCYkoACJ4zuPhKCWsJQT/p90pdgaqdoLkV98WIg0QBkYS/fBz0+RGCggEDAEAKGAAyBsBsiBg8LUYBgMDhQwBjy/Y8QowzISAIGPUgAT6DgNAk6q6c4FABUgd5qEJbamacyu6s0CUsrG/wzrQ9QBGgEs3ymNGWTECEK4AQX2VUdFxmijekCgr4oKb8x6++b1x4A5wcE6d7z6IAigW2WxsX52562NP500sGYBkAO2mR596DQbpWd70t0rvG77HPhNrMC0O546Z9p22IbVDz58RuzrbE1p91UySo9cAgKPvTYs/hjH/1Di+W4UGBQhwm+lmfbS3AAzAEBmFewCTGrVAB1KN58bROABH3bzpLm3s61gr6yxS5Agf9u5epCgU/t98ZbuAsDOUMOBqpjC5tA3SMQ1EOSPeoLoCE4ZJPeCASrMODqsQsE5idgQBDLLJJ1tshhBzhNACjmoDumGRhbGEgCshOrhoCBcUo4nRL2E2NMCWNinCXOkHA2ssKBXJOYcTaWz0mPGxSY5mDUBksdOLCQxoPTFBgc7CIhBJnx7iLhaCifBQwIRwNhoAlDJBwFgYghBOwi4UoMGGJAJEYMYqYYSLQJBRIGDLuhaBLikawkyJAwqBlhD5AuORz3Ur+6r0I2M2STkQcE67Su7fVzCwk1IAQgpCL0DRhM2A+6fI1GAUIzed0HIDCzE47lDeuZGYqsu3dAkK8rZoYMBWWcmW9QbY9vzJutep0X4IBtzFAYyGVYgAOnXFDkODiO+5QMAGDjetnx0sXWrbQIACRMc2drbCvH7Dk+Hxuh4JGr11bzfmHAoIWCpZThAKgAIScu13khliNmfd2zeNv/+Hu6JoM1INgwAZYseVlMTSQ+HIYC0xbUUODLt64t6C4T6gDCUjANDwW/eH0Izh+RcNV/4AsBBG6Hv2SOaXpe/AQGpx0YMEGyJSYAp/pXMEgM7B0MnCbREOynhLMk2oCzKWUQuLufBBqmKQPAOCWcTQIB+ykVbYE+y5sVllJrPgihaA12UXZNPIokQl6BYYgRu0h4YBczKBzFgCECRyFgF0VzcMVBwi4GBIWEIRTTxGDahCiaBI4jaNqrDX8EpRE8CQAwT6AwyE6AbptrWZYYzgcI7nsOBbIGCHGoAME2dyJmcFBByMWBWMK463LAEGUGrT4IpE6lOTQq5hqEZHKaih+ACfRZc9p7rNdvBQSTb7NdCnUZI+WHWgVt9D+A1afteFhGjGr8o7lJoQsHWXCbZHZ1QutZ6p3L9aN10wOEEkcCJcx9U5cBqJbi1w/WZy3ka6t8tHRhwOBQoW0mDhQ1fdvje6TFAE7uyN4E3//mEntcnA/nPgT3AgRZvvoZcAsFGzQFwV8D339UW7CUmV5vX4IBO7fmP6CP9ECQYQF1/eqlm9I9rzKw1QPt/gT5nAtlrA5pyVYY+NgD3qP91TAZxLKrX15Z4IAge6o7jYBpCOzzBPEbmFKtHciAoML/NDH244TTSYT42ShC3mDgdEw4G+XvfkzYTwl3x4RRP+9bIJgk/jsnZ1JLVh/LLWr7F1Cg0sf1c4yhAoRdJBH6Q8ADgwj73RBwZQg40r8GCUeRcDoERCJciWJyuBL09xEYiBUMCDHI5xgIQxgQwyAQMOkmSlE/J3NMnECkWzZ7QEjTvQMCBfF96AZPsm2ORYoxAE5JHU31XQ4RTAlgFaohSt8MrPAQRMsQokYTDgiQ+maCKMs6ArKFg16qzjhhN7l3U6IizgEB6Pgh5Nl5o0UAUIm6tTGqyZOZFOD0BtHKnmfVC3AQFA64hoOg4zvlejofJHhAsNlb5aho8imVbbFDvlGpX3CtUc73X3i23zBqCxQAFwgMrNALXWWWemTVAkFC2bDo+5vgIWt+BFuhYAkI7NghKGiD8nTNB0AFBdUSol5mqmO/cECwFLnSPq5CgSt7yJmY5loC0xxsCFWc7cprqwzgoMDVV/na9x/IduV7BAKmmB0JzwMEph04GwUITg0A9gmn+6kLA/tJNAvTlJCmpIyUBAjAkCo2TUGBgzV7Galq39o1DCokAiHEgFMCQgyIqgnYxYBXOpBwZRfxwE4/DwFHY8DRQNhPAbuJsTdAmBpAYMKUgBgYUwaEiDBEkEEBRekT4QAghFgDgusnlCVH31HR+k/uJ2kS+RkgEBoScghqjYYI0x4kFk2ATSOZpV8BGtk9wZafZu1BiCCboar2oCcgPRyY1sBS+57mr87MsAYIppK332dAsPdHNQZ+0zutqGUQyOcFKPLOmRkOPB7U44tpTsB80KwA0cnIWN9k5RAgeJiQ+zpSIpEngd2orec9TJCrkfb5S8kiPj7toGDLROzCgMGjHRLaEg0QqBu1goLbJ3jPDz4hexP4MJf6Zw0K1lJtLij0d09aAmAZClyMgsp8sGZKAM4FBEDfZLAEBEuDDJrjFRwsQIEPWdw1HbSxCJrVBt6XYHG1QS8OAUqhqiWGVR0KDMg1DgiWVhh8ATUEPSAwU8HpfsLpOOFMP48TZ43BmBhpSrKUcUpIKWnVcYEAZjnGXIBvi8aAREsQAoCRMixQTOJPGQKmGDBGwlkUHwPTGJxGwpVdxOk44XQfcWUXcVdNDVemgKPIOBrSPQNCHCL4vIAw7JZXMlgf3iI9oNoBlaACCpxhlDEA417gAJNsjUFJAUHrWM0JCJD9h1Qos/bLYKYF945NGzQHbbP2YJ6dANsCCDMTg3SSdUBYrMMCB1JXvlKWHRIlMiGVlRtmZoE5REomE9n4RXnXxVJXVv7l7Nl5Dwd5hYjCgdVroFJnVfRE/xuss1KJ+HgTj1wtSx63SMULAwaHIGBV64R6Fuuh4PsbKMgBMDpQsPWZXkNgeTcgkONzKIju86umJZhlzDn+LPgQ2BOylqABAju/BARr9mdLPS/cLwYUdDfj8RnqZv5wgJuywoAKEIQdZisMnD9BDwgmsPgBHAAC8x84mwQI7u4Txkn+eg3B6X4jEEypggFO9tfae63aDKh0wMsmBZKZLksI4BQTQkoIU0CIjBQJo5oydjHIqooYcKblfGAXRMOxC3hgFzGmgDECQzoMCDusAEIQv4MZIKQEpH0GBGLur2QwsHSQsNqvekkdDZfgAIjiG5GlrQIB9LNqLESbMAAQsLExJAG6k+IcDvQSMLC4YyHQ0SQsAEJrN/c+CK6XFEBAEfLZsRJYHsvYgYADC9MeGICw5snvgtwu7cz10WgP5HdybRCC2LQEfVZnmMOBFrb4HcDG1XYPC+Tf9YYiMR/cyEv2+2lZZl4YMLC0UUkAYD6A2dcX7qxDwf3ma9FkAHS1BIejGbrc3wsQAH0tQT6+3WywCQh61bgIUQsrL+4DCnIdaZAVnnSgP+CUmbnJzwR7uw1uCEbEa0sOGyBYcypsVxicpoS9agbOsqagOBSaD8G5tARqLpimNNMQLEFBz0ZdBevK0zVkQk6JEJAwESEiICUChqx4BpAwullg4hJYKTHjyoC8bHLaRYwp4YgDUjBte8CECRwCJmZc4QCOAYlZ/A4gyzsHljofAiGYD0ILCNMeSNoPeIJsvawrGQ4FTYpD1ddW+1IvsWzuhGmS/mSVaPe8RzgwQd7a3Rf9DRbeY7/iaw0QsrDjuXlBfkHIK6WIivbAAOGQ9gBwsjZIO6n2cxMggHUr7Ln2QK7jDAg9E8MW7XEvtTtftkOjZwh7jpcvt27LLo8ffksfCrbk68KAgVfJ99KaaPQd3zsavqFjnujVqWPSLph4zQDcNRkIOjBw3yaDynSw0BPWVBr3GJxoduwQEDTJawsOOVluggILqGKCn5MLBV024FlKFELJtruMJKpOdV0VotjiEeTlhqIdqFYYWIhiC3vrgCABYiZIMicsmgKJRdDTEtiSQ3MslGWHfcfCs4kxjkmDHOmqAoYI+lSEfl0XpFXF1bFqqY9N2Jy6U777+5RrKDR9sHOME4ODwAcTIxFhYkZIMpsbR5GEgep2TLYxzYAcgCkhgANjTLKs80pi0R6oAEzESCyajAlAVECIYUDoAIKBpy11lJUMU61F0L5H3mk1lZ0hKQLVttBA7lsVLLSJE3iCrP9k2bpXpwQFDpjyKsBDcFCabw4HUMhf0xpInqwda0DwPgh+5uu1B968gHy1H4sSFseyRWmnJcowIX0o+7uoiWEJEIgBhgCAdfM84WlMDKTXS3bWNQlrsmpWtFw+V3f6jFZT7qHg+pKmYIOG48KAQW99J4DcyL4hrFJas585Gj71dc/iDQ8+OhdsmNObt8O1je0bbQYD+uW+gcBrB5Z0uU691k29JYiawUNQYECA5hiASrOwJZ0HCup6cMsRDQoMkiRTGQSoqrN5OFTJSCzqW6CsX3eOYKWKqHxfgAGmzp4FOSCRagsaIBDhr3EC0uE4BGY6GBNXULCfXPyBlEpgIgcDbaIgA1wYAtKYEAZGSoSYdJlVqP0LEBwMbpiOFECmXG0GBEHNClDuIhDCEPoQgaI1CD7oUkrYTwQgqZNjkpFuEtGT1F8BPe0ByYwtsSgrpiBBcQYW89UMEHyAJFvJEAxMR0CXO1bmKgVVAgoo5AI5M8NSP2vrk0VwgSW+Zd69kN0xlvISJOKgmB4GgGRHQkJ5/+4bDoAZIOSmcwIOWNYezODAbqiag4Nag3zcjXmqMbDfSj3psQOA4E0MSz4Icm2BBNMkSBH7FXbIBN76W/XvIX8NCp5pNQULk9W12154MJh1Gy5kaomIcOv2scQpeOxZPPzgtYMDXN2etHjOPi7BgF1/SABmIQhkIOjCwAb1Wg8Qqu2Ul8wHC1CwajpoqtFvdl2eV39cg6Tu6gNnGqhAwP/t1E0O4KRmgia+Sp1i86qYDwGQ/QnyFsYeBmAahHpToxyUSLUFLRD4cMWjwsHEyHb2MTFOpySOhw4KfChjmyX3Uggi6G3kSvo5UMCYGESiNQihBMTJ2gRwmeCmetBbel717AqYyxTIQIACdZczkgY1yhEOA0nkQ6IcPKlNAguEcWIgJoTk9XsBExImljIOpj1gQiLGpOaFlIAUpC5aQDCtAdKUtQgCqUPWIkg/bSDBbRUNoGi12n5WN5pWWtk2G3D92BIn5BgA1qlz55ZVOjnWQQIQYrOevoaBHhxAvx58v0Xm13vVKAhA77sZDhwIcLWksSn76nfofSYQlfHQtAhElnuJrZHDSzOQuA8IZjLxWgQpG5dHahu1r8d5TN9t8lBRQcGX1ZqCRbBYefaFAYOlMlZLU6w/OjggAk4+V9Z5vuHqtcrpJtMxIS8naZ/XNx9QdWFlO7sP7UBvR8ScjwODch5AvBOPT9mE8IWBgqpe8jPr+lmFgtBAgZkFfJwCOHOB1dFSZgAR5tOU4QBQu3Gc1w+5gTn/1hweKKiZQHPuzAoZBirtwHwjowwEXMIWm9mAVUswJsapBhsyKDCfA/uXUl9AhxDEoY8IRxpy+GxiRGIMUYTnlBg70yakolGwVQjtygP/mPNs4d7VpvkVC25JYyDMAiJ5IDiKlKMoBpIIim0STQJhdP0gWIzcCGBKmHRUYCZwDGKjV/PCBEJkGew9IIQQEWIERZaVDGkUdb13ck2TtLlCArPufcAJBC6gIBWrf/qA3+2D3Svziyndk9VDighgFbA26WbpvxkOdHDcEgiohYPZ+21Z6cCBzarPDQdLJoWtYyFPqmHJFzeQELpahFzm1U2qpL4zJLimMMfFQ8nMy0AtN3qJqA8FvSXf50kXBgyy3WXtGq2hrGRi4Dgv6dAwypg73VQvQX5ep7r9eccF59l28zwwMN/CdCURFZVjdXzd2nXIfCCPvwfzgev49nULFPi6yU6F3NnOtYGm+tmKF5zk3bWQszp97r1Ipd4IfqYmg4gCgal7s2aAUG1m5LUDrLMQ9SHwuxpO+TNmGxqNBggeCnRnRJ8CERJzDjksbWOhgaHREBlDLKYFH87YTA32XX4v5/1eCL3Qx0BxCvQpLoyKfrZvebVDds4gwIdV9iGVrVxDjDmSYqzgon52AqsWARUcAGJamMAYKADBBJX6HwTSJW6sWhefj44WIel3Z+4q5oaE7AwbdCg2kxe2gb5cQaX/dS/MTgYOFFicFpkEgALgl/RthQOpS3vOWmaxCAd2jyU4iKuScW5O2DIuVhvFGSjIiRkkkEJCT4swgwSiPFZmTYLl300sV+uqGRslm/XYaClDwVvFfNBd0dW//S8NUwLKK4L1IhdBf+tOcdSwvRUskXsB7DfVPXoTbv+Z3L4FuEcYAPL3rvDbHBUs6IDQr5fe5kf2by0ZKFjashRR8qa5ugcoIFdHpa50St1qC9qHkjgSkno5e7tBd9Fps0JD6inke0md6X1NW0AGBgoDFLragcQFBFLq7HCIWktg/2yHQ9v1sBXMMmOWadggcg0xxGqjoykaKBQhn3dG1Jnq5Kqj1T6sbZJ0vyk2L5oX6KbEMY2AnTMQsM9LGzSF0AGEJAK+QELKnuYcRHswxCBtp5qcSLqsUX0QAovwmqijRahCbw/ab31gLYkg6f1grA+b8ATQh9zK/Nfpo+3ljdZAOMHeGyEBcr/dAgeMZuK0oWt4u7ktxVubSftbeq1B9jXo/mjjGGl8YI6qpv4nKpBA6o9yXkiAaBLs0eRKwivlbYW7lyVwx164M4eCntCvf8oLn+t0ccCg7SA54lX5H8jgihfuyNaW5qiR7DouYTEyHACzTuUbLR9zIGDX9LZB3qQd8DDQdvK1wcLK7onYrQnrag2aZFCwxYRg1x9M7pGbtSkzeGrryWsFFtSLbSay01Fq3pjGsYt8K2IGApVWAEHMBAYBCgkZBha0A+B1IJhQAKDd9lhKXMobUKZYAwIQFTpgAl9ryQCgqasvoKz/gqUZrDfagewritYsUWv8EhgRBE6M0bsgAFl7EFEDgmkQdIcCjATEjhYhBN0COqoJwYXoRjZ9OW2C9muu+vaB991S22eXEjMkMlLM2gS/O+F54MDgYqY9cNlezIbL6VaTwoGSNVqW1pyIug4r4rAvOh5ZUAgihYSQIaH2R+g4LBJqSDCtAZWNk9ZMcK1s6U0wMxS8pQ8FtcTrlL0tf5MuDBiQBf/IATCmUqMZEuT7ye0TPPkx2e/6kavXkH0Oms6PfKwGAKCoOL3ioCfoTDOQz+M8MLDkS7A+QOT2NkA4ZNhqtAVb06pNufPIJRL285sQaEGj0quvFpwWyiZ31kybmrDj4d3OwHK9YAYCecAwCDgAA+y0A+b1PrHO+vUfowABp7JUkRUK2voWGJCHiemAMhBYTW+NvdEzjbWz67nWrN+vumY2TUv5aTUfLaj0fCbupWweEADrb82ciqXexQZP4JHBAZgwFUAwQcaMkRiRVJA5LQJJczj/h6JJgPONkRUvg9N6OVAA3NiwMsj75DVdq1NxC3msJgVgGQ700YkIKbGs2ycboSjnLQOCl/oHUm/Z3eK1AGpfg4M3179mfl3SMMjx4oQ95TFRmKG88+Q0rF0tAvqaBFY5U5kXULQKltr3qp1oVlDwZdeqcRL5bwOTXhveAmcnXRgwyA3vD1URBgBQwMntW7jxsRu4+TYhrSx+O3AAaOCKzuNMrPSo7r5hwHXmuRNdXV4rV/lMLl54qs8BmX59x27TmrZgLVWDAuaCpAUCYKuZZaHecr2saU4SqsGxCwQ1QCL7Dpjd1oGAaQVyHcbch0ToI2/JnU0F7i+rCYFZ94bvAIEBQwsFPl5eJDONL3vkt8Wz30lR5UPbj80PwJ+335dz7n7ujds6uAM1VE5exepm6nIdcrlNU1LO6ffZef+c9XyUspVjE4Co9S3mAgKShfSdAwII2bEsEoOYEEhgbaJiaiAUEMnmBgDgSYSjh4UGFPJY0GoLt0Dxoh+RmhCI8rMW4UAhILB8YUalPchxKxpAyE9aGBN6iXO55lqD9uecVxGI5oORMPPNaKFgCbAogNjvfeJgQQIaaB/fBglek1AgoTYvSL5a4HdZ8J8PQEG5lmf941wh8XGhwMBemKIpsIoyQDi+/TxufPyduPk2Cf5gzWGdHkCOnV3Fo+48bqYxICyoc+4RBnrmgsVBwDd0qOFgcyq+BWvJP2lpo5Ulj9g1ILBjtT8BcLD+LLV102oKeo3o7bLAMgi0fgSqFWDUMFBpBvQv6zkDB4MuhtjqWVXV41QLQgMCOVYgdUIR2v7l9bjTE/IFAOQ4BRHqRMU50Zi4tA0KrKFuK6kl98Vqe8PAX2kDbCaK+U6lcty+o4J4u9bXoYcqoNQjMIcJO99Lfu35JD8WIRC0vzMwopgYJJyzzCwTyZsk5goxLRBYNQZOk2DmBohPQq7nVpvg+jr7sWJN2LWpB2xOk5gdEQ0IUMPBTF2e6jHSb93cAoI2V7df+ImCJcvFUrK7UveFXk5VPS2NqRUUUH6YhPGe3ITh3iHBKflKmWeauXnZiPqOhotQ0ALBln7i0gUCg0Y4AnCKLjx/50Xc+MSTuPnWZ3D9oUcBcNW5vI0slN63mMz+bZd5IHhVYGBN6C0l61BbzAcAaqfD+0tLT1uyk9m5ZS0BsK0eF8oFOCfD+ricdOpWhHxdZR7wL7q+3Nk3gIvguFcYaLUDwFyQWT2ZurWCgYXZfU/we6EfnMAnE1ig3AZ+104Pb207oWorba/8sTMrqfpZbuECBDjs12L1LNewri1nd76Gh7X63i3UN4CZentiRkzA6Oqbx2JGGLmp70nrKfUhwTQJBgll/w9CpAiKsdKUmeksR/aUTKoZYD6JaFXmq5MEdiaFDhz41QqAMy1wrWE17Wo7sdo8fp03bXFC9Kk3pi7lLc8KqQAD0asGCea4yJiDQm8VhoeC67okcTZWAnMoaIFg00TzAoGBvSyyPjXP/wEiHN8+wY1PvhvPPfZhXLv6KIqKvYYDQIU99Z2xPPXGZnCcCTFgHQg8DMwar9OIiwVv/Ag65oPZ9R0YYPevHZyrOpASZO3jmvq4Mq+gCBk7dy4gABagoKkjV67ZYDjTInS0Ao2J4L5gQK+7Fxho05BD5G6AACfURShBhZII/UhFEBHMBq5QYG3go0danIi1PiwFKe20lFr1Np3XbDP34bA2MjNN1R6RkBKttIeEVx6Y6vYgmrVH/j4pDGh7jAYNS5CQtF7XIIHUgRFldUOGBOu2bL4IC5AssZW1CbbCvsJADw50LDnobAe4dfwoEUXJxgpa7RK+W7S5ZkbXCVFv777YyBRQzfzb1BOMa+Os5TuPswUYyPwQPCSAxHFR91FYWtkAV5e+/IC8s17+3Lp9gic/IT5x1x661tdKWzla+WPlrKDxMKhdGDAw4dgCwvGdl/D4D349nnvsadEUcPG+XYIDYHntbC1aaqF+UDvQ+gy0TkUbnELmOWkP94ShG2zbcwtLm7akNaWEhwG5dh0IyrUdKKg0Kwt1Y6TSzVyojp0HBs5tJsCrBwNrpoAhzEHANAHRaQFsiV4k9zcYADBy5MjJNpvqbTyVILtQKiTABeXhTvS+Q6mJGkkU6mBRGQbUdKPX2xLQWMWGUHgAIYEWl4NWDp9BHDXbtrKVH5xNK7TYTr9QkJCcM+4MEvQd4WrMyRfo34UZ4sLLW5sV5PdL2gNllxoQcs0JqGUfLVr0BLqPZONXqobMsnNjQNlJyn4SVkBgZdw12LD6zWBr0MSofRLCTIvQXdmg9/IyiFDkz8ntE7zz4+ITd+2hoikof1egoJErq6bXJl0cMABKYRUQnn/5RTz+Q+/Bc1/3IVx/8OE8m/ZLc+T6umF6qZy1yvbx+JdmtAvaAa8ZaGFgq5ZAuH3hvJuFtb/zwtClSluw9Fi95tBcpAcDwBwI7NotWgIAHfNBqy24FxhofAZ0JpohAJgtL2Qugt6rrscVGFizdZfsqzagAQKvGRgozGDANABmBhDhTxg6IEDTHtgXCLD4/mgi9REnie0/jYDuEJjGPZCSHNMY/2wAMU2lTGvbCtv+E0RAFMFPLkok2dbUw06ODTu3TbVCQBhAnV0pg8FC3BXNQqImqiTNHEEjCVQMCgkjS6hgUkgwJ8Sl9hv1swllA/AxYBEShiBbPQdAt961dlQfB0JeviftrI549v5QVA/4+r2pIYEhSxJtXFwaW4rWQB+NEtdjWXvgx5/YAYTsg4AGEg6ktZHYx0Dw17VOiLW/gMYvWV3SfM7xlyfJAet3r0lQSDioRbC82aZOOibb+Hp8+wRPfKyGgrrcnYmplWEJCjZoC4CLBgaWmHH88kt4/FPvwXNvfgrXH3xDPp6p1+DATA82g5l1S09ZpcKp4xxUPWMJCKoGaxtvvVMu2gl79N9TzerxXkCjpSLPbts57Z/ehQG9aJOGAKjqFVjq1L266sAAsAAEy9oBO7bVb+DQigLJ+voL6aGgBQLTDgxagbIEzmkGOjAQFQaQRtA4gqaxBgH3PUPA/gw8jeBxD+hfAwOeRvmNgQEnpP0ITqIx4Ow8qbOmaV5eitY3Qv5OukV12A3yXioYIA7y2YEB/N/dUYEFBQOy/ScMFHS3yl0ckECYOpAQuWgSUpI8DRwxEmNQLUILCBNbON+6jPbdg8KobboECbayQRygRYuQVIsg8RIKDESddZvT4mTAqJoE5glgtber7wXUz2YGCbP3x2kJmPM7yjYb15m5zMTRNy9on82AoAd7kJDrrO0js17jK7i+QEVuzlvepUEjGVZaA3Q0Ba2W0Z9aNS84x26DBA0rnc0NBwABAFpTA+etGScc33kBNz72BG6+7dnOLokNxNwDFBy//OJy+XChwKB07OOXX8Q7PvUN+MibnsK1q4/ML51pDuz3mqzRG3tU1Vlmdle9x/0CQdtRF/X1zXE3Iy4A0dhrs6CsocD++dS+Fn4gbHPUBnnyMAD0fAiAGRAsaQjs2GLOtJz2cJwfCNZ8B5aWGHrtgAUhajUD7XK6NvmlgoeAwOrQgGCgYiaw8MCDaQZ4Ao17NQmMAgM6y6e0B9IIGATsz5DOTvXzqUDA/kzAQOEgjRPSfkIaJ/A4CjztJzAn8JQkWmJK4MRgDZnYbtkszaFgEIN8DgEhBPlOAbSLolYfBoQhIuwiwhBBCgcGBBh2oN0VAYajK8DuSM7vjsQGH3ZZ+8BxBw4DYogIcYchROxAGBNpGGgq+ygQS+CiBIQo5gbqAIKZGKS/rLSxzpJHliWMPU2CX9lgWoQI6fvRtAb6LykEksYREFs1BBZVi9ADBGTzgHv/uDPueS1B864XQFCht6BBMCYwQEisb6eDhFq6LwjmZtzzxg27Xb62NSnohIgzyDj7vvOhqJ/dm/Is5NHK73/htQg4DAhAkPGto0U4+fvP48Yn3yWO8lcfkfsuyCQAmPkUHEgiH9+Dh/Dg4jUXCAzmUHD96sPyUnitAKvaxsMBlRchn3OdwVf88e1bcszUpecyGRQgWIWBTqoI1V6sLOwaKMj22QXnOvnxHAZ6hOBSz9HQH2q1A/px3UlmExCsdfh7NBmkdQ3BvQCB1w60goITw28bvAQFQ5ibDExDMATSuAUa+levD0HvpxBA0wikfQaCDAPjCB7PBArOTsH7U/Aon5Eh4QxpHDGdjuBxxDRO4HHCtB/BHhBSQhonCSHMjDSlDAMeCnjirCkAHBwEQohqh41BACCEDAQ0RMTdIH+HCBoGxCsDwjAgHB0JICgY0HAkoGCQMBwBw5AhwQCBwg4cB1DcIYYBUyAMCRiTBiVKanoICgIJQCSwAkLejTC5kMja3raUtG3vfExBkYjq5Y+6smGIBJ4EEOzlkaFqDgji0U6IatYKYlMom4xRFIHD1AUEymMgIwvJKvmxyTSp+rXVIDSAAFXpV06KegPTgHh/gy2OiVXiuRNi1hwA/bgGAOyNk2sdHAAo2gSb9YtMOBxu2e6F/JsZIAAoWzs3gEDaPnqNgePjn3wXnnvsw7j+4MNFu+ID9i3Ipupcz68AtXx830e+vV8+XCgw8IX+foECuNljLzk4kGsdKVeCWwTZ8Z0X8PgPfr0e6wCBHfdaAu9D0GoIDr0RVDq7fN8ABSGiqymYCcmcq1XfgrYvttW5pB2wQxkIWrVXo/I6Hww0T1+DghBhEKRj+kEfgi0mgzYqoWSdZ86FuXoaKOhpCYZIWb3sgcBMBqI5EA3BViCgaY90dgrsT8VUsD8TINif5WPp7AzTqWgGprM90tke035E0mP2TwBhkjLvRUsw7W075lT8szrmhLkZAWpGIMSdaA/izr4LGAT/78oOcTcgHO0Qj3YIQ0S8siuQsLsi5oX8V46FoyvgtBONSdwDaQeaRnAcEBUQQhCnxRHi9BdY+m3ePElNDJEjRiTpYlOjPWDOQZGsvds+MAURxhkiGkDgAFBg8CR9YNIeLsJfIIADAN0WmlVTxEk370kGDeJ4SgYICXNheRAOLNl7K+9b+d1U3jfbwtgDgra1AQKj+CFUWgStZ5+WxpoqV9zxNWhNCj04INL6OAccrJgcfGbzXhSWF/YQVfIjvg4dDYJeJT5xb5Bx0T/K6Uy2BizibEZq5OODDy/+BrhAYDDTFKwlLp238i/oVbZV6u0TPP5D78FH3vQUfuf/9Dbci5agesYaFLg3YgYFq1oCpdQGCspOf7VPwQwK7EVdz1LtfNjRDiD/PS8QbNCitKPFEhQcCEZ0SEtgToXMtUPaUlRCYL7iIGfRLTGULJMzFfTNBgUE1FwQzHdATAZbgcAggE9fKdqB/Rl4PMN0eiYgcLrHdDpiPDsT4W9AcLrHNE5IY8K0T2D9m/bi5Jb2CWnSvRuSAFXWGqz5GASN1hhINQeEsBNzQtgpIAzyNwwBUcHAYICGiOHoSDQIVwQU4pUjYDgC746KFuHKa5D2p+cChJBkA6lAhDFxCXimsEgxiCo/1uYFpBIx0fpCCwdei+CvW4qu6LVGNulH4hxJDyhQAMhvc0xe0x6AxO9Cd1FchgOXFt+9BhKkofX5LSAUodv1Q5DsFy2CpoRlIMjmBNM6ODjQDBYYMm1JGlHBAQfV9Acdf8iN3e5zDw7W6kbrsht1lp0hxGb+ataQu6kfhGpn3vja1ytcB3hNQbdaNmqej+84+Wg+dyvpwoBBCwXn2l74gMqoQMH3146M9woFS2kNCOzzASjIm/ksqNINBoA+FKylnnbAvnaBwGtfeg6FrXbgXDpFp/U4JxScZ++C80IBUGsHgL7ZYKB6pYEHAnMkjCQwYJqCGGS7YTKnwWnMcIBpBE2n2X8gGQx4DYHCQQ8I0uke09mINI5IZyOm/YTxdKpgII1TBgSDgAwHzDkWcVpZsmg7IyIKCGUoUFgoQBArSBjORsRdxHS2RxgGpKM9wtkOw+kR0pU9prM94tGZAML+DHzlNaBpVA3CGdI0Fk1CGqRDxAngHShNGRBCiBhJACGSmhlUfppmyfal8OYFMRPX2gNABX4n2U7PpmUyOIiqtYqJMIYEJIEDX6NFxCpFqH6eFBwslKs4MqoIp1D88Fo4yO/TgfHJv595GWBjK2egdqgrws2vZNDcz+LGGCQc1BcuwgFqh8QwzJwxKWn+4VYr9LQHJOsorNSbAEGvq+HAxr3gQKxoDyo40HtL/RigGMBt0aJaJkK+/vmXX8Tjn3rPtkmzpgsDBs+9WRwNZ83VA4I1HZWlLhQ8jCLEOgGK7HdbGrCTh3lkvu1AsK4lADwUZHHcgYI257Pac1DgYeCghmAJCPwLtlZvC+14v1DQXX64EQrWUm8Pgh4QbPUjkFUGutxw2jdOhfpZfQbgoeDsbv6bTQZne4wGBmd7TPqX9xPGsxFpnzCdTV0gmEZZgTDtU4aBcUzWmnmzo47CQNZmT5OEBx7l2DAETGMSaNoF0JjAYwANCXEMSHsBBB4T0lFC2E8YjiakcURU80YadwjjBN6LD0S8MiJMI3D0gCy1nB6QerryGgGo4Qi0Y7BCAeIA8E6cM+MOu7hDjLKKQd8u8T8AIaD2P4gcMSkgxCQrBTwgDMBBJ1SpLz4MByY8Ad1fgkR1rxGAAiQOwrnhAOgLutVxzN5HndFyMTN4R8UMCE7wtksdAc2u4x3/tqfmcz63BQ4Ic+1BgC4t1fwe1B7UgCD3P4eQzvl1cOC+V3CQr12Ag7XUan8oiPn7h96zLB8X0oUBg/mSjoV0T1Bg6pdmFtz5zept17QWQCXE8+dzAsGalmAJCJa6eGsyAJacCvVmrVNhFwga7YCrx+1hXNfKfT5NwQwKUKAgD+gOCtpkqzVaEAD6Sw+XgKD1I/Bmg4BtWgLzG0ind2d+BNPdu10tQfErmGsJJoOEfUIaU60hGGU1Qq5fNcEABQ7kc+kzSWfbE3MOuDSOCSGJJmECENTgTKqRiIll4NahynZhjO4ZPCUEjWwUk5g5YkrSJiwAQ2kCpYSQxHGSOIF2EkmQ+Yr0z8i5z4a4AwVZJTEm5OWBa/4HLSBYNEUPCUABhba/+MQKBwOkf00JRXPABQ4ixN/gXHBAthyxhYNlrcHs3ay+SW7MRkC6MVMXEMzEoOYFcvfK/aR5SJsr/7nVMJglRcamEkBoUXuwFRCkFnKmtgrYxdQI+so/weFPDjh1D3BgPnHPfd2HZHVD79qFdGHAYJPA35LWoGDh2pVMQTqdJ8GE7uy31Q4AXxggWNEOrBYDB7QE7JZzOig4BARr5pXNG0H5srtyez8Btr/mU6AzAFahn5vS1Y85GgIq2LnYjqPPtn/BnT+BHTanwi2xCLzZINJhLQGlcdm5sGc6GGW2zd6pcD8hTaIdQDK/gXm7lFgF/X4fCU7VrnMg91ra5jBLUUXlviQrGZopIKeENBFiSEh7AS7aT0gWKjoGpDDObh45gVIZdEWLnGBBncLRFYCXtQcUd2LmUPNCUjCYGNn/wAOCj4EwgUESQWkecrmTfJn9pHIKAghyADoLtq/qeKhmBTMzmDxLNgPPApNUaBcfgUOp9476Y6U0NplJc0CwQEn6g3oVw7r2YC1lIMhQKr+bAcLCSo3tgMCiOWi0CLPUHd8PyKauwE/IO1/6+26EgwwFjz3dh4ID6QKBwQYBspSaju/VLxIx0d7CjZzo7DuzTtFu++s1A/bXwcH9AsGadqAtju9vPX8CryHoQkELBPkhzmTAqR5oluq01/mX6sDVW1VudhoCKARYtvXRlhOHLjlFyNK1qPH0DQ6sQsxO3GbRNAOA0w64OjwUnKhabZDGmZYAaa8z4BUo2EtsgrQfkfYTeFLnwHFCShJ/wFYTSLMsC6vsLLgL+bpEjMCEMLH6FIhX/EBZYdy9n/UrczUIUhl9fwP910tlaWQCp5DjKdAosEATI+0nUBgRKID2p7NhnKBzsyO9Z+7LjfYgDPIvAikJJIxmiuoAgsVAGAAMJOYoABjMJNWUqe1H1qX9Phg+mZi1/GfHdmEDgFl27OPaP7+8KjoHZ+R3qtIaUOq/lwvv6mzJXgUIjZOiCwW8tEkT0DctrI1dfmVDYPdb+LHrPgDBHpq9QF0m/HjW29Y9n9soo7YI/5V7Ht++hcd1byDZMPD86cKAwZaZ5eLs1Any45dfFNJ681MaXML1wJkNx323z7lBD5kNqPn76sEAUGsHsvA7wDVL/fGeoKDVEpxnVUabltq2Y0LIxc7aApvlFmCw+eOhaIRADQeu8ADql8ey2NvRsJgSgF7oYh+PIJsN0qRaggS/4iBHLxz3SONZDkhUQYFGKuRxL4KTRRPQagFE8AZwZMSdH9AmhDhgCqJFoEjgKYhfQWLEIcxWIGRgaJwL2l0KARGc+fkLKxXiLkhkRHIrFQKcU2IEokRN7GkXkBKYSco/7gHdk6ELB5zE72DYad8ReOWofTlMQEwSJClEBEDs/1CB1QCCj39hWgQGMDlNgj5m1o/q9plDQS9ZHZvWINiwpHlJRAgGDTCtQWNS6L0K1QRnNQN6fbNkL3vby2cRubX2oIUDJ88LHCw8bunYBB3HDCicFsHKn4NBNePYLKS0qfIrcEA1ps1AYSn592Dpc+/74j2WoOBduPmWZ3DtoUcPKFyWn3NhwMBisAPod2bmLjxkQUVBzAeffHexyVT3qZm1XsZi9/XkSPOO0mvUJRjQc+fWDjgY8I/fGpK3Pthz6gE2Q0FPS1BlqmmnNbjrQRKwyYTgAWHpTbFaTzqpiUGi0AGqJVhkE6mRDAJ6M4OBrEEgZKdC0Rp0gMDMBraJkWoMZlELLVCRi1IIDVMMv/lRm9cQAGKZpe/qmQ3rckneMaa9aAZoSMCVpLdlDC5egUQ9NAFXQ8GS9kHyILXk4QBAhgAf3yBE1by0MQ6IQDsXEMmiJNqGTKHTWLYZVN7bYQRCyILI/hJY/A4A1RwkgAfpTCGCQgTFnSxtdPEPDBAYqlVQ/5XgTFWkC/l3+rzJQuOuvJuz/kVFW9BNLRAAh00KqqbepDXIz+m8u3q9aRDk21x7sAUOLAXTQHVhYDl/Mskp5hvy96B71SLEZUho62Rt7Lf6csfnsin0r+98t98e374lERPf8oxoCrpjurvPCn9cHDDwpezaf5qObDNJraiTzx2r+uXpdU2Bq+zZGtdZY7ZZrH8rx74wMJBnylhPvb7Rs5AVjcE5oGBJS3BoFtKFJf1pUzfwkJAvsscVbcHW5OFgCFbiupao+RLc8RYGCEU7YFsdL+5ymMqeBki2r0FC1hLwVEGBAYFoBiZgcvbivE2zCM8E5MA6IRIoBiCMCLsopobE4CPRCIRxBCfGbiGqIedlidbxzByxvZ7z+KT5DB4Q7HOooyNSIIRhyJoOCoSwiwoOEiWRgsQ9IAunXGwW5eGThMfFuJe2DKX/EAAMUKevnfTzMKn2IAIpSkEtzLIDhEl9KyZSfxTVIjAwgwQGEKM5E7r+3dZT8+U8BlOvNSDKRFDK6RwBK0fEaqxTeLJxbullchMsUz32tQfb4KBXH605oXdNXX4DFa1zHUf6kLBVi9CHBAAFFIDDg86SLFi4pvruJ0Wajm/fwo1PPClhlM0R/6Cvwy8BjYFYOX1qrFazSipd7PhzJ7hhYSh1a2b7TR0IxHVN3zj5ev2j32cU2PoTuKUr9wsDLQhsGaMD3CDRpoqqfwGgoKmrLkG3/gT5mlpb4B/nn9ZOZIlI7MJ2W/1dRDVWz7Oaf1+yFPR+phmw+stQABF+Fqcgz06T29Ew/53UOU53QLTtkBso4HHsOglK3gIw7IBR1v1bndIuyj4HQ0Q42snvJ0Zv3wMA2RcBwMwfoX32Ul66+Wtm9ZRBhmbfKbpzzf4K0I2YgsHCLqrmwUHEsAPN+pPL8zjmNq3gYAI4JICjmhRku2fiScwLIVaAEAHdmElYadDvFjxLHBA5v68Ga/nVwTrI+35nf63f+ZS4+GD6Z4CQFytkrQFQhxFmzMc6u6aypS+YGRonuWpN/z3CwarWwMq8XG3lPgYKbD4XttGSlekeIMEGDSSw36SqqZuDMgFYlwvN9exkyfHtE4GCt91sVuctAyd3jvl0YcDACro0nteYwLmCjz/3vFSq2mSKV6l2gC4coKG5FaHWu860AsD5YECz3oMB64ZbZ8dExYlJfje3BXfTVvMB7hEKiPralCUTgnM4lBO1Q6FPNsAEVnusTJtkwKDaQatVGOV75GNUBmiIBdXMBNTCALx2AEXYJyf0mTMYLG6BrEIb3F81gFhvsUshiG09JYTdAJ6KZkCaQ8wBsxDGqZy3lBqqOg8EbE0tLJgWoTqXVyEoQCgEkNOQCExoPIugOzbqZ4Qo9dQkTgkURKLzNMpyxmEnfTboGpa276cRRKPAgkJDoChhf1WLEFFMDBOrTyPE3GArY4D6ffY1u7UfWh/smf9sXJS+L1+yLNSxbtWkoAF/8iZEuY+dEw6sQIfgYNMwVCpm69iXzQoG88x9SLDz54AEWKAnq1nfWAtm7Nl1Tj4ADRS0Y58eO759ghsff6eDgj4MLH1eShcGDPy4ZS/GEigYJFSVqks6/AvSwgH8/cgPwisZ840MOGGHgzCQy9XAgKfk1nQA9BveZzFvgUr1MZ8WtQU+VVDQHm+gYCkt2dqWoGBmQsi5q/rAIZ+KHFaESn1U4qKqm/qQH4ANBPLsDTUMaAlmwl/qzcNBykAAThkYiLlAwWTgUEwGZHZy1pj4wwBgEHU5swhFAEiuTZxQ597Afj9Cv2ff/wLfv9IGBNd/MkxENUfEfI5CVJ8B99tUws8yBjE1DDvxt2BW7UGQugyy451oENTEQFS0CESIQUw43IGEFABWO3buuwqs3hC61o1bMF1LHvxNa0D26yWTArAMB5a5JTgoDy5wYO3izmU4oMZHrKc1qCCp3vb6PONgvtZMC6TaBA8JhCxAtjgtwso3A4WlnLQZcwK9MRnMoEDPH995oYGCPgD4Y6l3QSddGDDwhWyXqvTSye0T3PjYDa1UXdKhdrQZHMid9DGmEqLcQNYBFsMw92xDHRiwYizBgCfjmelgpZFNdYhSLd1kwq17Uc+EUKXesfYB69RcVhfYsT4UdFchAFWddR8Dp5aEaA5y7qn8aDbIVoPDHATyd/unxysYYFZhL0Bgx+COFSBQr/hJtzb2UOALp5FrKIggozgUaBjgBiatngooFpZVtX24uW7m2LfUpksOgL3UCJaZNsLlWy7oN/Bq+arpdtRrQg0Qdm+9j8EBKIAiZ4GVAYGitFcwKJBQy6ZFABGCRiCNISoQULWRl/kblHd9/Z2urfaeXvVYFvid+mFYHCRUWgPYe7US+MjgAJj7HCz1gaVkWoMm0t/cpNC2IdzKj5Xbu8+r46JeWWkNlByCO5bcuOiBahESEGtQAGZ9fFFmAIflBhQKPvZEhoIeBMz60RZVgaYLAwbtkNOLiAVIox7fPsETCgXXvE3G6FfhwK6v7kRl1mGpskdX0/B5g94LDHgavpfGbutgq9mg0hb4NDMh+HPnmA0uagmAdSiQnFkdttqC/mNkhmFwkAuYn1bXR77EmQxmIICiFch1tQIDXvBnL3mUa/N3ryUA5nUa3CK2EEQNvrp82s+orf9S/d2EpL+egqjk4QewzmxG06ZgVHaXngd3PmYDtg6e05TPVXVin6st0FGBxCGTR7duyo/1sdJWspyRQZPSGI0SejyJ9qDSIiQFBwcJWZOg5gbpu5SjR7agUPwPyiTgkGag1297qQ0jPAt8tAgHQBGpqLUHW1KecNl3pzWYmRG4+EFsubU3L2wYH1MpBAAtFcvX4qh4j5Cg/gZFc+oKxrzqw9QFAnfcQ8E1hYItQNC+CWt1emHAoLXFZR5tKuWFOyd48mM38OzbbuJRR1q1Wq38Yg4ImhZmXG0jlvvdu2agEny5PM1ssCqHy6ZNDahctzq0VB3ebt5oC2ZpZfBdGZ1m3rhOSyDnF6DA+RX4euwl0/kYHJT/17Mb3HUeBKx+KiOGF/hrMJBnowZWKX8nZhGAQNESdFT/OYOxvLoZCryQ13pthTxTwT2rW6+BkXomV+edqJuaj67Kc1ahC6rmBga6e2hYHRk0ZcmZ6jrMx1DXJesMtFeXrRail1LKxeWErD0AkOvSNAbiYCd1SAYCK5BAVPb1iMFvC071mABWgX0+PyLfj7tJn1HBgfc3kApABQfmXNdoDwAHCOdKjdYAKG3WaA0M6JN/aL9Ysy8Hx8o8trrZI7OMgyzj0Lk1CQBALIYhBwpy+EBdLQABSJbUt1DQFmNtD5yt/HaBwMCXWLu1fzkYuHXnBE9+/AaeeWuBAumE/pdusDN1GQog5OctORg6ENDHHoSBfM4da2EgB+WpytypCKo/+td8KVWCz/12O6vPMzPzYnbHy0MaINDPW1Zo9FJrRrC2zajXVENbXnk85Sy1L/1MK2Cg5H0GTKCtwUCrHfAmA6AWpB4KOoJf/iwI/0rwR1ePVISZngNRmdk6oeWBll122r5qxyz1zKuVNp/Ksbb/zbQwK/DFBl8GWax7XHCC+GiYZquu764Goq1v+x5kLGDTFNj1Vt+JszoeRJInWoMEynUOq2cKHW1CDQqmBeXOWDDTmLrPvttncwLmibFhTT8gAET21BoQgL7gm2mT1PfgcJprDQLsXSYc8iVaGzerPBNy4KlsVXSrDmwMMEhoHRcXIQGQOnU35gPLGmdAACgU3OpCwQwI3MEWCOr6Wq67iwMGKC+Aqcqt7yUAt26f4F2fuIFn3iJQkHcqawRpuY/XHtjJziBdXYBZY7Uz2iVTwcxM0IEB36a9F8LKLJ9np3MuWxu5P3lQW+DNCPXDke2NNuj2VMszoOoAgZ1bgIKlul0qr6V5+OICAcAKCJh60Ooh+wEUGKjMAc5M0NMMAMjCKS2ZC+y4V3V7EFiAgJQFfcjmgSzkKcq23PpdjquDnD5y0r44TZDohnAbTkGKaJtKVfZwa5D15ijt4YQTofBKFf8BPiBUQITENIgBCDu7BihLOhXSnBMnGtMN8wSKrh83sMDUaQ/fFta3FRJ4Gqv2KB1K4ctDgmuT4pMwFkig0jak3yNRAwqAOClqNu193zoV9MmAoxkrqm2LW+2BjoPS9+eAoLddTgvapbxZUHf54rLWwPqAgQLcODor7sr4KbKijOEGCi0kmLnBgMBDAtPS8kf7auONjUTcUFsqddOMnQIFN7rmgzUg8OU+5Jjp04UBA6BqjwoOXrgjUPBhhQJ/oV/X26Y2fndtyJ3Pp2cCS78sza5m2oHcqPUyJqBu1KXZweYlh74U96MtWBqM1mzN3odAv68u3cz32xb22ZfrUBZyLu8RBpZ8BqrjreABls0EbWpNAg0MWB3JFttlBtoDAQ4DECISCBNL3aVJwvOmxHpM1ttPbFtSKwjo9tPjVHaZHDX4Udk9ULLKrg/PipMHRvkeCSi7TxKGGHLEyCHS6i6UdUjpIKGKg1hYAhgWF4IbUKA0iZOitiOlUd4tCgBYghBZW1k7hSDtNGsrhQRdwsxTa7oxjWPRGJi5YeaTYP091ZDACgm2VXEgkVEBtUBIJtQ28kEW5VvggCTfxZSoq7SIOoAAoKuPWEkHtQf3pzWYP25hLGV2Y6n0yRkkOHNDgAMCBYQKGizDNt6Y1iEf8v+jyJfGYUjMBwIFjy5Bge8LdZGqMm+tqQsFBm1iZrxw5xbe/ckCBRXbagvVQT/qv4v3dud7jeSJfs2RsKcdaBvzkPWuY6nL6dDSOthvZ9qCRjB2VyI06SCU1LOFVQ0BMAMCzdXmbaN72VqCAdiT2jJnM4EBwYqZYAEG0nls3I0zYAsEbDNR0wzosjvWvwID5RirRmBMQBoFBKYkgn9k2Q54SlKnY2Lsp4Qxsf6zz8CYklzDCeOkEKHahFHLlBQi5HMHDLQhogZ6AoAhBI3xIJsTDVE2ngok54YgESjls+xOuYtBd6GE7kIpG1CFIPcWiBgwDIP0c54HjbJlopyGfIw4CTQMKvA8IISVtrPvIcpmhaEBBdMm8IQeJMzMDda2tnzSNDwOEthBAgNlQKK+YOilFg6kjfR3kjOd3croVQOCzs69BiH7INjd7zGxPK/WGmhdoWgNgkJqT2swi1myUP7VYwoKHhLyyg2DhAYQAPS1CPYbBwma20rbbZdZOmmgwJ8/LxScJ11IMLCKfuHOCd79ySfw9GM38cjVa/n8VjhYS74B1sIS3ysQbH25u/5eK/LZQ0H2XO7OmBeSV7Ga+aBY8ZuLW7MB5toB/bwEBEAZ8w562bp6WoIB4IB2wJkEKCV5ij+eirbAhEmeffZgYMlz3qeswuhDAYbhIBCYRoDjDqCICfLocWKMqg0Yk0DBmGzWLyBwmhjjlBQKgLMpZQA4mxLGlLDXa6fE2E+MxIz9mCQEcJLvKcmzAMwCIkmxpJyDzvRle2n5uxuC/I1ybBcDdlGA4CiGDAxHUWBhFwOGGHDFQCHa3hMCB7Z99UDAECJCjBr/acr7UaCJNMm2usABAsaxWMcMDkLst+c0auFLexoodCHBfDvy5+CWP8qKByICUgLrPhCmITJTQ954SCEhcRH2Pis+q36CnoGgeadqDcIaIIiqP/t1mBbBRwGs7px7g7u9z6RBwDz5LJngz3NuFeJrSxlp4dwqPFG5SA15ub6TmhUqQNBrJtSAYPs2ZIhRQdOLuWPZObktq+eevQ8oqMq5XMxZupBg0EJBrtTmhViDA38vu8SnnpDqAYE9d81kcC9AAMwBoBeNr9UWtFAQsEGNfsiLNqfmhe7BgM/4q6AhaOuorRPThgD3AATJCf6llQamHRj3fRjIuvWOhqC3ssVBAQ071arE7A+wBgRMUSAgqfo/iTZgVCgYuYaB/TjhVL+fpQICZwoJd/fydz8mnI4TznTr5v0kZoVJP6fEujZfIddCJrvGyf4ctomSmQRUsMdAGKJ8DoFwFAlXhojdELCLAQ/s5O9RLKBwFOTYlUjYDdFBgmx3PATdyjrIZ4GQiGHQsMaHACFNJR7EuK/hoE299s1tT11IsPYVtf1UTEA6UFm0VQkqFAEEAQQ7rjESqAUE1ALfPtu7YQowuONrgFBmtSuAYL4ATotQIAHIEWPPk2YrFOZBj8CNpgA63nS0BlY/aMwPS8BgdVXV0QIgBKYi7B0gZEbkHjQUQMjlkSLh5HZ/9Rxwb1CgNZR/8sKdk/5Fmi4cGNRQ8GyGgl7qqeDb9b3dBnEnesLfjs+OOS0BN79r73UotTPiLVAQ3efsV3AICnJ5VzC8k6rAHTPhj8apUDKyFQi21E9rMtCP+q8p45oz4QoQYBzn2oEODMy3Ow7lfNYSxAIFcVDTgfMPID3vtAIch0pDMDZAsE+iAZgYAgNTwt0xKQgwzsaEs4kzDNzdT7i7TzjdTxkETveTwMSYspkhTQlpEu//NEpvLsoV7csrpgTSzieOhYQwiIo8RNk0ycwFu0E0AVd2MYPClV3EA7uAB3YxQ8JRJBwNCUf6uweGgCtRoCESi4YhhAwIKRCGEBEHnblPe9A0goNqE4iApKr7ROKnMOwkmuQ0OgXZVP9t2zql0tYtJLDGn3BaIYuPwCHVgBA0YE4YBFpJl0oyqxaJMyDYW2x93lTu9rmXfFMVQVb/ZtHEQEC1giFDgUKC3cFDwpbkZnESNbEPBzAB6+AgEjAtmBQ8HAQ3EVuDA19HlO9nF8sAOikMBHUMDSTvgmkLbNWCxVKpoaGu6+PPneCdH7+BZ5ymwFI/6NX2RJDVeV//ySdwFVcXr7swYGBdpacp2JQcua0Fy7n1OSGtVkhtjUHwakJBFqnUxEuHlMV29OuZDwKwuCysyoT/7kCBmxd9S9THahmOG8TY/QPmppleveSd01btJtu0BAeDEJnJwMccsABELRAswEA35cA3IvRzTP9hkNkYETjsGi3BDhyLliBBtoceE2PfagjSHAjujqIdOBsFCF7JMDDi7l7OGwzc3U84G1MGgWk/iQ9BAqZkmy5JWyTdo5qZczfpmXaEB1V46c6JIQAUA6JuKBGDbK9soHA0CAgMcRQwGESDcGU34IFdwGt2EUdjwNFAOAoB+yngbgMIEyfZxCgBMbDCgUQkDIMK5zQBCADtQTQBSXw6CACzgCxJ44JhZoOpNi00KfeDFhIcIJj5ogqgFKKskLDQ66opEK0CIy8rBEAk0ACkSnvAKCr3NT2yf5eszWxWC56rvm2oJBPR1RJHzuXxWgTJQgPIS+OHmRNYR1TycFXDge5kLQq+Bg6SlocgTrIW6XS2TwrULHEADiwLGQ5y3ZHT1HCGA8C0GnJNvXy+CJwWEGxJ/SNXry3LonMwVl5twYwXFAqefuxZ/NGP/qHF31wcMCDCrdvLUHDQL25DuvW5Ezz5iRv5uxfsrwYUHEo9ILDjS1oCDwUWw/+gwMwj+3rOzh0COh9b9x/oQddiHpiregBwsIxLwYjOFaZ4GrsagnbGmJPb5Ee+L0NBZTowCAgDEIasJTCzgYHApGBg2oIzNRHcHRNOU8LpmHA2KhhM8v3nz6YZEJyqxmA/JkwKBeMomy1NCggpJd2KGbKkMYmJAczgJPWzlIhCdraTHRHtr4BCCAEhEuI+gSJhGALGGHA6TNgNAVd2Cae7gLtjxAN7/bwb8K8dRVyZAo6i1MHRwBhZ/CEeGFjNEMAUGIP2vwmy46GYMY50lQCBJgLTqHZ87as8SVyfAeJ3EIcuHJBGouy1vx0na/9prAFBOojGRgCIWDXw+lKY4xslPS4hmSU+wqjaBsxm1d5ZrxWA9X4D9XvGOtv1kOAnULXzbomBIMs+nZmh0SLIzVM/HHAvccqlWTQrBJTJBBFSKj4HACGCiw9Gs4kaUDQAJqS3TNAOwQHseQ4OPFSUh5TjAPKS+ntNtFAGccT38nG5/i8MGAgUuCWJG9J5rF4GBR9+y0183Z//3fWkGssmBbvAe8audbq1d+VegaAXuW8zFHS0BXWGO05E5zUXOO1Ad8BaqI88WDg4WL5uCQo6pgPTDGS/g+JYyNN4WEvQaguWoGDYofYnmJsOOO7KsbibaQlGhltFULQEd8e+puDuvpgMXjkbcWoagzFlDcG0nzCNDgjGCdNUTAgpMaZR6jBNojlhFiiy/Qp6cGCbHZFuXkQkf0PcAUSIAyOEhBAJUwyIkcBTBMWEYQpIU9CVE0GcKoeAcQrYm7+DmhgSpC5SYoxRVl5cSQxm0R4wy7LLgQG2iWmrPZgo910CKtOCrFpQDQklad9xP4cDW+bo+4SFsM59o2gbGGN+VyhCtAcJBQRsqZyCAOsLLZsVRgDJNi7MgnNNvm1a124THw8JmGsRjBv6ZgZ2IijPpd0zOmOL1xoQ5e9rZgUPQAiqLWAFAqc9EB8IkeKJbKzuA8J5krN+VHAArZseHFDnt3lJ/cY8aG3Mkh8SW/l4CHwuDBi8+5M37o+0VhrBoOCZt9zEw+7+PcL2ac0kAQC2Q1g2WTbnQ3Ot/C3ZfTWBAEDtaNiaE2aZvzcgsLv6CJD2uN5a26W5p00Q2mbrlTWXgR0AZa1Ax5/A14v6HuRoeT1fglnmauTcoiXIqw5UM9CaDgwQDAq8dsD7EtydJuxHxl11LDxVDYFpCn7udMSpagd+7mzqagnMZDCNYi4YszkB+lcAIE1jBoI07bWa0xyMrAm0bogTMAEh7kBqaw9xwMQRHIPY33nKs2iyOAFJAyup0J9SxMRuJQRHXS45IEU7ps/WtprAeABR4UDIgAOKcAikoBKAKYAwqrreTAtlAozdEUj9TBiQclMtCDdMPCu4lI6ddNFCRA764wWlaigEDgYIxE4ZDoQMZIZu+Wi1BrN2wfq7ZnVok98SJhj5ReybGcI6IKjpQT43OahMCuU38tggUN9zvrR8wQHBRkDwO61uiR0DYHVSAmDut+YgINdr7x5rcODK2OanTW1wP/v9Wq4vDBhYmONZOvRmHqCyW7cLFFgchPPcvvtIKn3d239ajYC/3mf1VQMCefCrDwT52OH9IazsvcGpJ3cLkR/W+NTltXJ6GEozYDDhX+qkCXiz9jyvLm4353FAMNMS2Lr2nunAnA3DMHMw3LvlhyMDpwtQcJaKc+FeHQpP9xPGMeVVCsWpUAxePImJYNK/Uj2spgM1p7RaggYKfH0Vh0sRolJXRRgyB7UTEygwOBGY5PmRND9BwIQIGAMhTEn8ESjh1CbTRLi7l/vWfBYgxoMIYMKVGLWzJaRKXJnvwZD7JFFrWlBNQo550GgPOoDg62O2Q2WTxClR5B4G0v5os3+dr9sSCbZnlWN5Vk3bNyFae++yKcHVpHesMy2CB4Ty1QFCXslwDkCwzw0g2O637ObLrwoggLNJwPtc5FiFrnKWxunzJC/7t5gv6ge6myykLL+W5ONCujBgcP3L+oVeH8rr5KkusVaq7a1wdXul+vvl0MsQu1Z+RNOJesRYgYA78AXVEGxdmriqJSiDkf3bCgQVl3RewhltY1aV/UBNVjZbfWDxCawODArgAAI6kKzVATP8ssNqu9/s9NADAnIOhgumA7cMMWEZCiYHBacq5PcWi0BXH+wniUUwTpNGMoQGN+LsJ9AvovgBEDOIxR8gYQCmESHuwCRr7DklME3iUQ8Tbn3NifkZeHMC6WqMEEifCXnuwmhbtAactQZDkvLtAyGGJMshBxLzxARECjidDCkniSeQtI/UOYWsmpDlgLCVCl3TwihQsxNHNh73c0AApK8tbLy2JlFEkBlkJN2sKcE87ohJnk8GD3rOrmsDAzVFbUu+9P75N8o70Xkv+yVAgOXAL3WE29o+w82KhKvGpaJjzYBgJPVqaBDgr6+1mIdNlpbD8q2NKrk5Nb9r5dPSdZYMCp5tdxE+8DvgAoHBUhnvtVFeuKNLRt7ahFHuPLd0GqcJ0OPZKxiovGLXstXus14ttzMVHl4FH4IVGMgDUnVw7lzYW2nQAwIpN2Y+GC0oLKXWc7rVolh9NFVnP66EfVXu3rHy1H5mvMBbi0/gtzLOSxBd9MJ2GaJ+96sOGDRzMhwbKLC4AuJzIMcTA+Mk/gcWkXBS08O8OCb89W8iUV7k2ZgJdAZREHV0oK7Doc2Kmef1YjHiW0AwR0RZpQCEaE6J6ogYAyjK3JNC+Rc6L/eUSlkTS/nDJIGOxgRQYMTEoAnYI0noRAcHioxglj0ZBorAEEDTXuMJjBkIBA5kyaMta8TuKIdV5mkUDZCZn7b0l25qtHjmxGcDTudY0RrUYxQIOWKgfi0avZX3sHZSdJoCmgNC0me0Pgjyz8YJ8xUwmHBwYLDgUjUe5TEMMNUKkUEGquWb5wEEQPLN7LZFzqEl9XcHdC+9SZz7Kp97Y1RzvLpn5/ghuXZyW+TXs2+7iev3YF6/MGCQ7WAHruudb+vYgks8o6SVg1Dw3LZkL4QNodK45bjBAeDdbmoqXSJDO3xfMADUM2P97l+8pfXFXTjIJ7dDwVYgWGu7tfegt/d8gYJU/aUWBFaTbGBDUYLTVOOVdYR2MM8OdiF/z0DgNQQKB7NwxnGY7WvgoSCp0J90cM9OhyyhicdJQxU35Ysa4CcGILEIyhSDRiiUvI42OAZCSBKjgCKBJ0biUC1PlHX4YqfnNHSFitdCkBvJKufZgKKVIPlnyxcDEUgjIYYhZIAZhpiDIe3070DI5bOy+mQhnMcJiEigEAUQiIFIohZnk9/6ouvi80jO74Bs1YJqD9yyRoSh2nshxFh8U6JCk4FCt7u5vuP6zXrvt8pmgQATatVsPNZwgHqcWrzloefJneaAwHD7ByCv68/PdnAAZgUuLNaLvbO9cSovgWQABgRZi6DfIRAKdPaasLgDDgj8vhMEqdf8ZKY2A1aoKtXl1b+dcdyOzwu9fJ/e4326dbsER7r20LXZ76j520sXBgysisj9fy/Jh6G0XayyCs69XRluV+BAslWabkZ5i+RYBs7gzrcwYPdc1Q50YKB6wZYGBstDCwcZAtrv26BgDQh62uw2ZOihFQgzMwLQWX6Z6uNVuYOOMWVOVcFBdPbdzm8BuAFdf9/Z4nhtf4MqpHGSNdh+fwPTAli9TlybAnw44hCAkJDDDU9JbOxAFBV7IOzGhP1EEvY4ksAFM9KQMKjDnzgclt0WLZhRG+1wE3BZ33JRENugR+Q0CPZZ9lJogGCQAEc5hHKM2EVykRXLY1NixEg5vxMBA8v6ezHVCBAMqcwCJFohgc3vwPp7mkA0gYOsYslRE1WDkB1VKcD6Yd6gSTKwUDdtH3K92fpQ+xNWEwMSlEAEBmzZYLb2980JPnUdgFfeyyVAkONOe4BWyNVwUMqeqj50aKyyKJByfnJRVlm0CHrfg1oEQhcSQFSNWX6dBwOLomZpPAf6UODH+jVtw9pzgCK/bjrzQX0NL3yu08UBg/ZFsxfM/X8otZVq/QVo4ABlQqEPq+AA7ly1FfLKs32n8dfOdj+k+4CB6sVa8iUwByD3wgGzAWlpO+QtULAFCODObTYHHaDr9d9S/Z4QIatnbc31UO6at0+uIEVbQ+umDOSE7IBojoYGBBkSOrsfprLPgd/0iPX8lGRTpHa8DIEQGBgiI42y34A3CUyRMEyM3TRhNzGmIWCckmgdxqS7K+qeCGrD74U9ZvcZ2KiEcdULFDgwrQGAKlxyVDPDLlL+PgyiIRg0lLIHgnafhQDZmCnQ3PTArBHrEovGAuJ3ARQ4YJIXP6iQixQRokEeAUmW51WAoGsgOU1AEA0BccqaBH36al/Kxkbv0NurwK317Z9YfVlPS+9m9p3qAIJF+TsPHGSfg3yyMSesjV35VCggQbwCCZ0dK+EgAaiAgNhpDWi+5fVa8s20NLb7c/naaoKzbTzz8quYDzxYLdXbPF0YMKA0FdU2gKrzuOO0UMV+a8vrDRTY59C0lA8Ykq/W3mKcDndqMe/+c9tZtmoGgPy9ONjB9d7UfO9lhMp13Rkx5Rfr1YCCatBps7VE4p0BsQKn9qcrpgNRYSY3GBSHLXPyoiZ6W/ltkwGgBoFcR+WzxbYvAYyoOBnKk2SfAxXAeSvkBAUAtx0ytG6bdz0SwCzCEFMAhiQ29kAYAuPKELKPQuIokQ0VMuRZSXdbrDdHAjDbIMn+Tk3d9DZQstQK5+jMF/6vMVi72VIMEN+DfAw4Gsr3vHmS/dXNl4ZICJCw4FU7JoADg3XGiwSEINoEk2gjCDGpoCGAA4ljosUgSCPAsWgQWJfAaihjsC2NTLD3lhy8t9rpnGb9Csh9y11Tj3tWMFYA4PxCeLlnE5v2tTANaDd13lEPCPI4BwOWj/PAAYBWa1DSEihYmqwQACMvc4U5EBIVSKAAYOpDgkFAo0mwxxb42MxXBzUI/gIPBVvhoJZfjyLnbAGuyPriQrowYJBftrzkBe6F6UOCJanUJ3Dzbc/mSl0CiKoqtbMD6xqC9kVbWubS0wrYfTbDwBIIHFptYMuBmryt+RjcCxR0tQTnmGkCBQQIdV22lL1cZhkU6rLYjKJoCWRGv+QQln9c7pFnKE4zkI/LBjig4EwGoQCB0xKIGaGAAbvvecBq6oMCYVBjFgVxPAyRMIAwElfbJNvM374D+l37ioGCHbc0pf5nnw7F7gCWNUDeL8B/7m3XLFoFgwnx2/Hmg7KNswABEdwOjEF9GpoMsAiFyTpzEFDLdml9RYIKENFCDAUA8rbOCpNw76bfqhtwoKAPXk3Lfayp2f7Pc37KBMfaidQmugQKq8nNnirN3r3CQcdhtQ29Xu6P5fc7g37+oF/1PSfvI3IAEvTn2ScBwJrWYOtYD6ybDnpgsNRPZvKrAwP1vjcHZAEuFBg0AhFOMFcvTF0px7dPcOPjT+LmW5/B9auP5Bmj612ZtH0jzR0Kq59UT2k7R/X6HuwU54SB5qXp2tGblJdALcBBmf02uyHifFBg6TxQsLQiI2fNBp9qoGlu3PUlUGENqGgOOjtwgAD0plTzz65OWl+CbJPOfgaiMTBeTwmqKRAgYBPa7i+r9oB5bgCiAERTewfZcGdIjIFkljqBcBRsZl+KY97V7ez+kGDvbY7UpjCTuK/e7+V88920DSYACFkzQEFgQDYRI/cd+r3JH0SeiPbA/Fmkl7BqDSix7HUYJMS4LLGUrZ0Rpvxu+s25ODByiO2eE7Cvl0N9zjn9lv7WqTM/UdLJTr6zCekGBrL2U9/fQz4JS3BwHs1BzjlRrTXI8Rr8884xvtlKmVxfbkw/JyRYXZg2IYOCdQ9NaaXv9sZ9dOphDgXLY1lXfs00A83vNryDFwgMeipw7Tx2iQk+Tce3b+HGJ57Ezbc8g+tXH+3CRfFVaMhP/0ZC9bKVuAXLqeuxiqYjbIKBni9Bb6Dp+1/IZ3LOSxtSY0IAzu9T4IvZf4b8aaHAUk9bkK/bVopSFoUDkNZdx2ww/40fbIpGoAIBD1IhwurLtANgE9RzIAAj+w7YVtzM8+oiIkQdpYxpBkg03x1oVoz+Nj8leMtCUeWatq473SWeo/anTuO33dSbKJaaZGuZLPtWDgolv20/Yv1PhBgjgcCqTkjEKiAKIBCLiWIiIFAs73fkokHgBPDg3mPtDYfe3TYt9r9tqXdlpTVojlVw0AjBnJpZU750AxzY+Fk7I24sy8axLjsSkweFKY9jwgv1+9uDBFAJwcwo1V6ZgjbkfWmlwSIQLAj1ZfnVAYJeHa30swsDBtmBJ6uE3ezXXjx3/fHtW7jxyXfhucc+jOtXH67pMtsCym9bQFh6FW2m4qu8vXa1A3DfgXAJBmYvxyEybMvkNQTVDKP5DYUKHtj986ndZbIp4frsY4GiW89drz0wbUFXPe3BKkOAxrnL9scCA7l8rRNU/mwP8TO2Dgg08NSDgXw8uc22FoCgV0/EwBAoO6kNABBFxb/Ty9ole9T54izXlZY6H3d1X0rfTvXm9d9tjuZ7T3NkmgyG69Jm4qiOuT7VeUD7LG/6WKyXJtPMGvUPLJ7qVgcOEBLExJC49MuohJpNDZYjB/c90C+lBBaF3Xn6I1DFM/Ap553NDOrggGxYWICDXgVrMq1BwwrF9ZXrVUW9cTIvX1yChN4YtzTuce13IB/tgxvzSfFkCRKA6v2mnMcCCvaYKqud8mHh2FYgIE6L8qtcv1xHW7a/vjBgYAJuBgjupbGKOb7zAh7/5Lvx3GNP15VKtWcsd35br3ZA/jybza1+bxr+IAwAlb3yvKrInAmaQ0810CzMfv3vzblOS9FqC6pSchnU+/mZH2ppeisUEMq/PAj7cvl2Jaf/yYDg89XMN/MszdXHCgjYP/MbaLUoLQzYrpseCEr9wUok9bNSd4DY0V2WZ0LeBPz9hNUuj5/345y9joqXe0LNOdXZr339bfFZWapHuGt3gWZ1uZa4uVCsNTUgiBsCYWLO9dNCAmndyZLM6ATHAihofVbOw21/bJPvn9VxV9/6ri/uurgRDoAGEA4kv/NgOYaZSSEL2VZrQA3MU8fEsGUMrMY9BwtQUOhCQmtumK9uKPeYa5YPVdP83DIQ+Pfp+PYtPP7Jd83l1+x2K0BwAA4uDhgAGQ4AuFCiKvy0Io7vvIDHf/Dr8dzXfQjXH3y4+k0rTHIM9B4gNMOLp0eXoeZr3YEXYSBf01E19mCgR81tajUhW9NMW1Dvf5CL5gbqtbRks9waEOQwFBwqj5SjaJZWftVAAICDIOAFWfJmgHMIMbi/bZ1UxzYK/0h1vYVKYJW6lYxN0s+cfdx2oMxQmoUZN33PDzydvlhpo1rIogymld3clnR6P41smqkdN3t1Pt1jfftkxSSqNQiACLglSLBiVXWuRV8DBZnlYwYLB9NGc6DvS4fgIP8iT7gaQGjSoaXFUi4VmdQcRxGsB7UGiw840AcdcBSN8jSDhLm5wYABC5DQjK05/5YOyIY2vx0gAKdmUvvIQQF/L1AAXDQwAJbhAMDxyy8VKLj6SP0bnxoNwQwQgG0vanP/rt3HN5zBgDtH7eCwCgNLDe4AaUtqtAXz3RLngtCnQzXTGzz8obUIYWuz2JyzpbbJAMj9uvCQQMuRHfUpTug0wh9OSHUEk2SBVwVTb7axBgLRaQBCPoas2g7BYAGQEL4ToFH5KCU9NgoQJJvBTtmbnjiVzaSmEe22090tp5dU4H4b6s6Ok7JvQgkKlTeYooA2fHSYrfTwjp3k/DjItQmhNduA1kGB839aZGsP5tweFhepgBlcm5R26wFuVFBwqqzqfa8mEb0Mtp2kl/IYUE9jluAg72ygYMm+A+MwBLQpmxP8d261BsAstkGlNdj4sIUJ3Ow8owsJ3tzgtQgAMiQsxUkAMAeFTXkuHay3iiBDgZ/ULt6r/P68UABcRDAAKjiwdHznBTz+Q+/Bc29+CtcffMOK8Fj2MaAtL6a/1Zq6a0k7kM91tAMzGFih4y0Q0JuhZe1IAwPNDHlr8pEhD10nf+X7IhAAMygo9+Z5PfuyVv4G7rj9uqMh8GVuYSjPSt3n+pq5iWDGdSt14uHAzz4rrQDRDAaCLuuL5EBg0gA801hDQP4+yaA4juDxDBj3wDQi2bbC416AgBN4dAAxjbB9EjJQACXCn2+CGEudu/0SZEdJEe40yFbUFAfQsAPiIAARB2DYgYYjYBhAGguC2h0pKcp21goKu1nQKOT2snj+ibWuGWDUphzfRm1b5WEigwLntjJQKNoE1P1W29PgLTvo5Wu8RoHBFtFwYSY5r+zm/c+Cb25SAGo4ACHvJbAICL4CVtKhLYmrLNpvTJtkccjzCoVJ/qo5QbLaMS0sPmBhjGTd/qmCBKsv1FoE0yRkLYLARJ5IGVhkTbU+YuukDJhPIKHmg6zpXpFfq+XfPnJfTDBwiZjx/MsvFijwmoJe8jPr1szgGnfLMsAeDFieSiNtMBcsAcFaHpY0BEsvqkGBaQsAtHa0QwJyKfWCpvgBw2dpKxDo121QkH9f10cFAtX5e4eBJedB5jLw5VbrZdOBUT7koCAEmgFB9H+DeuQbDEx70QikvWoJxvyZ0igCf38G3p+Cx1H+TiOgxzGN4EmuS6NoBtI4gff6ObFoCyYGc9L9FJb7ZQgBFIMCAYGC7YEQQLuIMMhui2GIoGEnQj4OoN2RgEEcQLsrAhC7K6DdkexWafEEglzDIYLCDhwCYtwhaLTJKZALIlX+JgM5IiBxVqP7rnSw3QwAuqDAjXanOC9uggRANQqs+aq1X9vGoxoO0OBBJMheAXwYEIAGEjAfA9agoFqx4LQGvjpBbvmiCl/5rHDQbQfqv/9ds0JHo5WPOS2CmjMqQDCSNKOdwgSTBwxnolisiYW0BgXnSFscDXvpYoKB65DHd17E45/qQ4GvtMpxZw0OllJ7rmPrWos50DUZbASCsh/8CpX2nAi9tqCz3K63lTJQhOCsCtpHund0S6APctfdExAAVb1vcoA7Bwww+s6D3aWFCgKtMFl7TbOqlMr3LDScUAlhDgQxaH2lEZRG0QzoZ9jnaQ9KI9LZKXh/irQ/y1BgIMDjGfjsFGmcMJ3twfsJk4LAeHYGpIS0n5DGKYMCS0QkKb9u39jbxjmHPI66/CtqoCEFgTBEhJ2YFIajI9AuIg5R/h7tBBaOroCGowIKBge7I2B3BeHoikBC3AFhEEiY9qAwgOOAGPQfgClR1hp4jQJUczy5RsulWWpHPZC3HnapREgVs0NagYQQ/PUHIAGsQFJ2KgTm/b5ajuzhQBol39PKtAYIBBnH8ihWFbYv/lo+4N6VWuYSmdLeTTUpZC2Bd0TkWmvgx9TG4XiWp3bMdPVRfW8BAbr8kcysaL4IBLABAmHm/O4goWSi1eps0BRUZViQX4uFXoCmTrpYYDAzH7yId3SgoEdRs82CluDAn++lJSDI57wmoLPCoJpibgOCbnICb5uT13zpXWtbb3nAz4h9MuWfPbJ3PmczX7ceCtoOHdIQLNXJ/QDBTDuQajOB7V9QwYATIH6p3NKsKgaaDZgeCsRJUKAgapCeEAiDAgHxJILfQUDWDkx7EfyjgsDpK/L37G4FA9PZHtPpiOlsj3S2x3R6hrSfMJ2JScEgIY0J0z6Bx4Q0yQZOaZ+ySYEnXgQDyqsmAsJOwCBEAg0BcRcQhpBhIAwD4tGAsIuIV44QjnaIRzvEKwPi0a6CBDp6ALQ7QrryGmB3pCaHHTjuQEFMEki7DAgh7kBBNpYek8QkmEjFY5LliABlOKhea/1r7bo2U46hRMkD5pDQrnAwUwaRhCOKDDdTRgXJJtDlXUUeP6yv+3ehu9oKQH5TDwGCu9o0Bey0CHKPdf+i3rSFpbrzOV+TVWwDZ1LIcKCz9Rkc2I0zHPjPdToXIABOwFs4dYMuM3FAACGXx93Pm6ire/fTVijw31tAmEWOzM9eB4SLAwauQphIfAoWNAWLt2jhwKceHPhz1fceEABdLYGdvw8omGkKWijIqvIeFDQR+vRvDue7IjhL8dmell/sJd1FLfiWtQN2LVW/a+qiZ2/tpD4UlDv6srXLC6u4Aw4IUlrQDnCBAauXXjAfS5GlHqbEsr6evINa0RQE2D4BFtaXai1BhoI9aDyrNQSnrwAOCOT7KXh/hunuXQGBU4GC8e7dAgfjhOl0j+lUtAPTnt1n0RSkUXddNECYSrnFO9MKam2tIKCQQEG2VKZACgYR8UpE3FH5fEW0BQYFwwMPIF4ZEK7clWMPPAA6uysag/2ZaA8UEMKV14DDBOad7KcyHBXNXdwhhgEUgAkmATU+YCIwMaLCAXHtYOihYNa+fiY3lXJnSCABBNmeuDgvcpA+JgBYti+OcFsZ631nIYWBIjyzkOibGPqQ4AFB7mmgatrx/IJ3tAjW5hUoLKQsNLnENEhYMinYeGRy18NBeVoXDqSmunDQwtMsyFueGGph2VRJNusu3/MqkuyflWDLoFcBAViUKRUUWERD+8k5zQNeptlvj19+afU3FwYMfOErR8MOFHTjbzf32PbQTgPdKxSs3bP36K7/gFfKwwn6BS1Bz3yQN/pZhoK1rFJ9yUxluLaByOL+EMA6DKw5ktYPn0FBAmZaAr/kzYcnXtQQOBiQLBZhsTVrMVBddgMEEnWzQcFQmQ5YNQN7NR+IZkC0BGeAQkDWEjhNQTo7w3j3FOl0j/HuGabTMwWDU6SzEePpKN9PE9I+YTobMe0T0qjfVWMwjknr0G36ZFzgeoJFGWz9IgKAYQhFY2Bag11QbUHAcOVMgODKgHA0YDodRWtw5QjDA0eYzvYYHriCcHSGNO5FgzDuQVdeI9+vvAa0m8BRoWBI0vKcgJgQ4k6lkQS4GbOjAGGiErvAikPaXu1+Eb22HsEqXxg8UaUh0jdQhb7AZiQBEtmjgfJ8mAgIzDZPzfKqXm7qZtgLk5VZP8zndUyplu6ZcCvjwCEtgtyrqZfe8/x5P+c6AAfgIOaxSnOgkxsOOrZSM646bUGjCTiPU+Ass9yBg+y8qOf0+tnyeX8/oAKEakn9qyy//KT5ITy4eN2FAQOr7AwFvUqtZtkHKrEnVPT+i2lx5rogxCr95AEg6KnCZp26AwTuexcK/HbAK+YDDwXr8/O+VkA+y9817YDT+1Qw0I35refqhy+0a6tiQ12uBOc74D+rlsD2LEh6bElDMKHseNjuOmgpEvV9Qz0MkGkH1KdANQVdKNB/mEbQdAqkEXx6Fzi7Cz47FRAwLcHZXYyv3MX0yhnGsz3GVwocpNM9xrMR4+mEyf6djZhOJznmYGBkxj4x9gZOcHCgxfFyM1CZU3koCATspoTdnjDcpQoShisT4pWItB8Q94xpnzBcEX+H6WyHYT9h2o8Yxivg/YT4mhEDq3qXGYFZG0v+0hXpPwwF92g2ahQ4MJkaNAyy6bl1L4u8pG9B7nbbfJI2F0hQLURSM4MJeNUgIAiI5D0aWE6S5kneBflc7+7aCN01e3I17jgQaIQnm2c+BBLacMBdLQIaSGift5Jsvr8GB0ACh0F9snTFk2113VutoAGK0MLCkn0/P/AAMFSA0MBC/m0u0fxZK/fPcQoefLjOz/3IL01ePn7bR//Y4s8vEBhQIS0L/mDJO6QAKwIc8wptSe6T767PLfgrVM+9r+SnKgsg4M9tBYLWdHBOKKi1YWXVwZadIwFvJ/WHnXagBwMd/436YY0dr30Be/4STlNwCApsEyK7jrkGAstWL8Z/+x5TkFm0qZht9piXGTooiDPzQUdTcAgKTl8Bj2cFCl45w3h2humVM/EtuHtWaQnGV/aY9gnTmQLCmDCeThUQjKoh2LM58HH+u5TMXyIqFO4IGAnYM2NHhB0zhjGBOYppQs0T4ui4A9TZcTDVdVZPlGcOACilali2t4iuPFA+a8PM4MCZFdDAAVyfEa2BmIOmMN8C27f9lPNLmAJXGoQYipNhEnmHaPNhkueHIP0SunFTts0rO+R+ZQK0p1Fben/scB4fmyV3cJDQmBqsfJUWwUFCqX1fL/OxwmTrZjiw1QpIMrkxTQHkRjPtQQsIbR0sCurDs/CDqdUazB5RH/uCyq+efOykCwMGx3deVNL6sG6d7FMzS9+iPmoay0ec+l3/w2P1db7hOBVVzxq1957nf1N1gl5nauzm9tcBwqsCBMAqFJQsHAaCg9oBbyZoB7PWRNMkH8iqqPOWX2pfPvMpqACBvYMhz+DhPFDgU7sZUYYCAyWCOp6hrD6AQIFsJwxI/IEaCpD2QBqB/ZlqBmTlAZ/dle+jMxec7ZHGEelU/AjS6R7TfspQYGaD6WwSs8ECFOzVzyLp54nZaQvmhQ9ESMx51r0LhD1ERV6EMKSQp5M2ZakviiNkyBrFR4H2oBBAp3ukEDCeERACKJzJ0r8QtL/brFfOY4f8nli/Ze03IQzgAAwKByw6fKRk7aL+BioIYyCMDkoi0QyMfFecmBETqV+DOBdm7QE5oUhSL0yEpJoDVRhIPAFW6esYZlWEdYThfDtjqfPekrscaMgF7rGdBzNoOS1CXtEAzJZ+Li1jXIIDoHG41GWbzARZCVC0BwcBgVDGmnbDtC12Pz/Wbk3Wx/yErb2fSzOhTW0FHpBf/p6mSb99a0U+ztOFAYPHP/ku3HzLM7j20KO1TataXZDmlexTb1qHhUr1VH4QAFSltfQ7O+bhoPrtQh7bjmYwYMfuEQiAw1qCpXSvQDDbRVI/b43aZffs2vFc8mUs2oImEJFeZMdaKOh5V3UVGE13sm1+Z5oCHfRCcCsQiFxsAirxCbJTYUIVoGjaS3CiKvbAmfydRqT9iLSfJDjRxLLKIOlfLqsIOCWwE3S91QWlPAWupI09JM8HvJB/R9VMMM4v7T5fHBxVXaxQlsYRYYj6dwCPI9I+gsKIEHWp5ngGhAiapH5MLc4UZEtECiAEYNqLHT0MQFCHT9X1S35VGAfzAZDyDzGAkmytPEEE/8S8Kju6XdQpJwoAACZ5ZYogB0T2MRJRXlJol64+FDio0SyTGgcKCgl510FGo0UogMBoFC8LgNDNYnNt/q3e08p4r4Ag5etoDQwUehmcyYX5uFvO+UY9vwCf3wMln/76Q/LL3eP5Oy/iRisfqR6J23RhwODm227i+kPX5MvC0pxcDW0l+9Q0SrdS22srob6iNXDX2H3KYiZvl/LP6OSzIc4uDORzv7BAkEtiL3Ym/RUgaBwxu46bq6loCFZXlriUmkfksuYnqqBkrq47lNoZo9/mF2iggIr5gKiGgiGYk6EtVQQsYqFFKaSk0QzTCOKENElEwhyZcNLohBpwKC8nXFFFSkwBEcgcCLzTjE8MjEkEJovA3MOEI2Ni27eA9PJ5hVldWHm8r8HOlXkg8TWIgzoj6pJGC360lGbls7Jr1EceR41rMCLEKGYXkKxWoACS0H5gorJ9skn/QBgzFQoc+L7TmhUGUNfvoNUYLZfFhgvx3DfluQnGBF0BoAcS+y2Ml9PmdytnuQTvAQwS7F1zWoQVQMgjnMHNPQJC9se4Z0CIAFIfEvLMYkU25EqsgaAaf93x+poFmOhAQOsQSS1FbpBfdo+8NfNbnxH5WMmOpQJeIDC4/tD18oX8opkidPISlZX7+EaZVarex19XN1ot5BnJwYF1iPYaPZw1CoepdbkjrmgH7LoDJoMeDPSiGm5xKqTytfgQtEDQagfaAWsTldi1YfmFyTlZTrmc6xOqWYpQu7F9V1WxPw+g0hKALNJd8SkgqMmAnNc+AQNJixkIVCGNwSL0dB+D7GDVs6NaLVBQlTvJLDsxwpRgavoRADCIGv90ErV9INA+YWBG2k8YE+EK1ysRJpQVCR55gXruFEgDNWkZraxDAMIuSj25FQrDFQt+FDBcCbJUcQigQWIdUCApz9JMyxrT1Q9PGm43DAJX9t4kVZHHgIEII2xQFzhIDIxJhbUWllgUD7YNNtlSVTUXDNb7m/E9HuiPuQg6hJg09Mv8+unAfc3mnj+vJQveA3hI6GkRWkCwZMCSUGsAWkVrP3oqWS5KHTj6WTMxADI+s2t3r0WQZ9Z2e/IOGwc0B6vjsPs+25XV/8bfp/ccJ7fqGumnSn7dUfmVJ83tL38JaAxaS3y9tteukMRrKh5tlOPbJwUKvuyN+kNHatboaACh0R6Y1TXnbQkQWoehWRv27FIdzYCdX9EOAA4I3It6+CVdTj0oqIAA6ENBDwgWhFo3wmPrT8CsM5aEpe1q27nSlpK2gxihzBIBmSnmVDWV5MsDQaslIBOUoRaUMejOiCr8RTuQ8mfTFmTgsnNVxj0ERPDEoMSIPDSXEdLZqNcljVUQEa9M1RJFJMY0Jhy1cQsmzqGQVyIi2xYJsvlRFO2Jj2sQhwCEGgzCUOIahF1A3EWEo6EEQLqyQ9wNoJ3stSCaBYGF2YzMbxIFlrDQICfQApgEGihEmeEHoCxFEICpIiSy+B0kyD8DhCmp5sA6zmz8b/pGdW65Di157UFw3/sXt+8Zuu9Z9x1jaD22Ef5MOxcAXTrIeSXA3Ekxaw+4Mq6ul7Eahxwk2FOdFkHxG9UeEz0tgq8Py10LCk29zNKB8Rg4rCWYhWTv3n+eh83y6+PvVCi4rr/z91gf9y4MGDQg6T676GA5LTR4Vr/MK9Wfrz/ri2SAMHtG0R5U5zMguGtX911vzQeuk7nPvTDGM+1ABwb8C7jWYVYHsCUoOA8QZHJv2siZY6rBKwMZI68d9r/ZNinrl9UkuT47D0ZW3WxCwM42v3cfrDVIZVBA0RJ4X4L5JkhczAWmLcgagU5LWWAqIiBGgHdy/ZBgvYsiIQUCDREpBISdLAlMRxOihToeJ0z7CZx4FuVQ9ksovggGCPYZgFND+bzpG6l/DQgsTxLoaB4NUYIfRdUQxCp8chgUDIaI4AIh5X0Whp3Ug70TofeOce6fzFMxKQCyx4I2GHGJkEjqEGjhjVPSQEXsxiKbKjvhuNZHgNJP8mGiTZCwmmaQP4fvdoM4akP4crNHQA8QbJfBFe2BhwMp3zx7S3XlN2NjLoKX/I+qMci0CADPxiFtkzzmhBoULHMH0xwQzg0EM42B6wXUCRlFWIYWCpX8uvbQtRkEbJkIXRgwqNdMz89XTN51THOk9bEnqkrt36WvkdgKCGYPl7QGBK6j+ec5DcH9wkA7g+6lFUbNPgX3BgULQLCksWgAoYKDe0xWu3nZJYkd3VYbVDAAGRd1vJ9pqqo6sevtt05DYEDg1emEFgpQtAK2+2Guw9yI7qFBvdeC7Ezo2tfMB0OM4og4RIRxAh/t8l4IzAnTfgSPE3iyyIa2H8J8TwSBBH2GgkHqAUFbNw4GAGRTRW8PhbyhUiDZfGmIoh2gUO2lENSsEHa66ZKGRKZhAEUBBdEgUFFb+PqzOuUk9ZyKrTyEoTSy+hyEIMGQLFomheI0aAGwbBZ/6P1a6iuV+QV5Spw1DffLCwffueY75TEOc0CAjW1L2oM5HJjPwVI6NB4ZKNw3JOjOlV6bYE/ZGmVwcYx239c2bavu1VH5z9q6BwxQ83dHfuU+uJEQLgwY+ELmZl14czrVDAZwcvsENz52A8++7SYenUFB/3FLJous7iHbUxwqwMoSGW6zsSTgeh2sOrYNBtZAYKn/22OauXg+92pBwWxwOqTGU/PBLJTphpTHeIha0n8Wga0Cn+1iC75qcexLNk0YWH34urHz1TbJOsAbEIgZQa6x3RGz4cdBAaWmLv2DWOtEZ8Q07IrGzIBxGIBxB4x7xGFE4KSrF9xmSBovAAoCvZ0TAYjJwBwaq1UDdZv5c9S8jBTD/FwIYmLQ870dGGGgYMeHKNs5xwF5C2cFAlCzffOwcxqVjhpXbdDMAZRSrkPTHNimRraNM0G0B377ZqA4x9k7l8cj945t6TO5+YDqHbNr0Hy+F1A413uXJz0OEBBgIYDvGQ7c+2Tp0NiUfQ7sfqbVY/NfcPtLSKbXIQFxBgry7M7D1yYiSzDQOefH7er2ndu6YpTbNS1+rPKrpylol54fShcGDHxH8g4uwDIgWDIoeGIDFCzVbwGEetczubYDCfmGfpCPsw53aGtgy8sWGMg29WqAWu4pFsHsPKrMNSgQVbgDAqBAQW9gWnL+uU8NQXvLepCWA2X5HZBsFzcic84G+x5QDe7k7jWHAUKBAP8ZXkvQQkGus0Zb4DMwDFmIiSMeqRp9UKhI4HEPMIOTmiOmCcyM4O3uqWyGVGzxEGc9FGHPjSNB+31LalcY2PdiXoh2Is/yTfORj6nAFnOB+AWASCDA/CuGnTSIHhMTw4CZKPVaAyKBhDTleqUQZVVJVgWQao9ElW77agRQHoirDYKycoK7Unxr3+nBxKuSDr17biYt+fN7BAhWk+16GGQFQA8ONsqmKhu9caq8t5QBP8OV8/s4BAkgdQMlBmcoKBO4uVmzqfSlMdtnqKMdWJIlW+snwz/6k9osE9xNU/P7pXRhwMD3m2oNLKEEMev9DlKpT37sBp552008fPVa1zza+1373R4x0yJ4SGhXHax5pFbn5yAAFBjwQt/W5QPz40v5bxNBru/BgQ1OPW2B3HwDFHg1ZgsFa2aEc1HK8rW2Cs3KA2ZnMihwoNbH8mK5AC+ze+Z2L8/wg3nxM3BLFUOpO1KgkrpKBQp4crOZqoCyzM7UFgMQYhQhHgdwSrKhUHL3AmQVQ3LCH6iEQiXkW2dGnzaqWTeltXZ1fgEVTNi7kYHC+RAQyWeFCLK/Mao93DYPa636UF8DEXaEmOEALFH2Agl4sbZhSpRnv8wFwtl/9+oHKiK1W9xz9KP8m7XXgujwC+9Td5bs+r3z66lL4rQHaVqAg7IWo9UaAFSPUQ0ULE7WnA9Q/o0DqKCN4CEhR2mkUne21NKDgty/BcgD2oL83V9Xawd8WdpyHZI/vq0ZwK2FSe2h1WZr6QKBgXYOJ8is0wGNRkErNrFU6pMfv4Fn3noTjzx4bfMLVAd2WUpO2dPahBZ/t0yU54GBRT+CjZ3Og86hRO4fwDMoKEInVT3zXFAAdOgby2aELRoFqgelFg6ysEaZJISFWvFHK3DCfFA37UBdZw4IzLySElqQmql6KWY/CzKvawlEMFNULkaNnKYyO19I/dgQr+p0dX73A6PYWr7r/JZ+w0ABgg4UFHOfqcQnENvvuKw+ChEW+S8Eyf2kWoQMCSgxNVoNXtwwc84TDadJ6MHlpmagAAtYVBXXtJj5moSuR2D+AffhwN43ZoED+Dp0cEA8U4FvST43W8YwVo2BlNHVn0IC6fFlSJD/K1CwRIdartyhzXsPDGZq/gO3z5CJIr8MCno+BC0UFPhaftDFAQP0Z7nZNm514GaKtz53gic/cQPPvOUmHr16bebs0rMDVg9EHzi8UK0F7LYXogsCemArDPR+ezAtaFcOaQv0I0zAyceeTwHyuXNDwSyvPlPmcxF00D//wOPNCBEFBJK7l89eZd+0Y/mcm63oia0+GEWzAsx8MJhVmNlA7kK6UsRa4CIAfRWnfhbBV8xW51qjne9zDvOOy+ti0J2mf/T6TKVibvrYauqqe6kzE5ZZcN7yl2TjsRLcJ2h7BwTVIii3S7AnfWctGFHOfl3SWfa39q/zpLxts9twKNfTGgzMbrQAB0CpV3bLhp0pwX5jJoU1rUEvS1vGs3xNfgf1fnw+SADa8a0Aw9bUg4Iqj+6Emz6VUwsyKNikVuXXI17T7e69BAWHWvrCgAGwDgdADQhWqR9+y008/NC12Tp+NPdpU75npxGAw34Nvbzn+zQNvCj0l47Pfn/4hc/q8gU46Myt1k0I+n3V0dB+syUtaQu8I+bab8nsonX+K60SofQbSJ1W89EGkPztg7umHwK6DwJF6DX1BNR1VUkNDwfzevHHy8zYjjl4ala4bNlIa60vWtqi9EFHo2LnzwtTpa7OUYd6rs6cq0MVdnmZXp4lEmjiXIdkv7N6U1AABd1CudQhUGDB6qxySFx5T7vblS/Wk5UlrUsA7Q8MnE9r4JPz98lbD/ccEinq+QWTgu/eyChRpXOPa4zi73FOSIDKDj+htLyiPnQoC/P8NyeWJqTluyuApuM7J3iXyq9HH5prunvv41YoAC4YGADLcACUyrp1u1TqI1evdVQs7n5L6lcnTCwtQQLQB4WZSqwhx16DLmoM3EvjX5gtnYAgv1lSk1fXUk3U9aDdGbCX0pZBpzcb7Qk8ADOB54XdSmpDtuZCodYWzO7SmVFUA/N5hBjQQIIHp4V6Ult6f2Y/rwsOQyX0E7Q/JclBSrLELn8G590lWbNhy/B0DK3Wo1suJ3NO7OTbB/RxVZghgTT7RM2yTlXXE4AQgl4bZWknip9GaOs9jQDLioolaKjqez4ia/ZSvlbqeyoAII1RQKGBLQ8KgDiaGhtYnyrvbv3+VRONWWXq8QOvLFPZhVDEYipagwyXfqti/7QN/dCfXwN0rXuRtbVJgbU8SysUeimHLD9w3QRd/OfreAESvE8CaZarZZD+Pu3E6Rzju+TfHe6CwPKxF+6c4N2ffAJPP6aagvmjZ/c+DxQAFxAMesn3WYMCq1Q5v73SrP1bbcKSZsJS1XidBx1S+bQ2yjyLcxe1L8uhF2zpPT7o7ORmKTkDrTBb0BacO0ONyne+LwSwBAWHkp+x2IEqHgbmL7y7tAsBAPrLM4GuYDoYt2GW6Q0bZQX1OwgRNtyL/Vt28pO9DeRvSjJ4muCX75KdcUqYwBgnOT/qDoojS/wC24Y6gXP8ghLfaF6eoO1VVicSAnTJJgEUCAMRIsm+CUTAEGX76SEGuQ7Im03J907ESF2KFochw0JeiaF+HL5v5tj5hNpckZu3fKdspy8BgAxSSdvH2sPaqwXV/C55WEDhAnvaDBxyPTZdYlbTnZS1TA0cuAisReZ5sxWQNQhbk/kW9JYxkrtGy09wgM5aN7zgDLzwmqy9Pgw/ORSq7WkSvE+CjXs+VoKtfjBNgh9hWvNFfnCTloBgq2P4C3dO8PWffAJPP/YsHrn6aHWftom2Dim9dOHAYKn7MgsUvPuTAgWPPnSt2xhLois018k9S8zyHiD4321RHS3FGVgDgkMw0NLnUoz1Q9qCrkMdPEl31tiv33A++ABzge4HX3++GYB7UMArkKDvdv5sqd3pj2bXuNlmYzbpAhHgjjcQcAgKWjMBOac5DwS6RI/DIFAA29CoDwIjcwUB48QYOWGcGPuJMaaEMTHGBPdZ/iUFhcRybWLGlOSf9ctpafRG0RYEks9R94bYRfk7RIGFIfh/AUNA/ryLct1AQcGhwMJAvdDShBAEEiiywIFqE8j2m9D+mGfO52gjUg2Cb7MMCw28tqBg15qfgt5EjjfdYC11L1EYYNgy6Q4c2LI8FzXUZvBFc9W8P713dSs4sNcazPPtYWBtKXXA8sqO3piXm1Oz6jUJwetKlAJMW+BNN4uQ4PN1AAaAw2P00u9euHOC93zyCXzosWfxcDOpXTJ5t8mPe2vpQoHBWtdcggJfSWsirbw2dfINI981L1R+10s9WjwPEMh13AWC8+xzQM2XZW1B3/ZbaQvWhGP34eQqrD/ILAKBfl60lbvvnZIuHOkd9+XbAAKtNoDr39d/F+qmrQvn6FaEii67C3EOBEln9xAh7WFgSrpR4pRwOqUMAqcTYz8JBJwpKJyNcn4/ub9jkuumhJGBcUwKGXMw8FEQLdqhB4MQVDswBAwk2xfvYsBu0L+R8t+jIWCggKMosLCLAVdiAYUrMWCIAZFY95gwSGBEFngYAukW1lJfSDrrTxNk85wJxeY+1e22tc1M9eyhzgFdq1XI5ocO0JL+3nvxn+/NNvA22771bYUDQhbUoj0o5/yzqu2wPMznR62NvB2tQccREZg7IhLXoJC1Bi5zWwSdHw8rAeq0CIFLzVheDBAssBXgZEB7E8wnhfN8LOcLTTmWoOCpx57FGwwKXA4Ob6xV0pY6uzBg4KukraA1TYGlJRWPv5WHA6eF6qYtsnkNCOx8PrdBS3AuIHCZXwqistmE4AXmhlQvkaLZuZLJ0FzT0RDY+VZL4H9/SBtS50D/NDBQCYkVp0oudeKv7957lpFGe2K9zeL157I6KIg7gKKYC0zoq6CeFAjGxCLEVbifJsZ+nDIMnKWEs5FxNiWcjgl39xNOR4GA03HC2cQ43U/yewODZJsopQywFiLZeq0Ph2DhBwi6YVKg0s80BLIJ/N0gmoAru4ijSLgyROyGgCtDwAO7iCtDwFEMOBoIRyFkSNgNEVf0HkMEBmIMTBgSkIKI/YHddtZhyA6EEqsgApjKtBJBTQ8b2tBMDG2fq4R80RbUGoXaFESm3ie354B+J3/vkpuFVMOBXG9wYmVwgMAMXT8BcxAEc73BW6VJaKpgg/ku56xyRLwHkwKAwEVr4OcYa6m3rF1KK8eD+iBUDthcRvslQNg6Y1/Ml/s8g4LbJ3jPDz6Bp77u2Wz+7k1U1+CgTFw1vwfyc3HAYKFCxFGjQIFPrUAGtnWuXjoPsa2ZMBahwGkJ/O/Ok9/zRFbzUEB6fub9vOBlv7j+3A0w3UGkqzVotAN2nfu8PQa5u/3s4W7At5enV65DMJCFSKM5ANYbSzUDi+vrc7nCDAomFC3ByM4XQOHgzAHB3XHCXiHg7phwNsm/nzsdcTomnO4nnO4n3N0XSBgnxjhOSJOAwKgbKCVm8JTESVH3Usiw25EdpZl0b4QQxHEwylI/iqI9CDEgRMIwRAyRMgw8sAu4sovybwj4168MAggx4IEh4Ghk3B0IDwwRVyYBhKMITEF2OYwsE2NWIcMBiBTBEQUONHJfH954Q3saIBjAhro/GihsggS/KVEQ+Gi0YL6XuGHCz2PrykeSIGs68899nKK0nfoTEKukNS1CFco9LNRP09DtYbag4gIEgi362W0gl+PMWP43wIGxnM3wt06SPBwQ5toDC28N7i+Dt9+1muPDz92WvxfuFCh4WB3lvTba56GXWmCq87ec1wsEBvNjfvWBaAru7xlrPPxqQ4Elb669Fyhol9UBpTvcFxQcUka1mctv7UItVvW3AAN23UbtwMahQf90NARbgKCaURaBMjMjrObGRiZnOW3qqfhLUDEfqKaghYLyDwIECgF3dbZvnz0Q/PzpiLsKBq+cisZgHCdMY8I0TkgjChykhDQl3fk56YZKsgkTJ9mhMOc7JfhohUQRFGQfBApRISBIkMIoeyUYFIRhjzhE3B0ChkFg4DVXBAweGAJOx5QB4WwSOHggBQGZIeABZjCLRoGZS8A69WIbIOGNA0VwMJuzDLOi1fIBgToguNau0ojF/8AAgM0fwUFCiAD5ZZEutHALCH4h34pGrM3VUsh2gQS/nwuplgCuLzeQYPc5BLpVhnh+rMlxu5dCedp2zUG5hRsHDgyUXTgwDYHTHCzFyPGCeesEcQ1erIWz+eDrik/B1uSzsFE0VenCgEErbgwKntE4BcA2olyqxPb+FXfdS80fSFuE2joN1tcBcyAA1qHAYvifZ4OkefID2VI91TP+e4UBX2e9+ltspYNQMGELENRCw7QOCnPTPOqcRe3Lq9R05lZfRK78QR0NAxCK+WANCgQCJtx1WoK744RXVCvw86cjXjkb8XNnE073Ca+cTRUQTHvGNE7iS7CXjZWmMSGNZ0jTHpxS/mubLzG7kMu+KCFkMIDtnBh3+W8YjhAH2TBp3E0YhoA4MOKOMA0Jo8LAa44STncB45TwmqMBiYEHdgGJo3w2xY3aiCXZEG5fCdktPUC2V2YJeSxxChigNH/Jcn85R/uqJzxlGLBIgZwF8SIgIPZn2aphWgLttv/Xs8oS0c8gIYf9ZWfmW4IEYA4Ki6mTP+6UB7W/AVCvvpgOwYHCdQsINt6vpS3D99qs/F6Tl0PtvV9yPgWPbISCViMMLE9mb90+WS3PhQEDX8ocEeqtEtHQUkL9nkunrETXLJ0HCHodbD5x3q7mmudF939HefH9M3O5mnycFwjs+7mgwJ7VLduKruWeYKAGgUNAYHWw+nL3oKAJSVyHLDZgmAMBMZdNhxwsmbC0GTSPOhha/H7NRzeTGRBKfH+G+BRMsJUHtpywaArk3xwKfu50ckAgWoJX9hOm/YTxbMI0cg0II2PaT0jTHtP+VGBgHB0YTEjTWLQFU+eN0h0ViSJCHFRjIGBAw4AQd0i7K/I3RaQpIU6MOAUMA5CGBD5iXV0hQndkLXuaD2Wk9vr8PpBso0ssDm4TUXZwg2kJDAaWekrT13lyTopNG/NYz+wpRoUEAil4ZEBAcYYkMu1GQA7JnL83cNDLYvO3PV6NHzBhYntM8DZIINs2/oDmoEnEXPf1DXCQ5ClINlvXfOTvakpYA4RcBwtjpL90SzyXxfJ1brwmF1rbP1CWJH5IoeCQbKplUn1udgGKfHwIDy0V4+KAgdHlye0TvFNjR1ekxTUAmID2cLCUfL2+cOdW/r38Xc9XX3DPn902fj7nJjZShgIHPrE+qz1+aEMWoA8F9eY++oSlmTUw1xb0ZlpNZd0rDPQGvrX46WYnPAwHrZ/EAhTYEjd/3AEB672qWXMlPKxYWtZp0t3+fCqaAva+BU5bIDsx64qAhPyXVXtQVhQUXwLxGyi+BOOYcKr+BNPeQKAPBQYE0/4UPI4zIOBxn8vc1RhMIZebeQci3aAoRISUwIP/7RXAnAFziqAg15wS5VUNp4SyLJHsn7R7JFmhEIkxMHLQpgQJyssBSElNCqY1IFHlCyio9mC2R3qtJcjlNR+ayc/mxZyS/RMbQMgmDiZxNEWyEACAxgMgJsy8+jupfTe2vBdAeSfylsTAPUCCz8j6u5+1HT04gEKdwi+RVn9Cd1+TEq10DgiAgwS7V1Pmqk464+XytfW5ntl2KbVywf+2xCm4tlk2VRtqtSfRl49/+M//ocX7XhgwIJRdEp91+1EDrWBFjq7lBTRQC5q2XYkor26Q73J8rdEsFRg5AAedfM3gAMjOMQdTp3MfAoJNWgKgCwWzwWEJBID7goHz7i1u9e9XVFSAwPWqiuxTUJkPtkFBBQRLDlo6KIr93dWF1YdPrn4KJFAeADU3mFRsSFwCiz2QMCapr6Qza4s7kJxmYT8lcSRkdSLUsjOzxgNi9SGYwLpFc/7M6lPgNAQZDpyNXiDA+RtMCRwBcJCqDQGUgjwn6N8UkCZCiC4/KSFw0DgKCftJHBZ9ueSflLnEYiCMUwINQefhYkeWBYqt1sAJX2ra0EsWbcvF9p7BYAEE0RJB+5FCAAXpXwYHbHtYyKycUPgke/Wb3PaPxbZ3pIKGZuIgh+4BEurbdVPWGnTgQHhpQXvgrEGV9gB9QICOmx4SDqaOMG2Fbx4rO+fsfL7HhkemprL86jl/akk+9fLVG/tb+biWLgwY2NaTNzuFrhxZOnAAYNYIbUMbFDzzlpt405//3V0qW0pVqGR7t3oAAN9J3IWuDLkcS8/ysrcpizcZ2PGtQABgvmwPWIYCTYswoN/nMQdqGKjgLs8A3P0XXvjq5VQK8I5Ei82WBzcrlzcdlDIXkDgABe3M2Tvi+a2Aq8wH1CsvqPpnA79lyX8Gi9CbUObaRVgW4bkUhIgqRbNvqqj+AUnU/nbvcY8Yd0iUgFEFXhAfA2qGRfMxAAAadgha/jDsxKQwDO45sXp+nb958uXy5YXWg/xj7Jp6C/6zCpdc1/D1bz4BTvjFmH0J662qO23ewGAFBzBBD1ROfaybE+UIhbpjYeuHoslekd6ma3q7biLf5G46XYf77kACM9qVDfYQdk/ujQ1LcGDPY1glUV970AEEAJWJQe5Vjls6pEXJecz1U8ZPYBkKtqjxu89s8nPty67l+1ZV19Tj1v1agFo+Xjefu5U8XRgwuPGxG7j5tmdx/aFH87F6iCv2Kg8HQA0I+bfu64vOkfFRbTRf+YeSfzn9hj0llygAgP55ArIdqo3O16a1jtxGMFwFAs3XGhAA/Rf/XoCgqxlwMFDGy/p5biwrx1ybJipwsJpm6tDU+VsG7dUlW72UF/P35hEErxFYNCOQhq5lqHMd11k6kAVTt8dAGAgIuu5/nBghEpgDotm8fe4CAbgiwj0EJCp+AZwSaNqD45DrKXVMCQYCcr/av4AcIIS4Q9zJXghxF9QJMSLuCGGQVQsSLVEcFQeqy7WWGK6Zcx0CYNnwiKyOF8wJ+bvvC6WS5HgIczhYzZSaCMgyZUK2+Zuv5w19efs7409VewM0kJAFoUGC1oNoLkyLkPVzuWz5nq1g83Dgf4OUIzUeNC/kPFIur/kgAMj7nfh6ODR+yu1qoetzdxAI/KRki4ygGlb86FBrE+Y36z27bivRFNzIUPAo5hQ4TxcGDG6+9Qdw/eojpZPZANRUZm/TnFaV4xvm1p3iyHjdoACHhXP7TEvLz+4DAlx+14Jo9NRXvgNXHajTeSoguA8NAeCg4JxA0GoH2oHNP3FVa5IzUkY2g4OEuuxVMjXxStnOnUIDAVkwOm1BFvpF+BcNQTnemhHqyYS2mX6P+m8kZLv7EAL2xNhFQtpFTIlxZSfX+yiFRLLMTmbPE0IISCkhDoQ0DUjTFV2muK9WIfgli0vJL1X0qxQECOZLF+MuIIaAOJDAgi5lvDIEXNG4BkOUvzmscgi5zESlLnz9sJCjfEZZegZvTiBGNid4rYGGD2bwXGvg4eDVTpbnBQO2h2l7d5aCqPVSgOtH3IEEqv117gUQenBQ8r8CCFnNMtcgZEuB0yLk6nKaBH/OzvfSkilgKxCcdwdGAHm/DEC5kt29HaC17dd7dhnPPRQ8i+tXH64nMyvD3BcVDIjozwH4OgD/iJl/sx77dgDfCOAf62V/mJk/deheb3zwYSBNLnBOAYTcifRMvUbWVX7Tii/cKY4arfql/etTW98ebGfPbhq93fYXKLHIFh/YHF6yOfV3AnQC/7w+BJ20yaGwAwSt+tMGtXZAq2Y37osnfB+drNSjwkF5TK6Lmd24/d5LvWso5GIvKRPIz5ozFLi9DlptgUU5dGaEfnZKL4sgjCTrqSMxhkg4YkJiwpXBD3UDYphkxk2E3ZiwH2VJ4DQETGPCMAkUpBGYUgJPCdNk/geDW6ShkRCtIXt9RdsoNJEPxZ9SAvzESKAoMBAGaFyDgDgExCjRD0sUxDrgkQVCGgJJ6OQoS27tOdG9JUtL2NjOkYUrjmBKtdYgDLJzIyIYU4GDKFCw2vbyYbFuVtPMrlLe4BaaWyjY8v5M+bP8DQ0keEAAvBCaA4J7G2EmhyU4yHnI480E2/rY7C4Wx0F8ENgBAmBjiWkRAtx44jQJ1TNQYGEpLY2pQN9cMPNJOHDPnI/mHKEGBa9RCP5HHmCasR1wUJAnzW7CVz15nr7YGoOnAfxZAD/QHP/vmPm/PtedTHjZV0edrROLpUrN05y+pY4a3mehvny5UqnzyR9huIb3jd6BBOCw5rBLuM3La4fuCwYOIPYSFBzSEvRmOe1gdmj3sdpHRD4ZCFR1p8/ZpPGx2V+erYRa4ufBDmXNehsZ118Ldx0RWM0HFRTIdFniFVgo5KxFkHuYT8Esu1reIQZMYFxh9w4oENhMehcJR2OScMK7iCv7SeIe7G0vhDr8sWgIFAhGiTVXVnNqFMQ1KPCZBEDBZvSSaVkFI6YCIhJzQQyzMMm7KECwy1oCBYQhYAgaKln3VjjKeyrIb4cY1I9gni3vZ1DqW2EgxHocTlMDBwkY3PI9W60wW6kz7wOwPlAucu/PmulpnrzpLZcLfZNC20Je45Sv9ZpG5goQTMFfz44t7529GZbgYAkScsY8JGgeDRIw5clGDxIqTYINvFTt/HBQObikPdgCA3U32y4vgsuy/bIChfnNZ2P8cQUFj87H+QPpiwoGzHxCRF/+Kt1MP/QAIeWXa8l5yae5o4YXjp3KzTjauTf5F6Q+vwoJcI89lOUD5HjPMDB7a1pV31qe/Kz3/EDQMyGsOQ35ss9MCPlYoaTy4hnUuB3o2HzVpxIBj/wP/f71ZbADGBgGF3O+Tpxn/A4IshBSCPAwkH0L6tgF87pGmWXIZAoDBWBwCvIg87ghJBxFwtkUcDQkvGbHsi/CUaw2SjKv/7NJtAD7KeWNkvadjZM8GKzF6aAGDKzdbOnhbgj5u/kQHCkQBD3vN1i6osDgN1gaQsBukE2UrkT7LNs2Wz2Rf0F8G0HGCItpYL4GOeCROkWCdZkhpxwZkM3As7UPSEXU/UAqycG2vWs1JJTj1dttWZu9SzOTwoZ3iRUG/E6CLSB4EwNp3fW1B26cWU02LpUxhqxT3yMkmPmjBBLbkI02LYyxzSn3uSMzfFqTGagnOZlpsJx1n5fj28e48bEnMhTMgGBDO3yxNQZL6fcS0TsB/BUAv5+Z/1l7ARF9E4BvAgD8G3CV32gKzPaV4aAOvVndE460zJHRpoCtbSzNI51VfSHrlKe68V2ntf99o9tMlsslq6nXKavO2ToRAjMYmIPAIaJcAYTWJn6PULC0V8SmXNFy1RVR6eCg0ghMZUZDOjfKW9KqYEhTKZ8TDMj37dSfG/TrHSNrGMiRDSmCzexg9ehvpx2GiBBEvwoEYHAqkcgREQlDIFm+GFiW7bVbKu9EwI+cNLxyEgCYUrWlcvb4Tylv5wyU47mOV6ROcFNM7ywon9V0QBaToGzNvNPZ/qD7Kwxk3/tbM1fbNseQfQ2GoBACKqsQMKve0m81zkBeUsi2rC5JnZv5khOAmFextMv3Zv3A9wUv5CstEVVtP9fIKSS426+9ufm9a3/USTkopAGC5q8F7dZ3Jxet0R5kOEDo18ss5813k6MGDAoGFjIaWIAEAF5j7EFBM7qZE9bHWpfJ3qQRqJZEtz+pkskW195W/0tsY8dXoaCZAK4Bwi9GMPguAH8CUvY/AeC/AfB/bS9i5u8F8L0AQF9KnJ1MKqHVgQMgv1DtWCBQ8ESxyWgDdVUwB9QxZf9zKUkXFBptgifCDUzQXNdv9N7OgHJ8AQa2UH2HcqvdDqtr+1BQlthtA4Klvdct5bXK5FTCjW9BznWRnQ4O7IvBAMMkApPt2O7gwOqSohMMlkRAzDaK8gIgD/yN9iBEAKFAQYhZeLR8RJpdYvV1SwUOAsQxnoaAHVvY5AROIW/JzBrrwD5P2iYJRRuQmPV7aRODAGuT1tcwrfSh0PQdM7vb0GfBiexcgAtYFAgB6i9AAAXbTjm4zxKsaCCBAygQyL0cFARXfy4/VsdEsrOhyrYMB+bnUfYPIOu4MkOunEyKxmC5P9Sag1l/yP2xhgTvX2DtUk1M0IHuBgoOvlNMc0AwbQF1tAdYgANuJlEUwFjYbO3g+DOVdxVBAd5Bgu5NMduhEmhAQXLoqmNzWtUKLIHAOVYwURpR/Cls0tKTGXUq5oNn+lBwDl+xX3RgwMz/0D4T0fcB+MHz/L5eAtODA6DH1ce3T3Dj40/i5lu0UlNjJ2wrs6cxAGrh6BrUg0LOx6I2IR/oPKDJRw8E2uO+QyyBwNaOS6EMiN3zjbYAZbDdCgVru0l2Hwk3L8mSfp7aDU7KpTYj0/o3rQEgEkHlPkFAINdpFTc+VnXIec13PUP0dVSbEooAsN0Tcz2G2C0UEYmDWNDVKkE0BzFIdMxEwMCCykMgMMdc32Apv8UzYGZMVtdajMlgWyu/7fG8ohk4b6JmXVdeRaCHYzZB2HnKbRmDDfgFAE3GBmg9kcKHCjlbsWDnOzkSKMuKRgcEQQSb9YPF7bYtcSr9oRS4LuBMO1AgMfcJ/d2aGUEevdIu53iv/FbEHhBa4M7f9Xczr3yibSaFrWNRnj3V0yivTSj1xRUkAKhAQf60E5pzjLs+3+iMv8352bleYs6yoZg3O5Dg0kx+3QcUAL8IwYCIvoSZf0q//p8A/I1NP3Q2m1487gwHJthcOr59Czc+8SSee+zDsqQjjfU9XUNaxS5WsNnB8nc44WCNs0GbsKWs6HTEQyaCbpkOQ8HiXusV2DRQ0GgLTOBvgYLewNVWeWX2R620XCxHAw75q1d75uVoqYYDzoQwMx8UEG3r5/9P3r8025Ycd57Y3yPW2vteoHoi06yRWfoAGmioJoDMmkhqkQSIkUxEAnwYqh/foKvM9BrqA2ggWRdEooEEqlsmM5IgoZ7IDPdmgt0fo5kXNdFEA1nz3nP2ivAeuHuER6yItde5mexqHYbZOXvv9YwVKyL8F+4eHgPVsW03GCj7HDCEZViGlqqCwOq7gYKUZYQFQnKFhFG5Ui2X/z9LfVMpo9YyCtffTitwuDaIpipuFA6YAJar2tLEZJPoKTaQAKCCQrngpH2dqRt9vWja1ggKxrfy6Wzb4u54WcXwGA7keG+Ht7z2/gb+RuNM3+2XSn9eTrAv900OwB4ULD/30hmtwARyTgnm0pdrHmeQoMnk16ff/Sk+/nBiPvD3/h+6xoCIfgHgnwH4HxPRbwH8HwH8MyL6n0GK5b8B8B896aJ+NFsKsoyTBuaDz/DJL/8YP//OnysUtJqCaWEeFC75PADVvDGAhF2eznTQBxWwze9YK3C8nvwgaaUsKvMOEnZmhAMoEBB4GhQcDTDOtOM+9SJcurRWc2NHFTiwSaM2onPOUb3auL2Ofa9iq7EZ+5HjDqqAERToZrHtKhAwo8SoOH6tnU7KFaAvyzA4fBQ1si/+M69jJ8wG3/Ngo+9iG7n7BKqZBq7pMt7AAYndgVWIEWfwgZ8O9+1Ojxln6GQd2Znq9o6o55XVbTrS6BcLG1S034GDrHBqx5S2VWwy9bmH5oQn9lFlaWzVEhdQGGkTGpNDPYZ82OvZAGiW7sDAqVldXSKzzRFhNHXTQ0KVXz+pcQoGg8Sn5uHf9qyEPxxs/vFXce1Ga9DctBbSqzef4/u//BOBgm98s6N+7gqxb3b3aLZWMMvFOMoX8D6agmHlewoMnDEdTH0GRsc6wSYbJlCAu+YDuK9H9bgvsi+zKppcsBvZEACmMkqUBumkY+k9B0slu3Q3tkO3zXf6/Wd7n7EAB2jqOe1xdTdDxVZgMkHXa5o4t/Vo1Hmf6Xh6LRNQhR+wN6/ocSMhKSr3CqL6ZMPPs5aPnkmaFQiJwRaa2JnvGGi2TVW3s/LxA4m+TPyz7gDh6ckWYvO3vgffBgeyEVM4YIVVg4PyKL25zkOCTzt/HbvnCfMCuRlEDSQEiCHMQ0IndMu9O8e/w3uOoUbuMRpQnkU3a1ehXotsES3A2sSrN6+r/OqDF3XprAnB0v/gTAlfeWq0BigvvEDB7/+ZQsEJO3xz3XsSy71E60D86b6SDMwbw+eww4cd8ahTek9yHGlcdp15p+b0+5ya05ekgcBRuucQ1WcRkHL16mO/guQ0+c6tXMepPd3CNQYI9kynacWVSwMAZd9YkPXf+zSLrEbN707wO1ujX06a8laFfLbRhh3XLRJlqyfasYBo2JiLpu0o6mEtFi2L4KZoAuIhSBLxkJa1mefPIaotWN9RmbHhzC7qtElOgHphan4KvQZmVtb9eyH/nyLUZaHqtwb+PuXaT217UlByuIOmmTPvUYUhlfxtSHiBg/JsdzVNku6Z6/pgYk3ZzbQGAOrUj0HfdnrE22pm70NCQKPd9WbgcuyddEo78MQReznGT5MODSC8evM5vv83P6rya3h+m7+npOcHBvdIj1kK9a//VAv1d7C3x58YXY+2FV8BDyF7QDA/CACdFmHySNNRx4F24F6jGlVQ75cxGtVNM+g74dpxAW2ndcbZ8GyWgCoER1Dgzx/ak6eP5EGnAsKpTmIIBcejWGAwku1/u8uONAHlDh4EVKgXCMipbs9ZRkZF+Cdg28Bpk2tsNyBtyJv85u0GMIPTDUhJjnOAwB4UtL75JYnLY8TOIdOEeJmBEUBxAWIExVXaybJKJ78sQFwA+x0XWaqaLBBUADS8cpnJESJoMMND6kFbT32xj8wYo3fh3wdQFxnaN5enCjh/v07bNKu4Nko3kuW6LHHpdjS/PRxYDn2+75rpDrQGYHXy5X72j/UTNv1xojXwzz7rs+6W3xgS6lkKCEVjoKPz5hnTWOvcZ/N9gOCoz2/22YCiahFev/kNvv+rH+Hnv/djkV+j608ze44Anw8YzNS3XSpQ4Ap1qDYFOhpsX6T/3U5RQgsIJR/Osu22n1bx9DDgt50FgrtaiXlvMPWeH4xq/GjGmxCekupITH8PRslPdTI7Ss29ynXNr+Aol/tr9J/7aWLunDODR64w068QSR4IdPRfhH1O8t3+ONV9t0dw2kQLcHsE7HvaAL9Nt+dN1kPIWwLf9Htm+c4SKhmALJ50pDUIohEAAIq6boIumERBvy+6fsISQcuqsLCA1gugv2lZd9uwXiDxH2SFRosiWf5yjSZpWoYeEO6uTNi9Lwv8Ux6vvDz9rYJaPqKrn33BHErI3VH3mlPREqDCgsUjaNeLMRFdAWGUv/visU3epMB92yWdpaAayZ3WABpAbCY8j+weferMt2NAgBO8fts8fPP9+88GZHM50m9rp0Gr+eDffIY//NV/iF/87r/Cx9/4ZjVhAYeDlyba5AnYeT5goGkHBN5700PBB9+S3Xeg4IzH/m6O8sBBb5/Re0iO+xXtnsngLBBYKqMTN0qZggLV/Y3ts8JBPxLutQXtvbEbxYzu3KgxD4BAjm2hoGgLUM/r89cDyaxLPCv86ytoO15/jE+Ne1lpx7RbIbJoCmzUXzQEWwcEm27bRCuwPQoUqPDn20MBAd4EBtLDI/ItIT/ekDb9vG3gLcl2BwlIGcyMdEtl+iIn+fSBjiywEUUqUxPjGuXdxNDAQFgjaImI64JwWRGXiHBZEdaIeL0IECyXCgXrFbReBBbWC2i5iDYhLBK6OCywMNPEUbYx6zaUkX7JLe/r6eF74/reqiVb3psHhzE02M8zMVnL7ZoUqNY/ryXo4aAswsZcRvE9IMjzzfuLHsZLnjqtgd9dlrS2E82k0Dv5TrWYKiCpPNi5QU5/HWcatcmVe0Ag9Db+uv0J9xsecx8KRvsNEF79m/8af/ir/wC/+L3/tPjEEZzMG8kdBxezVS5H6dmAwXCZX5d25gP0BeQF7wAKnkKOZ52DZnCwu9d/D1DQp94+3mkL5BhTDbZOc0cd7CiVjs3BwThP7Tl+0wgIRtv7TrlPs1w2NWUAASMAmMHB7D7WJe1ARjvdfZ+sUOtNAjmBkpgCwPp9u1UgeHwA3x7Am3yHAsH29h3Sw4b0eAPfErbHR+SHG9KjmBTy44Z0S8g3AYJ0YwGEBAUFlrUULFCFlVXmJtohdAGloIAQlijWgLKsckRYg6ykeFkQlgXxsiBcVyyXC2iNiJcV8bpgefkCtFzA6wV0uSosXIHLtQACLSs4ZiBHUFyqE50tTlW81VvB3Ji6Ju/XJw8EsHfILHVPwSH7fUCzqNeoXj5llN6vGtus2spcFmKbAYJpowDUeCCzdKcNAR0oaKEXbYQNHnpHxPI+BlqDmVPiveRBooMD0Qh0s62OtLzAyf56ks4+Q3cv4oxf/5v/Gn/4//qP8IvfVShweZs62vv7lmvnU+aRZwMGe1KqD//qi88qFPilmYFOzcNfHgomeWjHgqP9s3t9CSh4n3w684CYCw5MCLSfXmdgcM8DXDonx+seDgZp1GGONAG9hgB4v873KSDg9E3DUeYoDG1uxipthgLGIWgzUfOcZZaAcwA0KKB8Ey3B7VG0AQ9vi4aAH94Ct0fkh7dIjzekhw3buwcFgRu2t4+6/Yb0IBqC7SELJNwy0i0jb7l8Z2bRJGQpAx/5MHG7YFXQZwgBohkgQlwDwhoQllC+x8uC5SoahHiNiNcV8bJieXlBvKwI1xXpYUO8Lvr7pQDCdQOlDbTdgKuUD4FFewCnEQpByosCmHUVxeY9nvOHmb9H1uet1wQUVKEhztiBoMmjKj2b892mJvlTfBvyy7r7FVvJ6isB2W1nDwm2/6AN91Bwz1TXm8D2M4B4blIwTW5xXnTC+6mq/i8DB/4a7cMP8uFQ8KRdf5bfV7/9r/CH/+V/jF/8r/9v+Pjf/Z/vn/8QCty+UoaDfV16RmAwfsiiKbApie977ckLaEwI00KfQ8v0Xs2570HK0+tN8jEwHxxDQR9sZe9weEZbYDlgjEcgu1GYy+pXDQOWD0sje3M/ktxta86pgHDUL2RwLX4GJFqRAoIWTPTXaIa2NrXQzAe5aA52UPD4FvzuLXh7RH73Dtu7B2xvH5Efb9jePiA93LC9e0R6uOH2dkN6SPL3KN+3h4R0E0DYMnDLrOGVGRsLECRua6sXCIC8l0gCCMtjQoSsZ7AGwhKAeFkQ14DlqkBwWRQMNqwvBVyWFxfExxW8JaTHFcvLhCUzQjOzoiYCgFXraQ4gssWxCMwZTQwKruWcIRoPDwTek79/p40KXruLZhQuEriM7os2S9X9Ptxwr2Xw8f37+tv0NFQ3ZkYBELh7lgEpc7H/Z2vzDnibdVsG6d64s9cajBwRCxyMgor1cCBPWEvlDCCcGB0fp5N99zAfAzhw/mgmO3YmBSdvXv32v8L/9r/8j/Gv//3/Kz4yR8MzaZTPJ5TFMwKDgfngi890nqcGfwDqy3mqWqor1CcDwVMq6N3KHkrFosb25mxx/vwzRDnSEpT9HRSU+P0tFHSDqcNbFhuoJfes/ehjBAL+OK8xGNtv3bW637P8tmGZ90Dgf/vjTXB4IOpNCn3ygoCIxGtbh3VBy0pZoc0zs/vL5Y90BkJOG3B7qJqCx4cdFKS3D9jePmB7J6aD27sbtrcbtocN29sKBNujfjLjITFuzNiYcWMBhMzAjetiSsnKjrmsjxDLuyGsJM+2BsKagUW3XdMNyzvC8hixPEQs14T4GLFsC3jLWLYMJHWCTBnLy9qOFwDhBYDHWn8pyKyEsKyQBY+iKytXftTWhfIO9d1mbt/rU94poCN36Dt04AcbsVO7/kDjV6J1aKa6p+67ZakI9l3FsTy1kOCP9aDQcOiJdnqUfBYaOAiLar8whgPt26gZwQ8AYZaaQVBotrWLmp04/+x9mNu8NqP8O4DQQcHH3/j37vetwynR75eeDRhwaIPMvPq71/jkl3+ssaMNCpwQtbT73Qldf4+RY4e/DoAZDJyx65TTvEAvlWuQ9+akDg66+4/PafN6JiTr3nwwXzVxlIyh99q4fV6PQKDsn4DAPSAAykCqfLfkVaj3oGAkNNrz9tfvkzkZirxgBBZnvcw2thIzQh9Rc/9AWvg2xXC7gTdxRuRb9TFIDzfkh5s4FT6qH8EECkxz8LhlPKSMhyxA8KAaA4OBW67agsx1nQX/9JFszQIZja6B8JAFCNYAXAPjxoRrIFwfAJjPwiCtAFIIMrMhBuRASCSLKRFp3IO4gLcNFG/guABrQKMSmBYjF3OCf7858/n3qgLV6rq9V6BCgmhT6Hj9AQe9vfalB4L+e69hKHLJH6Ak0WsMdqBQrnHcp8xEUQbOrcIY5Ka70NPFSVHyLH2z9Vc4P9DrhefwmK4vf99+3P9oHBk90OwBgUmmJDZQMIOXkVnDHcdDGSVrK4x7RUnPBgya2QdfvJbY0X/wU3z84UeQVcHMflSdWXaesJ1wHa4PsBOelu5UotMOidU5pA2+Ydf3KjXX+L2mYNo8XWryOQACPeZ9oKBcd9Jr3svdIQjoAaci+929D5Uj/UjLNs6gIKvAGGkIvOCwc+5mpwgD+ZkJ5g8ncsx4kHYWz3qJ4l+SAbAGIlJISAoHnMGZwSxTDdNt01kGG9KmZgL1H0i3jLTJ37blAgFeS3BTILipwDQgyBMwzKpGjyTjvpRYZLXSYtBhdmAgZEbYMhBJPgMhrBkpZNCSEZYE2jbkW0RaNpm14J6PssRboGUFWMqjTO3U8hq9ksY8pO/E3u/w3d55r4AIRNL3aFoBqGkhO3VQUIv7bGnjzhWl+RxlZA/H1BzpQSGgA+IZKBw87iw1PHsGDiQH2JsWUProFhCAsrjZvTTr95p9X01f7oPYDSHBh2J2rfrVb/+2+BR89I3fcX4fY6jZB5lzUDDItyy49Ef4kD6Y5v35gIEWfVk6+Xuf4uMPPypaAj9vtjiwlFNJX1anlh+kV//mb+s5XQWaVp4n2bnqeVaxngIIAJw9bpyGZpCzQFDOmUPB7O79QGW3/wAEALTTEptr2PDthMPo4B2R84+YJQ8JPRT0I8leaNi1Uzf6jTaFj+GEgZ1jQkEEh7cxl+lfT6pXNXHiUxEKbdrhv+10Jh9nnmeaiGodRlvOGVzhcPB+D9+t0xqYQ2ABBK8ZQl2DgMp7H66+0Wbb5/YJdZ8AoMwIcHCsjcqAyKeRD0KfZu26XAO1jh/CAUVIpL/caQ94DwjNYA97e32fx2nfBxwCwfv05w5W2Gs1TPsHBzMOEursg4FPgc/rSFswggI3a8zLx3/5n//LadafDRhkAK+/eI0f/MUn+PR7n+KjDz+SHaVQ8h4OdvNnHRwM0qvf/lf4w1/9h3pdnft8VHE6uJitUNjaluAaub3kuq3krAeEphJW9dQoH00eexiw40eVrAMCYA4FR/DeawR8dmZagXaE5DpDvdGptc8p6Ajc2/1EvXw0i7w8q/cpcJqCGRQw2mWN+7QliRAXA+3goBcMzHWBmlZvbFAaSocPSLAggzuyOfwGQlH2hyAqeAoBIQaEIDME8k1mB+QtIIoaQGYhMOk0OBYDdhLn/kiiQUiQUb/JSG9OsHDEgaoD4hoICxEiEa6RsFL1NVgDYVkC4qKzFFYJhhRXyWeIARTEdBCCfK9FEoAQQSUiIun+rk678qvlbO+zfV/lHQ/e79G7BTB8v0CrGbKxMmsmiGgoaAm+PRy0g6M2AABITRmQAwVGdXZszA4lr9J2/WNb8KRZsrHNDA7k0hY9Ukp5umYJ4EwMcIMm3vexsz7wQNv7lfbppfzyGBQGkPCL3/sxPv7Gvze8bpNfy+thfy2ZYIh8/OQvPsHPvvcpvv3hR4eDoWcDBq++eI0f/sUn+Gn30DWKnVNVNTZ4v4Ie0NiC/PV/+7f4w1/9c/zid3+M/8X/83ut+mZQaWa2nVHiTg1G/ksPCegAoaFOoBmv94v7DPwfZgv87Bf3aRf28SOse1AwG9megYFGK+CmY06Xm/bbynUNlNqVybiAYPvOfcCYYSqjpipAzkBB6ppihHR2KbfCI5br6fxkdh20XZ9QBD1852DCT4WjhBsm6chDQFgXhG1DXCJ4WRCuGTGLBiFa9MLBCP0SCeEBCCQzERZmrATcWEBAfAzMpKDP6zLtHQ8FDET4B0IDBEsQH4PlGhGXgHiNWF8udXbCRWYrhMtSpi2GZZEgSEtEWBdIqGWS8tAQyaVMQF15QetCfa/lq75H0xZg8H7vvVsiQso8hAMZ9KpINDl30FUMnQ8VCsp6GMCdtuD6mWb54VQEStUotNqEGSToReYZt9MmcLB/NgUEYrDZ0ahqEAQA6rPR0LvSDZh2adw3D/tC/xvn+3W2+5a+2QaldkAFBQ8JAErwvXuaH+7rseXP9dmAlMorHTT38nGWng0Y/OAvPsFP/+BTfOuD/UMbCe/sWJ2/QT22Tb/+bY1NXTQR5uw4G4HsSHJWibjdzbkFBV+hRpVJ71m1HscKyJ25o8vvjDYtp0dAAMzrcrnlAAaAaiagZjPvR0Pud+0YmqHL/oa6YlqzfKnCoIeDxgbrBiT30tEzzwSHbYuDejGTDwYLvTmEVfAX4Uckkf+Shg5WfwNmBuWMeNmvYyCXCgjxERQJaSVsDzJa394lpMeE5RKxuumK5nQoDohUoOBI829TFQOgUGCA0E5XlFgGEcuLWGIaxOuC5bogvpBYBvHlBcvlIrENXl5KwCOsV9DlhURE1NDJsq5CW0Y72yxaCBi9l1E6ercGB8Pr3QGBJnXA7NtGv0gWcNQmWo2o9AdJ639yA4M6GidtG2WwpZAwmg65u93gmckdG7o+pF3rpAKCnEsoJgbJvG7nRrCWEfidNDT9jvrDcsx79O1uFxvAeNDp+vUmX02/Psh3ZxY5AwXf+uCjYVyVPj0bMPjpdz/Ftz/4aEqhUzho5scC3Ig50RR8/29+pEtbfqtWmKBFN7DjWDonV8j9B5r4/NoA/EI+u8oEVFA4dbtJxW+210rl69BZDcHRbY+AYKcd6Ds9AMXWWG58z7ZqV21XJzOzUQ8HcEYFGUf9w6URFNxL9sRqFBATAgUA6onPGcwS8jdcrshszlu5zAgJIYDiO1Gv6/oEcV2QHh6xLRHxRUJ+uGF73LA9LLh8PWN72JqgRvadMyNtWcwNOddFFwd5t3cvtxUbRFzUHNAFOApLwHJdQIvENFg0+mFYooDA9SLhki3o0fUCurwA1ivC9aWESNYIiOFyBZOEQkYIOoMp1M5UhV7GaRZsUgQN4eCrSsOeZQIFp1aHNRUATOaGKnyJAOI9JFBdV4LcJXwL9LMuRrctt3fb7dgZIKg+RbUYTosAoC5LnAFbChsVxk+lARwMzUzv1b9T+836aw86QPccqIPOfgDY58VrvO5AwX+mg+adVmySng0Y/M4HHw0p9Bwc2NGAVz3V4Eg/wccffntPj91LsDT7Pkq9EszyWCuS0yh4bQK5yuTlS+OvsDeLzDUbXz0MlGx4KDipIbjb4XVaA8A1LP+cRUNgK6kZILAKCAcHrjMyc4L2k/ocaufstAmiEp49u5xjENBrCUajyX5LX1OZnQNikA6ROYOsc3S+EOH6Up3apBwCEVhXKlyWR4TlLfJ2QXq8IT9csLzYsD0+greE5UEWT8pbkimOWwYnLiGRObPAAWd1aJS7minCTzW09RGC2hQokPg6kIAABSohkSmSQIKCgEQ/XEFLxHK5IF4FEuJF919fSghkFxa5QMH1JTgs4LgCumaCrJEQ6ndIefbvsJ+3MMK493m/dV+/oT2H3DG7uBxdGyGLfqk5P90+ZEeR9OScsAskUG4AwWvXLI89IGQctwv3GADcYlTsZkh0fYXcr65ieVfANjea94v3tQJtH98/0pk+vu/f5VbuOaxvB2pMh34AuLvwSMs7hoJ+0HzvvTwbMADqwzZOXLgPB3V7TRYc6dPv/hQfffjtgf0p7ipKaZZnak53w9YJp376EexUm+Af/tDJxu8fV/azMHAYzdDd8x4UnAeCXmswGQ3ZfdV8IL2TX4+dCyCgzIVWOODa6bHll+V5AnMTDz+AVN0qvV/TqSlM2KyD5PqkpXvxZWaC/itvxx0WIHZ88mYENn8EKpoCMINjB5tEoBck6vS4gJYFvF3BywW8PSJcXyLcHhB1YaW8pbJeQtKVFPMm6yUICNRFlOSPy0JKOeUGBkZgYN9DVD+AqLEH1IGwLKIUCGGRtRJojYhLLOsklFUXVzUbLEsFgmWRbesFWC8CBUEWVEJUQCCDgwiAdtMObeZJcJoAE3Jfxfv1Ar+uAupFBkqwK7/Rt5Wd+cC3ETclUx8IfSrtA4A5MIuAqSKMdB2JGSCQE0SA882hORwcj+YNrs5DgpThBBTqTff9IoB7fSMw7h8tfZl+ftfHe+FPtNcSD9LID8yysYMCVAfaM+nZgIFVOPOWvR8YpMIBgAIIgEDBJ7/84xIHYaQZ8GrHcSz8exmuX0vlr5krXsEj2pROpvFZ35NNSe2OuzAzgYG+Qff1azyakk+DgrFjIQMaqe8QCHoYODs9i6HC3t6z5ogkeEoxK5Bex0FL0MI3ICDt3c0jxbigWbTGCw/9vYTxdEjqvnih0QgG1LKT+8oMBeOEQKGYtigBHPWZk3YwYQFRFGG5vQC2R9D2Qpda3iQqoi63HNKGJW1AznVZ5swCBkliBDTLLpvtQPcBKEswD19JVBCnUDwSZYZEXXaZKKjWYBFosGWVQxC4iUtdftlgwLbZyooUHQQE1RhE3a6wQKHEX5BOsxoSvQOqgaE5wn8V77e+YwcNVEHBA0KvXXONsraVe+3kXhsBQMQDSLgDCCH6oUtTT+9pDnab3EGsQH4PElzx7kGhPON9GOnzxINt/1D9vP2s9WWsSRj6TwHHUPBh1RTs+/J5uTwfMID17dzCgatIvhhaOKjHSHCkP9I4CB8357H7ntyPmXrmnq3LOv5mTfeDBjAGhfbbKJ2p5EcgcAYyXXab5DUF9lX+6khnBwU5Y9fRedVo42NwJuUGEAocQJctRQaYdloDy7DVlcBcRosCDToHXT3OGVyXvGUUrUNR5c/Kx8qkERjUCI0+lSIgeQoKVYMFjiBKMiLWJZg5LEBOQEyyAqGWqwj+LKsxWiAk/c05y++0SbmnVOtFWZfBnAqcmno2VQ4KBOXhre2FOntAnx1RZhIIEMiSzIiL+kXEqgEJQVZQVKdCVuFfTQaxAkGIAJFqECLYNEM8qUpU4Q8gxH+g9xtAVfh3+z2oYqctcADdt5UeCO4Z/M1J10wFnADKO0AQbb3Tsqnkp8HgyWsOdrc9sc2mQNa6Xnrt9oQJKNTvx/1jf2///ayj9Zm+3vsrjZ6hgX/dWCGHDx5jDwXmaDiDgns957MBA2APB36HmRT6AvG064M/fKRTOny9aCKDde3tbIE3+eU699cIGRiMPjup60HhTMqDWn9kHmgaxonrD5R0Oy1daz4YQEFOcy2BV5cObKejt7rLmZoUinbAno6DG9Lob2TYanumNShAoA8jywnDJDOSdp5i7KkCxHIx6jca3ybyZoRWaMBtt6cloKrASepxCEuBAZTPrELbbNEbzIubmYE1oRU4NhWUa7S7lEp5c79IUfE2fA83TXOyCv49uZgEFEDRxwtRkKe6ngeDisAv+4LOQICsk2BTN5kMPqho/HyUxtJHmy+Jln8unbLAIQjv/X59EK+oUt/aRgi00xo1wm5nOkCtt5Z6KNi1l0lbUXAu1ENW+xxIUxaFgmkPTNFmoODgoJ/yO9MaPKV/8aDgBe0MFCwfT0lHfSWw7+/9bY9Ss8YE2mcwjYhdKHGtE7Xrn0dasSuPoGCUhzP5fVZg0KeZSWFUMBb84VM3z5PdeSOBultet7v3LO2EZnfSESSU/Nyr8N39jyr3DATuDcjtOcxS3ydvQqhQgFNQQHkbd3KjDm7nWNR3hq3DZQ2Hqw6HLEu+gtSrX305vNYgKCXkzAUOpHxE/Rw1rG2GdJrV/YDLNeblWIWFHVpW40NrVy2PzHIDDwesgEA2YyZWALCy5lKW/ewOexK0L37ivOYyMn+ws6lrEAyMI9Q1x7bvdDene+Cpbc24QD7X0VQ/4iOgrIIYQAqHdd37932/Dexp2wjFz6AFhJ22AO4d9O/PmxDKtrPtpd9eehqIuYoBygpcAs8EgHMawEFV45fy4zEAnO1rGo0qDMS0PDARsuhU93bwUXLHnoWB/rnO9vv2DCPQATtfDapwMOv2GTW4X4ECl7/3aaLPGgyapKXq4cAK2wr1Z9/7FN9SKDhachdACeJyzxY/Tu3rLVN2mvzu8Tf701zlL/sHd5qpu/qK/T5mEGbsIGd4LLoOrjMfHELBbtTTC/0u2fYCCC6Ddo/GBnmsNQBKkL8CBzKoYiSqauASFZHH7/Ze8sIC6GyPHTjUx1E46EYc9RokzzoAC58G1Wqa7u0/DAwFy9fx/ntFdja/DfByu3HUpsv1Se4idbbG5S/v1voSnGsAo3fbv9eov4vmiKTe9TB9qC3wx41K4qy3vmnWSgNXPMrbHg40DwUO6DiK6Cid6Tv9YKloCly/2ZsdGo2CG1xZep9+czSAekq/2edtlH9ANAaNFsHVmxEcDKHgS/L6PwowKCPaARy8/qKNmHgPCLxAfV+1kq9NPrSoBwQeHDvqMo8UuLN8HZkR+mP7VNoYjwO39J1ba/dzI5g7HtW7Dm4CBdMpWH3yFMMsKlJ3vF+bXjQKbScXdUSObO+JEFWtzEBZwtYgoTTmk52kP2o0q0Ou5R4HKB1zqT8H1z96/7u63bSBdl9/zlM0TD6NnmukOZH91HSOft90fQ2MWss8WdbLWNnu49rnV/luvYbAthfzQTiAaTu+h5lde7kD9sOpvbkCdflu11KgLuY4BWg3s0dMEfJpM3sarYG2m+E6Cyf6n33Y5QoK/fufmWkt3TN8/UP0ne6EJl+9RsTatC29XUzKut+DtZdfPRScMdPM0rMBg1nHOkuZgc++eI0f/mWNmNibDHog8AWdc93ur3kveVNGJe52BO4VeqNjz6RRZQbOw4x/Fl8RR8R6lGrnBlQNQYWAoTq07+BOQMH9jJzIde+D4Do5S6F5KVTqiWzWOuHudS+HByhzWM6uqjb3GQn7tl7PV4NMegKjhvtNYPE71M4qAWUaomzT73b/rhEkruGQy3NphTK9DRGVtRQokI6eJWwyBbHFlzUltGDsmJkznxVgM73tACL08JJit6M81aAePeUdj/IS3M7pVF5L3P2epZlRf3Qo5zkcwJlnDKjtqQjVDIdQgdrMcqfu3qY+x30/5PePhP/I7NAf+z55eUof+hStWW9OAFQ7qTfygNAHgerl171kvjNnyuHZgIGl2WirJC3cz97UQv22c9TotQRlFIVOXtn2vhKMKoXLh5866YWsn00xst17QBiloXB3z1KOe4/KbPtmKuDqMHVHWwBUEwLQmhDKPkbVJjwBhIY26V4iERrntenF5uGSSS8zWtPeXpzvRO4HZj2X+ndotxzV19nqjzlXUGAAW8pIYGxJOqAtM7ackQC3LSMzsLFENcwsZrQM1t9clqH2KwzmwbsLrkFGXSchkIRDDsHMMro9AAsFBAKWEEAELFGgYQkBSyC3jbDE4EbhrNcTe32BBZJln8mPxrAfuaN8P/dunvKOm1o30IJQs6u2hWYthEHiMiVX6+xO08h1RG/HnwXrci3zuDCQcCYFQm03zkeHMdYagLkZBfdp1B/122aDLN/uh1rY3VHd4/b39fueCgUTeTAceDlNgveNk+2uvrJCwV+p/LIw/ZPu0jOid6w9Ss8GDI4adC8CSqF+tw3+ALQvftTx+nQm5nSz3wQ+z4VsnzwcnLnVTLU79C8YXazvTybpjFZmpC0oGXufUVCXdsJ9EK2sOKSd7eVdfhs4ANCHhAVc5zQYpRylPjez99q/I81J+z61btoU2gQd3TM3MLBlxsYZW2JsLGCwsYDBLWUFA/+XcdN9KTNuibGlhKRAcEsSy2BjhQ+nSZilsspiIIRAWEi+r1GiMsYALDFijaIhWGPAGqnAgP9bY8ASZYXGJQYsCgoLBSy6VDXp7BGz16uHJqISna8WDdi6lzRrq2ff4dE5NPzu2sMJKBjfyK5mMODgAAA4H8PxvTTSrvl4IE/UGtwVWIN+aSRcPSCMzLTAfrB1lJ5S6qMZDcPUPUvvFM89CMhG2IO/evMaf+zl1+ReHryc0sf13fO384zAoP09q/INFHz4NCgYkuH5Qe0/eLoHBU8m20FqOjLCe2sLaibPNr2uY7uXu8ZDnep5VKe6TWHBqW2qehRNXv18/KeqKO+nWmrWYZRFa7pRV8my/ksQAZ3YRvcCBCb4HxQGbpsI/secsSXgMWVsOeNhy3jc5PO2ZTxsCY+JsW0Zt1RBISs8sJoc5BPle8lX5l3Uw6L2t+8q6IMDgTUGLEvAJRKuS8S6BFyXgIt+LiHgEgOWCFyC/F4XwrIRrpErOAQSJiUWk0aQGSRM9b3N6u9+BF9K+h8m7doG9jMNRonkwVScAH19tSh6s9kI44uezLT6GhTJ8x5aAz+ixYkS3isGdsLVAKFM8a1NejqL6t9KUnnv+2NpyxVcS36Z8fmbz/Anv/wEP/nup/imaQoOUu9keXZ89GzA4MyL/jJQYOk0FfbJEyvtNg09z59Sec9oCsqx7wEFTeTIO7VrH6lt70g4WjVsl0wHRn7Uc3R8O4VtCAR2nB9RTQFBzQhl9Oa0FDx2X5qqZ0/ZheXadg9bMdE612LCyG1naiF8s2oKMioU3FLGgwr0LWc8qGB/zBmPG+MxCQS8uyU8bBkPt4SHW8K7m4eDjJQycspIW5bFkjbVUiR5tylxAwWjelfquAJBjKLJiRoeWdY4CohLQIgBMQoEGBS8WAOua5S/JeCFfl4i47JkXHLAGgOYGUsISMy4RtkGLT9Z+pghAa0Yva1/7PhX6+9uue8T77NP90bquzo0uxcR4NY2MDgQq4kfwXeA8NT8n9EOmq9BE6Wv1Rr0sQ32j+PiR9w5Vu6BIRw0h3xJOOiHBKVLcnk1mGnyfIpwxs/h8+7h4G/fvMaf/vIH+PPvVJ+Cp0DOkU9Nn54NGNyTGY1NZuKosQv766Bg947PvvQBEPjN03UFDi41unWvNnqCeX5fiXvtiwOZkVd1P9pqL9MCwamMkX+akouD42n//QwQlP0HlafrQE/N5T/wHh8+v7s/kznYmQkk6AqKEgWNLetZjpVQvfWFs5oPMlBNAgw85IybgsC7LeMxyd9/+7AVIPhvH5OCgfy+bQIC6ZaQNkbKurJiFkjICfKZWcIns66fYMtcu2iIFrSISKIYkkY1DLpuggQoDAIGa0AMAXEhPK4R0eBgjbiuCdc14uuXiIct47oEfP26YMsBWwReMMAcsC4ZFCJiZhAxlkigDITgtEFExXHRoKD4G7AsSvS0JY3379PeqSsINDHwnmri6q4pebW6nIvwnwJCkWyTMLuj5+jb1GHGDAiqwt4v2Qw61hqQ5m8oaOVyLn/3szN6HODpfawHBA8HABqfnxK90Av7M31xBwf20+DgN799jR/98gf48+/8DN/64Nvz67xfddqlZwMGRzb71857856jxml5eo8ITwLBrLIevV+/z2dhJE5HtrshjQ9u2Of7DBTMtAVnHJ2KA5XXEvSd0yx1o5sGBsr2/hjanz/N3Anh3zhpdeedHKGRz6tCCwUJDywa2yCOdEGcCS0M/O6dsjgMbpnFlyBxgYHHlPH2lvDulvD2ccPfP4qG4O2jbHt4TEhbxnbbkG6MtCVsm0EBI90ScrrJ33ZDtrUVckZON33cvUaFNNJhiCtsyecQF4RlRYjyF9eI8ECIq5gS4i0jroS0LrhtGbcUsSUxl9xSRr7INdMaSzlIEQZslBERQCFj8atLoYddDwUCALJaYcYwIucT3qfkxdfdJ9TBWZ1v/ARE6yJ10BbeQZFe7BwOqcRhcM9wFkxGoH10+GSGwhmTQg8HwAAQBmkkA0bazTMj59E2due28Unacn0vQDh4rt+8eY0f/fUP8Gff+Rm+eTT7YDKge597Ph8wcN99+ds8z59pmOMzU0lOO5zcKfgzJoMZDJxx7mum69g90VKtXas/tq80M4fIEcicgYKdtuBe0g6selcDjepzd7wfZbtS3Kk/Jx3xMJreIHUmkHJKERZAqTHNqDK35/e/R88y8IMonaSuoMhhAenCNYFMw8C7orafCVJPmMVhsDoVmgkhFyj4+4cNty1juyXcDA4eZZXFtAkQpNsDcroh3R7A24acbuB0Q8667gKnoikYagxCEK3BsiKEAFIgoGVBXK9ItxVxvSLniJwyYmIsHJHzhpVF+GfXiGVGQ1ZHRcKWCUuo/g4JwIpxTRSZ6tYpAAsIaIAtgYNU3utuzY4nvE/A6mmCX/SG2L1z3/NYeOLmel09de3DZgU0kID2fOYKFJLiE3wY4J4BOKU9qGehX2jpLBwA79dfSbbJfdd7nzh2/hiu3vm8lFNpf9wIEJ6QfvPmNf753/wQP/7Oz/A7J6Yk+nu6HA1/H2Xn2YBB/8CMNqKhrX1QRlZ60ExpPYOD6ZS9vs367xMYuFeJ7yd3oG9Euim7etqrvcppXf53FakDAjvmFBT0U62GHU+ABEvh0tk0R83K4myY3Omx3XafDiLEtdqBXsXc/oaHh3LNGSy5vBIV/wJZB0CiQ3IIVWUZFhEG2n+yqsPlnbfvOEH8D8ofy9TCnDO2pE6Fm2gX0pZlueXESLe8g4J0e4d8exRtwe0BKd3KQkw53URzcMeUgBAQ0oqsCyDFuCLwVc5dTdNwhQi3pMKbkDb5ToFw2zKWSNgSIeeMlEPxs7C/BMLalq6aDeR6ETJbIVg9NijISRby4tRqD+x9PuVdyk3lnRoUkJsR4JYBL+BOhCbAUHPpXqtgJ1X7fqMhVFMCmSahmBEU3jh0x95Ju7Y0yKO2DamrpjXo4oEcwAGozr6p13OXxx3hdxIIntrn+lkOUpTyoyAY2aCsHmdfIu1ry2j6pU8FCn7/Z/jWBx8NzR9lmz3noB8n7LfdS88GDGRlsFp0r7/4rKx9YFBQOtUODnzqN41G28A5EJDvenx33Jd1NgROqLO64zwglNQ1urN5H0HBk1MZFSgclDTqEGm8fwYBAyg4Oz2r3MnbZDGHgmH8hS4o006DMHm+MmMCBKiWQFYJVH7S/DERAkUpOnWkC3pMCEDKEhgoghEBjKL0jVZHLj4KbM6EAGeupgNddTFvoiVAyg0U5HRrgKAWpXb1ISNgRcYNASsoZWTKwHYDUUROGyjcZCnmGMCZ1LmSQXk8pW30HAEq+OEWK1K3DfskomJCAO+hwH6LuqVbbOop79KkBoUCAqSfrLktkQR1xF/rc1uPp3W4205OKwB2a2TIy8AOFoAGGKbPd2AWeUoizOGgXHkkaAf5GkdgbXJY7umPf6++V8+xfFl2DBSOIMHDBDAfZBJaKPDmg+EpEyjYA0G996u/+/W8/uJZgQHDlg199cVrfPKXf4RPv/czfDyY0tHAAeBGWm3HYzJhNNq+BwJ2XX/s1IzQve0j21Azd9ceBtips3wl9PcZ+SDM0gwIyj4HBW25dNqCo+ThoH2E/XGa7kEA99v9sbPk8snQmQjOPlt3TqAgV5VzI0CaztjVLVfPZIQoD07EAGzJZMUANo1BVlEPEIlECxRlNKJaA+nauTjVmQAkYpnfT0AgUb3HAIQgUQdDsLopwjLZ+QE6i0BWPaSg0+JClGWay+uJYOS6MiImGgOvOXDJ/A/sHnI/vb++x9B8r/mOoQZMkmcM7rn1mSA1LPbaAgDEsjR1qzGQ3w0QmN+Ee5e79wjs3mUxDcm0lgoIIaJfvbCZ+lcLR68fTtdpRnR1t5ocpD3OYAHVD6E+yHG6AwWtr0GrNQD2cFAEadd3+UHQ3VlR/v6Dc76K/recq03UQ8AMEuCOsduOQLeHgl118AOzLs/7vtgyifKOX/3da3zyVz/Eh/hw+pzPBgxMAP36i9/gk7/6IT79g5/i4w++hdKAQY3jZ4EDYFf/Ca2tHqiA8Js3r+WYA+o8CwJH6rBZsvCylmWrcAUSGuG/hwRneZymEfT0QGD5t4rYVMLB9MQ+1VXqeNi57GYKHEHATjswYuR5Eib0JZMrHMzSUFMwgALdRsyyfLGeu8uXOarFKH4DSTUFFMoLJdgrDmDcJN8xIAZCBiFmRg42WmYsIFw5YGNgYUZiwsLAkgLWyLiuC1IGHhNjTRmXJWg8giiBkTIjcgTnhLiubXY3Lf8QgCQqihxCMysBXnPQzUoIcZW8xxUUxa/A/AzickVcVxH6S0SIhLgExEVmKFwWDXq0BFyXiOu6aMwCiWuwRNK/gKsGQTIIsmBHBhQE9SVIN9EU9FDASd6lAsKT3mPpcdzyxQU262fdN4+h2K4UabXhTv3W61H5B3AnJCq8z2GhXG4ywpzO6vHmEAPqIzjQB/KA4IXs3ecFhn2XfNd7DQ6cmVFHyYf3NqE/ylh22gSglt0RKFgq5gM3qA2z/NERFOzf9asvPsMnf/VD/Pw7P8G/+Nf/u+lzPhswAGe8evM5Pvnln+Dn3/kJPvrg201lhJs2s3uXhN0iHwYHljIBn6t3KNBWkKcIUn98c97Zx3THMzrbFalTjh14QKrAnlZ7Eh81plOVsBmdDJIbiU8BwGdgpglwTlz2JP0nMJ4PPZwpYl2WTpfkooC3jnwQu6CBIAcFnSBhe14Tln3ZOE9zUBDBkllG4kap8HCwgknMZyEsWALALMIu68A0g8Ex4JrFP4EDI4eAvFi5MFKOw/DFgMjy26P4O4SYEGIA3RaEuCKnG2hZwNuGWEwIYovPHg78I1JECKJepxDL7AQDghBXhOWCZY2Ii0BBXAPWi0DBskZcLxFfu0S8vC74+kViGrxYA16uES+WgEsIuATCNQRcg8CBaAdEwyCaBcJiJoSiKchAvu2hoMBBfY/H71C0Jry596iagmpV6IXlAAi66aoVCury0cD71HOr47EAAxvMTzQLltcaBvygXY82q1Nx2x+XnEhfYv2YVfDBSLw+w/m+Czg3IPsyfbG/XxMqXdNMm1AOo3ocAPz4O3ufAj/Y9GZcv/9ef/z6717hk1/+MX7+nT/Hxx988/DZng0YvPriM3z/r/8UP//9P5OHto7YqbFGcDAzKcjJ9e3+7Zs6j/Q7/8W//yStQF/5huPZp0xn06tUIVGvFNxNGkiwbf5yE9JvmvcAaobP0dld7waCGTpWPQ0EfOc4W1Z3lkrZ6IO0nYV1wg4OOMlIfmaX6x0QgTq6NGFiZTGwwQPiHMCpmjBoWcWFTjXMDRxQANnAFUAMC7JWYO9PhsR4sc5GoouGIVZhqSPpNQa8jQHbFhBCwrZkpC0g3TKWS0C6ReT8Amn7Ojht4nfACXm7tYJzpDFQM0FYVgGFuIDiIkGNgkxTLLEMFpmyuFwiliXi5SXiugZ8/RLx8rLga9dFIEGhoP7p7zViIWhIZf0jNT0AQN5A6QZKW4WAGRSog+VT3iEpS5oWCOxW9XTOgruq5Or7DApKnb9T33emR2DQN0mMjNr1MbhMbdT35tvz0Wyh3cNoWfRwoM9HTY+G6lg4GOT45zxjAu2ftdv0pfvjNvppO0BpQOFAm2CgANQ1Nz7+4KNGRsn1qpzpTQj3oIA4i3z85R+LfPzGN+++v2cDBt//6z/Fz3/vx2I+8Gorb+MCSmXcwQEAcIUD71vw2Rc14pTFQTCb7IxGpwIU2AlRAIdq6120Pddx+FFArw0p2gRrVNw1moM082699yynI8Md2VFtv1Obernfg4DdqfoGOrVdf1v3vQ0SgwII8pykDT/rPmo/R6l5dnabXVlk0yYMUk6QSD96ie0mZbBgDAcRING7gomwqDPibsyTGC/XWP0FQpJ1CoKtOUB4sQb8tw8R13WTyIeXiL9/THhYJdCRRT3ctiwzFlJGToycV3VO3IdEbqYVmg+DC4kcoswwCCGIuSAGkJoILPqhBTb62iWWyIcvLwu+ftXohwoBFipZfhNeRPG98FCwqvlgIcCcC4vwT7chFGCrgZskrsHB+6sPW947RavD7ITqHApq23afFMAOCuoiWfs6r3faXxbOYY7lsqU/6EBBwNhAQdsSW6do/jfYt/F70FzyVzwHNHP7fsz6L6COwt+n//L37fsyy43P486x8bBfdscWwd1qQwx0GBhqE4CB2YF6Y0u9RT+YGUJBZyZqoOCDb02f1adnAwa/+N0f4+NvfLOobOE0BQUOiCrBOmFDGMMBIFBgsam//WFV78xMBFNyA46F54GHc1M5VR/JZTiyhwT732sTev+E3a0mv6eN6N4zDZ6l/V2b+d5XoJbgbIRkgsiO6wHhXjYC1yVP2TXG+j61UyaunSTroqjEpS5VYAv75/fl0o8y+4wSVaET6uUl3OxAc0At4CKigQMvW7bMAEUsxFgIuIaAdyHhsgRcNsLjJvDwsC0CBLcN724yndGHR7Y1F7KBQcrgzLKyYq7v42h1RdIeLgYFgyhgEHRtgzW2YZCXaOGQRUMgYZADLgvhxRJxCfK5RvlcImENwKIOimusmoKFIM6GpinIW519MNIUzKBg9O4s5axGYasPHQQ05rLgPt33oi1oNQUW6vq96742oMAtLJDvKHpABgRktM6zagya1sx53777dA8Q9NkLnKDtw3b2/eHzjX+32584qJkUKlHXL8PBwgAUvDbBBmwjrcisGEdQ4DbhLhR843cgps772p5nAwYff+N3AKCd6jPSHHRwANR3UuAAKEtb/rFfW8G9CYupDhzAgK9wI0o7cO5pPZztgAoD1Wt+DAk1P+QaYpvfUdrvOwEDg2c57DhdHkdAMNQOcAsDdswIECwNlzZFGQQJDBiFs9SCAofBPAt0RI4AolzNCeppTg0UjL97f7OxCtqV2QAOkJLaqnMLB9RpipiBuGINC4JqEwKALevURWJsBCxxwZYYL5aAh8y4bqGssPiYM/6d64LHtGLLjHe3VBZOerilsoDSo1tMKTMkfLJBgWkMBuVvmoNIhHUJusSywMAl1oWUrmssCyq9WCOWQAUGLiGUlRVfLBHX4FdaRGMyWIKYSpbefJDTARTk6lNwBgr6VPwoAoYQ0H33bcAiGdZtUidZq07fBp5c/wdAEJhL+yqjVa1U7ahUTA6GIgUQSl90sg/oAKH0YZLrpoz6Psxl7TANQcDdewoDE8dLObbrn5u+Wa+Hth/2oDCDBLlevVTwkDZ4qN48/RRNwT5K6zg9GzAAUDrWuqCHqeAHcCA7hnAAAK/f1LUVPv6nH5VblFFl8/sEDBQbtK+k85fTHFdqT6oV8iwkAB0onExfFgRmaWoymGsIjjrDAgieJgD0/uJC5k61AyCAZRDkOsgAXe6UqwpQOkMLTsM6crLvOlJSsKhhnalp7E0KUYRMOc/KZnICZ+GMhbRxq+DItYNluDrDWYIGBcKmPgQpM2IWQZmYsQXgGiNeMnBbgqy8qOGTRSsAbHm8FLMtzpRUU1ACJrEETQLGsQVMqx5CcL4N0KmTtvQyhkssyzYoAKhjoZ4jswyqhiBSBQKZwii4aeaC6lNgUCCit7bZ6hsyTLP3Fo58BpzA90PDUv+dL0HxK4itpgBy28y8bxNPqf9aaQI6INA2UNqEXY96YSR5K4CArn+Zlduuj6jH+T5Myuy/h35sAgNn+ujdqNuZFaqPCFetwh1I8CxgPhaA839rX135bOSP5reBgu/8ufoUnIcC4LmBQZ9smOa0BLJdxYtzSPTJwihbcCRLZ1Q3UxhoXsZ9G4+c6LUG5gSko8qzkABg55swS0f2tXtmj74zmHgoA2MoeB8gaE0L7bLYo8FKgnUMNklMkkYGUCDgZl9Vx6tJgbKEJ84JBQTkgsUWJTCh739ZgG1zWuVuZNXbqyfCRfKtnuHMoLxVzQH7OAcZyAIeFCLWuIKJkANhzYTEwMZ+aWZGXiISM5Ibuhqj9wABAABJREFUkW6pQkGC/GbW31qdM7hoDMx0YAJppBixEAcmoAJR0RgENX2YUCeqMwoKHMRQNDwxiNYhkFzHpiAu5btN8XJAYH4Dfkoic4UElgpFzBjU8PpuciewbLvTEBRtwbIAxSRlQj+UY6tzIWmUyxoOu4ECK/MOCpIDgnttwNvEiYFCrjSG5CNAkD9rvzIgYvM/mKXdvq6PcCAwBYXyAO/Zl92DgVE/PbjePtUhY7kW8TEkNOYGZ8pEFfVx8JjUfLrn0PuOoOCp6fmBAfO+0rht46kzdbbCZ19IGOU5FNiLSC2BuhdT6KxUpjNqKtnWRDYzoQ8AXkVlnsLlsGIAae5HHoZc1u+mI23A2Urm792ZOMq2O1CQ8zEQ+I7Qq1Mt+VIpbVXt8kxcAEG8Bqq2gNgYXgL9RLJOkGStgrypSUG0BhaVUHpbDUtr2xQOiBkoMxRQASF0naNPM7hS4C1wwK4z5QymBOIogJBuoBBBYUEMESsk5kFigcoCB768M5BX0u9eEKG8Dx1sShl1Sy2ng4pmy+mYY280p0T9Z+NpG0wHiMAneeJiwhOYqFAAqgGLpHK4YEUq/OV7rkBgU0oNCqYagtC5gg/eSwcEZSaCQYEuhCW2+m4bQjUhhAVmQmDI+wHX+t1oDbp2UGVbLf9RG7DyN1AokHAACAgkU7rRj15bOChlYT4HB33eeFstyx0owO/bnzpNo0HNFAgGg7adyXfSV8tO/ZT2VdfCUEhQwCHKFQztOZ1GxGsQRmkGBa//7lULBbuyOFdwzw8MjkjSmxQ6MwPATRjlfcTEtuNooqANtQMzMj2mUOrVZz6vxTnJrj0BhEnjupt2Q4y+QZyrVEfLGJ+ZfpU8BOBcR2g5HWVRovjJ91AOkI4wgxFYNASm1EsERJZY+xHSZzKwMykU4Q8FASuzLNcXbpDOkZGBhSog2LGW726I7SMIDgHBwwEFKTyz+ZqfgpVziCDcRPAQIVJA1OBJa/DvYu/5DvcOrKy7LnRY7qOIbmfmmpM7rvh/6I59QC0ugG7BiVoozxUGrI0WwrFj2/bbZjC4og+n3pEBAZvALBoAhQDTFDhQYNMuDEwIvj0kOEAYtIXi+Dl4J14vRaYZ4z0oe0AwUA5QaCSLrDmHg3uBwU71IaYNBUodbPqz8hAn0kTA3wWCUX/dXY9GGowy8LQ8m8kQdSBRNL1cAEE0vHZv6WPGcxNKRnbP1EBBmZ3Xpd4MNknPCwzOVJaRvwFrcKS/+IGGUf42Gl7rzQW2bQYEs8p1RM9NakfWhVD7/TNAMNPJ6HqzdAYCTpo+igNoozUY56EXRCKEKhSUUeykI+w7wVkWTWlkkBBMK+DgwNT0QP2eSyPW0SyFCgEZTlugWFHmfxPAAVwEkAICWVnYaE0/gRpRzxdprKaFIXCxzo0HXHlXDU0RxgoJZN9hkNZpdQDU+tXeb3j/mVbjTL9t7+qOo5ccM8CRRkM3aGO+UpR7+DabhxWGiVy5x/Je6tTD2XtxSxI7k0DVEhBKNMsgK1gUKAixwEKp+1178FqzWVu41w58qqYzyXsPyoDTFuhFM+01B8NX7Z0Rn+hfNbymhwXJ2rzuNedN+rWnAMEZjakDmDK4G0ECTNOoq2kqIJgjcb1fpyE5ejbmPRQcpRNw8HzAwFWaWQc6BAdmvHrzGT75Sw2jbJqCmdNK50Mw1BDMOqruevM0EOoKCDU4SnDPJKPHkk//eDtI2KfzEDC6jlenTcq45GWvLQAGPgXovh9AQd/OZyVrMt/aRO7gwPIRHBB49avKfgA0hgMmfQUWEMlGqAoE6tSmdoQKe75+LH0nwOAjCevqQZ0dMX/X/QI8/ZWP7cOjOnIPcJ+QRh3gvbp0dLmnPEtzrEFT1cMVPwF/CrCDqnZJ4qoxqE6FzlRAsaqWGyigps637aF1NHzfttDv6NuCh4Pg2wt825E8WayUp2gN9nVpZFLYv9+7sDBJcyC5AwVfpu9WGNhBAnQkwWgBwUVWrbF33PWAWt+6/Lz64jONaPiTGtFwBry2/U65PRswOAytCwwLgjjj12/c2goffnQOCABnStBm22gP9loDTCvnII9ewHYQcBcOuvPPqv9PNdZhsuNO0Lu/nfuzq/SdHLse7h4UjHLvc6Td3pRdrDOEewUgvZeOkszfYAcHrGG0s9yJy6pGg/UU9OI13sa+8xl2qkfvsamz7pqo759TqqYOALuwvjmDfZ3W75xz9SRkH9VwP3WPe0fKg0TeybLYeepCSqX96qJKZb8/NnhBjHqcU+sDfd8w6CemWo99RWmApHyvGhrnrVphwJsJYICw9zUwKDC/AoMBoHv9XJ08d/mbQIFvD2XfASj79sC63fvfFFDwxTXM0VE66mNOwIIXdGfSVwkFp+7b9o1VswetDzKqkHbpzSe5+Iex194NzBp+7YOPP/x2PcZrBRxQnAEp4BmBwaHKZVIYRlqffven+LisreBO61RhjUrynpZgV8FOVKQGCI5H3+PUwQEw1ZLMzz97LLrrO60FuY5yd7xTdaO1ZXv79W4aFjpYOMhedp9Pw5WqNfCo5UdJdQqizOkW5z/zW/FAIMM9fgo0mhDw++qWeefljusX+cn9PHwV+pwzoEso23bOCdhuUsY2jU/PZbsGZ53fz+BcQYGTCbD7QFmmaqnLdVlRMagKX23x5GzyMs0gimlkWQUsVDVPIQBxKccUlf0gnkDjFNhmSvK/M32543rn2eazMyHYNg8DDhbaGQnVp8AER689083D9RDupaP20HczBgG2zWvRzB/Bvls+o2sT5lzXpMas4IcC6LZNUtPHWArj8071d++h6ToLBYea1jrfifV3WUvdTyYtqs3s/Mf2PdkOCvrUmwzOmF40PSMweJoQtSkdn373p8NCnUEB9RX7q4KCXQb2HQ6AuyrUXXoS2T7lHHfsUdn3naA/dXLL/vb9NKx7zXpUQnbn8z5LdfTkR0zIrVM6aUQ4Vp8CUvOBdaBNnSng88TRikKGhdOtGiM7p3a8zdoMaatCf7tVEEhbhYC06XEZnDZdE4CRtw15S+DE4G0TX4+bLiKUMvK2CRxo5EPJbv1eyjG3SzEDEAAoaycQKAoUhGWR7xRAa5TZCMsCioSwRNkfCLSsIAMBXWuhwEJcCijQstbRuW6XeBCLZWQvyItKXCvLvXa489Hoj3MwoMcfOuDmVnuG7vu9ZLKAUNvX2R6j1yr4ZcQsH71GzY5tmhURmgXInixID47ZQUKYH/seqUy5LAVpbY2efu0i4KugN0gUcwHK72KTBJrjGp841/cbFMig9mBBpHt98yQ9IzA4qP5+FEPBzfMUm0z/ug+hoJdgXyUU9KOQw2P7Dgl4+vgYuEvuoxHgU+GkvyQwFfS7uw2yFNxxo47Qp0aJ7Io3ADhaiMXnJwClJzQ4aL3kNSIcAXWlOjmndABDk5IBgQkAB50ODnZahqLqt4V9c/PwNVpfEgjYtgIAnG7gbQNuD2Vbvm3I24b0IMemLYG3hHTbwFtCviXkLQkM3BKYGemmWoPEyFsqWcuJd3DQvI9AukaC/A5LBOmaCXEVjQCtAgFhiQhrBC0RcV3kc4myGuN1kWPWBaxwwLp0M+KqEjbLbwAIQfpY50DYqPlBVdswciDU30PBLw/WXNdve0q4bw8FXltwlIgIgdmt8VLbhN1nd06XVWDSe3RSP6MGeB6ZJ55uTujvd6e/2WlC30cv6HqQHgCOEnUFfCY9CQ70NqZpnMCBh4J/1kNB/xzv2Vc/GzAYjaR3c1+BFgo+/PbeIWtgxxlWmH77lyFV3zqfPEIBdg3jtPngTkN4H+eyu8K228/Dr+3h1snpPxOgvr2NLjAq1qIgJreWAFSRR+00rFHyZoWsnWbrnW1hYzV/u4cc1JkDk5XE61cRYSBqUxGzEAoDoLKOg6Ysx7NqApA28OM7+X57BG+PyLcN6eGG/HjD9ngTGHh4RHoQbUGybbeE7SGBt4x0y8g3MS3km/xGlkBHBgr3wMBAIEh0IllRcdVVF1dZWZGWgOUaBRaWiHhZEZaIeF0Qrxds7yKWy4pwWRGvK8K6gHIG5wsQE+jyQjW0ARRYyqOBAhu5z6YRdiYAPWco9O1lu2u7H00N6D9n00JnycK2i3Ogk/4QOABVR9kiQwfXHbUN+S7twlsH+tSwwl5dcPwA7zXyzncE3AQODvtBOz674/Sd2um9VqLRJHjVyXuk5hoGAb3scXCgyZZONk03YyCzBmU11jT/o9AY7B+yt8vUKR0/wUf/9OO51y6wE4r76IW2/SRl3svv+6osR9ea5QPo8uLH3qNzTlJ0l5+5LbV9H2ftpaG67ejtqMQjyJMOrj3fH9MCgV2/F/C9NsFMA7auermvV39096+Q4UYDflU9KtzjFbZ7QFAtAZlZQN8LkS0TjDJtkqKcUxhBfQg43RooSA+PSI835Icb0sOG7fER+eGG7Z18pi0hPSSkx4TtYUPeMrZ3CgebQsGWkW8JWwYyu0iK7r0mcAlqZGVSIhWSrGEQ1iirKepSy7QELC8ibkvAcl0QLxHxekNcItLjiuWWEK4rkBhR/R9izoio5Yl0k3foYg9QsMBDNgsgqhNjRB+AqHEOPBjx+09MfgODuu5goGwqSsZ95EXSfoQxgANIlqxuBkcEXrM2SyNYlu3nxv+Zuwh9fnZCecoAhjfFat9zBhTujnpPDIxm+7xDI7UgMAUEbwpsHTTu36/f3ZkI7DoEp40CAM549eY3Q/O3ne9l0e6aTzS1PyMwsNjdPtUX6B0NPxo5apxOJ0fRDbJPXspolPFEG6Yce/+l76ap9A20z3Ofr3v5H/0+SDP1ZkAbjGXWCVrnbGK2t8HOguk0MOA2NLHg9eDxgGNPASW/3UP5wEpHqZSwQY8ueVvP5Woa4AzkIKNj0jgFREDSPs5GVyG0BVnyL86D+ZbAt1SgIL0VUEjvHrE9iFnh9nZDehBtQXpMAgMPCduWZV2FzLixKD1vGiExM+piSq48DJJKGGMAq86PXx8zlkCy/sESEK8R27uAeIlI14R4jVi3Bfm6YNFrR3dxCgGZAsKSgJjH4syFIC5QEBeFggUl0JDNEChRCQdBuPSVAOd6g/Na5/GBdZBa22dpFyCXF5ZjrQ/hdl3HM21kBsso28fjzJ3i4Cj1/UzTV55Qfc+0pT3Mn+kXmxOsb30iIAB7SDjM89PTqzef4/u//BMZ1E7k11ArMDJ1yYbDPD0bMNDxZKfVksJ49cWv2ymJu5Ofog5qC3jqrALcqSj7Cv0kh6bRdWapm6ZCvhIzo6noZ1Vkk8ZZtAVH2bmTXdMKjEZIkkuu2SzndJ1cd80RCPjj/Pm92cHnuygKuvKZPdMZrUivePYjNxthE0WEGMVcEBbIKoHV050AAYGQgUV9G3IGwg39+guc63LCzFn8CLYkmgKFhQIFbzdsDwnbowCCAcEDM26ZsbFAQWLG5rQGAGocf9QRpWkLZE0DwhoICzFWJmyZsGTGdctYrlFmOnQFSIFAdAOFgLAl8OqeI3dRH2xKY3FQXFTwCxRwXMtvjksTYyCjgkCJG8D70fwZMJi1hlnv0Ov0AK8Zo/LfIoGUmTLcNopMXR2d9EdtS64No9eg3ZNtd+GAJloD4KTm4D4Q3J223ueZ0PTXkreI6qSRYRFFd06/ul3Oddc5fIbJAPAgeSgw80G93p2XMphdc0baPTMwaCsngfDqi9f45C//SMIczyJCOWF4dyGQ5rwA8xLfwcHheb4ij2DgCQ5Op1IHM3BOb/cA4cx1h8BCaNSwbvR15qrZwQGAsuLYbpW4nc61uxaNd420AmescBOdlOxj/513xx9BQgsqXDriAAMDg4SoZaFlmhMIW6n39kmL+BfQoo54cQWHDUQ6LbCroyJcWfwHsvBHusmyyuJXkLBxhYKHLGAgcMAKBxhqDVptgRj4IjHWIGs2rEHHYionIgHhlpCIENaAdMsISxRlyS2DV8YuPLF/rhBBUWYu0LKWGQocFnBYHRSs4KI1EBOD5X+2aJF/p2feJ+DV+Xv4tMdunqX77Vum7czuelU2VSEl59A4nyfbjIdmq4v3ktQ/a++dOcHKj3yUv/fscyRj9b6zkfGZa5p5j9lBlvWRZUO93ggS7DpnBpmzgeAgvXrzOb7/138qEQ29T9wTgWBk9jrK6bMDA59ef/G6W/tgUhSzoBCNzcZWGWsriIcDAE7dNLhNI9hPagW8fdOf11+vSw3c7GjWVGT3AAHYj4nGDW+X3yd4w/YgwG6b2U0J0jHvVhubtI8znfDuUpPGNlPx5rJ/IjScitfvm/Ud3twR9IuYOHi3aFAMqkGgTUY6yZZ8RvGHCBcgq7MipwW0LOBt0el+MuouAjWIWYK6AjZHwpxFA5DAxY/AQvYmRoGCW64+BlY+AnMiZAOJCQEsDpyBqiDOzEgEJCZhGXf/UkZRVl4scQ9IYyBEchCw1M/1gnC5tlCwXIGwVC1BWOQ58mRRKX2X/aJdR++wLJfrR+BFr1lNA0THfvVENKzi7bLs+s6piOCS2IEZV4K4m3pw9mLmKaBQL2jaAesvqfY779nnAAMgONCm3u0vPRQoLLD5+XhtgocEAOynGZK73iQdywH5zkQCBX/zI4WCj5r9s2d0O+Reds/uc/+jTc8GDNRBG4A0hPHSyZPxH/kXq3NwG3PAxDww0BAMy3o40r+jFXDClYfnD67fgEylYDumVtbehuYctPprD0KTnm2Yo9gFPpmJoG1nTkvgMuVHSNPr9dlssjwGhdG5+yZtHQAXwdtDQREg+jxnFreZeYqTEyJlZcEsWvFAAkyJCTEASzD1eABtAC8yerZL03IRzYEFMlplCmO8rODEoC0hbAviRaYjhi2BzBFwC+AtIMeMZQnigJjFR2AhEeKBZYSftWAiFTRpGkOjNYCcY6shyvUUeqDmhSUgREJcQp21sASERWIcxItMV6Q1ysyEi2oI1gtouYDWC7Be5fnDAo4XYLmopmAB63cGYWOBgsaBMtd3mHN9f2fena9PArdoFigq5cE6vnZw4Efp/nqlHN0NR6KHuR7DRR3egoM9y700guidSYH2fghNmvWncAOT/gHuOBMOTaldP/TUPpO9U7Dr8yTfGj+kvPwDbYI+Aw/65N29d/mr2xsoUE333gm2ucjuNh4Gei3XZ3/3ep8vl54PGEA6KQB49cVr/PAvP8HPvvcpvv3hRweaM1+YdfQPoIy+KhCM7GPAbooJtfbcmtqKOhWopn7vto1yv0vNvd3owJ4JoWyStFeBcddljO42teONnqHsvy/Uc7kENfdmZsxK9cyKfc3FcKcjA3b6Nm/fZQctvqTuredQ+hR/3kzF60ZjgbmAQoCubqeAwAzkQFgCIcaL1NntQUY3ejnCvuSZVZ2fa2jmnDOWEqiIISGS21K/WJ5SLsL9gQi3zFjI+xhU/wI/GraZCL2PwTXI72sgXGPARR0Q15cLlpcL4jViucr0xeXlBcuLK8J1RXx5xfLyItMVry+B60uQ/7u8ANZrhQKDBNUYJEBmVKivhAFBAjfLTTcakDua4qCy2Ooywa1i6Jb63qnv3ffGxwSY1t3RWLFRsPCupctmbs89Cp40a1/3tQU070932tZxj1wf405/47Y9ue8c9dfON4OL34b2nRrATPYNQMHBWPFPuJvaPP/6t78RKPj9PxMoGPan4+fhwfd+0PLZF6/xw7/6BB/gw2mOng0YsHbgn33xGn/8V5/gp9/9FN/6YAwFY1DQPabyarxkc6nIv36jpEVthZmmI42A+z2mQT3XnvH4ToPn0f82Y0MhgQ0EDrQIlhrnnCZv+2ccNs6JNsXeQ9EaoIIdqO3IRpqCe0KfxruHv3d5a0ZZKMjdmzuAqi3w1/FQkLODAa73SZnvdsgxUI2VAGgQmxYQIqTzyqY9WAnYHgEEFUbyGWJE1miDAVIfI9D4G4RQzQpxjQjLDbdlUzV9wLZuoHcJy+OGawYeUsYlMDamMivBmxKSe76ohWqmhACo4yGwkgDBEoB4WbC8iFhe2OeK9eWC9Wsr4osLlhcXLC+viC+vWL/2AsvLC8L1JejF1yoQvPw6wvWlaATiFVjkk6NqCigWLcHGjJRbIDC48e/s7PtKsDankKAD4KMFu4p2ABUKvCnJ9vnP/rul0B1gOfZztnJfYQcmiFEatblRe2P/2/WfBQ6au33F/U1zTtt/9t/nqZpvyvRihQUBBV3evEwrPgAF2XHndq2MKFDw4bfb52/60/2zWerlhTdrvlL5+Off+RT/+3/9L6dZekZgwPjsi8/wJ7/8BD/5rmgKoI070LxC+JFVgw1eBaaV+9UXn+H7f/MjuZ/THXp735nRtBw3Ep60e6k+36c83N2z9s82hIRCq6Ha2XYBMw6od+TzMGmgR3nO3B42w62pM+Fumyuse4GovB23dF51mlqdFVHhoAZZqt+LL8EECjYVLsmOy63w9CkS4SbLsiOCsMQgwopaQMgsbJpVe7CGiLC+AOih1i0HCKAAWlbkEIEYEeKCdXkHioSkYYfDGrEtGljoesPtbcTyQuIa3N7ewNuK7SHhestIj1uJY2BQIH4I43cXQRUOiAoMxFU0ArQErC9XxGssWoN4XRUILlheCAzElxcsX/sa6PIC9OIl6PIS4eXXgcsL0PUFcrwAUbQEiBeFgisyCLcsgj7pO0ncAYHCwJYyErhUn6N3RYGxJXk+ItHi2EJDWdUmQSHAu+S1Jq49FBDqX6nTB/VZqqe/5n7EXHwO6qbi1wOc62f8deapG2wBe63le/Y3O1PBncHU+/SjPou+/5RPBquJodEmSObkgwfvCziWF8AOCvYm2eMB4+5ZB1DwrQ++PTizpmcDBp+/eY0/+eUP9KE/qipMdmzaAUKvPShwUDxOKxy8evOb4h36v/wvvtMJxEGGTlFtfcEztY+lszbBcq4jepO59lnEIIWmYe4g4USa2vJOkK0v/6NO5hQAdCGHd2Gt3b724n2eU2mIRAEEiyyo7zmr3ZhRYi4MNRB6qxEUGBAw11ADvZPdpsUXM7CRzAZYQid0mBUKZJ+ovmXkui4vdBYCASGCNvG6J52qF+ICflD1+sMF63rB8vId4tt3WN5esL14xPbuHdLDhvXrNb7B5WFBujHSg0xxTLdcgh5Z1MOcxqNr0VxI1ENS/wEyH4JFQCCuhHhdJLDRiwviZZXfHghevhAguLx4kung1mkJNgUZ+8zuPRkwHL0jClUAEAsgpMCILCYKe0/R6kM/ZtBP6wkMCkIYAIEFuXrvel3bavVfGLdRf+oZRhjW/7LdwYGZEeD6msn5ozTsawbPMBwx+w2435+aw2yfyRrvhPSdRd3AY7ODZFw/ufk5S1MomMiM7tFK/v2OPRQcp2cDBgIFP2se2sMBqBZWQ8xoBdQIDn79xW/qetcWXOLIoQXHROvf2Ujds3dSu988iah0YOb/VUDBVWq775EWQfJ/YurNTDui17Z0VIFnbWQEAyMIOFwBE47O77iRC7nLEJ1ItERMXAChjLZMM6p2UjOXjlLfH3ko2MqjcBmJNu85yzvdIAJnC3JeJAKzAkIkcGKEoECZgRwYC5NqDxaESwS2B5Qof1sE4iozE9YLeL2Ary/BD2/BD2+xXt5h+dojlnfvkB5flmiI6eER+ZaQHrcmVHK+ZVlsKcv0RgnBbEXu3oMJpQg1VQRQgK6HEJrQx/EiWot4FdNBuIpzYXzxQpwLPRCsF9D1pQCBzTY40BJsmWX2ARQMsgj+LbVAsPHBu4EK18QKAzbiYMQscLAYUMK3t3H1I73eHgo6INDv/Rob5+q25mFgsiwgrNvc0U9Ww/tnbeGg3k9SrRtP7mtOaAdmgaj8be71q+In0jxBIyiaOA8geG2CXF87B+pAAdhpEZo09TObw0/J8+CFjaDg3rt8NmDwZ9/5Gb75wUflt82EaeAAaAAB2I+o9RAYHLz6u1+XBSs+8sElZhAADGm2h4HWg73meTj//c6zB9TzqDw7lch7oWsco9F51SL46TiWsROetc0J2D3D6Pf8zAkMeG1ABwJtJ9lqD8r5u5uG+qhEKC6F6hxKFgWPGBQiCOoJrqPABAnNzEQyi4CpmAn6ZO/HRqGSJRuV8ihIYR1hMINY8tQDAscgK7eqeSGDkBhYWEBhCYRleQGKCZQWIKxAvslCQ8sVdHkJuj2AX34deHgLvj2Cb4+gh7dYbg/g2yPy42O7yNLDpqsvbhJBMbPCgVuGeaCvtRG2LbMclggKhFAWTZLFkZpFki51hkEwEFgv4mi4XFRDsEqcBotNsMh3pogtixZgK5oBrmYEliLeUh4CwfS96LuROiBaHQqk2gWFgpGKQFNQc4rBAKlDJhQSVFwDOdW6nlMXlj2fq9+WiyJYKxiU4FjmQG0wbMd3kLArglnRuDN2fWqXt/r7+BnaA9tr3wOCfoD1pL7VeQ2rwhA2hbRoFTptgvWltvKqnJ9bABrAAntQkwu4Y09oRNDtQAsF3/7wo51P1Cw9GzD4VgMF1aEHaD2ju5panIBGpPvqi88kONIgYiKHgR3sgGSbl3gAA08hW3vGRlOACgrWGM8Agn/+3czpU561447iXiV8kmZgBAM9CJTR1B04IDMEwHWUoTRCYp3yRwwi9R4IsXiUmwlBlQrS/sl+iwGC7jx8L3xMmJpHv4UU2DS7HhBYASGBsVAAVG2dmLGoeSGrM5yotWVlwrBcQekmDop5A9INWF4A+QZ68XXQdgNvjwi3R1mC+faIcHuoSzTfHnfLM3PSSIo3W2Uxlyl+PgW/3PIaxVQTab+c8nqpSymvV52CuMr25aLOhGsXrKhOQcwgMRkkKdsjs8HGWbUFcOYDOc/DTf9OSMs26rHLgUKctIqVmATktln1ozp9s0BB3mpdzxkFBji/f/1WrZj4RgEGCqyVeAcJTjj1moRe+Pvkt9HgmGF0hhP9zGikfAYIHE7ptu66kz62mTFh/SvXtt0Ga8JYkwDsnBiH0yT7GwE7KPC3AsYaAksGBT8xR/wTmmdLzwYMRoldvd75wDpA8HCgmyRiYgmONLDJTEh2SrFdhe1h4Ihq58/H7s71m02vE01JfaqzGoT5WOdOfp54PHWf5SojIOhVqAYEvqP0zj731Kw+FKt+EjFAGqyKWGaiEoNDBOWtwAHpiE+UBHutAZNMUWMt66KS1JH+pnmK1AoXoA0j3IQUztwAQmJxSOSNwSFjY2ChIHZt5uLQGTKLkx8DSxbhvMQLQryAOAkkmBDS70gbwAlBt7NBwXYryziDM3i7AcxgW8MhyZLMsiT0JAVZWhlRYy/oYkW0rPJbgxNhqZELJThRDV2MEBUM3HeNWuinH5qGYAYErNoEMSXkHRCkQdVJXOEgwkb7tQbbd4OAoFWsfu61BQHedt1CgS2cBU5tPb9bxz0k1DwVYCAq9dxAGMTOqZqdSa06DxZw1ktbX3HU9p/aL5y5zk4wToDAds3Ms/fy5oWphwHA+tIBJDhBYu+19qmd+XaUBiZZ/5izZ7eUUWfnGRQ8NT0rMDgjzIaA4OAAECj4QRMx0SePD+2W/tNrB3p6PTIfzF74LjkVVrmY22Fe0bavXxnwjInhqWmW5f7S+1u1Qr3xHRhpCHogKJ+eyF1udp7Pwd1WO8ziU6CAoHpCyrXRMwAyzUHpL2vTzzpXvdEaEBADATlgC1kc1Rhim+6kT9xvAuBGrJmRAiEyizAzuMiYAkIkAYkNhBDYrW4YEWOUtYQAgJMIoiyfzPU7nIAiK/ecALAsDZ3MscBMCXslrWkLiho1Ru1kSQS8aWzcCodZVz+ErYgY6sqIWctlFJxoN/VwAgS92YD1nFnaRd6EaA8EEiT7CwV5314z4LQFfnXJSOJXIOLWQUExHSQFhG1fz9+njlvo4QEIV0AgcNGz5iLsCiAMZhuN4OCoL3gfWPDn3PMh6LUEMyB4n762BDrTgUGvSWh8OrT9j5zAJ3oToNs6yuKRQ6WHAjEf7K9+73GfDRj4orwXIQ8YmBccFPzwLyQ40kcaHKm92lytw9j7DoyAYFh5PQoCZerbUaqLCunvASDMtAcj/wvfrXwVkODTHqcGvyeOVqeBwDrKI3NCSamMpKTDY0hkOgcIti8AhAjOaQgHOZdTVHC45q8qg+TgAFHU15EBxADOKlyD2awlvzMBxQoHUIFm5gUDhI0SlkhlmuMG0RxIaGWdXkc6dVCd3UIgRAQQRcQIYJFnIXtHJqCm7wYY+nb0dcDbuvX3fJnuWEZISb8kBjhxEfwMFBBghk49rG2RUacdmslgBARWrrPkTQheU+ChYIlUoEDKF6W8gwMBb0K4CwXmZ1DArK3nT6rjrGaEvp5PAYGLiaG0B7Rw8BQgOLt/lmZq8zNm2XtAcLe/ZZTpwQCqsOc6EKga2c6Ma/fpB3JdGm3u5T+PdmIOBc31yU2zPnzY5wQGHQycYIMWDgC8+rvX+KO/rFBgaVRl+jp65Ew4A4JZ5TzTcMgd7wFh5y/BtSwKsXI9ogWHev17TjlPSfchowrzdvMECkx1fa+jbK7Zl6oVVABxqh2jAUKI0inr5JQCBzp6k1PFshzsn6mdylSQPRwsDg44A8SMLRBiRjErQO3XC+bCqqi7B4AAiGNdBINSVgHm59hrtsBuRIui2jbB5ZfeJej0TbWJBqr16l646V3e2X/vOvLCegxGPhVR0p97NC20mgiOgaDXCpjTpFmDPRQshFK+pqnBARQEBYkQqIlTUB0NeQ4FqqEZms5KKTY5L5vIyJXzHISJVUsWIGoyW3sjY2f/Z4OD4xd+NibCqdRd64x5oKlrPk8DILiX1YS6emfgGnwMqG2pwEAHCBYy3Bllpv3ivXz0ZXoPCkr/z72cnL+7ZwMGwLlOaZY++6INowy0AtbSCFrP+hDMNAR+cRZgOuAqiWp7L4DgV1Lzi6ZYRTAI8nBQFiiiAx+ML5M0H36a6KhMS+pHpLrtSVDQqVhpWpg6GmJj+gCyboKCdMIjOMgQnwPOYE4IZR4z7sIBKTQugRARkUieNxo0MBUBRiq8DBKanA9626RlVWyiGdj03Re7txNw1lbqPv1tXZmdazMJ/M3cj9HclKemBgkHHXnS5y0zOwyg9cQ6pXAfc2A03fDAA6KUkaURDFj5eSCIgQokGjTdg4LibMgVAKR+n4SCu/Vcn93quYJAU8+tYgMAsvEAPBxA/WPMrMBuZsP4jgMg+AoBocH9CRS0x9Sb+3yNBmL3+l6r+wYJu0FZBwOmqTWhnKk14XpHxXlP1X6O0pGmoGTdvbJ7z/lswKDYebrtZ0a+Fjv6p3/QagqA45c1c3yZ+RH05xytujecN+2OuwdBXgDfO97v/yo0BbMZIKeTV1M3v7024BgK7Nxi+x4kirqCGqF0kIdwwOK4RdkBVsAUDojVvp1r35qY6/mREFkAYdFR7jKAhAWdgBvAAvTYrd0CALidL/knpyN7/Nk0stv/QycPACONANACVQ8Dpn0xs4GNnUemmkDetDCAAufX0ZhsTkDBV1LPPRwQBHoBqJpMINWGFk9uy+3Pr1ITeU+4jbQFT7neqA+2TTY481rbRhOgWtm9KdcDw1h78NQmdQ8KRjLxnvx4NmDQgL4rWV8ofaUMaKHg438qhXpUZjsifioUDLQER0Dgt898J8Igx43CaKayctc8Axtn01D74AbTs9RMSdxdDa5DxNOgYDI/2vaLE5wd13eaFU4ob+IJD/EWH8GBhE9WKAAXJ0QLd0YkXxPrzAUIIIBJAGAACUALClJWFRR6kZBqlrUE5UtZJdC2M7t9OLGPu8/uvk/QG8euItjPoJWwfGolEm9+ao893EdlH+A1JF0+cAwCtn0HA0AxGZgpxgMBTPiHdrns4tMBDKCASz2r8KudxqSeN0BwUM8pRjn+CA4oVkgo5ZRRkUnOJWA8zc7OGAxxv0ogAM71mUXRgfGI3JXA4Pr77W0YdLuJfGTwTntgIND6c+3h4Ezf6J+hKCQxh4KpJo/qvqPbPUsw2FVCLcS+sDwUzNQvh2kABc3ukaYAnV3rRAUHxnbckXq3Tnvq7UnH17S8jE65q17bXVc+ex+O5pro8j+6idvWRg27n6FDKLDtuymnvO8Y7XfzCdhSrMgeDtTSqDYbYhL3AnUwy1nXN8jVrMIstkiTATESMggrAxzr4j1HoGBmhxSogAMrmOQyTY/1N3a/tyy2/OQDAOl5ti3nXL3/dRv0mSyKo49dMItj0H9fyAlxFbryCYQQ3DaBibpfl38OQR34ZHtQ4Ruy/WYRzly9x48cCIFjEDDNQA8DIuxbZ04DAqJqYiiOhmYWa0xj/ruHAKuLFQqG9XmUtJ4bHBwm1wFQ8UVot7fHup8Hlx2p/s+mM/2Rd6obX4OKin9n5XNfG5A4uOZsQDU06To48OeP4OCp6bM3Kr8OzAf9dRv4OLjnswED/4wNJPhCd++5hwJv63lK6pujaQsOTH7915JGlXHm2GUdUfmBMRSYmtMdtrvuKD21AY/ObUm5ZmDYDrzjxFeQKEaBAwrjTnMYudHtNnWqjYzMxsoaxZKo2mMNDohV2ETpgLQTMkDgII+YtAMyYFyYigbJL/d7BhQ2AAQqgLAFndIIVHBVCNgSsHEuMHBLIuAft/r9tmX5TBmPScDilnIBjNuWy6JDBhicq2+D93/wdaipu+a3oEK5CnRdinkJ5fcaA0IgXKJ+L/sJayRc3HeDhIUDligFHFw7iAR1GGyBYAnnQSBA8lXaH9V2ZxEM7fseCIC9qcAHL8rY+dhIobZgPKywx/X8LhQ8NQ36Dx788JrT90mzOrTPThX+gPOaIBPeYzioo3wucGD3rcJ/3ieXfML1aYMOjhkFBPyuo8HT8Dn18p+/EZ+4n/7Bp/j2KE6Bu4mXh2fl27MEAweDpVA8IJyBAv/9Xp2eVfoz4Sc9pcrv/avrgQConRLQOrB4IJDj2vPOzt7o89Wne9EY5ZgODnBOZXY3HUIEiY8VcwsHoyOL7bWUzliL0PxkUUCyOGeVIEgA/BoLFGJZSZDJIEC+BHZggAoBDCDrs4288HOuoLCwmh2CxvcnARCkXOCAuapKs472PRS8uyXcEmNL8mkw8HBL2JJAwk3DBcviSAIInEWDUIPxcQEEqzSDMAZF9Q8HBKTSNqiGgALJaDvK90XhYF0Clki4rhGXTbZtkXBLEWskiLo7Y0FADoycqTE1eChYYiizCWxhqpGvwPEsDSf80YJBcwys7XF1KLTZB2Y+UOda6oFgVP8KAIjzbKnDp+v5oXSd7zuZWs2opP6RzvQd+33j7/76XpCbDAhAsfPXkQlXuaB7gmsrM0C4l69RYr3IECY6RczIEjeSQ5+5KfVHwYt6udbn4CjrzwgM5voYRi2kV39XSWvkUzC6ggeNp+droh1w2+9VrgYG3IZ7QNA8V7f/TJrBwZHa7sgX4p7KrKzXbqvwOABg9aauhWZ20aCX9ap+KiuYHY2U2iVPHRSQRYU7mI7VTlMQnwMilIhxqooV4SHXUVcCWW0P1AXAosY/pfmtQJFCnavPLILNIGFLGTEzIoJGSZSRvajRrfBRIgHeUi5Q8O6WsaWMd7eMhy3jtslnSrJqogBBlgjKOYNTRtJFh3LKbpKIQYO9tP1wT8IiUxW4AQIBRIiRQDEghoCwCChsMeC2BMQt4LoEbImxLQEvVkbKAS8AABExZMQQ1cSh9yKBDCJdMyIE/dS/GKoWgEyr0PoJHEFA0cYNQUDL/AmxOcaJ2jrOvIMD4Gw9t0YR9nXcQ4Xt696dnbtbG2Z3w4Nd79F33O0fd/udVIe1txYQSO/nASGg9jfF3Huyv5wd9hTU6gdMI0H+WoPv+Tg7QAsVvYZgDAba+UzSswGD0rBK4I09Krz6oo1TMAOCEaUdwcFMgO5MA3oRc3o5U2mOYMDucU874BvOGbWV76LuaQ5G6Us7NTYaARW+qCDQuDf2cOAXKuE8XO98f78KBACOoaB33ILMVABqUBi5hgKB3V871AJxFHR0SaWJWo2owEA1iA80uiGAnOXumWtAH1oCFgYeUgIlSKhhJDwkAFHgIhfVen2h5jfwqBqDd6ot2LaEtGWkLckyyx4S1MQgSy1vYM7I6QZZREnXSxgIurLCYoiyiFJcIeslLIhLUE2BaAviEhAjI64ZzLG0o6SdeQgSxTFlYHEyUcwSJvjFZHCNhHWJWEiCEF1jVOGPNtYA9j4C/rsBgH5ttQFWNzSfpxb6cvW0dSzclVwHwFbvUI6/W8+P6ngJKOXh2OruBBBOpsYi8h72hP6Wp/ove0fknASZCyCAzDOfCtBGcvt4Agl3Ut9X12cYl9uhaWTw3Yfp74PvzaCi/XT11P0cpWcEBvbQqVRwH3DydRfR0NKYprDbPxzsEkosfJ9MfVVUXOicXlBtW4fpiTAwMyM0Jx8lbs+z5xqZO57ayHtbmj+byoil1QgwLByrCN+iNUAHB3oVLloE3dKX8YRS2tGRK9GuM233w03rsnyUOGcCCcjdudReo7yvUPNBhGgjOgAcqPgi5KAhrYOEOBbTgCzzmzNjywAtERuJkBeJKXCwBIMJwpbbWQE5O5+BLBCQE1co2BI2hYK0CRDk7bHAQE438LYJGLCssgigQII8ctRPCZJEQdZFCHEFhYC8rQjLRYCA1WeBGWUdSyIkyggUSj69g2N1RiQsUWNFOCi4qklCIkICS1CfBtQphRammKj6BuwAwGsA5CFr3XK/qZWIVtKTY10P4n0FvGA2AL5Xx+36XT3f1/GufhsUNNoC++2OLccXN8rh2POOR8QwHZk5d0Bw0J+VY11/m91ABYBOwZT9ft89SDh+AJcH6oQ7jaHGgHN0Gb+5XbunNR+MZNP+GrXunknPBgya5StNcGhlfv3FZw1pNee5sw6uvtMY+CkjgFVikaDcbfNOL5ZmDWfkKHJkJpjCQAcVZ1KTJ24B4auAg3IfnuermBMUAAocSAmWBku21rlV9qIpoOZa91PfKkcw0Al0f7iN2MpN7Ys5Pg7utIMTHUlbJ6z3Ms0CU0DUxYZEgSwzGxITtsAaIElG0FtmCYUcIiglRESUFfMA5JBxWUS4rjFjS8ASA0IXkMDerfkliI8BipYgpxvydkPWRZUEEsSZLisg7MqKIkKQkSqFiJAzeMkIcdH9AQmLgAOpJoTlefq6FtQUEAOwqmPiZRFTwSUErDHgxRJwjQHrQngRY5kuaNBg0wkXGkwlHPgENEK/F/a14HbPLdsHGpSZ+aDRitk2pwlr6vjBYjztBdrrl3o2AAKnQRiGq56kLxPlcAYFs8HNqX7NoMD6Mu0evLYAqBDHNAAI1H2WZs8567up295vs3MbkCjfWKHgB/j0ez/TBf1UK3VntFdgFtgDrd83SM8GDJBrXHAR4tK4Xn3xWpZOHi6IBDSFM/XqlRq16861knmtga2iZ027AAPQdBpHQV3OOBA2+wYQMK5kbeqrxchR0wChPotrNO8DBwO8LaWrHaKb9IMKB9oBkoMvDwhAUfMfeIMOhDLsYbrfgw7wQPc3nk55FGNvnAcCdh00IYBVoEZdbCiGiAWEyCTLK2fRIkRiJAUEUMRCXGzn7yz7G5AjkNaoUxI3XGLGTQXslsU3IUPU8raGQ328XD89BHgoSPu2xFGcEsUpMII5gXIAh3o9X8IlOBCo+CbYqN9mKVzXBS/WiEsMuASBAfkTp8QXS8QSCasDAvl0WgZSAdBEG9wvddy85/eE4mP9cfP0us0XYG9G+BL1vPensW2mpZhBQact2N97/nizNJt51Q9ygL32dHDILjvW15pwD2inLHoY8JAA7EHBNApnn6kfzPln7c1T+2fhqin4g5/i4w++5bRJJpeOAMGZpryfi3+gSXo+YOCjd+ko89df/Aaf/NUPpVAdae3Ps+/dfqLuRdRXQP5qpHBA1iYrHAATm1eXRg1ipBXwjWXmdXpWSdA8g0s7QHBwYPkYeQLXZzmXg/34x+2bag4cIABgdqpXxPEobOAEN83TKS3DYLTXqYfbbYN9sqG/av3f2X3JOvAQCyhQiFjjCoSIFMREsBBwy+I8F4lxAxDDIr4HgUAhqX1+/myJGY+abQYjc0BcAOYNkQPAqz5OdoadGzjLdMGcBQJ2ZdZoDNTHQM0J9heXgBCBuERxRFwJcY2Ia8RlCXh5jfj6JeLlZcHXrgu+dol4uUZ87SIwcFUoeLGIL8FColFYSGIjLBoLYQkatocTaLOlpxUE3LoFzrOye3/jd1d/dpqnQtWDfXbFRl0/ejNqjjmaujjqw/pDerNCOaZ+P6spGJkRZqkfSJwyHXSDnlEfd9Si+32tNlcAoICC7shw/dsEFIB9UR89h4eC0o93A7muNuDV3/0an/ylyq8Pvt3dsDM/jdIICnxfdAAHzwcMABTnNGa8evMan/zyT/Dpd61QJ43JV9TumCLaDRCcQ1ppSuTnxHZTYzqv2ObELjUV/Qkg4Omyfj1p4SvCd5yp/ZSe+nukPXhK6s0JvdZAtjk4IHVAY7HbV9uqmhH0OMbcO/tLJ1dXSj/SgIB3NvOCpBtxTkHBpcEMCQIBalKgEMFhAW03cFwQ4ype+YEQsmgQYlZbeWYQRURkRKjTHQgBSZzvdOTs09uHhLfl1+biAYi9f7uJc2XaAjIFQAV9TjcVsJj6GIS46vELwrIiLldQXLCsEXEhxCVivUTElbCsCxYV/h4K/smLBV+/LnixBnxtiQUG5FOmN64BWANh1VkIMYjmIJh2IN1AaRM40FUNxYnj3mJFJ96XX8nQ9hvodcG1mhF7ud68PX01dXx/L2/G8poF7n5joC04a0a4N+vgLBDs+r2TfV5xgHX/2fJA8r2Agu4YgQIA3HMT6/v0mZb3EApUfk1lE4CpYbp3hHX1+NWbzw/z/mzAoJAdMl799m/x/b/+U/z8O3+Ojz/4plSdCR3tnIjKjuDsx2HYoGdwAAggNHYsPfbelJxRwzgEgY787gZCgeuIuJpfjiChcZwcaA/kucb3uscLXmvQwgFgZoXyLgilNTYmjF7F+hWmdnEa+161U4weDjwUdMJFtzUhm71vjJv8T6EKCwI0bDMJBBEJFFAUfXteQekRHAQQQlyRA+GWCSEzFvsjsb3LdL2MRUfQC2WxzS8B/793AUvcJHbAY8DfB8JjJKQlgyIhbQHplhEXQtoiYlp1psKtmZkwm5XgZyTIX9BZCAIEcZUZCeslIi4BlzXia9cFLy8RX7vI93/nxYKXq8DA19ZqOnixBLxcIxbzOygwUIGA0g1It7q8cbqpiiO5UMSpDTds/cDo/dg7crECSh0uSxmbVmuvKaizA2z76JinQfeT0ghGvDOsbXdOudYKzmgLvHZxtM+ne31f/exg4En9X9pNxaSu7+tBAa7vM1DoblvSqH+/17f3z+ih4J998E33nK7+OVnEB/3eLsw8Z7z+4nN8/1c/wof4xvS8ZwMGZlezh/757/9ZsclMm9VMHdyZEMT7XODg1d/9Wjaj1hdgProG9dP/am5GjjX9dKi6aw8CU8/oo0RUz1PnOPbOTt1sDnvGI+2BXvYw3RPZTduz+1PQjVmcrJhbMHMmhHvprImgvS6qgGMT8gw3MxrmEEk7Z7seCqqwYc4iZNRGDxtZDzQSZTQXFAJIpveFGItpAWkTE0NcwHkD0q0AwhIIt0SIgRFJfA8CRRH8kbDGjCVkXCLhEgUcrkvAi3XD36/y/e8fEx5uCXEJbgpj7KYwLmB+WYIg9c9jz1GCFxG6qYlqNlgqEFxXgwH5fHkRLcHX1lhg4KUDg4v6HkhQJDEfrNGWn65AIN8VDPKtmA44JXDapL5N3kutT9Zg5b1wEvOIQYJ4wUQ06xHsrhHc6Hyixu/Ofd96fC/trru7d9ML3QWC0eDh6Fh3i/ua0V5FDnTC7zh3hNQ9X2ogyIMCa34YTkMwuHzjYzZ6tsnzjfr4Bgpmz+ZklIeEXer6r9dffI4//NWP8Ivf/Vf4T37xfxqfg+cEBgBevfkcf/irf46f/96P8fE3fqfAQuMnMIOB0TanJSDOxWdBD8IoVkIvQIG5UDxWk01AoIcA7yl9J8nyq3YTU3eqGL4DCJajkfbA0qwbmj2/mRNaKPBaBCsg8yvonucuEIx7pFFJ0W5/W/7SAUUBBbaodTIqRBNroQMEDwXbrQLBdoOprUUITTRXJYOirqcQwCGC4yK/VR3PYQGnCAqrAEJcQWEBLRfEeEGCCMpbZizEWEPGJS64xoxrzHi3Ed5tjBdLwNfWiHfbgr9/TPL3sOHdlvH2YcODBkHatiRTGm9JwjSnNvphztjZk0OA1DeLehhlRB/XiBAJi/oIXNeAl9dF8qJ+BF+7xKIV6M0G12I2COpoaEAAUHoEbY9AAwQ3UBYNAW83icWQc4GF8j4OZw2gvA8z7/Cylj6GlhUSmDruq6F34vPOfnqdZsTejdabqjXK2nT/SV3/5GojvVnRn00u7eFgtG90q0OhOQOCganndH+os4d6WDBQsJlBI1CA607PFO+p/h4YQ0H/PD10+7rayzmFgldvZND8i9/9V/j4G988zOuzAYNXv/0N/vBX/xy/+N0f46MPvlV3eA/dUSEfUnVVab968xt88ss/xqff/Sn+V//579rJO+FpKdL9httXkLEwwq7yt4FSJH/3U6jnEQGskfrQAoJzmRzCATCAH7Y7vH/qoWCfjocd/Xn977M20Np4tSsgCYbD1jGxaZAg7yIDpjImM830Gh1ARqMdFPB2KwKojFDNzJBbwDAbPctQG6RggGUVSIiLbFtWcF7Fdh4XEYjhsQDCGgm3QFgyYU2MNQDXJeBlini3JbzbAt5tGQ9bxNfWjMcXGW9vCe9uCW8fN7y7ZbzbMratRkn0YZMt+qFYVFowkD5Wox6GNtzxdQlYbEbBGvDyIrMNXuqMAwMAP+vgorMNngQE6QYYDKQNSFt5D5y2U+8A9g44gHMAgoAAtpuWP4CUgGUp9aBRy8MJfTfjBDrjpMJAjRMA9/nUuqw33vPJucs09579nubh3gFP0RIc2Mz3fSJwr1+UWGSKL422hisoeI0CWRTTWpplEOMK80v1+4A4yo+cBaepMwcNNAyv3nyO7//Nj/CL39VB8530bMBAoOBf4eMPvimdbvOmRlqB45E2O23Bqzef4/u//BP8/Ds/wUcffrue301l7LUHs4Y3qxBDGPD57EHgVKVBqdzynWSkCxPE3j5fzS5tLAhy26sAH2lHDnuMQYF4J8T36XyaTrIzbwAj0D5AD5IVC3snIbEFEmx+PbNpWVRjlAEQVwUCsewzWGgynBso8MLI4gAgpV0+awdEgJoRSDUHFIJAwHoBFtEYYL0AaQHC2gJCXBCXK9ZIstZAJtwS4xIZ10j4JxfgXUp4tzFum3w+pix/OeNRgeDdTYCggEHK2Bgl8FAalHMkXQchkEYhDAUM1ihAcF0kHsElBJmCGANeLBKk6IWLR7A6k8FaZhkwaHsQKMobaHsA0qbmgg24PUp5bzfw7VGgQDUFR2XvKggQo2oQGZwVELCAt5vAQc5AbMUhm9OyefiHKFoGg4IQ63aSFToNCHzYbKm/5+oxUKeYHjn3Afvpck9JO0g5ULfvjunufQ8KTveNpwd+MDood/SByRqNAllfmOCdMUegcKY4Z4NBycPeL6A85yCx0/KOZF0DBSof76VnAwYCBaIpKFAwsr3M1E1+NK37mEgK1RwZjeTK8e6FYMTkfRpU2FMVvrUTyb6TNjUFnCaamdEwqxaBSCtXhYHiV+ECRY2erwGEs62iS7OAR8ORkdt21Fn2upRTjkIsc/69gyVRXR5VAuAQAkVwDGKb1nuVGEKU60hjEOSnzUDVEnC6VaGU01SVLSpMMStwiOLguqwCCTcR/LhcgdsjaL2ImSEtQLoBYQGWC2i7KSBccFkitkDYMuMWJcTwNQJfX4DEi4RJ3jIeUsbGjJuCwsa5rNa42cqMqS7HnHL9tCiL9mmrIQaSsMULBYlWSAIC6yJmj6tqCtYo4aMX1Q7EgDLTYAkAcQLdHhUIbo2moJgLDAQeH6qmICeBhDtlDi1zDkHrCIFjBsVVzgsBd8fGflYCyfEGBRwWBQULYKWuJ0DRvLheYAi7fT0GHOBa1+au4Wc+lQBAJa/77J9unwfXmF3vULV+AAVTIOj7yMP+0Wn1bABlgyHTKBBPIAFgchrpYnI4UQAHDpT1EKdxOyGrGpO5piK/fk816cW8fgwHzwYMPvbmg1G6BwT+txa4kZZ3ZGxAw/swFO3B/fvfNRP0ld1X9JmWYODoJduhgj4pAFjydnFbjeweHLR+FZ4DPCBYekoktOmxJ0Bg1Gn6fc31d/dxvSZaLQiRPK0FsJIpfgAH+U4htmUhlgaFLYbMIMgAk670uI0fsawv0AmonIfe/UAdqXAIIuRCBC2LaBJuDzJ6XS9iZlivxcyA9AiYH8K2AmFBiAvWuOLFErFlYMuEjSGrNjLhn+SALQNJtxkMbAzVFIj5IGnVzWjDFVuSNQd0kSI1K/jVDgskaACiEraYFAIILQykG+i2HZsLbqI14Ntj8ScAZ/Cmmpq0Ff+Ow7J2BnMOUd55TlKuo3NiBBctQfUd4LAAFHdQwAoFBgR+jQwPB8CsLvv2r+XdafSaoDssLdsGzNzs24PCl4lq6PPSXXb3/UtBwfv0k81P6wu86UdaOHH9beYGOWcCCcB+UNqnDgrGjpQH+XeAUOMtVEAos/N+78d7+XjHI/TZgAEA7Kb1lGl5J6HAbX/127+V2Q1WqMzjwvRwMM2YJ7+naQeGlXxoTxvkqaRqLrDJNqy/CxxoHno4qPdp4aC5HdpGbrk6q6I80gychYFdx1nObwFhCspaZInlWWSJXa7RLQOJ7ZxkQyZTjfdwkEVwKCxyWLRcWRwHOcAFuxgURgcF9zQHCaIupwDe1O8gLuDtEXR7VN+Dd6JFiAtovYhjnI1SnbMiKBZIQIjICEgKBJl1eWcGMgcd1VYYKO+Da1kn92JNW0D6T5XrBRJCkFplaxYs6qAYg+6HQBOlG+hRYw/MnAlH2gHTyBgI6OJPZ5w+vY+pVIaJ0ytRdRBtQKB1LuyhIIOQtM5aWTdwoPtsKW7geMBntcuKvy7uo6sJah1ncnPrbXDA2JvUuLuwS081Q/SH99qCHgrq5g4KSvLb95rVoSAd/Va/K4OFHSSY0GfAFksrzty6jzT7w+ngs3TUh1vWntDfN5ruflALB4D/WDQG/BRSA7pCroITOHBkBBoq5UZwohupT2DA9o2AoNEcdEDQV457trNetVngpdrH6m8b6vb5z9qBhJrHzq+iTz0k9PuGOe1HJAdQMAOCHga4O+/IpGDJ+xZkkuewFQkNCjLEsS4UbylCCEt9btbhlr2fpKCQ1FM9Z7FL++/M4JRFRR3COJxwqmYJ1sIiE7ZR4ESmLaq/AQVweBChv6xFi8BxEW1C57BYAibZCDZEhBCxUCw2cV4jQPG91N19+QoU1KWLm3UKXMAh2pJ0sDnJTJCcRENgAYmcIyGbI2Gqsw1sf3HydN99ufZlauW6SyHUkbf5ClCQMnXfGQoCEFhAOTY6KFiQYTCgoKVlZ8tjZ651+X3qMWmdLYPcEnBJYIsUBLI7p5hLVN6UoUPXuI+g4EgcjjQF+lT6Ufu7naNhOdT1mb1ZtelnO7iYJQaKjd5DAkzb6mJSNKupqhnWA4LTLDcxBg783p4ytVRSK7OGmm6vKegGyfdiYzwbMDiEgdE0xZLa7Q0UfHjHPNGn2cvejfTnqrBDIGie4Yj4OntTDy4sisvd3GVvUnAVaQ4Hci9vTpjBAgb7jp7gCApGQOA1A74TbYpvcH1L1vHJs2pZqFZAwgJLRxhlWCuCxLlSGxwAACWAbZaa6NaBkMXiFPW4nEDqtW7lxv6BsQmvaU7FFJGKAAOqMOMsJgoKzmSh8Q44qdOdTm1EjGIbV6fFHSgEFWzOS57IhBoBISIM5t83K/ARHdDhqA109d9HIOS6fgFxLlM+mxkF3omw1wyY9kVNCAB25dhkL3MpR0ukPh2IqmXxZRgXeY9E5XeJL2HlFmLRyHBchlCQctUSmNbA6nJiHtbjYR3WCtXX46AAwMRlZAumISAcwsEkzV73ue2d8O6hwO1r+013vZ2GgZu6NrxXn0o/Ddf/TQDBDaj2flqAvZ3SN04cA88kdqaCfWrfzKs3v6ma7tHaQDQBlS49IzCYPOTdl1H0gw0UlIiJ3TSjM9qIeZjck2aDnnjL9U7Qbye0d6n3kyhp0PQbFZXXkDjoGABCOX2SC7/dvu+0Bk027kPBCAh2naj1E4N71AWTNT+ATi6QRVPIjK8soMDqtcU25D2CAzhoWtrnHkEVF1eETTzeVbhJ5OceDrL7DpAb+fJ2EwFngs2cFi0mgoICdIaDgYGMepcGBkpwpRBAFGokRnRAABwG4dk5hLHoH0qkQd4HfzLfi7y54EP9TIIeBJwZptcK+DKTsgruOzVl1kMBLcseCno/jhDBQTUIzkwzgoKUBQKKCSGrj4bW5ZGpxn00qanD5JwO2RiACyCIsGcE1oXMzY6APRyM0lMXbBtvn/dn952rJ/3fdAB4tu90x3hA6MyxrN+L8yIH56dl+6j0tyWK7hltNoDiIOgHtY3TYJVbll69+Q3+8Fc/2vsU3DNnDNIzAoNBgXMeF/AuDaCgMU3soWD2gr8SKBhqCUb0+1WmkxXWwUILGb7xkvvfne6299+fnE5AgXWmdn2ze/fTvXwHGIN6YmjnKqYDnZtvdobgxvlmdwgHcGCOn+q5HKKo5BGiCOXNhLcK2u0mqujtpqpJEXZkTqE66qUQOjjQDsj6EgcR3vQg0+JQhB+7EMym+i5lYoISkLwWXXVd/8Dd5N4ra+pwybuLMljiB6jQl82qQfA+Fx4A9PgeApp7DJLlfQoEFMTUQlSmgpqGxWtawuUKplAgwEBiCgW5+hN4KEgQx03RHuzrsPfbmNXhqBF4Sh0uBW9VV+uug4P3mU00A4LjSw1a+l0IGCUTjF9h2g14TPPq67T8buGAZLvBQW+2df1kM8Dyt3bwUGSVA4GiNdjBgaSd+fs9BrQ+PRsw4BB3lUpUZvcLeAoFbr7qUwu2Zsx1zDub10RT4PbpD/14SkMYgA1QnmkINlOtS6XeRiXWO10OIaHN0wwKVEPvHKbmzb5oC+z3CShIGnwHkM53f1HZFkHFxG+AEEg7adIbj+AAVd8awlLrUK71hwAgh2pKuBKwbeIoqI5rMDV0XMRngKgIRUrbEBAoxN1ouP9s06351Qj24e+2Xgxt7/qMp9OkLntfCjmMu9/58PdRqhBQYQDAsYbAAYEFlipagmUFLRdgWcDFF0MWiOJomoMWEM5AQaMl6ICAmcf1V3YigrAlGenHIFPuqgZADvNwUKopo1mboE/+zY7WMOi/30X9/v03/WQeH1MOIPR9vazGqg1wOML2ero7qQjyDg4636w9HMhzFDiw5zo5Yt+temmyS59pDAcH5m8nu8Yhr/9RmBKCvvs6Ei9L93Yk5gtY1C+1UG1UJ8e3UPCUOOXHQnwAAuU83lX606nkbwQ21P5u9ntNyLGmZJfXxqHmnorONX43McmJ1jIL4P1UCOehYJbVpCoCA4QYqMRniKxBY+7AAQcgUhRtgY7MCdI4Kd0UzNR+DpJ1D9QfgKKs+Mc5y2fUWQ3pBiQ9xqvao5oZymhZNAhek8CJYVPxOLmCHWgaZPOxMJaCes8XdJQGC933kBK8c6Df580Beh0xeVBzLIVwrB3ofTGIqobAgMFpDhotQe9PEAIQV52NEJEgRX4GChKwq7/36q5MUpG6y8yl/vZw8B6aZb3BGArq5e5oA2Zp1t8djZwBVFV+BwM2rCj9k+3XdnqoPT6Rz74ACzj02oXumJPyo2gPejgAqs+RHtvLL9nZAkYfdbPKt3keng8YFGHoF7gBmqV7nUmhTOlQm8xH3ntzBgQjoWmpVNzupQKHlfBJ3qijCt1Utv7Fj6Fgt3iLHsP3QGCU7kFMr1Uo12fN8b5bKVMEtUPrb/G+/dqoY/UR+iJRab8JLCDgOlfWfqWHg0wM1gE/M7Co/jZQlCh4FMSTPmkZOw97pgjmBAqLODdt4l1PtshP2sDbBuKL+AwUSBDbO223BhIobY1zXWNmSCzq9ywLHZV9Cg6cMrJbt4GzzJbYayMYI/OF7buXaCLcTWj77SLE63aEgBCCbKNQYIJCkCmPIUibj9Rd1wFBXMamgh4GzNeiOB3aLA6JRZB7B0M386CaDtoYBZvOPDAIkPq01xTcg4K+3to+DwdPTaMzzIXGoMAfOwSC2WDnq0h+YFeuWwUyQ/vTGRz4/PX+BP19zmZpYhoo1y198WBwOSM0IthshnqE5fGE/LJrGxQ0/fpcx+PTswED40FAhY2jrREcNFM6zHvzFBC0L1oOzPWYUSM4INTGtlSOpwZi6ogUk0p7Bwj0+3CJ1wILAy3D9H5PSEeOQ05d56tofwZpeQTUMK/e7OA1a0SDC2iKoLka1t/ftWc/8irjAQUWg4PI5GBBMhZAOouBqmmBggh7SuDgpuAVh7sECptsA48hwTQJ9rleSvQ+2m7FBEHZPPgDMjYgBSBmx6oydM3bVmAg31IR+nmTtR1YXObFVKOQwLZoksyxK2UGAPmEJiEUYW4bVKhHWUOBygqMBCgUUAgIiyzZnAMhrLFAQlgWGUmZz0Okoi0IyyLnL2vVBvR+A97x0msGehgAVfOALXlNoQUCDwlmOmA00xFHUFDMYECZDuo1Xb6M76UZFBDmYxsv9EM5dqDBcX+aqzZzXxUElBuGtn/1avWSBQcHenxprV6LbOf7/JoGYXzzmgdgKMgPtcizfWf6V+uXR4BAEa+++KydktgDxwQK7O1o9ZumZwMGO2Hi4QCoZgXkGiby9/9MwhyXkxwQDEfPPW11wtqDSJEueQILXsXl7+8ol7m99iiNYKDbfrTm+05LMNEa3DOjnNV8tNN2Wnr3eNBrDQApD3LfxTnQ5mkTAnPRMOhgHsQi1FMWIR9BYi6AFGkcdX6zR3UqWeZ6/UTcwAETIdqMBQISiWkhxAgEHenbPPycpRwUEpg13j4n0QCUKXtcpubBoGC7CQAoPBgkQKfyIQRQzqBAyFtCvgEcqjAvSaEgb/UPSZZXzlsWCNiS+ALeBGI4QbUMAgpZP6FlME1FI0AIBgJFkAMUAuIaRKu/RBXuAXGJyFtCWCLCIgAQVjQmBNkomoSwynENEHRxG8pvnaIJt2KlmAmoCnsKYxjoQxvrOQYE4NZ0YDMNDAqYsYOCWY/txxd9vbU6a1BQfAwAFAdEoGgASAVrUCdEAwHqvnttQQsFcyB4Ul9wsL1xxrN7zdTqrBCADhC4A4TSrzqftDOzFEaDLX/cwej/vlwpT6FfTeuNkm9bdp4AgQIL0z8KvkedT4GDAg8GR+nZgEFxWmvUXfZSRNgyBbz+4rOyIFIDBZi9uKHCbDzO7bUUndBnDRgEW6jD3Ze8qWPodtdXxgH6d8K9NRnoNbyG4AAGvpJ13wcqumbFwW7OL6hdzXEEByb8IVZ73S/zs20/gsi+Hg4Aktj9+iJTmDcP38EOH82uoWrbRKxRUsmUmiUYUpkfTmJeCAQgLLDAPWIK0Ln7InEFHMxDn1nMDXFVB1YxOUDn5lMJ6lM1DJxucsztAQgBMW6gsJXSjgASEuCiNBcTgUJBehBISDcFgwT9LZqDvOWiPShwUApmULaxlmkDBUGEPwVCXAPyLYoMXhhxJXCK+k4ADgTOYXfduMhUwrBExOuKsDoQWK/zaYZmMnAmAhAhh/q9LKvsBD+X7aFoB0ywZ9UQmFPsUdAii1EwNbMTVaAFT6G1BwKpiShQQBCmKVBAFQoCtSBwCgo6INj1ASfU84dqeOz7IPJfOBfhzvpbfA+cKZkimB0gGAyMIME9S73hcR+7z3tAGWyNBlbvK1uo9pO/1lV+G/nVdVGNpruDAobj9gM6eDZgUDxuMYeDV1+8Lksnf/Tht9tyGdhhjspvqKGw8wdaiqbSETXqL2gDoQIEncD0+Wtu2hJtrRAntAMzs8nR/YBd47nbIRzsszscLfXcw0FQbUH5rnAQWWz92cEBM4pvgI3ElkBgBYTFN0w/4nHP3nSys8dSD2+QdPrQvASCqjzregtEEvIXCgkUYxn/MOdqWmDW7wYLCg+2bXHRATkXbQJpSGDTKPCyFEAIcQMFQnq8IZsNPjFyzqDIje2fdZVEzigQkG8Z6ZbLd86MpIso5JyxmTnBHD1dVYkE4AYELVtZByGoUA/It4ywBnBmxMwIq4YWzoScGaGbdkkxiClhWRCXiHBdBQouK+hybYFguVQHQrcCZRPIqQOBRktANZxxBQHpJ0ztbzE1TCtQTAIOCMDYTa+dJXJ1z2bJLKBpPbW66oVnNQlgJ/Sjg4LgQMB/P4SCHgjuaQmGs5cmcDCD8SaKoGllUSCBzaGbCMQm9OV6bGsdaD6a/bZ91uf5PPtBl23vzLLNc0xG7/I8/tna7/bbeh+COBp+8pd/hE//4Kf46MOP5vVnYD5ooOCeugDPCAyKSOUxHLx683kp1F1EKEdwPQzMytBvJxjl6a9OS1HgQMe4dcqJgwP4ymAj6I5ku4o3BgHgjGZg6gQzI/jG67fmqfGROJiCNE5OFQhr+AYH8t5Yb5nQwgExI9l36KhB1QPiuQwwqmmhOA/qXZdIzUvkTvTvugjd0JeOmRT83PAELuFns1tvwYpvazptP3oTc4PtY+2Eqx9CqhqDXP84JwWFdr0AsvUCbg8CCI8PsOBE6eGxeY4NjwjqQ5A0+l8IYh4x58KsxnLzM0ibmCC23HnZO22KJRvRBmIEkpkeIWcsphpfBAqgmoew2n3NqZCAKCAgJoKI5XJBvC4I1xXxsiJeLzJ9cL0IHCwX0HoFDdaJ4BBFPTtyHNxpA2oHaxoBZGcC6ECgWc+jAwK4Y+x4n0zLBcDW6cESvCCpNbOpo139nAGBiDAqIagrKPi6KBeIDgpKY1EomEZ1nSXfxzgnnkNnvB0wuPOBol4vpkjOpZEXoclZIIA0j9YvjkDBpyf1uYP+tocCN3oH9vLlCBIIwKsvXuMHf/EJPv3epyq/nOajT50866HAeVdM07MBA3uPNoPMw8HrL17jk7/4gRbqt7sz9y/Kvh+ZSn1E3D6NTBg9HJQ7e9tYUY21lbbpAYYN6QlagaGN64CU5cx6vAeEWWfQb59V3s7XQIRhQAlD6rQHkVBmBdjRUdWImUgdlFX6M3SmQAUEUO24g2WRjhvHLsv+O7WfotWopgPhApa86E1sYSag1h1S/wi7TgsMhACBhTpq46olKM6LsqAQZ3EYxHIDrbLCIC4vQLcH8MM78HIBb4/A4wNifIewPCAtERQegUjeqiAOh6o1CCmCExBiRjpYAMqgwICgaT91GT8APPTtsIIJ6nMgABAQVvExiJcV8SIgsLy8YrmsiC8vVUtwedECwfVFE5GQ46oRCVVLUKYVKhyY9sB1qDm3ZgEDAKDd1jyvgoB+LcfKJ5f6AtRmRFQPLhbme83SruGuZZ9FNHRAMNISNIBKJ7UEIyA4ElITQVuPmw1OBsfbgEkyU2/PBCB24KKDPdMmQEMYo81PAQV7jv62T+13J1AwAwLfVnq589kXr/FHf/kJfvq9T/HtDz8qgEilLPperMq0ERSc8Q99RmBQY3t7OPj8zWv8UEnro05TMHpJI/uLr+6lejjy6qfxiMw5hgO5RK71ryzr6e7oG0CfA0+oQEuwhyDQgtCZ1IQl8h5Q99Jps0I1JZRVCRvtQS1gX9YGBKSAIA6IUhcCqLzLrABgQMBOhehzcabBAPO+rV6Hm+uZyjeD+/5IvuvGugqejKxLh53bDjzSUtfkMVAws4I5NaYbsGygfANMQN4eZPnh5a1Mp1suoOUdKATQKh7/SacCApCplmg1C2jxAQCw3BK2TFiDqNKDwlAflsAEUtTPJQBhjYgm/C8RcQ2IlwXxGhGvEcs1YlHhb5/x5bV+f/FCgGC9gK4v5W+9yPNeXwBhERiw0MQ+6NBgOmFKLQA0QODkSS/4/fse1YWj5AV6qTMnzwFaLZaHzB4GGgDA/vc908EQCM6aEM76LD25n6oFQdZfEoMR9d4eFGQbN3m3tQ5wH14ANKU908qegIIRTKLfBuA3X7zGD//qE/z0u5/iWx98JO2LvJyRMvA57u8zggL2cDRIzwYMgAoHlkak1RfFmPbtevt72HQ5s3vbd2CvQWjgADIapvaM4jgjdjG74Tiy3HAFyRMgMKh7dxvcqGns/CiOtAb+vDvHNPHFAaA4aeqom9Rr3ZkWTHsA7AHBfgfaQ4JkqIlZ1ublIKtnOvlRN3lmbr8eadkDMFYBQ00TXsBGWkTFDoBYZzekm0BCUs1BvICSLMXM6wW8XsXE8HABrRfEx3cIS0RaIra3j6AlIryN6hC4YVs3bJHEKXCVmQ68ZNBDQiJ1Cr0ljfVPxc/Ap0AkdmwFAiJxNlyuohlYXkSEJWC5LlheLliuC8JlwfLiivjyguVyESB4ecHyUoHg8kJhQECAri+B9QKOFwGCoilYSrAh1lkDBgI7MwjvfQGO+oSjNJIxAft6V0+w8+bCaadxL9elZoOHATvvvYEAaLQGVQV/p23fo2j/QHf8u873WXU0LZ9PAQWgahXm6Wg212ya4NEAdDRA+eyL1/iTX36CP//Op/jmhx/V0CmWS6pwMMrt7AnO9GPPBgwqPUnBvnrzGn/8V5/gp38gmoJZUbSjgJ6oDu7H1RboAcHTHODhQI5gBIwcZ4p37SwNoOAMCOzABzjdwkamEiPUnZPlxAlx18D63w6IGkBQc4KUpfs9AIRgz2cLwjBgCl5ZKlnVs+7dRldOgCufQSsziCgjhMHj9PWlbkdzTikld/jsdZS7FVAQLYJ5l4dAiBB7sYFCtOWS40UgId1A8QbkG7A9ihZhvYKuD+CHtwIIj++QH95iXS6Il7cIlxXx3YKkNvzt7SPC5YawPCJeItJjQrxkpMcN8RqdQ2Lcz07wz0N1NkJYg0xDXE1bsKi2QDUFTkuwvHiBeF0QX4imIFxfAteXCNeXCgYGBFfwchUtwXIBwgo2MKCITTUZKZ1cn8C9nON31P7wY15n1h74ptFue9/kQr/FawoG5/XXDN2+Hgbs3BEQAKhagiMgGLTp8pV12q4PDmKnHTjl9WBwxpO+PIym+nxPA4Vym7N+E72/hNcgzKCgA4K2OBmfv3mNP/nlD/Dn3/kZvvXBt5GzDHptoazS59G4n7Y0MyF89ub14aM9GzCwJIX6Gf7kl6J++fYHH819BQ6A4F79K57oAHy4Ue/bYKkZaTvtAeDq8R2wnoa1PAKBAxodpdKe+bji1TxXU8nddMIfod6+BQQi7VR2GgQ5yyABHhJA5X0UKFANQi/g7Zg+m1YXPETsSJ9Y769aisHosggalu99iGZjqnRQjmaTpyD5WWIAJVZnPlHbGyjEIA5rMUQsS0RcXoDSI3i5SUjmuALLFXR5Cbq+BT+8BV1egK8vgYe3uFzfIj+8xfb2EfG6YHmxYXv3DsuLq8xoeLhhe9ywPSzgTWcq3CwoUhfTwCUfuyAsUYBgDaItuEYsF+9IuJwHgstLMRPEFbxc6/d4QQKwZYGBLUuwKh9psEwtBLCl/N7vA1oHmgW4sIcDrwXSw4YjffezXLt+r9tbxXt7rL9nr0WwTU1LeIqG4Fi1ts/oLDVCtPZh792PubK2Ebbc52mgIJvPmUzHfXMLObPnGQ0sPnvzGn/6yx/gz77zM3zzAzeoLccoIFA1203lx25cJtDxp7/8AT7AB9NnenZgYKT1k++K+gVAp06ox/YvZjeqm1y/XMa9KK89MDjo626jPVBtAXs/glmDmtjd7jWgp8BO7cAq7Bw9j43ld/k81ZhGx1RnxBkgiHZBfRColpWHBKB2AEzOkcvubGqZrh5YGg2EfL0IfX0xZ8cysm8DLfVQ4IWPzWEXz38ugqhCQ01WS+z9LDGoJ7nMsFgoiM2eGDGI4FqoQsIaLoiXCwIn0HYFpUfRIMSLAMKLB/DjO9CDgsLDW4SX78CP75AeHrG9fYH0eEN6+4D0eMPycJNgSA+3YSAkAE0URB/tsA9cVKYaGhS8vFafguulNRnY30X8JjhegOUijpVRPjNFpAzctgoDG0v5JnUm3DJj44wt1Xew6bzAM+UfSQAnZmAj1t8MTgptXAcMpO9pbBqaq/yBvSbA9pdydXkcafpo8tnDAIBDH4IxEEza8b009DegqRB9cn/Grg+h2jOcBwW5CDsNQr32vT66Povl8eh5Rs/yp7/8AX78nZ/hdz74aFfC4ifFpb/zgOBuX2+O9i2ZfPyz7/wM/4d//S8xS88GDAhw6hfxKWjSgdDsgWBGo7958xo/+usfNJckVO2BH3HP4MDpD/YNhMY/ePA584noK9oZyvYjC6t0BgmjWR7tyaI12AV0mqbZMW67OmLWaZ1J1ZF6P9MilPunRi1ZO9i+A6LiELebnTT47iGiQIFqHcxEIcTOBRASlYMaFwyGaAoMCixm/qbhhiscyDlmvugXNLJFhMItg1RTsASZwbDEgIWAawwCDgoJSyBEIqyRsFDEur5EXF+AtgcNhvQIWh+B6yPiywfkh7fA7RH8IJoDeniL5euPyA9vBQ4eNqTHG/iWsD0+grek4ZS7UMp96XehjcNapx3SGoumIF5W0Q6oQ2FQGMAqWoO8XAsEIF7UkfKKBMItCQTckoBA0RKwlPVDyti4lvv2lDIHgYgRCdgKFEi5M0tcjBQYlLmJKVBNCccxBIZwcKRF6Mu3+dUPFTuBD2A65fDJMIBu/304KGbQiVNirwKf9Wv3+jQwF20u6XkW9bGAwlCbYu/B+3vxvtDrHZv89Z99/ICjZ/rx7/8M3/zGR8NnKwM2jAEB6CDBJfFZMPPER+ODND0bMJhBwdAZrIOCGZH69Js3r/HP/+aH+PHv/wzf+3/8+6X6GxzY+SOzQgGI3VWPVW1HletI2zF6jlFd8fku9is3WjH9+wwOhlqDsvMJsxd26RgSADinpgSZuaDju5H6ctT5dNsaZy/dF0nJnyr5974MFo6Zco2lkMhFqms6Ax2JcoWCjUXtvmW5Vs4irKqQwtAUFsh8DUic+ghYgmgN3gbCEgLWSLjGgFUh4ZYkCM6aSLUILyTYX94UEm7I2wOwfk0dFR9Aj++A2wPywzvQ7QHh9ohVZzfkx0dZa2FLYAWDtKUS2Yjd+y+QZlEKdSaEhDheEC6XOptglemGwWZTOO1AVghgMxuEBVvRDmTcNA6Dxl3CTWHglmT/pvu2nEsUQin343IGpKyXQEiBELJUTeHSLGs6KBx4M6NpC6KDAoshEALdDSoEdCLHt6munxqC+XAbD/fP/Qbetx37zB3Bwny+vQnQJ/dvToYbDAA2uuay3wYc5Lsy2oMYNb/maTzAqD96W/9oAPfNDw6CF2kePCDIIISKyWr0trwj47c+6Kfs79OzAQMPBTPT1kgVdca/4DdvXuM/+Jsf4l/93k/xzQ9a6LDq7mdElO1uNO8r2uyVl3za9TsyGMHAobagv5Gr+D6Z975V/r7SjeDgsIn4ofK9dBf5fUdYctV2YnTyXsPr+217W6F0HrqgDwWBBW18mQFimSKZg8pDjcKUiEtYBR/1EGo+YPUz4E5QbZzLKnxVYCkkuGe2CIKByAFCxhIhZoWYEYlwjR0kLAELMZZA5W8NC9bLgggWZ8XtUbQIlxvwQvwS4u1BYiDcHiVoUtoQbg+ygJOtzZCzwIAuCy3LQGtRjpY59msXaLwBulxFU7BcFAbW8odoJoO1aAduN1Zhr38M3LYWBh5Me5DMfNADwbnyXYLA28IBgRhAwAKGRN1lsEXJJFuHoIK2hwI/o4SK5sA7CjLuBhIaCPx7nvTDNDxnAgJ32+qddOe4Iyjo+7kn93G6g1xbJFWNBt3uzQ79dUYam6PnaPLm+u9yjCvLJ5lKuM1YHYi2231Rf/aFOOL/RKc81jR/mmcDBvegwNL7QsF/qlAwIrk6PrDRdwsHIJSASEdNdzRz4J5mYBdlrTl3cjfdvJs8SXYzap5hVKS1Gpo6UM0JQNNpteunQ694YgQy7eQGmoHy3Oc7xmE1aSqPn/Ghz2ifISJSQFBISAoIlBmJdM6EtdjMVqSIgbClcf/YQ8GWGbmowqvgSl1PaGFzg5oKwkZYYka4idB/iAIJl0B4GwjrEnENhDUGXKL4IKyBsCRxnFvDBcv10kJC3sRpMW1AviGkWwnDzGkrCzfxJms3lGWYs7PS28qH/QqGXXhiiTWwinkgrrAZBgYDph1IOvq/qangMbFoBzLjtiVsmfGYBQYeUy6gtSUp15t+AhiWqYFBDISFqQiGBYQtZCwDdbm916jkTNg7hcp31RaQjy4oMGBRLpUeO3X+XFvw5DSqhF/2mgB6M8JuuqKfzjeYyrfLEsZQcLev2wFDK9mD0+axARpXePCQ4B2xy1PekTMzKBhpC0bpqDykm+7i9nTbLXkoME36mdf8bMDgo39aSciq5tSpbLdvPNL+2zev8R/+qkLBeycHB3ePw74h9HkeUnPXQBrG8INrV6HLXH52mo4ODuR8+W1ag1PYDLTmhJkW4UnahV570F3qTEjmI/NGF92srPJWpoXKSnqUEyxkLoVYfDEyJNLiVtRF0mKDDoESdIYD6TEmP93DZFaTgoOCW8pIWaMKTipRNEETqAi2VbUFphm4xIDLxngbgDUG1SAQrjEiEmMNKgj1GguJw+JCqFMf0wbW72V1SM4AJwQn0MZdWwUsEQ4WdlhCD5dIhAoFNsUwZWBzMJAy46amgoeUcNtYNQSyXsNjygUG5C8XEEh6/tmytN5EtAXid1L7l9oYSP04ohrYRPjvoWAxbUGwzpxhkSxtQS2yFTdLWWrdnpgA9sU8Mp21jfbuImmnAxJNOoOBae5U6rQFZXMPBU/s7xK4aAJKXBNy2lKugAAIJFhXWLbzWLYcPctXkXw1bUzTAy21H6B+/qZCgS0DINqS+/d8NmAwqnr3xqZH6ve/ffMa/8Gv9uaD2Rrld9NMoLqX/hQgaJ0P22doTYV97aTdVzfWr6GebZg7Gd3OVGujhaOK1qBAQPdmjuBgqDI9AIBhqFZu97Un21W7m4T6rnV0wyGCWCEha+hBzqpBiNL/MYBMyB0cbBCnwyUGJCTEDHAISMgIGaKaHhSBCTMTZDL3PpeFdXyygIUhhAIGUbUDHhIui8xiuCgYLJR2kLCf2RAQ6AXiKveRx0t1hFvWcXA1dkikBgaEfnXCDBRHwaxmgH5GQQ8DGzNum2oFOONxa2HAoMrAIKs2Y1Z+IYRKyJkRA5fpiD6J6l9mJixBZ4gEeb/ml7B000eDlmWkrvxcaOuiKfCaAyvLXf2d1F1gbxbz3ymAeOyTswvH7q+1u0d/3/ae3N+bwhBITM7PBk7Dfg/Sb93r86TboXIfohYSwLWPMy2CgX55Im5XSQUwngXQP9SdROcOa5LXPBsc+EGcJfMp8FAAtHLySG49GzCYvRwvgs5EfAoEfP7FHgpGl38CB5f8jNLIxsRu39Bk0AFBbxLpr235tf3eWUWOY9EcdA96ZE6we/fhn0vSUbZMyxzBgcthVU9M7qSHHcVoH3aiLRQ0UHHHZlqWrdbno6x2gBDBlEAcKyCEiBBX6YQC3KIsAgdMAJNMwFwogENGArAwgYOuCplpCgg9FHizgqnEq/pbFirakkFCbiFhE2F2iQHLhgIJSwDWmLDEgKv5H5igo1YtLrbygKggSLF1oJP81Pz3s2hKZ89qoVAzyijGwE1H/w+ZsXWaAfMZEA3BHAbulxkJxCGLVM8oUyx9MiAIQXwOFjI4ENiiUlZ9TAl1ECXREpjGpQBBozXgVvPCeVzvB3W21FtOsNgf5YWY5ou9PavVkt1bCrm/1+gakofunv35XZjgUfKDon6H17KN+r6m3xuAgkGChSm3tW1tUORBIPiTh85/Bw9xIpmMajjKXVYZtXy/lz774lWZsl+g4NTotaZnAwZlGpr+LjahRk7RXTj4jZoPRlDQL3bT75PPdv+9dBYKmufiPS3PGsXoXgYIDUnfyWdvuzo8lpyvwQwOLOP3AMGZI6ZLPM+AoO9Qe1gApg5bTGXC0l79nbPYyDmDA0t0N7tXXLFSRCI91+7nfA6kslp3IFPdEtdV9DIHIGZkZh1liurTxwXwAs6m11lnKUAg1wqUEQPhgQjrMoCEztxQ/wQU6ne3L8r7rfPv4Zzs9qOXply1rta6XWGXoTM1vDNhN5Og3TfXDNy27LQtWj7DaQcCBylzAwLeJBPIx4qgMv3TysJ8NuQYLTNqoWAJGgvBmWEoZ9UabDuzTDEdOLPMUV2tVZpAOlMHOqW3AC5nbey+d7J2V7c1cEB7od8U3mDfbCG3u8Dhn+nEIM4f0S9O5dNscOQ1ojYwKnAgFwVQ4YB1Wz8D4MxTeeFf4MTMFW4/sN8HnAMCYDI7rwP1cqmDaz4bMNhV2fLCUZbrHZu460v43DkafmtgPuiJbrZvV1GcfPBpBAV+31OhYKdo7AfvXyKdgoJea2CdkcEBAHa5bK7IXWflAYEMLEahl3soqAAw61yLtzyLKC3OcuUxfIcnyxQLKBBASVTfHBQQ1N+AlyLpOK6IYRE/RbU5E0HXjsYeDpbod4qAT0HNERkeIm5aPplpZyPf/E+FiIW4gMItVUiIAVhiVECooFCnPYpQtFkOtp1USNoyvSXQD+or8ysn9rNWaxCnNrCTxXKwgEN+lobNIjAQMC2CgUDKjC2lRivggWkbtPulq84CAqJhMWBqwImClIWW0zW2jpw9FPjfxfSiTpxiPtg6IDD/glTqa6mrLkzvtK6aP4zWVbmearr8+Lq8ENMe8K5zmC/nDtyFgdF5PVycAIQzg7h7adT/9YMjqBpetBMtHHC5SKs58I5/UzgY9Pl7zUA7CCqDTdrv6y61kz8ASkTDP//Op/jon37UTH9tzuk+R+nZgYGnrHvVytPb51+82k1JPNICHGoIZiU+AQSfZuqzPkAGMAad0b6RTG8iqUEEmFUiQh35HZkRuivWDBKhBDxq4ADwJXUfErJ7SQdwMMpNBwXWydrcerbRGSAds7+9OdNrp8spSAeskGBx3zlkgC3WumkNVtkfM0JctVVShQF9JMri1xFDBG6pwEGkgJAJj8gIFPCo/gciuDICAbcklBFIFitKJIJw0el6PpXfiRtICETALVdHxSUU7YR3YqymBOqmRlITSwGoavmj0Y2xTBnFo8YQ6KcQ2qJGBgG9r4XXCtg1j2DA0kK1DGKotv8lRqyRcFkEDkQjQLgU34yAi5lYFAperFHXqECrSWigQE0H6SZagnwrvgWwZbPN8dCmeXIWf4hJHS11lasQpxDAW3YwiwIBhBMmAhxBwR4ShjDQHTNc10V23M1LQWcTlCzhv2WwR80AaQQSxWqJIf/U5+j3cXUz8emeWXX4WN1v75tQVzDfA0K/r04nd5d2MujzN6/xIw1etAvup1exvzPp2YBBsakB6Jfp9VqDUsFQ6e3zL2rwotbRUK9hv3EABH0F6CtVj3vcqo/OpukUxC6NGkEl0voMX1KRAACtKcKBwG49CAB7xZ5dYwAJpdNznG9w4EfSI0IaQEGZSuc7W5tSN7pGifgUBRZCEM2AB4Sg3RPLnHMpiyifLHBAIQIkyycTASFbp8egDLxcI5aU8UDALTEoZwQQHlNGCMBGYkM3AR1Dxi1QGSl7QAgqHL1gNGH5CDUxpEqZJiQfttTY201YAijOjHVqZJ0m6adL+t9Hqbfz228T/PV76yw48hHon9U/L+x5NR0BgWlOvJbgEkPRmJij5nUXOEpmLLSzOfS6hDKbo1kaO92KlqBsUyBgzjIF1E/5PPIrsBkyAeCkUGAuBh0cDBu7jeCJWiiYAMFTYECOn2sPvpw+QC+n/2yAFwpw7rN1JnlH7LPpSTLAbSuL73UQYEA02tenPRR83Pj6lIFeuYKR0qTf1PSMwMCNtSk0cDBKVvifv5Ewxz/uwkSe8iHwL7m7nf0c2Yh6/wdPtvdSpxjs9o1BY6bpINTR3pG2oF+VbZ70obyWgLkNV7w73N5ZXbxkvxS17iPt4Ho4KEP85kGPex6DAit4N+fe1LVFTcu1BXPODSBgUfBQ7QExy/z8YkBn8TsIizjhZyrlaiF2KUuZiWo+Y8mEB8pYArDlgEcNXLSlgMcgPgI2kn7caOhktzh1+uYqX29nfwSK2cFS6CqzV7v7faHrdc9AgaXeDOKDC/k8Hgn9WToDA37mhmkIzFzSA4E4Zcr0TjMlnPInGJkO0tZpCRKwbS0QDOrlrk4CsNgQpe76fX2aScgpFFQ42C0x3F+v00TsNBO78w/qCU0Gcf53GezJdWydEm+7dzrHXTrSmzwFCkYKkFETKIjVg4LPD1cIsABZsgosiv/o6Fn+1kHBKMzxDgruObFqejZgQOpYw2bnpro871BrAODzL16VVawanwL7vGMu2DlzYPzbp5IfhQOLilfu57QZPtkzFJjQf9YYADQNosmPNxuQq6jO+eYeFPR5udt8diYEe/6+01CtAmdHUxnNUtScdZc2EwMOQODAj276aVbTOfUu5dTabrMzN4SA4mwISIFnC5VsHvm81x7EvWkhxhVUHNoIW2CdqgjEzGqbjtiSRCbcYsBDylgXwm0L2ELGJRK2HMTeHgOui3e+C6chIWduhKxfSTDf7gvfE/L5yekMV3ih730ZQqCy7wwMnHG+lKmcNAQCMR/stQQyFXFiOkjbWEuQJDAUtpvWrxYGvBlhZ2c2OJgm6gQ3VeGvf8U5cQQERyP+09MYgR4eBrlsvO/rZY7hAKhmhToWcPViIgBnfaKdPqqLvk/023oomMkDHTaVFN3YxfspqMWx+jGggwRN1adANAX2LGPfAi5agmqK/UehMVCihgkfES6e1jwc1AUlBgsuYQADXWWJg0ow6tf6ymBpBwfUQlzfEMoucg1hd9N9DhqwL9eeA4Hdu4eEcu/7RFCfeBhoRT87P4GyyqT6ExRQUEhgNQ0QICYG1R6w3aZ0qAGAgwaWDo5iFPvrvdTbcg0SfPZDlE48RHVQyhK5z7IcIEs4sJkt1mLaCGFBCAtiBGImbBABk9TLfmExHSwxghlYU5JQvgtKRL/WQz80HvpVk5CLPd475hkkmFnBA0LuVfmm4ndqe6DtvPOg4x0toGSJBhXIax5stwl5E/5BBXBOrL4Ndbt9v8S5mSAQdpqBo5kY6xJFIxAltgMRioYgUNUORAwcDPMGMghoAkF1ULDdRPgXLQK3kDryKzBYvZOKEyLQCX8V/GreGkFBAYIOBnZmgf3LvZuve0MK308DY81BFZg1XHKASNkWEOqxu1zc6Rdt+64PdOf3UOABYPa0pftzvxlVngD6es3aN4AEQOTXnw6WARiZEOSGDgpO+Gg9IzCo7EUwQcMY+Rv85u/2YSLLMfZlQoHUHyc3P8xab9YwS8IIDnoQaKiZ7AmrGk1+szt2n47Wez8CgvKcg8rvNo9wZJyR5pDRSKfWfC4rGzqNAlMFBNYmRaQCOUtnlyW+IFRbJAqkJJoHs796PFcbrTBI1RDsku7jnCsc2MjOOYD1poXimBiiBJUJCQgbQlwRQlQoABIRYhBhvJAswLRlxkIRaYHCAWm8fzTz+T0kVE9+Hzkx7Lz3A4njXiY1J2iZZD0u62wBg4XMXAS+rfFQHbtq/T+CgvLqHRw0pq1A9bcK90wsgl9fSVQQiCSfl0hFO7DGMPQZsJG9TTk0we5hYNUVKQ0GZBYClQiGPliRj2DYBHvqfQmGDoYHpoN7UAAUKKAQinAXH4MAc0CUqYEqFkJsokzuoCBEDIHATy8cmRKeoHKfJROK/hNApw3oYED7xhEgaMcvJxpYKCjMUmM6oHnf2G87goJ9bz9+dvs2goVI9VH6S7z6ogYvMvnVD94sH+QuUKDghN36GYGBmA+c7gXepGDp8zev8cO/+gQ//YOxpgAYw0C9AtfPowh8fW0cOEbavWZw0DqgULl+0AyVoBegff3rbt9XeMuit/odAYG/5BER9+lpGmcvMBQcyIQRARzGgBBUOBeNQi49i4cDLIv4AGy3KRw0eZ51zu6cAgfYZEGgbQMWgFJnWihOYgkIazlfNAgRCwiRCVlH8QmkAlqD/gQgccSm7gvvP98/FNND1m0hZDwmRsjSZgJBpkXmCp1+0SdxndDvmYtveCmuowGJlnmZZQeZ7kiBNOocCzhklGGUQcAa7TMUKDBzwHSa4YFWYBifgVBMBT6Yk2kHbI0DC1TUgoAzG5RARS5OAVjqxwQKDpMHAmAKBbLmxAQKDBqCgwSKYyDwZoRGvDw9HfURPRT4QRPgVOwTQAAB2WkFPCRk7gT/QR+56+M6TcE9KGj7RiPmp8mHAkYuu6ZNsGd6pYPamfwaQkEfQdPy9o/B+VAqisGBag06k8LrL17jh3/xCT793sR8MPkcQoBFKpslRmeDMye7vWNkqwmoICB8M1KhyfYmqNOg9flKLp96P7d/BAk9OfeXfyoM3Ps9SvW+lr8IEE8AgaRTJtUSlFGDCJgacEnqCC0rYLMUyGkJ1HdhAuqTh+UGDqrfQQRn1oBOGzgsDhAMEiKINtEmhIhVR3AcxK0yZ9IIgBIAyRZWygykGBQa9qBgoYK3blXBUbjgW2I83Das+jsEwrZlABk3ACuCQIQKa/agoFBQQ/ubqWFecsVhzNaRcE42FO1dk2oMUCDABP6yhAYIrusyhAEL+7xEnUIYW38BDwKmFYhEZeaHTD2U78O1DVSoN5ELR0Bg35lbf4LeyXBXUK1O8h4Q1CmK1TTAYYEBgp3jtQQcnBlhBwQ1KmH/eZT6vsEE/2z/0bZeKDbqdQ8D5osE2Q5IsQaqdfJeHwmM+8lhH9n1j9Rcx8kK92738Ve6n8367EnvV2UFQ+TXH/3lOflV8jMyIbBbnGuSng0YAA4OChBU9nr1xWf4RKHgo4mmoHm5wO4F+5dLeasnTkiwzOOHQQowAgSr8419jexIEVa9dyq5BjBsWJ6Gu+c7oxk4BoF7TX1/lH3faZpn9XOYJxLIIxVQBmcsalERugGsU1dLCOOcZUlcTiAm0S6QzniYAQIAKobOwWhu5PSVs2gvrPOnIE6IFFpAyAll6WHrqEMEaWdNISLoyG4NERmkMwxbUBCHQtUoxIBNQeGfKCgIIDizw4LdmgKPKePFGvDuJsfctoyHmPCYAtYt46H4ZWRsAAICcsrVHJDnL9GbFUa+BdB3WjQGBIQYYCGGV10m+rpUILguEav6CrxYg64SuV8DojcPXPW7gcASWo2AXwo5agNo1jSwQEMGBfBgoILepscWIKhxCdhPlR1polT7RB0QlHpWGuwMCEjMp8VvYKYl0GMMFgwIQjW/2Rvdtdk7bbVk2bVZHh/SJVdPvNYQrTaBCK16XQ/wMGACrwcF2z/M/om+cqaqr8fugWC3ZPYoI9bXNLIlubDWsv/1m8/xyV/8AJ9+T8IcH4n1nRyzPJQ6eR/xng8YiHt+s0nmlQe8+uLX+OQv/wiffu9nzYIS7mT3Nc9frC/QmeqvtAStSvpyDRIaQJhpD0jVYL36DCjeqeVW70HBdtBMKzCsWEBbwR10+bNmo4y+c+m7xr6uWh5EHVg31EZJoLCoqj5rY9IRv3pysmmPyDrvAObsAIH1ORikKl5E7DvwIyPlKHGulq0EAKlGT8wWrpbAOSjo2EiNQKGO+kg7cQoRUUeAa4xg7cATa6jkLFEDG9NDDHiRQ4kmuHFuVyFc6iqEjznj5cp42DLe3RKuKeLhlvAQE5Yl4OGWsKaA26bnBgKljJwYHBjYJDR0liUmJa/MrUcVal2EySsQwiLTNEMkBOf9vy4y2r+usQDBdZUARC/WKLCgwYcucT+tcF1aP4EeBHrTQLP8cU7qPKgaHh31m6DfrWcwCGFcNASlPmTsBERft+KgOzYw8DAASB1aRCNgI/0nA4HtQ136mNG2VZ/jXRv1Qhrohz47QOgF/Zn+xWNCAxq6Qy2JNc9ETUTZgKo1AO3u6K7un2vQZ57REni5MZIZo/ffjLG0BHMCWewWCjKo/eUf49Pv/hQff/BNANyUy/Siu1kI/pB/RBqDUXr9d6+kUP/gp/j4g2+hLi4yprgGBrw9RhOV47g5Tza6l8TJBQOpTaYFhBPaA6C0pvfVFBxpBKZakv7ZdqlvxG2Td1fslkltL91HKysdiG9PoiKQZ3O3CQSNBRDBMZwHBI5gs/laZ7/G0qkH7kLR9p36ke9ByXTWU2Sqo0WkAyDBkQB9ICqgIM8aBBScCtjAgcMCbxeOIcq2RWAhg5CzmBwSFBgysEVGYkJaxksVP2bG45bxuDBerhGPKePdLeDdLWJLCe+WgHdbxqYahNsmAZa2KOaEHHRd+GTlxcMO2LQDgGoG7mgIXiyiFVhixIs14MUaCwRcYo1EuF9CGrvVIYObQdCaBXQGQVkdMmndqaP+ai5y9cUqsQMBACXcdhNhU+vDLo20Tn04bgcCdaZBrTPNQl8joX8CCDL2MHCvfcIdU5b+tXbp2nqgUc9gV+rKpWn03cDLhLWVRf1V+kvWexVQKP0FHQJOdwt5hrJRf5/QEgyBwGbL9TcdyQ17Zs6leF799jN8/6//FD//zp8LFORUzQ4jP7auPM9EiR2lf6tgQET/dwC/D+D/w8z/U932PwLwnwP4nwD4bwD8b5j5/3v6ms6c8OrNa3z/l3+Cn3/nJ/jog2+7l5Xc8b4g9zAwBIDuGnUb4KpUbUBtKAsA3gdCfveVnQBVmcuPMnKePnj785Q24IwtbJJK3olQhseD1EOBOa6hbr4b/dHmIhNXODJIyGSNVgABCghFg8QMcADlLB2imR9syeSctROvnb7XJNj9/foKRavQFMiIylU4+DV+KYipofx0I0AAQaGhCADbRwSYzdh7n6sZAgiyZHBcsUZZxjg5UNhYgYEZlxh3kPBuS3hQP4PHnPG4Rtm2RVxvGV9LWbQIt4RHPW5LXLQImYGUcnFKLEVgQFeeV2YeRJ026LUDqwr862raAQGD6xLwYom4LKIhsLUKXixxBwOjsMQhuAWMcgIeb08yB+iT7IS/B8emd7jXhrp3PqsLzZTDnWYglLoA5ysw8yGoUBmKxqloB/hLtE3IOQbzzYqEXOFgmA5Mtftj3VdvjydysNCBghRbCwu6rb9ms92ebTqQakfkJe+n5Uffd3TZ0L7l1W//Ft//1Y/w89/7MT7+d/+9oqUmMnWklYfTTvvr9IPck2YE4N++xuDPAfxfAPxnbtu/APD/Zub/MxH9C/39n9y9UhlxyeerN59X0vrw24cd9/Bl8qBpNEQ7K2C3pKlNtbNTGkDIbvtYe2DnMlqtrFV4f22f7moDPEme1hCg6eEJWiEHJpxR8h0Pd9vq7cf395oSIgUFBwmlI/rvyPu7nluS7L4T+6+IyP2cmhvfGBiIrCrNl7CkfqlTAix5pCb7xTBgsLu6yCaaLxJkey5tYQzo1rcDg5A0VIPsqa7utoEB+oVsajy+6HNOdVOCP0WfU5TG/gKsZ++MWHOx1opYERmZez+nmpL8MIBz9rNz584dmRkZ8Yv/WrEWySyRzA/BgQAHHQQsJoINCsFJwMzgOhgMoBBS244GK7NBwr+XE/Btr3RmqPGM63svIQMdDPiZYzM9iAOjOC+qz0JIQIhY4gKmiJVlaeTKuoyxNEh4I0dccsF9EUi4rIxzjvhkLTifiqoIuToryqtAQinc4MAtdRyLrS6wuAJ+dYGZCcyZ0KsDT1ILSfzEJS46xRZxcAkH4YjXgyWEZhpw8v9G+q8Br66sHPD3rb7vFQB52QIAgA4CZADHHATU7DSDAXlv6sHWf4DRImJfeyaPkhjZ81ijhtsHLFP1Ll3xUZnJ72779Ld9HwvotckVnA5Vhf5A9bNh0+b93oRqNvjK+YxAMDEt7BXOePYXP8dXf/K7+N4X/hDv/upn1feJWyZNpxp4X7btsfpxzzLcPvv454dV+I8KBsz8nIj+i2HzlwH8Xf372wB+ilvAwJVnrz7C1/70myq/fH4HCvyIVOY38sg+tEe3NlgCqDH+HSCwzbAdDGzUAz2ONWzfWEdZ7qpEd81fYk/q8kck9wRRq2eFAzsOtWiTwHw2MnZA4wylfs8V7108qgaBGcRoaZL1AlUVIWgT59wGd7O9UW6QUIl6DxT04B4WYNejzSarM2P9igeEPtTtdM36QbtiAAhOXo6pOqNRtBDNSU0riygoIYDjAoQFISb5OyZkEC5ZIOGSGXeJccmENxi4rEHNDAIJ1dRwSuqXkHDJpXNYLGxLIEWdmIUuDjqLr0sOiaaOhKOp4EmKLT9BEmfCRVcbLLraYIkuDPF6UfPApc9LkC+wOAJgWTLItjogrzJwltLux9Ez3k5KN+kAnFfUVQPD/l0iLqDCnY8X0EGAfXcGAtX8ZIrA4ExoJifgUB3Q1v+gZxFAVQm0kpvUxHWb/l4fEI67a7s325bPbuiXdJD0dvkKCnpdZ7BQjzMc90F96sastDOxPBpLhvLs1Uf46p/9foMCc0Jkc2ImNFO1Uwtu9IV69upn+OpPvom38ebuPv+xFYNZ+c+Z+d/r3/8TgP98thMR/R6A3wMA/C9kGxM1KPj1PxIoGMuh1LM3aLYb+ezjP99s6w5fK+hn0woCJrl1cABs1QO0uvjjYdJoJ5R9MwQcyVu1uMYITFUPdmFUa1V2jma/tAEF+2Pyxa5zIvv1XjUgcO2ERhUhEKqKUH9EB/feg5w3oADgABYGUOACpGG1Q2XAHhB4VXOCzU4VGuqs1O5dHmap1T/BBpsIxNQGnZQEGNIisLCcGigE+cz+jukOiAmXKErCJepyxkh4o8gqh4uqBqM/wloY59ICKc0yIMq94yE5U5+5cS+DofkNPKmZDtHFHBCFAFgIAgPne404eNFIgmsDgctZIGCVz9jAQKMOvtY118GfWE06pYASeiDYWz2w5yOwBwH22x4E/D7OTOCVgVth4JZnEH4fewZrp3VdJRgnNPa9DgquzbaHQmMWVhgs6KokryxMYKEe58bZ9rR/vUVtPhhPZuWrf/b7+P4/+Bd491f+VvVVkStrgCDvZSI08WXbAwQKePbyOb72k2/ie1/4Fv7P3/tnu3X4TxEMamFmJqJpM2Xm/xbAfwsA9CvEh1AwobVdIJjdPN332V/8OX7jX//j7TGBejOIS3V2uwkOqsLQ1AM5Pvo4CDOfhk09tw3xJklr1lDrb28b40b12HuwXNnrkLarFbazllol/4GtR6LrkNDJnQQEZ24Qu70DhaGjmsHCBhQGxzQuuQGCBVOy6+6UAnahcDlfgKwKRg2h2ysK7O6TdWbVjqwzViKqUEBJ1AFaTrItySviAgoLkBZwXHCKJ3BccLckrOyjK0q0xScpILOEWjYl4V6hwMdJWDNq6mRXbQBNUbeUzZakyMcZmGUuNEWgJi3SV6rKwFnNBZemEKwX8HqWkMOXs8LARa73etle44Pr2/kD+GiDMYLiAi4KCDGhRsW01QXUBx1idRDccxjcVQPc570icB0E5LyuwMDw7PnHzJc6/NTkRfqe7Me8fwFXc8NhmUHBbJJm+3Ylt76HIcqA9lMbWJiqCvrV+RDTlWv9bF93bOt/MK6M5fv/4J/j3V/9OzpWNAhh8yugAFDpAQGY+LKhg4Q6Pv7at/B0Nml25T9FMPj/EtHfYOZ/T0R/A8D/75YvPfv45w0K3n5nvtPeDewG0nmDfPYXf47f+B/+Cb7/X/4B/t4Pv9oaR22Yur/afF4PDrQ+WnYfqx2S/VTU6urv99s2RgcHVk8uqhrsVPfac8dbIJh9xzYRyZI8+W20CGfaQdmKDktKRU7SvAYKbWazDwsuoo+0Gee8xmZSUUCgtMhgZI8acz/oeyjIln1Pj5NXWfJmoYh9cie7Fk7KpiA5IRCTDGymHCwnAYXlThSF5U5gYbnbQEKIJ/FJCKmFYHYhmd/QsMvmuOhDNDPLagguXBM2OrFJlgTq6gAi6kIQmwNhoJax8EEwcLkHX87gyz14XeU1rwIGphQM1/Ta9ZTX4ZqGAI6pObzHRU9Un21zBlUVR6AgOCA4iEL4wKBDt4JAt80dYAbhh8+q3kf7XmA66KQqK+x+OPZBHRTsTdJ8JesERTdXNaD2FPq2gcJofqg2+lqJG9SDvYnmrX3ueJGH83v3V/6229bqY5DAfrv2yXa23ZjiyrOPfyY+d37SfABu/ymCwY8A/BaA/5u+/vCWL7UlHcckNIWCvRun7z0UvPurn+kPOLkJtxaJ2DXcyJ1G335vp1F2n80krvm5bY9vlbPGNjRGDwcGOhVZCywHwtFcoeuk/OkMp7jn/NQuEdlEQUCBTVXoH34DBei+pL26s+Q2nwWtfFAbLWnfZ7JdUwoKbIkbB7fErWTU5ZAU5P1CoFVs2BbEhot2Vu6aQO3bJntzYZR1BWfJ1MhZ9tmsiLBzCEH9DQghRVAI8urNCjGBTk8EFhQO6PSkKQnxpOaGO4SYsKSTOC6m3nGxFOCNRJpPQZzQwLLywe5bdjfXAk4TiSkA1EccTNTiC2wcCNczKK+gfC/g5MCAz58IBFzO8u/8iQCWMx+UVVSasuYHXUMiu5YsAY7SAkICSOI2UP9FNRmQQEFMQEpgUmdQik0dsGWm5gdgPgkH/gHA3Cygl71bBtxt9yQBGdRnIHDkaGj3zSCvG++1PqYa7KkE3dZxkDVlc6bMdZUcAWGso3UMblIDdKBQzQ8War0zNQCHtvqdAf1WT//5MXf64fFzN8GUiZgV7ZPHCacrz159pKvzro+PVv5jL1f8HsTR8H9JRB8D+GcQIPh/EtE3AfwCwP/+lmN994vf3ioFM/mnltLd6NeCgl9C6eAA2AKCL17qPDIRzGBgl1Y3T5dutwekAcIIB526UOHoaJowL52EOemodh8dZl0uZVW3ddlcB3+CUxSADhayq2kFBjRg6CJEEhAmoFD9FErWzl894ImAEjQiI0n+BKDJ1aWAODQLkaoOrNBhUFDWDL7IwJbXDFRI2BnYosZBiAQKATGJA2JIEfFuQViSmBTUxFAh4e4NAQczN6S7zichxJMMaGlBQZDET4w+CmMduLiDvNr3oik7FnrYRxuUhEQMymdZSWAAYD4D630zE1zO4Pu/bDBQTQZnlMuKfC9AAL1u4k9w+7WLCla0COiGlNSXxAU50u7TYKLz7UgJCEkBIFYzhL3n7v0WBCRxVbuGXgXw24ABHNyGIxXupucLbb7KOuj7+yk64cOe9X4mMM7CByjwfXFf+f6Qvs9y74mMZLwM7+gGM2c+reLRZG/WD/9Vl0F9rqo0oNsUDob9GxRMxseD8h97VcJXdz76Xz/0WE//i7/bNfa6hOPaTTwg05ugwDegatMK/We32NpcffYKTR+OHSCYPVQTtWFbXJ3tYd15SHyMcmmo89zw9VCvUWa19KJKmX0A1CArsl/DGmg/YFK33Zl6hg4ECNwpCR4WDBQCQVc7uGVw2QFCFtsyAeJ3AOufdPA0x8MQm607y0DGmeWfDW5rBuciKkLhOsDNQg+TBg66hIC4JFCKiJ9EUEoIKSIsEfFOQWA5gU53oHQSc8PprocEMzcYJFBE1BURrDETisrcflY7lqbGGCQANbZAzqCzS1XsVxV4GDjfq7lA/sblDF7PyPdnlEsWkFrXer3yRU0zN14vAQCJGBkTgBzAJNkyOee2+sNMAFHgidLS+3GECA5LNStUILDrpiqCDfYGAiMEdO+vDPzDI6DvbwOA0ZLov/N6euhxt0e3Dq6z/mtPOegGSmqTGwog/ZttfzONAhiXlcsuV2bytxTr+HQcYrJ08q5DHMaowzKDAzuG/1mdbLYl+wIFvBmr/v/LlPB6hVTSNts4wtz788YR6ioUTIAAOIKCvf1vg4abFYIjIOiux851mNi2rHvoVQO3/w4QBMJmuROAavtvuSGoUw32yjWxw5fOVVN3rJd6+F6A81mosw2tq82KqIcFC6HbQupGCU5kCZLyBWZPppLBOQB0qWpD1VXqigT1SaAgXvCrnkGRJYDIXKGgXJqN3NvKx2J+BzkEhCVi1YEvpARaIuLp3JSE06mZG2aQYJ/pLFiCSaVqIw81Tj+h2cwnN9DLxhZmWJcLtkyEzhSwBwN5RTmfqzKQzxfwJavKIuBUVGm55RpR4QoJAeJPUAIjlgJdCAmKsfoZUBJzQXXydCBly0Pr9QmLKARxqUBQYIoLUDRSZB4hYKK+yOv1wf51QXwPEGalfy7as7PZ7+hYU7OCM/F602/d91rfJbVr25v6eQsgyKDLN/XNFhfAvid1td92EDCDAzvn1zRFzysk0NAt2TcoGM0mB6f3eMAAw0l3QpcMbGzLAq88Nc/+3b/toWB242Yw4H+/a1TbbbcCAfBAKOjMBwcP1AhNoyngqu9E6zDbMRnVB3A8B6Lrdkz9T9q27O8t8UfKw7WOsHYEk2IQQW3P9gxJgxHFQNWCCGA11YDFqS6bLB4iYowNDkoQGZxI1AMKWziwE4gqeXNBSPp3CAih9KAD1AHPBkE5kckwoTNhOgf1OVgRlghKEeV8qUpCuFsEEk6L+CQsam4wSHDb/KqH0bGOpu3fnaNUHj64EHGpqwbqksKLriowGHDb8llgoNxfOmWA19xUA/UjYFk7Ob0uAEChIKQEoMgyRFdCoM5no6oDMYHiMlVXON2JgqTqCgdTVhZINEphwT0zTEZLa+3NBv61tdLjcq2H2euC/PZQtzVgvqXnmkFC19Y35UZb+019mP9snODINoJzEK/LrZut/qFwAKBb1t0AwUGA1sWffRes2asI43ncCg9E+OnHP3NQ8HRwYJVf7V+35dGAQVWTPCDsLPHbfNdR3LOPZUmiLBlxSsHQQKYw0O13o0Iwu+G3yGyj2WQ0M+xBwa7T4TUQOCgKBNPiKMHkY0srPVMN7KP2DdQogwXDc7dXneF9OfhwdhjyH5hpAaYWACsJKISgSyOJEIklql+xMLxR7PtlBXKQI1AAZXOYDAhRZo7ye4M/TCoIRUwJpRTQEnUglYyNVFzMAwDQVQFlHATXrHkJCIgBZV1BZ/E5KCmCYsCaIuInCeG0IKazqglLc1w0SBigoEro5jxn8RSAw3bdxQ9gRqnxBdySQl2hYTDgVYG8ZpTzpcKAKCkeCHj/egByPXKpcCDVDaBojofiW2DOmyGl6sDZ+WUoFIS7N8DBgkc1xYAtoFRIFQi6PBYKBazvm+kAnT/BzEF30153Nox34WiYuwYE/vsztcCb3G76wU9jn5+qwX4QHSY4oxTC3ODA6jKDmREOjtr1FBD8OLCFlSkkHKkJexNSLc9e/UzCKI9L9qndmVuu+qMBg9rBuleRjFogiE0x5zqVeJ59/HN89V//Iwku8WZvPtjchCNlwG2/2qhmxdmdOqkKQDfMbWxtu8Libb/7SyxeNRhhALDLs4UDwDkj7gAC6vH6533sZ8aO1T7Ozr4xqhhmUohDj0YkQXlI+5sAgAqrVz2QCYgs9coAEsua7hgSQohAuAggkDi4SZAbQrgDiqoIGGBzbC1cCiKAXCTtsfyTWcnYvL0dPZcsMrmDhLLmbuUCrxl0WcXskBLW5YyYIsJpQVg+qZBQoyyqrZyS2Ny7oD8zOPBQ4IM5cZGAQxZ90JZrKgyUi0KAOmGWdUUpZaMOlDVvYIAHO9aY+tmuX7uW1Pwx9NyrmaXCwBP9dwecniCc7lQZWIAk8SA8IBRQddRcmSsQZBZlwFZ1VBBQKMhudcctbdNK1Li/tqUmNhraUtctTfY5BALdMEKB7bdJVey+/x+2dJqc21y2bdMpzd3AfUv/PWxngoMU139XU4X9DgCKteOqJqNhRdim+uOkVOv47OOf4as/+R3JrfDW59GtcrHvDv/2yuMBA+5tWQSTTvSpUwho5gQv8ygU/Nnv43v/8F/i6ZufbRdtj9gMKobtuw3pQQ6IjhS5DHDgxfVfYtkDn+m+83MHAO9zQJj7GdgZjHAAcisI9Du2wkBuIdXBv6oIHq6piRdjo/dQMC6ns2fYx/dfhyNEkhDCFKRjrqBQ5DiBgEKMrKpBCYwUCJk1hn88icxeJXcC59iyKdpSt7rkTb3hzU9A7BTI54tegFUd5oIMoABQuKY8LijdwGh/FxSEGIA1o2RRImxgDSmiBEJYVoQ1ocQAOl86D/1uGWRUE0Lw56DtIkzajw81bLEa1HTCuR/k/UoM73Rp/hWdueCS6z0tuWyAABAI8MqJORqaIyaliLioanJaEO+SmFXMlPLkDXm9e8Ot4rhDiQvglniyMyGsasUoLNEhs7aVphY0B0Nrlxk8bY++rJD7LOclBzBYECGEpkNinThR/3qTOuA2dMmFHBTMegyDh1r85IXIPahq698brUYnvYc47T2oTKDgdfpxP+AbKHhFAe3zCgrmq6W/14Wb7469rc+zj/+8QcHbT/ubTKZ3NiAYl4qP5dGAgXlCb+AAqHZdm1a1uPpygZ/9xQt89c9+F9/7tT/Eu29+bucHxoFzTHyCHRUBOBxkN+cxcUxx9NlLYrc+LG6oHBvbpm7Un4c1LPLhWx94biS2eK8aRK1RoTbgMzOi9RUTSABk8AW8bKezftqaDI4Qyjpg63ztstrEexxcVj3NWICLwopl8bO1+RIbngUQQMgMpKADQCAsXj0gksGdCKy2eaIgUn0I4MuCQgG4LPJZjKBwBkUCpwg6X8S2Hs51tms+Bx4QAGzk9JIFDpgZuGRwZFCQQVZiLIjjHsUgvggmrxsU2Ex70XpHG6RcHgf0M/QeUlocgRqjQUGgmFOlwYD7ewMEzmTgz9P/bjBfggEI5NWvzohIp6WaUeqKDQWBoK+4e0NXctyBDQjUr4BVMTCV4KIAYFktOyDgFvPBPr/WBu28IkR9IJKdIxFy4I2SUK8BbBDX41APA7sg4DZusrUOQDBuD37nozKafCko1NjM3flM1f7tAX3Z3m+O72cSit/3tZRfXzcd9Jn3QaF+z/4evjstYasUaB2bsvAwKAAeERiIFN3DgYxDW9WgzsJhCSV+D9/7wre2ULDxK3AdnTkq7TSYmf1nlza9v8B4VvpAMIpbc9sPeQI+zs9gChZ25OGhGn/1GhTYNSTCvmNOXaAHAM5U0IOABwQQUEx1qSDdiLpeIVsfaYqCZhxjhh7LLTQyOCAFiL3aMmrEPsDDgicSeVm1jpEIa5DOncii8wXEARAKKzQAYK8ehAhaz5CsiPctI6LlOrj/S0l6dP+X4JSA8z1SOiPeydI8WiLKvTgP9s53ceOYGFOcO+D5a1CqmI2yonnq5wLWARUXSe1cbL3/X6LGSwi27G+mFGx+S3/HVgtku96lwoBfYjiuwKhQoMVmujENHWh1MGwwQLpCwxSCaja4W3og8KszOpXgpEAwqAQWCEpVAgsEtWr+CAOCrOqAhJDmru1N250/xyzAt+o5Rd7vUozBPRRUHwCnDMjTvK8IjNd4ozK4z7vPyJka6n7zJ7CbqOl6/OtwUL99DARjf9ZOaNJHN0Coq2vqd3f69mt9uttX/NjqQdyuZmpQWA5hCws76tGzj3++MR9s1YItFDi8mpZHAwasg47P5iWNa1ANnA3pp6+e13zXT9/6/PxC7TUC58G821BegyzZSVCtvkA1g3g4qBGvSoMDv1SmVbb9eYMs1V4PlAL/nYktayweDoAeEAiqFlgVW2/TnQUzaqZF9g+Y6qYFrGYXBQTSS8kNDmKQ2dzegyYzNzM16E90MzdZ1rYyIxYBhUiElYFEuQKCKQhZPc6TmReYUAIhhYi4vAGEs3QE4QJJoSzpkkNaUM73oOUEvv9EBp713APC6Yyodvbgl+vVWXbaDKpyPtcgQTtn9U0gPX8KBHaDrZkLQpB1/xS3bYQogCcqlsUUKC7DZA397MwBHgL60MU2CvUwsAkRXUNFi+IR1IfCO1gexnS4ewIsd1d8CU7IANYCBwMCB6smlTKFwIBg5QYDFkp629baeUbXJuPOc1bNW0CFgjAAwQwGRtOAHKsdd2cKsQsL/pg7Fe1XjM0exZlyUG2FsX9+b+3T3EC/l5wKcFDg+sFZ8qVjc+vw3sd5cabiKSyE2MOhV5Fdefbqozp+vfv20+48R7UAGKDgr4tiAPRw4HLswFSD2hgJkmVqLwvjDSoA+w5pb/aNq4qN86+rc2fYoN7HFTOSLu17Hg4A9yABHWl3JogdWWo85/qQbJUC2U7zB2PYVts8GhxY7TpD6AQC6n5o5gJ2+3agIJVSCGiAkG2pIYuDoMEBZ5VfiwzyY+kVhPFDHajkJmBVvwIOYQ4IzMiBkVRijkF8EZZAiPGEEBdZ2rieAf2b17MsiTvdoSx3IAv7e5J8ALickU6aD+B8j7KeNgF+kLmCwjgDB/qB9shRz5sYDBZE/RVzQ3aLKUcHv1kZf8tHIhxNDv0x26A//laf2yBUcDEQqNEMnV9BSFEgILb8EVRfTx0QFAcEe2YD70dgf+8BwVrKcRvTEu1zzTGx+Zyo+r14KJB8FKgmrw4I/GzeDe4zFcCXPVjovoQBNDb7Tcyd5Hy/mpERnXJQYeCX2KfVfQYF1Pb1tn7/2XDCN/Xxvo4uaRM75cBM2xzSkGzJ7yPf7RIGWkTDsb6DWnArFACPCAwsZKeVwoNq4ODg2csXeO8gTORNCoD5GIzf3auffW2y3XVvrhHJQ2AKQqVrfd85UZJ+hlDX5vYPk/7IzP9gcq5bmga6h2eQ2KZmE/vJ4ZyrD8HexZh9xq1uFSxUFZD6UlUjJPYBSdwgkjZRiqgHKPKMFQZSDEAuyIEFFlg637GH9smAxrLapVUFI6PsAwKkHik0/4OsSkJygEAjIORVwhPnC0jX9cMH/dElfcE+yyvKZa05FgwUvGw/k+yBfoAGtgP4bLstmbRB+sYVwofHt7LnrwCgqhMeAMZw0CFQBQGJRZAQFotUeNommBpjEhgQuGWHIxDIoN/7EaxFwLJlnZwDgQQ0Ojh/bY5xeE5aIipUKEhRJhE1qmSAW1IrQBBCg4GqItTf2nEi3OG8Pf6jyd97qDiquGZ77yY9FMGsMEEAVVMiu1nHA/q1EQj0712VoAMCPY4/h9l5Dec97uPNrCDnGVK72jgFBgAgLnj28kUX0bDutjF9DGqBr6NJqTvl0YABYCfbUn/244xcrGevnuO9H72PD7/8AZ6+/fS6+WDHOmZNcbP9BhrzJVR4GaoA6gHB1sFzweZBYt4FhKoYHkQo3A/QNDw49vmexDYBhBkc7HUUs86Ghy9UQ4l9wM1EwWpKEI8KBQRV7QwLs/YnGQIHVBiIRfqWwkAMWHNBZFthwB0rzCI0BhZTggeExFR9HzgUMIsMn4vMAlMQ/4OVCGkHEDhfJKNgvsgAnFZQuQBlBWn0P1yG9ML5AjrfI9qyvyuJhGbAAKBCQ70PHgYm/grXBvlZmakL1MUW8DdeBn/bZwMA1xJIaS4DUQhaOuoatVAjO0p+g6Uux6xBioblhx4ISvUnUKWJJdvkqiqCQcPKvAGCaXtyDX6WkTJC2goFVL8WciqBLGBRICBysEDO52DrJDjzLZi9f0iZfrf6ffEAB3L2rGYDYmoAoArBX0m/tjfRcQMs0Pr68fV1+nxftvMjGoCh/cBPf/EC7/34t/bHrx2HQ6CpBdcCzQGPDAy64kYjuwzPX77Aez/4Oj78yofNJlPLVho6IsM82wm3LyTsBjj3YNpbe62AQAzwAAg8AQQ4k4RTO5i3g/a4TzdXGGHA9vVylf/+gTnluGO5Mmvc2WrKA2vnVvRNhYQgnXTg5gVOQS5jZlEJCnTwKQFcnR8EDrjI4FnhAPJQ+cDQVnwa2gYIYmLITFiLOCCmIksYR0DICghRP4+BEMJJnBRrLoGL5l64AAoJllcgcEE5W+bBdQMKyBnBPnNxAo6yNx4BAyWX18H8D+IcGHbv6wwAnM+C7XMEAC37oToWDnEW5DVuQUA/D6c7MAUZ/DXE8wYG1O9jlUvTfAgYG4Xg0wIB0KDABniDgqS+EgYFfjVMoOZLEMnet9cGDWMGUetjZr3fQ8qt6GA9GxocAH2fRpCH2nvvw5QC+2503x38Dbqfm/RtM1PoDUCwCwPdpOHGspUQulI2mwXynr98jvd++Jv48Cvf1fFrdt7bOk9/80p5vGAA1NgGBPEp+PoP3sOHX/lwSlozCCizjcNne21y17NYS4Zrt+wGt4Hee0BQVWAGCKQOK2y0bZCwcwLA8OBgDgLu/XUHnKMOYtSyvA2tfTbNbzE7GsmadNI6RKLqh8gASpHZUSmiIrS49KixFTKzuGhEAjOBSJbtUWZwkJmFpFUGUJqD2F5uBw8IYoIXQOAdQFhJ4xwURgjicX7Rz1rGwYgQokACWEwNJYM0twCKDvbLf9aBgo8iKLCwtmiCCguc105ZABeUy1o9/2fqQpisIgC2wHBY9gAAuKoCyKoCDarklYARAmJqKzyGEM4WnrjsJTiKCxgCeYWBnFuUQnMmrOmmuV926IGgLUVszfwoJ0hAk/MjoQ7+bVlsv/rFBnxLXR2omQys/XiloJoS4IYPF0L9Ic9eLYfOeLP+wHV6df/B8boqo4DZ10fPffJSO2HeER/1b1dVT9oAwQgDM5+o9n6//x+tbeNVKlsywEevnuP9H7yH73zlQ7zz9lNVRff722m9H1AeNRgAclHGi7o33j/kxhcvrd5YF2ss9dHgvrEQOa993acHhDYQTgHBaBvzhrm/3hfTB/zTgUD90e3v23Wo7/vto83Qgjv55ZHtOviHXQPYUEQIcg0LiTnBlINMDRCIpKOu6kEkRI6IKHUZIjHVpXxyyQUQImi3oy/gGsUxA0Bh8bli7gDBOvpqIyZZmXFRSLAZn0nDiQiRTgiao6cDhZIBzuIcmC9ygjUpUb4ZFkjf3woLABowAB00jMUP/nLLhmWOfwUQYKmNWVMdi6+AZjbUJaIGAgWqCuQ+SqHlMqgwgKYOZHBdduiBYOZHcKQSeCAYVYJELWaGtJO24sDaRhyAwJQCUxPq/Jdzg4Aur8r1Z86eM0LGdNLA43Y73yNAADrHa+vXqge/wuMIClbvsdzSx836twcCQbdowL05GgumsDA5jTHz64uXz/FbP3oPH3zpQ3zurafI3NRlv1/3W/r6OlAAPDIw2CTuYODFq+d4/4dbKJipAdVvoBL+/g3f87G6AVwB9N72frh7GCCoiQFozirjg9SVW2xxg2qg9ar7To/QlykuuHwOe51SzboHoMOyAag2EqGaPah640qIwhgiItmyQSAydYAgS1tJrJo2CEBmsClKxx8Ly2qGEJEKIxOLilAYICAzTTv+mXlhBRDVxLAWaa8xKyTk0kMCWDv1FkxpBAWZLZ4kZw/ZSg+FBIUFLiuoaF6CARbI5SQwE4TPWUAarjiuF+msBzNEjSngTRHAsWpQVYJmCjAI2JgDKGgq4wYHXY4GbzKoqY4bBNh78RtQCJhlN1x5iEjYQIBtH5a2fysMuGZ76EdgJgMACgGkKw0mQIA20B8BQVRJoLYJMCxZVX0O2WW5nDxr7VEbnjW1/7fZN6tCWWT27ZdLTwbn/f7D9YITD34xGRRgMB1s+7n9YED75s8GG7cCgY0N3cRy9ps3jgeANy9bPymT2m/8+Ov44y9+iM++9XRrwnDHGn0XZuYO+f3rCe0eDRjM1t++ePkc7//oPXzw5Q/x+dlFRQ8D1252T4n2en2o9MpAPa6/oZsfuBUQZN8KCeDaUDqv1r1y4+D/EOikrm52gHYVp52TVBjmkNRAYb/D6vwnKDQosCWVRDozDAghIlBs/gjlBkDgOSAklf4zcYU6GwxMRbBSwB0gALgKCRQIcS0VBpqDmZ6SAkMk+c4WFvR892Ahr2AHBy3dcZaUtObUOFMVSm4JjmooY/+3RhY9mKaYPwFFuTcdBNjfaQFZUqaJGoDl1Gb83gQwmgRmEJDnECBqDjZhilcWx9Sa26De72MYsPtvJXQDn7yOjoXeZPC6QGCrEUwdIM1HIWDAm+fsIc8YoDHBQSDWBsnZ2etVVbN+ZchJsDNObYr/fOrBT23PGv3kFjPIgVP56wLBQ80Js/1GE0MW1sJHr57jmz/+Ov7oi9/B5956B0V9owDoREgqMvNX2ytEfjza3/kRgUH7O8BBwZc+xDtvPd2FAXnlzY3ek4rqMW4Agq5s9m8V3iQ6uREQrB20vXeoe6zKwfvxs1ukKO/NzMNrOzAfz1h8R+WlzQ4chlJh0M0AiBoomL2gJvhJarOnmwDBpGVKAckNFKlQzZJnKkKaDBLBwVk3a6ynREBhXACQPvF+9tgGjNItURtDMUNNEH6JWj9wBASKMsFetJ2pkiDKggz6VDKwNKdGcAY5MOhUBZ8hcb0IJFm2R58Tod4rbd22BDFIvogOAsxUsFEDbMBvToJNAXBQEFKFgFwUAlhWDYgJoL+vWe/FLDTxHgAA8yWs11YXHMGAdyqcrTTYv68zIOB6T2H3hBliQni95wtEaj4IAGlGQssmpmfJLhCRvHdmPswHYOBh/UutlvurqYhx+Gxb9vq5TV0eAARH48StI8TWxMz46OVz/M6fvo9v/fp38Jk3n1ZYAOQqs0ICEXX+auHgR83dk0jGx6PyaMAAaANrBwVu9cE1ILjFhGCN5yG2G28PAnTgtMZAGv2P3KBff7wN+WW48XuOiv43xjKr8qGD5exkXOldh1AdPQ/BdQYFVeZkBw52M0qr2OF6ZapgQDaDoVUggaPUNmSBhJAQicTUADEHRNbBIkg+hEwSmMjCGzPrgMwRHL2kLCrCESQwo1vNYP4ps8Hk4q+v3tg9T/WjgcV7q89mmYkiQkhISQeUbvVDAZeLvjdFYcdXgVlgwVQDy564Vyz7oikElrb5wDeggcAiJgK3WqBAIg6WIvL+CAFjbgJTAAzs9mb/oxmg3PjAP/SebfxM0NSBYxMStXsLoPoOVNgrOHy2bnmuGKjPFYBqNiBugBAA8AAHOw//tVn5XiluVtzq56755Pce3P/tTBxvUQhuGS8eUj569Ry/+6fv4w9/7QN85s13Wh0GEKiAAMDOeC+bphWbNP/Wj97DW3h7tw6PBgyscXjzwTtvzaHA3+xDKpzc1J+/UtI6uuFDq7TjjAO4AYLPGmiAYK47DSKa3DTSYfEH3CuT+r6ODCZ17Ovi1QyDg813vF9B/cGdjqvkVmE/y+kqaDJltkrJZnVCtA6NQpDjEQGcgFJAtNYZp5kZCgkMJG5QkFnhoNBmtkkpYGFgJTfbJIGEtXBdzUBog824mkEGrC0k3GCiF+9zPedgA4YOHmar9mveTZaODhairn5IQVSFGCLS6dRAoTo2NkWBq6pQqsNjUDjg4qTp2Um4dMykUCAydNSBPjgFYFAEVC0wEMiqBvg4Aj4fgV82KMGGSrds0JYaNtBnBwHXr7/dA7n+1A1O5kx4i6mgqT63qwOd/8DgU2LqgCkGYkawZ4/b8wQM093RJyBX9a09i6q+aQ9FKLZiGjDTgh6LgOZA6EqFgsmsXKo073cKnDKoB7K3U3AAHtQfHikAt6gENwPBDaDwO3/6Pv7wCx/gM28+3YwdUzV52O7HkFom4+N//f1/uluHRwUGz18+x2/+UKHAlILuJs6hYBcIhpv484+f43d/8v71yuwM1IXndOujNnr1wPv12o33ENEteZzUd/s7/W/OqlzrOvn+LQ6TBgfyyawSo18BjjuwYXZDY2dmf5pZwSkH4imtkBCidJw6ABFHoJiJQWbPIUQwqZlhR0UAAzm02WgkQgYdmhpWt6Jh1ehbfgZaILNcA4VxgDoyW1U4CA0WAqEueUyREEBIQbYtKSLpLPUuRhCxOrkJKNTvUUAKT8RXARhMDwW2VBJFAidhmdy3sdg9oQCG/u2dBCdOg1mvg0QPZJfCWDMX6gT4PucaYfCyZoUCubamJLS8BVyv+UOuLwCE0q4xQh+0XExUcq334g/MTAUGEQYD6N43daCtLuBmLqi+A7lBtgGCAfYtzxJQn6fqWMiDOkAG2Uovpg4wC4ezpjDbAQIPBUcTMmCn/xnq6+L+bMDB7O+3lL1J0aw+N0HMbPONdQGAP/zCB/jsm3Pz9wwQSm0XbRLZjQsOCn5THfGfvv30EJweDRi8eClLEj+wk4aT/Yd996BgDwiABgV/+IUP8L/77//hX8EZbIunPtMMto1x/+4+ZPCftXFv7gCac4y3dW0aqFXU1Xleiuuwhk7LtjvlgJjBOcu7PUcjnfFQlAG+gwTWDq0UOR/nd+BVBNJ/kYJEUQwSXjkTVbnZICGqKcpDQkIEc29qsBUNq7Q6ZBeVyZum/IC1liKzXzM76KwYaNt8idpjmAoQgwFCQDDzQmxhmiMR7uKKFAKWSBUUIjFi0HMhQgzQpZIJMSaJAQTMZ6rqK8KjU5uWaYbOndUCuQDrqgBWNFshay4CBwKXLGGG701BqGGIgZXL5lo+9DrWv4tcSw2xCYDqszkqNqLQtKWG/ZJUbJYaEramApr5DphKs6cOHPoTcH2GoM/QdKzqniHoschZFoI+rwSgyHJeQO/pzqqn4c2077WPdwZpk9C7qnYqTd/fEY/73t5PjnW+ZlL9ZZfPvvl0u3EYy6rZVkHAw4EVP3589EonzdPgftvyaMDAgheZUuBvrDld7JU9KLBH5+cfP8fv/+R9/MsvfIC/8+Y2t8KmTNrgTCm4pYxwMP7EtUZtx6ifPUA1GPfzsErUGqP/DGjKCG0+mVZiePUzTt4AQU38c+Rv4NYybyFB/A5ELQigIMDAIYJKkNmO2sArJATNdYAREiT/gUGC926PFMSuTW2gohBBWRWTDHF70BlUIK7xFepyucK45KIDWhvYSik1i7Kf7baBDJI8yAHCKcn7JcqglQLhlEQZOMWAFIAlZtxF+WyJQYPpzMwOEGdGihJTwe4y53ofuxgV03XjsQ4IElFQ4YexMQ+sDIUAMQ/c56J/A+dcsHLBebXgQgUXVQjOa/nU101AIQBBzEooJu87hUahQK6XAIABwczfY76a5DoMyN+lDfx1xYHBdd6HAQWB68+PJswyC10FBG5eMjUQEVW1oD7DmwFYlVBsZ9L2dgQF/1l9P6oFGLqRUS3Q4lXOVpt5mfV/rwMF1td351uv0fXvHzqy6myr72N36qGvo1Jg3zn67qMBAwtz3AGBSvLVRGYzPLSGONLsWAwK/vAL35mTnJUbYWB2M45odq8cta+NQuLOf/b9zcTpQJTopSyXl2J4b4epu/toZrvFQcK4r0HBkQ1bz7yGf6Yg6/FtoO8gIcssVU0NZAqCOr4JRFyHBFEUHCRwg4TMLIMpxwYIFHDJrEGU5IE/54IQJChSCFx992xwtIHuspa6TQY6romcvGnCHOASmQwtA90SCSEELFFUgiUG3CUBgFOFhYCTmhSWFHFnkBBkeaVF2UvU7N9NEo/N5jkz+tpssaA5+5lS4lQBH1HwkgvuC1fzwLnIgC9/S4Ki+1VA4aLwUBQOXu86yT1bFKQAIAaGRa63c7ZXgypTCUx98UBgCsyuEyFeAwZGU4FtH2CgjCBw6EDRdhU3HXcTzZmA9EZeKV0mWrd9hIBZX3zkED12oNVvy5k3beICoFsWDuz4Irxm8fEA/M9YlX3/P/UD27mMLXvEzg6uY5UJ09D3cjNNv3jVoMCUgltGm0cDBu++9TkAvYfmpk3RlcAOg13q33z8Ar//k9+cQ8EvGQT22ODWhrwHA/J3k+yA/QfvqCFuUp1UZJ80SB0AalY4i4pmRfe5pdTZzgwK9rzfbbumxmYOU0iQOgkMMEfrDWWpY/Wcb5BAKn0TEUiXPjJ6c8PotLiSDHbRAUKkgrUQ7nNB1YTURlFWc2TrL1ApAgWXXHDO6jhXeONENxYvc4dAOEV5NThIMeLJEhokrB4SSgcJiSCKgovSaKAgcCBTmJmPSdf2WFYNMM+jCd7n0vkKHMHAJ5eCNWcHBVyvz63XZtVrY9dniQGlFIQYh/3VhyBiUFpCr7SENvMfIeq1lhiOysBDYODas1JPTsNZh6DPm3ts043DxNiJOTjwDod7E7TtSoXJjdNNtT/ysw/MbfC+er6fvKVvHdXmzv+Rt31573/VqjyODWO957+9s8OV71UoUEdDg4JuVNzzA9LyaMCg2jgB+DWtgfqBcCZ08WSD9yn47FsDFGB7ox+iBHxaCPDltYBgBwaOVQj51NbmF2zhIOrbPdUAtv55FxJos41iBK/NNNDgwGylB7XOq/YG+UGQACJw0c/IlARqkBAiCBdwMCVBIKEzNzBhDSz+CIWQg0jdIsdHXHILYnRfHBykAqxAYUIuMtO/uPSOdamjzqarVO4GwrF4MIiBqo9BSgGnmPGXZ4GEuyVhiYQnSxQwWBUQYsBpZaQA/OXgwDgGXyK0yzUWGQTQDQrmsDlzHDznIqaCwvX1k4tAwP1lrTBwzox1LdXH4EHXg4FCjAUSnrkU7kzl3oQyml+WRLhTleAuClyZQpDUkbD5aBzAwGx5YYUDBwgHZoJDGDh6RoDNM+IHdIrRPZ4TEXoGA1fUAv93BwUDENzSHwEY4oXotgEQxpVf3b4Hv7P3ecG8jc9GAukm+7Pp0s/bMW9RFa6MO/beliR+Z/QpmK1ImZRHAwbEmjdA3oA8Hw2jf4BJTE1B8AAxQsE1JWAEgNcd+K9JPLNbuec/cQ0K9h6+sb1UB2Wtn1zleU1N1jLVgMnOibrBnskip3HTLUlHDBv8vcpAQTe7Mw0RbRniQSO3z/YgAQDn0NbWAxqVj9CBQlUNbBQ0BSGB1Jt+hITIoiZYSt5IHhBk5n2fM2glRGQQBQSDA9daRBIPCKWI/O1AwQZBgwSznY/RByk0b3pTDOJacK8z5FMkfHIpWGLAJ0upgLBEMS+kVZwXTyEgKSSMTnYWqdFkd/9cWHvM4BpJcGXoSo1SYWAtRVUBqO+AmAUaEGTxL1AgkL+5+mOYQlCYp9cAEL8OgwFpSq2eZlIIoTe3nGLAKYmaIgqBANKTZE6bDgicE2J1AqUdGJg6D17xF5AbLDCwp6Jdg4F6UfTcQ6+O+OdBNwyvNGwLfV4Fe35g/YJVi28a8DtRc9In9cChfTiodlRmg7d+y6/88mWvT77WF+/25QN0tDrrtRgmqR4YrBl2Joi93+8utcuaCYGCb/xYVueZT0G9E50P0F8LxcCF42xGsnqDDQa2jaxtDAT87NVz/N5P3se/+rUP8Lm3nu4CQBhoYdZQrjWu8Zh75ciO9aByAAV7/Yin7A4OLMWxUw2mfoau3hKRsLQf1gGXLPmK3jvJ0V4AloyJFCNsUQH3Y2aDg6OTGE/GQwIABDm+rUnjrIDgFYVdULgACgUjJCyasEdSNztAgJgYJFGTJG26JyAFxicEhEKo3olDERNFRgmEUJdU9ufNhRsYFusIqC7pokBYM4tysBLWxLhEwhIZpygS/RLFF8LA4C4FpBxwDowUSouNECSXg3jeo67Xl+tHXZ0ACy1ssQUs30CfqticCO/XBgZ7QHBxSoHBwObcqzdYXydAFQH9l0jMAoue75MldlDwJEWcgrxWlSCpo6GpAqEBQQoqPrDkpdjAQM1fcYPjYCnVAVfOZWIiuBUGgH72EmJt+zX2h/PJMbBnFx/Evs/ubzvunn+BLzO1YOyXbumTbP9u0tLJlLeX2Vd2Vd+uPtuKjuszRlEUQH0e23HkNTiT90xZ8IcZ62e5FSy4nzt6bTubmDKT8ojAQARKEQdCHacYvROigYAHBbu4P3v5DL/7pwIFn3/73e7wFazRXjcyzgNNB7eX/gCdow32VYP/0KWgmROM2q1hkykApg4QA6YakDk22dlwFQ/AujY6aSpp73NgXluv00n6z/Oqr+huFpuy4NUE/TvE2Nbkq4mB1FmxQkKWJZAxLoghqXogqZVjARIRLqUgJqceBEJas3i8I+ssfzLTCXbHC3IhLAhVNaj1d2/q3wGS9IgIhWVwz8xYSlC/BQn/LCsjGGskXHLEeS04pVBt6NXBLhap69piJgDzNt9kZBelEFzVgTVzdTg0IPD+A2Yy6B0N5TszIPDn7oEgKAAEPY8lhuZnoTDwZIl4Y4kCBVFg4EnqVQIBAmAJoQLBEtR8AABlBWWNIOniQFgyK+8nQFx6fxoAvOcj8BAA8GWEAeAQCHgDwqG+717ht7XjjWqlNyP8p1CsL38d9bffZ3+nCjGb7dzBA7udxBRHm+92yzHdb5pa8DOFgm9/6UO88zefbn19eAhLf1AeERjYVFLhgKWh+vwB3oTg4QAQ0vqdP30f3/rid/B551MwazxA88buth3V7xY42LtXND5M7mCmduhbDzs1yY//GsNilm39K36Jxe5Gg3dysoNmQ9TtHJJ0mmYekLlnVX9IXdmZaAMIAMCkV8c6uVs602vOWEADBqCZH9SMYLBAQfMypCTn6JQDaPheAYSAGBeEuIjfgCoIoQTEwgiqHiQS1YCoIAXgk7Wo/A8skRDPCguXrKcsV3FV80IIbvZM+4NlvQw6ZbugPT9imw8aQyBgKUDhUP0ZlhhQWFSPxFSX7aXKHnOY8XEaTO73kGCDfX1VCPAqQSncQcGeYyHQ1AFTSSzgUyRZdZAi4W6JOEXCXYq4W8QR8z87RV2dEfAkyb8l2t9RYCIAizlmqkKwBPWzyJcaWhrlMk2DDTCwrk0NMAioMSFwWxv1ZTAHXAMBeXFg4IFAtxn0dkqBruCRoFRUn+f6e6MZAX+1MNBU4XEk3M6sveQ+m3V3l+yWHz0qB8pFGZ4P3z3ZXTTXCbt2ewAyQoFffVDvwixmzF8L50OgwYGQwVQ1EPsiYHAQIUs6LIvV53fMB6NiENtVl+0HDWXvo81tGXb0nZ5PjtEvwekBIHSft8+qD8UOHByp8VsAOiBkfxr6pm6jACbtdkiUABGgs3Qyag6qAVRUeuAYXMNuCoIcRiHBvocbQIHZQQh634Xd5VweEkLtUNmWOaZFO9dU1QRWKEA4g2IClxXIlwoIZmK4FEbSfxcCUkhIMeOyBqSQkULAJ2tGIotFID4Ayxpwf8nVPyBFMRFcqHQe+QYJvoySej191jZUJPNj4LZUUk8eQEFh9d5nrqrGygYFcvNny7V6OOAas8HiNXgoqKqFM5fsgQBVWRAgNdaOfhUeCPZUgjsd/L1K8CRFLInwJEZdheBhwEwGHghW/VshQMNLmyrAWYDAck107fBoOaFrf+289e/qOEj9+wMQkGsVq2/AVCHwQKCqQIUFIoBiy65IwX3P9W9exTo6LfQhw/fchzrLhfuu3xCoH/jH/nv2mf+82wnH/fuwa7dx73wD9tuywX5VDNxFGL9CaOaDCgWEqVpQX68uG39EYEDMLpSnOa8V1FSd2MIBEeH5L57jtzXftU+4NDYk3+AAcTQaPtrW6aC+R2Ywu/nmjGIyk1OD6052Hszo1IPmNdvDAdDMKh4Oap12KrXZfAsxYzAnmK0SwWKpODjQmW0AyKBgBARLLe0i6zFz7YigdlkCqiwLKChwaWYHv5zRz8qcZ3cPC8PMLTRP7eqkdTlLYJigaX9jAsVLywxYBBIoRHA6AfmCFBfEuCAFwiXLGvqovgeRItZoYYwZy0q4X4tAQCy4SwH3a8En+rrEvJHbC6MO6DMPfeuA/IoFayPehyYXRogkAYLI5wUQXwTfGAsYQSl2ll3ShyEeocD+mYe9j0wYAiFnhQ35D0WXggK6msCbC0LzdZCYBGI+kJUYNAWCk6oC5lPxROM4PFmi+h+oQqCBjJY4AEG+1PwSZkYAZ0AzVNYslRZG2ieeOmhrVmpbs2WFfh8PBKN5wP6OTvWyueTGX8AZSQcg6E0K0SkFARZq3NSC0YxwtdgMpf36zf2Rh4JxQByh4AgWZiAw/vxRt3etvx+LOUfal9lt98XUBZ3rdkUcDbdQ0M1bfbAx+7m/NqYEoIvVTVzqQNKZExwcmPeml1/awSaNzH186F5zjchob85N3e+w28roHVHq/G0HEGyonYID+e9vO++9skfl3it2dl7+PIwIKhywOtpwqUDAaHLXNE2seTZ7z1qz09o+cvANKDANgBAEHHk2U3MKA/sO25kYGABVabVlDqSkGQFjAi0nhLR2KgLFBI4LEBcFhJPkXCBVEIjF/4AinjBwFwifhIJPQsYpBZzX0gHC5RQ3a/ptPf9Fw/3tSe82owawiXdgwZEsKuBYch2QrW1RHbfG9jSDgllYYvkdUSVQgFM0FazBQIp00/mYL4HBwCx2gwcCcy58osAQCWo2UKUhGBAAlM89EOS1mg6INQvl5dxgwJkOzGTAB+YC8uA6mgmgoFBDe/tgXM1XAGggMIcAoKoCQAMB+YEBBgTIOiAwFaHzOZDWMOtN9Ai9SdcmPQS3HHobD6C7xxN7wZFKcAsQHPX327rs9JU7fX/9PoXJ9v7oyfp5cirw5Oee2eoDyyI8QAG5evpIpNegAHhkYLAp6pC48TUg4Pkrl4VRoWCvYfgL3AYoZwu0/W6QaABsZF358tD0OnigJvljgARq8i/4dkAYvw+gefTOyg5NPyRqY1MNHBzYgUZAAMtM2yABqNe+S9Lj/uYKEP6z0oFCUE9vzmsDBCJQcOmDu0pr5233Vs0WnSeyXQM1IVCI4AtpSuEAXO5FRVjuVEVYmoIQLwoIlwoIMRByIKRCWLIDhCXik0vAfWF8smY8Wbmu9T8Xc9hLdWnftWiJviTrHB0MyCumYYLHnAzeSXLCD3V7KXD+B+xIve1XmESZCC2McWFGiu0cAOyex3gOFsVwXIJ5CrriQGMSeIVgBgQxAAsdAwE0JTVf7oG8NhiwFNXWlibtiFw7suZLXg1wF7Kmq/ZAEFO3koDHgV1+xQ3y6AbzbkWBBwX4z3sgADBAAe12IbNSncG1Os1g9fC+yI53ExBcgYH2E94Osu0bNlW7OgbofR8Awd/j5uXRrqWJPPb+2S8kToGNX12fXOvPXf/4kPI4wWCmuaBtsoRLH7qES/7zrlG4maq/6ZQv/e89oBANjYdC3/7Mg192rvtYY9mDhNq/3ggIBOmYuiUxN4zz/mGzKu466jDassb6fYJIkSxOojpwy/WN4oi1gQF7b+ugvYIwAIO7ZwILzvNbfyvEuAEEAurMrt4S74CYs3y/xiwudor6efM7ICLwegbFpUHC5Sy+CMsJlE5iZsgJiHcdIJziAp4AwsqMRBFvMPBJIlzWljegiw54F6uH/1G+hVkxVcAGVui924MB29eng67Hcp2dxVcwJSGAUIpEiEyBOgWh5knQ+A1A2y6Xfb/uI8AsMdSskbPQzxa10HwIIqGaCm4BAsr3QFnFXLCegctZICCvDQbyZbftuMpLOwoRlHOFA9g2p0RBlagtEIQtDPgAXQA6FcC9t7sn23RY8d+x7R4Gum1t+mT/fNCizb1C7yjt1cxarvRFs37Ijn0LEFyFgUkwoM3AfwMszOs+9O9AvbaUL/U+km73cXksi/D18WsoDxinHicYAKhxDczirhf9+csXeE+hYJtlajsL7dZ8+gurD/gtskz3CzSQsIcAQBqD/Z6OThUUrkBCPbRt1A+KAwGygZXMD4E2p7YXNvrIe3d8SKfn7l7Jaq+AINujXuu4eRjru/FeOHBo9R5NEUkG/Oq3kG061gOCnhgBDQ6K5FHgPHQAs+AydhuDdtAXgNMqkGD+BTGBzvfA6a6aGWhZBRDCIiaGdAcKZ5zSCRwXLJFk2WBhnCLjUhhLjMipZRr0kGCBgnxcgG6JoF4mL+PXgdvdPw8GexDQtrdLM1cMyF0q/dtmidynmq4A8Cnq2tJN+399CONlEpxoz6mQ1rP4DayiBFC5CBBcztVcwOf76kfAJYt6wCxOhgC6qITjxSrbC2dqQQcFsYFBBwTVxu8cA83ENZoCANTZv/xQ3da9xzCznXw+AgHc66x0EIDWBvxkBXi9PshqM52wEDazarjXPRjokoHVXYe6DXDwWmMCgNqBlNwmj+qwLX8Tnr/6CO/94Os749fkXJhxS9yCsTwqMKgy1+YDmU8/e/kc7/3wN/HhV76Dd99+B7PG0DWEbjbK7lhoIUwfVELfaIgqFDTHSbRzUBig6kx5GyRUFYGcmQENBEZv1woTtt8V88C2W9h/YOsxeXC2Qfvbm3lqOGva7rd555UBOIir90hhQEHBR5hjZiCIk1gFhDWogiAJk3i9KCRc5PpHyBKz7sR0qZl7D25XyJYq4nIGp0V8DyhUPwNc7sXEsJwQ0gLOSVSKmIB8BoUFlMRJ8ZQi1kIy2LMkGcrMODlI8LkG1lywMneBhPwywWbz394vX5pDotuGBgp+n/r5xI27+MbofreCgLu3r1O3mu2QJEUykYVtbhkPLdfDGKlQAhRZNEeANCiRBCbyCsEZVNbmP3C5B5tz4eUeYjbQ1Qh5nbcPv6pgAgSIbXCntAgE6CuCKgRpaUAQUlMGxqWE5hPgVy2YsmDvgelgP5bZrZg9z8D8vhkT1L7CKZn2s6XuO6/D2LsfqQZ76sCDYYAnfQ4GANjAwO1jg4z7vk34MUEiooIIz179DO/9+Lfw4Zc+kNxAFhdmLLN614MHcfi+Ai+PBgy24Tj1T5ZIes9+8Rzv/eh9uahvfq7z/t3AgP5N6ugmZbjYxZkSrpV68wb5qN4bBwwjLBgoPAASSG1T5H+KsHmyvbdrV90biPcoEMieWaFbfrk78F/7Tg8RflN76AXo2NNyyUBIMN8QWU8exEHVA0JapNPXY7H7Rc6rDOzGBnno8AH5LbedKWgQQ4EBXlUCXi8CCjGBLmdgOVU/hHBawWsA4iIqwprA6QQOCae4YEkLCiRYkkFCYVUGUsQbJSgAQD8vDg64iz7YIhK6+7HTqY8l+ltxbT3XpPjYCnnym9fqEWuz76MupkAOCvq0x/Z3UrOIwYAEJWIxF5x1yeFqjoPNobCc75v/gJoNOK/Vt0ASZazoghONs7VqSPekFQRIU0I1DwxQQMtpDgT63RZXINZ4G1vHQNo4Bs6ev71n9agcQVydnHTHIve/qARbF8u+7PU7M0fCURVo2gZuh4FxYug/kzPqK3hFTdgvbpBnU6MN3hg//fhn+NqffhPf/fU/wrtvfqaOX1N/NVc604dTa3gHvKw8GjCYkpOW5794hvd+/Fv47he/raTlZaHeVr2BgdGGbT93zamjm/KqLbre+LG+PqyvI8f6/fxgSCCzV+1BgtXDfsidyjXFwJf9q+5PYFumiwCG9+PllQx8+iAfdRBECJoCuMICi0pQA8wYJJSsNj0PCJJhkbOYAbpMi2py4FVPIsisj0M7qRrPvtYaLRmNrUjIa6ci0OWsnf8Z5fyJ+CHEBCwnMTOs96AgUjLHBZaTYYkLmCIKCGtBdS60VMYZkuWxaPUyIO0FIs8LIHDzrbSO42Fq6PRezMov47gt5LKElyb1KzBItKyPAdD0xi27oTknJv2cNMYA1jzEH7BIhXqfPAiY6SBfRB1YLzAzQY1RgLEd6DmY74AFxnJti1ICxaWZCcx0sNxJW0gJHFIHBKxhtz0MdICApiK20MPze7H3bA0BQaX60xt0sI0n39PPrqkEu7/rJyPUb/oPAgMHEDB1Qrw2Xtjvmo+BwsKzj3+Or/3k9/C9L/wrvPurnxEzFXE/BjykjGrRpDwiMJif5LOXL/C1XSiYqQOjajAAQLUrX4lKVgddP9D3QNJ7pjpVoVMUbIC37306SPBV89I++4cM1yXc1yoePvxmu9zetIFrHVjbOAsPCsg1s3z3oKge9qlBQr6AKIvM6gGBJLxxiBLhkC9nsfeuoVcQAG0Hq8DBgDZ9SGIBCgraAeigYCoCh3sBhsvSpGKdJSItzVkxyD8KYmvmuAAUEWJCChEcZZDIsCyPqOpAKRqbvohszxCzEjO7bHf9rerO6K+iTeyViQLVZON2n825TERzAQC7701FkPG35i0oGbhkCT5lYNCFLXbOhB4ErqkDrAGMJg/PqKhUKFCfgQ4KVBnoVIKgy1/DsgWC2g4EBgpUgSlQIOTufh75E3kfALvWAOqy6Nr2B+ne7zPev+l7V24d1kblYoSA/u9fJgzs+BccgcDOfrulk3BY8scAePYXP8NX//U/wve+8Id491f/jrRXDSVfTedeNa4V2rmqN076HhEYbC9Eg4I/FigANjdsTyGYrp/3ZTblnV50v1/vyFOD9IiXWzsPb1aodPpAJcGOBXTOK/Lij7V5lKSmo7XjhnJNkqzbbH/uYWDcXu9KpRf7/nhQeT+LsxC4DSJZB5HgIIF9DPsKCKIWyOqC0KkHFILMEi/6UKrZAVhBSGBzetW8B1y4i5HABSCf3CeqCmGDhKkIRAIEM0hwNmYoGFCIOluU10Dtb4MFVjQ1M4O/zg0MXGIbvbS3Jt3acxibldnssLMG6msAdfeT0GAgYHhv/+z7BgE5gy65KUQlKwgMUQn9wD+DAXMk5NLAYICBMR4GafZJQNWCmOT5c/4C9Z4udxOVYEHRe2xqUVUF6muqMFC02yoO9rwT5/4zZNe7vwF+GWHn4c8aH4Tb9oL5ID8zRdyuSR4LEVImg/AtzoMPMRPcAgR7MDB/WLbb6s8qFPy7f4Pf+B/+Cb7/D/453v2Vv6W+Kk2iJOYGCMPY0v1+t1z1tvJowMDPvonLAAWf150mDcKrBCMQtKms+6EduyGAzWPh6dvfrGHwvwoIgDSGWocrkFC/fwUUrF7+WEMDoslfY+FhD98H1eAl43eGQWmzTUUBmdn6WyF/zBncAKENPEQsaYAJiMR1BpnrTDIhmlnBZo4hgoIuOdNBl4o4BPJ6BoVzm+2HCOQLeI3AeqkrKKjeS8mS2MOB3u8NJKwKG+cGCX7w8N7oNoAMoEBkIWsbLDRntIhojmi2LSjgVNszbWRnf+1vU3OOyzU7McHfv+ZINg3eYp7X6lhKNXXxkM7YIMAnLbKYA/W1RSesqwo6ALgNBup5KRSQORPavaSgKpCoBLScmi+BX8oaEjioMqBQwINSJEqQQgFzU4j0/mVwd/+O5q4iXre06aTPrsFZAKuCTdVXIMiBAWxzxwD7Ej923vdlpy0dzM5vAQHZ74EwsPn+A4HgIb4HzHj2F/8Gv/E//h/w/f/NH+Ddv/G3BGg14Zz4jOgN4qCW4GHc2FEGbJx89vIFjq7+owEDfyF++vJnvU8B0DWOfoXBARR4xUC+sPO3Ta/7Abi7QXs3y203R8ndY9Xz3IGE7vd2QAHANI5CLXkbfKPuN0P/cBUePByMj8N0puqAoBQeBid0MCEbtz8fwNWvYiVZtLoSIZDE9o/EIjcXdUQLETHFBggUgSBqAucoMjNdxLygg7akbw5ATqC4glOSZY4UqsRMek1t2GtQ0L9SCGLn1sGEcd8GFJ1dsg0opiYYKKjaUIPeKCzIACTJncgtZ/O27f249/LKFDp4sMs9TEAd/BH2+vPNPZoMGp1+ZYN+lertOZWBv763oEFjCuMxWZGLLVAs1oCZA8blhYPPgPkLeAVopg70r7RVCRwQVMjzS1fNbBAXMRvFVFUBNsXAAcFampnI4KBwg4DajeGWZ8agTGFVocySoILngOAdBr1qcFOsALmQ2JQdwHxQHIHXXUFwZRDfhYLdehyd6/b7z/7i5/iN//H/iO///f873v0b/yv3fbm6LaOEUw/q0coUDvwqlGcvxRH/bbw9rzseFRgoCf3ip231QYWCPYq8BQr6G/fs3/3byY8LMR/Xb+fzazafPUCo3x0b9T4oALgeRwEAYWKz8nWwn/Lftf3d8kmgXZka4I6b3XvELIMCm+XYzKfr2PRvW9d+NEPdOqUxSJWDrFCQg0TQS8Wc0gQQOObmiBbW6n/A5QIKCSEtKOd7XVVw35sXiGSmX/QhjUXUhdzUAwpNReDMdeCh6DqzoNv8zPMc5NgUWjpog4UYZcBRJzQyudoBQ/V41wh5RE49APUOcXQlHK7d96pEDXLmWOxGYpjZjdB+9GzW+BFu+SlniWPhEhTV4EIazZJrQqMh4JCHgImvwAwC2C2haAmbGhRMgWDPbLAIFITTHTgkFPMjiA0ORiDYdzJtz8vMyfToOQHQnhWYY6awAIqoCEyMyFQDlnUKoaoGllxuvkRwmLnPZvr1It8OC9uBexzYr4DA3rFv8Q0YfDKubu9/YLq1QsGv/G1UQO4Wnc/hoPX1xT2f/TPZlux/iH/6//inuzV7PGCA/qTffevzqMvV/F63mA9GKNBjPPt3/xa/8f/+r9q2a3abOli6TvKg89ydqe/93tjwpqAAeBPHXhyFzc9NwaD/PR+EY+P0SH20rrGanQKgsxwPBbKkTu3hehCGrM0fvej3nj+BAKre6yvJUraVIKmOg3R0gRglkCTXGgFBoUBmbysoy4oCLhfxP+CConBAqhjYcjasl11A8DI0RaqDTVknKxq8mSzSdBBik6o7T3cdjFR5IHN0M4XBOb/1MremkKYgA0Jo70GyCqADB20DZBe9Ft9e96Xdo+evDf76PPrZv+UcyKtEQ3Tb6uoAgwUDihsAwA/8PBkcqjOvg4IHA0FdmnoHpiB+BCMQmMkgpCkQrBbNssKBQsGw2uSWZwQQQI6QmA8Fcsub06GY5LKDg7readLN1ePDQYGDwMNYAX67P9bRQLv72c7gfm3QPoACptCPK53J2JuDdfstkOCO9/2/99+oUmB9/u1wQNj23bZc9dmrj9r4+PZTHE1mHw0Y/PTlc3y9RjR85zbas/IAKPj+3/tv8Pf+5DeHQbrNltum4Wl5XSCo37lhn93GNwm/CewBKzAGYqp1cB1mZ6aQxm8rI3y0yU0Cq4Pnozq5MddbYVCwamfX1uFjWIc/P7CAATm1wOCAkFg6ukDSmca63QOC+CCwLWWjCMQsgBCWBginO1EQaqwDsVeTW9ZGNVCSBwRTDoYByf4uBYwJvIUeFgAZsAwcpvbt0RN+zyRhyXn8wEahi8PvzQ3ezNApCWPZ2HonYA6us//Oru+zEu6ZAMagQpPB3wZ+G/D9Nd9NeRx6ELC/D4FgZjIYFYIZEFBsCsEECFYfMtrA+crzcfRsAMAaNCmUKQQoIH1OCrXHntX/wGU5mV8uakAwQsEmkuzNcr+VG/v1q7P1K8UP8LPDGxx6CLDf9ebgEQ46UzGhdqLu90QpgDvGPhzc6sXpJ81P336KrRbel0cDBl//wXv4jo8dTTo4XWkftyQ+8lDQ3TR/V8YOcQSCmb1n/O7rlAcB0K2/ORmIOhPENiBTk5dLA4SArXKgz4JEYNzenAoDtv9OpzcG6gEaJPiyqg3jWgCcQgIXkRiFDwAhThQEA4SQxCdhOUkaZrNpX869D8J60ciGawcIBSuQQ5ullpaStziFYa+YkmBRB2WAV1iYQUNaGjB4CHAmierUaNki1UxhAEE0T+erFXA31tebW9RNAwA/+Osgv0lAZCaAyVJBXi/TwZ/VVf9TXUd5I3/rdQwp9UAwcSo8Wno4NRkcKARHQHAUwErOdftcUCCszAoEYh5KgZDV4zQF+f0UyFwLRCXgHWWg9nftxUNB9QMBKhCM0WSlTJSlW8tD+kJfdn2qrvfLGzV0ugptVHHZgYAzyLzGOND5pe2UZ69edFDAk3qP5dGAwX+nWabqJNaapUmcO5L5tfLs3/9/BAqqzcfKZODfgYHe9jpCwQ3Id/SAXCHb6Vc8te/95MZs4X7PKxBqqrBr3AGCmr4MDszXADqbITvE5PTGbRn7UMDOtroplolPfQvWIIGAiAiJAxLlFhI3htsBwda/GyCUReXttXmaryuwnkHLSWIhDMvfoB7wBggUCGXN4EIQg4lKg97O3c122z30iYVCpyaEZopQMAhBAWGJ24HOYGEY6EChVxbM5BBiky4t019tJ+NN9cCT240eUxKbEsClCyBkQGAQUNa1AcBF1IRiZoFs6lO5+ToBMvAzSu9IGOwaiRkmpNgDgb26FSIGAmMMiqKOhEdOhdeAwEJf23Ng4a9veh7sfFmSp+VAiAYRCgJZHXeTdxXcBYI2tAX0asEGCqZAMDEl7PRNt0ziblJfu4PeaA7e/bzP69KvLNM62z7yMLtOb1QPbqzTrYUCfvrqZ+Jz95XvdlBwlOAKeERg8M5bT2s8fgBt6V3nHOfeXy0k60g7R5Dh5s3UgREGptLqQxvvhDi7z+dwcFMa6J3eg/x18nX3hMr622TKjF86M4cDOZ4+I6xez0QI4qQAJkZQSfPYpLjtBFsinu0XQyBkBkJpkMDMMvDHgLUwUuEKCCsBaQIIuUCz90WkFEFRlsBxvoCixcjXQT9mUFpqKF3ktToqSuTD+w0gxLiiXDS6IrEEYgTgA2pxLm2AK6X5K+gAON71OugFAkJACKEBg8JCTDKoh3SuoBCWBJybmsAuGE8FBXN0dH4N8lvbNs6uzhUCzCHQg4A3CaivQLmsFQTKmoFSkNfcwVK9LpNrMhaKARm5XhOUItclOVhQKBAQcNdkBgQad2BmLuC9OARx0eRHUfwGCioAjECwahs3ILDVCAYE6wOeAwAILPn9YuEKB0eDoK1QkFcJK01EbYmpWZhwAxSMQNCZE670dXuV2/u+HWY22E63jdfgFjOvf6MgAEi7tr55hAR4M0McztOZGOY/cqU+6lPw8oXkVlCfAjMfXIMC4BGBQYE4yhSgRvQTOCCZedksi3R4MmglceIYZfZuyciv/J1tI/IzoxkM1AY2t7nuxaqe2vZ5+O0upsHWRnVYDkfbne8bANQ6tlUL7D/3DjAODjResHjAo6kGvGNO+DSlFJ76MRSdQQZdpshMVRmQdMaEjB4QWAEhFkYKjMwSXjcGOYbEQoiIMSLEBazRExFPsrwxX8DxJCqCxUq4nBQQzmCvJOQLoPAQ4oqQV+T7s1xjlih+Eqgvy0DvBkDOZXCgm0vH8hpErQkkcJAiQggo+rdBQg7rdkBUB0a+BJ3xxroagj0oAC1pT38ToCdUQaBLR/wAGOA1VxCwv8e4AofXobD+rc+/wpJdo5hiVVTCEhHvTj0QnO625gIfxpqi+g+4uAOduSBqvguBzZV1VUFpKwzW0hwKZ0CwMm/AeK/9t3vA0wRXn6YYIHRqwcahdL7iBHCD+Q0OiO1HvZlqnCjdcH67Pl/XFd3jvtu3e+ub9ZlgDwnOJ6FTEVzdxvOfqM3jSiCr27NXH+FrP/4GPvzyB3h3ohT8tfExYAYKqRdtBQNqqsHG58DPdNE+BzS4xP8J3//7fyBhKLtVBb06UJdz1c9GM8JgXhiP408A6EITe+/W3jHH2a72nF6ArffsrNxqhtjsp52oXjO2bXpNxd1P4IDKqp7tGeQSH2keRVSMC+JdTSQZHkg3x0DgLLbOlaVDJ5ZZzpDrsCtlCh2EkrkDBGIGO0BYi5oWHCDk0q9kSAXIQWIitKWOCSEkib9vKkIpQLlo8KQCxJNE2rt7Q5wVfUKeuHSAECkgpAvKmlH03orkq6BwWcUz2Yk7R+vsgTaTr34GlywrMpYIXERN4JSASDI4poSyZoR1VXPDRQbBLPEaOCZZ8+mgAJYPYOZjYPEADAZ2fAXKuopZJTN4XZHXDGTdrlBULt6p8LbzBlokQtkuikFcUgdG4bQgLFHgKLkgRAoEki77tFldwOZMGNrfHEKnDhTMlxyuzJsVBh4ImungGAhm7T4cDJbmf2PXJZIBMDUVgOy5RM07Eam9dmqBT1TWQYEzH5nTqb33beSW0q0KOJ7V75pyZ0DQ+YJdkffHyd4O2JBPTkMRQBFI0H3I5FHbrY4Zg5LgFerNebT3zz7+mUDBlz7Au3/z7wK6fsSqUHBdiHk0YADoyZJQUdxTDcyGCNSZruwng9izv/gZfuP/9U/w/f/yD/Dur36mHXz0G/CBYcYc5xtHxL5RzWSt6Tpe1zDY7FNo7ecQEB4CB93v3ijd1WM2QOjUA+QeDlh8mUFcI6lVPwNVMYmtA2uggAJkAlJs4IYiYUCz/lTUWVRg1Tu1kwxEm06yvT8AhEDI3AMCBYijoq5kWIkRFQhWVREuGp8/BcmzEOOp+SK4uPzeFwF3TyRY0uUsZobLWTIpavRDupxBaQUFkfhpzSj3F5GBIXAQFqBcLEZCbyobZ81cLFRzUThgWOjmsESUILPwuIgDHBUWBSEzOMs+VBhhSVUmtdUOtmTysA0ZDEyWDpaLrDYolzxVCPJlrSpJUZ8C73A4O18AHQjIe4GDsMQOCmKKCHcLQoqikpyaAkCnO1A6NUjQVNlYTlvfgXF1gQ9IlF1AIqcOzBwKueBTAwHQoEAYiBBAukJHlSN1ypW22/xtiFpCqkiWoZIaKOgr9FjSE0jl/EqTMSAVgD4Hje+fHmg6uFamUHAABNOJ3Gv34xFg6xfbuZJPdUvUlIRapVj3nTt2zCelTCQJl/7ktxUKnsph9GvWTm65xI8GDJhtKY3arWED6EQ1gOWk1hmuWLjx/OOftdjUHRT067UByKzAN6bO52CS79x/d1p/8m/qvt16XwWFCglVSXBkSf2A7X0CaJSsXrdwa9Sw5TQskfib843chWpWsN0KxCatdkowUNcx1vWMBEbzNTATUQcHgChCuailYgsHthew32kW09e00ewBAgXaOCquBIQicBGL+R6QKguEZKaGFBESqnnBkvc0X4QTaLkA6xNxVrycwef7Bgjne8SYENYzymVFpgBaM/JZlkqWVX0SqpROboDcDphcZXSbXct15dyk9WqbT3I8glybcoGCyCrmBbUUkbMcHZYboKCqB/ZPTQW96aRXCcZzBBoQdCsxgpgGKKiJJCXQEhFPAgTxbkFYkjgMeiBY7iRCYQ1ZvDR1YGepYQG2pgJVCLrVBejVAXMo5AMgkEt5bDbwQABsoSApFKQYtlAA71Ogr6YSKABbgrIWzEhUoBp10psPrkHBL8ukOJp1gQkQXIGB4f1u4K5Jf14TR9eBvvXNct7UQMEggfS9PasKBjRztPLn5yamHgq++8Vv4+nb72j96p0B0B7PMRHdWB4NGHTFmROAQTUAYHAgC3elV3v28b+RLFb/8F/i6Zuf7S/ZxGegRp1TKOhAoDaY/qYcFpPYAbSFw0Wy/SkokNqn2sCrg279JTQVYQcQyFPoxPzwIHAwh6UZHACwUaPCAbcBhijI3TALD6kDlIMD0luUmUEqrxocUAAoM6I6DhIxUnXCompHtVmVqAf7p1IdtYLAxcroACEWCalclzvmghSpBoTpIIEEEi7akSYDhpAQl4SwsCoHl5rql/MKJAWHuxXl/i9Bl7stIKQzwnJGvj8jpIhyviCvEeGSUNa1zv5lRk2wOAntNjePewDdgPlLK7KEY779l1Skvgr1Vf04Pr8pEDizQbw7HQJBuHtjG67Y8hdEAYUCcXLNGTUI0TUYyODd5YbjCgNrz7PizQUjDADYAMG4dNfa8p5S4KGg+hUEBYcuJkXpocDD/KeFgr2BuTMBDJO4hwDBCAMbteDGPp2aodTkaVYFwYMCd8qK3TRd/jsqCd3xnVo9QsHffLedo6/rAy7z4wQDNHOCFLvhpYMDc0R89urP8dU/+z1879f+EO+++bk24NVi0o0b9ONSI0rJT9THo15/fx/2BiWvclJ9tQfZbLU8hwSvItwICBWWZoDg4cC2Xyt7cGC/6R0SS/abNnBQaKscGOWFID4HBQDFgAQg6qI+ykBi6gChdaqEGKmbaQFzj+263foApyCs2C53jFmVhFxcJyuvHhJCUKlWO+UYCCGckOJJ/BFMRchnSciUdcljvlQ/BL7/pAOElM7g9YxyWRDuxQ8hny8IF8nwmNNa7fDQayGz694psM/+15wRyTkj1iV75GMh6LMwibYot37ifFgvctc0VE1StULjVHPQXBeBay8VkNxywwIKUc/LP6txc16dyYDC7QrB3ZPmPxAXFK8OxFNVB2xVwQgDmedmAh+TwysDIwzY43XUbn3/4Z0KTRkAmslgL5ZHBQI9XlCAoB0oqErBCAU1Z0U+jjArDbKd4C1llNQniuxrAcGRyjuZcb9+3976dDJQYIIpChUSAHBIqgQP41A3LrW6/vTjn+Frf/rNHgpc3Y/quFceLRgA/c2jGRwE4KcvX+BrP/kdfO8L35LcCuYEsteIjCjNx8DBgKnS/sevKqtuHLL72C251LoLJHBVDMRe71UEmelvzAx7gOBAoAXlcAQ+gsMtxeAASsN2zBvggMz+E0gAQNWC4v6ZehDsfQpIDCQSm2wqNI361jpcqWbmvhPdS0FbP6udtADLqg/2tQ43UZD484WRBkhIgVRZkGWPMTEon6qKgPVcAQF3T0B3b4Dv/1J8EE5nCbt8OSNe7hFOZ5TzGfFOHAX5khH0le/6ZXzTUgP69EAQAmmwnp01/CMQuFUK85JkFUIQyRlkwbBEYfCxHCjKawyS5rpobglaMziEbrnm3jn5ZZmmDpA6FFYfAucvQPX1BLp7o/kPRFl+yOnUqQMZAok5tyWGHgZsiWEux0GIRhA4apPjigIfgdAUAWDbLtv7Fia8tk9lc1mC2KsEHRhQS28dSbvJh0CBlYf4PF1RCDYw0H2nDZ43qwNOGZhN8h7SvzsR0g4p76n16aIoKCRYXx2iSP2qDDfHcx7OXZWCHSjw49JDy6MGAyt1ljzAwfOXL0R++fU/wtO3Pi/EttOAeAQDitMlIIA+C24w3bsx1nwL5AGugNgq3C29tCBBoLhREapkNaoIox8C2S8CGxVhGrXL739j8WRrZo89OCAGqc+BnW9dmVAYmaiCQGHxN2CgZl0sBYiRkDiCYx8nflUlIQNY0EDBFAXUbVSrLWc7n6H57RYXAYV1djaAwkpIkbe2WwcJUbdH80fwKkI8q4qwqB/CWQYudVCEOSpezuDzJ6DTGWGVjI/lsiIOXv018A9Qg//U4vIv3BT8iEKNbeDjGgCAX7IoB4zwMRgQub3nFrfA55OISVYnhCTmEU4RgTWIUYooRa6rD2Tkz0XqsQWbLg5BUgA4PWkrDKpD4V1VBUQhuBN1IJ06dWBV88Babo83sBd7Y2xrvoxKANAusbU5ABsQaNuaMuATi5lvgM38Q93uHA0JG5VAtB0A3qfgAApqmU0wRiVg8/kv0Vww8x3YUQf8gFrcGzMByenc1sfbFSC9nnUySAMkWJcZl6oigEv7nWElxrNXH23NBxPl+nXKowGDMcc70FTpfqyVi/fs5U8l+INlYbQBtR5waDwkiXztOKuDAQOB2pD09SEOtkEHd+bWmHViOpCmve1VBK5LXm5QEUZIqE6N3pwAR/mDvfjI7uXfmuPjHhwQg0i7RiIEvcYUzLognVHRVQgGAqyfFTA4NEhghsYjICyMDSjwTFEYTA8AkLmZHkKg2mH7frt0AxLX+wM0uy6tZePklbSTvouxQkKqYGCmhohleUN8EdalMzMg3YFOoiqIH8IZfH4CvtzLaofLGXG9IOa1phn2UQIB4FpiIDMfWOyCLhLiUWKm6phrkNDagw9uRNqGuBRQ6hMeWSwDWk7gvCJ458RTC3d8y3l4oLH4A9BVBTWh0cx/QBWBai5IdyggXFQdWHVFwaomAYOB+5yx5j4S4Rh4aNaWZsW3JaCZBh6iCIwgAGqgUFUCgwFQG/QVAvzf9plXCcCzJYlbKNisivL9zOYGDn3NqAz4fT6t78CgDgANCLwyMIMBPUvdNj+VzWmo2gm0yWChHhKkjhHkVITmgN7GOIOCD7/0gToazqGgO5euPm0kmZVHAwbAPnza2GrX5/nLF5ssjNMGeWAqyKXBwEwp6GhzKJ3tyRoc0QYSAKpL+oL7HQ87RypChQT5Aa1jo89dSNCT2UTtamewPamdcgQHFkKZmEU+I1aqlusegpxzUTWl6DVgiKmBWZYjts+5UxMMFFi1EJN1Z6CwKiSwAZoqCcxS7U45YB5meu7K6I2ymZ3NvlIIoFUcFtNK+CQyEgF3MYhXOHEDAyIsUWEhPsFiKxrW+87MgHQHyrqK4SJqAZ8NEO5BFn65FETWwEG+whUChwHdmwhcSucNCPiES0TNryAMnTiX1gVVQNDZ5ZAYiVxmRHKpkeNJTCE+j8ThOYwg46MTnu5ayOLlBI6n5j+g5gJOd0BIuDh14JLVL0DBwEIT3+dSYWBl70hYVOlSGNA2s2kv9jeodyCcLC2cma2mIECog/seCNhgVE0E6GGgfg43lzYgGFceALdBgW8X0+23qAJytfy2o2Xi13wHpoPoBAiu9fXAQX/PTT0w5cBAoSrGWr2CXkXget3lPCSi4TfaksQr5o+xEF2HmUcDBtawx2KqQYMCn4XxKeod2x5xAwSF2wWVwaFXC7b2p52rb+TnlABJES0NJNv53AAI9nMzFeFwVQPFjUTVReO6WU14QOngAKjDJ8sSNfLqDAXtAAMiSXAjBsCBOtNNUxAEFJi528fSOLPer7IDCpsETaRZ/szUEACLj2CllHbP63LIellMRTBAKOKUuBJSJIRLwZIIn6wFKQbcqVlhUUi4ZGAJATEAl0BYQkI8JQTOoHSRREwWOCmtoLuLwMPlXHMx8OWMGknQ5R8AUAMNdcUFJpqFOybNjVAhwOVN2E2ktG0EmwRKFuSINDzyJneCC5tMD6m/RSoMoeUtsMiEQZJftTDFC1iXIBaKyAW4rA0CLqWIPwEDFzUT3BeuMHBZBRbXzF1K5K5tDCUQoWQdfD0gEDqHVTJFaYCBurogjAO7/l1NAQ0CyG3fA4GmDMj96mCg86IfgMD+3oWBo3bxS1AF3PujVQXjwHkrEIzbgNv6fDEduD6fuU3u7F5wS429FgM2uw/arxNLlsQf+dTJ23M6AhW7CuX4VjweMKhNanLCdnE+evUc72sWxpZwibqvjBfYA4FvEGtxMOAaRf3+DhMQyWAFNLUA3B5cm7ECVG/eLYBgcGAqgvyWW9UAoMZKUPI8UhPkc7sy3CqvJ9eBgp3bTofQZQBzSy3hzoaYWz1NNejsg0GpuoGCnAoNfh5U71WG+BjIfSMZyEOvKhgoLNx7jK+FJXCSAgKydKBrEZ73nb0pCHUyPLn5gUg7bIMDyGsgpBDwSZLO/y4GLElMDZdSqoqwVF+EiCVFxPQEPi4Cl4sEUkotkFLg0pI2uQG2ztJn98qvKjAAqIN/mqZe5urRPXbC2E5PqlMqA0mc0qgOJi7lsqkGQ73lEAd1H+ptSY0sKiF3OQuWTYrji/MduJReHbjPGZeVVSFgBQeBhKI+Lb4tHEUgDEEuQb/E0KlLZA6sDQjsX135Qj0IzMwCDQ56IBghAPV1AgLADgwAVSFw97fej4PJw26G2b8CENCzmr4+VCHovscNBPydnvX9BVz9x2QyZPUUSKiqAZzp1BQbvU8vXr7Aez/4egcFV8/LF9edu6d1Wh4NGByeJYAXL5/j/R++hw+GLIwApn/XGadrEPY39G8vX482qNkjEdx+vXTECEx1hV4g21Ebij3MtcUpNHCdyPbqAzwktIvzaUDhmtlhvI5WqELGCAdauNWn1pPtasE96NR1CuQ7BFUWaicCgEHuHqnPQOD+bwWFwpZRUfwTEvfOY7EwIuQ9ELCWgoSANZTOr64wN9mYRY3wJQbn7Z2l804UEKjglANSFAXhbiX8ZShYooJCDDgTV/PCOZsvQsLi4yKU3AVP4pKBJQsomPQ7ztjH+zXO/IfBn922ek9oFhp8p3RtSh2r6ixUM0cuXD9vM9AH1jtErXOUFUSWo0CDENn76juw9r4DF11pcFFTwSULBNybOSEzzllMBSuXCgLyb//eF5aYGn41QWsHAgUpoAKjB4LdGAMKAt4kEAYIaKsI6o3oAUDvCTbPvLtnvlcbJhGb++G2HyYw+qWaB1DPcNanz2AA2DcPTEHhYCLonRGn/b/r+w0UiNEmhnq0lYEIUZBZx4kXr57jN3/4Hj7QSe14/EMgGMuVsRJ4RGAwrOLpLtCLXzzH+z96Dx98SS8qX/mOo0dvMrCHXvZtQGAyNTCnRStmIgBEBWgzrEaegTV6Y4UD2aHCQf2RBg3yPXcOAyToT7i/Hw4KbL97g9mhFg8DwP4MYtqJWFbMUOs/60Ro7ETIfC4CQh3A4gYWMlMFhaLmghKad3mkgIWbbLwWwn0uoMCgHJCZJUdALFjzqCDIwGBBbaxoSAGJQQ9giQIFgQjnUpDWgBSB+zXgFBkpAJ/E0FQEjhtfBHFcBFI4SRC+BTDFoHr7m6lG4YA3Hb67Ff46zwZ+Ur2KCIcRP+tNp83vVG9rq8Oe45oHA5ut7Txg7OusUCB1ixKMzIFBDU+8cnUgHH0HvDogbQA456LLDlGXH1qfcNFIkSMQ1PutD7XvcwKJcpRIoDASVZXgLjYokHaCukywRtikZgbwOQs6CKh5C26c/dcL2j/Hm/t3pewqAnaP5Ap076+uIPDfvaIIjH+PMABg02ePDuQbUKgTjbbNf/faGGA4S7Wvl/6LiRsgwJSmZmJ48fIFvvFjGb8+/9ZTWW59MN51J961t6FCB4DweMAAfeMQOlelwEHBrGHUL6FvEIxeMSjFORA5INjrZ90YvSlFbzoR1XGe3BcNDqr6ww4Cuh+TjR46OiWh7dKuFY2bmwMj1Q98pC4HCl5NOFrp0Gq6O6uYPUHEeX//6cxj6Ci8quAGMHGoiwILISKBKigUXXmwGiyUltAmUKgKApHQ/AUZq1VR4aCsrdM3SMilgYF/cC/qVX9eC6KaCMJKWGJBygEpyGw0BcIp8a6KEEngYRYbIcQoKwrlQqNbVjYODLNbM87WaFyy69WZ/dnYrJOqiUndv9rVullsjQoH3F5fX9cQAfUnsSBDs5gD3ndgpg6c1z6R0VpknxrNcPA9sdJBgC4nEHOBKAYeCk4GASkOTqlAVHUgUVMGkkFBBwLcYHAMSYz+WgIe/CeV/xROg9P9HwgDt0SSnY2Fm/uw09933DOoBJvPgU4psG0jFNw6DjBDTcpyzzwgADLOQAO6ffTxC3zzT76OP/7id/DZt57WpY5lc1AclrHP79WjbXk0YDCeJMOZDwwKxrJDj3W9sf3tSNG+kq0xsWsQk3qN/aI1CvtNg4NWD+5kRpslGUCQTT7qDnM8HEHBSgcM7ivbhuNUBQOFIVLXLiT4oErMMM/0LqHTKEX2T6r7+/rspNZ7M6uQToXcjFfOh6q8HPS1BM2YyIQ1SO6DXAgrqTmBpKO+zxkREfe5AChyrzKAVIDVHIek/cRAddAwBWEsZmOOQVYixFCw6GwxEOGUeVdFsNlkChKVMZiS4KRlGzRCSHKPw9a2PCtj51tnS+xv2dYOa9t37xNZu9Lz13rY8imiKG07zDuzo/r2szqgrAxGC0ksWQvVMVBhYC3cnAmvqAO2by5qaqjQt3NfbeC2JYekg3ognJIEwDrp/Vu8MhSjBhRCt5S15d8QWJAJQgblrEBgiYoMCnrFpb9Jtz1XcuH9bN+dqx/UXcc2X03Q/j6EgQ705yCwC6G2fXIKm8HabajHG/b1CkI7dr/FKwXdd8ff31TIHVMHhraUUWaBP//4OX7vJ+/jW7/+HXzmradaZ9qoxLcW/8wfPUvAIwIDoDnoAbL64DfNp+AtgYKjR8GrBF418FBgKkHb334Zh8ffYexPVQwO7LelbY2Ntv1tHfJonbUGYiTqN3rFQuI/DJG6RkjgFjOhAkJVECZwMFbUmSrq7BE7M5tbZjQeCLxtXCP0cUgglZpjXBBCRAoRCyRxUi4CCImoSs5EERGleobfF1lNERQOQq5XFJdsMnILqFSc41wu0sHL9Q+4ZBk4llgUFNSfYVARUij4JMpM826wQ4vszNuwtoNDml4euc/DJRw7uo2cqm9mzrZHUNBuS//7MmzQvN1RW8Z7VNexMzd1r7jn2ByJL7lXAO6LDPzjdq8OeCCofiTFzAftPgJAsGBR1GT/JYrPwCmKuegURPWxe1iVoKSxLhwQ1CBYCgNkMGBpvTm3eAIVDsQvowOEelNve3YAHeRrAjrS6a8+R36WY4f2UDADgj0Vqv7+9VUD/r77ez+Wa23Rf/oATHpQuTomcLuMNuAXBn7+F8/w+z/5TfzhFz7AZ998B8ySZTZwW712tLLAPyezlSbPXj7HkczweMDArWd+/vIFvv4DcdT4/FtPj85fvuo6v2tQsOmI8LBGNXZyY2Am30ECx2TnlYNr3fHsITEFo+5jHXbd0Hb0s7cWohkNEiwQh6462AJCg4OdCm47s3E70LzWge5Y3lO9S6KjS+k2znQKBQiytI3zBRQSOERQXBBDQg6EqIAQinb0hRFTUw/SSrgnCWQUCuGsd0WqYHdI6iaZkSWcryUvbK/yRwwCCYEIl8wbFcFCLSf1SbiPpbNN+yBKEvJWWUjBmcje2/U5njuMz0W9Jfp5veo7M6VZqV1/ByatfsCnq6PVz977kMRjvAFzJLxFHRDVod07XwwOQggVBAzulkg4paAKgfx90s+8SiBA0CJiLg4IIoC6CqVk+btI1EiMcDCu8LjhOam+PPassNwgspzo5i9UVyMFjFDQHWvmi2I39iFmqQEE9gD0gZPnrhz13+SPTeYD1n6NiBDcuDBxqTn8TTt7/52fv3qOf/xn7+Nf/toH+Oyb4lMQ0dSCCDt/2r0Fe+cCMJ794qd474fv421+e3ffRwQGcmWfvfxpDV70zttyUfu7u/lKe1+3eyeU3iHlz1+96Pa/BgW9dUz/ph4KiK7Pmmy/o/JpqTc42iE9HpEYFaqisAcJe4AAi3TX4ECb+OQJGmY4kw6uuLS9ctIGCeyP0h9VVYK67C4EhLTUjo7jUiEBUSINckyIDhBCJkR1Tgz6jygiEVf1gErRIDUF5wwgiVkhBlLntFLhAGgDTOe4mIGgyxQLB+Qig8N5LTilsPFFsKWP4rhIiJNoi0cR8Spw9XehKyaZ1xU55hFQmkltuAUbZQro0zf5tmz1kn2srcurr+NRPfvAVegiXI5RCEcYsLgD3nfgvJbOBGRAMJoNmimoh4JTCh3MnVQtOBkIqErwJEWkSFjCuCyVsMQBCPIK2LJUg4F8qc+MRLlszweberBTqqTvAlNxDl1sCgmuQ6q6WZTSua/PVQfVCRDswcBo2h3v96fu6w4+a2KILHUmNJ+1Dg7cfoEZxUGBtdo2LZgXmzp4OPjHf/Y+/sUXPsBn3nxaTcsM2YGIOp+F+nuzsaHrp6UYFHz4pQ/wT7//X+/W69GAAXHRiFC/Vdd5Frgbiv2bNAtiYQ2U0Rrnn3/8Av/kX78v37mhTh4KxpmQSaRU96XtoLsDBEfSb39e2/PcKxZYqb3X39LpF0E6khkkHAMCVR8EKujhgGfDh1XWQcHQ4W3W4w9R8KazIuvMQgDSIh1WWlCj+aVFOsi8AuEMssx58SSAEBMuTJK7gRgRLIGIAKSQ8ImqB58gi2mBgFCAlcRebfblGGTwySQZIyW50SQuwgAIkjZajnPJhCVynVmGTB0kBOj2IIGUiCxWfrvX0drWxlV5dita3ZpvjUHCfL9rxf+urxeAWreH1s8SE1m9LAKhhSIe4w2spQUjyk4huAUIALufDQjMT8RUgpnp4E5hYEmEJ+pLsFS1x8AAWAgOCM4CANnySsiKE1iMCoUC6LPy0Oehxn8oAtDMTRmQGBB6LB8yfrwHAxRM09LvAMEeDIyq1EP7s/59/7mfsYfJ9rY/1R+vzsVocFDFFTQ4aBW8DQ501zoW/ME//AB/51fFfGCEUSCmQWZ0y9dnatpoRrCzePbyGd774fv47he/LWkA/jqYEp69fI6vWZjItz4PYCsDgW+/SUDvaPLnH7/AP/mz9/EH/+ADfPUHX+iIEBgcAt1P7gGBfe6BYE8huOajMJ5TZx44mEn74vfz9MquLjNIsBZ9CAhlhXQW6OCgmRey++VWNmYDjZA3htGFmxlx6c0M7f6rVBpEKiWDgpRkXbtGxaPlBFAEF1EOKF5qEp0lnZBiRCTCSsBFzQuXwggUcba8B2vBfSSkNeBMMpivRZQEWYXgJGoHCDMFIRcxM/SAAOSS24oGog4SaiAlaqAQJCVkjaoHoDq5XlOifHvaTTA1aVz+PMKsA3ObZomCHlK3Fm64X1K8F3jIYGBcWXDNZDBTCJrjKFWV4ORevUrwRJWEU1QAUJVgqX4iAsx0kSRalO+1za+qDmTARbdEXluUSIODsjW1ycVUIDeFwCtpFsWSGSJ1yZ3gnIE0DhPUHbMO+BMo4JBuAoIZDMzMBjejp+/PiKYz61m/upl0OUAgrUskKCwRzNQM0r6dAU1fp9+Sqswmc36bb+afefOdYd+mGlzD5HpMf4sg4+N7P3hPxsc3P7NtG0N5NGDwtR9/A9/94h/j3bc+Z0In7GZ6GcjDwa32oD//WGw+RnJWPHg0S5l+9kAg8DCw12Bmxf/+eKunnrez891pcYFQpTRmbPM52DZ3jA0gIGt+8QIUCBwwte1lbRdLeq1No62qQMlbKPChfrNbo2+do82WdKbE5ni4nptaEKKqBglYJI5+ON1JzHwFBKSTmBjSgiXdyYBcCKEwUmGFAyDFKJJwJlwC45NVZutrFmhYC+Osr0v0dmwBBKmy1HkcmEop4qOQ2QGCfGeEBFMoPCjIPR3W0g89lh+Up+FdxzpdAYKxXAOEWZ2u1cvXSRwMue43CzzkYcC+41eNlPEkIbN6qVuo5+FhwMDMA4EtQ3yS4sa5cInNbCBgoH4g630Nd03r2fkVrChnhYTLPXhdNaJln4SKVUnrLoyPZgnI82a5L5jBSCCs+or6PDIVSVvNvJOYiIbBPzbTQY0lodtxHQiOzAcP7rfcRzyZWd/ax0arg6kFChnSx/GDAeGhv39L2Uwg7b32xc8NCr78Af7um5ow8MrA92jA4Lu/9i28W086wJnrAezDwbXy84+f4x/95H3883/4Af62I7lqh3LvMfw9mgxmPgRdmFL0kDAe10p3T90DtAc8m4draBPFNC9Yvant3xE2b5I+HQGCnH9TD+Rzkgh9UGigIH1NUc9n1m3EzrtjKKXF0DcosPS9GHwQxgx8RDqjASQ0roGCKQaXM2g5oeS1AwSUDMQLwHcyk0snhHhCCiSDOolpYS2MCJkVXnQVwX0uuISyAQSBA3EyPCXuVAQAFRTklPvzKKKR12WRRR3kxJ+hecMDLeoe0AbmPlnP/DLvXPr59lsIeygzSPg09WlRJ/sB37bZ3z7uwNGSwyW1imyXlfarDZIHAwUCcy40laCaDZxzofkRUD43EFgFACifKxCwAcHl3MJcr5cGAi5B1qzN1wtr7Z9Z1LKCZremMr34NthvVhyMg78pEBRkObBTCQqOgWCEgQ4EdhQrQCT9/mTV1DnAgVwH/U7bVbff3t8KJGldKiQ4QND6FmJNENcAwffLIyhcU8aulT0oeGFQ8JXvyPjoHVQPntlHAwbvvvV5mB1MgqNQbZjQ/6dwQPrpIKUDwL/5+AX+0U/ex7/4tQ/wmV992q1KMGD2pTY4N/vfUweqkuC+MzUd+N/gtq2MO+hDtgc82+U+faPw7zK4/Sw7ULDz1FbtVYQpIASvHoRhcYLCgQoERAOtkfgnUJQgS8wBXfxhPWepsJshmeOVwcGoPtTLFqTztY5SQYDzClovHSDQcgItGShJjhkTLOxwTHeIMSEGwpqBC8nyxkspEj0xBiw547IGibGfGKc1uHXyAWtqUfT2Aufkg57DgKAwVE1o2wC0ZZPoB2PbZo4CN5jza3mAO8FBGTp5+/0biH38fR9x0AeYGj/33/Ofd6Ck4ZV9/AGvyCzRzDS6QkQDFZnJwOe8MD+CFFpSrEUdEqsfgWbNpPWsQHAvHfjljHI5z4FAE2MdtfV6lSkAIdRnT3TsKM9NCvMBQn0PxAHR1IEAmJOuDyK1AwWWpp6hgh8eAASTfmqsJQ2fBzQaMDjYnJb77qHJdudZKKamMDS/i/R54neg5+ZVBAcIcg6mZu3DwHRCOVRpNqmcKQVfNyh46/OymgW4akYAHhEYVLnLpvH6ahfTeMA7kIwtLaA5U/35x8/xuz95H/9SvUP1V6qjVCcFDQ3sFhjYmA7cTa3Hdb/RyVk6htaHwPb2cKDAYw+dd8L0ZdzEDOdQI6WCgkJC9c69AgjS+k09ENMCB8DSLjNzAwUoNMiXe9XAHJuCRlcMBcQB7D19nHzqO0rOedcpjgJVSdXAAJezpOI1QFhlpoblrgJC9T9IC7is4HgS/4MUkQppfoVQs/JFisgReGKR9YKFWA7IiR0cAGuSuPuWsncMrTwb4MZSPxsGxhioriiQj/dJYG82/1dZDlxRARyrEmMo4vH97HrFYeSwt7Z9NM2kEGpegzGU8Z0qB31kygkQbPwI1LHQzAf5UkGgmgwu933GTC4tMdYNbZwimhmvQNfEZmnvAECkDoj6jNlsn8KgFjRA4Lqf5qIYTQroVYIugix2gGCAgdkZWf/kP5+11K4f3VFl9wbVYRPg+sNgddXB384lQAb9ERJqf6nnVX22DBQmbXqM3THWayqoUdunh4IP8e7b72BcvXUURRR4RGDQFS6otmoLbjOUoONW4F41IAJ+9uoFfvdP39fgEk8blfqb4+6U/XXNVBAmjbHZ5Dc/sT0t28caqpHxcA7+lhNtt43HAwbTw2znClQKGjtJn7zHbMZEPaCoAz5E2YFTEcgyKBKQXUeWqsgDlAwKAVykEyNmcFRZTG3wM68xNvODP6UQgFWOR4EkemHQduMVhOVOAEFnaZQkhS+bWSHKDK46KDpASBx1aRzLwJFCDabzpHBVEVYPCaokNOe5bXKecVY8i9E/llv2qeUBcv6ngYiHmCCO6n/t3GazRw8Aso8HAlRVwDtyHuU1sIBFPkBR9/7IsXC9l4F/PYtzoZoP+HLfKQQGBLJSh2ubnrVtmjprOH+DGMURUUFAlilSS1edkhv4JSslQuwBwBJUmV/BA1SCa0Cw1zRGR0Ibp8fiJ2DjNj8h8wPqLf2w7e+ysWslGhQU8FVTgxxj+0umFAD9mOJVhL1MmYCYDzooqDFhbjGeS3mcYAAddCg+CA6YJbjEN//k6/hXv/ZBi03tZlbWeKM/3ti4JuoA2cx5CgKGj3YD3RPR2b+Cfqc9fMEOVno4MNVg83DVEbb782qpx5kBgn5kPgg3qQchgTkreBNQJA5ADZYIAEVmKqQxAXx9bR8e/sZ6qWaLqVs5ANZcBZwzKBK4FOlICzdAMAXBHLviIq/rBVguwHInaY3NQbFkoLQYCAYIKwM5UEvhy4wSgDVGZAbeKH1IXoMETmgpfdPcs94n9RpDLj8IAm4sRwAwzr5vKbnw7jEf6rNwy+/7fUwJkL+BmsNAHTX9ig7LdEiEOQwEquqAz1thSw8T9UBQlYF86R0LTSnIq5gO8gWsDoczIKhQkNu1orhzHUKQ/oOCrMBJCRSXCgGUkrT35a6HAk08ZWqCQEFLTNVMB7erBBUIIPvdCgR7hXbf6KZZX4zGSATfJw998VghM5VSE/itD9b5DwoDxCSqwURFYHLKqzv00dhC7jz8OY2Ty+poOEDBLYmvfHm0YGGhj58AAQAASURBVADA6eKqIAAbQPBw8PzVC/z2j7+OP/rid/CZN8WnwG66NeTq3e0aFXAAA7pTn/aU4Z0/urzne4XE0GHOPqSwUwEhoDayPMBBhYXhPGZe5+3Suc7Gdd7e1CA8whWcCubmhbijHgSKYJU1BRACqLSgKERZlIMIUCEgUYOD0B6fDRwAdYAmvSL2SHEpAgMVDuwbDW+wXtS2CoGVUsBJvcPTSe3Aa++gmC8CDyG1FQwxYYkLlpiwxBZmuWbxgyTwuZtAgmV0rEF5Ur8WX+BAIvUB6GABQAcMVm4ZbGcD9Z7kfu17139ru82qa3e38wPQQe+h5+EBwD4zCABaymODAosBEYlqsCjvYOhhwOenGMMX1+BEqwtONFlpsHEsXM/iTGvLEVXBMpOBQYEHAqBBgakFYkaQmT5RkIF/Dwp0NY6P6WEQYE66U9OBS1Q1Uwl8zhnYPg9QCfb6oc39dv36kfnWMlD6wXTsk+nWGbaaWKSLtVTwAgpM0Mi5DgocJJiS4M0UVVGo7bS9JaLuPI6h4DuDUvDwScKjAYOZJCMfODgApoAQSNZ5fuPH7+GPv/ghPv/WO5qEiGqjHkHcOp4jGLCbpkNS3/C66H64IXOcQgQVbZDcAYINygXSIRWSjIFTONDqGByoCjaA8f5DOPohTNUDBQSAuhjfTCSKP6GSM4UkEFFyUw8QmmkhkzpAyawnxAhexWmQwhlY1T6qQCGdVpDOmAJAqyhIOYNL0JnWdrbVZFmA9NqxBXphjSSXc+d/QClVB8WQVl3BsAIhtSBJISGFiBgXcIpYmWrQnCkk6KVbN7H7Sxe9T9bsi+nBQGGzVM+vbBjamBcVbhns91Yy+E55b/+9wbyPi+DrJttTmMGAbduvf1dXmi/ZNH8B+XsfBCR6oSgGEXMYsGBHVR1YLe316oITNSDg9dIcC0c/gsFsAC4ol3XfbLAHBHU5bugGfYEBBYO0AMupbmMNCy4qgUHBbSpB4RvMBsBUJQDm49deP2SbpX9tb/agwNJT39wvy4W+2i9L19r6HuuzJMhZM62WK5AgVVPFwJ3yuMy9vj+CAnPE37uoOBgvtTwaMABQB/36tga58dsbIBgc1IRLX/oQn3v7KZhllsuQQXXWOKNrgACmpgKZIecpDHTpUIG9p0LPQ12JiSokdIAQYg2AAWoKCMLtcABYhR8OmEfqAfOBeUEYQK9bFKcolqRETLlXDyyGAa31ISR7EE3yNwfFEIF8Aa+x+SToCoQREGTcn88MymWVTjYtYKxVPfDLfTin6qDIPgYCaajlsMjv698cAk5xEWAASapnlpmxZfurqxJCqB3uWvp4/6ygIBH+grhYeH8EvacGDEAPDddKPwOzAdV9vgMGtygHXZTHXTA43vdavX2dq1kdbTWBOMO2iJBJB1SfZyJVXwNoRkP9fiD3XmEA3MwDpaALXax/E5deIbBYBHkVs8GoEjjTwaxQpOpHMAIBgj4XFqdjVAlO0lY9MLCpBMF8CQaVwLbpioNbzQZw+/n7+PB5bA8E/n4/BApC/T5jNx15bXf7/XMbF0Ltm2mAhEAEUHQTIYMo6p0Wgbriwfuv6dG3ZpAZFHz5v1MocCrLTPWYxIoZy+MBAx94A+jNBwYIFPobTi3h0ne+8iE+pwmX/CC6FwS0cyTcoVCywDtdg3Pxy3lIFtSdD7U6o2U3I4kQ1ADB7jG1lLVEKp5fgQMzkbTBvAeE1yk1bTSjUw9mgCDKgwcEaoAQZFBHySDK4GCpZSM4RyBkUIhyjXWWRTGJhH++B3ICRelwkVN1HJTlhkWz0jVIsGL+Bl3xwZksBG3J4LCqbXapgEApoVzuVbI9VVst1FZLFjvBQ0KI4BRRIBESSxEJNpeWKljSBEdZMTmAgvgjaEKgxGCFDIOFMRqgv1djGWf/IxQcRU3cM2/PilfBeRgwLKLirM631LuHggYBkaCDf/MDaH4DWxAIoaWwjoFULRiyHJbcw4DFFKh5DDKwrmoeaJEKN0Bg3ztYfihOtwXBRSLcwIC+1gE/xqtAcNVs8GmBANhAwazcYo3aAwL77AgKOnOun7CVjM1krVNzb+ijnUnBlm82SMgCZ9aHa53tGpqTovU6szFnVKPnUPBOreM2g61NiEkl33B4wR8RGExO0l8c6tP9MkKXcOmp5lbwzog+lOb4EzHs2Kk8gZbcN7b6WQODw+k5EciggCy7mczPidXBrgBE4gQHZFjWwzjAATNqcA5riKMPxczJsl2v24s3LQC3A0JTDwwQBAA2gKBAwEVkewMEm5FVQFDbrnXIFgiJXCfsIQFAC8Hszz3ntuRLCYE0noEHBFMvTKZFOg+dcAJWBwlVSZCOOYSIGGS/AiA7k4OkDbb4/tIhlxKRk9wcAwUuCgmAAoIlFwJaTgF3bjvt29oQgJqvYC+nwfj57HhjM7er7PMcyH688/lwT/aeS1fnqHWVJE2mDEjCJgMBUDMPRLJliWoa0LYYg4ICIO1ttZwF+rpJaqQwYDDq2mCNRTAG5XIqgZ54bYs+XwSFKCAgbzYwYOoAhrZY26RTtHCzQvAwkwHwMCDYK2OP3kHrFSA49PEyKHDBfsjMi3US18PBUT9NZsKUN6J2hohOSWBTeAOC+mWMKoJdohDm4844CR2hYBcGthXePRcrjwgMJrO8vfcU8PwXzyTh0pc/EEcNKGGSwgHQNPL6vfbnxplwCgS5b2g1V7oDA/v+9oTaeVV/Am6AIKwg6kGIYrc8gIPZigWgxf+eOlkO1Xrow70BBHc9A7iqCpLBkXWZz0j5UQLORIkBP0s1W0PDcgYFDT6k3t2ocNDiyfMICT5aYnSdc3eP/IkVpx4E1Ex2IdYlZxQXcX60Dtt1zN7r2zpiGuy5FBOidsxMoiYUc0aEzKoNFgwUmGPruIGabpiLH3j7eAGjTD1PbuQhwD6jBgj6nYcITfarPnOjz9oIoGZIBB5WZ+v3LGujpJ9u4dEDtXMyEG0KgYLBqAqUrPdb26C2OdjnRzBgA3++oAtbPAvI5c/LA4AVDwL2OoMBCm2lgfoVQPOAlCs+BKNTYUs/L+3uVhgArigEu59slSv/dgQCAK8NBVTzrAwTt/rq4OBaP60VkSXiLU11XTJOJM+5fkYhwhzISSHBn9/sZ65CwZGJYBwjD8qjAQML11kvkLejDLT37NVzza3wbTx9S703nc9BtDHRZtKuIVqpJoNrQNCZE6yROVi4cj6yL/WAYKcWoLPXPIEDeUxawg85Wl1ba+oBemXEHGDs/IEBFoD584H9TqDbzuiCfoAmkKCmDwJQFBJCIASb3XBTEVASEM3MoCGSywoKqaoIVTGYQIL4CbQOewSFLrxs6f+2JZFEQQlMA8HkICoCEVilXJ+4CUOHTZYCOkQ5v0FNAEUE7cCXFFFAyOqbUCagwPq+REIpbbZnt8GgYa9sBno3Y7FtRMNMBn0nvVcqE5u/g07xmVsbrPUd6owb6u3r3ORk1DYkcNCDQFATQrS2qKqAgcBGFTClwIPmXkKjAxiQ89YzHf1cYuuapyBA1MwErm2NMODb1oPVgXIjDNwIAr7sOayOm/0g2dpe3872gKBXdNtgfwgFfvLm+moA1/trCq2vZq5KglxXvgkQALQ+G/Px58URFOyYOzwUtLFl/0F9NGBQ7T0YAGEoz159hK/9yW9LwqW33+ma8ewyxeGm1H3qTKH0jYzzFghKRgcDG8VgdjoCATJAexKANOIBDuxNg4NS4QBwSgj3ZhLzuvXxv4G+fZG7SjNgsA+OEt34dzYzJP2g+O9Sc47sIKE0JSEEUREoQlSC4kChg4YMLisoLltIKKU3N9h7n5Apryox6r2ioTO3c6tTXO1A9MGzLHMc7nc781FV8N7hcDM78mpCCLLCIURAfRNGUGCnJoDlmneDcmwrSMYyG+gJW7vuUUe9V+rcawAERj/I2GqZPZC4pc5RjbGmCsi2EQTQnmXzFdhRBWw1gV89UNWAh4LA5gS037JO20BA/zZ/AehAAtduqpkghA0MlF8yDMycCAFMcWC8U7eoAOP3ZnkMxmFtE07+Bihog+h1KNiYFWbnSgzxBaMGCSB5ds2MUAFBIrhKnhZpH1x9EFq9GcMKBcxWH7ix5MgsDQcEfz1NCfsX6NnL5/jan34T3/31P6oJl+QGmLF+HgipbXH0WAciZ5+aNrA8aVw6MO7Uk1WKAhsgFEd5+sriBsQaHpCK8AGgnvd6Tj7eQZWr7OGmLQiMoAC0ffx+cl16dcFLiX61Q8cPvXjQfsPBQmYbiNA/5KwdfgHamuSIGKNGS74GCRlIvfRboxmq34Gf8dFOR0+jmjDM9mpGDVURAOnwLXkTUxAI2AOFmdnBOviJmkAhVrNDAwW5ic05jLpOfrz+7Z7q6yDR2mezULIdJEyOORbXTCQscx1o/CBE3Tbg9nqPsrKtXd+AQHFtYaoKrNU8UP85X4ENCKyXNngctI+ueCB4qCpggDBEKWQzE1yBgdpOSnMi9HEHPAgYqAG4Nv7UcYfdffFQCWxnwXuJjDxkXgXRDRBoLQwKXB/sfQradgcFnB/WZ1dpazD5Fha4J/2dEMEkr5RZJw+awdaWpOuVI/RtfQYFsyWWcpAdPO/8Iea7AI8JDDpbT/WNrxeqQsGvfQvvvvnZ/ptuIK3e/90eQ+MCXKcyUQmqM9tAqGAdbMRiupk9GDHGCIAbINgw62WC8ZWgKVRLPY0Z8Ehn6Qb+oZ3PICDAdcoDMLAO4jVPOdqsUOIacG3gYwcPt6+/1PabGYx+1ip39UJAVGmumwVClIQQ9eGyWV7J3UyQbSaXtrLwaHIgPm0hIa8CYwYIAa3z13s6OjFyva96nzXtczU/uFmh9yIfzQ5mJ56pCaR24kihgsIShmQ2RnFHnbvrvP2AP+9oMe+Y0Hei3bppP3MxdcW8pIM8f25i+tr19rPFDcgf+QpczhvzgF9FUNvAQdKumRPrxmdg4i/QtYGHmAgOohOa+lRXFBRs1CVmdr4EO2rNzmXvBm/rE1zbmYWKt32tGRwBgO2z+b0JZPSAOrRVv/oAduJl+wreQEGdMNl9Pei7Wc2/FRACUE0MkMmeNGdufmKAqAeA1FOfDeu3n+1BgS/+PRd4OGhZMDuDIPbKowED6y9mM/5nrz5qUPDW592XdBjzdDVc0LrN0xlwHQrYqQcOCNiOB2Ab31x/LrtOZKyHOhbaiGz5BmS01+REJPt2aghQTQteNDCpqjq+AJ39v13YJj136oLBgPkpDIBgcDCWraQ8/xzgZiYjtQEDWElSHVdQKAIKsUi1RS5OiCkp2DQfBJqqCTsmB++XkDX9sk95u15UKtTENmidhznJ1fu8tsGirT9vy81YB4ZOUfADwSwgzcQ3wQYDsXHGGgvCOoZDSZEZYPTROG2WZTOq0Xvb7TO9mXBdkLd5mlpFFq5XdSKivs66/4Pq3dXZOZwNjqsbX4HLebuCwN1r8H4Co1k8DH+fZXWLPL818NDkXhvsHcHArSYCUwVKaeaBqg44EChOSepVmu2lHm9lcX3HWMZkcvK9eXZZf8zuWB4KaLq5g4IKBHoCXb/dteGd5eI7JgPO+aa+GxgAAWht1+ZxVeEFgFj9xKSO/URVoODrDQq6SpXjm+QrJ29u8gJ5NGBQ4BuZXlR2PgUjFMyKoe6GxGaSzcOhYNOo/O+QBdwJ2uFo49r0gUMDHkChHVdNCfoVpgYItuZ2oosAaA+fTdLs7woNBgzkwntyvxQSzM6vQeGAhufPlDt9X6/G2HI9qMA6DVb1Q8AhkhA6QezJoXC35IwoynLAqHAxUROQL9XkQPHSQ4LGua8+CTWoEjUFAQAbkFU4mIev9X72FqimC1JzDtVuzJOodbsmBx0ciIa17c722QVm6crQSY4msIn9FcAwu5rdQF96MGghZXWY0O3kwGCzTvxa3cd6++dysoJgNBFMUxvbSpYrYYm7M3URCeX9EJ7YFIJZNMKYJJtnBwNLMxHoktdRFRDHVHlOuaBb6mqRMU0NsFUfNz2Dw+0L6LucThBSCBih4HBJoT82dW83f8/e1wqPQGDbuvaJAxDYdj639N+jgt/13+YTZjsfwIEdzCZ1sqT+fVk9Z+PXNRCYFZsU6Bl6RW5WHg8YsKdS+f+nr36G9378DfEp8GEiH7BsYwMFtTE44uw6Tx+roG9UUyCYlYHwhg8PvigtTRpVg4P6LQMEztNZV+ugx19qfwVqwMB6TADb1Q4GCFYTbuGXd2ou9UO/hG2v+KVyBOBC7DocUS2IdDkadPmZA4VICUHVBPJqgnmiV0hQJWE5iU/Cem4qwuXcAGG91DDMxk5AHvoiN5D4GUcdOGzWFBCW2EDBOZfNIGG2HFI6pYDm9OT8Z/xM3JeNOWAyyE7adLfUrnZuk3tXp4OxnTdZVr+gdda5nwcHL39eVQ2Gulf5WAf1o9gCExjwSYvKJVfz37X7SP45MtirQYe295CWU1MHLDBWSL0yYDAQm1LQlrICOQ8gUHo1wIJemZr3aZ63mkV1ti96KAhHQDCqCe1Ot/u4uc87/afWf7N0b6Z47Z/p5L3WoZpyZ79d/AtqN2twwOoTVvcnAAXE5mBOjRK0js9fvnBL6p8e1NmqGjAb48ZVCAxsVpOM5dGAgUhijVafvfoI7/3ofXz3i9/Gu299TvF2aMrXvDNHO85gn9o4raB9dnXwv1JaJ6mHrJ6uru63AM6ggtQj7jaKwTY+/KY82K2hbYN0yIzFAEF0CdZ4CqQONr10MHZStqbdLmF2+1Yzhrt1fo29ra+PQRSEACCoLW9v3XoIEcl8ExYJbcsumh3MKS1eZCB2kRarkhA0DPPZCSoAyDpdb3N2mfEAoKxZgpqsem4hoKy5DjBhkbDOYUl6kjK4sEuIU5dDWnhblaZt4CXb5lvBaEJrb7ZAa4N/aY6YXNpsnEs5hgIrDg4MCFjVEkvOI7PiA2iox5rV3wYH1kFd62fOghPnwS4UsSoDlpvAYIBVj+/uW+Ft+vXggUCv/QgFp7sGm6oMtKyGEi4bYVEQlL85BImwqaG0C4C1ACXvxbUQNaBwMw18umeLESEBt2IgEAsceINnG/ClEzY49+HjD5MY+fvo6vXQzIC7ysBeqQpzAwCGTBgAyLMV49Rv5GF1Cm6G5sYR50Ru+z77+M/7JfWH9aeu7t32CtKms0qbePGL54eHfDRgILNSGaSqTebLH9TVB93FA1rr3xtcR6lpNLptqNTJqvYTzPPGNCG7GoZ3Z+Y0Sq12DhUYzD7bnUPR3/Ln8rDGTR4UJvZeSxjCoObUyKixwAHJrBgVDowLAo8Isu24rHPLAMYIeWPxUfiss7IodwYLKViHxNNId0lhIcUTQjyJktAlwEngsAgg5Eubqa/690UdCtX0AAoIFGQQgjAGYqm3vjjbtM8QTYEBCCx4SAjrKoCQogKKRFY0FWEWWMkGXPYDrt7DTWkVA5sPzR4AcHPGBIBued54vPH3vNc90DnbeWCQ/VQq/7T1nwUaGpIV8XpBWWX/suYOBsqOH0HRZ84AocFcrPepAsFyEkVguRMzgb1Ppx4IYtK/5d5yXKoysBZpN6tTBVbWdN6ldfzr8Cyt9vzgYc8Ss5rqCiEHRmRZlZu8sR+qBtjMf4CCqhhsgIC7vnXj1/LLKhuwsEFYAUDNzrUPtX5T9wEX8R2MUVSAMBz2QIk2vxKDcmLnRE5x+wrnE+eX1NsEbyz1WDvjm/vbpq4vfvEc7//oPbyFt3cv2aMBAyvPfiEJkT78yod4963PScMG2sXzxXtEA9cVBADPPv7ZduNGgtW3A2larHP/2zR0dFM5NaTuvXWgPRTYOQ3nYI3cvx/KZgnOgQwsdcvok4VwBQSAhioIKRSi6qBQWPeHDPzO+6FW0aDAcgD4ULk+8p038c5C+PqQuIngYuQ3UAjENU6+h4QUImKKCMmrCJIhj9dLpyBIBy+DMl/ugRjB5/t6f+0OlAuABcAFCGgDjj8ZzhmIMjujwAihACUgZwZFEl+UNSOkCCQZ7BBFTeDkwjOH2CDB2oDOYmelts0ZBKgk34XxVRXhFo/82hqcZz47r3yuIOCi+Dkg6GABOD4HH1a4QoGr/+BEeA0I6nWZNTagAhwtcQsFpgqc7kDpJFBwuhMH0hEI9G8OqaoDRWfpljzLh8W2Vw8CNWcGy3PiQ2LLJeGjU6kwAEBkbn1eMhhxsB9U3VAhgNCrccG9N1io81YFgsNMs0fmKDufvcGyNYjt9ztpROEgoCleBgMhiYPqBA78z2x+wqtGg/Jbv6CxDGZjTl1Sv+cTp+fAFGqMhL3xzZsRGDI+vv+j9/DtL32I/+v3/+n8+HhEYMAsEQ1/60fv1dwHys+oSxfH74yyiy8jgQF4/vIjfPUnv7M9EBFq65oNvJPG5N9YZ8nU0gt3agB0tjRxyOqUAqL5gzLUqQOB+vcVUq/Pkh0/1N/rICFEBHVwKySHtUBFLXOIE9uDzHRAIlGurseqnRn72P86EdQK+YH1MlTZEujopamZ9Igkja4HhSWGDhISEWKQv6OqCDGexOdgvRcpuAOEVCV+G+CqeqADWowrKKwo6ypXOweEdUWBAmOk1mMbJKDIrLTkCghiMpMBKxQWEEpFBn2TzZPOwNXXgKz9ANX7uSvODMDOCWoXBPK6ccQDAM6M3SA+AIgCdtMEq2MeX5qJwUChKlR6Hgw0X4Wj8+CyiTPggYALo6xrrTdf8lYhGB0MXf1DIDXnEEKK4reyqEKQTluV4O4NNR2cwPG0AQJOd0BIkjWzaFjrIqm5VwcFhYFLLh0IrLnUZ2XNLaCVJdLaK1lBOoCqCZACIerAldEPFFGn/QR0yts0oqTuI72YDP7z5HLSV9u92y11Zq81HgbXtsR78lWbtQOo9nyvHAQ0QGEStSkkraMOxihAIu1DoyoCQ78/9OubcmUi+r0vfAtPRyjYGZ/qczE9X9cvQ+IgvP9DgYLPv/V0XjctjwYMXrx8jm/8WFInv1MdNUyCt+Ufk7JnUhga17NXH+FrP/kmvveFf4W//9//b3frIV7WvQQF+EYSh/0VBqwOIxDYLGkPCLqZPNpxds6lp/HhQeQy+FQMD2j3AEo9yNWDNcqXLZWLFPVhQ4MCe1V/AzP/WJ4EIhIpfTLpnGUM9CmGgakvWIWDsFLNuBfXgqgKQQoBKRT9WyAhkpgbJMUuYYkGCwnLKfWAsJ7BFEEhIaQFJSbQ5R5839QDnAOQVwQ6y0AYAsolowCIpYA1qyJj26mNGR85s0R5ygEFq2TbWy+6REoGX5hkmQ1SBBLkPh7I8Eex/CcwUGfYevENEGYDUZXbzUQA1AGVQgaFdQMJCGqKCX8159FBwQAAVSkYoi1WINDzCCleVwnungDLnSQwiouYpNIJnE4dEFxYrDMrMy5ZB/1i2TUbDLR/BgdQc0J7LgAP0P4+uL9BCPpYIogf0AgC7bydMgA0h0KS4+yHmQa6JaM8hCOugPCwvsciDSolAmRgc9wXsqquNrgDAhesEWQNAkAEm7WY/N8BAtBBwlg6nx7ff18pT9/eUwrG77b+e3pcZ4J+/vI5vv4DGR8/9/bTq5aaRwMG3/jxe/jjLzYosPMmBwfT0k/h2zcdUDx/+aIteXzzc+3zKtOX7n21W9UoheVY8prBgJkMDoCgHnOPQL1DyuA42TnbsPM81/fd9zf19lkf3XIye5iYxdQRUNWDFejhANxcm4vOblQ14EyQ54+xTn5+hIIasGXoDO2WVzAge5XOMVFQ8wEjhR4SlkS4ixG5MGJgZG6penNogIB8FhCKC7Dey9/BrT+/T+CLzgjPn8iAcTmD0oqQRMI2Bze6ZACxm7FaGXO0d7e5WFsTE0/1Y3LNnt39pKGdVG90m237gXSyTG/mkFcldze4euWAKDTW0+WZIVBnIqmrMQqDcm4hgGNCTVYVBLBMLXjQudRzaueyV0Kg6j/gt9WlhzOzgQagkvTGT5ofwd0bYjZY7lDiImaCJIAgJoNTBYJLceYAVQoyA/c547L2MLAWYC1FTAlc6nMATJ4FOwcQUKAZTKGOWQAgz+asnUWIKhBBnVrgU1PHsIWCRINKMAsKN5gRjvoceQ3iFFijwbqZPw9wMFOE3fJui/ci39E+aQQE69dV1ZA2zV3d63Gm9b7Wx4d+v+G7PO4HvVdgt+3ot6kGR/rgKx/i828+dY/oPh08GjD44y9+p0JB4RHyxwvrtu9ukwv/7OVP8d6Pf0scQd78nLNDOXWgyls9HICgNqC5pNSvNNgCgS0z28DACAK7FHqLNLezVl0/2y0UAAvhGaI+qAU11KceVwbKiKSmhYI5HCRdVpIJSDEgI0sI5ECV0iMBqzuVEQrae6cguL8bGFj2PIl1kGJBACkYCCQsOeCTyLhTBSFFIBFjiYS1iIKQibDEE6JCAYKAAtazSuBLdUqky70oCXkFx09kW0wdIHCKdfZKNyyHI9fI60x7cn+FUcUcgRCxWZZ2MIh6KBhn17yuGyCQzyaqhy2bjQGUAzhn5EjNhwIJiAXlAlAsooJA0l2zZrEUJSR07f3QLOL8HnyR69SWklIkUVaimh2DQEi0n5lcd+8E6p0L6fQEiAlBFQJbcSAQIAoB4kl8UdIdMgiXQSG4ZDMNMC654L4w1lxULUAFgwLGmudtf9bu5W89nQoDAEJvbK2+OuqbY11OikEEG0D8cyZQ0GWmBKNLT10KgIIui6FfyXKtvwFQ46L7oOtHs/C9PrJ286q4dpMkbqqAqRnV/KETH7jZv0HETuFu8Hf9vNZvE+5++LwbIzYnAB1fZn18g4LvfOVDfO6tp3W3o6WpwCMCg3fefrd7P572LCLi0aUhEJ69fCbBJb70gSx5LNnN0qVxVtuUd1zRIwA7Es+4XGymDoSIDQy4RnI1Chx80zE72jYc6GEAG+DgYXUzDVUKarrRkDRokDteiIgUFQJkudMa9DgzOKCgcjkDMQC5YA0AscxqSuYuH4PvGGvyIIzZ+NrfMTS7aMhUFYQUi7wWRlwLLpGwpIi7LICwsgBCZsISCKu+LukJQsygNQJhkRUM6wUUogwOl5NkSLycVU24r0vkSP8d2e1ryxmD5ngbvUrvfg19NUXtFT+IHhQ/uzalYLPP0FaK2yd4UwiXvo76+4wASu33aDbou/097ExLCNL0zalN1RQoqwcAXCywGMCFgGTX2yIUtuu98YcYYxK41QZ094aErw4JHO/Uj+BOgeCEQhGXIgpBLoxLmQPBZc24N5NCljTaa/YKQWvve5knDXCqGFCog4H6WiFAzGbmh5MiIdExFKTqX6BXbpaPwkwJs2RFwH5fQ0G+X40YgPVprQcItcN7WH8ZW3/JBUCUdjz4P3CIPdD4/pKia/t7o8owoSPCxoF8MIH0Y8dxxMJpxF8HBe+8/bRLeidK2z7MPBowALb3/OhCXjGxaMKKr+vqhs/rsh1nHhiXvAT0DcZHJJxVcpj5X4OBraS0f1NBUg9Wz1fZs+yf9AwKvM1vSpfZ0S4DPt2oPUikf7NG8gpofgdFzqEYHHAPB4iEyDKz83AA3YQoEYaDOicEdVRwgg5y4TpzGjvNS0ZNLxxIHQwDI2RVDbIoCJdMWDJPAWENjEVNCzkQlhCRljdAcQWtQc0LCVjPoKDL1e4/Ef+Dy0lA4HKW5XPqLU/OOc46ygfH3bft1cliLrsDkxm3NvEala3IvTYQEWcry1oBWVlh0JmD3GsdibpuLbZ3EvmvmRPMVj8Owv48NkmGjs4rpmFGZCdUQPq3wQkBt19ni1RIQVehpHk8grsnAgQTPwIOCWsBLpmr2eBSGJcyB4KtTwHXlQlH7Rto7RvF/Q111wBV9UBUMzWVxS0U3MW4hQIFAVvFE0keUXmct9kqvTlh2s8cmRDMX8ZkfQ8HO2rspt+0ezjrNw0oPKCwrJjoIIFMbRtUD/cd+Xunz3f1mpmH/aRzzG3AuD6e+TN7PkDBWJ2/Nj4GXdGxpUbp237cFXum7Nl5/vI53v+BLnl0qxu8rwKHoPJRhsGBmA6uXHW9+bs+A7swsE0sszl0fZUQwFZvRlvWUlUDmlyc2cO6S8Ly/T77ow7wUPpWPwMqAEMHvcAIIdabQ3V/d4nBEkAFwF2MiCiIheU6DXCAHLCGggTCWtysCG2JlnWa9mr3+5K57h919UEgMRNYZ3kKQTrvCSDwIj4IS2CUECoopOgcFM3/IF8qIODuCehyBquJYUzSQ96L3q4lsO9BtjdYetPCjtxaj521XQaVdzVJFGMVuVxNCtJMxHGUWST46jxpOb3t2P43O7PHFgg2Er1XQGwwnsQ/2Du3NuBrF2fXFJBO3a6n94OYXV879pgN0yJMpuRMBqceCCwgkXMsXFdWlQC4lIKLimKfXPIuEJxLwZoFAi76mhUMAHQzQd+exT+G6nbxK6BqNmsq2RYK7tQRN8WgsQigg78+G4Hce3MynJgOXE6KZjp4WB8DGswGM/Ns7VcHleBB/af2RRS1e2wTvUNIGJ0nr/T/nWmBCF0+k257X29/1HHMsnMioDkaOigo18hiKI8GDGaK+sEKnVbcPoWBF69kSYeRllxsG6i5UZkNss6rFYA4rgBbWWwjZe3AgPkVDA2iDC3aH712i3roFkTEYgs4qGHsQIGTq7sH1mS2zRf0f4GBaapR/R4HvU5OlQhROloZR2Q5I+nafVs2lVkiFlIMIGJQKLggIBZGhNhbAwihAKtfmQBRE5YoyoDwRN+JVliw7zBqR1pYOr/ChJUYpxiQAqlTWMCpMNYowLAkwpMYsZYifgcMJJYlYEtMiCfnd2CAkFcgnkDLBVifiDnhco8x7TO4XzooFXaz2snAX+V3a2dHcjzQTAlRfqsOqBoAyFY4SOTHghi35o4jv4hxRcWevX62bHGWdbA7z4ecY2pteHNNgfl1HX5rk+lwFq3QBydKsiQxQ9qhKQRrMX8C4BN1KrxXH4JzYZxXBwW5TIFgZjqQttvDwQi6zZeGnLOtd7wlLElMBzb425LEpL450TkZptD8CUjDiHeprLs8MjOHQ570LXp7rKtkZzaodv3W59YyqrHWp7q+9GH9KFVIqF9S5WNqbpis8urrd8MYADdpvDIGwLa5y/DipYxfH3xZliTujYGz8dKXRwMG3Zxh76QnF8nfuhcvJQ7CB7rO0+9OUJsQu8aHUD1S2xIX+8KksxqdB6uNaUuHLfOZVp1RZzx9vdo+FpLU0DHaoEvOU51YHlAEmMmhng9j25iB/QiOAGqK6BEQAmR2H2Jb62GmBa23+R1QALL6HYSggzyTRkcU9SAQIXFUIGCshXCfCygwKAeEwAhZHNnWTOLIpoEUCrurpJ2qzais5KKhZbkpCYWDAgLXTnNVKDgVcRi7KwFrZNzFgAzxTTAHRfM/SOqguAmSlFcgrTKjunsi5gRdBUAKBl3kQczvj9yIZlJom+YqgS8+qBGxC2oUdXZnkMC6xCyvoB3nxOoTceCvcNU3QiM2VmXAVia4cMm3BjuaniecMnDDtbRslSBqIaYtYVVImrwoVSDwwYmyqliXsnUsvKxFYYBxXwouq0DAuTMdlAoE57Xs+M1I8cqAqV8WmyOF4NSBuUpwp/Dbx/ToVYKrpgOfa8SbDiyZ3CQFvXx9v2/ZDQ402OPrCKCDqk2wDAp8f+r7UqD1p+bFAMBNWlBzOkQFBWifBw84PgGS8z3YLeNYMIwLUyiYwAwABN3+4qUEL+qX7PvflGME93avPBowmJ7l8PyMF9SDqsVB+Lau8/TmiPYTTqYKyYXwbIAwr1t/82+BAWu8KlZ1dfWNustpzjKIMjVXlEA6AzfFg2TpZEfp1bnH/t45j8n2Ls1ovQZBMhdSqJXvTAsZbtWCmBZIg6GU0tZJl6LyJ3ONBR8oILEEc0mBxGObClZmRGIkneWLE5eCRmYECrhkXXpoMuwAB4DrdAPhvJaqIJQIeQXV5WEng4JCWBm4K4wlBgUPRmFb2ugBQYMkZUnEhCKzLOQVSNl1nrkCWR2wZyYFX0bzwrWivh8+ARI5GCEXAZFKrksXkZ0srEBhx+CjOtpA7iFmNBH4JYoDBHSDtE/EdMP5NtMC9us3HG+Tp0EHJNYU14hJ0xwvNXSxD05kjoXmR5ALcN7xIxBzAdS50FSFcqgQWKly/o457AgIjswG3pfg5lUH3nRgbZbzVCWoQDDrb/buJ+23cYtPUJOHyZqxGhBqr0+d9adgCQVtxmMDhRESqskhxNrPVTVh79x83We+EDtQ4CeJvmQAH2lwPz9+9b83vF4pjwYMqp/NTG5x+80G2I9ePcc3fvx1/PEXvyNKAVfH3TkcANIAoR1HtS2N0pazdel3boGBjL4R752XECC3lX+QB9gAwSpOZFBjkhXDkhZIWE0PBfvhbH3xKaIB1ExiVT1ARJdNzISJ0bRgXuUhIYYo9Q8kMeAhD2IsQCaJxJaLBEWKFLAwkHLBajP5ILOvDSCQdLiBmp3WAEGUgW2na+BQMjsfBYWEoJAQiiasoWpeuCuMlVsMhCWIecIAwYIkpSWBkmZ19E5arlNlLsDC2Dho7XU2Y9nrXLvvc7svvqN2SxcFYA5yJpiyAEj8AfSdba3OxDdgTwmoEGCRJN1ADYyzyXH2eO2cD4p7Vv0A4wHGwKAmNtIshyu34EQtYiFXP4L7idlgzfypVILXAYK7EDpzwpFCcBMQKMhuTQcjFOReJXD3ZGNyuuUeAW6SZX2bDbBUB9XC0OBh0g3Z38CsX+XanAI1FcF+wiKnHkKCe1Y3mR6Hc9iODdiFghnIAH78ahEN7dfqlWJ05wUAR5DwaMDA5Grr6G3DSFnjRX3x6jl++8dfxx9VKJA4/kdwID8oXvfV+x+AS67d6K824i0FjhSbPdHWBtEa8cbZVV8LiyoAgki6+lAXb3AkIJDO2NlUA4OEguaqOsDCQekeZi4VDsRckAGWJEbaFSh6t/joHORadysiKGwAIaucn1kc3EppiWMihRoCduWCJRMuQYPABMZaAs5UOkBIoQWGsVlZbTc75bwWnFLAJQOFS6ceJIY4H07MCyWKT0R2gBBKy8UQYhQVwad+ngSBYe+kNTYEe3/NcDjur/dN7g5LWGVI+6DJ7M6vmGi54xkbc4c7blf8oD0qAAAsSZI5G+6lYQasQx2fsde4Brt2avd7FJxaEGEpj0FRMxtucxkIGLRohaPZYC1zlcBWGpzX/vrFoQO61WQg/jHNh2CJHhrMV6D3IejTk2MLBCqdHy9DdP4EZko4gIKbbll3zx0MmEe/3R9NOJUHKPDZJw/7VpZDZ2UE6Vu1j9CfjvVvhQQy3y7vl8BgRD3X2P/IxhRC/uevQoEd6SMdv2RS+45+3iaEot+2w7vh4LA8KjCwMnby1y5qhQL3hWtwIJdf5UXbUj/f3uTZjZbAJO7vocEaNMwUD/MlCJDGyaQqgcUXRoODEDSSIKD+BkmlVQcCVUUIei7apHhIHmIzwx26t2xitZTs4EDrZ7M+dSDiwAIixcnEIYmMGaIsPmASs3epsWgqKOTCEkCJI3IUM8NaQg0MsyTCZZXVC2uh6qOwFm5OhWXr7T0WURHQSVCFSlUPThEoEKXCzAspF9zFIGGWC+Oi+RdWSC6GNiMTSLD19bXDHb2hW2MeaudbyezeTDrfQYGokdzq7yn4AlvlYgINcqjhdxT2fLmWSbSbqet+/axw8OwGdoDg2nUYPq/OX+R+05v+ZMBhNBgQMNAIhdiGL77PRX0K+ngEXiUYlx/69jdOSLw6EHXGb6sERqdCcSK8bZXBzeGMvULAg99ANXe5tlL6CcZ0SejYl+y0i04R6O4LOSUn1P4WMygo+/3r2LdaCyky53ITMFYYkERwEta9VxEqJADil6DbXU9oGzBuPYICv58fvz73ljnKo5vc+sfCnNLtl4/g4NGBwd6kb4SC56+e45s//jq+9cXv4LNvPe2kF2lUXKXPgi0cWONrF3d7k+3V3+SZpFW00Y6NtfbN0xPSRkCojZNIGm2lGYWDbD8YROD3KyAoc68aQA46y/ngk0HJfq6THTt/1lChIB3gfBhThqYOBMzfgQhMmrWxFBCtSv+EEJJEbwsRJYjnfwmSWyCDqv+BxYrPQeIMPGGRb9fMqiKQLv+aB4sxFeHa+vDCXMcXUR/qJygsHTBzwFrWFhRJASHFoAoFIxbXMSvABaB6QwedrdlD3PqrfkDfto29p8Dfn0HitE7HO0/pOTUoccqF7sP1GFfq5KvnVbROSt3CgTn52j5HkT9viUO/qyi4OgFboC829umzaW2uwqTuk1nA1IBgFrHwWkwCoFcIxngbRw6FJw8Gqg5YHIJE+74DUxgok9wG48qCm+ISTFYejL5M7t7tK0Xk2olTC7oIsepXoHA2g4JyrY91b0QVcP4GHhIUAMSE68aIYLOw7Qz9KNDebLyo+zgoKBAoGMcvG7s8HMzKNSgAHhEYADy96P0eUjwUmE+BXUN/gW1GXa+vu1mzrtdvG4lvdHoxhcADgaUatoZahoPan16YCDquGyBIZ9vDAUHTHut5Bu+IGEILMw7Ig+5sXZuEUCk5SbA/f68UsL9oNmtk7v0OoBXXG0B1YBDHRSoKDrTWAUHMDCbnWiegs36DBJ29lcJIpCpCaWvD73PBmhhrDsiJcV5Jw8uGDSQsEZtO24eYte1r7XRC7XxMSk4sioYBQpVyAyEYJGgnTXr8YDMSMhlXr7FdJwBhkril3Yz9j3qfme3rLNBLVQ68cjHCQ72tB3Cwkf0ng74b8GeBvcaOdPPc7RUb6SfFvmfPqu2+97wWBwNyr7mZBBQIVlUHLrnlM5hFLbRiK2BGEJC/tzAQQDilMF1dsMS23PC28MUAOIuPSMnuHmd4MKwQ4BSl+owDE18YvZY6AaiZZHPGCAO2H+t97iHRDf6jn4cDyaL3Uu4bd1CQwVf72U0fa/eGe0iIRGB9zgNRNeESAVx46rBovDAef9Mk/aPkxg5ggIJf/06XJXGEg7H4Ccbhw4DHBAY6+JB2HHs2448mUACgg4PNoW2cBeq1tGNPf8cDhDVMtJtsDofWUG3b2FAZbcY685yNOjAaIATIMSMIRWmBwMgklWwmBRKnLy7YOCLas2oTSSX7uhyTLSEUy5Kt/rQnxVQIBwcsURPlWEE6Hz9jo9Bo1wYFNysgDQhCQTIaxhCRHCRkFlt/L/ECpxhRGHhigKCmhlOQZWXe1FC4QUIJfZjlsdTOvRDWILaOQrJqonBBZsJKhKgrKVIOmpNBZnekUjAKq3OTgIItP5Xh064HTwf27opfsbN38uJwHAuVKkASa08ylyH9AIBhluhUCWA7mx9m+zbg+8He4Bo6+HpJ1f36rnPxrMycIjdgYbdzkJztGc36pbUwmHlQAfpshxaYyNIl70GBAUBEA4EakCj4hF99/IFFVSgzFZi/QVS/gr2wxZ3PwMREUJcbdgO9AwGgh4Hx4td773Qu3/CmfYdrE141Mkgw58+Zw2GIsKWJZkLIer8FFHooMHh4nX5Wutb2jNpS7aLbDBACa9h2uwTDiL1xmHfFmxB88VDwuWFSe1R65cLgbn//RwQGOqQ6OHBtEkCzyRgU7BUjLznsYFKovyfHH71aPRBcUwlK8T4G24YqKxQmd49l8F8z10HFGp8Qo2YXk/YqdaG2AkIGG02TbKfShIHWV48JQwwQ6vW2/a7Lx3U/O7h+vzk55q6FjzbkulRUOwTSDoLU2YiJEEIExwVLiCgg5AkkrCwxCQp7U4MsZRxNDYVp06EDDQTstVMQFDACq2NiZJS1eYqvBUghg2pHbXAAdQYTUIhBWzG1TtyzErC1oDcg2EG0oQPxbz14yDm1fbolscN3iSzK5qROOx1WN0Mr1hn3syIPAXX75llr57lh8wcCwmhn3oN0eybXLANMzYTIDFaHw4IGBLNER1ZMITAIsG0ANjDQInHOTQXmN5CsXQ0w0MUcWC/YrCYwf4BRCbCbhB70Di/wpj84GLkmjqO7mWYNCuyZ946gaKsQzIRQTQT1/RYKDO4AbPvaqoy2Z9IDAvRXAwMWFgYgBHggEN+vGheB2zPSXSX306MJwYpXuj/35v74tb3G/sWg4PgBeTRgIOFbAwAJV2lNUWbRvaPGZw+gwL5zUxmu7axDM3ukNdoRCkppHRDzFgjsGct64KgPT9aWGRmiALDav7pXIQIzJZASQueI6GMbQDzfmC2GvDUiDwgWatlVDrHvNOr1mUHNDCK4ewF8V+Knx/1MosKCBsfhECsoUIgIccESt5Dgl5IlisipxanvTQ3sZn0NEqyTB2h3OXzR3mjNhECMEBogFGi0Rpt1WAeeJZudqAa6DCrUK9zNYCZXRy7gLbOH4b11GB4cPHx4MPC/GQwrh9/crOBxZaauAW2Qr2O/e47GQbv7fDz+/k9vfnP21s8c/TNoM03xv+1hwJSEEQhEGfDtpZUx0ycAzV9AGxjYNRVEMwmgOiLGDQywDPyXy0FEwol/QPecHg0ik5s98/WYTGs7FWnw8Zg6nIYEcwLdxixwEFAV2l4tqJMw2w/b+wzs9LXMPSBY01coMEW2aD8JtCXjAmS2fas+31L2lO5Z6YG/bSM7SVbz4EEFHg0YWLAW9rMXndH7OAWfG9Z5XpNhrsmy/lgzKKgzD225RrLdZ9hCgXVGY6mNtrRBw0L51vkiY1c1sIfCVAOR4NAcVnQHDnpNfe6HUVYkW4qDHhiszKTGzumI9XMj84OAJ66Qei3L7zonNQs8E0RJMDskxbSBBLEJuxlf6E0NAgdqK95Aguv4ySkI7vxtBl47Cp1JekAIBF2+KJAAoIKCrZeWbQIMADqvgrFpxhva6qzPjh7D3DEMQKpV38ugcvXHzTfJmt0A77bPZlD2uXcC9bOomaJ2i4A1Plv+rWlYBgD2mw0K2nekHfTA2JQlpyqpd42HAMD5k4QGBnsJjWx1S2cqUMUpKUxsYEAjEfZBh3K3mqCuKBmWEU6LKXj1+asfdJ93s3+/HcP+k+90PiXepOCXjBooDGqBDXd7aoEVxtCmJv1tfZ/l2cqBEVnTtYTmP8BuMmasXKA+BexBoF/tBlyfhBIRXrx8tpnUzp6zMHyvwv2oFHK5ep8fERjoEKtr882c8MJFhLI4BQZ5QN8ZdRfW/31DZ9eqMUCBm9oYuVoj9fRq3zUo6E5rKEf1aVy6Vz95zYDK1FQfMiq6EsABgc9J3ikDHhbGg9dKu86BiyiD7GwWwCawTo3w56LxbS4E6VDlo9+RrH8Xj2aVGzVcLamJoYOEZBHqyCkIzdSwpCCOZIncqgaLjTDODq26VGfEZbhxtQ9ygAAGQiE3exRQQGGNVtlgAUCFAwBTt8NbILZ+f7IvDT3IHoSM371lMcBeGfsn3znPBuv2vX1ovul3Z9A9Of4IAQAqCAC9GuCBYFaSXii7zCMMSB6D/ZgDNvibqaAqBEHvVVl7GNDcBSir8yPQENYWxbJo7XeeM6no8JyFMEQ87Wf5PQzMgaGXpw5gggiY5BSo6gFE3jcz6bUmcK2FHPW3XFDhAJBJVwUCABbsvRBLtleydkYbOLilEAHPf7Gd1ALH45ZBgTk9thdufflfF1PCWAguoYSFibRPbBaAPVl1K59+ir6veTzbe/TvgUnkvZ0b9xBI8b/vzQmFqHqvMqA24uzCPBNsWRppA2/XbAADYK4M2HYjIEVpfUzQrXTQfbkUCQ3sMrHdnCdAM/WxRcvT7HdIFr62gYGFrqWYEOMCThErk2a8405FKEVWNVw0NkLnZOYgYTtrNMVgey/kD3tRSJCLi1DaTFJegYt+JQwDd9ggYP9jD20rcWf/ERiAOZi0393/4dmAbGUWUmsGAEAbsG8t1waMUeov7ndH2PP7elPSrKqmCAA9CIhKsIWBMSKhOQt6dWBRZSARQOozINEpJdQ2PBhwBrrsndsoltNig7MPRFVMYUw65luqYDdg7/kH3KIk1HYz7uuP1ceVsD5M7gs2ZoSHFqJ5W5Fgaq1d5yLOw0CbjMnUydWH95+po2JX5NlLCdP/x1/UhH43jlszX6JpNQ4eiscDBjoTlbXWhGevfoavaz7qz7/1tMo7ctEMAfcG3wMoIPfK7fObbJsPKJFIsgtO7qi3PQPozAhCjMfHttNu10PhwFLpsoV5juADhaBbm8zmJOb2ny1xKlmXImrAY5aASLy6Tsr2s0yDY/jdoR5V3vThdQ0KNPsdxSRpcVeFBAUDS3xzigs4nmoCpJmKsCrjiMMidcvSvONZKVxNDONs0krXafnBRff3sGDFoGEs4XVo8Uo58hO4+t2DL5fX6a3tu6//1YNjzg86ggKwvYfy/fb3eNoz3wEPA+ZbYqaCMebAkTpA+Qy4hFzwYMAZuJwFAtZLzdrJDrpvfo44gEsAJTTlwPraKANzN4tX82TnG+DNfXpF5IfaBetWLGzUBTVcdUtXh4yJN6gF5Gb3BOk3VyVMCmKePepzf9llOq5osYR+3/6SQQHUDMHTQX46ZhG61US+P3728vlh3R4PGLjy7BfP8d6PfhMfutTJBc2TtPWvEznVbQr7u/XbrbEZdDBXp0eg+ToYS9hnKpIJaQaZsUZ2joU7AwEFDN7rcrDg6k/UJ1Ky4ulWInb57dQAwYfyBDZPHbHrOifw0KUiNUgouvKgFM32uGoF3LG1wzKTQk0/nC81y1+1lQ5FzAsyuyGiCgSUJMkNpQVYTg0SQgKC7MPpDhTOVUUoKTpfBGoqQuljI6yJcFlZwzLvQAIDJQyzzSszzSFgnAZS2ev5/gpGzBvLL2Ow/jQQ8ldZbjk3X/d9/4E5DERgmuJ413dgVAfWe812eQHK2mDgcpbnJq/g9VIB4ejZqSdgg3qMoLiAi6SblvDnUb7fRSokJ+3Ham7ogw4NpgBgM+gDwO6S1m6/7bJWm/QB/ZPg+10LvU4k/Z6ZAFIMIPPtCnzY5wJ9vyv3ug0D3tHPfh/uM6tHxxiTn3vxSrMkfvlDvGM+ca5fPzJ3+DGrgwJHTs9efYSv/ek38TbenJ4r8AjB4NnL53jvx9/Ah1/+AO++3cIcB9VTCjdAUJ+wrowwMHZaz5W0ujWo1tiGYxkoMDcqsG3m0GUqO3EPB0ADBF/MUWwGBUFVu+gbn2tQs2Kdn52PAYJ8KbZDDF/n2Tvu1YLqAWuzlKoUoLmD6Nes80GIOvsxfwOBAl51tmOfeT+EcdbjkvEwBQGElEBxAS0ngYTlBCx3CKc7cE5AvjgV4a76IqSUJDlOoe2KhkBYOeLOwjAH6kwNGVSXtTVI0CVqYQsJRwPRL2MA3psdd/scQMZRSPtbjj2WI6XjKJ/O1oTysGP/Moo3DwD7MEAEt3qgNxWkGKo6YOGJu5UFZi5wvgOU7zt1gMqKcr4HLvfgy1lg4HJuz8x6QfUl6FYhaPEmA3tmYqqDiMBBAZF+Rwd8Skv15emgwL93joKdCcD/7pV7ObYqDwRHz4TNrn2/G0HI4LoioMz63TB3aN3rd2Wb/Zv3u7Om6O3/4xjzmz8Upfupmr8Lo9YZ8BNbd6x64u145P7ZPX/28jm+9ie/je/+2rfwf/neP9tWTMujAoNnrz6Sk/7it/WibqMhekAA9n0H+oAQUl68fI73f/Bet70CAhqJetXAGl/Q7UUbq+0bIJ9npbsUCAyRstPkoWme4mgqAVrjNCggtE7KPvd06+vcXQ/an3/Obajtath69vYg6AxFZxpU2qyBKAPFYjWKLZliks4rQzqZGuIg91BgM6HCsNj8PkY/mV0UQFgScA6SItfMCimB0gm03KGc7kRFSKf/mbt/67UtudIDsW9ExFz70PXgJ9vqKmb2TzCM7pbFW2a1bd2KRRb9YhSZZBUF6gKj3b48Cv3mdwMGhG7rRlSVySRLhoFmFVVUQ/KF5ySzSoL8A/xkiCdZEmD4rSEz914zYvhhXGJEzJhr75MU4OodwDlrr+uMOSPmGN/4xg0oGzhvoHRVlkHcDFu5w5aLKPrJzWD18QslSYdsVgFPXA2cng4SEm4HsH0S5bsaT6HJj8GT8/Pj7571mFgN2cfj59O0OWflbs+7u+X4u2M2yCcfK2DxScEAmeWfT9gB3AomvO/ugv0KtKv/zfuDgICHe/D1Xp7vwX2gAYbtKmCcDWzrsHuEdI4oGwgF0tcUIpTyNp20BPmyuhpYA3y9nsBcY8ACgVeWvo5zubIew+cng4yAm3I3ggOrGNvQ5S4A0GJCs9wF4GnFLlsfkbtzUOBKxwBwpnu+FnaeT9VZDgr0ar/66Ut87YffxPd+/Xfw7qc/e/IrMp4NMDB65Htf+l28+7Z1mWqwfPdZDOUTBUgnf796/Qrv/eA9vP+V9/GXv/+XD6EGBjjsR2sAB0hSqldkoaUtSe0BQ6dSjwDOYpTVBE0whadxY3pJXdBAJVkN/tMRmfzpmPGmm2Xtqh73uPGlz4FMWpV7qyCqYNr9uwxouVQGV1HipJTlXHaZlYngxmj7Dq7S9IcX0WiUCXR/BWVCKgWUhBZFLuDtAso/B11eKHsgDIL/XQugzAHXKzhvKHkTN0Oiwc2QiXszJ2UR2Js5SeGkCmAn6TWwN+kYOYMEQFwOBhKAWWHftq7+nQEHC7ILvxf3hQGA+XhvAgwaj+eSSK7R2EWQh/elGNDta/DU8djvrFwEADxewD4TwYCUH9ZKluoquMUOePMjGlMNJWZAAIDFD1C97+6C64MzBAIMPu4uhACan3R/UALlJvcHC6swSwsH2kPXywAKcvG/5+6TKoU9DkACA8d5rORItIpvyq6TcZC7IGUJ5FgWB8TcAQLwyWTvYIyBDnLXQEFWHTFb9NMh8K6CgkBsg9EDGZ+it/pzYW9f/vQV3vvhb4t+/PTnRtZoMZ4NMPjaH31LkNBbn+8vBnAA4AAQ7OIxjiI3XtiXr1/hvR98He9/5bt49+0v6Pvjr0SAYJ75phvQNikz4I59BipJBUMG+41gG3WaRJ8X9UdBqVPZ3AAIIkK15/E3gKP7wy8bMETBxs8ON/Yce7BCyARkku6BSFVoe72r4hoAQLoA7b45ZWn+TlRzQWi6VWtd6F3l+SqwLSUCpYSadgUIV6RSkPYHoFzA1wcBA2UDKTCgu0/J47YLQEgbqFyUTSjBzZAPbobYEjqyCMzsrgaLR5D0VPKYBLnGPS4BGGMTooBc0fpnym6+LMksKHsfPb++tVEJnyn/+HoEBE8BB0Ldsv9tv5eIvCbH6rxiVcB4LvPnxu8+Op3+2UkDRUbAfsuAAACPGbCaEzMYsKqkxg7E1sYHd8Equ2B/GOMHrg/g+58rILgX1sDcB/sD2nVH23e0vQoQUIbg7L5AS8AGoCZwkvuKyOgKyH1ncQHaEjtd7sCpZ/YgBPAipAVbCmFj9BRtrIEB0F90X7l+hvQ7K9k1j+6mleNFuUvQpm4qeynBaxDEhkr22hkGNzAg+6PLuBxk79zjxA2zKJvt9+Tk54sR3gvnNHzncb0lDwoK/vAb+N6Xfg/vvvU5VTLp5sV8NsDge7/+O660AQj6paQrH8rtkt3W8bLHERaJm4CCP/gG3v/ydwRpLaJ5KeTrGpKs0M1tG5MZlUTYDJsUGAplCNFwLlzjDfIYGOjzGW+qQcHEMw+AgDECgUNp2rMp2jFsTqQgKVEHCCWB6tWDjQZwwE2UMDDWas+aYjVJb67swo+tpVoYFXConUsGXyvaVpH2HansSFsB7w/iStiv4OsGUrDQAcIFaFcgbZrlcAeqOzgXbHlDyRmZSZkE9pbQNWkjp0RoTNiCq4FTHkBCSVZAR9gEu+4xd97XjHHIF3zz+AEKbougnN2tQQNI8AWfeE1r+hMV/S1wcNY1cHwOf30GAytlHd+38UnjEEamgPy37KNzAaoIBiihN8dyQIyhm6G5CxKdAIJVQOEJIOBdQMEACBQk113l1CqvMxMaEhIaUDXjoJ80ek2QDaSuN3O3MaUjKMgXBwUN1JtLNetXAC8HD+Cm7IjbayjS49H4R4AwyDN/XVses8paoh5wzbq/uX/H5S/+3cjfjEkW6144AIJVHRjTMfEkKe7oWYP5D+hDj/V6+fqDDgre/oIGdNEJOuvj2QCDCApsDE1cliWNToZFb8aL+tZIv5BFz1Ny4BEBgjTTQAgYIaDJJm20QLE+Oz5Fcr2evT5fgAHfePGmGi6KXxB/L6L4VYEmRqQCe2MZ+/zw88ONo1anzs0AQkmEnC8A7X5moATaAS56TtzA252YsK0BZROBacGEjUG5ATuE5rT18CpuYZ13gHJCrQxkAu0VueQAEArSJnneyEV9tC/keXAxCEAo/XN5A6XNAQJSQU2E3IQp2pnENRBYhEyEggxMIMFK7lr2g11bYxSQaJDxZ/f1reBBYAIYJoTNdeCfIQcfVsDJgETvDyF7IkOAQMqdXZgt/tVYgYFbZYL9M48ABKAr9NNjn4CGeNvFolJzFUrrbWFpwwYGoAphZgcSibsgzfEDDgiCy2ACBAeXwRlDcK2oe3VwLO6DUdZRNiCusUAWY5CFVaOyeQAi1OWGXGT/a3wOlztxGZQLoHvfgIIUDOuAILamfpLMADTQTpUtxj4DEoDXqwdGA8h+wIP0bG8HkE0aZ8BnIMHkNdHN+8jmB5zL4DOWYAAExpJMtH6vB6MPpmPkYp3Oy4f+7qufvlT3we8d9eMjv/NsgMGh/NoUeRsv9q2+7fa5l68/CD6Zz+KQPxaL+lDS32UHCJZ3QAEgpBQ2JPiwQWVjnwgtewyKdwUE0vSFUxlJR8v/rOkTeOwE2UGDX9Lws50SNL9rJYgVrUKRWW70nIoIdqIOCnaAc+s3kV33VoHLnd/zaROaNJWMtgMpNdQm7I2BgzZUn2lIKYG0MubeGnJrA4OQL1ViEDRwi+qdCO/rww2AcAXa5gxCzhtyygKAWNwMLXUWQTo19oIpF0gZZoYwB7EcNvNYnheYKwOuhJcs+KpYUPwd+S37Hb1E6sZAQijnS6fln51xSOyfBdbBZDYGBT4xAmdlguNr8tlO50dL3q/AyaZ/alEmy1mPZagjEMjQmAF0oW9gwACOsQNJ3QVZFR1a1diBJwICCzJ8uB9iCOrD9cgQBEDQJl9TMhBg1Rf1AqeSQSkhbcXTeOkiQMD2umXwGABwUKABuo2yBOIyBpfaqs0xMMsLecXkhQcOAtBe8ohNiSI4iD8yLHncY0BQ+ro/0WUwiA4uDmZz8Z7slbBnlq7TMKczQND10Q3GQA7SdYz9xBP119dMf5lR68WnHh/PBhg4Lb1s5nOCyOJFCp95+foDyW749d/Bu7/ymcP3AQS6JzAGevEfAwhAZw0SOkjwnw6Hict4ygaEG4Omz8a/ZybP4iEsQ4KDouAJFNzqBLkaVje8QoJ/GnVk3kjpdRBKysjFGIOQpaDU2UrEW42DfAcAD37NpH5jBSkCkSj/fjVba3q9G4iT1JJI0uq4VgZXRtoq8t1FOf0dvO0iHPcraFdWoe6S1VD2BUB4ACcBCCllFO3PYCxChT4GkGAtfQslb+nL6L0zgB6kOjd6OVyb24TBSdnfyFDE17s7I1Z2lOvdmwQB4+v9evdjreICZibgVpT/LQveXrdxCwAA58ZS/y37HQMfY4paBwryXgQDSedn7IBEv1dQrV6QyMDBAAh2YQEQsww09RD6N+qOev+Adq1ou9QjMJaA9+oMwQoUUNZ0xEzIJYNKQSoZacuy361a6OVO9ra50e4+JaAgMgX5AtaYG8nUQc/UUSDQ2YInyAqbJxsW6B0LM4vx5JLB9sFCAcffijLPtoYRs84da7yByQ6TewYWbNySx4fOo08BBLfKys/PGV3H6IEe1V8f/USyD770u2PMnX30CamizwYYmN+EIzsA3JaUc8g7NM/zj76F733x2/jVX/nMqC3j4Zr5aqo3K7IFJLoNEIzycotdmQMbs2A7YwHGzQd07mntLhHBFtpSo4OD+a5l/c9QfwPUCsBBcc39HaJgLTkpGBA/YSNGSxGli4TN+eJzZEqg/d5BAgHwqmsA8AKSgnh9ECGdxCXhQZ+JpY5wBnJOB0rVR5UCTFUZh/6pB6RSgdJArUmVOM3t5ioUMMqD+11HgKC9GdoOoiw1E1JBTkViEFjcKREksNL0B1YmEZj7Wvn8IsuzPrPjqU5m/ApwdICAoYvg3EBoZhhugYN5PBbQ5wo3AABrImUKGejWOzAq8Die4tIAJvE4KQ9CZ7/mYLOUjC3oYMDjCgBYj4JD74IzQBCDCReZBm2vDgp430/jalJAYe4+oATasgTjKijId5swBeXSmQIDBQoMHBTk0Y3A+YIK4FqPHUsdEExMGHAgcoeCQQ16DysokDRuRtaOsIfVvKWEF2tr8s+embvXxDDQ423cgMM50IxyeQYnp4BgAgO00C9kwM72s+kYm5ezBwv9ZSn7v/47mn3AAxLupahv3xvPCBiotzwq8DNEdjIs5fH7v/ZtvPsrn4VnNSxH082T+uISB5DAR4AAeByCIVnbh4vehEur3zecndfsp3qk9vkMVprOQygqWtLTzOztoc03Lml37DQhEKzRJuezk3wm+mEb9TiFxgBrfGjJhJIvMq/93s+VoXP1cq0J7V4yFqTNckKiB1AiFZwZtFewKl8pztLdCzdHa7D6SwBAjZE2vaHLRdKqSumxB/sV2K5aG6EDBE7iWpD6DZsDhJwykqZzzSCBk7gaxJ1DT47peNrOhpvc/eu9gtqwy09A36rtsAEG+GuYjnEckfofqfojAIjU/aqwzOhKOx7jqWMmV58SYGaBZAcwYOxAq4tmRvUICCIYmAoTxeBCbm1gCpbXNo9nYgwBpTQAgrRlcSGUzZW/uAzukO5eyPPLC3UfCJvARVwJXCQrQcqBTx1KtbZH4xA/s3CL2ZqjWetxAcCWxcEkp5jSuuEQkfjv3Y0D4KCEgSfIwtStfn0tG1ugYtxAwRvJ5sAIDIAggoHhJpnnOcYYuI6xeImTu95T9r/4bXF/u5ubjzcIpVuEwTMCBrDFbeMqntX2n8bLjz7E1370LXz/1/6hFn+Y6J759/z1EBTCUB92YBIMIAAdJKBvxnFt4rMIcKaNZq+doc/F6IWFAlhJWeh9PTRNh419zIF+wxsosKj6uT0tEWGH3GR7kviCnWov8AJRgI0EGXAyjEUSd7DZxiUQPcgNTISUM1ouSClr0FMBaz8Euj6Ayo5UrgNAmFO2eBKqNIezA2BunoTQrru6HwR8CXtQwa2KT7Y1iS/Yrx6kRWUXyyoVUQ65AE0AAWnxlxkkMEQYNlXYPd6DJlBw7vu8NQ5+/2D129N4nJaVreAOHJjXLNFZPMRqrPz28voEAKgr/tlih72mvzkH5dp4ImEwzs8eF7E80YfsbAEwgoFWxZKzv4e2x3tvaHS97z0MzFVg5Ytrb3oUQcEK3FJK4hoLdq0X90oCCiyWYAAEMZ5Ai33R5Q64vJCUxLyB852CAmEIuFw8nsCbjTXg2prGFxhYaF7M6zH5QKkbIyVJ8KKleDYIa2BuSD8/NckHUKCdI6MSfkweDgxCAAcue+L7w+7QYw4/OBlpUxXYkRl4qm6xw9rr5i6fFD2i/vo23v305/rsYnzCcJ6d11iNZwMMRGSSK71jpOe8CP35y599iK/+6K/j+7/29/Hupz+DgXkYgEX4u+1TEIhtqolJMBZBfmAACRFDn8ZIzGWG7Vx8/jNLsuD0YYJONjynDCJVCSmDQM4aNOieY8B6yJtysGEWwpiPb4fvxNyeSEEBqVUAyZdmAnJS9qAJUJAzwWZxB9sL3cQZSA8yb2MOzB9aCviqFQovV+DhXsvG7siTUE2xyMutWvHDpW8ieGtV9kJZjBYsAGZwlcAtC0DsAEFKw3ITQIA0/osggSkhDxXieo/5rsQDJfgIOlhZzcdboAONFj5zlonS2howOIGF0WUR2Y0huM8sf8gFXQGAs2Jdc3zNrMjf5PznEb+zookHitgU0QoM2D8O760AgYOAsXxxfz30DYkjiZrsBEEe3gPgxYvS1oMLveOoxRJoiXBnDS4vgLKh5c3jCJAvCsDvPJ7gqi6DCA4aWwfSYDRMbii/pMaEM0v31qyysgFb9isM3Z6Dm8nAQKfudS3aLoaTdWZ9RC72/dfhhciYAAhcbk9y4ReR1fb+NKcn6xY/Vn+/669vS6ChHpenmyB2vnzMuHhWwAAIGCgEbKz8ODbkon4L3/+1f9B9MkBY2Hnx+29RrI/gdRPQlf6CRQDUekcdFXck2k79TzMldWPzx+tA1uFM4VPriFmmmxVUqYDlsYOXAQW5WQ35y81+5n+W6TJ2AjJJcZ/KhIqkoIJxl7O4KNTiLGyKSWoCbOUFOGmFxJRBexaaPt2L0r1egOsDyCK4y8X9stivyGUHc0NWIWsllK040q1hKVzQc6MMYR+wu1vD2AOUTRVBA/a9AwS1zNJUV55CXXkrHkNIYMshJwO4SSxqQtgrTzWDb51fJz/tU4Y/BAzQlK46Pzd2o9e2sPfa0Ge2p5XFEX31NpsUfPZvEu19vCJPOe/HxiS425ESFj9wGwCCbObaAwy5DU2MDBBgep211Deq7FcDrqZMZR+2qebA0fPtaYda4piytCB3MK2uA2ssRnO1z1RGliAVjyu4MgamwP/pPSsdR/nQcfQgEyByLRMktVc/x7qott+Acd8MANDXP4CCVlUJ12Clj/JynMRoRbuyd0Aw+uI5gIRBVvuWWbC2K3ZgpVcWemlMtbcR5yTvv/zZnygoUKbbfotoBAfhfDn8Oxt/ZoEBEf0rAP81xKzemfk/vPX5DgzIFbI/LkdTUPA3BBT8ymfPF++WqRGtIt8EC5AQic8ZQS7mNvy2bXJ7bXAjHNFnn5AKFKKubFJW5iJ3C5gIibLWWLDoYJLPAX4No1itCMFqQQDEgjwpyfVntk0qQIJT0tOo2DSXS6oYMsAJzIzC1F0Llwzs98IeZK1GuD9IFcLtAuwv5PmiqxzZ4yx4uQWL7Hx9D6lv1hmSm3qtdrmGtiZEshbmYshFjm/dHdsOryVPSUCCAR8HcEZlpqNg8nUNY0UxP2YeA6d0qje7Sd26YGiM2wE0YHgOPBGSPKL8zYfvVqH5j60J0OFefdr5Hid0fi0P9O+scMw65apgQVkCZgkWjIDA9t/cLdRAAU+NwRD3npYkXkhr+wxlA5mBVdO+BrHLKGKX0XKR1uRJ4mKgmQYCEAQUNNDSdbA3FcyVJ6ageXbCUh4ASKzlr70+h/S0sdM7GCVqsCR9nmyfcOugQEFZXwdbr8fkI9DdBuT3nuyX5J/pjNTR9XgcnwwMLN8jCsX61C2g77/80z/u+uvTnzn/PQcHDqf+G88Y/MfM/P95ygd1jykeCO4EUh/ctBADKLjFFJxZ5MvAlsgAKOr3zyOAhBtjdfzhb3v+CLMBhAjUJErVrknK2vYY2vGwgnNyl4LUV2AvYqPwYjnchcA9Sh3QqOSqvwERCqz5mhXN2QOguxZE14prQdKegI21sUx5AcoayJWkuBCsD33R3PC7ERAcKNsmVeas/awrGes8B4xuBnstKo+hdjAcHIhDVdkDZoueGgHC9cHp3JTzAAKsl71bLBHpEw3C8pzKfIQ9msYMCpZWE/UOeXkADdndHQ5zLcQb6/0y0/M2BwcAau1F5X+ghlds2VMCS+28+pPhvZvX9OT+M/rawAKrohdlX0dA4I/12AI5AgLdM6AEKqIMSFJvwoUM6xQ6iQ7txlP2vUYahzO0IbeyxpS9gqEDgryBycp9B1DAa9fBfePTluNAiG+xP5KAA5DIj5USMpudAK+gGpsTyfWP+6UzB32d6tPlox013H9krG4EChPTezpWboKn3p8xLkCNjRkcvPzZH+Or/yTor9XPhLgJkzUjM3g+hT/rwODpw8zfOII7QZ4LTX4KCm7+/hOET1Qk+psxEfHsKEskObMCBgZW7MHyCHFjNzA1BQcqVAAFBxaFm5BSGViDBnhhEQpBk6QFeyyLIQTyAxgD3QahkIC9CXsgxQPkCEvXQpLTaSzpSpUIOWWUkgUgtAoOeeGoVxG+m4IH9etCYw6gwjpSuWB2Qe0WINCFtf392DDqN2UBHlmp5ZQ7QNh3z2jgXLrbIBex9tqOKJiWQuvw17SjnsJ0zcFIwARC0jAPYZv6nChlOGCIFpaBhvh7q8EMMEbL34KzDAB4IFlgx27dA4+dX5jT8brq06WbYQG+IzBg7mDAlPu8zyIg4CYsQQQFwxR1TlN2gYNRAwDAEQSQNgijJPtMgYHHFVi8iyp9aa+8uXsLeQOn3AFBSEO0yp2PuQ6O3UPH0zDjLY4MCUI07OOuAmcJqIOEyBZ4lofFcuwj0OJpD8X19BUPtWgOLIHtbaNLyWVg9PWfFRs6ZQeeCGJP9zEgoOBHN0DBrfvPf//223+WgQED+Kckq/H3mPnvxzeJ6G8C+JsAgP+2vNaUyT24E9BZgyFQw+gXBQxvNruR7hnfmxCffWxlR602zSAIgUEYToyBMSFGi8dBOYsXmHVTUxMhYPgFiiqZNOBFXArGGgjzxB4YllgCx7gSdmLxYzLp/cNiBSQAJ0LBwEFiaUFs4IDJIu8rCiVAI5VrktbTrQEtye9XImTKyDkj5QtY08HYGIDWU8NQjgFgYtkpo6CUr7VyNmtjKcSBx0FCqwIOagWybgMA3MTC9n2hbZ1BJErFQIJZf4AyCgOpGjfNuM7zHtB940FrjwkjA4vh+GTHV1eHB35Cz2UCDB7QFf2z64s0gN8DAGgVx/1eXQnb9z7RuaErX8rioz+6F9bXeXV8BwO2b3Qv+fwe209xjmdgwACAugoMBCBlUfhayhgGErQDogMBVfYW8CrdDzfZc3PToyb/dtYCRQ3eVtzKJVga4nUPLMITQAGAQ9EqSqSGRq8NkVN3GQxuBGULxIVgwYYTUxCZgwWAO2wN2wMWQIUgJ0ktE8snN5DgesUYphBnFscZKL/1uScodGEK/paAgrciKBj3zwzaoxvhhufUx59lYPB5Zv5TIvrvAvhnRPT/ZOZX9qYChb8PAPTLgs90KWXQMTvhxz/7EF9zUGD9qE1LKjhQuuZQKMloHH9ONpH17Fc09K3NcmAHgDNAEDc6z0pg/m1KHSAgCwAw3wAlQd1I7lJATt7foPd7kNxiJtasBULJBN7ZWQNWWvAWOADgJXcTa1dBuzwNwA7PWuCcQM18k8IalAbUJP0XcoNWm5MULKlcpkzCFCk+AAWjgGNwmNL9K2tPmIm9r4kJd+Ao4H2ZWcABqrAHJHElBhCIE6whFFkQIvRm1i52vE9752zf2Np7c68+N98TK/eIDTtGSirvVGGEuUTQQrMLJAKGpzIGPo/j/jZK3pWruXqMIl6d19k56Xn5oxXJIvKMnCET5cb1HlxN7t5An88jgEB+Y7ohhnKQ2r/AQIDOR5R/7td/ZgMUGMTgVgdvDgSyg4TYErnq6YhroPc1iL09rKgZGzMwZR2Y+2B2Jc5jVckysgUlU48zIXtd0hazuxJEtQ0uBN8XMyjoYJIjkBwWdZTRS0PK9jq3DgiIMDTmeyzu4FHwurhfpr3I7j64AQpW8Uj6XXcp6Gst/L0af2aBATP/qT7+v4novwTw5wG8uv0lAOQPHS2h4eVHfxyKP3zODoK+qG0NDqKFM4MDO8YnOsEF1fQmoGBlNU01aLm2PmdKutoGDkhBARQUKDXMbWANUhJEnZoCBYbcoU1aterBAE2m22tnDhJwEBSxwU1lCUDakwnN5EGNe5OyyQYQKliKoTRopTkCiKcytFIbIGeso5YtQEmfu+thAgpGCdOKFtbo51noy/Ipi7NgkFwGzQChApEijkDh5rCmUVFJxj0RlBcvOoIuh7sK0K3SlDyQbQkYsogQZxjM4l4p2bC3B3BrwGsFAMw/P5/Pk5mCCXQhMCPxmgNPvu7zNZc5TYBgOF+dkoJoZwFsrNiANwACHLJcOBlTEJ6HeBCvSljHRkfgXvo8ljKOgECAAATQtzZkI9lIWrBr6IlhQYcKCkpWMJCSt6i2AmgCFgxIKJgY2AK55t2FYKmhbQAF2PcOCB6Tj7C0ZNvHtnaWsG37ifz1riuo//5TYg/m8RgoUPC9BgXj54AACg5sQQAC/01lDIjolwAkZv6v9e+/BOB/+9TvCzDoPqNXsffBW5939D8sieWFUluDA6C/DvTHW9bKkybL46OdwVNAgVGZwKn1ioALsO8ODkRBAqDuRkBTejj3NDlmBQgkl0gawUCdhQmUmnY4zKDakLPVOVBhoELBopNXTaK4MWoioDXPa2bmASBY4ZudgNREzCUFH57zTuT160W4EDIVpFLcT+nCpF4fAQpSjEaUv0SXUwgic8sxd0Ww7NPRT1JYrFZFASizIcpIejfIxxJgURtz8aUJCM4gYFCYK192nF8MdrOhwUkxkI3PlJYqKQtMZFOqabo/5mugx+Yw7zMQcNMv/4RzAeAKMlriPJ2PMyVx3mfX/uz6A+egIHfA4bs/ApfFdY3Bgu4a0KJZp0BA4wTMNcA67Vo7I2B9OazjofVAaWoQGxiwQlaxuqlkIfS6JXM2j9/baXxuusoaS1m76qIgwUHBAQx0oJAjWxDjCrj58wgKHpWR4d5DSi4jnwQOdK0HcHBrzPfCU3RGiHk4goIjM3dIS5zjfkJq8lPGn0lgAOC/B+C/VFRdAHyPmf+rp3yxsVq2AIgSXr7+cW89OSGtWFrSL7GF6w+fs8+EBY3swq1x5nJwS56O790aK1osbvgVzR3AATFrvIX6hjUCursUJGAxpaJAgJCbtoeGwPbdwIEkIwOloTQgI0kJZGKUSXgYUIgjL+4nZolwto2+J+hvLkrkssK/xlr0RD63AgtHVuESqtZdB2bBIspR5DWKbMIMEkLAmV1/PgNp44nK2pvl3xezK6nQ42FY85X1PCvPIBBjiqb/3uRzpUBfA3BGwB55AguUN7fCB6U6W+HxlB+ztC2Fb3EeQyqffffsHMJcKAIEm58p4wB6hLnprILE7d44B1sHf2Nxfy+GAwEAQ5zABAYO7gHLHnB3gDIBMUbghA2YQQAHRsDc64+VwAbstaOsyjSXOyZ/Xc557HthLAElLZWe7d60qofdhZASuQHQAw4Dg9TMaFImgXlkClZuNrv+Dg762sqyPYE58gW9wZDdkv1nn9W/TaG//NN/LqDgixZoGBiA4TsTWKCQQWSAH3hSfAHwZxQYMPP/C8B//xN/H6LoX77+AO/9wW/h/d/4Dt556wvyOjfA4wpyF7CILIK+fgIQAIxMwlPGgV7urMSj8QrD76Q+/3nE70+KR05BqvihGBXQAKhLgUh99ASLOE+UURLgsKnplU3CAEhVZULhrMGIDaWRl0oujVE1krfo3G6pzArtGaG+SyICKnv51Csk4BFgv3UjYABE0OREoMoAARkGKroLIiUBDIUUKOQssVoWo2BsQrtKSmRrcm1SkWCj3doyW6e8ABLOhNEbjKU/9MwqfUyRqs/eijud/j6ucj2DYvciOUHJsgOF+3NWAeg+/Hn+8Rz+nc5fzmGevyl/sIIEFjDMKQG6xywOBMBQbvbNVw7dFTO8GIBAACjdTdNTCy1osFkqoWcPbAMrYNkDBgR2BQASE9BdAqbcHwMAwLGk9VmLb/tcrPERFUm/Nw0g9B4YAyBI1pky3J/UW1bn1DtUGkPQa0UYONA9w+IPGQJwz+7DW1b+UxlfoiMoOP3NRxgFYMhusJgAT0n84j/Au5/+whEQrI5LU3ZQYDmeA2PwC4+Xr1/h6z94D+9/5X28+/YXupUBuei9dSVUOQsYMAppBAgA0D8j30s4S1VZjtnf6MrdfiM8N9rZP6ccl303pVE4KjX9GPvArUn71Zk1YO5AQYW05EjD4w3MaUgMJBaym1iEB7URIJRkgocOdfTLI0LHhgiz/vruTMj4uSiGxSJRgJdE8RtoEEEkrMK5IMraKvkiIMHqJFjmQypybdJ1YBFiASVXcLNV/IuMpXC7oVTVZ39W7dEDVqfqj5QJgK49qlbSq0cla5Z40nugBiscGKjzw4iBeE8EA0+fO+BptVmYF2rsAEfAfEEszBGzSLp7MIDqW3EHT4hJmNkUjyEJBYhGdmDz940NmGsLNAZqvdHmmI9NjCw2ADha/k/gtwD0e22+5/x9/XvVDyMyfQYICMISZHVVFnUhZJLsJ4sroCGWIMgpBQMDW4An3G8BqC3Xqr/QrXdTsG6FE2bFfNAHnyTmQOf18mcf4qv/5G9qmePP6+8fjxmPcUgX1jnLVRrHB69f3ZzCswEGcQle/vQVfusP3sN3v/I+3nn7HXmRMgB2IMCTkpdiSHCQwLYBSfPuB5CAJ6HAYVDYhMyhWIWxGJEJWIGDDEYVAVc72aARfiM4GC7MDeF1yhoATBKYiJxQSLIUAAI1RiWh6auChEZw32UmQgVhY4DzaKEAcLBQ1LUQrZTYaCVjpjH7lAF4zMJqzC18o1/TrJYtd6CQA4VZFFCUlJFLRirsoICysQjSPInb9cAigNsIEoAOFIZr/zSwwPPaPsJAzI12omIdlOpCeLIrS1WuGp19pmQPVjggkv7WHOc4gScAAubH+1zIvZCArO/Xfj9FK565gR6LIp8Ux8qlsP7e9DkDAvZbFsRJaWQHNIVwyQ7kzasPPtbN0AoOxViAWIUQePp9AxxdAfLeyAIAOGXvVl0xO1iQ3xLGYHb3daYgxhV4IaMlW3B2Uo/IxWXWDSGm6Z6CgkD5P8oaPGmo++Bnf4Kv/khAwTtvf77HN0xgYAAiB6DQWQL7Z26ED16/wm//4Xt4G2+fzuTZAAMbH7x+hW8oKPjC2+/4RSGgFzGJIAEYgcIAEmhgEUCElx/9JPzGJxgzuAgggThQ/Pa3Ir4ODhpQFKzsVwBJihdxg1dNm0fc/H0ix3lFMNRqz+5I2d0KDQRtiuiYxErlZkiKYuHuyyxEsFr7WICFYnRmIhAkhVHSo4JFo1OLOdKNOZRbXV9ql8lQK8WUfyYUBQp36u/cckImRklQClME2pakHXTOF3DbQbVA2y6CWlEwUEEpxCJYqVtVeLRIJTxsi6X7QBgeW1vPikhZFGEGqNYe+Fe1xXSCxyd4v4cqTMAKOMRBtodSEmWc26BkZdWr9o5ACMmx/Qf//up89KDL+IengoLHOmTGFt3DeyHmYKgUeBZgKT/SPz+NJWCIWR32PIABB1XL2IHN2QGkIgq+9aZFuz/KPbGqPLhXdgZhb292j3j0v6YVVoxxQG/aGttIbNOlCQEMQCiDCAg8aNhAQWAK5B4KQOAABvpz0mDCwWhanXSQicLaiKwamYJ8DggGMLBQ0p9wfPVHfx3f++K3g1HbmYI1K+AH9r8iSxCv1AevX+Ebf/gevvPl9/Gf/f7fPp3D8wEG1EHBd37jfXz+rXcOlbZc0dkX7P8zNsFAgrII1u8aQBfGbzqCYCYtC2rUJfMJQGg4gAdGlc1cqysPbq0XSnFXSReShws2XBmotavPVOB3cACpGeCAgJzjKOj6QJApAarIGXYfj2BhxSrsjVFJrgs3sWprIqcLDBTs3MQIDwBBnq8th0QWwNQDn+Rfws8TsOWEu5ywFcJdzsiNkZNkQ1TzdRJhywU5FXBWgFB3IFuBJXEzSCyCrM0hJz+uyxTEdsCZE4jg1kAUFK4VaNI9ylZ+knoJ5s7JJKRNFWoVq5pVkC1FmAnLVXToU8cMDn4BdwppLw0vMT3v5TBfovR4MyEDBaUMgIDKpgcMtD9wVPbDscPCTWBiVfuhadaA1xmY3AUGCK61Vx6MfQoqS9XB6864r03BAbxHwR4AwZPuiWaAQOqFFCSg9b4GPTYgDe65mCk0swERBJC5DBQIdBcC9ZLH+rcDAgCYQUHsXukyNJ4bDc8dHBiwDusS10b2zQ2WwMDiDAgMDMx+/+k4n2R870u/6+6DQ8zA8PudFYhjBgSRKTD9+AUDHSfj2QCDn3y0Pmkvz63X1C6WkwX+3Gg0EwIcQEKSQMYffhPf+9Lv4S/9n369C4TFprs9sn/HrWJT+AoSWGsneF6uUcitDgCBLYBRUxkRA28GBNtvAGDaxDYMhKhrg1ktQwMH3MCJFdUn6X0AgJNWJtTfHRvrhOcTWIgR0pkIjQklCwWam5VZFkXAJL3egR5gZQBhryyvsQCMpsL0cNXNf2mgwNKkMnBJCR/nhLud8HFh3CmDcMnAnhibAoRmfRtSBAhXESihAhsr5cncQElzrGH0/FSF7ZYFH0BE7xkA8H5VCr/1ACt9zvsuSq3uKsD2zmJo7QluBGlo1W7GA5iSlS00KdoYawC4Vex/z8NesywYteac4WiKzxXI6IdByJ25AE7newoIjLYPgX4OCKbiQV5FUH6wW/tyAfqxVr5o+3OoWEkBDKj1OacZLhiCCAauCggetFnRfWNc94p7ff7QGvba7wVjFZ5yH6QmAKFkUkAAqTDq7gIc6w0kYdoI8MBBAwKPtcruDAKF7+hrgNwjPMo+BwUcitW5C3QcTLrmyIPBdCYLre4Gz0p/BQhSWYOBmxb8I+NEd7z79jun8QLAmglYPY/LP4OCZBf9ZDwbYPCNH4j74PNvKSiYrpIF1K9Gom4Z94+RggTGy49e4b0//IZkNyjoWNZcfzKF1OMbSDlh785nIAEMtopCrIrBAAKrUOfaGQSyuXQFFIfQZOgXIfqshg/KjSRFkAqACqrSEdEjtvUGso6NOfUAFwb1Dcki2o0xYEjaIqvrwCKomS2ACqCSUNQqkszJDOwVkHBJiPubA0BgXFVI1jYCBABorSGpIHeBSMClJI0zIFwy41Ia7veEu8q4ZsJWMl4waf8GKc/cUkJOUnNhS9r5MWWwNXdqZbRsWhV/PTe5GMY+5a0zAoeg1HH9DmWAuYlC8+pvUoCJyiaAIW+a9te7Ogqz0bw/wVOi/A/R/cAREMxgIFbvOxkWvyBgRt0d3ACSWAMyBqRKjoq05NV75AnzvNllUNMsnR2gUEAoAoHgc/bzBkaFP05CH/U+WPmlF1UIrVfBrQ6Gu94LH+8dENzvDTs3POyMB3UnXGvDw96evP+zgt8tC/tngECYBPHxlyT3gdUbuMtZ3Ae63Jl6KWNX8OoOmAGAXTIHC/5SAAMxZsBidWYjaRWbYwqbLc2ZgVJ6bJjtvbB+UfGeuQw4hddO+oPA1tvn8cThRt18HuhsdGAGZjAwswHH3+9/uvsggoJHxrMBBhZoKNZpf33YRjxgx/6ZiUawzcsAPnj9Ad77wdfx/le+h3cj/XJwJdwQhqtPOYMRFITeHCNIYKeM5Xl/D818xpF5mNiI1XDEOwkwn3AAB5QA0vKj4eYQ+rCDCwqUV443DzpgEKZAwEJr0iipMqmSl/4LyQBCzriiia9cqASJuiYtbKTgydwJtQlAaMy47m2ymOSaZL0jtky435MKRhGQdzXhkrUOfE140Rh7TbhmxouSsJWEyg0beufH0iybQQHCUAuh4dAG1tZlCQrCenEPEaPcJhA5Nu8BN9DWpFLjdhHGoCpwsOyEXdO8DFic1AU47pOu9A/MQPTRAz1jAQrmzsaUaunXxIPLZF5UIEDhTec4z29mB6zb4CIYcKjeGIR9G8B/lCAL+tgUi77GgS2YqxF6UGEbgwqvGmR43Rs+3pu7DD7ehSEwQHC/y+sGjFuTvwFMbEFzpmArqTMCiYXJQ3AtUHcVGCi4y0ljcDogKNTTfg0IRJfAqPwBBwAt7OUFAzA003IZ2HAABcaYUQjQjgHlAA6lUw5yD32tbd8M7gJzLYxgINYHOOwDnMj7edD8yfheHn4nPrb5xcWId4oFGn7nyx0UPAW+PBtg8K6CgjgGsatvrlJzHOgpcDB24ScfvXIm4gvT7/Pi8j5G7RyOq/+LkhU0zhOC7iBB0wy9wlcDpXDDRCYhHn2JsKcNPYAC7heEJWyQ7eeI4TGbftECjTr720xRhBsqpwyA0FIHCnt3faMqQKAmiJqIcFVwUFHBnAQMUFMfaZ96BAUiKNldEDYKAddKSNQEGJSELcvnX2wZe0vYs+yBS1M/L4AXkO6PzJKO2VjOoQIo6mIwgOAR08ociJXcFXwXgkEwxhHW7AgkZI0pW1CjAoXQ84EUFCw7SRpQAI4ZAvM4YwNisJ7thRiwF787nZffauYCYdbQnqmuAdDBzJvMbxU3YMxArBmQkgecQanjtgLKUdGvzisqm/h+tDItnoBSBwTcGxbNgYWRJfh4l3iCBwMGVf59fK0OCK4KEJoFHS72fGMBwNe9YSudlk6q7BNZSq8UHNpKdpeauN2gGTuEnOABgyV1tkDtZ1h58SFjICp+YNrzi/sg3iPD6+FvBwe5X/+nyrvADBxiByg/AgbOLfizccAoy1f7by2ZASPb4mdvHPgnHwko+L0vH2MKRtB2HM8GGJyd5Lin1lfRXiYSQU8EfPjTTr987q13hsUCJibCXhuRw/je4vMuYgamghwk+A9ZtDs3gHO3+laoGphuyMe2LCbBZ6BAfcHMko4HwGHVI5QZYS0oKQjKrH67nDJyUkFJhD2x+j6B3FgqL6Ys0VhFkhgbxN/fOAnLQN3qAbq1tLPGMoSF2aGWTSIHEFtOuNsyamPULWvMgv4zcdcSuEigYgVhs1gJwWwCVtx/W+RbWi75TEieCjwAB2EZ2KCu5GXtmSsobw4UhlLOChZWPR88ZmFVxnfwqYcaBZNPXt7vinnYGzcKHBkwMfeAuzmmLI6hiuRifktwEl0E0ZVASVIEByAQ6OJYoClalQsQPJ/rzZK0BobRqxNWTSOMqYfXJimJxg7cN2EFHvYOCu73Dgrur9UBwUPlw14HZK/viXCBHDNlGu4XKfSVPO5mK4QXCgpebBmZgC2NmToGDjJBS6TXKSbgESB8a5zJK/fH02i8HNZkwVb52oyAbw4k5MAazGs3K+vZcr8p330e4b0bItR+a3WMLtZvy/WffPQBvvnD9/C7XxpBwZHJWY9nAwzEhpcFTDQq6XgRb15O/dwHr+Wi/t6XBRSAj+6GFaVzhuTOFtE+b5Z2M9mrxzGgQMmWKYCEFEGBFSwZKbelRfqUm/PsswfL9vx3u8tksryUBeBUQEqv5pSR8oaSM7K2Vk4kTZOIhFr91KaFaILPsKHiwkmVeELjhpzEOmpTAZxYUKlWRm6E1giXEHnf+HAr99NT33hlxh0noCSNlRCmoii4KQRtDa3BWKkc/al++Y774iA8IxgY6NbAHPHYfAhZ9sgBKGh9BYqfBbrvfiiaFYGBWebBF2+foQAOJp/88jpOMRNk5+gA4WRuJ/Nbzs36DUxAwH3J4bNLS1F+OCiUcV8sC5sdWIUYdwNP65WqnrJndsuyad11cK+ugY/3iofGS1Bwv3eWYK/NQUGd9lOmUfwLqyWPEmNDuGQNwC0JdyXhRcl4UQgvsoKC3IMON/1+IUgq4W6lxMVtNsQBnLrM4jWLDNOkqm4ZHytD5pHfHQyVGzEDKzDgQIC7zH6Kgm7osh2YlHH82nQKw5Y/Od4tPfbhR6/wzR9+Hb/7pe8OoMDAiMuiG7/yfICBWrgGDpYf0cdbqvGPP3qFv6YX9fNvvSMyKi6cobcTHTkv3FNoH5uxx2VBNlSjR0BC9L2t4hLiDE5u0qMiWij7yD5EV8WK7rslBAJKJ43QJvW5WordZgCBCFeyQijAtTJeeNmjqHgqGlK3wlpGbVUtIcbDNI2mqViV5S7fE3nOf07iZsjJfLJiiQENqWTcW5yFXVNt9lSSrHeGsAdEsmcku5BC8JWUmfZL8hhsR5cZwTZ195IVemFfcwtI3N1vL+2mdV94rMPYkEuo/PW6zfn8q6j7WfjerAha+p4RUMA+J3l9LGv71LnN8QHu27cIcwUDHl0e6WIHCJ0qhs/oEWNi8Zkx+Fb2HKMDg9p62eJYk8BAwb0GHj7sovQtBdHiCSSWoGntAgygwPb3cMkpsAM5e1zNpSRcjCGYQEFJAgouARSU1AGBsFD7OqZmFRMwLpw+zgWCFkBsBc6mG+e04uDMKjwaL3AOBljXbpbrpzKd+gfckcH2Xmem5TPjd1c6I+qVN9Ffh3nZr3C7ubGfETCQS9bTDTGc+FMW9MOPXuFb//jr+J0vfVfcB/6howRfMQKrYzwF4c10FCldRrRO8YHf6Me4BPm9I3V99O01PXQeP7fy9/nJqAA/3PyjopGfOm5f0gCxmNfNKYO06hvyBsoXAQjlTpRqlep7GYx7En/iERzcGk3AQRNr34SmWVOtsVdxqQ1IyQIZW/e/toQHLR6U0UBJah0ATdBEE0qVSeRjUmCQSCpFAn0d/VpEGvqG1WGfcyZfXU0e4JV1q4eULvPX0ySsu/tBwcW2YJcWIwpvjsI7+t8PwngU2MPvm4Adgs9kLgwc5nWGnw7zinMa2IAcmihFEJCPiqC9mVV4to5RgLMyBC08WhZOLFQkAYiSdvjgrzXsCgR6GmI/vrkOMtFhf6ckjFjR4MG7LePFlvDCHhUMDKCgZGwJuLNYg0TYsvYc2e8FENQHzcJpkEJfYY/NdTvitVoAOfJ1a329WAH4CYuwdvMAEQDI50bXgQOB8NzAQMM5EFhR+I+CRe73O6vsthmyutLEI/LmuuVsO374s1f41g+7/hpGZAvmuI3FeDbAgFhrDqAvSMIYbHjrhv/wo1f463/0DXz717+Lz+pFZZjQPV7EW2DA3vf3JtPjrCypty1VaJmoswcElvLDWLEJklpJFPHQCViwSQ7BQA5l4UWWvEdD6983UBAsT6OpWywDbMecKvwxoII5UL7aUQ4pa1rdFSh3QKvI5Q4pF2Qi3BOAmpDUes9oS2gQjaWcgGsipCSWV1rk9li8QRxDVksTJS9ta8XCo9qQrUAQMZBILi+xdPZM8huR7amY4SUv/lqN/q7b6XTMC8+KEMzdP4AFZRAO/t8Qt3Cq/FZUrL8e2YLblt3w+8tAtCPrRAYUnjK3mSJeMQQYFUBtR+H/ZMFvUzj55HDvM4b6HYeeBvbPShpzr80x0MrToVIiZxmBID/0vUsmz7wpWcDAf+uScSkjKPilkvFCXQlbgn/nzliCtisouIL2e3VHiRsB2itEWmaPbqBxsnL9HbTN7h4tv80JHRy4kTeyjb7O+ntn7p9bzYQGMHgDDPQ6LOPCPi7D9TkpEOAjUxBBQhzzvbgCBfNn/vhnH+Bb//jr+PaXuv7qcxqmJH8Hlm41ng0wAKAQTGINLM7AYlWOHz2Cgn/4xe8oUwBXvAYODt8Pf88LthIK8Ttncs4+Z8cT5UIDUBBFgEfYBPljBAsRIOhxOGY99PoJIFF0ywZODjC49wcwQDB1GfQgtzgsetxSxcomAGG7gLYL0KQPAeUruO3gcoet3AGkddQpgSqDkBxtZ8h1Sqhe2nXLDR9fCWlv+lxYAKBbWQYI5P0k0dbU67fLZ8bpV4a3lCan8yV4y9exCX3Y9NpXfnwPvYl16o2C0PeH1Z4nQigvq2ChFAxiMbgieLYeTgIQY+DWUeDKbIbzWc1/+vtsT9prKzBxNq8VJbwCAVJuW/+elDbwpuuwPrf5GtjxTE4MoIAbqs+LMYXGyP5r476U4qYJ19pwyQAyHfb0lpOnJ94VYQnuSsIld1BgDMGntoy7TBpzoO6DTNhIWYL9HrQ/CCCo90DbgesD+Pqg2S/agbRq+c3FPX/oKtm0lLdV6CzFZdDYAyCAgiR3+qlLIOwHW5UIAoBPBgRW++NssN2b/gVymdxBgPwfdUx/dfo9mzfO92Y0amf3gYMQivf/47D3+QADi6KPw/WcoIPgHfYxg4LlT2O9aMAaFIypJb0ssH3Gvnd+Lh3lNcISKMxsgn3nABT0OiQifU+UhQvmqCScEiQnCyQ/WG+2ASCYr7t1UGBAoYVeATFXHV2oshabYSLQdgdc74HtTgHCDm6X3lGtVWzlDrkUUCUQmoOjnIqWZ61yzmTd20iDrCSq+1oZW60DDWtD0q+SClMJxLK874QRKFisohTeobDuvR8EsckD9n3g6xmXmafH+TPz/WsAsS8riILLyZTHDbBAREjo7AKF3zobk7HU97cquJXAehQYEEHAK2Q++gFnm99wXm2azy0Q0CZFzfyUay8PCo0CezM+9s8c19qOY7aasxMs+wmQ/VXZgAAjMTkYuBSRYDkVXKsE2p7tZ4slsEJeBggu2YIME35py3ixZWUJBBBctOJnajvoGkHBA6g+dEBwvZf7/XoPMGtRrfW9DutsGdNF/WIpQ8l8LCusN7mDgkfiQuJ+OMrhIxDA/NoJUHySzIYxAHLeDJlag6yhpcAnkg9Jw7oODmzecTyuvtf6K+oBuZaLLz4ShP58gAHMAlZLWV+b3QlxPAUU3Brjjc+HhVz1CgCOCuHst8k30REoOEhQZ9ZjQMEorMgqUGAVWFs2dtoZHRxQ0HZ2DrV2diCCgrqD61VS46a2uoBudpX+bEVn9gdJt9segO0iAOHyAti0iqBFsG93uMsXKcncJO6ASOJKKAElJ5RcUXYBBw9VYgPuihWBSV4dMQ6rBOdFYDIdysCawqUkylUep0ULN7qSV4e9slJAjJ5iOXaiXO8Nw7/HxjUME5FEmIBBB5QRPAJrqjFMb5z/BIKBp9OsciwTVgpyguw6xlO82bxmAW9VNQ0YRMUcr/snueaA7Bkyq4HXgGA53+nyiG4j6WaZoOm6HFio5O3CE0ma7ZYJtaXTvewuhJRU2dMx8yDEE2yluw5SfRBQUO+B670Cgnvww8fyqOBguM+dLehn73Ed1pabmxgDdt6AKng1IEpY8cgEhGqRK/fQDASWxprNabFPZF1GWW1r9RRZbWvobgIOnUgMHIRdzPqDno2GYMS9wXiK/hqMCABD2vON8ayAAQCXyLM7ASBfXSLgw48+OFzUKJhuCSUbKwZiRp0zjWhTPCiI1e/rh4g7UIhKv5FtxLXbwX7bXQ/+O8eMBw9kpNBZ0cABoGVr5UYlb/8cWgtbhLwJi/0KsNKLBg4ArWEefIdaw55zAdU7iXje1T1Rd9CdNiYq0l0ttYptewGQVllrCbkyMjIKsVRqSwkfJ0v3Iila1Cw9rPtvh2tNZqV1xqFkLfqSx66MWd0a3n9+Qgh2o/P0mlyA/npUSty0yySvW1HPw4+dHul0N4GFyC6YmwkY/dPjufTj234GbgPdWzLUSz5PlrbNyV6Ls3lsbqv7bLDO+fa17q29z691bzHMfq13Hq+1x5FMwKbPd7oWymRm0jLhdjEKQDUhJUaqjJQaUjWQ+vQ9bP8uJeGSFAhkzUIIrgMJMFTXwfVjUH0AXT8GdgEFfP8x8PAx+OEefL0H7w/gh3s1Ah6/v6WEd6jmSYEaalVjjVTJhziCuYS0l5VexIvccg3YHrnlHpj38ymLtxhuCMS1JiCxosbIHKCD4fh94Khr7Hm/bp2R+Mnrl4P+ikD/APz9fgon9sh4PsCAdTuQYzW/SZNacqwK9ZVmH3z714/RmytQsIo8Vn38iaZ5y3qIwzejLbDeBAQ13k/YBCAyCsDK9XAACW5hZuEi7To2+b4gftabNoHBoKRpYWYt2KazCnvOIFS0PealX+W31d+YNq1KV3fwtkv1vrojqUsifeqX4CV8N/ltcy2kqpSrVkIsuUh1t0yS+lUarjt797nedQ5eT74XfIF3YbTmMpmkJoF1Y3ysw9xh7XzhjxbqSkHJcw7Px70DdGUK6Jq50gJia1yb5wEs2J4YtteosKP8OAjHIDQj0wHALe9bw/i8wfK2X1yyBU+c241rvPPx+gJvdo0zgF2v756kUudOvAQJBIxpznEofs+JgJa8RgYRQEzS3ROSspiJUFiqgY6dRPW8wx6O+9f2qzABGkxolT4TsKnbQB4n14GyBVSvaD//t8D1Hu3+YwEF5kbQe7tdJa4oZiFQkqubSnaF7/09UMYF1PiDHjOg7oGhlHTvK2Gl1S2A8wwMOBCw63TDPXDTYHuCkB9iBXjcO08d8SuzviGMMv4nr3tMway//L4Jhp/rQv/RKa5oMZ4PMIiroRGthGA5B6T1rR9K9KbVKYhf70FaowUTDjT8ZZa7MRLxeLdG/Myje0/lZQuHX7EJK6BgX5kZBaeZFSSwmmkmWMiiiNFBkNdHSA1UFzEdQO/wqG4GAwXcGrhKXAKrJLY2uW3fJUBu04Y/+xXYr2h1B112tFZBd58Cbb33gLkWXuSLKu+E0lj+kQjAvTJe1Ib7MvarZ17T3Ql9vUvuPeJLTm6Vy+vr3vOr8VSFtbc2gAGbo7Wa9t+bwIz8jR5IqX77qMwM0Ng5yPrTQI/b8BiQIDhmmt0s65ndiO/dGpHtkOOP85L33nxu87xuXVcAT762NUknwp2ATIw9EXKDNBYTpzFyI9TEyCx+/+hmWO4NlRMlERjC7BRmFDD2CpSUpdtilSDMWuB7Fzjfv7ZHi+7buazxZgyB/4MyBPeg/WNgf5DH6z3a/c/B9x+DH/Tf9QGsQYjtuqPt+8k9zaBMaLsxQUksjAyRD3mbroVyWqEltfSTKAdQ0ABP82wDQxQAXmAEIsMl1+2c4VoB38cG2fFuyID44dmV92R9o5P9YzNqv/RdfO7T7wyfX8UWdLYA/QY+HuQwng8w0CEpTgnmTjBwAAAvX/eKUIa05gWNiwYsFtuueTBfIjiwDZhAA0p9yli1SbVhDYCWIAHB0lkBBRMmET0yDUxCSsaokDdSSal0cGCAgBvAGUhSr5/NEth3OWgODAJEIBsoaHuvfc+tgXdhDZAS2l6RDCBchJq0FLtkCLdJ+2JmKzNcgVKxlQtyychNrS3S1MIEXHLGC8aQCmbKGOjK5ExZnTEDYwDgcb1sPdzlMy3tChRE0OLtpF2JYaKOTYnpcZIdj4L1SKos6gAUgM4sxHNeDQ4HNWsbmN0d4zkDt4GXHF8eZ8bjTeYW5zfP7QxgzdcUmK8rALBb32Agtc4gMZMA5ExAayhIUiQLjBLPT/8bBf98PeD+8ZwJDSQlHHIHkXuiziqV4znLNezXKrJZRRsf5aTNvogCMAASV9D1wTMPDq6D+58LODCW4PqA9vCAtu8C9qvck17WOiUxRhNrQGkDt8kRlLXqJIkL0d0IDgpSdxc4KCho6CxBa8L4WOGoM/ctcBsI2Pvxxfjek+TxE4YYkCPAjaDgLLbGvmzM7qufSvGi3wnFi2J8wgA4IgixM2N+UnwB8AyBgQ/1iRtJGRtKfD4UL4q58AdAcGPtszEDxhSgK4CRpZDPpEBf9gPiabAUfZNai2OgK5sIEmzuN4ECcAhiZIiPMzd29oCTRP2vmAMGA62Csrb3LfJoEcNICahdsFs5XxMiXtO9VaTUgJZQK4sF0hqyFujhVtH076RV/tIdu2uB2q7H35DKnTd6ic1pGgM1J23zjMGCX41ObWMQ7kTHm3eOL3CBRLoG3GNRciLsJ6Waua1BwW4EzEKhAQEQKD0kAoH9vRVQkHmzZ1gAawU8goK+p1YWt7f5fcKGtjmMLhwK7/XgyU86vxkIRBAwz3V5PZm0gydQsmyYXdt1JCY5tirtMgmK7Ocy7ptH94w/BwoJk7ABg9J6dM/S2BY5kXUAlftiKFa0XyXAUB+pXtHufw7c/1xcBw8/l1gCZQrq/QPatTrA59aG3gwJASBwAyH3dfMCG/JovSwoFzAl7zgpQGAD5+KgoGJkCZwtaEcw8GQgML3xVECwHAs9EeN47DOdLRhBQaT9V7+XAHzw01depv8x/ZWwAgVPAwQ2nhcwML+4sQaQiPVXr3uXxM9/+h1HYNHCmQHBQMEsjxUpe/LcdST9W+/SxuzmA4U7PAYumsI1gXJrY9bG/rn4qUME/Ly5AlCY4xM4wSv2MY3sARbgAFmVfBYLnvgCblXrEFjls95Gl1IFEYNRQSmhDvEGQr1SkiCrvn0fkKoyAw4m5Ddba5rWWMF8p4V7pChSyRty3lAhvRCsolysPNcFySIWAEcBTmEvzAVMfJhSIgNPAIOlOJJeWwmEJcSNlxXE+Nq12Zc8K7Zxb/SUcWsmxR0QaF2KZK2qGw3K+IqukBdms88nvn1mccd5PZ6VoMBFFRdwZDxsjrA53rgnzuY4++JvXUf/rSrzamAJ/tN9JP42XcsAUuZ+BOKe6/JD1qIL7cF6jOgT6HvI3CNYUOF53niL36eeYmtdEVNSQFCvQL2GYkX6eL1Huz6A738u//YH8Mc/90DDtlcHBbzvYq0PcQUJrTFysS6cEj9EWZQ/tN01ac0Sq12C0gGANLw6goK9jSxBDfdxa28Q2M03nwJ4HBREpiCuxAwCV+sh+4H8s2O59L7f5yV+9foVfksb+n3hrcf116HtdUhJlw+nR4HC8wEGK383M15+9BLv/eDreD+2TlZLbljZsDjDRT0ZWRVAgyrdpAvFfAoOMqgHZ3EHIRbfZ1N7E5rKT3V6PlPXA1jQYxtIMFDDCQCP7IEhoAgOgF7n3i9j07LLTWn/rOmM3JCKxgSwFE5KiQZLAxCQ0JCQ9n24oRPu5Y+PIYGHeixLhSQw0LSLYL6KQMkbilofpWT1S1KvVY+jwF2NoQFKWJK406KVZ49sUyRlkFKn24sGnEmPadYwzHFYSto83NI9U2qs4KBNio3VAibZ+CnBrWG03p1ydSw5xw4EbgOE2/Ozc+uPPDAe/poCGRg4bTx899Y8z5iBOMfH5ufX0Y9hVtj6vixkfv3UU1upx/EkUsBjAvsGdewsJEbXzFMLLvUA1ABKYsMjBQWxWBHfaxqiBRiq++CpoMCvncotygTKhFRycBcUTUm+k1bY2x2wXcD5AqRNM5PWoKC2kSWwPhM9vqDL4n7BjtfpKTzAU2RvxHOzS9Hcj7dAQZpYgtG6H/fDy9ev8PUfvHfUXycTO+ovHjeSf/Z2RMSzAQZMafCfEDf8+KMP8d4ffgPvf+V9vGsXFYrMpvWn8Ngv6DmqMv8gkVng5u9RH6QBBVWdERyYJWmbegAI9pU3OfcnvmYvEjpQSDwCBCZ422PTHcyEloCSxu1CgPQx5oZ0uQvzV0bBUGrp5YsbIKWN9x29pfF0nVtD62EKyHkfz4dlbajuQlmWC6hUcNsko0EBAu1XcEpIeQNTwpZ7VLNbFX6S07V6KjZzK6+vZ0VnCFoDKmGweA0cMDUBY8ofea37ZqlNqqD4aRS9DVNszVGrzdPAABQ49M8vf2fywT+maCPWW1leOXXA4+4OLeCzAjLD59JtwLGa6xlweeoY3DDo7hiinqVivvsVKEhJAlVT6sqgF5oKCuBJe41O96jpsq4QWEFAE0ZAe2YYIEBTcLBfJZjQgMD14RhkGEABFmDA+h+kRMoICCDIlw1UNi95TpcXwvJd7oDLC6TLnboPNqBsCgou8pgKdhYm0bpQOjDgnskzAIK4zd9gvLEJNoG5CAhuMTeHtT9T4gCskNzL16/w3h98A+//xnfw7ttf8E8YoOTF5I+gQKHmpMs4Nq1ajGcDDIARHLx8/QHe++Fv4/0vfwfvfvpzcmNQ774YN9CIE/hIvUQKxj9WdVPkYbEAoIKQmVFVoJDZ1Qw0Uh9+E6AQAULGKL+BNdU9j5kdmG+OWUjHQMYjQOj1/o09yBaUYODATzYSw0C6BHADBU8WaJQ0sj8lETQAsvkpT+J5mRu4Edp17+lA9p66Fag1YLsC2x3SpYHpKgBhfxBLJWVwvYpVFSun2SPsJpkPvpySDDt/UhMQsQSv1K9nliWuem13yPW1ro5SvKY7lCpL3QRBFfKdkqRsLiCFZ4T56NOYFd2ZRXtaB6B1Cn9+vR+jH2tkDkZ2wD4377Vofcc4GXXRj+dglD0gbAGFz+mcHptvHBYAfHh9wchEJmOOzbC6FslAQJZgVG9HnDsoyJNvP5n1PimELpbtfE3WvIFqY4wyygAzM2KnTS9ApOCAuKE93I/Fiq4SazBnHnBrHh8UxwAINIA4lYy0ZUlTvNwJYN8uHRTcfaqDgryB852CgjthCsoFTNlBgcUJRZbgrFCVjVNZZ/M+nMjjl3necm5EBkDg1wJ6v9FcYKwzOf6e/ZZVlNW1p1YFFPzwm/jel34P7376c1KVlsh1mJ76iR7rOgxYg4KXrz+4ec7PBxgE3+3L1x/gaz/8bXzvS7+Ld9/6HBBiDiQgEaBhR0xgIOZ5Dhe1ujKheoUU/GFQypAqgiLbwKIgSC29pkBAmAUBCJQw+KKNMQBG2ZCm53H4y+G7AAa3xGqsbp4IEITF6OxBYwEKzAASIaeMVC6uIOW0pHRpLwcaFHDOwIN1VnwApR2UyVOdSKObzwZr2iNRkloHdkyguxTqjlZ3pS2vUnu9SmSzlIEW4UWx9jrg++Gx9B0Z9h29/cI5ik81I1MW9gXivkgM7LK8EkshkkCLR0VwkIb89QTCbrEVYLRGaExoqRdiaYtE+ag4V0F+82eWZ5mCIqau9KOiPXN3HH7rSdd1/NzQDCh8/Wzew+u+jTr4TUkZEn1NYj7Gea2zOuR5ZwdGpiCCAqPui7IEUmbbCmIFWh8QBW2NyKIyB8LN/gSTYJJRQ8dTKzrkx6re24TrPpQ0Zk0Rtt4HlmZ81iExd03oKcfCFEjKMZULoOyAgIM70N0LqWh69wItX4B8UffBHVgZg0ZZAEGzTpM8sAStHQHBUwMF6fDHqPDPdum8fWf3zyF2JDABB9fBChS2etA5L//V/x1f+6Nv4Xtf/Dbe/ZW/IJ9BdWOGo8yZ9RjwqGH78vUHeO8Pv4G38fbp9XpGwEAE7I9ff4j3HBR8vt80gIADrsfVngGBo+75Jk14+TNFWq0CJCV5wU0VUILlEoeJDa4FwggQmrTdcpBgkx18ijTOIky7fySAAa9foK/lRMMNNPsr98qejmcAgdvEHlQFBwAaCCVl5JKC9f0goMA2q0Uhh2hk1ijklB+QpuIoBhKGKzdb8tr0h7iJEBucsCIMqUj/BSiNSda9DV2JC4CJZKD9eaJ54oLOjEPs9ubnXAQ8WaBRI88MSQYSwJrnLkDrvlZQhYDM2kCJvfpda+wdHg1wNMYYkjyNFQgYFe65wm7MrkzlhW69R0ULjQsQVkC/l+lWnGCf1xBr0JWwvbYKSnzqvGXK/WZquql9XifXLYKBmMkRq16WnDym4C5LtdCiFmBOoWqmfrc4IFAloBY8tSYX1uVNUOpjcMF6ssO90hXC3A7dup+a8gezND3S5kdcd7mXrFKpycEwiDQmJufhNcp0KFJG5SL33qaAQJkC0lLnXO6AvGks0EVYAg0WtniCvUlr6cgSND4CglXcRYwLmrOLgKiUj7f1sBcWl3yOOSL/7AgIHmcJdM1PwOHX/uhb+P6v/X0FBbpPyBpHqc5Ro3SlywB0fTaN6F7/2//oby/OUsbzAQYgvPzoJ3jvD78h9Mtbn+s3FYmm80s476e4OPGGtfd0vPzZB/jqP/lb8pOtAeYWIO2vTaybQprUeOyBsgcMRqUOEEBwn+o8VlXT4mZlM5shir5Rf52MXlPL1aqszeg6VqnLfAQI5jc39iAidok7IGz54kwBFBwxpEBSKhtaLqCHjzWoqAhNuW/AfkUuuzdbatddL/c5bSkXpgHU1NCuChB0vVTggZLEH2htdQcoFiltrE8QdLxUOAvQYGyDASAr6+xpmhlEeuy8ISdJ2apEoNbZA4tFIRYh+CJn7CQdILOyB4UYO7PWOjDmgIF09PvHMRfo8b8nWnV2MYzKVDaYAZDuSiB1JVB4HeF74ffC3IY5TWBlrjy5en2e/3ru/bUY3BrvpVvXKx4nqfuG3Oq/7TooNLEESeMM3N9/7ZZ7C4yBlxOeAAKwVv5hUDgXtsJi3Dzo14N0WebABhIMIMReB/75sJq6GLL18+H1tBW5DzTAEGWTOANjCWI8Qd7AaQOKgoFUBCSkgiuPrgNPMfZgQywBwSC/pv1wq+ZItPrPAoxX0mDYcw5wI9C4HUuQZKG6IepN68wIlWv//b/yd/HuLwtT4Cw121yT9qDoAGE5ZtcUUXevf+V9vPv2uydnKePZAIOXr19J9sFvWExBuKnO6LlwA0a/3OEGBfDyT/8EX/2v/uf4/b/yd/E/+cH/DGjiShAQoJS4W8ZAMgUZhRJ1gMBEnYdwFjEI0jO/cGAV3KCj4HJQtsGUugcWslVZg7egjaNnSzC4jvXfB/YAK9dCQdoIqFeI5ZyBXaqXkRUy2S6g612vt24WjJZWzaV5bYLToE9Twn4x5HO8h/dZGZi6I7Z6lbe1eIqOAxhIC1PS2/2lLihJ0rAMIFhBFtJjcd5AnKVCYy5ISf9lIDeSeAOCWOIKvKyjXk4Ze22okHoGq1K+cxlfwE/7MB6rBTAOeb/n6PffHmoXGIPloKADgtMywPGSLtiAs5oGdk52HufnML4+1ziYx+p6xSJQc2lpq3hpRYMswNAab5U0pwYCaDuoSXlvBwT1uqD5lQWr1fc+x/tg5WZrU/HpIVYjuAH0dziABOuiOjQ6syF0EEjrDR0vc3DJaZ8TDzDMpQOC7eLsQVMAwEUyEG65DgQMBJZgAgS3Sm4PRcgQLXgMwYGw1xDkbATQJ1ssvrzKLIlsQdIvnLIErXZAwH0PAMCv/vL/EGPr6hoMElajNLtROkzExiTfX370E3zth9/UQMZ3Tq+hjWcDDN7TlI533/rcdFERFE06vDagNff3BEqPGS//9J/jN//Z/wK//5f+c7z77/1H8r1WAWrSGSxlECe10o/sASl7kKD3oIKCHmclf6yt1hHOZIxrzswD69AUdMQ8eub+O509EJbAyvPGsYNBaQQIjS02jKXuATOaAofCotC2omCgavGSegVTBvJVUpTM93i9Exrz+iCWvbVsnRqynEeVHVEy77u4MnLwp5mit5iAheKnxW+FN8fjuVuEei/5lJDK5l3gmIvcuFnKuYobqQK5IedNlJ6yB5kkHSuRFMBqTcpTZ5KueTt1YRjL4cbGPzYq8yGnXk5hsqROTjXeMXNlPVbEOYMSDntusNDPZfeBwYhlqIFROcvrpK/T4Xyeci7xfIbPLK7XeNyx/PUy44B6Hw1rjVxIlIDXC2gNaMoWWACguRPUehdX2o7Bum/TPXAjQ2rFsvXmZrqGrYMO1LpOf0xJYl/y4p4IANkbI2nrdKtJIGmIHRBYsaI568BZgnpkCexxBQhWl2DdBwTq2x8BwVkswBlrAKxdCnoI/XzfvyH0QsEAPKbkwBIYMz3rHKDvgzgpklLTonPEP0UsQIFJLTY66jdQElDwj/8avvel38M7b78DhSk3x7MBBt/9yvt60ixKeQ68CH+PgT7RjTDTe8DLf/3P8Zv/7D/F7//FvyOgwG44jxLlDghU8zLQ/UAJiNkQFnSYAPfZzrtx3v+zAOzTV+sYnfZnvaFiHr0BD2KJkj+Ag9Rr9/uo0HQYQfOFkrsYuIov14+TGEVdC0X961bvnLJkCHDaHCDQ3afQHu7FkrIgqLqLha2uhUF4zUDvZDAzyChVVDCug190+Z2zN8JNRgYGAFi1Nqhvla3yYy4iGFtVgFBBqSgo2FzgI2/YUpHK0eqj35mlrAFJNovRqCVpwCEA5COVame5GjO1OlfbW16LiaJ1PcJrUNLLENMSWKzGSrHPJZCjcj7rnfAm52PjKdfqloKxbAPPNAjBhVljCdD27jbQv2F/T4CgBdbMWpiPHUvVuvcTOgcHN0cd758DKEgJrtbm2yVZwSKSe8lcB3ofUClSkyAXcRlQ6oDAMg1S8biCs1iCvXW3AbO+z23Yg3GcNQeLLMGtOhIOGPT31n0KjmMJFKgD3gEQLGIJyJS+sQRDnEGUd9H6c7pM2ZqucwBSgEAA14Nx+fL1Kwlk/NLv4p1//13RSfrrt8DBswEGX5jpEdJAQ3u6DOixxdq74A6L9vJf/wv85v/lf4Xf/x//7/Grf+4/AIffE2CgyK1BkJzyR9RwiD3oUaTyGWXhl7mot1VZX9CYg2/AwIJ0LJo8AoTK7CWSm4KDuURvvDw7S3VEAQnNAQInOcfWGCUxKoR2Lnq8kgg5X5DyBtSrAAQVklx3cNvF0mg7sL/QYiu7B0SZgLSbyIVjsHiOc+bh0cdufeIX4OJGJgQAt5J4Eo6swtGCKanuR4DAGkzJmyiC3EAsPemRdqS8IaWMBHEvVCIt90payKX32rBiLjIXqdfv572Y9kq2PSbw5LK5OJPLE54YKJHP8hI89N+5PTo7cFT6x46LcAXdZ/jUczp+4LHrNfugYwqa1SOw9EPLNsgAvE5ADDA0t4EyBtT2IyDYdwcDN0HxE/fq+NptSSL7eVIBsWUyOigeAHHZhsJFKNLwqGnjIy9WFABBA526DfYTQLDaU0t3WQAFBxAX1s3AwFxgCBgV+5sOGh7thmlHliC4q13nzHEmgOylAAJ7OjU77SvAtck1pgZVPsP8f/yzDwUU/PrvDHV8wixPx7MBBoAa39CdYZb4fAVugQJDatzw8md/gt/8v/1v8Pv/o/8dfvXP/Qdyow9RoLaogLUlprbLQqF2mgedPQBqTzXRXzlE3gNPswwoqd+VLMFB/f9K+zehmGoACEQYKV8AJSegNukM18hvxlBpVy8ba7xC8xaznJOwDgYQmLAToTBQmlpU+YKcLwIG6lUAQFMrqu5AMUG6A/uuvtZdKrVNQVNyabqvVJ7rTQc4YDgVqgsXxSpHe7jMwZ3AlIBqMRRXQBkCBwR1B2934NZAuYo1xU3cCyx7BFkXQ59n+wepZeBvJ6BpECCnEKBqp/borX0ep3I2YileuTb2qMAkkxo2XQT61Yu4+8YxaPFkjhRfBYfRJLg/6bndGrO/eU4787/JAgsBjxNo1WMJjiyB7O2m4DeyZA4I9qvsW/P5m8LAE/eo3bCR8o+FKiK4BQYAQNHlpm2Q/T3taSC+FS1YZJk+qahiyu46u1XS+BYgsLgaAwTMR5eQwZzIFpScgsJfF5bKNK6dswq+1EGZPzYek9c8sdWRJTg1RBUUGKMd3T6A6B247u/WZNQ5kv8MC555+dGH+NqPNOVRiyPJTfS4GwF4ZsAgDi92RKlf5AkUOIXjCyeL9uM//RcdFPx7/xFGFCh/UtsDkktOIfvrCQAnocdDkAhR8IEDGLb/vDEfM7+IYClzVjcgE6GR0NJgiWNoLHEDYLmhnD1o4lqI4GCcTj++xffFPvR7E7o7AoQUAEJujJpIA7QKylZAparL4NqLr0wggQygGSCw2ANzN1j09BR5zbrW5k7glAMt20EBB6EbrzlPTIT1kocJ3hB0xbn4HqK8hXh+iLC3YKIN2nQKXWgk6TyHJi4XpCzsAUlferbAPwZaokFB9xV5XDG+ierk8A1jn+yYJkwiOLkFIh6d1y1lPynl+fNRLL/Z+T3900Y3D8c0ahoYA8k8wyACgtafc5UmRCuWQONrPAjQaggs9udqb/q5BfAq+kFlHnVQMAACAwMRCBhNbXE0xg5EIAByF+EIBnJnB1L2IkUtugxU5swug9j11MDAyhUVXVAzKJibRhkYiAWmbP2UAxwt+inQfL0ppv1zCyD4zTBlHMR4gggKOLzuvxORdoUx0c4S2G8kZSGdupaYgq/+6K8rKHhn3CPDfM9P99kAA6uQJha6uhEmd4J+sgv0uCnUfRBBwX/85/7DacNw9+Ewq2TivjD6SGj+EjjJ5ohBIgCOeBiLIic6Xz+8oZK4SbWokIGESckkAHszOlTLjNpPJqGmIjhgUl+sHmr27aEydshNuCexAIoCBGELSBs9SXBXbUBK0vhph/SZTzkLi6DCszdcqpLZYGChsAMFi9rmuvfGSgYUDEAoSGDaHSB4wGHDAng1F7grYcRal9mEkmUiEAoAFeK+TLVThvZ9qCIp0n+eUwM4q0BiDU7MykIkeLonJYnST2Kndn3c1z3O9kzlvTkw6I8dBPTsmRGgdBAxv/fYOOsVMCtkf++E7v0k53frdVr87RYlN6AFRRJrEbTOGsBAQnAdDKDg+tBfs8wArSHgMQaP7Et5fQ/7EkdFRXFP0TE+wNJ3jRFQIGCf8awbU/oKCrwDImVJmwvPK+QW3Ktkz1jVwp1F8UuJY5EZrCBhbzHTZn2uOeyJTHQKCgppRojGqRzqSAQwd1DafmHXMrZfV90Rj/ixDnVwloHt0X0QGIMFcyGp71HPABJ4pD1odJ4v//QDfPVHfxPf/7Vv4523Pq+T6QyQ2hs3A4SBZwQMbIgwntwJSGCE6mL+YQ4L1ST7wJiC/87/4CZKB2QB3aK037NcH39edS6pH59ogVBnxIhDyuSg2Ka8eqvoF5WMFdqJefQpBfbAmAQFB0QEUpcCs1j7qGOf+37p5Hs7kQCPRVoXkeTi59bpvZw4BG1J+VTJtFAmIVhgCMKWW1VGQQUvswtY2pRV0PgEKFPApK4Jozua/3c6lhHeSKBEUn0xQ46JAq+lsEPdBtpcZt8HcAAAVHT9kkYOGMVolhsXoZxnoIdA/57lLM97Qxbo5nkexnQMDvurv25qks6BBCL4XR2n/7lS9AP0mSwwmlmeT3qO/vwJ1/PM+juJTTpkHMxMQetKYa4hsAIFj9b1WJ5nAAUl1BpYxQkoK+CFwSi71e9AQB/tPX8tgAGrQ9AWxYl2vWy30nCBUb7EwFQLSDUXQqwX4NUmZ5YgTXUkFoWlyBqyTHL3sDdorJRq8xr5Kyzk+oKJGGR6MFCnsWQvFRx0IECCGNRIffmzP8ZX/8nfEFDw9ufRi6/p3J7oRgCeITAADA9ouWIAzhqsYg50vPzX/wK/+X/9XztTEHOK5btJFYMp/QAADhNgePFYz62PqLR/zn8LOCJK+4zPY568IWndAEoNipJpQKrKIPQ8emMPyIByUlq4QSR1S9hTgzbi87bAtk3Nguk9jhh7InUvEHKV1C6qbew416RrnoMEouAP7ExCyrp97WaOOeAGEFqFRf1TKPVKubivl/ddroUWPMJ+hXAdRQHd7hiOaxXF31gyDRZFXo5L3EBIHYjUGgo9yW9QYj2u/lbZZNswC3sQ6i6QBbNGRTwJp3mPjc9H4bISNrfGWvABLvwMrCDst/D5A1W5sqiYhy28VPR+L0wWl70fPv+Jz9En8MicJ1BO8zzdsBgjzA+gwACjMg3cag+gfSSO4Gw/RhZrSCGMf5dNXAbW3bBYa+P+Goq4BZoq+iUYyBpLkKQJWcPIDLTgKphrEOwaTGjptnsAA7fcBhVwMGBLQwkuT0RmdFlyq46EA4IA3AajUFZgnkXfIxYT5q5bAwp1MNBO49mcnb6hlgN7EItV+ds1ggPGTJcJKPhb+P4X/wHefevzfSpkJZTTk0EB8MyAgbkTdCsdWAMBCLqgYbz81/9csg80piAGu/mIPrs3GRwUwOK9g29pEjYAjp+JI25USkKvp91vcKQNMY9ebjrqF0vBQZnAAXID78IakKLSVQ47AKAxdgIySRthrxZXCSVJZsMSJDgwIKTEQynZRAW5FKQCv8Hd5aBR3gYSkHtzGGcR8rUDhGogQeolcN3hfRe4qeupBZCQXWANAtiud6ie6MFdc1qkxT6kLNYgNWCXdE0GS+c7SgIQ7HcRWAJgrVwPFx9r4fYmStOzZfzIo8CLgCDM65P1m7D5nSj5eZ9HgPALnucY6HvCwjzlmpsQt7l7sBh3JsvjYLjHHcSRs8TQJHHhEUlqcyQcQ0ShTvXGXowxAbG+QAQEVoRI626YC2AEA1t3EWiWQYMUiZJTEmt/boH8FDBgVTyhvzdvYw+DaAxKNLAFUX4IM7AGBZI2elJHYgowP9QP8ImMbKy/psXsJMPM2F+tK3BW8eANwevp97iJ+tKAZuI+Nyu+9/1f+wd499MWaHhkCyBn/qgbAXhmwMAGwwTryBpI4R8e0J0UL/pPPSXxlFIgq3Y3C85OsT59gicMQbQ+Jj8UMS+RpM0jxUp8nIGWxVqIefTckPLmgUfUlGRoxgB0cMCVVFBJbMToWujBZzHLoVrFuibBibFYTakJlPjobmiMDB6Ch+audDkpSADEt9o2BQk9LYzTBm7XziJowNcIEIpWWtSAr5TUkmge9EVRMc8jVnybfbgxuvtkzblBLEjbSwEguMvp0b1zriTNVTXQkLcE06QUTeF0/+loNcl7Exg4U7DAeD4nwHg4hxVImD6zPMfhtxYjzO14jotzeON16PendARtN+cjwFNYGY+BIb3nqQcfUjnZi2f7sJSbgADbpbMDaYNV6ewg4AgGaoWm0cJbIM/lis/cBHtrQ1GsM7nhlS5ZixAF14EwAyovAiiwvhTZjJCkxaWsg2SVbBCKMUurSpO+hr448qCy3sueWzG7CSB4TNn0/acN/W03Hls/dsXtPa3j5Z/+c/zmP/1P8P2/+vekuB8w6Cg3GHGq2ZbjWQIDwMCBWDuMpNkACb2RgCCt3/yn/wl+/y/+HU9JBBYLcyL0vKd1pPJnC/5k3AQF8+blhjalMA0jJfFzK5VISg86QGDLo1cKTYvspAxob+jxyjVo9TM5VgWcNcgkPkOg39xeddFrIkhTnSvg1e2IqvsAadfSsnvz+vMWk5ABXDVw0SKMvXvdABK4ByqmTW76WtRPW0GUbwIEysJAxDQxmgW6txgcafZlUFeohBiF9ZmS6W4phucaLO7cgyLU+XGk4Y2ODoVxhnFWICoCGQr+R3v9pBw0EKxWnCjZm+MIZoCTc7T1mM8RGM/zKecY5xrP0c5pWuc1iBjHKVDxD6h7LzVhA8ombiYAXFQOVAInZRZIg1en8x7qFMyg9MxlMAECZwgsnVDdBBZA2EAHMLDqcDhnFFgA4V6bBxVGIDDLiWF5SOSIgQMvfa1sQexPcQYKthxcB/UKqg9uMPRqkxq3pGxwq1bf5ESe1tQDNC3omLinDHoUhOgVQju6qmytYPpIdVBoygduGtBs703fDTrIs6R0vPzX/wK/+c/+l/j9v/J/wDuf/qxduEkHvVlsgY1nCQziRRBBZlZZU9Yg4eWffii9D7TMMbPWKYhZDCsBQfa3IccAClKeqKdH/MLxdW5HUGCUJDf3VZoANMXgwixlKQ6Usiq+0gGCWjQuDLmpa+ECZIAOBe4ln3hTcFCZBSjUhj2JMEwsOLmpJQEg4mZfhKS/HbvVJRBob8ocACUlUGrOJghlCG1Sw9hbYBJa8CXmC3IGOKsvMRUgayGlJMF8DhD2Hbw/DABBAJPUGPCSsXMthGEvGdf5xJQvVwoTSFQL4VSZBMXI9txL5NbjPphT2+JnHxtBcc6KcegxMX32oGDD5580TNHHeS4U/Vlp4CEo7w3Oc3WO9trQdAvQZjVr0GBj2CWz5WkfbdWrYnJrYqSk1N1NmXsQ4ifYf1S2bhB4M6OLxw9YTQHPIEglxA2Unk0Q3ASRGYjZBOYimFmBPTCJrd2QC7YcIeTUChEZWzB0sVRQkBagYNOsgwwIGNAUaNof4HUkLGU0pIgu7yGMshREYL2uYO3aCoRaQuqePgONh9etZL4aWb5nZ+Ag95brnNXWpoQf/5t/Ke7vv/Rf4N1Pf9bdGTwZqBZbYP+eOp4NMLAI55i2CMCpIHcpEOPlz36Cr/6Tv4Hf/yt/F+/+8p9XocO66lIw6GgRhYsNdFBgwiOyBKuIVQA9ADGNQkSOEoKpuPdDt40cyqbOiuNQkEQr8TlAUMYAvMk1yN3Bl/OmF+8IDq4ODiBhxwoOkEUQJC2v2AaKcLYMWNdHHxO8ac61GlDg3sluJ5TMB5CQSZiEkgi7ZjeYgEhUUEqRokL1KkLQ4hLs77SLALUYhGpuCLmuxhZYASWx2sKJTIqQorI/AwO2JrO1N48bLMChfv40T+g8h+qQAXDcSh8cygpHa8SV4WQx3wIRcdwCCCsL7ZayN4t8rkcBfKLzZAvWA3AodX0D3El40iNAwd+zF5L2Ukl+7hSVUgB1ZGuq535r/x3meQgojO4BrUSof1vxoWWtgTaVJ16Agb22ZcdPAwLyXE/jhCGQPk2sckBkQI9D6qDgLicHBdspKJB4AgEDe2cJ2lX+tiDQAAoOBdN0PaVcQAJSGw0uFNlrhfpanbEDc9Cuv6dryFr0LqEbhHbbu1GZoW3wlnrox//mX0qg/F/8z/Hupz/T94cDguhCoAEUmH70OZ2MZwMM4ojgwIgeqF/v1c9e4Ws/+pakdPzyn1f6jhUQJGUN2kgfrtwDHrW7AAQhgtt/ghmSdyoAxOmj+eZhSYvyvugRFDg4qIPQkA2tPmAvy2vR+8I+WB6vPefcKctc7kAKDmZ7aHe3Ahwc8F41S0bcEEl3XrQMDtYC6zo0edIZhA4UxCpIziZEkHCXE0pOqBq4KPUS4DUSKkkjp1JyBwh198BEtgIyeRtoxaEuQtOOZ7O1CozKfaoUJ6+lEQQsKPd5RMbgAAoiILB2ubGYU7AwOcx52XTnjC6NzyeWAMBYChoYAixnwXgzvuJkDG4BeaH/fVbFElifY3w9jnielMy/JV+f3EErlsfSULtilt+RrJN+DcZ1DgAKOHWR0AT+aDrP5f5bzTFkF1gaIYKLwDMLAiCIDYys1kBnCQQA3Nc2gIEeTNgOQCAyBMAoC4wdGFpv2z2vDGImaJDhCApm98HmwEBscNq150p98Hue2hVo+3lhqQVD5TI0WUO8ArLcKy0YJYXKGg7ZaANt38HBUQcQxAAlMNtvqjIPMl3qmxiIGPf0j//N/0NS6v/i3xFQYCAgsAVWf8I4iAgKnjqeDTDQS+4jIiOhaQgvP/oA7/3wm1I7+lc+A4/8N6QF8SHZosgP04C+IjCQm9BcCmPu+UwlsaJDUcYJvVwyj8BkLnzkgvFEMQwXIY20eNPNrD5NKtwRq18XPZ1yh+2EOcAMDkqWoDk07I1QMrBX++hROLj8G1boyCAIUGgolHCtAhJKlTiEa2WU1LAVZREyvEZCTiQVFnkNEKxXA7JVXdQUyLyO5wDg12wej/uhR0B5SjWrNcDVUp4aZlBgfSOgbqW47kPL3OCP5vD3+rg697jNooKLAEH9sA4CruNn5XN6/vOFepMxA4Szua9S+2YwcBZhDnTXiDF9dR/LXFvOPzO4CUUvQaqtN8gCHBzEPeCs4vK4ZZiXZ6E/Ej8y77+BGTy0/U4OCIZqhAoOVoBAWIJea0BYgYbrbsWHLIagg4Gd2wAEIkOwGpEd8Nf0fi9ZQEFJwkz+OwEF9aEXlro+wPuv2OMt2Wn1T4Du7lkwUT22zNho2wvBKFy4kh3kcq+JwRZXFtZfWioH4KDf+/G/+ZcdFPzKZztLYGBxMFDpoA+H0z1fMgDPCBgAcrLKbgMYwcGr16/w3h/8Ft7/8nfw7qc/A27Sz8BZAlXOLsxdzgdAEKhED+SxyOK4SYBxU7C5CbJvSvcqWGoAJfRqjeOmjTnQS2vRfoq07TDphlaqkopsdm4N6aK/CT4FBzHeXP5uDg6ICNcqPIxcHhZQoOCgJKEfoYVuHBzw+OijQUGBWhVJGLsIEkomZGJhESrhLjNKJRUmhKLBUclKMM8AIQYqGougxWnYrilXoPRc9UMxrGGjmUUwWaP9A9MXukCQ327dGiiaKcPWSS5YkfodK4ozB0ta9ccBDJhyGdiIsEcSYU7XXZbX7VFWY5qfUer+vFe0OF6Hk3Er2nrSMksQEF5bxWkcz1f/dhCX/Pzk3EQM8q73UBEGRFgBAFby3BoOKTNkgICDm3GuB3G6F+K5WRCivHgutOO+CwZJbwVuMQUBEICeBAiuGkh4H9iByuxgwcCA/Q0oMFjcJ34v2+PACHZQkPXR3Ad3Gk8Qsw+2lCTjwIHBGSi4alfLHe3hHoeeFHW/KTe1iKCDg16LZGYHwvoO9H3uMSkp7LNJDwCy1qwygJoyA1HuZ61DA/LXvc7OX/w7ePeX/8Kkbya2YIoriKCgp/PfHs8GGMyMgT1pAH7y0St84wfv4f2vvI933/6CggIFAykPylEW7+hGGG4+QPx1ww1pbAHFw2tcgyhiCzqjtg+BLGTHpQxxM5wzD/LjCwrZjsnsACFeE/uV9oAADqiDA920SdOXAApnIuCJmlGqEix4RUJujIyGewUHJjRug4PxdOR1yWSQaors2Qg7NWnKRMl7MOzMKES4a4yNI4Mgv10apCmUAwTr9ggPVPQ9MHU7Y7u+vhemMbuYzt6L342/Z24ixGPWHv9gCtmAXRwcQIMxCTEewVgPjMpxrJ43uwDIv0M5h2OEw6JNYCBYvwcgcEPpn4zTJkE32IAz4CPPz87XzrNPU8i71qedzbqfFILXBVFQECoFdrkQLEWPRQrHP9sfmK7BrX0X5EKXO8pYrNIN29plMDMEZ4Bg1wyFnp2wvof9MpF8/gwUSAEiAQV3mYQVTBSYwB5cbGmIm8cWAMnSEfdrjyOo++OgwGqYTDJT9kCW/ioZx3UP170H9oXYEwNjsfbDmS5wBlt1gbmoAkCQi6YMkBot3uU3MgVDESoFCcoQGVvgoGBWAvrnLYDwrIABcKQ0P/jpK3zjD9/Dd7/yfm/NbBcWAFUFCNTgDSm8/3WwBMJNCGCoBhYXYoXQZH4kEfLaM4EogXHt4MCUBiX5XGawUpxS/UyR6Gyh2WgqvFv1jR5dBsM12hOwEahdR7eCudQyPDDLLgOM2GhCCxbOyFAroxGIRLBcdwbQkIhUsJCyCDrNIPTqJF3sds0JSMRoTPo7ovRXAOGuJWxZYg7uchYhpjEIESBYPrRVgSSIgjZXgrt6dB0eHSvANg//HRUC6rJw5gcEKe9MAlSspwJ2oFFf9zNle0K5n9XXf7SU7q3RJnBgx7pl/f8i4xY//ciYqwXaYC2co0+6km2tu8qOP6bBfoSxfLAqArPcY6DiHHMEvOF+uXVyE1UdXAlDVcJ27FcQYwjua1WmgHHf2hIQdKYgpCye7K0cmBljADs7IPetxRAJCOgsQcw8MHZgKFykYIHOQEGTzANzGTwJFJzsZzmB3Nfd/s5SJtrXdgYF2kDK1iHq46PFbroAvdEbdWDAWneGiPDyZ38sKYl/6b/Au7/SmYIOPtIIEEJcwQAK4hbC46zBswEGq/Hy9Sv89h++h+98+X18/q13fLG8XDI1cEo9BcV8OjqGGzygcgDgcucbwDvh4Xhve0wLAJAV8cnOFDBVUAU4WVGj3OeSiwhezYMmTuCmGyJDcnLjWIED0tr7gAi2uneGZAugwB53PQmrdVAIpPs2N+lqZp0VQQmFAaoVOyVcKyOXhr0l7A1ITWi/Xd0FImjI/ZU5ScOlmY5slb3eeSJNjaSGxiODwJzAqWJvhI0z9rq7f7IxOygQgCC0ada5ZCKklEE5hxuFu6I7E9IrAb9w/0Ta0NxAElTKQFJXBhHQkqTKQnWVgwP9jdW6u68y7N1meyYjugqszHOk1H3aMWBu4Yv//+tYziE5YKDSr7lVrIxjVb1SPmuBo+Ia9HgDUwIWiEikikFfn6oGRsWAND035W2K4oz9W+0b4PG9p9boYIw0eBEiezRAEFMOryGo8LpXjyF4qO2UITBAcOY2MFBgil/cgWlgCYQZgP+9ZXEj3GVRkDNLYP1UjjUK9iMosEBDiylorbe01vLTywDdOM7WXcEgOwCT1+xvd9voc2Nq3kQfkBmYFvScJIX6x3/6x1LR8K/+PQEFtj1cL5n74uhCaGEOwMwTPj6eETAQYtxuvQ8UFPzel9/H595+xy8SKbdPwYVgtL5Hi9pQxD+WCBXBYsVAGhtDzJ4FETcDEayJofiySAR5oiw+ezt+eBQ/bpPugupjFsUhIEGCo+T3HHGeCXOjrCy7QefhZYHLAhzY96yMcibkJjG6xh4kktKlewOoZLHSc8N1Jw9cuq9JSh2rFZJakqZNSJ2WTCQhDDM4YD4AhMYiTFCTAgXGpSShTFFxlxKYhcXYckJhQqMOEJK6KDIJGs9N0TOR3qwEKexgF+F0qy2em8KRh5T6z8j1FOEk1RrNyqz+3P3UrUrDpdWaALJupfTGUDcq5glQsM9N+2MVcDi9/sbjTaz8T3qcPB6HHRyUw/GHXgrTOZ5VDrQof2syRGUDtksHBe5CDJH/p3RyXjOJw+NMXePxvReUjsmdqn87KOBQkIiFQbiGgkSRJXjYm7MJlpr4GCAAOiiwQmTGEli/gh4fdO42ECYP+jtPLG88g4KQbeCsgfdDWOzJKcNoqFxqDEHpFSRhHShjcai0HWI5jK2p/HR90EhlqQEEiNH58qc/xtd+9Dfx/S9+W2Li5h9Z6CUzVGdQsNpeL1+/xJJO0PGMgEEfP/lI3Ae/9+XuPmiQBXEvQYgqBYVsBIL7fmIr44gIAeDagNbGm3J2I9iwgI+UNCCSrfsXSYliTAqZpRoeMQ8WJJXa/wbGymlPoSEBD1iT3GztAlimWIPcABzLKKdESE1qCgidz0hJ8pgzCAUZhcxvKX0S7luTwEEmrX3QkCoNVon4JdfsAaD0ZZIZSqBjQ7KF1OqJxh5sOaEioXLtFkmTOIekgqYmAqGnTHqUuAKEs/EmHgbzMEo6FoSdICClAu8cWa9qYV7hvRtMgW1hTZTxIWB0J80V87iFgmyTWJgVsR1nlYb51DFnE5wd+9Y8fsFjUzzeY+mLcz0JAwREY38BrQ1A22WoHshz9cCU4b0FnErOncrV0AWTEzabX9RTZYAAdhwFA6zHcgWlnzW3gaUeCkBAyDrQexF8AAWrcQYIotsgBhfe3QAEsd9BJstA0PsnljfmkILcqoKCULjIa77c2nvjHltWLg2VI2m7KEMUjEJb81XFSHO7xDW/oQ+IRJY2AE2vJyANkd774TfxvS/9Ht759Gc1xZz7xhncSeayCu6LE1Dgv//6Fd77wdfxNt46vVTPBxioz/CD1x/gGz8Q98HnFBRYjAeAocaBNS4RhUxHVGY0YOpdxV7+q1cA4FG9RtlpM93DTU+kxwShsjUOghZMEbiY8gUgK7qxHeIlqKCnMKUM7CrULP2GkgKEIKgjZboaxjJYql7r1ikgAIHz5huSkgjOlMS/n3kECDlJgNMMEMpO2JMyCAYQ9L1bAOFsyPUW2N2YNMgRQCg3yszglBTXVAEOOYGq3pStg4QIBm4VDj2QBIt1lt+QkSBWkxxLAJX1f8hJOkmKkr+CKoFTFUFVJcqd2lVcPboHBRSk7i4ienLFvD5Hnd3ChfAmdQhu1h8Axn04j7muw7Q/P9E84vHf9DqYUiDqVqIVDdrugsVYwEkDc6eSwvZ3gygHlwsICkJlBBAIg5M95M9vnXv4DeYOBkwZMcaSxav0QwcEjFCP4HjdYv2BpwKCN2EIDBAkdSmQsQRagMxBgfY8MFDQo/vbEaT6RUxOAh7k42rtrR11ZIiscqTVgkjJX7M1t5gOayhlQddnOkFsfiEPExFyY095fu8H7+H9r3wP73igfM9iGH8kxpfgAESi3rPxwetX8vu/8X/E3/79/2x9zfCcgAE6EvruV97H597q7gNA2fSBxgEAcnAATgP16KkmwW/043/1Ct/84XsAgKtVBwMPN+cE7PwxQSsFMoGhm6AxGrSCVyrgAnGGAwdwkC4A70IfdUsyqUuAYHEBUWi7kA2xEf0EGWjafhjoLYEB2c6sKZYpC4pJ2tI4ZWzKoLRE2JvczDsTWmJkoyQJ2DnjLpvFIgzC3hr2xHhoHSCURm6xtAa01K2WeSQSMJFAKsSkNLMENxo4EPusMil7wKgQ0EIqjGozv1tPD5tvovmGjk9nRG7qzSwBIhYwkEjAgbowUiIUDgChJHchAPI31av7DAmElIWm5rpL4JUprtbc1eQW9C1to3tgWbXvKdR+zK8fgh3D1Wj1cakS9uJpVcgnzIficecaAGc9FYBA65xUEVx1IRyaDi0sRqPsOXYbHK14kw0Ryg6XJU5xPteTvTn8lroMWPd7ZAmsh0EEBYeS5sx+fwFzQGEHA1Z7ZHYZXEI8wVkMwZCySHJ/jAyBKf+K2DZZUE9onMahtXVrxzVWF6vIQE0Tt6ZUk1wk60y5chvNoCBf/LUKCao+6zgZ3QhRJ1iMQSOAlOQTgCCf+Z0vvY/PvvWOSLNUJJX6VD/1YMfoQoiXw+IZPnj9Cl//wXt4/yvfxbtvfR63xrMBBi9fv8J7f/ANvP+V7+ELb7/j7YHnMbsUDBwADUzRyian/1oDfvxTAQX/8Ivfwf/0//xXD13GnCqMx1X+18EIMxqxV/pisgWQnZJTCV9PARyQ7yi3ILN2CtzVirRYhHxc0kERrCLLm1icZLm8zNIOmLMAhFaBpL0IyFK2hEHIChBKE/Ygq2uhEHngU6aMmq2ammYZ7FWZhDRaL2TW1tqCSbOERDeizfLZm8CbkuSYUq1RsxWydI4ExHKMwm+mBSJzYRZoDR/yeiThkhr4yEnjFhqrD9FqLACNpJxzg1lbF1CSCE+qWuiqXuFBRW0HKCEVUUhcd/W31r4+AFaR+EPFPDnp8fkKJKxGrJVgYwYjT+lZYONsPr/InOZSwk+9Lgacp6wDDul/hz4Dsawwh7LCLhNCK2KdUg1Kez6FeQ/JpRkt9bNhvwvAAQEzDo2NvISxXZ7Z0xKOFw8XwcCcehiDCreShyqlAn7FZToHFdp7CRgYAZoAgMdHLTojrtZVKyppwH7PTpnl4gAII0ugDBEfGKK7zg4FUHC27gYAT3UCRMOkJHpAGEX5yGfe+oJkBBM8Ho3IDLYWTwJDBsSJCwEQUPBbf6Ap+9qF8dZ4NsDgvT/4Bt7/je9InQJgKHRkBvXKpaCfAIUyl3ah7cZ+9dOX+OYPv45/8MXv4C/8irgnarzxdRPEGzQOs1IBmUMCCzChgbxfgAN0C9L+NgtyT9IAKJdBQRyazsQgs1kYxsHS75uybEAJxmyCVimLdZq0AJP62qju4JTF8k0ZJWVsIOyNFEULE1AVLOwkGQN7Y1wT4b6xllulRUEVcpAAdBoW6JApllWeR2WxujLLMZikW5sBhKzXfp+Wa2ClzY/LPc6/95Mfv5j1elrLWKKxvfROkEJMxGhJfJKSXQEg0cgeVAWCrSpQkEBFAwi0XXobbmMMgG5R0GLNzWUQW4cb9JwVsN0wywvDeiy9NmddH2+MJ1eP/ITzOrTTXaV1ugtFmZlQvdABgUWgx9bEeRtYAq8JoO6wWDiIeWxHbPvoKXsIgO+j+N7qstgpVR6PIc81bXHFvok9BNFAplN7lcLVfTbHD9wqThTjBx5lBzj+3dzYkXReZQS4Dvt+GGb964l1QICDLDz0NVH30cAQLQINT0FB69kfFtsR2Zt5uOGga8faot7i3iWmynQFqWEB0ImecrbAXp8Y6w9ev8Jv/WGv4+MB6TfGswEG73/5O3hXYwpI/0VrPQ5jDRpGZAyMF3oGBZ/5lXew6wXdWw/usZuf2/FmB+SmZhZLEol8MxifZBWxmQktASX1ZSGyHg5CN0cLksDeRnSov/6Y5TZbi4BXeRMckAHe0fuRizXLLEVcxohuBQlap31gERiojbzAytYsnxooOWOrjL2QlGANbgZjEYzm1FZRp7Fk0ZIZ1jKgv8pSPtmsdlvHeXNEANCBAU8CPRwjgE1Z6w4OShVBSbXhLidwTmgkcynq8eBkloX6W/NFXQsdeIk1lRQgNPmidZMEBorRR0iRi9X4PPtB3xse4/dWBXf8MXjLywhKBlfGWVaAHs/m5SVmp3l/krkxN2DrlhVZ4Nb8vek3maYiRRbxHWMJQjfClWJY9RpgVRDWhZDNBx2mP+8fmZYBTAUJCmwBjQ0+GREU+DEmn1wy4cgAEosY8g6rCgYW99accnirMFHSOAJjCjKJQWTtkG+CAfTiX/48AtEAhofJAn4x53YG/bPZZZezBkXW/ZQhssyDVKSKpAGBdqwR0QIg2Kd1sJFJWFMzGoxRsjqqzWgGs25DOqjrNkyg4IQt+PC1BOILKHgHI31xPp4NMBAkpOQNCXKcWYPViMGI/lqTi7wCBbbQc+ex2RqwQYk0jYgdINhm0KhEkfVAdzwZOCDSyPUTC5KbIEiWJkkzmn6K9dbn2YUu7w2DNcdqcTdlDIik8A5JMaZOxWZpf0rit0upYMsFVVmE2kQ5FyKxohJQ29HNYCzC3MENaUTGQAd23s7ZLaxuecXBTSj91euyrrwEAsx9HnEOxmj0MrB9LiVVWBvZnYG7xl7MRQIkJcZEd+0IDDWoSDIOcheiGmjlFThXyD8qVY9axs3CO3PDF/+pQfG2/rcVa9Lny/oP8e9BwQcQsJwbZH5vNDfI/Ka5WeGqA7gZfihcLytQZHnquq+H8sJBMVh54b3JPhlSAm90IgSOe0f+hger7iQuKADYk6bXUm/Qdra/D6eXJLhNipNK5cHe44BkPtGTM91T3vmUxs6H0V1QkrBj/nfqLZGlBsHugYQ9hsBSCruL4AwMsJxcOCdlBQwsWoGqhSvVPq9/jL0tQgDfkiGytbdeE9PaGxCIDJEBgjOdsCcBdzuJW6lQcoMRkN8v6g4dwIGyOoOuurGdLWX/uw4K8CS2AHhGwGAcYmHeYg3Cx8aLyyMo+IfqPqjohUKAdW9yUyrDaJAofqJQfr4JvQwAqiw8uj6Ag0xCL1swGlH1ugV2Y3EywaeAIPc0Q/KTxCEo7VBj/lCgRwXrPr2uN2Ja3FxkFeBSBmoG6c1V8oacN6HQNdBwZ8KWgmANboa90CFwamVtxREtLQAHa6tPfxSmZ4BgBQZut5XlIFDhwVklJXBiz5TYsoCiuyzdPi1yuRl7wACSlo3NF3Uh6L+kVlV+5OY2xW9KJAYqReUbLPXVr5G/3i1wD0rlXi0SwFQx8hYgjUzGBBA+wfxwMj8OSqbP7WnXLda8txREiTgfXQeVO2MwVxNcZQA8ee+0bqVXA7zM1soFzL0Oh1ymcX9zY9/zFSLkPfsKtuelXPhqRNaLAhjobdC7u8AaHFkw4br+wD6CgSlmwEDAwS11vlJyrnPcwJTxsnRNkfrkDQjYfksdBM6AMKYjPgUU2L+lPgCk0qkxQJXlfm7J3c2VWbZik7TqARw0xI2PswtloOA7vxFAwRPZAuDZAoN+7exaPgYO7JoJKHjVQcGn3/GgEmMSAGDuUT5bmT6PRF0YDRO4DQ4snZEXCkIAQi/gMZb1Ncuo9ZNS6pciWnRq15B5PrINKx+tfSsicCsYo5ScgAOh5EhvLjIWIW/YitCx10qe5hjdDI0zWsYAEmbf6dk4AwEzs/hYmFwEBUPGRAAI86Vx+rWR7jtLBVPBolkTMv2Ku5x7dJL6e1nZqwyJschkqY2hYMsK9QdlGiOWEYKTDIB45HIDYj788HP+swL+iLJHNx+KN+HIGKzcG4cKgE+Yn/zk43O8Nb9Ym94rUYa5DtdwuH49I6nxMdBsBgXWlfCsxPDNfcNK21shriQVPwsgrKLVWLkxfI+bggFQbIUau7CvykJsq99QhRXjZAwMSGbC2l0wVCi8XhdphiHDYOEC5fmiDBcHLmf87yEuJbBOuo7LZlZx/9lapwAOIiBYAMIKvBEoMHflUScouGOlWwI4sMvQTCawgIKUaMiss1N6FBT8++8M7oenjucHDNydIIF6hBEcrL8jDx0UvIdvf+m7+MyvfMEzYSqr8NLP3rJoXfcSgCqBJXsyARf8YQoOGmSTRXBg6YxpoSC8gh5rwGGarKFIy+lzGHqNwVpew0BuVmkDjJE9CNHvc694u1G9u2SI7E6hdCw0upc8D7izCNHNUCxYUYFYSTSABIvojsFWZyOfWEP2vWJrmaTRdW4h7iCukO2NCRTEjAmnhDm6EzhUaLTgU9X+SBBEuAAH2rO9wdKYTCDnKYYizrVb1VHB2t71/al/jwqYD78YD5MgCsn9m2RVIuNzQtJkcZfdi+vufIIdm6c5/TueY0aYH+UFoDm/js2uIUMizaGPTSP/HwEF0aXQeOw7INdA9wyRkEGkEp91Hg29INriWkYFDnSsFff9INwT+f2yLVanBz5iSOs1MCD1V9bFiPLEDqwKER3SCyeZEuWJzDcESE8prYMrwFw/c5xKUP52gfrn7IIpg6DHihUrIyCM6YgRFNj+XdWLmBnOqBNyswwl9qwpVEbOHQBbCx9AsQGUlVN9kOyDcYlxCxR0cPyU8fyAAXAAB0AHB/L++msfvBZQ8Dtf+i4+8+l33FJk9Rc19BQ20bFr+hkQYWaKQhSPVtxjLd3Mkj9PrTd2qbrwe1NqMfXCF710ZtY9LRX0eGUFHXyqk+/VbkhWS4+SAwRzZ/vVss/tV8S0yNVljME8rNG+yAXpcufRvpQ2BwiUCqhcgptB/KoV5IE9lhMs1Q27Px7cj3+rINJxazAKaAgWzSyUbdFa/F76efGzERTMneasSJM8IewpVGiEgQUgKzgg/ZtympiDXusiN9Z8ZuoKgvy/AdRGpRpL5ILHehsxncq+uxpmz5uMTehKV7anWLdRGdv3wgzjNIe6IhEAWM6/vd7PpyvqszkCNgelwR0YEKAR30Skkd0juAFoOdEGDJVNY466gYJmSqGGAkK1uxhiRcG4Z2Y3gg81B9NCcbvihwQBRqteQMI6zTGOJUOgG9a2VAd8AQygswNy/NFdQPsDMAACbYHMzbsd9pLF+yBD5ivR0wibpBHGcu8OClIABKM7YAAAQfkDGN1q+nuRHTJQHUvdW++J3jtC0xK5s0juZgb8cakPQM58RnDAjcGJPI1VQIDeBwSAqafYqxtpNT7QQMNfFBQAzwkYEI0nrgoxpneY0D5sSe5I63e/9F185q13JNgNk1Bis/qnry82gT3ONzmHjcAN4n+GVFUkpzVEK+1KLdtN2qAWJKAWUnYhrS/BBA3P1GkEDLHVcKs4NPLJ8gdbQEVs82vVFt0/OFxGpfzGamLt4WMvHILtAtQiLEIuIlBScTdDKZobnkhTv6RwktFrLSgOW+ZNabbViC83u0osG78SoygLQVXWxK36xpBAUWCvotRbFes0WrAzJpHnthForK9QGSkL8CMSKjHr3yURqEF6SSg4aKR+ZdIiScCpsjUhZlZ2LLKySqGas2ji9YvWZ7QgJcWKg2XOQRGLcvb9foMyiBXhZqDS9LH+AvMseqEyrMJlV3By30ghnw4Ojtc1Aq0IrnZzKTK68ufuWhALshcPAnADFGBKm+7XLyVojQvyuicxG8Bo/nFt+olElmGtRoJ+DN8hjW2wyp29WmeoTMgVtFtDox20i/IfGhrVHXx98N4Ft+SGTELkBjeVGw0idzSOgNIECpYNrFTBn8XVBLeCLYOBgejCmkvdr/pPmKNW5PloKM5j1gcRHMTFHzJvyblFeYkZjejgErXFNVDw3a+8j3fe7qCA7Ifned1gVIHnBAxOBztrYCPeiI2BD0Jvhc99+gvd/xxxxic48gr5x1EDa1ASiX+ZrYiOmqxMSEkEzkpBHC01E37ZjaG+QWIjH412XzXyqVethDgpfuWmuV4F+RtgmIY19xia0mx3EoOw3Uke/nYBmhZpypIfTpovvFm50WQV5RQcgPSmJb+RgX7jzWs04sSOxs0SzVmKDBUWy10AAkApg2oDJevNAKlImY9tpO2oZ6TFDA4t9dIL0rCAE4a4NIjhGRhCJZKkMfER0I4CrYOmWF8jpk6Zn3QVExODOlcpc0YhR4XkMTLEwdLkA3Ng193WZGAB9O853z8Gb73JPEtObtXKfJUSb4wUgEzSQmPRBTFfW2BkM1oLsS6wa22ApQv/OObncXjAIT3ebyBmBMTrP7p1MIKdGG8znaA9jUDOroP393BQoL/Z9gAIbnQ4tLbH13thCrjdlBe9g2UBZZE7FNtg07rGwJs0sLK1jIyQrFtfuxkQypr3e8syDQz7R7bg1piNiacMnp+sVIm+9pOPpHhRzD4Y2IJPMJ4XMFDWwFOVzPoL8QbxMjG04dIf9N4K7UzC3zpsSAdiHgGB05tBeNmY/eBGIcncNJWNBKEmpUUTiWAKONN/M/7c6APWG5uyVh8LjXxaBWrG0MgHAOqDpASlrBp2bGNq3RrRele/YRglmAu4bKD9AZQ30PYgrMESIFyBtkn+fi5D4aSmYKApOxD90kBnDGbE3oUCDUAiMg+tAVSSA4TcGBlJED3U3F6AA/mulZDtx51Lx54NbgxolgI1rcrIwihbwFFjAYfzcCtkOo9YW+OsTv4q02JYOne/WWEbUVBmtdKuClgbWJm1XnLy/XuLMYhAgBsOgGWuJGfzjOcd5xrnma7tkGvv/7TQVGcS2C3keN7zdTZQYEoD6ErB1/FkJJCwgF4n4HyfPNaEKJ6HxVAYIxItfbv+qf85jFleHGSF/QPGQkRPAQT6txsPxhicyAiihF7Zx2q2kCt+K1vNRL00dWxgddb22Kz6g6zQVVjIixlAzMyWgdoZ2khdAvZrm1ndBE/QCb/o+MlHr/CNHxyZAj+jyBwDY9bPjfG8gME0iMVek6o95ADBxqvX/aJ+XnsrMBGSUdUBSUQLXR5lYYnHjbASiLPlRSnk28+uBj2WeBU6U8CkVuNSBgVhM1G50QecSWLepAKZBDOOpXj3Tj2lAiqMWIcf+h4zB7+hBhWZ6yUEEVEiiR7erdZ4AdU70PXhSQDBshsoZVAqAqSSWQJjfXAguApwZAv8pgfATEoN6iNJVcI9AAQrUEMpYyfpSJegzZAg+2OvxmTIcedik1HYWwUzWx8iOgoGxQAGDnTJZe3nFQ/sgFuxYd69HgTfjJDv6XOTwgrKyiLRjdrORMi1dYs8J2QwSuOeVr64EboivQ0GrMPfPoGXuUS21444Vaw8Wtuh0JTVqI/ukLPr3PdNtxbjMDkAZYcSaUZBZuxVAanGDlghobM9EssMb1orINYNMEBQUgAD4XF2j5yxBjFexV1D6MwiGoc0Q318KiCIcQV11/LFa/mAsgGs/V9SnlplC0CwvgVjNcKxNHVD71vA6G4AlxFh3R6z3v3jgU1o03s2Mgg72PVBNjviDXSC6YOhBtjwYXlwAKivRVAQmYIICACsC6AdDjKO5wMMKGHwXXFUEzN7ALx8/YE2lJDWzIYoCSKQZQHYo4LNaLNgHSlWxEBO4KZ5uAZ89Zg9YrgDghgwZILVftOQuwsj2xAKEOaxXu4IEvrxjTpNSQwYCYYVgJBLBif1FZIyK7tMhHKV2vxFavQPu1dBQdulFDPXnrZGXiCJkEqRzV82ERQGEPYraBfhgbqDykVaS7fsLgarhUBGF6pFYQV+BCwcPcWjLFg1GSENLNICVI28hXRlmX/JSXLTKYlV0JpWZZTcdAMIwhqEBUegiZMoKlGuvXJcVn+hPOoeoPFetXz1ee1X3fT2NkbCr6LjI3tggVOmaC2A0/ci9da6JaVeDpe74t0bULRGw06SXWJ7fD9RsjHldK7hH4GLRfG/8VxrQkmMUlXJVi3Vy4y7lMDcUBpjywlQypwZmh44KtJ4vW2Q/peT9NwQIMdaOdEsCZE1jQnITcHAeo/MNS8i27FqRBQDAnPqgH8ABUHZ+5wx6wEzmZtrzlht8NCzwAFBBfYreH8QYPBwLy6D/aEDAo0t4MZo+w5W/0+UDZQJlJLIvLIFYRnckJbJZH0LygVcLvK83GGuQjl3uzU2DTiTlWEtTl6fXWDkqyt7oDbtzaR+MU7CTb+pTjA5YAsVXURJgasAPQyg4P2vLOoUTCyB68UBeZydsYznAwzORgAIxh68/OgDbbgkF9Vue7uvpRQwg5QCaiSqJXNfYCtGIYvfNwMwXtS57rkFShVKQ8DQGDcwnkLcnMPj9P4SCJNSplDrqHVquLAoFxFERSa+A8haJAa6uUxxmx9vuLzsoKDt3YfIdqVSQtsrUsni5+UG6wzIpVdEo/0KbFdvdUulgln8iZR2ERIGCIhAsCZDdjPFTR94IX2dVbkLiCANHhIr32IYvIU0S5dIYRCyNl9quO4UaPm0pOXjOKPiVxS3CZzoJz5TUrdAgVXbu+6Mh9qwc8PD3gHBVcFC1TlHhWtzBnqw2aUkXImxZanK2JjQUk/BtBoNJSuQicVb5rlPcQPGZLQ2A5c+18aMh72DA+DIGthccyJsWeZq1/hSSBVEAop03OyivefnN430SnyM+iZjER2oy7dLdo1gs9HnDCLp6plaGnp+rPaGVRc8KzVsDMHcqthiACwNU3e6K/uDYpANFBdEzs+0nmYvOUCIxYjqVQBB3UeGQP92sFB3tOuOtu9iMFQ+uBA4saS3ooHb6MAh63hpWU1FelMgpDpzuZPyxO1YcOrRJkZ+oL5iCKvo4mRl5VMAB6R7IGmbdOLevj7oBLnMfFC0KyOxaFyFvS7GaQcExhYcQQH3NY4swRlT8ITxjICBcTFt5IJ8SKrey48+xHs//G3prfDW52XTDzmwuvAKECywyly9tpFMKBDk+kuU+3FWBgSAObobAyC4tSEjIJg3O2OM4AbG/WDHNiCyk0TDpmQR/xL01kDYUkHaAvXIDM4N6dLQuIm/cL/C+jeMcxQfIrc2xGmk1ICWUCsLo9AaUhEA4TUUuHlgkhyzgLghcTtEIRPtcN7N2I1DMRM7ATsXFT4GJCgha7pTThkFouBWLaRrk3bNBRlF20QbQLAiJnNWSjz8rSpycwBZmkBB3Auja0Qem67/zq2nyCko2NsICh6quEMe9q5wW2uadrcCBtLPonHDlkdlGkeBKLe9MpDVrQZo/Y5+AnPfCQsyvAUKrlUea2PstXrV0SMwAFJKSKRprpzQsqw7dgClSdnxPQEFmgkiwjNr8mi26xqu+XgvdnAQlYnLgYQhcFWyXehJe6NQD+48AwSnrYohCtxKZY+VHm2WOE7AQUF438PreWALvAbBVRT/LZag7RXtWt1IOMoDvWeZMMTYW2yBug+wXZAud2jmNih3Agy2F2iUcW3AVe/Pa+vZIuZSq03u1XCqdhgAC1ksSyys0bRO86OBA1KdYAAhIw+ZTpse+0wv2HxcDoRjmjxAAAREEijvXRLf/gI0t3zNEMQ1jyfg+m4xMR3PCBg8Mpjx8qOf4Gv/+K/he1/6XWk9aU2J0BwcRNaggTXyWcsZhw1OpD5ASNqb5cfH4SV5bzAD0ScYvxMpMNuM9hwKEObmTdYpMJb57RuQlYo0alUi/FtibClph2XCJWek7YX9kKLfGlINu+8Qmk8dR1MGwUZtcuyUGpoGKnJrnVIDwA9xnRpou4CbujDUxwjt0+CVyuzbtAgZm9FVYBUc1KQMgpS/9RgGzYQoTejJRGuAcJd7+VsO1kpct2H93W00WgdmeawAwfGc+h6I+yHW1agKUqT1tGRUdEXbQYEp2atq2pUFLuaxRV0CBgoSKWPBWm9fH7MpwSTg4IztiBkGvTJkfORTUGDdC+f5Xiuk8l6Czrfpe5A5V3ELpqT3CkkxLbt+RdMb4pTnNSBSJpEhTBp15oBUDljgaqHU9wWpgnhkX0SXwRkg6PUDgFhJkFqTczaAYEreNsa4CId1kc92Gnpob7xf0WoIIjQ3wQIU1Ad1K16rA4I5s6kheVsDQBnVlNTntoHKJjFH3rRqE/dBAAUPVUDBXhnX1mB9Kow5M1m4loPyWLTDamxmRCzbfi4aFV2yAOARS2SxYN0WtUynTV9brX/cB1E32HHNXWSMEJHs8w9fr7sk3gQEn3A8M2CgC6cFe+SJPL786Cf42h99C9/74rfx7qc/p4rGMhU6OEiqvVsDMgNI6iJQusBcp4WgQXAA5TX6GoOARhBwqgB0eKMU7taWv4cxvctuglhxC+ipXZbW5cFijb1NagWhoUkGhNKsl5yRyp1aHtJ6mTb1M24Xof21eBHyDkoVRAxGRVK/W5wAo6EhIaEBNUmmxV7lWiQNPNqnDF1jD9TqsLaogpC7or85gqSPrAKBYMVQhhgGy4TImwhlBQjiS+wAQYzjsSLjHoRQHMtaABRdS+NesBQ6AF4i3Ra9kWSqJGYrkCgs0KK+sylZz78O7g4DBU2p12FEYkDjLlaBhMvLPQXZ2mtWuz8GZZ02vZjPg/kACuKcGyR7Q0BOkjgRpuF8G/e6F/PwAj8BmHmQ6God5LJ0FpE7I2CBq2c1GPyYem2GdEoFiL2iIJ0WFPIMgRgDYNR/oJWHoLPHlEVIoieWgmatdTdirEfA+4NkHKiR4HFGgRV0UDAJoeTXO3ltAtL28ZLWfJGAxE3AAMoG5Au43DkoeKgNV2UKrg0eS2Nt3Mf7cTi871FOSQgkhmYctcFqj2vldoW9GORKBobMBSAEqrIABXltfQ9FxtiBiMaQuBsS4j745hkomKvcziO4U6czOR3PBhg4C0ip0ys6Xr3+Cb72o2/h+78moMDrumvMAVFTaxSg0JnRJIL4TAUcWESrVOLrpWujNYf+644yzwQ/MC6TE4CKOiqJYLbKdcM5636w6POzLm52LPdn1oY9J9w1xsYE5BwQrQjYu1zA5SKbr1UwN6Q7oZ9pD6xBLkhFLATaMnAVhoBb63djHK2BmcBNOjSSBTTuFjOw+zWJzC5z6hHNjwGCeIEsCBIY66tTL4NKVijFMiHqFTlvDhBqAyr1vg7WatU7ZoKkqlywHGxQ+KP7DrvA6UV3TED0/cIsr7MDAbkqTSJaPOYlQ2MjiHC19fYr2IfM/el+R4nP6/5783FbhsWcVjdX5fNhysDicGxvhDTQRk37BMixTLmbq+CxTuI2WmuoROr+CFMId1m89oTJp0v9nON65BRkr66z3KPCRjDDg4Sle6hYjcgne4K6UkhJ2QOa2hUTzhsSVatEWkPQYB3bE9sI98Gjg4XZg6UXzqCg7pKubJVQ697jjFYpiWEdKCkY2LIGJffqqCib1DrZ7pDuPoWWL0DewPnOAw4jKHioIus+1nLU9+oqe1T+NYATUNFQmJBzEl268EKu7lFfP7tc+ijHEmsxpjkCcD3h35l0xew6tHgkIrmPPvzZB/jt2Do5NqCaAMGhWd6BPfVIlJvj2QCDwyALNIyg4LOKslglb9ULp4yBbhorDgQASGJhVxKQYD7YQuS0U7zI/rUJAJiQGViDacqDalZrp4MRSafSqroA0MtvBlAwd3Kzm8MZO5D2ZBDldtcSuAAvtn4mUqSGcJcvuvnu1Hqvgui9haoKgtaQDKFXRtasw+VIRwHFrLEajoBF0BC1rhGaWPnMNwSc596ttQgDmPuxDz0dqgY55gJu+wAQCgiZpSPdniSI04IWGfA8dzmffsy43uZftb0R2YJuOYRdoaAAam3LngiyV6nxYl0pNYDN3CD2D2BX7I0TamvqLsBBIJorYcuElJJ/b8tjwOQld1Bwl3vaormrVnqoqL/V/PA5A/fureiBfE2Zq271J2x2dW/MWa5xn7O95/9071MyMJP8voiK2SL8YwS5rYetL6NbhpnFMi16r9p+QPhM3A92tr4fiJw+LgqECgE0AYKbPQhiufJbKCrpWS3uQx9zL4NmzY6MQVTwsbJOU/INStMxcpF7L5UsmUplk0ykyx1ou9NHAQHGEnDZwPmCB40pMFBwbcDH14qP94b71npczS3Z13rPkgJWgCDK1NzA5svPE0i8VSkTGGU3a6aTg8cAGOJn4+9EfdEzT0ZQ8KsrUGAxJbIg4YfXgOCp41kBg84ayEK8/NmH+NofBVAwfFiVICAAIalvkhOAegAHXnBGX8pKD68WOVp9UeD7xoqsgv49UMYQwVJJamTvGliQ1YocEGugzHoRm7FGux8rAIOSGA1JGzDpcHDQ3SpbufOgJrQK2gwIVLRWgU3ei9uu7RVZBQRHIZE0TcloxESioG9sWm5Kwmm76aXQ8wCM2n2a9jgLL9J5aPEmJhJrxWhMymAtshQBAuUNW8ooOSNr0KY019FqjGmsxjgecw0O5xQzoO8Hv5ZamCmh7wkJOJMPsvr8t5xQuYJTQkkVVtFRajVIu+seRAhc9wboHo7DLPSSM7ZMuBRph33JPcq/kD0fc+2XJXptiQAPzCqQNts7AxRqRACQYFWNZQCAnBoSSSdOuS+toNQ4EhE2neuWe/S+xHNol8CkKaMk8yYy+r4rZBPI3puCTtZET8oUgbkrXCk8cT8Mx6NQcrjWBSDQv7n2ssNaatj3vln7w/H0/vL6AakDhFmB+ILxeD+tTkPLDVOS1EMihvSCLsgn976BgnS5eP0SKhekuxfA5QWw3YHzpccVlDsPNHyo7QAKPt4rHppkrtyUe01LZcsKgkjiQqzImC6LMEi6PraHDCTGPQEsZDcw7Im5ORjQK57e0htF750VKIhBomSycBk3wn1tp8fH2ALgmQEDGXLyP/7oQ7z3w2/2mAK2JZqGmJHapTCD2q4R8FJK2ZgwAwWmR8ssLXCkgqNgsU03LA03/zNr8KMV6DNQIOVxxQpkpRVnK1GzjDyAq3d1m+qzV4R8eg0eS8kLywAQmo8YaWfpnJwIeXshqLRUMCTlsNVdc537NkuAC4l2rYfIY8tfjnUNKFKJWhJVfP8LsBA3+xkYiFaT3YQTmCADBJYapcfH9QG0XZCK7IEDQNBiS1KyOaMmSXc0kBCthnhN4t6I+yLTyZ7oExVXxbQnqu4vP5AKtzuWPH0BCQ2XlMRMH2EbRPF2BdtaQ9LrnZSGN5bAwIGAAfnbWIKtZAcEd7m74AijLLKlKup2aQywBXBqvYjrLtlBhQkJogDEejew0lAzeUbFat7GdGw54a4IeLkUwiWlHnSbSIsF9YA++xd7AhzWJqxL9niV3jK6slmFo1K4tR+89HBSdkJ7EFhRoRkQUNvH7ACj+MN+X+11XwirItgSJE3jBCSsAuVSAjeS+xNduXSLVz7T9h1MvLz305aRSu4BhpcX8u/uU+JCuNyhlTugXIQx2F6ggoQp2C37QEDBv1VgcK0ND21MyT24EDhUt0wC7JzOX7BHFvgn7aU7aDvbE1l+6LAnYun2GRyc4EX5vSSBhoP7AKxgoHfVPdVpviJ9zPEFr16/OvmejGcDDJwtgAQavveH38D3vvR7whTMVua88dWHMIADKOWpFI8Fe9nYZmAQUOQBBIRIXz/+CtWTWNOcMhJlNBJlQE3v2QogCb3c2GhjxhX9no6V7PbWA7ZqY6dXJWgOaEiamy7f8fQyykhoSC2BduCuZFC5iODiBm476PJiPKCdQ0rIede4gzEq2VgCyuJbpCy9FAZQkIsr60ctm8lKMgFp9dlRa4+diC2j9TeJCJyz1Ge3+VzvwV6NsQKpgFvpACFbx7eMnCRYkSHFbTp1iIPkPOwLqy43Bw8BGHzCiz2RID0z3HdPEMooL8AU4ODAlL5F/QOW1SDfi24DA5DiMgAuSazxu9yL71g1PrOwloGUtlTB72oBe5kyatZqjSTFkvbWNFq/4aFJHMNDkpTJxvAaDPO8ATiQiTUMbN4vSsKdPm7ZWIOeJniI+j/z4871MtRqTqp4D0rh0b3ADgSWgKDJI64PaLEPgQUFWsGhxT63+UiJYc3m0RLCxOJOc7mZuhLv/o4swELvM0Jg8OJpGSNQdw82vXnfl8sBGNDlhdYr0CyEcpFgw92CDBuuDfi5MwUN93sTFqE1r3UR97XFxOSkRYig1UlzP0UL7jRQa8XHttxZpEwne+JMhuvfnLKAyAEsjIzTanz4Uc8++FUDBa2uQcFBl816KcRk6eOr16/w3g/ew1t4ez0BPCNgYOPl61d47wdfx/u/8R3PPpA0o8kPE6tBMZ+CA5D0cmfIxnn5kSCtIeUGkd5n9/0NlE9cyLPIUYt3UIWYUpF/GeKczQCqMAfMLKVda1OWorf8NV+WgYJ+02ivhQgSjGUk4GMSBF32hlQSqGrHvEZIHm+gyNXO2dqimvB5SFolbRGMZLXRUxBQtwCBCtvBHxrjCFIefz+AgqFpyyooCvBKilx2AQkaAHWsxlgUIGygVoVxyBuIdtkr3gKWgjUZD6TMUED87htc3eATMLBASWjGRErZA6mI3PiTvTe5ZTLp+sGoVnEj3ZU0sEndn0pe5fCSNbdeLey7AAg2AwTJcrjJC+3MsTSyJ2OPCxpaGBeS37vWhvsq87vPhK0mXFPDJRMeasLODZecDkxYnPsQB1ESLjp/BwWlg4LNgFASmtmtdov814C+W8AAsBRYgqfDahEutyRnTBsjypspm37coezwvo9VBqs0J3IwsF9P97fF0nASGWfVS8ne88+Fe21+rzVl8BgeP2D3ad7BpYB3Ac1Pve/pcif31d2nHCDg8gJcXmhcgbgTugtB3AfXXTIPPt6rMAVVAMLeBCRUN4TksLUxLtrUxBhSoLMHtk8N3M6gYMu6J1apoU+Q47IX8gAgkbIbYKF9yzB6nYLuPrgJCg66zH4/bDwSif1SQcF3v/I+/vY/+tuLo8t4VsDg5etXXub43be/IMFypEKZCBY/MIyYoWDRhxqm6m4FSEvilx/9BF//wdf109OizEI/bKD4uuexnKBNaxwi/yqQG3LeNIpb6ykwUBODmtRQ31lq/Vcm96dhAAc9370Cw3MU4KFBCsEAoFR7DW+o0NWgyyHegBm08WhtJLW+tYEKtelaa/VCs17IwEEuXgJVAIMItJU7gWkEApR0JWp43Zo8WdS0CaqzeAMt0wxjGvImj6EaY7rcAcxgrkASgGC9HKTwUlcO8ts03LgHEKB54sO+0H3k37dHq92QlK3IG3LeQEkC6q4IFqhQSS70MhFIrXCrjhiDsxr3gFqjUmO2QXQZREAw59nHSnzRfebrpvsOLJUSrdLkljqzlQNA2CphL8B1r7ivjEth7AoOYmCZzd9pYKzBzIuSUTJhS8L2bTr3zVmCGOjXgHYNRYNurI3dt2aZ0VSV87H9EFMO7bhqHbaHe68y6PUCrBfBU/Y2W/yOivlKZjrL0Fgb32OhxbGPbFNuXa5VubclZVGrlAaWzoMW45iNgO3iTIHEFdyNcQXcgw2vVcCBxRQYY/DQOiiYq2Pahq5NWp0DsjfMVVCyuA/uLNYkiaurpAgKFtkgXg3yhgyP+8K6PZrBZwxCKi5jZzD//le+K3V2JlDQ5cQNYKJnGvco696MoOAdL6O8Hs8GGLwKoOAdo198gbRXoaGpVbAG4MwBMYHRwQEoSczCH34D73/5O/jL/+jXZMMAA60ULYxVfvGwwPOwDUMJZG1F8waLEM7lIgpVfVSb+aq0OUwhwk49lWw13DdbTRmEeRSxPMue8DGqWl0Z18ZIVSo9pBLiDbg5DZlyRtM6A6xtWT0oajhHcj8nKI1gwJgCStp3feRh9GJLSlgVloKrpTbu6jfNYzoEd+uF96u+NM6JkgIVjyXQUs1NizppPYXGrQcocgOzvpcEILAFUc5U3qAMWt8Pvg8CSIifd9SfBHgYUDSWpFVlD6xEtIC5BEZC01r6Bfe1ouySSXGfqVccxFgPwFJZxYIS4XinPnlTsu6Td3qWBp+80a2y/ydlZRYTETbtlim17UV4CziQTJmSxLLfK+OaCC+alnpWcPCU+VtHwq1I/ENRnbilcC6ZsBG6D9+6B5rwN0v+5tokANTXXi1E3w/h88OesDV3VssAgroN6j72IlBg4IWGjBkLDYqGPY2qzYeg858ZgjTE15hb7+zekxLxeu+p287KmcNAgr0ewZT/gPZKsYJl2wXp7lNa4vhFr1dgcQVVC101cTtdq4CC687iOlD3gYGCGHsCwMFuHJaZYsGmd6ln0hhA3JKCAq7SEXbqKOn74TE5norsi7T3fcG9JbQVbHMdZfsJ8Ji4wc3o2QcL98F0neW3RrDqTLr2BnpsPBtg8HVFQv2kSYURT6zByQ9EmlCpcgMHLz96JYGMX/pdvPvpz8hnDuVHIy3YxpvdLAPmnlpkxwT8uJQLUhYlw7yJcM0VwAUAHBwwd392Yx5Yg1Il7kCa3EhVOBttovfkvaYWF2Mn4IEaSkr4WFvqJkqaIw+kKn5CKhe7wmqt34NeEDxPcb/KdQmBgW6FBFagxxjITWQKllXYTgukN0kTFK6lrO1mIrueuQgYYO4gQa9z00pAs/8T2svBfp8U9MQbn1qV2AMAVNhdGQ4QoqUw76uBJZqUQhS406BszlC9NlZzIVeAhbXgXLCVO6TSXQuoUugnNQZRr9RoVnjsWeDHouBvT93qToQOApb+eHWd7deuELxM6wgMxDrqhaWSBnG2JFkTUtI5lJtOwCVnNAZeTM2gzuZPicaASAK2NNLE3XXAoP1ehf8+Cn8D9jfWJ0Ulau6kNgGFR/fESelhiyWw0sO79igIHQvP9rPcIrqfEe87ZeeSgvJcvDcJijBfHCzM+R5kMFBk75IVU9qNKZBOqwYU4rwG16CBEE1L5Hwn/RDKC48ruHrFzl6M62PNOnio2mRLgcNc4juujY2sDIHFEBhbYAGnvt8PoOABpP0fDByQgbFJtrgMt0JsbVcAoCDBjAiNPZCgUDMGzQ2l16tF42Z2O2I87mpMbMHLj37yRqAAeEbAINIjdvkkcHBmDdRlgLS+uDHeAMCPP3olFRN//Xd6xURg9PXEWuUD+h/zjNluGt1Uhxs6SfU95IJ0aeAkjYQcoULAwaVk8A7vHtZKwgswuCXsmYMvWasbtipxiyn5zdNaE0tfwUVj7WzHwEMjlKb+Xs1+SASQFtFJ+eLXiqDotO0gyiCuLixioJK3Wi3bUZiSUeUd4QIdQUeEzm5xVxBIzsEuogYiUt4k8FCtNvmdLqys2xsAcK2gTHIfN+2LgQB6wrDXuDWky51gSWZwMlBpN6QBTJuZuZcsAJWHa+R08ES/cqy5YO4Wygoaqwh23gBmqbeQilhEAK5NUhEzyX6YKzVaVT6brqUZxip89rcxA4Mvvu2gq1nX3Qfbae3JorLrY+4b63+hmR4pb9iKdcsjr4NvrZdLSrpX3/wcjOHYHPTAg/yoPqzTAfdgla9qA6TQ7TMyYGaQeOnup+0J4iauA2tSdAMUtKtkIsx7WW+CDsKdxVLXkgIxLyqkLjKmJIF/lLsCDwwmgIE1GdwXuQ5AwYwfmcoEwM0IoOytk6U5krkRLgIEWu9/UBnCFrWGh2Yyqh3iTAZQ4Jkq8EyU6GK6Swkviqa3KmjcNFjVQcF+L91m69UzQtpDX4M5ldOuOeciwM4BWA9WZla3oxogXpqdVT+ZQp9iB05jkeIIsQ2RLZCGgb/1RqAAeEbAYOUzEZIgsAZW09hiDegEHOh4+dGHXhzpnU9/dvSZz/6fSD3qI/YdbQ/0n/kFp3Q6AD19ThVAs/LDm1QdjBWt8vZCorQBgCU17S5n7Jlx1wiVk1CtIM1jT9IEqQGI4IC1qqL68YoYz9iJsSd57b5WEGXpNkiMq3DW2AwcEAH0AKoEpiznndWqiJtYhYyzAakLIQ5+OA5Cza/Nyidbdzleu4LuaAiYYkCoSffLdTRvN/NQnKUmmFuFqzbVojZWY7RMBn1sD3BLi2rz95mSW8wR0DljVIM/dpWDPg9z0QRrq2dLbA5QqVVwuWDLFw1MFOWamlXiE+pdKnYe62+YcRsLu6zYgccK7gwW9swYTBY20uZ73jrnFY2f2AYWASH9FlqCWIMZF+cRWxMnjYWIoMYtwaatxmOwn9YHsLVhq/C3Whe1hG1tzB1FWe8D6hk1semYK9lgdTdTOHX3rINDlcFmroMOCuI+9lbnzs4p2C5WWrj02gF3n+pWe+qAwZqVzTESkUGb46c4yD9hFe5O73+YIZAUjHjNgovHFVjMyc7CdF2r9f6Q/h+twftoABhdCCl56qrU4BCG4FLk+V0mDaDN6kJImuJKyiApKLh+DFRhDZYZIWfyOwRQc9Zgz3IBCncjQhrTiCzh1t0OJqfmgPnHhu+t4JogUvf3b7l7/Ym/BuAZAYPZwjOrD8oacCqyiS3gY8jAjT8kr7/82Yf46o/+uhRHeutznfrzA5yDAs83Nj/hXFJUg3TmxhpUili5uQyR8eluFAAAsG2fAhKBMzzewFwKlyb1CZoymy2HoBwFB8DRD9dReKiiWOUYewNS0lbE0HiDCA40rcksoEPTEJosKeoWlzMHKfu6dNZHnpl7wEABskRx864WEcaCOubqYYskLpu4OJKCgJoO1xQQmp00NoH0PJwNSgnWYwMpOcXHBPcZUizHrZazKco2AQJTOi5odF/N142vD7InQpCkZEtUMLSnRS6+F3O5SOYCSaXGpuvGibxX/Xh9Q2BWDCRMoeDOftUc++5vdTDgpXJ7Ct2qMM7QTtfSQ8vmIMEAAiVpppPyhlIydgZqk7oL1RguPY95r9h5EIzxmM/jQX3j950l2O9722C/V9cKwPcyhNEhc4tp/BJzA1e9h7NagSxteaeN1veGuchcjrS+f50pUyuzrcW71Qjx1MCyCVBQQ4MOoOBOrXYBDcYYsIMDAwV02CscMq+sOyopU2osiLvz5utmMsAYoyyVDSvEPeBuJP26vKaljoN8iiPZ/QhgrmdRUsLF/0kg7V1IV5V4FIk1oevHDgpo/xhUr2j3Pz/EeoA162k4NRI3rwVVl83jkxgCBqloDYhuhwiY8No5nZUZZNkEsITVTuNr+siK8H/8sz+RmLivfG8JCo5RJON4NsAgjsNFIM3ziy4FZLjbYBovf/YnIygYRogPcIvULFkBBzMo8KhijdY/SzHia3KhyRrYQ9rMJH3ql+IJAUQo5QU2VgswAxXmUsgqQDFW5ULVIo40pCzOQ9gGqeq3s+SZU0le6Y8IoAogQ8ABJaAmEFWtpBiEAnXVMwCBlAESNwDDDSLFX6PvWILqshpABaSgQCyPAtq1AhvWm53D6wliGBo4mMfcLfLwW625ISgMUFVLGBP7JOcwxJUEV9KseLhexTpEP38vxZuzCNBaOkBoysqAhT1ga3olGRWUN2x5k1gT1nLITcrAzg1djPCOhVyG2IFZkQZqFdf78Xwst/4kMItSqGFRJkq7asBtKqCqFHMu2PKGLY8Bi1JQqlciPTuXQ2nhGoBNve8swapGwCNrQnmTc9WAUHElAFY/AKoMZG9UjLuzA8YIom5VGpRLSCMR4/FJAgq8gJD68Q+pgQYKyp3WDLiT/WOsjd6Tlvlxfj9KGjVlaEOpHrHPBhTQFZ1+Ge5XT92dUAGJK2BJLhI5Yz1gNJNKZdJqWHlvD4RNhBdblgJX+k9qWeTuQsgkQMFcS/vHwiRFUPDzfwu+/7lkg1iKqLsSJtntrpKt6wSLsUDQSRt58lvMgBviQQ6LrmDAyoYfAivHWIWXH32I93742wMomMHdY+NZAYMVa+DvkTZJAkRvmXicau8fmIKzYwWfm9+p3MQ32ZpaINduRVmakVGEVpQnBDVZARBQkhbHHqCmhv6nfkkU4FWWN1HCJV/AmgJ2xyyotfRKhs03lAwrEGP6z4razCBBuqNZDwbGphRugvRr2BkgbTNnkfG918Ek3AJTYMK8NfSueSp8atzB/UIP5WOluUxGKVkAwv4AkOYJa9DXU8ABtyS03jDNpAF/3Tdr87fHmMp1TKccoxMGwW9ruVKilpM++Oj1VxR0cdo1YEwBgrmjNN5BfL+Xfv31favUyLQowmTXg7pCFcVvvTBqdxeYL966663y6sO+hsVO2LXS2AK2GIPQOIfyx2iXO1Fk5eIWLOVQVIrEOk8al8BJ99HiXAwgOiBo9RBdTvXhkBJ4APDTegDw+Utwa5Vz4CMDAw0C7MDR9sUxeoVSAmu6rVcmnPeduuEoQ1JSw9v2vaGa6BkouHwKnC9A6RQ+lwuYsrQvbpIZYpU82+GeZIB6MaueppqRchawoAwfrQBiyE4xEGJ9W5rGjZgc2IMMmruAWqMtAJ6SGAt0dVBAuCtSw+JFSXiRM+5iXEEGUp3dBwEU3P+8t5i+Psie1uBPPyUrHpVSZ1Eu8HoRDLlWTEmY4EIALNhZNixZzMHZGMDB8T0HBT/7Y3zth789uA9WbMFj41kBg9UIWLfHGyjwYkVf5st5+VEHBe+8/fkRWEz0FU+08djitCNGsRYt5zdaI8eoYkoVlHakrXThvnUqU8CBPIqlkAQclKIWSkJDAyM7MGAGUmBFhDHoxY+MejO/8tnYtZaBNAuSev3qSVcrMwcg28WkbczKxgrI91oLtDBDo8zxuN/YfN9N6/lvn1IXhBYUAUAXU0ShmpvOI1ZpmyPNHRTMZZqTWGFWeMrrCsx+82HW0+2odEgECQMoCCzS7OJwpdqqZCRYQFNMpSwXdX2IokKSzAVLjVoW3nFfd0+vNUq4xw9cB0AAs6AMIESwq9c0Vr7zjpgIyitnrzTJ+eeiwK73nsaGu08pQLi6awG5AG0DqQuKUgalEqp16vkwEN18A8BpdQA4LRYOMpp4sgqXa8FJ3isbaL9qFhrpGjVQ0s1+qERJwyOT7DkhE3RvMQNJaWdmwO5t21VV5MNy7xoTMxcRGuoFXHrdAH1+ZWDfeyxHVWBQb9yPczxKbPwjbE1G1jLZs3NwkAkmixq7fLAA07NhWVSSNcPIaZRjVrHzRemPL0rCiy1jyxJXcKdxB6ntsvf2B2C/R9rvOyj4+P8LfvhY9/vDIfDT9rRlNVHOHfrtKiP2q+6ZYEDCgECXTytQMHdGXBo8Yf+PoODdJSh46ni2wGCqcwEA4oVOWaLJE+TOVB/xy9ev8LUfSfbBO299/vB7ZwzDk0bMWNAAorZX7ScQBY8EvrV910YjwVrRDdBS6uiTCLQn5I2UagXaLpb7L10KrGqR7Z1EEvG9N0ZL3Vc3VLzTCN4UGITKLG2FIaCkggEmEKOX5tU7Qr52rBFuzIAFkVUHBnBBsHNbGRjIkOwIaoxC3NOLALQkgZCJEtJV/aLXhVvBGIvrQ2cD8loBGyVoBVlou/T868E/XsAWaW8XOkQUs9+zCcwJ1vjoMEKK6yENCio0OElVOfO3xHmj26IOSZpGP6ceCEe0H4XNHNQZC+1EQBAVaLSgNHYm7mc2FIgRGMQmWrFuPq4PYLNw96tYVTODoH0rYkYDxSC5x84pAoTVOc11AmLkfdiMIgeKsrrViwJNG6kHJwIdOC72CaMBhQYoGdfT94BPISnoWOxdZ2EuwsTMRYTKC/B2B+QL2vYCLRWvLugBfhr0J/79NmR+hMsgRbCSsk2azmwliAtbPbbYG6TXV4mywQBIZZErgzQcggrJGyE1MAqSGjZyxebKlxftl2Gg4JcupRe3KhpsyFXTVe+B/R5UH9CMJbj/uYACBQbt4UH6QNQ53qu6OyfZOlhX2BuB7ctxiBUYdY3JkyEVRz//8qMPHRS8+/a73UW70INPGc8GGCxsNB+muxoEHFCSWgBgoX5evtaUxC/9Lt41UDCnF1EWqtyCxGwDWEANJdBcVXE1NIDI0tSiEAUATgzivoIHmyPZscipc1AKwYhCh8zgADBgMHZf7MfRNK8ADszfblZZA7yJFIz+J62NTgIO7ArEzmJN85GtBG5r8KIlcT57a9JGOuxmUveBFSKx6ntFK+e1JP8upQCXT7mbBRArzm20JDEOnHIv42pK0C+CWG0UMwBKBwbxNYuwtr4Lw03MUiRLLD5IamKIGHeq2MacKTG5mACjo7ubZEUPdnDAQrdD9tahCNMwT2O3QgCtKc+6Aw8fnwICE5Ztr+CruEyasQXVGKvWj2l+8ERoDwm0ZaRyFRBsMTnbBXR5ECtuuxOAcHkBytchSI6SZop4FPbivFbFg+YSw48UDxqucZZ1ljbhpqDjB4KryeZUYpT/cZ5Aln1YJPWWd9mnxkDIb9k5krucaLVvA5ClTZgBLzdsDEG5kyJCoQ/BgwMDaFnq5rFFO/PhfpT+AmMb7i0n5MZIel9KgS32RlHCNEzuSraOlJ2WYPbIBKnWCQYlqdFhZY0vSNgdRKhbFccaHBEUFII0AcspBBuKCwFXSU3k+4+FEYvAIICCtteDvJa0lwZuBG5pZI5vjJ6hQX19VdpbGeW4r/p+ARCYBpAW3wtMwQoUzOMxjPBsgMFyhAvTEBETqbXB+PFPP8B7P/wm3v/yd/DO2184ggsT6NwAtKAIco9ZYJbFSg0oLNaJ3dgTnX0YgZZqjaXtbEuolT1HOYV5tJSRNL+dKAFGWWkw4kVjsxkN2AUcZKrIRLjPtOxbDmBqySzWgJUIpaTWAIKxQ87aiiEc0L2xBBY3YNTkHgDBfZXCJPetYa8aiWyFShaAxeb0sd/w2bv0WSr3pWTk7UVYOy3rbJUZzQVQtc67Va+sVatKynWmwA5ElsBzvi2iGl1gD8MjzavEthZV3E7diyUvvumrXPxQ0tkU0hiBXkFZFa1aztivcqVSBvY0WpoFY3pULMIU5tkDaKeMGgvGu1o8QadU+eEeba+oD1fwtaLumkK3V/eVe155az1iXB9bTqCSQXtFLhltq8itCYMwu9Asyn2TFLik6V3/P/L+7ce2Lb/vwz6/3xhjzrkuVbX3PpfuZrNJBQmQPyCJLqa6aT8IViRRymOkZlMU5BiMZAV+yYP+gcDJS5w4QmLYBkU1KcRvFhnTVPwQntMUKdlAXoQECBDYYt/YfS77UlVrrXkZlzz8xphrVe063U1FD8HJbJyuvWvXZa05x+U7fr/vpRQHufFBfvz7Wr0JGiiowGeVJjb+R2P/P3IUFBWTsj6yFLZ/axbm+gBArpr9xtL7rLEizp5cdVlVH8i1tbCSNS8UKesGcTFuV6vh2kZYnQXd2TzoMSgYa5VgXi2Hz4mFqyrpiQOEiuBdDbtabafzCtpNFmtEZVfOfAShvJXzedmqqMIOtNh6E4uBgVSkkprtuyOlxjbI+hgu14ngpXoVWPvACyZXdC3/AyMYxmk1MmKZYB7J03hBODRQkCariLW12tZp+90C57XjqWv1IpEzUFzHQ61gaxsnD4HgW+2FJ9oNH3znW1WS+JBo+BY35I9RLYDPGTD4rNNU+2Suq2aLUv7w22fzh5//MeYPlxUGwFB9plqOtrKgMY2luu9JqSDBBYrGtUz9FPO9kWtythOB83WdaD7fbdD4qTJgK9FuRZzygIxYitjmkIpFKStIFByJmLWmKz5tK+u1OYSZj3jNmDyfvh+swXZTW5W8AYKCre+x2KafKigYl8SUC0tMjBWkzLm6mRUDCcADD3+zsG2xv5YMuKTCpjPz376ScgoQnCNcggNY2y5r+6USAKV0rJ4Sq0+BfkbbIJwBgZyNh9aTYLs3l2qVXKs7Wc7ggDpeciUptV5yzpVAmWvf+TOqT613nXOtLuVzqbyBhVZZaAooqVrpAo9nyaUz56XvxgoImld//dhOT2mye5higlTsRNVaZY+PKrXKlkg29rOiKaPeW5pi1eS73lsuR22htQqd5LySLZsuXC5P4tqWsR/93h6DgAeSwEsg8xmXtE24nfbU2fMTOYNJH2r7o5Ekf/LxUsQZNyRHpNe1atMCilaAsL7WxFsW483NsB/Irjv7BFRQsOBYYmGqCZtThjEmTrNZDs/Z3ATnVH70fIw1dTMpixYGbxt5nwtDsHato0p5RVAtZiFT2wtwXivWe1EnSFtrHEKUgndav87muWpdt9YO1XndCjXxc/DC4Fz93FmB0OkF2TBOSBxtbLcqQQPD8WGlgJxtrK/T8AwO1tevdfNvFRwXWE2OVtvpZuzl3gYFzQvlgVz0s68Pvv3hun81UPAkgfvyNfKTYYTPFTBol3Am2T2e5i3P/ve+8yHfuAiUuLyXTwEtqzBAaYTDKkmSeiBrlYNSiUPGTs1I6C4+NnKXlXdFiiHhJxaj5hqYowUFWRXCeuRltRU2JrD1W60UJaJ0zlOboLWEZxUNh8XbflaZEGoE6UXpvtmHasUfrpXn3z741I9Pg4JW5+SLAAEAAElEQVS5LkQtCGXOdkKZY7U5zfbvLQxlfV5CNSqx0uWQHJ2TWn2o/c/gyNW+GoBLcCAXJ+leTAoaZytNX7aG6jNtKP6xzr659K1Sy8eWpnBmpz/Iy6hqCTj3knNG/EUPslxYMJuesFYLnwYHzWPBNjgBV8v3jfjWevGlVIqoW21Wn3KTXN05Wzn/Qm3wGBS001MDBa1K8BgUlPT2mBanlGy6lAwQI1K0vkJ7t5dVsrfag0/owo0EXB5suA8VQ+nsFZAMKKzksaYsWAHCo779xcJ/yTxvpkGrP0D98yUnwsZJWOfpebFvRlgPdke4sFQv2U4ForXNcAkQclrtxt8atysoCWfzoGBxxisoSAYKrGJnMcaHJXFa6ryslYOfdD52rtD5TMbR5ULx9v6CU6te1t6jz9amXMuNbw0OkGLrSxGrLHinxmdKBbxDUq7R3DxomD/Zbqyuhi0wy6oFBhZXsmGVruZWDbtQpjQjustKwZNXrdw2RdNK/my8pPbx4nDRHCZX74hmlNUAbv24VlIe3iagRSf/4hoDkHnI3XjwEuvt+uMUDT5HwOCJAXexsrSNKwt86w8/5K//5tf55l/5DX7uK18jljOQ+KxrlUG1ie1CVTkkJMuD397+vG5I7VTa9esG4HqA2V4ToDG+JckBWyjzUtUKosgy2aJS3bWy8xdSPTECXhhWcCBS1mAd9YamY8pErZ70PCxZn3uIslYNTJLE+rGl2bW+4eUaBw2QmW1tazfHYpGpU84rKBhrAMpxNnezaYlnCWN9Tc2sxDvHEMyeeQhay5uXw9ehLfgK0EdthXPFoJ6s2gmyPo+1ZVSd65rh0lOucG9N5HoDSr0J60k12QlQRClJQZbzuNALWeVacapjSHI9GesZPNjDObeQ2on1soz5WA/fpHIXoGlV0hTrRK4+C42R30rsj0CBEQyt1F7aWG2nyafMjCojv6S8/vnxlXPGObWKgxY0RooTcozrpvwWOIhn9Qlg4KCRYZ8wEXrKK+ABEHfuYcXG8PSDNu7l2EDVTuStn984KKuSIpyNgy6klsX5c3XjouS2+iM8sJSuLZ3mKlkBwpM+/Zev7dJuuHoUNFCQxBEvKgWPQcExRgPtS2ZcEuOSidV18HIuNsOoPniCE7adIzdA7l2t7giiGVcckrETvgBF1mrt+RZYb7KRCKGsXwPQOzvQuFxwGIj0P8F6ZaBAa6JmlSXmiC5jJRta1eCBXHUeV+vpvEQj075lOW2XqiDeo96hweH6ykNqJNoGCioJ9IGhVBsX6i/Mntw66H4cR+Bb3/6QX/pHX+cf1P2rVQnaqG7r8I8QmdXf8Nkb3ucIGPxk14d/+CG//Ftf59f+st3UfLGZXV6XxG+poMF6/fXfxVlJ9wGBxDZnkjwAB2/5rGfb1h6DA/cjkOlZ5laZ64u1FFbntdaTquVy9dA5f964k4EE881XfLlkHV+i77c9552+LU1qP/dBz1AuKgecB3jKzc40WzpajU1ti9BxTkxLy1hv0qVzqa5z1tOMSetC5eraHnkwhMUhUpjF/ukx58DukYO8IFIZ8Y/xuCi5TdJ22nvsCqeOciG7amOnVVVEfSUA1rK1ONAFiRfgpH7vyntYLsBFc2j8EdJFGkGtjr8mw/zM62ITaWDjwaaZ89m45dJ9r467RrxqC+XlOF0zOFTP1c9VznWxwz56fXrx91J5FyUVihRyTDjnnn49yYiHRS7Aj8iT7/HJSxUpauO+tZlUrVVYizaXI/tJtYqvzP9L9cS6+HuK70CDAYI6hp4cM5Wn1MZMyWfL79XdM/vKf0hWafyMcVuazXQzK7oABXM0kmHMhSm/DQqOc+a0zsVYgYHNR3uclUioQucs/bAPdSNr74Va0pdMFMVLwVXr9ssn/2DdEMhi9tbrqqlWPUiViSjO1i7jLTy9Xl0eYM4ExBqMpAYKzP7aWmUrr2Dl0UwP/WWeWIdXXkGda49BgfgnvCMuQYHrzs9GLypJdT1pvzHltzf5dt++9W071D7evx5/7YPrj1MqqNfnDhg87DK2grpd3/r2B/zyb/0if/8Xfp2f++mvPnlCv/zex5/IsDJrrcog1XDF2QRuzNLLnjZtYakLZPs4KyyzebdrxIJ8krnxXTIC20uoMkeklhTjYovOMltLwQdI0wOYqB66Gqyj2InfixDLGSDk+v7WE1j9v1Z+euw7vwKDi3sBdvJRrESqlEcD1jwK7Pfm1fd8SQYOpsWAwbgY8alpm8FOKZNAH5wBmXz+N7siIn71N1CMFyGA1DRI9X19cxfATa3MXG9u3VAe9ggfTOALV7hm19pY1ev9rvdP28nKdagrD0FBnNbWQguAWu+iOhB7hs3b4C2Wc6saOG/uexe+CtJITnoubV7qnB/8jMc/N1/ci7XE/jYwOf+IyodIinit7nfnr32gfljHwWf8e1UrPHWtLnopgSvn1/2UVPCJ91jExoC1ChSynJUhjQDq8rkyIfnpe+7DWa1SEwkfkPzUm0eA82c3Qd9RXLhwbORsHHTx422uNTDuH7h7Nit3qSBzPSBQHgKhRma72HiK79e0whZMNKfC0oB5fAgK7sbItCROc2ROhWlJb81FFYhOGUJt+Yj5B3i1aO+YM7HlWFy0DYQGnN9WKShQKDUfoN2nBiCMwKwiZIRQrHXbfublWvU4Enx1Nayg4Ew2nMzcqrUP4rLmUZTVi+PiCV1W96rkVr0zNU3wFRRWUNBtHspEfTi7TNaKwWOw2NoAbVmLuby9BwG/9+0P+Rs/wf7Fo3u7fg5+YozwOQMGbw/Edu++9e0P+eXf+kV+9Rd+nZ/7ykNOweUkveyVX17tRrciZkxnKY6qJQ5arKbUQCE9l/grFyDXBbuokqXFbyoqM6JtwXoYjvJWMAqwEqaaQ10yBzfpxQxcLokrHkItX6rY5NV8vjfNWKi997aPtKpAqxK0iFK9rBRcjrJSEW9leDYgkigPAMTqWVBJTpegYIqZJeZVxdCu4Gqynlea/LK5nPloJxQjRNnJYUmlbtAFyUKn3kZ6ugBulRz48CE/8nG/AASx7pXNFe5cvisP2kgqUmVbbZESvOusHLy6NM4GItOE9pAbiXSZaeErpGil4UeZGs2TvaknLoO3PktSubY62vu2V2pk2bZpqlt5LLTfkdoYpgKNKsPMilPs5PaUjP9HVC4+qyK2miA5We197f1WoNYiqNu4rnNnBT9PvU+TcRiAaT717fe111PvaZFo9+LH3O9VrVJPgtrVcr3rKyh45CZYHroJNlOv9XdTm1/VC2BZx0119ywJLgDCg2j3y2vNGwlne+PqUxBLWb0KUoExZqZs861xfFrV7jTHB3NxueCKNIOxh5yDcy5BS8J8cO+o1caqZlj9Di4fgj20VQ6di1FnqLhHKshI9YZdrlON99SAgbsAB0GgZWGcrbAtPGtNsqzKlJZJcTkeSzLjolKygXA551Gs1tMX7QKtVQL6zVkRosFcJlsl6Snrac6AEezQCQ/3oH/ynQ/5t/7zb/Cf/KVf58985Wsr++ipmfZ483+bX/DZgKJdnzNg8MQlxin45d/6uiGtr3ztRwKBdsveAmOlIWT7q1kC2ylZS3P+cjjvKKtVbz3NxaYhlzVkQ6WmbzW+wDIjvkrF8gVBirooXy62F+XVEpczuWWZEc6VCqjlYt8RXMA5wWUhUhF4KeQs6+b2ABhgo6lVDLy093hGnpeLa1nLg4Y4itqCqHUheOpqOeqxmIRxWaVSZ1KkqJBLfmR2kmziuwtDk6hMkgkXaZBLsucTsZPYOiREjESql20IW6LLZbXgESBoMcDNnOlSakW7JxdmL0kFVyzIKLja1qi/R2Sq9+1Cs94258Y+j4udki9aUXJBkFzbSJ9lvNQ2i1YNqb/P/pCtbN4yRCqxrUkpVw7DhZLGFkT7GablP7cJ2qL55Hht4+RiTL9tFMODhfcxw/vB62nOlNIiZluPtv1OOUsAq6eEkVS7c8utSQGjnt9zTp95vx/LV21D6Iz5vwYR9ZR6QkxI9f//ScZNqRvc2+PmMUBYn9HjCN6L+9BAQazVtcYTaGmFU2vppcycrJ03LZEpngH6uKRVPnw5F32dyyrgvZkMLZ/Rh28VBpHWjqRWF55YQ+Tc9c5Z0LoWrZYYRaru6LxONVDVKplPJYE+TNGs4KBZXzfb+ss21frcFQ1UErhbx/RbttPO17aBKUGeUoSsYM13IK46v7KCxFzfU9uDHrs+/v53PuR/9tvf4D/+i9/kz1xUCkTMOeeSS/DWrPuXaCPA5xQYXN6L1pP5+7/wG/zZr5x9Ch6z6OGS1fkQJFxea7knFStj1f+imBmZquDV4cKG4iIS6yIUHUWckRfbSc97ig+W4NU29ip1fOzHDaxyqfVq5dWGfFVrS2FZTyPF5ZXFrC6g6quLWB2cdV0sF++tIcxLi1OvrVReaMlql/e7kWfk4iiQCmQpxDZhOQOr9S1UPkGrXqz+6A2opZZTriySUYE5KSE9cmurJcwlFvrqbeCKMZhVbNUJWoFYWqwe2chpbWZdVAoywtIW1vo7llwqObJmTvDQFU60OcJZ1SCpBVVlNfmod0Lww/l0K4qkKmkUawfleXogTzMm/qNfAlYhWNUp+rBK8JTPwlsM0URxj6SUTWJ7CTwvzHQUOGdMnOWdzeaYSoRd+/GPr8aRuRjfl/bJD35eW3zdRTrgpSSwke2aJLACIfshF++1KRLaifuxkVC915L9mpj31v1+BL6062vrIJxLxI/ig2Mqa/negAGr8+djh8/WJw9O0WwWv6EChFBNvII61GvdwM5pnOv4rc+58WFiOf++cx4J5mNy2dKrwGGpXgYNoMdUSOlRSmq2ep1qWdsi6dEmpsJKBlzBTqs4tn6/TewHiiCBdeyupkj5zE/IuaDIKjn/rDXKAWvQ14N8jBqgVfKDiOu1bXZx2TqbKj/GnT/XxncDhj6cqwTNTKrZTrcsCj23lhLWhmy28Gs78tG+c3kI+v3vfsiv/PY3+A//wjf5U1/+qmGX+v5LaSmM8mDYr8/i4t5egrCf5PocAoNzYffDyt785l/+DX7uZ772Vk9mldY9Kgs/1U54TKCJFfYbci014tZKYSk3ZOxxnbON6KJ6sIKCyRY7dd4Y4H5a3ddssaqL6AXnYF14H7/ruCAiZFG0u7gTpRhiofYmNeJcQNURxZB5hlUy1q422VQeJdStpczHpxVbmNUFgjqgWhYX2yTPJkVlzW74rEpCeSSjbCfRVB4uSO2/zBlQJIxU6URpxPlYzE0yCThx1qMs53hlLk6RBXlQ/m3gY0kX+fDxoVNju6yqkmvee5V0lbK+jh6gtRZapaC1nMRBmh7q10Nn44GLQXkBYlZDm6qi+El8FoCzVXJONEvnFlu9zp7GNXDp/DnVtxz3Hljxer+W3Fd9fXumlz4EVdkgj+2HH//cdiJr7P8ngpbWeVVP9k++11omvvQJQAxEryz/Jgd86l77cH5fzc+itQ4ayc/36yl9ycb+P4PWh6Zeqdjm3C7bSBWv2cx5nDOw7GxtCkApsq4pa8T54/FbqwaJ2rIrj2zH09lZtBkXPZhHFwD9/Nwu+wYgT2wvbR5by1FXV0THWVW0goKL3IrVuro9K5HaXjUPAFX5461Rlz+7goMnE0Gb+uZxW0sVoXrQrC2rizX3Qna4Wk43UNAPlWNSqwQrUAyWUlsPGZf5MLk8vd9YHg38s+9+i//5f/EN/o//42/yp79sRMMi5gfRwpfUEP2Ddu3l0H17if3xbQT4XAIDgMKH3/4Wv1h9Chp7Uxqp6+J6ChSUi/IOFx8vh9GSyoM+l9ZeukpZ+8ypoWTXobVq8Lh6IF1vp8RloiydeflfJLxJK3VdXq2sWisIaxRwWwDjYvGeiQsJXYImlykFXCCoJwukygm4vDWtbfAAgdfTyirHW1+PnF9TTqjvra9PtSx2kIq5o3kF77hQPDzMZXh8PTaD+hFfaqeiXFbPdesbSlWj1fAnBa2ul6UI0ErQdjpJFRQsjSCZM0s2H4bTYmXYsfIgWqnVXtc5wCV4Y20Hlxm8o3Nt0hvZMyME9WhoJz1nrQURLvXrUrItMs1UK9fcAbnsrbuziuKSOLnK4ho4qKeK0qox+QzycnpbLXGppJnHs+x2tWXVp/Mk6knqTIasJMD2vZcx5Mts7pOXUeTr+K5VkG64WIDfZv//WF+J9l7Lha9EdSk9s/z9qtBor/XS2vihfLX6WbhzLHTxnZH88jl3YEnncTPGtLp8NufRtjG0sdOcBIekLK6wCa6uRwWKVsKd9eucGOm5NAJiu2eck0tzrtyhi/XMKlxldQp96no8vyzm+elvUL0MLpI6r1k5B2dVgBkLrSTAxpVooG39ZVKfaW1r1TUqic0ZefylUNsGPF0laNLPtEBJ57bBY/r+hWTVwI+eEcijcd7G+AOOSSOePkqsbGNibSVdgILP2mPAqgr/9Hvf4t/5nW/w9/78N/mTX/4qqZRzq1Zsv9LKy3CPn9m6MT2uFpx/yQff/vDJZ9quzyUw+ODbZv7wG9X8wTZHyMKDwXU5PC5BQSu7XX7NY8pUuvzHVoLnTHpzIiz5oiSoDh82ZmfcTjnJU9JiUqUHAKGCg2WmXBLQ2mKt7gHB8DH/oEiu8Z4g0U6HpdSoXmepe5ITxXeoC4jKSnhp1wNQUCez9evOnvrrVUvh9k3BKh2hNxdGp6RixiS9U0andKp4l6tRiqxGSi5WdjOWglkqEpYGIkRWydRTUdGPL9uEa2qjyKrAsAlTbbHbs+cMCppqw0hbMMXMoRozTc2gqVYNHizuKnjN5gbnzA3OxpRW4mReF/lSLnkHVU2i8SxPy8k2Lnc+8a7vtvbWH5TQLzfItecu59PkxVA9RzNfBia17A05qyUuS/NxflhybSTHJtnrhocldrlQREB9D7ZIaz25yWUOQ3MlbJdz1eEwWFzwpcXvAy14OLvG/aj3Wgpkb+P2ASBK9pU52ob06D5Tq3CXlYkHRLILPsFjMDnFs6HXeNHTX7NKHo2dzgupODvpA7ui4E2qV4NgbRHTHz2Gc/t7qbbnP3KWnIm869wSIUshq6yl0jYPff13X+dtcELnzS+gU6uS9U5X99TO6dltcJne2rQf9lQuMjAqIC4enHoeyMuw939ZyVy5BK1KcBmgRTHfkibLXR+vrsmWIkJpqaVwXmOdexv4XgCC/EQ7KbuuVonOLchWiVnbtj/iefz+977F3/mdb/B/qKCg7UVWMVkXsIfj/HxbVtCkZ3zw4Gr748/wlc98DZ87YHAGBb/Oz186GtYyfxFB68RrN2zlFFwSQdZ/Y/1LAf7Z974FsFqFPqVecMV8wZ1A0kJQJTkIxaoHzgUkmuQQt0CcKTk+AAgs00MS2qVHOtQTmVtJWKtUrV0lm7Occ+Y2V+1TS+3tFd+dB4wL1XGMNXBq7QO23twleadazK4v5eJEZb3xBTBf+84P5GLZ6bEofTyDA7MvdXiXCa4Q/fmu50qObCfx0NzMqt95uPiv9fW9s8XLfQYHdwV7j2ZKW1TbKS7XRbuh/La4H6a0/vm0JOZ4fh4tB773uvaRM+5Bq0EbOLjs/jnB+YFSQYEx6dsp5+Kkvb7SR7yBi/bBA+Ol2s65/M76G88niLpJkj1oQHSy3vslUbaOtfXZ2iB/AAikHyh+INeSets020bdNmez861EsLAg/QjT+AAgrA/p0ve//o63iH7uomIgbn2Ol++V9W5feAS8Fa6UqmV0O4FfLLUXnIkHfhYVHCT4kaDg8AhMzqky/lN+0J/vvLIJzqzB69riMPc/izyvGE5Birx1sl/f9xOHmnWMUjd3J/hk8ya4vM6jzgk56088D32dv+FiPjfXwc6JRRs70JZLsIy1RdpaCY+Cqi4MmqRk8P0KglUc6Hl9sjF8AQrSfAYHF4DgshK0XnIhWXXeqmS+gsi2Jf4oFUoDBJd+FY9Ip5fjofFLClY9+CzFWzvo/J3f+Qb/wb/5Tf7kT311XS1KsfecBbQemDLGu7gc500OelktuBwNl/vj3/1P/y6fdX2ugMH6pv/KP6jZB6sZ7UW6olUNRN6uKMHDzzVQ0D71B9/5kH/3v/wlwJz8wMZbk/xdbgIO1n5hzNlIRM42yKAWeCTqkWSLMtkIMmZ9Gh4ChFZBWElhZd0UVmObdlqs1YNSmcvNJe2Bn3pdBAusp0pR/9ZCQrE8e0vbq1nl06mWfWsFQ+vJcD1BdrbRVMtZB/R+IBUl5cTilTEKk6tZ6UGJSdcNuV3W55S1StDKk0NQeu+q85rW00rt0VZmcpsc9t9TXdH69i4+5rqgtnZC68s2Fndb6O/nWPXe8YErXFswe6/sen92g+OiKiltqp7BQSnVdrqV/vWCk1JPVE+VXFcgJudKQVF3lkGV83u6JNhKfR0i1lJxzq1eC0VtHEisEsX+YouMF9yWVk7tN9BtKGFD6TbWZ/cDS628tLKp4qy91pmErAXYMPtzpakpMxr4aItyAx6uA9+vbn7tlLZ6BOQf916lEm+9MfzzBShqVYTPus+1YlBqRazdZ7MWL+vHy7bT+KjC1Ay9DlN8SwrY+vBj8FwNBfD1RC6ElHDiVp6Kwirp+zEFszNrn7JuGl5aOFqt2hWtZkV+zfHKFYioSpVYVqAiso7xIZz/6+pc7p0weKVXauyx4OKILCMyn8xxMMcHcd2X6wh1HdF+Q259/ubk+LhcDvZ9VYIoy3T++TGuoPqtNa2N4VJAsxldYUZwb62rT8hS8+Nq1YU0tPFLVr+IR7ykx/uD1CqoVNvokuxN/vt/7h9YpQDbq1qRqL3spxa0ptJoS8wZPMEKCv7wd2u2wq//2Gygzw0weAsUrOYfZSVWaW2gtqpBM+OhiHlyX1yXlQI4g4L/7Z/7NX75t/7SA0AQ6+RphLT2fd4Jg88W6FEcOQvJ2X8+C8F5XDAzE9KCuGgf44xoQPSsTafFwuaW+lbs/fnKznbhfLpZ30ReHdNKXUnEBQi9MZpaTy+bdC+39wSok3M5u6L9dLyljCMlTkgu66lVak8ZbydFCR10RvYpOeEQeu1YVOk9DN4xxsLglTm4usGyuqt5J2/5GPh2CvGObe/Zdo5NcHRqSYvBm4dB76ys2RY25IzIRVqWQfv7ozKcnJ95qx6tRkwxcVwMFLw5ztXG+Zy01kBL7hoMaMZS1jsWyZWZ7Wpad0WSTjBL4KpmcW7laqze+a1PDqyAQtui6UBMbRELtIjrS3JTAwjtPaq0ltfZTtbMmAJEa2vIetq3kzblfNKRYBt2CUMFBXvysLdy+WQ5GDHVnnYdJuZOB50KnesZhgGttZ0zCe3idzSSV2dgw0BBDQPyvQGCzGrrvRoHfdZ7lbOLp4rDO4dooZSIpISREts9vrjXlRB3WZm4ZJefwcHb6aFTqoFEsayg4DRHjvOFmdfF+Nl29ntVWO9VTGrY5WJHuGghrx/PVYKyjmNrN1a/lVIsCbEosUAohQFzEM2Bt645KeEJH4NWsWvgfAiOwVeQ7x29N/Jhr+DiBNMBmY8QxzWhs4WYrXyo+vyLCuJ7SpzR7bURal1Y16eUaltEau5CA3dxhjhRlmZpvKzI6awCa5wRq6Q92EljXHdU0QoMfHjAacmuKQy6Cg79ChJTOas9VoVHLmsLaYxprQIhnMmZIjWEzwBCqgDhT375q6Rczq3SCg4eadKqN8T5ILQeiD4TFHyD3/gr3/yxoAA+R8BgbR985c+eP3lBzNFWVm03re1rbb5dLCalIvNcP//Pvvct/t3/8pf49//cP+B/9FPnn9+qBE3605LJmg5VlepNrkZ0C85YpZjzXoFq+lGtNaEi3qn+Z30u8X71P+CiF9tQ7YNEt/ZmYjzzEubpXJZ2HunMsx/nK4AyPWw7KTekGrQu3DlSxiPl/pZ8vDPE377/ko1+gfoldMj2ymx01NFtOrpi7FyLTRbmZJtpym49efuUienMefByXpD64OppxUDB4JVtVz/nHYO3r1v1zDT75nVJXSvwl4sqnEHjOrEuUEIqEJOVh+/HhTenhfsxrpPdO2EIbgUzqkrnM3PtyTYW+pQLUi18JddflqBoKxdaSqWrhli2WdUy6OOT7IWVavNaaLK01r8usDKcH3O8nAiSjQvTVCNOTU6p6s1BsmQrzYa+svbr0lRbXiVsoN+T+z2HWDgtmVMsHObIaUlrK8bV57EJjl3n2fhMQtkNeyNOler/FhcDyXXySDDHOGp0cO425Grac0noWit2pbz1PhUbP0055IvgpFSPAMFrOIPjJ0i1l+FHGdaU0NWw6IKTklqFqfJT2mFhTrbBTjUX5DilB14BbQwtyeNUahWNByTB1WwMHiz+l++1jvBadj57I1CkZkAUcMpA4e1t5jwPQlSzKG+thYu56Kvapg+ObefWisHgrVoQtEUcg55OyHxAxnvK8W51GSwr2DR1yqrEAkro0Hhlhk613Vn0LL9st0SxyoeUXIORRsrpfg0AW4mvXW/rkbq31scVDIicPWMuPUH6obYLhgegtLWQmiHn2nbMMGfjJZ2WzP2cONaKUcOcxiWxuV7UzO/cZ1QBzg/3ovkoDz/fsM1ltUA4t88ouaYw/uSgAD5HwGAtjzxl4VqrB8I5xIPKtr208G3lpMs2wz/73rf4X/zjb/C//ze/yf/wS3/2QX9IBNPZA3PMvDme+OHLV7y6PTDNEe+ETVCuOs++91z3nk1QOrXTbfCe4GuZVRUVW6B77wgkehKdKL6oSRqzRX+eTiPH+3vmZSFXYkxx3UrAsnLGbGzy0z35dIDpZJNQFek36P4GuX5B2dyQN88ZizLGbFbPQO+FrVf0+Ao9fEL+5I9IL3/AcnuHAtc3Nzx/7x38dl95DkKMkWmaOJ1OLClT+i16/Ry9fg//7H3c9bsU11XmvqOLhS5ZTzP3DqdST1F5LWs6pfYxjeR0BgFnMND+HmrWuhM7CQuW9hdjYk5pDQEKTum7jr7rzK+/9eUUtDS3x0fZ8cVOfbenhY9eHzjej1CErguEzldQ4PFeWWo4VO+1ysQKnVZ/hiyUKkNbZWFZMH++GnhVFwKnghNvKK1kVoZ5gRyztWbyOcHSTtGFlHM1tknMMZGS+d5TCqLK0Hf0XSB4Z1UKbfLaRpT1+LAhzTOnuyPHVx+z3L4ijUcb9/0A22vKcEXePueYPbdz5s2UeDNF7qvF7nhxIh6c9dD3nXLTe256x3WnbDWix1fIeAfHW8o02nMftoTr52yfvcfmxRUubIh4lnj2lUhtY86JJSameWGcZlvkpfrnO6XzzoieavPMnPfKmgESVna9ricvW2mN5Wc23OncokEq6DoniDZAbXQKe9amWrGxM9f2wZxKBZiRZbb/5nkBKRz3A16F/RDIw3mdaUDAqh7V0Ie6l5RCzpl5WRiniSVZCV69R6tltmAguYiAFqxWY20+03IIXtOaL9DVsbukQkzpwVzUyiWwlp5xCzqndSyZcdh8PPHx7SfE1x+Rbz8m375CpiPBKZvNhr7v8b5yOlIkHu95/fGnvHnzhgyE6yvciy+i736JvHvXxljMTNFArpd6uJCMnl4hpzeU25fk+zeU6QQ522Gl36CbHbLZQzeA687rY05IMktkTQtdCGz3ezYbxXmTyRooUuYYmSgsRKZ4W4G3kHI2g6eYWFIkRguIOy2Z2ylyP0Xu5shpMV+IvvM8v97xhRfPuNlucV3lfz2WFdS5//izK/iTcwV8rRKsBO2LSsG/JCiAzxEweOtNv0UgaNpUedBSyJguVKlxn+WsXvivvvct/s4//gb/wZ//Jn/qp75qP7bedq/mpuZqDGgpmXEcefnqDd/76FPmmOmcsh888+CJgyf3ntE70/MqeOdx3uPU1a6znWo2neNq07PvPVst9CL4Yq99LsJhmnl9OHEaRxvwvlAC5yTAnMxJcZngeCTdvjbEPp8AkG6DXh/RJZO3mbxT7ufMYcks2YDR4IR959jEA3r3mvTxD4k//D7TqzuIGflKYbff47oeKSYFTNPI6e6WNy8/5TROJPXozbt07490Ca77PTJ4nCi9CqMXhqy16icE9zYpq5H6GrlwqIvRcAkQgq6nUrNEbajZFuicE2lZmOtpIjlLYgyukpqKnRqcOGjKJRVcVVK0ebukzGFcuL898ublPaqO/dXOxpRTFpdX4uI66sq56mAfbUMTJ0gGtJbbS6EFuAj1lO2sLOuUlSmRiy3W1qO2jT/WknZKzVCnBuAskXGeWZZEzIlcCsF7hmFgv92y3fQEX/DO4aqSpvnLd9i4fn2cuH/1hvnNS0ufAzNymQp5qyyT5/UUeXVKfHSYeT0uvBkT92NkXBIpZ5xalWc/eG4Gx7Mh8P6u4/nG8az3hPGAHt/A8c3F7xjpkjJ1V5Rn4GNhJq193AaG7F5EjqeJ++ORcRxZYqxMf0cIjqHr6II3oKBqQKCpYVRW4BC8kemaIVYpzZzI7ud6Yq0tu1Js/Wi2tumiH//4+bc/52xZBSllljlxOs7c3x3IOZFz5rDtz9wDsQjh5v1vKoB61i/NS6SQU2KZRsbTSEwJcZ6u9LWVopXCYRtIXIGvAe6mpFlVBF7sgLD6d7gn52PjFQze5Lm91upczsRp5PbTj5g//g7zR98nv/kElyOboYcX7+CvrtF+sPmZIvNp4vbT1/zgO98Dr/TPr/BJcG5Djj2ng52+x2RzJaiwC8q+U/TwCj2+JL/8Ifn21YM1TrZXuCSQA2VWStDz+pgXWx/jCPPIZhisWjHsLC66KDELUykcc+Z+mrk7TZzmVN0K7X6mnEgxElMkZ1gyjDFxO0XejJHXY+R+jMzJVFglvcPNdsP1ZkBwtbUla9x921vsTdiHBgJbBcw+yvlzb7UQKij4ww/5+m9+g9/4y9/k57/yVf441+cGGKwlooYIH1+1Tyu1rYDaOuwqQNCGCGrJ7Q++b5KRv/fnv8mf/vLZMbFN9IboBCErBC10Tth2gef7DfOy0DnhqvM8GwLPN8GIOd56SxZ0Y8S9Vv4tpaBSaknU5GMEIUkh2PGEmCG6gG72+LAluZ4UNpRuaxbD1E1lc41O94gz4FGcIx/M91tCh3rjMIjvSAXmUpgzjEu9d53Dx8LQ72Dcov0W7Xtcd6LbBLrtgB+61aNfCrjQ0fc9282GUgpLjcJ1JZvMJpuzntRFqFclusJQWLXQ8VFEr2p1E3SszGcDBZUB3ZQJdeE03/RS20VWQnVibZrQIn2lGHhIsY6JNgMdzgU6LxAhqpwZ2BWRz0vkeBg53p9wGuhCIARP6M6l2RYs0yZ8Q/Jgr8/Go1EdSjEia0mRZYmktFgbxwl95+m8Jzi3Es1yrifPuDDOC+NsRMh5SVayjqmeUC0IZknp3E4AckpWfkeYUiaEQHBq1rvOFl1Xy/pMI3N2lG6D212RqxOjDhtyGJDQk4oYsbRxesQhkgnVLtd40xB8M3Syqtb5+4Qu9EgY0E00aaAqOhihcSyel3ejhY75QEIr49sW5SVllnpaHk8jcZ5scwQWYJqEyRmZrKvVueCdbWzB+uSp85QuUIk3aCvkFCr4ikyzkU2LgHP2zKX655f6PNvzXepf2pi+HA/tyikTl8Q8zpzuJ1JeCL1jXq7W71sVACrGzfCC42w0ZstaQVLGiZ1IuxJqtcDjfG1FiqyWw64ZfanFrgt6TiJ0RrDtNDPnbAqJkh94T8E5vbBrbYXaMxcByeZk6bB5H6RQvCOIsq3VAhc6a0vVtp4fOvrtwOZqx5wWtO/RfgthS+l3jFNhTHCcK+E7OEJtmznfIerNrKrrafwY3V0hV89h98yqov2eWGQlcAYnyHzELSdcP6FeiS4wZUiLbayLFqZSuFsyd8eZQx0DjV7lVVBVXJ1DZPM3MdDkcKJrlsScilUlumCqKi01GtrAuOo5vv4RLjgLkGi8IFnBXiMxnqtIT4CCn7kABaucVy5+w9vX5wgYPH1d2puWKoYVcWcyolqPFUy7KwX+6fe/xd+ujlN/6qfPN7XUky1YOSs7AwbibLJttXDTKbINkB2bYGXTbWdleTt91laFCEvJzFmqHamhdEoheCHFREqRvOnZSMKXBU0LaUnM2RH9huQdKew4pcLxFJmqq5oTITjY+z391WCTp+txmz0lzjY4tldkbxKw5p1+mhPHGNcytxNP55TdcIU+fx+dJzrn2Xpl9947uN01OmxWgxDxgQ4YMkRRyhIpdfHMKbEsC9pFwNnJzQl9trO9d/LAMAjOhLFmGuSlVgycLUq2oFmvMVxswFof1vl/dqLPRgWEUiVqkQtuSZ19acE742wUlFwy+95xN1nlYJlnxtPI6TjhfWIYO7b7DSKyyrTOcq7mbaDrYiq1bF/rwNYCiAuncWQ8nZiXBaXQec+mD+yGntR5O8liwGCeF8Z55nCaOIwT4zybSqKSnEqV0pRa3xIRfF2Fc0zEnDmmyDSOZsFdNdsGphKhZFxZCHmhA5wbwG+ht7Gfapxv8X3tkZs0t/fCdacMLpCyf+tZurqZGDi29x9LMTJhGEzKuDGAZWNzYIowv3zNogeSBJbqElmom2J1U0xxIS8LJUfC5fNPhWmxeTqJfTRr6kqe6zrS0NspoSsIwWKksWrEPEcO48RpWphjJCN0ITBsNgzDgPOh9nbt/cVy3jib06d3502+jY9mtjaNM+M4EmOk3waWecbVUvm+dwxO2LQDRZqNnFy5EGfbcCAnvALiUKf2nxgwb0MbaSZellK4CGuloMkVB99aiuVJM6b2LL2zA07vzs6G5Gwn52Uhp2QbtHNI39u9vrqh213hN1vUV+5GSjiU7XsT2zhBzOj1O8jz98nDFcdYOMXM3RQ5LqlaAUOnjsllvOvAD8juBq2HQvEdbPeUzQ1p85ypeO7HxJJY18feCdswsNluYDkQSUxZKXPClYUchShwKo7b02TE0WjqAur7L1WJEdTWbAt7guSVKcEmBJ4NnjeTtRNQre2zwlCrsla9au0iqff3IZegtbdWbpQ0btq5hbBu8xeg4B/+wq/x81/5ubeVGT/B9bkGBisoaEmFNHCQqvsda0sB7DTw+9/5Fr/y27/Ef/gXv8mf+em3ezLNBaxtQCoZJNNrYe/h/a3jmRvQkuk97ILDL0d0OlrpquTKtt7hhiuKKEuxcuBcSSopq/WebGsiB4dPC5qT0QckkIIju577OXE/1Z7WHJmj9XS3QRmHwj4oV7v38WGLdHeWQw7mztXvbHGfUjVGKWtPfJTEcTEb09Dv6XYJ90XB7a8Zgqd//z3C1ZVlM7T7HRe6rif7jugDeZyJoad0PRFlWhYYR8LgwAVbMIudMGOxPvz6s7ShYamGKRfWqmL9zqDVZU2s/N1c9HKVdlqQkAEP2sQSMCO8RCoRzax8hLPRSY/ziU3YkFFidlz3jn0QvNj3FWKt6pjrZAjKpnNsOs/V4Fdy5CUPoslX15IgkHNimmcOhyOH45EUDRgswVNSV21vO7L3Zrud7OvHcWQcR+ZxZl6sv5lLtsoNTXfe1BlAfbYZSCTSkjlOE0uBOZnELlbJXq+wc8Iu2H/7sDEnRucgVzfGMJDFIWI+HfveNpWybWDkojrCmRhoELxybGr5NIviwmCAJs5WpvemeLifFg7LzGE5ckiFKUMRxaurANEkkL1CwHIGbF7aWMqrQiOvJeCcjDAsOaE54Ug4acyBgnOu9o4jp3HmeBo5TnafM8LoAykXVJVBbfNrZOW++uzbqLKPa7uhlJVncAhqilTMgbEQSSXiJbEPUsebHS42XtDltEYHN7+FUp00jT5SGfhV+WlOf5GC3QDB+CVaLdjFBbwISx0vi1B9DQyULMl8Raaal9CslC/np688Dm8MR1KKLOMIy0LEiH9ud4XvOoahY7i6ptvtcN2wpl0ChO1CH7YMumFZIuyfkbfvMIc9xwoIGrPfO2vfpLpOlb6n9Dt7Td3mvLYNV8Ruz92cuF+srH9crNXXeWHfeZbsSb3jargmpYmZREoGjrMuRKccl5njlCx9sqo07HTuUIUgwkBGTneU+QBxwYkSQs82bIlXW24GZYqQRel6x95Dr4UgmSDWzhSwqjW2pr21l2HtA+rhx120EM6HoXQBCv4+P/+Vn1v3wcdW4T/u+pwBg7rTf9ZV8lvgwNi69r2//70P+bfXFKsKCi4YoypYyRQIYhMmi7UoMpGtZrQ3eZMrBRnv0MMtHF+TD3fkZUbUodsdcvWCUjL99jmTVNIZQiYbqSWbc9a0FIIYu1wyCEpWR3Yd45w4xcJxybw6GdFljAknwqEi/7TtiKWw9zv6661piottmiX0NI+edkoxMxX75JwKp1hQSey7azrfE3bP6XYD3YvnuKGzTa4UW4SypUqWfkccdqTjSEFZ3EASzzLOUBy9doTe5Hx9bRU0hcdZ81/1/dKkPeCkrL7olrduJx5JM2WaKHEiLbOdHovxQdSbVan4ztwCsZMmy0JOEwUrd3pHPWEpspwofkDizH64ogTh1AlXrvBio+x3nmXqUPV0G0folZuN53rwPN917DrPvnMM3hb2vvVina6W2daxMvOVtMwsy0RcZlKKq/Z8EZjVWPSSE1oJnss8M0/2XkuOuJJBC77YUPVOV76AUlnbFJNbqTOCVMyIwDInpskkdEu2+bHY8Z8gJgedRQlhoFkmo878C7Aku84rQTPaOYJYW02WsZa8zVGw1CrDkm0zygWkEgGLCFwu6kDxnX1tMiLnYU7cT1YVK0BQpXSOrvcMvaMX2HgruUs1MioIxTtybT80G+sCa4ur5Eha7D57AS2Z4q3aMS+ReZpZ5pm4LAa+qqRyWRxp6SghIPV+ZwxcD85ZOwYQyZh0tZb+d9Tv9xxHpds4+l1HyMJ+53mxUa5c4boT9kHYu4KebpHlhMSxSohz5ZrAUoQFteyGYM8nF+v1lzhDnMlxrqoMcD7ggnlC+NDjXMBjVbcoNoZybenlAptq1hXTWfHR5mkDfw4jco/zwnQaYZoR8Tg/4K9f0JEZtgP9dkfYbFBfY8i9J4udtLurma6/QQ8ji9swqx16TtE8AcDUDr7yi9reGTP4blurwMUkjmFgSsL9FDnMhU+OM3dTXK3MB+9YklUinVOCFly3IaaZXFKlbxRSsdTJuBKic9091MirUuhV4PgGOb6i3L0kHw+2DoYOt7vCbZ8RhmvK5ookDgnCoJmeSMATRG2ONkkB1l5YMdij/WeNvV8rBp8FCi7UeW9dn91CaNfnDBg8ca1Vg5UlcAYHUla+wbf+xYf8zd/6RX71F36df+0rX1vNUppX/3rKa7I6aWXqTC3QkSUi8Uga7yinW7h/RXrzibFlxyMlZaTrcPtn9jPVI76ndwOzk0rC01VuIsXKjTHVAVmAGpazZCzFLdfTXjG9fUyFWMGREPEiQCDnxMYrwW2spE1jUVd9M6z9xpgLc9UozZUItaTCEAI3uyt45xn5ekcOipZEyql6lCc0L/hhR9fvGP09ZUok7ch+4DjNSFaKm1Df4UOwHtzF42rcjVYe81r7amJa+MYmD2KAQOaFkmbKfCJNI8t4YFkWm8wuIF3jR2TEBUrO5LiQpxmZD8xxpisLgYwnm+FP9UKXYQfLiaHbs5PC+wP87E3H/FPXfG8TiEXZ77a8d9PzhZue5/uO696zDY5t589ciLqQGdHI/iwFcm4ciCbpA8l22nW1V1hStLJm28RSYplniBlXMh5bHIwMVXvZJZrhSxzR6lhpfTAFP+D6HX2/5xgtXEqKQk6M0caEV8GEXWX1tvAuGPNNbNzmCuZUhMEXezbzATm8Rk5vyK8+MmlrjOA9ur3CPX8ft7mh3z4jdzuSnWON+yAO7baUHG3uiZpuPdvr8PV15WIVicHDdadcdcLew9aLcWqmgxHKqjIBF8i+N3fJ0JOdr8FbZ+mfqxkOy2iAIjln77EFLFWOjK+pgu7RMwtiPeIiVqZvToHeOUK1+Q7OxnCopFavBfKCz1u2A3jJfPn5hp+96Xh/gJ0khnyCu3tkPlDGA3mZyctMSomIsqDMEki+o3RiG1ZR82dIC3k6kacjZT6iabEqTQgw7HD9gKQN6jqCD3jXsaic1S2wGkfF7Cj+LGV+KlExLpFxmjmdzNJ96wfodvgc8L2ju9rjtxurMKrp/3N1rsziyH0Grlh4zZvTyDiaH8acMoqw9b4CgzPfwjtbH5N6pDODtpgLy1I4RQMVr08L9xUUTPUUtNQKpVXJbB1dssmzS23n5rrullrmUuzZFc7Ps3cg8xEZ78h3L0kvPyLdv6bMM+IUGbbo/gZ38y7sn+M31zi5YpDeFGd1zVnbBHW/DiqPchTK+jWt5fAAFFAegoKf/jlWA5HVz+ePd33+gcHjqxQoaeUoijp+79vf4pd+01IYDRRYW4HWd714KA1sdVLIJZGybUpyuof7V+jhFeV4S3zzKen2Jfn2FfF4Is/mPeA31dlwPOL2EywjnR/onTXPtD18tROyEzUkXExa2TISWuSmU+i9sg3GHg7uzGjuvJIxmZ2KmlQvN3lWG2BCJxZyklzLBzBgsBQz6xhjZgSm7EiS6WZhqxv8MBAo1p5IkZJriImL5poSHVkWMo7jkq0MXGZ0mRlyQvArOauVn9sppKHjVi1Y+QM5mdNZdWO0fPUJpiPp/o7xcMc4jiSUHAYYFiSCJiGVCbCNV6YZxgmZbjkdbwlxosvRuAGhQztjKJftFbHf0/k9zzvPf/fFlo0Xvvx8x4ISup6r3YaroWfbB3a9Z+NN3z1UiVzr466l/TqOShakOHLniX1A00JUW5icKp0zAJqXiWWp1YW4EJeFnKzq4wuUlZmYkMXsq3U5wXyA4z15PFgglyoybHD7Z8hwxW7zHN/3Vf4lTJWNfl74rLTsxBZErb4JiZoBgfWwg4g52x1eUl5+n+WP/ltOf/Qxp0/f2O/1ns07N2y+9B7+S/8dtGSrnHVblvpzUmN6i0ekbsACwRVyUXIHnTPQ6p3Qe2XjjZnepwm9fYWOd7YwjycLQvIeHXbodg/djpw2iO/NyKnmQEiJRjmJE9EpEmeyD4iz6G0v1q6gqIWUiuAr/2PTeZMW+2qMU1s1uQhZjdXvxNE7pffJnAFDonfWqrnphS9deZZ5SyDzzsbzU9cbnnfQnV4R7+5huice78jjgTzPxGW2Kop6Ft+Tt9eU/hpKR8GTx1TnfyGPR8rpDYz36DLiyGYstRtx+yurJIUeUkB8T1f9/rPq6mly6YvRuEdF2/OvVa9Sakts4jTPiKELtt2WTA+DgRH6DaWZoK2ugR0LwjGNvJ6PvJwy91NhivY+vAq73tqFna+qGXmYk7JGS+dziNWcbd3LFFMDYEZpYK6M2+qJYoof4+OsaaS0zbj6fEiuFUV7vkExu2cBlhHSRB6P5NOBfDoST9au1dOEn8zUyc0j7mZG8wyaEck4zTjdmLmYtBrTQ2DQ1v229cgjUCAUPvgXv2ug4C/9qoGCfwXX5xMYiPUHSjNpaddlElltK/zuH36Lr/+jX+LXa+BSzpAveqNtEW99nJRtgy/TwZjBiwED6mlF5iP5eEs5HWCeoWTUWUnTBWcynb712JqmNtKpg6rDF+wEYuxV63kJrUErFRDUfmCx06d3wk3vjSxU36ZqY0bbhIgFSjK9fI9aVolScxAiXUlG7PIdMQSOS+IoxYw65sTdnDhlJZwWhlOkuMSmc3RuQ/AFyQs5RbKPgKdEhTJZHzwl5mz9rhgjORtzPLizDrexbRsiXpPbhGr8ZLbRKyCoGQ55OpEPdyy3b5jevGY8nYi+g+01mQC6MM/3xCZLVNg4QVLBTQscj0y3n+LmEz5nnPM4p/jNYFa/2yvK/gXd9jk/tXuHfbfjlGDGFBld1xG8YwieIXjzUmiyuApsvGvPTVZTraJKJw5fOjT1+JJYZrFSZMnWJ15i7Y/n6mNhxK6SjU/QevZQOTXVupr5QDncku9fU05HSzSsipRyvMPdvItLkWFzQ+ivKjDQeiKXc0qemmlNU/y03IvUyI1i4EXTgsSR+OYTxh9+wt13P+L48R3LMRK2nniaEBU22yvC9QtIS+0Vyxnk1nln1aGMc9Y395orKLDfqSL0Xtg4wU136OkN3L8kvvmEfDAjHUTtvS6TARGa4Cg/6rna7y6C6dZzfGCQU8T8GTdOKN4j6ghdx27Tsxs6+s7Z6VBkVZqkbLTPmCCpcQC8OjZOuMqOMShjryxbz7wPNveIbBxcB8Hf/YB4fMXh/iVyvEPmE/E0klImpUhUJXUbyvU74DYkb/4Yp/tj5RsUvBS6EmGc0OM9HG/xcUY2G0Ja8CR8SWiJZtqTo7kM+oi6gK9GP7FQNfsX6YDZXC1bYukSDRjEaFyXNja9c/TDQBl62OzIw1BjtD1ZA0sR5pw5zYlXp8inp4WPDwuHccaBGSh5ZeuFbXD4Ul1h6zpFC7FKdo+nqqRo0uAhKN559h0P1kQjMyuDM3v1UGWsa7JIqYTNkghqZCZNpbprGljotK5JxWpP4gPaD5RlwgNpSeYeWzLMM+V0IIeA9x6ZOugCJYiVCPNCUYfW2O+grNbslHPeSduPVM9kww/+8HcfVQou9rg2wtdslZ+8cvA5BAaPeAZPyRfrw/zgOx/y9d/65dX8oQCs5ZnzT2s9HEkLVJ0s8xHJGVlmXJzQvKBVWyzqkX5jfb1hg8YFr2qVgn4w043dtTnHqUNKJjiP+bPZwtw2FCkRzdaukNqPI0e8esRbD35wsE+2YJfapS+FtWSaSiGl813JGZIUesQ21+neyrDRTtTFeVy/x29foGK8g/s5cpgzU1nw9yPDbsT5QCpWSjYr1A7VQNFIyYr0hTQmMtH8Ii7eg5RydiisQOAs7WpArKwAgBTNtCla37pVCsoyUcYjjEfS4ZZ495plXkhhQPqduZHFzHGZmLKNh84JSxB2biCLN++Keaa8eUM+zVZXzBkXAtp5/H5Ar67R51/AvfNFnu3f453rd4lS/fyrlbHWnnkzDArtvVUpkltLe1BKro2oSJFM0mytqLKQ4mzAqZRaaq++7yVXUNBssR+ZoJRWmKcSKU0+SM5GzKxl8VyXFc0ZrbyQXbdl2/W24GInEcmLnf4S9rNCTxJZ0wGb9EsUBrVNU1xtDOVCXsxgKi+VAQj27zXoaY52smsS26K24GolIZaS6Wh8gkC5AEASR3Q8ItM95f4V+e4V+XBvtrtxoYUwmXxS10VRqGFK8nBNKJhEOLfApxZMVvvJRczO2oWOQZRBM0Ey3mh2CHJOOfW6nqzbhmpujVaq3zhH7oSUFLJS4oIvjnT7CfLmY+KnP2B+9UPy3S3xfiTPkbQstiN0Dh0CcuOq54onu4HDOHNcaj++Eki3QegyuJQqOBwJcSJ5tY1JbTxqiBZlnc2SHR8gWZpg1zIpVGr6aKuGQBKr9BSx+Wxpj/aMMkpCSOKRfkvptmsrJ2NjaKqWwYcp8ulh5KP7kZenhbgk9p214fZB2fuMO36ETPdVYowpWfo9RRTFr8mF1LHkxdj+TvxqFtbOe9aqNH6BZUaYRFfaRl8SZOMSoIqqx4tUwHcGzpKyjeWwQZ+9a+3H/Q1lGvEtQbJWn6TfmMV9KWixlquL0wpAUK1pqdjYdsGqCPJwP6pbVAUF/ze+/o+eAAWX11tg4MfzC+BzCQx+suuDb3/IX/vP/yb/8Bd+ja995avUwuhboMDqgy1lMJ4NNJbRkHE93amKmRX1g2mxN3u0yYlEbIFuUa11sq22tuqQnGqv3bT+ROMcm1Slcg9qmcukCctaWvfJssZLXixFsfaVLBe8J7qesbKh46P+oKQFnY9w95J499Kc53zA3byDf7awv/kSUzZWvTnaRV4dJvrbIz50ltdeXCWENfMoIVHfl7cF2oVqwRsCQ2cyyFC5Ak1t4JWLqOdztjrt3jdAEBezV02LWaDOI2U6WvUm2omvdGK9Sxc4zYm7JXNcrBy66Rw5e9zg2AxXEE9wvCMf71juj3Y6G2ea37obesL2Ff3dLf72Ff37t8h8x/b6XYK/wfc71OmqajB+RPVsKBlJbcOum2M9IbcYWF0mXDzh0oTGEykukBL5PAIrEd1yE1a6TMk2ri5WvNLK/gA7q1xk75HxSJnGSh5UW7SW6mVfMhpnqARNe40V5AJrDK5zqHaUatKzZHtPSQUNPWG4xj1/j+ELL4njDCrkOaKdZ/v+c4YvvIt7/h5puGZxPcfF+shQQZRX03PnCGlaY74LNglW0FIMOEgcDagvk3lkqCKd+R9IP1iPd3sFmz2EDdn350yRdtXWImLl8Zxz5RbZnGzLqFDs/UtCI7jocIug9KjqCgra/VPOMdYOqx6VSiorReuykomniWV6w3L7CeXVR4wffY/4+hOmT16xHCfSOF2Mww6vAyFscZs97Mx9cizK/ZSqy15CMVAgRY0bIiZHteAiqVXOkeJt3pITEhc0dOADJQdEZ0uvdMFyW5xHXSA4T0JwWaq7pqFDC0SzllhaqozO2fwv6kg4FiNhmDqjlvpPMfHyOPHD2yOvDhOnxdZBI+4K+wD+zQ8pr39AfPOp2Wb3A+7KDi0lbMx5tV5aDxhNeumTKTmkVm8bR0s0IAQUZ8TFwgqqVaQ6jEYkQmYGtCbQ1qNixrIzwobiOqTfwea5racp1mqCgXsbvtXzoO9x3ts4L9mSJhcbu62KJTU/oriA1pTUy1VbgA++/UEFBb/Gz//0v/Y0KPj/4vr8AgNRm/BPXB985/cMFPylXzVJR8mWZdBY1+0qtvE37fCDisFSrTftl6EuIP0WkZ7Sm2VmUUfWsPq55+qUZt9xLpl7AUrdCFNcWweiilbEqt7sTW0pFnJKdnpeRnQ6wXIiH+/Jp3s7Gaqiww72N8hwg9s+N2lYfJQJXowElY93pFcfE+8PAIT71/gUcerZ797n1JVVj3s/zry+P7LpOiMybnu7Xb5VOWq/2Af6YUsRR4i2wHvv2QwDQ+dX7XRTGLRKgDQOQaqhUnlZMyLKUvkM0aSJJS0WMJVqrgCG4ptNdPIDp1PidozczomMsivmOtllod9cG2HyHTuVe4Qsr8kC8TSZjPRghMZ5WRjGE+l0oD/cIu/c4p+9B9sr24RcWFs9UIlL7Xmv4mQ5g/am5lgWynSyysc0Vj95k/HZYmHRs7YxWqXBfmjri2pN/LsoH6Zo93G4QvfP0ThRToeVTNdiZesX2yab5grsylrZEMSyCkqmiEP6QC6ZmBJTtFeyqBEprzcvCC8KwXmu9s/YffkT8jwbX+PZu/j3vky++SLL5gW3U+JUw7MEEC/VF0RhsdCuFvXdfBnWHrCxZtf5LaFH9w52V+v7ks2O7Pvqt2DpjOViA5E2t6vVNMjKKyrVPt2IxtZ60JIpyWJ9kULxQnFCKUaubODMbp/xREou63y1caGrx0ZOC4xHyvGO+Ppjxk8/YvrkB8xvXjK+uiMeJ+I822HFKX7TobsB/+wZ+vx9/DtfIm2fkzfXnKbMKcHrKXGYIkomJkcQz3YzoM7ItEw2fkyvuayZJ5ISxXuTirbkzJqWWtSyKmQ90FhsvFPPQo2nK8LQedJ2wCnEGBGg846+71FvKZgxFauOFZhquNCb48TLuyOv74/cjzMxJ7bBXFf3weHuPqJ8+l3iD/6Q5fYOAL/fIS6g+xu7l+ssM8zbe2HrQI+fouMbuH9DHg/V0c6jm73xTsLGTvJhsJ/nHA1Wm8Iqk7NJn7XJn1sLw3mQwKKOJJC1UOrwsnCjslZCtR7YVKrjaedsrVj5WfbCmwm7pMmeU8m0oCa5mNsWGPj11afgbZffJ67//5Yrtqu2E8ScANpCDXquFPzF/+RR4NK6Va5/l+apnXPtbS/kCgzS8WAP0zmKeAv9GHrolBILx3HhOJ/ZsEuycCWpiNac+4TBm4GJLwWSmRiRl+rb7vHB40LAhQGcxa/m6nqWK+mP5Uh68ynp1cek+zfk2cgvbrtFn72Pe//LoJ6uv2GWZidr1YPiOxvsQJlnlrsjOSbyvCC+x+9uGDY3bIJn33mmFIlx4f44ctsfLV3Nmfe6iFaJnCFkHwI7VbquMze6ipq7EOiCqxwKwVNJc/W9r/nqa3VgWgNYqHkHVGMbW+TsGTnncd2AJqDfkrstUyqMqazSJxRCcYw49hKYEbqbL1Ccw/uA7K7g6lP8/R1pOpJPJ1JMlJgoCvNpJJdPSXGxE/h0Ql+8h4tXdqpWrRtXugizkVqSrG6X7XRZbAPP80waD6TpQJpncipk9WS1TWoN8mlDs1onA1ZxEiXh1tAeKwp1qO8I3bX1LOOEXFk0rdR2zHnct6tq3tfUu7y2ugo1UCgM1vIqZn4zRpPOjVX7vu+fs/3CNe7mC/jpcJYr9jvicMOxOO6OkcNiUcSmalBKsTM2OVp6Z5yRONnryMk2fB8e9ktFoQ8w7Gtlw1N8ILt+DbxZMizVpId0PlE69ThN6xw3bKDN9KFW5oodCEpNXsyRlCNJEslBdlBKR0n+DPZyplR741Z9sMoBtbpDpfrPpMMd08uPOX76MadXH7Pc3xLHyUK1th399QbnHbrZ4Potur/C37yDu36HsnuO7t9ljtYWHCnMxTHlWMvgiV3nmFIhNGXOPJqZjjPdPNEcvnKKSPKWfChqXIoWhBYshKi4gLgOvCmQigsEF2qQm1CCQ7cb+uDX9+2dWVI7ZxWLVM7VgiVmxilyexy5PRy5P46kuNCreQxsgjDkCTm+In7yA6ZPXrIcRtQ7XFfbVY0PVXMzSqH2/xUd36DHl6SPvkd+/RHpaDkf2nW4/Q08fw938059nwV1rp7kq8toSiSBVF1AczKuj2iww10NdjrFzBiLrTM1QK+Us3OlZbsEtt2G/RDAC6KZgiXX5lzTPXMmt1ZUC8lDKZIqB8b2szX74C9/cz3Uvr39nefIOl/WffEnuz6nwODp62Gl4Amd58UieQYFaS1nkxeWk52op8Ot6XH7geQd2QeK27DMids4cRfh02PizbSYa9ZiVrVOYOOVm8Hz7rYz/wDHuvAQZwQjLDoVfNfTDVu021RgIMSYrBSYE8WZt3yJC3kaWe4PLHcGXvxxpBdnnIZuj9/cAFRXN0AyvQtov0e3V+gwoP6eOE7E00w+3ZFvX6L7F+yH97juioXj5EKJzWTHM/aO3leTIYzxKCoEMce+LpzL3+ZPoIRmo5otT73dX8tVNy5BmU4GCubaNmhAAFY2UamLEKqI97hhixfHMuyg3zJHYU6QxIEXQjD71W7o0S5AECYSfv+eMdf7Z3DzJXQ+ocuJPI348XAOoppHMoU4z0x3b/De2akgLfhuQJyrLO1MqVUMEaueuBBwXY86d/ZUiIk8T6RlIsWFlLL1Z0UpaqmCRd2qaza/pgtgIObZb772MLYc+FqiV83mDOkCne/peqrW306Mkpdzq6xKIkmLAZhkmxxJQeNKAHXSrSTRmG1BPC6Z+ymxXxzb4Bj8c/r+BSom35tSYRwLx2XhfmotBPPOaMFADlYOCTlSKp/C3qY7x/Cqv/izs1Nt6CgaqsTNZLbLnGoKIsBZbTE4c8xU9SbtqwqFtpBe3mvJHom1CoAYATAupGUiz5aWWkqsa4YZlaV5Ii0LuSYJog6pHBQr6SfiPDLfvmZ685Lp7g1xnsnqkO2O8Gw4BwANO7QfIGyg25C7LWz30G2J4iE4c8rMjr4oUxGWZSZJvQ9ZoN8iyw6fFxxpNSUrJVNisY0qLvXUSn29ZoIknYED6TfgbR0soV+fkbqOTh1asyi64B7MdXPUlMqrwTgnySpO4zwxjifGcaTEmU4KgxOuO8feO3R8Sb59ST7d2XoUI37obJ3aXpnNsQTGFJkq6INKqJ6PlLvX5DefMn3yclUKhKuNVZjq5otziO/QbsB3A947M8dKiTwrs1CrlOXM7RExACgm4z5F80p4M0ZO0SoiKjVNtPfc9IF31FMiZvbUOXI6kWqyZJlGcpzPUfN5sSrCRftYFH732//koc3xJSj4l5Ak/qjrcwwMGgvT1Akffvtb/LX/y984EzXWvuxn3NBKQlorB8X68afb1wCMd7doP+DFkemIwTNlMQvPpLyaC68WeD0Xbk+Z07SQciaocD2YjO2qS+yCr258qfaWDCGKCL7v6YcN3fYKHbZm1BITMEN1Oiu5UJYF3Z+Qwy3ucEfyM2lebGFf7NRVpzylmO/BnDIpK0ri+fY5en3EvbijjxFxat+bsqWPzSf6rRGCYgncTgm04PJsRkFxIUdXeyJq0kLVeovPKPXMl6jEwjzX9slyjppOM3k8UeaTya2W2UrtaTFOR/2ZolbuLXXy4D1aBOd6vOtJYY90G3JM4Bxd5wiuY7PZsNv0bIaADw51kOLCNGdiDMyygzBAMHJkUMyKdrrHjbeU4y0c7wwgqJJibWtMo73VaItGbvcl2Wt2XQ9dZ6S60K1th7Vs38aiYich11HChjkLcclWbRQrU5rVrq4025but2RzMTwuFu87VxmXikkPN505MfZKZWNvCGGLl2In9GT/lRTX8ic5rn1zo38vdH1Pnyw0qlNlxKJlD7nweopm6BRqAJQ0yVtmXPLqpOfUeCuWzme5GZ0TmIxfAdjJ1VVuTm3LUSVuxp/piEXqCRSmGJmyhdicZvtd7f13Kmw7xxa3ekrYPTWSZsE8QWJV7eRiQMWr0oVNWxQqH0HPa0ROduNLIaXFfAZmawOkeTKvB6eoD2gIVpEopbaMIilGsipl2KGhh901sr0mDdfkfk9yHUumRh9bKTtE8+b3nWWeeAcb15Fch3Mdp9PJwLUzEqB0G3TY4x3GYzGZTLVlxqpwWMYB1FCvRpYOVjnQZbJgomFj79n3dW0s4DxerR8enAGDt6idBcg2jinVSyQusCy4PLPRAkG47r1JUDUh84m8zJSUcZ3Hbzq6Z9e4F1+Ea0tdvJsSx9kcGrtqUWx3qhgwW6YKcDOuC7guIBvzFyj9FfRXyObaqn0+oFVhpGTy6I2sGmdyXCoZ2Nbp4hLibf5NMXE/W7Lo7WiJp06VTS9MUsgKOhd8UPpozosqHmImzwvxeE+eRmKLfk4LaKgtblsfPvj2PzGi/JPZB2c1xeW+9lCN8JNXC+BzDQzO1wff+RZf/62/bj2ZhrSeknOsn3hUXl0X78I82ml8PNyjOeN9T/EbclGO0RLAjrGw4CkeXFBC74hFYFnW3lMLIbHgH2xRLrW0WRnVrtvgN1f43bX1kFMhq7BIYiqRJYKELXplZCZfTyfafUKeRhDjGVgJ1k6dsZJ+7pdYnRyN/Xpz/UV8yXhVdPiUPJ+slKhqJ4NlZBt2xGwWuAnoHHQs1pvMHsneCJPiqhHHmYzXLlPwRNt0qvywNFAQJ/J4Ip/uKdPJZIjjyXgELXlQXV1gTSqIiuUxIIgDj8fhUb8ji6IeuuK46gPa9WyHDX3nTYPuTM6aC0xl4c2UuT0uHMeJOSZEjFB53Xuuumuubp4z7I+40xvceIdPE37o8f1gCgawRTOlSqBboHIrmgrBark23sR5VM03IWQsXlsTQkD8wCliORY51zKpee9bUp459YGdkPIFeSpmWyjv52TksEbImiz6eFt9+INWr3sVguvp/ICS0LSc++m1lL/GjacZWUY2YTDpWm+WvzFXcmuy6oFMD7XmaQ0Js3tqJWPHvndsvcWTy2I8B4CiAZHOTvO+EnVdR3aBjLk3LkthyZmppkreT4ljtMjnubbuKEZu3XeOLjdlAmvMdMuIaDwgi3Q2h1MRCMUowNt+h0SHsuCDI/Sd+V1olUTnWDkx0QixdeOvox5RQVINQBYL3vF9j99d4V1HcT1puCJtbhjdlrs5cXefuZ2OLLU83XnHdui53npuOgv8UhfovVJ8tlTFEOg7R54nurKgHpsH/Q7nCp4eIRompRi/IFvKYcmx5k2kta0iLlRlVUT6xRRSQ22RVC6FlIJ4QdThnV/lgkAlWxaT2wrVifNcHe0ksnfQ9YpD6R1sg6KLtaFEFd3tLSK926DP3kHf+wrx+ou8mQtvpsTr0ZxO98Gz8ZXgKc4khMOOcDUT9iYT12fv4t/9EuXqfcrVu2Q/MMdqQywOX0GoOME7c8B084yMo/G/UiUVphknO7yw5ra4RjAUsXCyvseFQPGeBc8xFoZUOC4J75WUMzKNxNORfDqyLEvbgNZDKWQ++M7v89f+879poOBnv3beppocf93Lzn9+a0/7Y16fO2DQxmTDjR985/fWPOqvfeWr515iu56sHNQbLAV4eIOX2SR90zTj1JOGSImFHDPTUpgjRAT1nq0GXJfZDJklLiyTyRq3mnmx8WyCGW3IdFgZ2PaSHIR+de2LWLl4QVjILChjsupEnDMBx7D/AqHb4ocrys075MOdDeTqvpV8t0qnxpg4LolUjYxyCZSN59mzL+Nch796TjndU1JC+429/zjRh4HrzjHlTC5mDtKRcNm4EZo9DsXVU5nUk9n5eRTIyVi7qRIH42TAIs6k8UQZD6TTwYiU45E0HklLrGVJxXd26lLn1/aB1Px0Lw4vDpcczm2gOLx3DKq1jD4QukAXDBQENeb2Ugozyt1S+KP7iU/vTry8nxgreLrZBN7f93z5euD93cA7z/a4+ZaOme0Q2HTBSpDVa6Aw10XNkdTQfLMylWJZFnYi8wa8vGUD5C5S5kIpyjgnphwZk1nfQsFprqfbypwp5vUv6uicIwY7BW+yZ4yZ4KqdbS6VY1fbL8W873fefAkWFXwWZhWCc3jvCd0OR0KWyax102wKipLIyxFfEjf9Du8yXj29V16ezI/ewIzp31tYELCaJm2D8mLj1+jcnVeYD2bMVFIFfKaqEd9RQm+s9mwGTEvKdQMvVfZWOMTMWHkLU8rEqpBcUwqdsgn2OrtgkcGOZBuj2qgt2PyYc4v+FpIzYNWpZ9jfsJHMphOG4GsEsm10pSqTStXAayuNgckcVazXXsG2iiLdQN5cEceFhY7YXfPpKfLR3cT3bkc+up94c7JNbwieF/ued65AwsAGZec83nucyEpUDvXUPHtFo+I1g4LrNzhf8M78C6RUt1IxWagVP1Ktdiz2uQIueNwyo4uBAleBlA6yzmqb34I4I1u6Cya9inkMSC1vZTKaI5oWXF7oigGDJA6VTK9KL8nWBRFTlbiaZLnZU3YviFfv8XoRXp4inxwWTjHVuHUhFTN68964BCUuhM3OSIe7K+TqHfL+XZbuijHCEjO+K+acieKbNq1Yj1/7LdKdIByRZaEQa3V3RqYD27BjyvBiY3LaYVCyGigIPtBVS3TVQgTmWJgkMiFoLMgSSdNs/61A8rw3ffDdP+Cv/fa/Zeq5n/3aW/tUEXc+yH7mvrauvhdr8Y++PnfAoF0F+PDbH/L1/+wX+Y3/yW/w8z/ztbeJGuuNlLc+X7TyDCpLGVVEzL4TbAEuonaQLcK8RKalELNN0MF5enW15A0lW1+xzCMbIltXuApCiMfqITAak9kHc17zA8n3HJeqCFYrmU4xc5ozb8bI3XFhHCdcKWwC7PyGm3f+BLp9jpvuIRmIyX5jut9LJF9/VsxlVUqk4nh2/SXCcGU5D/X0hlpJTZeR3vf0XVitYb0rdGmCCbIksm6BgtQTtJSKEFpbJicrx9bFybgVqRILjSeRpxNxPJHGI/PBgEGLN1XvcEjtf/ZVYtWhXY/6gZgtmCnKBp0TgcJOPCX0+BDoQ6DzSud1dZWUkum8GdWkAq+OCz94M/LyfiKmzBAcX7juuX8/MaUtqo4vPfsCfShseqFzBVcrICXF1aFSZELVXCqN3GQ8A20LnfdVshroUNgokoQ0JQ7pYAkc2Ta6dXq7pmYpxsfI0SpDLnDVDVWvXQ1cpsRh7bOX6qVgDoKhLhypYJ4DxcrVcy74bI6DXj1d5wn9DqaDubzNJ1w2QFfiyD5s2Ox2bJfCtgvcLZlTtMjtSNsoBU8hSGHjhauga0CTmw/o8WTAuFa86IwpTr9bQ55izsadqAmiubnd1TEdRMje1Bveu+oAbZvFrrMQrMEpu5p0qrFmOZRsxOGwqel2peaUWBMSsYqBuI5+t2PXOzau0JFx5NpyiZSSKMVDMMMiqWQy45c4XOjPVtvO47yV3nMSpqlwXIRXr49853bmX7w68t98dM8PbyfGJeGd8mLfI6o8229oEdKddwydpyB0JROiMIulSHZakKXgS0S94LuBUBxeEkELGkeYJ5OUtpYWs8V6z4txJHJGvafH7Mi9c/W1d5S4mC4/53U+UwTJsspLRaxtophLrJG3zSWQabJ1wxX6zvwRVJwRsJepykN7uHqBXkFxHXm4Yun2vJ4sFOmjw8xhtgPO4M+bYi5Un4Mr9J26xTnzPUibG95MicNx4bRAksgwCAsB8Rl1Gcjm7pgTeckk31vap5+MgxMXpIxmkyzCs36D08Bu6DjhkcpXWFVkLbCrRJvPS+aE0BUxfyOpaZ7r/mME5Q++90/5q7/zK/zDX/j7fO1P/OtvtwTEURHMj93bfgLdwoPrcwsMPqySjhUUwCO0db5hj2+aGXVUNngpSAmgxsSVMNj39RsLIZJAEc8cIzELpVSCU6iOeMHXWGAg9sTRwzzSE+HwChnvzNu9ZMxD3Fd5Vc/tacJnxWcQZyXvOWbup5m7ceLT48ThOJFSolO46j33G89V2LO/ucEtx3XjKL6DbBvDtnM1WpW19PvJYbH89QT7bsvuZl+DcM6VjFU1QeMLGKkoOcfc9bh5i8sLfrfHyQYkrAxZAfs55XzCOmv7rTe2nmjBJHHLQozG8EbNG51gEdLS9bh+U/9suvWII+DpiiMWT8gzoRSc80gwYlToLCvd1zZOKQXtlDQEDpuO10PH9abj07uRUiAuicOS+Uhg6Bz7PvDe9ZbkejbvPCOI9Ug1zyb/qhKwdSSpMb+tBVJlYGrELpxDnAXKOD8QxCRQoxxxpwhuIZVop1fBhFRizGuJExpHNNWycHKUHLnqtgw+MKbCris1T8NO2Tln2llO5ZyC2F5rri5rKQtRBF8serZTYRiu7OvjBPFomwrgu4HS79jsXvB8t2csyoyylGqrS823EAse68gMknHzPeXwEpkOlNkItHQ9+L1JDIcrxmxAZUkQi5i5TimVX9McEq01MQTIxTZJU3/V6Gu1NM7BV9IhEZ0NiEs25Uh2niwOpz3V2XgFHakAzjb2btiw2W8JZcGVWH+GkSgkB1N0+IwGmy+aEyCWaBhCBQa9SQdDB9oRtGOzD3zy8WuO+cQnx4Xvvz7x0d3E4WhSTacGZK83HddDx7Ntx/UQ2HaKdzYPY3HVTAs8jhml4NEEwQuh7+jEESShRHzfUfxodu5qihZJ0QipbiIvhVQJtHFZkD5fjGuTcF7O39LmtWQkY6TQ6oVBVbmk6cRyuGe+v2M+HUnzBCmhhVXv364ijtJt7ee4jhIGDkvm/hh5M1r74LhYZcc7485sO7eGKxVvCYul7CjqSWHL/Zy4u18scG6KTLngnWeXBdThg6+gshjpMEXiNBKnCXU9xQ8GCjDCtOSIy5FhiAy750x46Ab8sAPfuCGFZYnM80xajMwbc7YUXPGIBCT0lD4hvnJrXOCDP/qv+Kv/+G/zD//Sr/K1n/3XbQ1fm4WP9io4g4RH+xs8DQo++PaHT3z2fH0ugcElKPhadTQ8U1Lsuvz4OMnKDGoEVX++qaVHEFIFBgx7kh/AD2S0xt4a6t04pe89u01g23XGwpcCScldYnz5hvn1x5TTG/LpaBtm6GBTNddhyzgnIgVdoFsKPpg0aEmJtCwscWGeF+6nhXGJlFJq9LKpHaZU2HUD205XaZp3FsdcSpVtjbK2FMaYeD3aKWnJjjFlNr5j6AdzdawcAEnRwEaVcYmoqSWWniUveK/0Xaj2sYr4ehoRaoRw/TO1Pym1oiB2v7Nz1UHPg/eIV9SJLaybHX53ballmx3a96bWCB3ZdYgGvAb64liSEJK1OmIl7AVXA1iUczJj3UikU+LWw7MtkiNBzc3x49uRabGqgajaidZ1+GGD68znnKgQ2wStI632GyXZGFI1i15dZWDGokeNFY16I5/l2mLwNZegGho5J2Y9LODJsJzM/z7P1qZQrafWSNdt2A4bsvbVO94W0JxSlZIZX0bqxpdLIRZIiG3ARYhJiA5SMhc/QdgMO2Q5IKdEunsN82iAbf+MTjO+E/zzL5D9QKzOdxYHbKFQnozGkfj6h8TTJ8wvf0C8f21M727APXvX9PTDjjELp1RYsrKUTEpigU9yNsLyIqsXSGmkVKneHzWnItSWkebZ8hzmGl+cJgvUKgVyR0bxfagW3IWoQoxWnSgI4uuYrLwQWbDnFmcoHRLsuTeSr6iSq8+JVYeCtby63jg/vgff4/wGlx1+mMAdbTyoMgRH3kAflPeuB3723R1feb7lZ59teW/ruQ7C1oPzshInl2IVOnGCJCWq4NUTOkfoB3pX8JKQbGZoqgrek33j6zg8QkzZElN1WYm91vZq6or69zpvzdK6nVzN4bRQXVrB2jVxscyP8cByeGMqn2VCUrIWXDOAqyZw+J7se7IGxpQ5jZHTYrLj27GlyELXmQrm2eDZB8c21BjogmWlqOcYM4dT5M2UVvXAcTG10BAKwTuWaFWSedYKsjMxLsyjKQY8mSFszXgtzrWCNoMecPGEpInts/cYrp+ZisSZ5XNMmeMsHCQz5cVCqrKFOCWnqB/AL8ig6wHsgx/+3/mrv/O3aqXg3wD1q1dDblis7vu2V9X7vO5yD/e4x1fzQfgZfuYz99DPHTC4BAVfvQAFjysDlzc5c8E3LKw2lEXBVXAgxSI9/+v7/xcAud9RnJWxl9SSuBTnE50Wtq6w88LGC5tgG1I+zZxOL4n3H7G8/gHp/pa8zNANaG8OWrnbcorZ+ANZEJcZcmZI/YpmPYlBLIO+d8JpgXFJ1Sshrzp26nvfeI/ECV8iO+fpN56h+o+3HISxRsrez6n2bp1VEHxhGwLeU+1zJ2tRNOdBxE6+ZLL3Rryqp9jLTbKl3a2ZD1wMaFULrQkB7QY0Rlgqca/UwKh+wF0/Q6+fGTDoBzt51cXE+pAdTju8OvpY6GMhFSMumsVvQrNZpXoKAdA0E8cDw3TihSb6K89Gd7y3CXxh3/PR3cjr0QxbbnY9z7Y9u81A3/fGmPcKxUxQcDagVJUySU1zTLSKgYGAYOXm+ndzwLRAGdRX+aUtHiVnO/dpS/JrwS1maCVxROJsUc7qUB+hLBRmSpooztGJN4lrgVwSOadV4pnFCFd0A3MRpgRzEZaijBnGxfTwg8eqTZ3Qu2pmNY/k20/RnCine5TEpvf0yxa/6ZBhQ9GwphhKXijjgXi6Yxpfcfjke5SP/8iCZ9Sh1+/YYHA9czHAekgwRlbL5EGVIBZg1jv7yGJRxFpBpvXvHZodvoDmYmmTKSFpRJYWRbzYvSiC+GyjUQOdDhZ1ne2+a2Xr5/pcCthzcvZccZWZT4YQahWmgrtaMbBnr+alX7kTzTgIH5Ao9L2Nq2fbni+/2LHvbd15Nnjevxr48vWGd3Yd7+w8O50Z5jcEnfGbHdnZXM8Um6PZDNK8Uzo1oN73Hd4LLickK6Sq6FFvlYzO1qHsO5yYG2CZRpvD/WZdo7QbrE1YSbSX6yttyks1h6ptNCq/plRiZl4m8nIin45m1EaxexE646ToQPZWjT1W/5FTNDJtW6cGX3MUOse+c+c8BaJt3CIU33OKmcOceTMlPjrMvDpdtB86T+8s+n0Q8BiAiSkSY5VTTjMlJUvW9MKmq9H16sjTycDxscPvj3hf6IfAZgho3zNnOC21veIKRQsziZIyUTILQvCdtc6cyYMB/qf/+G9ZpaCCgkTtuD7apxpvox1mL/e2x+Cg/f1yf/y7/+nf5bOuzxUw+OARKGjX4yrBY0BgJiRniY3Wf1OELODVQ1B+9w8/4H/5//hf2c/o9laikkCsi4aI9R0DiY5ET2LrMl1e4HgH96/g9Q/JH3+P/PJj0ng0V7FuMB12vzP26mKTIWXwBbwuZGdadKfVaG1QXOlMTidwp3ZqSBnTlc+Jbaj695JtMC/WsnA+EPyG/XbHPiiHznFYEsclVzZ35lgJsoKiktkGK4EXceciVa4Lqqt+/NVPQJ0xr1urwW6q0lzmBKl/V6RYyVNcrklvxhorKZOKkv1gX7vdoVfPcNfXRohcLaWr0Y0Ltpi5gNNAkMzQ24mP6iFBjuRYKoEIlvGefP+KeHhp5jLTTPCBF37Dzf457252vLkZzKUvJpxzvHu949m2p/eu+pTVlpNrLnoFMNtWqgOc3Uip0js9t4ycMaBXkIBYrHJcWOaZnCJOCqGGa3XOKh1MBgiottGtly7VlKgsI1kPlZmsBjBa8FJurZwGVjyl2zIMV/huh0sCWRhzYY6ZJSWOM6RO0KL4boPzwQiMpxPzYST1E71X2G4Iux1hCKgm8IO9t5wgjuT5gIyvmV59RH71Q8ZPXrJME243IFfGr0ndhsOUuJ0zb2Yr5wfn2ASr+GxU2Thw8z2Md8h8tOpV9Yug2hNrBZuqaiFKpeZCQC1fVxC9Zk440JGwGeicDUOb/2YAtMyznSpTovj6zEqmFHdhySsQzECopPSwPF6tga1K1J69h5rF0HvHs23PTz/bMWghXfdsvHEjbgbHtUu40w8IdydiWpj6jry/xu9eoPvnMOzN4j8WyAmH8Vr64Bj6jhA6nFPzCknUMbdUs7Bs4UmdbfzqrJpRjgcL7gqBMmwsIbEbbJ7W1y+qlNUS+swvWNu2QuXb6BlMGPK9WENKNbGoLQS1kKVxMfOgU8wcFyvBq8C+82yDsguOXTAyq5sP6OlkKhpRa0e5sMp42+EnZZPqeoWrPvDOruedbcfVoOw8dJosXyVZiukSEzEmI1EXwXce7a1yBlCmE+l4i05H41EEZ2AxTfTDVU3pTMz1v0QmSa4GYQUk4MIWcV2Vi8Jv/OVv8rWf/XmKaAX01ACniz0KLESttWmfAAiXVwG+9VR7/TOuzw0w+OCJ9gE8rBJctgzSBSBof16/DhvMLlt4Shb4J9/+PX7pN3+Jf+9/8L/mV/7prxD9ppbwjAQlogQs7tSXSMgLuhTScscSR2Q5EF9/yvzxH5HemGlHiQlxnVULwo7c77kfE8elcFhSzXS3gI9BMgMzbpnpspG4tjtfE9mUT092+rfoZfOch1pqijM6H+B0SxltQPthhxv2+OGG7faKYbYwpGOVelHf29KcynJBfUeJHsn9Cl0FIASKMx96DR0azAZ1ZZdDLTFCTVCycoyolRAFsgaKQvaJxSemsGXeeJauniaGDWH3jLC9NqKh6tkmVKqHeW1riCgO6EKoz2cmLpEUEzHOjHMmxpF8+wn5zQ+Zf/hdllcfEQ92b2S7R1+8z7N3vsTV/n3ifs8xFrI6hiGw6yznweKB7RipovZ+65hCakukvneaXr6BAHX2vppZD7ISv6bxRJwnHJlercrSVUY/y3i2zBbBdd0a1iQi5BRt80qJFCMx2ZjINZmx5LKaBiFqBM5hZ/4EAtpdkaItDDknTnOs3ys4PIM6ejeYasapMdhjZrq9Z//sFu5fo32Pc7IqM0qNyuZk/14Ot0y398yHEykn/PXWAprcwJQdd3Pk5TFyWEr1lweP0ilsveDnOzi8hMNri5Sep5V8JerMMdIHsipZjTTnvLesA+dQ781p0ZkPiNkyFyPDLiO9HyjF5JcFm39xnpjGE8vQk4M3VUg9cbd1pkWiiyRWE6pmZCXVsrpWt5qRUm6ySIVdp7y7D1z5LZJTfa/3uNuPyJ/+EcvLj5iP9zZ/dzvC8/cJ7/0U7vmX0Ot3iX4gVnmlU8V7T991dCHgXN28xdQXZq/o13lcilVFktuypMCcHLNuTU7sBOk6nDc3zaw1P0Fcnb/Kqt5qYPSBXC6jPtu6EDoD9a6HkJEGJnwA1xtg8t265izlnO/SOV0jk3dBuOocfrpD37xBRosXB5Bhh2yuKb5HxaopWt1mnXhclcq+s+m4HjzXg2erEZcWtAjJdSQpTNWWeErZDhTFlBNh2CPTAe03pLvXlGUml0R645i9w1f77HK8J/kBxRFytH1BzPjYUjizGZ3V+9k29a/+iX+DljtWypnv8uQeVVuCRYRqeL2u++0SrFLwiz8hKIDPETC4BAVPXU9VCRLnG7/2bmDtlTVC/R9890P+xm/9In//F36DF3cvANOXX14OOwx2Ne40HV4zHxNCJOfZPNHv3rDcvSZNRyMquYDurmB/Qx6uuJ8zhyXzZjL26lBd9QaFbjkg97fockSWCRA2fiD0V+yvrtl3Wr/fTsfb4KyPLmKny+VEfvMJ5f6NGXbU3+2evYfsnnOze5fO2fdMSR+ELdnALKYn9wOlbkr4wXq7aqQbGa6Qfl8rIP3FScqwbkERyZW1X/v1UMvaSiwmw7xf4FQCs3Yk7yAMTKGn+D3aXSHDUI1U0hmgtCjdukFqZeb32ZGTs7jiOBPTQswJTm/g9iXph99n/t5/y+H7n3D65A0lJrQPbN65YffT7+O/8DMM736ZfvcC3dzQDd5cHsnmjrbeJ0XQtfdqplj+PKjqYrneL3VmYlJ5CxnzmB+nkXkcIUeCYps1NQGuVH5HKcZJ8CbH9MHX8KlUWzkncozEeSamSK7Ew5KMi5Kbg2QBxgNlOpmEjEK4dmzcltkX7qbCkhLTEsnJSq4bhbC5wV09J735BB2OlCVXG21T3ZTxaGYxg/XlpBTzoxiP9u/zRI7JdO1DsMCxq+ekzc3aQ74bF8ZU6INHcWx8YeNMxcP9p+S7T8l3r62cm7PN2RZdK1Z5sbK9ATb7Z4+E3symKmjLi5WNycaFkRxxJTJ4jxdT7BQBcmQeR8ZpZNMH1GuV5Z0Lt+tmqJ7VIK0uKCLN3tphPP0KDGK0kyiZnRf84JnLSD7cIa9ekj75HvMPv83hux9x+vQNeVoQ79i8e8Pupw50BZz2UBxsbohi6hffW1WrD47gdfUUAXOMlJIt7A3qhu7MjZCRe1+474QkW1hGXE7MpRAX2AdhKIqX+jNdrZ6IVnAubxvrOKO8ShdtfRhGcxIUR/GxoSojDHrrz6dqtQ3WQvPq8NV3Y98pG0m4u4/Qwyvy649JhztKWozHsb8xLk+a8aEnKOw7szpWEXbBse+U697Rz7f413e4ONomG3py2NL11wwaOImtf2NM1dLeE1S4Gq5w+xv0dE9eJsiJNB1Z7l4z+oCPC7K9ImrHjCcVa+shhVnPrYFcaiLmxTUnA4wgKyBYv+Rij2qNHBFwpZAqy6BZHjVwcAkKPmt/fHx9boBBQ0JPES5+ElCQ20kPaDwaivD73/2Af/u3v8F/9Bf+AX/6p7/KP//n/xzATtBUK9dKgAoKGkfKHMllIeYFIZLmESYL38m5Rrr25n8uN++QNs+Y3Yb7MfF6jNzPEUXYepsIG83I3cekVz8gvfkYFgv3yd0GuX5B//yLvNi9y3a74X4RlrpOdtVWueU8lGkkHW5JxxMi4I+3lNMBfXfC58x29w7aW1+uBeRUTpcx9DP40JNFED/QDIdwntINsL0ihw1JO2JRqgS8lhIdIuefWYCamkPWwhIjpzlyN0YOizDLhhOZ05SJ40zRxG4R3pOOZ3g2nZiyQC8TMc9PX8Xy4DuXWZwx41OMTJNp5d1iCY55WYjTzPj6nuMnd8RjRERY3hxJp4n9aaKfR8L7I64sVTpaSA4WCsWbw18BXDs9yTmfo7RKCdi/afuamplQT125wLJMzNNIWhY8RjikKIoZwjTbYnHe+rze4foeDbZBWdzwiVxq5HapHAJvHBnbCTO6WCiVucJF8niovV2LTR5uOkZVNt6IeGMuHFK2E3tQttuBYf8O7vkb+rTAOOKGbgWCktPKcG+tBCOtNta1ww0d/c0OhgF9/j55/w6TG7ibErfjwmGuFTMpbDy1D2xKnnJ4TWnBOGBeFhWISiXpSt1sALL3pGCVOTZ7Sr8x8xsEtyR0mpCYkJoTIXHGuYJzzvJNa7k2LQvzNDLPPd5vrRonxYpfhPPzbeFMbUS2DZlaOagLfq7tjBgzaRqR+Yiebgn3r0i3H7N89D3m73+H++9+xOGPXnJ6NRrHaGvk1P6dazSm6vAXSTqRxdEjaN/hnVQFjjufIEVtPuPX0nTMhRgzpzny+nbk41cHDocjkhNehE3o2IiyLJE0RrJGNj6YQ6A4G2MV4K6JsU2zUHkZqShJzc2T7Y1xh/xYjZXSGTj7jtg6clLjk73d/77mygx5wt1/CrcfkT75I/L9a+LJVERuu8GFfjVPc70FteViMc5BYR8cQz7hXn+CvPoB6fYlZT6RnPnHcPMe8vyLbK6+yNGZmdgcC2MVFHoV+mFDv3mGuxltbE9HjEqVWU4HS0adF1I3ELHWSJGAqqdzHYsNjDUuOl9wr5Zsclzkcu8qD/Y2qd+PHbdIFRxQ29+tHf573/mQb/xnX+fX/xigAP5/GBiIyJ8H/nfYYfw/LqX8ez/q658qjzwFElr74ClQ0EBD/f38k+99wK/89jf4j/7Cr/NnvvK1NYMeWlui4LAyrpds0Z45QjZiXiYSSz73G31ngKCy0PP2irh9Qdm+4HaMvB4jr8eFORW2Qem8DWoZbymvf8jyg3/B/NFL4mEkxYj2Hd07H+MOd7gvTmyuvoAfrjktmVTMG16FtcROPU2VbCe8kgpeFEJnkipRhu1zXBjwYsxZaJu7OcOJelx3VmuYPEzQ4sipoDETYgKX8BU4WdqYfWzaXit/2U+IuXCaI7enifspMhXP/Ry5m5JVQWZTFmxPiUkC2XXcYMztTlsoDqwmwZUl7QWSGmjLObEsC9NsttMbPyDdDnf9Ar26obt+w/zmnjil2quFfJopxxl3vEUPV/jNDl16yuSI9bXnbKVl7xzN00bFVzmmof7S3MnaBtF4EerWEnOaZ8ZpJs4zWhKeFrBSQ4SacY46nO9xXYcbNtB1Fr5SfTKWJMROyARKZ0mKUmoAUj1RtFjjcjqQD7cwT+R5grtXuMq83+/f47SY+dDkhFM0f4L7ObELSn/1niki1OFOt4Tgcdsrqxb5YH1nWJnWRdWIdt2A214RnkfyPpI21/DOl0hX73E/n8OuMoXe2e/feWUfgPtP4fCKfPeKPE8m+ex6dHeNbHame9dzlUpaEqIo2XmWbqD4DaHb47vGf8i4fsaNJ8o8W7BQSZTFopi1EkTVebQky8iYF/o+4TqLqZZiYTcUNf6GVtOZ1ZWuWlKt7QOqVDgRl8iyTMTxRJkOJjFejpTDLe54SznO5NNMKxThhLDxdNc79OoGvX5B6nYUP3CaF5Bs/gg5Vetnc8hcSb8V6Zss1UDBnAvjknhzmPn+q3u+/8ktx9OIp7Dr7HR91Tv2nSdPkSKTPUvfEZyBW5GHoCDXNTLXjS+mZMZqMXNMhal4cBvEPRThpQpqZQUF9nkv1goIaUSPr+DwkvzmU9LhlnhvFTLxNYLcWdsI0TU0a/B2gNsEJUy36N0PyT/4Nunj7zN/ekeeZpz3+N1A9/7BlD5hS+ev6bzpLI5LJualmmbBu9sXlGR/1+MdLsdKSO5MzZKShaJJNF6Ti6AdkhLed4DWPciE2qmuty3nhHwGBI/3Jmol4xIclFrplgqIv/XtD/mlf/R1vvnHBAXwI4CBiPw28LdKKf/ij/UT/xVcIuKAvwf8OeC7wH8tIr9ZSvl//tjv5QlewWW1gPbnh6Ag57L2ZwD+4Dsf8rf/i2/wf/oL3+RP/fRXq2TrgqcAq9uWV4ztXG1kS45reVxV7HTXdeYctr8yaxQfGLVH+hsOEY51UZxidcpDVqc4OZ5Id6+ZX77m9Mlr4t3JKg+dJ8bIVrDSrevw4th0u7XVETOEsIFuh3v2DqRoi13TjotYzO80IsMJnQOhFzSY1K2V86xQfr5PtrhZvHJBUV+IczFToTkhLlPIK3BqNa4zOLCfm3JiiYXjaeRwmpizcEqRw5y4mxIfHSZup8SSYRgj2XWEfiCEYJWIZmwizVnQSrhSpWhSLYpzjDU6ORFLYl5gt3kB4ggU9mpyteHTW5gj/WbD9p1rts+uGXY7tPM1eM9K8WVZSOpJRQmIASbvERVrtVC13Rd9wZUH0SKS2yKaLH1znmdijNVDfjTPiLSsC6f4Dg0Bt7lC+00dQ1YtSCSKU3LvSdlTtDfGtxifIYdhTdV0IiZB3d7jNq/Idy8phzfkaaS8/th68q7jKlwxb4JZHedCLBYYM2bhhOfq5ouErsfPBwYyfrdFtlfWVvJd5Xs0OZtAl5Btxr+Aod8DSux2LJvnnAiMOXKK9nuCKvvO82ITuAqCO71BDi+Jrz82trwKsrtBr16Qt89J/b5GnFsOgxNBl3E11Eo1LCf2e2bXIQSr8DiBvkfdgPMnyukOlsksoYGSpMpzA1mEGE2TvqSMz8U2AmqVqOQKEJ5+7jYqhJwLaYlMS2KZjeRmeSDWzlEF7Txht0OeTWjKhNAxPTtB5xneuWb4ypfxP/3fIz3/Mstww2GMRCyuxLvFWhSpShNTrk6MxgUo4mp+RVlN045z4uO7I999ecf3Xt0zTgtB4bp3lF1f/TNAnHA4TdUdc4N2glNHce4hKMhnm+mUjMh6qgqow2zl8xztq5VSpafUtcZW8uolh4rZdrs0VQ+KU7WcXkAEv+mADukG5Oq5rXPdjhI2xOq62NZSPx/Q4yvyJ39E+uT7HH/4ivnVHWU2EzV/taF4pdu/Rm5O9P0zQq0alGIumwaOHQcPN1fvol0wmWKsFUVxld/iKclk7KWUGnmOjQ/Ah4FShKy2rraN67J63QDBg8Z1KWgx3kQDB4KpjLR+zwff+ZC//ptf55t/5SEo+Axu4lvXj6oY/CrwfxWRXwP+N6WU5Ud87b/q608C/+9Syn8DICL/Z+CvAD8WGPyoqyGuVXZUWP9+KQX5g+9+i3/nd77B3/vz3+RPfvmrK3O5pWJCOxkaOJA4XWj7S01+c2StAySYzaxT010XcSziCMVzSsI4zRxjXkl/LZY5VNBBOsd+5mxyndIY8DmRY8LVmNrm4e0QYinMOVvZf/sCdd4Maa6eGQkxRXvPXW8a7VKM6a4jrhjZEDmnzTUAFYsRtpZsnvJZQIqSXcbHRLdEfEh104Wi1qf11hgzcCAexNQcS4rMsxHlMmZp2rwVjkvmboqmskiFzXHkxWniZloI3uHVKiPNYY+Szw6EKRGnieU0WoJhisypkokyHGNh6/dcf/G/j989Z//ul9i/+ohumei7js12S7i6QnY31ibZXLG4QKps4bIkxjgRUu3XOs/gPUUFc3asz6ldjZR1UWpN2UymxnnmNE3Mi5VWJUWIY/VmLyt3wVdjrbkocSks2ZjTaZkpOaI54SXgQ6D4eiJMhfEYGaN5GojA4JR9d83+2RZXHezy3SvjwojixLF93jN1gWeDNzvXXEjiGItj0QCbDd1uT5dO9JIIwaFdB6Fq0Wt1CFr/XVHXEbot/VWC4pjdhoXAcoqMxX5+CIVOxbTpnbJlRu4+Jr78iHy8s5+ze47cvE+8/gL32XN/KoxpphQITs3QyHd0vmUi1JCkw0zWhOiCCxaYY9HfioYenyMpzSZlTLECG5MdlpyYl8hpmujnGV9joJ1KJSGWM0B467nbvC+5MMeF47z8f9j7s2DLtuw8D/tmv9ZuTpOZN29XtwiSFmlEUA4GbRkgwapi2GYYJASSL44A0dC0SVNkQOS7QuFXh8MPckcFG1GUGAUQcPhFEikQNF9YVSBAWmaEbcmwzEYE6t5bdZtsTrP3Xs3s/DDm2nufvPcWqgCFzShoRWSczJOZ++y91pxj/mOMf/w/h2EkzrF5pIgXhrYOt95KrOk6Ntst8bVHDIcD0zwzuwDXT+Hx50jbp9xFxeGQiFkARTCKkBNlnoiHOxIdNgTxEzFWMn1ljpXTZX3sp8jtMPHiMHJzmDmMMysvff0xZTZFS3tKS/Y/z4mYE35p2Sj7ABQs1dVcxO1zTpkhiv7AmEU+vpaCriKzXHWViSul0Fq1IYUGFmqR2JYmaREs+2G1xVgvhThjhXS4uaT0l5RwQbIdc5YYY1u7V+W5vc4sBmvNB6TWQmkE4FKLTATliNWibthZzZBUu1+FQyqsM6z7nlXXYVXCNTVXmYCqAvJj0w5pzoyqVvGKURrShDEBXUWfJKvzOCu/Oz+Xaj3xOSXmchxZrOrUfvna17/Cn/xbP86X/8jD6bzv5PpMYFBr/T8rpf4O8L8E/m9KqS9zIj1Sa/13fl0/8du73gbePfvze8D3nf8DpdSfAf4MAJff/guXV/4sZZoTul9AwV/8wS/z/Z/7Ap+42oMxSuG0FknUpvmvEFCgjKMYB9ZTvaeG0Fi9prkOCnEnz4V6v6dqCTxGqebfLeYyrjG7UQrlOtx2Q4kZu+5QiOiK3XTY7QXK90cyH0BGsoHcyC1Oe7r1U1x/hd7sRXY0T7Jga5U+oWl90pJQWUkZWIuiWm0jgWiFzhBrU05svS5Vl3FJOXzmXDC2YKpp/W1pmC3gwLSeNg2Jl9o2D4jnQTOZ8ka+zs1zQAJrFhBRBY0vaniAOMWlWcRUhj3jYWA4DOQkAjmlFvZT4W4Wgqc3micrz1X/Ote/7U266YYLk1npSnAObSWYZuuZqiUWKdPHqhnHWVjrMQvxR1tRWVQao10jR56hyUbykrKhZAYpV+aY2B9G9sMoxMAqvW6dkyhPsmjhS9Y7FxhKYUiJuyEyzuKI6Np8f9CiBqiQw3xMlRdj4vl+ZhcTpQhX4I2N582N59HmNUwa4XBH2e2pMTVRno6Lq7eZiyVWxS5VGVU0nuICxXl0t8VbsCXKqlNVWhv6bFy1It9XhaoDeIVFRvdSgjImSpqpBqyr9EqxseJCeuE16uYZ5eYj8s1zao7ozQVqe0XevMaLaPjmbuKD3Szy4VrMdB6vPY860evwWkBYLJWpwJQjsSqUmei856J39FbTawjWCx9mMUZq0KaYciRu7ocRF0asC3gl+voitNRE0T7rube9MUyR+8PAfj+Sk1SEOiuyyxiN1R1hs8GkxyIjnBJ9jByK4i4bxnDFy7lwsys8O0zMWczPLrzF9UL2zOOOId3jYo9Z9ahe3BulcuOkirqUsavYIM+p2Qoj1ujn+8+26qjsUdl7ixV11VIZegAKOLVqc5V4EJuHRSrLJFgj11ExFRxSYTVC6GjVP0l+1NLfU1rGYDdGWkiqVSBNEE8NvyZqz5jEdjtXqZBJ9WEpSRiU77HbC0IumBCoKcv76ANuuxGFWyXx1xnFJpi2l7JUQNvnrsZjt2uC1zKNUAt5adXOM3WaKPNMSWItX3MS4JgmKaIqjdNOYtm3SOc/DRzUWk/Op+366rtf5U/9rR/nb/wGQAH82hyDGdgDAdjyyXP1/29XrfWvAn8VQL2l6qt//61KJksb4dVrAQX/xx/8Mt/39heOD2MRPFoYnyCB16rSBFMmWbhLNuh6iglMaMZi8cVRVMfKBZR30l4oFc2MCxk/ZdYhtdnWcpzRDVYy4Go85vo1lLWsrg/i4qYWv4AOulUj/q2pNsihU8QrXMZe5LE5U/DG0oVHdGsRi6lRlL1UOTH8201C1XQCJkpTSxaddK0xVWGqENyqUihhYkkmXNt+LpWs26FdqpRYGjgQArMG0/Tkq+jO65JwtmPtNLkYUhHRk95btLVsgqN3omqnWuVn4eHUluXVeSDu7xnvbxju75jHmawDvrvCKEi18OIwczNGYqr0wfB0HfgtVyveevQWV08u8a7iWpmztvthMuhZRpcOh4mpMZW1TuRa0VpMbXRzTxQy4llJeakSVKkU5CogZzdM3O4PHEZ5Dr5pzNfmQie9epn/jgWGnLiL8Pwggi3jNKNrYe0U18FinD5aXC+6Fjdj5MPdxDdvBvZTYuUNt49XlArhOrDtLwVc5ueUcU8CnBeC7MZfMRcrz9JYcYzTHlwgGUOyGmfXQKa0UcplcuXYWKm1ZZSNEIkhpUyqBZyhTmBcwSex9N46xcaBGV7C3cek5x9R9jeiGul7an/JXnnev5/4p8/3fP35gcOcWQfLm1c9xogngtOGrEQ9MmVxFn05yShkUZouRA6z5/HKNw2CRtQsCUpshEHbgrBmjpmSZ9T+gPYdWy3kO6NOQWIh2p5Ho6V0P86R3WHgbnfgMIyUUrBaUYrBrjq0VRivRYSrkR6Xdt0YFXfPbvnGix2/enPgo/3EMGWcVVx1Tub5tcHrKlNRZWIYPGa+QOWEW2/lvS1kv7Z3VJXKZ+9E8vt6HSjJ0FvF495xGUQvwKmCThNaV9GGUEoMU865EwsoWCoGVYh0S0yQvSqqoKXxX4xq7R8to6Eqzm36pjyMSYsyYnuNqg3aepTriBXxkYmZOUsFBZY2RG0GS6BsoPgN+smb6NUG+7hNymSpRGAshBV6ey3CUVUUUNdOkq5DFLLxOjiRvA8d2gdU8FQtbaI4Rw554lCkYluVwjqH1hPUA+SCqrmBA4X1MmKb26G0nF0nsvypPd5oBLyCBygVfvHdr/Cn/9Of4D/84Z96AAoU3/o8/LTrW3EMfhD4d4D/BPg9tdbDd/jav5HrfeCdsz9/rn3vO770wuz8Na7zSsH3vf0plQJOJDqQcr+Ut0RsRlFl3EZbsvYMsTCWQtUQsGRbqV7YsWKvC9op+r6yiTJ37jXUWrBKtN2D0agcqb4nW4faPpFu0jLDv4wIVSRLa1n/YjCzbNRDFPvdQrNu9dA56Uc6HEZbEc8xSpB6mhtCb/3SFtyVlj9b14nJCKC0oiiNsZbgHd6K3rheUDWykDMcwYFqWYJr71lrLep9OaHniZomerfC9gFnJFjtk2zai03PZRfonfT8F+1/hRJuR4rE8cB4/4L9Rx9wuH0p5fPVBRpNZzdYpUm18ux+4tn9TK6VR2vPizExGUe/VbjNFa731Jqa3WpB5UJhYpp2TCkzxkTK5Ugq08binDgtGu2a+IgsmCUI53oiZUm1ILMbRu73A+MccVSMlewMgHyq6JScKCkxRrgdMs93EzfDzByTyDwriwpLK0od+8JzEQJazIWX+5mX+xmlxFlu5QxPVo51uMD0azKQhglTXqC7HrO5YrW65pAqIStmJZ9hLDBkcEWjs0YpCM3pEqRErI5stwYKlMzYxyxTL2PWUv3ImbFlkUo1bwYjmgXq9pb88iPy/QvyFHHOo/s1OVxwNxXevxv55x/ueP9moFa4XnuebANWLSx2hTccDy7F4jeSmAv4mCk5Yyn43hAcrVKTxDZZqbbIRCp6ToVIpO4HfDcSFk0NU8UwDJpevzoG46UqOafEfhy52x/YHUam5mJomwXplCv9ao1aiUYERstcv7IMw8zHu1v+xe3EL39wx7/46J4X+xmjFE+2nk2wWKXpjEaPd6j9C+LhjoPRMFxLkd8YnAngEkp7FPU4Zt07y2UXmLc9ncqQE2ur2DjN1itcnlDjQVxUfWhqmy3mKN30QtQDUHA60KSiYq3BW0vvHVMRsR+ti6hZHhOtZm5V0jHuPCDqWtFBiLkyV8hJEZvvy5gyhykxpywGR0bUEI1SjQRZwXVUbch1i948PZ60S/tHFaH75aY5oWohGEf1sq/WXhKaLgQ2fUfXd2gXKNq0CmphKFJdu58y0ygjxp2G3nqMnmW6KCdaVARtsLYTKed2zuRPpc5/9vWL736VP/NzP8G//8M/xe9/5wQKPkvw6Ne6vlXF4N8G/ie11v/Xr++lf0PXfwb8K0qp34oAgh8BfvTb/c+vEhA/7ToHDL/03tf4yb8jnILve/sLn/p/lxtsWsA2lNavmqXcq3TL7h1TLuxi4X6uTDUTkmZSYgSiDHReYVGS1XQBUSpLTEYyFaPANOY4tVJcT9HyumOS8vAyui0MWfG3t8cPXpvffOsLVxhSkRGZBPdJEWzF6YpX0BuOo2jBOKxTomMuaveNzNeOtobinQtoa7FVgIpxBu9l0ztj0E1k6ZgztqBBqSQtCy9XRASoEY90ieh4gClTzQ7tV7j+ik1wTFV6wKvNmu060Dfr3HOt8FoqOUfiODLcvOTw4mPGl8+E7TsOaGVYX2+46MwpA5gTh0HGJFNVdKsVF5czYZ0purB2TsSadKHUSFGRjG6s8kV8JUOEYRg4BE8fPM4IU3tZN/X0aCRwFvm/Y0rsp5n9lIgxUzRYraXv3yZIyFGEonJs0xWFYUqMc2RsAkR9szNeOcVF0NgoVt7BdZiuI+bAyyGx6SwvdxMpCkh4cZi5GzNPVx029DJ62tTedH+DOdyiNjucXuNUZcqZmETbYD8bcahUnGWgFasNRtujoQ2c2OapLKCgMmWZONnPiSkmYhKA7HSVVsi0g8MteXdD2jeyrLGo0JNtx93dxIuDAJ00Z4zVbDrLo5XnjU3gUWdYlRHGkWo9NqyPFbm7SbgdpSZGoxgmRbSW0sA4OYrdtHEtO7XSisiFuTRXvmlmlRLW2aMpldbq2NVarlLlZ81zZDgMDMPAFCMx52XlkixktIAn7SmuCTQV2M+ZmzHz3v3Mf/Vy4J9+uOP9FwfSnFn1FvCsnZF1bUHfvyS9+FBaQ0qhcsL6gOm26C5iShW+ZItr3mhwmkfrgGfNlauUOBFUwdeIHm5Q8wGV5ya93fZdK+MvYHdx8cxLqGiAXWuFNloULL2TGFcMWYk8s1NVNDpm4UipKoRhmlOoVG2MmNZhmaIYv80NnM4VYlFMqRCTJC9OyQSPUsvUkjyUWDXoQKowVeE/pCJVQbkXMsEQTGsTl4zNExtj6Z2Vz6YtoQtsNsIv0MZSUCQqY4L7ufL8ELnZzUzjRFCVrRdtld441Lxv7ZFWgDcOY/3xbHn1fHr1erVa8IvvCSj4az/05Qeg4BP/7zP/5pPXt+IYfHra/P+Dq9aalFL/JvB3Ebz+139DAOUMKYjq1Mkj/pfe+yp/7u/8BP/uH/oy39/aB58JLNTpoagi/V/VJhFQLS1BHT3ib4aZQwafNMUGfJ/wzmMtWCsHOtpiVx0dmUOdibuR3GReK4pqOwmCc+bFkPlgN/FyiIyp4IzmIohp0pOVYxsMXZPPNaaJCbHY6Srp77WyZMygU8HWwtjYx1oJMU0bi2la8DWV04LKGdUU3VTNaNfhfU81Bu0s3huCNwRnRX1RnbLljCh0UQUo0J6Bro2X3A7AOuyp415yzdDj5j1u/YiL7SNM39GtOkLvcK2dYLSMg5XGfM+lyvTBNBGHA/mwEz+KaRTLWN9z2T3h9U3g+VXPzSEypULJhbtD5OO7kWe7kUeHWTLgxohWiMkQ2japXQM6nZprtYqc8TyT0kytHuEyn+4BcDJDgaY3AKUq5ua4VrVY5gbrW1hFeBNqbqYzSYItIpNjNHitWXvDdee4Cga//wh1+yF1PKB8z+bqdcz2dcbUs4+Jw5R5eZhBwRgLh5SI2eNtED0Aragpix/GeIA44roV3kBtGvKcGRQ5s1TRpUbgTJWqWDs86rF6JdbJcy7EVJlSZoiRYYoM88w4T9iS8U7Kv4xNLCnOIudsjTjR2UDMlUNKjE20wwXL9crzPa+t+a3XPW9tHf3uQ7j5kDoP6G6Fvnydq/41pgz3s0y6ZDjeS8XiMDhRYxtdNK7tQ9/ec2UuUK3wW5qC94PnurQb1fGrkNtSmsVlL7/SttMyFiliQZaMkp/RpgYOc+bFYebZbuTju5G7Q6TkgnGai97xxlXP65vAZdCY/TPqzUeUlx/BNIDzxG4l+yFGbNNfWaaNZP8UtDMYHIGOtUrkIYpR1v4FZX9LnQZZx90afJCRZzSpKko+bxt8cuZeK+EpKGclbhaDLZaiMioX6jyI50WOrVL5sP2GERXVoixzLoxtdPZuygwFktIUJcJRRRssolew8lq4I040DDSVXBVjlmz+2SHy7DBzNyViLnRWc907AZW94cI77FIVpgrY9Svc6oLVdoVfdRgn6q6xtFZVqk25M/JiNzFPEysDXnuSPbVTadMyCiA3sNRUGj/rAHr1YFdK8UvvfZV/4+f+hICCz3/pTNPlU/7Dd3D9S6tjUGv9OeDnfiOvcX5/zwGBaozOX3zvq/wbP/cT/OUf+jLf/9YXzmP8Z9/TE/PjRIpJkWoK1AC1UIoWpnku7KaCLhO+l6xwHRLrKrfdaI0zoLRlGhKkPeN8S9zfiXytC5QOqt9wiJX3bgf+Px/t+frzPbspsfKWN686fsfrW3qv2TSpXj3tJasPK7z1WFtxDm5jYcgCFGIqkvHmjLaKVBS5GNnYGrRxjf1bqKXJvGb5pWj+6yXJzP1qi3c9zhn5ZUWSVWl1lPWEJXDW5kHeRD1KJlYlVZBaqXEi726pcZYe5uYO92jCq8Kqc6z7a4y3rbd58hNbZKmLsqIvfmTEV8o8NztZi+lWrN2KNzY9Y+oBWD8/sJsynZcsd4yRwzQz5w7XWgUahIjpHCF0hBhJeZk8aFmJhCZUlYOGpl6GUsdD8/yS5EOU6qq2zGVEK0UsVebxrYj1CCGxoMIIccCbNWsnQS8XjdOaR53lImjCfAcf/Srzu/+U+eYO7R3hzbdZfc/v4nsuP3fkn7z34kAu4hoJku1VIzobNjiyUseKhc6xgSNpEZBlzG5s5LTQCGOKZp5TFEU3kNnKuLWeGOqxiNzwNC9Vj5k4R/EwaEbNTkvmXtpImnain6Ga2diisdE5I+0DrfncoxXf+9qW77kMrG7fI/3Kf8H0zfcpc8RfXeDeGQlvBi7Clkedo1RFLIXeatZOPgvxAEkUHClFDLGsSFfHkhvIrjhtMVZEpj4tiTjCwdYk1tS2Loo4TWpFrVJtcdYSOk8IHaYZbIlltYCoORcO08wYI0bBxcrhrRDi3nm84rdd97yxcazzAXX3MfnlR5TdLVTQzsm+a6XuoqxUJRpxUCtQRtxHg7L0OPb7kcP+GfPth8QXH8l+rFX4HcaKWl+pxKrEp11LbXFxAn31Phit0GiU1ahiMNngsmaOiTTtKPMgydVSQWm6K+K4aJqviGueAYv+QmkV1ErRCm0VzmoZ1zZw6TQbK0qNgYia7mUdhTVDquxT5hv3I//kw3u+eTNymBObYPn84zXpaaGza7YeVBqlNRMn8Zgh0iXDWm0IwVKNIVZNbH45Y8rsp8RuitwMEyUmdJDzoBQFShIr8azI4ITQSa1He4lPPXbOjp+FzvJL73/1qLPz+9/54ilpVeoBQPj14IN/aYHBb/RaQMFSLiutf7lwgn7xva81RcOf4vs+14iGr67qs6vUZp63HEVq6SLWZbZEqgc5YY1rm+HUWxznxNjKh7UWtNIYJSIudRqBCdRMGW5It8+o00z1Hfqxxa6lJ30/J755M/Dx8wNxzgxOPOc//1j0AoLV6MMLzHjXdN9X+PUj+s0lfdZ0Ee5jZj9nBiZiEXU9UQQTwtNxJFNJf1OYuU3JTUVUaf2xFFEqoWpCG4VxhtAHIWVq1bgHJ7LMq0EjKwmUeZ6Zx0isYkh1NGQadrJh0iwHbvDYeYud9/gugDkzGFl69qUK+dKvqd0WffEIfdhh5okcE+Vwj3r+AcZ5ri/fRl93bLzlce+5nRK5wqZ3hCZ3nHOmVCPvu83Ge+dY9Z28t1KZtNwfqxWdMwSrsapKH1ELqBAXO9My/WNnEZBM0YVA1684DBNTjngjxFG9DHLXQp0jatyjwp5+s6GzovcOlc5oLjvL1mvMxx8xvftPeflf/gr7D27QRrH93Auucmb7Oz2/9ep1Uqn0zjBECYbBtHKtC5h+TV1fYPyIckEMsaCJeIngVjqSpxI1GVKCrAsxK9CarAUQnPc3l2ckY65iA51Saq8hFt6agjeqjZY1edfmveE3BVyH6dcUF6hIufdq5bBmTe8sn7/q+a1XHdvhQ+I//39w85//MvfvPaPkyvqNK66BsL1i+9oll520BsYs97Gzit4o1LAXmeh5ahoFUsaOWQ7DKcuoXNevcCFI5egs9C4kZcWSOIiBk8oZq4TI1juDouJMBW0IIbBdrVj1nfgaaC0ViAaWcwPkgcLjlSO8tsYouAyWty8CT9eWSybM7TfJzz+Qkc4Kxll0t0ZfPKJ2W7Jfk22g5CrSeCw8FNmz1MQ877HzTmb9X3xIvnlGGZsdtLkCY8jKMVfLbox4M2O8cJ3OS9+qve5ykBmtMFWJhboqlHhATzvUdI9OicWFUcylxHnyONq7qIVWiU/GgMuazlZSLWQFzkLvHWtv2DrD1sHGFOx4S92/gPkgQCPPhO4RBsWYCs/uZ54/30t7zhuc0bx1FShVBNL0fKC+/AjmERU8XhfWlxsCEy6NKC/3Uyvhh8UscX6cI3NKMvZN07wxqvGVZgFBzUBKIYHyGB/rr82L+4fvf40/+3caKPj8F4+iRopT7nreZv1OwcF3LTD4xNXOcKUUv/j1r/Cn/vaP89d+6Mt8/+e+KOhZNRnJs4LAg69K2LbLXVfGHdW1as1iE2wdKo0Ev6YzkomMtjBXxOY1RmoW61tVNVbZ1rub8GWmzgdCHJgPd6T9juI6VOhQq0d43WG1lnJdLpRUqE7Ktb3VrL2mS3vM7YeU59+ENGO3V4TXM513dJtHhKgIc8KqCVcVqSqCtvROGNydlZLfsTesLVnZ5rVSmqOfhiLuYYqCIaEmjXIaJo/re5wR0Q7pM4ryXz07xMXEBdEVGGYO+xGwaL9C9VtUuEGNjjpGsTUd9+hpwNYZXaR9o5wXxnY5iaikXEhzIbsNdfMEEyO+VqK1qPsbmYE+3FM/fh9XCtcXT9lcP+LRyh2Nq5Q2bIKm00JIaryzIw/CKsNKeQwVoyqz0+QYj73aoAo2z6hsUFqC3aL2qLUV+dxy2sBaK7z3dH2P9XumIZGKZKXeeBmJypk6D9Ky6Q+YNNIbz2WwaCUTMp1VmDRR7m+Ynt+we/8Fd+/dy3jnfcQGz9XmkqvfvuW3Xq1wRvNimIXo6gS0KOPQmyvMdKCOAyb06LCCNmnhDHR2YZZXgpbeMKmSam5gsFXk2jOu54EP1daBtNvIBVMzQVeKUOPpLM0ETIvUcVhht1fkKaC6Hr25Eq1/BIg96h0XneNR7/ncNnBVD5T3/wm7f/LPeP7//ia7D3ciOpULq9eucPc3mOuJzsoh4otm6y29UZg0oucDZTxIedv3R7OruVQZs0MRfKDre/wyYaROzG9R+kRAQUkNHEiLwuaZoArZW6wRwqpxDt91rFcrQufF5EmpxjwXcqCqhU5XroPGXQbq1rJ2hpVTXAWDG14cjZY43GOosFphtle4x29irt+gbp4Q3Yb7uWAp4r1hBBwYLYdIzTOmzLLPpgHGvey/UkQ8KHTQb8l+RcQy7EdWOuCRMV1asqRbRiuTKfrY8tIo1JyJ0x413aOmHSaN5Cw+KtUGGTu0QUiGC6m3xSOrhfin4CgEFYxmKmCdYhU0VyvP1lu2rmL3L6iHj5k+eo90fwPWYx6/SWcDax/oG1eiAiUVilFNMVbjF47L7gX19hk6jtj1hrBZ4+cDvsyoPMF8wNqV6BcU4ciUKJUur8AvMs6LWN0kPijE6UQiNyJFvhhOyuDkaZLjeIQ1kPWP3vsaf+7nf4K/8oe/zO9754unWAJHgPAbaSPAbwJgcDzjGjD4yrtiiPQf/PBP8XsXUFBF8lIEcj4J1WrrIapyJolsPNoGUFYEQ0qCeUTbEZtG1t7zuJcy3pAqnixIMU3UaMArdKnUOKLjgJoHTJ7xOWKmSL4bSPqAcQG9ecxm+zaPV57XLwI3uwljE93a8/ajnje3QrQyLz4gvvdPmT/+CGJh9fiWqhW+63H9ChXWSBNb0VVDMUJA7NviVWmC2EaZrPgdxFKO1RZvOqw2wjhfrG5LRqcJNY+oeIA4YFYrcXesalHEbfPMMOUsGkSlEGNkv5+YDjMqJTbhAtdPmOvpKC1rtBKlQ61wSmGWn10LtWpyKcxFlNXmmBinRBwzyqzoHr+DdQEbOvLNR+T7WzH62d9S44Q53GEuHvN0/ZiyvWLMMq9vvKbXBa+KuGW2jEohLYOqLRaxQZ6dYR4PlHnCEsXUpsyNqZ/F0a5YMPUIDnSbzNBK+r3WaIIPuNAxjENzlqNJWLesNWfKsEN1K3TY0q+fMKZM30ieixAMtEwzF9KQSGPCWMPhmy+4+Pg9whvv8PjxbydWT2+lxBmslLSr8dSwwlw8htWMtg69uRCAquR9dlVhjcxfW10JZHSRZ5o1rW0gv8pZ2iMH6GImpKhFAlkA0BXvDaWqptzX/q3rRLOgiK8FVt5fNQIKg9U87j3WaC46y+Pe4J5/xPDxexy++YLh+cB0O2E7S8nlOKNPLUcgZ1XFa+idRu931OGeMuxExhygWXnHLIJeRcl6DD6IB8FSjleNZAccfS1ygpqkJZETpsz4mihErNFoH/DdCt91OBcwTjQwajsMVVVkVfGq0OvCo05zqR1OWToDerhBP38Od8+pdy9Q84jVoC+uMdtLzNVTuHqDevGUvfLUMeNUosPgncLTpgG0RjU/Dl0LrnECXOgo3UokitcX6Oun1P6KGC7YjaIXMuiJtXI4J6RZpSG0SotRp1aCVaBqJUaJE2oe0WmSJAmkMmA9xa9IyjVhotKAr5aJhTRja8FqQ98FuqQYbCUWhbaGziu2TnMRJElifMn08fvkb/4qw/NbcBo/7LDO8+jRb+PNbeDtRz0vD7MA9M7y+oXYMG+CRt+/JL/8mPzyBRSFtl7ic57R8wC+o2iL1g5KbrwUIVAGMsopemt53Isjq00jOo4nsGUcKAs2UIyntGApU2WfXsBeiPJ/5Ye+zO//3JdOaw+p0i0AYakW/HrxwXc1MHiVw/G1JhP5N/7IT/MDn/tCIx6dSgVV1WPV4LwvDqc/N8ViYqkE11FtQNkB4iR97HmPto71+gk5iNDNIVaqVnQkmCfyrKleEVNCpQM6TtR5RM0TpiR0rjAn4jyh/Mfoqyest095c+v57a9tALgfE9drz+98uuHNrcMfnpHe/+fsf/XrDB/dCDrPlbB5BtdPUP0Wvwl0upB0xVnQVuNVkZLefi8ZjjYU17eRHtXU8uTDe1vpraVzvfTIYkSVJM58NWJKlt+XJDa6LRAvRLspZ3m9JNbO0xS5O8wMhxlXMsUrrrevYalCVtoJE75fb1hdXuFcsyattVUhhFA5xcL9HLk/zOzHiXmKeAoba7l69A5hvcVeXZOffyDWtbtb4v6WvL+D5x+iLh7hHj3FdJewusT6gDOSEVslmcox41l4DcpiSdgk5LtsZbbb1YjNGp0UIOqPymSOLEUlTnG5LK9X0UqjjcYYQ8Uw5ZlUFASxslbGCI8lRSEUxgFHEanWJO2qmKH2PebiEd2TS1ZPNky3M/P9jOvkvpVhpu5u0OtbLvyFkNuy8AMqyLht2EigKQltHDWsUDaA0hiVCVbhqxyGzlScbp4YFEj5qFeQ2hTLkvHVIszwxS7aaoNDk3TFWogKStWSJStAaZQN1LBBX2hR/9SW6tfyPpMo0m1a+ffCG/R4S93dUIZZ3l/nCBcBv/Wsnmzonoi3QHI9cZD3Kp9D4SgQmwPkJBMQyjTRLxtIMTPlQtUes0zdtIkMjdxDqxEVwxwbMVkqhNSMTgmbI65GMIWilJTErRJejl8sykWvYjHVyQqCrqxNxXtFiiMcbtHjLfnFR+S7FzAPGCouBPzmStby4zdI6ydM4YrbKbNLhZmIT5p1VGxXsK0O7Qy+VioybaQVOOfoLy6pKWJCR6yKurkkr5+Qt69xNxVu50rUM72eiSYSioCJzhog0xlz5GxJK0FDTOjS4kQVzRJVsqhGWkd1PRNWtAhEOU3WuK1YlVFxQMdBDmFtWYc1fdgwV2m9WF3pdMGTxF9md0O5fcbw0Q27D55REOXUte/w3QVvbq/4nU835FJ5uZ/Zdpbf/tqGN7eeNRHun1FuPibeHWSqI8u0mJpbvI4TBSN7r1oxy5snOhKXXqGsY+Vk5HNtQe/vUPNeAASAtVQbwHVt4kU+8zLlcX6OgbQPfvLvCCfu93/uS1KdUmcVqwYKXkUD/w3H4FOu5aZ8dTGU+KM/zRfe+aIs2lLJC++gfV2qBufFA1GnFF+ARbZ4SEVGTHwvjFo7SUAZ9igtrmMXq2usMXSutmyoYMpMmRXzPmKIqDqj8ihOdFWY+7qRIvIwMb24Q20/wFw85cn2Lf6VxytpUaTCRWf5nquex66iPv4G8wfvsf/mC+bbAectaT9QxpEyHtBxJg97jPZ4XfEWKdEPtzDcUQ/30i7oVuj1I6rrycowZdFBSFmyqlrBdz22RgEDWYhVFrCqCLJvSF+1B7DMM8cGCvaxMDeSzt2c2U8FVwpKGbwzPHn8NmGzoX864vKEb5mV7TqU1tTmWliLkCeHmHi+n/j47sDtYWKcZ4KqPOodyjpeu3yTsLnArNfM3jF+BHcffEi8vSeXl5ib55jbZ9jHb2AevYGqEeue4tQGr0/9daXaIVoKtURcTWhdsFbY2OSCoaALEAuqWnGfrP6Bdr5xfZuoqJjSQEfr0c6loFJhLpo5VzorjGy0Fnb+NKCbpKvGAbCfE7kYvIHLizdw3/O9PIkR13dML3e44Fi9do3frATYpJkuwGQb0KnL+tYot5J+LjJ6q1wAv6KWKllVq3S4JhxTpz2k+egPUhdbX2NR2knAqkCZKXPLpCktw7Z46wlhTVQQq+wT2XuV6ldC5tWumZJpiu1EJKmKMJBRIgPcGVCjEFb9ZsXqtWtqhThFwvWGy9/+Ju57vpd08Qa3s5hzjSmz8VaIoimi0iQ2zjmBE7fIasUrQlTvSptGkOdlNM2oqPXQ4Ti+rNJ86ieXDEkORY+MI2MM2laMLpgqf6e0kE1zVY0CoPBa+Di2TKTdM8z9c/KLD5iff0C+fUk+TBhtsJcbVheXdE/fxr/xefLmdaJZc7uf+MYh8mKITFXR+czlKvNalUqY01CNoermW6A1rutQl4+woaObJ6IJDKpjCpfcjoW7WHg5FaIuzD5Tp8RaGeLxNDGE5iPFUjVQIrS2xAgLJMTBshqH8j3F9RzGxG4uDLFgDawwBCueJ3o+wP6FTKoojV5t0f0Fur+Ebgu6YmomDwM6zpTxQBlH0n4g7kbiLBbkfvse4fp1Hr91yX/r0QqrFXdjorOaz112POkt5v4b5OcfML24owwTWH+s9FFrm3QaZFJFRWYcZc6YMtOrggsyldUZxcqAPrxEjfcydZUTKnRiYe/FjXZI5QiG0sJ3OyMR/qP3v8ZP/rx49/zA5754vK+LzMaxWqUeVgv+m4rBt7gWP+rFenLh3lSl0FVkJYVF/ulVA+DIqp6SlHuGKKpdG79GTwe0C9Qo0rRq2AmSo7IOW4IPR/EWR0ZFETfRKjWltRlTivSjQ8AGkU9GQZ4jeXeLuX9G31/y+npFZ8VPIBjF45XF3r5PevY+w4t78jCJaYrVKG9RIYh8bZVRvlwzuhbqfIDxDu4+Jr/8mHK4A0BvH2F8B+WKokQ178Ug8sGrJOY6fauWmDpjs8Kp2vQUtCzSNvol44ptsefS5vblHi4mSfdTYcgKWw2+GB67Hr3uuHh8TZcO+DoLmU/mKE+jpjlRgJgS98PEs7sDX3++48ObPfsxEqzm7esV1Tj6lWF98QQXnBylcSLe3zO8vCfuBuJhwucsh6SxGB9Q8waVNxi1lvJs6++JhXBs7YwsUxlaUW1TgMwZFScZNy1O+uQ1y++XuWVtcMaTtWrrBKilGUqJZuzUwEEwAe27I0+BRvwkR1COmAt3U2ojV4667rl++3vpuxX+8a9Q755DgbBdo69eQ3UbcTpsQlpFOj7yjEpFu47SSKeq9T5jLuRyCvBeg5p21HmPmgdI4kePNmA6quugBqpbODhFWgFpagI2Y8sUDdhAnXZYv8aEdZtOEfJlqeD8WlocWTToq7akBs5Nm8W3rY1StUZ1G+yTN3hkDeu3nsi8/sVjzFvfQ3r8W7gpHR/tIy9HsX4JRgP6aE1OFmtdpQ3KdxQThHSYCnMqgDynxQhHtzaT0xzHl1UcIc3NwKgJJSVhoTtE6EcZIeiqpq8vjpewEFIrUoGoCuHzzANq2lFun5FffEh88Yz55R5KwWzW9OsV3fUT1m98XnwUwiW73cx7+8Kv3ky8//LAlArrzvH61RoFrIyiN1BcW1dV9pm2DrO9wG+2pKqYlcfZFc/HQs0ju1LYFWRUcSrgstiisxAZhe/jGpN5iYUa4Uw4o4la4ZyTmGsCyXVMpTLkysshckgZq+Vw3TgtU1DxQL55Trl/Ia+7uhBF2BYbVLchZ5FBVlWBC6gQUN6iraYOhTxMDC/uMc/ex24f89rl22h6pizaL5dB06c93D8j727Jc5TD1xps8KgQQImnQp4nZlWZ8sBULUo5HIW1hdq4WjZP6MM9aryDYSdGSlqjXKDYQPFr9lHGLw8xt1gpVbOFLvAP3xfxvb/0h4RT8KB9xekriiM/7DcCCuC7GhjIgfSVr3+VH/uPfpyf/mM/zZc+/0UqcvMyHI06jvMLCyLQNJW+48CBlFprZWoP7zDn41hVF9bUPKOcVA1qnIVBDlJedqujzaZJkRwzsal+GaswVqOsR4UVap5w2xXhaoObJ1IRFbJyuEcfXrLZOnwv41rBaOx4C4eb45yxXXfYNfSXW9ZvPkY/eh22j4huxawcYxSjJ10LJsloYL59Rry7R2mNVwauHgMywnaIIkAzpsLsKitv2QaF6gK6eKzKOIUYGpk2ptjc5RYijCgfVqZSjsZId2PkfkjcjZlxqnTWsLWBZDtMt8FvOzq2mHkngbGIrkKpBdJMxlB0T5xndsPIi/sD7358y69+vCO2vvrtkMBY1n1gFQL99gk6T4Rxz3R/S7i7J7a58pqKbNosI5iqFsnazptRJbUxzYVjEUX6VknDvKQsXJOaqalSoliwauvAn2bXFeJ7YLVu5WeRP845k0qmtFntOVc5ZH0nGew8SbWiWTErIy2t3ZybCE5iHzPzZcfrb/+rhMdv46Y7EY4xmuo6ogkU21FRTdpWHUvIy4GstJXxyqIoJVOOin4SsOoooFKNdzLKNg1S+g8dan2JMoZKOCOPyQGhShRnvObkKCOYPXpzKQdorRi/EvngqogFZlVk1A0B7TXXB4Q0kdcVYmOxHXl9het61NO38blQfSCGC6buER/uZ96/HfloP8sh6Q1XnQgTUTI1R9HTaFbkeAE5c6rteRS0zuSURVehVqyWA1zXBgjjKG3FPFHnWUjHaaaWLEBZGRHy066xUCu1RCiLwVZqI1SL74kkKgsIJacGNgSpGe8IFyv89SPCa2+hr1+nbJ9wv098Yxf51ZuRX/7GHc9eHsgp4zrLHBNbr3nUGa69IjvIZcSmWdZ9s2ivWpQStd9QcBhG0gGiTRzKxJgqs8pUk2TyRim0EonmHn1MrFTjF1ALRmus0TgrDrO1QjYe5QJxrBzmws2UGGKmsyLalbp2L+aRurslvnxJLQUXhYRs1pfkWpqGSaY6R3Ur3PYR+tHrrN+8J8UE4f50OkwDHG5wYcOT1SVTLjJ1U2eZyDjcSyywFuuNxOPtSsZlw6pxsBRznJhSJRZNRsYlparQpJ3jQUiM46mFoJynup4a1kxVM6TCYS7s59jiZW2qqVIpWEDBD3z+ixjUsYVg1OnrAgo+CQjOm+nfPlT4LgUGr4KCn+JLn/8Cx9EQvn1wUNu3ltsbW/NniBmrYdTgw0ZEMEoU0lxOIo4SZxSHxryLiNa3boI/maRhVhZnDabzGOOkDYHGz4mezDxN0K3lh+cZNe3wfo3TBj0f0LO4JCoX6B9f4Dc9xju6R4/xb7yDev17iBevs8ua3SCz947K2obm9lYkgE1RDo80HdGQGDAV9k1SWSthy1eQrCIZLEZKg62UqpuyO1VG9ZZLNBPE0GmIifsxcjvM3I+ZGKWcErHkZj5VtEV7h1IZPVWoo7gO1sLiKFlMJc2ROBy42+34+OU9ty92jIeINYacCn3nebLtebxOrIPlYvMEd30gvHZLPwwUBXGeMX2PWV9g1hcov8L4FcoHyWrbChF2eXsPzT1R1YSiUOKMXngAJR/vYTEz1XmRe+6k7y7eEhrv18ztYHGtLK3hqKo4t/FLbCc2y+0AFmZSlknSKtnsx7uJUkW4Z8gQMTxZP+Xi6i08hZJmSs5kIFd95I24pjWhkBJ+QcGiM9Gyd5GykY2glJLMf9pT7l5SdrfUeTpOLqiW1Vfrj1m/VuCspyZx8KxxlsCbUmNpF7QywtdxvQT4IoTgJTvSbR/CQrBa+Bmq7UvArYQ9DzJmaT0zmrsx8Ww/8asvRr5xN/ByLxwEe9E1/Qsa4KuN22DRoQcrHCJx/pTPYpFKhWvPzWoh8Kl5hDRIRSRNlHGgLEJJufld6GVstbH1jaHWRK2G2sBozflUCmGpXIS2HleY9QXlcI+PM8ZbnPf0rz0lvPYW7vopav2IfYKXY+KjfeRffLzjw4933L3ckXKmWzk+tpXdlSduDKlXVJspeQ9V2jwoBBwaT2mTKWWuYD3ZOCKWMU/sY2UmU/UMWuGtqJEue305XZaKmGLRcRASbzYiEZ9M0/JgbtWZzJgKuVYus3xdRpBqmiizWEnXWYSvals7h1kSrkOqrEPH9uItNAavLZf9mvDiOXmOmNBaZDmh570Qqm2QKta8h2a5rbo14briQ8A/FpChLh9Bv6E4T5ozcymklMklUzFUEmrxOEkzKg7SJk6tUmAsdGuq7ylhwziXM4+HkxWRAv6v73+NP//rAgWfwlo8fv/bAwffpcDgFVDwzu8/MrZR+tsDB1WhtPRHc11IIafZ7MV+c8qKKVc6v0HnjFohxkolH/vhMra0CNyIH7p4Kmiiccyuw3iHCqC6DcavMNrjui11HClKo9dbyTZqFoBQpPxZUahuhXn8Bnpzia8Vs97irp6gLp4yrR5xO2Vuh8TtIJnL1mlUUKz9Ct2vUd6jjBYGvWpiBtpQopAta63tHsh8udEa3QyGVMmopklwmtsWnwVlHfrcRKjKNMIQC4dRnAHvDjMpJcAxFYgoYssYizJovYxBVYiRkiUo5KKY2WH0GlsjJkdynBmHkd3dhEYEkD5cB775eMPTi57rVaDvOszmCf7pQBcT2a8wMYILmKsnsLmG9TVqfUnRnlSFHS4lu2UGU2yRBQgmapwkmKS5aTyIdbIsFJHWLSkKCa3nuA6rMnQmMBkxsOmdJVhLirFZWwuQMi6guhUMUjquOaJSxAUZgUI1AuaUGWIlYck2cqiWJ0X6vRZHFeN3as2td6nQqsrzVBJWpFgOVBndyk17wDRwUHRFl0ItSSY85lHaZ81RsTayXsQwNLlZqxVYQ7BB/l7r4+dgrpQ5yH0s5ah1kHIhs/TxW8tFy0y4vPd6moBA3mtMFdXWr8KQsvgPPDtkPriPvH8X+ehmYpgT2yCtutDUG1WK8n5AStDdSuytc5OvblWVYEUat3dG5HONhjii2mSRShNl2FHGoa2LfIw9ShuUbfbMWlHj4kCpIcu0i/Q+WvJQVRPr8mL9PY2QM86IPoGLE845usev4Z9+DjZPiKZjPxaeD5Fv3hz48MWeu5s9dy/3MiasMjn6xvCPmDQx37zAMYs9utFo41DBSItJW4oypJqJVfbnVGA/V+6GhI1Qlcgdr4Jj7curoo7SomskTBqwV7WiWtVFG+E3GK2PzoW5VmyV+FMKiLymbhWXtnq9+GYUv+KQFbdT5j4WlDZcJsXUWy6vPof1K/zlE9TNM/L+XoB56KFbUVGQo6ypJXYpg95cCo+hvobrOsyjJ5jHT1HrC4qVuDDXSEwjWUu7rJZyVlGU2ABFzgHnJclwnurWFL9hykI4nFubdSEcKqX4z775Nf78z/8J4RS888WjLsi3BQpesfz+rPPxW13flcDgE6Dg/Fo26QIQlh5rAcmbKlkJ+WdR2lp6PTKiptr/XwRbJGNzvoOQhaBixASkAosjGIvpkbay2awhW0f2HdF3mNBRrUXlSDQriurBX6CmUcYalRbmrnYtcMjc+fJ9woUsDB+ofs3YbRiLYRgKH+4iL/Yju1H4B9edQePp+0vs9gq9ucTNDSWHTmRntTkrBYoscLAGv3gU5IiuCdIsN8+aNp4lYiy15KPYy3LlJkI0zZndGLnbz+x2MrJkFQyTuKLlQpvkXZ6ZkMPKPJAPO+I4MU0TSTtquGRrLnm9N7x56fnGczlAUkpMw8gwjNzsRnZjZIyZwSk2q0u4GLGp4leXqBjJylO6DXp9zWQ8JCBXVAZ0wRh7Kl+37EfVIn3pOENTV6xppsZ2H2qb5TZWyEpZDj/db2T9KbHh7p1h5R2P1j13K9+seIuY9pRKcD02rKQEGSfJQNNI0JXeadbO0nvLIVbmormfKy/GinGFIc/ihUGmMzLu6TQEI9UCp5WAOU1TYNTEIuqLJRViEcfE0tT7ktb4RdRHauIoLCp06G5NDWuy33CYCrskmaM1UKvChg06je3f7RsvQbfgJX3i1EBBLLLJFBpvRE1T3q8cKDnN1FLb+22WyhliyWRVGXNixjBk2MXCi7FyP8v9MVrTeyv3zWmCrqg0tv6vEo2MsCK7vnmNSCrROcvFyvNo3bPyzeWzxiMoYB7Iw4467CnTIEZMTcJa4oyBshhNLffPCCsfmbSRDSeVm1gyc4YxVw4JJrdFXwWqW2P6S0ydxbRrewUXr8HqkiFXxij762Yn638aRlJJYs4VFG9eel7vDVtdqDffIE23TCViQkB1AbXaYIwRe2NaG5UmTpUrw5Q4jJHDYUY3y/hgDdvek/p6TKTaRxFNg5KP4GARSCNOgEbbgM4RbxzeaYI1jKk8EJmtWuSwVeiwK3lfenMp9tv9JXdD4Rv3My/HjDKazVR4FDte3yh6c0X3eItevw7zHjVP8rpGrL/RRuKp0lQToNNgA3r9SFxjQ0e5uCZuLwVMGEdMiVhHcrZkRkqKYt1clIyotlivaM/dBjkLrKO4Ndl2THMmNxErGQeXQ/wff/AL/IWf/xP85R/6Ml94ZfrAtHhsVNs6nIDyA0Bwft69Ag7kfPwxPs/nP/nv2/VdBwwetg++yCesO4+X3DBNy+RbMkjLDkuRkm9uBQQZSar8s/v/OyCGQ7adFIWmVBfWFG1QtjGRl+voDtYOceObHrpmTnKmTKoKw185IismC9PaU7skBMlaHn4WdSaxq+3RxGTOEJMm7gv7UrmPlRdD5OObmbv9gFWFOTnWTrMNnm1/ibl8IqWuUtCrrZSvjaOS0Aq65pZ42Tu2wbPyFpMHdJqp8yhe4hpKCHJIetE8P2YLR3RQyaUwpcRhihwOI8NhoJSCMYopJnI5Ka0vvV9VInUaKPt78v0N8e6G6f6OMUPeXNM9epu3t4+5e7LiMEZ+uVaGXUR7JTbANZNyZkyZmA2z1ujVNTpVlFlRYyYpxyFVpiGjbKLTmewK1Qjit0aET+R9SXWE0vq9cabGSUDBPB37wCwiJXLqyveaj73MGktVow9bNp3lat3xaNMzTzMxxmY6hAjtuB7drckpiSrfPKDGezZ+y9O145A6MIZUDcFLm+jQRm7GmDEl0evK1mu0k3DijELHAUPBKotVBtv3ZOUYUoFpJpVZsvci7zmWgtOOajv0enu09Nb9mrq6pHYX7FNlnyq3o5SEO6tRnQjqXHYXEEd0nITkCOj1Vhja2hFLIbbPbYzCGkMInnUQ3QVTI+mwI9VIyomMxrmeWCq5Conrfo4MRZF1JbX7UJXcl8s1WJV4Y+N4unZsvEaN9zAP8uyUEeDienA9aRbSsdGazjkebXqu1h2bztIbhHg8H6S1cmis82F3JCJLYJZVo4wVMbTjvpV4IBVFizqufHErjLlyiIXdXLibMuOYqCkT7JbV1QW6RpQz6PUavbpmbi2iMeUm1y2gTntFh6XfOL73rQt+55MVn9s6uv0H5BfvM+5eogy47QXm4gpdq3g3+E7233H3tv0bE/sxMhxGcUbVikPnmJLs36WUfS4MJlVEAdFlHinTgTKNVCXAwLjEyvdsg+eyT80dth4FiDCOagN6tT2W5c3lE2p/yaFoXo4zH+4mPryPFKW5WCsGDENRbJ1irSuuepyx+I1YWmuF8IaWz/eAFCHiQ0UporFk2zPT47KjpErOMM0wJ8hF4qN2lZpnFpCLziewB3IGGDHTi6kexznFDK4e7/Nf+Pmf4K/+kFgnL5UBxScnD06JV/3sc+4UfIETKPjpP/bT/Fv/p3/rk/++Xd9VwOD0oc9AQbvUK2iq0piuSpCiab3MutALlKxIg2Q7WRe+9vVf4H/zy/82AOsma7r0OFOtkMCYDm27k+DMq2UdJZs30RZGyQw1YorGGGHzznFmGEbSLO5eRomYjgiyiJAKnCYlYhLd8CFV9rFwyIpkClk79gl2Y2UXK3djoeYZTeVRb3ltZVl3l5iLR5haqCkKEcx2RyEnb8Sgx1vL003P9Tqw9gq/nyWYTgdKKRStKPNEmSdMJ4fmMraolfQUlyZOaU5z0zQxjoLe52hF0ASOUwCGVpJLMmpZDveUuxdMz16y//gFh3GA7j30G7dcvv07+N4nr2EUbDrDuy8G5gyPLzyXnSZoIc+JmU9FK0vtLkjZsEsD9+PMbi7s5kzWifWsmDFoIyp13hZRuqQ9V6qUiRuTvWY5sGkHgnANKotazeJ5oGo9lpdPZXHNxq+ZVoFHmxXjOHG/rxSEgDhXTXA9pl+jxr2AkP0t2nVsr1a8vvEUNF3wDFUy0uA93opwTUpSond1yU7EwEvPB0w6NNvbQDAOZ4C+w8Y2npvkIKioY2uj8x34lVSNlspbt6J0F4xKRq9up8ztJD3YKZ9+ZgierrtA54ixQY7CIOXg4jrmubSKkYxSOmfpQ2C76lg5BUMkmsxUZhHkElYwzvaoLFW8VCuxisKiMobOC9kzOENNll4lnnaG1zeWrc7ow0sRvcpRstJ+LdWCKuPJhUrnLNt1x6PNistVkDHHaY+e9qjxnrK/pezvKdMgxLYoRFHKwilRYBdbX4kFx8zZerBnGZ9S5CK6CYc58Ww38PHNgf1hwJTMxhs2XrPtOkzXU7s1SdkjJ8SoStBw2WnevPB4erzpeedRz3/7tQ2/43HPxfAh5f1/wvjB12FMlK7HvSb6H8UIMdD2a1SJ0kZSbV8CtRbmGJkbWc44wzwHSm6TGsikhuz9pc0oXghLjCjTJAqTWoP1+LCGXnG9DkduypzSMYvOFYzt0BfXUqWyDnXxiNxdMsTCzRh5vp95eYgo49G+4sdKVpVDqpgSsTmyMrX5jKjWRhIRtdbBoJTa2sSVOWexj9YVOyn6qPBObLJzTuQ5UqK0DjQygmm1A2ceAoIW9xfl15zq0evDtZHbao4pKn/9h3+aL3z+C0fn0s8eQ3wICD5xxi2VglY1eJWI/62u7xpgcI6EhGj4GdfSSjj+GapaEO4iXtNkgdXx9vP3f/UX+NN/+8f5X/2e/zU/+Y/+LNugj8pui3RlrPVITlRHVGeOmbM8R5FWFqdDGX3KOZInMSWZcmY/TIzTSIkRp2HrjTgnVulptk5kEw0SI5FDKrwcE/dzYcZQrUG7ZpysLEpbrLXMOR4Ja1OG3AnrWG8zNUUIQoxJRRBsZ6V/uF0FHm9XPL7oWcU7rMrkKBlypVJmQ55GSprFMrpkyBGjfFM+kw1gGmAS++BEzhmlwapKZzXBKLxuhjZpFhGRYU8Z99TdLfPtnvHFHcOHL9jf3hNVobvZ0U0HLn/L7+J3vfYWj1eed69H9rHivefpNnDViSeAFrIAGbFrHTPsU+X5kPh4P3E/ZarSrKZMQkmG0RzaslHYWo9TCTVHyQxTPJLMapopMVFSoual0iM/U7uIyYmSo1QOcj6Odxpg23kue8+u98zzTIyynsZUcWEjQlQuUKeRsr8Xvw3jeHT1Fkp71h0clKOYIPbESlFyIs2VGgtWZbzReK1lbHbeoeOApuBMwReHU0lIaF3PnDKHcUZrCWqJSiqKIRVWYUOpRUSQgOJ7atgwRpmo2MXMLibmXPFGJHg3STMaIevWHIWZDxTXUcKGIRXZF00zRGSYxap33TlcGkAlVJkpeSCNezSaUgrO+rZuGiFQGZSzWO/RxrYRV4fOllWNXHq4dhVz8xH19mPKXhjrygWqa8SwJPe/VkVwhotens+2s5h5j57uUOOOfPeCOuxEMXEayeMoB0YjzSmlUUaUTrVrPec4ixdJnMH5I8eCKll5yoVxTrzcDXzzZscHL+85DBOqFrbB8No6oK0jZAj1VGLWWqaVrjrLW9uAfXPL/Diwdop3LjreXFv6+29QfvW/YPwXv8L48Q2uatTlls5YcX91nuIDZdij/BrSLGV+Lfuza34gpTYV05hEta+W4x63SgStjFLCv2kVtpJmiRPzSE0zCoX2M15lrEpwcUFGUxXcHyZMy6RTqTjfo7qtgFHrKH5DNoFpTOKQ2vgmzkq8q8qS0exTpcSCSpl7Mluvue4s2UJnFQGRRi7t54wtpt5P+ejCqd1MFxLrPhCMOVpFm5oxtWCpnHJNgfyLq2hp01SvJvRWKZyV00jrpl0B/MHf9qWFhnLWiq0Pzq9PBQNnPLpXr+8EFMB3ETA4gYIv8oCV+UCM4OymnpX0FPkTJZfz66vv/gI/8bf+JH/zX/8PeHJ3DcBGJYrVR4OVWJu3d63HEUetOB2KWqoLS/lHCQVcOAo1cSiZMcNuStwdBoZRgsDaamqpeKOkt7tY3J5pBBRgzjJSNeVKVBC0wVqLNwZrjDCpVWaymZUXzQFoBHcr4zPYICVd4499r84anHNcbXpeu9xw6Qo2JmoaIU+UkhpbWERoSLFlBwIOrBM5VGdEIMkbWDlFMBVjKsaJK9qjjePx2nPVOTqnRVd8HFBpJI9ixRwPA3E3kO4Hyj4RbyLTPJH3I2VOrHNm/T2R77l+h0erLUMG4zyrfsXl2rF2YkttlCIrJcqJVe7d/ZS4HSI3Q2TOlc2cMEqz8ZZtZ1g7TbWKkqVXWtsYKccpFJlZX0BBnpa/K5RFo2JS5Jiw8ySCNymSa0GXjC4V69asvZDbOmeZooyJWg3eWHTYSCn1cEcdDuQcMbVgauHx9Vv0XsYRs+3Iysq6SCI8U+aKRxGMeGvo8Q497aiHe3JJlDwJ8SwE0ZQ3nnB0ymyVniq9fFsUyTmsX1O0lX3lOlLVpJKZU5GWUF0Igoh0dSokp0jV4FwvExBKNPKzdsQobYRFgttoWRvBGUzN6DzDfIBxR7l7Qd7vqdqiV5mqPX13xVTgQmk6LNoHUQO0VoxsqihV2gyrOqJefoP64huiHpgSql+hVltK2DBhOTR2fEHROXkua28x8wGd9jDeke9fUne3lGFPHvakKVKmU7VIa40ywsVYLm2S9LNzY7BnIavWnCk5U43Iex/GkRf3Oz6+2fHx7Z7dMOONohRHZw2Xq0qqUtGUkrNCGajO8Nra4cuaN3pFjsIzuTAZ+/JXyb/yyxz+2b9g+MYz0q6ADxSbSPcDaT0QVx16JWDcrIVYGbpA5zRXnezTRxvHfjDEVDCmEkxl5RS+7XNnaKqhSmTWc5K40Eiei7aD1hqdJ2wacSScV9TLjYyuAjHGI59LyvB9k9UW2faFsOeMZuUt1xhC33Gx7Vn3QcYicyZlw9SI1K6NAnfL6be0PNpZUQpMqXA3JhkBToWqIn2XuYiFTRBJ6pWuhDbCq/TJPzW3dZ8byM1nxEKtGyAwIvHujDqOopZGfrVxaG/qWxx4r55tx8NMPeQV1MpX3v0aP/Yf/4lTJf3buL5rgMG3QkIPUdUryAseqNI9vNmFr7z7D/jRn/tf8DM/+Jf5A09/N7/80X8OgNk/F1TqepTtAC3tgSKjU4t5jG0zTrox+nUjkEh5SMxSUgZKYZozwyz+9Icp4XSlM4ImnZaesG8ELGrGGgtVE2umd5o+ajLQWUvoPatVwFpLrpWpt1x1MB80oSYe9ZZg2zz4QopUMuteracIDwtvNKtguVxLb7WbbjEqE+Mi9yqIXjcRolrK0TlsaYO49v5XVrOxmo1XXHWGees4eNh0lt/yZM2b247Hay/20Uk8JOqwF5na8UA6TOSD+DmorLFYUork+0L8+JY5vM/KaPqa6K7eRF0/QbmADx19Lwedt4LkY1UidbyIMbWvtRZyFh+HaRrZ7/cc1o7Ja6LOkCb0MbClExiq5UguLEmcB0uplCjfW9zj1BQpwWGmiEszqplrqYsIPuJUR2fVkb8y5oqeC14rQn8pVQPfU/c7sdadZ3SW+3x5/Ta161GhTQZUmFMmBUOaNDWNdCrj6iyuceOeur9F1UImk52jdL1YbmOweKw6KfvlspTqYc4F67o2x1OpxlHbeKEzqqkR6jbKKpmmM6pxemozQpKWAa4T0HDW6hBlyHa4UGEeYNpTxgPpXuSsy+FAaQRIbTpct2YbPK4alO2wocN6j7cGpxCfkmlG7SfyzTfIH79LefkxZR7Qvkf5Xtoa/SWHSWbLxyyg3GoxqnJlQh1G6nxHumstiGFH3B3IUyRN8Zj5K6UoWqOdRWsx6VFayyFp7PGQZFlLKpLjRKyGaYocDgdZf+MgnJOcRcTpbL2KhXPbYwZA4xQE7VnrwtwB0VDun6FefJP03j9jfvd94se35LuCKRrrDCpriJl8mEiHCd/2XB32aNfjbHMiXXve3Hbcv7ZGlcRuTKyC46ozbLxiY2WfO62PZXK1jO/mpv+g2jQMclCbnDEx4lTG1sjV+pIpJmKMHEoWN8ZjAtOd4rfSbVIEHvVio/1IWfxqxXq1JgSPUYqUEged8URUqvT2BF6CkbUpAU/StmgkXmkt0xFzysSmb+GsVAu6YDBG7rlTCq8rpp3kyyRbqpU5NfDWzgMHwudQSuToZwFeuiQhZ4IAdqVAL/OeJ4rh6c/tNpwntEqzKCYu597ff/cf8GP/yU9825WC5fquAQa/5od+peRyBAO1AuUBYFCtb/OV93+JP/7zf46f/YN/kS89/d3iBjbeAaAPLzHWk3NEhYrvtsylQobKohRXMW2KwSAHkfRFK9ZanPPY3IxlqpDvRirBgOkMQcNFsFx1lpVXbJzB5RE1DfJ5jCO4HuWszHRrzSoB1uGDo+8tVmpVlKCZQmXukF6bhY3TmCwqfYvNcjVOSnntfi0z215V6nAHcScz6HFEldT6cxpjfSMi6cYmryL2kyNWy8hcZ2DrFJcWnq40oXpKdWw7x9uPVrxz1XPdO9YG9LhDzXvK4Y4y7EmDzC+XXCBLiT+4gOoUhYytBjMkzO0dYf1NrLfYoLHuCqcNtipMTqisjoe/q5lOQzCVi2A5zIaULZ0twoKnoEsiTxNpssx1QjNh8tScJhu/oLUXagt8NYuBUI2ZmiQLLGfgIE8zJnjyFDH9gB72MOxJ62uy3xL0CtfiwDAn5nYwe2PY9pfozR1lfwvDvQgMVZGyNcbg+0AIF+CljCocFAEGZdaSde/uUbVpLmThgggpbCId7mRdV021K6yqDRwo8ZMHSlEULe0YY91RLXApja6Kgs4RjD6OK26cYWUVzkoFTRCFkDIzwgdYKm0KdTTesapS00hOB9Q8kA93lMblUFkyb5UiqkZ0nnCbLWvj0b7DhoCzrskVF5gT0zAw331AevY++cWHlP29ZPTeozeXlP6SfYL7uXAzRXIB33uZ5NCVfP+MNN/D/iXsbymHA3mYSVMkT7NUhxoo0LppFaAozqBybesjC9egiutiTRHSTEGTC8xzIc2VPE3okuhUZe01Xlu80VwEw0WwBCMGUK5G9FwwWTe+VG1rOxIZSPMN6fAx6eU3qbd3mCHhq0N7LXP+Xkh5ZCXOrXMkDROq26MPd+gGFtfdFde9452rnjSvCFX0SLRSXK40l1b2d2dk6sVqRL+lJIkHyz3REi+qneUelARxlLiiHbUovBI1SbNMCFfIaDEeKkn4GSVh8sTGeepKsfKOZBy+XxFCh7bCCUoRBuWYTYAkvgW9VWyswpPQ49CURDXe9SjXkWvlKlvmLNMRkwxe4al0ujYABH2b8DkmC0qhbSAXgb0VqQpXwOgKSAXOW40Z9+jpHhMPQlxOTXVz2gko0M32vmmp0Colx2qAUqiqz1re55UCSWp/7G/9T/npP/rl7wgUwHcRMPh1XUtL4QwMLGSOr7z3S/zI3/tJfvZ/9L/nD7z+uyGNwgYfdwCo3Q34Drc15OJRtcdbx1wSqWoK5WhusZSO1HxAxwPkKAInvsd2F1ht2xytwdZMpyylGHqruOwsl8FwEQxuukOP95AnEZIxDp0nTNjiuxVDhouqwFis13gP3kmgrU4TrSVaD1nhKeh5L6NWaT6NUxpHKqeRI0Wl5EQcduz3BygD+u4l6rCTEpjWGO+xIWB9h/ahcTVqG02KeGfJGpwq+BrZ6sTrAS61xSrNtvc8uXC8trJc2Iwd7tHzPWV3QznsYB7J40yJmZrblIC3dOvQzGfArAyh6+i0o68ZFw/YtMcWL9MHyBzx0fUvRuqcMHPhwmlyZzB0Rx8KrRUbb1kbMEUC95wmnC5UJTK3ajnJyol9XHM9ihsdv1eFdVRzRn5byXNk3iu0cyh3j+pfMK+fE6/fpHRPMG2Dz6WSYgVmrA506wvC+hq9ekk93FNmaQeUW09ZrbEXV3SXV/iwISstFsfZQO+oMVCGO1L21BzIc4C6QeeIDUFIUCmRxwO5COFW40XZUkPRiytkI2638V5FFU8AKmvr0RicqaycPmVKrYrQWyXgpAXiRjNsYkWiWVDboeCUKIfmw45cRtR8oCbxGnCdOD4W46ALmD6geo9dB3R/gXIOlLTRtKqYWpiniTzcMt4/p9w+F3+QnNBda9Gsr4nhgtt94vkwcz8JNwIacfVwxzw+4/7lN/H7O+oQqVFsduX5tpaRQtQfVT2uA4Vp4PHse0fr0UJNicpMjoW5zIDHlMjawKPe4o0Q4zqredR7LjrDhdO4eUfdHcBblFtsoBWmZFyaCWUgpT0pHog1o7VDdz1uq8gmQxHyoPVWuA+5UGImjzNuHmX/+SBKnRUu3Iq0sqQLR1867gdNqoXOwVYnfI04FfC6CT9FmVBSCCjWXuJEDROUSJ5nas2kw46IoRTFXo9EvRKy7nK4tlFnY5wkVyBxix2d6/H9mi0ajMMFi3Na7LapzBpmpUnGQVZYVekNmHhATTKRonKU7Hs+oLotV+HiSKJcO8OQxCtkEzSXnWHrYG0hkGG4EyJ2S9ZKyTi3IhbFzCKgJVNI1ii8taIhUSZMmSiHO3GbXOLGuEf5Nt6oDRTT4rM66V4cQUIFzCenD979BX70b/1JfvqPfOegAH6TAIPaPLyPxMOFLbiAgkacoYn0fOUb/5Af+Xt/np/9H/7v+AOv/x5UE3NhntCzOK/ZNMtGrBI8slbgPAlFrqdRNdPmxXWJ6HgQzexpECa39zAd6FaX2LDBAp2yXAcJqE4p1l5MOMzhmRgeHe6FlKc1tluhtETqQia4HlxHUQqlMqZOmBxl9EhVqi3kFEnznjwtXuuCUqvxgkyNE2/yxqC1MkbANEbqcEMcbnDDHT5NonTmHbZb4foVtusxzrUsiXZvEzUO2KoxTTGvKyPXroATkuc6VLaMhOEFplh0nWT0a39PHfdC5krSg1UKjNEYK9lx8RltNbaz+D7gvcdXaZfocYc2WtQJjQNjJKNLWVTTpgnGhFWOK7/BKU3vPDEBSpzd1l7Rm0rZvSSbTPXC8xB5ZMkAQQhHFVDLgDG5fa80QqtCFaTVkDN1ruSUKbUQUyRq4KKDNybyGxbtrmU8MFf2MZGKzPP3VvOov8JeP5VSbz7NhatpwKYBmw74skX3G5GhqlYOq5worlBMonaG0nnKsG8kMMBYihbGdZpGoqlUXcV6WkPR0s9eTJ+UUuhc5JCv0mYx2rEyjt77JoHVRq2oqDShxyhja3J6ykiYVUdTosbTlDaPqtQ0EcuIyZI9G62xqzUhBPm3VkRu9OYS1V+g1xsZNzS2VVXF+bGMO0o6YJeKW4ryd12P3lxhrp+S+ivuJiHxvhwSYy6ssW16pJL3N+w++BX44OtwN2IzeOeEBGqNvJ4RAR5lFxm1hYxGU75TD763SCsLL0d4B3nOlGzojWfrFRrLVTBQkYqM1axtwe6fQY3UzlJDAO9R1kjVLovEsx726HGHrQlVNcV7VN9jkiaZREkigWysxphmv51FWTSPI8rtqXsvImurhBkPhClxSUR3lbUSJU0ohDKiJoNJDluNVHVKOsZdbQw4B13f1owQ8uZ5Zp72zFMijpG5v2LugSJxe3EbLLXKuLc2EluaNwVxxKSBLqzFc8N6lJqpVSS9fS1knalBoas5ClLpOEA6UKc9eTwIoLcek0ZUnrlePSJYy9oZYhUA642md7D2FTvt4HAL805mzqkQepncMA6nLcaAbmJ5zmg6b1l1jhATxoiGSKmZmkSaHRDpZyrad3LvdDmrFLQKghIwXY8dBtM0E14FBd+CiP8tru9+YKDUp5M0WEBCGyks8vUICv4H/1v+wBu/R2RKl5JximIfClhrMc5ivYO+o3Qe58WRsKgJNckEgtMVaxS6zZ5zuJdDLyWRXu3X6DRgugu23QV954lFgKAzCj3txK7zcEPd36HjhHeW0K1xnUd7S/WKuU7M40Qal4AkzN5MI3PVciQFqjSh55myVAp8B8ZTlZFxmtZLro1RC4qaMynO5MOe+bAn1YRZr1Guw/ZrTL9G9yu0C6fSV62tlFiIw8j48iX59hYXI65UahEZYX2fKBbKZkVdr+RnTgc5tOaRPEtmdnykRmGDbXbGEpBtMPI8jJEMNCV0msX/vBZRHEPIg2meiPs9825HShllAzpsWIctfViTg6G0OXpHQR1uqURUsGgXRAlRLeoiRlC91mI6pDPKKIw1pFKk1Fe0zIMb3TwkNCVF8hQ53O243+0Y04R5tGVlA+byDYy7QilRipxSJeZMGBOXnWOzXmHW19hHB6kczSPGB7EDpmKpGDKmJhHTUQZocsbBg9qgvKb2nYybzhM5yez4nIu0CnIkp0LRFaODtDaMOoJFa0TXQ0yARP61ZhlrNErJQbKsA4Ai5l3CSRHRp2o9tVoBHu21l2ke1/gfZRrJZaSo3J5z1+a+FcYKwVAFkUKufiVg2yqqaUG0Sjaea8bSPEKMEUExpVC+wzx6Sl1fM7sV+714TtzPmVKht7UJy1TysGN+9iGHr39AfnFPZwPbzYbVxabNsTuplplzIKChrQfVOAbKnNRFlTZt3l/LlEetYsM+JRSOtQn4zpBzRWuEhDndo3f3qGlHShOzNfjNBrtei4y3abLLsbk8tjK1QnQhsrO4kKXe02Lj8v6Wq8Ys8sGzlPjFeG2SPbQ/UHYHdIKVshTrUFoqkTntGZlYcY3ru5Z8Lcw7I2Zz/QpTxPY6pSz7YByZVCJXQ7Urqs+AprQES8h8yxpp3fySJLmpBT151HQHh0A2UoavSmJgZWH8C0gXW3fwXqGwlOqJZWY67Jn3A2UWnQ2VZjbdBavVhphFHt9pjS0z6l68QsrupoHrFs9zQmmHth3WO1xR1GaY1QXHugtseo+bE1p1EB3JWXK0LJG7pigVpxRR1lFLU/RUDSAUJG5rULVVDiiAEVDwt/9n/M0f/ht88fNf+Jak+m91fXcDA6UfcgtevY7tg/pJUPDmf++oXS42u63n3ja97+RAtP0K3ffUfk32K2HOKkl9chICoVU0053WT5yno/FMSRFyRhcZF7QuYJdAfhjFqe1wS93fYfLMOgS219esNltM6CGItvxhntnPA3UYSa3vLZr9S3uk/WpyvbVUsBasR4WORce7VI4mNtCKKKg2a+3Ad6I+iCJrg1lvMZtLzGoj78f5JnsrS0tRKWlm3t0yvPiIctij0ywmM9MI04E0jQwUuqtL1o8fY5w79l3zNEuvfsk6rMVYAQnKSK9aO43xTtzPjGkkSKn+1Hls2uUNWOQs4Ob2lnh3R5xnivXQbzDbUfwnXBAL3FjRyLPRVglPgYrRpklitvKetfJ+jUVbWS8FMKUcJber1pR0JqaS5TWmYeb+5T1TnggWuvEgVZ7G6DZaU5GRqUOu3M+FbWfpt6/J+9Iahj3GGNx6i+1WAhBUUzSshXMQrFpfXzmxsMYYqu8oJUvGEiNpTpRhbgz5hFYO1waqCzRCqUaTxBuj5JMQ1eJOWOsDYtS5qZZa3BtLbmoeBactxRRpVSDkLF1l4qNk8cXQvcd7i3dOPqM2KOvABdl3phFom5eF3GjZ51opjDFS3VpvyTmjc4Z+DRdPydvXOMyK+7lwyFVG1BpbfZnJJ0fKeGC8u2e6uSeaGW88q01Ba4M2Gm0N2hm0tQIErZFD1xn5OysGRYsnwyKmg0IkgXM6TmDoNGH0gFoosiXKgRX31Psb0rCjpBnrPXOe8WRcXZ/0NpocN22/C2gRl0AKKBMpsUgSaqXqoa1t+74IOJhmjBnJpaCmkRQjw/PnDDe3JMQ4S4eVxBATKNYz5InJKTr36Bgv0VbaSk68EoTwbDClkg+DxBNakmKbX0qSClVF/EPsUsU00vZQihM/Y39P1YqijcS1JcNWuumISFwwxhL6jnXoWfmAWXcwBfIqcNh57l++ZD8dyFl4DCZH9LzHuA4Bmlmewbyj3N9IkjdKG0GFHnx3jPUyjSUxwljLpu+5WK/YBIuxBaUiJa4wcSSVTJzms6OpHAmbYkpWEAp7Awe1SHlgaX8r04jyf4q/+cP/IV/6/BdOs3mfIYv8ra7vUmBwGuX7lv/qTE3wVVDw6v9XTaWstNlr1a1R3Qq7WgujOXRk7wlo/BwxaiCXjG6BatF0P2aYphkYpSiod38v2uHRH0tllCTz0Yd76jTineHi8pLLR0+wQex4MXYpVnO4u2Xe3ZKacx0LoFmC5HFmRuSUxVbXUJWlGE8xrul2LyIzFYoSy2RjULZH+xVsr5jGA7HfwuYKs73CdCtRJzPS210MYypQ48zh/pbD3Qux6J1H6niQue/hQJ0HYkr4+cCFBbe5kA3QTFJAMiqlFdoaanBgNMa2v2uB2ASHcbb1WRsbegF+pVBraaOEE/Nhx7i/Yx4nYcjnhC4FtYoo1zXlR8i1tHEpA50cSMoYWTvWnsClO1U0rDHkaW7EOJlQyEkqBtrKe1BKkXIAB1OayaqgvJTyS0Uy61pbli1aCLkJ3uwnTacMfXeJqVUklgG1XoOTwCqBs5FA0Q8nc7RpMrAtw7UO0wi5ZZqoaU/Mo5gHGShVi3RsbU5/CqqqpDSjU2tlxPmobHeswJ1dFU7yx64es56aMoWZahWqyOuj5EBIMYoRUZ6wXqTFbb+Wtb8cqNo0mXFzqlSxABMJpCh13L+4DrW5RKlGyrV9E8mB/ZQ4zImcy6n8vRCVl5l0LXbmmcKUZnBg+oANDtO5TwICa49gwQQvoMx6kV02ToR6loPMyEFJLZQ4kmcR2FkCu8qR2kh65XBH3e+Er9QFnNXkrqN4L22bhaFeFz8TUc4zzlJDy0KNGCgBsIAaa2T07qzVUeepAfzIvLtj/9E3GW5uxFLc96h+JWuwW1F9xyFNDKuei80GTHc0IgNHdRqlrchvGwsF4m5gmor4gfgVxfakqkg5H30qVFUkVZkLWCPqsVpZiaOxNoGx+CDGyU0zRx8PlKaEjt5pVv6S1WolzS5nKd7hfUCVwvjxx8zTKAXBWqHfSIKmdNNnmUVbZRYpZJCecT3uK6nE1FrEvbRkjBJAG4LHB4tRHTX3lNWaPI8yAt3eurLuwXjrcQdVAQfHccQGClRV/P33/iF//Of+DH/zX/8PPmkDcNoVn/H9T17fpcDglUupTwbHs+sr759xCt781x5UGVRD35UK2rB97SkA26dv4tZbVFij+w01rCiup5hM1QbrHDlGjJKRQzV1qHGFutrCYSdOee0BKx9QzeZVgpuUhlRO1HVHHVeoFOmCZ3NxgVtv0K4FGa1RaMIKLq+u5VCKswioLGSW9lXrpYSpRVTFdyJoFFYUvyG5FT4WwiLsQsWoNnpmFStzhRov0fMeXRLrVUe4uEQFGXGU3pdkAieUWtHWslqvePL4MSWOMB4og5MAFTw19VAKqxCwCyg6b/9oKccu2QwgAKFUCWJaS+D1Du0dOrgWDI4DRC0wWkyt+K5jc/kI6wMpF3m/vpMA163BhcYI1m10Dpy1rPqA63r0Qvw5vr6sMYyBWcbRjNJoG8UFMhrJfqNUE3LKGO9RwXKlK+aipxjF6vVr3Nu/Df30DaaLR6SQSN2WyzkRi2hKbL3lau3ZBM3Ga3x6TfwHasY5R1hvsf1GDh9jT+pny3tcWM4taxewalkmdYxy9Diy7egKFGUpzfwotW2x6HPoLOqDKs8CbhdSYa2UnE9VGpAysmkkqRbY8aKZUW2gGP9g3ttqOQh1mtA14TX0fYfpgriALsAAIdsegcHyGZfVp6QSpmrF9lu6a2kfdDFSlKHYjtn2mLmQp0IMM2FOjCnjNFx4y9ON59HaEuxISb+DK285PH6MzpXtdkt/ucWFgHINDGiNcsKf0K1SoKyTPW6s7HfrT99zIkiljcUZx6oCLhBTEoC+ZIUlQewovaOuOri4QJWMNZquX+G7Tiopxkq1sxWnq5b3pINDl4ppLn4lGqovD/aRCVb22ZEr065SQRWsNlys11hrmwKhKEWqTgzZ6FZo17Far+R12kF5XGMlS/JQMsp4gu24dD3uySjaG35N7bYcMtgkJkO59fedFvJq7zQ2rtDzFjVdwyQtRwGnAkpLW3tHwyWtxaLZedbbC8JqhXFeEhBjBPBbxwYovmOcZqp1qG4lolfmKCsn63peS4ydp2Mc16GH1YbqN9RuK4JfWbw2jHOsVz2rLhCcaaFSRliD98T9hj7O8F8ilt9KJLSX8+f8UrU82Ndfee+X+ON/98/yM3/4r/PFBgqq0g/2wXd6qVrrr/2v/iW/1Fuqjv/V8Mp3zz7X0tuspZU8FzOPyFfe/QV+5O/+JD/7B/8P/IE3/runA+kIDk6vs4w4mr/+3yH96V8Wwl4TByquI2tLzCJqsYyTGsBbhSkJPQ+oeBDb2hzPXrtVEpTm3IddNNTbjHOaT1lYQ7/qaEBz+py1nLKdV7O2pV2grBNrXCNBqXjRhh8zDKkQsyjQLcSxhVEerEgLqzSimlvg6ea0WdvGKn9wz9q9JkVUls9ehj21ycfWaZAS9PLeW3ZPTjICFrOMUM3NqGQRDSpFZti1lkzNGWzvMd5hQgM+LkhmY9zpUDxfO3oprXspBTov90WdgZtaWBD7soagHuWPacqHR/XD5jh49ExolaFa6rEttbDTAbQz2PUGffkY+/o75O1TJn/J80aCu58ScxGNgN5pLoNl5bUINlmFU1Xu75ny2TEwnH2GU8m/Od0df38GxNr/kexHsrpqG//kfCmB8AsWR8kS5T0sFapjG6P9+GNpV8CptJu+w9f/lu/VgDrL2vQrz2/Z/+f3yDhiVYypso+Zw1y4nRJDFBlkrxXbYLnuLY87S5hvMfcfkT58l3L7nLTficLhspYWDoGW6taS+SljZY0ZK2tyWW9O2nPKWDkMHsSBBnoefIbcyHaz7JkzY64HV6uUHYWE4iRcnWkmz5E0SHuupPSJfbRUO+wCss8rHeqUeR9jSeiPv3S/Fj0LE8TwrVUPPxkPKg9GxOXmUa14cEyFpswqonFLHLKNwNdbTWcQAuG8hyjgdGnVHmPJ+bWAnF8rbp7FWGW9VAUXI7wlDaj5YRyQFz46i1a3ovierC1zEqXb5TyQCTXkPGgkSHFlnaFm7L//r5L/5//Ph/oEy889G1MU3RnDV775j/iRv/uT/MwP/Xt86Z0vntb/q/v//HXa9ft+4Af4x//4H38qevjNUTGAVv98+K2vvPdLDRT8Rb701r/WxoqWBXV6CMtVls0KFLcSRGk8xQWqtkfrzFoWlAtVK0jgjMX4XnpdNhzncI/XEZ0vXws1N+c1nYUL8OoH+ETvqJ4CHzwMoIudqzbNv9zLr2bqMaaT/WcuMh60LGbhD4n4TDIKpwImdE3euG3yV9/P+fhnjvJaomgiv+9Wp+xukYZtB0tt/b0KmABKJwm6zkjfM2XOj3etpc+/ZDvat1Jt61cqFyT4tqBdF2C1BLoWmI9Oa8f5Yc0xS4ATX0O3IL3YYMe5/RxPjbN8PdMIkNHGfAo6Z4cTxontdb9BXzwihy3FdsQijO1FvOYYQ2sl5kLMioFMKcKUttqfYt/ZVj8x4w218SWrUlDEubBqc3pOx2eo2jrRDwLieRwvgFamCRW1wcVW3pQxvPIpzbylmqRA2aabIQGu1Fe2ZyONVW1P/ASVH67xY1bUnumDipU5vl5V5hTT6+lRpiR6+MsBFCU9F1nflqHaVrGNpeBsB2GLfvKWjCpvdtR5PjLsH+yBpWLV2gQLIF9AwQNAsACaBkZr64sv90yyVIVQ0OXeYZ3Yuzf1xMVkTS3tw1IgW1DTcUGYdihqa8hTOopwnV8P+RAW7U6g5vhVt3XbwM0CDIp2oIVjJHvJSkvu7JA6/rR6FmeVIteT70vM4lUQz0jQ0hpUKCVk3Fo1neulKmLERlqlGWyrVC3cmgcxcFlbpzh+/NYr1eTa9gAtXgpv4yw2w0PwoZf1LHE1K0tMixJu0zFQiqJFDl9rS3FBJhi0QSXbbJqh2PDponyLXkFbY1Lp/jf52R/8y3zxcz/w4P3/RqoF8F0NDM54BuckxHbDvvLeP+CP//yf5Wd/8C/xpTf/+7KYzheu4uygU6cgtGScfiX9fespyhCLsPlFAwBopJnS2kLkStUW4y2aNq5zXMCvvPMFVdtCPcvq6hFtf6e34lRCXpBmbYg+K8OcKnMR45mYG/lwKWEiDPRSZDukCrNaxsnabVLmNKt7/Jkiw6yhsa9j+/kalSSQq2aGUqdB7KKb7wCLuUzLurVJ6CY3XH3BvJINLMHuvDQrPVwroMCHVm5uAVrpxq04gSWUOStJ2xOYenC159GyhYW/obSVZ5SilIXzIsAi/IJjRvLKe17aSPgOFXpqK6NmG0iz/HujNEZXHEJkXIJoOpLj5LnYev48WuukLd1Fc10OcumTCkDQxxbC+VWXda5t44kIXK5w3FKNm4pu/f3ayteLpsOrr3l83YUX0L7SXrucvTZqAR5tvE8p4dso9envdZnrXrgGZ+9X+BryerJ9lrl42aupiFJpKrR+vqIpjwsZsMWAVCD7gOq2KETYqU4Xwpdp5eTPfMatpM754Wrdg/24AInTffnk2mMZaywZsmSnS2zQjShNq7RRWivSWLHqtg7ijHETOs4YL22uT3vPqqk1fgIQNDDzoC1yXmVrhyJG/m6pGJR2v4/4Wlb22fOox+ex6BXE9nFTq6yptqDrol4v5h1414nIlvVNgTS3SusZv+jVdfiJ77xyLfHyvBr1KdXQs5vW9oCjIAqhKUusTIXjZklI0giKGXDagdeo1KpzRSSRMf7UPjpuuPLg533lG/+IH/l7f4Gf/cG/xBffOQMFnwBAxz/8Wp/6wfVdDAzgAThAAqGqhb//3j/gR3/uz/Azf+iv8KW3vu/YI1rYnfJfz9KvluUcNzFQXWgqgaoFl9psclvVoJ4WdKmVpBSmqcCJrrlHuxbIOX9si/FO/WSg/bQS3K95C85K/AsCbgs4FlqmdHIUyxWmnEm5noBqFq2GpPRxvlza/s1BDfXgAFr8whOSzVstvdeqE4tAh2pIXCmDto4yT3KIx0nEXpaqwSIZWzLGp1Ob4fjxTln/MXgtwMA64VKcZWbLc6zt96dxQ9n8DzLPs411TlQ9BZ3czG9aNcCegtIy0bKoAqpXNvYRFLWWTm0tneJ6pgbUUguWgJQjSyVpMEWsdRE9P1xtfUxU827nCAy0UpT2+6rlWYnBjDqusU84wcGxFL+AgkXX4myZ0ripMgHRvOdle9SHr/ngtdXyXz9xcD94bZA1hWprRQhxAk4evt9TRipVgkJrNcNx1O3B74uM8S4HUFqsihvRrZZK1gpLAw9VhKZsqmi/BqXRxoLtUN2MXkrY5228s3u59IqXQ/8TYGD587JHl0PpU9afJAoFXCtlt1/1bD0u7bujQVMMR6OvGj2qVbN0/tb7aal4iAZIa4VYe+JE+HBqSdpWKWj2yGh7NItbMuZ8BsxOz722Saj2XBbAVsvDGKQkQQnGUFOlGnmNDDhtcNagbRCC5jGZ+g3Gy1eB7Dmv5WwNLwlgXtw9W3JY6qecBa1wXdoCt9rIxIOOkNvec13TfziLNcoc15YQ5f8CP/s//nf50tu/t20XfQQtD3hF7ed8p9d3OTCAIzhQAje/8u4v8KP/6Z/iZ/7wX+NLb39/K+e31Lae3cBzxNh6o7WV0ACK8eIEtwTwXI/BppQHcESyNS1EvljqyWP7LLM7ZXoKjZQ+TWvJSsU+n8ACfLLS8KBfd+pJPSzjiWlQKa1sV5bgKAFysW8WoNAyVCR7UklhjeibOyuHkDWLLWh9eBjps0Oq/TwZvbNob2QyQbXDOBlqSdIyqRmib1oLi3bE0qfPZ6Nwrx6y5lsHrqUicN5/XsrNDyopp8Nl+bWsoOXrg2fRQIFaKgcLSFhGRV3l1V470Oarl+kUSzWB6gLZeKZUm25BlayjyLOoSyXqmHppas10VYMRp0+tK6aKDv2yxpZnIO0DOchl7ZnWXaufXEtqUeI/BT05bGtb0XLls3W7LDu93KkjV+bsOssYj1SedlAcf/TyG90iqEYaFcrIhqilrcjTez0HG7n95hiY2/s+D9Tnh1DM4gaZSmUq5ZihmlJJFFJRpGZMNrW9HFwvh6aZZOw2J5lKWXrPn3jW+jiR9InK3QNw8HD9nd/r8wTiE+tvAQLllV/Gy0RCSAK8c+MbLHsqL///08GMfMa2p84qBgvILm0sWYjHrnEEwilrbrHxHIyVcg7STmsh5UKmEltPPmWpkJ7HIJs1yYh7ZEE3G3ERl8taZLS19ljTVsSncUs+LU6+8tlfBZrn66p5Y50ADqc/f9oae7i2JaFa9lQ1p9hojT9Obxwnu2qWZ3Dk1qjWPmig4HM/cAZWXrnOq92/jus3ATCABRx85d2v8WPNJfFLb/9eqPk00oQ+OwHONKnPN+/ywID5lcM1t7LkQjTJpT5AiqbK6NjRo1xJxq2WMi8KFqeuM8Cgj78XhKD1qxWGTz7686CyoPPSkHuGY2UjN0CQK4wxM5VKXDKn1jqQA0d+gkliq2qTABhrtOjoK5n1Ps58lyoytEVAQlIKW8EWQf3WdpJhZgvaofIkgbGcyvLnwlKLA93Ccj+OMB6ze/WgzFkbsayctQeqXkhEZ+ScszL5khHX8snDarnHx+eiTBvusCzGS6rkh5nKGQHvQaL96royjmpE1GpKEhjHXIgFxpSZGu9jeR4ANUHV+eiQmUrFGY0VjrX0xRvQrYiewCIUszzLk7e7BMBlDb2azS+B8DzrhlMW1LbLp/7+PFydH5fnsfnh65wARi3tfbeWibxfAQjn7/X8PS/vd1nnr1YIytkeTY1TkEptYLg8vMdVYsbUtPKNtiyALCMmRa7zqBxkZLCBw/oZQPBIvDyv3H1aVaY8PHjOr9MaFCt33VwjFfVUpVrW4KKbsgDqlk2ruj4R9FI8ZqUP9hScWh/nhOVjxcMd/76acPRYqdoek6Sl2pVbj/2kXijfr8fMekmsytn3OEqyL/FH5ImzALUKoVSqM+QiInJJq5Z8qGOlctmn5/Hys2Llq+uIz1j7CwBYqh21fX/hZB33DJ9yBmhRPy2lYrVoohbdWs6tegBSMZBWsxbOSKtmf/Xdf8CP/F9+Utrfb//es3Pq1Po48W4+7dN++9dvEmDQ/Kj/45/gb/7w3+BL7/xAQ8pSKaj1nIOwtBBOm1gY1OaIhEGsepeFHfOCEmWBp3bwnl9HIKCF2CSHKHLYcwILGllEpxL9J8HCq1nap12votrzrOkIYlrGtFQJplKEMFPkMwgwOL2mbqVoq8Xx8QgU9EOgYI3CKi0bddkE9WS8k5WYIBltqSahsj0yzym+GRNlmfNeSqRVvAbgbJMvo6RnXAEJWg+BXDX22H8+BuFW+luykmMAOE/XHjzA9ozavTdLCV1JABJ9g1OmcuSFfOJ19DHAHjOrNh46l0aCK7CfxXI55iJthfMpBrUILUswdda030uZqRZR6qsKdDtYjUJ+U+opG28H7nJPl59wXt7/VmXgs9X26YvwO72aX4KqrcJBFSn47/D9ZvjUKoEoenICBGdAeC4tmz1+lNM9liux9gIOcgvochh5TNMOOB7On9FGOQcCyz1dgEBpB85n3+OH92hJGAzLerSnic3lvSxmQ20qqC4AwJ4mPBYQs+yt448xrZrW4mBZ2h1mAdfuCAiWtsEykXWeeCxx5rw98FlAoCyt2G8Rd6zWZMRlMtdWPTBahJJ0lapBa9sKoHgI6j/terV69VkgYGkLvFrx4AzsZKrQPOqpDbJctoGX2vZoPnvWtq0FgKIM2goXSECn4ivv/gJ//Of/LD/zh/89vvj298vSOCffLuDgv6brNwUw+MrXv8qP/Uc/zk//0S/zxXe+wOImKBFogZTLTX2lH99Kzku/LLfIkXIllvLggF2CTS0PS/HLddrEIpFpaIdqAwvWNE+HZkmqGihISnSxT5WF0yEFJ5CwXAtKPWVRr5RQOWVMS3CczjKo9BkbFF4FB2CVbip9GpWEi2CTIhjhUzijpf/dNq7TmtKAgtEKqx8CBEo+MYuXEtrSB3XLp4KlfLyMTj0YsXsFGBSEGLr0AM8PjPM+4MOM+PSZlz23gDalmsVqez5LADoHCrqBlvMncx7wH/Qji7DeYxHAOabMmMoRGLz6LLQG20TSBS9ksOaYZTmjW3LbTlOktVArZCVvoqomwFrP3mT7zA8CI58ETeeZ0qvjzp9yJH7i+kQHdKmcVaC29d3emPq093t+Y+unv1+5158OCqZcSJXjus+1tW1qeXCPS1JgNbmewEFnTRM6qmQtxNATb0gMvZZqzPlzh4cVgVfbHFSpaiyf4/SZzu/T6euDBOLsqzl+tSIZbTkCA5a2VzkRn4+8iG9rb51GQs8BwRIXl7bkq4DgvDKT2r2uVeS+S+V437+dmGN1laqBqYSz6oEzmswCCKRtK5VL1Z7HKXM/v16NlZ+2vnN7Drn9w8qp9VHb+144QAII6qfGftvGWEM5xcZaJXGi6qPvQTxrvVal+Mqv/H1+9Of+ND/zQ3+dL33u93HaZWc8uPM2yH8N13c9MBBQ8GP89B/7KVGEqrXdxCZsc2RQLwh5AQanDDPVNnHQ0DCcAvlC1pNMW7Lu+krpd+nNLiUx1TK4BRws2bZJ5Uiy+f+y93+/tyXZfRj2WVV17p08BciLJbJ7/oMAiRFJ5Mx0t2FLETnDIemnkNPTJJURSSmUDAPOQxi/GDEQBEEcI5AISSYJDdHT5BAIAnJGHNKWnfDenh5NZD8GyKt5e4Z6yEMexXvPrlp5WD9qVe3a+5zv7abN3J4C7j3ne84+e9evtdZnfdaqKgMKGxCAgoCFbqCkjfMyRvtrjH3JdDIFGOt7taWK3OT88MZutIAOhgDd2MaVkQhqIkLJDYUSrlUEYGNGoVEIKgiVGwqLp5wTe2ywpCLUPBsVGpMua2+QFW98zxOwtemRFZDcD1XAU5zTYoAr6i8WUyZF3dnI8GSSHfsiUBhWBYQyeyLdS9Jwjs6jPwtA7UUTpWkgBoDsppabJCtlzXzeBBzkxiCSPqcmuS1GySPJXgjCkovhbdH1Dl28S9jTV9a+nA2XG79d7+3LHP2kwJax4nUHNCBUgpwhAGGcaFFn8/Zi+ED+Zu8/B70KCpzKrubNjn2ciNG2ikdKr7N6gRsTPpXlLIySNKeG9nlDZ2MfgcBAQ2tnmuwdzcecemjSYtcxvydbvQCUpMsPIddSvQrQjvkJcfCBvXx5GCH7nhMREJh+jDlXMyAwNtLAwBbAscwxBXKTvkEV2duagINHhRTMd/Zga4wLK1MJQoWd1skP0pVxXq+AgIGaOQ/CcoCYewgWCKE7DYVQInASNrWCJZmyyQyxHWyvjcEsSxu/8+EHeFsPRHrztR+FrVaw1TnRZi1zDV6yvNLAoIOC9/ToSYYMm3rmlsTVNdSe6p1AgQnsDApinDJOfgBLWsxRvoEF9bYtyYYSodTm8fvNlQHcoxqIjqm41wS4opmpvEhTvxjYgqbhEWnzTlABp+wuWQECkxzgE1D9JQtAeKy0X2HJKm4s7EHR/hU2wbwu8bbTlHRpZdj1y8ZritHWQCPbKpHIljCvBR2ALicaSwdw+3yK6wm7AyDMNAWJM1jjdTjHgNpWsfNkwcBWOziQzH2gNMYGgFJDRs/QJwYaKShgRiNhOAwcjPNmpFNXzIopwAiqgGNgtetPncvQpydY+EBuSG75OzhoCmb6r47qzN7frIZW7bD0pzJ65unOoKB7rByYmTYYosetJ8Fdcuo5NdS9+NXYn3mhc9zdvjuajwB8TkZHQkDquGqoMxrwBL1krIOGvkzG5l31Vqs97JC1GAbo+5/0VU02nyNIiGzkpjom6hqbR1ayOg61ia4BErABLaluBYGTMQYJhSoK91Dm9hK6MjoJMQ8iMsGmNwwEuIwswiCAMKtbE6ep5wclABWPcwaCI1kbAwl4/799H7/wTbNfb/REUaZRzqLdcoBAPueAWWJul1cWGOxBwVj8KGYaPzNBaMASFFxt8qoA/Nk2xilXimYobQYG/W/ztolqBwlKP1mSX8xRyInUuxrbYY+0Cb5xc8rLDH+s7woUXGvzLWqjoF4rq6DKa2Opu6H5BkLjFGJyIgiVEwoncBOhKEyehFNTZxBSIo2Lj0mXXiKFrDrtKPt8c4HdA6Mo5BHtz2XF7nRw0PMpLOnJGIWuiMzz62NixnSbaG0bkxfbpERDxVqFKnvIKYhJ5hg3oJKArTDVejRBn5sfoCE4hA8Ye/aJuSvQGFuN7Z37EpB5vPE0j0nrCsuPIFiYgNAPYLqnRFtq4CCWygIOZI4ao7Du60ZN82OazrMENjqbgVIbHufk7N4Y4uv3OjI+cVleBwV7L3TVlz4vr6Yn2pTjY4mbFu4I4YYh/JV9z4tVdl4zTzjKWWDf5rDB88bueGwNO5Brq21qG52PWdfYWJqugW82ZsBlfM/M4JSkr5PIlcnmLmxmf2MEAnEeH4EBc/wiEIh/jw0Q/b5RQ8kmO+T1la3aOjgApB8/+PApfvEP3sFXv/gePvv6m9LSVNRR2m/v/9AQwpNnT0+/fyWBQQQFb86ggGxyQdayT995YpBN9gAKauto0MMHdW9kjS0whdMmyU6q5BLRLma/NRHiymKAtladMSg5ITdgIx6UKzDGzgxNHk1yy3L3uKoi35kpMKGc69+qhA+i0DYWwW3McsqwKlMAyrw0PFbBrcy+Jrk29Lggx7hgT/ST9lnbehvNyM7erDAEYyJoBESRxj8UaBurid1xyjgRChFyFYYkem5WZ/OKga7wVtnYkWa1/SRminWeO3OpDBQ1KJflFT1x76hELyQO+eB9h9cV62IebuV9f2atu+zFIPO4ZAJXmduNjN0YWQ6vj8+Bc6BgOZarUhHo/IMyz3c7I0Lu2dBCElyhDhAimwQcj3/ssz4X2IHAy83N5nPTkvQs2U2WwyXNPxiZjR6Df5iszQxcZAm21obcjdnxaAx3PM70jHR6H9CcGLIDdUNKCY1JWFllzTr0kpUjhQGm9iA9afP4nuTI+N7qvtL1Mj4M1IRGEcwAVBuukTkA8O1nT/F3/+gd/MYXvoYfff0NSXhXXCTLjCXUsCx3AASzj5/Gpw+veeWAwSkosMmz7DzyaWVrnJsJaOvG5jvfex8A+hpz3htZQ8WrmJkUy55ljWVqgKPJSgXZyS6hUTSkAG/VAQKrhjaQgIXymCnJaBhnb2lO+FmVpheJUPIgtHoFLjmpUfNfoTHhUUl4XoFq4RsGWEEP5yTLeMCeR5FdSXEcOek9HuOB0YM9AgMW4hmFeU3/SRv1dWJ3YrKlGbataZ4IJzkgN3jGR2NhxmCbkt9i6CDGXYFzw27KftgqGpaX0mltonWmNrP8fRYKMK8XOqdXBi4mXkUanBJhYw27sGweU5LsEYAMkAJiO0n2jPvcr8iRH8kKTWmD5FKwMxFxPfxGhEy3wIG3GoBtY6vKXdmwBluKK/k0ManYwk5WIpMyx6ePkvFmYHCUlDezjxbOKymChDaABMtVmldD9Vj8KG/WBubehpW8Weig8pr5ugUKoo45K61B2LLAmlU2XdRQkLAlmQsepgrjGcfF23fnuMyA4FjPy9YbpueRtV4BzFBiyQuqtl0Z8Hf/8B3848+/ix/94TfF/hid0/oYk+wvf9A7x8IT7eOv/u6vHl73SgGDFSjYd9H+Ew7/fDMXNqQoiLIy4zvfex9//z9/BwB0na4CgIWRjZPFJn2nxLrXHSdO0UQym0RJgq6SwFJlcpekk9eUEAursXKgZkMUP2/mmScFJ/oqO+ZqPRur4Zf23BJWa6MZMOlH8niismYAGirL+etVPagMBQhqFCo6IJjHakXHrkIlEQxYWOHI6K4SnmKSZUpAasKCpNS7hjlhI0lyYmZs5i0S4bpUQhyA2qh0zkDBqsguh5qjomwGkVL0CPH7cO1y22Tt6GZvdD6dYATxIjU0vfJ65Zp+fU+WCri8ieHiBnCMeSwKHdUbwjBYvbO9TyKHxg8SpF+cbQshq5R4H2+wexsS0gdtqWnSZ1MPkDVcxtgSKRNCh+NvfWcA/SGGB1jMU+znqYDXAAQOQAKliWoPYbBYOuyHy11kiyLzFYFuB1Cd+VoZz1iijkkaZkq0z23yua+vvt8K9b6WIRIdWRdGNAKCuKLgiCWI4eHZ8YtMQdTz7jwhgJgAZjYFBJT0yG0A/+DH3sVf/eE3pM5s4WJlyZRwEPmmm7kDBvAYwNMb4fVYXhlgMOcUnE+/EWvtQEFjBwfGGvyLD9/H3/ujd/Cf/o3fwi988ycGb8gyUKMQzGVOrLGJU83C6MSRberg75ui4JTId5rLalx84qNP8pkGnAsl2W8+qeG37GtjLkoCgCSUY2I39O2Aupq92Dx/YP1qijp4iVCAwyzMx9Z4yKFYldVa4VWYYOWBmwKeY5tzWSZYckgqWwA32xAHkCNsZ887etFHBmE1b2J39pUgGnoCSTw5yalt4g2KR+gepClSiy1Hwzp1ceJuZBNLnD9hD05WU4sDKGCVg/lHtqogN9bzgGi+ZNl+ZzkO6u44g+ywKQEZiaDbQMt8KTmFlQCStNZAaJvuKYIeNjj2xgz8C0goSNhkyYfstaHAx9iRWGKoJW4gNhueGEI6S8yzYgl63ZA2lJQcJNiKoZKFKRnDDWF1zUvKXmS/zOk4YiFzIrRhTw75Ly9MnAGC2DZLpEzoe71YmI+oJ2bu677Wjatlhj6HWwA2rYOC2fHze03vZ13oORqqS1tjZE364caylBjAX/2hN/z5pE4Ikax+Mkdt5aOdgYSHgALgFQIGc6PPOonDq6Nh7qCA0ScFA/jge+/jV/7oHfyDv/ku/s2/1A+smNeqJkIPDUxKfmUw7XND/P0+t3DgusSfOZ3NCh40HpiZhXeuAFKfACkBqUrMLpEg2q1JtvUl4xDtd6+FhraY8MZiQpaYfN9/+QJuTImBDTzQsMCeij1KCprZgVXm81GiU6ynD1eSjVsu2au6BG5IwNbUY2+2OqC3YfakYwKTPXMu85wYqWLd6Y06PVySJkKSUJjx+76+HQD1JESDM4DSrSoHpIqUWH7HFubhUbaqjo0DH+4Gb98gBVU6/YgZBPI5aCyHhz+I+t+wz+Ax8aH+sSH2R+oeljWsUAKnhorkuQZlVuCQ8V3GvO0aVjaCBYTI0kWJbaOK8a3T+OtX+2z22fAsQMHZnLWE4GhIr8S4ZGFIJEsfckirz511kvNRKEzqvpfByBR5u6YqmvHWnpCVHOp4AMdgx+b/AAgc7MTcLJ3neUwQjmGdWG6xYUdF8qrY39scm529s9+PfSGlYm+IWUG6zDEJtaUkzIggXn9xubH3c3koKMCiPv9/W+5t9CEo4B5Drdxjqd/58Cl+5Q/fwT/4sXfxV37oDWy1w+AM9O03GQMtXzQRz7ztefIf0YAdAe/RsCvzg4k/C7JcLAqYkx6N2xjEjKxhi6pUamssyw2RnM4s+TyxJhZrA7CPfbpQL6rnMenKHh4BxOMarptQ/ZyodQsQvNhGtgDo8Uxvg8HwIeHp5UCa1Xnf3oU3DQMiKundddd67fvT9oZ4nAmXkvFY/y6ZlqDADQd1RWISQPo/IyjRRI6QWPtVYvjytS0pNBlYldj8o240r5rU+nu8W39ja/HTHfUn6uCGuNc/goPiAZYGFItt9YRkqbdRvWNcfx6HXXsV9LpneoM2v7ecxeJ7nWXuRoDeOKkxI8+HaZw0wXmf5DyvvNkWXqm1K4ZF5mJzuSTbj8FypxRQQxnEG/rEXucE7RkQxN1Xz/I8hj7VuTM7ThaOqsDAqkLDTf293Zed8Vjp+J1+H14V+Mz9i31+B5PYpUymQ3pf27UrcHBrdd5ReWWAwUNBgZUWRmGe5N/+3hP8nW+9g1/78XfxV3/ojUHRCSVPXbdkjLFHVyyCMmevBMChId3tKKgKMU5+Wy5nFCCAgY6LiWS24UdpQCXIBkOQde/3ZERbIpT011qYuyDjoE2dQZhl1bLMLS5oZcyL2C/hOtssxVaRzMsuheI8zrQ8imvad96mCbjNG1etSm7SBlEu0E6NYAAAtJ8mbREBVsmERwYOAih4tAAFxX6XzOAyhsNlrH8pKVUvr42ADaIYmTot3xojsfTJxsIgZJ1rM0A4w1QZ4ayQpEoUCgwcSHZQUF6i/gkj/WrgIC3AQaaEF42RfEWIUr6Ls6BG4P7R54AZm8TdE2zMTruv8n3m4nM6JQe2L7bmy/yyYV7NPTCmIiY5x1CIsHe9DetEyrEeRMqYJnK9YV62g4L0MF2y6ufdyqAFGLCwSK/bGN5zs96642TWloNsivGWBGoJHdEEHMnbcqTjYxtKRgjriCyT1Z8M4k5j63MW3u9E5OyePdZAgb2+LCgAXiFg8NDC6KDAMtoBe8/49rOn+OVvSXboX/3hN8RzIPOUgEIQvra2nlSXgdSSxGWhoEAHbR0/vk8AzgBBXAsOhEmlqN1AzwUA5/WyuZkanJOjpF86PXzmCHVgoG1D96Ciwhw8xVDmZW5nO4sdxWbPQMGyzmnvbWU1tpHG9Nj+AXAz5bRqF7ModvK2jArUlI31tddtMSdknTrJBjtFln1a+OCS+mEyhRQokBpU3T9fl4QM9aOw46dt61ySgIPcWD1xXTVAMr8LJVkOlsRDl13G2Y1eDCdEw0lpVIaFkocJMunOfdqnAygIpwrObZDpntQ6FeSUNRlNdoAkAlLrTMMjEHLKoFqRkeWwpMS6WoFRJnAcx8PmyhEgeOgcMMOD3NzQQBlHaL6PIoIAcmTOrkDufsWQoyL14i2JmZa5MhEgGIOwWn7qyzJDuxDaljMNDNlD9Ud0JG6xAvMy0aHnvWI2+mL4amNwoK8mkwABAABJREFUYh0PuVdhklwnYhTVJUSSJJ2aLKmNQOeh+n0OfRTqZ8zEAxFXsNI2J7OcMktJs6GOv/kooAD4BAODVbGh/eDDp/ilb72DX//81/DXXntD8g50EAwVlkzK7cgBHhlyuMpW2ZccxkNZbk+ctYLpsT/s1sqPOyGGuGucIdMMK+S7hvv26AwsN/iYAQMQqP87SnS2opI8ovdiWSkiK0O/ql7sOwuuf5eDRxAzd2Ic0wCBgQIz/DGuOQOCVVwztj/2lYVxkMn3HGAN50QAFss8J0yRPDbGQNenX3IAAgYMVLHbSXu+Vz7aPshKBFCG7KCUgQxkyrBMvwaAwUhN6tKUNWAWo2HLBJFFBqLnGBW89ZcAgl7XBAUCpkTnNsT9/pmBaUdMfwASkLQNqeCS5HAhan2Zpmw4I2EmyhkZ4kU/b4yN2sCgrUI/s8GKFDyAm/OgNJbNqHQOUJXlu6mpsk9tyPfZqCGn7EBX7L1Q10l3El3N51WR8GYfl6aJbE09pXTwO2vXspizBJXbLLqle+j0IJ0x9+OKCVjpP327XKFlXzo1D4AyASw5Ln08ZOlgZFUll4mWzsnL6PfV5nW2KRWAzpz56wQwsQYOVl4mp2AunzhgcAJWAQDf/vAp/vYfvINf/4KuI9VlIgQelkg9NoWiHmtJetAS2TpYRach6WxVZi/67JAl24Y3AgGiOIm6wgIOUKe9chfcBoBVyZt/YYABGEGDtEk/XwhD/OgO+7/8na+4UMosTzR1SmNmMyDC08CeFFQRQgHahpSFQs1TAtBR5vPMEBj9N58oOccz4zYZttkQK6yPgMsMhClRUZ5jp827Lc4b1syhg0sKLMF8eA4fMAbGFlCVM+ENBCZGTgWNBFAaDcwJsuFyAxAy/ZUmAHJXokbnzmA3rqCwdqQ4BuqRR2BjJwXCt4ZdtwNcpB6pAmlDzheklLGR3P9KUnciaIJtkmPBa8OWyPNVek7LfiLfMmAPmQemM7aWdKMqAS4bQ/N9yDc+W+0YaHPGjJTN7X0oLBirRZtmUHDURu/uhXxfDkzWLb0w99mSCT3Qe9B63qv35JWdKWYGcpZdWy8sK6S6kyS7uu5XL7y8fo86Y9jOWn+jRJfandhvtxXqk2dP8eWPCAqATyAwOCsffPgUX/nml/GbP/E1fOa1N2Qy2TZs6JnZgA54UYUSJ1LKu00zrMwbvliZEwhXqNioohkIEHUvCDRSSmfLjvSKHlIBNKzCO/DQsi3HGxH3QGLy8u1Q5uScGPMzpgLALlmyQqhPAtwbLVnPDZoSnGy5maykIFw0fGIrK6zMTEFcCmixwHjUq60Dj8dLH3owqz7PvQ+i0gH2m6xYmT0lW3NunnVJRywBd0Cgx1mTn1ZpxjSOBQGUQCkBrYLtuOssc+GSisbDSeOv2IUUUJpmIWqsOo0KegY4l7IIIRj40TZdCAIK6nU4PthO3DxsB23wM4jTRcIPKeOSL0jZ6GiZK7a3wdYYWQFC3BsjGvDYFoSxAbCbB1qbfckLhk51xnAccRKgEDflunXo0BlzsJrjNocsb2X2aM+S+VYx/KGZB8wDHfyxYjxnPWc/MV1n7brHeO5PUSTXda7n9H0mAkMPasprNhV67b363dqz1Bk0tn/YmRLimAxtXLZQmIKPAxQAPwAGXt5/9hS/8M0v459+8Wv4zOtviqJm1kNryPSiI9RL6rkJxSYS4HoqettWbHIeCdGMiuXaPlGOQEBc303h/vaYfXKxehher1i/zhrEz60VMVkzHg6zv8/+/pdEA7BA6LMCACqERulZsiQxBRq+xzCR4QlOshcAhWSnvo7aYvdW7k36jJvCzJTfLNAG2I6KtXueK/M8GRKkJro0a53j4Ti7XAI1ngIINgcIAgrOPW3mAlCWg3Ry9/0YQFFwwKyhWmt8E+oYVcAB1T5eRi13YzL2obwGgEPSBmvLAAraVdtyD/ORgJaF/UgVYDkiGNx27MGWWDc6kqZUjeMWteDRiMdxAY7lNhq2w/kwMXRsgKTQdJ6HMRjAlo53RvS5zg+f62cJfUcAeG63/z21e9ZBR6ym+8vUGY9Zl4168ZaO0/6w/nbHJh64Rf1vTfxcHblciBD14kpu4c/Z6/hV/tcZ45s17HCk41fl6bOneOf33sbXljv+Prx8YoDBCdujoOBtfPWL7+FzrwtT0NQYMytXoOjN5sElpZ13La+jUUTuxnCF9VaCs0LHZyDAJ0y8bvfEsx7oVwfyIIABCmDgADAcfT6xEX7vCaWzCtsq7pcb+2qKrPQ7se5spmNk8VlLDILGTxHqEZ2Y/RLAMWdgtde89bNRfZ5Vj1Gx7UoAUnPbgQ4u5dJRiUTFYUfqpqThDxg4AJKxBPWqtPsVqJZXoOAADGwalJkNainCKqQMZmMM4jWEQjl0IguDYAcFZUJmCa/ZeDHDD3Wy/op9WRT0OkNgfT0BHKrPj9sytwMAlSI9mDI4XaRduQhAaBWUL7jkC3Im5CaHX9nBYK0Rqi7fjcA/ysY8NsABg/eA+VCVJWuc0Yp8JtsNp7CjJ+m+HZKTsDrMx8pqvgPrOT8Dgpjg7Al/Uzx/ZdSs7fac6NXDfzMa+rPPdyBh6v+hM5eFhitEp9GUdE6nYOESwALQZRd6/ajf97WLIOkMHMXQTglAItqDWedbef/ZU/zc74+ggHc1eVj5xACDo/L+h0/x8994G7/1k+/hc68r0mJZn82AS4DJnNFCRXeKmZfscBiOs+xbK1F4V2h4xQKsQICLQFzKZUpz8qyI9xVj6pPRA30+c5NeY1nfXaQjgGBrM9Ng9Lrg0fj3CUr3uJ+GDYzeLRZvBRA3WCkan7VnHh08A2CZ0Lc6MbGfSDfRe/pKsKSicfx2fQs4WOntjwpqZF58nEJ9jU5MARCkmFw4e9aBeidu4O0Kbk0+B9S4Qun2BNQNlAtQBHSxRsWHNpVHyCn7uAOyc6BN9A3ARffhN9bHypwjY6Agpx4KMZBDXEHbiwkUKNjhCmwbuCooaG1sCwDUAkoJVC4eGhFQUYF88d+kfEFKRTeOkfrbimPWz+4ZmxVwl+/O5wSUOZJN1agbeZODlHyPlXg6KBfbcTDdTJSMZbVCaF7ut9oqOYYxX4a9HJyfcK3142C+B/2FUYch/I1zPSYXiL4ynWb6KxuzhGOwII8c9ZZ9FkEi8PJ63v+eqCUP/8RrFtcmiP165/c/PqbAyicaGHxbO/Xdn3wPb3z6TTdm1vlx3nW0J6+PcpzaY1nM19OyQpURbS+FSM4b7gBAjT+Z+w3As89VmJxGHirb/N7yJkxH6wfzxAwYWE1JDsPxpK8ZQOi54Eadd+HrBlIOYwkovcnua6YcRXEKSCicPeYnMWA6XT2x72fa0doxfm9AQAxwp+zXR9XCQcPRTODpddX+WfFY3Vfg0J4vDAG64Y+GMwKCtoG3K1rdgO0KMIO3q1amOcCjlMS7zgWoBenR46He8CPKCUnP8m2ICkqWuBGr1xpYH7+F/hfDIXN+RGQ+DOREUEBtQ3uhQEEZBHYOPbSnXGSVhIKdxA2cChgsSZhZwgvUKjgX5HxBTllZA6Al2WGO094oRMV8i7mLc+JsbjTWeLZ7/wIW4hHilYHWsuRVBqAwn54KwBPk5nIr238FBnz/C9rHvK3NGev2d2AUHBYbr6XO2jsyNAMD++3QsK6zbJ76McSus1L4TnQT6StTEr2QTF914ObOjg9ad3rmat1TaJ4Ii1IWF80fJQhT8M43Pn5QAHyCgYHFZN79KQEFAJDmQQ42chb0y50JIeflYFbFia+zcsUADALlnwUwEEGCJWrNAmf3m8uMvP01sAhELlwujCZwBhxSVqWiiD14nC0g9argoCYBCqIQ5dU21onJQasY8LxqYmiONmGV9BNDAzMQiLvudWU3KbqV8op9eKh8guLRn0aYNlOq/kvbj0BfPY+gbkq1b8C2oW0vBBDUDay0O0cPG+iMASVQkVyEBiA90iYBoEr+CkpI+aJ0p80RBnQ76AY9zQ7j7I4ep4VE5qTJGRRIuyZQcH1+uz11k/bUDZyLAITySPZBSAVscpI2WHgBKSt7kAXIJrp7XKwnDkH7HXPDmLgOopVB0OTbFVBgzrpRETxH4aXkwNqCLgsWuhKQ0OUiAgHbXnsHAnwVjMmILS29oae8wi+npyIYIHRHxWMe0WGJgGFycAaGAWGZJoUO3Ffk4PPjwgfvffv1owugTME3xH593KAA+AQCA4Is6Xjn997GuwFphVU/J3GtPmFT29aCP28c81BIOZfo/U/PORauhfCB+wl3tXo9OQYmz5B4CkKXc6DsgsClMv2dVegEENAEFigVNZZZQzdG32qOGQJgIEIMO5zFgENvLROjIh2alQpdAYFsHhArDc1ihIZ19CsFBuw8FpoYliNvZV10PD2JsAVgUNV7lpUHs1fNL56LATXGoNVxnA2sEcGoedL+6+BAN+WhBFR5n/Oj7h56HW2TIww5FDYOMRRjSz7jXgWWIxHZjxkU8PWFAoM727NtEhpprbMhzGCuQNJcCt/3oMrYJElczG48jsfknLVbMHaLOeJMHIlcpZRlbqQMFDmMtx4ABTB8uSg7wPYaAtjLQhw1s5kDEAgyYQCAEBgzC2sBIgetbzo1gIC4rHRaETMmj5rzwzvd5DJ9h24i1U3St6q9J6cFoNDfC7BgnQLAg4T3uPknheff+7hP4FBL1gwBs0MMhGOjgSd/sg8fjHbqo5dPHDCwJR3WqTbxVtRfBANuGFTq0vXPjoX/jPaayyECDWWFmhcIewUEPLY8e1cqrAMVe1aFQDuzxXLVqLvHmUsHDZRdIImyKuzUFXfKIFzlvqqEOWVk85rUY8sMDzUMYYd0O0YP9PFcxUKH0wZXQMAMb1w77wpuVGo7QLhgVwagRAkg+5t2Smns/GhwQr28jsoYWNjAvOrri25A6xWotVPMtpGRPbP0nALDyA0APSZQu8o41issNgtKyLaMUdtJDDVeQFPPMuBtp6ENGMybMA2Jk61Ju7guQQFreOSwPVn3Y1CAQJdHzoZQrpJ/wE3YA1/aqPdoeTQaq/HQMXcQwHWUwTYCx+hUjHNE+y8ycClLf6cscyRl5JSXQMHCD1XBWGSgXlYuduGBgRWY9GHcNKtFAG1gKa6EqW78dzkiWveH6KSeUdnBIAOjTrJckxOg4GPhhjqM+UP186qvp/DsMsxhegAAbc8DwBX2w8irp4tEw48LDMTyiQIGtiPUWaceAoLoKQKg7c+64Ec6ORppK7eoemBAjON1aa9QlkmFPCBubk2EzxSUCdwMEszrAu4GMWQeWcqC1s3oG2jIBUgKFDTjXbRPli13YYAi++QHRWHOIEqS6Ea2694YdpDm9xj93NXWvZExWCs6HAOBYHxHL6cu2ZddPzmzQt0DNJozBeNjfRArLmsBh0b58+PyQ6vv9UU3lkcGtNVxrqri5ZQcEFhfubK9vpANBUhAAdWr1K1e9QyFLKA6EagJsyObgdFAhcZkzSE8Y/1fr6DWwvJKfdW2OAMS21S3bjy4eQiBU1L3uTrgsfYS0EMJl0ciMyxgwAGCs1/n47FzGGy1RPw8yqXVc5org5dL8fkmYwIKlkDBQh8YcxUswRW91jvZsFaZbAAYwgPOClh4wIFx8w2zBiYrysm0csTGimNuyMeki3ihizxvhkjkIRf/nnLuQMHupe3f6eGF/vWv5s/vCXWY/Nu9rc5hzlHd5EA5bq43iBKePnt/cGr/PMsnBhjEbSIjU2BlpGK4x3GdHgvGAtBkrnqsBFZU/aJEGgyAKwkAewrKfhONkjxEgMCEwF0A67YXPm7dgzwwGLuidWUHAuLxImc36C6ACg7kVT/PBSlnDy3MIIFI9up3T1qVtLMJKkAFZsSm5KC5ujSOq4EAMOuGOYux3Rndts/qb6HfjkpQWIcgyXMyDCQEhTGXyfh4KMHqdgYKzIDa/PDSYIfusByZ1D2ubROlq8l7aBVMVRVYBTUS2j8DhfJw6JJshYwhZ8foagMH8fwDAQV9HGwsiJswIJpT4GD3VpskKWUJeHh6NeYASYNUyjxQq8fjAJj17TrAjObKGAZ5XBXvc5svua+qcM9WQcEKKHDKSC475GCBw/0fJCMLltRDBHEPiQUYIG6dETDHxPTP8PrRdJAZVQ46qLMsGq5U/UO66sbkkQObgFnv3tC5AHYg7yE6/shZEJ2nK4bqC9gOpJxkX5E//t6/wNvf+Dm3XzpK43NOa/Gw8okABk8mUDCXQ1DgSTT9b5uwwhjsjcasBM4mDaXUlQLgExrAKABT2cXdphABb1fMaNypOxPaoFQdUNhEr1b3cerZbl6Uu4fLavTcU0tZ1pFbaKFc5H0pkghmf+ei12XIjnqlC0f02iLVpp+T9ssuOcj7xTzs4E2u4p8OAngY452xDX3qLEx4jnk5NIVYBjbF6MzQfgdJUUHIDcaxj6xQiM3O9YQZIvuMmyrdaazjmDa4M8yJu4HTXRDFKCfJtuUKtCSrHYggCYcJyAmyS6SwNwYKOKiumLUeT3rsY2D93z1Rn8eBtXvZNrEu0yRKvmTTAUO5gJsqfyKt9/EY2N8DcIz0eFwBsvCKD+cMIPMmF5GrMGdMljyBV+UFRCNQCHk8zkgheMKzrBwkN88JgwMYmAFCWELK1v6YIBrn4xb0aABP1qd36R4bl5RApndMznJxRwXZtsYOTEK5wE4+Ygs5AOB6EjJgDcp4iKOP4z16voc8grOggCUF8Ge633b3lLnL+OPvfwdv/8FX8N5Pvou3Pv2GPA8YQO/HXV55YHAEClYhBEfKZkiiAMQNVgBPiIrIeJcpPSkBf1aKCTIYjQjQqTD97mbyy5wvMDMEMb68XUeBNKXWJEHL7sG6KUBUup3VUCHVelIiEVj1innrbAFvL0D5Aq7CGOD6QhScLo2L681dOFpA0KsEIQMLvWJjXwAYaN/o0R153cHLYUtumz2cmZKfh0HHzsfUAJJ63nP7I6jyTXkWtOY+dBTyRwIoYFvfb6VWV1wPKpas18T4cWvCGrCBSBkrQlIWQdqYoJbfdGYYlwRp3ry6wsG3AwQZK+ImJwfq8yVP5iXUYKuyfWYxA6AAwZqqr1QuAgj0dMF7xsCYgWayZPPGAGTwjN0gAsPcGVoUWbhyGRg4NpCdi7NvFNi3GHpwsBDkhfooHMgLgJAvtU5sHhnSHZO2ar/pnthHGvZ5Ob1zHfSOgwCVJZknF3EkuInuaQrUOfXxtnBTu0+3AujhD/mj69jQjytdz9bnQc9TKWDVC8iWFNv1N1gCQX/8p9/Bl/7wl/DbP/FP8dbrn5V+CXkHHydLEMsrDQxuMQXL4sIQmIJh1zVRLO35vx6pW5v00QgvFAEmlmBFg8XkGWAbgUL4bVT6pzG7A1AgQt0FkyuDWV4H5WUsyVCHBMqk9BiBUgWlDelSfLMcrpsqsioCnC8BxV+7gbTNdaKBjIpNDWbP4u2e0H78ggL38Tyne3k2rMawLCjrmV3ZlejJXBUcGUiw9mu/iKcqHo3FEyPVuD+Tzry7kFS68lRexoDeU1iTPFi9cKiR0KQvAwdE0GOa+0/HZX2dlZPQXDBEev8/v/pPpTUwiZcrHjl0vij4HXfDH/N4ZkAw6QEHAyZvd84dp8mDwTPWwBgFKAPnbMIiTEWWuzMn1Z3JziA3CoAOZOcUEEVAEPMLuKFdtwfpHD/P1XUOg3ITcNBYHJNykXBYLiK35QKqtevVVtWQah4KJqN6plO1b07DsfG3sR3RUSAC59wdJQt1XB6hcQMVWQZE7QoG8OT738XP/he/gt/5wq/jzdc+o+OT/vzQQCivLDCYz6M+UzXezzu2IP6rsPXhACTZKzAGrgi2fsiLo94JGACKgk34a3Kk3xFwSPDjBG6p/3aq/ymN5XWYJm2Ij5mAtq17wtyan5/uJQhNSg1oSemuLqzcGlLJcv9cQO7tXXoymMZx0ZqsnU/ZDaSBCsoNcyYxgAAWjtt75t1F78ZYnnuU2Zl3A+xZlHRRoKPt4lYFIKnXR9rHVKqAB/mx1HcACL3scksWhVKCHc7kmfmAECgJsgTL5sAUrx0270ljH3OtvX5RLiJQYAWK5s2E6g/xa0tk28lYB+VzGykly/vVsFK6q01C23a6fm7X3MaeCDj1q2XT2xw6o8zt9cQgyvOO54+zcEaJG2uQixsXYw5Qeh6P5ydEoPAxyM9yNcEsQxFQmyMyMQRtq0tAsNQ3gOscW0optpEh5xMaeFB9Y/MgaxgsF/087+8LlT+bD3NI4MjJimAg5kXcoe8t5MFl646CMXMcAm9tw5N/9V/jZ/7Lfx9f/7F/hDd/+DPy/CBnoPyRQglPnj09/f6VBAYzKLivhC6ONFpIRCNm2TQGcCXA1+eDIlhR9HPZUWAGErJskk6lqH3I8HgvN1WEaZzQMGPQ7A/R34BnaQPdEIvxPQESGEHBEehoEOGLAMGEtW090YxzkUQ/QFD8JmjYBcEBRHXlJgqjJwfFrO1gupbjN2eAx4TBGPcdqN7IDNgY3unZeDEFkAmpZB+jdGkdCFifWAuUMSA7VNpi3RrOj4moh8VyAVKGHBgBAZOAMDGlAJXAKSiyPDI/MYnUDI4nZ1ldcx6VkLFquhwQTCB1aIzq3Ds2kaY2zzm+74Vy7rR/UmA89+OdbbKcDl/9YYAhjNtc5v1H4mqfARSYrC9Agb3GedS22gHBwTwambgRaJrx5/RcQEIMOVjCr+UmxGRgBQNp3oNkHp+p/dbuFgzgsOvkUbjtBigwJ+QeXUMpoTVGwqj3liWGYOXH/vcQrsUC/E6gQN6PoOCMBbpb33MbHQUA2Hpo9Mmf/kv8zP/jf42v/41/gLf+8l+VZzhIM53w8oEEs4+fxqcPr3nlgMHLgYJVmeNsFbxd8eRf/TfytXkEpghsudiJZzB4BUaBZY0/N0D+g8REMzAgXZtU88T3r3MXVjMuKOJdAWKQy0UoU1LvA5qjtVl7baEZkCACO4COG0WUgAKTxqCMrrQVZFmWrdgUcnqPWwLZbNSYIFcIENLEoFNvz/raBDXkExglOHg1JuSL8MqRAgOwU2JWp5RMkSRZK0+MdAHadfOwuxs19eS5NRApWLOEugR4LNjaFj15ZjWa3YEXFkYXbRqrY8avbkKnyoXC2sxltarEfm8KVRq77Hti24LXkEFbXzvFrQ83/9KG+Z4ZzEBqGiNWcGBtclmZ2qP3ock4UvSup+z0cXOc3rZdG1oLTEzIfzgBBe2qbMO13phLFSl1PYGUkEpG2zYBCCVLroEl2l1fCDgoRfokF/D1hYME78MpI78/b11aZBbdYAaZmvKp3HB6guLoPZvBNIB9b+nyRd4flMlBUyp5yG8CCTDyPA0Lv8QExAgM47NsiAEB2LGeyhQ4EDVQcCNXouv86gCPmyz3dNNuoWNt6//i//4f4Ov/zv8Zb/3lv4IYtpv1wsuUaB9/9Xd/9fC6VwoYPBQUDMp6eYF5oTLYf/yn/zV+5oP/UL7arhI+YO7IWEFB27alAPCmIYRhL7Lqmd1OhwED0pXXMKHNG5gLkRhjE85tGxQ8bIOapNvFQuqTAPHIADCxUFU1IdtET3mg+UxxSTWDsKqXI5+foNlaJRnMlAwAyskNuCcGEXVvGHAqeVdiAtCUKBT3cth5NIt8Cxu72aPpiZ0LTzLLHvuUkgxjZSA3cDugri32mRfiNxlUX9o0/M1yGiKz9CVrnDwlAX7GNBlDZEAJkB2jds+kvfIMiW6gpGvsQ27HbPi5wY9sjmv+h2s40NTT9yYXlABqfdMn7SOXU5radNQeoLfJwY7Kjq0MCYCAyZb3jfOWaQIHtG7bnHQ2ftc62wQczidjuqrLECMl9dbVCHJrSOpUoLTugW5XaZe91g2+bC+ABOsXywtZ6pFYJtmKTsKcZL0EnVookU4R0S3IbXBCrF4rPbPSMakUWG6B52EYaxJDLTMgDHNjX0mCJf0J49ocXDvzKg3XvtmHGGe9z5vdWkKtAAQccBMdHPSyydTX3/g/4N/6y39F5Nr6flE4vN7DHTzEPr4ywOBlQMFYzrv2yZ/+S/zM+/8bfP1H/3f463/87/UvIoKsXXBscsxxRPOqsaLFLLcg5x09SJEejJ5ObNOE8skS3cxA2N/1Ct5y/z5JLFyEl93DAbIrtJX6cBAw0Z4xPkrBMxvo3d2AGA0eYn4mvNWlay8ktzKGD1ZlHMV+neqdQcFJtvSqHH4Xx3f8QVfeGh9mREM8zU+Wo8Dle0ayEIolfTZNDCwXX1kw55fIfc1YpLXxtBg2aTIoaF+XVQmZ/f73raJZi75bZumAwHMgYmz3Ie1JfT+JCAaAuBPdvo8BiLdWSPd2AJgT5mx2WXmizEFcGeJNC0l0y+4K80oZMkZDQxIKHcLsEXdjSY27B6rxdKYAqi3RzUNNxhaEhOa67Y3kiZzJ+zb2uZWcJeHPw5YAwZKL4Q4IJwHN5oQQsuvJWTMYGACAdMldv0yAwFYBDYBg2FMltn/PlniOU2viYCV2IOoOpI1va5jB4QwKVnqfkPU6dQbJAPtoht/6N/6nY87BskwAdvfJWB5qH18ZYPCyoOC0Q1VR/LHFfN743+Pf+dz/Sn7/H33ho1X4B+UH5QflB+UH5QclFAawvT19eALE77FlLxNef2WAwUfJKfBkMEpgYkWogiiffP+7+Jn/6t/H1//t/wRv/Y/+xx9PZX9QflB+UH5QflB+UFbFQndDSfq5vmLFeu/Ly+bcvTLA4GVAgQMC/ySs9yXCH3//u/jZf/738PW//n/Bv/Vv/JvA9Tm2//e3cfndz+H5F/8I/OJfSxLO9fku2ciopViWCTO5AJfHToPR5ZGvbUW5yNpWo3LD9plWR7lxoMWGpUaMw01J4sY4llUcT6yLm/kA6wxqKyF2udsi2fpz3gkQgc6LVOYBnSt1uLGkSK8/W1Z0trFT3FxlzhyfEzB73fdZ5Lvx9WVlF9Dlcd/10cY+0PUI20L7GOtys6PzNHbLy+LSv7hTn32nf+9v1Cn1eQ8Jp+Ptu7jL3rwJFbCs65Bf4BvpaF1vnMb38vXH2IZY14N6el0XG/ysdvuLR0EfrlS6sVxPmnQ8x4CRVj8K3cVwyeG25auN1f68ZDJu/DPLpF1/VGa9YiswiDwHxkMF8+6QlD4GfaljrMeX+5411xd9efr1+eH4DmM5rzCZdf+j/wHo8giP/m//NrYkcsdR7mP9Y87R7pNePkoi/isDDO4tPL3G9wS44vjj732AL/3hL+Prf/PX8NZf+itAfQHLeAVUgLKszWdbr08y6O26aTZwiBuuYu9uLB7JpL48Bj167MCAygWcik7w7EmEfcLbuoK5RMXburFsFQwWANL61r+Wh0BhXfKhcE9luaVrzGJfJD35eyurBL2oMDzTXpLq5pUSvrX0Hbv8yXpi6SLSbhWA0MeLGwHF4oSa+KgKJOYOmLBbHXxJ2ZwMFZQW4quNsRraARCkCBLCOC9oxZ6gNAKE2ZCyG2GsDWvv0P6sGZjEefcAUGCf20ZOmlAimyQBACfA4vfcej5BMMz8MvXWv8e6w+u/rqjFd+XZfTtgAsuEkbMiCsnyv5RkJUDKsuws6dLjAL7tXy49SZkDIIiG5GiuSRP7fHN9Ym1P6TTvZShRTr37Xl5OhxUxzH6mjCcUc28rhevksxt6ZdoxMK4qmbeKbnEHyAhk79WXpidJl6lTljG2PAVLRLzK/LIEbrn/hpzz4HDMfev7m2hiLRGJPo560u+XfR4PcgbsGIM5kT6eDfQyTvMnAhgcUS6WAJuodzRRwh9/+BRv/8FX8Dtf+E289Zf/ZyLcROLVWWKR7eBXLn2zmpTAdUOitFdgUXDNWKxYgstjpEePwZTQ8gVIF0f+Zjh8yZFPmknAfZ24eCXDcb22hrY1MFd5fvCALDEPdQv7pWPvGXizwrMnYZbPDhJ+zhQYNyCHZ1lSkN07Zg7bxwkODihl2JJfT4ayTH1SJUU9SVQ25YGP2by5ji21iistlgrZEwfH5VIzIIgb0wjwU0CQgqcTPfKoqCfjO57iOQIDBkaPfAIFA9MQy+xND8l5ARDYd0egYJF8eAQOQKSJdZI4x5z+/Ooar8OiL8OzqFUBA8Oxwkk+S7rpGYkn7zt42s6WdRsBQkh+zWW/E2Kcd6s5B0zzDhjBQPT+Vf6WbMG8pv9oxdNKdm/Jrb8N4xaWO9KkT+K13saZtbA6xT0ZAsPWovyksE005R1wPdOVrvvCuSnCJsoS71QuaC+eS5/ayq6UwaWAtw1kIDDb6ZMHNsATYItveOb/AG2b1tucwoMwQrRhVj4qKAA+IcBgLjNQsM4lAp4+ex9vf+Md/PYXv4q3fuhHwG0DpQpmMaDp0WO5VteO2/0IEMVgoCBOdpvoq+xZ9RpxeaRhg8sACFjfcwQHKQPoZ3TP7YmEE4F9gwxqsglJPD+971HPQBnDDohbwALy3VGnOsU5rwVf0HfW2cBe0bswSaY9gHFzmSlzeN67wTeAUgDB0ejrmvdIZVIcJzNCwJrinIHNIoRCWVaPxMOjllRnZAgm4BfBgDMGRutjzXZ5teKnyhKsKPyh789WDATjzrNRvQcQzJ/pmHhdCQDbWGkdkboB8ZUB5/U7BSqRPQAd3mtM3uLef3bKKlXZVMkNhwGEAkrbnoXbrmI8ws6IcZVMXF4bvWr7e1eOgPU8D4G+oQ6wBwT2+4OVGwIi9HWQ5/tlOQJtG73VqbB+7VyCPjk6jdDqHIH0oCcNDIQxX8lLbxmrfgx6sl1BNYHTxQGCyTP0OGdcX4Dy1gGCbYAE7O1ABAdDaPHSNzuiDDb97+2Qf9aW1SaRDOD9Z0/x5YceA7AonyhgEPuyLWbKHz97ip/7/bfx3k//Nt587TPgepXJrJusMC59sl8eyWSaqcMYR9Oy2oFtAAS5AJdHQCoyIXLpgECv53yBn7sudlAZ1vWyFtLnEhESss7JIkuGwpHCM1BwRsHoW27A5X76mf3pGBXICW073jsaLjVklGBAYbcDnVGTYZ21r3UHBm/F1ryL/QmswFm7Fm2caU75bKI6A2MQPZw2hYViTsFeqeUB/LWAAo9q7CaXANi4Iyq/3scWgliyDjf6ALgBBlaeJbcRaIQ9O3odcq/bvXUYAMtItQ79dlffyS0SEYiyEg8F4NpBgh7yJcdPbwAXcCsy/7iCUgFFBs5AwmpzIGAdrnvgnJS3U15AnJt27Z1gYFjKGfNbQh+P7MyiRMDpY8rApX92q12+ZNeMY2QEAhDooCGLboTqSUsduKkngQRCSgUpFTG/9Qq0At2pTAACyVJjB/x1E7C/XfWAtOkk2xM7MDCJ5hgCqvtNPwjwcR0BXTnNYyMaA9/+8Cne+b238bWPCAqATxgw2BXuSuL9Z0/x89+InWoeQwNwUQTMMmgAqDzS9dbUBcsOxWnVrx9i7zHpzIzGCSCQ9wUNstdGZaAx++toKPpM8c01ieU0O5LtajNkcmaNm1nEjbl2RsHigwf0s9w2UIY77+EOMOA/7vdeJdBxSD6zut0FEuw+Og4DVRljoveWFJVvoDljHHZSrvOBNkYHHrIDnjsiwt/QDVoNis3h0x1OflDhQfnZBVkONqJwjd/hiMVZPeyEWr51rT2Qm9L1VqKBm+8/siYr4x9Bs13zMv0msgIFCtJfBhI621YwJCYag8UNyAK0ZU7yGKrj1k+VtIqtwnVnyXlH5SXm6xIMWLw+GuYY56YQjrhX1qWBw1eH+mQVFtoxaSMQqE3GveojH6onc9NXIuT8SHzCvIGqAoR8BaoCglRkzA0gXB7tDmNb2oHoRAwJyJ0xMFvgwCd1JyHaLQBIDLz/oTi1HwcoAD6BwCAqEevcp3/yFL/wzbfxWz/5Hj732psa+VTqDV0JDTugXR73xKO6gS6tx+XRhXuHzkN8+RYgqAC2BtTWwcDW1EA0Dgpw30oH2BAFlxwRS3w+JRqAAgjIpQy0mhnoOfZ6TOuOSvw0M93uZ8r7LEPdDIeCBFGudQ8S9J7DRk/WnnuynxftWOVF7OjVoMCGbH4b94kC7efH90RSZ4O4ezmNeccMHSm5oTmu8PRvAgjsNLPPC3TAIL+z35CfhqgN3gGpBwGrW0VBwdwant5Hli8a+giaegSCd78B7uszPwmSgE37KhmwMpCQMyhrDo8lrPFFY8s9JOf73LcKFO4geADcPNDsIw0/AYUZpN07r2OuwIKm5zhHbU6HXKbDlShABwxWxVtyP7dvVfdlKKiHT81rjkCguW6UcW46N+7Vk3ZQU0nkICEnQkkFOZXbAEEBAcX8rLBb5ADS5uTJVNzp7KCgAPkijMGCLbauf6JO7bs/9R7e+BhAAfAJBAZzMVDw1S9+DZ97/U2ZcIIMkHS7LjIarMCVK+dHst0oJUGKEz3YE6yCMA7LDvNdgGBrLGwB5H1rujEnq4A026d+LESErBMdxKrcWJgDIlBj5AkojMZCQhCGFGJyizt6B326in8B/R7RQ40gxOPhZvgtKTJmDBubkOw7884u/brT2u16Sl/2TAdrok+MT9vnft0U276VuT+HCY7AgCs5/YwxGr1zD5hH79cBgY61hhoiYIhgAcDwWeymeQzn9w8ts/EHVsZ8zQCsAFPDQ/vKhpCnvpK+SUTeTyYjHSRQAAnAEG7gmPgb2Lc52U0r5jI8xeVPky/na2+Waa4CYb6GJX0DkNW5ao6NJ8HtjXWsyU4HTPPnoGaH4d45JDDIiOpHZqAyqxwpeHyInmyyw+s1MUoiZBByYtREdwEEZAvR1sEeONBb2QOQ5JRZeBEAl0eeS8Ypg5UVmUPIAPD+s/fxC998G+/+5McHCoBPMDBoGEHBZ19/E41FcVbQCA4yQGYkTFWWR4IGbflSKqN3bWUwGNOSwzsAwaYsQWNByVttAhKqCgKAoxO9hBFQIJCAQkmE4AAodKPBwWvqivKsnFG08+9nb1WeKfFwN06TkqVWBzAgAKI4oBgU7amijAjnwMDrZ+tMd22Bg4dR2dr7VezbKU8jRdCV2xAuaN3AzcyQK96pmfZ2GCYHBxwMPnz8rf/96CwCMunnqlF3zML0IFvRs5oe8bPViNwDBMwIIHxm3mIEASvPMP72nr4KUe3gSbJ7kqmpE61gKquMGGCwOWy5PEDYNz+AXvbGmNF3FNNrPAOFHcswNzbqnTvm/yI8MIOEFW0fwSza8VjFJhzW5E6dcgb+ZiDArPqT5aA2+/4ePVmygIGSE2pjJGIUlnNQ7gEIZIdJtdrDSINe8oce2wRgZAoCKIg6AgA++PApfuGbX8ZXv/gePvsxggLgEwAMZiRrf7z/bAIF4eukkykppVooyyFHlCQhBYLq+vKllXHCaFBi9qwllNwBCDYGtioTXV4FHGzMYA0xAKMQmsBl6hO/pARKrO/XQEEUIg8hiOh9HvbxHQ6L20hEj5Xd6MxKNiVJ/Epz4peCAd+vfFCwC68qPhwYjb159XrNih7d7aM/swd23+l1oPwnbweYgEBQeCv6067x36r3U8OsXukeALAggHtGasXN15sNYOMeUkiGjxMhMYNU+QpwwHBU0r2M0qFHiN5Hdt3MnESjsAJMRx6i9dNRH1k/WR8BAHFvPzF7HxFbWE7DDAQB1TqHrf0D62b9mfr3q1fvnWkek+U6BRDQmQjgUA7CPXqjZ9ary0FcBn0Uwz8Es9P4rB69Kg8BB6vxnoHA1thBwNaav79HT5ZNQEGpDSUTCiWUDJR7AYKxBWoXPIkb2NuFmZ0xFhkAl8eaU0DYDPS0EXh9+8On+Mo3v4yvfvFrnSkICN3eHoH2W+WVBwax2AS2RMMICuKEqdBJ00Q7bBCvOqXiHiLnR/DlSwvB3AkedWBgoYR7AMG1NmyN8VzBgDEFW2tOMwM9dmqxUqNAAQUFVLsAkCTXlMQDoxCVo5u9e2fVHU7KEfggsrCGKNmUJL5nynYACWD4sa6Bkh0OHYkBOPto9uxPKNHZuLvBCUoKQQECx55SNHTjPfvnK493FS6qYPeC4nOW2zpphbI+53CcVXNkdANoIE2mvxysZYAgkz2Y5Bwh3gMEa+sRY3ArV2A2ODGUYh7gPX102j/aR5bmuJFcfLOPKIAEZ1ykIjFEg4Gl6T0xM2f++XBtHkJBwlz26wj9mUMekCftTkyEdfBUgeUaf10KHcGAjcccxzf6/shoWxUPywN1y9k4b41RmQeHyfQkgLt0ZUkJtCko2AiPM6NUwiXfCRBstZcvAY+7dx7YhZhU6ftHaMJ5AAWWTAkIU/CVf/Zl/KbaL+aeP6Qm6yPnAH2igAEgoOCdb7yNr37xPXzu9Te8w+flLMQiOLkxmEQJJoIk6gESBzKBnJYY+X0WQucoMAICVXieT8DA81px3UZAUPV14+ZLcc4mu7wCJfVYaUqEzTz0kIBYcgLAQ/ghFpvPs9dV+UzyezkCH8YapMRK1QooqKoMN4JkCifqKywCVet9flSPic6Pxt+NtCqRVbIfwnWxn1dPO+uKWL9oDO3VlekNQ2dGjlURzve2783gXdENk4wBIaMDwpJkPNjGg6Hnhcj3DIacPSwgo0LDP8ogkMmGGk9TTEd9dMQSWPvtb08uwzp2bJ4im9xMlPE9fQMA29Q3GzNyE6CQSWTV5iwfMC6ATTMejb3L0L4nVp6yfTQnQtp3Me8jRUaCLMQxggY+YtDmSixCXbNBaswOBoytaa1ffwZgb5WH6JooA1ttDlg46NGmoVdL1rY5dwsYpNRQKKGy6MiNGYUIjxvjwoTHOcuqsMQoIMjp6h0gJMoolpTqydPdeRl6wxrpq5bIGYPrpJMiUP7gw6f4xT94B7/xhXfxWU2Uz3Cs/rGVTxQwePrsKd75fUnU+IzmFACjdxJLUqWUkinMDsUadVSPzPtZDAzGKMaJGh8Dgq02PK8N18p43trAGtgk78CAvQ1DvSlM+ARsTbJsSwaSvjeEvBGQSZA20Jc0xpKJdkK+UrhHxRQxKVPRwxs9pFGUohXGgJGZkEgATTNliJD4JTdEsoz5oEBjTX1cp3hoBIRzzHq14mPwgoCVru/lJTwhYE9/r2Kk8rddA4+bTluzIx78m9Ub3hR0bToOW5L7ZyIwi6EpSQxEZgCJxeAnIDOhwhgIGZOMqdyrnBZ9dwQKohEyhmCbAMHGY9+c9Uvsm2yefSLpU2WpWOcoK/hhAigxuErj5pCDvLmzoXfNmx7ak+ehAwIDcHNOCHXZlWvF0NiS1PiIpXwEwBUTYOPS6KrgwH6zHVD48xw9K3EO3at3RnDcmYHmdWdstb83XQngUF+ariwEpCq6ydiCjRmPW8KWGY9z8hyErEmKBhBKIlSVraTOy8DszGW10gKy0mKVhPydD5/iF7/1Dn798+/iM4Ep+JgxAYBPEDB4+kw2f3j3p95T+kU+t9eZ4pUvRfjYWAN09HltMRbb0V4sds+ZiovIdjNA0AQAPG+M61axNcaLxiG/AJ5n4ILa9pNdQAErgmUki4kmRtsUEGT5vHFDIqAmcfUSCJsqy7PifabXtQlR2Z/m7SR9k9TbzAT3VEtOyKgiiEn+pib3TkmuNY80qQflYQh0ZTJ7YGZkrDWr5D6L55tymanpzdiIBX2/KrEO5gXNceyjMsfBV0lTs+Gb46ZHS/GYzYixGjmo0RMjyAoQSiagSd5JJcAScImBSgLWZHfCrpBiWCEtn74uK2bG33NnCiIoMJbA5IAZgS0Y++VWnyRl7ogAVOsXQtb75CYAYgYIgICErd43nsA4pv7ZHfPIWDYg5ANVdvbGwz7m8aKvMop5O0AHDLF0HTgyNqZfZkBgY7AFR6Wie+5b6PtZN8w6wcch6Abgdj6T1Tve/xYguFtfKlNUMqFxch1RmcCJsTWZM48bS3iBCa0xioYVKgsIn0Oh5sCsygzKAAknx3EAA9/+3hP88rfewT/5/Lv4zGtvorGMfWQKVFw/lvKJAAZPn8k2ke/+9Hv43OtvukGwYsooJs4AItzEAJjQNFPfcN+18c44xbIUtkDFVYZ4OtzDBltbswT9X/MJ3ph9osdSdZLLLAn+gVlXBraqoCEpcHChDQKjAhuN/hEl5xu4LZRdBweWzNlDGpkIhS0hMgkwUOTNC4BgSV9GsarMgSY3aAYFc5Z/BAIrYxOV3S3vEzDPU4wIMLIuxpDI53KDW8rPpua9oOAIoFmRPiQBPUwDQNALUBJhE9cHaDIWulOx9pzIAJh2CumszB7qrTY3k0MDDdzl8wgUzHHlW/0BwDVoYjGYAy1s4EDzK+QzUdgCJOS326JlcewA7MIa8XPgvjkVw30xgz4mD1v+g4XdbKWJhRjE6K7BwVES7BkgMH0kssI7Ct8Ms43prvuDXpDh6Cxn/F6uOddFHQScg4KVzjQA11j3Lkj6uySftdT0VdiDigpOCVtjXIqEF2oDCjMa6VyZQqEPsROA1bWPxQffe4q/86138I8//y5+9LU3RZ4VpFO4z+r+L1teeWBgB0p87adlnWecFzIY40SZkbzggg4QbCRq6+uj98IWjJJO1miUmv7zxMLGvtJAAIJ8/iLkExgouNbmk3sFDIzibOZuHYADCQgj/K2fWd/UEV2vQIAJYfw8/iaFjpmBgW0iUlkAQkVDZUJFQmWJUxYS6eImY2DsAenzbWnlrgSQZ4rP+x1rJWcGxoyOeB982H5rE7CKVdLAjphyNwof6J8dlVsU7FlZ2kN32cQYilfMwhYAIJBHxDgxbEMVYuzCBjMm6KGdMSbu34ffpTDtGo3yNlfbQbV+x8wesq3oYGkGBbOXGsvssd5bJIxy7OnfAnGxbiY70sb75lXJhHRtHRRshEytM23Ktm0EpMaaDyWskEVAbYx25UxeuIcM5lVRMe8pxvNnYAAc6wQ00ws9aTNJulNv/6LPY//dCwp8rA4AYwWjsYYPmvjfApI6gmZOQOm6ipuwnchJ1asAhAQFZxQ3C9sb79lWAKMj850PAyj4YWG64y2YGY3OdcnLlFcaGMTzqN/49JuHnosNiH2/AgukoQQbk02pdwDdOA2g42Ct7YGgCUiAg4JNJ3MEBXFCn4ECAIp+e8KexdBGr33uh/09o+KaFdqc57CvkikDiALQepQky+JKlvBGSXKaHpsGyMlcOPdezUiQAoR5POJYyWtXyCZ0D1VwBsqO+sb6edWvCdKuqhQlp0DhEw1U9UqoM9RgJ/FaBTxBvNkkSbFVlURiGihzjQzdVSpECXDju63mCAr2vyGs7Q+wN/5E5PL2EJvtRvgBv9nR2Oge9JyMGwEcsMin0HIGCDYLWZk8nxixdX3VMCagbKkD6k2AQlbAUGixzC6RgugecrBVJGchhTmsdo+uihS+660H6QQ4ewnAw5+OCG7MZQMYEvdFR532Y0WjVqecaM8cNHb9WVUWttZQkNCSjKWUBmxBV02OjPV74hjqMWAWnLSIloFBfi3f6zsfPsXf/cN38I9+/F38yA+/MeQUWN/mhwjNA8orCwwiKHjzBBRY8Wxz7kzAUFhCBzPlGWnsKGByT3RAgO6p2gZFG8PzCWZQ4IrkQGnkScvZ3w8BBPbZsj+OXCPsQUEELWfJkDlZuCIhJQZqQsnS9pQYVWluXQ+0AweVuq4w8YjeRMz2v6XcZnZm6He2hNDe/0dAbO7vkgmpEUqSfiqcvM3EIsmRqo5lHoqibUEyD18uIhZPn6AeKVSZpgAQFvVNdxr+e4opO2AR3sG5kY+sQbXrJ/bgo5SUJPZ7Hs/GjtGZAUFPmt0/Y65rBAXzkuLO+pkRvU1xA7NMN2fZLDGuvyZcWDLoCycUqgNAsPraUkvxxnuJcmO66kxuquq+CApmZvOWPqjePpuvItzpZOasdFUCyTOS6OfWSKl/S96T7wUsSwi1MS/BwapYEuA2GAWL5uv+NSW7ruIqIM3J2LA0uwbGILZkx1Iz44PvvY9f+cN38Gs//i7+2mtv+BglBQcN5311qzx59vT0+1cSGMyg4GXKSkn5/gYIiUS7Qe2vjFHILEEnJu0IJdrjenOyVKI+sU2jpgVMjMbJfpc04z96sf6d3mJlLESp0hDnuqccGtEQy0ZSQW4qvKoQtspIWbxjTtBXcmp70zi4ez3q+USPMQ5J0/5fxUWfB4bgxfC9rAZpzHixjfkcc+lgh3DJ5CChsShqACggbEm9DvCQrBZXZ4iBMoO071ducFDBLLQ/Nx4Aglwn4Io5xGUjk3BgEK0uROPzE0KiJ3XKV5bJhYS2JSjY9xmF9hs4MDWZWBJ8ExhgAhOrYu0AwvqHWOve1Ms1BgVwrxNDO/pzY9sBDIAgjkcfo/V4gHRFB8PjyluYJybPERTMc6w2xrXyTSNqsv2oJCSS+eY5OYnwKAMNssTuMbOc38IApwZm3cnPxtNkZMEarPRVX23Xwzbi4Ey0vTsKOAXUOZk+u8+oRYcGONJXACD6qqXulLUm+kLGQJ5b9PlCChw7Xf68qZ5Nww0N6sgAIHVkeBN5BNqwwicp2BJQNjqTq/LBh+/jV/7oHfzajxlTIJ9/XCkEZh8/jU8fXvPKAYMICt46YQqI9sqoLq6eZfW733t/+Xn8OwoYY7/eOia3maBZSVA0SJ0WK0gODoaknAACgH0cf5XwN9Onq1KJQkwUAYGLId9CkHjO8D1jGqyYYjAhCxE8VKbdpKyQdcOMiYGbxwBjv5+Bgheb5nFosqetCnmxdcXdWvOlQ3PpiUoJtYmyflSS9JV6GbFtsVAwQrZk09bKAyMbVBsDWU6qtTXipQGVIJutACg6rziJQI/bv5InuB0lSUaAYFvCGiNACgRIbtU/01ebk4dLs5gHjUZk0dc+mJbLQATfJ8D2SCCwf1dyAmrTpEgBBMTk4RUPiUAyyWObrd1HS2cNnM1jcTQezKyMDYDW67LyKAzwx2z/h86zlBKulXGxjPmscqmd2Fjm3/NqPTpyAiUnAfo6VivZmd+v9KE5MXPb5P19DsRsbCOrWdKK4RxzdhY33CXjtsZoJHUqSMsQDnIIjU56bK6XfxZApgOmJLoGWVnBBpQkLENWcAAFuIoNABwb+l/5o3fwD3/sXfw1BQWr61Yg/Z4S7eOv/u6vHl73SgGDGRQcFaF11sUAw5wYBQDf/f77+Ht/9A6APRCw0sIHK1BgkwmLOlhsOMbMEkhRcPcA4+S8BwQc0aVANxIxTtqNEO2T8QyRs9Wrg5amljnlPUDYhThSn9zxdSX4vq1vHKfpmlmx1SZG1ICY7Rdh3o6DhQUo2GpFbZLoeRb/bUy4oAFJAIHvrx5zPULbhngwjXuzm+GV34S2Zxt3ueDCAOc+r2S/AxqWN5bGqOoNMfNOyOcVE4XGzY4IC7aAbBdKmUd9w6kFKODpzBD3UnViKzgYkuF88ttEks2VImvQtO5FO0hYlOTsCQBYnlhs836DpzUQ6IeO7cfB72UTVAHshiYsRmUNfzUBDpt4pMJg9DlUPUQFBwXGGpzNtYvuyi5wzoll/V9+l5R12xpL2AASdiJADk1LnbOJINuazOG9PIl2Ky9kRUpcCq1tTKGtIaY/5z0B2IU6LUTiORQWklP9JXIyjiOw113A/Ymfc76HlZV+lVf0EOykfGKeDid1MBUcVAUHtp24AzMoZsa+/ENlCmLpsngMAG4tVbzXPgKvEDB4SKOtuPcTvBRMgMBinwYK/uGPvYsv/d7nd1ja1eACFKyQN6AJZkmEV4RLBqQxjWh2qvW9WfByPsIxXR2p7biOPhqZjWyNct9wpLgHLnWIVJ0JTZu4yhjmMKF374AkPk5ad/Nijdr2DWUGI9SRtAnYkb9iNKj05bjSwChdo3VnULBa8hbpzNq6wgBCXBi6oRQZ7QsHBY9T2LNB2yToX+/v7VPFF+aBzc1ChHoHUJCxHUuMnUfjaADFDH4HAqN30k8ZtPGYAEHc1z8UIgEFrBQAUfKNeoQdI98zxFg8CeX2e5VEmnIqvnIO7MlD2joDgRhCsDE5GgOgx+OpJBQGMhpyE6CM2jQ5LaFx8yRbQAzlNVSyTglNyyWWSeZjd2kychIQKjZYFFdfJUTORh7RgkcylCDhfonBA5TYQzfEpKEcPWmSgaS7AG5NQu1b7fNnpQPkNW4o1JMqRR+I47Lf62QcQxnbtf4C9vuAzOcmrMCCjO/k0ARda22I+vZsdVGFhpoMHAASmg1yHp9mrYmgYMYBxtbN7N2t8lD7+MoAg3sabUqvcTf4CRgUUdMEEWMNAAkfGCiwQTszRLeKxUctyxwaW69MyBpHBo4naQwFHIEA80ZnBegKL9zXnrIMfyTy3eViFr9QZULHG1VnyUiAxf3GslqyGJXByqP2BKqF8vb+NDCn49kggshVPB5SD+as3JOItGuP0bzanktWJZfV8FPCoyyg4JITHueES7HDWczzhhtbUcxd0ncgS5UZQ9pbNGTBfAwUgDUlDBwbyFgnO8PCGQMHByMoiEdi704NhMiY2CzuACGhgwMdODnTQhG6ZZcrOCACSOXmkpOETYhRAvi+LNoI4BAIRE/MlOy9YxB3ZaSSQJVBqeE6JtUAFbIeXo33JSfUVn3+oK3DmGelNsblxhq1HjIKW5AjGJSFDBGgG1qJfKIlRV8jI2LrN1NLzio0JqTcQ45ziUnPMWQgeiC53oo6IDJZH0WPVaTdibRnhyw9RPfGsNTLlvmXs57zOYp9GGG4bgEUXsZpfmWAwUMabTqHSXc1o84WJJY/GgQc/IvvaSLIjy/oHX1VP8GpIptThrolQUnj5JpIJnvTCyCICs5KDU+IcVFgT4lGEDAb0ZXiWxV7MrOi7WRUOXw1xZaEptySZfOnaRfHEL9Lg6MXkoeiYhi9g5hQdeRRH9FpjDH8I+BPwIYkBSVUNDmPQY2PgZScLCnKcgZa2CRq31dC7SbkBJScccmET12y5xk8SgIIHikQeJwSLpkEGCh4yEnG0ZiUuLVtZEXm8WkgX2vOoMMjmguJZ90AXHi8R59ZGFgYZ5ymes3hA9tRj8CA7QmvwGB36p89xjh+Yt1aXGP0xKCUxQNTF4qYQI1RSbMR3FMVD5VU6TMg1L7N0zva5/MpADE/ZyD0/3CPMAbSv6I3KqmXTkBimU+FMzKae7fP2zSJivVLsK6aGnit7XC+OROVZFMj+ydAjfw66UeRpQzRDW5Q6IYcEXRdPJz6LpmQtU25MTKSvsqSxUokrIHLPzSh+M9H/jP1HKkzXWYy0jLpeAFVz2DbTsDCfMZGZKDm7ZvnFSwrRiNunx0rvZpjEQys2ENh6GhgC/y7gw55+hKgAHiFgMGtRgd73T+zjmZRVnJITAcHH3z/fV9H+qNhyYj91ove2P0EM8Bs1LIoOlaKgiBOlQAC+U3ho2neJxyAHe0bQcC8PSrCZ/coPAC6Nzf1WJwa/kJp2gEtDUl983aoR1uhRhrOvOZbCiG2wZR5LA1KTTMQ9640j4eTtM62NhXPjfS33YuLSKC1tkymjMlglywG/5ITHhdhASIo+FQhXErG42QntEk8X9gEMUh58DpkcFao302tWsQGWZLHKb4fgYIrR3RbvUp8iobC5o6xBETYgQJJXpOT46htCg4MEIQwwnDUrLWABCSQHPJDSU9fIAJR7g03ai9pfJbFMzejZe0K6Te7th2xAQYELCQS31v/Awdyov1fDRxAcktaI2wk8gBKOp4N1AjZ1wRoKYBlxOfUcE2Ea23ukN875wZ2KinjZiEr+0wZNwMFUUcAC1nSPrO5UxUgWMikJ/RS2P2QnEW8V/ZvgQFjriJ4Nrnoy07PAPTYBmN6GpvzM4IFRl9KPocjjkpnD0bdLN+F8zT8v70OnuXQXiMgmJksH0N0Gd3VDS8PCoBXCBgclRkQGHIOifUghm4AIsqICfjOs/fxd771c/gnX5BtKIGg26DX2z0pxLlZJjXrw+yaogCBAWcIAAwswVBvYweMPtP7RSYgu+LeKzs/bGgyNPE1Ptk8sNZIUb8AhHjo0wwS7BwHDiAhJv8AGBKDVtRbpAtnpTB4dkExxP5hlmWAprQbyzhVZlDTgUICpei5hbRhTezoitoSKdMyIcy8N1ui+LgkFAUDJQOPUsKnSvLQgb3PBFlm5krejr/e0/JoU/Ie4Me1ZtIfQLZplTEDWupAwYyW9AkN9PdyrqErnah8bR6NdQQQj5flBmoNDgi49boPyTqtt0PpOeKk0QWGHEcOJD2crJEAHtK62wqFBFtGSw9uUzw/wNtnciO18/4HsBwD6/8CUkAmXvOWZDviQnIq3xVASRmlNvwZAKKGklQem26VSzKPnm8Nl0y41vM5l6gvWYxAtCRjqug0ZLUyssC5LCXsQyYRIFSW8wM4rQ+xArBcGmpAwA9RW9TTEhGPxizaw6jP4qusElKHhTnoNgxHR1dmXHSZZw03YfQw49EJrvNqoqivARyCgWjQI1E0syJHskkIIGLS8x8FFACfAGAQi02eGRwgaVyfxUP59rOn+KVvydGWP/ramz5J2nSWQAQKpqJiaM1zGKJXo9/lTJ5p7vec6hqpzwE5zlRvAAEDzXsj9ss++yz4l/uZ7EnqVxmyHC/xHiQkoLU8MAnzqYC7MTBAMzEfK48mttP7we9jyqz3q+/axgH0NcnUztwpW2myGJkXtSEl4AVkCeXWGi45DVtPx2L0bVxL/qj0fIJLIXxKWYJPXbIAAmUGitK/F1XyhWScqF7VIE2e9zQfhG4ipeITso5XThkGFCpL6GAAB+j090q1xT7tTM3BnGpVWAJmnVus7IECgjDPfICIfOITMUDNAQI1CHsAMcSc1qGFxBLuNpmNRmzVnjksExkZZz1OQyH7pBQK/U8pI1EC5wsKCFn30782PT6coKtU5DS+cq14nhMoVZQt4QU1lAy82GQezfsbrOadswQH864QOSh4nDPsfJHOGPSQ1XzAkg2V9orPGQmV9JBJUQ88U5J8FnwEuQ9OwAoMeJhvHq+q4wWc6zSVE04ZSAms86VOYCGeIGnb1w9sm9qHuM/qPDtm3R3bHV/jtTGUM3xPgcmZgO1OPqdrAOD9jwgKgE8IMIhoMg7azBwkED549gRf+Wdfxm9+8Wv47GtytKWz/IHuz4rifMdEctXdBcPAwEJSLMo4TCijpAwA3OHFkd0jeHE3FbbWhyYhYkogSgBlcBKDk1LBJWc0ZQ8MJJi30NLIJLhK5RFtA9hRa3PscAYDs6fgCDwOonuQupCrmZejAEEVNDWAckbR5YTPGyOjgvTI50Ky2VHjhI0bHuWEuLXrauOokjEo5iGXoEjy1EW3rjW24JI1Vtk2UJV/4DoZKNPQ0eMmV3SkY4WUQUjgJO8zJaSUgZRldUvoG0muOgcHtFA2O5ZgADAhnGDza1X3yPFzBYg6QNCHkSYeyrOatC2yBwwxvjCljdN2ABhCM0M7amiHsR3W/7Edc//rKwUATdsLcC64pAKkgpII10rISVa5JH2lRxllaygEXBPjeSNct4RCTXcXTNhKP04dwDD35iz+RxqaykR4nKeQVbK4fQeyu1yRMMY7WQKUZYIfs1wNJOi+ESbzrCAhyjywl/so8yLT+xwbBwUOBgwIbH2sPoJOAxE4ZQHUKifNGJLW2dHImPbQXQcM8rjePgMJi/SQXR5H/NPezuGcuKHTEchdgQLCR2cKrHwigAEwAgJz4KWT1d6zIK1f+OaX8dUvvofPvf6GhgO6MkrUJ0TMsL2Z0E7TXvbTRIgG78zb2QGBGoFAeH/Tk5uQS0DXoKxKmcD5AkoZoAzKRUFCEZDQxjBD1aVlRs351tJhlYUJxYoBiZ7MHAbpTIFJZYxdJzUA5Oje6F2pmxlzqacoHqF4nxNQLJEyMR7p4Slbo55EGUpcVRH3qp+TC2PYoGRhCHICLgQFBFdVdtc+XjqGS6/bxkg7kVMBgYTZMUOVioyTKj/Sf1nH1sJbMbv+dA7aR2b8o1c9K2ifb3F8FkLRz3yG7VpEQGcPSM+fjImJRMiURfZ0LjQFObfaEMMDK9Bs+RHiOlYAAeTM88zGwI1NVmYtAzWD0gWcC3K+IOeCK8v4XxsjQw7oykjORl0qOUDYakItjK2mcQOexdzrcfkeo7e5V3TuZf0uJvYtnYmFPOU4z6AhKpYEamMQPL9C/549bEDkUG8j9UeXeXOq5rplDVctQXO99nFaysmZTuvAgBQUuF7TMeSUcVkAhc66xaTfYzk61fOhen7NAiiU0HcxDHEGCICPDxQAnyBgEMs8XATg/Q+f4ue+8Tbe/Sk5cMkQs4cDJmZg2LhjpQMnliAmpfhzg9BYPWgyijsgED2dlWFxwWHwdgW3BuamgWij32ZLLVQb5SJCVIoDBKSLwvuLG6CcMlK+4JKyLJEb0DaNaBvYyewMgObkuyXVO3ijIzCwtqRUxAimLLneepSqAYQMQlWAkJXi3WrD80phOaYtHR0dRusuW6YUl1RFQFAScEmyYuGSyBMOqW2g7aoK7zngXtDV28g6vtzaITCglJDKRcMJ6vm4obIwkI2fKkWlvUGkip92W+KG4YEZ0x0gCCzBPkxlSnoBaoZisFzHrVUQsW9MRK1Ju7iJp5eyAwTSumfDBEt8fVB3BTMOBiK4WcjMbp7pGFASD5RyDLtdgPQClAs4PxYGIV9QckFOhI2Aq+YfXFsTGj4AhK3o3Cv8UnNvBwhA/n5Hx88sz9zOIE+k3nZWACqhRRpCVXUwmLdlfsV6Zq0XbdceqjIwEPUcV2DbREbqdpdO8zELMgMFBVCAIEDh4iyphR8GoMDQ8MPIujks8fGine7vY7gXulWIodB4fQQDsS/tp4SXW5J4Vj5BwOBgsCCd+uXf+/Jw4NLgnTBcMKwUimp0ce/FJJCP90h6Rn+u4Hbx3OD1KJruWeFNlFrdAH3l1gB7nQUo1jNlUEriCZUC5AKUiwCFyyMRonxx5oDzBbRdwbmgTGi7MmD5CaYwVn2wpqzV4EQAFGO+K08acO+AcHVaPaWClDJKJmxMfmjKDBCKKumtMbYiy0ntLAsAnkQVE6jmDOq40sABQZIMceIKur6YAMGmHlAFri9krDbxiHi7yoMXLiMlUVhMBJSLKL1cHMixe0Fb92ZTlmQQAwnRi4pzIIzTAMTmuPtMuTuI6UZmTtqLhW28uHYGIUFzDTT3IGFcuRDjxQaQ7ql3DBMcAGhsm8iMyo95pSIz+zFgoBsZlRFSGeF2kXGOAKE8QikZRbP4c0uoTc5VKElCTszj9t235p4n7KVuXGdAYMmKlscyGNwpWXRXrL9hYSuaQlUh+dKpdrpb3iNIcdbT9Fm9dt02yQg2+Y4NHJheW+k0oOu1XEApDXpNxnAE0pSLhxhI5SkrQ2fgSJrbwYI5kEDAWbcY5KlvdtW2UI/21xBW6B/DZE5AwZfx3k9/DW99+g3/fO8C318+IcAgjNSUGPXk2ft4+xvv4L2fehdvvf5ZyLD3bjcKExhjSHkIKC1opcWYxAG215fxciIYaC/E2Lhx0deu7MJv5/Z70qHGS0sB5YsY2ctjMTi5AJfHaoAuovy2F0AuoJoFJJgQpeyKQ9XRkLATu2Zod2OcJrQdxd39htQNXvCgOSWQMhucMloi5CYAoTIhE4ccCXgi5So/QsZ8XA/ucVuypWGmlIGkHpB4QVfpMwsjtE3G7frcFR3XTmuzeeJhnGxZH4KSM6Vnyi4FZcepjCxC01eksb9WJSZ0TaBsFzqYWAIHDQdlCKpR0vEHPLSgH8WVC773geaEHNV5CWAO5CaCaMwgum56y4NxMDBtoExlJD16DE7FAQLKI1DdwOWCS74gB4CwMfkqntYYNaXulZ7MPdB6KZ+BhKyANEFBaa1AvY7hn5np2Q0S9VeVJ6PjPVRFBEplWCVzr7zPIVBhBuoICGx8ooxcn4t81OuD9ZqBaCoS8qFykXHLRcaNElBHto2CHLGzD8oUGfvmYAHWQpnlKxG40z6UNAOAePkI5p78ydNgvz4n37mMMJYPuqN8AoDBAhSoMDz9kyd4+5s/j9/+4m/hrdc+K56CLTUgE0Tf82q4a1n19yk9G+oSQcDOMxvXhw9xtXodlBpfX+zAAF9fuJA53aY0NS9mKyUCZRX+XEQAcgHlfw169Cl5f3khHurlEag8EmXYighNHb1TouxZ83RkfMI4OBCYlbkbnilPIt4jxkNVeXns3f61TeqUiyiyVFAtDyHJ0rIWEikZcM+nhe1kPdxDY8JUCkxBIVXGGjJAfaEe0HUABHx9LuMUQdx2FW/LDVmzAZLmmpIjAhcN6+Qiyq5u4FzA6q1Such9UgZzAVoTMNFqCD3Q1LK5BFmZ81QWoGC3RPHIE2XbdgmBPWi9HhpaiCsXBCBYXVdsxFRXT5JcA4I2eZ9cNwUFdT8OsS2U9uNg/X19gXZ9PgAEtE2YtnYB5UcC3vIFpWRsLIm8ff7BM+NZMvqGlsYYvYDTaf5FWr5tErqKMfrImID3MjUYFIwypfH5ASikDMIVltAX9eWurHTcUKdNx2gTBmd7ISzBdu1y8uLPfJzQWnd87tBrrGFSzkXqfXnUxy0XtBd/JrotgGs2hjRtGPN4aNBxFnIxsGDtj+fRhCjDabGv+0ZKkyzpWJmsPXn2vtuvN19/Q74n+ljAwScAGGiJxogbnjx7H1/65s/jt7/4Vbz12mecaieakad8ztOEJ644LBMK70pzFMRDDyeCgYnyNBRtCo2vzzu6rl3Jtat4P9wa2qZtsH0/W4NlT1LWWGImjV9nFxi+vgBdHoGvF9DlsXi/5YULkVOoyhq44nAvY2V8TgyOGcQhuUhjv3VcnsRm7HScUlZhVWDgCWKqDNAEGHDKEv7QZWYX0C4jOSYZ+RhaS27ESMlCBZt4OtQEFOwAwfVF93zMSA3UfHy4hALIDFPdxGsrBcgXcA0A4fJY+iYXoNjRzA1gBQQKGFzxo56Ct8Hgr0DBat4fhRJMYZkC61+M4EA3miIoqHUAuHDDZqbCQaWBgi4/zQxLkB+RmeCBmuG5MRYOpMulM2112wOEps/MV6A8dhB3yRdcsuTorDLiMfUuoQODef6Zv26eNw3GNnjipl/AXaYO5AlAlykK84Wy5n4sQlP3yHtMjo65AxYuCIDAdZvJjALptkmoh6uEmo70GgDRZxqGSxdld67PHVSjqG578RwoBXx5rI7RdR2im8DRkoEL79kdS/TvjorJT9v6nJ7muV335MNv40v/7G+J/Xr9s30crT4z0HtgecWBwV5xCSh4ii998xfw2z/xT/HWD//o1OnxB11p0nQ8C9Xr4nHz8wIl698vDOMqM9qQvSHoycMZAEGIU7etol2rCMxVBKg1Vop0qp8KD6Uk+zKUglQy0qV2gFA3yTPYrkD9lAjUdpXPzEO91K48pox5fcCuT5Zep1O33JOM5uTJ0McdiZOvVTbKMFlyWFM6MJUp7n7VzH2JH8qxzjF2uPfYYi7IMkaqIMAAAZoZiheijK4vOiAIISA0ZYFsnKZiCjtdlBJVBcitArkKm2PAFhBmQOcWlYv0aWqu2M0wO6ND+2f2Tub9mA3fn/z2njIrML9/U2cnOcOwfNYKsAQjuAwbKC0d83FmT7RdLZywHg9KJH2rYTXOBVQfO0CgKkCELo8EQLeiAKFoIu9lmaMj848QY9fAUS4ST7kDG9aeeE9udU/7hjxJOMroc30tRZ6syXnQZNCeA3JD3ueVH1P+gMnHwBAsdFvbKnjbul4DlrrN9VpKoEsGPb8iXfLg/JhuYwMI2wYqpYfoyqPebmMN6gZK3RHqSyIDMIrhmLlP4ueLsrMt07x/8uG38aU/+Ap++wu/qfar2xVCAAfDyD6MNXjFgYGWGJN59n4HBa99ZjLaq2I04tixpHHI8TnxHmtqdU3JjrG/Q2UWPZxoWOqG+vyFCw1aQzVUvVVwbWirU40ApJRAWamwrSKXjLZ1gJAfP5K65gJmFiFRL4Quj0SAZgqbEtA6FXk0HrEvhqz8mHE8J4MdUdTSGE8M45CcRwH5k66ymOOHPQNed7cjrBXcLh9ijJHaKgNs106JGluwvXCGgDfzgurOA9o1Txkdbg2pZB8PUQLwV7/eqmx/l4vYX8/+Z3jiH/Gx8pq8lOXnY0UVdKT9dXOy46pEkBBpUT6p2wnAfAgo6K/3jwdtAqCR1eC3Bi6aR7JdQZsCjrqpgalAy84g7HJ0LNHt1vxzZ+IgD2nK4mdjo2KC69HKCxsr9bIRciqw9ZVLDrwtzu7e8lred7I+j4sxBSYjNkaTbuNtQ90q4GzBuW5rOYFKBrluq0vdBgVMOwZuuw4Ji6bfWBk4tKYAm5Z9IeGVBZNyBgyOEsUBPPneB/jSt/42fufzvyHhb2awbeU+3/MjsAafDGAAAMx4+idPJHwQQUH04vW69e+nv9sKGPT7LGOuR7HZSWCaJUCtEgoPqLVZcAZA0JrH4Tic80o5oaUGbEAqBcSy1THZxvQAgBdIpQJFjrnlMGm5NfGI9L3FuMUgG2OwEgCLlXU609tsCXimvBbejdfB2hHYCb7KsjFcX3iikSk0iR9ed8l5PaFIww/AWnBDIp57PTNta0b/Tg/IQj0DHRpLSmBU2f1Pw9YSb05gbDImKfs8YgWsS3AAIQc4AbvYvjx8MVYIc3ghG3ofpiR1sH6LAMGvDXHXU0NioCCAltmAxTo9BBTUvhkTM4+sTQAFZ2PCiYENDtYoVZGR1sSYtEdArT4ewrBdgwdaO5OlOTpkbJuuBjief+iOhAGEs+RKS6pUefIkV73fUpZ0rFiz+h0olIvcx/JbnKEL7OCBvA+yPtWr67Pg8ChQcN324rp3djZjdXin1wCIbmsJqTZxfloTcOBXBt2mYVdcAgOnTKXrNw23OAtHCaAtOEAxt2oKLQBjOPrMYK9sC4An3/sOfvZbv4jf+cKv463XflTmge6W4OAA7YA1eFj5xACDnqjxVbz1wz+6z7gGdsrnbNkVxcE78qYi9RqUl997ivc1Q63zspyIpO27B4ICrh0c9NLAVQHCtiHZdCgAN/LcBADIOYuSA8CbKA/3VFsTZZeyCFdK4NopyF3RPmjGAARQYMzAMktf8wx2S6JMACyJsmosUKlby94fgMsQP5wSrIDjei9Znu6ZeRb17AEFlsDyPzzkI0si1rQ14HFTrqx22M5S6Al7qLbxDokHNFe9VsmO5AZi2if+xaV/RwolMgLcrHbeV8Oo3NJJ3r+0/+yeOKy934Fs+dtzUubSqsynWruBBBSAGngOz1iNixoIRkKDJktCAHW6aB3KozXDtl19lc/pHDyRm7M56IZ3Xm1hbMFDZSlQ5hbDJ8tzyWWUdeCw3ktZX+V5GFDjNuo2HYcICiIgiLqNW4Us8ZQwVIS/FbpDaqhenIns/RuoedNvrYLLBZbDQ5qDYaFTpiTjEECC9ylRD0eH3LXVXKcFMPjj738XP/uHv4zf+fF/grd+6EeCrArSd3Bgjs1d5Yglf6WBQTfIT57Jkg5JNOyJGuPl0/rrlXcUv1/RPUuAMIGBoMwMEPBMn8+goG5dkbHG3FWRxRh8mw1/AAUPLa0xcmtgJnAjcNWlYiTAxT1RhOmVmsa4VVB4QshD/x2HClZKzDPF7behMCDGU70vEIliYxYFWS7AJnFfTpqQWAuGzYJMkI9o0d0Yhg1xHhDyeQgomAt5TsjDPAFRZDcoxV1CYHwwLa5Z0NyulI4Vzg4xHIUYbnk7h6G/WK2HzXtKeix6JvDaaRvu7Z5ZbmhXIF2Adt10QzSZKzuGLawiGZeZTkl+UqH4QPlopUNsUybPlaiD/hDm4npTjgBIXoHWV3Y/Fc+YVB4lLJVE1mMiqy9T9s48lPeBvYigwFjCZqsNTnTbHUX0nuaohLrtdFsTtsvycjrQVvatXMDb1mduyqqDOzvKTZe5q44cQIKtd/fxbOdz3cKxWp58/1/gZ//o7+LrP/aP8dYP/4iM63C/2Gh1dmM4j3xG3t13rzAwkPLk2VO8/fvvyJLE1z8bPPjAFkRQsGIPVkooCtUANA5yCvyzCRBEJG2gYDKQTnueKbqUkFJDbUIzCywWoaCkEzcDK8qNEvVsXstI1s+IgjdwVFqV3zeAt2tPWlSAoBf1a2OfGiiwdk8sgWeLW597rHWmlpN/zinJ/WvtACFm6qZrz0WoG4YEqyx7VPIkrH5IS9id0FmNyTNbxq+jsjtIaLPPvb9DhnXMrqacNXO6dK8u9+1dV/emnI+VCRCYkgPDzQtl5l5LeOYdBntp9JefrXIVQojhMLRRpY80Bj2UlEX+ssqlGmtTm6QGKBVAjgbSudHMM53GKBT7zkC0h3peYEwSNYpaAQJfX/T9KTR+D9w5B1e5A2eA4EyGUoIokMAAKeshopM9lCAnYxZltHKXd/s7+uQxDBjqODA2iyK7TDI48aDbWA09DVzAgV7TdlFOO912WJoe5mXhOdVpAgY7YmRsHQAZQND8HfJl72FVTXSn/G0KchQAuK56e/Kn38XP/Oe/gq//zV9TUCBznwwc2O5gPIYO5Ptb4YTj715pYGCg4L2ffHdY0kFHymsGBWex1cgITJ+tQcY+rr4DBW4sF4aDSJeqmXemmdEpuRIDiuIBBpqcvDYn6FAZDUeKQhMzeUuRBCv9jLJtlpPOhQqAraH3GPyu7/rnww5zB6ED7xNuQ9/ExDBPrE+QPpoBQrl0oVIhR0oe47UEK45tC4bJR3lmOuIubOr9wLK/nQUKyngqlOUwYXlcBwIAEJeRDpnwtn21gYOcJRve9jUIu1dC45/OigC7mOgACo7yAmaKknmtV07Cb7t+3X1+pKjm61tgL7piFi+9AtAtn62d5SK3tyqG91BwaMxXoqQeeHKwJkmI+os61oXyQZ25ia2iBmDrSaLmKOj8g2bBo24vPweP2LajvTEMwET5oQ5+fLsIq0etQIaDg+4ItLEvja2bZT44AaclJTFwrAC9MShL6CvqtpQJaUo+nPWa3O5ct3WwPc37FPf5CMXGLOq1Pv00LJv17J2qPZNkTqpjsmOD/Dp0U6L99eT738XP/PO/h6//z3+tr54zILEKQXA48O8jllcWGPg2kT/1Lt56/Y37FBZwDgpuMQP+1cw68A7tnxYDACaExgDoPQhwS5hzRrtuoJSEns4ECvS0LenxaR/jp/NyRTX6UWhSyUsP1XYT8/ij1nvwpM4SqF62BO9tvG3ryi2lrpgBSaLark6LumCFJY5D3RdeN4A9mDFA0PoyMAcFC+ULBOOfADYPayGFAxjQeC9pTNeM3byjG+yVqG9pTUnWY8c46AwKbiVKrcqhAnpIjPPOYtjE+zKplQ1/GzhIEEPSKlDUow1GlTS7XvpR6GLKGbzp+nxWBsGW9ekGOs70HGjMPq5jvzE3EMyIdgCCVvseG85kfbQ5eBh+uwEKhnIL9N8qK6MV2B3JzcBOv1EpEr4JDE4C3Pnh1tDOdNtCr9nzZt1GlJAuqt8uZZQtGxPLsZj023nbm4MDAAoQoseCYentYQIuNzz5038poOBv/MOBKeihgY9Wnjx7evr9KwkMnjx70veOfv1zH90YAQ9jCA6uP0yIArqQJKWyUUDB0/Ckr+0q8bxWneJMutwmlVGJcWXFE0Zx7vvBBMXfx81ATFm5gbkIki526qJ6sFGpxf0LVsrNkXbeexDerizdlwGqulsf0NlCk6c7sZ50H8ueA5Pnw3pj4qTgPQFGF5oiWISNTpVx7F81VmgAldSXZQJYGVFTKt6nlEYwEPt/2h45sgQ9OUoZg7DufAcIZjBwMwnwHsW0GpyX/R2kn522VZmzPRm4iRZmkRZfYdIk4ZIMNJjhJ3IjQBoGoryBbU07s6wkYM1W5zYY1zM59l1EgT5+QzPCPKybxu6p0/MPnYPAXfNwV8/ZuMzJgyrLZPk6lBDX7u/KPXKfC8Dc9Ru1/lzTbySHEImXL3otF+n7uGlb1G1Hei22c8e8mbNj8mW7Ik76zc5bcBk76wMtXGuXY9Pg0duXiokuCKEIK0/+9Lv4mX/+9/H1v6Hhg4+tSF3swKVP49OHV75ywGA8UOLNpXfez32fvojU5NH9//T/ebsSh7RxFqViCioluTTKqAoQJe7ecaTRc/FEIFdi6hWQXuPewB3KbDZE0g0LYTG6+kxYgO6F+wM6FSrfB4+fSNqpdKgpCnlPbgyImyQDJaPw9B55uveRsgN21CArALEVDH0qhB3gQnx6yAs4We7lfSC3ArIt4wu0bQkMx6q+1v+zYrbxmMFAAAkUkin5FkNAhGGviTmxcO6zBy1/elmvRufgzrCNcVhPbGWlmlcAIassWwy4qOzkInNMc0QcHFTZOZImBgiT503AjrU6HM/J0M7F56GtlgjxeYqhQ79+mofGRM7LDimc8qf5PwDOZSawFUeAYFi+uDKUR7IfAQ6RRKasX20fgajfShn7XgHCx6bbZhlbgYEzhyfI6rwio4OCqdzp7Qso+AdrUBBDf8PndJd8xlMYf/V3f/XwulcKGPRGf+3OoyeTKmyLT7YODmIcU9Hsk+9/Bz/zz//+8e12y7kAKD62QeuTJiuy7NcyNUXVAQQE4acQTyRursTkx91joHkpUqvn+agq1G48H6oQggK8tWzJ2wl0kHAHQJA950PuQcZNr2hok7XrqAQm4zCbfVjetnh2zt73cu5EuCb+NuVhPB7U7wYE9PO4bAq6h/vdYOAACLiCiWN4lJT4cZYAqC3xbf6OPIjagQKjMwerDcSYG5BKBwmpyJkWlgRcNwfVdnLpPVS9VDGE94DdOFu5OQ9N1zx0Hq5KzkCtsKWrzHzq4Q71i3NQvvh45T8HcBP7vFwm/fZI5H/q949dt+k1y/aF9st1x+2MYGA00OH9TSZOioCCz4zXrtg9//s+WexO8+2jmV8ZYDCeR/3G+iJSgw/JbBXW4AY4sPt//7v4mX/+7+Hrf+PX8Ne/+SW40kUnHoYNXlbLuRAnDWvs137bzyVbnQtgf0dvJYIGT0IK3w/PPVnRsBPmOdZ5pAAohXia1f5s0mse7SK7elASgFCOU2KVJ10VdErylpIc2jkpx1lAbyjP3W+Zwz2z1KUEluLs+bNSkgsOFW/cOGpeXtlSGf6ecwaWQOAIBCxAAR8BhENwcHsO7D/W2BEwAIKeV6CrDOL3SreRJUKy7QKHPVDQ61nBgtyLQXnTPU0aEFYMSchn63Nykegnt2wDg9Sbcyvj/sZcPCsxDGcOR5iHwDhmt+48gAC9/10es3nLD9QDxs+tVlmITm67fpeP20633aXX5A+te9BfR8Y/XH8vAJjg4f659hz/7Qyg+ndv/fBnRkBg1x2BAmMLXP73cvkQUAC8QsCgg4I3sVI+TklboQNwYDS/bRbBjCff/wA/81/8imaH/oj/fqbjePgsKLWzTSdMOOKcSuOwjBstRaU55i+4UNlnQWjm72I/jH/uBWEn7MGINFoI36qEfAxX7vaMaWc0DoqXgrdGCwVBd4KfW+VoSeZwz1te17Sx0JFiWiqlhaKNAMByBPi/CwCwNP6zEty/X/29L7QHTIDLCA2PYmUDIjgwo9KBhNPLU9IvMQcgsQYMjMd7duGi1xg7MAOGGcjO4DwaOGvJxz03Uzo3iotNrg7veY9DoNct5yfMWIX38b6rwiEhVJeOjn3WwYM5SrQAAw/Wa+G7B3n5Q63mZ90Gdce7Hh6wc2eAwJ85gYKD5+3C63eUVwYYnCIho6MBOGughsnBgaSywBkFNfJPv/eBrCP9sX80Hrik1O1uBULY5OVmZrf84MbXB5Qcpkl62X0y1uueQmm6Q4i9L43GA7wcA0CLJZyR4gVMAfCgFCJgiKxJ9NYo/h0ffY9SPrjm5sZAVmI+w6xc7f0N49+Nez/z/TgEEAHBR/D6p+tc0YdPeHoF0M+bn6bcPbNt16PWRTR/RAegQQEDMDINwOC1D3N/Ag8cwEP/TQ9DkLMLCiD8fTz5c5qnxmpFdkufOYBaKx8BLAAAXW6r7zWo2DME8roHAAD28xSYQlYYvtt5uzfKPmwUGB5grds+kl6T+q6N/LG+PS73JrcfyN0Rc3kDEMh3U79Pci5L9n/uQaAAeIWAwT2NNuXoCoYNBOh7Io1hCkB48v3v4mf/6O/gd378n+DN1z6j15gQZf+NgIzJm5xj0AcJI/39fpLc3Fv7HsH7KCsyTu9/ZDAXQrTz5EbvTd7y8Dcg3hvlfi2F7wYaMtx7ULz2PGBUwkfrrIdm3FpzvQ4L3PSuVt5/6gafydiEY4owemdL7yM27bwVy2vOAICPwKDHA3N1x/PiDJEcEv3cutG/1L8XgKH/fqbkQ732H2H+VozSfo5xMP7z8eiIwMCSfU9YhiWoXYQfHjTnegccX7NiBRDAwuQ5RwYgeqrNDdP03ZFXG5+31CEP0B1W/tz02Mn9AyA4X45+9PsH2IDJoA/9ODGAS0cg6gYtvo/PneGDWF4ZYHCzWGcyrwGCev8GEp4++w5+9lu/hN/5wm/KgRUA4ha/cnBN1EJ3TNyzGNPkBco1KxS+nwDrCXcgfA9B2sBd7dqdKXEkMAMgCF6afxaAQ/TsXBCjJ6Hvywg2aAIX/hkwZi8v8jeGcubNTQo3KtoVCGBTqoGe3QEA+47GpYWsrfDemj11fuDCwDMdeeD99+7n4bIhwDVdc1YsEYyo/zDpjUnva4CBSNp8xDLc0w4rYz9Rf64xDjpsBkQo/AO64bc52cMIYuh9C2TLRwDrAT11z4LF+Rfn3r3zDuiGfZHwdxjzXxjxrksOvP45XDXd55S9iveaPx+efVIewkwuntEfduK0IDosi9DUrIMWZ+zc3FJ/V8+1E8ip7MCAXHO/PXBQ8FPvPhgUAJ8EYGAdFkMAWtjPplcB02uePPsAX/rWV/DbP/FP8eZrn9GkxKiegd1ZAPEZq+cPz54EaUJ+nUbuArs0EBzeYz8X10o6KI+FwO0ckOHL8PnOixvfh17Wly5s0p/HQuj9vQIP4Tc7AGHXTOEK8woj+2DfLxOhYpnyUvzt7GXpmLl3RR0EiOFXYbdDcwZwINueMNSZdAabPbh1r+EdjK6WoPZ31x2VFQuwAgpLdiH+aKhcv0Gifg2hGzAnXzyXoD97qPPSQTuu81zvsxL70PxkIgJRRqIsQwrrVx5AgR9oxP0ETgcLXDsLZmzEiv0CDucdcGD0J4PRvfzF9wuP89jTvz9ktctRoXHmzZh2fn80f+ZjEtbO+T1jGleJyGtkpxxfhX9WEQeDFvaMOsTyU6S2+0rekQMxgqd83N93OIYfhSmw8uoDAytHKNKVVQNTlk795i/stlHmySjJ4GGcAKskwwGI3AIBIwAw42+GYTYU/vckcZbWcw/5FqdUcq8ifBa9p1l5T/cQ5Tl+Zx8kixNPjxin9AgOxkQz7EDEECf2z0cGYsk+zKyCg5EmyyJXVCEwzKHTZYEpnwIBViDQmi3VZlStcmMO4y7je8ugjePCarR6Rycax0g+Dp76opwZ1JeecwEICAsQJoOCBQMCM1AAOlg4quthPUMd+eDaucQ+9f7Uvkw6z7Nel6lAh1j69QwotDqFzTRkFnJspGIHc49URwyGfGIDVl7+gpKO/fyyxt76efV6FoY6Yp9WoHMFGMIG0Q/Sc7OOM9kw3TUCwTju2eeCA4YYUlqBBa/gjRpOY8GWOBr6/DwxuJeeU/DyoAD4JAGDtQnq3xooCJ3Kltg0GChF67qL1+3HLgTtBAg07oJjxoKZZcfPyVhU5tFBRlDoPLzc7hE1KlHsI5uYcKAs9WIRMO2nBUDYGaaFRwuYBZM+Hhj7RZ1NQAVEtHGcPGms7YHDDBrsWrvmVrhliPFTN/rKFLAv77KwgJwBXxUINB3PxiwMAQQMxPH0seQ7xpC6kbdxI2Ifs9G4dW+dsPDEQ5k98KjoZyCwMrSxF6MKI1eENl52k45gNAPD70vkh9Yu6zgYkUlOmDmA6b2crPtX66b/WX3M7mYiAQkg2SeHCJnstSArUGCunncgQKHBj0tmy1n4CHMv5qb4dwv6OToi8bfayoXtXXvzPH5xC0DGa2ZAsAJt8R4jU7a/X6zHXXou6Liuj/p4znKSqcuPgUAawGEOeqzrkb7yasEeHFYyKsT8IEBm5emzeUniPZBpXT4BwOAY2cby5NlTfPn33sbXfvo9vPnpN+UayoPi5ZBTwCnuQTAK85LmCUAAkMkdGYHWOhvQWgcCFTyAgMbdYGyNFUAwuPVrADntmycOruqXOUxCO4HMuA4XkgTYNjxEhJxIaj+gah5AQ2cDeAca9C1c4U7GaGWbZjw8gAKi4TUh755l/zgKrdK7cYlaBAtAGM+VQLvCDYqY4iFMZc8KNAEBWwuAQMe0NTgArI2xcRvG8igd7Wi8iAhF3aAEIIUxaqr43FvX/tpt0oNjoxABQVT0s8eH8DfR2A5nK4LRJ9J7a93sfjaezHxevxMw8BCZWZU89XOhhKxDn8GySagBBQUIJZG+z0g5w6RfwMHmgMDYBAcKq04ExnkH7EBABACdrdo7H0N/RbyMPRCMZQVX9mO9//0ZY3MI2PR9DWMF4HC8op5b6TjpLrohM9jJjIAFRgbhSkAmRkrk+s/eQ8c9GYtsPbAYz3tsRc8xkpuvQNtcnqr9ek/tl/32/FfH5RUFBnsrswK8Np/ef/YU7/z+23j3p97DZ19/ExvPxqwvm/I7p+J3YixCCIu4WkTJXWnxCAzaCAbMcJhSM8OxNWEUtto6u6CGh4MCv3WOuUzu7sVn6kIkNCmFv0VYSqIAFrD0qByZTwzBCu+mxXgdliB4abrnOR2Y9WDI0pmGmEXOkyCfofxDRazMgCrdGoDA1gQM2GsL47lVxsbHY7l4/HKcZFyAkgkZhJKTLL5lqDPOSCzXM7Hg3eClA33eH9G3Znilm/ZeXbi8/zx8YCyAgQUiCxGIAjSAkBSED6zDQd1uAQLWvq1gbFVkS0Aav3R/l5xQtK8LJZRkniSjJMK1cQcHDcj2PhWkVCD5BkdA9aDQFHc2FmBKVnWno52HIeNY3cwPOShtcXE0/vLKA3A8AwF9XBDG5XicGvguHQeIHjgaywwBcwb8SiJcSTR7SoxMhEpAbrqICAoiIKCgqd7p7KkwCkB0esIy210JmlF/F20HsM+1sPL+s6f4ud9/G+/+9Ht4Q0EBD3d8gH7V8ooCg7FEMODGWf94/9lTvPONt/FbPymgYLk+W/s1Jty1+MXJc4E9GDAhrfrejIcAA3bDYsbDBKb/awoKWMGDGh1unrzW3JPrDbGDB2NJIEW7+neSz0oiBw3d2IyCtAQKYUZGjiSyn8c9NX16IAirMARRPwOdsKYDTYgpIHzKWZmFDgyYeZ/XADiKn8NBBgYQAJuNYzVQ0OSarTGutQ1jKa/A1pqPnSi8fdt9i4QwRjl4qSUlMXwknpYpOQAyNqkbYkxGGLRQPneAghkQnJHhPv3Cc5qCA9nfH71eoEHD3QIrVqfWupxH8GWAwPrcxqfpWHQwve73KCslCRiwPpdXwiXL55mAaxJwkImQm3ibZoBAGVmBqnmXd887/YwDGxDDkG0Ym30YEuG7ebzukblbZQUU59DNERCI4NjGzIy/jZFvYRJkJZZZz5njYWOX1PjLe0Lemus3GUv28dyIUJhQNQTXSOYlQQB3pg4cLSw3gwR9i1v2wuuPfX6GfT6XD9R+vftT7+Fzr7+506QPhwRSXnlgMOuSaKTff/YUP/+Nt/HVL76Hz772xoA8405YtLjZ4kCv5UPPEPstQDB7kxuzg4GtsnuiUWDMuFRtiwGEGtqWA8JJatCNBk1EEzgQIbpWdCNUm4OBkhO2Fqi3QM/Zc8j6hKYBudF39zgu3pJdnH2kAwnWPt4ljw1AASLoI+Le18mAHTgAPEg/G+uzNdYwAhwMPG+M61YHMBBBn42jhBvGHpjHJxGhZPVwMomhQ0NlGRdUFhenJfVe1XDQnYcjB+M7fDwxBXbpKvY7l7hXgROdPIIDv3YBDs6KyxbulyH5fgRj9/R9Sc2NhxgSMfiXkvE4gITaGNkAQgNqEgOYAVQFdNCQw73zbgYCq+RV0yvGPO489XDTB8nZ7o+DMt07hgXmUJmBgRmsRWdnJRum5+JYrfRc0jlleq6khJSa67iSCGkjAQfEygKtWCHyeZoJ4KRhOYIQb0TOyu1Awj19Z/LW1qG5OVH22x++j1/4Zndqza7FZOM7RWdXXnlgAIzCZJ339E+e4he++Ta++sWv4bOvv7GgP/snqyVTta1R9DyQ0YuaEfwqZGC058wQPF8osy40DVcFCrUxrpV3wKBNblBSSB2BQU7klOclm+EhJHQEbZ9tDSiJsTGjKJW9JbFDGwn1tvGYp3A4PlGwtcfMYaqTMAy0dLilgRJgjLVfNQkvkYCFVdyQVEETRnZBHjJXds38NB4BgTEEK0DwojFebPsxNgBhYzcruU5HdwXXmNBSQ2PNLUBSq9OQs5zeyYlhOQ/ECnwmI7wsCuQk5r82HzsP8wDYDeA6PJb52BtdrZI5Kk6N2/joZ8bQR1Bw3ckXD4Da5GhlZLIa/M4adJDwqCQ8aoyrAQQNJ1xyQmuMlOQw9aYAQTxQaV6N824x54BzxnHWJ2xOhv4+5lYAcOMcx8HKPB4Wr4/y1a+9LdcrMGAhgo0ZrCC5Ye3w2GdRx9W2BwZnOq6PHSGn5jpOQIKCa5bXysIUPGYGKwPHqYG5h+eYJXyaiYSH4c5Qso2lIrFoN3Aw32P/b9FBPejbb3/4FH/rm1/GV7/4NWEKHFx3cAD8gDFYlqNOHUHBm4eJU8Cetnb6jblnUx88c4y/9hwCo/SMJXBhCaDgeeO7lNlVvzNA0Jhx3drAFMzeDwAkPeGwGxsRJFN6jZPEzli+byA0TiiZ0bZuhIjZTzoklhgoJT1TXeUFACjt6xDZ0qOkIlNeI3jopScUqfDTcU5ESUIJEqa4IYCNLEaMARisdPSOBVJAYOMp3qkAuOvGeF6bjh/woja80LG01zh+Nl4rJWegQJQboWWgsYIBiAKszMhNvJkKgFiM0Vx2Cj3QnlaaGfEIDkguTszu/R/Rz/cUCv29W0Gh9ZK2TXUDBmHzUzinUt2bZp9Pzia89Bg0NzASOiA8UuZsy0nfb9hywuOcekgHIhdGR2cALRkgDf0wtWF2LuKS1tm5MAcohiBXBhmIhruXyCTNMoUm31/R2Tir3Wo1eAT38/PncIExZwIE4ExPBAQvtg6eW2ue1Ds7QWH0XWYAkY9LSQ4KrpVwyYxLJrSEJcg2Bq66RmgoOaGSDAgrGECC5+ykZLrPnCIMS21v2Q3TMf73dP0HHz7FV/7Zl/Gbar+6bqQlEngZ1uCVBgbAni148mwPCkzJL3+vH8/Lpc7iqBEMWB06W8FKAR6Dgm0CBRanjv9eqLGJwmKAoCs2oVBXyTmWkFNIExCJcMmCukvOaCzKD0h+hLtxoSUzGhO21lBSwlYZyISswi6XETJ3BmCV8n0EBExp9M/G61fe61ECZUkJlAglgISSE6jC44abMwmqGA0gHI1vGE8DBA04DBsY2/Nia3hRzRgJqPuzax2U3bV2QGclJwJqdcUGCEVtMdNN3ztrQeSC7d4ezPj2V0KPu9o1sWQalVTiDg5W4mIrH2x4lv03U6vxqwgKAiBY3SeRPoCl3aCeZGlek7V/MwOosmCGdQvswEqOgHkc5L0YFQHRL7aGT10yGrMyOGKgHpWEyhYvz3isHidy0lAOe6gkaz6M2t5lmefcjhkQ59R1iHjjUG+8HSbu7fo1yBEgRm8LoGCjnpC8cU+lWxk7r/sdoKAxdqBga8aANgdsW62oTeUl6DjgWM9FHdfYZCVpqKeicULL0vrxtxziPg15AQ6yXoKmDByx00Bxye1qX45VPwEI+m7fng8+fIq//Qfv4De+8C4++5rYrwgmLdn4o7IGrywwWM3TnlOwBgXxNxY78vvpNT4Ai0m4er4JMzCCAnu2rTioTWi+2cNh9T7tt558GOi0qMzEwHRAYPWMtGEmUmqTsCVCaQwkuY9MM5EGCS8I7S5CSGipvwqTwAO1uOsHB1b7z44Awby6wtpgStf6MpZ9gpElTtYpsUjaG7OPRe/JsrMGEXBPxly0bbWXRGNNLrwTFDzfBNRdqyi70VMd22asDZIAtysg9DSzMgahHxJ5wmUGNHmd3JnI2jdZPzDDG5ebxnES+9vzYwwcJKGVnDWAGiYHB/qZF+/P/mf0klOiARTEegF7hWpym5TB6A9X5kn7gBJreEvix1Xr3fu2e+AGzmZwbWXTPJvaCDmxg2hAjEvXpglAw6OS8LwCQAWKmVAxKuJtiozawhARw/V8s7qeJVjaaqU5QTnK0UNkCBCAnzRsyiTzydjBTCT9CTGQ8zjdwyT1toX8gRbqyD10cAQKbum5lgiJgUIiL5eccN0EglkeSCLGpifsml7bKiNlEj0cGDgCQI2VgZS2W+5BM5DH5Iyb5PaM+3Kc9sniosbAv/jeU/ziH7yDX//Cu4EpCHkMPvH399Rv7i6vLDAARr30/p9I9uZXv/gePqc5BTMomG39yvabwNwaX/9tAAUOFuzZ4ZIYD43rcodVBTdmVV1VGPs4fWV2T7I1xpYIj260xxOv9NU/hyZQaXHaUcutMPYZKDjK0rdmjv1hHrR8FjOPqykxNM2NEEAQ44YEoT4bxFNg1uQhpU39KQHNm4L25aRzGKh1UOCJcMoKrECBsET7PjLghrbof4KOi34HDaU4AyIsSdY+MVDgS64SHYdN9INGpCBBzxJpHRwQAwwBCKzj5n5VuOERIJjDB1avOddjWTfSvInW64Uknpq5TFyp57yokfMk1ASkyUWvDTvGbbyAUWx5h3wAcSmbxrKb3jcBm4CDrQHkoBsAGkhj1ZkBpL6M9J75ZrqKsc5HOluxBIwA28dHwwJJmQJPcGXBNIlJ2UBWNrCzg1HeY/GwuoEGTcA0BicTwNzzV1KS56RkhvVc381s6JmeGz5vYvCPSjMDC9O/8l4AAg15O9Jr8Bh/P6O3J84643anZV6p8e98+BS/+K138Ouffxc/+tqbXisDB1FAjDWwzx4KCoBXFBjw9P7Jn/QliZ97/U3Ycp54zWzIj4r97gZhMNxrXlJ1VEjjVBbPA1Q4RfuqUbDlZf0+liPQWH4XwUQ64Cd9ba9RbRqLs1yDmOwWjU4ihGxeWZolVP1+zwNgv9EI0KnQDPFI5iUe5uEAIyiIIAHAYllZVGiERqakpb7MCXK8RY8bCkMTlvWRAARb9w8Ac3pEZ5p0FNiWKSrIaeyhIgkzqKcW28DmDa3zQIzRicXHJ46JjYuCg5IgGdWJ8DgLXZoVPBQFBVlZhMOES/QpwyxeElMHCM7Zs4Rj5pwD84zmlQ82FWZQENmLFOoyMxlz/axuKcmKDErmugGbaWSNgzHLmHOSRDebF96HNMpRq+fj0WSSukw2NWSNJbmwUffSKxGKzotqfa4ep7AGfb4ljF5lHIdmRoaP98CYV7usZGe9HNPmurJTZl103kKN9mxhVhsHzaWCUbQNNRGK5iZtCSid6pFxUW9bxoNdL0n95TrTc4XEqfGxOdFz/nfUc7T/LoXPTdfFthKt8ymsREM9r6q5y2YAOxv0ne91UPCZ194E0Bltq90cUlhd8xCA8EoCg1ie2uZFP/kePvvpNx+UKOVINnbnSw4uYIpRlGhFj/ta1mo2fcuSDVvRXPEVTh7jb9Tj/10ahNL0SR0U9baAqhZ3A6AJhyIsJWdPbrvk5Jm7fWnWCArs/ZzwF3dOjEWUBHmWsrRZvRFmbNZPszGeFFukGq0kIrRqXrQqGO4sgolPZRZmtzYxHGFZX07kcXLL4G9TMyIoWGXASzvn+h9PnJy6ISoknpABNkDe2xhJgqhkwF+yrBApifBIE0cdEBTdb0LBgoG8kvrWveTGWJ4Tm+m+LQPg0TMnosFLb8SSdGrsgY5hbHI07kcswfGucmf1E6OR1HCq0w5Kmt/SAM4JFzVAzIyLgoWWmibVUjA6CalJUuEVTdgadBkqoRJ5IVdWotcJyHww4zivFBEaWH93a64xfIfM1TJMY9o8q/9BckMOBgDCbP8c7MOA5V7m5fuxEQXkKxOIxesmyN4Om0tQ1GVeM3ST15Ao4VoJMakw1QZkcuZzLlGGJI8quZ6zRF7Tc4l0WaIuAU4OBETHZUDbTLo0cTTGcW7vyj1WeaEivv29J/jlb72DX//81xwU/HdR/sIBAyL6jwD8IoD/j370v2Xmb73MvZ4+e4p3fk82f3gjrD64pwwU/hlAOBrsYNwsozuOu08enTAlyRJASjGLPCGjSZxS3GsxYEiKbFnRr2ZUJ0JtjEvpMbTG7MrNSszSNYbAMnVfBhDExD7PZNaYcSwMmXC1MTix0/fSXYStNtmatLFTjD1haBXqCWNkrrt/MCq5BvGkAEmYtLjhTA9avLrH7cYxO7LvFs+2SZYBbD6KFrPl4OnDkwmvmxiiSsIcWFjHQF5UZpdM+NQlOzh4lCQL/pITHmfCp0rGpRA+lfMACooyQH2DHXTGwEdnaJHWQWl7BSsGEJA0vBDYgwgQ5I79nkeHEs370UdAMNZvXUerX2UgswAWyoTawm8a9ERCFyQAqc+nMvZ3IsJWKxJlzbvBkFXel4z2LPc4rnZN1BdDtn/q/TEX5nGeRVAA/2wMOw75SJEpWICCDgw64LKEvFhiOwB4yDDuXxJ3C1zuXRJKgbBLF63/ZuxJyijNNp1Ki709ZJ8ISfhk0XX6vrF8bqsTHmMfTrVxAuBjFVf2GJj2vQrCviCZCI8nPWhjZzJE6KGxWHyOxw+OykKnNDC+872n+OVvvYN/8vl38SOvvXFyg4+//IUDBlr+U2b+P73MD62P7eyDd39awgeDggc8xc7/ngzPvHZ7uW3vLQQYjL/djwijd6UeDpPuLshZUbROuJRBJB5BJkZhiZluLEvsNpY11Lt1vkUU37yUJyqAuH+BGJ+wV8ENQBAFRfaOX8eE5zgjAyBZ7IsCYEMTYasM5KT5Fd2L2FpDQcKWmlCZDHUPaWpX73YHDAoOEmi56+OqVKV4d0NJ/dWUt4yrMdYSz+5JSpLsximhcfOVHPaLxgk5sa9CaK25kuvj00GbKbNPXbIvj3tUOlPwqSJL4z5VjDnAOH6kLEQKXo6d+meHTnljlX6npEZbXiNAIFaqnERw5LsOEOT3vR871CVnASIzYGcMjIDA3OuTHQG1bomyrEhQgJ31nhsxiBjUGJ9CRnbZUoNPkLwDEBI15ET4s2sdQLYtjYvjAsCX93YjE2QjQ+UodfBjAA19O/EeMlnPs7kc5RHNxfMJFqDA2t3f7zfOijk6R85A3HqbdJCjBz3Lfta4fgOhBOaDG2FrFHalPAYIj0saVivUNuo5YK/rbu3TsgIE8yZHw4qm0M5k8hRYMCDYi1s2wq6ZhvW733vfQcFnXntzZ39iiECedxt7UHh98uzpaZX+ogKDj1TsQImv6d7RR7Jk1D4H4xKR+eke/tNXJ6yix5c83oTRu0pJ6VeLNWaZqFttyE3i3wIERoDQkgjMljTBqLRBCVi7V8rkbLfDuJHRSkjiTmAgOJ3oAhO8QytzEpXFTKnIxiIZ0lbzIqg2UGJQFerf2JLWRPFHcGAlej6xTcB9oMD6BRgVdgQ8Pp7esA4qjTUojG49jAqtAEpDqj2OuTX2NfGm4GKJyix6No/U+DwqCY9LwuPUAcGjLCzQJbAExcJEpMbWDvJRXtvOk+/t0/0o4tbPKTtA6KEW8dA9xo8AELRvYp9Zm6Rfe45D8rlo/a71ms6xWNXT60cEpIKcsoADIlDDEC4hYiRtg21V/DzLrncJArBftKbsXd/b4CFjE43MIwVol5xcdszjpKEvFnMMGFZ3RAC6nW67qvVLkPAAaCcrM0NwtNvpQ4xkegn5L0QoyFPOBIXwCN3Y6TUPuu5Iz9l4RV3XQc+4tfhpW8OYrQDBrb035tIW8gHEnIJF+IDG+x6GLk7Kk2dP8fbvvY1P49OH1/xFBQZ/j4h+DsB/A+A/YOb/73wBEf0SgF8CAPwP++dPtdHDKYn73wLMYWmV/M0I4OCgfOfDjrSGAbrRIE+CJfOFqWfXq0BZfoFl5VNJuHDYrEQVVi09rjjuGpZ2yXrAPr49JNcEL0F2OaQhoXAFBqJw7I8r3aNZACHBU9gNBny5X2sdIBgYykj6qklJkIzyTRMMt6pLJ/X+rYV2mUdn3g9FT6jHDc2L67aFDoUfGBXdnI1POsiZs4McW/dMiUGUUJjwAg2pJSQwSmZne2YQZwptxeBICIE0fJCG0MHFFZt6dUnDRWBQvSooiCf7qQdt50QoW+CgAAmckvwmyVnCtn2vyEoEBfI+6Ty2ue5T3zy4CRzEQ2lgxxQrKKDWIJQaL+sq4psAyiDawCmDUsYlX5AzITfCBqP2WRgEACVlYbs26a9HifCiMcomQNs2KzJjtBobMzZdbvr4RCPzOIeQnBrTFZieDalkuMsck/k9AlBLVCZWilvzkRJDAav2vrJszpwdyEnJ0TlYM4RzG3Y6IOiWtQ4gje6MOiAT4REyWlaQUAQUbKbrynqHxI9b11l4JG6KFp2fGPqadR6wb/NZGVnO/t4TDV/f5xTE56wOjbtVzD6+99Pv4Vd/91cPr/vvBRgQ0X8J4C8tvvoPAfwjAP8xZB79xwD+EwD/y/lCZv7PAPxnAEA/JNzlk9BoO2WqP7QDZ5s7powiOADWA5ZINpf4pW+943/HYYiDcg+C46BAM6gbTiZk9J0RG6DGgzQ+pxRcof0pi2Vc8mfPAcacibPTFGfhmL2CeR38fEjR0QSNy0IrkwOgwoSa+q6BRB0gyNI/GtiDpNcm2PItBViLzf+jMjjzCmbQE70fW18f+006LwA6BnynR4wghwgT20OjB5TGteVj/W3rY1Fmswf6OEloIRMGluBinuwMCOqmhvf4RD8fOyLYufC62BGsRwNzYiQ9Wlo6QBQ+G0AAJMQwtweTQrN/mJiMVjsgaFWAgIGZMKnJ6jmxBqAMtCrsQb4IO9Z07qqBvTZGooyagXKteJ4TLrXhUSLfoTKeinnP+Bx52I91vCIgWBqYUIZwjNCN3vySCGiab6QhOEmkhadRjKEEgu93McnJPTLyeNIF5UQPGOgb5lIo/cwYcrmpTEqqiWyLvstoRdocl2Qe6bpbeg4413WrkOhKD6ycn6O9NlZlmEYHoZ3f0H0Kou2xEp8Zda5/r5+tEEoEBW/N9nEq/70AA2b+6/dcR0S/DuCf3XNtBAUzU2BIG5iAgFTGP/PT3cKAmaf/wYeyucRvfOFd/Lv/1x8fqDh7G6nAI9AQL2APVvcEI1avmjV7N26hbOiaE4E5+/p5Bg4PKQH2GfJn55LPgmEKgACPA8e4cBec3u6VfNhJcI2BCwdwANVvJKs18gQQSm14PhnX6DnMm7bEMm92ZB7Q+tjcDgqGNp+wIKZ4DNAlSJ+nyILkJIpN2R7xRpuyPuPmM7v6T/U2z+1SCI9zBwQRDBSSHSwzALQNVK8dEChAiGzB8nx4PTUSrYGUk+aUBSAYkOC2Zw8a+p4Hi/Ggab4YMI8sgbAZ8X2TG7PmnZzVWVkDpAzwxQFCyhc8TkU266myOVFpjKsam0wZn2Lgea24bslj2ts0z47Gh6ifPnqvdx2XaALrudXU8ydlrmIdjsBBzpD4PMhBdKP75WNmCiOoGQBB0AV9lQIGWTnTA4DOFwUI4v1TOEwubBtvO0qiO0Yfm67Tzo8MzsyCxr017F57g7xGBXF5uqp2aTuCvdGP7drPKVOw2mphXr67qpeVCCgsvH4PKAD+AoYSiOgvM/O/0j//XQD/r3t+F0HB8c3lZQy5EcDcN6agccCAvg3lb/6E7Jgo9dR7jbfeocc5Nr2sENBzD+xPNnAwomsOCmMEDPIb6N8c7nVUVnF02/VvXkYWhX9Q6pYg1g4SxLypCUxCsSJlFEAVgYCCpp7DRowcAEKmhXFttpELHXoMMyuyyqY+UtxGEfeQwnoTIFdw2udZx2FmQTJNIaEmeRNbMY9HvcPJc7d9ISzWabHsFACBfD7lEURA0K5iYHdsgajQnQEnVemUwCkDnAFKnqDIGZILo33BCZL4ZxPKxuGAMTDD4Yp1AQocwLTWAYyBBPBBnSFAgAicirQ9F6BlUN3AuSDnC1IuyExysqEmf9bUAYJR2dfahm2FbROuOD4AdmMUKegZaB6xbGfzStgxkXnb56QygxqWoasKgGpD0f0aatBlR7IRtw+fQU0ObVgteTV9EMfTdcGBHgDQdYEfXa6OQ6PhtNLWxjNmmsp614kP13W97cdh0BULutPpaywwDiLRsBeFvHZ7A+p177ZjfeMZENi1Q70WP42g4NQ+hvIXDhgA+D8S0f8E0m//LYBfvudHhoRmXEx6oyE8APNU5IK+s5soyojsvv1hP7DizRDzmQ8bWU2a2RuYX3cV1cL+GtC1eqVmhBwk2LX+WYhXLeg1qeNYsVkYZg9gzhKn2pPCPF5tNVl4JtpJICRV3FnjpJIsVlQxbM3W8VtipSYVMQ/G1ZKUWP8eDStNj90b2HsAgSm9WUnsBo+7YLcGBTzQg4xoOGDpkkkUn/4uMj1AP1cinkhpDM5cz2ViYdtAWwAE9aoa9do98W0TA69e+DBalIQhoCTL+7gBqYFT8UYKGGCXKwMHRMm952n/rT7vfB6FuRRBQds8lGCgwEML3MA671pbGJ1Y9yLhBG4ZlC5AugLt4gDhki8oChAKyZ7/tcX5BjzO2c8wuWuMtHN8h0l0MHC0TwNwPqcSS98yMBzJ7vs4LfJztpCsXHaApj8oApuSOusR2bM8zTdZ549pdQsjJrMO+uBUF2jrVR8YSOCUcUnJdV+1jbQCWPg4dZ2NwUrOo/4+1d3j0Mkr9b9TrCc6aLZ25Om3OYCFwbEM9QQmhiC0L5b3QyL+vaAA+AsIDJj5nZf53Rk9Mg+mdZ53vgEEjGzB+8+eDEdbDvc8AQNxEukUkA/CDjgDHTopOVEyfUowJT3cxx7oIul3FyHpszHecSWfM8oclbbeJHpqrWpWuFHRSumuYtWLpW9DLDipF0pZEttSVpBQ0BKhNN3G2LzuRsGr00zmPJ4gB+y3RAX29GFMJpoBwRwmWVGjO8VAQRkk83qAloQW5QTfca+qlrCdH5kZLZMzPavxidSzrWaIytlDBjMgCOED4gberuC6aeV0DFsgXpOoJyYCygVcEygXoLBuStMAXKzJHRTYK7GMo85L6lNxLQ8hd2AJCiYw06oAGmzXQA+N9Wdlo7BJ3VO5gOkKzhdlEPYA4ZILKmS5nO+9rwa3H1YktT+So5g8eBRz93mVxnm0MjS2Ktdl2sAAG4sgdVoBhMhMRap9LrZD4SrJbt4lMxkAVbCQbM7FfJCZ2XmAPpClsMnfg/pY5gAWTOfJv8ic6mNO9BwwgrA53BF1nt+Mz/X00B4gJMPqih69s8uD6QprBI0gIdZlv2toBwPelqk94U8A4+q8h4AC4C8gMPi4yuy0rATQYjhMnt/jAyanMH4Zv/WTi0TG8NsVslxNMqfWuH/ulKjP6DUh1usehAro68Q9gxyIVJ1fu9Q+AaAwhrr1ZWHGCoRktVW8N7Rp7yqOdWQFBAIQBBRwKqBUxHOwhDGQU74tsZ53wLISQVmEmJRpHvhh39E+8WsGBL7BDu3pbh9DYA/kYp8TIasSawYUGJ6cZ7HU6PUAo0cBjJ7NvKzPlh0KALiKYg6AgJpS8QoGuG4CFFoFK2MgzdB56AkiOibMQC7dqBfoUtoUeM+kHr2KTNLbKjjY9b+/X4AC7uCzMwUdFHj96wbermEOrttAReYS5wLkAspXoFyAluU+ESCkhJIvyPkCTqTAQEEdOq09eKdhjKxdQ/6EvQ+gcgi7ncwlqHHxuWT7MwC+y6MxnjNAKGyhK0IF4cKArgbcFdL/LJfIqHWTiSFckDQcgmlly47h0bF7KZ2g0mZz0L5LRY2iXUtd3826bnaXrax0nX3+QF3cy14XU2iThU6NHbH2GUhwXyCABL/zQTtWYGC+0v6eQcFBzxyWVxYYAGt7GFFiJNjcwyHp1J//hu6YeIC0Vsl2AyBYgIHZ4A6JVIMgYQ1/h4aEiRg+kzaEHOf4/Vym5V877/8QAKgXqdQuR2o3bsQeDQ5EeYsXJ16BC34qsKVwSBegXiUenLIcAY0eZqiwBKVx2WNcHz11k1QFGBIKzfvuND31eDDQwcByvf9KaXRvwZRX1vZJXoXRo+MKlJnZmT2CXT5Hq8IOxHh8Nc9tBAS4Phdj2lp/VbaAZ7ZAPTTiJHpKvx/BgWzgBCZQq6qQbc7ogTJ6BJ/6N7F1+tKCXPS5FpdQSp9PoOD6/LQdAk50TtVNgEIuoJQEIFweg3IBldoBQnoBygXcNtD2QjzTfNF5SL7NVrPNtBbj5GMVQID8MyB9NIfiHcd5NLBrRIDOoUwZjSxRtwMEtnMa2n5Fk9U3lpnlIKwBsocLuEr4MIJQW1Jq4MDGjlnYKdMDN/QCKQigrCwBert3gIF0Z9XgCA1LVo9K0KuDrpu/v0Pn9vdTHcLyXtMBNIAEhjEJxqrZLf2wMv3wCN8chabn93Mi/suUVxoYWBn7mRefkzMM7z+TbZRXqxvibyj88/seAYJpLbYbWVeGbbjH6eQdKjMJg9FNCCh6dd3inl4n//zA+KtS9rhlq10JrE9n0SoIJWhUtcSxS48HpyxMQr4Ki9A2CTXkMoQZKneavgbPu6421fHG7WOLczLlEC8dvFnGkMG/okdjPytgW1Gjo6LYszy7sWGWU/nmUI7VLa42MEO6vRC6vW7gYFjZKN6qhtS8KCIwrkDOoHwBN5MFTeDTMSSLVQ/hoybtZdskrAF65gAMNCza1OXEftOW3zG3zhQYKNBQAtfrri09/qLLLJU1oFw8fIByAZVHoLLJuLQLaLtKomLKoPrCxytryIvDuC3HSSqhlPrsCEyhN732VJ7d+CXE/RmQMhIlWSpKpHIAT76tSUCCr2gyb3T3HO0mGuVhB47V6O9WtXgi68TqaMjKdNs9ekGET5NXLRRkuSIh32UADXrtXQ7QChTYGBxc13+wdrp81Y5eQ1bvOSQC2QMExCBqO4AQ7Q6js9AzMFgBgMHuhCKg4MuHOXf3llcOGFhH2/teThS6lqcffnvXqUcUjHtwds9DhiAohhh/axUDEAiIG8BokG/Ue98JafrzGE3b/Tk+w4w/MAp5qIt7bXYNMHqh/uwQu05578mVS4gHJ6BlcLqErPLiAEF2tRMFaSBBEpNopOXD83ukryvBGOvdsQPzGvo5ZmosSuyvXb/PtGIHBbPikMunWRY9ymhUjpLyjCFYAQI1oGxzblLSrIqZqoy3gIMmiqxVqXNTQEEJwk2Lr0rcwBxYA8ryGSI4sAcFGQl/L2l1lkRDn2NabwGnfNgmV4I1dRYkZyBfwDUAhO0a5t0mY1IzfA8EC3F9lLFasm+hlidzpwNKBZEpgzhru7RtxiKkrBnutrQ5gINeMy+zPMQEY5MFqgo+40qWiZVazrsFoyNNvaEXgK4bPCzQ2xqvmVlIeXtbv3nPv4QejQCFrM40AgHoBmAUxgykYNFyJhYAwdi1GH5btWYHBBbtePLsKd7+/XfUfr0x/G5tE4/LKwcMgFXjg2DGeJN9SwlPnr2Pt7/xDt77qXfx1uufhYjaSeFuFE9DBvMGLQoIRnoxZFwfJIjNhvm+jhjgi5RZiGY0P8Vuo/F3rxPYe2sL4QcgAgJRQjtPTkECyiXEg4t4c6mAFRTYsjMoq+C5CKooSqDogX0sGJhpXu2VXTb1CBB2AC62HzaPxof18wH23g2URhw8w/g63ChQzpGxsPqo8XcA4KBg9Kg94fCI0q0yr7hcBBy4B5QEIGQM8424gSkHg3hSHqKEF3I5zEVtO7faQcF2XbeLk/QFJd1Ku3aAcCVQuRzMuwKhl3KghzUNzAz1Qb1ntm3NMN0xZ4BgWNSrDKEecBFP3lgEIiQNxeW0SM6byj4fSutqYGDeRyKCg7YdzzsHBZ2ZOtMNDABRNwDgbH1twCwAAgQAMDk+fEunAQ/WmQIOVX8aG6D6hnJxoNCZHdvaSx0gbj6PkABiYQ4OAUJ4NuFAthZMssnLk2fv4+1v/jze+8l38dbrn1Og3pmNewGBlVcSGIxlBAWrpJ+nz57i7W/+An77i7+Ft177rFKmOplXymB1rxkUzMp8Ts4JS7DmjOuYYLWMC0/G+a5iQndvOTL8J7FDnoSPgpAJVSheAKcsCWIaToCyA2SKupYxzMAXAQjBWxKKtS99jJRvtmd5xRosGWEZ5pkzqg/Wz2NO3ON5cVRs/ORtACMlGut45IUGEDLEbtV7O6LYeZsSwlrbjQ0AWQtvSrVVMZZDE/aKmI/qfNIHY1uOro0xY5O9EIaap3o0NnP79MQjogRw0j5K4LTJnDJQYfPO2KuU/W/PhXnZsapdVmfWbzlnKGFOoDRwybqXhGw4VTuL0BS46GfGTD1UDnb5HZGZimGqOQlUX3sYYZp3OjbSTQe6AcGwz/riCDjcW15GP/qcyyPLqbqLysUZJ1u5Q+bwAD1B1++jXUEMW+TKPX1UZhfr73jS71YWQCBe8+TDb+NLZr9eV/tFtAMHDymvODBYgIIJdT358Nv40j/7W/jtn/ineOu1z3RjbpP0QPMPBgY4BwVOK3ZqereELGRcs2WQG2CYadMbQrer6xx/u/dEofCM3XMGD239fAcDtT+TDBwo9WaJcg4QVCkOCWNcYXkIoAxKW/egSMECbNnTIm6v9Rsyj1djtGNzpmQqAw2L5X677HhgjJ0Czp5YDNXKigodjElr6omNYZ3lPIkMQTSYsxc1PzPl8b0raVXOqvxi3/bYOy0+m+5PoghZQxBiyMTDcjBgytHiylV3MdR54e+tXnObdmESALU5QPB+VAbB5h2nLPNPvUQORiGO1ek4xbECdmG2u+ZKfKYmUFK5aJ9kgDYwC2AYWIRmXqt85nR3GCuvH3Cb0VzlD1giq+2DEcIIrpt07u3m3EI/uFo1/QAMc3IJHO4p8xy4VzdOK3Pc4TCjb8CxNc+5QS5yf1anpgFEskRZHk7SvymakQkccPUx8zodsK8rxmCwX69/VnQaIOG8jwAOXmFgECz67CkCADfp1D/4Cn77C7/poKCvvarnXlHwOoFJ2CKNGA0O9+/c2ERQcOb51U0mYVyqVSs48IU8K0rslRmFzBaaEfgdCTz+0YTGecFbyrPq8CxWJU0qYJyL96MDBG0nMcv3w7p0izsmZQ1SV97RIM1t8XEK4xbjv4HJQWAHVqEdH7fIHthjdn2w94aGzxEAw1zmMNKKQYrA0aj1CBjPEr+sXknDBxp3pxDfNeqUHQAEg0QJQArfzaAgyg87OHCQQOQUq9DkrPfX70zxpgykJtfaMsrtKgajAR5FT2lsr/VDUnZEkyMF5F0ESJrirAI2efYQPUkur32EhcGfDeJKLrVHxvlhzwvUtcsEJaAUP8yKDSy0NuQjDEl598jAEZO5clqOGIIICCb91B99rh+AUR8tgcPUjuGjk2fd0ouUqD/b5UF1k+WrcNP8myogADa7C5gasF3756yhQ3N+WLaicyeBUwAHgLEHZ+3bM1NY2K+eVTKAA/kh8ABw8IoCg9CJM1Ognz999m186Vtfwe98/jfx1mufHVDYOGBHjzgCBbz7PtZlBCyq5BfJVbxtruijF9iuQh970mDlm6jYiqFj8k0YrpOAHIOIoemr4OXN6wwgwPSzKuvNPTk3BMyukDzE0DpwoHIZjBSbx4SQ+S8NDpU5iQUvwgVLyn5hnOVWx5Sl9YAlWzl7AAxe+tz3gzI7yfPYMUlnLNLE2rgSzEVWJdiSUetji73nDCbZbwIDMIsGaYyLL8yAGysfe+KRNVAlyqmAmEGZAS7gZooOATDaZxuAokatdW9t4TmS0vFIEE83JdEPRODcbCB64iJG0HbXGOk4yfd3UNlxXkSGwkBClvYjl860FNZclTr0uTMbt+Z/nPsOEjo71laAwP5e5a5MoMCM9C09sTfg20LnrO81gPH4Xk+Pe4hOFH0oupBSQroUdWCayIaxQMAyOVfkymmC3seWn8LKDhAU/LYODsyrj7Zmlb8zsZ5PPvxgZ784JvxG5mBYJXQfOHhFgcFB0QF7+uzb+NlvfQW/8/nfmJCWlI6xwuQ6RN83jOTi+55xrUBgkVwFNQAmgMIwyLXtWsHcXAjcGBx4JlL9BEaVSbx1cEB6ihtlyT4fld8eGFGS7YXvBQf2m7ED2ggOdO4ytt5fFvO2fkpXj++xhRzUaCEuZbJEP3nw/rnAYtnYFMPn1nfYa31ToKW3DuzzMZadEPogMjVGwd/qxNWzYt7HpAiJknrbAQwAg+HxLGpjCgwM2OvlkfS5bj5lGfu2dM7DOynvQIG1Z1ZFlnzJpifR54B4WzY3dKzK4j7JxnmTeyTtC2oOEJDTYBx24TR9jve9AgSgjiDBQhc4GaPY99FzPZkPPW7en0XM4GbJawnc1PO3eWjLfLmpd6rLWQMYvmvuz/NeDT2HeX8U1jzMX1nNwZfUE6vfrIDADAIeqg8BnTuqD4kY6QK0qwAU6WNlbFoFqu2tEJJzkzgxTE3Gvkwm1eL9R/VgDuBg1+jhOrvfk+99gJ/91t8e7ReR3MtCFPfkAJ2UVxcYxLBBKE8+NKbgNwRp7TYZSUNHAzgeOH/WYvITqTajSaOQ70O/S6g6awvgTIGDgsAc2MlrszC4oW9VdtRS489m9LN4Ulx5AAcz1ebvs8Wcb1S5jiGEqVLj3+bFmaLeIHkGdYOdXUFKAXvymNLAXPfrnYc83AMFaUrR6sqRmYlhgzNQsMq8PqGNncK32HnskjsEeTA08/MsyymOWwR2ERhEQGAMgSV7hiWkuDxSpkA3nkoJbBsA7UBBDhnxuLEyhHR1yQE4wBXApYvNRePmqStl9+xThm/qFAACEADgqlh/aEgLrXZ2YNscJJyVneGfEiIPS0yus2fpKlDS9rBWkbdNwJHm40gYoRt/1yVkPau5GrHuYc4DGPYnabeAcFhpsAIFuz7lNsg85Ru6YFn2YQFSQ7wCBREQzPpQ7rHQiSt9mJsAM+g4zJ78SaGk+mf2yG+tPFp+vgAEer2Agq/gdz7/63jrtR8FPFfhBIAYm30UKl6UVxAYTN6/hRGY8eTZU3zpD4x++Qz2oABYdbSjuqNCdAAOEiyZCjAKKXyfEtBIJ70qbGYwrvv7nCipI1Bgn52t8/VHZPLrBBho221JTqzzHYXy7am1S/yJpdauLAH3pOR6bZMBBKVgI0jwuh8YB662T8N+86Yhl8DKAhQM3wG72PK+TcGI27XmucTrjnIO5ueF3+sf+2un5aIAQFlDMTHpzpbwKUhAuajRL4Auh+tAoAyvQNglMNAFsReSfUcdICTK4Jy6x9v6hi9EtbMCjQQcUPIcE0oZ2IRq95CThuGkr8+Xyx32q62vP/L2V/eaExDPiocz0vC8Yb4b2LG5oaEUB0GAsGsGBuN8P6hDXCXRzuZ7YAkO57sVzekgZYD0w32T79AFw/UxMTeEJsZ7B0BwUs50Ymt8uP2wPHzFmBpbZrqG9vorhNeO7uPXnZRzUPDZcGUHMANrYLkGw/PvCye8gsBgXSxRo4OCs3IHCtPCxixoUom5PtL9rQsz6/a/UFkuciCND31r6hWrJ4c+hEJnZZChZ2JBt7oNb1IV3E7QbUrd8EtcmTy+ZgzBDgyktDfe94CEM28JGJTZzXtx23tSbEAqAAQFDsxBIGcFaYrUb33DkA/1k+fLroyQreIeshxqapPXJ/ZVXDpozzyqSyhLtmFaE64XYt5kynb864Cg70RpLIGDAD30agAHCgrigTad9Qxem4FswCe2nAEgGfZ9rgvT4ZQtkeybQFeZr6V4prxRvBRi4DDaN+sJkIuQzzAUt0KBVmZAMM/xO2PahyXnPlaWb3CrqGwA6vVuE3s0XBrm+kky7WG4LBaLqWvIBrBxvcMrvUdn2DPts9xUP8b8hQRO4uEzSZ6V6UNAdGJrfOgUGSCwRFvXh6YLc9izIIJlIv9NZNcoJkYnW2pKnq/iGyGRaOwe+rmD8j8FBeN1HzWEYOXVBAbTZPYlHV/YJxq+dAnGZ1R6yo8SqbfbupAzd/p0AgcRCKw+YwCJksT+gCAQJJmzrZmMuhIYl0NZkg2WgICyGocpMU1+mweP825KalYq80qGObdjFYdfPEsQPzQvAaM3ZV5YSuA6Kiqa+ueuopnvpDT3DA6oBgraSrz9kcE4A0Yx4W0W9EV+gryNn3fDssuwj4ZHwcFOqYU9I44AgbEEdQIEhzvusdHY6OEexXOZZJMe9jHcgLBGn/RvYXYqKG2gmD3fmp4sqTuHBmNH3OnlmDAqQ1O773SQJwDoPLX+ddA2gbqFMV6NU5QpGSpaggKaZfBGGWQ+tjm2zYy+NGoNClZ15ybJqbX6XhdDn+DGPJ374Lwh8hoSa8nqzrLvCLOMt+RqzQBB61ET8iIvYaUToz5MJYteXoACmvJvDkHBsHKH/DtnPOKy6jv6pucUnICCe/v2znF4NYEB4ArgybP3ZfMH36fAvPszcNAHEAiobtWp4TPJClUGgZOSDiJYjqgbwEmpplYHilQMnCkEpazqFbzpsjyNIedsQsE97yDQapaMM1ByKyAQWYHgPZ4qq9jmB6D/1U6KDoJOqN45m39VBqqVaFTeQRgOAcEcn9fER9KYtQCCAA6gipBZxs2VGXWF2Sb6c8UMxBINhZXFpitDX0TjH65bggDLw7D5FXb5a2RGuF+/BAS6EsTDBjKdIScRdiBwyhiQXOCHACXJwRHArACBSMIXRwAhCSvAXEH5ouu+eVhR4kmIC7AgFVKadTEvocvS4nUOFnI5YRkOGKDVGM+Mzj1gzu6xYu+sn4+YjCNQcFSIfL453ilBn9jrDEhjO1+GZQz6gmbmQsGC7choICGuhvjIOtGAACU9V6NI6G3Ow9E9VlBKz8MJp8aCwiqdwBKc2hKvWHKHUxLl//ZJTlz83Qlb8EA24RUDBqPAPv2TJ/jSN38ev/3FrypT0Dr1DwAx483LOGC7gZyTSaKnQCE+qYkrdl6CL38hneRNwUAjoBBSFiUsFOlV93TfdBfADVyKJCJpTHUlFAAQM3p3SYMzELDJr4pqiD1HunmmN08Uk1ZCXoPSFWDEPUYalDTN4MFKjMF6m/bPvieH4vZ16pHm4KU1BXo51N1XSdiWwaVTrwbuuAEoo+d5xgwvWIAVCJjXvHubVoYjgIBhx8WUA605eTbB+HP87IAhiIBgZgsADAmIyZcQw5dpSU6vRUQlVbISkCkjEUYGgeXcjGHbajcaImPIAggMKMyHgEWwIHURIzPsP3BkSANY6EmNZR+eOBrnFcj7CGDg1py/hxWzhD67rzNq83WL0MYhGLX6TiDhoTrDd2O08dFwkYMFO0SL2Zd1H+lEuf1eL546R0eA4IwlWAECOyfB2j4b5yOmiZLkxH1rDn+f26xVaEK2MY/9fjvP4BUDBr3Y3tG2TaQgpmjEjTU4mKgrdGdJJfrZ0dIgMp6Zm4IEYxHYlZ0AhCTx06TKj3Qr1lx2AIH40bC5CGr1jUX8mStlMBuTwAgcJqFpxvqhcZlB0lzcZTTfEncrafnZBCxWJU0K8kSRLsHEKkM6KnluOoTBkwmKCUA3KmEMBg+0hL64Vc68rZXSXbEA2q75kBeZp9QV04Li5MBU9c/JDX9j+RcBQOXOCsyJh1Ys8jusUiABCqQAg4iQFSAQkaSLEJDIzgEoAAQE2CY8zJfxsCtNMO5AoYHBQFGwoOBhtV3xMBcDfT1vZBWBrvxeAMNLje2K3bFrw9ju5oLdcjF/oyEMkc49G2bPaFXAQR8svfekE1U3DHW5G4wCL6Mzor7wMEJYSkmaJLkECgrgaR63WOYx0L08pO62V0o6BAR8BAgsZGD2wwGCPIsnW2GDNaygIRKmWzcvevP1z5ngWeUX97g/NAHIgUtn5ZUEBk+ePZUDkX7yXd8m0oEAJd+fYJiiE9WyRHkBDAxx5Wkg2E+gy2LwWJGsKixZqhU9HzkwyOnRpNsA2978uvVoTLJaUm1H5SB+SaUnnrlwTDSzeZdMQRFEgGR/Wx96J7Q+mZdKeq8AbHUAzd4aEKh5LScU5qCkrP2+86KGh1aIucwCJVednXg51/UQ3Dwg/nxmKA6B2gwCbLzDd5ElcKXlY6zeDdCXHLY9G7D7O4AB2wZmhkI1fGK5BbBYsIIEET85qd7CDONpmCRyASiDo7SyA4WDLcmZu+y1OsxDk82b83AGgnFc7xnbO42qX7OYr5O2Wu+pUNQTZgaQwbXqbZUZiNPb9glh7uzUkYzdy0at5qHPrQfoDCjgPDg8TJIQhTW1Tchu7jWyKiu9OB3uNuhEymgWMjAQMDNtExi463h1YDiY7MmHH0hO3Mx0D787sVexf7EHI3Y086fx6cOueeWAgR89+ZPvytGTFmOewIF1NoC+qgBRELFEe08+fL9/BzMyi0IWT0RXlwEcwCZ7mujRFnazy1WTrPquZLYj2Y5qA04Zg4GeNDQcDgDpFNidcbIzIbd4nqPc5u3zMIvRfWBQ3lxJx5Mm5VYLj+2gjbNylWEQYXUgEAR1EJgTofXXVLRdayYk1pdifaPCjYowzrUF5XqmfNscDoggwBSVjVcACfEsAzuR0g38vUAA8qMZCNxDjvi1trGWMhEJJH8Afb8DbVYCQG0ECqAsUxTaBegAwJYoezx6moMDWLBQHzdQvgMsfFzjau8PPGue5+jZ/AS6x8m2XA1AIW8DZdN5NpeNQQgyFZcVntY3+VxrnoHfdcZyDh4ZrgkY7HaRNT1pOqM07M6cMfY0sAovpRd3Cblq7CMY0Ot2YED15HhWyIl9sHHzDWHYsd+XjOk2+4UH2KvYt4s5Y6DgvZ9+D7/6u796WK9XCBhwb/RPvYu3Xn9DPjWaJoIDAENc5miHw6hMATz58H28/fs/p7/pg34UV7UiIS3Z1KWPIwflpTv+NTs3QCd9q06NMlegPB6273Whj17sVJZKiDrSZfMuB/Rr7/PkUcpkj23exSPh87v3kGWQs9G+2kb7PIKcABREEe9PqztoqLwMnlbcBTF4zQuQYNesiiebTcAHaowod6U8gwYA47kSc/0nenhnJCIIuKGAOXw+gLowZp4jMIUFvEXhM+CcERh0+gEqULW2K8YQDEBBdaMDBf2gHxXMDg6g9xUAQSAiJGSAJE3E5iGrnJncGDD1MMQMFhyI8hIsABjnI3BzTKV9+3EFMI5t/G1k51Zl7HxwmJMDW0J2T5EjAkZZWuVETKB6mIups4luJMmM68JIhpCU1m6pM+zVdUYAedwq5hMgzXniVoHL4uAz1YvSzBPduADe7DovAgFzkIJuHNiCUc4G+ZlK3Dqht7kzAu/91Lt48/U3QibAKEWn9mr6PDo/4jT/HN776a8J6DiBLK8MMOhI6GtyHjXgwjOAAy9xshx3rhnFJx9+Wzv1PfzN3/mbo6fF4x1nHVnDbf1JREgKFIQiDR6PA4WQWGVAIXg6zA24sCum1U5v0RgO+9sfCPRRslltZjD4sJ2x+6LSzlSABGT19tjAQttU6FVJqwJgB0jN227eesxbmJ4sX+u4jUBAv59pzeiVHQnY3NDY10579vdeb6hx5QaU9dgMj6BwNPUMXGJOQFROUQkHEGA91Bi26/ZHZgBWhv+8RXdcF+6ZdjKjWxTr5/8/9v7l6bLsug/Efmvv8xWGHjnCAlDFkR1he9pNggAqSwOxRYLEw47oCACJAsGgSLODktpTWhNPeuqwm82QKAkmqEIBUE+EBwmSLTmMzKwCwe4eeODHxBE2MgvSv2DgO2cvD9Zjr/06996sQkjx2Tsi87v33PPYr7XWb/3W2vu4ndQDwjDotcQBIEwAA2WknHXPD2khR7DgYDXImhnWkLOAcO3tYyq1b8BoZN4ogtjr5+KwvXfUDdZ55lnbOR4yW8hRYC2qgaxzMcbSLy1jlbD/fNVK89RBb2RkHbCc67iZ3ugdjMoscG1nBEldiWPDE91wDRAYGLcLdsDaKcC3dneqUxkA8MYv/G0ft7o3w3X2qm+jFXFq38Tbn3sbb7z2aKxYVx4MMHBQ8NqjKsQWOkDbSUA7EL6xANB0bgMKlH55XTs1ZmcDrbIFJsowTJKgJoIyE08+JSDZRkjB2x4UWKDaHBlbRaztsd0+8UMcLCBh36imVGE+gJp5XsRwcGhr305vl7aJoAlkJH+JxEFJiZBoQ942AQpGDdq/wCZ4aKUTdh867jJuLwCB041Gem9tGEMOH0uzf7n3RDcmDXjo7lHrPHqLfT15xhJoTkDBCAIOtHOybkC0HsNVKCAwl1eVs3NXz5gtWOXCy3vZOz7aOcdq/EkVbgsYsn+uYMEMEferHTpWIYIFrVwrb7MO6OZTO6Za+9n8i/cYOiUaew51mNTzBCj4Y6by08lO70QYk6jfDQhIrmade4VZk1ZfQm+APFSUEvnYZdqQVG9QYIMsn8DBQgRzveyFvp3pgzPWw2RtFnbr29WXBBkCAnxVjiOLwIyxsQeUAERwYGW0Vd6WSXFQ8Nm3rgIFwAMCBg4K+rKih0Oyh5zXdape14MCv4LP47F6CoBzOqmnR6vyMgW3CVgAGmrU6c0rFVQfDujZgMIsws3sIKAwcDCrkOt0ZGnXYdswh+faGnyjckFAhij3TCbghJQYiQiZgJxESeeckTLAZXePAEHIK1XYeudNq3v6dQYGrkoQCooj3r7p3MAIaH3i9tu+broBD8BadURQsKjfAggU5nb5YABxRbP9HU9qm7wWC0PdFFNgaEGt/3wDaOhL4wDr36aHrqElSK6uI8fVpkETF2HGht3YJFTAmjpQ3iQ3hjGl2JFXjCWAMZE5hCcb1uDKeef5LF39HCwEw9i+NAmN7DTP6VmMHoA2jOLWgIGjtLrjAAtA0K46rNq4Xm8ksHjSpBtgUQULpjvkr4AUYxU+KP14BgIMxA6g4FTPo02oZVmV46twuM4mu429cAworb2aAYCFEHrO3Q2gAHhAwKBpNKUqCAsUJeVE61BqEjUMFNjgH2FSTOnZ4J1Z6edmHcvq6dxPPJ2BGtVYqqcPxHuiVSrNpOUaEmhQvQozKxCQ7yrUhbFzwX6wCzkXxuGesBpFE279S4mwqVBvibDlBCqMDJblwkTYSBR1Vo8gJcKWNlXOujzNQEJq8y8ArEMnOn6DF2AsSVTQMzqQL9sjx/CUHe1TGAszUD1TsKKfR0PRxitXQMDBXRg/s10rhXygnYhDlA1AtsinK21y5QaaAwSrfttPbV8GEq8+X/+W8GVmRK6td19nFFbAzaAic0/mIgHEDlpnQMHGtBnPW8YSaMbT2hZ1iXXQ5Tln98xuNMnuHAFA8J65YxNW9W7lpg1V9dtf74EZOIroi5150B37UbCrrthZ9AaAU91Bzg4AWyZslAQwqJNhukO+V6AQGaFML68fZ7k370e3H4yWxQLX3T8DQGAiT/doQWEEjuf2qi+eU2Cg4NQWtuXBAIOhDJ0wQVSy7ml6+RIUOOCuE8c9tU5JR9pscBxjNSJAMCCgPFOk1kAcMrbJr70EfWYT+xogsKtwM7MKuBom9QgANCjZ0LEoWcKWDCAIMNhU2D+UM4gYGzFyMdaAsClwse9ZQUITR+StU3qTsvK6Z5Qgt2PY99lZ6ceAwvHK+GTtE/3t5H4zcHLGCBTzYLgyOjaGBgC4VLDgns7EtbFNX0w57cSqqMWA2rhY/QvaeWdzuD1W7xn7Ul9g18hCVHkGCmZtAK5vh3mblCpgyElfrwsB4JmAnQip8Dmj4I2sgGFVGmOjjZuxia1euDzXoqzHOoI0NGKVyhzkpWUTBpatPmCUmT5UwJUd2AsLy2jfVTf89Dga3bEzgwsLmLhSbxjDuCUZs6g7LgGFXkdeqx9tDGbGP/4ez0f3OXSl/3WTrnUCtwABNobMKHph4T5BUeHM0rCPs7HJubuBKbDycIGBF8P6q1/POnUEBZHu6UFBjKVFz83Ojd5QU8IBrw1xowSIuFUMIfxwSUmZQoqUsnmTuwIBLvq5MPZS9K8Ix36IEhBlYIYqKPpSDV8C1byCBGyUkAjYUsLdRth2wv8nMz6UCHc5YUuyuc2WGAcTMtl3wmGUYbLksc4zWiV/XaIGuTI+MRZf80aq8j7T1xbnduCmX3rGR/qlHau49XGzdTDmYM7mloUGSqlAYlcjat6Zja+BOqAyPfPuIqCIMd2J3HNj92YkXy2Cg8piBWAUgNLYVyqHXIGCAQRwZRYiKOjbw/6dT8GBtclAjhmXCBbc6CTy8UqJkYqGHgrrPJ6zd3274jhGgGltXu37wOGcVRHnQE4wzzOLDew8Zej8042hrDYvITNNzpHOP3MOxJkQ/WAg4f4o+GlhBwP3u+iRwsDORcBspzui3rC2me7YzFnIhHRfsCU5tqWkfwXwbZQkl5Jq2DLOR3OsViX2f+/Ime72sVzp79iNdgIh7Mch45dBOEgBAaSdRVFAQpgDet0IDs7bYOXp86d4/O3HIdHwHHTOygMGBiMguKZ7Yqe+3iGtiBZ98uCC4tYHR4VnlOhZUrOBw0jpZtvKEwD65KvJPfpJPVO6PSuwFzgwEFAQBVuvc9Q/9mjSelrC4ZaLCG8u2I6ELRNeOQruE+Fuyw4QDlSAUBLhYDFGEn8jp3uHJZ99R4Z4rengfhvfs+So6JkOiiG2M44VKnCz34ZkuDBIybVHW/oVAhHU9fHaHgwYVbuXEowonOGBtr1pg86nVER5seIoAQQE5ISsSu4AYwvqaQYKGnYrdk54bCEzhoTUgYPQRVVGOpBzc9sg45CP4mBh073xY6iLiLCpN5eL5cVwaBd3KyHsSe1z4xgC4zi2YKG9ejXHrK8BmWeZCIfVST8nAnYKyb16oXjTIjN8pbyIHquAoGcI9mJjUgHB/X5gL4yfFVbGoLjuMMfCGIeV3gAEeBooSAnY9oQtQxlIwpbYQYIAPh7G0ZmtzrnqyyDbYTxMTwK4WV9bHTzsxqquk2zelRJpH4ieZFZGIQCTqF8u2a34+9PnT/EltV+P1KmtUns9QHigwKAFBf3fVYmdaqCgR/mx1PXt1bvsAYMZY/PKe0p05fF4SzqK1xSEUaRW8mTq9xM6GhD53NJ8vUAXBQn3R1UK98f1wCCr0b8zUJAY20HYc8IrOWEvO/ac8KHCuGMJMVgfbUnuv+m9DCCQKkHfGwJwut7HBS3Ds6Lj+7i80fErIDcr05i2UYU+ZiPbsyq98Yh1ugQGLA/EKNsZu9OMVSH3ilMisG3dnQlZ5wZ8/lVgKkAgUPVkNG6bbOWF6rjE7ZEP9Xqtf83DyonAB2H3+VtZgqvbqJ3YMlhGVR/Y8hok7P63TYIDuGFGVuNnjzfjb23v55adf838inPrQEzMq6xBStWoWBik2LxToL6SF8AcnCovkSkwZqqwAIafHgfud8ZPj4L7o2AvwM+Ogp8dxjay6477oygguA4Y3GXycKLpDmMfq6Oh4UoFfJsyXQIOYq5T7ce+xH43vQOg0c/y22UdbVuZWAhuZ2jYg9z7rwAAFTCTAnGidhZwZQ3Cqc14ofv+9PlTvPntx3grMN3ttTS5el4eIDBoJ0APCvoxNkX2TEHB1ztQcKnE+3EIH0RaNFL1t1KiwAwc1DbGZL++zJJ97Pkx5meCbLTfflRmwITawEEppa5imNS55gcAKSXcZcZdJv+7peTKfFNmQOqVweWQ8EIm8CHggLXdJclYZTJ6bh0/7Kl4f4aCBcuadqoUmBpe99AX4yN9zg1om8a07VKv73RDZv1l9GD2o3is3eZQz/LEsZTP2hc8AjhTwgIIGIkJiYEtS70jIPC2qlExUOYMjt7vmriujQtBN95hQoEoxejxF7Y53tbbxiCGt6ytdvnYVmknAF3dQzrvLA/mqGAgJ+waxtpZ+mLLMl+dKUCl9WdjZ+2MtPP7y5fgZQgEJH0hgEAYjQzCwVwBQ5CXcoO89CsMVizBT/X7Xhg/O1iBQQUE9vcWvXF/JAUFhPtDdEc2toAFxKUDHlJIiXCPmtNkoRXgNv0IVGB3k14uYwhuA2FPBSgVHJDPCfb6FcHhpyUa+P643ePZ86d48zuP8dZnI1PQAgqt9eROY3mAwECK2gD/3Ixv6K3CwDsvKtJ69BKJGrNnu2LgqgyqUR7pXgBLzy5W3naEi07LpYl1eF1GT6sHBIW5QfkGCO734kItSkNuundV3swzI0JOBiSyXpdQbFt2JryyWcUTgEMsEwpYaWwU8diYhO5OBKG7qd1Tf9b/UdnNMvfPVl5UYHBdLBuIcd6qiFYJcADc+56O18KQxHqdAYLei+43fkvJVkar1UjtSmlS40PqfW1J2YIOFKyywvu8Cuh4ZB8T6fukHmoxN9cNv7IGLEaOEoFYGbLwKt1Ze2dtBStzwMKOFDBSEaNSQNgLsCXtVyLsxRgD6dce6J2NG4Dp2EV28P3MLfOILRGPE8lKC1RZEXJDXNOXlRen/k1WGA4AelDws1Lws50VHAiD8LNdQMF+HM4ymt6Y6Yz9aPVGIkJhAQhHYWcfC8u4bFl+NyCXSMaxskLykGt1I3CFHg4lgQANOcUQ3IaEIwnzlguBk9ECqGPzPkusXQMKPiNOrU2nyDYAtz37gQEDaXqPAVzu+h8APHshnfr1EJO5pSQaWQgrERkzV6ZgFic1Yw3MKfr6PAqf5e89akx1VmImcO9lVXCwZgkMFAjyr4Lt9y3mkRF+BlH4GzEKE8zoF+59lYR0EJKrpQgOIMdykjCICZdKV0p1iQ+ABiQ5dcsXMvh5nngZQyyzVRjDePSxbKrUonkRezKvwpQVDcoxlp7W7GPsFcCsk7ssJ2RabOE0t/MplkxKtWdyY78pKHDvzgFCjWvHjP145wakFzm3FMahRgtm+A1wAhpDJvDOYCLsi/actdleZyChBGNHKkDYOCmjkCSElW2eCFtgCZg7+KXGrWcHr8mPsGJGbifJd9iV6chEok9SccoaiSS3UOcgGzvwPuTlYFYvfw0K9qJ5SB0o+OkuLEFlDERvLHVGIuBg1xt3OeF+L7jbEu599yvRE/byx0JFwB0b+xXDY3LOcYVeBFodfo3+tfBbYsIByUupgABgs8oQO7At6nFSvabMajQDBREFGPEXQxDXhhQeGDCoRZ3E9oAWQ83vPn+KN78r9MsnX22ZgqHrVJfaHE0QQWPhRn2zCtnbX87PibAf7QA4VTUBBWdeXt+QlGoFDb0Co6KPk3xGMYsSqHG/+lfP0wt6+q8XcPucktDCeyJshXEQa50K7g/zBhgliVFORbwC84jpKMiQd8NTEc+AAjhgYtjaH3thLIWq2ceiE2CaRxBAwWpZpmVeRyp+5kmkYopClNGRCKmgUeYzqhGlVdDNCOtjesMSl4waKPANqRagII5/PzdcyaFuIJMJ2LSeWyKP50ZQ4Bnjzg5prBtKs092efTwFyXJZwBwENXMbQ7K2T5kAo4ETsKwbSzjz2rYk4YixLvs5mfX7sLcACJjS4rOq70UbCkJ6CJJfN0Pfaudgp+duZlr14zZmRNwOq9UpuO8yiyGnxUgHEjYGII6S2qMD+H9y4u9XVPCV8pSKRgwWTI58qWL5lBo6ED2OZiDAvtsOiM6N0dhpEwopSDlLJS7jmsGhb5DO56o4TFr2CWdWOsyH1sfE817qmwbAwkODuQt6zwItrFtBMvNqfkigHyPq11S+M1Kb496UPCJ1x61SYsVBfz/GYNZaZBgOP7s+VP85ncD0gql93giK+D6TT+TojKo0kqsaN0UHslacGI4JYpBiY1U6Io9sEkePaESwMIZ2p2CgoUwJFK6NyUcpcjk1pm3qeHvhdmvTdVo9OUojLts9aQOEJEr060AnMQIWJcdqEt+MlMT+22KKmAGrgYFMRGzMbpdDHv2sNYbrd6EJfP12f7uuXW5I95HdufOwACdpxmA5KrMwIDF2qXedozqkrCc6nJSBQVbsg2pFByQ7Vxpnml9453SY2NldDkc6Rr5RBmFgAOSh2HeeIImQRaGb5ivk0+ANaNA+1oFLbnQidFYMSF9KWBnTgrUoOjcz9rvuVTPM3fd3YcDrgUELzOvCrFsjQ3WsIqwajh4AAfCv3VL5FZctoOabpMs1PACe+itghkLNwBV78zyB5I6CdfqDEtgluNi6laedTGnQcFBonDMzpnoxKj3+iqvdK4bkPi87r4xBCfTPSzvpfg35uXU/Jz2ue1wRXDQgwKvu10b2nYtKxHLAwIG0noFvm3pDhgo+NMJKFjd2e2iAwRZbgWd7GesAR9m3NhZBqaa9LYKR0wn9OAJ2ec62VM/W1GPl2IThaRfVJlaR91lo1aLPk+BSCnueR3E7VKz6bOqgFsiYk5B4J2Sq9ccXCfkwZLAA1SPkrQPyZS4taHvowAKZgmhpujEfrF+DzH7oLz3o/b70rMDyeaMk2S+nmp0Y0OS5EeJlu8JmJWYk5eIHAS1fQ/MBqcCgnGt+CvKBMQlpD0oyKnmG2xJAU14z0V8A542IlRcJ2V4Yx1Slj0qkr6zQEMMO3ROWmghgoMtwwcFAI6kXr/M1ZJqUums/cMx7Y+mfyflmrE6AwVnyZKX5tWWqyEqYMmBAIOTzl8LIySG7T8QE93MXXXgMym+d0cIhcQujAl6kQRNOh+TTsycDOzLeBVuHYpV0D/qizZ5OTUrnOJS6Bbk1vDBSv8Ba0Bw6lAtgKY9z5I8M2oITv7SKVtA1m60uTmWyzPrKYLss2OJhq+/+miaNwKgYQ3sK/wQYf4EKQ8IGLSFMWcLLoGCs+6y2B1QUR84WnbT2pUyOAjYsnk8UMpJvxfxiJFkIHZFHo3xxnzSznIN+s1CZqVmMAOlqPKhzovRbYotYUiyg1NIIBKacFU3q58Jt2UY1+WLKsghec1o7D6beFaUbKjgIJReydq4s/93XpzubNibVnnHjVmixzm9V5ovlepLZA0OCJvARdesQ73XJHs9KC/sc25LVl8S48g8N4IUWALYunDZIEaAwIIp8LCCAQOhpem4F0Bw7LDX4TpbYGjMimhFZw1gr/fOst1uzncgjambEvXBm4ADOgqAIjF4HaimD/rXNS76IC5lzMqCxMz2Pqu9H6dL5Sw/5dp5ZR5wwtrjbgpXcsCSPmN9ZjJjQHpVsuZ4SJ9YOEffe1KkD+/CGCVKuD/k97giYVbqUsWqL/pli/3+BiswsNJ73nbr39I6ZLNwVKxbfI7JjuWAZJeTVENwyrwR1aWk/s6HkJ9jy0qJ6jMMO81a8sSWJH62yykY2jqyBrcQBw8WGMxKDwqi7pzRNui+c3e+/UI3goNd7gDzYY8ADqrXUyF7ZAbG549CMfPGx9ZUp7Ku8Zd6FG43Neo3J7FYotVptfzI6hDRvgiMxa7hMWwyo6OIe0bDARVZE+ZKIKlXVb8HBd4P4g0l3vcaACbnUfN7NDb91r2x+J51yWKXyi6weIkEaVRKFDzQagzdnnYM0swQylgQPpR1Z0odj7tUx2NLhDsFBBsBxAfouBdAEF96VQqa1xZ3Jb42mtIhrAHbezAKUr4D6ettKZINBJDLVgKlgnuVXkoMOpIaIA4gd90HAJp+sG14bW+Da8bIxok5hBlKDUHsNgcUxJVjYRRfYl5dLFE3TJ63rIM5I6z7bqgKsnBoLppXlUgTNQk4ErAVSSROwpqZU5FT8dVIwHV6IgKBnhmwXVStryLbcwkrCUYPxt+AYzNN25v0OnYFJPsdGa8FBeOOlbUWK1AQl9Q7qOmb8QGUBwUMznT+uytQ0I1AHZTxbvKbebTKHrAoZWKgJHgSVdHksqJhhQKAcpLNblIBHQAl8Xwsk/Ugqkk/LLNqlqAUhXtEtNScYwptxuD1S3XavcwTZtsgR7Ag5+m9FkI/o/0aAVKPtReuGQ1nAKEBBX27uFV0lgtk1Krca8z7oAQQqzdWOMSsw0MmEyz2f1QetgHLyuDEpYxAZdqbphQR0IOFLqYAEHK2RDeZN03uQdQYsa7J1u5jeIfFh3KkbYE7GwcFBRY6oONnwHEPKrszBnDGwF5wpSDmqD41ZQ0hgBD34ad8AGkH+E7Yh3yHu/yKvPXWPKlSZTKBsHFGRgGR5CXstN6Tf9YH0PljFHDPENwyPhJCZAcIBg42BXD7IXPJIyCM9zGvoLt/YlhSGoG03bmC6dtkxqKLQzjU+0BM0V60XkUYpFQSEhhbFj3xoS01TkVfIiiwNs8YAWcKgo6z8fN7neg3wwKWx1QUZBZGo2PrOdbKCiLrWLTMUv8eGNmi2fTefGmvvRnSWAJzSuvYjX0126ZfT9bxa8HBSTSljzBMy4MCBqvyzFYfLMIHcWAcFEwyqwG4tEkKEHxwBQjIHuv9EixiUe5UoLvKiWLLhZGRJIxwFGysG4moogcmyt7qfAIA+vXPcrydCkZVx2IxUgBXJ08BaDKs236dG00R9rR8yVIvXKagGwUXEHbbMPUOLGPdmByVnCMqOqp5H95hh/VtadgboR7HB55541F5mMGxHfau2tcga7ImENbG08n7A3T2TpRwnBNxt7/6UitM8wmMKUhg0P7TyhIc9wIIyr0CggPYd/CxA1zAXZCfrbMogfIG2iSUwHwA6U53XGQPReTtQx5auEcdd9tbACShj58eB3ZK3S6Q1/XDCghcu+fEoWhz07HZiu3mWAEcso0RiREK7E6V7fn9ZzJjQNPm1Cyenf1a1E2obpQZ28L3olOTGFtJF5N2V/phxgD0TE4Ebwjj1o/rTJ8BQE3elXMOvXauY+t9Zzq2f7mTyNNtLEG/tLexOwA8s1zLkxfP5NXJ+u4Da6WpNq/v0MMvXx4MMODur5UlKHCUugAFzJUOja9wtgeUXT2aBECUidgeCjnU8M1BDIUnZQRok6VGtvxnU+W2DZno853opDpzABAVXT2mn0+w4mwL5bkBiglXo4D1pRes2Ram9c1p1AhXr9ziWwpnW9My13Xc8Hj7CA4stEMJouiYgKMgZ102ScnZG4CasIu3q/MkZm+G67dpHfdzrwLdt4YBZHWFCgh3DHDuN0CiYayA1ijO3jjYK7ItVUDj+QSaZ0B8gPafKSi4d1BA5R4oewUE9q8cFRiEwDmlBKQMzhtwbBUgcAFr+IFzfQtg2l6poQWtO5SJy8oO0JbBOb7il27qBznebmDUj8vZ2BTIPNsgQGFjlkRTkuWV8pkH0N/sXZLX8woYaev+jYO2Q+NSbqhmwANzubHdKCWPAShUd6QkQuPUbKSreig1G2/tCoheRifY+FzL3Fyrx1b7Ssix23VsD/DfDyCQpoz2hsLLrp68eBePv/ebePszb+GNVz8JgJ21BhQc2Jd5VzfFoM+lUx8MMJiVuE3kTUyBDVJ497r8HKjRolFEfStZIgL0He6SFnU9QLjj+Xa8QLtjWiyzdyhMt+P1C6QecepP9EPd8ES/F+sWtAYJqIK3UsSr+sa6OhhAHY8zxTbbcreZ6CTKmlmUnSVvGXOTei+oMHJmZ28OAKTszYFR0Q3tcvB1zg5E0GPjQISmLas9+Pv99zcizz5HGK/44pd5Xanp657qnK46KLswBZElOH4GKpJsWH72UwcEvMvvXMrg9QAApyzgIG+g7c4BQnrlQwAzmA/1skWCJJbyIdylDWkTyjoTdHteS2yT8ckkcnRrP8iB9z8mzAYUSF8fLsZzU4Yngn4Je2DwXMd6jqGO3kutb4is40lmmBRI3yw3lugoxIzqMXY9lrOEcwyk9u+BeVldIMcuA+czvQVt00o2XmZb6l7H9ttS3wIIXAfz0dqZ2AiVnScv3sEX/+y38I1Pfw1vvPoJnXOizAZw0JId77s8WGDw9PlTfHkBCgwYDv04AwX2HvOu0LErBX0Ia0AkrmfKyItNXCJA2JhwNIqNcJhXiFa5rV6ysn6Bz6jkpkl7oQN6Wkr6oyJ+BmRPAU3sMmXYA4jwR+peuKFiz5RxHy6YKTX7DqwVhIR5JEQgYWAOnhCa0E7KksVOJG92HNgbqlT+rERaMzIDMzCQuvFwj/CM5gV8yeg4HmjG4y7J3gmzmlo/A53xCEqs3ZtAVh3Q/rMxdHDcA/s9yv4zYL8H3/+sAgNmsOUahBwD5KxWJgMKIJA30N0rKFxA2ysCFnQQLbRAzODtlWbVQiLCnmpeTuZ2Z8tr+2EmG3E8gAtjEsajdGORibAhi6F041nDQHu3xHFWehZweBtkogZMu2EKsuPL4XC73KQg4wUSZuCEZq+DjQivIL+0/McxuWY8rAz6KjAUM9nowbTpWGm7OTtjx/RvtzUdW0FNBdZovmvfG1iD6CFwme73EW0MlQNPXryLL37/t/GNX/8q3vjoJ+Q6wOAZ4ouwDOgDEx2O+Rx++vzpeDCUBwkM4lumVu8+oPBXcXsLCsrhlKaUHtXtwZsQYMBEMuhEQNpU+comLpzEMB1EnjUdFZtN5ihw9qqdWcbpNUK18rRnSqKPT9kznUHQOlZQoN+te7gVSnTXN30fnu1AhXBa18bbOTWi9uCq3IqyOKZMfKkSSX/LpkStt9d7F7O2VEPbMjXRk+hBT3wLoSUgWf8vmmTdK+0Def/LcU2i6kDDrPR9HROhRKEFlsBXHUxCB/c/q2DgZz/1MAKXQ4AC88gYHDs0oxDY7qQzSwGKAG8uEkow5TmEFsqBlO+Qkiz2zYVclg4NGRUWYG3zcNUHwDjnZDzbeXZpPFSlS1P0+eBxo6BNQQK4MoMvO7dmQLMHBIniOaFNF8EOYOuxe/BfUnUMRO5pKvMDyMnVebF2xXlo9fngdBRNdVR8tboDhqSydKOebRyYAKwTWlkiu97YgbCct9qWMZftyYun+ML3fwff/NRX8cZHPi6hOe0YBwdUQwoGaRjrsY3l6fOnePztx3gNry3PeXDAwF6dHN+SaIUmnysoKA1T0AxcQHZPX7wr11koAfBlWIImdTqkA7ZmOyVdq61I8jAF4iBBYnlnAjcrZ4Y1d5+b37t2nz7E6ZUUrwKLCg9GqRNG65uTW3t3Ya4MTJk1yrv7u6qu7RVfGLJLIiS5iEneXy+KW5mbiSI39kb1OYAxs3pGR0dWoA+FnCnsJYulxYEB25dRAcZjszIFW6ReDgDoMkRomKCyBMdlUHDcg/e4bHHMMUApsnMfM7BtrszwM4BeqQoOd1XRDaGFtCPnO+SUXZYkZzEYLdDFPrC+PgOd146F9HkF/FsAKmaQ7G2esrf/PAwUi3nZcX6dAU1jgUzuzVD1cn/WrqQn6bTv9JHNK+pkfN3XXvWJjM/kO9ZRdJNtlHVBCSa5s+mkqpfq21sZ7ecIpl9Wz/ZgwBkDq39p9/agYvPYBn6+rPcLf/47+Oav/THe+MjH9Hx9dR7b7Jb51ocUYumbY2eZfXz7c2/jD/7lHyzb/KCAQQQF1zAFUjrjyPF7aZDek5/8NT7/l/+ZnGe7vBHB90MjYw4SiJODBKJdlmeljEQJST0nET6luFMAB9CBR/QM+3qfeNb2z853xKqx3x6xhok67zTL+FPQo+yIb1ajeRYiMSaktc4uqCelV8aDkrD6Rcot1Je1jgbOSL9nktUHBUAhUXJGkSbI3xxAgu18uOlzoqG9myUnWT9HLwIjlTtbntQmHwHOWE0Kh/ZI58R+roAtMjjTMihiVWBcdBliaQCChxH4uAwK9ntpSxGP34vuhkWUAE7ye63OGhwwQwI58NACbA7nO5clptoPJU68SduBEWg2IBm1E0/HQuXXjFIJzy8cgYLKtTKGK6p7NrekzwKQCcxABAPkoKF1Alooj4vyQ+E56OZbK88Uu2lZ1v0c4uulC9l+EDopZR+ju5SdRZCxqYxbBJGR6eh1be+sNHIdmYEoS3EnUMQ2to6mXMYVPQH41q/+Y7zx4V8Cyg5OG0ACDvSNcio0et8JOHAZ6koEBZfeIvxggIHRIz0o6Dto6MIhr8ByCjpQ8N67+Pxf/T6+9Sv/Ff7O974onlRfzDgZrI9AoSSAMohI13Ark0CEnJKg3TR64TNE3lBrM0XPBXRoHMtDIkedtEb1+gStE3W5MY23z4REQYG2pW5ecwE8xAZ0QyF/J0q5T86Z1NHvGJ+hzxSFmRwkmKe5aR+XoptLAcHzbI3MsO2wPTH2fwfULiqPPo/FB3rWvrbvrG/jy4lY2ygV7InX0JcRhBhQNEDQbVbUgIKQZDhlCnpQEJcspiThAdVtKAfY8netm3f1jJL27t0roEN/s/qmDPBWmQhKoGAIqrFetB0AyiTpK+YSnYyDj3w3v7P1v8qDA4UI+lNriD7IOeZAAR3QaXTbjfITvhMwyG//jo/Yd+v5PTGSjS4Kdbf79I/o9ZGOAQVdRLpPBuvxZHtnmK5Fr2vNEev65QpdG+1Esy14DwSijM8Aj7brjb/1iw4CKIADYgKjAEwqM5ZvIP0wh5Yyvc6Y9Fl5MMDg8QUkNGIqnAgON4MZQcEbH/5FPUcXunQDPJu0PVCAAgKivVHwbECB1JJgnUDl7eDSKroZEOh3pSt10rICCFtetlLq1iaKn3N2QEAODnIVVDPOSVSIg4nOyJ2WRlIX3sNQIjhowYnVNemxAog3F8BYpKXrU8e6xryAnq1ZeRFVSVZlOHgRM16zV9TW33aMSMBDBA7hfC8x2WkCgKHvPagbFgVQsIcNjcohOQVW+pcTzL5Hg13UyAPgcshcOXY5Z78PzIGCg1QAzvAwH+0CEIiAw+bfFW32/m37nBr5v24c6py3+Z5dnrOClZwyAEIJoN8YhcgGns0x+2VFwbfM0yJ5+hTszEroQ/OYz06P94/92iTaWR2PZs5HPQRAdFH87FWKuhWiizo9xCmrnhEQ4KAxZR8fAQ0BSKeBW2nKjHUZ8wXGPIKmzSdgx+UYcX5qXyIwBVBmIYYUGnAgtaX2bnh2A1Ng5cEAg1WjGyPqHxeCH9kCyAA++clfB1DwH1dAYEqxG+QmrAA4gm2Bwo7qbQfFgvvWEwGqR9iXBRJ3j8+R+NECAfP2StG/uuZchbGuP++Sx8y4qzCK0CU0CWUKHGi70zbkxiBHdI/QxggQuFPqKzp3Ce3jffq8jwlQMICQjd2BzZQKylaU6ZQq5WMN1HrF4QpwojQW7YtgwFXApE8b4GDnAM18HbyZIlvz+RwCyx4FPj8Y/cZFvvIgpSZkwO0w+vHaealev9XjXGTTHJQCpgLad2CDvBshKRFfZPWPJPoGVu6attrxRmkD72sMVB56gO9AQVcqVUOU1yGgrqxDbNcAgbbNgyxdkCE/7Uwml33ag16RA97vwcYqhVUsL6ODWHUQJ3vnhm2eddcBhex6ilKu4zNhNqc90PThgvlwpmDRfuujSSHfltXm3QHnY0j6W8YgsAZhVYLXRzqn3hekOybeBgqABwQM+kafAgLAB2u6X4Ge++Q9ySlwpsCQrt+L3QuJ2796HbINropzUGKgJJnevVJvDCnmwtoLe0PHRVbgaDafYfX22I6Vw3eqw3HAs8lLbKM1RuuTDH2TL0EjFUxKCax/7eU4dtzBwqwfgoGryv0Cm3BBqUWK3X6XsELs1+Bputen/lc4b0qZAm0selDMgREINLvPmwgQ0G0hvAJD2j82r/r+9HpHanE1jwZl1dbVvDifH8YkBGBAKcuqgeNwVkjiobqLx8rb6+YQpdDDlkOgnhwfu7eZC6SfUkazcLsBgFe0MRr+lRxfMQbDfE6b9DvdO1Co4L9lFJxN0/FZvjSMazsa3XMNCLjEgFwJDPwoz3TpjPXiBgQg6Br7Xv/Wpa0vrX9U11DegG2T/TFM9+gxUNYQbhKg4I7ZS+rbi0Doevvgb8A0loZL/c4KAuxvU6dSQzxsbEIFCE+eP/FtlOOOideUBwMMYhlAwUQwLnmiT977Ib7wl7+Hb/3dP8Ibf+uXMGbIBmUeUW68leYh9PT7Eij08Xv5sqxjMwHd0ARkbvRvBAN9XDiyB2YMbH1vYwSMvtM6BqHkAARkq9uK3EFpFFZVmmmiXAHMPd1LZZj1od8i+xIBQ/Q0Y58jxO5hBri7+4KWPvXCJ9QpRy+p9HNsUpTVgMZLPbyjbRz6VK+Rdox9uTKSkdodDPxR5Pn6bgMGJKclb+DEtR0ZYwjBlDogoCDfVSVt97dSimyqwAV8qH4EC3uwmi+zdgFLAPYy/Q/A49Wx/1OgtA0oEFoDNIQfZuGfph3RGAfPcBYGacZy5qVeMA2rn3vmpXn+RA92TggiU8nlVPfI4871j4UOSFkCDrqH8p0DAjK9s92B8obUMZkVyN2qb7WzVoAoyE6Zvf879quHQrAeK+aqzgwAhGJjwiH88+T5D8I2yq/Lebg4A7w8SGAgZQ4KRjptHLgn772LL/zF/ypkh5bh2kEYzCMEBgqMg9LjHY0yqUABWBrISzRwh9BjkliTLNYkjdnfA2UXKq/sB9jePFJGsOMAJyVQFoGiTEjbJgK73Tmlx47W7xpET9vm1B93itW8MJoY9ZvKLKzQ3G/iaU4YBr8k3ONmGtWAQU+jRk8qbh9sQj5RKNR73XrMklmR8tCnAEK/WmtmxeRjMq+tpKzKZQORGHwuRb03oYTJ+sPKcYhn1/Q/QKHO0/CUV6uY/lRwYEr0gK259/Ddqi1+n9D3Idz2gfR/N59puwPKLv2dcsvkTEI/kcaezjVgNPZTAMAY9MTsHreUzmD1c2SYzz0jGfSOAQGRBUbZd/DBYC6ie8q8/2Ve2HwmpE3mTdqy6NC8yRio3mHVO66TbKfNwGD6+EQHrT6wafsMEMnxSV8AS1sQAfB0XuuzZmB3OTbdfPnBi3fx+Ltv4u3P2jbK0PpeYGFDeXDAoGn6S4GCH8rmEraOdLK1a7y+AQW+PAXtdba5y4HB4+iBAtAr8r5Vo9LjY2+E8xpAUO53lH13MMD7LpnTujWjCOpEOWal4zOJYCb5S3cZabuvwmoCaUK6bUC+A99XAGEhB++LowNMAM63qln8xu15jeGYGP4BgPUCFEMcM1R/wXNwsNYbIzsO+G9NQl9HpbJ/zM08mhlWi21bv8p17Rybdl18I6IqL19aoa8MpVTbQMYKsNKavUKMGibGiK2/u/coINTb+z6wCHwcoe7t+A8gwPpwBgQWTMFN/a+5AgJ875u+5/ufXWTHKlgA4pxrcjEGHdUCgSVQnV1zWtby1NDhXMQLjv0aQwOWNxCXsKre4f2+dULu9fPL6p1tc5CQtg3pbnOdYztr8rHL+NhOmwoQ+NjhYQhgdNAmfbcEmkDtizgOM1Bw7PU5FilIwGKNhz6Y5p+9ihUcPHn+LLxb4fXh92tZgwcHDKS0NMw1gADQvam//9u649THFFFCBoNtf4IJUOgUjzyitL+njhYzsKD356AMG0W+KINwmtKbhQwmwlnulSnYdxz7ARyC3osZrMJLAZW6SQw4323CHOzZhTVtGenucIBAxw7eN/C260tzuFLMpThAQMpgVmUZYmXy3F5wzqd3H9Pz68PYG3U79aQvIfbooWHhOcwMUnyXgBkiu5fFWYE5IA3zBUC7zXA0qL33bUaMgyHq+nco7tkDgDADsleynl90gVcEw0BNyj0rkRGYhRGom/9dHYd4be/lXwsGYv8byL62/z1/KLmyb94FoTLOCyZnaoiunnN+wD/Ntm1fysD85svrOfbvChDY3yt1TikswIALjvvddY48dwEMVOeklMDbBuwH8pbFsTkEXKTtADZZfsslgHXTOaWAtsPlgnUljDslq96JQBNYOoKj3q/3dA2jYTgAIlP9s4zVbABBQrNSZKyg2K/vfQXf+PSf4o1XZRtlDqGqW1iDBwUMBrZgklC4Kk9evIMv/rnuTf2RjwMomqwmcfv6kMsd60ooPjNMdi5GySvqj0DB0WSWx84ma/TKZkbnRlDA++HCWe4lx8CXL3bp0p5jkERQeT9AW0baRVhJ9xxmVcCpMNJd8CjtPgC4UKWmoTF4bHWFDoIdu8LeNP3dH95raORMAVwduogAY+Y53GCQTpOuYlEvR3IMUp3fIRHL8zg4yTxLCryYIW/vyoHGbFyWheFQL9/aGo1NV88Z/b4qwxiEfj83YF09VkZr1f+zhDfg9v4/kudLcJY+lK2dqRqECUCbMoWTPjhv/Fg/z3w6meO8XwCDi+cMgCuGwBYsAe8/G5nJ+0P0zf3hTgjvB/gol3XOfrjOwV1GKQX5boPl78ce6TmYRudYOwy8Bbm4St8CcwdwNXeOKl+MC7pnZrQp+R4dAJoVa7FUUCAvXGqK5abcwBo8KGAgpZtUfEHYKeHJ86f1LVYf/YQqlgSQbEriw1WCAPoAmec7sVyd8QiPlKKThnvvGQhIdAFE+skZPFFJ/NEsclajxVX4zJsthYW+OwJLYOc0gtqxHQAoMSgRqOhKWgU5CRiElZLGVZU2d0HNd5AYdVGhCUg64XoFJpUMH8v0uB86AnCzciqwXWm8gpE+7IHhVYCgj02iempWKM43a4PGuCX5T+08BHRJTsBW/YSufx0cACHLHmgUVD8Gaa0ylgm905NPvLPhQBl+pZxDsto5KIhhnGv7f973QN//tsXzFCCkXAEa9jGHIiaSWnnZedhd23jdEXQZGOyOXyyxf1GdH/PKe0dE+v1Yg4J9BxdWR+Rc39R5WiDslexlyEcBUUJJjFwKmAlcCFySxOk55PkctiqJVB52dUisfZrUN9XjC1ZgAgZmujKC76YERqwx+La8MtWVFPDl1pEBQLVfDgo+2TxzzFe4jjV4MMDglC2YKawQk3H65bXXRZHbGmlfS1rqoAT86XFw8/Z1fbVPGKXEe09qChJsjvXtSQvvqRPQevOXSC46KbO696iXjwJKguJFEEnZANvAZr3V71Vl4U023msMP9TKyt+ZIW/OYwz5a9eUcK9lAttZyOAaoxToSDtuy51tbxMAIziwecNF9WkYs0XSJ5ZLH628z6TQS2WWv6HH++QvJgMHYegT2vGNfXsJFCwAgYMv63tKdUysb9H1/zEBCAomjMUBguGIYcVb52E0Eg76OqAxM0zd96lczWTq2sI1NCBfa4jNMvVnIYNr9M37KlE/llK9eNPds3IBEKyYMq977OsY9tMcB8qybXO/YqoCgcA2dYxBY78GpiCM+4Q1OCsPBhjMytIYNaBAEjUevfZ6wFItOOhZAwCC5oqMGbiArSuLUF4MyASipPJ1QcBsEE0R2aQ6m7DTtoX4s8bVSOkoskRBYnBipFRwZAJxQsKGsu8aKqjo/KpHapwsJUkQojSJ180UExEs21gEqFLdlhTk8VjA98Svz+2UmSoy0v4bymw52iImfjUtPmEnmgS8HqhF+trKmQL+IJRi6hQT0IGC5OPRb8Y1rNi4dknp2bKv5lgAyZ3xr59ZDbOt9iA5Bkl+dHBgyygtodM8+/dTbgg7MbOCg269eakvTqeElim0Z1wx35YGss/ZWOiMPmejkZ84XtsWEg7t9ARGTZQ1HSeMyITe7oFHSqDEADFSSuJE5ATu5G/WRgsh1L+6EionIBOS5h5U3SP1IZvLKYeckOsz862twDlLsCoNKOh1nK2OMPmzjZf0XP8+AQUme5Zo6E7tol43rXLQ8sCAwfXesoOCz76FN8LmSEwZYKVgAFFEBS04AHSNstrz7U63ca3dGTO3ZaOqC+g7TCD5at87oQdE2ZiC12uMkndFZoKtv5sBznkHpR2UCUUzfI+UwPfmbSTPNZDmT3a7QxBWTQpaCapnC1uCoa5QIKXK3HPV32aeLM88VS5BmbErM+6dHS5rxTtLGAKuUtJeVnH2VRJeFs+SwxjK+XAcNmwO1Jeo4CM1HTecMtBFYR6F3eFASfaXaJbT1c14zt6BAaCjPq9Mnms7Sv+GTPq4XvxkT30JjR2yC1wBsBGwS/a5GC8D6RGY6653BUIE7sFLj31/NvR9PkAno77pzqyosXbA37BClzrLuqigByqnzkOYJ7NVTxqUadskD6qytZEn1lLewEHHEHZlQLIAiW2rGpiLyD0giYELRyTdZfBBqmdGo+2GHkC6y558mLYNdJeRt6wrotqVCWSrE+L+BkR1b5XovKx0rP1mQCiEfCOYnwK2HhBEpqDZ9K0DAfpM+25Jq/HFUABkR0Ndkvjo1ddvZHYuhxMeEDCYKJi+9EzBZ9/CG7/wt+3Heh8C/LWWZZdu7MEBVSaBOQF3BNr3mhhWCvqlXVgp/Ji52k/WkIw4b5MlA9bnEOmOcaa8uYD37Ag6BfagmILbMo49I2lCUNqKr1CQbg0Z0LZPqwop5QTSdcVLQd1eqaBge6UuWQx7H0SDBVCXdNMLXx42sWGUoMiCp1NKJT/OsuavYXUulKpAzOgcejwDyOIhbanW/aAKEmLdzizUyiBFUGAga6sbS7VbxhLibnC2tzwotceI6tJHColQPh6Vxen/Tvtn+lfHT2W3306aWROAjcXjApQE1kGmcsi2yT4PBBzYqgned2nzfu/ggLZN2BsDqNb31/T7rO+BanhPZHdqRE5CnUOJLMOynnUsx3ebKNDuQF0rX9o2Y8CY9XyWLarNaUq6a+WuOoTqaFqCm8+S3hExxuB4eT2DlHQFVK4roAwQ2BJp6nZG7A219de0r9vQTJ/MeEmfT5fgWn22TZxQ22jJ5A9oQYGDhDpmT54/w+PvfNl3NJTxUJ0SwN1QmnDCuRP9gIDBSRliMhEURNUE/y7bZupmlZTAdIBKeFFL3iSWDhLvRT2XpElRtgymAQjA2jCtlElH/w3FKHeNk9rbvahsInyalEh5lyU+hywzIv2XNXOYS0HS1QqWVBTXF4/PrasTztYUm6D6Zkd3H/KNjlyAO2M1UNqz9nMRwWr2EqgUc+PpGLjvwcHKG1iVKz1iC604W4HKJFDe2pBGvgvfDSwcp8uaAcyNkW0YFBVgtz3sSF2a0srVSCK5UopAYXzfPfy1wf3rwWe9WKGE/iV7KRBJNSiL/oPCDUscUyBApeh2yAeIDqAIbc45iWyWwwGR7w+h+4EMgJm57noptMPL97s+Rxq5AAE3UrmnZcYqxucvckgcEKz2UBhCIzaKgbm5k9hNUqDFSXcJDZ4t6Kc6FxNw7Eh5R7IVCtsO6vZOSSZzCz0T90uJYIBSmusZ21Ez7JWyMtLX9HPNGQtOGKBMzeQecU50gKB/4dOQaAgAaXNZNFk1AG5vEXZQ4OMWwMFkLOdlDQ4eFjC44NHNQIEpur6LRDkRKG2yyY55Skp3cb6Db7Fb1JMyRUYVIMCSDy3BKRoGK7Os5A7tA2hi7U1d4wYk2g9siYD7ve+6R5qhTbqckRTA5FdkOWMuxXck82Shg11BcBBcMmBAk90Pzfvvdj+kuw8tAIEIyUBnX6KoT+PQRT0dAQiwsaAAAMohdWGeeAPXl0vJUb4RkJUuL8EMl/x2tFI5Y7/i+Ftim1RkBAQdS8DOEnQKSY9ZLJOT7iuB+C57ZfSZG0AQgQKA5vXBPWnpW0URkEj6Pb41MNlnIiTKSDmLlJYDsFfb0gFOsuqGyqExfDV6Uf725InBpOvufexjv5s8blf0t/a5dPcEtM+o/pNyyxLP4Z7XAoIZ2H4JGWPfE0DBGiUBCL6R0L3olryB738q/W2rFAwgBB0Tdz0EzvWL5S0tdUwEAwaMN91uuwtRyv2v16WS0BvCODZmZ7r8ZCwGlq4LJXDeXP6ijXry/Cne/PZjvPW5t/HJ1x6hoNqpKThYldUqiVAeFjA4KT948a7sCLUABWWBDpJ6NJQ22Y3QY0ebUqybvo1OFI4ABKUmlbJJYfMbYK4MZiCgWT5GmtAYJrLRf5IEVMT71IlNaiR94x3dj5xsX4NShjXIMKbDfgvZ2qd0Z7JdyUJIYLuDv1zJGIK7Dw0vNikTKnuMbS9KH4P2v10cWqlQ0oz0ASBYvzJXD/ASs9P0w9xw+M/xS39fG6/ut9O8h1XoqfdQPI55WSE1tGXaUCAOXAQDh4EC/cuo4KACBa3/pOuMmzN7lGCgQACCgAFGIkKmFiTktCGlTcCBAnRC8iVoLUBX+buj6tmWInMyrBRBOVpQdtLPTV9bf/sP5+PfjmOfbDfpqWvmno211XMwQjXsMwXc71POxFDKBkWUNtn4TTdXo0M2MuPwncsB0h1YSfVLej/6Jbxno99J1YGAe+hiPmP4hNGGT+r7BrS9adM6yfi4Dg91ZXP0mioHxy4825yfufyFsB7E6QTJrpoGyp/9+Cne/O5jvPWZt/HJjz5CYfibN4ERHMQ2vUx5OMBg0glN9qbtHd2BgqOjRNsbCEGQ/F92KpG3V9xj8TempRIAgtDcVPYQo5Oburln7hBrRe92fEmpx+sskahJ3pI+YbDsBGZsBsK+/UFILRYr+Qj1dagwY4pKF9fHWlglxLd7+m7CDjQI2QTEKTNTWGMctI5LDB+oJxOT1azerDH9SEtPwNrUG5gpyhmzE8cEnfG4hs4L87ZZxjpbNjfUpwOTnXdyWSH1gCCjgBwQlMI4oH8DKDiYFSxoyIBVlgr7HDkm0CB7mI6Qk739kZUpAIgYuQEHjJRk0WtJhERQgJArg1CsXccaoN8lEMZtqq3/6VL/+qB88OPcJiGeeKLANLx4ccwtVKTyVOcC4VZZq8jP8j9KzZ/iA5R2kB7nu1ckZNk7Iv73qEAdV+qWa3NnQkJt3UWwa1+XZNjqzzGEIjkVW/PbJT3uwMxCdsroNfqOKmPnwIByw9I9/fFTfOV7j/G1T7+Nj7/6CAfrE5SOy07CVXBgobJl4TJH71oeDjBYlGtBQSl1V34b+gSAWOimCBAARXWuoCTmyXxUBsGWKdkrM804oUOnWtr9+gMI6BC+n7sSZL9hNJ6l0oC6lhh3izcyhhBEs1EMcF2ijQqqIXZjBkCE0oOBiJZPE9ygfRJ6rGEJAihgFpqZtX02RiYokzADAZXRMdowz0XjcnhnQs2eeWNhvPysBhSM0jsqova53NPGgU3ol0DZPPaQQQH2DhAczAEcVBC9HwUHGByukeorOAjK3l4rbAllGcCWCJQEMGw5gUgARSIgE+NIhGzgXK9BInAiYQ9sbI97zf/pAAIzmAIw5OLK3diLy/0a+lb7t36+blzrZ5V/Ww8wo61x/fxrlvH24YLu1cKDrAUZu1beuEsMNd1hssa6LwSzvHXTXz++75WxjI7ILXqlCwkMOUlpq+GkGfN4Nl6drvRjqldcd8oPt+lx69vZOERAQMmhxh6YumfPn+Ar3/sSvvrpr+OXX30dxRlhAImQWDeUW4CDly0PGhicgQJBiC0oiHFT6HciAoGR9Nykiu0AKYOQGoAg8ZuMBlVHmhsjOgbQItkZCJgh/EvxQcAF2oxmXArGwbvmUoA7DT+EVwQ33nW4Z//cnrJrcwaoCsBJcls/PmXoJmlvIgBNopoNpiWqhXFostlTBUZg8ShtXJRJAIQ2nHblxPiPzE77u/ztvJTpzXnxeQQJ89p1YNKe5+OQMCx/Uo9FQACcFdgDQ3BEQMACAHYu2A9hDeRcxs4cAMFcJ1H0bhJhI0ImEoBwFGyZsFHClhSIM6MQC1ugAIEBJNZQQ5IQAxMF+asMgit6A4bWt1HJD73b9af1aWzE7HNfJuxd8xQzsMDUE529+wBo5yAPxq81jJEpGpa9dfFrd4qax9b2pYm8tfrjEKOveq+OBwObjoVv8tW9/jr2z4lOiQCAp+1c6MhLnrONU8+KxBBKfw6u1+ORDR3lLzWgHIDIFoCnL57it7/3JfzzX38LH//I62KnqObjFI0nJAY4TssIDqzeN5aHCQyImnWe/eoDMzoRFBwKCAaDxOLBOEjQH/cCfekcgTRJCslilyFJKk4yYECbQI8ycTrBW3RPjVDPhj+asjp32EGLLxHzzXhKpfksnhiV3CIWyFbvGwXCmZvSArPYliK90bTJqMZszCGpFwm041DSyCIwV2MxYXNWYjR9C2NviGPb7dzGgFyinSPN3BuVdt5crGM/jzqWpgBN2GBXMFAKsDMrUyDnRECwK1tgYGAvEtkspeYfrIrkEYjc3IOwpQoSNk7Y6GgAApPcW8aZwMooMAEJAiDyTP5s3JmBXJq5e3UfWj8C728M43OjJ9r83o31SR1v8kon425OkOVVHS7e18kdad+DMnLOvorEgULR3IMAFBrW0sN/i3aaDAFLfWKMV6MXFewAeAm9WHWdsyIrsBDqfbUen4yNjbpudSOGHsIYvPP8Cf7en7+Jf/qpt/Cxjz7yMUpgZ7EzVXAArmw22f/KkM4A7JPnTye9UsuDBAZPnj/F4++cgwLoWEdQUEML7VQqbILA3vn3hZEBkHovhyoomQu5TrLG+66egCuFMGhDrG/hTc8Mqd8TjVryEn1ZEe4NtslhUgQaBQQTAXEv5gQpT4FM8E5MIZVS636Edgyei/0NczuR3I2IsGvda6KaeDcpjMPVbE5UjFFhzTz/l2B1WoMz8TZDvw7v+GjmTQQK9ZqGBu+fHeZRDwj6PILIEhwGAoowCT/tAMFeijAJXETBsY3fOEcSEaCGPSVgo4S9AFtK4CSsw0aED3ECJ937Piex68RgYmRQAAZo8w903DmOe+/taR+e9ls/Rnrs5vH7ILzR2Ty057+MV8qVpl6uLvH22LNs/ORgBAjZP9dVJClDAxGjA/K+9EjnEEVwUwCNaowh4Vg6Tm8EOmkECz6Pwphc1OM2Xg0Y6JwhDrYndMfTHz/B73z/Tfzxp/4FPvbR11tZYqFtMhgHxPbYzwWOEUZwEIrZx9fw2qSHpDw4YOCgwNd5zkGBDYgIRgUFBziwgPKBiFBUIN55IUjrKKwsglCcBFVQqCyC2IkN0diuPAGOxmcGBHge9rCEMPjkaFF/X8R0VWiZqIKF7J+zG1kDDpFxmKjE2o5JX0cQEIFYcw7aLHcr1lsu0Cp0pOAggYRRhCSqGUgwqvlqNicqaaBKWyPwIVww8cavYXSmijcOjrU3tYfav8GorTzLwGr0YzJdaVBk7hc1+KaofnocA0uwHxo+OLgBBPYZOAMGCgo4oVCRhSEMFEg+ATKAo+BAqjkKOYXkYHmxGVjYAyZCVoq15gGp/OXc9lf07qZ9VfsrnjOA1eEGaATE5KU9zGg2cIrGZuKNXj0Xb/BKfawR9EhpZU8eJx962ZMqUCN794Rm2WnVIQoW9G2pUYdc0h/2t9UP1l0MBk91iNT5Vv3H1akAsAOuR0Akzl6cR4HtWTFPKx1Q+3vBVOvtfuf7X8I/+bW38LGPPMJRqg2SezMyk76ojlFI8qQOkL+4jmrz4OAA0oHRaf6Db/2jZT89KGBwCRT0SNINkX4pCgri8is5UZZY/c2LZ/jP/uJNAEKtGotQTBkFFsEmmtHcZmwrmmvLzICYAo+IMgKBI9Q3xnUNOPiXWBT1G0MQneDsQm8IugUOdm6Puvt2WB8PnsgEANTzxvGZFUvRMYdJqDWlp9kMg4K1okJOI5vTKua4umFRLnoyeaoABiYEYV6dlNjXvXcDkM8t25EnTQYjsi7DeKBlCWy1QQmgYD/G0EEPCvYDnpcg9+H22aEkkoOJCYUKtkRITEL1H0lBARwcICd/18GWEw6SiiewMAYFlT1I8DyguC8CrL8IaDeT6FgpDnohgNMoS9eO28jOQQFsiNNnAHxMqWuy+XJhPg5zcRKiM1aofq/hnrKQwUvyV9vG3t5m2WlgV9s+qP3Sl0E3X9AdtjLGz8UlndeyoqLvar1z0HeJ4WzI0TCRosdB8zbER0+BzYkuL2Cfj//4V9/CL37kdexxF0j4IkQUYiSWMBszjETw0E9svoMDMJ68eKcy6eE1ALPyYIBBEz5YgIImhBDYghIGZmZkAeBv3nuGv/+Xb+KPfvUtfOHbnxLGwAaELd+gIrZIs5UwMc9mVBSKSPP1a8dLMdRf28UQFmPn4o6ReVwnjJ2AgSSZ4bNlZK0Rbj32VXMCntK/C+UT+vjmpW6KsAyTp2SCbvRyC9Yim1OXnXKjkM/iumdsQAztREbHPwfht/ZegUG8bytIC0ukrlC2sVidGoVb6ryfgoLC9fOSKWhBQWG4lwPI78YUHAByItjASxgBKIWwpzk4yDmpgiwNOEDSCQDRjLrfJfpk4Ut9gq5fvK8mYwZcHrfVmKVESAX1M4AVdd2AheXDTuj1MBcbI1SqM2HjHWXxpeTQ6xP1BXf6gj8wfRHraCtirtVz2m3YKCEnEnbAx4cr2wuZs4c5RtSGK4HbdPksXNozNdZmAPiPP/y6j409xgBABqMo8AJLXY01sApZSAHh+ifPn+Hxt790FSgAHhAwaHMKpMxAwYotGJQE6rl/85Nn+Ad/+Sb+UJEcIEotCkAxDyag5QgSEkTfXUKaDbLUOk+FmaGKm0NmOMD62SdjyBTvS8wQNxAjQMG+z4CDXuP/1WJGqlFnnfEH0Ai11K2i/8XWLsjmqUzqtCXCoYIm4EzYnIwK1iwebTkhPZPTjkJf5iBzFqudbQBk7ZuyIjw8punLuWdmZqBldrpbDKU3eLF+rojUsB+QebQaD9i9Tn9n/5toXTMD1rNyAL6klArXhMQADpik/r3srYqPwQKwXcVkTcZtNmbOwhWuiZccdMLEI227aj0nZ/otxqxjuMDCRMW/y/n7xMieyyL7rtHWPgCNPAIIoIGn83oYn9DPM8PPDNdn71+/MTat+5ZUhyjrSGV0MkD1+8vq8l72VmwNMO/3pDcuWie3WzPWgFtwULdR/rq/W+FSeTDAoEVCtBSn6BmMv1WhMIT6Nz95hn/wV2/iv/y7b+EXP/y6K8MDKuA+KLrpS8MiVEUVJxJ1SnLlqYhQt8LMXJmBSPNWgFDXnccMcVN+s3ewWMwtZownUKXbFsBB2iJ/80Lxx7XsUeH0a93jmETBsOfch+fvSdjnXTfE2Vm8AChA0BSsABCqR2kx7aRMTkT+K+PVxjAXrEDnfbMChzNPp++f2I8WFrzM5HAHGLQtC7W1DOVoe1jHohIpF0Is1qdh0HIiHGUEA7mLd1zYLVjrweAkb1HkxLAkuqT1PpQdIBKAQAw0a7e6tsPbeJtnCuCqMevH6361gRPG3JiGTQAcvE7bYlVgtKAU1wMCYxdXhtdKL4/3Vj0DAd7+US/E3/vSe/orw28M7mzlS0wU73XbTK9lEsfAAUFO2Ai+EiYnAvVORqIKXo1F6PrAypAIPgHiFxnUML3s7g4IGGDw8Nz67FA5CCj4UvNuhXNZtvJggMEbrz1Cm0Q0oumIk67xKAwU/O9/5V/gP/pbn2woUu7uV+ObLYtgIAGo3t3AebnRGSdPzxCcLRnbi9C/IkBoPgNVmOxzVDqy3K/WswpT/70FDUCrEKx4skzX1n4THKBF+33X7A48pKP2RAoKKkjYGGCSLHZS94uLMQUhHo1IOYfwiNb3QKvQIrVpw9QnC/UhKAMDzPMNgEzxzfqm77sz9mbLaQintB5NVR7WJm76edzGOOoMSgJ6KZG/nAqwuSAxzi0z9oMABrYsIQELKaRMDaNgmEDmETXzasv2uQU0pO13ttwrL+12EE/QvSeMTuehzTGhbgUE4njNjOWtY3ZpAyfPjSlQkCB9bgl88b6reemAbjEvPwiWMTb5vqmLLCN1oBA2r5r1zUwX9Htf9Mbf9Ff8DmvjBX0GVP21JWo+bylhCyth+qWyvQ7p9XlNYJzMB74NhIY/0hdBcGK4Rqf9tBTUfjfW4Mnzp/jydx7j6597G49i+GCyUqEvDwYY1LJ+MZIV14MnnlAEBb/4kdfHSa2Dl3V9SJFHDyyCbYVuHs2qUlHYqwfQGhlbMnZAvt8fZVgyth81O9yOHxoDjhOun3zxb1KPx+jNXpHPAAMA3/wpWez3pAxLQhe8tG8opcIou9Pp3GZRrshJDTNjywQURf7KrTGJAluHe6hyTDM5j0qXa5jgkifm48VRGQeDtugjUzozxmbLCRnsGwLFcAoQAWoIN0weM1ULOmz29sFM8p6J+kZR2XxlSwlAQdHEQQEEhJIsTkqnOwz382ijhESybJHce1PvDsEjtXpMtKMowxa4x3bOFHKk0W3DJpOtmccq97tuzMwjNbp6K+zx7S3JMtusXmlSZouAmjiL2+dlT1NbvWdM46W2tp74WjYjqE/a2ffDmeFebtj17wkAMOMfc1cMDJj+mukx+xz12JaSzzXro7hUVi8C7+xLZQ3McWn1ua9SO1FzNjbS5jUYjW1wB0T/Egnz1uRyTJ8lTkAEDs+ey7sVvv65t/H6a4/8WXSFbgYeEjDo2AIrkS2IgwWosZ6Agx9pTsF/+Xffwn/0tz7Z0Wrt4GnStCfEzQCCHOKpgm7qim4CcfVkzMDMQIEZIPtnCWH3R5F4cRkFq3SaO6nWNqRtApUTNUK2pREoyHUVLPg9T+LK0/aHfrZry1H3jrCUW+YKELakyk0V8H4wkAv4IE9Wc3AGjfWrN+yBOa7PiOgc6Cnby4Ag7ghoY2PK1oBa7/m042BNnbE0jI1ZvdGkIRQz4IHCDvNPvNTW45wV0u4tUAVbEvZUsCk7REyaDChtI0pgFmNQqNLYDgpS6DsExmDSPgMEM4rXWBKneVFZnr70WKSC7aqQzUDOvOa9FJUvXnqv146ZtIm9TeKhEvbOK42hrwSZmzEMGfvulrk5cywiAFoB1pnh9v7t5LMMgf21gmu8+wAC7LcIBHbXWTzor3ivMx3W66+SgU1XwwiILQqmLTDlilwS+YrUdNOlskDn8KFlUob2Yj7/Is7rAUGf7GkG/1KJ5zAznj1/hq98T1+49OqjxUVpTT/gIQEDAKctvXAZKRL80XtPPdHwlz78ug4gmlgjUAcx66EBIJgi5FqrMwVtgABoQUFMBIvhg8MU1/C3goKf7WKI7o+CUook6HWIu5bSMQZa/5SQiHCXaQAJA6NQAntA0pI+jryKfffF6pkoUNKFnUGwcgDICpgIshaeC3AklhUJSVeFqKEUHcBtTBrALC69ogP71SCRzTFAYAbG8j1iWKc3NN1DnXGZedXMCTsBBwo2liVrnDCwJQ1A1VvP5l5zjOEsypYIGRkHSYZ8LrohEQObG1TGVtJVRmXGKkVGxBgC+TwHBDVsUut9SZ6iQjZ5Wm3pXI+NmzbNYtr9mIk8yJzfiyRKHupVHyjVQ2U0Xql463PaWsRo3siL8xMVCFmIxJiCGDoY7lt4CQjOjvX1qveza+q1Z4yAgYH7Q3/TG5zrLyCnqjNyEt11l1M4N8kqGEDAAdj/HmyvZy+yTBY14TVbjoHqETaAcDL37K/PP6CZg/L7HBAc4DG3CDGvqMpDX9558RRf+d6X8KefEaZgXi6zBg8IGEgvcfhnbEFzltKkfWZpAuHdnzzB7/+FLEn8xY+83iA8QGKFq0E0dOfUD8vNDY9eYm8aUNAcP7/QliHFvwNLEECBCd+MHtyPoLjN+JeCnIDCqQEEr2ypCqDGnMWICS0qgkRNYyyZpy/XgIV0+RSJVybCwYxcCJxY5bwmGpYJLWix3+F+LuBtfPAalmC2K2ALDlrvp22rgS3t17Duv7CEEGxJn8wP3T64QJb9lYQV/Tjl1fTECGZNdCgTNs7gLABoA4MLnWaKN+Nhj4g0L3BxBcyW0xQMmDI+y1uchUkiKDADOQNx+xwHW2EAAQAASURBVFHB9YrWHvqUApBTgLzpXJeNnBjMSTZmqhpBGUD28ALUgV3S1rN28Tg/e1BgyaSeJ7EIDbRtaoHdqqxCK82brDtgtQIE5shUHTXqLbn34pmsDKYhKxQcRMrMsOgpgoS+EvvfM4fSE/pQR05JxpNr9C9uBwVAXRLqxWSg+Uv2E6D1eldBwdc+HUABa72pud1pm4EHBQwuF8UEzXeAkMB458VT/N7338Q/+ZSCgtJSq84I6PXD4M1KmHP2kfkyrXtLMQNymk2ppQcF+2xyH7KUB8lofIkfp1JwZ3A7EX62G8OQKpQGoJlgPnEdacc6L/puFnqY5y9cVw4wtjAAvgqhqSrrmLT3vRQf3BdGZgYKbBOggmp4TLf1no94JzyArQ1JvVA4VW+vVZPlfPJ3m/SPGVRbvWDHhr7WtkpiH3tCk7U/b2nwvq9dTx7rApztn7EGAmd1j8+Vly+1x2KxkFwNzVVmJ4KCuFfD+Zjpcz0pU9+lojkYMnY2aDaLRjC3JfLQ14y2vnWOenuviClbSTYAQEAGEzbNGL3ut9l28vG3GSiwkGcEBfd7mQKCqc4CsJECBlXaKV/WEyndHu68VCIoqAfX58/sSL/ks5WHICf2nYQp+C0FBY9+YcUUDE9a/vKggMGEIJiWyBoUAv76xTP87vffxD/71Nd1b2oASddGU6VWRRme3BPjkqxri4EWmwiFangjs7GJXHeDywDvQRmpMBcm9fAFJSMRCiccRQ25xX+LAICVoJUJbV9Kke2FL5Q2z6DNPWgzhyfXdgfbLHX5a/QzMHqffm66Dry5up7RpWgNSzSK0QuLRsZCPEBlceTaqBTlWFR67UMwgC3zbIz+vLDSb6Af+yWNcVvY4UKWi2ZUdeyXjQi25VIfP71Ur2h/IgjwY6GuOKtveGAh22GSHBxYqOgaqWzCcjw3YnLeOGZGNwPiiRo4LgUoSQG2UdZAA+aM3WLUMKRJGQdAu5qjl0oG4bD380JkJ5cQhkuEDfDkQ2FAqyzOEoV7g2pzWtjCutQPpfvtCg3d5w7YM1dlC86DsZryPU1zpTbLlbLwD8gTXmNui4HWHrACL+/c2VLeWX7bsAdEeKbsrVDra2FcIuCHARSswwdS1lCvLQ8GGPDisxXz/uuAiiX+4Yun+O0/+xK++htfxy9/VJiCug5M9zXgaqgt+3trYlctIDhTfsB6UjmjYcCXDJAQAH2H/BHAwQZgD4kz2lKjzBIR7o9KXZZSlAFgHCQK7xW0QhdDCdauGLOLgnaX63fL+r1t5cK8P3qw39PQcg0N66d7OnrliV5T2nBOO6f6Zau3FOv/lylnmNOy9zNUMRK8TxKgO7ppv5u3gdYLjeMgVazKywxuDM/Ffelj2CWWOvtqme2e2b+/w47Pzo/Ps2eKTSaVVV1JUSRBNTPkJWeJwIdtJcvYpz15PY0+v/Y2ixGYahyalwBoF3Nt86wfZ6V3LIzttPdQcIG+bZRAIHBhbNCcgyQqZQwH1TYdDOQgoC1wNs9ev/tKFaXtGTXGX0hYlOQ1dnqxsH2nhgG4pK96XXWXE+4y+V9LAG3/JdxtLSiw1T6XwlmnRXWGRzW4Ym6zFdHJHOyHyS1VveagIM1BQWQKZnMljuSl6f1ggMGy2Gig7axCwDsvnmmnfh2feFVfbekvt5ZudB9ED5dw222mqc0Ahq89IIheUSyslEEf6z0A3OUkiTCZkQsjI8nfreCnR5J49MFIqSAdNnlYkXLBXSbcH2kavxsWHgMuYACWCYh32dahkwOAuiZ93PcAGL39pusmEtdvjtJfO6Om7V4rUHBNrLqHW7EWOcnqB1vrD0ZY2gewJjRZmKccRtHqZ7tZIvQuX00ARZvYaX+Dl5PV+NuyKtkhN9CPBH+ZlO0FX/eHj8puNLxWOCS+DZu3aK+0oICac2KJt++fmbpzVoCl1qU+k0k3PTJWQwGCJZraZmRF70tJkk63nMD74WNm7BsstyNxFUZwHbjJmEVV0DBkga6OY5ZD+30zq5lzYfccu8CPR4dnxjo24ECT0Q0gIAs7sIEcEGxN1mi7odOd9387wofWmgtLXF/r4is7yMJpwH4IYEgpYfdVRwk5seYCJNwfKjtFdFdMPOwVRwQDBhBMV91lBQP2Ku+sS0azOBYfmgAGX/5LFVxfpbsRpgu0n9XJg4o6AS4cg/2wZ1C1GyavVR9oe0lyCn4rJBqe5Q/dWh4UMFiioEnnvPv8KX7zu7Kk4xOvPVJFIxQPKUpOEE+DVOEcXCfGmfcWDZD97SnSadWIXJ/FWK9tfyCoWV9Wkgq2QtgLgUhWKmRibEzYSVcwkMRJN41bW5avJCemKT0aPZ7VksXIEFi2fPu57jAW1+ADaCj/KN8z5b/aMS2uTO2BgPdpEOQzQLBaDsQh1ARUAV+FePYkGwFRAjYdxb1IfNnW+u+l3QhIhHx89tkmQHXpW10GJ+CAPIlNxix4GTYW9hcG2DD30Gf9EX6JcuY2hNsrZxT3oLiCIuwOXahHACfGEkA8SSZyeYWOB7Eat6LKuMiYyJp1M62OJHScGDiS783Qb97UtMsMfxgz21An7tGwJeiGTe3qixllDdT5emmupmCUIuuYUZ2ZLRPknRLKCujSV6DmINxBAMNsUva7PQItWxA3LoubQR0GFhiex7GR5W+Qf95Su+/KK9tcT8mYt7pqpqei0zIDBBY6+JACh7jHxMww23joELX9EMbG5x61TqT5Xzy7QTgP2v0RwEdQb3L77otn+Mr3voS3bPXBDQCg5YDm5cEAg15hzIrN96fdjlA25wwUpKSxPlUuBZ3CgSjeFSM8Mz5EYeDRzrAoh02+D8f4YgUnmQgbsiS9HWL498LYkybAJcau69D3QtiTJlNtYzLVKm7aC50ZK9/HgNB9rpvTxLi/IPDgIXVevT3jUlmeEX4Yvc76t1eul+hAiQGaMIvHw1yZo1WIJxfGroaGSF4dnErCDo27FnLvCRjjt9KOYGhCP5tSs7Xxd1tutnO1vrd45BbYhgYQUPVKpF8YvhMaG0N2Hr2214Rn68y+P2f7iszuGeI01zyT7Hn6jExhxQkJXU0BIBSu1DYSgwpEwx4J2AroAChlWZKjY5aSJImmLImkRokD8xj5uPVuBAftmG0pOSjoKetokJzRuWK+RqMEyHw1w2SA1h0Nu8ZeGALgLjYpoPVLdDMwz7Lv34ES90ywZM9XOIV9PUgTPlNI/nx5HdUCsjmYbsIJyrjVcGSrs33HWn9u/dwCYwljmYMnDl3LJFyyGQBcZxJGloAAvPue7lPw2bfxxiLRcBUKM1DAOB/fBwMMZmXG1Dx9/hRv6t7Rtk0kUY0e2G5eBKHEkikXH3j5m3XkVgkkPbqcvexmJvQx6cj+MvT5TMioG5kcLALQvhEvhfX05FnykhGfQna8tocX6+lRvVXrywgEVvuOm3DFXfl6Sj/2S8OeXDDWsatX50ZzNOvfAZydFEtmIwOFESBgHuI5SAwNHQUbS98fVJevjRvmtLsErjY46o3LTLFtwauQXAP7XAGBKRdSMECT108PnT3x/2no7f6ESf+e5lasQEF9hm+tS4T4uuGkrxtOJPkDRwAIB+nugUWUbYHuXZEJmTMyiobkclhZIvJ9MGN7yfGK+zTMNm06o6xjoqj19bXz1fsHARCg1VO22qQv18hXf+5dopqPEu5vMfa4csV3XNxsx0Vavt/FVoYA1+uoGMa08OYtYOBSHs6sT1LoX9ZrLQ8ngoSeBY6lv/9G9fmWS2DO1g9fCNP9dX33QaxSvOsVvtZpeVDAYKV2rI+eTV8oUdd7J6qJIuBqDAwgCL2sk1BHscwmy4QRsAkW2YRqHOcTLyaAMcwD0O+Jhteo9iDhQGpftLSFXc62aqiAEWHaxDp7oZIBgUjF+RIbb/Oo7JbGue/LxYBeymw+u+9MYFbC5RvLBCE3AT8mIZ4trPXfdWfAdtvZSquabjhTeLHPbQMg8yZi6ECUWg0dGIDL5mkkVXzemANUdoBZgYHOLgcHJViUzmBf4/JcW669l7EPRMIaGCiA/k1Z/26glGEvyDpIX4hjpzGwQzw5Yll1QDl5aI4OYEu52fRotU1wLJe3sG7B8oohyDP5CDrEuyN24aQeU2/R9BV4Cgr6e4zXd9+7ZyzfDZBkV04GUHIFC/tGzTtEzrZmvlVHzV6QFN9XEVk1+U71XRV0Xf6Ni4e1mwiBmHJb0TAKCkz7m/X2Iqdaj8q+As9ePMWXv/sY33D7hebmZrpOhilW5bQ8GGAwlYXw+Vnz6snXAVaXmetAESWZJAuAEAc/B09hVvpJNZtoqxjv2LYg8CzkNTM0/knKHsgN7W1qh+6wdmxqyEr7NjVbpjh7QUyMZV67Cc2MhrPY2AoYnbW5tr2WaqJI63vh4kW51oZFb6BXuomqEjyYVRkAtCXcMXxDoAMMTvmlXsoz6/ve22mWMRkYcCBhtLb6cwYIygEqBZL5fQgQMICg4EBb7p+nNP/LDsC8wePtHRBUGO2gQAEBUwKxbCqMdMixtCGlDCJC0pyOxLI0l6Dv3WD4PhYemsvt9uP9eL3vl14BAzsQwcC1rOJZYb7kLS70VXe4Yd4uPVPP8lnD1YM2nWUODLMsj2TOvlEY47oXWMm9Wx0Vc5amO2bSyArMYvbmmV+vk6XEHBfXF8FWzBiFvtgRa9OmzpUBApDsU/Dl75hTO7dfIIeoXke79zWAIZYHAwxioe5z8z7qVz9ZlSFQlSAlAId6JIRMWZR/AAhxoC2j9IwAjQPeTzod71DXoJB7BeRCoLAW9qIofYmJhj/cK2XUddjQOB0DzNkFzgQSGOOE9bH63C4UQGhBQKKq1FpgMG/7bIxi4cXnIZ4Xz1vM9BnYubZEb8CeVwUdrvg2314V2MxT4hrmmdGqAJpYrJWYcDlbdun5ATQHBBY22M4AAVfGoAKCo2UL2BLyxLvmIyxiO8kF4Nnbk7pCZ+9bDrkJKef6AicQLHzAlORzKTKuxhjwJgkFChCQNuSU62uNS31lcykjQGAWGbubjFds8my81q/JnocJoszMKOuzVRrnnVs/Xpr78bD3ugH38NutsmphT5ODqJcsTwpcdZOAhVRlSm9mYxDbUutedZPXawK6rJ/jEr/ohbvO8ra5x2SdOO80Baua0+kx+9j2xHNGoS/9WOdUE4Mz6QuRvv0Yb3/2X+CNj34cdNy3ddTJx/ZyFLJXctVxWQGEVXmQwMDKFBQwq/Lr6dJDOpQKQEn20ScCKVBQh9yNky8rW/R0FO45GGCvQ1OfoIGom5QcJiXpBEiqLO9SAitgOHRyVrCAChaCUJrHW2ntVuxnyZO9Mpu+S34mbNZWMxxR01oFegXmE14T3ciMA+Kdp38BG6vbAUGtV73W4rIxBG9KzB1tRA9i8v51pVWtno2isIp3ytpA2Myo9MrO8gg2ugAIDAyUQ4753DsqCOAiRp6VJC7KKnh9u4kff7vUrd7AjtRuLFXWFQOQ+Z4EDFBWEEAZoF3mRslAyhIWSfkUIOxEvnRRwkK62qBQY6gGGvzKsbJmxPFayc4l2rqBTy81jetFM0Pf/22MYjFQOJFXYC6zUV5NVkGDXvIVJIif8YHqpssgoNNJnf5d6l5rp7bPHEnStpoz6bZC5T06FX3px3uzsAaAp8+fiP36zFv42x/9BGCAPoICPgCyUEKSpyk4MFBwLSCw8uCAQZzsU1AQOnZMtCrqkVCgLJMonTDwAMJOg/N6mPBbXaKBnCZ8dXFeAAN966gvUKyiTJLXGykjUwKnjLskdTbfrzCp4kOTt9B0Qd+fYdL2ysyFMLbPjZAJ3dH27yp2PTy4xpV9nz9rY/ytV0bh2n65463C0Xhf3d8S8w+AavC9Xyn08+WNgYZHW1P0+xkjczFkYGxAOcSAR0BQJBsf+w6wvGwLRwceAAEJE1A3MATXAIQOEDQMgo1tSvV4yj63OW8OErBt4hmlAuZDwAKzbuBTQJwGgHCn9yqWo6PycCR4/s4RwB2Am8aq36jpEgAwOQJGNu19QNrJtaMn3Bj9IJcULfJLyKzrJPkCVkYnqyGVsaiAK9LxH4huio6LtT3q3XJcrXP7tjkosPwWd1bmtoJDHc9sBVDHLGudn/z4B3j8nTfxjU9/DW985GPAcd+NDYCYcEoMogJOG1bg4FqQ8OCAAXACCs6Mcbha+jr5pCY2FiG5UJtqW8XzGn82MgPlaJ8/o3CHWG+4l9RQP1ThcyOpVCul7G0go1pdMCOaH43e2I4R3PRIu09iiwCsgf4xfr0soX0mfPqdIpXcC2i8JvSPAalmqG4MLbTXJt2dUbRP7L9VPghQFZ6BNTl24bm3KL8ICGxulaMFBODKGgRAwMeu/Lr85kDAmAOgggUA3LAHnTI9TsBBv6U2peqAG2AwEABUEGBA4djBeZPvKpfYuAIE64eUlXUoIHnblNQ/MAhZQy8G3AQ0x2RfA3vXjxHCOHlT9GCTOxDO1U6c9+UtZVLRweOfOkVRL3bM5aCLrpRbYNRLaWsdGU0Udb1EinBfWi8d53qpHHM9G/uneVodnSa3JepZSgJIzaHUOdYyCXKvc1sRnl12PP3xEzz+3m/iG7/xJ/jbH/6YyCXCmDRX2/tYhQcjLheZg6fPn84ro+VBAQPr4CUoaIzy0Uz4iBRlkkpX0sF10KPRKTuMvpkWi+91YQJP+uqNp01mLAR0KZCtQfTJijqBDRjMMrq5M6Ly2bzh8MwFszEImhmck3g1gDZmbY9Vo3EaVwZNhRQ9WNB2cByj2Mb3WwKwoHDvJuyRqlLg5m8dzcaL6Id44knOAdrezq+eIbAwgZ93jICgHGJ0e0Cg17N5W4Abfo/7RpBwKcfgHqFf4OwBEYFxX4GDglsuAhKIE7gkIGm78ib16wECq7yCq2FIWQA+BQZB50pKG8yblevg1HczPoux6cdn/BsMz7X0/AdR+pBkzwCcOiM/H/l1vaT6iPrfer10hU5q2zdpWzkutAvetrGMuhVBtzpopb0BPcylYZzdMTlzRgKD8/T//X/GF//st/DNT30Vb3z4Y42tGuwUo9oqZAEHKg4zcADUnIVX8dqyOg8KGABx9YFmbzpK7CZRP4EAOYcSiA+ASgsQWD7/4L135T7HfZ2svcGZofOZN90nfrny5WmsF8CoeDsaNsZiRUAnxjOChhuAQW3TWtCIy7reRdPuFgrQFUrK0q7QnjRpj1BmKqQTNC9NCWCuX3cfBXWplK/14AIrofdtckLi8wKIcP+ZqDE2bRUmBuUl55aNDx8CDLDf+/lsYGEGCI5jBAKzfJErkg/1/cPN+Z5PYICaCJxNHnkACGTnFlV42seUWVcqSDhBNGQFCKAd4M2NENHe7IvQhKYQwlGzsXG9MTH2nSd67f4Q87IAtKfzt9VBUoc1EGiclJX+KaUyRRN54Vgnk2FgopdSI6e9I+MO1wekk071KbDWqV29EfQPpyzzWNkCB6RpU+evApxGB53ZCi0CCv453viogYIzO2UGP0k7AU2YLzXnIEwTW7L/1ufexv/mX/7BMIZWHgwwIAhT8KUICiaKtCrRCWIG4MtASLYejgDhyXs/xBe//zvyczkCMp6g5wlVN2UI+JgqbbZzQqy3ifPOSkpOv1pcLxpZ6G8DcJAfvN1DmdBtp4qjr69e11DSTb0rvdxTx9YOjmAhbyKoZUel8QLoSbka4iCU3CgaRdtNO9u+HeKNvTLsf++EvgUJaTivehAngGV47mh45go/KBQLITCD9/s6XreCgggIJkCV+/5YlaMEilW8fhyl6T9OGXQc8Bf/pOzAkhLA+w7aIHNnvwe2Owc7tN2BwaCjKK1bAQKM7h0MU+z3sKjsdCzm4+GXTpR+vc/53Omfy8Pv5/O3eXbUb1NGryadWh/ySvdwCC35/UIxBigYV0mASU2uSA/8P2h9xPa7OSSxHcBFPUpdfbnXPWqcBXxqqCqlBiAgOJQAtH3zMFtkdb75qX8qOQWz0If3wVHBrWXDEAlYYQUmTh3I36fPn+Hxt+uOv2flwQCDFhQ8AoxKDOCAopFeomcEJcIOEJ785F184S9/D9/8tT/Gr/yr/1SFCVii+Q6xN2vFY6w3ULvMSu2qwub9fkrrSnPWcVzPKQDESAZjOwUOQIOSh9Kj6+j5z5RGNCx2faAeV0uPkHP1/oJQUkqAJZ3lTYBTEiEVClmT0JwOFpZH2lKCF3KE9p3EwWcKfaL4B80cf5uBhAjdZ4qPJr+v6tbUqaNFJx6Tz68A4tAry2vK4lzu58iq6DxzunV2/7PljPGZRbMAZLMCwHTifu/zAuUIAN/AR2By3JN7yb6Pn8/mRv/78IzzuTEye1fO36ZuIzPgSaezkJKHlq4IJ3X1ZEDCQu6QVH0T9VEDGICWaZj2U2hfcDSmhj/qI+2Pi3pTGiHANDop250c5wJhnFTvFMjcSpuAWwuTAA3jDEBAwrS0+uaND3/sco5WBNYGDjgBVEBMElKg7PkGT148xeNvf8l3/F3xo1YeDDAYQUHHFgBj504oNTl+AJZVRglP/u1f4/P/ze/jW7/6jwXJATLZKKG+5HwVl+8AyCIBjI+9ZoMfe/XgDCSceW9dcVo2xG/72O0AHPQ3L1E44zOi998DlRu8y6a+9jx7vgIXIgK2u8YrxL6Dtk2UyRBj3iAbUnHLInCIV9IFkZh4I/Z98AD7c4BhmVNz68b7OTcEUw+xudmC3ehAworZuWV5oVRNWRtArjdPRF4AUSl4O3Z6r8k8C2MPG/tbirWnOkgyV7JuIs4BGM5YnFgHK2cM0gUwcPM86OZAZcD6c8rci24e0IKBFVCEOSIx6XSmd/b7KtMxfDTTPfahYwxExrOGiHIF/9bm96uHtN09eFmGv2IxsGrzzgCNGfntTkDottVn5k1GZYPOLQg4sJBVA0htLM9thZV21VoH+pvzAzggYzGUvSCCsdVPXryLx9990+3jJVBgzXoQ5e2eHolsAYKgzv42oKBFZE9+8kN8/l//Q3zrP/kjvPHhX/IJZnkLrly63p5SeT1TEAQUxigoKGATVGUQGsE02v6CwZ0qXUrVEJugAg1wWJaoBHuh6wBAU7dwXZ+0RP1zk1K7SZN2bJJnnfB5k/vlrSah6fWk93NDEAXTWARetLFX8Pp9lqjUbPgTqVUOm+Ze4TXHdvdL9qwtQG9IgAZUxDrHMwywHUfXtnhSDbswEJKWAM/sh8aEuSi1v4GTzWUBBfJGMe0vU1YnCrhvOwKQjeyRg9hY136VQgS5Q5cUYVyzvgqLSEC/dtU8dfikH/We9fZlOOY5E7P6rMbdPgc6PYKYCmDCHA736Nvs504AQR8ywH4/MgTHDj7uq4zv963O6dq9lGmro7abKDV7U7CNH1ABQ9c/0zJjLRag5Vod2eicI8yrTV80bfWM13ER/b3J9rJU9hYcBG/+alth9Z86rNE22W+pggCC/yWWV8A/efEOHn/vK3j7M2812yiHoM20PBhg8MhfKBGVuJaVYuxL4xEUPPl3/x0+/6//Ab71K38ooGBCHy73s5oNcnwGS2iDYai3KOKtIMHjfb1w2nETDNsdLEz+aGhsy9DGCM9AQ/wej1mZIPYVCODjmNZrKPvhdaVENTmJa+yXUwr9DXCxzTw2EHa5j8WYYQrW6hhjcCaYygjVxjTtGr3v1vMuk6TKGMNtY7AXQBtkrMyjkgMa14zn9QDiTHG2LWoLpTb6pUqM9FlcCoiq1y/fVfHkO7DFPbGJYs5o6WTzzE435A85JREIaP2mYMDr3u5vMITDJv0yy6CfluiF6ncBSTbW7Tg358bz+tKNdRxnz50BwEcXc28ALlXjcss8vgUUGEtw3AtzYMY25jyp3jmV7b32d5RtTPSPG2Q7PumzoW87Y7/SP3Iqh8tG/Wi60YFo3gDd/4K3O8372lv5SCy/U5F3bTA7dd947VIp1MTkK2yFfV+BAvt8Qf4B4Mnzp/jin/82vvHpP8Ujy7mjdoXCqjwYYDBFPzGMAABXZwEDT/7t3+Dz/+Y/D6AgoDO7d/RoX7ZcAi2HLTkbQQEXARI24fmIbe1RPAG4DwKRQLrJyMxrv1gWXoPVST5HpmA+FSmTClsCkMBlR7rbJBUkAXTs8Gl61D0azIAJAcAeY45eYvS0iTtwsMgPuAQKmuV9kxwLb/dJ9natVIjHTgygfJwYEmBMKG3uO/nOxc/jgurlJ/P6NR6fquHjUkAZ83BQOYAcwVqYb2dzOtSNUvQQq4G/ph9WuTHTzZJi6erWeP0LANDHrf26ps2Xx7nP1B9Yj5TBLMapzmGrs+gen8fevslzI7i1U8Mc9raaI2I6xKh40zkTUFDud2//XO+EZ6psS1WTOwHRUWlk8gPWO6t6ybNrvWTOHGLPjx2ETXP3DuCgupvdrIgwXa738vqZ4T8BBVeVgicvfoQvfv+38Y3f+BO88eonzlbbTsuDAQZSKm0/38FKJkBTqKPmuFRQ8Hf+DwIKLj72BnBgsVkgrNW9beB7UMAHO2peCqlv73uoJ36AMjUCK9ULxrTfjKarg3/uvIeZYM4y1okS+OBBgfAhQuqurSlG//w+BPGaciUoWCWHXpOkBYzesi+xnK0oMQV27BrDrL+zZvSv3kEg4xj6rH/vQbaPrdKlCcChRWZ3L28zT/IUwKwAADAa1P5eEWx0c3bFFsyW0tZwnxn+xdjKCfL3hvGNcethb4btrj4b+yk4+LmW3jMNn6sRHkHBdEXK0VBTzW0piRMg+kbDs+9D38zqcQpYdmUr6jsRARygMxAwvVkat0u+tvRz5gxcds+sn8f58OS9v8YXvv/38I1f/6rs4wNzjLS9MQF3UR4QMLi2Uy3OLEs9RPrMWLOEDwwUfOSX7aLL95w+hzvDL8+TJVIaA9SlL8wJKKQCk1RxsKzlZvWI9T6UM7jsYw+YIMySggqqcs2m1MwIV6/dhFau2RugMN5zTtN5F2TyOk0z0GFMRkXuFk5oJ380Fq1ynBmIni0A0CVzAXVMuV57gsyH3f9CXsXVCaK98bT6mAL1nI/7RQKUAgQ9tybcjX0b8xOqX69lUH71jJg018fVHbzN2IPmdryWmF5WLnn+YUz99qskTvRagCVzvM8TiOWE/ZmNqzTv8tgi5vIomJvtzSCAAE3SmlPW+cTIXJjLjBasMWmYbjfDEIC4tcEdFatL1TlCs0N+s702S3EZX8k3UGU8lqpvdO4lOgFxo55ZAZMzHQhAgTRP6zTmRmQgZ9gumg6+iRyoC+tBChDCnNWVUD5OZzZiWTT7J+qmmJDaPw+SE/eF7/8uvvmpr+KRgoJ5YZzZzAcEDHA95WKC02T4Jk00DDkFcrJfM13StMpwtvvrvSs4yAAIurJE6qEJLk5cWHO6z/YbH0K3G60HFDEuWZGz0cOL4l46l8FrN0Vx+gY874I42Vfnr+sRwxp2PwcFKSxHTMlXKJiQkm6LK8K7VUHd7pqNkDgCij4DHAhzhtt54Wvf5sIzKKgeFOzhDWh9LDT2ASXVtTJmcaMf2jYFahm837sR8XmRJ+Jr8enQB/ogue2VuQnEBch3jeKi2FdYgIgbytzYT8ZIwc2lug/1NECgbwuhnCUsP9MTF0CBx9yBGlK4NK4ARNBTjVnHvRl090bKJnMrj5lc/6xXK2gJBoTt0iLHmVTX7PfiiJj6TwWETcEEy2q8/V7n4qbe5g4Loxs4qLpi7hh47fscC/98G/txNShYFdMzWV+Op4wppSThS8sxyJvomm0D5TtJdM6brEqw3/KmuiZVPUN5BAVn9oELpg6kO60FDTioV3bPkXnx5Cc/whf+4ndkx8RJ+CB0ZHe/sTwcYNApg6ZQAtvO52aRoUaAGEDBk5/8SFYf/Mof4Y0P/2Jzrfyl6d/2nfFdlQzpEYTK9xUKJAo+J8lkLS8BDrhISP04wEU22uBCYNKwgnmMQVgiSjbFFT329m+XsNj3R9NQjUXb18ZIBOVfeFAGs4RIouTGHimLQJrQKiiwJYtQQW5AwWzTox7Y1Qpo5dgFJoIDVoMilDLkmcfI1ngbozdpeSG9hx3P1w1+qAA1scKYBJoaEQBDwl1sP08NycJ7WVDTDTW+WFbFUfVsc8NAXJYGnXtl5x8XVOly9UFbV/Z6KiunsscowKbLWff7GqEq0UOeg71+RZA8qiwBuC1ZJ3sGJQEflui5KsEzdYC3GstL81mNiuyxY31wVF1DyhoAIYwBX6kkDIO+e4OS5xtQ3ur7CGDyfi7ntXrX6ZO2mD7pz63MJ4DKhAJKm9szW70XAYE7I3mreucWUJBy3YHVwkRxvFZtRNTn0YH0itbvsT+iDFNlKlpQ8MlxS2l/qIa9uZwRBg8IGACuIJp4Z6BrfN/8CA4S8PTFj2Sfgr/7R7I3td2rV0qhowfaCMDUazYAoTtSOUAgUg+RXGngTpCsTThKGdjVYMYJt92BbBOXLPvjy452CdjQxABXI3wRCFj7orG+spA+s/ekelVB3TMcHHTriSMgiJuNmJCCErBtYhBT3HI1LGVbCYoVA2yqUJkEtAG5KlPztJQC5v1e48SytI+PUpd/9mWWVW31KUWmZFc/XlDybVZ+BAXRe+kMyJnCOotbD3HQ2o6LO0PiRPfMQj21gafnTp8Zk0fjEmG2/BsFCeUQJ8HGM4C9hlZvHjFpVw8KJmM6DfGQAfGQaBnndM98GcgN238PNPW0X3Q+ax+x74RJAnjuXgHtu1DrKQH7fdU3pdSwhjk3xyF6Z7I0mbZW1qdz9gb94U2wMTUnZkg4lOOcDMjFcNmkDmFFCyUN46TUAoKgV2hTYKB/DSRg28CUW5bAcoEigDuzC76KgUeAkHIHBsLeBNIhft4SFPi1ARQZSL8iJ+7hAIOLWcE6iTrm4Ml7P8IX/vL38K1f/Sd18yKjePz6qkBHQNAq13HNuW2Tieq1qOKiVDS+mcF0AHyASD1k3cuA8iYeat5A9ma5Q9bO8r6LoOp5MXsY6D33rktO1hpLtYPnGhXcku6cPAMY48+T+0zXrselapRahiCGGbaK1tnBwBXGMJYI2AJA4EQyJgXVmECN3X4PywcxkCBADXUuqrdISgS4IenrMAvbGDiy/piFT6Kisj7ovZcGKOl8pfH11TcVp+gx8e70ljNjunrOiiG4oS4yhqxGPMgYm4xpDkFg6cw4NjXtafUi85MDo1DPTadjStFg+LjV+SlgIMStb6Sox7wZ65MCIHcgiSpIIt0UbBNGwQxj1DdUVL/o5j6yPe8i3wLXy/pVxfaKAdocDvtsKwoiQMGV+k7HwsfGnI7ASp4BAkRAcEHGgPmcF9kwYw+Y8+rz0O+BADgNFLS26JQpuAXwd+XhAINZsQ5qDH3yLN+nz3+IL/zF7+Kbv/7P8OgjHxeajD2Q1pRBCMOrNptnTQbDh8Cz3VXp8AGkTTxTRehMuglSEqNPFu/UjY4obIBEW1iTfBy6e1kVJDqJ+w2brUQvFEC/W6L86ZaXfVAlek8BjFA0ggYQdK/yqXccqDw/HlH7rM7MIuzLN7MlZw+akE9gdXi/r16SCdxBHpc1cICcRhalB2IdY0La/sazvKSsUvAy4zzVPvFX3Pp8ViV247CdzPQr7tUxBi/97AoM2FYjcTBkzEA6WhlTz5vKAWyoGzzpfZ0Jguxux1k2d2JbOUAGOMrVYyqx6yz0tIGC3vhE5sdAHlXjM3qjJwbAnBD9bGyJgIQsBj7JZmGk6+/Tdtfsc0B3RUFCzb8g62tLRL1xB83TEnNWJqs/HITMdlUFLuu7qFt0PHpG8uqQQSNjwuw0tuBEP3IAswB0xVj14tnG5YItevITtV+f+j/i0WufDM7PxKm9sTxsYGCl6RwZjCcvfijrPH/9q3jjo59weoVX+1lHQQT8pRoRyU0Hpi+qvFgFFFxUQMWrsa2SmTKQCpgPUNrkTVoGCI7KFMQdy+zd47PlctMyM/yBppqtJ5evL8cgLEvsr7BEb/WmyHIVGLjBM46ArRQHCb6xTwlxewv5BE+TlAIGJRkfIjDJdrzNCoWgWENnartbQHCzwqLs97DvvrNc6Bt7pTAD8tLGQG5YV9Ruac31apvievh2sLhyXM6e7VPUnklAoizTAJAWOlNQgKJMEh/wt3AWA3wEbISUM3gXWt3HU8fYx5OLAD7b+dFi9O9nPA34zSjqmQFCu3vg6ZwGnEkRBixPQAK7M8JcQMlyENqluk1+RTlGIPx+SgAXDjiAlv00/abPXC0bnZbZdvCm38I7WPqxAGXVNSFc4PIVwEAA2nw2Jlp3QOeMhnrYHRKzJfmiLZIlib+Lb/z6V+uOv72T+j7KgwEGMcnpNHuZEp48f4bHf/Zb+Manvyb0SzMwi4ke2QFg9L703he9rypR+icoMGagTEBCKXIsm0AfdStlZQkMFMw23JHHqMDN1pJ3+QZnO8pd3EDmxnIxM50IxdmaIISDBxU845WgLmlXVMCWFBQYOGNJTJPY6rEM+TQ5ITFGm7mOb9xGOvZBH0oxA2KK61KMU8/vAQEnPa5g4FAgcLAoIyXdqw1hHuZtaQ7Il7Nk8mugwZkpKZMf5XkhiRa1z4gEINixrNRspgzKWVpeDqDsFSAYg1DSCPhokuNzREavAoRh10erXwQD0RDFHJkrWJ8KdA0c5Jea1zAWxfUcC7jpX+lbDj0uORmSaFhcNuLovd8VKav6Cpk7hg+ifkM8p9Nz8bdBV0UW0nRIH5akrt9nYGDmjGjtz+Y2+X8MZoKFEwwk2FzilE5t0ZP3fij7FPzGn8hbhLV9167aAQzMrmv7YIAB0IKDerBVVU+eP5UXSnz2LTx69fW63jcMzDRu2iHBOlFELQXnq3pjwLzvdYYkghiYnB1O+Jaz5QDzncbRDkf5rAYKmwAH8QaOOboHqmDNOmy2dlw9dADVS+/av47Xz55y7lGECGW4TVR61dCfxsp7hTkZl742MgzZH+NndN4mlaIx5uMiQCDbQTCwOb5WfqsK1RIWa2WCF2NKrEuybFmCu2pA8t0EEGwoaMFAYSGXS+GGMShhfQHzaOx6abhW/UQvf2ZAZ+XSs4hIx0oSM5MzBjLaKcnbGo4IEtKGlDYFBx1AUFDAdIDK/Zw90ERFSbSrMXeE5NBmPHvW7YJXOgUEKcPZgY71eV/zugEKWzg2yc2wnISKHKsHH598d2lUr9ALIXmUumcZewGg1XHhumazrf5RQc8NLGSf3DkBZc4OWy5ByohjcJ2+tzkqB4js1fAKEjhV8JWyO6t9efKeMt2f/pq8EOkl8gnmm/+15UEBg6ZM4ixPnj/D4+98GW9/7hsVaXFRzzAMDE86uUfpq8nBoxcGTAQXo9cDIqS0iQeU5SouR33pkqFmlmMcjjEX4K4Vamvfuo/aSeVx+x4MTGJnp1nk15QZ/drUzXopPvM6ENALauyBKCMtJQ01MCK0SV+8UwHCAaIDnI5TgEAsOyLysfvOgXTUbWSXQG3mzXRxZ45hg7y5AXFmQP8WkAIBAQEHAhgoAgSKAgCbq8x1jnp/vQxL3DRsFSd4+fumACVlasrCNCIB2qkowaUgIQMoSX4TgJA7BoF8PPlIQNLfKEnM3ZeJbhUMRFZu1uyQ/e5gIKUA7qLnOQEEVD9HQ2Q+3qUQUENseuWoC7foQPS5GXFFh984sA99uSTHl0rMEWDd7jkaxWZlCVodF+t0pZ5rdFzPPsacnHgs6PqCl9fzhep4JB8H0TcOECBOZww1WHny4h188c9+S9598AtvjPltzSwM/WLn3RD2eVDAYP0aVRKmQF/NHN9HTZbcEQYGfWxnGiqg6SSJFC3CsUWN9fY0UKKkRiqlDaRggdVQERfPIyDbnSwgfe4Fa1VWBr9hR4Jn3p0bFcE1NNY1SDWWAQ13oZoekAHtONh5M2BAgamLYlX7XkGC9j0UEDiboEzOEO4pO6A5IQ4SlMEhoLIHsXQhm365GkellZIwBMGA2HcGYZ8AAmcLtF8OBQKHdhADOGybWWYcMFbjpuH6uRabChnkijYncno2g0EEZGIQK0jgqowrQCBspuzLARypAr6UQce95vdcGMvVro+WI6OVvjSW5zkhuQEDhdHoGOBkblt10Bood0L0xBEsAEAwxDEmfkO5WR9MQIHlRqBPolwBiOWDWt3VM48rRuCs73s9s350fWVRUjtBRANIEObL9EBuQg2AOrXf+4ow3a89GnTh5MkqG8n717ua0sXxfFDAwIt7kUAEBV//3Nv45GuPGmFyoTHkBgDUd3VrjABgPwEEzXc/r96Tm7vKEaNEASCR0qRKi5rBykQAbcgWdkQLFtCBguFV07F/7Mm9wY95GsEb79vfeOWxUWel0xXXbnzmzynNnykAmMXLZ3HrWT2qFypVTQ1IEBbBY9YOEDQnxLzOssFYHYnR3qmCEzUz2553CN1QqgakpzcXgKAA2EsHCApwgFGKhRAqGNgLOwjgAhzMFSwAiNvQHld6GvmMzuzKy9xT9tpg2TuU5DdKAhZ2ImyJsCtISEnYhSMRskSEGoCwJQHdSJKhT8e9AoTcjmWRxLwKEm4bxyZHJqwwOGMHotNhBsnCQAMwuHFuA3V+94ABMBGtRmq2knZWhnrcqA8qzV5Biku2Mhi+j0FkNhD0nPzYPiOyjUCj52ZhgUMVSZ+H04TftFrnOt2MPXvb2PQJM4gR9tVRAGt+aWeLJPz9Zbz9ubfxxmtv+POiLm6a3HzWnqQEdyCvKA8GGMxjLRUUvPW5t/GJVx8tk5sYLUgY7g8ME2OWxGWeWYzdRgO2pv4UVaIKKpUKGFZgQSjUTelTq38nWMtyYvC5/l0xIfL38lPqk6y9Qdl3XT3TQz22nT27j/GVrvUreSCScQz8iKP4GLOuIEFj1gCQQ6gnhHkcqBVNNgzAQDyd0kgeAw2QXVGcs6RCM/j7hCE4SssOMAP7URwM2DVcWIGBdNIcHMz7DwBsY7n79SnLcum+dk9KBBQx7PeoQJkSIYMFFLCABNmrQDcfZ0Yh2b0kMgilQMFBBxBsLCMjxBwA30uMo4EBM0SWzR7G0jxUCwFFMDDLCfHn4ca5rVUDojHmJqwZyy3yKJ9HY7kqEZjEZ/f6DcjK2l0AEBeeNNNzxVMsFiBgosevDU0WrWvhFiTMAEJG7dcS2vb0+TM8/vaXnOmuWRd67qLZ0emi0EsgkvlMERqO5cEAg6ZooyMo+OSrj4LR6EoH92aebG94DBSYwvWJVepEMk/t6hiuTyz2r0RcET4RqNSkq5S4pb/DbaqwzQd/alzDbyvGg7trz5SEPL9+Ts3xURENaHtRZki9b89KeFfF+u9QJWn0auIRJEwT27DIB5ntj+AV01rOcj00+WwW74yA4GBgZx5CBgezMwQREOyHMAN7AAMVHNT+Kt3kLCeu6T0EsN5azu4JAEe8p4LkXQ9lYuzKBuxE2IsxBsKCOEAI7cmkwEABwgFgYxnLCBA8B4E3ZYR0HA3wWScB4xgCmNHVq7i1gQFzNGZgoPDcwbh2XltxvdLJXjWbt8td0xWhQmfyNwMfsU4VyJhThEbP+TV+PzGjK8JqyiJirbcjALhJd7PpkFF3y7bUAFgmoDMGBGRmHMEdNbF458VTvGn2S0HB6ACNfWnHB3BACbgyJPSwgEGYGTNQMPPWAR3IMLCF0VqpMAh2bU/tuXCDm2M2qYrehyHx3ChER5hlcSd1IkK2rYoBQZwIk650DINeZ+jUSq+zZyGAFbvBfRtCO4A2Nr0qEZzU9hiCbQFNvYbD5+WtO29laJa341JJPBfoQlVZ9YltM5BAaQOhZnPHpC62NeV9ZUMjpwmWJx6lGZO9jIAACgB2LlNAsAdGwZS+GWubI+VK6tEuSFeEE172nknnREqSXJkKJHRAskvlwcIiMMMBwkbiZh6q+FOCA4QNoqcFLHSMUAR7H8QY2meMS0cHMHCrkTopKVQxRJlbQN4MWdumS/JVwpdeJ8g1l/VC1hCR6DitN0TOYmjPrnF5tJNv0HW9gzPr26irrT236GtrR2IoAJCKyH4Y7FRChhiaI7AHVud3nz/Fm999jLc+K/brUsKpt9f6j2fgwCuKJz9+Ol4cysMBBiGG9OT5M88p+EQABTNkG24AoHbsjJmaG80RFFi8tnCdZLOYrt3Tir3GJCxi82bFeKocj5OQO7bgslHtddvM8B8nde7p5lXMeIwRw2PE8fdZ26w9TVnZHT792iiraT3j8xQYjspJvNYeJNj6+R0W6iGAsuSCeJWt706SpWIctI97cqU5+1UGlkfgKxF0/PajCDDQa/ajOENgQCECAvNOrfSswTXlaqN/4z0TyJW8sTiakQtmwsGMLZs8EracwDvLy8X0uzAjAJOwBwzLOahgz5MUgQr2EMYwJr71pRk/YBjD0lLVTSjyAtN4yQDPSk6EI3wf7P9K1y1kKRpI0wnAuV6Ivw/1c9mf6wXTCb2uA6psIlxzSdfZ5x7URF0X27bW0fWVUfcBZGWq4SwiYbAMIEDrm0E4iJFZxsbAgScjAnj646f4yvce408/Mw9/X2vHDBzUd0gJOH3y4x/g8XffxGt4bd5heEjAAFBQ8BSPv/0lfP1zb+P112qnRqM+6057YQ1z6+H25zx78bS5H+sNS2AKTKB7UFC9NTSxXbu3lT5W68haY6rAXIjkOGHmoccyQ/R9EpocQxN/tu8A3NO0cmZEkht8qy80NlzbN2tbBA3Wtr5PZmUmMMdJ/QBgP07QfwQKpSa2kXqZGeK5JrLfxbDsqB5OTS7NYtQW1XfPgFvwGQ1JBKJ9YiGzjO+1LIEBAnm2gd4ADi64pdcmp52Va55hdRKAoMr0YAcIiUnaS8CWFATpHDm4sgc5kfervHNVQEeGgIuiZFvW4TpimM7i3S85frP4dWQYbRxnQODMa10VPsZKXis3/cqUWXJqn5fSh6OA6/SC1KsaMNMNbR5JPG90kqxco+9i21agZqaXY7E8F6DqZWZ2hmBLkgy8JfI5BUChH6EQIzHp9vwVHADAV773GF/7tDAF/eMv5nT5BeRzNJ779PlTWbL/2bfwB9/6R6u7PCBgEEDB2woKoqdlxYWuK+p8KDgwJKp+gn5/9uIpfut7X2rv1aF4VwZ6kPX6CApm8V3gLBFLfoiCA2AwrLPfzspKEKKQRwMC1Jhn9TTr/SKy7eVTjCnpb7qczLxrsCuGmVKwpLOdrB/I++RsVdQsnHaWDb+DQz9yAxYiUDhKyybsk7X0NTbaJnbNwiZNnfXvYFR8LrdepRsSrizBWS5BD+gulVsN/1mM2kpvLD4IcAFU5U6wuC0AVc77wUAu4KNlDwoCQEikiWIB6IEDuJNy7dhJncbxKwEIrCjs3oMFMHjoq2JzOL4eyuVkcm2UkzNDOdMN0l4eGCc51j5npR98w1WQ6wY5LvphphusnS+r76ytq4Tbs6RYeTYAcAAEsqR7SyTjVQQcMKiGE1g8ed0hxfskJlQCwJ98+uv45Kuvy307GwS086wvMfeioGUNnrp9/DresG2UF+XBAIMeFMRxbbz7cMwKkSmICg7knHrSOwoKvvrpr+Nz//WvTpHkLPnmKAGhqrCthKyP8/bFmARL9Kqe+Bw4WIlCE6nFXgh6L7IX8hUoOKOPPT48EX4DC/b9SNTEjS25jIiwo0Xm0u62Qf1yuZUCPdOru91bz6lggZ0mlGcrUNAJ04YdKlAgUyCIMVHrm/H5s5ioHbJ53OeuGEtg3pCBAld2HQDtSyJTUBMX40K5JqdguKYzrbeEH+LzVvmOXHSZotB/QtkyFBwA0Ndj5yTjYwCBuCYoEmpGv61UOXvumBQ2jtstQGAGAq7pJqL5vM/lXDZmLKEcXzsKUR/8PPWDnRedicg+Aq3OW+k7YK7zgMu61+ubzFArYAlzTUApIatOOMCgIitmSqhXNPgIx2ySffLVR93xcB6svm39gcrIsv9QEceT55LIKEseH8lZJ6L7YIBBXNLhfcUjqpqhLNUfU3AACCj4yve+hD/59NfxcR00IhoGzV+6o/eZPssmJtbCBsxpXbkvuRbqlaRnh4fJnUBNaKKlifUZ4ZkR6Y+Cz7Vepkj8HqNAxfpl3X2uJpIRUgI2Skik9HypSkH2CxdqWGhDFfKDJZtX15hGivGatfH9Kctd3sN5DkT4eqAAaLgBMQYackGuMKizJFCr2syo9KAgKnhAPS7NNwCEfk+JlE6v8+GSsV+tQLiGLbDSzut63SXF3NdhFaaycjAjF6jyruAgszA/FlqwWLB0V5v0ZitVLpXZioEGBMitrwIC0VBfKtEQmi7ry0w2+mdcAgR7KVMwsHNx/QCYnmgp/DP9kBMBRwVcogOocxx0boZjcq5eE8Z8AJ6dDu31Xl+/qc7VevXhqwMVHGzaXzPkOFuRhe7YpSl2BgoQfkvd93d//BRf/q7k3PkLl0LmzKw8GGAQQQGj87zeRzFQ8LVPf11iPuE3QvW2lCkSYGH4gGTS80FO61Gigae6loqz47VUYbHDqfMMeiU/E4AeCMTnGxiIgm5r5IFe+ONz6+ecCIWrcAlIkBhboSLryVmPJcaGhL0UFXZqUPk1CvqMOQjYTc7F2quwsgMORABhFbJ0UhPeuD8wxD6zXkdAAAyA3exSa3plYAd6QCD1bq8lZVzEY9bxigpMH37o2OR8Td+Ox3qG6rqinlcvqLri4NoS67MKsfX14wIcigL4qMlt3n0qzHGlyhmNEn/pAQDw8iBgxvCcsYHACAquZdGa53agQFa6cAcCEFbDtDrCdMhR+Gr9YH+Tgu6s7GEPElCgDkbQfWj13pnOA3rn6GJ3+GqWeplRbG1eEtAzFyr/7hiM4ag+jBB/+yDKs+dP8Zu6uiEy6Zee8WCAwaMLMZOzYvOoH7R3XjxTUKCdyvWlKWSannXSaIZJgWyBSVxZAyIC6drVzJBd7ViMHQ7dxCgB5ahGOZZLIMd2ZZVwfD05pYlQTHICZmDkDBDU73LuLDvas3eToOyjiLB7pySDUtX7BUiXi7J/Nu93NlH7hCQrfd7BBmqNJrVK2e5z5p3NwEJ9Hl0EC1LfChiANlHqTFDjk/vloTNQQKQeMSo4sEnuS+1yZbzOlMAMiPXG5uWAAbTebb8ezMM7eS5tOwu09by0EkaeK+NygJ1J6MelgoJF3WO9L4zLCgSswjuXymrlUiyz/JsYUrDExxk4ljoHqj06DZE1mIAC0xG36IdEcB2RNa/oAJAT3Imo7IB8B1D/xiWLaXzeWb7DWYlT2xJV5RlU2SrNfchEPs9khSp5KMqYQ89DAvmmdMNW1RjlzpPjEe3KeXjp2XNZ3fDWZ8R+Nfe70O4HAwy4+7sqsVNnpQUFsmTk9YZ+CfdZgAPjJYui2y0RUBKQJVOcEmFDAkoBC4vmHrPcbwQHVs4ouWGyzxgH7r+fg4J43gz990JfmF8q7twXE0hLPJLPbZayAYJLqxaYuZG6A1zBQgAJ2dpkccTQtlNPSwGd5UFY2a1e2q9t8qRuq42x7rHMQlaxHX6e7l1i95Y22s3JFch2gz8Sh/HM6Kzqfk2JbegZj1vj6305856pmw8ZtZ/3QFlc2/+3AICX3w66BcPxnJkMWP29jkEOogwcLODIto8+tAGZ7HNlRt9v6fWDgYHIwB/qjbd6TRkCItFTyZawyv2aul2h92J9+rLSX03+Q0iKzAC2lECas7JlwkZJmFBlBGT/jHNQUJ3ONqwg4SFqwAH83L6OUozp/tMOFFw7hA8GGMRiGcB96RGXlTlTIKCgyd6kel0i8hh4IVW8ieHpp5CdCU3HRHCAQzZksZoQRBltKQUKn1CSJvIEqv/9Gt0zAU+pImuj9OzcpLPXPX/1CNy7msSn7bdE4zIiyzdocg5UaGyr2kyyP8CWyEHBlqgBBNOX6gxFDb1+26y+GQ0LZEpygzIVASBkootLmICavAhA36neAoaeXQBEuOOKCDnPKtU+Kyr+3ggcwVO6BgDMDPpq6VdzZvjSO6XXzM5T/Mrz8y5tmnPN8r1YzsBMc68IThbZ+/U6O4+bc+S86+oXM+6BOeOxWp58mYGqckDGcOjuqVyETeJUlRwLfSfXJZGbvcAdmNbIc8M69DriTD/I8VlIof7WfD9ZxXIrebUEAVSf1SZE1uTHLZODAtNLPShICh6cKQh/IyCIYKAP11WSp4ID64KZHXvXw98BFHAgauGHluXBAINLYtcjroa2CecI0lL65RfG8IRdlwMg8CQ5ki2LDdUeBKBoIpOuGcmckSEbzwjllEFHQc62tKwmhEWAAIw5AWfldAlYuN7QtqPvBF8GJ7+xg4kMYTUKcyPsR2GkSfA5Cro9KyYhbplc6LZcQcGW0iB4lAibAgIHCgYETIhx2TDZPCHb4QbkQMGUJBAUZQAIWYFD70WuExiVIUBc7aDKOSp+7s4huWnvDc5KNOQGBmZruYcjC+NeZaH9G887e9fFywADbuywzfX297tUWQ8OT7Hz7iY3numEGa19ZrRXTACwBgJXzQ0tbUiggoLZnh49GGiAMI0OzrJN0PkPAKzznxib5gwQyTyVdulbQQ9L8JMOKWDgSOq8yD4gd1n01UxHrAz2SkfYNY3z0K1SmN13pfckufZCx4Trz1ZDRGdly6nRS1sW3ZW9vtCNsypLML7vpgPYfZsAMexkdgw+EfvmTkFBV66BqA8GGFiZecMWi3GF1ikCO/7OizZRY5YYYsWTUQgAS+KSbW5zMEDKHsjuY+L5k6I2yglbljXnucjGKzsDdBRsnvgDHDoJ6j4CSplla+t8iC+yCnmVkEMORuwckSXyRMQtEWKsEZgrW3/URMgjCo+AwFiCGSCQYyp4UqUudjc3XLFEo5OBZumYAIURJPQAQejX6h2xev+rxDFK7e5zlgS4K8DAoWxEMAQyV5XijR7WIkchGgfgOkPf95X/Dvu9PRAVcATSfuxGTw2YA4LG6Nvv+nf18qAeTNTjzeVSTzeI8Q81OQIZ1LAGZ+GBa8BAnAuxxCRJ4BwQjDsAjvMfuE0GbO4381/10gbZx4NSxlYYGUVyLo6ElEQfyf4PwH4QUg4rFVINRd5lkae7xUYDUT/I32CQuyWMdl5vvK28jN6bXVvZgvU+ClNAYPXT8w0QwK5XBjSCgyhfVoPexxJ41gIEPzu0550OFMy6Y7FoYigPBhgMtokgsTH7PSo28/TDsXdD9uYbv/BIBC7cqi8J0sGMGj0QT18iChomE4OuACGh3bqWtoSNgyBS0mVB7AjeQIJla/fzepWLcOtGM75MLMVEI2pYipiseG1m7zX7F0SGYAshgy2JUrQd68z4pwAE5nunLyqj3vrZfumFO5Dg48AgXQqIRKAAEGbggJL83hsF2QZVq8NhA5PCo+HoQMEQMgnGIAKAGUiyPrKxmCmks42YZm/Aa07AbTRuI6/K3IyGvQUCBTT9zVa8xNwcntzHZFOntitcQDxAhixhBCx5UxkfoiEvIOYMrEBBPOcMFMwAgQHhGRhYzX/gNhlYzn/VSxvZDprAljL2wnqMNfQpSxg3CsmIYcXCNQxn9PAvAYC4+ZFcS821l4o5P0MdumMGBIAWDMRQZuOohHGZAYLhDa2Qapjs9XYmAnurMYX5mvSgJbYDto1ym1OwYiF6wD0rDwYYTIsabGCkXGJHPXshL6z4um7+0A4UY8iKAkAsKoB0X/sktgQWlsushlzpJGYFDBCAwGzLgIC8JVkKROv16ACanboAU0LnAnG+IxhNFdnZ5h+zlQyXyijwpCxBFbwlO9Apw0y2DTG1wrUyXLA2xDZJKECMCpTxGfepjyCBCiPn+t4Ii8c6QPDcBO1DjMagL43HHePDhHPq2IwCWiCwAkmxj2L/9P228mCi8qqudpCJzjWnibz4qdoon5fRqlELR+yFQ3brqNB6kFCNPLmxi+MLzI2hAQVWRRsBgiTkKTgo5Fn8UlWqoI/mCYrXMgQrdsD32kcLBlKiOsYfiAyI1enf9ZKJsCGDc7uj5s4AJ8ZeCg5kcGHspTKbKwdiVWbhgbN9Kvq+vbTrYdVxa3nsQzoxx2MG1maJhaaXUqogoIIKDGBgaV841Di8HtkBAlXHE3y++uBCs5flYQMD4GKnvPPiKb78nce+D8IwYMytotPPdNyrIjvAlKpgUpI3vDlIIFc+hZv8QzmeqjHKRGBIuIBzu079LNZ5dVdM+qK/z6W46dnWof294vP69yMYEOiFrqfkTGEmwpDNa7Rcb/Dk4X1D5U9VjtQYFN9amCRUEkFCKUDKsheDsQjEZQAI1i89SOjLLMFstRd8Dwh8OdQMDBANIGAay5wqKO0km/Pm5sVXRdvc77n6pmcx+a02rh2W1PwGwHMvQBYyIgcM/pbCVJUlowIISzoWYNeObwwbFa6KdQAJ0OWxSWSRTAYTOziIq1dsxYkvv7txzON4OzMGNEanAQMUDdIYq2569QYZOFiAkMx7zXFqQEJyvVRBAjXOy65zpn9vAnBZN1ifAHMANft9dq/V864pFXzY80YZPBuXKTtg/zDaFSCA6GBjqFRgwMQiNwEgSB1lrJ6+qG9hXOUUvEz59wIMiOg/BfC/BfA/BfCLzPzfh9/+AMBvQ+bXP2Tmv3qZZ5w5a/bT07BN5CNnCjpAMFGMACCbBwSf3d+9bp8TcsoOEgo0kZBF+ckrc3U/A+WJjtS+snkzoGDP5Mr+XHqzWiyzZLRVWb1gKTb/1t3ZZvsNmNDFnIGqKEdFeD0CB6YcmT4keqAGwmxMfGMXJvV2VEFSHRNWFmHjjD2EGTZNTOxBwqrMlp9dCwjMO7lkKJZ9xIckxcb5HY0+qzkN874qsE4W2kSBZXvHDqDxcwQDMCCQ/ByCyFgDHhQ4sAJypOxxcxtfcF2TPwDBDiQcrC/CwQQghLwTW+IHzSGx3JOplUI73mfsQJSBfs17LwPR8MT5XyFTX4n219hHd4yqj1KQAW6BsugledcEstzDgAIXGav+RWxn5drll5delLQqL6MnQzc1zJyNiTEDCMcyXQAD8fXdQCNzY6XvvQOIis7vChBI9dizzn7pk5blluWm/74Yg/8rgP8lgD+OB4nofwbg8wD+5wA+DODfENH/hJkvza+mzBKl+s9Pnz/Fl/q9o2eAYPH+dSpxixvAcboqLgcLKcNYhEwZytrhYGqFEcAWhPGO59SnVeMuNDLq45leWuiqeVH4bnRjvL89JgKVePysUPiwosF7VuAW9D2AOKAKXmNc4IYERMiUBBRAlWSpRuRIwYCUmiNiGc49ixBBAqCrUoCpvYyKcBUyqN7/ZaXU9Jf1X+gjKsc4p/kI31uAUF1sn3UBPMxAwUzJxYbPJuaMMUj+m/MLBrrNaJABhOS/EWUBDBEspIxs44w6vgXKIiCMNddqmLwZQGCIYe8BgjXb9ANPDNbLeqF9FnsExM6aQaFuLwNxbHoZ0EpRJwMgmcclguUIkIuAXns9dAk66S6lBnS9rF4AKtvhIGeCH28pszE505eRdRlCc66D5k6KM5iAyxZxESdyAN7AwLJZHUqBvaxF5jQPAOHpi3ea1wD4tdbG2F79m+h6cPDvBRgw8/8DaFGgls8C+BYz/xTA/4uI/p8AfhHAX9/6DFr8BeyFS4/1LVOvw2M6nYERZerTvfWsjv7lyFWpUTBEOERRccqNQCb97gorCGOlQGO8NGzi0Q1ufCXtsiO0vL8EsXVmuHw+8ZBdqdcqmbBFo3cKBKKnq8K28nDHCoQxgY5JMCScNhmPlBsjchi7Q5ojot97FiF3IAGAA4VVmb25MQInMxSXPMdoLBwMmIdi/VTUHLrnUg1KndczkCD1J2bwYXJS9M8YYrupBGNFqZUdyrkyAyAHAJVFIAcIiONoYBxJO0W+J/3rr11WI2dhviYPqMD3KjPjRwoQZ6tXNtDFce4TR2egz4yO/c3xbxxfMzpl74DebXLQMDCUZOl0x7w4UEi6Qipxo5fqG2yrXqh+1GV9oFWxUQYQ9FjQVS+tt6xS/TkrnWnAwMdnEZKjAOjsIZEV6O3HEI4bx8ZBMdc9N4kTQCz2Q5eO/uD5u3j83Tfx9mffEvvlDQx9Omn2DYTBf3A5Bh8B8KPw/T09NhQi+l0AvwsA+B/U4z1bUL9Ktzgo+OxbeOPVT8L2E56yBHFQo8IEVOG2XS2KPXg5+p0pgYqFGlqgkDvPxjxXRoyZVkGsLRmefHr00tIyU9FTv68RdGrqULpzzkpE41a/PvY9oO4eCJiBm3m6E2ZnqEAcH2VzBLDtoiBTRupAwsHyAp5ShBrtWYSoJB0kAAAT7jAXyJmnZIbCqWZTPJ2xMC8yNf3VggFXSmz9pl5LMa+yNSwAV+PPpRp9vYaBds7HOOj0pR6TY5NF5gYIGFCDLp3A9tmMu55LlAJoCIAhbXCwEACB/J6rzAWQYJ74oXlApcgLug6qIQYPM5BQ5JaH4CABBDCuGuczWrpnB+L4DmCgHCPjE7xSB3uz4vO/9p2FPg0ssPbfDCgwKICB1nEB3r8+qL9Rc04s1+gq+Vy/3KozL+fkdP0+AwK9zZgxbbXBsFRwKntgwwrACVQOMBGevPcMj//st/CNT/8pHr36ushhQ6sEsD1Yv+tZg58bMCCifwPgfzT56R8x83fe7/2Z+Z8C+KcAQB+mpqntAAI2iE+eP8Xj77yJtz/zFt549fWa5NHFfRwUmFKdGR9XsHr/prQDRcGrWQKFgNpbik/up35bEyNfFZp8bsUgSs8FT6/ZXWecaBNSAcA4+WbhnV7IRLjWiLsxZMxqnKrQNUI4bYs+TYWOQGo8qBqRlAeQkJIYopJGFiGz0tDcgoSCyzjFquQeSUddzkIqkbY0g1GN/qH9F8DACiCAgX2vIMD+2vkqG3IsgAQ7BjTg4Kb8gtB4v0qBgAEFn3cpVfCgYyUG3oyX/t0EGFgIQeSuji2nrDKn16rR45SRKKMQPNfHlhsXRBCIujy5G+NTWQx2OI7xKkZt7ICACAV7PRho2KAgB7NckFW/h/yNRg46Bs1ZhZQ9RJMioAC5IwPcrgPi5w9eR7VOTP95uM30bzDqfIGtnNmKJhw3q0HnSAIyvsQAFR0nBpDx9MW7+OL3fxvf+I0/wRuvfgIou1zHaNmGxjltE36vldKfGzBg5r/zEpf9BMCr4ftH9dhVpU/C0ZoIKPjxUzz+7pv4xqf/tHZqrawP3hIUlKMd5Pi73aMv0RABdaA6CnSG2p3i0+uIxgnE4fNATa1ijdbGoc4r4eu8PG/TZEIvkOtQJnU7FbKm73sPib1NPdU9FPU2yeuvRsO9yuRGA2kDyi4gIWXxnlKesggb5lndQDUkq3IptNKHC2JYxQy/9EXrUaIHCNqffBzgY4ezB9q3vN83AMBBgo4T9yAaAI4ADIBT6nhsd+ex5ez9VRmCXMFDyg4CpLMSaLtzAIddAULekHJ2w8cpA0VBgo0tb6pUs451RlKA4KuJSl1ufEBBAddchKJjc+v4zlbVzNiBy2Mr4+uGShkfufw4lQHpzjFUY4BKdNR9AMwn+onahNGr5B5dp0Xd2/8G4Crd1OslAE1Id1K3pf7sHEWv29UgIF53rX0Iv1uOQcowTfLkvR/iC3/xu/jmp76KRx/9uDIFyfuGuNT2OVAoPp7w/69jDf5DCyV8F8A3iOh/B0k+/B8D+G+vubCPQUVPtIKCrykoiJ7ObFKWc8MEdDQs6vFuAngiYkSGnec6BQtAjZnadXKwa+OkrOJZK0rrWoXeCN0aGHBzbKIoGpDSCeFEwPqY90B5R+p75sWGep96mymD1auktFdPkzNQghHRHJHIImQE7zIBMVcEWAuizVszFhEMxFhm40E24YJgQAI74EYkMAPl2DUeIr85G9CxBRxp6eNoDb717yxUcEOeAffz4h411JAqSCCiChqUUXODdajHZKxCyuC8gZMAhGFseQNKAdF6bG01kbE0cbkxc81FiEzBLWNrajomil49tnosgoHI+nAEe97RowxYWKCXgzQL0UR2bZLTMYzlLfLe/97GAead2pdIyWAEAx+Y/ozHVjrKrr3CJugHrVJgB/y5ScYxZTx971184S9/D9/89X+GNz76ca8LQxXOhC0gAJom7eMXa5FGD7op/76WK/4vAPwhgP8hgD8nov8LM/9dZv6/EdF/DeD/Dnn3zO/fsiKBwr8pKPjoJ0bjiIgGgTZRJBqrFgmuUPrF6axeq3aE17YRRPtuNNBkUjefeyEa2tgh2NnvsYoToYxIvJlRE0Fs6tyf3941fJwJ1wIEAI2360bOjq+KJbiZEQneZmNIKANF6NTG00z6W/ieSXIRCiAJbQxNIg3djHOfx/02upDYpGD0GkDQsAMKBBpmgIt8XgGBHgBEoz/pY74BFFih3ogckL49lM2jJGN9BMCwAgrbnYKEe2ES9nsFDxsob6BcXNFy2mo/zgBC2WVsdblxIR3DDiToyLy/se1zB5Tp6Zmf6ogcFQwY0PuA5IBDGOcMLFjf11BE/X8sM2ZgzWbG8y/rofDcoIeWK1v09/byhR69RoeG387YmqlNCHaAxcUXgNA8K+HJe+/i83/1+/jmr/0x3vjIxyH2qfY7E9XnUWrs2AwcnNYplH9fqxL+FYB/tfjtvwDwX9x6z2FazkCBHge6SdeDglWIIB4/S9YC1oKZEtiiGFTjpyPNra1aUfWzsjD4Pc3eJ5ZJfUMyWd/u2fNjcpjWX/503w0ENe0KtwzPmWW9D15QVIAhz2NgDPr627PVW3Ja2ijpwZDkakjM00yHGo5UcxG6hDbbt4IBxHX0wGhAfGStaj0YiCGTPsbcJBOODEGJgODYvb84ggS7xwwMTObx1Phf82aaRfEFuDEpUfckpkCTmiJEKWK8jl37n8C5VIVoQK+UyiIUAUecZVyxRUCgAEH71wEC1bEH2QqiNIAEGybgfGyniWsrdsDYgG5crwJ6N8hA0+8d6xLDNxxZBTsXEorwcXwJuY7fZzoIgW0b6m/lJEEVwLiyJdT/al3q9Wy9/wgAXE9ZOdH9tQ4qY2TJtPHZBaCMJz/5ET7/r/8+vvWr/xhvfORj+owEeSOfzPnmMpuFDhB6cFDH7ELr/4MLJbx0aYefJdFwAgoGFHp1XHSk3XtPdpjgs3tH/iMIIIAGscufjga7WMWJ4PUZ5YuksiazPArorJwkinE8nnKrNFKdmPH8QZA6ELCs76UY+KyolykrggIlnTeptxoR1u+03bUA4djFS42e5pGqcvLEN3JjAmoROqMVzAbHqzfSLEWLSw07QFDBQ+dJGhtwxhBEQLBQ1h80GJiWeD9LNIyMnn1MEOBAqYIE5gYgkLaPtjsJC5XiY8gG7CkJQGAGJ/XAKLcAQQEGJ8kIhybkpTiuQKPQrxpXM/yzcbV/4BYQ7PcoFgKKYSAHfIfrmob9WRWq8tmAhT6HYybHIcwTx6ufE9wZ+UaO4/krYL8qQYcMOSemT2KSqtXxA9Kp3MnHqa6PhaiCTQhwtanIB4Bta8btyb/9ET7/r/8hvvWf/BHe+Mgvn9SPfQwdtFhugYXFva3srMHT509Pq/tggAEQQMGPf4DH3wmJhs4CnMWwPiBlNzPAl+rdvEkkLNnqj116LlDb1dOKK2MaKGQ5zO39ZsWAgSkLD42o0dX627kzAHG5PRcUxwwM9G2YFCIC417q7IaE3MtE3lxJc8rgUkYq2jw+j0sTwJLF3YCEmIcRE536OnFoa5yrxhA0zIGC0LAawYyHGYuGJeiMxy2gYFmuGb+fR7ExMo1qyjpl0HHITnwFMgeVASJuwSqXImPMChj0XgIQbE5EBkFAgyt2j7OPBmY6rl14LC63dfC3AgTHMWd+TsZUHnU+/4EDyBlsIAuocgAoWMbABr603Ha66YPSO9zrnV7nKBMip6SaW9EzJ6ftmeRrRHb1Fv2eAhN1Up7827/B5//Nf45v/cof4Y2PfGxVMTgv1eWREHMFB5B5GEMKT58/w+NvP8ZreG1ZhwcDDEwomyWJH/1lF8imTAUnUJca2yRL5uDqC/zg3/138wpcudRrVsR7NaHTOEMnhDQRytbLPxfIi7TxzEO8QIkxUDP5pZKV9bDlTVABdsQevBQ977RMBLMBBDNleKJczKOg4wBwdJ6mAoS8DZ4mH2kECHwARY1JCkbDQELYDfMSddlkPWsbmlim0/s8xJs5xptPPMphDgyVUKMb5hqtpu3PAxxcomCtTLw9Zh7AAcqhXvwO2YZor3OIg9HbNmknFQkvHDYnVLnSXvMZpEKwrPyzUZ3lLjVJy5PVBQ0g4DIwP6tQkPXBJY/bveh9b0GCqz5L6qQqZ3bN6tZ9CGAln4tQ1U00vFSyHlP943KtOShN/om2+8xBuahfZ3W7kplxYGqgwPohz2VIQMEfCiiY5HRo5ccLA3tQ61cBAWD7+MiOiX/wL/9gWfUHAwxg4YNvP8bbn3kLf/vVTzQDN0tkGYvGbxoEliDrSJPQO/+n/7XdUWJDB2DUEABPnJrtCneOLtvfiNuBX6VgDiGAHpX3ghmAAAdAAFVI8pHHe3fFd6qz/cWD8YfG+Sw+OSSPAe6xW3/RFej9qnDBTDGW0ioW9QYBrI0JJ2ELNolDI2/SX7xVKhoEJM0mDp4lBc9yuhtm06hJDosZDmcNBBBE5sAMiIcODADE/IuT4m8EHPo9tddG5bUCA9d4XqsSxyve5lqQ0LNXy+eEl+6assUueQf7DsosxoRtlzmuYQvSnQstpKBK2p+4GlOdp/MxreMYc0OmIC/uN3EG8vq53897O2c2Xq6jtEnxHEsGHS6JgOACCPgA9Q1jrm+QdJvnI811TWBjnRXRe1+T4n4NO9DU14ClXtswB11uGYd8MgEFH6/M1JB0fhmUE3O7dJNL3THxc293OyaO5cEAg3ZHw9fPjceZEmEFB1DWAAVIGU9evCsxn1/5Q/yd731JWQWWCcpFH1d80hFOJtKlOBo6oTs9MeQ8dBTdEgwANbxwHODC4FLzDPgwr+pEUD3BR4FBOlqw4MJYhdbzHUJsGMBAa8o9WuXV9Id5tnotjmM0dPH8S97tChwkgPcdZFJSyNfZGxXNxQBCBpO1oQq0G6JVEmmf5ORGpQMEblSOMem1izOvCiX1DE0/ets7NbCy8yHhbOa1XwPurEznd5xvx5Xzf1Kvi/WIrB4Vj/NShsi0sQeUAM0Yl/HUBLQPajwtfKCMgOcqKUtwDSg4LQsQ5W2Y9F2t+nEug7NQQB+WimAg6Br5+YPRN5TUQTNwbucaSOjzULSdTXJkuQAqbymLuRcTO70OKeziGQCAgwLLlTC2wB3V2+v75PkzPP7eb9Ydfy+UBwMMIiiYvVCHia5a/iLf1GMgArPEZD7/3/y+JIJ8+Jf0RAKQwZBEJ+z3YOtOReWSEQohA1gQI5fS0klNZUL9ZoM/q/8kzt54EDPEfgIIxGOJtPxcWFnfU49dhJWIhWaHCAEVHoS2AgS7h1J/h9CZEWH3ZVT2ubZ9E6DgPdYZuxlt3niYeSLMERwUvXfKorRTbqhoyrkChMgUGEjQ88LD22ctMqAHBmGxfPOseEKW9hXlrX1evpsagWld+8Sv+qW95gyI6Xzyu4b6LxNgF4BnWu+uvjOKuLuJTzsBByLTvnysAQhac8sq75/X19Xb1oO7diwbliCEI4cdKfsSQDGAGiZouoPGa/zHNRA/LWeg4OegayRUoPOX7RXExb3wqGsknOm+XaNrPHyS0eZXzNp+Sf/Ofp8lZXerPZCyJzr7Bl1kyZJbvXcKxzpAMNisC3btyfNn+GLcRhlQ+zg2wcqDAQa+zfFZLH82mJ1SY3H1xdtjwpP3nsrmEr/2xwIKjHJLkkUqL1fRePRxVI+46D2wawaqCIo9zsek8YRvQ4KnrMK1jIPdqxdUE+CFsMaaMhKQSw0ZeFgkATh0lcyJgu6SiOTz5QQhytvcgKixa+q6mhcr5Rg/Rxo6gDqGbUlaAiXIjVEBoMDowDj4XWlCX9HbHEHBskRAqmEByunUWDezLspDTNwK95eDk/G5Je9gAgIoAlot03X5RtFfekYXS3YFvSpcKjgwPYCkuUZxA5rrx/Jsbw6O4G4GCmKbw7xz5gdwUDztj063NfP7JfJ87HpWR8eAybJMQEFkB/jgpo0XdU1Kco3qGmMPrionSX+nwLipyOJ5Z4AAECclrPqw5dDN7pykoUi9Xz0mwGG606zV/yRx/smLdxQUfE1fGGiJiOflwQCDN147AQWReo7HgHawXclngBk/eO8dfPH7v4tv/vpX8cZHf1kSnGwr5SRGiXOSl16wDGLKkhHNVOAb4ZQDlFqBp6zPitnSq2KKskeKLqhHrXuWxDpesRInhUgMPB/s4MZZjpcoEcU3RfvFd7XrEoWmKxnsur4sEoKGUM61fbFaijk81xKzKtFhRgVgBwgA4BuXLDP5utLFqM2YrOsc6lgO9UgWcefp9Z2xt2Mny2d9c5ZmTnaKM/bdIJu1Pau14fJbAAYXMsSXZQVmzsAScwAHQAUItGhPV07GcLrMedUOSlNngvrwz6y8H4anASVBR9mzLaSRUfUN4PtQeN0zABwXu2tValy+hhKGjbGuKVHfaN3kz2Tud9ddrFu435QhCHtBGEvADUtQGQIA4HznLIHnacU6DvbMJkjbyU/e+6GAgt/4kyZ8QBxA7qI8GGAw3evaVxRgFIYQJ5xt6fnkub3F6mt49NFPgFmWGrGh1KwUTynyzm8+QKyZvHevIFnc0ACCMQhBYUfDRb3R4FEw60+twav0cK7CCgAHCeVfxOORLGuh+Ujj5ILoZbmWZLIn0CaIXmL/LBN9oigA1JgfCYInFYwGFFieQd5aULDdVUDQLTFqEnVsvK4pwWDYFdPVG9eUPmv5RElwqbFqIAAEIvhrVC02cbENsY7r1QPSRwrcOqAJAL7CYtEmv5e1T7+3Rp8axcSNzFwRJpm2r6Xb2dq5VTBAE7YEmIOHwerMxvhSu/t21ApCxi6yYBdKDCGgAz76+ynYblgffaY5E8A4psP13bwN95WDt8uS/A3hgVJEhlmTNvf7Cg6ogI5dQv7qZyVg0DPIVcd4XSd6JuoY+36qZ+JSxQkgOGMlT0NPJ4Cq0Q9ngMDzkZQhCPWsOzbWjaXMTg2G3PqCi3lBiHPTmYLf+BNNNET1YOy6/19IPvQyMAPr8IG9xTAeAzSR8btfxtuffQuPXnvkTAEHpCaoLoGSUYBqlNOmDAIDdyQAYeYh2Hp5oFWUE8+hVyKUUQUob42idHrdBNb6JCQjUlOP4t8j7ScgowAbVHAXSTWeDHRBUJVO6wEB+Va2a6Rt9+/Hr+ukeX9Z7oFT0hdi1/0Sraaxl8MbJnx8HLKTpWcH9x7kbcU887qTZHZDQ/2yJ3vGNvbT6OmH+a+G3xIsEXImGjmhdtvZVsau8ebi+LDX18InnoQWVmWwbRK01fPokgGe9ckqh+XCTn7DfU7Lgg05u75Pmk15pLevmZvx9wnr9VJypLrC33Zq+QQxWdIYyv1enJGUQMd+wRGxmHmUw7ZtUb/Y35vAQGQGTK/EfrjASE6BwiwkE/ucwntYNLlwyRAYIEgZJjusTmfNL8ByrFjZbQIcIDThgz7R8CSPK5aHAwwuCWzojBEQ1M2Enjx/ii/pOs9Hrz3Sc8zzq8qI0wZ50a50NHFx4ZDBErAgXpzmI8ySx/q6TzwJMWwXkhXVQ46xSYqJS1kF+hJIsDpYO7slRW2XdsZqIaiUtxoyMICg7AEF0LASqsEzXSlu7Y82p2DttQGYj4H1waxc43l2pV065NkKV5b23OY+20x8T+h8knneronujP/S8NvxcL7+zrNxuWB0fKw4zDW2JD0FBGGL8nquAod+KScA39kw3w2sRFeRsWqzvnupMoKCWfEQHaUWS/Vecyx3r/Q38Xs138/An197LkMOqBcJr2R6RXcN9eWVWXYHFT2zgY996YgAL6dfpPrnYGDKRGr/Dmzkqj9WDsAEXE11FiUFBeb9B3BixxUQxJCBhxI622R/fVRJk+RRAcLT58/wxe99pW7uNytXgNuHAwxmpVNOK0Bg/54+f4ovffsx3vrs2/j4q4+wM3SfcwJRlsiEDU++093SGERqaFlXI3CBvmLPlZ1tYiLJiRUoAFd6OsPheeiBomfM7KwEl8AMBPTfLH2aLDmK3nazTe0Maa+E9AwM9Bm6wVAVN2AtnR0qEdrPi89iONjaY0uWzAjJF7ndbByu9fC7uXa1B7q4fn3e+l7zt9y1nr7fIwKAM+Nvhr9TVPYPQH0fBAcuYGoTpU+8lhSmTvjn7IAB8QAILr3+1legBEDhlYnz92xcLyz7rOedz424Ekr2PDmaca4fu7Hv7VG85gLjAyDIjf6+AgSX5Mdkx+TGHaN2iSXpfgukG2pRCRsxOUg4lHk9GseDgh6b6pfes78WBPQAILAFMyZFvi5kcCKb03FQICDHDQgE0J3yABLY6kYpbOG8NTLWY6bmrZ1qmwDGk+c/kCWJn3lrBAU3MpUPExj07ABwCggKA89+/BRvfvcx/vQzb+MTrz6SvU1IfouKzPdIz6/I1ZbtqmCADAykY660Oi8HwWMaPJ1YuoHt/Eg5dhLLJEPsRV/GozkOA1gAWsAAeObx0iT18btOSGnbqnBq6KDPzC2WhRtptptp69Ej9TGwdkVv0/vV6GrtJ1s21BuW0MeXyvItcP61Zx7G3l3FFaf3HOZ5C6ZuBQAcZMQMf31rJKufErqR25fezF5FHF+NTgBsKR0R/E2E8hZCQtJEmZQiaKjjdxNgAPxcrawDxdDbXed38jZVrN14cEE7zt0mM8Oy2EWWfDMXWm+fe3noQXPH6DTs0C2ys2JpdO8F5gJKurW0hQiOXc7f76uOCfswDM4I0KxqmOqXmW4B5iDgLAx5iU2ZlRVYp+DFX8u+RTDgrEEKrEG9531A3a6BOAxfoA7sBV3Pnj/D4+98WTYvevWTOr8DK2d1vBIgPExgoKUBBWHwelDw5MdP8ZvffYyvffrr+MRHX8dhWq0bHCJ5uxogg5eJQGZEDCS4wpIcAr6krIDWy2kacC3K4/p/jMNCDVkwcM2St0DxNfkOMTFysla+2cXLSqT5TEgt2ZDaBBwTkLKKt/VCNDA9E6MZq+JAoDRG4BrjsQRvet8ocPG30zKra1DS5wDghAbu4v5+rwZAnYCAoJAYwNEBgMLsxt/BgDMD8t1+A1oltuwK0+v+XV5HLHCFAOL6NkL9LQFIHWDIATAA+uKkABbqX4MwaMc8fG8qbX8pN3LDM8VKPWCYjHP4fckcTca2Nf72sM7o6PmnoM/ueavcACPo6nZrtHCkbPS2w97+2bAJpl/CVt3OQr6sXgng4KrwY+hT7vv72vDRCsh3emk5HpEZOJE9QBZ2mEw1osRS2wKVDT38zounePPbj3VHw0fhKtV/lM4Zskl5eMAgCsAFlgAsOQW/+d3H+JNPfx2//OojHGiVHYDq2YCRwuAVVVqC3AQk6NPdyFylrICpgbl1MOP9eKbwWNX5HVqjGOlBYwd4kgNh7EP/zJjRHmm6TQ2/hQYuJN7ETFxPvOnGDuHvimKLdWyMx0vS0zZm1cuMY5a9vy6VqwBAPH5J+dgxU0D+24oJyIMMRBYgzn0HCWA9J4IEOGPgXQFUQI1WfrxZwTDmRD4ucMaAK+uq5yeqgCFTBQsZATgQIaWtCUeAj6n8OaMHYJXDINUq0qcDaO8ZgQAY4hDafKA5KwB082FG/S89z+tYn5XcAOf0tP0d5Ebf82ArtGBvhuSiLEAECnfBKWF/8ycBnqcgFQnhg6ZCLTDqvf4+7Oh7AsQ+PJGfW8sUUA33u25MTINUkN3anJ25YeL8cTrfASAxI4Hcfr312bfxydceCWiA7biaYHaGEd79cYWuenjAAC8HCj7+6qPRM/IbypdE1Su6LzyhQA0oELLmJOgNfDAaZRXuLZ87BP0SbZ+/QXL0kMi944XhmynLSR3lpFZAml3/bCzOEm4agNAJTsHgtYYWTUtQsYGubulpG7eZ4hsA3AoshD5ut3M9G7kJAAh9dx0I0FbeYBiO0Jel1FCAf2ZhCFiPWZ8XrgDAjL+ACJbQsSoxI4RnSWRDD6gVytpcYd6ADGEFHDQQK1tQZS3pb4lIjusxG0/7DMoDq7AC6+Tzfy4nl8ECgBVgGFsfO0L/hrG/EgTMgHPBGPaRar6c3JCDsqyOeXV8nKEpxxwoWNjB+nKrMkSx7870ifZJv1KmZYK7PrPzopzEPgauy/m5VKaym05tTwXbrT7rgcFReB6KA4MYzly/8/wJfut7X8KffuZtvP7qIwl7N84RVSBAVMHBFeVhAYOpRzYHBT/48VN85XvKFHz0UaMgzVMCqoontGBhL0J56gr11tMBcHRAIVFWZReVVXwCLiO5ExpwuFu8l064SBVyo/w6I6jnNNT55H61XlfSaQ4Q6m5fFss2w3UEgYmfPcEN49g0VdG//oqbU2q6Kr6sio96A1KMPjVPswMK1/TPqiw8mZdjA86VkIUFzoBAKWgUlYGAvXADANi/s3+HPfNC863J2ZtO7v0LQKAGMGwpgAUCUjEwwBUoFFagAPms4KCXwSlYd6A3AgU5K7IKuR1zIAD8K41NNEodzb0EAh5io6nM9GM8ODgfgNzk5jMBtInMZHjuh73giy2PaSIvPPTbQpes+sfPCX0Uz+1DJv39ri036WKHng07M2MEeiDggC1cvJd2nOqUlUFIYPzw+VP8vT9/E39i4W8WoF1wAg5i3S+07+EAgz6EcAMo8AEr89hp8xidaIfSrTJFCdC1uFWYWqBg1zoVVG/o904T2rFlLrq/pyUuxdH7p+arAxSfhpFqB9yTli8BRCzKUkhDYmHj2QQgYDR2YfZxOsA+DpHGvtQFJqbNGJC+zwEyXilxo/R2mLdJSClXg5K1Xzx/pBqPBig0fXQFiPI+aynTlwUC1djXuTsLDVifmqF3b59FIfVAYJ+AgN1YYLCACh03O3apmPExY279DsjWCz1Y2BQURKCwqZLMxcaZBViQ7JsDAjJxE3pwoEAGDN8HUPDxzqMXdjLmy/GejXWQG2fROtanAc8T/fVByU1WjyYRO6iOYwTKSDm7vrOVJTV3aeKMLMqw/W9v8MP8tzb1qrH0By41vu0ERP0ZDa2VmV62WRBtxwwIxLrZeNl1QBuS82oRgUkYgx++eIbf/f6b+Oe//pbYL734AJ2DA5Q2pHBSHg4wwO2g4OOvBqYgCJV5UMAo43bAUF2CKFtTQDam4qV0S0vscw8OUM/xx0za15DV3QkzEOP3DYqpfzb576IoU/jRQE6sW2fWprWfIucPiMa2th4nUq7czJSWlvYycpHfE7GCAbi3ab9lHbRMWTYrAiAJWFcYD++MRT0XVOcsaexWVmAFsMzYl3DNURg7y4uE9hKNf2mAQA8C5Hud7zNgsNgsswEG8tfCOzSAhQgUNt3cRj5L+GGj5GzCAfFydwUJhxovEHCoLI6MERREdEuSA0juAXLdlbEb74tj3Y73NbkgDugC2Ot1luWAHAYKMQ//ALgoN6Yr3n84p4KF3hHRqvn/9Vc0Rwcdol9KxRfTMEkchmv1onyvn2dm80w/98+esQGRsYngob/26G5OJD8mBn70b5/h977/Jv7Zpyz8zSik4bNbwcFJeTDAYBbT60HBk+ctKHAhCsgtGqYzlH0UmSgFdeCi0S3qilMwrpUlqPeZoVErU7YAc0rwnL2uPzbMGkxJ8wTEkJ8fQQQQBWpe+YtomVuFdkZjz2LZMY59dA235aSrOLYZkh0ISo2RuXqbRxLzm9QIHQ4ScB0dDVz0jJo45wwIhO8fJBiYMQM7XwYDOxcHAhEEyDH9Hto6OD1HO9crKBBQllRgKjCQsdpLBAnHABI2OlomQdFsDxLMw7V53oOEmEi8AgkyniNQMAm5ONY3Ar4VGIhj63ITQIABvVX+x0pmpIp0nvtBYziHiJAKKxiov8fQ3ag7gJn+6LPxbW7bb72xlXPapbLnujA8Pey4WHV0pyDt+I16ugcBVv8RQDSXDfdT0wIi4N2fPMPv/8Wb+Ce//hY+9tHXPaeAGXX+GlDgFhz01W+2NJ+UBwMMAAxCBrSgwJYk/nJINLTf2wHskDcwoAMfPKq/+WsQ9IKRIVDlQuOAEM0n9ApVTidUc+F4L68vrD5az1CHCG4iOGgFh8/mVPP8M2pzBQT24+Xj2Vb/VQx7SzylpQ8KhoTVOClIkOMYQIIYsc6AaKU8HAPU401FK5A9AwItmGpB1oxGnoIt7ev9KE3/xjCBfIYeL1NmoP3OFYigsjllNom7IqCAkZOCgWJ9KSGexITEhELFQUMiwl4EEOwE7OXAlgg7teGGLRMyCFtOrjgLWN/2zagrHeBvAJf5YJ9pXG2kYyy1HYGCj+M149yAhDFpcBpaC2PbAL0JyOvH9YOQmQgWrH+JyMF0ggGBkJBNbegO2p9rxVR/usao9vrv5XRfe5i0fgOr+nPQ12f1bWyL1uNH7z3D3//LN/GPf+0t/PJHLHygcxYMQOZtRgAK+nsm6BmmX8q88qE8HGCwUKpACwo+8eqjOmiT25TZZx7P3YsqtvCDJSJKfeoDWsBQnzyZby0NNqlHPD6jCKNu6j2DWFovQY8NVCJ7WyprMNa9b0dL5XXt4OrZREYg0ti9UjODFT1YYG2EWopavE2y5W6Dx9kaEyIzIBKjTkrNgVTYkgiueEw0GhDtj6racBEY2Ky5Bgxcww64lxgMx88TEMRxmMVHxwHS8TuEIUAiFGYJ6xgoCwAhEfv3wkW8pJRwMCMTgxPhYDFizALuDrDP5y0RjmLsETsQoKJhJ2IZW5I5bwp1BAmiXGNuQuOrno7z+Rj3IbaYE1LCGBlgiCBvxvj8PGSGiJAPQh/OGVeTBCcInWd+QVdYHwFVX9ifWSjx/eo7WLtUlzf6G6PzNGtD346hDd3BWS0PsyfhdxJrjr/+yTP8w798E3/0q2/hlz76+vjsWl0FDBUopO55DTg4KQ8IGLShBBO8Z7550dv45KuvN96rFUeo4RiHD9EQ+2EWoWyq0M0YG+gGMPSVXJT+mb0wmBD0VGE93t6832w5pjk6GFjQ8PbbTKD8fhOuLdZd6sAuyLMs9515qdTMOAFoYtvzwk6hzWLXmajxODdO2EiM5JZIXqUNqQMxo5CEFQoLyPP19IVdYfYGRD5aPy12uOv+RkMBBACADhzocZvL1wKCA2JQDHTZ8bOQgQECqd+cJejHuz/Wz43+mH2Xy82CkXr6I0DYkLCXgpQIzISDGVtWGWEBeLwzdmJsmcCHAl6b1wxJ5CJYorezC7YkmQJIYKJ6zmqMAcz2LDgbYxvTPkk05tvMAEEMAdl4GsCT8MKYC+LPXhb2EA7KPN8jAoWNCHsxkFBZOAAOFoCgW070A9DqCOBcxwGY6rkzHdfURVkQgE91XF93b8EV2Lc/rZeNWe6D2ROTBwbw3773DP/gr97Ef/WrEj6wa4nIsgXC/WgSM6ghheYnSqes78MBBh0iB4Bnz2Wb4wgKZjgpgTyBcEWx9EYOmCTyhN8y6CJw6Mvs3pdAgCmWeqxWpU0EW3gKQWCTC7O2gXqQ0AtWDzBmbbL21DoCuIoVmCm2HiDM2wSAtT3FFD47Lb0lwpFIlviQes4kBuXg6m1GgJAg8VROrRHhhZcp/WkdM6+ndIi2LfTVallTn3U+AwSMMc5sHuWMJZitLLhUEkkf5EQ4inj9hUcAYGV1PHWK9yyOG0sxJaiWOrHIGpOsatiPIuOSCNDjlBgokl9iczEChEwEBustddWKgoKi40uQ7+AQ0wWuHl+gG08E8NcBAgsFrRiCFSDYy8j2yPOvlJligEA0aQzlJAKOREjFcjfGsAOAxugClz3slWOz0muxPcD1us2Yi5ley0TYFRRlSPvkXMKut89hoG/R5bWda/nK3SQ6CuO//3fv4B/+1Zv4w199Cx/7SGUK7NlpMvE846VjDYDARNTgybI+DwYYRGTOkG2O3/yOvPvgk5q9eVZIr5NVBkoVQxQABSVopR/knkXcweOKkJM6zCixaFRnQjMzqMBtCiEWf+cITFFT8LypAQ4xHgms0fmKuZiBmEtZ7733aqU3aIlbQ9N7nQWMjRMKsVPSWwagBoVZAABQ3NPkYkLHbkTAJGzCxMsk1GM2VZx+HvBkm1ndswPgFhDMEjZ7luAsPDObM7OSrO4eCyMHAQIOGEknQlms479bb/y3TEYE2rmYTpRwCeDA5DZrO3MB9kRgZmwg7Kk4e7AlWbWQWCnbpONGsiysqNeM2fhqf/fjC8zH+Jpk3FVCYcwhsH/GrtXQULmK7Yl/+34vhyZhRpaGSI9H5sa+C1tDzNghAHs3XaDPvPc+uawLgDovgZ+vPrtH1Wc71brbaiRpjwBL2ahJxnVX2dda/3/J+7uYzZLjTBB7Is/7UXvnGwuS+k9jAzZg+GJHI4k//VPFnZFGFJtN0bsw0N3VTbVAUaJA7YwGGnhm1h7bMODFrtdjz+wMR+JSvSS3upo9gAdLscUmtRxp+qvqZpOiZPvCwMIXa0Nd1U1hAe/FXo1Y78nwRURkRuaJPOe8XxUlzTcJVH3v+c+TmRHxxBORee55WQR/vTkjVjwo+MCDT7gcL712/6Mb1sDKzbdvrl5yaYABUEHBzbcFFFz/6A089siVhSKWvhV2wIQ+sSgEwVNcb+jKpLFKQBDeGgIE4sEQFR8bizzsETNwikG1siZQ/ZSyfjpZvL9Fr1WHr4CgDvX3MdC9oGDk4S72WyYOA1AW4ZhyoaQPKQm7M5nykpcVwEBATiXbHVm8zIlQaGi/8Ih5maUtmKtnGVR3maS0DQiMJbDzGPEsjrXkTSIFUKl6XB4IJMhYESCgxsMAAkj7rmqaPYq6L/2y+N4D8mPMn9sDhUpza/tlxpyoAQdzEgM2ZcKcGBMDxyzybAozZ+vGFiCI47yzf4FFH2/1r1+rw4cNDOgdOTe5IZZHsBcU7JWVCvhI+lLlRfbDlGSRIckP4SL/zMLgiAHtS/vsPfIv++9Rj5WQiO431hAxwBFmqfYTMVXmiZVJ4JqciLnNXbiXEoKCn7mO9ykoILJ/fmG2GiLwa+SsFbOPz335Gh7BI8PzLg0w8KDguS9XUOBLYQEQgwNL9vBGX+jG6pX94XffKPfz5+1YTKopfaJMBAjsvFMo9zge3yqHoZPoppT108laIavxyJSX7ALQ0nij4j0CX79R6T9Vb8psb8moyq4+z6hiKkaF1Es4KFNwTLnS0FQd6PLtjISFAQFga16tLsG6mJ7lAIHtvwgoiJplghjJSceRgYOUqIwnDwQwdcxMc7cOFJzgPfky+tJtBBLkt11HCzDqqe2+sBphDw5mAwes97VxSEDSsSE4Uhv6Pvcvo4YNmFV2ra4uHGSgwIO9ogN4yZ7tWWTKl56RGfWJf0YFDPqsnq+Orsu+ni0LMAIBJ+kvaD/mFlgmFdgomTUCCBNhAS4B2QeITQBk3Hk9fgpI8GxBn4/RMwUGCmzarZmuCBREkxAN02XUDy699LEb+Hv/7O8N63epgEEBBT+na0cH5xk4AKCx4AoO5Dcw60pfDC7oPQF4691b+Bu/+zwASDw6C0VZEmdSO2LXgMJEtJpFC6xn2UZlzVswYdqaVlaVRDSdTPan1AoZUOl6u4d4FO6+K0DBYtbFawHVkQzA+kVOluMGENbAwam09MwiEMxiNWegeJqcGLOCBVujoqxXkTn0MO3dtsJYRdE5T8oDAtnXgYLVO0qpQKACzonFCpZeLgCUME21bUZVPtXoXKREsVNgGasehbP6XJiVaEYpNTar2woOZChQma0iMyNw4f4dMUAGCsAV6HnmB6jP6RfA8cVYH5uiWRJe1s6336uhxHa/vfvqe3dAQP62YMCOnZLouibvMwzsVf1jYRLPeJyCY2X2y/rLbh3vww5RcqbdoQcF7cfDYqZgT4jhljLpL33sBp7onOa+XBpgYDkFBgr64uNc1niWYZw1rkwQOglZYsdMBGIZGG/dlikj/+RD13Htyx+WOb2pTN5bgAR7wCjcwLkdTJ6hAJxC1/s13h4QInMDN6P4ew8KwixyuCQysz4KEMxT8NS8GXSDpQmOkuR15dEmB8F5rKKEjLo22lqUhirmxpul0GD1Xmev5HqWY1qp6wztX1RmqVCiAw8zoTUiYXHV7pdHXQUFA+NACeIVu/E0J8LBknMtlGCV15c+w7aB0ydsJl7dS9lXhzj5q59VA4xn1iwfDLdIjOwq071c387QufkWXtnZv1sMkB20n6uJaoSSEOcdGgPTxvpk5mLsR0nX5fdK6CY+vn8MjEBBzBrU6a+Z9+kswIMBama9FJ3l3nNU9WKEOzDZA0w/nprrB2OrTywMZzu4Ux598ImQJSgrtGIdEESyYYn4139Ocu6MuRqVSwMM1kGB/DX0X/ZDBHSChgsMIMgcJVUWjLfeuYVPf/15fOZnr+MDOmWkXKP3OliszXWRgQVfiuLTHjUFcIDQ1iBFqAz17KjEiSfmAg4AoYCPWS/yioHN467CASwTKPvtvnhBQ2oFKjsg0Jd9OQjLMk3UeKo+BglU5yc7g1kLhXHH0VK8VjeLT/cZ1kCbUNkLt3mY5LedAfH1NyMSFQ9oPCjwzwltJdULLPelgkpuxtMBjn1SIHAI6jTqm8gbipRg30ZrJTJ8EcM2Ys2i3UtGwcn6aO56OSF+RpPVzShrVPh+29O/PeDzr1rAnq87CEcDd5C4vSwqxAr0ZGpmDQNBwEAHpgEUQO3LKL9jBAC8nG/hwtI3hZrlhhHsi2cKUC/ZvWhWP+vFb9eFs8wZWDoGh5RCHRABAj8lszxvYPhHdaLuh+mREg5IS0AAq58DBHaf0QJMVm7pOj7XPxrbx6hcGmAQvXS/jIOPw0CPVSYBJQElK1PAyhR86rWP47P2wQqz64aoDfGTiyPq38NEASqjZhc5w8zJ+IeqJIllARcDCLJt5zCIUslkbhSD/pbsdSpIPE3iTRRAMIrxdqg20ZJ2bOc699vrsxiA5UwGK3U+svaNy1oGesPp2JG1DPuGnagxuTVlUBC7ayND914wzWCg2zbV3BiRAbvRgwLvORPV/aIodIwRSpxb+olaMDrV+/QA1ZfRehS9AgPa4dJ6KcPbD4vvR98ijY3g5fFo/vtaGa0vMlLQ9TpfVw0XdX0r9V3v3wjw+e3Sp64/gZb9OazoAAsDWd6Blbwi49E0Zf/OkbwCe2S2DU8xo8lh6Z0Yv+3Dg0K5FYQtfzieEut1lc+RagGBgYDlmiZ7dIC0xfYaLqEYBPJTjfrSLh00rDBiCPx2f98eYDagwMIHO0i5SwMMRjGTRiZo5RgAELnkW8atO7fwya8+jxefvC4fXHINWhgCbilf6PV2zD+z748MBQ/loPw+QPIcAODAklR2yMBM4unNAA6ZMRPjwLKu/UxVMaRZ43NQUEDL3INDMvROiOxpkx9AbibCTkAw9MI7unct/tsvWBKt9V4XRtlfRrFoO+YBQbyqW1so2DcqWzF6AwFihLhLltXCylhpfZQYABCBUQp/ArGS8n/LcaeEqFy7vO8J7PIirwIYe9dyrO47S5VZMoZuQTaMmvke2sDvH5W9ORgGBLrIIzJZiFIW0GL9Cijn1kkAqg7wYaD5BNamlwMgDsf4c6MyQ0NRJp+N7qrgJWI3ZJs6R6ayC1tTYj0QsL9rOuowtfqpfNZ74BCEH5ayMgDM3SHZPkG2Jq03wfTpEhyED/H14AEo2FkuDTAYlsbALXY15zFUOBm49ce38AuvPlcWR5ph7IAM+rNUDWq/oE3jCXVugldgkz/XKQmG0OoSSyYRuqnPPqeyeM3BTUk7au6CgYS66ImABKQ22afUa4WGX1Lw+8HAHvQNxHRY1IaegvarKNZzh7dZFP/ItVXPDBBIW7S0nxfUHsX3b+SNaW9A1Ecq9+zBAVDHWTle7tu9d6eE+veNjF1j8J2hp+68iMLs77tVGhq9YUjqtwP8fsYygU9/LhiWfnpg/xxfRu3Tv0sECqLROupf69t6vQN9TicxO+YALUDAVJ0EMcLiKJwZq3YP4x7AkBo/RS5ZX8avauodmLnUu9VP1XFpHRk49mVPmDDKIzokWuinHgwc1hwBoHEGegDZ/5a6tCX6um0vbx6Hlc+MowMDtGHDUHXIucsp2Bs+8OXSAINwxlTnyRBcxzYnVteeIVM6Pv4Vyd58/OErwmqhTRSaEhWgMKH7aFOv5VEVhUfevSyvKTlmAwuEM14Chf7DQzLfmVanOPp6nS54VdjkWwMxEGg+utIl28SJM8t6MC97y0oOdo5o51HZiv/ZZoT6e6PhazrKro+OZbTrsVfjXr1jGzvsUcGi4rEislP8B23M8Ns1EVWZonPhpQUVofnwR5Aw0HwBtTRibWGud6xggMkl6dmKgdSwCXIulYt9EmdXrc3Qw9qneNeAXl+i/vU3sn4V5ocLK2TggBmwYU+FVVQnASiD4cLj222MwkPASB7r2UXvQSpm2/s+ikaLZbmXjovqz5Mcl9hR8Z/tjnST10sm73uZoy2g6E/qjXvPBJylWPaWtovDrfO3b+Ljv+3slx10JS93NeXSAINFoTVAwKEyA4Cbt2/h2m8/jxsfu4Grj8hXrHQdmKKogEpnshOGjI4FgD2iXdWq7A+qLXLeKjk715ShKcoDiUeRIQ9uV75Lmx8m8q+/RoH2uQK7v77mhM2Ojzxs6p41KlvJej2oQpfQuGYXtjxsq7M/tgoE1l+lKcn1uS/2QafVikZ1iECxa+8RAJgaJaRywgzKufwG5zqrgc3HR23cwTxdGQupe4dUfjORHNe/y88SiyGqn56uAMHVAsxU9gFd2ILiHI+1Eo7JE/oWwCJB0fcrE1XWh1rg1+gUdwtr6hKK9FWj+Hdv3PzxvYDW6g608tfPoDkQYd7jxKTxR9P8s6Kyto5Fr5v6Dz7ZgmU9CDCwv2DRXCNFIbNRi42AZutwtEzjlFowsGWz7GKihPO3b+G5L1/DDZ2S2AO3veVyAoMAFCwUXWnk2tjnd97EtVdfwMtPfQFXH/wAOB+l0wCAklD0et+DTQNqPJVyq6bsofn6U7ySk79GqVK5oF83/5BkkQ5TkszjxW/az7FyeUZfotyAze+1owqbz6QltNRZZKyALZ3bJm/2bR4uKON3YKlsou4Z1WH0vXbgtBh7WNz1NW9sBQQMcMgeABAqnjw3AIDyXA1/AQT1d0XGpRf0TwwMpHJdLxfjTyD9LYAg6djTfWmSbUpIlIA0wcaCAYa6eFALDlrmrWX4+vZeK/fUv/21fbzcGX6/9MAawxjddi33w48P+WvXjO/XF9az+jZl0GLxJjs2d07MMiz6Z6+XynmdXrL2Iqy305628vWs2929aNk/ZwT0QEDkzsmVxBr1Qrnb67dv4tpXWqdW7yRrxdjGjnJpgEGUkLEKCjrv5/z2m3j2tU/gSx9+EVcfeD94viuKCmg8mDqVTSTZlsbVJzR/fU32KJ6mNIZM7uG9I9lPrWDmpVDmScCCX0DFrvfCWR7b6fQ+9hiFBHo2IG1k1aaOOvOGqtx3Z/MsAQC5Nmu9Gd9udqy55ymB2q7c77n93nSN7t17gyNgYO1bz/FMwLHKQc4AsgAEkw8DB5wBnltgAC7MAc9zGTy8sl4tGf9LCTSJ2ZOVAwmeLQBNdVtBASsgICRwquCB0wEgwmTAQt6igPY+JKct0OQ6pEHXt7ND7l8fJ+rGWwMGtp+zxxO18yJjZHVozt1Rby93dS58DfdICGCsl8pKj+rE+I9/AfdPJyV9v4gF2FoPIGyzvQ3kyhqzsGxzBwTysbVRABqWzhVh2apTe+Oj13H14cchcLm5e30gt/oiKpcGGPgyomHEA+roUGac37mFZ177JL70s5/D1Qffpwqw3E29mMocAAAd/7SCBUA8HrskmOS9tnhOWKj8Z2+gf8sbOYRuH/7hisodiAgBg1aKmRpluVodtABAtjWu55gAb/ib3ynul0JVA60w2HZTkSBOrfu5A3JWc99mDZDQjfoE1xaREI4a5/tRRmCgOaV6NBHIitt5BQgYCMgzAH/uLBnx8wyej3KvLOdzOV8BQQ8KODd9xgCQEijpwsJpKsYfZuynA2iaVOnJcU4HUXR6PqXJAYW7BSjIvQ46Da0CBfvXM3x17HNbx41+uC9l5d7RkSi+PfI8W8PjmByNfYYeaPm9IXO6vQz1oLI3ueolmW3gfjtmITvg7gGDq/Vq+5g+srbYAwAWgGDgmLR9cJ+kP2jzvi/o+Ke635Bs0E82Fjjh/M4bePa1T+Dlj3weVx/6gMhlgsiSe4tE+5gx4LIBgxWl6EGBp0jPb7+poOCz+OAD7wVy/QSIGJpZGxjaGQoM5uMCLKCcs7Ps9VAD0MEa2pAXbWOwFTT0SH7pQbcGcr0+UeJaL3A2vQbUxat5BrJja/LcDvwGGQ8UVtcm1g7WLn0fFUBnCkzPK23XJb5Zm8hfOb8RpAWLE5d7IB7CMjIEwM6QmY59UiPfAITCCMxLIHC8K8Z+PoLzrH/V+HPW33I958oYYF6ZPKosASiBHCggM+xJ/vF0EBAwHUApIR3OlkCB3PWOOSA61nyFJMyDhSEsHMiuLZd5PbV8P/uyLyPvdOTZL4x+7ijnzgGS0soaABdWXZc1ACXMI7tV1kzOtK2rbpoKWPB5ITkvk0jvhy6yOoXM5BpgBmrbuXZZbZOVNtosAQjzfVTksDneahzTe+fvvIlnvvZJYbof/IDq9gzKGIMDYBPnXB5gQNFmVZAhKHj7Fp752i/jlQ/9Bq4+8L7i+dR7zCoE89IQ5aMzRFaWTEFceq/qBO1jSLEzfvJzO3HLe02Nx8QMbyDDR5cqdJ4qxVQ1Gb3sQQCbkRrFqtcFwpfK1jgisAEBy7i1gKzUtJs3HD3rIEyP9T3s7eoMAVefAiL8kLioYenG86ZxGIXJjBXYAQZwFDaA5yP4eFcM/vGuAwSOMZgdW2B5CcoW8EqflfBc0rZOqRh2TGrEFRRQSsB0AAwUHM7kGCXQ4dCABNAEoqNjIAQkYE6FiahhitrfQJ1pY/3bAIbvQ18CLc3sDy+91c7bBxZ9LPs6kB3JGHBhOZOTSrqfa08q7VlzQ6YFWEjGCIHAiTTUgwIWeqaOo0br22igh4YgOa+1G7BIot1TFoDgAjagBwHz3XB/+1jG+Ttv4Zmv/wq+9LOfFaZA7RunSe7PSf4qOGiZsPXaXRpgENFoEX26CgrCAaGdUgah/rXOC5Cin5YVCtyItlsGvdvt/lk9ILBn94lb3mv2oAGowtr6H8MyNEh91npkkMx4WHza77/HeDWASj87xVXaY0OZGdNAHWBowhIFWNRnegnzyr60Y7Rvo22jfSND0Xg5HfAdgYHKoDlmYD5K3xgoMDBgbMHxrijt493Sj+z6k+e5JI3t6TNKJDkG5Ay3/uXDmfTD4UxCBsoaGEjAdACOGnI4nAnLR5MoxJyWIIEPpX9L2CLq165PC2C4z325PN4ZgKBfTwIA3vj3x1XO2Fid+5QXUkI9rh8bFqfLC0mh8wL3d7ttF15/pIdG7aXnDzP9d+pea5fheTjBHhTG7RjXwZXzd9/C0//Vp8V+Pfh+kdEEAQOcASRQPkr7I4tM6LVpqVQW5dIAAyBCiq2AkQ8fNKDAx3gGndFnEVvnBQNh2N4hAlxB7lvINaTTe6PWec3wQMIBB7tm8E6L99hSVMUQdSyBxa95GbNmY2zMkGFdYTGc0koKCpL68qaEzOgcDhgCBm8onFc5bKMuLGFtVlqteFbAJjRffTv7uW4wwj7w+TQ+Z0B/43gUZoCzGPv5CDbGwIACZ9mnYKCAB87IdwU4cM5gnfvKXH8DaPMN3KIYNFUjTBOBNOcgnQkbgOP3xPjf/Z6Ag8NB2KHpIHKnjAFPB6mT5SVYH6dJFGKehXnIwvyxhRV4xgg0AygGy/r7+9KX2p8AQvpf9u8AAdafAQAoDFDP6DS5JOsyBqD2ncmY/vY5IcmDcsv/cOwByOWFDJi7JuQ3bMpeXy7bqLZpBJZQ2quc5+87Kn04BQidg0iHbo6a/tmFuY7rdP7ut/H0N/59vPLTnxH7xZJZxpwAyiAmsIEBzmAkgAbJiINyaYBBSB8BZWCUREPNKSigYAe11izMUnaaEbOnL07oNuv9QsRqeNl7zcG1UalK1qN5rZczhI1H1AMHuZFe7xGw+732DpGnUuLO3Mas1fiYcgrj1nZvoAKG/r3TpM1flZWPVVMyY0C7FRlg9GfQdgYU9JkctJ2FnsLFfPYWbzB82zuluGk0iuc0N4DMAAE8IHBMwRogkP5jcM7Id+cKBIrByc33KrzBIQcMUiIH6FIBCpyzggQCDlmO2Vg5nJW60HQo70jzEZgOAnDs2EGZkzSBeYbNcCDOS2DYgT8BArMDCmo894LmQV82/dkbIx7kAewBAitA2yeTfr9lzEI9BsZLTojqmgLKBsxd08byYNeGA71jx3rdU9o0L/Yv9OuGbvX1oJIf07ncPWMbvYM/vymBiebaF70dOn/323j6X/xNvPLT/xhXH3hv1QGoTAGU/7V8A5Ae847jRrl0wACAAwMtirTZB6986Ddx9cH3L8MHPc1k93YdVeh7l6S4WRZMAYcDlLOm3Dh03wvw6mM8mgcqRQsUYW7odjlQFaVu6xtuPa3Uewl0lt6KGR+vpFi3wS55zWLX/t0HHg3bO+o7ExHYJ7f5WLUpMlVY7BLbSszalBhPBSTUeHTANnh6urS/JavWvJTSzlulV1K94Sj7OpDQzLYxI+KnFrb5A03IwPphPoLnu9L+ul2OdQxBPs4FEHgwIH3sgIF/H/d1oHkikM4PTCkDOcmMGs5IB+kvyox0dhCwoECA8wwyEMAKSKaDAATmAhAKADiweqxZ+sz6lajmlKRJ6saI+7OwbF3fnNqfkafb92cEBFbAtpexRpa0Xz0IKIABaPNDgKF8ARD6Hyg6pcgYJWUABFiTMjwFKEyHhrWjgLWTNnY66gSdI23Y6ncPkMq72/meLfH9sVY6fcqBPgVah6xhbHsdsLPQAKSdv/stPP17v4ZXfuofiVPr34OkPUSPZ8gX1AQoFNagCyms1exSAQMpS3pO1il4o8w+KKDAXxOBgghN2nhaG1jNdR5g1Ngee4oPaBE+UA2oHXP3PSXuDueZDanAVJP4lqzDuIzATe+xrCqqrSS2LZpT340oVaCgIIAdWKDpTIz63e8pYKggAWkCZgEJdDhTxL5kEspUOWJADUVDjUoFihFs2pB2KKLGM/KGA1iAgRUD0kxJ5Nx6klFS4T2CgjIeXUhh8Womi5SA2RYBBjISkq4swUjImJFUK+W7RwEH8xGEA6AfIpZz3V9OZRtZY9QKcGliZXykzzgdADbjxAGLwHpTApXkLQ/87kd/tkA6ZN/WmAE3W6SXsQYgWJ/eJ/kCnHF08oVpKqEeHgBxpAk4duE9AOmi+sbrQqdv2NrxPutPUv0pG+pY6DGe0wIolOmvPaN8EQcBwOvf/UM8/ft/C6/8tX+ID/7IT0rf9awKTe6vsgYkx+S3fXptG4JdGmDQoMmOBZDwwS/qOgXvjw1+OX8FFDTnjY63QABAAwbk0s542nMjyg9oAENFvCteNOCQrRM8Q/1uENv0MATnLT7aHj0rYjZG1OWGsuqT2eQ1Y2q6vAcASmIMfDIbmfKiBMwJnI6ayZ5EoVq82lgDzeY1Ort4nB4gWJp6E4bh6pECajCUc2FPK+4xJP6cJUjdFb4ZGZEeFOTK4shUROsTZzTgQN9KoZSaviFKITigTinSxviy59NB7kdIUu/EoHkus1IYBxBJyIEOZ/Keh7MCbOhwJoqaSRVk0vaqACEGfNYf97k/I493oy+NHcgeEDQJoq4vWUNBJ8jXSLbk97Z8ieG/KzNLFIQL4D7WawLnpGH91oqxqZHz5Az/RXSme1HNW3Ihg559tfdgRv163qTxfQ2tHKvRFkeL6zub03FCef27f4Cnf//X8cpf/b/igz/yk9sXMNdHGFCw/ah187aqL5cHGDil6afznN9+Q1c0/K0VUEB1UJhnZnGZ8QOHR/wKVaE3DeyLARrFbu8X0ex2r74YmgUaGhBAQwUCG6AB2FTgDTL34GcUJjCFZclv/jrNbrd7lr9z1N5z+cAMpQQc5xqj7pXYdJB6pEkozTwXGrrQ0Ycz6QuLlULRf+4o6aFRIem/BiRA9y86CMVYNI3Zglq5TcsWDI2IZw0s5uxBaA/GVoowMFkVmyTrcc7alkJNUkqYjDlAKukXnDNo8KHeMNfAxplPStR+NAarBxXLdsvSnNbHOdew6zxrCE2Ot2Bu2ZeSjzDXc8RaoHzc+yL96fvS9m/2ZbCwVA/uLDznc0MG8nVPsgWAMpc+MVamkS8zmPPs5K/L8XHX+XuPu3Xp9Q+Nv+oYad7T9KT8doDA6j8fJWSSNWTCCZwTkHJlEewZCeA5q26V92Qde8VJJOO5orIEDA0oeOC9jtlK4flhUUBgrFwFqeNLLhEwkLf0yRrnt9/As1/VZY4fehRocgVWGohoBzgokGxcpVGCyymgIDKodk830Edzx71CLTTgXRQqUM6hsvAMO8BQKNRUlXwvyI2n4ViDENBseDC90uIVaro8/6hGbBZFVhLYvBKbDpKkph6fXKNeEHNLR/sMbK2/eJwzwAIAbOEQ6VezhjVhzVaSLyOjGBZfAk+8oZ6XjEH9vQ8ULEBoVMyTmSbxwlOC5TIRNGZPCTjeRTocwCkDmEr/MBMIE8iMS5bjq8UBAalCKrMTAAcKDmeNd1oNEbmFkgbK0d53Sjq+UMGB9lv5nNKoL2EAoQN899qfa305StjdYglsOqmeU+RrPjay1YOBU2TLXqnKFmDT5Yt85VwAgoTxcmXpDCQAlaHEfp0ix9y47h2li+hG67aUKlvm6o9pkndWAFC6PqOGrxpw0NP7FRyMSzyGI1AQvETRO3tKzTdYL5cHGMALn6xT8Ozv/AJefvJFXH3oMTX0IuyFGSgJGal2ov6unwnZakTfWRUVErMooh1U7LCsGFUAbYxwT2hhRlXKjhVgBxbMILbK1xT21GCruMotIJB6xvUf32NFWTnvpigrNV48c/1ErS85Q2fyoFDFSafyZAcSSD/0mrS+KS09TrbpQFgaFHlYMRzkPMgt4nkxv7kPaZlnWfY5ULBVTFnZFFs1/sUZ1vvbb7b3J0vky2KQvdcWrFuwOe3Nv2/nMTbrGmgde0DgF0IiR1c3qyaWd56wh6IWRYlxX8rN5NxmURoHFFbuXc/nfX05ZHzMWcg1DJTnYigbGfOgOygRKIjkanGdhdlGRVma4m8lAZvAXAwsoEbWxiIQ6pQF/b8DBJT32dCJ5Rl+rGVIxQ0Y+/pPwCbYXSlx3taKt08Jr7/rQMGD76v1LWPOhzRTyyT0AKKMa2UzOOP122+u1vnSAIMGFNx+o4KChx+vQgjE4CABsk4qLwCCZXsuHxgIiBc0S06dqjFd6B6bZw01HLn8554RW+IeFHgAwsGC2OS/ye6pQHZAgVOJCRZaEHAhiL5uUcXatlp8c4C0vfuYpqOsaxGvtCQ+cQ6Vlp8Tf0/FOqcJM1SPU7KqId5E721SbS/zMMu7EO0z4EEYofEsy/6uTdcSYa2kBEAXm0mVqSI1+vKbgEkNTAfsqFPGkug3UMh7SgBQZYfbNiAA1FURB8soF1q3p63XSnEWyo5lX8qLWaXrrlP7dBco6EKQfbvujZVvFJqoyFVJCu1kxzONIZsTecA+DGnvYZ63q/ew+v5AFBLoQrHNuLN7n6QHIfU1x2Gk0/r9a2OOgkTEfnpjdE+gjMXXv/udkmh49cH3Vz1j93A5TpugoC8sOXfXXv15PIKHh6ddGmBgA+P89ht49tUX9IMSj9bjRMUICjyMmAPqAIIljtROPH/nLXmcp9cLFJ4Wyl3m95J6AACZoVGFjOPdBhwQDiXJRlZ9y0rx6vO0umUt7E4ZR8Kw3D+X6kJjYpzkNxXDrff2IQcTummqAhoJjR2bpnKNZPEq5WbvoC9iIIcOqWTRS52TUtf2DuNB3ysueb9lshSAxuCQ68dqlC7uHUhFuYKDHjCMzm+28/r+vljoSzYkZK6AtIw38+aMMqU6ZdB7moB60c4Ylfho4MXJEwMAuKM0XwgM2KnyboP8lz5TvI9jxwraK+dRf+R2XPf9aftW77EC8i5aLHfChUGFxRNQxyW3Ijn9oHIO1SfZ1otQBmBHLkgoU8BSrhyzA6AFdfVmy4ftAQR23gYgGOm/+ihuwMHiPSJ2yoNRm9HkQYFbv6Efb2VpZ//ebsxErNPTv/druk7B++yCeg8DBG67X721f0Z9mHyF8dlXX8DLT30Bf/eVvz9sp8sDDIAaPvjI5/XTk6jGGU4lcJINIskmVXpYjlkM0G3b/d/5Fp7+F39TNqgO9qZzm7hivQcjAwethyXoGB05HxtPDkD14vT+stCL3ngBDnx19gnIoqhCad7D3ssSuvrivYrm+FSNyCFVDxMAZhKq2u5LeswzIJMCBdtvVdoIy5A3Lt4jjQRel9sNBd4ZmWb9h9Wn+4qsGIsFzXdCPxXQpTCSFNxybpVCB0iBqX7YiLNOcW69L2EAajy31NKtjlf3OUW+FqN2561S0F5+OoMk+7xhaWPUCyDg7ucp3IWCDubRI1LiW+WUPm0A3HopwC5BdBSgzkJaOhDTmeiI411JnO3CQMK02XoP3pCOATBFxtwMqP7u2R15RRoCAep0SNER3pnwxefF9MdPYafK8x24seeOQIHOYKJovRPPEvhjjh3gaL2GoE0aoKrllZ/+jCTKl1Oo/u3vOQAE7dRaOVaYdG8fB+XSAIMQFABNJ9jcaZBTmLKyCRhTAQUUgYI7b+Lpb/wqXvmZf4qf+u1nYkPJXC00ZzdvFA1daCChAAQFAeAMHO/CEl1wPFYPz6b9sMs87oTfvG5wFvJixZBS56E1Qm/7ew+7/zKetmVTTEFMNhm9q8Ok82tHuQjAgrYuVdqjDNYo6RU6ep/AB9Sd7bO2oIFxGYGFNWPhjy0SYh046MNebnpSSbY7WH20jYF49kzZcYIiHoCDXYGdFSO8ABPdud54RfHbhoKNvLRIQQfnr9Kzo4QwwPVdBWzNMReGathLzh3rcxCG0W4fORA2boOEZeplKWfQluZPnS6wfREbELE8zb06XZmNsayhueJM2C2MJdVcH/nim7IkxnAae8K55HMt2AB7hZG+uwgg6BiChh1Y6AcgBJwr4+bqw48Oz1+GDDxQGIcqFqBgA6BeGmCwioSKsu4UJgCLBYuSFMPOfjEZSKM+87ufrosjARKD9mVBHaZ2P7NL9jFWIgNJVnVLRqHb+u8517XhbUqdZibTdLZYD6B8PMTFfc1LCIv3zCI6EFgYVDnsjCec8h54fU3xHmSJNzrK2rXXiLYuTdwNbOoFbQRidsSmyxf8VgW+o/fs/r4ttuJ9tfL2Uu31C0PilEFpCBfGQkBNcsbIMyxsAyBzz9equP4Gfy7FRsBWEuASfND4+AgQ7AV6o3MaIOCvtX1TTXzWcGboQJg+IKoOhJvVVGageAB+H2RI/nSG/6I6oACCVgeQgl/OM6BhRczk6pkgNGluw5CWHDoaB1E+SxQy8AmtnkG0T4AvdENSYL5TL/RtNBhD7GVxcX7VLdzrm+iezCcxBVYuDTBYMgVrQu1+K0Iog68BCgoKvv4pfElnNxjrwL0iXYkHj6YqcdmeG5BAZ+9plrAlFXZyU5QkOU7nOe/xEkZljQ7cMKpyeb2uafOtOL15Db7teto6oKyLElm/+1JZJUc170wYGgKCNYZgI364WufFPHgv8HAgwIOFe8uHYH+/pjJB3VeM4anvCmA9eS/yaKLz7zVuXyqzAhaC4xfuW8tDsv4kQslr0jNXAYIDjcYymn6AJYlC5aQLxy0WMRrVdcXQL8I7F5B5qUyQQ9CDG2YBrF6/jcKQwDgycgp76L/k2YEBv6RzBQPTEgTsCiFgLE/pLDxvAQRG7KQr57dvCih46gsyO29nuTTAYJFoCJQGW/+gTU0YJOddMevshtd+ES9/5PO48vDjZV47AP2cZVw8RWvTwOocmGVoAazTjZgLSCD9OhydvaeZz0zGGnBd5KQki/VAARh6CaPEr13etTvuY42jGO9W6dd7EJ0ZAJsmzr1jGqhXVINY9CJr2At9eReKhd9+Y0MB7CwLE2ehgbLt3seMS1QChTOsX++9un1DanIFdO8xmIUtKhd5ABgk7cHJVDm+55oTgcTIgwtB0ml9XNdAyNWp7EOWXW6TeMNc38k5D9CsiX7NigIWgBZYGyO3ViJ5AZby7fddUMa9fHtgAwDUre2ylwUJdRuw1G+Rbju41Rqnujx6+KE198XIMHTQM3uNs7DNNHHPRgcAYPWjXubUvn0L1159AS8/9UVcffix5ph3UKNyaYDBEo05ULBo2N47kD/s8gHO334T1159ATc+eh1XHnliobT58ANDj6c5N1J6LB99Ya/kLL/Bz0tWoMAKFMgUxdF9Qc0BhTIFzWfrdlOdwmG5lfU98K7Db7MDRVBKO2wpUQPII5Zl0ZYdBToY4EshXCJuBrCIB44YgR4ErALOPYajGz/N0qX2DhcAGXuMffOOI1nRa6wu3d/c77D9K1VLKLfV5+h+ajYXf7kxkMHnp+036njgLRBxkTJU7Dv72yct+yWW1SkJv7yquqA6Jl65Z/GqG+cGuG8yAqyC383WtPoc0EzJNF3Wf/9gxIL4pY/7WTNdzWuJQh2RXnO5BJ4VyMnYgBYIcPIsQUftRwZ7hwz7tl2w0eG9RuNQHIbzP34d177yPG589LoDBd7JSBiP5csEDKz0VG7pLFootuBiAMCt22/i2m9/HDc+9jKuPnIlPjUd9mepY2ncOFJu5a9jDlgEpGEUJtkvtGG30l2HsotCWPOwo9hhAAL6BLw88qoHlNcpC8JwCAjgjIAznv05wweMvN6N+o48gf4+Vp8VgWsVdMcAlJOGl4/LDrqx8TICmWCosWf5W5tajFIxOVz7ZwESyvtEdWw3F2CAqA5B3V+mlRIhYQJIh6a/zh6o8lNyJ3pw2QGH7xdIGPd/avu/oJ4aEuKAWQQCoOz0SH3wQGYw0HkjansHU7Rbll192bYL6Kl5VzRlB/iWX0kkzxwAp+u1LZ02YgSC/QUArILq0hKr7RTXuzXLPPgdFQLh5ts31X7dwNWHH29tjeZxbJXLBQx6JdgpwEb5WfG/Cbj19k08/9vXcP3nbuDRh68Mlhdqlx3anhlIRQmWz15SrNhKJ5qSM/TcsQr107OzeFMHBQtO6PzXHL2iKHH6IOs7ZAEMBEQAoKfSIhSt97Z9o1Kp5Q06uTmnAwSnKvvIu+72LxT93lhhUC+OTrVY86klUtq9onIexkIGWJO9uTX+zXY5T7btmAcJzd891e6r7MGAggMZRQQQI1EFCUQtaLBtECHRVK5tasPe6KAmAa8Y0t1lzUBGoHGxo9vT1aN41Itz74EFGbFbozG/yowFpWE39B0idjQMpVbAEIEFAO3iT/bISKe5cEc0q4hNnxl7ELABZRl5O77hbDY9tKNbokkUvc0J79PZLStv3L6J5798TUDBI1f0RP22CVB1zcaYuTzAYAMUZFQlB2Ch2ADg1h/fxAuvXsMXnrqBRx+6gszjzj0OQjR7FnqpXpBs6/AFaGq8IQMM7EIMu8CCCRtYaPrO6yB78U7IC/3vAYBV1IOAdKgAIEDQI6Mkb9O1V982K9sLhY/6br4zlssL71T6AyV/ITo/un0Y827p5f03cwAA6IDBfhCQ8xIAZGf8y3DSc6yV5yyhMP3QreSGdWO/cY67jp2ISnNPIBARpkSAxs4FDHB13CByk8gAA5BIz020AhYAUsCgtVqMmZJbdB/Bwf0aM8M6XXBMh/lWgYe75qXuldmiv4AWnHk9VkCDHnefDC9gQa8v4aTDUpao02ee7WOrTefQbIIAOx7Jk0Uy7HkFIO/X/0AFAV4+jpGaWLmtXfrm2zfx/FfEqX38kSs6Bdb0cAAOVsrlAQbALlDglSNQB/kbt2/iF159Dp9/6iU89vATyKYNB+VuHitB2V4bIM6IOZDgo0dVwSlgmCZUX5wLCKihhqWQFVSuFfQoPixdjkDZ14MAFZhNVibX9rY2Gfg8wxKRci2wmuo5TiP1NHV/yr2UffdY9j/3dAHzEIyMHx55crXt7cn9eM+NoWfMenwGN/uYVVnp8D/OWc5Rw2/HObNuc9kGgPBDfV2x1Xcpybp7RKRAwbYrcJhAOEyq0Ij1OGOiKiNTBkDQfVWWEhFyAQkGNkSeqDQlgwsoWOZ47C5brBK0EhtlywjvKaN7LJhSHR9gLAzbvcppGwLCqtPjAQNb+LPTYwCaHIW1jz+FIcFIj1FyoYKqxzJiHeb1V6/X9pS6NDT76um+2obziYvTEYv9euHV5/DFj97AEw9fKV+IlzcagIOVcnmAwUIQqTVS3HpIQO3cN2/fxCd+5zm8+JGX8IGHroTKre98fx8AYSJW7kQ0RtqyN5WkH/lj3lBMn0q8tQiahF4rovaC1giW97S72hQhcl7EgIXhvm3z2AA1SolrmyyUV1ydrq2sndi1U3s8ZmNalewRe/+coZke6PSeCqTuV/vcjhmgYKCtliVn0vSD7miYALh+6cCAGfrsAIQBgeMsx44OAMhvvR8YObOyavJw69u1tZDKbDd9l2QsQCIkqKEn4JCoAIZDyiACDhMVoHAkIEHCDEdiTESYSU27goDk2QZUZsGPHRK1WcBCbeaL901/9ZaxH9qBwf6+eVuxrhtez/nzhvkhbOffBxnVEJDt93os9U5PAxiWYMEezr0eW6nQQo9tOTK55tWshtWCdoraqlcXRW+5g153EGo/HbMBh30uyJt3buIT5tQ+9IQsk6UyXZqhr9GGQ3JpgEGfWOU7faQsmRlv3r6JX/zq8/jck9fxgYeeKErOykho50IltULUC58X4lU6yBmJYsxIPvMrOJfg6VMiko/eYQkYQAjQOVDM9B60jUFiWh63pXmhUTzay3KvsMpDwvrUn8aWVBBQ//o2s30tmIATSG5u3YOJ+qxFFZrzASAPrmnqTeHupbBulB579qExr8Rmr9ByGyLwzACzeChHzg0YOOaMGXD7cgEBRwcIBCBYfbj5G5VE1P5N0j8GEA7J/04OEACHOQlgmDMOE+FACVMSI5M17JCJXcgBZdE8IsKUufwmp6cjhumifdP/HuUzjfRC74A09wquOcXIR7qpt7H3Jpumi8yw+bwRr8daxqcFbkuwUB+l77JTf5X3Rucg7gEAgR7r22Vdn7dtM6N+TMoOWV+lbqwVx1QfMFjMEQDwTbVfL37kJTz68BWVdwYSITGanCZyrIFvo6hcGmAwirF6b6gfBG8YKPjwdXzgoStDT6f3/IHqdQHLQdOExso90O5YfRf546JjIljOC26StIhChqFea9ne6jGVdUe75waGxt4vFKC8FB4zOnY+9LfFpEvbaYP4uHQkaF7AJhMsF5uGvt9k66Arm7CmqOyaiqZVABcAom2mKtgOxHE9J7tzwK7u/r0GgGFP6VkBYKzgPEgbAQLPDhx125iBY65g4MgZOQtgMCBgx+bMBQjMmRc0qOqopkyp9lfSvjMgYEDhkAgpZRwo4e4sxw5pLkzCgRMONBcWgTXcwIwSasgEpFyNEZsxAjuPSiBA5nYq5YX7Rvun7Hf9VA73xtxfegGDb9eNDH7zHLfdyySAe5bLXiZF7bBzYKRvjh1YSAmgzCFDas+rBnSKdZf+jWTDt4sHAJnHSbWhE7NXf3PVNXZt0eHc6iEDtVY8sE6g2DklAQWffO15/NaT1/HoQ08Ik6dtZ7/BXo8RLKSwVS4RMFh6uob8es+1BwWSaLhs/X6PPyXn04VQzmkFsdw7L9kdL2QAnKDJzb2wtegcC5ahXObuJ7/j9/OKaS0z3QTIx6V9Ypp5nJZk42PT0g51/6j49c+NjC9torFpO+bj09aGjZLShkgd80DkgNiIddB29IAhd+eDHR63cdEct/t1xiQyRF2T9ArPTlkDbFm95JxrHx0HDEEECI6zZwk0nKC/v3fM5R1sX3boenb6Z3JjO6VUAIG153sOCXeJMSUBBcesfynjMElbHbMBBBk/nJLUn2SsHSiVzmEGUtL8iaR9wYSUGMTSJ0RAYm76qXRDPxxP6B97vvyNQcAeALDH2/cOSHY36uUQwCJPRPZxAQG9TNrxUfFyB6DJFbH9PgRkcngEUPNFRH+lHDELlSG1+5dn79BbpT03dFdpO73Q2q5tk4vpawBFZyetCwHFkyfV4wJoqdTZ3s+e2w+/b96+iV9+7ePFqeXSEJJXQ/ob+m4TyXtVcBDc1JXLAwwC6sjQo0eNISjo0DfKuf123XEvgunv3UxNWQA5OakKnvz1iVlL0LAEDEAFDUD1oIEx8LY6RorIv6u955FzmKBmlDRrfNruazFqYBmuaRCz0wDeiyu0IwwMoFNI6wltsHMSiWFoWIaaDW9tlx1IEO+yooMCKCIgADE+/jgRLbORx/p31cj0HhB4fw6BsQRH5iZkIACBC0NwnCsguDtnBQEtGJgzGubASmZGIsJd+2J3ImCeFRhUkJA5I5EcP5skRJCZcJgI+UjIKeOQDIolzMyYkTGz5BxgZmDK4Fm2Z0JZSDBnyUFg4tKRCR1A0PYd9eGe/hn10Wofct0w42Xnb3qwTv56Nm4tURSo8sju2bnpN7jfsTzKtv51OSJAlUcLAUVyWEJBQAPWC1jQZ1R2LzaSvc6ydo7arm83O9e3m7/P6vqq+gDTz0cdPPZ+x7l1TIp6V5BAVjfTNQ1A697SbX7zzk186rXn8Vm1X2bnkt6DiApQyBSEFLoATVQuDTBg97coSfRCK4mGn9wABSNA0HsEW4LaU+ZeUOu++rC1QXh/POU90SX3jsG7oXs/zwh4BbSWqOaT1Kyf1uLSVmp8WrddItsyiY1PBgt9e5nwCivQsjJG/5kn6hvVFJx5ATNakFGWbkU71rxHNIo1hyEEHfTeKzoFFHA+DRTcnZeAwIcUvIGROnuDY30ohj/ljCkBmZMzOhk+Y+Mw9VO4Mg4pCfBMEGqigAPZPkypUqkQgzmBMBPXPgsAgu+ji/RP30f+XK+XejDQG7SR0xHpl1NAwJosAheXx3VZnBcJpSKDc8gomFddwTpfSF/Zn/vBnPTFpxEf9Z0A0TcGMCciHMGgxODZ6ReulH9TcVf/4kxQZcAA4K13BBT8prNffY6CxxX2OwrprZVLCQyqonT7gC7R8MqqF1DuOxAQv/tUobX7RtO8RvJ4V/96b39k+AAsjJ/sW9JdQKW8rHiPb5QXcBEwYEDgIklrQJy4BvTKKFZORwImYhwTYcpVmI8Jui1e5ZEDkGAI39B9R/+x0Z2sMcFS32pgTFJ7kODL2rSnyNiMQAEzF8NiY3POdUwyQ8FAHXuWi2B/AQNubd94m+9ZglJPd8LRHTpQPZ7ceMssWtL6MqttzyyGXACIHK/1E2A2M+FgbZEZnEhkLjEoMw7JaFVtMyVTQXKfxNR4WoV6vQ/9Y+cXY+UUVB8i2MvK7QUDW0Cgl0PpF9cfWPZrX8ZJpEL/pwSkXOVwToRjRgHsEiYiJ3P8Z6Kr6r4lcKr3WX11EAFH+Gm3KudEwMwCMg0gEDBlwpwYExuGXYID0SXLZ5X7Anjrzk186msVFKwVg9aFY2PHKti9V66/dMAgdx1sg+UNnZL4W09ex6M2zxOmyNnyRQBIB9ngIKJQIfhzTq7rQIABDOn1viQSsGBK1rxkoKXx7B0qw8CL0ISVye2Iko9GwuXfw2evj8CATJ2r79lTz+N3bhVEynV/InLKaKycUobEnE2YmVpBNuOfGMipCjGwBAhkyF6oO3IGB0ADENrB0hqgPWXofQagwANi/xcIFiEKBlqiOv7Es5dwwAyZgmYKddIYi/VZoqrpcuYCBsp9XcJhfRa1+QeE5pxRKKmpf2rH7cGFFcn9rZ7YGjgA7kf/2PNPZQg8ILA/a6G6Xg6PeT8ovzc5VF2jiaPI++WQk4SCIqB+Fyy6K0N+uz6YXEhqtJjWWi5TFM4ETtO5QB3HzLaOBmNOhMkZ8VPL6LIRKLDTe7agL+zGO7CfObg0wKDpVK5KGVBQ8KqsU/DoQ/JBpERVAD040F1dR6mS6gajyUeJQbMIyuwzEruyhurtPfp54fGN5E/KHXJXA4jMLuYX0+pWDBDcHTwvotg80zHyTCxxbU0RRXHp0QIf5i1MiZBnLu88JVVAbCyB/mZSxWTbRkvLJ2wFAMh7HZBUuJV2zoRjymMK0BS9ggMBkCgGB4AABGqFkVC9AD9EYm+h3+5AwUoZ6XXx0ET5EmvoaWb19hTUaHsdkiX82dU1ozkzYSZJ5ss5l74oBmVq+9F7ek2/uUTEPsfApi6WKYzeKzVvUunpMrYHGnbkkVmxPrpf/WP3tB2jRMJ7YQn6/B2Tw6OFejYAgQcDvRyuyaBR8l4ObdXKzLEcHiZ9KRCOOSMlEsOqcggoSIA0wpGW/Rnpp9aBiVmAHgT04ZJVPavFZgck8qhZZHxGCw5GA23BQsJywex4uW2T0PxNBwoec0xBk+e0YeyNNQC143JULg0wAOBQeS22ouGLT72Exx++IkLJ3IIDoHh5cn3XYrT40fZ9AA54VvooqyCxXSeD5xhUvwcFfdwvKolqL6ekdVF0DvXwUiLMDM38NRpMrqMkhgIYf848ylKOmI4oTBCV3juJlFH/zokCZeXnAyXH+WR17VURFalgpUw7gUYi8WAyGnBw2EDjgLaBAwdA9VD9hhkeO1byDLoY4vg5rdHZKs6pA6Edk8TAxKiLwEzWr2L4MxMwZeRcmY9EMkNgSlMBdDY9MetXAj3QWyv9jASbvrg+dVGS1ezYIcnYPainWTLclYL2uSJe+W4V3z9tTsHKNR1LAMTK19+iX99jqwxDkmHYYD8oWAMEazIoDhCXfpx1gI8MVHYyWAVCGsV73keoA9M5I4v28G0ZhGNHQCBahGtdv9oPVnAgIyR6z97hstDIYkx2oKAPLfqp1G91iYYNQ2DXOafDX9uXAg7ctaNyeYBBBwqY5YNIL7z6HL7w1Et47OErmkQGAHXgJac9fHjB31PuK7SW7Zo0xJDJhRW4goPDlECZcUwZk2ZBGW02QxVYFoQrXhq0AjRUrtH+gmIB5NnAjnjJ0HvbYBDv2QyRIlIDBQoYrJgH1lPNUS6EF749JZH8FykZ27c3UabPkt4qpo8uWyH3X9JxCQgAmN2YlCAnSgyWQMCcS3iFZpkCmLKM5UPSRESSuPApXmdU/PoFti2Uc88KbC94RCQzECZoYlvql1CuibkW5fDTeS9jOUUOgRoqio/F13jmzu/bK7OA1jN7feRCVO4lClDAti7aCwb8bcbvHr/MaKVOY64sZHtwoGA5+0LZqY4hKNv691t3bumURFm8qK1H/V1MmL8H2jFuREZJ690YJ5cGGPQOqoCCa/iCMgWAczDJcgda723qxkKztL1tFCMrHpclobECB0vyyBBlNWHCEVk6aZYEqUYZQ1aX68GBUBqihC3mOxJiH+Mtg75SIW4QGPVXG8OYkiKU9pobGsbAUEKtdwEpWvcSmGeUFehyiUkz0iSZ6XO2350HEpTIsFh/jIxLv2319+EVAGHC5lbxBqfsa05Aqd+9FMt1EYhZx4TkPSgwBA/BAatSOJsSDoCMyQQcsiiuIwOHzDiQJZKm3Qls5dnAKlNkZbwsct9v42WSe+XbA4LeIwvX+NBzgdbrsva+l+L7xzM3xWGj2kd7WIOJhHmcWEOFahUlHs9ljYpUHB8sZFC27b24vnRGI39bsifv1wI72+flr+5r+9bap59yPCoXBQR27BRA4N+tvNfKuIxAat2/HJMGCHow4MdkIsI3b9/EL+niRY89fAWj5tmzBHxfMsQ+rg27SwMMfLnpvpL4xCMCCmp2uPMYVVmuKQEbP8JucWnxQ6Ka/EWaidoBBIYY3DNlD6ZJ4oU0M6Yp4ZjlU6LTZGvSE1LjlRFyUsRbFG6t69r8Yl+iKS17CgXWzKN5AxPJ1RtAU3d5fr+PWoFNRmmqgWPgbOODg70yAiIBXjc0nore/oBPK3S9wYHts/q5hJ/e6ADxmIu6sI1zU7PT6XXZNoDQjceSZa9jkgHQIeHAwEyMAzMOYHCmXdnuVoXRWhR7E0ir4h3Pgd89xTQAA8D9AQR7+8XrmAi8lTwmMg+uBXAWkjS9dEgkV8y5CUtODGCS6ZrErDNr5EEj/SGA3LZRw0Rcv2lgU0nXZM+rhEj26v7RPt3uAIF3yiK9s+WoRGUtd6B3suJxuR+oHpIHCro6Z6JN1qou5FTr9q07Agpe/Igw3VsY1TOgazrGWINbb9/Ez3/lGh7GI8N7XhpgYH1sTMEXP+pBwdijoy24broYAIjwxu2bABQpsygDv2ylX5KyZLkDxTs+AJiQFSAAB41105xxUAV8nBEKONB6ZmtgIPLK5G8snP6Ld1ZC/eCOH9BmAQOSyzBNtIzzFeVTFRUSQoRvJfI+/efW43eLldGpgGAtUeh+AYI9Tml8DpX/mbmsambhsGQeIrXjUQwS+5QLHCYxPqOkNwBN4huwnJ1ipY8H+zhwP5ai5Nd2Js3GuhyuT+z8Ub+cAgb2EgX9eV7HNE6IjXvUfmnDkvLOBuCYjYlbhiU5KWOURW8Z+zhl1vUZWMFbDBCsHjlTI4+y/97kzs6JWAG7pgcB/cqJa2X2esnGHiprKeFTWjKXAEostXuvxWJNQybrdIZAMUHVOQMwQGiZAwD4xa8+j88/9RKe6MIHo7Lm2PpDzK3T/Pdf+XvD6y4NMABiUABUhRGV7JHi6Bx3/1949TkAwIEIDC4JXIvsYnb5NSwKNOlxOiScscydt/n/MZ0LzBQYWQYwrXtmQCS4sZBGS5vKMacwB1z4GdRIaOO16zSoouwSFqW9lt6mf5+9mcL9e/bveCHvE1iAAWCbAjzV8Fw418EZmz550TNbrHVrKG2iMmXOrusX8OJEJQeF3X67yK6N5pD74peRdd8urK/hwUGqYMf/iIy/7bd7/Ln2hZZ2amOnjV09PECQ63R1hY7hMXBQGJ6JAHUsjOWZwThkySE52JoODctDwynRF83ML78D3bLmaPjEvD16BWhZAnJ6BUmcqfq+Mr5YBryAqswltGpJg2mlk7d0yCiMVb7dEbADkwcDCjI8EGgZyNoO3n5t2aS95wD2aeaWSR+VSwMMak7BoFG78VcG9MZ9Sc+5+fZNvPCVa3jpYzfws1/6GZ17TTCWf0IHDCAJfpagJcyAeF1Jva9EhAwSSjf7RYLqGvYLIe8MwZZAjyi7aJ2D5X67JlDq2nDcr5oBDw50G3bueHWxPrPYXx8VX6UtJbS22NMWMwAs6T/ZVzdGxmfL8IxVon9P6+9lYzSeVrU15cHZnceDdjWvNcqi31oVtK0SLRTRWunbY2Swm/budp7CyCyet6OOa23fl0jXRMDNvuRS8pdc7lImBsjlLfEgDLQCEpDicJC9x/dL3uDetV+V1V+/qUt80XcBRH8C9Z0OUBYrVeYyYi0BG8fjHo90ZO9MWKjAgwEfwrLcAQMDk2MF6memlwyBmRFf/p2/5JxaINTwkT5ZzCJx2+caPtgDCoBLBAwMFFz50Q4U0ECBBPcYDZ3zt8/x/Jefw42P3cBVbdQzU8RUBRbQOB0vGYSZNQkMSuVBAAOzIN4DEWaQMAmOyjUa98gtfVsNqyqeIAY3+vjQGnUr+6uH138QZNFOAQ/oazL6GEn/FTevlPqlSEfxxfj9dDsAAPYeHgTY+xgQ6D3RERDYCwL6MbWX9lse22PK2mJtwgu9WDcio8fBeSOjv8NmbpbRq+0FUxdt070lun//3tQck4Ol/d0JnqFU57cwPP2X9DIpqMOS2THdMk2EAspM1ziZi75k2gN2YJ+M+XcCYlmTc3bqjr5MS91xhlZ3ePDjwcJBWUvZ3zodUKdq9G4j3dgnt0bMgIUJkvZf+Vw0kRxDxxB09qjokbYZUFsibrGot/wwtTGXGbj1xwIKjInYI7OXBhgsQEGnxJcd4KFyp/Zcy93843Nce/Xn8fJTX8TVB98PzN+TA/m4pCcp6cdaHN2qG+K1SWb5nDybIOsamPDPrEu5civsts59v9QnEAv4SKC9MEfx29JGDtFSd+0pypZTbfmlp7qkqbufq1PfrISKxyNyVOMPxADAzokWDTHhhrvH3oSftbbaTV/vaO8VRnbjVvfBcv4FK3uxyq58ttE5XbN5DRIlfVkRNqDQByV3ye7h2R1zKkpd3eyTQvAEgOEskV5b7+vf5b7Il9uIMuEb1ucUfeHeXfJfYHO6cMYAT7XeBnwknClg4czpmZGDMXIoemdiNKMgChEYK2AsgwcChWEpbcTV5gRWmszGWL39ZxytjmW7HqN698I0vHH7Jp7/yjVc/7kbeMJN2d8CB5cGGCyYArSgoDYZUBaM10Klk5x4M+P89ht49nd+AS8/+aKCgrv1muOf6o+g0yhJZxIh6V9jFRikn8V14EAHswGF3AEFZplSJkKC0DMAzBtfSuHm50Ct+qies7Vlbzhlf7fDlZrhvDx2Gk2tG9M+inpETW/Vfc377xXeyfHpE23ulnGPDvf7OpPkfo7BLzXjPmjtSItE591r6b9jC8RWxZ0XKc34fkuXwNp7dagO+mRB28an1Xv48109m1ixcxKFRXCfkLd6qSvYroLZvkgvW9XYtpXOwHKOdleaFrxAuKe51w7dENU9+lLigilxgEHOaxnKvlJrn7Tvw4lrQCCh+22MQXkaF3tDnGNA0MuSszOghPIt1qbRpWfYfTtHKlkCI7j59k08/2UJfz/+8BWANf8CsVj5cmmAAbAMH5zUQbptSS7nt9/Es699Al/68G8JKMjtWoWUo7ULtQYkg4y1o0DUgIUpydDLUKDA0LBCDBQsk9lTihnQJDF5YT/8R4kp1cDVvxF1HtHm3mjGHsI+K1iV07jOIUDwx4P7hkZzw5CPAIE/p795ZLxPtP+7yzYQCJSLttcC7A7GeW3s0Xk7FNm9xBPWjLkeK8afUneNeXR2HsXnFSWadHe7v/PBQnjdv+GGPV2WwfnjWPEStbQyou/sAEI9x47585dvdb/kqL/udF3g608NkOmBT68H5Zr6rgcbC65Ge/Sh6bXR7IEQCDg7k6yCvY1h2TeUt6C0tqUf8zbO5woa3HhmzVM5v/0Grmn4+4lHrjROpYGDtXJpgMEYFAw6Cxh22PmdN/HMVz+JL/3sZ3H1gfcCeW69KgB0bOmeeqAqM1FaSfclEFEDFiYFCxMRkKbCKMxKL2QQcuZCp5kwGJgwQQGW3sGgWtZE2mZ14Nt+T4OFgKADDP5+/e9BTZr6WuHB76IUooN7yornEh3e2t6uzPcDJgyM8hoI8ACAi1pwiqo7t+Gvi382Bgj+HC2bU3/9G1EwajqDXg15as/xyLbZTu02lK1zTF4LFmYHOrzxW5qP+9ur6+Nm6ppmTVYWAHvojZ/wBt2pI3k5TVa66ugV7Pb5BFhjVAEHEHRf/WrlWA/KdfHzQxbR241OB8qMC9koeQMAwDOQByDAZI5XZGhQSBkD1jEqO+vYJbYxq+9scsAziDLOb7+Ja6/+PG783HVcfeSJhiHz4GCtvy4NMFh4uLZrDcF59FaYgjfwzNd+Ga986Ddx9YH3AXluFaeV2aG6sr6sj/dgqcAABQdVgRESOMk26XkpTcJJgcBJmIUqAB4g1PjbiC5smqhD/v10GW/8+wSZYs5LG2KVeSnPiTx+ouWgHFHCI9o4OndP8YHb/lb3SI+H9QPGdRydv0Hn7wYCDQB2SsrOYeGsFsfBdXrYLOOfs+vrnEUuunpx7qO6KyW5NLYSu7FxD5EHk4tJ07FIR6KF50yOdB+Z69cdr7LogYJ6XNTKLquCberlyyl9BgyNwCljLRo9o7E2Act6b4lIX0fu6heA0cV+e9RA3ptzAkaHKdW6m+6TqtRciYEOBFqmQaq5rQebkCliHVgNqMrJ7OwBZ1DO8ACAuJW7BnA3jbK0GaWobSFnN6DjVMa03UsdTmZAFzU7v/1NCX8/9UVcefgJqRNVWSvgYKNcHmCACBTMFRSogR95UABw8/abeObrn8IrP/OZwhS0Bs91bhZUtyXgCxoUVD0XqgqtAQs0FRQoGa8J7MECRFiKUJgEoQKFYRvZjwBI1d0blFgPqE6hnd2z6o5OOBrazIGttXjyPc9Ej+45Lr2ya2KB/b0C4W8M0FqVIgUdhQU6D2U/EJhlYZwOALAZf2Ygz7rdHXN1KaBgj8EzkGzgwAEDAwOyWk4SJk0ZNRhQ0L9pMjmZTgMKBSTUdUhM2TZAwZrZ999Kn4W6YGRUg7Y6hXE55dxxWQkJubrRCe8QlUhuG72oDpId97rPQIMxrD7vwnRgP9XWh1Oi0rPLdnbvAC1AgMrLLhlzbbPbRljJRxQAjFxtBQAiBiir/oCMWxJa6ebtt/Dsa78oOXEPPSqyCmEdFuBgY/hcGmAQ5xR4wxZTPNZp53e+iae//isKCt5flCDKvXLTwWRMgh23/b3gjChTU1S6TTYQHHvQMwsgAqepiZHWUEQU+R8VN3AtU3cHHdZ6ldgnDKcosM7YlnCMtVn/fiuA4mQvPTh/IbAjRshH7RaxQDiEX5/VKIuoTqM4/gIMANuAYO6OVTDA8xHMavTnYwMCWLfBLIafMzDP4o3lGWWJPM8kAHK/QSlGIiVluOs2bGxPkwKAST3ICTQdgCSggNMETIfKtE0HYRUikEBTUZ6NUiUGQA0ty6pgjZat1O2Kdzfqp+54oxeacwZysgAN6yzSap2i81eft2T/Fs+/gFy3+g4oxs8ZvlXdFwEG6GwCSiaUOyrU6r+e+aTiEA7kqtiGzinqw3D2+l1b9WE0Uvaq7MlHfTcZo2VMEmmdDCCQGH9KOH/nW3jma5/Elz78Iq4+9JjIIGuum4Jgz9Mm7w0G5dIAgxDxOWO3ZujO33lLQMFf/wyu/sj7WmMX0UNASRDhufWU8spXZMgvvaUeD2ADJaJAO8FZEZpyn5GxXFNYAYNyr9SzPGJAR64VLyBde5WfTbuVvYvzRszD4rwB2GiSqGh8/8b74bq/jwWWOhcmUI9vMQeNgj4FEGQFAR0gON6VcWqGfz5KXzV/lUE4HisIUKVYgARnAReWCZ6Dfu+K9Wn9S2rUKyi2v2xg4XAQkJgm8HSQa91fno9lPx3OJFRFk4KArOAgjQECEcBGyRJKDrwp5NIPK/3U91HZ7/rKb+9h2UaGPDLii/2dMQ+vXxqvos+6+90PWSbdtyc01LOq9W/Vd9TlkJyi+9rcsh1ydILe69urh1DkwmWA6jPO9T2YQTy7MZr03kmebQBBuY+bd4Tp/tKHP4erD31Argc0nJL1N1WbsqNcGmBQh4Tv1O7vFih44H16i/0Dgb3XVL4KFCuQZoBosmEUUy0U6ZbQAI3gEDojNSqRUmrAz7YwrFLP2gaNMumNRa9c+vqaR4kOIFi7+bh0147or1MPxRQS4EFFp1QiMOHZiv48cojfxwEBBwgqs0BFsAHwvAxJBGV1BkGkzPKM0ne5sgU8z8jzUc473gUUJCwAwfGusgVm/OvvfFfO5ZzBMxd2gGdu+jgCB20/JlnBDwCR/CZlBNLZoQAEmg5SRwUBOJxJn+cs+3IGHc5kvB3OwDkrg8ACJkhWx5P20JBEWZjYUtZq33mAIFdtG8ELA4EeADTnDq4L7mnn+nr0Dkto2J2+Yn9fp7/uVYab38kBgrXQ0L9Oeq9rx82SJsGXBoKtXg4wyQkS7ipj1MABpWaMnb/7bbFfH/pNXHnw/QocMsAOTEC3kQs42CAMLg8waATJQgi6TV3Hl/CBBwXWqP3AGD7OJWQFRlFO6a43ZeZKUZbO4HEvNDDlORAcuRFgeHDgNZc2cO9Q2i6kxHaAgPsdf24bR/8MEtV8+5mn6fZxF8NmDyIoLa8HGm+GPGjw7IAxAw1QqMakp/nqlKNqfMrr7G2LniUATgYF7EGBBwMGBIwhON5tAAEf74IzIx+PBQwYEOCcZeaMjXW3xq4PKTRxZgMEKSElAQQGFIgS8vGIdDiAEsl3oQ0gZH3Hw0HeczooVer68XAm9QZAupKe6UlQkutG4KD0UfXMdsXy10IDXo/0YGAABMLze6ekXFf1TGFygGVyaK+TXKiUR9fg/skuO7n1eSQlNBTpvTWwUJ5RwWV99v3TeaV9Bs5POe7Lmp4v11ZwVOz9AFcQB+AAwPmdt/D0f/VpvPKh31CmwM5T3cgswJYmvUcFB6UNBuXyAAMnTMtpicoWuLLIKRjeNxgwqIMFkZEEYsEDFstxNUrNx1t1P7s4Kw/YBQBVePx9gKWQlPaw3wPFo4LAo3cMhGURhwbaWHTzvB1F36NhVgwQmKE2GhqdEvLAIWjXhfeix3lOTX+0NJ/S0Dp3uMYBAbCjPDUOKOg8jwGCGaDyvisKDcCumQb+mLZeUXKeKXCgwO6xAAXzUViGo/R5Ps4FEMy6z4CAgAV55lo4LaUEHAGaEkC6dr+yBdNhki8IsgMOELDGgOQZHO/K78MBxngIODiAEkv9D2f6DgAONU+gKM3Gq++9Mm7BwSl94/dvAYIIDHRAYSGP6PQOMGbpHJPJwbmNwTed1jMG9yi3AEo4CEDJH7GwUMmZ6oCCTzjlDijQNClD8P3Rdw0LMNLr7j7DsFmZZag6LKfKcFmbWDu39l7sDNkzUvhO5+98S0DBz3wGVx/8AGqIAXUMs17rQwo+H2oF814iYGB0mgMFni1w5fyOMgUf+g0XPtDzHCpjQBS79dxaS/qy4j0DTiitDDziAhp6b9gEB2hQdjlu72G3dBQ6gDCOyFkF4l6AgI9FazswxwIV1iWo68KoG1gIwBR3Cqj0oYErbVd0YKEKatCmVl9jbCwhCOmkOGD1TIEQIACBsfHlRI8NaNq6pYRbsMZ5FuPgDUKu+QMlbBCAggUgaO5b38eWoc3qTQmkyqApgeYERsZ8lGgBQxdqQRaFillp5AxM6vXPAsxoOrg6C4tg70uTp2r3fNzXl9p3co9R3ywBAYCloY8YghVAwPNcwzS9E2LP87Kp1/c6pwcAIWDvZPT7IrM+2VRl9V6Agtzq4rquvOeoTe39e2Bl55Vbdm3R6XJWhorMHq994rG9U8uQGDtCkmj49Dd+tYKCtbsYa6CAoIKD9XJpgEEzt3tFwZ7f+aZMSfzQb0r4wK5p0FYADlhQqg08SqkoxKb0g0nvGVJyhtRxVwxZ80LOQ+6Nmh3f8oABEcJokUZfX2C/wnGAoElMs3frktMAhAlq/e9a7raxaFQFQInUc7QQQVooH2mDBMypZLmX9iWJH3Ou7UmcFM3nlu5Tqo/ZPBZIovokn7VpAMJGHLAossIcLDoB2CGsJxfHTy7Ga0pANH4vWHqWoP9oDWeGX6M+5yzswf0qI+pWNu7fc5oSt99iiukWKBgxBANWUm4TgHVgAdhl1w7Q7mRV7r+U12hbSiuzfWKpBwRESTxZc0RUTnE4tPLpZRPHFsBHoUGrX6TrelA8Ylg8cBiBK1886DDmdnby7uua8yo48MxmTabcAQqC3KhhccyB7hieemmAQUPV6XbPFpzf+Sae+dovy4qGD/rwgXVUdtRLBw6S3JublaiSu9RooVRswUKIrI49dQcAx27J5ZL8VI0a5mM1+h0FHtLlQAsSRmUQf+wpyFWGoAMEPRgoKH0Qg27L3MYMMddEtZSA4wxJVNN96nVY7L5RPs7YExF4ymrohcorfTvnQveRtVtA9VVwEJRyUlUO7mUd+AyObxbVKmV8QqdzVm9AdqKOWU1iokkTnkgVK2vWclbwxAyezJs4StuxZPjjaEo/g5jASJgOMhQZGTQnYAKmSYCH9agowQ6IlJ+pePNEqQkllFyDkoxIUg/NQ7B8A0yT9GGyqY36bmkqsilGqcsNkYfCJ6/F31u4AJhoAEGgdB0oGN5iAxR4QCC3XAL2Rj6BpYy6fR4I9LNKvKzKK43rTW7mDU3KmqQEch8u8nJagLwN2yNa+XQAAUAD4O3eRR9u5fxF+k12LHSc7F6GWeT9x4aU7DwNPfby3TO/RX/bfgtbSgPGoODdb1dQ8NBj9T5WPz/G/bNLroE5LNsOwSUCBlEcqZbzO2/qPM/P4epDj7aJUWUO+lQbjghgU8JKW+Yj7HtcNYM0izIt9zpqjMpshBu1npr2Jchmlbi6PuN4dDF1q6sIBqVp4QFLtWyAHLfpqyCW2Cfa3E9Q0GSyDwo7MABAPAuprDvL9s3CFCta54QSJvanc5pEgIvsenBwb17lmA2ITu7PiwxTVwys2thUtLIABwZoXDsIywIdq1XkSX8a99H/tVBHgownSgn57gxm/d5HbpMPiTREsGpA9F3V8EfJh+lsakCBrWGA6VBBwXRWtulwkPeeDmUaYzFCZHk6qmDNi+tBQckPcf2x2hd2nnvXkXxH7XBKzH5QdoX15MRdTN5ITv2+YTEwQAk8M2iicj9KSZ+jcpoRymgon/NR+gkVwINSNehpox3XdJu85JIRiBy30YyDyPFaAQU2xdZAAU2HCmIdcOV0AFw44fzdb7lEw0frKzWgdmvcenCwXi4PMACGQnl+500889ovKSgwpFWTkErjclaNOHWJXQIQOBGMmuV0gM9aZsrOW9dpYBkyrzpnMeysn02eAMyzIGTm1jv1xU8b6kFCxSX6zEkzzYOs/bmctNl2i3gaHCjYWQQ0VaUjimF5PU20rXBWCud8zwb9fpUe3S+Ec8szDYSZfVZ8YRvs/FzGcJMLY+DAxmWCePo6FjHPNZlTE/eKN04EpqMoqaOMSbIZATprgdJyuiJZH+Y2hJZdOCH5T/fa84ACBqLpih4Q2HoGtp8OZ81aBjic1WOmZFMFB+aFLUDBDkDQ9EPTFx1Y65mcxTWVTRJG597Bwf0oa+tO7Ck0+KJUG16oYdidlargACgAXu7lfozqHuXXbM2W8jkY/vxR6RKhw4W5PCg4nMl+G6uprrvBNj4BcDqr45JImILf/TS+9LOfxZWHHq2hGV8W+mOgF3mHLcBlAgadkPmvJD7z2i/qilCPttf0bWlwFpDYst63LqlMRTFzmlThzsDZe0DHY/HgMKvx1ilhpKCBj3fVwwdwECVBNhidAISlTLPSUqgrKIKeZUAaZa3nN8ZzNCg8e2JGvHieVOvMWby1ea5ARe0RZQBTKl6J5HrNei2Xx3LOIEwFENBh7GH6cEITSmj+OoXj45h9eGVtVb0g2amn+nxW9Naa/e4Ftj3ThgqMwUHXKDq2ra8FILDOWyZjuQzQ5rnWT5mENE3iLarxpDxXj9zG7OTWNGAG6WyF6RCzQxEN3ZuBqA/7BY6avjucSZ8dzqS/gsWNjCnwgID3sASDfhitK7FY778JDe1gcnYkNzOhhH0K+VCIMk1iA1pGUo8VGZ3QyaeeaOyReeicC4BfGhEzlG7/dDEZ9eG+po+B2s+9fGo7LZKG3b2l3ZeUfW8HdoGCed0pWxSnh71OkfpJnUmN/4IlsLFLCTgcJNRVAOxU76/65/V3voVnvv4r4tQ+/JhVYFy3WrF2k7kdwxu5eJcHGHRFPijxBp597RN4+ckXceXhx3fOKbAFTqCKd5L4TDpIKMEaM52JgrT9ZxPAMxIz+FiNK+XZAYRUaL1CAVKqnsMsWdV7P4S0SFjcUyKkuKC+5G+pRZ7FezPa7aB1ngmclLacIICAM0BiPGg61H2AxjMVKBy8p7L9Hh4IAFhPQLwXQCA3ujgoMK/U6tacg2o89HcjrCGj4ErxSKFKkcrUPkAM1QIgmCerY00UgihUOhwkt4VleiLlLKBWY9k0H8FnPyDbtgbC7JLgrG9H2exR8X1l25FxKIzB1IIBCxk4GhYHoV35JIYgAAM7219a24FpoGVy6kd4W3Awyl86FRxYCMw7HZ6VpEA+Fbh7+bR+aOUTJQzJDU2/LqMny6fLgRoZVzvu77/l6dZGO6FMUwEHDYsb3d6zA/q8VZ3SAQJjCEoegQ8Z2DtOB3A64PwdSZT/0pMv4urDjwMYA9f7XS4NMGhW/TJQ8FUBBVcffnw5oBaeWD1eFFyXyMiOUZC50poQRAIKwOplnyUkzuBZKdjDmXpcLvYXJBeVz8zuHdhekPw7rQnTWvY2zFjrfdXYyxwyp/QNTU/6Pj0Vl2dRll0WdIlDBnG/ve8aGhY77ox7CAaAkxkCaQcBBLzFEoyS2rwx0vMWxmgEEJo2cL/dJ2lrO6ayZDKHy7ymCu7SQUMDslgQnb2nLJfMzuBT9/0EWMJWkH9CQOx99aVXqvb+o76xkEJHv6LxtlLZ3gIDw7bfNDqo49WHImEgTdtYBj8Am6KHCtQKoEjlpgbY7BxGBg56P7eYUZOQSARKPAYIQCuf3VRFsnuZjANL+TxVNoElCLDjERDQY6F8+vvu0lmuZH+unlOO2SydSa/V/Q4HrJreHrys6ZMgvNWwWeT0jtMXPP2AJMq/9kt4+SOfF6c2GqN9//QOZdB/nklfK5cGGFhpQMFHPl+QVi/8cbzXihslLAsmsc85gHQeWBE8ZyCr0qUZAAP5KGzC4aBZ34ci2KsgAVjQXsPi6z0CAx31Hq6Z4G/pgXIUb+sye4nbzF7qjAWAopQouo+cvPaWMbAZxPfkdCdwwMnMgLRDZQfYG5lSn3tkCDyV7UHpltdaTqznkXHIbGtRGCCwJDI1SAugkF32uuYLTGdq4DLg5tSTM0zUAYNh1ndURuPxBJYmnwgEpF0pbPP97V1DAfWjSlDP33I9AoDgvPhFqEcqAJ/gbPEAsr6hVPphBBLI9QV1IA6dbK4tbETfD9lcAYGy28nKibqqHJoQ6qri5GRd04IreLLzyjoYe3Xtmk7xYKBjB9izA04/LRguAK+/+y1hup/6Iq786NUVlmBq6r34HHuzDofaLSLcfPsNPPPaJ/AIHhq+7qUDBgtQYAoBTgm4fYYPh8Oi9IkI8/kf35StwxnABxFEZkHbeS75CKyeWAMS7CM2HiQ4JVuEGmgzitdKhNYjpeu3PeLe8pLK6+faHN0UH2/wTSmRu4ZNeQHNQG5mbKy9Z1fHVYbEKZkhCNB7euMDoDFAZQGpLWZAt09mB6IxOXhf3dltelp7KgZFdnBd6c+zX8Z8ec+1AwsVACtwAMuyxEA3716e7OfeNzWM+rPvx9Gqnd57ikBYCAKAhhEYgYDVdu4VMC8PcVbmUNvOgAKhGPm6ah001FMTSLmsYe8SnK29rN0BZS1r30jCqDF3jOTavQEKQKtTgIbxMblsjKO9CzqZtHqNimu/0bLlkaMyBIV6PLr/avF1NIYzzy1gcPqpYUK33rGru2ymxriHbNYWGEgTynj14BbAs7/zC7jxsRu48sjV1fA3lf9szFAwJnPZB84lvP6lD7+Iv/Ol/93w3pcKGLx+580KCh55QnZSahWCKhpGFXsG2u9T+982xolw6/abeO4rH5dTDj9QPK6Se5Bypcw3QYIKvsV4DSgALV3fDeym9HGwEQAgN6D9dviVwvWy9aEWDwjsPbziaWc79BTgtqdZ6rEGfqL3jgCAnsMRG2C/y7jZAgLAMG498lJDCnsMVNt95P4HPNVDVFSFu1gNPzrPogAAlDBEDxqg17RhC5T7Nd7WHvrZ2sEq2/zd18aL9l0F/8D+dvVn03KfWJu6w4CCV8hSEfnTtCGh5C4hK6Cz4wOg0N3DwELpRwsJ6bHRR95oh0wW+bVyEXkE9suk3w4/cLZdtr4f0egiIHRQ5PiKfh28h08iNPC6BwyU1Rvt3DJW5TkvfexlPP7IFdz1Bqq8sKse1XEq5NQEEDdjUo7Lu5/f/mYNr9vsvEG5NMCgAQUlfOBAQQcIChgw2YCTyW7QEBFef/smXnj1Gq5/9AY+/M9+BneZMNGkzuEBJrAlpmf0HhsbIOCB+czF3hmUZL8BBf89hgbZ6/aiNKh9ibYrFQs0Bg9VAMO5sIPSeKE6eqgovs5waOOOwER1wnYYE1dG7wkgflfdl0fGH8AiJKD7Fl9t8wbKbTefvL5HIDD6DXQAdqWk6k7ooyfRXe6e9aj3UlEEwQOIBgD0/e2PnVKcAdiK+4/bcNx+99J2kS7m8tv9b0BBT2JLBrXFtqIwD4AS6qG6j9G2vwdl/T4U5sftQ/3aZVlkqAcVwNCYCs65f7IIDOQRcHLjQKI/vlY40Imp9f7JvWv/nvLzRL260KeOueoTCBdGvy7pDGpnzJgJmp0t+sBDV3C34JelLZK/9XxQBQkE/eQ4DHzKgm3nb9/Es7/zC2IfbcrjSrk0wCAKH1RQMIWAwMAAq+dTxKdrtTffPscnfuc5vPjUS3jvQ8JEHGfGTCSAEABIgYJ+5AOscXVjDfhMk3ryKlCQOG8VfuqEer0Ehg0Ye8N6Tvu3E8wIvXcN1IAFf7yhRbWcxd7lKXO6FwsJkX2imZp98nfskTb32mP4h/eu9VkPDfSmeAwCRgxW1EqRTY5SAKOuTL5exjqYg+R1PuLfa/u2ypb3PmwPoGmIvW0SldA5XfHQPKiqv3ugAFiopwAFFw82sNDIzcLIOwNeXmYFOHT7WxDSnl/YpLP2ec29dhQucodWBvbIYXdN3betb4aMVceqlH3T2bLNTtWn3rnw4a0ICHhWoOQRtHbIgEBvgwDgbubh+LV6J72WiGo0IQQIM15/+01ce/UFvPzUFypTsNHPlwYY7AYFOQYEzbbT2t+8cxOffO15fO7D1/G+B57AXZ1/fzdzM9YnoAMKE6aknTShAgUHCDxQGMZ5gQXiXy8rhv8CnrCVEb23+EjVYkTn5f4AEDRXrWn1oB5DxqM5d/lem9PUIoO/cZ7uqNf5evb11r97QEDrnHN0+uKa6No9ZQ+Tu8OvO7nsMUn3412aujNa0ENUQJX3yhJcPwVgwQMFOaWCLYK/jtGs8BAB6vDY4DwfBlocH19TqtQZCO7P7cuF5G/d2Thdt+xkVtaAw2YZMIoGAqgCgnF4oAMCne2ZfdU8MLAFwwKAaoafzdYwS/TAAQQP6G/efhPXvvI8bnz0uqyD4HMOVgT90gCDMKdggNByXgKCerzgaXzz9k186rXn8Zsfvo73PvgEjg4M3FXaISk/O5OgOXLg4AggJWqBQjosGQXrLJ/p7QADEHgFo7LqCXtgsETDQ9rW36crYW16NOrqvFBEK+fuKtHg7uq6mnm+YtTL9SuPP/XYwvPdAAIRCNh93goqyUHt9rb8RaIGe8ve8HJ0WlokaLpjVHdZ+zW0rJ1nXj3qexpYKOdxNXXZUIGvBy3bkoJfstkDh1qCW7sjfrPrzRV562VzEwhEZUOGTpO3ceEADC0y732oxDtT3SfK22svoEN7/ekSBof2Jo/tTXFAO5sDAMcg5kX6L7OOc2JMtAQISKRUAvDm7Zt47svXcONjL+Pqw4+hzAz7N+pbCd6oWYdhDArmroOy/raOe/POLXz6a8/jMx+6jp984IlFZ93V7aRgANDHOrCQCKDMBRwcIUpmUhohpdrhhMoUFLAAlIHfeAVDnmnFG3aMQZt3AX26XoeBYdmgbtsyadsExe1Mgda7CCUdlZHorxnm3ZbxHsuWV78GBnogsOirQNH010Qhs3LOWj2D9rkfTRb2eW9sg0MNblV3qYxoEuts51j7mGzKbzM29RoPAMozC6VfwUQPFHw99+YyXLxQ1z5tEnIa2N7vp2wNQaj+ztGxoDRVdwm1JecPrv/9DS0RvE+std9APOD70ocOO8eptS81RyCr5W/sChwocCDA7IxUgxfy2NsaY64SZPwxKQgAif1hqe9E8jwkwpt/fBPPf+Wazm64YrU067RZLg0w6LORfaetgoJcOytn6aS3bt/Cp7/+PP7xh5QpCMbQ3Swdpfm8mhXaKihjDygzEpHSQC2rkMpvCsBCebvqFexB9wtPv4qQBwBR8uXC4KCXp6XR2lOiwdis4thV25f7QVvvjZzuedaaYPXvtFWiVS57MCDn1fO3wIDvx9xca+qhXme3mO1jOsywVelNn86+z/Vnn8PQf2Z5T6EOGZoZ8E04GehOdg6VNp5slb3ynypPAPa1vxLaQ69UAdgsAaiXXzwq9as8k2A/grbwfb5jeaewz8uxHdffz7F80WctWf7xON5T/Ejw7dn0nzte95sTNlX7jaXuLPU5RX8GutODAJOtrMa9ZwN6Z9MDAOa2fXw7e3uT9OEEG66MxMJEZ2aASUATM2YIOLj5x+d44dXncP2jN/DEI1cM/kIWhcsePg+b4NIAg74zSyeGdM42KPhPP3Qd733gCeRAeQKSfGilKCjbRaagfB4CYzKg0LAKBirqYO+35SaTeNhu8I9KYzg2jH9rOJY5Fr3BKc9wG/vTlWJlFRnTNft6Uc9nEysHh73t6g/3CgxAaaSRUvb3GKmotVBB1D/szuVGSdXnZP3BkHFsAMAbf2YxbGbkZZ/+bva3dZM6nw4MfH9Y+01FjMkBBXIAQfYTtaDBAMOUSnRfZIktSasqVZOtWb0t+6oBoP1dXqwygdndsy8VUOj24H17ORnJU3SPVaYrfNbp/bF128imRoDgovqgyhNrf9k5LYAz50quqTrT7uHBRKM7sa5XGkPtdOeunDQni8xVnmDnosqgf9YcAOrjzMWmlLYkSThMUMCqTHQGA7mCg5t3buETrz6HLzz1Eh575Apy9mwLqX38Ny6UUBuzoDa0HVN+dx3Zg4L3PfBEo0QBYHZD5+5cG9cjvKkok+rVmEczN8xCxyqgC0EEgx9Ao4Aig7pFQy88zg3jUvZ19+oHuJVooPdlWsQQODbZkaEe3HPdi6/PWe5b3te3uxnBRkmVExUEEAqqL0+KQENXSpw6ONYbjabP9FVGtKT3SEwZHTsw0AMBAwF12+SCy1cSzQuSOuvf3NUL9ZzwnV1j2DAoS1JoCxq7JscICaRAwAMDAQuUCFMGjgq8j1xBwkEztczrAkSpSh5BRdgZjKTsgTKxpRNk7Xw9lZZ9ZX04Ygo2Q0Tc7cAyFOTvswbIY8M9qBiWsju67+jk6PqLyH/ZMnly+42BLQBA9aXpDK8z5Rp259y73gydpk721uTObIh/hrcjPo3CWLEjZ/Bc7ciUSACu6hkDB6TDOIMBJrx15yY++dXn8eJTL+Gxh6+I/No1OnaNmV4orK5cHmDgWALfgY2HbJ1oJ7l9TfigAwW9ZwV0cSDXuXcdSzAR4e5cPRrAdbRUWWJEiMFCP/iB3oONhdDvbcMFVeGsAQA/qNG1A4BFW3jls4dK7Uv0uZJedqduR5TDNHUj3SsAr4yas9xGn3FR2R5nxL3nYbJlU9JcH3kvkjlmF/bQtCHtzEuPsFdOs57nx/GxAAA5fpxzCAaOORcQIMBgCQoqIOAGCOyJKPgxLCBAAUFaggPbZ9uHlHAkYCLGsYACwmFK0uaiI3FIwDFLv2cAIPl0AFDBge8TDw76drXzpB/b43tZgBEQ6EG5v66XzeY+zUPDn42R7sfRHOiOPk9x5n6MLS65Z3n3uhLAImTUh4u8vpTrewcLAFwknXqdKcf7stCZunOLkYtAgAcAa7qyL8Y4cwaOYLEbDMw6jg0I1LEgO4gkUf6Trz2PF5+8jsceuqLP4vLGGU1eIqgLuPTl0gCDCBRkd8z/Rvf7rTsCCv7Jh67jJx98woGGpYdlxVOro3J0LuHUAYae/ixGZmXwy+/qza62R6Cg9lDMo/c2BWDepb+vP+bLvN48pUSfcx/Fn4El4vegwV+3VwH5ezaKSEtyY4mIw2QgAJWWrvLYeJ/2HN83fT+uKY6+NMoMrbKy4kGd9WUFCAYWloDgmDkAB6z/5L4GCGzceEMUiYbvUmtn+5tUDhJVsJASkLLtYxwSIXMW4JWSfthMQcCcwcoSGDNnSvWQnDlw4MDGh+uu7TYPwEHfZ2uJocDY2NTtevpWPgiAkwG7l9Ol8XfH2tcK9d1FZdzk1IeL5DzSY9yEjLy+BNA5Vy2YJ3KJpTuYu/J+G8Cs15fA0nn0bT9q86iQm8pis9+mTJgTK6CtBr53pN68cxOfeu3j+NyHr+PRh6+U84xDz6g6LEr4jsqlAQYmQNHiReh++/LWnZv4FZ198L4Hn2iFMShrBnJUKSLCEdUoHTXMY2BhFCv1YAFoBUDuO65nAwx8dZzSiZiANYq5vne9f6WU9a977hql3JcUvEzjWbqNKDYNtPHpsq+cVxWPP6cHbBYW8v0ww4E2FgAgMWo0cWsfLkrcInUzPt4DBZaGhqjtO/sMrGIR3Sn39wamJsLW0oBgMxrludKnPSg4ckbOopw8IDhmLmBg1t8ACkiQ3zYWxhKUNG6QnIK37pqSgQP5e2BjDbi0Z0qMAxKOOeOQUumbSRtyhig1Ns2I2PD3sxw8Kwd3/laC7C5QsAEILsLYRQbI54b4ukQGyht0/w6eicobQO+i8u3DRIDJs+nCFjQ0ISOnLyM5JaB09nK+1Wn6cgTI1nJzgHEfjErRSzsTM3qi9K07N/Gprz2Pz334pQIKClNA9XemFhy4YRmWywMMHCpvkrcwNvTWqL/xs9fxvoeeqGhdtTARYWKhdfriO34bGTIoEY56jQ2Cow76u3rcYqVARcnASib2DrmMvIw1IGDvNoo3A6Iw7ke8OSo9QGjBgfvtKOf+XK9w7sIUgsz7PaYqiB6wWf/IfkgGcGLw3La90dKJq/EYxq0N5fMYqUfKyu9jbsFBojFdX67jUo2wlLGba/8VdsDCBhugwLMGBgTm0v/jPk967pTs3FSAgJtTWIT2YAHVxMhMQAZyUmUHxgSS90iEmRmH4K3J/TWPclg/hw/2sHM9kPOljTbuzwcx0G7n7WXvevAO97w9AL4FBfF7+PP3lmQUG7q8krwMHc06DhIoDBk1+hJtbgmwoiuBTX25R1fK/vUk3XK/gaA2jos6CjPzIlzav09RLzqG37pzC7/y9efx2Q9fx6MaPvDFswZl29vHlfa4NMCgonLd5DFaSwS8cfsmfvm1j+OzH76O9z94RQSVjA5WqjER5iwGYspCUVqcctLObKoQoPFS3M4Skk4QY6QVt4EPCKtQAUQFC1Gi41rpE11GnsYpYKCNMa8BhLj9/f6IKej3JxeSqWwBF4WzSGLLrbKRY4SZgeQMvwE2iU1L2x8Bt68ChAnUxPqyeSU27mgZt4Yd3wEKoqTK3JyjP5jr7BQW6J+JkUFIOoYBVQDdGMYsXhiTgCBKVMblCHD4vhqBgh4QrCag9ckayNJxJQiq9ZkMVHE4Rpq4sYE9cjMZEirzRhb2MZpZjiWKktdiQDDqHztvaCuD/Xvj1KNEUWAJ3mXfUmalDbdBfJMnMgQI2/IMjNg/lcmFjFrbswsdERJxI7MNSABALIu09YD+CC5O1VEV3L3qSTm+DgRWdb8rE8m5i1BpCaG04ZPChti41ePfvnMLv/K15/HZJ6/j8YeuNmO4L8YaEFVwsFUuDTBoWIIAFMi4FAl+4/ZN/JIuc/zoQ1cEzZMqpaSDwSlW5IRjygUcACge/gxBfVESzkhZHFnro6MoAgrEbfjBjFUNT1QhWCujOehr7ICdaxnpUSIaUPcBKN6l7Ze/XV0GbIqVNlu5Gnxkr2zYKRk1IkYz21/LbvYUNKvgJHkks2a6ZxYvRduBSPs1CVs0g5tYn49Z7yqdwEYzTfS0RXHf8VMWQn4QZHyXTGMWRGtj2IMWG8M8EzDJVChKhAMSkLOOPblA5kfLGEtMQGk/GR+lf7JsJ6Li+s3aR5lZwciyr30+gWxLaKH0J9Vwgm2XXAO4ZEQipCRAbpLXwiElkCrQw0SY0CrU5EBBUtq5nQV0Wt8A7TmZWnbH6u/Dd3vKFihYC+2tMXojILAG5teSStdnH3Any04m2fVtUuDHAgbyDAUEus/JbGJdqnpmBesIAf0E0aHuq+RFv47KKCcjCgusAYE1IiVS08tQie5XUHCgFI7hbxtTEIACG8fLkFcLDoB/Q0IJa53iZ25+884tfPKrz+O3npREjcwo8dqk3qBFAomBmdQQ5ARONdN8ghoPDw6S0JqTOmJrnkQ/CAExUtD7QgHC0bzQmcsUOJ+oshXD6p8xGux7QYGVJXPAISBoE9LW65qL90qlzmU+L/MCEYsioQqBvRUFCjyWhEARDD//15TNFCD4tcIYG4tR6QXWU3zAMpbdvQlYx4MpuUySxEgQRWnsARLCMXw2JVBmoaFmBheUZEiPbFArVS9Z/YdJPK+zCULlOwiezdVNSZR8zoVZSVFGKWoIwecaWJ6BgYKzKRVQcJhkuuIhyT/bZ6BA9iccHCg4UCpTFWsCW23/KQAEHgxYzTcXq/LMFxwJog7ICXhAbnfq+SeAgjVAEIGBi8pwPbH+nBK5+7nrPb3FVe5zrvtMZg0cqCosgN47ZVNGAQjgml903KhznINR9aM/Z20tj1GxYdSv0dGDAjtPxjKFY/hb79TwgQcF0ZiN19zYri9wiYCBlWH4AMDNOzfLVxJtSocpVssmN/aAWAaHLZBymEi8TB0Yh0SSFZ0IpHFO2UYxNoBP9lqrM8qABzqAAE2sAhoBsDIHBs0EYpRVvDcxxpekgfU+r0wMgv5Wj3EtDr6nePp4zm6xDza2uaWX/SIee8u+hUFriRbQseLpaVIhjbzR3vicQln7KXMASkZ+CBAgYGAxhhNhwoQJuWQ+U5pAc8aBGUcN1RxzVq88I2dZdjWDcZwZiVITSvDhBUwJc2YcdACOQkaeEegTDiNAkNR7SgQFAU55TgkHEi/rkGSbUCnqQstuAIJT+wMwdkA3uM4cEntHJexjrE4Gw7JYLezjRZBQQz+j3KZRSZpzcUrpZfm+gYJBKWG9KEzoz0unyacxfRanN6re67+RTgTaXIC9gEDOWa/XKYDAxi8AGcPUsgQTEd56pyYaPv6I2a/949beac/qrJcGGIyMnDXBG7dv4hOvPofPP/USHn+4Jmpkp3BJaWobJ+bxz8z1M5fay2dTUmXKqwBBMqRFIeyZ2jNwtMo79okqUbxqNLeYCqMh9zDas7IfVRiESkb1JgH1SLUhQNUTN6+8UTT6sr5uOzNvgTas4G9RaWf9u7o4znK/LZgDOE9yQ2CbWJ+8ejEm8oz9oMBvS721NO/YtkXpBlds3CZlB5ioJhcxF4CwGMMM0JSUCciYMuNAScJbc8YhMY45CVsAwnFmHJICA+pDSu30RWA9z2AZSgimJ8JYAf/bQMK9A4J0Ql9EJJI6r1KMhEIFCawAYS840IhOs2jNKLcpYiinXL/Sl2xgqsxm5gLoq+x1+1Eq64rru/sov152vdz6UJIfA3Vfu8gVgIZ6H8048mW4ANUKKAjfy+nxNfvaz5DaAgR+zYYpGMPfvnMLn3rt4/itJ8V+heNWy5bZr+83fs9LAwzWyhu3b+GFV5/DF56StaN9SdCGIjTKlbkaO/OAmSvdfpBPWxUFO4NxyBp6UIr6YDFB7TnrsF1zWrVE8/dHAhAtFNQXYxgOCmJAls2NBZiZmeqymzDakQrVLAaClGJGky2enbIxYyHz0Pd5HcvZCWMgIH9Rjm+toAfsBwQLpoAqtRcyBUT7AQHVevu3jQS7RI+oRAwqZmM3bhUkzBtjmBmgQ8JBjfqRMzhNOOa6ENKRGe9Jcv7MjJwqTW3rHADLXBPbN+rTkVGwUIEZAwsXUCIctI8spNDHYPuQQSK9f6ptPwX90oMAa/uRcvXDssSuGSWfwwDCGqOTeQkO5JrK9Fhuk4UvOWPogMhx6acJVWZrXyxl1vYDcMf0/HK8Dd/tjba1M4Xc7xTL7QjAA2gAQW9kgVgX7tKD+teuixwmu9e8OG9832h9hjXdYoDW9AoAnKV2DL/1juTE/ecfEabAhyG3vP9TWGFfLi0wsAa79fZNvPDqNXxRPygRMs56boYmfKnilAQr87gAMJWFM85UmLNXsMQ4MIcggbMY35kZmGhXhzUr920IQD8+oqREAwBWWO9l5LivIycZHFJ3vV6VXBPP1MfYh0Rkv2jdKOMZwC7Ks6cSewAAbLEFS2YAWIKBdt86IPBZwT5mTVYHqiyBfVFzDRB4I9S+7Rr75ZVuBQsFKHiQwHJ87sbwpNMTDwoSDkQ4YJJzDCRo4tuRa9LbkXnXcslA3Mepqfv6sscHp1APrl/2gAG4fSMwcJH2l3OpOYuhmeaETYCQsxlags0msbAlQ2Upy70s9MOQ8OUaQDjA6x5zPKjIq/VHNiaB+lwDY/tUDzqZ9uV+yO2YKVgCAWDJDCzXIbFjbT1G+s/KAV2e18BhqucGDGhXIrCyqld0PHp2AADOFM1OVHPivvDUS3jikatDlsCXlthx4/UEkHApgYF10Bu3BRTYV6a8hwYsvQLLAjcFK2yC0XT6V9v2LMnSGfbxpZyBaSJkEM5Yphh6kICknrnFgN1zIwZhfeU/3ecGf79EcDRFx8/x9tNzDsXw67ar4xmqYTiDJStWINEnLELbBGjp7y2j0ZeRgpHf+tcpE8C3i/y95w/xdGAgZAg6QNCzBLWOaM9DI7L6RztijwCTW9RUvxOSdIcZrKwJiQYSDkAZqzJ2BSQ045gI78FUptP1WfLH7I1PO6sFiMfyouoBver7xRiBSIlKm2Ixu8CYgR6QTbRsd5S/XNv8hHbXyur9yp0kbBMAhJw1/yDFCaMeIKTEjv3R+xo7iaXz4fWKsZRI6gS4ODmXc9tXPWWBslPldktme/YOWLIBIwDQr2Aq54yNNrDU9813CkoYoVa2zyba0tO+jpFe8d/uCMexXnOYpG3eui05cV/86A1cdfYrekvuN6x/9WRmA6v7wMGlBAaAgIKf/4qCgh+9suIhLBtqIipJ3qZgiwemp79nSuIlk1CDnKpyFVKAMCtI4EkUbDWs+tsGZlquieDrYmUkBP2CHmX/Svuc+bfXET1ealWBAtdloU9dIvmUpJ1S/4UX4I4FlJ2cQ4vjXpmMFo3yQEB/XhgMRN5puScCo8Tdd+T3NBQ1jVH/EoF028axAF5NrmUJmbGNWYjh4qTHU/dxmImQs4Jgq7Y+dvQNDQDheI7G8nhN/FZ5jsI1ETOTQgDmwNd9ae9ZPvVu442SePCoAMFYHKH2EeaDlHCPU+TVIVmCBO98RHoFoBNXRqRNed1qmlEz7ZXXyPjvXbocWNdzpf5ojd2cGZiqzjqA0K9lYOeXsdzFENbGs9XRxnMffvRgQMZrBUpnidSpfQ4vfUxAwWIcN6UeYVQGKztHtk90feP2zdX2unTAgIgqKPg5DR9ESmKkIJySlWVuUwMUZvWmzxT9S+IPFZAAFoPKLFStAYUDmXIVoQZEsIEq3FslFAY3Vj1p0AtLwKxJHdxgOUxlxXEAjpLi5k8IIPp12v2996y5Pip7mBNgnyLp26yN1dX79YZI9sVgwN9nRFcvgGg09qyxnKGindq4TGMtY9eQSBqOYwMKYCCnmjjHIGUVuJ6TUFbuA1rQAFS6mrtxEhXf3v6vb3M7b094ZtzWXTv3bIxun9rGepHWPZcBx7pOtgEEa4eGRYDLOXAhBmAMEozF9EDBhztNZjNIdAjLCYw1oC/3j2RVzuve/T7I60hWN79V4sZHt6tu79BtQDsuqRh5Kgca4OAWQljTzVF911jGKBG2Mmdyi2+9cxMvfOUaXv7YDVx95AmA51Vbxc4hMHAKoCS9AigLngGEW2+f44VXn8PDeHj4XpcGGPjwgYGCqz96Zaks8lwVBfNYQTTKlYB0ABHh1u03AADvmVS55o6eBTDZb26BAlCF2yvTapB3vGdUxQAcbK3xvlZ8U/gPipR9lojDLYYdAQkgzlJfo7VGSTVbX0mMFEhkhPwzFgBAL248/QEQsOvXY9fVSAFoxx/QjkFetOamu0b+5XwLeLTTsQmsQEGCvlTAgoxbAOrl+nFbWANUQ6VvJzWOqsnAQpOjbVup6mntu2jbHACtsF1R3+KE0IHYCQNeuqYIlwMFJNgHtXpFTVrhKBdE3qIyCd7wN21bvr9R2933j3szMFfnBWj1Sy+n3c/h4kWnyuuaI5O6/Xt12UX1mGzXNpr8OdTpPAgzs1WiuhsIsO1+iqwHt/a7JMjq/Z778rO48dHr+OAD7wXu/quhjWocAgX/TEmmIHv2Cihj7VyZiC88dQN//5W/N3y3SwMMgDZ8sAAFhrrysSiNqjACJd14XwlER7x+501c++onAAApHwEiTGmShk/VA5uNqoUmERmjoEIKXnpg8ndbUUVCsjBqbmefK3OCXLXsAS8OyH6npIAYVABQ77K7/8aSZCMlsJieM1Ace9qlBVpVoO05FBwrz+/BQ/OIaphCr3UNDCwAQzBPzDd00FCs43fBIsD2U/WCHatQrnXbYlgqCKzGSq/vhu3arLZF0lTQH4t2BKr3n/cY//vfjhY+ME1LHIMEourNFRYB1g+irBOhhBqA3ugvjVUPyIA2VmxyWmR0RT77V+51TgYWlLl/7qhEstqA9BP1FtDqrtP1Vi21Xepd9uqtUel1hVqJZjxHU2N9zkvRLx0j8PKTL+KDP/KTwPEuItsEqjIMpDoGdfyxVoyo8quJgPM/Fvv4xY/eaKbsR+XSAIMGFPwlDwqqt2aggPKsSmVWgNAqmaaoknz9nW/h2a/9Mr705Ofw0//830O6+6/AaaqeQZow6bmS5OWoWogR9GDBtoFA0VqxAzs8LiA2bKNz6nbYnPL4QD4ihQR0jMKKkO3w0TZLX+WtdxrRkCNA0RivAZDo//YGDMDpYKA3YE1DnpYkVz00r11bNoHQGz7b34IJJouVBqEKV5gWZn9cv/KOdvEG1e/ba2Tw76G9asXa9iJWYl0Nfm0vHoME/b0ACUABYCqpANAkjfplsCPg5QJNQzlr2YFWGEZedHTtRcuafI7kbE1H9df1Ze2dOilbhL2ac8o+Wh7sdUqvDwIQ0DNd1dFQxzTPzjbNpVIffOB9wPy9XXaJS7hwkvGXJlA+gpPMKTNAevPtm/j4V2oi/hp4By4RMBiGDzwoyLMq5dmFFBgoQKExb3YHnL/7B3jmG7+KV/76Z3D1h35cDs/fA2VLPkrKB7lYjwMK5mU1cV1Uj7kHCEDsdS08Zfs7AAQRa9AI4OB+UeHgrF6JNPXvhWygcbYGqJVVkzOoeDSzKDp1377A6Djtsoj/rRq4FY+29w5CTdaP01MLtb86wND/ptFxtGCggpGV3gqU3MKTj961tYIr97u39mjbQu9VGAEFCirTtm/BJBABPLcgwc5xzyisjj6j74uS3LYiQnvkEhiEeaQSgwP3QTYHxjQ4tFsurazpHnZX9ixKBQhj3evP8yUOU1bduxXu8kBAtjMK0LXjAOjuv1rqgT4mZ7YmTQBlMGUgTaBcgbyAg4ybt9/Ec1++hpc+pqCA0XwdNip/LsCAiP6XAP73AP5nAN7LzH+o+/8SgP8awP9bT/0WM39qzz1jUODCBxEoyLMmB2SUzusU0evv/gGe/v2/hVd+6h/h6g//FWC+K+9w909RYriUgDSVzpLQAzWMgtGzHiwAFTAAY7q+bbz6MxK09u/AyCAwZOW58YPDQRTB+L1e5CnBwrXiA7GLY90jI+S9ef/BNQs3xflyoRHboLe3AEGjIPrnbXjHe977xH5qjNp9KsP+GXn8F30v907N+KQk+sGR3G3JZb8BBHCWe3iQYHyAYxLq8wiW3kd93UYycYG27uVuddGf+ymLzbbefqFjNkI6W4WobYsOaNk5AETf6naHEXaFStvn1p+bIUQfPmwYgQ4IFKDgHNP5e3I/7yi4d5L3ZAF1zEBKMnQzJLKACZxlUvnrd97Cta88jxsfexlXHrlSQll/URmD/xeAfxfAZ4Nj/w0z/+VTbxgmGvrwwRooYLdPryVmvP7d7+Dpf/m38cpf/Qf44A/9WAEFAEDznxYaByCXAEIVIBhgMKDgjpl34Kc8TcHAjsvYowo91zVattl2191rGSmz+6WE7rnsp71PKxtK7z4AguG17jwPTnju8swHfcz94vknFjrhgxXRs07x9YfP6gwiTdUkFg/eecnUsALZyTRcO40BAgCQbtf62/mVSSjPY3/8Psnaol73F7CdXO4FyO0poZ60FQTNeDp2pmO+2IEFO6emVdBK48V6NwoZNvlrnp1eAAHnkJoOn+8ilGMS60aYgTTV93CpBx4cvP72LVz7nV/AjY9el9kNrqRqKMPy5wIMmPm/BioNcz9KG0/nYWcUAFD2zxW5gYHjEQDwL7/7B3j6/O/glSf+I3zwf/hvA3e/pw9QZTDf1YFVjb0HCjWhqwMKbqA2gMHdW35vtE1obNbp6tONUXnAel2GZeMdTohJbz9qx1ja8MjWzjslfj58/Miz3wMKIkDRgQZiLiCA9Rj7ZD0A7Scy52Vd/P5SvR0KPZ/6CR9gz5evFgAgiaEvzyrxM+cT2zWUwPOx3sNkd5ocSHBGngjE7j1Mljmr1mU19PbX7UcFCAVgNBX1ZbRyf/fuezzrU1mWU8/ZWzaN/n3SIQujToP9abnP+hi4vzq317d93toaENB9XnZx90/LY3LOi/GLw0HsG9Xl9mXu66zDMeH8zht49rVfxMsf+TyuPvwYOAuYMK5lAxf8hcwx+B8R0f8DwH8P4H/DzLf2XNRQ582URB/LcZ2UZ0iH5NJRPAtAeP3d7+DpW38Xrzz2f8TVH/y3wQYKgKJ08vek80hzC2ia3MCKwcKCVQC63/1A3igjTzQy/M2+07xPue99VCL+CRcChxcAHCP6uDmfgn2oDE93n63nhGWvYt99D66rts1zuR8bG1YWzK/JTWb4ObvjPZDw4MB7LXkf+3BS6b38FHj5QDH+C0WZUt2XJmXzqYIIVhk1Wz1XJkHIAwMJfDqjZeDArgcKwBhfsxHu2ZVPEYP4ew8rnS7nfza6IZA7T617GSbHHgTAgcvxE0BBqVQQCvT7doAAuY6LvLKBapNBb28g49Vy2Cgl4HgUcAuAWG2MGn4w4/z2G3jm67+ML334RVx56FGpg90+TQ4ajMv3DRgQ0b8A8MPBof81M//24LLvAniEmf9/RPTjAL5MRP9zZv7vg/v/EoBfAgD8D4DiVfWxHRfXaSgfoHakE7IeFGA+tg82vaiojtMEpASWD4EXoADAgYUOKAAtsyAvZC/mXnLFo4oUwJan2XmZjUHR6xYeJtB6g71h6OuwVk5Vumk1Mhp7nAsjkxbHUkQvY6lA5HhnhJrzctdfjjbewzCcYlQLTzi4lQsXNKCgAwQFDPi+dudwruAC81ySs5p+j8aI37+n9H3njHwZSQYEiADrsxKeQzX+TnEiqYeVJpFdl5ErNlzp5HluwgyLcgpD5MEBMJaFLeMfhI0ArID3lbCRe9aC8YnG0VbfjeS+1rbbeQ/6IGJ/gKpXOx1bGYA2fLDQtfr7+6NvR7q2YwMMCHgGr2/b3uYsxjBQ5q/Yc3Rq4vmdN/HM734aX/rw53D1oQ8AzCrDDhwUZ3TcR983YMDMP3WBa/4UwJ/q7z8iov8GwP8UwB8G5/5nAP4zAKAHiK1xF4sXRZ6wggGPrnme8fqf/GELCgqzEAiXdd58bBQV2+8TX/SdAACkQUlEQVSUwLPzZFZYBSAyQHrOeou5nyPGYEkzrw7O3ohYGRiDe41Lj8pqvLoX4O5cfy17IwJIbM5fM1A2ZfEQ6yeNF1e2wdrYKaPG4+zaJWQXuli2p6mlsibJ5Xni7bg+tltN0zKXwJcIFCggKGDAgICNhY51aMaLq3f/3LWV8vqV8cgbfKD0C3mgQAmYHSNgQIGSGEVNLDOsxtpia8AyBAVN/8Lu0sipnedeaPiMhQHekT8CnCbDcruuXwJZrlXaAHNr4PPPSta7sVDOMQbIy7LXr6FTBhRdCwSAAfWc1TLQtUADAmQ/N+xd3sHeNU863q3vrO9ZzkvyH8+zhBS0HkwJ53fewtPf+FW88qHfxJUH39/VTYRDNEmCJOaP3/YvVCiBiH4QwH/HzDMR/Y8B/E8A/H92XctOWfkEEGApkEF5/U/+UHMK/kNc/cEfE4MfUa5a+OhQXdKBklIFCkpnihGaClAo53VGaEHv7PVYAnoxBAF2bg8CPAAoMzRQDYedVx639EgAAGtG6QKFgeol9mWFem7aHE64esPjFM0Q0PVAgcklq60kqi0M/EZp3qcHFFPnFbAaPhV2zmDCJp27CgqOdysgiMCAXWf5C5mrUXJywd0C++zGSJ+BX5ekvVv6iFKqwEENPzjJtcYK5CzGgPXzuAft/zwvwcEG41S8zChHCGgBwV4wMHyY00EBIIjAQC/HWwamkeNyH9dHa/Jcd8T1v8/yDaDId6lNAemtPAMC8BtZ7uX4XwddCywdzQhsmWxZ/S1MUKqRQVNb5/N3v42nv/E38MrPfAZXH3y/1oFleDODTV80K1v8OTAGa4WI/hcA/jGAHwTwVSL6fzLzzwC4AuD/QES65BM+xcz/3a6bduhtkRSyrIV4XzzL7IPz/xVeufof4+oP/mXpmJFS51boAACzCaB11rEduMw17gmU8AOABatQardGc/ZV6ujD7BVDr0R6sLOTUgawpJX3ehtrnsaeTPYNhoA95WylxJBTVTQ+Uc0pjBKjThPkKzaTxKXt2crfSVzP/FHx3FulNohVnxLLNCAAvV1hEzxAUC+aM0inLDGynDLXKoseUCbNKPp5o890zPSAoAcDBgKYcwsIBn3NPuEuJeBor1XBFk010YoygxKpMhdDQPZOfpYAZywm4/mcA3ue8yjLPReAQBu9BwSnxqJL/6W2fbvckC3qmU02e2bPA3on6xGYj+RYDg881hMYhOE1vqzNHrkbnKeMXpHltRCSXWfjwztmeo6Pz48SUXe9I9AyYz0bIBvrzKtvp1PCiPbO9jOlkmMAQOzXv/ibeOWnP4OrD36guzALuAYacLBV/rxmJfyXAP7LYP8/B/DPL3hT/Zs3vacSC6aE17/7LTz9+78uUxJ/8MfA87EqVE/hjB7rOruxX7Mq8JxbRsHq6hKkyuB1N2INVWxNAeNosAWsQDk3YgV6JeKBQA8COtrSe4WhgjhZAAbv24cLKC2OsTP0fYjHKxhihn0xEmkCcRJQZ/Fp5gIQfFzPkta4ZvIIOFjzOEY5C6P35VwdVmMJmuQmcgaGKkBQLxkHkvF/vCtVPJzJ0qqadMRZ5zwDGoNP2JslL1UQINAAgoA9yEE4wT65C5dpzUhgzMIgzOrRnDDtsYK7qRp+50XicFb20eFMviypxp9t7ZGOal4kB6Prt6jPfJV8hvpGbsgwHu1BwQgQBGBgM0dkZZYK9/UcGfz7IdM+TOAZIR2TJdynckxEYNxtQ0iAyLHJ8KzPMjk2HeBkvQH8QAsWBiXUscBqaGDE3kT33BzvPQiiVNiu17/7HTz9e7+GV376H0tOwUppmYP18hcqlHBPpaDvlbABiacHiKY/f+eb0qh/7R/igz/84yUcwfNxqVDtHisysejsInzuuNKhDUgAmsFbLkgJ3Ht4URlNQRtknw+VSQ8IAuZhFGcGOkQN4JSvskWlj0cDlUlhOUEf1CkZ27Y6O+oZmMGTMgAjChpYxPXERu80WIFxiZYejq91lCFaUEBliVzLnzGQQODJAMIswn/2HtDxKP1tbWFTtXIG4QjGQbYPAB3viqHMWaIh2TyMfWULFNh+AwecTwMAZPSxeo6kRp/SBEwHuZf7a6wQTQe55nAYA4J+WjHQsgh7WB6gAmzbbbkgNrjcOVvlJFCwV4aBC8mxHL6/styuL9HKMfltSjV8lCbQPCOUYZuOZ3KsurY81eS50PL3Sce67S1mYHeORhAmKQB3OihbQHj9T/6ogoIHH12M4aYoi1nBAVbH4qUBBvFqc/6E1MRmz7/7lsRkfvof4+oD7wPyUT0sjUH1CjVxMSJ2P/dHHpvbQVCNf67XuPgRgHbgAm0mdRdbGpZoatkaKHDnNh4GOpbA6r4z+axXHvearCSeem88at2KDVV2BlDdMmc1JHrcqmshh3mWT9NmXDg+3ZQVI9MAgm4+dfOu3XYZzwQV6kkEugEKztvMGT1AEPYgVwaqKBwNiOgHV8iS98qYnwEcQTiAWdvyeBdNg85JPiMK8fqt9pxzZQaGzVWZHcs1IJLfpJQwJQIdzuQcAwVq8OlwKEoSadK/wgiEStS8rB4QFA+MAiCwBAXDPqpnSNsoy8Paf+SSv/TFbfCd5H3vTh4FKihwMrsXDNxvOZYSy3Gh8k03plTHnILU0i95llwD6wfOVYYN0OWsXayAAjrevC7dYIEXZTB1dy8gaM7REs2WCgHBAOAuQMFoLDeVqOBgq1waYLCKxEsymICDm3fexNO/+2lJ1Hjg/SJcOpiGCtUDBAB0OKhwusecwICWahuA8APXx6j9oOyN1SJx6IKovldQfQjF3v8+lZGiiTzI3rNkjT0D2J5yFj1jy/vr49NSsW5fb/hXQEEBBN4b3Y5bl55sAG+bUFuAAmdQcp4lcwkxmNKlwwE4HmvowWTiaAp4qqGFwh7MoPko9EFKoJzBx7vgnMApA5gktECM8qXMeVsIIjAgzbwBCHqWYAUQSMa2JJXWfphqH6Xpnvqn9NGif6geJcv/qA5JDw7KLJMyA4VLW4hItixkmCdyj+UioOAUGb5w6e/V67+LKFwg1q9butWu647vBU3DmRfAUuc4xisCuK//yR8qKPiMhA8inQPE43anjbg8wGBYrJGkA8/f+Rae+fqn8KWf/WxZ/EEUKrUKNUsWV7JpYH0i0HQoLAJgA8SQ7cpg6WPlewUp8mB7BFyQNBfjXpQJYBEUOUWvo+mgXgeASRmMea5Z+qjInWC0Xr0fUD13SX7zArVt6PsyOuapyAYM9Gi72y6eoW8/i1M6VB7Gpw2tN0lrvRBOLSgYGJzG2Ljf0frubYNVQS4evWMKLMmWkYF0qGwBp+pNpoMsDX4m3jYxg6eDxFenAzAfQfkgM23moyim+aheqJ6nY5+mQ5iYaH3r/671b/1LOrUstWDAtqdD7S/HENBBlaeBg+lQcggMAMhHZKj2p2cNor65h37Ri6rHXkCCGCHmVPcVo6PPQgUODNapp4rXS1hsjvNE8gxKU5VfYw2KbtDxYsmbwFB+pbQgYQ9Y70t07CT5DWYfFGDoc4UcU1BkuLuumfInD2grNmIGd+SX2f3LeLdnZadoF/et+5u6+YTKsx/odI4kG3OacP7d7+Dpf/FreOWv/1MBBaZv7L2b97s4SLvcwKBppAnnt9/EM6/9Er705Iu4+tBjADLKqmimbHMHEIzmAiP5qSlnP1Ap3DzrTLJBpu9aCabmLPaXfSuDrVfGeRalyVzocrhBTOoFAiro5kECOlsjA7N6UA4AgXIJtWDCIszgkwJHq7+tzbcfMgC9oPVAwO0LFQqwCgiamQkmpBazdgJaQIEZnYiSThNGRqdZp73U3dHVi/euR0sEuxgirmPUAQVOc91WmplJKVv91Dgogc7eIyyCggCa7sr1x7vCDtiU3QISxOiQo6lpg6Ie9mNvCByAKwZgZ7hgyQ5MTb80YICmuE/KLIS03h92lOoZbHEpi5Fr/geXPnEAoagTCwHpXSgJeMPUzjCZDvLlPKCCg4PJbC4ybfJbZVfkv5ddcC4AH5oz4/uPDn8G8rsG4v32CAzIxaeDgV6nrjllxqI1+2L2dgEb1+67MkPK6p3e8wMludAzXufvfkfC3x/6DVx96NGlA1IqdHGmwMrlBQaeFgRwfvsNPPvaJ/Dyky/iysOPq8Cn1uNSpC3fU2g9Ls8iANp5nkkAKlCYtFn3AoS1AbuXLusT4/w9rE4ewDimg8zAewWfZ2DqgE6f3OSO0Vbmrit0cMqoP7b2vqN26RRBDwTk9BaVrwICF9MzloDNC91DS68ZHzU8Jqb931GOV3W4CETTwjh5cGBjVUACFzBQQELOQBKQQOmgn3vlAhLo7D1iJBQYsCUx5ly+Etcku21NY4360PfVGoPTMQM9GMAmGLB+M+ZgvS9882/1BVl/AABNqnI0rMIpBgjGAjTJpGrUE8p+1s83E7O0cdKwZp/z1AMElXNM0kcFxFteTdBXdRity20p04mye69yKwf3AYEeBFxElwKtPrV2WXPAtorX8aZ7rO6ejQTA6dDmw6QJ5+9+C09/41eF6X74cT3XOR8rxRYBOwUcXE5g0MUKz2+/gWe/+gm8/NQXcPWRK82pxUB5j4srGCgfWMrqdZnfNp3Vc4LFSOTPOBml1jV1m2n1OLCCyhGgefPsfX28hwDU2LT+JjvX2AUPfgwI+CmO/p5APF+6f4c10DSg9xb5AQElGa1Z0CuTkMLzqH0vINjrjaYJZoC8Ecpuhx8dUZPZ7NbSREABBokULHRAQbz7I8CHMobhv/aW/UqHDEpH2FdIy1z6+ajnVlAQZscDDkBu5PtY22MboBkQqGGcQZjAAIKyNexZnHRYAAH7l12HbPWBVd+uKZJJAhYKUNC+YHBt6wIQqLRxYStNxxhAMCBn1xhAUBbTz1jwfdLILWe3PYumZ9Z96zIr7z9oAAMbUdkjtztlVvZXA7qQWzu/BwDe4HbPKZun6E+gbaep0+vTYZduH3/Iy/RETVTm6T0NyH393W/LMsdPfg5XH77S6pjg/RpGERAG6kRwcPmAgUdQlHB+5008+zu/gJef+iKu/OjVFl1ZuACoDVZyDsa0LADwpKGE4q3VGK/eCIRqQK1EAy8aqMMPiADtVL2+pEN9j+4qu0v0jQT5U1kDioBAkIXbexuL7NutJJ61MkLCnsaLFMAapegVSQcEACCi8Ex4T2IIHOJfGCI1QjWPsAQJALQGqinOIM2oo4KIpFqAGikHFNIBZYwWA8QuUdHGelYWQA0/GDj4Md/OsfcrJBZwCbR9O0qcjby6AJQVpVniqAEQ8KxAlEuw0f67295eQ08kPVcofIlA1vaX4wQCpYO0ZdEfAUCgVPqgyXcq+kVDFRKDQPLy2wMFa3c9h0Yy6/RDu/T5XN6vKfcisx1giBZ0O8n7d7S7N7TAut60c4dvwhlwoRR0Z5ve9Hfdo8+j77EUINDrFwA4vKeM3/N3volnvv4pvPyR/xxXHrniZAEtMPD63tgq0ndCKlMUpe+3+/LyAIMudFBAwasv4MZHr+PKj15pFAVgBrbSsmT7JnZKVBQp5VloWVMdh7MCGESILfbrAEbz17T6ssmXceUOCPQg4BRKrNzEKQJABs501gAicrVZAw+NYumUSgUKweA7dZqQL5E3MkokCsDC4psVQGt89H5DMNB7n30OQTFUA8+0hpOLQXKjqTFSwIrywhIUgLkCAz3WggVCoglpmkofswe6HijYB8fysRonC7dxBs6MWXOhC1fjPVOhwg9XubbnvW2/AgQyYhDgAcAImI2KgQFrdyK5SQJAjLLcbmZp95QcQKCpTCUFswNiBhBqAqm1Pfq2N8fD9neMJVDBAuAMPjsA37M71gt/1vLaU+tAKKtAZPQ7T5lU1u6zngTQfk8HWLbTmj4vNqkHQoPxbcDl8G8BRHj9nbfw7Gu/VJ1aZR79M7g8arI7AzZLiM0ZFHAAzmWMbpXLAwwwAAU/dx1Xf/SDGHkOTfHUbDqoUDta1hA9tPOcIl0oSxNUz0ScWvzA7o3gRlxptTgF2Qx0p+TL3kMNJ8jf6H2WqBqI0fTy2h1lIOBjZF7OaK/X43lNUO28zliFYMCHC+y3G2cjw9QbpXIuUJoyb5opKZ7OBpy36l5FYU4FCwoU7Mt0AojZ0d7ZxcU5HOcVCDcv48ZV0MfRePZezynt3YVnrA0zfz/b2uoqN7W2rh/cYiSu+2wafmURjMlRxU1J29K3LWvIcqxXioHy8mryOdU+aZmB9h33MJlort9ZdsjqSZ9Tdn+b80e68R71Yr+9Op63ysKz36FXAPDZD+D122/i2u/8Am587AaeeOQqzOXsx259VsdU0eQAgiW2VnBw8+03Vqt+aYCBnw7Wg4JGWefYW/Al8rbKtibk5bN/q0H2ZKyBX1WKW6N6SvKHVqR7x3Hc7GSBWAhBHfjRN9052Lf87VUsA2fRcy8gYKVi7Tt3qm79/N4Q+eMOrTcAYWWq4So7sAEIIgNlLcfom3d9zMzgQmcTATNXnyIpDVaT5bgZ2y2zQAAdnJfrAIPzcgtoACpwQFfpVUJ+2ScXat+83b6A74uWFViT/75YjDxDmBkqNzFkoCDBWAMCEnPDIghN1wOEZfsWkODad1WvdEC98WpD0A/XDu54L6v3UU7rkwLZA8aGvTes+HPSgdG5WyViC0Z6pWHDgNff+RaufeXjuP6xl/How1dw7Mb68lEi/xbOapmqBCCDjYlCKon4j+ChYfUvDTCwBj6/HYOCWRXEPFAkza1o4GkBuHX7FgBIR7r4LTvFuRBs4ATFaWU86Bd5EoPzdpfee1jQaQMg0L/HiqBFZTQdalRWv0nQ3Dg4r2mXgXHy17rt5RTDSuftAQPo9wXGyhsqf40vY93EoW61FSwK7Q31ah1goO6cfswDhKQLXBSGt7SAV/VjI9SUwPvzTtDCoDsbN5Lbvk298bdzojZd0/XNcOHavokrUGDiBUhI4AYggIVFsHyEZS6CBwn6Nr3Bj/Kgmu2BTEYvuFPm7rtsbuqpGCgsWMDvg75rAYGDTIv2O1Fvnwh87e7PfvkavvjRG/jAg1dwnLdsFalMV2Aqgw2YVH49OLj5x+eSc/eRz+PvvvK/Hb7FJQIGBgp+PgQFRjFmVbw5t0q6LcXtKsqTiPDWnZv4hVefAwB8Ly8ZhaowK6ovgKHcekNwo4HuhGG48EpEty3fannr8Mz2inLdBooeKpPB/hMx+MXLQGltt+XScNnfPpt9QVfjYsar1/XFUG69I7e/E+pSNdX422tyzUcoAECoZwMNALtM+xZctK1jhrK2n8U7w2ouhlA7xqKci2V7bxv/vv388V2F2/dklraT/A5pP+baLrMCgh4gAAhZBO/htbqGgGaGiasQ0DSgNxKnyt6O178/ZY/sAUu9t7hurKlO02sDAOvG2lBfd+e2D4x1cbRGxppDAQCff+olvO/BJ/C9Oa+HDwCARE4zgJRIE1NrHoEHB+dvv45rr/48Xn7qi7j68GPBzWq5NMDg9Ttv4doOUGCMQWEQzPg5BRNRs9+6cxOffO15/NaT1/Hv/t9+Ft+ba8JXzyoUsAA5uOpdrZZ2eEfjo/GwBre/OClYny9DfRodbrY3lspfvcWfV1n4BoP27D3PPqHNjm3R2WuAYAEGvC5z1Zo3Pmwz9R+usdMbA89lVwEIPQAQ19exDrXYB10XB7ZK06a82N1T/ct2Hxj9ABAAp7WV/cpuowABux9QjL0BBJC+iwcIZM4BWhZBK2lMAoD6HTVfMao/EgF+mcJ7Caffz3IKiBh2Q7+f70Vv1bKAJa7RUpOwh/KbEQ3l03T20JlwIGDkRPzkA0/gOPMCAMvfaqPIKmshrczKsDBsoW1AwMHNt2/h2m9/HDd+7jquPPyEk/q4XBpgcO0rz+PGx27g6iNX0IcPPCjIzJqkxAoYOPDUWg/rrTu38OmvPY/f/PB1vPfBJwAAR0UMPtmrAAO0YAHok8T84Fy+y0J4AsNU67r0ukbn+XPvV9n89kA57/49c2+u8f3UmwvgYPsHfTDKH7Br+pBByBB0+8y49X04D5TWcZBPNnUt4/swMpBwDBrQtj+tKEzf52vDbjRu9wCivW0xKtYWx9k5BajtYGDKvHunHsp+AMVTs3bqAQKgoR1tSg8IrA1tKmRleu5f2dsq98MYl2fufOj3UyfRcNy2547O88AMOE1fDx2Fbl/vOBwzD/VDqStXx5SL3SHYpwINHADA62/fxMd/+5raxyf0pus9fWmAgbz01YamAdecAumMJSiYuXofkYf27Tu38De+/jz+yYeu4yd+5AncVQVyV5chtRDCkhJ0DFLvgZ34bgsPEjsMktsZeWT9fS5eLnaDewEKo0tTdCTYFQn3RfvEylq7j1C/3+fPa/pZ//agoDBd7uS568xR394N+mzUH9PgwFZIuQcfwLbRHumq/r3K+ScMvf417L2O4PIuE2SNASLCnBlTosZ7zKjgwPZZiEF+uyMdQEggGSONAarIz8DCKSV6/dAjD/aNZmLcizq4z/Z9d1lEIdxbpK4/AJ+MCwgTNgYH96qrFyHGzmFgLB0FszGhTVKWoOYUlEW0YSOTwMgk02Bff/sWXnj1Gl762A088ciVOnYprb7cpQEGVzpQUGcfMGZU41+AvAMFs9vvvZE/eOcWfu0bH8d/+jPX8RMPPIHZDfy7GZp7YExBO8Bs0NWYbWuQ9g64xpu6gPGJKNjo3v7cXZW513JBYBDZo8azRRfuG3kMA6+ir1YINlbKCISN+uUipQcFZjjtGZ4o6L+WNzKyl6n0YIYSlc4wIt7acKL6/YIZHAKaPaWABgUWBhiqoaggwMbURnTD3bsDfP63l2e3MWYM23uH4GJftZblz1A/rGUgtA5Zm0/THO/vFYCHHVUpJdTV3Oporwcqc9AxYm47CoFNiUqYa1L7NYFL7oouYYDzOzfxiVefwxc/egOPP3Sl6x5afbNLAwwMEABopiQ2iMxQGtfjPSgQIMH4zru38OvfeAH/4Ke/gL/8w4/iqJ86tcFVp4WhIjhwMyglAWyJSKPuKMoikK49BqY3/BHabAZfQEtHHt1W7tL309Ds9Va9Mo9o8Y4NXMTW5br2rz3C958/fkqJmkjyhNV4mNep49COgdQr0HexPptAQ+/bQIEBggoaeHhO2R/c8i8CjojafOq9RO3rCcDR5wqhLu1PiVSJxsWPo2bsBIzfaLz40rAJbv8mc7Jh7CNZ78/pZX4Ufunrciprcz/KSM6tROxUJPMlDMaxzPskXP/Xz96x/SYLF9XVkZ7u82N8X/k73VVbE42TCYQ5y7sSy3fuJpaQFjGQlUb45u1z/OJXn8fnn3oJjz98RZ6VZWYRoxAPw3JpgEFRas7oN3Ea/U+AAjcozUDBUaXiD+7cwq//3gv4T/7qF/BjP/S4TBmxwaSBQA8UJlU4fhAlrivUEdUuGCmSkbKIwwfunRArgy3q+V69zFPigqeunzbOaW8NP1CViv+s61TO7c+pCsUrE3UsGpou+3twO11tVJeRfjPj769hrp5Mdtfp45pjNnwEHOhvBuYkn8ueiEofTajt3YOCGR4wQI/pc3R8ZNf3fhjkoL/3Lgy0p0TMTGoSxdz+RJi5XkMEYGZMBBwhY0G+FaZhAeYyBvzY6sfFYkwAAO0HBHvHAxB47jsBwF5ZP0XOlyGocb/eT1nuS5SvFLI/AABelXN59j5Zr7qbF/0LnK6re4Ym0tMesPU6+jjol4kIc2K5aU7lfbg8R1iDb96+iV967Xm8+JGX8OjDV0qIK5NWojbhsFwaYFCp9QoK2P3eyoa1zjFQ8H/6q1/Aj//I46WTixdi23pDSozjvDIIXbFMZKvnCa+1vOYCimHLm4yVRlefoB23DETey5l2JQXJAKnxFOp+8yC95yjnUAMeJuc1TEQ4mtFVw9CAPKc86tQzZxRcYxh93Ncrqqvuqe9ULmxZBGZ5D6YqzweNe89Z19oyCjwLi1BulQhTrn3s+9ZCaID0nfWPdZNM6XWGKqM5buf4chGQ0IIBDoAA19WtFQgkIuTZpmixTtGi8o42DjwzMBE1wNH6vgeKRWYbL7NeswUG9rBJe9iAERDojYkH+zNzY/y9jPfyHcm2r1fUl/dThpvj3RgAlu3YyLYBYwB34fQtEe6CyzkTgKMKbckn4Q097dzo1LTHvhLq6g09DSydteOgrU3OKaEY+wJytHzzzk186rWP43Mfvo5HH3pCxpN5GwoO0o6uvDTAIMoA7Ts0VF5uMHzn3Vv428oU/PiPPA4gQMg9MHAGA6iMAs/LARhmG6/JDYc/LwQELuI9Avs9yDXDsPYF160SfdrcKxMzJqZ/TBEl1Pn5E1VDacpkIsIxCc1MJEloBhQ8SDCQXTxH3wa09CbW8hVG5/lFXMp4AhcmgbkdZxkGECS8cACBk305D7paHjAbOAAwMa96e5aUCxhYiPcDbX/6cRCxCqOSqApeBQQVCJixT0yVLUjOOoJWp8VOqP1sDEIPCBbGgdbBQJSktgML6JvVe/bNNKSZO6NiYIDzUs5Nxnv59rLds0IeCNa6RKBg50sGJZLfcqxrvRFDNNtGrpn2x8ISqDHVPkaWfjqiAsIjC7tAXOUckKTTSE8D7fdI5ISVlzxBTwNj1rZc191+lXVx9Xrrzi38ytefx2c/fB2PPnyl7Lf8Fw8O+rr25dIAgz6mE5XkYrNEVdkSgO+8+wZ+/Rsv4P/8176Av+KYglExj6Si9ArXJ6Ka7cyS0NRPh7LSzzVvntEhx1PCAiOPwYOBEY3cK4zIe/Tn1e1Aqdwr3azPa8EA3G81LmZQsgEFMRyFds6iTAQkKM3MdX1ySpqRrgibEoNnBxDYAQFeZwWKHfNSu2VBiuXQNiMqc+dBaJKVLDOeAdBE6k0SKPMSICSSPOXMQM4KFmpC7loZgwXu/rbXra0ZYOPdrk0kv4tRqPP/NJNfGmBrbQwqfSsG4pDSKiCIwECfH7RMJr5gn6JNQIvAAbAEBSNA4MEAUGXcg4EeCKwBvBHgvy+horwWJmK3jbKdUj2UQAIIigNg58g2K4CUb0zINdLPwvISqxOAOjaOrCGDeduhs3IRPQ2MdbWc5+4xvPuy2BguABbAt97RKfVPXsfjD10dXtvMnFkplwgYVMEDlmyBKCBTALJCFCut8ta7MvvgH/70f4G/8sBjIoBKy/qYbV9svwcIkrgiG5MqOT8lyoCClRFgiADOqYDA7rMHEIzAwCnKI/Is1rzI0bEUWd2Gal5SzMgGBkxxsHibpmBIaGfWr9mxBwiknnUizfIVL3xvaZccxsVmojgUH2Wte3bCWLGM6kRXkDAGCEDS30qUZcbMhJQIxyzBCt8nSS1nCPg6UBCBgfZetDhvSjKFbwEOXDGAF+07pLQABMYSHBKFgOCgc4k9GPBAYNGPunFqljq7E7ONUTXWBg4AOi1XpwMFa4Cgl+ktFggYM0G+nCazdizY5/jsMpVT72MfdazhIh0fsPFSw0lwDNNRAfuUuQIBx5RN2eWgFJ3Z6upZ6zV1r3mc+SQdDVQ9DVwcEPg8ChvLBmxtHH/7nVv49NdlnZ3HHhKmoG/zwho0FRiPvcsDDPacRDKAmAQ1ERh/oOsU/OMPXcd7H3hCELpTqjPzYpBYiSgedt6kT3qyKVGAG4xOxawn/CwHW5+YEg2wPUqnDRWsg4KLKJOxglmp08I41f2mgPLslJEpGUZRHGY1TaFAPQ8kGQMzRIHMqjx6CoAbSWpLZEwiQFAQvTM4GN9W3sv9Lq+lYJLh+kKfH4GENBEyEw4AZmIcmHHM4iUd9N1pzg1AAFIxHpkJOWmegcVRWNsz13aX/pA6FQo2+76LjYX3vkoIiKh6hanmEwjr028TEgiH1AKCw5QahkDAQWUHosXIov7r+w7AhfrP+kZJHVXOFSAYZZG4DRtFJV6Zdx0U+FwR6dc1GV/qgbq98sLumlNWPG0ybn3Q2wMEA42aTZ9RWaQyBjNv5jEY+Peln51iutpPXQUupqOBfaAArg5NTow7PmS7dPz+gQMFjz58pdFBo7LHVl4aYGAlCofZN+std2ViQiaWFQ2//jw+86HreN+DT2BGjd1izkBRnHIf38EHalmCqGxNw7nIvGlKWGSiA5XZoESFHZCEOP3d7K91T4kKOBBhtOU0Fannyrb0x+X6qlzs+FY5JY/JlEPd5s4wUVEci2sH+/sSZUM3n1HQ/7yXuWVUmiWyUffVZ8Z1MaXgQwigumRLTa5FBQpMzWIpM8uHe5iByUDCVGfeHDIwIcl4z4wjchlXM0kS5THnBiDkTGXb6iH7DBzoeJhotX/bMBCVv2b8y3YACCKGwIcMDkkU6IHSEAxMJ/YZXL9tiHMJ88hvpbaJSp8Rau5T0j7rwQHpPfxsFEDHY4f+yQvyShnlCNwvUODP3QMOmlwCJ2e+/+Vve05/XI7FMtUmm9Jif+/U9TMaTilb00+9ru7HkDX3wded4plUhykVXTShYwocKPDj2RcbBnte8dIAg1F+TG0bUr9RBPabb9/CpzQm84EHr2BmiS9nvdfZpItLKoMgd6hl8oHHlbI1B7eta7vfjLo/f3ZhiUJ/Gbo1eiyRJKRpLPmg95L9KPtlpTeIFw1F34rUy1g3NF8aWIXTU8xNCu8eoHOCtrFqdG3jvUurj+33CiXyNHvjAmARj/bo3IOClCooSLRuXCJjsyfE4FsnWxDUdUMDDPptNlaAi8dq3iRNhANP4Emm286QGTWHNGGG7DuoF3rIST/3qh4omQeKwipg8l6peXA7+rIzBt4QXIQdOEzSX73iLP2sfTB93/vK7fSAQEFCVtBlOkhWogEyycp1SZFFzhUc2BTVieX0GaxTVUmmaAJN3kiy9XGzIUs4pkfeoLBoRba177pwzsksAGKWaCG7K2BAfrfnxbJd9ZBdajIN1ORT2U8LQFCvWQKCSD+vhRF6/dzfzxy5sAS7bTzbc/t8mESaU/B1sV+PPXSltI0HBKNHlkT9+LDUYeXYv7YlREQEnQpGeONtmef5W0++hA88dAWZ62plxdtCG7dlSGMZVXo4VWIQe6bAWmKLG3D2XN3HqkR8PNyYDctKhyaeyTGunxRN9c6sCmTmKmym6LMey4xiBOpx8RitnkUpuvPKscUQpAtlOkcKBYg9jKXBiUFBn6RmQtnHoxMFHmcnjFNgcBbUNHyvbgOkolCojkm5sgI4MUBUqWVWQwLpowmsgKGChOmQkBktSKDUxLCPOWM2gMkxVW39m507spsxCFmC0/rJwIBnBgwMGHAr36cvAOGUPtrXTwCVkIFhuWKfmVyoR+aazz1A0P2khpytb0l0zTFDwBEk3j0xgCmFSaU9OEhoQ0OFpjeQoHLlRdKHjPaWrRlEQAwA/LVj1oCa49bPwGlgYA0InJJwaCOkz61p9HN5uZXb+Dvq839gaj/u1OfDWPjgs09ex+MPX10A3bVH7lW7lw4YNHOMu2MTgDdu38Qnfuc5vKgrQjFb7E2vJ/kdJXcBChQgjAKwT2X4sipqo4O8BCJs9BLc4Jyk3gdQyWI+QEMgE+Hg5nsfLBlR+UrzFAEXOiEb5LK9WATHGYE2IbEtOWARdhELXVkkoQ08D69MIo9iFJNeo6F7YwO3bwEInKGpRkbbh3Np0PJ5163lJcu33M0jJoCSxEQdYMhMODigkJmRk/vNkk8hgKF6/9MhyTUu3MCZMLOFFXyiWw1JRdnvViJw0BqCquh7Jb+WRFj6xylMY3FS6TftJ2f8/W+M+uce+sY+IyxjoAYjEgHeTmf2rEEFCIb8suY/2Sd3bJaMhTgpMzDldtZJmhY5IzMTpokcmJP6GPMDIGB6agfdD/kElmBhDdDL+UsAAGyDADt2yuJmQGv8w1de0clA7CDy6oUrj+jfFRUMGEv5rXdu4lNf+zg+9+GX8PgjV8q4Bqo8rFRXxuGOOl0qYLAVG7z19k28YGtH6zxPQ/CiJGvmax+39UABqJ03AgZ74zm7hk930sL/9uvDcgUMUEV/hopgZ+h2lrjXGerCPNsLIFFzXM7Ra7pKjdZCKPtORVSuRIoEGHsTkSexN0ktoqLbfRUcLA1ONTbEuRoZ5vLbWJzNWDHpKmeFQUiyj1L55vukfw0ozEZVQxQ/A8jKBHES1ian9oNiByK8B5Psw/Z0uX72iy9+nFCnQLf6JJpauIcVEDDY/i59hiUIKP1ywT6B9gm5PrH+YUoFKDAqaOwBwqxMAbOggARgJpR9tvw6w8bz1CaVMmOaas7ITFXmpZ+k8aIZSYDKp7EG90kuy75APrvma5a3jhYok3NocBzl+NqKh8DSAEud2rLbnHuVu3Hqli2o9kr+Ti5cWdhJUFm86MUnr+OJH73ahMD89b6UIWxyin3g4NIAgwgUFA8BwK3bN/HzX7mG6z8nX5kqxdN8aRm3LauDUQUKAHA2tVON+ob2Uxj18l3131v885pnUfvcSaUua8ucmc5TyYoX4aCTl05uM2+rJ9JfE1170RIvg7zuReyZzz7KI9gCBBWxO8OTj8XotAaneqfVII3FtXik8lJuQInxYWURPFhIRECStR5ZDQMYyMnAAZQVEA/zzLxLB4Q5adKhvRawWMZ1a4526S8nAyNPLoqneo9pjbEZAgGegbwEAWRCjiVg29sfDYuD2i9szAEROB0ckyDvkKmGGeR7GEuAANQkROsPUoBnIU5LKq3gjVZXQZTXFPlcWwb9IqUHf8Ayya/x1t3vvUuay/F9hr+3Cf6e/W9//t6yZVxLClbwrP55vl3OlOmUAzKWv3nnFn7pNfn2wRMPX1mEwuIKyDHVLjI7AxUcrJmkSwMMPCCQv7XR3rjtv0d9pblOc6gKFVtjtvLvgGWCF6AUkgkv6rrdFxGv3SjVlV7g+ud6xTz64BIgHqJcT805hfl299zz4aW1zwD3z+7LaYt8tGVkdOTc9VXuerquoHQa5xAYIFh4onluDBDluQUCHgR4Q9R22OJ9KdJonYdat1uwQGkaAgUBAwoU4H/7HIaOOZtIcxrq+OjHzWoJFPewDxQEWD/sYgQ8EMjzEgQUYBAAgo1+sPYm99vavQELHigww8I/rP0xuTCDJSYYQLBcA/meBpVQZ5LTKmjTV5omQoYAOwsHSfV5CPSj17sX+QuaqD2/AQH9vdyxztgD2wY/Eo1yjn9uV6+L6F1fzG6cWvxzo89CnymDafLwpoa/v/hRtV9OHvr7wdWJFXwmttCQnrnFhOESAQOgKhTAYo4SPnj+yx4UdF4BUEZqFLPtgULLGMjv8jeoUw/Et3IW73Ww9nWQ59PiYP+5ZsC9R8CEMMuAld9tLf09QhQdNMza0FxbOc/KKDGoVyJAi47NAJX9K0ZolKxWktnsvizGf5chMiNUBlSXI7wV0wacZq2GqShAxxx4sEA0VaCgf1OaNAhMyMmtk1Bo5woWuGy3s1Y8i9brGz+O+sSuUl20yrFve0+X9uGakuXDGTTPARswj9veKnyf2l/a2No8a3hHjDLNrMBBWQPOLswwCXbQd83ZMZgMTSasumctzGmv5MF+8zYregq4N7lzrbG5cy0PbM3Iyz5anlfuG+zEUufebx3blz06v+qj5ft4YPCmhr9fUvvVOCHAcrxSArkzbGwh+7yR7YW1Lg0wiLKMb719E899+RpufOwlXH34MSAfx0lFPdofJHdZOUtLYAAEhnlQ361409p7RmXU0eP6UFtv70m4WjSDvLvZ2nfi633317XUr/+m7s6yiBcG3kJkiGz/6vQ1OiFcsGaQijEK2AMgMEx9W3nlZ2NWa2RAoFCtBhAqIOB0QMQqTMoqTERASpJ7AnJjH7CwmxkjqR2FTFSpPS8Xl/H90YK2nQmCG2yA748QgPVswaLdV9q8a3eWBfdruy9AApV+kX0TmLMLM3AbZkiaE6LtneBeAbGTYg4MMAZqvcyFemmn3K1R7mt93exb3HNsvb1h9WeN2IC9eQOn6tJyfLDf2rRnU9bqs2C4ITNuzH59/CvX8PLHbuDqI0+0DggwsGFzyTkipwvKMtMNezDu70sDDMqYDkHB40rpdh5FVAKPi3T/5Br6THuR9aHNHGZXTgUGIcLeJa/rJ4XOQLOv9S7WvL96afvM0buOmnpNALd9l+2ypURaKrub6rNmmJg3wwVbgGARSoCbTjp3pO4IxNqmTm+q2rEzSuUaAtHdJVigBKQJBLumHjP6u6zboffh2iLN3+U4W45LD8aWf12bbOUFlLDNTuBl+4F7a2vO0kZMICgoMJYAjLLeNrEqaIZnEVbDDKpn1thLALDpqaXd3f7yKgOwf9GydoeRkR3sDvVff+qIZQqvv7DejMrAMbIyUE67db3pmYEcnBHj/I9fx3O//TxufPQ6Pvjg+4Hjn27ariq3WcZTEueAVLcnQmGjbt2+udqflwYYeO+vAQUPPSaKu/EuVBn03kKfSAQ0XlUBCwBSPjbb/Xzzvvh9QwQb7o3csB10Z1cWMUFKwQPXakDBvtG5cVk1GisXnv62cWkEtPNINo0UbxipCwGCFgywPottUYCd/cyyALzUO3kgAKRJQwgd7e3ZhYhZANB6ulFiHVC1tTOgTJHa19P8OwVee5igGeUDnAC2ivF37cv++XuKvZPXFQrIWMFAAwB2AAREYYYOJJA+y4CCPK+83ZDRs4Szey2rCXkrt18zyqfov/FtdujFi3gX3didinfdVmqP/tt+H2d/nLG/+f/9PVx79QW8/OSL+OCP/CRw90/1SMAsOjmUIWUhQ000JAbSJLkqkH65efsmnv/KNTyCRwZvcYmAgSn3BShQyrF4dTy3SgRoB1RPEwJVYJFwfuebsu/4p8257bVLcdojooss6MV278bfL5PZV6TzkhbHdyqcFQMBrBuQmH9cJTH31Ume3G22BmpB1TljsyuJ7URAAM5LMFDWq+092kAlubZiAEgKA1MqoMEDBpomVRoKGPoxb4BBz6/jmzrwjI6R0J9dnYZ19x69P76WBxDK7rI95VAuYOui7QlA2zNLe85Z31VBYgEIfDJAKO3Xga/R9EfS8337Tl4mIiO2WlYsZ6RbFv3nHr2li/bqqg0a/89K5y3GwOJ4rIu2Wj/ULbptY/jZV1/Alz78n+HqA+8D5u/V6/bYK2IQTQDpx7qSnq7g4Pxtybl76WM38B/8s783rOflAQYAbr59E9c2QEEYfwTCxibzonTf+TvfwjNf/5Rsznfd4OkBwsWQ+oIiarbzYD+GwrIprCeWVUO+p1wIcATP7M7jUXvvrW/XTk0/lN+5E+IADPjtKJbtjJj3YjnnMSCw+3YGjYOlI4vxn4/yN1WeqLSRzkqQDMoly5DMC+5ZBiAEA7JnQ4m2Fe93NPvHsf+x8ZfT87jt/L5SjZX2s2Lt14MJRx5YigEA0DTpLIMMmI/GA4AAjEGCHlvolf63VHrxHmEZ6og1nWNlB0jY+by95c9Md60CgjTYH2zvLMs1MpbOxys/8xu4+sM/KeGDcmxkpzKM7SuANAHABMpzAw7O77yJa19+Djc+dgNXNJFxVC4NMCig4Of+iwYUVADgQEGe0StwoA7GpRdEOH/3D/D0N34Vr/z1z+CnvvJMZQx6ZQlUmrXuiCsdZmvVjm9pVw7PQWjE4vsNKrFxvJZ7JyZ33mkTvVNzLg2OhcpgjyJdAIWgvRuD33qv9nsECvxzGgPlDVsACMJzm2r3+wwguLHZLBfpQQAqaPDXBOChhFumAHicUHxbNLH+QUilff8xcNpqp1FpaOCU6j09QEgT/Ne5OGclWFJ5Jw8OhOLV0IWhiPrN78IiABDFjnl1DC90S/giAegZ6Yz+/C2gcB/1yf0r2/pk1Qnp9Em0v+mHrfv5stJ+4XRZAFd/+CcE3Hf6xF/TJL16wJkmUD4WcADOQAZu3rmFa6/+PG783HVJZNwolwYYXPvys/LSDz/RZiznDAsftKCgCyn4DrMZvToYXv/ud/D07/0aXvmpf4SrP/ITcmw+Np5UGRajQbZW1kIGEVLEEiTsFvzoHkEZJmfuKIuELn/faW0GtF6/ELLTBVeu2r5udV+5eQS4Bv0Seb4X8X4sMc3uXECD7+c94ACowzkV77Y+xwOFAYAAwI59KGC4XLfdp1FhYJ3aX/PyL9IOWhbMgOys90xJDf7GmNkqETiwZ5XxAadHluNslNQn166My/L8srHYt8vx6PTEHr1wr/I/vO9mH+wIP0Y6ozkn0Bkj/R7df1S2nDkPAvLdjVkziJNeMVXwanooA6+/8wae/eon8PJTX8SVh59onZpBuTTA4MZHr+ODD8mUxDZ7MzsAUNmDsi/PsA5Z0moZ//JP/hBP//6v45W/+g/wwR/6K8B8V87N8jdiF5qyNXBG1Ko7tuqxuvO3PDD52YOQQIHmfiKiP7ZjGZSVATc8Egl9b3DSUpBNeZsnRVPnBQ/ocDlnCSaaazZLDMDWV8yj2k+U1EYoJ+2rkNVzTCLspMaq+c518+huX9BPvKfvgJY5cCU0qPoeFy6Dthoa9r3vEBVjSPTe4fvYeCpf9ZnqmCnrPqBlUprEy/HYIQsrWAyiXDQDaxltTYkN+B4dsZiJETExfUjLirZ7U8ULOA+7mKU1oDkCdc1mLNc9w1VrskNHrOmHi+r4QfiM1MYM7ZLW39gnSr5fJrVtMtX4/J1v4tnXfgkvP/kirj70qNhHSpth4UsDDD740Aec4XcZ48yOFeg8OmbAxXwZaDrx9T/5Qzx9/nfwygf/E3zwB3+sggtAGhhuulJHAQKodM9GOQW5rwl3X/8w4aofZCMKtr9HWCe/e6dX7OuyogAWSjvwbCmlNtmOqG6vKPBwep9/xlY4wp8beauUap/6WHGhAQEcDjruAJrMgzTjjwoA8gxMBxnHChJKDxa9nytwiErX55sAwXIU+tusX/UXolA/pkbT6HqQ6QFB6sZHN576a0lnfqiEtvcdjaVFrHjMLF3U6IehmEEIZg8jtSrjO+Xal91AMzgvColZacBHn4jr7h/1Y3s9Ldhj+d316ak6Hthw8HLj2HF/bTPu5F4EBzpleUzcvPMmnvn6p/ClJ1/E1Yce0/tIXgJ5hiIolwYYjDPHnXD0Wc8mMEEy0/mf/BGevvV38coT/xGu/uBfFkE7OqR/PLbTleQH0ODQYFA1dR6zBaH3r3TkZgb7SOj9uSMWYSNre2FUImW2QiXW28YL35TjAOBpx4YlWMbGI0XvlTyb9w2AFST0mfpgm58OWD+SSxQrdfNU8Kj+Jeac3bWtYWAi4KDPUXBaQQLAlKtSdCCBkobEChgQEFGxSi5Mw6JeDUh07zDos63FXv4synCcBOOD87wEB0Dp+2acnMAOjMAAgLpGwJ4wV1TuxfPXY9kSWAd5KgIKtoHCqeOjVnVdnkvxXrv9WHj8G+DOMVohOxCFxAKwxzOaPub5OAQLRV5LxS3t9lQdX24gl/WADgAflY0eALEi5zOAw6E4FcJETQAzzu+8iad/99P40s9+Flcf+oCCB4CR0YSvBuUSAYO1j9SMlRuxWymsgILv4Olb/wFeeeI/xNUf+jFF33ZFFa4GKABl2lKpUhlIsVCFFJEOEI4M9ylAYA0ABFnbRSEMlMHCQHgFEg3gkeF05y7efosmtIQvU/LWvtNUDbFT7myAoTcCeg475c9zWrAKAhKsvi7eaJRwVMeujLxEKpPSVTEgAweLFerY8ICQsjIH2rfTQUMMHUjIuYCLyizU/qY0LcFdGXPj+P5JfXyRErWh9U3ZrgaDiKTeXcyaIm8fLWNUQKMdd6BxMVsjAAIA1WRC96y9Y6IpESgYzMjwumExk8WAgIEA22/nujHQyHrf93ZsI4cjDGfZjzUm4G63L0qGtdM9IAhAIJlxl5PrPeYKCCRKkwAc234v79uOE+bOwVgwO0s9L3VeB9C9ri992T8Dgb5vbtTmv0iyawuczt/5liTKf+g3cOWhR+VdKQNckybZ28egXBpgUFkCwIOCYbxXPcNFo/7JHzlQ8OP1wKDzGr3fAYXmccYueGPbn3SvYMDO62OHI68gAAGhcRgpCW/k+3a+F0OyQiWSU+5FOc+dki+hgtQofqHbE5BUsJhFMeg8debKJAjFr1QdFCBEfXtKbgnVpCCm7rNbXbyRmYFk3gAjdV4iGzgwoJBnkH1mU40D6TS4ChpmrZqCA2M+pgmYZxAFa6hHSioyChcACW2Gfa1rv6s3NB4Qyo4lKIQHhXoPDwrtnAgIUD9lk2ysdQDg1LyiiCEcsU8RKPCAoGcGPCvgwYABAbvfPEsfe9nudAyv9fVFZRgI+jHVLzelVENYxv74a9y4LAtwTYHRLjrOG3yV9zmr/BvD5vTGrAvWWfLeYt0KhxVW9DywQ9dvhWhOkKX6GXb5e/7ut/D0N/4GXvmZz+Dqgx9QpyI14ABYjNhFuTTAoLAEzb5u22hinwDEWQzADJz/t39Ywwc/9GP7HtsnMgUIE2gHybB+7n6hIj6BIeBecSAABHvAQFCPoeLwwCZ431M/7dp/xpWmyYEBiLJ3v0vS5CwGgIhEeVAS5aoggDgACMVYlP/kmTb9zIlSXQRI9wfx5HEWe6dQugzl1oNwcWcLfylYoMhg2Hk517U7PEhQICHbNutBQcKEFhwMQhFt1XeAwVFJqbl+cxpewBIJiHMMwRYYWAECEQjol5Ouv7Gzr7viPz7mWQFnCFtHhpesogOCuwGBBwPdOJGqBPqjk997lV0A7YwESmAPBmfVo5RgRhmA7CeR7Z4xxPHoxkNT/Tou1NAXL5tzJX2hY6Bk8nMLDuyZRafavQMwdy+6fuWc6BnNuHX65/y738HT3/j38cpf/6cCCvx9S/3s5f9NCSUMFgCSlcIyLOnCwACng6xrgAQG4/y//b9LouHV/xgf/OGfiDttLz3orwHi6/YMlD/rMgIFETOwAxD0yuTU92sEEQDnoyqcWZTMrGg4JV0jRgW5jP1Jvro3WffV5D3TPxHtyUbHo/Ua6+piqe4DYAuMaGUbQ3HKolBDKplZFHsPGDivA4URSJjQGBIDCZY7QYAYkzRVgFAenQsIM2NexsRgxsSiRAyQ3+9jxR4QOIaAOiZoFC7yYGAIBPp+tTr0/SoVkXe+SL8CKLlNBZg5h0JBSdFXnGWKegH2g7Y9BRREgKADA15uL66TOnYgUbm/5fPogTpmFCxSt6/oXjPUxav/8ymLqayyc7eeHxYPRIP79TlRfqn+1//kjwQU/PRnJKfA66tSF+cAyee4hlW5PMAgipd4ms6yw80gOHDw+rt/hKf/5d/GK3/t/4J/54d/YpEEJmXQSdEz95Qg9lwNoBsUThEXJW1KxU9ls3vpu9XpbdXz26SQ7Vx7np3nFD6RenreCIzo0J2lVz6LOfROEDlzUTLFA8m5fDCktFkPDg77+6V9/gooMMPRC2EDDty9NjzLlsZ3IMGHGWw7AAvMGZTmVaBAnbcJ5216T7SZWTHLVwBlHGm/O/kg331TywQsmncUJvJgACiMzx4wcCoQyGkag4D71JeNc7Loy2nRlzV5bAkOTIGLTAMLZnRUIlBQqudYAuwHBVuyelIJQOEmUARi9sid14eUmnr2ANKd01+zNj06fIetd7TS6fvlJQkLZtHercyAGYCChx9dOCetrdnH/FweYDAqVI1mEbYE2JSO1//k23j69/8WXvlr/xAf/JGfFIFxmeLAgBoaDIiLLODR5yYGmKEqYm+Qh1PZ1KNz15g3QXp+oZAPrZeI6dBRyYEhcPsqg6B1P7jzpr7tlm0Wou/SDkuvo/yeJiwUhm9ANRaNcfH3dQrCG5TWmKQOFExjI1KE0fbFDMKmIum9S91XkmR9Yi0vP9rEyYyPAoVJ+z0CCvOxYRNGIAHTGeA8UEwOxFgYwhmM1eVsI8V/apggAgPTIQYCaULeA+i6/gNQPTKrw0X7sAA7Lv3HrHKnekj6z1gEasABw2RJMbjRwGVlxuTIUCdvk4DiQr3nrm7OATBZNRkTAF7fc23th3hfJ6v1QPt8ODDg73MRMCAnlPNPnpLqnzcCA70Xfx/0/aL4Z3dTKLlzVCR88DckfBAxBaey3FouJzBYoHlpnEKVqgCe33lTGvWnP4OrD7wXzDM8mi9I32WLl3KImy7GY319xvciNdqlNOsV2Hs4mg1wnjvVBDSgxh+BJsYst8sCEnzewnQGzrPUljOAQ2wIAJibWM7tjAMXcHCobEO5b9cap6yU1imWRql0FHQBBc7bpMNZa1ymQwUF00GOHQ5FANl7l2aI/P4eDBTD4xYR8Qbm1MWTrL98Mm3vba6ABWGNHFAIGAXW5Vc9m0Bd7LoAhQMqkHD1W53B4rsvyjQfeHq7WIHpsM4IGADcAnGjfnP1u1DfuX5j9n3EVcfYPgNfTNKdpq+y3vFAOk36AEZda4Jw1FUkATqcgY93IewggAlLcDApG+TBfYaAeq03ObETwBAbwNAwRuAPDgD4/XuBojt3deaJv+cFgEAEAvp39Anry5G+oeuBoe1YHqd69cLgT7pMvyYaPvRYO77LdV19/o1lDJqGsEa07QzQBOaE89tv4Jnf/TRe+dBvyFesmAFF8ZIx3lG4i+fsRIprlGPQSXWddS0dKCHW+ageMEyK6J3htyltNEhYrKxDrvdp9ut5U3edP3cleZHWpjv1FOUUtG9XQqUCVMUPVDAALABBMTTmWdp+BQqUkihVIulbInA6oDcuDSCgKTYqBQiIYFvrMDD8TG7wwvJKNPlNqPkrF5uxWX4bpIIF/+GwOs2tAgU6nAF7gQIgbJQDmAUwSIW2zaeXid6z28oTOBUIpEMMAtLU9RXC/gJcn632l3slsq8jTnJ7ae3SNzFImB0Q0HBhzlKnKdU+PKAymf//9t48WpPiuhP83cyv5J6e6fVYNksVludMd7ttjyQQQkBVvULCshAIie4/phFFISSEJA4gS6OxBZYs21osvLVlA1rMIqSiEGO7bQESi2nLvPdqY5fcdnt6xqfd1Aay54/u6TPTtup9eeePiBtxI/JGZn7vvapX9ch7TtX7MjIyMtZ7f3eJSHXOhWxb5cZZ//LYEeIGmFIE+crSQwLs/dgKJeC+OJSZRtplas9N9ygLf/dzGQBAlz8UBGQAIN2pRumwlzTwGfl8J1Wxv0sBsPNHngxbErdtPD8DvEDbOhuVxyH1WT/AwAQE+WC5gZ8PJ0LdgbnTz3cMUqH7lLnGBeJvuv/rDavfBnk3UtChgUkQwskEdcy498hTJfwjVkqBgwCJ/CQ0re3n2yBJPT/kgJRco6Smz7amqIepDDY/G4CAvVUgcRtYQkYDgkzAsBIujZIyDdT0kr4a0FwZJ2krkcxuJ3EqJXwchvACX4EE5g0eqBlAIWj/0fVA/jp3PQQtE4iAQX5DzQvADkLU2qPFxLOzBAa5BpQVJwcCrMYut/7I0ByLcZom4+SrSQTyY4UcJDRTgCkBCOSPrhVrDVcE8NQdddtMgQ2vQMUMXjoaD9xaOuoAgrgIcmuPBvlDwP2AdudBgH1nD7ifmeBW5bSEPjBc8Ku8w4W/0v4Nt9GyP8S2AuJKyZbE6ujm8fzhfbj8D93hRXObtvi2VOgGJ1Lnpjufp/UDDADkgCDxEfr0+YO7ccXD1+C+t305Hv4AWRiZOdbd8H8zRlcdw67LBCe33q/uJ/5McTFMkmutYZpxEwaAiMAhsyCoMqzjlEuWAi00EiAwK5oOhfQwlRwIAAkY0AKHxUIwFBB4AZMLmgZRyIiAYR8bkAsc3equnWDiqg0H7mYAgXxaEEREDizUtbcuRAAgVgXmDUEIJfO98cFqAhrAoHopWiRc7RPAAKix13OxBxhYjDsBAJKHCI223AiDrNIYkFnBWt/4AIbFoGN8EMYC5vg0YXwcSBBAx2AHYJupN+lTPLRGQFwACN7s7y0AzAxsIAcQJD5Igkz96XmWW0gsCq5xbXA/MxlrEVBj6y5UHsNs3wEW+uZNuNUh9CVf8NVToW7WR5Q0DRCqKyax0GRAhanC/OH9eOdj17ljjjdt8enUypuMpebnGiB00DoCBhVagCDzET5xcA+2P3Q1dr19J+bO2OqZH5SFQBgcR6AAmOCAJ6/oDrJaTcoXbP5ezchMIBPTpCTS5nvtv86eGfphJsrSEsEgcQt5WzJLwSxbo4pnpZc0T/eQ2uYjoMDWPqPLQNwK7XuJwGlSYTNVvxMXAgMyCiFpAD+OrgSXuQoWA1dSDhCq8Nu1taq8oBKrQjNN4xXCeRepVYF5gzQEwQUBAJNoZWuN/VCbu2JmrH37pSDB3C2gx8QDghZIU+PSMLcAAaDBwkrGBX5c4kUllhzfjBopSKgqgOAsCRLcG3b9sAcLEnXIDYjECiSnVzLAtUurJgBPE5DghsWDAcvSI9fS3uWuP6C9Bq30LgGv0gAkfv0uQa/f1ZjCvvuZlqDPwcDxAAJIgQrXG4y6Vpg/tBfvfOR9uO+SuzF3xlwCCFqf5w6KbeN4nSgA4SXKNWHQ+gEG4j8EFFOJPt/5g7ux/cGrsOuy+7DtjDkAgcVFpiamPUnLhGxy2Ei1YZCJcXWosGA7AEPfh5m4lW5bIRLddmKXT4VntYAYAjAoJC2TQXWZC0X71ECgR/hwyWrgWzb1P6aZ5tk0Cgx4gcOIAgnINdS0TfpSL92Un3EwVQsgILACDBEs1EFARbBQVZMQr0DejRAsA968nX5zpG1J42SNlOZAa8DaDcqAfDFAUABZyRqQgTMZowScDRiPVnMK45E3oQIwBYd+bliAGaMihGMzKm+xIHZjAwJqEoAgQIAR/MFcgZrGASEBbVUdeFTctqp2ogRBkAL83M3ofmrLYjcVP1ktP02BnpVuzQFkwnGIFl8S8APKHw4Ajq3bwKxD/YpUlsG5v6942H86+Ye2te6no8dgObyFZdtxAyAeKtU31usGGHQFgc0fWMT2r1+Jey/bha1nzKmjHVSQkLuU0vyfyPiS76kD4InyA81iglspAi29q2BFKD9XAA5GWRbIYCtvxzuTOI0NZSFCGtWWyk0eSHMzqTMdsoAdSxDlQqjLLy3zpmH3r2gdaNrCJwqs2HcNkEid3lnEaoqKVgooN0MEDCWwECwJDRRwiFaFCBYA9gJKf1U0aB4BNISWdM+jIiPWVoO2pS+u4wjIgpBv2qAsWAYKIECPg/R5qCUnfzopYRXkhP1U+o44AIXGgzIBCbLXiYg8IEO0Ivh/RDUqggcCLuaDK2XVdCdUpWBN7oklRFlABTAA8MGL+Zobvr6AXGgjGVseIsTz6w4h3fWudrndwr71yedSrMCx4tED38WTV7i/AgoOLmL7Q+/Grst2Ye6MbdGilf2NJZGfhzVADGaKAMHvyOOSsulp3QCD6KOyQMF27LxsFzZvmpOTN5NuCdMjmADdjxgs1CaefJ//sUx3wmoHsGT1iIK7PUlNF4iZ1gECQlldAKQDrBSsG+ZWt1n62GBGrT29uSYq9wwwkAijDrO0WA+0IJoyJ2BAAwEGMPX+BQcuFIgaqHDXoBBzUFcEUhyiCu6FHCy4dGc1cC3ULojUslC7sjLAALEjZcB51gaUtnO2AEAGxDQImAWMHfP+V/mCVcCDhJoITOzOKfNWhJqcsCIwKna/xc3AiZsh7pJqgQSoRiYfjhMrZxu0zfRdix4/e1ELbzHO8r2WwLbeOyRPXp+uZwvPr4hWSRZw7YEBgIUDC9j+wFXYedkubDljDkczNFCSY8GNBYIcM+1VBPcU1eV+wXoCBtTeKjZ/YAHbv74dX33HLmzZOJcwFyAKPEH6gGOKElUMWKBBnonWhuVSH66c5XjyqlSPHCR3lLGce+ZXxTqsF5YAMUHMLFpNByVMxxBE+ZyR+mjBVBJKlmbaNGWBJMJI5t8UDG4cgMibX9yn0cQ9K0fB0fpIFJonAquuCMHKYIGFJgMLRJ6hxE/oWoABIO9LrwMTAuw5QrDnebA1KKND0G+NvnbprMZkGAgIa9z3uwCAmfsdvt99JYmAJXDod55Kf7l+b3zjKwaIAVZWBK5cmj/v2FkHiBzrFkDBHpwFJq9BgjQuBwhtYMBYpbUD9FgACm6AQVq6zV26ar3ce5pm46/d91dTFojwX3xhATse3I6vvH0Xzt84h6NhAxm3niFAWaL8TZk/Mndy60EHrRtgwMpiABCeOLCAKz0o2LxpzjQ5ArGTwzYb9syWxWfYBg0AsLRMcJjUuTAxuw6IOZ406PvqIAN41rZnLqqcrWjuPFv+u70UMByhG/7KDHhH7dRftDTS/DoDBF0WAg0IBAwAMT/g5pk+jnbaMQeW1O/ad/5RuKOia0TAIPeoSsHCrJaFkJc4WQtAG0APmTEKC7jrbC3q/nX5ZrcErFZfJ/VWE136eonZC35JZ/CUOgCCA49iQRCA0Kg4BHEzRKuD3nXi3iPbH72XCOqW6mFABxx3Uktgm6W1ytLX5hkdKi13nQFlHpjTic4TV+p9AGJfTBtg8cA8rn7oSnz50ntx/satOBqsWzp/KruIgCrMR2XB8nPEWaqc9eCJg3vQtVrXDTAQtMYA5gUUvN2BghAMBoO5C2UTr6I2KNB5ZKBmMUaVxFjbGm8vgmOBhLtpWEFdaNrUIjWDVRkK4UYqP2X5rVPXjBcaTApIx8OyJOXzBSgHsA2xEmgLgSWkogbL4V4fLfm8os0uiWmbKIAFZ2XoBguWZQFA4i931xTTVT0qfdXFJBNBwUlyDhCK8QBsWwJyECD9t5L+FQogQPq3cQCNKkLNcg+oG8K0YtTsGLwAsbBdMYy7E/yUAQTpfwGnGiTk/Ki0dhK0YK0RY1lrnmEJn/yxfO1Y+TteZ773eFCf5i9UylYMlcjasRK58MQL87jmG1fizkt24tzTIygo9hU7UBkAgQII8Gvbn5oNAmHh4B5sf+AqnEGbinVaN8BAFIwFDQrOiKCgKzo5J0IchKgxuYS9BxcARP/kNHsur1OJWoPcElrDFlgXkJ4FZa+CAaRIpvGwAA5MYUOxHZbZOkfxs6J3i7ElQMAn6PlSAgRAPygQykFBLrDyr1NOu8Y6tNmBgQAY/MBqwJCDhZjuf1uAAYhCC3n/xx4b0vdpf6vf/m9uAQD6XQG6z7oAQGopKNexztox9ROvVmXWRO593nrgyuRecMAcrQci9AUggPVuBgSmX1JSVjLX3XVb8Fuafw7irPIsfnO8+UqJuqyfZviC+T5vSi5k0qAjB52zyIb3fONK3HHxTpx7uouJK/V9UrYClQIQam+dElNUVUX5uOuyXbj5f7+5WId1AwwaxEbvLIACK0Cs71z3RjHCvYcW8IGHdwAAlgz4prWmLgNeCxN0LNZ8YbXz9pdvldOZuf9WoGkH5K874DkVFlhl5AnxgWTk8X6fkEfdq6zlXapSBzDTYMD9jTG9faBgtSgXYvm4L/lrojSzBRgssGC5IuQegAQ0uPfEjiyNs04t9YTuIx17ASCxAOg2D7GydAGA0pLXAmLKbXCwXJo23AYHQOiURgCAAAQggARTSZEHB85loH8+x3z5vXaRCS8xgJ1Fy+UTQp05CjezkL5i1qIVICtHtqLqIvM501XPofLhixd/FW/YuDWAe6BfTuVWpxqMKVJwsPifXMzCvT6QsWu81g0wmH9hATseaIMCp9FxWIgN2AeTcWsRtIgjg9x3cBHXP7IDX3jrTvyrP7g4THQ9UUqD3Yeku4T9chZkvgjz90+Neubuesvv2meA6A3cMkgvrFpdlASRMJGoxbpK5T5u0WItMKHLG0JDGKe+J1RXlIwFVd7ULGZndpooe5+z9F/Nbgsk+XuAE1JdGm6sa3q9pJmXL6DLunAUqq89YICvizyjx9Lqxrqjb/vmVVm7twFAO1+57FlIgwJpuxxQ5O5TuOfSEdJzd30u+BrE+ZjMHwEIKr2Tv3QoFPIe/Y5cyIf5mqXr3RpAG6gB6TiW+ng5/EBoyLyy4hjrTDTn6zwfC2XsgsZdumi9ronSxpbdOe33D5UP55y2NVE8u+YwZaASxKiY/Dd3GA25QOGFF1zMws63x0D8LmSwboDBjgd89KYVUwAfyRwsBfZ+ZouIgf2HF3HDoztw20U7cc7pWwEoBlQw6ZUGswUC1O8+wW9pV0Aq6Icu3hJjbT+Xg4o25c8PJcoWqTCLRBtVDFjyWNqsBSC0CRzIwYM9bl14oc9a48J63Au8wufBATABOatVxe5348ZtAoArBwpcOvvfvu8rBRi0AOyoR1+9h1gXAGBJj0+yG0Ke7wZXmpH3Bfj1zbM+t8pyAUAKTFW6mm8uXwQEpbmYu2KADMgWQCp78KYtItF33Q/Qi+4YlcCwBb5llSnFZqTPZnVYJR4AtEGDxQ+s58XaFZ9TZfqLo9MUUGggYVnBkrc54yQANYYc80ifJ1Vkuz7ZrYTy892Ksglx7jTk4gpkdwvY7TBiBhYOLeCah67EPZfe6+SjL7RrxNYNMLjn0l3YvHFrjCOACH5O0FHJ9F4ytT95eBE3elDwhtO3BrNew+mkKQIBI22I5p8vYqAbufdpXSVhP0TzSrXiTCvJGMKs/EHzhUpdVEHQu2th2qKhuXvU0tzkGSvIDigseCjQkNV/qGFBeIaUIwfbgIFJFZwiINmuWCkFpUE8ma4iCKSR8Yj3XP4AHKSzqTxeXVQCDIEaTtq/lN2u887K6Gjn3ZQ64yeWKfQtWg4QyO+XwIA817ZquT+l4FqLuGM8u4Q/0D6foW9nRh7fEtPT9+u1r9f9StY8kK57IDW56/iVmhQolWcywFpSKvIgXSlbtpw64jCesv0UqkxtbUh4eGY1yMerA+MFYuN3X3xG3GCtnmWxIrjn9x6cx3u/uQN3XXovNm+ac/KR+0H9ugEGWzZtTTpUQIHV4Qj3DcGtEp48vIgbH9uBW9/iLAV6oJYVVFNY4MAwIGBZAGyfqlWWlS8tC4iL31r4TWJS10wCLVruHuHkEwh+2suBUxVFJuL25kaGEU26qWZXN8BSyEdYYpuJi6bvylak2tFnXRAED0RmxZF/AXAgAT56YgI/7jUw4RhYJydPTz3in4CS8ZvAj3FNYYxJ3ZeLIWChdzNbx21LmJtxHbO+c0YqvXM1QICUY8ValN1bUi+k6Ua9StTrTlS8q+9sDBH2mi/EtPg+vfbzNV9a7ys9C6AKZrZ0HPWaF5qqtS8vdsJdjy+H8eOIaMJY5VtOdT5nQfOlBEUEADNqEJamki8DCkpJDF0zUKGwKAd8LVJls85HKg0uUP59D+/AnZfsxOaNc4kLy02w8uCtG2DAaPvUNA1iRgVQ8IbTt3bbXXrKKiV3BV4BbUBgWQWGbMWaRQMYwhA0M5gFLPRRm0mwZxAcGIT7GFAEDBU5hlE1wJLXLmpyZvDab98TkOC+MIjinvMQ7GVpeGT/zsm65w0E7nf2l4SjMWGDpNdubkzUnNgANx9EkZnw6oCFIQFRuVWoi1Zd6BcC03IwcCJYAiR96FyxKMYW5IBdMrQBQWmrpoCBLiAgIECv93yd94GDoWSBfjQRBKTrXCqq1rtPqiSuyK9Z1x4ZJwBTzwOgLAvM5pZTQO84ifUTPlx7ZjCt4k3JN1V9oK0JysiRUq+ZKPvbla+nrN2H5vH+h69yoGDTnHuMbd5m0boBBqXDUoQqULLdTM3FOA7eBNMCBRYtg//1gYIkr2ElCM+GPN2gYDUAwaxMIgEKs9qAW0zCgwIfxu00hXgaXFW5QBs0HEBCVRGY5dO1DvE7QOCOPw4Agew95xZJlfJAxqHbJNs+YQ2m0jzB4lAT5KsRG2SO1rZ2iMoFNE48c5Txn4iW2AEWNFCoYpBBMr5VRSY4WM096LY2mSa2TcyOtJsJ6I8LkOdLMSqluIBc8OfzQb/Tqqem0pwQxp3HHeRUOhujCxD0gYE8DdDrfwXrGm5dy4ccK4q7vaoqlleB3FZNnzcMvwIJjgRURIAQysvWgMTlkI/VqeH5Abk1I1tO9X1NMU8amwBVDhB3nkg1zWFfrfVC6U+idJ7tO7SA9z98Fe64WIMCV1eZV01PddYNMBg6V8mv9oo5LEIojW7fEQ8KLuoABa6IngrZj0iyjlh3A5Y+QJUDB7WfwKtJApJyWgkoCPcSBtJ+d2nbUjDpC5Mg9ZspMBMBCQ1LekyDmBgrd28KxximFYVI/7qBv0bnGMZjgFMBoE8ZC3wrX6k55WkFARwUBlaCIklzgAFwJ+ptYAC1e840JxOwAZQElOVgYQIbSNb+uaRDpM5NHKOQtowp2gcGLCAwKwjIrQA5ANDav9b883GXZwIo0HXuG/+c1NwLn3r2TFsEV0NpfmVMs4tcBigoAQK9prvWc99abjc6pYrIfTuiin3hwIED+mELvs+T32+VtxL7fYFyF5Km0pbdYi2GVq8nnzVPhTftO+y21N9xsQs0tGiIOFk3wEBTy1rgEVLFXiBm4ECQ+b5Di/igDzQ8twsUoGyK6fQxcZqsJ5OYpRJzlkfKwZwl5i2W+5lJTMoVARHuZ5YEjuBAtMFKq0dSB0HqeaOU5aGq/MIlvaC1cDf6wSBhMhEgRMERy3OMBFVkEEAY3E5wMMuRZzkoiMcEZ2CAYrFRm7S1WotY6os4Z6W7Go59HYQH4qLOd9ZMagIrwMC1HYQ2BCwkVgW0gYLW0AItAxkMsQhYQGAWENDapaLGV96jxzik6ToJQFjxWMvvOJ6iJSNMZRdZTpwBBAEHWrvooWMFCmY9nyOJIQqxA/6vZ6RWPNGw+5TcnwVA2veR3ffXydyMcyqk5Y3umA99Zvyu+/m8lfwWKMhZnrZw9NG6BAYh0EQxuQAOQI7ZUroA9/ktibdftBPnboygoISuSt2rTVGtRylF/aQy6ch1wPuYQSGC3W1tcyYtZwLmMHgcTJ++3YVo9jbDl8XphbFnHgGtCzoPaF2DAOs+JSABSDX/kDCALGbSztNdljMXx8UPoL3vHOobAhkoqCkFBBoMuDyRGaXCZrjAEGJlRUi+GeBKSawIDA8cPMPPLQ0CGCZEvgyaCSxMZH4QF60KAKVuiKHAS9EscQF95wb0gQAL5PksrXFFlmflYxsLYG/LlbFlj1njWSsGf4LT/jU4kG2wNbvIeiKEczFkYFzwbXy2RHq9ltI1yO87kKi0dlcTDKw2EEjz+GtlXcrbnfRA1h3meQZGWonyvPk8k3ksdSNy7oMPPHxVAgooecaoQcecWJfAQChZxABImCghWYB7Di/iukd34IsX78R5G+dSH3BhRKse2MdsT4Z8h5eSBwlomAR/lfurI9gds6bWrgW95c090x2g1mb4hClFzbDxakzj7zUMoM5dCl6Lb92PWmWTdeJQH2XCVAxm4WIKYrqOM6hArd0Kk7oKQqUmwqRywmTiV1EFLxzktxcwWnBUZH+i2NUBZe2yp62c/PZAjdObjQIIwbKQpeWAYXlgwbmvNFhwwY+MKcW8wjy6zsSwKN97HtJXyRJQsvRYwC5hskZaaTzz3xZlw+fHU8ZH3AZ+7IiC4athbgEENM56wN660JDjEUsNMPF+hilcvAzqCkvTxrnPPDhg9o3ya9mZKyLYlzQL5Lu68yBMn4P1Y2ERWC4IkOfTPP56CAjQgEeXiZRKgHEokMxlixXDooHt/kOLSaAhqXyd1JFlXQKD8sBEoUdwwWn7Di5GpLVxzrsa4oG9elC0POs63c2/wD/DZnruL/SGgfgeSoVFA2cujsgmDp4w64l6l7X1zeUhf99fK+1QlzH1lWVvhi8FMDZNNHdGwBAb2/g9MqWdDEOoi5nkWxndNsZoKZhQ3JVQwzFTES6T2rGbKCiilaCiKEi05UBARhAeWtBArzWOiE21t+9zp/qTtWGOVVFndXLBC3cvJAADIHhAt1ywABhR7xUh3yGjQYMflEA6NsZaL0PMuMl3G6QXlgEC3NwoC//eMWxmH0PSjaIqtMsPhxrDCAoYjCmR+60AAlUI1gNXB3fLWRoJ8KGq3PhC68oF1TFjqQImYEyZghvIuRG80K+4B+SH1vRSaiHwf2fYgqy77Hi6A5LWSb3bSYlsyad0SVec5ZRVwANg4xFJ0l883XfIbUm865Kd2HLGNrNemoay3nUFDKhjQHUC+8wLBxdw7Td34O63uU9bumf9YklVuFZhk47eT/yvRj7O6qhNje5aWSz831rlS/yVkAh2VUFGsvUNsE89AwBW2rzF9F0eDvfj85TdQ/Jsfh5CHzCwgiHzYKI+k2JuIegDBAiCPhf+KSCoA3BwvxOB41rtODJzFBr+WgQLJYNWXp2JMAHivmuqABLtuAKLoPGgQQQNI/qsm6oNFpjFf9wNFpjjzogN0kQMm0dC/cDA3zO0NRkfYDYQULLolEGcH6PGj50GczOOnW9EmLX52IUGU+Xa6sdQvp8gIEEAgHZ1gh2wmRJCmpzNPwWwoa4wAbCEBlQ5hWAJAMG7E4j9WRnw7qPUMhjWqa9yDgiGrE+gBAxmF/xDhb50r8vTnkchT+tHaikKtyn9q/PpsvP8RvEmDQmQTGSLrgelSXsPLeJaf3jRnA80tEpPFEwRET3TeN0Ag9wk6NLSiahp4YV4TOSWTfGDEiKIq4QXtHux7nAlDDrkRRcZOEmoeFiIdXbL1ceum3WOg/x2wWnuZQ0ccNAFyyM246fOD9oAKXhIn1d1yUBEXtc+6mIorYONIAI/ugxE4Ay1EAggEG2zqrRQycCAAgIkkhU5OGhbEPoaTFBWBBEwAAjuN3sBI4LGBgzeItCIX5tmAgsAEusCfJlhHnntUn72Nsu4KAUEuuvZQUA6Vr5mOQCwxgtY8ZgBbswExLm0OF6kgAJT5YSp/xss/o3rX2raAAFwDF7S5PhtBkAeIEyJMakYSw1jSvHci4kHCRuAsHMBYhmEfejZwCYDaK9NYPU0/b4DpYBuYa9/l4R9QSb7ZzpuIgruLhqQJZEtVt0qALsPLuCabzj5tbXDUiAubVnHUnTJRS60boBBHiCUTICsExYPLODqh7Zj5zt2YatHWi1AQEroBmYYaTJkFqhyc0qCFI1FmKclqC+5R8nNWT+PmoOJDZVoEpS+Myu0G0igF0i4PGkj+7ZlplpCm6mUTNCOQXfHEIggCSBAhAwZ1oFmGoVLMzWBAGmLgbRLCZy0F0ukGGAAB6TUGgcImCpEi0IbMHA1AYhQZ2Ahfm3UBgvuWlkgZGyhmxSBQ2hVR7MsTaxrN4AVD5BfBzAXekyBtmapE7BRZtkJ5hJgWWPlflaRoYulwI8TZUDBNaL2eSpQVQNw1i0XaOgDfTlstnEBwnBm/2BtaFjFKMAdnMWESZ27hFy98uORXXNjG2dZi67JFhBI884q6Iea8mcV8l0CvirfMl0CljDOqSccrUWTijrfvfvgAt7tP4i09Yy5XrTR+OdZLVwtL806zFjnE5aqbELkpkN/iYUDC7jqge2477JdmFOfnqwRl35cH1TkB0M+FSrllqjLfWeChezailPQheWWjnhF7d1lBriwgEVLENQKSGSgoQ9M5HW0vvpoUa+vkJBoDloDzQMKrfgB7S4IC4inbUHTNBAho9ODcGF/jIjWPmcSOqmw8Q12TwgYgNdA5Z4BCAhHTbBQGZYFCyxIDTVgiLVWcylrRnJIUj7JhalTctkCAHlaEQQ03SDAHB8gGaOWdWAFY5RYeRD7PQEK1cSlNQ248qChWYpjQ7XbDUA+bIDd+HDlhLnbzeC3k1bxADcBCdolBG/hEZcQ0Ab1wMrWINAv5HMB3wcUs2JWTajnAj0X3hZ374oVGAIQ+kjqsCGXLepy98EFXP3gduzy8iu7HSgsSUIwJzUUc/dVd90Ag9xCYAUTzR+Yx/avX4ld7/gqtm3aDPA0MlUg5AwxfjGptVw2zAoDM+pffu7FxSDvQnoKFjzrzPKaJy0mZWhwkT9HSf525LwS+lkZAVTUul6FOrZqmJKe2DmT6TNBk7qXWAdguwuombatAzyNwqSZmmAg1UZdIBhP5TD2xv/pDmQLbay0oAGqulbnvithkwCGo8PBQlVHs7blhlDjLAAByEGCIx18mpOtBUZBoC18vfEAjYzBABDQMS4AVnVsqK79ZLTHJVh3ming+524Asg/59NB7m9tuBkqwLuAEH6zF/oNEbpcQvBp2joIYKY1J+OV/y4Jd/2sDp4D2uBQP5+/pzN+LHsuf9YqI81sJw/dibsK2ABAdCXk5S0cWMCOrztQsO2MrYgal5qnIfA1jimRAgjoXp9C6wYYaEAg15qZzL+wgO0P7sB9l34F2zZuBqbq228KHCQRxUCYWXkXVpx9v/MYUW2OHfXOwq5FzsbDVv62VSE+Z8czOOCQWwwsN0YLWGSVmPXM/S5mY+1JD8DAApFiHWgJHwEACiwwuzwtoTNtCZumEfeDRLorx0qP6ZYT7li73qmqyAj87wgYBoAFZcIuuSGSuAXyqEmNMSsGBHQAWUWlrZxJSUPiAbQ7YFZwxk0U/Pr3DGOSuBgrbxusKvC0CuMBKKDgBX8CEphjP1dN7Hf2sQUBJMh41KiqoASiYWexSYBBqH50D8Gw7Cx7jbniYno+noZQL8V/WXny8vU7OrIU04bcizRbn6waKeFecfZRa2bMH1jE9gd3YNfbd+KC08+N8qsFCqbKWuWsiSTrVRicnzxd/bF+gIFCWYmG0Uyx8MI8tj/0Ltz3ti9j28bzU1AAQHu+Hd8zBH6ORFtlnFjUGvSBICbZLpc8n4ERw9KSCoz0r+WeyYGDtmTk4KVPd+vTREMeCwhon7QSRCTCxtJERcjnVgUlfFgEjuQP5QFopkoYZa3Lt8PlY1cpMAB4rZNagGEIWCAIQIj3NFhwXZYChtC3/re5xbKLrK1/mZulFBDYit0wQVkBBOQAIDyP1RmLqnashAhc1RG4+YP2qfbCHgIEaoRzBahy9Q9uBjmlrlIWBAJomoC22lsSxLoT40Gk2/wMNy073WPVZV4311aWqRsACmmtIe97NrP1bRctlneSUJAtvv3zB3dj+zfe7eTXaecY8kvIzw1MwzpmYqCq4WE/GH5cIvMzaf0AA/83Z/QLLzyBKx66GvddchcuOP08YCpflZ9F1CiSBTHNv06fUZ+2YdLaT+T2XOnuByEOQgSReSa2xkyIBBtjEg3g8kiZuvzemmfVM363NFIBAuratA5ogZMDAtFWhwACAQNKU3V/DG21i7x2ylDggKqWkApgIQMKLVN3o7VYBMGl3RBQQECDhkSPLMwJAOmWPyBbH6nwd/kzAJCZ/ocAAS71P6DGUfU/MPMYkD9WlJsKwBJQ+X5m9pGBNZhzgFCDwaBpE1wJALtz0f04EnMK4HgKUN3a1SDjEYGCG4f2GqJVXENCmSnbmyySNRWyts3e3XNC09rzxTYtw1o8MBCBlqLgnz+0B1c8/F587eI7HShouuROBaBRIJ5B5O1CufUAnbhg/QADS/ObP7CAKx66Gl+7+C5sO/38hNkD3chTa0Em5QM0EJ32ot1ZAMWsiHhZYMUgY4JT3l9iZtXpJFu4olBJ83aAi6z83vGRVybMSTEwdT1oV0Gfn3ppaSZA0NJcQxW1KbugrXpQSlWNYHUU1wCQuBiCoKpiTILWZotWBV9m4oaQOuRBHckYKzBgjEfS/7qN0Wyk2p31dcjfYQ2Q/ltJv+t6hcZU6mcdxoAb6ffG9WXjgUbV9AIETCYuboUYoKYNEIiiZYYITFO0djVIn2frRa/HZa0V3QddQj30YVfe7F5efpJ27HhUd/4ZBH1P2UP7u/jOxgGD+UN78c5H3o+vvfVL2HbaOZ1WavZuhLBWuQKIwVXtLJ1iPRBg2WMtWjfAIAh9oA0KNgoosJiMTQRDc9DMIR+k5Qr8roVQLHP2ZzoByWosxpa2qGI1LLdDS/CTCQjS6G6keQGYyF3K7NNAWgBBC3sMAgSWgBoECpQAC0JJj9HU0lzTNK5VfqqA6VIEC8HGWys/W5UABQAtq4LLllsWgBwwuGeNsZG8RbKFg70jQNZzOy6gZQ0A0r7W1gANFoAZ+lvTFKh9n4nghgcJ4o6A8OXK7TSQnlB5oHABlpaC9QBMbYAg3w0Xhi6MXwCbq4ANthUl9801YWj2qn+KuzVycKABXVZGUo71ruVSh5AuCuhOwV0or+uZ3M1cSB9aJjVTzB/ag3c+dj3uf8vt2Hbq6zvcB/Iqloc9mGcANaiZ+jUOH15QATzF/MHFzv5fN8CA1GKfP7CIK77xbg8Kzm2DAq0FauKM0bZIM5IOk84sAt3IO2gBme9I87VMdUAMhussBxgakW0RVdnmNA2oaiWQXEqaJxM6UfMpAwrrPTEtW5xdY96ntSb3CoFsXTSUMaoxsnaQBFpaUkFcXjgJWJD+Y45AQXzglkUBCH5s9r9ZAQor8p6AxF3Qp4WkpIzcIrCtXQHsTaGGNSDk6bEItIBAtgY6+xiIfTyN4CArIJ1nQfKXiRt3QqHzENQ+aryBc834dmuAAESQoOsk6WmF2/VrV6B9vyTg+wCCAnBAxmOyOb9SvlK4kV76MdK9wC0hbczVAfzDBBvmWA8BGHZfzB/YjcsfvwH3v/k2bDv1DS3Xo/lOkjko382N9SDUbg1418L8wT3Y/tC7cAY22XXEOgIGwijmDyzgim9eo0BB5gvWgED7LHPKI0M9zR95snx/qLbea1LrFvBdwl0vPM7L7vKl5nUyfK1DF7XTnLKJq/ziiTYLpBotFLAQrRUFIFFwVyT5NOWLqQtc9WiyFugqEVWVF27qnaJFMjstv/HaIDdO+PgxdgeTlN+l7xGREnpOiCXabRCUqv8ToLBkBjZqsBDem42Zbmsf5fOIpU+s+dkVIJgDAcmPlYMBoUQACyjQ1gKXKembxDIzkIg5AQcAMoAAOJDg7rQLmH1uu/fmgDfmSwS9Bmu6DNX/LOUIZTxkFlBg9R1XGSjLhb3mMZXFE4yyC6AivLNlDcjeKSb8Qp1aQELkRgdwvPzx63H/m2/FttNeX5RDabp3LTFH61Ilo+jqR6jBaDB/YDeu+OY1uO9tX8ZN93+iWIf1Awy4wfzB3R4U3NkGBcEsrEzD/jnAEN4GPXHkKVz+Rx9y+UtBIIO0+7a2FO4YaJthLKohAt9YpGmgldYabHDRCyAKlOQqLOBcC41fR4pCiTWIKGiwBNG6ClYIqw5oL1pzDnSZSfWzVRX7Teom0eQQMx4AVCBSmrDPR/XEv8uDhEkV5yawPMEmGm5g6rq+3k9O5N4nTHfq2zJFGSwAqSAEIkjrr5VuRHqt5poFApJ0nT8HAyo/gAFuAketA2zqTNgjAwNAOpc9IAjXOXBQ/VcGDVpAK4CQtak1dxvD1GzFcuTuGXUvuMIA5woZCM7S9OXxi7QxWaCkmmtFoW71bQLW4tgmACMrj5UluPSuBDxwk4IHzoBE/jldqUuHvLn/ws/hglNe1xNoiNSV5xYsgiuJWVkOHDhYOLgPVzz8Xtx3ibjXXwauhIUDu3HFwwIKzjNAgcX0m1QYdHTUE0eewuXf+jDuf9Nv4Ccevrqw6JLCk6sSAMi1ei4I6xULf+sdGWN197r9r6ZA6ovkzoQIaSFiaGE8gOlyxnT7zN4AWsAhxJH0BQsl4+tEvDu/nh0gSYrxAKGCq6/0NRFIztv2vnCq4e77+sq4BAGlBd5EFrwCC0IlwZebvaUdVIVxDoLOg4TwPh9QByBG3gewALhP9CBlrMulDtBbBKlDAzaBxAIjFPrYcg3kgYbuAffXArVdYAAoAlqgPDdDW6gy+RKxsU5dgr8/g3smj9GQewWLTJc1psUfZuQNQMFKA7h5CzUmLnNSTgC1Pr+7l4GG6VIChBOlQVvFuABCCiZ9N5ZqXhIZmn2SxeQ9F5z6+n5ARRRkF1vlKLcUA5g/tB/vfORaFYjfXf66AQbvfPg9+NrFd5RBgRFIFqjHtP/Ei0/h8m99BPe/6ddxwalnm3mGAgHrHS3zHNAPBLq0K1XWckytnL9jCFgBoknYoCRKGrBNfWJyr+qEcbPW1sjtEc8ZsxVQFwTutIchB7RtVdyDkhY4EGbOwGTix1uYjerb2o9BsCI0QD1RZySg3c9Z8BzVkzRfPrdmiajOiJtpZLQFM3CYKwosCBGvHBiYJmZLoFgxM3m+Ul9M7HQqWD5MLTUHATrfjEAAcPPHi3G7ztKWnpglCwQEK6M2+Q8N1MyFvygxynJg+r2XyQ8AJPwgmc/TtJ+JCAxvHdFKRSnwFmqOkrbqedCbW8EUQNYWP0AB+CT4VLU3n4ott0TWCShYDrqs12FO+HEMSkSj+JRUyv1eOLQH73z0A1E+ohGNtPiadQMMvvbWOyISGuAWGEopKDgnu1swK2ttZmA8QMtnB8wOCJYJBopAIKsTW0zA6OtWmxF7Kl8sOaoXhhGi5I/6e3LACxDAggCFAALygDp1nzll1rzkFlIECKFC7brlvw2K/mF4IRTnQGUxbqOPCWj51l2x3cCvBRySii3DlCvP5aZ1M+5Eg4YZaeiZAVIfg1qC3cxkCN6S4A/3DQAwIDambAkAoM8SmAXMtYBpTwyABQa6dscIELBAgJ6nOR8o8ABr/YemSL/k7ZNYF0kKAlABaiBaBqtaBd8a8TSmq6yJ4ylVD1Wo1M4RBfZ0mnYVekEcrHyVGiPhYRInJMGQzNl8SEwHxT5bCc0f3ofLH73ObXk8/dzB/GDdAAPnM4kTtBgzQHFQ4QN7KN++5gf2iSP7O0ABAOU5LpEzNc/AAC0yNSVDWAwqqwMU9AGCHIBoK0N2Fm4x0GhJTNjavOdN+3UdzWKeUZD6zaKxMUfNofbH+ioQQFyBmwrWXvK4XcyP/9QAK1IHIAiDYVp55jLRpj5moJqEvqMhJl9/TSXXjwcOSf6SdQeFMZlFOHfRapVToq6tX5Y5OndxdAl9XYYl/CnL3+cG8CbvxK+9DKtOEhjYAgf+lgUKSoBgFjCQA4HC2tfrvi+40MXhaB9+3FFDdR3Pb0DEAyRFaiVYqKrBnhck8TSz9LW35rldIrk53gDHM5LJW9q5BmTJ53PGl6hy5ah884f34fLHrsf9F30R204/N3Vb9/TRugEG/eaXTPhLJwo44CaZBE8cftKDgt/ABadZoACxnFgJ978cMuLfFRjIdJqYB2UicqPMQNrkLJHr2ufs00KwW+LHrkK5rJ/P+4Ib0++alJXTjIyhj0lopB3mqi8rAQoCBqaNAwns6sjeIkDTqTfXey3BR0fLkDPQsZc8XxxqYWV7xcP95LqbEqhkaHxABF1Uq/nJclhtj2aoyu0CD4Gs2JN6AmvtDIogX00wMEDzNwP2+gLQdNlDNH5/PWQ3TNOaHypfK30gqSBBDkqMgATtWjCA+DJBgQUIEmWAm9Z6n2WtW+TWfVzvPPXgQAS7dr/lZKVnsUpp/kKwsyrLdA1lz1vuz0EBigBm2i5ZmjelHQ8CClS++SP7PSj4gncfZMRNp067foBBiYgi4hZwQHVgwC4CTC1CuC2Jl3/rwy46tAQKqto2y2hXgfigpSrav6h4apwH2gcmSUpYCyP2EezhcBUdEZ8BwvB2FfkeqlgjRLwnkboZSqcmInjdhY4iw9CR+UmUfgdppC6/XVnCLOI7ufIahMZ5XeCgLi2w9Hjg8keHkKS7y2E7Hrob3Q5cDZYbYxeEBR5I3Td9zENcFUJG0GoIjEzqnV1nQZPLoaF703Nh0Bl5PrOpv0/oZ2N+TOdCpeaAwNrInwJA4Madf+SDXyP7ssdiZlCgXQ6rAArIAG1aAfAJ7q92K1o7l1S8gbmFtCtQVJd/DMFA5/keluAnsn+H8koWsPbcTUHB+e13DaB1CwxCwFpiCaj8OvNpTCkjptp16r/9Kb+P9A1tUCWMnKoUpAW11wjk8s/FyeIC1txzMWjNCUKtFfiFV6cTOmHYYp2oUg0xRrg3ybPcNO2od7GYAMDUfcAllOXfH9sn2oQDLOS31VHt61/VShMYBg5CU3Lmr1H3LNHvOjgxFJYGJHaDgjoVApTd98+H++6l6bu6qAUoM2uWAAFtSvYBZSGINuTjYCIcBBwKFochwCF5LtxrygBsKJWEPjC74PfXgwR/Dv7kHdTOP2jck/QCJWPfMe4haFrxKO+AItjgQAfI5daccJZG3zdehJRCkrtDcwWgj0puQ58Q34fZwYC1a2RmIGBtX9QC30ozQEArkLRg/o/P2X3HVUksFwCGmrPzR56M7gNvKViOG2vdAgMAgPj6wrUABa/t64AxVJg/tA+XP/5B3P+Tn8e2095QKtT/yU1FRucnMQ/ZoAahrPJlQWvE7huDrbMNasWgtSnYR7w760B8hwYLiSboNUvyz7jyVOSx5X+s4czWvh7aD+nK8QLGiEOITVBtNL6najEN0otcmIfsXvBxBom24PNQVbk+UQGKVE8iKMg/heuvW2AgCAktLDKBouuM/gXZdzZ9sBRoMKC+5xDKsIBDZnEgboB6Q7gnrGymQ2yAeMKoqmdCQ9wLltugsGVsucKfAWX5yQR9JvgToS9j7OvE+bhaVgLMONbZODtLQLQScPGbHQRwBQ4nuZLPP1XuyqUUo2IpHq7lP7DjwDvcWtbWQo83RJfgCtAWQgH/rvlKMC5jPQNI17TcV8I72VLaZxGYFQTMCAAGBZF2CX5zfhRAJBnrIy8/+e3Knj+8PwQazm08P61jl6vdoPUHDPKBygW43l7lVwdx40DBH3rzy+nnZXk0+efDgTQZ6bRgPWjAoR5tS4JYDPIzFQKWSCZVxKVdR5AO1Qgp37vs65EfLevwSxQewQzp85A8nwkTmkQBF7qlZEXINcccDMjvEiAQbUFAwWRDSEc9ifcmE7DXErkSpkMxgFGnKzDACiyAfDCkBgdJZEBrpDvJNjwqgCDjxaqPWYS+DRy6QUMsm7hxgZGABw0DAyNVWqAhFqKOwMBBAX7+Hne5fSzhXxL8iauoUkChPLb57z6i7G94OhH8euy4DRJk/TE5d5m4Fxq4fBMKoJwpjgNVDF46CsIkBtp5F2uwFoqlsJkGZOCUqAj83XjIDhhVvm5oaQ3rexkAcPk6LAF9AGAZMSShfgXBL5QK91J6LvQ7rAZZOWZaV+BsoR7zh/fHLYmbNrfL12hxAEhYP8CgZeIDbPNuLrgbPHFoP975mOvUBGkpod0iS+spxhxU5Tx6a2PIn6SoclTVNbNXTD3cnyXqHUjjGDwQyA88If2sgAG9FVKBBncrgo9AQ10LhjlZgwFAaQ9dgKByFoLoOqjKgKCqW5aDIDjCPQrPMRD+BaWJo5c3cR/3bBPSh7oE/qatUwRUlAoYEVUmcGiBhg5Lg+GeEDCRgwYgzi1Xjpq/M+y+6fXH5sI/pBlm/y7NvwTowvPpWALZeDbJn1UYU2kiufGkWrEsRgIUGrEMcBDiAfB5wY5mCq4FIEwdOBBLX+VP8tNAYOmocy9aAAFAEnsABEUi4RF9VFq7wGxugJLwHyj4Ow+Pkvml3l/249vPxmvquR4IDjxxvcHIX37n/KF9eOcj78PXLrkL2zZujvcSJVWPnfRTsQrrCRhIK1OA0DIFCnkmN39wL654+H342sV3p0iLGUWTDgqDJ8+10toLaubvJ2T3E6bUASYCc5f6agGi8pdMyoFZKOBAimkEX6OyFsS0tr+695Q6oUSLLDAQ7WoQ5kEETDZ4xhMtBIATHJ2AoKoBVPEMBSLn71MCRHTtxkuSBqKIM7QsiZZhVr2cUsNO2CPLEdiYxI8ooaLvEXnwkAEHCzRowBD/dgMGSJsy14T7k1mkgOiq6CNKj7zt3P0R1u8yAUAfkGvSMZQW5kBg+DiqZsraUuOoWxjHEH78CEQ1KoKLHJEA52bJV7ACNY2zDlBVBAhO668AsNPYxQLIEQiE+KHcSthMgYniBytZr9JowA4EHCL4e11I0puqDsL7Yc0tlT9Pn1XAd7kOSs9Y5eZUFWRL633kPgPgjzmeO2PO3AkV4thkMlPHOHpaP8Cg5BcsAQPU7iuM37wG9136Fcxt2mwEGpaZXDlAxL+u9KzJYIyBGgAwOsGFusf6npknBrA5pqAmlAEgEuuDcQBKCxDoGAegP9odaI+X5TvUTEfiB/LdBkkMgXIPhFMURfDXbetAVcMCA1MFBHJgIHkAoEEEC0M1zVTL9H0uXRAYIFARB+Hi8kbgkIOGqlomYJCKc7QW5YFyrd0Us5AW+kCbsSfmf9cDywIAjTFOuimFsUO8XOb4+b/gbOxknNrjVhMwzUGCbFHmBkzeikC1izWoJu67LbJDidm5Ldnlxwb3u5J1W08iSBAg4BtIap26Tmi0CF3+OtWdURL6Ssvv+2AaJ+81+Hs+r6y69gn1wQJ9uOBvb1nMn/NWjNqQLcaz8wf3BPm17Yyt1gvdH1nbRJ6nZ31n0LoBBqzNVyWzodyD+wrj9oeuxq7LdjmkVULDBSbAk++LrzOebYOMDpRWBBH2MwHN95bTDxysb0VwSwhEgMBgYAOCsKDsfhFAACkg0O+dxS/dq0HIuBtgoOQuMGIL2AuYqbIMNE0UKuF3uO+uRfhojTN0ozS3s6Eul6XfEHFqPSByadJFoBZosAQPiFB5E7YGC8GtALQAg7M8qcYUIupDna01UWK+hrWvBe57XACzAjct+IeMmWpxJ0m4cxwzaR6rMXP3o6XAX1eufu43uU/fEFATgaqJq01Vu7ggbgD2vz0QcId7iQuijgLBByvKAVvUE3RK1notNjjju+Fn26zf+4VUynovtxjpdyhBaVoFWoK0x/wfyioISwsodJTT+UzyvnaeljXaAgW5/EoKkLXp+17iUiRmpQ+gYB0BA2RMwwoMk85bOLCA7V+/Evdetgtbz5hDA4CKkaDmz+h3hSGgExqoSQ0ICLGtDZGKpz12WBZY3U+sHB0CIMQaaKGQaY7MDbAhFRrl77kntekgwwRI1N5zroGANj+H9LJ1IAh+dv9EaEx9tRvmRLg0SqiIQAn6thIuU9kDrvp42tHeWrVVtNBatnqF/1gJfQccUuHDCiy4awss9FkX3B8HGuIY9oylAXZbTLcA2ktrd6gVwAIBOQBojZVvqtR6tcYrfFwv9DkrS4Ebj8r3/5TZ/SYCNYzal9X48as8oIN8rEeO+m2mAFcOjHsrQiv4NDTY2b+SszFa7kXfGZ3UPZa5sJZgX50nGePcSqTzIb8Gii7iPvN/eL5HaA8QnkMEv884MJ+natLZ+wsHFrD9gasS+ZW8hfxsJJnD5OdHBAgu38vAYhA/oNM2KwKRqcy/sIAdD2zHznfswuZNc0EjnHXsjvqCLd9iSgML9sCkM/cMdezKWsDKcTLmTB9IBALLvRxQaDCRCQxS2qhVfkuQDDFbWtqEaYbOTNBZICErwdM0HdaBJhUyU2YTDAgQcIAiHig1DaDJX7dbqDsAQBr6Ks10GqT/DXKWgEp9KZ40YOAABKqKZgQLcCmEYGGA5Mnq6X42VqpNhs9XxkB+p6b92UBA0wwDazJGUv18jIC+cXJUi66rxuiod/VbY1QxvELixqf2VoOGZaz87sEq5hUrggNvqRUhrEcVsMjiErLcQX2uoFnWn/6thLp99kMOBCzrgAKIpfLdRauKXfNuoIrWS6tVjlB2mnyQLRYtHlDya6OXXxnFWCOvSFANEKcAoYfWDTCwtIzcN7x4YAE7HtyOr7x9F87fOIfpLAA5I3n2GJ8Qf8yoDRZdguu9Wie59CpN0n89K3UJivnMBiaAxFg7xGedNMJgODP4obV1gBHN0dGV0LYONNwNBgQ4TAHIXu8oePx1fysDOAiaqP/r9oyLZhkBQx0YghJGBFQNezN1v2UB6r4ABp0We5zCzaojWDcnvYsDSIP98iDAVloGArQloGnKIA2AOTbAysfnKNLxOQp3emfdAEvEqImwxGWQwArANQRUTbQi1OTH0oMdYj8uYl3QOxtqRmLdyeNHpJOTXtdpHWStN5XeK+Rbwj2xSbX+JgJT3SyJtSFNOBloWmjg4oEFXP3QdtxzqZdfGklDDY+kUQQJVbCkigWB0aU+rhtgIKZgACnT9xrgwgvSqffi/I1bnUl4Ba9bWuEsXOkk7tsqtVKiDDnoyzx0xeUVgVFHllFFi4oGEl1gIryvZZoujJbSIErxJBYQEAZjaaF97gILECxxY4IBLWi0ENJIv28oj4YmuYx1aB4lQskBgzJYCAJpCsc0ILEIMNwQ7n0pYHBp1m4J18vDKRdNUWbZAEDSOt0BAwAaEMdFC/8IDFQdB8pKOUuwJgcGltj3PxOW4MZgqUILJEyoQl05BaaBAAQ3W5k9QAB8InuXgrf6sFgc7J0Ncb3GNRbAObL1NWBtuWp0afC21QdQlh+9bozxBlKh3zJiHGeeN/vzq1MPS7bsPriAax66Enddei/O27S1lcfNE89bZX36NLE+kdgQvAWha8GuG2Ag5mD3O2X+Cy/M4+rQqXNBG4TKPytNc/uPQQOypNSTvylk6Hps+WtJCelsAgVNJ/yKk0wDgS4BUgQTklfzIKN2Oi1vor62zNFAGwxYJmkrfmDqX2AJoKWGi4BAgwHmdCwbNVGsOZO7q6Y+oWoiaKjJfaVewMKS0l5dGpeBglIeUrCggukswJANRL9bLZKlDVoAwKUbIMBn7AMCpbEAxCqUvrvJBqBvPCp1wUwxqM8raDX8nJm6T+5SRWBmTEBYqhrwNI6DAIS6AyA05OIQAijIQMIUiEAB7qIiILgqC2tZDUNCrbUkiQUh3wX42Con5Oc8KXl+JdQlsNNb8WVVH8ydEQTMsjZy2bLn4AKu/eYO3HHJTpx3+tbkvhSrQSLYg3z2L3aoIDsHhdDViHUDDGQi5trg4oEICjZvnEujyYUGTj7NzJcGSv2+XKWJb6FjC9e3UfWwOizHWpKH2iQH3VD6N4AABQ5SQMFhsrqy2vmTcvJ3d6z2vO8sZjXUNN2nlS4F7TMVRJaFQAMCEUAyjZoODphPtSpcc+i/aUWuXxvGkgcKALAUzNmUmrgLQAFAAha0zSUdY26NcbzXTbqpWvjre9Ln4Z7qd3muz2XTBwTyMXC/+9e15K+I0EzjGKBy9azgBHTdsANx3oLg0jywaxjkAcCkcuZj6fcpuTGW+xogECQOwVkRhO8DEcy5Ovi0rO4rXTc6XyLoC+BO57fGOH+X0GrwJ6CgVOTgyMhUGVwz6TvW6d31kjEbSktNBN57Dy3g2od34I6Ld+K80+faG0U0SIWbUyFgFT4+iuRETASeq4bMrvOxNs8cD6LTiPH+ta7FSCONNNJII50cdOYDZ+G55541Yc26sRj8l5/+bwlC/fyzt+LjT9yET267Be8/6wZTM3R503L6gFID4FW3/j187V88jOsf2YHb37oT523capYVyzTSCmULPXl4ETc+ugO3vmUn3nD61lb+vu1U2m1oRVo/9eJu3PStq3HLm+7B607dAs4g7XQAYKwzqKw/oPL8i7vxM9+6Gr/6pntw9mlbVMCcfzbTUAGEIDkgD3iLf0Ur2HdoEdc9sgNfvHgnzt8016pbPtu7TJS5yVrKv+HRHbj1op045/StLUsBYAeylfzXuRvh29/dg08tvhc/e/4deM0PuhM3tbZachtp0ubOivRv4Dvf3YNP77kWP7f1Tpz1g1tUtHwcJ9kO59IpuCGAGJ8ApGMG/8xThxfxocevwm/5+Wlylx47tU7K5/P+Iwv4yONX49cuvAevP21r6GNgePxGl4XmO9/dg8/sdf3/6h/YvOL+FpdC5d1j335pD35h8Rp86oK7cPYpW1x3qDiQwQGj/h3i3oH/+6Sa/+dtnAvpmvrWgPSR+6vnHrDv4CKuf3QHbrtoJ871/KdvW6fe3QF078J55vAibv7jd+Ozb/wyXnfqlhXzGyDdwfPsi7vx09+6Gr/u54/Lr/NqK1fKh0J68QJ4Sviz7x8jiy/bbksp/VW3/j0cuPG/4s7nb8endt+Mn9vyWbz3zOuzZ7N6KqtrHhNk7TgCgM8/eyt+54H77UpgHQEDWfgM4AszgoJ8UZRIz93rH9mB2/2kKJ0D0if8rUwMBwo++NgOfO7NX8XrTt2CJSWMABsA6IWVL8Q84vq5F3fjY0+8B5++4C782CvPw98spd9bD++Z0ZgkQXHPv7QHn1hwTPFHXnke/ttRf966EkIAOgVRziiBCByePLyID/pFefapW/G9JU4WWr7ochNjl0uGGdh/OAMFuo0VYdrEOUMVUDeEKRg1OzPxBBQEV80MeEbP/vSaZ1/ci08tvhc/v/UuvPoH3ffSm4adaVrM1L79fabtKmtsDgpe60EHs+uXKTsTN+DqWisAo/3gznfs2yh941/1zEtOaP/6m+/B607ZjCUVRj0kgCsH3/l8fvrIIj7yRw5UnnnKlhC74e6ngEDqngcPlrqtIjc/f2nvtfjY+XfiNT+4GQ1zp0/Z6mMgBQOAzGFX/i8sXoPPXHAXzjplSxYYihYoENJzXahB8FAEXrH/kBPat7/Vzc+w9rl/rqeAODyW5H3y0CJufGwHfustO3HmKVvwN0vuTr71Ngdqku7KVvMKKW8R/vALW+/Cj3z/efh/j/bv/ajJxdMIkfKhCA+RuBrhb59945fx6lO24G/9mRD6bJ8lsOI3rm5LUxswACloeOqw659b37IT55y2tdXHegykj1ogTdKNaXfHc7fj03tuxse3fBbvOfP6dvlqEIkorG0H3hgV+w9zk7wouhaYGV989lZ87Imb8Bq8tv1yT+sGGEhX3f7srfiEBQry/ANAgcVc7n7+dgDAbRd5TV4vNKNeQ4CApn0HF/Chx6/C5978Vbz+tC2Jdgr0WwL6tl8989Ju/NwT1+CTc3fh1T+wGUvTdhmlwLikXSpZ1sxRRKH08c134Ee//3z8rWcqEldQ0lwBZAw0Rta7/I6JPntkNz78+Lt8/2zFEiPxnwHRBy9rTvtgu0iDAhnf8DxFAemYhDuEhtlxYyf43YKaMoM9QBCmOQEwJcazLy7iF70meeYp0VIz9ZVrj0OsdGksdADct33///zWuwIo0O3WOxosmjKjFuYv4xMEAOOZI1ETO/uUrSHWJgi4GVyTeTD8lGP5v+ItWaX5nBNVDozVHvyEGBWPZqqKgqXml/ZG0CSALKcq658qERjub96XNYDnXtqNn1+4Brd4TXhZ4Ff9yGMFZH6KUpJ3R+ivAp9psgSt+TMznjqyiA8//i782oX34Me90iDWMMC2iMX09N0WH9H84cdeeV7gD0A5QK+qKJQdxqGJvITDOADPv7Qbn3jiGnz6grvwWg9aa3K7QwTwBiDBvoxpHJclcLSQab7qq6ktZed4Swplfd0Yikk+ayWLHidpz6f33IyPb/4s3vPa600AIWDRPc8JOEjqwW1w8AUvHz99wS343ZeFxQCx0Z/cdgs+8LobO90CRKJFUchXIRXkYeL5Yu5+3iE5ADhvY3tROnG2fNKg4JzTt7ZAgSaLQc4CCs48ZXNxIc8SlNUou+Kf/tVe/NLea/Gz59+BH3vl+fiegAIfEaU1rSm7qHoAWAqMlrHkNQGJrA8LtgKePxzdE2eeshl/O52azDWMYcZc827MF9KTmumq8Q0fzBae5K8nHiAwEAQ/AEzUuE08dJ8y4ztH9uDj89fgVy68B2eK+6Zy4ySHoKYM14OFGO2GLnr+JeeekPEN7ewwuSZakQ4izEAbADx9JPb/WaduScrMhbx1KFzXuSo5KDjbly9Be1HwU8gfLFAc+2eKlKmlmuredv/09Knuu/w8CSC1fj3/4m58fP4a/OqFbfeZnqcABgOBcJucpUDPT03LBQJAtAQ8eXgx9P+rT9mCo9PIR7rcNe49Nu/QfONP/mpP4A///JXn4XuNVhqMIFvfV60AT/9MYgUj4Lkju4Ol8qxTtgSh6c6CcPOH/BzJT9yQPEkaOLEgCCj43Ju/GpSG5ZA142R8AThQ4N0HdmCkfo6K+XL6wjO34hPzN+GTF9yC97/uRvwOXgbAIAcFmnwQsPsNikg2X5VIzTRCDQF3PR/NO5/afZM5EMztQW+Z9rIMgjafzHy200Yzvph/6tXhWhUkwCFodsLURfMD8OyLi/jEE6mmqrUrp/i6vqkq8ppUFoHd4QP/k7/ai1v2Xoubzr8DP/4D54f7FeJzDsHGSG55l47kxtTXy0dzy+L+9uHduPmP341bLrwn+CTrhgLCrzmi+uCj9Qq3NsdKr+Wuh33KPCsxI6Ux1lsr9XSpawEm7u8GhvuoGdz4/sy3rsZvekuQHrch7qCQXrAaPPui01R/6YK7cdapW1pMLifLYpAzS13EM0d246OZzzaW1fmqznzS9ud8TMqvq5gCoQl8f6g6k8FexTKTkH/nLP1TAlKp5SUKfcBZsj7qx/ec07fOJPiHRMlb8xNITdIl14AGBdauDiCCglvedA9e+4ObO3fXyHuH7rBpwC3+kN93faP4cBbDAdiuGyC6bwQU5DEdgGWdlGc1GPZphjuhM6bGsBB03E7eL7T/sBtfALjmrBhTYO600HUWa0nI3+43APjisx4UbLsF12Xy0aJ1Aww+8YRDQte97ka1WOJq8cqdE04ce6zx5m2ZyrrTpZwvP38bPr37Znxi62fx3jNvwKd23xQOFInlpINt+ZaYU+bLPsN+8Vkp8/VE74/WE1UWdtyzFs1SfpGHvdS+jOdedJrqZ9/4ZbzWa0ryga34rGgHfvEF7Tsu/oDgOZ2A3/7ubtziNYFX/4D6dLWnko92KGmf4euUpmqh/IQUM84BAal7+w4t4voskEvnscgyoJTcUvsOLuKnHlPmX3WvpNEB6X7mrjP7nz6yiJ994t349Z9oC+1ZqCTgnz7iQM1vvPkrOKen/JljDCrgqSOL+N/+KC3fuWHK9reJZX0oTKxS/1jtHeJjzoX9U4cX8b8+/i7cdpHbZ+6eRfIXsDU9JGlm9bH30EJrfrJiMCV3ZbCAEtDRlYmlQCxZQ4IBcxJFQq/3hhl/6kGBxR+6eEMXEACisP/OS23Q1wcEukBAPtaitGn+7OqFJJ8mayxLsU/Cf77w1p34V39wscnTzPJCOZQk5lvAv/TcbREUnN0PCoB1BAwEFAB5J0bkzuy00WASzgSc5Qj60vO34pOLN+Pn5z6La8+8Idxq+yAzYeFvJ0zQWMT7Drno1s9n5uvwvMrbAJjIqpBPaiIOomXKfvrIIm72mpiYf7U26j6UyMaz/jVSYeXn00369nf34LN734dPKJ92Hw311xJRAAW3XHgPzjl1S3im/3sBbUCgwYAwgX2HF3DdozvwpUt24vyNc61FViTzXnvQ9h5awPWPxfL1ePbFuQiwY0GQaE/RfYcX8ZHHr45Mq4efz8run5Ly37JzRebTlGLnPXnY+bSt8oN7xSqi4/RlPTRPHnagIyl/Bg2vT8Dv99HpX/Tjmz/fCmqcERTvPbiADzx8Fe645N5QfgNn5gusIoBKDgoKM4I/neHPQGCncDjLgbOy7X9pIVhqzjp1S7DMkATQQiw27ANrEYJocyUi1E3Rt7+7t5M/5P2T8wPA0vb9NRGe+250z5196pZeAFAS/umPOMb7fSD47Xr3ga6bMZ6t+dQh6Pcecvzni5fsxGY/vjm+NQNj8zwZGJB3BkvBBcMsBULrBhhcX2q0gADEzlIuyZbgBiIj+tKzt+EXF27GL87dgve/7obEB5agukyYC8kC1qQnzd5DC7j+0bammsdGiLCoVd0j8IhVyE3Zzx1yTF27J3T50ZTt8pe2F2nztY5beO6l3fikj75+zSmpz7mP+vzcVBGefXE3Pjb/nsRnuxwwoA/iIYr39x9adEz34ntx/qa54uKSsmal3QcX8L6Hd+Dut7kPdgm1T93045aghvhTM1udZe/BBXzw0R340sWRacVn2jSLEiiBmDlTXE2atfxZxkDcQx98bAc+783vXQw7YfYqTwkoVuROpLvukatw19vuDePbdZT4rLT7gMwfd4w74Mbfued8puAuc7xGFBQGB0WEGeHAKoZXMCrCk4d34yOPXx0CnbVri5iBmjBhCnxgEgJl3d8NyC1ZaSc9/9JufGrxvcF9mVPbdRUFfkjr0PxF6fnXb74H556W9n+fK0ffK7lyRGn7QuJe7B9fa8gt4b730ELCf0JeoqJV1UouzeMveVDw6QIo6Jqa6wYY9K2/xIRvqAQ5I/3Cs7fi5xcc0vrAWa5T9WBNgjbHxZeXtkA1rDQBmRSJtUHqxO26Z2UluyLUxR5hiipQaSLACFmbO8zYpV0RTx9ZTAKt8roMJctvCwDPvOQW/ee8ednatggMAwKSXfb4AsC+Q47p3nXJTmw5Y1tSF/tbEHadS7R4wJ1t/pW3u0+jCmntP6bZgl9tAmyNswilO5VQAsrgwpVnD5CVuvfQAm54LGrCyxnbLtp3aAE3GKBY05B+LmVxW/qUpp0J9VIZJSabC5Td/pjaey5Nx3dovUsk/bx4YAHXfONK3HPpvdiyKVqaggEvszYlYAHOQsCIlkx9VkfFztL0wcfc7hvZkruB/Vyo28GJQFQgADseRtMzL+7GJ4Q/nNoGBXkfdfn6LS3/KbEEeUtZe82231Vy5bSAAaLSZloSdcaMhgr0PQfb/EdIu5G75lLXSY+yZf8zM1oKQh1mfuIEpdy030f5ZNad/PlnXCBjV6dOSGIEyu8tBWHvOxg1gc3G4TyyIOtcgFjvSBJdpdykvipM6vzZLhO2JG2oKAixXFiJefY2ZZ616tb3PYl8z7ZvAZ7MzOOlRd8LAvyFtgQQUTh7/K5L78XcprkWALAYSQAiA/jDwoEFXP3gduy6bBfmzjDG1yih1VWJxSAr/4VFvNcLpS2btlqPFLbekrpu10JS9hxcwHUPX4U7L4nzc5YjXfto78EFfOARL7SN+S/UtaS7NCdhund7oRrKKzzfJSSsOeC+cncl7jXGd1ZMYHXrvP/g204FKluAQIQ1kQkWWpH+5AOL4UGf0oTDwV4U17+zPrqyN0hF63Kd9Vp/6vBiEogJlNe6ddGn1Yul6YuGJj+r5Qdoz7M9Xmm765I2f+6KoRkqxHcfXMB7v7kDX1bzUz86KYGLHsQpt29/xp1T8Jk3/jKu74wpKJe3foCBlbgM5O469aP4bNaprUMmClwri6tqkWiS92RMK6Gg2XdTg7Y5TpsfN2+aawmARHho08kA0zWz0/Q++Gi6KEvnP2yYNcoQcdF//q07sVlFX/dG4maCWwt7Leh3HxRNzDHdFggolNP1OxJj/sACrvz6ldh12b3YdsZWlEcxLSGEjmS3FT8GAMwfWMC7H9oehFLZ/ZDVjLuv5XHRhDXTsuaZRUOsCiWhXaLShocSMIia9i7M/VBaviXkgW4LQn69cGABVz2wHfddtgvbWqBvFvTUbgGr8jWoFM2/8cLbuQ8MQCBwIbunrQoi9MSS0oBDHAJQtkAmsS9GM2Wt7z+0iA8/flViqUxa3QG8hrh2EktQYn638+flWnXQ9bDmv1WmRUMCbxcPLODdhiUxqUsIupyRCLj96VvxsT/28uv1ChQYY9ZV23UDDLo0uq78mm57+lbc/McfxS1v+mXc8PoMaeUWhtJ78nT1nHxPe2fHpBDq2PIdKOyvb5kfXfksG33t6pjvSQLdMg6wW5mvW+bfAk8cctSskFg6dKAVYnUA2AzAYiiWO0AvysDUS4BC1avYg9nG/PkDC9j+wA7sevtObNu4GWj6T3TLxyfVolID5vyBRez4uhMa285w5mXdHxrnaWYvZFmXNJWYVhLb0tmW7tv5/OwtrlBeSQkI6+sdu7AtAwVdwt66bnUON3F837ET2zZtBtiP7yx+llKjqMLCgQVs//qVYXylFrnWHMY5dxWpGCo55Q6E8BneBS/0tKUy3+II6HnSniNAeU3ngbZCxVMlOzR3S7gPsQRZz8rzZhVUcp/QnllQZxXS87Nr/oftmTPqVZ3yS5U1xPq3boBBRb18qZNuffpW3PQt16k35p2KNErWutaU81/AawIPbjfNjxZZk7A4oFSYdFyupA5oDPXWXMgXLOCghKRLAIY1cxlAoslY7pVO8272BsukWKHdP10WgU4olZ/S4xs6f2AR2x/0oOCMren37rtIGLO5yT8Ci/mDiy2hRNkz5t5+9bvK+byySljzM5lv1jDOIA97QXHPNOnz3WpNO9fkS9ad9NIYLyXwk/HdtBVgHj7GUhxVNoggwvwLT8TxVZYmyv7XRoNsg1JskmFViJbKzP2ULa7cOmRjnnaPxvW7y3SP+maa1CfYc0uQtvR1ldNT5YQWDyzgXQ+2+cNqkWUJKlGXbClRn/zKy888iy1aN8DAZurDSDr1lwd0av6+IfcWDiwETU9PilndtqUJU5x0WpvMnmkdvNQCBfIgBZ+q5fPMTcz5rgmLLEuEDrQa6kvT1GIKKs/ug1HoaaHRrTUOAwRAKjQu2LTZFjI9ZAkZAQuWUHJ70oz3tMBC/D/9FVu4mM1PSe8FvwMX21BQPMhcWyj/yq/boMBcZaXxKWj+0v/3XfoVD8pmH18gjnEOAudfWHDj+46drv7hQJJw8K2UYGvDyICCJPqLIPQyUGYeBZF3QfZCKzZFr9+tWSCdRTNZggAsHrQ17T7hPVQWzCK0l/OuEv9fTlkWzSq/Sqxe07oDBn1pOf3W07fio75TPzgQFAwtG/Dm5YxpDZCdg0kzxZUw3dJiXThoM3UTWAx5Eacvs0CHXcHuYktBgbIopf4zm5CBTkGQWwpCNyxTePSVH+vk35QPnH5vS7BockLGmp9UeCJ9ukz62b7+n4WsZ9P6d8R0dI1HhysgAQVntH3ms5BlFUrHN3fPNRnQswFCF6Pfc9AJPROU2dOi5376JnP9DhzkIYK9S2gfT/45lPI6Wetr1jK6aKj8sox/Xe9Z18Cgj5YLCoZSaVKslpVqOZOuRJbbcsiiGaJdJvlVvlndK3ld+/KU6l9+fjZf8fyBBVtoo+AamJGKoEDVoZNammdy088fCZRcHtPtki2rBVpLNH9gXtW/ILSHADQiExzo/p87Y+vMFr4+CuW/wwAFnZRCgRJIyPs/r7+1Zi1XU4lK63fW8Szln339zkaryT/XovxZ5NesfbZugMGstFag4EQvXybQsS5/VvParLSwmvXXgtULmiQQbdbyu4LVvLAPoGM55YeyyuCkDQr6AgoKr+gs/1jMf4npKIOatIK+D/oAQgayOvt/wPj1UWf5KwCV1vqV9bVcV41Fq7l++y1BJw//PF7lH2v59bIEBusXFCxHp2kvy1nqvxz0bjGtlZKuxzHtf6o8KLiqXf5Q90GP8JgJdCxDiAwWqsuk4zP/Z6x/Vz+VdpeU+n8lJxiF8o35s0o0tP+XawFZKKzftdPkZ2tJnD8jKCjRyw4YrF9QAAxz8K+k/JXTiV1+n4O1RyitivvgeAiNklBdOWs/9uO7uPqgRo3bcen/B65aZv37x+d4gvoTb/1qsls3goJh9LICBusbFAyhAZrqCV3/41l+yZJyrDXtY1n+4knNFGcrf3Z9+MQY3+WDsxOr/49n+TO4b07I+g+j4wUKgJcRMBhBwVj+iV/+y0VoH4/yZxOwx6f/x/Edy18eHU9QAKzgMKeTiUZQMJY/lj+WP5Y/ln8yln+8QQGwRsCAiH6ViP4PIvoTIvoDIvqH6t7NRPQXRPQfiOgtK33XCArG8sfyx/LH8sfyT8by1wIUAGtnMXgcwI8z86sB/J8AbgYAIvpRAJcD+DEAFwH4PBEN+X6LSSMoGMsfyx/LH8sfyz8Zy18rUACsETBg5j9k5iV/uR/ARv/7HQDuZ+a/Zea/BPAXAM5ZzjtGUDCWP5Y/lj+WP5Z/MpZ/rOXXbz19a+f9EyHG4D0AHvG/TwdwUN075NNmouOBtE7mSTeWP5Y/lj+WP5Z/YpZ/PEDBR7/10c48ZH0UYzWIiP4tgFOMWx9j5gd8no8BOBvAv2RmJqLbAOxn5nv9/bsAPMLMv2eU/z4A7/OX/wzAf1hGNb8fwP+9jOdOdFqv7QLWb9vGdp18tF7bNrbr5KPltO2HmPmV1o1jtl2RmX+i6z4RXQ3gbQAu5IhODgPYpLJt9GlW+b8N4LdXUkcieoaZz15JGScirdd2Aeu3bWO7Tj5ar20b23Xy0Wq3ba12JVwE4GcAvJ2Z/z9160EAlxPR9xHRDwP4JwCeWos6jjTSSCONNNLLkdbqgKPbAHwfgMfJnTu+n5k/wMx/RkS/A+DfA1gCcD0zT9eojiONNNJII430sqM1AQbM/D913PsMgM8cp6qsyBVxAtN6bRewfts2tuvko/XatrFdJx+tatuOWfDhSCONNNJII4108tGJsF1xpJFGGmmkkUY6QehlDwyI6LVEtJ+Ivk1EzxDRsg5UOhGJiG70R0//GRH9ylrXZzWJiD5CRExE37/WdVkt6joq/GQkIrrIH23+F0R001rXZzWIiDYR0R8T0b/36+qn1rpOq0lEVBPR80T0jbWuy2oSEf1DIvo9v77+nIjOW+s6rQYR0Yf9PPxTIvoaEf2d1Sj3ZQ8MAPwKgF9k5tcC+IS/PumJiN4Id5Lka5j5xwD82hpXadWIiDYB+EkAB9a6LqtM5lHhJyP5o8xvB/BWAD8K4J3+yPOTnZYAfISZfxTAuQCuXyftEvopAH++1pU4BvSbAB5l5h8B8BqsgzYS0ekAPgjgbGb+cQA13CcFVkwjMHAfbv/7/vc/AHBkDeuymnQdgFuY+W8BgJn/ao3rs5r0G3DbXddVgEzHUeEnI50D4C+Y+T8y8/cA3A8HVE9qYuYXmfk5//u/wgmYmU9nPRGJiDYCuATAnWtdl9UkIvoHAOYA3AUAzPw9Zv7Pa1qp1aMJgP+OiCYA/i5WSX6NwAD4EIBfJaKDcFr1SaulZfRPAWwloieJaJ6IXr/WFVoNIqJ3ADjMzN9Z67ocY9JHhZ+MtCrHm5/IRESvAnAmgCfXuCqrRZ+DA9zNGtdjtemHAfw1gC97N8mdRPTfr3WlVkrMfBhOZh0A8CKA/8LMf7gaZa/VOQbHlbqOZwZwIYAPM/O/IaL/BQ5Vdp7aeKJQT7smAP4xnLnz9QB+h4j+Rz4JtqH0tOtn4dwIJyXNcFT4EoBdx7NuIw0nIvofAPwbAB9i5v9nreuzUiKitwH4K2Z+loguWOPqrDZNAJwF4EZmfpKIfhPATQB+bm2rtTIion8EZ4X7YQD/GcDvEtGV8kmBldDLAhh0Hc9MRF+F86sBwO/iJDKj9bTrOgC/74HAU0TUwJ2n/dfHq37LpVK7iOh/hlsE3/EHY20E8BwRncPMLx3HKi6blnlU+MlIg483P9mIiDbAgYJdzPz7a12fVaLNAN5ORBcD+DsA/j4R3cvMV65xvVaDDgE4xMxi2fk9OGBwstNPAPhLZv5rACCi3wdwPoAVA4PRleB8Mtv87zcB+L/WsC6rSV8H8EYAIKJ/CuAVOMk/IMLM/46Zf4CZX8XMr4Jb8GedLKCgjzqOCj8Z6WkA/4SIfpiIXgEXFPXgGtdpxUQOkd4F4M+Z+V+vdX1Wi5j5Zmbe6NfV5QC+tU5AATx/OEhE/8wnXQh3uu7JTgcAnEtEf9fPywuxSkGVLwuLQQ9dC+A3ffDG3yB+sfFkp7sB3E1EfwrgewDedZJroC8HMo8KX9sqLY+YeYmIbgDwGFy09N3M/GdrXK3VoM0AdgD4d0T0bZ/2s8z88NpVaaQBdCOAXR6k/kcA717j+qyYvFvk9wA8B+d6fB6rdALiePLhSCONNNJII40UaHQljDTSSCONNNJIgUZgMNJII4000kgjBRqBwUgjjTTSSCONFGgEBiONNNJII400UqARGIw00kgjjTTSSIFGYDDSSCOtKvkvEP4lEf1jf/2P/PWr1rhqI4000gAagcFII420qsTMBwF8AcAtPukWAL/NzP9pzSo10kgjDabxHIORRhpp1ckfG/ws3EFb1wJ4LTMfXdtajTTSSENoPPlwpJFGWnVi5qNE9NMAHgXwkyMoGGmkk4dGV8JII410rOitcJ+D/fG1rshII400nEZgMNJII606EdFrAbwZ7rPfHyaiU9e2RiONNNJQGoHBSCONtKrkv/T2BQAfYuYDAH4VwK+tba1GGmmkoTQCg5FGGmm16VoAB5j5cX/9eQD/nIi2dTwz0kgjnSA07koYaaSRRhpppJECjRaDkUYaaaSRRhop0AgMRhpppJFGGmmkQCMwGGmkkUYaaaSRAo3AYKSRRhpppJFGCjQCg5FGGmmkkUYaKdAIDEYaaaSRRhpppEAjMBhppJFGGmmkkQKNwGCkkUYaaaSRRgr0/wOe1CskuE727QAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.sim = mp.Simulation(\n", " cell_size=mp.Vector3(Sx, 40),\n", diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb index f84a6b0ec..a15e36dea 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb @@ -17,17 +17,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -53,17 +45,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.20124611797498096\n" - ] - } - ], + "outputs": [], "source": [ "waveguide_width = 0.5\n", "design_region_width = 2.5\n", @@ -198,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -252,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -346,1594 +330,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 0; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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eXv99tuQWzn+P4hLoLfjxZB1ddO6RPik70Y1E30HTNLZer9t70uPtqV38uEgkChrH9lnkni98FBqP43n1G7Tw2ZRZFrx3VG7h8Zbbfjwcredafn+4B8Dj9qizQy8Av+fPLHD8jAWtoN2L0Uv0pd74tuIX1GazscViYZeXl3Z5eWlffPGFzedzWywW7X3pV6tV6w2woLvG75k1Q+uOpbUs9kzwJQsfCR47MXTHvR08Uw+tPNYX+DHxOXA8gMeCLwnfzwv/nf3Pss/FM3qJfsgncDQa2XK5tMePH9vjx4/t6urKLi8vbT6ft9bRBZ/l3bsCdvgax89RpV3kzmeCx/33ETzOpMNJPDxTbzKZtJ+jRxClKzn1hmAHwHn8KKjp382GBewxiRi59x00TWPz+dwePXpkjx8/ts1mY1dXV61YeNopL46BqTsmqy7jHHxpHF+y8FnQjgWPx+FtiUTvAUSMFaCVz1z3CB4KYIdQGsuzZ9AVFxCHFEU/n8/T/PFtxsfVZma/+tWv7Oc//7l9+umndnV1ZU3T2HK57GXdeazrRMUkbOHRunLhDY7jSy69d0Yek1itVqHg2aXHAhwWvHdIKEAWbCkFmVXfcSeAYMSez3PXcEkcUhT9kydP7MMPP7Rf/OIXrUs3pJPaNI394Q9/sN///ve2Wq1SK8OWrkvw/szpJxS8B+7Ywh8jeA/Yodg9GMmBSBdv1vFEhT5ooVmIfoxd0XZ38fkc4jbwHHsbM/H7NkROp6X/4IMP7IMPPjAzs+l0auv1+qU07FWC49N3333Xvv3tb9u9e/f2JpXwIhRmcRQarWBUTovFM3hTCi6+OSZoh4J3d36xWBxkH7CuHo+JRc/VdyzEKArv7cHzw4VCTuQhccfhHUP0kKt/HEXR73Y7u7q6av9eLpdfeoNeB9C9vLi4sIuLC9tsNgd5ahcqz+7C4JbDQucJK5gH5/QcrmaLATWshsOAnQserbtnHFzwOE/+GMFHc/Ezd97TeejVoOBdyJzlwA4Fv7/dbq2qqnCBzsjTEDFF0VdVZXfv3m3/nk6ne4UWtxm/iMbjsc3nczMzm81me25mXdfta4eF7+9xVR2LndenZ9c6EyDuzwOJKPj5fN4Kfj6f7xUXOb6sNA8xsuo7FGUkOLTOvt2oio6j7Zzl8Pf5/Eb1D1EsQMR0Ru/xROIEjKHgQTC05l6OimNxFB+OUc1sz7JH43YWOlvZaH07dund+qFL74L3x/X19cFEGp6mWhI8Vt+xdY4CgX6+0LtBwft4ntOZmBHgbfmxZhWQsvbdKGXXgQvJxYAXN1rsKIJtdrikVbSGXbaeHS6AgcU6nL7yix/r6HEc74K/vr7eS825ha+q6qAAx//mDgYtciR6B915fOA54mfeXvS/GI1GBylSufbHIdH3gC9EHvv6+B6/70SReXbb0X1HofNcdRaf2fObTGIe3sXOgudIvZntBRDZ04hm6OG5wEU0UXDoOfixs5XHQh4kynrgttGV5zvnchBQxEj0HfBFiFYRU1l8b3cM5vlvogo7Fj0KHl1vzm2jS8+C59ScR+pdHH4cnDHgm2GwS4/7jNbD9+1iR5d1HL5NPrYIDzZmAT8F8o5Doj8CTLFhSs2tYhbEK5XVRneQzZaT5jFvNI5H4XsuHqPdZrYnyKjMlj0X3GfpZpYYqfdtY4aDswyR2Evi5xqI7MH/A7GPRN8TvJhRqJ5KY6vovzGzcDzfV/AOCx4Lb7C01gWPS17hQhgYiIxuaMnLZKOIePFMvFc9B/F8G1jBhxF7PkbMxZ+dne1liThNGMUUFMTrj0TfAxz/oqXHWW9sHR0MYmFEPLsRBRf9oOj8YsdFLLPpsTh+93aYPS+QwaIfTgnicfCSX5ng/TzhWJ6PyY+HK/O4aGm32+3l+H3bLOxsPC/Rl5HoO+ALFFePwUo5tGj828iddqFzKs4st2w4fu+aHosWHvPlvk9eKjuaoccLf6CV51Vy+TxFBTnoDWTn2NvJ0X3/nr+XufqiG4m+JxiUiibEoGtuFt+FBn/vnQcHzNjC4y2m3F3HtBwL3sfwXi/g2/SOxzucqKQXi28ywXs7sly67y8qt3VYyNw5crCPf8Nj+ChHL2ufI9H3IHJbOert43K2WvxbFH+fOvSShcfnaAEM3AcHEqN71aMLngXusHyXS2z9ePHZiUTJ9Qxu4X27nNLjz/BzThuKHIm+Byx4H59jAI7d40j4WJmGLi9btkjwXhnIa9thWS3PluNIelQBiFF2r3jDHHg0hufAHQbo/HgdzrHzGJzPs8+gy8TPxTzRuRNlJPoOUKhcKcdpKV6Xzuzwxg7+2iy/Rx7nw3FBThQ+L9CJ7cU8vGccUPRZ/T4OJ6L72nHNgrvjSNSRme3HJlj4WLrMtQFRAQ+KnsUu4ZeR6DvA9BZOjEGRcyENCx9B6+7gheuWEN1qFH20Gi+vNhvN4MMhCNfvc+7f98n3tOPAHQueOw8M3nG0HYcIjgufU3sen/Dj5I7SUSCvHxJ9B57ems1mdn5+nt4FNko/IVG6yd/3ix8nkfD95fDZP0MriLPYstl7UR4eg3ZRJ8PpPxzHmx1G3vEYvVbe25gV02DHgIFMH26UCnYijv3+0OgUPUZl67oe1NTapmlsNpvZW2+9ZQ8ePLB79+7Z+fl5ewcZFI9Zvi5clGKKLB8KPnOvsQKOo+c4BIlm6nHOHAXPNfwcK0BxRvEKPFZsF68ZyGJHeFgU5fQz150zCCJHi2h0sNvt7OHDh/bw4UN78OCBvfXWW3Z+fh6W3kYprMjKsdBxzXxc1w7Fj52C7wvz4FjxxxNncPyO7UIhopVnrwI7GJxjwNaeOzc8h1GHF20DQZceyTqdaBvikKMW0ZjNZm3A6DYzGo3aNezfeecd+/rXv24PHz5srTyK3uy5xfTX7pb63yj2SOjRDTP4tlk8BsZsALvyXO2HFh5d5lJaDtuEYEYA4xgOj+cxVuGf47a888L3cFulFChmQniYJfHnFEV/fn5uP/rRj+z73/9+e/EMKTK63W5tsVjYdrtNZ8GZWSuMKI2Fbnwk6OjBnYNv26xcz8+ijzIKvp0oYIgFQN5mBEUW1RlksYoowo7i7Yu7/Ch0Fr8E301R9G+//bb98Ic/tPfee+9ltee1wS+cTz75xD766CN78uRJe8G5uM2ei8jFGYmAU3A8S610aywMnqF1x2KbaJlqjtJ7e7BNXFbrwUGcKuugyHGogMeLv2F3nj0UPMd8zqP/BXYSnJ3I1hwQMZ2Wfui8++671jSNffzxx21MA+/3xkGpKKgVBejwGT/LtsnuPE/eyQTPHVDUDm4Lp9G4VoHn2+MxR2N37AwyYXYJFb2CqLJR1r4/Stl1MBqNbDab2YMHD2yxWLRpqKqq2hr0qAQU01YlSx+t7MqBLrRwHKzjgB1H6M2eCzErusEIPRfM8L65go9TdbgNjnMwmcXPhgLR0IatvQTfjW5gmYAX83g8trt371pVVbZcLlvX2ANRuECjw1aeRY/uPOetzQ4FNxqN9sbpXYtlmh1Oyy3Nh48q7fB15tr79jkdF6XkfHtYz9DXHccoP58DuffHoRtYduDFOXfu3Gkv5PV6vTc5BK01F+DgZ56K444isq7Z+DWaf88WDl10LvzhcTy2gQNr6NpztP4UweM2WKBRcQ+mQHEbbOVxKMMTf8Qhcu87cMsymUxasXDwKhJwlKbj/DzHAThg5fsuubMoUiygydrBqUD0LHa7XVhlGKXGzOKZcxwARJect8NWubQd3EYUxJN73x+JvgMXYMmt5eo6fnCnwBaMn6PUGIo/ishzAI4tMHY4mBbEY+Tj5g4oGsPzPvD3ZvsrB7kocQoyttefM8Gj18PnJcrVixiJvgcYyMoCWJGLi2KIglP4jBctC4XdYtx3JDSujIvahMMJ/y1W27H3gUTb7+POZ+PuyMJHXhD+NjsvuF0JP0ai7yCydgwKuqsgxcwOxMUXNLu/kVhcIO7Kc1u6Ht6O3e75JJfo2LH9eGxRlN6JOq3IGnN78BlFjx0TdyJo2bNsgNhHoj+B6CLrk2fOfo9BKBw+4Pv4/cj9jUSNz9n+I8vI4mMLHGUbnEjskegzy477xE6HhxvYIUrwxyHRH0Ekbr7gRqPD2WFuUf19s/2UXGTdS5FtfF1yjfG32X4i8UfDldL7TikOkXkqCHtJ2P7s/Ivjkeh70mUpo7852BZ9ly17NozgQFf0zGLBfWCbuG6eMwD4zMOWKC6AHQtH1zGdhsMan5TD3gW/7iLyakQZib4HJevCHYBf3Lj8dBStz8bypcBZKShXGlf7PjH6zYE13E9J+FkenqPrLHp26z1FyETibZqm9ZYi76bvsEY8Q6LvQZf1YBE3TbMndr7A0TLi77N9+8WNc+mjzIBf5B7c404GLTzfkZZFFLWBA5O+7citz8pjI7e+dJ5xbJ8JviR8cYhE35NjLiJ0mdHKR+PUKHLOrm4keC4GisSIHREXtkTFLFjcg/uPBIZE43gO4GVufNaZROC5yGoi+m5ryEj0JxBdVFk0HC0+f9/MWq8At82Buyyanl3cWYCwNJ6PxFgSE2Ye0LWPxH4TLjamJ7NKx67zIp4h0R/Bse4juvF+0UbjTRc+p6n8e5l1jUpes1hBFK2PLHrkLh8j+FJlXNdYPDp3+H0XPa8+xMKXtS8j0Z9AFGmOXPfo+yx8fy9Kf2VjX44f8D65jJcFyELyv3l+QBS048BgJHg8DxyQ6xoq4DnjbWDEn8WeBRjFIRJ9TzJX21+XiNx87jg4Jx9ZcTM78AiiIGFkbVno/l0WfTTdF7+Lr7OJONH5KQ0ZsuAeByerqmqnNEfTk2Xl+yHRn0BXAOqYMWxpXB69x9Y62gbn/yNhsYdQqs/n/bOVZ8H7PrmTi3L9uJ/svKLwzaxo5SX4biT6I+GLqq/w3SqX0lVudTm6X0rnZdtDMN/OFYO8raz4Bj0MFH62ncg9zwp8eEJS1qHi+YmmM3d1xuIZEv0J9L2wOJLPr6MOJBJ8FBTz73PRitlhVR3mx1Gs3JZMOOzWe9s4FYedEHcE/B0Uf/RZyeXHKL6s/PFI9EfQ56LKrHIWyY/y99xBHCNUFgrOmXey6Doep7eNg4XRb7mz4W3xdrvG+Li2XnTOI+9A9Eei/xJAMbDYsWgncudLgs+i8JGF5fexY4m2xVF3bEM0zx6JxJfFG7hNbPm5whB/jx5LtG1ut4iR6L9EIgH5cxTNL43lS26+f84uPlpQ3Bd2IliGy9a8dAzYbn4uxRmyoB1XHUaeSxbfyDoyESPRH0nfcXxk7TP3PfoOb89fl2ID6C5nltPsuZeBaTcP8nln5Pn9qE14LiJxclAuAwUezSvoOtddU4RFjET/gkSWLhK1v8bnrLiGt8/izoJk+B4KHgXl+3Ur77jwszhDdsyRu95nrI3fO0bw3m5eeETC749EfyJd1ozFj0Rj+WwfkcfQNX5mq44W1exZcA+j/nzDycyFj46/JHj+HXYmWdAuq/PHoQjPJ4iKg0SORH8CmRVCK4kXeIksd+8udiQiDtaV2pS1OcogRO56Vnjj24g4plPz7fBQBLdlZuGkHlygIwp4ihiJ/gWJrFufi75PpxBZ8NL28HUUR+gaX6PgeWpwH3y/Xd5Cn84Q245Vf9EUXn+NvxE5Ev0LEEWibxK09izaLIrtz275PEDn23N4Oi/OYisFF/sGHBk+P9G5i+IIvPAHz9ePXHwJv4xE34NsTGtmB2PaY+hr7UueAwvdLV5ktTlAx9vbbrcH+XrefuRFlMSGcQTvVEpBOmwf1vZHd/qJ5u1L8N1I9C9Ayf3uO671iztyo6N8eXRxd6XWeAptFgg0ey7OqC0YSItc70h0GBzEDjI6HixgwoAd3h47up0VB/Ik/DIS/Ylk4+bSM/8eRZi57yy0TGCY/uOy2WiqLLva2Zjb94O5ffQqoum1eCxcM4Dbx2EFDkXQykfr7uEj64wk/ByJvgdRkMwvsvF43KbAsrnnTBYLiFJcLHi8sHFc7uN/F7aLxduWzUjDDqI0ROE2RPfWYzffU3KYFvQHzglgl58X58B91XVtk8lk7zbdURuycy8k+qNAEeIF6S5pX9GbxTn1yNL7/sxiV5oFi+kvfjO6g78AAANOSURBVB3dWZfz42aHAb9IhKWxNR4jtiFa4opvvOm/5+g8BvJc+Cj+m16T7zYj0fcExe4X+2QyaV1zvvV0ycXPYgEl9z5y8SMi4ZdWj43c/si9z26Z7Q9393nhDm7Ddru19Xodzq6LOtUoL++in06nNp1Ora7rdtzPK/yKQyT6HrDg67q22WzWjkHrurbNZrP3m2jCDBIJPZtcEgmfOxZ22fERWfroEaXOIs/GHxhgK82vZ8seLYCB55k9C16A00U+nU5tNpu1wmeLL2Ik+g4ywftY2AUf5Zlv6sLL6suzSHlJ+PweuvZROo1FWHLtMcZgZgf74oU3fZ94rvF8436jNqC1d1dfou9Gou+Bu65uXdwVreu6dVWz8Tg+R/StssssfLQtFnFm2T2YFll53i8G76JHnxgDuvtdNfZR0Q0L3y1+NrYXMRJ9B2h56rpuBe9/43iYf4fPfYgq0qLtcAEN/97bEwXqOJAXTZjBfWN6jt1uLI1lz4O9Du58siq8LIaBn2Fng8E9ib4fEn0HaOUcTNVlFWZ80XVF8bN98+u+qUB/ZuHxg/P2kej9mDPrmwXOon3xe3y8pYd/JxvzY2xB5Ej0PfD6d7N+gkduyuIc24lkKcFSqjDrvPo8uE2lfXalKHG/vF3cJ8cboloBcUjVcdHe7AySN5DIJS5Zq5vmRS5ebFuf111tKHkbJc8DXx+zv2y7keUvdUYDJjwBEn1PjhXK68yp7X/dRJR1CKJFohdiYISiV8RDiIEh0QsxMCR6IQaGRC/EwJDohRgYEr0QA0OiF2JgSPRCDAyJXoiBIdELMTAkeiEGhkQvxMCQ6IUYGBK9EANDohdiYEj0QgwMiV6IgSHRCzEwJHohBoZEL8TAkOiFGBgSvRADQ6IXYmBI9EIMDIleiIEh0QsxMCR6IQaGRC/EwJDohRgYEr0QA0OiF2JgSPRCDAyJXoiBIdELMTAkeiEGhkQvxMCQ6IUYGBK9EANDohdiYEj0QgwMiV6IgSHRCzEwJHohBoZEL8TAkOiFGBgSvRADQ6IXYmBI9EIMDIleiIEh0QsxMCR6IQaGRC/EwBh3fF69lFYIIV4asvRCDAyJXoiBIdELMTAkeiEGhkQvxMCQ6IUYGP8P0DRUdxwEtBoAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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R+D3pWSBevIJVbLWpOh77mh1evCg4zMOfKnh06fl22348fJ97nqaL28ecf+TaozeDbfHv4d/wM3/tRLl8WffTqBJ9mwt2F/ELab1e22w2s+fPn9vz58/txYsXNp1Omw4ArXx2j7lawft+0bqjS10r+Chwh1Nk2cKz6NEd93ZgaS/vA4cB3OFxft4DeHjM/h2O3jM1wwHRTpXou9yT9vt9WywW9uTJE3vy5IldXV01wp/P53uuPubvs7p6s3y2GQseV77BtFyb4DmwVit4HKJgO1jwOJbHAF22KEjmxXAHwB0Bn59sSCCOQ+59C9vt1qbTqT1+/NguLy9ttVo1gp/P5wdBL7ZwaPnMDueQRxVlbuFdaNn4vUbwLmQUPI/jPVq/Xq/32sKi5+o+P57IXY+EjODfMysf5ea5I5H1P56i6KfTaZg7vutgAcmvfvUr+/nPf26fffaZXV1dNWN8HPtG1r2Up448Jy5n5WWuMAefVdyh9d3tdnultW7NIwu/XC73XHoXPU/N9Tp+7Fg2m006HmdrjR1bFPH3ZxwOmB2uwYeeE3c06gTaKYr+6dOn9tFHH9kvfvELG41GB2Oxu852u7U//OEP9vvf/75x31nQ0Zg1E7zDFz9beA+UneLS+z49xsDW3WMRHIh0wfOqO2jhOWLPgTt/zR1cKbfuLj7/nqP5WCR0dnaWfl+002rpP/zwQ/vwww/NzGw8HttqtXopDXuV+Ph0t9vZu+++a9/97nft/v374ZTSyGpzZ4DpNxY6lrC6gHkcj8JnCx9F6bPxO2cf0KU3MxsMBntFONkiHFyIw8fNx+zwAh5OVJXIHYd3DNFDVv44iqLf7XZ2dXXVvF8sFl95g14H8GK+vLy0y8tLW6/XByvS8EST7IJH64Z57+j+cmjlcclqtu4seDPbc+ld8LPZbO/hwcflcrln4b2tmeAzK+/7jdx5z8lzQQ2eH85ucMQfv+9DCUyRZuIXOUXR93o9u3fvXvN+PB7vFVrcZfwiGgwGNpvNzMwa4ZnZ3hiTK+dK49nsRpLRena8GAbeiiqy8JngPcU4nU73JtN4G/kutuzas5XHY4sEh8fr54rPF28HhyUcG+DcfFT4FMUCRExr9B5PJE7A6Aqr1cpms9mesH3cjULmYBZbKhQUL2sVPfNrFDu6yOjSu1uP4/fpdNo8uK7A7Pp2VN7WqOouyvuz6DhYyV4ObgPH6TiUilx83hbWO3AZtNz8OpSya8Etp4sBy1NRxJxy4jEpT1ZBK45LU6M1Z6G3LYTBgkdL72N5FIoLPhN7TSeDLjZaZa6Pj9rNz9yJ+HY8peefo2vPK/RK7O1I9BXwhYg3lHCRsCjY6rHbjON1HqujhXWh86IRKBKeE4+Bu6imAMXJgTvuhNiLQaGx8HG4wPGLaGpv5pLjtjabTROtxzy9Hze2h4OAIkaib4HTbpjDxltEoZuMFx6m47h+nu8tF915hoNfuP1I8B6sw9QcRuo59cXtwhQgdjRs4XmmYRRtj5bbNtu/aQWn8CLw+9E4XoG845DojwBTbJhSG41G4RRT/k1J9JngWQxYmIJLXLmFR+F7Lh4nApldu/NZ0I47Me8osJPhRTPRvY+yE+ja80o8DJb0IuwhZA/+H4h9JPpK8GJGF9gj+tFyVH5BZ+P5kuDZlccLHq1tVnzDS145XNfPt6/mUl608Cx6DApy1B4Fz+fGt4eix1w8dwbYYZjZgUuvXP1xSPQVYHAqKpOdTCYHOXP8LRbfRGk5XJACBYegO+9ij2rpcQIQRrQxD+9LbeFCHFGxj5ntlRjzirnR0tg4lo/Wz3OBnp2dNcLnoiX/e/Q/8HNRyh6IMhJ9C1w6Gq1eM5lMDlaRcTByzQU4mYXnAhgsq0XBYx19Vlprdp2W806Lq/08LuF/933yON6HCujas+DxdRTE45w7n2NvJ+fw8XUmegXy6pDoK+ExauSmo1tuZgedBbu8aN1RGFzTjq582/RYrLQzu3aNudPJSnoxYMZDCXTrcfyc5eW5w8zEyJ0ERvV92MAFQSh6ztH770SMRF8Bu61ZYC6yavxbtPpR7jqLlLvYcLYcBu94mSvsaDBlGNUH4KIYbEnRpffnKHDHovfXeGxceMMBzyi67+8ZTvlpTF+PRF9BNE7lBSJ5PBwJn8XPeWsHBe8i43Xp2bq7KH2fkeCx+AfFHq11h6sB8RieA3eZFUeh47azMTiP6zEewVV6LHK59vVI9C1gII6Xf+abPUTW2yyeeGMWzx3HgpvtdruXlnOxRwt0cv7dxYMLcnDFXzRhBzub9Xpti8Vir/PhwJ274wyKkb0XTvP5OfKyXHTRcRjB2+Y6fVGHRN8CBr6iijl8sBXPCk6iCxUvYhQeiz677RSWrUZ1/lFqEDsnHB/7/vwZl/1mwSPcmbE1j6r4OJ3Irr13AqXcfeRRiByJvgVPb00mE7u4uGhuF8WWEl3TyKL7M1v3KCiFlpYFiJ+jsHy/PIuPxY6ltS4uzsFnngSP4x0+Vn/gXH0/1iz45m3hcb0/I23WvVTdJypEj//c4XDYqam12+3WJpOJ3b9/3x4+fGhvvfXWnvCjMtXtdrvn8rKrmqWa0AJy4CyrgOPoudcCZFNjo1p6nKaare/PgbsoZuHbw04t+hzPQTSmd7hQB89z5EVxRyRytIhGC7vdzh49emSPHj2yhw8f2v379+38/HxP9BxU4o4RL3qcrIITRjAPnz14TG0W3wwjK6stCZ69imz4wFNxzQ6FH7n1HHDD4QhvA7dfMjJZ0FSUOWoRjclkcuCy3UX6/X6zJt4777xj3/zmN+3Ro0eNlT8/Pz9YVMKFwwU62BGg2PEZb5iBf8NJMhz46vWu19RjVx5TcTUWPkrLeb6f4wVcdMPHy4LnjpDTdB68w8/Q3c88C2wLx1Ek/jJF0V9cXNiPf/xj+8EPftBYiy4FSTabjc1mM9tsNgci8vE8XtDokjrswrdZc7ToaNlR7Dx2Z6Hz+vRc+x61B291xUt6O1GtAkf//TnKybPgM5c8SwFihJ8n9WBbJPgyRdG//fbb9qMf/cjef//9l9We1wa/cD799FP7+OOP7enTpwcpJf8OjnfNrl1WFwyP11FkkeA9Ss5WEwUfWfdoSa0s7oBjeA4Osldhtr+kFqYmo0pCPCecP8+Kd6L3fNyYjmzLnIicVkvfdd59913bbrf2ySef2Hw+t16vdzDWZWE6aC252AWfsTPIctNRNSCP39ty8JwdwI4nmkCD+8VAIc+ei9x7Dlry9moFj3/HkmIWPQpf1r6MUnYt9Pt9m0wm9vDhQxuNRrZer5uLD29lxRV1ZvtTQCPXHsfyWaUau9Qsdp6tx24utgUDdvyIlr3icTOv5pNF7n0bbZmezOJnQwH/HtcgRFOSRY5uYJmAF/NgMLB79+5Zr9ezxWKxJx60nlgcggGsKDqPn6FQXDiR4HhRzWwdOwwumu13Ptl8eK6O4yKcyLXPBF/ygKJAHJJdaxjlx3OQWXqRoxtYtuDFOefn582FvFqt9sbtKF6uEOPUXNRRcFQey2i5uq60Mq6z2Wz22oedD8cSsA2+T3yNbcBofRYnKAketxFF3J0s3+/b4EpDjl3wb8Q+cu9bcMsyGo0a65jVq2MajMfQbmXxsyi45e99H5yWyyw7tsVTYVGlXyR4s2vr7lYT25UFyjgyj0LFYzGzA2scWeXSdnAbeD6yCL7IkehbcAFyxNosd2uzajtOg7FVYovIASsM0EVRc0wXRpF6fmA7fHvcEeHfojE87wPPm5kdeAvekUXW3Z8jwWdpOuwAFcSrQ6KvAMfVkXX155LoMTjlLrTZoTgitzpyi32f6F0gUQQ9cr9xrMxkhS5RdV00Fud2Z+NujIXga2xX6bxEgpf4cyT6FiJrx6Cg2wpSHKxkQ7Hzxc3uNY+j3ZXntrQ9vA273fUklzY42MjPTiTOyBr7tvA1n088V35ueFuR4EWORH8CmVueEVl2fM2BOxchfs4puKy+n8WDz2wFo/bjPqIxdltaLnPD2UvBbXFbsTPAdqHwSxZelJHojyASbJSKcguMFyMK39+XrDs/zPbz//4+Eya308WWWcgseo5j9SiGgbDgOa/PQUD+PXtJfM5r3ncxvXwsEv2RRNYyEqiLyswOLFdktcz2LTvuy7fhz5FrHYkF94Ft4vQbWl8MCvr+omwApxnN7EDwXArsWQEcmnCHxq/bYG/m2N93EYm+ksjK+2t+nJ2dHUzCwd9Foi/FDaJIOYsvc7NR0JHbHY2xcQ0837+ZhYJ3sCPh9GJk5X17TCTi7Xa712FE5yOKofAQQXyJRF9JyXpElh7F7jlw/g1a4OzixIsbI/VRZsAvcrfM7HWg2H2d+8jl5qFCFpjEbZesPHc+3AlGQsfjxw6pJHZZ+zok+luGhYYXeDZOZaKgVknwkeVlb4ItcZYRYOFk0Xo8jixwx9N68dhw+/x5dk6w8+NiJ+4ERI5EfwSZtTPLI+IcwMu2i6+jCDrvvySWLECYVcRl42oWfBQg5DqC0vx2jkscQ9ThRYLPagbENRL9ifCFFVlaBsWMbLfX8/RZ8P73UmeDr7NYAccNuO1ZzCDq6NoEn2UFcF+83+y4sF0+bImqC3HSkKx9GYn+BDIX1SxOIWXWHt3kGsvnHUKUHkS4jBe9j5IHwaXCkeXkTABXx7Gnw/uICnwiy49BON+Gzyvo9XoHsxezAKM4RKKvJHKraywKu/k4fsZts1hK20GPgIOEbNnZveYgneOi4Zl3GJPg7ENpthyeHzxmdMnbzh/+3h/r9bp5jlYZkpVvR6I/gZqLK7L42bZqfu+fsaiibXD+n8XuKTDcB46Vo+WycP9s5Tk6b2Zh2g+9h2hacRSz8M/x2NmlVxDvOCT6I4nG8pGLjxe8f1YK6qFFxN9FhTrYjpogIVp2FygPC7LxPLYd28bpPt6Ou+DReYpqDCIvCo+Tz2u0HkHWaYh9JPojOMa1ZzFwB4Dbw+2z4LOsgH/fJ81EY2MPEKKF5WBe5I7z8bFbH7UNLXkWsed2cofUNs7H48oCjqIdif6WYVFmKTy0Wiwu3l4Wdfd9sEhRLG5ts84kajM+s1fQFqzLPJKS+Fm4XOvPuNchoZ+GRP8VwRe/i8fddRY+/o4tKz8QtpBtQbvS9ngogam56Nii+ALuK3udWfeowjBy72si9VlAVEj0R1NjWThFxiKJxveZa+/fj4TKguj1rlN/UeqNt40eBJcNZ9mEGkteijOULD5XGPK2fCgTne9Sxyj2kehvSFsnELnvbNmjjgH/XrNtbwsLnS2n2bWXgWk3DvKhdc+GFf4ceRRRIC7aBgfzokBidh6zqciijER/A0oXNlrjaJzvIouKa3gf2d+zwGJkOdn6cowAx8nRsKO0X3bXa8ba+L02weN5wM7KPZRSjEIcItGfSM1FnQXmSoKKtnFswIqtepSCM7sWule6oehLxxm58pHg+bdoraPvsmuPbUXB8/yBSPgiRzf0PoFMCG0XvRMFz5jMXebPatzo6PcsrtIYO3tkY/csFhG1ict9M68pqvHn9e5LNf/iGln6G8LC4XE2v2/7nLcdPbf9NssA4G+4s2ExY5ahFtxPFvDj1/ie98Vj90jw+Bp/I3Ik+hvQZnHZnT0WD6hlnQkSCR2r6Nqm+OIstlJwsSbgmFn1krcSDQXMDmfy8Q0u5OIfj0RfQWlcy2PaY7dTs+9SHICFzstKRd/l975tvx1WJnZ2ndESl1x4s+sZcrUz4dCl7/f7B5Yd38utPw6J/gaU3G9/35ZyM8sX2ogEmF3cWWwAf5dZVXxfagePnc2sVXQYHMQOMgvQYbt9vI63x44Ez/MAJPwyEv2JRBYvs6RtF2E05sbfRoth4P5dWN55RGWzPFWWXe1SxN1/g0EzF2UmPIdrBnAf/jkORfCYo1t6le7WK+HXIdFXkLm57nZuNhs7OztL554zWcQ82i+LHi0iWkauxHPReNtwuixXvGHngc8Idzy4pn3p7jVYfIPnDucEsMvPgTvcl9+1l+/cG90yS8KPkeiPAEWIFyTOZsPv+euImrQbu73RHW94mzgvnl9n68mVUnFoxaPVbqOxNbcHF7KMlrmKPBwew/f7/Ubow+HQRqORjUajpoIFaj4AAAMsSURBVAOI1uQTMRJ9JSh2vDc6BsHQPc1c/yyCzX/L3Ht2Yxm0rCh6XnAimtPubYiOI7tltj9wDI6WHjscf6xWq3B2XdSp8gNFPx6PbTwe7wkf2yBiJPoKWPDD4dAmk0kzjh4Oh7Zer/d+k81Yc1joUZms/y4SfCloxqKOLH0k/CiyHnk2NZYe21JayJKDe5FnERXmuOAnk0kj/OjGGuIQib6FTPA+FnbBs4XE51r4905J8OxOc5Vb5O7jZ/x99jaycXwpbcZtYeHzvqP98eKe3BGwtXdXH28nLmIk+grcdR0OhzYejxurNBwOG1c1c835M6QtX42/iSaVZJa+JP7IuvPYHvePnQ0XxkQBtJLoedhR2l9UdIPCxyEWCj6KLYh9JPoW0PIMh8NG8P4+msHmv8PnNrJCmmg7PHTAbWBbOEofiTway0d59Mjy+vg5Gkdz59PWyaAHEw1p/Lh9f2jxMZUn0bcj0beAqTkHU3VZhRlfdG1R/Gzf/LrkPWQZgZpIfRZM9H2UrG8WOIu8jlKaMhJ+5N1EY36M8meFSuJLJPoKcMWWGsEjt2VxajqRLDNQ85p/j/soibEUX8j22ZatiPaN34uGAdkwQxzSa7lojy8Wv2NE7mnJWt02N7l4o06g9LqtDaUhxzGdUO3+su1yR8MBziy70UHCEyDRV3KsUF5nTm3/6yairEMQDRK9EB0jFL0iHkJ0DIleiI4h0QvRMSR6ITqGRC9Ex5DohegYEr0QHUOiF6JjSPRCdAyJXoiOIdEL0TEkeiE6hkQvRMeQ6IXoGBK9EB1DoheiY0j0QnQMiV6IjiHRC9ExJHohOoZEL0THkOiF6BgSvRAdQ6IXomNI9EJ0DIleiI4h0QvRMSR6ITqGRC9Ex5DohegYEr0QHUOiF6JjSPRCdAyJXoiOIdEL0TEkeiE6hkQvRMeQ6IXoGBK9EB1DoheiY0j0QnQMiV6IjiHRC9ExJHohOoZEL0THkOiF6BgSvRAdQ6IXomNI9EJ0DIleiI4h0QvRMSR6ITrGoOXvvZfSCiHES0OWXoiOIdEL0TEkeiE6hkQvRMeQ6IXoGBK9EB3j/wFj47cE1klYMAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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Ww8qz4LX8h0GJuwj1mDnRx9cZf+PrXoWFfhwW/RGwJc8SVFi0Ep+FS18neO6A4x53tri6oi1Qq4v6fNnEGi6jaVjBgtdYnkuCLHZeQhvr3XMVQi09HzOX/jS5mV1zhhOJ+N00w6I/gqyWrj3w+BsEwLGnxs9Ztx1n6RHD393dHTTiaImLp85yTR5luqoeex5suESHUKKsB0Dr/tg2vsOWnsORiNjzjPi6qMvPAyz/LcJCfygWfQP0xlSxQ7RY7z1ryMk8BG3A4VId/3u2aEVE7MXUiN/xgEoVJNfOcTxY5VYbcSB6rhDgnOBB6D5gudnS80CFF64LDwKZ+Pnfde18xnH88Vj0DVHLxTE4t8ZGxF7snFlXWEOdQKNCx0CigudSmbbb4p0TeGXxNgt+Op3G7e3tgZVnb0Vn7fFjsSKiWNdeS3V83lVTaCPuH5QRce8N8OfKYnyLvzkWfQ1Z4wzHv1zW4kQVv/M2Mveek136jDpN2kXEQXaeH0x5enq6N5tOrTyOB5UCPH0Xr+l0evAEXrj0ui+EDhqf8znjfMtKc3qNIvaX1VKR88DnbryHYdHXAIHwM+Gn02nx0ummepOqteIlpnghDB0E+Hu4qTlpx331LHhMrOEn0uI4uGUX02an02nc3NzEzc3NnqVHXA6PAqED9oGn3rKVZ0vPYtdmJG23ZeHzdVfRZ+255ngaib5J2eSpgRtxvV4X3XHX19dxdXVVPJOeRc9JtqxOr33o/DMEpm2qOA59UAYEDat7enoak8mkePa8Wnne9m63KwYwCB7nc3t7G/P5vLDy2DcLnveBR0Zz3M/nqqLVGn/VNNysBJptz9b+eBqJvu0XdDabxevXr+PNmzfx5s2b+Pzzz+Pdu3dxc3MTs9msSMKVWbgsUVWWxAK6CAU33rCFh+DPzs5iMpkUbjdbee7eg1s/n8/j9vY2rq+vi8FsNpsVsXxEFPsaj8dxenp6sA8sRw3PRFt7MyvND9XIWoL5u1qaZNp+T74Pdu9rWK/X8fbt23j58mV8/vnncXt7G5eXl4UrzMk2jdsz8UdUJ6Oy7LR2vyGZBqs7mUwKS89WPqJc8EjcwcLDa8HghTh+PB7HZDKJZ8+exdnZWWrlI/Yfg6XniL/xeXDWnxfn1AGQLX8mfIv/eCpFP5vNDuKxNsCu6h//+Mf43e9+F3/605/i5uamqGcjw85TRTOXnTv2GC3p8Q3Mf4P7q/PjEbtD9BzLY+ILW2EkIFnwnLxDmRDn3u/3i+2fn5/viR6DCkIFzrjjOgDus4fnwG3Bw+Ew/W5ZhyN+1t9NcypFf3l5Gb/97W/j97//ffGf05YBAK2gf/vb3+LVq1d7K8ZoBlnRmj5bLc6kcy88r5bLv+u69SiVQeS8Ig6vfRcRRZ4BMTxWwbm9vS1CE263ReKu2+0WnsT5+XkhegwueNikTvjhf9PyIgYuFj0GD/ZKMjffAn9cai39xx9/HB9//HFERIxGo4O10Z4imA232+3iu9/9brx48SJOT0/3LJZOYkHZqiyxpNaPnzBT9uQZfjAFu/VZrRzfxX55RVskImHV2Z3nGB4uPQaVs7OzPdHDymPwR18BJ9l0LXyu8et5cA9BXehjHo9K0e92u5hOp8Xvd3d3X/gBfR1gV/zvf/97jEajmM/nhZUajUYREYXIeIaZ1pi5HTfifnaZPnUmmxGnVpFXwuHJNGwtITy17jc3N0XF4ebmZi9EiYjCuvd6vRiNRkUcj9fZ2VmMx+PinHmaLO9XrTyuja7BzzF9RBSP4q6izMp7oDiOStF3Op2YTCbF76PR6KCk9FSB8Pv9fpHbgAVEbMqWETEut5rCY9B56JzMUiFDEDwXXufFc6edNt/wBJ7ZbFaI/fr6uqjFs4WHez4ajaLT6RRuPYteY3k+H+wXyUzOX0DEHJro4pw8ow5ewzEufFn1w5RTm71nq8ddWm0BdfqIe8sFC8fryvf7/UL0sGBwsflm1CfPQPAcm7P7q0LPnhPPVhZWnktyl5eXhei5rwDCRXkP55IJXq089xboIhns2uN8yzwUfJYHxTq09NmkMmLuccmuBogI1hmC5wkuJycnMRgMiptwtVrFcDg86NJTEXBcDmGp6Lm8xSvOcuyuk3gQw3PjDVx7brHFwIV3rser4DmW51Kkip5bhnHMuiovBjBeHSdL1mV5ERU8n78F3wyLvgZOUOGm4ho2GmSGw2FhBVEe44km+B5iZrjynIFn11fXp1dXPuJ+xhwy75vNZq+fHu21Nzc3ReKO43gIj3vqYeGRqWerDFecKxP6Apqx1zAlm/nH8NJies5lnY1285th0TeAbya21iz68XhciIKtHpe1kMBjq8cz47jspoLXWBf74X1q443203PnIJcGYeFZ8Ox5IJxQ6549aSciiqRi3VRcPo5j/h/4+upaBhZ8PRZ9DVldnoV7cnISk8lkL5nHDTlZfMvuLr+zK8/datqbzjc3l+ZU8JgUxJl6HA/2pRaeW23VInMMz1OBdS18Dhm0RMfz+7UVWclq9jh3naufNUCZHIv+SLjkpo+N4pqzDhTaaFOWoMtWmAW46SPuLT2Sdtpph8abbH18JCAhdtTiz8/PYzKZpCU1rQyUPbKaJ9Lw8/Rg8XlBTL5W3IlXhuYuNJfgTH4zLPojybrkIH7EyNpayyLQZhz+d30sFF6awNKFLFXw2niDPgGIGAlICP7i4iLOz88LK8+Z+ojY26cu5cUVHXyHl+/ieF5zEmq9tYGJ4UFBFx/hyoGtfT0W/RGokNllV0um00d5Vhm/WOQsdqAtvbyIJS+AUVWHh9g4F3F2dhbPnz+Pb33rW3FxcRHPnz8vGnB0hh7iaH7ijj7bDnE8Z+y55Ijzj7gfRFTwZS6+Dgq86IhafFv5eiz6hmTJJnbBeRacWm9NyOkDJ7ISVVaaUsHzHH/MlsP0WI6xMSNOZ81dXFwUouc2WwgYYoPAdFluJPAiYm8Q405Dzk9oHM+5D7bQPLBmU5A7nU5xLdTSW/D1WPQNaJJdhpi1Zz4TfZax1ng0i1/hUvPEGdTguQ7PFp6rBpqlh4VHLI9MfcS9G42+AyQKsYAm1/uzgY/PX6fNYvuZxce1zDweLuGppWfRW/jVWPRHwp1fmWVhkbGrm2XieTv80nKUrsuHrDzceWTqs2WuEMNzey2Ens2R5154xPG8Hj4/U48Xz2CXntuEs67BTPB8/dDKzNeWBb1arWIwGBwsP27BN8Oir0Etsv7OXWHsfnKZTuN1Ts5lIs9W3GVLyyvXqruN2jdXCeDSn52d7XXbIWnHtfiI2EsU8n75eXoQbJbXyDL/fH20zFZm4Xn+Ag8ayDFo9t4NOs2w6BvAGXitmbNgYYGqXE2+KfW7/NALFjtbWX3GnT6FRnv7sboOhA6xn56eptNbOUvPMTxbeM0XqOh5mq+27eo6+OotsdD5OvMA2ev1iuul5TpTj0VfQzYzjt1gtjrcp4+bEbPS8Fm8I1bebDZ76+fr0tr8XDu02GrJDDc8J9EgdrbuWFYL1l2n5HLugD0KFTxXBGDpdU0AtvLcTFNlodnCc7mQBc99CmXlPlNNrei5OQRWrA3g5hoOh4VlRIYbzSvcArtcLg+aatiVhziyWjtbcyTJ2LrqM+24Po6+dS7H6dRYba3lJbVwrvAWdEmt6XRaDDS8uk7Efl2eS5McvnDVAceNfAA39TBs4SP2JxWt1+tiIHVd/mF4EY0GfPjhh/H8+fM9EfF0WtzUmpWHwPVZbxBZ2bPo9Wm1+ByXp7RUxqvWIknHy1zBnVfB65RcDDgQPC+YqYuBZL0FGIR4mxyqQPgYQJSsGYkHD7b0WZlPfzaHHLWIxng83ptJ9VTpdrvFDfbixYv4wQ9+EBcXF3vPicN0Woh+sVgcZOI3m83ekloR94k/FTwsKguehcJz2DlZyBZ+MpnstdRydp4fQZU13kDwfAycN+AVdtgVj4g9weOdPaCytl1NekbkZT3N9GNg4P1nZT6TUyn609PT+MUvfhE//vGP9xY9aAubzSZub29juVwePBMu4v5mh6vNCSpYOIge8Iw4lN80OaePiWaRYL9otuEHUXAffTYfngXPwuSynDbgcGku4nAVX2wLXgAEyaXGzFOB4Hl+gVY1OPGnFQB8nsOpY2bstZlK0V9cXMTPfvaz+OlPf/plHc/XBtyAf/nLX+Lly5dxeXm5J2h+sixeyCovl8sYjUZxd3e3l/SLiD03muN4deezzDR3qvHyWtxwg8QdHj/FGXr1NrT+r2U5nTKbtQxjezzTTuN4eApq4XWVHM4DcOIuy/RzglUXF7Hwq6m19G3n+9//fvR6vfj0009jOp3GZrMp3PHpdFoIA+5vr9crXNkysXHyTh+CyaUodYEhOp7dxw+74PXvs3Icu8q8Dn5Wh+dpuHjxJCOdfQdPICtB6jWCWPF97djjeQZVgteJTLb2zXDJroZutxuTySQ+/PDDODk5ifl8Hp3OP1bIQRwPq8aZ9Lu7u73WW83aQ1z8qOtstR2+wbXhhh9cyU+r5bXv2XJqNl3d+bLGGy7P8UzCiDhIrmHbLHwuteF7ELpOHS6z8mW5jKy/n/djDvEDLEvgm2Y0GsX5+XlhTdbrdczn8yLHwTd6xL0lYzeYm3H4sdT84pZUtrDcBMMr7rDY0VWngww/p4BjePU0sJQWVwUi4sDTYIta12TEyUecE/IbWZig3XaZ4LOpzboysAVfjR9gWQMmqvANOZ/P9xZ2hJAhXBYJYLeVY2Dc4Jyg4tiU+/d5UQpeRJNverbmPFhrgjGbF4/vYJCBQMuy45xsywYxdsl1ijG/o98B2+RrXRbH60IkaulNOXbva+BVcrDKLbvPEfuC4huU0Ziaf8bn2TXVabnZMluaM+C4mZuoWJjcIKOtrDxjDudeJnQdRHjbbKEj7mv63Lmnz9zDMfN14jo9D4LZYptO5DXHom8AzyJjF5rdUo6ZAVtadt3VAgJNUGVTddmqazINP/M2IdBsXTvtudDOOnW9ORHJ8TuX5jiLHxF758BPCOLcwHa7jeVyeXCtdADVvAa2l/2fmHIs+ho0c94kS8ziVrTWrvvQOfC6zBb3y2di5xyCzuDjRCHnDrIBht1luNra+875AfYasF2eC8DhiFp5bLes8UurB/rgDH74h0Vfj0XfAI1pWazac84NKrzyi24rYj+ZhZ/ZivM7f4/bYSFo3Te74Vr6wgChA0q27FfEfWttJnh+adMNP7tOS4k4X4QIm81mT7Scpcc22dthwfP1N/VY9A3RbDqLkwXPNWd8r2w7fGOXeRL8fVhFCLDMneWkoZbscAzcf8+JsbLptmWNN+zSc5mPlwhHpYGrDOxBRNw/wFLPHaLHIKJuvVp5u/j1WPQPQGvncItRIuPklIqfhaYWni2/wgLRbfM7x8OcCWf3n8tbbOW17h0Re/usmh5bJnhM9tH+/4gorDtPStJrC++i7Djt1h+PRX8EZdaeXeSI/bnejNam1cpnlh3by16a8OLZadpbgX1xaFEleNThsV0WfFkfvQoeC3agU1CfloMeArj4OJb1eh2DwaA4d1wXXqCDr5s5Dou+ASzELMOOmzWrNWvirszSsxj5+zyllCf0cOdemcixPW575Rq8ij2rCmj8znPhcRwaw0PwPA+AJ/1g2+ziczKRew647KeCz2hjI9mxWPRHotl8vhH5hsZLYaFnbilbcM68Zyu/suj1+BC349+0bZUfJqlC0gGHW4W55IdYG/til56X5kJcz1OMuZ9AV93hAZQHzawWn3k3Fn41Fv0RZO49u8ns6uIzmSj5HfANq51usLR41wafiMNKAMfsmvHWx2GzmDjxx008OvMOYF8QPCYAIY5XK8/5ju12u2fZWfhcisT1ylp2Ndwx9Vj0Dcmy8NpEw734WbmOYZHzjaylNhY819m1J52bcjKx88MyM8FHHHoZ2Yo33N6LAYZLczwnAHG8rivIScUsVMKx41yyJif2grg6YeHXY9EfiSbi9OaF4FBay1xOFnpWklPRs8hV8AyEwz3psLLa0KL96tg2RMU9+ix4Hsi4045n/WGfWcdcdv56HeE1ZTkS7TDMpt9a+NVY9A8gu1H596xGX2X1Ge3P11Vj9BgiYs9Sss7ST2wAAAhASURBVNhgfbn+ngleRcUeBtfi2dXm59Ox98CDSzYJpirxqH0K3W63CCW4xVnnDuj6A6Yai/4IqhpttDkks/KZ1c8SUhwz4zM6mGQJLrbwPBMvS9phvzwYcTts3QSaiNhLvPG+NbuOwQTHzYNYWRJSB0yO8Tudzl5i0S7+cVj0R1Imcm0hzchuSHX7VQQ8m6/T6RQNLfxvHAdDfMcIviyXAGvP/fRaEeBe+rIKgA6AWSxedb342HDsmmvQbVn45Vj074FaJm4b5Z5xpeqGxACCZBf/jvKWDgzZnHtutNF4mq17ViLkJBnviwcb7kbU7D+2xQuDaLJQha+9DXocSGKCwWDgp9Y+EIv+gWgiqszFh1uOG78qtucBQhtkdrvdXv2aP8fCzyahZILPSl5ZLoFdei4PcvzN20blArmCzWZz4O5nJUkdcHh73BAEsL5BtsKOhV+NRf+eZNl8tfawihBEWSybAfc+a0BRDyNrTWWhl1n4sp85b8DHmMXcsOz8u3bQ6eBT1XzE6+zxTDyQLQ9usTfDon8AZTV7zt5zLA4LhQkkSEYBrq9niayIw0k1OmhkE0/YYutAwJaef6461+yYeKkvWOZ+v1/00pf1yWsYkE3T5YYkXpyj2+0eiN2Cb45F/wCyG0xLdvoZzHlnF1/r1ZoM1P7ybHKNHoO68DpPHf+uba5ZRUF/LxMY9rFcLvfifG1RVuFz4pCn6vJLXXguMWbHaOqx6B8Btc56Y/NUUhZ6WZNKZvERJmRZcQgAHgQLJIvr+T07Dw4d+LOchMO/a6IPoYYKPnPz2dJD+Fillx+QobP59HrpxCUPANVY9I8IbnhYvt1utzcnHILKbmDNC5RZWw4Pysp9vF0uc1Udtx4Htsnb5q7AsklF2pqcVRE0Sckz+Ni6Z6vqYh88oOiMRVONRX8E7N7WxZCZew7XFNvSv9V5DPw5PqaquDw7hrL9ogMui925zJb1//O22NJzVaDK0nOXnfb58/ZZ8DqgNOmVMBb9g9EkEt+ciNszIXMZL4ujNRGnZb5sn+pyZ++6H8AeRubWc8KNY3Ctr+M7KO2pdUY2n/eFgUPn6mvtHceJyT3oOPQS2A/Doj8SLZux21uXBc+SeFqCUxdd6+pZTF2V2WdB6u/seXAFAZ/j8h1bePzMf9ckHTcUYZu8lBh7DuzK42e+jtyLwLMGuRvQD7tojkXfkMyd5xuTBYC/qfj05zJrrMLkz2Uehg4ybLWbnlM2kHCTTNapxwOUttvqfrJkoK4TkD3tB5ab17vPJvbY0jfHon8A2jbKN3KV5S0rubFouZ1VBV/mRWg4oYMGU5b8Uy+Cu+b4Zz5fbL9sHgIn1/hawHXP6vA8sYctvD7SCy927y32Zlj0R5BZbxVjmejLrL6KWBtpymCB7Xb35bwm55ANPmrVs3ce6DRfock1nkevrbos/GzNAJwfknZlgmf33tn75lj0j4Am6hSeQIN3Fk9VQk/jcf4u98Nn22Ay91rbV8vceC7NseAhaF5gU5en1vXzsxwDv/Ngpiv/8EIdvFiHrg9gF78ai74BmQVVS6TLX+NnfF+9gjp3Hfsou3nLrHqZ4Fm8cNXRTwAXPqs64Hi0KYbLcjqfPltAgyfP8HXiqbo4HgwWyNLDomMZLl1wk5fWttjrsegbwDcSx6y8zntE7K0uU+Xmq3ud7a/K4lf9XlZjh6Utm+TCE4K4rs7HiHPnxhtepSd78kzEfj1+OBzGcrksBg1uwOF9aJae1987OzuLZ8+exbNnz4qHaGjZzpRj0TeEE1MQ+8nJSZG1Hg6He3XliGrhZ6LPhF4m/rp3Pga28BCYdr1x9lzXnOPz13X4eP09ZNfxGbj1bOWzJbWxP+xH3XoV/mQyKUQ/mUxiPB7vJfMs+mos+obwTY+FICH40WhUOsUzy9aXWfk60TcVOsPeBVt5zZyr6LPWV7bw+lw5rZdrqy0sfdZ1h/3xNdYBhVcEGo/HhYsPS69JQwu/HIu+Bi1FwZVF/DkcDvcEn8XomcjLBM/7ZPRmbiJ8de25uabslSX42MPRJ+NoK6xaWxZ91oTD+1PR84sX/WSrrxl8W/p6LPqGIIYfDAbFDQrLU2Xhy34v24f+3PTf+Hc9Dh6QsiWx+PeySTTc887JS530oqLL9qv71DKg7kcfiKGJQzfnHIdF3wCuhyPbDCuvDTkR77coY13Cruln+Diy7D2/2AvQ48e2Wdj6XlUqK9s39wVk5TrdPr+y6bpO4DXHom8IZ+1h9TMLX8YxJbZj/l71mSyHUFU6LEsushj1Ouir6hh0n9m1y/aXvbJ59E2vV9vp1NywXoPo/1OXhf+qOKaOXzYIZH/T7dclFcuOpS6pid+rwpiyBOcxCc2Wkl4QW/qG6E35kJj9mH08NlWDQNVnIupzCI+1/2P2UTfYmHJs6c0eVVUF843Dlt7UY5E/feqncxljnhQWvTEtw6I3pmVY9Ma0DIvemJZh0RvTMix6Y1qGRW9My7DojWkZFr0xLcOiN6ZlWPTGtAyL3piWYdEb0zIsemNahkVvTMuw6I1pGRa9MS3DojemZVj0xrQMi96YlmHRG9MyLHpjWoZFb0zLsOiNaRkWvTEtw6I3pmVY9Ma0DIvemJZh0RvTMix6Y1qGRW9My7DojWkZFr0xLcOiN6ZlWPTGtAyL3piWYdEb0zIsemNahkVvTMuw6I1pGRa9MS3DojemZVj0xrQMi96YlmHRG9MyLHpjWoZFb0zLsOiNaRkWvTEtw6I3pmVY9Ma0jH7N3ztfylEYY740bOmNaRkWvTEtw6I3pmVY9Ma0DIvemJZh0RvTMv4frjneH/0A8bEAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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G8Fq6h3rPTfbdYdI/AKUHTuNMFaNg8TLRjl36TKlnwgFMeK2ph1IP0Y7deq2845JZbvPNZM/Sgjguzy3IJthkvfSwD0bm4mdKvv6t9LORw6TfAZlLmT2M+ne17llLK3blmfA6TTai6c5n8+FBct6QmtMZdGrlmfQ86DBheW4Bjs3NQni/mqGAlc9EyFJcX7rv+jejHUz6LShZmE1FKBnhtYed1tFvqrjTSjsW7bjjDa9Kg+3w8LB26TFHHlYe0Mk1pfQgjg/tgBfLgPfAZbzYX2mar4qAWQZE76vx+jDpd0BmZfi19Pls8kyWf2e1HgODTitlwmOVFwh3PE2WCY858iA8rLGGH9n8fJxDqRYAAw7PzouIBuFZi+BrUGSufcmTMh4Ok35HbHPrI+6n7bTyTi07C3aZaJcRnl16kBtk5/nxauFVbCsRHgIez9oD6XUZLFh71QmQBVBPBd10s3SfDqpcA4AKPuP1YNK3REb2TGHmeFVdfLXyrNJnPe3YpeaZcrxeHVfcsXXnLjj4DsfNGeGRnsMrPA4mLK+Ck/XTi4jGBCAMdBi4sJ9SWKT3Gj/rvdb6B6M9TPodoCTWhzOLR7N4Xt17LsThSTfYJ8fRSngmOhNf215lHXu4sSaW5+LKOwxAOA9OCyJLwAMLavB5MOH5ATxRKbu3rC3o/dX0Hs6JX7P9Gfdh0reAWne2Ytk0UnyHFewsNcdCnlbaAayUlwivLn3JwvO1sEqPBhkouWXS41yyLAEfC9abS2R5sIuIe7F8qeyWv89ZCxUCPbPuYTDpWyCzjFwSy+TWktGsr532p2eLqIRn0W4T4bXPnRJeCYVzYMJj2iwaZGBAw8Cj9fw4Hubhc2kx6xK4Lm6nXaq1Zx1DY3qgVM1ntINJvwX8ELOF5lic3WC27LzQBFt1FevYJY7I8/AcP6tCX7LyauFxHI7hMXX2/Py8Jv319XVt5bEPTglqXz3oGFpmzNZey3Y3WWodRLFPk/vNoBXpu5guwQOGktSsRxzELiatimMau2cdb3B/uZdeSaXPcvGZlYd1jmhOWIHHwXPl0RHn4uIiJpNJ3QYrIhqpQa7yUyvPg9628IeFSW4tlll4HQzxisHD2B2tSN/1Efb6+jpevHhRkx3toqBuq+qsnoHW0rMqzu4uT1gBeZEay9JyqtYjPcfFN2zhkUK7vb2NyWQSFxcXcXZ2FmdnZ3F+fl4PZPg8RMD9/f1GHT9IPxwOo9drLoKpNQmw1jgn7RScFeXwfczSol00Qm8Sdu+3YD6fx9nZWfziF7+I8XhcW30uTeUHlyfSaI94raVHvMtuLixr1p++FMNz2ozJxBoDCI8WWOPxOF69ehVnZ2fx8uXL2sqjBRfU+v39/cYU3dPT07qGn2N0FPFwMQ42gOsMdCEQJrJ6TVwmzJ/LUqbGdmwk/XQ6TYWUxw7EshERP/vZz+LHP/5x/OY3v6kXduC8Na9cg+/Chc7KWZGW44KViLzFVbYgBQiPmXQcw3Mcrwo6CA/R7vz8PF69ehWvXr2Ki4uLuLy8rK18RNSdd0B47amHwYWn+nKWgn/PhUWaftSW21ktBKfsshx+157P18VG0p+fn8cPf/jD+MlPfhLD4bBYVPEYAUv5hz/8IX7/+9/Xca7WnsO6soLNbjSLeCBCRHMuvsbv6E3PhFfrvq3ajj0ONMSYTCYxHo8bMTy3wAJJMfAcHh7GyclJTfiTk5NUvGNPQgkPsiJ0weCk58xiH6frOKZHNV8pj68DgpFjq6X/8MMP48MPP4yIiP39/UbXlMcKPFzr9Tree++9+MIXvlBbN02daTUaiKZpORW1SnXsvLYcUmMZ4THgqGgXEY2sASrsLi8v74l2yMlDjFytVo1FLRHDo102FsVACS2eBc5WqEjJVXjq1mMrxe3q3qvLb6I/DBtJv16vYzKZ1D/f3t7+yU/obQC73S9fvozBYBBHR0e1hYKSzmWmID0eeLXuHCZppxkMJKzMg+yYD8/xu1p3Di1AXuTgr66uauuu7jy62sKl5/CCrTz3x0cIwYVEWnjEpcTcGAMeklp6rdjDfeK0nWomWrxj4rfHRtL3er04Ojqqf97f328IM48ZnLKaTqexXC5rCx/RrC5Dk0hYvywtB0AgAxE4D8+CGcjO6TElvObh2cLf3NzE1dVVnJ+f1wo9VPrxeFynG7niDt4DrDxieZwLi3fs1uN42ssvoql74FpZwOMFQnAdeOVcP4uTPLiZ+Ltjq3rPVo+XTeoKYLXVLa+qqmHhtRBHV56JuLOkiHOznna83BTy4kp4raXHww/ia0ruxYsX8eLFizg7O6tr6+GFYBDCflmt50aa3BsfFp61CyV8lopkl57ba2nhTRajI1RQS2/S7w6n7LYAbmyv14vZbBZVVd2zNFzIA+EM71nMioj6QWd9APE7x9Ds1vPkmVJprVbaQaF/+fJlvHjxok7LcX0BziciGmW2WnXHugUGfU1Lqlu/Wq0aKwCxhd9k5TP1Hn9Tsqu1Zy/BKMOkb4HMjYxoxp4gHVt8Lb7Bxi49SAYLz1YeLjU3qdDqNS680Uq7ly9f1m79xcVFY6VZ9jb6/f69hTGU8MhORNwNhCUrz95gr9e759Kzp7JJgS8NAhnx9X9jlGHSb4Gqw5pz1s9x/l2/w3EzW1WQXJtYIi3HMbyKdmzhkYdH4Q228Xjc6FsPYQzE47XuuA4AhNfcPxMe2gA0DBYrOWxQ4VGrPNum3TLSs6U36bfDpN8RXPetfd75MxHN9ei1hp4XoeD4mfvSZzn4rAEGhDQU3lxcXNRk55Jh7XXH6Udtt4UYnnWIrNKQsxRMeBbuuJZB59LzQJkNrll5rk7btXu/G0z6ltACE46vYX2Xy2YbKHahlfCZO8/uNJfUbmuAwRYeqTkQ/vr6utFRlz0VdelZpdc22WrhudKQMxSahtTBi+8Xu+Rc6aj3nAcDHhxKCj7uk5HDpG8BzjOrEo1JJ+zq8wq2vFS0rjrDK89o/JxZ9ojmhBZeWZYr7XgNeeTh1+u70mIWEFWp57XqeTBjpV77+vG14n6xZwPya02BFtrg99iHzpvH30sxvd37djDpdwCr7lxogpJUuLRwddWF1jw8NsTPWTpOibFaNRtgsDt/dnYWr169SgtvIMSxhT86OqozBdARQFBe/AJWHg0z0TST23shBMD54xjahZfTjNi/uuel8Ckb+Ez83WHSt4DG8VxkgleNTSPingvNJbUs1qHEN6ud5xlm7GLrfHgo9TwnngnPBTKcljs9PY1nz5416up5wQq28iA8SI+QgefEczjDll574kc043ncN7bwPO+eNQNW67URiQm/HSb9FmSNG3TtODzQnJNH7K9TY7UXvc6BhxoPQkTc70vPLa60vBZpOdQW6FRWXgFH17mDt4FjQvjTVWxZsYdHg+9xx1ydH6D5eM2xZ659VrTDKwRpyzITfztM+pbgsllWpUFctnbcG57bVLMbz8U2XM7L1WcR99t1sUo/Ho/j4uKirqVHEwwuvOHz5Vl7yBRwii5rY80ltiA9W3kcR5e6YtVeu/jgldNvmtvXrAgGCRCaCc/EN7bDpG8BtfJw25nUIAwLfNo1dtM0WH5g1RKyhUdfO4h3eA9xTbvecA5eZ+7p6jfs1nN1ocbymu9nVZ4n1OD4mVvPlpld88zKq1eACUU6v8HFOe1g0reAuvXa5IKLaNgDAOl53ru6uVlsyhYMeXAQD6SfTCb1HHnE71nGQJtwcGoQXge312KhENkBHFfXtoNrv2lCjQp3THTcg6woh9/rfUGMnxF+W3GPYdJvBTfM0J7vIBQ3tOBXJrySgB9kXchS37O1ZRcbsTvPaONpuiA5u/JaSw9Vn2NlDiPYyuvgkk2Z1ao7JTr/rMTcVG7LLcZA/KwVucm+HVtJz/nnqqo6N7W2qqpGQQ1Pe2X3mIU53tjVZcJzJZ0SjPPgKK7hXnv4mcMCnQuPNBxEOk4Nas4c1p1j+Mlkcm+JK51liVl33OqLaxY0bmf3nKEDBM9l4LUCQHAMNHbvHwY30WiBd999Nz73uc/V6S1Ocan7rk0idN470l+8dhziciw0gQFAV8HBA8417hHRWO4KzS+4zRW79LrQJMfKPAipV6GNQBH/c8GSahSch+fZdzxQ6dRahDq8acsxeCglwpv4m7FTE43RaFS7ko8Ze3t7NbG+9KUvxde+9rV455136rp4CGJw4yFWaUGJKvB4oLWSDiWzaF/FxS8ogMmWrVZhERYe1X7aAIOLf5SYLNphMOLz4PZXCAmylFpE1ATn8AU/c30+C4EgP3tBOnWXm4oOBoP6vEz43bCR9IeHh/Hd7343vvWtb9UPTJdu6nK5rDvEaoksx61cWqouLYNVeJBcV5eBMMeCmZaqsnVF/K797GDleREMJldEU7RjCw+yM9n0/64pNVXmWWjjASsi7g1Yes+zTsIgPwa8wWBQHycLGYwyNpL+6dOn8Z3vfCfef//9T+p83hqAHL/97W/jl7/8ZUwmk9oy6USPiGY+nWNUrl/nhSZQK895dlh5EB4EAniQYcGOC20ylx4xvFpTFQlZsFOXnr0L9mQ0f87uuYpvug8uBOLCJOgHuqovvI2qqu4NqtyQQ2c9Gk1stfRdx1e+8pWoqio+/vjj2k3lmWZMDCYo6tHhxi6Xy7rABeWz3KQSuXZ2pTn25bnv2Xz8rL2VrnaDSj9NySF2R/zOKr1WyWWEx/VyBiAT2SKa025BVPaUuD6AXXtuq8199oGsgs/I4ZTdFvT7/Tg6Oop33nmnoaizC8sPNQius9TwIPOcd5CdU3CwwiyYcaUbE55n7cGVh9aQWXYu5eUcvFp5nirLMTwsNO9bZ7xxLM5Exb64PRfn6iPuqux0UNUGoyh55tp8zh6Y/JvhBSwL4IdmOBzGkydPaiKztWfyR0TDxcfDqblvxO6s0vPiEGwBef4+pwfh1mPTxhcRdzE7z85TwY7TgiyW4R5wm6ySSs/iG8iuegQGC+yH43AWFDVbgY33w+eiPfc4xWzk8AKWW4DcNwiJBxMPsE4PXS6XDWJENAU8KOOImTX3zcIWl/Ri4wUvsj52vV6vUeHHhNd8P9bk45CCK+02xcl8DLbuGscDmupj6Dlm/fP1HmFf2Wo5XX5e28Du/RZwxdlwOIzZbHZvmii7z/hOVoiTqfLYP4jGYhdID1JrLb+uRc/xbkTcs55wmdm74MGBa985dudr1Wo5Xb6LXXGcB6f4+P5oSjMrQ86ERJ77kK30Y9JvhknfApxeynrOc1wbcWcZs/QVq9hIO0VEIx2qpa3cTJMr/zJXHnUUOmkmW0yTVXdcE6cktXsPhwuaWmPScy0BT03GQMb7jrg/wYgHxWww0hmOWZmziV+GSb8FnGIqdXLRmvHMMrIIBfUa1h3kZxeYe8SzNdOJO5yrBrSmXwnPhOLzgQeDASebMIP9a9yNnzEIsqCmA5hWBaogyHX6+B8w4XXaMvapHoSRw6RviTaqMAtTCh400FcPrbXUMrLFZfLr3HR8l0MJtfDZYhTQHViJj7ir39cYWWe/ZeWxPJjw/dKZhxjANDzKBDgNL9TC64Qmu/btYNLvAHYddYuI9MFV5brX69X149kkES2AyWrbtWEEl/myes5kZ5dZK+LYu+A2YGzl8aolsqqu4z6wDsL6A5qHcDEOwgYmLF8/7gvOCylL7QVgS98OJv2OKBEfQhpPHtHPM9G0tFaPUYpNWdhissOlZ8JnWgL2qfMFuMMOu/bsbmvhTaauM+GzngO8Wg5q+VHIpHoCh0rYJy8Rni0EYsJvh0nfAvogMdF5y0jPJaaYBw7RrjRJRNX3iKZgqO9Zhecpsvi9Cnba9IKnAGsIgWOD9Fwww4VEvG+2xigi4qYduH708VutVlFVVZ0ZGQwGjcIdWHwV71S1N9rBpN8BaoGV+EzkTOzjz3CtOoNr2fHga0WdTlfNfof3fK5wkSEO6tx/VPJxRoCVeh5INA/PhURcMYh6ApQFc8twFDQtFos6FODKO1wLVyRqrwKO5W3l28Gkb4ks3uRXJT1/T2N2/pnrzkF4WD9O+XGVG5OO3XudA8CFMKrSc3UfF/cglmeXHmTXIh69HxzDw8rzxB+010bYwGXKWlCkOgbFh6wAAAliSURBVAhrBLxMll363WHS74hS8UrJ0mSWn4nP77PiF3bX+ZUJr9NWmez4WZt2cu4f7+Emq2bAuoCKiDgWBgxu+Y2KQW7ggc+D9FyYo/X1CAMyoRFioIZSxnaY9A+AKvYgPk+z5Ve8z+J4tvDZBBaN3XlQ4PNhq8jehxKeJ+5o51oemDhEyKrjcO14VSvP1j6rHOSUHV8jW3mtcuQVhbhoaJM+YtyHSb8DNKbXWm913SOaCy6W4nkmPpOe4/MsrQfVG4ClZgVcl+Eq5f1Bxmzw0XPgVCK73qysZ52AeVEQTj8ilufCHj4nICuO4nM28dvBpH8ANCVXcuu1Uo/j7WwgUHUen8NxQAJVq7cV9DDhYCk1r83npt6Glhcz8XBcDhdUaOPyZR6s1ut13RADk5o0TOFwgicB6UCpxDfKMOkfCC2iwcPM1j4jPL/iM/p7fvgzQYtTg5r/55JX7UG/afppaTACOFzA5/B7Jr0eC+fNwL1icXG5vOuIw/eYP88CJ2sBmTdklGHS74isGo/Jz5aTkRFbrXzEHZlg5diFViKy1c0se2ZpVfhSK6nCIJ8LzwbkDEG2uAX2D3KWVPZs0MIggO8jn8/7VSGTj2dshkm/A7JqPJ2Iw1aUY1f8vInwvG+QKxP++HOcLssU7ozwmfiVxcY4DivpEc3JN1nmAkRFuTH/XQVDvRbeGKqFsN6h1Y0m/maY9Dui9ICCcOiaw2CSZV6AWs4254DXrJRWS1pL4leWKWDyZB5Gr9drVOHhd6xJzGazBtH5mJxbz8IIhZ5vRHOhT/WcjO0w6R+AUiwNUuPh1weba8m1Ek/ngmtWgI+t71mYUytZqvhjK6mvGsurtYe7X/JatN5gsVjE/v5+/V6bkOhCGFpqzIVH6/W6dv8zz8TE3w6TviWUkJmVV3cVDziDPQG1oPxdPg7/fdO5YZ8c45bCCyaZKuDYJ2cL8B3N5fN3cG3z+bxRUgvCr1arWqzjgYkn84DgXISklXqbrPum+2T8ESb9DlAru83SR0Qd10ZEw0qWavW54EUtPlCK83nqbBZnZ6Tnv+nnFOzCg5hKfNyT+XweVVXdq+SDSs9z9VerVWPmHi+Sof32cH/1OkuekXEfJv2O2Kbeq4INsMWMiMZDzPuOuE94JS9eVQTMkJG5jeCl7jYTfVNfe9yDfr/fsPBa9KP9+LFv7gHAy1hxCpMFTH014bfDpH9NMOnZikfcEY2LUrgMVePnDJlV5+m2WVyNz/GAsMkdLnkTiLd5jj53y9GafAxsHO6oJ8PEx+94Yg+3+MJAwPeR7zl7WXwMYzNM+tdE5vKXXM9tDyWHBxHNTjzqimexOG9crJIRPvNWOF2IfSjheW25TSvYZGlI3r+Snj0IJjyujb0gJTwGGc+4aweTfgdkrnEWK++SPtokRpUstarjXJGmn+FmFHoMdo01rYc4Gyk4XUySV7BhYnJtAXs/GEAwkMEjYoWfXXrt1svnmS0FvkkDMZow6VtC00KZteX57FkKqpTaapN2ygjPFl33mb0HmCAaE/PntdMt98sHObPOORHRaAmGe8DFOhwCZV1/lPC93l2NfzZTsKSlGPdh0u+IzMJyWkkFq2wgUKIq8bPcOl4z912Pid/xsXQ/Ec2ZguwR4LxZUFO3XlfVRViic/mViHxf+HhMfNUC2Lpz9xxtl2X3vh1M+h2glprzyxnp+QFn0kc01XEmTymEwKuKf22U+Gy/rJwzWLwDwXW5aG2GqTG7FgploQO/zwpweH88l0Abf2gHHbv322HS7wglPYtQSgZ170suvRJeLbt+hoEHHO40C4EArKpeQ+maSmq9Dm58fLjf2tYKllhr8Nlj0QGRQw/sU/v58SIX2WQiE78Mk34HlGJ5JbaKewCr+3hImah4z9aO3VwFEwiv6mkgjlYNgK8JrxyqaMcc7EvJDkvOK/Bos03teMPnygRnQB/gxTJGo1HdbFMXusiIb+Qw6Vtik/ucVYSBsPxQc54+U9ezTY+tVWdK/DbhhYqBmLqqsbRumFAEqCUGMbM21TwngL0KVvAxoGiLbl7Hj5ttai99F+i0g0nfEvogIXbFQ49ac661Lwlw+ru2hMdxdXDh46mrrmIj/sbv2bpjpRm8cpzOXgJ+l60tp1aYq+90QMJ56GCDOF478qCl9pMnTxqttbP5/EYOk34HcCUY92GPiHtpJsYmVT6L4TeJcxnhNU7OLL1u+jduca0r5PBKNgDuAUipvfF0oUpNB2bTeXEdWQNPtvbotAuLj6WyrOC3g0nfEkx4kB1u8XA4bCjP+HwJSuZtqTo9D0DjYN7HNtIz2VWx5zx8tnQVt+XSxSSzVBqHDuqBlPLx2vZLY3teRINJbwV/O0z6LdBSVVi3g4ODiIia8Kyub1OR9XelQSD7m1p3vM+8hYxg2wYA3TiVhmNl8TbH75mirlZe43hAS2tBfm7LlSn4LsNtD5O+JTiGBbExdTRz63clvmJb3j3bjxKfXWclGv+8KQTIUmlMQI69N3XrUQ8kqzngUltt563dgbaV4hplmPQtgIdxvV7XQl2/328QvuTWP4TwGTIFv/SZjPylV32fqfx8XC6+0VVmSzXwbQRLTgHifpdes2Oa8O3R22JR3Hso8nQYl9duwraH8E1Z/NL3NomFbTc+VyVgRvTsmvT42bWpSFlKhWaDQRb2GJHeDJO+JTaRZhM+rYewpAtk3kCbv0XkpOSf+TObzif7WY+hx2tzDsY9pDfF7n1LlISztx2l89z194zXFSrbokTkbcc3NsOW3mhgV9IbbzVs6Y3tMKEfP+5XdxiG8ahh0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx2DSG0bHYNIbRscw2PL33idyFoZhfGKwpTeMjsGkN4yOwaQ3jI7BpDeMjsGkN4yOwaQ3jI7h/wGrk63ybdJKdwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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+SJzd3d14++23cejQIdjt9prO8qVSCUtLS/j2229x7969DfnxlaJXqVTwer1obm4W1XXlchlqtVqUzNZyzc0cfYS0SQfv9PXDot9Dfs8vX6FQQDgcxr179/Dpp5/iq6++wvz8/LbPI/HY7XZcuXIFf/nLX9Dc3CyGYWz33PX1dYyOjuLatWtYWlp67nWlGAwGeDweuFwuUaRDAiWv/FbjsSqtpq2EL23MwdQHi36XvAinHZ3haYf/4osvNghwO6xWKz744AN88MEHOHjwIEwmU03Py2QymJ2dxd27d/Ho0SOReVdNjDKZDDabDR6PBzabDSqVSjw2m80iHo+LwpzNni9N3a302kuhhp0vMh/iVYJF/wenXC5jbW0NT548wWefffbcjrsdZrMZ586dw7/8y7/g6NGjMJvNNT83kUjgzp07+OGHHzbMx6sGdfFxu92iuSYAEZ9fXFxEIBAQ2XnVoBRe6cIizdEnlEoldDodt+beISz6PzClUgnJZBKjo6P46quv8PXXX297hgc2msaHDh3CBx98gOPHj6Opqanua1NXIGIzk9tms2H//v3weDzQarWQyWQiW5BShP1+P3K53KY7faXoqz1OoVCIMmA6QjD1wcvkHxQqopmcnMS3336LL774AjMzMzU9l8TicDhw6tQpnD59Gna7va7r53I5xGIxLC8viyy/rY4yXq8XfX19cLlcIiqQy+UQDocxNTWF8fFxhMPhTRNqpIU6lZ1xK7voVu70fK6vD97p/4DQiKqZmRlcv34dn3/+OSYmJkQfu62gXVKr1eLMmTN444030NzcXFN4Tnr9aDSKZ8+eiaq3rbDb7di/fz96enpEDTwtWnNzc3j69Cnm5uaQTCa3vO9qZ/pqUKddPtPvDBb9HwwyqycnJ3Hjxg18/vnnePz4cd2jqjweD9566y0cPny4pmIaKcViEcvLy7h16xYWFhbE76uZ2yqVCv39/Th69Cja2tpELX4mk0E0GsX4+DiePHmCcDi8pdce2BiC2+x+aWFg7/3OYdH/gSgUCojH45iYmMCNGzfwySefYHh4uKYMNoJmyg8ODuLw4cOiG289lEolrKys4Oeff8bi4uKWj3W5XDh16hSGhobQ1NQkxmBlMhkEAgFRjUfx/c2QipfO81t5+Sufw9QOi/4PALWQisViGB4expdffomrV6/i2bNnNQte6rzr7OzEpUuX0NHRUZdZT1DHW8r02yxMZ7VacfLkSZw+fRrd3d0wGo2Qy+XI5/NIJBKYn5/H7Ozslmf5SqQ59puJn0x6LrjZGSz6lwydXUOhEB48eIAvvvgC169fx8TERF1NH0kYZrMZJ06cwKlTp+BwOOreDXO5HJaWljA9Pb3lgiOXy9HX14fLly9jYGAAVqtV1L/TAka98Gt5H/Q8qmXYbreXltsy9cGif8kUCgVEIpG6M+2qodVqcfr0aZw5cwY+n6/uszwApFIpPH78GD/++KPw2lcTXmdnJ86ePYuTJ0+KDD8SI72nqampbct9CanzTtpAczOKxeJzvfaY2mDR7wL6wu0k/5u+4NFoFHfv3sXHH3+Mr776qu5R01I6Ojpw6dIlnDhxoubGGFJoKOYvv/yCmzdvPncOl0YGTp06hbfeegs+n08MwqDr5XI5RKNRzM/Pb5vUUwmZ9Ztl49F1pIsDUx8s+h1SOZet3uYZVK56//59fPrpp/j666/ryrSrpKOjA5cvX8brr7+O1tbWup13ALC+vo6xsTGMjIwgkUgA2HieL5fLUKlUOHLkCM6dO4eBgQFRSVdZ7x6NRhEKhWoy7aVeeFpIt2uFJe2qy9QHi36HULZZNpsVgybIxN2OfD6PaDQqBH/16tWaMu02g8Jz77//Prq7uzdMrKmVfD6P2dlZXL9+HU+fPhW/rxTVvn378N577+HkyZNwOBzCUSgdi7W2toZgMLhlXJ6QxuapQGc7k12607Po64dFvwOoiCQUCiEajcJiscDpdMJoNG4ZO6ahD+FwGPfv38c//vEPfPXVVxti4fXidDpx6dIlfPjhhxgYGIDNZqs7NbVQKGBpaQk//vgj/vnPf26a+ed2u/Hmm2/iwoUL2LdvX9XIQD6fRygUwtLSUk1DNyjeTpbJdua61CLgxpg7oybRN/IHW03ANLySRkM7nU5xtqfBi9Ln0ajldDqN5eVl3L17VzSk2M0O7/V6ceHCBXz00Ucit76WTjiV7yUajeLOnTv45z//icnJyaqP02g0OH36NN555x10d3fDYDA899nQYri4uFhTbJ6q5WgcF/W9q8fbz468+qnpG8JJEBCiLhQKCIVCePr0KR48eIClpSXY7XYkk0n09vbC4/HAYDBsGNxIgyhmZ2fx8OFD/PDDD7hz5w4ikciO7kUul6OlpQXnz5/HX//6Vxw/fhwul6tuwUv77NEiVE2oKpUKhw4dwsWLF3H8+HFYLJaq3wkS/dLSEmZmZrYUvVwuFzMAqdSXpv5uVpRDSGflseDrh837baBEExrqEI/HMT4+jlu3bmFsbAzr6+vQaDRYXl5GOBxGb2+vOOtKq8yGh4dx+/ZtPHr0CH6/v+60WkImk6GjowPvvvsu3nvvPRw+fBgOh6NuwdMx49GjR/j73/+OGzdubLoIdXR04IMPPsDZs2fhdDqh0WiqPo7er9/vx8rKyqaClMvlMBqNaG5uhs/ng8ViQTqdFkeCVCq15b1TjJ7P9Dtjy29KKpVqyNW0XC4LR9jU1BRGRkbg9/sRi8WwsrKCiYkJzM3NYW1tTXi0FxYWsLS0hNHRUTHbLpFIIBAIYHl5GYuLi1heXq55zhzwfCac2WzG0aNHcf78eZw7dw79/f2iwKUe8vk8FhcXcevWLdFYU+pXkMvl4mzd1taG9957D5cuXUJ3d7cYH12NbDaLSCSCYDAoEnuqZfMZDAZ0dnbi8OHDwvHo9/tRLBYRiURqsizpGNBo3829YEvRx2Ix/PLLLxgdHRUVTY3wIZPoy+Uy5ubmMDExgWAwiNXV1U290vF4HJFIBBMTE2I+PM1Ur8WLvdl9AL/OjPd6vThx4gQuXbqE119/HS0tLTuqJ5cW83zxxRe4e/fuc1NxSPBWqxVvv/02PvroI/T19cFgMGz6uqVSCYlEAjMzM1vmGuh0OnR0dOD06dM4efIkWltbUS6XMT09jWg0iunp6W3fg3Snb8RNabdsu9P/7W9/w9/+9jcAvzpzainv/LNDOx0lomg0GtF6eTMnEyWkUDJKsVjck53I5XLh6NGjuHz5Mo4dO4bOzk7YbDbRqKIWyuWymAc3Pj6Or776Cv/1X/+F8fHxTZ9jMBjwl7/8BX/9619x5MiRbVtslUolcVzYrDJPJpPB4/Hg1KlTOH/+PPr7+2EymURLa2kIcLtrFQoFFv0O2VL01BiR2M4b+6ogDRul0+maQk/lclk0ftwL7HY7Tp48iZMnT2JwcBB9fX1obm6uO7WW/hvOz8/j5s2buHbtGu7cubNlqq9Go8Hg4CDef/99HDt2DBaLZdvrkGk+PDy8aWWezWbDwMAAzpw5g4GBAdEXP51OC4ce1eJvBsXnaVHdbCAmszlbil4mk20w6TQaTcNNFXkRziLpuddkMqGrqwsnTpzAxYsXMTQ0BK/XC41GU3f9eLlcRiwWw8jICK5fv47r16/j/v37YhGTnt2l9+Dz+XDp0iUcPXoUDoejpmvReX5hYaGqk1KpVKKzsxPHjx/HwYMH4fF4xHSdYrEItVotBL/Vd6xcLiOXy4mhluvr68hkMmKmILM927p8pasor6q/DyQ2o9GI8+fPi4y3lpYWmM3mTb3l25FIJDA8PIyPP/4Y//Ef//FcEpD0v6V0YXvttddw7tw5eDyemjL7CoWCcHKSo7LSgWcymdDf348jR47A4/FAr9dDoVCIUCjNqKMhFltdK5VKIZPJIJlMIhwOizDpTlKPGxEO2b1EpMJwuVx455138OGHH+LYsWNwuVw73r3K5V9nyM/NzeGzzz7Df/7nf9aU9SeXy9Hf348zZ86I+vhayOVyWFxcxPj4eFWnpVarRXt7O/r7+9HZ2SnMeGmJ7Pr6OmKx2LZHKXpsJpNBIpGA3+9Ha2srHA4Hi75GWPQvmMrCEuC3Ypl//dd/xbFjx0QTy92Yq8FgEN999x2uXbsmzu+bNcMgM1+hUIi+evWEAnO5nOiFRz4g2sGpk09fXx/2798Pu92+YbRVqVQSRwO/379tjL5QKCCRSCCdTiOZTCIajWJ9fb2u3gONDov+BSMVnU6nQ3d3N95//328++67GBgYgNls3vXZlNpmf/7553j27FnVa1e7J4fDgaGhIezfv7/mIwVV1S0vL2NqamrDeb5cLkOj0aClpQVHjhxBe3v7hjAjOT+TyaTIZdhupy+VSkin02JybTqd3nJqDvM8LPqXRGdnJ86dO4ezZ89iaGgIHR0dNXnJtyObzWJ8fBzXr1/H3bt3a54Qa7fbcfHiRfT29opy2VogczscDiMWi4kch1KpJGbbHT58+Ln22GTWp9NpBAIBzM3NYWVlZcvZ9QQJnnL12c9UHyz6F4RcLofVaoXb7Rbe+ddffx0HDx4UY6B2C82Q//LLL/HZZ59tO766crjllStX0NnZWVdKbyaTQSQSQTwe3+B112q1cLvdOH78OE6fPo2Ojg7RQw/YaNZPTU1hZmYG0Wi0JjNdWlYrrfVnaoNFvwto8AKADV1cqDacykYNBgPcbjd6e3tx7NgxHD16FF1dXXA4HNBqtXXnzVejVCohEAjghx9+wJdffinM+q0GQUrDhIODgxgaGoLL5ap5lydTOxAIIJFIiCm1er0ebrcbBw8exJkzZ8Tr0lmehlmura1hfn4eIyMjmJmZQTqdrkm8tMOz0HcGi36HqNVqGI1GmEymDedMmUwGrVYLo9EIi8UiCksGBwdx5MgRdHZ2wuVywWg07onYgd/KYx89eoRPP/0UT548EX/bquiFuv5cuHABly5dgtvtrtnioPN4IpFAKBRCPp8X+QRtbW3o6+vDwMAAenp64PF4RK+BynP86OgoRkdHEQgE6uqYKxU9l9fWB4t+B8hkMtjtdvh8PjgcDpTLZSSTSSF6vV4Ps9kMu92O5uZm9Pb24vDhw2hvbxfn5b1KJCHB37t3D//4xz9w+/ZtxOPxqju8NBmnVCrBZDLhzJkz+Ld/+zecPHmyruGWdJaPRqNIJpNwOp2w2+1wuVzo7OxER0cHWltbYbFYoNVqheApzh4IBDA+Po6HDx9ibm5OVDHWQqXIWfT1waKvE5lMBqvVira2NvT09IihkJlMBvl8HjKZDCqVCnq9Hl6vF729vejr64PX691Rd9qtyOVy8Pv9uH//Pr744gtcvXpVjKGqJgISvMlkEnPuPvroI7z++utobm6uyawn4a6vryMQCMDv90OhUIgd3efzwe12w2w2i6MLFS8Vi0VkMhmEw2FMTk7i4cOHGBsbQygUEjX0282ll96H9H2xM692WPR1QiGozs5OtLS0iFLTynRWs9mM/fv3o7e3F83NzaJj7F5Ax4mFhQX8+OOP+OSTT3D79u2a2k3b7XZcvnwZb731Fo4cOYK2tjY0NTXVnHmXyWSwtrYGv9+P+fl5hMNhmEwmNDc3o6WlBU1NTdDr9RvmzJE5TkMxZ2Zm8ODBAzx58uS5AZm1ir7y85A2KWW2hkVfJ0qlEg6HAy6XCwaDQXxBKfZcKpVgMBjQ3t6Onp4eeL3eugVPO14lVGASj8cxOjqKq1ev4tq1axgfHxfda4Hqzjs6Yw8MDODo0aM4ePCgOMNvd2+UIhuPx7G0tISJiQnMzMwgmUyKI4zP54PT6RTptVLBS8//c3NzuH//Pu7fv4/p6Wmsra0Jjz05QOlzrJVCocC19XXAoq8Ti8UCm80GvV4PuVy+oSCnXC5DoVDA6XSiq6sLzc3NVXvJbUdl1h61oVpeXsbExARGR0fx8OFD/PTTT1V72tH9aDQaWCwW9Pb24u2338a5c+fQ09MjTO/tMu5IrLQ7j42NYWxsDNPT0wgGg3A4HDhx4gTMZjMsFovodVf5fun8Pzs7i19++QW3b9/G2NgYotEo8vm8CPVJd+ntzunSz1xabcfz6reHRV8HZrMZbrcbVqtVOMetpC0AABcYSURBVKaA33ZWpVKJpqYmdHR0wOfzwWg07jh3PpfLIZFIIBqNIpFIIBaL4cmTJ/j6669x8+ZNRKPRLV9DrVbj8OHDuHjxIs6ePYuenh643W4xVXY7aNT06uoqRkZG8MMPP+Cnn37C1NSUaPt96NAhEcWgRUQ6XJIEmUwmMT8/j7t37+L777/H8PAwwuGw2J1ph5c2aaHn1gIPvqiPbUUv/YKoVKqGLq2lBButViuaOEhj8iaTCT6fDz6fDyaTqa6QHO2qlG0WDocxOjqKn3/+WYgkHA7D7/dvMOUJqUnvcDjw1ltv4cqVKzh+/Dja2tpgNpvrir/TbPkbN27g6tWrGB4eFr39qOQ6n89Do9FULfulRhfJZBIzMzP44YcfcO3aNTx+/FgInu6bBC9Nz93ueyYN23HMvj64iUYNGAwGOBwOHDlyBIODg/B4PFAoFMI0LRQKUCqVcLlc8Pl8YmRzrVDIb25uDiMjI5idncXKygqmp6fx5MkTzM7Obni81PyXvgbwa1vsK1euiC655KSrVfC0+CwtLeHGjRv4+OOP8dNPP4mcehIp7dDkna88ktCxYGJiArdu3cLXX38t5tRLBU1Cp5nzpVKp5h55JHZ6f1xPXxt1NdGgHe5Vh7LGSqUSWltbMTg4iBMnTuDYsWNob28X01mz2SwSiQTi8ThKpRJsNhuampqg1WprEhl1jw2FQpiamsLt27dx7do1PHr0aMsGmtV2NZlMhvb2dnz00Uf48MMPceTIkbp2d+k9UaLP3//+d9y5c+e5IhqycCjuTxl2dK1MJoNQKISJiQl89913+PbbbzE6Olp1rh2Z5LRj19rLnhZb8tirVCr23NfIlqLX6/X493//dwwNDUGlUm04x77KUF/1QqEAvV6PlpYW7Nu3Dy0tLeKcLk00SSaTyGazIj5fy5eP0lBJGDdu3BA7YS1FJ1Jzns7v7733Hi5fvozXXnutrkQbKel0Gs+ePcPNmzcxMjJStWCHdvhSqYRUKoV4PA6TyQSVSiUcjo8ePcL333+P+/fvY35+ftPqOanYaQGho9NWUAiwUChArVZvCBEyW7Ol6K1Wq2i33EhImzuQg06r1T6XoqpSqaDRaGAwGJBOp1EoFLZcGOn32WwW8/PzuHPnDr755hvcvXsXY2NjG865dA/bvZbL5cLQ0BAuX76MCxcuiOkzO4FM8ocPH+LHH38UffArQ4DkwygWiwgEApiZmUE8HheCHxsbw6NHjzAyMlJT7gAtoNIut9ttLrRYSO+HRV8b2+70zNbI5XIx2EJq5kodU2Q5UGLL3NwcfvrpJ3z22We4efPmc800a00rbW9vF+21jh07BrfbLfrO1Qt121lYWMCTJ08wNTUl7kt6L1LHZTqdxuzsLDKZDFQqlTimPHv2DMFgsObxVHRckO729SD9rJnt4ZDdHkGizmQyUKvVG0Zb0d+CwSAePXqEb775Bj/99BNmZmZ21D1XrVZjaGgIV65cwfnz53HgwIG6nYdSyPEWiUQwPj6O+fl5ZLPZDbPg6X+TdaNQKJBIJDA5OYmpqSnR7ioWi9VUw195fVogal3wpOPB6d6Y2uABljWw3RdKmmJKE1p0Oh3kcrnohz8/P4/x8XE8evQIw8PDG8zeWlNPlUqlcCxeuHABZ8+eRVdX165z+ovFIpLJpMi2i0ajUKvVG1KLlUollEolVCqVMKWpXVUmk0EqlarJF1GNndTE06TbejsEMzzAck8gh1YoFBIVYzKZDOl0Gmtra1hcXMTExASePXuGQCDw3PNr+bIbjUb09PTg/PnzePvtt3Hw4EExV26nZ1lyVtL8uaWlJSQSCdGGu1qmHOXfp9Np0aByuw62tVDvxkIhPj7L1w+b97uEsueku3kgEEAymUQwGEQoFEIoFEI8Ht+26eNmeDwenD59GhcuXMCJEyfQ0dGxq247lLZKfePD4TDm5+cRiUTgdDpx+vTpDTsphRaDwSCWlpawsLCAeDwuEolehiVIRw2dTldT/QDzGyz6XUJlpisrKxgbG8Pw8LDo/x6LxZBIJDbEueupIrPZbOjp6cHx48dx9uxZDA4Oisq+yjz1rb70tKOT2NfX1xGJRBAKhcSsvUKhIDr8mEwm6HQ6YUXQeX9qagq5XA7z8/Nih39ZR79SqQSdTge32w2LxbJnDUkaAf6kdoG0z9vs7CzGx8cxOTmJcDiMdDqNXC6HYrEocsq3c1LJZDJoNBoYjUa43W4cOHAAr7/+Ok6cOIHu7m5YrVYRj5aKvFLwdB26v7W1NayuriIQCCASiSAajSISiSASiSCXy8FqtYp6Aa/XC4vFIpx15OSzWCxIJpNQKpVYX19HOp1+qbnuhUIBOp0Ora2taGpq4p73dcCi3wV0lqdursvLy4hEIkilUlU7tVbu8uR1ViqV0Ol0sNls8Pl86O7uRn9/Pw4cOIB9+/bB4/GI5JfNPNXSmnVKGKJCnYWFBYyMjIgUX2piSb0BTp06BbfbLToBSYtn6Pii0WhEEdDa2tquZvZR2K8yH6Je9Ho9nE4nzGYz7/R1wJ/ULqCdlEJVqVTquVJRqdApdVWtVkOv18NqtaKpqQkulwtOpxNutxstLS3w+XxobW2F0+mE0WgUu670uiRy6SDHdDqNcDiM6elpPH36FBMTE/D7/QiHwwgGg2KqLi1EDocDLS0tcLlcaG1thd1uh16vF+WxZDHIZDLkcjksLy9jbm5ux74JQq1Wi+k95PGvJ71bp9OJ40e1pClma1j0u4DOyvTFJW+yUqncYMrTbq7VamE2m9HU1ASv14u2tja0tbWhpaUFbrcbdrsdJpMJer1efJnp7C7NWCMPejKZRCQSQSAQEA7DQCCA6elpjI+PY3p6umr6K8W4jUYj2tvb0dHRAZfLtUHwdN/UpWdlZQWjo6OYmZnZceGVVqsVi5zBYBARD2nV3XYolUo4nU40NzeLEmemPlj0ewB5kamuXJrJRju70WiEw+EQvfW6urrg8/ngcrlgsVig1+uh0Wg2hKDItJaa7MlkEvF4HLFYTBwrJiYmMDU1Bb/fj3g8vm0IjVKLPR4P+vr6NpQCVwo+n88jEAjg8ePHePr0KYLB4I4+I6PRiH379uHw4cPo6emBTqfDysoKhoeHMTw8LIZX1PJZu91udHR0wOPx7DghqZFh0e8ShUIBrVYLq9UKq9UKvV6/IUlFqVRCr9fDbrejo6NDNMrct28fXC4XTCaTMN/J4Udn80wmg3g8jmAwiIWFBUxPT2N2dhbLy8sIh8Oiwo9+NhO61AdQLpehUqng8Xhw8OBB9Pf3w+12P1ewIi2PHRsbw3fffYfp6ekdfUY2mw1Hjx7FmTNncOTIEbjdbsjlcqysrMBoNCKRSAgfxHYolUpYLBZhHfGI6vph0e8CckhRy2tqQ6XRaIR5r1AooFarodPpRFtsm80Gk8kkusXS+Tmfz6NQKCCdTiMSiWBxcRFTU1OiJx2NfgqHw5s60raqtQd+baHl9Xpx/PhxnDx5Ep2dnTCbzRvMZDpGJBIJjI+P4+bNm3jw4MG2E3Oq4XK5cOLECVy5cgWnTp2Cz+eDRqNBqVQSnYQDgQDC4bDoKLwVCoVCOD0pmsGirw8W/S4g0Ws0GuFcIg+7NGxGHXHImx6NRsW45mKxKMpUqflkIBDA1NQUhoeH8fjxY0xNTSEUCtWUCLPZ31UqFQwGA1pbW3HkyBG8+eabGBoagsPh2LDLk58ikUhgZmYG33//Pb777jusrKzU/fnYbDacOnUKH3zwgWizTZGBYrEIhUKBrq4uDA0NYWlpCaFQCGtra1seTWhRpX/5TF8/LPpdQN54qjqTyWTCk05VdwDEVFUy37PZLOLxODwejwg3kSlNWX2jo6OYnZ1FMBhEMpncVRKM2WyGz+fD/v37cejQIRw8eFD0qaeeedIFKpVKCcHfuHEDz549q9t5Z7FYcOLECbzzzjt444030NraKroC049Wq4XT6UR/fz8WFhawvLyM6elpJBKJqjkNZDFptdoNjU6Y+mDR7xJpJVo+nxfprdK2zHK5HJlMRlSi+f1+TE1NweFwiKYcNAhyaWkJ8/Pzwim3m/uyWq1obW0Vcf/+/n50dXWJuD85DoGN7bXn5ubw888/4/r16xgZGUEymazr2lqtFl1dXXjrrbdw6tQpIXipz4By5w0GA1paWnDo0CEEAgHk83kxsrpS1BTqpLLeRCIhFlQ28WuHRb8LpOdnCqPRT2UzCGkBzsrKCiYnJ8VQCNr9yTtfqyd7s3vSaDRwuVzo7+/H8ePHMTg4iI6ODjgcDhgMhucch7RQkaVx9+5dfPfdd3j48OG2XXerXd/j8eDo0aMYGhqCz+ermkVIQlWr1TCbzejq6kIkEhFluYFAQDQmkTbLoCzBeDyOaDT60nL//8yw6HeJtHVWPp9HLpcTuw/t9FLhZ7NZEWOXJu9IY/C7uReDwYB9+/bh+PHjYhR2c3MzLBYL1Gr1hkw4qTkfiUQwMzMj+tI/efIEsVis7vvRarVoa2tDf38/PB7Ppv0CaQGQy+XQarVwOBzo7u5GJBIRuzylM0s/TwphUtgym81yv/s6YdHvAfSlpt1d2tyxmhf992guKpfLYTab0dHRgTfeeAPnzp3DwMCA6KYjjcFLBZ9MJrG6uipmy927dw+jo6N1JcwQlE5stVphsVhEA1G6JvB8dEEaAXE6nWhra8PKygqCwSDW19eFL4FyFsiSSqVSSKVSwqrirLzaYdHvEurESt7kylj3i4ByAVpbW3HixAmcOXNGCF46Zoruh9KH4/E4lpeX8ezZMzFbbnJyEtFodINZXQvk1FQqlaJvfzQahc1mE2HLyh1fmuZLjzEYDNDr9ULE0pRjCmdSQ8xcLicWBpopyGwPi34XSItlaNyVTqcTIamdDGPcyfU1Gg2amprQ2dmJ3t5etLW1CXOedlYq/iHhRKNRUYjz+PFjjI6OYnFxEbFYTJTMVs6j2w7ajZeXlzE8PAyLxQK5XI6WlhaYzWao1ernxl5V1hBQjT+N/q6MhGSzWSF66jmYSqVgNBrZxK8RFv0uINHp9Xq4XC40NzfD6XQiFAohlUpt6P22l9ekfylUKC3eoXRa2gmpvx3lASQSCayurmJqagpPnz7F06dPMT09jdXVVVEdSIKXDraQ+h2A560Y+ls6ncbi4qKYkhMMBnHo0CF0dnaKibbSphd0X7QQLS0tiZg93Y/UIUrXqBQ9xf2Z7WHR7xKFQgGDwQCXy4Wuri709PQgmUzC7/eLRJN6zWSpk6va3+lv5P2mHTSXyyEUCmFxcRHFYnFDAlAqlUI0GsXKygrm5uYwPj6OqakprKysiNBX5eQZaStu8lVQfLzazk/HBqr4i0Qi8Pv9WFxcxODgIA4cOIDm5max69Nr0ax7qg6cmZkRvfcqF01q70Vde+h6HK+vHRb9LpBm5NlsNnR0dGBwcBDFYhFqtRoLCwtIJBLCHN2ugQadi6UdXqul1RIkTMqgW1xchEwmQywWE2W5FCqktN7FxUUsLS0hHA6LZpZSsUvbd9P/pp2/1saX0u6/tAAuLi5iZWUFhw4dgs/nE6m/uVwOkUgEk5OT+OWXXzAyMoKlpSWxy1ciDTFSHUG1SbnM5rDodwGJlCbbUBGLXC4XVXPz8/OIxWJi16p2PiZxS1s6V86Ik0YESKR0RqfEnnQ6jdXVVTx79kyku5IAqTovkUiIuv9Kj7rUlJf+HtjYsbbWXZUWo2w2Kxpprq2tiSIfjUaD9fV1LCwsiJRjGpxRS5MOivGTs5KpDRb9LpE2xrBYLGhraxO180qlEgqFAnNzcyKRhLzilR10pLu71LQm81pqZgMbHWCU2x+Px5/rSEOhua2sDOl5me5Hmukm7bG3k6nFdOwAflsIvF4vVCoV4vE4ZmdnMTExgcXFRVEavBlU1Uj/2mw2GAwGFn0dsOj3AKmZb7FYAGw0Q6XnTmnYrJozTCps6e/pHC2d+0Z/24x6IweVVkSldbGbczPN7pubm0M2m8XCwgJkMpkoMCLH3XZHCKqyMxgMMBgMorMQm/e1w6LfIypjzXa7HV6vF83NzaIclvrhV0Kiknrmpfnk0oq9Wqe67hSKOGzlS9jp6+ZyOeHcpJHV6XRahOdqiXRQmrHNZtvQ3oupHf60dkll5p00SUWj0UCj0VQdo7yZcElw0sdLrYIXlfCz19eh90sZdslkUnj76ehQC1qtFhaLBT6fDz6fT1QJMrXDot8FlTswJZhQuiglmaRSKWSzWWEiVzPt6fWkr1v5+z871A6chE/+hlqPDUajUbQb6+3tZdHvEBb9LpCmh0rTQqnPPCWaBIPBDYUjUsdcNSqPAHttar9M6DOT/lsr+/btw9mzZ3HmzBn09PTAarVCo9H8jnf7asKi3wXkrKPKLyqdDYVCWFlZwfLyMlZXV8UkV+mkm+1e91VDOviSIg617vAGgwFtbW24ePEiLl26hIMHD8Jut7MDb4ew6HdBsVgUbbDS6TQymQxisRjC4TBisRjS6TRkMhn0ej0MBoMIhUmdcZVffDJVt5pgUyvSa/yezr9KpGFHCq1RHT8AMVqLypArZ9JT+FIul0On06G7uxtvvvkmrly5gsOHD8NqtVb1kzC1waLfIeTlplp0+hJT8ku5XIbRaERraysMBoNYGHK5nAjhSWvvpbn0FN+nzLjK7LzNFgS5XL4hlEdHDvKQk29hL6bMbgb1tifPOvWzo7wFACKvgMKZVC6bz+fFIkEOUKvVioGBAZw/fx79/f2w2Wzc9nqXsOh3SWUoTbq7WSwWMVSCBE6il1aLARCz3/V6vZjeIm16AUDE6jf7oR2z0s9A9ec0g47+VmlxVIsQVOYGUGpuZbYgCdZkMsHn86G9vR0ul2tDnr3UuUl18LQAkOhlsl+n0RqNRthsNtGQo6+vj2fW7REs+h0ibf5AJbX5fB5GoxFGo1F8uaX131IPv7R6jL7oWq0WOp1OTLiR1qDTzi19fmXkQHpWpsdIU2Ar72UzkROV90sFN9JJPpSboFQqRQ87GpPl8XhEMw2K0UciEXH0kfYUpHJeCnUajUY4nU50dnaira1N7PB8ht89sm3Oea+eR2kPkTbDJNFR2yzpz1bddCoLbcjhRf3gKttpV0vSqdyN6YeOG8FgEKurq2KmfK1ne0oaknYEktYIUCKS1+uF1+sVwiTxk3lPzrtMJoN0Oi1+KNJBVX5qtRpWqxVmsxlGoxEmkwkWiwVms5mddjuj6gfGot8DpEKuFGLl34hqZ/RanHebJelU/p52e5pFH4vFxBz6ysdKU22lxwMAz1XcSU17crQ5nU5hykuz46T1BJULE/XmIx9IoVCARqOByWQS48EqFz+mblj0jYi0maS0BVY1wZMZL62tJ/OdTPjKrrb0951Mj608OkgtHfbM7wksemYjlcKX+gekacW020p3buZPAYueYRqMqqJnG4phGgwWPcM0GCx6hmkwWPQM02Cw6BmmwWDRM0yDwaJnmAaDRc8wDQaLnmEaDBY9wzQYLHqGaTBY9AzTYLDoGabBYNEzTIPBomeYBoNFzzANBoueYRoMFj3DNBgseoZpMFj0DNNgsOgZpsFg0TNMg8GiZ5gGg0XPMA0Gi55hGgwWPcM0GCx6hmkwWPQM02Cw6BmmwWDRM0yDwaJnmAaDRc8wDQaLnmEaDBY9wzQYLHqGaTBY9AzTYLDoGabBYNEzTIPBomeYBoNFzzANBoueYRoMFj3DNBgseoZpMFj0DNNgsOgZpsFg0TNMg8GiZ5gGg0XPMA0Gi55hGgwWPcM0GCx6hmkwWPQM02Cw6BmmwWDRM0yDwaJnmAaDRc8wDYZym7/LXshdMAzzwuCdnmEaDBY9wzQYLHqGaTBY9AzTYLDoGabBYNEzTIPx/wHtW5djz4fpdwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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hra0N7e3torquUChArVZDq9W+keFXzT2XQ3qe552+dlj0e8iv+eXLZrPw+Xx4/PgxvvzyS3z77bdYWVmp+DwSj8ViwdWrV/HRRx+hvb1d5OhXem4sFsPk5CRu3rxZ9HrlRKnX69Ha2gqr1Spq78kZR1V2243HIgFT7f1Wj6MGHNUuIkwxLPpd8ls47XK5HILBoBD8N998IzLXqsFkMuFf//Vf8fnnn2NoaAiNjY1VPS+ZTGJxcRGPHj3C6OioqIIrJ0a5XA6LxQKbzQaz2SzM+EKhILrvBINBxGKxsk48adouXW8r4VOo9LfMh3ibYNH/wSkUCgiFQhgbG8M333yDmzdv1iR4o9GIS5cu4a9//StOnDgBo9FY9XMjkQgePnyIO3fuIBwOb/vYxsZGdHR0wG63i751VAsfCoWwurqKjY0NxOPxLb3u0rJjqd+gtNGmQqFAQ0MDt+beISz6PzD5fB7RaBSTk5O4fv06vv/+e+E53w7pbjw0NIRPP/0Up06dEoVBtbz28+fP8fTpU/HzrUzupqYm7N+/H62trdBqtZDJZCJb0O12Y3p6Gi6Xa8sYvdQpSve/lUWhVquh0+nEEYKpDV4m/6BQEc3c3Bz++c9/4vr161hYWKjquSQWq9WKM2fO4Ny5c6KtVrWk02lR0kpZftsdZVpbWzEwMICWlpai9Fm/349Xr15hcnISm5ubW8bnKedBelYvFwalhCjpTs/n+tpg0f8Byefzoqjl1q1buHbtGmZnZ5HJZCo+lwSg1Wrxzjvv4MKFC2hraxMdbKp9/UAgIFpvVcJqteLAgQPo7+8XGX70HpaXlzExMYHFxcWiEdql9yx14klbYpdDoVCImQJs3tcOm/d/MMisnpubw+3bt3Ht2jW8ePGi5lFVra2tuHz5MoaGhqoqppGSy+Xgcrlw9+7dih57lUqFgYEBnDhxAg6HQ7wWld9OTU1hfHwcfr+/YsZgafvrctDCwN77ncOi/wORzWYRDocxOzuL27dv48svv8T4+DhisVjV16CZ8sPDwxgaGkJbW1tV4Tkp+XwebrcbDx48wOrq6raPtdvtOHPmDE6ePInm5mZh2ieTSXg8HkxNTWFubq5iN59yAy62O/vzDr9zWPR/AKh9VDAYxPj4OK5fv44bN25gZmamasGXS8Lp6empyawn0uk0wuGwyPTbKkxnNptx5swZnD17Fk6nU3TcyWQyiEQiWF5exsLCAnw+X9XFMdLxVeXGZAHbh/OYyrDof2foC+71evH8+XNcu3YNt27dwtzcXE1NH0kARqMRIyMjOH36NKxWa83mLzXkePXq1bYLjlwux6FDh3DlyhUcOXJExObp/QSDQczNzWFtba0qXwTwevHbai4eQe+JS2t3Bov+d4Yy7Z48eYIvv/wSN27cqJhauxVarRbnzp3DhQsXis7XtRCPxzE6Oop79+4Jk7ycsHp7e/HOO+/g9OnTaGtrKzpfZ7NZ+P1+zM3NVd3Jh5A2ztzOOqBee7zj1w6LfhfQTrST/G9p8czjx4/xxRdf4MaNGzUVz5TS09ODK1euYGRkpOrGGFJyuRwikQiePHmCn376qaivHfDazNdqtThz5gwuXbqEzs5OMQiDpuNkMhkxTbeaJp10n1KzfisxS1+Hm2LuDBb9DqHOriR6aTZZJehL7ff7xQ7/3XffYW1tbcf309PTgw8//BBnz55FZ2dnzc474JfRWFNTU5iYmBDhNel5nqbSHD9+HBcvXsThw4fR2NgoyouB1/XugUAAm5ubVZn20hFf0pbZ2wlautMztcGi3yGUbZZOp4s6v1azu2azWQQCATx58gRfffXVrkx64HV47pNPPkFfX1/RxJpqyWQyWFxcxK1btzA5OSl+Xiq8ffv24ZNPPsHIyAisVqtwFErP2eFwGF6vd8u4vBRpbF4ul4uFdDvB007Ppv3OYNHvAMo28/l8CAQCMJlMsNlsFZNKqP8b7fBff/111dVyW2Gz2XDlyhV89tlnOHLkSE3tr4hsNou1tTXcu3cP33zzDV69elX2ca2trXj33Xdx6dIl7Nu3r2xkIJPJwOv1wuVyVZVbQMMzqNlGpZ1b+tmy6HdGVaKv5w+2nIBpnNPi4iLW1tZgs9mQz+fR2toqJq6Wfjlpjvva2hoePXqEb7/9Fnfv3t3VDt/W1ob33nsPn3/+ucitr6YTTul7CQQCePjwIb755hvMzc2VfZxGo8GZM2fw0Ucfob+/H3q9vmxsPZVKYWVlBa9evaooepqlR1NtyFyvJmohdfTV8/dzJ1T1DeGsp9fdXrPZLDY3NzE5OYlnz55hbW0NFosF0WgUqVQKra2t0Ov1Qnw0fsnj8WB5eRlPnz7FTz/9hEePHlXd4qoUuVyOjo4OXLp0CX/7299w6tQptLS01Cx48is8e/ZMdMUpdd4Bv/SxHxoawpUrV3Dq1CkYjcay3wkSPYX8aCT1Vu9Bp9OhqalJVP5FIhFkMpmK3zepec9n+tph874ClGgSi8VQKBQQDocxMzODu3fvYnJyErFYDBqNRnR6PXjwIGw2G1QqVdHUlvHxcdy/fx8vXrwQM9l2gkwmQ09PD/7yl7/g448/xtGjR4t60dXyvnw+H0ZHR/GPf/wDt2/f3nIR6u7uxqefforz58/DZrNt2WYrl8shkUhgfX0dbrd72+KaxsZGtLe3o7u7GyaTCYlEAqurq0gkElXN4qO2WbzL186235R4PF6X56ZCoSAcYfPz85iYmMD6+jqCwSDcbjfm5uawuLiIUCgkPNqrq6tYW1vD5OQkrFYr5HI5QqEQPB4PXC4XVldX4XK5qp4zB7yZCWc0GnHixAlcvnwZ7777LgYHB8WQx1rIZDJYXV3F3bt3ce3atTdy7CnjDQC6urrw8ccf48qVK+jr64NWq93yuqlUCn6/H16vF9FotOx7AH7psON0OnHs2DH09/dDq9VifX1dtPSu1C6LPPws+p2xreiDwSCePHmCyclJUdFUDx8yib5QKGBpaQmzs7Pwer3Y2Ngo+kJLiUQi8Pl8mJmZgU6nQz6fRygUwubmZtnHV3sfwC8z49va2nD69Gl88MEHOHfuHDo6OsRZuBakxTz/8z//g4cPH76xEJHgzWYzPvjgA3z++ecYGBiAXq/f8rr5fB6RSAQLCwvb5hqQ4M+fP4/Tp0+js7MT+XweRqMRfr9/y376UijHgZNzdkbFnf7vf/87/v73vwP4xZlTbUrlnxlpbrdWq4VGo0E2m0UymdzSyZRKpRAIBESHmb0yP1taWnDq1Cl88MEHOHnyJJxOJ5qamkSjimooFApIJBIIh8OYnp7Gt99+i//8z//EzMzMls/R6/X4l3/5F/ztb3/DsWPHKrbYoj78o6OjW1bmyeVytLW14ezZs7h48SIGBwdhMBhES2sKAVbTvz+bzbLod8i2oqfGiEQ5J8/biPQsWu0Zk1pD7dWiaLVacerUKZw+fRrHjx+veqpsufuKxWJYXFzE3bt3cevWLTx8+HDbqIFGo8GxY8fwySef4OTJkzCZTBVfJ5fLwe/3Y3x8fMsQpMViwaFDh3D+/HkcOXJEOB8TiQTMZvMbiT5bvR/a5akVNjvzamNb0ctksiKTTqPR1N1Ukd+iqEN6hm1sbERvby9GRkbEUIq2tjZoNJqaR2YVCgUEg0FMTEzg5s2buHnzJp4/fy4WMenZXXoPDocDV65cwYkTJ2C1Wqt6LTrPLy8vl3VSqlQqOJ1OjIyM4NChQ7Db7cJayWazYjxYpZAdzbPLZDJIpVKIRqNIJpPQaDQcZaqSii5f6SpKk0KZvYXEZjAYcOnSJXz88cc4c+YM2tvbYTQaqxpKUY5IJILx8XF88cUX+I//+I83auOl/5fShW1wcBAXL15Ea2trVZl91DDD7XYL/0CpM85gMGBwcBDHjh0T+QzUPJP66dFgyu2+YxQRSSaTiMVi8Pl8Iky6k9TjeoRDdr8jUmG0tLTgo48+wmeffYaTJ0+ipaVlx7tXofDLDPnFxUV8/fXX+OKLLyo2wwB+2fkHBwfxzjvvoL+/HwaDoarXS6fTWF1dxfT0dFmnpVarRXd3NwYGBtDT04PGxkZhtZC5HovFEAwGKx6l6LHJZBLhcBjr6+tihBaLvjpY9L8x0ooyEnx3dzeuXr2Kv/71rzh58qRoYrkbc9Xr9eKHH37ArVu3xPl9u571+XweCoUCFy5cwPnz52sKBabTaSwvL+Ply5fCB0Q7OHXyOXToEPr7+2GxWKBWq8V7I3Pd7/fD7XYjHo9v+1rZbBaRSASJRAKxWAyBQACxWKzujp27gUX/GyMVnVarRV9fHz755BN8/PHHOHLkyJbZbrVAbbOvXbuGqampsq9d7p6sViuGh4ernnMHvK6qc7lcmJ+fLzrPFwoFaDQadHR0YGhoCD09PdDpdEW97UnElMtQaafP5/NIJBJIJpNIJpNIJBJ87KwRFv3vRE9PD9599128++67OHHiBPbt21eVl7wSqVQKU1NTuHXrFh4/flx15MFiseD999/HwYMHhRe9Gsjc9vv9CAaDIschn8+L2XZHjx7FwMCAyFSk16SOuRsbG1heXobL5aoqQpRKpZBKpYTnngVfGyz63wiZTAaTyYTW1lbs27cPp0+fxrlz53D48GE0NzfvyXmUZsh/++23+O///m9sbm5WvCfa5Xt7e3H16lU4nc6aUnqTyaQQvNTE1mq1sNvtGBkZwdmzZ7Fv3z4xxRZ4PXKaEnLm5+cRCASqKrahkJ30iMSx+uph0e8C6ew1aWiPfq5QKKBUKtHQ0AC73Y79+/fj1KlTOHnyJPr6+mC1WkVV3m7J5/PweDy4c+cOrl+/LhJvtktplYYJh4eHMTw8jJaWlqp3eTK1PR4PIpEItFotCoUCdDod7HY7Dh8+jHfeeQfDw8Ow2WziLE8lxqFQCCsrK5iYmMDCwgKSyWRV4qUdnoW+M1j0O0SlUsFgMMBgMAhvOQ141Gq1MBgMMJvNaGhoQEdHB4aHh3H06FH09vaipaUFjY2NeyJ24HV57OjoKL766iuMjY2Jf9tKGGSCy+VyXL58GVeuXIHdbq/a4qBkpEgkIjrkUD5BV1cXBgYGcOTIEfT396O1tRUGg6Fovl00GsXa2hpevnyJyclJeDyeqp1xpe20KjXdYIph0e8AmUwGi8WCjo4O2Gw2kfUWj8fFcEWTySQec+DAARw9ehTd3d0wGo17OopJ2mfv66+/xs8//4xwOFx2h5cm4+TzeTQ2NuLChQv4t3/7N5w+fbqm4ZZ0lg8EAohGo7DZbLBYLGhpaYHT6YTT6UR7ezvMZrNILCLHHZ3jp6enMTo6isXFRVHFWA2lImfR1waLfgeYTCZ0dXUJEx1AUV4+DVhsa2vDwYMHcfDgwR2l0FYik8nA7Xbj2bNnuHbtGm7cuCHGUJUTAQm+sbFRzLn7/PPPce7cObS3t1dl1pNwqUfA+vo6FAqF2NEdDgfsdjuMRiO0Wi2USqVIrc3lckilUvD5fJidncXo6KiYcUeDLStV2EnvQ/q+2JlXPSz6GtFqtejo6IDT6RSdYIHXrZtI1EajEfv378fBgwdFRdxeCZ7O0isrK7h37x6++uor3L9/HxsbGxWfa7FY8OGHH+Ly5cs4duwYurq60NzcXHXmXTKZRCgUwvr6OpaXl+Hz+URtfGdnJywWC3Q6XdGcOTLHqUvu4uIinj9/jrGxMbjd7qIBmdWKvvTzoOIbnnxTGRZ9jSiVSthsNrS0tIjzPADh0Mvn89Dr9eju7kZ/fz/a2tpqFrx08ZCSzWaRyWQQDocxOTmJW7du4dtvv8X09HRRE8pywhkYGMDhw4cxNDSEEydO4PDhw+IMX+neKEU2HA5jbW0Ns7OzWFhYQDQahcViQXt7OxwOB2w2G3Q6XVG7MDK96fy/tLSEJ0+e4MmTJ5ifn0coFBIWktQxWsvOTYU3bOJXB4u+RkwmE5qamsQIp9LQkVKpREtLC/r6+tDR0VG2l1wlSgUjbUM1OzuLyclJPH/+HPfv3y/b047uRaPRwGQy4eDBg/jggw9w8eJF9Pf3C9O7UsYdiZV256mpKUxNTeHVq1fwer2wWq0YGRmB0WgUTstyQyXp/L+0tISnT5/iwYMHmJ6eht/vR3NvJkoAABeNSURBVCaTEQ680l16O+FLnXi0GFJWIbM9LPoaMBqNsNvtMJvNwjEFvN5ZyQrYt28fHA7HjgQPvO62G4lEEAgEEIlEEAwGMTY2hu+++w4//fQTAoHAttdQq9U4evQo3n//fZFLb7fbodPpqhJGPp9HLBbDxsYGJiYmcOfOHdy9excLCwtIpVLQaDQYGhqCWq2GwWB4owqQPpNsNotoNIqVlRU8fPgQP/74I8bGxoTgS+cGSMVc7W4vNe+ZylQUvXT1ValUdZfjLI2/y+VymM1maLVa0cSBvrAqlQomkwnd3d1wOBw1h+RoV00mkyJp5eXLl3jw4AHGx8fh8/ng8/mwvr5etp+81KS3Wq24fPkyPvzwQ4yMjKCrq0tEDaqBBL+0tITbt2/jxo0bGB8fF739qOQ6k8lAo9GULfulElmq5b9z5w5u3bqF58+fw+fzvWHSS4eFkNOv0udVaRoOUx5uolEFer0eVqsVx44dw/Hjx9Ha2gqFQiHOkrlcDmq1Gna7XTizasmwKxQKiEajWFpawsTEBBYXF+F2u/Hq1SuMjY1hcXGx6PFS8196DeCXtthXr14VXXLJSVfL9J1MJoO1tTXcvn0b//Vf/4X79++LnHoSKe3Q5J0vPZJQue3s7Czu3r2L7777Di9evIDP5ysSNFkddK18Pl+VdSQddkHvj+vpq6OmJhq0w73tUNZYPp9HZ2cnjh8/jpGREZw8eRLd3d1C0MlkEvF4XOy8TU1NaG5uruq8DLyekrO5uYn5+Xn8/PPPuHnzJkZHR7dtoFluV5PJZOju7sbnn3+Ozz77DMeOHatpd5feEyX6/OMf/8DDhw/fKKIhC4fi/tTFhl4rmUxic3MTs7Oz+PHHH/HPf/4TL1++FK3EpJSa5FI/RqX7pMw8uVwumnAwldlW9DqdDv/+7/+O4eFhqFSqonPs24x0QKJOp0NHRwf27duHjo4OGAyGojpwKvHMZDJQqVRFVWTbId0Jf/jhB9y+fRtjY2Pw+Xzb9ouX3iP9X9D5/eOPP8aHH36IQ4cO1ZRoIyWRSGB6eho//fQTJiYmyo6rlu7K8Xgc4XAYoVAIKpUKqVQKbrcbo6OjuHPnDh4/fozl5eUtC39I4PR50yJSybynktxsNgu1Wl0UImS2Z1vRm81mXLp0CRcvXvyt7ucPgVTU5KDTarVvmOwqlQoajQY6nQ6pVAq5XE7s8OXCbiRSmgLz4MEDfP/993j06BGmpqaKzrl0D5Xy5u12O4aHh0Xsva+vb9uutduRyWQQDAbFqGrqg18aAqSaglwuB4/Hg4WFBUQiERFhmJycxOjoKF6+fAmPx1PxdSmGL7UaKm0uUkcf3Q+Lvjoq7vTM9shkMqhUKnHGpAktZP5Kz7rJZBLBYBDLy8u4f/8+vv76a9y9e/eNnb3atNKenh5cvHgRn3zyCU6cOAG73S6ShWqF6gdWVlYwNjaG+fl50eRTei90pqeGlouLi0gmk1CpVPD7/ZidncXMzAy8Xm9VR0HpcYE+w1qdxTRDkKkODtntAfSFTSaTSKfTUKlURT3pc7mcOOc+f/4c33//Pe7fv19x9NNWaDQaHD9+HB999BEuXryIAwcOoLm5uexAyWog553f78f09DSWl5dF8RC9N/ozWTcKhQKRSARzc3OYn59HPB5HIBBAMBgseySo9Pq0QFS74JHQpWOumergAZZVUOkLRTt8OBwW8XPKwkun0wgEAlhaWsLU1JRIP5WavdWmniqVSjgcDhw/fhzvv/8+zp8/j97e3l3n9OdyOVH1Njs7C7/fD7VaXdQpV6lUFk2YlcvliEajCAQCwqG5kwUMKE60qRYKk9baIZjhAZZ7grQDzMrKCuLxOGQymXByra6uYmpqCpOTk6IgRko1X/bGxkb09/fj4sWLuHLlCg4fPgyr1bqjWfTS+6ZY+vr6OtbW1hCJRGA0GtHb2yvMbun/P+XfkwOTrJvdJsbUurHI5XKxEPFZvjbYvN8lZBqHQiGsra1henoaGxsbos7c4/Fgc3MTwWBwx+OtWltbcfbsWbz33ns4ffo0uru70dTUtONuO5RLn0qlRBvp5eVl+P1+2Gw2nD17tmgnlYYWV1dXsbKygnA4LBKJfg9LkI4aDQ0NVdUPMK9h0e8SyitfX1/HzMwMxsbG4HK5EAwGEQwGEQqFijrC1FJFZrFY0N/fj5GREVy4cAHHjx9HR0fHG7v7VgU6BO3olKNOPe02Nzfh8/kQjUaRzWah1+tht9vR2NiIhoYGMYyC8u/n5uZE51va4X+vo18+nxcdiUwm0541JKkH+JPaBVQIQ2f2yclJzM/Pw+PxIJFIiDBeubbX5ZB23bHb7Th48CDOnj2LkZER9Pb2it29NPusXGiQQlqpVAqhUAgbGxvY2NiAz+dDIBCA3++H3+9HOp2G2WxGT08PHA4H2traYDKZihpfZDIZbG5uIhKJQKVSIRaLIZFI7Emu+05KaYFfjhkNDQ1wOBx71mOwXmDR7wJKzqFurmtra9jc3BROrdKc8HK7sdRMbWpqQldXl6jD379/P3p6etDa2orGxkZhxpa7Dok8nU4jHo8jGo2KQp2VlRW8fPkS4+PjWFxcRDgcRi6XE+2pz5w5A7vdDofDAavVKjIKSZDpdFpkY4ZCIYRCoV3N7KOwX2k+RK3odDpYrVYYjUbe6WuAP6ldQGG6YDCIQCCAeDwukkykcXoyxSnMpFKpoNfrRdquzWaDzWaD3W4XtenUistgMECtVhfFoaWFJpTMkslkkEgk4Pf78erVK7x8+RIzMzNYX1+Hz+eD1+sVU3Vph7Zarejo6EBLS0tRAwwqjyWLQSaTIZVKYXV1FUtLSxUHUlRCrVaL6T3k8a8lvbuhoQENDQ3QaDRlk6aY7WHR7wKKL9PZljzJFD+Wil+hUECn08FkMsFms6GtrQ0Oh6NI4DS5VafTiVZT0li/NAGIhjf6/X5sbGzA6/UKx+HCwgKmpqawsLBQNmZO92MwGNDd3Y2enh60tLS80QCDYvSJRAJutxtTU1NYXFzcceGVVqtFc3MzWlpaoNfrhXNQWnVXCSpfpv57nJRTOyz6XSBtnNHQ0ACDwQCtVisyyqgCTKPRQK/Xo7W1Fd3d3ejr64PT6URHRweam5thMplEi6nSElMa6kgmezQaRTgcRjAYxMbGBpaWljAzM4P5+Xm43W6Ew2HhtNvuvpVKJVpbWzEwMFBUClwq+Ewmg42NDYyNjeHly5dVteQqh8FggNPpxNDQEPbv3w+tVgu3243x8XGMj4+L4RWVUKlUsNvt6OnpQVtb244TkuoZFv0uoLMpdb81m80wGAxvlI4aDAbYbDb09PRgYGAABw8eFL3ppOY7CY76wtOQRq/Xi9XVVbx69QqLi4twuVzw+XwIhUIIh8MIh8PCA7/VfRKFQgEqlQqtra04fPgwBgcHYbfb3yhYIQdeMBjE1NQUfvjhB8zPz+/oc2pqasKJEydw4cIFHDt2DHa7HXK5HOvr6zAYDIhEIsIHUQmlUgmTySSOJTyiunZY9LuAMtVol6dQVzweF1aASqUS3XHNZjMsFotYHMiElzq0stmsOJuT0GdmZrCwsIClpSW4XC7hdd/qnoDytfbALym8bW1tOHXqFE6fPg2n0wmj0VhkJtO9RKNRTE9P486dO3j69GnFiTnlsNvtOHXqFK5evYozZ87A4XBAo9Egn8+jubkZAODxeODz+ZBMJis6CGmRbWpqgtlsLhqGyVQHi36XyOVyIWryesvlclEpRk62eDyOUCiEQCCAQCAgeuxls1lRpkrNJz0ej2ig8eLFC8zNzWFzc7OqCTBb/Ts5D6k/AE2esVqtRbs8xfSj0SgWFhZw584d3LlzB263u+bPpqmpCWfOnMGnn36Ks2fPor29XXxGVJHodDoxPDwMl8uFzc1NhEKhbUOBWq1WOPAorMjUBot+F5CTTupwI/HSri2TyUSdOPC62i4cDotJNwqFAul0WoTXKGV3YWFBZPftJgnGaDTC4XDgwIEDGBoaEmOjW1tbxeIjbVNNs+1//PFH3L59G1NTUzU770wmE0ZGRnD16lWcO3dOtAunkCPlJLS0tGBwcBArKyti8i2939L3rFari5KGuC/ezmDR7xLpnPVsNotUKiXMVIo/JxIJ4YgLBAKiqy2d6eVyORKJBLxeL1wuF5aWloRTbqdil8vlMJlMcDgc6Ovrw+DgIAYHB+F0OkXcX6PRiPg2WSTUpvrBgwe4desWxsfHa04f1mq16O/vx+XLl3H27FkheKnPgBZLalIyNDQEj8eDTCaDlZUVMTxEKmqyqJRKJZLJJKLRaFFzTaY6WPS7QHp+lhaiJJPJonZOAIqq0lwuF3Q6nYg1S3f/aDQqvvA7EbxMJoNGo4Hdbsfg4CBGRkZw7Ngx9PT0oLm5GXq9Xohd2rGW8g1WVlbw6NEj/PDDD3j27FnFrrvlXr+1tRXDw8M4ceIEOjs7xfGh1KEok8mgVqthMpnQ29uLQCAgxlttbGwgHo8XfYbUQLNQKCAUCsHv96Ozs7Puq0BrhUW/B5BwaG46ZeOVEy6F30KhkPD+U7JNpVBbJWQymQiN0YjoQ4cOob29HUajUeTsk9il5nwgEMDCwgIeP36MBw8e4MWLFwgGgzXfj1arRXd3NwYGBmC326HVastWwdECQCFNq9WKvr4++P1+sWjKZDIkEgmRB0GFQuT7CAaDSKVS3O++Rlj0u0SaFivtv75Vg0dpP7i9RC6Xw2g0wul04sKFC3j33XfFFBtplEB6DzQ9dmNjA/Pz83j+/Llo3UUJM7XkxlMkw2w2w2QyFWXKSQuOSv9OiUtWqxUOhwNutxterxexWEz4EqjOIZlMivr9eDwuFgjOyqseFv0uIEcedZOhYphqu7/sFdSQs6urC6dPn8b58+dx5MgRtLS0iA4+pXPl0uk0QqEQXC4XZmZmMDo6itHRUczPz8Pv99c8A17q1Ewmk/D7/QgEAmhqahIRjtIdX5rmq1AooNFoYDAYxLkdeD13gBZK2vkpE5JGhGu12r39UN9iWPS7RKFQQKvViuQcEtlOer3VCuUJaLVa2Gw29Pb2YmBgQAy3kNaZUwovCYd69dGIrKmpKaysrCAYDApzWrpQVCN+yiB0uVwYGxuDyWSCXC5HR0eHOF6UdrohQUsdobFYTDToIIcofZZ0fJL6IeLxOAwGA5v4VcKi3wUkOr1eD5vNhs7OTthsNvh8PsRiMSH8vdz1pWmy1L6KinesVqtIpyVvPL027e7RaFTkAUxMTODly5dYWFiAx+MRjjMSvHSwhbRcd6tjC1Udrq6uFo3FGhoagtPpRHNzM3Q63RuLkdSv4HK5iqoVaZgIvR69hnSCbjweL+pEzGwPi34XSM+jLS0tcDqd6O/vRzQaxfr6uhhYUa3wpYIG3hzoKN0haaqLSqUSHW7S6TQ2NzextraGXC4HvV4PlUolKvACgQDcbrfI15+bm4PL5UIkEikaJAmg6EgAQJj7FB8v936ofl9a8be+vg6Xy4WjR4/iwIEDwqlImXTUhGRjY0NUBy4sLIjee6W+j2w2K87y5DilykamOlj0u4TOotSIYnh4GPl8HhqNBsvLywiHw8IcrdRAg87F0jFUpaaw9PG0s1Gd+/LyMgDA7/eLSjbygFNa78rKypZ1/9J7oPAYOfKog0414qKSY6/XKxbA5eVlrK+vY2hoCA6HQ6T+ptNp+P1+zM3N4enTpxgfH8fq6qrY5UuhmgBq00WNOjlOXz0s+l0gdeRRFd3hw4fF7q/RaLC0tIRAICC66JTuktLdXdrSudyMOGkdPQCxM1MXG0rwmZ6eFh57MoepOi8cDheFwaRiKd3dAbwxjlv6+pXIZrNiCAY10gyHwxgYGEBLSwu0Wi1isRhWVlYwMTGB58+fY2FhQSyUlVCr1TAajVVP4mV+gUW/S6TCpww46UQchUKBhYUFBINBMROuXEcdaf92El9p1Zv0ufSrXEWetFpPOj2mHNL7oEWEzG7pNaTDOmuFjh3Aa6ukvb0darUaoVAIi4uLmJ2dFQ03txM8OU7p96amJuj1ehZ9DbDo9wBpyMlkMgF47aCi2HKpQ6pcOEwqZGl8XNpSip4nrdnfilodiNLr0mBI6T3t5txMHYOXlpaQTqexuroKuVyOUCgEj8cDr9dbVe98qrLT6/XQ6/UwGAxcXlsjLPo9gnZrtVoNvV4Pi8WCtrY2tLW1iXJYGnRZSqnJTOfn0oaa0vHMv1YeAGUGbuVL2M11KTcgn8+LkdVUk1DOaVcOSjNuamoqau/FVA9/WrukNPNOau6r1Wph5m/VsVb6d6rIk/bVA4qtgt8q6WevX4feLxX1RKNR4e2vJf2YciK6urrgcDhElSBTPSz6XVC6A9P5Op1OC8dVJBIRWWPSPnflRCUV9lZNMP7MkBVBVk0lf0Mp1NOvv78fBw4cYNHvEBb9LpCmh1IxSCwWQygUgtfrxdraWlF4jJJlaFffSsylVkG5bjh/RshPII1C1PKenE4n3n33XVy4cAH9/f0wm83QaDS/4h2/nbDodwGZq1T5RRlim5ubcLvdcLlc8Hg84jyfSCSqvu7bhnTwJVlE1e7wer0eXV1deO+99/DBBx/g0KFDsFgs7MDbISz6XUAjqKPRqKijDwaD8Pl8IidcLpdDp9PBYDAUJbhsFfMul5Sz0y92pdTZXwvpMAsKrVEdv1wuF3F7yq0vrVOQzgtoaGhAX18fLl26hCtXruDo0aMwm82iuImpHRb9DqHzaTweh9/vRzQaFbnt8Xgc+Xweer0eHR0daGhoQCQSEamjZBlkMpmi4RjUdot+p8w4aYspYOsFgUxn6f2Rf0G6MO3FlNmtoN725FmnfnZarVaMv6a8AkqjpXLZTCYjFgmqWrRYLDh06BAuXbqEQ4cOoampidte7xIW/S4pDaVJK99oGINery8SOX3hM5mM8ForFIqiBpvk9S+dNlOaHVeaKUfOQjKhKQ8+Ho+LIwZ5y0stjnIRAun7ozM5/SotmlEoFGhsbITD4UB3d7cYhkl59jSgg0J00sQiEj2N+TIYDGLM1+DgIAYGBmCxWLhufg9g0e8QabGN2WwWs94SiYRoh202m0XKq9SMJcefNExFo54aGhpEKy2pCUuCp+dKIwGl462kGXTSFFhpLfp2Iiek15UW3NBceLJI1Go1lEqlWLRoTFZra6topkExer/fL7IT6XMh66dQ+GUIB9XV22w2OJ1OdHV1iR2ez/C7R1bhnPf2eZT2ENrhpDXf0rCd9N/KddORVtPRzkm7u7TgBXhzxy29nvRn9HrpdBqRSARerxcbGxtipny1Z3u6lnSRkaYJUyISJSGRMEn8ZN6T8456CNIvinRQlZ9arYbZbIbRaBQLp8lkErX4LPiaKfuBsej3AKmQy5nc5XbUcmf0apx3WyXplP6cdnuaRR8MBsUUnNLHlqbaSq0I6Wy+UtOeHG02mw0tLS1vTI+V1hOULlpUQ095DLlcDmq1Go2NjUWDQEoXP6YmWPT1CDnzpGY9UF7wZMZLa+vJfCcTvrSrLdUckOOtFkqPDpTJSK/D7BoWPVNMqfCl/gHpEYR2W+nOzfwpYNEzTJ1RVvRsQzFMncGiZ5g6g0XPMHUGi55h6gwWPcPUGSx6hqkzWPQMU2ew6BmmzmDRM0ydwaJnmDqDRc8wdQaLnmHqDBY9w9QZLHqGqTNY9AxTZ7DoGabOYNEzTJ3BomeYOoNFzzB1BoueYeoMFj3D1BkseoapM1j0DFNnsOgZps5g0TNMncGiZ5g6g0XPMHUGi55h6gwWPcPUGSx6hqkzWPQMU2ew6BmmzmDRM0ydwaJnmDqDRc8wdQaLnmHqDBY9w9QZLHqGqTNY9AxTZ7DoGabOYNEzTJ3BomeYOoNFzzB1BoueYeoMFj3D1BkseoapM1j0DFNnsOgZps5g0TNMncGiZ5g6g0XPMHUGi55h6gwWPcPUGSx6hqkzWPQMU2ew6BmmzlBW+HfZb3IXDMP8ZvBOzzB1BoueYeoMFj3D1BkseoapM1j0DFNnsOgZps74/wGMAabBFhiT1gAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 63; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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hl8theXkZP/74Ix4+fCi89YW7PPAqemG322G320ViUj6fR01NjSg02u5epX9udlyhs/zbtK7eJ1j0u8hv9eUjp936+jru3buHL7/8EtevXy9phyfhWCwWfPzxx/i3f/s3tLW1lbXLT0xM4Pvvv8fKysob15Wi0+mEGU+NPchMpzJeKqvdDDoubVU7QL4Uad09Uzos+h3yNpx2ZNKT4H/44YeSk3CAV+f4Tz/9FP/+7/+OQ4cOwWQylfS8RCKBhYUFPHz4EE+fPhX9/4qJVi6Xo66uDg0NDTCZTFAqlSL0Rtl2Pp8PkUikqKClERAAG9J3C6FQ6dvMh3ifYNH/jiGHlt/vx+PHj/GPf/wD169fL0vwJpMJZ8+exRdffIHh4WGYzeaSnxsOh3H//n3cunULoVBo29dxOByi9p7KYzOZDILBIFZWVuB0OkWefiEkeHKKUpxe6rmXPk6r1XJr7grhZfJ3TC6XQygUwtjYGL777jv8+OOPJZn0UuvjwIED+Pzzz3H06FHU19eXHNcm0350dBS//vrrht8Xo7a2Fr29vbDb7SJhiVqKUXMPqgvYzHSXdhSSliFL3w+l+ep0Omi1Wt7pK4B3+t8puVwOkUgEU1NTuH79Oq5evYrFxcWSnkvCtFqtOH78OE6ePIm6urqyjiKpVErs0JTlt5lpD7yK/Q8ODoowILXH8vv9mJubExl8xaripCKnRCFpq+vCnZ5q72mn53N9ebDof2eQORuJRDA9PY2rV6/iypUrWFhYKLkbTj6fh1qtxunTp3Hq1Ck0NzeX5LgjKC7/4sWLkiwLm82G/v5+9Pb2wmKxiC478XgcS0tLGB8fx+zs7JZHhMIEp83CcVLR85m+Mlj0vzNyuRzC4TAmJydx7do1fPfdd5iYmBChsu2gXbG5uRkffPABDh48WFJCjJRsNgun04k7d+6IUlfptaWoVCrs3bsXw8PDaG1t3dBLLxgM4sWLF3j69Cm8Xm9JHvlSGoWS8HmXrwwW/e8EqdNrYmIC165dwz/+8Q9MTEyUPZvObDbjyJEjOHDgAJqamkQLrVLJ5XJwuVy4e/culpaWtnxsU1MTTp48iaGhIdTV1YnYPFXjTU5OYnp6uuRCIGKzY0Shw48pHxb97wBKvPH5fHjy5AmuXLkiilpKFbz07Nvd3Y0PP/wQnZ2dZZn1RCqVQiAQEHPxNjvL19XV4eTJkzh+/Lgoo5XL5aKLz/LyMmZnZ7G2tlZWhxs64hR67gvf62b/zmwNi/4dQxVka2trePjwIb799lvcuHEDc3NzZX2h6bFmsxlHjx7FsWPHYLVay74fasgxOzu7ZdMLhUKB/fv34/Lly9i7d6/oqQdANMycnZ3FyspKyUcTeh+FLbWKfQ5UbsuiLx8W/TuEPNwejwf37t3DV199hWvXrpWVSy9Fo9Hg5MmTOHv2LBwOB7RabdkmcCwWw9OnT3Hnzh1hZRQTVk9PD86ePYvh4WHY7XaRHUeLmN/vx4sXL7C2tlbS69JzyWtfWHRT7LGUf8/CLw8W/Q4oDCWVg9Skv3v3Lv7617+WnXgjRSaTobu7Gx999BFGRkZgNBrLvqdsNotwOIxHjx7h9u3bbxwtSGx6vR4nTpzA+fPn0dLSsqGRSD6fF/P95ufn4ff7S7p3ghaNrQRP9yq1BpjS4XhHhUhbO5f75aPdyePx4P79+/jqq69w8+ZNIfhKHFTd3d24fPkyTpw4gZaWlpIbY0ihtlvPnz8Xba0KBalSqTA0NIRz585hYGAABoNhQ9iMSl/9fj/cbndJPonCoZWlnNdp0eRdvnx4p68QGuCQSqXEoIlSK77S6TS8Xi8ePHiAr7/++g2TvpwvskwmQ3NzMy5cuIBPP/0UXV1dFcWv0+k05ufn8cMPP2BiYmLTe+ns7MQnn3yCI0eOoL6+XkQG6H1TyJH64W8HZeFRj31ph5yt3jOLvnJY9BVAZ3H6YptMJtTX14vxT5sJP5vNiln29+/fx9///vey6+ELaWxsxOXLl/HZZ59h7969qK2tLTsfPZPJYGVlBXfu3MG3336L2dnZoo+z2+04d+4czp07h87OzqKhQFrQVldXRYeczbz/FG+nGX8Att3lpYsL98irjJJEX80fbDEBU0784uIiVlZW0NDQILrD0IQW6fOo+UUsFsPy8jLu3buHq1ev4vbt2zsSvMPhwMWLF/H5559jeHgYdXV1ZQuenG7379/Ht99+i+np6aKPU6vVOHHiBD7++GN0d3cXTfih+DyF6rZyBAIQE21olBVNtd3OWpI6/ar5u1kpJYmekyBez3GjEdWTk5N4/PgxlpaWYLVaEQ6H0dfXh8bGRrHjAxBid7lcmJ+fx6+//opbt27h4cOHJbe4KkQul6OtrQ0XLlzAF198gSNHjpTV4JLI5XLwer14/Pgx/vnPf+Lnn38uGl5TKpU4cOAAPvzwQwwPD8NkMhX9TpDoaZptOp3e9LWVSiX0ej1sNhssFgsAIBgMirr7Uu69sH8eUxps3m8DzYeLRqMbWkf98ssvwuGl0WiwvLwMn8+HPXv2wGq1QqlUiiQVp9OJZ8+e4ZdffsHTp0+xtrZWdpYdIZfL0dPTg08++QR/+MMfsH//ftTX15cteDpmjI6O4ssvv8TNmzc39bS3t7fjs88+w6lTp9DQ0LBpwk8ul0M8Hsfq6iqWlpY2TcihcVodHR3o7u5GbW0totEoFhcXEYlENu2NX/haPMeuMrb8psRisao0ofL5vHCEzczMYGxsDGtra2J22/T0NObn5xEIBJDL5aBUKrGysoLV1VV0dXWhoaEBMpkMwWAQa2trWFlZwdLSElZWVratS5dSeBY2m80YGRnBhQsXcObMGQwMDFR0hk+n01haWsLt27dx5cqVN3LspZ1rHA4HPvnkE1y6dAk9PT1bZvhJvfbkxCtWJWcwGNDX14eRkRH09vZCq9XC6XQik8nA5XKV1DiT/CMcsiufLUUfCATw6NEjTExMCI9wNSwAJPpcLofFxUVMT09jfX0dbrcb6+vrRXeicDgMj8eDqakpGAwGkUfv8XhK2rk2uw8A0Ov1aGpqwrFjx3Dp0iWcOHECzc3NZTeRyOfziEajePnyJW7evIlvv/0WDx8+fGMhIiFZLBZ8+OGH+NOf/oT+/v4t22WT135ubm5LP4XBYEBvby/OnDmD48ePo7m5Gfl8HgaDAV6vd8PEoK3eB420qsZNaadsu9P/5S9/wV/+8hcAr5w5W53T3hekvd3IyZTNZreckppMJuHz+cQORzH8nX4hGxsbMTIygkuXLmF4eBidnZ2ora0VCTGlkM+/mj8XCoUwOTmJq1ev4m9/+xtevny56XP0ej3+8Ic/4IsvvsDhw4e3bbFF5bhPnz4VLbLptQm5XI6WlhacOnUKZ86cEQtJKpVCKpVCfX29sCS2+txI9DzLrjK2FD3tDEQ5OdT/ykhNxmg0uu1wBgAiE223FkWr1YqRkREcP34chw8fxsDAAJqamsouk6X/w7m5Ody6dQvXrl3Dw4cPt6yeU6lU2L9/P/74xz9iZGRkU8edFBp59ezZsw1HBSkNDQ3Yv38/Tpw4gYGBATQ0NKCmpgaJRAJGo1G8t1IEL22FzSZ+eWwpeplMtsGkU6vVRfubvc+8jQQQ6RfdaDSiu7sbR48excWLF3HkyBHRgqrc+vF8Po9AIIBnz57h+++/x7Vr1zA6OiqciNKzu/QeWlpacPnyZQwPD8NqtZb0mmTpzM/PF10klUolOjs7MTIyIgRP1ko2mxXJOSTozZCOtUomk4hGo0gkEmVZPtXOti5f6SrKjpPfBhKbwWDAuXPn8Mknn+D48eNoaWmB2WwuaQpNMcLhMJ4+fYq//vWv+K//+q83euRv9n+5b98+nD9/Hna7vaTMPvJfOJ1OEYYs5oQcHBzEwYMHYbPZhD+CHiNtkb2VtUQh0EQigWg0Co/Hs6HPPrM9HLJ7h0iFYbVa8fHHH4smljabreLdi0z62dlZfP311/jb3/627VAMcl4ODAzgzJkz6O3tLbloJ5VKYWVlBS9evCi6y2s0GrS1tWFwcBBtbW3Q6/UbHJA0btvv9297lKLHJhIJhEIhrK2tobW1dUNKMLM1LPp3gLSwBABaW1tx6dIl/PnPf8bRo0dRV1cnHlcJ+XwebrcbN2/exI0bN8SQis2GSNDv5XI5Tp8+jRMnTpSV3ZdKpbCwsIDx8XHEYjFxD/Q+LRYL9u3bh56eHtTW1m4YbZXNZkVKs9PpFM/fjEwmg3A4jHg8jkgkAp/Ph2g0WnXHzp3Aon8HkNhVKhXa2trw6aef4vPPPxeDKHZ6No1GoxgfH8d3332Hqakp8futxkMDr6yNoaEh9Pf3Q6PRlPRaVFW3urqKmZmZDW2xqEFna2sr9u/fj/b29g1tq8n5GQ6HRULPdm21KAGIjgLxeJyPnWXCon9H2O12nDhxAh988AGOHz+Onp6esgZRbEYymcTExARu3LiBR48elZT5l8/nUVdXh4sXL2JgYABGo7HkKj0yt71er0hWonwOhUKB5uZm7N+/H/39/WhoaBCTbyjBJh6Pw+12Y35+vuQuOyR4aayeKR0W/VtEr9ejoaEBXV1dGB4exsmTJ3Ho0CE0NjZW1MuukFQqhcXFRXz//ff45ptv4PP5tnx8YV+9jz76CF1dXWWdjaX5CVIHHA2zPHr0KE6dOoWOjg4YDAbhvJPW3c/MzGB2dhZer7ekkKe0rJbun2P1pcOi3yG0IxbuNnK5XOxqGo0GVqsVnZ2dGBoawvHjxzE4OCiKc3ZjEGM2m4XL5cKtW7dw5coVkXizVdxbGiY8fPgwjhw5ApvNVvIun8vlEIvF4Ha7EQ6HRTadVquF3W7H/v37cfr0aRw+fBg2m00sJtL5dtK++PF4vCTxclLOzmDRV4hcLhejlYBXO14qlQLwylttMplgsVig1Wphs9lw8OBBHDx4EL29vWhqaoLFYqk4FFeItJPu119/jefPn4t/20wYUufdhQsXcPnyZTQ2Npa8y9N5PBKJwOPxIJvNorm5GSqVCu3t7RgYGMC+ffvEqCvy2FNyTTQaxerqKsbHxzE+Po61tbWSnXGFZbVcaVceLPoKMZvNaGlpEfPh4vE4kskk5HI5tFotzGYzamtr0dTUhL6+Phw8eBBdXV2wWCy7OmKZ6uEfPnyIb775Bvfv30c4HC66w0u997lcDkajEadPn8af//xnHDt2rORptvS6sVhMhNmsVitOnjyJxsZGdHV1obOzE83NzTCbzSKxCHgdZ3e73ZiamsKzZ88wPz+PSCRSsnALRc6iLw8WfQUYjUa0tbWht7dXtJlOJpPIZrPCrNfpdLBarejv78fevXvR2toKvV6/q2OY0uk0VldX8fjxY1y5cgU//PCDKHYpJgISvNFoFHPu/vSnP+HkyZNobm4u6d6kO7XL5YLT6YRcLseePXvQ2NiI1tZWNDY2wmQyQaPRbFjgKAHH5/NhZmYGz549w8TEBNbX14WVtF0arvQ+pH9nZ17psOjLRK1Wo6WlBb29vSLRBHjd1IFMWIPBgJ6eHpGQUm7O/FZQ2GppaQk///wz/v73v+OXX37B+vr6ts+tra3F5cuXceHCBRw6dAjt7e2or68vOfMukUggGAzC5XJhaWkJXq8Xer0eLS0twvLR6XRQKpUbrknx+GAwiIWFBYyOjuLp06dYXl7ecJYvVfRSpJ1xebbd9rDoy0SpVMJms8Fut4sQG4WngFeC1Gq1cDgc2LNnz4b5bqVCiS2FZDIZpNNphEIhMfrq6tWrmJqa2lC+S8+Viqe/vx/79u3DwYMHceTIEezbtw+NjY0i530rSLChUAgrKyt4+fIl5ubmEI1GUVdXh/7+frS2top2YdLdnUxvOv8vLi7i8ePH+PXXXzE7Oyu65QDYcB/l7NxUeMMmfmmw6MvEaDSivr5etH6WFuRQKqvVakV3dzdaW1sr6j9f2F+P2lCtrq5ienoa4+PjGB0dxZ07d4r2tJMm/5jNZvT39+PixYs4d+4c+vr6YDQaRS+/rSCx+v1+zM/PY3JyEhMTE5idnYXH44HVasWxY8dgNpthsVjeMOcJiuUvLS3h8ePHuHv3LiYmJuDxeJBOp5HNZkUnYfrZrjbVDDUAABeiSURBVHpO+plLq+3KbShSjbDoy8BoNIodXlosQiapQqGAxWJBe3s72traKhI88Lrbbjgcht/vRzgcRjAYxLNnz3D9+nXcunVr2yESKpUKBw4cwMWLF3H69Gn09fXBbrdv6N+3FblcDtFoFG63G2NjY7h16xZ+/vlnzM3NIZlMQq1W48CBA6LXXaHgpQk4kUgEy8vLePDgAX766SeMjo7C6/WKczwAYXHQQrNZynDh50T3yjt96WwreqnJpVQqqy7HWbqT19TUiDAclYDSFFWFQgG9Xo+2tja0tbVtmO1WCvRlp2wzn8+HiYkJ3Lt3D8+fP4fH44HX64XL5SraiaeweOf8+fO4fPkyRkZG0N7eDpPJVPIuSIJfWFjAzZs38f333+P58+dwuVxIJBKi5DqdTkOtVhftsy/tALywsIDbt2/j2rVrePLkCdbX14VJTzPpa2pqxEJaimOucCgGe/BLh5tolABl0h08eBCHDx+G3W5HTU2NMCszmQxqamrQ0NAAh8MBq9W6oahkO6jh5sLCAsbGxjA/Pw+n04m5uTk8ffoU8/PzGx5f7MxOf7fb7fjoo4/wH//xHxgZGYHVahUDJUq9l3Q6jeXlZdy4cQNffvkl7t69K9J5aZEjv4NSqdxQ50/io3Lb6elp3LlzB9evX8eTJ0/g9Xo3dB+Sjp1WKBQl79rSVlmFs+2ZrSmriYZGoylr5PC/KtTYIZfLobW1FYcPH8bRo0cxMjKCtra2DTPYI5EIgsEgcrkcamtrYbVaSzahKdbt8XgwPT2Ne/fuid1wq+kwxQQhk8nQ3t6Ozz//HJ9//rlocVWuN5vi/qOjo/jqq6/w4MGDDfn7ZLLTDk0psbTw5fN5EZabnp7GTz/9hBs3bmB8fByhUOiNe5fu6NKkm+1ET6+Zy+WgUChKckgyr9hS9DqdDv/5n/+JoaEhsaJXgwlFY5MymQx0Oh1aWlrQ2dmJ1tZWGAwGAK8dSPF4HOFwGKlUSpxvaefainQ6jWAwiJcvX+LHH3/Ejz/+iLGxsQ0x6+3uUeqwO3jwoOhaS6OjKyEej2Nqagq3b9/G2NhY0fp26a4cjUYRCoVgMBigVCqRSqXgdDrx9OlT3Lp1Cw8ePMDCwsKm1XNScz6dTm9oh7UVlMqbyWTElBwWfWlsKXqLxYLz58/j3Llzb+t+fhdInVDkoNNoNG+kqCqVSqjVauh0OiQSCdH2CSgediORJpNJLC4u4v79+7hx4wbu37+PqampDefcwpr7QqTm/OHDh0XsvaenZ8uutVuRTqcRCATw5MkT/PLLL8JZWBg7Jx9GNpvF2toa5ubmEA6HkUgk4HQ6MTExgdHRUYyPj5c0wYcELz0ybbe5SM/9NTU1b+QFMJuz7U7PbI1cLodKpUIul0MikUAmk0EqlRLnaOlZNx6PIxgMYnFxEXfu3ME333yD27dvv1FZVop5S+b8hQsXxEBJipNXQj7/qmPu0tISnj9/vmFCjfRepI7LeDyO+fl5JJNJqFQqeL1eTE9P4+XLl1hbWyupYo7eKx2nKNGmFOizLcdnwXDIblegnTCVSiGZTIrpNnSuz2azSCQSWF9fx+joKG7cuIE7d+5gbm6uou65Go0Gw8PDuHz5Ms6ePYu+vj7U1dVVXMBDzjufz4epqSksLi4imUwKiyOXy4m/k3WjUCgQjUZFWWw0GkUgEEAgEEAkEinr9aWjvkv1wpPQCxdXZnt4gGUJbPeFIvM0FArB7/dDJpNBq9VCJpMhlUrB7/djYWEBk5OTePLkiRhtJb1+KZ8xVbANDQ3hwoULOHnyJLq6usRrVQolz1C2nc/nE9YL3R85y5RKpYjHh0IheL1eJBIJUXBUCZXUxJNJz8k45cMDLHcByoX3er1YXFwUk1fJnF9aWsLExAQmJibgcrne+HKX8mU3mUwYHBzE+fPn8cEHH2BgYABWq3VHrZ+lsXSn04mVlRVEIhFYLBb09PQgk8mIHZ6g/Pt4PC4aVKZSqR0XvJS7sdBMe/balw+b97sANWt0Op14+fKlGH3l8XiwtraGtbU1+P3+sspHpbS2tuLUqVO4ePEihoeH4XA4YDabK+7+Srn01Dfe6/VieXkZfr8fDQ0NMJlMQlAKhUIsDB6PB8vLy1heXkYoFBKJRO/CEqSjhlarFc1KmNJg0e8QaZ+3mZkZjI+PY3V1FX6/X7SRikajRYdKbEdDQ4MY9HjmzBkcPHgQTU1NUKvVZe1utKOT2KkOfn19HT6fD+FwGJlMBnq9Ho2NjWLaDIXB6Lw/MzODVCqFpaUlscO/q6MfFTbZ7XbRo4ApDf6kdgA574LBIFZWVjA9PY3Z2Vk4nU5EIhHE43FRYw+8Lp7ZDGrAodfrYbfbsXfvXpw8eRIjIyPo7OwUu3spU10ppEX353a74Xa74fF4EAgE4PV64ff7kU6nYbFY0NHRAYfDgaamJphMpg2NL1KpFMxmMyKRCFQqFSKRCBKJxDutYc9kMtBqtdzzvgJY9DuAwnRer1eMoybTnhJNpCIvNvWXwl/UR6+trQ19fX0YGBjAnj170N7ejsbGRuj1euFAKyZ6aYJLLBZDJBJBKBRCIBDAysoKxsfHRZca2tnVajUcDgeOHTsGm80Gh8OB+vp6UTxD11UqlYhEIiK1NhgMlpRAtBn0HmgxrHR0GDUqMRqNvNOXAX9SO4A6ugaDQQQCAdGDnUpsCz3L0pCXXq+HxWKB1WqF1WpFQ0MDbDYbWlpa4HA4REMKvV4vzOzC/HZpCiwV6/h8PszOzmJiYgKTk5NwOp3w+XzClCfxymQyWK1WOBwO2Gw2tLa2oq6ubkOjTumiRVNsFhcXtx1IsR30GchkMhGyLCe9W6/Xi/6ExZKmmK1h0e8AShkloSsUChFKIrNepVKJ3xkMBiH0pqYmOBwOtLa2wm63w2q1wmw2w2AwQKfTiVg4LRwkcDqfp1IpMQqKnIXkOJybm8Pk5CRmZmaKxsxpQTIYDHA4HOjo6CjaAINi9IlEAi6XCxMTE6K0thLUajXq6upgs9mg0+lExMPj8ZQseqVSCavViubmZj7LVwh/YjuAdkGFQgGdTgej0Qi9Xi8yyvL5PGpqaqDRaGCxWNDU1ISOjg50dXWhra0NdrsdtbW1MBgMwgtNVWdEOp1GKpUSAxsjkQjC4TACgQA8Hg8WFxfx4sULvHz5EktLSwiFQmIQxFb3rVAoYLfbMTg4KGoKigk+k8nA7Xbj2bNnomttJej1enR0dODAgQPo7e2FRqPB2toanj17JkZ8lyJ8hUIBm82Gzs5O2O123uUrgEW/AyglVaPRwGg0wmKxwGg0bkgjValUMJlMsNvt6O7uxp49e9Dd3S3aYNPuSqWhZLZTPJzmta2urmJ+fh6zs7NYXl7G+vo6AoGAOGNHIpFNz9lSHwCd0ZuamrBv3z7Rf7+wYIWy9ILBIKampvDTTz9hZmamos/JYrHg8OHDOH36NA4dOiSm4TqdTuh0OkSjUUSj0aJ9AgpRKBSiE/FOhnxWMyz6HVJTUyNaXlssFmG2khWgVquh1WphNBpRW1uL+vp6mM1mcVannb1Q7IFAAKurq5idnRVRgaWlJSH4zaa7blVrT/fT1NSEo0eP4tixY+js7HzDEUa58NFoFC9evMDPP/+Mhw8fwuPxlP35NDQ0YHh4GB999BFOnDgBh8MBjUaDXC4Hq9UKmUwmGoQkEolt05Jramqg0+lQW1sLi8XCoq8AFv0OoJ1eqVRCo9FArVaLkBqF59LpNJLJJGKxmHD41dbWitRZOvtTfX44HBaz3cbGxjA6OorJyUmsr68jFouV3EKqEJVKBZ1OB4fDgcOHD+Ps2bMYGhp6o+EH+Q4ikQjm5+dx69Yt/Pd//zdWV1fL9rBbLBYcPXoUn332GU6ePInW1lYxl57q4Ht6ejA0NITV1VV4vV7Rm2Az6HOmPzkbr3xY9DtAGnqiLx950alEVCaTiZ74JKh4PI5QKCS85XK5HKlUCoFAAEtLS5icnMTY2BhmZmbgcrmEx73SezSbzXA4HOjv78f+/fsxODgoJs9Qww+ppUEtrm7duoWbN29icnKybOedyWTC8PAwPv74Y5w+fRoOh0N0BaZFUaPRoKGhAXv37sXKygqcTidmZmYQDoeLFt6o1WoYDAZoNBqxYFZ7XUglsOh3iWw2K3b0WCwmGjxQo4lIJIJAIAC3242FhQU0NjYK0VNZq8fjwdLSEubn57G0tLTtrrcVCoUCtbW1aGlpwZ49ezAwMICBgQF0dXWJrDtKwCFzntKJl5aWcP/+fVy7dg3Pnj1DKBQq67XVajW6urpw7tw5YdKTZSMth6W+gi0tLThw4ADcbjey2SwWFhbEwglA9LNXqVTCB0Lz6SlywiZ+6bDodwHKzIvH4yIxRhrKA17tuIFAAGtra5idnYVerxfdZkj05JSLxWIbnlsOMpkMOp0OjY2N2L9/P4aHh3Ho0CG0tbWhrq4Oer1eiJ0ch5lMRuQbLC0t4dGjR/jxxx/x+PFjeL3esl+fGnuMjIzA4XBsyO6T+hxkMplo093d3S1yHQDA5XKJz0HaLIMWKapofFe5///KsOh3AakDLh6Pi12eGkJIhU/ttWinkwovm81WLHbg1e5pMpnQ29uL48eP4+jRoxgYGEBTUxOMRuOGJJ9Cc57Kf3/99Vf88ssvok11uccKyvIbHByE3W6HVqsteu6W7vhk5vf29iIQCCCRSCCfz8PtdouSXbJGKLwXDAbh9/vFY5nSYdHvEGlrK/pSklkvbQ4BYIPQdhulUgmLxYK+vj6cOnUKp0+fxsDAABoaGjbs7HQf5GSMRqNYX1/H7OwsRkdH8fDhQ4yPj4uEmXIKhBQKBbRaLSwWCywWi4ih0+sVhg6B185QSqltb2+Hy+WCx+MRlXzkA6CqQCrrjcViIieBk3RKhz+pHUCClzaXoF3tbfVhp9Reg8GArq4unDhxAqdPn8bevXtFHn2h4ElAoVAITqcT09PTGB0dxejoKKanp8vKkJPeB1kvVI/g8/lQV1cnXr9wxy8UPvUb1Ov1bywYUmsqlUoJH0o0GkUqlYJGo9mFT7M6YNHvEErOoTg9FcbQzvZbCp9EptPp0NzcLAp1HA4HjEajCB+ScKQdfAOBAJaXl0UTy4mJCSwuLiIQCGxwjhXbnTeDfBtOpxNjY2MiTba1tRUmkwkqleqNsVfUv55ETdYHNdqknZwSnpLJpIiGkB8iFotBr9dzF50SYdHvANrlyTRtbW1FQ0MD1tfXEYlESu7hXs7r0Z+UP69Wq8XoaZvNJibZFPoHqJY+EonA7XZjdnYW4+PjGB8fx8zMjLhnOprQ7isN523Vl56ON4lEAqurqyJqsb6+jkOHDqGrqwtWq1WkG0vLjdPptOgyRB18PB4PotGoELz0fdBuTxN0Y7EYstksi75EWPQ7gISh1WphtVrR0dGBvr4+0W8uEAgAQMlz1gpFLf1d4WNol1epVNBoNJDL5Ugmk/B4PDCbzchmsyIHgObJ+/1+OJ1OzM/Pi3x9p9MpJseSuGgxkzr9yClZ6JyUQscGEjEVA62urmJoaAh9fX1obm4Wuz6d1WOxGNbX1zE3N4fx8XExIFMatiMoz4F2fHo9nk9fOiz6HULxY7PZLJpWAq+82AsLC8LDTMLfTPxkSheef6V/l36xaRem8tRAIICFhQXk83n4fD4xJ55ab1Orq/n5eaysrIgMv2QyuWE8FCUa0fVpp5daANtBlXm0W6+trWFlZQUulwuHDh0S5r5CoRBJSTMzM3j8+DGePn2KxcVFRKPRoim5ZLFQ1x5po06mNFj0O0DqvNLpdLDZbBgcHBRebK1Wi9nZWayvrwvhF5rH0t2d8vDpmpvV0Et7xdOXn3bz1dXVN7LWKANQmgdAz9vKupAuNoXmfSlQsg+Z4lRAROE8tVqNWCyG5eVljI2N4fHjx5ienhZ+ha0+93w+L4qZShm7zbyGRb9DSCxKpRImkwktLS0iF5/i4nK5XOysxYY50DWkab3FOuRIxU5QYg31nZcuFHReppTgYkjbT9Pf5XL5hteg62xl2m9FKpXC+vo6JiYmRKvw1tZWqFQqBINBLCws4MWLF5ifn0cgENiy955CoRC9BqhkmZ145cGi3wVol6adB3jdYCOZTAovNJm9ADbU3BPS3bzQa05x/0Kn2mZIxVwq0gVFeg/Fcg7KgXICAoEA5ufnkU6nsbKyArlcLpx3brdbhN9K6SNIoT2pVcOUBot+l5Du+DqdDhaLBXa7Hc3NzVhdXRXnW2nhilSYlF++mXecfi/daX+LcCAtVuWE6kq9Lp3f8/k8PB6P8PCHw2HRamw7yIdSW1srahd4ly8PFv0uIT2jF3rWpQ0qCgVdaF4XnqeB1w68t5XwI72v3byetDsvhQfJ+15qMhCZ9A6HAw6HQ0wJZkqHRb8DpOZ4YYIJnbPD4bBoGU1x861i3Zvt4u9DfrnU+Qi8njFfquCNRqMIi/b394s2Xyz68mDR7xBp7JraT9N0m9XVVSwtLcHtdosy0FLOxcXOp791dt/bQmq10EJZKt3d3Th79ixOnTqF3t5eWCyWiod2VjMs+h0g7UxLu3s4HIbP54PL5cLq6ipcLhe8Xi8ikQhisVhJwn0fxF2ISqUSacGUCFTqDm8wGNDR0YELFy7gww8/xODgoJjSyw688mHR7wDKQCPzPZFIIBwOi2ITaj+t0WhEogwNe5QeDaQUy8Sr9ItdLLb/NpAmGVHGInnZAYjRWuS8K4xGkGNOLpdDp9Oht7cX58+fx6VLl3DgwAEx6YfN+spg0e8AqkX3+Xyi4UMkEhGmPM1aUyqVsNvtIn2ULAP6kxx4lAFHrbCl4pHG7YstCIVZe+SFp+YeZGnQ6/9WaatarRb19fWora0VDTs0Go3IuaeFj8KYFNKk5CWqr6dYfH19Pfbv349z585hcHCQTfpdgEVfIYXhNun5VFoIk8/nodfrRZEIiV0qfuB10gll8lGTTWmKqTRWX+xH2oePfAwksFgsJmrRyXlWaHEUcyIWvj9KNiqWBKRQKGAymeBwONDe3i4KgMgMp4w8qqCT3iPt+tRNR6/Xo7a2Fu3t7RgcHER/fz/q6uq4z/0uwKKvEKnparFYoNFoxORXShoxm80i5ZV2VxKk1GtN8X2qJ9fpdCLUR6audLqN9BrSxB26pvQnmUyKphPSrj5biZwovC4l7EjHWEt7CVDHXbvdLib3mM1mKBQK0e3G6/VuGAFGnw0l5dDiRxN0Ozs70dbWJppy8Bl+58i2Oee9fx6lXUQaopOKklo60U5O/1Ysb12aekvDLGn4RbHmF9If6S5fmLFHoTGKJLjdbgSDwbLaS9G1aKGRltxSkoxer0dzc7MY3qFSqYTFQuY9pfVSO7FYLCZ2/VAoJAZ+qtVqmEwmMd6LxoBRE08WfNkU/cBY9LtAYYLNVj9EsTP6Zmf2zXbizZJ8gNcLEuXkS4dXFj622PFAas6TyKUVgPSnTqcTwzeplp+Q1hAULk5UfhsOhxGLxZDJZKBSqWA0GoXTr9jix5QFi74aIWee1KwHigte2uOPjh5kvlPXG+lZHnjtvyAfRDlIjw75fF5kMrIZv2uw6JmNFAq/8PgAYENUYbPqP+Z3C4ueYaqMoqLn7AaGqTJY9AxTZbDoGabKYNEzTJXBomeYKoNFzzBVBoueYaoMFj3DVBkseoapMlj0DFNlsOgZpspg0TNMlcGiZ5gqg0XPMFUGi55hqgwWPcNUGSx6hqkyWPQMU2Ww6BmmymDRM0yVwaJnmCqDRc8wVQaLnmGqDBY9w1QZLHqGqTJY9AxTZbDoGabKYNEzTJXBomeYKoNFzzBVBoueYaoMFj3DVBkseoapMlj0DFNlsOgZpspg0TNMlcGiZ5gqg0XPMFUGi55hqgwWPcNUGSx6hqkyWPQMU2Ww6BmmymDRM0yVwaJnmCqDRc8wVQaLnmGqDBY9w1QZLHqGqTJY9AxTZbDoGabKYNEzTJXBomeYKoNFzzBVBoueYaoMFj3DVBmKbf5d9lbugmGYtwbv9AxTZbDoGabKYNEzTJXBomeYKoNFzzBVBoueYaqM/x+PR/0ife3UjwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 65; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 66; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 67; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 68; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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0XiVUE8EDNBqDLf0fgGKxiGg0il9++QXXrl3Dl19+WZPgCYvFgr/+9a+4ePEiurq6oNVqa3peJpPB2toanjx5gidPnoge+GrI5XK0tbWhq6sLdrtdnN0p6h8Oh7GxsYFAICC8BSnSKTpKpVIIulrMQS6XiwEle72htxlgS/87p1gsIhaLYW5uDl999RW++eabugSvVqtx9OhRfPLJJxgZGalrfn0sFsOjR4/wj3/8Y8c5+mazGQMDA+ju7hY188CrM3g0GsXa2prYvpPP56vOyZPOvKNzfTWoxFej0bC1bwC+Tf6Ooek8S0tLuHXrFr799lvMzc3t+DypCAYHB3HlyhWMj4+jtbW1ZoFQAO/Jkyd4+PBh2der4XQ6MT4+ju7ubpEVoJJdn8+HmZkZrK6uIplMVhUzTRAmC07TfCrP+TQ/QK/Xc4ddg7Cl/50irXG/d+8erl+/XpPgKxkdHcWFCxfgcDjqsoi5XA6RSAQrKyvCylem6AiNRoP9+/djfHwcTqcTLS0tAF5Z+VgshuXlZUxOTsLr9VZNs9F5XqFQlKX0aI6e9GdTk5NOpxOtt0x9sOh/h1AVmtfrxZ07d3Dt2jVMTk5WLXutRqlUglKpxMjICM6ePYuBgYG63PpSqYRIJIKlpSX4fL4dH3/w4EGcOnUKg4ODMJvNYrhFNpuFz+fD/Pw8FhcXEQ6Ht/QUSPDUtUitynSul07opfZlmrDD1AeL/ncGufRk4f/nf/4H9+/fr3k3HVlEg8GAS5cu4fTp00KItVIoFODxeHDv3r0dI/ZtbW04d+4czpw5A6fTCbVaLVJ01Bfw4sULbGxsbLlQUxrEk3oJ2wUNqa+fz/P1w6L/HVEoFBCPx7G6uoq7d+/i6tWr+PnnnxEIBGp+DRJmb28vTp48icHBQTHoo57rWF9fxz/+8Q8sLy9v+TiTyYTjx4/j/PnzOHjwIAwGg7C8NAhzbm4Os7OzOw7qJPeeYgFSpGk76ePedEfjuwKL/ncAFd5Eo1HMz8/j1q1b+PrrrzExMYFQKFTTa0jP2z09Pbh06RIOHDhQcwedlFwuh3A4jJWVFdErX83K9/X14cqVKxgbG4PVai2bLUCvsby8jJWVFbGgYrvrJ2g+vnTIRrXHSot2mNph0b9lSPCbm5uYnJzEjRs3cPPmTTx79qyujTXSVVLHjx/HlStX0NXVVXd0O5vNYn19HYuLi9vW8zudTpw9exZnzpxBZ2enOF+TEFOpFHw+Hzwez45HE6mIafgl1dtLF4XQY+n4UOtsf6YcFv1bhppnJiYmcP36dXz55ZdYWFhoqGVULpdjeHgY586dw/DwMCwWS91WPpVK4ZdffsHdu3fFphyp4ChIeOrUKVy6dAn9/f3Q6/Vl472ptmBlZaWmQCBBpbo0UotEv9Vjt/s+szUs+rcEWbXNzU3RPEOFN41+kB0OBy5fvoxz587BYrHUbeULhQIikQgePHiA27dvv7Yei5phenp6cPbsWRw7dgwWi+W1jTNUMjw3N7ej6KXncorQ53K5Mve+mjUnT4Atff2w6HdJIx125NIHAgE8fPgQn332GW7evLnlYIpasFqtuHDhAi5duoT9+/fXHbwDgEQigRcvXuDZs2fCta88z5vNZpw/fx4nTpwQM+wqoZvZ3NwcgsHgjj9X6t5Tz/x2Z3r6GTs9hqkOi75BpJtUyVrVkjOm/HUwGBQW/quvvoLH42n4WgwGA86dO4dPP/0Uo6OjdafogFdn+cXFRXz99ddlAzkqBTU4OFh2Y5EWz0jfXygUgtvtRjwe3/bnVnbVUQeddHBmtefwmb5xWPQNQqOdstmsaACR7l+rZvlpKozf78f9+/fxxRdf1NQeWw2ywFqtFu+99x7+9V//FWfOnEFbW1vdgs/n81hdXcWPP/6I69ev4+XLl1Uf19fXhwsXLmBsbAwOh6NqPz6l6vx+vxjHvVX0H/h1x3zlZtudIvcs+sapSfTN/Ivdym0vFAoIh8MIBoNoaWmByWQSPeSVK5koQJVIJPDy5Uvcv38fN27cwE8//dSQ4AGITTNnzpzBp59+ivfffx8ul0sUt9RKoVCAz+fD7du38fnnn2NhYeG1918qlaBSqfCnP/0Jly9fRldX15bHh0wmA4/Hg6WlpR2tPBXk0B576XvbKa7BZ/rGqUn0XADxa4GITCYTAylfvHiBhYUFqFQqdHR0oKurC62trdDpdFCpVCJfHYvFsLGxgaWlJTx58gS3b9/G06dPax5XXQ2LxYIzZ87gb3/7Gz744AN0d3fXvdCxUCjA6/Xip59+wrVr1/Dzzz+/VjVH9e5HjhzB+fPnMTo6CqPRWPX1qI12cXERL168eC36X4lSqRRbc5VKpfjZteTf+UzfOOze70Aul0M0GkUikRAfrmg0ihcvXuDevXuYmpqCQqFAX18fDh06JNYtt7S0iFVTa2tr+OWXX/Dw4UNMT09jc3Nzx2KV7bBarbh06RL++Z//GefOnUNHR0fdJam5XA4ejwf379/Hf/3Xf+HOnTuvWWay8h0dHfjrX/+Ks2fPiq0z1aCR3AsLC5iamnot+i+FFmJYLBYx1IOChzuJmM7+1SbwMDuzreiTyWRT3knJupVKJSwvL2NmZgY+nw/JZFJsW5mdncXS0hIikQjkcjnm5+cxPT0Np9MJi8UC4NXvLxQKwefzwev1wuv1biuEatAaKfr7oUOHcOHCBfz5z3/GiRMn6ppoS2SzWdGue/XqVTx69Khszh6JvVQqoa2tDR9//DGuXLmCoaGhbbMCVIW3traG9fX1bd+TTqdDW1ubGK1FCy0jkciOlp5ET5aeqY9tRR8Oh/Ho0SO8ePFCTClpphtAsVhEKBSC1+vFxsYGfD4ffD4fgsHgazPmw+Ew1tfXodPpxN62RCKx47m2lmsAXjW2HDlyBBcvXhTRc4vFUpdLT0G22dlZfPPNN7h27RoeP378mtdB/4/1ej0+/PBD/O1vf8Pw8PCWbj09h/oG3G63cNWrBfFopdahQ4fQ29sLlUoFj8eDWCxW02es0tI302dyL9jR0v/973/H3//+dwAQu8nfdaiVs1QqoaurC21tbUgkEvB6vVsulKCuMiqd3au6cJlMhqGhIXz88ce4dOkSDh8+DIfDse2iyWrXRvvhnz9/jm+++QZffPHFllF64FWP/MWLF/Hpp5/i5MmTO87Vo7qDJ0+elL1u5e9ArVajra0No6OjOH78OFwuFzKZDEqlElZXV0U6biukVp7P9I2xrehpegpR697zPzrSD93a2hpCoZAY7rgduxV6pVUcHx/HiRMncOzYMYyPj2NoaAg2m63u7rJYLIbnz5/j5s2b+O677/DixYsdswYDAwP45JNPcOrUKVit1h1rEGj5xqNHj7btzLNarRgZGcHp06cxMjICg8GAcDgMv98vjg7biZ5SejQGm+r0mdrZVvS0sYRQq9VN9wumYNxvifQMrdFo0N7ejsOHD+PixYs4f/48BgcHYTAY6h4EWSqVEAwG8ejRI3zxxRe4ceMGZmdnxfel8QIpTqcTFy5cwKlTp+B0Omu6waTTaQSDQSwuLm6ZldDr9ejr68PJkycxPj6Onp4e8dq00nqnzxfNG6B997FYDOl0WizVYHZmxwOh9EPB0dLfBrLuGo0Gx48fx5///GecP38eAwMDsNvtYpx0va9JdfT/8R//gc8///y1o0llnzpdx/DwMD766CP09PTUlBXI5/OiAo9agSu9FpqWOzIygrGxMbFqOp/PCwufzWZrys/HYjGxz87v98PpdEKv19ddo9CscMruLVEpis7OTly+fBmXL1/GsWPHRFS7kXFQJPjJyUn853/+J65evbrjcktqmBkYGBBVd1artSbrmc1msbq6iqmpKWHlqwXvBgcHMTo6it7eXlgsFqhUKrG+KpVKiS2020GrudPpNKLRKDY2NtDT0wOHw8GirxEW/VuCRGGxWLBv3z68//77+Mtf/oLx8XFxhm7UXS0UClhbW8P169dx69Yt0c++lTtPX5fJZDh37hzee+89tLW11ZwZyGQyWFpawrNnz6pOyFGpVHA4HDhw4AD2798Pm80mBmDSXvpwOIxIJFKT6BOJhLhJhMPhLSfsMtVh0b8lZDIZOjs7cebMGXz00Uc4deoUenp6ykZONUKpVEI0GsXk5CS+/vrrskj6Vq4z3YAcDgdOnDiBQ4cO1dylR1ba7XZjcXGxarDTYDBgaGgIBw4cgMvlEseVfD6PVCqFQCCAjY0NRCKRLefoSUmn0yKQR//lCH7tsOjfAu3t7Th58qRoUR0aGoLD4ah5v9x2ZDIZPH/+HN999x3m5+cBbN/wArwSvdVqxcWLFzEyMgKr1VpzOpDKjP1+/2sehUwmg1qtRldXF0ZGRjAwMACTyQSFQiGaj0KhEJaXl7G+vi6KwXaC6u6rTdZhdoZF/xtDm1jUajUsFgt6enowPDyMM2fO4OjRo+js7CxrUd0NVPf+9ddf4+uvv96xrFV6M+jv78fHH3+MgYGBmiv8aCyW1+vF5uZmWe088Oq99/b24ujRoxgbG0NHRwfUarVov6XqvdnZWaytrdWcEt5pwAazPSz63wClUgmVSgWDwQCr1YrW1lY4nU4cPHgQ4+PjOHz4MFwul+jK2wsKhQLcbjdu3ryJ69evi1z5dlaevm4wGHD8+HEcP34cbW1tNR8vpGOxAoGACMyp1Wro9Xq4XC6Mj4/j9OnTooJQoVAgl8shHo/D7XZjZmYG8/PzCAaDNWeGqAuPBd8YLPo9hOoarFYr7HY7WltbYbFY0NnZieHhYQwPD6Ovrw8Wi2XPrDtQ3i332Wef4fnz5+J7WwlD6oJfvHgRV65cqas1l/LloVAIa2triMfjMJvNkMvlsNvtGBgYwJEjR3D48GHRhKTRaETl4sbGBubm5jA5OYnV1VUkEomag3GVFp4tfn2w6BtELpfDYDCI/LBMJhPWvb29HQ6HAyaTCQ6HA0NDQyJV1chI6u2gfvi7d+/i888/x8TEBLLZbFULL43eF4tFGAwGnD9/Hv/2b/+GU6dOwWQy1fQzqRQ2FAphZWUFfr8fGo0Gw8PDaGtrw759+zAwMIC+vj44nU4YjUbhBaRSKWxubmJhYQGTk5OYnZ0tOxrUCou+cVj0DUDWjHaxk9VWqVTQ6/UwGo3Q6/UwmUzYt28fjhw58psIPpvNYm1tDT/99BOuXr2KH374QQTTqomABG8wGGC323H69Gn8y7/8i2jPrcWtJwsfDAYxNzeHubk5pNNp9PX1obu7G4ODg+jr60NbW5uoIlQoFELwsVgMa2trmJqaEuXAqVRqV6KlXD9TGyz6OpHL5XA4HBgcHER/fz9sNpvIZ9NZHngVxOru7saBAwf2XPDUB7C0tIQff/wR//3f/40HDx7sWIADvKoLuHz5Mj788EMcPXoUvb29sNvtOwqeBmTQQJC5uTk8f/4cbrdbFN4cOnQI3d3dsFqt0Gq1YvkF7eajoN/09DSmpqawvLwscvOVY7br/X1QcI932+0Mi75OVCoVXC4X9u3bh87OzrItq9JGGIvFgr6+PnR1dUGv19c9Lbfa46mdNBKJYGpqCt988w2++uorzMzMvJYfrxTPvn37MDw8jKNHj+LEiRMYHR0V02y3Ewq58qlUCh6PB9PT03jy5Ilwy41GI44ePYr9+/dj3759aG1thVqtFq9JrjcNA52fn8fTp09F8I4ET+uqKEhXj+Vm0dcHi75GSEQWiwUulwt2ux06nU5EkaXz8IxGI7q6utDd3S2CW/X+LIJen2bPkYV98uQJ7t69i6WlpbLrI+iaTCYTDhw4gIsXL+LixYs4fPiwsMQ75eKpy5LE/uzZMzx9+hTT09Pw+/3Q6/UYGRmBw+EQw0NI8NLpuDRcY25uDo8fP8bU1BQ8Hg9SqVTZgBDpe95pQAa9V2mrLZ/ra4NFXyOl0qtNsC6XC21tbVsW0igUCjgcDhGlr3duHUFprVAohGg0inA4jGfPnuHmzZv48ccfX9txV/mBl8lk6O/vxwcffICPPvoIo6OjcLlcYh7dThQKBUSjUSwuLuLevXv49ttv8eTJE/h8PhH1V6lUYiioyWR6bSgoTQwOhUKYn5/H/fv38eDBA6yurpYV4q+280UAABcwSURBVNCATHouzTKo1dpLC3WYndnx/770DqxSqZquxlk6cVWhUMBqtUKn04nCFPqwlkolKBQKGI1GOJ1OtLW1QafT1T23LpPJIJPJCMt4//59PH36FF6vF8FgEF6vt+qOOWlk3mQy4ezZs/jwww9Fjtxut9c1dCMSieDZs2e4du0abty4gZcvX1bdSafRaKDVal+brCQtwJmZmcF3332Hu3fvYnFxEclkUkTr6ZqUSqVIF9Kyi51qDKQFOmzla4eHaNSAwWBAW1sbxsbGMD4+LgpYqAY8k8mgUCiIqTDt7e0wGAw1W3kaKOl2uzE1NYXFxUW43W6srKxgcnIS09PTZY+Xus4ECd5ms+HSpUtiDj7N0KtH8MlkErOzs7h69Sq++OILzM3Nie9Ll1SSta88v5OF9/v9mJycxI8//ojvv/8ec3NziMViwlOQvgey9tL3t9N1kujpudxPXxt1DdHQaDR151P/iFD3V7FYRGdnJ06cOCHq5Ds6OsQY7HA4DK/XC5/Ph0QiAb1ej/b2dlit1rIA31aQwAKBAF6+fIlHjx7h5s2bePjwYdmgykq2smrt7e34p3/6p7IRVxRBrxVKA965cwfXrl0rEzxdsxTaMptOp5FOpyGTyUTxzeTkJH744QfcuXMHy8vLZVt46T1IR4tLLftO1lsavKOsCQfxamNb0et0Ovz7v/87jh07JqxFM7hRdB7N5/PQ6XTo6urC/v370dHRIYJ3+XweyWQSDocDXq8XgUBABPEomLVVFB6AiMIvLCzg9u3buHXrFiYnJ+H3+2uamCsViFwux8jICD7++GN8/PHHGB0drbkXXgrN0nvy5Alu3bq17dgren/UJefxeMRE29XVVUxMTODu3bt4/vw5NjY2dhz5TRtrKEOxk3GhACGJvt6pQs3MtqK3WCz44IMP8P7777+p6/ldQIKi+AUtZSB3XS6XQ6lUQq1WQ6fTwWg0wmAwIBKJiCUX0qg+QdYrnU5jeXkZDx48wA8//CAmDkuFIXWjq0Ff7+7uxvDwMD788ENcvHgRBw4cgMFgaOh9U4bg4cOHePz48Y5z64vFIoLBIKanp5FMJqFQKBAIBDA/P4/nz5/vuOOe3gfdROl3XsuEJnLvSfTSlWLM9uxo6ZmtkclkIoqfyWSQzWZF1J2+TmdNcuWplfSnn37Cl19+ibt371a1ajt96NVqNfbt24fLly/jypUrGBkZEWnERqD10s+fPxfWmd5jtcyAXC5HLpfDxsYGSqUS5ubmEI/H4fF44PF4EI1Ga/IKSfTS9dS1TFyW1kTI5XIWfB1wym4PkEbwo9GomPVGM/ABIJVKYX19HY8fP8b333+PJ0+eYH19vaEYSV9fH86cOYMLFy7g2LFj6O/vh9VqbTg9SKW1brcbDx48ELn/ashkMiiVSigUChGso9r5VCqFZDJZ95h06Q2u1v10dB0Us+AgXu3wAssaqCUgRx962sceDodFG2kkEoHb7cbs7CyeP3+O6enpMre31tJTp9OJkZERnDp1CmfPnhUNLrUEDbeCzsaBQAAzMzOYmZlBIBAQKUDpdZHYqfgmn88jGo2KnfKNfk4a6Y2vdOmb/TNaD7zAcg+gnHQsFkMgEEAikRC95jQldnFxESsrK1XHQ+/0gdVqtejq6sL58+dx5coVHD16FO3t7WJRZqNIp9fMzc1hZmYG4XAYarUaNpsNhUKhzIWm51DQjVzyvdgeW+/z5XI5VCoV1Go1p+vqhN37PYAaYCKRCAKBAHw+n0jneTweBAIBxGKxhuocbDYbTpw4gUuXLuH06dNi4UWj1p3KVqkBZnNzE8vLy5icnITb7YbRaMTBgwdFtZ20Qy4SiSAUCiEcDosCmreVwi2VSlCr1WhtbYXRaKy5DoFh0e8aijgnk0lsbm6KohqfzyfmxiWTybq7yDQaDYaGhnDq1ClRI9Dd3Q29Xl930IqOHyT2aDQKn8+HtbU1uN1ubGxsIBqNigk6ZrMZRqMRGo0GcrkcqVQKPp8P8/PzePHiBSKRiEivvS3y+Tz0ej26u7u33aTLvA6LfpdQ9Vk8HkcgEIDb7cbq6io2NzfFeVdafbad4OVyOfR6PWw2G4aGhnD+/Hm89957OHToUFkzSy3QzYhKejc2NkQ9QTAYFFH2cDgMlUqF7u5uHDx4EP39/Whvby8bfJFIJLC8vCxGXdM6qbdJsViEVquF0+mE1Wpl0dcBi34XkKucTqcRj8cRiUQQDoeRSCREzp3SW3Q+riZ6lUoFrVaLtrY2DA0NYXx8HGNjYzh48CA6OzthMplq2jRDQTkaVhGNRrG5uYmXL19icnISv/zyC16+fIlYLIZcLgeZTAaLxYLDhw+jp6dHDPswmUxiMIh0+65SqRRlx3sROKtWTlwLVIGn0WhgMBh4pVWdsOh3AYk+m82WNSLJ5XIx9ZXcamk6jUROE2za2trgdDrR3d0tRk11d3fDYrEIF3srpFVsmUwGkUgEL1++xLNnz/D8+XOsra0hEAjA7/cjEAiUrc7WaDQwm81wuVwYGhoSAzWkHgXFKwqFAjY3N+Hz+fakB4NSbeSR1Cp8mUwGq9WKjo6OXcU2mhkW/S6hD2tLSwvMZjNsNpuoQ0+n02XdZFTBZ7FYYLfb4XQ60dnZie7ubnR0dMBut8NqtUKv14sKQGlvujS1VSgUxLHC5/PB7XbD7XbD4/FgcXERU1NTmJmZqVoRR3ltjUaDtrY2DAwMiFVT0n54+lnJZBLr6+tYWlrCxsbGrlpYW1paoNPp0NLSIuoDaGFFrc93Op3Yv38/ent7a17KwfwKi34XkOBVKhXMZrMQsUKhEG5wsViEQqGAwWCAzWaD0+lER0eHaL+1Wq0wm81C6NQ4UpkioyBcMpkU7nYkEsHGxgZmZ2cxMTGBqakpeL3estbVra5bJpPBbDZj3759ZdNqK/vhM5kM1tfXMTExgfn5+bKmmXqg5i2Hw4HOzk7o9XokEgmsr6/D7/fXPARDpVLBbrcLz6Te9mWGRb8raMQTLbJwOp3Y3NyEVqsVJblkUa1WK5xOJ1wuF5xOJ2w2G0wmEzQajUiNkQWmUlRy2ZPJJKLRKPx+P1ZWVrC4uIiXL1/C4/EgGAwiFAohFArtOCOPrLdMJoNOp0N3dzeOHDkiRnpJ016V7bH37t3D2tpaw7+n1tZWHDlyBGNjY+jt7UVLSwv8fj+mpqYwMTEh3utOwlcqlbBYLGhvb0drayuf5xuARb9L5HI5WlpaRNSdZsdTHluhUECv16O1tRXt7e3CulMASlpVRha9MhtAa5/W19dFdoDq2yvZLjhGX9NoNOjq6sLw8DAOHjwIu90uAoXk0udyOWxubuKXX37BnTt3MDU1VRYPqOf343K5cOLECbz33nsYGxsT8wj8fj/sdjuAVym49fX1HbvxzGYz7HY7LBZLTSO/mNdh0e8CsswKhQItLS3QarXQ6/Viok4ulxM3BSpfBVBW0SbtK6dps8FgEGtra5ibm8PU1BSmp6exsrKCSCSyozXc7ntKpRJ6vR4dHR04efIkTp48ie7ubrE0k2IGuVwOoVAI09PT+P7773Hv3r3XxnPV+vvp6OjAmTNn8Mknn+DYsWNob2+HRqNBqVSCzWYTQ0PpGOPz+aruqZfJZDAajWhvb0d7e7uoV2ArXz8s+l1CwqcPILnmqVQK6XRaVLNRuyidzQ0GgyghpceEQiGsrq5iZmZGTNDxer0Ih8M19dhvh0ajgcPhwMDAAEZGRnD06FEcOnRIjPCmOXM04mp2dha3bt3C7du3sbKyUndeXiaTweFw4E9/+hM++eQTnDp1Cp2dnSJuQOuv6Fgjl8uh0Wjw7NkzuN1upFKpsjSnQqFAa2srOjo6xOxBHpPVGCz6PYIEQ5Y6HA4jFouJbTOUHrNarbBYLGLYhkwmE+uhPB4PlpeXsbCwgLW1tYY2v1Si1WrhcDjQ09ODoaEhHDp0CPv370d3dzdsNhs0Go1I+0mXWNy7dw937tzBwsJCQzccs9mM4eFhvP/++zh58iQ6Ozuh0+nEzZGClWazGX19fVAqlWI70PT0NNbX14VnUygUhOhpH14mk0E6nX4tFsHsDIt+l0iHQCQSCfj9frjdbgQCAVGCS/PYKV1lMBhEswz12QeDQfh8PgSDQXGz2G1qzGQyobu7GyMjIxgbG8P+/fvhcrnEeZisZSqVEqm/+fl5PHjwAD///DMWFxerNgjV8rNdLhdOnTqFsbExOJ1Ocf6WjsWifxuNRvT09ECv18PpdGLfvn2YnZ0VK7Pi8ThyuZyI1FPPQDAYhMFgqKlwifkVFv0eQJFu2tPudrtF3T3l6il1R1N36O/0XGm+ejfWnbyK9vZ2DA8P4/jx4xgZGUFfX58YskFiz+VySCaTYmX0zMwMnj59isnJSSwvLyORSDTU/WYymdDb24uBgQE4nU5RYCQVprR7j0ZdUdESBevsdjsWFxexurqKUCgkrjcajcLj8YhdgTqdjq19HbDod4F0+qtUQOTeRyIRUa1X2ZteOQRyL1YvK5VK6HQ6dHR0YHR0FKdPn8b4+Dh6enpgNpuFuChomEgk4PP5sLS0hBcvXmBychJzc3NYX19HMplsaNw5XYPFYhFWWPr7qrTI5OorlUpotdqyqbhUYejz+VAoFMTRyWaziQ47WpjBoq8dFv0uqJx/R+d6Om9SCa50EcNWwzKlN4BGrkOlUkGn08HpdArB0646WkRBgUZqA15fXxfrol+8eIGXL18iEAiUbZ6p9zrovaXTaYTDYUSjUVgslm2n1ZLwAYiaeqraKxQKSCQSiMViSKfTUCqVaG9vh1arRTweRyqVQj6f54abOmDR7xL6wNKHlarqKi3PdqOdG1naSM9TKBRicKfdbkdfXx8GBgbQ09MjxEaufKFQQCqVQjgcxsrKCmZmZvDs2TPMzs7C4/EgEomIjAPw6+CMem5IpVIJ8Xgcbrcb09PTcDgcZek16kGoduOTnvel3YF0VEqlUlCr1WKXPXlX2WyWi3TqgEW/S+iDrNVqRXGO1+sVZ3Q6z0ur4SrdfOl/K/8OlG8Zkj6e3GKVSgWj0SgyAzSmOx6PQ6FQQKVSiXFegUAAy8vLmJ2dxezsLJaXl8W0H2larnIfnbTmf6tUGTUYJZNJrKysiMIjuVwuRntJC2oqC4mko8UpqCj1Pqg6kRqAaAhpOp2GwWBgF79GWPS7hIRHiy4OHjyITCYjgnV+v18IaqttrNKgljTnT5a82jmYHk+BMGqUSafT2NjYgEKhQCgUElN5aRIvpQVXVlZEnT7Nt6ObEv1caUERpfVoNt5WVp/iBZubmyKeUSqVxO/D4XBU7RykgiVaZ021CisrKyJ6D7zaF0CvS8cpXl5ZHyz6XUACIUtvt9tx8OBBUWtvNBqxsLAAj8cjzqRS4VMqT1rgQ2IjCy0NbFXubqOVTrRxJxKJiD10q6urwuWlgBjV6NNZuPJGRDcRupFJF1JSeWwtZ306TsRiMUxPT6NUKomdd3Rzki6nIA8hkUjA6/VicnISP//8M2ZmZhAKhcrSlxT0pH/TzYRd+9ph0e8S6ZneZDKJZhaLxYLW1lbY7XbMzMxgZWUFoVBIjIgmwdJrSEc603w6lUolhC/tzZeKVWoBKXsg3QBLj6kMLlZ7H9L97lKPQ+p+15NloGm5CwsLsFgsou+Agop0jVQYRN18d+7cwcTEBPx+/5Zlx+Q96PX6uiYKMSz6XUMWRqFQCLeVJrpYLBaRutJqtVhaWoLf7y9zqaWvQyKj4BxZMaDcwpFbS3+XCrHy/F1rFF4qbJr0Q9WEdNamGoJ6Xel4PI65uTk8f/4cXV1dYiGHtDiJLPyPP/4o1lmnUqktF21IMxZU2cjUBot+jyBLQ/XkZLEpmEVjskms5AJLP9SVgiPrLl33RGdYei69VqXwG4F+Dv2dvAj6eqOjrmnZ5+LiImZnZ2E2m5HP56FWq5FOp+Hz+TA1NYX79+/j0aNHWFtb2zZtKE3rabVaTtfVCYt+D5BG5uk8TJZHWp5LAyNisVhZGyvwuoVWKBRiCyx9nVxzqcWn5+4VUoFXXuNuXjOTycDv92N+fh4mkwnxeBxyuRzBYBDz8/N49uwZpqen4fF4yqYHV0JHH5o81Mh04GaHRb+HSKPqVFuuVqvLik0AlLnkUgtNrrS0yIWgx26XMtsrdusxbPWatOtudnYWfr8f2WxWFAitrKwgGAxu2zqs0WjgcrnQ19eHAwcOoLe3V7QFM7XDot8Dqllr+nflmCsKplEwrtJlrtwoA6Ascv1HTk1Rn/7S0hJWV1dFia3P5xNNRttZ+N7eXpw4cQKnT5/GkSNHRGUen+frg0W/SypFTq43iT0ej2Nzc1PMnJd20FUTsTSgVu3rjVbvvU3IcykUCojH4yI+EYlEEIlEyjIa1ZDL5bDb7Th9+jQ++ugjjI6OwuVycaNNg7Dod0Flzrya6Gm/3fr6ulh3FY/Hd4yq/9GEvR0GgwFWqxVWqxVarVZU3UWj0bLCm2poNBp0d3fjxIkTuHz5Mo4fP17WucfUD4t+F0jTYlILTdVrtC+OLJlMJhNjtSggRzcKQponl/63kWsD8EZiAJVQQZFSqRRTgl0uF0wmEwCI8mDKTFDRktSbkclkUKvV6OrqwpkzZ8TiTurNZ8E3Dot+F1Baiz680iIaSs/l83m0tLTA4XBgcHAQNptNjNKix5CloyIf6Rpm6WrorZpUql2XtE+fRmanUqldD+fYDrlcLmb6WywWmEwmEWWniTfpdBrRaFSMA6fFnhTjkJYAq9VqHDhwQAzU7OjoYAu/B7Dod4HUpa8WzKNCndbWVvT398NoNCIWiyGZTCKVSpWJkLbiUKRfOhpbOlBTeoSo/De18VJHXTabFZ4G/aGblDSmIA0USv9Lf5c+Vjrqiq6JvButVivGctHQDpr8q1QqxTiujY0NMSxD2n5MKUi5XC7GfB05cgTDw8NwOp1cebdHsOh3AeXkAZRZ+spacOqCc7lcIoJPFp7y4dQeq9VqRdGJdB4+CYuODlKhSMtz6Q95G7Rnj7rTKvfJbxVIBMqPKuTNUCUc9QbQH+kAj8HBQfT19cFmswlXnPri/X6/qFakQR10EwIgrDyN+urv74fL5WKXfg+R7XDWe3eiSb8R1WrRpTl1qnunP9VKWaXNNtLyW+nNYzsLL/1DNwKy9JFIBH6/v6a0WCXSQh26UZCVVygU0Ol0YmMNjeJSq9UwmUwwmUxlbbRUkUgDMRKJhPB4qD9epVJBr9eLG5/ZbBatwhylb4iqASEW/W9IZVS/Mo9frYd+pwDeVi649OcBvzbaxONx0V1H664qX6Oyrl/qatPxgv4rjS+o1WqxucdqtYoa+MojgPQ6pd4DeSI07lpaS0/eg/R4w9QNi74ZoXr9yr7zrWoLyBMh0VOLbUtLS9k2HuDXwCPFHxrJNEi9CDrmsMj3DBY9U06lh1Cta0/a41+ZTmR+97DoGabJqCp69qMYpslg0TNMk8GiZ5gmg0XPME0Gi55hmgwWPcM0GSx6hmkyWPQM02Sw6BmmyWDRM0yTwaJnmCaDRc8wTQaLnmGaDBY9wzQZLHqGaTJY9AzTZLDoGabJYNEzTJPBomeYJoNFzzBNBoueYZoMFj3DNBkseoZpMlj0DNNksOgZpslg0TNMk8GiZ5gmg0XPME0Gi55hmgwWPcM0GSx6hmkyWPQM02Sw6BmmyWDRM0yTwaJnmCaDRc8wTQaLnmGaDBY9wzQZLHqGaTJY9AzTZLDoGabJYNEzTJPBomeYJoNFzzBNBoueYZoMFj3DNBkseoZpMlj0DNNksOgZpslg0TNMk8GiZ5gmg0XPME0Gi55hmgwWPcM0GSx6hmkyWPQM02Qod/i+7I1cBcMwbwy29AzTZLDoGabJYNEzTJPBomeYJoNFzzBNBoueYZqM/x9go02ILMAifAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 69; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 70; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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tt15ax+5yufDBBx/g4sWL6O7u3tZbT6/r9Xpx8+ZNPHz4UGTAVe7ywItkH5fLBafTKXIV6GfpWpudwyubaVQz9envdI1XdZx60+DuA7vkVZj25ERbW1vDDz/8gM8++wx37txBOByu6bnAC2/9hx9+iL/85S8YGBiAwWDY9p5LpRISiQQmJibw7bffYmVl5aXrSjEYDHC5XGhpaRH58yRg6vITi8U2rY6rLDveygqh8CgLvn5Y9HvAry34fD6PUCiEe/fu4b/+679w+/btmgRPmM1m/PGPf8Rf//pXHDt2DE1NTTU9L5PJYGlpCY8fP8bTp09F84pqUK89l8sFi8Ui8hfIHI9GowgEAgiFQi/ly1cKXdpSrBoKhQJqtZpbZe0Qfsd+w5CJGwqF8PjxY/z973+veYcnjEYjzp49i48//hgjIyN1jaeKxWJ48OBBTceIpqYm9PT0wO12w2g0QqFQCCcctdxaXl7GxsZGmZOOkIqdch8qew8Q1K9Ap9PxAI4dwKL/DUPJN+Pj4/j6669x+/Zt+Hy+bZ8nFcng4CA++ugjjIyMwGaz1RzXJtN+dHQUjx49Kvt6NZxOJ9566y243W7hoSezPhAIYHp6Gh6PB7FYDPl8vuo9U4YjOfw2y/9Xq9XQ6XQi0YdN/Ppg0f9GKRaLSCQSeP78Of7xj3/g2rVrWFpaqum5JEyj0Yjjx4/jzJkzsNlsdTm9qNPN8vIyYrEYgM2PMSqVCt3d3Th8+DCcTmdZcU0ikYDH48HY2Bi8Xm/VNl4keKrgo0e1cz0tDNRXj3f6+mHv/W8M8rTH43HMzMzg22+/xVdffYWFhYW68szVajXOnj2Lc+fOoaOjo6Z4vPQewuEw5ubmEAgEtv353t5eHDt2DP39/bBYLMK0z+VyCAaDmJ2dxeTkJILB4KaWgrQZiDT/QbpQ0deUSiWf6XcBi/43hjSf/rvvvsPf//53PHv2rOZ+9iSYlpYWXLx4EUePHq3JUy+lUChgdXUV9+7dK7MuqgnWYrHg5MmTYrItVdJRcc7S0hImJiawsrKyZQIR7erSbL1qgpY27GTv/c5g0f9GIC99JBLB5OQkrl+/ji+++AJjY2N1tbQulUrQarUYHBzE8PAw2tvbty2XrYRE/8MPP2x5pNBqtTh48CDOnTuHoaGhshTbYrEorJXJycktE4ikDjyp0Kt1viWL4FXWObxpsOh/A1DSysbGBkZHR/H111+LfPpaBS9Nlunt7cXly5fR09NTl1lP5HI5hMNhLC0tiay3art8e3s73nvvPRw9ehR2u12c5cm0pyPC/Pz8tpaK1JSnOP5mba6l5j5TPyz61wxl2q2uruLBgwf48ssvcevWLczPz9f1oaaf1el0OHHiBM6fPw+n01n3bpjNZrG6uor5+fktd2cy6y9cuICuri7RXouEmk6n4fP54PF4tjzLA3jp3C7NxtvMjyHtv8fUB4v+NULlsevr6/j+++9x9epV3Lp1q6awXDXUajVOnjyJ8+fPo7e3F0ajsW7Rp1IpPHnyBHfv3hVWRjVv+/Hjx3H58mXs378fTU1NZfP5yC/h8Xiwtra2bfPKyl2ehlqQqKWvL23ZxX3vdwaL/jVB7aHX19dx48YN/Pd//zdu375dU7/6zejs7MT777+PU6dOwWQy1S146rX34MED/POf/9z0aNHW1oYzZ85gZGRElOZKxUc99GZmZuD3+7d8TWkWHt0vDbbYaieXLg5MfbDod0Glg6lWkdEZnurhK3PpNztDb0VnZycuX76Mc+fOobOzs27nHQAkEglMTk7i6dOnIgOv8l5MJhNOnz6NkydPor29ver4avJPTE9Pbxvyo5g8eeKlZ/mtBC3d6Xm3rw8W/Q4hbzs5umoNH1Eoa3V1ddNquXo/xA6HAxcuXMBHH32EgYGBTbvTbEUul8P8/DyuXbuG8fHxTX+ut7cXH3zwAQ4dOlT2OtIzeTabRSgUwvLy8pYpw9LYPA2vpOSdrXZ5Nu93B4t+h+TzedGrjVpfU4x6M/Hn83kkk0lRqnr16lXcvXu35maW1WhubsalS5fwpz/9CcPDw7DZbHVnqeXzeSwvL+P27dv44osvMD8/L74nFVVnZyfeeecdHD9+HC6Xq2ppLuXaBwKBmnZ5Ghii1+vF8ytfV4q0TJcdeTujJtE38hu7mYCz2Sz8fj/8fr/o/koTWqX54LRrUVrr1NQUbt++je+++w6PHj3aleBdLhfeeecd/PnPf8apU6fEpJ16KBQKCAQC+P777/H5559jdna26s+p1WqcOnUK7733Hjo7OzcNBWazWfh8PiwuLoqefdWOKzKZDFqtFhaLRUwDSqVSZaG9rT530vHaTH3U9AnhJAiUxatTqRS8Xi+ePHmCubk5mEwm9Pb2ouv/zK+j6i9pHTnNsL9//z5u376NycnJuufIEzKZTAj+r3/9K06ePAmn01n3Dk+TZe7fv48vv/wS9+7dq+ppVyqVGBwcxIULF3DkyBGYTKaq16NWXvPz85icnEQikRBfr0Sj0cBms6G7uxutra1QKBTw+/2Ix+M1+TRoVl4jb0g7hc37baBxUYlEQpwj/X4/fv75Z9y7dw8LCwvQ6/UYGBjAW2+9hZ6eHlitViiVSiH45eVlPHr0CPfu3cPk5CTC4XDVSrNa6ezsxJUrV/Cv//qvOHr0KFpaWure4bPZLNbX1/HTTz/h008/xa1btzaNy7vdbnz00UeicGezjjulUgnJZBKzs7MYHx/fNCFHrVajpaUFBw4cwOHDh+FwOMTE2/X19Zrun6IfbOLXz5aflGQy2ZBvaqlUEokmCwsLmJiYEHPUqPJsenoaS0tLSKVSUKvVWFhYwMzMDPbt2wer1SqKVnw+H1ZWVkTMup7dvTLzzGAwYHh4GO+88w7effddDA0NwWaz7Ujw8/PzuHHjBr744gs8ePCgTGzUehp4cYS4cuUKLl++jL6+vqreeiKfz4v4/PLyclXTm+bQHTp0CGfPnsXBgwdhNBoRCASQSCSg0+mEo24zyIIi0TP1seWnJRwO4+HDh5iYmBAVTY20AJRKJfh8PiwvL4u5beSgqhRvJBLB6uoqnj17JqapRiIRhEKhmgdQVHt94MXO6HK5cOzYMbz33ns4f/48Ojo6YDAY6hI8xeGnpqZEbv+jR49e2pFJSEajEZcuXcLHH3+MoaEhGI3GLe81Ho/D4/HA6/WK96fSVDcajRgYGMDp06dx6tQpuN1u0S9/ZWVF+Aq2+pxRcw4edrEztt3pP/nkE3zyyScAXpzDGmE0sFwuF+dFh8MBq9WKZDKJQCCw6RhoaglFzityNO0Ws9mMI0eO4P3338fZs2fR398Pq9VaV1iOil+CwSDGxsZw7do1fP7551UHVhBarRaXLl3Cn//8Zxw/fnzbFlvUJvvJkyebXlcul6OtrQ0nT57EyZMn0d/fD6PRKObPUwbhdoInx2gul+Nz/Q7YUvTUPYWotbzz947UZPT5fIhEIuKDthV7IXLCaDTixIkTGBkZwdtvv43BwUF0dnbuKNMuFovh6dOn+O6773Dr1i1MTk5ibW1ty+f09fXhypUrNbfYoijAo0ePykQvFWRzczMOHjyIkZER9Pf3o7m5GUqlEtlsVsTpK/vcV0L5EdIHm/j1saXoZTIZDAaD+LdGo2m4AQM0SunXRLq7GY1GdHR04NixY7h8+TJGRkZE5pt0WGYtlEolBINBPHjwAFevXsW3335bNglHenaX4nK5cOHCBYyMjMDlctX0mtlsVrTFqpZKrFQq0dnZiaNHj2JgYAB2u13kNdDU2kKhgFQqteXiSvUK2WwW6XQaiUQC6XS6bCIuszXbHgilHwp2nPw6SB11Z8+exR/+8AecPn0aHR0dsFgsOyqPLZVKiEQiuH//Pj755BNcvXr1Je+89P9SuvAMDQ3h/fffR0dHR02ZhoVCAZFIBGtrayLXvtJMNxgM2L9/v/DWSwdZUhZfIpFAMpnc0gdCRwEafx0IBOB0OmEwGGrq489wyO61IhVGc3MzPvjgA/zpT3/CyMgInE6n8GTXCwn+yZMn+M///E98/vnn207BoVZUXV1dOHfuHA4fPgyr1VrzLr+ysoLp6emy4yChVqvR2tqK/fv3o6urS3TLlabTxmIxBAIBxGKxbXPuo9EoUqkUotEo1tbW0N7eDrvdzqKvERb9a4QET5Nn/va3v4mutcDOk6IKhQI8Hg+++uor/OMf/xBZf5uZ8/R1mUwm+uo5HI6aIwOZTAaLi4sYGxsrS8ihRY12+d7eXthstjJTnMx1v98Pj8dTddGQksvlkEwmkUqlkEgksLGxgUQisaf+lDcdFv1rRCaTwe1249/+7d/wl7/8RQyi2M3ZlHb5p0+f4uuvv8bi4qL43lYNKQDAbrdjZGQEhw4dqvlIQaOlPR4Ppqeny6IbpVIJSqUSbW1tOHz4MLq6uso62EozFj0eD5aWlpBMJrf9/ci8p7n1uVyOPfh1wKJ/TWi1Wrz99tu4cuUKLl68iKGhIVgsll1fN5PJYHx8HDdv3sTc3ByA7Ut1S6USLBYL3nnnHQwNDcFqtdac0kttroPBIDY2NlAsFkU+h0KhQEtLCw4ePIjBwUG0traKkl/ywicSCayvr2N+fh6rq6s1OU1pgCV57jlWXx8s+leIUqmE2WzGvn378NZbb+Hs2bM4ffo0uru7y6IkO4Xy3r/55ht888034hy/VcUafa+rqwsffPAB+vr66qrFz2Qy2NjYQDgcLvO6q1Qq2Gw2HD16FGfOnEFvby+amprEYkJRkY2NDczMzGB+fh4bGxs1menUQINr6XcGi/5XgOrEyfOtVquh1+vhcDhw8OBBnD17FidOnEBXVxfMZvOOvPOVFAoFeL1eXL9+HV9++aUw67fa5aV99d5++22cOHECDoejrqSfZDKJtbU1RCKRsjJZisnT7yptj10oFJDNZkXzzbGxMSwsLCCVStUkYk7I2R0s+l1CAieUSiX0ej2amppEuM1gMKC1tRVDQ0M4cuQI+vv7hXd+L4Y1FAoF+Hw+3L9/H59++mlZE4zNxCF13l28eBFXrlwpM7+3Q3oe9/l8yGazcLlcIh5/4MABDA4OYv/+/Whra4PJZBLJN/l8HvF4HCsrK5icnMTU1BT8fn/N4eDKBB7e8euDRb9DNBoNrFYrzGZzWY84jUYDs9ksvme1WtHW1ob+/n709/ejo6NDPGcvoKq/u3fv4urVq3j8+LGYLlspBKn3vlgswmAw4Ny5c/jb3/6G06dP1+xEJNFRnDwajcJqtYoS356eHvT09KC9vV0sfFLBJ5NJ+Hw+TE9P49mzZ1haWkI8Ht+y3Xbl61f+m0VfOyz6HaBSqeByudDd3Q2n0wmlUimyytRqNbRaLXQ6HUwmk9j1uru7YbVaodFo9kzw2WwWXq8X9+/fx+eff44bN26I9lTVRECCNxgMsNvtOHnyJP793/8d58+fR1tbW01WB+3wVE3n8XhQLBbR19eH1tZWdHZ2orW1FU1NTdDpdGVZhNJz/Pz8PMbGxjA1NVXzWb7yPqR/56Sx2mHR14lSqYTL5RJxZ9odKS5NwtFoNHC5XDhw4AAGBgZgsVh21KyyGtRnb3FxUTTW/PHHH2saYd3U1ISLFy/i4sWLOHbsGLq6umC327cVPO3SqVRK9L9bWFhAIBAQqcP79u2D3W6H0WiESqUSHW7JMpAOxXz69CnGxsawuroqehXQUaneM7vUscepuNvDoq8TijvTB7zaqGSZTAar1Yq+vj7RVKOebLHNPrz5fF6coycmJnDt2jV89dVXeP78+UvVf5VmcldXFw4fPozh4WGcOHEChw8fRmtrqxDnVvdSKBSQyWQQCoWwsLCAyclJzM3NIRaLobm5GW63Gx0dHXC5XKLcl65J90DhOeo4NDo6itnZWUQiERQKBRHmo9+7HuFLy2x5iu32sOhrhETU1NQEh8Mhdm7qvS5t32w2m9HR0SHaZ9WbHioVPMWgafLM9PQ0xsfH8fjxY3z//febxuJJQE1NTejv7xeNNwYHB9Hc3CzO2VtB7a+CwSBmZmYwMTGBsbExzMzMwOfzwWazYWRkBFarFTabDXq9vkzwRKFQQDKZhMfjwc8//4z79++LKbbZbCSDmP0AABenSURBVFbE9BUKhXgvga2rFqVOPGnMnkW/PSz6GimVSjAajXC5XKIdVrVmj0qlEk6nE93d3Whubt6xSU/n5lAohFgsJrLsrl+/jn/+85/Y2Nh46f6kyOVy9PT04N1338Xly5fFzl5rYQrV4K+srGB0dBQ3b97EvXv3sLi4iEKhAIVCIcZFGwwGsYhIW2KTlZBIJLCysoJHjx7hzp07ePLkCdbX10UBFz1PqVQK0dfi1KPvcTvs+thW9NJVW6VSNVxprfTDpFAoYLVaYTAYxLlaOl5ZqVTCbreXmbr1nDFzuZwoGw2FQpiensaPP/6IJ0+eYG1tDcFgEGtra1WLZ6QCaWpqwpkzZ3Dp0iXRrIKOIrVAY6lmZ2dx7do1XLt2DZOTk/D7/WL3VavVKBaL0Gq1Va0GaiKSSqWwvLyMu3fv4rvvvsPDhw8RCARE5h19vlQqlbgm7d5bQVYVh+3qh5to1IDRaERLSwuGh4dx5MgR0Xk2k8kgk8kgm82iUCjAZDKhra0Nra2tMBqNNResSFtNjY2NiZRUcng9f/78pedUM+cBiLn0H3/8sQihqVSqmgVPJv3i4iK++eYbfPbZZ3j8+LEQu3QTkMlkL/kESHz5fB7RaBRzc3O4d+8erl+/jtHRUayvr5eZ7fQeVe702yFNvyUHIDvxaqOuJhparbYhqpkoa6xYLMLtduPYsWM4e/Ysjh8/DrfbLTzMsVgMPp8Pfr9ftHtyOp2wWCw1mdBk+gYCAczNzeGnn37Cd999h8ePH287067artba2oqPPvqorMVVvWLI5XLw+/14+PAhrl69ip9//rns/5xCY5WDJKmLDQCk02lsbGxgbm4Od+7cwc2bN/HkyRNEIpGyHVnqB6HficS8XT691HmnVCqr+hKY6mwper1ej//4j//A0aNHxW7RCCYUfZjz+Tz0ej3a29vR19cHt9sNvV4vzqrpdBoOhwN+vx+hUAgymQxms1nMad8Kmt8+PT2Nmzdv4tatWxgfH6/adHOze6T/C6VSiaGhIXz44Ye4cuWKqIWvF6pgGx8fF221qjW0oNAkWYLhcBihUAipVEq01n769Cnu3LmDBw8elA2+qHw9aTiPFlvpArLVvUp9AtS4ldmeLUVPlVcXLlx4Vffzm0DqhCqVSlCpVC+dW5VKJTQaDXQ6HYxGI0wmE+LxeFmcuTL0Rl9Lp9NYXFzE/fv3cfPmTfz00094/vy5+KDTLrrVOZW+3t7ejiNHjuDixYt49913RbPJnZDL5RAMBvH48WP8+OOPwndQeZQgsz6Xy8Hn82Fubg6JRAKFQgFra2uYmprC06dP8ezZM6yvr2+5UdCOTrs25eVvl2wjtQiozoFFXxvb7vTM1qjVahiNRnG2p7ZParUaGo1GJKhQcUooFMLi4iLu3buHL7/8Ej/88MNLu1otTimVSoXe3l5cunQJH3zwAd566y3Y7fYd/5+Rt35+fh5Pnz7FwsKCEF7lvSiVSuHTWFpaQi6Xg1arRSQSwfz8PGZnZ+Hz+WrqnEyLK1kUtTQgBcqnBNOoa6Y2OGS3B5Cwc7kc4vE40uk08vl82XirVCqFtbU1PHr0CDdv3sTDhw/h8Xh25CNxuVw4deoU3n33XYyMjKC3t7dmP0I1yFT2+/0iF17quJOe45VKJVQqFZRKJZLJJBYWFrC0tIR0Oo1IJCKmAdXz2tIISa2hN3Lekc+CnXi1wwMsa2C7DxTtTvF4HOvr65DJZAgEAmK0VSQSgdfrFWbvxMREWcpsrfPoLRYLBgcHMTIygnPnzmF4eBhtbW1bTp2pBRqCsby8jOfPnyMYDIp6AuAXzzrF5kloyWQS8XgcmUxm2y62W0HCp7/XAjvvdg4PsNwDyKkXDoextraGZDKJUqkkzHmv14u5uTksLCxUzY/f7oOuVqvhcDhw+vRpvP/++zh58iTcbjeMRuOu8vnJWRmLxeD1eoXDzWQyYd++faKISOpjoOy3dDotWlbttkvyTjYVuVwOlUpVVziSeQGb93uAtGXU+vo6AoEAwuEw1tfX4fP5EAgEEIlEdjSl1mg0YmhoCO+++y7Onz+PQ4cOweFwCH/BTu83m80ik8mIsOPCwgKCwSDsdjuOHz8usu3IMUkTclZXV7G6uip2eEqjfdWQg7WpqQlarZZ3/Dpg0e8SMu1jsRjW19extLQEj8eDYDAoxE6DQIHaTXmVSoWuri4cPXoU586dw8mTJ19qOVXPPVIoLJvNIplMIhgMwu/3w+/3IxwOI5PJwGg0Ynh4GGazGXq9Xiws5NWfnp5GsVjE2tqacFq+rqMf9QNwu91obm7m9td1wKLfJWTah0IhrK2tYXl5GV6vF+FwGIlEQpxztwvBAS9MVr1eD4vFgu7ubpw6dQrnz5/H4cOHYbfb6+q0Q4kuNGPP7/djbW0NgUAAoVAIwWAQwWAQqVRKmPPd3d1wu90vNb7IZrPw+XxIJpMYHx8XZv3r9PUUCgXodDoW/Q5g0e8COuMmEglEIhFsbGwgFAohkUiUxZor+81Xi3nrdDo4HA709fVhaGgIb731Fvbv3y+m3NSSfCKteY/H44hGowiHw/B6vRgfH8ezZ88wNzcnmljSEIrjx4+jtbUV3d3daGlpgU6nE/39SPSJREIk48RisV1nZko97vX6A+i5Wq0WFosFBoOBzfs6YNHvAhI9FclITXhysFE6L7WGJg849dFrbm5GS0sLWlpa0NbWhq6uLtFqymq1ivFP0kYdUsgZl8vlxBmdmk2OjY1haWmpbMS2VLBWqxWtra1wuVzo7OyE3W6HyWSCQqEQGXf0mrlcDmtra/B4PNv2pt8OuVxe1gq7XkcgvW8mk0n4HZjaYdHvAmmll1qthslkgtlsRiaTEUKkAhK1Wg2dTif65tntdrS1tYmHw+FAc3MzzGYzDAaDmOJaWcxCGWyUuppIJOD3+7GysgKv14vV1VUsLCxgYmICk5OTCAQCL903LT4mkwkdHR3o7u4ua4BBCwslFaXTaayvr2NqagqLi4tbzprbCrlcDrPZLI4P+XwekUgE0Wi05mIutVotSpepVVmjh5TrhUW/S0jQTU1NcLlc8Pv9Yr59Pp8XO3tTUxOcTifcbjfa2trgcrlgs9nQ1NQEk8kEnU5XJnSp6Uu7OTnhyMSmTrSzs7N48uQJxsbGxGioraa+UNMKh8OBAwcOoKOjAyaTqayfnbT+gJJ2JiYmxIDKeqGwY19fn+jzT8055ufna97tVSqVuE5nZ+eO5/01Miz6XSKXy6HT6WC1WuFyuRAMBqHRaMSOrFKpRPVde3s73G63aMRR2XxC2mKKik6y2awY1hgIBODxeDA7OyvKb2myTDAYRCQSqSqcyvx/qvs/cOAADh06BJfL9VIIUFoeOz09jTt37oguPfWi1WrR39+PkydP4ujRo2hra4NCocDa2hosFov4HWvJ5FMoFDAajWhra4PT6YRWq2XR1wmLfpcoFArR9rq5uRl2u13skmT209nZ7XajtbUVzc3NZSa8tC8chQCTySQ2NjawsrKCpaUlERWgDrRer/el7jkEXU9askqQ4IeHh0UKb1NTU1kxkbTjzdTUFH744Qc8fPhwR7u8wWDAwYMHcenSJZw7dw4DAwMwGo0oFosIBoPQarXC6ejxeLbN6tNqtTCZTMJCklonTG2w6HcBeZGptJNaX2s0GmGu0u4pdbjRg5xlwC8z2knsq6urmJubw/j4OMbHxzE3N4dAIIBUKrWtGVzNrFcqldDpdHA6nRgaGsK//Mu/4NixYyLRR3qcIMEvLS3h7t27uHXrFjweT91nZ61Wi8HBQXz44Ye4fPkyent7Radc6rpTLBYRDodFiDMUCm0qfLKazGaziDCw175+WPS7hIRPHz5KUaXacplMJvLS6ZFKpURfeGksPBqNljW/nJqagsfjwcbGBuLx+I4dVgaDAU6nE/39/RgcHMThw4dx4MABtLe3w2g0isWH/BDU4uqHH37AjRs3MDExUXc2oVarxf79+3Hp0iVcvnwZAwMDwqIgS0gul6OtrQ3Dw8OIRqOIx+OYmJgQwq8MbRoMBthsNhiNRlF7z068+mHR7wHS8lBKhKFqs2KxCJVKBYPBILz2drsdVqtVdI+lohy/34+lpSWRp+/z+XaUugu88DU0NTXB7Xaju7sbfX19OHjwoGgGYrFYxDAKAGXRAK/XiwcPHuDbb7/F6OhoTf30pchkMrhcLpw5cwYXLlwQgpea4lTEYzQa0dXVhWQyiWw2C7VajenpaYRCIdGGjMKGJpNJWAqZTAaJROKl6zLbw6LfAyjzjcY8ra6uIhwOiw8y8IvDz2AwlJmowIveg+SJ9/l8iMViyGQyO2pCSll9LS0tOHDgAI4dO4bDhw+LPv0mk+mlzrU0eYbO1aOjo7h9+zYePXqEYDBY9z1YLBYcPHgQp06dQl9fn+gXWFkCS/F6s9mMvr4+AL+MBZuZmcHGxoawmMgBqVQqkc/nEQ6HEQwGYbPZ2INfJyz6XVKZ7kpJMJFIBIlEQnSEAX6pAafKMHKekegon32nJapUgNLf348TJ07g2LFjGBgYEGOmKkdqSZ2GNEH2yZMn+PHHHzE6OopAIFD3vdDIr4GBATG3T+qsJOjf5Ahtbm5GT0+PuIZOp8Ps7Cz8fj+i0ajY9amMl+oG2tvb0dTUtKP3q1Fh0e8B5KQjk5NM+1QqVZaRtxm7PZfKZDJoNBq0tLTg4MGDOH36dFnra2lWH70eHUfi8Tj8fj/m5+fx7NkzPHr0CBMTE2ISbb33QTkJNLdvuwIjWgg1Gg0sFgs6OjrK0oilx6RkMoloNCrSnqlykfrwM7XBot8DKvPI8/l8mdgrG0RsZYrWuwBQrz4S/Pnz53Hy5En09fUJ4UmTfcgyyWQyiEQiWFlZwczMDMbGxvDs2TPMzMwgEAjsKOuOOtlks1lRixCPx8U9SIUpfQ9I+JSebDabhYORFidKdw6Hw6I7ETlMqY6AqQ0W/S4hhxTNodfr9WXDG4lqPep3ijRUqNVqYbVa0d/fj+HhYQwNDaG9vf2lGDaJnaIHGxsbWF5exsTEBJ49e4apqSlRHUhnaPrdKmfBb3ZPwAuH4MbGBqampuB0OkXPPqojqGbqS68hLWKinT6dTiOXy4kkHtrdyVJJp9N1VSA2Oiz6XUDio7x7h8MBt9uNtbU1kVEnNe1r7f222b9pIaHsPXJ6uVwutLe3l1XkSTvMAhDn4Wg0ivX1dczOzmJ8fByTk5OigUY8HhcCJ3+DNN9farlU/i7SI0MwGMTY2JhovhGPxzEwMICWlhaxKEqtI6lfhFqOLS4uYm1tDZFIRHTnASD+TtmKsVhMhEBZ9LXBot8l1LaJptscOHBAjLuSyWQi3EWVbdWEL813p+dJH5XfI2cgZaeRR55Cf3q9XoS/AAhxUIbf/Pw8pqamMDs7i7W1NVF5R/dGpjjtytJMQelCUk34UkGSgzAcDiMajWJoaEhM/5FaIdJwp8fjwcTEBKamprC+vo5kMll2b7Q4UIgxm82W3ROzPSz6XUBCVCqV0Ov1ItuN4vI6nQ4LCwsIhULCJK22S0oFTX9SI0rpzl75miTKTCaDQCCAhYUFZDIZ+P1+GAwGKBQK0QPP5/NhaWkJCwsL8Hq9CAQCIqRIlYIkdqrxp7JVElo1/0Q1aDQWhdyoS242m8Xw8DBcLheMRqM445MPYHFxEaOjoxgdHcXc3Byi0WiZ4IFfIg6080vfJ6Y2WPS7ROqEslgs6OrqEjswOaTm5ubg9/tFCI92TkK6g5PgaDeXOsCko57IYRiPx8Xsd5/PB6PRKFpvU1cfmnobCoUQiUTKYt9SS4Jep7KtdD3jpqTQgrOwsCCuS4tIS0uLGFgZi8WwvLyMx48f44cffsD4+LhIzqm2QBKUlku/L1MbLPo9gARDSSIajQYGg0FU0ZGIfT6f6KojDWVJBUYNLKR/SlttVZ6rZTIZotGo+HmpUMnrLT0TV1LpoCNzXroo0M5K16vXEZlOp+H1evH06VPhlU8kEtBqtchkMlhdXcX4+DgePnwopuJs1o6LfChSXwo3xqwPFv0uIUFKvfhkjku90TThls6f9GflB7tQKEAulwsfAJnTlSFAqZldGRmQOt/qESjdF70eNbaQvuZOIg+l0otRXiRupVKJUCgEjUYjpuKMj49jdnYWwWBwy/575EPR6XTQ6XTQ6/UcrqsTFv0eQbs1mfqlUgkWiwUulwtutxsejwerq6uIRCIvnT+lHXjkcjmy2aw4j0t3eRKd1Ju+XSitXujMLL3HvXCS0dgsr9cLlUolOvpsbGzA4/FgZWUFkUhk2w675O+gNmN6vZ5N+zph0e8RlaY67UgajabMxJcKvHLnlJr8NGiCrv1riXyz32Wvry8drEEtwrPZrPDuJ5PJbY8OVETkcrnQ29uLffv2iYo7pnZY9LtEKpDKMzclwlCsmrzlZKpvZpoTWzXD+D0inQRE8fdkMil8Dtv9fg6HA0NDQxgeHsbBgwfR1tYGvV7P5/k6YdHvAumuLe1lR2KnKTder1fEw6m5Ri0m82ae69+r+ClhiHLpKZ5fSzttnU6Hw4cP47333sOpU6fQ1dUl4v1MfbDodwnt6iRkyhGnZJjV1VV4vV74/X5RebddjPtNQ6VSQa/Xi6Yh1Khjq6gCIZfLYbfbMTg4iPfffx/nzp0Tgpe2+GJqh9+1XUAil5rs0kaWoVBItHdWKBTQarWiO4007i1FmoQj/XM7qlWzVVohrwpyaNLvTP0DyRSnTD2VSoVUKlUWyagMX9rtdhw5cgTvvfcezp07h56eHpHYw2f5ncGi3wUU1qKkG4qNU7FIOp2GUqmEw+FAqVSCw+EQI6GkITzy0lPqK2WZSeP1my0C0k40AMp8BbQgJRIJJJPJus7PO4UGUdDkGYPBgKamJjFht1AoCD9HIpEQg0IoTFeZnNTe3o6RkRGcOXMG3d3dYhgHs3NY9LukMoZNOz59eM1ms6g2I5OWHul0WiwWVFNO8WetViuaXlQKWipsaRyfHrSQUH4AtZemCjVKqa30SVRzGFa+XmV9AH0/n8+L3vY9PT3o6upCS0uL6OmvUCiEEy8UCom0XOm90tmemng2Nzejv78fQ0NDwlPPTrvdw6LfBdTuSa/XC/Gq1WoxN53OslarVXju6cNND+nuRpl8dP4l0ZO4pKKmxYW+Jm28KX2Qh5x2e+lzNxM5UXl9uleyRuhBOfoajQY2mw2dnZ3o6uqC0+ksqwGQtgSjbjjkzKP7oR2eJtL29PTA7XazSb+HyLYx8948r9IeIhWiNCdeWgFGbbAqxVYtrk/mPZm30l2+WolrZU68VKSUShuLxeD3++Hz+USYrFbTXrqLS9N+6d6o243b7YbT6YTRaBTWislkgsFgEPUD0qNQPB4X1gc9yFIwGo3iWGA2m8XRYKs6fGZTqr5hLPo9pjJmv1VKbGXprPTrlV+rdv3NviYVayKRKJumK61ak/5Jz6EFo7KvH6UWS017Kil2Op2w2+3Q6/VlPobKY4B0kaSYfTKZFG3FpNWJarVa+Db4DL9jWPSNCJnPlXXnlYKXHh2k52s6ppAIKxcqatdV2XSzFihuT1aE1Nph9gQWPVNOtZ2+MpRIQpSW27KZ/buBRc8wDUZV0XP8g2EaDBY9wzQYLHqGaTBY9AzTYLDoGabBYNEzTIPBomeYBoNFzzANBoueYRoMFj3DNBgseoZpMFj0DNNgsOgZpsFg0TNMg8GiZ5gGg0XPMA0Gi55hGgwWPcM0GCx6hmkwWPQM02Cw6BmmwWDRM0yDwaJnmAaDRc8wDQaLnmEaDBY9wzQYLHqGaTBY9AzTYLDoGabBYNEzTIPBomeYBoNFzzANBoueYRoMFj3DNBgseoZpMFj0DNNgsOgZpsFg0TNMg8GiZ5gGg0XPMA0Gi55hGgwWPcM0GCx6hmkwWPQM02Cw6BmmwWDRM0yDwaJnmAaDRc8wDQaLnmEaDBY9wzQYLHqGaTBY9AzTYLDoGabBYNEzTIPBomeYBoNFzzANhnKb78teyV0wDPPK4J2eYRoMFj3DNBgseoZpMFj0DNNgsOgZpsFg0TNMg/G/AcqRA21VkYvPAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 71; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", "n = Nx * Ny # number of parameters\n", @@ -1980,22 +379,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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+ygdes5gPXL5otKaQu5rpWPMewCmwXzwywIqOWVn3WP2KNs97OkNl44g456NVId9zHV4+PkhrfXR0FzZwlgFprIl4JoHTqTWMZXZdlIaaKqrG6TQ3xpSXaIEJUEEgIj8HfqCqd3pcWwusBWhvb1+1bt26CT+nv7+fhoaxV0z99OZBjg3n/6xaa4S/uNRfW/wXHxrh+Ej++ZZq+PJl1fzD4zH29ilfenWUukh+zSQVGyIUrR09/vr2GGGBT11cTV00TCKlxJOpvLZ+L7c+MsRQAr542an7DcWT1EbCjCRSeUmmuirke6+I8X6e8aQS8ah5TTU/v/egmC6xWpzFVeo4u7q6HlXVTq9rZWlWEpH7gHkelz6vqj91X/N5IAH8m9c9VPU24DaAzs5OXb169YTj2bx5M+O9v+eeu73PDyu1Z17g6znvSB7jzi0vMZQz/n8wGeLjm52s8erFs2k9+2yvtzO098msZzXveoGDJ4dZtvLVnD2ngeMDMV440s8liwovmpf2xYc3ceGiZlavXglA/0iCl48PsnzeLHoHY2zdczzr9Z2LZtM83jAo13g/z1RKA7Hgnp/fe1BMl1gtzuIqZ5y+6/YiUi8iRWkoVtWrVPV8j690Yvgj4Frg9zUgVZv5zbWe5+fOqvZ9j2vOn8dX1qygo7nWbZKqIiQwnPEpffve3rwRTIU01kToG07QVu/EEKkKEffRrJRIptjfO8TC2Rm1hliSmirn19tUG8mqJYg4zyqWICQGY8zYCiYHEQmJyO+JyN0ichh4FjggIk+LyK0i4n/d6dMgItcAnwWuU1XvYTNlUGho5oeuWOL7Hk210dHd0nbf8hbqqyPkLsuUXvDOj1k1VfQPJ0Y7dyNhYcRHh/SBE8MkUsqZGfs1DMeTozOWRSQr6TXWRHxPfjPGzAxj1Rw2AWfjjBaap6oLVXUucAVOR/FXReQPShDTN4FG4F4ReUxEvl2CZ5y261d28JU1FxB1O1LTcwO6ls/xfY/mnElk480iHk9jTQSF0dFN0XCIZFILLgSYtu+4O1IpIzkMxpLURE79c5jTcCo5FKsz2hgzfYzV53CVquaNqVTVHuAu4C4RKXqpoaolqZEUw/UrO/jHzS+wpK2Bb//hKgDPlVMLyS1kx5tFPJ5Z7hIUx/pjtDU4S39XhYVYIjXmsNP0rnKZNYehjJoDOKOKqsJCIqmWHIypQAVrDrmJQUTqRKRTROYUek0l6B2MZ9UAEil/Q0frq6vy1jzyaqryWvCukHQ/wNH+U0OgouHxh7Pu7RkkHBLOaDq1L8NwPEl1Rj9DKCTMaXRqD7k1HmPMzDdWn8N1IrJHRLaJyJuBHThNPk+KyPunLMIAUVV6h+JZawzlTmorxKuATTdVpTuoO5pr+dBrF3vuvuZlbpNTeGcmh0hViJFxlu7ed3yQM5pqRucaqCqq5O3TMLexhupIyFZPNaYCjdWs9L+BNwJNOP0PK1R1l4jMBX4F/MsUxBcow3Fn/H/mGkN+RgdB4U/fubOI9/UMsvNgn+drc6X3cT7Wf6qPIuKz5pDZpDSSSBGtyu9wbq2P+m7iMsbMLGN1SKdU9TlVfQTYraq7AFT1MM78g4rTO+QUws0TqTn4XLRuXlON75FBZ7XWURUSjg1k1BzcPoex7OsZylo2w2lSyq8dhELC4rZ6X7EYY2aWsWoOIRGZjZNAUu736VKrItc+6B10ulgyO2i9doGbXR9lVk0VIkI4JIRFfK9LFAmHPJfybqipIlkVYlFbPVUh576t9dW01Eezag7RcIh4snDCGoolOdo/wpmt2SOVCsVXFw3i8lvGmFIb639+E/AopxLCtoxrgZiYNtXSySG9npJq9rBREVg6tzGr4J2Ijtm1o8khFILFbQ2c1VLHb/aGOGdu9lT61oZqjmYkh6pwiH6PrUnT0sNYF8zOXjbD+hWMMZkKJgdVXTSFcUwLJ9z5BOkO6WRKR/f2qYmEuaCj6bQ2xCmkqTZCY00VCpw3f9aYs5PbGqJZzUrRqtCYe0mnV1zNnOMwFEv63hvaGFMZCiYHEbl4rDeq6raxrs9EJ0b7HJz+g3R/w+z6KCsWNI27PefpOK+jibpIeNylJlrro7x07NRE8vH2dEgnh9w5Dqe7HLcxZmYbq1np/7h/1gCdwOM4TUwrgK3AZaUNLXhym5XS/Q3ts6qLmhjA2a/Zj9aGao5lDmUNjT1aaW/PELWRMK0Zo5CGYt4d0saYyjXWJLguVe0CDgAXq2qnqq4CVgL+Fv+ZYXqH4kTCQp37KTvhdvwWOzGcjtaGKAOxJEMxpykpUhUikVRSBUZR7TvuDGNNz2lIuAnO1k4yxmTyU6otU9Un0weq+hTwytKFFFy9g3GaaqOjBWvcnR1dVcaCNb0ia7rfIb1PQqEd4fb1DLKwxTqjjTFj85McnhCR20Vktfv1HeCJUgcWRCeGYllzHNJDRqtC5a05wKmJcOmFAb36HVSVfT2DLJidu6ZSRY5MNsaMwU/D9geAG4BPuMe/Ab5VsogCrHcwnjWqJ5luVvKYXTxVWhuyl9BIL77nNf/i+GCcgVgye6nuWMpqDsaYPOMmB1UdBr7uflW03sE485tPLVYXC0B7fVtOzQEKL76312MY62A8Qb1NdDPG5Bhr4b2fi8hbvZblFpElIvKXIvK/ShtesJwYcvoc0tIT4CLlbFZy+xyODmQvvueVHDyHscaSVFuzkjEmx1gfGT8M/Bnw9yLSAxzBGda6GHgB+GZ6W89K0TuY2+eQIhySsm57WRsNUx8N5y++59Gs5Dk7OmYd0saYfGPNkD6Is13nZ0VkEXAGMAQ8F6TtO6dKLJFiIJbMW1epKlz+IaB5cx0KLL63r2eQ1voo9e4cClVlOJHM21PCGGN8tSeo6h5VfUhVH6vExACnls7IrDkkUxqI+QGtDVGODWT3OeSOVtqwvZv127o5NhDjNbfcz4btzveClHWehjEmmKxU8Cm9dEZmzSGWTI0OHS2n1vr8xfcyaw4btndz8/onRxNGd+8QN69/kjse2mNNSsYYT+Uv2aaJUzWHUx3SiWQwag5tDdGsZiVn8b1TyeHWjTsZimcvxjcUT3Lnlr02x8EY4+m0SgYRmS0iK0oVTJDlrqsEzv7RQWiSaW2I0jMQG10yIxIW4okU6i4Zm7s3RFrPQMxqDsYYT+OWbCKyWURmiUgLzp4O3xGRvyt1YCLyaRFREWkr9bP8GE0Obp9DKqWkUgSjQ7q+mkRKOTnsxJgeWpuuPcxvrvV8X0t91JKDMcaTn4+9Tap6ElgD3KGqrwauKmVQIrIQZ//qvaV8zunoTTcrufMc0gVvOZfOSEsvoZHud4hUucnB7WP41FVL894TDYdYs7LDRioZYzz5KdmqROQM4F3AL0ocT9rXcYbRBmbHuRODMUSgscYZBprMaMIptzZ3CY10v8Po4ntucpjtLs/dWh9F3D/fd9lZXLqk1focjDGeJN0uXfAFIu8EvgD8t6p+RESWALeq6u+WJCCRtwFXquonRGQP0KmqRz1etxZYC9De3r5q3bp1E35mf38/DQ0NY77mX58eYcuBBP/w+nrASQ6D7gSyqUoQheLc15fiCw8O8ZGLqnnVPCd59Q0nRmP7zhMjbD+c4P9eWUcipcQzRjI1VFchRQ7fz88zCKZLnDB9YrU4i6vUcXZ1dT2qqp2eF1V1yr+A+4CnPL7eBvwWpykLYA/QNt79Vq1apZOxadOmcV/z8X/fpq/72v2jx0f6hvXeHQf10ImhST37dBSK80jfsJ71uV/ov/zP7lOvffaQ7j7Sr0OxuJ7/xXv00z/crkOxhP7qmYN67w7n676nD2oqlZqyOINmusSpOn1itTiLq9RxAlu1QLk67oprIvI14K9wZkffg7MT3KdU9c6JZitV9eyzEJELcJbneNzdM2EBsE1EXqXOjO2y6R2K05QzjBWCsUnO7LooImTNdYiGQ7xwuJ+7tr1M30iC+U21/Pfz2RUwp9ZQ/viNMcHjp8H5jep0SF+L80n+HODGUgSjqk+q6lxVXaSqi4CXcXahK2tiAKfPoTln6QxwJpyVWzgktNRlz3VId0pv3XOcumiYc8+Ylfe+zDkbxhiTyVeHtPvnW4AfqeqJEsYTWL1D8aylMxIB6pAGdwmNnMX34skUj+3r5aKFzZ5JLPPvY4wxmfws5P8LEXkWp1npBhGZAwyXNiyHW3sIBGeLUI+aQwCGsm7Y3s2eY4M8d6if19xyP13L57BxxyGO9Dk1icZq7+GqmX8fY4zJNG7Jpqo3AZfjjBqKAwM4HccVI+lOMPNqVip3zSG9blIsY92kO7fsHU0MAJt2HmHLrmNZ76uJhG0CnDGmID8zpCPAHwA/EJEfAx8Ejo39rpmlbziOKnkd0uGwlL1D12vdpFyxpLJ+e3fWOWtSMsaMxU+z0reACPCP7vEfuuc+VKqggmZ00b2cdZWqAjBSqdC6Sbl6Mpb0BmtSMsaMzU9yuERVL8w4vl9EHi9VQEGUu64SQDypgehvmN9cS7ePBNFSnz0yyWoOxpix+CndkiJydvrAnSE9djvGDNPrsdFPIqll728AuPHqZeOuj5ReRymtKiw0VPv5XGCMqVR+SogbgU0isgsQ4CzgAyWNKmB6B9Mb/Zz69B1PpWgIl7+Avd4t9G/duJP9vUPMb66la/kcfvXMYQ6cGKalPsqalR1cuqR19D1NtZGy95UYY4Jt3NJNVX8lIkuBZe6pncBFJY0qYHK3CFVVkkkNRJ8DOAni+oyaAcAXrk3ywHN5S1IBNvnNGDM+Xx99VXUEeCJ9LCI/As4sVVBBk+5zSHfinpoAV/4+h0LG2r602TqjjTHjmGjpFoyPzFOkdzBOQ3XVaDI4tXRGcH8MIuIZnwjMsuRgjBnHRJNDYPZZmAq9Q7GsoZ+jNYcAjFYai1ftYVZtJBCLBRpjgq1gs5KI/BzvJCBAq8f5GevEYPa6Sun9EMIBrjmAu/heLHtgmc1vMMb4MVafw99O8NqM0zsUL1BzCHhysMX2jDETVDA5qOqvpzKQIOsdjLFsXuPocTo5BGG57rF4zcOwmoMxxo9gl24BcWIonj3HIRH8DmnI73Ooi4aprrLF9owx4yv/LK6AU1V6Bwvs5RDwDumz5zSwuK1+9Ngmvhlj/LLkMI6BWJJESgvsAhfswjYUEkKVNerYGFMkfvaQfgXOEhpnZb5eVa8sYVyBkTs7Gk7tHx2UGdLGGFNsfmoOPwK+DXyHCltwDwqvqxSEvRyMMaZU/CSHhKp+q+SRBNQJr+W6E6nA9zcYY8xk+Cnhfi4iHxGRM0SkJf1V8sgCwmu57uFEKvD9DcYYMxl+ag7vd/+8MeOcAkuKH07wjG704zYrJZIp4okUDdU2JNQYM3P5WbJ78VQEElS9Q06fQ7rmkN6vOQi7wBljTKmMW8KJSERE/lREfux+fUxESjrNVkQ+LiLPisgOEflaKZ81nhODcaqrQtS4u62NJgdrVjLGzGB+Pv5+C1gF/KP7tco9VxIi0gW8DbhQVc+jjOs4bdjezb9ueYmRRIrX3HI/G7Z3Mxxz5jgEeS8HY4yZLD99Dpeo6oUZx/eLyOOlCgi4AbjF3WAIVT1cwmcVtGF7Nzevf3K0ptDdO8TN65/kT19/DkvnNtqy18aYGU1Ux96aQUS2Ae9U1Rfd4yXAj1X14pIEJPIY8FPgGmAY+IyqPuLxurXAWoD29vZV69atm/Az+/v7aWhoyDr36c2DHBvO/9m01MCXL62mOhIac7e1UvCKM4gszuKbLrFanMVV6ji7uroeVdVOr2t+ag43AptEZBfOXg5nAR+YTEAich8wz+PS592YWoBLgUuAH4rIEs3JYqp6G3AbQGdnp65evXrC8WzevJnc9/fcc7fna48PQ+2ZF3Du/FnMb66d8DMnwivOILI4i2+6xGpxFlc54/QzWulXIrIUWOae2plu8pkoVb2q0DURuQFY7yaDh0UkBbQBRybzzNM1v7mW7t6hvPMt9c6QVuuQNsbMZAXbRUTkSvfPNcBbgHPcr7e450plA9DlPvsVQBQ4WsLnebrx6mXURLJ/PDWREG9f2QEEf0VWY4yZjLFqDq8D7gfe6nFNgfUliQi+B3xPRJ4CYsD7c5uUpsL1K1ci8KQAABRLSURBVDs43DfM3/zyWQA6mmv5aNfZzG2sAazmYIyZ2cbaCe5L7rd/qaq7M6+JSMkmxqlqDPiDUt3/dKxY0AzAnR98NVcsbePgiWGe6j4B2FBWY8zM5qeEu8vj3I+LHUgQHTo5DED7rGrg1AQ4sOW6jTEzW8Gag4gsB84DmnL6GGYBNaUOLAiO9Dn97ummpKGYkxxEsHkOxpgZbaw+h2XAtUAz2f0OfcCHSxlUUBzuG6G6KsSsWufHlK45hEO2l4MxZmYbq8/hpyLyC+Bzqvo3UxhTYBw6OczcWdWjiWDYTQ7W32CMmenGLOVUNQlcP0WxBM7hkyO0u01KqjqaHKy/wRgz0/mZIf2giHwT+AEwkD6pqttKFlVAHOobZvm8RgBGEinSA2qrrOZgjJnh/CSHi9w//zLjnAJXFj+cYDlycoTXLp0DwGDs1EiliM1xMMbMcH6Wz+iaikCCZjCWoG8kwVzPYaxWczDGzGx+NvtpF5Hvish/usfnisgHSx9aeR0+6Qxjbc8Zxgo2O9oYM/P5+Qj8fWAjMN89fg74ZKkCCor0BLh0zWHYJsAZYyqIn+TQpqo/BFIAqpoAkmO/Zfo77E6Aa5/l1Bwyk4MNZTXGzHR+SrkBEWnF6YRGRC4FTpQ0qgAYrTk0evQ5WLOSMWaG8zNa6c+AnwFni8iDwBzgHSWNKgCO9I0QrQrRVBshmVJG4qnRa9YhbYyZ6fyMVtomIq/DWU5DcDb7iZc8sjI7dHKYuY3O7OjhWCLrmg1lNcbMdH5GK70TqFXVHTizpX8gIiXZPzpIDveNjPY3ZDYpgS26Z4yZ+fy0j3xBVftE5Arg9cB3gW+VNqzyS9ccIHsYK1iHtDFm5vNTyqVLxrcA31HVu3G27pzRMmsOw1ZzMMZUGD/JoVtE/gl4N/BLEan2+b5payiWpG84wRyPkUpg8xyMMTOfn0L+XTiT4K5W1V6gBbixpFGV2eG+9A5w+bOjw2Hby8EYM/ONmxxUdRD4Kc58hzOBCPBsqQMrp0Mn0zvA2fagxpjKNO5QVhH5OPAl4BDuLGmcCXErShhXWWXWHF4+PkgiqaPXbI6DMaYS+JkE9wlgmaoeK3UwACJyEfBtnH2qE8BHVPXhqXh2WrrmIAI7D/ZlXbPZ0caYSuDnY/A+pna5jK8BX1bVi4AvusdT6nDfMJGwsK9nYHSDnzRrVjLGVAI/NYddwGYRuRsYSZ9U1b8rUUwKzHK/bwL2l+g5Be0/PsSs2giq+YnAmpWMMZVANPejce4LRL7kdV5Vv1ySgEReiTM6SnBqNper6kser1sLrAVob29ftW7dugk/s7+/n4aGBsDJTLf8dpBYEv7s4vzpHJFwiJpIeRJEZpxBZnEW33SJ1eIsrlLH2dXV9aiqdnpdGzc5lIKI3AfM87j0eZxZ2L9W1btE5F3AWlW9aqz7dXZ26tatWyccz+bNm1m9ejUAvYMx3vyNB5jXVMNHVp+T99qzWutY2t444WdNRmacQWZxFt90idXiLK5SxykiBZNDwWYlEfk57jLdXlT1uokGNFZhLyJ34HSCA/wIuH2iz5mo3qE4y+fN8rxWZUtnGGMqwFh9Dn87ZVFk2w+8DtgMXAk8P5UPH44nGYwlaaqLeF63DmljTCUomBxU9ddTGUiGDwPfEJEqYBi3X2GqHO13+tyba72Tg62rZIypBH5GK00pVf1vYFW5nn+kLwZAU4HkYPMcjDGVwBrQcxxJ1xwKNivZj8wYM/P5LulEpK6UgQTF0T4nOVjNwRhTyfzsBHe5iDyNu9ieiFwoIv9Y8sjK5EjfCOGQ0FDt3eJmHdLGmErgp+bwdeBq4BiAqj4OvLaUQZXTkf4RmmojBZfltmYlY0wl8FXSqeq+nFNJzxdOcxu2d/NfOw7RMxDjs3c9wZZd+WsNWs3BGFMJ/IxW2icilwMqIhGcCWrPlDasqbdhezc3r3+SWNJZlbxnIMYdDzmrdly6pBWAUAhClhyMMRXAT83hT4CPAh1AN3CRezyj3LpxZ952oLFkivXbu0ePrUnJGFMpxq05qOpR4PenIJay2t875Hm+ZyA2+r01KRljKoWfneD+r8fpE8BWVf1p8UMqj/ZZNRw8OZx3vqX+1Mqstq6SMaZS+CntanCakp53v1YAC4APisjflzC2KaOqtDXkL88dDYdYs7Jj9NiWzjDGVAo/HdIrgNeoahJARL4FPABcATxZwtimzNZDSZ7aP8h1K87gkZeOc+DEMC31Udas7BjtjAaI2AQ4Y0yF8JMcZgMNnNoqtB5oUdWkiIwUflvwbdjezVfveZYDJ0aIhIXVy+bwl9efz9Y9xz1fbzUHY0yl8JMcvgY8JiKbcXZney3wNyJSD9xXwthKKj10NT1CKZ5UPr9hB19IpJjbWOP5noj1ORhjKsS4pZ2qfhe4HNgA/AS4QlVvV9UBVb2x1AGWitfQ1aF4kv93/wsF32M1B2NMpfD7UXgYOAAcB84RkWm/fEahoasHT+SPWEqL2DwHY0yF8DOU9UM4s6IXAI8BlwIP4ezSNm3Nb66l2yNBzGvyblICCFuHtDGmQvj5KPwJ4BLgJVXtAlYCvSWNagrcePUyaiPhrHO1kTAfv/Kcgu+JWLOSMaZC+EkOw6o6DCAi1ar6LLCstGGV3vUrO/jKmgvoaK4FoKO5lq+suYA3X3BGwfdYn4MxplL4Ga30sog043RI3ysix4GXShvW1Lh+ZQfXr+xg8+bNrF69GoDewVjB19vaSsaYSuFnbaW3u9/+hYhsApqAe0oaVUDZLnDGmEoxZnIQkTCwQ1WXA6jqr6ckqoCyZiVjTKUYs53EXTJjp4icWcyHisg7RWSHiKREpDPn2s0i8oKI7BSRq4v53MmySXDGmErhd/mMHSLyMDCQPqmq103iuU8Ba4B/yjwpIucC7wHOA+YD94nIK9LrOpWTiNUcjDGVw09y+EKxH6qqzwBe+zS/DVinqiPAbhF5AXgVzryKsrLEYIypJKKq479I5CxgqareJyJ1QFhV+yb9cGe9ps+o6lb3+JvAFlW90z3+LvCfqvpjj/euBdYCtLe3r1q3bt2E4+jv76ehoQGAZEoZjOVXVEIC9dV+cmnpZMYZZBZn8U2XWC3O4ip1nF1dXY+qaqfXNT8zpD+MUwi3AGfjbBf6beD147zvPmCex6XPF2OTIFW9DbgNoLOzU9NDUScidyir16qsDTVVWct3l0NmnEFmcRbfdInV4iyucsbp56PwR3Gadn4LoKrPi8jc8d6kqldNIJ5uYGHG8QL3XNnZXg7GmEriZ/jNiKqOzgwTkSpg/LaoifkZ8B4RqRaRxcBS4OESPeu0hG0CnDGmgvgp8X4tIn8O1IrIG4AfAT+fzENF5O0i8jJwGXC3iGwEUNUdwA+Bp3Em2n00CCOVAKqsQ9oYU0H8NCvdBHwQZ0vQPwZ+Cdw+mYeq6k9w9obwuvbXwF9P5v6lYLOjjTGVxE9yuB64Q1W/U+pggszWVTLGVBI/Jd5bgedE5F9F5Fq3z6HiWLOSMaaS+Nkm9APAOTh9De8FXhSRSTUrTUfWrGSMqSS+agGqGheR/8QZpVSL09T0oVIGFjTWrGSMqSTjlngi8iYR+T7wPPC7OJ3RXpPbZjSrORhjKomfmsP7gB8Af+yueVSRrM/BGFNJ/Gz2897MYxG5Anivqn60ZFEFUJUt122MqSC++hxEZCXwe8A7gd3A+lIGFURWczDGVJKCyUFEXoEzOum9wFGcpiVR1a4pii1QbMluY0wlGavm8CzwAHCtqr4AICKfmpKoAshqDsaYSjJWQ/oa4ACwSUS+IyKvByqyhAyHxWtjImOMmbEKJgdV3aCq7wGWA5uATwJzReRbIvLGqQowCCI2x8EYU2H8zJAeUNV/V9W34uyvsB34XMkjCxDrbzDGVJrT+kisqsdV9TZVHXMXuJnGNvoxxlQaay/xwWoOxphKY8nBh4hNgDPGVBgr9XywmoMxptJYcvDB5jgYYyqNJQcfbF0lY0ylsVLPB6s5GGMqjSUHH2wvB2NMpSlLchCRd4rIDhFJiUhnxvk3iMijIvKk++eV5Ygvl3VIG2Mqja8lu0vgKZy1m/4p5/xR4K2qul9Ezgc2Ah1THVwuWz7DGFNpypIcVPUZIG8xO1XdnnG4A6gVkepy70AXtmYlY0yFEVUt38NFNgOfUdWtHtfeAfyJql5V4L1rgbUA7e3tq9atWzfhOPr7+2loaAAgmVIGY8ms6w3VVQRhUdbMOIPM4iy+6RKrxVlcpY6zq6vrUVXt9LyoqiX5Au7DaT7K/Xpbxms2A50e7z0PeBE428+zVq1apZOxadOm0e+PD4zovTsOZn2NxJOTun+xZMYZZBZn8U2XWC3O4ip1nMBWLVCulqxZSQt84h+PiCwAfgK8T1VfLG5UE2NDWY0xlSZQPa0i0gzcDdykqg+WOx6AhpoqQpYcjDEVplxDWd8uIi8DlwF3i8hG99LHgHOAL4rIY+7X3HLEmNbWUF3OxxtjTFmUa7TST3CajnLP/xXwV1MfUWFzGi05GGMqT6CalYImWhViVk25poIYY0z5WHIYQ1tDdd5cDGOMqQSWHMbQ1hgtdwjGGFMWlhwKCIWgtd76G4wxlcmSQwEt9dW24J4xpmJZciigrcGalIwxlcuSQwE2v8EYU8ksOXhorKmiJhIudxjGGFM2lhw82MQ3Y0yls+Tgoc2SgzGmwtn03xzVVWFqo9akZIypbFZzyGGJwRhjLDkYY4zxYMnBGGNMHksOxhhj8lhyMMYYk8eSgzHGmDyWHIwxxuSx5GCMMSaPJQdjjDF5LDkYY4zJI6pa7hgmTUSOAC9N4hZtwNEihVNKFmdxTZc4YfrEanEWV6njPEtV53hdmBHJYbJEZKuqdpY7jvFYnMU1XeKE6ROrxVlc5YzTmpWMMcbkseRgjDEmjyUHx23lDsAni7O4pkucMH1itTiLq2xxWp+DMcaYPFZzMMYYk8eSgzHGmDwVnRxE5BoR2SkiL4jITeWOJ5OIfE9EDovIUxnnWkTkXhF53v1zdjljdGNaKCKbRORpEdkhIp8IYqwiUiMiD4vI426cX3bPLxaR37r/Bn4gItFyxpkmImER2S4iv3CPAxeniOwRkSdF5DER2eqeC9TvPU1EmkXkxyLyrIg8IyKXBS1WEVnm/izTXydF5JPlirNik4OIhIF/AN4EnAu8V0TOLW9UWb4PXJNz7ibgV6q6FPiVe1xuCeDTqnoucCnwUffnGLRYR4ArVfVC4CLgGhG5FPgq8HVVPQc4DnywjDFm+gTwTMZxUOPsUtWLMsbiB+33nvYN4B5VXQ5ciPOzDVSsqrrT/VleBKwCBoGfUK44VbUiv4DLgI0ZxzcDN5c7rpwYFwFPZRzvBM5wvz8D2FnuGD1i/inwhiDHCtQB24BX48w+rfL6N1HG+BbgFAJXAr8AJKBx7gHacs4F7vcONAG7cQfgBDnWjNjeCDxYzjgrtuYAdAD7Mo5fds8FWbuqHnC/Pwi0lzOYXCKyCFgJ/JYAxuo21TwGHAbuBV4EelU14b4kKP8G/h74LJByj1sJZpwK/JeIPCoia91zgfu9A4uBI8A/u011t4tIPcGMNe09wH+435clzkpODtOaOh8jAjMOWUQagLuAT6rqycxrQYlVVZPqVNkXAK8Clpc5pDwici1wWFUfLXcsPlyhqhfjNM1+VERem3kxKL93oAq4GPiWqq4EBshpmglQrLj9SdcBP8q9NpVxVnJy6AYWZhwvcM8F2SEROQPA/fNwmeMBQEQiOInh31R1vXs6kLECqGovsAmneaZZRKrcS0H4N/Aa4DoR2QOsw2la+gbBixNV7Xb/PIzTNv4qgvl7fxl4WVV/6x7/GCdZBDFWcJLtNlU95B6XJc5KTg6PAEvdUSBRnGrcz8oc03h+Brzf/f79OO37ZSUiAnwXeEZV/y7jUqBiFZE5ItLsfl+L0y/yDE6SeIf7srLHqao3q+oCVV2E82/yflX9fQIWp4jUi0hj+nucNvKnCNjvHUBVDwL7RGSZe+r1wNMEMFbXeznVpATlirPcHS9l7vR5M/AcTtvz58sdT05s/wEcAOI4n3w+iNP2/CvgeeA+oCUAcV6BU819AnjM/Xpz0GIFVgDb3TifAr7onl8CPAy8gFONry73zzQj5tXAL4IYpxvP4+7XjvT/n6D93jPivQjY6v7+NwCzgxgrUA8cA5oyzpUlTls+wxhjTJ5KblYyxhhTgCUHY4wxeSw5GGOMyWPJwRhjTB5LDsYYY/JYcjAmh4j0u38uEpHfK/K9/zzn+H+KeX9jisWSgzGFLQJOKzlkzGIuJCs5qOrlpxmTMVPCkoMxhd0C/I67tv6n3IX7bhWRR0TkCRH5YwARWS0iD4jIz3Bm3iIiG9wF6XakF6UTkVuAWvd+/+aeS9dSxL33U+4eCe/OuPfmjL0I/s2dlW5MSY33KceYSnYT8BlVvRbALeRPqOolIlINPCgi/+W+9mLgfFXd7R7/L1XtcZfqeERE7lLVm0TkY+os/pdrDc4s3guBNvc9v3GvrQTOA/YDD+Ksv/Tfxf/rGnOK1RyM8e+NwPvcZb9/i7OswVL32sMZiQHgT0XkcWALzgKPSxnbFcB/qLNy7CHg18AlGfd+WVVTOMuTLCrK38aYMVjNwRj/BPi4qm7MOimyGmcZ6Mzjq4DLVHVQRDYDNZN47kjG90ns/62ZAlZzMKawPqAx43gjcIO7RDki8gp3RdJcTcBxNzEsx9k+NS2efn+OB4B3u/0ac4DX4iy0Z0xZ2CcQYwp7Aki6zUPfx9lXYRGwze0UPgJc7/G+e4A/EZFncLZ43JJx7TbgCRHZps5S3Gk/wdlf4nGcVW4/q6oH3eRizJSzVVmNMcbksWYlY4wxeSw5GGOMyWPJwRhjTB5LDsYYY/JYcjDGGJPHkoMxxpg8lhyMMcbk+f9HTBbXmfnkagAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "lb = 1 - np.min(evaluation_history, axis=1)\n", "ub = 1 - np.max(evaluation_history, axis=1)\n", @@ -2023,22 +409,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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FkEgkkMlkkEgk1Kl3F5DoXwD8fj+Ghobw2Wef4Ztvvsky6bczcdPpNIxGI9566y188sknOHPmzI597jLx+XwYHBzM8tZvJViDwYBXX30VFRUVWat8Op1GOBzG2toanjx5grW1tbzPnwuW7qxSqZ55UdPLAn1jf3DC4TBGR0fx5Zdf4vvvv4fb7Qbwew58LjKFcOzYMfzHf/wHzp49C51OV/C5BwYG0N/fz3+WKfpMs7q8vBwdHR2oqanJEn0ymYTT6cTIyAiePHlS0PlzwUSv0Wh4DgBRGCT6PzDhcBjT09P44YcfcO3aNR4qA7Y369nDQCgUoqWlBa2trVzwhZjWkUgET5484Q+ajatq5rEqKyvxyiuvbBp/FY/HMTc3h66urqy03b3A2mwx0dOevjBI9H9QIpEIFhYWcOPGDXz77bd8lcz3BheLxXjjjTdw7ty5rNh4vu/3+XxYWFiAy+XK+fvM4zQ0NKC9vR1VVVVQKBT856xT8PT0NAYGBrY81sbj7nSNQqEQCoWC1/UThUHf2B8QtsL/+OOP+PrrrzE8PJx32inb55tMJly6dAknT57Mio3nQyKRwPz8PG7dupW1OufaTuj1erz77rt44403YDAYsgQbiUQwNzeHkZERnra7XV3ARsHneh1LP2a1+tvV/RO5IdH/wfD7/Xj8+DFu3ryJa9euYWhoqKAxValUCjKZDEePHsWxY8cKyrpjJJNJbmU8fvw452tYVODkyZO4cOECGhsbs4Zcsuq/oaEhDAwM8Pj8fuXKP+3pQS8zJPo/EH6/H8PDw/jyyy/x3XffYXZ2FtFoNK/3Znrya2pq8M4776C2tjZvwWc2mBSJRIhEInyVzzx25kpttVrxzjvv4MiRI1CpVJtW6UAggIcPH+LBgwcFJ+VsZw0A2dN0iMIg0f9B8Hg8+OWXX/DPf/4TnZ2dBXu6mSjVajXa29vR0dEBi8WSd7dY9ppkMsnbYOUakcVEptFo0NbWhra2NpSWlmZNsREIBFmTf1iIMd9uPju9hh0/kUhkDekg8oNE/5xhU2Tu3r3LE2+2qmDbCiYmqVSKN998E5cuXUJdXR1UKlXB1xMOhzE4OIiuri6+rci1ynd0dOCDDz7gefaZ1wIAgUAADodjV2Oz8yGRSCAajVITjV1Aon9OsNXJ5/Ohp6cHf/3rX/Htt9/uajoNE6LNZsPrr7+OU6dO5Z11l3ktABAKhdDd3Y2vv/5609aCnaeiogJtbW04ffo09Hr9pmMkk0msrq7i/v37WFxc3PT+na5lpyxD4LdQYCwWI9HvAgrZPSdY8UxPTw/+67/+C9999x0XfL5ZZpkmLSumOXnyJCwWS0GNItlxotEoZmZmMDo6ygW/Mb9dLpejra0NJ0+ehMlkynmtyWQSS0tL6Ovryzs2X2hvQSZ62tcXDq30e2Cr7LSdYJNienp68Pe//x3ffvstAoEAN5/zrR5j52dC/OCDD1BXV7er2HU8Hsf09DS+//57TE5ObjoH+7O+vh6XLl1CfX39lg8WVu8/MjKCYDCY116eFdDsJGL2PZN5v3tI9LuETXFJJBIQi8V81PN2sEktLpcrqx6eOcwKWbGYkCQSCd588018/PHHOHXqFMxmc8H56IlEAk+ePMGNGzfwt7/9DVNTUzlf19DQgKtXr+LMmTObrInMh14ymUQkEsnbgbexcCafhx6rp6dVvnDyEn0xf7FbreBMvE6nE2q1GmazedsW0olEApFIBHa7HX19fbh27Rpu377NBb9T8Uzm9bB9r0qlQnt7Oz7++GNcuHABFoulYMGnUim4XC709PTg2rVrXPAbhSqRSHDu3Dm8++67qKio2NKaSCQScDqdWF5ezmtktlgshlKphEQiQSKRQCgU2vE9rO6A1dQX8/25G/ISPYVDsvPZmeBHR0cxNjYGnU6HhoYGHDhwAHq9HhKJJOtGDAaDsNvtmJiYwMDAAHp6etDf349IJLJrk16r1aKtrQ2ffvop2tvb+ZSdQj+T0+nE4OAgOjs7N3XTzRR+c3Mzzp07h8OHD2el2m6EFQgNDg5y7/9WomTTgaxWKwBgZWUFDodj24dFpiOPheyIwiDzfgfi8Th8Ph9CoRBSqRSEQiGCwSDGx8fR1dWFhw8f8gy406dP48CBA1Cr1XykcjKZxMrKCnp6enD9+nUMDw/zGxbYnRXFkmL+/d//HWfOnOGiKYRYLIaVlRXcv38f169fR19f36ZtBhO9yWTCe++9hxMnTmRl3eUiGAzi/v37uHPnzrbbFoVCgcbGRpw7dw7V1dVwu90YHByE2+3Oq/0X+35J9IWzrejZjV5s5lM6nYZQKEQ6ncaTJ08wMTGBlZUVhEIhHn9+/PgxZmZm4PV6IRAI8PjxYwwMDKC6uhoGgwGJRAIejwcejwdutxuLi4uYnZ0tKDMtc2Y88Fue+6uvvopz587h/PnzaGlpgcViKdgSSyQSmJ6exo0bN3D9+nU8fPiQ95gHft9qpFIp6PV6vP/++3j33Xdx8ODBnGb9xnDd0tIS99rn2s9LJBIcO3YMly9fxtmzZ1FSUoKlpSUEg0GMjY3B6XTu+BlYL/xiuzf3g21F7/F4MDg4iLGxMT5nvZi+ZDaHnU1KXV1dxdraGpxO56bVyOPxYHJyEhqNBiqVir93Y//2fPbumft2NofdZDLh4sWLuHLlCk6fPg2z2cxn6BVCJBLB5OQkvvrqK3zxxRcYGhrK+bmB37L7Ll26hD//+c9oaWnZMvafGfJbX1/PqqbbKHqhUMgjAJcvX0ZNTQ1vmjkzM8NHVO9EZhpuMd2T+8GOK/1nn32Gzz77DAAgk8l2nE7yMsD27el0GhUVFTCbzXxM8k5joP1+/7YJNvmYo5k3sVqtxvHjx3Hp0iVcuHABhw8fLng8MxsV7fV6efXeX//6V8zNzW16bWaLrX/7t3/Dp59+irNnz+7YcSedTnP/QOZxNwqyrKwMly9fxltvvYVDhw5BqVQinU7DYDCgpKQkr1qBjWFE8jkVxrZ3DquUYuRb/PGikylMu90Ot9vNw1CFkpmTvtPrNnrLX3vtNZw5cwYnTpxAc3MzqqqqdtxT58Lv9+PXX3/FzZs30d3djampqS2TZpip3tjYiMuXL+PVV1/Nq8VWMpmE3W5HZ2dnVtfczM+kUCjQ2tqK119/HQ0NDVCr1Xz7IpVKIZVK8161SfC7Z1vRCwSCrLxqmUxWdMkQiURix9V9O/K5iTMFbzAYUFlZiebmZrz77rs4d+4cysvL+RjsQnE6nejv78e//vUv/PTTT1mFPFtVz5WVleHixYtobW2FwWDI6zwsIWd4eJj7OTZGAOrr69HR0YHGxkZotdqsz8+OsZMlmU6neSvteDyOcDiMeDxOzTQKYMdvKnPVo17jT4fMzLp3330XV65cwdGjR2EymaDT6QpugsFgU3D+8pe/4KuvvkI8Hs8S4Vb/lk1NTbh48WJBUYFYLIZQKLStNXT06FGcOXMGBoMhK5+AiT0cDnNrcquEHvYQDofD8Pv9WF1d5dsCmlGfH/R4fI6IxWLuzddqtbhy5Qo++eQTXsiy206vLM13eHiYd9BlK+hWlgfL7mtubsbly5dx5MiRgubcLS8vY3Z2dpPo2flKS0tRX1+P6upqvkVhlgCLdKyuru64hWL98202G5xOJ+bm5vjDcTdbn2KERP8cYKsYE7zBYMD777+PTz/9FGfOnMkyfXd7fIfDgevXr+PWrVt89cx8yGTCfh6Px9HR0YHXX38dZrM575UzEolgZGQE3d3dWVENtn2Qy+U4deoUmpqaoNFouCmemWhjt9sxPj6+Y0pyLBaDy+WC1+uFx+PB2toafD7fnifnFBMk+udAphPKarXiypUr+PjjjwsaRLEd4XCYt9zKnEqzlTCYmW80GnH06FHU1dXt6EXPjM1Ho1GMj4+jr6+Piz4zlGaz2dDW1obGxsZNITnWF39qagpDQ0N5+U8CgQCCwSCi0ShV2u0CEv1zwmaz4bXXXuN16fX19fsi+EQigcePH6Onpwezs7MAds4NSKVSsFqt+NOf/oSmpqa8Y+WMZDIJt9vNHzASiYRvJ9RqNVpbW9Ha2orS0lK+yrOHBlu5p6amMDk5mVdImGbZ7Q0S/TNEKpVCr9fjwIEDaGtrQ0dHB44ePQqz2bxrZ10m0WgU09PT+PLLL/HVV1/xDjzbDcVgv6uursb777+Purq6ghxirNowcy+eKfi2tja89957qKur2zSRJpVKwefzYXx8HBMTE/x6822rRewOEv1TgpWLsjZWJSUlsNlsOHr0KNrb23Hq1ClUVFRAJpPtS7iJxcn/+7//G//4xz94Tfx2q3zmz+vr69Hc3AyTyZTX+TZm4WW2+FIoFCgpKcHx48dx5coVdHR0bCr5TafTvOqwt7cXY2NjWb8jnh4k+n1GLBZDr9fDarVCr9cjlUpBpVKhsbERR48eRVNTE2pqamA2m/dtJFMqlcL6+joGBgbwz3/+M0tAWwk+0wR//fXX8fbbb+cdk2ew5C273Q6PxwOVSgW9Xo8TJ07g9OnTOH78OOrr62GxWKBQKLjoWYhueXkZQ0ND6O3tzZkdSDwdSPS7RCqVwmAw8Bp6VoEnlUphNpthtVqh0WigVqtRVVWFlpYWHDp0CCaTadPs9kLZWODicrkwMDCQlQ2Xa4XP9N4zwbe3t/Py3EKGW7JjeL1eLC8vw2Qy4erVq6ipqeEPt4qKCm7SbxS81+vFyMgIfv75Zzx+/Div2ntifyDR7wKJRILy8nIcOnSIN65IJpMQCoUQi8WQy+V81lpNTQ2OHDmCmpoaqNXqfZnIwt4fi8Vgt9vxyy+/oLOzEz///DNvQpFrhWeC1+l03MH20Ucf4eLFi6ioqMj7/Ey4a2trsNvtSCQSOH78OA4ePIjq6mr+YGNFWpnvY3UAzNnY39/PZ+URzwYSfYEwwR85cgR1dXXQaDRZjiehUIhEIgGZTIaqqir+OpZnvl/EYjHMzMzg5s2b+Ne//oX79+/n1Tpbp9Ph6tWrPP+9vLwcZrM5r3Mysft8PqyurmJmZgYOhwMmkwl1dXWorq7edqhkKpVCOBzG4uIi7ty5g9u3b2NhYWFXqzzt+3cPib5ARCIRysvLUVVVtWl2G0MsFsNsNuPw4cNcCIWwVTFJMpnkhT+Tk5P4+uuv8Y9//ANTU1NZoa5cRT7Hjh1DY2MjWlpacPbsWRw5cgQlJSU5z5OLWCyGcDiM5eVlPHz4EPfu3YPT6URtbS0OHTqE6upq6PX6LT3/rP59aWkJd+7cwc2bNzE6OrqrIqaNUHltYZDo84St5nq9HhaLBVqtlq/qrOkGu7GNRiOqq6tRXV1d8Ex4di5GKpXi515dXcXY2BgePnyI0dFRdHV1YXx8HMDve/hMq4P1n2tqasLVq1fxxhtvoLa2FgqFAnK5fEexMzH5/X5MT0/j4cOHGBkZwfj4OIaGhqDX61FbWwuDwQCVSpW1b8/1wHI4HOjr68O3336LBw8e7NsgjELajREk+rxhgmcrfKbIGAKBACKRCGazGTU1NdDpdLvOn2ez4NbW1uD3+xGPxzE+Po6vv/4a33zzDe+vx9g41VYoFKKhoQGvv/463njjDbzyyisoLS3NO/GGnX99fR0TExP46aef8PXXX2N8fDzrAVhSUgK1Wg2xWLzlOGzWLLO3txdfffUV+vr64PF4Np1zt/F5ysgrjB1Fn3nTSiSSoiutzWzJJBQKYTQaoVarkUgkePtrsVjMvfcmkwnV1dUwm80FZ7axjrls7zs5OYlbt27h3r17WF9fh8/nw+LiYlbDSSaUTG+9Xq/HW2+9hYsXL+LEiRN8Nc73AcScbTMzM+js7ERnZycmJydht9v5eYHftjparTYrGrGxvx6zULq6uvD555/j559/3lLwG9uDFQI7F9XX7ww10cgDtVoNi8WCEydO4NixYzzRJBaL8f7ukUgECoUCNTU1qKqqgkajKWiVj0QicDgcePToEWZmZrCysgK73Y6BgYGsuDvw+4M4c4VjQtFqtXjvvffw6aef4uTJk9zvsJ3pvZFUKoXFxUV89913+PzzzzE8PMx/J5VKEY/H+YNGoVDkXOXT6TSi0SgWFxfR3d2NL774Al1dXUg39KoAABTlSURBVFs6GzdeY6EzAAqdkFPMFNREQy6XF0U1E5uKmkqlUF5ezmewnzx5Ejabjd+c4XAYS0tLePLkCdxuNwwGA8rKymAymfJu+8Ryz+fm5jAwMICbN2+it7d32+aQW62EzDPPynNzORDz2ccHAgGMjIzgiy++yBI8gCxPO6sU3GheR6NReL1eLCws8Ll43d3d23rpd2OeZ67uu91GFSPbil6pVOKTTz7B8ePHeeeWYtg7MRMzkUhAqVSioqIC9fX1KCsrg0Kh4Hv5WCzGe7s5nU6IRKJN/eu2WllZM4iFhQXcvXsX33//Pfr7++F0OnN6tHM1mMzsenPkyBG88847eO+999Da2lpwxIARiUQwPj6O7u5uTE9P5zw3g8XqfT4fD0nG43Gsrq5iaGgInZ2d6Ovrw+zsbF5huUIaXWa+jkRfGNuKXq/Xo6OjAxcuXHhW1/OHgN3kzH/BEm6YmNkNplAoeOadyWRCKBSCTCbLumm3EvyTJ0/Q19eH27dv49GjR3j06BEvS2UOQdbxFdi8V2Y/P3jwIM/nb2trQ319/a5GVLNz+P1+9Pb24vvvv+em+FYi9Pv9GB0dRVlZGfx+P8LhMGZnZ/Ho0SM8ePAAQ0NDWFtb2/S9bnXuQhcU2sfvjh1XemJ7xGIxtFotvwFjsRiCwWBWTzt2U4bDYXi9XszPz6O3txdffPEFuru7Nx0zs8FGrt8xGhsb8fHHH+Ott97CwYMHoVart50+sxPRaBRLS0sYGBjAxMQEgO0LdrxeL+7fv494PA6bzQaPx4OhoSH8+uuvOcNxT8NKJMEXDoXs9gmhUIhoNMrN3Xg8ntWXnpnC9+/fx40bN9Db25vVpHI7NnrGbTYbnyt3/vx51NTU7PkBnU6n4XK58OjRI+6lB8C3dBsFKxAIEA6HMTExwT9HNBpFKBR6Zg5fct7tDhpgmQf5eLuj0Sg8Hg+vFlteXoZIJEI0GoXP54Pdbsf09DRGRkYwMjLCw1aZnvhc580UnF6vx/Hjx3H27FmcP38eR48ehc1mK7ghZC6TOBqNwm63486dO3yVB7BtU4tYLLalwJ92TTwTPGXjFQ4NsNwHkskkQqEQXC4XlpeXeQZbMBjE+vo6lpaWMDU1hbm5uU1m+3Yx6cz8AL1ej3feeQcfffQRTpw4wSMEu+kAu/HfMxaLwel0YmZmBgsLCxAKhbBYLPzczL8Rj8f5eO6dRko9CyEyBx458QqDzPt9gBWhsJFOa2tr8Hg8cDgcsNvtcDqdCIVCeQshcx+t0Whw4sQJXLx4Ee3t7WhsbERJScmeb3Q26jkSicDr9WJmZgarq6s4fPgwDh48yJOQxGIxYrEYlpeXMT4+jvHxcTgcjuceuk2n09yfolAoqP11AZDo94FEIgG/34/l5WXY7XbMzs5ieXkZTqcTXq93kzd/4/+zlZcJnf3Z0NCA9vZ2XLhwAcePH99xws12U1+SySRfoVnGH7NCVlZWEIlEYDKZ+Lht1lJaJBIhHo9jZWUFv/zyC5LJJFZXV/f+pe2RZDLJexWUlJTsW0OSYoBEv0eY6cuSUSYnJzE7OwuXy5VzP7xxtd+4J9VoNFAqlaitrcV7772Ht99+G/X19VAoFPzG3krcG/+fvS4SiWBlZQWzs7M8jTccDvNhnGq1Gk1NTWhqakJZWRmUSiXEYjGEQiHPWdDpdAgEAhgeHuahzOfZyy4ej0OpVKKysnJfuxAVAyT6PZJKpRCLxeDz+eBwOLC0tAS3270r89dqtfIx1EeOHMGBAwdQWlq6qUvuTj4WlhHn8XgQi8Xg8XgwOjqKH3/8EQMDA3C5XLwi0Gw246OPPsLBgwdRVVUFrVa7qdEHM6UzR0o9b9hwDmaR0L4+f0j0e4Q5uJgY2J87rYI6nQ4VFRWoqanh/fQsFguamprQ2NjIK+LYXnUn052dN5VKweFwYGBgALdv34bdbkc4HIbL5cLCwsKmYpfS0lJUVVWhuroaWq12yyad0WiUt6lmon9eq7xer4fBYMjqu0fkD4l+D2Rmxmk0GlgsFmg0Gvj9/qz56QKBAFKpFGq1mou7srKSd6CtqamB0WiEQqHgte4bzdWNKy9LBWbx/6mpKczMzHAH4vDwMHp7ezcNj2DZfolEAkajESdPnkR9fT0MBkPWAybzfLFYDKurq3jw4MGm4p9CkUqlPFU5HA4jEAgUZDnI5XJUVFTg8OHDKC0tLZrU8P2ERL9HBAIB5HI5bDYb6urqMDExgVgsxh1nrGjJZrOhoaEBzc3NqKurg9VqhcFg4P3qZDIZ30PngvkOgsEgN92j0SjC4TBmZmZw+/Zt3L59G0tLS9smx2Rm+5WWluLcuXOora2FTCbLWQ+fSqWwtraGX3/9FY8ePdrTBF+dToeGhga0tLRApVJhfn4eDx48wNLSUt4ts1hXoiNHjqC+vp63KyPyh0S/R0QiEeRyOW+cUVtby4WVTCb5A6G+vh4tLS2or6+H1WqFWq3mvfG3MlFZWC0ajSIajSIQCGB+fh7379/H4OAgN919Ph+cTmfOyrxMQbAVHvitXPjYsWNobW2FzWbLadaz8tjp6Wl0dnZiamqqoO8mc4vT0tKCCxcu4NSpU6isrIRcLsf8/DysViuuX7+OhYWFvI8rk8lQVlaGyspKqNVqMvELhES/B1hyiEwm4+Z9VVUV39uzsFJNTQ1aWlrQ0NCAsrIyqFSqLb3NzJvPwoDz8/OYmJjA7OwsnE4nVlZWMDY2hrGxMT7sMZONNemZpm/mhNy2tja8+eabqK2thVKp3LIwaGFhAb29veju7i64ay2ruT927Bg++OADvP3226irq4NcLodQKOTpw263Gz/88ANcLhf/Xrcz2Zn1xJpwEoVBot8jTPgSiQQKhQIajQYqlYrvt5kImbkfj8eRSCS4Kb/RlA6Hw/B4PFheXuarend3d9495XbqOmM2m3H69GlcvXoV7e3tW47ETiQSvOPNjRs3ClqJMzlx4gT+/Oc/49KlS6iuruZZhOl0GmazGa2trfB4PPB6vbh58ybvObgVzOfBGneQaV84JPp9IPPmi8fjCAQC8Hg8vNouHA5z0UciEVgsFr7as+y7RCKBcDjMa9F7enowPDyMpaUleDyeLbvG5hsrt1qtaG1t5ZNnmpubYbPZNjXIZM7HlZUV3L59G1988QV6e3sLbmElEAhw9OhRfPjhh7h8+TJqamqy5vUJBAK+Pz937hzcbjc8Hg/u37+/5f5eoVDAaDTyrkTkwNsdJPp9gJnRkUgEq6ursNvtWF9fRyAQQDqdhlwux9zcHCYmJlBVVYWKigpYrdYsL/b6+joWFxe5OT82NpZViw5sNt3Z37dCpVKhoqICBw4cQENDA06dOoVXXnmFJ+CIRKJNx0wkElheXkZ3dzc+//xz9Pb2chEWkoxjtVrxzjvv4K233kJtbS2kUmnOQh+pVIqysjK0t7cjGo1CJpNheHgYPp9v04NGrVbzzrvxeBx+vx9KpZJM/AIh0e8D6XSar/BLS0uYm5uD1+tFOBzmN65QKMTo6Ci0Wi3MZjNKS0thNBohFosRDAaxuLiIubk5rKys7DhHPh9MJhNaW1vxxhtv4PTp06ioqOCCYZGCzOtnW4uVlRX09PTgyy+/xE8//QS/319wNZtMJkNjYyNee+01HDhwgDsJc5niAoEAEokEdXV1EIvFUCgU0Gq1ePDgAZxOJ29ACvzmiGR59uvr63A4HFCr1dDpdGTmFwCJfh9gWXlsrtva2hri8ThvIMmIRCLw+XxYWVnBxMQE35ey5Jr9qEMXCoVobm7Gm2++yQt0bDYbVCrVlh76eDyOcDiMhYUF3LlzB9988w36+vrg9/v5awqhtLQUra2tWbPstkMsFkOlUqG6uhodHR2QyWSQy+UYGBjA6uoqz3lgAzcikQiWlpZgNBpRWloKjUZDBTcFQKLfI5lmMeuMEwwGt9xzslTW7eLSu81pV6vVOHLkCC5dusRz9tlQjlxTb4Dfh1BOTk6ip6cHP/zwAwYGBnK2qc4XjUYDq9VaUHqsQCCAUqlEVVUVotEo3G43HA4HTyVOpVIIBoNwu93cZ+Lz+RCNRmlvXyAk+j2yVZHLXm7EvQj+6tWrvH0WC8VtHMgB/GadMB/E+Pg4urq6cOvWLYyOju558kwwGMTy8jJcLhfKy8t5NGM7WBREKpXCaDTCYrFALpdnbStYTUEoFMqqGCTRFwaJfh9gCTpqtZpXqD3Lm1EqleLAgQPo6OjA2bNneew9l7XBzHnmR3jw4AFu3bqF/v5+LCws8Oace2FtbQ0DAwOoqqqCSqXKup58uxD5/X54PB4e+WBEo1FevcheG4/HyZlXACT6PcJWKNYZt76+HgsLC1haWnom55dIJNBqtaiurkZDQ8Om0VWZ3XMTiQSi0SjW19cxPj6O/v5+9Pf3Y3R0FKurq/s2I97v9+Phw4dIpVLwer24ePEimpqa+FYjl8nPrjEWi2F+fh6PHj2Cw+HY5OfI7ICbSCQQCAQQDoezpuwQ20Oi3wdYVl5ZWRnOnz+PcDiMX375BUtLS/sylTUXzGxnVoZYLEYkEoHb7YZKpYJKpeLOrUwn48LCAmZnZzE8PIzBwUHMzMxsKfaNRT6F4PF4cPfuXXi9Xni9XgQCARw9ehQmk2nLUdasT9/AwACfipsLZkWxCb6supCceflBot8nxGIxLBYLzp8/D61WC5PJhK6uLszMzOypSAXYOtTF6t7j8TiWlpYwODiIWCyGmv8/PJNtMwKBABYXFzE8PIwHDx5gamoqy0G21Tkzh0js1BMvF4lEAo8ePeJtxKLRKE6fPg2DwcCFnxm9WFxcRG9vL27duoXx8fGc0Qy20rPrZhl+tMrnD4l+nxAKhVCpVLyllU6nQ0lJCbq6uvDo0SPeuKIQcqWZZgqP7XV9Ph+mp6exvr6OX3/9FVqtFlKpNCvTz+/386KcfEOD+1XI4nA40NnZCalUCqlUimPHjsFoNHKfA1vhu7u7ce3aNQwMDOxoIaVSKV6uzHL5ifwg0e8TLK1UIBDAZrNBqVTCYDBAo9FAKpViaGgITqczb+FvJ/hMjzarxAuHw3A6nXlXwu0UFsyc8JN57kJh51leXkZXVxfUajVEIhEOHToEuVyOcDgMu92Oe/fu4ccff0R/f/+24UJm4bC/y2SyvOYGEr9Dot9nWGqrXq9HXV0dj8szTzRLzd2JrWL8+8Vur2Ev53ny5Alu374NsViM1dVV6PV6OJ1O3L9/Hz09PZiYmMh7KyQSiSAWi7NSiYn8INE/Bdiqr9FoeJeX6urqgkNiL1v8ORKJYHZ2Fj///DOmpqYglUqxurqKubk5OByOvJyerGzZYDCgtLQ0rxwAIhsS/VNEKBRuit+/bEIulFAohMnJSSwsLCCVSsHn8xUU4VCr1SgvL0dDQwPq6up4GJDIHxL9U4SFlLxeL9xu91ML371IxGIxpNNpXoxUSH88g8GA1tZWPtKLVQvSSl8YJPp9JjOkFAqFsLi4iMePH2N+fj6rYq1YYT4OoPAaA9aB5/z587zlFgm+cEj0TwmWEDMzM4OxsbGnmqjzIiCXy3nzTRZtyDeSodPp0NTUhCtXruDChQuoqqrKashBFAaJfo9k5tizFZ7NtlteXsbS0hL8fj+kUinkcjm/0XdTmLPdqrZTF91CcwT2QmaqrVAohFKphNFohNFohEgkgs/ng9vtht/v5+XHmat+Zv2+wWDAyZMn8cEHH6C9vR1VVVVZacZE4ZDo9wArqY3H41z8rJjF6XTydtQWiwUtLS0IhUI8sy2ZTPJiEdYqm4Wg2EgpRq7W1Ozv7HW5RM9Mab/fD5fLxbPwniZ6vR42m423pmai1+l0vO6dlSCzYprMGQHAb9mN7Oc1NTXo6OhAW1vbppZbxO4g0e8BtrJnlnhmNr8UCARQq9UoKyuDWq1GNBrlr2epp5FIBKlUiufvK5VKaDQaKBQKyGQynmLKHhSskww7Rubx2DWw/9jrA4EA3G433G43wuHwjs0nN37GXGSuxqxQRigUorS0FI2NjTh48CAMBgNkMhkkEgnEYjHi8Tg8Hg/W1tbg9/s3Jf9kpvpKJBLo9Xo0Nzfj+PHjqKioIMHvEyT6PcCywyQSSVb1V2beOquCCwaDXGxsZWOiTKVSEIvFkMlkvP2TVqvNapXNhMV64MdiMd6dhz1kmMWRuYXIXOmDwSD3nheyrcgUd2Z5LPv8UqmUf1adToeDBw+iqakJ5eXlvPotmUzC5XLBbrfDbrfzJKXM7Qc7HusrWFNTg8OHD6O8vBwymSxnjz2icAQ7/OMXr5u5ADamx2bmvAeDQYRCIV7ckvl9Z/498wEilUr5Cpk5amqrlX6jtZF5PYFAAA6HA/Pz8/B4PDxEtp3nPFd1XeZDjSESiWA0GlFZWYnS0lJeM69SqVBSUgKtVps1aZe19/Z6vXwuABvikUgkIJPJ+INOIpHAYDDAaDRSWG735PzSSPRPESZUZrZu/K5z7dHZ33M1nNgo6I3OwFz/lqFQCGtra1heXkYgEOAr6lbXu/HYW3XeSad/m2Sr1WpRWloKi8UChULBrQyRSLSp1DXz+2APq2g0yn0dUqmUd7cVCoXcv0GC3zUk+mIknf6tNXckEuEdZ3K9JvPvmVsE5onfKH626kskEh6O201mXKZ5n+tBQewJEj2xPdut9LTavpCQ6AmiyMgpeqpUIIgig0RPEEUGiZ4gigwSPUEUGSR6gigySPQEUWSQ6AmiyCDRE0SRQaIniCKDRE8QRQaJniCKDBI9QRQZJHqCKDJI9ARRZJDoCaLIINETRJFBoieIIoNETxBFBomeIIoMEj1BFBkkeoIoMkj0BFFkkOgJosgg0RNEkUGiJ4gig0RPEEUGiZ4gigwSPUEUGSR6gigySPQEUWSQ6AmiyCDRE0SRQaIniCKDRE8QRQaJniCKDBI9QRQZJHqCKDJI9ARRZJDoCaLIINETRJFBoieIIoNETxBFBomeIIoMEj1BFBkkeoIoMkj0BFFkkOgJosgg0RNEkUGiJ4gig0RPEEUGiZ4gigwSPUEUGSR6gigySPQEUWSQ6AmiyCDRE0SRId7h94JnchUEQTwzaKUniCKDRE8QRQaJniCKDBI9QRQZJHqCKDJI9ARRZPw/ckQjlgJfyMgAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", @@ -2065,17 +438,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta)], need_gradient=False)\n", "frequencies = opt.frequencies\n", @@ -2086,34 +451,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", diff --git a/python/examples/adjoint_optimization/01-Introduction.ipynb b/python/examples/adjoint_optimization/01-Introduction.ipynb index 22771aa8f..5c8b0a984 100644 --- a/python/examples/adjoint_optimization/01-Introduction.ipynb +++ b/python/examples/adjoint_optimization/01-Introduction.ipynb @@ -17,17 +17,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -51,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -149,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -174,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -210,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -234,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -251,30 +243,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.9/site-packages/meep/visualization.py:357: UserWarning: The frequency parameter of plot2D has been deprecated. Use the frequency key of the eps_parameters dictionary instead.\n", - " warnings.warn(\"The frequency parameter of plot2D has been deprecated. \"\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "opt.plot2D(True, frequency=1 / 1.55)\n", "plt.show()" @@ -291,19 +262,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "f0, dJ_du = opt()" ] @@ -317,32 +278,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.figure()\n", "plt.imshow(np.rot90(dJ_du.reshape(Nx, Ny)))" @@ -359,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -377,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -393,22 +331,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", @@ -450,19 +375,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "resolution = 30\n", "opt.sim.resolution = resolution\n", @@ -472,22 +387,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", diff --git a/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb b/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb index 750031271..c2e360d25 100644 --- a/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb +++ b/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb @@ -13,17 +13,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -46,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -143,22 +135,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "x0 = 0.5 * np.ones((Nx * Ny,))\n", "opt.update_design([x0])\n", @@ -178,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -209,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -226,46 +205,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - } - ], + "outputs": [], "source": [ "x = solver.optimize(x0);" ] @@ -279,22 +221,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(evaluation_history, \"o-\")\n", @@ -313,22 +242,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.update_design([x])\n", "opt.plot2D(\n", @@ -350,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -378,46 +294,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - } - ], + "outputs": [], "source": [ "evaluation_history = []\n", "solver = nlopt.opt(algorithm, n)\n", @@ -437,22 +316,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(np.array(evaluation_history) * 100, \"o-\")\n", @@ -471,17 +337,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Achieved an improvement of 28.0x\n" - ] - } - ], + "outputs": [], "source": [ "improvement = max(evaluation_history) / min(evaluation_history)\n", "print(\"Achieved an improvement of {0:1.1f}x\".format(improvement))" @@ -496,22 +354,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.update_design([x])\n", "opt.plot2D(\n", @@ -531,22 +376,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.imshow(np.rot90(np.squeeze(np.abs(sensitivity[0].reshape(Nx, Ny)))));" ] diff --git a/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb b/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb index ba833ce4c..f2008a8ce 100644 --- a/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb +++ b/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb @@ -17,17 +17,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -53,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -81,17 +73,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.20124611797498096\n" - ] - } - ], + "outputs": [], "source": [ "minimum_length = 0.09 # minimum length scale (microns)\n", "eta_i = (\n", @@ -113,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -151,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -208,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -286,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -318,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -340,22 +324,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "rho_vector = np.random.rand(Nx * Ny)\n", "opt.update_design([mapping(rho_vector, eta_i, 1e3)])\n", @@ -372,22 +343,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "beta = 2048\n", "\n", @@ -420,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -468,1594 +426,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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ED3iG6AHPED3gGaIHPEP0gGeIHvAM0QOeIXrAM0QPeIboAc/Ee/4+GGQrAAyGkR7wDNEDniF6wDNED3iG6AHPED3gmf8H5Xh9SXsZLCkAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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Pzz0OVdGXet2uZFZL3bmH0rYtLRg5Zpu73a65Oy3HvuwGZwtHala+q+CzsXCN4TPRdxE8x/Gw7hA8jo/vvKuLcWA0gO9pz52ahhGc0MvWt8tGLeANMJqZt8gfTlX0uMd5HxkMBnFxcRF/+MMf4vLyMtbrdSwWi7i5uYnFYnHgDmc16qUhvWwIi/9WsWv8XhO8ZrN1LJ4Fr1Z+uVw2nRHXAQyHw4OiH73D7WazObDOasXxvZJY2f3PknLsdZSy/uY4+qvqDgwGg7i6uoqvv/66Ec319XXM5/NG+Ozmq7jV6um2ITAdh9b7ymdZ+szCc6KMBZ8l7ljwyNijjSz4bD/YBy+bxaLWY83G0dW9LxXslDwIdwCnUxX9r3/965fVjtcKxKtXV1fx5Zdfxnw+b4Qzn8/j+vr6QPSajdc4FWQJKDzrTDkurT1G8BF3q99A8LDkaDOeudNiC4+ORyv8uIYfx7fdbqsuty6fpd9lS65wMlFHOjRx6E6gO1XRf/rppy+rHa8FcCGXy2WcnZ3FO++8Ez/60Y/irbfeaqw8W02O6dWdZdQScqIuE/wxMTyX2aIN7M5n1p0Td+rSYyxeS3sRy+McZYtj8ggEF9dkFXVaiMOVjPjNbrdrsvz4u1TfYLpTFf0vf/nLl9WO15If/OAH8dFHH8UHH3wQ6/U65vP5wXi0jrFDeFpCGnFnybSMlKvmuPBFLX1WV5+V2LI7z2LHoyZ4PGeiH41GTbu5yo7R7Dq3jUuCs7JdtdbaMWCor1YQZWvfDcf0FS4vL+OLL76I8/Pzxl1Ggi1zVXGhqyC0qATWmYWuK93ozDkWuwpeh+Rg3TnxeHNzc29oDu1kT4Nn7fH8fK3uy2J0Pla+ISULHWGIjvFDyHo+9/t9c6w8QmKLfzoWPaHDe7PZLP74xz82n52fn8dkMonZbHYgvoi70IB/z9us3VtO7zqjgitNlQUcw8PCI9l4c3PTCB9DcxiLhxi1zJY9DE3gZS49H692etrRZZ1FNoyJ33Fngs63NERqa98ND9kRuCDX63VzgV9eXjbWZjKZNOPOEG9EHFjB7MItxeylW06VlrtSwWuWHoKHKz+fz5vH7e1tU1SE0IPn2bOFz+bkZ8enSTWesw+4wEctvFr20vZwzrUi0q79aVRVjTryPoKE3mKxiMFgEOPxOPb7fQyHw8ZSwoKVlpTmQhtYUb3VVO0Gk9mc9cwtVrce1h2C15EGtI3zCupllKrvsvoDwMeqf3N4k5Uo67Y5LxIR91bnVVff1r47/TLlR4KKvOFw2Fju6XTaCJ6HttjKZWWsbOGzmLlm1dWC1gQPKw/Rc/KOBcVtK8XxOqlGaxBYcNzBRRzG9vx+xN0wH59HnDN+5vdRr69r+WVFUaaORV+BhcUVYSx4xL88Tg74ws/i9dLSVmrZs0y3xvFZ8k6r7dhyauKutJgmexZa4VdaaENDGvVQBoNBc05rY/wQPh+vrsFvN/94LPoWdFgoIhq3nifAcOGKxvPsqqvo1aXnzLeKhWv5s8IbHpZD0g4xfK2mXtfXy8b+ayvlssXmxJ2KnouWIGicTx7a1DoHbkNpmW6LvTsWfQXNxvMFzRVr0+n0IJPP34XA4N6zK43X2TLVat3VwrPguZ5ep8myhecpuuptZGP/bDl10Uzddna86i1ExMHKumiP1ter8HHcWTIvE77FX8ei7wCLkIfYIBYMbekFnsX0nKHXZB3H7io6LbxRl54Fv1wu702RxXM2PVdFDzLPIptdyMerOQmu4sNDE3sc83MIBfFyfF9K5Nnad8eir6AFNtkMOK6WUyvN2WsuwCll5rPsPFs2LgHO3Hlk6CECtEETdqg14Fp+HQ5kz0KtvC6RnRX48DPgyTnsznMyTzsIDgfQEah777j+OCz6FvgiVQGpxedqM/6+jtOXEnUcTuDi7jJTjuN3dp+5M+Hy2mz1XFQZYv+cGVexZ8tj67FmNQV6TnnIELUQ+K6WMUdEM16vFt9iPw6L/gjUgqvl1zXmcWFHxIHFz6w6nksJO17Eg4fm2ibP8Dg8hyO6ph5X3EVEKnhNnkXcv0c9DweWMvPaWaiLz+LWc8RDdFn9PX/X5Fj0HcisN4uJxc8XO7uumt2GuDl2ZdFzlhoxPASuiTte206Fp4LnSTtcV89tKll4tqx8XvC6JHbeLh8n/w5j8Cx83o6GO7XiHFPHom9BL2p1yzPrlsX2OvSWCQMXNY+F800oIPhsyWq28CgPzmbtcSjCFj7i0Lpvt9t76wFynbsWDOmIAx+TjrOrQNkjgpXH73VbbNlt5U/Doq+g4+wat2YXutaea2IOr/k9vZj1vnLZ2vQaw3Nng46pVBNQm6VXu8WVxtl6rEC9mMxC85BcFg6p4LkeQF17W/njsOgrcL08j6lnbjqLV0tm8azZaHWntfiEh+f4NZegqkuvE3pY7BhW1Cy9VvfpLa7W6/XBUBqLU4WqrrzuQwULNKuv8PmyyB+GRV9hMBjEZDKJi4uLe3Pa9UJXtzSLR9nNVaFngocI8QzXmy98FjwX/JRq6fFddol3u13qVeitqjkvgfPDsCj5uFX0WWyP7WWi5+9k2zfH4am1BC44TK2F4J89e9a81uo7tpJwPdXtZdc9E7jeMhpuezYezRe6Dh/WblnNs+VwrHqzDs4dZLer5iIftczc+fHffNw4F8cItWbVa56BKeOptYRa5+FwGG+++WZ8//vfb0Q1m80OxuQRC8N66sQbTWLxMJzWsmsHwPGvVr5lmXnNzut8eA1HOHGnMbxW3OnQXCZ6biO2XXLJNdHZlVIStS08MHf0y5S3oNbk9vY2nj9/Hu+9914j8NlsFhcXFwfDXOgcMeykmXodguM75rBF1RJTzgFE5HPgVey8pl1p4UwVPLv2Oh6f7ZsTmRGHC4FyzkIfgEWKCjycryzJyfD+deqxhd8Ni77Cs2fP4p133on333+/EQWGwCAmWHq49ZzgY9FrRp5jdp04opY9Ig6SbzyikAke7ePFPSLyW1WXim9YbCwsTWSCLDGpw2kR9zP/nP9goetvEJqUKhvbCoLMHVXR/+xnP3tZ7XgtgFixBPYPf/jD+PGPfxwvXryIzWYTi8Xi3oXJll4vuJKVRwfCY+KcjS9lyrW+PRt/z9bRU7GXBJ/lDNiylqbLlpKUmYWHZd/t7pa3Bhp6ZEm+bA4DD5/a2rdTFf0vfvGLl9WO1wqslDOfz+NPf/pT3NzcNC633tJZxRqRJ7G42AZJs8zCR8SB18Bz63XRi2zsvZRgg7dREjtXtmlHw7PnVPDYh+YfOKEXcbeOfUmMKn7AYcVgcDetOVvwI/NAzH2qov/pT3/6strx2vL555/H73//++ZCxh1ucKHD0sPas+VjEZQSdlmiDmj8qqvmZtNydZYcC55jeJ0iy+LUpF2Xdfo4SamWOqtOzLaTuff4LpKk2apDau1NHcf0LVxcXMTz588boez3+wPhRMTBRc9Z7GwsPlvqSQWvM/NY3NkcfE0cMuptdFkIQ5N2WflxSfAax7MQOeRQbwRtzTL8fC6yVYORZ7Cl74aH7BJwYd7e3sZoNIpnz57FarWKs7OzWK1W6e2as4te3V19rfvTYajMumcWN+JwiEw7gawuIBM8ewocQ5css3oyJcGzt4DXKk4OFRg+JxC53tbL7v1xVEXft5PHgom4K8NFLLrdbu+tZ4ffaRKrlMlmkUVEk8xigZQW3tChMnbd8TpzvUvVf1nRDdqWPWqhS0nwmg8ohQgsdvU8Iu5ui1W6J4AKvm/X7jFURc8VXH0EYsMFrgLMCl4ywTOITzkRVnKncTGXht94uJDrA/C5Cp/bxILf7/ep212yxBoyZILXBGAWjkQcjvGXxuXZtdecRjbr0YKv45i+BU6QZUtbdb3IIAQVGJ5ZKLx9Lp/lDkZrA3h7pQIXLfTJ2p2VEPO2shi+FoNny4LxttnL4TZgv9rxqeDt1h+PRd+CJrZUnNnn/Du479lwFVt3zpprHM0dBo9jYz/HHAe+X7KO6sZHHC6QmSXteB8almQWOeIweafnDB2jnutaYY7d+u5Y9EeiQsmScNvt4Yw7tu7ZNlT42QWsWXkmc4kBdyDsPWQdF2+PQ5O2LL3G7zqWnll5hBgc7mj7ueNs87Is9u5Y9B3ILqws8QZrjKQfCz/b1jFhgo5ha6mstpcz8RwelMpXeT/ssmsyshbDq9g16ZmFH7UEXOaNlLwT0x2L/kjUOqv7GXG4XLOOneuFzJnzzNpie5yAY2GWxrU1M88dVK3YRstfs/1ngufhRa4W1JLgiGhmJZbOBc5fW8eouQvTDYu+QsmCZPExW3oAcahrj9/guZSIwzZ02I9LZvFQz0O9Es2k429NrHHiLhvuqwmeK+aywhnsgz0P9XyQnMz+BzoikXVSFn87Fv2JqMA0vq9diJpM4/ey6jQVvIoeqKvO7WJhov5ArTy/1v1yZ1DadumW2zoigG3z72HdMT1Zz09p+LEkflPGon8gfOFy0g4iUve+RNZJaBzPFzmEA0qFNVkMn9UZcAeilrSt8EbnAmT1DNiuJi75HEL42nlmw4VZQjEr9DH3segfCY2nWew87syoK8vvZ0JU2B1WV5kTjFlsDEodSyZ23vZgMEjDhWwCUBa26Lb0bw5buCISk4ba7nBj4Zex6Ct0vXCy4aaIQ7HXhN9l31n8q7F8FrfDC+HfqzBKeQP1UngbWmmnC29mLn3p2LJzVzo3cP+ziUsWfDcs+g50uYjYRdX3QUn42f5g7TRfMBwO7xXFYNtcwKJWlo+DY+qIfDagHje3vUuRTJZZPyYG10pAPM7Pz9NJQ3btu2PRfwuo5S9diOo26294NICH4DSJlw0jZglCtuDZ8CBbzSxLr8m7WueCbWaJuFL8re9r54Bzkt1Xz8LvjkV/BLVkkcbKaqnxe95WDY1zuRMoWUquvgMQM8fJLOBSLA+0wEiHKbPOg/9W0cPL0GQhf4fbwUVIyF/UBG/asehbOPZCYjdYra1mqUu/599wOzKXF3/rvvQ33DbtGPi7vA+27Jyb0ARcxGFBjbaJx+izDiZbbyCr/Nvv99V70ptuWPSPgAqOL/LS51lsn5WdMhon85BddtHrNF/O5HM7dWhR3XrtVDLBczs0HNB2qtjxHi/plXkeSOJp/cAxHpSx6DvT1R3n1zykxs/4TH9femg72E3PEmOaPOPtZDP4Sp1LyRvh7/H+uVMpHQO78GzVsSowBJ9l5fX4LfDTsOgfSGm4Tr+jwtcEn4pE42XdB1tm9ijU+qlbjolAg0G+FFZtn9gvHmr9tYPhoTuN7dmdZ8HrMBx7KZzYLHVYbR2VsegfHb3o4JKqGLLMfSb40rAbhMDuNE/I4WE3zaQj647fZivaMJkHkSUy8YxtaSfA24OYM8HzakCZNS91kBZ8Nyz6R6B0wZXcfP48s5jZNmpeBJNlxzWphtCAZ79pVp+3p0No/FnpeEshBG+HY/jMtS+dZ65JKFUjmjIWfUe6XEjssuvrtqx9zcpqCKExN6yqWmLNyLPga8dQisH5b7X43Ba0mTsS3R6X1GbZez12ntij04PbPBVziEX/iJQSS6W4XwXdtm0WvHYi6kVklplDgtIIQNYetfaaL8D+9Th19ALbgsDZsuvtuPl3ujpwdqMPu/ndsehbOEaUpd+XhJr9TmN9zoq3dSqZm6udAfbP1p+r9LK26ehAaf+lv7mTYNc+G6NXdK5+aepu5uabHIv+EdAhJRZo12EltrI14SsqbrZ6nOTLxtI5wVdqZ2loTDuIWhJSz48W5rDg+TzUBF9bEdeCr2PRt9Am2pLrm30nK8jhfZTyARmaJ4D7G/HNpBwsSRWRT+Flj6NkmUti52E4zv7jfV4ZB3ByUYfj2IvIBF9ahqu0lr6pY9GfSJbQKokl+7vWmajVL+UE2i50vi1ZKQNfaleWmVexR8SB4DMBlo69FjKo4PlmlboUl6388Vj0LdQsbdcYkjPZtQy+7lOHqLILG9vEQ9ukpa46dKgCzIQIa479oU3ZjSwyj2K32zVTYkthAW+fRQ+RTyaTmE6nTQeQWXoLvhsWfQdKItRHRF7CyiLS6bIlq6sZ6SxZBSBUXqaaF5vAGnRw+dE56Lz5kuVVoestpnS1HG4X3Pls2I4TnEBvYQWBT6fTmEwm6c0ra52iuY9FX0EtEruxPHwE8ZQEzKj7XBvvzoaiMmsKwXPMnK0uoxlznsDCyT3tpNiq84q3Onymrr22JVu+S5OWEDK2P5lMmv1B9OgAeLVdC747Fn0LatlxoU8mk6awJCIaa6q/ZTSLre/x7zQbXrNmWoijQs+e9TtayKPHjuWwWISl2Bpt4jH47MH742RktpQ2/p5OpzGbzRpXX2/uafG3Y9G3wOKD24mLD4IfDodNocljodZdLX7JxWehaeFLrQPQaaxoAx+3ZtDH4/G99fEAV91lwtcZdDwKoMNy7E3AyuMZbaiFP+YQi76CutRs5XmO93g8bqxlVtVWs/il/UaUa9izC7s2/s2iy7wA/n7NtVfhc3zPbWTPI9s/3+2W43pN4qEz4dJbdfVr96g3ORZ9C2zl9/t9jMfjA8GPRqMD8fDvTt0fKIk927YOhbH1Vndep69mi0tqDkPXty8l0rg9tf1rElPzJZw30fezzodDMIu+jkXfgiab8BoWfjKZ3BNNbZjv2H3jd12GBzW2V+uvHoAWx2h4wkLSSS6lzDm3QxOMuj9N4un+2OXP2sIPj9d3x6KvwO69vg+Lw+KJOG4SjW6z9F7pmdEioawIJusMatWEGt7wxJcssciix7OO/3PnoiMW2TClbl/Fr22y4Nux6FsoCf/s7Cx1i/l3D9ln2+uMbBiw1glkVYXaji6jCKUcQ60dvD/uzGrhTEn8TuIdh0XfAbZE/B4nrdqSc8fsp8v7Gj8zKjZ+nb2XbYP3kYm/a7hRakfbvrLX2pZaeyz+MhZ9Bb2QOMHVZikf86I7dlu1eoCsrbUOqyS67LNj2lHbV23bNa9A22dyBi0WysuN/j81kTyGla9xzEXcpS2ntvfYBOW36f1kn1ns90hPiEVvzHeXVPTlxdKMMd9JLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RnDls8HL6UVxpiXhi29MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjesb/AWnng9cTMqCuAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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tubARJzbjXK/XsdlsTtptdTusbFss/L4OLpzYzCw5hwZNSb6srm/hn49F30Bm0TXW5kw7xFpquinNltO22iw/gIQdT6KZTqeVWw/Bl6y8Xgtvub1arWK5XMb9/X08PDxUSTycl8/HVQFO3vEggeuG1ed/Y5BoE6uuTVCy8nbxz6dR9Jx17QNamtK6tZa0uLQFobBnwOUqHjRKrn0meNzkWoefTqeVK98meD4mrvXx8TFWq1Vty2249QgruOGHk4WZlT8e6xtn4vssWfZzyKz8pRN7TIvoX7XrKcu+ZjXs13mO7P2I8z4Ljnk4HE4y25zhzpaQ2u2+W3teE1ksaiTmOCTQ7jcWXUScZOohdn6ez+cxm81qbj0fjwWPOJ6320Ycz5l2HmS4DIiuPrbyLMDs+9ZJOW1xuc7Lb7Ly2f+5yWkU/WjUX+9/MBjEYrGIP//5z/HXv/41ttttTSiwiryijLbIZoJXi68P7bRjawvhcZa+i+DVpc+Sd/BYIHielotzchkQ58A1N21XpXG57mbbNlg0xfIl4Zsy/VV1BwaDQSyXy/jmm28qV//u7q5yix8eHipr3TQDrm0A4IQdt6jqyjdNLj0EDwuM0AweRWbh8RlY8DyRhgcaWPj5fF4rAUacNtzgu2Oxs6fStnd9U0mvzaW3+NtpFP3vf//7N3Ud7xTY3+3ly5fx9ddfx3K5rISzXC7j7u4u7u/va2vGlUpt/JpLd1x3B5zkgujgzqPhpimGZ5cb7jyy9Jl116RdRMR4PK4l77i7b7FY1Kz8bne6hTXDos52wc0m6QB8N9rTnyXvLPTzaBT9r371qzd1He8EsF6Pj48xHA7jxYsX8bOf/Szee++92G63sVqtqlieG1i4Fq9dd5ng1ZLBEgKITl16iL1k4XH9WYYeYi814PDsO1hk7fBDY442+oCsvMjeAwYxtfb8t13yM+bVaBT9b37zmzd1He8kH330UXz22Wfx05/+tBI9N59kXXgR+Z5uQONbhstySKCx6BaLRdzc3DRaeCQKYd05WYfX6tLjOjBwsJXnlt7ZbFaL5bUpR/MR+JwcpmRTcfU7Yg+Iy3z6XZnLcEzfwN3dXXz11VeVu4893eECA86yc9cZ3tPZY/q3vL6czpaD6CB6FqE233DTDQSOUAQNOJylR387L3dVytbDo2BvIuK0LRlw1WE4HFY9+uyV8KDBgySe+XvSgaHLa5Nj0RMaW87n8/j2229rwoD1g5ubrf7KNyiExYkvjkPZzYWIkSGH2ODGQ+wqeHgb6KWHyO/u7mK5XMZyuaySj8hD8HXxtUD0HD5oXR6fUROUnIjkTD0PZHzdfJyMrLzLYUA2WOhrc4pLdgRuxO12W5W8VqtVDAaDSoSDwSCm02lERE0Ix+MxRqNRbDabiPjOwqFZJ2u24ey8ioK73yA6FjssLq4xIk5i+Lu7u3j58mWVeETijnsJYIGzOJ7Pi+QdvBjNVWheA8fmOj+79uzWc09CRJyECfqz0gP/B6adRlXj5ugjSOit1+vatsuLxSIi4mROOQQAIe92uxNXP6I+eYXLcdrbjhVveNkrnRfPbvV2u60JHhYe7j331B8O9V1k1K1XjwKDUsRTd58u9sHNRVx94Ho/Pm/JyjflQXiA0BZnHlQt/Hb6ZcrPBHH8ZrOpBAyBsDjwHsp34/E4XXmGwwSImGes6TP/jpa3uAlI43j00nNPAdx6TbRxeU7jeJ4rj+8Dlp4nGWUr7HCooAk8TjoOh8NaaJBl8VnoWfOTelMWfjMWfQN6s0U8WfjZbBbX19cxm82qRB+72JPJpDYBJ6K+vpxuQsHWXde009LWbrerhJNNnsEDLj3Kc1r7ZitcEryKlFf0yZbVYreexc5VBv5us8Ycfo8HKV2DgJuhSuVQc4pF3wBbHbisECfXy+H6IpE2nU5rNXwWvWbndT27trIWT41lwaPhhifPcPONxs68ai6X5ri3HvkKDHjZOnq6ai433vCgogOICj4rc+J6OQTQVYVKwrf4y1j0DbALzEkpbZbheHc6nZ4sN4WbV6fGahyvjSsqCm3r5V56ba3FbDkNL9jT0OabrJ2XBxuu//OAwstqsWuvD/4+ueTH1r2UnMN77GVkS3Vb8O1Y9C1oGyk3raAPfjweR8R3oof11k0iSsktFjv3vOO8XBrDa54tVxJ8JsaIqO14g9V24LEgvNBmH+7f11mG8CJwDl3kgwWvic3MMvMAgH8DXslnNBrFbDarBjceYC36Ziz6FnhWmAqW3XJYLyTy2PrwcbgHXfeK10kofPND+Cw+tNXCpedOOxYiu/QcnqC7Dw+dMsulOZ6wowMLPp9aec5H6Dp6mdCz15yhx8AFb0XnPujkJZNj0beQdd7xjc118+PxWL2ni2Fws4pOPuHtoVjwbOVLc+E5dtfWWi7L4Xph4W9ubuLm5ia+973vxc3NTWXlEcfzugDs1vMS2ToVl4XPy2WDrOxWqrOzV8ODJ6w9vC5cC2f1LfhmLPoW9AZCAgoPvrmRvUZdnxNQ2m4LYfCDG1R0Hry68vyA6HUBjMlkUp2b23ph2SF4tfIQDgYbCJ0HF56Zh5yGJvF4zjy+y6zOXmrG4e+APxvOpfmTbJkxc4pF3wFNOpVuVl4ZJmswYQHw7+LY7Mrz8tewsFyS4xVveGMKDkdwXl0MAw8IHlYe69LxCj884OA1D2jaY8+DmYYonIxUy5zV5rlEt9lsTkTPXkd2PJNj0bfAQuVuNF7qSmeCNa0Bx+/pzQnXVevh7NKjj15j+IgnAfIzJx95BRxeGx9uvSYKNXGHa2JvguceZKvgZC49l9rUI9LYnrv+MKgdj9+1PHOY4URedyz6BtiNB2oJcTO2LfaoM+0Ai0IHFZ0iy4k7XcQyIk4y5bpqLk/e0Y47XAcnCjmO180veF4Gf09NLbb8+XTVoOx3dV1BNCVFRLFPwKJvx6JvQWNU7UyDSGCB2K3WwUJr0tpOqhZe6+K84g2mx2LAQU+7rrjDy1zxLrbcLKPtvDgHL7ahpTl4Nxy2sOA5BtfGIm2nzWr27HXwA99ttiah4/luWPQN8MwzXXCSE2w8F720ymsmdO5b55BB21whRF6BFxael9jiLju18LplNScNOW6GN8Gi15l5ilr3TPD6+Zsy+Xwcvj5YegxAWXhg2vHUWkKn1k4mk6p5BTV5bk3lyTj4GSeycEy+sbV3XDfO0P3h2Y2Fdee4mle7gTvPQte4va0Oz15Ftix2xFMSstQ1yL+biZsp1eR5YOQBEnkETZJmr02Op9YSegOPRqO4vb2NDz74oLKgED5be7j9sLS65p26qipq/hkPAMhKa485rpXFrm21ulU1z8GH4CPqgxeLXVt5I+oLd2o1gi0vU8rOc0uuegLqAXGzkf5/ZWVPC7+ZfpnyFjQeXK/X8d5778WPfvSjiPjuBta2Wyyasd/va1NgtWRVmqEGYUHgXBXgxJcKBjG5zpBTwetW0ux+a+JOk3caR6vINGeB3+UYvql0mSX+sumyWWmUOyT1O7fom7HoG7i5uYkXL17Ej3/849jtdrFer2utsxANJ/eyZZ3ZK0Czi85U091uspudF+AoWXc8oxmHBQ+LrS29WZYevwO48YbLdDheVmrTzwBBsjeknoI+NDmn6xHwhB5tYzY5jaL/5S9/+aau450AlhlLYH/88cfx6aefxgcffBC73S4eHh5OkkcQCMebauW19s1LaOsUUb3JecIPN9qMx+OTtfOwLj0nHpGXwTFV7Cp4nahTah2Gq89uN3sQ+lm4rh9xmi/SWF6nJfN16FoEXI1QD8Sc0ij6X//612/qOt4psCjGarWKv/zlL7FarWrdcVh6ileOiajHmBFx4tpnc9GzOjNuWu1y0wUveBMMzdCzMNkT0aYbbvDJxI5nnmugn5GvX6f/8rHYQ0CjDXchsoVn956/V05Ycs5CKyemTKPof/GLX7yp63hn+cMf/hB/+tOfKkt8d3cXEU8xLD9HRBqnaimOE3g6oQTH4HbdJsHzTD9OJOL8EJD28LPg0b/OU1cBT57RwUQFm3XG8WdBwlPhGn5WhuOBhzf/yFb5sfDbcUzfwPF4jOvr63j//fcrK8/lLa7ZZ5UObTvVLH0WO3Osy8k6ncrLz3zD47oj6qGHJux0GS38DYcmOjMwmyLL8+1Z/IDLepzA459ruy4GKlwPnjHJRlf6sXt/Hi7ZJeBGW6/XMZlM4vb2thL54+PjyYKRerNr7Vmbb3jWmHbx6Uw1XWgjW0OPharZb52WyzE8BqDD4VAl2NRjYZHqbDlukVXXnL9HrjbwAKKWP2uh5RBHd9DVHgQn8brRKPq+fXkcT0c8LdaA9e73+31NcCx6dWu17qwtpxH1nXE4s80rz/CeduzCqwh5bjuLslQm5AUw8Fkj4kQ8ajl1MOGHegwcFugKQdzQpD0Nek1IYHJpksuSWX9E3+7dc2gUPc8c6yOcvDocDtVrtlag1HnGLmtEPRsPtP7NpaisLKUttBFRJcfwc3brs04/rv1znK6uOMi8BxU8PjMGimwBUN2KazAYVO21bKV14OGZgtnOO67Td8cxfQMsRnW7ObON3+VsdETdanELK4uJBwEWPWfMSyvQwJVGJlzfy8SpSUeUzlh0KhotT2qeQl36bEkxTbhFPO1rj25GWP3RaFSriMDK60O9rtL1mzoWfQvacsoDAG42/PtwOFQz0PDMljPidCDQmFnjXi51RcSJ2LURSEWvjS6Z4LmUpq48C5qberJaOgTLi29qO7Auuom4Hq/RV8+DJI7JlQseQJzAOw+LvgNqBbMBIJtkgxsaAwDgjDSLTTvV2MWGmCOiJnYVaTZxhX+O82YPHE/baHFOzl3oIMKC59l9XFpjK4+MP16jjZmToPj8nN/QTjy18BZ+OxZ9C3wTaeytLj9b2Yh6Fl+Pqe6+DgDqGbD4mtDWVxYln0u9CY3fefKMJgazLL2u0INltbWsppYdx9fmJDwjEajuvGvyl2PRd0QTXFpPhwXE+5nwIiK1TJnYgSYGs7JW1+sttdNqiMCC1pBBJwCphefdf66vr0820MD3wavg6CCD7w/XnmX+bdkvx6LvgMblpQRfJlB+5uNlWWqNzdVV5ypAdkw8sxXENXIFgMuCHJaw4LPEoM4L4JIc5vFD7Fh0E12DSE7iGFizgHsLMkvPTUo8QGkjEugyIPYdi76BzIpkcT2LS29IjqUZtq6ZK69iU2FkCUGtCkAwXPrTeJgbe7T3PyJOeum1Dp8tvAnXHht8Zq49zrnb7WI8HtdCBryH82ipMqK8Z71px6K/EBU+l770Rswy94x2pWm9vyQ8nE+f2QKjky1zkXng4ay+DlYsKC5T4tjcD6/74ulSY0hs4py8WAZ3BGoTU5awtOgvw6K/AE3u8QAQUb4p8R7Qm5RFlnXzlRal4BBBl8/iqbjcXMSeAZcWWfD8rOfKYvlsiS7elJM/I5foNExi4WuyTwdA9X4s/nYs+gtRsbE1wutSo07m7vPvZZaWz6uvOW5Xy67bX/MS2aXzZuLRv8kacHheAHsUWXVAv6tS2U0HCW44yhbasODbsegbaHLHVRBs7dlVbjtO9u8sSacWjy0uJ+oQP2c1be4i1DBErag23SBRyc1EWkrT7HrW7KPJQM1jqKfB/x4MBrW2Ygv/Miz6DpRuosxis/A5IaV/0+aGQljctcaDCA8yEBpEpxNzsrIcC0vFrp17EfV5GLg2DhuyPIF6LNq/X8pZZKsIceMTdgXGQwcJ04xF/xrRUhOEj0fbDZll89kiR9SbbUolORV6dozMwrPY+JrQh8DX1zSgsJg5UYj3dMVfXmuAVxRiS88e1Hg8rq1cxLkOW/t2LPoW1BUt3UwqWP55m/uvxyj9W8mabrR5BdetXomKM7OuWp3A32TdcPAYIGAOIdjrQfut7uKDBT54nT5dc0BdfL1mC74bFv0FtAkfwD1vEj6T1e41L6AJMI6xsxZeHmC4hbepLKiWnvMJ2i7M1vvq6iq22+1J/oFDA/wu75enu+Lyvnn8uXF+DBgqeLv33bDoO9IWf2vWGdl7jsv5WOzulzLZ/F7pdTZphl13LnuBLMbOyoJaitRqRDag4T0cixfSxN/w5pzYn4931GFLD9AHwS3PmZU37Vj0r0DbDC9YdBW+WqWm8pUej8l+TxNnbOG7ZMpLXX98fLW+pZ4CuPvq6usW3Ng/Txfp1F58lOy0AmDOw6K/gCzWLgmQn/f7fa3/HO9logda9mqK+9lCZzE8/47GwPz7WblMPQE+PwY1rQbs9/uTFloMBHDrIXj8m3fI5aQoPtO5A6U5xaLvSBZrd7nhYKWADgTZTcx/W0oQKpllVqFnzULZtWXxftYBh+uCF6MlP7j3bOkhesTwHMura6+5Aw43kFzMJixZ+M1Y9GfQdjPxzZm9B2FoBl+Fz5Tif36fn/m19gSUMtz6mtfa0xp6KcOP5p1scNGkHy/QqRZ+s9mcDGB8Dp0tWNrOysIvY9G3UBKwvlbBl9xwTsCVSneAxaPxPMMC0dZdFbyKXwXKSTh11ZF5V48CgsdnGQwGtbo+i54X6eSaPC/WyW26PHmIlwAvLQNuF78di/4VOOfmyrwALd1lySn8ftsAUHLdS8k6fR8CZ7ees+RZeYwz+whjslwAX2O2YGc2n34wGJy0E/PEnjbhmzIW/SugbrFmtc/JMGduN/6tXgJntTM4HCj9rNQSC2vLC1/yQ936zKNR8fEgw8LXbjr+jDyXgHe04YcugW3Bd8Oif0XUqvLP9P0uxyqVydpiVa2lZ7E/D0ZwtbUjjyexZIk7HFuX/tblt3jqLjyaLLzQYyIHwJtUYq4+HqUlsO3id8Oiv4BSvKyCyzL1pZu+dI4mkWcDAZ+b218j4sTV5j54XtZaF7/UeJ0FD4HyCj08h15zHhnqHSCGzwQ/n8+rFXazPews9nYs+gvIrGhXi65/yz3leF9jf76Zs4agUtafByN1tbmsprvg6NbZuAZd9CKbT89TbLOluHj5bl1xCJ8PgwcEzktw6Qq7mXtv4Tdj0TfQ1gCiLbDqhmfWuuT6Z6/5OrhGncXNpYGI42h+7+rqqorfOYbnRBsDYfHiGVghR1fK0b32+PwYTCaTSfU5ebUctvKI3VX0bOUz1940Y9F3QAXPc9chgojmsloTWRhQOn8m+uxm1ww6ugF5gwndQ44HEN5pBpY927lGd7DRzT0jnhbXzGJ+zI3HeXkJLnbn2eojiYfdanXWn4XfjEXfQknsuDFxw6IVlf8uO1YTmegzL6NN8Hy8LHbX5av4nMPhsLbfHVt3uNwsSC6h8R58nFvgJOFms6nNh8/Oxbvk6CKbukV1tg6+Rd+MRd8CCw039HQ6rRJfEd+Vl/jfgIVVcsObElx6DU3C52Nz4k5Fv9lsThbKxEOntOLYvOusls+yzSk1lsf5edEMnTMf8bRvndbks40reW88d+Sdh0XfgAqe14WDwOH28kovWTYd8LJPXa+Br6X0UNS9Z9FtNptKVA8PD9WUVsxRh/eSxfClVW91bzlQasjRBTA4saeddyxwDSNcrjsfi74F3IzH47FyPTnpBMFny0xlryPOE75arlIMrmSi17ns3P/Oy1ZxAjJb9ZbFrrvH8nVpTiF7ZNWB0sKe2Q492dJgphmLvoXSLDlYfRb830L0/HulZ32tlQAVHfe7w+XX2jwExMlKhAXZ6rdqabMehmzxTRU9J/hY2OxtaemwVNEwORZ9A+ze688h+qauNf0b/fk5N2fp75qSeBzboyyXdeBp/zvgKayl/fCaXOtM+PycVSlY+Hp8XSFIXzuZ1w2LvoWS8HUVl9JNfM6/u1xLl5+pZ6Ki0xVuMsuLz6gCzEpkJc+l1IlY6kjUJKUKudSYZAt/HhZ9B3AzaTZe3diux2n72SXHUUrC5zhbX2vLbUQULW0Xy1pqQ9br48/VlKjsksy0+Nux6BvQm4mzzJnVarP0pXP8rWmzuE2DVpvQmpKJfG69jhKZeEvJzNJ7pplBi4Xy6oP/QXbzlv59Dn/rm7R0bU2fR2nKIZxz/ed8T6Xjvi5vqSekX4xFb8zfL6noL2sWN8Y8Wyx6Y3qGRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZo5b3B2/kKowxbwxbemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvSMfweFx02FjS2mIwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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6WWiVI+XGq1tPL1YC9Ln9pB4zS1tnm+UHkZjtHjoFDkOQfk/45ButtC+f+fZYX3pjd5wvz6kmTkvPTD1jYY63Pj4+TnvQMXm3LVvPWfoq0+n6zN7MuNMYajbuSM5L0vq8hMIDjtQys2wyj9LcwLtHkH4P5OJ0T1aS3i8IuR54SmlzvfN8r4/hzd7MtZc7z5ckt7LEFAj58pxm+EtyK/WdiMecQb/fTzp+Jgf1mcuqDVqgykIUIaer9++L2vzhCNLvCRKe1kzkzdXeSV7fLeddYLr8dP199lxWt9vt2nA4tNPTUzs7O0u712jLqn6/X5q4I+HH47GNRiMbjUb2+PiYQguzN000mjevygBLgLLM8ki4aLH7r6pFliGLXwiqBo0G9kOQfg/48pYnPdVqLMNx3JRvj2WLrJ/n7jvTSJxms5kSdycnJ3Z+fm6Xl5d2eXlp5+fnycqzjObLg9yaSy+10M5msxSHc/KOyK5Xr9cruOsiuV/EhFarVch5eJD4dPFzcwsChyFIvwO8W08rRhLTIvu5dr41VpaUGXK58EwM6vyUu3rFnSz81dVVsvLHx8fW7/eTWy/ra2a2Wq3SBpwPDw92f39vt7e3dnd3Z6PRKG3IqTichFfjjo7f7XbTMXV8JvLo/WjByYmK9LXM7a/6ORaA/RCk3wKWiDzhWdP2yjXfE5+bfEP3njp6ksJnt7UDLSW2cukvLy8T6bUFtdxpEfLl5cXm87k9Pj7aeDy2u7s7u76+tpubG7u9vS3sfmtmqTzHur8ShLLy6/U6eS5qjvHlzDJC70JYb+H3aVcOvEYl6euaPeXDqZ9zmW7p05+fn1Pii4IbX3f3wy9UEuOoK1p4Zsz1PS28tpyWW89YXmIZMysQXnvbi/A3Nzd2fX1t19fX9vDwYNPpNFl5Jd5EeCUI1aKr5J2unz3wZRZ91/FiOdc/1+sQ2B+VpH9bAYTXn+tn//t3eY7cv5vt91k82dljznlxk8nEptNpYQNLn9Wne0+Xnll73y2ne0ShD1tlSfirq6sUx/v95s2skFhTHX4ymdj9/X2y7re3t/bw8GCTySR5JQojKPTROZXAk5U3+574ORJ6L0WLGFtxfY0+J8Apc/MD+6OS9NRR1xUaJrFYLOzx8dFGo5Hd39+npJeI7917r7DzpTqfqc+59WZvRl6pVCaLmyO8yEjCs1Q4nU5tNBrZw8OD3d3dFeJ4lenkisurUGXg/PzcLi4u7OzsLHkSZpY+83w+L92dliRXuMCNNnIddl59GAq8d4dg9RY8Pj7a3d1dioPlCt/c3Nj9/X0ivVx1aumZ4efUWt8mS8KTLFTaDQaDpLTzmXoSXiRieKGknRasu7s7u7u7s/v7exuPx+n6Za3lKfhE4cXFhZ2cnBSsfKPRsMVikY27mdWXp6Kafm6TDZ8/0c8vLy+FCkQsAG+HStL/5S9/eV/X8VFBVme5XCYrPpvNbDKZ2M3NTSK8CEMrT408s/K+P963idLa0SJqEAatbZWF18BJWV+586PRKJH97u4ueSrM1JtZIqHOK8Kfn58nK9/r9czM0nl8TzwtvFlxQGe/309Zf8p3fYWEP6uM51uTA4ehkvS///3v39d1fBTQwzefz63ZbNrnn39uP/3pT+3s7Myen58LLj316dzpxSfyWNPXv/lzCiKKavDS0kt4c3l5aVdXV3Z1dVVKeC02su7j8TiV5G5vb1NoMplMUlONhlJK7NPpdGwwGKSFRoQ/OTmxwWBQiOVzE2xV5ttsNum6SHgS3/f0U0tAkvuKQDTfHI5K0v/xj398X9fxUeL8/Nx++9vf2hdffGGz2czG43Fy00V0uvNel08337ustISy8HJ3KXXd5tKrbCbCKzuvGvzNzU3K0it+n0wmqXWWMTzHbKkPXxl7nY91ebnkjL+pKdBnkzvvd9bhFF7dg7L+e9+S7Ikf2B0R01fg4eHB/vrXv9rt7W0ilNnr2e5mRXmr1+YLIgOtpH7HiTcihwhPF/v09PRVll6NLBpoOZlMkmVnDZ6E97p6kVDk5Lml7mMsz4WMjUf6nEzUcXcd3+LLSoXuo47Pn7X7Lc8V5D8MQXrAl/d6vZ599913NhqNEjlltdim6nXtuYeQ1o8/+950Trwh8UR29cYrEWb2prFHLv3t7a3d3NzYt99+a99++22K4VmS06LEeFzCH56b5+x0OoWEmy9LclCGFiNPeHb86RjyNrwl11fJcf0C4zP8+j+Icl41omRXgfV6bc/PzzabzZIl7vf7BRedpKc7yqQcf8eadW7whYQvIpwIyK2oPGmosnt4eLDb29tEeIlupKfXdlQih65fn40hBQdw6JwimS9HsptO16ZjsitPCyatu88LMCei3/nuQ+ZJfPUjUI1KVrNRoq5gRlxudKfTKZCGJadcso6WVJlsluJEdBGc02u195zIzmEVtLRS2UlhJ8KrrMhWWV67mb1S+vH8JDw1/BytJbUhm2OUmxDZOXpbpTz22ZsVS3UkNHvxvdaBbn5gN9TblO8A6sb1AJoVh1B2u930Hq+u8+IUvV/urppXSHZZWE8U3zyz2WxSt9xkMkmCm+vr64JL//T0VCgp6vrNrGDlVRok4ZlhV2VA+9zxxRwBB3vQyqtGr+QdLTPvb1kyr9VqvdrPLzepN1CNIP0WiMgim09OSYOullFPesbtVdadO9D44RR+FBW75ebzuU2n01cqO7n03IqKiTZ91UKkra+46KgyQLGPCK8pO5qNzyk7VBD6bbEp2KFHRJIzdmfXYqPRSPP7uOOO3hfYDUH6PSBXngo51cmVbOLYLLn0FKYodufOMzqOrDsHTrJSkOuWk7TWE9679CQYB3D4uXokPLe8Uied39SS4iQuir4ur89TJrRhLO+TdrTg7Xa70O/gW5ojrt+OIP2OUAxMlRwFK4r7Pek1Hppurqw8N5SkNeTGkiR8rnlG4huRXhae46t92ZCkl0uvlz4P3XDlDkT48XhcECkpnqdmXwuKPo8f5qGvuTq//o0CJ33+ZrNp/X7/1aaaXByC+NUI0u8A1tOpVlPtXKTnvDyVmvTw+zFTnDFHV96XAj0xPOHl1t/d3RWm3sjy5ioN3BCD4h8KfnyZjAq/0WiUziVVn5mlMiJn4bO5xudGfInO7PVCwNZj/b3afbV99mw2K4QCQfhqBOl3hFx1SmOlhR8Oh8lt5QQZxv6Mb5Wgo2UnOTgZliRQEk0z7Siv9S49vQ0l4uh6+0YajdhSqCIxktx6Dc4U4Zkg1ALHcCZHeH2eXL3dW2pZdr+QytoPBoNXzUKRyNsNQfodQCupMpSScOfn5zYcDtM8d46HYtyc0517Ynhy0MVVAo0Tb3KEZ0KNZTmGGlq0pOVny6wfjKHziuiy8NPpNLnVWqh862zOwivv4Unvwc+uxU6hTaPRsOFwWJjcyxblQDWC9DuCpTe/DbQy+Iy7zeyVqyuyc0eYMsvOh15Taym+UT2ec+1kmc3ejKw2KyYgtVipW+8HP/iBXV5eJre+0WgUpvYycTcajQp71XvdPnvmuYAxXvfz8Mvq7PQIJACiRl8LEGXFPi8QyCNIvwfkvnrrrXq2SlvM3ItsInkV2X2nGcdzqR9eM+1IePbza3Eqi+H9qOzLy8tk5ZWM9Jl6kYsxvM6l5BzPxc/FJKL/TH5suO6x7ocWPYqQhMFgkLwbT/pANYL0W+AHQ3BuHbviuMOL/o7vKXPhSQomtjhiy8fwsvKcaeez51ygKK/17bJKKspTkfBFMXwZ4c2sUFL0hOdnYzzuR4bnZgvw3lCFp4ShmaVSoaoHZccJvEaQfgeQ8Ox559BK79J6l9c35vguMrbhci87Zek114498YxnGX5wEIZ0AWyVpZ5fmXqp+3xZjll6v7hQ1cf8gQir6bhm9iopx9mAOQ099fd8n6D7oySe9xgC5QjSbwEJqzo942WzYiON9woE6sq9EMWP1dLDLNWbLK52oJHlnc1mKa6WVWfvOlV2moXPnW9Ui/dDMx8eHgrnUr7At+Ly/nhtvpkV5vhTrchsfa4/Xj/7Pf7YkedzAxHP744gfQW8cq1qH3a2qnqILL4MldswQxaMUlcl0vSz3GxZeMlb2cZKaa8I7/X0ItZ6vX61+YUqAiyJmRW7Bv04a3bGecKT7L67zmvtmdWnolALBX8O3f3+iNZawPfTszXU79Iql1gEptpMdW66uyw/KRuvchg3zJCuPPc7bpChBcZvgEGyq/bO7aqZcOQGHBT7yNJrgaHYiDkO5ihYThPhad25kQf/Xsfk4uCtu98phwTn9egVqEa01gIcEGFm1u/3k1JNhNJ2Tkp8KdEkovs4l8Iaue6+YYUbZvguMj8jn1bSb29VNXTDa+l9WU4hBMd6szuPcTvzBrL2ntyUJHM/AOY6lOAUfL88RTnemjNR6hOkgWrUy5RvgXcRl8ul/fjHP7af/OQnqT4vUimmXSwWBVeZk2g84UVubZYhkYsfsslJNDntvEp/bN5RNp5DLLnfHKfsyKr6shwJT2WfwOEfJJqSe0y+5SbqmL3uOpS4R/fP7w/A+F/ehq4jN8UoiL8dQfoKXFxc2Ndff20///nPkyxV9Xm5yavVKhHEP3heXCOLyn3gPeF9okvIzdFTYo67z3DEFbeRZvNMjvC8HoUQVPZp0SNhGZNLGCQPgqU26v/1t3TV9Vl9jsN32flmIcmYfUdiEL8alaT/3e9+976u46OArPNsNrNms2lfffWV/epXv7If/ehHaVKMb//Ug+rjSpatfEsq21JzfeF04ZkZZ++7rLtektKy3ZdjtXRc7knvFyAlCL2eXrV/Ly7iQqJje2vNYRkcCqrj6t+oEfAbg5D0bNvVwsfNOoPw21FJ+j/84Q/v6zo+avjutul0mtxyWkVaZv6N3wMvZ+FJdhLCq+rk0pPsdOk94UVM6vdJeG5p5UdesZ3YS4k5o09xuxY47W2vxUMLoUqEXMwELpC+R173RMegnJghTMT1u6GS9L/85S/f13V8tJB1FmnG43HKUGvTStaxRRoly2RZRTYtGpKhemEJu+JkUTmlh7vHcnimKgt8+FnvZh2e7bEsAVK3T5WdrHtuMWFSkC8tBmZvcgE+PmeJj+EAxTsCtQhUF+qzh6XfHRHTb0Gv10vZ6Ha7nXrL2Y2meJ0CEpFBQhuV4agVLyM843c/bYclOf2Oo6jMinVv37CjshzbY73wRtdCCbHce+Ut9DnpkrPi4HvbZbFZ9mNtnok/3kf9jRYOP5Nf1QmOFAviVyNKdjtA1nO9Xr/S2SspRveYFpCJKc6Soxutrz5h5odvcAiHhm/4uJ1yVTa3SOQjK+8Jz7q5WXHDCp3DW3gvKiLhpZzzjT96eY0+LTwVdmwg8lt1K6TRtl6RxNsNlaSPG1jcWJKZa85ul+vvdeC0Xn6Ao5fqkmSMnzlmi1bdZ885NINEYh7C5xQ4kYaWMtc3QA29knZaxPjZc9l2xeEcn6USom+79Yk7feXOO9JOyNJHPL8fKkmfk5TWDTnXm2TQe9gs45Vovt3WzAqZa5KDrbvMUnPKDs+7XC5T6ZDeB628BEAUAuk6udiwJEaJrY7Jcp8su45VVY+nqpFjsJVv8KIo5hb0td/vJymxSpQUSrHPIVCNiOm3gNaGxPekMHs9AIPHkHvLRJ3+jXE8h2yI9ByWafaG7GZvst6+w43JNUp6RdbNZvPKnfedhDp+rgOQ1j23iYbftFJNPsw9UKfP+6gFUdfFRCYHeOqYDBfC2m9HkH5HsAbPrju++O96sCVE0YPsXVefuGP4IFefmz0qlFitvt9pZzabFfTvJCjzCfRCzOyVRfftv2aWvAdNydG5y1x65iR8ApKlRCUCNQJLvQu+Y8/MClJjZe3Ldt8J7IYg/Z6gxWdiSoQVMfXwsgOPIMn0wFPe6vvzRSxq/fkyK87Vk7Vn15rq/hyywaYdZtVlfRnDk+xl4hvf1ptr5yXRqeLjPWQrs6w8t/pibiAs/H4I0u8BPVg+067EG6fgUl7qRSYkay5P4MMG1rxz18KfzexVeCHwPH6iD7vdmP1nQtJn6csIrz0BuNU1E24qe/J8TIBq4dECpUWEY8O9Wx/YHUH6PSGr4t3xbrebEmOsP4t8bLXlosFSmU9GsT2VxyOpfVjBMIRWPJcz8KU4EVDiI4YTHO7BOryugQ1AmsGn+XtnZ2eFDTSWy2VKSMrqs7lHi2az2SyULTlJOLT2hyNIvydydWdZelopDoSoOgYtFbPysrD6yrHROXmqJ7fcXm6rxZ1n/IDO3MQbWmDp4X1/AGP44XCYCK8pu5yn3263bbPZ2GKxSFUM5h5YPlSikY01fufeIPxhCNLvAWby2VcuS6+HloTXA+yTeHwJtOIcFVU1MprZf5bL6HKzXOa70kR4LU4cgMGhH7wGlhp9T//Z2ZldXl6mmfqqpWvwiPIDuk86PuvzLNVpQfHWXYipOfsjSL8naJXLBkIo1lX8KkKxnVQPKh9Yn333pCubQMNJP57scotZMmNFQNfLz+ZLdMz6630szcmtZ18AFXPSxovoyt5T1OS7+3SPqf3X9dGTyg3YCFQjSH8AfPmOsbOZpYeaULzPB1Xwi4EnnEjHCTS6Dp8XYH1fKj7FxNxCi4k7szc1cy8s8jPoaIV9b4Dq5/oq6TBlsvJU+v2+LZfLggiJLr6+svzJRTEX6gR2Q5D+LcDYPJeM04PLnxnne2ufe6j9e3xoIE+DcTtn+nGTTO8ie4+jTEnI8zLxx8XFv+iSK3eg8/jSpB/OwQqCwg9WEej5+EWJTT6BPIL07whMzNFK5ax4zi3VV2+5vLXTvDqzN623tJYkuUpbnoB0k+lZeOvJphnlJtico8y6lwrvOr6KOQ0vcNL16Xp0TlUQfJ8DQ55ANYL0e2CbG0lrKHJUyXRzmX0eR628JD5LdbKUfostfZ/bM4/XQavua+U6h0KGXN4gt/88zyGL7YdllC0wevmEpax9s9l8NXwkXPz9EaTfE7mHy9fHmeXnVx6j6jj6fr1ep6y8r897vb6sriciXXl2+VEUU7bFlMIGJvrYOKSEoBYC6v5VhhPhWaLjv+de+nddL/MlnU4nzSTgTMEg/e4I0h8A737zqyydyCrrTPEMVXTMwvsXXX+/UNAKs/7uE3Usx3m3mQ00vg4va65Ym9et8/savwaKUB67Xq8LTTZaFDRNKPfi3AEtUvI2ut1uYRBJ2Tz8QDmC9G+BXN2eoBvNzTCkevMkNrNXsbB/H9/PkpbIz5idffaU8ebKgqzB87je1WebsK6N8/f0Pi4s0tUrey/SayCnhoTqJUJT5qvr7/V6r+bohaXfD0H6A+DJ7htf+B6z1xaIAh6z4hDKXNde7vyM+9mgo/PJujOm9mEC42s/ooqZeiXxdDwdg1UI/s4Le7TRhkqZIr2ft182gtvMkmxXxwyyH44g/YHwtXq2hDKDz/ebfU/wXDnMrNh5x2PncgO+bEehDUUwPlOfS5yxTKifeU06pqD3SzffarVejbtikw43y5QiT3vnifDaZIMDQ3V/WK3wJbrA/gjS7wkS0FtnSkTZYEOCqu3Wt9uS4CRymbX3x2Y5zS84nuxMCDJfoGPk/j2n0mOZrNVqFeS6TMjN5/M0SFQLhR/FLdfe7zXPciHvFRfI0OHvhyD9HvCE91aedWLW6HN/w/FW/hw5wpe10OasHs/rLXtOG0CUueg+u+5j7kbj+5n0TK5poVgsFim5KJGNSM94nsk5HpeqQ988FO21+yNIfwCqrD3daT6IGhrBY3ira/aG3N71l+dQJd8ts9g50vvjC0rMeQWcxm5RGuwXNZ8T4HUul8u02K1Wq8I+ABLc+ON6ibPKkRQeRYvt/gjS74mcta+qzatsJzL5+XBl1lYgiUgyWmsm0UhwxuiMhcssPf+Wbjyn5nCEN0dvsUlGf0tRkEIO/Zuf2eelv1pAKT6Svl/Tc7izTc4TCuQRpD8QOdJvc+llqbUQmL3R5/s4mm69X1R8/d5bc7nHuQEc7PjjcbhgcMYe+919ll/XWaUn0LnY0OO7Bv0WVrpXFB2pkYfTeGJG3mEI0h+AXR+wXKmuDL78pO8prPGiHv4diU3y5rLzWhT4e9bVVVbjME3WxHlNWrxySc2c/p7HYMOMP56fgHt+fp769DX+2m/jlatyBF4jSH8AfPzsXWjBE3GXMlPufXqIfelM7992jTmisV3Xd9fxPVok/GfwmgRO6GGDjxJ4bDdmjZ/HYMJOm1SK8BcXF3Z1dZVGcGnmfSTy9keQ/i2Qq3f7uDuXSCMOeVh91p/lQf7MEILuPpNxfltoinVyn4vxuwhKN1wJtuFwWNgCjOIe77UowcmW3cFgULDwn332mV1dXaXdbXJWPoi/G4L0O6JMGSf4GFlf6bp6srKmXjUuO9fQkyvdyZpLUEO5LxcGWW9OrGGTC11+1uK96MdvvaWhGSI+N+jYbDZprz8OFdVnFXk1Hkv71V1cXNhnn31WID13tmESL7AbgvR7giIaxq65WXjKQufCAV9a87/3JTUv3sm5+T5+Z51dhGWIoPPT4vuWVyYVvRuvuXgalcXNNSm91SLDzTb1+eQpKLnZ7XaTW685e3LrLy4u0gx9Hj/i+f0QpN8BspbMxKvBRQ//ZrNJmzfwb6qsf071VhbTV1l6fwwRV0Sez+eF4RY6t2rnZR4LPy+FMZyJ57fOlqXn/nKbzSaV6XQd6v3nNtm08hqweXFxYefn58mtz1n5IPp+CNJvATPlcsO5jbT2lGu324WJrma7tXr6hcG79kTO0vuSGUlP/bsaXKoIo599+UyJNRGV+8n5DS0Ua/seewp8JMiR2EchQ6fTSTvjcLOM09PTwry93HSeIP7uCNLvCJakZPGOjo7M7HvCDwaDV9n73DH4lch5BLm/y32v9zNk8P3yanCR3n00Gtl4PE6z9Nij7ktyXhyTm3hLwqu5RtelBYgbX3IIhpmlujx3p9WLOQIO7vCLVhB/NwTpd4RIJjeXllBbWvmas//73FePXRYNwWfs9fee+LKyvsllMpnYeDwuyGFJer+Rhd+uKqeO4x58uhaWAP0cfX+u3Nw9Ep31/7DyhyFIvwMY03N0kxJb/X6/oDv3f+u/3yXptK20V1av32bxNZ3GN7s8PT0Vxk+ZWUrayRvgJpIke5m7ncszeG0AF0+/ay+33vKty7kwJ7AbgvQ7grJY/awHlWW2bZY+92/83S55gG1eQk6YQ8lr2b71vsON1leWNzdhl2OxScQyEVNOs+CrIWWtszmiB+H3Q2PLQxaTCoAyUm1T25U9lH+Ph9UnEnOqPD8qi0o8Vh+0qHkLXOZm+89UVrnYp0JRZdGD7FuRvUFB+gNQlXT72FBGOKr0yqTCXktfZXWFHOlz11OGXcgdZN8ZQfp3iV0f6A/5gPrrqdIMbLO+uZ/1u0OvJ4f36RXVAEH6vxc+Zkufw9suWPv8/tB7EyR/JwjSBwI1Q5b00akQCNQMQfpAoGYI0gcCNUOQPhCoGYL0gUDNEKQPBGqGIH0gUDME6QOBmiFIHwjUDEH6QKBmCNIHAjVDkD4QqBmC9IFAzRCkDwRqhiB9IFAzBOkDgZohSB8I1AxB+kCgZgjSBwI1Q5A+EKgZgvSBQM0QpA8EaoYgfSBQMwTpA4GaIUgfCNQMQfpAoGYI0gcCNUOQPhCoGYL0gUDNEKQPBGqGIH0gUDME6QOBmiFIHwjUDEH6QKBmCNIHAjVDkD4QqBmC9IFAzRCkDwRqhiB9IFAzBOkDgZohSB8I1AxB+kCgZgjSBwI1Q5A+EKgZgvSBQM0QpA8EaoYgfSBQMwTpA4GaIUgfCNQMQfpAoGYI0gcCNUOQPhCoGYL0gUDN0N7y7433chWBQOC9ISx9IFAzBOkDgZohSB8I1AxB+kCgZgjSBwI1Q5A+EKgZ/j9F1MKG30RJswAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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22dpnEfhzXjfp9UwbrrHfZBXzQUn/ApCZb/vPCXspbtqHW7bYXl1doVQq4fj4GJ8/fzaz60zePUV4p9MJv9+PTCaDQqGAr776CoVCweLW2+fxaeVbrRYqlQrK5TKazaZltFUeXyruxONxxONxMw/P50hyyiYeTtjJOQX7ANG06zxtiEkxH5T0fwVMs/SzEr7f75tM/enpKQ4PDy1u/azttqurq6bV9s2bN3jz5g1yuZxpwpkGltdqtZqlD8Du1tMd93q9JpaPx+MIh8Pw+Xzm+PYhG9mmTAIzwSfn6JkoZGzPv5PPVdIvDiX9C0ASeBZrb0/ayZ56Ev7k5MQQnom0fr8/s7w1Y/nNzU3s7OyY6Tlm66dhPB6bPEKlUnlQbNPhcFgy9olEwlh5Cmly/v76+trSRQfAWHSSmGPIskdA3iT5N3w+wwV7E5RiNijpn4l5E0uPxfDSwn/+/BmfP3/G2dmZEb6cJY4HfrLyoVAIyWQSuVwOmUwGsVjMtMY+VOYaDoemzVf22EvS0yX3er1GSDORSJgZfLbW9no9XF1dmeEceih2HT2KZtiFMO2eEYU0peCmWvrFoKR/BuwEBqzad/YuN/vfShkqCk7Spf/8+TNOTk5M5pz18afgcDiwtraGWCyGTCaDTCaDeDxu4mz5+pI0d3d3pqZerVZRr9dN/mBaXZ6juel0GolEwlQDJpOJZSKv0Wjg6urKeCmyVZkTeSQ+XXrpwvOaSnESaemV+PNDSf9M2EkP3Mf009pqZQcaRSnq9bolS390dISzszNUKhW0Wq2Zu+6AnyxoMBhEMplENptFOp1GOBy2xPF2wk8mE9PmW6lUUCqVpuYPaOUDgQDW19eRTqfNTSUQCMDlcpmZ+3q9bm4e9qk8WmkO6XAqj0rCt7e3lrid6sJ20ivhF4OS/hmQjTT2DTfyq/25bK0lOc7Pz3F8fGyJ4TnYwmm2WcMHTtFlMhlks1kkEgn4fD5L/GsnC5txSqUSzs/PUSqVzM1G/o106zOZDPL5vCG91+u13DxqtZohfafTsfQVkLC03mtra1hbWzPz8/bS3ng8NuU/qd2npF8MSvoFIUlMIvPnhJ30Mn7nfDq31BweHuLk5MQM0jCGn3ewxOfzYX19HZlMxujdra2tPUgQuuPVahUnJyc4Pj42K7LkUA3d+kgkgnQ6jY2NDaOpFw6H4Xa70e/3zfYdJgKvrq4sfQUy/JGk9/v9cLvdFg1Aekyj0ch4A+raPx9K+mdAEpnWmPVvu4otFWB6vZ6Zljs7O8PJyYmlvZbddrPG8ITD4YDP50MsFjNu9/r6umVTjRwIAu7r8vV6HScnJ/j06ROOjo5QLpfR6/Usmv0ul8vE8Zubm9je3kYul8P6+jr8fr/JT/D9UdWn1+uZIZlpdXvG9JL09u48Tt1JDX0l/OJQ0i8A+zgoxz1JeqkZZ5ePbjQaZlru+PgYZ2dnOD8/N4mzed154CcCeb1exGIxM0XHEh0776YlEvv9Pmq1Go6OjvDhwwd8+PAB5+fnxrUn6am2E4/Hkc/nsbOzg52dHWSzWUQiEayurppEXbfbtWj3yfdDbTzg3spTypuWnM+x5z7s+vmKxaGkXxB2gUp2rckFi3LDLPfPUYHm7OwMFxcXxrrTIs6asCMcDodpt+Ve+d3dXWSzWYRCoS8acUimm5sbVKtVHB0d4bvvvsN3332Hw8NDVKtV9Pt9S2nN4/EgHA4btZ29vT1sbGwgHo/D5/PB4XAY0nPlFiWySXjpZciFF1Tbofw18yMUx5TWX+vyL4NHST/PBtS/JciE3LReeiaXqGTDOfPJZGLcVS5q6PV6FsKXSiUUi0UjSNFut407P+8QCd3jSCSCjY0N7O/v4+DgALu7u0gmk4aQPGeSqdvtolar4fj4GO/fv8e3335rrPzV1ZUhKktlnNLb3d3FmzdvsLW1hWQyaZpx+DmhR0NZbnuIIrvwaOFJ+LW1NbjdbhMqSUVd2Z7Lrzp0szgeJf1zL6rdrXysbv1SrzHt98Bs72VWt3EwGKDdbpv98KVSCd1uFwBMacnhcBgBiUajgWq1ikqlYsQlOWu+qAoMCR+NRpHP5/H27Vv85je/wf7+PvL5vKmbA/ckub29NVn6o6MjfP/993j//j0+ffqEs7MztFotC1E5NptMJo2I5t7eHjKZjHHr5fHlZl27Nj3DBNbmmbzz+XxmaaVsuZXxv5w+tLf1KubHo6R/qEd7WSE3t5ZKJZycnJiM+/X1tWlPZTKPXXatVguNRgPNZhPtdtuS3Frkg8vXiUaj2NjYwJs3bwzhNzc3LcsqSHZm1S8vL3F4eIgff/wRHz58wNHRkRHIuLm5Mcdn2BCLxZDP5y3iG8wVcBkGYF1oYSe7LNFxaWUoFEI4HEYoFDJCmmzflcfjjcTpdJobJIkvwwbF7FBWzwhOnzHrfnp6ipOTE5ydnRmBCbqulMTu9/tmnTRr1Yzb543dCRnD08J//fXXePfuHba3t82yCoYgdq/k48eP+PjxIz5//mz6AVhSk6DmXSqVws7ODvb29rC5uWnq/jQIsjlJWmGeq1xRzbXUkUjEjOFySIeuPXA/qCOFMwCYxRe67OJ5eJT033777c91Hr84pLXg2CfVXQGg1Wrh8vLSUmIrFoumL55DIqwv393d4fb2Fre3txYxx+d8WJmlZwxPl17OyVPzjvE1z/vw8BAfP37Ejz/+iOPjY5TLZbRaLfT7/S9Kg3ydeDyOjY0N7O7uYnt726Kcax8colWWtX0O39AzCQaDiMfjSKVSSCaTWF9fRyQSsVh5kn0wGJjrx1Fi/ow3TdXIWwyPkv6Pf/zjz3UevwrQHe71elhZWUGhUMDvfvc7xONx1Ot1s5P99PQU1WrVMpBir4HL7rvnkp1gHT6fz2N/fx9fffWV2U4jO++kku3Z2Zlx5T9+/PiFwOW0c2IrLxdisDwXCoVMvoLvkXV0aZVlFYNbb3juyWQSmUwGqVQKsVgMPp8PLpfLnAu7FeWDpOe/5QINxfx4lPT//M///HOdx68S//Zv/4ZSqYRcLodWq2WWPTSbTctWmYe6w2TcuQhkqypJk8vlUCgU8O7dO7x9+xabm5umQYYWvtPpoFwu4/DwEB8+fMD333+PT58+4fz8HM1m80kRDpkgZM2frbZyrgC4X31FgtJFZ22fHlMkEjEDOqlUyoziyi02FAKVFQBJev6O+RB5s1XMDo3pH0Gj0cC//Mu/IBwOmzXNjMvlXPtfw+Iwwx0MBo0yTSaTwdbWFvb29rCzs4NcLmcm6GjhO50OLi8v8fHjR7x//x7v37/H58+fUSqVzLTeY5ADNblcDrlcDolEAoFAwBLH8yubZ/r9vrk2wE/z/LTyHMFNpVJIpVJYX183XgMTnnTv7aSnS8/n8aFx/eJQ0k+BdF+LxSKKxeLP8rqyLZW6c6lUyrTVZrNZCxHD4bBJ2rH+Xi6X8fHjR/zlL3/Bn//8Z3z69AnlchndbnemmxNzBul02mzAkW49j8Hw5fb21vQicF6Ax/H7/QgGg0YLn8M54XDYzPYPBgMzzccqA4U35A2WpO/1embphT0ZqlZ/NmjJbgoY28+qUvMSoN5cLBazkCSbzSKbzSKVSiGRSCAWiyEUCsHn8xnFGS62rNVqODw8xPv37w3hS6USOp3OTOdAXb1EIoF8Po9sNotYLGY24BB2tR/22l9dXeH6+hoATBI0Ho8jnU5b5u5l7oGxOTsX5YPxO2v+7PaTPQ7cda+YHY+yetGykmJ2cFCG46obGxvY3NxEPp+3kCUajSIQCMDr9VpUY5jVbrVaOD09xQ8//IDvvvvOEJ5NQ7PA7XabBB5vNKFQyLLnjl8p0d1sNs0YLXv2nU6nyUGkUimTuKO6DvsY+JDSWt1u1yKxxRIdN9ZeXV0Zr2IWgVDFl1hOUz4H7BbuJeF0Ok38LAUst7e3kU6nTezL5hX7aij20LfbbVxcXODjx4/4/vvvcXh4iHK5PLOFJ7j6KpFIWLLrdreeu/Wo9nN5eWkSnOyX9/v9iEajZvlFNBq1hAl02dmvz14GaeVJepYCuTyj1WqZZqJlbRV/DpT0T+CvZUlk8ws3z/zmN79BoVBALpczXXVSMEJmzfnvfr+PSqWCw8ND/PDDDwtZeB6PHgfDiGAwCI/HY8IdSXhuz+WUILfuADAttuFwGJFIBOFwGIFAwJTw5H56xvG08IzXWf6T9f+7uztcXV2h0WiYsd3hcPiFFJjicSjpf0awYYVTa5lMBjs7O3j37h3evXuHQqGAbDaLaDRqEl0S8gbE5p9Go4GTkxP8+OOPpizXbrfnrijwnGiZmWxjvMy2WA4ZnZ+f4/DwEIeHhzg7O0O9XjdiF7T0wWAQwWDQ5B+YI2IML136Xq+HXq9nKcmxLCeFShhSSBmuYDCoI7dzQEn/M4D1do/HY9Y553I57O7uYn9/H2/fvsXu7q7peHtIplrG8ZS4uri4wKdPn0zjDYdm5gGbceLxOJLJpEXzjoMug8EA/X7frNmyi3f2+33ThMNxWb/fb0Zm5c2DHXckvBzFpVQ2E3y08Pxeincyj8DEpmI2KOn/iqDiK0dII5GIEazk1Nru7i42NjaMyg3JIZNmds09bpUtlUqmAef4+Bi1Ws0MzcwDt9uNcDhsNs5yoIYNMdyc22g0zF496vlVKhV0u11Lnz3FLpmH4Htitv7m5sZk4ZmYs++ep3WXCT8AJmlZLBZxcXFhBowYOiiehpL+BTDNrWRjSjAYtEhYUVuOnW6pVOoLV5rHnFZ3ZhxfrVYN4Y+OjlAqlUy5bB5IK59Op5FMJs3NhyWydrttWnpPT0+NeCfr/3Tr2WNvX1pJ686QhMo6LPMxnpfuvBynlVN7cqMuRTw56mu/Yaq7Px1K+mdCijwQlHYOh8NIpVLY2NjA9vY2tre3sbGxgUwmg2QyaUpY9gEWwv6hZa2aijc//PADfvzxRxPHS/XaWbCysmJ6A0j4UCgEl8tlGm46nQ6q1SrOz89xenqKs7MzXF5eolqtGqIydJFqOHTpGYrQpecSjHq9buJyLsiUXXbyIfMTdPErlYpRHup0OprFnwNK+gVBd9au2yZbT2XcXigUsLGxYWlQIVFmsUh2C88W26OjI0PAeUDCJxIJ0wAUj8fhdrvR6/VMTbxSqeDy8hLn5+dmbTUTaKPRyFwDvm8pUU3LTreesmHUFyDppXKQDGvsrj1BMRBqFNi38Cgeh5J+QdjXMtHic248k8lgb2/PDMbI0VQ+n7BP6EkwiXV9fW1ELP/yl7/g22+/xcePH42Ax6ylRarXMOxgfiGTycDv9+P29hbFYtEQ/uLiAsViEeVy2Uhz27feSAksrp1iDwHddQqDss7OWJ7xvFTskUS3C3Lw31yqQUESnbibHUr6BcAPuN/vN80zjF+pWZfP53FwcGAIz1hZNtg8RVS68yTg8fExPnz4gO+++85o2s0yRAPcz8hTxIIxPFdfRaNRTCYT1Go1U4Mn2ev1utHyY2wuJawZzlAKi2OwdM1lbqDVapkSHV16ZuztEliPTSjK2ftF5MaWGUr6OcHsNFc0J5NJRCIRs6DB6/VaZKxIeLafAtMtu0xcUfHGLs3FWvynT59QLBa/2EIz7VxJRJ/Ph3A4bFpj+aCi7WQyMSO55+fnuLi4QKVSMUq9dpFLejZSytrj8QC4VxmitaeFp2Vmlp4uOUuC/J7X4jEiy+ulIpnzQUk/J+gaMxPP7jnG53JKTY6+MnaflpGXdXA2qXQ6HdPmSo18Zs25N/4hC8+bDz0RuvIc5JHjrW63G7e3t6jVaiiXyzg+Psbp6alR1rGr2krNOxnHcyiH8lxcWslkoHTFOchkVxqyd989ReaHYn7F41DSzwEKRW5sbBihyFwuh0gkYtpVudE1Go1+QXjgPi6Va6ppDdlpVqvVzKNcLqNYLFqSaPYNsDyuDDvYE0CCU54qHo8jFovB7/ebLD1XaTFDf3l5aVmcOS1elq/HISAA5sZFwnOtFb0FDnGxO5G5EQCWiTp71v4hKOnnh47WTgGJJGNFSkFvbW3h7du3KBQKRv9duu6SeKy92914Ep0ZcmrnVyoVlMtlI5XN7DQTX91u16IYw3NlbTwYDCIajZpddrlczkzLxeNxk3+g6IbM0nMJh9TOs09ZymUVdOnlxlmKX0gLzxq8LMfRS+C6Krr10yz9LP9XWo+fDzpa+wicTqdJfsXjcWxvb+Pg4AAHBwfY2tpCIpEwCx+A+1idcS4HVeSH2L6PniId5XIZ5XLZbHqlJJdUiZFWUr6OXB2dz+eN0AYJL4dnWDuXlrhSqaBYLM60GpsVCt7YOBTE82NZjlLfUu5K3qjkEI8kPRNzs5D+oWUkKqbxOJbTlM+Iu7s7I021vr6Ozc1N7O/vY3d31yTAGJPKTjKW45iQA2DaT9nddnl5aUQ2Ly8vUavVDNHpDkv5Z+D+Q87tLxxfJdnZACTJLgU32Et/e3trsumyyWXaTnoJ6dKvra2ZJRVc7EHJb1p5HsvumZDwUgyTYQ/d+1lceyYTdYPtfFDSP4J0Oo2///u/xzfffINYLGaUaNPptMlU80PLDyktHkUq+Xtuc+UOeC7KYKwud9HLGwghrbvH4zFz71woKXXz6IFIwQ25GoqtsLz5FItF1Ov1qVl6QmbqSXhaeXuWnosrp6kA2y0+V1jxd9Peu/xbgltsOb2npJ8dj5L+H/7hH36u8/hVgF1k19fXcLlcODg4wO9//3u8efPGdNBFIhH4/X7zgZXEpisrB0YoKcXS28XFBU5OTox1bTQahvB2yCQdrTuVZbmdlglFNtjEYjHTACQHdJgYGwwG6HQ6qFQqOD8/t8zCP0V4Ts+xIiBLdLTwJPxTq7btq7z5lQnOp1x79huEQiGzbcd+3RTT8Sjp/+mf/unnOo9fBaRbLkUlmPhiPEv3VLqkjGWlYi7nxa+uroxVpYx2tVo13W2PafHZs/JUqd3d3TW75TY3N81CyWlyWvw6Ho9xfX2Ner1uvI2Liws0Go0vll48VBUg4XlNKIzJstwshLeTnNdxlvo8cJ9UpQwX5wUUs+FR0n/99dc/13m8Okh9NwpLcL6bHWeMm6kjV6lULL3rj22qtVt3jr5yr1yhUMDOzo5ZGc2EorTu8lwZYnAe/vT01NT8WRXg6/KrdOlp4QOBgClDUuqKWXq69LO2xN7d3Vl20c9afguFQkin09jc3DQlU3ulSa39w9CYfkGwoeTm5sZS8qpWq6b7jC4vNd1kvPvQ8kp77O73+03n39bWFnZ3d7Gzs2Pc+Wg0anbB2SW1pMQVLTzn4Y+Pjy31eLmpFrBm6dfW1oyF50YauYabTTeL7Oib1m//GJxOJ0KhkNkBQNLbx5IVD0NLdo9gWjlI/ns8Hhs1mcvLSxwfH5ulEiQDLb6UdJb73uRx5bQaicYlFxsbG4bs+XweyWTSzOFLK2d3nRl6UABDSlxxvZXspedXdtoxaUeXXhKeHo0sK856XSXmaa5hXoUzA1zEoZgdj5Je75hW2GvB0mVmCY7CEv1+36jBSM03fsBl4on5ApKMW21k7X1jY8NUDujOswuQ52OP3zm/3mw2USwWcXR0ZJG46vV6XyzelK21JLzU2Gc4Q0+GYcqsQy8PNdPMSny2FEtZL3kM/cw+jUdJr8mRhyHjeWq2sZOObbKyrRSAqZOTqIzZKanF9tloNGpaaGnRSHbO4lM7Xta0+f1oNLIQvlwu4/T01GzbpcQV5+GB+7ZYOaDj9XotngRr8czS2zfQPAbZzSfDENmc8xQo+MEEXjgcflBPUPEwNKZfEHSdOTZ6dXVlYnYm6OSudrruwP0svqx5Uy6aViyRSFg048PhMPx+vynFybXQPB/Zy8++gFqthouLC1xcXODs7MxCeOrXyfidvfRSDIPlSFp46dLPSviVlRXTkszSKK/RNNUgO6jJn81mkc/nkclkEA6H1TAtACX9gqBFpfWToo60YPywA/cuvOxZDwaDCIVChuzxeNyQnC489eLdbrep/wPWCTOeCxdAsnxID4Qtvo1GAzc3N3A4HEYrXtbg+eANSm6RpTvPMuMsLj2PTe9BiofQG5Etuo8da3V1FfF4HPl8Hpubm0aQROvz80NJvyBkI8lDHXR2y06ys7GE5OYjFouZxRCydXYymZjYmWEDbwDyK0kv22FbrZapHkjrzhsS43cSU7bqcr3UU62108AbnGzmYRMNtQLa7fajLr70ANbW1kzLMUMdinEq5oOSfgFIy2TvKZcuLHBv7SiDHQqFzCScdOEjkYiF7MD92io5Z89YWm6BYW2c/6Z1lquead25x14KWNKVB2DyFGwsmtZa+1RVhzcSquzyPfr9fgBAt9tFvV43eYnHbpycIgwEAojFYpa9fhrPLwYl/YKQ5TySnYm5yWRirLysdctd8yQ8P8Sym05ucaW1bjabRjLaXiZ7KBHGkIJLKCTRGbdLb4I9B8zKs/Q4a2stAEurMEuNLDH6fD6Mx2M0m01cXFwAuHfz5aQdry+vKVd3R6NRRCIRSwswoFn7eaGkXwD2FlW6xiSRfI78nUySMVPOJh/5wae1pWpstVo12nUkPS29rLHLSgCrAfwaCATg9/vNoAzPlUm1fr9vPBfq8kmXfhbCcxSZSsCFQgFv3rzB5uamcccHgwFqtRpWV1dNIpSz+9PCI1p5rshiMtMuO66YHUr6BSEtvExUeTyeL5Ri5YSbXNrIhY+DwcDITVE5tt1uo16vo1KpGBUdLm20k9AeQrhcLkPyWCxmsZAsxdkJz2y6FMDga83SWitVgzKZDAqFglECzufzJtN+e3trLD7blO0Ku7Tcsl+ANyy5Ikv+Xyhmh5J+QUjSM1Eny1LAfcMJXVj+nFlxTt/JNlq2zHIhRKPRsFj4fr9vEeWgRWRcbo+Bud+epLcTXkpi2SWuZNLuKaysrMDn85mBoEKhYHb0ra+vm7l76gt0Oh2kUimjECT7/6lJwLLm6uqqURvm71XyenEo6Z8BORRDd93tdlt03kgY1rpZ76ZMFt19Wi+pEU8CUn56WiOMXLjB9l1un2VjD7fQys46iliQ1MzSS4krSlPPUoenYGgikTBru3K5nBHgZN7A6XSaNdbsTaBXcnNzY5qNSHo5+cjqxDwtv4ovoaR/BmSHmSQfcN+xx3KatMrSO2B8b9/qylhXLnYk4WXFgOfA4Rx281HTPpVKmfVZUi6LVlUulJRJPPvGmceugVygwV4Ddg9SJ5Ceheztl7kOfs/qgOzak6PLcsCHoYxiPijpF4TM3pPIMrkk9ev5QbYnqhgOyL+XgzL2vyVx5N8zM09RzEQiYTTt2ehDt15uj5UVArsuvRSxnOU6MLxgTkHeYIAvNf1lf8FDbrq8Dgw7mNtgOMBEpcb080FJ/wzYk3WM8YH7gRzG81LskVZfjtHKnnT+vXyu1JqX9WuWszicQ+s+zaUH7t1khhksCTKOZ4wvvYnHZv7lZB5vWkxW0hXn7xi6sN9AtvRyIEkmDeUWG5fLhVqthsvLS+TzeWSzWYTDYZMAVcwOJf0zIK08XXbZYy/77+0rmAEYwozHY0sMS4JKb8Du0sshHenSy0UW1LBjWVBazk6nY+r/FP5gGVC+DonM9yPfu/05Uum3VquZzTnU2KdyDwVFqPxLl12GQxJOpxPdbhfVahWlUgmlUgnNZtOoBckEn94AnoaSfkFI116W7Fh/ZsxuryfLBBSHTXg8kh/A1BuAvAmwlBUOhy3tqdTI49itVJhlIozxO6sDtPKS8NPClYfAUiTHjM/Pz82sQK/XM5t0+H2lUsHZ2ZnR56OSEBOVdtUfotVqmWnGZrNpegsU80FJ/wzIshLjWa5NJtlkqyzJJIkv3XqZ5GK8Lj0Ifi9n3Un6ae2pMqRgwk52+XGbDnfb8xxJUFpvwLqZxw4+r9PpmPBjNBqh2+2i0WiYDUCszVerVcvUn1xXbY/zJamvr68tswS6rXYxKOkXhF1hhl1vjGFJbvuiC/6tvWefVlxm9WVvvNwZ5/V6TcNKKBQy5S9u2plMJpbyHmP46+try/ZYro3udDpmEaYc++U58rxZSrOTUur78wYjk28k/WQyMXqC1AtsNBpf5BKmWW858cfy5bIrOy0KJf0zQNJzci4cDuPu7s7U6yVIbnbSySy8dKdluEByr62tGU+CMtRsp/X7/SZbLpt7AGv5j4RnAo1JNE7S8bx485FDMMwpyJsVv8p5eBlCsNegXq+b6gEA020ok4cy0fkQ+Hpq2Z8PJf2CkJaewzTRaBROp9Oy301m9Ulwe+1edvbJ59O6c7UWyc4bgGzsoXWXW2Mo1cVsOTXtSHQ+nwm0WaSrpUS1XY9PJgx5vH6/j1arZQZk+DN5DrMq6HKYhzdBqf6rmB1K+mfATvrBYACXy2VIL0ksy1rD4dCyy03G9PYH8wayJVW23LLezgSYXUyDFp6juWy8kc0+Mu7n8eTM/LTHNNjVgmRowfOV5zeLhSd4XSk6wsYfHbyZH0r6BSEJSZebpLe31trR7/e/kLyy1+dJYLtQhlybRXdaxtWDwcDE79fX14bstK4yuTht5RSPz4d87iwNO3a3nz+TQ0fsFZi1AQj4ycoHg0Gk02nT3mtXzlHMBiX9MyB77xl/M/tNBRrZYCK3z8qavSS6tNYrKyu4ubmxZO7Z5CK9Bdlww0QXCc82Xrr6fK7daksFGxKTVnjWVVN2yPdF8P3xuLN2/fl8PiSTSTOfn0qlzHox+TzF01DSPwOyUYbxt2yXlT30jEVvb29NSYzZcCkgYXftpUAH3XmPx2N239k74Ph6tO52CW6ZT3iI9AAMKWfdIDvtusj38Fhn32PHcblchvDb29vY29vDxsYGYrGYtuAuCCX9M/FQHG4frJH99SQBcJ+NZiJMlqFke68cy2UYwefbJbLkgzcDPk9acuBLvXy797HItZBLO9hvwPc4a5mNVQS/349kMolCoYCDgwMUCgVkMhl17Z8BJf0zIK2jbMKhVZUDM/I5Mm6WLbrAvTa+jPVlbDwej78YOZWxvCS61N6Xgy72cEK+l+deD5JeKgnZQ51ZXs/lcsHv9yOdTqNQKODv/u7v8NVXX2F7exuxWEz18Z4BJf0zIeNfWltmp+WDxLPHtPZ42V73lvVyvgbbdWX8TwFM+XrSpZeEl7P+L9nGyiEg9i1IkU+Ox/LmJ0dnpx2HLn2hUMA333yDb775Bvv7+0ilUvD5fOrWPwNK+r8CZCmOZT0OyLAWTvdXCm7Y+/n5N1KTXirI0HrKOX77ecibhrzhvBSY05CyVuFwGKFQCH6/H2632wzasAPQ6XSaBqJpWgOBQACJRAKFQgFff/01vvnmGxwcHCCbzZo+fsXiUNI/A5KgJDRJYG+0oQX0+/2WBJusa/P5drFNSXg5dy9Hd2nh7Rl8quDYa+j29/EYHvMGuIqLyzqi0ShisZhlwy378uv1OsrlMur1OprNJjqdjvFEeH1CoRCSySQ2Nzfx7t07o7NHwttXUivmh17BBUErvrq6Cr/fb2JtxvHM3MuaOR9SCWdaZ57cKSc14mQsL+vdMpyQijsUuOTKLSm7JfXr53HxmWDzeDwIBAJYX19HNptFLpdDNps1Gv5UrQV+6sJjH36pVEKxWDSTcpTI8nq9CIfDSKVSyOfz2Nvbw97eHra2tpBOpxEMBtXCvxCU9M8AJ94mkwlWVlYQCAQsyTlJSGmN7bG2PJ7U3JNfZdluWk1fxvVSqIIuNXXzpX4+5a1nRSAQMNt55FZd1s4zmQxisZghvJTmkos+S6USqtUqms2mcfO9Xi+i0SgymQzy+bzZ0BuLxczCTsXLwPHEXV6HlR+BPdkmG07siTqZsbc/H7gfsZWiHA+V+ezZd3lsehB08eVUHafbLi8vjbXlLLscwyVk12EoFDJim7lcDul0Gslk0iLeIWf57RUHtgVL4Q5501ldXTWCnhwTlgs7FQth6oVT0r8A7LVu+8/lQ9bA5d9Nq/XbfzbtNe03AHkTkG4/FXg5y35+fo7Ly0s0Gg10Op0Hh20YwnBjzebmJra2tpDNZs3uPS6hWFtbe9AFlxN4MqfBEEPq9nNqUOvwz4aS/teAWdtOn3Pcac02LJmR+KVSyVh6quZM0/Bj9YEJNsbv7H1nNWHajemh87RP6/E9y5ud4kWgpF92cPyWM/XM7NtXbBMy5PB6vQgGg0aww+fzPfo6gPbC/wqgpFf8BBkGPBVuAPfkl+3FilcBJf0y4aFOt7/Wa8xz7KdCHPUQXgxKeoViyTCV9Fr8XFJMqzY8BSnXrXi9UEu/xFhk2EYJ/6qgll5hhRJ4OaHdDwrFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZVp74veNnOQuFQvGzQS29QrFkUNIrFEsGJb1CsWRQ0isUSwYlvUKxZFDSKxRLhv8PokYq5r3fTWkAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 63\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 65\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 66\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 67\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 68\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 69\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 70\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 71\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 72\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", "n = Nx * Ny # number of parameters\n", @@ -2094,22 +467,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(10 * np.log10(evaluation_history), \"o-\")\n", @@ -2128,22 +488,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", @@ -2170,17 +517,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ "f0, dJ_du = opt([mapping(x, eta_i, cur_beta // 2)], need_gradient=False)\n", "frequencies = opt.frequencies\n", @@ -2191,34 +530,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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FylPf78vKn41kwtBOQZ0cSjwwsgtZeYX8/dMv/R1KrQj+b8TUSGFRMX9ZspPuiY34dp82/g4npAzr1pKFU4fyxMLtzFq1j6XbjvHEjX24olNzf4dWp77KyuMfq/fz+uovycwu4PIOTfntdy9jZPeEkBvTq0frxlzbM5HXPvmSe4Z0pFFMlL9DqhH7uRjm5nx+iH0nz/Hf13ULuf+sgaBRTBS/H9ebNydeQbHCLa+s4dfvbeZcXuhPb3Lgq2wefW8zVz2xjGeX7uLyDs2Yc98g3rl3MFf3SAzZv7cHRnbhdE4Bb6w54O9QaszOIMJYQbHy7NLd9G0Xz7U9E/0dTkgb3LkFC6cOZcaiHfz90y9Zuv04j4/vw5CuLfwdWq3bnHGal1fuZf6mw0RGCN/rn8SkYZ3pkhAe41f1bRfPsG4tefXjvdw5+JJqD+kRCOwMIoylHiwkIzOHh6/rFhYjtPpbg/r1+PW3L+OdnwyifmQEt89ay/S5mziTe/HIoMFGVVm16yR3zFrL2OdWsXz7cSYO7cSqn4/iyZv6hk1yKPHTUV346lw+b6076O9QasSnqU1EpgATAQH+qqpPi0g/4CUgBigE7lfVdR7KPgncgJPElgBTVNUmC64l2fmF/GdPAVd0bMaQLqH3KzaQpXRoxoIpQ/nLkp389eO9pO44wR/H92Zk9wR/h1ZlhUXFfLj5KC+v3MPmjDMkNIrmkesv5YdXtKdxkLe/10RKh2Zc2akZr6zcw21XtCcm6uJRYYOBzxKEiPTCSQ4DgXxgoYh8ADwJ/EZVPxSRMe77EWXKDgauAvq4i1YBw4FUX8Ubbv7x6X7O5CvTRne3swc/iImKZPqYHlzfuzXT3vmCH7/2GTcOaMujY3vSpEFgHlhLj/bbukkMgzs3Z92XX3PgVDadWjbkiRud+2Y8DZEdjh4c1ZXbXl3LO+sPcUepsaKCiS+bmHoAa1U1W1ULgRXAeECBxu46TYDDHsoqzhlGfSAaiAKO+TDWsHI6p4CXVuyhT8tIUjo083c4Ya1fu3g++OkQHhzVhXkbM7jmLytYvOWov8O6SMlovyVTsB4+ncu7n2cgKC/fkcxH/zWcWy5vb8mhlMGdmzOgfTwvpe6hoCg4h18RX7XaiEgP4D1gEJADLAXSgJnAIpxmpwhgsKru91D+KWCCu97zqvo/HtaZBEwCSExMTJ49e3a1483KyiIuLjzaSefuyuf9PQX8vJ/So1V41BkC/zvef6aIV9PzOXi2mCtaRXJ7z2ga1a/Z2Z23dS4qVs7kK5l5zuN03oWv008UUejhUNE8RvjTiMAa4jqQvucvThTyl/V53NOrPkPb+u7MsCZ1Hjly5HpVTfH0mc8SBICI3APcD5wDtgB5OElhharOEZGbgUmqek2Zcl2AZ4Bb3EVLgJ+p6sfl7SslJUXT0tKqHWtqaiojRoyodvlg8VVWHsOeXM7w7i25OelsWNS5RDB8xwVFxbyYuofnlu2icUwUv/1uLwqKiqs1zSbAoqXLubTfQI6fzePE2TyOn8nl+Nm8bx5ncjlxNo9T2fl4OhQ0a1ifhEbRbD961uP2Bdj3+A01qHHtC6TvWVUZ+9wqzuUV8tFDw6nnoxtRa1JnESk3Qfi0k1pVZwGz3CD+CBwCHgOmuKu8A7zqoej3gDWqmuWW/RDnTKTcBGG889KKPc4sXNd249DW9f4Ox5QRFRnBT6/uynWXJfKzdzcx+c3PiRAodg/ezjSbm8jOL+TyDs3cA30ux8/kfZMEzjpJ4MSZPM7mFcKS1Av2US9CaNkomoRG0bRtGkv/9k1JaBRNQuNoEhrFkNAompaNomkRF039es4B7arHl3mcsMdG+62YiPDgqC7c+8bnzE8/wnf7BdfYZr6+iilBVY+LSHuc/ocrgQf5psN5FLDLQ9EDwEQReQznR8pw4GlfxhoOjp7O5fXV+xnXP4kuCY04tNXfEZnyXNqqMXPvG8yA3y3hTO6FN9XlFBTzi39vvqhMbFSke5CPpkerxgzrGk3WyQyu6NODhMbOgT+hUTRNG9Sv8k1qnqZgtdF+vXNdz1Z0S4zj+WW7+XafNkF1g6Cv7+CYIyLNgQJgsqpmishE4BkRqQfk4vYhiEgKcK+qTgDexUke6Tgd1gtV9T8+jjXkPbdsF8Wq/Nc13fwdivFCvcgIzuaWf8f1sz/of/6gn9A4hob1Iy+6Ii019QQjUtrVOJbypmC10X4rFxEhTB7ZhSmzN7J461G+1au1v0Pymq+bmIZ6WLYKSPawPA2nUxpVLQJ+4svYws2Br7J5+7OD3DqwHe2aBVanoilfm/hYj007SfGxfKdv3Y6dFahzMQSDsX3a8PRHu3hu2W5GX9YqaC4ttzupw8TTS3cSGSE8OKqrv0MxVTBtdHdiy9xkZU07wScyQrh/RGe2HD7D8h3H/R2O1yxBhIHdx88yb0MGPxp0CYmNY/wdjqmCcf2TeGx874CcZtNUzbj+SSTFx/Ls0t0Ey6AQwTuKlPHan5fsJDYqkvtGdPF3KKYarGknNERFRnDfiM78ct5mPtn9VVAM1GhnECFuc8ZpFqQf5Z6hnWjWMPhnuDImmN2U3JbExtE8t8zTxZuBxxJEiHtq8Q6axEYxYWhHf4diTNiLiYrkJ8M6s3bfKdbtO+XvcCplCSKEpX15itQdJ7h3eOewHlnTmEDyg4Htad6wPs8v3+3vUCplCSJEqSozFu2gRVw0dw4OzpEkjQlFsfUjmTC0Eyt3nmDjwUx/h1MhSxAhatXuk6zdd4oHRnYO6hmtjAlFdwy6hCaxUTy/LLDPIqqUIESkoYjYeL4BruTsISk+lh9c0d7f4RhjyoiLrsfdV3Xko23H2Hr4jL/DKVeFCUJEIkTkhyIyX0SOA9uBIyKyVURmuKOumgCzeOsxNh06zZSru9r4/MYEqLsGdyAuuh4vBHBfRGVnEMuBzsB0oJWqtlPVBGAIsAZ4QkRu93GMpgqKipU/L95JpxYNGT/Arp03JlA1aRDFjwZdwoLNR9h93PNw6v5WWYK4RlV/p6qbVPX8lEiqekpV56jqjcDbvg3RVMUHmw6z49hZpl7bzWdjzxtjasc9QzoSUy+Smcv3+DsUjyo8gqhqQen3IhIjIhNE5EF3lNaL1jH+U1BUzF+W7OTSVo0Y2zt4Row0Jlw1j4vmtiva894Xh9n/1Tl/h3ORqv7EfAbIB74G5tV6NKZG3l1/iC+/yubh67oH1ZjzxoSzScM6ERkhvJgaeGcRlXVSvyUinUstaoYzC9wcoKkvAzNVk1tQxLNLd9GvXTxX90jwdzjGGC8lNI7h1svbMefzQx6Hdvenys4g/gf4nYj8SUTigaeAfwMfAv/r29BMVby59gBHTucybXT3oBlr3hjj+MnwzqjCKysC6yyisj6Ivar6Q5yk8DZwBXCDqo5Q1XfrIkBTuez8Qmam7mZQp+Zc1SXwR4g0xlwoKT6WGwe05a3PDnL8TK6/wzmvsiampiIyGegJfB+n72GRiHzbm42LyBQR2SwiW0Rkqrusn4isEZGNIpImIgM9lBvpfl7yyBWRcVWtXLh47ZMvOZmVz8M2iYwxQeu+EZ0pLCrmrx/v9Xco51XWxDQPyMSZF/r/VPX/gG8D/UWkwjmiRaQXMBEYCPQFxro31j0J/EZV+wGPuu8voKrLVbWfu84oIBtY7HWtwsjpnAJeXrGHqy9NIPkS6xYyJlh1aNGQ7/ZL4o01Bzh1Lt/f4QCVJ4jmwLs4HdNJAKqao6q/BSZVUrYHsFZVs1W1EFgBjMdJNo3ddZoAhyvZzk3Ah6qaXcl6YemvK/dyJreQh67r5u9QjDE1dP+IzuQWFjFrVWCcRUhFU9+JyI3AA0AR8LiqfuT1hkV6AO8Bg4AcYCmQBswEFgGCk6AGq+r+CrazDPizqn7g4bNJuIkqMTExefbs2d6Gd5GsrCzi4uKqXd4fzuQp01Zm07dlJPf3q/pUosFY55oIt/qC1TkYPb8hl80ni/jTiAY0jPLugpOa1HnkyJHrVTXF44eq6rMHcA+wHlgJvAg8DTwL3Oh+fjPwUQXlWwMngKjK9pWcnKw1sXz58hqV94ff/meLdnzkA9117Gy1ygdjnWsi3OqranUORlsyTuslP/9An/lop9dlalJnIE3LOa5W1kn9V7cvwdNnDUXkbhG5rbzyqjpLVZNVdRhOB/dO4E5grrvKOzh9FOW5Gfi32t3aFzlyOof/W7Of8QPa0iUheH8tGWMu1LNNY67pkcDfPtlHVl6hX2OprA/iBeBREdkmIu+IyEwR+ZuIfAx8CjTC6aPwSEQS3Of2OP0Pb+L0OQx3VxkFVDQ56w+At7yqSZh5duluVJUpV3f1dyjGmFo2eWQXMrMLeGNNua3vdaLCmWRUdSNws4jEASk4TT45wDZV3eHF9ue4YzYVAJNVNVNEJgLPiEg9IBe3D0FEUoB7VXWC+74D0A6nc9uUsv+rc7yTdpAfXtGeds0a+DscY0wt69++KUO7tuDVj/dy56AOxNb3z7D9Xk01pqpZQGpVN66qQz0sWwUke1ieBkwo9f5L3CunzIWe/mgX9SKFB0badBzGhKoHR3Xl5pdXM/uzA/z4qo5+icHGgw4yO4+dZd7GDO4c1IGExlW/cskYExwGdmzGwI7NeHnFXvIKi/wSgyWIIPPnxTtpWL8e9w7vXPnKxpig9uCoLhw9k8u76w/5Zf+WIILIpkOZLNxylHuGdKRpw/r+DscY42NDurSgX7t4XkzdQ0FRceUFaplXCUJEurmXvC4WkWUlD18HZy701OKdxDeIYsJQ/7RHGmPqlojw4KguHPo6h3kbMup8/151UuPcr/AS8Fecu6pNHVu37xQrd55g+vWX0igmyt/hGGPqyKhLE+jRujEzU/cwfkBbIutwMjBvm5gKVfVFVV2nqutLHj6NzJynqjy1aActG0Xzo0Ed/B2OMaYOlZxF7Dt5jvnpR+p0394miP+IyP0i0lpEmpU8fBqZOW/lrpOs+/IUD47q4rfroY0x/vOty1rRJSGOF5btpri4/PHzapu3CeJOYBrO3dPr3Uear4Iy3yg5e0iKj+XWy9v7OxxjjB9ERDj3Pe04dpbFW4/V3X69WUlVO3p4dPJ1cAYWbTlKesZppl7Tlfr17KIzY8LV2D6tuaR5A55fvqtkMFOf8/YqpigR+amIvOs+HhAR6yn1saJi5U+Ld9KpZUO+199uKjcmnNWLjGDyiC5szjhD6s4TdbJPb3+SvogzPMZM95HsLjM+9P4XGew6nsVD13ajXqSdPRgT7sb1TyIpPpbnltbNWYS3R53LVfVOVV3mPn4MXO7LwMJdQVExf1myix6tGzOmV2t/h2OMCQD160Vw7/BOfH4gk9V7vvL5/rxNEEUicn5sBxHphN0P4VP/SjvIgVPZPHxdNyLq8LpnY0xg+35KOxIaRfPcst0+35e3CWIasFxEUkVkBbAM+G/fhRXecguKeG7pbga0j2fUpQn+DscYE0BioiKZNKwTq/d+RdqXp3y6L2+vYloKdAV+CjwIdFfV5b4MLJy9sWY/R8/k8vDo7ojY2YMx5kI/vKI9zRrW9/lZRIVDbYjIKFVdJiLjy3zURURQ1bkeC5pqO5dXyIupe7iqS3MGd27h73CMMQGoQf163DOkIzMW7WDToUyf7aeysZiG4zQnfdvDZ8o3c0ubWvLaJ/v46lw+D1/X3d+hGGMC2I8GXcJzS3dy80uryS0sJmnNMqaN7s64WrwkvrIpR3/tPv+4OhsXkSnARECAv6rq0yLSD2fgvxigELhfVdd5KNseeBVn2lEFxrizzIWceRsymLFoB4czcwC4rE0j+rdv6ueojDGBbOm24xQWQ2GxMwx4RmYO0+emA9RakvD2RrkpItJYHK+KyOcicl0lZXrhJIeBQF9grIh0AZ4EfqOq/YBH3feevA7MUNUe7jaOe1WjIDNvQwbT56aTkZmD4mTC3cfP+WVoX2NM8JixaAeFZcZlyikoYsaiHbW2D2+vYrpbVc8A1wHNgTuAxysp0wNYq6rZqloIrADG4xwDG7vrNAEOly0oIj2Beqq6BJw5sVU128tYg8qMRTvIKbjwiuG8wuJa/ZKNMaGnpMXB2+XV4e18ECWX0owBXlfVLVL55TWbgT+ISHMgxy2bBkwFFonIUzgJarCHst2ATBGZC3QEPgIeUdULjqQiMgmYBJCYmEhqaqqX1blYVlZWjcpXV0Y5X2ZGZo7P4/FXnf0l3OoLVudQ1ixG+Cr34rupm8VIrdVfvLldW0ReA5JwDtZ9gUggVVWTKyl3D3A/cA7YAuThJIUVqjpHRG4GJqnqNWXK3QTMAvoDB4C3gQWqOqu8faWkpGhaWvUHmE1NTWXEiBHVLl9dVz2+zGOSSIqP5ZNHRvl03/6qs7+EW33B6hzKSpqnS7dAxEZF8tj43lXqgxCR9aqa4ukzb5uY7gEewRlyIxuIAirtuFbVWaqarKrDgK+BnThDh5dc/fQOTv9CWYeAjaq6122emgcM8DLWoDJtdHdiyozSGhsVybTRdhWTMaZ84/on8dj43iTFxwLOj8qqJofKeJsgBgE7VDVTRG4HfgmcrqyQiCS4z+1x+h/exOlzGO6uMgrY5aHoZ0C8iLQstd5WL2MNKuP6J3F9r1aA047niy/ZGBOaxvVP4pNHRvH3bzXkk0dG1fpxw9s+iBeBviLSF2eIjVdxrjIaXmEpmOP2QRQAk90EMxF4RkTqAbm4fQgikgLcq6oTVLVIRB4Glrp9Hetx5sMOSQe/zuHSVo1YOHWYv0MxxpjzvE0QhaqqIvJd4HlVneX2L1RIVYd6WLYKZ7jwssvTgAml3i8B+ngZX9A6ejqXtP1f89C13fwdijHGXMDbBHFWRKYDtwPDRCQCpx/C1NCHm51JyMf0tiG9jTGBxds+iFtwrkC6R1WPAm2BGT6LKowsSD9C98RGdEmI83coxhhzAW9Hcz2qqn9W1Y/d9wdU9XXfhhb6jp1xmpfs7MEYE4gqG811laoOEZGzOHdAn/8IUFVtXE5R44UP04+gCjf0aeXvUIwx5iKVDdY3xH1uVDfhhJcF6UfplhhHlwT75zXGBB5vO6kRkaY4I6ueL6Oqn/siqHBw/Ewun+0/xZSru/o7FGOM8cirBCEivwPuAvYCxe5ixbmBzVTDh5uPOs1L1v9gjAlQ3p5B3Ax0VtV8XwYTTuanH6FrQhxdE615yRgTmLy9zHUzEO/DOMLK8TO5fPblKbt6yRgT0Lw9g3gM2CAim3HuhwBAVb/jk6hC3MItbvNSH0sQxpjA5W2C+AfwBJDON30Qppo+2HSELglxdLPmJWNMAPM2QWSr6rM+jSRMlDQvPTjKrl4yxgQ2bxPExyLyGPA+FzYx2WWuVXS+ecn6H4wxAc7bBNHffb6y1DK7zLUa5m86QueWDemWaGMvGWMCm1cJQlVH+jqQcHD8bC7r3Oalyqf0NsYY//LqMlcRmSIijcXxqoh8LiLX+Tq4ULPIbo4zxgQRb++DuFtVzwDXAc2BO4DHfRZViJqfbs1Lxpjg4W2CKGkPGQO8rqpbSi0rv5Bz5rFZRLaIyFR3WT8RWSMiG0UkTUQGllO2yF1no4i872WcAevE2TzW7TvFDb1bW/OSMSYoeNtJvV5EFgMdgeki0ohK7ocQkV7ARGAgkA8sFJEPgCeB36jqhyIyxn0/wsMmclS1n5fxBbyFW45SrDDGbo4zxgQJbxPEPUA/YK+qZotIc+DHlZTpAaxV1WwAEVkBjMe5+qlkHokmwOGqBh2MFmw6QqeWDeluN8cZY4KEqGrlawEikgRcwoXDfa+sYP0ewHvAICAHWAqkATOBRThNVBHAYFXd76F8IbARKAQeV9V5HtaZBEwCSExMTJ49e7ZXdfEkKyuLuDjf9A2cyVOmLM9mbOcobuxa3yf7qA5f1jkQhVt9weocLmpS55EjR65X1RSPH6pqpQ+cYTa+BBYA/3Ef73tR7h5gPbASeBF4GngWuNH9/Gbgo3LKJrnPndx9d65oX8nJyVoTy5cvr1H5ivzf6i/1kp9/oFsPn/bZPqrDl3UOROFWX1Wrc7ioSZ2BNC3nuOptE9M4oLuq5lW2YpnkMwuYBSAifwQO4Qz8N8Vd5R3g1XLKZrjPe0UkFedmvT1V2X+gWJB+hE4tGnJpK2teMsYED2+vYtoLRFV14yKS4D63x+l/eBOnz2G4u8ooYJeHck1FJNp93QK4Ctha1f0HgpNZeazZ+xVj7OolY0yQ8XqwPmCjiCzlwrGYflpJuTluh3YBMFlVM0VkIvCMiNQDcnH7EEQkBbhXVSfgdHC/LCLFOEnscVUNygSxqOTqJbs5zhgTZLxNEO+7jypR1aEelq0Ckj0sTwMmuK8/BXpXdX+BaEH6ETq2aEiP1ta8ZIwJLt6OxfQPXwcSir7KymP1nq+4b0Rna14yxgQdrxKEiHTF6VzuCcSULFfVTj6KKyQs2nLMmpeMMUHL207q13AuUy0ERgKvA2/4KqhQsSD9CB2aN6Bn68aVr2yMMQHG2wQRq6pLcW6s26+q/wvc4Luwgt+pc/mstquXjDFBzNtO6jwRiQB2icgDQAYQXrcqVtGiLUcpKlZrXjLGBC1vzyCmAA2An+JcgXQ7cKevggoFC9KPcEnzBlzWxpqXjDHBqdIzCBGJBG5R1YeBLCofpC/snTqXz6d7vmLSsE7WvGSMCVoVnkGISD1VLQKG1FE8IaGkeclmjjPGBLPKziDWAQOADe6kPe8A50o+VNW5PowtaC1IP0L7Zta8ZIwJbt52UscAX+GMnaQ4Q3UrYAmijJLmpYlDrXnJGBPcKksQCSLyELCZbxJDCe8mkggzi93mpbE2c5wxJshVliAicS5n9fRT2BKEB/OteckYEyIqSxBHVPW3dRJJCPjampeMMSGksvsg7ChXBYu32tVLxpjQUVmCuLpOoggR89OP0q5ZLL2SrHnJGBP8KkwQqnqqrgIJdpnZ+Xy6+6SNvWSMCRneDrVhKrF4yzEKrXnJGBNCfJogRGSKiGwWkS0iMtVd1k9E1ojIRhFJE5GBFZRvLCKHROR5X8ZZG+anH6Ft01h6JzXxdyjGGFMrfJYgRKQXMBEYCPQFxopIF+BJ4Deq2g941H1fnt8BK30VY23JzM7nk90nucGal4wxIcSXZxA9gLWqmq2qhcAKYDzO/RMlvbhNgMOeCotIMpAILPZhjLVi8VanecmG9jbGhBJR9c39biLSA3gPGATkAEuBNGAmsAjnEtoIYLCq7i9TNgJYhjOs+DVAiqo+4GEfk4BJAImJicmzZ8+udrxZWVnExVVvios/p+Vy+FwxM4bFBtUZRE3qHIzCrb5gdQ4XNanzyJEj16tqiqfPvB2LqcpUdZuIPIFzBnAO2AgUAfcB/6Wqc0TkZmAWThIo7X5ggaoequiAq6qvAK8ApKSk6IgRI6odb2pqKtUpfzq7gG1LlvDjqzoycmSPau/fH6pb52AVbvUFq3O48FWdfdpJraqzVDVZVYcBXwM7cSYaKhnk7x2cPoqyBgEPiMiXwFPAj0TkcV/GWl2Ltx6loMial4wxocfXVzEluM/tcfof3sTpcxjurjIK2FW2nKrepqrtVbUD8DDwuqo+4stYq2tB+hGS4mPp29auXjLGhBafNTG55ohIc6AAmKyqmSIyEXhGROoBubh9CCKSAtyrqhN8HFOtOZ1TwKrdJ7lrcIeg6nswxhhv+DRBqOpQD8tW4cxrXXZ5GnBRclDVvwN/90F4NbZk6zFrXjLGhCy7k7oGSpqX+rWL93coxhhT6yxBVNPpnAI+3nWC63u1suYlY0xIsgRRTR+VNC/ZzHHGmBBlCaKaFqQfoU2TGPpb85IxJkRZgqiG0zkFrNx1gutt7CVjTAizBFENH9nVS8aYMGAJohoWpB+htTUvGWNCnCWIKjqTW8DHu5yZ4yIirHnJGBO6LEFU0Udbj5FfVGzNS8aYkGcJooqseckYEy4sQVTBmdwCVu48yfW9rHnJGBP6LEFUwdJtTvPSDX1a+TsUY4zxOUsQVTB/01FaNY6hf7um/g7FGGN8zhKEl87mltwc18qal4wxYcEShJeWbjtOfmExN9jVS8aYMGEJwkvz04/QqnEMA9pb85IxJjz4esrRKSKyWUS2iMhUd1k/EVkjIhtFJE1ELpqTWkQuEZHP3XW2iMi9voyzMmdzC1ix8wTf6mXNS8aY8OGzGeVEpBcwERgI5AMLReQD4EngN6r6oYiMcd+PKFP8CDBIVfNEJA7YLCLvq+phX8VbkWXb3eYlG9rbGBNGfDnlaA9grapmA4jICmA8oEBjd50mwEUHfVXNL/U2Gj83hc3fdITExtEkW/OSMSaM+PLAuxkYKiLNRaQBMAZoB0wFZojIQeApYLqnwiLSTkQ2AQeBJ/x19pCVV0jqzhN2c5wxJuyIqvpu4yL3APcD54AtQB5OUlqhqnNE5GZgkqpeU8E22gDzgG+r6rEyn00CJgEkJiYmz549u9qxZmVlERcXd9HyNYcLeWlTHtMHxtC9WWS1tx+IyqtzqAq3+oLVOVzUpM4jR45cr6opHj9U1Tp5AH/ESRan+SYxCXDGi7J/A26qaJ3k5GStieXLl3tcPun1z/Ty3y/RoqLiGm0/EJVX51AVbvVVtTqHi5rUGUjTco6rvr6KKcF9bo/T//AmTp/DcHeVUcAuD+Xaikis+7opMATY4ctYPTmXV0jqjhNcb1cvGWPCkC87qQHmiEhzoACYrKqZIjIReEZE6gG5uE1EIpIC3KuqE3A6uP8kIopzlvGUqqb7ONaLLN1+nLxCG9rbGBOefJogVHWoh2WrgGQPy9OACe7rJUAfX8bmjQWbjtCyUTQpHZr5OxRjjKlzdid1Oc7lFbJ8x3Gu79WKSGteMsaEIUsQ5VhmzUvGmDBnCaIc8zcdoUVcNJdb85IxJkxZgvDAmpeMMcYShEfWvGSMMZYgPFqQ7jQvDexozUvGmPBlCaKM7HxrXjLGGLAEcZFl24+TW2DNS8YYYwmiDGteMsYYhyWIUrLzC1m2/Tjf6pVozUvGmLBnCaKU5dtPWPOSMca4LEGU4jQv1eeKjs39HYoxxvidJQhXXpGybPtxRl9mVy8ZYwxYgjhv04kicgqKuMGal4wxBrAEcd5nRwtp3rC+Xb1kjDEuSxBATn4RG08UMbpXK+pF2j+JMcaAJQjmbcjgqseXkl8Ei7ccZd6GDH+HZIwxAcHXc1JPEZHNIrJFRKa6y/qJyBoR2SgiaSIy0EO5fiKy2i23SURu8UV88zZkMH1uOqeyCwA4mZXP9LnpliSMMQYfJggR6QVMBAYCfYGxItIFeBL4jar2Ax5135eVDfxIVS8DvgU8LSLxtR3jjEU7yCkoumBZTkERMxbtqO1dGWNM0PHlnNQ9gLWqmg0gIiuA8YACjd11mgCHyxZU1Z2lXh8WkeNASyCzNgM8nJlTpeXGGBNOfJkgNgN/EJHmQA4wBkgDpgKLROQpnDOYwRVtxG2Cqg/sqe0A28THkuEhGbSJj63tXRljTNARVfXdxkXuAe4HzgFbgDycpLBCVeeIyM3AJFW9ppzyrYFU4E5VXePh80nAJIDExMTk2bNnVym+Tw8X8PfN+eQXf7OsfgTc1as+g9tEVWlbwSgrK4u4uDh/h1Fnwq2+YHUOFzWp88iRI9eraoqnz3yaIC7YkcgfgUPAY0C8qqqICHBaVRt7WL8xTnL4o6q+W9n2U1JSNC0trcpxzduQwYxFO8jIzCEpPpZpo7szrn9SlbcTjFJTUxkxYoS/w6gz4VZfsDqHi5rUWUTKTRC+bGJCRBJU9biItMfpf7gSeBAYjnPwHwXs8lCuPvBv4HVvkkNNjOufxLj+SWH5R2WMMRXxaYIA5rh9EAXAZFXNFJGJwDMiUg/IxW0iEpEU4F5VnQDcDAwDmovIXe627lLVjT6O1xhjjMunCUJVh3pYtgpI9rA8DZjgvn4DeMOXsRljjKlY2N9JbYwxxjNLEMYYYzyyBGGMMcajOrvM1ddE5ASwvwabaAGcrKVwgkW41Tnc6gtW53BRkzpfoqotPX0QMgmipkQkrbxrgUNVuNU53OoLVudw4as6WxOTMcYYjyxBGGOM8cgSxDde8XcAfhBudQ63+oLVOVz4pM7WB2GMMcYjO4MwxhjjUcgnCBH5m4gcF5HN5Xw+QkROu1OgbhSRR0t99i0R2SEiu0XkkbqLuvqqW18RaSciy0VkqzvV65S6jbz6avIdu59HisgGEfmgbiKuuRr+XceLyLsisl1EtonIoLqLvPpqWOf/cv+uN4vIWyISU3eRV19ldXbXGeHWd4s7MVvJ8pofv1Q1pB84g/4NADaX8/kI4AMPyyNxJinqhDNh0RdAT3/Xx4f1bQ0McF83AnYGQ31rUudSnz8EvFnROoH2qEmdgX8AE9zX9XGG3/d7nXxVZyAJ2AfEuu//hTP4p9/rVAt1jge2Au3d9wnuc60cv0L+DEJVVwKnqlF0ILBbVfeqaj4wG/hurQbnA9Wtr6oeUdXP3ddngW04/7ECXg2+Y0SkLXAD8GqtBuVj1a2ziDTBOejMcreTr6qZtRudb9Tke8YZmDTWHUW6AR6mOg5EXtT5h8BcVT3grn/cXV4rx6+QTxBeGiQiX4jIhyJymbssCThYap1DBMkB0wue6nueiHQA+gNr6zwy3ymvzk8DPwOKPRcLap7q3BE4AbzmNqu9KiIN/RhjbbuozqqaATwFHACO4ExSttifQdaibkBTEUkVkfUi8iN3ea0cvyxBwOc4t5r3BZ4D5vk3HJ+rsL4iEgfMAaaq6pm6D88nPNZZRMYCx1V1vR9j85Xyvud6OE0WL6pqf5zpgIOif80L5X3PTXF+PXcE2gANReR2fwVZy+rhTJ9wAzAa+JWIdKutjYd9glDVM6qa5b5eAESJSAsgA2hXatW27rKgVkF9EZEonOTwT1Wd68cwa1UFdb4K+I6IfIlzCj5KREJiHpIK6nwIOKSqJWeH7+IkjKBXQZ2vAfap6glVLQDmAoP9GGptOgQsUtVzqnoSWAn0pZaOX2GfIESklYiI+3ogzr/JV8BnQFcR6SjOFKi3Au/7L9LaUV593WWzgG2q+md/xljbyquzqk5X1baq2gHn+12mqiHxy7KCOh8FDopId3fVq3E6OYNeBf+XDwBXikgD9/OrcfrYQsF7wBARqSciDYArcOpWK8cvX0856nci8hbO1Q0tROQQ8GsgCkBVXwJuAu4TkUIgB7hVncsACkXkAWARzhUBf1PVLX6oQpVUt74iMgS4A0gXkY3u5n7h/hILaDX4joNWDev8IPBP98CxF/hxHYdfLTWo81oReRenCaoQ2ECQ3G1dWZ1VdZuILAQ24fSjvaqqm92yNT5+2Z3UxhhjPAr7JiZjjDGeWYIwxhjjkSUIY4wxHlmCMMYY45ElCGOMMR5ZgjABQ0T+IiJTS71fJCKvlnr/JxF5qBb393cRuam2tldqu78o9bpDRSNxlik3tdRQCd7u69OqxldTItLSvbTShDhLECaQfIJ7h6uIRAAtgNLjJg0G6vyAWA2/qHyVC7mDyN2NM6qs11TV6zuC3X3UmKqeAI6IyFW1sT0TuCxBmEDyKVAyN8FlwGbgrIg0FZFooAfwuYg8KiKfiTO2/yviuFRE1pVsyP3lnu6+ThaRFe5gZotEpHXZHZe3jjsI2hMisk5EdorIUHd5AxH5lzjzZ/xbRNaKSIqIPI4zauhGEfmnu/lIEfmrOOP1LxaRWA91HwV8rqqFpfb7FxFJE2fOhstFZK6I7BKR35eKO6vU65+LSLo4g9U9Xmo7T4tIGjBFRK4WZ5C+dHHmGoh21/tSRH4jIp+7n13qLh8u38yvsEFEGrm7mwfc5v1Xa4KRJQgTMFT1MM4d7O1xzhZW44woOwhIAdLdoYufV9XLVbUXEAuMVdXtQH0R6ehu7hbgbXHGl3oOuElVk4G/AX8ovV8v1qmnqgOBqTh3sgLcD3ytqj2BX+EMmIaqPgLkqGo/VS05gHYFXlDVy4BM4EYP1b8KKDtoYL6qpgAv4QypMBnoBdwlIs3L1OF6nAHprnAHq3uy1Mf13e28APwduEVVe+OMpHBfqfVOquoA4EXgYXfZw8BkVe0HDMW5QxkgzX1vQpglCBNoPsVJDiUJYnWp95+464x0f7Gn4/zyLmmG+hdOYsB9fhvojnNQXeIOIfJLnIHLSqtsnZKBC9cDHdzXQ3AG+MMd2mBTBXXap6obPWyjtNY4w3CXVjJ2TjqwxZ2zIw9neIx2Zda9BnhNVbPdmErPIfC2+9zdjWWn+/4fOHNDlPBUz0+AP4vIT3EmFip0lx/HGRnVhLCQH4vJBJ2SfojeOE1MB4H/Bs7gzGEQA8wEUlT1oIj8L1AyfeTbwDsiMhdQVd0lIr1xDq4VTasplayT5z4XUb3/M3mlXhfhnPWUlcM39ShbrrjMNoqrGMc5L9e7qJ6q+riIzAfGAJ+IyGj3bC2Gb84mTIiyMwgTaD4FxgKnVLXI/SUcj9PM9CnfHERPijN3xfmrkFR1D87B7Vd886t5B9BS3HmXRSRKLp4kyZt1yvoEuNldvydOQitR4DZbVcU2oEsVy5S2BPixOCN6IiLNPKyzA+ggIiX7uQNY4WG980Sks6qmq+oTOCOEXup+1A0ngZsQZgnCBJp0nKuX1pRZdlpVT7rTY/4V5+C0COegVdrbwO04zU24fRY3AU+IyBfARsrMBeDNOh7MxEkqW4HfA1uA0+5nrwCbSnVSe+NDLmzuqRJVXYjTJJXmNpM97GGdXJyRW99xm+eKcfo3KjLVvRhgE1DgxgkwEphf3XhNcLDRXI2pBhGJBKJUNVdEOgMfAd3dZFPdbf4b+Jmq7qqtOH1FRFYC31XVr/0di/Ed64MwpnoaAMvdpiQB7q9JcnA9gtNZHdAJQkRaAn+25BD67AzCGGOMR9YHYYwxxiNLEMYYYzyyBGGMMcYjSxDGGGM8sgRhjDHGI0sQxhhjPPp/KvkibOuY6voAAAAASUVORK5CYII=\n"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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", diff --git a/python/examples/adjoint_optimization/04-Splitter.ipynb b/python/examples/adjoint_optimization/04-Splitter.ipynb index ff4b04f57..5c82ed9a1 100644 --- a/python/examples/adjoint_optimization/04-Splitter.ipynb +++ b/python/examples/adjoint_optimization/04-Splitter.ipynb @@ -13,25 +13,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.9/site-packages/meep/simulation.py:5441: RuntimeWarning: quiet has been deprecated; use the Verbosity class instead\n", - " warnings.warn(\"quiet has been deprecated; use the Verbosity class instead\", RuntimeWarning)\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -58,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -128,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -189,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -228,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -278,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -339,22 +323,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "x0 = mapping(\n", @@ -378,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -426,1600 +397,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 4\n", - "Current iteration: 1\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 8\n", - "Current iteration: 13\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 16\n", - "Current iteration: 25\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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1x49UHNBOsOom4/ls7fm1dbdc7cyrVlffD4MNCzwlfH49HMeDGz8P30cqPei8Wlz0ewBrX6/Xw4+5XC5vFNawux0Tv6afeD7PwTvd5pq3quZzyhK9NuvMEimmD7w/H3sdPPVIBf80ZsBZB11s4xH510um6Ita+KApMZ1va0XeYrFYC8hpUC5WDqoudcq1Z+Fy+SpEz5Y+5t6z6LVpJwsXx6euY1F2tvh8W4N+LPJdLXvqu9t13YCTJlP0L/vl6j8otsTzZdn2WqkfT9axMfRHy2k3fW6suCWW6uKqM82Lq3ARGWfh8rSALT3uZ9Hr1IDn9Wrp9ZKVR9eAnH6GlOD5f4elyLHltKkyYp/770+m6L2j6Atiota/F4vFRp6cI/csdHbVtXuuegLcj4+Phyemlt7sRfQex3PkXqPzOqdXwWMlXCzFxl5KTPD6Heo0BqT616f+B/iOedqQ9Zqp9y0iruodUOvLqSpeHqqr6zRop64671PPaTQVPS9H5fk45/vZ2mseXefwOvdOzcfxOmabgo55MwpbcLXc6vWlBJm1QnC1WoX1B7H+d9yyzHlBpuj//ve/v67zeOPwj2k+n1upVLJut2snJyfWaDRsuVyuFaLwHBuCNdvcqcZsvZmmLjHlwJzmztXl1oo8nLe2uzZ7sckmC11d9JgLrrBwWXhZYo2JHN8Dzi313ev9OqAhngLwWRh8x0XribcrmaL/1a9+9brO463g4ODAlsul9ft9K5fL9t5779mPf/xj+853vmOLxcIGg8GGeDHH5tZZEL4OJHDTuVpN8+4sSLiuKlgWFEQQG2jUguu0hOEiIXgLWuxj9nzKh8AljtXzyrKsqUAeDyA8wHCAlL0ovg/fQavVslqtZk+fPi2UwcpLpujff//913UebyUPHjywRqNhZ2dnZmY2Ho/DD1Jrzrm7SypvPp/P1wJqHFSDMHG8TiligUTun6/bPPP7anbA7EU/frwOZyDYi0CcAkVIy+UyDI4pT0GDt0rWsTyQcSNRXqeAba+xPBndhm7evGntdtuePHli7XbbPv74Y1ssnnfJRWzF8Tl9Jg8fPrT333/f/va3v4UcPJfZmlkQO5fHmm3uwKLBPNxWC5y6Zi+Cl9RC7IvFYmO/Ok2p4bwgeAhY78NrxVJyGsiLFdmwxU4tIkrFANhz4d1/uB9BvV63TqdjvV7PTk5OwuXmzZvW6XRsMpnYD3/4Qzs7O7PVahW8AZ/bP6e0Jd1VyG9J03ywKtyLvtFohOwGl8lqNDk2p2UhwxtQYcTSnerOo5kH70irRUEQJgJ/bPk1W6DnqEHFWIqOn8Ni5wFIqxMZTYEiGMoX7klQqVTCuofbt2/bO++8Y9/4xjfs7t271uv1Qhm0Y2Zm0WBJpuir1WohRQ+rp4EgbBcNt5J/XBw0i82ddb4KYXLgTx/T56kouDWX7mPHwTadJmggz2zdk2B4MNBUnX4+/iwYhCBY7QaE19TPGZu3c9PRSqVizWbTjo+P7e7du/bOO+/YvXv37ObNm9ZsNnf6/xaIqOgz3ftYGqTIzOdzG41GYZtq/DjNbMPtjaHBMraGsc0rzGxtcOAVeyw6fo4+Tx9PzcPVFU9ZZrN1b0A/DzcH0ZZh6oXwa/E5xDIgZhbiJY1Gw3q9nt28edN6vZ41m80QUNwl3190fE6/BS22mU6nIZAX61TD17HXwvEchCuXyyGQps0wVdCVSsVWq5XVajWbTqdmZuH57CXEBoIsdGus1HZZsc/ElhmdgPjCW2fHRB+bHvB5I+WJQGK9Xrejo6PQTBTdiM026wmKjO9PvyexHw5+hLFouVl6AQkLElaSo+GInKuV5vQY3mM2m62lyzj4lnp+LCXGcQLeOlvn1amOuxxhh1VHExFcIEoE1FjMqcAlvkctbFoul2GHoFQjUScbF/1LwGW3IMu6sDXWWnMzC94DW3sIg4/FgKN5/VQUnQcNFStua8deds2x2YYuKGJXPCV67v2nXkysqo8vnDGYTqdWrVbDZ8/a9cfJxkW/Jzqv5fu2AfFjs0v8zblkDcaxC68BtazCG46gcyQ9lvrjFt3csJPdc7b4HJeINfrkAGOsB2BK7BxsRH0DFwzp9GPXKYzzHBf9NZBn7qiDBP7mvDlucwGNpvl2mbdqUA6WMTZ/58042FrHxKtWnkWP19AAXiptFxM8Zxs4eFmtVteE72LfDxf9G0KtHIsZ8PxcXXzOrfPrxVDXPhYl59w/hMrWXy19TPRcLxDb1ism0izXno/Ju6GHk8YjH45TMNzSvyFSxTpqjfV4/pvd5SzLp56EWtJSqRRc6dlsFiyzpiV5OgKry4HEmCfB57WPe69LfnWprpMfF/01kCeQxz96TZ2x8FNptlgBTwoWOGoLEDNgFz0rNsDBNHbTYzEBdAXmEt9tgbws9z61tBiDjefi98NFvycxC7uLBYoJNxYRT+XStVgma75s9iJTAEuulhq3Y1Z1NputBeM4Rabvz9H7XVJ2MbHr35yyw2rG+XweBhht+OHshov+Jdi3OIdFz8ExLTJJ5dlj6Td193E+iHyz0HQwUfFyXb/W83NPQB6ktPwWm3Iikq/WXkXO3x9/h6niHOTss8qenTgu+i3EXHeOesdEn6oMY2usZbicTlP3nf/mqjmNosfeW+fvKTSar2W4MU9CzwtpOi3F1dr7mHsfe03+DCjQMXu+6hFNM9CABNV+/LpFHwy8DHdPVOz48WL1WGzBjT7PbN1q43rXBTc6n9fVdboqjT2QlPscs5DsRWRVuqmgNH3Huf1dLD3eO2saw8HIer1upVJpbWvwVG2/s4l3w42AHw4sC8CyzlarFSwYfpC6tNYsvs48JurU0lqNF3CRDbvgLCp9LY2Gc5AsFsnnz6+5cm3jpfD7c7cbjQcwKnqtFdDn4n+AtQbodVCv113wO+JLa7dQKpXCOnptosFWHq2zts0xU26nuvOpc4EwdEFMqokG3pP77V1HE42YR8Pnz9Y6NmWJPRcDW+qz4Xar1bKrq6vQGITn/Vh156QppinfkWq1at///vftm9/8ZnBT0UCD22Vxi+rYOnfNO+smEmYWFREPENvaZamlx3Nivfg5FabiVo+AO+6o+Pk6NTdP1RKk3HutDuRmHLDs9XrdLi4u7OLiwk5PT+3x48dr7bLG47E9efKk8O2yfvnLX0bv93ZZGXz729+2n//85/buu++aWboxJu8GyykxFj0ske4iq22tWfwsKG6aaZbdGFMHGt1ZVvvfq5ghdk6Z8X2pc+PzAzGvJRatx7E4njMZqcaY9Xrdms2m1et1a7fbduPGDWu32/bll1/aX/7yF/vwww9tuVwGr0y7/XzdefToUf7OOT/5yU9ezdm8paDoo9/vW6VSsR/84Af2ox/9KLTAHg6HQTjcwppbYGsOHOB5aIGNnvexFtgxl5pFyMLXqDvX6nOqTpcBs2ufep9tW1ppIU1KzEzsmFTsA/8TXReAgYCnDgcHB2EA+Ne//mX3798Pr39+fr73b+LrSKalv3//fmEsPc+DsdlFp9MJvdfYUk+n042+9bw8VgtmIBTek043u9A93GPTgX03u+C97XlXHhY3W3h+39jjMbHHsgR8zbdTGQ6FPSZNHfLn5DJgMwsDcdFZrVb5Lf13v/vdV3M2XzHwg4oFymB1cJtdbhU9746Dfu0seg2wQXw8LVCvAO/L6TYMGDhPRLp5owqU40JAKQGyQGMCVsGr+LMEvotnwIOpWXxPQY6NMPi/FG0uvw0P5O0Az5O1ao074PDcOjan19QailngKWiQjAW/7waWnONWqw3hM2zFMZDo2n68J65xv75WKjjJ76P3b/s/6Lmm0odmL7Zaj2UYYvDny0vsuS/zeq8ST9llwGLiH6mmntStj82vtViGX58XqbCFZNEfHh7a1dWVVavVIH5+LX4/MwvPxWvxRc8b05msHyh+C/Bc8FnAavViy2mgf8cErx6BHqv/j9hrpYhVIm4bXF7GK8jyZt4mMkX/No5Sr4PYCM0WDcewC86Ci+WkNeiGrralUmnNVefXxqKXyWSysboNrnpM9LCAWZV5ZhYi2ngtLKvVuTSacLI3Ag8Anx23+b3gIfCcO+8cP6834GwnU/QoPnGewwLSRSAcSNMFLnrN+WgISAcavEdsj7pyuRxcfL6PvRLUo2ueHB4JFqywy1+r1Ww+n1u1Wg0DAA8KHMjjSH8qtQevAAObCj8rd+4Cf3X4nH4Plsvl2g60mK+brQ8MOjXQKjgzWxO8ehfcI05deQiSg3Eqegg3VhuvgueIPef0eXPN2NZYHOXHsewF4Dkc0GTrj8+zy6Ig53pw0ecElmwymdhgMAjbV2NebmZhEIDYOMrMF3XNORWlg4YW76BHPo6L5ein0+laK2usddcCHZ5KQLxZhTxZgwQ8ENzHFwxsLHycr9lu/Qicl8dFnwOIaTab2Wg0ssvLS7u4uLD5fG6Hh4fhx41dbeFe83O5AMdssz0WCx2WH643Bg+0stJBQ1ekIUd/eHhok8nEGo1GUvBsmfE8tuJamKP1A5xZ4GukI3kQ0GAgu/27BPScl8NFnwP8KGHlz8/P7fT01KbTqdXr9VAEgwYSKB9l91yj9/w47mNXf7VaBasOQXBJqRbn8EDBlhgWGBdODcasd2pg4ON5gED9AaY8uGDvP1xwLkgncnUfB/6yUoHOy+Giz8li8XxF3XA4tLOzM3v27Flwo/Gjj225FIvkI4rP92kunKP1eIxz53qJZRe4+i5WRRerAoyJXZ+DAYJFf3V1ZcPh0AaDgY1GIxsMBjYcDm04HIZBoFKpBPFjAOOagpTVZ3wg2B8XfQ54rjwcDsMqr8lkYpVKJfzosRKPrT3P2bW9lFprjeTzwhOunuPX0zLVVPBQ59GcNst7vFYMYtCDyPv9vl1cXFi/37d+v2+Xl5dB/Cg24mmETjVi3lEs7+/kw0WfA07V8Zz+6urKDg4ObDwe22g0Co0hMbfXJbDoKIMGECxisxettACsu9nm1tUp0eN8YwUxsc/Fx7Blzzo2Fgdg4Q8GgyD2i4sLOz8/t4uLizAojEajsDoR4udgomYAuK7B2R8XfU6QDsMe9XBjS6VSsPToEcdbO0H0uixUy0hZVFrjj7/Vwm/rZcdsK0NVi546zsw2LDNSfJzOHA6Hdnl5af1+P4j+8vIyZD54iTHHHhAf0bQhvxdnQdzq746LPiew9pjHYp66Wq3C37wdFHeA4bQZN3XkaD7eQ9fHa4Sfy311+mC22aJbYwpZn2/X7yE2x+d5PsSPuf1gMAgDACz9aDRaE7wOHtyrAMHF8XgcYgX9ft9X1OXERZ8TzrlzqgpLZ9GlRbvZoCgGVh5RbbixLJ56vW7L5TIEAfGaurZc6/xV9CnhX0d5tU4JNAtQr9dD5qDT6djR0dFaRB+rCyFotuKadWDRz2YzGwwGdnp6al988YU9fvzYTk9PbTgcer5/R1z0OeH5MiwPLLYKDeKHWOHuY/EM/+D5x75cLte2idZSWhW/uvaa9nvVosd1LHiIz9RsNtdErGLXvL2mHLnqbzgc2tOnT+3Ro0f24MEDe/jwoT1+/NjOz89tPB5vpD99AFjHRZ8TDZTxHDM2H2YBwt2v1+vBynH+XOetWsSjP+ZUg4ltotfbjAYVU+h0RGMRLN5qtbpRjcifNes9NJsAj2owGNizZ8/s0aNH9tFHH9k///lP++ijj+yzzz4LaxKq1epaIZDzHBd9DnRuzfXkHFTS6Dc/B0tjOWqNOayWrbIoeIFO7Jz0/PYRvdYI5P1uOMfO1YLL5TLUFnAqMPVZUueGa1RFXl1d2be+9S27e/eunZyc2MnJiX366afB8msLc+c5LvqcsMvOZbbspmrFG55XKpVsMplYrVZba1ul81geBDi6r649u9QpkeZx7fMInb0dLRvG47gPt9kDSL3mLueJ77bValm73bZWq2W3b9+2d999187OzuyPf/yj/fa3v7XT09OdP0+RcNHnQItrID4A95Otv/7AUYHGll0Fr8ErDB5axKMLelLiv445/C7fjU5FYoOA2fYGGdsEz54Tpkt3794NNQ937tyxTz/91H73u9+ZmVm73Q7rFRwXfW64Oo5z8GgywfN8XUZrtl7JxvNVjk5rayw8plOGFBzF1/s13pAV5No1tZe6re+zbUDax9MolUqhJz7u+973vme/+MUv7L333rPlcmnNZtOX7xIu+pyUSqWNHVq1J57ZejRbXXS8Bs9vMRBwbhrVbei4q9OBRqMRAmWxHW40hafC2xbV3vVxPW7b81JC31f0+p63bt2yn/3sZ/bTn/409+sWARd9Dtil5H3YIXwIGYtHzGzD0mMgQLQ/lp6C8JHP5gt7Aa1WK/R6R3pPd7rBXPplAnRKLF3H19uez4+lshP6XrHX18/EqxC3vXeRcdHnBIG8w8NDazabYUXdZDIxsxfdbrSLbCyAFeshzxVtWsfOFW29Xs+63a612+1wHpzbZ8sf80SUbYKKHa8Xff627EHM5dcpR2xg0dfB37EpjbOJiz4ncM0helhbTg+pW8/WPiZ83B9btcZLVS8vL63b7drZ2VkQfbfbDefQarWsXq+HC6YfHHfQ5bd8vjoF4dQkw4NUKkuhdQSxtGLsb9yXOj8OaMKix6YtsSBq0fD96a8B/Dhh6ZEyarfbod202fqPFIMBovAMfqzz+XwtD4+oPtx41K5j0Qq/b6fTWUtdNZvNcIEXgsU/Wt0XE69ZfAWfxio4WMmr3rgCMatUmK9jtQX6PfJ5YoqF/4k2DsV9ThwXfU7wg8KimW63a/1+P3SfRZ08jkVu3mxd+OqualoO0wQ0txyNRjYcDtdW6WHdPi4Qul7znJ879WhhEc+Jee84TU1yyyy28lpuzFON2O66qekHzo/Fzh1/kKbj94z9n5w4Lvo9wOKZZrNpnU7Hut1uqJfnuTRbrtlsZqVSKQgsBv/Q8TzuQz8ej4NoEDxkFx5zegwMemFLDyHhWjMMsb3hud9frEyYewXwNAPvr4MABx91+qGCR+ZitVrZ4eGhtdttM7Pw3KwCJWcdF31OIEAWfa/Xs3K5bKPRKAiQLSUX1WDjCO4Ow+jcnwcAnl6MRqMgEB5keCkvC4t3ecXrax08C5itMnf2ZUHidTSrwZ6Ixhggfr6NAUk9JQ1sTqdTK5VK1mw27caNG8Hjwvk5u+Gi3wOuBGu1WnZ0dBR+gOxKs9VHug2VYZjHc4EOSKXC1GuIRcd5gNFGG3wsexUYAPjzxV4nZUlZ9Ih3sMeRJXb2BDAtiA1MsPTlctm63a4tFotg8eHqO7vhot8DWEJuiFEqlcKPly09B5ogoMlkEgJ4LF4VPkfU81STxRppxCLw/D6xz8jPjb0GH8PBOgx2GPjYpcd11hSErb16JJVKxXq9npVKL7YS53iIsx0X/R6w+4s+d2a24aZynT43wyiVSsFq8e6yGuFXwW+rfMPfGjNICT72GqnnpfL2GoXHbd69l2/DFed5PQf9arXaxneF72C1WoUttxqNht25cydsK1b09FweXPQ54Xl1rVazRqNhi8UibFvNEfNGo2GtVitE0i8vL63ZbNpoNFrrBovOO4jac/pPc/wpwaZ+9FrswuxSZqs585joNe6AwiQeCDhCz1MODhhymzH1lPDamEJp1yFnd1z0OYkJvlKprO3zxr3h0McNXWH7/X60FzxvCFEuP9+gkot8lFQF3S7nnxJw1nO2gQGCz1ctNdx/3OYVhovFYm0FIix+tVq11Wq1NlBwCXRsXwEnGxd9DrQ4B0s2sfDF7EUbLfTPQxnt1dVVKKVFoQ1308VlOByG8lvuEqu94bXqLKv+ndnV6mcF7fRxLbhRix4LDPIUgC28pvV0ytRut+3OnTt2584dOz4+tkajkRlkdDZx0eeELb2ZhS2f2dJpQQnv68bbPsEbgLVHh1h4AYPBYK2RJC+24dx1rDqOpwKpuX8qVoDPabYeFGTLrYLmNt+xYhzOAnA6k8WuQT2+DwNAq9Wyk5MTu3fvnt2+fduazaZX3+XERZ8TTk/BVeWacJ1/80Ia7pCj3V7ZK+BpAXaF4bbRGACw5JZ3itUdYoDe3iV6z3NyM4vOzfE9sIXm21rcw4E9jeTzJiEQPa8hqFar1mg0rNvtWq/Xs06nY41Gw937nJS2uIMeIUmQZUljx/JzYgOCLquNbQap1h7Hal+91JZQfL1te6hYRSBPb1JWm691ma9eVPwobOJKPX0ttv5cNOREiY6ELvprgL9DjXZve07KM4h5CKmL7gPH8/7UnD+2+Ec/Q2pBTGyOzl5AbHENXjdWSJSKCcRSgXqMW/hMXPRfZ7i6LuWFZOX5YxH9LLHybRffW4uL3nEKRlT0Hsh7C9m32ORNWNtXXRjjHsT145b+K0Teohqn8Lil/6rjYneuA891OE7BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BcNE7TsFw0TtOwXDRO07BqGx5vPRazsJxnNeGW3rHKRguescpGC56xykYLnrHKRguescpGC56xykY/x+93eMiATmXjwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 32\n", - "Current iteration: 37\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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xGAzQ6/WMEO298GTTDwqez5Hvw2Nlvz8ez7m8jLpzgJAehf25lDeHin4N2OQil8shHo9jOBxia2trJuUW1JrKnh/byMcoeAqO22UxZSVdZbnqbjqdmgGi0+nc2QSz0+kYwcqqN5eIKXi5K649HZDnKeMAm7gHXxhQ0a8Jl8NGo1GMx2MTnJMW3jUnlbvXBKXi+Lt0vWVk3Pd9ExBjaktOEWQ+mzvfcm7dbreN6y2tt0v8clCwg3wuzyVoVx7lYaGiXwPZ/MKOzAeVzNrPtyPnEhlgo+A7nQ7a7TZarZZp3sGlvRww5K4wg8HARNMpeuni2wE6Wmk5CNA9p+hdJbUv08dfeTPMFX1Yq5tkgGyegO0Amm2pF6XP5llOubsLLTZTYcxz2+v5ZRCPouc+9xwk7K2wbXddptJssdvCVsFvJnNF/7L/RFcTCYrhvi6QRa+1SqFK0IKWRc8JGhhcn9XOc9uRdBm4YySdVW2NRgP1et2IngKWQUG+PnfUlZF1262X52HPxWVjzyCrvuz3qjws5opeNwf8Eikq+TtxlcXKx12vJ0VtB8zo0ktrTdE3m020Wq2Z7bHt86Ol52tT/LLE1ZWv530peArdjsJvgtjtdOmbXoz0UFBVL0HQnFtaRj7O1k1BkXta2XmpNFn0IuflvHFxit2QUyLfhzl3O9dui52Cl58R2EzBA181htyU831dzBX93/72t9d1Hm8cKdLRaIRIJGK62tgtlRkhZ+CLwS9aE1l0A8wW18gKNopZzrcpUFn4QmsvBwa7N58M5FHIjMTLfLycw9uCd7nvmwS3zpKZCOUukXn/4B/96Eeb+d9fk3g8jvF4jFarhXg8jg8++AA/+clP8M1vftMcI11zCrLT6ZiiFApfLo+lhWd+nRa71WqZNJodZLNdf1kkY69Ak806ZOceWT0n3XpXgE4OAACWGgxWHRxc3s+qz5WDG72qaDSKZDJpdvppt9v47LPPVjq3t5HpdOqcz8y19L/5zW9ezdlsCLVaDZVKxTRq6HQ6xpKziIVWmCvGJpOJOYY5ewqQxTW08vJ5smRVtoaat02WrMKzW2pJa28H6gB3vIE1BNPpdKaeQIqMr+16jUUiXlXkrjJlWnM2JOE22LlczjQSbbVa+Pjjj/H8+XNMJhMkk0nzf1B0Tj+XTz/9FL/85S/x+9//3lhQNsRgtxxp8RlYA+5uPGEvQaXVZupMClu64PZKN762LL2Nx+PG6tG7CCqWiUajZuCQ1tP++3g8npkuyONd4uVgZ79f0PE28lxkizEuSGI9BMVeLpfx5MkTfP3rX8fR0ZFZ2ZhKpdDr9XB5eYlWq4XpdGoG302dttw3c937SCQSym9p3sXNTSm4fxyAmYUqrtJT2yLO+91VAGO/lrSC9nbSdixBxhPs2gCJnWt3zfeXFY1c7msvJpINRO2sB/DVBiBsIhqPx83mHuxMVCgU8PjxY3znO9/Bd7/7XTx9+hTFYlG3rr7L6u59WFN2vHhcmyMwwAYAiURixn1fx321V8ctqiuQ4nANBrTU/J2uOr0B+fnmYWcn5rnG8rPIXXtkb0DZN8AuauLAYPcWpCsvpy3b29sol8s4OTnBu+++i6dPn6JcLi/xjStkrqrDtg3QqrzMjikULDMFwHJitKcNkUjE1P5L958WNRaLYTwez4jK9XpyAJLnKC28PSWQnoV0v3ljhyD+ZOcgWbosPQJ70JADA6dGsVgMpVIJh4eHODw8RDqdXvdfEFrCacpXQLrL971qzBYTxbnofHguAGasu33Otlcgz11aWbuDrxww5LEylkBR0oqzCSgFznZgmUzG9AdkpyC78adt8e3yZ7mACPiyF2GlUjHLmHmMjCvc5/9pU9H96ddkkWt7X7isadD52JVm9gVOSzmvdTbFRatLQco5dFBLbx6TSCSQTCaRTCaN0D3Pm2kKmkqlZjbtWGZRkkwbMrDJTMloNDLvx11/gNnBWZmPiv4B4rJQdiqO1ttVoGPPj+3pgBQ8019s1S1bdnMebt9ki2+68bLbry12252Xn0d+XjuIaIt+a2sLw+HQvKaKfD1U9BuCnRsPcl/tNJdrJaC02hQtrSfdc1pwCtd1k8KXNzlYuFx5+TnkZ5P37ZoBOdi5AnzK8qjo3wJc+Xs7lecS/NbWlrHQsiW37NcvhS8FLX/aHoGdPrQFHpQztx+TAU5+lul0OreRqLIYFf0GElT4YgfFZM7bLniR83LbVafYeV+KndF3Pn+e2F1deucJVQ4GvG9XH6rYXx6dFCn3Qtgj5ZuEWvoNxA7MEXvxjEwByoVAdj7fzo27avhlk4/xeIxEIhGYBrQj87JgaF52wvZgXAVCdrmvsjoq+rcAW6CsEJQ1+BTiaDRCLBZzdriVffK53p9puUXBPG7oKQtwmCd2ufdBqUSX8O0uQ8DdNf7K8qjoNwRXvb7L4lHwTGdR4HadO1N2st11t9u9M4e35+1ybi8LcmTKjvc5WMiUncsTWJSy49oGLl9m/ECFvx4q+gfIomCXzHW7XHEusuFP/k2+hhR+t9u9I265ss1eBCNr6mWenkU5dnEO9/OTW3wtyrFLKy87Ao/HYzOY6FLZ9VDRL+BVluFKlolu83wWucsUetB8n3/jTc7lZf7bHlRk9Nyu5Asqw+V9WYZrv4dd4gt8VQLMz8PWX8CXZbgcbJi71zLcu2gZ7pq8yjJcW7CLrJ88fl6fffvil5/B/ixB9e/yuUHLe4PSgBwA7IIf14IbKXa75FfuBUjhc0twAGapLYuQtEJvOXRprQNGuOetoqO1i0Te3NJaKRb5GtKtd7W2Josi6fZUYZnPIgcAWeZLC2+7+EGlw/YUgySTSZTLZdze3iIajcLzPF1ptyK6tNbBvE0+ZH0555WsDZebSrpwCcx+zNVAI2g+Ll1zuZedPXDIQel1N9Gw79sxCfmZ7OmGnELwd24U2mw2MR6PjWHSJhrLE05TvgBe3MlkEt/73vdwdHRkasEZ3ebFJtNbcm856bJLi8l2WXLPOLsPni1SW3TSJZYlt652Wa6IuByY5kXOXQOQPFYSNIgEHe/C9hhk7ED2xkulUqjX66jX6zg7O8Ph4aFpl5VOp9Hr9VCr1WbaZYWRn/3sZ87HtV3WHN577z38/Oc/x/e//30Adxtj2r3re73eTMMKXrjT6XRm11m7MabciIKDwaLGmC6Lb5eo8jVkg0y+FhC81Zb0EKTYF7XIvs/AmR3ck16AFH82m4Xnecjn89jd3TWNMf/+97/js88+M9F+Dr5h4vz8fPV2WR9++OGrOZsHCqPAzWYTsVgMH3zwAX7wgx/caYE9mUxmNqNga2sKyg6y2aJnv3u7Bbbsiivz53YzzWVaYEsBDodDc9FLz2NZwd9XC2ye6zLPs9+bg9lgMEA0GkW73Z5pgb29vT3TAvv58+crn1tYmGvpP/7449BYejkflJtdVCqVO5tdADDWU7rotMD2whBevDye3gE3uuC+dHKzC+alXd6E3MBCnn/Q+/EnPQjZ9941b583GPD1HyKc5vAzhp1pQN/7uaIH8DD/u68Z29rY5aGy4WRQVxhZaGJPDbhhhm3le72e8Qx83zeegdzpxv7/0ZLK95Eeg9z0wiV2O+q/SaK3YZ56U873vhkOh6u798qXBBXOzCtekcfzonPlpROJBNLp9IwgKVgZA2i1Wmg0Gmg0GittYCkDe3ZxDgcsu0mFvVBHTgn43E0QUlA1YhDzUpiLcD33ZV7vVaIpuznYopaP25bPNZcOmgNzDp5IJExpq2uralrqdrtt8tEseb25uTG16HYFHl17xhzsVJadWeB7s+YA+EowHBBkIExezA9Z/K7A3aLzfZnP43ruQ/x+5or+IY5SrwPbci46Nuh4uwyWj8mKOg4Arnk0m0DKllWsYU8mk+h0OiY6L6cUcq893/fvpL0Gg4EJWtq76chgpL3nnSv/LwfAh3iBK3eZK3q7R7oyi7SStkcwbwAIKlIhFBlFapfbsvV0t9ud2TTTdu87nY5peyUbWto738pUnkzt0bq7xC+tP3/fFLc/7Oicfg1kEI8iiUajZscbebOfZ08BXCW4PFbGCGRAjUUrDM4BdxcGDYdDdDodZLNZs+qN2QLu0sPYgRS73B1X1gvIoh0Z5KP7z7+73H4dCB4WKvo14dbTdLHZSQbATEksCSp4sUtRJbb1lqJPJBJm80s7TUjrzOh/Pp9Hq9UyBUGyGIiBQxk8lFtk9/v9mSIfafk5NZDegmtwUB4WKvo1YHGO7/uo1+vo9XpIpVLY2dmB53mmVtzOnds3wL1NFZHLTLe2toyIWIxCkdlNIylIGcxzpQYpdgrWThcy3Se3z5Z73svn8FjpQfC7stGB4M2iol+DyWSCTqeDy8tLnJ+fo91uI5PJoN/vo1gsmsU4XIVnC5+4ykxta0/3mcIHYOb0tpW3pwKy/ZW03q7yXgqV7r0tYlnYw2mALCJi8RCrC2UtgYwdLCrlVV49KvolkdHr0WiEVquFs7Mz/Otf/0Kz2UQmk4Hv+6hUKsjn8/A8b2attz13Z0COi0FkEEwex/eV+8rZzS1dRUHSzZZitVOD9Ar4U7r60v13ufIUfrfbNYJneXGz2TQ3Ti2CCoqCCMqe6IDxcqjo12A0GqHRaOCLL77Ap59+iuvraySTSTSbTdTrdRSLRRQKBdPhxd7pRfabsz2CoAIg+btcNSZrCeznu6YUQbUEMlAoB4V5pbkcEOgZsHqw2Wzi+voa1WoVFxcXuLi4wOXlJRqNBnzfn7H+riYd9rkp94uKfg1GoxGazSbOz8/xxRdfoF6vIxaLodVqoV6vY2dnx1h7pswobnaUkX3kAHdKTyItukzRubIFL1M4s0zUXQYl7WlEt9s1FYT1eh21Wg21Wg3VahW1Ws2Iv9VqmbUFnFKsswpuE4qEHhoq+jUYjUbwfR+Xl5fmIgaA29tbNJtNeJ5ndm+VnWHZgCOXy6FQKBiPwLZ0Mv3miu7bjwWl/YJYVIq6LLaXQOtfKpVMPIALiq6vr3FxcYHz83Ocn5+jWq3i6uoKrVYL7XZ7JnAoA4HK/aOiXwJbCAzkca56e3uLyWRiLnLZJ072g89kMvA8D4VCAeVyGZVKxQTK5HvI+TtxCV3+bh+/Lqu8hivTwBWJcgrAUuJms4larYaLiwvUajVcX18b0csVhrT+Mm0o6wlkFsL+36jlX4yKfklkIM+ubedFGY/H0ev1nL3e2C46nU4jn8+jXC6b+nlG0WnxmfOXUXngrmV3if1NlU673ld+B8lkEtlsFuVyGU+ePDEpRNYM2EuAZVbA933zPbfbbVSrVZydneGLL75At9udOQcOmGFfNzIPFf2ayGo8/pQVacBscwsurEkmk2ZOKy0cX6NQKCCTyZgAILFX77lc/YeAy8KyriCRSCCbzTqzCjKl5woOdjodDAYD+L6ParWK58+f4/PPP8fZ2Rmq1SoajYaZGsj35Tk91BVvbwIV/ZrIKDYtv50nJ9JiM9BFF1Xms3krlUrwPA+TycR0kZXpO/LQBA/cda9dQUm5pkPGBYIChLIHwHA4NKnBZrOJ09NTPHv2DH/4wx/wj3/8Y+Z9mBWRDU4UFf1auDrjMM/N3+VP2YRjMBhga2vLLJuVTS7kfLZUKiGXy80Vz0PGda7zMgPy87mWMwdxeXmJSqWCra0tFAoFXFxcoNlsGsuv3EVFvwbMlbPwBgjecdV+HoAZ15ZWTBa50AvY39+f6VxrC4OPbaIVc6Uk12F3dxfvv/8+CoUCfvjDH6LX6+HZs2f46KOPUK1W7+NU3zpU9GvA+Tlz78Cs6Ome2o+7PAMeK0VP4fd6Pezv76NcLiOXywEAtre3N7a/+30MTK6B9ODgAEdHR+axd999F59++il+9atfAQA8zzMbeSoq+rVgND6dTs9Yexv7ArXnrLFYDP1+38QE5EaNLGf1fR/dbhePHj0CMOv6ykIdu4TXvr9pLCoMInbFIgB861vfwk9/+lO89957GI/H8DwvlC2wg1DRr0E0GkU6nTZr1V1zUHteb/9NBpj4WLPZNBZftsmWy2FLpRKy2SySyaRpsHHfufqHQNDnWObzJRIJfPjhh/jxj3+89HPChIp+DWKxGDKZDAqFgllK2+l0zN8pwnkFInaQT65PZw0A22Szlv3q6grHx8d49OgRisWiEf88d3/e4GOz6JznPe9VHLsscrrEOodNC3y+TlT0S+Ba7up5HkqlEvL5vFlNR7d92YvNXsDC1XNcyNLr9XBzc4OrqytUq1W8ePECtVoNJycnODo6wu7uLvL5vKn4kzUB9nkve06vSiivUoAUOKc7ynxU9GsQi8XgeR7K5TKKxSI8z0O73Z7pJ89W0sDyHVhlg0oZ3GNemgtYTk9PcXBwgEqlgnK5jEKhYBb4sN6fJcAPOej3sktsg46V32GY0f3p7xFp6Sl87j7DenApZGA14dsXLttY+b6PWq2Gzz77DDs7O+b9d3d3sbe3h3K5jFKpZFb5ZbNZpFIps+Em5//2+9rFRNJyLjpnV/uvoGXC62JPURbFL+SKROUuKvolsReXpFIpFItFVCoV7O/vo9/v4+bmBp1OB77vA4BZKbaq8PkcBvvYjrrb7aLVauHq6sosz83n89jZ2TG3YrGIfD5v1vNnMhmznFfu9S6768jaf35WDhLsyCuX9crnyhJX1tknEgnTupvlxNyrXm6rvWwRjnxPrklYNKjofD4YFf0aRCIRbG9vI5fLoVKp4PDwEKPRCOl0Go1GA5FIBO12G5FI5I7wgeXFHxSEY2qv3W6j0Wjg/PzcNOSgwLmUlxtkUPR0+ehByDJXvg/FLpt8cKpAL0TWywMwxUqZTAbZbBb5fN7cuNSYXofs4+9q8R20ZJfnyF1rORgpq6GiX4NI5MvuN9lsFnt7e3jy5AlisRiur6/heZ6xbsyx9/t9RCKRmUDfKlF1261llJ9NK1znJ4VLkcmaAlnbLgUlPx/FxSChXAMge+Hz/bhFVzabNT0D8vk8crkcstmsGYDkTj0coDgIyAHA1ZorEokgl8uZ9Qmsr1eWR0W/JtFoFMlkEjs7Ozg8PEQikTBzaXbLubq6QrPZRDQaNeKyN4Ykq4h/meNkBkCu9pNVgbIlVtBnlPveBy0oAjCznNj2OOh12L8z8JjL5WaCkBycKHquuOt2u0gkEjg8PMQ777wz857K8qjo14SluNlsFru7u2bZKINntF68KH3fn5nbLzvPvw84CKwKd8pZB9eyYg4IvLFlWDabhed5yOVyppMwPRK5p1+v10Mmk0Gz2UQ8HjeDh4xTqNVfjIp+TRjsSiaTyOVypmCHc1cZNZeLcnq93p1IufwJfLWxJJm3Ou2hYg809Dj4fTAGILsMMfjH4KH0Sthgw/M8jEYjZDIZlEol7O3tmW49ynKo6NeAAadYLIZUKoXxeGw2n+D8NZfLmSh6sVjEixcvUK/XTYSfHXPkZhN2ld7bghzQRqORCXByjs6pg3TXZcaA3X85aBaLRfi+b9YtKKuhol8TBrvS6bQpxKF1KxaL2Nvbw8HBAU5OTkwDzVqthqurK1xdXaFer5ue8Le3t+h2u2u54EGewrxjXxZXz0D5eNBCI/v3IMHKlCGnAnIAYCAvm83qfH4NVPRrwgszEonM7GPHIJfcG+729hatVgvX19emMWS1WsXl5SWurq5M+yyKX7aEtvPhNi6hPSTrZw9K9ipBWnqKnFkDOfeX2YdKpYJvf/vbeOedd7C7uxtYdaYEE1kwR9yMCeQbxK5G42P8yQGAS2bl6rmbmxvc3Nyg3W6bFlr8ycflSjv+jd11HjIMYjKAJ911GdTjfJ5xkHQ6bW58jPERZkhOTk7w9OlTlMtlpFIpDd4F4/xiVPSvAXthjbTgsoMO01K+76PRaJgpATeK4LSAu8/KLaflvnRyye59IuffstTVdsVl22+KlYE73qTYPc8zcRDGRBjZt0XPPgapVGom2Kc4UdFvCoPBwFj5RqNher5xf3lae7ndtGuPOiB4nr2oRsDlgts19TIlx4i7jMrb5bd2aa8cIGzLzki+HDCUlVHRbxJyfb19k8IO2mvOjgHMiwfI+67luPKnfT/o5tpfz3Xf9hrsQiC15C+Fiv5Ns6j09m3rfnPfyO9Nv6OlUNE/JOa51IpyTzgvKE3ZvSEWCdzlei/Kfy/zuveJ/f4v2zZr2ceUl0Mt/QNnGaFvEsuIWIV+b6h7ryghwyl67UCgKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCEjvuDvkddyFoqivDbU0itKyFDRK0rIUNErSshQ0StKyFDRK0rIUNErSsj4/6QdvrwRM+prAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 64\n", - "Current iteration: 49\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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SnmZ7uo/er9eensU727Doz4F+RSRGOVbrflqmU3ZfoVAQZbYpCYdE32q1UKvVkM/nkUqlEI/HRe+8bDaLQqGAWq3WtUeXl/rjog7I4UHhYuklejbkTZFpz/LyzNpsNkXf+mQy2dXmutVqCcFT3nipVBINMxOJBFKplOhrf3JyIoQuz9qTFgO5iBJdzOj0Ff00K8JcJuiiVe+T+z1Xpp/FXuvvXq47ealeq9WQy+UQi8Wwv7+PeDyOdDotRE/nC/ynnFe5XEYul0MmkxEx6jS7yyGq6o67w8Civtz0Ff00aqupl7y0/53WhTPoteSl5jCvNe3374W6CITW4zTDl8tlZLNZxGIx7O7u4vXr14jFYsjlcqhWq6cGZ1reU9PHcrncJXg5V0B9LszVp6/o1WWI9AqJql+I6qjuOXU2nuw6k4NdSqUSstksjo+PcXBwgL29PRweHorGmLSnp9ek5TqtDiimnYpG9lptXFb6DepXof7AWcCqHgKaieVY72mFzrZaLeEmoxvN0LlcDul0GolEAvF4HMfHx0ilUshms8IfrpUrTvtzEru8f79KSSnkBqTaA+pVpdyGi/mevtb7Z8+e6ebbkkXTaDRgMBgQDAYRiUSGFrXsN9fyS5MLjJJRyuWyCIIpFosiqo3+pj15KpVCOp3uMsSpU0d7vR8V4pDbV11lERiNxiv/GYdlLJfdZ599pqtvzmw2i1JLZrMZP/nJT/CrX/0K4XC473FUKJP215RvT3572W9eKBSQyWSQTCaRTCZF8kmxWES5XBb772q12rUCoJldLgwBnN4yyF1p1Zb5ixbDpN6NQedus9kQDofRaDSQSCQmeq+rwFguu7/85S9nczaXBEVRcPv2bfzoRz9Cu92GoihCWPK+uVQqCYMZ5dVT4Qx67snJibCoJ5NJHB0dIZFIIJPJoFAoiP05udNoD65Vv11d/VVeWWglz8zKrDfN86B6BW63GzabDU6nE4uLi1haWsLJyQl2dnbw9u1bdDodkQPBqbf/oe9MbzAYZuNquSBcLhc++ugjrK6uCpGrI+8o+YSMZnKEnDxAkPBpBlcURczgdBwJfRjUBkQ5i09GHUFH900Trfc8S6xWK5aWlvDo0SP88Ic/xPLyMsxmsxgEaLWmKAo6nU7XiktP/OIXvxh9ea9X0Q/r5puVC0k2aKn39vSTZrmLcNWpz4/uUz9H/VNuk0332Ww2BINB3L17F59++ik++eQTzM/Pn8vnuISMvrzXq8vOZDKh0+kMbI4wC4IHep+HLCx5r6/1uPp1pvHZrFarqAvocDjgdDphtVphtVrF1kceqNQ1BWW7CK2U7Ha7qC/4wQcfsODHgGf6KwIJh0SitccnBkUCqo9RXyPybCwLlvrJ2Ww2USXI6/XC7/fD7/fD4/HA7XbD4XDAYrEI4dNxZrO5a1CwWq1iUKCtkdVqxerqqigVbrVaz+DbvDJw7P04mM1mTUEA2pF0w3DWMeq9qvOoke0O/Z4rDygkahK2w+EQYna73XC5XD1/Op1O2O12WK1W8VpyhqAsfvlxAEL0BoMB4XAY8/PzYlBT9/QDONkH4P70Y3PZikHIAlWLWb3cl0VGs65c2ksWo9lshsVigd1uFwL3+/0IhUIIBoPiFggExOMkcHkpP8y+Xg1FLFJ8gs1mQ7vdFoOCXreh48Lf1hVCFjmJXl2fj+43mUxiKW21WuFwOMQenJbeVqsVFosFFotF7Mlp5qZqv4FAQCzfqfKv3F13WlitVthstq6IQrXrkhkOFv0VQxY2LaNpuUz1+Olmt9vhcrnE0tvtdota/VS9lwxx9Li8VHe5XLDb7WJGp8HiLD+bPICx4MeDRX8FoRme9t+0fCex08xOIvZ6vWKW9ng8cLlccDgcXT9J5DQgyCsCdRddrYAiYLKageo9+rRrF+gJFv0VQu3jlvfptIy32WywWq1imU6NOaghhyx6crXJNzpeqzTYpElIwx7Dgp8MXh/pmFmzbs/a+VxVeKa/Qqgj8JrNpiifJd/fz8Ul176XS2bLqau99tT9gnumkWwjL/F5th8fFv0VRBY8ublIzBaLBfV6XeQLUIWdYrEoDHm0lKe9u5Yhj/b3tOS32Wxde/xpQ1F58sDGwh8PFv0Vg4JVSPDUSruXy05RFLHXt9vtYs8uG/7I0i8H4mi57MgmcBYuO+D75h3A92HFbMEfHRb9FYL81nL+PHXCBfoH58i/k5DIWEePkdXf5XKJ1ttyYE4wGBT+enLn0QCiDhYaJUBHKzjHbrez4MeERT+AScNwKbVW5izDcOWqOb0Kbmql46rdbmrkQYJWBmThdzqdXaG48lZA7duXXX3jhOEajUaEw2E4nU7xeeSSYRyG+z0chjsmk4bhXkThBi2D2jQTbuj58k2dNKOVcKNeAdAx5FakLYWcdEPCVyfctFothEIh8TxmeDi1VoNhU2tnDXm5K8908u/9BqGzSq3t5efXmunltFo51oCKmNjtdkQiEdy7dw8ffvghNjY2Jj5PvdFX1Zct2WRaDNvkY1aKMPbKkLvoIhrk8qMyYwA0txHqFQf9LRsdCbvdjkAggDdv3qDZbMLv92NhYeFMP8dVQ59T+QBIDG63Gx9//DGi0ago4kAXIV2Ycovner0uDGfqfXKr1RKuMipxTU0ka7XamZTLUlf2uahyWcOuNIYlk8mgUqmg1WohkUhgaWlJ1MzzeDxotVrIZrMoFovodDq6XbH+8pe/1Lyfi2j04f3338evf/1r/PjHPx5YGJN6uZN/XF0NVy6MmUgkcHx8jOPjY9FQkgpjUr086hbbK45dRit/Xl0Y86qVv6aaeC6XSxgVFxcXsby8jEajgZcvXyIWi6HVasFms2kaVK868Xh89Bp5P/3pT6/GFTIkJpPpVAnsL774AqFQqO9xnU5HlK4m0ctGKrlX/KAS2FQsU10Cu1KpiO43vSLT5Ci5y14CW71KAbgE9qj0KoHdV/TffvutbkQvG8Go6u3c3BxWVlaGvliHaXYhV8+lZT61nqYlf6lUQqFQ6Gp2kclkkM/nUSqVuNlFH2bFzjILjCV6cH96ABANJrTaWg1Tlqof1GxS3dZKURTk8/mutlZHR0ei5XS5XB6prRWJ/qq2tdIaBGkvfxU+6zg0Gg0W/bhMq4FlLyjiTG5iKTewzOVypxpYUpvqQQ0sKWFGblF9ljXwzxt5daNGHqTVz9diknh+rWMvOj+g1WqNXhhTry47QvYf92PYAg/9LOlU6Yaqu8pZb5VKBYuLiwiFQqKqbDweF62q6f8ki562D9RUo9PpiG2LjNbe+TLR79x7RSSO+3rjnMssfrd9Ra/XLKZR/lHj/lNJbFpuNhk5JNVkMol6dYFAAOl0GoqidHXVAf6zHSmXy8jn88hkMshmszAYDKIfHr23OjZ/2M8zixcyMzx9RT9ohmNGt0j3+rtXuGyn04HRaBRBKQaDARaLBR6PB5lMBqVSSSTakK+eRJ9Op5FMJkWL60KhgHK5LPrlyUk5soWfudroM2rhjBh276iF1jJbtheYzWY4HI5TgwDt6eWgIep5XygUkEqlEI/H8fbtWxwdHYmgFZr1ZVsCMNmWTm3fYCv6bMKGvAlQZ3hp1Y2b1vvQe1EEoFbDTLkFFMUF5PN5Yf2Px+NIJBLC9UeGvnq9LoyHVDGHXptuvYJ96PyY2YNddmdAq9VCuVxGvV6H3W6Hx+M59ZxhoukGIYewykKUvQpy2K/agq8oCnK5HHK5HAqFAhRFQbVa7SqHRQOJHBBEAUe1Wq1rVUDPp/Bjes6gz8krgPOFRT9lWq0WFEXB27dvkc/n4fV6sbKyAp/P19VldRDDrgxk4csJNPJrqF9L9s2T20524cm+fDmHgMKKKVCoUqmIlYW6zFahUEChUBCrB8pB0LvnZxZg0U+RTqeDQqGAvb09fPPNN9jf34fH48GdO3cQjUYRCoXg8Xhgt9v7uu9k//ooM77W/6zX8Vq+Y3l5LvvuaTBQz+C0/KfZXl4ZyMlDiqJAURQRapxKpXB8fIx0Ot13Zh/m859nduBVoZfo2ZA3JHKgRavVQiqVwrfffou//e1v2N7ehsPhwMHBAd59912sr69jaWkJPp8Pdrv9VGspeSk+SsmnfsFBo7yG+n3lwUQrQUdOy5Xvo1UEDQS1Wg3FYhGpVAoHBwd48eIFnj17hlevXuH4+LjnFuAsfefMaVj0Q0AXOgms2Wzi7du3ePbsGZ49e4adnR2YzWYUCgUkk0ns7u5iaWlJNHOU20S53W54PB54vd6uOm9aCTS9GMdY2O/1pxFVKFMoFLC2tobV1VXcuHEDh4eHSCQSSKfTSKfTSKVSIrtwEmOg/N3xwDA8LPohkS+qZrOJWCyGly9fIh6Po1KpiOVxKpXC1taWqBZLAqfKsfPz84hEIlheXsb8/Dw8Hg8sFkvXzHsWHoDzDLSiAW1xcRH3798XkYFHR0fY2dnB1tYWtre38ebNG6TTadTr9XM7N4ZFPxTqWaTVaiGXyyEWiyGXy3UZwxRFEY0i5U6vVCsuHA4jEolgbW0NN2/eRDQaRTgchsvlEsFQo8z6Z/1Ze92nfkydgES18H0+n3juxsYGotEorl+/jps3b2J/fx/ZbFbUEqD8AC0DI7kQaQtRq9VOhdnyzD8cLPohURuSyBVGVW/ofrJak69bURRR/ZUaR/h8Ply7dg337t3Do0ePcPfuXSwvL8PtdgNAz7LV58GoSSP9ogzVOJ1O3LhxA+FwGJubm8LdSQZCWez1eh2VSgWFQgHpdFqkHieTSezs7OC77747VcOQ4hRGqUCkR1j0Y6DOigO6Zzq64OQLmDAYDLBarTg6OhIVc6hgxrVr18RyX46Pv8gciEmyztTCo0hCu92OcDjc9/h2u41KpSLSi3O5nBD9ixcvsLq6KjIN0+k0jo+PRQq0/H7yueg1l0QNi35MKEJO/lvrd63j6vU6stksnj9/jmKxiGw2i2w2i4cPH+LWrVuYm5sTVXyAyXP2Lwp5ABz13I1GozCCzs3NiRVBtVrFw4cPUSgUAAC5XA5ffvkl/vjHPyKVSnW9Bg2eWjUH9AyLfkx6pdwOs6ykpJhUKiVq61EATK1Ww/r6Oubm5kRv+ct4sfbbngzac8vHGo1G0VCDWF1d7Xq+2+3G0dER/vnPf4ptQi6XYwNhD1j0Y0AJMLRXHzX6TL7ga7UaDg8P0Wg0UCwWkUwm8eDBA9y7dw/RaLSrSwn5zgd1o5ll+q2I5DDdUT7f5uYmvvjiC3z++ecwm8148uQJfve73+Ht27fTOekrBot+SNQ57zabDS6XCxaLZeKQ02q1irdv30JRFFEPj8JZI5GIiO67Cr3bplVpiH4aDAaEw2F88skn4vH33nsPL1++xB/+8AcA/1kJmM3mU3t+vcKiHwJ12qvRaITL5YLf74fD4UCtVpvYWtxoNJBOp4XhL5fL4fDwEJubm7hz5w5WV1e7usFqxd9flMX/LNFaGciRgXItPOLGjRv4+c9/jtu3b6PZbMLn84nsQ4ZFPxZGoxE+nw/hcBherxf5fL7LYDXJAEA+6EQigcPDQxwfH6NYLKJeryMajcLj8QhR66HISb+qQv2O+eyzz/Dpp5+OdJxeYNEPiXzhmEwmhEIhXL9+HTs7O4jFYlObRai+HSW6nJycIJvNYnd3F3fu3MHGxgbW1tYQCAQGvs40893HifHX+nkW0KqH/POU6MRi14ZFPyTyBWQ2m7GwsIAbN27g6dOnMJlMYr84zUKTpVIJ3333HeLxOJ4+fYrbt2/j8ePH+Pjjj7GxsYFgMNhztlfnr0/KuAI6D+FRAZOrYPM4D1j0Q6AWsslkwvz8vAihlfeU02q2QO9JIagUoKIoCo6Pj7G2toZwOIxAINB18/l8sNlsYy2Lzxr199LLh9/LzSc/NijjkJp86BnuTz8hahEFAgFEIhEsLCzA7XajVCqdet4kaA0aqVQKX331FV68eCFi+RcXF7G6uor19XXcunULq6urWFxchN/vnxmxywwK2OlXF2CUgZQKhTKnYdGPCFmMDQYDgsEgrl27hmg02pU0Mu24b7kEFhWqODo6AgD4/X4sLS1hZ2cH29vbiEQiCIfD8Pl8cDgcIpZA7gdPn0POi5fPWc65l2v/y4JUd5Sh51gsFpFjQDeHw9F1/HlsFWZxwJsVWPQjIgeOuFwuRKNRbG5uotVqIR6PI5/Po1qtTnVp2W8QyefzqNVqODo6wtOnT0V2m9VqFYK3WCywWCywWq3CBkCGL4pgk4VPe2STyXTqteh85D55wH+2PA6HAx6PB+FwGEtLS1hZWcHKygrC4TD8fn9XJiFzcbDoR0SeQaxWK65du4ZHjx7BarXi1atXODg4QDKZFG62szwHEilZ+/P5/MjHDgOJXq62S5ZyWfR2ux1erxfBYFAIXi16p9MpOvnIgxENLrQqIduIusafyWSCz+eD1+sd8VtjCK6RNwGtVgvJZBKHh4c4PDzE3t4enj9/jufPn4tccRI+LZOveuloSiF2OBxwuVyifzzd5DRjqiDs9/sRDAYxNzcHv98Pp9MJs9ksDJmUxlwqleByuXD//n1sbm7CZrMBuPiecTMM18ibNkajEV6vF9evX4ff78fCwgKCwSC8Xi88Hg/evHmDRCIhutCctTVZa888aIAZpSDlMDSbTVEoU531psZms8Hj8WBubg7hcBjz8/MIBAIivZhmeEqxLRaL8Pv9qNVq8Pl8WFtbg9VqZcGPCIt+QqxWKzweD2w2G5xOJ9xuN+bm5rC4uIgnT55ga2sLr1+/FtZ9LWgPTb/LQpu0pzy93jDW72n69YeB3JGUE39wcACHwwGbzSYs77SVoNLcVIrL7/fDZDIhGo122Rp4ABgMi34CyMpN1W4tFgvcbjdCoRDC4bBYrgYCAezv76NYLKLZbIpecpSoI1fcmYRxtg2zUFqabBLZbHbgc0ulEuLxuCigMYzfn+mGRT8haleWwWAQbiq/348bN27g8ePHohpsIpFALBYTcfXDGN/UaJWxnrULXb1aofsmHVg6nQ7C4TDW1tawsLDQ5Q3g0NvhYEPeGUHfKy1Py+UyUqkU9vf38eLFCzx//lzE7VNxSLkwpPwaekErPoBcjsT6+jp+9rOf4fPPPz9VTIM5BXe4mQXS6TSOjo4Qi8WQSCRE7Td5yU/Gq2KxKOrEp9NpZDKZgT3jZhXaBlEAj8ViETUJqFqw2+2G1+sVQT2yK48GgXA4jIcPH7L1fjhY9LOAHAUn32jp22w2hc/96OgIu7u72NnZwatXr7C/v49kMin6zM9qbDn53ik6j4RLNzJ6UihxKBTC/Py8uM3NzQnx2+12ESNAUX/kz2cGwqI/T+T9K81Eo8aCU7htLBZDLBZDKpXqWhlQRJyWO/CstgbqGVVu1imH7JI/ngSqjgy02Wyi4w+Jn27kshsEz/ADYdFfRuTGkXTr58Y7TztAv4QZuYKvupovDRBySiyF/aoNo8xEsOhnAbnUE/2tRhaFXun3PXEG3dCw6GeJXv7xq1jnblyGKZPN9IVFf9lQF4KU77uMDMqf5wFv6rDoLyODIuZmZRAYR6ijFNFgxoJFzzA6Q1P0bA1hGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ3BomcYncGiZxidwaJnGJ1hHvC44VzOgmGYc4NneobRGSx6htEZLHqG0RkseobRGSx6htEZLHqG0Rn/H317AblXWxsZAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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2FAWgevHkVKOltGKxGLLZbIdVJhGTJSfrT8Uvu43n5etiLics+nNgHAdbv2PIr6mbXqlUxOIahUIB1WoVrVZLiB6A+F42m0U8Hhfr2x8dHSGdTiOfzwvB0xr3p1lmS3vPcoiTG4WLo5fo2ZE3QU5r5ft5qZvNJqrVKvL5PJLJJBKJBI6Pj1EsFtFoNDrOXa/XRcpqPB5HLBZDKpVCLpcTq+PW63VhtScV42ahXw36il4u0qAn6KGVreeg72rp1gD0suKDQnc0lTeXy+Hw8BCvX79GNBoV+egkYLKwtK7e8fFxxzieuu5y2rAsdqPRONRkIeZq01f0k6itpu3yyg/nJBh0rH6TX3p9d9Tza48/iuCB3vFmsvCVSgWZTAaHh4fY2trC1tYWDg4OkMlkUC6XhejpuOSUI088hd/I+XeWS2sxl5++opcXB9Qzcl69lmGniWo/176mcTU56+r1uphWSjHv169fY2dnB3t7e0ilUigUCmI1HdnRJnvZKcYud+Ovk+D7VdS5DvUHzgJW9RCQJZZzvSeVOttqtUSiixwyKxQKyGazSKVSiMViiEajODw8RDweRyaTEd168q4TJGzZQSdnz10njzuFAbvNdJTLiV2X+50Ufb33//73v3Xza2kdYQaDAYFAAOFweGhRyw+eNi5NwpNTVinBpVgsvvU3n88jk8kgkUggmUwimUwKsdPYXCtgGurIFp/Ofd0E34tJDh2vOmOF7D799FNd/Xpms1mUWjKbzfjkk0/w5ZdfIhgM9t2v3W6jVCqhUqkIT3q3uDnNNstkMkilUkLIFDcvlUoiIaZSqaBUKnW8R7H5Qc5DuftODdFlmHZ6VtENwmw2Y2pqCo1GA6lU6lTnug6MFbL7wx/+cDZXc0UoFAq4efMmvv/976PVaqFQKHR0JxuNhhh3Uxoq9RLkRR+bzabwqFOXnSx4Op1GoVBAuVwWoqYxvZx1N0jo2lTcXsk0F8kkz28wGOByueB0OmGz2WCz2TAzM4OZmRk0Gg28evUKR0dHaLVaomwWpwa/oa+lNxgMurL0WpxOJz7++GMsLCwIa63NvKP4ORV6kJ1+sg+AHHRyt5686jQ2p/TaUSINcuGNXqmugxqO06C13ufVsPh8Pnz44Yf4+OOPMT8/L4qWuFwutFotMRRqt9sd693riZ/97Gejd+/1Kvphw3yjhAPPEtmh1c2bfdEe+27XN0wkRB4mya9dLhdu3ryJzz77DD/60Y8wPz9/Tndy5Ri9e6/XkJ3JZBLx7n5ctNgHIVs3OUcCOPtrNxjeLL5ps9lEARGHwwFFUaAoCkwmU9caA7Q8lTYxiib4WK1WzM7OYn19HY8ePWLBj0FfVettGSDiqt23VsBaK0qNmDymHcepJltceaMCoLQOn9VqFSKnsmFURozG4GazGWazWQiffCDyJjdQlGikKApWVlawtraG2dnZkWsSMBynH4jZbO6ZADKuY+isu9rasT391b7XrTvdLYlIFiRZb6fTKTa32w2HwwGXywWHwyE2+pw+s9vtopKQLHht91+OfFBjRRONjEYj5ubmxDgeQE8/yGXviZ01vD79mFw1q68V0SDPvSwy2uTuNVlkKt9NVX39fj+CwSCCwSCmpqbg9/tFIVCqEmy1WmGxWDq67PL1DUL2SVChzWazCYfD0bG/Htb0myTsyLsmUDebhNrNqadN5KHvkDAtFgssFgsURYHNZhPddJfLBVVV4ff7xRYIBBAIBOD3++Hz+eB2u4U1PytfEEU55HvlVNu+8NRaPaDNESC65aWbzea3hE5dc1VVoaoqPB5Ph1UPBAKiyq/WotPY/izvje7rKi/iedGw6K8J2m59L+HL3yNPOll0p9MphO71euHz+RAIBDA1NYVQKISpqSn4fD5h0amHIFvbXsOJUQXaLf7fyxfBjAaL/ppBFpDG0d2gz8jSy952WpzD5/N1dOc9Hg9cLtdbJb1lJjEJqd9+NN+fxX46eEDEdOWiPN/aGYPM5GFLf83QTrDp1r2XP+/luZf3pfdp/E8e/X6FQU7bvdceR548xOP508GivyZoJ9hoc827OfJoPoG29j1NIKIZgVQ+O51OY2pqCl6vF6qqiu6+1WqFoihn6sijCTPy2J67+uPBIbtrQr+QXbdkoF4hO7PZDKvV2pFCK6+6S2E6+a8csnM4HGcWsqOS39p0XaYnHLK77pAVpCm5vZJzZAtpMBhQr9c7suDkGLicnGO327sm51ADQD2AUZJz+llqufdCq+q0Wi3Y7XaoqsqCHxMW/QCuWhqu3P2V01O13WJgtDRcagDkHoDL5erYqFdAKbiUfkvpuJSGS1GDbmm4csKNXAZLTsMNh8NwOp3iGntlTerdEchpuGNy1dJwge4C1064GQej0SgKifSaGddvwg01DjabTcy004pdDidSAyTn3lutVmQyGQDA7OwsnE6nbmeDjgtPre3CsFNrLwtaC92tUGa390dl1EaDKtpQpp/dbhcJQXLykHbCTbc0YqruqygKNjc3sb+/j+9973t4+PDhyPehd3hqbReGXeSDuqGXtQxTN5GfZ5eXKgrJogb6lwPvNuSgiAD9+9mzZ9jd3UWr1cLU1BQWFhbO43auDfo05QMgYbhcLjx69AgLCwvC8mvTW6lOHm3UAGitL4XFqFwWlcyicllUE2+S5bJ63dck6ZYu2+28kxheENlsFuVyGYqioFAoIBwOQ1EUkVHYbDaRyWRQKBQA6HcW3hdffNH1fQ7Z9eH999/Hz3/+c3x/QGFMWs+9VCqJJZtlbzXVtu9WGDOTySCfz6NUKok4ubws9KQKY55VjbyLwmAwwOl0wuVyCefi9PQ0bty4gZOTE+zs7CAajaLZbMJqtXZEMvTC4eHh6DXyPvvss+vzlAyByWQSRRVNJhM++eQTfPHFF0OVwKYy1b1KYNN8cLkEdiqVQjqdRi6XExVxqZQ2lcAuFotiAQyqd99r+NEtLq+nEthGoxGhUIhLYP9/2uPUvX/y5IluRC/HfKnqrd/vx9zc3NAPa7/FLkh45JCSq+KSuGkrlUqicaAVahOJBDKZDEqlkugNdJsjT+ejngj9mxe70B9jiR68Pj0AiC57t2WtRqkE041ey1oVi8W+y1oVCoWBy1pRmq1elrXS1gCk6NN1ud9RqdfrLPpxmcQClsOcQ15/jsb3xWIRmUwGsVgM+/v7HQtY0vLT2hLX1BjIC1hSI6BN2LnqDCoUon1/UAbgaSYGdXNqXuTcgGazObroG43G1X8qTsGw5Zi6/YaDvNrDxMzb7Tfr0clLVW9vb3csVS07DwEIKy8vi0VOQtnqXyfhM93p1b3vG7LT6wymUYQwjODpPa3Foddyw6LdV1sWmvLgvV4vksmk6ObLxzw5ORE9hFQqBbPZjHw+DwDC0Ujn1nq0B907NxJXn76i12t8cxRGaRj7JaUAbxevpPcMhjcLR3i9XgBvMiVdLheCwSCKxaJIopJDicViEalUCvF4HLFYDMlkErlcDuVyWeQFyPPrtWNi5vrCyTkTZNixYy96eZ4p9Ge1WuF2u2EwGGCz2eDz+cSYXk5jbTQaKJfLyGazSCQSODg4QDQaRSwWw/HxMQqFAiqVCk5OTjrW0Dut6GX/Bg8fLi/syDsFFIYji6ktRDnJ89C55PXtaX45gLfyAmhpbHICHhwc4ODgAIlEAul0WmQDyqvkkvOQsgepQZDj/XQ91zHh57rBIbszoNlsolgsolarwWazwe12v/WdQaIYppGQx9/UfZeXsZatvNxdr9frHQlBx8fHyGazIhFITgGmBoCSjChJiFbjpe9Rg1Cv10WosVKpdDgTe92nNm+BOVtY9BOm2Wwin88jGo0ik8lAVVWEw2F4vd6eE0u6MUrPQGthuyUCaZ101EiQJZc3SvWVrTzlCBQKBZFaTA0EfYcaB0oioo2yB+m4zMXCop8g7XYb2WwWOzs7+Oabb7C3twdVVXHr1i1EIhEEg0G43W6R893rGN1EO+i88l+i377az6gh0G7aoQNZcLLi9Jn8PUokkjMK8/k8stksMpkM4vE4kskkjo+P+1r2Qfd/2inBemWskB3zH+REi2aziUQigSdPnuAvf/kLnj9/Drvdjr29PaytrWF5eRmzs7PweDximad+izUOS7/koGHR5h7IDUmvSTra8bzs7ZeTiWgokUgksLu7i5cvX+Lp06fY3t5GPB5HtVrtek10zF73wkKfLCz6IdA+lPV6HQcHB3j8+DGePHmCV69ewWw2I5fLIZlMYmtrCzdu3IDf7xfFIuU12mmhR7vd/lbW2KSHBPI9dNt3UhmFRLPZRDabRSQSQSQSwcrKCvb395FIJHB8fIxUKoXj42McHx8jn893DANGFbf823HDMDws+iGRH6pGo4HDw0Nsbm7i4OAA5XJZpL+m02k8e/ZMCJvWZfd4PPD5fAiFQgiHw5ibm0MoFIKqqqJeHHEWEYDzSrQymUzw+XxwOp0Ih8N48OAByuUyCoUC4vE4Xr16hRcvXuDZs2fY3t5GIpFApVI5l2tj3sCiHwKtFWm1Wkin0zg8PEQ+nxdx7lwuh3w+LzLnqFCkXEI6GAwiHA5jaWkJ7777rvABOJ1OkQw1itU/y/vs9Z72c/k6afhAZbIooQh4U0lnaWkJkUgES0tL2NvbQzKZFDMHZZ9Bs9ns8C9Uq1U0m00xH6FbMhFb/uFg0Q+J/BC1Wi1Uq1Xh2e42y41CWqVSSdSTpyqyHo8H8/PzWFtbw8OHD/Gd73wHs7OzcLlc4viT7nYPwzgTRka5PpvNhoWFBQQCAdy5c0eEO+VwYKPRQK1WQ6VSQS6XE7MMKano+PgYu7u7ODg4eOv4lCehnXnIdMKiHxOyRFqrrA2Z1Wo11Go1sZ/B8Ga12FgsJtJiS6US7ty5g3feeUd09+k4FzlT6zTn7WZtjUajWEgjEAj03V8W/dHRkZhnkEwmsbOzg52dHeRyOVQqFaTTacTj8bdyBbSWX69zSbSw6MdE9mgDw4eV2u02arWaGPvn83mk02lks1k8ePAAN2/ehN/vF1V8gNPP2b8o5AZw1Gsnx6fH48GNGzdEnkC1WkUul0OxWITBYEAmk8Ef//hH/Pa3v0U6ne44BjWectYkw6Ifm15LKg3TrTQY3qwqQ9ZLToCpVqtYXl6G3++H1Wp9a7GNq0K/4Um/vHztfiT+fjidTsRiMfztb38TY/1MJtPRw2L+A4t+DKgqC9VvH3WiivywV6tV7O/vo16vI5/PIx6P48GDB1hbW0MkEulYpeQ6rNqqjffLUM9g1HtbX1/Hl19+ic8//xxmsxkbGxv41a9+1XXcz7Doh0Z+EGnGm8PhgKIoPZNOhqVSqeDg4ACFQkHEs6kQZjgchqqqsNls12LtttM6KLXDKIPBgOnpafzgBz8Q76+vr2NzcxO/+c1vALwpZW42mwfOD9ALLPoh0E55NRqNIsnGbrefWvTAm4SfVColstuy2SwODg5w9+5d3L59GwsLCx3dXIoSyNd4ER7/s0Y7FND2FGgdApmlpSX89Kc/xcrKChqNBjwej5h9yLDox8JoNMLr9WJ6ehoejwfZbHZiIaJ8Po9qtYp4PI69vT3EYjEUCgXUajVEIhGoqipErYciJ+M0ZAaDAZ9++il++MMfjryvHmDRD4n84JhMJkxNTWF+fh4vX75ENBp9q2TVuFAOgDylNZ1OY2trC7du3cLq6ioWFxfh8/kGHmeShSxGFY5WrGcpPOr1UHzeZrNdyWjHecGiHxL5ATKbzZiensbi4iKmp6fx/PnziYleplgsYnNzE9FoFBsbG7h58yY+/vhjPHr0CKurqwgEAj2tvXb++mkZV0DnITwqYHIdfB7nAYt+CLRCNplMCIVCWFxcRDAY7Fjd12g0TiQNlM5JyT3pdBqZTAbFYhGxWEyc2+fzdWwejwdWq/Utx+NlQPu7DJoEpN1X/mzQjMNJlP+66vD69KdEKyJa/WZ6ehoulwvFYvGt752Gbo1GMpnEV199hRcvXohc/pmZGSwsLGB5eRk3b97EwsICZmZm4PV6L43YZQYl7PS65lF7UMOWL9cjLPoRoTCRwWDA1NQU3nnnHSwsLIhJI2eR9y2XwKKKNkdHRwAAj8eD2dlZfPvtt3j+/DnC4TCCwaCILFDeP5XQ1pas0tbBp/PRXH/atPX/tBNe6DsWi0WsR+90OuFwOGCz2Tr2P4+hwmVs8C4LLPoRkZNHXC4XIpEI7t69i3q9jqOjI+TzeVQqlYl2Lfs1IrlcDrVaDUdHR9jY2BC57YqiCMFbLBZYLBYoiiJ8AOT46lYNV66zrygKrFYrLBaLEK52nTzax263Q1VVBINBzM7OYm5uDrOzswiFQvB4PB0zCZmLg0U/IrIFURQF77zzDt5//30oioKtrS0xXZTCbGd5DdQYkLc/m82OvO8wyA2GLHq5h2AymURx0GAwiBs3biAcDgvRe71eOBwOkdBEjRIdmxoWuXEB0FHKq16vw2QyifoEzHhwjbxTQGWz9vb2sL+/j93dXTx//hzPnz/H7u5uR/43dZOv+5JSNIWYBO50OmG1WsVQQ55mbLPZRKGRQCCAQCDQMSwhR2a1WkWhUECxWITT6cS9e/dw7949WK1WABe/ZtwlhmvkTRqj0Qi3241IJAKfz4eZmRkEAgG43W64XC5sb28jmUz2LPowabqNmQc1MIPEMmrDRKvrkGOzH1arFaqqwu/3IxgMimiEqqowm81ot9sdvZhcLgev14tqtQq3242lpSUoisKCHxEW/SmxWCxQVRWKosDpdMLlcsHv92N6ehpTU1OiMGSpVOp5DIoz07+1BTtOEwKUJ7EMOsYk4/rDQOHIYrGIZDIJl8sFu93e4XugwpuVSgWFQkH0BHw+H8xmMyKRSMeS1NwADIZFfwrIw03VbhVFgcvlwtTUFEKhEAKBAHw+H/x+P/b29kQhSLnmPPCf1WlPyzjDhstQXpqseSaTGfjdcrmMw8ND0YMaJu7PdMKiPyXaUJbBYBDjWa/Xi6WlJXz00UeiBnwikUA0GsX+/j5isdhQzjctckhNfu8yoe2t0HunbVharRaCwaDIhpSjAZx6OxzsyDsj5JJZjUYDpVIJyWQSe3t7ePHiBZ4+fYpvv/0Wh4eHSKfTYjVZ8lbLx9AL3fIDzGazKCTSbrexvLyMH//4x/jJT36CSCRy0Zd82eEVbi6adruN4+NjHB0dIRqNIh6Pi9Ra7TJT5XIZ+XweqVQKyWRS1IufxDTei4LCcdpCoaqqdmyU1EOhPCpWYjAYEAqFsL6+jvv377P3fjAs+suAnAUnb9T1bTQawlt9dHSEra0tfPvtt3j16hX29vaQSCSQzWZRLpcvbW45idpqtcJmswnhUmyexE6pxOT4nJ6eRigUgt/vh9vtFuE+Oh5l/SmK0jHfgekJi/48kcevZIlGzQWndNtoNIpoNIpkMtnRM6BMOm1BDTrnWaC1qPJinXLKLmUCUlkxOTPQYrGI2L3L5RLip42iIYNgCz8QFv1VRFsTXs6EG2dxiknSb8KMXMFXW9WHGgj6K6f9ah2jzKlg0V8GSKyDKsJe5eKXk6Df78Qz6IaGRX+Z6BUfv4517sal37PJv89QsOivGt3KRV/lMN6g+fPc4E0cFv1VZFDG3GVpBMYR6ihFNJixYNEzjM7oKnr2hjCMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzmDRM4zOYNEzjM5g0TOMzjAP+NxwLlfBMMy5wZaeYXQGi55hdAaLnmF0BoueYXQGi55hdAaLnmF0xv8DppoVKdxWrNsAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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ISHRaIQJo62MnbxBBZrZ2u+tCoSBETzM4DQD0Ow0cWu+4vDRg8U4uLPpzoFcTiUGP1z5HZjpV95E5XalURBEODTo0w1M2G+2nF41GRQormeu0x73cGXdYtAk5PChcLN1Ez468MTLKLN9N8PQgIedyOcTjccTjcSSTSRSLRTQaDWHiA9939ikUCkin04hGo4hGo8K0l/PYtc68UQV6ES26mMHpKfpxdoS5TNBNSy2g9bxWpl9HV+2/u4XuKCmITG5qxrm7u4ujoyMkk0kUCgXhNKNrrVarKJVKYh2fSqVQKBRQLpdPpahqd9zVA4v6ctNT9OPoraY1eWn9O64bp9+5ZFNTz7nG/f7d0DaB6PR3muFLpRLS6TTev3+Pt2/f4s2bNyJqQEKWr6XRaLSF08ghR2mu2lZSLGJj0VP02jZERoUsnl4pqoOG57TVeNoQmpzskslkEI1Gsb+/j93dXRwcHCAej6NUKuHk5EQMGiR6cuSRs4887uPIq580eg3qV6H/wFnAqtYBiUrO9R5X6izNyhQqo4eqqsIRF41GcXR0hGg0ing8LsJuNHNrlwnk9CMHHYX5yAqgz3DZoTAgbQyqXarI23AxP9DTe//kyRPDfFuycGu1GkwmE2ZmZhAOh3WLWjab5VRbbchNLkahJBg5GaZQKCCXyyGbzSKZTCKRSCCZTIry0mq12vEmp5/aNFn5d+Bqi8BsNnPU4P8zVMjuiy++MNQ3R9sjp9NpWK1WfPbZZ/jVr36FYDDY87hmsylqw+v1ugifUexcjpvncjlRdEIeeGoQQevvUqmEcrncNvtTbF6OmwOnlwza7bBJ8JMQVx81h6HftSuKgmAwiFqthlgsNtJ7XQWGCtn95S9/OZuruSSoqor19XX85Cc/QbPZhKqqbcKiRBnKOy+VSqjVajCbzW2bRtI+69QUIh6Pi1Aaxc3lPHeyCOSQWi+0VgXQHu6bFMZ5LVarFS6XC263W+x1HwqFsLi4iFqthlevXuHw8BCtVkvUQHCBzvf0nOlNJtPk3DEXgMvlwieffIKVlRUhcm3mHTV2kAUrp8USJGSawWmQkGfwbs62TmgdiNo0XKA9bi4PHuMUX6fZ+6wHGqvVioWFBTx48AA//vGPsbi4KJqWuN1uYa2pqopWqyX+vyZpADwPfvGLXwxu3htV9HrDfJNyI8kOLW2OO/2UU2iJ87p27fXRc9rXaH/KW2XTc4qiwO/349atW/jiiy/w+eefIxQKncvnuIQMbt4bNWRnsVjQarX6bo4wCYIHul+HtsUX0DsZZ9wDgt1uh9PpFI+pqSnY7XbY7XbRY0AeqGhJRD0FaZlEnYFrtRocDgcWFxdFy3AW/ODwTH9FIOGQKdtpjU/0Czlqj9HeI9rZmN7XYrHAZrNBURQoigKPxwOv1wu/3w+fzwePx9PWMUgWPondbrfDZrOJv9MsT1mGdrsdkUgE6+vrmJ2dhd1uH/M3eaXg3PthsFqtHdfJwPCdW846R71bdx4t8kwrJ7Jolwfamdhut7e1/6KHx+MRzjV6zu12i39T2zC73S4GiU4Dh+wEpWsh0ZvNZszNzSEUCrVlIHa69kmxxC4K3p9+SC5TMwjZVJZFoxW//Hfq3CuLTV6Dy7OwzWaD0+mE2+0WM/jMzEzbw+/3C/EriiJMee310HXI9KpZIIdps9mEoihoNpviWo26DB0W/rauGPKMKecK0EwuP0+iVBQFDocDiqLAZrMJE5uETmIn77hstgcCAWG++3w+uN3uM9kfnnwB2pp+TrUdHBb9FYNmaFpf009yipGQSegul0uImXr0k+nucDjEBh30Gtlcp9eSyU4DxVl/NkBfBSTTGRb9FUI2m+VZmsRPs6WiKMJMp1mbnGyy+GWvO63R6XmyCuROv0DvpKBBi5JktNWaZ9GhyCiw6K8gNAvKs7tW8PLMTZtzaEVPM778IM87LRtkRi1C0nsMC3402D4yIJPq1Z7U67pq8Ex/BZGLbGiPPdns7tTLT1sFWKlUMDU11bbHnBzCk60JoldyzziKbboVGjGDwaK/QsjxfxI7ldRaLBbRnIP61lMdPxUMaZ1zvRx5tM6n9b2caXcWgjSZTG2lwSz+4WHRXzEolEUzM22lLcfv6VEsFkX2nBy+04brbDab8PTTXnzakB1t3Ol2u+F0Os/ss9FnAdDmQGT0w6K/Ymi749BOuEC7d588/NrkHG2WnjxQUC59p+Sc6enpU8k5FMrrlJzTLUFH+5zcK5Cah5AzkkN2w8Gi78NlSsMlgZPw+12ftuClX8qunNRDwnM6nW3pt2T6y/F8bVxfzqvX+gfoeTlNl4qf5DRcl8slvkN5wxFOw/0BTsMdksuUhkt0K7LR/t5ttu1XcEPHyA85b54SfygHwOfzCfOf/ABkXZAFIKf6yok+3Qpums0mZmdnoSgKp+EOCJfWdkBvae2k0cuTLrfP6sa4LRAqzJFj/uQ70BbcyJaEXFpLgwI1+FQUBUtLS6K09tatW2O5ViPRU9WXcZYbB3o3+ZiUJozdTPOLbqJB++RRmzGgs/NNm9QjWyDy4AB83wcvEAhgd3cX9XodgUAA8/PzZ/o5rhrGnMr7QGJwu9349NNPhTlZrVbb9owDIPrL03ZRcrtsusFp3UnhMtrPvVAotLXZot1n9KCnXVa3z3Xe7bL0Whp6SaVSKBaLaLVaiMfjXdtl5fN5ABBLBKPxy1/+suPz3ESjBw8ePMCvf/1r/PSnP9XVGLNYLIr4OJmosiOqWCwik8kgFouJHvbaPebkxphyB9s+/0+nnHHaZJxJ6IY7TqxW66l04fn5eSwuLqJareL169c4PDwUSwL6PzMSR0dHg/fI+9nPfnY17hCdWCwWNJtNZDIZWCwWfPbZZ/jqq68wOzvb87hmsynaVlMcWfZOt1rf70dHO85SC+xYLIZ0Oo1MJoNcLneq/TXtI0/PyS2wgc4dcIzcAttut4sW2PF4fKT3ugq0hul7//TpU8OIXnaCUdfb6elpLC0t6b5ZSVjadSmJjUx46p5bKpVQKBSQz+dPbXaRz+eRTqfFAEH98WlnG+1SQDvL82YXV/cz6mUo0YP3pwfw/SAAdN7WSk+MuxfNZlMMADSr05o/k8kgmUwiFovh6OgIR0dHSCQSbeLnba26b2tF0aer8FmHoVarseiHZVwbWHaDMs46bWBZLBbFPvO0geX+/r7Ypvrk5OSUR77bBpZaU59ef5nR9vOTkQdp7es7oV0uDUKnY0c53zhoNBqDN8Y0asiO0PZd70a3yrVOr+v2u9lsFkUr8jnr9TpCoRBCoRBmZ2fh9/vhdrtxdHSETCaDUql0KsRYr9e7blWthZYfl5Ve195tC/BhzzfMtUzid9tT9EYtZhjkP2rY/1RZbJ060RLaXvBWqxUOhwOBQADJZBKqqgrR03qWnIaZTAapVArpdLrNIpFNYTk3X+/nmcQbmdFPT9EbNb45CIMMjL2SUrqlzpIVQDu7AN97qT0eD1KpFAqFghAuna/RaEBVVaTTacTjcRwfHyOZTIoIAYUFyckn56+zoK8+nJwzRvSuHTvRyczW9rxzOp1iEKDZntb0cjJQo9EQ4cF4PI6joyO8f/9ebJiZz+dRLpdRrVaFs092/A2L1r/Bg8hkwo68EdBWeMmZeuN+H3ovctB12jBTzgAkEz+bzbZ5/2OxmPD+VyoVkSpLDj/6nQYE2fN/lZN9riIcsjsDGo0GisUiTk5ORFWZln6i0DNIyIk2shDlAUfbuoo8+LRDbjabFdtiq6oqkn2oHRZFCyhsSCnC1GFHtgrk18uPfp+TLYDzhUU/ZhqNBvL5PA4PD5HNZuH1erG0tASfzyfEN0rLZy2y8OUCGvk82nPJSTkkVBK5NkRIFgTlCcjJQuT9JyuAag2KxSLy+Tyy2SxyuRwKhUJb5iBzsbDox0ir1UIul8Pu7i6+++477O3twePxYH19HdevX8fs7KzYqLFX+E5Oqhlkxu9W396JTrFjeeCgnzSLa3vo0TKiVquJB71Otgqo/kBVVeRyOaRSKcTjccRiMSSTSV21A3o+e7fPz5ymm+jZkacTOdGi0WggHo/j6dOn+Nvf/obt7W04nU7s7e3hzp07WFtbw+LiInw+HxwOR9uGEHIWGf3US7/WUnrPoY3KyIOJds2uXbvLf5cHC3kpkUgksL+/j+fPn+Pp06fY2dlBNBrtugQ4y9g5cxoWvQ7oRieB1et1HB4e4smTJ3jy5Alev34Nq9Uqbvg3b95gYWEBgUBAtI+iSjB5VxmHw3Eqa2ycSwLtZ+h27DiyCmWy2SxWV1exsrKCtbU1HBwciBk/mUwikUgI/8IomYHyd8cDg35Y9DqRbyoS/cuXL3F4eIhSqSQcZ4lEAtvb2/D5fKJrrNwyKhQKIRwOY3FxEXNzc/B4PGKvOeIsIgDnmWjl8/lEqevm5qbwERwfH2NnZwfb29t4/vw53r17h2Qy2dcJyIwXFr0OtLNIo9FAJpPB0dERstlsmzOMau6pcyz1h6fuscFgEOFwGKurq1hbW0MkEkEwGITL5RJm9yCz/ll/1m7Paf+mLUCi5pk+n0+8dmNjA6urq7h27RrW1tawu7srwofkZNQWC8nOw0ajgUqlAlVVUalUTjkzeebXB4teJ/JNRJVxdPPJlWvktSZvuKqqoskjmfh+vx/Ly8u4c+cO7t+/j9u3b2NxcRFut1ucf9wmt14GLRrplWWoZWpqCqurq5idnRUWgFwQJCcKUYehXC4nyooLhQISiQRev36NV69enaoloDwJuecAcxoW/ZDI4S6gfaajG06+gQmyAo6Pj0VaLDXNWF5eFua+XId/kTUQo1SdaYVHmYQOhwPBYLDn8c1mE8ViEdlsVpQTq6qKeDyO1dVVRCIRUWmYSqUQjUZPDQLamd+otSRaWPRDItem0787/d7puJOTE6TTaTx79kw0y0in07h37x5u3ryJ6elp0cUHGL1m/6KQB8BBr91sNsPj8WBqagrT09PCIiiXy3jw4AHy+TxarRay2Sy+/vpr/PGPf0QikWg7Bw2echITw6Ifmm4lt3rMSpPJJJx+1FuPEmAqlQpu3LiB6elp0dP9Mt6svZYn/dbc8rEWi0Vsp0VEIpG217vdbhwfH+Of//ynWCZkMpk2C4v5ARb9EFABDK3VB80+k2/4SqWCg4MD1Go1USBz9+5d3LlzB5FIpG2XEoqZX+Y93HpZRHKa7iCfb2trC1999RW+/PJLWK1WPHr0CL/73e/w/v378Vz0FYNFrxNtA0pFUeByuWCz2UZOOS2Xy3j//r1Ys6ZSKVECGw6HRXbfVdi7bVydhuinyWRCMBjEf/zHf4i/f/jhh3j16hX+8Ic/APjeErBarR2biBgRFr0OtGWvZrMZLpcLfr8fTqcT5XJ55Peo1WpIJpMiTJXJZPD+/Xtsbm7i1q1bWFlZadsNVpt/r23GeVXoZBnImYNyLzzi+vXr+PnPf4719XXU63X4fD7RPIRh0Q+F2WyGz+dDMBiE1+tFNpttc7qNEi7K5/OoVCqIxWI4ODhANBqFqqo4OTlBJBKBx+MRojZCkxOthaX3mC+++AKff/75QMcZBRa9TuQbx2KxYHZ2FteuXRNZeeOaRSgHgApdqtUq0uk03rx5gw8++EAkuAQCgb7nGSXFVcswOf6dfp4FZPVQfJ4KnVjsnWHR60S+gaxWK0KhEFZXVxEKhfDs2TOxXhxno8lCoYCXL1/i/fv3ePz4MdbX1/Hw4UN8+umn2NjYwMzMTNfZXlu/PirDCug8hEde/qvg8zgPWPQ60ArZYrFgbm4Oq6urCAaDbWvKcW22QO9JKai0E46qqohGo+K9A4FA28Pn80FRlKHM4rNG+710i+F3C/PJf+tXcUj9/4wM708/IloRBQIBhMNhhEIhuN1uFAqFU68bhU6DRiKRwL/+9S+8ePFC5PLPz89jZWUFN27cwM2bN7GysoL5+Xn4/f6JEbtMv4SdXn0BBhlItZ2EmB9g0Q8IeYxNJhNmZmawvLyMSCQiQmy1Wm3sed90w9MmmlSxBgB+vx8LCwt4/fo1nj9/jnA4jGAwKCrdKJdAu+Wzti5evma55l/u/S8LUrujDL3GZrOJPenp4XQ6244/j6XCJA54kwKLfkDkxBGXy4VIJIKtrS00Gg1RdVcul8dqWvYaRLLZLCqVCo6Pj/H48WNR3Wa324XgbTYbbDYb7Ha78AGQ44sy2GTh0xrZYrGcOhddDw1u9DktFgucTic8Hg+CwSAWFhZECXEwGITf72+rJGQuDhb9gMgziN1ux/LyMu7fvw+73Y6dnR3s7+8jHo8jn8+fWRqo1kFH3v5sNjvwsXog0dNsTQOGPFBYLBY4HA54vV7MzMxgaWlJPGTRT01NiZ185MGIBheySsg3Ivf4q9VqsFgsoj8BMxzcI28EqG3W/v4+Dg4OsLe3h2fPnuHZs2eiVpyET2byVdpHrhNyCTHlzJP1oShKW5kxdRD2+/2YmZnB9PQ0/H4/pqamYLVa0Ww2RdddatLpcrmwubmJra0tKIoC4OL3jJtguEfeuDGbzfB6vVhZWUEgEMD8/Dymp6fh9Xrh8Xjw7t07xGIxsQvNWXuTO62Z+w0wgzSk1EO9XheNMrVVb1oURYHH48H09DSCwSDm5uYQCAREeXGj0UC1WkWpVEIul0M+n4ff70elUoHP58Pq6irsdjsLfkBY9CNCW0wpioKpqSm43W7MzMxgfn4ejx49wvb2Nt6+fSu8+52Qm1VqvdSjbipB59Pj/R5nXF8PFI4sFApIJpPY39+H0+ns6Hsol8soFAqit6Df74fFYkEkEmnzNfAA0B8W/QiQl5u63dpsNrjdbszOziIYDApzNRAIYG9vD/l8XuTWyw045I47ozDMsmESWkuTTyKdTvd9baFQwNHRERKJBAqFgq64P9MOi35EtKEsk8kkwlR+vx/Xr1/Hw4cPRTfYWCyGw8NDkVevx/mmRQ6pyc9NElprhZ4bdWBptVoIBoMiG1KOBnDqrT7YkXdG0PdK5mmxWEQikcDe3h5evHiBZ8+e4dWrVzg6OkI6nUapVEKtVhPeavkcRqFTfgCFHIm1tTV8+eWX+PLLL7GysnKBV3sp4B1uJoFkMonj42McHh6KzSQLhUKbyU+bT+bzedEnPplMIpVKXdp20bQMogQem80mehJQt2C32w2v1yuSeuRQHg0CwWAQ9+7dY++9Plj0k4CcBSc/yPSt1+si5n58fIw3b97g9evX2NnZwd7eHuLxOHK5HIrF4sTmllNYTlEUOBwOIVx6kNOTUomnp6cxPz+Pubk5zM3NiQgIbRJCOQKU9UfxfKYvLPrzRLsVlHYNrgdKtz08PMTh4aHoCkuWAWXSdQoHntXSQDujypt1yim7JHwSqDYzUFEUseMPiZ8eFLLrB8/wfWHRX0bkTSPp0SuMd55+gF4FM3IHX203Xxog5JJYSvvVOkaZkWDRTwJyqyf6txZZFEal1/fEFXS6YdFPEt3i41exz92w6GmTzfSERX/Z0DaClJ+7jPSrn+cBb+yw6C8j/TLmJmUQGEaogzTRYIaCRc8wBqOj6NkbwjAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGg0XPMAaDRc8wBoNFzzAGw9rn76ZzuQqGYc4NnukZxmCw6BnGYLDoGcZgsOgZxmCw6BnGYLDoGcZg/D/g5eVOfbppIgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 128\n", - "Current iteration: 61\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 63\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 65\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 66\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 67\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 68\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 69\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 70\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 71\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 72\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", "n = Nx * Ny # number of parameters\n", @@ -2060,22 +440,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(10 * np.log10(0.5 * np.array(evaluation_history)), \"o-\")\n", @@ -2094,17 +461,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", "frequencies = opt.frequencies\n", @@ -2116,22 +475,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(1 / frequencies, top_profile * 100, \"-o\", label=\"Top Arm\")\n", @@ -2153,22 +499,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", diff --git a/python/examples/adjoint_optimization/05-Near2Far.ipynb b/python/examples/adjoint_optimization/05-Near2Far.ipynb index 676d68bb5..f2a0df62d 100644 --- a/python/examples/adjoint_optimization/05-Near2Far.ipynb +++ b/python/examples/adjoint_optimization/05-Near2Far.ipynb @@ -11,17 +11,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -47,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -176,22 +168,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", " simulation=sim,\n", @@ -206,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -254,1594 +233,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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lkHo2m6UI6Pb2NnmdpI8mk4lev36dcrf1el2tVivpUrvdTjKNF3xwcJDSKUQt5PbJ75OqOz8/183NTUq9eNqAlBZpMHdgKpWKJO0UdZEJcttuYDFy9/f3yQARlZKCoYbE94h8cvn53CiMdOv1ul69eqXj4+O0mAggngoKxwTiceVWyCu7eAAuKAcHB2o0Goks9nUD+DU9n1ur1RIBUNSjCNHtdtXr9XRwcKBWq5VI33OJpBFYZApc5IaHw6Hu7+/Ts6F0/gyQV6/X0/HxsU5PT9XtdlNqYblcplQDaQWPFCBbCA5lxrBBtnhobjzyyi+eCV4YAlupVFJhqt1uq9frpeIeniMK73l4DCbE5PPHvTAKTk6El3nl3TsO3Ptx+eC5+DzEiWz52CjotVqt5JmTauJzEHGe+sHg7JtDjB1rQFoF5wGDKz11m3h9gGfHsJNK6/V6aSykVyqVihqNhmazWfKiuT6e4mKx0HQ61Wg0UqfT2ck3V6vVZPg9ipCU/u1dAqQy8HiR9aurK11dXe04Bfs6CfIIlnFC9Mydy4V3NrhOey6fKMINIXPN85TLZb169Sp5xUXgs5MuE9vv9/WHf/iHOj09TSE0oYiHEPtyK96mw79RWCYR4shbRlxpva3FFwxyfnh4SLlHcm4ePu9rKcuRe2GudHklHOEmtdDr9RKpE8YdHR2p1+up0+kkolsul8kgkEMGOdlISgpG+IeSViqVVCzBo3FjIT1FAqSBWq1WCsuWy6XK5XJKgRweHqrT6Wi73aacMsbFc3OQGx5kHgFxT+YSZZGelN69FS+M4Umxbu4JuRHJWwcJ0SlKkb/n/5EfikoYm4ODg0S6uZeWkwDhMREDREQ1vdPppPnnedAPDF2z2VSv10vy4sU45pA5w/NmHfDyFotFGmceuTAX++Q510nXQZcb0hqj0Ujj8XinuyX/ft5S6XrrJNlsNnciDYjXOWHfuL1bhJTgwcGB6vW6BoOB5vO52u22ut1uus7nRmGe7mAw0J/+6Z+myuFyuUxVYvp38149V1LCLw+B86o+Xh9KLWkn90il23sGUUS6AvDOEBBXcgT08fFxRzg894jhyKvM5KQhOoi/0WgkJSJlgZBRac29UPJd/X4/5dxQVASVzzMGil8oFgUYUjN4du4V8izu3bRarZSyISeLl07Bh7mdz+cfdRTg3XkI7vlQL0h5KOqeCd91Y+1kKin9nu97XtjTGFwbeSFlgDFyY99sNneeudlsph8vJLoRJJpxGXp4eNjJkzNOoh6fb8aLoca7zUke4+BjQs8w0pvNJuWoiYiYJ2TUCTj3Jn2ec7l2Qyp9iAa8drAvqvC1xzi4blFDQD8hd3fOGKcTdx7Zch+iGJwa5vqLL77QYDD4/2C3Xw2Febrtdltv375NXpAXDFggCiwodN4i4oUT93AhXDyH6XSa0gQIlOcInTBZIATUC0kQGNVZyEt6UmQnXYyFe7az2SzluvB0Kbah4OTSuIaHv57jdOJxBXOD5UUkcpSuIJDMcwpFrs2LHr6WpD7wXt04MX6vNu8L610ZmIc8j5fnnfm8e+DuwfGMEIB7tnyeaz43Bkk7GxqcICAo2qx8DSELCDkPyT18hgw8jwypEBLn68gYIF5qDblxzGXGjRw5d0mpX9pbDb1Vr9VqJRlgnNwHXUVP6fUmgkMOkA8n8jyt4LqIwyQpGSW+x1hwEpjTfUAm5vN5kilIF7mVnvreiWaoq/xOebp4p0wghaXb21tdX1+nfjo8TYpeeKruwSB8eLqETHi5PtEsHgqZ54By0uE6EC9WWFL6Pdd26+2khzJ5CxdFQe8M8Dz2YrFISuBk454Tz+phoCs2ZOUtV3nhzJUd5cHb4f4eEjup5k39eNd8jlyn98J6NOH9o5ABaQDG7WkDFBWjyjpiUFnbvAeUeeMefj3SFu7hOgExl3R1MF7mIS+cIq/75spDboy/f9ZDZO4JgXkeOPfakDN/Hq8beFhPp4l71e6t5utMvtg93VxffC2QZ8ZGBOeGPvdyJX2kL3i4/OkRiefoKZJ5NJkTMOvKdZz4PU1ExOnF7yJQGOliyWazme7u7nR5eal3796lXUneWeBJec+9uYV0b8O9LoTYvSx+v2/xc0WjKnt/f58E2ntHvYjnIQ3XQojoiHDD4gU091pyMvSWH/8hf8fvHAghOUPmzHOcnt/250XpEVI8dC9+kFrA08VbQmmZd/dY8xAVovD/zyME1hPFBuQhKQ55vtw9Y0CRMb+eGywv3jihuJfmHjLzxN+RU2+bQjZ9YwGEjjfsEZy3mTFnkLQTc55b9Tw50R2y5j3QkB8dNE7E5NPr9XrqXSfdlRtNlyFPDXlul3XHAOR94Lmn63PObjrWPG8NRF/cefNI1Z0fuja4HrzjHRToMGv1nOf8OVAY6S6Xy0Sul5eXaXPAzc1N2qaXW+I895qTLh4XYYmTLnmwXKn3FQo8dGfBaDGDdMl5urfnqQcnTDwa0ijsPBuPx8njZvydTmcnJ5aH2B7mrdcftkq7IrhX6wrsBStSMTyvt4gBz6f7M0AQ5MKZY7wGzwEydpRhnyA7wTt4Hp7V197XaV/BJM/hYTyeKwj5/OzzcJjr556P+3mPOSSGN4ZCuxHiu3lKhfSF537zMXpqzT1jOktGo5Gur691d3eXjLt797R34QH7eGlFrNVqOjg42Cm6sqGCefPiFrJElIYR8JRa3nGQy5yvnTtFXthzzxVjj0fsBM7a8dxuWEiFOEcwp61WK5F+EfjspOvWh0Zxwh8XAg9TmGxJewWQnj5yVHgR/J3rIFQoIyEnC+G5KpS9VColL4+F8G2bEC9FJw9fc48C4qZXl1DUc3hcH+FyIve2IhTYPWxCWN9Pzv8zPs8749FRjPOCnW9vJirxLg0vVC2XyxRleBHPUzu5Z0Nkwr89x5cbAOCkmYej+f/nufoc+4yM9xjneXHmi+f0dI+kHTLmh+s6MTG3KLoTCrLjFXb6bXMv13P43tZIqo52RDYS8Xyk3iSlnYj0vpNW2Ww+dGMgr77xwecF+cPjZE0fHx9VqVSSd0kEgJfraw2QrVzG8py3rxfziW5DpG48fQ29q8ULq/V6PRXT2PVG5Jg7ZZ8DhZ6n6/DcJZaJSZGelIK/S0qFjH6/n3pXEU6avn3BvEAiKV3Prek+S8lCeTGNcNGLGHkuzr2JyWSys9slzx1LSsrqhQ76XD0PlR9Og0fLuFBGLxJQYPP5cyWniks7mjf5554mSotHDQGwXdQVhecj+nBPRVJKf6AETnCuPJ4z35e7Bk6mnrLwdiTP8+Yhsf/kpAy5uLdJ6P/w8JAUP8+7Y8hoKfPzGXxNmAPuxTpK2iFdSR91SXh6hTrEvl2CyN10Ok1j98jJc8SsNVvfyXl6WsENvzsCGAbPv7tMuPHnWt6Gmeuupxa4F1vvIXtPQXm+NneGXA5z8v9NoDDSrVaraZ87FoYNBh62SU8kgSK4d3Z0dKRXr17p5cuXarfbaZvjPi8CRfE2HK/6ehXb4USBoLHPPU8reA7XD6thXAhOo9HYaa9xxcg3RpTL5eQ15OkW8sHSU8EA4cWT5Hl9a7KHbhRL2EjiGy3I6zUajY8OHnEDRG8xxT0P6Xh293CY81zJPdrAe+Pz3q3gKQvP8Xu7mRs2T5fksuWK7LvI+LwTl7fkETkQTrPmyJhHH8whZArJeEgtKeUm9/VIsyb8fZ9HnstGnlPm+e/v73d62XFKfKxeHMQjzruFnNjcW18ulx9tpthHcJ4apGtH2j1bw8naUyrNZlODwSDJrnecsOU+Txn5GDwCIVL281SKQmEtY7VaTYeHhymM8a20rjh8J89tERK8ePFCb9++1enp6Y5XBOHm1VSujfATMqI8npD3MXv7i/SkyJ4/zZUby8t1IW9yt06+Tn6+C43wyb0FjwY8VPI0jIdp7nn6M7mH4WdI4OXmFes8tYMCsC/fT7/y/mUUwPOwzBkpH8bppO7ywrq6XHiOzz1+91I9P8i/PX3kz+Xky//x/JASEQHGlrTPer1OHiHpFb+2jwVZJI3kcuJz5ON1rz1fX6/ce9HVDRFkyud9vTebTSJQ98zx7CFR5DifO18Txu7ebK47LoPoJPPK3OXpCK6Lk7HdfqitdDodHR8fJy5hbshje47fi83e9eS7+eh55lqfO7UgFezpsuBelMpDjLzQwJ8c9PHy5Uudnp6q3+9rvV5rNBpJehJE/xPvBQHAoiOo5FidQN3D3Ze/c3LIQyYPQZ3g2AHDgRueukAZ3PhI+ojcqcIStj/XLuPk7+NB6cvlcrLypEogQ+blU83sefiMkSDM43AX4KkFFAlP0hXE75GnB9zLdWVyAs+9XeYozxH6Z/B8fN49N8g48Ix8m+58Pk+59H2edk6K7lkjB8yDRz8eOeUFPObQ14i0ght5lx3kn0jq8fFRjUZjJ3XmLW5e3HOZdrlmflzevGOG+7sx87n1ThgOhsKQudeO7DjpwwWumxAyHSTMEcbZUyNeB/EUkM/x50ZhpEsIBYl4bgnrCIn5zhz+3ul01Ov11O12k0XOe/lyL8EVWtolABTN82p5vm+zeToZbTab7ezacs/Er53vCnJLzeddQBEi7pd7mdLT2cBs+kAAIZ/c83Dj4FVa6UngiTR8h0/ex5obEhdW5gIi8vQDP35tyMXbsPLiRa7Uvh4oYt7fStVdetqF5qGtk2e+vn7SF4SHYXHZ8xCbOSaXjvPAvDl55l04zCHPTFcIBivPTe+TB+SHwjGpAE8F+We5XqVSSafUeWubyzKOSb7Tbp+8swZ5i6F3uzDnpC18HZjPPJf+KT32SAxj2O12085WUj6kOrzP2TuZXP/cuSsKhZIuXg5hGSdQeYEIZfATt/AOvOcRa+/E4wTjCiA97VRBAJyY+H6u+BRMqOri2eHd+LPlXoxXSr0Q5eTjaQZX1NybcC+GkAsiYuxOKm4AMFB8DtLlWUgNeEju12Rs5NRywvXKPooEOTshMu/0BTNn3l+bG0ngygdhu1HAsOVefx7p+BwQ3pI6YHyS0u89CnPj43lB6goQq5MNz+SpHm8LhOR8DjCwePGeBuGa6NB6vU7HfNJTzXrl8+aRDuSPHHuaBxnmND3kfR/xshZsZqLVLCd/ngH4Onoazb+TFzD5PnrPenkkUiqVdnbB+XxRcKTu4nNWNAp/GzCN3L5zxtuoEEqKS15sohJNuwvFGifrfQrshS6sJUUuabe6T2iEgXCBOjg42EkN5N/z+6OctMNhnSFN70jIvQcPdRgDhobPoKx5x0cexnqLkfR05KMXdTwl4x6Jh3Z4RX6uA8qAJ+FrhZfvu9e2223aUVav11OzuuervQ2LOZN2W9ZyTxcvx8kgr3znxVTC2263u1MM5HueAnPizXPBHpUxDuYHA+tjxlgQBfAMyCxk4fUJT9dgsJlLfx1NTm4uU6y9G0XP4eZdNBRZPdfrpOvE6T3pyLT0lCJzEmZM8/k8zSkk6flvyNcdMOYl1wdyxBhyNtEwrz7/ktJhNxjLfYb+c6Jw0vVGbdqqvGfQlRjPCkXx6jGFG0IlFJ2JZUEQfMIxwh8/r9S9IBYNcvSNEt62hXLyfbxC6SlXyPjzLgDvQJCUDE6eN8M6+9moEA+VacaVF9Wc/EhJeKuaF3G8Pc1/fHOGpEQE7KwiLGfsOfEyJm8toseXsbFDi11DEK4rKl6Nk64/C4Y3LwS6AfLDg/CMqFwzP75VNK83uEfoc+CbELyA6zlp5s0jDp/X/PMQquezyd/6y13peffDyakTMHf5+lAc5KByJ1P3Gv1ddZ7nRYZZA7xH70X3yAdj5MVCT4+wbn5uBuN1OcEwwh+eUiRnjdyT24Vv0D/kyD1dPlckCiNdt4zeV+hhtvTU4kOuk+PuPC/pRQSICEVxy8WEYsW9T3JfZd9zkVSqfYcarVCLxWLnOp5383YuyJftzb65IC82eC7UhRMvAg8RYUOQxuOx7u/vkwB5isUJy8O4PEfpO9D8wCHfO+9FGQh0s9mkIiHP7UUcjCTeCs/pZIBXwlxLu50bXqTyuc7Dd99R5N9jzH5ymLcMoawQBevIc+StZGxxZcvtaDRK88/c4lWxVp7LdKfAC0YgLyS6DHJfIi1I9/7+XqvVKnmEOBN+T/8/l708Z53ncvMuBE+DoMeegmPdXI9YT4ePyaMb5iCvDXhqkgiMtWGsHP8oaadvn/sjdy7feYG8CBR69gJbJvO3N7iniSXudrvppY8otJM2O73G4/HOGQHuMeahGwSMonJPxoCl991cvMbFPVeEANLy0BfBweoC91g81PGQyAtT0u6uG9/SWK1Wk3fKZgy8RPdqXbC9/Qdj4CE68+qFGT/PAs/fPZz5fK7BYLDzmiGfS8+pel6U3CMtVIwzb4vydIt7Y/zeDTFr7OkWCM/z2/zpSg1R5orvnh3FovF4nA4wuru702g02tkE4/OJjHuXAGPP12O9Xn90mAvz7QaIz5IP96NIt9vtR8eSuiw5sbgM5pGR97KzLv496YPRwAjc3d2lw/kJ/SFB0mCeW+Y6z3Wx5Plc0pLeGbNer1MhGI+d8VLYnE6nO7UOX888dehy87lR2DZgXlLIiWJ4ny4YvC3h6OhIZ2dn6fXThGgcRINVxcoj9F6VzIHQ5gssPZF9vsV3s9nsbFLgsxAQROOkK31cPXbC9VDaD79xEsAwOBminK6EeD9437kAu4fhXqJ/xivFeLzuBfj3CAHJ4/m25n6/v7PdNU+3uLLlqRBPSdTr9Z02Pp/PPI/sBReQG3DSBB4mMweQmof2+ZghyMfHRw2HQ11fX6fXnvPm2n3FQM+jMi/75t2/456+/w6CJOoh5+xGEBn0rhA3SlTz8dbzeWU+WSMnSuaD9ARjub291fn5uc7Pz3V1dZUiQWTa9cl1yJ/ZnzEHOuCpHb+GR8N0AJF6w1B62oLvkKbgZbWVSmXnGAGXp8+BwjzdyWSif/zHf9T9/X16I6+knRCi2+3q5OQk7Tg7OTlJ22IpfNEEzY4ZBN/DhOdIlwXMwz8U2b1NiJ6mbcJ3iH2xWKTmbgQey+2enSutpykwFJISITC+nGx9jE76eS4R7yCvMu9r3fHw3JXWr4kAevjriusnZkG8kGDeSsZn3HP3qIBuB78m8+OFqdybfe7/815M94bIIfqzuWfuaRDGxIEyFxcX6W3L3kXhZM5Y8vn0kBh5Y229yOrGhvX0MJnrecHTyTfv7GG9vMjFLkX3dhkbRsSdGGQaveDFqLy23g9zwpChT6QQ/TnQSdfHHJAx+u2yTu66VCqlyJjDoPz8CsZO3hvwJuJms6nr62v96Ec/0qtXrz4aw+dAYZ7uaDTST3/6U5VKJd3e3qY8FLnYg4OD9BJG3rXlRwhijb1a6mfuPke0+/5078GVzns33aKv1+tEkgj7drtN22DxDBAIz2dJSsl/EvicHewH3qAwnPjE0Xx4P7lX5ykE6ckD9/YaT12g0JABc+keQX4GquddJe0YHH/TgJMXrTvkdJlnD+e8gOEkzdz7c/OZ3Ftm7fz/8Nzy6/l5BUQbeDsoMtFNbqAY8+3trS4vL3V5eZkMP2uN7LDWTpBet4Bk88Itz8eacO08VZavO3PncuE1CMZFioyDbqSnVkY32O5t+4409IO02mKx0Gg00vn5ud6/f6/b29sUonvUwNx7HtYjDHcQgOe9WX9PQWGkarVa6iZifNzb31rsLWx++tpkMkme9Hb74Y3dzlefE4V5utPpVD/72c/U6XR2tuy55UewSCVQ+dxutymdQLXWuwkI3VzpuKaHYE7MfJ6WE9ILFBJo6fEcLMUm8kD7UgGSkpKhiN4fSIsPaQGKDpPJZMdaQ9A8n3umkAV5Q88P5/D8qhMvniak47lB/t8JyJXSuxsYvzefo5ye/yUHz3N7bjnPfefryFq6rPh6AwyFe4hupFgLz6VC8FTsfewQJl4ux5CiyO79QZrcC0XH2GCAPEz3YpVHTPvW0FNAEKJvuuG+i8UinZlAGxmH6XNIT7lcTpEF84gM+1kIuSPhBenc6fENHqzVZrNJxMtY0FUcrlxO3cB4xMY46dGl1591gytKpVLqpvA0ixst5pBnRDaLQqHn6Q6HQ223250eOiaEw8NHo9GO8ND+Mp/PdXd3lyrGfsi5k47nqRCY53Jp+QKzcQDi9fC6VCrt9G3m3Qf820MniA3SoXncj3/EU4R4KWKQ2vDcmoeXePzeRoVQOyHT0eBRAl40SuehpBdwPDXgIbz/lEol3d/f77w6KI9MeFY/yxVjh5ePQnkeeZ/HgyK6wu7Lj7qHjee43T690QDy8RSOXw8DhXIzdg/RPR3A/dx78sjC25aIQAiL3dnAKLDOXNONOzlvPGdPqzHf2+021U687XC73aYiqhf4nHjzQpYbCv+M5+IZJ2vK+uR94sxVnq/nd/yfRza53vKMnsZbLBbphDYcHHfQPJJDrtzLzbsrPicKI12I1EmCSUQop9OpJCWSor1HUgqRmEjfZ517Pbl391y4kJM0wkUfI/lVSIdTjqjY93q9nV1yKHmeV/NdMhgKz7+tVqtUjaboIz0VHvw5mDu/po8RkvVQ2YXV39eWF5Go+nox0nPhOTkBiAWhRiFRUH6Xe+dOWDnpevHjV4F/x0kXOWHePD3BZ0jtMNd5emBfhdtzyT5uDCnE5NV1yNe/x3z5llo/AIY1cFkl1M5z+RgL6SnqYm4hKZc/95zztkl0wmsJRIFu1Ola8LQSBO8560+taa6LGIKcsPNOhtVqlU5Rk7Qzh96Ng2xh5N0g7sspfy4USro8tLR/iyCuv/RExHhaeMLecrZvovyaWHSAd/0pbwllIafM2Mrlcir2HR4e7rwC3fduI8i8VQCvwK+PIHv6xMcg7eaY/bk8F+ufz70CmvEpKEBqvhkFA4OHsFw+Hc/nrUdeSGP8eMG+cWVfEY/PexeBrzPP40UsnjuPhvJnzA2t5wO9x9SLTJ4DJ+/LuDw3mkdDfIaQnvTCvhQI80Ju0U+x8jcYUMzbbrc7J9/lnSOuI77uzFWp9LTDzeU5Lyz72HzDA5/BWDLm/JXyrBvPguHiIHCvnfhLAHwMzJP/m+dAlvKx5cBY0d8NCbP7Ee8+T7/l+XHG6rxUBAoppEm74bakHeLxHTBeqUYw+dP7VaWPX/2yTxilXZL1fBvXkJ7OrUUBer2eBoNBagtz8uBISj8bws8h8BAQAeC5vAld0k7Xgodr+S4g5pBre745L5whrBSMeF6EkPv6IeakeFiH/ExcvDgUjrUjKmB3l+cpWWdf62q1mgwn64i3TQqH6zPu3IvLyShvA3Mlzg9scdnjIPZ87P5ZNlJ4/YCxY6A9HIaYmReMMw4AaZb7+/skG9KTQ4DXjdfo12dekBFSYRg817m8kAf5+Pd9SzcGiJcEUF9gPrgeeslny+VyOj0QOZnP5+ktFsitryVr5GvPuJg/nC2exw2sp2MoDGIQPMfOdb0fm/F7Csg93X0k/12j0M0RCBTChGD66WF4f56z9N1rHhbmLSdurVwZpad8nrTbs8rvXGGbzWZ6O0W73d65BwrtOTvpqd8URUS48NDxpBgXn/XddnnjPgLP/f16LoDuwecGJg+rSClwriidInjC7nFWKpWddh0PGXnOnLRcuN1L9Hw4z+bC7kqIN5yHlsiPt7PxeSdLJ2QnbJSwXH4645izjPM0kXt+EIy3LBFt5N6t737rdrvpzFZy175Di9oGhOGF39z7Q16d0P1MamTL88qQrrcf5sU8l0eMkL+aKk9PuXOCw0REyP2n06m2261ub2938t45obnRxOFgXOip691z3RzuUeOdu1GBnNnk5O+A82JyUSg0vSBph3Sr1Q9n7B4eHurw8DB5OfP5XOPxOBGlexkoGhOPt4CyeN7RPcB97SmMy0NJwqt+v6/BYKB2uy3pyQNhgbyI5d/Hw2OcTjLSbtjnlpidcL7f371+yJXXw3jhJq/2ejjtc8+/XYkhAEkfeUVejMqf1efVQ3/uy//xfF40c08jD+2Yw5wwUS5y3xAr8+Unv7lnnl/b0wr5llfPQT/33VyO3Fgjc3yXyAJvzNfAjYj08Zs1ICN/Jmn3LNput7vjkXrRzTtMSFnkGzlcfvZ5hD5OunbYjMQc5Skm5GuxWKTzcj2F8VxKMF9rdxJ8PnBe3LPGGeFzrCdzIyl1kPhn87koCoWevSA9vUob78zDHa9ko2Tu5aJknkNEAT3sdE9F2v8qcB+T57RIH/jbHCB/925ZOC92UGTwEMatvBdyvEld2u3JzPPSLmguOBCwPxPPyv2dQCAq0hlc8/7+XtvtNvU/0z6WP5uTbqVS2VFoChvME88ESXgDv3upbhCc0D0VwxpBCr4tNu+Jlp7SKLlB3NcCh9HBeOfExHy5HLrH55/xOeFa5PY5BWuxWOzsqnQD4oYKz91TC27IPTWCp+kOSC5Hec1Bejpn1+fpueKjb0bKN614bpx7Mi6vd+T5aP+3F7gYp0dw7pyQqsGD9RoHKSqPFpg/Nwxe8HYuKAKFk657IU4enBxE5dW3o3qOi/CK3Sh5fpOF4v+kj0nXx5OP0QnBc5PS7uEZngJBETl5C6+Ujgw+w48f6ehhO90L3rvKvzmngPYY31fu3jOKl3c2uMeAl0k3AwqAAOdVc58X6alVyotz/N29FbwO+p5ZC19PvCr3TPNiV37ua07cfg1CRiddSMdJ19NNfgId5Oq5cgySb5DJQ2bveqhWP7zFgHUbDoepjcqdCfqV/Xnd69xsNikqcaOBc4DHC6G7UYDQfZMD9yQ3ixFHXmi1Yk4wlI+Pj+ndf+ibb1v3uob08WuWnuOD3AN20nV5c8Ilj73dblO/NEaSKBrewBEgTeY7Id0ReY4PPhcKPcTcgdBziMh2u00eHWE832Px8UK9+DOfz3faUtidxo8nzPPQODcE5Nko9KB4+9rPnIAxGCia9JSOoGUFgXUlcJLxzgGek+cih4cVJ70Bobvn5cSOYQBOONyf1/+41+cVXQ9B85CX53TSYm7cEyWK8TnkWTh3F1LAa5F2j8jk+3nRiLFiwJ1MPVrwSryH4B4p5SG45yn5vRfQXA489+hzR5uWtzjlabA8lUHeF3hkR+RAEZTDhrg3rZTIcd6qhzFlDr3ThshxOp3ueIUYYoyRH49J4dU7ByBq332Y11H2pWjwunlWvH3PkdNVsdk8tdkh917HITphrun7J5e7L79cFAp/BTsTCiHiJa1Wq/SuMISLfCpKTZ/sycmJ+v2+arVassL07+bhsRNMLtzAyfP29lYXFxd68+aNXrx4oUrlaY93q9VKu5darVY60o9QjK2Q7jH5jx+/6F6Xk5wrRKPRUL/f1/HxcXqPE03gEFi1Wk0nXfnRejwTHoOH4XnRCUBOeV6Q33nByglaejqTQfr4BCsMjbeNebFGeurNZg4xNu12OykbXh3k6WSNYWNefQMAB6LgbXv7YZ4O8A4bD8uZJ5dP71bBkCLjno/P59nz+553Zb3cE2WNma9+v5/OJzk7O0tb0dfrdTLInL6HHOZGwQu0kOxm8+FsZqISfxURc8q//c3V/kZvvM/FYqHhcKjLy0vd3t6med5XrGJMpVIpRXoUs/khf93v99ObPpbLpUajUZpnnAeMgbfoEXGS0sERg9iLRmF3RHixTtLTywzTYKxvtN1uJ4+OsLXVaun09DQdhkO+lXNFb29vdX19rcvLS11cXKSFxhPaF054rnU6nerm5kbv37/X9fW1vv/97yfyz8NjFJ0ff/U6b8TwTQiAvDTKK+0W8yC3Xq+nk5MTvXjxQicnJ+p2u6pUPuxU81en93o9XV9fp+P1pCcjBbzLgPOJPaRn7gkXIdU898xZGMw5z5uH3J6P4/qQP2s8GAx0dHSU2qlQft8ejQLh0fHmVrxe9/iZ806nk3qRIW6em/tMJhPd3t4mT989OSeHvCiKwaDrwV/K6X2p3veLbLkBy1McPLcfgsTnWq1W2pBzeHiok5OTdAIfnSfk+XlhI14ehCs9dYXskzk2EXBPOjt83l1++MmPgeQ5Hx8fdX19nfQIGXE98PSCp2og+E6no7OzM52dnenk5ERHR0fq9/vJAZrNZrq+vk4G1Q/Q8rNTSFWipzg+PGtewCwChZOuez6SdrZkdjqdHc/EC1LlclndbldnZ2d69eqVXrx4kT6Pdb+9vdU333yjf/qnf9JoNEreK97Lp/I23trCEZT0I/qefA/DDw4O0jZRioO3t7caDoe6urrSaDTaOV/UPYQ8z4nXWKt9eFX92dmZXr9+rZcvX+rw8DD1C282m51WJ7osLi8vdzwd+kCZ+1arpaOjIx0dHaW96gi6e5UILB4k0QLeT73+4aD48Xisy8tLXV9fp+f04p1Xt/EsiBrq9br6/b7Ozs50fHycwkV/SwcRgb/u3UmXuSDSwRPsdDo7Z1r40X/UAG5ubrRer3fyl05ShLrMj7eZ4WmenZ2ls549T+9pE9IE5IMZF54dkR7j4SB0SWnOu92uXrx4oVevXun09FSnp6fpFeR+wP96vd7pYoAA8SDdO5e04zgQsfF5WiaRtWazqaOjIx0eHqYNQXl7IPOEkwHp3t3d7RQMP6V/ECVOyZdffqkf/vCHevv2rQ4PD5NRYe0Gg0EyAHjrRBnb7XYnvYKesgbcE8P6O0W6hFe1Wi0pTS7c3t/Y7XbTS/GYED7X6/WSAB4dHSUrL31QnJOTEzUaDY3HY/385z9PnhuL8W1AufCMyZ/5KVWSkmDku5Rms1kiUi+WbLfbpIjVajV5qBCB91E2Go1Euqenp0mwvPXGixje9kQxhVwawFvEc+71ejvVfs8TQroIKkrEvSCZ29vbnTCZcBUP03Pm3gIHuQ8GAx0fH6eIRXrappwfe8n48x2AeMO+8aLVaqXvkgtk/iSleXl4eNDd3V0yJL7TCiKBVFi7g4MDnZyc6PXr13r9+rUGg0EyQnjMkDMdG5Cuv90DLJdLjcfjVIREViSlHObx8bFev36tL774IhEuNQ3fRQfpkZ6A6EhJ4ekSaZKC8BQcO+SQGeb/4OAgedqkAFkDT79A2PTG4iy5h/0peMdKr9fTmzdv9Pu///t6+/ater3ezmE8dIRQMxiPx4kn+AxrQxqCa/O5SqWS3jbB2heR2y19S9XuOyvp5ZXynUHsaW/Z9/Dez5f3puZV0Lxd65cF1/b+0m+rwvq991Xh943fi3JcB3jva/65/POfujefyed13zX9//Iio3/G77/vXt/mzTAW34WW5z39z3xcz81FPh/+Hf8zl5O8q+W5cefzuK9fd9/n83HtG7fP5b41e04ePjUX+2QwH69/Nh/bp+79bfpAnto7c35ZcP18A8c+OeHaz90jn9d9a8AauQP3HeHZSSqMdAOBQOC/EZ4l3cLfBvxt+K7c+1/Fun6ucewbQ1GtKUW3xPyy811ka84vi9+Wsf8m25g+x71/G3Twlx1HkXMdnm4gEAh893iWxYsr2QUCgUAgSDcQCASKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgWi+i2/LxUyikAgEPhvgvB0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIj/B8mADxmWxVGTAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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e4mZGz61er4t8RQ/IK3hKugDENePi54LSC4vESLf+qs4GIJM9Go0ilUphaWkJy8vLSCaTMtkqlYpYb+5uNhwO0W63cXBwgPF4jEKhgN3dXWSzWaRSKczPz2NhYUHcQQYoGCTRFh6vHQwG5bm63a5sMPfu3cOnn36Kg4MDlEolTCYTxGIxZDIZpNNpcfWXl5eRSqWwu7uLW7duYXd3VyyKi4sL0QAfPHiAWq12ZR9RbtHWIvCUsGjN0GLk4tcBG35+VhCNY6A3H07iXq8n1urp6SkikciUm8vP0H2mFUfpgkSl+3YW2P8ks0gkgng8Lp4A5Rc+D6069k2n05H7kXC5OfBv9TPrDYeWKy12d+Ph/fil56C20jmvLkMwGMT8/Dz29/exu7uLra0trKyswO/34+bNm9jf38cXX3whshLJpVgsolqtinzh8/nwy1/+El999RV+8zd/Ezdv3sTy8rJozJRd3GBeIBAQC5ReUK/XE7mqXC6jVqvJPWnlHhwcoN1uPzNHOb8oJzWbTWSzWdHM6/W6kC4Nr6vIkfMnn8+LMcONkRsp12MwGJxaC17CM9JloCOTySCbzUq0sN1uizXAiaizBJ5n7utIMUl3cXERi4uLSKfTojPp6KgLbYU/fPhQiHpnZwebm5uiC3Jh6IU1K7Cjf85N5OLiQoJbjx49QqlUkkAWLZlIJCKWWSaTwdbWFvb393Ht2jWkUikhDrpJX3/9tbj91PNIoiRSEj/7WU989gvd8lQqhVQqJcE/AKLfVioVCRZp4qGFp6Ud6pfMwKAlxMmuF7TOLqE1yC+dRaCtSB3o4ELUC5IbRLvdFktaW/HaatZkyLFj5J86N7VvXp/Pzb/jnFtYWJBNme1otVqo1+uo1+toNptTsg0AaQuflZkwtAr5e7/fL4HCTCaDa9euYWtrS6SHbDaLbDaLlZUV3L9/HycnJ2Jo1Go1mZvcPEqlEh4+fCib7Hg8xtramshMel7r7zo7g+PAuEW/30epVMLjx49xeHgo7ajX65dmH7FPmX0Qj8eRSCQwGo2EyJvNJnq93lTG02XQm6Q2gKijz83NYWFhAalUCpFIBP1+/8pA93eB75x0OWBzc3N49dVX8eabb0rgiXpXKBRCpVKRgdGT8EWuzwVJN1UHrTQJ0TKddV1aJLVaTQgkHo8jmUxicXFxZnvcKLCWLPjFABmlklqtJsTJNqfTadkoaKVvbm5ie3sbq6urSCaT8kzcsXd2drC/v4/Dw0MJonDCuSlKJDA9EblouJCXl5exurqK1dVVyVLw+XzodruoVCo4PT3FyckJzs7OUK1WRdcEnkxqTuhkMgm/3y8WFS0cV+PmWLiTnUTjBub0GGspR6fy8TOasHg/9x5sjyZrWvvchGKxmGxk/BuSJY0IuvpbW1vY2NhAJpMRHZJZDblcDrlcTjZh9huvSeuaLjDHh5sBEQ6HsbKyguvXr2Nvbw8LCwtTmTaRSATRaBTJZBILCws4PT1FPp8XEidhkiRJbPl8Hul0WjZcl9h0UI39pH9P2anT6aBareLi4gKPHz/G+fm5aKiXrWWOq96IeQ96Qtw43YDtZeC9tDzCsdra2sLu7i4WFhbQ7XanAmu873cNz0h3fX0df/Znf4bd3V15QGYtAE86nwGTbrf7Qp3r5ktSkri4uEAgEJDUl0ajMbWwLgOtslarhUqlIl/VahXVahXxeFzaGw6Hpd2zoCdiuVyWSClTsJgKFI/HkUqlkE6nRWJYWloS8mOATk/GSCSC5eVl3LhxAw8fPsTFxQW63e6Uq0RrTJOSziNl30UiEczNzSGTyQjxcjFTI6b1o7ME6vW63DMQCCAWiyEej4s2zUWiNWBaixyrWdYUoRc+/47XZYBxNBrJZqODXroPdOR71qboEjufL5VKIRaLTVmiJE0tZS0vL2N9fR3r6+sSuKPnwr6jvttut0VWc9ukMweCweAzJBUKhZDJZLC7u4vf+I3fQDablX7QfRePx5HNZiUfnV7GeDxGMBhEIpGQjZAudrFYlLmXTCYl1e6y9ccNix5Uo9EQfZQyRqVSEWngKhdeyzDNZhMXFxfw+/2SnUMdGnhqKLxI7i7nmQ6wbmxs4Pr167h+/TqWlpYAADdv3sTe3h7W19en2vNdwjPSXV5exu///u/LYuaBB7rTkUhEtFxNEpftkDqAAkCiyYVCAd1uF9VqVSxEunfPGyguXmYXlMtlLCwsoFQqTREurzOLeNlmXqdareL8/BzHx8cyiQCI1jg3Nzc1yWe5066VwdxaWsTpdBrFYlHur9NftBbpRuHdYCI3HE7uwWCAdruNVqsllmsymZTAIt0+TfDMT6b7rRPV9eKbZT1pApmlu9Mi0s9Di5aWrp4v+lquVOXeX0sX3OToOdC9pyvOjSqVSiGZTEpq2HA4RL1el/Ss8Xg8RTzuxuCOATdqtksH/Kjnbm5uSp6pexhBewRaTuFcpRfCdL7hcIhGowGfzyfEND8/j2QyKVa+Hj9eT88VWsr5fB7FYhHlclmCoNyUL1t3enzq9TqOj48lMEyduNFoiBHGcQJw6XU5B8gj9BCp8c/Pz4tnSSlsd3dXSPjXgnSJcDiMTCYjE5ERY7pAlUoF9Xp9KsDmRjsJTbhaD+QXA1d0vRgoe5FTbXpClUolsQAYZKCmSxmDk2DWdZrNJs7Pz3FwcID79+/j4uICvV5PJrmOhPd6PSEwarrpdFr0JjfPV08kHSQiOWmL8rKNi2l8zAsFgHq9LonlDGi5QSQSA4lKZxUwMMiFqrX0WRYmn0WnErnuK4CpTIJutyuWrrbk3UCNSxaz7ssvvdlNJhPRhRnYY6qZDi4NBgOx7vgMPBXFrJZ+v49msynpXJpAZo0Hn4HPqi3fUCg0deCDf8fvDGw1m03Jcy4UChJAY8Cw0WhIWyaTCWq1mmRhkJCSyeTMNrKd3JBpnJRKJVQqFfl3q9USy/0ycDz6/b54o/l8XuYlg+g68KvXwCzi1ZsCx58bVzQalYMz8Xgc4XAYiURCske8gucpY+12G7lcDg8ePMCXX36Jg4MDnJ+fo16vS+CFg3XVgFHfo0ZLy5idzURyQru5z2snSZqRWK1JPY+09TUajQaOj49xcHCAw8NDSY0jYdZqNdEOY7EYEomE5D4y8s6Jo4/SagJ0j0/q9CZNvgCe+U6yp3VLjY/BOW6Ao9FISFb3Od1XbmY6q0EHg3S/aAuV4PVIXLqvtQ45S6PlItPXdhenaxnr+7rQXgrngc5q4OforZGA2AZKD5Rb/H6/BCOZxqdJxP3O+7t6M5+Bz6afhfONHl4ul8PZ2RlyuZzIWvRWmINNEp5MJgiFQpJzTO9pYWHhhec6wRzrarUqlvSLyAD0UvWmqYOb7Bt6TtpYmQXOV31YhCTOTbTb7WJvbw+rq6uIx+O/niljg8FAdt2DgwPcuXMH9+7dQy6Xk+OTmjCe55YweMPjlXqBcxJqK2dWmtNl0Gltg8FAdLJsNotMJiPWxqxTPXoSFYtFCT6VSqWptBm67a1WC4lEAslkUsiVhwOYoD8YDOR0FSUON2WJ99ffSZS0kmYtAvaVPh+vSVeftdcZB9zYdL/TyqaO6t5vlgXr4jLdVZONe61Z1vM3gTuGDEbqVC9en4TM+akzIYCnqYLsFy27uP2h83JJKLMkFt0HepPVrnSn00GlUsHFxQXOz8/leH2r1RLLl8Tvpg5yg0wkEjg5OUGxWJQDP67ExTlFKzGTyQjhn52dSRzjRdOxtETE/tL6P+/JPuJxdcpos8ZcS1n8O85vEi417+FwiEwmg42NjW8wY341eEa61LtKpRLOzs7w+PFj5PN5CcjMcv8uQygUktSPQCAgCe2uZaBxlevqQlttkUgEKysr2NzcxNramiSr6yi6/gwnYL1el6g19S09EbV2TdeHkyoUCqHX66FQKEg7uOD0AuDkZ0BFHzrQ2RokQj2hgaeBCZIpLVlGt11C15uKvjb7VGcXuGPJn+nTeYQO7OmxmjUuz/u3i1nygf6dPtShr6efh4tYB275M20g6Odg//DzWg7T80b/PTdydw5zLJkL7Eo2roVbKpXQ7/cliMR81Xa7PaXDa6PE7/ejVqshl8vh4uIC165dEw3YnXtsq/Z+fD4fCoWCnL57UQPHnV86s0OvWda8mJ+fx2g0QqlUEjnlMuhx09KN3+8XCY8pd887C/Ay4RnpBoNBJJNJtFotOaYLYGpXc4lyFkGy83d2drCzs4N+v49Hjx5NHSvlZ9jh+novGv1km7PZLDY2NrC6ujplbc4KnjE3lQchmDLjVoKaZZEmEgksLS1hfX0dmUxGdDwGFFKpFObm5mRhAk/TlqLR6FT9BC5SvTDYH24+qtYgqQ/zOtql1Z/h37i5z7MsMU3wtKhciWRWsNCdB1dBE5gmv+ddUxOuJnxNdtwIZ8k53DT1ZqdPK9JS5FwnodDCcjdIBuook9E74e+oPeoNgu2gNUttlJol+5y51nxGbT3z3zrwm8/np0hXB0Vdy5wb6ubmJhYXF6884HHZGPBZ2D5+5/0ikQiy2Sx2dnYQDodxeHiIBw8eyPPPmg+ulKQ3X2ap0Mt80Ta/DHh2p0gkgvX1dYTDYTQaDeRyOSGlWZiVT8uUl/39fbz33nvY3d1FqVRCp9PBxcUFgGnXQg+qtlDoGj/PQorFYshms1haWkIymRQrdJZmSBmAx3g5cdvttrSdViTbwAWZTCaxtraG/f19bG5uIh6Pi5sGPLWKZ3kD7mGIWQcItAVGstSEoo8z8zrhcHjKEueYRCIRORNPq0tvOrT03UCKbpM+tjyr3105wXX/3b/XcP/+qvuw//TC5zjSg6ClyH7jwtWBNq3582/Zl1oD5+8ZIKJLrcfNbSN/PqtQjfY+eD3OWxo2CwsLCIfDcurLPYxEK5KkwxNd5+fncjzYtXYJziNmPnCtMBZxFVwi15u83pC5wYTDYayuruKNN97A4uKipHjdu3dPsp70uM7iD/ZnMpnE+vo6dnZ2cO3aNSwtLf16BtLC4TCy2aycQsvlcjg+PpaCE7NcTkasOeDRaBQ7Ozt455138O677yKTyeDo6Ah3796VTtZasHYTObl8Pp8QwVWVhfx+PxKJxFQGgd7pZ+lu1NZYrITHc2mlMC9SL8D5+Xmsr69L/uDKygqCwaBURqPFAzy1HjXRuUSsgwzsN22xkjh5+IOLk1aLzoJw3WYepGBecSKREM2SUfNGo4FOpzN1HT2mrjY7S/O9zEJ1LbNZfzeLhPVc0J+bZRHTch0MBrKxJJNJzM3NiZU5Hj/JKWVuKgAJSnHT16lmjD9EIhHZTNvttmiLrnxxmffH+e0GBvV8YrxB5xnHYjGRvPQhGs5zekncaHUxJkb69RxziZdzh3VPGEC8CvoIuA6Eu7EcSlkAkEwmsbW1hWvXrmFlZUXm3eHh4ZREyTkyy/BgfGZnZwe7u7siGTL90wt4RrokzdFoJAcBmEjOnEY3OKQt1FAohKWlJdy8eROvvvqqWM3UkHQEW8PVhZjGo13ty9pLsnUDRleBKU20KHgAIhqNyu95X0aLb9y4gZs3b2JnZwepVAo+35OTXIFAQI6y8rNcnDo44x4+4ITTJMOJS8uaqTOcbAwe8oCIrndLySIUCsmk5cENACKp6ACam8J1GSm6Y6UtFXdzcwlHj9VlFqCeA7Pgku6s9jNopI/48hg7dUVd4pIGg64xoXN3efKR7j43f72huFkZvLYecz0n2E7qlel0WsgyGo3KpjgcDhGNRqdqTHBucIOg/svNcxbZarCtlEKed6yW81EbIjpVTkP3BQA5Kh+LxXDr1i1cXFyg3W6jWCyKNzgrW4Ucwg2JtVSYCvq8TeJlwtN3pHFSMXWMuxMnPgffLSZD/WVhYQFLS0tSCo/XcSfHLLDT9bn4q1xP16Jpt9uycGbl5upn0AVXeLSSGp8uhBMMBrG8vIybN2/i2rVrkrDNPtBZAUx7CYfDU1HrWRFiblZc+HxOTnSePqP7yLxkFgt3Fznb4/f75UAHA4qanLTFoi1yrZlSXtEpWNri1G6s/tIEpMlU5/fqf/P/szZKrUtqzZVt19amznOmNUci7fV6U3WUORb0GLiJ8DAFrdB+vy9zgV4NN1P2jf4/oWUc/o3OFiDhsoaGnkvb29tot9vw+/3I5/PigXE+MquBVjnjBHosZq0VjrkuWqO9y8vWF5+dRtNVwSzOR6a8cXOPx+NYWlrCwsLCVLaRJmp3459MJmIUMdbyPCnkZcPTPF123MXFBR49eoTz8/OpNCoOBqO01Be52LnIWTVsOByiUCig1Wo9t/NIVM8jZ41er4d8Po+zszMsLS1JIEvrUBq0jlOpFFZXV9FqtaRINQv9sEITFxr1pWw2O3UyjRYKo81ah+NimlU1jJsLj6lyo2D7SLo8surz+STflMRO70KPHT+vi8HMCppxgbpBPY4lg40kUjerYBYRzyIeYpb8oMEgD9vJe+nMD7ZVb2Jau3fdeL2Ba3JxLXW67tyAedJrMpmINRiJRKZKl9LK5AlKvTnRG6GWDEBOhTG/WgfyWCvX5/Mhm81id3cXkUgE9Xp9KuWSNSLK5TLG4zEWFhakDOcsq1VLNvTsdBzDLVQ+C64Ff5kXqceg2WyiUChI+YBms4nRaCQBMXezpAfCzZSbSy6Xw8OHD7G/vy9F4l+UE14GPCVdlvk7ODjAvXv3pAQbJ7q2bGn2M3BB7bBareL4+FjIJpfLTRGFa91ol1Mfx3xeHjAAqdJ/enqKtbU1LCwsPFOsXLtedK0WFhak4HOr1ZLTeNSHSbo8WcX6v3RHufOzDcViEb1eT86zM82FRy51yUpdiGVtbU10dJ3qxUCLq4O72rD+Ga0GnWiuXV1N9pSRuFj1e7D057Ul4mqU9ICIWW3Un3Mtfq1hu1IFNwK2ld4DczcBTD2LDoZRRmDA0JV3eG9tjbsbPqU2PR/0plQsFuV+vC7nDE99cdOqVCqyjriBAJja6CaTiWTHMPebwdNAICBZCyw6lUgksLKyIsV7XOnGlXyazSZyuRweP34s9UW4JmatMU3WOj9/1mf0GHa7XeRyOSHYi4sLMb7ogdLboRftHtxhfYevv/4ae3t72N7extzcnHjPXsAz0mXaytnZGQ4ODnB2diYak3bLSLCsluTz+URfpJU8Ho9RLpcxmUzkvVIcHJKVSyBcMDpYdBm4SIfDJ+8bOz8/x/n5uaRzzcpg4OeoObFAyXA4nHLZuHnQWqFFS8tebxr9fh+VSgXHx8eoVCqIRqNYXl7GysoKwuEwisWiFLyhtUR3lqUh19fXEQqF5Lgvj342m01xx/g7Hg/lKSz9ckx+plKpyMIlcU8mEznbrwmw2+1Ke/TbLUi62k1mYRb3NTmEa21y8Wtydj/jyiy6spsmVQbOdPoTPQXWIgiHw+JljcdPDo9w06M3wiAcN2Dq8mwbn11XzGIbmB0zGAwk8KWPDXc6HTnJGYvFpNJbPp+X4+UkyZWVlWeenxIH5y7nI9cD31nI+RqPxyXtbJbeyfGgPq2LwNM6v0zeIR/o9ehau1quY592Oh2cnp7K9avVqtRa4TNFIpGpDZCbnT4swdrO9+/fx6uvviqlHr1KG/OMdFlJ/vj4GOfn51LN3c024AAEAgHZ0WjJkYSY1sIOrNfr8hkdUCJm/f8qK1eTt3adCoWCpJfovE59PVqaweCTt0HQsnHrRHD3Ho1GU3UcdACMBXyY5REIBOQ1NoFAAMViUWo6tFotjMdj2fFZl3Rubk5IhaShtSy6YTy5xOOijKxzfPQ48YgzK1Lxi8/N/qbVQmJhMJHZHDpgx4pw2uK9SjKalaWg5xHHkf3P1+OwbgYj9tT4uRnx2jxtRe9mMpnICULq37VaTU596ULZfA7qrywC3+l0pvrI5/NNjT1JgzEAbgBcJxcXF/jqq68wHA6RzWYxGo3klOdo9KSa3sLCAtrt9jN9wNQunWmgc36pBfMZ+HeuEaP7mFpusVjE+fm51Ih2yfIya9eVF3T/awuX67per+Px48dS60JXJGTch4V6mKrpBtipg/P9jCcnJ1JO1au0MU9eTOnzPanC9MUXX+D+/fsoFouXFp+hDsYIO60S5qoy3YaDzgntapsuOJlmkbIGLSEuBr5i5+joSI4BU/7ghGQwQRMFLQq2x42s64g7J7fWMWmZM8pdqVTg8/kkCj0ajWSy6xfr0bpg35CMK5UKisWiuJB8VhIDC2wzwOkWkuff0kKp1+syRsvLy0LwtDR0JgPHUBftYSAlHA5LTq9b8tB1M/XzaVLRn9Hf3TlFQqPlq0+ccY6xbaxJyzfpNhoNNJtNKc5ET0G/3cI9+EELSwc9KVXojANayHxOEri20kiqjx8/xnj8pAh5IBCQND22m4dxdIBaEyi9KU1o7tzU/3azRTR5saDTgwcPcHR0JLVFOK7MX54VINOB1svWoysNMkOGGyb/hjEgBhDJE1dZ6Sxp+fXXXyMUConMcNVG/7LgmaWbz+fx/vvvy+J38xJ1PmMmk8HKygrW19enzlvrIIJOadIa7VWC+CyXVYOTkK4nXZ9arYajoyMhYwDY2NgQzVnnZTIYqJPK9eTRUVWdjgXMrgHgVtDizs63HXMiMl0HeFo9jDmkw+FQXsJHguMCZNSYAS6SiCZcYPo0FYmXpMOEc2rTlIL4d1zg7vjwuWOxmBTwDgQCU8Ej9osOqOjvrsyjfw483UQpcZDsCR0cIyFRImLmibboWIieXoD2BHh/LV1xU2WWiB5/kjB/zheeVqvVqddU8Zo0XkqlkpANx556MGWaWRuW60m69R50QJCbMecon4Hj2Ol0cHZ2hs8//xyff/45jo6OpEA/vT29ebiepraAn7dm2c/0ykjq9JjC4bBs/pT0JpOJeCEcD/IM12ypVMKdO3dwdnaGt99+G2tra5e242XCM0s3n8/j3//93xGPx6fe6cVBp460uLiIzc1NqXVAIubEp1WnRXjtmvCes9rhDrwGF4c+Fst0GrrfdGfa7TZef/11LC4uTm0IDIjwSK3OwNATWVd7AiDHRemSkQR5TRIBLWjg2SpM2nIBIJYBia9Wq00dXNB5pJoEXLJ1rUkuALZT67msL8vXrtBy1fqfvr6O8MfjcdlI6QLOcjfdsXZ/rj+jU/eoT5KcaAly7gHTr3XhoZHxeCxlPvmuMcpdbjYEMH2U1e1X3ldvpiQmbjaRSETSmbQs5br1w+FwKvOCxKj1cZ3KxzncarUAQCpr6cMxnKec80zH1EeRA4GABHe/+OIL/OIXv8D9+/dRLpeFcLmW3SCju0b15nTZmuWc43No78Hn80nhfH2EnpsQYxP8t14vw+FQXoLbbrfxl3/5l3LP/+8tXT4kddhUKjVVi4CWYSwWQzqdxsrKirwQkpFjRlT1ySx9jHLWru7e3/23C519wHKLbDcj7Qw88Vo8zABALJ9QKIRUKoXhcCgaIhcNT6w1Gg15id9kMpHX5VC6GA6H8rZSamRcGPrV4Gw3SdHV4Wi96re2ckEy24CE4/antgr4XWtvmjzL5bIQm+5jkiqJ1CUDkiI9COq+nB/8OxKP1tHd8dbuODBdN4EZCgBEjuI4ukE27ZIy0MdgFbNH9FFurT1qC07LWexbRvU1AdO67/V6oimybXojZn9qyUrLNSR7vg26Xq/Lpk+JLJ/PS9FyvmyVmQy8FjfHVqsl16FnxPbV63UcHh7i008/xd27d5HP59Hv90UrpsFCI8ON3XAML1uPl61hrSuzrcDTwyusjTsYDJBKpZDNZsXwoHWu08dI4Kzj7c6t7wqeyQs6Is/FrdN5tGDearVQKBQkgZtFz6k36mtoC1brYlo3exFoeWNubg5zc3MiZ3Dh+Hw+NJtNef1OOp2Wya9lAD7LZPI0H5PWBgtan52dyWt24vE41tbWsLa2hmQyifH4STHn09PTqVdEu5Fw1xoleegi63w2foakA0ynFV0m0WhSd2USWo2sN8G0m0gkIp4ISZ/jxig/MJ2WpUlLE4tugyvV6H/rz+u2kwR1qhrbBkAi+yR9yj0MrPV6PVxcXKBcLgvhct7pNrnuOdvFfnfzRbUcwZQ1QrvPurynfl5ewyXLarWK09NTzM3NodPpwO/3o9FoSH3ddrstr1Hn5kMtXs9TBlZ1RgPXAQmcb3nWNTooe9Cr0OmFl8VRLluPeiz1GtcxC1rk9Xod5XJZ4jvcILlutCXPDVp7cN+kbb8qPH8Fu3Yr+F0HfRhIqFQqMhn6/b5Uwefx2lkCvLsovwm0xT0/P4+5ubkpCYNEqt8FlkgkJNVEW1m0yPXxRkoiLJ93cnKC4+Nj1Ot1AJC82sXFRQQCgalILQMSJEWSmQ5GutFe/cW+1rotCUqTswudgTDL5dKkwpoTJFLKQbpotJsRQQuP+cq0Bl13lHqmPgnotkf3D9tGAqG7TvJjG3hN1lnQXpMmXy0pXGWhzZqTun2aLHSfUqICpktgagvfDSrpN43wi/m2R0dHGI1GePz4sWiX5+fnovGn02mRLzg36VWyrWwX+1xLX/1+X+Z/LBaTk26UiZj+5vf7JdPj27jslwXEucFywy8UCmIQMYDHAKN+jTuJX/OPNmS8gqfHgIFnH067q8w1ZRI4XT6eJGE606y3QOhdS+t8s+7pQg8uU0/4aqFYLCaBk2g0itXVVdy4cQN7e3vY3NyUnFwdteY9NdFRC67X6zIZ6vW6FPxhKhjfxDsej6VMH0ldWzSuns0Nx61GpXUwrakCTxcSU4eYcQA8TbVxNzHXonTlBx2xJslqDVPrtfw/i/roLBCXuF6E7Nx5xevR4tPeiH4Gto9He/lzejCuV+Y+u+4f3R90yRnsmeWdcaNxtUqOo0u4fC667RxnnePNLAemW5ZKpak5DACpVAqNRkNyrSkz0ErVlcL4TCQ6blCMD1C/1wFIXlNvlC8C9pubbqbXs54bzGTiQSbGKLSUpr2MWfPKa3hOum4UWssLAGTi8TikFvb1wr3MHXAj166W9CKIxWJy7Je7ab/fRywWw9raGvb29rCzs4OFhQWpQasnF9vM2hBcuDrxX6eJMdtAB2RYq4Gatg6u0dXUmqJO+tfunbaMNOHo03N8sywDRrNem60XAP9PSYPWv66TrIM7dJfdQAj7SV/bzWoBnnU1Lxv3WZ9hEAuYrg3h9pvOl9Ukrp+Pm5ImYpcgtA5LEmJxoF6vh2KxKPmsOouFJEGy08fhucGyb/i8+lSd7mfq16yHoA/F8HN8ThIUr8Hqc7rMJ8H26qPw8XgcuVxOUrQ4Z5nz7RpDL2IA6S/OJRduUJwBajdwrUlbzxFXyvIS35ulq3NUWY+TxxI1wfDfJNxZZOvmarIzNRFepdm4An0qlcLW1hZWV1fFQmG+aTKZxOLi4tQbJNwCPRxwbQFrDYqv+M5ms5hMJmJ98G0YPCHDOgzUI1mo2u/3T7nJJA6+7ZRn/P1+v0gkWuogIWSzWVy/fh27u7tIJBKSj3xyciKyhhv8cGUUlvNjn9A6ZACQn+GioE6oMzP0IqOGqRfoZdKGS0AkEv6MQSq9ydNr0JkqnHs6ik+NNxQKyWGFcDgswU0d7HI3Beqa6XQaW1tb2N7eRiaTQavVwqNHj3BwcIB8Pj8VWKNRQYuTBzK0x0HLlsSYTqelhgaPzXOz7fefvK+MfcA5lkwmJVBNC1wXXU8kElKvw53P/D+PME8mT44X7+7uiqfAqmG5XE5S2/T6ugpuIJjjrH/vrmOuLf1/bj46O4dzT7+h2A3Ce4XvRdMFnmqoiUQC2WwWy8vLEkSiKD4ajabOt+uBc90u9zt/903F8mAwiIWFBWxvb2Nra0smq7YQudPTVdbtmRVp1mTFTAxOWrpmPIG3uLg4RepcQO12W96BBWDqNM5k8iS5n+RHwtY6pg5mkHSXlpZw/fp13LhxA+FwGKVSSSboeDyWI6960ej+Z6rY0tISFhcXJbmc2rx7Hr7VaslipruvCZP9NR6Pp7I0tFaq0340kbquvxv5J4FxHtGC5Su5E4nElBxDfZ8/JykHAgHJjeZ8cq0zv98vRb03Nzexv7+PxcVFifDzzde8hpZiuIkyA4AERNJlW1KpFBYXF7G1tSV1Emih0t2u1WoolUryYkoSOush0zth2UoSrvv+P+2V0Pjx+/1S6lLnp3NuRiIRnJycvPDRWu01ELPI2tVgdVxBB/Li8bgYLwwm8jXxuui5yx1e4HuxdPmAPCq7vr6Ovb09JJNJdLtdqT5GTVNrYZe5Hm4A5du4Dj6fD8lkEhsbG9jY2JAjv1yI2s3jiyW587Nohna9mBFBXY9BHW0hsiatPoXHQB5zc3k/pmTxzLkuM0nrh8dxfT6fBLOYY6zzUwlaoaPRSE5dMdNAp4i5/a4PHdBSolVBcIPS1a601MG2aw2abdKBMTdvV3slmui0ZTsrzUxLDUwnY1CUG4YuegM8JQNduUsfXHDnNZ+B1j4DSYlEYqZOzv6n5s9x5v2onWqvQ8+f5eVlbGxsSCyA1n6v1xN9dWFhYareMTccfWiEtRhcHZmyGN+vpmUKXalMnzikNLW+vi71U74JZm3yXJ/u2nc3ZL0elpaWsLa2hmg0ikajITnGWuP9tbZ0NfTD6p2JBESLQ0sK2qqYZUW6Irn7Ny/SuX6/X6xNWj88J8/dnW49XzXd7XYRi8WwvLws1rrOzeV1tdXC9uocUS7+ZDKJZDIphyt4HVp+1WpVrBldd5WbgS6byPdi6YIsJEUGHu7fvy8WbaFQwOnpKYrF4lS03rXk6DKSLFjhijIQSZ7tYB/o++tqalpLdXOQdWL8VTq+lnhIvNpd1fqu1kb5M24ymuA491gwiKTDftcL15Wo9BtnG40GlpaW4Pf7p2oU6GuwEDx1efaNHjfOG25ALMidTqeRTqenTkhqPZabn64hy/7kBkWy1WuKhMu3U7O4FOUFelSMIfCewWBwSsvW1utl0OSp2+CuZ3ddux6wGyilbEIr/5tywneB74V0gacpTJ1OB6VSCXNzc3LaTOtmbiCMC4kD7J7M0uDPXjQHTxO4tqA0OdBa5LnzYrEoRzf7/T7W1takmhPvrXVpne7DwxTu/XUb+Jw+n08sMlap4vVIEMCTQxo8YNFut6UAuyY8yhyFQgH9fh+Hh4fyTK1WayqtC3g2HY2bou5XBlgoTzAlSR+pZv5nv98X+YGLFoAQn14Q2uUkAepFpev1aqLRBEiNj/1DfVunKTISrz0C5ogzs6Ner0/lrrKtJEStEZJ4W60WisWiFB6i269rhZB8mbXDDATON1qZDDpGIhGJMeg6vfp0mjuX9Bzj87EvODcplbFdrMlxfn4u9a+HwyfFduil6k3T1ee/qW5K6WsWtOeoDSG9UfPfPIbeaDRQKBQkmMj3KZJXeE+v8b2RLvDkgdvtNi4uLjAYDOSwgT4vDUwnyXMBUWultXUV6br/vqwtw+FQDi7UajV5S6i2ACjQM+DHxQA8fV1Kv9+fSjbXC0e7pjoNSZMoU3s0qXBBZbNZZLNZmfA6fWg0elKEhFkbTJfRQR/2I3U/LmR9Qsfd6NxglLYStWXNxaAPQHC8qIFzk/D7/UIYiURC5gKLt+jPc+NjPuss0nWDlvwsyybyvV1092ndk2zZlzrgyDxd9o/WXfW81CSn5y0NAub6arJgME/3M91/Sj0cR91v1FsZEGNQLB6PP2PNa1LVmifnM4m+2WyKNOTWDq5UKjg9PZVXu7NkKa1nt0Ie52OtVsPp6SkKhYKQ/FXQsgv7VgfUSLg6n1gfM3c/zz4k6VerVZRKJbTb7WcsaK+J93sn3X7/ST1OLpZUKiVVvLi4OGm4s7MgeDAYlFeU03Vy05Ius4JntaXX6+H09BQ///nPsbW1hfn5eTlKSSlAF/2ghkT3ejgcTh2bZDtIplwATEHSbh0XAy0/rZVykuljncxZplWkDz1oK4cuJDU3N1DB9rnpNcB0TVNudCRQTW4cI5KJmxrHojgAhJxZ83dtbQ3pdBoAJPDDFyjq+/K4sNZ+3WfQR55Ho5FUnmJgEoC8YpwbobZ6OGdIUDrwyPtQrtB5scwuoXXvuq5a/uIm5OrqnKck/Vl5wXxJ5rVr1/Dmm2/izTffxM7OztQrxLnBcuPn8WJu2rwXx2cwGKBcLsvBAvYx5y83JaaCzc3NSV0UBuP0qTiuga+++gqffPIJzs7OpoKOV0H3kZaGuC4SiYRk5wyHQym8znHk5stsjlAoNCUFcp18H9athmekq61VLdLTpWMmA9+FNh6PpTIWX13CPFm+qjwSiUy9woP1CkqlkhAxLawXAQfys88+w/r6OnZ3d7G4uDilx+o6AXNzc1heXpZINJ+LByCq1apESnWgbHFxcerUjqsf0gJ0I8m09hgAisfj+Oijj6Rcpg6Aafec+aLU4fx+v5wg47lzEo6WEEjWXOzMUAAgBz3oagPTtUs5trymtg6ptS0uLmJ3dxerq6sIBoOo1+u4uLgQN5AZHel0GgsLC/IyU/3WC741mW9U4Hu6xuMnB1uYGTM/P4/BYIDz83ORCrhpuboxrVCty3PjZfCI1iUANJtNlEolWdTUXPX1tKU2Pz8vb83V2js3G/YnN2JuwNlsFvv7+3jnnXfw3nvvYW9vD6lUamp+6KAkP8taG5ocWbieFiADbZQNdPH2xcVFeZU7NzHWRtGbIK3ri4sLfPjhh/jss89QrVafCTpeBW42TInjhplOp6e8vF6vh8ePH+P+/fs4Pz+XFxkwFXNhYQF+vx/lclnmE7N93KwTr+F5yph2XfXi5KmktbU1/OAHP0A0GpUjs0wfW1xclLcBb2xsSLoKazPwLQuffvopPvroIzQajW804LR2z8/P8dlnn+FHP/oRbt269UzuIC0dLiB91FUf83348KGUseSCYY2FTCYzJUHoAI8OTmgC5L0zmQxeeeUVIedwOIzbt28jl8tNWZx+/9M3cFy/fh2vvfYaNjY2EAqFUCqV8ODBAzx48EDePAFAFjjdRhYi2tjYwObmJtLpNEajkRRX5/FSunp8Dq0h64mtpRrWANje3kY8Hken08HS0hKKxaLIJCwGtLq6imw2O7VZafIoFovI5XJS1GU0GslGsbS0JMdVx+MxTk5ORAbhQnTnKd1VWuvUpnlce3t7G9lsFoFAANVqFY8fP5ZaGdyIaAEzH5fP+4Mf/AD7+/tYWFjAYDDA48ePcefOHdy7d29q49TZL+l0Gq+++ip+9KMf4e2335Y+06RH2YZftEx1TMKVDnq9Hh4+fCgbdyAQQDabxd7enrSRb99lMRv9lhOXtHw+H87OzvDZZ58hl8vJs78IaHyQPN9++2388Ic/xNbWFjKZjGxU9J5OT0/x5Zdf4quvvpL6wgsLC1hdXcX8/Dy63a4U+afU4lq5L2qQvUx4Rrp0fxKJhLhABFNyMpkMtre3cePGDSQSCbTbbayvr8ureVZWVuRV5SxwDjxNe2K6WSgUwtHREcrl8qWJ1lqj1MEPtqfRaMjrVmYlv2thn9kXfP0Oo/+sv8oFNBwO5QBEMpkUHdLVA3VwYtakpgW4vb2N4fBJARK+oFOnVzH4try8jNdeew3vvfcerl27hmAwiHK5LO/MCofDUgOCbxBg2hLfl7W9vY3NzU1x7S4uLuRFnfw8PQu2kZau235ajDzMQQt6MBjIRsIShKlUSir7M3dZnzrS1aU4DnyWRCIhpMFC5OVyeaquLnVWDVr79GgoB6VSKfG0rl+/juXlZZG41tfXcXR0JG/x0Me+WcIzlUphf38fb731Fm7cuCFlCI+PjwFAXGXOaRJnMpnE2toa3njjDfzWb/0Wtre3JStg1rzUshCDlO7hHf68WCxiOHxSbzmfz8umSO+D1ch4kGJWTrTbb4xf6EMZ7vpzoa8ViUSwurqKt99+Gz/60Y8k7YvSHvBERqHFnU6nkcvl4Pf7p94YzgJFx8fHsg5dkuW80DzxXcP3HL3zpeVUDAZPXpGhXbaphvieFnvW7pJOHXETqPUOLw3+fzqV1hW/DThhGPV+EbC91HK1nqzJZhaZfhvwfpeVz+N9uXjdoOCswJletPrzOufV/byWM2al4ribHTCdhaLHG3g2ouxGxS/rBzfNiJ/lfdlm9yi5u+m6/ef2hXuYg212swV0u/h5ftbNS2aGy6xov14bmnh+Vej+mDVXdTu/yXylhf9trUgdR9BvYOHv2HbgqUSpOUJnT+j8+st4ZzweixH0EnFph3lGugaDwfDfCJeSrqfZCy9idb4sYfvbWrgaL8safVnXepH7XIar7j/LsnqZ9/42bfqu8W3a/G3a+0369lcZw5eBlzlX/6usP8Bb3nkRmKVrMBgMLx+XsvjLEYcMBoPB8EIw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cIPuf3Pk9aYTAYDP9NYJauwWAweAgjXYPBYPAQRroGg8HgIYx0DQaDwUMY6RoMBoOHMNI1GAwGD/F/AcLJdDNXOWCOAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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ut+/Lt0wWQqVSEUum999/H++88w4+/fTThuVrvV4XYkiDj4IvPp8Pb7zxBn7t134NR44cgU6nw+bmJi5cuIBPPvkEq6urDy3ZqtWqsGDph/yazW1KAkC/ZaEg8ZWX8fu5b/m1dP/ZbBYbGxvCb0uuAK1WKwY7TQrUFvTdsv99N58uXTPdAwkaiRpNfpVKRbRPc7u08qHv977l9pP9grJ/Wv4eup5isdjQLiqVCqVSSfQbGYPBgKGhIZw5cwZf+MIX0NPTI1wYP/rRj3DhwgWEQiHxneTHllc0xWIRKysr2NzcxO3bt/H222/jS1/6EoaHh0VAqZ0/G3gg5na7HeVyGel0GhsbG1hcXMT09DQKhQKSySSmp6dx48YNBAKBPdsvn88jGo2it7cXHo8HPT09wnrfy43VTKVSwc7OjujXcl8slUpIJBLQ6/VIpVJCkJVwKxCKiS49sL6+Phw9ehQ2mw1arRbpdLrBsiMepxHIQvD7/ejr64PX60U+n9/3zFWpVLC9vY1cLoeVlRUMDQ1hdHQUXq8XdrtdzJ6PEk0vl8sIBoP46KOP8O6772JhYaGtJUUZAmQd9vf341d+5Vfw9ttvNwRDaEk8Ozsr/FHyZzaLUnNkvRmNRgOdTicsebvdDp1Oh1qtJvzZqVRqz4gyBcQANAx0+VlWKpWGJThZ+mSJk0Uov4dcHbIoyfcnryro9TTxkFVGz41+ZKtT/k3tJ2cckHC1gzJlHA4HHA4HLBYL1Gq1ECTZEmz17OkZ0fMnsSDrlKAg3NjYGM6fP48jR47AbDYDAHw+H/r6+tDT04P//M//xPr6OgqFgmgDOU4hP4vFxUW89957MJvNsFgs6Ovr23fAul6vi0wau92OWq2GtbU1ERcgt8d+Vw5qtRoWiwU+nw8qlUq4g2i18yjIY5R+k6Xb29uL0dFRpNNpqNVqxOPxAw+eyRy46NINd3Z24qtf/Spee+01jI6OQqPR4O7du1CpVLh37x7S6bQYbI/rX6FBLwdQ6N/3m/xcqVSQSqWgUqmg0+ng8XhQq9XEYGge9O2gwZ3P57G8vIypqSlsbm62TJuRr5sG78jICL785S/jrbfewsDAQMPEYbFYcPLkSbz22muYn58XARvqaO2Whbu1mcfjwfDwMCYmJjA8PCwGUSwWw/r6OmZnZ7G4uIhYLNYycEMTBYlAPp8Xg75Z2Jp/N1uU7a6XhFe2jOm+W62M2vnU9/oOuhcKNuVyOXEvza8l18no6CgOHz6MgYEBkcWSTqexvLyMe/fuYWlpCbFYrGHl0Ey7LBDZHdHX14dXX30VJ06cEOIOAEajEUePHoXZbIbb7cZPfvITLC0tIZ1Oi1Veq8mjUqlgY2MDN27cwPj4ODwej3DH7NXP5aAmjbdkMonNzU1sbm4ilUrtW3DlTB8S8+ZVyuNAbjONRgO73Y6jR4/izTffxPHjx1GtVrG4uIif/exn6OzsbLing+TARZca8vjx4/jzP/9zdHZ2Qq/Xo1wuo6OjQ6QuLS0tIZvNimXl40CBr42NDdTrdTgcDiSTSWSz2UeynCmfN5VKIZ1ON6RWtVt6NUPWSygUwieffIKFhQVxHdRB5aUoCa7dbkd/fz9efPFFvPLKK+ju7n5oFlapVLDZbDhz5gyuXr2KjY0N4SNtzpjYy01DuaQdHR0YGRnBiRMnxDKzWq0imUzC4/HAarXCZDJhZWUF4XC4IUeU/KN2ux12ux0AhIWzWy7vXgLY7nppNUAW/G453I/6HSS6FOWn71SpVA1BHbLwvF4vhoaGcOzYMYyPj6OrqwtOpxMajQbZbBZWqxW5XE5kqey26mh+Vs2ThtlsxsmTJ3HmzBnY7faWPuSenh688sorYum8ubnZYCzILiOy/rPZLBYWFnDnzh0MDg7CZrPt21Chfl4oFITPnFZFj+IbJ5/78vIy4vE4UqkUNjY2RFzicaEJwWKxYGhoCK+++ipeffVVDAwMQKfT4cyZM/jiF7+Ivr4+8fqDRjH3gsPhENYTWSgdHR3wer3C1aBWqxsso0eFlsPlchnxeBxGoxHVavWRA3LkY93Z2UE8HkckEkEsFhPBhv0ku9fr99OQ5ufnMTk5iY2NjYYoPH0GCQhZ0iTATqezwZKRoZmbBrzJZEIikWhYJdCA2s+mknq9LlKqQqGQuJ5cLod4PI5YLIZEIgGj0QifzwedTodUKiUCXbIPmjo5bQrYa2m+X+heaKlMk/N+Nmk8CvRMKMhWrVZFewAPsg/MZjMcDodImYrH45idnUUkEoHb7YbFYkG5XEYoFEImk2mbz9wMuVJIIMnPDwAmkwnDw8Pwer1t+59arYbVaoXD4RBZBLRqoxgDCT/9FAoFbGxs4Nq1azh8+DB6enr2lddL7Z/NZhGJRBCNRkVWzH7vl6hUKkgkEigWi1Cr1SJl8HFcCwRNWvRMbTYbvF4vOjo6YLVaxYrJ6/UqYuESitdeACAyE5aXl3Hr1i2sr6+LjvlZRJc+mx4WZR3stqRrh9yZwuEwotEovF5vQ0fe7UHV63XE43HcuHFDRJWpI8q+JhpkJJjZbBbRaBSrq6vY3t6Gx+NpmyFBvlgSpGafaHMGQisoJzYWi2F6ehrxeBwWiwXFYlHkXlJKDUXbaVnd7DulSLNOp3vkHMv9QIE22oDQvIHiSVIsFpFOp8Xgp0mbJhRagkejUYRCISGOBoMBNpsNNptNJOCHw2GRGbHXJCj7d+XsF+ojtImk3XtzuRy2trawtraGaDQqnh/1D7lvyP2F0ttu3LiBkydPwmaz7XmdlAaaTqcRj8cRDocRiUQeK+uIVqkUFH6cMdvqGuk6MpkM1tfXcevWLQwNDcFutwuLfr8xmieF4tkL1WoV6XQat27dwr/8y7/gZz/7GQKBwGea0VrxWR+abP09avSUOvLm5iY++eQThMPhBhGSo+YajUYMWBpMkUgE09PT8Pl8MJlMGB8fh8ViadhBRfdIE1VzhHy/y2qyVnZ2dlCr1ZBIJFCtVsXGBBqkZC3odDqxk4qsTPKrUn6vVqttmAieJHLg7CCg9pA3ZsiTJWUWUHvTxhF6BrQKkTdnkAttr4Am3R+AlsFEeVdds6+6Wq0KN8GVK1cwPT2NSCTykOi2ymKhvk6usM3NTfT29jakEe6HQqEgJqrHmQifhNA2I/fL9fV1/PSnPxUT33PPPQeHw7HvwOGTQrFvoyV7JpPBnTt38K//+q/44IMPEAwGxaB91qCH5fF40NvbC5fLJXZN7dUZybWwsrLSthPSTEx+RODBoA8Gg7h69ar4/8OHD4uZWYYGRjuLr1XUuhkaxPKys1AoPJTPS/8v5zI2DxLZcn8Wn+l+oAmD7lmGXGPUN2TBpXaiACpNVrLFuhe75SEDD/sc6XPT6TRmZ2fx85//HJOTkwgGgw0+bzmNsN090067hYUFnDhxYtfaHrIrjFI0Ozo6hMg9a5BVvrW1hZ/+9Kdicjxx4oRYlShl7SoqurRB4N69e5iamkI4HN731t5WkFA9SZ+eTL1eh91ux/j4OPr7+8W20b3cCiSa9+7dQywW29eSkvyiJpMJJpMJtVoN29vbmJycFBkNtAVYXmqS744sZtkCat4q2wrqfJQ9odVqG6xn+Rrl31qtVryPrK/mgM1/Z+RMCbLc5cwJeUICWqec6XQ66PV6sSKg1+/2LOTnJj9LCtyRZSYLPG0NnpycxPXr1xEIBFCr1WAymR7KS96NarWKWCyGu3fv4qWXXhLFY9r1d2oPm82GgYEBjI+Pw2azHdhkS+0AtE692wsy/MLhMG7evIljx46hr69P+O//x4kuALFbyGg0fuaKPhRdJt/jZxHvdmg0GvT29uLw4cNwOBx7LrdIcBOJBG7duoW7d+/ua1MGfZfVahXRb5qZKbgl7zQC0JDS5nA4HkqrIWtco9E0+HllSBz0ej0sFgucTid0Op24ZhrY9LmUxmOxWOB2u8UkRAn+9DufzzcUBfrvBt037TqkQUm/qXZEPB5HNpsVucbAg0AcTZQkfHI7thJAelby5zRPoHa7HR6P5yGrjHyywWAQmUwGarVapK1ZLBYhzPsRqlwuh7t37+LWrVtwu91wuVy7Ci/1IYfDIYJw09PTj72rbzd0Op2oYZFIJJDNZh/7sygtcK9qbgeBYqJLSxG3242jR49ifHwc8/Pzj+wvBe7v1T979ixGRkYwNzeHqampAxFdnU6HkZERdHV17cvvU61WkclkMDMzg48++ggrKyv7WmpRYKq7uxsTExNwOp1Ip9OIxWIicNNK8EmoKcuBLLDmfOLmFDL6GwkuTWC0lZjSo+jayZ1Anb6vrw+jo6Po7e2F1WoVfvpQKISNjQ2sra0J0f3vJrzyNZtMJgwMDKCvr0/UadBoNMhkMtjY2MDS0hI2NjZEcSC5nUwmk9hwILcBVS+TN47Iz6o5DYwsbBJQ2mLbDPUTql7W0dEBu92OZDIpAmz7ySgol8tYXl7GRx99hJ6eHhw/flzcdzuoL3V3d2N0dBQ///nPD0R0zWYzJiYmcOjQISwuLuL69ettazrsdq0UJzl69CjcbrdwGSqFopauRqMRaS+nT5/G5OQkdnZ2HukBORwO/PIv/zK++c1vwuPx4P3338f09PSedRUeB9rdRknye1m5tdr9Enh37tzB3bt3kUql9rXMJkthfHxc+Jii0SisViuA+7uNLBZLy00P7TZstBtcFLCjYI/dbheDmcRADmrKW3adTicGBwfx/PPP4/jx4+jt7YXRaES5XEYsFhNFUNbW1vYVNHpWIT+oSnW/ROLY2BhGR0fR0dEhVgJbW1twOp3CIqUNNXJgq1QqCZcNVd/S6XSi/kOziwJon1e828YcShPz+/2IxWIAgK6uLng8Huzs7KBUKiEWi+2rRm2tVhOZDHfu3BFV+naLY9DkQFvmTSbTvld4j4LJZMKpU6fwxhtvIBKJwGKx4MMPP3yksa/RaODz+XDq1CkMDw/vWgf4oFBUdGmZZLFY4Pf7Rafd73sNBgMOHz6ML3/5yzh9+jRqtZqo0rRf5O2ge0FFaOQgyW5QACUcDjdsf2y2OuXvpqVod3e3WAGYTCZRWKdWq8Hv98NoND70XRTUaU4Na/Yv0n3Tb71eD4fDAY/HI5ZrVLoxk8mIzSAU8ab3OJ1OjI6O4uTJkxgfHxebB7LZLHK5HAA0RP33k7L2rCG3K1USAyA2rlgsFgAQQc1MJiM2A8jZGrT1WKVSifc5nU5RQjMajQq3GL2H+kkr4aV+I2dOyP3RYDDA7/ejUCgIYXG73aIwzerqqrB6m7eMN/drqkRHm2D28wyp3ciPvR8e1f+v0WjgcrnQ19eHgYEBZDIZbG9v486dO/vOmFCr1XA4HOjq6tpzMjkoFE8ZA+4XvYlEIkin0/tqKLK0yGHv9/uhVquRzWZFIvZ+IL/ZfgNvtdr92rTJZBIOh2PP3FxautD+e9o1JftXATQMTp1OB7fbjZGREYyOjqK7u1vUPTWbzSgUCqJsY/OOOIpI03fL19HsIwQeuHg8Hg+GhobQ19cHs9ksckkpv7JVwRmdTgeXy4WBgQGx1VWv14uTFuLxOEKh0EPbhJuXzc+ay6F5QpShimehUAg9PT1iw4rRaITb7UZ/fz8GBgawtLSESCTSsHtKzmwA7ot0Z2en2KG2vr6OlZUVRKPRh9Il5ewJuR3p35uLGcnfQasP2kVYLBaxs7ODkZERBIPBhs0WcpqinKWi1WphsViET3o/xgb5laPR6L5FlIyw/fYJKpZVKBRgtVrh8/kwMDCAlZWVPbMz5GtNp9Oi3Z8Gim+OqNXu1/+8evWqKE3YCrlDyLuEKpUKIpEIAoEAotEoNjY2HqnxHmXQF4tFLCwsYGVlBV6vVyzBW0EdyOl04vjx48JfHY1GxdKL/HFkUVYqFWGhDA8Pi7Qb2vGk0+mQSCREShAV3CH/Mglec3aEvAPHarUKy4Os1cHBQYyNjcHr9YoiP9lstiF403xvWq1WBHLsdrv4TCq+HgqFhHDL+arkk6R2oxS4Z0F46b7oOql/yteazWYRDocRCoXgcrlgtVpFAIbag3ZUtnLxyEG57u5udHV1QaPRiBMM1tbWkEgkxLKfcnp3dnaEmBF0ZBQVYgceHIVDBgyVs6SSlsViEb29vRgeHhZ1aovFotjqbDKZxLZ3KnnZ2dmJU6dO4fjx42I1updrjWqMLCws7DtO86h9oFgsYmNjAysrK/B4PIhGo6hWqw1HIsmTXivxr1QqCAQCuHLlCl588UW4XK49jaknjeKWbj6fx9TUlPDntmp4Ws5Seo6cJxoIBHDr1i1RiHttbW3f56c1L632gkrg3b17F6Ojo2I3WjvIt3b8+HEA9yuqLS8vo1gsiq2Z1WoV4XAYa2triMVi0Ol04hQBl8slSutR6lYikUAwGEQkEkEqlUJvb6+wuiORCNbX1xuO9ZGjyWNjYzh06BBcLpe4bovFAo/HI/yTyWRS1IIga0HOKyXhoA0TVMiGXAi0135nZ0dE6eVts8CDUoBUeYuS9dv5Mw+C5pWA7NfWarUN+bcAGu5Bvk9yx9BWVbkYjizc9FtOF1Or1SLty+VyiUI55Jqp1e4XGFpYWMDCwgKSyWRDyl86ncb6+jrC4XCDL3lzcxOBQADVahV+v1/46GkMuVwueL1e+Hw+5HI5sQV/YGBAbIHd2dlBJpOBwWDA8PAwzp49i4mJibaBO5lqtYpoNIq7d+9idXV130bQo47HarWK1dVVTE5Owu12Y21tTdy3Xq9vyLahCm3NwkvtODk5iampKYyOjorYiVIobunGYjFMTU1he3t712WIXq8XFgQN8Gq1imAwiBs3bogTF3azlp/U9d67d09U2d8rfYbSuM6dO4fR0VERwCBrYWdnB0tLSyLtp1aricLPdNgkDXa1Wo1UKiW255JPlYp1zM/P48qVK2IbqlzXwev14oUXXsCLL74oKqWRlUTikM1mEYvFEAqFhI+RDh+UfcWUIrW2toabN29Cp9Ohr69PRPITiQQqlQrMZjNsNpvwVVLUnQJ25DvOZDLClUFitd/l4eMgW7TypGA2m8Ue/FKphHQ6LepjkK/dbreLvOlEIgGtVivS9zY2NnDz5k2xjV3OV6YJiJ4hbRemeswGgwGdnZ3wer0wGAyiOls0GhVZB7Q1nibEYDCIK1euwOVyYWxsDOVyGWtra1hcXEQymYTb7RYZFyS4KpVKlF6k7ckajQYjIyM4deoURkZGRG4t5fbKBed36+/Ag00V6+vr+PTTTxGLxQ5s8qRVGZ0+k0gkEA6HUa1WhaFCq2EKILaiVqtha2sLU1NTeP3111sGqQ8SRTdHVKtVbG9vY2FhYdfoJlm6VGaO6rmSf4qix3SS534t3cehUChgdXUVs7OzGB0dFed8tYL+nZb2ZrMZ3d3dQlxocwgNls3NTVHgg8rYkTjQQAsEApiZmcHm5ia0Wi0WFhbEcULz8/O4ffu2KKEnL+VNJhM6Ozvh8/ngcrmEz43akAJA29vbWF9fF+elyac1NC9tt7a2xK6evr4+YfXRwHQ6nfD5fMLlQctY2Z9ZqVSQTqdFyUEKVlE0/6BEl6xa+iFBJRGk/FbKpqG0K6/XK/Kmqa0oGEZLXfJjy+1Fz1D23ZNVnc/n4XA4xOrHZrOJPHCtVovOzs4GXypNfqlUClNTU6hUKhgbG0Otdv84nEAggEqlgt7eXvT29qJSqQhLHkDDqlGuKjcwMIDh4eGGQk7U/2iC2mvpTdc1OzuL1dXVx0oB3S/VahXxeByFQgE6na6heI/BYBD1jMm42K0kZLFYxOLiIra3t9Hd3a2oi0Ex0aVl9c2bN0XpxXZoNBpxuJzNZsPW1pbo3JR8L1eZOojUJHoIpVJJzK4DAwMiEt1qySUv8eVgFmVAlEolFItFGAyGhiUQLT2pw5Nw0gm9gUBAnD+VzWYRCoVQLpexvb0tAgJyxJt8dHTsDXXQWCwmfOGpVAqZTEZUh8pkMqKztio+TpYxpR/NzMzAZrPB5XKhu7sbvb29sNls6OrqEveUy+XE4YU+nw82mw2VSkUcy07Hvezs7DS4Np40FAi0WCzCz02BItrhRUXGNRqNsNrJB2uxWLCzs4PNzU1sb28jkUhgZ2cHuVxO5DO3ai8KfqlUKhG80ev1KBaLsNlswidM16jVapFMJpFIJERpRPm5UgCa6nnodDoxgdXr90uGRiKRBiu7Xq+LfkVpbCRGFDugFZZcyEkWoHaZO+Tzvnv3Lq5cudJw/JQcBHxSUC40CSpNDiS4Q0ND6OnpEfUfdktHpVoMU1NT6O/vh8/nU6wGgyKnAatUKgQCAXz3u9/Fz3/+c4RCobYPRKVSweVy4eTJk3jppZdgs9mwuLiIcrksUpnk/e4HhVxbIB6PY3JyUlgML7zwgqgbS51LHnTNEXt58FWrVezs7CCRSIhlKlnrzR2dKjiRH478iOl0WohV85KcroPcAX6/H5lMRkx6W1tbYicVfVYrl0Lz85FTlmiFkUgkRBaKSqXC+Pg4XC6XuKZUKiXSfChASCd50GRkNBrF6Q60/fggniVtWDCZTKJSFw1Yk8kEvV4v3ArVahUOhwM9PT1wuVzCup+dnUUwGGyY9NsFZunf5MmVTlGu1e4X+yYxp+CZRqNBIBDA2tqaiHc0P1taOkciEZGdQGOBakDL6VMkgDQR00omkUiIfkFiS4ZEc9qa3H/l/r6zs4Pr16/jnXfeweTkJOLxeMPuxYN4lmS80CRFq7q+vj6cPn0ao6Oj4qieVCrVVmvq9TqCwSB+/OMfY2dnB7/xG7+B3t7ethPMk0Qx0Z2ensZf/MVfiNzGVtBWxxMnTuDVV1/FyZMnxd5ustIouHCQgkuzKPnmKIf1gw8+QCqVQjKZxOc//3lRrJpcJ7S8lC1WagPqLLFYDEtLS+II71qthnA4jGAwiFwuJ/xrlB5Du51o6Um70+gzm9uBBkWhUEAwGBQbFsg/HQwGEY/HG1wAVINYDgK1Qw4WkZ89EAgIa6O/vx9OpxPValXktFIAkk7kJdGnQUk+TVn4n+Sz1Ol0MJvNMBgMItIufw8FFR0Oh8jKsNvtIg85FAphaWlJHOW0XzGR25OMBo1GI05/NplMYvVAQhEMBhEKhUTaXbPFSM+cLFu9Xi8mq3K5LFxxpVJJBAiz2SyCwaDIH1er1eJYncHBwYYt7vR98vMF0OByqFbvF7e/dOkSfvCDH+DatWuiOh3wwJ1zkOOUPpvyoMfHx/H8889jcHBQuLXIFZFOp1teRyaTwbVr13D16lW88MIL/7NEF4CwzKgjNEMzlt/vx6FDh9DT0yMKbtBpt/Teg97pJBcVl8sXhkIhscWxXq/j9OnTcDgcwiqlWqB0bI2cfUHpYzMzM7h9+7bYPkrtMzU1hbGxMej1ehgMBnGqbzAYFJ9L+//l5V+z30r+Gx0QSO6NcDiMQCAgfJB0bzRg2wluuzxW4EHd4e3tbTidzobgE/nc5ILwsVhMnItHecM0oZBf8UlVnaOgGX0uLa+pPjCl5KVSKbEdlHzrZrMZtVoNkUgEy8vLCAQCuwpuuzYiAaMAYqFQEJOy0WgUVhvwIHBKftFWy3T5tXRfcgnKQCCAxcVF9PT0oLOzE/l8HjMzM7hx4wbW19fFqmR9fV3sOCM3B5XspOulzAwADcWWkskkpqam8P3vfx+XLl0SR22R2FL6FhkPBwVpAaUzOhwO4SqhmikbGxviXpqfDU1IlHZHz+ugeSqbI9pBDzwUCmFmZgb5fB4GgwGbm5tYWVkRVt9BW7lUlEeOrtOsSsXXZ2Zm0NfXJ4SVfM2VSkX4+UggS6US4vE4FhYWMDk5iZmZGUSjUVH+r1gs4vLly1CpVNje3obX6xV+ZDqyBGjcDNGugA1Z6XJAhDoXnYwci8Ue2of/WYJYtOTd2NiA2WyG3+8HcL94Ck22yWRSHNAo192liQQ4uA4vT4r0bCjYRD5Uqj1BPl+y2oLBIDY2Nh55u3rz9zf3W5XqQSF4KiJENZPln1abB8jNINfGoL66sbGBy5cvo1arobu7G+FwGBcvXsSVK1cQiUREP45Go/j000/FZDM2NiY2vFB7kSuCfJ00DiKRiDginfKyaRMN8LDRcpDWbqVSQTKZxOrqKubm5oTbbGVlBcFgsCHv+llBcdFt9wDoQYdCIUxOTmJ9fV0U8Y7H41hcXEQ0Gj3wWp1UfchqtT4UIKGUMJfLhc7OTthsNrF3m04TkN9DlkIsFsPq6ipu376NO3fuIBgMiuLX9NmBQAAXLlzAwsICfD4fCoVCQ6CMfGTkFiCBb7dqoKUnlQHMZrOIx+NIJBINFpvsy/sskK86FAqJdsxkMkJsKU2MxEf2dVLwZTdr+3EgkZAzI0jwSSTo+/P5PHZ2doT4UqYFVe76rBkyJJTy/dEzsFgs6OzsFClackZIMyQ0dCYZ9Qtyb0WjURFoMxqNCIVCYgswWZ1kQYdCIdy9exdGoxH5fB6Dg4Po6OiA0WiEWq0WRXtoay8F4ygbharS0apFLrpDVQSpfQ8KCi5ev34dsVhMbH0OhUIIBoPiCKB2feqgV82teKYsXVqq0lJ8bm5OLDcpIf+gzX+VSiUKM1NUm1KyjEYjBgYG8IUvfAFnz55Fd3d3w4YJGuTkjsjlckin0wiHw1hYWMDi4mLDKRKyoJM1nM/nsbm5Kc7nAhp9xOQbbidQcuI/5WlSDqp8YCCJDg0UylUlS/BROyMNZNpEQUnoFOWXjwOX7132zTWnqT0J6POoreSatvIKplAoNPjyybdHf38ca4lcAOS2oI0h1P40idIBqHTYpBzoa7XLjfoAFdSRg0oARKCV8qubMyHocwqFAiKRCBYWFoTAUvoaia2cTUPtqFarce7cObEjbG1tDYVCQeTWU5YKZVEcJGTcbG5uIhqNikwdudD8QaaUPg7PlOjSoKDBR+KkhB9XvgadToeenh709fUJi61cLsPpdOLo0aM4d+4cRkZGYDab2w4MslaoAAkVHpGDDc1RYvI55vN56HQ6satILp5N6VjNyClqtFylZTtZnGThypso/H4/Dh8+DI/HIyqFBQKBhqN6mrMqmqEAH+20oqAYDVj6Iauarp8+iyLoB7kMpd/NS3W5LgZdJ01YtNTO5XJ7rixa7XgzGAzo6uoSFcoikQjm5uYQDAZFLrSc3ud0OsUxP3JmQ3N7EZQDTZkXNFlSH6KMkGbXkXy9su9Wo9GIVLZ25UTr9TqsVitGRkbw1ltvwePxYHp6WsQOaIWwvr6OeDyuiI+UJhDyj8vBwHbZJU+TZ0Z0Keghp6TsVWn/IKjX68LHdf78eZFHCdwPJsj72uVlefOgox+n0wm/3y9OPaai2LK1Q8JGgkkFcIaGhsRyD7jvIw0Gg2IPPbkn5Dxfs9kMt9uNjo4OcSIt+S1ptxW9tq+vD1/5ylfwxhtvCNG9fPkyLly4gKWlJXHQZLNlKD8P+s6uri6Mj4/D7/eLvFQSHiqqQ8fZNxfUloND8rbXzwo9g+bPlzexUP6u3W4XOwOtVitcLpcoNET1ZFsF0+jz5WeuVqths9kwPDyM119/HS+99BLcbjcikQguXLiAd999FxsbG6IPUMAzmUyK59bR0dEwwdJraUKw2Wzo6+vDyMgIfD6f2NFG7qyVlRUsLS01ZCBQP5N96RT49Hq96OrqEn27neBSOxqNRpH5cObMGbFSIJ/vxYsXsbCwoOjynfqmHNsgPXlW6n0Az4jo0sMncaEk/Uc9xvlJ4Xa78fzzz+P5559vKD8pP1DgwVKr1RKKRJQCS0NDQ9jY2EAsFhMWb71eb6gtQVtDT5w4gZdffhkTExPipAAKGKysrMBgMIhlIxXO1mg0wjfY3d0Nj8cjardSpTTZPWM0GjExMYGvfOUrOHXqlKgYZjKZxCnAW1tbDbmzJFj0GRSB9/l8OHLkiDhhgzZFkP+bAnmhUAiBQEAkr9OAbPYry0LfLMK7ZVfIGR3NgkufJ7+GrHO/3y9qFtOqwmazia2wHR0dIiBJ/nj52uXvIZ+sz+fDuXPn8Prrr+PYsWMiU0Gv12N1dRWRSEQUei8Wi0gkErDZbDAajWKTCQWk6IBJEk+TyQSPx4MjR47gc5/7HIaGhkR2D22ouXv3Li5duoQ7d+4gEomIZ0+iS3ELOt9scHBQ1MLd7YQU+ZkYDAZ4vV50dnY2uB8oYHrlyhWsrKy0GWUHg3yPpCvAg+JQlAn0NHnqoiuXDHQ6nchkMsI/8zTQaDQYHR3FxMQEOjs7RUBAtr7lDQ0067c7sJJOd/D5fPD7/aKcH4kSbcKggT46Oopz587h7NmzGBwcFC6MarUKt9sNrVYrEvUDgYA4gp0qiPl8PpH+s7Ozg0AggGAwiHQ6Lawe2dIj64SCM3LxGtmqbZUPLE8sVMOA/IjkkjEajeJ4HxIF+mwSc9lvKLuTmq3r3XxzJBTNgtv8ufQZlLvrdrvR29sLv98PjUYjYgrZbFZshiFrmAJdrTIKaAKWU8TIj05pYrIfnd5Dn5FOpxEMBoXQWq1WdHZ2NpSIpIAjpVH29/eL8/so86FarcLr9QqRp35KzxRorNpns9kaJp1Wtall1xdNOM0ZMrLLQqfTYWJiAiMjI/jkk0+eykGVGo0GTqdTnG5CGQ6xWOzAarXsl6cuumThud1uuN3uhsTqp2HlGo1GDA8Po7u7u+H8JHnQFgoFYbXl83l0dnZicHAQbre7od6tbBVQ/icFm5qXQVQUxuPxiP3+ZrNZuDHoc+x2u0g1kn3dJAIUjKQAXiAQEBFcWTgLhQKmp6fxwQcfIJPJwGg0YnV1FRcvXsS1a9eERdeuRB5dE7kvNjY2hBWRTCZFPij5eQ0GgxB8GvTNAilbo7JQUpu1yoWVl/XNwiuLLgXF5EMm5eAfWUGUt0tuBXmDhJyyKD/b5naRywdWq1WEQiEMDAygUCjg448/xszMjAg2ylkN5AOtVCrwer0NtZ9bBR/JKqYav3I/oa3XdGyPnF5GbUv/TbnhsmuPoIyPeDwuLHSTySTygOWjouQVR3d3N4aHh2E0Gg+0HkMrqI3IR93R0QG1Wo1gMPhMpI89ddGtVCpIpVIiF5JOIdhLdFsNwCcBLTvJv9zsqwUg6jFcvXoVq6ur6OjowAsvvIBTp07B5/OJDkzBQNr2S3v2m8WMxLxUKolaqvJykDo0bZtVqVTC/SIHSei0CjplmXJkm7MR6vX7Setra2t45513cO3aNWg0GoTDYWxvb4sKTc0DUG4L+fqpyhidQks7vlSqB0XdTSaTOGiTRI+S+8lap+i+3O4kPHKxbRk5W4P+m/6dfuTTXslqpHajlRUVIacMD1o9kJjJz6WVf1BeEdBqaHV1FfF4HLdv34bX60W1WhX1G5pdZ7Xa/YNIKYc6FouJOg1UbaxSqYhsFGoneVuzLJq0xZt2vDVvWybjgfpmPB5HZ2dnw4YV+pxgMIibN2/i+vXrSCQSGBwcxOc+9zlRrF9ua2p76qtPIh2xmb3GPoluLpdDKBQSgeTmw12fFk9ddMnft7GxgWg0Co1G07Ajp50PT651+iSFl6r6p9NpYeXQd9L30rKTttoGg0GxZDl06JBI/aGTioPBIObm5rC0tCSqjDU/fAqoUD2DbDbbYPXRpg2n04nu7m5R+4EGkbzjiT6PUmfatQ+V5Nva2hLW0141BeT2kK+bqoc1By2oqA25X+i+aTMAWfRkMSeTSZEpQN9Dotyq/CM9E9kPSX+n8oxU2Fur1SKXyyGZTIqAHkX46T7kAznl4G5zQZ52fZO+n9oxHo8jlUphfn5e3H+7gS+vXqioPICGYBhht9vR3d0tti6TSNJ10WYYOYDa/F1U9W5paQk2mw2FQgF+vx8Oh0NsmU6lUpibm8PVq1cxPz8PAPD7/SK7o5VxAgCpVApra2uiVvCTYj9jn66lUCiI0o/5fF4Yc0+bpy66AMS+9Gq12rBHnsREbli9Xo+Ojg50dXVBr9eLUwvIh/hZKRQKuHz5Mq5fvy42Z8hWl0p1f6/34cOHhZ81FouJANT8/DxqtZo45iUcDiMejyMSiYgBWKvVhKuBOi1ZGFSYhVK+ZH8nZUN87nOfQzgcRjabxezsrBhUZC21yjSQU7earQ95QMtBwuagGYkbvUde8pKYyG4YEhnaBEGWLfm4x8bGRJEcmgCWlpYQDAbF8dpkMVEeqdFoFJYdfXahUBBpXbJgWywWdHV1YXh4GP39/TAYDEgkEpifn8fc3JwIZsnHx8v3QG4uWczlqDi1hbw5AWgM6MnuqeY6BtTOzT5smpSbMyJUqvu72I4ePYqvfe1rOHfunKj/IT8r+VRgch1QVo48KdZq9w+hnJmZQSQSwc2bN+F2u0XBcyp+n06n4Xa7cfLkSXg8Hpw8eRKHDx8WZTHl/kTW8fXr1/Hxxx8/MdcCpaP5fD5x1hxtaZc3X8hWtsFgaEiJe9q+XEJx0W2Xj0n/ZrFY4PP5AACxWEw0qkp1v/rY8ePH8fLLL+PYsWMwm82Ix+NYWVnB/Pw8VldXsbS0JPJMH9cCnp+fx3vvvYdz585hYGDgoes3GAzw+XxwOBw4fPgw4vG4ONAxEAhgenoat2/fxtramjjDjawQ8t+Sr5Oiq7QddGRkBMeOHUN3d7dI3SFIpPv6+vDrv/7r8Pv9+PGPf4ybN28K/7K8dKb3ULCrq6tLRLrL5TIikQhCoZBYdsnCDzzYMUVpbHQMOblXqH4EbXoAGo9DosAR5Y6q1Q8OJT1z5gzOnz8vyndWq1UkEgmsrq5iZWVFWCgUhOvq6hIDTg4ukuVKPvZYLCYKyni9XgwNDWFwcFAcYJpOpzE7O4uLFy9icnJSiK184GOzn5xElFY4FMSiiZ+CYIlEQggAWWP0+STiDocDPp9PBGkpI4XS0ejZybUMqDhOV1cXTp8+ja9+9at45ZVXhK9S7h/Uv7q7uzExMYFQKASDwSDypgGIM+2owhy5WMjlY7fbMTg4iBMnTuDYsWMYGhrCxMSEKOXpdrvbug5Uqvsnmrz77rvCMn4cyC3U1dUl0ifHxsYwNDQEt9uNXC6HTz/9FBcvXsTdu3eRSCSES4iKsAP3ffG7WbgHmR/eDkWLmANoO9tQpzx06JCo4JVIJLC9vY1UKgWbzYZjx47hxRdfFEdskCVCEfNwOIxbt27he9/7Hj788MPHTg8pFou4du0a1tbWMDAw0DDwCLX6/tErJpMJPp9PnBVWLpcRDocxPT3dsPuMBNNsNsPpdGJiYgLPPfccOjs7AUBYdv39/Thx4sSupxyr1Wp4vV689dZbOHToEC5cuID33nsPd+/ebSivR+1uNptx+vRpfPOb3xRlKclXNzs7i+XlZWQyGZjNZhHEo0wDALBarUL06LDM1dVVXLp0CRcvXsTKykqD37HZVwk82I+v0WjQ29uLc+fO4fnnn4fX6xUC5fV6xf1TpSy5ODUd9tmcXkbBQ7nYPZ14TEV4qC3dbjfMZjOKxSK2t7fFySPNucP02bJLyWKxYGhoCC+//DJeeuklDA4Owmg0ivxp2jIMoCHNj86Os1qtGB4expEjR4TvP51O4/r16/jhD3+I69evN1Tgo3Z0OBw4ceIE3nrrLXzxi18UxfTbQeU0T548CbVajfX1dQAQVd/C4TBu376Nqamphm3h1E9p4vL7/Th//jyOHDkCp9PZEOtoBb2fjtT5LNt/9Xo9Xn31VXzrW9/Cc889J/LcyTdfq9Xw+c9/Hq+++iquXLmC6elp7OzswOFwoLu7Gy6XC8lkEpcvX35oTMjIxoJSKCa6NLAsFstDnZseIuXHvvbaa2LJSUEdOnrZ7XY3iBFFwk0mE7xeL0ZGRlAoFPDpp58iFAqJqPh+kaPZyWRyX6+Xt212dnZCo9GIcoy0K4xeSydiPPfcc8JaUalUwofrdDobUtV2w2w2i3OsKpWKsLRkl4xKpYLX68Wbb76JX/3VX4XX6xVtMj4+jhMnTgixMJlMcLvdInVIzryQB1ytVhPZGpRqRnVmm+sL0DVQbijloMonCss5rpTK5fF4UKlUGqzvVil5snVmMplEXrBcw0AWabVaDbfbjYGBAXR1dcFmswmha+Uvps83Go3o6urCL/3SL+HrX/86jh8/Lg4KrdfrOHr0qLCY6Xuq1aqoeZHP52G1WsVZeHJbdnd3I5lMivP+5Gen0+nQ29uLN998E1//+tcxODi4r+CUTqeD1+vFyZMnMTAwIKzler0u0jEphUq2goH7ok2FbrxeLzo6OoRPfj+QX56e7aOOv1qtBp/Ph2984xt4++23xWpQhiaW06dPixz4RCIhDgzV6/Vi8pibmxMGWPO1aLVaZLNZxQqYA4BqjwZ5YvJP1uhuKRvyAGt+nRwZbQfNtOTH+SzLBrJk9yN+MpSxQIOvuX1pCSifYSW/rlVQYi/kyH+ramG05GzVrvTaVta8fM2tvpNWGXJgsF1/kv2ccgGV3e5pt+//rO+jbBE5s2Ova6fdYO0mgN0+Q27fVs9AzoFt/m65Psbj9A25bwEPVgettgjLr6PJdrfn1IonPf5265tA+40z8rjY7Vrq9bqwop8gbS9YMdFlGIb5X0Rb0VX8NOC9eFLJy0/CR/NZrmWv7z+IJO39pHgdBI/a1s9CgjrxLF270s/vIPvo0x5/Mkrqzn5gS5dhGObJ01bFn/x2EYZhGKYtLLoMwzAKwqLLMAyjICy6DMMwCsKiyzAMoyAsugzDMArCosswDKMgLLoMwzAKwqLLMAyjICy6DMMwCsKiyzAMoyAsugzDMArCosswDKMgLLoMwzAKwqLLMAyjICy6DMMwCsKiyzAMoyAsugzDMArCosswDKMgLLoMwzAKwqLLMAyjICy6DMMwCsKiyzAMoyAsugzDMArCosswDKMgLLoMwzAKwqLLMAyjICy6DMMwCsKiyzAMoyAsugzDMArCosswDKMgLLoMwzAKwqLLMAyjICy6DMMwCsKiyzAMoyAsugzDMArCosswDKMgLLoMwzAKwqLLMAyjICy6DMMwCsKiyzAMoyAsugzDMArCosswDKMgLLoMwzAKwqLLMAyjICy6DMMwCsKiyzAMoyAsugzDMArCosswDKMgLLoMwzAKwqLLMAyjICy6DMMwCsKiyzAMoyAsugzDMArCosswDKMgLLoMwzAKwqLLMAyjICy6DMMwCsKiyzAMoyAsugzDMArCosswDKMgLLoMwzAKwqLLMAyjICy6DMMwCsKiyzAMoyDaPf6uUuQqGIZh/pfAli7DMIyCsOgyDMMoCIsuwzCMgrDoMgzDKAiLLsMwjIKw6DIMwyjI/wfsmkRpwv/wyQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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iga2rq6O1a9dKqSQDne655x6pJt1dRkYGxcTEDHi5Op2Ofvaznw0oFUksQNnZ2bR48WK7T8XFdLnf/va3UoN+f4HdbDbTpk2bhv0CnzdvHuXl5RHRjafq3333HT3zzDMUHh5udz9UKhWp1eohrUmOxCQIAqnValKpVHb3JSIigp555hkqLCwkk8lEVquVzp07J6UjDue0efPmfmtkYvmpr6+nLVu2SGlT9sr23XffTTk5OQNKYSO6kdr39NNP95vx0dcUGxsr1UK7Kysro8WLFw/qeGm1Wnr88cdtgroMHMbVEe0GLDKZTOjo6Bj0w4uDBw/i2WefxapVqxATEwOtVovLly/j1VdfRWlp6YCXq9frERsbO6AGdkEQcOXKFbz11lv4+uuve7UZCoIAlUoFrVYLq9Uqddroq52YiKBUKnHTTTfBz8+vz4cUg3X16lVkZ2cjJiYGnp6eSEhIwNNPPw1vb2+89957qKiosOloMpLdK+WgVqsRHh6ORx55BI8++ihGjRoFAGhtbUV2djZKSkqGdf1+fn6YMmWK9DDQ0fEW/24ymUBE0Gq1UKlUMJvNNu27BoMBX3/9NfR6PZ599tkBPVhTKpWIiYlBUFCQSw++uyspKcErr7wCq9WK2NhYGAwGXLlyBTt37sShQ4cGtEyR1WpFR0cHzGbzoJYzVEY06AqCgMbGRmzfvh2HDh3q1UvMVV1dXdi1axcOHjyI6OhouLu7o6SkBGVlZQMO6CqVCqGhodLF5arKykq89tpr2LNnj90HX0QEi8WC+vp6HD58GOPHj8edd97psHdcd2FhYQgKChrWoFtSUoKtW7eisrISd999N8LCwqBSqXDTTTfh+PHjqKurky5kIoLJZJIehPwzI6JeAUoQBGi1WiQkJODmm2+GUqnE9evXUVlZib///e/48MMPhz3o6vV6hIaGOjVvR0cHvvnmGxw+fBj19fWwWCy9zgsRoaWlBX/961/h6emJjRs3Dqish4aGIiwsTMrGcZXVasXevXtx+vRpREdHo6OjA1evXsX169ddXlZPJpMJhw4dwrhx47Bu3Tr4+voOepmDMaJBNy8vD2+88QY+/vhjNDU1Ddlya2trUVtbOyTLUqlUSEhIwJgxY2y6fTrDYDBg9+7d+OCDD9DS0uJwPqvVivb2dpw6dQo7duxAQEAApk2bBjc3tz7XpdfrERAQ4PI+uaq4uBgvvfQSsrKysHDhQsTHx6OiogIWi8VugP1nD7gie/slprmJqYTFxcXYv38/srKynErzGqzAwEDo9fo+5yEidHZ2Ijs7Gzt27EBubi66urr6PC/Nzc344IMPEBYWhqeeegoajcap7SEiaDQajBkzBmPGjMHZs2cHXKO0Wq0oKSkZlh+umpoabN26FWVlZXjyyScxceLEIV+Hs0ZkaMeuri588cUXWL9+Pd55550hDbhDzdfXF3PmzEFcXBzUarVLt88ZGRnYvn27TQ6jI0SEjo4O5OXl4fjx46ipqYHRaLRbQxe3ITAwEPHx8c7vzCC0trbi2LFj2LNnD/Lz82G1WqHX64e0f/8PnSAI8PDwgF6vh9VqRV5eHvbs2YNjx47JEnABID4+HoGBgdL29GS1WmE0GlFTU4Pjx48jPz+/34ArampqwnvvvSelcDrzHUEQoFarER8fjzlz5ox4LbIvTU1NePvtt7F+/Xp8+eWXI5YeKGtNV6wpFBYW4rXXXkNmZuaglqfT6aRCNlwmTpyIuXPnws3NzalCKO5jSUkJ3nzzTRQWFrq0Pk9PT+j1eqlW3dc6vb29ceutt+Lvf/+7U4F9sMT2zClTpiAiIgKjR4+Gh4cH9u7di5qammFf/0jT6/VYsmQJ7rvvPoSFhaGsrAznzp3D2bNnZVm/j48PUlJS4O3t7XAescxoNBro9Xp4enq6dOdRWFiIt99+G/Hx8YiKinLqzk4QBLi5uWHu3LmYMGECqqurnV6fqzQaDRQKxYA7zhARMjIyoNPpEB4ejqSkJJfuXofCiDQvtLS0DOr2XxAEpKam4p577kFxcTE+/PDDYelhpdFosHDhQkRFRUnrdWbbamtr8cc//hGHDx92aX1eXl5IS0vDggULoNfroVQq+3x4JwgCpk6ditjYWJw5c8aldfVcjo+PD0JCQmC1WlFbW4uWlhabWraHhwfS0tKwfv163HTTTVCr1Rg9ejRiYmLg7++PnTt3oqqqSpaeXHJTKBQIDQ3F6tWr8cgjjyAiIgIKhQLR0dHw9PSEyWTC/v37bUb+UigU8Pb2hl6vhyAIqKmpQXNz86CaXkaPHo2pU6f2OY9SqYQgCNDr9UhNTcWFCxdQWlrqUkeagwcPIjY2Fr/4xS8QFBTU7/zidRETE4OFCxfi2LFjg34+Y09gYCAefPBBxMfHY+/evTh8+PCAj2dtba0sFRW7+kptGPIkiu+VlJRQenr6gFNAlixZQhcvXiSiG/m/4ohcQz2NGTOGTp06RUTOp5k0NTXR7373O6f7yHefbrvtNjp9+rRLx7KxsZFWr1494H0UBIHCw8Np9erV9Prrr9MLL7xAd9xxBwUGBkopU25ubrRkyRIprain4uJiWr58ea/+9f8qk5ubG61cuVIqc91ZrVbKycmhu+++m9zc3KSUucDAQEpLS6MXXniBXn/9dVq1ahWFh4cPKpXu4YcfthlQxhmnTp2iefPmubwuf39/euGFF5xOkxTLRW5uLsXHxw/LeUhPT6eamhqpzN1zzz2DWpaYdz9MHMbVEWnTHTVqFKZNmwadTufyd5OSkvDcc88hLi4ORITAwMABpbk4Y8qUKVKbqbO3H99++y127tyJhoYGl9YVHh6O1atXIzEx0aXveXl5Se3NAxEdHY0HH3wQa9euxaJFizB79myMHz8e3t7e0jiw8fHxWLlyJW666Sa7y9Dr9YiOjh7wNvzQqdVqREdHO3yANWXKFKxcuRJxcXFQqVTSWMLjx4/H7NmzsWjRIjz++ONYuXKldNfkKo1Gg9jY2D6bFuyZMGECVq1aZdN13RkNDQ3YuXOn091vxesjPj4ekydPdmldzoqJiUFAQACICPHx8XjuuecG9EDMzc0N06dPR0hIyDBsZf9GJOiq1WqEhoYOKOimpaVJJ1V8cj6YgVH6EhwcDC8vL6fnt1qtuHDhQr+5it0DuEKhQEREBNauXYu77rrL6afGAKR83cjISJe2UxQUFIRVq1ZhzZo1mDRpEvR6Pfz9/aWB0YEbF3tiYqKUG+poOzo7O4e1bX0kGY1GtLe3O2w6EXOmExMTpR8e+j43NiAgAHq9HpMmTcKaNWvw4IMPOnXL3pOnpyeioqKk/FxnaTQa3H333VizZg3Cw8NtzmF/FYmysjJcuHDBpSYjb2/vYQtmBoPBpv11ypQp0jgurtDpdAgNDXXpWhtKI9Kmazabce3aNZeDpZubG4KDg20Sw9va2lBVVTUs22kymVwaAEPs6GCPTqfD7NmzkZKSAh8fH3R0dKC1tRVarRYTJ07E7Nmz4e/vP6DtDA0Nhb+/v8u16+nTp+O+++5DZGSkdOsj/hCKF7ZWq0V0dLSUmmbvQrVYLNIF8a+IiPrNJAkICJA65IgdEhQKBbRaLTQaDQRBQFRUFH784x8jNzcX+/fvd2kbAgICnM7P7cnf3x9PPPEEEhMTcf78eRgMBnh5ecHd3R3Nzc04duwYjh8/3ut6FARBaiN2ltlsHrZOCJWVlWhvb4ePj490/ev1euh0OpdiSWdnJ8rLy2EymUbk7mxEshfKy8uRkZHhctA1GAw4d+4cqqqqEBYWBgC4dOkSrly5Mhybi5KSElRXV0vr6o8gCEhKSkJsbCzy8/Olv2u1WqxevRo///nPERcXZ/OaHLFgD4aHh4dTnSm6U6vViIuLQ1hYGJRKpZRzK9aEugddcUhER3Q6HUaPHg1fX99/ySwGPz8/xMbG9nsMxAAgPlAThwkUj6lSqURYWJjUDOFKcHJzc4OHh8eA90Gv1+NHP/oRlixZIm0LcOM8L1u2DL///e+xY8cOmzSq2NhYJCUluRR0a2pqcPXq1QFvZ1+uXLmCS5cu4eabb4YgCKiqqkJeXp7Ld1hdXV3IyMjAihUrnM7QGEoj0rxw6tQpnDp1yuXvWa1WHDp0CNu2bUNxcTHKy8tx4MCBQXXx7cv58+dx8uRJl26vbr75Zqxfvx6TJ0+Gn58fIiMj8fjjj+PZZ59FfHy8TWcCsf2vPw0NDTh9+jTOnz9vNx+0vLzc5V5pFosFRUVFKCgoQHt7OywWC9ra2nDx4kVcvHgR7e3tILrxjrLGxkaH7wsDbgSE22+/HampqT/oPM2B8PX1RWpqKm6//fY+g644Zm1XV5fU2eXixYu4dOkS2traYLFY0N7ejoKCAhQXF/d5PO1pbGy0GdtZ1NbWhvz8fJw+fdqpOx2lUmkTcAVBwJgxY/Dss89izZo1iIiIgJ+fHyZPnoz169dLQyA6w2q14ttvv8X58+ed3zEXlJaW4uDBgygvL0dxcTG2bduGQ4cODShjJicnZ0AxaCjIWtMVA05+fr7Lt8Ki6upqvPHGGzh37hwCAgJw4sSJPnt7DUZNTQ0+/fRTJCYmYuzYsU79Inp6euLBBx/ElClTUFRUhMDAQEybNs2m55grv6qlpaXYvn07jh49Cj8/PzzwwANYuHCh1IZbWFiIjz76CJWVlS7tm9VqRWZmJjZv3oyZM2dCr9ejpaUFubm5yMnJQUtLC8xmMxobG3HkyBHccccdSE5O7tUtVvzvuHHj8MQTT8BoNGLfvn2ydRYYTp6enkhLS8Pjjz+OhISEXudNPBaCICAvLw9HjhxBU1MTrFYrmpub8c0338BgMGDq1Knw9vZGbW0tsrKycOrUKZebYioqKvDxxx/blMXW1lbs27cPf/rTn9DU1IT58+fjkUceQWRkpFPL7L4/UVFReP7557Fw4UI0NjYiPj4eEyZMcOoOSrwuiouL8cknnwzb3U5zczP+/Oc/4+rVq6ivr8c333wz4G7CDQ0NyM/PR3p6uvyde/pKbRjK/InuL4Zcvnz5oNNHVCoV6XS6YR1TVkyn+tWvfiW9dE9OBoOB3nzzTQoLCyNBEMjNzY3uuusuOnr0KFVXV1NmZiatWLFi0KlaKpWKPD09Hb4nTa1W07333iuNNtaTOLZqR0cHHTx40KXxkeUejcyV9c2YMYMOHTpEHR0dfY6hfO7cOUpPTyeVSmX32InH1t7nrkxi6lpWVhZVV1fT0aNHadGiReTm5iaV1bfeemtAwzQOVklJCW3evHnQaXH9TQqFgnQ63aCPJQBasWIFNTc3E9GwjDw28qOMib8mZWVlKCgoGPTyhrPBXkREqK2txa5du1BbW4vVq1dj+vTpUCqVsrQDtbS0IC8vD/X19VAqlbBarSgrK8Px48dx5coVfPbZZzh8+PCgszfMZnOfNVOTyYTPPvsMDQ0NWLlyJW655RYEBwfD3d3d5rXiGo0GycnJuP/++3HlypV+O8CIDx7tvfZ9OIivQ+85kI09wcHBuP/++5GcnGzzlNtkMsFoNKKjo0N6vc6HH36IrKwsu+XRZDINWUeBzs5O7NmzBy0tLVi8eDGqq6tx7do1WK1WKJVK1NXV4dy5c2htbR32MTnE8m+xWHDy5Ens2LEDBw8eRG1t7bA+UBVf7zQUzp8/j7KyMkyYMOFfs0ea0WjE5cuXpfbYfxYmkwllZWXYsWMHTp8+jVWrVmHp0qX9Djriqu5BXPx3V1cXurq6pICr0+ng4eGBuro65OfnIysra9jS5Xoym834+uuvcebMGcTGxkojnCUmJmL+/PlISEiASqWCl5cX7rnnHuTn5+PDDz906iGHuH/DzdkHlhqNBosXL8Y999wDT09PacjNwsJCHDlyBAUFBaitrUVlZSWuXLkia8+mrq4ufPPNN3Bzc0NISAg8PDyg0+nQ2dkJpVIJg8EglQl7ZWqoiD0v//KXv2Dnzp3Iz8//p3nVkaioqAjbtm3DE088gdjYWPlSyPqqBg9FHVscbDkzM5PGjBlDnp6est5ODvXk5eVFK1eupJycnD5fce7McenrjaxWq5XOnDlDS5cuJV9fX/Ly8qLo6GhKT0+nn/70pzRr1qw+B42W67bdw8OD7rrrLsrKyiKTySTt15kzZ+iuu+7qdztUKhVpNJphf/WQQqEgjUbT722pIAi0ePFiOnfuHFksFrJYLGQymSgzM1O6lZfjuPZ13HQ6Hc2ePZt++tOfUnp6OkVHR5OXlxf5+fnR/fffT2fPnu3zdtlsNg/qtTQmk4mys7NpxYoV5OXlNSLX4VBNnp6eNHbsWOmNxHK8rmfYa7r0/a1GVVUViouL/+lHpGptbcWHH36ImpoaPPfcc0hOTpZyGen72kT31CsRfT9ubnV1NfLy8qSn2t7e3oiKikJ4eDiCgoKkmlVFRQW++OILFBYWSp0gxEFwampqcP369T6bV9RqNbRaLcxms0tvUnVVe3s7vvzyS4waNQqjR49GcHAwrFYrJk2ahCeffBKlpaU26XM9iU/xFQrFsNZ2xeX3lzUwceJEaeg/+j7Xtra2Fu+//z72798/7DVyNzc3qFQqGAwGh3cJZrNZGr7UarVCrVZLdwuFhYX4/PPPoVAoEBYWBoVCgba2Nly/fh3l5eUoLS1FS0sLPD09ERcXh6SkJISEhNjNx7VarVKgEMu3xWLBt99+i9/85jc4cuTIsB4LObS3t6OoqEjK9adhbBoRyda80D1X8Ycygrsj4nB1VqvV4bbm5OQgKysLiYmJ8Pb2lgqleKGKywEgDe5dWlqKXbt2Yc+ePaioqIDRaIRCoYCHhwcCAgIQERGB0NBQKJVKVFZWoqioCDU1NbBYLFAoFGhubsbVq1eltmZH26bT6TBt2jTMnz8f3t7eKC0txfHjx5GXlzcszRFWqxWHDx/G3XffjUWLFkkX6eTJkzFz5kwUFRU5DCD0/WDhw/1jbG8A7560Wi1mzpwp9XgkuvGK9pycHHz11VfDFnB1Oh0mTZqEmTNnIjIyEi0tLTh8+LA0Dm5PYtD97rvvANxo+xdT1YqLi/Hee+/hm2++QWhoKCwWCyorK1FWVoaGhgapZ51Go0F4eDjuu+8+PPjgg4iKioJKpbIps2Jbu1iRoO8zJjIzM5Gbm+twf1QqFRQKhdRJ5IdMjEdD9Np1p8jeI+2fYRQqLy8v6PV6tLW1ORymLigoCDExMdJrxLsXVpH4b7FNsKKiAidPnkRRUZHNfJ2dnairq0NRUZH0t55BSKlUSiN/0ffdbh2Ji4vDhg0bcMcdd8DNzQ0GgwFFRUV45513sHPnzl4jTqnV6kE/zKqsrERGRgZmzpwp5ep2dXVJPxj9Ge6L05nliw+GxGMrvtkkIyPD5ZS8nsSHeD0fqnl5eWH16tVYu3Ytxo4dC41Gg46ODkycOBG/+tWvHD507uzsRGVlpdQr02w2w2KxSD/upaWlfe6zWCZOnjyJ2267DaNGjbLpyCFus0KhsCnb7u7uiI2NRVBQkN1xsAVBQGBgIDw9PaXR6n7IRiIe/SDekfZDExcXh3vuuQcWiwWZmZk4c+aMTQELCQnB0qVLceutt0p5jI5qamLNVxxd//bbb8d3331nN9G953K6XzRWqxVarRZeXl4wGo0Osw18fHywatUqpKWlSWMoaLVaJCUlYePGjTAYDNi7dy+USiUSExNxyy23IDQ0FAaDAbm5uTh48OCAXv9jMplw9uxZZGdnIz4+Hk1NTThy5AhOnDjxg7+zEZnNZpw4cQJ//etfMX/+fPj4+ODixYs4e/bsgDMQ/P39kZqaimnTpkGj0Ug/vBcuXIDFYkF6ejo2btyIiIgIAP8X2O68804UFxfjd7/7nd3AJY5hq1arYTKZenXDFmun4jLtCQsLw2233Yb4+HhpnNru3+uJiODm5oY5c+bgvvvuw/vvv29TKfH19cWUKVOQkpICpVKJvXv3DmrI0X9VHHR7UCgUmDx5Mh544AH4+PggPT0dGRkZOHbsGMrLyyEIAlJSUrBs2TKEhob2+3RYLMQajQZhYWF49NFHoVQq8fLLL9tNIhcvEC8vL3h7e0OlUkkDdEycOBGxsbGor6/HgQMHcOrUKZtfao1GgwceeACrV6+WAm73bYqIiMCaNWswduxYREVFITk5WbrYgRvJ57t378ZLL73k8itTlEolysvL8cknn8DDwwNXrlxBfn7+gN+ZNRLMZjMuXryIN998E9988w1iY2PR3t6OioqKATWLRUdHY+PGjVJZEl27dg0nTpxAWVkZ5syZYzMCmHgbr9Pp8NBDD6GkpATbt2+3aZ5RqVRISkpCWloa/P39cfnyZZw/fx6VlZXo6uqC2WxGS0tLn2PohoSE4N/+7d/w0EMPOZVe1r0shYaG4oEHHoDBYEBWVhaICJGRkUhJScGtt96KsLAwNDU1oaSkBOfOnfunuLuVEwfdHjQaDRISEqQ2rqCgIIwfPx733nsvysrKUFNTA7VajZaWFly8eBGBgYHw8/OzeZDWl8DAQDz55JMwm8148cUXe6UbBQcHY8GCBZg1axZCQkKg0Wjg7u6OkJAQ6PV6uLu7o6urC7NmzcLvf/97fP311zCbzdBqtVi+fDk2bdokpbPZezASHx+PuLg4m8F1xEDv4+ODtWvXQqPR4D//8z8d1sbtUavVaGtrw1dffYXW1la0tLT8U7Tp9WQ0GlFWVoaqqir4+PhIg5Sr1WqXgm54eDg2b96M1atXQ6VS2fRei4iIQEREhE3+dfd0NvG8BQcHY9OmTejq6sKf//xnGI1GqNVqzJ07Fz//+c9xyy23QKvVSjnDNTU16OjogNFoRHV1NbKysnD48OFeP+6+vr54+umn8eSTT7rU44yI0NjYiLq6OrS0tGDevHmYPXs2goODERUVhaCgIOlhr4+Pj9RcIlda4z+NvlIbhiJvQkyL2rNnj5S6AzupGxqNhry9vaXeNfbmkWMKDAykvXv3ElHvXiomk4ny8vJo06ZNNHfuXJo1axYtWbKEXnnlFbp06ZJTx0NcZn19PT3xxBNS2pdCoaDJkyfTtm3bqL6+3qllHT58mObNm0fh4eE2g2w7Shfq7Oykqqoq6uzstDuf+P+VlZWUnp5OgiA4dS4UCgV5eHiQj48P6XS6Xt8R07U0Gs2Inlt7kyAI0rb1LJuCIJBOpyMfHx/y8PBwKq1NPGb33XcfVVVV9Xmce54PR2WlqKiIVqxYQWFhYXTbbbfR0aNHnSof9fX19O6779KkSZOkbdfpdLRu3TqpjDnbE+vSpUv0P//zP7RkyRKaNWsWzZ07lzZt2kT5+fm9UifFZX766acUGBg4YudWoVCQm5sbeXt7k0ajcTgPAPr000+JiPpM43TRyPdI64unpyemT5+OxMRE6dZrpEar8vf3dzgoOhHh7Nmz2L17N8rKyqS/79u3D3/5y1/w61//GqmpqX0uX6wx+Pv747HHHkNpaSlOnz6NpKQkPPXUU7jjjjugVqvt1hB7NmVMnz4dK1euRH5+Pu69916MHj3aZr7u87a1tWHv3r24evUqli5dirFjxzrcxvb2dnR2djqdUE/fD31oMBjs1gZVKhW8vb2hUCjQ2toKg8Fgc8spZouID7KGsjlCHFSIvs8g6X5cFQoF3Nzc4OnpCYvFgpaWFpvbePp+wB8xu8LeObGHiNDe3m7z+h57SktL8fHHHyM2Nhbp6em9RhETj31cXBzWrl2LoKAgJCUlYdq0adJ6+mq39ff3x+rVqxEcHIzXXnsN+fn5mDp1Kh577DH4+/s7fX4PHjyIX//61zh16pRN2/bVq1cxfvx4h2UpNjYWfn5+w/IqLWcEBQVhxowZiIiIQEFBAbKzs38QY4L8IIKuu7s7Jk2ahHnz5uHChQsoLCwcsaDr7e3tcFxbg8GA48eP2wRc4MZDpBMnTuCpp57Cu+++i5SUlD4LtPj38ePH46GHHkJCQgLmzJmDBQsWSON79nUxiJ9ptVrEx8dDp9MhKirK7oAsgiDAZDJh586deOmll9DU1ASj0YjnnnvOpt23+7xffPGFlBLkTKARA5oj4riyYv6pGPwEQYCnpyfGjh2LhIQE6WHc5cuXh6TrrPget8mTJ0OtVqOwsBCFhYXSCGoKhUJqMxfT9+ztmys/AuLxysnJwb59+7Bu3TrpR7T7ce7s7MSuXbvwhz/8AX5+fmhtbcVjjz1mM2/34xcVFYVp06YhMjISWq0WgG0ZcVRe1Go1FixYAIvFgoyMDMyYMQPjx4/v8zvifgiCgIyMDDz11FN2e5GKqYhLliyxOy6tn5/fgAbXHyr+/v6YMWMGxo0bB5VKhfz8fA66opaWFuTk5ECj0aCxsXFE24DspU51D0iOfgwEQUBRURFefvlljBkzBsHBwf3WJLRaLWbMmIHo6GiEhoY6HADdESKCWq2GTqeDyWSSxm/tvk0WiwUff/wxtm7dKg2BuXPnTowfPx7333+/TU6x1WrF/v378d577w1p7UQc20EQBKmrqCAI8Pf3x+LFi7FixQqMGTMGRqMRn3zyCV555ZUheaNsQEAAHn30UaSnp0Oj0UgvMf3iiy+kUe4MBoP0wsihrGHX1dVh27ZtiImJwaJFi2yOs8Viwd/+9jfs3LkTzc3NaG5uxtatW+Hl5YXly5f36q4s5ovrdDopf9wVKpUKU6dORWhoqFNvTBDLbXV1NV5++WWpU5O9H+CamhqHx42+z3MeKZ2dnbh8+TLq6+uRnZ3t0ss5h9MPIuh2dXUhNzcXNTU18PLyQkdHx4htS0VFBQoKCmyGxxMDp4eHh/TOtJ7EAnn48GHs3bsXjz/+uE1BtRd8FQoFQkJC4OvrO6DkbHFQbHEQc/GBj0KhgNlsRnl5OT7//HO88cYbNmMOl5WV4aWXXkJXVxdmzpwJb29vGI1GFBcX46OPPnLpAZozzGYz2tvbpYAD3HhgmZKSgg0bNmDChAnSvEuXLsWRI0eGJOgmJSXhxz/+sfResoiICOj1ejQ3N2P//v0wGo0wGo1Sx4mhzrK4du0a/vznP0On02HMmDHQaDRoaWlBVlYWXn/9dZvXOpWUlOC3v/0tmpqasHjxYoSFhUGlUkk53jqdDvHx8fDz83N50HtBEODn5wcfHx/odLo+X7skzg8Af/vb36ReZ47ueOLj4+Hu7m73s/z8fFRUVLi0rUOps7MTubm5aG1tHdCbaobLDyLoAjcCb3V1NXx8fDBhwgRcunSp1218d2Ib4FC/l0t8ffr48eN7vURQo9Hg4YcfxpkzZxy+Xr2zsxPbt2/H6NGjMWfOnH5fB9K9CycAp9vZxO+GhYVh1KhR0kUhDpT91VdfST2HmpqaIAgCtFotfHx84Obmhra2Nrz55pv44osvEBYWhsjISISEhGDhwoWYM2cOrl+/jitXrqCwsBBVVVXo6OiAwWAY8LvQegY0b29v3HnnnUhMTLS52CMjIzFjxgwcOXJkUJkPgiBgxowZ0o+nuKzExESkpaUhKytLGgVtMLUxjUYDNzc3aLVauLu7Y9SoURg3bhxiYmIQFBQEDw8P1NTU4Ny5cygrK0N5eTmuXbuGtrY2REdHo7OzE83NzTAYDLh06RL+67/+C/v27UNKSgpuv/12JCYmQqvVQq/XS6+qcuUHWtxvrVbb73fFcmcymfD1119j+/btfXbCSU1NxcMPP9yr5iwIAkpKSvDGG28MeLzbvogZEn01QUVGRiIuLg4tLS2orq7+wQRcYASCbl997MX2p9TUVKmW9o9//EM6cSqVCrGxsZg+fTrGjRsHtVqN6upq1NfXS7etBoMBFy9eREFBwYAv2v379+OPf/wjtmzZIrWfiRISEvCHP/wBL7zwAvbu3Wu3V87p06fxs5/9DHfddRdmzZqFxMREREdHOwymYlK61WrtlT7Un54XUktLCz766CO888470gWj0+kwc+ZMLFq0COPHj4dGo0FVVZWUpK9UKpGSkoJ58+bB399fWl5HRwfq6+vR0NCAuro6XL9+HaWlpcjNzUVmZuaA2t3F2r+Pjw9Gjx5t084ptrPOmjULAQEBqKurc+kBVvflBwYGYvbs2XaXP3r0aKnHlKvLF4WEhGD27NmYOnUqoqKioNfrERAQgICAAPj7+0u1P6vVivr6evzjH//AyZMn0djYiGnTpuGWW27BqFGjYDQaUVBQgH379iEnJwfXr1/HgQMHcOzYMdTV1eG5556Dp6eny9snEq+1/t51RkS4evUqCgoKkJWVhX379qGwsNDuvL6+vkhPT8emTZvs3vkZDAa8/vrrOHDgwIC3WxAETJgwAXFxcdI1KL7oMyQkBCaTCd999x2ys7Nx6dIlaT+DgoJw22234a677kJ4eDgOHjyIy5cvO1zPcI/5YVdfqQ2DSJeQiCkYf/3rX/tN8bjpppsoNzdX+l55eTnt2rWL1q1bRw8++CBt2bKFMjMzqbm5WUpLsVgsZDQapamzs5NOnz5NCxYsGFS6SWRkJJ08eZKI7KfVtLW10WeffUY/+clPaMKECQ5HT/P29qbp06fTL3/5SyooKOj3WBkMhkENqGwwGGj9+vU225CcnExZWVk2IyiZzWa6evUq7d+/n7KysqitrU36rK/1W61Wqq2tpddee40iIyMHfHzDwsLo0KFDNuvrniIVExNj93sqlYpiYmIoOTmZYmJiHI4aFhsbS8XFxXaXf+DAARo1atSgysbrr79OtbW1/R4rUVtbG2VmZtL+/fuppKTEJjXJYrFQZmZmr8Hfn3rqqUENSG61WslgMPSbBpWfn08bN26kadOmkbe3t9199vLyogkTJtD69evps88+sykvPff322+/pYiIiEFdf6mpqXTmzBnq7Oy0ub7FMmy1Wqm5uZkyMzNpy5YttHLlSlq3bh3t3r2bKioqpH3OycmhKVOm9Lu+Tz75hIj+xVLGxIdEHh4evW41xbFjJ02aJKWfiO2VK1euxKJFi2AymRAQENCrFtizpqdWqzFlyhQsXLgQR48elW59yMnaTPck8Ly8PEyfPt3ufB4eHli8eDEWLlyI7777Du+++y4+/vhjm84O9P0YCdnZ2cjLy4NCocDzzz/fq/bcnXhiBjIADBFJPd+0Wq20jLS0NMycOVOaRxBuvAwzOjoaERERNv3rxWPQfZnd/yYIAoKCgrBixQqcPn0aH330Ua95+yLW6E0mk8MHGx4eHggODkZJSYnNsTKbzZg4cSI2b96MKVOm4MyZM9iyZQvOnz9v8xDSYDBAr9c7fJGjOFaB2PXV2e0W3X777VixYgX8/Pxs5rF3rLrv06xZs3rdyVC32n1qairOnj0r/T0sLEy6lR5oeehv3wwGA3bv3o1XX30VBoNBGp2u+/p8fHywbNkyPPbYYxg3bly/d2J5eXloamqSluPKtQfcOM+LFi2SBh5yNK+3tzdmz56N5ORkNDQ0QK1W9zonCQkJSEpKwpkzZ3q96Rq4EZfa29tdfog9GEI/B2TgjWo9iBdZf0/zezbK9yxw/RVA8XODwTDoB3Jubm59voywe7aAyWRCR0dHr/xT8b9EBJVKNahbRWf13Hd3d/c+A/1AEJE0yPpACIIgvXnC3rLFwNiTWq22OYZtbW122/bEY22vrIhvfnA2GPSk0+mg0+mGfGQ0Oc6bPeKx7h4gux8bhUIBd3d36flEzyyZnrq6ugY9nKi4785e747+H4D0PKKvZXh5eQ3169gdbrRsQZexoTTQ2h9jMnFYOGV9kNZfzWKoL6KB1mRErm6P3PvXl+7bMpzrHcwx7q8G09f3+htBazDL749cx3Okyos9Q30t9EfuWCDnseaaLmOMDT2HUVy+4dIZY4xx0GWMMTlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRlx0GWMMRmp+vlckGUrGGPs/wmu6TLGmIw46DLGmIw46DLGmIw46DLGmIw46DLGmIw46DLGmIz+F2T/mJp2nEIuAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 63\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 65\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 66\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 67\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 68\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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CQgL/zYIFC/Dkk0+iqakJtbW1s27BKhQKqFQqqFQqCIIAv98/rY/5dgmFQnM+vIulDdwqWq0WKpUKkiRxf+ts3hd7zpWVlXjyySexYMECADesulWrVqG0tBRr167FSy+9hPr6elitVu6KmYrLly/jV7/6FVJSUvDggw/OGDXBysGQ/hq9kJaWBlEU0dTUhM7OTtjt9phHDX6/n7uL8vLysGnTJpw8eTLiaPhWSUpKwtatW/HAAw/g6tWrOHDgAM6ePSvrCHDORZc9oIyMDDz22GP4+te/jsrKSsTFxWHRokUAgFdeeWWCY/520Gg00Gg0EAQBCoUCXq/3ltwUfr8fLpcLXq8X8fHxE8JMYkEQBAwNDeGdd95BW1vbjL/V6XQoKCjA7t27sWfPHv5ihYs9AFRWVuLxxx9Hc3MzxsfHYy5XJBQKBQwGA3JycrBo0SLk5uZCrVZjeHgYzc3NuHz5MhwOx4z3wMr7t06096LX61FSUoIlS5YgNTUVPp8PXV1daG9vR29vL2w226yIryRJSE5OxhNPPIFly5ZN+i4hIQGPPPIIKioqsG/fPlRXV6OzsxMul2vae2hra8O7776Le+65B5mZmTGVidURC5tkIYe34qbx+/3wer1QKBQQBIG/y7OFwWDAo48+in/6p39CYWEh3G437rnnHvzP//wPMjIyJtzPXDLnostmilesWIGlS5dCr9cDuNFIFi5ciK985Su4evUqampqZqVh9vf346WXXsLY2BiysrJQX1+Pq1ev3tK5gsEg/H4/AoEAgsHgLcU7+nw+nDp1CrW1tdOKvyAIUKvVMJlMuO+++/D4449jwYIFk6IdgM8EeNOmTaiuruYRGbcDu35aWhqWLVuGNWvWoKCggPtgS0pK8MEHH6CxsRFjY2MRLUd2DgCzbuXJDbP2gRvPMJJoiaKI5ORkLFu2DPfffz/Kysq4j/jatWvQ6XTwer08Fn02OqKVK1di48aNABBx1MXeqyeeeAJWqxVvvfUWAoHAlPcA3IgUqa2txSeffILt27dHjCueDkmSeBw+e1duhStXruD1119HZ2cn+vv7cejQoUmTkLeKKIpYsWIFnn76aSxYsACCIMBgMGD9+vW45557uLhPFac/m8jmXmChKuEoFAqkpKQgKSkJSqVyVl5Sp9OJo0ePoqGhAXq9Hjab7ZYtwWAwCJfLBbPZDJvNxsNuYqGzsxOvvfYarl27Nu3vwoWVBXBP91vgxuhh8eLF+PDDD297+Mz8TTabjVu0SUlJ0Gg0fFJTq9UiIyODRwLcLCThAqBQKP7mRZcN52923zArLCEhARkZGdBoNGhvb0dXVxeCwSC8Xi/Gx8fR29sLu93O6/Z2EUURpaWlUVllWq2WCwlrW9OV4erVq3j11VdRXl7OR1fRwhY/DA0Nwel03rLodnd343e/+x32798Ph8OB0dHRWRsxCYKA5ORkpKamQqFQTDhvuOtODu5Y7gVBEDA2NoY33ngDp0+fntVZ41AohJGREYyMjNz2eWw2G3p6ejAyMoKUlJSYRNfv9+Po0aOoqamZMTSLWQtjY2Ooq6vD8uXLkZaWFnG1DkOhUCA+Pn7Wemev14uRkRGMj4+jpaUFwGcCyvzjoigiGAzyP8OfWygUgs/nu2NRErNJMBhEKBSKKJjM7xgMBjE4OIje3l4+U86OAT7z58+WPzma583itOvr61FXV8dHJTM9D5/Ph48++gif//znUVBQEFNkTiAQwOjoKHp6eqJazTcV0i2EJUYLixx644038NRTTyE5OXnWrxEtd0x0e3t78fzzz+PFF1+cMZTrTuLxeODz+bjYxuJi6OvrwzvvvBNVI2Kiy2J19+3bB51Oh3Xr1k3bE4e/5LMBi4OdCjbRxxaN3MxsWXV3munuIxgMwufzwev1RiVos1mmmQTNZrOhtrYWL7/8Mi5evAiXyxW1CI6NjeGdd97B9u3bkZeXF3WZAPAFHXM54Xq7dHd347nnnoPFYsE3vvEN5OTk3JFy3BHR7ezsxC9+8Qu89NJLszaBNleIooiioiLk5uby2fxoOXv2LJqammK6nvTXteBsRnVsbAybN29GSkrKpGvPtqUbbfkCgcBdka3pTsLEVs4OhuVyiPS8JUnCyMgIDh8+jAMHDuD8+fPweDwxl6+pqQlnz56NWnQFQYBKpUJOTg4KCwvly19wiwwODuK///u/Ybfb8cwzz0R9n7OJrPl0JUmCz+fD8ePHcfDgwbtecAEgJSUFa9asQWJiYkxCY7FY8MEHH8RsxYuiCJVKhUAggJaWFhw4cACnT5+OOAkniiJyc3MRFxcX0zWmItJy3KlQKBRRhRf9vSEIApRKZdQdXSx1OhNarRY5OTkRXVxerxd1dXU4cOAALl68iEAgAJVKFfMcxODgID788ENYrdaojxEEAUajEWvWrIlpdeadwmaz4Y033sCJEyemnWCcK2TvlhQKBf/cDqIoIikpCS6Xa9qMXLdLaWkpSktLYzomFArh2LFj+Pjjj2M6ThRFmEwmZGRk8PhYt9uNoaEh+Hy+iJNrubm5MBqNtx3rLIoinzRjQ+dIjZGFlWVnZyM5ORlerxfDw8MYHx+Hx+OZEOnxtw4TWFEUodVqkZSUhNTUVGg0GoyMjGBgYGDKcDA22aZWq/nk2u36dpOSkqa0zNjqSbfbDYPBgJSUFL6EfWRkJKZr19TU4NixY9ixY0dMHUZZWRlKSkrQ2dkZ9TGxotfrERcXh/Hx8duqT2Y0yDlKZMgquswfuGLFCpSXl99y0HN8fDweeeQRrF+/HkeOHMHBgwejziEQC4IgYOXKlbz3jrYBXrp0CXv37o05VM1kMmHDhg2oqKiAy+VCf38/9Ho98vLyplymmZSUNGUymWhhiWMMBgMEQYDdbp80SQbcWM6bkZGB9evX47777kNubi58Ph86Oztx9uxZnDt3Dp2dnTGn4rxbYUuF8/PzsXz5clRWVqKgoAAqlQrd3d2oqalBbW0tBgcHJ9UVE2qDwYBQKASr1Qq3231b9WI0Gqd81iqVCvPmzcPSpUvhcDiQlZUFnU6HpqYm1NTUxDTi6ujowN69e1FUVISysrIZf8/ei9TUVKxcuRJHjhyZk05XrVZj+/bteOCBB/Dxxx/jL3/5yy0bXBUVFaiqqroj7pA74oApKCjAxo0bcfz48Yg5AqZDrVbjy1/+Mn784x8jOzsbWq0WH3/88ZxMxul0OsyfPz+mIZrD4cB7772HkydPxjSLq9VqsXr1ajz++OMoKiqC0+nE4OAgRFHEggULpoydFEUx5rjKcFQqFRdcrVY7pYUriiJSUlKwbds2PPnkk1i4cCHi4uIQDAaxZMkS5OTkwOv1YnBw8G/CbRQNoVAIWq0WZWVl2LlzJ5YtWwaj0QilUonKykqUlJQgISEBb775Js9CFw6rR+b+USgUt7xwALjR9qcSCZVKhYULF/JoiYyMDMTHx6O0tBQulwtHjx6NepIrFArh1KlTOHLkCPLz83ls/UwolUoUFRVBp9PNyegzOTkZu3btwiOPPIIvfOEL0Ol0eOGFF2I2uAwGA+6//37k5+fPehmj4Y6IrkqlQlFREQwGQ8yiW1lZia9//et85jEpKem2RGc6VCpVzP5Su92OS5cuxSw8BQUF2LVrF1asWIH4+HgEAgFkZWVBkiRugUaCpUaMFZYIPTU1FUlJSVAoFHA4HDyQ/+bIBFEUsWTJEjz00EMoLy/ndc6SeatUKr4Q4O/BtQDcEE22olGlUkGr1UKtVvOFExUVFdi9ezc6Ojpw4sSJCaLLXAoejwc6nY4nSxofH8fw8PC0CcinYrpQPIVCgeTkZCxZsgQKhQI6nY67q0ZGRtDW1jbjEvRwLBYLLl26BIfDEbXoAjcMlViT50SLWq1GUlISBEFATk4Ovva1r+HcuXOor6+P6TwGg+GOTvrdkauGQiH09PTMuKT0ZgRBQElJCbKyshAKhaBQKDA2NhbzeaIlEAjwtG/R+n40Gg0SExOjzvnAJiE2bdqEtWvXwmg0QqFQQK1W8wm18BylU50jFgRBgMlkwtKlS1FaWsqHy21tbZNiTRl6vR6VlZVYvHjxhKWZkiTB4XDgypUruHr16t+d6Lrdbly9ehVXrlxBSUnJhA5Qo9Fg8eLFqKysxKeffjrBkmThXSydYmFhIfLy8uDz+dDS0oLm5uaYg/9n2k9MqVTynTpYpI0oili7di3Onz8Ps9kMi8US1TWVSiUSExNjWoYbCoUwNDQ0Z64lh8PBwy9DoRCysrKwePFinDlzJqZ6dDgc6Ovri+m9nk1kFV0W49rb24vDhw/HNEMK3LC2WCq5pKQkOJ1OtLW1zdlEmsvlwunTp7Fjxw6+CmgmDAYDNm7ciLq6Opw/f543QKPRiPz8fGRkZMDtdqOvrw8WiwUqlQorV67Etm3bkJWVxWNgWSMKBoNcyOLi4ngjYXXp8/liWgHELKI1a9Zg8+bNyMvLg9VqRTAYRFdX16TVOgy9Xo+ioqIJURxMWEZHR3HlyhWMjo7+Ta9Ci0T4/Y2MjCAtLW1C1EZiYiKKioqQkJAwKR5b+mvCdK1Wi+zsbFRWViIxMRGLFi1CfHw8Tp06BYvFEnWdMeuZnZuVgbURZpGzqAW2sCU7Oxvbtm1DV1cXzpw5A7/fj6SkJGRlZUGr1cJsNuP69ev8fRRFEeXl5di4cSPPcxwNQ0NDOHPmzJy9j+x9dzgc0Gg0GB4ehtVqjTmplcViwXvvvYctW7bwTQvkjMK5I5bu8ePHceLEiZiPCwaDaGxsxJtvvomNGzdieHgYp06dmpNJNODGC1dXV4eGhgZs3bo1ql5RFEWsX78ekiTh0KFD6Ozs5FbiypUrkZGRAafTiQsXLuDTTz+F1+vFvffei0WLFnHrJLwBOJ1OtLe3w+FwIDc3FwUFBYiPj+eC29bWNmWCaI1Gg/j4eKhUKp4LIT4+HvPnz8fy5csxb948qNVqeDwe2O12OJ1O+P3+iPGnXq8XDoeDuzPY936/H1arFQ6Hg7/kf0+w+7Hb7bBarfD7/dy1wurB4XBMCuljdej3+3neWa/XC7VajXnz5mH58uWw2Wzo6OiA0+nkLgv2+0ghgmazGe3t7SgrK+PZzJxOJ65fv46enh7o9XosXLiQJ2di7UilUqGkpATbt29HcnIytFotKisrUVZWhvj4eAwODqK+vh7nzp2Dw+FAfn4+duzYgc9//vNRz2eEQiG+Cm6uRjperxenTp3C8uXLYTKZ8P7776O5ufmWOvrjx4/j5MmTeOyxx+agpNMje/SC0+nEiRMnogpxujnLE3NL7N27F6dPn4bL5Yp58UGsdHZ24ujRo6iqqora2tXr9di6dSvWrFkDi8UCtVqN5ORkvqQ3FAqhoKAABQUFMJvNKCws5JtihgtuKBTClStXsG/fPrS1tSE7Oxv33XcfVq9eDaPRiIsXL2Lfvn3o7++fVAa1Wo3CwkKsWLECRqMRY2NjfLLHZDLB4XCgpaUFTqcTHR0duHjxInp7e6fccHJsbAxHjhxBWVkZ1qxZA7VajUAgAKvVCqvVyrfNsdlsEVe1hYvA3WQNh48cIi33jYuLg8lkgk6ng81mg9Vq5ZsYer1enD59GkeOHImY3yMYDPJRTUNDA5xOJ+bPn4/4+Hg4HA5kZGTwc6WmpiI5ORnj4+NoaGjA1atXJxkTAwMD2LdvH5KSkrB48WJYLBZ88sknqKmpQX9/P4qLi/HlL38ZSUlJ/BjWERoMBixevBhxcXFIT09HWVkZUlJSoFAoUFZWhtWrV2N8fBw+nw9Go5H7+aPFbDbj6NGjcxouBtxYcPTcc89Bp9OhtbUVvb29E9pTtJnhLBYLjh8/jp07d0671H4ukDWfriAIMJvNUQslc8gzPyNwo7fr7OxEX18ftyTmEta7njlzBtu2bYu6ISoUCphMJr6jA/BZQ2ApHE0mE9xuN7Ra7aSFBoJwY1+3Tz/9FB9++CEGBwdx/vx5tLS0oK6uDkajEQ0NDTh79mzEOlCpVMjPz8fnPvc5pKamor+/Hy0tLejs7MTAwAD6+/v5zrcjIyOwWq3wer1TCqLX60VtbS2cTiceeOABLFy4EEqlEhaLBcPDw4iLi8OCBQvg9/vR2dkJp9M54VwsJpJZh+wjd0wvi71lnQArD/O/MhQKBeLi4pCbm4sFCxYgLi4Ora2tGBkZgdFoRDAYRHt7O44ePYqzZ89GtEzZYqBAIID29naYzWZcuHCB75zLJjPz8/NRXl6OzMxMDA8PY3h4GN3d3ZNEl+VHsNlsWL58OSwWC86cOYOuri4EAgH09PSgrKwMS5cuneCLZffMQthYJxLe3vR6fUwTZuGEQiGcOXMGn3zyyS3v9hIt4+PjOHXqFI9jD29jLEcIgKhGv42NjTCbzSgoKJDVxSCb6LIb+uSTT6KKX1WpVHym0mKxTJikCN8ccq6RJAkdHR3405/+hNzcXCxbtuyWH1D4sNzj8aC3txe9vb1ISEiIKHYul4tvQMle3mvXrmFkZASSJGF8fHzKTodtSDk0NARBEDA8PMyTs4yPj/MNMf1+Pz/3TOLn8Xhw8uRJNDY2IjExEXFxcdBoNEhNTcWCBQtQWFgIrVaLQCCAzs5OHn7GrC2lUsknBdlknUKhmNCpziXspWTiz/5klj3zZwuCgLi4OOTk5KCyshKZmZkYGRnB2bNnMTw8DK/XC7fbDYvFEpX/MhQK8YUjdrud+101Gg2SkpJ49jaVSoWhoaGI25UzPB4PTp8+jba2NgjCjX3TmPgMDw/jypUrcLlck/J1sORNvb293M/L9gK8VbFhddXY2IiXXnoJV69enfMOlHVkkVCr1TAajXz5/EwG2dWrV1FXV4eCgoK/T5/u6Ogoqqur8cILL0SV/Ss3Nxf33nsvAoEA6uvr0dPTc0f2rwLAd+kVBAH/8i//gqqqqts6n8fjQXNzM9577z0+5F+0aBESEhImWMNOp3NC42FCxWI9p6uPQCDANzE0mUwYHR3lgu1yuXgWrVAoFLO16XA4JkSMJCYmIhQKIScnB0uXLuUTPkNDQ7yMTHTDCRdjOUQ33Nq++f9ZeYDPFoHcc889qKio4Okum5qabjkGmeWsYKMzhUIBt9vNV6r5fD6YTCZu5U43MeT3+2Gz2bhfl9Wd3+/ne8Cxa7L7crlcaGlpQW1tLdLT03lceCwbU96MIAg4c+YMfvrTn+LIkSN3NNkNy/9QVVUFpVKJU6dO4fr169MeMzw8jF//+tdwu93YtWvXhFHpXDLnoss2equvr8fXvva1qGLjsrOz8Q//8A948MEH4fF4oNfr8dZbb2F4ePiOhCOxCQu2PfcPf/hDPPjgg7eU7NntdqOurg5/+MMfcPLkSfj9flgsFsyfPx8bN26cED1gNpv5duhMLNjM9EzDp2AwCJvNhitXrqC/vx9ut5uH14Unmp6NpC1WqxVtbW0oKirCvffei4qKCj5ZyKzxcIEPt3KZ8M51/t3wpeesLOGizyxgNsIqKytDRUUFEhIS0NzcjPb29tte9MHcKew+g8EggsEgHwH19/fD6XROa+mGn4t1FqzTlCQJg4ODGBwcRH5+PrdEg8Egzp07h+rqaly4cAEqlQqjo6MAgFWrViEuLu6WkvMfPnwYP/vZz9DQ0HBHVyAKgoCkpCRs2LABu3fvhlqtRkZGBvbv3z9jEvSzZ8/i9OnTyMrKwubNm2UJI5PN0mW970yoVCq+L9m8efPg9/tRXFwMo9HIh9V3ikAggFOnTuHZZ5+FQqHAF77wBe47C7cY2TA6/OExwT116hSef/551NTU8IUhJ0+eRHx8POLj47F8+XJoNBqYzWYcO3YM165d4/cca5KZcN9pKBSC3+/nK5Zmux5HR0dx/vx55ObmIjU1FXl5eRgdHeXRDcyFEX7t8OQxcoguE71wy57FsrJlu3q9HpmZmcjLy4NWq0VXVxfOnz/PRep2Ce/kmCiyTin8eUVDeP2xe7p27RqOHTuGgoICpKWlwePxoKGhAb///e8n+FwPHz7Mffj33nvvpO2oWHtmnUJ4e/Z4PPjoo4/w7LPPorGxcVbq5XZgse6LFi1Cfn4+VCoVKisrUV9fj5GRkahGyG63G4A820zJ7tON5sUaGBjA0aNH0d/fD4VCgbNnz8YUzzgbTDcL2t7ejpqaGixbtozvpMBEjS1mCM/wFAwG+az03r178dFHH01YiefxeHDkyBGYzWZs2bIFOTk5uHz5Mt59912YzeYJPkf295nqgm1uWVhYCKPRiN7eXoyNjc16/l0G8+PW19ejuLiYT8x5PB4eQ3pzmeVME3mz2IaXgYXJATfC7LxeLywWC65cuYLLly+js7NzTiw51m5EUURaWhqys7N54iC32z2tWIRP/LHOBLgxOtq/fz+sViuKi4vR09ODw4cP4/z58xPmQaxWKz766CNuDFRVVfElzsBnW1WxhPXhu4KMjY2hpqZmyj3/oo0gmC1CoRAsFgsaGhp4593c3BzVVj+sTf5d+nSjxe/349q1axgcHMQHH3wAURRhtVplXc/PhplTZf03Go0oLCzk8bLhkzKiKHJrhSV1ZkHj1dXVOHHiRMQVdCz8qKWlBQkJCfB4PJMSxzCBYmI1HWq1Gnl5eTwtJdsr7la3UokGFrzOfJX9/f0zRkUA8rycM12DTeZZrVb09/dDEG7s99bd3T2nWeyY+y0vLw9VVVWwWCxwuVwzTgQFAgF4vV4IgjBh9OD1etHW1oa+vj5otVqeaCcSdrsdx48fB3AjJHDFihVIS0uDVqvlk47MeAj3x+v1et6ZRzo3+72c++SNjIzgyJEjqKur46GMsSRwl5O7TnSBG73PzZM1cqJWq5GSksIF3+Fw8O2hU1NT8cQTT2DHjh2TcuwysQ4PjA8Gg+jr60N9fT0uX74Mt9s9rQA4nU4eLA98FvHAhD185n8q2Kqz5cuXY/Xq1VAqlejq6gIQWXzYizUbEyFutxsDAwPweDywWCx/UxtUstEKiyHXarVTClasaLXaiJnb2PNIS0vDkiVLEAgEMD4+jq6uLrjd7inrjrUD5pphbU4QBB6pEE0svNvtRmtrK7Kzs5GdnQ2TycRjh6fKBZyYmIidO3eiu7sbL7/8Mk8dqVarER8fj8TERAQCAR7pIQfM2r3dFKdycFeK7p1GrVbzFWTBYBA9PT0YHR2FwWDAypUrsWXLFsybN2/K41ljVavVfPNClveWCdJMlpckSXxfsvCJnvDwo6nQ6XSoqKjA5z73ORQWFsJms3GLKBxRFLFs2TKsW7cOBoMBzc3NqK2txfDwcGwV9lc0Gg1PEMRC0v5WBJfBhJdtxMnC4m41ciY1NRXr1q1DRUUFrFYrjh07hsbGxknJcZRKJZKSkmAwGLB27Vp0dHRgbGxsWiubxd6ydsEmSGPZl41NHGZnZyMjIwMJCQkz7pAiCALmzZuH7373uygqKkJ9fT1sNhtMJhNyc3OhVCpx+vRpHDt2TDbR/VuCRDcCRqMRW7Zswa5du6DVanlSb1EUkZCQgLi4uAmiOVUDFQQBWq0WRUVF2L59O7RaLaqrq9HY2AiHwzGl8MbHx2PhwoWoqKhARkYGz7ng9/vR39+PhoYGtLW1RbRM4+LiUFVVhUceeQTl5eXQ6XTo7+9HT0/PhBdYrVZj69at+NGPfsQzU42NjWH//v349a9/je7u7piG/QaDAfn5+UhPT+dDu/CQpvBZ+7uR8KTWKpWKZ7RKTEyEwWDA9evXY8qIJwgC8vLy8N3vfhePP/44kpOTEQqF0NLSgv/4j//AO++8w4Xc4XDw55OZmYny8nI88sgjPPdHJGtbq9WiuLgYVVVVyM7OhiiKCIVCcLvdGBwcRFNTE9rb26cUbUEQoNfrUVFRgYceegj3338/5s+fD41GM2NiHUZOTg727NmDnTt3IhgM8qTtbrcbycnJOH/+fMxZBP8vcNeILpshBXDHE2AXFhZi7dq1SE9Pn/Rd+Kx3NLBA+0WLFiElJQWJiYn4wx/+gMbGxoiiaTQa8eCDD2LPnj1YunQpzxolSRLfpeHdd9/FCy+8wLOCMfR6PVatWoWnnnoKa9euhV6v58t9m5qaJlwvMzMTTz/99ISY44yMDDz++ONoaWnBq6++GrW7QavVIjc3FxUVFdBqtejo6JiQeCVcEGby78qNQqHgFjpbqMH8mWy1WHp6OoLBIM+iFg0ajQbr16/HY489NmH5eFVVFZ5++ml8+umn6O7uBnBjIrWxsREtLS3Iz8+HyWTCfffdxyNVzpw5M8HVplKpMH/+fDz55JPYunUr0tLSoFar+WjGbrfj3LlzePnll3H06NGIQ26WJ/ipp57ie/BFE855c7vX6XR8kQUjMTGRj7J6e3ujqq+5gt3T3bSbyV0huqIoIjMzEyaTCRaLBYODg3c00Hrp0qUoLCwE8FlYT/gDY343lh4uJycnokCHo1QqkZaWhg0bNuDKlSvo6enBwMAA98WxHKjr1q3Dt7/9baxYsWJSWj2tVouEhASsX78ep0+fRmdnJ58YUyqVyMnJwdatW7Fy5UoYDAZ4PB58+umnqK6uRmdn5yTrnC2zDl9hNzQ0xNNZRgsTB7akeHBwkCfH0Wg0MBqN0Gg0fMnw3ZL+URAEvqLOaDTyqAWv18uTyGu1Wm79xZLMnq0QGx4eRmZmJoDP6vnm80iShOvXr6O6uhpZWVlYtWoVDAYDVq9ejeHhYfT396Ojo4M/a7VajdLSUmzYsAGFhYWTtr9KSEiAyWSCwWCA2+1GTU0Nn1Ri1zeZTFi/fj02bNiAtLS0qIwIs9mM3t5eKJVKZGdnw2g0coOA1Se7x/nz52Pp0qV8ou5OwFb6sXDTwcHBO27QAXeR6JpMJuTn56O7u/uWfYqzQVxcHBYvXsxX6oSHyjBGRkawf/9+HDx4EHa7HYsWLcKTTz6JjRs3Tpl/lPl5jUYjX4IpiiKPSFCpVDCZTKisrMTChQv50PzmfAzMxaHVavmEGju3KIrw+/0YHx+H2+1GY2MjXn755YiZ2Pr7+/Haa6+hqKgI+fn58Hg8aGlpwfPPP4/jx4/HlLnN6/Vyv3cwGOTZyoAbFoZWq+UTM+H5F8LrhYnGbFsk4SIXHgsb/h1bQOB2u+Fyubgf0uVyYXR0FEqlcsL/R4PP50NtbS2ee+45fOtb38KSJUug1Wpx/fp1vP7665PCmXw+H06ePAmlUgmn04ny8nIeNhYessXcNawDDhc9hiRJUKlUKC4uRmVlJRobGyesYGS7SOfk5PD8zdPh9Xrx/vvv409/+hPa29uRmJiIXbt24YknnuAdSnh9AzcEj71Hd8qAUiqVSE5O5vMvse4VN1fcFaLr9Xp5aA6LFLhTKJVKvtLsZtFj/+7r68Pbb7+Nuro6AMCFCxdw4cIF/OhHP8LDDz88KWtR+EvBVoaxFzg8SoFFbbhcLm79RiIUCsFut08KIevt7cXBgwdx4cIFBINBXLhwYUrfr8/n4y//kiVLMD4+jvr6erS2tsZc/yxEx263TwruZwsirFYrn6Ri98s6G41GA4VCETEE6nZgHRFLCB8+CcnO7/P5MD4+DqvVCo/HA5/Px618n88Hl8vFn02sOBwOHDhwAE1NTVi1ahUSExNx4cIFvhLxZtxuN44dO4aBgQGUlZVBqVSivb0dvb29vMysfqbaEJPddygU4u9T+Oo7htfrxejoKNxuNxITEyccG47L5cLrr7+On/70pxN2nlAqldiwYQMyMzOnfE+m215IDvx+PwYGBmC32zE6OnrXTOrdFaLLElTY7faIs+xy4nA40NraymeUw2ENKzExcUJDBYDLly/jN7/5DeLj43k0wM35cd1uNzo6OtDe3s4F5uZrd3R0oK+vL+JW2+HXz8rKmrBkWJIk2Gw2nDt3Do2NjRHDk27Gbrfj8OHDOHz4cIy1FJlIIhDuyw1fdcY2fVy4cCEWLVoEtVqNjo4OXLhwYdZWfwE36mrJkiUoKiqCz+dDa2srrly5wsUoEAjwoXek8t/uMmm/34/m5mY0NzdH9XuPx4OmpiZcunSJ56QI7yRYZ5ydnc3bYCTXQCgU4m6JmyfTBEGAx+PBlStX0NHRAaPROGFbKhbuaLPZcOzYMfzmN7+ZtNVP+Dtw8/XZyKG1tfWOhX0CNzr8sbGxqFelycVdIboMv9/PJ1+msyy0Wi1SU1OhVCp5POJsTs68/fbb2LlzJ1avXh3x+4KCAvzgBz+AQqHAiRMn4HQ6IQgChoaG8MYbb+DatWswGAxQKpUTwqgsFgsuXryIvr4+GI1GnpGLJaBh7oGZOp3U1FQ8/fTTsNvteP/99/mwaboMTNMhxwqi8HwHoigiOTkZ999/Px599FGUlJRAFEV0d3fjyJEjePPNNyPmkwU+22VXqVTyHRMi1ZdarcaCBQuwY8cObN68Gbm5uQgEAmhtbcWrr76KDz74gG/jLUcnH2sdh7sD2LFsc9AHHngATz/9NN+leqZziKIIjUYDnU7H907TarXo6enBwYMHucsAAO8gWe6O8+fPY2hoCBqNBpIkIT4+HmvXrsUzzzwz7caOdXV1OHToUFT3Gi0KhQJGoxEGgwGBQAAjIyPTui6YjtxNggvcAdGdaY19QkICFi9eDJVKha6uLvT39/OKTUpKQlVVFR544AEsX74ccXFxE8JjzGYz2tra+EKAW6W1tRUvvvgiKioqIiY4FkURGzZsQHFxMV9FxhqA2WzGn//85wmB4WxtP4urzMrKwoMPPsgbbV9fHzo6OmC323nOiemGZQqFApWVlfj5z3+O3bt3o6amBsePH5/SlcAoKCjAkiVLYDQa4XA4cO3aNXR2dvLVflqtFikpKTCZTFCr1XxTRhZDmpKSAq1WC4vFgtbW1pjDypjVzzJcffvb38bKlSu5RZ+fn4/S0lIsW7YM+/btw5kzZ2Cz2fgkZHl5OcrLy/lqQKfTiWvXrqGpqQktLS0YGhpCMBhEYmIiqqqqsGfPHnzhC19AcnLyhDpgSdxramq4FRjrfcybNw8lJSUwGo3weDwYGRnB+Pg4D53SaDTw+XwYHR3FyMgIbwuJiYk8gb1er4fFYkFzc/O0yb81Gg2Ki4uxbt06bNiwAWvWrEFaWtq0ZRRFEfPmzUNlZSW8Xi8SEhKwYMECZGVlAQCuX7+O5uZmHD58GNXV1Ty2l3VAbIIxNzcXmzZt4sncy8vLsXLlSn6eSLhcLuzduzemjTCnIj8/HwsXLkR6evqEMEq3242Ghgaez5glkddqtcjJyUFubi78fj8uXbo0aRulcOY650ckZE1iDkwfDqZQKLBx40Z84xvfQEpKCvr6+nD58mUMDAxAq9WitLQUa9asQXZ29oQhzfbt2+Hz+WC329HS0oLf//73eO21127LcqutrUVPTw+Ki4sn+awYWVlZeOihh7Bjxw44nU709fXhz3/+MxoaGjA4OBjxvHq9HitXrsSePXuwcOFCnsLx+vXrGBgYQF5e3qT7m4r09HTs2rUL999/P86cOYOf//znOHr0aMTf3nffffjnf/5nrFixAnFxcfB4PDxXQktLCyRJwvz581FaWors7Gwuum63m1umiYmJEEURDocDZ86cwR//+Ed89NFHUU+UsKG6TqfD2rVrUV5ezvPrsvs1mUzYuXMn8vPz8d5776GtrQ0mkwnr1q3D6tWrkZ6ePqFDCgQCGBwcRF1dHWprazE2NoZFixZhy5YtE3YtZtdXKpVYsmQJPve5z6Guri7mOFKtVov7778fX/3qV1FVVQW9Xo9AIACLxcItZ7agwuv1oq+vD5cuXUJHRwcEQUB5eTlWrFiB/Px8vuKtvr4ezz33HD7++OOI11y/fj1++MMf8utFgyDc2DF39+7dPCcw2+opFAqhvb0dv/rVr9DQ0DClCyAYDGLbtm14/PHH+eTvdBEc7Dl2d3ffdtSCIAh49NFH8c1vfhNlZWVISEiYlNVv7dq1+OIXv4i6ujpcvHgRHo8HWVlZKC4uRnZ2NkZGRvC73/0Ob7zxxpTCGp6/Qi5kE132orDtxcNhPiCTyYQdO3Zg3bp1UCgUWLJkCTZu3Aifz8eXJoYT/rKq1WoeBqNWq1FXV4f+/v6Is7vTwXzKPp8Pw8PDKC4ujureWBB9aWnphK26by6vKIooKChAcXExj2+Mj49HcnIy3G43n1iKBb1ej/vuuw/d3d04f/48rFYrrxdWr1/96lexefNmfoxOp0NycjKWLl2K8fFxSJKEpKSkqMKiDAYDdu3aBY1GwzvGm+/zZsI7EaPRiLy8PD6KuLmDUavVWLZsGUwmE3p6epCWlob58+dH3NpbFEXk5ORg586dKC0txfDwMHJzcyOOFth1dDod8vLykJSUNKGupmon4eUrKSnBd77zHTz44IMTfhNuTYdTXl6OTZs2YXx8HIIgIDk5ecL5dDodtmzZgrGxMW6VsWcgSRKMRiMee+wxbNiwIeL5p0Oj0aCkpATz589HXFzchPooLi5GQUEBdz3c/AxYhERpaSlKSkpiui7Lo8xGeLfy/mVnZ+P73/8+Vq1aNek34RE7ubm53HXk9/v5pCy7h76+Ppw4cYJHodxcFlEU4XQ6ZZ3wE2aokFmTf7/fzyfKpoLFqrJwKWBig59pYUL4ZIPdbr+tYQMLq4n1Yfj9/mkTbbDh9e0kj4712uH3Et5RRbLgp2sPNx8XDAbhcrli9omGP+fpCM8nEI3lH8vvZ3pOU8EWTYTvMsGuPRWR6vjmugwEApO2OAKir6tbgWWAm6rst3rtqe4lFthEa6SQzXBm0olonrMkSXz58ywyZQOUTXQJgiD+DzGl6Mo6kRbNMCPa5bWzca2ZuNWyzHTt2brHWK49V9e81XqeyzqIlrut7HI/u+muebvXvpPv383IqTvRQJYuQRDE7DOlis/tZkAEQRDEBEh0CYIgZIRElyAIQkZIdAmCIGSERJcgCEJGSHQJgiBkhESXIAhCRkh0CYIgZIRElyAIQkZIdAmCIGSERJcgCEJGSHQJgiBkhESXIAhCRkh0CYIgZIRElyAIQkZIdAmCIGSERJcgCEJGSHQJgiBkhESXIAhCRkh0CYIgZIRElyAIQkZIdAmCIGSERJcgCEJGSHQJgiBkhESXIAhCRkh0CYIgZIRElyAIQkZIdAmCIGSERJcgCEJGSHQJgiBkhESXIAhCRkh0CYIgZIRElyAIQkZIdAmCIGSERJcgCEJGSHQJgiBkhESXIAhCRkh0CYIgZIRElyAIQkZIdAmCIGSERJcgCEJGSHQJgiBkhESXIAhCRkh0CYIgZIRElyAIQkZIdAmCIGSERJcgCEJGSHQJgiBkhESXIAhCRkh0CYIgZIRElyAIQkZIdAmCIGSERJcgCEJGSHQJgiBkhESXIAhCRkh0CYIgZIRElyAIQkZIdAmCIGSERJcgCEJGSHQJgiBkhESXIAhCRkh0CYIgZIRElyAIQkZIdAmCIGSERJcgCEJGxBm+F2QpBUEQxP8RyNIlCIKQERJdgiAIGSHRJQiCkBESXYIgCBkh0SUIgpAREl2CIAgZ+f89pJR5iKMRAAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 69\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 70\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 71\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 72\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", @@ -1951,17 +317,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ "f0, dJ_du = opt([mapping(x, eta_i, cur_beta // 2)], need_gradient=False)\n", "frequencies = opt.frequencies\n", @@ -1972,22 +330,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(1 / frequencies, intensities, \"-o\")\n", diff --git a/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb b/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb index f30f97f89..407781c03 100644 --- a/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb +++ b/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb @@ -11,17 +11,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -47,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -176,22 +168,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", " simulation=sim,\n", @@ -213,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -249,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -308,1440 +287,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 0; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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Abqs14Ties2ld0NwfFhXgLijoGPV8epx7v2ZVfG4VfrTnsq17hNdtt1j4NbPThefadxe7sNB16+iOnXL8W6KsftIC88pjuVyu73lVcW0Fwnv6yLsKLg+oj7jvb/1uyif03LqWWuLr7gZqvb8luFreQhvcLy43uF0QZ8tqT34hyGhWrH3iJEFtdY//fWm6fgzYvarqamHCAIhEmo1g++QeaKFZjBNcfgjMfD5/c7tYS3y5fQ0eUxmYOjGLTqv/+j4HL+bWmJVWaYX76YTBlSq0DbcFZPvpsxKm6mpT/cTfVQw0sOhxmmnr3KLPbk6nRL8V9DTwtBa1BkD3dxUTzkRbtGqzei61gRNcF0i0b/hZxRbHsD+of3H5xY3Z9V8Fl/uLcemOS+dG57cX7/YxYIUNjAliY/M2iDNB/I5rN61Fi23zarUan4sAhwDqhDrxKiytybu3boefkd3jIoNuG1WQOFDx1WmtX7uXbh1dEHHHc/9hK7Yvxt26XYoXJWecmtFMiXuLewKOu7io51Z7uC0oBw22FZIFtacGGbxXd3Q6Vswx3os2NcNt+SCul8A/nEjyMXrdxH2CEmO659513sZzn/i5E/v9fhynzh9+1g8WoW39nSsz4msrGL8H3e/T1axBywC8KGBQrmVphqPbEX2QC4sl13/wTAW9q6CV4bnMCO06Ica5uMbGWR8vKMBPA3MLn4WWHZLH3bKlE0oei/tIq9rW4TI1Ft6WIPD4YZNWzduVLvB9K6DosSyy7jZC99JP5aHfEFdFgyULAycFjAq4K0MhE2QB537rfeZsXwgvrw8VxqmMXAOTW39uDeO9WpphH4YAc1mC/YJ3s63dEddzeb5xLrcWmPcQ32/iE2kchXVStVBe5UsAiOh4mhE/3xPvU3hBOadRZ+MMvLUtUcHV+3510i+Xy/gpHSxOvEfvmVQnZed1WemUGMG2WiPTLEcFV+2oWSLeg0XT2gqrjVtCxm078XZtMtp3tlUrixuGt1vXqfOwMHAbHGwvl+vnH3C/+KJYK1vX4KlB0gk8ixmyXQcnNToHU3Oi/sU7LhZeXS+3bIr1u1gsxgcCqX04y+X1xYkM9w/JDD+/4r34JkSXHRFZD56JW/U2g1DBwUsfAO2yMC5J8CPtOAAAPo6jLtd4W8KLhcsPoGYR14XPHwPmNjTz5LG3FpHamdFtWeuCBESTt2xunGhT+4m+cbmE2+K5nhJTFxBaotvKlPmcLApTtuA7ODAuvrij2R76hbZ4F8WJAeygpQ5u657sC0KpF4N1R+OyexZBzZZxnCstOJvek03r80DYr1igVXgPh8P4WEhNAHj8LvBjDKwrujN6L95NdLXQzffqqlE4a+BPrMCIrqaposYLgs/hivj6s6vJ6e8129CHpPDEa/v6O1fPhdPpeFq0HEtFRh+ogrmZykq0rzwfbEPO7nRnwpk7t6XndYLsMk0OFjzHVe0nrzl7sj209ARuZeTcngYKl+EqLlnQsbbOw8e5UoWznQY1jL9Va2+VE3T9gZb9Efg0aPCxyHjRNv8bHt1RaCDUQMvCPTWHX5vuoquG4cyDt9UcAYEKm2uHDaqZB5yptYhV6N33bjyu9MFidKsNPXaqX+6YW+27Opa+WriMrnU+tVmrfRa8Yfi47ePj3Nim5kmP48wKGbVuuafq89p3NweuH7fmSl8q5u7+cW6/xVTCcg8s1CqESBo4E261AVtrn9ycsrjjjiTO0FnMIb58LtYD9VPOupHwuCz8PTLed33gjfsdZ0KtReYWMYSXM1xMCj+VHreL3RKbW6LB7wPqqCqgrUDCX3X8miXiPCwm/De249Q2qiWEVW8/4svt4ljdxqrj82Llc3D2gfe4fqqA6tj4fSr2LLh6bvYT/r2KgRP6ll303Dpv+j5n/9b7nG01M22Ns1VbdQLVsoHO460xtdaVBke0i9IBdpz6r6QwX/pkMm7LjV3XUUtv3oN3fZ4uM5V96e/uMTTgLYj+SxAc77YbOMaJJ5+X39vKqhl1Al4IU+LC4+ZyhV5IcPZwtmbbuLFxucYJDWeRrbIHvudPFikqapp9TonQVHtMq26o/tbaBangturcXG/UGj2fk2vZrq+uLIB2WxfP1AbOz6a+om98Hjys5nK5XN2R0grqHAjcbW1ahoLf8H9khj9p8JoKuFO+NxUI3ouuoquG0+ipFwdYuDBZKiIus3MOpTVMlyW4C1OcCbksjdvnW8TwVbc1ruYFGzinwle8NDPTWjFnBzw+dnqtWU5lL2oH93s9D9uGa4MsFi1xmcpmbi0cJyiuzxoo1C6uRn3Lv6b6p1l7S9R1XejzR3jecI+5+oULRlP2QJ2U7cDBpbU+uJR3y95qY+6fC8jOzlOJCF9XUe3QUiPbt9Xm16brv2DXidJMsaqubrXCp8b0P4Oq4KF9dmBMhF4l1bpZKyOEcPHEAYg/juVJ5n7rcyPUFvx77QeLl24ndXwu2+Wv3G/uJ2d33Aft7y1cVsFtOhF2D3KZahfHOltWfbza3hq7tskXzNg2bvegQe9em7hrCPAr/tn5ngq0+pj+G3sVPxaYVtDQMXJ/UWdFe/BzZKW8lt3taWonzlh5/nTXwEkJ+7m2x2sCpQc8AhV3PPBzoDFGHo8LHD3oJro8Qa1PyLDja8arWSWjBuMJY6dzIspZqNa2sA3ke/s0Q+MFqXVjvmWotQ1sjYHtoeI4DB8vPrnsQeuoOEbb1No2OyI/KKV18YTPqZmsO7faje3XQkXSZeaw79QnErktPZ+7oKYBThfvrYyOd2iaNaOW2Srv6C6G+8RtO59ozZeKrs4d2tOHx+CrfgJNs2FeYxxIOOjqTlJFWhOnW0LItpnSDhd4YCsEjJ50LS/whGiU0exnakupN2DfwkV6FTznlFMZI080RBYvfMyYH+zBmRU7H7ftonJV+2E5947Z2VAF0p33Hlrz5Bzd7XLQV/7etdk6j8ueWnPn5l771Aquru17UKFHFulESPv7qXPBbbaySe273jLYqhPDTpwE3FPqc+PiuWm938HHueswPG4dAzJtpz0uM/+adK/pasbLmaabAF4EVe1PX1W9zebw/dT2t+ptXYnPrcfof4flGhtKC1zPRd9b525toaYEQLOBWw7rbMvH6rZL50rnQPsCZ+YtOJxZL8Tx33h+Whfj+Dz8VYXVjV8DvDs3257r9c5ufH6eP9hEbebe35q7e+aQx4Tz6VhvJRWaPfMOhb9XzueP/9+Pn9GAT43pjgmwz2upZWpdcsbudjY6t/w3N3eYd+wIeNfbU3CrOouuyyoUNr4u9qrrT5k4g7nsCMDZuJaHNrmPDt7istiy0KrguqxM7QF0UbnFrfU7Z0vndDiGbcrn5y2j1uncHGgQYKfX30+JQFVdfSpQ510Dgo7DjVN/x2LKn1bkNvUY9QEn3M4m6BfOx9kgb9lVJKZ82fmFE1BtU/2I2+ZdpcLiq31EzRTii/bd7VzaFq8ZZWq3pTscFVsOkrqLcsHb2aiVNX8tul9IczUUvsjCUZeNiTY0e2Lh4CiJ9zvhYWdwDngr+vF53EsFhG3gREIXrHuPirgTqNaL+6xixRmmCoATdM06eAyKlks0m3LC6HYDejx+1v4wPBZ325tmeG47zj9PJQvoh/M33tay3V1wde22gqqKEXxZs/RW4Gz5orbd8nF+XwsnuPyMB97laJ8wJj0Pr3togbbDc6sJltr3N13TvVwuV1c+2bC8GLkmqhcgOMvlOoxbjDgG5+N//czH3XPLChfdXVTXfrptuMI2cAuWx6WZ8zAMV880aG0JHa6f7j0t4bs1Ln0fsjG+8q5ZBvrTChi62F2WDXu2SlUqttwfrWfqfLZQodPx42urdvup88Z9H4bhzf3mLhC1RHsq29Xgi/O7C9i8Lnl9tvrv+ob1qYERx/Hxrn/6iUaUPVar1die2gbH4ZNwU/P8pel69wKeBKaLAC9ciHJRkWtvLgOEM+pCY8HVjMXBjuTEA87FD+TBcRoMwK2F5QRQM0O+Us2Zot7H3EL/pufUwOX6oGWF1jbQCRaPQxezHuPsxxd7NMtn4dUx6nn4yj9f4GwJJ/+sQbLq2jdcNu76orZx53Pj537rbZQuAWHu2b2pTfVWMEV3nCqGrbID24O39yy8PG5OwFzZA/3G/Ou1lsvlUofD4Y39sc5/sxfSkOm6Z91WXV9F1X8e56KpTozLopyRp4yrDqQZs/udHgfYQXSbNrUlZnQLzPU7XuBOULWdqrcPgtdsUgWDXzzWVg1Of98qU/B4tK8c3DS74q0i96nq+t818cJvlTic0LPv6Hn1PmDur2sf/dR22V68I+I5AW4e9aVz6u4+aPm8BnSXSbpS3tTa4EyX57JFa13qmtHgyIHBrSfsBNCH+Xw+Xj/gpOp4PL4R5K/Nu34iDbQERCcaRXwuU7CTY7vADtE6j96PiMlpRT11dCd2nJHrcew4+H4qwt4KEM6mai/tNx+jT1er8v9DiseGNvkTebxtw5aV+4M5c7f5tYIF982VdfQiKJ+La32tR3w6H+MSAH+KDmPn/rmgzkLtMlw9h+LmCDZ1gavFrZ0cznXPrWIcaF1fW+tAj9fdaNX1HUN6PL/fPagKY+THrbqsn/uqyQPed69dvyTv8uwFzXJc1oHvtVbntjwspJfL5U30d1FTRddNjjoWt+MWMC86zRj5vLccVUVUxZSFRe/mmLL5FGyrqT4xWDSw+zBcf0CBBVdfrT61hLclJpph3fNMZXw/JYJ6DAt7K6hPBdDPuVjD86s2VHHmvk6JiUsG1Pd5B3NLePkrzqtritdc1fWD2PV4Xa8uE4e/6VpTO/DfwKfU07803f9HGv9TSRhYtzlVHxctLziuv7itjjsff62qqwlk0W1dUGstdo7a7pzu9+qoCouHy17598j4OfvnvzuRaQUL2IUzV4c6sauh43g3htYOAm3zV4yX51/tyOPUAKV9mpobHMPj4TqtEzHXJx0T26nKP1cWf+fMWu3N10Mw35pYcJ80ELXs7urQrflx42ll9C7DZYHlsfOu5Z55YvtjF6RlPJ0bJAXYBfOYp/5DyNei638D1qfpq3PrVgcTok7pIroW/tXwXKxfLBZX2VZLJPhcKpqoF2lW7gr9rq+8EKYis/bPCbDbtjvn0v+IwOfmbR9nT/oJuk+Zb7UXB1vN3LXv3AbfD71arcaPLyMjqqpRjFxGw23zAmPR4cV3S4hasLiwr0MYVKyQSOBnzs41cKrwc7lCyzyaFbo2nc9X+QeUOz9H4D8cDmPQxy4D7+WyEO/EOIHi/zjB7+FAAvuxbqAdN7f84Q2+DqB645K9HnQVXXZK93e9nw+G4YsMmEQ4HISW/0ea3gTPE8mTyQ9FxtYUx3NxHQLLzocro+wsug3jrIDP4zInZyfNPnVBsKC1amTc1nq9Hq96V70VPBYGtjnOp7fW4Py6PeSxwiaot+sCc4EObS8Wi1qv17Ver2uz2dTDw0Ot1+tarVZj3/b7/dWDWbgN7murTKRBnn+n42j9zEFtuVyOfeRApnbGHMHX0E7rPnb+JKTOEQuJq9MyGuCdvbiMwcGeSxyHw6F2u13t9/va7XZX4ouxVH38Z6tcToQv7ff7en19HdvB2mObso05EYBd9C4UV564dYeC06OvSddbxljIOLpo1FGH4TIE2jqdTuNEwQEQbTF5cBi0hwWMRYFPkMHZT6fTVTucSfDx2+32amvIfdUMBY6jmThvF9kenK2wjXA8t69O56I12lmtVm/EgBcW2kMWyQuaBVKdV4WMRZ+zWggPtsYIEFqbZLsisK3X63p8fKzHx8fabrfjYw3P53Pt9/urPmudm+3FdtXAprVJ3Qm0sl9OFnBvKIIb+qj1ab4dEhkZAoir/Wpw5d0U7wD5djgO1loHxrkxJ3p/LScwOP5wOIwC+/LyUk9PT/X09FQvLy/j77kNzB12lfv9fkyYsL5wHF44Xuef/2UPz6mWJnjuXHKgWTLs8Zu9e4FFaxiGq+eBVl1fUeVFwFEWj2vTCM4Rm+9gYPGo+ihceMKTZjjIfnnitQzAE4eFgPuLdQvPW0xegHpRsOqjk242m9psNrXdbmuz2Vw5KosuFh4/rQqOyn1G39brdT08PIxZIo7Ff0+GHZAJQwR4i+Zu19NtnRMnLf+wCLgPzMAeCHAfPnyoDx8+1HfffVfb7fZNpvvy8nL1qMP5fF673e6qLML95DnSq/itLT1fS3C7De4v25l3X0gAMCc4BwQJzzHg/4A7DMNVpr/ZbMZjVYA4gWCxxFxzEsGBGMEY62Y2m70Jjjgf/I2ZCsI8L1pW4OSIbcnzpGsMvswv/L6lG5ywsD9CbKFJvRhuKPwXk//j8VjPz8/NVP6eQasATn2dgoX+1nmmjlWBcVmQtnurn602p/p0z9hbfXTljVa797R/C9fGLVvw9+48Ggxd2Waqn5/Td23D2XnKxvz3f2b+3HvUDrds4eyqx7g2P8cvPme9Olt8qn4w5/O5Hh4ersoZX4Bmh7qJbggh/I5oim73D0c4PiW171l7uYee25LfMreywW+lzfD/ew1+CQ36XJLphhDCl6ep4n3vlQghhN85Ed0QQuhIRDeEEDoS0Q0hhI5EdEMIoSMR3RBC6EhEN4QQOhLRDSGEjkR0QwihIxHdEELoSEQ3hBA6EtENIYSORHRDCKEjEd0QQuhIRDeEEDoS0Q0hhI5EdEMIoSMR3RBC6EhEN4QQOhLRDSGEjkR0QwihIxHdEELoSEQ3hBA6EtENIYSORHRDCKEjEd0QQuhIRDeEEDoS0Q0hhI5EdEMIoSMR3RBC6EhEN4QQOhLRDSGEjkR0QwihIxHdEELoSEQ3hBA6EtENIYSORHRDCKEjEd0QQuhIRDeEEDoS0Q0hhI5EdEMIoSMR3RBC6EhEN4QQOhLRDSGEjkR0QwihIxHdEELoSEQ3hBA6EtENIYSORHRDCKEjEd0QQuhIRDeEEDoS0Q0hhI5EdEMIoSMR3RBC6EhEN4QQOhLRDSGEjkR0QwihIxHdEELoSEQ3hBA6EtENIYSORHRDCKEjEd0QQuhIRDeEEDoS0Q0hhI5EdEMIoSOLG38fuvQihBB+JyTTDSGEjkR0QwihIxHdEELoSEQ3hBA6EtENIYSORHRDCKEj/wvOLPq74CxucwAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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6/bITIttfn9leCXilEUuEt6fYgnffpxux/C4mGirbbqRB1Jr2VVOKbPRu2cVl8fNWo3NHnevw3PlQLpfN2QCurb8xoFvFskDW2YTWPfuVt2pgmwt0zYyqz+emhtk12Z5sKl7ZMNfxMhFr+YTPY/HMYLFWEcJ1uGxub2736gsHODe7rvoiq7NuF6wyw9YAWa3vt77wwD6pbIN92XUzu9569rCUd90yNieSrUDi8/G82vitd/3xuDQrq6ZLlb06coO5rEDFm6/HnY1/rk+/DDAMw7Olm2w7lnacVtBm7ZOJ4tyAU52bXS+zq7JRO9RcGy9tTxYePa41OOv5OjvRczRWda1U9wDzrIqXtfQ62h/m6l1lj3N7nF/SR/gcfcx8AWHOdjPx8yyDrq7HcfEeQvwu/5iSHauZW8TzrR3aiFm2W+3zVMHORjwNzCpzQnm6Rai6XjZK6xIGBwA23meZJ2cGmciw+LINmulmdmav5wRFfQJ4Kl2t02VlvWUmou1eXZ8fs50pOgDO2bt06or2QRtz3GhstmZbWiYe9QssWsZcNpm1H8ccx3GWWWYZa9a39Hq6FU1je27rHduIY9iWTHt60F10s0w1E0v+oeGIdgBXoqlBUAkL26Blfk1jLJnWZu8vmeYiCFvCkYlMS+yqa2eDT3Ysl8HH8bequEzNZlo2ZINmZWfLtiXXZ5urgU+vqdfOzmvZq2Vx7LTiQmdGS2jFcnWNl8R/lulXyZQep5knl8XLENWgoyKPRxbdrE5/+UyX0VEz+21TOEhv9OCuJ99g0G9IReQ/Mcc30VSs2S48n0OzDW1cLUPXjSvByTpgqzz1kY72L+lw6oNssNTO3hpoKl9H3O6dbIlvZpseu2QAya6v52VC2/KRUglmS8Q4283K0nbO7Ghl8y3hzt7XOK5u/FZ1wSPHo+7fjoib3RCc/LBY6vIG92OdFfPMjr/AkfULFfpevIvoZpXUjlB98QCfVRux4WyekqCh9Z/hadYzZyPO461v2Q6GKqOvyqtea8YzZw/bXQ1mresplVDOZbpLytXXmXi24kQHJSUrIxtAsrpkA0p2/aysJTOzqk3RXjwIsaBy3GW0BHeuXVgg9e9yucR6vY7r9ff/mMHrrNXNsFa7aLwOwzD9Fw1OSrQfV+VUu3uyG+6Zv3rzh9i9EHEbYLpWg5Et4svIqM6OiJuRTTMoFkUWbYywc/uG1VaUw4KbBY0OJnMNPddZtdNp588yXB3Zq7KqgSwbnLKprb7mY1s3YqodHZnYqYBUx2f1qAZYHbyyelT+1XpmGS4+4/pymfocmV9WBveHJcsLc0KbfYVXBW0uW10KJ0Aq2hEx/duiiOe/Nse+VT9E3OoCl5/92M97001058RGhRBZJAcYytE1TRWdiOfTQp7O8PW+dbTTgQHPecqkApxlSEsyLtjPnV334KrY8mdAgx/HVDOIzE861a1EkAdEdITW1JztrQR1SQda0q4qKuyXrKxsQGP4RiejvmYBy7IxHiR1yYr7A28VW+KDKu5gCyclGs+cCX8reg3+F0jZQFvZnon/9Xq9+Ye0urtHeY9s991upLWyJw4o/am61tpSlanhOR5ZaFo28jFLpmgcAFV2l93s40cuhz/j+nNH5LUvXQ+rOlnVYVE/XZLIBjTuqFw+P+eOCrHlTpzR8vFc1vaS91Ee25cJoz6PeL4DJTsG71XfVNQ2asVnliGyIHJs6CwgEy62Octycc2lIlslPFXW34qtbEDWOGNb1X7snkG8Zb/HoX2ot/B2FV39Zk3m4KwTtLKcpQ7jjIszuiwz1HMyYcTnfF52o6M6XkfgSmw02+GvleoUSuupHYxFr6oTyqy2e/Fx/Dxro5a48vEtn6ldeq7WdwlzQrJEeHFt9Qm/rmJYs8aWGGazFC5T/wV55YeviT18Vg0QWm88annaP5AgZHE2R3Ztfs6+y9abM/15jRnvS+i+vFBVWrd2tMrJRq6qQ2Trq1mm0iofdlUdiAOTbcrK12sh+KrrZudU6Bqulpl1bs6S8F513Up052zVgU1tmNvBsHSgzUSyEkutFw+YLRHScuZ8kg0uVTtVdrYG8yW2ZgLTamdcE2Q7g1C3Vlad+aCKCe2nmS+qmezcDIjP44Gh8vtb0315gRfIWXCrjArv6XMVUj6vavyI20yuGpHxnAOKM02d3jFVBl01bCX6LBicmWiZrSCFn1EHPELkMpv4ejifly1YmPg6fI3K/1lWocLYmtJWN8IqtINV9uiNyZZQZx1Wy6iWgrQsFhpe0smm0irA+rqambT6kr6v9cn8zRn29Xp9tibL1+f3dDDVfsg28HNuQ7y/pN9nPuA1dyz99c5yIzr/C/ZqdM8qnTV4JTRVoOkjT9X5poFu0cnEAcdm685Z9qv2at00GPh5Vo6el/lAn/NNtiU+V19nA0hle+YzJZshoIzWOrSe06rLt16j8oO2pda5mkm1yq3qkf1fu0zo2T4850fYkh3Xiku2kX2UxX1281F9xkKd9T/1fxa3Wr8sFrUulc+rNuhFN9HVAK3Qtaos82kJq46I/J5eg9cI+Ro4T4OQA0Z3V2hQqb0tu6uG16yAbW2JPNc9Ewwth8tXqg6/VMR5YNPrVINGRSWOL1mS0utom84tr3ytH3QtVuOt1W5sg9YZj7rLoWU7l5XZjNfadhrnVZtyHRi1tbVstKR/Z77mGWk1a8oGzJ50XdPlJQWARsSUl3crrNfryXHZKI7z8Zgdyw7m6cUSe7PpsgohByAHn+5c0IbWa2mdNLOIuN1TzDcKWtkgl6lZhm7Fw2O2v/GlPmO7MF3l/0SQrc+hPno9HRD43NZnlc/Zt4g5FZBMoCq4rmp3JejX65etTbqmn8UvXydrS1yvFedZ3GU3m7g89S//U06tr4rm3Awi4vmPMVVt1RpQAOrPOxiQvGV7n3WpsxfvsqaL5xxM+LYYxFa3NWlgMHBy1jhwKgc4ixaOUVoBw59lwbAk88KjHsv1rX5Bahi+fIMnW5udG5iyrIs7aivQW1mxnlNtSWutJ+MY9Q0/r/4dfJUpVYOmTnF1ayLHVGavrklmCYAKJZfZ2qam9WGbcV72DUu1N1ubrXysfEucVxlmxPP/Lcfil12PE5u563H74Vt0Gaj7e6zpds10dUoW8TxQdeqiZSgc1Ov1Ok6nU5rtcIao26wUBMSSvYpZVpmN1HjOWU1VrgZ29rq60dHKmrmsrMNrx1K7s+WJyuYKtmOuQ1TnZsdXIr7EjtYxHKfarjoNb/kvG/j0G1QZVcxnsallzMVXNQBn187iPCuXY6W1ZMXfHG39SLveN2mJOZ/HN/qQ9WYzl9a68Fvxh/oaMD8qc1NFNFArC8qcmzVYVX6rw+tXk7PytRwdeCJett+0olqj4utkX9xo2cqf85S08gmyCBxfdSr1gd6dx3vVlzCqDsTxUl0L5+ux/H5FFostv3F5EJqsPkxLuJagdVIfzAmtZqSVT7SsbGbB5XP8V3GBY3S2p7ZmsVjNbHpntBVd/zFl9pydVTkUr1uNxJmDZqpL7WI42KtskW2FPfpL/1XZWaatdcsy4mzGoNlCVTc+fhi+fPMMAVnd3c/qw1latoaoAlbZmNmJc/DZ3MxAbW6JQ5a9VQOmls/+0bpkAskxoX7WNqx8wTbyLIkHpyw++HkrDlt15Tit2q3qF60lqKqueC/rv9mauA6YqhVcJvt/bnbx1nTNdJH2cyfNso+I51/31VGbWTKCqaO5gbIG4E7ENs4tOWggVLZx3augqTIdvsaSb/kp3GE54+VzqyxYM6ElwasC0xIZtg/nakdtZejqf6XVcZcKu9allUWxKPPrVjy37MP9iaydM/v4WnN+52OrG7V8vg5UGhvVjpu5vtfyR8YSreBr6+DQW3zfZXmhakS+mz73b8A18KttN5mQtaZ+XJZOAZGt6Xa2VkBp2bAr6zAAgbDZbG52cWR2VlNDpcoa0HFgFz7ntbBquUSXArLARhkZc0sp7FveIQL7ddDIlhKqcvlGrZarMZN1aBVdtpPL5Pe5zixYPFPLULFEX7m7u3u2w+d0OkXE77/Y1YoN1INvQnM7ZnGt78H/vAOBl0546p8NuNlgkPVfjnXuQ5U+wD98s7laznmPbLfrjTTuFLpGp8Gke/94pM+CqdWwPHpnP/WWbUbnz3A8GpE/1+DHtRCMXI5mt3jkjomfidztdrHdbmO9Xj8r+3Q6xeFwiIgvW260g3AHYlHZbDaTvZXI6GCFgYa/eRSRb4nCQKFtdD6fJ/+hHK4To2t4sDkbhHDjFCKDRxU1LZt3ysAfOtCr3Vl2pwMZ2gt/cz6GzSiLty+p4MHezWYTm80mdrvdzTXgD9jL12Pxw3NNFPTXy7hdWdDxiPLQBrpWrX0z+79lvI201X+1v6jveecJtzX6XTY7qP5L9lvz5qKLCp3P5zgcDnE8HqdGzP4fmGYcPLJByMZxnF7jPXQOND4aB/8ddxzHmwDVrTbc6CiXBRHBDntYfHmg0CUBtgc24nqcWcIOCON2u437+/tJeGHr+Xy+8aH+k0oeQLh+uA4Ehrfl8fncKVmwNPtn8eQ9kCwOnN1BCI7H403w64A3DF9+DIVjA/7n3y5GpsY+RX359wIA6gV/ot222+3NQMQ2q2BpnKH9WYRZzDXetO3hg+wmIIsjD3Dr9Tru7++ngRkJDOLheDze9JksEUD8Xi6XyVb4neOEBxkWScTh4XCIcRxjHMfJX4hjXnNu9Tn4OotfxBa2f8E2iOhms7npQ6wbXEfuB1qP/X7/ot/T/lbeXHR5NNput7Hb7W4cB2dxkHLwc6CP4xjn8zk2m02cTqcbEdPOgAZtfWssm1rwiM0dFkKFOtzf38fDw0M8PDzE/f39lIWxLfrHNnIH4I6Ecj98+BAPDw9TNsMd9nA4xH6/n0QIPjsejzGO403Qcva13W4nEUf2qMLMA9jxeIzj8TiVfTweJ79q5sxl4xF+RpnonIfDYWpP+AU2wB7YjAEIgxDbDV+P4xj7/X6yEeVyRsbZHAv4brebMkfEBmdvKG8cxzgej9P1eIaB2OaBEj7gGNfZA+yEeHE8c/uxDxAXj4+Pk/BqNno4HOLp6Wm6Pv7Y15zJcrbPfucvjSDZ2e/3z7J21I2TDZ1hKZzxQ1jP53Os1+ubG5s6I0E8s72YBelsRcVcByD0we12Wy6BvQXDTGr9ann3OI7x22+/PQvAG2Nm1ld02tH6vGLpGo6WpetAOsXRcl9qq2b8S8rOpl4VVVk6na6ukx2jPsmeK5WtVbmZ7ZnNr1Fuy97W86zu3+pnReNjSWzgsSp/rg++pOyl5Wd8bb9dGnPVNTjR+O6776as+ZUoDeomusYY8zeiFN3uXwP+K5KNst9S16UZwltc58/QRq2s8Y/InzE+qmv8Gfz9NbzEp998LWe6xhjz6pQq3vX3dI0x5u+ORdcYYzpi0TXGmI5YdI0xpiMWXWOM6YhF1xhjOmLRNcaYjlh0jTGmIxZdY4zpiEXXGGM6YtE1xpiOWHSNMaYjFl1jjOmIRdcYYzpi0TXGmI5YdI0xpiMWXWOM6YhF1xhjOmLRNcaYjlh0jTGmIxZdY4zpiEXXGGM6YtE1xpiOWHSNMaYjFl1jjOmIRdcYYzpi0TXGmI5YdI0xpiMWXWOM6YhF1xhjOmLRNcaYjlh0jTGmIxZdY4zpiEXXGGM6YtE1xpiOWHSNMaYjFl1jjOmIRdcYYzpi0TXGmI5YdI0xpiMWXWOM6YhF1xhjOmLRNcaYjlh0jTGmIxZdY4zpiEXXGGM6YtE1xpiOWHSNMaYjFl1jjOmIRdcYYzpi0TXGmI5YdI0xpiMWXWOM6YhF1xhjOmLRNcaYjlh0jTGmIxZdY4zpiEXXGGM6YtE1xpiOWHSNMaYjFl1jjOmIRdcYYzpi0TXGmI5YdI0xpiMWXWOM6ch65vOhixXGGPM3wZmuMcZ0xKJrjDEdsegaY0xHLLrGGNMRi64xxnTEomuMMR35f6dO8jQtLTsRAAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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SFtF46u1puNfQqAxY59SHNTk3IlecOuZSeeFrNV8ZG53YHd3vyToiAxi33XG+vJ2+6Em0yINgaYZKnw7nNWFXeqwNs20/px2yLZ5PZ9W5GhfNu8he/ffg1hIAHthIzFTfHBtXcjyGmZbbCsdW7eCcehmI5ETbVSBsbS3jAqTv9qD/MJtzW2KWSvJmnzT2PJ7Hqf3c3ubjxACzXq9/v6T78PBwtsmZakeOxAHlqjKJj2TJp9JcvbjK0D2lmnSc4GQkQ5IBqs7XIh2lpK10kNGbqpqpne7PcWHq7DXCS3VC/V3XZTDwcdb92X8HycmJyp1f7ZTadcVGpf014vV76RoeaFt1uVY7v3Z9/d+d0oMydy20FObXVCbH9VJpjEGENVsvDVBd095IUpxn2gO35fmxFBJ+bdVEWVKiXXsGpFKDl2/UTvmpFvM4du5bbKMEgcqCrAEza62qYUGPax2tkpXKnr9b0tU2lapzw/BNyr4/lFGQBsHVY6Y//NA4qAiZZrqjqA2z2WzYX6l9hJfIhwGEBMBUmaSr4/Wv2kOCcUdi/9jWS/UokrdvdeMxVU/KgBvgSZJyPHdIXU9/U52QRnypfsm/q18kOV2fmQ0dSaTLcWMG5SSi8bt0f2ZLurfsTPdkECR5/pzx43U1HxyrVkBvzS2J2slVf2cGqH5ThLA0pDnkfmqfN4oD9y8qX+5oYdupmPV/7sRwZd7yMfIFn/5039f8q5yhY9RGBSD5oR7K6YXuD0foCRpFKRLPpcUKd1aqJ68ZugL0qEnDaV2TE6hJms/nZ2kTr99SVPx/Kwhosr2WysU09duDhde9v/bzeDx+9s4A9VF/Zw1PCw1cbPAMRKRKZ1fQEEkpXaXDtMa7pcT5ewYo7gVlOUjnKMUnkXB/NWuILO34/DuhsXzEc0i4ug+fQNTc+zkaaxcaLYFBBcix5Ph5mxkEdH32Udfj9Rn03HbZ/paQYRbHXQxfy4YuZSEtNdpSvbzmJZui0Kh6epqRQkhEq/3rvdD1MWClIfq9ojxT0arn6Z2n1iw7SBGzjOBPrPi16BiMvLp+Kx3iRDFF+6l0ks5J8mSbZBxSGiQTjd2llJkKgESuayvaO3Gwdqv/+4dzx1ROH6+vtp6o0hhS5bT6zzZozHz+pNT50IWefNL5VDokac5JS9V4gNc9/ckl9ZPjoPOdoEkgrC/SFjXP2sXDDEzXvhTcNbZqr9QtA6jmTWUB1p7lg632uB3zfq6YqZxdmeqaDOguuNz/3Yf5VBv9kbsOaE++tuLBgmUWjQ3F1qWM7NdCV6WrtEMDTYXCNJERmI4gwp1MJvX4+DgY8mg0ahIuHUIfTbw/wTWZTIbN6FRLrg78ZS40WhoRFyGYgns9W4QrlVT1RLoC62hUALoOFY8rtVba1qq3utORMNVvvk1MRMIMRXNEwuU1/GcRhOaR7eK8M11mnVtEczqdhk34DJrqH8sRIh6/n+bHSZfEW/W0CMwnyPT0oatTXoNZnMbX0+9WxqMx9axNdsD503jQnngttZuCgYRLe/Hsg7beIj4nW5XjmHH5zgU9PMGFco1Li3jVDu2X5/ZLcoULJvZbHz4NqrcM9kL3hbSbm5sajUaDQYhEWVej+mUtzleoZVByOE956NyKtpp4kq4c39WVjIcqRW3RdamoqJ5bNTBXiVXP01i1qep8tVn9YXBh/1opl8aMKsdTfVc6viix2+3OXvGn5/5dVasvajdJxRf8RHIOjhHJgQtObDevoXMYsBTkPDVvtfFr12dfda5qgyJe2gjVHDMC3pdZGonGU3O1hTaksdIxTu58JaUIzoMKr+VK2lWjfidfoU8KJHzNObMOvt6SpKuHh1xttsoXVT8GPe0NVv24NVYaT/XLCZyLeL/rhbTValU3NzdndVwRpoycjk9HFWm0IjSjmTu3G5DX0zzCVp0/pEEFSRUkovY0VoZIktTWGipwT0F1XRIGHy7QNfkeCY2XXogi0EG5JYhKhu3nyryrmIeHh+EVknzvrO7pKRxLCT62TpKeotMZqESULVxSI606HVWWE2YrY+C1NP7sF+2J6t+3Rp1Op+F9Hcx+tPPFFRfTXgY83o9jyHn3jIH1XJZqaNeaV75shpmbrqcxYKpOgXIpK3BVT7EjwcM2aUzoAz5XLDHs9/thb7Ce7vSgxn5yXHkN7s/VW9t6oRvparD4og13PCollgZaNV2+KWixWFTVuQKlIdD5/SO4EhXo7HIeT8VdZVJh6lWJSodk3HJC1t9EoAKvK+PTU0B6DJXn6Xgaa1WdGabuJ2fQC9bVT91DK9F6kQ3f+8uAyS17rsyZanOcq+qM2GkjzHJaKpRj4mq3Vcag0tdYeEDgvAlcK/DMRmQkstDLs722LsUpFXw4HIYArD7yvRXsC3dLkKC4LiK7Z537kh3yRTZ6+5wyH2V0LM94UPX0nSU7Dwwc36r2omDLB9UXzq/OYSnAXw3L41niY7DxYCvi1ZOmrczrW6H7ljERAFUr05+q85T7cDicrUDLCWTAqumxZsdaYcspGeGVevBF3iR8/cv3lnpw8FSR7dO7ZvUWe6ajIl8qBxkRlb6uz2920DhWPb0PlQZL9cg0T3+fz+fDG6GoPvi6TX+rFdurdJFlGaaCnm3oZ86DFsFY52RpRyCZ6VzOt/7GFJ8lK91Lzss6JInEy0H7/X7IxBSo1HfOM4OViFb2rJ0jHF/Nr4KYL/qqH5xP/V22SlXMGiptRjaqjEVvg5MN6Zoibe5kUZ89SHq5hsRIctf9XTUzKPj1mI36/lrNo78bwstzDIqtchLnltvcfpflharz57arnm8daUXUqvNXupF0RQoiWh3rpQuqVZ1LY/boSeMTQbkRVZ0rDzq8pzgsL4gwqmpovx4akRpSLdLrT/7uVe4J5co5gwnrjiIt1qK5lUyEKdJW21krlUL2dwUzdXZC09xqzDT/rItyUVT94HW4DcsdSHPFOWMmouNZD1c7WiWIVnlGY0qCk1Jl/7m4Iztkf+QHTiq8PudH86k51LyoD1LU2tqohSnNs8idj+tqTr1UoTUW+qnarHv6ljCWvnQvloY0Lyw/SHD4dTQXLkKk7JVVsO+yF98aKeJtZbTkAs3xpdLVt0A30nWlVdV+AkVGpxfj0DBlDJpQTYIMrFXn0h5bRkWPgpdqkWqPvxDDi/VMD7204aTjv5fD+leX8Petr0+RE+rarOWx/kbVydKDBz5PvfwRT16DJKXj/QXgl8oAXsahEzBl9bnRzwoK/ncFAxI9x5cBkjsP1A+2k+mpAqp2uLRIRMdqDv11l7pmy+E9HW/VZjW/6hfrwSxj+HY1ki7JlCUD+ifHoWWjDKoaA46zb/Gi37cEluycfuOP9lPl6h4ULiovXl1dDR9fQOQ1SPQkedrMt0Y30h2Px0P9kFuERFxUAjI4Da6cjV+JQif21IDRVP8XmM7L6amyBN7bjcXT0JYxMU1S6tZSmnx6iUbRKnuQMNxwXLG10iupYz62KRJpqX6SLslO9UyBCxNUxSwXsR0kW7abWQ6VLceE53Hcda6TE6/Bj8iHaLWzqoanEvnVNb4ow7KHj59sQcqYr8X0p7LchmSDnGf2iVmQ5pNZH8dbc6J6LYMM7fFSiu727j7Dv3t/dA/2y2u3VU/+eSlzdLvnlkuRrtY5qNgp2tjn0+k0fOFlL3Qj3el0Wsvlcvg2Vm4b0aQodRMpVT2pM38yzB2EJQmP4F4r9pVpOQXJmmQgI2FNyv/uKs13S2gHgfrkW81aCxKuhKjWq2pYPOF53u/WYgxJUn+TKiIRkJxZI9S4MXhKgVBVOCkylfUSkuDn0InZN/1LJ2zVtB1+35bjs/0iaN/ayAVHZl5M0X38ZMe+k4X9vUR2bDtfh6oxoKqmeuTYcEsXlabs1Z/4U39pzx4c1W4d4z4jv+J1+XALA5KuxcVqzQODIOea2+P4xQOaD5bJNO7yefnecrk8m/9vja6kq6//1uB5qquFs6rzp3Y4cDJgrnq7AiIxyUmq6tnfWOvUZLN+1iJbQec44VKZ8CNDUD+phFuLDK4qdIyCUtXTFwVyLGm8Miwaq6sHLzW0lCfJx3clML3m7z2d9rFv9cnHmH3xMoDg5M7zfJ5I1lyEamUv3g9lZBQBLL24cvPx8/FuEUhLZTrhKFjTlnROy25cHCiDVN98/YJzxbF2GyfhOlG6yGEmyfnQ+STdlp14n2j/VLms52p+xRX+fW+j0Wiwu7u7uyEY9UB3pXt1dXWmBJjmSRHSIBWd+UQLV4yrnlItOo9PXNXzGhSVJA2ROwv4Da5ucC01TJKiQqXB8LyWytK/bI9WklWnddWrftDwuX9XxzF9I/HT8He7XV1fX9fV1dWzr2JX37R7gucRLcJVgGi1hePhRNjKLASqO28HnZ9KW3VH2iaDc+ta6pPI1xeUdD99XbvGT+pLBCc1zNKT+qn6JN+ZQPvUPXwB1b/a/VLZy22PmYeOd7slSXpgJEGqP/xWbX1YOtNc8XrqJ8F+eHs0Vq5y+Xi/rsmSmb4YVwpZ11gul7+v8gIjogyKpEnFOp/PzxYguHVGK5RcFKh6cjqllFIBregqyACUYnNFVG1VGscn2FyJ6VgnXNaQaMSX0KpZeeCR46q26OmlE4/KN/o/AwkfheZeUhm1lBwX7vQ7zqkcQj+35p1zQPXmzkqibZUeqB5bQYpjxnPcedk+kgp/T7LgfZgR+PHa86wvNb25uambm5uhxqj7nE6ns7FXqks713iwtEJRwDf1ae78yUmOhavrFpjJ+H5pJ14fY5GYvhfNx0m+5iUKL1+Q/NkWbvHjee6ftC/uHOKun6qnDJHtbq0/fCt03b3AQWFdUZjP50NU4raPqvPn9Ol4mhgdQ+cgUereJAlOKtWE7sXiPFMXJwYnYydRqjyv21E9yajYPrZ9PB4PNXB/+krEztqxzuV15ditcomO1zH+khIuwOi+bCv/xn46qbma91S4ZfgkDVe6l+yN48t7tUiT48V7yaaYSbEd+h0DGHcStIhK/fTxZBmEdsHFPx5H8uaKPzMh2gdJlCRDMmYpyevorOXSXkR83G9cVWdbvUSCEl28rmyU99euC5Ju1VNpjmUNXzwk4frXyQsKVK2M+Fuj6z5dpto0KBkeN/9LHUi5esStev7lcnRoFulJ3q7OWkqUKRvTRHckKlx9POX04zxoyOjp2Pq9/vUyAmtWrgomk6dvMdDf3GF0HRknVZ8/HcX702k1n75wxj7SQXRPBiM6m8bdFS7beintv6TE3NYYYDnWnA9XmFTJ7Dvv21LmHF9dw3cr6Bq6p2zhaxkTU3evA3ufW8GNgVXzQZL28Tsej2fzwnFmgF4sFs/2NFc9EW+r3Kbr8Z6uuKVU2W8GvVZJj0+C+v57+ajPbU90fbUjt224cTDd16PCIg4nMxo306/RaDS8jEWEW3X+rQs/lT4wzeOGa3/axRcWqs5fokJVwojK+iEVKg2xZeBsX2vsRLi6disNrHoiNpULdC5/z1V5prauEvl7tc0VCJWb5otqyg1eQUXzpKyIY0UFRudnSUrn6ng+gaR2k/RbpQ3aKm2MKpr9IolpoU1zoX5wJ4MCFB9IYabGYOHbnVrBR+c4qdCuaIO6p4sf1Xm9PRxrEq9KViRe+vultnLcdf3WfLFtrr45z/qZCpfv+GUZ0oUfs7ZvXWLo+mpH1maqnqeATAu0v7HqfOuQzmtFZP3enUHncPKdRFzZsV5GJeElBU9h6SS6tqeDMh6qLh5PFSNy8Pt41iADIumSuN3Zqs5VBRWDK0JXfU6gVc/fx6v2eymiFfxc5fs8kXA9K6EypWL0a3sG4u1oBWaRAMUCbcDtu6W2uD/ba7OcX80/CdOFiqtXJ0benwFDbfZtW1xM45qGzueCo5O3L6r54qDeHsa+u9+15oN/875xzHlvjptEg6/VtOZO1+a7OXqgG+lSsXAAmGa70VK1Mq1oqRLd49Kk8h4e5bgPVWCK7E7cIgVNnJ5zV5SV03lJQqAykoEz/eMC16V+VT29g5f7ELlgwRVu7z/rlUy/vN0kYDo0F4J8pwfRSqPZRo2HOx7RqkXrPCoytw2vYVLVsWau80W4fn3ZiRMPAw6DmttlS21q/p08vLTQghM1yZLn+c4R9ptky3ZqPPjK0ZZw4VwwwGvcdH0+UOIBodUvt/eW4OE9NBYkW5ZkNF9e7vL90t8aXUmX9aOq5xvVBSob1ps8tWgRr+5F+PWp5vj4atXzF54zZWfqRsdgNNXjuqonHQ5P27i0R1J9JOHKUNgGEpArRqkUGqY7psZFi4LX19eDM/i+Z3cebufyMgGN3MshOl4OqDZrDHUdjq0rFld2TI25eEVivlQrZNu1cMV5pCN6BkWnlv256mplEyTUFuFysU1zIVLj+zK8Ruokxb6ondyOSdKV3cgG2SfdS+fQR0+n09kLe1r2zyAmpcugyx1K6n+r7HDJhznWrUyJvMJsyIm0xRO9Cbeq80KapxiERy0ZHNNlV1gtwnbQKagYWUMW4VENcMM167DuDFKy4/GP+z+5WspyipSiaq98DLT11BcJ9HQ6f/qLUJta40Mi4cq6+qh7efbhRHJpOw4J0c9naUFqTm1iLZD9IvnTkVxJeltIAEzdnXhZW+XuD6phb7MrXfZb/dG5vtDqGZjawldq6hqaQ/adj33r/myfjtVYcLHQv5FXbZa96ToUN5fGRfbH7MgDgvqmNunRaZ/LyeTHN/ZxrlulHffrFvHSVxiA2DYGyUsq/XdLulQ++r+rCyoXr1G6k10i7ZYCJum6YnNnVS1X+y312LKnvFS5cho33BZBthRGq1Yp5aM26WcaC8eDZQwSFY1bv9N1fFFPzkMDbaVy7B9JlgbONJvz4sqVpEsy41i7wmHfnBTVB/aZ9WVmBewn1THHn87MLVEsK7BdtGWe7+pbC7PeZ1ee/hpD9o99YSbp7RZobwy27ku6DklcxzCr45yxX1U1vNmM894K8B5EW/7bElZUtfKPS5mSwPIer0le6oHupMv0RhMkw+C2FxKvzuegOplQcdAhvL7INJBpvytCKl218Wt1NR3D1E3tYIrO9Fht97SWxuF1UXdMV51SFyQm1rb8+lVPb2sjwTFVpfF6Ss+0k/Ooj8aFJHHJPjgWHpA9MHOBR39n+3mNr2VEJAwumKosxLl30vAAyHKRkyPHmyUNkgUDtwiXRD8ajc4etPDFLF6bOzP0+0vB63j8ce3ASzQUF5eIideh/2oH0nw+H77PkOsD/nG7cbGke+n6GituZ2O5wDM+cY1nJCw/9ULXt4ypjiVyaC0UMO2V0tW/+h1rmVVPdSpdl5vTScDH49MjmNriohdSawJ9hd8jdctoZKBqN+uummQuZFX9aDhSO9pSpvN0TRoqVS5JjsqDgYXHsG7FRSuqPzk6+8Gat9dXqQhbWQXHn3VtzbeTG1NatZ9j4jVtzgnHTMdyoah1Hzo6n+jSvZnye//4L5W2dixwbNUe2QAVIRU5bZsET2XqKlyE5NmFxlk+xjZQZTNAM9thmYsBpmX7LHvIR3VNllP0M/e8k/B0LktaPtetjJhlK44Z7YIlNRIt597FzbdE9/fpOvF4hNdgyeC32+0wASIHLQBxYOXkJF2vkelnKQaRLhXsbvfjFzEqgu73+2f7bP0ptfH4acuNFIoChatuKhVhOp2evYfCVRrTyFbdioansSYBqp96PFXjKRLiu3O144I7KvjYtYxYRk7H4iOp/iWEvljDwMl0WPdgv70W1woynuZzjDWuIipmNrIdEsDhcBi+QURvt1MbSco6R3apbyDQE5UiNq0LXF1d1XK5rNvb27q+vh7sRjXQxWJRj4+PzwhdfdD4yLZYP27Zi5dcaPNqk8ZW5SmSFp9EVH8UKCiUXJ2v1+v69OlTffnyZVhEYy2bXwzL7IEBVot3k8nkWblO88Lgp36yDs77trJN3Ys7lHoo3m7vXqg67yA7KqPTAhSdk0ZeVWcvSuaE8mEGphycoKrnj9fKsE6np3ed6r0D6/W67u7u6vb2tm5vb4cJvL6+ruVyeUZgapO+nkcLZkrdqs6DA1WHrsHI7mTLjID1Nhoq1YwMfLlc1t3dXb1+/bpevXo19ON0Op3tZlDA8McmdQ+NnwyXi0ByEn79j0ieXwOucZKC1kfO4qqNCp+1a6bBJB6v12u+9X+NdeuhF9mXghDJxUsYHHONiwhHNq4xWC6XNZ/P6/b2tl6/fl1/+MMf6u7urq6uroagzi1ZJJjD4fDs3bcMuq7cGDhoP/Q97pyQmqeg0e6bw+Ew2IlEh86Rf9E+d7tdrVarWq1W9fnz5/r8+fOwg6eqzuyewY0BXdfTnOhhC3+qjLt8uDNCvs71BhdJVMusz/dE1yfSRKQcVK8p6WtFRJRUrlV19pq20+l0Rrh6i5mMiSTqtUQq5sfHx1qtVmf7aufzeX348KFevXpVb9++He714sWLurq6Gl5oMp1Oz74O5dOnT/Xx48fhBR+3t7dVVQMpSSnqHvr4AqGIgmQkI6URKiOoOq+bz2azurm5qZcvX9bbt2/r3bt3w6s1FWT0rbVygO12W6vVqrbbbd3f39eXL18GhyCZsybs5Rr1h2Mkcqt6+q48fcOwgp2CSNXzr4VnzVztlF2xLMBFOX5EIFJsL168qLu7u+GFNAoKGtv1en1WclksFgPB+6q4HN1FgN7Rend3V8vlst68eVNv376tN2/e1O3t7WADh8NhGB/PCPjoaqvcoLESOXrpgupN48nSm8Zeouf+/r4+fvxYq9WqDofDYJcaM76PQz4kO7y/v6/vv/++/vnPf9bHjx9rvV4P8yRfV3DWN2xovgX3CRctFBskYXIDv+hgPp/Xcrkc3iSm7ELZCMeafvet1W73hTQ5p1QRU93dbjeseHJBSgZGKPIrkkmN3tzc1Gj09DiwJkCKjYtT/nNVnTnSeDwevsjveDyeqVzdS2pjvV5X1dPEa8vMbDY72y4jQpLRMx2ighNZqB1yOKaTrM1qHER0y+WyXr58Wa9fv643b94MKpe1RP6sYCbHIwEo+FGZs95MBck2aE78yy8fHx8Hpee7Aqqev1Bb4+qpM2ulTuws+VTVWWC+u7urN2/eDAFU7ZLK05iw3KESkMoBdFgKCjmt5vv6+vpsHhT4GCCktDUWrK1ThYlw3AZY0uG3O7PkQFXMQKv+adz8i0gVSPgeEu4YUClFP8tf/CulWj7HwKiAIWK+ubkZXhPJ9rrQEFjj1Y4iqfPr6+vBX2UXPF/974XRpdXk/49f7dk4KRzW3KrOH89lnY/tah3LFU2/5tD40+nZ8c86iPv5MV4f9EUUtslraGzfpcWXS+3y33k/vMbp9ahLCz88hvfhIgod4dJ48P/+d7//pXmpas+3Kw2WhS6Nl/f/a+Pj9d5LNtNqV6sPPMfbJJu5ZDeXxuPSuFwaH+9fa2xav7s0/+5bXjNmn91ufo4vXVKSX+tHa05a8+680Zrvlj99g6/suSiXu5FuEATBvxEukm7XJ9J+Lr6mZr72t1/jPpfwS+/Rozb0rdHKNoLLcAX2Xxn/Efv9lv70tXt8C274lojSDYIg+PVxke377pUIgiD4N0dINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdMT0J/4+6tKKIAiCfxNE6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOuL/AS9u6RDcsyw1AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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PoxVyDjBeLDah5Iwg24fnKqfXQ3aVAxrk48CUBQ3/RzCwhevx8bGz8WxDHi/Xe9lL7IW9jwmJXKYwGCuUOvN2enra7ZTgXObQfV0ul3F3d/fnJF02gqNUvBc31zJReUwwq9Y4hBe18n7enGbnlc6IZ/LJKSolAM6342Ock8kkNptNr3aFCoLArWgyceR0NpNmRF81Wdnb6DJBeZHLKS9beiKiV2LxGPnLaT3k6b24JlwrVJckWGX29h7PiwNIJgTPWd4RkGuFVpxDtWXOe4l4/N1tw3GxMQJrrpuTwWSVObQWMDTPvi99JmgNjXEmQWdtntdcpjChZaU7NDZkFNzPhMu84pNe4HYW5LZAePb37GP27Sy+CMy+ns93eYBxcmaS99ObXw6HQ7d7pBWa7V4gqvA0FAMDaWHQTObhcOht7nfKA2n7iTAmyquQdg4mhbTEW22YqFw7495sF2LPXzYw2mXyd/S3urHSj3h2Uhb1TBbeF+sUzyRmJ8qk61Qrp2pOce2UNujspH6AxKk97c4LPrnePaQAI/opdm4PfWDOyS6Yc8o+Qws+vmfOIHwf/9/kZuJlLuijFzKt+vNcR8QHv/s+/N92l0k31+j5u8nVx2Ricztzicj9zusLXsSyjdkebHcmYC/4OfBhJ/i6S22UpnL5xUGM4E4QQOH6ftSNCZj4OuScx4ESoH3mU6KZ0mWQvC81lwv4OeK53mo1hoF5ZwEDykA6Zc4GlxUHKsUKJ+K5zrVer3tRmkU//p/3/mYDzqo7Ey/tttPZ+E26di4cDMcdKg3YsfMeSCuErKpzMBhaFIl4foDECxMQkssVbnMmXd/bii0Tlgn34uIizs/Pu7asVqvuen5SzZmISywfGy/6Px6Pu4cZSFFNupSh8qPD+Z4mSvrooMucMqbYuMdxaE7d1qzAaU8uC3D9nBkN2att0IEeX7JKdInCTzb6QZlMuD4H3+T+2XetdF26cTmLvrmUiH36MWPanYOGxUQLNCVdIktE9Igwop8GUti3EWRkdemFhzy5WcnlFM9Gl9NW1O50Ou1+51gmlv+bNLNjgKEUk2Pz1hjGJY+Brwv5uJ5MO/K+YhSiVWwusTgtnU6nHyh1QKDkuhHxgQOAl9J+Owf9tHpmDlFds9ms934BL5pyLf5mAnTgoj2j0fMDDFnlEUxMuu6jgxZtNkmjjL2I5zozfTLR5GDFfXJQNKHmQOmA6R0UOTuyfeW+W5n6WLIK7z/3I9S5zJAJd+iamVw9rnmtgWsw3kOZi/kC/3QWjW1a5Volt0JT0rUazQtkThtAHliXCiDFrAD5v2tFEX2iyvv/IPmIZ7WU2wzhQgpeXMoprRUm7WZfp2tpJgeUqJ3MaexQLZBrmHCtGCHFvIWJvjlI5Hopznp2dtYpBGq1DpRuN6otlx0YR6s8E67nwsHZtT4/Eurn/Pf7fRccSD+dkpp0GXvmA7sBufbIvQgstN/BhfFiHPwiHJydvlnJ5cyKMXLwzvY0RIjZFqzUTbrYEjuFaFte08g1UObEGZ4XVa0YXRpwm10b904Xrp3J2Go6kzD/t5+7hGWS53qMcS4r+Pc/NenSQZQKg4sCcO3IC1AMOik/7xuw8+UI5ijpOp1fPBLx/NIVSDkX9h2FTb65RpfTc/5nRzE5cz/XDiEMK1yncll52sAcfEzcdp7cpiFnzkbuv7k8ZOeiP5kQPH9O/xgjL9oMpdqeh1yr9BiyAJPJx2kpduLFIc55KQPJ8+o0GeJ2ycL99VgTKHL25Xv6flbyzpzcbveF42xLPDXJ9REptgvboMfLdjEUJDJ5cQzzkLNBnu600MHOvTWS62clylgcDofei5b8VGcug+CrFmO5vJBryC3QdPfC09NTXFxcdAbIIpVrY/v9vktbhiJTRHROj8GRVnoRwsTk487Pz7u9grwch4W18XjcvYzHislR38hprJVixLMScqpklZXLK17EsBGhrlzy8KvzMBgbjjMKlBllCKvroZVdG2x+3SM1VNqfidlz5Z9NNhAYc47z5wDrvmWl4kA4VK8fUrEQOeCa3N8ZCdmMMw33kRQbeLWc47Iqz4E71xZd4xxagMz9pF/OrEy6pNYuw3gcua4DKF9eU8iZg8cQ5OwOv+PFP/gc57L90ud7/of8OeL5STK/w4U+0FeXQPgff3cZhO9PT0+98f7UaP7CG9fkqId60k26Ti8YtK7hMoKI6BHQS2oFoyRlRFl6QlG5OGNWAFZRmSitiDBqjCTXwCAFFgNcv7T6yCTtelouVUT0a8aut+12uy5FdpmFlN1tRU3kF9oQkBiXTAIoCdqYScTz4kDh8oOdxlmRa7ZDJScTmVPzrFCHAhT3zUod4iUTy/Vwv8jIKnS/3/eeuvLDPowxY+ex8s4d2ux1CJM/du972MadFeRAwWIXpY8cvD1mzkryoqpLdg7kjGkOAiyAOoCxIwgbGqo7O+vlnQn5PcgcZ/vhXny3XbHrZb1ex/39fedLLdD83QtXV1fdQPi1dFaFLg1E9F8ggoH7jVMc44URIu9QET8jp7+ZIHw9DMypl1P+iOenyfyaRL/+kHZ6tT2vdgMTOU4KGWKkGDqODRFxH5dsRqNRF/TYrI4De6sewS8buAMD45rrfX4vq8clzwFzTf+5RlY3WQV54cVBxgqQeff+bo9Dti23x8TmgOjrek7ICGxvfhvXbDbrvT4xInpPdpFBePuUSY45xK7oj9UktmL1yNivVqvuZeN3d3fd+wa4DyJkNpv1SgVZHdv/vNhJv13GIohTbjCpDyGXvLge/kgbHKCwE+47NJfMe7ZPfIi3uf0pSXe/33eRBccnGuXIysCzQORJz4rIpGujcOQlvY7op1csoDEJfnm302AvhFjRoFr8eLDJA2P3axwhLu6b0xqiv2tx/J3+Oq2y4s/vKPUODu7p9w17gYP+ur5uAqWdp6envUURp9NO3bK69di5lMEcYPT5+IgP3wpm0s3H+3f+74c08hw4y+IYVK9TUAdHL5q5dMNDM9gtC1rsfvCcM3/YkPeK5jprRPT2hnusEC+uKXNdbObx8TFubm7i5uam+zSIvEhJ5uRxcQ3aCplzs6Bh/l2eYy6cKeSyEMiZpUUNPunSI/aY58Tvncgq2RmYlf+fsrwQ0X8yxwaE42XFlhdPIp4VAoPIxHhCXYNy3dHE7LogZEvkc+oV8dMjjFZdtMMBgknmCTBUlSc6r7Z6L6OVs9/V69TZH6lD8PKCBQsnkAzO7dp2dhja7jp5Tkv9RJ/J2u+ecG1ySE16fhw0GU8v8DCWQwt0KBurINrmIJDV6JCKdjptpea+u1+eY0QDffdcZVXsWqf76XGgXZQK2JcMgdt+bTcsDPsakPJu9/wJI/44JOyb9jAnkJevc3Ly00Iur0HklaR+Aox5MekyXr6PxRVj5rWTXDtmjChpQb5ezHU2MZ/Pe69ddd8IttlGf4kK/63RjHTtFDkyZnXrR2rzq92YMDt7xPOCDMaJAsg1vPwMvYOAC/c2llzzwuBR2a4R+w1kue5l4s4136enp17tz/tCXce12kCJoFYoG3AvjJX2s3iZVZPr0HkhzQs+JhGPP4Erf2wPffX4W53Svojnd6jm2rivw7hD1ibxHPwAju3tTfldAUPpac6mVqtVj+yt3rBlSGNo5d1E6LF1KQYiYSwgXRNyLr9NJpPuo4C8D5vxdU2eDw7I7R+q0WY/9Uc5Ufe3uh5a/IJQHYxyeQqRwv0IOFaqDtRuP6KMxXE+u9D8YbK17XsMOL4VmpHueDzuPU0U0d+TBynznZ9RlJ5Uzo14Vj5Wb5QXAOREO7xpOuLjW50wqAz/zxNodU5QcdrofrjtWf0DyhT5/QeQbcSwokKJue6aSYSXY9OOvAfTpIDTmzTsfDh1/kBCQD8j4oOa7H7//BY2xsmk6+Ny+YDrMc9WrLkWmRdb8xgaTquzgs6Kydd1YPOiIv+HCHOQoi3YrvfZDtmo1Tu24XZGRM+enFFxnIUB27rY2sX9mZuhGnqG/+eSzZCN4gsu43kh0OUF5p058XgQnObzee9jtFC3zA/zkEn75OSk++DPVmhGupPJpPs0z4joDSjkkOuGJs+8OGPlNOR0fDnNiOiTrlPbvDshor+A5lTe181PLs1ms56y9eIIKshqgL9zD9ewbThe4HGbHFz8PwIXpRHaYEc16ZpMHBS8K4DzTWwEgaGXs7su6/PyCrhVlZ156G8fg+fd9uH7orTyfXJbray8vYnAk4mT8/z+AOafmiF18qFFVeZyqC5pvDRmuVRHoHWNNguR/BCISdeljcPh0BGxa8+5NGb/428mWu9ycCaJbVq9mui5l7NNky4+SPu9eBfx4cvnKR0y39fX111pswWak+7FxUWv5uiCvn/24B8Oh5hOp100x5iBlVNEP6riCMDGwLHA50VErw1D6ZdrSRcXF11NySmzDS6rcVL7l/Zz5lSSYESfTVwOPlyTD950Ddtpm1V3ViJ5f7IN3gSFA720W8FOaEdw8LP6oW2elxzwchkgKy8rWe7txVD+hrMPlTNy7ZkFM8pbzjwYP9ei8zZDrmVVnLMj5swvcvLYWwUz//kj17Ep7mn74lgW+WzbfOVtla6JssOC363Q8zUof2WFbtuBZJ0VDWU5OePkehYWlPf8MVqMQV7gzTXk7Xbb+5ivT42m5QVqLqPRqEu5iD6kt3kFldTn/Pw8VqtVp9xcT3RaY1IjEgJPrh3KdZ6IDx8ZzumYHYT3AUC4qAOnY0MFe9I8nCI7Gd+5FgTPeBB0MB5fn2u7DAHB5E+kRV2ZlHa7XVxcXPT25jq1w+nsHDigiZU5yKoMYzfpvgQTrYNfLk94wTQrY6ftpMtWPW5bVnEOTrZTyIc2EoSd6vIBpgRKAk+u2zpI549p95i6BBcRPXL08c4Eua+zOwe1bKOuxZq8Xeayr9Af+s9OihwYrcQzSdNG+6mDVK4RYz9+RNufZehxcx3fC+TOUgkQrdCMdInK3sKFYgCswjLYJiVSeG/45xo4St6vOLRoltVL/kyoiOdonCOoH4jA4EnF8msm82TnWm0mEkjEi21OwfwpqY7aHGvHoc/+pGPujWPQfn9xHrVMky5qNmcKOENebBzqJ47o9uX+mwSHCNeOyv2Hfqa+6vKCf6YdXgxCBRkmdqu0nFLj+BCuP0XaRIRdu/TjrOGlFxNxvhU018hBO9exrarz/DhIrtfr7gMFctnDJTQrXoIYawgEaD/kgdLkUfuTk5PukWDGzz7K+fZTRIb9GtLN/mfCzrt+AMGeawxlvp8KTbeMRfQ/usSrthHR2yvLpBC5HNX82U6oDl8bkmJinH55m0/ezuXyBgbNhFohDqXhTo1fWv0HQ7U6H2NHtOJgJZt7uc2+r0kRw8olDxObycN1Rb+P1AGJc14qg+S6XiZKq6ysTE2KuZRggvTfnMH4XOOlNNUKy9emf9zDO2b4P3NyPB4/SHP98hvmy0FhqM7qhyE8pzmgeY7y7h5s22NjYcIccyx/R+3ll9nQ9mzzZA20Bd/CDlDHtIfs1qrYO3UcMPBnk7VFg23VuzyYH59H7R1hhU1RorHttEIz0mUBx68LZICJxHwEiD/SI6JPDDmFyMRjhRcRPcNm8n1/yDEX80n1KCH4o4Fcu8tpGvfJq8Y+JytvKw2Mf+grk4Br4yzcuXbrVNNk5L7zgApjnB0812Xpi9s/tHjm8XFdz9cm1WPecr2PYxjTXwuTrFWj5yWi/0kiBHjbpckJ22McUMA5QPh+9NvB3bX0bEPYaU6tTdouS2HfJtj85SwoK0uXIzxGTrtzKYXvbjdPtLnc44w07yV2duXAYRXtLX6MEw+auDRGf7y9jUwNZc4iI/PGmFj42Vc+FZqS7nq9jvPz88GtO3mwIF0TiGu1wGorInoTbpVlo3qpFghIl/w0kVW3lSHIEXqoNu2apAMG59oAcw0u3y/Xse0stCcvKNnBGW+rA9rlckuuq0EGtCHXHE20rltbJXoByzVO1KWVtEnP9Udgm8hlCs91Vo1cw7Vs2svvDhBW2S7DOGjmYLZarbrjIV3XRX1f+wHzlNvM/X1PruH5MMFhh/iPSxNZgOT7YhP0zfczcaF6yQx9X/pJu/NiIv2wjeQSAWOKDzkADGWY+X0hftouXxs/cP8+NZqSrjelM1g5ylOwf3p66tXLuMYvhUlmiKQ9+TaCnLrwldNjXzenn/nN+b4m5ILDetM+xxLBuRdBx2NJX9wvq6GconuVnLHH4JgbDJqyi+vrJjEM14RmonWAyaoq4plsOS/PrQk6z1vuo+83dE4m6uzovo5/zgrRijATOmOS64hsryK7gIxzqpwVrVXXS7Vyj5m/ey3BgdP3w76wKy8qRjwLiLxjw+Oe/SrvfECg0HfPeVbD+Zr52j8Hi43NZtN7GAQ/zPdxwHH9uQWav0/XBu9U24qLRRyc1ulzjox2GP8tkwAG47TF236svnK9lrZBEkOLERgtk5jfsoWy4Hp2lPyWMdfFPDZWX/xucqV/VrZecLAzmDT88ENEf3Eyz6GzAhMYc5nHjWt6zIbq1UNlGvfLvzvzGSLtoRox7cPZslqyWmYenGabfHN7h5DTVpN2VvaQsd/zkMd6aJ5d9uB+fnqRefXcWslbJUY8q1neIUEgc9lpyB4ceBzcOZ82MPeUCU26/M9zMeTH2edNnPv9vhNsflx5KON8qbzSAs0X0iL6W3hcPzPpOp2GtLIhM4CeUC8iDUVOCNIvu2DzOm1y21y/xLhQ3/makJUf1/XLdkwWucSBcex2zx9QaaeDCExKdpxMPjYyP22E03mhLxtedqDJZNK968HH5nvx3aQCfKznzqpjSM3mvgxt7clK1GOTFXe+j491zdrK2MTG766NupSSA3U+l+P8tBnvNsDWh9JvxoBr2O5sQ7a7vDhs8sxZgK9jXyJIMBa0BV/Nu3IoXSAyOM5zOh4Pv70t+6rH1hmN7Qp/G41GXf8fHh66skKug3sx3NdpiabvXnA90w7hFH9ocYeJi4ies3BdRz7+5p+5BrDS9VYSaqheRGOP5VB6HdF/PJOgkN+fGxHd3tpcD7Nz8X//3aTrGq/76ZqnyYaxYcuPXzHISi4ljiGF7FVmHkqhXVbDVrmMidvgRRDbgh8Iof85BY14rrvm+0REL8NwmpmzlVzDZNxy2ukyhVW6U2Jfn+M8Ti7hOHgyF94ZwlyQcTC/L5Gv4UDjLZC5tHA8Hnv7tjmPuYGcsVUH3bwYmseEOWPOeaBiu/3p7Wb0x7uM8K9MiLms4J+z+rUf2/f8KlITuwWB58d22gpNSdcRhkHwEylOz00YTNxoNOoU2lAKmsnX/7dKysrUZMMWFPZaskiAY1gd2DABBu0HPSL6CsbOmCM8Y3ByctKNg4ndtTF/5TQNtYxj8zd/npkzBysc5sJzkB/eMMExFi79ePsT1+M+/J3juI/J2WTFIooVG2OFTVhRuQTkubd6s71llc55LgExp26jr+uF1iGVG/EcNLz9EXtibF1PzbsscknAGYf3s7qs8JKN2Y5tmxHPWRB/JzvKC8FuA/2yTz8+Pva2cjEmeXHQc5MxRLbuM6KNoJUFj0s6DsTOUnJW9qnR9OEIfzCdJyIiehuy7RSQI4NkxZENMpO64UG2CsxBgBVY0vHRaNSt8rtteRJJb6yIc+qUa1I+H7IwcZkAnQ1kZTdkxCg29tjmNDWTKPuXs4oYSv/cR6e7Q/3implQ6Q8O53tzLX/nPKf2VvgfOy8TTV6ccnCxbfhe3N+ZRyY0/s5OFK/cn5w8v8vYQcnEDNl5ES7XQr3bwu1wfxhPK3FvL7PS83jluXUfsVHXa+0P2BVrB9vtttv54zn2eJpsf85fPcc5M6atXhD0XDlrYyeSdyR5n3AL/C6kayf0JEIeebM3AxrxbNR5dwDX9wu2bURuh1M8r8j6o7OzMbqu9LHN6TYIzneaTn+zysBJrbozEdjhMTBvZMdJCB7j8bi3MGM1R8rJz1zLisRpqsc5K0aM2g8H8HE1Ef09tu4HQYNr0fdMLJnYvL3N/+dBBdrqLMnlnCEiw25cyvBYU/KyHZkUaBfZCXtc3TY/Vej1AsYkz6n3pzp78Vg6YHHtbPec66cR/dCGFbeDgAMafc12n/dZe874v9/hMPSEp4MiffETe/zNmQcqFy7h7w4Cnkv7vInW2V8rNC0vWGXS2f1+373Mxm9swphwfj/g4B0HRPTpdNq9/8DvFnU09A4FiMETGfFTfe3h4aG395AJwWAvLy/j8vIyFotF9+QcAcMpDuQwVB/NhppfIGJl7CjskoIXMnLJJdcQUe7z+bzrG/e1smJsvZXNgQFyxQlw2vwOCoybNnPNHCCGlOeQ+vUYOHXn+Lz4lUnaqpU5yGTkHQVW4Q6AZAROp8nAmAN/goRLCdPpNC4uLrovhMbhcIjlctmNmVW8bRY/wiYYf4uBrFJdBqN0ZsLLT43lWj2ZH/2H5LyA5pfgEKjv7+/j4eGhqxNzPz9sRNBhLQOb4jgrdC+MeS8z1/Y8Mz+cyxygwl/KdBnfT41PTrpDaUNOK3B2NjWbTJxKYxx+xyuEe3V1Fa9evYqLi4sucjNBfHqCSxWkQhzjdyRstz99QuhisegcBGO5vLyM6+vreP36dVxeXnbE7fPu7+9juVx2atI7IvxBkBixnS+iX1qI6NdQh1RbjvB2sMVi0bX39evXnbMfDofeU39uP3sc/bErVkVOc1kUgdB5mjA/Ams1zVwTnHIajdJ5adeBlXPenpaPs+LLioe2eiudMyu/ZhHH9SOpXmg6HA69hyEIRMy75+HVq1fd/1wz9eIuO3iw+1ySyeUZlw1sN0Nq1w8eOe0nYECCXH8+n0dEfHAu9oa988kSfB4bdsR1nFHY7rAB+wj3QATd3t7G8Xj8YO+t/cg7MCiFjMfjmM/nMZ/Pu/u6nv174JOTrlMDVJTVBgaH+vIb6q3WHBW9EorCWiwW8e7du7i6uorT09PuUz4hcr8oxzVQiODx8bG7Z0R0yvnVq1fx/v37eP/+fVxfX8fV1VW8efMmXr9+3alGNmQvl8u4ubmJ7777rtsCNJvNIuKnl/k4Akf038LlNMoKKy+sYFTe1+myhA338vIy3rx507X/6uqqM2gyDBssH9K3XC7j7u6u+/BLCNyEy5ycnZ11Rs3HbL/0Mh2I9+npKW5vb+Pm5qa3vSni+YU8XNu7Dpxye6xeOs4OzbUZm+vr63j16lU3h9io7Yu2mHjH43HPTiKety1h36PRKGazWRyPxy4QXV9fx7t37+LNmzexWCx6ryB0IPa2L5fRTDLAKTfBxCUjlwhymcvHcF/mJdvuxcVFXF9f9xS7x2y9XsePP/4Y33//fXz77bdxe3vb7ZONeFbM2BntIoC5tsp4MUb7/T5ms1m3MOftdbYv5oFAe3Jy0ntvCqQLh1jU8YCQ+epTouk+XStc0hbSCFQVn6oa0f88rIgPF9+YzMVi0ZELpPvw8BD7/b4jDtcsOddbfPLCBItnZ2fPHxM/n887tXJ9fd2lXaSaTrHoB4snEc+Ewke/4+SUGzxGWQm47ulUHyOxwprP550if/v2bbx9+zaur69jPp/3tp2ZqNbrdTw8PHTzYaNEPTHetI05dDaQFyhy/W6328Xj42On1lyK8VhaiXIsLwCnvzgW6Sokxty5tET7z8/P4+rqqhuTi4uLLi1nrP1gy263660T0C/SZu+2scpymnx1ddVlGldXV92LtplT5gEi8us0UXv87B0EVo/5bXe0zesLLjO4fEP7OYeS0unpaW+bFy84Z4EWEuVBJubHpQDX4zPp428OuChTPvCAoLTdbuPh4aETUDkDyBkx96Ckc3l52ZUzecUj59r/W+DEkXMAv9ljGhCg0x8TqgvpOaIPNiytHA+tglodcL183Y/dMxuM7zHUdisLp/tOz33eUHs+1t/ch5fOzWOSFzmGruta8Uuqaqju5b+99Hd/92KgHWao/fleQ/3+ueOGrp1r5bltQ/d5yYZeutfQHPzaefiYHwyN7y8ljjxWmbyy7Q7tdsjnZdv/mC8NlRzzMbmGn8fklyjSfM/cdnA4/PSyHi+U/gZ4sXHNSLdQKBT+g/Ai6f4ujwH/HH6p+gP/Tmrwqe/Rojb0qWHFU/hl+E8esz+D37ZAKd1CoVD47fEi2/8+b3woFAqF/1AU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDFOkWCoVCQxTpFgqFQkMU6RYKhUJDjH/m/ydNWlEoFAr/ISilWygUCg1RpFsoFAoNUaRbKBQKDVGkWygUCg1RpFsoFAoNUaRbKBQKDfF/ARDS0YFA5B4WAAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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TFiKQhEOQEYJOvpgwkFwhISZFEbx6t9AIhhefKJTRgQE5uvBxXwQQ5USRfS4Z82PeENcmh97v9xM54+nhpRKCE6kgtITr3I95z/Pa/OsKxedRHsaKYXHFZb295QjSca8J0svnFuRKzlgZt8uPpEJkwbNiEHu9nqS3UVev11O/30/jYI4ZHzKDHLgBRzZZf0iR/PlsNntHF7g288T8UfTDqCJn6AJGIC/skSqhOIrhQcbRFz6Pc0SEQpSSrxvE7GTKOjLvrpusj3/foz4vgPFZ5IbPIvcUQTEK/J65/9gpBUeppEuOU1KBMJkgwhLvQ2UB3DMkzEFBUQCsNUKJteM7Ts7SMVfoRORhoucVyb95DtG9dUJPPB/p6AGTB+SeGBhIBjBm/huBxpJjAJg/9/AolLgn6gTInDkBohT8+LVJrzDnGAuIg/wnQo/x4PpECnnLHPPvRRxXOp4LJcfQuUfjypjn/FhLlIqiFOkHPO48d8dYWEfWlGciheVrylojr6SUOp1O6hRhbt3D9cIo3hkyhiHE0HlaAs/Y140xuceMM4NX6nqH3HFtLxqSWkA+SSWgu9KxgwICnkwmyRD4HHpayvPUeRcL92btXcYwTnmR0HXfIybvaCGthwx2u91U7KOmgZEtI4/rKN3TRdm83xFBpYUs98TcK2BCO51Oqm669+e5tLxZ2xUcIXDyytMdwHNJ5NcQRumYU3YBpS0J0vS2FfdUPPRzi+5CkBsevBsPEcknQ6YoDmRAEcRTIv5DHhrB93xcnu+FCD3dwzqgIBC3K/up8NG9S57V7+PKR5cLZOikTWFmvV4nA8Uzu0ftrYZ5zpH5Imd76lkwalTRCVPxnvI5I7rykNe9auSSdXPvbb1eJ7nhXkRTHp67fOSGxKMDjzZYT9aP8XqfbL/fTwYIvWNcyACkm+dF3dD6WuMJs4ZOqj5XrD1FQgjXayPIoPME8oAxo3d5OBxqOBwWOndY23zePjZKz+nibSJ07vb3er3UM0k1GUEjfGm32+r3+xoMBhoMBgWLScXZc2coredt8N5IGxCWe+jpHgeeHQn43MvBeAAP4SjIeIjqYb2Hy06qnjOUlCy7tw/lXhNK5Vafwo/3M7qH5HlOzytCdF749H5iL2pivMiRgVO5dZ4TpXQDA+ESwnsRkPvg2fpa8rxO1pvNRp1OR5IS8ea5RJdHV0KPingOj8qYF78OwPsibId4kUOXfW9d4xoUfb2Q6evKeDCk/kyMOe9M8WKaF1S9Pc9lE28ZWcuB40DXBmvluuYODGPz1A2GxMfG87tse0oEHct1AMeBucHo4pj1ej0NBgN1u90C2efyXxZK7V6Yz+dJoAmVqXyenZ0l8rq/v0/hYj6hbBigJ5BiA+GjW/PcG3Qv0fPGeUECRaJ1ZjKZpNYc2lg8P40X5ATj9/biT16kycNqfk+oijfkqQEnwrwv1p/JC1I03EOMCJ4LHJFEnofLnyk3Dl7Q8rBXKlasWROu5+kcv48rA2sPyfBMrDOpFM9pIm/b7Talb07dh/Gwbp5r9CKUk5/nkJkPJ6fcs3Tvmr9jPGi/w/gz93lxzceLDkCWPiekpjz/nxctXSZPpQD4yQ3cqdSdd5PMZjNNJpNUWHW58mtDlqxRXkTM5ccdEOSXlIu3+nEd5rrVahV4otfrveMI0XqYO2UfG6WR7nb7drMD4QJtV3i7CAmeBFtPc+Vki2an00lVZFd+4ATgVo3F9XA/n3AKd4vFQpPJpLB1EtJ1qy0dLbl09BjcCrt3C2mhNJ6r9dQLQpSnQlAaD/m5HnNIKOY7qTqdTvLE3EMjR+3pAM/Fojxe8fcQjs+gNKfab3KPxgsjnn/1MNplApJicwN5fPeKkReu6Z0QuVHLw3DkIQdjy58TeWQNz87O1O/31e/31ev1Ct0DpEa4jqfV3GnIIylPTyC7rIunsng2z6vjqZIPhmy9RZNn4Dk83eO/5xmQAU8xQbjj8Ti1s+H1Onn6d5lDT5e4t+06IhU7W4jWdrtdOjYgj6a8GHx+fp566w+HYxEY4yCpUJsoA6WRLpsjut2uJL2z5ZIwGA83J2LAxOY5UA/RWUwnVPfOEORTVXBfQPK4ED2E6593QfXOCC/sncqfohzSMdz01i3Glefw8u4LF04nAdqYIIFut5uMlBcoeV73mMjr0mfsuV7CUveIvOgJmbtH695VXkTyDhHG49fmdxT2qD5Dut5c7zIFQbNueT7XQ2BkkOfwkJj/5/k98nKnwb0q2seYE3KzeHhEdzw3z+kpnMPhUMiTIjOnogknTI+SWL+8EOqFSZdflyMiR49ivMccz9Zby8bjccqrOwGe0i2XW0+dMMdeRMuv48bPuYCx42h0u93CDkQ/T4JdfZJSTroslEq6t7e3SWHJW3lo40lw3xJISEOxBI8Zb8ZbWbzIQ7jpoZRXc71YAtwTcmGjR9I3O7DAeKVO6tIxNHIv0AVGOpIPB5BIx1QCgsJzPFZY5Nm8gd1bl6imuycM0eQ5My+25TuPaJRnPbgexkg6Ko2Hs6fCNyddPPBTaR9+5+2FTroYCcgDTxxP0outkJ53iQDmIG9p8iKfF9A891mr1VJf9vn5eXoe5iPPG7v37HnYTqeTWvWq1WqhkOjnUbghgxw93cBGBZcpPE/IxWXUPV03OjmxoQu+ocNTCq5v/myu06yXR3EecXnR0Xd78oMxYuu17zzz6AYdJwrYbI5bqO/v7/Xw8JBqQJPJJMlGGSh1G/CbN2/SRLfb7XfyiYQAHiJ6HtMP7JDeEmS3200C6iGPhzhO6JCk74DxHWV4aQgigkNoRgjYbrfV6/UKXpKnCIAbFZTFvWQP/wkZaYhnXk6FdK6Evj0ToYPM/GQuT0tAjJ4qOPUcFAGZJzZE7Ha7gpcKWbuX4pVp9y6ZD5TEe6HdG/L5Y0s23guki1EjP5gb0TxkxljkEY/nxX0emBcvuCBrkFSv1yukizxNA6lQwIGUIH6Xf56z0+mklAo563q9XujIoS+b5/B1IMKB/Mlj8j3mTjp6+b5Jxv/fU3bICnNAwds9cvQmd3T8jAz0zh0kZBmj7nrMfLNZaDqdphP07u7u0tZyl2HG4EZrNBrp9vZW9/f3abfm4XBI54s4X31MlObprlYrvXnzRtJbRSAUBPv9PjVz+040Jysm3gsml5eXqYEfAeXHld7zwf1+P21aoO9yOp2mzQgYBhdEBMLDS7xDSNNbbzwt4mG3V7aZCydNzw1D9OSeCes4S8JDQA/V3cpD2L4F14thrmSe1/TCCbnth4cHPTw8aDqdFtbNw1TGzd98zphbT0VIKpBmblg8/1qv11NFGuKAUKTj5oU8t5fnphkbRMr46WRBZpyAkSGXQ3B2dqbJZKLz8/PC5gw3rnjyeP38N2Nzo5HXCvi7pwi8V5d1xOh6zYP7QpRed8DgILf8d+4tIn+QmP8OHcuf02sn9MmS60bvZrNZej7foo/c+Q8eOhGzH+EqHVM2yAqpJ9Z6Pp+nMy44UwQdevPmzQ83vXBzc5MWkgqkK9V6vS603LiSeA+h56MkqdfrFQQAIfPvI8h4qMPhMPXSTiYTScUDQ6TiBg7/l2eAxPnxVhbGjEWH1Enau+Dm9+K73tS+Xq/TYTQcFCId26EwWH4Nrw7Tx5t72b6DyMmJ3N90Ok0H4PgRiO7JecGFeT9VSHTj4+SBwLMLyw8swVva7/eFOUdxIS4nQy/mucGUjptT3HP1tE2e6/RiDmNhbjGunHXR7/dTSF+pVJL36TKD1waZ5AXIvA3Q1xMDyHzlGxkYJ86C1wg82iFiwZCfnZ0V5gsjTnoJOd3v9+l0M/coT+kJ8oExQOf6/b5qtVpKM55q+8pTGoyTFMHNzY1ub281Go3S+Q2eKvOUINcnLcnJfsjydrvVzc3NO+mmj4lSj3Ycj8dpeyTeLgd6sLic1erhmpMPRMLuMG8JOiWsebHN29Q4UxMC80Xmmp6j8lA172DwYoSHrFhYP4Bmv3+7uWK73abcK0Tingljcm8cwSFMJIT0Z8douGfkYeSp4/O80u1EPZ1O07GLruB4VCigRyO+a8rH5WE84/L2InKmhKsYJ59/7/bAcHiYzfqRcqILhjU91bedk2mtVkvP6rKD58zaoaiMG/n2Dou8GwHCk5Ranbxo5EVIz4syt5y9QHqHtT2VrnNDg0HCcJPT5LvD4bBwLc+Te6Th8o9eusHF6XGnijE5kRPR5t0hbsTda4UrHh4eEnH6jlM3bJ5aIFr0rc2eriA//IPqXvD8Kw+M5fRigFdkIVjOkmXiPO+C1Z1Op4lQTvV0enrBiRcvAIFnUb0FizAJj9jDTU834Cnxe/dWZ7OZbm9v9erVK41Go/T8KAkFFO8awDvmmfE47+/vdXt7m4QEQ0VO0RXFCchzpZAW7U1eaUdQPRyl+R3PhHwj3gv5VU9FQJoesnqBjf/G2LGuecjL87iBg/xyBeW58HzdQ6YYKR2P42R8p1qlGIeTGGNAJuv1esoJVqvVwmYB3+mVH0+IwfC8JvLnsurG04tBkDt5f5630+kUzgPGG8abxHjiANze3qYUSavV0nw+T7lhzpHwMXg0gF7kuuB1B0iX9fS8L+uVF3ZZB09vMH6MlvcBkwIknYJMo0+kiryo67zjdSDvtvnYKH1HWl4RBe7JIKR+NCKL76HJdvu2n5fJl467nQidXZDzkAhFcML1zQaucHh/+fc4gyDfIMFYVqvjGbUeDm232+Rtcs4rBC+poMQcnUg+ihPE8Na9oOanc3n4CknhbdB+5a153kaFguMh7Pf7QqHC23G4l59uxlq5UnrrmG/f9DCYaIeeU8JTL3y6EqLUFJBINbjn5CkFiME/48Uvdwj83AdODWMrLKRDOgIngPmC5JkD1oQw3+UH8mYdpWOHA4TFs2PYKB6xLpwKR09q7t3TJjUej1PHAZ0Y6Fun09HV1VWhiOkdBci6zx/P4/l4P/eDZ+bv/j33nNFx78X1Aq7LJB45ctLv9/XkyRNdXV3p4uKiUEDECDkPOfd4jaMsfC8H3nieKq/eQkRMmjeaQ8x+bCEEzHUJN1g89wDJX0JWnhf20MhzSyhtnu/FUqIALLS3MHmOFE8Qr5P8HcK4WCwKaQK8TPogCQkpAEhKljw3WF5kckvP+FHmvNiHcnkul3QG+a/ccEGcjJXPQe6e6/OiqOdiITw8HI9EvA1KOnqqfBeF4qBxz9cSxXhhlX8he+TKPWOKMHiTrpCe8+T6GAFCc1r1OLuAe6AH+fx7VwPX5vndKHl0hScLabLVFdL1DgUPpfMcKs/mXUF5eM8zeoHTU3C+pj6//N3TTBgh5gqdlJRkwfvhGRcGm/VGznknHe+VI32J3kpKHr07ee5Ze5cGz/wx8dFJN688+2tLpGOOlhwg5EhhCI9QOuYoK5XjAeIeEiMokK6nB6rVaupcoGGeqqkXkUCezHdv0UOuU+1Xef7XF/dUrtoNEFYXMvaeSEJXDIuTKNfxMNmr5S64/uOdIB6+ksqgxQavyL07npNCBakI7nWqwAZpuDHgWsxh/vodyJcx8sxuIHNj40U1lB5SgACcHKRjRwMpHe7l7U8YRtaFwmi1Wk2GBOL1lyh6326+BjnhORG4LDlJM5cYZ2SEnDgRlRcR/dreCuetYi6zrk+eqnJCRRdcTzy64nNEn97Xvl6vU18yxs27MNwIepTkhb7hcKiLi4vk6bLdl44FUgce7UjHFAcePvJVBkrzdJvNZpoUFgEBp/fVd8ogGJAshMBnpKPSeKiJsCLkkFO9/nbnEMqA5+QtVnku0IUm//Hfe+GO+3ne0fOG0rGi6yG+h1SeBz+lmH5dLwLxN37vxRTIHG8BYyMdDxDxlAY7jGgTo3hTq9UKR/uRm4bQvJWJcZAq8LnyroC8WIXhxVP252Ve/F8U2jsnJBW6H/wafm+/pofJeHOQKZ6eRwL+BgeMT6Px9nQuTt/ynmJSF56vZ12J1giZIQT3sn1+csJzOXGS5bN5pw0pEAgvLzz7vLl8Pyb/7rCcWk8v4voxmThMdC3hJKHHdNK444OukMflDdxEFqwF803x0l9eW6vV0jGtV1dXSRfKQKmk++zZMw0Gg4L3RdGBUMFzqijF4XBIu4t6vV76DvkjJyS8MYSNPJ0vEukKr1g6oXqo4d6Gp0Wk4rZbv6d7Vrl1pVjhpMvuITy7SqWSPBUPsfEUEJpcOXMy8R+EUHr3OMA8n8UYUGaIhjF5fpt5cCJE8T2f6Z6ipDQnXg3PdxLloV+e/3MPlLUkSnCvH4Xid17Q8UjDc76QgRcl+TvRAO8P2++Pr7NxEs2J0a/j6+VtZfl7/3y9PP/sZwTnnQROdPwwL8hau91OBsajTvdm8+KsF79cB3IH4BRp56SME0QKgKKgr2mlUjlZZPdzOHyLOwZLOkbFGEh/4wZrz7xh5MtCaaTbbrf15MkTtdvtdDIQngMT3+12C2dz+qJ70zeeFp4yC+ML656l79Cisgnhs6uGBXchwUvzxTklPHmemnF54SFXGgSfYpTvYvIKPtXk4XBYID0sNSTmXo4XErkuREkY7ooOcbvHe3FxkV40OBqNCrk7nm+9XheOtAQ5qUvHzSnMQZ6/PEW+zKl3MuS5cjeUEL4bKU+JeHrBK/AUknJDlFey+T3P7oVHSamgxVujmT/OvGC8jJF14hkgEy8CebTDPeggIQc6GAzSvbydztM7TlSdTqcQevNs6ANdEB6tOInnOuDdQp7W87QRHifv8+PZu91uIm3vMuE7rJl3wmCcaHnMC+6e9+bVQxQPSaf4pib3vstAqTldjmJkAfCg+AyVVfJSfgALoQlkRa+j52ukYxjt22GZYC8yEEqzw8o9bFcCD8sYiz+b51CdmHzvuIdNtVotWWjG432mnhKAWL2HktYgFIK+XicC5ts9OVI1kEzeb+lpESrxo9FId3d36XCQ3Lh5KJ93ong47/lsJ3cPOd0rO5Xr9u4X98ydIPO8KKGm56+9L9bHxzPloXseHXgbmXt45HEvLy91fX2tq6urdGg2xoa5Iw0BEboBpPPAHQrWFgPi27upU1xeXmo4HBY26eQ5c4x4t9stvIqH1Id3VBBBMjceOTjp4pigYzgY3jGAx8muRvdKSb9wD9cZPHGuBbmzTp77R15IR3ghmlQP9QZJSZ9xwriuP9vHwvf25gi3ZPzdwzZeqeGhfq6QLiT5LjBJ6bOcAuUbCfBwPezwEMYLIuSMvP/PLTv3cW/JCw5Ul6XjO7TIQREOMW4vpjGG8/Pz9N80hvtxdHlO1T1eD+mdiHPFYZ7xAvCuIQ68BBSxVju2pZH3hMhcEb3dy4krvzdziAGA/H0+8nYf5CJPC3mE5J/JoyHu78TCXDpxQABOZHhqfB+jjpfLwTzeDpfrA0YPnfBUSp5/h4ArlUoiau6Jp0tO04uoPDOyjWfMWuNw8DfkyeXRPcxc5iGu4XD4TleDpwZwcpBd30Tiz41D5q1x7lDhtPiYct3LO3/QddYz7/rJI5qPjdJIl4lnMfyHhWWzw8PDQyJdVwafnNybdAWRjsUhmqcJQ7Cmnr/zHDOGAXK8uLjQ5eWlLi8v09t9PTQ51UvswkqFtd/vp9DQT4CCvHxXnOfwICwXeO5FccuVk8N73LPM86/MH0bPCcpb41Aqwi+viOOxsSaspadFPHyTju8Agww8/wnRcn+u6x0PELuP0T1pjzhYB368PQ9vzJ8bw4QMOVntdrvk/fF393J5Xi/KetSDwfE2Npd71xHmNJdtvodsQ3SkMZzkSWG4lwvwCvk+HmC+G43xSO8eJE6Kz6/vxhEQPXr0x/Oga8wZ8kTPsreoeb6eKNUNt19/v9+nQjCnkPF8rFvOP97BUwZKO2XMNzLkD0jBBgvlnq6k5J0irE4YHjZyLVphpOM7r/C4EBQXEve+IBu8vYuLi9R0TajovZj5K9q9UIIQEzpSCOR3Xi13QfKcsverSkdPgO2tHnJDvvSx0meaF9XwhJxEyCcTCuK1OMHlhOZpHU8nQLi+vZn7OfmwXvyLJ5MTEd/1Nfe8t3td7rXzfTfQPBty5dFN7mFXKpVEHC5jPm6fF9aBz+Y77rxnGALxPDnznRcveeZT6RPSVPS2u8eYp6o8zw/5UGjisKd8owzzTPTCulKY9ZSDG0ccGebSDaDnwtENCswuhzy7y1hufF3u+awfqO5vT/Y2NmSDTT2eJvvYKYZSX9czm80KGwDy/Bm7XggN8JDoqWXyvGiUh56+GG6hIR8Xcm8S5/tOdnjIXvH1c1r9QG2eEU8KIiUc9AM/2LSB8lHUQJDdW4KE8NqdhPNWHsaAR0qhBa/a88dS8SWQXAOvG88ur7T7euZzRthJwcb7LcknM2ZkgPGiUJ5DBW7MPOx1RUbx8iIP3+fveJ7IHwTmc+oRmHvKTsa5Yp4yfCizG2SuAVm5w+HbeLkGKYV8vb2w5cVI5tTJG9nhO6TJMCrIxXQ6TUSEDOOFMgYKWJ6Oc7kh+vNDmXxuvDfcn81TLcgm38mjDr+WrzsdTX6gOqlHXzfXV0jXO5k+Nkp9Xc9isVCn0ynkfTyn4lVO3thAOxD/fpdQ4LGcn1tiOgv80JF8YU6FrNKxbcWFhOtymhEH3Gy320LVnjDUvWvv3cQD8WoqAuX/790ZpB7y3BbCSlqD4yd9rnMv6hTB5OTuHgthon8OEsBrYo7wzjBqPt+eB3cPLW9JOlVl/q7fZT25r8+bRxM+fjd6kFlen8jnByLFQ/O6RC5XrD8FP8J2JzT36CFsz1czB6wZ9/ZQfr/fp7nzqEBSKmb7d6TiG33zjpNTG4p87b3DBKL0jh7IEN12PT1leP1Zfd2JCCBnCnbe05+nSbgOhE2RuCyUevYC5Jbnfvg7uRwmYjabJWEkRPW+V1co/v/Uv9JRMX2XFyGIdxigkL4HHCIkXHcvj98fDm/fv/Tw8KCbmxu9efNGk8lE+/0+hdkuAMBzy4vFInl8EC/K6TlQ93Q8xMtbqlAQb6/xwp0fqeg582/Ll+IJ5s8CmbMe/LgSQbooMPfyOYAwPNXiRs67LTyU94q7h9QQQE4GLj+MnbH6+D1V4XLrxJ2nOZxw3PixTn46m6TkqXkeNzdizWaz0NPuaSLmz71AP+2NufbaBc/A5zmBjAOkkA0+xzsNPap0b9oNvKcwuMfhcEjnkKB3g8GgUPgDj+lxTpp4qsyFO2y5M5Vf2+ftB5fTzeFeSV404XcI32KxSKEoiurHBkpFRSH/lId/bm29sumLQxiYpwMGg0HyEAjD/XAcen6327cnIN3d3SXSnc1mSVC73W7y9B8LA1EqT3vkYaMTYl4c87n0FiTSIv76HSIMSMu9qzxf6YTuikTEwhq59+55aeBE4uNH6UnP5EU27omnS7qJcJLPIjt5CgCy97cru6fuspKPO89Deq3AC47enueeWj4fXpxkLBAG6RjfneVrmqcXfP7ce3OPFU+SVs38OTD2nEI2Ho8lHb1fb8Wq1+vvFCTRR+n4Bl5CdkJ8z8t7+nA2m6nX6yXdPhWhPqbXGNz8NLX8zTHOM77u6N0PunvBvYL891LR4/FKvOe9fKJdmBH4/X7/jsfnhORegC8KQuE5r6urKz19+lQXFxfJu0VwWFhypSygt6lQnEDJXAkYu1tX9xw8L42XgOAjiC58WHjvTfbP+g9hKdeUjl6vV4lz4+ahswstn4c4WFNfAxd45hkCJZLxinUuI6fCzDzlcupzPK9vDGAtkSHP/edGIU8V5MbeldlJGhl18vaUgN9DOr5Hjd/7fSn+kfNn3fIw3OXTyXC/3xciC8boqS3PIdMtsN/vC7l5vHTG5MQmHVsN6RBarVbpkCYcD+aMMXrBjWfxufE5yg0Na0iNqFqtpnnPuz7cuOYpCi9Wl4VStwHTmwd8MrDITr4uvJBmXs2FzPkM1XNydXk+ViqeV+q5OIjAW8WGw6EqlUryCHIlRBF8wf29T77YThancqAYmNzi46nk4RXzgaLzdz9YxD0bHw9AiUizQIyr1SqdueBGzsNLL6Q5+biHjaJCyKwvZOyhOflu5MLzp9yb8eJd5+kE5tA9/VwevAjrXr17q0RXTmzIYO6BIsu+4UY6vtUDuapWq+lt2MBTEL5W7hVixPP2Pnc4MF65rENceY6ae/PsLpvuXDiB5bLPmlar1ZQyQR6m06n6/X56h1kuv7lhzWXdnQfkwL3uPDp00nUyd+cCw4E80P6Gs1YWStuR1mq19Pz586SMFMrciqOM0jHXmXtUvvOKVIAXZSAdPxj8VNhNvyyWnVA+z+Ox2K4UedHEvRsvztE9wJi884Drsj3Zd/IQytF6hcLy7HgO7qHmW439DQfe7cEzuTGiAMjh2BAbeW/fLuqRhHeh8IzseMrfQ9doNNK8oAhu7JrNpjabTTqBiuv73zHOeNaeL80P73EP1/PA7v1Dqr4tm/FKSmEyJO/eUB5J7ffHXXzk97leo9FIm2vyZ/cIDLKA8Jh/GvwhB+SR5/Xtt94hAgnjSNAz7Z083v2AXOYpPhwdj1pywsdp8C4AdyoYq7+pmEhHOho11prtwWdnZ++k1XyMvrGFCDHfmcn38Ma9A4k2Tj8n+WOjNHrvdDr6yU9+omq1Wnh1BvkkJys8R3K6CIekQnHAixJ+Sld6OCtUsFgQdb/f12AwSDlBckPr9Vp3d3f6+9//rt1up2fPnqnf7ydh5p5+ZgJKM5/P02Hj5MY8tHKPw/NV/L3VaqX8mHtpGA4a2T2MxGCh/M1ms3DQtHTs0XTiR2lXq1Xa4bPZbFKRw3freWO5dyDkG1D6/X46au/y8jLtuHPPCSPhxEl4CIGwtp6KyAsgwMNR7wrx6zkR8Tnf1YSBwkiv1+ukhD5ecpqem0WpiTioGUByzEm1Wn3ncCM8arxqz91zHXZT5QfJkzqo1Y5tXKfCd2TJ3xDM+rF2XpSD7JnrVquVahsYKNIOzEVeK3n16pW++uor3d7eFtIPp3Zjkos9HA7ps/4aLuYDp8b13yNh6Ri1eSqGZ0dnvf2TnuPdbleIwD82SiXdn/3sZ9rv9+lQG0hjMpmk14jgWXluiSKDVDxTlzTA06dPdX19nSaOEG8ymaSQjw4IiI+DSTiL1MOS9frtSftv3rzRs2fP9KMf/UhPnjzRcDgsHCfHBg4UczQa6eXLl/r66681mUyS5e/3+4WCBt4vHhmKz9miuVJ7wQgh9fmDZGq1WiESIOwbDAYaDAapewEPC8LY7XbJ+PE6oNvb2zR/fJYx4TEwVjzF8/PzdJj09fV1OguAZyV/6GkYjpLk2Tw95Lno3BuEtPMWLIxYnqvF8Hh/NRtmnDyZC4p1nrpwp8CNAWTFM5BKu7q6SsrOQduXl5dpO7qkdC1Ih+4BIjzv28WgNBqNdwpYfng33yMFx5xCNnwOgsZL5JVQ9/f36f8rlYqGw6Gur68LrWLSMdXEm1Fev36tb775Rq9evdLd3V3acCEdt+Sjd2zmYAyM+XA4pAPg0TdJ6bVXvHbdN60A5Mnf58eO0MvLS11cXKSt0jg03W43GcSyUFp6od1u68WLF8kj9EMtxuNxCj9ZrHxjAaGqC16n09Gnn36qzz//XC9evFC329Vut9PDw4P+8Y9/6Msvv0xeW7V6PBzbCxHuVbOQ1WpVDw8PqRNhNBrp5z//uVqtlq6urhKJ9Xq9gofw6tUr/e1vf9PXX3+txWKRvPpWq6UXL16k1ATernTssSUVQIjlLWF4spPJJM2fv9GU65J7xgOBCBE2Vxjp2N4lvTVm0+lUr1690mw2S8/NOnBIj7/t1kmSMyrYMp3v4vOinb+Zgjex5jlnT8lAJHnx040BoXzekeEtS5w7cH19XTgfwTsaVqtVgfg87eUHIxEOU/wjWuM+q9Uqpb6ur6/14sULXV9fp2q9p7DcsCyXS93f3xeMK06Ip9+azaaGw2FyJgiXSbV5MdaNuMsAcoXD8M033+if//yn7u/vtd/vU5vjxcWFfvzjH6cis7d5YTy/+uor/fWvf9VXX32l0WhU6Dd2Q+o1jrwOst/v1el0dH5+rk8//VSffvqphsOharW3b4v5+uuv9Ze//KVw3oo/Aw4UW4l53k6no6dPn+r58+caDAbJyBLxID/OVx8TpR9ivl6/PQ4QYSaPt9lsdHt7WwixWJj1el1o5mYREYaf/vSnev78eSIuPEssIxYUQmMXGTklFNcVFELmEB5CLk9N0LuIVcVbhBzq9XrqGyR94cKPl0To5TlXCBHF3u/3BS+VENsLQJA5Xs1gMEjkkp8PwXeJGK6urjQajdIrst2rxNi5V+seJgoPwROO+g48DAipBozoZnN8FxsFF+lYDccrY469yEZoTXjtbYWkS6RjcQ8j9OTJE11eXhYOVYKoVqtVoUjnUUOr1UoRGPKbv+MMb5d2pMFgcPJ+j60Dc+cH23vqge85ofruSYxUXigkyvJilBehcTzw2InMSAVKSiTlhSvSaP7W6DwS8Hu73knH18rTokb64ZNPPtGnn36qwWCgWu3tsY+t1tsXaN7c3KQeeOnYMXKqW6FWe3tO99XVVUoVeu82TgROUBmovKdH7YM2sEEUfk+vCrug5BV8fhynWsO4phe+vMru1/D75vfkvvyb38uvBQkgrH6tXOjyQp2P5dS9H5sjxszf+byH5Iz11P1O3d9bwbjXqTXICcP/7nNzqnPksWfyboj8uj4/j8nGY5/Nx+5FrG8bm48pl49T8puPi/vkPc7vQ97JwP25lz9rvsan5Ntx6v7cz4ut+bw5Yfoc53rmJH/q3vn853Po0VOey2eseZ/xqXXI7/ltc8R/f4SWsUcXu1TSDQQCgf8meJR0S9+R9j68xwgkfKjcy3e933e952PX+xi5olP3KmNeysh7/f+CMubpY67z++7zXe/1ofXou6BsrvhQCE83EAgEPjweZfr3J/sCgUAg8MEQpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRJRf8/fK6WMIhAIBP6bIDzdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJeL/AeMwyKNJeJOSAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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HY0kKXlkikdB4PFa32w3639bWVvAouT/a3dHRUZASJpNJIELu1263dX19rXq9Hp7VGgoWgiUApAPGkzGlr+nvbDarbDarra0tzWazjfrzpn/J2KPvcU1L7Da3AOlavTOa+bbGqNfrqV6v6/r6WltbW9rZ2QlJvt3d3aB37+3t6ejoKHhRGFkkJPT4yWQSPH1JwXBKCnkPchx2TpAksvPHSlXM13w+HxyTdDodSIl+sJIbzwt593q94FHSB/QxfRuNzjKZTDBmP1TbtcDw2utZuTCah4BobRQRjW6tpNftdtVoNIIMN5vN9Pd///d/dnt/KGIj3dFopKurq7CIbWhgM6p4LDYUi5JrVMsjKyu9DZui+CE6LmDC4m21Wi3t7++H9lljYds6nU5DQi2Xy621x052Jhf3YnEul0sNBgPd39+r3+8rnU6rWCwqn88HOQMykd5mm23YDqnk8/k1Dc9qfITKhHPoacViMSz2nZ0d9fv9d5Kg9Xpd9Xo9kLgdG/57U4lY1Ku1P9YDxbuLEnlUh4yWLvHc9CsEAPHaMiOMV9SIRrVXe32iNKKGYrGo7e3tkGDF60P35DlsmG+9b6IKKiWQFxaLhYbDYVgPRBmdTkfdbjdEhxiR4XCofr+v+XweEl61Wm3N87ZaO4QVdWJsAs1GltY40WZrCPr9fljPf46zs8m7tEk21jhkjFYb/V50PK0MRduI1CSFyhMScnEgNnmBLKe1rplMRqVSKWi3kIWtrbSWlM7E8ysWiyoWi6H0A53MhhfSW0J7rH2bYAdzMpno4eFBFxcXKpVKQRflfniHEAREZ7U0JiJWmMm+tbW1ZqEJ9/E2ms2mJKnf76+VKlnrv1q9yWjbMJSSOSYshMZEZPFPp9OQrIB4IWvb70xUkhgPDw9qt9vh+SxJ8rxECY/VWtv6WbxTFrfV7DHQkLNNePA7u5CIDuhfwmmy/ptKBmkz84ixgvQg8dlspna7vebBElqXSiVVKhXt7OwE3dwSLnqxNVLor8wJW/mBpINUMRgM1Ov1QvIKiQYyHgwGYT7s7e1Jeuv9W8/UylTWGbESxWq1CgnEZDIZvHaMNo7F/f29Li4u9PDwsObdvs/J2USwm9YInqpNQkoKz00ewsI+L2OZSqWUy+VUqVSCtMa6nk6najQaoWror0peYLJSb8nCZsHmcrk162qTO9EBsB5FpVIJITSLkw0XUUGfzrSDK71/s8Rq9aYOEwGecqNqtRoWIvWgpVIplKEQGhM6QhS0z5azWc/K/jd/y8Kn72wihnB1NBqFRW5DTbTHqCGjj6Q3EsRsNgteW3Qhkogi01+v19VutzUcDrVYLELUYY2bvV90QwnPjDEgYkkkEmtEG63sQGe2hEl/2aqW8XgcJAp0QeYOBp6IwoIFa4mJe2AUSH5R/8zf2KiCZ7UVKf1+P5Alfc46wOPmGZbLpYbDoYbD4VqEguZsdVtkCoiQsWMubaqY4Hs2CSlpjVC3tra0t7cXauTz+bwKhYIkhTIySu4uLi5CbfyHtFw7z+0atLBRARo0kUA+nw8lgmwg4l/GCSnMJp7z+bwqlUqoL0aSmE6nQcaLC7GWjPV6Pe3s7Ly58f8tfqYzDg8PQ4g0GAzU7XaD3oKHwmBRvnJ8fKxqtapUKhW8HGokrUYXTaRZr8lqRVEQug0GAzWbTd3c3KhUKgUvirIwvG3uKymQLsk1q/1SAhf16q1nRZkVYX0+n1+TFyDbfr+/VmdImzFeVt4gIceiY9Lxt3YRspjJ7Hc6HbVaLbXbbfV6veBJ49FawrIhqfVQouNgvXDazsIfDAYaj8driwEPhmvaEJlnYa7ZcaB90tvdU9L6LjCuab1j+xxWAoMArVFBiybRiuePBz4YDEJNOobGaulo7q1WS6PRKJCFNWbMGRwVHBDmHfIIpVaMp62Dx8sn42/rW9E80fx3d3clvdVxbZTU7XZ1e3u7lkhF2/8Q8VrCja7L6A/Sy87OTlhjlGyS9LTXwTkpFAoql8sqFApBmqOayEZ6o9EotDsuxOrpdjodVSoVSW8WAxIBpT5kt+/u7vT69evgSTFZmKT5fF77+/t6+vSpqtWqVqs3xfaE2CzI6I/FhyaGTbr0ej1ls9lQQkPSBC+KsFzSWnshV7LSeBl2E4RNHFmJIZ/Pq1qthkmCgUKjRFvFu4A0CBGXy2XQ9SSt1T1CSnwXz9fKHhb2mSBriMKWdrFQbAJjUxJR0hrJQXR49daLARgn+sqGuiw8rk17o56qva/1Um3+gKiJttuEHL+zyUD6wxrc6PNaI4dRQyKDuEkyUnserYigvZBKuVwOkQ99Alkjb0CS3NeW5rEGMUjMz6jht5EKpMycbrfbajabajQaofoiWom0CTbCjP5dNDlHBFGtVrWzs6NE4s3usul0GpLxNtGHI7Ozs6OTk5O1umyiFJy54XAYpJn3OV8/NmIj3cViEbaNEuox6ahpTCQS6vf72traUq/X0/39fbDA0lvPiEHY3d1VpVIJE77dbn8U4UYztu9rMwuh2+2q3W6HRMZgMAgGg3ZBIjZ8Z0EyMSBMPJNisShJa94qWne1Wg0kQDhqdb/hcBgSWdZbgxDxvCqVSihsR0qQ1jeNsHDJGGNIbLvRFKNVBdbQ8bx2AdsxBCQ28OhZFFFdkTGg/rpUKoVEEZUaGB++w3V5/mKxGIw6hpT727635AvR4sHzfPQdv7dS1/7+fuhnIgzkkWw2G8apWCyuPQdGEg0frRyihIRYN+VyOZAqxjqbzQZ5oVAoBO3Stns8HofNO8w/K/dhZHgu2y+0gUQbc6HT6bwT/byPwN639jatW8a+WCyGDR/JZFLtdjvwRzSSIgF8cnKi58+fh91ydrcj5Nvv99Xr9T66tvjHQKzyAjtcGHDE7XQ6rUKhEBYJGykoDEdLk9YPlkG/kbQWSlnv4zF8jGBudeXhcBgWxcPDQ/Am8HZtQmxTthevDF1usVgEgrPJGlvcTyjKLjXCftqElod1x2uBcHZ3d8OmjlqtFsrKIFsIbblchnMV0HUlhZCMUBnvxoab1FNiEGySA2ODVGGB7lYsFkNfzufztS3gaId4NxgiPJfZbBZK6Vj0LDprmEkCUQ1gk1m2LXYceT4ICQIlGSYpRCS7u7t68uSJnj59Gs5cYByt5ME8hXD5O6QArg952g0j/EvoTx9bOQTDXCqVQtLVRlCE1ezkQmLI5XJrO7IwRlYLxqDaLfzU8drkno1yPrS2HoMtrWMNYwB5Vms0Ab/j3Jb9/X3t7++HHavUkSNfknzECMWFWEl3MBiEB8ejKxaL2t3d1Ww2CwNPKM1ihljspGm1Wmq1WqE4HqvFITR4CptkhGg50PukBq6BfspkI0uNvGE9TbwFQkMMAe0HlrQgOElrybLV6s3ec5swY6LYjRUkI/kplUo6ODjQ0dFROEDF1gxTeoP3h3drTzDDs1qtVur1ekE/JMmB91mr1YLHbpN46PG03xo5xrlcLgeDsFgs1rRQ+i6ZTAZPZ3d3N2w6mc/nYc5sbW2tlS1RScDfsxjRWAmTmQMQLGclEHrb7eicZcHzY8C2t7fXogk8TOYVfcszEbZbTXi1Wqlara6ddYAhwZjYsj3WEm1DeiGyIVNvd6BRqYLXb+cgxtU6M3aLOZ4nem6j0VC9Xg9nbnzsKWNWpnlM9uM+RHKsbeqOqVXGeLD+NmnrGBLmIZ653dWGExQXYikZ41+2fUI8TBR2uJDFtZ/bQScsaDQaOj8/DxY/nU4HfYcssc1O25Db6nTWG33MQtvQBeIlRMNbiJZjWZ3S6rsseO6Np4fXg5fFv9bis9A6nU6Y7HZXErIL12L/PSRiJznhPx62DSNtPSOf42FzsEur1QqTNJVKBeLEO+UsCpvNtslN9FmkhWKxGMJjW9eLzggZViqVULGCp4vRRZJAOrDjzPgkEolAVFaHtrXAjCfGCy+cdnc6nSC1UCWBHEDfRWUKjALerg3VLQnZU8x4tmhVh63KsTo+kQrGBgkCo0k/QWREEBhfu/sLY2BlB1vxgJNDfe6mtbZpHdm+sX1gpQS71pAAOp1OOKNlPp/r5uZG5+fnYYtyNAqmz7jGarVSq9XSw8NDqL5hAxPOoC2//KkRq6YrSYPBIEx4W+KBdppOp9XpdNaOZQOE3IS4bAYoFArB8tuJaS2vzUJjxRl8FozdIcV3bLbc6qlWM8VLpZTFhqM8OyEiyQxbUWB3h9EvVoezRfG3t7e6vb1Vq9UKyZdyubx2LbwW+owzA+zvqFiAbO22ULsg2H12dXWls7OzsHWbsbOhP/1pJRRbD4zna2uMrU5IJplFhofH37FJhDMNkJHa7baWy2XoW0jCygbRBJ8tN5LeeOhIOfQT5FssFoPBhADZDp1IJHR2dqZnz57p8PAwaK22KgEDg8FjzkEMksJYM7doO+MnKXiZRD94vowBkSO5huiZy1Y+sREOBkbSmkxhk2zM4WKxGLxrm7C0a0V6d7eorfLYlIgEELDNfzD/kSgvLi50fX2tVqsV5jGcwjolIYmxaDab4XwLCJfKIuZXXIiNdBmEaPmX1TFHo5EymUzYEWMz6ehZ/LfNnNdqtWAdNxGnDf9tFpzFn0gk1o6/i35f0poXQyhfKBRCRYAte2LRSwrbDuv1uu7v78NJXHgVh4eHa144Pyw6ZIHFYhFOZuMcUevlkmRAE4X8OAMVUrF6HZsXWGB2I4Ml3NevX+v777/X9fV1COnoexYsbWTBULZG2RceP5/b0jBIjv3+9Xo9JD2sYYRcWPjsxIIIiaSsjENfsMuObbcQhjUwEB8kyed2zDFOzLder6fr62u9evVKBwcHIQ8RTUQxnrZckM0M9DXJKYwN64N5NRwOg1dtNWqqeawcEt1swr92Czfbx7vdrg4ODrS3txfKIK3Hy9zHo7YbklgT1mO3sB4uDgGyjM1H2GS5jY6t18uBSZSoWVmA+3Ot4XCoZrMZjFO9Xtfd3V04twIHzRr1uBA76UKcqVRKk8lE/X5/LbQhoRKtYbWCPpMUbYm/Q8+z3ukmWJmB/4+2c9P/sxgt0VipwhI7meeHhwedn5+HQ5cpxGYHW7Va1d7eXlisfNeGSqlUKhSj397e6u7uLmxbRFJAKyQpgiGhNAxNLPpD9touUAiJk6Zev369dk+2CqMd2mP0ohGHfRbK3axmZwvTMRIcIoNxZkFEQ2GkAysV2LIsrsd/Q7pW85e0Nl+IMIbDYUjIMd4kaCqVSgh9pTebBahZ5QQu+jQqj0HstCP6Q5KPSErS2njaLatETBz/Ga3CsIRCmdf9/b3+9Kc/6dWrV6ESpVqt6smTJ3rx4oWeP38eDITtF+Z2VH6xUs2H1o8lYJwd+5mNSrkX44+MwNbz6BGfeNMYCYjXzjMOy0HPj3NDhEXspBvV+Gw9q/UsqCstFotrkxHLBplQJsYp9rbzrScTbYslF9oS9XKtxZXeeodMSJupj35vPn9zVuz5+bl+//vf6/vvv9f9/X3YxFEul0PyZH9/P4SlTGBbdkWNM+fORjVE6Q351mq1cDYESTe7oEko2aJxq9vZ8cFotFqtoJ2lUqmgPddqtZCt39raCl4Iu9Vs/TBesO1vjCahIN4PlS02k24PPokaU0sEtgaZcBb9Ea8KA0RkA1HZjQgsWBsh7e7uant7W/v7+8FDarVaYePLYDAIWuH+/n7oazxV6Y1cQYUG3jmeJ0QivT0PFvlstVqFIzbx7pGrIF10WlsrTT+hWz48POjs7Ezff/+9zs7Owkl1uVxO19fX6nQ6khQkiuicRi5iUwx9Zh0iWy8NrDdrde0oQUejUUh3Op0GY8y/1jO2hqBYLIaqDbxzCNaWcFod367xOPCzvQ14U1LHhry2hAUPActkdTG8DbQ8FoH1GkHUo2US2ckahd0dA6GjiVEjysEm3IPJx5sILi4u9Pr1a7Xb7TABl8ul7u/vdXV1pWfPnqlUKgWCIbxH0+p0OkHLhdBYvOhy1WpVh4eH2t/fVyKRCKSBzjmZTJRKpYIMYQ8Litb3cn/CfciF8JC3SBwdHalQKGg6na6d5k/YbPVqScEQWPJFioBUIXZK0BKJRJBx0Mnxehg7Sg5JsEVDc1uKReUCpISWHo0Q8Azta5843vPo6EjSG3Lk2Mn5fB4MFImZqGRkq0HwhCFEDBD10iTwGBPeaIE3j0TBUYU3Nzf65JNPQoQB4RCBWGmKczOo9mBHVjab1fHxsT755JO1GmvuieaOxAQ54o2+bw0h30S9cNaMlQeslLdcLkMfR8+flt4mPjnc/+DgIGyIwPng+zZpGifJRhEb6UJ49l8rmmO5OL82lUqFInNIDiHdWlZ0P3as4V3hJdj6UCvoRysYPqZszHq+1Ct2u13t7OwEy8p9rMxgEw4QidWqCH3IkrMY+ezm5kbX19dqNptBI7UZX7zc3d1dVatVSW/P+mVB0T8Qls0WR5+VfuaNCDc3N2F7NecZ4PGyY+/6+lpXV1eBoJEgMJ52rz+arh1XrmtrgxkfSHW1WoX3rdlt1qvVKpwLQAkf/Usoz5ZRklUQoK1xpsKAhUr/4RUvl8uwoQHZgWvypo6Li4tw0Let190ES2RsCsJTK5fLoQxvuVyqVquFtWCfj4QRB84fHh4G48Hz2QSlrezhOrY6AYNl1waSC5uEbJ9sWh+bYKsbIF4iGuv12rGwdek2T2ATlCRXibxOT0/DiwYWi0V475xdg7TjMfnxp0bsnq7Vh6S3Jxuh4yEVlMtlLZfLcEI9GXG8XTrPZuLx2iSFbD2kJ72tlrC1lzZkeox4rdW3bcaDtMk7iIX78PYHFiiEBHGRzcVzsyVabEagCN0SJkCK4R72ZYqj0SjUaUKOJBEmk0mQSRgXawju7u706tUrvXz5UtfX14Hc7A48Jj+vQOE9YcvlMmh9hP3Su68GYtLb0js7/rauGIOGrkm4ilHlXAruQ0iKV2vP8LC6r22TjS7oI1srnEgkgjbMzjHOtuUzpBc2ZdBuWxXCnKWuHBKjvzhcBq2cA1vsePGckCovEW21WuFVOkRCRAREklwbA2M3qjBf7SaVaILUbtqIjutjiMpA/M7OPcY5ukmIPouWyyWTydDu4+NjnZ6ehsPIk8mkut2uut3uWqTDfaIyVZwEHBvp0sH2xHlJofyGrXuU7BCy53K5EAJ1u92gFVoR3FY2EO7g9WLlbG0o5ESoQUIiGvZsAveyIj4TgGswwSjhQUOFAGwxPSGrTQbhWeBB8WZc+tESEYkTvosHwXMWi8W15BQbK3gFi9WFISveBMxhJhA+UgaZ5MFgoO3t7ZB1txlhjKCtppDWo4Do80S1c7bv8nsqLYhWbCmTLRnCU7ekau9tDb+9t918Yk/tSiQS4QxhFm+z2Vx7GWcymQx1u69fv9ann36q4+PjtYNnrKRlD3ciCiCPQUUMmjSAlPEUeSY7brSdbci279ilV6vVQskbBo+tw+j8NhKMzu9oPuRD4BpUDbEzkkoRHBeiPJs8Z71t8lIxFuwIfPbsmY6OjpTP58NYspmK3We2qgUpz5ZwxoGfnHSZHHafuj3YBJJJpVKhY5bLZZgknBXApgC7Txriw0uR3h5eQqfaMxHQywh5IY4PwS5OJputp4zu249aTSYcmhyvsCYTjsdrydNOarxZvGWuT4JJUiAJ6lO3t9+8oJCQjLZDYlaDixoc+wxs4WVh2DpotuHiSdpr2ppkyunQhCFkmwTZlChjztgaTwwmhLTpCEj7HCxia2DYes2GEjwvDJ3tS4iLRUwiy775hGuhIeJUWC3XeoNW+0Q6SaVS2tvbC7v7IAS73RstmJpfScEzpvrFOgXWCNvzCyqVSki8DYfDEAFG562dzxh4W+ttSZc19pi3y7XtPODHHloEF1hjaJ+Ha7AmyuWy9vf3dXx8rP39/XDqIE4ChGvPhpDenheytbUVIhTbzp8SsXm6+Xxex8fHKpVK4XUyVq+RFMI1wmiIkn3UR0dHYVOADXmspoSXhGUmVCEEJ7FDEo42PObh2klnE1xWF7SkK62fwI/nYa041p5QLnqSFKTLItvZ2dHR0VHYzcbpaywCtEe8rui5u5JC4T+elPUYraGSpFKppMPDQz158kSVSiW83JJrQTzcw3pUVs4hS8z96R+qGWxEgHdDKZ4lWjwaxtCSLuNpN52gX0NIeL+ExyzgVCoVdGSuYStZSIZZXXO5XIayLa4FqJd++vRpOF/ZVofYCMAmf6gCQJawBwDZg4Z4Lvt6+Hw+r8PDQx0dHYVzTBgDazTQSpEuCoVCeNMEf490Y9cXa8B6oNLbg8QtsVsjE4VNpiGx8c5B5hNJVutMsdbsBgraksvlQgL58PBQlUolbISxCUqShpvWIedyxPVSSilG0t3e3tbR0VFIqtgDxwF1t41GI4jhLMhyuRxOFbPhsl3k1nO2p2Vx0hSLwGpTXOcx4rVW3tYkWjHfTjjagRdhD/qGQEqlUkiUQEaQOWTCs2WzWe3v74cQNp/Ph3CfBcxOJfQrvAUbMmFoMplMOBbQhuk2XGUhf/bZZ/r+++91dXW1pifbRI7dGhrtPwjZVgdAOFQdWG+Wz2zdtjUG9K3NQFvPiueAyCFcSskYd/4bMkenttqtBXMM0rWJKBvqlstlPXv2TJ9++qkODg7WtFGuYxd8uVzWYrEI85K3T+DlEmUwtryrjpdvctjO8fGxnj9/vvamajRc5pM1fuVyWeVyOXiDSAwQlU222bkdrTuW3kYjOB6b5oHtPxKaq9Vq7ahSaobR0nn2aL6FaJAoJXqoUbSGv9FohB2uRHyML1UilJjFhdhIF8skvdWhbMiAlb2/v9fl5WXI3LLFVVKQJ2w9pfWUJa0tBjzBYrEYTsFPpVIhE2/DyE2Ea3UoSJvsMh6S3T4rrW9jxFtncTDRqM1lUwS6crSyAf23VqutEUkmk9HDw0MgXrLKJMlYxBgF+h1PyRIasKTLhCa6qFQqodyNa0BSbFG1Xh/EQk0zhfSMFwvaelB262n0WEmSRd1u9x1t0Rpa2//0F16ePe0LI2gTOxAGUYKteolWweABUwtOn3IYP3XX9NMmzZowu1QqBc/VHtCCl0cFC8RB9MP4nJyc6OTkRAcHB+HoTuqMbcUIxjiXy4VX+eRyObVarbCrj7I8623avIWNGJn/xWJxrSoELzMqM1g9m7nO8xOxSOtyFuRr1zfzy2rgGBqS7d1uV1dXV2ubejjnl/GLcsRflaYLKI8ho2o9FkCB/eXlZQibeeEfHoqtDWRRWqJFa+X3lKDVarXwfjMmr51cUbCo0MLYOYbuxr8kwaL6naTgoT958iQQLIeREEKS8CO8AlzDbkhAsyMZRp9g2dGuOIXLJqGiHjkEuikpY714IgYroTBBMQyMJWRkDQQH8EBkkK49T8B+z2aqMUZ41Hg+3JO5ED2/who9G9ZG626lt4X7EFmxWAwasbR+NgQkZr0u681D4OiFVsu1uQfaFY2UaA99wHGi/DDeNrQ+Pj4Ob1BBpqK/rFGlDSTpdnd3dXJyEsJvtuBT3xpNQEJQhUIhrAWiL9a3lQGI1CxsH6xWq+AQsc7smRY2dxJd3zYfwPMSCUwmk3Ag1uXlZZAjrY5OH9PPzOO4EOubI5rNpsrl8lqdqbT+6ufBYKC7u7sQ7nQ6nbDNlXBoU/hnr2cFfgaWU5c4tITvbYINpThpimMS9/b2QrZ0b29vjXSjyZJk8s3ZqScnJ8ET4uQvwkdOr8czwTOzXhietD2Ry8ocEBIaVrlcDkTAgoYIIAm8DklrYSSEhfyCtxGdlPy/1dPpbxYnEgpJLOvF43nYqgNbToX3hZdGWGoPK0e/t1GHLRtj4fJ9fjDekAvjTYmf3YrLs+Nx8RzR/rDjQMIND8xGduyMguzxxvmxhila4meJzEZgeHvo2TYstx4dei6yx2q1Cp50u90OZzFgWGypJAaiUChof38/bDiwzgBJP2uINxEvPxg75J1+vx8Sw9Y4PZaco52cPEjirF6v6+rqSnd3d6FiwRK31ej/ql/XQ90kmxYeI08SXI1GI3yPzQeEpbbkCNhyEjuoTE57Nm80w2tJA1hPD9Lm/FykgUqlsvbeJelt3S8JiUQiEQ7fZnsoVQwkDvBQOUyZxWhfZwRJ2MSdLemxk4jXtvPMkBHkbSUQSNBKJEQDkMemUFHSmh5vQzX7pmZ7ji91r3h3drwgC64ZNWCQlSUfPB/63NaWRjX2aC01fYlxhYRoO54ixjC6azGaMOIzSJfyMZtvsHOO8NgaIp4PI9Hv99dyF9GSRCIl+pJokpJAxi6VSoX8AZGffbMFkh0v22Tsow4F83JnZyfkGWh7v98PXjRSQ3TO2AiXvoB4kT6sM2HngPRWz2UM6TuIFWmBIxyju9fsXGWtcpRAnOcwxHq0o81QSu8WKEc9XmtFi8Xi2uBbkngMEEH0XFDrxT1GuNGJTVttssceU8di4ryCu7u7sKmAkMyGwSzCROJNnSDvm6I2lMzqdDoNRM1Epl14BbQR4uUoRe5n27spHMSbs14cYwTJRyUU2282zCczjpeLXiopSDX8t/WgWEDWS+N58USl9TI5PpMUFp31BBkrEmYYMu6LPmk1PYysLYWyNa2WdO0Pz0/pkdWuLeEwJ20lAHOe58A7twaG69sT4aJki2ODEbca8Hw+D2uI6zDetJmoi2iQ61vHwnqn1pBHjUC0DNGub+QlvGJbUmmf6zFYw010QE0751hw2I2dpzbSg/w5n+SvSl7ggZlY0VrKKKymxxZOSBOysp33PkQng6SglWEd36fpWpLEC7eF3NZqS29fM89RiK9fv9Z0Og3a7+7ubij1om9IlrRarXAgznK5DJtF8FhKpVLoH6u18pw8G4RrS9qoi7SlaVSR0G76wS4uiB/ytLt6rCcDaUUPHcHLRbcmQUe7refNorEhujUyRCuQJwvaVqHYN2lYT5/df9aztJtpaAdt4aQ762VDfva5Wfg21LdyiiVbq+Hao0BpD5IPRGmJ1+7CsslG+pVEFqF1s9kML2olkZtMJlWr1d7x/HBC2G7ebDbDWINKpRKMpC3nslIGUgNbuNHsLfg+UpjdYWo3+nzMuo5GMbZaiHm6KaFn20JOw+5UiwOxbgO2HqENG+znTFDIw2YypXWCeF9HMXjWekKSLNTHdtTwPYrJqXqgtIU3VIxGo1AZsFqtwklOFxcXOjs7083NTQh9qSektIXJQigffSNFOp0OhEu/ZTKZ4BnYGl0WHoXghPVoxzaUZkLzrw31CEX53EoFuVxOvV5PktYICQNoQ0CyypZguUcikQjVDBDUfP7mRDa8fqt/spjx2HgNDZUDvK/LvtLIeuRo+gcHByHBSH/bFylaY4a3T/JNencnIvop8gSkaz1nnsMaAhs5MAbRhFp0TuC1Wk/ZlmghA3GoTbPZDDW4zBHyGTbCSSaTYXt4vV7X7e2t6vV6MMj25DXK1Kz0wRZmSh1ZL3YzTXSN2kRq1EjyXFYa2gT4g2ulUql3Kh4g0k2RrOWfx5yunxKx7UjD67RWDDc/GoIwUdkzbj0NwpL3WScb8tmibjwGFuemAZHeVgwcHR2Ft4lKbyUSJttgMAjhKtoSJ01xOr2ktZO+bNhsDZDVNdE+pTfhLvfhO8AmpZAqrPTAcZfRfrKRAgTDoibUtx6WlVdoq61WIOHEfZAZ7Ft4bZTD2FCGB9mjyW1KTPJseN9ku1l0EL8NJSHRcrkcXvOTTCYDibFoiYJY9GjulnDtRp7Van3XlDVszC+cBMY1mUyGhJHVnGmvldbQGR8eHsL5ya1WK1QLbPJU7VnGbC+GdEjoRT1vO3+Yo+xQa7fbajQa2t3dDU4HJG3fZMKJahhSyBlD8Ji2a5OzyAXWKL1PYrBJSzxq1rSda5sINRohw0vvI/kfG7F5uhAZE8AiWjpmPWJb2gHp2pOigPXg8D6imqz9Hd5IVHfCk+AVzs+ePVvb8WR1UwYaQreHgeBlEeZyPqoNjahQwJvkmEQmhpUFmPQ2tLNHFaZSb7egoo/Z17dQN2qf04boZK+5Nwewt1qtoJVGCcL+Nz9kuHlxJ2SK7mhLgaIlZjwrkg5zgGgluvPPGhmrceOl2w0ytvbXViMg99BXSAtouUQlUa/JGkv6YDQaBT0fsuPvoh5jNJeB4ePlp+QEOIqRJCvPYj1oW+2C/ELf2XOpKd3bNAftoeQ22rQVLBhQS3DL5XJtWy+/s+dSRNcX42MTmVYK5HfkKTZ5qna92Q0gVvbbdF/rlNijSu3a+KkR250ymYz29/e1s7OzVr4DSUUHxy5mGxJtOtnLem50qtX++C6EZEnQep72WpagyfTyud0pZ5MhNmxCT6M2F5nCasQUh+/t7YX3NfEKesirWq2Gt/muVqtAYFbLxKuCLEgQQX7VavVRq0//4NmQ3KAM5/Lyco2Q6Wc0SPs72lyr1cJbiCFdtL5erxfGgTZjTPBkIT7rgSJx2Oy29Fa7JUGHvJDJZNYOs7bziWsiv5RKJe3u7objExeLRTgT4OHhYc3Tt8lc+7vpdKp2u62zszMtFgvd39+Hg+kzmUx4Fc6m0NmSbrfbVbPZ1MPDg+7u7sKh8Hh0EKl1POwGD3Rz5lM2m1WtVtPx8XHYuWXPJKGfmKNEcHb3mI1imPOQLCeh2TMs7M61qFNj1xTjaeUC2pTNZt85PtL2l11rzInHqpEYJ/Rwu2EHHqDtcSA20i0UCvr8889VKBSC2E0oxEE3LHgbgrChAtKzQjlZaKtZsmiZXJPJRM1mMyQrsIzs4Y8m00hu3d/f65tvvtF4PA4vHCS0PTo60uHhYXg1i6RwVN/NzY0uLy+DtwMYaJsAs0Xo6XRaOzs74YBoaxxIYrCTztb2YrQkhbDO6pVRr8IaDwgaYzAcDnV1daVmsxl+0JlZ9BgMwnkiA3baIcmcnp6GjS2Mo9Un0aajiSrOJECDZ7HYcxdYyLSfnVhUe9hEIH1nvVrOHWa+sC2WBTiZTMLRouz0s1EO17fVCqPRKNSF3t/fh9O8qOnGk6bGepM0gZ5JVECyyW5hZQ3wGd5vsVjU7u6uDg8P1+qR2Xa+t7eng4ODUCdujRcJSunNi2ObzWYwhBxYX6vVwvNWKpXgBa9Wq1CixnvIzs7OdHd3t9HLtWsUfZ0XSHIUJ2VpyWTynXfa2TVq5RDGwCZ7MeQYPt60zIYd2mHluzgQm6ZbKpX0t3/7t4EIyVDbk+/Zkoh3azOghFLRAzmwvLx6mnd2QQoQSaPRCAtkPp+v1a5a/YcBpS0PDw86OTnRp59+qs8++0yHh4c6PT3V06dPw1tp2cRxd3en//iP/9DXX38d3s01HA61u7ur6XT6TrKAUAoN9ODgIFh36y3M5/NApHYvvj1dC23WJrRYiPRJVLvCq6jVauFM1pcvX+rs7Cy8z83WepJgtN4J5FatVvXixQt9/vnn+sUvfqHT09N36pjR4SCNTqejRqOher2+ltyh7MyWSEFULDAqPGwZmK3eICtOwgvtFyJkz709BIk5Mx6Pw4Yc+ny1Wq3tbrIeP/Os1+up0Wjo9vZWlUpFJycnwRg9efIkHG4Tzc6zMaNSqWhvb09XV1fveOf0N+uGHYhIS0RShORWc4dw2L5uSx2RhGazmR4eHnR5eamHhwctl0vV63WtVisdHR2FDT42EWhlldvbW7169Sq8IcU6UMw1jAtrbz6fhxeR4rnOZrNAhhzy1G63g1yBd08Ckf6HG3DebMWH3b3HtnYcIPiI6qA4tN1YPd3PPvss6J8kyrrdrgqFQqhVtVqbzUqyQ8lmKCUFz+jFixf65S9/qcPDQyUSifBCyHq9vvZCRSw62e6ovCC9TX7Y8rJEIhFOpT86OlKtVguhK+159eqVfv/73+v8/Dx41J1OR9lsVr/+9a/DM9lSKVtyhHeFFweR2tOXWq1WeIWOzWpDinj0eORHR0ch278JkObh4aG63a6+/vprtVotXV1drR2jmclk1t7swP22tt4c9HJ8fKwvvvhCv/jFL/Ts2TPt7++HhNemEh/eeJBMJgN5UMZHmZPd0Satl8QBS8qSgi5qazUlhaqCg4MDPX/+PEgJtorAJrPw6CEC6U20wpGILP5NMk86nQ7eUz6f1+npqQ4ODt45hByQ9GPM7u7u9PLly+Dt22w8ZJlKpVQqlcLr5yEWjCsOhNVQo8dn2oqMm5sbffPNN/r+++9DRPXw8KDpdKoXL17oyy+/DDKGNbz9fl/fffed/vSnP+nbb79Vp9PZmJxmTbHTdDgc6v7+PowZfZzP57W3t6fT01Pt7+9rtVrp9vZW3377rV69eqVOp7N2jgZRVDTfY9daIpFQtVrVZ599pk8++eSdNx4j68WF2DzdTCajw8PDIHpLb7xXwmneARbVkMhiMpB2YwTh5d7enn71q1/pH/7hH3R4eKjJZKI//vGPqtfrurm5CW89IPtKssCWCNl72HZLCpnsRCKxtpUXzxmi5G0CDDhGgndSsVjwkHgensmek0p78FYIq+0WWrxl+gkJg9eXHB4eBm/8fYDkDg8Ptbe3t1araz1M2giZ2r34JycnOj091fHx8dqZsJs2sdiwnNcU2XA7qmeTfMX44kXZZ2YckaNs9QvXta81whBFPRtLbLPZTE+ePAm70tLptFqtVtA8KSFEv4eobf9hpKmVfR9I4B4cHISqAUJ/5gLPsly+3UgiKXiH6MjRiMZWD9nPIGjeUILhgPA7nY6azeZaYpgxZYdmMpkM1RJ2G7YF97SEjWyC42BLNZ8/f64vvvhCmUxGd3d3SiaTweDZ/uW/rcbPurBlj3t7ezo5OdHz58/X3ugBH2HY4/B0E5usksGPVsCGpYsK6zYbb0P8tUZ+oCNsyUm0PvKxBFI0G/8+WG8hmkEHlO7YcyX4G1vU/qGFF4XNlkdrah9LjNkyuQ/t7oneC1Jngdvn4FnsvfgdPzaB8qFxY+xt5tneJ3qdTeNlyZrPbEWBvZ7NlH9M26LhvU3EReeP/W9bGWCli49FtMLjsTXBM23S638IrIGyz0ffI+NE5xLrzFYyfMxa4t8oF/B7W31kPfL3bXawbYpez66J6NjThk0G+D+JRy8WG+k6HA7HfyE8Sro/2yvYo/iQhfwQHrNS/9nrfsw9PuZ+P6YV/Vhv4qe6/o99v/+XEHff/NRj/TH3+s/M+z8HP9Va/kuZp+7pOhwOx4+PRxk+vuPSHQ6Hw+Gk63A4HHHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGJH+wOeJWFrhcDgc/0Xgnq7D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y8f8DK7QtpWdmlUMAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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YOmu5XL7jyf0pQMfL5XLhGaW3mVDCZowJQ0KvXiwWYWDb9mDwkwwi9OSdJpOJut2ubm9vtV6vVSgUAvGiG0N6t7e3ur29VbPZVC6XC+EqyUTeA3mn0+kE/blYLKpQKGzoWRDX3t5ekH8Gg0Ewan43HA7V6XR0c3Oj8XgcJCBrOLwnBmGrErZVNaTT6UAIhHv8fVtWmt/byZr+sFUSkLXNdm+Lrmz1iyUBoiaSp1Zeol3wysrlso6Pj7W3t6ednZ3gfdEOeKDj8VjD4VDdbleDwSB460wQ19fXymazGo/Hymazms1m6nQ6Oj8/D32L94lGPZlMgndbrVaDBg0Z2eSjfc+oFm6jMJwCbGybnGUjApLMPwZoz0QkjB3bf/a/dtLAYYMros4WEfRwOAxcQdvZa/yciM3TnUwmarVaIeOJbpnJZJTNZkPG2xJoVHeU3iU8CAvNClLms+CH6Lh8HiIgE9/r9dRsNsPf6FgGCSEr4fhsNtvQqnkPjJnEFsDgFouFer2eLi4uNJ1Og16Zy+XCtUmijcdjTadT9fv9YHzolHg1tDEEhFFYoyOUT6fTKhaL2t3dDeFbu90OkgceI8kjmwylXfi31XRtH/JfPGkM2Yb+RAi2pJC+h5xsaRYT83w+D9+H9PA28cCizwHs81pN1iZhxuOxbm5uwsSFpJHP51UoFLS3t6eTkxPt7u4GLReSm81mQccfj8chaWjDaQgDr3Y0GimZTAZ55OrqKiQTs9lsiD6IkPDAJanZbAYd145B+mMb6drnpA0YP7wL7bharYK0NhwOg+19TCnjNnuLwpa/8dxMzNHkm+1LW07KvykVI7LDa6ZPf0jVxY9FbKQ7m810eXmpZrMp6U0DFotF1Wq10MGEv9YQJYVMMI3MAN3Z2QneGoRrvWK+h5Fuw8cQ8XQ61dXVlV69eqVisbhR7rRerwNJMRgqlYokhZDIkpGtvcUgeC+SSNwTnRiJgfdk0OO1rNdvysNIOlDihIdJCR4EAZlZL3G9XodEAiSys7OjnZ0d9Xq9EJ1AFqPR6B3DtH3F7+4rUaKP7YRl+5yJBTKxpT+QrNUBCYnR9TEwvHikDlspYr0ansfqkYwZvo+xDofDUFVTLBZVLBZVqVSUzWa1s7MTwmLa2sorEC7tT1ujJc9ms5BcHY1GYdKeTqcaDAbq9XpBQlgsFkG6wMulSgQ7s2RDP1uJhzHC+yJ9SFKlUgmVGNVqNeROkGyQsl69eqWrq6sg39Ce99nWNoK1NoJjYvuI9ufvkD7tCKKESxvv7OyoVquFSotkMhna++bmJkgVcSA2eYEOoo4zk8kEApPeZBz7/X7oUJsAs94JOmulUlG9Xle5XN5IniBLEBJZ0Nl0ng137wPGTNYd4yW5RSiXSqW0u7sbdLZsNhtqPBno0tsECs9nBwlEzP/jufHstuTHanh4RlyfSAKpAOJBK+W70R8MnHDcJrAGg0Eoi0L3ZMBzbTvxRUNRKwnRj0w6jAnemfAbrx8DwyO0/cWERfJnPp9vZL/RLq13hP5qjdpWztioievzfUs6tuRPepOkwjO32jMTIYTL79GvyQeQmSd6IMSmfApCtF4444kIhN/ZNrbaLtGkTTTbsYmenc1mQzIYiatcLgevm7ris7Mzff/992q1WhtRz32w0Yq1wajNYSM4Y8gMxWIxjAE7cdCfTPZWZsrlcqpUKtrd3VWz2VSxWAz5kfl8ruvr69CH/63khdlspm63q0qlEl4abwAtjAFHrStGZGdkGr7ZbOrg4EClUkmSQi2opI3wx+pAwJKdXWkTBVlqQj06HE+BGsxqtapyubyh00LOaNe2ntQmV6S3hm49K7x5QkV0TspcMLLpdBrKgvB8IAUK9ZmYuIakjYQR3h3JKLwyPFqSdni6NtkWNWhbB2090W3Ac8V7JWSHnGwi1Wq+1tu1yT9IKeqxkmziXtFEnH1Wm2jjGaNZfrxXHAOIq1AohDZCc7WleNQzW/2T61HCdnNzExak2JyCTfzZzD6TiB3jTGLWg7WeIfY4m802sv4QOBNXs9lUvV7faAO+iyNyenqqly9fhrJJnuV9sIQbtUt+bN0+nipjl5JQEp9wBP2OHMcillKppEqlonK5HCQfauIp8/yh9cU/BrHW6Xa7XdVqtTDIdnZ2Qhi7u7uryWSiXq+nq6urEK5gTHyH7+3u7uro6EjFYjEQMhUL0UTNNkH9QwOD77PKhQRPuVwOZWp4IjYRhUeC1zCbzTY8WYiBd8rlcmHFEN9HV61UKmHmZeCg6w4Gg+DRU0plPVDpDdkUCgUlEgkVi0VVq1WVSqUwMWCEdlUcBnEf2fDD723pkTWYqG7KBGOjFjyRbd+PLivFK+adMpnMRshu+3vb9y2p2jGyTXu2koMlYp7NJmmiSbnoPez78O42Wcj79/t9SQqrCKm+YIyhGUMmxWIxTKaUr+EBQi5MhowTNHmIH/mFaMTmG5LJZFitZb1qxhcrv1qt1sYCk+gEsA3RhOo2u7PPgadKNVC5XNZqtQpyC99BH08m35Ra7u/va39/P0gjREQkM8l9UB8eF2IlXRY12MLzTCajUqkUZq/hcKhcLqfpdKrb21tJm8aO8VWr1aDP4JVZzSdqGBb2/983OCjzoTbw5uZG19fXajQawWPH47DeGh6Y9SSZufFOqcWkxIkZGI+UQWYHEbohXjKGiXeCBw2pMkE1Gg3t7++HhQ2r1SoYE4smbLZYUogCrBQSjRrwPIlY6Gf7ObvoA6nFVm3gtZFJ5jt4OYyTfD6vcrmsWq2marUaSJfaWDxF+3mIrVQqhYQa70S0ggds64JtvSjPybtZyYP7ELo+ePBAJycnOjg4UK1WC3WfSDaMSfoX0mW829DXTvo2qSspeHLVajWMB9pytXqz5BvHwEZYk8lkI/mJnkz/MfFYMrYlhUweo9EoLHBqt9u6ubkJK9eYvO/D+2zP2qutrqCN0Mqz2az6/X54xui1WMjx8OFDPXjwIETDTGjj8Vi9Xi+0Ra/X+8Elbj8GsZIuOpANGdERy+VyMKy7uzt1Op1QwLwN1pOyGhUzsZ0t7/v+h7QbOp9SMLzwSqUSSnXsfgM2m26/b0uFRqNR0O/y+XzQ6zB8EmWEqFRPkLCB3PkuhrFYLEJIjAfdaDR0fHysR48eBSKg5tOGquv1OmigPAMelU3AWQ9yuVwqk8kEzx+t2JIukw3VDjZBxfuS4GAy4L0ymUxIDOH512o1NZvNsFKPsq18Ph+MCV2vUCiEidlqkZKC18cYjCbArPcO2ZGsxDviuYi6PvnkE3322Wd6+PBhIE/ajO9D5sViMYwfnIi7uzt1u92wGAHtGMeECZmf6GIT3iWZTKper2t3d1e1Wm2jbM1GboytnZ0dLZfLjRWgfJ6xjDNhiZsFIZQtjkajMKm9z5GJ2u82MH6sPW/bUMd65gCZp9Fo6OjoSEdHR8rn8xtyHCvtKIljNWtciHXvBUjHJkby+bzq9XoIu1KpVJipqdVEgyG8IbRBC8YrZqUKme/7iNcSow01t8GSOvrP1dVV8LqazeZGmRsJJAYwJEopmaQNb4DlsRg8xsw9qXG2yQ77fQyQtsQ7L5VKOj4+1ieffKLDw8MgTRBKcn36Bi/Xeg/8e7VahZVTNmO8Xq+VzWZVqVSC12WfD8/w9vY2EIj0VsvFS2ThCR4/iS+0au7RbDbVaDTC8lhWx+GtsSKOSYM9AqinpboB47NVMSR2KfDH8+O/8/lcvV5vY5Uj46tYLIZVWQcHByqXy2ECtN47YwTCtMm9crmsw8NDPXnyROPxWJlMJkxUPEdUf8VbRYNmDB0cHOjw8DCUrfH3xWIRokdLMkQrvLctEbM1vrRdr9fT5eXlxqpSbPp9ZPoxdmcJdzKZaDAYhCqlbDYbftdut4PERtvy7EhzyGmJRCJIcMgJ1EdTHvchufGnRCzVC5JCdpn18+iH2Ww2rHJB/Gdg4vVYvYlVX6wmQSOmrMkuFbUJnffpblani8J6rRTIcy9qjS1ZWe2ZkJlVL7Y8h5C+VCqpXC6HfRykt4k1ngmhnxmfGlk8O2Z3rguR7+/vBy/SSi+8pw1Zbc2zTX5BxrQ9CTW+LylIGHY1IBl1QngM0k5QhMlokHjSkJFNnFarVTUaDe3t7YUEJll2Se+sVCNMRmbgnVhsYI2e56LPyDVQyoZ0wfWpRKDd0JRt0ouxsF6vAxlYfTraF/RhvV7X/v6+5vM3O9fZlXG8J2Gy9HZDnaiHh5ZpvdflchnGGbXaJLStVo43bZOWqdSbTY6YgNBTbfmbtbVtdmTHltW7rS4uvU38IRsOBoOwQi+RSOj29jYsEsFLZUwhjeCIkK/Au2VBD9457WnlrJ8bsXq6GC5GiNETykpvsvTRTTroJGY1VmpB5LlcbiORZJMWdjDY2RAyR4C337GwJV3S28oIvDzCfsJSSSGkJGnGc1B+NRqNgveAx4eBSwqang2r2OADLY2t65AKrLEz8KIlWLw/mrOtf4zuN0t/sWyy1Wrp/Pxc3W5Xw+Ew3BNSYZLEA+TZqRm2fcP7Aat/s7SZCZQJEo+XjLSNfvCwaV8mUeuxsU+FvS+fo2aTBS0kXXgv+hXiJIGHt3h+fq6Liwt98sknYQ1/dGKD6G2FDeMSw8dZoLrBRki2JheJYzgcBrmlUqlslEVFNV1pM3JB/6ZGmH5kHGIjfN9GFrYdogtjtnmvH2N3gO/bd6ZmmT5GS+71emEihCcgdrghnX6zKRHJ+U6nE5wm+vBDHvpPjVhJV9JGhYENM5mZSqVS8CqipEtn2LBQksrl8lZ9x8ImRvCAMNbxeLyR+IkStf2x1yCLbEt0eC+r51Jyxk5St7e3gSTtmn0bfuEFV6vVUCM7GAyCgff7/dBGeIs8H2SHDIPeShbXhqTRMNp6/0gDr1690unpafAsIAauYcuTkBRYDt3v9zdK/+hDiJZ1/RgH3gg7ljEJQBJMKHi6tDm6MfsO253I0IohN7s6TdIGYbOngfXGbYLJkgZ5ik6noxcvXujRo0fB47flcHzXyi60r9VJLy4uQhUANmC9PsrRSIRaOQX9lpAabZ6xSL+yGOD8/Dzs+Gc3jsHztaRL1t/WPtsStm12Ym2I98dJwVum7aM13FFA8EzK19fXYaMo3s1Ke4lEIiT8mKQuLy/VbreDrMA4sHmGuBAb6fJyDPRkMhm8IAyUekrrveGp2DCf6zAQrd5liYSwww4A/mu9OluDeN9z2wwy97EkbAcf32EfhNPTU33zzTf6/vvvdXl5qel0qp2dHa3Xax0cHOjk5CSEicAOVkI7dv+6uLgIIS1eB1UAPHO0+gOSazQaYcMSm4yzZW2E+XgIZ2dnur6+DiRIAqZaraparYbQD83M6us2/LSJSxsm09/Uf/IdVuhZD9X2AZIAE7JN8iElkJSym8bYBQq0tZWPbOJotVqpXC6H8J13pv0Zx+12W69evdLx8XGY3GxCzuYGmBSRa9h17/z8XK1Wa4MI6T+bzGWyZWVcoVDQwcFBGC9IRnyX/h+NRmGf5OfPn4cVb61WS91uV59//rmePHkS5DLGNe1uf6KrDaP2uQ22Pe6r37YSoK2eQLaiht9GstEo1iakqTxiDQB6PiQetfM4EDvpMqPYMJQf9D7r5THASXxYjRix3e76ZbXAaO2hHcQYul19ZQv7o89M5zLQ8FQwYAjeeopssfjv//7v+vLLL/Xq1auwiIMJhpMU2JgEEuGZ7tuNCk+BsihKzSBApA1LvJJChQgkbb0Z21ar1Sps8MLeC1QFpNNpNZvNsN1lLpcLCU4rfUCc0qZMQ3jLfSh54m9oyBierQ7hGa1nhBbL3hOs5uI6JKSWy2UwWrwcSySSwgQBAWOgJCL39vbCWOS0Eyoj2u222u122HOX57f5AxwCpDa2hnz9+rUuLy91e3u7sS8JEzsRBlKNpPBfEnnHx8cbkhSTD+WF7XZbL1680IsXL3R2dhb2dcADpB9IXEXthTZhHw7ez46daPLaOknYGP28TYagL6wd46Gz1NkuLIKg8aIpKUS7jspd8Mx9th4HfpHTgKMJDFuPh/FApHa7vWiGFIMlK43h2NDHNqadWRlIeHX3VTpEBxLXZF/Om5ubsGG5NS6qNdizgW0W7RrvbrerVquly8tL1ev1MHh4b0js/Pxcp6enOj8/D7t6UbKFLlytVnVwcKCdnZ0QhuExQrz5fD4ko6ykQLtZL51Md6vVUqfTCQX4FKofHBzo6OgolPghoVxdXYU6UFt2xQo9KxExqZA0smVQnAhCIo2aVr6D1wjp7ezshJ290NQxbsaQXeXItRkr1qOi/dLpdFhRuF6vQ6300dHRRojPPTqdjlqtlh4/fqzd3d3QprZ9GVMQL1soWsIlz0EVBHsxtNvtjQmZqJEKIPRddGh0fSIuTlsgiURClDFZLpd1cnKix48fBztjLBOu39zcqNvtBsK373SfDUW9/GiyzbaRJV0iP2rPo5IATg6J40ajESo32HWPOvQo10STd3EidtK1IT6wXialL+xTi9dhl4TaUic0YjwkS94Yg9WQ0ZbsrlvSx9UNRsM9MqLs4h/d/Ygkid0UG2+c9ycTTIaWvXmHw6Gurq708uVLffvtt0FTtaExkQGE8+DBA5XL5aDb2RpKEkucgFCv1zcGoNXNIeoXL16EzbW5Bp4nuh7eF/u8soTVep/Ww6XPIE+7eCSVerOYgyQQhogXT39xkgFGTJ03v6NtCC1ZWm4916jXSX/c3d0Fb4p+oL339/fDe7MlJs80Go3UarX07NmzUK7FM0Xbl0hkMBjo6upKFxcXQb6h9nl3d1eNRkO5XC6UNl1cXGxch7a4vr7W6elpqIKBrDjnjWQoFTfYH5MMpIruzt8tWVE1w8koVjKyhLYNUZLD+6ff7Vgm0kVLJ6KM1uDbsQHhHh8f6/Hjxzo5OQl5HssZVqaK5lDiRGyka8nWzmxRrQZyIEElKSRcbNkOnWTrTSFWq+/YkDmZTIZlt1RLYOwfanyrKTLQII5tnYmnQagGoVpNFG3VLjzgupxC8fLlS52dnW1sEm7vhVfEqrNKpRK2N0Q3hWzW63UYoHhE1guDEAeDgc7OzvTVV1/pu+++0+XlZfAYKOnCm8rn80ErJMFBcoR3g9gwXpsMtYkQJpxsNhu0Yp4LD4jxQr9zL0uCaNo2YWWXSNsaVRvl2FpwZCDaDZ2U8+kgH3RC7plKpYLHVavVwuRjk6tEMa1WSy9fvtTFxYV6vV6YdOr1evDWaOtutxuqNux4hPCvr691dnYW9tqwY40xw/XL5XJY4EHERJLMLpCxpBQd73as2+d5n/3TFti15QRro1aXJrrlvrbkkNJSzhJ89OiRTk5OtLe3F8rimERZvGHvY/M4cZJvbKTLC+K98v8Mmn6/H7wyDI8SKvZksBlLK4KjE0Xr9bgvRksyxC5EQHuNJsG2gY6xxerRzUi4P+ER98NYGeScdEx5j93z1CYAqFqwiRU7cbGyC++TGZ6SNXRDkgmUftnjhNAObYnY69ev9fLly42zzyQFr4Nk0M7OTiiYh9yRPNAIKX+yHg+wv7Per/RWJuKd7ZaNyAfot1yL8UCyJVo++L57JxKJjS0WGTeZTEa3t7ehTznKptvtBrlCUpjsXr58qfPzcz148CB46JAufYJs1Gq1QiUKCzrYM6BSqYSJzi43xwO0kRcbM5HEnEwmgaSZ0Fhgsru7q/F4HJ6HiZtxet94tkvFbbXLhwjLOiJsvG4rQ3A6bHtHq0SsV2xlRE4NPzw8DJvHs+yXCIcJkg3ZuQbjxm5JGQdiI106n2SHtHlAI0dokLmVFHZh2tvb0+3trW5ubjaW7FljonMwUjtj4hHmcrkwm5N1tlUH74MtS0FHIonFSh7rBfNMNqRiIFHsX6/XQzUBYSv3on14F7tIgcnFyiTM4FY+oY1oL0J5EhJ4fVb/tqRkaykxCrRYns8SDl4jhICXhXHSJ7Y+FmO2K+XQeZEz+LEbvkA8Vgu0yRqbNGFyok14F6vlQuAQtU20cC0WRPR6vWDIvDMyA/e3hGjHg60MsYtbOPTx0aNHG0fU2xVwEAWVG3iv9HfUC4bocDZY/ddoNMLqOmqhrQZux4Gd3O0iCku4tnrhffZjK3IoE+WkCXI2NvLhXaK1tNgTKxr39/d1dHQUDh1lLwsqeChBtIdmYmupVCpUQMWFn/1OdEYul9PBwYGq1epGsbyVBzhmptfrqV6vB6IqlUra3d3V/v5+SDZYrcbqvJBRNEmCpMCA4RpkQt/n4dqkky15gcRtOCZtTiYktOxiBFY9ocHZukfCJ66Pvod2TZi0Xq+D10etMZMVoaKtMc3n8xqPx2GrO+ut2IRXLpdTrVbT8fGxjo6O9Pz5c93e3m6E4YlEIiSmLOljrFang9ysxIBx0zdEC3Z5LO1oDZXr2HFlS4u4N33JtZlkqbKAVAlPbc22TQjxGQjTbhhO1GJLINFjj4+PdXJysrGk2JIOEwBHpvOMDx480JMnT7S/vx9ONkbLpGIA6Y1rMpZYFIG0ECVFG3UxyWez2Q1JixK2bTIW4xEZh0mSscME+z5d19Ym8x784AhYjR8pybaz7XMig4ODg42Vityn1+uFFWhEi0yQNjokirLj6udEbPQO6aLbQbhWJuj1emq327q6ugqbhDOAOPyuXq+H4nEGRtTI1+t1KIdiQFer1UBEzPAcZf2+PUBtNtUWu0flBTvb28QD291Jb0ub7NJfq0Pa508kEioUCmo2m5rNZoGAWRFmr4lGSBSAZ2OXVRcKBfX7fWWz2ZDkiYaJkgIZPHz4UJ9//rmeP38eNnmOJt2YtJAy7LNbY2dyoG34PpOB9XqoVIjKLdH72WfYFn5yLTxJKhjsvgsYG/eH1GxEY5NWEEtUW7Ta+vHxsT7//POQ1IxKJIwbHAl0Zk5pYJ+MVCoVchzIbyQNcR7Yeater2tvby8QL1q4zWcg6ZFnqFaroc7X7rFrj6NnYox6u3YyZ8KzE/d9VUBo5XafW0q9WKlqk520jSVznseWiDUajVCCyfeofb66ugp8gc2wOpDdChl7cSFWTXfbXgo0Jh1C+RS78KNrrVarYEjR0w/sLGi9KbQhTplgE2QGsK352+bt2vCXTWns7IyXahdaWI0QL4LEEImzRqOxUdaCUdMmkEImkwkeP4bFsdj2+HqMBS2P98e49/f3Q/lTIpEIa/LtrM6/IcpKpaKTkxMdHR3p5cuX4V42WcnqM7RQS3o2aYKuiefIvTBYwlbbtra6hPA+upKMayAdQe6E4fQ35CC93SjGkqZdLmzHBeMBsmdSoUwNIrIGfHR0pJOTk7D9pH1f6zkyJrgmTgXeGmPBHjkOaeBxknDb3d0Np6hA9Hh7vCMTVy6XU7PZDCvZkOxsxY+NJG24b/sKW4BA6X/s2RIvjhHjBI82nU4H/ZXKo8ViEeSGaDRhr0ebM1ZWq1UogyN3wTFCUccKW+edrZwRB2JdBswgsOGb7Ry77JQQlhpYtDBCTWbyqN4D8H5szWOxWAyetfWQtkkLNhTk+/V6XbVaLWixEDnPE9VzE4k32/iRyc7n3x7nXq1Wgx6MPMBEwoCTFAYV5zuxrh8NFUJgsFHlQXUB3g1EyyRAe+I98c6WWK18QnmY9X7I2Nv2tgkTPDJLItGJEq+c71nvlh97eoU1IOu52aPBrT7HBGCrM/ix2qhNtOJpEY7asFh6a/QkgCAkG37btrSyC21OsrhWq4WohmoSe0/6legGrZwNjR4+fKjDw8Owao53x9bsUum7uzsVi0UdHR0Fz5rNXxgzSDFRDRUnhsmh0WiEGma8ZbuPQjQ/wO/oe+Q2GykSsdnP2JyEdbR4nlQqFap9GBvdblevX7/W2dlZeDfr4FkZyU6ocSE20l0s3mw4zb9tp9hEWHQXMbZ2y2QyG2vErfdgr2WTbBAfISQalu1QG34Bm4CAeCBbtGU8VauvWeNiYFBwnsvlAlmTVYY8Wfdv9Ss8Dbw46a2UwDHgNoTEQ0AysRuSs8AAjwACskRvy5psaMekZCUDS4zRCc9qfzY5aJNUkA7Eh86KJ4dB8hmIB9K1CTL6h4MyOZ/OvguEwH3sezHO0BnRPpmcITpLFDZ5Z9uGvqf6gWvQxhA5+ydEywiJBiAx9nq1E4318pAW2HeBtuN92fAFSYO66Wq1GiKV4XAYFjzMZrOwh4eN/uwkw1FZh4eHISKz2rOVoaIJRentDmKMBUlhXNpNge5zivi3Tf7agzqpDLm6ugp7hTBerDTG2CCJb2XOnxuxku5gMAheK8ZvYWt2MdbFYhH0WGDDOmA1H2kz8w+B2sFvNchts5xNHuABsDSZsptqtbqhPTLQ7BJSwnlCQTxevESK1q1Wy/NAXjYMt5umE0Zyf3QzkhKSNkJnSwwMaor/SY7gHW2rabXtHTUqK+fw3NFtF7eV/Ng6Xqvf2SoEdFjCUj4TrTxBR7fXxWvDQ7dJN77PfayuHT31wToK0Qk6Og4hILt03UokSEe2CsBWhkAGJOx4VluKaMeDPb7I7paGPSAzsbew3XR+Op2G/+92u5IUKiuQKqz0RNS0u7sbkm7j8XhjRaHtawtrb7bSw0aV1pbus+3o5M/4YMMiNk2yqwmjtm2jCfbljQuxnxzBslEQNWQ7Y1nDwailt+Vn2xY1RA3DJjuiBmFnwCiimWbuSTLB6o5W5mCnrFarpYuLCw2Hw6DT2e/Z0i+WFLMAYrV6eyoBJw1j3LYuFw8YrzY6qZDosxtU8xlW+UFqeLsQoKSgi0KgdgDbdrPtEj1JA4+d6gue2y4e4dmo+IA07MQI8fB962HitVGTvC3cp90LhUKQYJj4E4nEOyu1iGAymUzQs3kePmfHFtdGjpHeaoc2/Ka/ojvToY/bQ0a5H9e3/W3rdrEvdpaLnqKBdwrR2g1tIN5MJhPyKkQg9CGyh9VB7YkitiQvmpiN2iY2CFHaPVfeF8FG7ZJ78u44dXZnOzt+on1G/9htI+NCbJuYS5unHtynodjQglnbbv0o6V7CteBveLnMksxuGLYtjbHftQPJhj1WQ7ayCIZ7e3ur169f69mzZ3r16pWWy2VY0hk1WGnz7Cp2w18u356fhiZnZ3fIpFgsBiOyJWdWB7MkbQvOMRC77SJesy0d29vbU6PR2NhInc9aQrBSjM2eR3U5iJmd/ck6s4AD4rTr7CE/3iNavRDdR5nwG4+Sk0jYKJ3dukg88j4sw8WbjNYO24UWNoIik86+FjYCispNfDbaHxAEHmo04cf7o/9C7kgSyHfX19e6vr7eOAcNvbder2+UVvKD1NTpdNTpdJROp0OiLZlMhmOeGO9RGY2ow9YLQ6BRaQCCjJ7eEbXTD9m21f+tE2Ht+n0lbDay+FDJ6E+N2PdeuC+ct39jEFo9DvKxoep9DWXDIVt8z4Cxu5ptu4atuWw2mxvbF7IlH6SAB7der8PAPTs706tXr9RqtSS9LcBmMPAsvCcDhs2p2UKRgW9PHsYA8ZS4PyEpz1YqlTZqSNH2GLC0kTUcm6mmdIxSpKurq3cIGkKwui/kQFjO50hqkkw8Pj4OCaC7uzebtK9WK/V6vXcy6NLbI2XI0rM4AmnGGhpkAkGXy2UdHR2p0WgolUqFJdbn5+eh3+gX6W1VBUZJ/9pxZ7VF21Z7e3uhHC+akLJylV3MADnTLzgc9th25A/uRwLKfp5tItmjg4ofvFlbA2srMQixu92u2u12GBtUlCDH4FjgDNnl5fl8XtVq9R35IGrr3BPb4f2xOUum9xGvTUrSP9v4gnEXtXFbYREn2YLYFkfYQmoQHZTS5u5fGBEdLWmjdGhbg3I/QnFmX/YiYCaF9LY1eir1Zo062y6SpKBz7X4GZP9tPaVdikmG2/5EQ2bbNniqkjaWpJLoib4ng2+9Xuv6+npjqSZanh340QjAJpx4Hrtah5IgewqBrUKAXBnseKZ2orPeIcmYWq0WDk/EcKgjjoaY0tuz4EqlUvC8kDtYVcV37LvSBs1mU3t7e8HzZwPvXq+34bWiteLtSW9XpNn+sxOXbSdkCdqJ7y6Xy4361uhiGht6c9ru9fV1SAhR3M97WjuyWXi7haH0dil19H52wsQWbJTEZk79fj9s3IMjYB0PapupruGYdFtja2HflffgGe0SY2vH0e/DDZA/UQr3s3rxtjER5aAoL/3ciHUZMCthokQXlRuYDRnkDCI8TTwu+x07O0K4aLL8F0/MLhqIhkB8plar6eHDh2FTZ0iWEN4uj7XeHB2PwbMhR3Q3fwyXRQ+1Wi1sUUkCimXGtgrAehK25tGe82Q1LxYbQCT2PXl/ElDck9CczVys9skzWL3SZrulN1pwpVIJHhCEISkkgfAmMThkByYLPE3Gjl2MYhdbWK+PihDuYzP2dkMcPgsBM47sBMMkwPi0P/zOhsKE6XZjfapNSGqWy+UwVrclJanJvbm5CZ74+fl5OCF4tVqFSMJWCdBGjJfpdBr6mlVbnIpsV6vZSJAFO+xvggdtJ5ttXiwTDvkKIi+ON48msmgr7mtlHDsh2VV8UUJkciKBZ+1xmzcfdfwYG3a/EvoyDsS+Io3NZmx2lvDJlpdE9UBbBsO+ATaRY0PjXC63NbkkacPAbQgJonquPdiQgYABQ95WX8QzxSg5YtyuiIMUSVQ1Gg3N5282rq5UKuHQQEK2Wq0WloUy4dj9Ydm+EgK0+me5XA4LM2xCgXYjkUO7QkaLxUI3Nzd6/fp1WEzCd2kjm3yw8kK1WtX+/n4o0WPjEbz1er0ejm2xXhd9UywWg8TC2OF0aHuUjPR2YkL/tQsIomfX2fugwTLpoKGzSoo2vb29fWdiZhxFtXk2CspmsxoMBqG2eblcBkK3kz3XZexAuuzL22q1wom7bFBDv9oklJV0cG5snTcbwjSbzVBSZ5cRs2KzVqup2+0G4kKGI1Kx0pOt6lmv1xt1xjaZFm07G01gV9bL5pm4h92a1U768/k8THBWKolKCla7tnbB/bHJ/5Yr0nZ2dvTZZ59t1FSiYdribwiQTiCzCuxmLdYDo6Ojy0rn87k6nU6QFiaTSTA6ln5G6wlns5kuLy/15ZdfajKZhM3B0XnR7uz2g0wIHG9zcXGh9XrzOB1mdt7HThYUytudkOwS2tVqFTwHVt6wHaYtd8Hg+SzX2aaX4R2xJHQwGIQKCjYwb7Vaod5R0oZnaENdvMZSqaS9vT09fvxY9Xo9EI/tW6tJY0D8DQlBUsi+k3jjAEZWuNnIAUOmZhMShQiYvGnnYrGow8PD0HY8PxNOt9sNkg2/tyVNVluVpNFopLOzs3B0D+WBdgl69Iw6+sGWgUkKei79aPfVYBxQj02lAbLL3t7eOzu12aOVaBMb9eBArNfrsFE5BMfm+MhGqdSbzXn29vZC1p/SsXa7rVarpdPTU11dXW31cu3Emky+OXGj0+no7u4u1MsS/SFTkHCz3ADpYhtwA4tYbNIaO6Jczu55srOzo/F4HCTEOBCbplsul/UXf/EXymazIcNIedXFxUU4pyl6fDogkYC8wOxrGxSjtEtrJ5NJOK7ZrtYhvLWaK17HfD4Pz9Jut8NGJJ988kk4pgajIhSnKPurr77Sf/zHf4Qs8Hg8VrPZ1NOnT7cmtGz2u9lshknASh+U0GF8NvNu5QwbGSQSiXBNVrJFKz4wACor5vN52BD72bNnOj8/f6dwHIOlHpMEEJ4cm0l/+umnOjw8fGcPWCvDUFdplxDTlySAWHZaqVQ2NgeyWX8I2a7Is2ODeyaTyVBrHY06rMc6HA51cXGh2WwWTpBlsrbRhE2G2a0zX716pZOTE3366af69a9/rXq9HhJ5tq7b9gPLehuNRqhJtwtlGO/r9Xpj208iAojEVnYQ/lsPj0ncetnJ5JvVYK1WS8+fP9fFxYWWy2VYpMTmR1aTlhQ8YfZffv78eTh4NXq8FuPd2t10OtXl5WV4V0iaSTaRSIRValauwCFjcQ/jEG6wC1PIBdAHh4eHqtfrIVotFAqaz+dB+nlf1cRPhdg83WKxqE8//TR4Eng/t7e3QW5gNysan1B9Op0GI7TLMyUF7+rzzz/X06dPtbe3p7u7O11cXOj7779Xq9UKZ2QRGqG1oQFtE/sxPrzK5XKpw8PDEDpjHKlUKhDl6emp/vCHP+jbb78NZ4r1ej0Vi0U9ffpUn3322cbCDggLErOGBfD2mRgoC+p0OuEYaTw7ElfSm7CblXN4DduAxwjhc1bXN998o4uLi41tNu1mNJCu9UaZ+HZ3d8MAZ1nutgUkg8FAiURCvV4vEIv0tqyM5dcQLkQZLRmzGxjZAzFpR66dTqdDqE2FgZ2IIHE87U6nE7xDNG/aGuKw9cFIG/l8XsPhUOVyWX/1V3+l4+PjsM/GtoQNUUe5XNbh4WE4gQLJyCaHiI7S6bQqlUqo9oCool5sNBK0k4xNiLZaLf3xj3/U119/veHl393d6bPPPtMXX3yxocdDiEgf3333nb777jv1er2tyWn6isQri3IYC0SttMEnn3yio6MjpVIptdttff311/rmm2/U6XRCBIdHTzRpE4lEkOv1OkgsT5480aeffhqcJfpxuVxuLL76uRGbp5vP53VwcBBCPUnBS5nN3hxFcnV1tZGskrbv8E4ohDd3cHCgv/zLv9Rf//Vfq9lsajwe68svv9TV1dVG+Io3iPfDLLhtFYyd8UgqrVarUF9KKQ1GICmcmcYsi1fCBjXUM1rPCl0Vbc7CLqDgOW0m3VYa8C6EbmyGzcYr7wPEy4kSPHdUx7XJGtoVbwb9uVqtvrO3xLbEEd7WeDwORGHrY9kQhiQQWiulWNabw+MaDoehfpX9Yq2Hxf6r1OxGPX/ILJFIhPdAj59MJmHiJzpJJBJhiS7tZCOs1WoVvNf7CNfC6uHo3oVCIZR/WTnH5jkSicTG0utt2Xg77mxlABEDu3KR9JMUdP2bm5vwfEygyCUkTNmxDLnlvqoiW9UCSeJkQZ6lUkmffvqpfvOb36hYLIZl7zbJh/3b/sOhsfX8jNl6va6TkxM9evQoHK0lKSRfee44PN3EB+rUfrIiNjwc/h39fTTE33jIDzQExmpLoEieIdJvu5bVcd8HK8Rj9NsyqtG155ZYLWH8UHA9W0bHO92XGcbz/aFZWTxHm9iMXn9bG9psuPWoPgS74GTb6qHoz32gjaI/9tl+SHmQ9QJtJpzrbbtvtK2iEsTHwvbzNpuw48r284fa6H3vamvXo+9hV7BFv2cXjmxrhygs4QP7HfrIvhN9cd/yYov7xizXjDoA/Ds6Af8EuPdisZGuw+Fw/A/CvaT7ixzBvg0fmiE/hPtmqR973Y+5x8fc76ecRT/Wm/g57/Fz3ffPFb9Ue8TR1x+6148Z938Kfi5b/nMZn+7pOhwOx0+Pexk+vrVvDofD4XDSdTgcjjjhpOtwOBwxwknX4XA4YoSTrsPhcMQIJ12Hw+GIEU66DofDESOcdB0OhyNGOOk6HA5HjHDSdTgcjhjhpOtwOBwxwknX4XA4YoSTrsPhcMQIJ12Hw+GIEU66DofDESOcdB0OhyNGOOk6HA5HjHDSdTgcjhjhpOtwOBwxwknX4XA4YoSTrsPhcMQIJ12Hw+GIEU66DofDESOcdB0OhyNGOOk6HA5HjHDSdTgcjhjhpOtwOBwxwknX4XA4YoSTrsPhcMQIJ12Hw+GIEU66DofDESOcdB0OhyNGOOk6HA5HjHDSdTgcjhjhpOtwOBwxwknX4XA4YoSTrsPhcMQIJ12Hw+GIEU66DofDESOcdB0OhyNGOOk6HA5HjHDSdTgcjhjhpOtwOBwxwknX4XA4YoSTrsPhcMQIJ12Hw+GIEU66DofDESOcdB0OhyNGOOk6HA5HjHDSdTgcjhjhpOtwOBwxwknX4XA4YoSTrsPhcMQIJ12Hw+GIEU66DofDESOcdB0OhyNGOOk6HA5HjHDSdTgcjhjhpOtwOBwxwknX4XA4YoSTrsPhcMQIJ12Hw+GIEU66DofDESOcdB0OhyNGOOk6HA5HjEh/4O+JWJ7C4XA4/ofAPV2Hw+GIEU66DofDESOcdB0OhyNGOOk6HA5HjHDSdTgcjhjhpOtwOBwx4v8H/SKPiYUcxD4AAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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LYDCIfr8Pt9utItDGBcjFNT09jWQyiVarpYTV4eGhEv4nJyeoVCpvzRjhs+oaPYA3BJwucN1u9xsC4yz4ez4P5xXnwTABD5ydjWH8/m63i3K5jL29PbRaLYyNjcFqtapNPxAIIJlMYmZmBsFgcECb1Dca+lsbjQby+Tyy2ayyavr9PrLZLDY2NuDz+TAzMwOXy4Vms4lUKoWvv/4ajx8/RjabhdfrRTwex9jYGHq9HkqlEmq1mtqg4/G4GhMGQPm8usth2LMWCgVsbm7i8PAQxWJRuSyM2SP8vNVqVRv/+7AwdfcC712XAcPkA+9rVAYD50OhUMDe3h6cTify+Tz6/T7Gxsbg8/nOHJf3hWmabjabxZ07dxCJRNBoNJTAHfYSyVkPr5uu/HfgtZB/XwNns9nQbreRzWZRrVbhdruVgOQk0P1knPyVSkVpFWc9A3dX/i19Uel0GhsbG0in00o7dTqdKhDQbDaVH7tcLmN5eRnBYBCNRgN7e3vY3d1FrVZTKWYMflUqFWUVMPWMGgXHjAJkamoKU1NTKJVKKmhYKpXQaDSUBnyezc2o6QGvhR/fPceQC433SJP5PEK31Wqh0WiozYTuHX3R8tpcpPr3vk1gcDMvlUoq+4FphHTdcMwCgcDAnNStCN5nvV5HpVJBs9lUvmObzYZarYbd3V24XC6USiW43W6USiVsbGzg0aNH2NvbQ71eh8vlwvHxsZoX1O7j8ThisZiyFLlO+GxvS2nk5lIul5Wg1gW3Lvx6vR4qlQqy2Sza7bZyc3xTuIaNa5v/fZYlO+o9co51Oh0leNPpNHK5HK5du4bJyclvfN/nwXRNd35+Hg6HA+12W2k0DodjIFCmD7iOLhRoSnq9XhW0Yt7eqJ3uD8FisaBcLuPZs2dYXV2F0+mEx+NR17NarZicnMTly5cxNjaGYDCIubm5AXfHqA2Ak5n/zv+2Wq1qY8pkMuj1enC73eq61Dzb7Tb29/fR6/WQyWTg9/vRaDRwdHSEdDqNarWqggUMLlIoMfvB4/GovEVOZprZvPdOp4NKpYJaraYyBYw50W+b6EaNVTfvuZB04UA3BO/7bTmg3W5XBVx1n7VuovK5jPeqB5mAN7Vh/TPUlrjh0Dz1er3w+/0DvsVut6tcA3Ql1et11Ot1lbXTbDaVP7nRaKBYLKLZbKJYLCKbzSrXUaVSwf7+Pg4ODlAqlQY05n6/r1wwzBdn0HWY0D1LIeG9OhwOzM7OYmVlBeVyGRMTE5iamlJCld9Xr9extbWFtbU1VCqV96bs6No4N2IG3XWrYNi8MGq8XFe6vGm328hkMmi329je3kapVFJ/8z9G063X6yqfkD5F+u1qtdobpqfRD6WbjyxUiEajCIfDapKVSiU1Cd+X0O12u2rDmJychN1ux9TUFDweD7rdLjweD1ZXV+H3+1EoFODxeFQeqtElQoz3x9/pgpdCkFATcrlcavE2m031zMfHx7BarSoVj75yn88Hi8WCZDKpFni9XlcT1+12IxAIwO/3q+ASF/3R0REODw9xfHyMXC6Her2uNjYuet6v8T3xeUaZo7rA1RPtdUHLa5znXVLTpbalFwroAp7fa7yPYe/I+Bz8LBc6fYwOh0P5vGOxGI6PjzEzMwOv16uuRV99uVxGo9FQ38v0JgYZ9/b2UK1WYbFY4PP5VJoe/ZH8W32zNKZJGosX9OfTzXKjgNE1cr/fj5s3byIajaJer6vsDY/Ho4K/9XodqVQKd+7cwePHj1EsFt9bgQTvhWs9GAzC6XSi3W4rlwznBq+pKwqck3xuu92uNkaOWbVaRbVaxc7OjkpLNQNTA2nZbBYnJycDE4nBKJvNNpAaNmrR0ccai8WwvLyMqakpWK1WZLNZpFIpNSnfl9Dt9Xool8vY3NxEIBCAy+XCzZs3MTExoZLk5+bmMDk5qTQAapA0J42anm6i6Qtad1f4fD6MjY0pf2E0GkUymYTP50O1WlUCkcEuaktcgBSIjGRXKhUAUD4ymrYAEAwGEYvFlKZWr9eRyWSQSqVwcHCAQqEwcA3j+zCa0fpk1xf9MH+qMfKsZx6c5/McM6OPX9fsqPkag2c63BiM/nXjMxB9nvLfLRYLjo6OkEqlMD8/j2AwqJ6jXC4jm80qjcrv96uAFABV5HF8fKy0fLqGXC6XUlKcTieCwSCSyeQb84GCKBwOw+v1DoyR0SWmb4jDFByv14ulpSXMzs4qtxezSWj5HBwc4N69e/jd736Hzc1N5Y54H/AenU4nYrEYpqenEYlE0Ov1kEqlsLGxMbQijfeqb+Ycx0AgoNYlg520KIbl9X5bmOJeAF4Lr3w+j3a7rVJObDYbIpGISrVpNBoDPkT9Ozj5PR4P5ubmcOvWLczPz6Pf72Nvbw8WiwWlUgn5fP69PkOz2UQmk8Ha2hpCoRACgQDcbjfi8fgbaUHDAiaEQlgvi9VTe/RgUyAQwMTEhCpKmJ+fx9zcHLxeL/L5PNbX11Gv11EoFJT2ZEyTojDsdDrwer3KMuC9VCoVlWbHCis9Je6sYJbuCjEuaKN2zw2F19XRv0PfjIZtmgyU6j5fppwZP6+/B91Vxevw+3T3hi5sjXNO3xiNyoD+WZqwFKb0AzMbpF6vw+v1Kguj3z/Nh2XWRaPRUJsm/fAUeG63G2NjYwiFQlhZWcGlS5cQDodRq9Wws7ODra0tlEolxGIxBAIBpZVzPjA9jrnURmtAH2e73Y5QKDSwSfFZqtUq8vk8Xrx4gfv37+PZs2fIZDLvvSjJarUiEAhgenoaq6urmJmZgcViwfb2tirOGSUj3G43QqEQgsHgG/ns9H1Xq1VlfXCdvi9l7SxM03Q50avVqppc9MlGIhEkk0kEg0G0Wi1sbW1hfX196EvkgE5MTGBxcRGzs7Nqt0un00oAvu97bzQaODk5wfr6OpLJJMLhMDwejwqs6Sk0uklJ87HZbCqznhPF5/MhEokgFAoNCDen04lIJILp6WmEQiHEYjFcuHAB09PTcLlcKBaL6HQ62N/fVz5d3fepCzbmE1+9ehULCwsIh8Po919nL1QqFeXmoX+ceZ+cgE6nU2VD6Fq0XpDABW70o/H/M89W15S54HU/GzCY7UDocmFZuB5UpBZu/Dw1HOP3A1CLjAKGeb66D1Z3P/D/MxBGAc1rRCIRLC8v49NPP8XHH3+sNF364Pnj8/ng9/sRjUZVKbTD4cDVq1fx8OFD7O/vq3mvv0cKfLvdjng8jqWlJaysrCAUCqHZbMLv98PtdiOTySAQCAxU1HFO6r5iujC4BllUQSWAG5vRCqGw2t/fx9raGtbX13FycnJmSt8fCu8vkUhgdnYWs7OzaoPe2toaWXZst9sxNjaGS5cuYWFhAU6nE6VSCUdHR8jlcioXmi46jpFZmC50G42GWnx02IdCIVy8eBHz8/OwWq1IJpNK9dcbbQCD5rluHum5oO8zR5cwd3Vvbw8PHz5UQTO6HIaZ0vpkrdfryOfzODk5UZp4PB5XgoQL2+VyIRQKYXJyUpWXjo2NIR6PK7+WxWJBIpFANBodqB/XtTIAqkTzk08+wc2bNzE+Pg6n06kCEgxK0J3h9XqV+8Hj8SAUCiEej2N6ehq/+c1vcPfuXRwfH6tNgwKafjL9uSnEGOVnibQeYHI4HAgEAsrd1G63USwWlUaoCz5qLhMTE8q1w7xVpjXRLKcgdbvdCAaDqiSXc4qVb/p9BINBhMNh9S64SdDdA5yW3tIlw8Xq8XiQTCbx4Ycf4oc//OGA64mCmmPKiiiOGd+l0+nErVu3sLm5id3dXezs7Ax1b9hsNvh8PkSjUSQSCYTDYfh8PrhcLsTjcdRqNXi9XjV/uJnTKmBWQiqVQiaTAXBa+JNIJAC83th4XaM/nsrH8fEx1tbW8PXXX2Nvbw+1Wu29ZCzocA1zPdMa0dfXMH8/Yx9zc3P49NNPcenSJXS7XWxvb6Ner+P4+FgVHlHj5/eZhekNb3RtxmKxqEnOum76ovSKNaPZXiqVsLm5iampKfVC9vb2kEqlkM1m1eJ733Q6HRSLRWxtbSGRSGBubg4zMzPq95wQnCB6bXe/f1rMQXOy3z/NDXQ4HMqs1xPC2cjn5OREbSy6OU2BQW1bN7EpKMbGxvDBBx/gk08+wcTEhKpEotBlGhoApeVwYtOkXVhYgMfjQaPRUItVH1u3243x8XFEo1H1XZz4FHSHh4eqdJuBLjYzisViqoJP9/uzpJgbQDwex8LCAlZWVjA5OQmPx6MCORsbG9ja2kI6nVZNb1i6y83J5XKhXC7j4OBAuVb4zpgJk0wmMTExoTYAfVGyjwU3X11bTyaT+P73v49PP/0UyWRSpejp8xyAMnH1pjgUuhMTE/jkk0+wvr6OYrGIQqGgNjC73a40dloo7P3gdDqVwKOfeHx8HJOTk2qz4RykAObmwTnC4Dbng1HL5XMAr4uc1tbWVO+Db8Mf2u+fZrBks1ns7+8jmUwqxWptbW3Ah6xvUFbraWHJ4uIiLl26hLm5OeUOYeCSBRZm+nF1TO29AAxWB1mtVuVTqVQqaLVaytQZGxt7w1cKQFXxvHjxAjabTVUGHR4eYnt7G4VC4b32XdDhRGdhAPNg9QnKe9V9hfTvNRoNpWn2ej2lKXIh6UKbbgC73Y5SqaTKp5ke9urVK1UFRT+nHjBxOBwYHx/HysqK8g3rphgXM+/ZGOXnfVATd7lcyj1CLY9Bw4mJCSwtLSkfJYVFq9VCJpNBOp1WGj/fi9VqhcfjQSwWU2Wl1HD18l8WaiwtLeHjjz/G5cuXkUgklLvg5OREVUvRKmLamMPhgNfrRTgcVhkazCLQ0454Xw6HA2NjY4jFYuo9cUxYQpxOp5W7CDh1lzUaDRWoYWtPfaz1fGy6JIzvwuFwYHJyEsvLy9jc3FSZJvoGyCydk5MTvHr1Cna7HbFYDFarVa0dNtxheavuV3e73UrD5hpkCSyDTJwHxvmsz+V6vf5Gv4r3Dd8Ly+x9Ph/y+TxKpRKePn2KFy9eqCZL+jjSGqD8AF5bKJQzurWtb45mYbqmy0UBQCXZHxwcYHNzE36/H+Pj4ygUCqqGe1hxAatKnj9/jlqtpkwrlqN+G309ie5rpGYxbKEZFx1N1lwuh+PjY5UrOyq1x+l0IhQKKROKronj42O8ePFClVGXSiWVk6n3eqBmxGBMs9kcaAqim+F6IMnoI+v1emqsd3Z2lCnJxciWgktLS/D7/WqCMx2nUCggn8+/4XfVtROmH7GUulgsquISRsu9Xi8mJiYwMzODSCSi/r/L5VKpetROdb8rfZbUDoe9R95zLpdDoVBAKBRCKBRS/leHw6Hq/Tc3Nwd8vgxiPX/+HB999BGCweDA+HHz5Qanm7N8B/R5NxoN2Gw2FcAk1DypbdMNk0qlcOHCBYyPjyMUCiEcDiMejyMUCqngrA6FJtOumOlAjY9zQN8UdMHLTczn8wEYXW77vqCSk8vlsLOzg3Q6jXQ6rapZhxXm8JlrtRpOTk7QarVweHiIzc1NHBwcKKF7ntzvb4vvpIk5dzGLxaIG59mzZ+h2u5iamkKz2VTtAIc5yoHXrQcPDg7UAhuWZjQKTi59AZ0XVmzRNBsmcLmg+v3T/NGjoyPcv38f//Vf/4WdnR34/X6EQiFcu3Zt6DUsFovSnqrVKg4PD/H8+XM8ePAAGxsbODk5UdqWXs5LE58FJ8fHx9ja2lJtCanN6Br6MGHLcSmXy3j8+DG++uorZV5TGLpcLhXQZOI8M09yuZyqjGOhht5ngpkq+XxebaxsBcmqN2q61PSpyfn9/oHsBY/Hg2azOeAL1rMVLBaL8kMz4q0H/RiR1wOxVqtVFaQEg0GEQiHU63Wsr6+rFpTMTslkMvjqq69w48YNRCIRlQKpCy6ONS09PfjJFL2trS0cHx+rWIe+MTEdsFgsIp1OI5VKYWtrC7u7u/je976HDz74YCDIOGrdVKtVbG1t4dGjRypNjeM8MzOjtENeV/93jsnY2Jh6B++CMbh6nrXKcSoWi8jn80in0yrWM+oatICeP38Ol8ulXFC0To1ZRWbznWi6hEK1Uqng8PAQNpsN1WoVVqtV5TPq6URGuFio1QGv68zfJkTP+t5RcOLpaUGjJjeftV6v48WLF/jlL3+JX/3qV9jY2FABD6fTqdr46ZOd16LZabfbUS6XVRObdDqtWmEy+Eatm8/EhXpwcKB6XiwtLWFiYkItTKOGbbx3Vrx98cUX2NraQqvVUgvH7XZjdnYWN27cUC0vGfjb29vDy5cvsb+/j3Q6rZqVEG66FHTsJEezn8JZ9+NTMBgLHqgFGlPW9FQt3dfLwhGjC4pNVQ4PD5Wm3O12VR653+/HhQsXcOPGDaRSKRX9Bk79va9evcKdO3ewuLio0iGNmqYe6OSGUalUcHR0hM3NTTx69AgHBwfodDpK2+X7ZNYLs0eY7sSKuMXFxYFeBcPeaavVwvb2Nu7cuYNHjx6hVqtha2tLvafPP/8cFy5cgM/nGylQ9SClbuGdV2kx5lifhS6k9eyfUQJTT18sFovY29tDr9dTOe16rjkxM4BGvvODKfXFUSwWB9JX/H6/anQ+TBtl3iEd6novh/MMJhfou2i5nAjUOprNphL4xu/udDrY29vDv/7rv+Kf/umf8OrVK7X4q9UqHj9+jAcPHuDq1asDQlC/Ny64ly9f4tmzZyp7QM+OYJR+ZmYGPp9P9Z2l//n58+dKQFNrGyYUjPdfrVaxvr6Ox48fq+bhusD9wQ9+gFu3bqmm3plMBs+ePcOjR4+wv7+vTHI9YMGxpkuBEXEegwRgIBPCmCFhFCYUAtx06K/j5ghgIF1Nz4M1vvdWq6UCQ5VKRfVY8Hq9mJ2dRTwex4cffqiCfHt7e2rDKJVKePLkCdbX1zEzMzOywbzR71+r1fDq1Ss8fPgQGxsbqrlMIBCAz+eD2+1GtVrF7u6u2ix4751OB4eHh3j69ClWV1exuro6sPHoc6nb7SKXy+H+/ft4+vQp0um0clPk83nkcjm0Wi38xV/8BRYXF0daP8yj5xifpXQMm1OMO5wHzlEGLzmnh61tzgPOFTYJYsaKvpl/13ynQtcYXGNUPRqNwm63q+grfZrDoo0sa9WDSOfdfc+z2xrhhCmVStjf30cul1OajXECsq6bXe7ZgJvk83k8f/4cqVQK4XB4QHjTrCqXy3j+/Dnu3r2ryhV5z7x/ajqffPIJYrEYisWiyv7I5/M4PDxEr9dDKBTC4uIigLef5dXrnZ6scP/+fZWMTk0zkUjggw8+wM2bN5FMJtHv99Vnb9++ja2trQHt1qiZ6GWbfE66K1jNx3tkTjZba+qBWJqoHo8Hk5OTmJ+fh8vleqN9pO7r1cuYgcGYAYUyBUs+n1fZM263W1WB3bp1SxUD0A3WbDaxvb2NBw8eqNMpzoLjz0ycZ8+e4eTkBMBpGtfc3Bzm5+cRCoWQyWRw584dFUjS1w3L67/88kssLy8jFosNZCxwnrRaLaRSKTx//hy5XG5A4+v1eur4qRs3bgzdNCjk8/m82lCBtzfPMUJl4W3oWT8UniwAGgZTT2dnZ1U+eqfTUQcl6PPGzKDZMP4oNF19BwwEAkqraLVamJiYgMvlwvr6+lDnOX1yjPTrCe2jBpcvlL6ld4GTjFVq+XweyWRyqBbW7582BGHFi66B8P6Yw8oafk40aiFPnz7FL3/5Szx8+FAFl4wEAgFcvXoVn376KaLRKAqFgvIF84gbdki7ePGiqqEfBe/r7t27uH37tipNBaCEI3s60Fx/8OABfvvb32J9fV2dUUW/sZ7yp/tSdf8mCy48Hg8ikQhisZjqPZBIJFTqGhc/fbr9fh8ejwcLCwvKl8dAXDabVcnwFLq6wOX755hTODGnlcFAXu/GjRvquZkfC0D5Zo+OjvD73/8eq6urmJycRCQSOXMu1et1vHr1Co8ePcLW1haazaYqyb527RquXLmCUCiEbDarNt90Oj3wHUx/e/jwoTo25+rVqwiFQgNtEZknbmwyzufmBj/qfEGOTS6XQyaTURr+u2i6hOP9tjXKe6eVMioriQJ3ZWUFN2/eVAURdCvR8v1DlKxvg+9c6AKDR/kwOLOwsACHw4GpqSk4HA4VdTQ64PUotG5S6QtJh35ABhs4mc6L/vd6JsAwmL7CBj8U2DSFmNrCdC+9gUelUsGLFy/w61//Grdv31YalRGr1Qq/34/p6WlMTEwgFArB6/WiUqlgY2NDVRCVSiWsra3h9u3buHDhwsiAC6//6tUr/Md//Ac2NjaUhm61nnY/S6fTeP78ObxeLw4PD5HNZnH//n2sr6+rqDjwekPVJ71xvHQByCCdz+fD7OysqrdnkQa7rOnfQ0E9OTkJh8OB6elpdSAmA1R6fwTj3OE/9awC3gc14/X1dTUHo9GoCmrSH633CdnY2MCvfvUrpe0a08N4vXa7jb29Pdy+fRtra2tIp9Ow2Wzw+/2IRCKYnZ3F5OSk8v1PT0+r3hhGwUGN7vbt24hEInC73VhZWVHBLt2KDAaDqskUx5DZFWyWf1Yzb36eedjvqumy+o9jP6yEW9fS9TUxqhjC4/FgZmYGn3zyCT7++GPE43GVGbS1tXWmwP4uMF3oGicNJzy7/rTbbbjdbnXcSSgUUl206Hca5psxmov6SyN6hzK32z1gfp7nhehRbb1KbFhAjROHWiHNXu7gLG+cm5tDLBYbKCOmGbe2tob79+/j6Oho5KThguG9Ma0nHo8jmUzC7/crTfXk5AT379/H6uoqJiYm1AGa+r3TnNva2sLGxsaAlk7Bksvl8PTpUxSLRSQSCdTrdezu7qqOZxxz/bvPyhDRtS2+X7vdrqqlQqGQOrFAT9wHoJ53bGxMZXwweMLve1vnOX3j5uLWN3L2smXp+vHxMVKplIqi68KbGunW1hYuX76sGnsbr0X/6v3791Umis/nUz03EomE6iKmV10O0yx1/+6DBw/UOXB6Zo3dbkc0GsXCwgLi8bhy/+hClP03hmmw/A5aHnrmznljKFRYWADFvh/6eHPe6K6RUZkOnO+RSARXr17FzZs3sbS0pPzgh4eHKtg+yo//rsH094HpQnfYrk8NjqY2K5G4m87OzuLq1auqoYcefdYXE/1Fxkmq50p6PB7laGeV1LveP80ZCoRhQpfPpTc01wsXvF4vkskkZmdnlbZLodvtdlEqlVSFHV0qRnOMz0azr1KpKLMyGo1ieXlZNULnpEun09jc3EQmk1FHwRjRNRk+i/5cDDhxcwAwIHD190FhTatg2DNQOHOh0eWgCwMGV/UNBnjt7uAJyewpayzC4XWGda1j+qB+v3rAj1219vb20O+fRsYZ1DG+cwCqBHqU6d3tdpHNZtV7YDFHMBjEhQsXcPHiRXV8Oq0eNooaFq/gddhsn4c2sryXc5btGZPJJI6OjlQBBseLwUOuLWPKGO+RucDDXGpvg5opq+vYB4HWgu5+0+eCvs75ewbOKB9mZ2dVox9mejDn3zg3jWNnJqYJXS6SYbmxFFC1Wg1HR0c4ODjA0tKS2ul9Ph8WFhZw+fJlpNNpHBwcDORa8juA15qtnn/KFC/mefLoETaiPq/ZwYXLfFhqsMNMLAb/1tfXkUql3mgU4/V6kUgkEIvFBs5eozbJKDuzBZhep2u8nHzsCXFycoJ4PK58oZcuXVIZBF9//TVqtZpayKM0Z252ly9fxocffogXL14MnNRMGMAEoAoVRmkMzAvmuOhClT9Op1P1V+DRKXqCP1OsKOgoKKgZ61ob383Y2BiKxaLKkGDE3Xh9ploxGd8Ir0NfNfsKDxu7UCiEDz/8EJcvXx4peOle6HQ6quLP4/Hgxo0b+OEPf4hLly6pDl9sSq/32R0mDDl+zKzR14duocViMeUvpxXD4oxUKoX19XXcunULXq/3jfvmd/DsOl2pOQ987na7rbr1BYNBdcyQ3hhft6yMVgrXkMfjwfj4OC5fvozFxUX4/X4AUFk7bE3KNLNh38FKxnd1k3wTTBO69BPx+HBjAItm7dHREdbW1pBMJgEAY2Nj6Pf7qosTB3FY5Znx5eg5rOFwWLksms0mDg8Ph2orZ6ELck72UT475siy7R21eeY4sj8uzz4DXu/q7EhF04nNyjkx9Y5O3NVTqRQODw+xtLSkXB7j4+O4deuWshz29/fh8/lUieiwicZnm56exueff4779++PLPeka4bCyjj+HC9qqBwnll9S4HEhs//GwsKC6oHQ7XbVSb76UT66VWA8yZfVa7Ri2JmNJbv8rC6wXC6X8v3pJqceYNPf0bBxczqdWFlZwZ/8yZ+oPs+j5hFPoh0fH1ftCz/77DPcunVLNSai5n50dIT9/X1ljnMtUQjqCgWb+uunLHOsrNbTvgSTk5MIh8PqyCyOXzqdxtraGg4ODpBIJIYW/TCDhf/f2BbybfBYoWAwiGg0ilgshnA4jFwup4KenN9nFVDYbDaEQiEsLS1heXkZ4XAYvd5p+1ie78aMkGFKgz5+rAQ0i29d6PLlUGM7yyRhrfWTJ09Ug5K5uTl1qm48Hsf8/Dz29/eVIBiG7t9lJ6vJyUnE43FYLKeNpt92wqwRLhQ2laa5O+plsVKJB9/xb5nWcv36dVy+fBmBQEAJL7oK2AXJ4/FgdnYW0WhUmbgnJyc4PDxUuZLAaSZFOp3G4eGhCpBwvMfHx/G9730P3W4X6+vr6PV6iEajA60bh0EBcvXqVTx58mRoFY9+38YFovu0PR7PQGtE/f3T5ROJRDA5Oala+LHvbzabRT6fV2OplxPr75c9E8LhMFwuF8LhsOpaR62MwTG9raOuZfMZaZHobhJ9Yxk2Z2i9XLlyBZcuXRqZpwucCnK9RaPVasWlS5fwve99T/liLRaLyl9ng3Jq12ymxD4L4+PjKlhLrZkniDDoy/sOBAK4dOmS+t3e3p6yBoDTij1WHg57Bs4rugjohjpvDiytjnq9rnpH9Pt9Zc3x/LizBC7fWTQaxfz8POLxOHq9nqpW297exqNHj/DkyRPk8/kziymM/Y/NcDeY2tpR95UNo98/Tb7e399XGs7y8jLm5+cRjUZhsVgQDodVmeVZ6JFZarqsmtJf7HmgFkNtLBaLqWDCqKo0PQWOkyKRSODKlSu4evUqFhcXMT4+rvoV6GlKbCbCaDy1GaYYPXz4cOBvWN1Fvy6fi66C8fFxXLlyRR2dHgqFBrTpURONAUOXy6XOvzK+P2OmAq9L040LU/eX6lkN/HuOL3Oe+TxHR0fY3t7G3t4eMpmMaozE8WXDFragnJ+fH+j0pfcJ5v3q5isXNivd9O5i+mfPCgTSOmBns2HFMoRaM03s5eVlBAIBLC4uIpFIDLgk9PeqN3ehaT01NYUbN25gcXFRdYJjoQmP/+l2u6rCjEL3gw8+UGb5kydPsLa2prrZsevfWQUIoVBIdZaj8sNc5fO46nT3Gf2ynU5HpfidFfQEoGIE7DcBnG7O2WxWBYB5kvFZ96TPAzMzG0w9OYKT7axBpW+X5Zg87XZpaUnt5vrOdNZ19cXNScf0KfpYzwN3QwbPZmdnMT8/r4TuqOt7vV7Mz8/DZrMhHo/j+vXruHr1qhIKANQJvexXwB8ePhgMBpWAb7VasNvt6lgXPaeSZiKDI3p1HlOwAoGAEp70BbN7lW5Kcuzq9brqsk90AT0s2Vw3QfVgHO9PF2R69ggLEo6Pj1WQrlAo4OjoCMfHxygUCsotZYx0MwDEpkD6hthoNNTpycbyYt4X5yXHgn2A+Xs+IzXgYWPBz9RqNdRqNbUpGMe01Wopt8fU1BQmJyeVUsANh59lsYbuSuNc9ng8mJiYwMrKCi5cuACHw6G635VKJezu7qJQKCAajSISiShlxe/3KxfD6uoqPvroIzx+/Bhff/01MpmMOttt1PpkVsn8/Dx2dnaUxcX1NKp4QYdjTouOvmjd3/42IUgLyW63K0XlxYsXeP78uSprPqtkmHOAcQL9PX/bmKbp6tVJbzNDONkymYzSIDjJer3euf1INGepDbI3wLDa+1HQBKXfMRqNqsXCRHnj4uOCAU5dImNjY1hdXcW1a9dU43I+JyPObFfH3E+bzaZOp6BfGjhtWJJIJJQABaDMY+bd6pOWZi/T8GjC8TOs3jLmXFLLGlWQwfE1jhU1XPZlBaC0at0PrWcN6BkY+im77C2gCx6jxsl5xQXGrA+9AxwXlp7BYHxfukbEvGne67CA7TBoqfBedBcONxdehwUgPHnDmCalC3n9/fb7p/m2gUAAiUQC4+PjSttjyTU3qW63q46VWlpawtWrV1V5MZWFcDiMaDSKaDSKZ8+eDcxNapT6+7VYTgtDWCV4eHiIQqGgfKPnWVcUdlQ22A96VIaBESoTVKTK5bJq7cqez8yIOAt9nb5rkdQ3wfR+umf5avTPctcsl8vI5XLI5/MDpjjwbjl21DLYqOS8WQt6wIwanNfrHThUUE+v4XUymQz29/dRqVRUyz2WNXMxsq48lUrh7t27WFtbU/41l8ulghmxWAzRaFRp28lkErFYDKVSSZnw1GT0ggc9O0AXqDSd9f6zfE59A+GJCnp2hXHM9BQfaooMTvH5KHD4ncBrE1lv1MNiBgpaPamd1xllWVBwUpOkIGArQrfbjUQioYQ5o/tEPyWCY2q1WpUw0C20YTms3JgDgcDAvOA/dV8xo/+6oOUz8H3w/dE/PTY2hkKhMJCFkEwmVRtH9hjY39/HixcvVADJarUiFAqhUCggHA5jenp6ICAMQFlwLBtPpVKqMENP5ePzcA1Q09Stm/P016XQ1Y+jf1cNk5shXUHsH8FeLefRlvWx/h/lXjDyNn8NP0PtlFoP813pOzqvsGWwhYuIL/y8O5ve1UqvzTdq7Hwu5q6y8Us6nVYlqsO0YmZSvHr1Sh190u124XQ6VYoQNRFqvAw46ZpCMBhUR/cQPcVNX9x6ty79OXSBYrOdHhh64cIFxONxdby20Tw3jjOPBjemdDGKzmKReDyuSmX7/T5OTk5UZgqzSng9XbOhBs3v181Dfl4vEwagAqmxWAwWi0XlsvKsMH1j0A8KpR+d/S6oFRlT9nhfsVgMi4uLKlCpzw3gdSHLqKwXfp8xA4dHJ/H4HQZj6Zro90+LMvb29tQpGmzcw9aYbrdb+TiHXZO9ere3t1Eul1V1GvugGN1KXId63xM+23kygrjpU3OnO+q8WQTcZKvVqmr0z6Y2o6rXjJxHFn0bfCdC97yf018uj9xg+tR5oqV6cCMWiwEADg8PlVB/G3rwjBHlWq2mGm0b09boM97e3sbDhw/x7NkzNJtNeDyegaRz/Rnp32KjcX4nP3t4eIj9/X0sLCwgEomo4+enpqaQTqdVihNNa54VpvvlhgXKKCyAQdNK15IDgQAuXryIhYUFVd0zTIPQhYqu7enaJAU6Nfjr16/j2rVrSCQSqFQqePr0qervSiFoLK7Q3yW7SGUyGdUjV/883SwWiwXBYBDLy8tYXV2F3+/HyckJvv76azx+/Fi1UtTdMYzO8/+POryQz2S1WuH1erGwsIDl5WVVrjss3UrvRzHsnejzQj/tmTm9jA9MTU2pceh2u8jn89jb21PmPseD19P9rsY5SIuSlXbZbFZtjDxtQs+X5hrM5XIqjc3Y9Oltgpf3wcwVAMhkMmf2AibczMvlMjKZDFwu10B/jXcJjP2vELrvkkjNycc2ilbrae0/uySdx/fDclueOba/v38uf7DdbleaXjAYVL5Q4NSvSuHGaC8A1aRmZ2cH29vbyOfzbwQH9B8AKhc3Go0ilUoNmEaM8uq5i3pOKwM+1WoVx8fHeP78ueq9oJ8IcVZv1GH3Z9R2eSZZsVgcOV66VsqMAWYtcCFQG2KviIsXLyKRSKjn29nZwe7u7hvvh4uZp+7OzMzA7/crfzPT//RsCEINlPmcPp8PwWAQuVwOW1tbAwEcauosOaYrQjdDR42j1+tVmrtRy+WP0awfBgUKfdPPnj3D8+fPcXx8rJoWUfNlu0v6itmfhGuDgt7r9SprST8hWL8/PiOrNHd2drCzs6MaTukB0UajoZoK9ft91aSHWRNbW1sDuemjYIXb+Pg4bLbTo7foajoLbhTZbFadgsw2q8OKKc7CjBQxI6YJXV2DGlaOOQw9us3DHJl/+DahS4Gid/7v9/sqOEUn/Cg8Hg8uXbqETz/9FF6vV51q0G63VVqPUZBSY8hms6jVakoDSCQSqqxTdzFQm1xeXsZHH32EbreL3d1dFb2nwOBx3TR7dX8Wx0PPyKCJOjc3p4TKWdkeXOjGnFam1jF4+bZ3xWvz2CWa63wfus9UdxdYLBbE43HE43H4/X71/LoGTRcRzV6fz4d+v6+qAvXgl/43DDjFYjElpIxHFPG9+P1+1b7R4XCoLJHzzFXOJwo9vbE6LYSzcneB11H9fD6PnZ0d3Lt3D7dv31btGJnypVsQxrS5WCymApdM75qbm8OHH36IixcvDmjh/HE6nYhEIkgkEtjd3VXHLTEFMRwOD/ipORZMKXQ4HGqOs6n63bt3VaB31Hh5PB6Ew2GEw2Flkej9IkaNuR6IS6fTqoHPeX25wGD5Of/bLEwTutz9Oah656dRg8Tdl4UBFDJ6T9lRGIM5zAnUzd+z0lssFosKUDEIxvr+mZkZZXrpQQaePXVwcKCix2xgYiz35Zj4fD7VNDqZTOLFixc4OjpSh1gyLYjaJjUNnidGXxbNYJqh/GFByttMWvrC6G9st9tIp9PY2NjA0dGR8km/LXjpdDoxPj6O1dVVuFwupNNpbG9vqxOBWbDBxU+NjE164vE4yuUy+v3XVWDsyMYj75nRwZQ65h0Dr3v3sniA38mgEAV/IBDA+Pi4qtFn314m29OlsLe3d+Y84/tka8eNjQ1Eo1GVpcL5q28ww+CmxTMDHz9+jLt376qG46wq7Pf76iw3tttkl7UbN27A4/Hg6OgIrVZL9QBeWlrC5cuXMTc3p3J29QwSuqwmJibUplcoFHBwcIBcLjeQ96wH8mZnZ1UANZFIIBgMIp1OjyyN16HFw8ZT9P0zNmA84cFIr9cbOBdR9+WeNT91Nw9lxP/YijSXy4V4PI5oNKpyJhmU4n8b0U1sfpcxb9EInfmsxGk0Gnj58qU6Y0tPQzvrqHb2KG02m1hdXVUZA+FwGAsLCyrvlRpAr9dDsVjE/fv3VdNyAKrSiUECfWfVTWaPx4Pp6Wl89NFHKq/UYjntRubz+VQvWQpbLgg9+tvv99W5cexfoWcXjHo/zE1lDnOlUkEqlcKDBw/wxRdfYH19HbVabSATwmjC0ecaiUSwsrKCjz76CJFIRBW55HI5pf2zVSNzabvdrnKzTE9Pqw2Hgo/NWsbHxzExMYFIJKLSqOgaYZ4q5xFLrdkeku+JlWA3b97EzMwMisUi+v3TnGr9fLNcLodu97TFJS0cXQjoQovFHM+ePUO/30c6ncaNGzcwNTWlKsSojZ/1Hmg90bWQSqWUcNUDsfr75zV4/6urqwPH0AcCAdXnQO9apl+XG0K3e9qI5/DwUPnw6T/mJskNa35+XmnebK6+sbGBtbU1rK2tnanlct4DUE3Gq9WqKnWm20yPbxjRc31p9bLgadSa5jyni4bBO/6T9/ZtY5qm6/V61cmlfOnszaqX9RozAmhG6+WmxsnPPqQTExOIx+NKOLGwgocJslKIi/8sbbfXO20PmMvl8OrVK9y8eRM/+tGPVGCJNe56Mvvm5ib+5V/+Bffv31dtHDudDjY2NlQzZaMA1E1cmuX8Po4B/aKsxmJwjcE8/bOcjADO1R+V98Cm5pVKBevr6/j5z3+OO3fuKK2bwpt9hI2VZawQWlhYwAcffIDV1VVleurPobtkuPGy+o5J93a7HblcTvlpPR6PqtOPxWJqs+t0Our/ZTIZZLNZZQUxL3l6elql0jEFzu12Y35+/g3BSU2SGSi1Wg3r6+s4PDwcaJaj/w01JJ5hlslk8PLlS7x8+RJ/9md/phqf06V1FnpPW4vFMpCmxh92eNvf38fR0RGWl5dVVot+dpz+o2+Ww4QK0/U2NjawsbGBbDar1pDD4cDKygomJiYGfOs8+t1ut+PevXu4d+8eHjx4oMbqbc9ptVpRqVSwtrYGAEoBYwUdG7JbLBZV4q63owRe5/NznPQqQuPzOZ1OjI2NYXp6WlmpwOsTUs5q6v++MU3out1uzMzMYHp6GqFQSB1ncnR0hC+//BIPHz5UGq0OfYuj6PdPE81/8pOf4Mc//jESiQRKpRJ+97vf4d/+7d/UUTPA65QdRnPPUz3TarWws7MDALh586aqdtIbxjDl5s6dO/jyyy8HAk6pVAq3b9/Gn//5n2NpaWnoNfTULv25+E9dQJXLZezs7GB/f19pxLogAF6bm1NTU+cKTACDRRTb29v47W9/i1evXg1scHqSvu76oYkajUZx8eJFLC4uqmbaoxY6/97hcKDVaqmz0MbHx1VTIl7T6/UiEAioH1YwMcJfqVRQLpdVhRPnCzU9Fknwe5haN2xcuHitVisWFxdVe0y6t+jf5t/rVWPA6aZFBWJlZUXFBM7zDhiEnJqawsTEhBIMenqV1WpFuVxGKpXCzs4OKpXKwEGR+qZuzJ4YRb/fx9bWFn7/+98jlUqp/99sNvHFF1/giy++wCeffKKayNNiGxsbQyKRQLFYxIMHD9Q6eRtMA9ULl6g4uFwuzM/P48c//jH+z//5PwgEAjg+PsbPf/5z/OxnP1NHGumb0NvgJnzp0iV8//vfx/j4OACobB/Oa7Mwzb3AoFAymRxoaTg1NaWaVOhVQWelO+nYbDZcuXIFP/3pT/Hpp5/CarUq0/zevXvY398fEBxcUHr/2lHmiH5t5gAyGGNcRK1WC7u7u28cVNnv91UqztsCUqMWib7QGdlmo2/9PDEGjtiHwBhJfxtMfWInKLpqCCuhAoEAACgfMnBqyUxNTWF2dlaltp0Vqefv2FeCPXGj0ahqa8jz0vT+qzxHje+OqVDUlPQKtn6/rxL5aVa+7RRn3c/JTImpqSlVUAFgwBwtl8vqeXTYmOe8ApcwY4Qa2d7enopH6Bkv1Hj1s+uM+djngeO4v7+Pk5OTN9pRNptN7O7uqoo+Qu3R5/Op+WKc96OuRytTT+/jPKX754c//CE+++wzlRKXSCSwvb39xjmDwzDKDovltGfLpUuXcPPmTYTDYdUkihWgesD328bylkjfe0tio5tAN3P4wvU2dH8ITPVhxJhm/aiyRD0f8rzQrDKeBED6/f6AlmX8W/r1vgk0fYedhqBPMAYIaNq/6zWYM2x8H7rJys/qv+NCfNfm1kbXg/F6xucb9vfGfw67t7elaw37XhZfDLs3/VrDTFp2pPtDFjKvq89f/V3r7/msTeS8sNBh2JqgtTFq7BmXedf1xL83/n+9vwrXM62a81inw6CiZVS2OO/epTDjnIx8IaYJXUEQhP9FjBS6fxQHUwLfvDLkLA3ofXEebeI8ror3wdtylL/N7z8LM/MdzeK7HItv+z2f51rfZN7/oXwb6/mPZW6KpisIgvD+GSnhzTsYSBAEQRChKwiCYCYidAVBEExEhK4gCIKJiNAVBEEwERG6giAIJiJCVxAEwURE6AqCIJiICF1BEAQTEaErCIJgIiJ0BUEQTESEriAIgomI0BUEQTAREbqCIAgmIkJXEATBREToCoIgmIgIXUEQBBMRoSsIgmAiInQFQRBMRISuIAiCiYjQFQRBMBERuoIgCCYiQlcQBMFEROgKgiCYiAhdQRAEExGhKwiCYCIidAVBEExEhK4gCIKJiNAVBEEwERG6giAIJiJCVxAEwURE6AqCIJiICF1BEAQTEaErCIJgIiJ0BUEQTESEriAIgomI0BUEQTAREbqCIAgmIkJXEATBREToCoIgmIgIXUEQBBMRoSsIgmAiInQFQRBMRISuIAiCiYjQFQRBMBERuoIgCCYiQlcQBMFEROgKgiCYiAhdQRAEExGhKwiCYCIidAVBEExEhK4gCIKJiNAVBEEwERG6giAIJiJCVxAEwURE6AqCIJiICF1BEAQTEaErCIJgIiJ0BUEQTESEriAIgomI0BUEQTAREbqCIAgmIkJXEATBREToCoIgmIgIXUEQBBOxv+X3FlPuQhAE4X8JoukKgiCYiAhdQRAEExGhKwiCYCIidAVBEExEhK4gCIKJiNAVBEEwkf8fpltjLw1aZKgAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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Bx+Np2xDOgj/nM5nzpVO4m3/v3HDOeg6+562tLTQaDfT19aGnp0fMe7/fj6GhISQSCQQCgbZgmvn9nJ/Hx8eyiVYqFbmXw8NDvHjxAh6PB8PDw3C73ahUKtjY2MDDhw/x9OlTHB4ewuPxoK+vD8FgUHzo5XIZgUAAzWYTg4OD4o+mUOLmbgYbuz1rNpvF4uIiUqkUjo6O4HK5RKnpDBLSl2+63H5XuJZp9tP66JQL5jvmfZ31Hrlhbm1twePxtFmTVFy+bb51ocuXm8lk8OWXX6K3txelUgm5XK7NlOzGWTuxmd7Dv5vXe1v+Ge60BwcHKBaLYrKZWpTpE6I/9OjoSH521jOYu2uz2RSNPZ1OY3l5GQcHB7Db7aK10I3CwFQ2m0Umk8H09DR6e3tRrVaRSqWwurqKYrEIh8PRZn6WSiWxBFwul5iapqZAbXhwcBDDw8OixdO1USwWkcvlkM/nL7S5dROcZpoT/8vxoOBsNBpi9l9E6FIT4ufp3uGC5TX5d75H8z7Pu06tVkM+n4fNZkO5XIbf74fb7YbH40EkEsHw8DCGhobg8/leEwJUDhjsrVQqKBaLqFar8p4cDgeKxSJWV1dhs9mwt7cnlsWLFy/w7NkzpFIpVCoVuFwupFIpOBwOVCoVsRr7+/sRj8dRr9dlPM01dl5mRaPRwNHRkfhMmbZFxaBzQysWi9jf35frvQ2Fx/RBm2ub/3+WJdvtPZoWKtdoKpVCJpNBNpvF7Ows4vH4mZvR28Iyocso9fj4uEQRGRQqFApt/jdzwE1Mv1RPTw+8Xi98Pp/k3Jm+wbflzwVOtPT5+XnMzs7KdRmhdzgcSCQSuHr1KsLhMAKBAEZHR0XjPsuMY5DI/H8Kby5ubk7U/iiUGT2nsNnd3YXf70e9Xsfh4SEODw9xfHws6U0UXoy01+t1+U632y35xdTEqL1wXCloy+UyyuVy1yyDbmPeGbgxNfzO3FtTOFCzNn2zZ2HmaZspW/zOer0uQqPzXs3NBnhdAzd/B3gleOmG8fl8CIVCCIfDbd9hvn/6jilszSg73UylUgnZbBalUkmyPmjWF4tFbG9vI51Oy4ZObZmbTa1WE8uMgpwbkLmWzhIqfP8OhwOjo6OYnp5GoVDA4OAgEomECFXOlXK5jNXVVczPz781fy7vg/fKjQMAXC6XuBV5r510bnZcZ5Q3LpcLx8fHslEkk0n82Z/9mXzm//dCl3D37unpgd/vlwkRDAYl3YaLAmjXjsydlYEZr9eLvr4+CSQxgECt6G0JXZpajx49QiKRECHr9XrRaDTg8/lw/fp1+P1+ZLNZeL1eXLp0CYFAAI1GQyZLZ1J+t0VvRssZROLv8989Ho/4GGu1GorFIjY2NrC3twfgVfoS0+aCwSBsNhsGBwflM9SK6I8LBAIIBAKyedHsTafTSKVS2N7exu7uLo6OjiSdzRSEpg+987m6Be3MBWUG2/j+zaDWWYUPJmZOMTdDChzeA++301XVOf7mPZr3ycVM90ChUMDx8TGKxSIqlQqAk3SviYkJxONxeVcc02KxiEKhgGq12pZaR9fP1tYW1tbWJLjs8Xjg9XrFvUXBCqAtSGcGmwG0WQzm5mY+VzcXg6mRB4NB3Lt3D9FoFOVyGZFIBOPj4+Izp380lUrh/v37ePz48WvpfL8LvA+udaan1Wo1ZDIZACdBMVPw0nox0ww5HowR8Huq1SrK5bLIJeZgW4FlQvf4+Bg7OzsIh8MIhULwer1otVptwSgKBZqVZnCDUPj09/fjypUrSCQSsNvtODw8FLPrbWYu0Im/uLgoGQl37tzB0NCQLIqJiQkMDw+jXq+Lb4vPRw2rcxMxMzHMyCtNqEAggHA4LBkZvb29iMfjCAQCUjyxu7uLQqGAYrEowsaMxNNUdbvdojFzMnLCtVotBAIB8Q1yER8cHGBzcxNbW1vY29tDLpcTrYrPYC5m01fYKdD4O918qaZAo9/UfP8m3SwgUyh3Buj4d1OLNzd1c46Y2pH5M/MZTAHFeWneq91ux9bWFra2tjA8PCyWR7PZFF98oVCAzWaD3++XlL9W6yS7YmdnB+l0WiLzFJzMzGB2RiAQQCwWk+KHQqEgKZOtVkssLjNOYI6NOe9Om5s+nw9XrlzB2NgYWq2WXJuWVqFQwPb2Nh4+fIjf/va3WFxcFO3/bWAqGv39/RgeHkYkEkGz2UQqlcLS0lJb5o95/9xsTDeT2+1GMBiEz+eTIopyuYx8Po+dnZ1vJbf/NCxxLwCvHNh7e3viC+NLjEajiEQi4q+kWV0ul9u+w8waGBsbw927dzE+Pg4A2NzchN1ul2j726RcLkseZSgUQjAYhMfjQX9/f9viMZ+ZG4aZDkYhbC5SarX0U9GHFgwGEYvFxJ87Pj6OS5cuIRwOSw4ttftSqSQmKxeN6U+uVqsSdOnt7QVwIqxYUcbAmtvtFj+ow+GQxd5N+6P2aC7aTm23m8DtFLzUdM1N5zTNlr5FLipqxd0Ced02ANMX2OnGMk3wzs+ZWq4pmMxrEafT2ZaSRa24VCrh6OhI3BFutxuBQAAAJMWLLhXmI9OML5fLYuWEQiH09vbiypUrkgvMFMJkMolyuYyBgQHJETZT4PidnCemRty5qVBQdW40dDVlMhksLS3hyy+/xLNnz7C3t/fWBZfdbkcwGJT86JGREQDA2toajo+PJQBpvgM+h8fjQTgcRjgcFl+5mYLG2MTR0RGy2axsFm/TLXkalmm6nKRHR0dSM06fLB3/wWAQlUoFq6urWFhY6PoSqUkODQ3h8uXLshM7HA4cHBxgbW3tG5Xgnnfv5XIZe3t7mJ+fRzweR29vL7xerwjczoAYADHFmT9ZqVQkD5Nafl9fH0KhkExwPh93d7/fj0gkgunpaXFbMJCTTqexvb0tC8oUuITlodeuXcP4+LgIXZbrHh0dSeSdpiy1beBkEvb09ODFixeS8kctl9oPBTWFoOlHo1nPPFszKMbPm75qAGIqdxZBOJ3ONldItVpFPp8XH3W3oglaHeb3A5C/8zlcLpeMCa/N5zB9zXynfA4GHQcGBnD16lV897vfxd27d9tKnfns9LEHg0H09fWJ0HU4HLh+/ToePHiAjY0Nmffme+T7dTqdGBgYwPT0NK5du4ZQKIRCoSBlyZlMBpFIBNFoVFIcuckcHx8jn8/j8PAQpVJJFBgGA7np8k+3TIVqtYpisYjNzU08f/4cCwsLbfPibcH5w4Auc8T576urq3jx4kVX/6vdbkdvby9mZmZw6dIlyVJgUQQ3P9MN9zbv/TwsrUgDXvmlOHHdbjdCoRCuXLmC8fFx2Gw2xONx2Y3oPzK/h4LFDESZmsW30XiDJtXGxgYePXqEcDiMYDCIQCAgPrdOs5faGzV4avqHh4dotVqIRqNiLlKbcrvdiEQiGB0dRS6Xw/7+PkKhEGKxGPr6+iR3NR6Po7+/H4FAANls9rWAHbWYwcFBfPjhh7h7965UYlGQulwuSWvz+/3y3VyM4XAYAwMDGB4exq9//Wt8+eWX2N7eRrFYlHtlbwwWMXDsKcharZPKpkwmIz4/BmpcLheCwaBoI0x7MsvDAYjmEgqFEI/HxV9aLpexu7uLdDqNfD4vOcC8vtfrRSAQkHQ7Zl8wTYj30dPTg1AohEgkIoKSGwS/y2Y7KT9nbT/fMSv1PvjgA/zwhz/EjRs35P7o0mBgj+NBQUeh6HK58P7772NpaQnr6+uSg2u6Qvg+A4EAotEo4vE4otGofE+xWESxWEQgEMDAwABGR0fR19fX1guDbo6trS0cHBzAZrOhr68PAwMDiEQibVk4nRo+11ylUsHOzg6ePn2Kx48fY2NjQza9twnnEjc/U7HhRtIZQ+C893g8GB8fx3e/+13MzMyg2WxifX0d5XJZApH0w3ezwL5tLBe6FLg0b1iqSJ8lfaFra2tIJpOSxG76pI6OjrC0tCTmhtPpFP8jo/bnRbu/CSxcWF1dxcDAAMbGxjA6Ogqg3ZQ201w4eelHbTQaKBaLaDQaCIfDYsaZLobe3l6MjY2hWq3KeDB1jVoTMxqYSsbiBI4Vd/tbt27hww8/xNDQUFu6G4Nm1FKZwWAGvqiJMmK8sbGBdDotE97j8SAQCGBkZAQDAwNyj/QBOhwOlEolbG9viw+ZQS5uNvF4HENDQ/D7/VLavbu7K4LXZrPB6/ViYGAAly5dwuTkpPjTK5UKtre3sby8jLW1NXFdtVot2cy5OXk8HhSLRaRSKbRaLYm0U5P1er0YHBzE0NCQBEnpo2cmyt7eHjY2NsSHDpxs9vF4HB988AHu3bsnG1s3HzHnhJmqZ1puH374IRYWFqSHR6dFQcuK1YnMD2bcgBkUY2NjGBsbk83GdDP09PSgVqtJPw02EGLDIpfL1ZbxYPregRMrZGtrC8+fP8fLly+l9PttQ+vo8PAQW1tbGBoakiyUZ8+eYXl5Gfl8/jV3lt1+0iPi8uXLmJmZwfj4OMrlshSd0IXDtMt3geVC1zR/WRnEEk1OHprUzHU0Axv0KbE0MZPJIBwOI51OI5lM4vDw8Ftr79hqnVSMZTIZpNNplEqltsg7Ba8ZqODPgRPfcE9Pj2gdLD+keW1+pr+/X/yyBwcHbQ1RMpkMXr58ib29PdRqNTGNKWypXcXjcVy9ehWJROK1kmQzjcY0oTs3D2Y3UPDSJOMC8Hg8mJiYwJUrVySyzYohumT29vbaTHZ+N11LzAahn40bEP2Nvb29mJqawr179zA7O4uBgQERHru7u+jv75dUJgoXCnYGahmUpB/cLD/n+3A4HAiHw9JfgkGsnp4elEolLC8vY29vT/zIzFGlz5Vxim5WBy0ICt9Os9jlcknq4eLiogSV+Z54H3zmly9fwm63IxKJADhRZkKhEKLRKMbHx2WjMYWmx+MRwUqBzmwiWjpm7rs5n825TAuDpd/f1lqj5ZNMJuH3+6Xh0dzcHJaXl9s2Pz4j5Udvb6/ID2aa5PN5eV+0ZMzN0Sosb+1I/yMA8TFtb29jaWkJfr8fsVhMKmtMjdGkVqshl8vhxYsXqFQqGBgYQKVSweHh4WsRzbP4JlkOZrCLk7XbQjMj3hTC3LnT6bQIiW5VYdR+otGoaI77+/tIJpPy32Qy2VYRRgFpphSxnpwVZeZ4mlqUGUjqFAaNRgOZTAYLCwtYW1trazLSbDYRCAQwPj6OqakpMfnZapFBzYODA7lPM73HHFN+LpPJyOIwNTOfz4fh4WGMjY2hr69PTGav14ujoyM8e/asLW+VY3J4eCgmON8brSbeCxclE+XZUS4SiYjvnjnD8/Pz4rum1bW2toaFhQW8//770obUnAd0DdAs7nx+anXHx8eidZtdxji/qtUqDg4OpH/JxsYGxsfHMTAwgHA4LHEAdinr9MdyI2LaVb1eRzgcFl9pt3lgzmGn0ymuEd73NzHLL7rumHvNXhPpdBp7e3vSd6KbS4NriRs+N6nl5WWkUqk219W3YQ1fhHfSxJwTnpHEnZ0dPH/+HM1mU1oZ7u7uinnXDSaob25uygJ7E4HLCdapgZ2H6eCPRCJtte3m75ia/fHxMba3t/H111/jk08+wdraGoLBIHp7e3Hjxo1T749VTsViUSrUnjx5gtXVVWSzWYncmkEPug1YtplOp7G2tiZpYQwqmQusm7DluBwdHWFubg5ffPEFdnd3RZNkVsXo6CguX76MwcFBWaCHh4fIZDLY3NxEMpmU1o9mI2wK2kwmIwL04OCgzbVA4cZeBh6PR/zo/Az7HVQqlbaeB9T4+XzlchkOh0Mq68xUKmYX7O3tyfjQ380SbGrKIyMjmJ+fRy6Xk7m2u7uLL774Ajdv3hRBbWqznDPcqPiHmSzlclkEy+7urriOTLOZlWy1Wk062q2uruLSpUu4desWbt++/Vr70W6USiWsra3hyZMnElyilj86Otrmx+2c08yZ7e3tlVz709bnWWuHz3MRgW1qvM1mU/z3p1W92Ww2EbRLS0twu91IpVIS8DODrlZqtybvRNMFXi1qCk+2j6OvieYfJ0G3F8TFksvl4Pf7RXO7yE5q5oa+iWZMU5GL86xJR8GyvLyMX/ziF/jlL3+JpaUlacvndrtx48aNrm38eC1GlUulEhYXF/H8+XNxN9BUZa6t1+ttEza1Wg2pVApzc3Ow2WyYnJzE4OCg5Bt367tg3nutVsPW1hY+//xzrKysiA+M2tilS5dw8+ZNjIyMwOfzSZOcjY0NLC4uYnV1VYoqOvMpqV0yL5UBNy4KUyjSv8qxN10hZlMUsxqRQo3ClPnODIR1uqC4wZjZIHQ30FQdHR3FrVu3sLy8LEFBZgWsrKzg888/x+XLl6UDV6fgMwNUZvrVzs4OVlZW8OTJE2xtbaFWq7W1D+U8MtPJSqUSCoWCZAFNT0+3VRZ2e6e1Wg1ra2v4/PPPpfn+y5cvsbm5ib29Pfz4xz/G5OSkCNTTMN0une6/8zB//zyhy7ECXmVNdFZCdn4375tB70bjpMUptVzztBfA2gAaeacHU5oLiu0M6VtiZJyO+m47E7VIM5rMgb/oLtotAnoW3K0ZVKMG1u276/W6NND+p3/6J7x8+VIETLFYxJMnT/Do0SNcv34dsVisLe3MzDMtl8tIJpNYXl7G7u6uaIGcvIzsj46OSkCKQSu6Bjg27FjWTSh0UiqV8OLFCzx9+hRHR0fy7B6PB5cvX8b3v/993Lx5E6FQCM3myQkQ8/PzePDgAdbW1pDJZESIcoz5X/p8KVAYrAIg49BoNKTnaSAQeK0LFL/L4XAgEAjA5/NJvrIZ7TZ9eKze6pxPfF9M8C+VSuLiomsjFArhxo0b2NnZkYAqi3Hy+Tzm5ubw/vvvS6/Z0+aP6XJiRdSDBw+wvLyMbDYrObz09xeLRWlraG4utGSY9XDnzp22eWMKXrqJHj58iLm5OTG9GbSja+6nP/0ppqamTrV+WK1I6/JNNF2ut/PmHTGtA1bzce53G1cqA0zBZK8Rdsl71xoueedCF3jlG2IAiPXmNCNbrZa0D+yE2QBmtZU5sc+7/pvudJwwTL05PDyUlK/OCUjN4v79+9KAxtT4KBBTqRR6e3tfKzSgab20tNSWw2laC81mE263G5cvX8aHH36I/v5+CT4kk0kx8+v1OoLBIC5duiSugLNoNE568j58+FCECwXZ4OAgbt26hRs3bkhRy+7uLh48eIBf//rXWFxcFPPbTCMzv5vPyOek9sS8WuBV8GdoaEiyL8yCE84bnrAxPj4urgZTk+H5a2YrTY6hmWViCmX6o1ke6nA4JG3v5s2bcspFOp2Wz62treHRo0eyEZ2lLXL8WeiysLCAnZ0dtFotKbmdmJhAOBzG/v4+7t+/L/7uTtfVxsYGHjx4gGvXriEWi7WdiMB5Uq1WsbW1hYWFBQk28700m03R1G/fvo3x8fHXzhDje2Q2AdPuLipACZWF8zAtMDMwe5pV2tPTg3A4jPHxcVy+fFniIbRozFjS/2ihC7wq4+QiYMMY83gat9uN+fl5ZLPZ10wL0zdMP9h5Qtf0Y76pM53fzT6qmUwGg4ODrx16CLzq+Wv2hDC/hxovK2vMgzbr9ZOjUp4/f46PP/4YT548kc2lk2AwiPfeew/f/e53EY1Gpa1ksVjE3t6e5CW63W5MTU1hdHRUBNtpFItFaWLOMkkzv5d9BRj0fPLkCT7++GM8ffpUBK7pw+N4dLoA+Ie/z54a0WgUgUBAjr9hkrtZgEHhSVdHrVaT5PdCoYCDgwMcHh6iWCy2BU/M/FfT/0vhxHukOUtXzo0bNySdzDwclN+7vb2Nzz77DNevX8fg4KBkFpxGuVzGy5cv8ejRIzkbLhQKYWBgADdv3sS1a9cQDoflyKQXL15Ijw1CLfDx48dIJBLo6+vDe++9h3A43Ba8Y3+HYrEoz27OU27wtMQ64dgcHh5if39f+hK/iaZLTBfYaUoP1xmDaWc1PqLAvXr1Ku7evYvLly/D5XJhf39fnrdTzrxL3rnQBV75+Ki1DA4OYmJiQqqp2EeWO15nsYRZwmf6zbiQTGiG0PzjZLoo9Csyz/KsXZNmL7MIzAAFq8CYS2kme1N7X1lZwX/913/hs88+k8Bit/thruzQ0JAEOdgvotk8KfelDzKRSODy5cvS+KPbRtFsNpFMJvHxxx/jxYsX0qMBgKQLPX/+XPq9ZjIZPHjwAHNzczg4OJD77FxcpulLTAFIYer3+zExMYHR0VH09vZKKpTP5xMhYv7X5/MhkUhIA3FG9iuVinSSMsthzWc1n5n3Y/Z9qNVqmJubAwCp9uJhm3t7e6hUKvK89XodCwsL+NWvfoUbN26celwP5/v6+jo+/fRTKaN1OBySLjk6OionNPDYJrOfggm7zH322WdyPhsPjeQcZYoi/dPcwPl+zGq/s5p5M45gnsb9JnDtkG7pnVy/5pwwN1sTM6D74Ycf4jvf+Y6c3sHKNcaO3rWGSywXup2ThgPKwAqDCKy+CofDqNfr2NnZQSaTaasdN+EOyJfFydCtlJSNasxrX+SFUENmVkEsFmvTKEx4DywgoNnL+/L5fIjH45iYmJDKNI5HvV5HJpPB8+fP8eDBA+zs7JzaU9Z8Vi4eFhMMDQ0hEAhge3tbMkIePHiA2dlZDA0Nob+//7UINDehZDIpbgLz2o1GA4eHh3j+/DlyuRzi8TgqlQrW19fFEuGYm999lu+cgrher4vvlZV5DPwFg0ERQGYgjfmxLHtlRRnNfrP0+Kzrcz5ROJumKI/TKZVK8Hg8SKfTcsqDOXfo211cXEQymZTgVuf4cgy//vprPHjwQPz0fr8fwWAQQ0NDiMVibSd8mCmI3e6/Xq9LhszExAT6+/vb0sZ6enqkA9rg4GCbq4spivQhd7sOFYXe3l7EYjEpqjgr0N2JzfYqL5hWEDetznVqukZM14AJs3X6+vpw48YN3L17F5OTk/B6vRKgZACObrluQttq7ddyodtt16emQFObpgt30/Hxcdy8eRPJZFJ8Wpzs5mLqJnhNzZfJ8nS003x80/unOROLxeTctNOei6auqfEx33FoaKitcoguB0bSU6mUZCp0c5nwuWj2FQoFqXKLRqO4cuUKJicnJWjSaDSQTqexuLiIvb09Kf3s9oxm85vOgFO1WhUBm81mYbPZpO1jZ2SYfzddOp3PQOHMe+E7ZV8G5kN7PJ629Di+C/4ON1Dzvvm9zEboDKRwDDsXuTmnWMG1vr4uQpiBys53zvs+68j7RqOB/f19LC4uShqe0+lEKBTC5OQkpqenJa7BucDjgE4bQ5vNJvOApz0MDAy0zdne3l6Mjo5iaGhINmJmhZiusG4CjkKXJemcZ2/qXqBmyoAvGzbR0u10D1LgmuucP+d6npiYwM2bNzE2NiZl7fTNm0G0borLN3GP/K5YJnQ5qbvlxlJAMWc3lUphcnJSFlkgEMDU1BRu3LiBTCYjk/+0qDivx0wGsw6fHcIYhX2Tihoz39KsJutmYrVaJ+36eOQJd1reE0tgWTnUmUrDs6CYLcDnMp+Zk69YLEqxxMDAAPx+P8LhMGZmZqQhDFOEmL97mnZP7XF6ehoffPCB5Dd2/i6DR7lcTprSnxZVNoWk6cvlO+J4sjNUJBKR92T2FKYApKCkkDXnEl1HwWAQkUgE2WxW/LbczDuvzzS00zQqltya53R1c/XYbCctOD/44ANMT0+feiI0N656vS6Cw+fz4datW/jBD34gDfEpPNLptGQvdGqhpruMKYMstDBdN7S6YrGYaNFs98mAHI95unv3bteTpvkdtDjOSjnsBpUlZqXQgqFiwmAn32c3gWs+s9frxcjICG7cuIGpqSlpIMTMou3tbezs7IjQ7bbZOxwO2cyswjKhy8Xj8XikablJq9WSnrvz8/NIJBIyiYGT5tDXr1+XHrLcGTtdFaZA4oIKBoOIRqPSivH4+FiO/H6TRh2mtsadtluCOAXE9vY25ufnxc9Jbdfj8SAajWJoaEj8frx3ChYm/ff396NSqaBQKMjE7OzoxGbSqVQKV65ckTzceDyOu3fvShHF+vq6+JFPEwh8tkQigY8++ghffvnlmQdPchGZR6+b48VNg5Obm2U3TTUcDmN4eFj6K3i9XrEWmL/NP6ZVYPpgG42GdKaiheFyuaTE2DxhgYuXm7tZ+GFmiJh+/845Z46by+XCzMwMPvroI+kV0A3+Lk11v9+P8fFx/OhHP8Ldu3flEFSWVLNHL1udci1xLlGhCAQC0mPXbDbPsbLb7eK+GBgYQCaTkWeu1+uS8re9vY2BgYGuRT9mUxzey5sILFoNfPZYLCa9hhkwNIOr3caa67qvrw8zMzO4fv06+vr6AJxYy9lsFsvLy5ifnxcXQzcFg+NH5cYqvnWhy5fDxXeW8515pXNzc/B4PCgUCtKO0OFwYHBwENPT022NbbphajIul0sWcywWAwDs7OxIHuZFhS5ftJnWxA5Opz0LK7NMUzkUCmF8fBy3b9/GzMyMHCLIHZ4aVaVSkWPP+/v7pYhkZ2cH29vbbY1G2NScz2WOdzwex507d1Cv16Xqr6+vTwT9afT09GBqagqzs7N4/PixLMzOcabm2blAuECZZ+t0OlEoFNo0Xv6Oeb7YpUuXMDY2hnA4jFarJRkIzBRhribdBcxppY+dVYJsGsTADwsSzLaMphBhw3H+vNNNYm4s3QQBqxRnZ2cxNTX1Wq8LE95TNBrF5cuX4XA4cO3aNdy5c0f6PthsNjkZZGdnB3t7e/K+zXmdSCTaCl6Y514ul1EoFCRwxfsOBoOYnZ1FLpeDw+EQlx2VIObsUgvv9pwU9GYP5s5ugKfBCjy2luzr65NuaGzoRKXirO/r6elBIpHA9PQ0BgcHYbfbpYybqXtzc3OSHtcNrmnTorLC3WBpP10zgt2NVuukEMD0nc3OzmJyclKKB/r7+6W2nEUR3eCitNtfNdAJhULS8LszZ/YszIANa9wZTDitKo2LmilAzWYTsVgM165dw3vvvSfPxPPLzCPOeVKDy+WSRHtWpS0vL+Phw4cyyanlsXdAtwBJPB7H9evX4Xa7kc/nEQ6HJSBJbb0bLIXlWHdLwzNzbk0rwwxashkPx8WcB1xYZhct+ogPDg6ws7ODtbU1rK+vI51Oy0GO5qbq9/sRj8cxNjYmgSJq8vxOs+eveW1en9V/7NhGDdC0QE6bt7QO3G43wuHwazmuJnRVVKtVhMNhTE9PIxwOY2pqCrFYrM0XbDYM57lgACQmMDw8jDt37mBqago+n0/cCj09PXLcUrPZlAq5ZvOkV8a1a9fQ39+P2dlZzM3NtWVP0Cd6VgFCOByW1pJsekOBeRFXHXNueYioz+cTJeWiVaLsTcJgcDablUZA8/PzePHiBTY3N9tytjsxY0JWZjZYdnIEJ9t5kWT6djc2NiTyWC6Xxc9FH9h5Jo05mGYwhUelv0n7R+6Gvb290lD50qVLcszQadenk99ut6O/vx83b96UHE6mrPEZmYyfyWRweHgobesotMPhsJhBOzs7otVygdJfxu5Ups+STWnoP+NnmNvcWTpKgcT7MsfJ3GBME9z8OQUuWxjSPO98/7wOFyzLcNnJP5fLSb/cg4ODNp8fYck4m3Nvb2+3ZZXwJBL6YTs3fmY48KBUBm/NAgw+I031bmPB32VONMe/c0xZJWWz2TA6OoqRkZG2puac1+a4mOax6Z8dGhrCzMyMaNb5fF7ysjc2NpDL5doa94RCobYMCea1Pnv2DE+ePMH+/r6cg3ba+nQ6nYhEIpiYmMD6+roEUOlDvkj6JZUrusyYBUFhfxEhSGHNDnD5fF5OS15dXW3rs3DaPdDVZcoCK4SvZZouH+oi6VmsINrf34fNZpNGJzRjzXaJZ8GFwslAfw8nykUG2JzkTNQ3cyi7BRK4YABIg+irV6/i+vXr6O/vF62LEVWmYK2srIhwsdlsiEQiouUy37dQKIiGzJ6w1CjNkwLMoJ3P5xM/MjVcam5m0xwzgs8Uvs72eZ3j2zlWfDcUYADk1AwzsAO88pHzdw4ODqSwgovSPIrI1D4J5xUXWC6Xw+bmpvSiYF8Gmpi8Jjdjvi+OA3CiRXUWUHQGbLvBcnazTal5DfpoW62WNA83j2syFQnzHdJaYLCPB7rGYjHE43FEIhHYbK9OqODG1Wq14PV60d/fj8nJSczOzso5YcxEiEQi6O/vR39/PxYWFtrmpjlGHAub7aS73vDwMEZGRqQBDUviu1lDndDNQ0WDDezNYpnz4Nw2M31WVlawtraG3d1d6Qp3FpznvCersLyf7mlVJZ2/y0oUmpn7+/sIBoNt5YsXvSZh5Jkv5CJC1+zrSmFCAWgGdDoXVyaTwc7ODsrlspzAEAqFZIExsMGmMl999RWeP38u3ZQ8Ho8EM3i2GaP7zHpgBZHX65XGLKbW2pk5QLgpUPNjN7fOZkGMMNO10W1BmSk+5lEvzKelZspFbJ7Bxv6uHEv6+mh6dmYmmEK627um0OHmSt8jN52BgQH4fD4R5KZ2w7lhZrsw+Mc/puVkwo2ZWRPU7juzcyiIWYDQGYQyI/b8Tvpue3t7pRG31+uV0yM4pyqVipR/Ly0tSeGG3X7SzD6TySAUCmF4eLgtIAxArClqx+l0Wnocd2vITi2TfZbNNcLnPAsKO77n03LQz8PMJNnf35dqxNN6tXTSmY9tFZbn6V7EdDCj0jQ7c7mcJOpfNABmBmrMhs4XbQHJiU+Ba/rjTH8fhRHvO5/PY3l5GU+fPsXu7i7Gx8dFsHUuWHbiX1lZwerqqjSBcTqdyOVyovHSj+33+zE0NISRkREZEzb56EzzMXNQzcXNRWJG/s1n4bhFo1FMTk5iYGAAhUJBrIZu75C9Wink6ENk+S2fn6cURKNRJBIJRKNRtFon/YJTqZScMNH5jhj0YMN2LnAKWjOQw+vx86FQSIpBbDYbDg4OJBhLDdmcT+zWxUwb0w3C+cnxNbNkYrEYJicnpd+v6Vpg8Izpad2yXsyqOHNcea4ae5GEw2GpQPT7/Wi1Tk7CWF9fx+LiojTOYSZGJpOB2+3G7Oxsm0ZvPgMVhWQyiVKpJNdjKXPns1C5oH+dc+qimQwMTHK8mfZ20c/TDZnP5+FwOOQYJrPc+yJyxkphS34vyoC7wQHhjsjzwpgAflpeqAkXeV9fHwYHB9FsnvTjBC5mTjBHOBwOizZmnrPFU3Z5rUbjpOXe5uYm5ubmsLCwIJpot/4L1IB4aCF9Y2Y/gM3NTWxsbEgRBdPIEomEVF1RY2bHqFgsJq0neW/dxoYCulvBgMPhQDAYxJUrV3Dp0iWk0+m2BPbOdwW8SsHhmWnUJs2jhHp6esS/fevWLcTjcRSLRTx//hzFYlH6yZqBV2pXLpcL8Xgcg4OD8Hq90nAmnU63adMUJCwT7u3txezsLGZnZ+H3+7Gzs4MnT57g8ePHbX5mbnaMzHND6jxx1nxujhX991NTU9LsxrQMKJzNlLdu78ScF2zszso3lury/bPqjC6q9fV1bG5uSj8MUxgyyNb5rnktttnc2tpCJpOR63CTNPPrefoGezpzrnBzLxQK5/p2uf54eggAHB4eXkjwcjPngQBMCaR19K6E6UX5vRa6NLVoOtEEpfA9b2Dt9pO2fLFYDIlEAo1GA5ubm2fWlhOn8+Ro+KmpKfT19YkfCoAc3WwWPHDyHh0dSfPuw8PD14IDndFSljHykEEz35XPzjLGVqsl6U00+1utlgif+fl56b1AH+1Z/m8Kgm4J6ByD/v5+DA4OwufziQ+5G9SCaT7TjKdGRAHIwpCRkRFMTU0hkUjI721tbWF9ff01PyJNYR57PzExgVAoJEE+LrjOkk5+ZnBwEDMzM5iZmRGt8eDgQI484vUYpGV/AlZonRdNt9lsUmHIaLqJaUV0uno6oTXFefTs2TPMz89jd3dXypC5sbGnB/vsMg/ZzAlnGlk0GkVfX99rVYadQU0W5ayvr2Nra0tyhk33Ga1PNpNhRo/dfnJSx/LysvS8OIuenh4p9XY4To6lYqbFWTAwu7+/j83NTXi9XtkATGvs9xXLhK4ZPOlWjtkN04xhNJ877Hk+WdO1EA6HJe+TPR3O67nr8Xhw9epV/MEf/AFCoZCc5NtoNCSth/dnRkKLxaKU5DLowPxRlk2aYxEMBuX8r0ajgWQyKaahy+XCwMCAnPprmm9cICyY4CbAxdZqtTAxMSE+3fOamNB/SmHNxc/Az3n5i2Z0n+Z8tVqVLA023KGWR38lA0mDg4NIJBLiuzRNeTOf0ufzSRoYj/ExixvMd88CBOayssrLbL7N77bbT3oNJxIJDA0NweVySUXTReYqx5xBP56ObG46Z6WSAa9Mbpr5X331FT799FMsLCxIvjeDcWaqG+MMTGXk9zgcJwdPjo+P4969e1I4Y/r8OR4sVmDlGwsWisVim+ZuBrqY0dPf3y8BY1YofvXVV2emdNI6YHaFzWaTU6E7rYRuc40l6Nvb25KVc5ol1g0z3sH/t4p3UpFmt9vbOj+dNkh8wYykU+vrVtHW7Xr8Q7OdPkcKsLOimzSVI5EI4vE4wuEwfD4fGo0GRkZGJKG7M+KfzWaRSqXkDCqm55jnVvEFs6vU5OQkenp6MDQ0hJcvX0o6GP9tenpaAhsMHtDs5GGR3P1brVZbRNw8afgsk5YBSvobq9Uq0uk0Xrx4IYKHQuqszYpd4q5fvw6Xy4W9vT0sLy9jc3NT2kvGYjE5S4zjFwwGkUgkMDw8LAKai4iBG6Y8UUhSiwuFQtKSkO4W9segEKU/lCY3o/9scO73+0X7jsVisoElk8kz5xnfJxvOvHjxQoQfz2XjuJ5WBcj5w5TGzc1NPHnyBF988QXm5+eRTqel/0ar1ZIz6Fgc5PV6kUgkcOfOHfj9fmxvb8uGNDg4KP0cxsbG2gLA5ubHCkkeHJDJZGQeM8vCzOSJRqMYGRkR4cXycwYUz9NWGaT1eDwyd1kpaK7b06Arj0VSzFA5rYqNmBk2fCc8yskqLKtI42KLRqPiv2QmAf+/E05Elj/abDYxf05rysHBZPVYsVjE0tISjo+P4fV6sb+/LxPjrMTpQqGAR48e4fj4GDdu3JCgDxtMj4yMiHnHe83n83j06BF++9vfYmVlBa1WSxpvd9aqcwG5XC5EIhFZOPfu3ROt1WazSXMQpjExl5dd1zpTY7LZrPiBJycnxaw/bbFT03G73WJRsLnLw4cP8fnnn2NxcfHc45Mo4CORCK5cuYK7d+9Kg/PDw0Mp3waA3t5eSa6nhup2uzEwMICJiQmUSiXJmKCGGA6HMTg4KKXc9KWzTNput0uVHt1KQ0NDuHTpkhQdmAGve/fuSTASOOnjzCblDodDFvPi4qL4szvNZTNAWSqVsLCwgFarhb29PWkGztQ1bn5nvQdqkvl8HhsbG9ja2morPgAg58rt7Ozg8PAQIyMjshmx4IHzmtkUoVAIgUCgrSKU90F/Oecpj5lnChrjIWyrSffWyMgIGo0GAoGAVII9f/4cc3NzeP78+ZlaLueczWaTAo5isYi1tTWUy2WZ60z366aY0fWWy+Vkg2ST825r2nxO+uw57xmc5e9921im6bLHJ0txgZOO8Ht7e+K87wyOUXMBIP/tLEflYLL5OaPhmUxGAl5Pnz7FysqKZDCUSqVzd9NmsymCbXV1Fffu3cMf/uEf4vLlyxgbG5PCCLNvwsrKCv7lX/4F9+/fl1zbWq2GpaUl/OhHPzq1lt3U2uLxeFcfMIMd1WpVmgJ15hublkGr1bpQf1TgRGCyqfnR0RHm5+fx85//HPfv3xctyyy24DPzPukyCofDmJycxM2bN6WYhdfuDI7x8ywWYMDz8uXLAE4EM32GtDgGBgYQjUals1u9XkcoFJKOb2wGDwB+v196BzPfmSXJ0WgUY2NjbdqeKUDr9Tqy2SwKhQKeP3+O7e1tOU2ZmxjfJTUkFvHs7+9jZWUFL1++xB//8R/j/fffb8sZPgvOY2YkMIPDNJnZuyCVSsm7YR43U/7MP6YbpZs/mb/HDYaaNTcPh8Mh65aCktVgfO9ff/01vvrqK3z11VdIpVLnBtG49vL5PJ48eSJuGQpNbph0OxwcHGBjY0M6oHEsGGzk9U6znKnNRyIRjIyMSJMpsru7KyccW4FlQtfj8WBkZATDw8MIh8NS7LCzs4MvvvgCDx8+fC2dBXgVWDiNVquFWCyGn/70p/ijP/ojRKNR5PN5/OY3v8G///u/Y21tTb6XWjDN5/OSp4GTlK61tTUAwN27d9Hf3y+VcaY/qFqt4rPPPsMnn3yCbDYrn19fX8cnn3yCn/zkJ5iYmOh6jU7/kvlsHANmcVAL3draQj6fF4HYqT0zeHiRwATHhv7StbU1/Pa3v8Xa2lrbBkfNgCYhN0BquQMDA5LtQEF3WuCIQVIGI1nOOzQ0BKfTiXg8LlorXUKhUEhMUsYFmMvNAycZtad2TLOXKWDsbdxtvDnmfLeXLl3C9PQ0lpaW2qoYzZQvpq11Blnr9TpmZmbw/e9//0IVlMCJcOBmQfeE6RfmBpfP55FKpZBMJlEoFNrcSKf5KM/S4Gw2GzY2NvDpp59ifX1dFJ9KpYJPPvkE3/ve93Dv3j1RWvjs4XBYSoG/+uorWSfnwZ4SdAeY6X0ejweXLl3CT37yE/zgBz9AMBjE/v4+/vM//xP//M//jO3tbXlP3JjOg3nvMzMz+M53voN4PI5WqyWpqKamawWWuRcYFBocHJTgEE8eyOfzWFtba6vdNlNtzsLpdOL27dv4i7/4C7z//vuyawPAl19+iY2NjTYNgy/LjOKe5mIwr83IMBdw5yKq1WpyWoG5i7ZaLekAdt7znLVQzM2ChyfyPszUIL/fj76+PoyMjCAajb6Rr4pmOX3o1EiI2VCFfnnm3/L0htHRUUQikTNPpeW1uEmw4orHezOtjMKTRQ6mWcjnNt1UTLY3+zH7/X4x783KrrPMfH62r69Pqg/Z95UZJKwOY7vFznGmVXJRgUuYHz08PCylwfQJ09qghcZN18yRfZNr8XnNOdrpAmEvFDY2Mt8f5wNjLZ3z/rTr0b1grj2On8Nxchbd//pf/ws//OEPZeMZHBzE2traa2Xp3eiUHTbbSa77zMwM3n//fUQiETlVhI2EGOS0wr1gOyfS99byLqjVmGYOXzgrfi5S/tcJX6B5/Dg1kNOqXcx8yDe5DhdutxfD1K1uOy+1tfOi1+dBN8NpPSw4ptR8zhIuZ12DnZ5OG7fTAnIUVm/a3LpbOl1nxoTplzb/vTP1yfz3TlP7LN/2afdlFgB0my+dSoJ5v+Yx6m8C37OZV9vtPTMQ9E3ecyfValWshE64IZ4279kw/E3XEz/f+e8sa2cGCH3dpivhTeHcNKsuaUU0m80zOwZ+Q059IZYJXUVRlP9BnCp0f2+KI37XZObTduG3yUW0iYu4Kt4G5z3b73q9bzp2VuY7WsW7Gotv+x1f9Hq/y7z/pnwb6/n3ZW6qpqsoivL2OVXCW3cwkKIoiqJCV1EUxUpU6CqKoliICl1FURQLUaGrKIpiISp0FUVRLESFrqIoioWo0FUURbEQFbqKoigWokJXURTFQlToKoqiWIgKXUVRFAtRoasoimIhKnQVRVEsRIWuoiiKhajQVRRFsRAVuoqiKBaiQldRFMVCVOgqiqJYiApdRVEUC1GhqyiKYiEqdBVFUSxEha6iKIqFqNBVFEWxEBW6iqIoFqJCV1EUxUJU6CqKoliICl1FURQLUaGrKIpiISp0FUVRLESFrqIoioWo0FUURbEQFbqKoigWokJXURTFQlToKoqiWIgKXUVRFAtRoasoimIhKnQVRVEsRIWuoiiKhajQVRRFsRAVuoqiKBaiQldRFMVCVOgqiqJYiApdRVEUC1GhqyiKYiEqdBVFUSxEha6iKIqFqNBVFEWxEBW6iqIoFqJCV1EUxUJU6CqKoliICl1FURQLUaGrKIpiISp0FUVRLESFrqIoioWo0FUURbEQFbqKoigWokJXURTFQlToKoqiWIgKXUVRFAtRoasoimIhKnQVRVEsRIWuoiiKhajQVRRFsRAVuoqiKBbiPOfnNkvuQlEU5X8IqukqiqJYiApdRVEUC1GhqyiKYiEqdBVFUSxEha6iKIqFqNBVFEWxkP8Pmthc52NdzVgAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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9yuqsVCpIpVIoFotwu91499134ff7MTY21ieyesbBSX2zWCxiY2MDiURC+U273W5f1gI/y/Z1Op2w2+1vxa/LscX3OSwzgf9Nw4X/PWii46RWLpeRy+WQy+VgtVrhdDoxOjqqJrdvG9NEt9lsqtmcsxaXEMZBT06aienz4UvhLEUf6NuCQaFcLqf8z8DLzkaLly4AfSlKy+Kk72YwzGKxqI5TrVaxu7uLp0+folgswuFwYHR0FKOjo3A4HDg6OkKr1YLFYsHm5iYeP36MSCQCl8ullvCZTEb5ghkA4+/WajUAx9FczvC6uDFCPzs7i/39feUj52qFkwx9oG+K8Xd0QeAg0JeKp/HPU/xobVK46XLS09j0e3jTSLu+EqAfsdfrKVfIxMQE5ubmMD4+rt6tLhZ0fTSbTdRqNeWbj0QiKhLfbDZxeHiImzdvYmNjAx6PB0dHR0ilUkilUmp16HA44HK50Gq1UCwWUSwW0Wq1MDo6ipGREVy+fFn1MQrXaZ652+2iUqko/yzbkhazLnxcceZyObWKeFvo7ilei2P+pH43TEt01yPbv1QqwWKxYHp6+q3d9+v41kWXM2qj0UAqlYLT6USj0QAAeL1e+P1+uFyuvsE1KIgCvBRhuhDcbjd8Pp+yiFqtlhoEb8ufa7FYUCgU8OLFC5w7d05ZvsCxwDudTiwtLeHixYvIZDIqy4AWy0nLOE4cOhy8jUYDlUqlL3Pj6OgIPp9PpW3Rt5hOp/H8+XPlo6QYcvkJQLXL0dERKpWKcjvwh4E/PQjJewGgfM9cRehR8pP854PyTvWBqb9nPdrOvF0GX18n7vSPttvtPmuMA5TWz7B7PclK0u+V1hL7G900+nKYE7Mxl5SuGv2n2Wyi1+v1vZ9CoYBMJqOWwHa7vU+w9cmaKyyuPvjMnBAYTNPfwUlGCS1xu92Oubk5vPvuuygUCpiYmMDi4qJy3/CeOEFwdfe2YJuxbR0OhxJd/d91FwOfTbfm+c6Z60s/N9uOAqy7vb7tgJpplm6tVsP29jZCoZAasDTrA4GAslD1DAag37cLvEwC93g8GBsbQygUUhZNtVrt6+Rvg263i3w+j/v37yMWi8Fut2NsbEylDNGiCIfDKJVK8Hq9mJubg8/nG+gS4TMZAzX8k5YeBxsnIIfDgZGREWXR1ut15PN55XvlAObkBRxbslwaRqNRlMtljI6OqsASg3R+vx8+n09lfhSLRaTTaaTTaRQKBVQqlb6NC3pnH9TJ9WfSrWh+Rn9uCi6flQJJoeV1Xvc+dZdSu91WVrOeEsS/NwZZ9Pujha1f02gEAMfWqM1mUwLM3ysUCsoinZycVM/WarVQrVZRqVRUsAuACppmMhnE43EV52AKJa1Uulu8Xi8CgQBCoVCfBVyr1dR7sNlsajLVN3roz6QLkrFf0nf7/vvvIxKJoFarYWRkBDMzM/D7/eh0Omg0Gmr1+uDBA9y7dw+5XO6tjTtddL1eL3w+nxrn+XwemUzmlViPHoQ1WvZOpxN+vx8jIyNqdcCUxnw+j6tXr76V+z4Nplm6hUIB9+7dw+zsLBwOh1piUTytVqsKyNDa0i0U3QJ2OBwqhWl6ehoOhwOFQgGJRELl372tl08/5qNHjzAyMgK73Y7V1VWMj4/D6XTC6/Vifn4esVgM3W5X3Z/L5VJ5i8ZOr08kenCO/2a32zEyMqLySV0ul7I6FhYW4HA4kE6n8fTpUzx79gz5fB6lUkm1nT6gudyOxWI4c+aMsoRpITEIMj4+jkAggG63i2w2i8PDQySTSRUQ0oVCR3cNGC1S466tQb+vW8J6fvJJO9P4py4g+p90XdEqovDrgjrse/l5vks+x6AdaPp3caJjZkEikUAsFlPfzYBbuVwGcLzK83q9aLfbqNVqSCaT2N/fRzqd7nPb8Dk46Y6NjWFhYQHvvPMOJiYmVPbD48ePsbu7i6OjIwSDQZU1YXwn+lga9H74eY/Hg6WlJczOzirx0/3l9Xod2WwWz549w7Vr1/D48WOVifQ24D26XC6MjY1hamoKo6OjaLVaiMfjfSsufh5An99ZX3l4PB4Eg0F4vV4cHR0hk8kod8ze3h7+6I/+qO+63yamWbrFYhF37txBPp+Hx+NRwstdPKFQSKWBMVjD7ATj4HO5XJicnFRZAjabTUVzS6UScrncW2s4+qB3d3dVGo7L5VIJ6R6PBx6Pp09YdStNtwjZqWnJ6X5pPZBmtVoRCoUwNTWFQCCAaDSK999/H1evXsXk5CQAIJVKqeeOx+Nq6amLLu+Hy6hAIIBAIKDyGpnv3Gg0Xtmi63a74fV6X9nkoXdKXWz1gWy0EPV24Xfo98dr6mljJwk0RZRWzrDAiR5c4X1xRTXIiuXvGZ9R/zFaysZBypxe+j8pDFw9VKtVeDwe+P1+eDwetdOORgctSP3+dLdIKBTCxYsX8YMf/ACRSAQAcHh4iMnJSdy5cwfJZBJ+v19lu+jto28i0Zfug1Is6UbT24W/X6/XUSwWsbW1hVu3buHu3bvY29tDrVZ7q249q9UKv9+PWCyGs2fPIhqNotPpYHR0FJVKBYVCQbkqeY/8XbfbjdHRUQSDQdXOnDwLhYJaFdfrdWxubqJQKLyV+z4Npli6AFQyPXAsmh6PR0XRJyYmMDExAY/Hg2q1is3NTTx9+rSvQQmFKhgMYnp6GjMzM2og5vN5+Hy+t576wV1w29vbGBkZUX4h5nsag1G6INCvR1HUcymZcsacXg4ur9eLSCSC+fl5tFotnDlzBu+//z6Wl5fh8/nQ6x3vkMvlcnj8+DHW1tZUWxstGboWLl68iJWVFbWjimk/xWJRuRiYDjQyMqJEymaz4cGDB0ilUio3kss4/gD9Ew3bQM8t1peA/Hcu+2hB0T1A/6X+LHpwjMtmbpM25thSIDmR8BoUXC7d9XbS/Z7G9DE9zUp/VvrNbTabemfvvfcePv30U1y6dElllFBIubQdHR1V+a4Wy3GO8eHhIZ48eaK2VevX558ulwtTU1M4d+4cFhYW1LZuina328Xm5iacTicikUhfqhgtw2q1qtxRtCTp06dVyP/W3x/vgZuV9vf38fXXX+P+/fvY2dlRwfG3id1uh9/vx8TEBGKxGCYnJ9U7X19fH5r/bLVaMTo6itXVVSwvLytdyWazyOVySKfTaDabapLb2dlRWvO2Jo0Tn+tbv8L/B5PQ0+m0MveZ2B8MBrGysoKpqSl0u13lH6Vvy+iH0gcbBQDAKzP524SpWmtra3C73Spjwev1KsvXaAHynnm/zLFl9DkYDCqfFYXF7XZjfHwcCwsLyj3ARHtanr1eDz6fDxMTE5icnFSpRfQv8h7sdjsikQg++eQTfPbZZ1hcXFRuDz5DuVxWATdufbVYLMp/HIvFMD09jZs3b2Jra0ul+QEvd/no23QpqnoAh1kFtO75Q/eM1+tVuxJrtRrq9XrfDkWr1araWs/i0KP2tVrtla3InNT5jvj9fJ+6ZUSXkL6biZOAvmPJuM3WbrcjEAhgcXERH374IT799FNcuHBBxRp6vZ7aoOB0OtWGFi51rVYrfD4fPvvsMxwcHCCVSqk0Pb0vUYAmJycxMTGhfPCc4MPhMObm5pQ7amFhAePj4yo2QOGlK6NYLKr3HAwGVXojXTKD3CnM3IjH47h//z5u3bqFtbU1FAqFgdv4fxv0wKHuYuQ9cJPKoPiIy+XC9PQ0vv/97+Pdd9+F1WpFPB7H48ePkUqlVByE7kwGYM3C1B1pANSsT4Hw+/2w2WwYHR1VW23b7Xbf1lB9JxJn2t3dXTx69AjdbheBQADJZBIHBwcqdWWY7+63od1uI5vN4vnz5yofMxaLKYtDXy4D6Its89npwGcBGQoXP8slPjtaNptV1q0eNWZnpLWs1zbgPbAw0I9//GMsLS2pQa6LDV0jXBLz+gzmud1uOBwOFItFtQuKHZSCQKufbUQR47VqtZpKJ2I/4AQTDAYRDofhdrvV9nCKKCdUn8+nRGV6eloFMpnKF4/Hsbu7q5bvegBmdHQUoVAIbrdb+SF13zHbiql/rJdAy5A+duA4G4H3xd+12+3w+Xw4f/48/vAP/xCXLl1CMBjsCwzqmRRcZXClx8n2zJkz+PGPf4zDw0N88cUX6j7Zjxh556TJzAkGCDkRz8zMIBwOY2lpCePj4yqzR3ddMUOCFjqNIL5HY6U14KXBU6vVVE2O58+fI5fLfSuCRUOF73dkZASNRgPlchlPnjzB7u4uqtXqwC3bgUAAS0tLWF1dVavFWq2mNrVUKhVl0BlTCM3A9Cpj7Ox0eNP609OYfD4fxsfH4fP5XvE3cVcTK1dVq1WMj4+rJQ83YHwbywRjR2COn95BdR8cOwFFt16vA4AauACUxaxbylarFdFoVPmmaKHpBYKy2Sy2traQTCZV6hoHNcV0cXERH3/8Mc6ePfuK31nfDkqrU/fp8t59Ph9GR0f7fI4UVt4/g6HM6KCF2Ol01LMa07Yo7CMjI0ocyuUyarWacgu0221V8+DChQu4evUqlpaW+vyV3NZ59+5dPHz4UL0jfgdXVIFAQOU3VyoV9S50vzA34+hbQrnUpsDTF8hn4fZmi8WirFc9ZY0TKcWPIq7np3PiWl5exkcffYS9vT21/AVe+lfb7TaSySQ2NjbUiojpYbVaDS6XC7Ozs5ienlY70fT7cLlccLvdqi9yUuQ71DNmjP1Z9/+Wy2XE43HkcjkcHR19KwZOr9dTq8u9vT3Y7XZV1W5tbQ2Hh4d9KxvgpRFA/WCBJxo6tHCr1eor/dhMTC14A7z0lwFQQbO9vT08f/4cTqcT4XBYWau6YOl0u121+wsAotEoer2emr1O2wm+SZaD7m9kKsugSkXssLR0aCUcHh5ie3sbNptNVdrSByDwcotxOBxGr9dDOp1Wk1O9Xkc6ncbW1hZevHiB3d1d5PN5lVsJQM3209PTiEQifctLPjOXx/qPcaBRVFjYhlYN25efHxkZUUVQWMWJwlatVpWly3vQrW0AKgWJm0r02qxW6/FOuEAggPn5eSwtLWF0dFSJ7tjYmNrBZfShM2uA1hwDYPoPBy0nNor1yMgI/H4/RkdH4Xa7ldhyg49u3eVyOTx//hxbW1tYXFxUeaD6+6Tlr0fb9ffR6XTUJonp6WnE4/G+1Q0nmIcPH6q88YWFBRULsdlsyuVEt4KxT7KfcSssV0osU6n76Y2BRE4WPp+vbyfeNzFuTjvuOIGWSiUkk0mkUikkk0kcHh6qzUdG6PLhu+p2u0in01hbW8Pu7q7a5KRX8OP7MIvvpJIvX5jFclwBan9/XwUcpqenVQ1ZPfpshP6pXC6nggHDot6D4MzNDn/a36MFyC2W7PBGwR1kmX/11Vf4n//5H+zs7MDn88Hn8+HChQsDr0HhDQaDapfZ2toanj59ihcvXuDw8FC5KXTrkfvIR0ZG4PV61dZfWjL6oKIQGAcn77vZbGJ3dxfXr1/H+vp6X64uB6PH40E4HMb09DScTidyuZzKPGEeJCdCXfj1AcV3rPtnWTeh2+2iVCqhVqvB6XQiEAgolxStU6fTqa5HPx2FnfdJn2O5XFb3o4suAJXrzbZhcHFsbExZk8lkEqVSCcBLK7nRaGB9fR03btzA2bNn1YYdozVLq1e3/Pkn26LVasHj8ai6zVy1UURyuRw2NjZw9+5dTE1NYXl5Ge+88w7OnTuHcDiMYDA4UHBJpVLB2toa7ty5g1qthoODAzSbTXz44YeYm5vrCxga+zTz40OhkApYv4mlqBsY+jbtk9An/0ajoURz2O/yvbOdHA4H4vE4nj9/joODA1QqFeXDNVNodUwXXT1tiAGWQqGA3d1d9HrHSftOp7PP6mDepBF21kaj0ReB1X2Hw6B1p1ver0MPajA4M2xS4LPWajW8ePEC//Ef/4H/+q//wsbGBqrVqgqK6bVXB12L0fpSqYR79+7h1q1bKh+ZlqDuk2PAiUVtCoWCWhGwOArdGUYLm7CjV6tVPH78GPfu3VN1bHlvXLpPTk5idnYWY2NjqNVqKtmcS3EGPIzfz8moWCyqCZj+fooSRY07hph9oEfVaT3qWQz8fk4ceo0P3tOgCZr9UXev0Oc8NjaG2dlZpNNpNdnxubrdrspDf//99zE7O6tS7YyWJt8ZrXIKPd9TPp9XdWsBqAAhrX9ORrlcDolEQi2zp6amlKtqmOA2m01sbm7iq6++wqNHj1Cr1bC1tYX9/X0kk0n8yZ/8iZo0hmUA6UFN+pTfZMVozAJ53Wf1HaAMoA0br7RybTYbarUa9vf30Ww2EY/Hsbe3pwJ+uuCa6csl3+lxPXpaFQcri3jQl0fLZNDM1Osdb5usVquqs9FPeRrnvr7MPC20gvQBbBRMfne73cb+/j7+7d/+Df/8z/+Mzc1NlS5Wr9fx5MkT3L9/H++9957yqeq/T1g57NGjR9jf31czPQXI4XDA5/Nhbm4OkUgEXq9X5dkWCgV1XYoBrfNhEwYHUS6Xw5MnT3BwcKCsRwDK4pmbm1NZJwDU1lUWvTa6ejiR8N5pferLPLYtLRauKvSt4vxO9h1au36/v89XxxQovTaxnvWi72AitFwpynrbTk1NYWVlRb0P3TffbrdxcHCAp0+f4sMPP1TpYIPaln/P+2e7HRwcoFAoIBAI4Ny5c8pfnEqlsLu7q9w07LN0o3i9Xpw/fx4XL17sy/QxPhd3Vj59+hTZbFZtIy8Wi2pl+ad/+qdYXl4eKNwcb8zHPame8yDedLwx7sAJmeNt0O8zLjE6OqpWoPQJD/LFf5d856KrbyKgX5LpSxMTEyrthqcT6A1G0eaWVloSp519+ftvAsWKJRhLpZJy2Bs7abPZxMbGBr788kuVC6gvz4vFItbX13F4eKh8anq0mPnBGxsbuH37NnZ3d/uWVmw/l8uF5eVl/PCHP8TCwgKsVqvKSywWiyiVSmpffCgUwujo6GuXhZ1OBwcHB1hbW1PLaVoptHDPnj2LWCymUnLW19exu7uLYrH4yrvi+6CYcRLl+2eEnktrtoXL5VJ+Sk5WDPhwdx0AhMNhLCwswOfzqWszys+lKcVXX90w11YXQoohA75cTTAvnCltiURCvQ+u0tbW1rC/v4+5ubkT25d9tNFoYH9/H48fP0Ymk4HVakU4HFaB5G63i+3tbVy7dk0FGnUrjSd83Lp1S+1SYzYMYVCKNRIKhYKawCwWi8qh//LLL3HlyhXMz88P3BTDLeKpVErVdnjTnHiuVl6HPoYrlYrKWhokmuwn4XAYKysrWF5eht/vR6lUUgcg8L2+iQvy2+I7P4Kd0H/LcoWzs7MAgFgsBpfLhdu3byObzb6SD0jLpNd7eYQKBXiYtat3ltO4Ioy/y6AMc4kjkcjAkyJoDZdKpYE5hcDxkol1hvU0K1b0evLkCX7+85/j7t27KJfLA+81GAzigw8+wOeff47JyUm0222kUik8efIEe3t7SCaT2NnZQbPZxMTEhHI9DOv8vV4PhUIBd+7cwbNnz5RFx8mCmymY5pXL5bC+vo6trS2VL0xrVreEjJYO3xUFku8/HA4jGo1iZGREifDU1JTKVqEbiZOSxWJBLBZTOxLp300mkyoRnm6OYSsmHT6rnhrGCZ2+cb/fr4o38bOVSgVPnz7F3bt3ceHChYErIP2aXPreu3cPjx49QqvVwuTkJBYWFrC6uqqCoMzZ3t7eVpXI9Hstl8u4e/cupqenMTo6ivPnz6st6wzm0pXDbfLGd8Li6MMCVPpn2A/f1NIF+t0LJ/l1ORFylUvXzyAjie6Y9957Dx9//DHm5ubQ7XbVyoE+5N8VfidEV/fB+Xw+TE1NqYpGU1NTsFqtyOfzqNVqfQn4/F09RWlQ1FVHz0HlLH/aPEM9n5O+0ZM6nc1mU8Vk9DQiPVDDVCvdCqT1/vz5c/znf/6nOvxy0H3abDaMjY3h7NmzmJ2dxcjIiFqC+3w+VWycOY7BYLCvsPWgiaLdbmNjYwM3btzA4eGhWtLys7VaDZlMBh6PB8ViEeVyGbu7u6rcoTG7wTjIB10PQF+1p7GxMczNzak6p8yfZZ4l3QYUES4pKS4sAk8/8WkFV++LFAUWGu/1evD7/SgWi8hkMqrv0Pqy2WxIJBK4ceMGPvroI5XSNaiNO50O0uk0bt26hZs3byIej6vdgHoWAuMaZ8+exdjYGLa3t18RKqaS/frXv1ZBxpWVFQQCATVZ9Ho9VcfA7/erfGa6cpiZwL46DPp09f7/JoYLYwFsdz1Xluh9hu+MqYiDxrPX68Xy8jI++eQTfP/730cwGFQuorW1tb6x9buA6aJrfEFsEA64brer9o77fD6VX0mLTffJ8feBlyX5dGEzFjihUz4QCChxosXxJp3G6/WqIt+6RaGjZxMw6KCLEI/1YRK7XrKu2WwimUziN7/5Da5fv469vb2hx9TrSf16/YRAIIDR0VFljeXzedTrddy4cQNzc3OqKIrRLUK/3fb2tnKJGN0Z+tKdVaf0dD19+cv3YcwUMMLJkwWms9msisbrW60phnoEXN/tRZ8jI/3M/z5pwA26T71f0TfIPqXv+9cnQtYl2N7exu7uLi5fvjywyhqt04cPH+LGjRvY2NhAo9FQK5DR0VFVVUsPlDIjYlBgkoVbrl+/rlYzel6u3W7H+Pg4lpaWMDU1hXK53Ofq0q8xaDKmj5y1QOj+OEmgjbCvhkIh2O3HBfe5JVkfp3qJT720p3GlSANqYmICly5dwuXLl1Xcolqtqk1FumYMEm2zg2lvr+LwKRlmWXU6HZWnycAKzy6amZnBlStXsLKyooTEaEXpKUm8DmdwbvFk0vT09DSmpqaUYL7JvbPjjI2NqY43KHWGA4EJ2bqvmcGZWCyG+fl5BIPBPtdCq9VCOp3G+vo6EonE0FMw9HQopmlRQOh35ffTD72/v69qnw5bcjHBnAPWCH2pPB6Gx/oY82DZDmy3Qe2kv0M+e7FYxN7eXt8BhBx8tBL1Wgt0A/D0hN3dXezv76sqUryXYWIyqOiL/hycYMrlsiqnOezEDIvlZbGgYaugTue4BOTGxgb29/fRaDRgtVpVLeapqSm1mYV+Wz7LoGcgrVYLiUQC6+vrSKfTfYVtKOasiKdvI+ZzMxd80CTFdqL7JxgMnpgpMQy73Y7R0VFMTU1henpajSFj9hHRVx36O6TgsoTAlStXVK44d9XpmkIf9qDnMhvTLF098q3vpQZeCi/zILndlFtQ/X4/VlZW8NFHH6FUKuHZs2d9dRl0q4SWjzEf1ePxYHR0VFkB7XYbmUzmjXav8YXr3znstNdu97hE4uPHjxGPx1Vwh503EAhgamoKExMTr7gpKCBMTWIqDJ+R6Mnu29vbqog6t+9OT0/j/fffV5HqUqkEl8t1otVJ0bhw4QI++OADbGxsIJPJvNJGDDYxoDJs+caJzxhJ18VYn0AtFovyHSYSCXVUElMDK5WKEiT687lbjyUpDw8PUS6Xlc+Y38/71P3TTB1k1sSg4Kq+GqMAD5qwLJbjSm7f+973cP78+b5DJQf1j16vp6phjYyM4MqVK/jggw8wPT3dV6WMZ+bpAWNjP9DLLtLvqk8K+oYbVq/TD4VlLvmTJ0/wve99T+0GNT4fs1/Y70+aBIxwFceSriwc5Xa7ldGg18PWJ1Z94uTzjo6OYmVlBR9//DHOnTsHv9+v2owTcDKZVG5JY2CX36Vn5piBaaLLwNWwM8y63eNCFgcHB3jy5IkKnHDLZyQSwQcffKAKaus7kIhx+aFby6FQSO28sdlsiMfj6tjw04quHgSgD29Yak2r1cL29jYePXrUtz+dNQfC4bCq26DvimHbNJtN5QqhuOm+ST1QRdE9ODjAzMyMmqyCwSBWV1eVJbW/v6+O7xlmpbAzzszM4PPPP8etW7cGntjMd8bynMP8bazYb7PZlHDyWfUNA3rOMHdglUolxONxtYOKlpkxz5PtzQpaXDpzlcPt0foWdD3ljvmtHODGAA+NAt2lYYT3vbS0hM8//xwzMzMDB7K+WopGo1hcXFT+608++QSrq6uqFi5XFPF4XBUb4opBbzu6zVwul1q9sQ/prhPWOInFYgiHw8oa5g/P5tvd3cX09PTATT+8Lt+1PiZeR6/XU/UvWLqUpzizSqC+e3FQW3OydLvdmJ+fx8cff6yKrXM1VygUsLW1hSdPnqh4xqB3xnbjcfZmYaqlyw5y0vIonU7j7t276rC9M2fOIBwOq8pBV69excHBgTqOeRBsYHZO1gbgkqhUKqmC0m8SRGMAjj7GQdt/9WdJpVLqdFa6D1h8/cMPP8Tq6qo6LZaWC/OOW60WRkZGMDs7i/HxcQBQKTu8d2Zy8Ojvw8ND1Ot15at1OBwYGxvDhQsXYLfbsbOzA5vNpgq/n4TT6cTy8jKWlpbw5MkTFXjR0V1DugXJ9mLNAVogFEd9AFCsOAnoW5J5+m0qlVIpXJx49BUA34V+TA796RRwWsvGZSr9wdzaqg923X+tW1yDJmkKwfLyMpaXl19JuTLCFLTLly+j2+1iYWEB586d68ss6fVebh1PJBIqyMi+yOLzFGnguDg6d7NVq1X4/f6+9xUIBPDOO++oLIitrS0UCgX1rPl8HqlUCs1mc+BBjexXdNlxB+Rp4yJcxWQyGWXhs5Y2U/uAkzMbLJbjQlmrq6t4//33VX9mgHdjYwP37t3D3bt3kU6nT3TP6XEQ/t23jamBtNe9lG63q+rp0rdXKpVw4cIFTE9Pw+VyIRqNIhaLnZiOA/TvbadIAlBBFu5OOY2Vy+/gxo3x8XFVUGPYDEk3B49WAaDq2l69ehWrq6uYnJxUuZhMaapUKshkMuqIlDNnzqgUpW63q0rUbW1tqdQdPcndeOoAhXdlZQXj4+NoNpvqRAoK17CORouMImh8f/r/60t24GViO4t101LWrUlG1vV7ZedntJpCq+9KMwZHKbD6ZKhbzVartc8VpV+f90Nri+2pBwQ5uZy0k4oTxuuO8tbbIBQK4fz586rmyNjYWF8qH1d/LLqtV3Dz+/1YWFhQY4MrnkajAb/frwSIkx6tfZ/Pp6zpM2fOqEJByWQSQH9tXiNGvy4NGWaIDNu4YHx+Biaz2ax6P1ydMcXtdd/j9XoRi8XU8VV0jzx+/Bh37tzBw4cPsbe3NzQFTse4qvm2MU109X3mw6wFAMr3uLu7q3Lz2JlZMo+VnE7CGAihz5ibBliQ+zTox31Eo1HMzMy84howXpuukeXlZbjdbkQiEVy5cgWXLl1SkwaXq7RumXh+cHCARCIB4Fiop6amEA6HlYuASzSmLOlLPD2YpS9lw+EwAoFA3+m4LLdo3A5Mi5TbeY1perpIGwcIhY6TFP2v+iGNuvCyDegqoq9W321G63hQrjODS5y4KJ56iUKj9apfn/fWbDbh8/nUTiZO2Po1jYn9euCNfVdvM31C07+D72B8fFylDHLS0N1XRl+m7lLghHz16lXMzMyg2+0ik8moYjAsmFMulxGJRFRGBDebhMNhnDlzBpcvX8b9+/dx//59pFIpzMzMqN10+iRKaEjMzMxgdnZWBRWZeXSaurqdTkedIMxUNb2dT9IHtg3dhqyyVigU8OTJE9y8eROPHj3q8+sP+w7dlWRmHq/p9XSNA9gIB3ylUlGBlHA4rDYg8KWexo+kLw1ZU1NPUzmtlUu/H5dCLCR9Uq5rp3NcwWlxcREzMzNYXV3FhQsX1C474GXpw3K5jK2tLayvr2N/fx/ZbFad/MvSipFIRFlxBwcH2NzcVBsB6MtjTivvh/dGC5BWtS48ejU3fcCzhCWDecPa19hONptN5dXqpfX03Xi0pPQgGtuM1iwtU97voJQh/R44+fB7j46OlNWpl7zU80rpm2RAjp/nkpxF5AcFdIywTYvFInK5nDpaXbda9Qi63j7D4gJ8dzy81eVyqXKX4XAY8/PzWFhYQCQSUTWqDw8P1UppZ2cH4+PjmJmZwdmzZ7G0tKQCV3xO5m1PTk5ibW1N7QwcZG2yP/G0l2g0iv39fVXk6LTnE7LflUqlV441Oo2Vq485ulESiQTW1tZUxs9pUwX1+hlmYXppx5N8NfpnORtms1ns7+8jHo/D7/cri/W0MxP9X3oxZz396HVw0HLJSqubOZO8X31wsX5nPp8HAHXcCP11urh1Oh2kUincvXsX9+7dw+HhoQqYjI2NwWazYWZmRvkMx8fHMTc3p6wMWj3RaLSvzJ8eWTb6q/TIsL5c53OwXfQc40Ed2GiFsU4CK4FRcPUNKLrI6O4AACqgM6wK2Osi5RRnXcgppExJ4lLU+Dy8rs/ng9/vh9frRblcRqVSQaVS6bPQ9bbkn/R1Mr+W98P70K0p3pPREubv6BOEx+NBJBLB1NQU0um0ysGemZnB/Py8KuPIlLa9vT2sr68rP63f71d5uT6fr6+EIwAVcJ2dnVXpcCwkzxNN9P7KMaGfNqz7Rk9T90Tvf3TD0W1yWp8qg4ylUgl2u12dpJzJZPom+JPQ34kZbgVi+uaI0zwcXwoHbC6XQyqVQjgcRrfb7Tu94CT4IukWYGCC4nua3+fgoP+vVqupyk/DUt9KpRI2NzdVDVoGeIyiQYsyHo/3nezL5WehUIDVasX09DRmZ2fVEjEWi2FpaQnZbBbpdFoVAqfvzii4xuvq/8blvNFqdTqdmJ6exvLyMh48ePBKcrnxPVIguGnE4/GoSZNiCkCJLH3dIyMj6Ha7apcXfdJ62+pBL06CFDP6X42BLv5Or3dcNIf+R4vFoiZELj/5PbSWWXCpXq8jkUi8Mjnx+XXh5eS3vLyMWCz2SiohP6+vKga5KmiF8/3oOxfHxsZgt9sxMTGBM2fOYHp6WqV2VatVlYO9vb2tAp9Op1OV9lxeXsbi4uIr75oWP7NE6vW6qpurn4KhL8npEmMOrHGcnEbwgGPf7MTEBCwWi1pdDQuQ623J7BYeKJlKpdQK400K25gptuQ7q6d7ms9wGVIul5HNZpFKpdBut9Ux1a/7HloFPLKaPuK9vb1TiS4tF4qdxWJRAQru8KI1wI7AAyzv3LmDr7/+Gq1WS51FZgzO0AdWqVTUmU0MSgDHHfPw8FAl+vM4o1AohFgsht3dXZXuwjPYarVan/gOY5AFw7+nOExOTuLixYv46quv1P2ftFLhBMfk+0wmg0ql8so7GRkZwfz8PM6dO4dIJKJqvLLo+SC/Hn3P+hFCejEbtr/+PBQ6HmPDXM5UKoXnz5+rrcu6mLJwytjYmDphgCsWIxRJWn4zMzO4ePEiotGoChANmjhet6GAfYMuMWaq0J/KYDJXTtwcs7+/j0Qiodxn7EO6W00PKOoTF915LPVotR7XwTCWetT9sYwrMJtAF/DT+Hbtdjumpqawurqq2iuVSqlaJSe1DwtOJRIJ2Gy2voye1/mE9e/5LvidqL0wCH1JRqFjUe54PN5XVm8QFI7x8XGcO3cO58+fVzvEHj16NDRCS7i8X1xcxPj4uPL7MTiRy+VQq9X6Kna1Wi3kcjmsra2pYjPc7qgfLaJ3Cqv15VleXq9XWfG6L5q+RS7FGJFmbmmtVsP29ray5mjZnVTMmm3EH6O7hQG42dlZzM7OYnt7Wy3bhr0rpuawQhxzLzOZTN/nWUmObhIWDjo4OHitIHH1oleUO+mzbN+pqSmcOXMGIyMjcLvdSKVSiMfjr9xXIBBQp1On02n1Xk9yR9HK5/MweKi3DwX6NC4SGgf5fB4vXrzAw4cPsbW1pTa36O+f7cBNIvrGDbp82L9oFRtFiUFIpnK12204HA5MTk4iHA73pb/RvZfP51EqlZQLIxwOK/Hb2tpCKpV6rWHDkpnvvPMOHA4HOp0ONjY2kEgkTvQNM8vp8PAQOzs7cDqdyo99Wr/wd4lpoqsvxU67BOFg5rKD+96TyeRr07101wKjtZ1OR9VLeF0gjmeMffTRRwgGgyp/kQEa4KUPUbfKc7mcqhNRqVRUcEv3Aet+QK/Xi/n5eVy6dEn5FHn2lNPpxOTkJKLRaN+hkrqFzG3GXFrRN8kUMVbDOimNiYLJNuGExGi9ns4zDE4QzWYTDodDnXtWKBSQTqdViT3gZbFwpslZLBaVUztMmPj/XE2wDxknAWMQ0ejT57X1bbycoHna7szMDPx+vyrgM2wHmn5N4zX4rGzbbrf72txofrZSqSCbzeLZs2e4fv06bt26hb29PbTbx8cW8fQD9j2r9XjbdjQaRTQaVf2DBW4WFhZw6dIlzM3NqX6ktzEnchZe4hhjkIwbijg589l4QonL5UIkEkEoFFLLfVYsGwZXoVNTU4hEIrDZbJiYmFDb/E8KdHOsJRIJdboHg8+nFVzqkNE3bwam70hzu90qDUz3xQ2CL5hixiNNeIbaMDiIGLTiMpQDkEeNv+47mIDOABV/hx1FP8CQ/tlkMonNzU21/ZBVo3hsuB7U4mCZn5+H1WrF5OSkqo5VrVbVEn91dVXt3uFyke4W5nD2ej1V55U5risrK2pn0TALkoNPT83iDjMewMlavMDJBUL4vnjuFtubgsVC2TxaiW4jvTA2hVfP4eV9AnhFAHkvxklUD+5RqLnjibUwuNnFZrOpnNmrV69iYWEBnc5xPWFjMM+IHrSjtb60tKT8sXoq3LAt4+w/XLUkEgk8e/YMN2/exM2bN1XBdL6nTCaDbDarVlqcnK9evQqfz4dEIoF2u62EeH5+HmfOnMHMzEzfYa+8f4fDoc6EY9yD/TiRSKhdjhQqr9eLSCSC2dlZlR7IY+FpJb9OxDi22D84YbOduKtumDY0m03k83ns7e0hEAioY6H0SWEQemyA+d3/63aksfGZjxcKhZTY0uLQz7DX6fVebu+0WCx9g2ZY8QpGUTlzs1p+oVCAy+VCPB7vO7562EutVqt4+PAhGo2GOh3B7/erSk1nzpxRQgocW2C5XA63b9/G7du31YC12+3q+GdjPiwzIUKhEDweD2KxGK5evaoCdbQAWbuW95XP51Wkln5Httfh4SGePXuGsbExlWp30pKWbcYJhalH6XQa9+7dwy9/+UtVBYs5r4OEV7cqQ6EQpqenEQwGsbS0hLNnz+LFixc4ODhAuVxWVqWexJ9Op9WWbKPg6hhzKo1BQR1+Dw/zDAQCCIfDqvbq0tKSsh5jsRiWl5dVlSqeuKFXhxsUPORPo9HAxsYGfvnLX8JqteLy5cuqoDjf80l+dj4Ha4I8f/4cT58+VXm3uoGQSqXUydec/CcnJ1XxF/YHZp8w9ZArFqPAcKKrVqtIp9PIZDLqeRm05erQarWqjRWdTkdZmalUColEAs+fP8f6+vqJVi5Xoa1WC1tbW2rVs729jUKhoHK86c4yjnU9YA1AFW86KU2M/ZyV17jypIHBDBozLF7TLF2Hw6HqDfCheVQJz4YybiVk49JXxU7Jl8CO6nA4MDExgYWFBYRCIfW93GyQz+fx4MEDuN1udDovT5rgbDqIXu/4uJq7d+9iZ2cH77zzDn70ox9haWkJKysraieMHtVdW1vDL37xC6yvr/cdRbO+vo5yuTxw6cwZl7u3jEEk3bfNoE4ikVBnPumdjJ8pFApK6JlCdhIMUNntdiXqN27cwL/+67/i0aNHKtjEttbdHMY0IqavxWIxjI+Pw+FwYGVlBZ9++inq9boKFlarVSSTSTx79ky5IFiYhNaXHuzTl+l6H6EQ6KsIPe2K/SedTqsaB6urq4hGo0qI9PPl+Ds+nw+Tk5PKPaQfPaRnhXAirdfr2NvbU66eer2OTz/9FJOTk68cy37Se2CJTm6W0bNkAKi4BCuwLSwsqCJHxtNHhmWxDHLdVKtVrK2t4eDgQAlmrVbDL37xC1y+fFmtxiyW47Kk0WhUfU8ymcStW7fw9OlT5RM+CU4+uVwOX331FWw2W59Pmj74cDgMh8OBfD6P7e1ttaWX44PCy1USdzAOmhxpDMRiMZWKyRXwaVw/bxNTfbp88KmpKXWQXjabxZ07d3Dv3j01Q+vQvTDMFWCxWHD27Fn8xV/8BX74wx8iEAggl8vhV7/6Ff7lX/4FW1tbKgCmLyu4Q+p1sFxeu93GhQsX1FEqegUpprvcvn0bDx486JvlC4UCHjx4gM3NTVy5cmXg/evpQjrsPBRdHhPOsof6oNQDYvSXRSKRgdWiBkHhcrvdqFQquHbtGq5fv66CJRRc5rvqIkiB4zJ1YmICwWBQuV+YQaC/06OjIzUh0sLi87Bd9BxefdOEEX6On9V3v/F6FstxEZ+pqSlcunQJ0Wi0bweY3uZciU1MTCAQCPRNAHoqF9Pt9LzfcrmMUqmEUCiEK1euwOPxnDr/lD7+SCSCkZGRvnev7+YrFovY3d3F3t4eLl68qCY8fYPLMFfSIHq9HjY2NvD1118rnyzb8f79+7h9+zY+++wzeL1e9T3MGQ+Hw8hkMnj48OErAdNhMCsjHo+r59PjB0tLS/jJT36Czz//HKFQCOVyGdeuXcM//uM/4vHjx+q96qmIJ0GX1+rqKj744AOMjY2hXq+rmhbJZPJ/p0+X/hO/349IJILFxUWEQiFUq1VVJUv317FhX9cYbrcbP/rRj/Dnf/7nmJ2dVU72ZrOJ69evY2dnp29gUZR098Kwpax+7Wazqfa1MytAp9VqqQMc9Tq0vd7x0TfxeBydTmdoIZSTBonujuDGC/rF3W53n+ix4Mnk5KSa0U8Lha7b7SqrQr9fLvtYHEbfyMAKViyOTl/poOditJ+7mvQdYiyywiU525KCMsjXy8+xHgddIXoUn98fiUTUbsJhbaDvQOTzUBx4X7SM6GvV+0Oz2UQqlUKv13ujhH8AKvMkGo2qiYvLX75nm82mdnTRX2zcbHFaOFHTd6/vaAT6+7X+9xRInknXbDZf6ffDrsdVJtBfnAo4flcTExP45JNP8NFHH6n2m5ubw97enqo/fBJG7bDZjk9XWV1dxZUrV+Dz+ZDP51XO/0m+9m8Dy2sifW8t74LLPH3w6MERY/Wp08KXb+ws3LEyzPf7piklFDVjOhBhAGKQJcZ7HDbQTwstHfpdjfmovJYuDN+kMzGgM2jThP4nr00YuDKebHwSfE/DAqq8lv6swz5j/KzxM7ob5bT3Zmxr/TrD7ksPOH2TAA2XysaTOPT3rLf1NxFbI3q+sxFOtoOu0e12++rgnpZh74qCbHxPTIs77fZ9IzS0OFlyLHEyPU09lzdk6AsxTXQFQRB+jxgquqZujjgpmf23SWb+Nr7zTa5zmmu+7eXL657tt73eN227b3pdM5LZzb63b/sdmNWnfpt+/00xWyf+V7oXBEEQfo8YquKmH0wpCILw+4yIriAIgomI6AqCIJiIiK4gCIKJiOgKgiCYiIiuIAiCiYjoCoIgmIiIriAIgomI6AqCIJiIiK4gCIKJiOgKgiCYiIiuIAiCiYjoCoIgmIiIriAIgomI6AqCIJiIiK4gCIKJiOgKgiCYiIiuIAiCiYjoCoIgmIiIriAIgomI6AqCIJiIiK4gCIKJiOgKgiCYiIiuIAiCiYjoCoIgmIiIriAIgomI6AqCIJiIiK4gCIKJiOgKgiCYiIiuIAiCiYjoCoIgmIiIriAIgomI6AqCIJiIiK4gCIKJiOgKgiCYiIiuIAiCiYjoCoIgmIiIriAIgomI6AqCIJiIiK4gCIKJiOgKgiCYiIiuIAiCiYjoCoIgmIiIriAIgomI6AqCIJiIiK4gCIKJiOgKgiCYiIiuIAiCiYjoCoIgmIiIriAIgomI6AqCIJiIiK4gCIKJiOgKgiCYiIiuIAiCiYjoCoIgmIiIriAIgomI6AqCIJiIiK4gCIKJiOgKgiCYiIiuIAiCiYjoCoIgmIiIriAIgomI6AqCIJiIiK4gCIKJiOgKgiCYiP01/24x5S4EQRB+TxBLVxAEwUREdAVBEExERFcQBMFERHQFQRBMRERXEATBRER0BUEQTOT/BfXpVbzzDxjIAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 63; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "lb = -np.min(evaluation_history, axis=1)\n", "ub = -np.max(evaluation_history, axis=1)\n", @@ -1822,22 +355,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", @@ -1864,17 +384,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta // 2)], need_gradient=False)\n", "frequencies = opt.frequencies" @@ -1882,22 +394,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "intensities = np.abs(opt.get_objective_arguments()[0][0, :, 2]) ** 2\n", "\n", @@ -1918,32 +417,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.sim = mp.Simulation(\n", " cell_size=mp.Vector3(Sx, 40),\n", @@ -1965,32 +441,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.sim = mp.Simulation(\n", " cell_size=mp.Vector3(Sx, 40),\n", @@ -2012,32 +465,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.sim = mp.Simulation(\n", " cell_size=mp.Vector3(Sx, 40),\n", diff --git a/python/examples/adjoint_optimization/Bend Minimax.ipynb b/python/examples/adjoint_optimization/Bend Minimax.ipynb index ea883612e..8e9837dcd 100644 --- a/python/examples/adjoint_optimization/Bend Minimax.ipynb +++ b/python/examples/adjoint_optimization/Bend Minimax.ipynb @@ -17,17 +17,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -53,17 +45,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.20124611797498096\n" - ] - } - ], + "outputs": [], "source": [ "waveguide_width = 0.5\n", "design_region_width = 2.5\n", @@ -202,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -234,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -256,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -292,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -350,1594 +334,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 0; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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azcfrQpcRsWfhMXU2m0XHz6rDb7EvTheWLH2WqYCgcU25Go9hS45/c/BPO19zPBZ9hZLQebmo0gMd1f2vCR6CygpveBXbtpr60rp3auFrguciHBU8exEQvK6+o50kP/KKl9bCfvV6M2rp24TedbJO36mKnudA9wG+ySMiXR9Ot2yhyFJKScfGWkev69JrlF7r6kuTadTdznLxqKvHLDp+3LUueBkRe50KxxXY0uvQhyP/OG+17rUJSjoUyrId5niqon9X90kLQXjq6XO5Zm370mKPY/a53W6bWWdak473WsDCqarMMmWuPI/dS/X0XFOvll6Xv8pWw2ELj/bf3t7GYrGI29vbg8dds1BZ6FlZL0+80UdwadFNlpIsTbJhF7/2SG/er2mnKno816yPDAaDOD8/jy+//DKurq4a8S8Wi8ZCck4bC0XqmJT3x5um1FjssPIcoMN7nTlXW+iSBc8z5yB2bJhYw1YebYT3oZkC9iqARtxV9Oza8zXJUOvOWQ3+rRYG1fZpvqG/qu7Izc1NvHnzpnGNr6+v4/r6unGJdaHIiMPptfpcdl3iSoNjnG+HqDU1xx2CCp5dY7QbngkEf3NzEzc3N7FYLJrIPQueA3dohwYMebIMW3p9MEdpXX4VqwqdO05d8Vc9B9Odquh///vfv692fFTgSTFXV1fx5ZdfxvX1dZPeglgwBoal10dVl25OWDwN0mU5eBY+i5/Lb0uBO3bpkZa7vb1t2n99fb0neC7E0aCidjY8RZcDbZq50MIaUKo6RNv5lVN92bUs7ceUqYr+V7/61ftqx0cBxqaPj48xGAzis88+ix/+8Ifx6tWrWC6XzbJREJKO5dWl51lt2D9beRVxbdJMadZczaWHhX94eGisOzwVuPacptvtdnuVdjpHX916DbLxmJ4LabT+AO/bgnilWgZb+nejKvrf/OY376sdHyWvX7+On//853F5eRmr1SoWi0Uj8C4VdjyRBDdn9ripzGUvzZZrK75RwcOlv7m5iaurqz3R89RZCBQdlbYN/+ZxPIYPeK+pSu0Q+NpkwTv9bg3tQEx3PKavcHt7G3/+85/3IvkRcRCQUnTMymN5zsFnS1xlz5zLHjipxTc1wUPoLHiOR/A4nsfw2iZ9qAULFcfWWoWSRS659Jrn19/U9mO6YdETmt47OzuLr7/+uhFFRBw867303DbeOC2nz5fD02f4lQN0nI7D8bIJNJng1aXHBpefH02lUXptE84ZufbVapVWyLWN5bu49FqQY2E/L07ZEbi58LimzWYTi8WiqSSDhUbN+263awSpItdgmBa16JJWnIrLHjRZcue58q0k+K+//roJ4HF6DuLkgB0Kf7ChE+ICHI5hZBH7kpUGpSWxNADIbn5pvsJz1Xv0iaqqkYLqIwjo3d3dRcRbS4j3EDVEzoLkohu26pr60vp5tqo6H740k40r7doED5den1CDdk6n0zg/P4/z8/NG9Jyei4i9aj10AKWpw5lrX3Lrs6g/kxVZaU2EO4Bu9MuUHwlu5uVy2bip6/U6xuPxXsmuWuNs/ntmRXnMnLn26s5rDj4TPIJ2meBRQchz14fDYdMZnZ+fx2w2a4SPtuDcYcn52qDj4VeGA5il1XuyoYFWcuK7/LvSq8Vfx6KvwAUuEfvzubm4hpenKq1wo5Zdha9LXWn+ncXSRfDX19ep4OG9sSfCFh7CR2cE70YDdzys0CxGRBx4PFnZLefiNQvC+1Gx1/5t2rHoK+BmRLEOwA0NUbNA2CXPFrvIHijJY3iefFMav9cEz2m5TPAIrtUEj7bhXODGA47U80QjraDTGEfm2vN7tfqom9BOAp/zq8XfHYu+A2x1eCYc3PLz8/OYTqd7aTUN3HH9fJaeK61nx22AZcWQA7Pl1MJjy2rq2cJz+2ezWSP4s7Oz5lwi9he31GnGOg7nWAdPE84CkXxeWeQ+K+EtRfg9ru+ORd8BtliZ4LGxtdcy22zFWg3YZZaQLRpbeMyHRy09LLuW15aCdhjD8znAwqNNWQyBpxuzW4/rhFeNbZSsfW0srh0Ef6+08bUzORZ9C7A0sFKc2oJrfHFx0Yiex+Q6Bx2C405B6+a5JiDibQ4+mx57d3fXlNOyO59F6XUMD4s+n8+b4B0LHq51JnZeQ0BX1wFs4bOxvXZqXQTP14Q7o0z4poxFX0Fnx6lwZrNZzOfzRvRw8dXNzwSgkfmSe5stgMFTZDFTjmvps8KbzMJfXFzsjeF1uiw6HHQ2vHaA1tZj7M8BvGzIosOWtuuO/fPkHo30a02AqWPRt6BFNhyJ57EwIt4afdeyWR7XZkE6dadV8LDymAfPYmeXngtvhsPhXvWfWnjk4zk9h/hBtmpQSfQRsefW88KZNdderzdeszx/m3tvF78di74FLr5BRJ5z7hz11sdBZ5H4WhCLZ6dlD6Lgde1g6fkZ8vqQSa0izKL0WmYbcfgUW14PXx9xFfHWrdeVgEqBu9J11vdt7n3J2lvwdSz6CpngVfT6HHiuk8+snLrxWsKK6DyvzacPlWSh60MmdfIMu/RceMNjeLQ5Ihe8PvWGrTxXKbJlx/7QlmzWXalUt1Sjz3GALFVnsXfDoq+AG7aUb9fpsLosNK9XB3CTwm3np8xA5Nljplj07MZnD6XQgCNbd47S47x4DJ9ZeBa+LpoJgerYnbMRu92uWWFH0foHfs1Qz8jCPx6LvgLmles681qXXpryqjcvu+3quqvI9akzLELuHDhXrrEHjjto4Q0PQ9C2kuB1STAE7EqpudK6efg3rgOTCR4eAv+OO03+Gx/L1LHoK4xGoyZKj1dYSp1jXnJH2SUtBeZKmz5Qkh9EwYt5aIYBQxCNOWRpObStJnisn8dr2LN15+FMROztF686Bufro4IveUd4r/t1qu44PLWWwE2HWvvJZBKvXr2KTz75pBE9rCVPN43YnykWsZ9v51y7PjsuW1abLTrG68vlsvm9VsCxtdWJPVx4ky2GURI8YgU1wauHo9eDz52vj+bmWfgZ2nmWXHvTDU+tJVCQAsbjcXz66afxve99rxnPX1xc7Ik+4q1wIt6OX7UzyHLtHJRDdZ268Biz68QWwPl+navPrrxW/kEstTE8C15z/lqDkHWA/IphTSZUHgqxldfx+2Aw2MtwZF6DaadfprwFjSY/PDzE69ev4/Lysrkx5/N5nJ+fN3PMcUMjN65jWh7Ha65dRc+FNVr1prPYeMUeXgSDLb2ui8+dFLcbVX4ll57z8Ryh1+KbiMN0Gj/vT61zKUrP/xfcaeL4WadiumPRV7i4uIjPP/88Li8vY7v9Zo08BMggeo7C48bkklq28nDrWez6LDleUnu1WqU3NgTP5b48mScTO3dQWmnHlr0meK620yyF1uljSjJPzOFUH59HCU3taVwgC+C5A2inKvpf/vKX76sdHwW4cR8eHmI4HMbl5WX85Cc/ic8++6yxivgeblbOjeMzULLybOlV8PpcPL65uW5AV9TlJar1MVdoC0fS+VHVmi0oCR77RCGPzhvgzoTTkezal657xGEAUC062s4diIV/PFXR//rXv35f7fiogBt8f38fNzc3TR27Fs5kbjfI6ubZoqrYWfB8Q5fSY1wopBuv28fWHe3iNmnKMAvaqeB5qix7NVpopILnfbahXgPHFHQcb5EfR1X0P/3pT99XOz5a/vCHP8RXX33V3HhwyXFT69NqIw4fHMkCYxeaBZcF6nQBCl4+G2N4nqfPU2LRDn6oZkTseR6c7+dz4biBdjQlwWsNgq6dl6XneMtgUcObylx8i/84PKZv4ezsLF69erXnsi6Xy4h4+7AHTGHVaDOvLsNWFdF5Lq5RK8jWnYXH03KzRTg0FccpxMxbQdvVXdYqu0zweq66ISZRitaXovYR+dp5+J6F/m44ZZeAG/Dh4SFGo1HMZrO9B13gRs0Wlcii11piyyIDHCRjl16LYPjhFzx+1ymxOE7E4VBDg48sKj4m9pmt84/jaOfG6UUNQrLIeW49dyK1MlvuaEqBPNNOVfRdxl5/S2iRCG543MDr9TotXWWLqaLXdeTY1Wa3nS0YPlMrj/awtefoPPbPIuCgF7dFxY7z1cKb0jJeKnhNL2YLZWoGQGfh6Ww8FT9/xvTtPn1XqqLHzd1X2KXebDbFohu1bnyz1ixpxNv6co5Q60QWde/5fVYQo+3jdnLAjcG+YNF5U3GWBM+ix/fUfef9ZTMQ8Vqy4pnXACz+bnhMX4Fdz9rCF2rd8Lla0Yj9m340Gh2k5XBcdX1LC1NE7C/NzVF6HtfruF2DdThu25x43q9G6DVSr/EJrtxj9569Byb7m8YDNDZQCwyab7DoO5BFmTVXXAssQVQQ0Ha7jZOTkwMBsqXn42rHo242twlZAxW9pr4iolniCsdkDyLrXLIqOx0uZAFJ3ldm5bNUnlZHlq4H78di74ZF35FapDiz1BFvRcXFO/x9dWOz/et4Vz0NHfdqcC7rmCAYvFfrntXS875LgtchAwtdRc/nkoHOUc+bha6vbfs032DRPxNZ/hlobETHqzwUyD7LfgcRatCQrbFGwLmdEbEnQp0L30Xw/Dft9HifbQtsoLPgzoivJToA/I4zCdnqRBZ+HYu+IyrkzLWsCb9UZ66CZ6GqhY44nJuulp3Fru58xH7KMRMkCx/H5biAjuE1W6DReV0BGMfg/UfE3rwFvZaaVdBFO0q/MzkW/ZG0BZEi9sfwOg7l70Xk5aaDwWCv1hzf45ScjtdZ+Npp4Jgs7Oxhm+yRZGlHzVRo9SDOGZs+26+UBeDrAMuPtjGZ95Dt19Sx6FvIBJ6NKbWiTD0BdUEZtcY6NlZ3vjRu18IWtF/bqQE7delZiCx4LOChlXYcw8gq+FScfF7aAcCiZ9SWFbeF745FfwQ14evNlrmn7NoC3Oj6+2zMnll7/oyPzcLhNqhwsplyfPzS5BkcU8+TXfpScQ/2rzEKBD41joEIf5ZK5Otp4XfDou+IjtdLBSYRh3XkbWgUniPhmm7L8voc6c7y2hGHa9JnY3kdY2M8z659qa5AvYhs42vEbcV5q0ufpTKztJ9d++Ow6DuQCb5k9bP8MltE3Nyl4F3WAail53ZlQcPscw6GZW4xt4uHEzxbToOKul8tIFLBZ+N57iDZi8hc/My7stiPx6I/gtLYXv/GaOqNg3SZ667uu1ru4XDYRLoj8lJptaiZWLgiDm1R74Kj9pl1Vy+C3fvMGmeuODwUtfLr9XovfYcOQjtgczwWfQs6Tuw6plexZmP3Uo49K7phUL6bDSkyUfD7zEry/rN0oXov/PvSev+lAFttGMTnDcGXOtPsGptuWPRHUOoAeMuKaRgOTKm7XLqJWbD4jlrNrOPRjkDbnrU1E70eB2T5ffUg2tBAIFt+9mi0nW3XzJSx6DtQEk0XV7MkfLyWrDn2y0IotUW9jS5tycST5c35WDxEyUpstV3YD36r7eL2l7IY2AdfA+2Y9Pq5I6hj0VcoiUctq4pfb7osop79Td3w2vf1N9oeFUFJFBohz75f8irUpefzz4KS2Xny9dP3aAsXKmFokwU2td0mx6J/R9iFLo2TlSw4x5YONzYElUXNs2MyWTt4H6Usg/4mE6jGNdSyq2Xmc2wbm2sb0E6uVMzSlxZ7dyz6J9LmTqs7rJ8pLAq2mBjXlix99r40fFD3vXZubccpBQRL1YQIzvH54rxKcw00q8Gi17Lj0hDJHGLRvyOZFax9rzQ2x5iXJ55wwU3tpu4ieE7H8feytoBSkZEWw2i71uv1XnUd8u4sfnXLVcz6ul6vGy9IP9NztuWvY9E/A1nArRS4yjoJDnbht7D6mUveFjNgsZUm4GTnUPNcsvQaDz14Hxxw0yAf/5Z/z3UB2SuOkVl4W/njsOgrHDMuVzLx16w94Py0LqJZqvjj9pRc45IFrLnytXLiUjvgBXCnxVYe1lo9BYhZH4XFVXo6ps8ChhZ/Oxb9O1ATUhZ0y171PcNlsaWsQBaoU0ufRdGxfxy/lDkAKvIs6IfvcUCSvZTM6mMfPLlH1/PTMX3p/Ew3LPoOtLnTGU+19LVjZhZb30fsz8GvCb7NpY84TOV13S/Ejd+w1ddhkE4Z5hV5auP2tmtmciz6I8jEximljJL7XBN812Nnlj4TKSjl2zOXPst/88Ydi7YzYn/RTd63RvdxLK311/UCuG0lL8N0w6J/Apm15de233LwLttnFojjziVzaUvud8Rhzr7W4WRiL9Xjl4Y2pViABvBwPF0NiMf4vDqPBuy6pELNIRZ9C12ErdY2otsNWBqPl4JUKviSd8HHL9ULaIGPtouPwek+7QSUTPDZcdVL4uW32MJzx6KeSe0cTBmL/hkoBeW6uvZs9dmqgVqkvA2tjGsjE3zmeZSsfO14XJGnnkuWbdB947dc78/P17P4u2HRt6DWJJtVxnPBu1h6FqsGtRheIJIfq6WCa4sTtHkr/HfuYLTarhb4w795IQ1+zl62cg4XIiHQx+N2LkzCfk9PT/eW4uJjlOIUZh+LvoIKHjcnbjp+cmzE4ZhT96PvIw4Fx9FsWFpEvGuR65IgNfin77PIOLeDJ7jg37qWnXaIpWWqI/bTkHjKT3aeGvAbDAYxHo9jOp3G+fl5TKfTmE6nzVN7sRCnBd+ORd9CJnY8E346nTaP88ZDLruQFdmUgndZ1VnNytdy7CVxlwJ22cKcmg7ka1RanjrLz/P+dAyvwwe+/mdnZzGfz2M+nzfiPz09TR+0aXIs+hbUlYfop9NpI6bxeNwsD12LLpcoBetK1We6/1LaLaIejc+EngULa4Ln46vI2fXORN+lg+P/g5OTk5hOpzGbzeLi4qIR/mQyaR7drZ6FOcSir8BWHjedCn40GsVkMtlbKRa/7ULJ+qoI+bvcPqA3OkfteaxeO072Wssm8DVia6zizybo1LyarFMZDr9ZS38ymTTW/uLiorH2cO8t+HYs+hb4Zt7tdnF6enogeNSLd0mhAb6pM0GVRKadSpe0le6nJOiSuLNOg8+Lg3ga4GQrz0HBkneRufYRcdDpnp2dxWw227P0ftpNNyz6Fnhduoj9stLxeBzr9XrviS/HomLORN5lLF+7ybN9a6691MGU2sKoR6Tvs7a1xRV0/6WYymQyaSw9p+9MGYu+At/M+nekj7io5CmiB5n4s1duQ/a+bd+ZoPG3UlVfqQ18fBZ3ljevZRS486mdKz+Oi7MnPJ7nLILJsehbKAkfdeRtqbSuZN5E6d/cttLfSvsoeRZPbUdpmPHU2oHaMEbjBtwJ8HDCgq9j0XcgS4fh5npXwWe/e+q+anED/ncm7HdtR224UXLvu7RL94NhQxY7sOC7YdFXUIu12+32qsdKQbbn4CmC67qfrp5El33psdv+/S5tyKruOIbQJahpIgYtF9tzF/8/T3G/P3a+zXYfm7J8yn67ehU9Jr0YFr0xf7ukoj/umcrGmBePRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZFr0xPeOk5fPBe2mFMea9YUtvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnvGv2wYXnj4q9DMAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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ULTz17ZS7np+fh8YWuvR6DoLE0+TdysoK1tbWsLKyEkp0k9R3vD5NIJ6fn4drsITXsIaE57w7de8BPMjW28/PLprWqqvox2v0z4eT/pmwD+2kBB3wsKuMfel06S8vL3F+fo6TkxOcnZ0lavE8Pt1ohhI69LLRaCTcegpxtLFmHFib7/V6gfSXl5eJHALvizoAO9BSx1wpYfUzGCdS4t/ZfEAsHHDCzw4n/UdCrEwXS0iR5OxLv7q6euDSdzodnJ6eJuriWgpUFR2tPAm/srLyoCb/WPIOuLfyXHh00u7NzU1wsXkMHWjJ3nyr8IvV2vlZxcqZNvyxJbxJHoPj6XDSPwOTLI7N0KvgRnvSu90uLi4uEq9utxssLZtpRqNRcOFVyKLz7FWEs7a2hqWlpUSJblzyTq+Psl9tn6WXAdyP9+YgDu6EUyqVEhtQqPLOKvWsxiGm/VfC64KhMb9jNjjpZ4SNy20Zy9aaVWHH2vvZ2VnIjtOychIOPQBOyAHuRTeFQiGQhuU5Sm23traClbdNNXp99l5sAk9DC1p5HkeHcXDjCc0ZaLcfFxOSWBOAsTIfkCS8x/AfH076KRFLwsUGQ2pcattjT05OcHR0lFDZsQ7PUdo6AYcgqbLZbHCtKbVtNptYW1vD5uYm1tbWgvJOhTiTCK86f7bPXlxc4ObmJgiOSHj25nPjS/blx6bdaAZ+NBol2o7tvH/W7vVzjqn0JqkZHY/DST8DbNZdG170wVbC04KyH56bTrZaLZyfnyeaWFTxFttSmptQcuwVk3dsqmHy7rHWWX7VujzFOCzVqXqObbrlcjlM39HzsBWXn4N+NhypBcTFPPREtEU3pkWweRPH9HDSTwFr4SmV1X3smL2m1aIF5T7yh4eHeP/+fSD92dlZUNqpLp0uMGNndeWr1Sqq1WogXqPRwNLSEprNJpaWllCv1x8k7/Qe9F6YvOt0OmFBOj4+RrvdDhtWkqDFYjGcd3FxMYzc4rQceika0vClGXjenwp7VJmn10noohBTNzqeDif9lFB3+OrqKgylpCXjJFwAITFGC6qEPzg4SGwLxQy5Zum5gNzd3YWEXbVaxdLSEpaWlsLYK750iypm0h8rh1FjT8IfHh7i+PgYFxcXiak4dOkXFxfD9B3mDDKZTOi648w8WnmS3pb7VMlIa69ttRaP5QEcT8dE0ts4NS3QerJ9CFWm2ul0cHZ2FgjCzDZr6Noae3x8HMjOYRQsiSkxgHtJbSaTQT6fD//mHnTcV55jr2q1WoivOeFWCa/H1f726+trdDodHB8fY29vD+/evQuL0eXlZRhbVSgUwnkZRjCE4Nw6NuNoIs+GPppMHDc4NPbMWRGP/X9x4k+HiaR/brZUHzj9t/35xzxH7PfA0+4lFvfqzzTDTSKTvGoVmdCisu309BRHR0ehRZWZcdsPr+ezCwDFN3St6cpzwGW5XE6UzVQKa/UCSvhWq4V3797h22+/xXfffYfDw0N0Oh3c3t6GYZTVahXNZhPr6+tYX18PLbqlUgkAQoa/2+2G8ynhGSLEXHu7IYb+f8bi+XEjxR1Px0TS001NO+iu9vv9MDrKDrRQ957xPGvezIizPKdadtseS6iuPZfLoVwuJ+JpuvTVahULCwsoFotBCqskskpAjsDqdrvBwn/77bf49ttvcXBwgLOzsyAGyuVyoTKwubmJra2toOWvVqthECY9Fh1mqYTnuKtJ5TrCLlD2M9EknhN+NjirHwEtGC3j0dER9vb2sL+/H+bU0UXnAw3cx/OcE2+bZ8YRXmNdxvFM3mn8XqvVUKlUEoS3e7oD900rOrue03D29vbw9u1bvH37Fvv7+6FnfjAYYH5+Plj4ra0t7OzsYHt7G6urqyGWBxAkukwaan3dJiaV8HYPekLLdF6b/+dgIun/+te/fqrr+N4RCxNoVRiztlot7O/vJwivgyU0htaOuV6vF8ge20+e51LrTne+Wq0mkmfMzlerVZTL5SjhgfuEI/X0zNBz3t7+/n4YsskqAhevXC6HhYWFYOF3d3exu7uLjY0NLC0toVwuhxJdr9cL59ckoS5mWmaj56IbfKpXYufojWu2ccyOiaT//e9//6mu47MALVW/30c2m8Xu7i5+8pOfoNFooNPp4PDwMGwwwYmwlMhqPE43mp1zJHtMyKNuKonAbjnW37VjjlnzSqWCUqkUJby68tTSc+TW4eFh8FQYnnCMNjUGxWIRjUYD6+vr2N3dxatXr7C5uRnc+lwuh0wmE5J1qvhTMQ4/U5XuapMOrxtAIhzQWXr8rFj2s2OzfAGYHhNJ/8c//vFTXcdniXq9jl//+tfY2NgIlt7uJhN76FR6amNba90BJGJ3uvMk/MbGBra2trC1tRWEN2yiiSXtSAydl68bZHBmPhOKVAGyLFgqlVCpVIKkl249m3cKhUJ0Lh6ttFXPaS3eCox0XJYVO3HR5Hnm5+cTdX+r6ff4/unwmH4Czs/P8ec//xnffPNNsJpWMUeX3HaIqfZcLZNCs9d052u1GhYXFxPNM2ygWV5eDnV4TrONEb7f7+Pi4iIxK//t27d49+4dDg8PE1thUVSk17C8vBzc+q2tLaysrKDRaIT2XJ6LhFcLrAsb742LWbFYRKlUCl15LG3qAqkvTQJms9nE73w/u9nhpI9ACcxZ8/y5loz4PR9MItYwotDsNfXzlUolbDvFfniSnYMwarVaKMvZpB3Pxym2x8fHoRT35s0bvH37NhCeu83y2nThaTQaWFtbS3gX2rgTqwqoS64ae/49yc6dctmcwxCBxGc4xNZjnZozNzeXCJUmVT4ck+EluwhIYG0pBe7rzyzNxd5jG3KsO6+TZnQXWQpuqJ+nZWfiju2rTILFptNQNHR6eor379/j73//O7755hu8efMm5CJUG6DXpMk7ehixYZpAcrKNzgfgcXVBy2azQVTE5pxSqZTI9Ov1c6CIFSxls9kwaEQTouNCLMd4TGQ1XT/HQyixY33q2mprZadM1HFi7erqamJUtartuJ20te42S0+3/vz8HPv7+3jz5g2++eYb/OMf/8D+/j7a7XYgvFpqHo+z9Tgjf21tLTo9V++fhGf/gXbk5fN5AB86A9mRR00Bj6dxO0lPt127DDOZTGLwCImvQ0IdT0c6TfkzYWWh/Arc15kZ+6p0lFNmWHOnpHVjYyMo3ZaXl4PoRiW1dhss62Lf3t6GZOO7d+/w5s0bvHnzJtTfe71eQh3HawM+ELNSqYT2XG3N1fFXvD/bf8DefxKYC5v2C1BXwDCBrrnmBGKiHtXjc7YAwxMd4+XJvKfDST8DrERWXV/NYqt1Z1xLRR0n1aq0dWlpKWTmi8ViINwkBRoJc3V1hXa7nUjcHRwcoN1uJwjP61O33u53x4Qhx2VbaazW/nu9XmI8N4BQkuNiwt4ANgIBCFbdlhltHz4JTdefCwytvcf108NJPwM0LmdizVp6AMHNZQ96o9FAs9kMrry17nTlSbZxwy+4oJAk3GhSG2cODw+DSx8b8qFxd61WC1oAXosm7vS89Cp0CtDFxUUQ9tCjmZubC2699t3Pzc2Fa4klIjUnYlV5dPEpelIX3/F0OOmnBN11DrKgZWY2mjV6AGFRYN292WyGZB3JHsvK20GRlnjaLcfk3dnZWRDeMEsf2+DS3oO69dTV6153PCdJqVOAOp1O2H1H9fp639ruy8+Ibnls7LWFnpsxfa/XCypHip7cvX86nPRTgp105XI5tJouLi6iWCwmrG8mkwkJO+2MW15eRrPZxOLiYqJZxsbtPFcMqgWgW6+tu2yNpeiGZUUek1ae97C+vo6tra2gq9d6vOYNKC1mP4E2EOlCx2EftVotuPa08la9qOfQXEksWcm4XucYcNCHJ/OeDif9lKBMlvXsnZ0drK6uYmFhIajLSDSq62q1WmLQhermNW6fNLwSiFtc7Yff39/H8fExOp1OILxtZyXhK5UKlpeXsbW1FUQ4q6urYRIOYdtxOfbr5OQkbKpJkQ/zGwsLC6jVasHK0xMCEFqQ1YsAkrMLGNqo9adXQ0vPacLq4mvC0TEeTvopQCtPwuzs7ODLL7/E5uYmqtVqQp5qS3OcZ2fls9MOhKDV0xFXe3t7eP/+fUjcMY7X69Y8RLlcRrPZxM7ODr744gu8fv0aGxsbQXUX65bTgSCtVgvHx8dh8g/n4mnIQ8IzT0ES6yxBTdYB9+O16frzc9FY3+YStAzpeBqc9FNAh0qsrKxge3sbr1+/xtbWFqrVaqJMZ4dZUm8+Ldk1a67de7Tw79+/x3fffYe9vb1Ea6xeB/Bhdp/uZru1tYUvvvgCX331FXZ3d7GyshJKdMB4C99qtcJAEKr7aOV1Sq669Zyhp4uIjtLSspwqHZn043uHwyFyuVxiYxCS3pN5T4eTfgrQZa9Wq0Gfvrm5iY2NDVQqlUAw7Szjy058AR5ubDkOKl5RwlNm+/79+zDxhgIZzf4zscaBGOyee/36NXZ3d7G5uYl6vR7m6SvhGcMzb8DpPxyRzTFhOuhDcxUsZ/L6b25uEi+Nx20IouIwegXaOahbd7ulfzqc9FOApCd57PRZtd52/ptNTinGPbBq4ewAjP39/TDxhoo7ZtC50OimFLrlFafg6KYY5XI5DOHUBabb7SZceh0ccnNzk/BoOJZbQxjmOUh4zhfo9XqJYSJAMgyJlQq1XMhjUBTklv7pcNJPAZ1kw9HT3OiB5Sj7GpeN18w0oXVpVagxjmWb7P7+Pt6/fx8m3rRarSDAYaKRrjybeCi8odRXtQHUBQBILDDcU49k13ZcHQRKK2876DKZTKInQLfsomuuqrrY4mh7GRgeaPONavQdj8NJPwVUVhqbPKvZd/vwxgQ2+lXVaKozJ1mYMT88PAybZTCu5uRabmRphUDawEOyq/ut/fCxnXg4FtsSniIcyoutUEm1+d1uN+yR1+l0whbcVlVnvSEr2iHp7Ux9J/3T4aR/InTABUnP+rq14E8hvFoutqbqTrYsS3HEVavVwsnJSXSi7nA4DC62bnFFTT+Hb6hnYrvmdGOOs7OzQHa7uLDkZgmv8TuAEK/r9tskPDfGZDfduDq7Veqxucm2Ldv3ulBnMry1NgI+MNrwoe6ybuUEJF3Qx6Bxuo1zdSdb7lqrIhi+SBq6tfPz80H1t7KyEnrh2R7L3Ws5XkuFN1bZ1263cXR0hP39/YS6j/vaAfd76ul21fR27u7ugsvNrL9aeEp2dTccJbd+njqUI/YZxxZYx+Pw1toJYE2epai1tbWw0YNNfNmpr7EHUa26dd1p0dUi8qXbWTNxxY0o8vl8Yi799vY2dnZ2sLm5GfrhdSNLK321ZUCGEBT7cMQ3xT46zdYSfjQaheEcKtXlfdGlpxegPQoAHpA7tohaAY/vdDM90mnKn4jBYBDq8OxEe/36NdbW1lCpVJDJZELcCuBBWc42kzApx7nznId/cnKCk5OTYNFJcpKEHWysR2uHHN15zrOzI650C2lNjmlSjDH36elpcOnHDc1kZUCHW7Ikx/hcBTTq0lutPD+rcclMm/cA7gVSDCt0Eq/+jWM8nPQTsLa2hl/96lf46U9/GmrtTIYtLCyE4Q4AguW1tXh6Arr3HZNkFLqw7q2uO+vYNlHFRhmN37e2tvDq1auEnJYjrsbtdmOFN9yxR4U33GePCjmd+MOKBS08FzPmBTQ80Xq6LpBcRHhfdswYobkR7VyM5RIcj2Mi6X/3u999quv4LMAHj7u1/PjHP8ZvfvMbvHr1CsCHGJ8PO+NiDnawdXkgSSzudsOaN8nFuvf5+Xmw6py0a6fv6IhsuvSbm5vY2dnBzs5OlPBW4261++OUdpznz+SZDrck6Wml+XdKeIYmSni18PbelPDWrVetvu4JwKEcrEA4noaJpP/DH/7wqa7js4DObs9kMiFbn8/nA4G1dm7HNulxSFrG7kp4kp3WnbE6LSFdWz7IdKH5wLMdlt1x29vb2NjYCJtKsqoQG3GlYYYq7Wwd3ir7SHY2Cum8ei58lMaS9LrJh3bX8bqGw2HIi/D6xhGe79HPgGVTbQN2PI6JpP/5z3/+qa7js4etY3MB6PV66Ha7ia2mAQTLp/E7O9P4lbG71qpjklS1tLprrQ6wXFxcDOW4mKJtHOG5CNk6PIDgKeggENbidZNOS3iGKMxFaEJYN8bQ6+NnpvG8JTyrKFT+6bBQt/RPh8f0TwSTZ3SxmcSjkIUlLbqwTJAxg31+fo52u42zs7NAChLGjnzS7D+TZnb6DodxcMcba/FsCczq1mnhY8Ib6vXpXUxy63k8Ep4JSN3VhwTWhiT9t15jbIIwPwdeF117ViVsIs8xGV6ymwBbetM4lLVtill02izLcuwGo/XTGJekiD3kat3tqOzFxUUsLS0lBnHQnQfwIC5WTYAKZVReq8IbKu1sWY7Wne64JiYt4TnYgouZWmsunFpy0yqH9Xj0M9F4nhr/mEDKMRkTSZ/WD1LjaS29WS09BzWSRIyFlWSsxTO+1SmuREyrbwnPHnW+SHZtheUxY0o7LkCsmzPEGCetZVmM16HH1LIjFzNm+lVHwHsj6Vnmo3WO7XRrlXb62TDEYRORio3S+qzOgomk90kk94gtArpnHNVmJL3uWhtL+PFht3p9WkHtXKP0ly2rqgZkjJ7Nfpgjr9tOUUNAxR/LaBQDMdnG9/Hctv9fPRveD607t+Gmys62uWrFQUt9PJdKka21J0hqJT2be2I5AsdkeEw/BWxyjKQnuVma0k4w+wDTTbUxrRI+l8uFB1wn79CyMZ9wdXUV+s5ZZmRWnDE8Scowg1JfWnfW4ElwbrOlhNehlJq0U1ktk3YaAvE+SFISlZ6Dfob8TLV0p5+5boLJhKIKc9zSPx1O+ilhRTcqraVl1YcWuB83nc/nw99ymgyPyRiabi9de5JFM+bMvnPevRXg6L5wuijR/dYedu271045HX7BGD2WpdeNJ/R+uICw68/OBVQNA0MQ697rZ66iJPUY3LWfHk76GRDr8Y41g6hVpyXXBxu4n7Kjmna19DHlG/BBFKTbXGnMrW2nzB/ozjG8Lk7UITFJeh3nTc09hTeatNOwRQlPcjIXwdFZ1Wo1JAlZ7qSq0YaSejx+dvqZqObfST8dnPQfGRpf0oUfjUbI5XLBusZKUprNVouvzSwk2NXVVbQOrzvY6HZPmoSkF8E95lTkQkLxmm3zTCxLTwvNe9HkY6PRCNUGNv7k8/kQLtDiMydhwx1N5OlCRU/EST8bnPQzwHbS8aFkOUutk20giY11UmutQyH5MGsXn0126XZV1ovgsZUsfNEKc0S3HYBBMms+wApvdGKNEp7DN6knUC2B5iAymUyoLDCcsANDASRCHuYa1MtxTAcn/YxQkmqjjZ2LF4PtKuPxYgkplr6U4LHJMfw9j6UjrwEEsugsfpb+dMMN2w+vO9lQZ8A4XMtyjOEXFhaCYlBbkVlizGQyITQh+ZXIGu4Q2pzDBSC2k67jaXDSzwC17io6sdNw+bex0qdmqsf1kcdcdmbctTRoJ8qSINwsUkUt3FuOhNeBIKwKMGmnOnoSni69ztVXwlMxyHl8lAhzvLaGDf1+P4QUar3t8BYluh0h7pgeTvoZoaS3CTV1z2Puqrr5MddcQwF140l4OzOeBFQLSfJrjK07yCrhmbhTUZFOvGF7rGb+2devgzG5k4/dr69Wq4VNNHit19fXiZ58LVfaECjmTTnhZ4eTfgbYWF6tfcxdjz2gGpfTump8rguB1t3VulvC2+YcbiJJV77RaIRsuh2MqQ1CdsSVSodV9ccFjxUAHQ1OmbBuXgl8EBPRqltdQj6fD8lKhhr8LHlOfnaxqTqOp8FJPyOsbNZadCb07MNJsuuIa35VS68u+7j6tZ6TcbV15XW3GYp8dPAE6+WsClgLrxNvNEtP60sxD1WDsVyBynjtAqiLlcbt7PTT91ipbqz33vE4nPRTIpa5j1ny2OQXJbNNxqlePRbb053WRYZQC0/xipbjrKJPZ9zrzD47tTY2AIPn02w9tfA8l912m9DFjp6KLiR2IbV99hriaGOOE386OOmfAX1ICX0ANdnF36mCL+au23hW6/ixc6ibrdNtVL5La6u6fJLH9v2rhecQS4pvGMMD9xt06ghs3ZiTpNXNKFW2rENDNBkZK3FSwKM5DX52Tvjp4aSfEdalj1l8a6mU9Epy/V6VenZRiX2vY7RUpkpZLV1+AMFSstHHNg1xsIbuPmOThDrBxibieG067Re4355aPQrdloolQs1t6OJIqP7fST87nPQzQAlus/Ux4tvE3DiLHltEaOVtwtDGwbS8JKKdVEttPFtoeW2cvU/S677vJBUVevaedYNOAEGn3+v1guSYNXm19LrVtI7UouXnNWmiErifR8i/i/XeOx6Hk35GWOksCUaCjHuPvpcWd35+/kGt3S4mVrhC4mttW6W7WoKjO68LExchu7MOCcj3aRnQagFs2y3JTqt+dXWV6ITTXnwmDHWKEM/NKoFKibmLj1v658NJPwPUAivROEDzsWYcHgNAaICxunOV8irR7SYP9nuNpUn2WPgRyy+QTCqv1aYhS3rrzvNv2JyjWn72D6jrr9aerj6beNTNJ1Sf4FZ+djjpZ4S61lpntj3htgxHa013mZ5BrAnHWnZL8JhWncSyi4/mFwgSiteoMbXG/bxfXbzUW9AR2CS0Tt3ReJ+hhu4xz5edvKPnY5ii+Q8n/Gxw0s8A1d3rHDnu9aYquxiJNFa2pLTniMXxMU2AFfvQyttEoYVWHvh765lwkSKsohBAyBX0+/0HoYY206i1173qtddfLTnPbzsUXZE3O5z0M4CE5APNjDl73a1enjE7iadjosZZYZ6HX5XgSk5inFxXE16aibeLilYGLPnHhSz2Hqx3wvIdxTkkvZYJSXZ167lo8d71e+v5OPmnh5N+SmgG21p6Nrio6o5Wig+vdfVjpOIxFPr3TNIRGsPr0AytbeviwnvQngE7dmpSeBArM/Ln1jPJ5XLo9/uJ2fR08ZmUU02/DuQgtFKgs/u88WY2OOlnQCyRR+IDHx5q9o1rEo3/VmLbTjsgqebTf8fcc3XtdVIO98JTIinpuXCQ9HqtPG7sGnkdSn67MKi1p2fDhY+fj+6Mo4pEnsdWOnT+vx2K6aSfDk76GWFdfL5o7WgJGbczhufDP06Xr1/tgmD/XhcPzcKTUFbwYrPvmkzU2J7HnOR52ISlTbpptx//XpN/XJzGtQcD956IzgFgP4HdXsvxdDjpZ0BMPENrpEMvYjX7mNus39tWWzsxR4+jP9fhGppTUFdcSUXCqyiIx3uupj32Pr1vm3CM/b0mA9lAxGYe7dzzuH56OOmfgUmyWwAP4l4rMbVEt2S3CbNxJLR/M+69lsi2bh+L4WP3HJsRME6KHLtW9Tx0kdPj0MIr4Tlzr16vhy2trIfieBxO+mfAkjyW0bY1cCX/OLLHjjMusx+Dkk9DiUkJw0nuOo8TGwyillYFRSoPtuFM7F5sT4Hu31etVhPbeXHenu9LPxuc9DPClo/0exKHMTx/p4RT/Tv/huSwrreGCDa0ILiwaD96NptNfGXyLraw2FCD16bn5HVruUxzAhqD86sNHZgw5DXn8/nQiQfcD/Ik4Tn8QyfxNBoNJ/0z4KSfATFxTqlUCgTThFoul0uUzrSHfVw8bzPjCjuWi+B7tGHFdqXx57xGTRha7yRm5fUa7IRaZtc1qamWnvfDZhnbOMNEX8ylbzabWF1dxebmJlZXV1Gv1xNbVHtMPx2c9DOAJOAWS7VaDaPRh9n2zJqzLBUjfMy1H5fYs9ZWW1utFbU6etbAtYddf65dbVoys/E16+182T563YBCN75U+S09EV4ba/MqHtLddiqVChqNBhqNBpaWlrC8vIyVlRWsrKyEmXuexJsNTvopoS5toVBApVIJbqpu/mDLUTrEMpZcs9ltQmvWWv9WNR3BxYRkJsnZwcYXpa9sa9XOOh4DeDgzn1tscS85bhfNurm29tLF1/tQ0tMTsZt66vgtzvOzu/VWKpWg8nPSTw8n/Qxg/FosFgF8KC8tLCwkNqy0BLfZarXi4/5NWNKrjFaTZDw2yaUts6p1180sOSFHe9p5bp10y91zdSIPR3BxPJZaeOuJqKWPTfTVBB6Jz3NyYeF5tInHST89Mo/UYr2NaQw0K69S13EueuwFxDe+sN9rdjw2aEPfo4uMkt/2zLOzjYMzuNU0PRN6FCSgJTvFMRzFpdtPqyfC69I8hb6sbl/zJOo1aI4g5uU4ooh+QE76Z2BSHXwaUj8VtjQ27ppiZUC7k611+zWxRljpK914nYdHN35cq2/suiaVBdWbscdUojvhnwQn/T8L9jOchdBPwTQPug0VNEOv22rbRCP/xnbLxbLy2vASE+vodcSubdI92nt1ks8EJ32aEcsfTLK6QHws9SSCOz47OOkdjpQhSnqXMzkcKYOX7BwBk5psHD8cOOkdAU7udMDde4cjZXDSOxwpg5Pe4UgZnPQOR8rgpHc4UgYnvcORMjjpHY6UwUnvcKQMTnqHI2Vw0jscKYOT3uFIGZz0DkfK4KR3OFIGJ73DkTI46R2OlMFJ73CkDE56hyNlcNI7HCmDk97hSBmc9A5HyuCkdzhSBie9w5EyOOkdjpTBSe9wpAxOeocjZXDSOxwpg5Pe4UgZnPQOR8rgpHc4UgYnvcORMjjpHY6UwUnvcKQMTnqHI2Vw0jscKYOT3uFIGZz0DkfK4KR3OFIGJ73DkTI46R2OlMFJ73CkDE56hyNlcNI7HCmDk97hSBmc9A5HyuCkdzhSBie9w5EyOOkdjpTBSe9wpAxOeocjZXDSOxwpg5Pe4UgZnPQOR8rgpHc4Uob5R36f+SRX4XA4Phnc0jscKYOT3uFIGZz0DkfK4KR3OFIGJ73DkTI46R2OlOH/A+yhdKsE+nRSAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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zs7Pwb5I9Tm8eV0lgbF0qlTA3N4dms4nFxUU0m01Uq9WI6o7v0ySaztFnHuHs7CzkD9Rb0TibpFTloHoydo6gvj9O1MPwgD9jG3Bcz7/j6XDSfyIMyiLbDL2tw7M/nRJXuvAkvY6hsjG7/apTcGjhFxYWsLy8jMXFRdTrdVQqlYiVj0vgsUzXarVwdHQU4nguOno+rc0rCXVBi9PI00Ir2fXfWk3QbD3/Lq6pyfE0OOk/AeLKd3ExPa0fXWRtT1WXXuvuvV4v0i2npFDZrxKeUtvFxUWsra1hdXUVi4uLYbqtWnmFVd+xLMj6v3oYdMXV6jIpp4MzSHoVC9F15zVb6z1oRJgq/dy1Hx9O+mfAutaWSJqlZ0xL6842WLrw7XY7JO/Ozs6CdWUyjOQAor3pfPhZHy8UCkFqy4aalZWVoLwbZuXVtWfn3iC33ibv1K0nyeMGbACI9MyrdJfH1HCD0Iy+W/nnwUn/TMRZebX2lvQkFq0pX0zWccgk++O5L102mw1fbe8+S2WcaEu3fnV1FcvLy1hYWAh98naUFq9TXXsq/yj8UVmvxuBU33HSDr0P9gjodB1N5Klrz+NYKz9M3+BqvOfBST8mrBBGk2K2Zq+tpSQWG2Y0UcYGFrr/OgWWIhueiwk7demLxSJmZmZQr9fRbDaxsLCAZrMZZthbfb1+Flpm5hXYRXdxcRGuXRNvnMunE3SpouOxOBOfll6lwbb8Zpty9B5rlUITgE788eCkHwODas1AtC9cH17GsXTtWQ7b29sLWnYdLsnjkvC0qLSuqn4j+Ti3fnZ2FnNzc5idnUW1WkWxWIwIcQaV6bgYMZan8s4O/czlcmHUVqlUCnP1OExEN8OggEh737W0p6U+zfrbSkfc/XbCjwcn/SeEdfVttp26+vPzcxwdHUVIz6Qda+DsfyfhSbBSqRQGWOZyuWBxOdiyVCqhUqk8IOSgOfgacrTb7SAG4jCOy8tLAPd98rlcLpyD+9frtB1qCOzuNyQ9P5uSnlJg4D7rz38/JmW2/3Y8Dif9iIhzPYGHbbaELgBaDjs6Ogq6+uPj47BLDt+jRGDNvV6vB+tNQpP4Sn7Ov6dnMIzw2lhDwqvnwQoB3fBSqRS8iVqtFqbtcLNMuvLaKWi9F83a64x+JgGHkdg77J4PJ/0YiBPZDHsIaU3p2rM3XTvldKYdCUDi09JTWTc7O4tyuRysuM6tVyIN06erGlCbe/b39wPpLy8vQ04hk8mgWCyiWq2G6bm8DiYXu90ugHuPRjP3DHEGkV7vK3MDExMTkek/w5KmjqfDST8C4tR0VmXG31sJKuvfrMOzPVbdei4e2lZKq89SHON1utRa47Z17GG1bFrVXq8Xkoq7u7vY29tDq9UKWXtWCJgk5CaX1PAXCgWkUqnwGVKpVERWbDP/dO1tcw6AB/Px4yz6qPMHHQ8xlPS6yiYJtrQUl/ii5eaDal164D4+5YgrO27q/Pz8QfKO79c6PDenLJfL4WXn01vdum091evXbP3Z2VkYu7WzsxMZuUWrywWHAzio7iuXy8hkMiEPQUvPxKCOulIrz74AJiG5hRVjf34WNtvwOjQR6cKc8TGU9HEJlFGgySz93v78U54j7vfA0z5LHEH0Z3E7vHBh1DHYfJ+WwTgEg33xqnSzwhdddOgG23idpLGSVPtVPws/w+3tLS4vL9Fut/Hx40fs7Oxga2sL29vbobGG025YFSDhV1ZWsLCwENR9AMLCpfvP6b719rOQ9MxJ8N5p9p5eBt9rXXsn/PgYSnr+JyYd6nJeXFyEDR5omVKpVNgPnrVwACGGb7Va2Nvbw/b2dsjWU+mm/fA2VtV5c5qwi5OtPpbgsha+3W7j8PAQW1tb2NjYwObmJvb393FychIy9tlsNjTtLC4uBrEPx2xlMpmw6HU6nUgG3rbRqkxYFzCW+nQbK9vB91jOxDEanNVPAJtNKFGlUo3ZdkpgmTHnjrKMlzmmenNzE7u7u5GYeVCPuRJeRTB2Z9k4Vz5OJ6CiGSX8u3fv8O7dO+zs7ODo6AgXFxfo9/sRwi8sLGBlZQUrKytB7EOhEBN2vB6eS8U0OkZreno6bKtNwVAqlQr3QjX8+lns5xlUynM8jqGk/+tf//q5ruNHR1wYoLGlatKpVONDSldV3dV+vx82o9jZ2cHm5iZ++OEHHBwcoN1uB9Vd3MOrJOFxeWytuQ8jPD+Lusv8DB8/fsT29jbev3+Pd+/eYXNzEwcHB2HjCu5zV6vVgpyXTTtzc3MoFAph22gAAwdaUFzEezQ9PR20BKVSCdPT0+E4JL7tX9DtrblwqSfhxB8dQ0n/hz/84XNdxxcBJo04y31tbQ1ff/01arVaIHC73Y5ktplZ1x1f+QDTtedQyb29vZCxj5tppwo8Er5YLKJcLqNYLIZsvfaSx5WvbIVBO+cODg6CS7+xsYGtrS0cHh6GhYgCHG57pfPxmbyjdWbIE6dCpIVPpVLhs5DsFBhxYw3N1nOR0u2vGEKpBsBumOGu/9MxlPR/+tOfPtd1fJGoVqvY2trC0tJSII22umpnm1piAJEdYbjPHHvkNesPPNwbj8esVCqYmZkJYhwlvVp5HgOIt+7tdjvkFZTwe3t7YSHTRUw3tmQc32g0Ir34tsLBcxM6P4+E18oDvQXKdlmx0Fn/DB24ADDEYv1f76Hj6fCYfghOTk7w5z//Ge/evYu0h9rSEwnPJB4fZPbKs3POzrYDooRnDM8S2dzcXFDhUYzDcwyK4Ul4lgmp8d/a2sLm5iY2Njawvb0dhmzSawEQ3G8m7tbW1kJbbrVajczH1yYYG2Nr0i6dTgfCUx5cLBbD4sGOPC5SJLruTqs71vJFDYBvfDE6nPQx0LIZ3XIguo+6uuEsQ2kveNx+8ToySl1S7YenCKbRaKDZbKLZbGJubg6VSiXW0iuU8JTVctur9+/f48OHD9jd3X1QPdAcQrVaxcLCQiB8s9nEzMxMiL9Vg2DPaxdELmIkPEmvVp7v41ARDgbVDj1ujZVOp0P1xM75dzwdXrKLAR9sWkCCCSTWpDm0Ucc8AVESaAJKj89zaOealsdWVlawuroadpOldVTCx8XwnFd/dHSEzc1NvHv3Dt9//z3ev3+PnZ2d0NzDhYjXQ7e+Xq9jaWkJy8vLgfC6Xz3PZ++JuvvZbBb5fD649mzOUSs/MTER8hp2ipBadCvw6Xa7oWRKnYTH9aNhKKtVJeaIgtZJm0RYUwbix14DD+fZZTKZiFtNK/vq1Susrq6i0WgEuetjWnpWGY6Pj7Gzs4N3797hb3/7G77//ntsbW0F4Y1KY5kUnJ6exszMDJrNZhi8MTs7O3BDDN4DzajzeHTdubOOduNxXJfW5TUU4ktjd56DpOewUFr7UfYQdLh7/yTEqf40ptXYOo7g1rKTEKxZ67Sb5eXlUBNXK/9Ytxzd+na7jYODA/zwww/4+9//ju+//x4bGxvBwtO6AvdJuKmpqaC6I+Hn5uaG7oAT139Atz6fz+P29jbU+qvVKiqVStgKWz0UtfIkvu5Bf3V1FbHidO81R6J9Co7H4aR/AuJiRiu5tbBZdZbjGOcWi8UHTSyLi4tYWlrCwsIC6vU6ZmZmAlEGbUyhbj1LhFtbW/jw4QM+fPiAra0ttFqtBxZeFyHW5Dlth4o7quU0d6ByXpunYEcgj08PplKphJIjm4kYqzOO1957O1dPLTnde7r4+pkcT4OT/p8Muv2qn2cprl6vY35+HvPz81hYWECj0UC9XketVot00T3WLadu/e7uLjY2NvDhw4egsmOZ0M7Lp7dBt35xcRGNRuPBPD17PnXrlfQsN9qwhRoGttDy/XYbbk166jmU1BwXzsGivV7Pk3kjwkn/CWCtvTa90PrR4rEXvdFohFe9Xsfc3NwDN9iKcOw5VVp7dnaGg4MDbG5uBrmvuvTWGnIholu/uLgYFHdPcetJWG2qYQUAQERjr/kIEtQOz7Rkt2o7Padaetuw5HgcTvpnII6M2hmmO8ZyAAbJRTd6dnYWMzMzQbDC+XePzXUn+VRLv729jc3NTWxvb+Pw8PBBDE+wXbdcLofEHctz1Wo1KOVsV6RtK1aRDIAwAYfHZ88AFzBtXCLh+ZUZet5X/Wr1B9Qg6DZfTvqnw0k/Jmxft7X0dG+1D311dRUrKytYXFwM20XrHDtL9se65ailZ/MMm3pU368xPK9XCa+z8ev1erDKwxJ3JJ5aaIqLtD6vvQKpVCqittNY3hLe3ldNkOqkXSb9KMl1PA1O+jFAAYrWy1mjp6XL5/MhSbe2tob19XWsra1haWkJ9Xo9lOHieuJ5Dgt1rWnh6dJ/+PAhSGspF9aJNcD9hhkk/KtXr/D69Wu8evUKzWYTlUolWPlB5yXZtazG0VZctHR8l52Hz/fyRdLyOnkv4zwd9RJYsnNLPzqc9COCD6WOb6Zby+RYLpdDpVIJDStfffUV1tfXsbS0FBl1FefGD7PuOtOO3XJbW1t4//493r9/H4ZgUFrL6+E1cyAGLfzr16/x5s0brKyshKEYmryLs/C0sHSvaen5GWyvP3C/XRbfZzfmJGm5cNhEnt4HHkt1+E760eCkHxGqoGOjDa01X9w8cmlpCa9evQqEn52dRalUiri8cV1yRBzp2MTDiTcszW1sbODw8BBnZ2dBWkvS64y9RqMRrotufaPRQKlUiuxxN4jwrJGzTs7hmdohqL31dOd7vV7YjVc36NTNNHQ0tnbv0VMAMDCn4KR/Opz0I4APpUpL2QFHIjNTTzkrE2RUtw3adAJ4qOKLIw2n1nLE1ebmJnZ2dkJ7rGbSWSasVCqhDs9rosyWCxFVdHpuzZirKEattO5Ppx6LZulZYjs9PQ2vdrsdNtOgpVYtg7bt2oWILr5tsXU8DU76EUALX6vVQrltfn4+tJzabaL5e8bv2pQzyKprPzlj4G63Gzai4NTa7e1t7Ozs4ODgILTHsh+eU3bK5TJqtRrq9ToWFhZC5UCbeFg/1yEYmmVnDE6ys2PQSnn1c+iutboFN608ZxIwEagLB+v9mjNR8Ni6MDnhR4OT/omgXJXDJbgFtPaZc5QV+8dnZmYetMTarDi/WreVrjS3rz4+Pg5z6ff29rC/vx/2jme9mrV3VfrRnaf4h3oAq/Qj9Dq44Oiuukyg2TId30vLyxieO/EeHx9HCM9cAC08Ca472NrF0cn9aeCkfwJY5mISbG1tDa9fv8ba2lpkmozuwkphChcDa7G0A882nXQ6HbTb7ciW0a1WK+w+02q1cHp6GumWozCmXC6Hxh1261ETwH3ttFtv2PCNTqcTZgIyBtf59tpdSAKT8N1uN1w/Z/xzzBjHa9ukaNwIsEE6hWElU8dweGutIK5hRoUsCwsLePXqFd6+fYuvvvoKi4uLoddcy266mYMOnQAe6uXVfadVPD09DWTRr/yddpgB9+o3eiGM25lPYMVAW2Tj6vBKeHoY3Epbx4SR8AAihOfwECYbj4+Pw8YeXKSoHdCR2FoCfYzAmjDUKoGT/unw1lqBJX2hUAj7tc3MzGB5eRlv377F69evsbS0FLaA1pKduqk2AQXcu89KdpKj1WqF0VpKNsbRdON1myjNIVBdpwMw2KX3lKYdxvBMuvG6lLCMuYHoFtwAIoTXz3N6ehq8BN26i805tPaDGpv0/0dDAJYHnfSjIVmm/BHYh67f7+PNmzf4+c9/Htzm9fV1LC8vR5pSbJ3dTpdhsomWlLHuyckJPn78iIODg7AJBglPy0oBCxNeJDsFQHZM9draWmQARlwC0VYJlPDqltNKt9vtEMdzIWOykRl03eqahGcYwvczS89JPazLa6jDa7Lae0KnDFEEFNcY5BgMJ/0QNJtN/PrXv8avfvWr0PHGLjjVpwMPH1TdoMFad25gya2q9/b2QhaeVpX1a+0yo2vLraYqlQrq9XrYiGJ1dRVLS0th8Ia685pTiNPSM/HGWjrDCXXrAQSLzIWCWX5KgrnzLRcvWnidL8hroaegU2957yzhOaCEYQFnEzrpR8dQ0v/+97//XNfxRYAP1vn5OdLpNL755hv89re/xdu3b4NLywYS4N5V169KfCUHE2O07vv7+2HvOG5XrZtoKNl1rzrdLprdcdT0Ly0thRIiS3GDhm7ECW80gdhutyODKjicku9nHK89AAwH1FtRqS6AoNGPa5/l8XUwBxN+XCxo5e3mIk76p2Mo6f/4xz9+ruv4IqCuayqVCiq2XC73oOOMru3d3d2DWey6CJBQrFXTnWfZ7fDwMEylJUF04dCwQctxjUYjQnidS8/E4mOE16qBluZUfKMLDz0GtfKsNmg4QAuvW39paU6JzoWSP9Pae1xSVTf0pIvvpB8NQ0n/zTfffK7reDHQWjTVYRSw2J1a1WWmS697wLdaLZydnQWxi+0WU1kqB2ey/q6E53grVdYNE/+o0k4Jr6O6VQ/PTL2227LTjZ6BZunp0sd5LDy/XpduaMF7qp6AhgSM49nFF6d/cAyHx/QjwibqmLG22XVVstH1Za2ddWtVt6kUledhWYqEr1arqNfroS/fjtWyBIgbRGEn1qiWnhl6ZuRJdqt+U6Uds/yPEZ7HARDJL/C6dIsvXp/V1NPaUwfBduRhcwccD+EluyFQC0OoxdRYnQ88ic/SF2vvWm/X2F3dXkLjd25+weGZ9Xr9wVgtuwnFIGtqCa8NNEzW0eJqHV5HY3ExY/VBPxPDAl3E7ELGl80PcEsx6hdsjkQXQd4X3eLL8XQMJX1SV099UIH4zS01QUcCkMxWr05Fm2bCqWrTbjggSngO4iiXy2HHG07asbG7zX7zetV91u2i2CFHhZzG71Z4QwIzacf8hCrtuOAxv2Gz70p41TLwOmlgVAasyTzgPtwh4TWeT+qzOg6Gkt5X0Cj0waJFsuo1JT1jZXWdaUmz2WwYE81jq/BEt3YuFouR2fEkO3A/SYZut8bdNjuuG0Dq1Fl2yzGzrtJaddOtll6Vdt1uNywqcYlIEt1u1mGvlYspXyrmYfaehNd5BI6nw2P6MaBuqJa6WOaKmy7DpBhHUXEyrGal6aqqC8t6tG4UASC0rAII20sr4dXC01rr9zZ7rlJY/o4bS2oYw/wEPRsNDdSlt96STsWx23LZ8EOvl7/TxZCEdzXeeHDSjwGrYtNGGU3mxW14aTPh1I8r4bk40JWltp7xda/XQyqVwtXVVSCA7WUHoltOWeurbja/T6fTQQBDMGlnVXrMYSjh40Q1uqjpZ9XNOwYtULxmDQd0QRw2LdgxGE76EWE19HSVuSuLblapCSggOi1WN3lUq2WtoPaW9/v9QLJut/tglpxem0LPSdIx863dcqlU6kG3HAkf1zxD1WAc4W3STptkdAgor1dDkTgZrjboMKb3zP14cNKPgTjLpA+/qs90dp42ivCh1T58dVWVCFrGGnQ9+lXPbUMFegy8JtsJqIS33XKtVisIb7j42Bhez28/t07I5WKl/QQ2cafHsaQf1EDkeBxO+mfAkl9JCtw3h+i/9eFXRZm6upppJwF1YbHxOa8FQCxBuIMsLTvdZN1iG7ifSnN1dRXKkJzWc3R0FGJ4SnMHEd4udPp5SXodvKFSW3XV9ftsNhs5hu784xgNTvoxoS6oFa/Exc5aY9aHnwsBCR+nmNNdYDRfoAk5AJG4lxtJUgVH0pM8ts6thGenHa27SmvtBNq4z6s970r4fD4f2eeeHowm8TgUUxcCHautpHf3fjw46Z8Jtfa2RZSw8a3W/fmwA9Fee5KcGzsyYajEtwIY7VEnIUg8TvIh8dTi8hiatNP2WDbPWC29fj79nHGE5352dMuB+x4HLlxa0aDLH0d6LlieuR8PTvpnYJC1t6QH/mGFb25uHsTttmyldXQSXTd41OYe3RVG41t6E/l8HsViEaVSCaVSCcViEYVCIdKdBiDSD0+hEbvlHpPWxhFeXXrqDDgunK2+PC+AyILJ+8JjMqnJOF69Iyf9eHDSj4k4otvYntApr3ShrXw0TkRDi65fNdbX4+ss/lKphHK5HMZ0VyoVlMvlB4SnlbUjtjVLr+2xgwjPa9AuOLXw3KZa25K10qD3i5oGHp+LmH15qW58OOnHgGbLLeFtjZlxq7qqdhacuvmayNOv2mOu9XXG8LTstOrVahUzMzOB8LpdtJbKLOH5omxYB2DEZejtFFsuPiQ8F5pCoRB6BID7ufj8vOra69AQe2ytiDjhx4OTfkxY1ZtVu/FhjiPKoOPYxUKTg1aOqrEzLXyxWAwWni/uhqvbaFlFoU7L4cQc7bhTd1sTg0pAW5lg/kBzCPwbrW4w7xBXruM5LcH1b+zfOx6Hk/4ZiHPx4xaBuNhfCc1jKWzDjx28qXEu4+ZSqRQsO2N4nbkPIOI96BBMJus0YadkV8Lr9amSUK08XXltjNFhF2rFdSHT2r3eY1uqHJQ7cTwOJ/2Y0JjUkj5O426nwqgXYIlkyaDKO6tso+tMt14Tdhq722uj2o5Tbxi/x+0kq+EAoQuQZuttss3OEeTXQYtd3O+ZD6Hs2bezeh6c9J8Ig9xQtUy2iSROasoSGo+p8aztJ6dbT9eerrwOl+Diwk48u900XXvdeYZ5A56PcTYXDet16LXZPe2A+yQlPxOvwyY+teEmzt3XUqZvXDk+nPRjQl1edbmVuDZZFyfiURLpMdVltplrLYfl8/kHu+eySkDZrrarkvDa/hvX+quEJxFtbiFOZqwlSFYG+L2V3mqfAq9Np+ZYF97qFAblARzD4aQfE3GZ5Uwmg6urq/A3luSaBCO09ESprhJem3IYH6tIRZV1XGxUuKOEUsJb4Q9/xutkn782D8V5J4TKanXBIfnZHsu/0/Zdtd78aiXNQNTS60LkGA1O+jEwSF5LK8wSXdz7AMQ+rDzWMAuvbr7G+cD9vD4VuKjIZ5jAR5NkVqOvbrctUQ6rNNiEplp6hgt2MeK/dWMP9Zj0Wt3Kjw8n/ZhQK6/NILS0tOAcRMGHVrPg+kBrCc6+NIRQWBdaFW46FkuHeVgrGbf46IKmGgISmMpCtcjqinOx0PLg3d1d8EaA+5DDeh90+dXKq1Q3zgNwjAYn/YhQK6/NLUp6/i6uW4wxrcaqmguge88FJS4voFNt6MJrll4HX15cXES2xtJ95W1OwuYnbN6B8T6ASL5ArTqvTxcNexy+T116kl/DCcKWCuMETo6nw0k/BtQy21ibLnLcfEGbFOPPgOiwDC11kdjqYmucfH19HemHt9te86WdeTyvZt5JUDuUQ5N3cZqEuIy7fv5UKhV2ugXuY38d0qkWXtt1+be81zqHwKfmjA8n/Ziw1p4vHRtON9haPJWaqpxWv6p1J6H4+36/H+rWtmvPWnkm6jS5x/Py2m9ubpDNZtHv92Nn7XEx0SoAFxCVC+vn4zl4jXHegBLfHoOw036o4aek1/vpR4eTfkzY8pq+6N7amrV18/kzW/4CBgtYeGxaPyUoLaWS3ma76WLzxQWEX+MsvYqJ7KSgQeOyBiXZ9Hi2Rm81CzpPj3oEio+4oaiTfnQ46ceAkjdOlANEN7e0ca/G9DZ21uMr6W1XHQkbVxdnDd4O3rDJQ+YXaJHjkoU8tl6jFRvZUp7Ne8Tdn7jQQd9rrTubiKrVKkqlUqRN19370eCkHxP60GpMqwMuGF/bEpkSWHMAcXV8WxZjss56DCSjnWmvsbxdbDQ5p4S351fYpqBBhNfr069x3oM9h87UI+ErlQpqtRpqtRqq1WpkGy/HaHDSjwG1wJpQ080jtPbMr1aDr242YS2XkoTfW0sNRLPoWh9XUjKPwPNr2KHn5/Hsz+KstWKQQk/lubwe/Vsb7sRZ+JmZGczNzaFWq6FSqUTGbjlGg5P+E8C6vjbejeu6U1h3GIhu8mi7ybQrTbP79AJub29Du6qW2Xgs61rbz2JhE4zqrtsOOY3DbfONlhQ1fLHXze281MI3Gg3Mz89jdnb20e24HcPhpB8DSlJbtuN2Vcx4q7W1pSyNXQfNvbew7rIm8XTzjbguNwCxQz4I+731BEhcWm1bYqQcWHeV1Xl2ACJ5BwqGdBIQ23OLxSIqlUqw8PPz82g2m5idnUWpVApjtJ3wo8NJPwb44HPibKlUwsXFBe7u7pDJZMIDPT09HRkXzQUAuK/Lq9yWJTT+zibA4mLlQZn7brcb2UKbpTutieuiFCcYstabn1kXKl30tBnITvzV+rwuTioYSqVSYcFQt56xPN37YrHogzGfASf9iFAicEdZym4LhUIka64PtZ1rZ1tSB+1yE1fuszGwWnrdjVb3ne92u0Gow+vq9XqREETFNWq9dVcZjp+2L/5+WDMQgAfJRh2nTdLT0pfL5chL5+35+Ovx4aQfA6lUKsSd1Jjn83nUarVIQ4vN5GsMazv0tNFGE3w2yWZjao3pVexCa0/yawutkp+6d7X46oFoGy9FMTqGWl/8e912yg4AZdjDa1WFIhdCWnsduWVn9esUHsdocNKPAXXvgX/MmM/n85GH2A60tA0kqndXUYzN6FuCx2Xc4xKJXHysBr/b7cZq8nm9QHTfOB1/ZeN02wEY1wlo8xN6nbaMyPuix7WLh01iOkZH6pFuJW9lGgBLNNsBpjXsQQKUuLJVHMGJQd9bEY8q3uI07tpmqzG11vBtglLd+kEtvtZDGVb3t/qDuPtijzdICOUYiNgb5aR/JuxDrD+z/7awVvsxko9zPbZ+b0uKcZ6IeiEkdpz1HkRGm3x8yr0YdG/GvQ8OAE76z4+nEP5zXUOcyMd2z9lrs4R+CrEdXxSc9A5HwhBLem9RcjgSBs/eOwLi5MGOnx6c9I4AJ3ky4O69w5EwOOkdjoTBSe9wJAxOeocjYXDSOxwJg5Pe4UgYnPQOR8LgpHc4EgYnvcORMDjpHY6EwUnvcCQMTnqHI2Fw0jscCYOT3uFIGJz0DkfC4KR3OBIGJ73DkTA46R2OhMFJ73AkDE56hyNhcNI7HAmDk97hSBic9A5HwuCkdzgSBie9w5EwOOkdjoTBSe9wJAxOeocjYXDSOxwJg5Pe4UgYnPQOR8LgpHc4EgYnvcORMDjpHY6EwUnvcCQMTnqHI2Fw0jscCYOT3uFIGJz0DkfC4KR3OBIGJ73DkTA46R2OhMFJ73AkDE56hyNhcNI7HAmDk97hSBic9A5HwuCkdzgSBie9w5EwOOkdjoTBSe9wJAxOeocjYXDSOxwJQ/qR36c+y1U4HI7PBrf0DkfC4KR3OBIGJ73DkTA46R2OhMFJ73AkDE56hyNh+P+FVbJ3YDogQAAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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YVA0uRqXCTpLCa/T52e12VQAUCoUQDocRDAZRKpXw9PQEi8Wi2mXNWhwkiOjnQhc1T0bhO/pLZ3g6vxcKBdze3uLk5ARv377F8fExbm5u8Pj4iEqlMtRIchROpxPhcFil2K6srCAcDg/lEbzEYDBAo9FALpdT1gYtPK1WSzkYeWXcJM9JX43Kb7mp73a74fP5EAgEEAwG4fP50O12YbVah3rkORwO2elnQET/GaCbVjfxX0qp5cUzlUoF9/f3ODk5wU8//YS3b9/iw4cPyOVyKs/8JRYWFuByuRAKhZ4V0ry0I/Jr7/V66ix/fX2Nq6srZDIZlMtlAFDTa7kvY9YUXr7r894ELpcLXq8XPp8Pfr8fT09PsNlsSvTUMFNP+RVeRkQ/J0Y3nZHY9fAc3exPT0+o1+vKWfb27Vu8ffsWFxcXSvCTFNFYLBZ1Lg6Hw4jFYohEImpH5GmxL70PWoQeHh5wf3+Ph4cHlEoltNttOJ3OoVLiSdpR6yLnSTdGlXpc+NQks9vtwuFwwO/3w+/3w+v1iiNvRkT0n4lRAtfFzr8nZ1mhUMD19TVOT09xfHw8tMNP2hCDzsOhUAjxeBzRaBSBQEBlCk4KZf4VCgVks1k8Pj6qTr/UwYbMb2pTPU58RmLXRT/OuUe9CegMT+d5oy4/wmTIp/YZ4J74l0x6frblZj156a+vr6cWPMXlyaxPJBKIx+Pw+XxTCWMwGKDdbqNcLuPx8REPDw/I5/OqtTcdH3j33FHxfv0MT0LX+weOEj6l7NIZv9frqcXG4/FInH4ORPSfiWnPljR4krzj5KV/fHxEvV6fSPA8wYUScdbX17GysmIYj3/puqiLTT6fx8PDA7LZrMoHAD5aEz6fD9FoFIuLi4hGo/D7/UPHB84owfM8+lHpw5TCTKKnGD2F6ybN9xeeI6L/GeBm/KjimXq9rlJrKQ7P02rHoYe4QqEQlpeXsbOzg93dXaytrSEWi8Htdr/o3ebX2u12VcpvOp1Wvfy73S7sdjv8fj+i0SgSiQQSiQSi0ag6Wxs9L88u1Hd5/l7oK89v4IVMlKdPuzzv3Cs7/fSI6H8Gxpn21OCRBH98fIzT01Pc399PVBNPxS2Ut04i3NzcxO7uLnZ3d5FKpRAMBieKY/PFqdlsIp/PI51O4/7+XrXgAj567CORCJLJJFZXV5FKpRAOh+F2u5UjjxYQXfBGpbB6KFNPXebOQirq8Xq98Hg8ypko4brZENHPyDjzlMOLZ6gBBvfUv3v3Tpn1zWZzrFlPKba8v93q6irW19exubmJjY0NrKysIB6Pw+PxjPWq69ZIq9VCsVhEJpPB7e0tstmsmoDrcDiUNbGxsYH19XUkEomhhYWLnQueF8vwwhqjHAdeq0ALG/XOp2pBcujJAMvZEdHPAXdAjQqHUfJNp9NRgr+8vMTbt2/x7//+7zg/P1dFLON2eSqTdbvdiMfjWF5exubmJra3t7GxsYHV1VUsLi4OdbWdVBSdTgelUgn39/e4urrC7e2tWoSsViv8fj8WFxexsbGBra0trK6uqoXFyJlGoudjqY0Ez/+WqhP7/b6q0eeip/x8Mu/FiTc7Y0U/abbVbw0jE1S/wfjNyrvI8J2L96Wnarnr62ucnJzg+PgYZ2dnuL29VTHwcVANejgcxvLyMl69eoX9/X1sbW1heXkZsVgMfr9fnXdfEgX97OnpCdVqFel0GpeXl7i4uFDXRM6zWCyGjY0N7OzsYHNzE4lEAoFAwPD4oO/y1OEW+CR42tH5hB6+ePJwncfjUe2xaCjHPOO3hBdEP293Et2jq5/7PgcvPdc4h9qo350EPjWWTHJeXEMxeGpTnU6ncX19jYuLC5yfn+Pq6goPDw+qPHbc9ZGX3u/3I5lMYnt7G/v7+9jf38fa2hoikcjQ2KhRDi49rEiOu3Q6jfPzcxwfH+Py8hLZbBbNZhM2mw3RaBRra2vY2trC9vY2VldXEYlElPCMXoOf5ak6The8fp1c9LyluMfjUZ81TQ6ScN18jBW9JD88hyrh+IPMV9q5KAbPBX9zc4OrqyvVvTafz6NarU7Uu51GSlNYjgS4traGxcVFlSDzkjebC4zi8ZlMBqenp3j79i1OT09xe3uLcrmsiltWVlaws7ODvb09FRXQ/QV6GysuenpvRn3wuQVFz8HP9CR8sppoipA48eZDVD0FNGyB5qlVq9WhszjdiJTVViqVkM1mcX9/r9JZ8/k8KpUKWq3WRNNoKPGGKufW19exsbGB5eVltcPT+X2SHHjeM//u7g5nZ2d4//49Tk5OVPutXq8Hn8+nogKvXr3C1tbWyOYb3NrSY/P0HowGX+jXSgsm/S6l4tJ10zlfnHjzMVb0P/7445e6jl8cfhNR00cq86QJqqVSCblcDtlsFvl8HuVy2dDjTk0wSqWSmkBTLBZRKpXQarWUOf/SsYTO8bTDb2xsKA99NBp90UNP6M7EfD6P29tbZdKfn5/j5uZG+Raopx4JfnNzU7XXGhcG1IXPzXl9qs+oBYrH6kn4tICQ115M+/kYK/o//elPX+o6fhXQTtNoNGC1WrGzs4Pf/e53WFpaUp73fD6PXC6nmlno5jmdY6n7S71eR61WUzv7pJl2PDS3vLyM7e1t7O3tqXZXfr8fTqfTcFCGHkmgUCFNxLm6usKHDx9UyWw6nUa5XEav11POwrW1Nezu7qpzfCgUmiiPXxc8iXeUWU8Y+X+MEnZE8PMzVvR/+ctfvtR1/Cr5t3/7N2QyGayvr6NeryOXy6kxydVqVdWW62WpelPHl4Yy8O65lIFGwyWXlpawvr6O3d1d7O3tqfp4t9s9VkB0Le12G6VSSUUOLi4ucHFxgQ8fPuDu7g65XA7VahVPT09wOp3wer1DyT7kKJyk445eI0+7uj7E0uiaR6Xw6u9rkiOMMB4504+hUCjgr3/9K969e4der6emrnQ6naH56sDwTkXe5klGLtGuTq2iwuGw6hoTj8eRSCSwsrKCtbU1pFIpxONxeL1eQ2eWvvjwRhjUhYfChNlsFuVyGa1WSxXTOBwOhMNhrKysYHNzc+p0XoIET+9vErOeo3v/KeQ3qhxXmA4RvQHcTL67u5v4b6a9GXmb6qWlJSSTSSSTSSwtLSEWi6kHlcoGg0HlqTcyc7ngqUaf+uT/9NNPQ332eGMOsjAoJLixsaEiA36/f+LCFj0Bh557nFmv/z0JnubWtdttdLtdZebLKKv5kZCdAXS2n2Q2HDHtjWixWIaq43Z2dlS2G3W8obZQvLLspU41tMM/PDzg+PgYP/zwA3788UecnJzg/v4e1WoVzWZzyApZWFiAx+NBLBZTab2UgEPFNOMacLx0Fp+k7Tc9uOBppl2321X/TxwOx5CFJUzPWFVPWs8tTA/ls6+trakhFFQsQx1vKDw1jWncbreRy+Vwfn6OH374AX/729/w/v17pNPpkaOsaXBFIpHA2toalpeX1RALYhIHHv2enls/STUc7fLdbhetVks5QMkJarPZVB2A1+uVabVzYM6tfApecjjNAmXXpVIpvHr1Cm/evMHR0RHW19eVk27aTq9kmZRKJVxdXeHdu3f48ccf8f79e9zd3aFarRpmRwJQDTiWl5exsrKCZDI5sVk/qgEGF/0k74OHFRuNhmqBTTkNJHqXy6Wsn3HWhzAaEf0LfG4z0mKxDM2WOzo6wtHRkepLT2GxaW9mamaZyWRwdnaGn376CcfHx0in088EDwzvzJRfn0qlkEwm1cIzDaOEP+nf8l2+VquhVCqpvveNRgM2mw3dblf5HmZpBSZ8RET/hXG5XFhcXMT29jZev36Ng4MDNXmGC82o0YQO3+k6nY7qtXdycoLz83Nl0us59/y5PR6Pqtqjwh3qdqu/htHrv7TTT7PL0zirSqWCYrGIfD6v8iGsViu63a7q9hsOh+FyuSaauCsMI6L/gpDgt7a2cHh4iNevX2Nzc/PZ+RmYvvinVqup1lvkpS+Xy8/O8FygDodjqM3W8vIyQqHQxLPqeYWhfu3TCF437WmkVbFYRC6XQ61WAwAl+kgkMtS1R3b76RDRfwHI+RSPx7G1tYU3b97gzZs32NnZUQUzsxSQ0Lm82Wwil8vh6upKxeHz+fyzcl1dnDTvbmdnR02y9fl8E8XkjdpYz9K+itfedzodtFqtIeFXq1WUSiW1KDidTtWjLxgMqtp6YXJE9D8zXq9XTZzZ3NzE/v4+Xr9+jb29PSSTSfh8vqluWt1Ub7fbajLOxcUFrq+vkc1mVctq+l1d8BST39vbU9NsFxcX4Xa7nxXT8NfWq+j0PgL0N9Ps8vR8nU4H7XYbrVYLzWZzyItPFY0Oh0MdR+LxOAKBgIh+SkT0c0IZZ7TLUXjNarUqwVOXm1evXmFvbw+rq6tIJBLw+/0vzofX0XPqy+Uybm9vcXZ2houLi6ER1gQXPE/COTg4wNHREfb29pBKpVRxEf2NLnjeQ4AqBOk9z9Kokpv2vMsOPbrdrmpC0mg00Gw24XA4kM1mUSgUVNfgSXIJhE+I6OeA6rt9Pp8avsDbPIVCIRX7ph52dG6muvBJ0W9o6nhzf3+Ps7MznJycqCEZrVbr2d9bLBZ4PB5Eo1EsLy9jd3dXRQ7W1tYQCoVGLkCDwfDceKoUBKDKX6dptU3PyY8JvP6eHrrwKU6fy+WUV9+s3Z3mQUQ/A7z1dDQaVa2qKL7u9Xrh9/sRj8eH0mvJHJ12dweGPe80JCOdTuPs7Azv3r1Ts+Or1Sp6vd5QOypahGKxGFZWVrC7u4v9/X3s7e1hfX1djbA2qpGnUFqz2VSFRvV6Hb1eTzWrpLz9SdEz8IyaZ9Lv0a5PKbnUz6BSqaBerw9lTcouPxki+imh/u+Li4tYW1vD+vo6UqkUQqGQGsHE56yHw2E1iokq415ilJlKLbioiSV11D09PcXd3Z3qXsuFHggEEAqFEIvFsLy8jPX1dZXym0wm1XXrVgcJst1uo1arKU86jcu2WCwIBAIYDAZwu90T5zMY+QV4JSJ/7/Q97fjtdhuNRkM9qPBJmA4R/RRQ7/VUKqV2S0qdpWQRavFEE1enNeOB584zXqPP59dTz/yrqysUi0XVUopmvsXjcWVlpFKpoYy7eDyufApGzjpqC1Yul9Xwi0wmg2KxqJpUdrtduN1uJdhR9fz6c+uNM6l5Jnc8ckuD/z45+6gQRy9tFl5GRD8FDocDsVgM29vb+O677/Dtt99iY2MDkUhE7eK81dMsZjyH5tZTWmo+n0cmk8HV1RUuLi5weXmJm5sbFAoFdDodtdhEo1EsLS1hbW1NzahPJBKIxWIIhUKqa67R7k6CpyGW9/f3uL6+xs3NDTKZjEqUofdMc+q5yc6jBfquTbs7ib3T6ajmIlz09Df8fM8n5fDzvzAdIvoJoc6wGxsbeP36Nb799lscHR1haWnJMFY8y86jh68ajQbK5TLy+bzabW9vb3Fzc4O7uzs1kIJGPoXDYeU4XF9fV4Lnc+p5Pz2j16fR2blcDre3t7i8vFSx/0KhgG63q5Ji2u22YcUbF74udr6768Lniwc9j9GDf76yw0+PlNYawHcaGiMVCoWwtbWFo6MjvHnzBru7u0gkEkMpq0boN+ko+v2+OrPWajWUy2UUCgU1PTaTyeD+/l6Nj6b+fBaLZcjHsLm5qUp0E4kEIpGIcjKOuza+w/PGG9QwM5vNol6vq9Acr2vnZjvvY8/Ta6n5CJnl/ByvN8zgHnz6Gf9/wy0qaZ81PVJay9CTWCjPOxaLIZFIYH9/H9999x329/eRTCYnKkqZpKT06elJDcR4fHxENpvFw8ODEvvj4yMeHx+HOun2+33YbDaEQiHV+GJvbw87OztDDTDGtbniixsdJSjR5/T0FO/fv8fFxYUarDkYDODxeNTfcoHTDk7mNpnk7XZ7KM5OLcR4Us/CwoKKEvDZd0Z5/bw9NjUT4Z+lLAAvY86tfARGHuj9/X0cHByoXnWvX7/G2toaAoHAs+kswPQ58+Qdz+fzuLu7w83NDa6vr9WuTl10K5WKiktbrVZVGbeysqKaZlITy1gsplpVj3p//DrpOsrlsnISnpycqGk31DSTRKa3oiZx0/NzT3u9Xke1WlVNQmkjoU6/VDRjtVqVo46Ln18vn2LrdrvVNQjTIZ/YGJaWlvC73/0Ov//971XvOkqu0dNOp4XCb5VKRTnnzs/PVSrtw8MDSqXSs9AU98yvra2pQRTb29uqAceoiMGoczx1y81ms7i+vlYTeGiHpzAgCdXtdis/BvUOpOd+enpSzS/K5bJ61Ot1tTBQLkMgEIDf71c+AgoRco8+LSS0wFAylNfrHTllRxjPWNH/8z//85e6jl8FZGbWajXYbDYcHR3hD3/4Aw4ODtSMOL/fPyT4aU1KMllpZ+Ujpej8nMlkVI98ahVlt9vh8/mGsvxoHj31pacCFN0CGVcaS0cLao99fX2tGmfSbHqK+/t8PiVSqgqkvy2VSqqvfrlcRrFYVFYKdQ4eDD7OqaP30e121efhdDrV58IjAmTS04ITCAQQDAaVn0JEPz1jRf/nP//5S13HrwI+oQYAfD6f8nrTz+bx0tMNTsMw8vk8bm5ucHZ2pgZaPjw8DI27ovAfJftQHv/Ozs7QaCsaRDGpBcKz7eh4kclk8PDwgEKhgEajgcHg41w5LlQSHHWyKZVKqqtutVpFsVhUzS9I8O12Wzn5vF4vgsGg2vHJRKfroXM/z7TjrcGplj4QCEiF3YyMFf3R0dGXuo7fPNybTef4QqGAh4cHpNNp1ZK60+moppm8W240GkUikcDGxga2t7exubmJlZWVofP7tAsQ7fIk1mKxqPro2e12NUGHinRCoRBCoZBaBOv1uvI1UHsreh5Kk6WjCfkCyDfQarVUkg3l8dORh+YDUkGPxWKBw+FQYUkuenHcTY+c6b8APKRFTi8KzTUaDSUKCv/RDkvnV4oeUKdaCseRAGcRvN6EkgparFbrkGVDk3Y8Hg/8fr8aFU0DOqvV6pDY6fxORxMy0akSkc+3o9fgxTW0GFAkgI42FJok0fMJP4QsAJMhIbsx8LDUrDcUTyrRs8kWFhZU4Q7V1tNOT86ucDiMxcVFJBIJpFIplVlH4bhp49T8Oih+TsMuPB4PIpEIHA4Hut3ukKeefBoAlODJlM/lcigUCqoYRx/fTUcUm80Gt9utUpQ9Ho/yQehlu3zBoOsgayMYDMLn803dPFT4yFjRm/UD1Z1zk7Z/HvUz/UGOsWAwiGQyCYfDgcXFRRWSs1gscLlc6iaPRCLqEQgE4PF41Pl9Fp8CiZ52XV5E5Ha7lZedT6cBPg3mJMFns1k1zLNWq6ke9TwfnnoL0BEhEokgHo8jFoupllfU/46m7fCUXKvVqkJ75MQLBAIqQmHWe3QexopenCQvw7PSJvk9MlfJlKd4Ox9dzUddUZkuVemNmm4z6TXyNlcU+/b5fAA+dvmh1FpgeGIOTe2lFlaFQkFlB1YqFZV0w5+bnp+GYtIRhVKDg8EgHA4H+v2+OhLw90Wfg8Vigc/nQzAYRDAYhNfrNawdECZDPrU50XPCCb1yjURApirtfoFAYCj9FPg0z52Kdnj2mZ43Py5kqC9Ieu467aILCwtwuVzPMuH0kF6pVBoa2U0Zgs1mc+j6CZvNNlTHT6O2U6kUotGompHXbrdhsVjQbrfVe6X3TeG8YDD4zLSfpa+gIKKfC+6R18tCjYpa+MjlwWAAl8tlaCHwYhL+PC+J2+j7UZYIWRy0+OiLFx0BqAvPwsKCKrUtFAooFAqo1WrKMuAVb7xdWDwex+rq6rMUYZ/PB5vNhn6/j0ajAQCqyIiPsiJnHR1z6HhDNQDC9Ijo54AXlHCTmDzV+igqIz/BpK2ljL5O+zsEicXISclFTznytBvz0B7NxCOTngueJvgsLi6qkdfUfJOb9ZTCS113KLJBMXqyPqiUl4Z4Snx+PkT0c0BhL36j0i6ul7DqO/8sDiheEDSq5FTPrzdadMYtONz3QOfzTqeDer2ups6USiXlpdcHYVIiDwmeBnpQmnAwGFS7Nx0f6DOjyjvgY6iQCoscDgeCwSCi0agk5XwGRPQzQk4xMkl5Xjk/w09qotNzvvSa3APPH7rYycrg1sa4BYCeX680pFx6LvhGozHkpQeGBZ9IJLC1tfVsMGcoFFKCt1gsKuOQFgBaQGw2G7xer0rdpYxEClXKeX4+RPQzQjsTpZ9Sn3kqt6Wa72mSR8btvLrYeQmq7jHnDr9pFh8SPH3lVYAUk6fSXr1FltVqVSY9F/z+/v6zHZ5fg15/Tx5/r9eLTqej8vU9Hg+CweBMyUjCMCL6GSHRNxoNFcrq9Xoq/EXx7c9hhurxdd4bXh84Qa83apef1IfA3x8V0JTLZdRqNZU2S89HOzM36b/55hvs7e2pqkQ+mFMvBKKIBhc99eDjeQ2UDSjx+fkQ0c8ImfZUOFMoFPD09AS/368y6mw229AgDGC2Ah1d8LzzDE9iAT5FCPTHpK9NouQ1AlQeS4VAfJEhgcZiMSX4w8PDZ4IfZ/HQYkUOPYoo8Px7qgEQr/38iOhngIuCN6xstVoIBAIAPu309HXW7Dk9fZf3iNdDZLTI8Necdb4c9bqvVqtqhyeznnZjiu/HYjGsr6/j1atXODg4MDzDj4KujXwC/L/19yiC/zyI6GeEYtiVSgW5XA739/doNBrw+Xwqq46EyHfbl3bdl87weniMvw7lt/NFZhbB6xNkqSBnMBio17JYPlYChsNhNUDj1atXhl76SeD+CG7+6/6KWd6TMIyIfgb0824ul1NdZlwul9oN+YN2ReB5vN4omYZudp4nzwVAzjraFflRYhbB88WGRN9sNtFsNpVJT2mxJHy/3/+s3Jfv8JO+vh7W1L35/DMxGo0tTIeIfga4+Ut15LlcDqVSCQ6H41laLXdgcUeULnh6bj0kp3vo+bBM/THPbkiC52OkyJlGHnW6FrfbjXA4jNXVVWxubmJtbU0l3uhe+kle1+j9c6cl8KllFn+vwvSI6CdEz3HXz7ylUgmlUgkLCwuq642ek9/v99UkHN3BR69hJHo9Z56b9Tw0OKvgdaFRaStPlPF6vQiFQurcTXX+fGIOvTe6lnF1AeOugRYdaqhBiU9UnEN1CcJsiOhngMfoqeMrzVejm1bfoXmOPp8wo595R4keGDZ/uehnFTw36Y0Ez3f4UCgEACok6ff71SSdaDQKn883dIafVPD6saLb7SpfAnXRpeo7sjTIky9ZebMhop8Bfu6l3Yg6vujDGniaKgm41+uN3PGNUmsJLnj97D7r7s4XJN7EgvwSVAnY6XTgcDhURZzX61UFMLz7rtF1vwTf3Y367DUaDZV/3+/31fgu2e1nQ0Q/I1woPIxGZ2A9RZWLk4RBu+NLwjfyXs/rrBsV/6dJNDwpxufzYTD4WBVIiTlOp1N1xbVYLEOfBXcyAi9nGvJIyOPjI+7v7/Hw8IDHx0c1JddutyOZTMJms6lmGtNEB4RPiOg/A7pJTrtkvV4fEqgeTqO/0Z179Jz86zTXoj+Hfo38qKHPmOPDJkj01PDDZrMNdbalvvc0xYZMbnp9/fiiOy55+W65XEYmk8H19TWurq5wd3ennKPtdltl6FGqb7vdhtfrFdHPgIh+BnhM2SjxhkQFAI1GQ4XVKKOMt6AiMdK/GwmfXpNCWaOOAEYLhtGObmSh8H/nU2QpKcbpdKqjChXbUEbiOAvFyIqhr3wcdjqdVsMyLy8vkU6nVTVfp9NRhTaJREK11abzvTAd8onNCHmQnU7n0A5HNzWJnrz85XJ5qAsOPwP3ej243W4VAzfavXQHnv4z3dTnZ3XuoKOW0/pceD0vgBYFPmJKb145GAyG/BnU7lqfQ6cvjHpGYyaTweXlJY6Pj3F6eoqbmxs8Pj6i2WyqSrter6dSgfUqPwndTYeIfgYoDZWPeNJNW+CT+Uo57LxVFu8ASzvZKOHzczwlxtANr+fXA8YdbynRhjrTkED1ZBd+BCCBU8yeOy3p78kSoPJi3kyEHtShhyfddLtdVKtVPD4+qoEfZ2dn+PDhw7OBH4PBQDkRqRef0YhsYTJE9BOiZ9HRmCWv1zvUqJFnl9EOZbF87P9GxTkkUJ75FgqFlCec98PjdfE8v55mv/FxzdxJyPvI1+t1FQKjnZIPlKDr1Z9DHzNNo6Ypds6TZqjFFfDJCtKbeNLzkvVTLBbVHL/Ly0s1w48abRrt5JKKOz8i+hmhYRTUw53CVjSiCRhOpaUGk3SzkqCopXQ0GkUoFILP5xua5Mrj8SR26kVPwuLnZr6Ttlot1Go1VCoVlTzEi2f0zjcAhp7DqJSXdnh9kEW324XF8qlfP3Xw5Ysh78RDNfqZTAZ3d3dIp9OqlTYJnq6Ht9GmabWzdAUWPiKinwF9p/d4PMrMp97tANSNy1NyuZBo96eW0jSuicTCz/82m21o8AQ9dAHw8zIfN1UoFJDP51XtP5n4ZCYb5RTQteoOQX5e59Vv+qgqsgYoEkChPVqMisUiHh8flZeenHa8oIgWEhqeSZ+PJOfMjoh+Ruh87nK51KQW7n03SsOlG5qHqhqNhkpIofgzn/7idDqHxE4jpvhiw+vMedy72WwODaegun9qZU3nY97cUk/9NcoToPfPa+BpAdEXCX1B4Uk43PrgPgGqoSfBezwehEIhRKPRkSOthMkR0c8AmZzkvec7Lpn3upOJvqf0Vu4Jp52PcvhJ9CR8Ejv5D/h5nAuLdj46OtCuS+dwWmAqlYqaSEPmOhe+kZNMP2ZQCJIWGv5vep9+ev88k5Fi+3zx4dYDLSjUoINGe1GOv/TJmx0R/YzwrjGBQEBNdCUnVafTGSq+0U1nHh/Xu+HoMXQSA53p+WAKo1JTXnJLCxE/+1PvO7IEeEIOOee4A40LmoRO7ato8kw4HEYkElFHFGptZXT25osmWUp+vx+9Xk/14KcWXKFQCEtLS9jY2MDq6qrqmS8puLMjop8RMj39fj/i8ThWVlbQbrfhcrlQq9WGmk/QGZd2ZMC4VTV5vfmxgXZ3chjSg1sCtLuSuCiphl7HaIGh3Z+86WQ98EWGwm3kT3C73QgEAkrodByhmfHRaFQ9QqGQOnvTsYdnIPp8PkQiESSTSXQ6HbjdbpV9B2Dod/jE3ng8rubfCbMhop8RaigRDAaxvLyMbrcLp9OJxcVFdU6lZJJ6vT40643+nhpr0LDKQCCgrAYSF42HJvNeP8+Tw4+H7siSoJ2Z+wPob2ixcLlcKJVKygnH4/9kYtOCQ7s5WTU0ZooPlvT7/WpRIsHz7EMuftrR/X4/8vm8is1ToQ+Npo7FYmroZSQSUaO4hNkQ0c8I7fTBYFDtXrFYTLWJJtFXKpWhUU3k2efmt9vtht/vHxI+H1rJRUoPPvNNb6DBz89kgtNiEolEEIvFsLS0hEQigXw+r2bK08IEfIq18wWJiz4YDCqB8+vUz/R6Jh73C1AF3+LiImq1GhqNhqpb4IuNvpBIC+z5sLyQ1SQpT2Mg05jCY1RbTw/dxNdTR/kZ3cgrz8N2PHSn5/wbVbNx7zl37FHcnsZTUQiPvOe8uy6Z9HTmpgVJFzq3NCbp3qNn/JFjjxZF+mxoAaHPYNZGnybG8IMS0c+JUXmqnsXGw1l62yuj6bR60o0u8Fky0nhaLveckxXCFyXgeW2Bbm3wRWgeIfLPT2+CKW2x5kZE/3NjVN02rioOGD2Ugn7Gv36ua+RC4w4+Hgngrbh4w81Ze/gLvwgiekEwGYaiFxeoIJgM8d7/DHyOks8vYTrzePy4n+uIWf91I+a9IPx2EfNeEAQRvSCYDhG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmAzbCz+3fJGrEAThiyE7vSCYDBG9IJgMEb0gmAwRvSCYDBG9IJgMEb0gmIz/D4TKmORnchcDAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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zLPynhUX/hZCLTNrtNur1OiqVCrLZLG5vb3FycoL379+LppPK2LiaCMhbv7e3h8PDQ2xubsLv94tEnHF7eXqeIgaZTEbcbCZtKyj7Tq6fl/PlqSKOlugkcmXMXXYqyn301TL3mMfDov9CdDodlMtlFAoF5PN55PN5ZLNZpNNpxGIx4UCjwpZx1lav18PpdCIUCuHw8BAvXrzA9vY2gsHgyCo6QpkkUy6XEY/HcXNzg3g8/iBioFxhUBmtyWSCyWSCXq/HYDDcqXectZb76dE1kNhl4dPPmPlh0X9mqMcc1abf3Nzg7u4OyWQSmUxGWNlsNotCoYBqtTrUXluOh+t0OphMJjidTlEy+/LlS7x8+RLr6+twOp0PQnSTrq1UKg2F6gqFgiifBYaFR2K32WywWq0wm83Q6/UiHk9WXhaufC0kdsrPl4WvdOKN8iMws8OifwLUMspGPVev10UBy8nJiWghnUqlUCqVUK/XRQ15q9UaqiaTG1PYbDbY7Xa4XC4Eg0FRMru/v49IJDK1lZctfbfbFR154vE4UqnUSK+9Xq+HzWaD0+mE2+2GzWaDRqMZSsKRQ3RqVl4O89HWgF5Lv7ealWfv/Xyw6J+ASY4x4DdrVavVkEqlcHl5ib/+9a949+6dSK3N5/OiY4xyCS03pXC5XPD7/VhaWhI97paXl7G6uoqNjQ2srKyIFliThKH8ea1We5AMpGZhTSYTHA6HuA6v1wuj0YhGo4F8Po9arSb29GoOPNrPU/NMegDDvfMmbQdY+I+DRT8jat7naajX60gkEjg9PcWvv/4qmmDc398jl8uNdZZpNBoYjUa43W6srKyIhJv19XXR506eDKNsjKH2OwwGgyHh0Jz729tb3N7eIpfLDS3rgd+2FFarFV6vF+FwWEy2dblc6Ha7SKfTaDab0Gg0wsqPqnuXRU8luPIcPPk61d4jZ/8xs8Gin5FZxT4Y/N7i6uTkBL/88gvevHmDk5MTJBIJVCqViQ4qGk7h8/mwvb2NV69eiXBcIBAQAyvkkNksv0e320UqlcLFxQXOzs5EzbzsPKTlfDAYxObmpui+EwqFYDKZUCqVAADpdBoARNebcTF98vobjUbh9ZfHYinfQ0KnKj7mcYwV/SwDEf9IyNZmXBknOeVoGUt7U0ovJQ89LenfvXuHt2/f4uzsDPf390NJL3QueSUhf221WkWW3eHhIfb29rC0tASHwzG1AEatUrLZrOjI8/79e9zd3Q3V6uv1erjdboRCIezs7ODo6AgHBwfY3NyE1+tFv99HMplELpeDxWIR02jGJePI/gmz2QytVis8/mrXKDv92MrPx1jRzxsiUdubkqCeKvwy6VijCkomHWtSfjo5vfL5vJgYSz3hqEVUqVRCIpFANBoVyTZXV1dIpVIP+sspr1H+WqfTidTa9fV1rK+vY2lpCXa7fa4P/2AwQC6Xw/n5OX7++We8fv1adOShvACDwQCXy4W1tTUcHR3h+PgYR0dHIiRoMplEbr3dbofJZBIOOTVk8dI8PfI/NJvNoU47o94rp/vS78H7++kZK3qeAT4MlYpWq1UUi0WkUikkk0lRgWYymcQHv9VqiUSbaDQq4t7k6JrlpkeDHoLBIJaWlhAIBISFnyVTTVnQk8lkcHFxgZ9//hk//fQTPnz4gPv7e5H9p9fr4fF4sL6+jqOjI3z33Xd4+fIlIpEIlpaWhMee9voUstNoNCNFL4caLRYLbDYbLBaLWNrTuSel+rLIHw+reko6nY4QOoW06EH7XxK9wWAQoorH42LpW6/X0W63ZxI8Jd6Q4P1+P2w22wMLP6pwRe1n9XodyWQSZ2dnwsJ/+PAB8Xhc5PjTaOjV1VW8fPkS33//PV69eoVIJPIgOkCWm5be46IZ8kx6u90Ou90Os9k8NOhinAefmZ+xon/37t3nuo4vjtKxpdFoRBxco9GgUCiITLmrqyuRUJPP51GtVjEYDMSHWafTieV9oVBApVKZqi+92jVR4s3GxgZWV1dFeGzUtY96nnLhZR/D27dv8csvv+Dk5GSopTWdd2VlBYeHh/juu+/w6tUr7OzsDKX1AsM3FmXRjPI6yPNusVjgdDrhdDrhcDhgMBhEG2+K6yuz8eSfcVrufIwV/V/+8pfPdR1fBbRcrtVq0Ol0ODg4wN/+7d8iEAggk8ng8vJSZKolk0kUi0U0m03h8JRrvQeDgUhUeWxDR+Vwio2NjbFz5wjlHrfZbIoy3bu7O9HZ9vT0FBcXF0gmk0MFPSaTCYFAANvb2zg+Psbx8TG2t7fh8/ke3HDofMqutUrIeUeC93g88Pl8Q+OoaXKtWnyfji/32ZOPzUzPWNH/67/+6+e6jq+SX3/9FalUCqurqygUCqJtFGXPfarurAaDATabDSsrK8J5dnBwgJWVlQfe+kmJQc1mE/F4XDgSaSpOLBYT2w65b77BYIDb7cbGxgZevHgh8vjlDrpq9f+UgacUJF0PJRcpk3p0Op2YfCun7SpvIHLrLXIc8lirx8F7+jFkMhn827/9GxwOx9BMdmXl2VNis9ng9/tFeezLly/x4sULRCIReL3esYk3shh7vZ5ICPrw4QNev36Nv/71r7i+vkY6nRbTX5WDMmw2G9bW1nBwcIDj4+MHS3q135sES11ylKKXawSWlpYQDocRCoVgt9vFKGra08vluPK5qAtPrVZDuVwWI6snJSIxD2HRqyCH+WKx2Gc5JznOqJHlwcEB9vf3sbOzg7W1NQSDQVit1rHHkAVfqVQQi8Xw8eNH/Pjjj/jpp59wfn6OdDo9JHQ5TGk0GhEMBrGzs4MXL15gf39/7Lw74HcrT3PllWOkNRoNzGYz3G63CDlubGwgEAhAp9OJyIey3kB54+j3+6jX6ygWi8jn8yiVSvD7/TCZTLy8nxEO2alAe3tlGuq8qOUUGI1GkdpKPe1I8JFIBIFAAE6nc6Lw5A9+vV5HLBbDmzdv8OOPP+KXX34RglduSeh6qJHmxsaGOP/a2tpUk3A6nQ4ajQYajYYotqHflwQfDocRiUSws7MjuvI2Gg2Uy2Vx05AHUyr/Tt1uF9VqFZlMBul0GrlcDqFQCE6nc+gmzTeAyYxVNc8Af1qUH2SbzSby2KlrLT3W19fh9/tFttq0NJtNsaT/j//4D/z7v/87Li4uUCgUxm5JHA6HaMBBKb5ut3vo2slDr0wiItHX63XRacdgMAj/wMrKCnZ2drC3tyeGbWg0GmQyGQB40FZLzVdCBUu5XA6pVAq5XA61Wo339Y9gMU35DIwKQ037XrX3GI1GeDweLC8vi/lyu7u7iEQiYinvcrkmeumV19hsNnF/f4/379/jp59+Eha+UCgMvZ5EIneycblc2NjYwO7uLra2tsTyexKUY0/LcxofTTX2JHjaqtB2odlsilRf6rBDjsBRNBoNFAoF0Wug0WioZjGytR8Pi34C8zjs1N5rsVjEMEmqf9/d3RUVcy6X61H7VBpOQSm1P/30Ey4uLlAsFh+8VlnUYjKZ4PP5sLGxgUgkgnA4LNplE6OuhybYUA0CAFitVlHnv7W1JabrrK2tidVDoVCAVqsVXnl5WzCKbreLSqWCSqWCarWKVqvF8fpHwKL/DOj1etjtdpFZt7m5if39fdGtdmVlBR6PB1ar9UFOOTBdtl2z2RRZdu/evRN7eLXaB/n9AMQSfHNzEysrK3C73cLKq+2T5e9brZZY1vf7fXFTo7Df3t4etre3sba2Bp/PB7PZLJKEqFnGpNHT8nW3Wi00Gg3hMGTRzw6L/hOj1Wrh9/uxurqKtbU1MR9+Z2cHkUhEhK7U9qWzWPtyuYy7uztcXFyIsNwop50MRQwikQjW19dFiu00ULitUqmg0WhAo9HA7XbDZDIhHA5je3sb29vbD25qZNHJyo9y3qnBM+rnh0X/RMh13tQZlvbutFfe29vD1tYWVlZWEAwG4fV6hwSmlrqqRM3612o1JJNJRKNRIXg5LDeq6k0O0e3t7WFjYwNut3uqm02320WtVkOhUBD7a7PZjFAoBK/Xi83NTbFy8Hg8ohiHVg5yt9xJffFlKDNvVCIQMxkW/ZxQvj09LBYLrFYr7Ha7iE1HIhHs7u4Kq0f55kpH3TRiUy632+02kskkLi8vcX5+jlgspjpZVs2paLfbRSLO3t4eVldXHwy4HHUN1BornU4jn8+j0+nA4XCInP2NjQ0sLy/D7XaLCARdA7X+VobppkF+L72f/A9cfTcdLPpHotH81meeWlW5XK6hZpU+nw/BYBChUEg8lpeX4XA4ho4zrceZxK7c86fTabGPp9n1yuIeZQtrg8EAu90ucgL29vbEgEu5V52aiChJJpfLDVUZAhDhx7W1tbGCpxBfrVZDvV6fydIPBgM0m01UKhUUi0WUy2U4nc6xdfjMMCz6R0LVbzs7O9jc3BR7YdlhR0t4h8MBi8WimjI6qQmI0vEmJ6KQt/7169d4/fo1Li8vkc/n0W63Rx7X4XAgGAwKC//tt9+KVFt59JXazajf74sZe1S4Qw03KH04FAphaWkJLpdLVBzKx+x0OqjX6yiVSiiVSqICcVQegHwt9HWj0RDx+kwmA7fb/aDdNzMaFv0jMJlMCAaDODg4wPfff4/Dw0P4fD5RVEJLe7J0MmpimqbxhbJMVg7P/fzzz6I8lvbySv8ANeKgGxVFD7a2tlSX9coVBU3RTSaTiMViiEajiMfjqNVqoi7e7/fD7/fD6XTCZDIN1daT4OmmkU6nkU6nUSqVROotXeski08jvKn4iRp6yH3z2eqPhkU/I+Sh3tjYwPHxMf70pz/h8PAQLpdL/NxgMIhpL9Oi1vlV7YPbbDaRSqVwenqKX375RXS8SafTD5b1Wq0WDodDtM1eWVkR/oX9/X3hraduP6OuizLh4vE4otEootGo6PFHabZULisLXj4GOf7S6TTu7u6GJujI8XZKgR4nfJodQANCyuUylpaWHlw3C18dFv2M2Gw2LC0tiQaRtB9+TBda5fOj4vG0D6a++RcXF3jz5g1ev36Njx8/CosrYzQa4fV6EQqFsLGxgc3NTWxsbIiCF/Kqj5tm2+v1RNMNqsG/vLxELBZDuVyGTqdDKBSCxWIRwjebzUNdeSksV6lURPOOk5MTXF5eDomeEoamEWqr1UKpVEI+nxc9DT5VmfMfERb9GOhDqExXXVtbw/b2thgQOWnuu3y8Sc/LtNttlMtlsf+lnnunp6d4//49Tk9PkUgkhsZYk5OOpt5QA46trS2Ew2H4/X4xkWbcSoTag93d3eHs7EzU4t/e3qJYLEKj0SAQCIiSWSoKIsFTWI5SZxOJBK6ursSxaKpPpVJ54MSbtMSnLEBK0hnVaptRh0U/BuUyk/byW1tb2NrawvLy8sRElmlaWSnPSfPhc7kc7u/vcX9/j2QyiUQiIRpt3t/fi+ESdDyTyQSPxyNuSkdHR9jf38fW1hZCoZCIl0+ypjS59ubmBh8+fMDbt29xenoqJvF0u1243W4YDAY4nU64XC6xRSCxd7td4eW/u7vD5eWl6AZ8c3ODbDaLSqUyU7hO+XeSu+uw6KeHS2tVIMutbDARCAQQiUREIgv1q6MP3Tjn3LjQHFl0eSY8lZFSws39/T1SqZRY0sp99yjNl6w7Ta6lrQc101QW0NB1yyuVTqeDfD6Pi4sLvHv3Dr/++is+fvwoLHy32xXWnFpfORwOWK1W6PV6ccOi5fzt7S0uLy9xdnaG6+trJBIJ5PN51Ov1BxZ+FuFSnsO4ZpyMOlxaq4LaBygYDArrubu7i+Xl5aGkkFHvG3dM4Hen1N3dHRKJBDKZjAhl0WRbasJZLBZFN10SCPWlp7TXFy9e4Pj4WNTDu1yukdVyymvq9XooFou4ubnBu3fv8OOPP+L9+/e4vb1FqVQSJbPU9dflcg2N0Wq1WqI9eCKRwPX1tfADRKNR4XRTeutnFazJZILVaoXD4Rjqtc9Mx2Ka8gnIFicUCmF1dRWhUAj7+/v45ptvEIlE4PF4Ju7lR32YSRzlclns06+urnB9fT3k3KJOMZSEosRgMMDr9WJ9fV20t3rx4oUYNzUqEYiQBTcYDET+/ocPH/Drr7/i/fv3YsRVu90W03soLEn5BwDEKqVYLIqQ3uXlpfidqP5dbgGu0WhUVx+TsFgs8Hq9CAQCIvrAop8eFv0YPB4P/u7v/g7Hx8ciu46svDL+Pq4STabRaIiwVSwWEwKhwZHJZBLlclk4D2l/rIRGTa2vr4u+9MfHx9ja2hpZNDPOj0CRgbOzM7x9+3ZoSd/r9UT7arLyTqcTVqsVGo0GlUoF9Xod1WpVTPS5vr7G7e2taCJKRTLK81ITjFn6FlgsFgSDQYTDYSwvL8PpdA7dPHipP56xov/Hf/zHz3UdXwVUmNJoNKDVarGzs4O/+Zu/wdbWFhwOBxwOx6MsC9WbU2JKNBrF2dnZUDvtfD4veuRPgjrSrK2tCcF/++232N7eht/vH8rpnybNt9VqIZfLIRqN4uTkBKenp2JJTxaeEo9sNptw3Ol0OlHXXq/XkU6nEYvFcHd3J6b5VKvVsdtEWfgAVENvsjef5hEEAgFRuORwOKZq+MH8xljR/8u//Mvnuo6vAjm2rNFohKOKhjLSh39awVOeej6fFyOwbm5ucHV1hfPzc0SjUTFkYtqKMepKEw6H8eLFC3z77bdC8D6fb+Yinn6/j0qlgvv7e1xcXODi4gKxWAzFYlEM5VRmGjqdThiNRrRaLaRSKTQaDWSzWcTjcaRSKWSzWbF3/xTxc0oIoroHWtVwQs50jBX9y5cvP9d1PFvUJrLQkpwq0SjMRkveWCwmUkjlIRPEKM8/8Hty0O7uLo6Pj8VsOdnCT5MDQNderVZF8s3l5SXu7u5EWI7EbjQaYbPZ4Ha7RS0BAOTzebRaLXFTy+VyKBQKwjM/69+Rrn3c8l4emmG324c6BHO7rOngPf0cyDF16h5DJZ/khEsmk7i6uhJL+UQigVKpJDrHjjquGiaTCV6vF5FIBEdHRzg8PBRORTXBK79X7psph538CmThZS89iYs89Q6HA0ajUYQVc7mcCCM+1rqThZ7GiUcDMGkFsqhh5XngkN0YyLs9ykkn15VnMhnkcjmUSqUhzzwl1FCcXa1n3TTQIEsagkGttpT98KdtgEFLcqqWi8fjKJfL0Gq1ol0WlQo7nU7Y7XaRk9BoNFAsFkUr6lKpJIZzPha1Nl5qkMeftlls1WdnrOj5DzoeasucSCSEJc/lciiXy0L02WwWuVwOlUpFtbnFNOj1ethsNtFfj8p5A4HAgwaWk6ApMZQDQIKvVCowGAwIBAIi2YcsPPk0Wq0WCoUCqtUq0uk0stksisUiGo3GzDMClIlM0ybmkOB1Oh2L/pGMFT17RB+iHLVUqVQQj8dFTjmNjKKYNXm3Hzs4Q6vVCscVhQ2pQYU8TFLZ4VZOTyU/Q7PZFIJPp9NIJBJIJpOo1Wqi1ZWcS2+328VI6mq1imQyKdKDc7ncowVPjPNdjHreaDSKBzfOeBy8IZoDeeoK5Zdns1nRzpmmvjzGg00992iZ7XK54PF44HK5YDQaRTELTcolYVA7qW63K3rQUV966jVPfe0oJOdwOETjSsqldzgcogKPUn5pK0PvfazgaV9Ogp1l/DS1I6PiHmZ2WPRzQMIulUrCoVUoFIY+zHq9XlhaQN2akd+AlqskZIPBIMZeWa1WGI1GdLtdkebabDZFKSstkUnoNHxCdi7K37fbbRH+o5p7t9stnHVyph21tqJx1+Shn0Xw5HQzGo1D7bU7nY4YTT0Jk8kEm80mViH0e8t/R2YyLPoZkT9Y1NGVkm+ozJMaaZAFJiGrNcmQBS9bQLLyJpNJVMY1Gg0kk0n0+31ks1nYbDbRZRb4XUSULCNHFKiaTavVwmQyCZEHAgH4/X7RAMNisYgmGK1WSwyXoKYV5LOYRfAU46fUXb1ej16vJ1J3aTzVJOGbzWa4XC7Rk5C2HqP+fxh1WPRzIMfn5cQa2ckki11N6PJr6Wv5vdROu9VqIZvNol6vI5VKwWKxiH0tWU5a2ivnuHe7XZFs5PF4EAqF4HK5sLKygnA4DJ/PJ6w77ZNp61IsFhGPx0VBUKFQmHqyDFXieTwe4Y/wer3Q6/XCGZhMJofaWY86rnwsn88Ht9sNi8XCOfePgEU/B2qWm7zL8n5TXrrLnmc5u08t95wccTT2qVqtqq4O5PfRAEiy7JRVZ7VasbS0BLPZLFJYaW4edbyh6yF/ARXPXF9f4+7uTtx0plmKGwwG2Gw2BAIBMY2XynwBIJvN4vr6WqQ9k7NzlOh1Op3o0COLni377LDo50CtpFYWP4lbp9OJ2m/a0yr3o8oRTyRasoLyQ84CHFePTjcZSp/1eDxYXV0VrbPk2XnyCoNmxsnFMxTHnyZ3g7r3hMNhMcuOavvdbje63S4SiYSIChQKBZTLZbTb7QftuuVVEi3vZUcj7+lnh0U/B7IFl0VOTit6UHabyWQSS3Lah9PWgBpikJUmy0dWXnkjmGZ5rdFoYDQaYbfbxdBMavNFYT9lE0tqYClnEt7e3iKXyz1ovKmGXBuwt7eHb775BkdHR4hEIlhaWoLVakWz2YRerxfddROJBHK53NAQTKWAyRdhtVphs9nYez8HLPo5UHrd5ew9eTlvNBofiJ7aStHynZyBstip2QR5t9Ws+7hrs1qtou311taWGIdNe3plm2qK5edyOdze3uL8/FyMySJn2yQsFgsCgQC2trZwfHyM7777Dru7uwgGg6IvH1UJUstscspVKhVh3eWsR0K+gZKvg5kdFv0cKFN05WIROTlGTpCRq+lkzz/ta+WxzbLY5X5watchfy3vf9fW1rCzs/NgQi5NnpGvnXrjUc7B1dUV4vE4SqXSVMt6s9ksthD7+/t48eIFdnd3sbq6CqvVKkRKYUgagyW32xqVnafmBGUeB4v+CZHntJFIlMt2uW6cRE8PeRkvN34cF+On50kMVBlHFp5GV5H4SPDK5Bjax8fjcdHTjhpYThOek/v0UR/Bzc1NURugHH1Nfg2aAShHIuS+ffLqQr6BTpvMwzyERf9EyOE7nU4nHG7UmKPVaglnGb1Wtuiyt13+cE+7lJfr3l0uF0KhELa3t7G7u4udnR2srq7C5/MJAZJFlSfPJJNJXF9f4/T0FNFoFOl0euoyWRL90tKS6K9Pk2dkwdO/sngBiL/NqHx6ikwob5C8xJ8dFv0TQB9g+lpucUXecHmII32A6SYhe+Tl4yhRS0SRowMWiwUulwuBQEA47aj9NTnt6BroBkODKNLptGhTTYMoKpXK1JVzBoMBDocDXq8XXq8Xbrf7gYWXf3e5b73aAEul/2AwGIiko2q1KjIC5foDZjpY9E+IvHenZSrweyce+TWyU44suvKDrubBpudJ8OTcoj1yIBBAOBzG+vo6VlZW4PV6YTabMRgMhjIG5ak55Lg7OzvD2dkZbm9vZ07CkWvcgd/aejcaDZFKTElKtI3I5/OiAlEu3Bl1Pornl8tlUbvfbDZFnz7623LYbjIs+hmRP1jKJJlRVW7K908zpEF2VimdWCQwStMlCy9nvvn9flFnTwk1ZHXJb0AiSqVSoo1XNBpFLpdDvV6f2L6Lfl86brfbFSXF8Xgcer0e9XpdOOnIWpOz8ObmBvf398jn86jVamLFo/Y3ochCsVhEPp9HqVQS46x4iT8bLPo5UFo4tSw5YprlulpHV1nodD6K85MTjApmaGltt9uh0WhEZ5xSqTS0rKequ1qthlKphEwmg3g8jkQigXQ6LUZN0fknta+i11DF4dXVFYDfqvP8fr9IpKEGHNT2+/z8HLFYDLlcDtVqVfg11M5Hlr5YLIrGHc1mk515j4BFPwe0vKb6boofyzn3arFtpWd61JKUHG50I5Er1cjC07Kewl5arVbE2kulkjgP+Q6UVXjVahWlUkksmWnUlOxln+RQpGU7tdGmYppkMilGV1PLcLq2eDwuegXSEMpJM+moAQgV6aj5G3iJPxkW/RzIYSeLxSIeAETyjez5VraEkuepE7KFV2b7KbP76Hw0FpuKZCqVirDo8oNq6+WMP7WQoTx9hq5TGTqTfy5HAGjbQEMr5Sk4Go1GVO7lcjnRNbdarT5IwVWDMhepNFhtLBYLfjIs+jmQU0Op+YQ81KHT6Yh6ejlMJaOWLy/v5ZWFOlQUI0cDyCFH4TW51JeSfei6aN+sjBiMChHKwgd+79Aj+yMoEkArCnm8Fd0Q5eGWtLWo1WriGqfJ9qNzyqFNZnZY9HOg1WpFbrvH44Hf7xdhMCpxJespN9KYBtliyU5CEle320Wz2RQiAoYtoSx6Evso56Fa0YpyVSKjVuRDNw0KUZLFpy0J+SQADHX2mbaBBvB7fzzaRil9J2zlp4NFPwdk6Sk+HQgEMBj81lqa9sw6nU5YWQrjKZfHyq+V6bzKnvAUbpOFpMzwk5fy40SlTGtVCoeuQ5k/MC47UM5MpKQk5WTZWeoI6Loo8YgGbnATjcfBop8DjUYjykhJ9P1+H+VyGfV6fcgSaTQa4ahSin5ULT2JQk7FpdcrP9xyGe4s1XhK6z8qG07t62mhpfioG9s0UAFRIBDA8vKyGL/NOfizw6KfE8qEczqd8Hg8aLVa4sNNFp6W5OQQk4WtDInJCT6yxRwnEvl18t56FiZZ8FlR3tBkcU6znFduM3Q63VDyUTAYFKFJ5XuY8bDoZ0T5IaN9vezBp0w0OQVVFqMy51xGdqqRkOXnlSjz9JWPr41ZOgPLN0S9Xg+Xy4VgMCgsvZyNx0wPi/6JkK2t3HKaOsnKe+xJgpSFLy/xxznVpmks+TlRrhweM+pK/n3tdjv8fr+w8k6ncyjkyeKfHhb9nNB+W25ISZ1oqTCEauWpyGaaZBfZ2j+VmCft1z/1+x+LzWbD8vIy1tbWEA6H4fF4uNBmDlj0cyJXusnJM2azGVarVeyt9Xr9UFMM5THoX6XXflSITZk4I98o5GYdsod8XoFOer+cLiwnFsnvp+tRXpvyGPT3tNvtWF5eFn321tbW4HQ6Od9+Dlj0c0Jjk2m/CUBMpPH5fKIajNJMlR51ObdeLVwmv1Zuja18D60gKE5PKw25DPVTQnUANptNTKCRW4OR0KmkllZDtO0ZDAbQ6XRDPfKppdba2hp2d3dxdHSElZUVduDNCYt+Dihk53A4EAwGYTAY4PF4UK/XRcYZZZ1RogyJk94vZ9ypheLkc8mZecoBjmTdm80mqtUq8vk8crkcMpmMGDopt5lWZuFNKqqhG4y8wpDr+B0Oh+i463a74XK5xFQeWo20223UajUxWosm/NLoL6PRCKfTiUAggGAwiKWlJSwvLyMcDmNlZQUrKyvw+/0ij595HJoJS7avz/37laFMe6XEGDmnXU6WobAaoL4cHtU5Rn6t2tRWWi7TvLp8Po90Oo1UKjVUlUbXoEyHpYQiuRstbVMcDofoQEsipuU39btzOp3wer3w+XyiiYbNZhPttZXpuYVCAfl8fmimvdFohMvlwtLSEkKhEEKhEAKBADwej7gGZfdeZiyqFoRF/wQow2Tyvpq8+cqe9cDvll4W/ShrL/sO1FpLUaUbWdNSqYR8Po98Pv9gei6tCOSbA5XgUrmqTqcTSUfBYBCBQEAImVJgdTqdqPijJpdk5WliDr2WLH2j0UClUkGpVHpQbEOrJp/PJ8ZtUVkuJ+E8Chb9l2JUR1tlcwx6Tomytn4cg8HvLbUpctBoNEQOvjx/r1gsIp1OIx6Pizl1JHrKNAwGgwiHw6Ixhzw4Una4Ua6C3OiSBK+s5acoh1zZNxgMRFqzxWIRvgEW+1yw6BcNZXxfTumlEtdsNot0Oo10Oo1isShaZOn1ethsNvh8PrG/9ng8otHlqJvVtDcmtX9nOQYzFSz6z8G4HPZPdS5i1nP2ej3UajVUKhXUarWhefOUaUijrO12+xdxoCkjHcxMsOiZh4xLAlL6HJhnB4t+kZhUGfeYJfQ0oT21r8ddm/L7cb4NZmZY9IvOU2095t1WMJ8NFj2jjtqqgJtT/CFg0TOjGfU5YLE/a1T/8zgNlwHA4l4k2CXLMAsGi55hFgwWPcMsGCx6hlkwWPQMs2Cw6BlmwWDRM8yCwaJnmAWDRc8wCwaLnmEWDBY9wywYLHqGWTBY9AyzYLDoGWbBYNEzzILBomeYBYNFzzALBoueYRYMFj3DLBgseoZZMFj0DLNgsOgZZsFg0TPMgsGiZ5gFg0XPMAsGi55hFgwWPcMsGCx6hlkwWPQMs2Cw6BlmwWDRM8yCwaJnmAWDRc8wCwaLnmEWDBY9wywYLHqGWTBY9AyzYLDoGWbBYNEzzILBomeYBYNFzzALBoueYRYMFj3DLBgseoZZMFj0DLNgsOgZZsFg0TPMgsGiZ5gFg0XPMAsGi55hFgwWPcMsGCx6hlkw9BN+rvksV8EwzGeDLT3DLBgseoZZMFj0DLNgsOgZZsFg0TPMgsGiZ5gF4/8DS6B675U/27AAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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F/4VRdqE1Gg1RV390dITz83Ok0+mxm2mMRiN8Ph9WVlawvLwMv99/K1J/1+p4c3ODer2OfD6PVCqFTCaDVqvV91i66eh0OlitVvh8Pvh8PrhcLpjNZqjVajQaDeGUW6lUUK/X+xyGSPDy9VDBDf0M2YEBv+0EyFqL/PN4pZ8cFv0XYFAArd1uo1wuI5lM4uzsDL/++isODw8RDofHtrVWq9VwOp0IBoNYWFjA/Pw8bDabWEnH7UIjowzZ1nrY65lMJjgcDrhcLjidTpjNZmFxRXn5YrGIUqkkSmeB37f2slMuiZvadJXbe7rBWK3Wgb30fAMYDxb9F0BeLTudDmq1GgqFAq6vr3F5eYmjoyMcHh7i5ORkaGOLErVaDbfbjYWFBaysrIjoudLzbtTjCTLmKBaLqNfrfVts+VhhNBrhcDjEKm+326HX69Hr9dBoNFAsFpHJZJBKpZDNZlEul0X/vWxpTSs2vQ5118nHCVrlLRYL7Ha7CBI+dCXkLMCi/0JQ4U0ul0MikUAkEhFn+PPzczGtZtwyW4vFgmAwiM3NTWxubgpjjEmvqVKpiOuhNN2ggBqd5X0+HwKBAPx+v9hVkOBTqRRisRiur6+RyWRQKpWEkGUbbErn0XlfdtClLT5Ze9ntdjidTthsNhgMBhb5FLDovwDUJkopsdPTU5GWk33kla2jSijdZTKZEAwGsbOzg729PWxubo59lpe3/NRYE41GcXl5iUgkgnw+PzCAZzKZ4PP5sLS0JGIHZrMZNzc3qNVqSKfToqgnFoshk8n0OebK9te0YpOLMLXgzs3NiZ+nld5ms41c6Zm7YdF/QgadoZvNpqhyOz09xeHhIY6OjnB1dYVkMolSqSRsqIahVquh0+nEYAifz4e1tTU8fvwYu7u7WFxchMPhuLODjtJndI2UKqSoPZX7Kld6o9EIr9eLlZUVbG1tYWVlBR6PB3q9Hs1mE7lcDpFIBFdXV7i6ukIsFkOhUECj0RCrNq3cFJSj36vb7aLRaAirLEKlUontvc1mg8ViEb368s8wd8Oi/4QoLa7q9TpyuRzC4TCOj4/x9u1bHB4eikq7YU40MjqdTpyl/X4/QqEQlpeXsb6+js3NTayursLtdt/phKO8PuA3o81MJtO3JVf2zBuNRmHCsbu7i52dHSwtLcHhcECtVqNWqyGZTArR0ypfqVREfYBGo4Ferxde90ajUZzzO51OX/GNnNrT6XTCINNoNLI/3pSw6KdgnDO28sNIYri8vMT79+9xcHCAo6MjXF5ejqx2k9HpdDCbzfD7/VhaWsL6+jo2NjawvLwsimIoZTbO9cui6nQ6yGQyQqyDjDYNBoOYe/f48WN8//33wm5Lp9OhUqmgWCyKkVdk2FkqldBut4WQSfRmsxl2u10cC9RqNVqtFnQ63a3iG3LjMRqNMBqNMBgMY3kBMLdh0U+BMletXNFlqDU2kUjg9PQUBwcH+OWXX3B0dCS2vePU0tNgCp/Ph42NDezt7WFvbw/r6+uYn5+H0+mEyWQSQbFJ6Ha7SKfTOD8/x/HxMa6urm4V49Brb21t4enTp3j69Cm2trYQDAZhsVhE8Q1N0I3FYkilUmJbT6OpSPQ0ZMPtdsNkMonIfa1Wu+WtR78/3Sz0ej10Oh0X50zJSNGP6nT6I6NcCYf53d3lmtNqtVAoFBCPx3F2dobDw0McHBzg+PgYkUjkVnvsqL58nU4Hp9OJ5eVl7O3t4cmTJ9jZ2UEoFILNZhsrNTes9TebzeL4+BivX7/GwcEBwuEwCoWCOMvLK/zTp0/x/Plz7OzsCJMMrVYrcvvUpEPBSHmKLQAhXJvNBpfLBa/XC7PZjGaziXa7DaPROHRugDztlgtzpmek6O/bt6z8ENP3D2k6cddzKVfl+zzXKKiElIpM6HwcDodxcnKCw8NDfPjwARcXFyJgN65bjEqlEoaWm5ub2N/fx6NHj7C4uAi73T71itfr9ZDL5XB2dobXr1/j73//O46Ojvp65g0Gg1jhX7x4gWfPnmFnZwfBYFAU4tB73Gq1UC6XUSgUUCgUUKlUhDsOiZgKbFwuF/x+P/x+P0wmE6rVKhqNBnK53MDtPaXv6IYg/85sfT0ZI0XPM8Bv0+v1hE8brU6y7RMVmSgj9Kenp4hEIshms2MF7GQ0Gg1sNhuCwSDW19eFqaUs+HEMPJRdfJlMBmdnZ3jz5g1++uknvHv3DtFoVDT26HQ6IfgnT57g2bNn2N3dFVt65TGChmFUq1VUq1U0m01RQ08pOJPJBKfTCZ/Ph/n5efj9frFyF4tFYbIxaFdFBT1yiy4zOazqCSkWi0in08hkMsjn86hWq6KenCBfuXA4jMvLS1xcXCAejwsHmUl3E5QTp3p6Kq+VV7thOfhB/1av15FMJnFycoI3b97g559/xvv37xGLxVCpVAD83rizvr6OJ0+e4Pnz53j06BFCoZCorZehCjrywCPjSgBCpFRc4/f7EQwGEQwG4fV6xXtmMBhEYc4oeFW/HyNFf3Bw8Lmu44sjf5BoPpvJZBKlpbR6y5FpMqZUir7T6YjBDMlkEtlsdmybahm1Wg2r1YqFhQWsr69jbW0NCwsLcDgcou581NZW2cjSbDZFff/FxQXevn2L169f4+joCIlEQhheqtVqOBwOLC0tYXd3V+T/aYUfZA0mi77dbvcFJ9VqtTjH+3w+BINBhEIh+P1+2O120Xcgm2rIQy7o96Q/2S7rfowU/d/+9rfPdR1fBeTZVq1WodFosLW1hZcvX2JhYQHNZhPpdBrxeByRSAThcFj0uMt94lRPT44x1Gk2KRqNRrjKbm9v47vvvsPW1pY4A49CeSNoNBrIZrNIpVKIRqO4uroSbjwnJydIJBJ912gymeD3+7G1tSXiB2RyqbS3otejNtpB1tRzc3OwWCxwu90IhUJYWlpCIBAQ9QR0nOh2u2i1Wmg2m2i1Wn0ddvQa8hfBK/9kjBT9v/7rv36u6/gqefPmDZLJJFZXV4XvfC6XQzKZRDqd7ktH0YfzPg6tKpUKWq1W2EgvLCyIwN3u7i5WV1dv2VbflUFoNpuiVffk5ESYYpAFlrJGQK/Xw+VyYWVlBbu7u9je3hYBQ2W3nrzKt9ttcYaXRU+VdHQDW15extLSEvx+v7iJ0Ew8moZLFYnyDopeQ+7LZ6aDz/QjyGQy+Ld/+zccHR2J8tBGo4F6vT6y5XQa9Ho9rFarGB9NBpM0b25xcREejwcmk2loEEtZR1+r1RCPx/H+/Xv8/PPPODg4ELZblUpFBCNlzGYzgsEgtra2xHBLpeCV0ArfbDbF+GqCDDPn5+extLSEpaUlBINB0QFIAq5WqygWi2IaLu0Y6CZKxxO6MTQaDbTbbS7QmQIW/QBk4cRiMcRisU/6ehaLRdhNUbBucXERoVAIi4uLmJ+fh91uv7O0Vr7ucrmMWCyG9+/f46effsJPP/2E4+NjpFKpPqHL23SK1m9ubmJ3d1dMwRk1A56OM7IYaca8TqcT5/ilpSWsrq5iaWlJ+OdRapPGX+fzeRSLRdGYo2zpbTQaoj+/Uqmg1Wqx6KeAU3YDoLP9p95CqlQqMUF2Y2MD29vb2NjYwNLSkqiys9lsos58GIMGVMRiMfz666/48ccf8fr1a9Gbrzx+kODVajVcLheWl5fF7oICd6OgqTWy6Kmk1mKxwOfzCR/8tbU10YJLzTmtVguVSgXZbBb5fB6lUkms4vKuotvtolarIZ/Pi1HZXq9XzMob9D4wgxmp6kEtlczD4Xa7sbq6ir29PXz33Xei4EYWxqTQGf79+/f4j//4D/z7v/87zs7OkM1mR0a9rVYrAoEANjc3+87xhHyOV3r1yfl5CghaLBaYTCbh1bexsdFn7EFttNR7n8vlkMvlRFpTeXPqdruoVqtIp9NIpVJiaq3L5Zr4PZp1ZnMpnwDlyvFQ6SKPx4ONjQ08f/4cz549w6NHj0S3mslkmmjFop8lf73Dw0P8/e9/Fyu8ckCFcmCkSqWCw+HA8vIyNjc3sba2Bo/HM1alH630tVpNbMt1Oh3cbjfm5+dFbGBlZQU+n6+vio9W70KhgFwuh0KhgFqtNnCxubm5EUMwaVR1vV7n9N0UsOjv4FPMqHO73djY2MAPP/yAP/3pT/j++++xuLgIq9U6daVZu91GNpvF6ekpXr9+jZ9//hknJyfI5/O3flZ2nQV+j9gvLy9jdXUVgUDgVqfesJuQfJ5vNptQqVSi3311dRW7u7tYW1tDIBCAzWYT7bCU16/X6ygWi8JHj6L2g6jX6+IGQb4D8o7goceK/VFh0X9G9Hq9WOGfPHmCly9f4smTJ1heXr4lMmXPghLlB7zZbIoqO2rqSaVSQxt65L93OBxYWFjA8vKyiKzL5b2jGo0ajQYqlYoIrOn1evj9frHK07bebreLmnoqvKFioVKpJM7yd2VEarUaKpWKSPPxSj85LPrPBHnDUx378+fPsbe3N3S23KSrlTyc4vLyEqlUamjQTn4Nq9WKYDDYF1kfx4CDehCowaZcLqPX68Fut4sZepubm33neDngRnGASqWCUqkkdgqD6hzkm9WgMl9mMlj0nxCtViuMIubn58V2Vy5rlavr5E69YQzawlIt/dXV1cDxV8OmxVCKbmNjA5ubm1hZWYHD4RgYx1D+XafTQblcRiaTQTqdRqlUwtzcHLxeLwKBgCgZdrlcMBqNYudAApZvGJSmG6eVm6rxqHCHx1VPDov+E6HVavsGOq6urgqnm7W1NczPz99aUac5z7daLSQSCZyfn4tOPqqhlxnUNmy1WrG4uIidnR1sb2+LZpq76Ha7qFQqwvySbLoNBgO8Xi8WFxcRDAaFQYbsbqu0/c7lchOJnnYJ9KX00WPuhkX/AJC5A7nWUIfa6uoqtre3sbW1hbW1NQSDQfh8vlt+9OPkl5Wlr/R3mUwGJycnePv2LY6Pj5FMJm9V2cmrIRXNWK1W4XNH8+5cLpeozRj0esDvQzkSiQQuLi5wdXWFdDqNTqcDt9stOujcbndfpF4uHCJz0HQ6jXQ6jXw+f8tffxgUD6jX62IYh9Vq7fv9mNGw6O8J2TK7XC44HA4YjUbRCru5uYmdnR2sr6+LiLhWq71VaDOO4Adt67PZrDDAePPmDU5PT0Ut/TBDEKqQW1hYEEeN9fV1eDyevsq7Qa8nOwFdXFzg/Pwc8Xgc7XYbdrsdDocDXq8XLpcLFoulz7qLfgcy2qDmpWQyiWKxKIZg3GVkQjcNeSAHdUKy4MeDRX8PzGYzvF6v8H4PBALC6NHtdota80Fb+UlQrrjdbheZTEak5+T2WFrllVF7vV4vovTr6+tiS09nb+W2Xj5qkJd9LpdDNBrFxcUFzs7OEA6HxUrr8/ngcDjgdDqFPbXsfHNzcyNW+Hg8jnA4jHA4jEQigWKxKHwGBoleWQwkz9nLZrNwu91TeQPOKiz6KaGJMjRgYn19HcFgUBTXKMcvjYOyV3zQh7jT6YjUHFlcHR4eIplM3mrh1Wg04jqorVU+ciwtLYlt+LBrJG8AeTDHxcUFIpEICoWC8N83m80iPy9H6ikn32q1UCwWEY1GRccfHQ2oqEfeXYxa7clKnMxMgsFgX40Dl+OOhkU/BUajEaFQCLu7u3jx4gW+//57rKyswO12w2KxTD1RddAZmm4EdI5Np9PC4urHH3/E+/fvcX19fSt4p9frxTDL5eVl0chDu49QKASXyzWymYbcbcPhME5PT/Hhwwecnp4iFoshn8+j1+vB6/VCq9WKwRuyHz0V4DQaDeTzeVxfX4tpvMfHxwiHw8hms6I9Gfh94MUoyIAzm82KKr5B6UkW/mBY9HegTHdptVrMz89jd3cXf/7zn/H8+XNsbm7C7XaPnJmuPCPfVXxDkJd8qVQSRpvHx8d49+4djo6O+jztgN9bdD0ej8iVU8aAAmwOh0ME2YbRaDSQyWRwcXGBw8PDPp+/UqmEbrcrfPIMBgPMZrPwoqe6emqmoSg/ZRjoaEADPia1EGu322KqrnKXwNwNi/4OlIIPBALY3d3FDz/8gB9++AE7Ozvwer0jK9iA8eyz6fGUliqVSojH46K9l2bD0bioRCIhXGtpbLTL5cLi4qI4t1MtfSAQgMPhuHPoI52ZU6kUzs/P8fbtW7x58wbHx8e4vr5GoVBAp9MRAUyj0SgEPzc3J3Yk7XYbxWIRyWQSHz9+xNnZmTDwuL6+FrP6lC4746J0K2LGh1trB0BnQ7kkVKVSIRAI4LvvvsOrV6/ECi8LnrjrQzhIdJVKBeVyWZhI0JaYptmSWMhPnlx7gN+KbGjM1crKCnZ2doTgFxYWxLFDGSMgwch/32w2kUql8OHDB7x586ZvMAetyuSGo9VqxfAJmjpbLBZFX3wikcDHjx9FpD8SiSCTyaBcLqPRaNyyvJIdiEZB025o6AV74E8Gt9YOQPkB0mg0Ykv/8uVLvHz5Ejs7OwM70Sb98PV6PZEGi0ajYhWsVqtimGQkEkE0GkU2m0W9Xu9zlKF6/sXFRWxtbWF3dxePHj3C2tqaGIQx7MihvNZWq4VMJoPz83P88ssv+PHHH4UPvjwym+oS6It2B2QnRuab4XAYV1dXCIfD4veifDwJnq5hksi7VquFzWaDw+EQI6uVj+ebwHBmcym/A3mlDgQCwslmb28PL168wKNHj+D3+0XEm1ZM+YM26kNHveFkEUWRcdr+0ljner2OUqnUZy4hYzAYMD8/j7W1Nezt7WF/f18U2lAb67Dfi66RrrPT6SCfz+Pi4gK//vor3rx5MzBmoNFoxNAJGjhJxTok6kwmg+vra0SjUSSTSWQymb5cvJJJBWo0GuFyueDxeODxeG7tYljwo2HRj8DpdOLPf/4znjx5gkAggMXFRWxuboqBjcS4Ygd+ixFks1lcX1/j+voasVgM0Wi0b1XM5XLCLooi4MptL42aogYeecwVdbQpUVbzyddUKpUQiURweHgotvTxeLxP8HSToLJa+fxPZbR0jqd0Gm3lR+0ax93W0zWQcajf7xcpR55rNz4jRf9P//RPn+s6vgooUl+v1zE3N4f19XW8fPkSGxsbsNlsIt99l6BkyDSSjCMLhQKi0agIbH38+BHxeBy5XE7YQN1l02U2m+Hz+cRsOXnyjM1m61v1hvWYK+21EomEGL91fHzcN/iC3hv6otFSlMNvt9tQqVSiN15uornriDhOak2Zt6dR3W63WwzuZMZnpOj/5V/+5XNdx1eBPGyBVhSbzSaaRubm5sYKbtLKRSmrXC7XZ59Nee/Ly0tEo1EUi0XRPHKXSIxGI7xeL3Z2dm7NlhsUrLtLUK1WC7lcDhcXF8IH//r6um/0Fo2Ukrf1AEQwjppoqBaeWl+n6YC7KxpPNuFmsxlWq1UM36DH8tb+bkZ+gvf39z/XdXyzyEMeyM6ZvqfSVTrfXl9fI5FIIJFIIB6PI5FIoFAo3HrOQbl8oH+Ff/bsmVjhA4HArQ+/8rkG0W63xc6DcujRaBSFQkFkLigyToLXarXQaDTC9YbGepH99TRjuwb9rqOg69DpdH19DOycMx58pr8HcsdYPp9HLpcT5pD1el00lkSj0b7hEmT1NO7kG5VKJZp4dnZ2hOB3dnZEI8+wG8WwG0C32xXZgfPzc5yfnyMWi6FUKqHT6fQNipQnxdJ5npxvaCrNOLuUh4Cui66F6+0nh1N2I6DA1bDz8M3NDSqVCuLxOC4vLxGJRITwKfJONeLpdBrZbLbvnDwMWai0lfX5fGKY5NOnT/sET5bddxlw0HN3Oh1x3dQ8Qyt8t9uF0WhEr9cTKTl5qCTtbJQ97Q9lZnHXik83IuX8emZ8Roqe39DRdDodFAoFfPz4sa+fncYyyWOb6e/GhSrsLBZLn+vOd999h83NTTHTThm0u+v/GbWlplIpkSak5plerwer1Qq73S628bKnHe1gaET3oLl1kzCu07D89zqdDgaDATqdjiP2UzJS9PymjqbZbIqquZOTE7x79w7JZLLvXD/uwAxKhWk0Guh0OphMJlFlR9bUjx49Er35Vqu1L6g4rFGHgorU+EIWV7FYrK+UV6fTwev1QqPRwGAwiD4CaqstFovCoopW+vsIflqMRqP4osYeZjL4TD8lJIZ8Pi8Cc1SIAtzdHipDQytNJhNsNhtsNhucTic8Ho8oDlpZWRGDH2VHGhnqxut2u2IlpsGS5FpLE2LoWjudDqxWK6xWK3Q6nRAUpSUrlQqSyaQI7A2bTDspdJOj93Kc59LpdMJzkHr2J6mRYH6DRT8l5OYq56Wp+QWAaD4Z9mGmc6ler4fFYoHT6YTP54PP5xOVZl6vF36/X/wdbbvlHYS8mpPY6WjRaDTEEArZebZSqYhyXrvdLm4k1BdPq3y9Xu8r0JEn005zhqf4AP3udJOinchdwqf8vNPphN1uh9FoHLuRifkdFv0UKEc5UcRe2Qc+rNuOGlVoC09TahcWFhAMBuH1euFwOOBwOGC1WmE2m8WqVi6XRXqMUmS05aZVWBY7xRNI5BSgMxgM8Hg84nVo9aTZcO12WxQL0dFAHiE9KXRkkQ07aDw1TaoZlaNXq9WiEs/r9cLpdPbNsWPGh0U/JbLAyK9dWdpKkPgpv2wymWC32+HxeBAMBoWxxcLCghhcKQv95uZG9I9TerBQKIjUHwXWaCWmgZJ0Y+h0OqIzjnzsfD4f5ufn4fF4hMUVnZO73a4wqZCr7KjLblKMRiPsdju8Xq8Yt00lu5TqpGPJMNHPzc2JmnvyBBi00jN3w6KfEvn8fHNzc2u7q2wAoXZQ8s+jWv7V1VUxWcbn88Fut8NkMkGj0QhfuWq1imw2i2QyKYp6UqkUCoWC2KrLqz1t86myUK/Xi240u92OQCCApaUlBAIBYWKp1+vF9pvGRVHQL5lMilbeSc7xKpVKjOGW/QLNZjMajQaSySQikYjYOVEVo/x42VHHZDLBarWKab5KbwC+AYwHi/4TQR9Aqmaj7bzsVbe+vo7V1VWEQiHR806GkvKct1QqJQw0IpEIYrGYED1NhqGtu1yVNjc3B4vFArfbDZvNhkAggNXVVeGi43K5xI6Cct4kQGqcub6+RjqdFuaVk/z+VqsV8/PzwtBjY2NDNCvVajVEo1FotVrhhCPn/GWffHo+su6mIw/Ppp8OFv2UkKjkyjXlqkNfWq1WOM3QNNdQKIRgMAi/3y+2qiR2OUiYTCZFeu3q6kq0q9Iqrxz4QOh0OthsNpjNZjG2ant7G5ubm+ImYzKZ+lxkZQNLcqyNRCLCvHKSTjgal7Wzs4P9/X3s7u5idXVVzMmrVqswmUyiZp9GW9ExRYlsnEEpxVk1ebkv/K5NibIybFBASW5FpXQYGUjSCkulvFRYQ0GzUqkkvOXC4TA+fvyIaDQqVl1qV1UKXt7Ok5PO9vY2Hj16hI2NDWGISUcI+eZElXqyAUYymUSlUpkoeEcFRdQFSN76fr8fBoNBtOSqVCqR9iTzDXovaIWXx2lTSTAdQ5jpYNFPibzSj1pxaAcgl+7KW3eVSoVisSjO8NSoUygUkEqlRLNOPB5HNpsVZhoUS5BbXikrQGk4Msbc2trqs+im2XLyzoRaitPpND5+/Ijz83OEw2FkMpmxewSA34J2Ho8H6+vr2N/fx+PHj7GzsyPO8pTK1Gq16HQ6YtdDdQOVSkXEJ5R24IN2VMzksOinhIR81weRovrUoEJBOQqYyWdT+WeKxaKo28/lciJ6TiuubGZBjrQWiwUul0sU9NDsvOXlZczPz4tJMPKNSG4Dlnv9Ly4uEIvFRCXeOOh0OtjtdjEfj+YByIIHIFZsSsFRfYLL5RIBQ9lOS1715eApMx0s+ikYJPhBppO0ElMnGm1laXubyWREDTk1wlAakGa+l8tlUWhDz0c3CRI+pcQ8Hg9CoRCWl5exurqKlZUVLCwswOv1ioo75Q2KjheFQgGxWAxnZ2c4OzsT9fjj9gtQdJ0i9Wtra1hZWRG2XfQ7yo1BVIlI9f5Wq1UYbiqtteSZ9pQm7Xa7vM2fAhb9lChFP2ilJ+GTswx9T+24sk++/LMUzKOoPG115deifnKz2SwKfEjwy8vLWFhYEEFC2k0om3OUK/zp6SlOT0+FTx8VzYwDdQN6PB7Mz8+LegO5gIaeS7ltl002h+2aqOGnWq2iUqmgVquh1WrBaDSO/z+NAcCinxpaZelDO6xjjP4kJxkK1FEsQDngkcSoFBsFA+lxFBR0uVyioo8MPIPBIDwejxipRccAel66DpoRH4/HxeSZ09NTxONxlMvlsabIEmq1WgQrTSaTOEZQDYOcEqTfV97ZUDGRXNcvvw8Uc6Dx1qVSCc1m804ff+Y2LPopocg9+a4rA2MEnT1J0LRyKyP+yry+vIugqTHkMU/beZfLJUZDh0IhBAIBMTVWrqGn65B3EBQ3oMDd6ekpjo+PEY1Gkc/nB5YVD4OumX6/er0ujic2m010DsrBTEpLFgoFZDIZZDIZMQCDCovk1+92u6JhKJvNCuddm83GdlkTwqKfkrm5ObHFJuErz/UkeFrpqOiEymLl7bq8rVWmp6iHnKbJkCkkldJSNZ/b7RZndyrdpSCiPAqqUqmgUCiIcdGUEpRz8uMInq6Xbixkh51KpRCJRGAymaBSqdBqtW4FLGu1GlKpFKLRqEhHknsu9RPIux2anEOiLxQKEx0/mN9h0U+JXCwil7ASJFyl8Om/aatLxwQ5wEW7CI1GI4pRyKTT4XCILb3X6xX18xSZB343rGw2m6KhRe6yKxQKyOfzSKfTSKVSwrI6l8v1OdgqA36jICFns1mEw2FRaFStVhEIBIRrrVqtFgVAsVgMJycnwrmH/P6pJFem1+sJP4BisYhKpTLQR59X+7th0U8JlYXKpg6Urx8WjJKFLyMXo9BjSfS0m6DXMJlMfV9arVZE4EkotHUmgVNzjvxFf18sFkWGQDa2VN60RkGRdVqJgd9iGNVqVcyR9/l8YqvfaDTEzYH8+RKJhFjlh70mxUMom6HM5bPgx4NFPyHyB4vKa81msxAh9Z5T4GyQcJRmlYNuBnKlHO0KaKtOH/5KpYK5uTlRKw/8Jng5z09fJG5qtaV2YOrQk4NnMvKxRHnt8u9DxwGq3pNNO5LJpGgX1mq1QvQ06IOm48g3rkHIefqH9OWbNVj094D60i0WC2w2GywWC6rVqjiP0ghl5TYfQF/Unv5e+XN0FleW+sr9/IVCQUTKaYtNKzkFvORuPIqOK6e+jnLRHYZylZU7D+VgYSaTEfEGjUYj6gKy2Syy2awov72rCEje/VBwk5kcFv09mJubg8FggNVqFY4ulH6ibeqg1RPoFzkJVq6Bl3+OIu/UJ1+pVGAymfoaT3q9nigAKpfLKBQKKJfLIqc9zJNerjGQYw6DUIp80L/LKUG6aVGqjc70dMOiCb3jDsag95oKeQbZZfH2/m5Y9PeAWmatVivcbjc8Ho9oJFGpVLfSTsDtGXLKqD79DD2Wtu+U26dVTl7t6IhA1WrkmkPtqoMCY/L10JHlroj9JJFyeacj1wQAEDeDu65Nhir+KGtBaUkW+eSw6O8BBfMsFotIo1FRC21x5dVn0DaaVnhaZSlHTY0pcmBP2dknb2+puo5W9En96B8y9SWnHunaGo1GX2yCji+jrk9pokH+/9SSzNNqp4NFf0+oOs5qtYpzPQXY5BLbQcjVaXKtPv23Mocv5/Xlf6MjABW9kJi+hhy2fKOT4wejrLGA37fq9DMajUa0C1MREq/008Givwdyv7ycp1dGmO/6gBPytl75YaYVTd45KPPok7zWp0QWudwPr/z3u55D/jmTyQSv14tAIAC/3w+73S5SpJyqmwwW/QMgC072nZej5MQ4H85Bx4CHSk8Nawz6VI+b5meV6PV60VBENl8Gg2Hq55t1WPT3hMplyQPPbDbDbDYLy2kKjk1qG608xw/6XtnSSzcYuulQFF2+YUwrvrseNyzuIBcqyXUGo/riqaR5bm5OdO7t7Oxga2sLi4uLfas8Mzn8zk2IcsXTaDTC4TYUCuHm5kZMqkmlUgMNMO6CbiJ0IzEYDGJ+G41nVgbzZEFRao+KcarVKmq12ifb9lMTkFykpNfrRVcgnc0p0CgP36DiIIpnGAwG8f7Z7Xa43W4Eg0Fsbm5ib28PCwsLfWO5AQ7gTQqL/h5Q9N5utyMYDEKj0cDhcCAUCgmrK7KszufzKJfLA2fAKctuSUB2u12MuaLpM0ajEXq9vk/4JCoy66hWqyiXy6L4Rb7xUDMLBfrkFtZBNwU5biF/T+lKuU7B5XKJ6TN0rdR2LNt5y4VDlKunjIXNZhPdg/Pz8wgEAggEAlhYWEAoFILX6+Ue+nuiuuPu/+XDv185lIOmaTLUUprJZIR1Nc2lr1QqohlGKXpyxKFJMJQGJBGRsy2JnlbRQaInJ126BmqmqVarYmWllCLdJOr1OhqNhijgIb89qven+XZyMxBdKwnV4/GIQRRWq7VP9PR68jy9TCaDUqkkqhi1Wi1sNht8Ph8CgYDoHnS5XOL3NxgMQ9uYmVsMfJNY9A+AshKNClGok03uFZdXWoLOvrTSUz2/XN5LE2hI8HIPv+zKQ4U81D6by+WQz+dRLBbF61Nffb1eR6lUQi6Xu9WuSvUHbrdbCFk2x6AuQypDttvtA0dx0dlbNv2kikEqD6bmGY1GI4xBvF4vvF4v7Ha7EDozMSz6zwmJSh4gSQEsZQ5d2VJLhhnUuiuf5cexf1bOtJNHX8nlseTZRy222WxW+Nvr9fq+EVhUO0+iV7b/0q6ASoMH1cbTTYkqB2lnIfcoUEchBURZ7PeCRf8lUDa03FWQovzvQTn5SV570DXINtzKlZ4CfrTVplWXvPZoiz/oOiepfR/2nkz6PMxIWPSfms9dJDKoeWZSqBWXzvW06tIKTivulwqeTVrjwPTBomcGQ/EA+dghHzl4HPQ3C4v+SzDOtn4QylXtPtv7Ya8/6TZ6WFpPeX3jVh0O+m/Ovz8oLPovzbTlrp/i9ad9nYc4UjCfDRY9M5xxA4zMN8XA/3FckccA+F3Y8pme+WPComf6YLH/8eGwLMPMGCx6hpkxWPQMM2Ow6BlmxmDRM8yMwaJnmBmDRc8wMwaLnmFmDBY9w8wYLHqGmTFY9AwzY7DoGWbGYNEzzIzBomeYGYNFzzAzBoueYWYMFj3DzBgseoaZMVj0DDNjsOgZZsZg0TPMjMGiZ5gZg0XPMDMGi55hZgwWPcPMGCx6hpkxWPQMM2Ow6BlmxmDRM8yMwaJnmBmDRc8wMwaLnmFmDBY9w8wYLHqGmTFY9AwzY7DoGWbGYNEzzIzBomeYGYNFzzAzBoueYWYMFj3DzBgseoaZMVj0DDNjsOgZZsZg0TPMjMGiZ5gZg0XPMDMGi55hZgwWPcPMGCx6hpkxWPQMM2Ow6BlmxtDc8e+qz3IVDMN8NnilZ5gZg0XPMDMGi55hZgwWPcPMGCx6hpkxWPQMM2P8f8CdP/4WqEVKAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 63; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 65; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 66; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 67; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 68; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 69; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 70; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 71; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "lb = 1 - np.min(evaluation_history, axis=1)\n", "ub = 1 - np.max(evaluation_history, axis=1)\n", @@ -2029,22 +413,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", @@ -2071,17 +442,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta // 2)], need_gradient=False)\n", "frequencies = opt.frequencies\n", @@ -2092,34 +455,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", diff --git a/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb b/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb index 9978da26d..076ab1998 100644 --- a/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb +++ b/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb @@ -11,17 +11,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep.adjoint as mpa\n", "import numpy as np\n", @@ -45,29 +37,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "foward value 0.6369810999723368\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAL6klEQVR4nO3dX4ilhXnH8e+v69bgP9gl7TJai22wAQl10w6mECkWm2i9UW+kexE2NDBeRDA0FxVvIpSAlMT2pgRWlGzBWELU6kWoMSLdBkrIrqy6um02hA11XXcRA64UbNSnF/PamSw7O3P+zcw+8/3AMue857x7Hl9evp59z3veSVUhSerlNzZ6AEnS9Bl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIZWjXuSq5O8kOS1JK8muXdY/kCSE0kOD39um/24kqS1yGrnuSeZA+aq6sUklwOHgDuAu4B3q+obM59SkjSSi1Z7QlWdBE4Ot88kOQpcNevBJEnjW/Wd+689ObkGOAB8Cvhr4IvAO8BB4KtV9ctzrLMALABsY9sfX8IVEw8taWP8wR/+z8jr/PTlS2YwydZyhl++VVW/Nco6a457ksuAfwO+XlVPJtkFvAUU8LcsHrr5q/P9HVdkZ30mN48yn6RN5Nk3Xhp5nVuuvH4Gk2wtP6zvHaqq+VHWWdPZMkm2A08Aj1XVkwBVdaqqPqiqD4GHgRtGHViSNBtrOVsmwCPA0ap6aNnyuWVPuxM4Mv3xJEnjWPUDVeCzwBeAV5IcHpbdD+xJspvFwzLHgbtnMJ8kaQxrOVvmR0DO8dD3pz+OJGka/IaqJDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDa0a9yRXJ3khyWtJXk1y77B8Z5Lnkhwbfu6Y/biSpLVYyzv394GvVtV1wJ8AX05yHXAf8HxVXQs8P9yXJG0Cq8a9qk5W1YvD7TPAUeAq4HZg//C0/cAdM5pRkjSii0Z5cpJrgE8DPwZ2VdXJ4aE3gV0rrLMALAB8jEvGHlSStHZr/kA1yWXAE8BXquqd5Y9VVQF1rvWqal9VzVfV/HYunmhYSdLarCnuSbazGPbHqurJYfGpJHPD43PA6dmMKEka1VrOlgnwCHC0qh5a9tAzwN7h9l7g6emPJ0kax1qOuX8W+ALwSpLDw7L7gQeB7yb5EvAL4K6ZTChJGtmqca+qHwFZ4eGbpzuOJGka/IaqJDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWpopN/EJJ3Ps2+8NPI6t1x5/QwmkeQ7d0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakhz3PX1HjOurR5+M5dkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpoVXjnuTRJKeTHFm27IEkJ5IcHv7cNtsxJUmjWMs7928Dt55j+d9X1e7hz/enO5YkaRKrxr2qDgBvr8MskqQpmeSY+z1JXh4O2+xY6UlJFpIcTHLwV7w3wctJktZq3Lh/C/gEsBs4CXxzpSdW1b6qmq+q+e1cPObLSZJGMVbcq+pUVX1QVR8CDwM3THcsSdIkxop7krlld+8Ejqz0XEnS+lv1F2QneRy4Cfh4kteBrwE3JdkNFHAcuHt2I0qSRrVq3KtqzzkWPzKDWSRJU+I3VCWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWpo1bgneTTJ6SRHli3bmeS5JMeGnztmO6YkaRRreef+beDWs5bdBzxfVdcCzw/3JUmbxKpxr6oDwNtnLb4d2D/c3g/cMd2xJEmTuGjM9XZV1cnh9pvArpWemGQBWAD4GJeM+XJab8++8dLI69xy5fUzmETSOCb+QLWqCqjzPL6vquaran47F0/6cpKkNRg37qeSzAEMP09PbyRJ0qTGjfszwN7h9l7g6emMI0mahrWcCvk48B/AJ5O8nuRLwIPA55IcA/58uC9J2iRW/UC1qvas8NDNU55FkjQlfkNVkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDU07m9i0gTG+S1HF4L1/O/ytz4t2ez7k7/Va2P4zl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyPPcN8CFcA6v5yZfONZzu7tfXDh85y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktTQRFeFTHIcOAN8ALxfVfPTGEqSNJlpXPL3z6rqrSn8PZKkKfGwjCQ1NGncC/hBkkNJFs71hCQLSQ4mOfgr3pvw5SRJazHpYZkbq+pEkt8Gnkvyn1V1YPkTqmofsA/giuysCV9P0gbytypdOCZ6515VJ4afp4GngBumMZQkaTJjxz3JpUku/+g28HngyLQGkySNb5LDMruAp5J89Pd8p6r+dSpTSZImMnbcq+rngAfgJGkT8lRISWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGpoo7kluTfJfSX6W5L5pDSVJmszYcU+yDfhH4C+A64A9Sa6b1mCSpPFN8s79BuBnVfXzqvpf4J+B26czliRpEhdNsO5VwH8vu/868Jmzn5RkAVgY7r73w/rekQles5OPA29t9BAr2TY3zlrHxn25Tb0t1pnbYonbYsknR11hkrivSVXtA/YBJDlYVfOzfs0LgdtiidtiidtiidtiSZKDo64zyWGZE8DVy+7/zrBMkrTBJon7T4Brk/xekt8E/hJ4ZjpjSZImMfZhmap6P8k9wLPANuDRqnp1ldX2jft6Dbktlrgtlrgtlrgtloy8LVJVsxhEkrSB/IaqJDVk3CWpoXWJu5cp+HVJjid5JcnhcU5xupAleTTJ6SRHli3bmeS5JMeGnzs2csb1ssK2eCDJiWHfOJzkto2ccT0kuTrJC0leS/JqknuH5VtuvzjPthh5v5j5MffhMgU/BT7H4hedfgLsqarXZvrCm1iS48B8VW25L2gk+VPgXeCfqupTw7K/A96uqgeH//nvqKq/2cg518MK2+IB4N2q+sZGzraekswBc1X1YpLLgUPAHcAX2WL7xXm2xV2MuF+sxzt3L1Og/1dVB4C3z1p8O7B/uL2fxZ25vRW2xZZTVSer6sXh9hngKIvfgN9y+8V5tsXI1iPu57pMwVjDNlLAD5IcGi7PsNXtqqqTw+03gV0bOcwmcE+Sl4fDNu0PRSyX5Brg08CP2eL7xVnbAkbcL/xAdWPcWFV/xOIVNb88/PNcQC0eJ9zK5+d+C/gEsBs4CXxzQ6dZR0kuA54AvlJV7yx/bKvtF+fYFiPvF+sRdy9TcJaqOjH8PA08xeKhq63s1HCs8aNjjqc3eJ4NU1WnquqDqvoQeJgtsm8k2c5izB6rqieHxVtyvzjXthhnv1iPuHuZgmWSXDp8UEKSS4HPA1v9SpnPAHuH23uBpzdwlg31UcwGd7IF9o0kAR4BjlbVQ8se2nL7xUrbYpz9Yl2+oTqctvMPLF2m4Oszf9FNKsnvs/huHRYv//CdrbQ9kjwO3MTi5VxPAV8D/gX4LvC7wC+Au6qq/QeNK2yLm1j8p3cBx4G7lx13binJjcC/A68AHw6L72fxWPOW2i/Osy32MOJ+4eUHJKkhP1CVpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGvo/PL0GIHoV1ZcAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "nz, ny, nx = 25, 1, 25\n", "c = mpa.ConnectivityConstraint(nx, ny, nz, sp_solver=cg)\n", @@ -99,22 +71,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# padding to show the side with Dirichlet BC\n", "fullT = np.pad(c.T, (0, c.nx * c.ny), \"constant\", constant_values=1).reshape(\n", @@ -135,22 +94,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "r = np.zeros((nz, ny, nx)) + 0.0001\n", "r[:10, 0, 8] = 0.999\n", diff --git a/python/examples/adjoint_optimization/Fourier-Bend.ipynb b/python/examples/adjoint_optimization/Fourier-Bend.ipynb index d0ec785b0..d681f2ad1 100644 --- a/python/examples/adjoint_optimization/Fourier-Bend.ipynb +++ b/python/examples/adjoint_optimization/Fourier-Bend.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -59,17 +59,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.20124611797498096\n" - ] - } - ], + "outputs": [], "source": [ "minimum_length = 0.09 # minimum length scale (microns)\n", "eta_i = (\n", @@ -84,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -115,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -158,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -189,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -259,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -276,22 +268,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "rho_vector = np.random.rand(Nx * Ny)\n", "opt.update_design([mapping(rho_vector, eta_i, 1e3)])\n", @@ -301,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -343,2006 +322,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1\n", - "Starting forward run...\n", - "Starting adjoint run...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.8/site-packages/meep/adjoint/filter_source.py:91: RuntimeWarning: invalid value encountered in true_divide\n", - " l2_err = np.sum(np.abs(H-H_hat.T)**2/np.abs(H)**2)\n", - "/usr/local/lib/python3.8/site-packages/meep/adjoint/filter_source.py:91: RuntimeWarning: divide by zero encountered in true_divide\n", - " l2_err = np.sum(np.abs(H-H_hat.T)**2/np.abs(H)**2)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQkAAADnCAYAAAANUaMiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAALeklEQVR4nO3dO2/k1h2G8ffwzrlofdHCdrC2ARfuLKRzG/fp08dfIR8klRs3qQJ/ANc23CadFzAWcBEgWS8gwHCkuWhmOLykMMjMyjN/rVZDDkd6foBhrbVeckfDh4fkGdJVVSUA2MU79AoA6DciAcBEJACYiAQAE5EAYApu+D6XPu6Roij05Zdf6rvvvlMQBLrNlS3nnPI812effabPP/9cvu+3uKY4ALfrGzdFAveIc07Pnj3TV1999dp/xnvvvSfPYwD6kPDTfmDuuoEHAfuVh4ZI4FaYfPfwEAkAJiIBwEQkAJiIBAATkQBgIhIATEQCgIlIADARCQAmIgHARCQAmIgEABORAGAiEgBMRAK34tzOGxjhniISuBXuJ/HwEAkAJiIBwEQkAJiIBAATkQBgIhIATEQCgIlIADARCQAmIgHARCQAmIgEABORAGAiEgBMRAKAiUgAMBEJACYiAcBEJACYiAQAE5EAtpitcn3z7Fz/nWeHXpWDCw69AkAf/flv/9Q//vWLPjod6pu//OHQq3NQjCSALf7zy5Uk6fnFQnlRHnhtDouRBLDFX//0e/39H//WH89+p8B/2PtSIgFs8elHb+vTj94+9Gr0wsNOJIAbEQkAJiIBwEQkAJiIBAATkQBgIhIATEQCgIlIADARCQAmIgHARCQAmIgEABORAGAiEgBMRAKAiUgAMBEJACYiAcBEJHArzrlDrwI6RiQekH1s4FVVEYoHhrtld6iqqq3/bnN5VVWpLEuVZanlcqn1eq3xeKw8z1UUhcrSfqaE53nyPE9JkihJEgVBoMlkojiO5XmenHOtR6P+8zeXQ6i6QyQ6UhRF809ZliqKotmI24hFWZbK81yLxUKXl5f66aef9OOPP2o2m+ns7Ew///yzJpOJ1ut1sy7XBUGgIAg0Go10enqqDz/8UMPhUN9++62ePHmit956S2maKgxDed7+B6V1COpQ+b4v3/ebX7exTPwWkehIVVXK81x5nmu9Xjdfl2XZWiTW63UTiclkoqurK4VhqJOTE2VZJuecVqvV1nWoRwhxHGswGGg8HmswGEiSptOpLi8v5fu+1uu1oihqLRJ1HIIgUBiGCsNQvu8zkugQkehIvdGuVistl0stl0tlWfZKQ/7XXV49kphOp1osFpKkOI41Ho9VFIWiKFKWZcrzfOuf4fu+wjBUmqYaj8eK41hlWWqxWGgymcg5p+Vy2epIoo5DfbhTVZWiKJLv+3tfHrYjEh0py1JZljUb7Xw+12KxUJZlrY0miqLQarXS1dWV5vO5iqJQGIYajUZyzilN02Y0s029F0+SRMPhUEmSSFIzOsnzXHEct7Jnr0cyURQpSRKNRiOVZdmMLsIw3OvysBuR6MhmJGazmS4uLjSbzbRcLluLRH2Ik2WZVqtVsxcejUYKw1BZlpnnROoNNQxDxXGsKIpUVZWyLNN0OlWWZQqCoNVDjTiONRwOVRSFfN9XFEUKw7D1k774PyLRkc3Djfl8rslkoslkosVioaIoWl1uURTK81xVVSkIguYqxWAwuHFjq0MRBEEzxK/jkmVZqycPfd9XHMcqiqJZ38Fg0FpUsR2R6EhVVSqKojmZuFgsdHV11WokNg8j6hFDfWhw2+F6vWeX9JvzKG2FwvO8lwJRX4lp4xwOdiMSHdqcs7B5paPLPaNz7s4n/a5vpG1Fzvf95nWqLx+3edkY23GhuWObE6m6mlR1rLa9RrxW3SMSAExEAoCJcxI91OchdZvrxizKfmIkAcBEJACYiAQAE5EAYCISAExEAoCJSAAwEQkAJiIBwEQkHgBmMuIumJZ9j9Vx6PM0b/QfIwkAJiKBg+JQqP843LjHOMzAPjCSwEERsv4jEgBMRKIj3NMSx4pIdKR+UE7Xd8cG7opIdKR+Lmf9sGBCgWNhXt3gTbwfzjllWabJZKL5fK7lcnnwEYVzjp8vXokZCa5h70+WZTo/P1eWZZrNZs0TxXdpcyPm54rbMCPx9OlTzWYz3lR78OLFC7148UJVVWm9Xmu9XjdP9JJ+HbVtvs7s5dEXZiS++OILff311zzF+Y6cczo7O9PHH3/cPLTX9315ntc8R/MQw//ry+xyHTjcOR5mJC4uLvT8+fOu1uVeOzk5UZqmiuNYg8Gg+ToIAnme1+kGs2tZfVgH9I8ZifrBslT/bpxzury81Pn5ucbjsaRfX9sgCBQE3c2M3/Zz5JOiuIn5Dt08Xsbrq6pKi8VCFxcXcs4pSRIlSdL51Y1thxb8bHET5kl0JMsyLZdLZVnWXP48FEaGuA0i0ZGqqlQUhfI8b/bgtwnF615h2vb/1VdS6u9tfg1cRyQ6cv1yZ5fLrdUx2AzC5qiii1BshgnHgUgciX2ExToH0VW4OMw5PkSi567v9Xf9Hut7276+Hoy2z1O8yt8D/UQkemzbhmUN17cdTmyz6zKotfx94ZLr8eH2dT236x4Um7+2RgS7vr7+33ad4NyXzb8HI4njwkiiI865l6Zgv4rX2Uhfd8NmtiV2IRId8TxPYRgqCILmkKCOxl2wV0bbzHfpbfd82M45pyiKlKapkiRRFEXyfX8vryt7ZbTNjER9vwPeiHdTVZWSJNEbb7yh8XisNE0VhqE8zyPA6D3zxOWbb76p999/n4+K78GTJ0/0zjvvKIoiDYdDxXFMJHAUnLXxP336tJrP57yR78g5px9++EHff/+9kiRp7iNxfWr0TSG+75+5uP4+8zxPSZLo0aNHOj091bvvvqvHjx/r0aNHOjk5UZIkB1rTe2nnRm6OJD755JP9r8oDNR6PdXFxIc/ztFqtlOe5iqJoPgm6eWlwH7Mijy0o7Ij6ixvhdqD+ePgHH3yg5XKp6XSqq6srrVar5jZ2+95Irkenz9EgEP3GjXA7kqapHj9+rOl0qrIsm/tctvkab37as6+BkOwJXTg85kl0JIoijcdjDQaD5jLoPuZJ3KRvN5bZ9RkOAtFfRKIjvu83cajva/nQN4ybpoyjH4hERzzPk+/7e5tEdayIwfEhEh3ihis4RkQCgIlIADARCQAmIgHARCQAmIgEABORAGAiEjgo5oz0H5HAwfT9g2f4FZHAwRCI40AkAJiIBAATkQBgIhIATDwLtIduOuv/qs/t7PPDea8/4Rz9xUiih27aaDYfvmvdnq7PG1+f1w0vYyTRQ686wWjfv+9Q+r5+Dx0jCQAmIgHAxOFGxzbvc8k9L231a8RrdVhEoiPOueaO2UEQKAxDhWGoKIqap7fjZZ7nNa9TEATNncY3o4H2EYmOOOfk+77CMFSSJBoMBsrzXL7vE4kdnHNK01SDwUBpmr70zBJ0h0h0xDnXBGI0GqkoCvm+3zw8GL/l+77iONZwONRwOFSSJArD8ME/u6RrRKIjnucpiiKlaaqiKOR5ntI0VZZlKsvy0KvXS/XhRpIkzYgiiiIi0TEi0ZH6DV8/xDeKImVZpqIoiMQOzrnm/E0cx4rjmJHEARCJjtSRqE9e1icsy7Jk9uEO9clez/OaWNQnMDkv0R13wxuUd+8e1SOGvj3p+xhcvwzKSGLvdr6gRAKAZESCMRsAE5EAYCISAExEAoCJSAAwEQkAJiIBwEQkAJiIBAATkQBgIhIATEQCgIlIADARCQAmIgHARCQAmIgEABORAGAiEgBMRAKAiUgAMBEJACYiAcBEJACYiAQAE5EAYCISAExEAoCJSAAwEQkAJiIBwEQkAJiIBAATkQBgIhIATEQCgIlIADARCQAmIgHARCQAmIgEABORAGAiEgBMRAKAiUgAMBEJACYiAcBEJACYiAQAE5EAYCISAExEAoCJSAAwEQkAJiIBwEQkAJiIBAATkQBgIhIATEQCgIlIADARCQAmIgHARCQAmIgEAFNww/ddJ2sBoLcYSQAwEQkAJiIBwEQkAJiIBAATkQBg+h+L8aoSlgK/7wAAAABJRU5ErkJggg==\n", - 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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evnyJFy9e4OXLl3vuO0XBVlHa2TcvSHnfIiMfi/DfYSaDPSUZJOQuVNsjk52ouCGM+zUajcatuASPbQ2cx2aTHWKzGj59Kh6PpEhItT8drqWn0yn+8Y9/4PXr13j9+nUosWaWgOnDvI1aeRui8iooffmyFQi7sQt435Yuy7KQkQBw62K+DCTyuOxBSU+CSw0eM2+3q/3bxPaw+N9mK0LVcKZ4kiKhf4xPh3/DxWKBr7/+OjSq9eJgo/mxGZSzr30eK7X2XoKvsvRXyWKgkiLEZQZndStadplhMyK2Y5TFG31MyPg5X35txYCBUZ5DQlEsSZH429/+hul0qn+QT6RUKuGvf/0rzs7OQrxht3vfVIUR/Bj2dWuEMRHIa23vvQvrQfjMA/HZFLtU4FgoFHzM7/n4wX3GYgXPPufvYidtNZ0pnqRI/O53v8Mf//jHcJ0I8fH84Ac/wOnpKVarFWq1Wpg9/YxqawVIrGKS99Ylj3kOqd2T9CIsdgw2lhAzbisOTPHyVqvV9lKrdtdpXsv9mFBwWUOhkEgUT1IkxuMxXr58WdRYvtM0Gg0cHh6Gpi507e21M4D3RsqIfkwgYh4D34stQfKCmrES6NheCVun4I9psyIMcPJyfD6DYoXiPkFWK0a24YxEoliSImE7HsuT+HCscQHvDLfX66Hf76NWq+Hm5gZXV1e4uLgIbeN9wZStLfAzsRWLmNHEZmlg/0pgvs1drA7Di5F9bKszJ5PJrSts2eWCj5lwLH7MPr5CcVBXqschKRKp9Ju4G2voWZah1Wrh9PQU3/ve99DpdAAAk8kEr169CrEJ3qw48N663nwcM36LNyh7sR8KhBUKHxy1x/BixMDmfD7HZDIJm7nsVcGZmeBSwYqcLaKy47UeiPdG7vJCxLeP6iQKolarodPp4NmzZ/jRj36Efr+PUqmE6+trtNttAAj1EYwTsCbBr8sZ8LRRftsfE4hvy/apRl+XEKvkTGUl7Bb3LNu/cI8fOzeP2XNQVOw577r58YmHRyJRAFmWoV6vo9fr4eTkBM+ePUOv10O5XMZisUC1Wg3NW1jRyO3XLH+2+yNocDajwFRlXpVmlmXhWPa9mEDYcfPm+0vaWASA0CDX9pXgVnE7bl5PIy9wKW/h80MiURD0JHq9HjqdDrrdbrgILoAQ+Fuv1yiVShgMBlgsFsGw2X+SDWHoutvdpYvF4lZTGr8bE4gvSyyxrEisQpNei1122D0nAFCtVvcqMhnU9JkY3qcEworLh25oEx+PRKIA6HKzOpHGwiXDdrvFyckJ5vM5NpsNarUaut1ueO7Ln+v1enDV6YFMp9NwY5k3KyYB7MUa7gpy+liAvWq4Xd4A77fA09uwu1a5r6Pb7aLb7aLdbgeB8/EOPo6Jh98EJnEoFolEgfiSY+sh9Ho9PH/+HFmWod1u4+nTp0EkWNXI63Hai/fai/eMRqO9WMVisQhCwmVGbNbmrk2fPvUZBWu0seCqveRfuVxGs9ncEy67o9UafiwwmpeylVAUj0SiAGio3MBlm6nQy2i1Wuj3+yiXy+h0Ori+vg5egK04tB2aGCScTCZotVp7OzBtxsIuNbww5AUPvUDYjIT9XQD2fgvFy+7i9HtSvFeTEgcJw+MjkSgI7rCkO75er/eCgYxZVKtVHBwcYL1eB2OydQKEfTJns1nYom1f556Q1WoV+jD4GIPt6QDcbmcXq5D0xVbWwDlOu5/Dl21bPsaDkFgUj0SiIBjYY0u41Wq1N/Mz7sBrbfrZ2ddDcHa23aD8OebzOSqVyl6bOetF0HjzNmHlbaTyyxYAIXNhr9vBq5EzFuGXLrFshhcj+5vz3hMPi0SiIGjY7BnJ1Kedqbkd2hJzvxkUpBEzu8D4xGQywXQ63VueAAhBUutB2M5P1ouwxhurxrTxBLbi5/U6er0ejo6OwnVE+/0+Wq1WEIo8TyCV1eC9PIniSYqEjUBLvT8cu5ZmXQMLkJbLJRqNxl4D2bzZlVAg7IxqL9TbarVwcHCAdrsdUqXVajV0td7tdnu7TulN2PHa4CXhOX3AkUuLer0evIejoyM8efIkXEf0+PgYR0dHODg42MtspAw9b/+I/4zEohiSIsFZRgLxcdjAHmda4H0zl/V6HcTDrv9jRpQXN7BBzWaziXa7jYODA7RarZBqrdVqe8sX23HaehKx0mcKkY1p8DgUiWaziW63i5OTE5yenuLZs2fhQsO9Xi+Mx3s2/rf513yQNLa3RDw8SZE4PDzED3/4Q20V/wQ4ez9//hz9fh/NZjNkJxjtZxUicLtuIGUoNgBJkaA30el0Qjt+CgHPa0uyY923rXfhL9PHGIZN3/JCwaenp/j+97+Pp0+f4vj4GN1ud0+sbH2E3SdiX/O/1f4d9H/wcUiKxG9/+1v8+te/llv3iWRZhm+++QZnZ2fodrsh7mC7YMewbndeOzi7gcoGDQ8PDzGbzfZKo33Jtu2I5Y3SGqYVEnaxojjV6/UgEkdHR2GJ0e/3Q8CSGQ7+P/JdtH29hH2d4+G9xKJ4kiLx05/+tKhxfOc5Pj7GX/7yF3Q6ndCQBYivvy1+M5Zfp1tPYrPZBINlPQZLv2ezWSjXtlcSY+1GnmDlrf15Thuw5JXI2+12CFTGOlfR2PlYOzw/b9QItwCyLEOtVsPJycleiTOQv4/CR/T98exSxM7QrVYreAilUgn1eh3dbjdc7Zs1FNznwTb+7LcZiwVQbKyBM7vCmggbKGWcxaY8feEVidVk2N/kvRpRPGqEWxAsveZsHSsuugv/7+F7TfjPVqtVtFqtIAL0LrhbczKZ4PLycu/73BgGvBcqBjqr1WrwOCgQsX0dsaBr3pLJN5bxKVj7e1IFV+LhUJ1EQbD0mi4/SRUNeWKGk/celwPtdjuck7tF2SSmXq8jy7JQpRlr52+zKcQaM8dsS67tDlSbbo31rrBZErao4znsfew3imKQSBQEswE+UBhzo71QxLwE/7qfaZka5fKDN4pEo9FAlmWhXJyNY3h+LxJ+r4f9rK39oLfCJY9dLtiMjg1Y+loNu+TIK/ASxSGRKIhY7cGHXMLPC0MsGwC8v/o2Z2c7w7NJTL1eB4CwOcwWOdnx+HPyePY9e+1OigQ7bNnjeMP27+UFZfOWIBKK4pBIFIwVBr9t2s6uecaVJxIAbsUFGGy0szhn7fV6jXa7vXflLSti9pi8Z4WmLcwCbm8q88uVWPGUrx61v8nGKe6KVYiHRyJRIF4cGA/w3kQsLhHzJGLvA7ev6MVj2r0eLAv3TXBYOMcxMFZgU5j+WhxWhKyA2FgDsL/fg78dwJ4YxNrnxzaGieKQSBSEFQArEnkeBbnLIGx60X/H7vC0mQqKATtltVqt0PWKqVNbVcnvANgrwgJwa98Jjdp2yOYSiFkdnoO/2X7HioS9pF8qaCseFolEgdhYBPtD0uB4y1ti5GU98gTCfxfYTzdyv0e73Q69N9l7gv0p/TU4gf0rddHo6/V62CNiO2PbRjkcB38ne1xYj8VeFDjW8EaexOMgkSgYnxq0fSE4wwL7LeRi2Y5UqjTvc3amZ4Pag4MDHB4ehga87XY7pGj9xYCZyWARFoWOVZfcO2KXMDbewSWPDaZaD8dfKkBLjM8DicQjYIXC7p/wOyQ/JZIfExIbfGTPzF6vF8Sp0WiEjt2c3e31N1kDYdvmb7fb0FWr0+kEsbAt9K1I0Puwyw2bZvU1GFpiPD4SiQLxdQi+F6TPMMTqBeyx8ryJ1HsMJtoKSFu+zToHAHszO4AQ8JzNZphOp8GbqFar4TIBBwcHt7qBx3pW+LTnXfUQ9nP38aLEt4dEoiBia2obo6Bg5JVYc+b9EKHIG4N/jbs5O51OiDXYz3OpYS8MzHJv9urkBi9u7LJZER6HgmT/Dj7dmhIHPpdQFItEokD8bGnrBfwttkwA0oHKlNHE4hM2TVmv10NZtt0N6lOfq9UqxBxarVZYmjSbTXQ6ndCU118BPE/4bLrVC+h9fod4eCQSBeEFwgYk87IWqSKrDzmvx8/mFAp2sIqlY+lJ2LRmrVYLIsEWer7hrR1DzBvwY7zPTk8JRLFIJAqERskO1gwi5pUde2P4EKFILUG8gTLjkVfY5QOOdhnCJjSsu4j1sLTjzvOIYuLgf+/H7JwVn45EoiCsMTIgSJHwUf1U6s/Pwvb4941PWKOLxTlsJsIKBL9rL/rLIiu7bdwKgh1nzDvywuF/R95jURwSiQKxSw577QsvFD7IGTNye8xY5D9lUDFX358vJkb2fb+l21ZHcoxWYKwQeA+Fr8fO6z0SBS6LRyJRMBQK/ge3MQpfYWjJW6v7IKc9z6cakp/l/RLFGmyeQPjx2Pf8Z2LCZb/vf58oBolEwXgX/D6f98/z1udeFPLc9PvGA3z8wHsVXK7EMhM2SxO7Voi9j3lGMaGIPRcPj0SiQOwsyoIl38PBxwD4PSDe2zK1LCGxGEZeANG/FostxIKJ1vuJBTy9R5LXR8MeM7YUEsUjkSgIKw4sxaar7Tc0WWKegTfSmFjEvI3UEiYmBim8YOUVQqWCl/Z57LembqI4JBIFwtmTXZxsU1y7b2O3u12ZSGLGkicQ1iPJC0beh9gSIJZdScVUYsTGEfsdajbzuEgkCsJ6EvZaF1x2+Bk47wrc3s23633vQeQJRR6pWTu2G9V/l2J331k/5jVZYYgFcyUWxSORKIjdbnerHyQ3dQH77vp2u90rbY4tIWw60IuEdeHvMm6S8kr8Z2LPvXHHvJ276iDyUsB5xxPFIJEoEOtNsCck4xF+W7V12SkkMcO4b+AvFvyMxShiBho7h3/slxuxJUJKIFLLDI8EolgkEgUSc5VtpN9fk8J+J7U295kK32cyZuAp44t5LHlpST+72zHeFZvwY/OeiD2+lhmPh0SiILLs/bUwGo1G6ASVZdmt3gv2Zq+Qdd8MQp5rfperHtttau/vOl4qppH6u8TGLVH4fJBIFESp9O7K3mwZl2XvLoxDkeC1NNkv0i4/YjM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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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ApAbQ9Pt9TCYTzOfzxnLR9nWLRDmyIvHq1St8+umnpa7la82HH36IDz74AKvVqkkVcpHxqUuRYGCQcQBdcCoADBjGMmx9RRdDR+LHlKMGGYF7N4E1ERrn0ABiKq7A43UgDXcqpzhSTLSLlPGN4XCIxWLRvFQkLBBlyYqEmsa2JN4c/t56vR7W6zVevnyJ8XjcuBb8T6/djofDAdPptDVLQZ/imnLk4o3zGvQrgFYGgwKhL239ZqqSopAat08B4meySlSrQrXgitcdMyG0llh6TQFgwdd8Pm+5Gnq/FopyZEVC+/vNm6OBQdYdcEqTZiJoTbBXgZv1anu3LrQoEjyXxgPUvVB3IO6bwe35aEGwrVw35tHp11yk6rpo+lInZ9PFYJBzOp02lgL/b7HTM7pQvNf5fN4SFwtEeVwnUQia4sD9U3o8HjcuwfF4bEX0NR7ACD8XG0WCizimM+PwGgqILmZWQsax+HE4TSr+AdzXVej+opou5b3MZjMsFgvM5/NWoJMWx+FwwGg0ahWJ0d1S98QBy8thkSiERvxjHIFPXv49FxLToswK8KnKxaZdm3yq93o9HA6HVqGUWhS6b4YOtNFahlR9As/He6EwsZ6C/Rn8HK2noNvA3g61IjTOoTMv4r4f5nJYJAoSm5TURdAaCPrkAFqxiNSAGACtNKlWMgJtgYhTsOJ5NR6Q6hSlm8HjdVdxrSRlDEWH7GpvB69FC8EYxNXuVQ1Spn5XpgwWiUJo+7Sa/DovIZUV0MWqT9a4M1asZEwNtYlNUhQkHqMl16yF4Hk1TcuXVmZqf4ZaIrpJjy76mM5VYYsCGHteLBRlsUgUQjsr1+s1bm5umqemTpziYo1PdU07arBSOyRjkZS+YrESFzHdFeB+By6KBGMCGofQWRNxQAxjERqYjcIGIOleaW+HCkRM0Xr4THksEoVgg9dnn32GFy9eYDwe43Q6NW5F10LWxQXcuw9caBqP0FoHtU60hZz1Flp7EbMRuvcGg6s6/EXbwHXuplo/Udz0HrQXhQKhQVfeJ9OiGhy1SJTHIlEITmm6ubnBy5cvGzdjMpm0LAc+WWNrNGMBamkAaImEzsaMT14+ucfjMYD73ggVJsYBdCANn+o6pTtuFsyJUxqL4Kur10LdHQpiTONqEJSNcRocNWWwSBSCJvrNzQ1evHjRDInVQCSf3nyqa5Ukv6ZiD3GIbsxa6Gg7fo1zKWIrdpxWFcUhpk1VIGL8REUitrzrebn46Wbwnnu9HmazWSuDYkuiHBaJgnCYy83NTZMKZN0D04W0KHRoLAOCcXxbqmGrK8DHhajHEp3ZwAUf2711/qSa/dpw1jU7U6+1a8oUz8n75TkoMhx+Y5EoT1YkdOy5/1G+ONq8xEKi29tbAOn6Ca1/4FcVAh1CE9OpKiKppqnoxqgwEJ0xwUWrU6ZoqVBYNN2Z6j7lZ+r9apGXip3Wcez3++baFotFK83q/4/lyIpEbOM1Xww2ak2n00Z4dbybBvz06RvN/RiMVFTQ1X1QF4NPe3Ur4kQsWg+6qGOmRNvAeY7YBs5YRCzqioVd+r26NBQk/m50LH+8d/O0ZEXi2bNn+Pa3v+1W8S/J+XzGcrnEarUCcF8HEWsAuEhYcMRybV082oQF3M+0TI2xU2uB6U3dyo8ioZaIpkRjIDHOtEiJQ6wEBdASFrUk9N7iOH2KAgOqKhAxrmGelqxI/OQnP8GPfvQj18t/SXq9Hn7729/il7/8ZRNvyGUpaAkwDagb9eh8TM2IcNEySKmBSm3DjtWM6s7wM0mMgairwjkXsVhKrYeu6lJNeer9cXQ/29i1zFyFxgJRlqxIfPzxx6Wu42tPr9fD73//e9R1jZubm2ZEvQYM6f9zoWrAkC+tedA+iBj3IKm4RHQ1NFsSX/x5rM7UQGWcfhXdlfhKuRip9nXO14gC5odWWTwI94mhxTCbzfDd734XNzc3+Mtf/oL1et3aW0IXmTZoaTMWFxaApBjEIKRmDvTP8fri4o0l3BozoVBoO7kOn6G7pPGHKBAsT6flpC6GWkzqAqnQuRu0LB6E+8Twd7hYLPDRRx/hr3/9a+MKcHcuLrg4GyK1aLlAugbBaOCSnxXNfQ38abAyNbVaKyM1y6VVlLQetP8iZT2k4hHMWGh5dyzQYjyFrpJFoiyukyjEZDLBN7/5zaaIiq7GbrcD0LYEtKhIf6Yxh9S+Gio2sSuUw10Yg9D+CA2gcloURUndGgZbFVo+HJobLROeIxZ+aapTh++qOPHcOuDXrePlsUgUYjgc4urqCrvdDpvNpokvsMFL4QLn4ox1E2rqp1KaKYHQJ3xq9mVMSUaB0k7NVOl0tBxi0Va8nmg9xAY3Wkep6dx6/ebpsUgUIs56nM1mjasRC4qAdvkyF6Y+2dXV0M5QHhuzAbpI45M4FVxUi4ExBhUWTduqaxTjJPyqMQpN6apIEK29mE6nrWG4bFu3SJTDIlEIjSGwKnE6nTbuhZr9ugD4JI9pz+haAG0xIAwS0sRXq4OkMhp63So2XdOtUl2ssbVdLaZowRAeNx6PMZ/PsVwusVwusVgsmhmZFomyWCQKojMjubeGPvU5U0FR94Go9aCxBQqENm/xvHyCx0KqeH08pxJdBS12ilYAi594jbrHRl3XrdSvBmBp4XDozWKxwNXVFVarFVarFa6urhqRcOCyLBaJQqTKpYfDYbOoNZiYasKK/RjRxeB7UruW01rRNKYKBb/qNRC1cGJFJAff0tLgffE8nHKt+2ykBtgAaFVwLhYLLJdLXF9f4/r6GsvlsrXvhkWiLBaJgkShiNOagIfZgTh8NlXroAtZn/qaNtUFyr+PwU69Rn2fpkd1I2FaBfwsCh8tAZ2QzRLz7Xb7QKB43RQWCsSzZ8+wWq2aAcCp1nPz9FgkLoQWOkXrQF+5KsMYmIwCoX0OzJQwQ8H3xtqKVHZENxLWgKPWMwBoshG0BK6urjCbzVq7jm+32wf7edBdYZyGLsZqtXLA8iuAReJCpEqnU5WSMSjZ9VkxBpHqxdA/xwpGFQv9zFhopdOoGIhk7IFZG1oCy+WycTkANF2v2pHKPTeAe5FgsFItkZTVY8pgkShIV29EXPxRMOJn6PviMUxXakFVtFS0QjPVyxGtkq4aCi3X5g7gXOCr1aoRCXYRsxcjNokxo0N3Q3ftimP13QFaHotEIVQQdBGm+i9iQJF9GlqroMdo8VU8p5ZW6zwJLefWIGi8tvi9Wh46cIab8DArQTdhOp02sRC6FVqHoe6GDt/V3ck0A2SRKI9FoiA5KyI+7fmE165PWgn6fuDevYiVmfG9XYNnumomYoaE4sJMCT9T9+3UXcZ0O0J+DtOourUfRZDXx3uIU8C1HsNCUQ6LxAWIQqGLnT9nyba+L06kUrM95VrE0uqYAo01GKnMCM/DzAWAVos6YxGMH+hsia5AYwzSqjvB4i/+mW4Ig6apqVzmabFIXJBU7CHWAOTKs/UzUu6IFkd1zZKI1k3svUg1ljE2QctCrYZUXwjPE8fgabyD1s/xeMRgMGj2CaWFMZlMkjUg5umxSFyA6CqkrIGuY2JfhYqFpjW1PFutBhUHoL2Q1eePIsGv2qNBFyQ1lYpdp3wfLYRYjMXKTa3poJix2Iy7k3tjnstgkbgQXAwaWExlNKL/raP2+fPcOWI6VRctj9eS7q7UrMZKYqxCezPY3Ul3h+JR1+0Nhm9vb5tuWKZVGZ+gtcM9SLQmw/GI8lgkCpIqoFJXQBc/cD9lOxZGpaaYp4Ki+h79bD2enxvb1YF7CyR2gVZV1VqwtBJUiDilipmN8/ncqtjcbDatWZaxTZzxj/F4nBQvUw6LxAWIT+dUMVXX+7hg48yGmLIkMbUZg4SpGRBqHehnaLl4HEzD8mrtENU6B8YeKBI6y5Lt5vwcWhB6LZp2tUiUxSJRmK7/4I+Z0NH6iMemBAK4L93W4qooELpDmF6jBiG1VkELqzR2sd1umzoHioRuHxCH3kYroqoqjMfjlkWhtROx8tKUwSJxQbhYtf4hWgUpMzsGK4nGGeIxKh4qEqnBtzwujqRjzEEzE5p2ZWHUbrd7UE7N43V/DdY/0Iqg1aEVnPP5vCnK0gCpRaIcFokLoQLABcvFpOnCVEqSAT42a8XaiMcWkC761LBdvS59L92JriEzsXRb9/fQrIcOvFVLR6dRxepNioS7QMtjkShMqi6BT+PH4gQaK+Cf4yQroG016IJKtaPH/oyUlcHFrwLBQKdWYkaXicJA8aLLkXJVWAvBcXXs/6BIxA2AbEmUwyJxIdTHV5GICzg+rTVQSKuBFgW/xvJtnk+rMEkqzpDao1MFIlZy6mfHlKreV0xh0sXguLrZbNYSCLabs8zbU6kug0XiwsTYQyoOEa0BLZai2xGf2KmnuooR38uBNPxsjTtQIGIcgrChLFZ06vaCuqA1lqI7ig0Ggyb2QGHQJjGt5nS7eHksEoVIFSVpDUR84qt4dBVZaaxA3YMu4eHTPA7DjTUYLJhK1XNopSirLTUDETcljtmRWIzFOMR8PsdqtcL19XXjZrAfJLXJsSmHRaIQ6rtzs5m4IIH0cJmu1Kc2Y6krkMqOqAsQh8gwZcm0pQ64VStCg4zxXlQk4vZ/Kmh6bTqLYrFYNNOoOB1brQiNRVgkymKRKAT9b0bvKRDc8zISLYjodgDt/TJS2ZCUNRFjDdqKrWPy+dJYhN4H4wjxpQNt9F6iSPB+OBOT8Qi1IGJX6WPj/MzTYJEohD41GQugNdFVSBVFIj5FU9mQGJOIIqGikhpLR2HQ+Q0xBavbC7LIKWVBpM6rLpSmPZnZ0I5SplDjgBwLRFksEoWgSAD3e1NoV2PXf/xUubYSy7FTboaSS3+mxtSlyrW1S7Nrw2I9X7w2/Z3wc+JLRUGH48QhOebpsUgUQisKe72/DY6NT9bcsfqVpAKT8e9z79c4xetYI7EhLfZTpBZvV+YmFciN07L0c1O9LqYMVZep+/+4J/ctEhcz/y7Fmy6CR/4dX/uY1/2c1PV9kb6UVIo0933uPOZL0flLtUgYY4CMSNi5M8ZksUgYY7JYJIwxWSwSxpgsFgljTBaLhDEmi0XCGJPFImGMyWKRMMZksUgYY7JYJIwxWSwSxpgsFgljTBaLhDEmi0XCGJPFImGMyWKRMMZksUgYY7JYJIwxWSwSxpgsFgljTBaLhDEmi0XCGJPFImGMyWKRMMZksUgYY7JYJIwxWSwSxpgsFgljTBaLhDEmi0XCGJPFImGMyWKRMMZksUgYY7JYJIwxWSwSxpgsFgljTBaLhDEmi0XCGJPFImGMyWKRMMZksUgYY7JYJIwxWSwSxpgsFgljTBaLhDEmi0XCGJPFImGMyWKRMMZksUgYY7JYJIwxWSwSxpgsFgljTBaLhDEmi0XCGJPFImGMyWKRMMZksUgYY7JYJIwxWSwSxpgsFgljTBaLhDEmi0XCGJPFImGMyWKRMMZkGTzy86rIVRhjvrLYkjDGZLFIGGOyWCSMMVksEsaYLBYJY0wWi4QxJsv/AaohBMgemUmIAAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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vv16vcX9/j5ubG9zc3GAymYRJ2Co2ekZHt9tFtVrNnKvB3ApH+dN7oSeY8T1oBYSRA/AY2agZzBSLRaIgdDYk+y34n1+H22qTllqs80p/WuLUsffqP9DthiYYmaDkYtSSJZONFIn5fI7tdhuqI81mEycnJxgOh0EgOGhmNpsFr4N6OzRi0vIvgJDgpEDo2SAqej4L9DgkRcL7v28OZum5heAC0dKldkPGcxZ0mGw8D1JFod1uh8lSOmFKy6HAYzfnfr8Pd3R1VU4mE0yn08xRhNqfMRqNMBqN0Ov10Gw2ASB4OtQQRfGJDV16vgijCkZN+r3xlsmDcYsnKRJW7G+O9XqN169fh1kMTAA2Go1gXeZWQE/R1gG1mqBUgeCQF7Zpx92cehcGkCmfqu1aR92xssIogKdy9ft9nJ2d4fT0NOQhKpVKZpiNNp0BjxEPhYx5CYpeXNbVYwf4muwojY1b5tsnKRJ/+tOfMJvN/A/yDcCynjojmeWnAHBroFOd2I7N8DseokuRYLMWrdYnJyfodruhYYvPEVvBNT+gVRW6HBlBUCS4zWAreL1eB/CYG9H8CfA+GqDtmmeNsrLCE8cAZASF26FWq4XBYBAEib8rUyxJkfjNb36D3/3ud2HasfnnKJfL+NnPfoaf/vSn6PV6GI/H6PV6IXymR2Gz2WA2m4VOSjUlAQhNVHrOJqMPigtbv7UTk3dsljbV9k0fBidqs5rCPIgOyWW0opOrGEUwImBOgePxKCLNZjMz+YoH/y4Wi4xoskRarVZxcnKC09NTXFxcYDAYoNFoZJrVTDEkRWIymeDLL78s6lq+1zDEfvXqVeibYMWBW4/NZoNerxcW6+FwCM5ELlIVAQoFtycaWXDbEidD1SbNh/aJMMIA3osbcxt6iDANWjRG0b6t5VoAGYMXoxwauMrlcohUFotF+F4VldFohPPzc5yenqLf7wejmCmWpEjo5GNHEv88pVIJt7e3+PLLLzEcDkMpkVsBNQ3xbsvp05yizVBdtxJxWzdfSxOf3AIwiqAf4d27d7i9vQ0DbVmJYMTBfAfzIRrBqPgw8alRCY8OZMMYr3s4HKLX6wWXJb0XFDpGCtVqFZ1OJ4jEeDwOQuV8RPEkRYKJMgvE14PtztPpFJvNJkQF7XY7RBK73S5jmd7tdqGsSE+C5gLollQxiCdTqedCz8yYTqeh5VtH4lMg6KjkloFRRDw9mzkNNUJpExdnXo5GI4zHYwyHwxBFrNfr8HE+n4f8C3Mg/X4fp6enYWvGvIoFonjskygINjEByJiadNYjjUqdTieIQ7lcDmVH7drMa+vW0Xfxid16UA/7I2J3I28KvCZuFbjF0OulsYv9FmziWq1WmXLpcDjExcVF2DJw+0Tb9Xw+D/kUilS73c7MueB7ddLyOFgkCoILGcAHvghWMDSsZ7VD9/NcNIxAmMSLB8eqcADZM0H1oYf2csujZ3HEh/WwSqHTuNnfwb6LzWYDAOG6R6NRJiKoVqsZfwQFot1uh60Et1axGJrjYJEoiPhcDXUgqtmIfgKiFYW4a1NPxtJRc8wJxBFGfPKXWrrVuUkrd14lg7bt/X6PxWLxwUle7PRkXwdt27x2lj13u114nXa7HbYfzL/ojEz9PZnisUgUBEN+TfDxLskFzHCfUQIXrN7JtSmLC0i9F3yteKiN9n6oHVxbxFkh0RH7tF0zR6FTtBhFTCaTMP8BQCYK0XKsih9fiwlc2r4BhMoM8HimqV47r98Ug0WiIA6H9+do3t7e4vXr1zg/Pw/bC+YLONORyUL1P+hw2bzj8DRxmTeaX92MTCoy4uCkaopSr9fLmLJoYtJx/kyAxgNiGOW02+2MuMVTrygSLKvudrvgLmVUw98LS7MUCjd6FYtFoiA4Y+HNmzf44osv0O12sd1u0W63Q5ShMyLoeaAHIq5i5I3n1wGy2lxFgdBj+JgHYcKQHgV6MfTsDOYRuJ3gMBomP5mspAmKCz8+dFjLtDphu9lsBsHSkfrabk7B8rajeCwSBcFI4ubmBp9//jnq9TqWy2UwCXGyNSscehye9lfQw6DnYegcCrontZGMC5B3eOB9SN/tdjNj6LRrlA1VNDAxyUlnJm3mFCFWQngiGLcZarri7wHIzrxgVEORjKdl0549HA7R6XS85SgYi0RBsDPy/v4ef/vb31AqlbBYLDAajcKenbkAht1ateCdWsuAmqjkImZ4ToGgRZrejEqlglarFbYhADKTpjTvwcoJrdwUBx0vx4hEBYJJVlZn4ihI8yIUClZ76L9gE9x6vUatVsNgMMD5+Xnwj5jisEgUBO+g6/Uak8kknGx1f38fxr/pHZmLS6cxxdWQ2DSlp3tpVyWAsBBbrdYHsykYtegofm33Zk8Jy5wcastro7B0Op3McN08L0dcqYhnaXCbQWs6fw/j8Th4K7zdKJakSPAOYFv210eTdhxTzz04KxtMKOrxeDocRhd/PCdTIw8dY0dPhrafa6s58wNMiHKhUgToxtQmMCYp+fwqEHzQy6GLn+9fJ2HFcz450YolVQoYR+h5GG7xJEWC/7D+R/n66BBY4HHACxeuVjI4TJYJQ+3cpM+BC0wXuA6i4XOqSYtlSA3vdfuiWxf+2/PvNGBxYlacCNUR/XF7Oo1aT43oY/6BWxpavOfzeUio3t3dhfwHn8s5iWJIisRwOMSLFy/cKv41YdJS9+G8a+cdzMP2awAfJAs5wSke78ZogWVMWrqZj9CkJHMO6mTU8Xp0RMZlVW3+4pwIuiPpkKTIaQTEhGvs3WAOhe+P9m6WVRlJtFqtzNmj/r9YLEmR+PWvf41f/vKXVuyvSaVSwe9//3t89tlnoUV6v99/YKFmdMGTrxh+Mw8wn8/D5wEEjwMfulXh0XkM15lY1MlQjEJ0jqbmP3Q7ADyeCM7npKeCw22YgwCQqVZo7oSRCHMnFIjJZBImc0+n0xA1VSqVzDmiFojiSYrEj3/846Ku43tPuVzG559/jru7O7x58yYMjOWC06PuuFC5iHiKNzP+OqUqr1IQuyo12tA/qyAAj9ZxneZNodGj+Q6HQ5g9yf4KRi7MZzASig1ejFa4jWEH6WQyCQ9uNdjkpgLmG1bxeBBuAZRKJZyenuIXv/gFvvjiC/zlL3/BP/7xD8zn8+BS5B1ak4bM8lMgdPHF5iRNQGpSMn7w82rGiqsk8TkXOlsinlTF6AVAaO6iPVu3FrELVLcaNExxS0WHJcWIr6/Rj8WiODwItyAGgwF+9KMfhfMpGo0G3r59i+VyGRKAGlHoHZfRA5BtxNIzNLiFYF5CxSAWAi0j0u0Zj8DXiIVzLShiADI5EJYtt9ttiG7iHAeTrRQJzcGo+UsnbGt/Ced+uiO0eOyTKIhGo4HT09MQKXDi1Gw2C8lFTSJqFMdmL9qnua3gIBiG+7zbark1tmwzB6Gj9fWIQQqF9nroABrNC7CEy45Q7SFRe3UcpWjbOudZUGQ0gmEVptvthrKqRaJ4LBIFUa1WwzCV8/NzzGazsNXQaoL6IDgfgg1YGurrOLm8Hg8tZ+pRfrvdLiMkKhKxGYvipb4LPelLZ1Hw9TSfoYKigkUx4kxMLesSRkWcBzoej0MfiUWiWCwSBaFbBM5ZoE9C76BcRNr0pXdzigQjh7hSwbu5Hoqjd3KKhooEFzgf/H7gsaLBRc6f4baEjkwVD90q6IKmi1Ot4/EJX/w90T5OUR2NRmEehXMSxWKRKBDtkjw5OQnlTt23c/gK8GgY4qG5wONUq9gUBSBzB2eJVUfZrVarTNu2DqrRQTi61dEFSTFh/iCesK3NYno2iLZ+ExUmXgN/R8xB9Pt9nJ+f49mzZzg9PQ29JxaIYrFIFIiWKVk+5CAXhvy6CCgSTy3APIHQCgJfs1arYbVaZSIPPo9WSPgxbiLTLQl9DTofU81deqoY3ye3S8zDMNGpUQErMuxUHQwGuLy8xMuXL3F9fR3O3fCcy+KxSBSELsJ4jgJHuuXd3QFkqgpPNWNpuVEPyAHeC8t6vUaj0QhdlXo2qM61jI9R0FZ0LVfSy6ACxvfFhU6bNlvftWdFvRN8LUZZFIjnz5/jxYsXODs7C9OxPDG7eCwSBaJ+BY0IAHyQcOQ4N832x4YiLW/GlQwuQL4ucw3b7RaNRiNMtKaFWu/q8faCpie1TjOKYMTCqIRzKsbjMU5PT8MYfc6kmE6nmUN2KGDA+wrQyckJzs7O8PLlS7x69QrPnj3DcDjMnANqisUicQTi0J5zFGJHpFqgGVUQ/TuFRGc1aCOWdvOqj0E/F0cV6nXQ/grO59RxexSWWq2GXq+Hs7MzXF9f4+rqKiMSq9UKk8kkTKtiboUu01arhdFohKurK7x8+RLPnj0LCUtXNY6HReIIxIlBfuQdPW7j1p9Tj4IaomJ3bF4FII5kYkHSUilzEXE1Qkfx60wIjSC4yK+ursJBv8DjCWU0jvHwHiZr2+12+PnLy8twcpcmPy0UxWORKJB4cG2ce4iTeLEFWX8m3pvHbeIqIGpMis8L5SOeo6nbHuY5tKNTrzc+iOf6+hrX19c4Pz8PI+wAhLwIX4tzKFarFUqlErrdLobDYThFXMfq6+/QQlEsFokjoOIQRxVc6LzTq8X6qcXByCCONLi4YgOWdoNqAjO+NnVL6lxJJjmB9zkFnjA2Ho9xeXmJi4sLjMdj9Pt9tFqt8L06bYv5knq9HnISrVYLJycn6HQ6H5y54Rbx42GRKIh4a6APIJufiH0Gcdu1bhn4ZzUwcaqVJi61VTw+jVxH1OdNjuJzUGg4QAdA2DKMx2OcnZ1hPB5njiLUs0MBBKHqdDqZRraHh4dMlKHTuPRaLBTFY5E4EioQunVgq3fenZylQyAbdRCWShk56NF9XOC6vdCSK5A1OOk2iLMj9O7P12k2m+j3+xiNRuEwYw6e0SiI6JZKk6X8ndBDwevSIbu8bm85isUicUTytgTs4yAqJBQB9U/EdmyNJBjmM5JQf4SWXPkxHgwDPI7aZx5EIxuKBOdacpsQeyzixi+dTMV8B/0S2mi2XC5D1MKpWo4kiscicSQ096B9DvHcyThJqAuYnycajrOMGW9jdHsSD6flQ6MIbit0BJ6O+GfykT4GNV+x54PbBxqy5vN5cG1yCpXOydDX4jak1+t5MtWRsEgcAQ25KQqaF1DTUNxPwbuv5jj0o76GJjw17NdoRD0W+nnCWZYcAKNNYLxWbgE4fu/+/j4Ihs67pONyMpng3bt3ePfuHSaTSRAJRkK0j3P2Rq/XC/0tFonisUgUTCwQOsKO2XwAwW3J/X+cJ9BE3seatABkQn5+TxxBaO5Dqyt5bk9eA6MGDs+hIOx2u8z2g7mGu7s73N7e4u3bt7i5ucFkMglNYsBjxMKTxhxBHB+LREHEyTvgccuhf+ZwFx0wo70a/BrFIi5Z6tf4Wtr7QbQ1XUUCwAevqzkGzSXoYBoOjmEuYT6fZ0qZ+/0+nCF6e3sboghOAOfsDG5RmHjNEyxTLBaJ7xCp8mieyYqoQGikoInRvCpGnEjU14vP5VBzFWdAUFj4/Y1GI0z25vkbOiCXW5HpdIrJZIK7u7swU4LbmWazGQSy3W6j2+2GcqpF4jhYJI6IGpR0OlWcQIz7LGK7tj6fJi/5d3Vs6haDDkpGBSouQH7TmZ5UrrMoNdnI5CQP6eHYO+YktNWcYqNRFM/yGA6HGI1GGVOWu0CLxyJxRLTCoF6BeEqUikncEAY8RhLx1kLFJM5J6NRq/jnOV2jZMv7evORlvV4P38OeDxq2KEpsENNhNXwOnSVxfn6Oy8tLnJ2d4eTkJDMo2BSLReII6IKlCMR+BV2IKgDqfdBEIj0G/DPRkidRAYjFgp/Laz3X0fhxdEMTmL4+txDMo3B+JiMRTdryoJ/xeIyLiws8e/YsNHl1u90QkVgkiscicQTyko1a8tRcAcuCjBx06AwH1XBMHU1UcfKScK+v0QkjFzU1qSjkCQTfg7aIM8GoSVB+L99HfNI58yssd45GoyAQV1dXOD09Ra/XC3btWBhNMVgkjoiKhVYQ8hKP2v+QN2SG5iU9WEdfh5ED8x+6t9fX5XNphKFnf+jzqg9Dbd/aE6JbIM2p6HTvTqeD4XCI8/NzXF1d4eLiAqenpxmLt3MRx8MiUSDxLIe4eQvILtg8YxPRiCNe2LGHQr+fk6m4SOPX1SqJXrcmFvk5Pgct0+12O7gv2dylnZy8Ptq8S6VSiCLOz89xfX2N58+f4/LyEoPBIJRQnbA8LhaJAqFAMEFHP0BcgYjNULF7kt8Xz3ygKzH2S6gAcBwdp03N5/Pw4BF7HC6jB/WomGnTVavVQqfTCQcHdzqdMAciNlfxWrndocAMBgOcnZ2FNvPhcBiG0/hov+NjkSgQigTvpPQC5LkJtRT5lEho8lOTnLHzUl2ZcSlTj9qjWHBUPj0MaonWYwHoYeDA2zyB0NeNt0Js3OI5JIPBIJzUpZ2qGkVYKIqn9BG7q72w3yBcvHklTiBreMqrUMSff8qSHXsl4u9Vn4RWHNQ1qYfvaHKVItdsNtFut8OD2wtuDdRjET/U+k0DFQ8fZpIyHrZjkfjWefIXa5EokHhR5zkrn+Kpr8UiEH8u7/Xj64ijEf2o5Vktw+rZH3mLOu/a4m3UU1O48sThY78f87WxSHxXyFvUX4W8WRNP/f2p1867jtRDX1sfH7vT571e3vvJe+S9X/OtYpEw7/kqQpLim1y8FoLvBBYJY0ySJ0XCBysaY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JUP/L1UiFXYYz5zuJIwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJP8X1WXdkeR2HYQAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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ZWuc2xMN0VSg0PqGiBSB0aK7X60JNxO3tbYgP8HztdjukUIfDIbrdLo6OjrBer8OcCU7opgVAN0W7O1muTWHi3yQunNIybgtF9SRFwqbd22O1WhWGxpZVLOqrjHi0G10KuhG6c1dsPdBy0JH4tCLUoqDZ//DwgPF4HLIZdGs6nQ4GgwFGoxFOT08LcQgOtQFQsHq0Z0RrKjRIqruSsdRbS7fjDIypjqRIWLHfHovFAp999llIKepmNloApelELmxmKuIR97prl3ZysgdDYxEqEACCBaHpV5Zes65hPp+/sgXgYDDA+fk5zs/PcXp6im63i0ajERY5F7+Kg/aMPLVXqWZstHw8yzIcHx9jMBiEAbv+/7JakiLxySefYDqd+j/KW+DFixf429/+Fp6WtCB0G7z1el1oeMqyLCw8rYuIJ1tzn0zd3JepTd07U2sxtKOUcQRmUmgR0NKhq8JKzZOTk2BFMIipnaJc8Nohys5TFTLOwtQZF4yn8G/T6/WC1UJBsstRLUmR+PWvf43f//73YfCIeTNqtRp+/OMf40c/+lGoduz3+2i322FBcNDscrkMW+RRIDhjkguOcQFaC9ooxZoFTX1SZCgIOuuSLy3cYqCR90UroKzGotFoFMbl6+QrpmR5HzyWAgYgHKM7qvN6nU4Ho9EIl5eXODs7C6P0TLUkReL+/h5ffPFFVffyg4ZP2KurKzSbTQwGg7BYWD+w3+9DcFPNfKYCGRNgcxbFIN5ER92U2J3gaH2+WJPAWgy1dFhnQYGiW0NLh5mJuJycIsEyb+06HQwG6HQ6Ye7lbDYL/RoUQ1oug8EAFxcXuLq6wsnJCbIss7txAJIioU1CtiTenFqthslkgq+++iqMc+t2u8HP3u12YSQ9RYJWRLfbDQFKzp/USdZaeq0TsOPiLG3WGo/HITBZJhC8ZwoFgFfiCYxv8N4pOqxxUFFhGfjJyQn6/X6wIvI8D26W7koOfFODMRqNcHFxgfPzcxwfHxeEyVRHUiT0fzrz5mjT03a7DcG44XCILMtCelDjA9zDgiXWFAd2brIpi4tYi6G0DoH/7eKZEA8PD69s98dFz6ChujhlwUaK2mQyCaIzm83C56FADIdDnJ6e4vT0FL1eL0yrYlcoXRy6LM1mE71eD2dnZ7i4uMDJyUmwPhyPqB7XSVQEi6Tom7NKsdVqhUKpxWIR4gq73Q5ZloUCJgYMT05OgsnO0mgABXFYLBYhrUmhoEgwMKkb9aiLwfiFBh21zoIWBBd1nue4v78PqV2ej5WZw+EwZEO42DlIhhO0GSQFvnkw0Wo6PT3FcDhEr9crTLky1WKRqAg+0Rnhj6dRM6bABclOSUb4B4NBEIher1cYD6eDbzWTwSpJrarUKkhaD1q8pLUMFCwGRbV5i+djSvf29rbQyMXahuFwiNFohLOzs2A57ff7YFVRuOii8Fidts3PaZE4DBaJCuEC1F26498zmwE8ZgfoasTTrOPMBSsZ4yEw8cxLHXhDeC9avKSNYFyom80G0+k0LPTxeBysCAYh6a7QAtLpV0dHR4VWdF6DgsZ70O0Fgce+F35vwagOi0SF8OmuL/rnrDwEHofKMg3IhcrFGovMU9WacYepLjQu5Lj8melVTXXqLl55ngNAIb7BACiHyrB5TNOxWtjFeAvrPJjuZTqVVhVFKd5CwFSLRaIiaPLPZrPQWcmFwqyD7vRd1neh1oEu+HgHMN3URisZdfBMq9UKAUaNQeh2fxQJWjY6/IYdnsxosBybi1s7UMvuXz9ju90OWRWtBNXrMNbhYbjVY5GoCBZK3d3d4a9//StOTk6w2WxCVaVmNbiAdFYEz8FeCx1+q52jOhNTG8hoJXQ6nVBJqQ1X/D1H7fPFUmjNmHAGJV/a1s37Ltt0h+6QWjN8H/8OwGOBFeMTdGWWyyW63W6hVd58/1gkKoI++O3tLT7//HNkWYbpdIp+v19outLxczyOAgA8zoJU8dCJVlo9yWlWwKMLw+sxIKoujlZyaj0E753njvcWpbAxhsBFT/cCeIyL6M7pwKPbQ6tCS8UpflmWhXSoXY7qsUhUBGsAptMpvvzySzQaDcxms5AG1X0oWO6sgUkKAwVEOynLNvvRITUqCowVHB8fB5dE3RAubO3SpOXAxi8KBNOrtVotxCHorjAOwVSstoarRaENYMDjpO3JZBLcsGazifPzc9zc3Hg61QGwSFQIp0c/PDyEAS1M87FsmRaFLlI1q+NWcRLHIzRgyVoLlnXHw2u0q1RTpow/sJybMzdZ16CuEQWCborWcTBgG7tIOmyHP2McgrEOWhhMsTouUT1JkSjb8MW8GTomjmPmtVdDBUILoYBHd0L32tDz8hjg1UE02s8Rb6JD9HstDddp1josh8Vfcfu3dqIyI6Kfg/EU/h3ijlTWTGhfCeMWd3d3hYnZjklUR1Ik6K9aIN4OWiHJhae1EgzisUSbgcW4eapsDoW6CtopysYwzTJovwfRad28vqYgtcOTFkS8jwe7W2lFAI/WAT+PWkmxAKm1QteGFgotC93rw1RDUiROT0/x4YcfulX8H4RuA0fKx0HH1WqFLMtCPIGLlE91Lh7tcQAQgoWaIWDgkTUVAAodpIwdxJv5UiBYKs2FHE/2pjjw2pouVYFgLIW1EySOP3AaFkfv86U9JTrazsNwqycpEr/61a/wi1/8wmbdP0ij0cAf/vAH/O53vwuTorU2QZ+ujOzzyc06AWYU6NsDCLEABgl1Lw5OsopjBvGcCQCFOIbei47Wo9nPn+s4/diCoAUSx0YUxir4GVm5ySYx7QFxIdVhSYrExx9/XNV9/OBpNBr4/PPPMR6P8eLFi1DCDCBUVzLtqSnNeEcrLhg+qQEEF4HCUBa30NgEC5vidKRaDvyqcQeWfAMoVGYym8HOUd1FPN46AMAr7gbjEFq9SddG42FxubmpBg/CrYBarYbz83P8/Oc/x2effYa//OUv+OqrrzCZTAp7WTBWQItCR+pr4DI+t7oAcWAyDlDGQUouVp5bdwjTOZqMb2hFqDZ/UXC05Vs3Ddap4Ly2BknVYqIlpeLAv4/H11WPB+FWxHA4xE9+8hP0+/1g+n/99ddh8ApdAPZI6NxH4NU0pVY16kv7HoCidUAhoBABj1YL6yp0IxzWV3DTHR3Gq12ruoGOno+1FHG6U2s7dH8OzZxQpFgqztSqfjZTDa6TqIh2u42zs7NCNWGj0cB4PAaAQo2CLjaKgVYr0n3QYKXu1KXn4nm4cCkQscXCRcqnP6+jWRLGPWK3QTf2UXHQ8mrdb4Nf+bfQJi7NXtCK6Xa7YX8Pi0T1WCQqgh2dJycnuLi4wHQ6DSXHOktBezPW63VYmCzd5qQqWiNxelN35tJFq1WU8V4b+jTngtd+DlouWrClrgmP1+7WssAlBSae1k0h0YngtJra7XaYzs2KUYtEtVgkKoJ+vI6lz/McjUYj+N9c1LqA+dTWtm5aF2XioENoOP1JXY/NZlNIf6qlEbesq0gwNarCotO246YyDVTGLoZeU7tVtUxb/1aj0Qij0cgj9Q+ERaJC+ETWfTS5+HS8HfDY8cnjmH7UFmu6HOpe8GmsNQXaSr5arQoDZXUgTby4NQ7Cn2svh9ZvxDMf4iCqZim0fJz3qX8jilO/38f5+Tmur69xdnaGTqdTSN2aarBIVEhcpdjr9UI7NBcMUNxEh+jio6+upda0RDRAyXoFXZQUibgFPR5Kw8XKn/Ppz5JybfaiBaHHxuXgPAc/S1nlZGxtjUYjXF9f4/r6OozU974b1WORqAhNSWpmgsNu41F0avpz4WtaMo4/aAaDwgAUn9r8Xqdbve6prDGH2WyGyWSC8XiMyWQSejk0a1JWuEUriJOw9b5430CxnZ0C8cEHH+Dy8hL9ft+zLg+EReIAUBR0QIsGFXUvUL6Px8Wt1XHptAoCRSWueOS/dSCMVn7yvDwX3QsdV6duRhxLYKk2p3pz+O1iscB4PA7BRy3ooktFgbi5ucEHH3yAm5sbjEYjuxoHxCJRIWV+Orf344LR11NPTS5g/l5dDP0+nmmpcQ+9DhefdmjyHCx2mk6noaeCo/a0RJxixgnZZ2dnODs7w/HxccjQcPz+y5cvgwhqOjjLMgyHQ1xfX+P58+d4/vw5zs/PgxXhasvDYJGoEC5aXbwkbvGOBUJ9ei5y9es1rqDH6HU1kKnXjK0Tvo/pU81iaEVknAVhN+j5+TmePXuGq6urMEZ/t9thPp8XrIt2ux1G63NDHroZNzc3QSAYi7BAHAaLxIEoE4vYytB2bn0vrQGdqRAHHPXfsRCUWS2a7VBXQ+daajWkZjBYoj0YDIJAfPjhh7i6uipYEvP5vLBnSKfTwXw+D67P8fFx2NqPVggLuczhsEgcgJRAaGBSrQoSxxY0u8Hz6AAaAIUNhPUVnz8OfupLG770uHq9HqZqsVDs+voaV1dXBVdht9sVtupjBWdsSXCXMt21K2WBme8fi0RFxKa//kyDfsw+MPvBvTnilGh8DgDBgtAYg2ZT4lgEr83zaDwjHi3HwCIXvAoQy6ZPT08xGo3C9nwczael4BpYrdVqmM/nISZBsdF5FFqZqWlaux7VYZE4IBq85L/jp7kWKbFUW4VBrQkAofBJ6wl0PH+8cxgXfFxnQYtF4w0apGQ8otlshkwGdzvnFoa0YFgtqhO0ODBXy8R1r1FuWcjBNtpJaqrFInEAYteCT9l4QnbcK8FFquXauuBVKJgxoQg95V7oiEK9Js/NfUnVImFvBvDNXAnOttTht5qWpRUQZ13UMtDJWGpB1Gq1UDthkTgMFomK0aIqPj2Bx8VOMzwukOJXEk96ihePxgzK0psax4gzH9ovAjymNxls1ApLLmK6COwZ4aRrWhCcNcFybo7lZ0BUG8rU2gCAbrcbUqUWieqxSFSE+tBqRcQ1CnG5dBxEJPFo+dctnlgQ4vRpWSCVwcX9fl+IkWh7N/DYfLZarTCbzYI1sd1uw9Rs/f14PMbDw0Oou1DB0e0NOadTp2R/m89q3i4WiQNAEVDzX1u5tfqybFK1NkppNWUcaNRUqIpDXH8R107EAlZW5anXZFGVuhcs42alJIBgSbC0m5OxdX6FlnTXarVXYjD69zPVYJE4EFowpWKh8Qkdj8/JUyoUDDrSzI8Lqfj7OG2qAdK4CIv3wylUdDkoWrpLGMuyGSjlU58j8fv9fhg7B6Aw2Jf7auiYfAYqAYRj2OkaN6WZ6rBIvCOUmdJx3QQFg9kO7p3J4zQIWVZfEAcpNaCogVBOqI6H83JmBMuydR8QHpdlWbAQuEkP6x00XsGXbvTDtnfgMbXKnc05aNeFVdVjkTgQ+gRXi0Cf8FrVqJ2jcdBSTXIuOL0O8CgcGgjVSdYaFNVCLh7LYKWWaNMF0uxHq9UKv8/zPHSC6rStp6Zg8drtdhvD4RCj0QhnZ2cYDochtuEu0OqxSBwALmgu2DgWoWnP2P3QlGS9Xg8b6WhgUwVGr8nz6SRufl/W08Fz6fvpZmglJo/R9KiOqKOFQPHTa1Mg+dnYBXpxcYHLy8tCebZH1x0Gi8SB0NZuAK+kKDX2oIuI742rJykWzCrEwT51ORhb0CnZ6nbE9QtPDaxVgWC9B4AwN0JndzK2UebeMLXK7tGrqytcX1/j8vIyzLZ0F+jhsEgcAM0o6Ii6uOJSFxKfolpirWPsuIBigeH11O2In+jxvh6xUDFuoFkMihsFDEChyjMumNJSbn4mvtg9OhqNcHl5GQRiNBqFLlD9jBaKarFIHIg4qBiLBPD4hOZC1AXaarVCjEDLn7no45iHpiw1lUn3I67HoIDpz+MZmAAK9xcHWBlDUSuAvSX8zK1WC71eD6enp8GCuLi4wMnJSciOOGB5WCwSFaJBSC4mrREgsYAoamnofhmcWK2zHrSOQmdUxmZ7nDotu67euw7JYUaDvRh8dbvdwoZBKoI8H0fms8Wc8ywZrGQxlQOWh8UiUSHqLnS7Xex2u0JXpT7h4+N0kVEodNMdCgRjB9oURsGgSLDQKc/zUKvAdKS6HxrYjKdas5aC4nB8fIxerxcavJjV0BF5Co9nLILBytPTU5ycnASRsZtxeCwSFUKR4BAW1iPEIhEfo7+L06SaRdCgYmxJ6H4XmqKMeyn0VRazAB4LnDjL8vj4OHSAsqZBty1U4maxdrsdzkGhYW1F2UwNC0X11F5TB+8i+bdIvLB1qrUS93nEPwNeHYCrFkPcrKXvo1DE+3XqDlyxOGg2Q0un2f3Z6/VCe7gGGWMXSqGrolPDGb+IxSEWSfO98OQf1iJRIfGCLfP9Y163KOL4RRxT0K8pYYlb0vWrBlO1r4OLOl7YTwUZNR7Bc2lsIx4CrO+1OHzvWCTeFeJF+114KtgY/7vsvGW//zYCk4qPxE/6N4kblB1bZkmZ7x2LxPvGdxWgNz3mbSxiC8E7gUXCGJPkSZFwhYoxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJknzNb+vVXIXxph3FlsSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYk+T//u/ZdAZqqOQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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rMFCGbg1FRns0Op1OaOTidXGxsw6EML6y2WxCwJPj9dipqg1e6l4B3uKvbCwSJcHFd3d398aMybjsWGsp1BWJ6yHinbKYzVAx4PQr3U9T50Nw8WrJN92E6+vrXBxCB/j2+32MRqPgZlCsFotFmE4Vt7bzM7VOhJaQulU8Dw26xqP0THkkRcK+33fHdrsN05xevXqFm5ubMIvhbfn/OEMRF0np+HrGGbiYdWMdHZTLVnIdUvtYZSXrFRqNBrrdLobDISaTCcbjMQaDQUhnsplMC6l0uIwKmgZJteBKKysZb2FgVN0jUx5JkfB/xnfHarXCX//6V7x+/RovX77E7e1tWBQ611F3vtI9OBqNRs7s1kIkigJrHlhNqSPt4520mMLUYTI6dp9mPy0NxjIGgwFmsxmm0ykmk0kY5BtbC7HwUaA0mEoromiwjPaX9Hq90Hbu6dnlkxSJP/3pT7i7u7NYfAf8/e9/x1/+8pdcpJ4xBa1PoFmt050YwDse87uPq0vBBi2toux2u7nshd65425TxkmWy2U4Ry56tVT6/T6Gw2FwNdiYxcWtfRq8Pr5G068UL1owOuOC6dpWq4V+v4/JZILRaBT2KIkrS833S1IkfvOb3+APf/gDGo2GXY9vybNnz/D+++8H81mLmPT3e39/H9wELh6OowMeCqfoRvC1ulEw05s6n5ILly6FPigQOrmKlgarMDn9Wsfb8fgMvKrwUNg0DsLrZgl4pVIJ76FloSP42u02xuMxLi4uMJlMkGWZ3Y0zkBSJm5sbfPbZZ2Wdy48aBuYGgwEuLy/DDMlutxuCfowN7HY7dDqdIBBZlgFALoXJ2Q+aztTMhVZcaicmqxp1OK4GDbW1nKY97+Aa81B3QQfcxBYIx9dpKTiPx/JtigQti9PphHq9juFwiNlshouLixD7sLtRPkmR0B2dbEl8cyqVCjabDV6/fh0qDjudDkajEXq9HqrVapidwIAiA34cIquDarUpSzs34yYvphC58HTnLW7Nx9iDDpJhYJSLVjcUogABDxOkaIXQVWHBVVxPwfOmC8UUqG4KzPPPsgyj0Qiz2QyTyQS9Xi9UhZpySYqE5wp+N9A64Bj7LMswHo8xnU7R7XbDggEegoq0Ig6HQ6hP6Ha7oXBpMBi80XMRD5/RmIMOjWGKU8fsa2epdo9SIDSFys9ibIVp3bjTk4HTwWAQRtrRsuD74swGLaBut4vxeIzxeIzhcBhKvh2PKB/XSZQEF3Cj0cBwOAzZgXa7HYRAp2Jrn0Oj0UCn08FgMAjFSwz+aUm1CgMFibGFeLIU3Qya+1rsBOSH5Op4fD0Wuzxvb28LKzMZeJxOp5hOp0HYAOSsCLo6FLhGoxEKtTT2YVfjPFgkSoQmuC72ZrMZ7vbaxdnpdHJmu1Y3coqU+ug67k2H1jImEdcjaPxBG6e0/kJ3Pm+1WsFi4fvY5cmNehaLRVjsFLbRaITJZILJZBKulyJyOHy1eVC73Q5l2Ppexj8crDwvFomS0LHzNMNpvtM850OzHdyhW3skdIoUF4/WPdAy0Z2wuKiLBsvG/R9sJ9cAKZvGKAy0IhjfYBWppnY5GFfPm0Ha4/GYy8rQ8jkej+H3FMc+dDKVRaM8LBJngn/4um9m3ILNWIROsmZ9gWYvGEeIg5Y69zKOK2mhlpZqUyAYIKUgaTaCk6K4MxibuChUXOAUxKKiLlpNtFaYNqXAUbjojsQDfU15WCRKgnfzzWYTSp7b7Tbq9XpoDdeshnZwxiPuVQi0szSehxk3R2kVo5Zk69xJLXhiQRabx+im0E1gbINxDaYuGcvguWv2Bci7NBQlXjtrQvh58Wd4hF35WCRKgqnN+XyOV69e4csvvwTw1V2XqUlG+LnYdLq1Bid5x+ZXpjc1CKiTrACEkmhaCSx24h2cKU/e2XUPDgrJcrnMlW9r6hR4EDatBtXJ2fwcdXO0GY2xEcZojscj5vM5bm9vc/EOXrNdjnKwSJQE//hZoNZut7HdbtFut3Mdn1qqrfMf2c7N11I8aElQJDQwybszBYEBUAC54CgLp+gCqEvDRc2OVa3SpAjx+PEE7HigjVo2vFbdoIdiw+wJX9vtdkM3qqdTlY9FoiS40BeLBb744gs0m02s1+vQZq0b3PDurSY6F078vB67aGMfBhJ1oxy6G9ozobGJolmXmjZVqwd42M8jlRHRRjaer3azqrVBMaLQtdttXF1dYbVaWSTOgEWiRLjQ5/M5Xrx4gd1uh36/H3z3drudW7R8D2sKiH6v2QsNVuprKRKs2izKcMSmO10guhcMUurkap4nBYLiwNJrZl80s8KYQ7zZjsZWKEq0VrIsC3t9eJOe8kmKhAaarN7fDp3jsNlscHt7G6oW2ezFO7NOjOJ7NCj52OLWHbppxmtXaVEHpf6/xouUYsDAqvZ3aEeqBjvjncB4HXRp+LekHalaIcp4B2MeTKVyxibFyZRHUiT4R2uB+G7g3RxAyBDoguVi5m5bLDCK9+BQk7tIFBgXYKxAR9nRpYjrKDSuASAXH4g/W62deB8PrQTldTK1G4uTHlvneWrLeqPRyLk5rpUon6RIjMdjPH/+3K3i3xIGG4GH4SsMOhbt0qWLQWc9xBkQHVKj7eMsfWbqMp4JoaPg9K7O+AF35uLzseVCi4SfxfH82nDGEXaagaH1QXT0PjtTme7UXbs0CGtXo3ySIvHrX/8av/zlL63Y35JarYaPP/4Yv//978P0Zy46nduoKU66GIwH6MLRkXBalESYVozHwDGwGIsEg4p6XN6tgfwgGAZAtTdDuztpBemOY0XDbPkZWpQVb0bMfhJ9r/8WyycpEj/72c/KOo8fPfV6HZ9//jkWiwVevnwZag60FFrnP/AuHA+GoQ+vd3TOoeCijguOdDSebgeoo+YUPZamNtlXQcuEQUqNQbAeROMbcX8IP4Ov5eh9PigSrL7kebiH4zx4EG4JVCoVTKdTfPTRR/j000/xt7/9DS9evAit0nQV4qEqGtjTydlA3jrgotedulKLif+vGo/QwTRxWpRb8tFK4PMsuWZ3KM9XC6JoUcTl4WopcSMiWkyaPdEReNrsZbEoDw/CLYnRaISf/vSnoS6i0Wjg5cuX2Gw2uUImnU4FPFgBmkkAHqobtfxZXQmKDd0JzY7oBsJFcy91rgSnUXEwDvCmQAF4Y0NjHSRDoYv7Sfh5uucpi6gYdGWzF+MdHqtfPq6TKIlWq4XpdIrD4RBiC5VKBXd3d6F1mgLBBcUFTVNfuzUpEBQJBiQ13hALwG63CwKh2QfdBEjH4DO2AXwV+1Arg+J1Op2CGDAQqcNu1LqI+0xia0PFRDtiOUtDB+Ga8rBIlAQnNXHgzHw+x36/Dz0LWjyl5dc6pUqH4Wq2Im7+0kCo7hpGgdA+CR2nH9dA0Myn5RC7JTpXgu9lupaCouPz9P0UBX6NU6u8Tu41OhqN0Ol0bEmcAYtESeg8Cc5YWK1WOTNed67iouJdVQuSigRCMxVclDwWXRXgwb2IS6/1bq7pVZ47t/DTzEVRkZVuRRhDIaCQaAk5rRc931arhcFggOl0ivF47JH6Z8IiUSJ6d+b+GFx0mg0A3swwcBHp9KqiqdUAclWMOoNBxUPnYmpwVJuv4rgGz5VpWd31PC600p4MHoezM3QIjtZS8H06aXsymeDi4gLj8dgj9c+ERaJEdI4C27FpenPRaBm2ZgO4wW+86a9aBHw9j8XnVST4MzXbNZgYxxz4cwrYer0OU6goFPGMzHi/DWY/OHOC11n0+1ERHY/HuLy8xOXlJYbDoUfqnwmLRElwUWh7NLsxWZ0IPMyq1HkSHG2vnZr6c23DLspkFDVSabcnn1frhc/FPRUseIrrGVi7ofMktAIT+KrUm2nUeCo48FCgxdmYT548wdOnTzGbzdDtdkONhi2JcrFIlIya4VzsXMQaL1DfvOju/tiYOgoF79qPNYNRKIruzBpkZNyBY+ooEuyt0N4Muha0BIbDIfr9fsiQcCqX9oxo7INl3pPJBJeXl3j//ffx9OlTjEYjuxpnxCJxBrjYVTBoKejjsZZwjVGoixEPdaG48D06cJfH1slX+lmxBVG061ecLqUb1ev1wqbCXOCn0wmr1eqN4CMH4+ok8YuLiyAQ3JuE7pYpH4vEGYktg/hBdOFroRXv4Bp8LJoBqdYGxUQnW8UuBoOJzGBwnqUOnGFGBHgQGm4oPJ1O8fTpU1xeXr4hEkxjUqxWq1VwORiHePr0aXAzOG9DxxaYcrFInIHY/+cfvtY5FN3Z+Rq+V90FrWjkMWPXRI+n1kaRIGl7uo7D01Z17avgDmPD4RAXFxdhoY9GI7RaLRyPR6xWqxB8ZFxmtVqFku9erxeyGdPpNAiEWj5uFSgfi8QPBG3C0kecZdAFTp9e6yr0WHr3LboDa9aDXxlQ1BRlPMOCwVAelyPr+v0+JpMJZrNZcDcGg0Go7qRFoPUfnDbFlOdoNMJoNEK32w17fcSxFVMuFomSKcoixPUIWl7NzXs03sBsCBeaTg6jpcE4x/F4fCPuoBWQsRURj+RXYeDdn5kMfp5uCMzqSG5FyAYwCpiWpZ9OpzAtnOXX3NZPszbaT6LiaMrBIlES8V1QFxm/6l1T7+gqEHFQksdSoaAVQZcgdmFUIDTToKlUFR02eanLo9aA7tTFyVTxBkLaoaqj+0+nUy5tSnFg0FSnctuaOA8WiTOgAqEuRbzHRiwMWp0JPFgeXFhFnxF/r1YE/x2b9DwWC7c0VdlsNnPTsXRUv7a7qxWglZ4qQqmMCqnX6+h0OrntAUy5WCRKRgWiaP5DHKSLRQJ4qFxMBfNSIkF3IbZINDbB9KTutMW7P7MbOhqPr9WRdBQYFlDppj7a88Eek7hjlbM2er1eThxNuVgkSiJetBqk1CCj9jpo2jIum9a27aLFUyQc6lY8NnVaxYqwV2S/36PVauUyHbRkgK/Ei1WZtCY4zFZFggVZd3d3oe8DeOhuzbIsiAK/18Y1Uy4WiTOhQqHZCF34NL+zLMuZ6nqM2BoAHk8TPpYliIOocXEXnycaMwGQW8Q6UGaz2aDT6eR6N3RUHesueBydgsVYRZyxccCyfCwSZ6CogErFgiY6YwK8g8cZh7dZEzHxa9SNoYuh/SH8np9F0dJ9R3Uwr87ipBhoKpOZjdVqFawITgan28JsDAusaMXoDFBTLhaJHyBxyTbN8GazmRMJ3cavUslPu36MoupLNeN1WC6FQgusdG8MxhQ008FMiM6tZNaCIsP3x0NugIdaCjZ69ft9dLvdN4qqTHlYJM6ABgi1VkEXpKYzGUTUEXJqhahQPDbwRbMaWiwVT7LWgKUGRlmBSZHQuIQKTKPRyE2p2mw2uT1BaYnoXhp6nayXGA6HGI/HGI/H6Pf7ufmWFopysUicCW3KilOQGqwE8oVMdAt2u12YasXmLRWLx2IVRRkETVMCb86S0IWtJdoqaDqoN74OniOzH9omTveCAct+v4/xeIzZbBaGzXBPj7gRzZSDReIMqCBogVJcTKUFTUwjMn5Rr9fDfAbOpKBo0CLRPo94rJ2OkNMaBLU4tHZB51fGQVS1AmJXidfFn6l4UVi4h8dwOAy9G0+ePMFsNgsDcItmeJpysEiURFGfBMUAyHd0FomETqY6HA6hdJsTsGu1WhCIohH2cYGWjr7Xha/v0/mXFBa6K5oRiQfYFDWoadwh7lFhi/h0OsWTJ09weXmJ6XSK4XAYAp9FLe2mHCwSJVHUkal3bt71+TxfG7d0c5Fz8WqPx3a7DWKhE6q0tiLu39CshQqDuib8rHhmJvBgAWnJdTzmn+cXiwdfr7Ms2QGqAhHHSEy5WCRKRE1xTXHqXRbID8EtQhczg4NF7dzx+HuKSCwURAus4jmUGijVgivNaGRZFnb14r81fakLnS4TrYjZbBZmUGgcghaUBeJ8WCRKRPsfOp0OjsdjSCGmfO3HGrR01y0KhO57oUKh2QYOkOFjvV7nYhRxYFP3xlArhxZDp9MJHZy9Xi8IhQpEXDTG3wNjEQxWjsfjEIco2k/EYlE+FokSoUhwCEu9Xs+lEOMFoIuiyM/XTEE8+yEejKtxBS140poF3TtDh85o3ILnwgVOYaB7oI1eXODxIteUbpZl6Ha7YWdyHZ5L68OWxHmpvKUW3oXy3yEaT9CZDUqRUMRf4+Bn3N+hwcf4NWp96OQpfT5+jW66AzyUT7fb7WBBxC3ijy3u2JrQ7QofszxisTTfC4/+Yi0SJRIHH/+RhqW3LQoNfupDf1b02XHVZfxc0fdaus3sig7I0bjD286bx9GHNrsViaP5XrFI/FAoWsD/KLG7UXTcop8V/bxIQFLiEp8HF3KRK/F1rydlLVgcSsMi8a7xTVqqv8l7vu0itgj8YLBIGGOSPCoS3u3EGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJOk/pafV0o5C2PMDxZbEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJPk/YGMj+SoWdtQAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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ikSgJmtSM3Me+vfr1mlLU6kmKgfZ1aMUjrQ5mA7hrl4oDj9P2b34eRYkuAjs9dYcvbSvnTIx+v492u41arZZzkdQC4qLnuViqzWvUOIhmNjiyj/dgysciURLsYaCfHw9qodmtsQJd1H9NH0NRY1TRz/k9xYLn0FZwioTu70ErggIxHA6Dm6EuAy0SFUOdN6F7fHDxUzwZe9lut7lZFJoqtViUS1Ik7P99eaxWK9zc3ODly5d49epV2MyG06kpDNqTEQ+X0aAhgNxkKtZJxANodNo2UNw8pR2jHEpDs5+xCF4Tg6TD4RCj0Sh0ezJVeXd3FywSrfnQeIL2aPDvWDyl4/a1OlMDo6ZckiJhxf7ymM/n+MMf/oDxeIzXr19jNpvluiG5IHSRc/HTdQAeMhQ6FJdb87GxilWUOhNCA6ZciHQ1GPfQWRF0GbQZizGIs7MznJ6eYjgcot1uo1qtBpHRpjW6U5rJKcqmaJWlWkAqELwnLbwy5ZAUid/85jeYzWb+R/kS+PDDD/HBBx8EM1xTmJoC3e/3Oeug1WqFykamALWginUSfMJzF/B4RqUGTOkGMGbAjXp0KA0XrIqYTvkeDAahHZwNa3G1ZFwApt/rYo+Ly+LBPBRBFoT5/2O5JEXipz/9KX75y1+Gkl7zt/P8+XNcXl6iVquFnb/7/X4QC33iazel7vitFZRaSMUx+kX7blB4aO4zoKgb8/BF0dBsibo+PKf2fFSr1Ufioy3kXOhFWRzgIfhKWGfB41qtVrCQ1N2wUJRHUiRub2/x0UcflXUtX2vYFNXv93F+fo5ut5tr12YWAvg04k83grUFrVYLwMOO3dx9XEfFxS4GLQggPxKfqUZt+9apVTT5dXydxj/ijX84nk737ASQ28NDYyvaJ8IqTM18MMOjrgZ3N3dMonySIkG11zkG5m9jvV5jPB6Hbf3o3/f7/TALkk977uTdarXCYuVC1RhEr9fLzaQsyorooud0qfl8jul0GjIYOrlKaze0OpKLW/cFYfBRm7gY6KSFpI1pcQ8Jryeu29jv97mRepx3YYE4DkmR0BkF5ovBgB7dicFggNFohMFgEOZDaKCQhUs0vTWGoQIRj74H8jMr+HSO997QidcMUuomvqyqpDVA0VKBUNHRfgsKC+MtRaXbjF2oVcHfE4/neXXepikf10mUBJ+UzWYT/X4fo9EIo9EI3W43tFRrSlDLrrkwYytC27zVx6dFwsWnBVn61NcYBBc3z611G/GIfboYbCePN/sBkBMWChsFDXiYVaHNZUA+JqE/8zyJ42GRKJFKpRJ6HYbDYYjYs7pQpzFpkFJnSOo8Sj5l9WmsAT0trKIlw8CiDrqJR+VpClZnTrBhjF2e6/U6VxzGmAarM7lTGbMujMOwP0S3ItSeFN17hBZQ3CruCszysEiUhM51YGaA8QldCFpSzTgETW++4nH36hbGY9/iV1zExGvT+gsNqMaZDC5wLZ7SClJ1FTqdTkiXsnSbRWHx9oHa4cp70bZ1LeyyNVEuFokS0ZgBzf9KpRJMdh0wq/0WRalDzQboXEh+dlzUpI1h2gTG2APPRwuA2xAye0I3g4VXm80mpFF1XgQbtigSakmwAUw7TnUYMK0FCgcLvdgd6k7Q42CRKAlNFXIvTTKfzzEej3P7asb7aWj3JouWtPdCXQstnKJVoj0QdFUYWGw2m7kuz3jiFYVE51owlao7gjGWoJOwdfNhDuGly8KAZrvdxnK5DOP4tCuV52EWxoNnysciURLMBkwmE7x48QLtdhvz+RwAgl+/XC5DXQSf6DTPNbZAwdHPLnoByA2g1cV7cnIStvdj4ZSKBF9ZloXPoUWicy60w1ODnUxdxiPygPxwXwZm4w16tKeEmxTP53OLxBGwSJQEqx1vb2/x8ccf43A44OTkBJVKJVeExCc7NxLWWgP66EV1BTqhGsCjegkuStZeUCDUImHBVBz7oNmv8y0YP1EXRi0UTc9qyTl/F0B+V/F4/D5djmq1mnNpKBIOXJaHRaIkdKTbixcvsNlsgq9P/5sLudPp5BY78LB/JvB4eE3sp2vhEwOEFB9+ZrPZDPEKdUV0ngWAR8NpdbtAFQhaPXF5uNZVFAmbCgZR10l3D9O4hykPi0RJcIGs12tMp9NQeq1DYfh0Bx42ugE+rUxkujAexa/1A1qEFe+OxapHFmXF07EUnXWp2QUdSEt3RwWJAsFgp8681FSrZmD0+jWmouJHcWVrfTwnw7xZkiKhPqTV+4vBACSfjsCngUCd+cAAIgWDNQP39/dhsTFoqbEGFQguVh1mQ+uAQUUgnx2Jh9WwkpLXyi5RHUOnpdtFlaAs8gKQE4n4HihEOmxGtxTQgTTMqvj/YrkkRUL/E5kvhjZMMfCodRBap0Dznk9zdTmKBEK7LbMsy7WTM0OiTVbxdCetyuTn8xq5gLV2QwfZNpvNXBUoO1LV8imyHHgeVoBqrEMng7OEm9fhVoHySYrEaDTCO++841bxLwjLruNBruqjUzxobjNQqFvvaepT5z5SaNRVYcaArgpjGkVBQn4OA5hFMQ+dp1k08EbLxJky1TRsbCWoxaIDc1WM9CGlMRD/XyyXpEj85Cc/wY9+9CNHkb8g9Xodv/rVr/Dzn/8cAHIt2QAePd31SatCojEEDWzGQqLFSFyMjEtozUVsTfDcRUFFWiq0IrRYSpvNWHSl06+KZl1qmbhOw1KhoIUTBzf9/7FckiLx/vvvl3UdX3t2ux1++9vf4vb2Fi9evAgVhGoN0EQHHobfqolfVE4dx4t0AelAXKZO1XrQBqpYGPhZOgWLbg+tFlZlxhvzFKVL1YWIe0k4BCfeO0TdKXWd4vs0bxYPwi2BSqWC8/Nz/PCHP8Qf//hH/O53v8Nf/vIXTKfTEMTUZi4AOYEomhUJPIzh5+KJU5gaC4hrEOKt+VRQ9LMZ42A7O/DQ4anFUgByAUi6Epoy1f4Rvkf3FaEVoVsNUKiYOfHgmfLxINySGI1G+O53v4tOpwPg00X4ySefYLFYhBoJIC8O/DPhezSdyWpGFjJp1SKAnBXBP7PsW4fUaO9HnI5lLYf2k+i5aD3QzVALIu4d0eArhYSWBgVCMza8N8Y9WNptysN1EiXRarVweXkZcv50NW5vb0NZs1YZMj3KpzgXs6YddT9NFk7FO4QzjUgR4Hv0vXFHqYoEBUGtDI1tqLVCS4KLnhWZWk6u8zDjzElct6HFZcPhEMPh0MNwj4BFoiTq9To6nQ5OT0/x/Plz3NzchNFtzHzwKawLQOMI2rnJJyz/rHt0UBQ4P3K326HRaGC73T4SiaIqS7o+OsJOaxy0yUwXOgOUFIz4GO1QjesjVISAhzmXnOJ1fn6OwWDgMXZHwCJREuzD4NCZ09NTTKdT7Pf7ELBbrVY5VwH41M/nwufCjgfO6KLRBU1rgv0XGsOIX1qWHRdgaSUkKyA1lsDYg064Kopn6YQsfmXtQxwP0d/XcDjE+fk5+v1+LrhrysEiURK0CLgTN4fg0q1gVSWAR7EBuhxx7EGLs7SWoSgLolYDj2UjFwVEi6SKUqRxBSZLtSlyGkuh2Kj7QzTFqwLEa+XxzWYTJycnOD8/x9nZGbrdrrf6OwIWiRLRegNOfVosFkEQaI5zfD4HxQJ4JBKaDlQXQPs6FM1m0P3g4ozdD4oF0cInpis5cIbVkrFAqJWjWZS4xZ2Wj6Kl3v1+H2dnZxiNRt7B60hYJEpE6w7YUs29NWgBcL4Dn6y68OI2bm2/1iey+vhc8LpYKSgUilgk4tQo3QJaDSoQLAzTlKUGUXUEv9ZRkNgtUWuHrhn3HHVm4zhYJEpGy5rjDX7jhaoCwcUT761R1Jyl5c+Ei5Rdp7EVoeKgLenaKh67GdoRSmuIJfx0NTi8plKphPiDzsjQwCivQYfoDgYDDAaDUM1pkSgfi8QR0H4LXaDxS/30ohiBioNWTMZpR+BBaPQYZjuKCrHUfeF+HRqoZJpTp2BxweuAG7aNM4tDS4LipMfSgtAp24PBIFdEZZEoH4tEiehi1+5PzTqotcBFR1QY1M0omgmhxClMjsRjkZSKQzz1imnZeJq3lk/zOH7Vsu1Op4Nerxcsi91uh+VyiWazifV6Hf4OQG6aOHtCut1ubtcvUz4WiSNQNONBW7r593yfCgBTm+pu6GdykaqroSlJCkbcDVrUOl60U7i2bD/VfKWBR47k5zlWq1UYisuKTgZnNaCrE7b5c71fUx4WiSPABaMCocNgm80mttstGo3GoyErmnnQz+HC1k5NWgo8Ts8fi42i1Z9FcY6n6iBUIOg2UAxYncnsDO+R56tUKoWj/FUgPJHqOFgkSiZ2N2hia5xA3QkuLvXd9XPieEXc0akzGYq6POPP5M8pBkXHxOeM70drMTRVq9OzWOqt1aTcLV13EY/7SmxJlI9F4kjogoldA37l4ouH3arQcJHFYkBhUYHhIosXPa2KGE3ZxmhPiFo0WozFcxalcykghIHOeC4n702Fz0JRLhaJI6CLjynDuCSai65Wqz3agYufoU9zLii6G8xw0OWIU6Yp6yAWoMPh8CjYybjHU4NzNH3Ka9PpUuomMXWqVaT8nKIhOKZcLBJHQM1zmtRFqVCKCPsiiiopi+Ai1ixJUWCSaPBSj4v3HNVpU9qYxcVLt4IFWNwUmJZK0THx70WvTy0hC8TxsEiUjD6tdUAtn6LxHEotcCqyKIj2X1BcWAeh5vpT10R4Lpr8vD7t3dCKzEqlkguO0oJgjwd/ztgKKzTjYb4MlGpMhjMlioTOlIdFoiTi/+CxHw/k+ytid0KbwOLgpk6OovBoWzi/j4WlyOXQeAGFghbCZrPJlXXHLgmAIBBavMVaDFoYrLWgeKnLpIN3tbhKqzQtFuVikSiJp8zrojiAPl3jGge+V90OjTNoxyhN/SIxiAe8xLGK2P3gU53v1X6TuLhL2e12odMVeNgRTAf9avCUo/B0Lw+O6deAqCkPi8QRKVq8GiSky8CUYRzA49NZU4nAwx4fT2UCilKjAMJ5OcOCi1KtAM6iVLeBw23089XKUatDU70MfKr4MD7D5q7hcIh+vx9qJp6q7TBvDotEycRP/fjpTfQpTqsgrpeg+c9FTPb7fXgix+XbfPrzWrS/I27dZtMV3QAdNBO7DPE9xAVg/BwNSMaxFbo63W4Xo9EI5+fnePbsGQaDAVqtVs41M+VhkTgSWs9A+KSNMw8qFPw7TUNyoXMRFrkjjE3EKcq4pVzLse/v73OTqRhPYHl20ULXyVoUMgCPtgvQ43iPHDIzGo1wcXGBy8tLnJ2dhTZxd4EeB4vEEYg7OPl3zADo05wWBesptNxazXY139Ukp+uhi1ef5Bqb4J8pQjpTQofd8n28bhIXZmkqU3cj03uje5FlWRCIy8tLXF1d4eLiAsPhMFRfakbFlIdF4kioq8FFyphEbL6zMlGj/7rItRErnvwUZwSKYgLqcmiVJ69Tqzc1bcmfx3ECLdfWr/xei6hYadnr9XB6eorLy0tcX1/j8vISp6en6Ha7OSvCAlE+FomvELF/zievZhhin14LnLT1m7MbGLeIn+5xP4QuenV3tIZBRS1GrQJaPnQhtKdD4ft6vV5wMa6vr3FxcYHT01P0ej1kWeaA5ZGxSJSEPlXjJid9GuuiVVdEv6pQaPs24wWr1QrNZjPEFICHOZXr9Tp0YGo8pKhAKz5vfC/8nuIQd7NqQVZcEMUgZZZlGA6HuLq6wrvvvotvfOMbeP78eQhWan2EOQ4WiRKhQLDbkVmI2GT/rMWpIqKb4uj+Fxxzz3Fz0+k0bMnHF7MUukcGiYfrMkhKS0L7SygIHBjD7f8oEkVTpVjV2e12cXZ2huvra7zzzju4vr4Owcp48K2tieNgkSgRPj1brVbIOHCBphZAkW+vgU+dkB1vlsO6htlshul0islkgslkgul0ivl8HoQinhWhn1s0iUrvpdfrhVFz/X4fvV4P7XY7uAoaNOV9UCToajx79gxnZ2cYDodhGlW8FaE5DhaJktAKRu5CxQIpWhJPHadfFY1RaJxCFzhFg5bFfD4Pr8VikdukVwOUOpGKlgnfw3LpTqcTCp5GoxEGg0EQCI79VzehqCaCI+74KirBtgVxXCqf0V3n1rsvEQ38acl1SiSAp1N+RSXVGpiMC6jiGIZaIPpePaaojJoCpwucLkY8CbvoWnlP8fi+OHah4mCReOM8+Qu2SJRMvLC/7M996hxxL4gKSfy+pwQnXuDxXM7P262pQcy4jyV+j3njWCTeJj5LfJ7q5/hrjynqaC36+8+DxeDoWCSMMUmeFAknn40xSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSeqf8fNKKVdhjPnKYkvCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgk/wesbaHFdAKREwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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+SCTitl+DwQCdTse1qi8WC5cDURk0GCQSAWCbpezMR4qFHe3mLQMyWbhtO8Fw306XthOm2Y6dz+ext7eHWq2GWq2GcrnsogjglUDM53P0+31cXFzgyZMn+OKLL9But1+bWMXrsoQ5mUxwfX2N8XiMZrOJFy9eoN1uu+3DfD5HJpN5bRJVr9dDq9XamHK1WCxwc3ODXq+nk8MCQiIRALZhy0YGFIvxeOws1AzBbS+Ht2Xc2zm5bQwev0+n084TYQWCjVa2zXw0GuHq6gpnZ2f44osv0Gg0MBqNnEB5E5YcLsOqRLvdxsuXL9FoNNyIvFgshsVigWw26+Z18rk43er6+toN512tVq4pTRWOYJBI7BhbTrSNWrbEaT0O1n5tzVDEJhkBuC0F7/CpVMoZmDgyr1QqoVqtolQqIZfLuW2G3QZxm3F+fo6zszO8fPnSjf/nc4TDYWQyGVQqFVdCTaVSWCwW6HQ6uLi4wIsXL1xUwaQlANzc3CCXy7kGN0YszWbTdaky58F8jSKJYJBI7BhGEcPh0FU1rJ/Ba5piRcDbCckcAxctgA3LtW1F55h+fk1vAg1M7M2w/RKc5v3s2TOcn5+j0+m4cidFJZ1Oo1wu4/DwEMfHx6hUKohEIq634+rqamOrwPkRoVAIvV4P7XbbRT2cnNXpdDambtvuV+UkgkEisUN4p+YgmHa7jU6ng36//9owGZuc9M6YtNezlQjCXATH9HPOZCqV2vie4+844o55j36/j1ar5drBm80mRqOR81fQOLW/v4/j42McHx9jb28P8XjcGcSGw6H7bLtOKRTD4RCdTscJXL/fdwlK67Dkewd01F9QSCR2CKsFrVYLL168wOXl5YYpyet9sFsLrxBY7Lg3bjPopqQo5HI5F03QMGW7RZmMHI1GuL6+xosXL/Ds2TO8ePHCRRHxeBzpdBrFYhH1eh23b9/GrVu3NrYZzWYTq9UK4/H4taYywmQtJ3Mvl0sMBoONcXp2+0R3qEbZBYOvSChJ9N1Bv8Hl5SWePHmCJ0+eoNFooNvtbvRG2MQkf/6mMNt7Bqidpk3no91a0BbNhWkP9+H3dtRds9l0HoX1eo1UKoVyuYyTkxPcu3cPp6enqNVqyGQyCIVC6Pf7uLm5cY5J2+JtLeHApqGM4mRH+PH90ZnJgcFi9/iKhFT7u2M8HuNvf/sbHj9+jE8++QRPnz51d2hbqrRDbe10J69Q2O5LGqR41kWxWESpVEKlUnHTqpPJ5IbZiclARjCj0cjlEew2iCd+8do8du/999/H0dGRc0uyMsPBNN5+CzvolkLlzYXYORf8/UQigb29PdTrdbc9ErvFVyQ+/fRTDAYD/cN8B/z973/Hn//8ZzdKjof02hIlAHfOhJ2zYHMPwKZAsMzJrQVNUvV6HXt7eyiVSm4EHQ/ToXHLnqTFuROdTsdtPzgDM5lMuuvXajUcHx+jXq+jVCohmUwCgIsY7FmeADYEkJEOzVTJZNLZvvm+WGXhe8vn87h9+zbq9brzYYjd4isSv/rVr/D73//e7V/FPwct0naxxONxZ2TifpwlUDuliZGEPeWK1YtsNuvG81cqFVSrVVQqFTddis/LDtNOp4Pr62tnWup2u+j1eu7POUyGz8eKBMudnKTNmROMAmgxpwDyddrp4Nls1h1AHI1GN8795OukCKZSKZycnODhw4fY29vTIT0B4SsSnU4H5+fnu3ot33vYHp3NZt3oenZecro1Lcr0BHBeJT0GsVjMCUOhUECxWEShUHDJyUKh4IxNTPYxkWiP5OPcS9qfR6ORSyjaiMWOx2cEYCsjtu2d0QjH1NGvEYlEXMcpxYtzLzudzkYOA4AbjlupVHD//n3cu3fP2bjF7vEVCfr4bagr/nEoAuFwGMViEScnJzg8PHTH9vX7fRe18U7MxWSPBrTmJXtXTyaTGwcGh0KhjaYwRhHNZhMXFxfO2UhfhvVh8G7unRfBygjwqjzJcibdkjx/I5VKIRqNujkTBwcH2N/fRy6XAwD3fmksWy6XLlrIZrN477338ODBA/d3pCgiGHxFwjsLQPxzcMHlcjncuXMHP/rRj1Cr1RAOhzEcDnFzc+Pu0DRDcfR8KpVCqVRCvV7H0dGRm/+wbaK1HahrHZ2sPrA/otfruf4Jb8nV3iDo3EylUq7CwE7PyWTipmdfXFyg3W5jOp26Ywjz+TwODg5w69YtZ7iKx+OYTqdot9tYr9fOeUpPSDweR7lcdiXWQqGgg4MDRD6JHRONRnFwcIBHjx7h0aNHKBaLWCwW6PV67vwN3rmz2aw7SbxYLOLg4ACHh4c4ODjYGFZrOzft7Al7IK+3w3I0GjmXpzVree/WjGAYRbBBizbrwWCARqOBZ8+e4eXLl+h0OpjP526cHashd+7cQb1eRzabRSgUwmg0cjMker0eRqORew+pVAr7+/s4PDxEpVLZqMyI3SOR2CFM/t2+fRsPHjzA8fExUqmUW1RMUjIHwIN8OTuyXq+7OZQ252CP+WMSEIArX/Kuz0oGe0K8A2uI9V3YxCgnTbGvgsaw58+fu1PFWTZl5HNycoLT01O89957KJfLSCQS7rxQmqhoxea2lgnSYrGIVColgQgYicQOCYfDyGazODw8dHvzeDzutgOZTAaFQsEtIibwmIfg0XsUCFYO7IwKfqZQ2P4HNpBtM2cxgrDVEyZZaeMOh8Ou8YtJx0ajgefPn+Pi4gI3Nzeu05PRD2de8nXzcGSKAU9Wp+17sVi4ciuFU2duBItEYodwf88EI5OLzEHQEGUP6mWykmPeKBD28VxIXkfjNqMSXwc/27u0HVRjy5WFQgGJRALz+RydTsd5KVqtFhqNBq6urpy9HPja68HxeEysplKpjQnctgxcKBTcdoOREKs8zKmouSs4JBI7hL0StvEJwMZRdiwXUgjS6TQymQwymYzzJdiFbS3c9gxROyDXHgWYSCScddvrwfBGEMVi0W1t1us1+v2+80F0Oh202+2Npqz1eu0iHJZ4bd7EiqIVTD7XarVyp4LN53M3mbvf76NYLMpxGRASiR3CDsuzszN89dVXrtlqPp+7kfoUCruYbeRhBcH2dmw7HtAmLunKzOfzG2Yne0gvRYTbAHovmGCkE7PX66Hb7aLf77sEKK/HCohtKrMmKNvHwQoOhZClWk7lms/nLolZrVaVnwgIicQOWa/XGAwGePLkCT755BOsViuUy2UAcJEEF46d/8jEJM1G/D1ek9sKe+o4J2/bEmqxWMRsNkM4HEY6nX5tgXPMPisZjADYgMUBtYyE6JYEvo5CONiGJi87fdtuiWwlhcLE6Immsm636yIhHhuYz+fVCRoAEokdM51OcX5+jj/96U8YDoc4PDx0d1uWGjmxabFYOOvyYrF4LWT3eiLscYD2IB9OkALgDsTx9mfQzWnzHRyOY63cdGfaQ3/sY+ki5XGB9FVQyCgS3oOGufhtNyr7S0qlEu7evYtaraYtRwBIJHYMnZVPnz7FdDpFo9FAsVhELpdDuVxGpVJxFQYuGuDVwBo7pcqeRM67rjVGMaSPRCLIZrNuFsS2fIU9m4Mt3OwEHQwGuL6+docIcVoW8Kpdnb4IDti1I/rZxMVIgs/hHaZD8xcbzWgZL5fLaDabuHfvnhM7sTt8RcK2L6sE9d0xn8/R7XYBfN1CXiqVUCqVMJ/PNxqqbGLTbiHs3MttA3HtXAk6Je0BwG9KfHJ03WAwwM3NjTsUx07v9p5/wW0Gz/HgQT/sz+Dr95ZluX1i+ZcDgCkQNzc3Lmq5uLjA1dWVDugJCF+RsHcx8d3BBck2fP79clHTtBSNRt2i5eJhspALj6LCfADNT+yPYNIznU67rlPvYcCMRjilipUM5jnsAcF2GA7zJ5lMBuVyGfV6HbVazbWn2+Y0vm/C55xMJu481Ovra9edShdmLBZz3aq2+UzsDl+RoGNOreL/HOv1Gjc3NxgOhxuH56xWK5dDsDMeOPMSgOvp8JYbmQ/gndzOfGCjFENz5jt4WhZnUFiRYKjPPIidnL1tC8OBMHaGBedXsKvVHkfID/t3YpvOGo0GGo0Gms3ma9saRjI6DzQYfEXil7/8JX72s58pUfRPQIv17373O/z61792J2ezQuE9Uo/hPcPwbre7cUweW7q5v2d1gFFFPB53+Yb1eu1yEhxdx/H6tmrCCMEbXdgyK69DWFKlq5Ij+mm7puixh8T2iRDmPjgy7/z8HFdXV+j3+xsHANmhwPq/uHt8ReLDDz/c1ev43pNIJDAajXB2doazszMXPnOqtT1ol3dvtmDTk+CdPM2cAOFCpinK+7V34hUXnN0O2MQor0lxoZDxNbOSwWazSCTiTh9nlMDoyOucZPQyGo1c5aTVarn3SWHg++NrF7tHg3B3xK1bt/Dzn/8cf/3rX/HHP/4Rn332GdrtNubz+cYgGisUDNe98yKtfZpbjWw26/wNyWTSOR+t9dp7F/Y6Nb0VEnaAptNpLBYL13dB+3g+n0ehUEAqlQKAjVwJp1TZQ5B5XesK5VRtJinZY2L/7zFq4RAbsVs0CHdHZDIZnJ6eOnt1Op3GkydP0Ov13CzHbDbrhqvYXgXe/a1HwOYZ2BjGqVE0MdHCbUul/Ay8ygvYQ4FsvoODZmazmUuiWuMVE6F0kvJoPu95pvRjWJGwxxzaA5G9UQw7Z1kt0f/J3SOfxI5giF6pVPDgwQOMx2OEw2E0m00AcBEAHY7MZTDUZ5jPPAOjBzZh0Qdhj/QD4O7YNFbZQ4Ft2dPaue2EbFZJUqnURgmS+RD2WLA6QscnKyLes0TswUOMOuwhyPb6wNfNYjyWUCP1g0EisUMoFJy6NB6PXShv51FyStNsNnPNVSxj8i7OFm4KBH0QFl5jPB67Kdw0aHEqNe/k9GBwUdvnYe8G/Rl2qI0VGPs7Nipg5ELXJbcbdp6Ft8uT0VMqlXKt9XJbBoNEYsdwn88zNJnsY1VhPp+7EqStUHDfzy0AW7h5poYdmc9ogHt+JjgpEByx7xUJLnrbrMUWdrt1YbTAnAPHz3kdn4TvzYqDLYt6c1/WxUlBrVarmpYdEBKJAGAirlKpAIAriXJ+AoCNOy0FwLZgs4GKCT16Ljiqzt7xbQ6Cz2OFiFGBPYOTfw7AiQ+fo9frbYzB8w7StTMy7GAcXoufrduTWCt5JpPBrVu3cPfuXRQKBXWABoREYsdwETCUt9Osp9Opm840m802JmVzvL1NUnJKNi3cjDwoNhwCY4/bY+mRd2V7kjgfz9fIxCHFgXZtGrsYfXj9DyxXMqfCbRATojxbY9vfDaOeZDKJvb09fPDBB7h9+7YzaIndI5EIANvzYO3LANxdnr6GaDS6MWOCfRi0b1vvgLdlnFGJt1rAidS8wzNqsbMeALjtiO0CbbVaaLVaG12k3hkRdGNyfgW3SpysbYfd2IVvzWGVSgXvv/8+Hj58iFqtpvJngEgkAsCG4twqANgwO9kFYY1PxBqbvFUK+wG8mj9hDUm0bvNaNo/A79nLQYG4urpCq9XaOorfbjHY8MXjBnO5HEKhEHq9njvmkH0bjFRshFUul3Hnzh18+OGH7mAezZEIDolEQNhFZUN7uxBsyZDRh60sWA+ErS5YzwEXvjVmWfu1d2IUxYeHCHOEXLvddv0nXk+DNWuxZfzo6AgPHjzA7du3USqVAADX19c4OztzpcxOp+Ps19yeFAoFnJ6e4oc//CF+8IMfoFaryR8RMBKJtwxv7wQXLICNkB6AS2baBi3mN2xiEMBGZYGVD+DVITzeATasXnhnO3hH8dvXzaayWq2GDz74AB999JE7XGe9XqPb7br+jkgkgvPzcwwGA7fFymazOD4+xqNHj/Dhhx/i5OQEmUxGUUTASCTeErz2adt85S0X2gQkj9yzCVArJtvas/nhPbHcTriiXbrX66Hf77tReFaAvK+dJ2/dvXsXjx49wgcffIBarYZ0Oo31er1xwjnNWq1Wy1m+9/f3cXp6iocPH+K9995DoVCQN+ItQCIRADa031YytB82uWgnUjHCYAKSOQTvpCkArx3gw1F4NtHIvAYFgs1Z1hzlnUbFx9rBM/V6HXfv3sXdu3dRr9dRLBadkHGbwfJsNBpFuVzGbDZzA295HKB3OrY6QINDIhEw3olSdnhMKpV6bRIV7+LcOthj/rx9D/Q6WH+B3VLw+Ynto2DewV6PDV52DB3zEGwbPzo6wvHxMarV6mvHEAJwA2oODw+xXC5dGZgGs/39fefytCP7RHBIJALERg9czN5JUWyCYjWA2wwrFiwP8rH22t7kpBUIKyCMMKw4UADYSGav7W0l54zOarXqDvOxA3a84+7saWWMiOypXUzOcg4GfR2KJnaPRCIAtjkSgVelym35CFuWtDkJPs4mIK2xiVjxsXMkbD6C17QzIzKZzEYJlSeCeztCc7kcCoWC6y+xfRp8fjt8187EsNsddpDy9bLb1esJEbtDIhEQ1n5ss/feE7q807EBOAOTt+Xb4k0u8mc0bXFR8ms7oQqA693wnsfB+RB2pD7FhF2ho9EIg8Fgo9+E0cFwOHRnd/R6PfR6PcxmM0QiERc5TSYTpNNpTCYTVzFJpVLKSwSERCIgrEjYyoadZm3vvl7fAxcoF741XG3rh7Bfe+/w1rINvMpncKtBgchms27EvnVccltDR+XFxYWbZMXZFuwp6Xa7uLq6cvMsu92uOz2d7e88HnCxWCCTyWgAbsBIJALEKxJ2r0+ByGazzj1p+yvscJhtrdb2OXg9Gz3wZ94ohXZwTtRmQpUH9ljrN6/Nyd/Wk8Ep3+Vy2fVdUESazSYajYab2+mdX0GDVSqVUtLyLUAiETDck9s8hZ1ExVCf4be34uAdWe+NIJhPsCVXmy/wnt9hz+2gONiWcnuUoPeov9Fo5EbR9Xo9tNttVCoV5PN5hMPhjcG+rVbLTcXmSeIUBTovvfkIbTWCQSIRMG8Ko61g2JH5XNQ2+mA51Hs976KyPgk7Oo7XBLBRirVVE3aB8rBgHv9ncxORSMQNvh0MBuh0Ok4kWD4dDofodrvuGqzY2K5UWrtLpRIKhQKSyeTW/hWxGyQSAWLv/LbSYH9OuzM7Rr2j8jmin2LB6/Kx3qnYFAgapuy4e1umtCP07dxKDpnZZs+2yUf2iQyHQzc5C8DWA4352EgkgnQ6jWq1isPDQxweHrrZlpolERwSiYDwliStPfpNY+2TyaQTCN7tx+OxO1T4Tc5Ie10ueN7t6aq0j+NjbbMXFz2/9vaGAHitQsKfzedzt9Dt9ay4JBIJ1zl669Yt3Lp1yxmr1CYeLBKJgLBiALxqB3+TSNgZl8wZ8IN3ZdtfYaMJm6CkF+FNZ2J4u0GZt7Cj6fhhn8NCYeB2hqPyrMWc74sCUSwWcXh4iLt37+L09BT1et31bmirESwSiQCwCUQuaD+R4NwHe84Gbduj0cjlDziw1joyvRUM22putxwUF9tEZgfZeudRekWIi9j72b5X/szOzYxGo8jn804g7t27h6Ojo9dcmyI4JBIB47VS2xDeOjJZZbC9F9Pp1B3EQ/syj9ajUDB6oDsS2JxazTu+d7S9FTD79bZEq52LwRIqhcu+Nu8g23A4jEwmg2q1ijt37uDevXuuuYvDfSUQwSORCAAmB7mYeJd9kxmKjyFeoeDWgYfh2PwEP08mEwwGAxeV2PkSrF4A2KiSULS2vQZ+b8UhkUhsHFycyWScW9J6Lfj7yWQShUIBR0dHOD09xZ07d7C3t4d0Ou3EUSIRPKFvcLLJ5vYvwBqS7EnZb1oQVkBstMFFbj0MtoPTRgscm0//AkfRXV9fo9frYTAYuOQkf982fNkOVOvpoI+jUCigWCyiUqm40iWPLuRpYvYcUts5ure3t3EiOcVEArFT3viXrUgiAFjWtA7LN/3etq+J3Rp4cwf257arlKVM+h1ubm7cYJnBYLCR1Oz3+y5CsUNveYJYoVBAuVxGvV7HwcEB9vf3US6XkcvlNgb12m5QG0nY/IrXNCWBeHtQJBEgfj0W3+Ya3s9v+pmtWDAC8Z4BasuddviM9TTYVu9SqYRiseiiBtq5t0UCXtGz8z23zfgUO+WNf/ESie8pb/p39RMQ74c3GvEmVBklvOnu//+74CUMbwUSCfHN/KOdllrk3wuUkxDfjBa72IYM8UIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF+i3/DnoZ28CiHEW4siCSGELxIJIYQvEgkhhC8SCSGELxIJIYQvEgkhhC//D/mr4re9HpYyAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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Op4NIJIJ+v4+rqytXeAXAuSTAqwndtvWcA2zG4zFWqxXa7TZGo5HSoCEhkQiBm1wICgYHsthMhy0ssm3chELAFvJIJPJajYNtUc/lcqjVamg0GqjX66hUKshms+7nbTNZv9/H+fk5Hj9+jKdPn6LVar0WI7BdqGwAW6/XmM1m6HQ6rtpzNpu58vFsNuvem+d5rty71Wqh3W67OReLxQLtdhv9fl8iERISiRC4qaOTgkGrYjgcbrketofDf1hisdhWaTRjEJygzVgBg4aMRVAgcrmcq6y0JeF0M549e4YnT56g2WxiNBo564WvbUuwI5GIG0/X6/VwfX3tLAE2fi2XSxeTSKfTzsW4urpCs9l0P0+LqdvtujZ0sXskEjvGP/maKUp/jYPNdvjjD/4AnhUOCgYA51okk0k3b5ITo/b29lAul5HP55FKpV6bZkU34+zsDM+ePcPFxQWGw+FWloLxDc7DLJVKLsXZ7/dxeXnpuk5Zkp1IJNx4O1ov8Xgcw+EQL1++xNXVletSZYaG8QmlQcNBIrFjeKdmjQQbrqw1YeshbH/Gdx0SW3kJwAUHOWKfcydLpdKWQPgtCA6VOT8/x/Pnz/Hy5Uv0+33X5ZlKpQBgq1Jzb28PhUIB0WjUHepOp4N+v++mXwFwfSa9Xg9XV1fYbDbwPA+DwQAXFxfodruuJ4SuFQvDVFAVDhKJHWKLqLrdrvO9mR70V1cyu2AFwi8SLGNm8I//Ttcil8uhWCw6ochkMq7CkelO28q9XC4xHA6dm/Hs2TMXh4hEIshkMq6Uu1qtol6vY39/303YokCwOIwZC7txLBqNumE4tEyGwyE6nY5Ld9IysvM1RThIJHaI34xvNpvOFKcV4R+y4v8IwjZasRaBIkH/nx+c28CaBFot4/EYrVYLZ2dneP78uauJWC6Xbv9GoVBAvV7H0dERGo0GKpWKK3p6+fLl1p4Q+57saD0GaYFvhYPulXWrCEfcaZRdOASKhNT7h2O9XmMymeDy8hJfffUVHj9+jGaziV6vt9UbESQKzDzY/7Y7QG39QT6fR7FYdMt62NXJn9lsNu4QA3AWRKfTwfn5OS4uLnBxceHiAwDcvImjoyOcnp7izp07qNVqSKfTWK1W6Ha76HQ6ALAVaL3p/yNbUMbfDa/HzvGk1VIqlRCPx3/4P4z4TgJFQqr9wzGZTNxU6T//+c94+vQput2umyTFwKFtnfYvD7aFRHYWBGsj2KzFWQ52WrV/whQDgyx/Ho1GaLfbuLq6cgFHukGRSMRZJbdu3cL777+Pe/fu4eDgALlcDrFYzPWScFalf2ivfW92sxfH89n+Ej6G4+2YiWF6V+yWQJH4/PPPMRwO9Yf5AXjy5Ak+++wzvHz50i3pZdCOd0wALlXJgijbc+GflM2CKQoEg4iNRgMHBwcug8EsAmdpspNzNBqh3++j3++j3W67D7tCkAc1m80im806N4O9HsxWMCBp6zhspSffGzMuzLrQorDYcfqFQgG3b9/GwcGBc5HEbgkUiV/96lf4/e9/71p4xd8P04ZcgsMhuPzgv7HykIVEALbmKdgFNhwem8lkUCwWUS6X3Rbw/f39rW3cViC63S5arRZarRaur6/R6XS2MhE20GjH3OdyOVQqFVQqla3UKUWBcQZaH3a5EAu52PnJCdl0MRhv4O+BWZSDgwN88MEH2N/fV0wiJAJFgkttxQ9DKpVyGQc7xyGXy7mdFABcGtIWRzH9yMPD7eGMOTCtya8LhYLr6ARejbLnSj7u3+AWr/F47A4s7+x24Ew0GnUuB+dW2upMdrQOh0PXGZpMJrc6P/P5/NYU7sVigV6v5x5v92/E43GUy2Wcnp7i/v37Lr0qdk+gSNiFtbIk/nHos0ejURSLRWe2M/A3Go22luuyvNouseH8h729PTdujlvIuQHcbgGjBcFGrXa7jfPzc7x48cIFJu30bH8TFf8fYLbEH9tgfKPX66HVaqHX67n3SVHhaDzOrMjlcliv1+j3+25SNq0XWkmZTAaHh4e4f/8+bt26hVQqJSsiJAJFwua2xT8OD18mk8Hx8TE++ugj1Ot1xGIxjEYjdLtdZzUA3y65mc1mAOAG1dbrdRdzqFarbkiMP0XIOIFdhjMcDnF9fe0W9HQ6HZdVsCv97B2bbgOtIFomzELM53N0Oh2316Pb7WI+n7s+knw+j4ODA9y+fRtHR0eoVCqIx+OYTCZotVoA4HpUOJzG8zwUi0Xcvn0bx8fHKJfLr83aFLtDdRI7xvM8NBoNPHjwAA8ePEC5XHZlzDS3GdDMZDJYrVauNmF/f98JRK1W2xruYlfx2cpJxgvsgBvGH+zsCj4WgHNtgFfLjVmxyRoIBl45Z+Kbb77B2dkZut2uu+ZcLodGo4F79+7h5OTEZUMAYDweI5lMYjabYTgcuu1fq9UKyWQS1WoVjUbDpVjlaoSHRGKHMJV4584dvP/++7h9+zYymYzrhLTLeKLRqPPDWZ9wcHDgApIMHPoFgs/Bwx+JRFzjGKshbSm4nQNBmH7lTlDGP7ikmGXWs9kMrVYLL168wNOnT3F+fo7BYIDFYoF0Oo1yuYzbt2/j5OQEx8fHqFQqSCaTWK/XbinRcDh0pdgA3FbzSqWCUqkkgXgLkEjsEB54OwmKbsJsNkM2m0Uul8NisUA8HnfmN7ePM6Vpl+b4R9ED2HIZ+H07it9f1WjTrPyaaVW7FzQajbqW7vV6jV6vh4uLC7x8+RLn5+fodDqYzWYu88LRePV63V23dac4mapWq2E8HgOA2+LFrAzfg1ze8JBI7BAb6Wdrtq154OGyrdvpdHqryzKbzW6Nu6cVwRoF/5Qouh52IQ/jDLFYzFktdrpVIpFwAlEqlVzcY7FYuIEwo9EI19fXuLy8xOXlpZsXAcC5Jsy0MKhKQWQa2L4GW9Ank4n7XbBdnWsBNZUqHCQSO4SBPs6KYPu0nVfJ1GEikXCFR/l83lkPtsiKz+nffuUfd0cLgwczk8m4QCEHxQCvhtdQqLg4mHGE4XDo4gfMZnDxj20FZzzFXre1eqy1QhHkOLxYLOZiKf1+37Wa12o1t5lc7BaJxA5hmvP58+d4/vy5a7Zi/QJTkXYnBhfX3BR7sNknxiI4j8JOtNpsNm7XZ7lcdkFCZk9sdaQteKKb4XkeZrMZRqORG4jT6/VcXwctCBZOsSYin8+7QjFr4QDbO0Oz2Szy+bwrxOKKwvl87lKhBwcHyGazLqAqdodEYodsNhuMRiN89dVX+Oyzz7BcLlGpVLZ6KWju02JgrIDZChYvWVeDbgX7MOzeDVY/plIplMtlN606l8u5zV60KKwwWauFd/XhcOjKuO0cTuDVFCwKhO0ZoTtjLR47vIaTrWz2xO712N/fVyo0RCQSO2Y2m+Hs7Ax/+tOfMBqNthqXEomEGxLLqkNWYVIA/I1fPHR2ua51M+jC8C6cTqdRqVRca7YtYqJQ8FCzx2M4HKLdbrsxcqzOtNOwotGo6x/Z3993Q2j43niNVtgoFHYUPwXT7iKpVqv48MMPcXh46Ko4xe6QSOwY3pW//vprzGYz1Go1Z5az/wJ4Vem4WCy2Jljbmgb/3dlmAWy3Je/ydDds3MJWWdoCLFZnstSa9RW8w7N021og3ErOOg4Kk90FArxyj/yvbcu7+/2+c8G++eYbNJtN7d4IiUCRsGkx/XF+OFarFQaDAYBvg4HFYhGFQsH59nQlZrOZq3Owuz1pMVj/nvEAmvzpdNp1btKcZ2yDlggPJw8ri6SGw6GLd7DYyW9BMKPCuAIF4vDwEPV63bkavH6bPaEY2b2mXFTU7/fR6/VcP0ksFsPl5SWur69dcFTslkCRoDmpP8wPCzMa3ENBK4B3ZLZfc7I0W7q73e7WhnAAzk2hENAiKZVKzgXgZ7Zn2wyJ/8ByRwfrGezSYduNakf0FwoF1Go1F2CsVCpIp9MAsJVdsfA1/ZWgducG06G9Xs+Ve4vdEygSrJhTq/g/xmazQafTwWg0clYCXQhWW3KUPu/kPFj0z/lxUzzALxAAnHvB9nKmNtnBaQOfFAUArrvTv07QP07O7vCoVCpbRVOskrSzOv27QuiCTCYTdDodXFxcoNls4vr62s2z4O+GvxdNyw6HQJH45S9/iZ/97GcKFP0DMP34u9/9Dr/+9a9ddSUzGX6XbrVaYTqdbtVU8A7LfgseWqZKrTnPoic7E8Iu9GVAlDEPOzqP/23rLPgctraBr8uOVAYqWXTFoijGN5hxsULB+MpoNEKr1UKz2XQj9Vl3wWvyC4zYLYEi8fDhw11dx788yWQS4/EYX3/9tRtdZ9OTiUTCVVkCcIdrNBq9tkzYrvejqwLgtcyHxY69s1/7g58UGFoA1p2xE6hYUVmr1VzBFbdzcduWXTJEy8C+3mKxcN2vXOLDzV38WWZOGHPRDWv3aBDujjg+PsbPf/5z/OUvf8Ef//hHfPHFF7i+vt7qVWDMgKXLNqjIu6q/jJtVi3b5LuMONuZgsx8snrLpUzZs2c5QWxXJzk4eXA6n5UIeAFuDa8bjsautoGVgsyl0Z7gDlUJoYx8UBBaCacZlOGgQ7o7IZrO4e/fu1pKcR48eod/vw/M8N5yF/jxjATaFyTs7rQiOr2PFIsug7eQoW4nJGgoKBEWI8RAGC2mteJ6HbDaL5XLpgqjAq50e3EK+Wq3Q6/VcBygXD9nyc74+Bc/GPCgMdJH8A3HZUSqRCAfVSewIHuparYYPPvjAFUY1m00Ar5qiWFfApqbZbObmStgmLrZxs3OUPRKcTMXnYXpxMpm4rAZdBlubMBqN3FZzZk4YmIxGo66l3VZKUsxYN0GBsCsKaZUwZWvb4e1nW/sBvIqBJBIJ59aodyMcJBI7hP/TVyoVHB8fYzKZIJvNuq5IO9Gah9zOubQl0HbOA2shmKHgQeahpTVCK4SbuzjOnjUKTK+yMpKHMpFIbFkibEZj7IBuiu0Z8S84tuXjtjv1pkIwVmHSimBrPa0jsVskEjuGDVCVSgWHh4fwPA/z+Xzrzj6ZTLaCigxuMlVpXQy7ro+LiO0OUdtizTs5LRYrEuPxeGtSNq+TcQ+6JKyUZByBQcnJZLK1P8O/c4Pvzd++btOathWcGZRyuYyTkxPUajUngmK36LceAvT1q9UqALiuTE6QomDwTszZCrRE7D5P66Iw7WirMm3qkaY9x9kD2Brhbzd60Z1gtoXxhOl06gq77JRtG0/gQbcZCVoHNnhKMfEHyPm6mUwGR0dHeO+991AsFpXdCAmJxI6xvnY2m8VisXBWAi0Km4qkONiKTLoZdqcnD6qNM9jmMHs45/O5e5z/tWw1Ja+X9Q6DwQDtdhutVgvtdtulZW1VJQ+4DbjSAqAAMQB70+/GDt7d29vDhx9+iJOTE2SzWQlESEgkQsDfIs3qRAAuk2AbtHinpRVhR+ezfNruwKDLwV4HOy+Ch942jtlmKwoERYVLhDlk5urqCu12G8Ph0Lk11i1iipTXWigUXIp0Op26Qbl2y5f/98Jg5b179/DgwQPU63XFI0JEIhEC9o5pZz7aORH+4iceXvth4wz+VnGbcuRr8a7O+AVnSPKQUxyAV5u/bbETBYKb0O2sTOti2FH6+/v7rlS81+vh/Px8KwXL1wfgrpFxiI8//hj379+XqxEyEomQsAff/zXwqumLd3nWPDDox3oH2yNhA4J8Dn72l1vTnbhp0hMtDWtBsCLSWhA3VXsy3nJwcID79+/j9PQUlUoFANBqtVAsFp11xEU+dhFRsVjEnTt38PHHH+Ojjz7CwcGBC8yKcJBIvCX4Z1ba+RFMffrjBaydYPCRaUfCw2jdDFuBaQfg+kuzWVLNIOVgMNiqqLQCwetnuXa1WsW9e/fwySef4OTkxFkS3W4XhUJhq5lsMBi4aykUCjg8PMSPfvQjPHz4EHfu3EE+n1dWI2T02w8Jv6lN/H0Xdp6lNdHpDtjaCh5ellMTG+Pga1rBsK/FlCjLpTn4helROzyG2K5QbgH/8MMP8cEHH+Dw8NB1o5ZKJVd7wXqNVqvlFvLUajWcnp7iwYMHuHv37laVpVyN8JBIhMBNAsG4AeMU7NS0VoMVi+Vy6Sor2drtnxbFg8U+D2ut2CInf9DTVmkyS3JTmzavjW4GrYjj42O3satcLrvmMO4SYVDV8zyUSiUsl0uk02kcHBzg7t27uH37Nkql0lYZto17iN0ikQgZ6z7YYbLpdHprqK2/gpHxBTsY1s6YsN+3r8HH2wPO79vMiLVM+HNMa1phsSldu4qQawjZbEY4f+Lw8BCr1cqVnGcyGezt7aFeryOfz7vH2BF3IhwkEiHCA8aDZzMW/mAk78DWmmAFph0gY58X2L4D2+Al8CqbYkum/VkR1mawloPXaa0QBiu5RKhYLG4tMLbXZmtEisWiez+s/2D9BgvBksmkS/XKmggHiURI2HZve6D9qT5rOVhxsG4H05p8jpvGxfH7NnsSjUZdUNR2Z9omsmw2uyUYLNGmkNC14TxN1n3Y8msrfFb0gO3sCgOw4/HYPcZuPNPOjXCQSISAjRUwcn9TKTOwfXj9E66taPi3etmyZxv4s92YN8UoCGMadmEPZ0PYnRv2ILN8nDM5c7mcExs7aYuLi9n7sVwu3YhEigldHk7A4sxMsXskEiFhLQnrHljLwroA/LCxCZvKBLbjC3786U5ixcT+LOMadDPYbZrJZNDv911jFy0R4FsB6vV6aDabbsHwfD53U7NZccn9oRxVxwXJtlksn89jtVohn89vbT+Xu7F7JBIh4S+gsnEF1jSk0+mtlCQHwjDbYIXCjz8+YUfP2WCg7cRkfIHWA78GXjV82XiJFS02qfF9sX9kb2/PzaSwItFsNtFut50lwf2h7AUBgHQ67d6b0qDhIZEIkZuqLoHtTIKdcs2MBzsuba3DTesPbNCQr8HHWPfFNnYxw8I7N0WCLoB/ipU91BQx/sxwOHRl2WyJ5xJgLgJm56ptmc9mswDgJm/Z2Z9i90gk3gLelN6jhRGPx7dSo3aKNRf62MEuxB4sW4TFjtObRuazf4IfbO+2S3o4qs6uCeT12rbz0WiEXq/npmhzVoZdvsMmNKZKWU9RKBRQrVbdkh/1boSHRCJEbIemvevbD36fqUjWFQBw1oFtCbcj7gC89twUCLovDEDSGgDgpmTZ+gs7uZsTqGwBF1/rpmG3w+HQVVpyQjaDn7Zmw/M85PN57O/v4/Dw0Fkhmm0ZLhKJkLC9EsQeZBs3sOv7mJWgKxKPx52Zz2YvW1TlbxrzD761E6r9rgofYwfW2joK/8i5m2o1GANJp9NumC5jKvw5u8i40Wjg+PgYR0dH2NvbQzab3Urxit0jkQgJf2m0PaD+jVd0OTKZjKtLYJERzXTuzWQg0Z8J4Osx4MiRdezqtG3fNuNh4xZvmkkJYCu2YmskWH5tp1LZ+EckEnECcXR0hJOTE9y5c8cFPDm8V4SHRCIEbJcng4Z2cIwtoAJerfJjtSIDmAxmJpNJJBIJZ8L71/IxUAlsN3H5B9faVKO9PluP8aa+E4s/E+F3n2x8IR6Po1Qq4fbt2zg9PcXdu3ddabat2hThIZEIEf+BtMVNwKtqRLob9rDyoNsx+slk0rkeVnzoonAUHl+TbsNNVoINhFrL4aauVWC7toIZEm7+ooj5rQKWc9frdZyenuL09BSNRkMC8ZYhkQgBm7Vg7MFfMWkPpP+g+IXCbsGywUg+N/sghsOh64FgANMKAK0N25/htwpushrYvcqUJcfWcVkQN5Mxlcmf5xawW7du4e7du7hz5w4qlYqyGW8Zke/orlPr3T8B3r1twDCoWOimf7MDYuyKPgYZ7ch6Bgs5aYqj6C4vL9FqtdDtdrfawm2xlF0c7Bcu6/6wuataraJSqaBUKqFQKLiZnLQM+MEYS7FYRK1Wc+lOBjitaIqd8MZftkQiBGxQkIfvpgMRdAf3uyoUBH8lpt9FodXR6/XQ6XScSLAngwt6mP3gZi+mPFn4lEqlkM/nUa1WUa/X0Wg0sLe3h1qthkKhsLUwiFkWHnwWi9kOUwZg/TM+xc6QSLyN+H38v+dg+AOJ9jn937PBSP9UbaY3bbrTLu2xfRpsuiqVSiiXyyiVSsjn885i8FsCN4mdTZHailOJQ2hIJN413vR3vUk43vRxk0ViLQG/lQAEWz83IVF4a5BIiO/mO/5feCM66P8SvPGPqOyGcOiwi5tQKZsQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQiYQQIhCJhBAiEImEECIQ7zv+PbKTqxBCvLXIkhBCBCKREEIEIpEQQgQikRBCBCKREEIEIpEQQgTy/wDWge0Rr/byaAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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37Ha7KBQKuHr1Kq5cuYLDw0MRwciLnMxwms2mqFrQ9yrPiFCrt1zylGdB0uk0bt68idXVVbhcLpw7dw6tVgtut/v7/8UwT8AioROyI5N6MlIe81abxWpVKSgHMRwORVOVbPlmNBqhKAomJiYwPz+PS5cu4dy5c6J1elwLNs2LbG5u4uuvv8bOzs4TTlH0HBpJLxaLQgxoKK3T6cBoNMLhcMDlckFRlJHnUv/FwcEBrl27huvXr2Nvbw92ux0PHjxArVZDKBTihiodYJHQATlSkDsP5a/JlYunZfbVkQVFC/RBJi52u110Qy4vL2N5eRnxeBxOp3OkokGvQ9dYLBaxtrY20n4tb21oBsVms6FWq+Hg4EC0gMsGNjabDVNTU1hYWEAikRCj6MCjbtJ8Po+rV6/i888/x+bmpjhxbGNjQ4zSMycPi4ROdDodNBoN0dcgo44ingW665O7VL/fh9lsFlZ3Ho8Hfr8f0WhUNE3F43G43e6xTthyFJHL5cRwmOyURaPcfr8fMzMziMViAIC9vT1Uq1Wk02lsbGxgf38fjUYDFosFyWQSP/7xj3H58mWkUim4XC4Mh0OUSiVcvXoVH3/8Me7evSts/nu9Hg4ODlAqlcRUKHOysEjoAHUt5vN50dasjhq+S1+APP9BdnQulwvhcBiRSATRaBSJRAKTk5MIh8PweDxjz7Sg16GO0Ewmg3K5LEqm9ByPxyNEh/wwTSYT8vk8VldXhV0/LXiDwYC9vT1kMhkcHR3hzTffxOTkJPr9PtLpNP72t7/h6tWrKJVKI7kZakPnXgl9YJE4YagdO5PJYGdnB6VSaWTwihbG87w+LSbaBkQiEUxPTyORSCASiSAUCokIQt0OTe8vC0Q2m0W320UoFILD4YDNZhOvOTMzIyKS4XCIXC4nej+2t7dRqVRG3K96vR7W1tbQ6XRweHgoyptra2v4z3/+g0wm80TOQ96WMScPi8QJMxgMUCwWcevWLayvr6NSqYh+A+pefBF3TPJq8Pl8iEajiMfjiEaj8Pv9cDqdIxGEusLQbrdRLpext7eH9fV1HBwcwGw2Y2ZmBsFgEMlkUmwvvF4vbDYbAAi/TMpjyPZ49Po0pLa7u4ter4ft7W30+30hRuPs861WKx/OoyOaIsHh3YuDqg7lchlXrlzBp59+inQ6LfwjgPGW9N/lfcxmM+x2O/x+P2KxGKLRKCYmJuD1euFwOJ7wnJT9HhqNBvL5PDY2NvDgwQNsb2+jXq/D4XBgZmYGKysrmJ2dRTAYhKIoIxb+5GwFPDq/Y1xehd6r1Wrh6OhINJCVy+Wx7ddGoxGhUAihUIiP+tMJTZFg5X5x9Pt9bG9v41//+hc++ugjfP311yiXyyNeCc8rEiQQVOacnJwU+QfZlJbei8SJypTFYhHpdBp37twR+YRGowGn04mFhQXEYjHMzMyMDG/JJUyr1Srcvo+z4pebvmjrRcNp47YUTqcTZ8+eRSQS4f+POqEpErdv30atVuNfzgtgbW0Nn332Ga5fv450Oo1WqyUW0ov4+VInpdPpRDQaxdzcHBYXFzE5OQmfz/fEXZ+2N+12G5VKBbu7u1hdXcX169eFWS6NaodCIWFn53a7xVSqHI2oB7Zk12u56YuG1pxOJ1wul2g1V9vV0WukUim88847CAQCz/0zYr4bmiLxhz/8AX/5y19E5x7z3alUKsIngpDnLOTFoe5X0IKalmw2Gzwejxj5Pn/+PM6cOYNoNCoqEnSHJ0+KcrmMbDYrkoa3bt3C9vb2SMs1jXLXajUAeML7ga5Zdtei0qjVahX/Tv/m8XgQCoWE10Sz2UQ+nxePo9c1m80Ih8P44IMP8MYbb4i8B3PyaIoEHejCvHhkh2pZGOhzmrU4rsOSWrAVRYHP5xs5bPjMmTNIJpMiDyFPkJJPQy6Xw/r6Om7evImbN2/i4cOHKBQKI7kEusuTZZ48VyJDC7zb7YrjAl0ul4g6zGYzAoEApqamMDc3h8nJSXg8HvR6PWQyGWxubiKdTiOXy6HT6cBsNmNiYgKXL1/Ghx9+iFgsxvkIHdEUCWpckUNG5sUgJxjVMxbUUyB7QsoO0iQMkUgEMzMzmJ6exuTkJBKJBEKhEPx+vzgblHIQ1D1JW4vbt2/jq6++Gsk9qBu7ZPMaOmVMbg1XVy3IpLfX64lzPenw4ZWVFSwtLWFychJutxtGo1GY7sRiMQSDQezv76PX6yEQCGB5eRmXL1/G0tISRxE6oykS8hgw82JRFAWRSAThcBgGg0G0INOhO/I5GrQ/d7lcCAaDmJmZweLiIhYWFjA9PY2JiQm43W4hCrRFkHMGlJw8PDzEvXv3cOXKFdy4cQMHBwfC/m4c8hyJ7F9Bw1302lQVOTg4QKvVgt/vRzwex9zcHM6fP4+FhQWEQiHY7XaYTCYR1fj9fng8HkxMTCCfz8NgMCAWiyGVSmFqagoul4ujCJ3hPgkdII+En/zkJ5idnUW73cb+/r7wuKxUKuKubTQaRTmT5h5WVlYwMzODUCgkeh7kPIEc+cmnZZFPw8OHD7G+vo5cLjfiYTEO2c262+0KoaD3orJpJpPB/fv3sbGxgWq1Cp/Ph/n5eVy8eBGLi4sIBAIj7d80hCafZVqv14VTViAQEDMljL7wb0AHAoEA3n33XXz44YeIRCKiuWh7ext7e3vCgAYA7HY7gsEgEokEUqkUkskkwuEwXC7XiDiMs4CjSJAikm63i1qtJsx2yQZPCzr6z2aziW0HNUnRaPfBwQHu37+P1dVV7OzsYDAYYHJyEtPT05ienkYgEIDdbh+pXlCERHb7FotFnF6mKIo4HZ3RH/4tnDBGoxFTU1N4++23sbKyAqfTiXa7DZ/Ph4mJCSSTSdHKbDabxQlfExMTCAaD8Hg8wqthnHGLuuSoTohSLoRKlDRiPm5LaTabYbVaxYnltF2h96lWq9jf38e9e/ewtraGvb09dDod0ZMRCATgdrvFtaorIsBjmz5K1pJg8PF+pwcWiRPGbDYjFAohkUiI+QlaRDabDcFgUHQeUvWCFqmccxjnZn1cgplyGjabDYFAAIlEAtFoFNVqVUQb6mP/SCBcLhf8fj/sdjsqlQo2Njawt7eHer2O/f19pNNpbG1t4fDwEO12GxaLBYqiiFbqcQNk6muTXbPl74NzYacDFokTRq5iyEa1VqsVbrdbnJtJd3nyjaS7v3p7oX7tcR8UPTidTsTjcZw/fx7D4RBOpxNbW1soFArCpRuAuB6HwwGv14uJiQkYDAbhx1mv14VPxNHRkdi6GI1G0SBFXhbPEhHI/SJyv4Xsj8FRhX6wSJwwcm9AKpUaCcNJBKgyIX+uNrgdNxMhz2Go7e3MZjMcDgcikYg4zXxhYQFbW1vCr0GuctDdnRZoq9VCqVQShxVXKhWRmyBxoYjI6XSKLs9nqUzIWyFKtNJULJ3yxT4S+sEiccIMh0Ps7Ozg888/F8fq0SIglydZLOSoQe1iRa9HX5Ndrehxcps07f8dDgeCwSBSqdTIkYGNRkMcC1iv11Eul5HL5ZDJZHB4eIhcLodCoYBGo/FEiZa2Sy6XC9FoFNFo9AljXXk7M24rIZdq6ZxTp9MpRtQ5mtAHFgkdqFar+Oc//wlFUfD222+L+QqPxwOXy/VEeC3PWqh9MOXogf5OUYjFYhnbN0FC4fF4xPPIhLfdbqPRaKBQKGBnZwfdbhdbW1vI5/PI5XIjVRG5h4PKmHRATzweH4kk1ElV+jf6Ux5TL5VK2NnZQTabhc/nw8rKCuLxuDDWZU4WFgkd6PV62N3dxaeffopCoYCzZ89icXERs7OzI63a1EpNi5fao2u1mjCXlfftcjWCBqjkpKB6gEq9SKlz0uFwwGw2i+E+2mLQ7Ik6yUneFbFYDGfPnsXS0hJCodDIOaDjUPtYdLtdVKtV7O3t4ebNm3jw4AF8Pp9IuLJI6IOmSKizzcyLo91uC+fpo6MjlMtlABC9D9Q/0O12hdXd9va2yCGQWQ31GjidTgQCAYRCIeEhQYNfwOjWQ17cwOhipd81nSper9eFQMgRBEFJzlAohJWVFVy8eBHT09Nwu91C7NT5EUKOIDqdDqrVKg4ODnDnzh18/fXXePjwoXDWWl5eFklR5mTRFIkX6ZTEjELNTYeHhwAelRzJsJbMW4bDIWq1muhFoEGsw8ND1Ot1YfTicDjELMf09DQMBoNIHj7L707dv0A3B5oUJYFQvxYlRN1uN+bm5nDp0iXMz8/D5/ONzIzQ9zvuWmimhL7P1dVVMVOSz+dht9tx//59VCoV9pTQCU2RoFZgHhV/PobDIQ4PD8W4NUF30HK5jEwmg+3tbcTjcTgcDhHW5/N53Lt3D9euXcPq6ioymYxws6KTx+12O9rtNoxGI3w+n2jpBp4UgGdZZHR3pwQmTYbKyANqiUQC586dG2m/JoGQTz8noZEjGDr3M5vN4u7du7hy5Qpu3rwpZkparZZw3+b/g/qgKRK//e1v8etf/5rV+zkYDoewWq346KOP8Pvf/x4Wi0UcCqwekCoWizg6OkIwGBQCsr+/j42NDaTTaeTzeZEXkAesTCaTOLiXDr+RG5meVRzoeunu3mq1hK8EvYfctWm328VBPwsLC+IsUXKcokRko9FAvV5Hs9kc2a5QW3c+n8fDhw9x584d3L9/H5lMRvRtDAYDMcvC6IOmSJw7d+6kruMHD41Hf/nll+IsTVrotJBpcdLxf61WC7VaTSww2VEbeHw3J8PbWCyGZDKJeDwOn88nSqvq3MPTGrFIoHq93sg4e7/fF/0KVqsVPp8PiUQCyWQSPp8PRqNRiAPNeRQKBeRyORwdHYk8CjWTycY3u7u7yGazwutSTo7KRj3MycNGuCfE7Owsfve73+HGjRv485//jL///e/I5XIYDoew2+3iEB2a6qROS9nqzWazod1uA4DYajidTkQiESwtLeHixYt45ZVXREmVbOaA0R6F46AogrYHJpMJHo8HgUAAzWYTZrNZCIeiKAiHwwiHw3A6nSIBSw1WtPj39vawv7+PYrGIarUqpkjJHater6PRaAinbTpsWMZisYhKCXPysBHuCWE2m+H3+/Hmm2/C4XDAbrfjs88+Q7lchtvtxtTUFJLJJCKRCPx+PxRFEXflarWKSqUiXJuoqkEngy8tLeHChQtYWlpCLBaDx+MRU5TqwSpgNKJQT43KJ5GToW4ikcBgMBC2dtR2TQnKcrksTuoiY5tcLod8Po/Dw0NUKpUREaB8C/VmyG3qagwGA9xuN5xOJ/tK6AT3SZwgVK5cXl7GBx98gMFggAcPHsDj8WBxcXHEuNZms2EwGIgRa4oaKKFHZceFhQUsLS0Jfwl1LgJ48qxQuha1QMgNW9QcFYvF0Gq1YLPZxFka5JwFAIVCAdVqFcDjowur1arYIrXbbXECuSxAz3qUocViQTweh8fj+T5+JcwzwCJxwtBsw/LyMqrVKmKxGBRFQTKZRCKRgN/vF81MtBWx2+2iX+Do6AjdblfMYSQSCeFMRdsLLW8JdRJT3bFJH+SEFYlEhF2+0+kUuQVq56ZtA4kB5RPGiYD6PZ4Ft9uNxcVFFgkdYZHQAZPJJNqN/X4/TCaTsHGjszEotKYTwRVFQTAYHDGjcbvdcLlcwi5fDsfHDXlRy7bRaHyiPVq9eKlS4vV6xdkYVIFpNps4OjoSEQOdPape/HJSVuZZc13kvbGysgJFUb7DT5p5EbBI6AC1MYfDYdjtdgCPFr3sLSEvamq5djgcYoycxIMGwsYJhHw3l2cs6O9yN6Q8+wFAzH6QKxVNghaLRVGtUHdhqs1uKKoh/wuq3jzNDYtwu9147bXXsLi4yC5VOsI/eZ0wmUxiAIp6HcbZ6xPk2ARgRDjkwS3gyWP7yAFbjh7o71oiIb9eq9VCoVDA3t4ednZ2kMvlnhgTVz+HRMJut4tzP9rtNqrVqkhiamGxWLCwsIB3332XOy11hkVCJ+gOKxvDjFvs8uPpMbLXxHH5BRIIGgKTj0eQXxMY3ZrIr0Ht0tlsFul0Gpubm8J0ZtzBvjKU00gmk5ibm4PX6xXOVul0GuVy+dj+B7PZjHg8jp/97Ge4dOmSiLYYfWCR0BG62467e8tbBnUT1LiFTshVBMoV0PPlSVCt6gY1OlWrVWQyGTx8+BAPHjzAwcGBSFZqCYTRaITD4cCZM2fw3nvv4cKFC/D5fKhWq1hdXcUXX3yBGzduoFAojFwfRUvhcBg//elP8Ytf/AKxWIwNZ3SGRUIn5InMcUlEdYVAjjSOc6iSk49yuVH9OHoPOZKQHaHa7TZqtRry+Tw2NzextraG3d1dlMvlpwoE8GirMDU1hffffx8///nPkUwmhdv23NwcYrEYHA4Hrl27Jqo1AGCz2RCNRvHOO+/gV7/6FZaXl/lgnlMAi8QpQhYIajSSh6Joe6LOW5DQjCsxytsU9XuNe08SiEKhgN3dXWxsbGB3dxelUmnsJKgao9EIj8eDH/3oR3jvvfewsLAgGqGGw6GYLfF4PIhGo7hx4waKxaLYYtDzzp49C4/Hww1UpwAWCZ1QG77Q5/KClduUDQbDE30H46oaskDIfiDqE73U70cDXeQfkc1msbOzg93dXRSLxbGToOOwWCxIJBK4fPky5ufn4XK5RioTRqMR0WgUb731FsLhMC5evIhsNgubzYbp6WnRUOZ2u0VVhNEXFgkd0borq+coAIyUGofD4YhRLfBYJOTtiRxJjOvAJIGgOYpyuSycsLPZLEqlkrD4fxYURcGZM2ewtLQ0YjxDkElNIBCAzWZDIpFAo9EQZ4zQcYUsEKcHFolTxHEVDLlRSb2tOK5HQn49+lzORcgCIbdTl0olFAoFFItF1Go1IVDj8idq6Ii+VCqFSCQijGfU3yPwKOKgobV+vz9S1mUL/dMFi8QpQN0qPc5/koa95I9utzty2pXc9zCuYiK/HpU4O50Oms0marUayuUySqWScKoGICztyR1bffK4fM0kEuFw+KlTm3KzFQnXuDIwPZbRDxYJHTnOd1JtFDMuoUkTofKH+rXk95GhbQwJRL1eR6VSQblcFj0QZKEXDAbF5CeZv6i9LmnBW61WsVVQb32OQ0vMAIxt62ZOFhYJnVDfOdVNUWqRoMiBDGHI74Fs8+lzdS+EWjjUicparSZG0avVKprNJobDoZgVobM0PB4PcrmcmASla6A2cfogJ6lKpYJgMDh26zCuTDvuWgEce6Qhc3KwSOjMuAYp4HFlwmKxCIMZqnDIdvokDmSnL89yqE8AAx5HEZSorFarKJfLIoqgKobdboeiKPD7/QgGgwgEAvB6vdjd3UU+nxcuU3K0QALx8OFDbG5uCrcq+TxQdS6ExEaeLVHPfnCOQl9YJE4BxwmFOolJC5ESjdSERC5WJBLyoTyUDKTXkp9fq9VQKpVQLBZRLpeFwS4JDk2X0p/kxUleETR/QduhTqeDQqGAu3fvIhKJwOVyAQC8Xq+YO6FIiM4Qoe+DTgBTFAU2mw1Wq1UMlzH6wiKhI8e1V6tFQ92dKftgypUBEgr5gyY5KaoYDAZot9toNpui3Hl4eChGwIfDoag89Ho9WK1WYVhLH2SQS1GJnEeoVCpIp9P48ssvoSgK+v0+pqenxUFBJBD0vjTDYbPZ4PV6EQwGEQwGhcBxTkJ/WCROCXKCb1x1gqCIgMJ22nZQWE5RAN2VaTiK8hW0XanVamLs++DgQJwsTv0XZHZjMpnQ6XREg1U+n0e5XBZCIV8nlWrL5TIePnwoDhhqtVoIh8OwWCziGL9MJoNMJoNyuYxerwePx4NEIiGOObTb7U+Y6DD6wCKhI+NMYcZ9Xc70yyJAVQayh6OwXfZsoCiDPicXazoMeHd3F/v7+0IkyDafxtAp8pCjCHq/cdB1FItFbGxsiEVfqVTgcrnQ6XRQLBaRyWSQzWZRr9dH+iOooYps+Hi4S39YJHTmuIhBbpiSZzfIkYr+brFYhJckbQG63a44J0N2vpYX/NHREfb397G7uyvu6FTeVLdty8a1z2I7R4nJcrmM/f19BAIBmEwmeL1eEWlQObXf74utxsTExIgVH89tnA5YJHTkuG2F3PAkRxFWqxUAxFkbDodD3OGpAUr2aJBfh0qNzWYTlUoF+XweBwcHyGazODw8FEYw8vuqP/823xclMilqKZVKYuvQ6/VEotJmsyEUCiGZTGJ6ehqBQECY//I243TAIqET6g7IcR2T6m0G2daRQLTbbTgcDjgcDthsNuH6RKd5ye5TFAk0Gg2USiXkcrmRvgfZwPbbioIauYQJPO7N6PV6ouricDhgsVigKAqmpqYwPz+PSCQCh8PBeYhTBouEzqg7DGXk7ksyp5EHsxRFgaIo4hwPu92OWq0mDvCReyfoPbrd7ojtvVypeBECATz2x3Q4HPB4POLYQVkYKFfh8/kwNTXFk5+nGBYJnZDvturW5WdZpBTSkz+D2+2G3+8X/QvUxSgPgLVaLZhMJjSbTeTzeTHlSbmBF4HdbofX60U0GkUqlcL8/DxmZ2cRDofhcrnE0JfB8MgM2O12IxQKCR9MzkOcPgxP+Q/J5/x9z8jbje/6fHWbs9p3grYcvV4PzWYTxWIRW1tbuHv3Lu7cuYP19XVks1nUajURWajnM7Sw2+3w+XyIx+NIpVKYm5tDKpXC1NQUwuHw2BPFaAtFvR3qaVbmxDn2PyCLxA8Ira0C/Ts1QLVarSfO68zn86L7kjoxaSq02WyKvgjKKQSDQUxPT2N+fh6pVAozMzOYnJxEIBAYKWGOG2KjP3ku49TAIsGMoq580CwFnWZOh/hWq9WR9mng0YniLpcLPp8P0WgUoVAITqdzxIGKF/5/O1gkmOeH/R1+0Bz7S+XEJaOJOrdx3L8xP1w4kmAYBtCIJDidzDCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaGJ+ytcNJ3IVDMOcWjiSYBhGExYJhmE0YZFgGEYTFgmGYTRhkWAYRhMWCYZhNPn/iEOWy33IY9IAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 65\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 66\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 67\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 68\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 69\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 70\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 71\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 72\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 73\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 74\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 75\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 76\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 77\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 78\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 79\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 80\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 81\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 82\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 83\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 84\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 85\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 86\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 87\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 88\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 89\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 90\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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Tp3H06NEp92BM5Pf78ac//QlXrlxBNptFd3c3Ojs7EYlEhNdcuHAB77zzDpqbm1FeXi4EYFdXF06fPn1DbUcgEJixGIssHAqJEspms0gkEgsaEhPnB/j9HU1NTXj66afx0EMPFbUlPB6P49///jd6e3tnfG02m8XFixfR3t6OXC43ZagEg0EcOnQIK1euxAMPPAClUgmfz4eXXnoJFy5cuOH1fN0HKQ0KiRJKJBIIBoMLdgIwDAOZTCZ0ftLpdGhqasLjjz+Ohx56aMauT/zQvrW1FadOnSr6fXO53Iybybq6uvDrX/8agUAAdrsdb7/9Nl566aUpVzL4vR3890QWF4VECUxshDs4OHhTy5cz4estnE4nVq9ejV27dqGlpQUVFRUzrmYwDINYLIa3334b165dE30tf8tSbKUnx3G4cOECBgcHoVKpEAwGp629SCaTcLlc2LRpE230KgEKiRLgd1JevnwZHo9nQd9Hp9Nh/fr12LVrFzZu3Ij6+noYjcYZG9vwzWreeecdHDly5IaCJoZhYDAYYLfb8YlPfALV1dUYGhoSGusWo9gHGLMsi0gkQlvGS4RCYpHxo4jBwUEcPXp00oTefFMoFKivr8cDDzyAHTt2wG63Q6PRTBsQE1cOYrEYTp8+jd/+9rc3NKuxWq24++678fDDDwsNc8vKyoQOVAcPHpzXeRalUonKykoKiBIRDQlqHTa/+FoAn8+H5557Du+8886Cvl9FRQXuu+8+7NixAw6HA0qlUqhLmGhi74pEIoH+/n4cPnwYhw8fxrVr1ybdDjU1NeE73/kOHnroIdjt9kknrtlsxrp16/DPf/5z2luHuVCr1Vi+fLnQsYvmJRaXaEjQL2N+ZTIZ9PT04LnnnsPBgwcXtNxYq9Xi3nvvxZ49e+BwOKBSqSCRSKZtJMM3ffnvf/+Lf/zjH+js7Lzh+CwWC5566ins378fOp0OwOQLCcMwsNls0Gq18xoSDMNArVbP29cjsyMaEh0dHYjH4xQWN4nvEn3x4kW88soraG9vX9AlPa1Wiz179uArX/mK0IF64iQlX5eQy+UwNDQkFECdOHECvb290/agqK6uxr333jupc9VH/zb4Z5LOp/Xr1wuNeelvcfGJhsSzzz6LY8eOQS6X063HTRofH8f4+PiCr2RUV1fjkUcewRe+8AWsWrUKKpXqhsrGTCYDj8eDU6dO4fXXX8e5c+fg8/lmXLbUarWwWCwztvufz0fyGQwG7Nu3DzabjW41SkQ0JCKRSNGtz8jcSSQSoS/EbMNYoVDAbDajsrISO3bswH333Yc777wTFRUVN0z08QHR3t6OP//5zzh27BiGhoaKfq9i2v0nEglkMhnR16hUKnAcV1QV5fr167F27dqij5HMP9Hf+MTdeTSSmH9qtRpNTU2w2WxIJpMYHByEz+eb8SQDALvdjhUrVmDDhg24//770dDQAIvFAqPROO3VluM4eDwePP/88zh06BBisdisjneq2wsefwvj9/unnI+oqKhAXV0dnE4nqqurhbC6ePHitLc3Op0Oe/fuxcqVK4X3J4tPNCT4oTEFxPwzGo34yle+gr1798JisWB0dBRnzpzBq6++ivb29ilPHL55y5YtW/Dggw9i69atMBqNwqTkTDKZDN566y289tprsw4I/v2nwt8GuN1utLW1Tfraer0ed911Fz73uc9h9+7dwkON8/k8+vr68Oyzz+Kll16a8vvdtm0b7rnnHgqHEqM6iRKQy+XYv38/fvzjH8NisQAAHA4HnE4nrFYrDhw4gNbWVmFfh1qtRn19Pe6++27s2bMHmzZtglarndW9Pz95+vbbb8Pv98/puMWeURoIBPDSSy/h5MmTwsXFYrHgC1/4Ar7xjW+grq7uhuXXNWvW4Nvf/jYikQiOHj06aQRls9nwyCOPYMWKFXM6VjJ/KCRKYPny5fjyl7+MiooK4aRRKBSw2Wy4//77UVZWhvr6egwPD8NgMKC5uRlbtmzB+vXrhaXH2SoUCggEAnC73XOaPOU3h/GTm/zoMp/Po7e3FwcPHsQLL7wgVFvK5XLs3r0b3/zmN1FfXz/laEAmk6GxsRFf+9rXEAgEcO7cObAsC6VSie3bt2Pr1q3zOglK5oZCogSWLVuGhoaGG04cqVQKi8WCnTt3oqmpCalUCgaDQWiEe7P4DtRzIZFIkEql4PP5oNPphPmHU6dO4ciRIzh16tSkZjQmkwnbt29HbW2t6O2CQqHA5s2b8eSTT4JhGPT19aG8vByf/OQnYbVa53SsZH5RSJSARqOZ9sSRSqUwGo3Q6/XC8yjmi06nu6nnV/T39+PAgQNwOBzw+Xzo6OjA5cuXp9x/YbPZsGbNmqKOX6vV4r777gMAnDx5EtlsFmq1mp4DukRQSJSAWKXlxAfV8P9/PvDVkLt27UJHR0dRG6smYlkWbrcbBw4cAPDh3g6xLtYVFRVwOp1FHb9EIoHZbMa2bdvAcRzOnTuH9vZ2VFRUwGQyFfWEc7JwKCRKwOPxoKurC3fddZdwb//Rk2m+r578rs3HH38cUqkUx44dEypqi6n+LBQKRXeulslkWLFiBcxmc9Hfh0wmg8lkQkVFBcLhMFpbW3H16lWoVCo8+OCDwuMCyeKjkCgBl8uFn/3sZ/jpT3+KDRs2QCKRCA1t57ticSKZTIbly5fjmWeewe7du9HZ2YmRkREEAgHh0YHJZBLxeBzBYBCjo6OIx+Ozbh1ns9mwc+fOWe234Bvk8BvMPB4P+vv7oVKpsGLFCqxevXq23y6ZJxQSJVAoFHDixAnEYjHs2rULVqsVVqsVK1euhNPphFarnXK3ZrFfG5h+JCKRSKDX67Fx40Zs3LgRHMchkUggkUggnU4jnU4jHo/D5XLh1VdfxfHjx2cVEnK5HDt37sTWrVuL6p058bj5lZKBgQFhgvX06dN4+eWXUVtbOy+Tt2T2KCRKhOM4vPvuu3j//feh1WpRXl6ODRs2YN++fdi+fTuMRuOkXZt8Exi+NVw+nwfLssjn88IoRCKRQC6XQ6PRQK1WQyaTFdWeTqfT3bC02tDQIKxezKbnxZo1a/DEE0+gqqpq1j+TQCCAy5cvIxgMCv+WTqfxr3/9C1/60pcmbSwji0c0JCZOnlHV5cLI5XIIh8MIh8Pwer3CMmJLSwu0Wq3QlcntdqOjowOXLl2C1+tFMplENpsVfi8ymQwajQZVVVVoaWnBjh07UFtbC4VCMe1JNdW/819PoVBArVbPqqbC4XDgmWeewV133TXjHo+Pvmc+n0drayva2tpueM9wODyvW8/J7Ij+Jic+Z5IsvHQ6jTNnzoBhGHg8HthsNvj9fly4cEEIh5kmGlUqFdra2pBMJrFv375JBVvF4F/LcRwCgUDRPS+0Wi327t2Lz3zmM0V14Obxez5cLheOHDmCgYGBG16TTqcxODiI1atX0yiiBERDwmQyweFw0Fbxm1QoFEQbvU4Uj8fx3nvvYWBgAEqlEsFgEIFAoOgiqHQ6jb6+Ppw7dw733HMPKioq5nTMmUwGAwMDRT+sd9u2bdi3bx+sVuusTmR+D8cf/vAHHD9+fMoATCaTuH79OnK5HK1ylIBoSHz961/HE088Qel9EwqFAhQKBQ4fPoxf/OIXRb0+Fouhr69PaAwzW/wtDF+MNBfxeBzDw8NFvX9NTQ0ee+wx3HHHHcKtKf+/LMsim80iFoshGo0ik8kItxehUAjd3d146623cPLkyWnnPvL5/II/n4RMTzQk1qxZs1jH8bFXV1cHvV6PQ4cOoaurS/Tk4zhuTuHAk0gkwlzEXE4slmUxNjaGUCg04+crFAqsX78eGzZsgFwuF94zk8kgHA6ju7sbZ86cwdWrVxEMBoWQYFkW4+PjGB4enjRRORWlUonq6mpqhFsi1Ah3ERQKBRiNRnz3u9/FY489ht/85jf405/+NO3S4sQVjdliGAYajQYWiwUKhWJOXyOfz2NkZATJZFI0aBiGgV6vh9VqRSqVwujoKDiOw9jYGK5cuYI333wTly9fhtvtRiwWm/PfU1lZGRobG2c1GUrmDzXCXQT8z1GpVKKxsRE/+clPAGDKoJDJZJBKpcKQfDarC3xAVFVVobKyEgzDgGXZGZdCJ+I4DqlUCmNjY2AYBgqFYtr5EP6k7enpwQsvvAC1Wo2RkRH09vbC7XZjbGxsXnp5yuVylJeXAwAtgZYARfMiKxQKsFqt+P73v49IJIJXX30VuVwOarUaOp0ORqMRUqkU0WgU4XC46HtxlUoFk8kEu92OxsZGmM1mpFIppNNpyOXyoobqfDBFo1Gk02lYLBaYzWZEo1Fks9kb2uvx8yeXLl3ClStXwLIsMpnMvDf5lUgkdKtRQhQSi4y/CjqdTnz3u99FeXk5vF4vqqqqUFVVBZ1Oh3Q6jd7eXly8eBHXr1+fdtlTLpfDYDCguroajY2NqKqqEprR5HI59Pf3Q61Wo6amRqjiFMOyLFKpFMbHx6FQKLBq1SrhYb7BYBDj4+NIpVKTCrny+fyct58Xa926dUKxF40iFh8zw1WKJiUWUC6Xg9/vRywWg0ajEVrR8/0u33vvPbz55pvo7u5GJBIR6lZUKhUsFguam5vxyU9+EmvWrMGyZcsgkUjg9/vR3d0Nl8sFlmVRX1+PlpYWrFq1Cnq9ftr7en4UEA6H4fP5MDIyglAohLGxMbjdbnR3d+Pq1avw+/3IZDIL2vV7orKyMjz33HP44he/uCjvdxubNn1pJFFCcrkc1dXVAP5XVMQP+flSab1ej8rKSrjdbmSzWRgMBjQ0NAjhUFVVBY1GA+DDTtWpVAqhUAgdHR0IBoO4ePEi+vr68PDDD2Pjxo0wmUyT5igKhYLQuTqZTCKZTEIikaC8vBxGo1Fooe9yuZBOp5HL5RYtIIAPu3g1NzcLx0ojicVHIbFE8J2oC4UC5HI5dDodampqAHz47M2xsTFhKXD58uWwWq3QaDTCJCfHcchkMsIuSpfLhfHxcTAMg2AwiGw2C5lMJjzohh9R8KHEb+7iOA4KhQJyuRy5XA6RSAQ+nw99fX0Ih8ML+lChqX4m+/fvF3aAUkCUBoXEEsOfCFKpFGq1GlarFTKZDCzLQqvVwmw2Q6vVQi6X37C3Jp/PY3R0FG63G+Pj48JcQTAYxKVLl1BbWwuLxYLa2lrhqV58TUYulwPLssImsVwuh1gshp6eHpw9exYul2vB5x4+atOmTbj//vtntZuUzD8KiSWIH1XwRVF6vR4SiQRarRZlZWVCQEy8ZeCbwvh8PoRCIeGkBz6se4hEIvB4PBgeHkZ5ebnQ2JbjOGEiEoAw6RkOh9He3o7XX3992hb/xdDr9XA6nVAqlfB6vUV3xOIfObBx48Y5vS+ZPxQStwC+pR3f1m6qp4KzLItEIoFIJIJsNiuEDN/MRalUQiKRIJ/PC1vOJ37uxLmJUCiErq4uvPHGG2htbZ3U4HY26urq8Mwzz2Dnzp1QKBTo6+vDH//4R5w8eVJ0VKJQKLB//348/PDDc3pfMr8oJJYw/sRlWRYMw4DjuEkdrPiVKf5WI5lMgmVZqFQqKJVKIVx0Oh3sdjvsdruwRMp/XT4kcrkc0uk0xsbGcPXqVZw6dQptbW2z6iUxkdVqxfe+9z088cQTQrOYT3ziE6irq8OvfvUrvPzyy1MGhVKpxL59+/DDH/4QlZWVc/7ZkflDIbFETbzlACCMACbORfD4WoVsNguNRgOr1SoUNalUKlRWVmLt2rWor6+HyWQS7vH5oOBXNoLBIHp7e3Hu3Dm0tbUhEAjM+RkdjzzyCD7/+c8LjWJ469atw49+9CMAwKuvvjppK7rZbMbjjz+OH/zgB6itrZ31+5KFQSGxRE28TcjlcsItAr91m1/G5DtWJZNJ5PN5mEwmrFixAmVlZeA4DkajEU6nE3fccYdQVCWRSIQdprlcDolEAsFgEC6XC5cvX8aVK1cwMjIy501mDocD+/fvh8FgmHLZsrm5GT/84Q9hNpvx2muvIR6PY/ny5Xjsscewb98+OByOm/75kflDIbFE8SHBN5PlO1HxT+3me2Dy8wjj4+NIp9PQarXCEinfqn7ZsmWw2+3Q6/WQSqXCcikfEHzBVFdXFzo7O+H1epFOp+e8IYsvDRfrs9nc3Izvfe972LJlC0KhEJqbm3HnnXfCYDDM+WdGFgaFxBLGL0fyJxu/vTqVSgmrF3xIpFIpxONxcBwHvV4PvV4PlUoFg8EAo9EItVoNhmGElYxcLicUXnm9Xly/fh1Xr17F8PDwTfWhYBgG1dXVQvXodKRSKaqqqvCZz3wGLMtCrVbTUucSRSGxxPFLlSqVCul0GplMBoODgxgZGUEsFkMul5t0QvPzGPzkJb/Emc1mheVNvt8DXyjV39+Pvr4++Hw+JJNJ4RZhLv0oGIYp+mTnR0tTrdiQpYNC4hbAjygUCgXy+Ty8Xi/a29sxOjoqTPzxH1epVFCr1SgrK4PBYIDJZILJZILBYIBGoxFqI5LJpPAAYZfLhaGhIYyPjyOXy0EqlUKlUgm3JrMpxeY4Dm63G8FgEOXl5dOWUnMch3Q6jVAohGw2K4x+ppqYJaVFIXGLmHiFDofDGBgYmHTl57dT80FRVlYGvV4Po9EoPNfDaDRCpVKB4zhEo1H4fD4MDg7C4/FgbGwM8XgcLMsKbfn5LebpdFp4cE8xZdldXV04evQobDbblHMMHMcJVaAXL15ELBaD0+nEunXrsGLFCiEsaHSxNFBI3CL4moeJJdORSASpVEqoluRvNfiwUKvVMBgMCAaDCIfDsFqt0Gq14DhOmIvgr/oTt6MrFApoNBpUVFTAaDQil8vB5/NhaGgI8Xh8xluQcDiMZ599FhqNBo8++qjQHJdfURkcHMSxY8dw9OhRXL16FSzLwmQyYePGjXjkkUewbds2WCyWWTXLIQuHQuIWwYeERqMRekPw8w35fF4ozebnEvgdnXzjmXw+j1wuB51Oh2w2C7/fj6GhIfj9fsTj8UlzG/zSJ39rwt+efHT+Q4zL5cIvf/lLDAwM4OGHH4bT6UQul0NPTw+OHj2K48ePw+v1CsHEB5ler8eaNWtm/SgAsnAoJG4hUqkUer0eNTU1qKyshN/vFwKAr5zkTdz8JZfLhUYyiUQCsVgMIyMj8Pv9SCQSkx7yA0AIlHg8Dp/PN+npYbPhdrvx+9//Hu+//z7uuOMOFAoFdHd3o7Oz84ZKTn5ZVqvVwmAwUCeqJYRC4hbCP8dz7dq1CIVCYFkW7e3tCIVCk7pQAxBGAPxyZyaTQTQaRaFQQCgUQiAQEEYQH8WXec/HtvBoNIp3330Xly5dgkQiQTqdnrYBcG1tLXbt2kWjiCWGQuIWIpFIoFarUVtbi/vvvx8VFRWw2+14//334Xa7kUwmAXwYEPyyIj9HwTe45YunEonETbXtnw2O4xCPx4V5ialuWYxGIx599FFs3ryZumIvMfTbuMVIpVKhqrK8vBzLly9HQ0MD3nzzTXR0dCASiQgjAJlMJjSQ4a/i/CrFbJ4UPh8mbkb7KLlcjgceeAB79+6F2Wxe1OMiM6OQuAVJJBIolUqUl5dDq9XCbrfD6XTixIkTaG1txcjICPL5vFDWze/X4NvbLXZATPTRAi25XI67774bX//619HQ0FCy4yLTo5C4hfGVlXyXbYfDgbVr16KrqwtjY2MoFApQqVSQyWSIx+Po7e3F2NjYovaonIivu+BvgfR6Pe6880489dRT2LhxIxVRLVHULftjYmJPiXg8PqnwiWVZRKNR9PT04J133sGZM2fQ29u74E9oUyqVcDgccDgcsFgsMJlMQpGWVqtFQ0MDNm7cCIfDMeNeD7Lgpp0pppD4GJv4u+VXOiKRCN5//32cPn0aHR0daGtrm/FZnMWSyWRoaGjAnXfeCaPRCJvNhk2bNmH16tUwGAzCPg1gcrctsiRQSJDJOI6Dy+XC2bNn4ff7heYz0WgUXq8X3d3d8Hq9ok8QYxgGdXV1+NSnPgWbzQalUok1a9Zg06ZNwsN0AGqFf4ugkCDFyWQyCAaDGBoawujo6A2FVhNJJBLYbDZs2LABSqVykY+UzDMKCTK1j/7+53rF578OjRhuWRQSZHY++mDgqfCBQMHwsUAhQQgRNW1I0MI0IUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFEFIUEIUQUhQQhRBSFBCFElGyGjzOLchSEkCWLRhKEEFEUEoQQURQShBBRFBKEEFEUEoQQURQShBBR/x8aNAygNv6ccgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", "n = Nx * Ny # number of parameters\n", @@ -2382,22 +364,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.plot((evaluation_history), \"o-\")\n", @@ -2409,22 +378,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", "Ez_coef = opt.get_objective_arguments()[0]\n", @@ -2477,32 +420,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "src = mp.ContinuousSource(frequency=1 / 1.55, fwidth=fwidth)\n", "source = [\n", diff --git a/python/examples/antenna-radiation.ipynb b/python/examples/antenna-radiation.ipynb index 177d2fa83..6bf5e69fa 100644 --- a/python/examples/antenna-radiation.ipynb +++ b/python/examples/antenna-radiation.ipynb @@ -32,17 +32,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import math\n", @@ -111,30 +103,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "f = plt.figure(dpi=150)\n", "sim.plot2D(ax=f.gca())\n", @@ -150,19 +121,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "field decay(t = 50.01): 4938.653280565278 / 4938.653280565278 = 1.0\n", - "field decay(t = 100.01): 7.375043707445242e-11 / 4938.653280565278 = 1.4933309322336344e-14\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "sim.run(\n", " until_after_sources=mp.stop_when_fields_decayed(50, src_cmpt, mp.Vector3(), 1e-8)\n", @@ -178,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -194,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -227,17 +188,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "flux:, 1.227787, 1.227651, 1.227260\n" - ] - } - ], + "outputs": [], "source": [ "npts = 100 # number of points in [0,2*pi) range of angles\n", "angles = 2 * math.pi / npts * np.arange(npts)\n", @@ -271,22 +224,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(dpi=200)\n", "ax = plt.subplot(111, projection=\"polar\")\n", diff --git a/python/examples/antenna-radiation.py b/python/examples/antenna-radiation.py index 83b9b09ac..4b5d0b778 100644 --- a/python/examples/antenna-radiation.py +++ b/python/examples/antenna-radiation.py @@ -1,8 +1,9 @@ -import meep as mp import math -import numpy as np + import matplotlib.pyplot as plt +import numpy as np +import meep as mp resolution = 50 # pixels/um @@ -104,7 +105,7 @@ # integrate the radial flux over the circle circumference far_flux_circle = np.sum(Pr) * 2 * np.pi * r / len(Pr) -print("flux:, {:.6f}, {:.6f}, {:.6f}".format(near_flux, far_flux_box, far_flux_circle)) +print(f"flux:, {near_flux:.6f}, {far_flux_box:.6f}, {far_flux_circle:.6f}") ax = plt.subplot(111, projection="polar") ax.plot(angles, Pr / max(Pr), "b-") diff --git a/python/examples/antenna_pec_ground_plane.py b/python/examples/antenna_pec_ground_plane.py index cdd26c4ff..f16e7ebc8 100644 --- a/python/examples/antenna_pec_ground_plane.py +++ b/python/examples/antenna_pec_ground_plane.py @@ -2,16 +2,16 @@ # positioned a given height above a perfect-electric # conductor (PEC) ground plane and compares the result # to analytic theory. - -import meep as mp import math -import numpy as np + import matplotlib +import numpy as np + +import meep as mp matplotlib.use("agg") import matplotlib.pyplot as plt - resolution = 200 # pixels/um n = 1.2 # refractive index of surrounding medium h = 1.25 # height of antenna (point dipole source) above ground plane @@ -194,4 +194,4 @@ def pec_ground_plane_radiation(src_cmpt=mp.Hz): plt.legend() plt.savefig("radiation_pattern.png", bbox_inches="tight") - print("norm:, {:.6f}".format(np.linalg.norm(Pr_pec_norm - Pr_theory_norm))) + print(f"norm:, {np.linalg.norm(Pr_pec_norm - Pr_theory_norm):.6f}") diff --git a/python/examples/bend-flux.ipynb b/python/examples/bend-flux.ipynb index bf9a54c7e..8a451fe8b 100644 --- a/python/examples/bend-flux.ipynb +++ b/python/examples/bend-flux.ipynb @@ -18,17 +18,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -53,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -109,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -136,33 +128,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,-11.5,0)\n", - " size (1e+20,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sim = mp.Simulation(\n", " cell_size=cell,\n", @@ -204,21 +172,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "field decay(t = 50.050000000000004): 4.825189380557789e-09 / 4.825189380557789e-09 = 1.0\n", - "field decay(t = 100.05000000000001): 0.02880180987942578 / 0.02880180987942578 = 1.0\n", - "field decay(t = 150.1): 0.0268934650933857 / 0.02880180987942578 = 0.9337421921042788\n", - "field decay(t = 200.15): 2.3158397338096397e-13 / 0.02880180987942578 = 8.040604890819488e-12\n", - "run 0 finished at t = 200.15 (4003 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "pt = mp.Vector3(0.5 * sx - dpml - 0.5, wvg_ycen)\n", "\n", @@ -241,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -258,43 +214,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (-2,-11.5,0)\n", - " size (12,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (3.5,2,0)\n", - " size (1,28,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "field decay(t = 50.050000000000004): 1.697652269444903e-10 / 1.697652269444903e-10 = 1.0\n", - "field decay(t = 100.05000000000001): 4.6910710639105265e-07 / 4.6910710639105265e-07 = 1.0\n", - "field decay(t = 150.1): 2.992872733686264e-07 / 4.6910710639105265e-07 = 0.6379934758846679\n", - "field decay(t = 200.15): 0.0039278135652722765 / 0.0039278135652722765 = 1.0\n", - "field decay(t = 250.20000000000002): 0.00015009081939073738 / 0.0039278135652722765 = 0.038212307406279115\n", - "field decay(t = 300.2): 8.806226395655623e-11 / 0.0039278135652722765 = 2.2420174097660296e-08\n", - "run 0 finished at t = 300.2 (6004 timesteps)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "wl = []\n", "Rs = []\n", diff --git a/python/examples/bend-flux.py b/python/examples/bend-flux.py index 66bebed80..f56ab6492 100644 --- a/python/examples/bend-flux.py +++ b/python/examples/bend-flux.py @@ -1,11 +1,8 @@ -# -*- coding: utf-8 -*- - # transmission around a 90-degree waveguide bend in 2d -from __future__ import division +import matplotlib.pyplot as plt +import numpy as np import meep as mp -import numpy as np -import matplotlib.pyplot as plt resolution = 10 # pixels/um diff --git a/python/examples/bent-waveguide.ipynb b/python/examples/bent-waveguide.ipynb index f03241857..aed11b6f0 100644 --- a/python/examples/bent-waveguide.ipynb +++ b/python/examples/bent-waveguide.ipynb @@ -18,17 +18,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "from matplotlib import pyplot as plt\n", @@ -47,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -122,36 +114,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (-2.5,-3.5,0)\n", - " size (12,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (3.5,2,0)\n", - " size (1,12,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "f = plt.figure(dpi=150)\n", "sim.plot2D(ax=f.gca())\n", @@ -167,18 +132,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Normalizing field data...\n", - "run 0 finished at t = 100.0 (2000 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "f = plt.figure(dpi=150)\n", "Animate = mp.Animate2D(sim, fields=mp.Ez, f=f, realtime=False, normalize=True)\n", @@ -188,32 +144,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating MP4...\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "filename = \"media/bent_waveguide.mp4\"\n", "fps = 10\n", @@ -232,38 +165,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (-2.5,-3.5,0)\n", - " size (12,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (3.5,17,0)\n", - " size (1,42,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Meep progress: 307.35/400.0 = 76.8% done in 4.0s, 1.2s to go\n", - "run 1 finished at t = 400.0 (8000 timesteps)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sim.reset_meep()\n", "cell = mp.Vector3(16, 40, 0)\n", @@ -299,37 +203,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (-2.5,-3.5,0)\n", - " size (12,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (3.5,17,0)\n", - " size (1,42,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "run 2 finished at t = 200.0 (4000 timesteps)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "vals = []\n", "\n", diff --git a/python/examples/bent-waveguide.py b/python/examples/bent-waveguide.py index 1faaad84e..b2d0b4e57 100644 --- a/python/examples/bent-waveguide.py +++ b/python/examples/bent-waveguide.py @@ -1,8 +1,4 @@ -# -*- coding: utf-8 -*- - # From the Meep tutorial: plotting permittivity and fields of a bent waveguide -from __future__ import division - import meep as mp cell = mp.Vector3(16, 16, 0) diff --git a/python/examples/binary_grating.ipynb b/python/examples/binary_grating.ipynb index a49b08fe1..ce4a3a291 100644 --- a/python/examples/binary_grating.ipynb +++ b/python/examples/binary_grating.ipynb @@ -26,17 +26,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import math\n", @@ -53,30 +45,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fused_quartz = mp.Medium(index=1.5)\n", "\n", @@ -146,26 +117,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "field decay(t = 50.01): 0.10609306658233127 / 0.10609306658233127 = 1.0\n", - "field decay(t = 100.01): 8.493199823356459e-20 / 0.10609306658233127 = 8.005424008330923e-19\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -181,36 +142,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (-2.25,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sim.reset_meep()\n", "\n", @@ -248,33 +182,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "field decay(t = 50.01): 0.1031398354415896 / 0.1031398354415896 = 1.0\n", - "field decay(t = 100.01): 8.275841626038753e-06 / 0.1031398354415896 = 8.023904237006029e-05\n", - "field decay(t = 150.02): 7.578862246277466e-06 / 0.1031398354415896 = 7.348142658778572e-05\n", - "field decay(t = 200.03): 2.633198313201328e-06 / 0.1031398354415896 = 2.5530371479917354e-05\n", - "field decay(t = 250.04): 1.0595609940381944e-06 / 0.1031398354415896 = 1.0273052981922272e-05\n", - "field decay(t = 300.04): 4.182093600425728e-07 / 0.1031398354415896 = 4.054780175399971e-06\n", - "field decay(t = 350.05): 1.7897453529966721e-07 / 0.1031398354415896 = 1.7352610127153491e-06\n", - "field decay(t = 400.06): 7.323581231104047e-08 / 0.1031398354415896 = 7.100633038387535e-07\n", - "field decay(t = 450.07): 2.934107857572782e-08 / 0.1031398354415896 = 2.844786250637789e-07\n", - "field decay(t = 500.08): 1.1841535133169714e-08 / 0.1031398354415896 = 1.1481049084934639e-07\n", - "field decay(t = 550.08): 4.9984066703627664e-09 / 0.1031398354415896 = 4.8462426267816536e-08\n", - "field decay(t = 600.09): 2.35073055720754e-09 / 0.1031398354415896 = 2.2791684194016495e-08\n", - "field decay(t = 650.1): 1.1816026525464885e-09 / 0.1031398354415896 = 1.1456317023267471e-08\n", - "field decay(t = 700.11): 3.9576427413983696e-10 / 0.1031398354415896 = 3.837162163827255e-09\n", - "field decay(t = 750.12): 1.4213834548284144e-10 / 0.1031398354415896 = 1.3781129752076983e-09\n", - "field decay(t = 800.13): 8.16132061584665e-11 / 0.1031398354415896 = 7.912869533778332e-10\n", - "run 0 finished at t = 800.13 (80013 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))" ] @@ -288,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -326,22 +236,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(dpi=200)\n", "plt.pcolormesh(\n", @@ -387,58 +284,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "field decay(t = 50.01): 0.11139427530016409 / 0.11139427530016409 = 1.0\n", - "field decay(t = 100.01): 1.824305440068526e-15 / 0.11139427530016409 = 1.637701250941967e-14\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - " block, center = (-2.25,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "field decay(t = 50.01): 0.1082498119209954 / 0.1082498119209954 = 1.0\n", - "field decay(t = 100.01): 7.512926070352665e-06 / 0.1082498119209954 = 6.940359467632024e-05\n", - "field decay(t = 150.02): 6.732414916367269e-06 / 0.1082498119209954 = 6.219331744687766e-05\n", - "field decay(t = 200.03): 2.3712635292765586e-06 / 0.1082498119209954 = 2.1905474819736332e-05\n", - "field decay(t = 250.04): 9.494875348809632e-07 / 0.1082498119209954 = 8.771262675023706e-06\n", - "field decay(t = 300.04): 3.8220269083178343e-07 / 0.1082498119209954 = 3.530746927400933e-06\n", - "field decay(t = 350.05): 1.5689447663324306e-07 / 0.1082498119209954 = 1.4493741268368232e-06\n", - "field decay(t = 400.06): 6.492618040375044e-08 / 0.1082498119209954 = 5.997809996301509e-07\n", - "field decay(t = 450.07): 2.9338161615346314e-08 / 0.1082498119209954 = 2.710227490903943e-07\n", - "field decay(t = 500.08): 1.115652431764312e-08 / 0.1082498119209954 = 1.0306275936798441e-07\n", - "field decay(t = 550.08): 4.421515879496221e-09 / 0.1082498119209954 = 4.084548324872104e-08\n", - "field decay(t = 600.09): 1.6989786783603636e-09 / 0.1082498119209954 = 1.5694980418075362e-08\n", - "field decay(t = 650.1): 8.779199940057175e-10 / 0.1082498119209954 = 8.110129509014344e-09\n", - "field decay(t = 700.11): 2.64630974046326e-10 / 0.1082498119209954 = 2.444632183189964e-09\n", - "field decay(t = 750.12): 1.5056115102443512e-10 / 0.1082498119209954 = 1.39086755304776e-09\n", - "field decay(t = 800.13): 6.39132960275918e-11 / 0.1082498119209954 = 5.904240838241642e-10\n", - "run 0 finished at t = 800.13 (80013 timesteps)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from meep.materials import fused_quartz\n", "\n", diff --git a/python/examples/binary_grating.py b/python/examples/binary_grating.py index 74fb36992..4b0182bc0 100644 --- a/python/examples/binary_grating.py +++ b/python/examples/binary_grating.py @@ -1,9 +1,9 @@ -# -*- coding: utf-8 -*- - -import meep as mp import math -import numpy as np + import matplotlib.pyplot as plt +import numpy as np + +import meep as mp resolution = 60 # pixels/μm @@ -139,5 +139,5 @@ plt.title("transmittance of diffraction orders") cbar = plt.colorbar() cbar.set_ticks(list(np.arange(0, tran_max + 0.1, 0.1))) -cbar.set_ticklabels(["{:.1f}".format(t) for t in np.arange(0, tran_max + 0.1, 0.1)]) +cbar.set_ticklabels([f"{t:.1f}" for t in np.arange(0, tran_max + 0.1, 0.1)]) plt.show() diff --git a/python/examples/binary_grating_n2f.ipynb b/python/examples/binary_grating_n2f.ipynb index 892af4622..c3992260d 100644 --- a/python/examples/binary_grating_n2f.ipynb +++ b/python/examples/binary_grating_n2f.ipynb @@ -20,208 +20,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.52 x 0.04 x 0 with resolution 25\n", - "time for set_epsilon = 0.00124812 s\n", - "-----------\n", - "field decay(t = 50.02): 0.10083813086471559 / 0.10083813086471559 = 1.0\n", - "field decay(t = 100.04): 3.8807013552778427e-16 / 0.10083813086471559 = 3.848446338701171e-15\n", - "run 0 finished at t = 100.04 (5002 timesteps)\n", - "\n", - "Field time usage:\n", - " connecting chunks: 0.00231314 s\n", - " time stepping: 0.169821 s\n", - " communicating: 0.0808802 s\n", - " Fourier transforming: 0.0359166 s\n", - " everything else: 0.156131 s\n", - "\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.52 x 10 x 0 with resolution 25\n", - " block, center = (-2.25,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,0,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.105644 s\n", - "-----------\n", - "field decay(t = 50.02): 0.09814324428153094 / 0.09814324428153094 = 1.0\n", - "field decay(t = 100.04): 7.939880373487791e-06 / 0.09814324428153094 = 8.090093649962981e-05\n", - "field decay(t = 150.06): 6.627624476069797e-06 / 0.09814324428153094 = 6.753011401434805e-05\n", - "field decay(t = 200.08): 2.146203858444386e-06 / 0.09814324428153094 = 2.1868075323532677e-05\n", - "on time step 11243 (time=224.86), 0.00035578 s/step\n", - "field decay(t = 250.1): 8.032368775670381e-07 / 0.09814324428153094 = 8.184331824846705e-06\n", - "field decay(t = 300.12): 2.965177550201506e-07 / 0.09814324428153094 = 3.0212752511988307e-06\n", - "field decay(t = 350.14): 1.3086054960500633e-07 / 0.09814324428153094 = 1.3333627858237849e-06\n", - "field decay(t = 400.16): 5.285162741753926e-08 / 0.09814324428153094 = 5.385151856803365e-07\n", - "field decay(t = 450.18): 1.8336367759809585e-08 / 0.09814324428153094 = 1.8683270452330267e-07\n", - "on time step 22548 (time=450.96), 0.000353856 s/step\n", - "field decay(t = 500.2): 7.33127235003134e-09 / 0.09814324428153094 = 7.469971472515275e-08\n", - "field decay(t = 550.22): 2.8980535977675605e-09 / 0.09814324428153094 = 2.952881391870729e-08\n", - "field decay(t = 600.24): 9.12195167093687e-10 / 0.09814324428153094 = 9.294528357723631e-09\n", - "field decay(t = 650.26): 3.6982320415205274e-10 / 0.09814324428153094 = 3.768198278540582e-09\n", - "on time step 33740 (time=674.8), 0.000357411 s/step\n", - "field decay(t = 700.28): 1.7964764774382362e-10 / 0.09814324428153094 = 1.830463717181505e-09\n", - "field decay(t = 750.3000000000001): 1.1056722890352689e-10 / 0.09814324428153094 = 1.1265903192109368e-09\n", - "field decay(t = 800.32): 3.1235531072986875e-11 / 0.09814324428153094 = 3.182647089124699e-10\n", - "run 0 finished at t = 800.32 (40016 timesteps)\n", - "\n", - "Field time usage:\n", - " connecting chunks: 0.00979877 s\n", - " time stepping: 9.10302 s\n", - " communicating: 1.25385 s\n", - " Fourier transforming: 2.74567 s\n", - " everything else: 1.15844 s\n", - "\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.52 x 212 x 0 with resolution 25\n", - " block, center = (-2.25,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,-100,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,-90,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,-80,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,-70,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,-60,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,-50,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,-40,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,-30,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,-20,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,-10,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,0,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,10,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,20,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,30,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,40,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,50,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,60,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,70,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,80,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,90,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (0,100,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 3.14788 s\n", - "-----------\n", - "on time step 597 (time=11.94), 0.00670513 s/step\n", - "on time step 1213 (time=24.26), 0.00650265 s/step\n", - "on time step 1848 (time=36.96), 0.00630352 s/step\n", - "on time step 2483 (time=49.66), 0.00630129 s/step\n", - "field decay(t = 50.02): 0.09814324428153119 / 0.09814324428153119 = 1.0\n", - "on time step 3124 (time=62.48), 0.00624995 s/step\n", - "on time step 3763 (time=75.26), 0.00626324 s/step\n", - "on time step 4390 (time=87.8), 0.00638796 s/step\n", - "field decay(t = 100.04): 7.939880373488582e-06 / 0.09814324428153119 = 8.090093649963766e-05\n", - "on time step 5016 (time=100.32), 0.00639027 s/step\n", - "on time step 5655 (time=113.1), 0.00626925 s/step\n", - "on time step 6286 (time=125.72), 0.00634788 s/step\n", - "on time step 6913 (time=138.26), 0.00638669 s/step\n", - "field decay(t = 150.06): 6.627624476069556e-06 / 0.09814324428153119 = 6.753011401434542e-05\n", - "on time step 7542 (time=150.84), 0.00636212 s/step\n", - "on time step 8176 (time=163.52), 0.00631072 s/step\n", - "on time step 8807 (time=176.14), 0.00633942 s/step\n", - "on time step 9430 (time=188.6), 0.00642652 s/step\n", - "field decay(t = 200.08): 3.77258555623287e-08 / 0.09814324428153119 = 3.8439584750336235e-07\n", - "on time step 10053 (time=201.06), 0.00642324 s/step\n", - "on time step 10693 (time=213.86), 0.006254 s/step\n", - "on time step 11327 (time=226.54), 0.00630954 s/step\n", - "on time step 11966 (time=239.32), 0.00626743 s/step\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "field decay(t = 250.1): 3.5244910064367445e-10 / 0.09814324428153119 = 3.591170265705177e-09\n", - "on time step 12599 (time=251.98), 0.00632365 s/step\n", - "on time step 13244 (time=264.88), 0.00620512 s/step\n", - "on time step 13874 (time=277.48), 0.00634975 s/step\n", - "on time step 14516 (time=290.32), 0.00623725 s/step\n", - "field decay(t = 300.12): 1.0847045885564748e-11 / 0.09814324428153119 = 1.1052259342934691e-10\n", - "run 0 finished at t = 300.12 (15006 timesteps)\n", - "error:, 10, 5.981324591508142e-05\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import math\n", @@ -424,22 +225,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "freqs = mp.get_near2far_freqs(n2f_obj)\n", "wvl = np.divide(1, freqs)\n", diff --git a/python/examples/binary_grating_n2f.py b/python/examples/binary_grating_n2f.py index f1b41450f..77de0bc3a 100644 --- a/python/examples/binary_grating_n2f.py +++ b/python/examples/binary_grating_n2f.py @@ -1,10 +1,10 @@ -# -*- coding: utf-8 -*- - -import meep as mp import math + +import matplotlib.pyplot as plt import numpy as np from numpy import linalg as LA -import matplotlib.pyplot as plt + +import meep as mp resolution = 25 # pixels/μm @@ -189,7 +189,7 @@ ) norm_err = LA.norm(ff_unitcell["Ez"] - ff_supercell["Ez"]) / nperiods -print("error:, {}, {}".format(nperiods, norm_err)) +print(f"error:, {nperiods}, {norm_err}") freqs = mp.get_near2far_freqs(n2f_obj) wvl = np.divide(1, freqs) @@ -221,7 +221,7 @@ plt.xlabel("angle (degrees)") plt.ylabel("relative enhancement") plt.grid(axis="x", linewidth=0.5, linestyle="--") -plt.title("f.-f. spectra @ λ = {:.1} μm".format(wvl_slice)) +plt.title(f"f.-f. spectra @ λ = {wvl_slice:.1} μm") plt.tight_layout(pad=0.5) plt.show() diff --git a/python/examples/binary_grating_oblique.ipynb b/python/examples/binary_grating_oblique.ipynb index 8454e37f7..7a155ebbb 100644 --- a/python/examples/binary_grating_oblique.ipynb +++ b/python/examples/binary_grating_oblique.ipynb @@ -29,17 +29,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import math\n", @@ -57,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -139,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -168,40 +160,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "Meep: using complex fields.\n", - "Meep progress: 9.32/200.0 = 4.7% done in 4.0s, 81.9s to go\n", - "Meep progress: 17.79/200.0 = 8.9% done in 8.0s, 82.0s to go\n", - "Meep progress: 26.91/200.0 = 13.5% done in 12.0s, 77.2s to go\n", - "Meep progress: 36.730000000000004/200.0 = 18.4% done in 16.0s, 71.2s to go\n", - "Meep progress: 46.550000000000004/200.0 = 23.3% done in 20.0s, 66.0s to go\n", - "Meep progress: 56.34/200.0 = 28.2% done in 24.0s, 61.2s to go\n", - "Meep progress: 66.34/200.0 = 33.2% done in 28.0s, 56.4s to go\n", - "Meep progress: 76.15/200.0 = 38.1% done in 32.0s, 52.1s to go\n", - "Meep progress: 86.03/200.0 = 43.0% done in 36.0s, 47.7s to go\n", - "Meep progress: 95.34/200.0 = 47.7% done in 40.0s, 43.9s to go\n", - "Meep progress: 104.71000000000001/200.0 = 52.4% done in 44.0s, 40.1s to go\n", - "Meep progress: 114.54/200.0 = 57.3% done in 48.0s, 35.8s to go\n", - "Meep progress: 124.45/200.0 = 62.2% done in 52.0s, 31.6s to go\n", - "Meep progress: 134.25/200.0 = 67.1% done in 56.0s, 27.4s to go\n", - "Meep progress: 144.12/200.0 = 72.1% done in 60.0s, 23.3s to go\n", - "Meep progress: 154.06/200.0 = 77.0% done in 64.0s, 19.1s to go\n", - "Meep progress: 163.6/200.0 = 81.8% done in 68.0s, 15.1s to go\n", - "Meep progress: 173.31/200.0 = 86.7% done in 72.0s, 11.1s to go\n", - "Meep progress: 182.85/200.0 = 91.4% done in 76.1s, 7.1s to go\n", - "Meep progress: 192.52/200.0 = 96.3% done in 80.1s, 3.1s to go\n", - "run 0 finished at t = 200.0 (20000 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "sim.run(until_after_sources=100)\n", "\n", @@ -218,25 +179,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (-2.25,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0,0)\n", - " size (0.5,5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Meep: using complex fields.\n" - ] - } - ], + "outputs": [], "source": [ "sim.reset_meep()\n", "\n", @@ -283,48 +228,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 9.58/300.0 = 3.2% done in 4.0s, 121.4s to go\n", - "Meep progress: 18.59/300.0 = 6.2% done in 8.0s, 121.2s to go\n", - "Meep progress: 28.060000000000002/300.0 = 9.4% done in 12.0s, 116.4s to go\n", - "Meep progress: 37.59/300.0 = 12.5% done in 16.0s, 111.8s to go\n", - "Meep progress: 47.0/300.0 = 15.7% done in 20.0s, 107.7s to go\n", - "Meep progress: 56.58/300.0 = 18.9% done in 24.0s, 103.3s to go\n", - "Meep progress: 65.96000000000001/300.0 = 22.0% done in 28.0s, 99.4s to go\n", - "Meep progress: 75.53/300.0 = 25.2% done in 32.0s, 95.2s to go\n", - "Meep progress: 85.19/300.0 = 28.4% done in 36.0s, 90.8s to go\n", - "Meep progress: 94.8/300.0 = 31.6% done in 40.0s, 86.6s to go\n", - "Meep progress: 103.02/300.0 = 34.3% done in 44.0s, 84.2s to go\n", - "Meep progress: 108.78/300.0 = 36.3% done in 48.0s, 84.4s to go\n", - "Meep progress: 117.64/300.0 = 39.2% done in 52.0s, 80.7s to go\n", - "Meep progress: 126.28/300.0 = 42.1% done in 56.0s, 77.1s to go\n", - "Meep progress: 134.91/300.0 = 45.0% done in 60.0s, 73.5s to go\n", - "Meep progress: 144.69/300.0 = 48.2% done in 64.1s, 68.8s to go\n", - "Meep progress: 154.22/300.0 = 51.4% done in 68.1s, 64.3s to go\n", - "Meep progress: 163.89000000000001/300.0 = 54.6% done in 72.1s, 59.8s to go\n", - "Meep progress: 173.19/300.0 = 57.7% done in 76.1s, 55.7s to go\n", - "Meep progress: 182.75/300.0 = 60.9% done in 80.1s, 51.4s to go\n", - "Meep progress: 192.55/300.0 = 64.2% done in 84.1s, 46.9s to go\n", - "Meep progress: 202.20000000000002/300.0 = 67.4% done in 88.1s, 42.6s to go\n", - "Meep progress: 212.06/300.0 = 70.7% done in 92.1s, 38.2s to go\n", - "Meep progress: 221.95000000000002/300.0 = 74.0% done in 96.1s, 33.8s to go\n", - "Meep progress: 231.65/300.0 = 77.2% done in 100.1s, 29.5s to go\n", - "Meep progress: 241.43/300.0 = 80.5% done in 104.1s, 25.2s to go\n", - "Meep progress: 251.13/300.0 = 83.7% done in 108.1s, 21.0s to go\n", - "Meep progress: 260.92/300.0 = 87.0% done in 112.1s, 16.8s to go\n", - "Meep progress: 270.72/300.0 = 90.2% done in 116.1s, 12.6s to go\n", - "Meep progress: 280.64/300.0 = 93.5% done in 120.1s, 8.3s to go\n", - "Meep progress: 290.5/300.0 = 96.8% done in 124.1s, 4.1s to go\n", - "run 0 finished at t = 300.0 (30000 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "sim.run(until_after_sources=200)" ] @@ -338,38 +244,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " iteration 60: trace = 166.7770726644721 (1.83568e-09% change)\n", - " iteration 55: trace = 177.9876051266832 (7.99025e-10% change)\n", - " iteration 51: trace = 189.5448069917852 (5.38133e-07% change)\n", - " iteration 48: trace = 201.4664481059231 (1.8864e-08% change)\n", - " iteration 46: trace = 207.5342915143063 (3.85266e-10% change)\n", - " iteration 43: trace = 213.7703137192983 (4.51554e-09% change)\n", - " iteration 41: trace = 220.034335071049 (1.36799e-09% change)\n", - " iteration 41: trace = 226.4741893511782 (4.77871e-07% change)\n", - " iteration 41: trace = 232.9432676387035 (5.52227e-08% change)\n", - " iteration 39: trace = 239.595834286208 (3.53174e-06% change)\n", - " iteration 38: trace = 246.2788666241614 (2.25977e-09% change)\n", - " iteration 37: trace = 253.1530499209222 (9.10884e-06% change)\n", - " iteration 37: trace = 260.0589101770202 (1.34992e-09% change)\n", - " iteration 35: trace = 267.1635561942985 (5.91227e-07% change)\n", - " iteration 35: trace = 274.3011759549739 (1.72579e-09% change)\n", - " iteration 32: trace = 281.6452308165144 (1.0722e-05% change)\n", - " iteration 31: trace = 289.0234421086738 (1.50935e-07% change)\n", - " iteration 30: trace = 296.6161748479355 (0.000100081% change)\n", - " iteration 59: trace = 296.6157305576143 (5.87185e-11% change)\n", - " iteration 29: trace = 304.2434860777851 (2.93797e-07% change)\n", - " iteration 28: trace = 312.0938090067969 (0.000189191% change)\n", - " iteration 58: trace = 312.0929296806977 (1.19518e-10% change)\n" - ] - } - ], + "outputs": [], "source": [ "# Calculate the number of reflected orders\n", "nm_r = np.floor((fcen * ng - k.y) * gp) - np.ceil(\n", @@ -409,22 +286,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "r_angle = np.squeeze(\n", " [\n", @@ -463,18 +327,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mode-coeff:, 0.061047, 0.937862, 0.998909\n", - "poynting-flux:, 0.061102, 0.938344, 0.999447\n" - ] - } - ], + "outputs": [], "source": [ "print(\n", " \"mode-coeff:, {:.6f}, {:.6f}, {:.6f}\".format(\n", diff --git a/python/examples/binary_grating_oblique.py b/python/examples/binary_grating_oblique.py index e4644c8fe..4b209cda7 100644 --- a/python/examples/binary_grating_oblique.py +++ b/python/examples/binary_grating_oblique.py @@ -1,10 +1,10 @@ -# -*- coding: utf-8 -*- - -import meep as mp -import math import cmath +import math + import numpy as np +import meep as mp + resolution = 50 # pixels/μm dpml = 1.0 # PML thickness @@ -148,7 +148,7 @@ def _pw_amp(x): r_kdom = res.kdom[nm] Rmode = abs(r_coeffs[nm, 0, 1]) ** 2 / input_flux[0] r_angle = np.sign(r_kdom.y) * math.acos(r_kdom.x / (ng * fcen)) - print("refl:, {:2d}, {:6.2f}, {:.8f}".format(nm, math.degrees(r_angle), Rmode)) + print(f"refl:, {nm:2d}, {math.degrees(r_angle):6.2f}, {Rmode:.8f}") Rsum += Rmode nm_t = np.floor((fcen - k.y) * gp) - np.ceil( @@ -168,13 +168,13 @@ def _pw_amp(x): t_kdom = res.kdom[nm] Tmode = abs(t_coeffs[nm, 0, 0]) ** 2 / input_flux[0] t_angle = np.sign(t_kdom.y) * math.acos(t_kdom.x / fcen) - print("tran:, {:2d}, {:6.2f}, {:.8f}".format(nm, math.degrees(t_angle), Tmode)) + print(f"tran:, {nm:2d}, {math.degrees(t_angle):6.2f}, {Tmode:.8f}") Tsum += Tmode -print("mode-coeff:, {:11.6f}, {:.6f}, {:.6f}".format(Rsum, Tsum, Rsum + Tsum)) +print(f"mode-coeff:, {Rsum:11.6f}, {Tsum:.6f}, {Rsum + Tsum:.6f}") r_flux = mp.get_fluxes(refl_flux) t_flux = mp.get_fluxes(tran_flux) Rflux = -r_flux[0] / input_flux[0] Tflux = t_flux[0] / input_flux[0] -print("poynting-flux:, {:.6f}, {:.6f}, {:.6f}".format(Rflux, Tflux, Rflux + Tflux)) +print(f"poynting-flux:, {Rflux:.6f}, {Tflux:.6f}, {Rflux + Tflux:.6f}") diff --git a/python/examples/binary_grating_phasemap.ipynb b/python/examples/binary_grating_phasemap.ipynb index 295e97635..b0db5216a 100644 --- a/python/examples/binary_grating_phasemap.ipynb +++ b/python/examples/binary_grating_phasemap.ipynb @@ -16,740 +16,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00244403 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - "time for set_epsilon = 0.0124252 s\n", - "-----------\n", - "run 0 finished at t = 112.0 (11200 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00123405 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - " block, center = (-2.3,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.66533e-16,0,0)\n", - " size (0.6,0.035,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.014374 s\n", - "-----------\n", - "Meep progress: 266.34000000000003/312.0 = 85.4% done in 4.0s, 0.7s to go\n", - "on time step 26634 (time=266.34), 0.000150194 s/step\n", - "run 0 finished at t = 312.0 (31200 timesteps)\n", - "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", - "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", - "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", - "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", - "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 7 iters\n", - "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.875,0,0) = 1.875 after 7 iters\n", - "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", - "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", - "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n", - "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n", - "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n", - "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 8 iters\n", - "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", - "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", - "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", - "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", - "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n", - "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", - "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", - "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", - "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00058198 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - "time for set_epsilon = 0.00720787 s\n", - "-----------\n", - "run 0 finished at t = 112.0 (11200 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00100899 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - " block, center = (-2.3,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.66533e-16,0,0)\n", - " size (0.6,0.07,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.016 s\n", - "-----------\n", - "Meep progress: 302.72/312.0 = 97.0% done in 4.0s, 0.1s to go\n", - "on time step 30272 (time=302.72), 0.000132144 s/step\n", - "run 0 finished at t = 312.0 (31200 timesteps)\n", - "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", - "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n", - "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", - "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", - "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", - "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", - "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", - "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", - "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n", - "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", - "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", - "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", - "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", - "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", - "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", - "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 10 iters\n", - "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.375,0,0) = 2.375 after 10 iters\n", - "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", - "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", - "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", - "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000844955 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - "time for set_epsilon = 0.0124938 s\n", - "-----------\n", - "run 0 finished at t = 112.0 (11200 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00101209 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - " block, center = (-2.3,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.66533e-16,0,0)\n", - " size (0.6,0.105,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0164981 s\n", - "-----------\n", - "Meep progress: 285.47/312.0 = 91.5% done in 4.0s, 0.4s to go\n", - "on time step 28547 (time=285.47), 0.000140126 s/step\n", - "run 0 finished at t = 312.0 (31200 timesteps)\n", - "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 7 iters\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n", - "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", - "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", - "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", - "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", - "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 9 iters\n", - "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", - "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n", - "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n", - "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", - "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", - "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n", - "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.25,0,0) = 2.25 after 8 iters\n", - "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", - "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", - "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n", - "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", - "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", - "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", - "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00107813 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - "time for set_epsilon = 0.011832 s\n", - "-----------\n", - "run 0 finished at t = 112.0 (11200 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000978947 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - " block, center = (-2.3,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.66533e-16,0,0)\n", - " size (0.6,0.14,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0274839 s\n", - "-----------\n", - "Meep progress: 281.47/312.0 = 90.2% done in 4.0s, 0.4s to go\n", - "on time step 28147 (time=281.47), 0.000142121 s/step\n", - "run 0 finished at t = 312.0 (31200 timesteps)\n", - "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", - "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", - "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", - "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", - "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", - "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", - "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 9 iters\n", - "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", - "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n", - "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", - "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n", - "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 8 iters\n", - "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", - "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", - "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", - "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", - "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.375,0,0) = 2.375 after 10 iters\n", - "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 10 iters\n", - "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", - "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", - "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00116396 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - "time for set_epsilon = 0.0128059 s\n", - "-----------\n", - "run 0 finished at t = 112.0 (11200 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00101495 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - " block, center = (-2.3,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.66533e-16,0,0)\n", - " size (0.6,0.175,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0251269 s\n", - "-----------\n", - "Meep progress: 266.41/312.0 = 85.4% done in 4.0s, 0.7s to go\n", - "on time step 26641 (time=266.41), 0.000150151 s/step\n", - "run 0 finished at t = 312.0 (31200 timesteps)\n", - "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 7 iters\n", - "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", - "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", - "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 7 iters\n", - "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", - "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", - "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", - "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 9 iters\n", - "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", - "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", - "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", - "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n", - "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", - "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", - "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 10 iters\n", - "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n", - "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", - "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 10 iters\n", - "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.5,0,0) = 2.5 after 10 iters\n", - "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000806808 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - "time for set_epsilon = 0.0111492 s\n", - "-----------\n", - "run 0 finished at t = 112.0 (11200 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.0011282 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - " block, center = (-2.3,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.66533e-16,0,0)\n", - " size (0.6,0.21,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0204818 s\n", - "-----------\n", - "Meep progress: 266.28000000000003/312.0 = 85.3% done in 4.0s, 0.7s to go\n", - "on time step 26628 (time=266.28), 0.000150227 s/step\n", - "run 0 finished at t = 312.0 (31200 timesteps)\n", - "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 7 iters\n", - "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", - "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", - "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", - "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", - "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", - "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", - "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", - "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n", - "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", - "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n", - "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", - "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", - "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", - "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", - "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", - "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.375,0,0) = 2.375 after 8 iters\n", - "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", - "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", - "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.5,0,0) = 2.5 after 10 iters\n", - "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00195003 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - "time for set_epsilon = 0.01247 s\n", - "-----------\n", - "run 0 finished at t = 112.0 (11200 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000983 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - " block, center = (-2.3,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.66533e-16,0,0)\n", - " size (0.6,0.245,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.02367 s\n", - "-----------\n", - "Meep progress: 270.11/312.0 = 86.6% done in 4.0s, 0.6s to go\n", - "on time step 27011 (time=270.11), 0.000148096 s/step\n", - "run 0 finished at t = 312.0 (31200 timesteps)\n", - "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", - "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n", - "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", - "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 9 iters\n", - "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", - "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", - "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", - "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", - "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n", - "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", - "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", - "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", - "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", - "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", - "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", - "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", - "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n", - "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", - "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", - "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000772953 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - "time for set_epsilon = 0.0116861 s\n", - "-----------\n", - "run 0 finished at t = 112.0 (11200 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00118494 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - " block, center = (-2.3,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.66533e-16,0,0)\n", - " size (0.6,0.28,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0194418 s\n", - "-----------\n", - "Meep progress: 266.44/312.0 = 85.4% done in 4.0s, 0.7s to go\n", - "on time step 26644 (time=266.44), 0.000150135 s/step\n", - "run 0 finished at t = 312.0 (31200 timesteps)\n", - "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", - "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", - "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", - "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", - "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", - "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", - "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 9 iters\n", - "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", - "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n", - "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", - "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n", - "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", - "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n", - "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", - "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", - "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", - "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n", - "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", - "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 10 iters\n", - "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", - "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.0010891 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - "time for set_epsilon = 0.00840378 s\n", - "-----------\n", - "run 0 finished at t = 112.0 (11200 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00111604 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - " block, center = (-2.3,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.66533e-16,0,0)\n", - " size (0.6,0.315,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.021673 s\n", - "-----------\n", - "Meep progress: 266.16/312.0 = 85.3% done in 4.0s, 0.7s to go\n", - "on time step 26616 (time=266.16), 0.000150297 s/step\n", - "run 0 finished at t = 312.0 (31200 timesteps)\n", - "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", - "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n", - "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", - "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", - "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 7 iters\n", - "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", - "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", - "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 9 iters\n", - "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n", - "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n", - "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", - "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", - "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", - "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", - "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", - "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", - "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.375,0,0) = 2.375 after 10 iters\n", - "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", - "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", - "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", - "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00112605 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - "time for set_epsilon = 0.00722408 s\n", - "-----------\n", - "run 0 finished at t = 112.0 (11200 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000966787 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", - " block, center = (-2.3,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.66533e-16,0,0)\n", - " size (0.6,0.35,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0155971 s\n", - "-----------\n", - "Meep progress: 266.89/312.0 = 85.5% done in 4.0s, 0.7s to go\n", - "on time step 26689 (time=266.89), 0.000149884 s/step\n", - "run 0 finished at t = 312.0 (31200 timesteps)\n", - "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", - "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", - "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.75,0,0) = 1.75 after 7 iters\n", - "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", - "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", - "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", - "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", - "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", - "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", - "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n", - "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n", - "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", - "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", - "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n", - "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", - "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 8 iters\n", - "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", - "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.375,0,0) = 2.375 after 8 iters\n", - "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", - "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", - "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", - "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", - "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -877,22 +146,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(dpi=150)\n", "plt.subplot(1, 2, 1)\n", diff --git a/python/examples/binary_grating_phasemap.py b/python/examples/binary_grating_phasemap.py index 6f23df160..50e99b178 100644 --- a/python/examples/binary_grating_phasemap.py +++ b/python/examples/binary_grating_phasemap.py @@ -1,10 +1,10 @@ -# -*- coding: utf-8 -*- +import argparse -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np import numpy.matlib -import argparse + +import meep as mp resolution = 60 # pixels/μm @@ -148,7 +148,7 @@ def grating(gp, gh, gdc, oddz): plt.title("transmittance") cbar = plt.colorbar() cbar.set_ticks(list(np.arange(0, 1.2, 0.2))) - cbar.set_ticklabels(["{:.1f}".format(t) for t in np.arange(0, 1.2, 0.2)]) + cbar.set_ticklabels([f"{t:.1f}" for t in np.arange(0, 1.2, 0.2)]) plt.subplot(1, 2, 2) plt.pcolormesh( @@ -168,7 +168,7 @@ def grating(gp, gh, gdc, oddz): plt.title("phase (radians)") cbar = plt.colorbar() cbar.set_ticks(list(range(-3, 4))) - cbar.set_ticklabels(["{:.1f}".format(t) for t in range(-3, 4)]) + cbar.set_ticklabels([f"{t:.1f}" for t in range(-3, 4)]) plt.tight_layout() plt.show() diff --git a/python/examples/cavity-farfield.ipynb b/python/examples/cavity-farfield.ipynb index 2314b9090..2c4b502b1 100644 --- a/python/examples/cavity-farfield.ipynb +++ b/python/examples/cavity-farfield.ipynb @@ -60,119 +60,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "field decay(t = 50.025000000000006): 61.714437239634854 / 61.714437239634854 = 1.0\n", - "field decay(t = 100.05000000000001): 47.39669513065221 / 61.714437239634854 = 0.7680001187827836\n", - "field decay(t = 150.07500000000002): 38.753487484300784 / 61.714437239634854 = 0.6279484868965497\n", - "field decay(t = 200.10000000000002): 31.933885594488228 / 61.714437239634854 = 0.517445949810579\n", - "field decay(t = 250.125): 26.076764607720868 / 61.714437239634854 = 0.42253912980630065\n", - "field decay(t = 300.15000000000003): 21.47655605320123 / 61.714437239634854 = 0.34799889643015885\n", - "field decay(t = 350.175): 17.53631126728562 / 61.714437239634854 = 0.2841524941593938\n", - "field decay(t = 400.20000000000005): 14.443563832839763 / 61.714437239634854 = 0.2340386541443446\n", - "field decay(t = 450.225): 11.896373926780527 / 61.714437239634854 = 0.19276484496791815\n", - "field decay(t = 500.25): 9.718573927428102 / 61.714437239634854 = 0.15747650569495178\n", - "field decay(t = 550.275): 8.004609807436562 / 61.714437239634854 = 0.12970400712486385\n", - "field decay(t = 600.3000000000001): 6.537545155581314 / 61.714437239634854 = 0.10593218455831122\n", - "field decay(t = 650.325): 5.384562849608928 / 61.714437239634854 = 0.08724964676743095\n", - "field decay(t = 700.35): 4.396534866374258 / 61.714437239634854 = 0.071239973384229\n", - "field decay(t = 750.375): 3.621135834360022 / 61.714437239634854 = 0.05867566806611696\n", - "field decay(t = 800.4000000000001): 2.9581397060851486 / 61.714437239634854 = 0.047932701623751385\n", - "field decay(t = 850.4250000000001): 2.4364448081077583 / 61.714437239634854 = 0.039479332828510356\n", - "field decay(t = 900.45): 1.9900002176534468 / 61.714437239634854 = 0.03224529472619754\n", - "field decay(t = 950.475): 1.6390396477862001 / 61.714437239634854 = 0.026558447603141393\n", - "field decay(t = 1000.5): 1.3499743126254105 / 61.714437239634854 = 0.021874530061475092\n", - "field decay(t = 1050.525): 1.1023166078985005 / 61.714437239634854 = 0.017861567847054106\n", - "field decay(t = 1100.55): 0.9079059458801253 / 61.714437239634854 = 0.014711402817379026\n", - "field decay(t = 1150.575): 0.7415977350408866 / 61.714437239634854 = 0.012016600461919312\n", - "field decay(t = 1200.6000000000001): 0.6108108814904085 / 61.714437239634854 = 0.009897374241924183\n", - "field decay(t = 1250.625): 0.4989128271302527 / 61.714437239634854 = 0.008084215775848247\n", - "field decay(t = 1300.65): 0.41092341533267496 / 61.714437239634854 = 0.0066584649186230875\n", - "field decay(t = 1350.6750000000002): 0.335555319091018 / 61.714437239634854 = 0.0054372256168855465\n", - "field decay(t = 1400.7): 0.27637506434209563 / 61.714437239634854 = 0.004478288658275236\n", - "field decay(t = 1450.7250000000001): 0.22572291785727622 / 61.714437239634854 = 0.003657538299843885\n", - "field decay(t = 1500.75): 0.18591483342900764 / 61.714437239634854 = 0.003012501478496957\n", - "field decay(t = 1550.775): 0.15312742040508032 / 61.714437239634854 = 0.0024812252570738393\n", - "field decay(t = 1600.8000000000002): 0.1250809574092981 / 61.714437239634854 = 0.0020267697965649983\n", - "field decay(t = 1650.825): 0.1030217208117877 / 61.714437239634854 = 0.001669329340422537\n", - "field decay(t = 1700.8500000000001): 0.08413043895503002 / 61.714437239634854 = 0.001363221358210797\n", - "field decay(t = 1750.875): 0.06929274173262323 / 61.714437239634854 = 0.0011227962990825323\n", - "field decay(t = 1800.9): 0.05658712763643502 / 61.714437239634854 = 0.0009169187983795317\n", - "field decay(t = 1850.9250000000002): 0.04660765052217251 / 61.714437239634854 = 0.0007552147051296399\n", - "field decay(t = 1900.95): 0.03807289009086582 / 61.714437239634854 = 0.0006169203154689755\n", - "field decay(t = 1950.9750000000001): 0.03135838502348845 / 61.714437239634854 = 0.0005081207319727313\n", - "field decay(t = 2001.0): 0.02560950778551407 / 61.714437239634854 = 0.00041496785729525993\n", - "field decay(t = 2051.025): 0.021092935013554397 / 61.714437239634854 = 0.00034178283003134775\n", - "field decay(t = 2101.05): 0.017223354025599415 / 61.714437239634854 = 0.00027908144019399826\n", - "field decay(t = 2151.0750000000003): 0.014185939167263606 / 61.714437239634854 = 0.00022986419064602556\n", - "field decay(t = 2201.1): 0.01168408783845677 / 61.714437239634854 = 0.0001893250325379764\n", - "field decay(t = 2251.125): 0.009544955140265965 / 61.714437239634854 = 0.0001546632452176994\n", - "field decay(t = 2301.15): 0.007861650796955479 / 61.714437239634854 = 0.00012738754736479216\n", - "field decay(t = 2351.175): 0.00642066767842841 / 61.714437239634854 = 0.00010403834119878947\n", - "field decay(t = 2401.2000000000003): 0.005288325829931077 / 61.714437239634854 = 8.569025444397566e-05\n", - "field decay(t = 2451.225): 0.004317908714773744 / 61.714437239634854 = 6.996594164842606e-05\n", - "field decay(t = 2501.25): 0.0035563445246973805 / 61.714437239634854 = 5.7625811459451985e-05\n", - "field decay(t = 2551.275): 0.0029053004098988015 / 61.714437239634854 = 4.707651142661528e-05\n", - "field decay(t = 2601.3): 0.0023929347699027303 / 61.714437239634854 = 3.877431079232002e-05\n", - "field decay(t = 2651.3250000000003): 0.0019544194427344327 / 61.714437239634854 = 3.1668755807421446e-05\n", - "field decay(t = 2701.3500000000004): 0.0016097324307071902 / 61.714437239634854 = 2.608356330718304e-05\n", - "field decay(t = 2751.375): 0.0013258509849590956 / 61.714437239634854 = 2.1483643767354885e-05\n", - "field decay(t = 2801.4): 0.0010826046664947212 / 61.714437239634854 = 1.7542162173350262e-05\n", - "field decay(t = 2851.425): 0.0008916728224396508 / 61.714437239634854 = 1.4448366740789007e-05\n", - "field decay(t = 2901.4500000000003): 0.0007283649985088117 / 61.714437239634854 = 1.1802181646420878e-05\n", - "field decay(t = 2951.4750000000004): 0.0005998993426679592 / 61.714437239634854 = 9.720567334002778e-06\n", - "field decay(t = 3001.5): 0.0004899838939467707 / 61.714437239634854 = 7.939534343385189e-06\n", - "field decay(t = 3051.525): 0.00040357507547706214 / 61.714437239634854 = 6.539394889237914e-06\n", - "field decay(t = 3101.55): 0.00032955488895076876 / 61.714437239634854 = 5.339996663521688e-06\n", - "field decay(t = 3151.5750000000003): 0.0002714323244167271 / 61.714437239634854 = 4.39819816168404e-06\n", - "field decay(t = 3201.6000000000004): 0.00022170111231405412 / 61.714437239634854 = 3.5923703144727217e-06\n", - "field decay(t = 3251.625): 0.00018260060353425382 / 61.714437239634854 = 2.9587988111310567e-06\n", - "field decay(t = 3301.65): 0.0001503954748659794 / 61.714437239634854 = 2.4369577297124076e-06\n", - "field decay(t = 3351.675): 0.00012284487711379126 / 61.714437239634854 = 1.9905371029600284e-06\n", - "field decay(t = 3401.7000000000003): 0.00010117728681778103 / 61.714437239634854 = 1.639442751862314e-06\n", - "field decay(t = 3451.7250000000004): 8.26234434523444e-05 / 61.714437239634854 = 1.3388025095573772e-06\n", - "field decay(t = 3501.75): 6.805354196313139e-05 / 61.714437239634854 = 1.102716722488808e-06\n", - "field decay(t = 3551.775): 5.557995527325353e-05 / 61.714437239634854 = 9.005989158977281e-07\n", - "field decay(t = 3601.8): 4.5777693507503735e-05 / 61.714437239634854 = 7.417663605964786e-07\n", - "field decay(t = 3651.8250000000003): 3.7393438370402545e-05 / 61.714437239634854 = 6.059107081411959e-07\n", - "field decay(t = 3701.8500000000004): 3.0797909234401674e-05 / 61.714437239634854 = 4.990389706514627e-07\n", - "field decay(t = 3751.875): 2.5150148826942176e-05 / 61.714437239634854 = 4.075245591122396e-07\n", - "field decay(t = 3801.9): 2.071514647935486e-05 / 61.714437239634854 = 3.356612715906121e-07\n", - "field decay(t = 3851.925): 1.6916336856910708e-05 / 61.714437239634854 = 2.7410663717511034e-07\n", - "field decay(t = 3901.9500000000003): 1.3933210839234033e-05 / 61.714437239634854 = 2.2576906575574684e-07\n", - "field decay(t = 3951.9750000000004): 1.1476555260458193e-05 / 61.714437239634854 = 1.8596224439178076e-07\n", - "field decay(t = 4002.0): 9.375123704517074e-06 / 61.714437239634854 = 1.5191135370989156e-07\n", - "field decay(t = 4052.025): 7.721326456512098e-06 / 61.714437239634854 = 1.2511377891254968e-07\n", - "field decay(t = 4102.05): 6.30564330583996e-06 / 61.714437239634854 = 1.0217452492283747e-07\n", - "field decay(t = 4152.075): 5.193524218131245e-06 / 61.714437239634854 = 8.415412098736288e-08\n", - "field decay(t = 4202.1): 4.240401634668664e-06 / 61.714437239634854 = 6.871004297103679e-08\n", - "field decay(t = 4252.125): 3.492891885354038e-06 / 61.714437239634854 = 5.659764621673968e-08\n", - "field decay(t = 4302.150000000001): 2.8538219922205117e-06 / 61.714437239634854 = 4.624237244745873e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "field decay(t = 4352.175): 2.3504340518958436e-06 / 61.714437239634854 = 3.8085643441407335e-08\n", - "field decay(t = 4402.2): 1.919486550538664e-06 / 61.714437239634854 = 3.110271496255195e-08\n", - "field decay(t = 4452.225): 1.5810178663207582e-06 / 61.714437239634854 = 2.5618282156276092e-08\n", - "field decay(t = 4502.25): 1.3020179000714368e-06 / 61.714437239634854 = 2.1097460469675026e-08\n", - "field decay(t = 4552.275000000001): 1.0629751847546947e-06 / 61.714437239634854 = 1.7224092648324764e-08\n", - "field decay(t = 4602.3): 8.756223798546496e-07 / 61.714437239634854 = 1.418829076338557e-08\n", - "field decay(t = 4652.325): 7.15412237809199e-07 / 61.714437239634854 = 1.1592299465217191e-08\n", - "field decay(t = 4702.35): 5.892719179602837e-07 / 61.714437239634854 = 9.548364115711901e-09\n", - "run 0 finished at t = 4702.35 (188094 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -250,32 +140,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.5, 207.5, 79.5, -0.5)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "d2 = 20\n", "h = 4\n", diff --git a/python/examples/cavity-farfield.py b/python/examples/cavity-farfield.py index f7a061355..c643a8105 100644 --- a/python/examples/cavity-farfield.py +++ b/python/examples/cavity-farfield.py @@ -1,11 +1,11 @@ -import meep as mp -import numpy as np import matplotlib +import numpy as np + +import meep as mp matplotlib.use("agg") import matplotlib.pyplot as plt - resolution = 20 # pixels/μm fcen = 0.25 # pulse center frequency diff --git a/python/examples/cavity_arrayslice.py b/python/examples/cavity_arrayslice.py index 7e7627531..55c4b1d05 100644 --- a/python/examples/cavity_arrayslice.py +++ b/python/examples/cavity_arrayslice.py @@ -1,6 +1,7 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np + +import meep as mp # set up the geometry eps = 13 diff --git a/python/examples/cherenkov-radiation.py b/python/examples/cherenkov-radiation.py index f2f17b5db..92423d9fd 100644 --- a/python/examples/cherenkov-radiation.py +++ b/python/examples/cherenkov-radiation.py @@ -1,5 +1,4 @@ ## moving point charge with superluminal phase velocity in dielectric media emitting Cherenkov radiation - import meep as mp sx = 60 diff --git a/python/examples/chirped_pulse.py b/python/examples/chirped_pulse.py index 57064bf18..493d8bbba 100644 --- a/python/examples/chirped_pulse.py +++ b/python/examples/chirped_pulse.py @@ -1,7 +1,7 @@ ## linear-chirped pulse planewave with higher frequencies at the front (down-chirp) +import numpy as np import meep as mp -import numpy as np resolution = 40 diff --git a/python/examples/coupler.ipynb b/python/examples/coupler.ipynb index 47de7645e..df8feaebb 100644 --- a/python/examples/coupler.ipynb +++ b/python/examples/coupler.ipynb @@ -25,854 +25,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000570059 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 34.4 x 7.84 x 0 with resolution 25\n", - " prism, center = (-9.09425,1.32149,0)\n", - " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", - " (-4,0.06,-10)\n", - " (-4.108,0.061,-10)\n", - " (-4.215,0.062,-10)\n", - " (-4.322,0.065,-10)\n", - " (-4.429,0.07,-10)\n", - " (-4.535,0.075,-10)\n", - " (-4.641,0.081,-10)\n", - " (-4.747,0.089,-10)\n", - " (-4.852,0.097,-10)\n", - " (-5.062,0.117,-10)\n", - " (-5.167,0.129,-10)\n", - " (-5.271,0.141,-10)\n", - " (-5.479,0.169,-10)\n", - " (-5.582,0.184,-10)\n", - " (-5.685,0.2,-10)\n", - " (-5.788,0.217,-10)\n", - " (-5.891,0.235,-10)\n", - " (-5.993,0.253,-10)\n", - " (-6.095,0.272,-10)\n", - " (-6.197,0.292,-10)\n", - " (-6.299,0.313,-10)\n", - " (-6.4,0.334,-10)\n", - " (-6.501,0.356,-10)\n", - " (-6.703,0.402,-10)\n", - " (-6.803,0.425,-10)\n", - " (-6.904,0.45,-10)\n", - " (-7.204,0.525,-10)\n", - " (-7.303,0.552,-10)\n", - " (-7.403,0.578,-10)\n", - " (-7.502,0.605,-10)\n", - " (-7.601,0.633,-10)\n", - " (-7.7,0.66,-10)\n", - " (-7.799,0.688,-10)\n", - " (-7.898,0.717,-10)\n", - " (-7.996,0.745,-10)\n", - " (-8.095,0.774,-10)\n", - " (-8.193,0.803,-10)\n", - " (-8.291,0.833,-10)\n", - " (-8.389,0.862,-10)\n", - " (-8.487,0.892,-10)\n", - " (-8.585,0.921,-10)\n", - " (-8.781,0.981,-10)\n", - " (-8.878,1.011,-10)\n", - " (-9.074,1.071,-10)\n", - " (-9.268,1.131,-10)\n", - " (-9.366,1.161,-10)\n", - " (-9.463,1.191,-10)\n", - " (-9.56,1.22,-10)\n", - " (-9.658,1.25,-10)\n", - " (-9.949,1.337,-10)\n", - " (-10.047,1.366,-10)\n", - " (-10.144,1.395,-10)\n", - " (-10.338,1.451,-10)\n", - " (-10.436,1.478,-10)\n", - " (-10.533,1.506,-10)\n", - " (-10.63,1.533,-10)\n", - " (-10.728,1.559,-10)\n", - " (-10.922,1.611,-10)\n", - " (-11.02,1.636,-10)\n", - " (-11.117,1.66,-10)\n", - " (-11.215,1.685,-10)\n", - " (-11.313,1.708,-10)\n", - " (-11.41,1.731,-10)\n", - " (-11.508,1.754,-10)\n", - " (-11.606,1.776,-10)\n", - " (-11.704,1.797,-10)\n", - " (-11.802,1.817,-10)\n", - " (-11.901,1.837,-10)\n", - " (-11.999,1.856,-10)\n", - " (-12.098,1.875,-10)\n", - " (-12.196,1.893,-10)\n", - " (-12.295,1.91,-10)\n", - " (-12.394,1.926,-10)\n", - " (-12.592,1.956,-10)\n", - " (-12.691,1.97,-10)\n", - " (-12.791,1.982,-10)\n", - " (-12.89,1.994,-10)\n", - " (-12.99,2.005,-10)\n", - " (-13.19,2.025,-10)\n", - " (-13.291,2.033,-10)\n", - " (-13.392,2.04,-10)\n", - " (-13.492,2.046,-10)\n", - " (-13.593,2.051,-10)\n", - " (-13.695,2.055,-10)\n", - " (-13.796,2.058,-10)\n", - " (-14,2.06,-10)\n", - " (-17.2,2.06,-10)\n", - " (-17.2,2.56,-10)\n", - " (-14,2.56,-10)\n", - " (-13.892,2.559,-10)\n", - " (-13.785,2.558,-10)\n", - " (-13.678,2.555,-10)\n", - " (-13.571,2.55,-10)\n", - " (-13.465,2.545,-10)\n", - " (-13.359,2.539,-10)\n", - " (-13.253,2.531,-10)\n", - " (-13.148,2.523,-10)\n", - " (-12.938,2.503,-10)\n", - " (-12.833,2.491,-10)\n", - " (-12.729,2.479,-10)\n", - " (-12.521,2.451,-10)\n", - " (-12.418,2.436,-10)\n", - " (-12.315,2.42,-10)\n", - " (-12.212,2.403,-10)\n", - " (-12.109,2.385,-10)\n", - " (-12.007,2.367,-10)\n", - " (-11.905,2.348,-10)\n", - " (-11.803,2.328,-10)\n", - " (-11.701,2.307,-10)\n", - " (-11.6,2.286,-10)\n", - " (-11.499,2.264,-10)\n", - " (-11.297,2.218,-10)\n", - " (-11.197,2.195,-10)\n", - " (-11.096,2.17,-10)\n", - " (-10.796,2.095,-10)\n", - " (-10.697,2.068,-10)\n", - " (-10.597,2.042,-10)\n", - " (-10.498,2.015,-10)\n", - " (-10.399,1.987,-10)\n", - " (-10.3,1.96,-10)\n", - " (-10.201,1.932,-10)\n", - " (-10.102,1.903,-10)\n", - " (-10.004,1.875,-10)\n", - " (-9.905,1.846,-10)\n", - " (-9.807,1.817,-10)\n", - " (-9.709,1.787,-10)\n", - " (-9.611,1.758,-10)\n", - " (-9.513,1.728,-10)\n", - " (-9.415,1.699,-10)\n", - " (-9.219,1.639,-10)\n", - " (-9.122,1.609,-10)\n", - " (-8.926,1.549,-10)\n", - " (-8.732,1.489,-10)\n", - " (-8.634,1.459,-10)\n", - " (-8.537,1.429,-10)\n", - " (-8.44,1.4,-10)\n", - " (-8.342,1.37,-10)\n", - " (-8.051,1.283,-10)\n", - " (-7.953,1.254,-10)\n", - " (-7.856,1.225,-10)\n", - " (-7.662,1.169,-10)\n", - " (-7.564,1.142,-10)\n", - " (-7.467,1.114,-10)\n", - " (-7.37,1.087,-10)\n", - " (-7.272,1.061,-10)\n", - " (-7.078,1.009,-10)\n", - " (-6.98,0.984,-10)\n", - " (-6.883,0.96,-10)\n", - " (-6.785,0.935,-10)\n", - " (-6.687,0.912,-10)\n", - " (-6.59,0.889,-10)\n", - " (-6.492,0.866,-10)\n", - " (-6.394,0.844,-10)\n", - " (-6.296,0.823,-10)\n", - " (-6.198,0.803,-10)\n", - " (-6.099,0.783,-10)\n", - " (-6.001,0.764,-10)\n", - " (-5.902,0.745,-10)\n", - " (-5.804,0.727,-10)\n", - " (-5.705,0.71,-10)\n", - " (-5.606,0.694,-10)\n", - " (-5.408,0.664,-10)\n", - " (-5.309,0.65,-10)\n", - " (-5.209,0.638,-10)\n", - " (-5.11,0.626,-10)\n", - " (-5.01,0.615,-10)\n", - " (-4.81,0.595,-10)\n", - " (-4.709,0.587,-10)\n", - " (-4.608,0.58,-10)\n", - " (-4.508,0.574,-10)\n", - " (-4.407,0.569,-10)\n", - " (-4.305,0.565,-10)\n", - " (-4.204,0.562,-10)\n", - " (-4,0.56,-10)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - " prism, center = (9.09425,1.32149,0)\n", - " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", - " (4,0.06,-10)\n", - " (4,0.56,-10)\n", - " (4.204,0.562,-10)\n", - " (4.305,0.565,-10)\n", - " (4.407,0.569,-10)\n", - " (4.508,0.574,-10)\n", - " (4.608,0.58,-10)\n", - " (4.709,0.587,-10)\n", - " (4.81,0.595,-10)\n", - " (5.01,0.615,-10)\n", - " (5.11,0.626,-10)\n", - " (5.209,0.638,-10)\n", - " (5.309,0.65,-10)\n", - " (5.408,0.664,-10)\n", - " (5.606,0.694,-10)\n", - " (5.705,0.71,-10)\n", - " (5.804,0.727,-10)\n", - " (5.902,0.745,-10)\n", - " (6.001,0.764,-10)\n", - " (6.099,0.783,-10)\n", - " (6.198,0.803,-10)\n", - " (6.296,0.823,-10)\n", - " (6.394,0.844,-10)\n", - " (6.492,0.866,-10)\n", - " (6.59,0.889,-10)\n", - " (6.687,0.912,-10)\n", - " (6.785,0.935,-10)\n", - " (6.883,0.96,-10)\n", - " (6.98,0.984,-10)\n", - " (7.078,1.009,-10)\n", - " (7.272,1.061,-10)\n", - " (7.37,1.087,-10)\n", - " (7.467,1.114,-10)\n", - " (7.564,1.142,-10)\n", - " (7.662,1.169,-10)\n", - " (7.856,1.225,-10)\n", - " (7.953,1.254,-10)\n", - " (8.051,1.283,-10)\n", - " (8.342,1.37,-10)\n", - " (8.44,1.4,-10)\n", - " (8.537,1.429,-10)\n", - " (8.634,1.459,-10)\n", - " (8.732,1.489,-10)\n", - " (8.926,1.549,-10)\n", - " (9.122,1.609,-10)\n", - " (9.219,1.639,-10)\n", - " (9.415,1.699,-10)\n", - " (9.513,1.728,-10)\n", - " (9.611,1.758,-10)\n", - " (9.709,1.787,-10)\n", - " (9.807,1.817,-10)\n", - " (9.905,1.846,-10)\n", - " (10.004,1.875,-10)\n", - " (10.102,1.903,-10)\n", - " (10.201,1.932,-10)\n", - " (10.3,1.96,-10)\n", - " (10.399,1.987,-10)\n", - " (10.498,2.015,-10)\n", - " (10.597,2.042,-10)\n", - " (10.697,2.068,-10)\n", - " (10.796,2.095,-10)\n", - " (11.096,2.17,-10)\n", - " (11.197,2.195,-10)\n", - " (11.297,2.218,-10)\n", - " (11.499,2.264,-10)\n", - " (11.6,2.286,-10)\n", - " (11.701,2.307,-10)\n", - " (11.803,2.328,-10)\n", - " (11.905,2.348,-10)\n", - " (12.007,2.367,-10)\n", - " (12.109,2.385,-10)\n", - " (12.212,2.403,-10)\n", - " (12.315,2.42,-10)\n", - " (12.418,2.436,-10)\n", - " (12.521,2.451,-10)\n", - " (12.729,2.479,-10)\n", - " (12.833,2.491,-10)\n", - " (12.938,2.503,-10)\n", - " (13.148,2.523,-10)\n", - " (13.253,2.531,-10)\n", - " (13.359,2.539,-10)\n", - " (13.465,2.545,-10)\n", - " (13.571,2.55,-10)\n", - " (13.678,2.555,-10)\n", - " (13.785,2.558,-10)\n", - " (13.892,2.559,-10)\n", - " (14,2.56,-10)\n", - " (17.2,2.56,-10)\n", - " (17.2,2.06,-10)\n", - " (14,2.06,-10)\n", - " (13.796,2.058,-10)\n", - " (13.695,2.055,-10)\n", - " (13.593,2.051,-10)\n", - " (13.492,2.046,-10)\n", - " (13.392,2.04,-10)\n", - " (13.291,2.033,-10)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (13.19,2.025,-10)\n", - " (12.99,2.005,-10)\n", - " (12.89,1.994,-10)\n", - " (12.791,1.982,-10)\n", - " (12.691,1.97,-10)\n", - " (12.592,1.956,-10)\n", - " (12.394,1.926,-10)\n", - " (12.295,1.91,-10)\n", - " (12.196,1.893,-10)\n", - " (12.098,1.875,-10)\n", - " (11.999,1.856,-10)\n", - " (11.901,1.837,-10)\n", - " (11.802,1.817,-10)\n", - " (11.704,1.797,-10)\n", - " (11.606,1.776,-10)\n", - " (11.508,1.754,-10)\n", - " (11.41,1.731,-10)\n", - " (11.313,1.708,-10)\n", - " (11.215,1.685,-10)\n", - " (11.117,1.66,-10)\n", - " (11.02,1.636,-10)\n", - " (10.922,1.611,-10)\n", - " (10.728,1.559,-10)\n", - " (10.63,1.533,-10)\n", - " (10.533,1.506,-10)\n", - " (10.436,1.478,-10)\n", - " (10.338,1.451,-10)\n", - " (10.144,1.395,-10)\n", - " (10.047,1.366,-10)\n", - " (9.949,1.337,-10)\n", - " (9.658,1.25,-10)\n", - " (9.56,1.22,-10)\n", - " (9.463,1.191,-10)\n", - " (9.366,1.161,-10)\n", - " (9.268,1.131,-10)\n", - " (9.074,1.071,-10)\n", - " (8.878,1.011,-10)\n", - " (8.781,0.981,-10)\n", - " (8.585,0.921,-10)\n", - " (8.487,0.892,-10)\n", - " (8.389,0.862,-10)\n", - " (8.291,0.833,-10)\n", - " (8.193,0.803,-10)\n", - " (8.095,0.774,-10)\n", - " (7.996,0.745,-10)\n", - " (7.898,0.717,-10)\n", - " (7.799,0.688,-10)\n", - " (7.7,0.66,-10)\n", - " (7.601,0.633,-10)\n", - " (7.502,0.605,-10)\n", - " (7.403,0.578,-10)\n", - " (7.303,0.552,-10)\n", - " (7.204,0.525,-10)\n", - " (6.904,0.45,-10)\n", - " (6.803,0.425,-10)\n", - " (6.703,0.402,-10)\n", - " (6.501,0.356,-10)\n", - " (6.4,0.334,-10)\n", - " (6.299,0.313,-10)\n", - " (6.197,0.292,-10)\n", - " (6.095,0.272,-10)\n", - " (5.993,0.253,-10)\n", - " (5.891,0.235,-10)\n", - " (5.788,0.217,-10)\n", - " (5.685,0.2,-10)\n", - " (5.582,0.184,-10)\n", - " (5.479,0.169,-10)\n", - " (5.271,0.141,-10)\n", - " (5.167,0.129,-10)\n", - " (5.062,0.117,-10)\n", - " (4.852,0.097,-10)\n", - " (4.747,0.089,-10)\n", - " (4.641,0.081,-10)\n", - " (4.535,0.075,-10)\n", - " (4.429,0.07,-10)\n", - " (4.322,0.065,-10)\n", - " (4.215,0.062,-10)\n", - " (4.108,0.061,-10)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - " prism, center = (0,0.31,0)\n", - " height 20, axis (0,0,1), sidewall angle: 0 radians, 4 vertices:\n", - " (-4,0.06,-10)\n", - " (-4,0.56,-10)\n", - " (4,0.56,-10)\n", - " (4,0.06,-10)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - " prism, center = (-9.09425,-1.32149,0)\n", - " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", - " (-17.2,-2.56,-10)\n", - " (-17.2,-2.06,-10)\n", - " (-14,-2.06,-10)\n", - " (-13.796,-2.058,-10)\n", - " (-13.695,-2.055,-10)\n", - " (-13.593,-2.051,-10)\n", - " (-13.492,-2.046,-10)\n", - " (-13.392,-2.04,-10)\n", - " (-13.291,-2.033,-10)\n", - " (-13.19,-2.025,-10)\n", - " (-12.99,-2.005,-10)\n", - " (-12.89,-1.994,-10)\n", - " (-12.791,-1.982,-10)\n", - " (-12.691,-1.97,-10)\n", - " (-12.592,-1.956,-10)\n", - " (-12.394,-1.926,-10)\n", - " (-12.295,-1.91,-10)\n", - " (-12.196,-1.893,-10)\n", - " (-12.098,-1.875,-10)\n", - " (-11.999,-1.856,-10)\n", - " (-11.901,-1.837,-10)\n", - " (-11.802,-1.817,-10)\n", - " (-11.704,-1.797,-10)\n", - " (-11.606,-1.776,-10)\n", - " (-11.508,-1.754,-10)\n", - " (-11.41,-1.731,-10)\n", - " (-11.313,-1.708,-10)\n", - " (-11.215,-1.685,-10)\n", - " (-11.117,-1.66,-10)\n", - " (-11.02,-1.636,-10)\n", - " (-10.922,-1.611,-10)\n", - " (-10.728,-1.559,-10)\n", - " (-10.63,-1.533,-10)\n", - " (-10.533,-1.506,-10)\n", - " (-10.436,-1.478,-10)\n", - " (-10.338,-1.451,-10)\n", - " (-10.144,-1.395,-10)\n", - " (-10.047,-1.366,-10)\n", - " (-9.949,-1.337,-10)\n", - " (-9.658,-1.25,-10)\n", - " (-9.56,-1.22,-10)\n", - " (-9.463,-1.191,-10)\n", - " (-9.366,-1.161,-10)\n", - " (-9.268,-1.131,-10)\n", - " (-9.074,-1.071,-10)\n", - " (-8.878,-1.011,-10)\n", - " (-8.781,-0.981,-10)\n", - " (-8.585,-0.921,-10)\n", - " (-8.487,-0.892,-10)\n", - " (-8.389,-0.862,-10)\n", - " (-8.291,-0.833,-10)\n", - " (-8.193,-0.803,-10)\n", - " (-8.095,-0.774,-10)\n", - " (-7.996,-0.745,-10)\n", - " (-7.898,-0.717,-10)\n", - " (-7.799,-0.688,-10)\n", - " (-7.7,-0.66,-10)\n", - " (-7.601,-0.633,-10)\n", - " (-7.502,-0.605,-10)\n", - " (-7.403,-0.578,-10)\n", - " (-7.303,-0.552,-10)\n", - " (-7.204,-0.525,-10)\n", - " (-6.904,-0.45,-10)\n", - " (-6.803,-0.425,-10)\n", - " (-6.703,-0.402,-10)\n", - " (-6.501,-0.356,-10)\n", - " (-6.4,-0.334,-10)\n", - " (-6.299,-0.313,-10)\n", - " (-6.197,-0.292,-10)\n", - " (-6.095,-0.272,-10)\n", - " (-5.993,-0.253,-10)\n", - " (-5.891,-0.235,-10)\n", - " (-5.788,-0.217,-10)\n", - " (-5.685,-0.2,-10)\n", - " (-5.582,-0.184,-10)\n", - " (-5.479,-0.169,-10)\n", - " (-5.271,-0.141,-10)\n", - " (-5.167,-0.129,-10)\n", - " (-5.062,-0.117,-10)\n", - " (-4.852,-0.097,-10)\n", - " (-4.747,-0.089,-10)\n", - " (-4.641,-0.081,-10)\n", - " (-4.535,-0.075,-10)\n", - " (-4.429,-0.07,-10)\n", - " (-4.322,-0.065,-10)\n", - " (-4.215,-0.062,-10)\n", - " (-4.108,-0.061,-10)\n", - " (-4,-0.06,-10)\n", - " (-4,-0.56,-10)\n", - " (-4.204,-0.562,-10)\n", - " (-4.305,-0.565,-10)\n", - " (-4.407,-0.569,-10)\n", - " (-4.508,-0.574,-10)\n", - " (-4.608,-0.58,-10)\n", - " (-4.709,-0.587,-10)\n", - " (-4.81,-0.595,-10)\n", - " (-5.01,-0.615,-10)\n", - " (-5.11,-0.626,-10)\n", - " (-5.209,-0.638,-10)\n", - " (-5.309,-0.65,-10)\n", - " (-5.408,-0.664,-10)\n", - " (-5.606,-0.694,-10)\n", - " (-5.705,-0.71,-10)\n", - " (-5.804,-0.727,-10)\n", - " (-5.902,-0.745,-10)\n", - " (-6.001,-0.764,-10)\n", - " (-6.099,-0.783,-10)\n", - " (-6.198,-0.803,-10)\n", - " (-6.296,-0.823,-10)\n", - " (-6.394,-0.844,-10)\n", - " (-6.492,-0.866,-10)\n", - " (-6.59,-0.889,-10)\n", - " (-6.687,-0.912,-10)\n", - " (-6.785,-0.935,-10)\n", - " (-6.883,-0.96,-10)\n", - " (-6.98,-0.984,-10)\n", - " (-7.078,-1.009,-10)\n", - " (-7.272,-1.061,-10)\n", - " (-7.37,-1.087,-10)\n", - " (-7.467,-1.114,-10)\n", - " (-7.564,-1.142,-10)\n", - " (-7.662,-1.169,-10)\n", - " (-7.856,-1.225,-10)\n", - " (-7.953,-1.254,-10)\n", - " (-8.051,-1.283,-10)\n", - " (-8.342,-1.37,-10)\n", - " (-8.44,-1.4,-10)\n", - " (-8.537,-1.429,-10)\n", - " (-8.634,-1.459,-10)\n", - " (-8.732,-1.489,-10)\n", - " (-8.926,-1.549,-10)\n", - " (-9.122,-1.609,-10)\n", - " (-9.219,-1.639,-10)\n", - " (-9.415,-1.699,-10)\n", - " (-9.513,-1.728,-10)\n", - " (-9.611,-1.758,-10)\n", - " (-9.709,-1.787,-10)\n", - " (-9.807,-1.817,-10)\n", - " (-9.905,-1.846,-10)\n", - " (-10.004,-1.875,-10)\n", - " (-10.102,-1.903,-10)\n", - " (-10.201,-1.932,-10)\n", - " (-10.3,-1.96,-10)\n", - " (-10.399,-1.987,-10)\n", - " (-10.498,-2.015,-10)\n", - " (-10.597,-2.042,-10)\n", - " (-10.697,-2.068,-10)\n", - " (-10.796,-2.095,-10)\n", - " (-11.096,-2.17,-10)\n", - " (-11.197,-2.195,-10)\n", - " (-11.297,-2.218,-10)\n", - " (-11.499,-2.264,-10)\n", - " (-11.6,-2.286,-10)\n", - " (-11.701,-2.307,-10)\n", - " (-11.803,-2.328,-10)\n", - " (-11.905,-2.348,-10)\n", - " (-12.007,-2.367,-10)\n", - " (-12.109,-2.385,-10)\n", - " (-12.212,-2.403,-10)\n", - " (-12.315,-2.42,-10)\n", - " (-12.418,-2.436,-10)\n", - " (-12.521,-2.451,-10)\n", - " (-12.729,-2.479,-10)\n", - " (-12.833,-2.491,-10)\n", - " (-12.938,-2.503,-10)\n", - " (-13.148,-2.523,-10)\n", - " (-13.253,-2.531,-10)\n", - " (-13.359,-2.539,-10)\n", - " (-13.465,-2.545,-10)\n", - " (-13.571,-2.55,-10)\n", - " (-13.678,-2.555,-10)\n", - " (-13.785,-2.558,-10)\n", - " (-13.892,-2.559,-10)\n", - " (-14,-2.56,-10)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - " prism, center = (9.09425,-1.32149,0)\n", - " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", - " (14,-2.56,-10)\n", - " (13.892,-2.559,-10)\n", - " (13.785,-2.558,-10)\n", - " (13.678,-2.555,-10)\n", - " (13.571,-2.55,-10)\n", - " (13.465,-2.545,-10)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (13.359,-2.539,-10)\n", - " (13.253,-2.531,-10)\n", - " (13.148,-2.523,-10)\n", - " (12.938,-2.503,-10)\n", - " (12.833,-2.491,-10)\n", - " (12.729,-2.479,-10)\n", - " (12.521,-2.451,-10)\n", - " (12.418,-2.436,-10)\n", - " (12.315,-2.42,-10)\n", - " (12.212,-2.403,-10)\n", - " (12.109,-2.385,-10)\n", - " (12.007,-2.367,-10)\n", - " (11.905,-2.348,-10)\n", - " (11.803,-2.328,-10)\n", - " (11.701,-2.307,-10)\n", - " (11.6,-2.286,-10)\n", - " (11.499,-2.264,-10)\n", - " (11.297,-2.218,-10)\n", - " (11.197,-2.195,-10)\n", - " (11.096,-2.17,-10)\n", - " (10.796,-2.095,-10)\n", - " (10.697,-2.068,-10)\n", - " (10.597,-2.042,-10)\n", - " (10.498,-2.015,-10)\n", - " (10.399,-1.987,-10)\n", - " (10.3,-1.96,-10)\n", - " (10.201,-1.932,-10)\n", - " (10.102,-1.903,-10)\n", - " (10.004,-1.875,-10)\n", - " (9.905,-1.846,-10)\n", - " (9.807,-1.817,-10)\n", - " (9.709,-1.787,-10)\n", - " (9.611,-1.758,-10)\n", - " (9.513,-1.728,-10)\n", - " (9.415,-1.699,-10)\n", - " (9.219,-1.639,-10)\n", - " (9.122,-1.609,-10)\n", - " (8.926,-1.549,-10)\n", - " (8.732,-1.489,-10)\n", - " (8.634,-1.459,-10)\n", - " (8.537,-1.429,-10)\n", - " (8.44,-1.4,-10)\n", - " (8.342,-1.37,-10)\n", - " (8.051,-1.283,-10)\n", - " (7.953,-1.254,-10)\n", - " (7.856,-1.225,-10)\n", - " (7.662,-1.169,-10)\n", - " (7.564,-1.142,-10)\n", - " (7.467,-1.114,-10)\n", - " (7.37,-1.087,-10)\n", - " (7.272,-1.061,-10)\n", - " (7.078,-1.009,-10)\n", - " (6.98,-0.984,-10)\n", - " (6.883,-0.96,-10)\n", - " (6.785,-0.935,-10)\n", - " (6.687,-0.912,-10)\n", - " (6.59,-0.889,-10)\n", - " (6.492,-0.866,-10)\n", - " (6.394,-0.844,-10)\n", - " (6.296,-0.823,-10)\n", - " (6.198,-0.803,-10)\n", - " (6.099,-0.783,-10)\n", - " (6.001,-0.764,-10)\n", - " (5.902,-0.745,-10)\n", - " (5.804,-0.727,-10)\n", - " (5.705,-0.71,-10)\n", - " (5.606,-0.694,-10)\n", - " (5.408,-0.664,-10)\n", - " (5.309,-0.65,-10)\n", - " (5.209,-0.638,-10)\n", - " (5.11,-0.626,-10)\n", - " (5.01,-0.615,-10)\n", - " (4.81,-0.595,-10)\n", - " (4.709,-0.587,-10)\n", - " (4.608,-0.58,-10)\n", - " (4.508,-0.574,-10)\n", - " (4.407,-0.569,-10)\n", - " (4.305,-0.565,-10)\n", - " (4.204,-0.562,-10)\n", - " (4,-0.56,-10)\n", - " (4,-0.06,-10)\n", - " (4.108,-0.061,-10)\n", - " (4.215,-0.062,-10)\n", - " (4.322,-0.065,-10)\n", - " (4.429,-0.07,-10)\n", - " (4.535,-0.075,-10)\n", - " (4.641,-0.081,-10)\n", - " (4.747,-0.089,-10)\n", - " (4.852,-0.097,-10)\n", - " (5.062,-0.117,-10)\n", - " (5.167,-0.129,-10)\n", - " (5.271,-0.141,-10)\n", - " (5.479,-0.169,-10)\n", - " (5.582,-0.184,-10)\n", - " (5.685,-0.2,-10)\n", - " (5.788,-0.217,-10)\n", - " (5.891,-0.235,-10)\n", - " (5.993,-0.253,-10)\n", - " (6.095,-0.272,-10)\n", - " (6.197,-0.292,-10)\n", - " (6.299,-0.313,-10)\n", - " (6.4,-0.334,-10)\n", - " (6.501,-0.356,-10)\n", - " (6.703,-0.402,-10)\n", - " (6.803,-0.425,-10)\n", - " (6.904,-0.45,-10)\n", - " (7.204,-0.525,-10)\n", - " (7.303,-0.552,-10)\n", - " (7.403,-0.578,-10)\n", - " (7.502,-0.605,-10)\n", - " (7.601,-0.633,-10)\n", - " (7.7,-0.66,-10)\n", - " (7.799,-0.688,-10)\n", - " (7.898,-0.717,-10)\n", - " (7.996,-0.745,-10)\n", - " (8.095,-0.774,-10)\n", - " (8.193,-0.803,-10)\n", - " (8.291,-0.833,-10)\n", - " (8.389,-0.862,-10)\n", - " (8.487,-0.892,-10)\n", - " (8.585,-0.921,-10)\n", - " (8.781,-0.981,-10)\n", - " (8.878,-1.011,-10)\n", - " (9.074,-1.071,-10)\n", - " (9.268,-1.131,-10)\n", - " (9.366,-1.161,-10)\n", - " (9.463,-1.191,-10)\n", - " (9.56,-1.22,-10)\n", - " (9.658,-1.25,-10)\n", - " (9.949,-1.337,-10)\n", - " (10.047,-1.366,-10)\n", - " (10.144,-1.395,-10)\n", - " (10.338,-1.451,-10)\n", - " (10.436,-1.478,-10)\n", - " (10.533,-1.506,-10)\n", - " (10.63,-1.533,-10)\n", - " (10.728,-1.559,-10)\n", - " (10.922,-1.611,-10)\n", - " (11.02,-1.636,-10)\n", - " (11.117,-1.66,-10)\n", - " (11.215,-1.685,-10)\n", - " (11.313,-1.708,-10)\n", - " (11.41,-1.731,-10)\n", - " (11.508,-1.754,-10)\n", - " (11.606,-1.776,-10)\n", - " (11.704,-1.797,-10)\n", - " (11.802,-1.817,-10)\n", - " (11.901,-1.837,-10)\n", - " (11.999,-1.856,-10)\n", - " (12.098,-1.875,-10)\n", - " (12.196,-1.893,-10)\n", - " (12.295,-1.91,-10)\n", - " (12.394,-1.926,-10)\n", - " (12.592,-1.956,-10)\n", - " (12.691,-1.97,-10)\n", - " (12.791,-1.982,-10)\n", - " (12.89,-1.994,-10)\n", - " (12.99,-2.005,-10)\n", - " (13.19,-2.025,-10)\n", - " (13.291,-2.033,-10)\n", - " (13.392,-2.04,-10)\n", - " (13.492,-2.046,-10)\n", - " (13.593,-2.051,-10)\n", - " (13.695,-2.055,-10)\n", - " (13.796,-2.058,-10)\n", - " (14,-2.06,-10)\n", - " (17.2,-2.06,-10)\n", - " (17.2,-2.56,-10)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - " prism, center = (0,-0.31,0)\n", - " height 20, axis (0,0,1), sidewall angle: 0 radians, 4 vertices:\n", - " (-4,-0.56,-10)\n", - " (-4,-0.06,-10)\n", - " (4,-0.06,-10)\n", - " (4,-0.56,-10)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - "subpixel-averaging is 6.81898% done, 58.0995 s remaining\n", - "subpixel-averaging is 10.2289% done, 37.5564 s remaining\n", - "subpixel-averaging is 13.6388% done, 27.3374 s remaining\n", - "subpixel-averaging is 17.0487% done, 21.2318 s remaining\n", - "subpixel-averaging is 20.4586% done, 16.7154 s remaining\n", - "subpixel-averaging is 23.8685% done, 13.4232 s remaining\n", - "subpixel-averaging is 27.2785% done, 11.4875 s remaining\n", - "subpixel-averaging is 30.6884% done, 9.75372 s remaining\n", - "subpixel-averaging is 34.0983% done, 8.27854 s remaining\n", - "subpixel-averaging is 37.5082% done, 6.93392 s remaining\n", - "subpixel-averaging is 65.64% done, 2.22818 s remaining\n", - "subpixel-averaging is 69.0499% done, 1.90412 s remaining\n", - "subpixel-averaging is 72.4598% done, 1.64002 s remaining\n", - "subpixel-averaging is 75.8697% done, 1.32216 s remaining\n", - "subpixel-averaging is 79.2797% done, 1.09069 s remaining\n", - "subpixel-averaging is 82.6896% done, 0.886299 s remaining\n", - "subpixel-averaging is 86.0995% done, 0.703019 s remaining\n", - "subpixel-averaging is 89.5094% done, 0.498196 s remaining\n", - "subpixel-averaging is 92.9193% done, 0.325508 s remaining\n", - "subpixel-averaging is 98.8867% done, 0.0483677 s remaining\n", - "subpixel-averaging is 6.81898% done, 59.2108 s remaining\n", - "subpixel-averaging is 10.2289% done, 37.546 s remaining\n", - "subpixel-averaging is 13.6388% done, 26.994 s remaining\n", - "subpixel-averaging is 17.0487% done, 21.1849 s remaining\n", - "subpixel-averaging is 20.4586% done, 16.5219 s remaining\n", - "subpixel-averaging is 23.8685% done, 13.1793 s remaining\n", - "subpixel-averaging is 27.2785% done, 11.4351 s remaining\n", - "subpixel-averaging is 30.6884% done, 9.73853 s remaining\n", - "subpixel-averaging is 34.0983% done, 8.23782 s remaining\n", - "subpixel-averaging is 37.5082% done, 7.17582 s remaining\n", - "subpixel-averaging is 65.64% done, 2.2864 s remaining\n", - "subpixel-averaging is 69.0499% done, 1.91744 s remaining\n", - "subpixel-averaging is 72.4598% done, 1.63739 s remaining\n", - "subpixel-averaging is 75.8697% done, 1.36288 s remaining\n", - "subpixel-averaging is 79.2797% done, 1.06936 s remaining\n", - "subpixel-averaging is 82.6896% done, 0.882767 s remaining\n", - "subpixel-averaging is 86.0995% done, 0.708544 s remaining\n", - "subpixel-averaging is 89.5094% done, 0.495993 s remaining\n", - "subpixel-averaging is 92.9193% done, 0.328165 s remaining\n", - "subpixel-averaging is 98.8867% done, 0.0471172 s remaining\n", - "subpixel-averaging is 6.81898% done, 58.5489 s remaining\n", - "subpixel-averaging is 10.2289% done, 38.1212 s remaining\n", - "subpixel-averaging is 13.6388% done, 27.0431 s remaining\n", - "subpixel-averaging is 17.0487% done, 21.2724 s remaining\n", - "subpixel-averaging is 20.4586% done, 16.6743 s remaining\n", - "subpixel-averaging is 23.8685% done, 13.2109 s remaining\n", - "subpixel-averaging is 27.2785% done, 11.3869 s remaining\n", - "subpixel-averaging is 30.6884% done, 9.75682 s remaining\n", - "subpixel-averaging is 34.0983% done, 8.26105 s remaining\n", - "subpixel-averaging is 37.5082% done, 7.09754 s remaining\n", - "subpixel-averaging is 65.64% done, 2.24059 s remaining\n", - "subpixel-averaging is 69.0499% done, 1.91605 s remaining\n", - "subpixel-averaging is 72.4598% done, 1.63231 s remaining\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "subpixel-averaging is 75.8697% done, 1.34937 s remaining\n", - "subpixel-averaging is 79.2797% done, 1.08291 s remaining\n", - "subpixel-averaging is 82.6896% done, 0.888016 s remaining\n", - "subpixel-averaging is 86.0995% done, 0.710327 s remaining\n", - "subpixel-averaging is 89.5094% done, 0.493508 s remaining\n", - "subpixel-averaging is 92.9193% done, 0.332601 s remaining\n", - "subpixel-averaging is 98.8867% done, 0.0488103 s remaining\n", - "time for set_epsilon = 271.702 s\n", - "-----------\n", - "MPB solved for frequency_1(2.2349,0,0) = 0.687238 after 22 iters\n", - "MPB solved for frequency_1(2.08443,0,0) = 0.645247 after 8 iters\n", - "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 3 iters\n", - "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1eb3cf6322754e07ba3761f3426e295a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=177.5)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 44.68/177.5 = 25.2% done in 4.0s, 11.9s to go\n", - "on time step 2239 (time=44.78), 0.00178698 s/step\n", - "Meep progress: 93.64/177.5 = 52.8% done in 8.0s, 7.2s to go\n", - "on time step 4687 (time=93.74), 0.00163464 s/step\n", - "Meep progress: 142.64000000000001/177.5 = 80.4% done in 12.0s, 2.9s to go\n", - "on time step 7139 (time=142.78), 0.00163171 s/step\n", - "run 0 finished at t = 177.5 (8875 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy\n", @@ -984,37 +139,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MPB solved for frequency_1(2.2349,0,0) = 0.687243 after 22 iters\n", - "MPB solved for frequency_1(2.08451,0,0) = 0.64527 after 5 iters\n", - "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 4 iters\n", - "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n", - "Dominant planewave for band 1: (2.084124,-0.000000,0.000000)\n", - "MPB solved for frequency_1(2.2349,0,0) = 0.687243 after 12 iters\n", - "MPB solved for frequency_1(2.08452,0,0) = 0.645271 after 5 iters\n", - "MPB solved for frequency_1(2.08413,0,0) = 0.645161 after 3 iters\n", - "MPB solved for frequency_1(2.08413,0,0) = 0.645161 after 1 iters\n", - "Dominant planewave for band 1: (2.084127,-0.000000,0.000000)\n", - "MPB solved for frequency_1(2.2349,0,0) = 0.687236 after 8 iters\n", - "MPB solved for frequency_1(2.08441,0,0) = 0.645241 after 8 iters\n", - "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 3 iters\n", - "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n", - "Dominant planewave for band 1: (2.084118,-0.000000,0.000000)\n", - "MPB solved for frequency_1(2.2349,0,0) = 0.687238 after 27 iters\n", - "MPB solved for frequency_1(2.08443,0,0) = 0.645249 after 7 iters\n", - "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 3 iters\n", - "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n", - "Dominant planewave for band 1: (2.084119,-0.000000,0.000000)\n", - "trans:, 0.12, 0.000001, 0.406761, 0.587083\n" - ] - } - ], + "outputs": [], "source": [ "# S parameters\n", "p1_coeff = sim.get_eigenmode_coefficients(\n", @@ -1058,876 +185,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000232935 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 34.4 x 7.84 x 0 with resolution 25\n", - " prism, center = (-9.09425,1.32149,0)\n", - " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", - " (-4,0.06,-10)\n", - " (-4.108,0.061,-10)\n", - " (-4.215,0.062,-10)\n", - " (-4.322,0.065,-10)\n", - " (-4.429,0.07,-10)\n", - " (-4.535,0.075,-10)\n", - " (-4.641,0.081,-10)\n", - " (-4.747,0.089,-10)\n", - " (-4.852,0.097,-10)\n", - " (-5.062,0.117,-10)\n", - " (-5.167,0.129,-10)\n", - " (-5.271,0.141,-10)\n", - " (-5.479,0.169,-10)\n", - " (-5.582,0.184,-10)\n", - " (-5.685,0.2,-10)\n", - " (-5.788,0.217,-10)\n", - " (-5.891,0.235,-10)\n", - " (-5.993,0.253,-10)\n", - " (-6.095,0.272,-10)\n", - " (-6.197,0.292,-10)\n", - " (-6.299,0.313,-10)\n", - " (-6.4,0.334,-10)\n", - " (-6.501,0.356,-10)\n", - " (-6.703,0.402,-10)\n", - " (-6.803,0.425,-10)\n", - " (-6.904,0.45,-10)\n", - " (-7.204,0.525,-10)\n", - " (-7.303,0.552,-10)\n", - " (-7.403,0.578,-10)\n", - " (-7.502,0.605,-10)\n", - " (-7.601,0.633,-10)\n", - " (-7.7,0.66,-10)\n", - " (-7.799,0.688,-10)\n", - " (-7.898,0.717,-10)\n", - " (-7.996,0.745,-10)\n", - " (-8.095,0.774,-10)\n", - " (-8.193,0.803,-10)\n", - " (-8.291,0.833,-10)\n", - " (-8.389,0.862,-10)\n", - " (-8.487,0.892,-10)\n", - " (-8.585,0.921,-10)\n", - " (-8.781,0.981,-10)\n", - " (-8.878,1.011,-10)\n", - " (-9.074,1.071,-10)\n", - " (-9.268,1.131,-10)\n", - " (-9.366,1.161,-10)\n", - " (-9.463,1.191,-10)\n", - " (-9.56,1.22,-10)\n", - " (-9.658,1.25,-10)\n", - " (-9.949,1.337,-10)\n", - " (-10.047,1.366,-10)\n", - " (-10.144,1.395,-10)\n", - " (-10.338,1.451,-10)\n", - " (-10.436,1.478,-10)\n", - " (-10.533,1.506,-10)\n", - " (-10.63,1.533,-10)\n", - " (-10.728,1.559,-10)\n", - " (-10.922,1.611,-10)\n", - " (-11.02,1.636,-10)\n", - " (-11.117,1.66,-10)\n", - " (-11.215,1.685,-10)\n", - " (-11.313,1.708,-10)\n", - " (-11.41,1.731,-10)\n", - " (-11.508,1.754,-10)\n", - " (-11.606,1.776,-10)\n", - " (-11.704,1.797,-10)\n", - " (-11.802,1.817,-10)\n", - " (-11.901,1.837,-10)\n", - " (-11.999,1.856,-10)\n", - " (-12.098,1.875,-10)\n", - " (-12.196,1.893,-10)\n", - " (-12.295,1.91,-10)\n", - " (-12.394,1.926,-10)\n", - " (-12.592,1.956,-10)\n", - " (-12.691,1.97,-10)\n", - " (-12.791,1.982,-10)\n", - " (-12.89,1.994,-10)\n", - " (-12.99,2.005,-10)\n", - " (-13.19,2.025,-10)\n", - " (-13.291,2.033,-10)\n", - " (-13.392,2.04,-10)\n", - " (-13.492,2.046,-10)\n", - " (-13.593,2.051,-10)\n", - " (-13.695,2.055,-10)\n", - " (-13.796,2.058,-10)\n", - " (-14,2.06,-10)\n", - " (-17.2,2.06,-10)\n", - " (-17.2,2.56,-10)\n", - " (-14,2.56,-10)\n", - " (-13.892,2.559,-10)\n", - " (-13.785,2.558,-10)\n", - " (-13.678,2.555,-10)\n", - " (-13.571,2.55,-10)\n", - " (-13.465,2.545,-10)\n", - " (-13.359,2.539,-10)\n", - " (-13.253,2.531,-10)\n", - " (-13.148,2.523,-10)\n", - " (-12.938,2.503,-10)\n", - " (-12.833,2.491,-10)\n", - " (-12.729,2.479,-10)\n", - " (-12.521,2.451,-10)\n", - " (-12.418,2.436,-10)\n", - " (-12.315,2.42,-10)\n", - " (-12.212,2.403,-10)\n", - " (-12.109,2.385,-10)\n", - " (-12.007,2.367,-10)\n", - " (-11.905,2.348,-10)\n", - " (-11.803,2.328,-10)\n", - " (-11.701,2.307,-10)\n", - " (-11.6,2.286,-10)\n", - " (-11.499,2.264,-10)\n", - " (-11.297,2.218,-10)\n", - " (-11.197,2.195,-10)\n", - " (-11.096,2.17,-10)\n", - " (-10.796,2.095,-10)\n", - " (-10.697,2.068,-10)\n", - " (-10.597,2.042,-10)\n", - " (-10.498,2.015,-10)\n", - " (-10.399,1.987,-10)\n", - " (-10.3,1.96,-10)\n", - " (-10.201,1.932,-10)\n", - " (-10.102,1.903,-10)\n", - " (-10.004,1.875,-10)\n", - " (-9.905,1.846,-10)\n", - " (-9.807,1.817,-10)\n", - " (-9.709,1.787,-10)\n", - " (-9.611,1.758,-10)\n", - " (-9.513,1.728,-10)\n", - " (-9.415,1.699,-10)\n", - " (-9.219,1.639,-10)\n", - " (-9.122,1.609,-10)\n", - " (-8.926,1.549,-10)\n", - " (-8.732,1.489,-10)\n", - " (-8.634,1.459,-10)\n", - " (-8.537,1.429,-10)\n", - " (-8.44,1.4,-10)\n", - " (-8.342,1.37,-10)\n", - " (-8.051,1.283,-10)\n", - " (-7.953,1.254,-10)\n", - " (-7.856,1.225,-10)\n", - " (-7.662,1.169,-10)\n", - " (-7.564,1.142,-10)\n", - " (-7.467,1.114,-10)\n", - " (-7.37,1.087,-10)\n", - " (-7.272,1.061,-10)\n", - " (-7.078,1.009,-10)\n", - " (-6.98,0.984,-10)\n", - " (-6.883,0.96,-10)\n", - " (-6.785,0.935,-10)\n", - " (-6.687,0.912,-10)\n", - " (-6.59,0.889,-10)\n", - " (-6.492,0.866,-10)\n", - " (-6.394,0.844,-10)\n", - " (-6.296,0.823,-10)\n", - " (-6.198,0.803,-10)\n", - " (-6.099,0.783,-10)\n", - " (-6.001,0.764,-10)\n", - " (-5.902,0.745,-10)\n", - " (-5.804,0.727,-10)\n", - " (-5.705,0.71,-10)\n", - " (-5.606,0.694,-10)\n", - " (-5.408,0.664,-10)\n", - " (-5.309,0.65,-10)\n", - " (-5.209,0.638,-10)\n", - " (-5.11,0.626,-10)\n", - " (-5.01,0.615,-10)\n", - " (-4.81,0.595,-10)\n", - " (-4.709,0.587,-10)\n", - " (-4.608,0.58,-10)\n", - " (-4.508,0.574,-10)\n", - " (-4.407,0.569,-10)\n", - " (-4.305,0.565,-10)\n", - " (-4.204,0.562,-10)\n", - " (-4,0.56,-10)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - " prism, center = (9.09425,1.32149,0)\n", - " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", - " (4,0.06,-10)\n", - " (4,0.56,-10)\n", - " (4.204,0.562,-10)\n", - " (4.305,0.565,-10)\n", - " (4.407,0.569,-10)\n", - " (4.508,0.574,-10)\n", - " (4.608,0.58,-10)\n", - " (4.709,0.587,-10)\n", - " (4.81,0.595,-10)\n", - " (5.01,0.615,-10)\n", - " (5.11,0.626,-10)\n", - " (5.209,0.638,-10)\n", - " (5.309,0.65,-10)\n", - " (5.408,0.664,-10)\n", - " (5.606,0.694,-10)\n", - " (5.705,0.71,-10)\n", - " (5.804,0.727,-10)\n", - " (5.902,0.745,-10)\n", - " (6.001,0.764,-10)\n", - " (6.099,0.783,-10)\n", - " (6.198,0.803,-10)\n", - " (6.296,0.823,-10)\n", - " (6.394,0.844,-10)\n", - " (6.492,0.866,-10)\n", - " (6.59,0.889,-10)\n", - " (6.687,0.912,-10)\n", - " (6.785,0.935,-10)\n", - " (6.883,0.96,-10)\n", - " (6.98,0.984,-10)\n", - " (7.078,1.009,-10)\n", - " (7.272,1.061,-10)\n", - " (7.37,1.087,-10)\n", - " (7.467,1.114,-10)\n", - " (7.564,1.142,-10)\n", - " (7.662,1.169,-10)\n", - " (7.856,1.225,-10)\n", - " (7.953,1.254,-10)\n", - " (8.051,1.283,-10)\n", - " (8.342,1.37,-10)\n", - " (8.44,1.4,-10)\n", - " (8.537,1.429,-10)\n", - " (8.634,1.459,-10)\n", - " (8.732,1.489,-10)\n", - " (8.926,1.549,-10)\n", - " (9.122,1.609,-10)\n", - " (9.219,1.639,-10)\n", - " (9.415,1.699,-10)\n", - " (9.513,1.728,-10)\n", - " (9.611,1.758,-10)\n", - " (9.709,1.787,-10)\n", - " (9.807,1.817,-10)\n", - " (9.905,1.846,-10)\n", - " (10.004,1.875,-10)\n", - " (10.102,1.903,-10)\n", - " (10.201,1.932,-10)\n", - " (10.3,1.96,-10)\n", - " (10.399,1.987,-10)\n", - " (10.498,2.015,-10)\n", - " (10.597,2.042,-10)\n", - " (10.697,2.068,-10)\n", - " (10.796,2.095,-10)\n", - " (11.096,2.17,-10)\n", - " (11.197,2.195,-10)\n", - " (11.297,2.218,-10)\n", - " (11.499,2.264,-10)\n", - " (11.6,2.286,-10)\n", - " (11.701,2.307,-10)\n", - " (11.803,2.328,-10)\n", - " (11.905,2.348,-10)\n", - " (12.007,2.367,-10)\n", - " (12.109,2.385,-10)\n", - " (12.212,2.403,-10)\n", - " (12.315,2.42,-10)\n", - " (12.418,2.436,-10)\n", - " (12.521,2.451,-10)\n", - " (12.729,2.479,-10)\n", - " (12.833,2.491,-10)\n", - " (12.938,2.503,-10)\n", - " (13.148,2.523,-10)\n", - " (13.253,2.531,-10)\n", - " (13.359,2.539,-10)\n", - " (13.465,2.545,-10)\n", - " (13.571,2.55,-10)\n", - " (13.678,2.555,-10)\n", - " (13.785,2.558,-10)\n", - " (13.892,2.559,-10)\n", - " (14,2.56,-10)\n", - " (17.2,2.56,-10)\n", - " (17.2,2.06,-10)\n", - " (14,2.06,-10)\n", - " (13.796,2.058,-10)\n", - " (13.695,2.055,-10)\n", - " (13.593,2.051,-10)\n", - " (13.492,2.046,-10)\n", - " (13.392,2.04,-10)\n", - " (13.291,2.033,-10)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (13.19,2.025,-10)\n", - " (12.99,2.005,-10)\n", - " (12.89,1.994,-10)\n", - " (12.791,1.982,-10)\n", - " (12.691,1.97,-10)\n", - " (12.592,1.956,-10)\n", - " (12.394,1.926,-10)\n", - " (12.295,1.91,-10)\n", - " (12.196,1.893,-10)\n", - " (12.098,1.875,-10)\n", - " (11.999,1.856,-10)\n", - " (11.901,1.837,-10)\n", - " (11.802,1.817,-10)\n", - " (11.704,1.797,-10)\n", - " (11.606,1.776,-10)\n", - " (11.508,1.754,-10)\n", - " (11.41,1.731,-10)\n", - " (11.313,1.708,-10)\n", - " (11.215,1.685,-10)\n", - " (11.117,1.66,-10)\n", - " (11.02,1.636,-10)\n", - " (10.922,1.611,-10)\n", - " (10.728,1.559,-10)\n", - " (10.63,1.533,-10)\n", - " (10.533,1.506,-10)\n", - " (10.436,1.478,-10)\n", - " (10.338,1.451,-10)\n", - " (10.144,1.395,-10)\n", - " (10.047,1.366,-10)\n", - " (9.949,1.337,-10)\n", - " (9.658,1.25,-10)\n", - " (9.56,1.22,-10)\n", - " (9.463,1.191,-10)\n", - " (9.366,1.161,-10)\n", - " (9.268,1.131,-10)\n", - " (9.074,1.071,-10)\n", - " (8.878,1.011,-10)\n", - " (8.781,0.981,-10)\n", - " (8.585,0.921,-10)\n", - " (8.487,0.892,-10)\n", - " (8.389,0.862,-10)\n", - " (8.291,0.833,-10)\n", - " (8.193,0.803,-10)\n", - " (8.095,0.774,-10)\n", - " (7.996,0.745,-10)\n", - " (7.898,0.717,-10)\n", - " (7.799,0.688,-10)\n", - " (7.7,0.66,-10)\n", - " (7.601,0.633,-10)\n", - " (7.502,0.605,-10)\n", - " (7.403,0.578,-10)\n", - " (7.303,0.552,-10)\n", - " (7.204,0.525,-10)\n", - " (6.904,0.45,-10)\n", - " (6.803,0.425,-10)\n", - " (6.703,0.402,-10)\n", - " (6.501,0.356,-10)\n", - " (6.4,0.334,-10)\n", - " (6.299,0.313,-10)\n", - " (6.197,0.292,-10)\n", - " (6.095,0.272,-10)\n", - " (5.993,0.253,-10)\n", - " (5.891,0.235,-10)\n", - " (5.788,0.217,-10)\n", - " (5.685,0.2,-10)\n", - " (5.582,0.184,-10)\n", - " (5.479,0.169,-10)\n", - " (5.271,0.141,-10)\n", - " (5.167,0.129,-10)\n", - " (5.062,0.117,-10)\n", - " (4.852,0.097,-10)\n", - " (4.747,0.089,-10)\n", - " (4.641,0.081,-10)\n", - " (4.535,0.075,-10)\n", - " (4.429,0.07,-10)\n", - " (4.322,0.065,-10)\n", - " (4.215,0.062,-10)\n", - " (4.108,0.061,-10)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - " prism, center = (0,0.31,0)\n", - " height 20, axis (0,0,1), sidewall angle: 0 radians, 4 vertices:\n", - " (-4,0.06,-10)\n", - " (-4,0.56,-10)\n", - " (4,0.56,-10)\n", - " (4,0.06,-10)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - " prism, center = 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constant epsilon diagonal = (12,12,12)\n", - " prism, center = (0,-0.31,0)\n", - " height 20, axis (0,0,1), sidewall angle: 0 radians, 4 vertices:\n", - " (-4,-0.56,-10)\n", - " (-4,-0.06,-10)\n", - " (4,-0.06,-10)\n", - " (4,-0.56,-10)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - "subpixel-averaging is 6.81898% done, 58.5777 s remaining\n", - "subpixel-averaging is 10.2289% done, 37.5198 s remaining\n", - "subpixel-averaging is 13.6388% done, 27.1639 s remaining\n", - "subpixel-averaging is 17.0487% done, 21.1306 s remaining\n", - "subpixel-averaging is 20.4586% done, 16.5868 s remaining\n", - "subpixel-averaging is 23.8685% done, 13.3518 s remaining\n", - "subpixel-averaging is 27.2785% done, 11.5065 s remaining\n", - "subpixel-averaging is 30.6884% done, 9.72629 s remaining\n", - "subpixel-averaging is 34.0983% done, 7.75935 s remaining\n", - "subpixel-averaging is 37.5082% done, 6.95236 s remaining\n", - "subpixel-averaging is 65.64% done, 2.22683 s remaining\n", - "subpixel-averaging is 69.0499% done, 1.91287 s remaining\n", - "subpixel-averaging is 72.4598% done, 1.63956 s remaining\n", - "subpixel-averaging is 75.8697% done, 1.36389 s remaining\n", - "subpixel-averaging is 79.2797% done, 1.08924 s remaining\n", - "subpixel-averaging is 82.6896% done, 0.888155 s remaining\n", - "subpixel-averaging is 86.0995% done, 0.678355 s remaining\n", - "subpixel-averaging is 89.5094% done, 0.499442 s remaining\n", - "subpixel-averaging is 92.9193% done, 0.326281 s remaining\n", - "subpixel-averaging is 98.8867% done, 0.0483846 s remaining\n", - "subpixel-averaging is 6.81898% done, 59.1477 s remaining\n", - "subpixel-averaging is 10.2289% done, 37.4525 s remaining\n", - "subpixel-averaging is 13.6388% done, 26.9655 s remaining\n", - "subpixel-averaging is 17.0487% done, 21.0934 s remaining\n", - "subpixel-averaging is 20.4586% done, 16.5002 s remaining\n", - "subpixel-averaging is 23.8685% done, 13.1996 s remaining\n", - "subpixel-averaging is 27.2785% done, 11.4347 s remaining\n", - "subpixel-averaging is 30.6884% done, 9.76244 s remaining\n", - "subpixel-averaging is 34.0983% done, 8.23291 s remaining\n", - "subpixel-averaging is 37.5082% done, 7.1923 s remaining\n", - "subpixel-averaging is 65.64% done, 2.28558 s remaining\n", - "subpixel-averaging is 69.0499% done, 1.93198 s remaining\n", - "subpixel-averaging is 72.4598% done, 1.63656 s remaining\n", - "subpixel-averaging is 75.8697% done, 1.36626 s remaining\n", - "subpixel-averaging is 79.2797% done, 1.08377 s remaining\n", - "subpixel-averaging is 82.6896% done, 0.8822 s remaining\n", - "subpixel-averaging is 86.0995% done, 0.711633 s remaining\n", - "subpixel-averaging is 89.5094% done, 0.493514 s remaining\n", - "subpixel-averaging is 92.9193% done, 0.328464 s remaining\n", - "subpixel-averaging is 98.8867% done, 0.0470705 s remaining\n", - "subpixel-averaging is 6.81898% done, 58.5464 s remaining\n", - "subpixel-averaging is 10.2289% done, 38.1292 s remaining\n", - "subpixel-averaging is 13.6388% done, 26.9734 s remaining\n", - "subpixel-averaging is 17.0487% done, 21.2453 s remaining\n", - "subpixel-averaging is 20.4586% done, 16.6093 s remaining\n", - "subpixel-averaging is 23.8685% done, 13.2456 s remaining\n", - "subpixel-averaging is 27.2785% done, 11.379 s remaining\n", - "subpixel-averaging is 30.6884% done, 9.74246 s remaining\n", - "subpixel-averaging is 34.0983% done, 8.25901 s remaining\n", - "subpixel-averaging is 37.5082% done, 7.07729 s remaining\n", - "subpixel-averaging is 65.64% done, 2.23432 s remaining\n", - "subpixel-averaging is 69.0499% done, 1.91417 s remaining\n", - "subpixel-averaging is 72.4598% done, 1.63028 s remaining\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "subpixel-averaging is 75.8697% done, 1.34979 s remaining\n", - "subpixel-averaging is 79.2797% done, 1.08361 s remaining\n", - "subpixel-averaging is 82.6896% done, 0.888317 s remaining\n", - "subpixel-averaging is 86.0995% done, 0.711677 s remaining\n", - "subpixel-averaging is 89.5094% done, 0.492528 s remaining\n", - "subpixel-averaging is 92.9193% done, 0.332357 s remaining\n", - "subpixel-averaging is 98.8867% done, 0.0488405 s remaining\n", - "time for set_epsilon = 271.364 s\n", - "-----------\n", - "MPB solved for frequency_1(2.2349,0,0) = 0.687243 after 9 iters\n", - "MPB solved for frequency_1(2.08451,0,0) = 0.64527 after 4 iters\n", - "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 4 iters\n", - "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "19ca7d2433564cacb893306433113d88", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=400.0)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 44.980000000000004/400.0 = 11.2% done in 4.0s, 31.6s to go\n", - "on time step 2255 (time=45.1), 0.00177414 s/step\n", - "Meep progress: 94.72/400.0 = 23.7% done in 8.0s, 25.8s to go\n", - "on time step 4743 (time=94.86), 0.00160783 s/step\n", - "Meep progress: 144.34/400.0 = 36.1% done in 12.0s, 21.3s to go\n", - "on time step 7224 (time=144.48), 0.00161273 s/step\n", - "Meep progress: 194.36/400.0 = 48.6% done in 16.0s, 16.9s to go\n", - "on time step 9725 (time=194.5), 0.00159943 s/step\n", - "Meep progress: 243.5/400.0 = 60.9% done in 20.0s, 12.9s to go\n", - "on time step 12184 (time=243.68), 0.00162705 s/step\n", - "Meep progress: 293.78000000000003/400.0 = 73.4% done in 24.0s, 8.7s to go\n", - "on time step 14699 (time=293.98), 0.00159097 s/step\n", - "Meep progress: 343.84000000000003/400.0 = 86.0% done in 28.0s, 4.6s to go\n", - "on time step 17201 (time=344.02), 0.00159902 s/step\n", - "Meep progress: 393.40000000000003/400.0 = 98.4% done in 32.0s, 0.5s to go\n", - "on time step 19681 (time=393.62), 0.00161343 s/step\n", - "run 0 finished at t = 400.0 (20000 timesteps)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sim.reset_meep()\n", "\n", diff --git a/python/examples/coupler.py b/python/examples/coupler.py index 3e2c4dfd3..56b321a1f 100644 --- a/python/examples/coupler.py +++ b/python/examples/coupler.py @@ -1,6 +1,7 @@ -import meep as mp import argparse +import meep as mp + gdsII_file = "coupler.gds" CELL_LAYER = 0 PORT1_LAYER = 1 diff --git a/python/examples/cyl-ellipsoid.py b/python/examples/cyl-ellipsoid.py index cf4be178f..090e31296 100644 --- a/python/examples/cyl-ellipsoid.py +++ b/python/examples/cyl-ellipsoid.py @@ -1,5 +1,3 @@ -from __future__ import division - import meep as mp @@ -41,7 +39,7 @@ def print_stuff(sim_obj): until=23, ) - print("stopped at meep time = {}".format(sim.round_time())) + print(f"stopped at meep time = {sim.round_time()}") if __name__ == "__main__": diff --git a/python/examples/cylinder_cross_section.ipynb b/python/examples/cylinder_cross_section.ipynb index 991295a92..62ab123ea 100644 --- a/python/examples/cylinder_cross_section.ipynb +++ b/python/examples/cylinder_cross_section.ipynb @@ -19,94 +19,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000299931 s\n", - "Working in Cylindrical dimensions.\n", - "Computational cell is 4 x 0 x 8.88 with resolution 25\n", - "time for set_epsilon = 0.052002 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "06bb84a8b26649f389aeca44c7d1e125", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=15.49778699874878)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 0 finished at t = 15.5 (775 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000163078 s\n", - "Working in Cylindrical dimensions.\n", - "Computational cell is 4 x 0 x 8.88 with resolution 25\n", - " block, center = (0.35,0,0)\n", - " size (0.7,0,2.3)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (4,4,4)\n", - "time for set_epsilon = 0.053905 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dd6ce618a7f24cafa0fee3b7c304ff6d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=105.49778699874878)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 35.94/105.49778699874878 = 34.1% done in 4.0s, 7.7s to go\n", - "on time step 1803 (time=36.06), 0.00221964 s/step\n", - "Meep progress: 72.2/105.49778699874878 = 68.4% done in 8.0s, 3.7s to go\n", - "on time step 3616 (time=72.32), 0.00220671 s/step\n", - "run 0 finished at t = 105.5 (5275 timesteps)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", diff --git a/python/examples/cylinder_cross_section.py b/python/examples/cylinder_cross_section.py index 489ce3ff1..159614982 100644 --- a/python/examples/cylinder_cross_section.py +++ b/python/examples/cylinder_cross_section.py @@ -1,6 +1,7 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np + +import meep as mp r = 0.7 # radius of cylinder h = 2.3 # height of cylinder diff --git a/python/examples/differential_cross_section.ipynb b/python/examples/differential_cross_section.ipynb index e26b07def..88d22e2d8 100644 --- a/python/examples/differential_cross_section.ipynb +++ b/python/examples/differential_cross_section.ipynb @@ -19,290 +19,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.00184703 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 6 x 6 x 6 with resolution 20\n", - "time for set_epsilon = 7.05405 s\n", - "-----------\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c12404a3f62e47c2b85ddfd51a7bf713", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=60.0)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 0.025/60.0 = 0.0% done in 8.0s, 19114.4s to go\n", - "on time step 1 (time=0.025), 7.95255 s/step\n", - "Meep progress: 1.425/60.0 = 2.4% done in 12.0s, 492.3s to go\n", - "on time step 57 (time=1.425), 0.0715737 s/step\n", - "Meep progress: 2.85/60.0 = 4.8% done in 16.0s, 320.9s to go\n", - "on time 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go\n", - "on time step 571 (time=14.275), 0.0708588 s/step\n", - "Meep progress: 15.700000000000001/60.0 = 26.2% done in 52.4s, 147.8s to go\n", - "on time step 628 (time=15.7), 0.0706377 s/step\n", - "Meep progress: 17.125/60.0 = 28.5% done in 56.4s, 141.1s to go\n", - "on time step 686 (time=17.15), 0.0701739 s/step\n", - "Meep progress: 18.575/60.0 = 31.0% done in 60.4s, 134.8s to go\n", - "on time step 744 (time=18.6), 0.0700753 s/step\n", - "Meep progress: 19.975/60.0 = 33.3% done in 64.5s, 129.2s to go\n", - "on time step 800 (time=20), 0.0718821 s/step\n", - "Meep progress: 21.400000000000002/60.0 = 35.7% done in 68.5s, 123.5s to go\n", - "on time step 857 (time=21.425), 0.0706058 s/step\n", - "Meep progress: 22.825000000000003/60.0 = 38.0% done in 72.5s, 118.1s to go\n", - "on time step 914 (time=22.85), 0.0709217 s/step\n", - "Meep progress: 24.225/60.0 = 40.4% done in 76.5s, 113.0s to go\n", - "on time step 971 (time=24.275), 0.071352 s/step\n", - "Meep progress: 25.675/60.0 = 42.8% done in 80.6s, 107.8s to go\n", - "on time step 1029 (time=25.725), 0.07002 s/step\n", - "Meep progress: 27.1/60.0 = 45.2% done in 84.6s, 102.7s to go\n", - "on time step 1086 (time=27.15), 0.0704253 s/step\n", - "Meep progress: 28.525000000000002/60.0 = 47.5% done in 88.6s, 97.8s to go\n", - "on time step 1143 (time=28.575), 0.070285 s/step\n", - "Meep progress: 29.950000000000003/60.0 = 49.9% done in 92.6s, 92.9s to go\n", - "on time step 1200 (time=30), 0.0704149 s/step\n", - "Meep progress: 31.400000000000002/60.0 = 52.3% done in 96.7s, 88.1s to go\n", - "on time step 1258 (time=31.45), 0.0700102 s/step\n", - "Meep progress: 32.825/60.0 = 54.7% done in 100.8s, 83.4s to go\n", - "on time step 1315 (time=32.875), 0.0714932 s/step\n", - "Meep progress: 34.25/60.0 = 57.1% done in 104.8s, 78.8s to go\n", - "on time step 1372 (time=34.3), 0.0706952 s/step\n", - "Meep progress: 35.675000000000004/60.0 = 59.5% done in 108.8s, 74.2s to go\n", - "on time step 1429 (time=35.725), 0.0702772 s/step\n", - "Meep progress: 37.1/60.0 = 61.8% done in 112.8s, 69.7s to go\n", - "on time step 1486 (time=37.15), 0.0708871 s/step\n", - "Meep progress: 38.525000000000006/60.0 = 64.2% done in 116.9s, 65.2s to go\n", - "on time step 1543 (time=38.575), 0.0712132 s/step\n", - "Meep progress: 39.95/60.0 = 66.6% done in 120.9s, 60.7s to go\n", - "on time step 1600 (time=40), 0.0702636 s/step\n", - "Meep progress: 42.025000000000006/60.0 = 70.0% done in 125.0s, 53.4s to go\n", - "on time step 1684 (time=42.1), 0.0479748 s/step\n", - "Meep progress: 44.575/60.0 = 74.3% done in 129.0s, 44.6s to go\n", - "on time step 1786 (time=44.65), 0.0393623 s/step\n", - "Meep progress: 47.125/60.0 = 78.5% done in 133.0s, 36.3s to go\n", - "on time step 1888 (time=47.2), 0.039338 s/step\n", - "Meep progress: 49.675000000000004/60.0 = 82.8% done in 137.0s, 28.5s to go\n", - "on time step 1990 (time=49.75), 0.0392874 s/step\n", - "Meep progress: 52.225/60.0 = 87.0% done in 141.0s, 21.0s to go\n", - "on time step 2091 (time=52.275), 0.0396056 s/step\n", - "Meep progress: 54.75/60.0 = 91.2% done in 145.1s, 13.9s to go\n", - "on time step 2192 (time=54.8), 0.0397233 s/step\n", - "Meep progress: 57.25/60.0 = 95.4% done in 149.1s, 7.2s to go\n", - "on time step 2292 (time=57.3), 0.0400305 s/step\n", - "Meep progress: 59.6/60.0 = 99.3% done in 153.1s, 1.0s to go\n", - "on time step 2386 (time=59.65), 0.0428728 s/step\n", - "run 0 finished at t = 60.0 (2400 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000314951 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 6 x 6 x 6 with resolution 20\n", - " sphere, center = (0,0,0)\n", - " radius 1\n", - " dielectric constant epsilon diagonal = (4,4,4)\n", - "time for set_epsilon = 10.8833 s\n", - "-----------\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ae846a20eded44459b066e949664bb21", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=150.0)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 0.42500000000000004/150.0 = 0.3% done in 4.0s, 1422.1s to go\n", - "on time step 17 (time=0.425), 0.237267 s/step\n", - "Meep progress: 2.6/150.0 = 1.7% done in 8.1s, 457.9s to go\n", - "on time step 104 (time=2.6), 0.0463833 s/step\n", - "Meep progress: 4.4750000000000005/150.0 = 3.0% done in 12.1s, 393.5s to go\n", - "on time step 179 (time=4.475), 0.0536454 s/step\n", - "Meep progress: 6.65/150.0 = 4.4% done in 16.1s, 347.7s to go\n", - "on time step 266 (time=6.65), 0.0463018 s/step\n", - "Meep progress: 8.825000000000001/150.0 = 5.9% done in 20.2s, 322.4s to go\n", - "on time step 353 (time=8.825), 0.0462615 s/step\n", - "Meep progress: 11.0/150.0 = 7.3% done in 24.2s, 305.5s to go\n", - "on time step 440 (time=11), 0.0461603 s/step\n", - "Meep 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1.0 diff --git a/python/examples/diffracted_planewave.py b/python/examples/diffracted_planewave.py index ce5409e47..572f12443 100644 --- a/python/examples/diffracted_planewave.py +++ b/python/examples/diffracted_planewave.py @@ -1,15 +1,15 @@ ### compute the transmitted diffraction orders of a binary grating using mode decomposition ### based on two different methods: (1) MPB eigensolver and (2) DiffractedPlanewave object. ### Also, verify that the total power in all the orders is equivalent to the Poynting flux. - ### for normal incidence, compute only positive diff. orders (total transmittance <= 0.50) ### for oblique incidence, compute ALL diff. orders (total transmittance <= 1.00) - -import meep as mp -import math import cmath +import math + import numpy as np +import meep as mp + def binary_grating_diffraction(gp, gh, gdc, theta): @@ -172,7 +172,7 @@ def _pw_amp(x): t_flux = 0.5 * t_flux err = abs(dp_sum - t_flux) / t_flux - print("flux:, {:.8f}, {:.8f}, {:.8f}, {:.8f}".format(eig_sum, dp_sum, t_flux, err)) + print(f"flux:, {eig_sum:.8f}, {dp_sum:.8f}, {t_flux:.8f}, {err:.8f}") if __name__ == "__main__": diff --git a/python/examples/eps_fit_lorentzian.py b/python/examples/eps_fit_lorentzian.py index 4d3de9833..68852eb65 100644 --- a/python/examples/eps_fit_lorentzian.py +++ b/python/examples/eps_fit_lorentzian.py @@ -1,13 +1,13 @@ # fit complex refractive index profile over broad bandwidth imported from # a file to a sum of Lorentzian polarizability terms using gradient-based # optimization via NLopt (nlopt.readthedocs.io) - +import matplotlib import nlopt import numpy as np -import matplotlib matplotlib.use("agg") import matplotlib.pyplot as plt + import meep as mp diff --git a/python/examples/faraday-rotation.py b/python/examples/faraday-rotation.py index 426744939..9a607d381 100644 --- a/python/examples/faraday-rotation.py +++ b/python/examples/faraday-rotation.py @@ -1,5 +1,4 @@ # From the Meep tutorial: plotting Faraday rotation of a linearly polarized plane wave - import meep as mp ## Parameters for a gyrotropic Lorentzian medium @@ -41,9 +40,10 @@ ) sim.run(until=tmax) +import matplotlib.pyplot as plt + ## Plot results: import numpy as np -import matplotlib.pyplot as plt ex_data = sim.get_efield_x().real ey_data = sim.get_efield_y().real diff --git a/python/examples/finite_grating.ipynb b/python/examples/finite_grating.ipynb index 2f6600f25..fb961a2d2 100644 --- a/python/examples/finite_grating.ipynb +++ b/python/examples/finite_grating.ipynb @@ -20,114 +20,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00181007 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 14.5 x 9 x 0 with resolution 50\n", - " block, center = (-5.75,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.610417 s\n", - "-----------\n", - "Meep progress: 9.18/125.0 = 7.3% done in 4.0s, 50.5s to go\n", - "on time step 918 (time=9.18), 0.00435765 s/step\n", - "Meep progress: 18.490000000000002/125.0 = 14.8% done in 8.0s, 46.1s to go\n", - "on time step 1849 (time=18.49), 0.00429697 s/step\n", - "Meep progress: 27.810000000000002/125.0 = 22.2% done in 12.0s, 42.0s to go\n", - "on time step 2781 (time=27.81), 0.00429364 s/step\n", - "Meep progress: 37.160000000000004/125.0 = 29.7% done in 16.0s, 37.8s to go\n", - "on time step 3716 (time=37.16), 0.0042805 s/step\n", - "Meep progress: 46.44/125.0 = 37.2% done in 20.0s, 33.9s to go\n", - "on time step 4644 (time=46.44), 0.0043122 s/step\n", - "Meep progress: 55.75/125.0 = 44.6% done in 24.0s, 29.8s to go\n", - "on time step 5575 (time=55.75), 0.004299 s/step\n", - "Meep progress: 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cell along direction y\n", - "time for choose_chunkdivision = 0.00184488 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 14.5 x 9 x 0 with resolution 50\n", - " block, center = (-5.75,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (-4,-2,0)\n", - " size (0.5,0.5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (-4,-1,0)\n", - " size (0.5,0.5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (-4,0,0)\n", - " size (0.5,0.5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (-4,1,0)\n", - " size (0.5,0.5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (-4,2,0)\n", - " size (0.5,0.5,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.655086 s\n", - "-----------\n", - "Meep progress: 9.15/125.0 = 7.3% done in 4.0s, 50.7s to go\n", - "on time step 915 (time=9.15), 0.00437325 s/step\n", - "Meep progress: 18.43/125.0 = 14.7% done in 8.0s, 46.3s to go\n", - "on time step 1844 (time=18.44), 0.00430983 s/step\n", - "Meep progress: 27.73/125.0 = 22.2% done in 12.0s, 42.1s to go\n", - "on time step 2774 (time=27.74), 0.00430151 s/step\n", - "Meep progress: 36.980000000000004/125.0 = 29.6% done in 16.0s, 38.1s to go\n", - "on time step 3700 (time=37), 0.00432353 s/step\n", - "Meep progress: 46.13/125.0 = 36.9% done in 20.0s, 34.2s to go\n", - "on time step 4615 (time=46.15), 0.00437322 s/step\n", - "Meep progress: 55.32/125.0 = 44.3% done in 24.0s, 30.2s to go\n", - "on time step 5535 (time=55.35), 0.00435253 s/step\n", - "Meep progress: 64.53/125.0 = 51.6% done in 28.0s, 26.2s to go\n", - "on time step 6457 (time=64.57), 0.00434152 s/step\n", - "Meep progress: 73.79/125.0 = 59.0% done in 32.0s, 22.2s to go\n", - "on time step 7383 (time=73.83), 0.00432269 s/step\n", - "Meep progress: 83.04/125.0 = 66.4% done in 36.0s, 18.2s to go\n", - "on time step 8308 (time=83.08), 0.00432687 s/step\n", - "Meep progress: 92.32000000000001/125.0 = 73.9% done in 40.0s, 14.2s to go\n", - "on time step 9237 (time=92.37), 0.00431051 s/step\n", - "Meep progress: 101.59/125.0 = 81.3% done in 44.0s, 10.1s to go\n", - "on time step 10164 (time=101.64), 0.00431663 s/step\n", - "Meep progress: 110.92/125.0 = 88.7% done in 48.0s, 6.1s to go\n", - "on time step 11097 (time=110.97), 0.00428761 s/step\n", - "Meep progress: 120.25/125.0 = 96.2% done in 52.0s, 2.1s to go\n", - "on time step 12030 (time=120.3), 0.00428792 s/step\n", - "run 0 finished at t = 125.0 (12500 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -246,22 +141,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "grating_dft = sim.get_dft_array(near_fields, mp.Ez, 0)\n", "\n", diff --git a/python/examples/finite_grating.py b/python/examples/finite_grating.py index 36be29976..e83b25a19 100644 --- a/python/examples/finite_grating.py +++ b/python/examples/finite_grating.py @@ -1,7 +1,9 @@ -import meep as mp -import numpy as np import math + import matplotlib.pyplot as plt +import numpy as np + +import meep as mp # True: plot the scattered fields in the extended air region adjacent to the grating # False: plot the diffraction spectra based on a 1d cross section of the scattered fields diff --git a/python/examples/gaussian-beam.py b/python/examples/gaussian-beam.py index eeb6a13ec..42c00f4d4 100644 --- a/python/examples/gaussian-beam.py +++ b/python/examples/gaussian-beam.py @@ -1,9 +1,10 @@ ## launch a Gaussian beam - -import meep as mp import math + import matplotlib +import meep as mp + matplotlib.use("agg") import matplotlib.pyplot as plt diff --git a/python/examples/grating2d_triangular_lattice.py b/python/examples/grating2d_triangular_lattice.py index 61ce7ed8b..44e7bd916 100644 --- a/python/examples/grating2d_triangular_lattice.py +++ b/python/examples/grating2d_triangular_lattice.py @@ -2,11 +2,12 @@ # triangular lattice using a rectangular supercell and verifies # that only the diffraction orders of the actual unit cell # produce non-zero power (up to discretization error) - -import meep as mp import math + import numpy as np +import meep as mp + resolution = 100 # pixels/μm ng = 1.5 @@ -120,4 +121,4 @@ t_coeffs = res.alpha tran = abs(t_coeffs[0, 0, 0]) ** 2 - print("order:, {}, {}, {:.5f}".format(nx, ny, tran)) + print(f"order:, {nx}, {ny}, {tran:.5f}") diff --git a/python/examples/holey-wvg-bands.ipynb b/python/examples/holey-wvg-bands.ipynb index 9ed14d556..1ab325f21 100644 --- a/python/examples/holey-wvg-bands.ipynb +++ b/python/examples/holey-wvg-bands.ipynb @@ -17,17 +17,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -46,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -138,35 +130,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sim = mp.Simulation(\n", " cell_size=cell,\n", @@ -190,30 +156,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.3050132495988499, 3.480691775115153e-06, -43815.03294539187, 0.011878313829683136, -0.01179628264240186-0.0013935764266843182i, 5.295036558380241e-07+0.0i\n", - "harminv0:, 0.43960436531181446, -2.7363657356159734e-06, 80326.31741985629, 0.01745119452862526, 0.017126214985327293-0.0033521561348289074i, 9.312403773661967e-08+0.0i\n", - "harminv0:, 0.5005873843994147, -0.0009565526575684324, 261.6622202858719, 0.00018410949058675333, -0.00011025456283318074-0.00014744570491736274i, 1.242711680938466e-05+0.0i\n", - "harminv0:, 0.6404729374549927, -0.005167317620050075, 61.97344391699944, 0.0031051734641799815, 0.0007710721023072853-0.0030079145692141806i, 3.514904299909929e-05+0.0i\n", - "harminv0:, 0.700226243241067, -0.0037510897788062153, 93.3363748313049, 0.004369347983945716, -5.4869994286284585e-05+0.0043690034434110386i, 1.3205854700252928e-05+0.0i\n", - "harminv0:, 0.762852889565266, -0.006722784235381431, 56.73638055721298, 0.0316955396820757, 0.02965548904826291-0.011187457488026218i, 4.404983351452081e-06+0.0i\n", - "harminv0:, 0.8234265979478261, -0.006957003127114023, 59.179691521096714, 0.000615608025293912, 0.0006152489526665892-2.102301236585398e-05i, 0.000399758756541917+0.0i\n", - "harminv0:, 0.8243479880387249, -0.0005179204557115729, 795.8248983486759, 0.030110588821435868, -0.005890629837883119-0.029528766301466433i, 5.666365518267213e-08+0.0i\n", - "harminv0:, 0.8878738611231001, -0.0002512055373818404, 1767.2258947331713, 0.0032343510475491863, -0.003225648132430831-0.00023710930923945473i, 8.018541351827511e-07+0.0i\n", - "harminv0:, 0.9495092084271758, -0.005235806245342398, 90.67459374301981, 0.005382664822832947, -0.0016788910104550295+0.005114137812962859i, 1.7544167778335838e-05+0.0i\n", - "harminv0:, 0.9758414410168742, 0.0009253350005759116, -527.2908948702515, 9.453933579227094e-06, 3.226576270516566e-06+8.886285258249198e-06i, 0.00012202208460776741+0.0i\n", - "harminv0:, 0.9858676174323542, -0.0037464233316342127, 131.5745085596497, 0.0012188508341391812, -0.0004042226659787743+0.0011498701631883445i, 2.708082943685142e-05+0.0i\n", - "run 0 finished at t = 306.675 (12267 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "kx = 0.4\n", "sim.k_point = mp.Vector3(kx)\n", @@ -233,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -255,22 +200,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "kx = [k.x for k in kpts]\n", "fig = plt.figure(dpi=100, figsize=(5, 5))\n", @@ -319,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -349,132 +281,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "Meep: using complex fields.\n", - "Normalizing field data...\n", - "run 0 finished at t = 1001.0 (40040 timesteps)\n", - "Generating MP4...\n", - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "Meep: using complex fields.\n", - "Normalizing field data...\n", - "run 0 finished at t = 1001.0 (40040 timesteps)\n", - "Generating MP4...\n", - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "Meep: using complex fields.\n", - "Meep progress: 929.575/1001.0 = 92.9% done in 4.0s, 0.3s to go\n", - "Normalizing field data...\n", - "run 0 finished at t = 1001.0 (40040 timesteps)\n", - "Generating MP4...\n", - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "Meep: using complex fields.\n", - "Normalizing field data...\n", - "run 0 finished at t = 1001.0 (40040 timesteps)\n", - "Generating MP4...\n", - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "Meep: using complex fields.\n", - "Normalizing field data...\n", - "run 0 finished at t = 1001.0 (40040 timesteps)\n", - "Generating MP4...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "kx = [0.4, 0.4, 0.1, 0.3, 0.25]\n", "omega = [0.1896, 0.3175, 0.4811, 0.8838, 0.2506]\n", @@ -487,80 +296,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import IPython\n", "\n", diff --git a/python/examples/holey-wvg-bands.py b/python/examples/holey-wvg-bands.py index 456793bdf..55a2e19d2 100644 --- a/python/examples/holey-wvg-bands.py +++ b/python/examples/holey-wvg-bands.py @@ -1,14 +1,10 @@ # Meep Tutorial: Hz-polarized transmission and reflection through a cavity # formed by a periodic sequence of holes in a dielectric waveguide, # with a defect formed by a larger spacing between one pair of holes. - # This structure is based on one analyzed in: # S. Fan, J. N. Winn, A. Devenyi, J. C. Chen, R. D. Meade, and # J. D. Joannopoulos, "Guided and defect modes in periodic dielectric # waveguides," J. Opt. Soc. Am. B, 12 (7), 1267-1272 (1995). - -from __future__ import division - import meep as mp diff --git a/python/examples/holey-wvg-cavity.ipynb b/python/examples/holey-wvg-cavity.ipynb index 393bb3187..fdbbf007d 100644 --- a/python/examples/holey-wvg-cavity.ipynb +++ b/python/examples/holey-wvg-cavity.ipynb @@ -18,17 +18,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -47,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -105,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -128,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -144,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -161,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -184,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -200,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -223,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -246,45 +238,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "f = plt.figure(dpi=150)\n", "sim.plot2D(ax=f.gca())\n", @@ -302,46 +258,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Normalizing field data...\n", - "field decay(t = 50.025000000000006): 3.95790820471984e-05 / 3.95790820471984e-05 = 1.0\n", - "field decay(t = 100.05000000000001): 6.062401894821014e-05 / 6.062401894821014e-05 = 1.0\n", - "field decay(t = 150.07500000000002): 1.7734570553698603e-05 / 6.062401894821014e-05 = 0.2925337326918046\n", - "field decay(t = 200.10000000000002): 7.468025297259177e-06 / 6.062401894821014e-05 = 0.12318591586016357\n", - "field decay(t = 250.125): 5.439633091350728e-06 / 6.062401894821014e-05 = 0.08972735865627277\n", - "field decay(t = 300.15000000000003): 4.23291165174129e-06 / 6.062401894821014e-05 = 0.06982235300759884\n", - "field decay(t = 350.175): 3.476628845824414e-06 / 6.062401894821014e-05 = 0.05734738320127583\n", - "field decay(t = 400.20000000000005): 2.847134687922034e-06 / 6.062401894821014e-05 = 0.04696380638100359\n", - "field decay(t = 450.225): 2.3289750979705154e-06 / 6.062401894821014e-05 = 0.03841670576079938\n", - "field decay(t = 500.25): 1.9177580551625897e-06 / 6.062401894821014e-05 = 0.031633634464268874\n", - "field decay(t = 550.275): 1.5662051283234017e-06 / 6.062401894821014e-05 = 0.025834729460304812\n", - "field decay(t = 600.3000000000001): 1.2900383339035116e-06 / 6.062401894821014e-05 = 0.02127932717567875\n", - "field decay(t = 650.325): 1.0534058195581604e-06 / 6.062401894821014e-05 = 0.017376047280172294\n", - "field decay(t = 700.35): 8.676272594305365e-07 / 6.062401894821014e-05 = 0.014311609069859468\n", - "field decay(t = 750.375): 7.087820739725053e-07 / 6.062401894821014e-05 = 0.011691439899060524\n", - "field decay(t = 800.4000000000001): 5.837770400453704e-07 / 6.062401894821014e-05 = 0.009629467827662155\n", - "field decay(t = 850.4250000000001): 4.808205960298484e-07 / 6.062401894821014e-05 = 0.007931189722684065\n", - "field decay(t = 900.45): 3.92681229771858e-07 / 6.062401894821014e-05 = 0.006477320979120792\n", - "field decay(t = 950.475): 3.234259653563735e-07 / 6.062401894821014e-05 = 0.005334947615938655\n", - "field decay(t = 1000.5): 2.6407774313354585e-07 / 6.062401894821014e-05 = 0.004355992026182594\n", - "field decay(t = 1050.525): 2.1750627194785985e-07 / 6.062401894821014e-05 = 0.0035877903794809614\n", - "field decay(t = 1100.55): 1.7768960210318136e-07 / 6.062401894821014e-05 = 0.0029310099393934598\n", - "field decay(t = 1150.575): 1.4635346961931978e-07 / 6.062401894821014e-05 = 0.002414116915349122\n", - "field decay(t = 1200.6000000000001): 1.1953143759707475e-07 / 6.062401894821014e-05 = 0.0019716844853058654\n", - "field decay(t = 1250.625): 9.845036591681545e-08 / 6.062401894821014e-05 = 0.001623949840754035\n", - "field decay(t = 1300.65): 8.038713471789712e-08 / 6.062401894821014e-05 = 0.0013259948138141452\n", - "field decay(t = 1350.6750000000002): 6.621009853390941e-08 / 6.062401894821014e-05 = 0.0010921430100249761\n", - "field decay(t = 1400.7): 5.408528044424668e-08 / 6.062401894821014e-05 = 0.0008921427741445288\n", - "run 0 finished at t = 1400.7 (56028 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "f = plt.figure(dpi=150)\n", "animate = mp.Animate2D(sim, f=f, fields=mp.Hz, realtime=False, normalize=True)\n", @@ -357,32 +276,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating MP4...\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "filename = \"media/hole-wvg-cavity.mp4\"\n", "animate.to_mp4(10, filename)\n", @@ -400,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -459,71 +355,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "field decay(t = 50.025000000000006): 0.024304124551377593 / 0.024304124551377593 = 1.0\n", - "field decay(t = 100.05000000000001): 0.00029478773644369007 / 0.024304124551377593 = 0.012129123837418002\n", - "field decay(t = 150.07500000000002): 8.914669657976724e-14 / 0.024304124551377593 = 3.667965755825354e-12\n", - "run 0 finished at t = 150.07500000000002 (6003 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "field decay(t = 50.025000000000006): 3.95790820471984e-05 / 3.95790820471984e-05 = 1.0\n", - "field decay(t = 100.05000000000001): 6.062401894821014e-05 / 6.062401894821014e-05 = 1.0\n", - "field decay(t = 150.07500000000002): 1.7734570553698603e-05 / 6.062401894821014e-05 = 0.2925337326918046\n", - "field decay(t = 200.10000000000002): 7.468025297259177e-06 / 6.062401894821014e-05 = 0.12318591586016357\n", - "field decay(t = 250.125): 5.439633091350728e-06 / 6.062401894821014e-05 = 0.08972735865627277\n", - "field decay(t = 300.15000000000003): 4.23291165174129e-06 / 6.062401894821014e-05 = 0.06982235300759884\n", - "field decay(t = 350.175): 3.476628845824414e-06 / 6.062401894821014e-05 = 0.05734738320127583\n", - "field decay(t = 400.20000000000005): 2.847134687922034e-06 / 6.062401894821014e-05 = 0.04696380638100359\n", - "field decay(t = 450.225): 2.3289750979705154e-06 / 6.062401894821014e-05 = 0.03841670576079938\n", - "field decay(t = 500.25): 1.9177580551625897e-06 / 6.062401894821014e-05 = 0.031633634464268874\n", - "field decay(t = 550.275): 1.5662051283234017e-06 / 6.062401894821014e-05 = 0.025834729460304812\n", - "field decay(t = 600.3000000000001): 1.2900383339035116e-06 / 6.062401894821014e-05 = 0.02127932717567875\n", - "field decay(t = 650.325): 1.0534058195581604e-06 / 6.062401894821014e-05 = 0.017376047280172294\n", - "field decay(t = 700.35): 8.676272594305365e-07 / 6.062401894821014e-05 = 0.014311609069859468\n", - "field decay(t = 750.375): 7.087820739725053e-07 / 6.062401894821014e-05 = 0.011691439899060524\n", - "field decay(t = 800.4000000000001): 5.837770400453704e-07 / 6.062401894821014e-05 = 0.009629467827662155\n", - "field decay(t = 850.4250000000001): 4.808205960298484e-07 / 6.062401894821014e-05 = 0.007931189722684065\n", - "field decay(t = 900.45): 3.92681229771858e-07 / 6.062401894821014e-05 = 0.006477320979120792\n", - "field decay(t = 950.475): 3.234259653563735e-07 / 6.062401894821014e-05 = 0.005334947615938655\n", - "field decay(t = 1000.5): 2.6407774313354585e-07 / 6.062401894821014e-05 = 0.004355992026182594\n", - "field decay(t = 1050.525): 2.1750627194785985e-07 / 6.062401894821014e-05 = 0.0035877903794809614\n", - "field decay(t = 1100.55): 1.7768960210318136e-07 / 6.062401894821014e-05 = 0.0029310099393934598\n", - "field decay(t = 1150.575): 1.4635346961931978e-07 / 6.062401894821014e-05 = 0.002414116915349122\n", - "field decay(t = 1200.6000000000001): 1.1953143759707475e-07 / 6.062401894821014e-05 = 0.0019716844853058654\n", - "field decay(t = 1250.625): 9.845036591681545e-08 / 6.062401894821014e-05 = 0.001623949840754035\n", - "field decay(t = 1300.65): 8.038713471789712e-08 / 6.062401894821014e-05 = 0.0013259948138141452\n", - "field decay(t = 1350.6750000000002): 6.621009853390941e-08 / 6.062401894821014e-05 = 0.0010921430100249761\n", - "field decay(t = 1400.7): 5.408528044424668e-08 / 6.062401894821014e-05 = 0.0008921427741445288\n", - "run 0 finished at t = 1400.7 (56028 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "freqs_wg, psd_wg = sim_cavity(N=0) # simple waveguide\n", "freqs_cav, psd_cav = sim_cavity() # cavity" @@ -531,22 +365,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = plt.figure(figsize=(11, 6), dpi=100)\n", "ax = fig.add_subplot(111)\n", @@ -586,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -627,45 +448,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sim = sim_cavity()\n", "f = plt.figure(dpi=100)\n", @@ -684,21 +469,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.23445415345698725, -0.0003147812363599428, 372.4080827818086, 5.812143030902256, -3.763107501450195-4.429450140163555i, 4.306293489743804e-09+0.0i\n", - "run 0 finished at t = 450.0 (18000 timesteps)\n", - "Normalizing field data...\n", - "run 1 finished at t = 454.0 (18160 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "h = mp.Harminv(mp.Hz, mp.Vector3(), fcen, df)\n", "sim.run(mp.after_sources(h), until_after_sources=400)\n", @@ -712,32 +485,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating MP4...\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "filename = \"media/hole-wvg-cavity-res.mp4\"\n", "animate.to_mp4(10, filename)\n", @@ -753,17 +503,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Resonant frequency: 0.23445415345698725, Q: 372.4080827818086\n" - ] - } - ], + "outputs": [], "source": [ "f = [m.freq for m in h.modes]\n", "Q = [m.Q for m in h.modes]\n", @@ -785,316 +527,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.2340278559161547, -0.0018734141261924503, 62.4602570900316, 4.626071402369734, -2.8205983802960652-3.6667098872005526i, 4.878861173642782e-09+0.0i\n", - "run 0 finished at t = 450.0 (18000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.23453222952425626, -7.30923087629776e-05, 1604.356419256047, 6.010010961462772, -3.965258312230556-4.516299178994806i, 2.5784755758812747e-09+0.0i\n", - "harminv0:, 0.3203380792800332, -0.0028107745362353324, 56.98395142519764, 0.08939729348630548, -0.07611073206285417+0.04689384338623845i, 1.5030156708333963e-06+0.0i\n", - "run 0 finished at t = 450.0 (18000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.23454147739266504, -2.0401831532648347e-05, 5748.049556662018, 6.042373374596647, -3.9957221571700208+4.532601950396154i, 2.948523805875737e-09+0.0i\n", - "harminv0:, 0.32588515566889864, -0.0018520908802034915, 87.97763628993552, 0.08796924097874716, -0.05533533077673982+0.06838558712335283i, 1.0559310146633343e-06+0.0i\n", - "run 0 finished at t = 450.0 (18000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (6.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-6.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (7.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-7.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.19490287264916165, -0.0007098826592777302, 137.27823190347084, 0.021882941703098496, -0.012928672972631956-0.017655383109633823i, 2.500625566886593e-06+0.0i\n", - "harminv0:, 0.23454251746066898, -1.9496236307475725e-05, 6015.071672339521, 6.045187754860183, -3.9989015117156104-4.5335506715057345i, 3.135084966927066e-09+0.0i\n", - "harminv0:, 0.325392318417585, -0.0028756252341051265, 56.57766432120712, 0.10346319679053909, -0.06988052223995311+0.07629774375162895i, 1.7085893425281652e-06+0.0i\n", - "run 0 finished at t = 450.0 (18000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (6.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-6.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (7.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-7.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (8.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-8.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (9.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-9.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "Meep progress: 397.32500000000005/450.0 = 88.3% done in 4.0s, 0.5s to go\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.19654202912381852, -0.0001319711534889467, 744.6401123571296, 0.010139002924627917, -0.009080829279710545-0.0045097582971114i, 3.34141550460465e-06+0.0i\n", - "harminv0:, 0.23454237393497965, -1.928288781118331e-05, 6081.619522749968, 6.044742625704984, -3.998564131543015-4.533254713442919i, 2.0904183509113547e-09+0.0i\n", - "harminv0:, 0.32477010784223226, -0.0009459772540947283, 171.65851844557693, 0.10284612856999271, -0.05368440033289738+0.0877229235874676i, 2.3129729952416795e-07+0.0i\n", - "run 0 finished at t = 450.0 (18000 timesteps)\n", - "-----------\n", - "Initializing structure...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (6.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-6.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (7.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-7.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (8.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-8.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (9.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-9.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (10.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-10.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (11.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-11.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "Meep progress: 366.1/450.0 = 81.4% done in 4.0s, 0.9s to go\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.1689088120846072, -0.0008445701020748057, 99.9969165790139, 0.00035136553844277774, -7.403109585265841e-05-0.00034347800286486707i, 4.7246283165373895e-05+0.0i\n", - "harminv0:, 0.2345423978640612, -1.9291522826319817e-05, 6078.897969217604, 6.044913362697281, -3.9983665938157515-4.5336565974910785i, 1.6528012731386283e-09+0.0i\n", - "harminv0:, 0.3112255725190105, -0.000515974557591914, 301.5900376672834, 0.014525642975353973, -0.013494086961301489-0.005376236688268417i, 3.824912357074424e-07+0.0i\n", - "harminv0:, 0.3253120677284504, -0.0007471267911785511, 217.7087420565448, 0.10327042908975875, -0.0574294536238316+0.08582912897642073i, 1.5876314759103416e-07+0.0i\n", - "run 0 finished at t = 450.0 (18000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (6.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-6.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (7.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-7.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (8.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-8.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (9.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-9.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (10.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-10.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (11.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-11.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (12.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-12.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (13.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-13.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "Meep progress: 331.45000000000005/450.0 = 73.7% done in 4.0s, 1.4s to go\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.18634339022479032, -0.0005848109413962261, 159.31934325638528, 0.008396135658688452, -0.0015504592340709706-0.008251737402667624i, 9.722082433583947e-06+0.0i\n", - "harminv0:, 0.23454234757429138, -1.930720606601693e-05, 6073.9587792330785, 6.0453078999997, -3.9988080338829404-4.533793325015162i, 1.6246192346348785e-09+0.0i\n", - "harminv0:, 0.3251536244537306, -0.0007666444458027871, 212.06285797404294, 0.10960733464915637, -0.0601241424854098+0.09164526883198224i, 1.4407296748106877e-07+0.0i\n", - "run 0 finished at t = 450.0 (18000 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "N_vec = np.arange(2, 16, 2)\n", "f = []\n", @@ -1109,23 +544,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Resonant frequency: [0.2340278559161547], Q: [62.4602570900316]\n", - "Resonant frequency: [0.23453222952425626, 0.3203380792800332], Q: [1604.356419256047, 56.98395142519764]\n", - "Resonant frequency: [0.23454147739266504, 0.32588515566889864], Q: [5748.049556662018, 87.97763628993552]\n", - "Resonant frequency: [0.19490287264916165, 0.23454251746066898, 0.325392318417585], Q: [137.27823190347084, 6015.071672339521, 56.57766432120712]\n", - "Resonant frequency: [0.19654202912381852, 0.23454237393497965, 0.32477010784223226], Q: [744.6401123571296, 6081.619522749968, 171.65851844557693]\n", - "Resonant frequency: [0.1689088120846072, 0.2345423978640612, 0.3112255725190105, 0.3253120677284504], Q: [99.9969165790139, 6078.897969217604, 301.5900376672834, 217.7087420565448]\n", - "Resonant frequency: [0.18634339022479032, 0.23454234757429138, 0.3251536244537306], Q: [159.31934325638528, 6073.9587792330785, 212.06285797404294]\n" - ] - } - ], + "outputs": [], "source": [ "for fiter, qiter in zip(f, Q):\n", " print(f\"Resonant frequency: {fiter}, Q: {qiter}\")" @@ -1140,22 +561,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "idx = np.zeros(N_vec.shape)\n", "Q_fund = np.zeros(N_vec.shape)\n", @@ -1184,89 +592,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1.2,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " cylinder, center = (0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-0.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-1.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-2.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-3.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-4.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-5.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (6.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-6.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (7.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-7.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (8.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-8.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (9.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-9.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (10.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-10.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (11.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-11.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (12.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-12.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (13.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-13.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (14.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-14.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (15.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (-15.7,0,0)\n", - " radius 0.36, height 1e+20, axis (0, 0, 1)\n", - "Meep progress: 184.5/700.0 = 26.4% done in 4.0s, 11.2s to go\n", - "Meep progress: 363.225/700.0 = 51.9% done in 8.0s, 7.4s to go\n", - "Meep progress: 547.7/700.0 = 78.2% done in 12.0s, 3.3s to go\n", - "run 0 finished at t = 700.0 (28000 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "sim = sim_cavity(N=16, sy=12, fcen=0.328227374843021, df=0.1)\n", "sim.run(until_after_sources=600)" @@ -1274,22 +602,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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8QeEbHgIDYfMd3DmbbiAoo6LJrKjTdgBURsJ4z0gTmqLqtaSxw9VXc1nee5ITueSjcqecARnGMRk8BwQ6k0avF4SoAR3rV0E9laxLI/KqIlVfVT0XKII9pKHwPTRDQOYFmBGGDj9z8XcIIQsAHABvAniJUvquwvMPe/+/LPj9ZQB/G8BD2YsYxk5vB9u9be5vf3XlFfzgbz8Lm9owiYnfe/a38eDivcH9272d4F72byHS9hQIN9ADYkMhej0+8C9sXsRTL3wS5zffxYnZO/D5Z/4vHGzOu3KoHHMsupawX8QtEX/D4OAeOVkvtC7iqd/5sYEczz6Pg80D2O5uIyw3x8j35CTc36Jyc7MXFDW+IjeYkKOkxv0/2h6/++znGDmSQXhyRmWSlVVljgg5IDw5tq7gYy9+Cuc31+L9KvRQCsGNxr7Hss4gc+i3ZJkvtC7hY7/7EzjfWsOJmWP4wid+PSIH5No5dF9eQz9aUHGfdlOnuNC6zLTHMXzhR36DGecSjR0dx0n6KpOuSoNbRnd8PMfXV5DQU1lkjVwrQmfJ6ysgqX1z6yxRMQPDmiM3o7ti7RGSI5p+CWNZchwL4RXpQusyPva7nx6Mj6d/EwvNucE4F+jrVP0lzJYnK/O3qr72HuXqq8Z83I7JpbOyjGE1nXWxdUmsr6LPxcoqvpbpUCLhdVFHG1yPjY+nf1NNXzGI2ZQqNiODHRl7NAWEjjCLLBuEkD8B8CHOTz0A/4xS+s8k03kBwDMAPkMp/WXO7z8C9+CAFyilPyqZ5muCn05hCQ38A5lUNDQ0NDQ0NDQ0NMYA/wq4f+E0XvvWG5lWmfZ7+Nn/A+C/AHAKwCSA0wD+MYA+gP+JEPIZyXSmvf9FNNNfipjJWE4NDQ0NDQ0NDQ0NDQH2dfgZpfSfRi69CeB/JoR8DcAfAPgsIeRXKKXtIZTtAd51z4Nz/8X/6g1M1SaD6xdal0KuxC8+81s4OjM4HYx1JW73dnD437jJX/p7fx1Kh5NhvGwiNyn3/jyxxUkbRQXhZzKnConinUUuUylXcF63Ny/8iCNj6slnke+8YiW59wGpcIbYMwLEN3kmh26EnskTviEKt5MMtWP/zrK3apTC7di/yw09c3Pg/p117KadqJZwLV/YWZrsvHIVpK9Kl7m6cJ2h9u2CwrEG2fP2rWQIQYsmkZwp87dAhwGjp7Ojf0eTGjzIKYDq/BRNqFy9JR2WpWhzlB8m6+YS/hr9nhDWz7vfw3ZvB4d/5UEAnk1Zn4rkKhd+tt3bwZF/le903n1NakSglP5Hj9g8CuB9AP4k5RH/HTciduC3cCt/6bwEa5OYqrnJrrUu4hMv/jjOb74rPO5YdIIImw4Xo0JqZAdfpQomrnyVQFMUadJ+i9jzgmtpsbypBoFbAl5ayhMkT15lA1/SGuDIyP6dagDlODBiJGRmv6fsPWBLLkxLFml9GijGwC9jDKelLVW2AmROul40qSmrnWX3ivG+i5B3TEeeK2Zcj7geizyXidTwDiHIsq+Gd01yfnJzL1F3Fa23otfz6KvY86NHaljwbEpZUlME9nv4WRK+4/0veBlKCP6hAqJjoPzr7+QqEQfcNwuP+ekVLhIGT9rA44Fj4O1JiCZB2d+LLIrSMmyRGSdPIFxvRelIMX5CtxZQb2mLEWVAsdyF948i+rNKGlXnJ5Mc+EaYECXvqU0/PjxrwvFyDwxrJ3JPhXpI9hS0Ic1DpevkEZp/hg5Fm0N+7A5pXh0TaFIjxgHv//SjnYBz3v/vFfzuX/9GrhJFUDqhGYZhVCYKVaI501JdoVUBIQXKWkZ7C1Zsy4BMPQjDrzJkJ7OCx0Pe/qBkjPPUfkXGhoyhzULi1CBuNlm8NFnkTvJME34ZZN8HNdJI9XYpmBa5PGMJ11PuLcbAVzBcq2jTUolcQeUvan4qbQ4tsJ1GfRwnoLQFiZKhSQ0HhJAlAB/0voqOaWbx+3CPg/5g9AWbhJAGgI8DsAH830WV8ULr0h710GhUjTKIqspxkImGruoEVcTKaOGKu+KVNdnV0FInqIzkLglJxtAYTLZCJMlU1Mq2KLymaCQthBW9Sq/Ur4boFSyqzxakQ8vw1oz1YqdGsRiyLt63pIYQ8n5CyNOEEDNy/QSAF+Hug/k8pfQC89tPE0LeIIT8c/YZSullAL8JoA7gXxNC2L1K/wLAEoBfp5RuFFX+p154bv8QmpFb0S8aFRm9aeUvKBwredIU/1ZFOJJq6Fnm9zskxZMXHXqm4rlI89IMC2mb4xXGXiYDK8/YzvCsUhnT5B+J1zJU5K1JLYUoBI1BRm+NdJtFQ9BCv5UYbhhDBbo2rY0rM3BLGgMj54HTSMN+PijgHgC/BuAKIeRlALcAHAfwCIAmgNcA/N3IM4twj33m7bP5rwE8AeBHAbzhHTTwAIAH4e7P+YdFFj50KMCeIzQZB/BeGviqYTqqKEARE1C5iV61XbK2Y67woSES1iLDKPJ6aQrZOF4xEuVUfYldESAI2p2QQd0QQzyu2fuqBqXZx47oRYNJBx1QTt3IQlRWKRkoyml3r72zyJO13VXbTPL+QshO2X2ZOoUtwA0dWcdeaX15/LFHekYm/AWA/x3AJQCPAXgOLgF5FcA/AvCYimeFUnoNwOMA/je4HptnAMwB+GUAj1NKbxRZ+BOzd5RLaEY1nKNsYz+KMpTnqJOvHG0vPSkWXQfRMle8IVXVS8NF2V4afgLqeWZF1WNXo+I6F/el0vYRyYRkFeityYwkfVSUfkqSl1ekPHKPqn3go4w5tmoiNar6csTbft96aiil3wLw9xWf+SyAzyb8fgPAz3qfUvHFZ5/fgx4aNZQ1Ge3J+GCBIsora2obpMXyZ9k4LnMsKK8oMqFnGRS2uA4kwj+KnHxTTx7iHYUqeD4X0vfTFBf+Ei5z4rGoGeGnWcwq9sBbQ0HCaRa0wi30oIZWhQta6U3z1vC8U3m9NSJ48iV7kPlyS3udQw8leaRcuWNtrJJWIeORkZfjFShyDhXKOkwvZEmQbldFZOqHGiHsZ0/NWOPozOFhF6Ek7C3ll44SNlSnYQRXWjJPEHmODS2jHrJ6aWTSiSK3l6YgjIPBkrEfsPWXWJeyp6CNKipow8K8NUqnncl5awqDbD/LeKofN9lRnDOr6vNVHYAxkthv8spBkxqNOEZpEh4Hg0kFw5BHarNjhWF2eevA3zhd2ObxCsLt0p7Je8pbJi9NGYZ3hf07yUuT/KB8Fow8lZwaVanuLcNbxulTPN1SZDhW0jtrFJCvfXOG3gXJqOs2fqYl6V4eZOaOYdgU2nYoDyW154XWpdxpaFKjoVH58aIFofSJosj0JUPPsrx1m0XeECy2KCnyJx9bLRFeNowXHxaeiaR8WV6Ym4SKTvID3PIWSmgqODFK+CK/IfZHoII+KSWfWMektbOSt0mVzMkgTT9yvcFFtmvJ7Teu5GeUFoLHFGuti3jqhedyp6NJjYY0sk1IKgpjhFYiykAG5ZnbmEpYBS3VwFCQtezwCfmjWMusj4RjnFUxsl6a4UEtNGwIkAxxG1sM21vDK1L0eOcQytnTJULZbaykV0okrzRJr+Rs00y6c5S8G6WgXPuqKt3kv0j+/Oa7udPSpGYvYlRPzciLvSpXWch1xHGRqGjfkKxxH76YLasivTTR+9Le2SIB1ffxZEKsnAKZ0sZtUS9Y3UsoIAQr/LvIW6PQ9/Ii6f0t3PsT7ksaM6reqMJeTimpc6o6mbHs/SYjM7/sYexVm4eRyyc0b90+jxOzd+ROWpMajTC00ikH3IlWFPesoMhU2kt46lXBakDGMBqmsi51xVAcElKKR4pjIKW/9G94XprCPY/g7UtR7M9FGPLKxCxcxjJe1Fj5BnKFPjYg3eX2xazemjL6aWWQ0a2FH6kvGHNFzE88CGWs6EAIjUL6OEto7pw7gS8++3zuNDWp0RgRjJsyKri8Zb+3JZrdMEJeUleTc9SBwDgqWk7pTf5ZvTSJmWcMOwtflM8vCUV5aVSwHxdcipS5cm9NAZ4LJUO3OG8NUDwpFIZm5WnjMnVqRqTq3KLHceHz7gjbHeNW3gRECc2Xn/t8Iaf6alKz31GVK3y/oKqjQ1mwp+UUcXJOHiQZuxxUsmkcSDfwJdJIJzSyG3czIE/svtBDV1Q/kWvDQk59i+Wc4qWRlbGKBYmYwS7hrclZDvnxVfIeExkPm2qaqkjz1mStP5GHKrhUzelg6WWtMNQwDVXPWcOYl/cDMrYbj9AU9d5FTWo0KlAqI6w8Mh8Lm4DgvSQ0QXFWHONcBdGRnSQqCj3LG9KT+TjfPMal6EWGSdeG7qUR/DZuXposRk4VhlEl3profSPkrRFdkyxjXjKdWQ/kIXJ5ITv2ht1/y97DGbpMw3Nz7lyKP9yiPJS8l1VB9rXWRXz0c0+XQmgATWo0RgIjTHqyYoRXgYRvXU9TTMpGUAUHBMiEq+TYM5R4nG8WL41q6JmsgYeKvDQZw85yGZay8haxNyxxISLDfWmQ9dbk8DjFD7UoOQytCG+NQr8PJ5gyvjJ4a4AUPSCDNCLH+14UUvVUDnJL+HPJSJ/oN8Jzc3aMh0xlExpAkxoNjeGjivC0vYhcx+SWFHZZZGy+VFgd53CAEXvRZj5jUIHASUOBtIg+ReQhfLL4cMHhhqFx+h7PAFbo78rFKXvPZloIGpvtqB8/XgaGEV6mMVKogtAAmtRoVIYxC9MoFXtMAWc82Y1IrJAqIa39SyMvJXlpeNgrYWdZvVQQeaQkVsJHAZJtlovYqJ4+OE5haDIoyVuTCVXoJKa8wpeuhh9ITGNvYEzkqURHDb8uYoTmky+VQmgATWo0ho7hD7jsKKHsRe83ybxnSHBf1GCQDdUpGrKTQZnvaYmiai+Nn+1eDDsrfbIvU++MgOd1r4ShqaSb9tiwjXlZXVRG388SUil8JuOhJYWfelbG3kxtjxSNKgkNoEmNxn7GUFZyR0jxZDX4U8nMEGRMO8a5yFCssrw0/nUZYzCLl6aMsLOy9tEIcy5yzxSNfLKigHRUvDWyKJLYZEWWk8FkvDX+9bSxJLqnDG+Ngo4RHu9cGVK8c5mJ7CiZlMOdhzRcQvORCgkNoEmNRqVIWO3dryhzpVBFwVaxSVUUeqaKveylUfSwDPYj8A58KCPsTM7wq+RgAEDcxpn6c5ScyH4UIWzjgsPQgOKITdFhaJwN5sqyZmjj/eCtEYagVXVYS1H3qmLPhdBlwejYWANC805lhAbQpEajctDI/wVjpJVmBftGEnMf0fhdlXpNORVJ6YCALBjWXhoWWY5ITSAKiXWW4ZjuuNGcY+XXT6JQD80IoyyPTaT+MxHuhDZM7EdZSEnBe02Gs7dG4cCArKe9BYllKWv5Bm+uvlvIXJ5TxlFZdFRCyTaWBMKE5nhlhAbQpEZjKBiBFZWKT4CqHHvRDZ5VpiGcvKNsNFKqHHbG9dIwJeA+zzyrbnCkh51xCY0KZPdbJBGacej7iQa9JLFRlTOJ2BQUhibsV5JhaEJDP2lsKPaxoXhrRlV3DQMjK1MZIZfDwvDskgutyyFC85UKCQ2gSY2GRkWoMsRBQqmWssqdYNCW9cJNkZemjLd4l72XRqUoiYajQjx/YiYSoUhFEhpZFE1o0o5vlv0oy6FGbApBXmIjKac0scl7aEBauar21ijq3kr21iSGoJUwRsvQvbkwoguPexQfe/FTQyM0gCY1GmOMzPHmhb/7IqPSjE4wZRn+qCr0TA6F7jVRzLkKKMmXZjTJHg4gWT+F9oOiCY2MgZu0h0Y1XKeAsLjcaSaVW+bFnLkPukggNjkh3dc4766J/Rb9m5uhfL3n2tOnhALGW1F7h4aEUueeaH/NPJ6zPTc6Nsjo4Pzm2tAIDaBJjYYksq3uDhEj4QL2ka1OhBNT0bHow4xdznO8r4wxWFQ/yOJlKXovjZ9U4lgUG4FKE2baSn3S+4YKklWJ0CglrLBPiPcpMg8feYhNFkTaiUtsCjg0IPlYX7EsxZJvybIX6EnlgnM4QuI9MtdZVGzQh1D0vCP4LTtZGxG7BBgd2ySP1zUBJ2aPDY3QAJrUaOxl5N18qQSRghh+2Jm0cVgkigg9U1h9VzaCcvUD2TCWtGQUV/Rj1xK8NHkIjQJwFfUAACAASURBVAhpe4eSjMK8nhHRKn6RhEaWvEjfl4HYSB/VLQGZehcRG87vWSH1vhJZb01qZhnKmxaCpgrFPqnk0cqqt6o4BY2FLBkfiTC0ssJkq7Q/Rgdfeua3hkZoAE1qNMYJWSZ3fyLIMyFIg3I+oltzKtJoWEahspVXT5WGRUiuCqdCYQVX2jDM6KGS9tJIP8N7QLBCH1xyvNwSCE2SMS0he2qZCwi7GlxX8MCoPl/0XhtItqeo3nltwiM2Be81EZdZ0VuT1WORZ6wmXZdCBftmuLmWqWsLlCM6f+Wto8S2Upiji0Be+2MMT3g8OnNoqPmPX41paDAoNQZ11BVKJURNBgXsp1CFZLhO8ZD00pSxwTh0f9RLwzcKCg1XAuRkLXvTfGEhhXwyQoIzvJI/sullQlTGMg5HSPoOCMLQskO8KZ6Er6mO4SK8dUV7a2RQ6DuWFFHFgQGqGJn5LAElzi97YT/NKGDErTaNoqAHTDKGXj8lHRIwdLkqQLWhZwnJyhoGZRKeQgmNT2QSVrmzEpqE+4Sn2OUKv4mWU0xmZJFIbpLyls4guoItODUrln/GNvC+J3sw8hm/qqd90Sjhkc5IrpyleTPKCkEbY5QmY4kH7Ehlvw/aLg+qrh9NajQ0VFDEquUwD1MofNU7RZYi9xxkRjWhZ9zfshr9SaFnSV6aDJDaFwPEw86ichbdnqkHPhDBRzEbHjlROL5ZlQwpl19mg3lWROWJEhvJ8ZufHCh4a2R1VtLYq/zAgIoMO4nxm3BDvrxVvItlY9znaY3M0KRGY+yhV0rGA4Wtikq9C6KMPiEbtjIklOml4e2jCW5N2b+R57QsLtLIS0oaaUdRy5RXQG54eaiXkSNf3ve5pLVHojcuGoZWrbemMlQVglah/hqlo501xNA2THHQpEZDY49iKIqy4tWt0l+4KQkpYz/HXhMVL00l7U4jpIe9rno4QNqJX5kOfMgQrpR02EHSJnvlds0pg+oJYUmkk3dfBWOY30f53prMIWiA1BgcagiabEhhGRiCJ0Ib7xplQ5MajfFCCS7tShXtnnJp7yVZFJAldEUFZbzzIcsJPCIvDc/4lTHuU36Xfgt99PeMRDVxX1BaG6vsR5EqjEiOZK9Gqu6SaRNeWJYoDE2lf8v2u2G9W6vscTyW2ENyV9iGpdgQo35Q0YhC15rGnsBYrwBVFcJUtZIc1n6aso9yDn5XkK8UeSWM3Ezv+FA09sraSyNqR56hzDWcObKntRmPqIk8Nankh5cXp314ciTJDpSzt0bm2uBHiXsiSHp3UkH7w7hQGYtpuriofTVlht3l2ldTMKqac0YpDFgRY227jCD2LakhhEwSQp4mhPx7Qsi3CSG7hJBtQsg5Qsg/JYRMK6Z3nhBCEz73liXLyGHfrnKlYNRWjsbxnQl5ylz0Czf9ZEsOXxGHngnui1wXpSsF0V4aYagS5Xw4zylBwRiUlC2X10nWu5FaCBVZCgi9km0b0V4hFcNRsu+lvpSxiBA0CSS3WY7xPTR9xSRTpfdF5US7KqBtET72cL1Ywy7AEPETAP6t9/e3AHwewCyA9wP4HwF8mhDyIUrphmK6/0Fw/XamUpaAC63LSvePy0oCBVFW4OMi21hgmIpyFFz1pZyalBFlvMAuavSG7kkzCiXLI9ozlBJ6FRr3hGSvcymZyODerPWcYNzHZAnKRdwy+fIRQ4FoiGTxr0dkiv4feiSH3EC+9skKUZnzylIElNqxYIyC/HsEe9n+yCLbsLCfSU0PwK8A+EVK6bf8i4SQQwC+BOAsgF+ES36kQSn9yQLLWCgoCC60LuBjL35q2EUZb+zpiUBWruEruP1wEIIYYWO/uBOl4vKlT2b56yRx43jqfYPr8bJ6RID7kIikycrLMf4TdYOcPEKCE6RBY/fnNziSiScB5ZRVgazGEhwQm6D8wbWENqsSQ9Dzo2E8yrbriLRTGRgZPT/eGMYcPQLLm8MBpfQ/UEp/iiU03vXLAP6B9/VZQki9+tKVg7XWRXzkc0/j/ObasIuSDwmr8iqDSLvB9zrKjFuX3E9T+H6TKvssI6MsARCGbgnqgXvymWR4HTeX9DAl6ZdLpp5+Jt5XkmiYFiGPTL0xqcQv8dpJ0Mah/lyhR6Hovi47LkuVca8uho0wRi3sW+beUYg8GFPomuPjnPd/A8DCMAtSFNZaF/HRzz2Nt26/gxOzx4ZdHDnsWW9IueCfIjVChwSUiYr7TGWrqrJEPqv8golfmQCIvssgJGOKJyrriW5RiMLNpE/GKojAJsgUJzZsXSiO69R2ykDUssot+16linRX5R6SYc1vI3BYwLiEXY0ctE2UCk1q+LjT+78H4IbKg4SQnyeE/B+EkF8ihPw9QshS8cVTw4DQnMedc8fxpWd+a9hFKhVKK6DDhmCCGX4IwghiBDbdClHUaqDURmrF1XtV2VX3BhV5NG70JDBEjPqkDei8+3npRsuVeDoWjXwknon+VpVMacjSTkXtExPJKezDEqF6RY3pslfyR1lvjSHE5Ho0TkAbK/tjD2I/76lJwme8/3+fUtpRfPZfRL7/S0LIz1BKf1UlEULIa4KfTqmkEyY0J/DlT76EheacShLZMOR9J0mxyXIngxXI93XoWRhjVx8Zy5t35T4PeMai0uo7J/Qs9Ld4RV8Mfqx+fDwmeSnArxu/HJk3ocvuq+Hsp8m6t0RRprBOC+9nEOs7hbqIysPq8NDfDlc/8vfdgC9n3rGR5yCITGOLaeNx2FM5DmWsEkXWh8TBDrntjzJR0fw7LDm1pyYCQsgPA/g7cL00/0Th0c8DeBbAcQCTAB4E8AtwQ9j+HSHkRwouaip4hObYzJGqi1EOJEhH2iZcjT2CMsJTCjk9q2CknACW5XlppIYoRT0aimFZ0TYUGfyZjgoWPCP0tkT2kLAfJofkNGIFFJQNcjIl1YFM/1duqwLD6iSR/vLVgjFKiyt6D8WeQ2b7Q/eFXNCeGgbeu2R+He4M9POU0nMpjwSglP5s5NJrAP4RIeQNuKes/S8AflchvQcEZXwNwP1pzycRmpHdTF8Cxr38o4mkfRYalSAp9EzGEOSudoe/C99NE0PS9eykS2j8i0LJoqeQsddEoBGjPiArgpVY/zoxEJaPel+T9oZw2igtRC5S/tgKsLLXQrGtIivccW8M5znV/heVYRhHPu9ncL0YGb2PGiHsJftD5WS+YcqtKaEHQsgRAL8P4ACAX6CU/lJBSf97ABsAThNCThSUZiL2tIdm3KAnZ70/qEqUeWKUeyFyvei2JcnGf9q+IdnN51EITvgilIY+vHtUxjj3UIe0/SZCAlS04ZDStuN8kt8+h9bB0HPxPoEmNQAIIQcB/Ee4oWO/BuDnikqbUuoA+J739VBR6YowUoRmLyuRvSybxmihyHCELIakrIcmFqLF3KM4Xig4RCUouxH5IPI7MhrMEU8Vbw9SindLCsJycuTi1IHyKqiQpCS0lzANBRRJWnRIjkZV2MtzewWyDTvEbt9rCkLINIDfgxvS9QKAv0tp4S1/wPt/u+B0Q1hrXcRHn/9EtYRmRE4c0dAoHSpqQfiulryqpdh9B7EJSOa0q9g+kohxzDWWM4JLaKKIkIBce4fccpMQEQjvPQl+yyNjqJwRchZAQGzyQqa9ZN7pErmeK+SEK1teeQXjLfc41tDYg9gjtty+JjWEkAbcfS6PA/gDAJ+mlNoF5/EAgNMAdgC8UWTaLLISmqSJaC/Fg2pojA6KMZRSN5xnNfCFk5vC3hPhc0kgfE9ElND4csXkix82kK7DaGxfDRF5NphrYY9NPA1+Trz2iHiaYnJFiE0sLdn2lWiDoC057Sy8VwG8/pi2jyozNBnR0Cga42AT7ltSQwgxAfwmgA8D+DMAz1JKuynP/DQh5A1CyD+PXP9hQsiHOfc/BOBzcGeef5eWflbECM1zn1fy0JTaUUs9DWrvdt9xUB4aKqjYyModiiWA0iq3aLN9JA1iiAlZinEfujfpu2gTfKS8MULjv5wy9JJKhtgkeTgGBUgvX9KeGh6Zi94X1YVC74oCGRGkkWl/Rln9UQqa4Owl7Om5sUybpkRbbJTaZD+ffvbTAJ7x/r4G4F8TvrL9OUrpNe/vRbhel+jemMcB/A+EkHcAnIPrlbkTwHvh1vGfAPhviyy8j0RCozCBRU+2GKVOWjn2MFlKR8ZTbxJOLFI5NWWkUeipTHKnTRUGlRPB4BmuaXtpIhvqQ3cIZQjLLadnJMKwgpPCDAB5wygS9gHlfTdNCIakTNS9N0Wu+DgTt7GwvYJ30OTsn1WQGOXT+DJgjxxmkDjOMsu4B3R6Vki8q2avIretWLJ9tZ9JzQHm72eEdwGfhUt6kvAHAI4BeAzAkwDmAGwC+H8B/J8Afq3osDYgv4cmin1NZDQU4Bt14w8h4RrnY2WL9sykESDBhnpKCGMkQ42wpXoz/L95RM0AiDPIk00jcLaktK0wBI2ky8H+LgwJFBxwwMrFXmcJqUz5o88BiO8XYnL12ystnaL61h4cX3tm8QZAfsKusZcxyrbiviU1lNLPwiUsuZ+hlH4VwFcLKJY0LrQu4RMv/ngyoalqNUHwhunB7xlXn8d10tMYC4yUYo6OEYk3uIcgu3qe+X0gnMMBwDeQxc+nlzG+7yQamhZNg0MClGWkTOhZPMwsdm9AbABCAQq/fVLkS9zzlCBXIAfjrSHEK0I0lI8nr1wbcYlo8LwCGU36LnomlWQmHKE9QvPE3iI2GiOHsmypqrxOFUTB7Oc4m7HGUy88V5iHRmMfYC+4yovYJzBSKMH4KTNcRoYc8Dbcy7ZPUtmZfTE08vFuGNwX8YQkktfUEDvxnhr5tKJlMLiERihXar1IlIFLQsM58Z8r0UAvpa+OKaGQ3X82jhhb/awxjtCkZkxxfvNdOUIzKvtD9oJy1pBHUr8bsTj1dI/NaJU3F3LXPYe0xIzjAsa6T0wC439g+PNK5F5P8IRIyR0JtRPtqQn9LSEriRKu0I9CahEjNmyd5Aaf3MRJTw6M2DjPh2RZRsrrC6QQ4RGxCTSqwajYXhX1O927xxQnZu+Q99BU0Zn0asz4YVSUnUZJ4Lyg0kceg1PhGGf58DQGwj0Lg/95Xo2A2IhOEwOEupB/RDMAxwl/gBiZEcoo0rsMUZOTSZCGIpKPqo4g7zt4Ykjoixp7B3pOGT9UYbtVSKS1hhlTfPHZ58cv5EwrvEpQTUz3iK1Mji04ngFmnMTbUrZto6pdZk+OfJvKGcgq/VBEQuLGv/9/uvGfcAw0C9Gk7nCu864lpeEjejwz+yjzP0+mEFnjJ56cNzc38W9qRDQlPA6Acl+MlGeQU/JYGdvQs5FD+bpd7zuqCPvQ5tKkZkxxdOaw2gOj4nLOcmrPXkZCu4xcSEMaRiHcJGO/Kbyuy6iL3GOCY+STyAsqs5abu/eE/d1BLLyLh4SjgEUEJu7hEIWdRWWLpMQeFBB4ZZzwB3B/ix0qIEiTV7cMUYvvoUmWVemo5KCMHMKV1l4yYPKPHfCQQOKUUMY8UPDYzKw7RmGOGwWdrYDko6lHxMYpG+NmQ1XcLvukF2gAKL9z6RC0OEZp0pBunxFRhikofbVv2G1X9qSkGKIVB2+1HJFrlLPyLyMXvwwUcXs8tM2FvTlqdCdCRMQASilo4qEHyfKISGPIx8CRSZyqTPtEvBxsyFxKe+VC2WNm2IZayfKNjwdD1rAeIZtg2Pp8FFF2+yjanBdal3JnqUnNfsMorGYMe2JKwygp4rKRqy3kJ4nCPCHD7jvBanvCfgllcMJqYrfE+6TYAEoa45IhWWWD+j6VtPaMeDQEsVosCQi8NQDSN9ZHwpd8Lw11wmQGDLmhTthbw00rKgZ7IltkL02KTCEPVAJIEmkpGlIHgUj0w+ijCeQyfj1n6FkaqR+RMZI7GSXdm2ev3YjP6/sJo9AWirbmWusinnrhudzZjoCFq7GnsJ8IwQhjJFb8shLoLAo5wwp6/O6kCV20eV3ldC2VwgzKzjf0ONd4ZfA3pUff/ZKtUPF6TjoimJVB6jho+WLEvtMwCXAzlWuTxLLxws9U0wjdGDm+OS3+TArJN4fKJtle7ndHtSAMIi8XFR3ewLvGqUvufpqiELSJ+p4l9cUaBdKWmEzWkMHhm3wjMTflxV6wc0ZIBv9F8uc3382d1vB7uEb1GAHFlqiUpWNGR2dQJiKHwVv4Xo/C64xzjG4RKNpwGfrKlUL4U9KxwrLHCAPxthC9/DG6rwbJ/Y5vwCetmrOhWwrGqYgAwH0FJfvxbwyixNg0kozqaFkYL81gDw11P54MIW9Nmhyc/APHjKQ8SkQtdoqbM/g7fKPgby8b6XfuYLCfhlfHWV7G6ZeJ2/c5f0t4+4aKUdVlnOPFC0XBc02uuXDYXjdZyNZZEfZTmVCwMX1C89bt8zgxe0furEfAutXYcyhiYO4nlEQypVfEhtEOokkm7T0h0WRkvRjDgPJEKjbiiGjDd+Yy5T3mOc2oTAiF4noE5MG7m72WGK4lQWxihCbIJEJsmGe44OYVfy9NmjxSSPSgRa+pktIECI9vFv2mCOqkeGkUx/qoGLdp3iiZ50R9bxgySurc0rw0o7BQOwoYswVhltDcOXcCX3z2+dxp6p6gobHfULa3pmyUSFpiq4FZDIRCjLmc4TbRk6ikT81SPV0r8YbQ39wXO6akEfVqhMiLt8fFJxiO90DIWxPay8LLwPcesd4kD45HbtgPS3JCXpA00ml4HjG+PFFZhPIkgUNaQgcEyKQi6T3zvsjdp9T/JMpVxPgvYFyXejpl5Qszo+2l0RgjSBLMKKH58nOfVz/VlwNNajQ0qsCQQtCGEr8cMmAGKiaTHCqTe1UTaeSwgMR9NbJGYICEkBvAM64zhqDx9tbIhGbJgnccmX89+JsJQZPx4oAvqW/808j3gAhEH5DZu+GwoWV0UNehz+A36h8qkFJ+7tHUEULDkyX2TFr5Q9ciJIvXJry2yoJQH+LspWG/S4Gp69DlLF6alPyj+2mqPiRARWdlaKvwyXuRtqkYeeaifRF6to/AIzRFvXdRkxqNVLgR94OPFHQImhpKdJ9LnSiUN6xj2HHrQuQ0BEJQkDHXRBox+j3jM9aO/jWRcSo83YlPYrIdeiAgKImhTuH7E2VgEPJqMCn7f7PExt+LAub9KYmGEZs3S2iAgZfGvy/msRGXO1ynJNgbpCJLInh9Q1iejG3FQ1KdJpFkEblk+zJ7e7RvZCmrTBnEN3OvZjOyR3WuKy5M1v2uclJjAdChZy4KDj3LZPdJoExCA2hSo5ECXmfeE6eXpKGMVf8hrhiNdZulrOiP1L4a2ROeohCRSiVPFQVfpZPIB+GV9bL6Zchr4JrrsRA06YnYT3JAArgfJlaLJQtShwREyYt/LbiP81v0Gg+RAw+yyCKFiAcvCD1jr5c1Lti+FNQ1p9+FYGTo30CqUS0sn8S1KpC2n0bSgzkuGOrcU0Yb74PQurLsvrIJDaBJjUZGyG1qLHHw7wPFwqKU+O0y99YMzWBIkCmrYZAkSzQELfW5hDComLcicj1YzU4JQYsZlhJlICTyf0FTA9dA45yC5l1TmThjkWEYkAHfWwMgcS9EQLL80DPfS+OVkzrhD/sbHBoKQUs81tknNtT3JDGEJiKHLIIjB1ivkichdxwUZRhHQ7Rk+3mMUPNAg/4Q8tLwCA3Xg5NShkQPpcy7dxSRVOfDmsOUQ2IVUIJMpe5dGkUM2W4qi4RWQWgATWo0EhR5Wucu7ySTfabEJJFXuae2l+yKp+oejCIM5CzemjzZpdV1ItHJuSrMM+KixIa9V1yQIAX2E/wW3QeRVO5E44z5zXHi+0z8UkS9NSIrPjGciyUCNPKJyEhZeX2PFE8ezso/ZUgM8xlci5aR1ybh+g15aVTk4FaEoN6861wvDYtoO8m0b2K/Zr00Kf0uLR8gmdAosz/F8ZgyVos2sivx0qjoYBUdn1K2vHp53xEaWZRkI5Vl81VFaABNajRyIrcxWTKBKSsuNHuB0lf9S80+ZsAprArx3jkxygS0iPj7GMTyhjflclaxE70nvAQTjFLGsE7q12wNxI1MkWctY5uyRnKawcyeOkZ5e4PC/dL3bvjJ+Z4O/70uFIDDEgL/Xo48JFYrSCYvoQ+f7MTSi4E9+YwGhMbhyOETG//e+Ahl9/ewe1EYWaKIEk/e30oQ9x1+X0sehUHtBF67KKL9Q7J8vPHHXMvywt1sYOQbVfD0udJiTLjdKplvy/CuFYhK7Y+c8o67h8aHJjX7AaNufPJQwADdi/uBili5ylQH0iubJfYzWW9N1WEdohPeEsNzeBCYg+yqe8xbk96W0TtDT7C6gcRfwilMUcY4ixnMA+M75q1hjdmEkKLA6PflYKsGYWITOjAgZU/NIPSMhghNPPyMJTZ0EIKWEnoWkJhAhgGhCfgIxaDlvd94ZQ3/HyFeQd0KCLEMiRER6ehtCPedMFlI/l+UIsDz0rD1K6JMKUgjNFVvNvf6+nD30kiGC2eYi4uYZ8fdSyOyP0Z9IbhIVE1oAE1q9j4yHampppQS7x3BvS+Fn+BWMQonNmXJWcomzepIadZ6TiQ2st6a2DtQ4sQm8che3oo55ZmDkRC0FHDfNZMEJ0JUQrJwvDVgPQ/xWgpsee8f38PBHuU88ITQcBGj/dEnBEEZWe8L5XpkWGIT8tKw1yHIk4bLliRDqMqi4NZVpE5Dnhs6KGMqBpkm7hEKYRDal9bnvJRj8gz+jpO0kHz+PYkQjDlpD40YlRraZei60g4FGbTJniY0CieHjRwSyl6YvcdgGIQG0KRGY5hIU7AZFfBIKhRZSK4YSiv9hFXkQk8NK8pbk6XNk7w1VZGgSLvJEZvwE+5/kc3owff4qrXwKOTIaVtR+5BvaKI8j26M2Lh/k4AcRMkaXy7K/k8ZT4z3ceA6WxwAjjMI66K+bNzU/K8skQl7a/jhZzRMcJLS9jw1QZicQ0NljcoQIjZsMgCnPOGykWi9KREaRXC8MzJ9LniWRdQDGSU0URIZC/vjlI33Pzi6syovDetdg4SXJg25xmq2xc4QOGUWzr1p3kz2Vtn5Y4yPcs5so5RkMxUNLqGZPVpJ3lYluWgMBxUeY0lAq1tdKWjFYdxBQVK8ZIJJk+kDQbtRRzxJJJ0ehUi6VZAISvlGUWLfpig1NA5w64/pm349EdBB3YTqiIBnkPFWyAc7Kww3H8cBDAOAw5U7MIoZS9I3mA3/km/v+d6aoFgkkoD/d4phbBhi49kvL6WDZqAOCAxQ2N53AkJt0KCOHABmWC7KkBU6+A4AhAKEEDhREQTFJazBHLybJrqfJiqPW+fUcfMKnjVcykIEYWNsWSiCSLdw+b1/HeK1EbdPO0GjEmoPykjtAUlU8dAYKYYhdQBiDtIT7hVjvDTeH0GP9YuDQbtwe2zIc8YQGQjGRNqpa5Gy8k8orMIwTtGJlYedMShCt/NIGiCWK0FPj6yHpgQk2kxJ83EJ5SgSF1qX8IkXf5xPaCqwETSp0YgZY+OKUghNGcqlwIEtJDZJ6fu/ZSC4USWcSqzKQgKxkSFq8dVaNkwohzxsft6YCsiNKJYqcr9fwlCy1DUFA3JDXIJACIX/S7TU1PsnOrJZcgPfyGRZQAwK9ZFGbPx7vLwDYkN8I5YM5PKN91i4kmf7UmZPCjzHAaUwvB7pONTjURwPVLBq7u+JiYaWMf+HhYBPbEANUMcBMdzjoIlP2GLPkIDAOA4deGUSyh/UD0fw8J6TAZkKe/SQ7p1JIzTRvEUGGAkTGiciPktuKImn4uuPUCidR2gC4hnP1CsWZ4xHTu8qzTMj0BmJOjFKAIZJaKLZFqXb08ovIDaFEppSQp+Lt5EqXQwuE8yYeuqF53B+893KPTQ+NKnRKB95iIEkAdhPHpoock0+kiTAzyc1/yIImz8hyUyO7P1585OCgmzRkDT/3RuRfRasIRze5B3NjzA1TQb3scY0GTwVhDMBMY8AIcR1MIA1ND1jPVFumt4urLHMM6xD5MaTxuUIgENAiA1KDUa2cIiTw8jjUKaG6ODwZELIwA8WtcfZunUcwLYBx+YTm2j5fe8YPEJDiPusbQCGybglooQVAy8NXELj0HBtEwoYxCM/bPn94jOhZoTa3j4eB8Qre2h/jwiyRCboS34pRGNkcFw1S2i4/Q0cYsOQy3go3cArNcCA8BGKgbfG8xZxCU1mEpMkdwRF6D3Z54sy2CX2GKVHA0iQNAnsCcM+I6SJTZ52r3DRepiEBtCkZn8iZXCkrvwXoVRHJPZzX4FHAjKFcnGSLsNjI2sosOWNErXo77w8suSb9ju3To0BsZFChEB49iWB46YQyOwxAsLeyGbNnBbm/U8oheF5ZyiJPxMqd4hwMWULskuor6jnhp1cbY9E+44bB6AGcWXxZYoQNTb3IISLue74deRQGAavpiPkTHgAwIDQ+MdDuwSGCfujbll9bw1iRjiN5ewmS0P7aVj4oXNcsN4Z3/BnCQ37clAfrEGfRGhCmQ6MeUKp2z+EHhMgmDG8n30PVLTsRLTvLvAyeYlQO0xoomNA0gAOERqRp477oIT+U9UbzHUuAaia0DDIRChkPExFLTppqKGoSJAEmy+JiJ2YvWNohAbQpGZs8Yd/+IdoGk3xDQmKZNfZHaTz5S+jSRret6jxwqycsQhNjmTwpJTyYlfWos/46bJGUHQCYCYH7sANrW3Gf82tYJnnVfYsJU5gYmNBPl1O2wGD+HS4K5veRe9/b5XVNxQM06sfdrXTN5YTDAS/HEH4i5dH0HdovFx54DAGUEguROQxAJCBTIYRlgeIyxT0N7Esbp6DTAd9yq830/vfPRXK/z3YxB0Ypa5BSuy++5vTB2jf9Rz0e6C9Dmi/711nDASzBlKvg9QagNUAzDocqwaYNVCzBodYsClF36boU8AOjOjB+09MQmAQwDIILBOoMcT0jAAAIABJREFUEQMmcUD6XcDpgzh9ENv28vb2bACu8e/VEzFNb9ybg5XyGOhAVrvvytXvuXLZPVC7H7QDMU3AqsNoTgL1CVCrDlprgloN9GGi51B0bYqeQ7Hbd7Dbc9Du2+jaFLbnIjANoG4amLBMTNdNNEyCZs1AwzRg9ndB+h2Q3i7Q74B226CdDmiv45bL7nlHNHurz37fMDy5vDxCvxsExKwBVg2k1gBpNEDqE4DVCMpuW010bLe8HZtiq2tzyk1QNwkmLBPNmoGmZaDmXasZBBYckP4uSG/XbaNuG87uDtDvgtp2QAqIabn9w7JALLdcMC3pNiKAm57fZw2DaWMDMCxQ0wQ1LPdvqw6bGuhRB30b6Ht9zQ6IIGDA7WumQWARwDKJ2/+oDWJ3AbsHo98D7K7bLr0OaNe9Hur3huXKVWt4cpkA8cpBCKhpeWNOcvzBiejHpDGdogtDesOvU4TyCLywwfdIK0QXJZTCA0UgjDwJsoh0eiBTgl537Ig8vPnFyyuQiS3X4LvU/CkzX0bTkkpXJS85iE8QFNspYRulOHtJCMrSE+b+qMc36DvxtmNtyv9m+Wfx2v/313gNf52YJw9+Oq2trfRyC6BJzZhibW0NDVIX/Box2IDQCl6Hdpl0LrrpsPHYgfK1Y4oX8JSUP2n4ipIHXsdlBwbxjU5+WYm//Mfb/BodtEkrlGkKM63MbNlj6bCKU2LTrftHPC/eqlakngB2gvBuYZdIo23obSQm7Oqzv7k4SN8I2pIaphtC409yxCM5xPB+Nwb3e0UOiusbRQ67adlGqD/xXOCEmeR4kxCXDLJ9k5WNudcnNCGZDE8mBIYPJZz+Swd7FFx5+gPjhzMmqOERGcMANSxQswYYFno+maAIrfabnoFnEoDYPRC7B9g918Drd0B3t0F3tuBs3UZ/ewe97V3Y3R6obYOYJqxmHbWpCZgzMzCn5kCmZ4HGFGhtAk6tiT5MdG2KXZui03ew1e1ju+ugazuBsdm0XMN/pmFiqmZiomagYRIY3TbQb4P0uyBOH7TbAXU8ouU3uGm6hnyt7hIpz8AN6jkYE14fcGxXzn4HzvYmnK3bcLY30dncRq+1A7vTA6UUhmnCmmigPjeF2vw8zNmDIDPzoI0ZOI0pdBwD7Z6DzY6NG+0erm13cLXVwY2tLnpdG47jwDANTDZrWJ5r4NDcBFan6zgwYWGmZsLstkA626A7t936bW/Dae/A3u3C7vcD0mKYJkjNhFmzYNSsQR+lDpxeH3avD9qz4djeAQcGgWlZMJt1GBOTMCamQCanQSbnQBtTsOszaPVs3Gz3cWWri8u329i43cHObg+O7cAwDNTqJg5O17E008DiVAMHJ2qYbXjtYjgwOtsgnRZo6xbszRvo3bqF7u1t9NsdOLbt9qtGDbWZSTRmp2BMzcKYnoMxNQtqNbw+abr9NNQ+jmuUekQWdg/odV2CF21zwwKpN9w+btUBawJOfQIdm6Ldc7Dds9HqDAibQylMQlA3DUzVDUzXLTQsA03TJWsWbBi9XZBeG+hsg25twt6+DbvVQm+7jf5uN+jzZr2G2lQT1tQkjOk5t36bUy55NOsBoadmDbZH5O3IuDOIy09rBnHltXuuzL7OimwGogRunfnGOLE8HUjc/4V6w9OFFMHChV/PxCMAIYMxCJlz9WsoX/939l4vLzZf4TwIhOfpkF43PTlMCHU6fB3I7HFiD6jwZWKJIju/MASTMvNK3A4AAluALXogJmMHSM2bYOpPdl7GoB7ZtGVsgsT0OQRDaK+ISEzEXgo9A69tvDxitpxkmdl5msZJNxAZE0wbdtAP7mlfaeNdvCOQD3E7iIFvm/Z6PX4ZJaBJzV6FgNBwbkQwYKiDIDY/GCSRzbKEgFAD1I2DCV0f5Jew0hEsULj5sgZmuEwUFA6Iv8mXHaD+KhGBu7IaVXTwys3WAc94ZfNLO5YVJKLxiXcPRbgiWBl4aQ1k8MMQwtXlfQk2UzOTKIdADjZTUy+exWGyNkDgeGfHOu7JUrZnoPowTRDTAiWmt3DnvYAxWN2EOwF6hIaCBEY69X4nhMAAASGm94AnJ0uufJn9kCd4demfrhRZBQrazsDAACB+evBW0b1VUM8oCYc5uZO130rEJFwvHfFXc0Ohamwokpeu0w/IYeiEK0JAHG+ju9GAv5rdsQdeBdsZ3O97R2oGQc0ksIwaDMPwNrdTELsL4jhwOm30NrfQvnYLuze30Wl1YHcdGJaB+nQNzflJTC51UacUpmm6q/JmPdis7lA3umur6xrT13e62Gr30LUpDAJM1E0sTDfg0BpqpoEGhSuf03M9Ab1dON1doNcDnD6o3XerxjTdlXLD8owiC6hNBAalwzQ/CGASAE4fRs9dgSOmBWr30bm1hZ0rN7C1sY3uZhf9Th9GzURzto7JxUlMruxgcrULd/o0YBAD9fo0ugYBCNBxHFzf6uLK9R1s3drF7tYmaL8Po1ZHc2YG7U4flmFgrmHhwASBAQek1wXaLThbm6A7LfS2tmG3u+jttOH03D5q1ExYzQYs0wAxjcDrAQC033cNvA6F3e2hv9sJPVebnIDZ7aFmu/eZxAAxajDqDgCCds/B1VYH72xso3Wzjd1WC06vC2JZaE7PottpwiAEMxM1t+4MgrrhEk3S3XEJzfUr2LlyDTvrt7BzbQe7m104PRtWw0J9to7p5SnAoWg2J926thoB2YVhwWZUFvH6o0+saa/tGbAU6Pddj4lte3ao5ZIZuwdSb7rjyrBAaAMOJeg6FLd3+7je7uH6Vgftrg2HAnWTYHqihoXJOgACyzDg+KrNX+W3u6Dtbdhbt9C9dgM7V29i99YOuls9OH0HZt1AY6aB5oEpTCw6qNc8bx4hLlkz63BqdTgw0Xcoet64871G8LSK6Y07xwQapuV5GIhHaFjDHIEuBHVcYxwWYNhu3RkWQgtCAIKjEnxdF8ynxNUXvvHv2HFd5RMLw/2bEGOgg1mdaBjMVBJdIGP/9g7XIP784N9r+hWPgVFqJut0eOGhdsTI9RfLPJnEcwsFJabr0TQIQ2wYu8Pw5E2a3xxvPqeeFzHqRaA0zPdIoPmZm0RzcvQaAVuX/Puj7Mvg3BMu3+DviE0FAHDcNjMYjurbPUx9DRaAo3VFQGG79lIoz4Ryi2y2gNAIbD92zg5sxxRyx+7XLflgKk1q9g34A44Svz96CozakRt8Ze0boXCNviAQnsQ7K2Gfi67csCu53jMkbHCSaHH91eVmE5ZpwnFstNs73mU6sKNjwkWIjYd6o4l6rQbHcbDb6cCx+2EFwl2Z8RQko+SsWh2Nhust63R76Pd63PziSbn16VEamKaJZrMBAoJur4dutxciUH778FYH3YnFm2tNE83GJEzDQK+7i067DRq4lr19A7btrsIyz1MAxPKIqkMBk7p1bdXQdxy0O73BXgICUM9Y8KcMAxSUDM6oclco3XZv1CzUak23rtttUM/KIP6Ea/gTra8gTUZm/ycT9XodtZoFSh10dtuwez2XYPi5Oo4b3hS0oeHN4UbAhUAprLqFeqMJQgg63R66th1IEZoVfcVOPULjT9zUPenKMgzUm00Ypolez0anb4M6trti7FVjz6Zo9210+hS2V9aaSTBRs9x9BxSABcxOTsOaBpz2Djp2zw0XAmD3++jvdLBzvY3dm7vYtR2YhGByx+1ztakm6r1OsKI+0WzAaDZhG3X0WjsgxF2x6zkUO7t93NzuodNzjYLppoVGzcRcw83LIAD6HZB+Bw3iwKpbsB0Tu5027M5uUK8uQXSNNRgmYDXg1CbQo0C/7xI4b8cP6rU6rEYDBqGgvQboDkCaHY/YONjd7GDn6g42OjbatoMa6WO+3cPBvgOzYaE2NQHS3ER9ahrNugnaMGDWaujaFBOmAcsgsG0H3fYOOrevwe62YVgNUOqgOVlD13YCQ5b02jD6bdSpDdQtdNoEu90e+u1d11NkO4BBYNRMENOANdlwvR0T00Dd84h3u3DaW7CwCbvrjiHHtt2FFf/QAJNgkhA06hZAbXT7bdDeBGrGBEyDoO846HX6aG/exu7NDTj9Dsz6BAzTgj1Th2UQNAwDUzUTc5MNTBk2CEzs7nTQbW+h19pG59YWtq5s4cb1Nm71HPQoxUTHxnKnD6tpYWLJccPQmpOgtQlgcg5mrQ6HEvR2O+j23dVQA8QNCTMs1GoWDN9I9YxGarshg5RS0H4fZqOJptWEWbfQh4NOvwNq1mB4YdB9h2Kr4/azrd2+N/5NGIRgtlkD4J5qZxoGZqcmYTpdOIaN3U7L7cO9Dno7beze2sH2xg52Wl3YlKJpGi6hrxmoz0+7bWFagFlHc2oOxsQk+g6wub2Dbp+i61D0HaDds9Gz3UUvkxhouLFvsAz3NDriNiCIY6NhmajV6nBsG93dXfQdj5D43l1PRxFqunorSACeLnf1Xd2y0KjXQClFt7OLfrfj3hos9HhhmD55JMRVAqZL39183H7eaE6AEAO9Xh/dbtf7zdOJPkGhNjPfAkEokbf4YxgEzYkJGIabTqfXx+Aode8RuLo4qtPdUysQeKmbEw1YhoF+v4fdnW3Ps+kuqFE/tNQZ2A8EcEmMSQB49UYIGs0mavUmbH/+9QXzSQ8L6hIaSjAgVZ6s9XoN9VoNFBS7ux3YgS5n7I00UAdWrYZGvQYA6HS66Pe6oXyEZCXkTaEwTAvNRgOGYbjzeGc3/ozAmHcXDylACQgBJiYmYBgm+raN3U4n3N88QhOzmcCQhTT7K0RIPH0eIjGRPW2B7RctuOGGfnKlYq9KtEVB2PekhhAyAeC/A/DjAO4AcAPA7wP4J5TSi4ppHQDwWQBPA1gFcAXAiwA+Sym9VWCx0xF1qVKmc7Lw3cG+ZwTsarv/OLNqEVqh8gYFS1QCFj5IgFVG1HC85zjDgONaN0yC1eVDWFpcxPLyMqamJtHtdHD16lVc3djAlSuXsdtuB/mEFgwibvfl5RUsLS9jZXkFM7Mz6Pf7uHH9OjY2rmJ9/Qq2tloCQhPGgYMHsby0hJXVVczPz4NS4NatW1jfWMfG+gZu3d5ETBFywvtmZqax6pVp4eBBGJaFzc1NrK+v4+rVa7h6/Xq4biKrg+5kSjE5NYGVpSUsLy1haXEBtVod21strF+5jGtX17Fx6SKcngPAW41kJlSfjFBQ1Gp1rBw+iqXVVSwtLWNichLt3Q6uXruOjWvXcWXjKrq2723xKRmr61xPhkEoVpZXsLRwEMvLS5ienkav28W1a9ewsbGB9fV1tHfaAz3HEJpoKOLS4iKWlpawsrKM2dlZOLbttdk61i9fxNbNmwBx3Nh/26/n8MRICTB/4ACWVw9hZeUQ5g/MA8TA7du3sb5xFRtXr+L6rVuDuTAIsegHhMZfYZ2emMDK6iqWl5exsLAAq1ZDq9XC+rUb2Lh+E+s3WyCWayTY1PXWtPs2bMetNwoCy7AxNTGJ40cO4dDKMg4tL2Oi2UB7u4WrF9/FtYvv4MobPfRu3QIIgdNzsNN30HYoLALU2304fbfWreYkjtx1N5bvOImlw3dgYnoOu70+rtxsYe3yOr79zkVsdjZhml4LOexxya7XqG4SNE3g0NwMlmeWsTQ3jenJCfQ6HVzf2MDG+mVcuXgR29vb3qA0QGp1OJ4noEeBjmdMOpTi4MEFLC8vY2VlBQfm50AoRWvzFjavX8XNi+dxu70Fa+oaDIugY1Ns9x1s2Q5MAHWDYK7v6q75xYM4ev8DWD15Fw4ePgpiNXFrp4NLt3fw7XcuY6v7Lq7c3sVWven2ZccBpQ4IMWBaBg5M1jHftLA4WcORqToWmgs4ONmEBYrWrRu49M4aLr97ARffegf9bs8lMxNNNOanYR5YgjG3CNqYAiyP1PS7MDvbIPUGGnDJPXUoqO3Aqtdw5M7jOHTHURw+fgwz8wfRB8GNnV1c3+3j4ra7l2Zuog7T8lbj/QMKCIFZb2Jiso5D8xM4c+cRnD5+CIfnJjE/2QDt7+LG5Uu48tabuGD3sHXxKuy+gx2b4lbPhg3XvuxYBgyLwJpqwpg9gJnV4zh49E7MLixhZnYelBDcvHUb6+vr2NjYwI0b12E4rjcIFkHdagB2F0atDtppBzpmamoKK4cPY2X1MBaWl1FrNLDd3sXGrRY2Wm1c3uqhbRJY3t4jSqnXFm4SpknQtAwcnJ7EvSeO4tihFawemEGzZqG9dRtXL72LjXffxsVvtgAQOH2KfruPHZuiTykc6qDec9zwxEYdtbmDWL37ASweOY6lI3dgYmoG7d0OLm9s4PL6Bt65eBntrR10bQe7fbevmwZ1F0dM9zshBMSxsbJ4EMsLB7CyeBAzMzPo93q4fv26q6euXMF229VTroFOAuOOUAJKnEBnLMzPu3PC8hLm5uYASnHr5k2sr1/GxpXLuHX9aszT4XupiWEE4WAz8wewcugIlpdXcHBhAYZpenPChjvnXbsW6DSXnJgIvBfBPA1MTE5gZWUFy8vLWFxcRK1ex9bWFjY2ruLqjRtY37gGmzqAN+dH1/T90wPrloHV5SNYXlzA0uKCq6fabVzdWMfVK1ewfukCep0dT6Tw3OK+uwkAMWGYNSwfOYLFpRWsrB7C1PQMer2uO7dcvYr1q1ex096NzW8gzsCgDs0Ji1hZWXHnhH4f12/ccO2BjXW0WluD+7164nlS5udmsbyyjJXlZczPHwAh7jy+sb6O9Y0N3GTnXxEIwfT0DFZWVrG8vISDCwuwLAutzRbWN9Zx9epVbKxfCdol6qXwbRYKikZjAocOHcLS8jKWlpZQbzSwvb2DjY0NXL12DVc2rgZ78Py8g3oKgQb9iw2pDDtTWIJjDuqGXRQEE6oetR19L19AlDiLuazN6bdDBchMaggh/xLAP6aU7hRYnkpBCGkC+CMATwC4DOB3AZwA8F8CeIoQ8gSl9C3JtBYBfBXAXQDeAvASgAcAfAbADxFCvo9SeqNwIbjwzUyvQzKbIQP/8qDkDAtnWDvxOrUInts4Zoh6q0DuwII3KJhB7Bigpt/ZOQSCWR2ZmpzAg/ffj/vvvw/1Ws0rrfv7sWPHQCnwve99F6++8gquX7vK94ASgnqjgdP33oeHHnoIU1NTYFcNjhxxT+i4ePECXnn5ZVy6dHFQTxElaJomTp46hTNnzuDggQMh4/vQoUO47777cP36Nbzy6jmcf/ttbyGLCRNgCM2xo0dx9swZHDp0KFTW1ZUV3HP33WhtbeHcN76JN7/zPc+bMLiHxfLiIs6ceRgnjx8P1Q+Wl3DnnXei09nFa988h9fPvYp26+ag3bw0iUdK5+YX8J6z78U9990PyzQRnBxECI4fPw7bdvDt734P5/76ddza3AIlriFFCGAQd3WJ2A4mG3U8cO/deOD++9Gss3u+KI4edev6/PnzeOWVV7Bx9SqnvVxFW6vVcPfdd+Phhx/CzMxMSHEfPnwYwHtw5dIlvPLyX+HC298DpXZ4Y61XdmJYOHnXPTjzyKNYXFwK6o+CYHV1FadPn8atW7fwyqvn8Nbbb7urfX7suONNCtQN3Tu0soyzZ8/i6JEjg7Y3DKysruKuuwm227v4xuvfxuvnL6DdJ4Gd4cb30yCUanVpCe979Czuu+cumMTdNE0MwMQyTp44gd7uDt44dhTf+MMvYPdmC7WpLdQ2CToOdffjNEzUp2tYOnkEj/xnH8N9j70fVmPCPejBrIESE8dOmHj07Bmce+M7+KOvfh3t3kW0uzb63iQ3UTcx16xhrmlhcWYSD59YwXtOHcOE5YaHgbor9UcOHwLoQ3j3nXfwyte/hvVr1werhF7IWa/noOtQUGLg1Km7cObMGRw4MO/2DbgLvocPH4ZJgFs3HsAbfzmDt7c30bx0DRONFqxOHwSARVwDuznXxL2PPownP/4UVu+8G6jVQepNUMPCqlnHacPE2fc8gOXlc7DxMv6y3UN/dwk9qw6j1kBzZgaL8xM4NtfEgyeP4NHjCzg6N+l6HXo9wLGxvLyIU6dOYWdrG6/+5dfwzVfOodProj47BfPgCoyDq3AmD2KXuJ4hAKg3p9FszMCw6gAx0HAoiEHQqNXxnrMP48zjj2JyeirYfwLDxNGau1H/wu0dfO2d67i9+w4uzE9ge3MGdvcgnF4Htak5zByYwEPHD+LpD74XH378Ycw3LZdM2+7epkMLB/DA6btx5fQ9+PPaF/Hyta+ifqsDixA41CW7Ew0TzflJNJZXcOLs+3HvYx/E/MISbDo4QnppZRV333MaN2/dwquvvIq33vouuo4DwwZqtRqIWQuMd2IYWDl8GGcfeRR3HD8ezAvEdPdOnTIttPs2vvm9d3Hu/DraPQe3dvu4Ve9it+fqvZkJC4vTDdx7dBUf/r5H8NC9d8GA6zEm1AZZPIATdxyDffa9eP3wIXz9D76A9vXbMBsmTC+ErWYQ1KYs1OemcOD4STz0N5/CvWffh1pzEjBM+GsZh48ehU0p3njzu/hPX3sFb19aB6XePjLG1iaEoGER3HfqHjz8wL2YmmgO9LPj4MjhwwB1cOHiRbzyyiu4vL4BSgmIQ0GJ4y0KOCCOOyfcefIkzp55GPPz8wP1A4pDh1Zx33334vq1q3jl61/D+e++6e5PI4ZnrHv78Dyv59ETJ3H2kcewevhwoKMAYMVrs1arhXPnvoHvfOc77p4DQRjP0vISzp45gxMnT7KKFSvLKzh15ynsdrt47fVv4bU33sROtw+YpvduJNd7bHjjdm5mGg8/eD9O33UKpunP764+PHHHMfRtG29+63V885WXcfv6hhc25+8zM4K5ZmJqBvc/dAYPPPQwGg3vcCNPDx85egwA8PY77+DVc+ewce0mU+QwmalZFu65+zQefug9mJmeHvxAqTcnAJcvX8Yr517F2tqFOLHx5gWDACdOnMTZsw9jYWExVHerq4dw7+nTuHHzJl599VW8/b3vwbb7CGOwUHr48BGcfe97AzvCx8rKKu66+y5sb2/jG9/4Br79xhuu5yZCQHybZWFxCWfOnMGpu+4OR4WB4OTJk+j2enj99W/htde/ha32rlsG3iI1BjaGS2iiIdkIxvGAkGBAkA3qeeBSbD92UZsTNsiWw+0zJHJIRPricVbk8dR8BsAnCCE/RSn9clEFqhj/PVxC81UAf4tSugUAhJB/COB/BfCrAH5AMq1fhEtoXgDwKUpp30vrlwH8DIBfAPCTBZY9BWEjGr5bmEQ6lG+gEMK4ed0OG7h8I7Gd7sZoj9Dw9nrwGHngAnW81zwQ9z5KBs8zqwKWQfDYI4/g9D13+9KEvOy+DHedOoXp6Wn80Ze/jFZrcyAj4z168D0P4b1nz8IwTVDPEBkUy03nyJEjmJ6exh//0Vewsb7uVw5TVwR3nroT73/ySTTqdS+sywnJTSnFwsIiPvDkkwB18NZbb3Pb4vChQ/jgBz6AmZkZOH64wOAfUEoxMz2NJ973PliWhXPffD0oJ4u52Rl84ANPYmlx0SvPICbaT6dRr+O9jzyKZqOOr/7pH8NxnCDkzA8hakxM4oknP4jjJ08GoQhBP/G4qWEYuP/e05ianMQf/dmfo93pBaIbAEBtGHDw3jMP4YF7TwMEbl7eTa7zzy3YiRMnMD01ha/88R/j1mbLq14jdLTwvffdi8cefRSWZQUrq36/8uti9dAhfPD7/wb+pN/H5XffBkxvZQ9wFahh4fipu/Dk938IExOTXh155QH13/OI+fl5PPn+J0AI8Oabb7rx245/6pj7/9LBA/jgB57E/PwB+C9rhOGZAl55piYn8fgjZ2A1J/CfvvkmOn0C03Bj+R3b9YoszM/iIx/6II4cXvUiRCgcApjU3wBKUavX8dBj78dko4avbFzG7o1NdLd6MLZ6sEzX6J87vIQPPPUs7n7kcVDiHisMwxysSlIABsFD95/G9NQUbr70e+j0HViGGz+/MF33NqNbeOzeE3j41B0gcOD0++HwT+rKd8fx4+74+NM/xY2dXffEKe8wBH//wj2n78YTTzyBer3urtp6OoRS4vmBgYMLi3jswz8E2tlBZ2Mdk5dbOLDb9zZwG5ifrOH+9z2Iv/ncj2H64KK7Cd70X8LorgZTmJibmsRTP/B+UGLg6uafgToU7a1pGCbB7MIU7lqZxoPHV/E33nMS83UTTq8bjD/q92tKMTE5gSc++CQmZqbxl1/7KxhzB2EcXIU9vYwDf/0GTr78MqZu38at6Vm89tDDuPHg/ZifXYUJwOz30CDA448+jofPPAxiGm4f89qVwPeIERxbPICZ2Vlstru43u7h1o47fhybYmK6jpOHZ/Hs9z+Kpz70BGqEgtpdz9AA4Hs+bBvLR47ibz33Y+jc3sL21b/ArkPRcxzM1UxMLk5ianUBJx77fjz24R9CrTGBPvOyTwr/b4q5uTl83/u/D8QgePPbb6BHgJ5DUTcsl5CZ/z97bx4j2ZEm9v3ivZdnZdadWVVdfZNsks0mu5vN4YxmZndW9sxirD0sS8JalgzZsiAYlmRBWMsGJEHSwpJtyIAWI1uyYawhr7QybAhar6W9Zg9Zy+HMamfIZh9sdpN9VVXXnZVZlVl5Z773wn9EvCsrMyurDw45ww/IyqzM9+J9EfHFd8cXJjOzs/zIV77C9MxMsHY8ligUG08lE3zulReJJ1OUr95hOhWjkU1gCLUxfzqT4HRukp/+6o9y9tRx31mATo2RAK6LaZm89oUvkYrF+PWNHVp7bbJtB9uRJDMxUtMpxhdm+cJP/jGef+1zSMP0+QnSq+yn+nfuhecZG8vwK7/523RKZVxHF+YwtOFsCl578Xlev/ASphA6ZUoG3F7jd3xxkcxYmn/zb96isLureKKr+KZAIA147uzzfPEPfYF4PKH5VNRXJ6VkZjbHl370K0jpsnzvQ02HVoRPLZw4xZe/8mNks+MBr/dEi1B0lc1m+UNfUDLh5s0H6jeCAAAgAElEQVSb/voSobSjyclJfuTLX2Y2l/PbEQiV2uTJhESc1y9dJJFM8vvfexdHOhh4+xrVY5OJGF988w1OnTyho26eiRW0YxoG51+5wFh6jN/7nd+k7dhg6k3mhglWDMOK8/qbb/LyK6+pSLXPy/H5nURw5vRpJcd/71uBTAgGESHh/Msv8saVK1imoeSmL3sDObWwsEAmk+H33nqLjc3NqGGjcqc5feYMX/ryF0klUz6fCppSdDQ9NcWXvvQlDCG4+9FH9DQEQD6f50d+9EeZmJgIxjqsD7gqyvn5N98kHotx9d13CAyBINUrmx3nS1/+Mgvz8/4a9Z+j7bp4LMali6+RSqf59nd+XzmnenSmaDl6J/KMg0aHp8OFizRIhKsydaThIlytm4WMp0BGC/+zNAxkr0fZz3bQzzdMta68qM0zhBE2AAyE/xs4A/yWEOL/0KlXnxoQQsSBv6T//YueQQMgpfx54CbwFSHElRHaWgD+I6AD/AXPoNHwXwM7wH8shMg/Lfz7gi+9Dho0QhLU/u9VkH1jxQgI3Qu1+xa5fie4xrP0VYlPw78/rJyqfQkaD63cBxW7gs2HQqrKMd6Gy5dfeomzZ88GKEZRVeFxzSTn8nkuX7kSwtdDV3Dq9GnOnz+PYZqa2UZJPmgHJiYmuPLG54gnEqF2VL9nZme4dOmyMmj87z3M1Eto4ZNMJrl8+bJKQyA63qlkkiuvv042m0VKqaMcSkj6I6z7ZVkmF155hcXFBTyh67UlkFx5/RK52Zzqlwjw8PlhCMfnz73MufMXlBfZUh5ZYcYQsTivXn6D4ydP+P2K9M3DTwuiE8cXuXThFd+bpzzxEuE4nHvuLC88/5x/vyEM/J71zNnsbI4rV6708Fr1z7HFY7x64VUsywr1LRjrcDuZbJYrn/s8ybGs2sCrCx9gWExMTnP5yudIpZTwEh5D9V54fZXE4wkuXbzIzPQ03jkZQqdTxE2T119/nYmJSd9QC/NmHx+U8ffKC8/x/IljKrVL5/DHTUHSMvny599gYX4+cp83Z7p3/miceulVLn7tJ0jnJhmbTTM2mSA9k2IsP8bnvv41Tp+/oMbHG5MwPfp+AsnJ48f4+o98nnwmwfxkivnJJLlMkul0jMvnTvPymeMYOn/eXx0RElCCbXpmhjeuvIFpWvoHU5fvhdnZPK++9hpmLIbrKkVFRHARiq6BRCrNpa98ndmXLpBZyDI9mSSfsMgnTBbPL/CVP/ZHyGTHVaRMCH9MhAxaBUnMMvixNy/z45fPsTiXYWouw0w+w7mFLOdms/zYhbNMpZO+we+NdYQeDVUQ4OUL5zn36quY49PEifMT//M/5E//wv/KF69+l4v3P+Qr17/HX/inv8Af/1/+Ia3dKjKtqrOdu/AaL184jzAN1ZavGIZoVSvcU+kkX7lwhudnFI4zeYXz4lyGH798jh978zIxy/DXukD4xpw3x7guY9lxvvJH/z0Wzy+QT5jkExbTk0kyC1lmz1/g0le+TiKV1natCOge7/BWgetKzFiMV197jdlcXkdzwKuOZpoWV65cYXpmJliDIkobypmh9m28fOY4l86dZjodI5dJMj+ZZH4yRT6T4Otf/jwnjx/zlX5fwfRGyZtjKTl9/gJvfv1rjOXHFK1PJhibTZPOTXLxaz/BqZdeDXELETQV4gsAC/PzfPnzb5C0TFVpTa9F0xA8f+IYr5x7HkOXqw7fp8UGQghc12ViYpIrr18mbpq62Ip3KKjDzPQ0ly6+RjyeUHMWWss+6QuBlC6pVIrLVz7H+NSMKk1t6n00hkVyTPEwz6CJ8AWP9vUaNC2LCxcucGzxWM9CVZ+uXLnC7Gwu2o7wxkpgaCVWCMELzz/HuefOIhwHQ8gQP4dLF17hxOIiyND4iCg+Hs87fvIEr15+AxGLB3JFl9w+d/4Cz597ORhfj5eHaUD3LTeb48rrlwif4+Ot9cXFBS688gqWZQZyM0RFPn/Rxt+V118nlQwdeaF1kImJCS5fvqwNGokQRt+xlkAiHufipUvMzM4EeKoLiCcSXHnjc9qg6ZWb+jJD7fo3TJOXz5/n1OnTWp7it4MQXL5yhfm5ucichVSZiA1w9uwZXn7pJUVvSF9vAg7qVX31LtWil13j6W1SR1/D1U4VNfTogP7L8O+PIBsebyn1esEv+91fR3268NhGjZTyTwE/BawD/wlwWwjxM08LsY8BvgRMAA+klNf6/P4v9PtPjdDW11Fj+baUcjv8g5SyDfwqatvyH3l8dA+BXuLwQ39uqLRk2KDQl/nEaATESohwI/tnQtU3/PK4Ql9jospRhheF13aPkRUuwxtadAC4amPgqZMniMVihwYpPSZw+tQpUqm0/jLwHBw/foJ0Oq02qB/wVkTbkVJyfPE4k5NT/kL10Jqbm2dqairksRrWjsvMzCy5fC7qSUEyNTnJsWPHejxN/dtxpSSTGdMpQDpfWXtfMuk0p06e9C4ehhBSSuLxOCdPn9GKv+WfX2HG4pw+exbT27B/CBiGwelTJ7AMtHqkKvsgHU6dOE4iFotERAaOEZJTJ0+SSY+FflFjtXjsGJlsRg3ZCHO2cGyR6dmcX7JWCgMMk9n5eeWxdJXwGjJISCmZnJxkYX4O0GeVaOVlYnycE8ePB17Gga0oIyKVSnJiIUfMFMQMVT45aZlMZNM8d+b00OnyR0KqPU6nzl8kPTdNajZDOpcmNZsinZ/i7IVLxHRKZp/eRMbIMAxefO4MxybHmE7FmE7FmUlZZOMmJ3OTJPWcRXhIP8eHlJw4vsjE+DieE8Q7ADM3P0c2O6HHWjdBwGmklEEihJRMzOTInbvA2LFZMvNjZGdSZOYznHzlORZOnVUb8MMD1Xs2kW5zejzDxRdO8UI+w3NzGV6Yz3J2OsWp/CQnchO+QdN/oIJiJ8lUSq2PeIKv/eL/yZn79/re8vLyA376F/8xHRGHRJITp06TTKVCGkh/OvMMmxOzE5yem+LsdIoXF8YVzvkMrz1/kunxTA9/8XhiKPItBK7jMH/qLCdfeY7MfEaP3Rhjx2bJvXCBiZmA77h6nHrnQwjlSc5mJ8jPq6ihK/GVlInxcU4ePx7x0gZoRelESkkyFuPk7ATZuMlMymI6pSKBxybHePH50xiewRcaj34Qi8U4+9rrpPNTitZzaUX7c9OceuUisVg8iGQMASHguTOnmcimSVqmPvdHVWM7sZAjlUoyGi+XHD9xXNG8Ls8s9f7Ihbm5IHI7lN8ZSFcym8uRn5tXaaJafmKYTM/mWDi2eCh/8eRRJptRKXJAWJZnxsY4dfKkin0M5VOqX4lYjFMnj4NURU48fm4ZcPrUCQxzNPXQNEwlQ2LxiGzBsDh5+oyO3A6Xv95vp06cJJNOaxrz9ui4LB5bIJMZG1luHjt2jKnJSSK6hZTk8jlmZmYDJ9fAMVLrZmpykrm5ee92Xy+YmJzi+OLhMgEhkK4knU5zXKfaSSl8PpFKpjh96lRfo6gXJBCzYpw6eUJnE0R1i0DP0nrCAb1L9yzslD6gu5mh34kaNL5DW4Ta0d9HEFXP9g0rv7x5CKfI9U/XsHmSSA1Syl8HXgb+EZAD/i8hxL8UQiw+DeSeMVzU7+8N+N37/rWPua2jwzAi8ZiDL9K0shaBgxurPc9FUBPdI+peyz1UjrnHm3MAp0FWek/FjtnZGTIZpeyOoP8BYMViKq9WGP49mWyWyampAP1DWhPCQBiCBV9gqBtjiQS5fD40DsPBY05z+TlMHW3wbvX20Iyi2HqXTE9Pk04mQ0apy7GFeSzTGmmAvGdlxieYnsmr8s2mCaZJfmGRpPZo9W0qQlvqczKRYCGfU8xKn2MwPTFBNpuJIj4UJ4FpmiGPo4J0OuXnOQ8aI18VCQmC+WPHlVGjX2Ysztz8saHtRPFR77lcjng8HniagIX5ucALPxQCATeVzTAzkdVRGoO4KThzfPGAIdKXnYf6NTY+wdy5CySms6SmMiQnM5w4/yJjE5OD7w83pd+TiTjPnTzORMJiImmRjpvMT48zlUlpREYQLEJgmKaee60oS5UuNzuT8x/Yy10OsCeN1dzpc6TmF0jNZknPpMjOZzn98jnliPE8gH3OkBC+51H9f2I+x5ncBAvZJAvZBDPpOKdms2qv0uG9UqkywmA8m+WlrSIn790dev25pfvMf/gRkxOTTExkFc8ZwSkgUIeenprJMJOOM5eJs5BNciY3wckFxV+ET3e+OdjTiNAeYJfTL58jO6/GLjWbJTW/QP70OX1XtOe9c+B6CAEzM2ojuW8sSMlCPofR7yyWfqDvm8qkmZ8eJx03mUhaTCQsnjt5nGQi4fd/aDP6PT0+zonzL5KczJCaypCYzjJ37gJj2Uk9BH08wxxcC7FYjDPHF4mbag1aQjAzkWVK8ykvSjsMlLJpsLAwr/DX8xOPx5XTiqPxl/z8glL+Q7xq/tjxA/0aZG6J0Jyl06nIb8cWj6ny7kcQLuOZDNMTEwjXRaCcOAv5nD9nkfHpwyN8/pJMkl9Y1JFyEylMpmZyZMYnIngfhpNpmhxbmAdcXylOJ5NMT09Hnje0GX3RwsJC8Fwd5ZrLz+lrRp+0nC6OEe7EsYUFhCEOcZYpjL3bJqemyGSzQT+EwbHFRawBzqmDLSnIZMaYnZ0harCFdKiRoiGe/hbW3cJlmsOGjKbJfnqhD/0Mm169cwgtPUXD5omMGgApZV1K+V8CXwY+REU2PhBC/D0hxN8a9HrS5z4F0G5u1gb87n1/6mNuCwAhxAf9XsBzo7UQjtJECbzs7vd7IJFISwTU/+GCAKoUdBDNCGjyiCQVOYhJ4Tw5ORlsKBwRDMNgZlZv+tMehEwmQya8oXAkfCQzMzMYISUlkUgwOaGZ8xGampqaIuFvlpdYpsXMzIxuZ0QuD2QzGca0kaddSMzOzo7GmEPtpJJJxicmAsUIQ1fwGnSIa3+wLIupqSmElP5+momJLKlUSj9tNLwEMKvTW7xbUql0YBwdYbSnp6ex4nG8pLh4PMGUFoRHgcnJSRLxuL9uhJSKro7IdMfGxsiOpfVJ6gamIZidncEcckq4hKiQARKpFFNz81iJOFYqgZVOMjN/jLhniA60/KLCzDRN8rlZ4pZBKmYRMwTjqQTp9NHWGVIyGxpXCSRTKSYmxkOdGA6+kJ/NE89OYqWSxNJxkuMZZufyykgYVQuSkqmJCabHM4wnLDIJi5RlkJvIEskxHzR/fsEHQTqZ5LlbH47wXHjx+jXGs1lSyZSuh00fvqnB570Kn9xklpRlMJ5Qe5qmxzNM6YpZQ2le80qhefTsXJ7keIZYOo6VShLPTjKV85S2ETqhh2RifIJkKhWhv9nHWDvpdJLxVIKYocqWxy2DfG4G0wwZfEPmwpvzRELRuJVOKppPxJmamyeu+Ut4jQwjN9MwyOVm9X4aA8OA7FhaF4s5AmiZ4BucUqUmTUxMHn5vD0xNT+t0NcWnrFjcV9hHAy0TshnS6XTwlZTMTE+PzDE9Hq3Wbha1J1IZ1VNTUyr19whgxeJathm+Q2JiYjKUAjaiTBCCWY/f6lvGxsZChQFGcCrqa2ZmZpTjT1NJPB5namr0XRLekyYnJkj4Rh4Yhqkcb0eUCb4+EtKxZmZnlPPgCJBIJFVRChk2GujjrD4MtC7nLUmh9leHD/cMjJceHCORnkNkUGjd9FbGfRbw1Eo6Syn/QAhxGfgW8CbwVwdc6rlH/tun9ezHBG+VDKrepuuXkv2Y23qmsCsr/KPuL32/0fgMPoO+MLKd9pTu+zhgdOPzhws+ieOSKFdGum5sv49j6DP4xMIoUZnvF3wCl8Fn8Bl8auGJIzUeCCHOAr8JfA5VD+6XgX/a5/VP9PtnMASklK/0ewEPRmtBEMmLFIJd9vlG95co0efIHJ2mJvyczDBoD6FvaaMLD2gLPJINcERvQaj6hodzuVym3e/gqiHgui4lr6689s7WajXqtfrwGw/gIyiVSrihQ8Ta7TblilJ2jiIWy+U9Op2O1zC2Y1PaLel2RmlJXVMN90N7VIrF4ki55eF2mq0W+5UKSFVNCdSY+YeNjQi2bbO3t4cUQqexmFQqVZr6vKDR+qawKpZKOs1IfddoNNQ5AyG8R4FSaRe700HRn0un02Zv9+gV1CvlCu1Ox183UtPDUTWPeqNOtd7AlWA76pyaYrGEO8Sb5qe3hN7bzSZ721vY7Q52s43daFHa2qDTaunLBkUgoik6juOwUyzSsV2aXZuuK9lvtmk0jrbOEIJiaFwF0Go2qVT2gy8OAQ/lvZ1tOtUydrNFt9GhtV+juF0AfcDpCC2BEOxV9ilX6+y3bWptm6btslOpcqDoSd8m9Hy4kkarRXUsPcJzoTE+zn61SrPVDOW2D5hb7/kan51ylabtst/ust+2KVfr7FUqGsch/fai5JpHF7cLtPZrdBsd7GaLTrXMXrGgrxmhE3pIKvsVWs1mhP6Kj7F2Go0W+802XVfS7Np0bJfCTkkfitgzFn3Am/N2W9G43Wgpmm932NveotMMzs7x3oeRmytddopFHFdiOw6uC9V6g3rj8WSCn5kgoN3pUKkc/ei5vd1dOp02Hp+yux1KpaOMtZYJ1RqNRiP4SghKu7sjc0yPR6u1WwVhqmQhIdjb28O2e8sYDwfVjxL+Ic9SUqmUabY8/jKiTJCSYrEYkQn1ep1qbXSZ4PWttFvCdmw8Qu90OpTLe0Pu7G1HQblSod1u+9+7rqPk+FFlQq1OrVYLorZAqVjCdY+mM7XbLcrlMsG+5UMixQPBi/x6rMcrPKB/9jJ9Qtf64B170JtWFgaf7wXr5sDh688AntioEQr+Kqpa2B/W71+QUv6MlPLPDno96XOfAnirZJAU82LU1QG/P6u2jg69iyv8vw4R7sp9vtH9ZxTZY4besHmUYPtVJyNMwN7GM70JLKKIRTal9eDU7wXBYtT5nMViiZpW4kdl0na3y8b6On6ZUKBWrbK3txugf0hr3mnxmxsb4S/pttvsFAqhcRgOnnDe3i4o4eDlpEpVR3/EZnxsd3d3abRaAWPAYGNzSzHsUdrR19T299ktFRDS0aeFOxQ212l5CnK/myO0pT632m02Czuok7ZVBZTdSiUwRkbCSeI4DhvrG5HvG41mYPgN4pWh6jjeWG9trKkKK/rldDtsb20MbSeKj3ov7BSUIeoxYmBzc8svazochH/N3n6NUqWK7Uo6jkvHkSytrdPRJ9EHd/RHxmunvl9h++4t2rtVmns1WuUaq7c/oq4VqsPEg4dxq93h/soalbZNpWXT6Dhs7e6zV9dKx4h7J1zH0XMv9G2CbqdDqbTjP7BXqBxgTxqr7eW7NLc2aRarNEpNqltVlu/cVc4Y/xA/I3KneoQnJNX/q1sFHhTKbFZbbNc6lBodHpVqOHJE14GrNoDvV6vcOXdmhDvgweuvU66UlUIo3chJ6gOfAzhSslKqUWp02K512Ky2eFAo82hTGyM+3fk5ID2NaIeEMFi+c5fqlhq7ZrFKc2uTwtJH+q5oz3vnwKtcBlAqFel2OkHkTAg2CztK2RpxrxXAXq3B1u4+jY5DpWVTads8eLSmTkTncLbgodjY32f19ke0yjWaezXau1W2796iXtWOpQEpbL0U3Ol0WV5dp+OoNWhLSalSZW+/FrRzyApSZaJdtra2gvQ8Ieh0OuwUdvQ1h3QsdE1haxOn24nwqq2NtQP9GnhGuzdnuyUajWbkt431DRzHGc0hoC/Zr9XYrVRUBSy9aXyzsOPPWWR8huxjarVaFDbXwVGyRUiHvdIONR3RHNVH4TgOG5tbqFR49cxGq8WuNrJHakZftLm5GTxXCBzbZnvbM/pHn7SdQoFuux3pxMbGhk8bh3XKd+Ls7VKrVoN+SJeNjQ3sbnfg3dGWFNRqdYrFEjBAhxqkZ/W2Fl5HEQMl/FvAA/rqhT70jEO4ehp90tSG6axPCE9k1AghXgW+C/w9VHWvvwG8IaW8+hRwe9bwSL8fH/C79/3Kx9zW40FfIlHewV0qfKP7SxTZY1ZM8RcTfya4zF+onsESrpoRVNvyvvOjNboyVGCtq7rofqQnkjsZXlwegYeqaIQXp6GqKj1aXaPb7R6utGkFcGVlhWbT81ypA9IA1tZWaTQa6myaIYzMq2KytramvDmhKBTA9vYWe3t7eOUeh7dj+KdSR/dICPb2ymxsbvjlJ4e1YwhBrVZnfWMT/9BMzRxqjQYrjx55Fw9DCKGF8KPlh+rsFccGuwuOjdPtsLK0hDOCUgYqIra8sortomvlCL3R2mRldY12t4tX3WzoGCFYefSIWsRrqsZqfX2dWrXme4+GtiMEmxvr7BZ3lJKgyzHjOuxsb1Pc2UEY4hDho9opl8tsbW8DegOorlBU2d9nbW0NcdicoZT8ZrPF6uYOXUfSdaFlu7Rsh0q1wYOl5RH1REG322Hl9g0a27s0izUaOw2axSaNwh5Lt66rA/j69yYyRq7r8tGDJTbKdXabXXabHUpNm2rH4VFhj5aes6FCR6qoyOraOpX9fUVXrouhbytsb1OtVvRY6yYIVCIhRKhctKBS2mHn7i3qG0VqW3WqpSa1rRqrHzxka+WhX4Ldh8jBbcFY7+7XuHFvhQc7dR5s1/hoc5+Hu02Wt/dY3aloWjwkSiMlrWaTR8tLrB2fZ+2F54fMDCw9f47t509Du8XqyjKtZjOkHPSnM4kq/rBarLC8vcfD3Sb3tqo82K7xYKfOzfuP2N2v9fAXjyeGxLRUJWK3Vh6y+sFDals1PXZ16htFdu7dolIKjE5Dj5PXmgiaQRiCarVCYWsLIdTWIOF52ff3WV1b1x7zHoLtoRMhBK1ul0fFCtWOQ6lps9vssNvsslGu89GDZdwep8CgOel2uzy8+R6Nwp6i9Z2Gov3tXVY+uE63GzK+hoCU8GBpmXK1Qct2aNkuXRc6jmR1c4dmszUiLxesra5RruzjVYYS2tu8ub1NuVw+NLVNShdhCIre6fKu45eFxnXYLe6wubF+KH/x5FG9VmN9XR8SHRrHWr3OyqNHeNXNBjaj+V272+XR6hoIE2GYPj+3XVheWcV1RosgOK6jZEi3E5EtuDaPlh/S8QzmQ/oGsLL6iFqj4TtjvUM81zc2qdXqI8vNjc0N9vbKRBV/QaFQoFQq4R2/MHiM1LrZK5fZ3t7ybvf1gkp5bySZgFTn4jUaDdbWVnU7QaSm2WywsrLi6zHDQABdW82ZNIzQXr4ePaq3HLOIrHxfPzugu7n6s/87USOnt2qt/7mfQSNCWUJmNErzDA0aeAKjRgjxd4F3gTeAbwMXpZT/g5RyNO3o+w839PvrA373vr/5Mbf1+NDHQt9zK3yj84u+QfNX4n+WSbNns5ynwLsugcESttZD77jRRSFdhOOo6iluaDFoJh8pC+h7WPH/9zaiSe159U5ouH3nDg+XlgIUo6j6TEAIwXahwHtXr0Y8D1IvyJXlZW7fvu2XiJU9od6gHahUKlx99106Ia+M1P0uFUvcuH6ddqej92XKEGbewW+qTGSr1eLatWtUKhV6F3Gz1eLq1feoVqt45SelVAcC+iOs+2XbDrc++ID19U18350X9UHw3rUb7BSVAiP9+ZH+YIVxvH/3Q+7evqU8aXYX6ehXt8PNa++w9mjV71ekbx5+UoKA1bV1bty6jSu9BApwpUCaJncfPOTe/Qf+/a508XvWM2fFUpGrV6/2yDn1z8b6Brdu3cLRUa7+Y63aqVWrXH3nu7TqVW2wOUgtUPf3Sly7+g7NZtMXYsob6j0qOByx02lz4+ZNiqVdvLLQUhuSHcfh6nvXqFTK6mwLqQ8x7KUhlNH3wb0H3F/dUFEa29HRGknLdvjOd99lc2srcp83Z7p3/misfPg+N37n12nslKkXG9TLbRqlJvVCne9983dYvn1LjY83JqEx8pasEIJHaxt88+3vUqi12So3KVRa7NRa7Da6XL+3zJ2lNVxXzW9QdjlMAsqg2S2VePfqu/qEbQnSUYUQBBR3Crz//vs43S6Goc9mieAi/YNm280m19/6JsUPb1HbrLJbblFo2xTaDmu3N/i9X/kN6tV9ddq6lP6YSBG0CoKu7fLW967z29fusrpVZW+7RqlQ4+5mlfulGm99sMRew1NegzUSoUfXRToud27d5u777+NU9/it//CnWT13jn6w9Pw5fvfP/zlEs4yzv8vdWze5c+s20nGDaJ7E77vCXyn+e40Wb91a4n5J4bizrXBe3ary29fu8tb3rtO1XX+tS6QfjfLmGMOgXt3nrf/3N1m7vUGh7VBo2+yWW9Q2qxRv3+L6W9+krdPJ3DDN674LJIYhcLpd3n//fYo7BUxt1CgFR+I4Nu9efZddnXopIwSv3rwKaq4rubO0xvW7y+w2uuzUWhQqLUVrtTbffPu7PFrb0ApgsPb8UfLmWAiWb9/ie9/8HeqFuqL1cpt6sUFjp8yN3/0NVj58P8QtZNBUiC8AbG5t8Z3vvkvLdug40l+Ljiu5v7rBB3fvqzOnIutQY6VpwzAMKpUyV9+7RsdxFD16fEGYlHZ3uX7jhk4nE5G1HOV3Bs1mk2tX32F/rwSurXiU44Br06orHlbTMiHCFzza12vQsW1u3boVinCHDUW4evUqxeJOtB3pjZX001+llNy7/4CP7j9EmiauFCF+Djdu3WZ1fR1Vi8NrJ4qPx/PWHq1y89o7yG4nkCt2FxyHu7dvcf/uh9F+hObM58FCsFPc4ep71wOD1zc6Bevrm9z64ANs2wnkZoiKfP4iBNVqlatX3wulvuHrIJVKhWvXrtFqab7gp7pG+yZQKYY3rl+nVCwFeKoL6LTbXH33XSqVirb7DxolUh826ToOd27fZmV5WctT/HaQkveuXmW7UIjMWUiViZjLDx8ucfvOHUVvCF9vAg7qVX31LtWi55z29DbhOBGd7kBZaO/df7n+/RFkw+MthKSV+7wAACAASURBVF4vqEp/YSNraBTpyeBJCgX8dWAf+MtSyv/tKeHzccJ3gArwnBDikpTyes/vf0K//+oIbX0TRQE/IoTISykL3g9CiASqIpwD/MaToz0qCPZkmW+0/zFFqQ2axJ9jypikLUL5slL6e2N8Yif8mdDq0t+7Eil0pMYNUqLUNQOqW/jEa+jzRLRHQYTCkp43Rwhs1+Xdd6/SajY4//LLfincYA0oAfngwX2uXbtGtRrauOt5D7Rn4Nb779PtdnnttdcOVL7xvH7r62tcu3aNQiF8zJCMfH7w4AEucPnSJVVFJZQaIzRDKZWKXLt+g+XlZfW7PrE47CjZ2Nzk7e98m8sXL/klnntxqtZq3Lj5PnfvPQgxqCiUK/t8+zvf4fLFS5w+dRKUjw6vFIcQgna7ze33b/DBjeu4dgfsrkqV8c4BEQbtRoM/+M7b1Bt1Xnz5PKZp+sIEoWrJOI7LR3fvcfODOzTbHVwp8Y/+EQJTmLi4vHf9Ju1Wi1fOv0wyHqqqJoKza5aXl7l+7brKC/aqzEkXgalPvhbcuXMH27a5ePE1stmsnlMRoYGtjQ2uvfcOm6srQQTKN1otEDYrD+4jhMGlK28wO5uLGIVeW+Vymes3bvLg4UPUgWIg0KW4JWBIdnZ3efvb3+Hy5cscX1zsmXslOBqNBjduf8Tt5TWlSEmJ44LjKoXCdqFY3udfv/U2X3jjMi+de0F5yDV9eP70bqfDR9e+y41//Wvsb+zQ3G3SqHao2S4xR2BWWlQ2dvjOr/0KzW6X85/7ImZCjbX0vWGAlNy8c5f/799eZXmnTGG/xV5dRXdsV5KOW2QTJu98uEyn1eTV506SsvTc+yEWRVWrjx5x7d13KBVLiEQKXBvh2sRigpghcCQ8uHcX6bpcunSJqclJRV54skudML9XKnLne99i+Z1vUd8o0ig22Os4VGyXuJAkG11u/8EtkpP/gi/91E8yf/YF7a315kuNe6Xe4F9/7wa//PZ7LG3sU97epVuvYMQSSAl30zFmUptMpGJcOTXL8Ym0VpL0AZQap2ajwbXvXuX9a9dpdzvEpaRlxfi1P/MnOLa+y5nrN0lV9qmNZ7l/+XUK585iNPdwd7dwyiU6+3W+963v0KzWufTmFdKZMX/MIDjcda24x7srRd5f3uTuVpVKsU69tIPbbdOuT7BkCH75W++AdPh337zIRNLy+4rwDgk12VpZ4zu/+mt88G/fp9zoUu44dPRaHC82qG+VWH7nW4hEmpfe/FEmp2dRK0cipPD5VLlc5vr16zx8cB/LUHMYMwSia/vOgVKxyLff+j0uv/E5Tpw8GaEJUBy/2Wrz/oNH3Fjeptp22G122am2I3SWsMr8q9/9Fv/OH7rCxZde0Pcr3iNQxoLTdrj9zu/z3m/9GpWNHVqVFtWOQ9eVuFVJfLfJ/sYOf/Abv0Kz3eXFy58nlgyqfxlSzynw0b17/ME71yiW97H12lPrEGypHAw3P7qP3W5x8fyLpFO6QpdWZtEls9fX13nv2jV2dnfV+V5Cnb2CYfkHTT94uAQILl18TVWkAo1HwO9KxR2uXX2XlQf31di6ruJVgGI2gs3VFd7+1r/h8uufY14fKeAr9yFl/ebN97l71ys5LiP7XEHxsre//W0uX77M6dOnAx7l8ztBu93h1u07fPDhXVydcuZKzwBWxm2z3eH3v/cu9UaDF59/DtM7s0brB0IIHMfhozu3ef/ae7QbDRUpd1Q6ugSE3cU1DN773ndptdu88urFoKqpx6I0ZsvLK1y7cV1HxA568qWA23c+UjLhtVdD1dCCxgTKmL12/Tobm5tElGjfUDK0bBZcvnTRPz4g/CyBy97eHteuX2fpwQN69QCvrUJhm7fffpvLly+zuHgwSUcYgnq9zs2bN/noww+D+/0Ih4JqdZ9vv/02ly5f5rnnno9033tat9vl9p073PrgDrYriaSbSU8QG6jNzq6Sqw7qc1+QEbqJnmfoIrwDyMK6nxCIkMNGSJBC6nLg/Ywak0CJFD3R9j464lMCMfpm454bhfiXwH8hpdw49OJPKOho098Afh/4cSllXX//s8DfB96SUv5Y6Pq/BPwl4FeklH+tp61/BvxpVIGEPymltPX3/wD4y8A/kVL+p08B5w/Icf7n/5ufIyEGleIV7LllvtH8BYpyl1kxzV9J/jmmhCrn28bmZ+t/G4C/P/7fkRBJPCIXUmrGa2vCdqLE7x26aWii1Wc8RLR2P9rjBgvE+9W/zwARCAfpMx9C1r+DZRjM5XPkcrPk83nG0ik67Q7F4g6FQoGtzQ2V/gF6kfkDRSTcKQT5uXlyuRxz+Tmy41kcW234KxQKbG9vU6vuhxZyv3WhGp+emSGfz5Ofm2dyUp0oXC6X2S5sU9guKMbcC5G0PtV+Nptlfi5PPpdnenoa07LY399nu1CgsLPDTrGEf8CpJ0jDual6jNKpJPO5HLlcjtzsDPF4nHqtxvbWJsXCFtsb67jdNtgd6LaVF81jhKaJiCXAihFLpJlbPEF+bo7Z/BypVIpmq81OsUShWGJrR200d6SnqCswBFhCIHAQjo0pHebys+Smp5mbyzM2Nka306FYLLJT2GFre4tmoxl4cCKHuRIxGHKzs2rO5vKMj4/jOg57u7tsb2+xvblObW8P3C7CtZFerrow1GndpgVmDGlYTM7kyM8vMDe/4B+uWqlU2N7ZobCzQ2kv2PSrDjZTSjtOF+HYOmXEYSydZn5ujlw+r8thx6jWahR2SmyX9tjeq+LG0zS6LvWuS63j0OgqD7EQgqQlSMdMJjNpTh8/xnw+x0I+TyqZoNWoUVhfobT+iM0712isPGR/eYvycplyuUXTlVhCMJkwyS5mmTg1y8SZExx/9XVyJ8+QWzxFamyclu2wvbvP6uY2H66ss1TcZ73cZKvcotm2EQIyqRgLUylOTaY4Np5gKmGwkImRH0+Rn8gylkrRbbco7RQobG2ytb5OvV4HK4aRTGNkJnDjaWQiS0dYtG1Jx1Xe0unpGfJzc8zP5ZmcmEBISXW/zH5ph731ZSoP36e1dI/S7Uds3y+z0bKpOS4mkE9YLEwnmX5umuMXX+TEKxeZP/s8UwuLGLEk5UabjXKTD1c2+P2PHnFtZY+djX1q2yvYzRpGLEF65hgzi7NcOjvDq/MZTk0kWcwYzCRMpsdSWNKlWtljY2WVzZU11h8uY3e6CNMgNpYiOZ3FnMphTMwiE2Ng6XKudhvRruNWijh7O7R2q3TrTaTjYsVjLJ49xcKpExw7dYLsxBS2MNitNym1HdZrLiuVFu9v1bj+sERpvUijtIHbbWOlMmTmTpE7Ns7lU1N88cWTvHTqGMcmU0ymE7jdFntbm2w9uMvqBzdYu/ERuw922dxVES4HyJgGx5IWc89PMnP+JMkzLzBx9lWmFk8zPpMjOz6JFIJypcLWdoHC9ja7uyUMIYgbgoQliEsb0a5idBq4tQpuqwF2l7GxMeYXF8nPLzCTyxNLJKk3mxQqVXb2G2zUbPbaLlvVDkt7DTb3mtSaXaSEVMIiP5HgxFSaM7PjvHRqkRMLc8xNj5O0TJr1fXbWV9h5tMTa++9RWVqlslKkul6l3HawpSRlCCYnk0yenmT89DzpU2dZePkyM4snyS+eIpnO0Gy32dwusFXYYWVtk71anUbXoWUrz7dpqHWXiZuMxQzSMQOj02BuKsvczBT53AzZTAa726VUKrFTKLC1vU290UBqOSdD/CTgxwpmpibJ53LM5XJM6PLc5fIe21ubFLY2KZd2fH6CF6WRSvkUsbhuM0Zmaoq5hUXy+TmmZ2YwTFPJhO0COzs77BSLEZkSVkTRabdCQiqdYn5unlw+x+ysOoeoXq+rdnZ32S4UcYTqk8TE1oaxJ+VMQ2AKiFsG87lZ8rMz5GZnSCUTNJtNioVtCtvbbK+v0m0rOpHdtm/UIATCikEsAVYcw0owt7jIbH6e/Nw8mWyWTqdDsVSiUNhha2eHRrNF5Nw77Zz0+bGWd7nZGXKzs8zPzTE+Po5j2+zu7lLYKbC1XaBarUbkvmdA98LkxDj5uTxz+TkmtROmXK5Q0H3b9QoPDdIFdPuZTJa5+Xny+Twz0zOYlkl1v8p2YZsdL+UwrFeEjJqwzpJMpZhfOEY+n2d2Nkc8EafeaFIoFNjZKbJd2MF2XX+MfPrzHdNe5EQbztLpo3sZ0Vev3ualcB+m+3lrwHOMyg7/1f5fB+DnM3+HBFbQz17DMsDowKi2ZYef/R9/jnkjz+bW9mOFcR47UiOl/Pcf995PEPxd4KvAF4F7Qoi3UWfJfB7YAf6znutngReBg+51+CvAF4A/DnwohHgXeAW4ANwDfvZZdKAfHDBoUn+eKWMiuCDieJAokx6fafQNJfreAFApaNor0Ev0fSI1BwyNvgTOweeiIgQbGxtsbKyTTCSIWSaO49BsNjSPcPGTXQ6sGR2t0c0WtrcobG9xL3GPeCyGKyWtVgvX95qNZuDv7u6yu7vLg4cP9ZkDknanqzb8Raq5+YgEYxPiIdVqjWq1ysOlZZLJBEIYdDodtZm8NzTrMUXhRoZXIGnWGyzVl1lZWSaZSGAaBt1Oi3azqZVzxajUDQbhHH3PGyxQezjWVlZYXVsjmR7DisWxHYdmW/VLaiYWdoR4M6k8SopRuq7L5tY2mxubJO7eJW5ZOK5Dq9lEhiVnuG9C0Z7ABOn6c7mzU6BYKPDg3j3i8Riu69BuNXG6XS3otGfQi9CEI38ejhLKe3uUK/s8XFomnkgqj2Wnq9JKfC+XonMpJEKYCkcjSLNAQq3Z5P7SEssrK8STSQzTpNt1aNsO0owjY0k/7csUgrgpkNLE0XQaMwUx06TdbrO0tMz66grjY2kV7WjWae8XoV7G2S9jtzsgJUbMIG0ZGI6LKQRWysKw1KjbrQar9++xtlMieX8ZI5nBMWKUqg3qXYe9ZlftJ7DV+Bh6E4wXofRT4xzY3KuyvbVJQtpYOLitFs16Fafdiq5/10V2OwgzDnabWNwCS2C6AseV1Cq71Cq7rC89JJmMYwqUUd2oIJpl3P097HoL15YkTMGYZWAIiAlB2hSYlqLJcnGXxu1bLBX3SE6tQHyMmmuwU22xvt9is9yk2ejgdBR+6oBUAyldHNtlr9Gh3LIZi3Vp1posNXaJt2uIdpNWpUy1tIdda2K3OkjHBUcgDEG7LEgAstPGSGXAizh2OjjNGm59n3a5ht1s4XS64Eq6rsvaw2W2Ctvcf/iAxMQkJFJ0Ehmc9DR1I0W5ZVNpdnBsdSq9OuzXAClxOi2ajSSb5SY3l9YplUrksknGDAfRadDaK9ApbtIpqg3TpmWQNgWTMZOulKRMg4QpcG2pxnZ/j/3NJSrlPUhPYMQSOBJarQ7trtoEnjTV+UmWATEBottGOJ5zwFtPytv84N49lh+tkhrLYiST2Ji0hYWbyNAykv5mfHWLjixpPuE4kpbtUqrWuf1giZVHj5jJpjHdLm6rRqtSRFZ2cVoNQGJYisbT2oGSNA2MmIHrONjtDt39Mut3b7G+vkZi/C5makxV86s36GjjOmaaxF3pp0GaQh2Aa2ralxKkYbJd3KWwvUXCMonFTKTr0m42tfIY7BeUhhlEazxHU4hnlPbKlHb3eHD/AYl4DCklnXYLu9P2DY0oU9L3g05TUvyvWt6jul9l6eEDEskUhmHS6XTpeBUZQzxN3azSBSOyS0Cz0WRpaYnllWWSqRSmYdKxbdqdru9Q9PZoetGAsP9cSpX62Ok6rK6ts7q6SioRwzJN7G6HVqOuU5ZsLz9ArT0jjIfh4+g6XTbWVtnY2CSRShGLJ3Fcl1a7rY0ptfYkUfnWL7VpZ0c5x+7fv088HkdKl1arHaqyN8igicrkcmWfcrnMw4dLJOIxQKUhdztHqAYqJbValdr9KktLSySTSQwh6HS7dLwKrr0GTfh2L4CESltfXlZyPJVKY5omXdvRRRu8yEfP2PQF4dOXNAYV+5Ah/cjbWxhONwvWf1T3Cz1by2vZ90B3enSYHsPyyGfqjA5P7ZyaTyNIKVtCiD8M/DXgTwF/FNgFfhH4m1LKQYdp9murKIR4E/g53c5/AGwD/xPwt6WUR6//+BigDJr/PTBo0v85U2J84PVCeuvk4IJTFwitsYrg//BWLD/VSwSKs7+IPCMm0mC0fZ2eFfm2N1KiF1ar1aIVal9ldgiVstTXmdJ/y1in3QoYjodT+ETevhDqv2aW3W5Xb9YOQtsjgReR0uLEcVzq9Wbfa4BImLcvc0ELb8elWa8RMKbghHt1KJpEmmZ0rE0TYVp4e5lUYr1isLR13wwzMIS0wm7oILRAKzFhlIRn4Chjrx1i7qpgA3g59Iqx6k3hAp+G/G3lApAunXaTTrvhCwgRObQVjV+InRnKq4SfagRSCOyuTdeu4Xm7hE6FjGxc1nOs7jOUR8rrmFR5+LaU2C2vSpq3/8b0bzcExEwliOKmRKJ+U1EttHEjsISg1ajR6XbAaSMcvdlWupiWhZVOkJ6xMeMGyY6LYRnEMzGSk2li6ZTyhJqqD81WG+madEVXRdJU2AvLEKSTFq6UdBwLQ0AqbpJJWFh6s6krASuBdLq0Ow7tro20bTAsjEQS6dhqikxNC1Iqr57dxhAGcTNGzIqp/VX+tNjIto3t2hjdFqLbhFYD6dgI0yA5nsDOpZkvt3G7DkbMJDkeJz2bJjGZIZYdwxgbxzFi1No2jnSodbvUuw5Nx8V2JaZpEE+lcSdmkbaNEYuTzGaJJSzipoHtSLquRMaSuFaKVruJ07GV9z4eg5TOx+8GW0Gl42I32ljsIztthD6IUNo2stvGbrSVEQSqoIEJRszESiUx4zG6UmJ3bMyECVYKGUvS7UpsR2IZBrGERWp8AiEM3G4HYVnEU2lM08B2JU3Hpd51iDfaODEDs2MjjBhGKkMsWyMx2STTtjEsg8x+Jxi7yQTJ8QTCNNSejVYDYSWQDbBjSTAsLMCylHIkND0Kp4uwu2C3tadWgk5381i/MC2kYdGwbUTHhpiFjCXASuDVGLEMQSZhMTUWIxkzcCXETUV7luYrUirDd7/eICZtRKeteJRpQixBLJ0iOanSymLpGK7tYsYNEtkE8UwK09IeYMcGp0OrXoFWCzcWxxKmyjp1lKpuxM0D0YeYIfziFnoCka5B23Zod3UEm6Dvwd46i94jEsL8QvErSde2lUyQ2pARQvMeEFIrpiZE0oJ8J5PnPHFxul0afuVMLS9CDrrIodUyKiu9/YYIlVLW8Ephe1GQyKGKAQ/v5elBKN4AR9JqtvXzHHxZ4ehEO1PxSOGEZKBpIryqarpNiaTdaukSyQoXdQ1ajejRQSJp8UEfkVIbe93Q154m0zM/w0AY2F3tkPQ6LIzgs5/23Ecv8JR9qZyWrmPTqNcOXtfzvH56lpKZas6kVFVAIzpXpD9K3zmgM0UczB7RDdC/euTtQePRwHegRnS/XsRdBEafamihZ33M8ENt1ABIKZvA39Kvw679OZTRMuj3XVSq2V9+SugdCfoaNIYu3zzQMg57EEKELQykcBCyd1HQf4/HYcaA/294gao8b4nDgZoV4YIDPk4hr4H3tfCe0a8Cxyj46Wcchrf3/AP3DFq4w8ZDauET/irURwj1U3lThPac9kajwkUZDqS46U2QQgi1ydVFadRmaNl7Xkc//Ssa6VDPk8pzayhjw/SU/rAD50CULzzPpsbRDe3XCBuCYaNURt9D4fOgbV2UQer7DTytIbjW0BVYDJ064kmLA2Tr0ufLADcBGBKwlMh3HUJp1epyL6XSD+HbJExlPMS1EhdWFkwRpHcIL73N6SAcpVRJw0AkUsTGMyolKpMm3emqUqmmiZWMExtLYWazmGMTiNQYmHHVTz3mhgDTgExc4ZUwDdrpuE6Dg7hpMBYzySZM4lrRk8JAGDGkFVfdNi2k2Ua6tioGEkpZ9JwYKu2vqfphmBjCDA+kPx5KcW7jOjbCtIiPq71tsWxKGYeuxIhZWKkE8YkxYpOTmOPTiOwkMjGGG0/RcSWOq2glYRjMZOLYriSesOhOpXBdF8M0SCdj5CcS5LIJUjFFXy4qzUckMxjSRRoGMdPCjDcwEzFVlELTmmGaqgqj4yKMblCuWaqiAhgCMx5DePsMDIFpWZjJOEYqjZEaQ6QzkMwgY3G1bwGHVMwgl01guy6FhEVjMonruBiGQSxuMjkWZyYTJ2Eo5dVxVdQhEU9hSBeBStFLx+LEsmlSuTp2s43btcEQPl3Ex8eUkunYGLaKyhiuNlB7z4mQXmlhnW4pdeTSspSzIpymaliIeEJFbK04GDGkMDCEJG4IJpIWhlBpXh3HRUpF5wnTYCxukImbmKGiTdJzKphxRGoM03GIC4GZjJOc9iJoiubNeAwrnSCWGUMkUmqNSIlwukhtUgjTwjBjmEIQMySOBK+AhmfUGAJihlAp1h6/NCykdBBuIOukZin+BmfPSeJHHyThqLnU3x2QBlKVZlA8yGveiBo1nkzURov0nYTec3SrTsipJXv5Yo9hE2l7QCUsz9suzME8HVdH7WSUxQqBxFARbQPlQBEmmE4PHl4FMy/CH+K3As0ffLNtgHzTIxx2PPaOQ2+f/QeEYZgO4CNE71iGFI3BMCzycCDzxaCfvuI7zrznht91NoPSl7wIS5gGogWaAtyH4TzgR218Dtb9wmuAYB0Ng48pSgOfGTWfWjhx4gRJI+n/v9Mt8XeWv0FR7jIfy/Pfn/mb5GIzfe9tuS2447WzSFLonHFNbOFzQCLvHvR4qQD6sPP+EDmTRj3tQLuR5/XiEiyIvgU5e42D3p+f1HsQXpx9nzGCkdM7noMW+aHP4sC49J27kDEQ+S0SGtaGjx9dE/73MiIQB+DiG1IqahSE25UwOoDXk8ABuuxtM4Svj39QlCLiyesnPEL96dcX9YTgmUG1ntAzvDE8ENlUhp8I58Drc1LUZ1XwQDq2v/9JOqHcZmEgLAthxRHxJMTiKu3NjCHNOJgxXMPEdlVqmeOqyk+OjIpRA+VZNw2l5FmGwBIS7A5CGyHeORrCz9mXoUIMBBv4/RKeByOVwje0XbBtpJNX+ff65bv5daRLGR5pRDyJNBMqnc9KYAuTrk6V6zqSluPS6Di0HJeu4+JVnzUNiJkGKcsgE7dImoJkTKVmGXYbw25Bt43otpAd/ep2FC52VzsCvDQqI8AN/DmI/C4MsGIIK6ZwjycV7rEkxBK4VhLXStB2JK2uywuOpNZRB4T2wztpGqTjJknTIGaq9MWYIbCkg7AV3sJpK7xbjWAfmUcfhqlwsWIKLzMGljX6HA2ZY29vidpfoveWWHFsKbBdFRFzNN310popPHrTtGaAIV1Fb9qgF04Huh1kt61eth2le9P092kI0wIdPfL25XkODD9yKjxngwdusK79Cp/uCGt6BF7Yy5PCvMPbWxpxOPUozxqDSJujRhuGQSR6MaQvg3i69z6Mr/vnGQ2RL97/PX2LjPOwPvco8P372kPbkbZGkMtHedaIMLBo+BA9JZo18PT0pZHwPGAshy8aPHcttwW6FsKJE4sR3bQvDNADWq7K6vAKQz0OfGbUfErha1/7GmMx5e1cra7z1X/+02x1C5ydOM3v/sy/4kR2cSCDqHfrvlHzta9+VbUzqrEAj2cwDGU4h8AIRsDwEwf6NHkU42ZQWtmTCJ1hyv1jMNID/e/X/iBG6gk46GGiA5jYYTj0E3B9FIeRDJxRjaBBhnZvfw4w5OFC9CCzH9E4GxTJHKTceIpPWGkIRyl7+uCnxBim/9nP+zdMvzSrKmITlD/1mwIMoT3choql+R57f8OxE2xoRirjK4xbv/71g7CS3Evb3h4vbSBII1BW1efASHOkMtK8QhWON4Q9wx5Woi1TEBNKicZXnru6AEQX7E5g1LheOXAnotx7e8j8VE5P0TdU6qZvSMTiYCkDEzOmDc0YmHFsF5WW5kSNzMj0avzDkTz1ro0A11F4+0UsvOiKZyD3yW0P8y5jAB/zBi76hU9v0i/HaoBpBhvnQzTo6KlVdCcP0JrQ42doNAwIRYiidOd/PmQNKCMt5LAY5Mzo17+R1+8gRe4ofCPUh0EOnsfhcyNcM9ho6MMLBzXXjwf24+sH5PRgxb33u8dyNg5L934WsvkIcvmp6CPPWGfymz3M8Brw7DDOdbvpGzVf++pXGYtnDlw/ELcQ1Lvq3LpodbujwWdGzaccPIPmYWU5atAMg0MYqCqzOMBKf1oQiXN/Bh8LjCBcBn53WNNHZOIjwZMaNP1+7/eMoyo8XptDDdN+RmWf0pfDH9LjYAwZNHq/kPra6OmDJHxj78F3noIZfcQAvDwlUqqNw8FhvSK4Z1SDxsPVA0/J9g0aM+hLJNIVPDH82RAHkjiU0ixUQQTRO3yRVkToOTqv39s4KyTgKvxcNxgrD+ewIi3Upvjw/5Fn9DxZaNykTqNzRfR3oy/O3gWiZ2zUmPmZTIb+0zvmHrjuYMMmsg5CSnHYOztwHWmJoUnCOyAxTClhevPuifbL22sgQHipWm4vKff+MxQEMpjTo0SIR+F/R+UboTXoy9fwujyMnwx77hOCTjY64k198O397jP5/v2Bp5ENAUPn71B6+T7qd58ZNZ9ieCyD5vsBo26g7wcjLtC+htgPCTx2vwelAAx5Tj9mduToy2HwNL2Woz5v5LZCyvyh7UaVy4jHMxKlCdpThgMh3S2qysuIUht6wQEDQAi1HchFKZW9PfQ2TAffD/NEho0BjmbI9IIR9hoHyrkXtZH+huaDirTA20KlDCyjRy9UhoHwztNWWPd2PKy8Gyr64EU6hKEVYXTee68REBp/35gxTNWGEdqvciAail+UxcPfW3m9+Ite/NEUF45CCBNhqFCcjGzRkAHO/eahN7VsEAwxznoaBHXSkh8lU+dX9aG3UB8jz5HRvnmjpJZBn6igxkmGfQzdXAAAIABJREFUx1jKg0tTiMCwidw6qjw6An95GnzP689hbQ2LgBwFQnxvJN7eD7Qx2tdQOwI8lmH1AwIj9/tJDIUBBQp+EOEzo+ZTCmvVDX76V/7k0zFofkCI/ZkYNk9ikA2Cp+VJYYjQGSZcnkAY9gqf75shOSTCNIqRdkCIPq05GZYa2ZOjfvDCsOCJ9i/wmEdTbbw+9FdIQGrDBgKTxcC/PVCch/b/CIbcMIMmYsx4SOgzqrxUoUjqkPr+wJM94wUdEQgFAYQQvm6sojXegZMDIs9Cl/jtjdYQMmwi90UjSZ5hIwYYlwqkxkPocx3lofj313OCsfGfp6MQUugKXOGy8cOMG+/7YYZN77MHgZSBEaMNG2QPvXno97sdgWfiqaii8Neu1BvLxUBe1gf/nmt9W8F7eu8xBI8LA3AayhNHMQIe0zB4UnhqvP0w42yA/Hmqhs2ziBQ8A8PgB8aQ+wTpkJ8ZNZ9S+Mn/52dY3n803KB53NSax4CPdXEOYS4/TBGbw71oPW7sPjBSlKaHjkbKwX2WMMSgGXLTM0ElAoMiMxA1aIIv+zfTt3+hPQNehMFTagcZNASOcNDGTUjvDnTnHiOrN4XEpyEDvwRoPxjZoAloTkWegrQz6aXVCS+m0dMvL0KDl7oVRAS8YTMifRs88yrao8fQEOB6qWhST6Vn2EQQwEtX8w1MI5iHvnPXg4sgSJ3zzkwJN+9Hafq2pcMyUkdrhAwZAE5g2HgPk9JPo+sLhxk2g4qVeJER/7MMZkLTnGfc9AavBo6QVwnRw9enbYkcGkUMQ+iBHt/S7956jDozXJ7IsBkJDnEKfD8Nm6fB23sNNf/7Pk6jQ/j0D1PE5kh7kJ8xHs8sffxj0j/D8PGN3GfwVOFQg+bTAKOG2Y/a7KeZKR61WsmhF4qBgqT/5sTHHLsRlPWjtzMihHAeuZrO04ajGDR9+xjCO1x1q3cTNMF8HqjE1PM8T4EWPbeGDRrRe9+zEDoHIjRoAyaIeAx8hSBipIhQOpMIjAHDezcEnlkkvL71bc37Nxx5Ce+T6cXJ6LkmiCoMbFvKCC6GISK49vYhHNEIt9SbgtYPN9k7br6l94wizr17tbzXEJrz7w1DOELn4x1uJLwWiH7ux8eGrLmDRVU+Ji9zONpKD/99HD71RGv1KaQM98F5oOwdIocOXPqY6b2fJnhsHeUZ6Uw/SPCZUfMphdPjJ49eFOAIBH+URfdJ8TiMCkcuP/0Jg6fhVelbNvJpw7NgsB+XkcLjj/NoBs0Iueq6tQMGTTj1yL++d1xkVMnkoDHjmUYKp9EUhGgkYoS56JtyFlJUQ/3zMA6X3D5gigi/Fd+w8V6eMeClQnkHEAbI9yrS2ijpKVjQvwCAF8UJp5+FFOtBBk4kDBPFbVgfIkPWC33HqmdMIxGxoxg2wUMHRZ0OQlCp7DCa0y0f6E/wuceQ7O2fd81QGLDmhhk2I8LHmgXwLHjds1J6Q3PyNJyKn9hsixFl5SfSsToE96em7x24+CkYz0eET6bW9hkcCr/2x/756BGaZ+WBfZbwhPhKRN/F94lkNkeAp27QjHzTkOd+QtLORi148NRhUDn0oZGZfnBAJdRfHzRogjk8fC57rzxwRwjP0ehrRK/rASU6UFAPVNUKGwTh3w62EBgG/heB8myIoOqZHwUJ97Fvd/ReDl05IRylEYYRefVGazBC+1sGgY7UeJEY/7Nv4IT60dPHfrhG33uNAEJ7lKKmBDCaYdPvvn6XhdMW+0Rthr0PahF6IpCRvnjX9Fkjh8Fhhs3H7fUfNVrzcTlxnjJ//4E2bEaEQfrHE4/Np1GX+xhx/syo+ZTC8eyx7zcKwFNgXs+Y2D0m8lSYyVNBaJjwePaC9cAYHMUA6MW9L7P6BDFcMZoqdTQY3L9DDZpRozR+gwMUUwiUWYavweGqoOyP5+POYVhhDn/uq6z1idJEjIMoXSoDRfjNeUq/F+HoNWj8a/v0Rx4YFY3PgYjMsPSznldvewcg9NSQMWP06UdQ3EB9PrhCQ8afGBSt6YHwHAyapyPBIA9sn2iNh8LQ1sJGbj+ceuljRPz6rb+RIzZPk5cNNtS/fzACPz+KfO6Zt49F3g6TmZ8A5f9j1T+egiP4BwE+KxTwww5DmMJhG8ie2SL4BDCjTyI8qefq0PkaReA+ztw8DWNt1CjNU4JDx/r/b+/Moy+7qjr/3ZVEMlYGwJakKhRBREBsBJGA0KIJQ3cqmJAJsQVc9GoUFRCVbhpUBBQFBJtBcQIaXR3IWCShGyFMLcgcQMSBscjvVymGiKR+VWFIUqf/uPe+d4cz33Pu8N73s9arX7377rDPPeees/fZ++wbOrMZpBzUy1JX9mpKrHbfzkUBGGb8K4OmHnpmk1vEXIb6b0Yleal4O999YimTNP7T3K8e6rS45OLX1lvPRWo6XdvQL8wMXT6ShrGjl6z2W/3+bluKrALLoaMhf/26RV00UoMroLOzLlW17VoAjAt/RVDc38rYKv4xtjvbdcp2VGRCa11ncfNq8vhgldtz/7qYiRezN8ZZ3XNme/a8LxKQECG0r7Ls33cR+jolDgjC0UajTzuWzleyuXVT73PQU0Oi8GrcOcOAJrreJRdZXPHJ7+Hw8bMdbGWKHQQ8vGva+tEep9lmVPINBk3nJY+a61q9QhZZF38TeQ2tXpraPlX2sIBBs+NEwdLTUYWeAejUQ72uFgbWtm21ELTlGptu+Flt/U0VelYaCNY1KFXyBll6nZbyNj++LHwiHSPLw1vTB9Uygn3bed17YnymZNEeumFo5e+LXWvbtWXzeyaXKdczzPpbjcWRxrBUSV10ZCjT3MPQghlZbxrLiNzY2ofdV13c+zzrpRmSYFZxXYoXE38/TfCl51xnMV6ascI8Yr00ppn/kHJUM+ZdAVofNL002RYP1xTRtpemsd3nXNUpa8aA7rNQclveDseamuJv3WipbVvsp/nNJ8teKxtdTFm8aBm62rU1uZ6LeltqGNStdtfgcET7Bjo3JNbDPNrEi2NMDfReTp1Rx54syWpWX20eWu+rXiS/98CNvc+1+rVDehMVF+r74M+4s05KxvU0eqXfFX4SWi9TnU0Lb1/mmcGAMvYaTFsKqC7srNxuTOtsk8EwU955SWiMrDXZGr93DmsZpR5uisXKlZoxUF2hbgQ0EgTU1g3ZX4pYu3blrTEZL9W7aXTHtk/buKfNhAG+ZbESFJIYWVc6bPfU5onRbW94IZsydNpGjKw+Mph31m6N8x5MdawLLYvDqNSM/VkNmxmndk6K9wSRfwa3IdYDVQbNF2/Zi13bT+99Pho1hAxBDyW3j/t9lFmyxuJcQ2YwX4JmcgfqzlohWlZjIDjUw0NhsIWe2a7fUTZrM+spZjRNMVT17/XQM88ZaV1Jm4kAWkaAaI4xKdN1qhC0+gs1OzFuy9/qoWc2+bvhb8U/dcNGV5bOMS75G9uk+Tzo6iQ03s1Eow0d7m6vf/eidq8bm2MmXhzXbxtn7dC63IT0WRF1ZczwNoKXqs9Y1CsEjWt0J0vdoDnjxF247vGX9T4nEwUQsm7kXEszBBm9e90X80WULZWBoPt/iAzVcUEZjQKUUBHHPtL4v/YdN46ySSmRSPF3m1qqzW3lf2HQyPLYjqLducA2NBehbwPUHeUJBTjcelYaHppW1jPrc3UYUMUaHF15dIaMtjw2FvVR3TUsEwYs6lVgfV5dba2xQLm6lmO/Phm16nL5JK3wJWahdeuYrIvYU5QxCEtdxqDLskHWA896bxs01198DU45+qTel6enhqSHoWdhZOr8/UMFR/bm1AlU5ie1nqZNsFJi9nAop9IcKpOmzcWEnXX+39rm440JLJdu7/q2pR8rcB1F5TGpewnqRkz1/8pLUztGi2EBfdvP5iqPF06vRn2bpb6MvxvQljGht6NKGLD4HuOtqTGVWXvXuhqf40xtb4wyeva5+TKm0oAC4D/2jbwuSGfQeL930QGNmnVkCh2A7eGbyYPpzUihZ/oTZvbSpBpQUxsmoxs6rvtiUFa0oVyeZXHNkNfChtrtzJrWU3svbUrx0hgwltNCVeJ60be1PtWOHa9GPTTKJ1lAmeGsY9i0DRrZtgw9cxk2resvw8z8yhNQ4y1Z6oawzRjQKNg+64Tq3033ONpLowmNMz4XrrszsiEz1b6sUxfTHmvWIgQtxYTw6GMdrDpmToMGoFFDUjMXQ2PFmUS2s1jjOaZTNr2wL+QU1kHTsWA446Dp7Y0yrRnQraWJQuMt6iiXesXTmu54cWwLozdP811nAHjWiVU27Us2A8/R2HFp2DQMNV8HhLZM9msrqzFgM6BbRmgQmrU1vhnIYr0YsTgTSZjbUbiirXuWI8bMWMV1AhOakxib+rIKes4IZcht0AA0ataPCXRqk5hJsLEKHZYvverCf0BP5nEau+243uMSZdxoFMteGYVsz/jAC6FNSBUg5p5lr+v9jQiqmk5eGTRYbKqF2QWs+0G1+L/0yNTXuiy+V16ajtfM4bFZ1ItqyOoq03KTvc5U/cDceL3TxaMdtg/1Ufq1z0hEmY0Z2qb1jPQ+TVDf26PMY/fNZMkU6qLVRwxh0AA0ataL3AbNOhkDvow9MNbxrp8JdIgeZJ/xG7vucg9MLqXOiUWJrmnqXS+GT7n0MlRGQPujPXOtfG7FzqxMN4ybCI9gMwNVXabaPpoymc/qUz/No1XdAAtebxNA7mdmbGUtc/nm48XwlHNKOsHY/fkUyV0/5VgylEED0KhZH6bgoQH8B6WxB6+hsNTL7N6kPIVBI7LdJL/XWV761veZ0MxAt9fTxMptsjIWv5ehTD7ZtQyymNRuaX1QezeNPaV22xtWi2NbrJfRhJ/VvTROg01Tnvo7axpyu8salCJ5IaNmmHfVlw9to1ErW89xJ8c4kPjZjO47pjDGTaHPDsB6r6ei4+RmZjrUxoGNwQwagEYNGZKJPGSrzjCzffMaDKeLPfSsW5e+ddse4B0Dfqlo+6K8ZvZD2qHJa7Q0AOpnbBsD+it5hhGZZivb76AxbbOdo8Ki5LvK1DHUuie3X1t7NfNv3uuDqms7FePAttiSZ3kl+7MyFw/z9Mnft8/HIzVzRta5Nrb24ezLzxvMoAH4npr1YIgZjCm5mYkfNDJXHJ91DxF4vYMiRkEuMbxDRADr2zQayn9gaF3xPpfaMSLFOXRGTEs2YxnVYUCO0GxXC4GlnHu2l0l37vD6W5axupMW+vTn2vpbk1n0dad6bsh8yPROoTEMGoCemtVnKi5ZKtDrhddC4mngDh+Zlry96H3va16kzgr37j7RVAvrFwZKMx1yW6KFQdM+hzYMzUQViuW59sQ3vXZdBk0aXdNZ6v665bGuxAe+tOtLZ4ROfH3NoNjLMrlQYdu9n4pOQIZhBN2rY9BctAc7j7/bINemUUPIOrAKnjRT5zzbsk1/vUADr7TMGqPHt36siljTsOmuRWkbDkvFzapwGstUM2w6a09M7dB8f5oyHG4ZOI5yOe+LhwxlHXSNFked5lSIsrTVmU6emZ6RVZgMnG3/TGLY3NrfNWgG8NBUsLWtMkPNyLg6rdiOeRU6dDJZJjW7an3RoEc37aMgRr8IEegoi1olOeB4415tJb7tmWiv/al993n5o0k22bbMEGY1XupenDKzm+/7XEwvP22XQ/e9ZajpXpiqvUZdZpd4RiM0oB+Ouf8++9Rlsj0rIzOpPoWsHrl0qYSG5+6rLzEbNAPopDRqyAKBWnwIMbM67cPY1uccOpNSdo/sWDrDRqsghwzIbQOl8wJHndJf0faEaPZxDvK65fuWsDTtsW25oPUkGY023UsrbeuFXLJYDFGncdonS5qOFXy+VmvcXKWykNTYdMW9B24cxUNTsbZGjYj8oIj8NxF5j4jcLCK3ichXROQqEXlExPmeIiLK8nlzjnIYCbSI241ztTroQNY65jhScbEoKSvTlpIqYuEhTL0ISQWMcqWHMzRrOXwokcbHjLS++dzTVhazurLfWTeT4tn1XFPTm8OeZSr3ddC9l2YZjfW1qNOe7TOwvUXhap8pmLPxVcOeCjm2jNPxkA3OGusILl1x1/bT7QZN5nu3ztnPrgdwGoCDAD4E4BsA7gvgfADnicizlVJ/FHHeTwH4pGb7h2MFzU1WpTNrTPbqdiwrYwiQEo+MUympZ6BK7bnxPZ/vOgF1GMA2GD0cIiiU+m3Ncun2rb53lGqNzO2yyDYoHG5mCNP2X7VF9aa33TcFKi7frg/T9065akadyQPV7gtN9RSS6cjQd0el483VHr1YY+V7BRGo1U0JrQ7nW4OUMTNdXV+57vy3jOKhqVhno+afATwXwOVKqW9XG0XkaQBeB+DlIvIOpdQ/Bp53j1LqBenEzItNeV7pzoOQ0UjzTHWez7bCHzuAGRXfumFWKf3+6Z3dqIUdoS9XzbDR0pLDtO6kLduiWMW+9tTHNYOmcQ73onuBKkRvHFuWqZS3i/5dO8u1R7517GFUa7009sQDQejKpytXEgaeRCBkDfB5PnecMEyWMxNrG36mlDpbKfVXdYOm3P6nAN4B4AgAF40i3JxgZhOyLoR4HY0z/H0NGt/F4X50Bilj9iwx7yPbWmtntqXrF7zCsA6j482IpbPupLumJjirm46GnDX5G2jC7lLgU1+2+jZs72WQaMvWt7yG5633c0zICrIiutxqlCI9nyr/njqqFLFMJSxrlQeEVS4bmRYpn+cYxdh37YLWmPFcXN85SrOupGMEtIyB3us4mjL6LaiPDMXS/V9XLs09CDYeTOuAbPVlPEcAKUNdpjKmkdVnlcf2Acrm1T9lfJ7XOfzMxhnl369EHPsgEXkZgO3l8e9WSr0vmWRkXvANy1CQhGElxIpuzUkfOu23ttbEGKLVB836k+pa9b+NQ/RGQlCbq5ezFlJnTHoQmdWtETJYL5PuPEajrvp/6meqbYC2jaDECtGa94tDwjByrLaxQhbQqGkhIvcEsLv8ek3EKXbXjgeA3xKR9wG4RCn11b7ypSBksJ/7upp2WedclklD421YdOtn2oaA6/gOTQOlY4wa69hk2AQ+ay25jWuGTO3MqPxbKBbwQLtWCNAswK97NnTraizo6si1gN615iT4mQusq1aZuv2n5rjQ9mdLvU3yQ2U/G6ukf8xFb6RRU0NEjgTwRgB3AvAWpdTHAw7fD+AFAN4K4IsAjgHwYwBeCuAnAFwnImcqpe7wlOUzhp/uGSDT6uLhvtQ9hHM30qbBxBbh5sgYE2uk5TTuNMpiUHvutdakXq62t6baZjnWKVurDk1Gmklhtir/hnJ3sp9pvE/GdmUIqzOWtTqvxVjrHGJQ/DtGgEcoR3Bdeayp8SHAc+j/MtFETEmZn1x43YTuzUyJ1j9yZkBbA2Zr1IjI1QDuE3jYk5RSH7H8/ioAD0dhlDw95MRKqb8B8De1TQcAXCsi7wHwcQA/CuBiAJcGSZyYwcKARh4wemd1S9mx0IvRZHb3I9KI61POvmFkprTB7d1MoYH1rGbtEKl2ymPvMurL030eNQaAb0rgXu/caIfVVbK096v+awjT8iWwTDZDzdzfBbTddnlMyQIM/aKxT02dzrmvcR53YIJzDMgcZBySlPcjckK1/tuoE6sDjb9jlXO2Rg2AewC4d+Axx5p+EJHnAfhFAF8F8Bil1Dd6yLZAKXVQRF4F4DUAHgNPo0YpdT+DnJ9B8T4dYsDHcBu9Y1kIok+Jy3UoGvoo+qnXmrRJNVC4QrCKnYC2oq85drEtFOM7TgK3V7+FoPHKNAwb0zl1a2ls3g2toab5zXfhvC1b2FBlchFTT6HbTbhCBjvf9bPcXucMJbcB0EfOWU3+DINx3J6Id2NW+scKMlujRin1gFTnEpFfAPBiALcAeKxS6vOpzl3yufLvuAm85wY79Ci0HebQLu2Ql/ylJLfx0r7cUManpf60i89DMSi82vJpw7RaM/FR2bLqZWwabp0ypkIXVtfZrjlm+cWwPRBLmboGTd2wCXzGnPVkCKuDTZnsMdGwuKrNUzVMPzK4kjnW+Da08q+pP07YRTLw+DZHpmHajoiIPAHAawHcCuAcpdQnM1zm5PLvoQznDmZza//YIvTDMsiFLmYbDHZEI5AzHt/Sddpm7fsypCKky/DV/qvLkKUtsyU0ybVNG36lR+vRaJ9eJ4tpLYnuU7ta81jHNRY/JiiPz32rnaW7SVdPhjr2CD3LQuq27vtcZi0jlfnBGXDsTaZ/TG6N1XxYa6NGRP4TgDcBuB3A+UqpD2S61AXl3xsynd8LgcLG1j6cc/UlY4oxf1baQOm3PmJIRpntm0zdN5Xdxr3opQwGKuiGY8KvqpNZH4JUfXTbfc6xPMhmqNnKVPvdZgB4yGIrS8g96Y+9DvVtII1nyuqJGpMRnvVpeDB8yz0FWTMxmX5+3ozRntfWqBGRHwdwBYon+BKl1Ds8j/vn8nNaa/tzReQurW1HichvA7gIwLcAvCGN9HFsbO3DWZefh70HNoKOm0ZH6yZGzrmUbRaMORBMYWYr1fqDFPSd5dbJbFXgbYZAQPlV66WTdYVXk+FMq/wnW0guho9u30BCyqK7F0Bgm7fVjaZMNiOtb3seI+xqSs9mmzH7rimUf0VYZf1jLnICM15Tk4DrUKRd/hKA80TkPM0+71dK/UVrW5Wc4KjW9t8D8Nsi8jEAGyhevvkAAKcC+DaA/6yU2pdK+FA2tvbh7MvPwxdv+TJ2bd8ZbNgEwY5Sz4BZv/wyvA0Xn5ts7cloyQJq6y3ap821rqazqLxcc2IoR1SqYBfl2ijjO2s6bdpxf6Puf7V+B+46jF08304cYDuXKUlAn6QIpt+XX8LOXckTUzctmRZtKiQsKyr1tma/UuH3CSvsg72vzLNWKuux9dMM6VX3kJlh3xNgQF1kaNbZqDmp/HuP8mOibdSYeCGAh6Iweh6IoifcBPCnAF6plPqXSDl7Uzdozjjx7rj2vEtxv//1sLHEycKcZhI6DLWofkrJAnJ2qg2FqqYQh+KSMaR8WcrbXEwPBBg2OnQJAIz7OLb1QWu8tQyb+rXb25Yn6m4yGWm6c7num+F3uxHQao9ehpQmtDB1fxHskYkw5GwGTc7QM1umus6+jv6xl5KcKkTUwWjrhTQM5YWaSOazGJgJLS1ra9QopaJakek4pdRv95MoD0uDZi/OOPHueNdFe3DK0Se5D5wqGTrJQTuVlZohsSi/q4wrM1bf+o3xKNmOMaUudrHwYrQMgRjvRn0f08/arH0OA7XHve6Upy1nSPrjclt0P2Ish10Rdk7mOA1zu9epY6D1MGJ679f3mDZW5X9dlcwVKvdEEwR4w5dwRsE7tsI0DZpdeNdFe7DzhNPcB5KVYB0W0jdnez0M3iHDV0JmiI0nLuRdht8Y1pzUL+MK6UmBKeQqZu2CNpOXqS59yxARhqYrizX7meG4ZLKZ6lFzb2KMyVRhdBFYkyCkDD3zaJ/ZJrR85K3V5eD99domQiCrDI2aFaVt0Fy/wgYNO8p5kEx58FIWcrQJneEyoS7U970fXrQU3LKc3sabzRCwXlVn4LS3KdgNA5f3YllnnfL4yqvZr3EuZ7twyd/6PcZL09jZwzjrSGHy0vR7jtNl6kuMts4yKP4D9l8Ma5oH1GHSMaERmaRinQyawUkxuzVmaEN7lrf34Olad+D5Ppes9ChjjOeh/lust8ZWLx7emhCayrg7Hl+1DR7XcdGC1dqqca2J7hN4mTLvWAOTl8ZgzMQpj57y18ueY42Cph69kgPUjuuvPOu9NPpdPevY9uzFhJ6lWk+Tk17raXrWYeddSjPP6ra2IYjzhkbNimEyaDgTYGf0+5Npxn/0cg1AcBlHyaBUI2a9DDzLmdNbU7+MybAJSUpgvKpBidekdfamo+B2n7dQ48S4f/vcscpRu7y+oUohdWAzaLTnHNZLEx165nkPRg09q8F+us+Jx1Vj16Hu+jD0/aFRs0JsbO3D2Zc9bq08NFkfmCm8+8RGHyUvKQnWjoRiKnf2OvMMQYs0XPz3t3hrkho21YksM/cJ1w7pf0vtXSyRbVbjxvXxPV8UIbPemdaaRCUHsF3SaNAEeGl0pDB4hgo9q2Mq5xB9euceTMArMZnxzELG8YXGURpo1KwIa2HQxHQoVUc5SIcpmo9p14Su/uRly3efxntnwjAhaF5rTmznbJwsdB2F/rfgwbIhm1kBtIat2dbTeJTdKXMKRX6xvacxYjs+tYEHz/o03XfHehp72Fl8uKFZZnNZ/NZX6Q70a3Pez6ptuxfjrCHK29cmLEfHI9nz3Na6ChijU9BX/5j6xOoEoVGzAqyFQRODNqtSrkHF1DmOpcTXJRgh1rkR1hLZzQQMBMOGoPnO9LpOE9A2bBnCPNp5kllAl2JoUwgjkgY0L2TI+JUyLKkyTlxGjvd+Ed66oExwDnzuu2tdTAIvTbf/sbTX0MxunYtFyJvaS5Mr9KxX+GWtjIN40pv1OEhGRicDj9GD6h+kgkbNzOkYNBdfk8Wg8e54p7K4blKdR9w9Mc60WUN1YmaDUtyryHqP9FjYt7sWmEeQOmFAH1F8vTV9DBuTt0YThtYxbHKEKwHpwnW815xs039SXqPCM5wyaTihyaBJ6KWxGzSBXppYRk0QUMMn4UNoX1cnWs4E5Us97hh+i/c+TUQvAaajm3i2l7mFxdGomTFDGTRTJXr2x9ejESBJ3GHuxcupmFLHFDUwJRkIhrkHQeVzDSyusAybt0Z3uaTKoiM1cig+4XY2ZTB0Rj+hARZ9TpvcPgZNgjA8o0HT894EeSAAt5fGVdYAeYcLg03wvEXU8ZRSOWcde1Il5Yi8X9PRQUgFjZqZsrl101obNPNj/DC05j45QtAsim0ug820KD7Hizg915oYj0k4E2xXcO2KYJzX1VCO1IaNDzkWzaf4BJfDNlOdKUQ0yqAJ99K4DTCl2WY51nm8WShfAAAgAElEQVRBj2crJkFAjoQfpix2uWbwrYZqhmd0xJcg65mOgUfyQ6Nmpuy+6uIZGzQT6GSSdaoTKIuOqbi4UxJbptz3wncRsuscPqEnOiNOq0S4DZtw5XEAw8ZXyU1t2AxNoEGTxEvT16DxuYSpXbnCzsoyG5V827MR2MZyrR2yX3SifdcYTLZMGcIMR2OieskA0KiZKXsP3Dhzg2bAxXmp6D3wjRS7vLj6EJ1tzELdwHAdyzZ3GXveg1zemhBi1lRZwh3SvPPEYdjkCGVaxcxAvgZNCJr7HxWeZKlDazuKWSeSoh8f2kvTwV4Gp5cmd4KEARTfXm13zLWei8Mz3qPsSYvW07ChUTNTdm0/fcYGjen7GjKVTjNb6IPnjH4IvrIOqfSm8NYAUUZJ8bsuOULCReW1K+q3N+k1Gx4Sb570XR8S+QnEcw1NIVGPheOAX7tMGHbmRhN2pvPSNA7peQ8aVx/BS9MQwLNP6tEf51ojZb7gBMYZYNh6nCzUsWjUzJTrHn/ZzAyaCTKKm3hCnUzfVLHmHeLO2weXt8YU/tJ7oXVCb021PTAMbbktPAwtDncYWnHl9Apkeo9ND+Mk5TlyeWjal4kxaPrgGXZmPMb1LExlLU1dJo/tg6ylsWIpXx/P6qS8p+OOQ2QcaNTMlB0nnDq2CImYkJIfTAbZUy+oj07ra9ivrSz4DH45ZtBW2VsDBA2OQestot9zYZkBzGHYZFcO+hoyQ5+3RmR632iDJlrBjVTyY8/rOmxQL00P4ztH2/ftp71CZf3k83o/UR+yJJ+hPjJ3aNSQgRjggZvNLMmKdT7awc80g2p5v0lfItNoRjHW2pq+3po++L7jw0fZ6GHYeHtrptgfeNZZr7AzLwXWMywpddhZY1PPFM4mRl9LU2OIPqkmr/t9Q4C+v1qxMWkuY+wgfdRM7gWKrL59oVFDyNhMNfPO1LGUKyhhQDZjp7pSRm9NuY82nXXKkDstfuXKsX6qX2hWwKx1dPrm+DLnePu6fx1kDjvTvJNmuS7Mv70Hi5NbkXeF2dUva/t9DfvqJKycUbZ+bGztw+6rLu59Hho1ZAKsYIc04U7W+D6XPi+3c3lrhlqk6u298Dx9mddJyxDemoCsSCsZhgb4lzdFqGFISGUS76LHCzaB4RMDGM7lRYqws9hsYJm8NNZ+wAcfT+IQyVpCn0NXG9AkfCiuMmHjbMJjczzzLlP1Ivm9B27sfS4aNaTomLLOpEz4gYtec2Kh6jStis/A60yGmAGMSP+bk77v9qgrMUEKTR9vje4euhS8OYeh+TKl9tv3mFCSehJnGnam2+YpY18vTXQ/0HftUB98n72x22+W59oyCVUfm3tfZSCjNAkZ67mn/lgZNF+8ZS92bT+9tzg0atadFDHMZIl2dnCA8LJFWEduA9WBbSZfQ6/Zz8VJfMJW+mcGcytHvjPDEXjW6SDeGs1VfRgkG1q7nnt4OPyFiPQ4+XppesgxbtiZfbt3eVO10wgvjdf9i8nwZj1HHG5ZE3niUjD0mDXGuLwO9Ky3ukFzxom7cN3jL+stEo0aMhHm1ukkljd1WR2dzSjhAa4y9rkHBuM8dTm9FXNvb0aPmd9JeWv8wtB6sY6TLoN4aXIpuwm8NCHl932uPJ/XJBMu9UuYJlH61HHOPjUSZ5+bPAta6nF3wnrH3OS10DZorr/4miRZfWnUkCbrqDgMQWSGMCch9WWcRU2c9jg0+9LQ9GzjdmXH7K1JrSQVJ+0qR9nf09OD3vfAZ8Y/tD2nUPxCz+HjpcnaTjMQ0Ma0yQEytMXFPXB5aUzHRV94TG95ouyDIaR4CW6QAbvOmd0mQqKQs8qgSfXeRRo1q8iYCmNOVrVcuYjpdHLHOOccYAb0XHQXXyf01tiOCw1Dy/menjG8NcB6TLzkfKfHIF4aDWVbTBJ2Zntmenpp4g2aROuHUuEKje3LZMaXFWYFdZ6NrX04+/Lzshg0AI0aEkBcKE9IZ7riMyoRCkPyGcOMoVkNIhfi5mDw2P3c3prYMLSJeGs65Fi0W2fs8jlkSJqcZCwCUjhrj8mQCSzWSxN2kR5r3RLSN5FJKowhdrrvgUT1nWM/F9nJq1+lbre5DRqARg0haZSeMTrP7MpayvN7LqKPXW9SEaXg63F16HZvjUeWncxtZpB1UxGZ0AZJDpHQQ9U7nW/nhA7ZUi8aHzzjmeUSudukV/nMfYyrnr3kjzXkfOibsro3metvjAmIJKnZJzBxMnGGMGgAGjVEx5Qe0FWbaZmq8ZMjTMlU1r73oMqWE9BO7cpIfHuPUXaTZAGbrLdmwPadIx17+xKx6Xw9GTctbA5vxQhemkSL/PvVbyJPXETfpr9opr5Xx0DZ3YKh7pCPwPocyqABaNSQyTGhB3cQBlpvUmdKRmtJtELheqfF0O9IsMXsh8RHx76Ys32asb01U8GWdtczNCwsxG1C4X0+TMVL43OvQmStnrmhUvr69jeufivkklMcM4dq87nXDU2aeZS3Y9BctCebQQPQqCEzJldnPum3IcdiGGT6lrUKzTGG6KR+j0sPZcBr8XzEYGxuhx7ZlVIqVn1D73J4ayxvaE+CzVuTyPtYte4k2N66nuj+G+9tDgXQ1ZaGXChfli84O6HzGANWAyZBUoQk98b+LDr776Ar5e5XpkMuHWGSBmoPNrb24awBDRqARg0xMdWOaOhsIEOGZU2FHnXv3Snnfi/PUBmGqku4FHkfb03fNUSm02YKvQtmBTP5TJ5B73nPEKy+a0w6v/l5aQZRJDMnRQBgL69OpD7lnqp+UDFWmF1Kptpfetb90qD58mAGDbDGRo2IPFJElOXzocjznisi7xORA+XnvSJyTmr5V5vIDmnqxkIIuTu01IuRbYTWS2w99koxmmuQ9ihLyvj3vqF3se1i7LeTG387bNgvp1JmqIPEHsJkpH7e+oRguej1rORqo5VXqk//E0imPnWQJB59maqyH0N0vzltXadp0Nx9MIMGAI4c5CrT5gsA3m/YHoSIPAvAKwHcDuB6AN8B8GgA14nIryilXtNH0LVFJH9HqZS7g/HZJwrBIDPmLvnV4SQDhoJYZmbNZbUfFyNIt7wCVQzcnmVd7O9zubr8ujZbbZNtgDqcrrwe7dJeboXRB0nbM65q99TnVAF11rjGgMZ08OJxYNjF38FYwh29Dk8cepfBS+MfSmYJPUs8jsWE2rmPCxEgfyY/PzI9A15Jdrhusc7m1n6cu+dnFgbNuy7agx0DGTQAjRoAeL9S6il9TyIi9wbwchSGzE8qpT5Ybv8BAH8H4JUi8nal1Of7XouQlEQpgQ46CrtNaS2VfP1vgQatSTkNUVqTG68BRmsKA74uv60s2Yx0oFHmVJMSNuU+a1kyk32NE4ZbUK2RebmWLXFIaFB9Zw53jdk3QZa35W/+Ya2DZPEja8s5V1+CvQc2FgbNzhNOG7R1rJAfb3SeCeAIAK+rDBoAUEp9FsDvojAgnzmSbHHYBqg5knRGpee5cs66pkgJujxZovPUybBI3kTQTFvikLygLE0920OQkqUzIIddg+RN5AJqY8KAyCxoRnyzntkSBHid17BtLFyhfDm8NCn6zORraQJCzwad0c/RtyaSP9X4lG0MTRwtMFNiPY1tg2ZoaNSko1o3c4Xmt2rbuQPJsgL4zUp5D0wqYPCZMyFpQzPfi9EyuThmRr2yoCWnZciVbTjJO2t0jDEh4SN36pdw1knpDZri9XxOZ3rppokxXgCbSaE1h54N2A+1Q8+G8MIFkL1PntD4MzqBOof/szttj/Su7TtHM2gAhp8BwL1E5CUA7gzgZhTra96ulL/GIyInATi9/PqJ9u9KqQ0RuRnA3UVku1LqQF+hD912q32Hlvj1waV+rPM8rhebtR++zv59HkDLTKpp5tNnEPGc4ekMkp19+oZ0GGY7NelQvUM42pt0YmneSq2t045SEB7G0FVqWl4Rn7I2yuy8pE7UmjHRKq9VAdLcG+/L6+pWX2Zn3YaM/Q3xLfWsbdMpnl1HmwbCnl2bQhCyXsL4DBdSaE5kvm55due2yZY5Zb9V+64p76ht29J/9XumNW3c9Uy36zSmHzP1YcD0+uz2/9unWh6oESB0fGqfKG+/5Z2KPVDn6Ndf+eKQ3eVRNpS1rkdefs4bcMrRJ+HQbYdqV23JbDDanPqoBzRqgIeVnzqfFpELlFKf8zxHZdD8m1LqkGGfTQB3AXB3AJ92nVBEPmP46Z4AcNrrftBTNDun/tkPJTkPIYQQQghZXx586dmjXn+dw89uAfAyAGei8NLcGcBZAD4E4P4A3iEiJ3qe6/jyr83MrIydE8JFJYQQQgghhJiYradGRK4GcJ/Aw56klPoIACilPoFuqNi7ReThAN4D4BEAng7gJX1ljUEpdT/d9tKDc9/PPfUG3PXYu1hOYA8/O/VPi9Pf9F//Accddezit82t/ThnzxOw98AGdm3fibed92bs2H5q89Q2V6K3q7S93eSPj1h4q1S3HD99KXaccLeWCH1DOTKF69TKuXlgH3ZffQn2HrhxWR9VOUJCGkzhWLX/h4RkNfb3YN/WJnZfeWFZjtNx3eMvX7arocOxeoSrbG7tw7lXXrAox7UXXlWkq3SuYfEMQWv8ljDsrvZ/BcHm1v5auzod1z3+MuzYfpruBJ4XNglqDk0qzt4jFAtlf/XWn/Hvr7KEdUSEnbX28+qvtDI3t+Xvs+z1u7l1E3ZfdfGyvzr/Ldhx/PfVdk8fhlWcpmfoWf2+KtV5zq+74IpaWtrAcNIRw+02t/Y1+t1rL7jSmV5X32/V/h/aX7dP071g63u3rBtbXzH0V+0T5A2nLK4Q0WdJNX60nvPz37J4zsNDR2HZ7qFPtWW16IztfdvluPyc1+PBlz4KQKlTfs9xrav6hZ99/dabca/XPtAgux+zNWoA3APAvQOPOda1g1LqDhH5AxRGzWPgZ9Qc9Dh/VctbHudzcuxRx+K4o44z7+BqoJVQtfNsbO3DuW994jJ7xYVXF4u9QtbV+CoKprSsmisZ9zF0NBsHNrvlOL6p6Bhl6BOf3vdlia3OcmNrHx6352ew98CNzWwitgWIJiWwLVvQIAksyhmRPWjjwCYeVyo6Z5y4C9dffC12bt8B2+BhVwwCNANNOYOMuNrxNx7Yh5++6qJFOd55yduw88SdRpn0g5/OePE15jzK7VHPN27dVGtXu3D9xdeU9bGU3Hg+Fz7ZwBIZNRtb+3DuNT/r7K+mbtR0+t0LrjIvso3ps2xKUa/61fRXVz9h0a7eddHVzf6qc7zmuw7dM1z7f5J+q5YCfePAZuM5v/6S65bPh678QUZNpr6rsX9lCOxr9LvvvORtrefccNnU/Zbue0dmzTaplSNXf9WRLXASNcCo6TzntcX0vfoob12quJJxP8/1NDo98ZSjT1rsdpxGN/VeU3PUGq+pUUo9IOPpq7U0mqkyLTeWf08WkeMM62qqp/DLvSTLxMbWPpx1xfnLFyZVCkIoSsV1LImwlsNnQKnJn+P9Lb5sbO3D2Zc9Dl+8ZW9TQagjEjZI+lK9oDFBXW4c2MSj3nLOohzXX3JdrRwCQOUrh4myXJ0XUi7Kq9AePIpy7F6UY6EgBMtdnT+CBPdpY2sfzr78/GV9XHyN/Tkf8nkOKN/G1j6cdeXjzc+5j8yd/br17nGS7jm9DlsqCI3+ymbQ6Biyr3UYmPX+qniDuGYiKZRe5Qs4tqqPVn9VPOe6+gjwWgxB26A5sImzLzu321/1RTMxkZRWfTffTO/RX2XGKylR5yBJp1+NjKkc9aQAY7POa2psnFz+9aoppdQ3sTRsfqT9u4jsRJEk4MspMp+lxvnADd1BN/AfmJodYOKOw6cTT3SfigGpqSDsOMFzQOqhBIS+P8O1v9agiR1YTbO1uSlnbs/WGTS5sJXVVnbHfVk+H3XFU2fw91PUjeeJQVMmq0HjlNH1HIfI7NjXca1gRSdJu09ft7r+6nRfgyakPWfuA3QGzenbRxoHe5S1bdCE9LuhSnuvNPHe/dWXu/1VKrwmOvsZcOkMmsRtPqQ9l57YORhmNGr0XFD+vSHgmLeVfy/U/FZtuzZaop6YPA6bWzf1b6jWh97w4CQeILwMmlQDoU0B7ElHQdDNTLkGvBBF2Bt9OU2DWjKDxiMVZh6WM7d1g6aXYea8omeZRPQf3X4lvRUE1/Ma8nvou2pq5fMyaELahjHk1VYew+8BfdrGQUO/q6tLU/12pDKEsKSsuxY6g6Z4g3jAc+vbnnWiJVL0nAZN66pZ6dHnJTNoGmSYnHD1Vwdv6vZX23f2l8NKZJigBetzngvf0LPGTwknYGzhvwOwtkaNiDyr9KDUt4mIPA3Ar6JoAX+iOe6fy0+7Rv8ngDsA/IKInFnb/14Angfg9nKfYfB58RqAc/Y8McqgSfIiP6Wan1DKMhYKW+2BsylsgYNmMD06Qy+DxpdU5fSIGW63hRQGjddLKV1l6xlfbVIQfNp+0POhe1bbhmtoWdoGTW1A8jNoDPVu+vgcHyh3m42DN6U1aJwow6cf3hMwrnqPLmuauvXur/pOsrTvg1Yh9ldlBKqzBtNp0ASu9zJfPLDP0uxvUhJtBo2rP/J690pfRd9zTGo/H1k8NEA+z1tZRqNB0xbDNBnhS189qkVnPE/toYkpYwCzXVOTgGcBeLmI3ADgSwCORpHK+R4ADgN4hlLq45rjquQER9U3KqX+RUR+A8ArAPytiLwTwHcBPBrAMeX5Pp+lJD1YLEJPGqqVIlbdQCvuvlgjUIXUtAyaRA/4otOp1l5U516UsVW+kJj+qhweCoKCLDuc+n0YbE1KrZytMkpN0XlUH8+GbOvOGtnK6qsguZSiet1CVx/XNsoRZLQEePaMdaz7rkMXk35F0+B3ZT9qyhrz3CZQflTEwBqgADZ38nxeXedw7QLB5tZm06C5aI8+iYmL3sZbv7o19lemGV/TcxvYnuvf3XVr7q+A4hnW91c2g0an7GvKPESfFdBfAc0+q9HPtMkYiWCk7oltTMC8NdyjHDj2lhv0v7kWzxuw9lcx9d8bD6NVQ9aQ/kyss1HzhygMjvsBuC8KI2U/gL8G8Cql1EdDT6iUeqWIfB7Ab6DIngYAHwPwUqXUdUmkTsBHv/LJxf93bT89TUNtKYWaHZDMsClpGjSaxamDKfuA1rCpZNDubvEIVNldfAb89nVClQQTzvpEZwDxCtWKVfDrhNZrVEy6RkFw1WmbFGtMYhUimAwai4GpVQhCn9vwGV5tPbfaVYyCMFaSDxPLJA0Wj7LtXvUpa8K69fXQdOpV99yGPJtRSl7LsKmdx9lfpRw7EvVZunruJGmo91eG8/gZNAZ81phEjEfeEzCudmAzbFJ6lQ2EGDTD9FGO8hnqc44GDbDGRo1S6tUAXh1xnLUVKqWuxYhrZ2woCD6y/2N47JXLZT9vO/9Sd0ONDePJuJZDn23ntO7+ORVgH4+U49pGg6Yh09J74VQUeoQpGXF5pVDWR04FwVZO3bk94tL9FARNfYTIUbua8RwuIozzTuiDzqDxDgOoX7vd3l2H+s1smwxYnaLjM7Ca13QYytynvzLVTe25bU/AaLMZAlEyaMuqm5QwGjYdIYy/Ow2alqdV219VsoSQsl+ukn/oQrV8n2HDfk5Pq+34iLU0zv7KdyLGpez71FcPo9U5ARMasuTdvtL2y0k8NFZZE+hUHmXsGDSRHuUxJpbW1qhZRz68/+N45GXn4nZ1x2LbjhNOtQ80WVygvufUz/qaDRrLrK9vp+wUKZ1HymrQWOT1mgH1IXrWqFnGbjmu7b6fonN8/aun7KZ26lNvzlj2bRYFwdJunbJ7eC5aSr7Lc+GiG5P+1oCQMxeedRXRHqv2twhl1A2slnL0HkBDJgcCyqfzKO844bRaq4oz+KPK66XkGiaSItf8tevVfX03nbK3+2WtYVNI0bu/aqMLmV38lq/PCuqvgp9Hv/1TjEX9Pcqxyn6cB6NBrbzJDZre3tW4fsXa75p0xKR6Yj9o1KwJhUGzG7cfvh1HyhENw2ZBQMPsF6vu82B6emguvgY7j/d4nVCk4ZYrrMPLQxNCyAxozNoDg5KwcWBfV0EwliN9aJJ5ljysTr08NEaPhe533c/+g4w15t2Cd5Yzm8cCiB+EE6AgrVCtriEQRchsb6KybBzcrwmR7a6VG5xA726QQWNQ8rXGTQTe90vTljcObHg858VVnOd1yGc14nr2Wx1DWVsfoZOI9WM8ZK3JFeuN03mUd0Y952kmTO1HmMOhjQZNlvDYeP3JGTniCpGdkPFiYm2zn60TDYNm25F4+wVXjC0SiofO9umiHVh9DJo61axC/WOUUPNbUMiOrjyqHFgtBo3nomMtuvI5yhtbTr1hVoUAetRpQGiSEc8yVufRGjQH90fMQPu12WJXZa5Ty2ygSV7Tvjdu3VTLAmgwaGSbXxuuZA75eAnqnv3Ur5Xr6WnKnHFHh82jPAi+79dw1GuUh8bSzqp2Xf94iena11ReWzkWng3PZ9mEpqxWeQP6repcFVaDxhhG5vvxwFBW7zKWn2ba5rpB4zlhqv/BUJ585dR6NrbvSGMAeJezZ/uFI+lSBGOtaaSnZsVpGzTvvfg63P8u93YfaKGfsh9HSLad2Fnu9jn8dzZ5pJpnBAzlcIWcGQbrvuXss+7AWg7nheMU/JiyuurRatDErrNItdi22tWjLToNgRGU+g5jGDQjlVvr+bNlB8uFT8IPC8Zy+LLIFmkvdzIFqLpOq8x+nlif81uebYeHCojzUlkNGluyiZxKtaasIZ44U4iTsR2Y1ogBUaGU3d3ivOihIbL1c/hfMHIc8kW2FROttqRLM4JGzQqjM2gecrcH4dbbDkadbyzLOzh9aCTO8vVUkIyGgK1DnWAZgw2aREp+0vZnWkMTko0qlsR1alR0ZmLIVPQyaKZQ1hJruzJOGGQ0durnDrhPvQ2aOo31LgMYdjUl2GxgZni2HUZcnz7M+HyYDNfIRfveRBpxsYaAs5w5FH9LGb2yGYZiK2Mmw8bUrpYBfRmek4zQqFlRPnzTR7UGTZZFqbkUCl1SgAwGTW9Fv09Mek+DxjQ7Fjx4etZhlKfJxpAz1/X3OqRU2HzIVE6/2PoBiSyn1aCZkMHiIrpdeYUEJmhDnXdA6a+b9fnozLhn6gPU4fGej8RGnNPgN3ioGoSs4/FO3hJmxJkMAf91UhbPY98soDavVHXtEufrJPowoPHmEyIbGyXReMdf44T9vMcuaNTMlJu/9a/G3z7+1U/h/KufiDtwB47AEbjqcX+Ne5x4Or5269chULj1tlsX+x6q/d9EtEHTM0Xq5tZN2H3Vxdh74Ebs2n46rjn/zTjl6JNw6Ltb1sN9H0CvjtTn4XOUc/PAPn05bjtkEGwcRd+FuT4iPH9DlVFTPmM5TPURS+Yybh78yjDlsJGgjJtb+7H76kua5TjmFBy6/VsJBIzAt0yttjVYu2qTuJ2N3q4SlafTrs67FKfcafuwz0ebiLJpy+Gqj7EmAizl65bjzTj56JNx0EMH0eJbRl89xDOiwFQfVTmS6R8JJlP1F3boV4Z2ZStXXY/8+q0349BRxffQEHebXuuLqBwuWJINETmAI3ACThlbEkIIIYQQQhLxDeD4Y47D1tbBKKuNRs3MEJGvADgWwMbYssyEe5Z/vzCqFOsB7/Uw8D4PB+/1cPBeDwfv9XDwXoexE8CtSqnvizmYRg1ZaUTkMwCglLrf2LKsOrzXw8D7PBy818PBez0cvNfDwXs9LPNZgUkIIYQQQgghGmjUEEIIIYQQQmYNjRpCCCGEEELIrKFRQwghhBBCCJk1NGoIIYQQQgghs4bZzwghhBBCCCGzhp4aQgghhBBCyKyhUUMIIYQQQgiZNTRqCCGEEEIIIbOGRg0hhBBCCCFk1tCoIYQQQgghhMwaGjWEEEIIIYSQWUOjhhBCCCGEEDJraNQQQgghhBBCZg2NGrJyiMhxIvJzIvJqEfmwiHxHRJSIvMBxnHJ8jh6oCLMh9l6Xx+4QkTeIyE0i8m0R+ayI/A7vcxgi8khHu/3Q2DLODRE5RkReWLbJb5dt9PUictrYsq0SIvJeR9t97NgyzgkReZCI/HcRuUpENqv76HHcU0TkIyJyUES+ISL/R0QeNoTMcyX0XovICxxt/feHlH9VOXJsAQjJwL0AvCny2EMArjD8dkfkOVeZqHstIt8P4IMA7gLgHwD8LYAfBfBbAM4SkbOUUt9JKega8AUA7zdsJ56URvW7AZwJYD+AtwLYBeDnAewWkTOVUl8cT8KV5EoABzXb9w0tyMz5TQA/HXKAiPwRgGcC+BaAdwA4GsCjADxaRC5USu1JLuVqEHyvSz4A4POa7R/vJw4BaNSQ1WQLwF8C+Gj5OQfACz2PvVkp9ZRMcq0isff6jSgMmlcppZ4JACJyJIDLAJwP4LkAXpBe3JXm/Wy7SXg+CoPmgwAerZQ6CAAi8mwAfwjg9QAeOZp0q8mvK6X2ji3ECvBBAH+PZX+8F8CdTDuLyNkoDJp/BfBQpdTnyu0PBfBeAG8Qkfcqpb6ZV+xZEnSva/yFUuqN+cRab2jUkJVDKfUFAP+l+i4ijx5RnJUm5l6LyI8B+HEAXwPwnNq5bheRXwSwG8AzROTFSqnb00tNiB4R+R4Av1x+/aXKoAEApdQrROTJAH5CRB6klOLMKpkUSqk/qH8XEdchzy7/vrgyaMrzfFBEXgfgGQCeisKYJzUi7jUZAK6pIYQMzTnl32vbIWZKqa+iCEU7GcDDhxaMrD0/DuBEAF9QSn1C83sVmnrucCIRkh4ROQbAT5VfdSHXbOtkdtBTQ0iT40TkeQBOB3ArgE8AuKo+Y0t68+/LvzcYfr8BxWD7wyhCIIgf9xKRlwC4M4CbUayvebtS6vC4Ys0Kn7YJFG2TpOOpInJnAIcBfBbAHqXUjSPLtOrcG0W41FM1dQMAAAi8SURBVNeVUpua39nW8/BTIvIAFGuXNgH8X3p900GjhpAmdwHw4ta2V4jIk5VSbxtDoBXk9PKvbiCtb7/7ALKsEg8rP3U+LSIX1ENLiBW2zXF4fuv7y0XkRUqpF40izXpgbetKqUMi8k0AJ4vICUqpreFEW2l+rvX9RSJyJYCncPK0Pww/I2TJmwA8FsBpAI4H8CMA/grFzPdVIvLgEWVbJY4v/95q+P1Q+feEAWRZBW4B8DIUi9vvXH7OAvAhAPcH8A4ROXE88WYF2+aw/D8USt49ARyLwnvwPAC3A3ihiDxzRNlWHVdbB9jeU/J5AL8O4H4o7v1OAD+LIsPfBSh0DdITemrI5BCRqwHcJ/CwJymlPtLnukqpJ7c2fRLAk0RkA8D/QOHBeUyfa0yNse41WdK3Dsq1H+31H+8WkYcDeA+ARwB4OoCX9JWVkJQopX6rtemzAH5PRD4G4G8AvEBE/kwp9a3hpSMkHUqpv25tOgTgf4vIewB8GsB5Zbp4vlesBzRqyBS5B4oZuxCOzSFIyUsB/DcAjxSR71FKfTfjtYZmjHtdudhN5zmu/Lsu4Q5Z6kApdYeI/AEKo+YxoFHjA9vmBFBKvaM0bH4UwEPAtXU5cLV1gO09O0qp/SLyBhRenMei8LCTSGjUkMmhlHrA2DLUUUrdIiJfA3A3FKE9+0cWKRkj3esbUYT27TD8Xm3/8jDijEvmOqjW0twt4zVWiWpxOtvm+HwOhVHDtpsHa1sXkeMAnATg37ieJjvspxPBNTWEOBCRbQC2l18P2fYlXnyq/PtAw+/V9r8fQJZV5+TyL9utH2yb04FtNy//AuA7AO4qIqdpfmdbHw629UTQqCHEzWNRuOG/oJQ6MLYwK0CVRe5cEWm8gVlE/h2KcKl/A/CBoQVbQS4o/5pSFJMmH0CReOGeZdrVNheWf68dTqT1Q0TuiqIfANh2s1CuU3p3+fUizS5s6wMgxVs7zy+/sq33hEYNIQBE5Am67GYi8hMA/rz8+tphpVpNygXuHwDwvQAWb2UWkSMB/DGAowC8Sil12zgSzgsReZaI7GxtExF5GoBfBaAA/Mkows2Mcr3ca8qvry1DcAAAIvJsFO/seB/fK9EfEXmYiJwnIke0tu8CcDWKiaRrDO9QIWl4Rfn3+SJyr2qjiDwUwNMAfBPAX44h2CohIncVkV8SkRNa249H0Tc/BMBXAFw1hnyrhCilxpaBkOSUGaWq+NRTUaRP3IdlTv79Sqnza/u/EcCTUWTf+QyA2wD8AIBqtvbNAH6WLzLsEnqvy2PuBeCDKNYofRrAPwJ4MIAzAPwdgJ9SSn0nv/TzR0T2ooiLvwHAl1C81O3+KBIQHAbwTKXUa4wnIA1E5GgUC9MfgmL93N+ieC/NQwB8HcCZSqkvjibgiiAiTwHwBhTK3A0oFOi7A3gQijb8GRT9wNfGknFuiMg5AH6ztunHAAiAD9e2vaj+zjUR+SMAz0SR2vmdAL4HwKPK4y5USu3JLfccCbnXpaH+JRTJGT6Kol+5K4oQvzujaPu7lVKMTugJjRqykpSKnu0FeV9WSu2q7f8fUeSMfxCA70ORR/4bAD4O4PVKqSuyCTtzQu917bidAF6IIrzvFBQLVy8F8HtKqW+nl3Q1EZFfAfBoFO8/+F4Unq5KGX+VUuqjI4o3S0TkGADPBfBEFEb6NwC8HcBv0nOQBhG5D4BfQWEs7kSxruAQgH8CcDmAP2Eq5zBqhqKNn1dKvVFz3C+jSC3/XRQZuF6klPq79FKuBiH3uvTQPA/Fu8S+H8VLvu9AYei8HcArlVL7Moq7NtCoIYQQQgghhMwarqkhhBBCCCGEzBoaNYQQQgghhJBZQ6OGEEIIIYQQMmto1BBCCCGEEEJmDY0aQgghhBBCyKyhUUMIIYQQQgiZNTRqCCGEEEIIIbOGRg0hhBBCCCFk1tCoIYQQQgghhMwaGjWEEEIIIYSQWUOjhhBCCCGEEDJraNQQQgghAETkSSKiROTTInKUYZ8zReQOEblZRO46tIyEEEL00KghhBBCACil3gTgegA/BOA57d9LQ+fPUYydv6aU+vqwEhJCCDEhSqmxZSCEEEImgYicAeAfAAiAH1ZKfa722/MBvAjA9UqpR40kIiGEEA00agghhJAaIvIbAF4K4L1KqZ8st90bwKcAHAZwf6XUF0YUkRBCSAuGnxFCCCFNXgngEwAeKSJPFREB8GcA7gTgBTRoCCFketBTQwghhLQQkQcB+DCAAyiMnBcC+CSAByulbh9TNkIIIV1o1BBCCCEaROTlAH6t/HoHgDOVUh8bUSRCCCEGaNQQQgghGkTkVACbKJIGvF4p9dSRRSKEEGKAa2oIIYQQPb+DwqABgMeIyAljCkMIIcQMjRpCCCGkhYj8BwBPBbAfwB4ApwH43VGFIoQQYoThZ4QQQkgNEbkTivTN9wZwIYD3A/gnACeiWFfz0RHFI4QQooGeGkIIIaTJ81EYNNcopa5USn0VwHNQjJl/LiJHjiodIYSQDvTUEEIIISUi8kMAbgDwbQD3VUptltsFwPsAPALAc5RSLxtPSkIIIW1o1BBCCCEARGQbgA8AOBPAM5RSr279fh8U76q5HcD9lFJ7BxeSEEKIFoafEUIIIQVPR2HQfBjAa9s/KqX+CcDvAzgWwB8PKxohhBAb9NQQQghZe0RkB4B/BHAMgAcqpT5t2O9OAP4ewA8AeIJS6i3DSUkIIcQEjRpCCCGEEELIrGH4GSGEEEIIIWTW0KghhBBCCCGEzBoaNYQQQgghhJBZQ6OGEEIIIYQQMmto1BBCCCGEEEJmDY0aQgghhBBCyKyhUUMIIYQQQgiZNTRqCCGEEEIIIbOGRg0hhBBCCCFk1tCoIYQQQgghhMwaGjWEEEIIIYSQWUOjhhBCCCGEEDJraNQQQgghhBBCZg2NGkIIIYQQQsisoVFDCCGEEEIImTU0agghhBBCCCGzhkYNIYQQQgghZNb8fyKIwaBRbm0PAAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "f = plt.figure(dpi=150)\n", "sim.plot2D(ax=f.gca(), fields=mp.Hz)\n", diff --git a/python/examples/holey-wvg-cavity.py b/python/examples/holey-wvg-cavity.py index 0868401e4..745b64a41 100644 --- a/python/examples/holey-wvg-cavity.py +++ b/python/examples/holey-wvg-cavity.py @@ -1,15 +1,12 @@ # Meep Tutorial: Hz-polarized transmission and reflection through a cavity # formed by a periodic sequence of holes in a dielectric waveguide, # with a defect formed by a larger spacing between one pair of holes. - # This structure is based on one analyzed in: # S. Fan, J. N. Winn, A. Devenyi, J. C. Chen, R. D. Meade, and # J. D. Joannopoulos, "Guided and defect modes in periodic dielectric # waveguides," J. Opt. Soc. Am. B, 12 (7), 1267-1272 (1995). - -from __future__ import division - import argparse + import meep as mp diff --git a/python/examples/material-dispersion.py b/python/examples/material-dispersion.py index 860a81886..ecab6cb87 100644 --- a/python/examples/material-dispersion.py +++ b/python/examples/material-dispersion.py @@ -2,11 +2,8 @@ # simulate homogenous space filled with a dispersive material, and compute # its modes as a function of wavevector k. Since omega/c = k/n, we can # extract the dielectric function epsilon(omega) = (ck/omega)^2. -from __future__ import division - import meep as mp - cell = mp.Vector3() resolution = 20 @@ -48,4 +45,4 @@ for fs, kx in zip(all_freqs, [v.x for v in kpts]): for f in fs: - print("eps:, {:.6g}, {:.6g}, {:.6g}".format(f.real, f.imag, (kx / f) ** 2)) + print(f"eps:, {f.real:.6g}, {f.imag:.6g}, {(kx / f) ** 2:.6g}") diff --git a/python/examples/metal-cavity-ldos.py b/python/examples/metal-cavity-ldos.py index 3f67eff78..9da8dda72 100644 --- a/python/examples/metal-cavity-ldos.py +++ b/python/examples/metal-cavity-ldos.py @@ -1,9 +1,9 @@ -from __future__ import division - import math -import meep as mp -import numpy as np + import matplotlib.pyplot as plt +import numpy as np + +import meep as mp def metal_cavity(w): diff --git a/python/examples/metasurface_lens.ipynb b/python/examples/metasurface_lens.ipynb index 7fb38949a..e42fc948f 100644 --- a/python/examples/metasurface_lens.ipynb +++ b/python/examples/metasurface_lens.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -194,940 +194,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00247622 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00647688 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00136495 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.03,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0103281 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000896931 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.0044961 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00125909 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.0382759,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0100789 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00088501 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00477791 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.0013721 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.0465517,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.00952506 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00219703 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00522304 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00136685 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.0548276,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0103021 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00116801 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00504303 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00156713 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.0631034,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.010608 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00124407 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00682497 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000928879 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.0713793,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.012069 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000815868 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00467396 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000994921 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.0796552,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.00999308 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000801086 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00456905 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00162697 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.087931,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0103731 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.0009408 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00609112 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00112414 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.0962069,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0113389 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00133109 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.004987 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000950098 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.104483,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.00973797 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00119305 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00534606 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00095892 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.112759,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0165179 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000835896 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00442696 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.001477 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.121034,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0124168 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00136709 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00486684 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000990152 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.12931,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0103049 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.001513 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.005023 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000887156 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.137586,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0100081 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000860929 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.004879 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000782967 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.145862,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0097518 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000844002 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00591779 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00101209 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.154138,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0102739 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000947952 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00474691 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000870943 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.162414,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.00949287 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000854969 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00593901 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00108814 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.17069,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0101011 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000795126 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00502586 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00118899 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.178966,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0093739 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00110197 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00501704 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00141692 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.187241,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.013217 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00104403 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00458407 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000869989 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.195517,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.00961399 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00112414 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.0046351 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000927925 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.203793,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.009727 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00181007 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00469708 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00114608 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.212069,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0103791 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00141215 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00565505 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00124907 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.220345,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.011498 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00082016 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00524902 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000906944 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.228621,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0162721 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00110412 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00537992 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00147796 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.236897,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.010345 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00085187 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00499296 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000822783 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.245172,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0116842 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000720978 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00519085 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000974894 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.253448,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.012033 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000780106 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00697684 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00137496 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.261724,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0110409 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000945091 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - "time for set_epsilon = 0.00621819 s\n", - "-----------\n", - "run 0 finished at t = 75.0 (7500 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000971079 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,0.27,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - "time for set_epsilon = 0.0130391 s\n", - "-----------\n", - "run 0 finished at t = 225.0 (22500 timesteps)\n", - "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", - "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n" - ] - } - ], + "outputs": [], "source": [ "gp = 0.3 # grating periodicity\n", "gh = 1.8 # grating height\n", @@ -1141,22 +210,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(dpi=200)\n", "plt.subplot(1, 2, 1)\n", @@ -1193,339 +249,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "phase-range:, 6.086174\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00169206 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 60.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-30,0)\n", - " size (1.8,0.109864,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-29.7,0)\n", - " size (1.8,0.123415,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-29.4,0)\n", - " size (1.8,0.136671,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-29.1,0)\n", - " size (1.8,0.152579,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-28.8,0)\n", - " size (1.8,0.169076,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-28.5,0)\n", - " size (1.8,0.186752,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-28.2,0)\n", - " size (1.8,0.0500621,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-27.9,0)\n", - " size (1.8,0.059489,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-27.6,0)\n", - " size (1.8,0.0692104,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " ...(+ 192 objects not shown)...\n", - "time for set_epsilon = 1.79243 s\n", - "-----------\n", - "Meep progress: 12.530000000000001/125.0 = 10.0% done in 4.0s, 35.9s to go\n", - "on time step 1253 (time=12.53), 0.00319322 s/step\n", - "Meep progress: 24.92/125.0 = 19.9% done in 8.0s, 32.1s to go\n", - "on time step 2492 (time=24.92), 0.00322858 s/step\n", - "Meep progress: 37.88/125.0 = 30.3% done in 12.0s, 27.6s to go\n", - "on time step 3788 (time=37.88), 0.00308826 s/step\n", - "Meep progress: 50.44/125.0 = 40.4% done in 16.0s, 23.7s to go\n", - "on time step 5045 (time=50.45), 0.0031845 s/step\n", - "Meep progress: 62.02/125.0 = 49.6% done in 20.0s, 20.3s to go\n", - "on time step 6203 (time=62.03), 0.00345439 s/step\n", - "Meep progress: 73.87/125.0 = 59.1% done in 24.0s, 16.6s to go\n", - "on time step 7388 (time=73.88), 0.00337615 s/step\n", - "Meep progress: 84.91/125.0 = 67.9% done in 28.0s, 13.2s to go\n", - "on time step 8492 (time=84.92), 0.0036253 s/step\n", - "Meep progress: 94.47/125.0 = 75.6% done in 32.0s, 10.3s to go\n", - "on time step 9449 (time=94.49), 0.00418097 s/step\n", - "Meep progress: 105.32000000000001/125.0 = 84.3% done in 36.0s, 6.7s to go\n", - "on time step 10534 (time=105.34), 0.00368885 s/step\n", - "Meep progress: 117.42/125.0 = 93.9% done in 40.0s, 2.6s to go\n", - "on time step 11744 (time=117.44), 0.00330608 s/step\n", - "run 0 finished at t = 125.0 (12500 timesteps)\n", - "get_farfields_array working on point 996 of 1000 (99% done), 0.00401804 s/point\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00165105 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 120.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-60,0)\n", - " size (1.8,0.09101,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-59.7,0)\n", - " size (1.8,0.115166,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-59.4,0)\n", - " size (1.8,0.141974,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-59.1,0)\n", - " size (1.8,0.174379,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-58.8,0)\n", - " size (1.8,0.0533026,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-58.5,0)\n", - " size (1.8,0.0730401,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-58.2,0)\n", - " size (1.8,0.0933667,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-57.9,0)\n", - " size (1.8,0.117523,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-57.6,0)\n", - " size (1.8,0.143741,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " ...(+ 392 objects not shown)...\n", - "time for set_epsilon = 3.96254 s\n", - "-----------\n", - "Meep progress: 5.39/125.0 = 4.3% done in 4.0s, 88.8s to go\n", - "on time step 539 (time=5.39), 0.00742406 s/step\n", - "Meep progress: 11.540000000000001/125.0 = 9.2% done in 8.0s, 78.7s to go\n", - "on time step 1154 (time=11.54), 0.00651044 s/step\n", - "Meep progress: 17.45/125.0 = 14.0% done in 12.0s, 74.0s to go\n", - "on time step 1745 (time=17.45), 0.00677692 s/step\n", - "Meep progress: 23.16/125.0 = 18.5% done in 16.0s, 70.4s to go\n", - "on time step 2316 (time=23.16), 0.00700984 s/step\n", - "Meep progress: 29.25/125.0 = 23.4% done in 20.0s, 65.5s to go\n", - "on time step 2925 (time=29.25), 0.00657387 s/step\n", - "Meep progress: 35.27/125.0 = 28.2% done in 24.0s, 61.1s to go\n", - "on time step 3527 (time=35.27), 0.00664574 s/step\n", - "Meep progress: 40.980000000000004/125.0 = 32.8% done in 28.0s, 57.5s to go\n", - "on time step 4098 (time=40.98), 0.00700592 s/step\n", - "Meep progress: 46.58/125.0 = 37.3% done in 32.0s, 53.9s to go\n", - "on time step 4658 (time=46.58), 0.00714546 s/step\n", - "Meep progress: 52.46/125.0 = 42.0% done in 36.0s, 49.8s to go\n", - "on time step 5246 (time=52.46), 0.00680792 s/step\n", - "Meep progress: 58.76/125.0 = 47.0% done in 40.0s, 45.1s to go\n", - "on time step 5876 (time=58.76), 0.00635169 s/step\n", - "Meep progress: 64.7/125.0 = 51.8% done in 44.0s, 41.0s to go\n", - "on time step 6470 (time=64.7), 0.00673928 s/step\n", - "Meep progress: 69.12/125.0 = 55.3% done in 48.0s, 38.8s to go\n", - "on time step 6912 (time=69.12), 0.00905871 s/step\n", - "Meep progress: 73.92/125.0 = 59.1% done in 52.0s, 36.0s to go\n", - "on time step 7392 (time=73.92), 0.00833534 s/step\n", - "Meep progress: 79.69/125.0 = 63.8% done in 56.0s, 31.9s to go\n", - "on time step 7969 (time=79.69), 0.00694212 s/step\n", - "Meep progress: 85.4/125.0 = 68.3% done in 60.0s, 27.8s to go\n", - "on time step 8540 (time=85.4), 0.00700793 s/step\n", - "Meep progress: 91.31/125.0 = 73.0% done in 64.0s, 23.6s to go\n", - "on time step 9131 (time=91.31), 0.0067687 s/step\n", - "Meep progress: 96.35000000000001/125.0 = 77.1% done in 68.1s, 20.2s to go\n", - "on time step 9635 (time=96.35), 0.00797192 s/step\n", - "Meep progress: 99.73/125.0 = 79.8% done in 72.1s, 18.3s to go\n", - "on time step 9973 (time=99.73), 0.0118546 s/step\n", - "Meep progress: 104.79/125.0 = 83.8% done in 76.1s, 14.7s to go\n", - "on time step 10479 (time=104.79), 0.00790892 s/step\n", - "Meep progress: 109.58/125.0 = 87.7% done in 80.1s, 11.3s to go\n", - "on time step 10958 (time=109.58), 0.00836883 s/step\n", - "Meep progress: 114.53/125.0 = 91.6% done in 84.1s, 7.7s to go\n", - "on time step 11453 (time=114.53), 0.00808508 s/step\n", - "Meep progress: 120.01/125.0 = 96.0% done in 88.1s, 3.7s to go\n", - "on time step 12001 (time=120.01), 0.0073038 s/step\n", - "run 0 finished at t = 125.0 (12500 timesteps)\n", - "get_farfields_array working on point 446 of 1000 (44% done), 0.00898336 s/point\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "get_farfields_array working on point 897 of 1000 (89% done), 0.00887155 s/point\n", - "-----------\n", - "Initializing structure...\n", - "Padding y to even number of grid points.\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000861883 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.8 x 240.3 x 0 with resolution 50\n", - " block, center = (-2.4,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-120,0)\n", - " size (1.8,0.109569,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-119.7,0)\n", - " size (1.8,0.161122,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-119.4,0)\n", - " size (1.8,0.0609619,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-119.1,0)\n", - " size (1.8,0.0983747,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-118.8,0)\n", - " size (1.8,0.145804,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-118.5,0)\n", - " size (1.8,0.0518297,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-118.2,0)\n", - " size (1.8,0.0883587,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-117.9,0)\n", - " size (1.8,0.132253,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " block, center = (1.11022e-16,-117.6,0)\n", - " size (1.8,0.190581,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", - " ...(+ 792 objects not shown)...\n", - "time for set_epsilon = 10.2552 s\n", - "-----------\n", - "Meep progress: 2.2800000000000002/125.0 = 1.8% done in 4.0s, 215.4s to go\n", - "on time step 228 (time=2.28), 0.0175505 s/step\n", - "Meep progress: 5.0600000000000005/125.0 = 4.0% done in 8.0s, 189.8s to go\n", - "on time step 506 (time=5.06), 0.0144061 s/step\n", - "Meep progress: 7.79/125.0 = 6.2% done in 12.0s, 180.8s to go\n", - "on time step 779 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progress: 69.79/125.0 = 55.8% done in 108.3s, 85.6s to go\n", - "on time step 6979 (time=69.79), 0.0198233 s/step\n", - "Meep progress: 72.57000000000001/125.0 = 58.1% done in 112.3s, 81.1s to go\n", - "on time step 7257 (time=72.57), 0.0144427 s/step\n", - "Meep progress: 75.16/125.0 = 60.1% done in 116.3s, 77.1s to go\n", - "on time step 7516 (time=75.16), 0.0154471 s/step\n", - "Meep progress: 77.78/125.0 = 62.2% done in 120.3s, 73.0s to go\n", - "on time step 7778 (time=77.78), 0.0152936 s/step\n", - "Meep progress: 80.57000000000001/125.0 = 64.5% done in 124.3s, 68.5s to go\n", - "on time step 8057 (time=80.57), 0.0143637 s/step\n", - "Meep progress: 83.37/125.0 = 66.7% done in 128.3s, 64.1s to go\n", - "on time step 8337 (time=83.37), 0.0143218 s/step\n", - "Meep progress: 86.07000000000001/125.0 = 68.9% done in 132.3s, 59.8s to go\n", - "on time step 8607 (time=86.07), 0.0148534 s/step\n", - "Meep progress: 88.82000000000001/125.0 = 71.1% done in 136.3s, 55.5s to go\n", - "on time step 8882 (time=88.82), 0.0145593 s/step\n", - "Meep progress: 91.74/125.0 = 73.4% done in 140.3s, 50.9s to go\n", - "on time step 9174 (time=91.74), 0.0137382 s/step\n", - "Meep progress: 94.64/125.0 = 75.7% done in 144.3s, 46.3s to go\n", - "on time step 9464 (time=94.64), 0.0138319 s/step\n", - "Meep progress: 97.55/125.0 = 78.0% done in 148.3s, 41.7s to go\n", - "on time step 9755 (time=97.55), 0.0137471 s/step\n", - "Meep progress: 100.47/125.0 = 80.4% done in 152.4s, 37.2s to go\n", - "on time step 10047 (time=100.47), 0.0137389 s/step\n", - "Meep progress: 103.35000000000001/125.0 = 82.7% done in 156.4s, 32.8s to go\n", - "on time step 10335 (time=103.35), 0.01392 s/step\n", - "Meep progress: 106.25/125.0 = 85.0% done in 160.4s, 28.3s to go\n", - "on time step 10625 (time=106.25), 0.0138042 s/step\n", - "Meep progress: 109.14/125.0 = 87.3% done in 164.4s, 23.9s to go\n", - "on time step 10914 (time=109.14), 0.0138697 s/step\n", - "Meep progress: 112.01/125.0 = 89.6% done in 168.4s, 19.5s to go\n", - "on time step 11201 (time=112.01), 0.0139446 s/step\n", - "Meep progress: 114.66/125.0 = 91.7% done in 172.4s, 15.5s to go\n", - "on time step 11466 (time=114.66), 0.01511 s/step\n", - "Meep progress: 117.28/125.0 = 93.8% done in 176.4s, 11.6s to go\n", - "on time step 11728 (time=117.28), 0.0153079 s/step\n", - "Meep progress: 119.9/125.0 = 95.9% done in 180.4s, 7.7s to go\n", - "on time step 11990 (time=119.9), 0.0152988 s/step\n", - "Meep progress: 122.79/125.0 = 98.2% done in 184.4s, 3.3s to go\n", - "on time step 12279 (time=122.79), 0.0138704 s/step\n", - "run 0 finished at t = 125.0 (12500 timesteps)\n", - "get_farfields_array working on point 236 of 1000 (23% done), 0.0170414 s/point\n", - "get_farfields_array working on point 472 of 1000 (47% done), 0.0170053 s/point\n", - "get_farfields_array working on point 704 of 1000 (70% done), 0.0172601 s/point\n", - "get_farfields_array working on point 937 of 1000 (93% done), 0.0171973 s/point\n" - ] - } - ], + "outputs": [], "source": [ "gdc_new = np.linspace(0.16, 0.65, 500)\n", "mode_phase_interp = np.interp(gdc_new, gdc, mode_phase)\n", @@ -1575,22 +301,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "x = np.linspace(\n", " focal_length - 0.5 * spot_length,\n", diff --git a/python/examples/metasurface_lens.py b/python/examples/metasurface_lens.py index 71451d6f3..b9d55fd0f 100644 --- a/python/examples/metasurface_lens.py +++ b/python/examples/metasurface_lens.py @@ -1,9 +1,7 @@ -# -*- coding: utf-8 -*- - -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np +import meep as mp resolution = 50 # pixels/μm @@ -167,8 +165,8 @@ def grating(gp, gh, gdc_list): gh = 1.8 # grating height gdc = np.linspace(0.1, 0.9, 30) # grating duty cycle -mode_tran = np.empty((gdc.size)) -mode_phase = np.empty((gdc.size)) +mode_tran = np.empty(gdc.size) +mode_phase = np.empty(gdc.size) for n in range(gdc.size): mode_tran[n], mode_phase[n] = grating(gp, gh, [gdc[n]]) @@ -197,7 +195,7 @@ def grating(gp, gh, gdc_list): gdc_new = np.linspace(0.16, 0.65, 500) mode_phase_interp = np.interp(gdc_new, gdc, mode_phase) -print("phase-range:, {:.6f}".format(mode_phase_interp.max() - mode_phase_interp.min())) +print(f"phase-range:, {mode_phase_interp.max() - mode_phase_interp.min():.6f}") phase_tol = 1e-2 num_cells = [100, 200, 400] diff --git a/python/examples/mie_scattering.py b/python/examples/mie_scattering.py index 3dc54cdca..86f9887cb 100644 --- a/python/examples/mie_scattering.py +++ b/python/examples/mie_scattering.py @@ -1,8 +1,9 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np import PyMieScatt as ps +import meep as mp + r = 1.0 # radius of sphere wvl_min = 2 * np.pi * r / 10 diff --git a/python/examples/mode-decomposition.ipynb b/python/examples/mode-decomposition.ipynb index ee0a2f252..5fb11dfc0 100644 --- a/python/examples/mode-decomposition.ipynb +++ b/python/examples/mode-decomposition.ipynb @@ -17,381 +17,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00183296 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 33 x 12 x 0 with resolution 25\n", - " prism, center = (0,0,5e+19)\n", - " height 1e+20, axis (0,0,1), 4 vertices:\n", - " (-17.5,0.5,0)\n", - " (17.5,0.5,0)\n", - " (17.5,-0.5,0)\n", - " (-17.5,-0.5,0)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - "time for set_epsilon = 0.447863 s\n", - "-----------\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "on time step 2379 (time=47.58), 0.00168153 s/step\n", - "field decay(t = 50.02): 7.54740931333862e-08 / 7.54740931333862e-08 = 1.0\n", - "on time step 4877 (time=97.54), 0.00160183 s/step\n", - "field decay(t = 100.04): 0.001735614282175414 / 0.001735614282175414 = 1.0\n", - "on time step 7353 (time=147.06), 0.00161556 s/step\n", - "field decay(t = 150.06): 0.45397875691800166 / 0.45397875691800166 = 1.0\n", - "on time step 9819 (time=196.38), 0.00162256 s/step\n", - "field decay(t = 200.08): 1.5637205312092566 / 1.5637205312092566 = 1.0\n", - "on time step 12317 (time=246.34), 0.0016016 s/step\n", - "field decay(t = 250.1): 1.2378300815914483 / 1.5637205312092566 = 0.7915929073555166\n", - "on time step 14830 (time=296.6), 0.0015922 s/step\n", - "field decay(t = 300.12): 0.031049012038060042 / 1.5637205312092566 = 0.01985585750034836\n", - "on time step 17280 (time=345.6), 0.00163305 s/step\n", - "field decay(t = 350.14): 8.860450697395457e-06 / 1.5637205312092566 = 5.66626230234599e-06\n", - "on time step 19780 (time=395.6), 0.00160038 s/step\n", - "field decay(t = 400.16): 2.7848020268498908e-11 / 1.5637205312092566 = 1.7808821789251224e-11\n", - "run 0 finished at t = 400.16 (20008 timesteps)\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00132203 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 33 x 12 x 0 with resolution 25\n", - " prism, center = (0,0,5e+19)\n", - " height 1e+20, axis (0,0,1), 8 vertices:\n", - " (-17.5,0.5,0)\n", - " (-0.5,0.5,0)\n", - " (0.5,1,0)\n", - " (17.5,1,0)\n", - " (17.5,-1,0)\n", - " (0.5,-1,0)\n", - " (-0.5,-0.5,0)\n", - " (-17.5,-0.5,0)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - "time for set_epsilon = 0.664247 s\n", - "-----------\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "on time step 2337 (time=46.74), 0.00171202 s/step\n", - "field decay(t = 50.02): 7.55116423679346e-08 / 7.55116423679346e-08 = 1.0\n", - "on time step 4808 (time=96.16), 0.00161879 s/step\n", - "field decay(t = 100.04): 0.001737568234822459 / 0.001737568234822459 = 1.0\n", - "on time step 7266 (time=145.32), 0.00162736 s/step\n", - "field decay(t = 150.06): 0.4546391533256169 / 0.4546391533256169 = 1.0\n", - "on time step 9726 (time=194.52), 0.00162628 s/step\n", - "field decay(t = 200.08): 1.5661544172637174 / 1.5661544172637174 = 1.0\n", - "on time step 12196 (time=243.92), 0.00161985 s/step\n", - "field decay(t = 250.1): 1.2398082542830435 / 1.5661544172637174 = 0.7916258068914784\n", - "on time step 14700 (time=294), 0.00159784 s/step\n", - "field decay(t = 300.12): 0.0313140484926616 / 1.5661544172637174 = 0.01999422799405148\n", - "on time step 17203 (time=344.06), 0.00159832 s/step\n", - "field decay(t = 350.14): 9.723724333805377e-06 / 1.5661544172637174 = 6.208662585643395e-06\n", - "on time step 19681 (time=393.62), 0.0016146 s/step\n", - "field decay(t = 400.16): 5.154329670663798e-11 / 1.5661544172637174 = 3.291073736949327e-11\n", - "run 0 finished at t = 400.16 (20008 timesteps)\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 9 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", - "refl:, 1, 0.00033425, 0.00041190\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00108004 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 34 x 12 x 0 with resolution 25\n", - " prism, center = (0,0,5e+19)\n", - " height 1e+20, axis (0,0,1), 4 vertices:\n", - " (-18,0.5,0)\n", - " (18,0.5,0)\n", - " (18,-0.5,0)\n", - " (-18,-0.5,0)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - "time for set_epsilon = 0.462233 s\n", - "-----------\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "on time step 2327 (time=46.54), 0.00171948 s/step\n", - "field decay(t = 50.02): 7.571674211765607e-08 / 7.571674211765607e-08 = 1.0\n", - "on time step 4775 (time=95.5), 0.0016345 s/step\n", - "field decay(t = 100.04): 0.0017406599773709985 / 0.0017406599773709985 = 1.0\n", - "on time step 7173 (time=143.46), 0.00166846 s/step\n", - "field decay(t = 150.06): 0.4552933938871408 / 0.4552933938871408 = 1.0\n", - "on time step 9613 (time=192.26), 0.0016394 s/step\n", - "field decay(t = 200.08): 1.56824628067331 / 1.56824628067331 = 1.0\n", - "on time step 12052 (time=241.04), 0.00164018 s/step\n", - "field decay(t = 250.1): 1.2414144426139568 / 1.56824628067331 = 0.7915940614129616\n", - "on time step 14512 (time=290.24), 0.00162657 s/step\n", - "field decay(t = 300.12): 0.031139198594396233 / 1.56824628067331 = 0.019856064049472478\n", - "on time step 16935 (time=338.7), 0.00165142 s/step\n", - "field decay(t = 350.14): 8.886312411745595e-06 / 1.56824628067331 = 5.666401075684586e-06\n", - "on time step 19391 (time=387.82), 0.00162891 s/step\n", - "field decay(t = 400.16): 2.7609192234474572e-11 / 1.56824628067331 = 1.7605138028843823e-11\n", - "run 0 finished at t = 400.16 (20008 timesteps)\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00079608 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 34 x 12 x 0 with resolution 25\n", - " prism, center = (0,0,5e+19)\n", - " height 1e+20, axis (0,0,1), 8 vertices:\n", - " (-18,0.5,0)\n", - " (-1,0.5,0)\n", - " (1,1,0)\n", - " (18,1,0)\n", - " (18,-1,0)\n", - " (1,-1,0)\n", - " (-1,-0.5,0)\n", - " (-18,-0.5,0)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - "time for set_epsilon = 0.693091 s\n", - "-----------\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "on time step 2308 (time=46.16), 0.00173349 s/step\n", - "field decay(t = 50.02): 7.571396797724319e-08 / 7.571396797724319e-08 = 1.0\n", - "on time step 4692 (time=93.84), 0.00167819 s/step\n", - "field decay(t = 100.04): 0.0017395257849605922 / 0.0017395257849605922 = 1.0\n", - "on time step 7113 (time=142.26), 0.00165247 s/step\n", - "field decay(t = 150.06): 0.45384932485125673 / 0.45384932485125673 = 1.0\n", - "on time step 9546 (time=190.92), 0.00164416 s/step\n", - "field decay(t = 200.08): 1.5552337783783405 / 1.5552337783783405 = 1.0\n", - "on time step 11961 (time=239.22), 0.00165646 s/step\n", - "field decay(t = 250.1): 1.2260556872108188 / 1.5552337783783405 = 0.7883417298775754\n", - "on time step 14416 (time=288.32), 0.00162956 s/step\n", - "field decay(t = 300.12): 0.030083252819861385 / 1.5552337783783405 = 0.019343235234531443\n", - "on time step 16863 (time=337.26), 0.00163533 s/step\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "field decay(t = 350.14): 8.582297003142968e-06 / 1.5552337783783405 = 5.518332434942239e-06\n", - "on time step 19312 (time=386.24), 0.00163389 s/step\n", - "field decay(t = 400.16): 5.083050063922431e-11 / 1.5552337783783405 = 3.268351121606029e-11\n", - "run 0 finished at t = 400.16 (20008 timesteps)\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", - "refl:, 2, 0.00007150, 0.00008424\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00330091 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 36 x 12 x 0 with resolution 25\n", - " prism, center = (0,0,5e+19)\n", - " height 1e+20, axis (0,0,1), 4 vertices:\n", - " (-19,0.5,0)\n", - " (19,0.5,0)\n", - " (19,-0.5,0)\n", - " (-19,-0.5,0)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - "time for set_epsilon = 0.496261 s\n", - "-----------\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "on time step 2138 (time=42.76), 0.00187101 s/step\n", - "field decay(t = 50.02): 7.571674212930823e-08 / 7.571674212930823e-08 = 1.0\n", - "on time step 4454 (time=89.08), 0.00172765 s/step\n", - "field decay(t = 100.04): 0.0017406599772782168 / 0.0017406599772782168 = 1.0\n", - "on time step 6777 (time=135.54), 0.0017222 s/step\n", - "field decay(t = 150.06): 0.45529339380861855 / 0.45529339380861855 = 1.0\n", - "on time step 9069 (time=181.38), 0.0017456 s/step\n", - "field decay(t = 200.08): 1.568246280713512 / 1.568246280713512 = 1.0\n", - "on time step 11341 (time=226.82), 0.00176058 s/step\n", - "field decay(t = 250.1): 1.2414144439515642 / 1.568246280713512 = 0.791594062245601\n", - "on time step 13493 (time=269.86), 0.00185906 s/step\n", - "field decay(t = 300.12): 0.031139203524192197 / 1.568246280713512 = 0.01985606719247225\n", - "on time step 15734 (time=314.68), 0.00178549 s/step\n", - "field decay(t = 350.14): 8.886463070035761e-06 / 1.568246280713512 = 5.666497143543454e-06\n", - "on time step 18069 (time=361.38), 0.00171313 s/step\n", - "field decay(t = 400.16): 2.7650862223635545e-11 / 1.568246280713512 = 1.7631709103148714e-11\n", - "run 0 finished at t = 400.16 (20008 timesteps)\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00128293 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 36 x 12 x 0 with resolution 25\n", - " prism, center = (0,0,5e+19)\n", - " height 1e+20, axis (0,0,1), 8 vertices:\n", - " (-19,0.5,0)\n", - " (-2,0.5,0)\n", - " (2,1,0)\n", - " (19,1,0)\n", - " (19,-1,0)\n", - " (2,-1,0)\n", - " (-2,-0.5,0)\n", - " (-19,-0.5,0)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - "time for set_epsilon = 0.755732 s\n", - "-----------\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "on time step 1883 (time=37.66), 0.00212453 s/step\n", - "field decay(t = 50.02): 7.569671091769381e-08 / 7.569671091769381e-08 = 1.0\n", - "on time step 4139 (time=82.78), 0.00177353 s/step\n", - "field decay(t = 100.04): 0.0017391263700697627 / 0.0017391263700697627 = 1.0\n", - "on time step 6309 (time=126.18), 0.00184352 s/step\n", - "field decay(t = 150.06): 0.453841743379235 / 0.453841743379235 = 1.0\n", - "on time step 8560 (time=171.2), 0.0017771 s/step\n", - "field decay(t = 200.08): 1.5555871736720326 / 1.5555871736720326 = 1.0\n", - "on time step 10769 (time=215.38), 0.0018108 s/step\n", - "field decay(t = 250.1): 1.2260941054634609 / 1.5555871736720326 = 0.7881873328700771\n", - "on time step 13056 (time=261.12), 0.00174983 s/step\n", - "field decay(t = 300.12): 0.02968036484241766 / 1.5555871736720326 = 0.01907984672588669\n", - "on time step 15299 (time=305.98), 0.00178368 s/step\n", - "field decay(t = 350.14): 7.2347918961150245e-06 / 1.5555871736720326 = 4.650843114781525e-06\n", - "on time step 17653 (time=353.06), 0.00169945 s/step\n", - "on time step 19969 (time=399.38), 0.00172778 s/step\n", - "field decay(t = 400.16): 8.407569561060486e-11 / 1.5555871736720326 = 5.4047562896870926e-11\n", - "run 0 finished at t = 400.16 (20008 timesteps)\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 9 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", - "refl:, 4, 0.00004024, 0.00004337\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00212097 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 40 x 12 x 0 with resolution 25\n", - " prism, center = (0,0,5e+19)\n", - " height 1e+20, axis (0,0,1), 4 vertices:\n", - " (-21,0.5,0)\n", - " (21,0.5,0)\n", - " (21,-0.5,0)\n", - " (-21,-0.5,0)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - "time for set_epsilon = 0.565803 s\n", - "-----------\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "on time step 1905 (time=38.1), 0.00210044 s/step\n", - "field decay(t = 50.02): 7.571674211765524e-08 / 7.571674211765524e-08 = 1.0\n", - "on time step 4047 (time=80.94), 0.00186782 s/step\n", - "field decay(t = 100.04): 0.0017406599773711756 / 0.0017406599773711756 = 1.0\n", - "on time step 6216 (time=124.32), 0.0018447 s/step\n", - "field decay(t = 150.06): 0.4552933938912802 / 0.4552933938912802 = 1.0\n", - "on time step 8357 (time=167.14), 0.00186832 s/step\n", - "field decay(t = 200.08): 1.5682462811448037 / 1.5682462811448037 = 1.0\n", - "on time step 10518 (time=210.36), 0.00185132 s/step\n", - "field decay(t = 250.1): 1.2414144444964543 / 1.5682462811448037 = 0.7915940623753525\n", - "on time step 12633 (time=252.66), 0.00189134 s/step\n", - "on time step 14831 (time=296.62), 0.00182053 s/step\n", - "field decay(t = 300.12): 0.03113920465258198 / 1.5682462811448037 = 0.01985606790653486\n", - "on time step 17006 (time=340.12), 0.00183974 s/step\n", - "field decay(t = 350.14): 8.886516913937694e-06 / 1.5682462811448037 = 5.666531475815538e-06\n", - "on time step 19110 (time=382.2), 0.0019017 s/step\n", - "field decay(t = 400.16): 2.7625870947574233e-11 / 1.5682462811448037 = 1.7615773287476016e-11\n", - "run 0 finished at t = 400.16 (20008 timesteps)\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.000883102 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 40 x 12 x 0 with resolution 25\n", - " prism, center = (0,0,5e+19)\n", - " height 1e+20, axis (0,0,1), 8 vertices:\n", - " (-21,0.5,0)\n", - " (-4,0.5,0)\n", - " (4,1,0)\n", - " (21,1,0)\n", - " (21,-1,0)\n", - " (4,-1,0)\n", - " (-4,-0.5,0)\n", - " (-21,-0.5,0)\n", - " dielectric constant epsilon diagonal = (12,12,12)\n", - "time for set_epsilon = 0.863971 s\n", - "-----------\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "on time step 1966 (time=39.32), 0.00203556 s/step\n", - "field decay(t = 50.02): 7.568972576123606e-08 / 7.568972576123606e-08 = 1.0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "on time step 4042 (time=80.84), 0.00192733 s/step\n", - "field decay(t = 100.04): 0.0017392532865220022 / 0.0017392532865220022 = 1.0\n", - "on time step 6101 (time=122.02), 0.00194315 s/step\n", - "field decay(t = 150.06): 0.4541781140774447 / 0.4541781140774447 = 1.0\n", - "on time step 8223 (time=164.46), 0.00188516 s/step\n", - "field decay(t = 200.08): 1.5597082712382018 / 1.5597082712382018 = 1.0\n", - "on time step 10355 (time=207.1), 0.00187692 s/step\n", - "on time step 12479 (time=249.58), 0.00188403 s/step\n", - "field decay(t = 250.1): 1.231655150453248 / 1.5597082712382018 = 0.7896702051053925\n", - "on time step 14552 (time=291.04), 0.00193036 s/step\n", - "field decay(t = 300.12): 0.030221199579030786 / 1.5597082712382018 = 0.019376187288562084\n", - "on time step 16622 (time=332.44), 0.00193299 s/step\n", - "field decay(t = 350.14): 6.594515383229233e-06 / 1.5597082712382018 = 4.228044118785151e-06\n", - "on time step 18688 (time=373.76), 0.00193665 s/step\n", - "field decay(t = 400.16): 3.0985772121881132e-09 / 1.5597082712382018 = 1.986638956353199e-09\n", - "on time step 20781 (time=415.62), 0.00191162 s/step\n", - "field decay(t = 450.18): 2.4580489315863303e-13 / 1.5597082712382018 = 1.5759671067429584e-13\n", - "run 0 finished at t = 450.18 (22509 timesteps)\n", - "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", - "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", - "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", - "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", - "refl:, 8, 0.00001847, 0.00001902\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import matplotlib.pyplot as plt\n", @@ -517,22 +145,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(dpi=200)\n", "plt.loglog(Lts, R_coeffs, \"bo-\", label=\"mode decomposition\")\n", diff --git a/python/examples/mode-decomposition.py b/python/examples/mode-decomposition.py index 60492ec58..9cfd58a1b 100644 --- a/python/examples/mode-decomposition.py +++ b/python/examples/mode-decomposition.py @@ -1,7 +1,6 @@ -# -*- coding: utf-8 -*- +import matplotlib.pyplot as plt import meep as mp -import matplotlib.pyplot as plt resolution = 25 # pixels/μm @@ -112,7 +111,7 @@ abs(taper_coeffs[0, 0, 1]) ** 2 / abs(incident_coeffs[0, 0, 0]) ** 2 ) R_flux.append(-taper_flux[0] / incident_flux[0]) - print("refl:, {}, {:.8f}, {:.8f}".format(Lt, R_coeffs[-1], R_flux[-1])) + print(f"refl:, {Lt}, {R_coeffs[-1]:.8f}, {R_flux[-1]:.8f}") if mp.am_master(): diff --git a/python/examples/mpb_bragg_sine.py b/python/examples/mpb_bragg_sine.py index 606b7a7a4..858ad8d51 100644 --- a/python/examples/mpb_bragg_sine.py +++ b/python/examples/mpb_bragg_sine.py @@ -1,4 +1,5 @@ import math + import meep as mp from meep import mpb diff --git a/python/examples/mpb_data_analysis.py b/python/examples/mpb_data_analysis.py index 8893f6133..9d7a27daf 100644 --- a/python/examples/mpb_data_analysis.py +++ b/python/examples/mpb_data_analysis.py @@ -1,10 +1,10 @@ -from __future__ import division - import os import sys + +import matplotlib.pyplot as plt import numpy as np + import meep as mp -import matplotlib.pyplot as plt from meep import mpb examples_dir = os.path.realpath(os.path.dirname(__file__)) diff --git a/python/examples/mpb_diamond.py b/python/examples/mpb_diamond.py index 818f6141f..b7aaaa1cb 100644 --- a/python/examples/mpb_diamond.py +++ b/python/examples/mpb_diamond.py @@ -1,4 +1,5 @@ import math + import meep as mp from meep import mpb diff --git a/python/examples/mpb_hole_slab.py b/python/examples/mpb_hole_slab.py index 88ba31528..e95faa480 100644 --- a/python/examples/mpb_hole_slab.py +++ b/python/examples/mpb_hole_slab.py @@ -1,6 +1,5 @@ -from __future__ import division - import math + import meep as mp from meep import mpb diff --git a/python/examples/mpb_honey_rods.py b/python/examples/mpb_honey_rods.py index 445642d6e..f6cd9489e 100644 --- a/python/examples/mpb_honey_rods.py +++ b/python/examples/mpb_honey_rods.py @@ -1,6 +1,5 @@ -from __future__ import division - import math + import meep as mp from meep import mpb diff --git a/python/examples/mpb_line_defect.py b/python/examples/mpb_line_defect.py index 8799cb4c1..8f1941b64 100644 --- a/python/examples/mpb_line_defect.py +++ b/python/examples/mpb_line_defect.py @@ -1,6 +1,5 @@ -from __future__ import division - import math + import meep as mp from meep import mpb diff --git a/python/examples/mpb_sq_rods.py b/python/examples/mpb_sq_rods.py index 78ee0b3e0..b752cf03d 100644 --- a/python/examples/mpb_sq_rods.py +++ b/python/examples/mpb_sq_rods.py @@ -1,6 +1,5 @@ -from __future__ import division - import time + import meep as mp from meep import mpb @@ -41,9 +40,7 @@ def main(): t0 = time.time() ms.run_te() ms.run_tm() - print( - "total time for both TE and TM bands: {:.2f} seconds".format(time.time() - t0) - ) + print(f"total time for both TE and TM bands: {time.time() - t0:.2f} seconds") ms.display_eigensolver_stats() diff --git a/python/examples/mpb_strip.py b/python/examples/mpb_strip.py index f14ead8b4..b7303c299 100644 --- a/python/examples/mpb_strip.py +++ b/python/examples/mpb_strip.py @@ -1,5 +1,3 @@ -from __future__ import division - import meep as mp from meep import mpb diff --git a/python/examples/mpb_tri_holes.py b/python/examples/mpb_tri_holes.py index 113d0c7dd..0365546ff 100644 --- a/python/examples/mpb_tri_holes.py +++ b/python/examples/mpb_tri_holes.py @@ -1,6 +1,5 @@ -from __future__ import division - import math + import meep as mp from meep import mpb diff --git a/python/examples/mpb_tri_rods.py b/python/examples/mpb_tri_rods.py index f7d4a79db..f1ac47447 100644 --- a/python/examples/mpb_tri_rods.py +++ b/python/examples/mpb_tri_rods.py @@ -1,6 +1,5 @@ -from __future__ import division - import math + import meep as mp from meep import mpb diff --git a/python/examples/mpb_tutorial.py b/python/examples/mpb_tutorial.py index 33734e400..7e2df4c34 100644 --- a/python/examples/mpb_tutorial.py +++ b/python/examples/mpb_tutorial.py @@ -1,10 +1,9 @@ -from __future__ import division - import math + +from scipy.optimize import minimize_scalar, ridder + import meep as mp from meep import mpb -from scipy.optimize import minimize_scalar -from scipy.optimize import ridder def print_heading(h): diff --git a/python/examples/multilevel-atom.py b/python/examples/multilevel-atom.py index 1a9aea6e2..0dc2f8784 100644 --- a/python/examples/multilevel-atom.py +++ b/python/examples/multilevel-atom.py @@ -1,6 +1,5 @@ -from __future__ import division - import math + import meep as mp # This file realizes a 1D, one-sided Fabry-Perot laser, as described in Fig. 2 of Optics Express, Vol. 20, pp. 474-88, 2012. diff --git a/python/examples/oblique-planewave.ipynb b/python/examples/oblique-planewave.ipynb index 4083cccc3..b889a4151 100644 --- a/python/examples/oblique-planewave.ipynb +++ b/python/examples/oblique-planewave.ipynb @@ -23,17 +23,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -49,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -107,123 +99,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "Meep: using complex fields.\n", - "Meep progress: 11.120000000000001/100.0 = 11.1% done in 4.0s, 32.0s to go\n", - "Meep progress: 22.75/100.0 = 22.8% done in 8.0s, 27.2s to go\n", - "Meep progress: 32.68/100.0 = 32.7% done in 12.0s, 24.7s to go\n", - "Meep progress: 41.97/100.0 = 42.0% done in 16.0s, 22.1s to go\n", - "Meep progress: 51.26/100.0 = 51.3% done in 20.0s, 19.0s to go\n", - "Meep progress: 60.370000000000005/100.0 = 60.4% done in 24.0s, 15.8s to go\n", - "Meep progress: 69.64/100.0 = 69.6% done in 28.0s, 12.2s to go\n", - "Meep progress: 78.49/100.0 = 78.5% done in 32.0s, 8.8s to go\n", - "Meep progress: 86.27/100.0 = 86.3% done in 36.0s, 5.7s to go\n", - "Meep progress: 94.66/100.0 = 94.7% done in 40.0s, 2.3s to go\n", - "run 0 finished at t = 100.0 (10000 timesteps)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "Meep: using complex fields.\n", - "Meep progress: 3.87/100.0 = 3.9% done in 4.0s, 99.5s to go\n", - "Meep progress: 8.57/100.0 = 8.6% done in 8.0s, 85.4s to go\n", - "Meep progress: 14.43/100.0 = 14.4% done in 12.0s, 71.2s to go\n", - "Meep progress: 19.23/100.0 = 19.2% done in 16.0s, 67.3s to go\n", - "Meep progress: 24.62/100.0 = 24.6% done in 20.0s, 61.3s to go\n", - "Meep progress: 30.2/100.0 = 30.2% done in 24.0s, 55.5s to go\n", - "Meep progress: 36.06/100.0 = 36.1% done in 28.0s, 49.7s to go\n", - "Meep progress: 41.77/100.0 = 41.8% done in 32.0s, 44.7s to go\n", - "Meep progress: 46.78/100.0 = 46.8% done in 36.0s, 41.0s to go\n", - "Meep progress: 52.68/100.0 = 52.7% done in 40.0s, 36.0s to go\n", - "Meep progress: 58.67/100.0 = 58.7% done in 44.1s, 31.0s to go\n", - "Meep progress: 64.54/100.0 = 64.5% done in 48.1s, 26.4s to go\n", - "Meep progress: 70.48/100.0 = 70.5% done in 52.1s, 21.8s to go\n", - "Meep progress: 76.5/100.0 = 76.5% done in 56.1s, 17.2s to go\n", - "Meep progress: 81.32000000000001/100.0 = 81.3% done in 60.1s, 13.8s to go\n", - "Meep progress: 86.11/100.0 = 86.1% done in 64.1s, 10.3s to go\n", - "Meep progress: 90.9/100.0 = 90.9% done in 68.1s, 6.8s to go\n", - "Meep progress: 96.07000000000001/100.0 = 96.1% done in 72.1s, 2.9s to go\n", - "run 0 finished at t = 100.0 (10000 timesteps)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "Meep: using complex fields.\n", - "Meep progress: 5.2700000000000005/100.0 = 5.3% done in 4.0s, 72.0s to go\n", - "Meep progress: 10.35/100.0 = 10.3% done in 8.0s, 69.4s to go\n", - "Meep progress: 15.610000000000001/100.0 = 15.6% done in 12.0s, 64.9s to go\n", - "Meep progress: 21.490000000000002/100.0 = 21.5% done in 16.0s, 58.5s to go\n", - "Meep progress: 27.37/100.0 = 27.4% done in 20.0s, 53.1s to go\n", - "Meep progress: 33.26/100.0 = 33.3% done in 24.0s, 48.2s to go\n", - "Meep progress: 38.52/100.0 = 38.5% done in 28.0s, 44.7s to go\n", - "Meep progress: 43.32/100.0 = 43.3% done in 32.0s, 41.9s to go\n", - "Meep progress: 47.58/100.0 = 47.6% done in 36.0s, 39.7s to go\n", - "Meep progress: 51.89/100.0 = 51.9% done in 40.0s, 37.1s to go\n", - "Meep progress: 57.2/100.0 = 57.2% done in 44.0s, 33.0s to go\n", - "Meep progress: 63.230000000000004/100.0 = 63.2% done in 48.0s, 27.9s to go\n", - "Meep progress: 67.82000000000001/100.0 = 67.8% done in 52.0s, 24.7s to go\n", - "Meep progress: 72.65/100.0 = 72.7% done in 56.0s, 21.1s to go\n", - "Meep progress: 78.24/100.0 = 78.2% done in 60.1s, 16.7s to go\n", - "Meep progress: 83.16/100.0 = 83.2% done in 64.1s, 13.0s to go\n", - "Meep progress: 87.56/100.0 = 87.6% done in 68.1s, 9.7s to go\n", - "Meep progress: 92.73/100.0 = 92.7% done in 72.1s, 5.7s to go\n", - "Meep progress: 98.45/100.0 = 98.5% done in 76.1s, 1.2s to go\n", - "run 0 finished at t = 100.0 (10000 timesteps)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for rot_angle in np.radians([0, 20, 40]):\n", " run_sim(rot_angle)" diff --git a/python/examples/oblique-planewave.py b/python/examples/oblique-planewave.py index 2742fb9e1..b5dec9451 100644 --- a/python/examples/oblique-planewave.py +++ b/python/examples/oblique-planewave.py @@ -1,6 +1,7 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np + +import meep as mp resolution = 50 # pixels/μm diff --git a/python/examples/oblique-source.ipynb b/python/examples/oblique-source.ipynb index af62389a5..3974ac34e 100644 --- a/python/examples/oblique-source.ipynb +++ b/python/examples/oblique-source.ipynb @@ -27,17 +27,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -58,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -110,33 +102,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1,1e+20)\n", - " axes (0.939693,0.34202,0), (-0.34202,0.939693,0), (0,0,1)\n" - ] - }, - { - "data": { - "image/png": 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nkRibyE2lN3H7jbeTk5NDZWUl1dXV/hmMe8jfA0z+vyMkY24FCbkQ9lNbW+u8MmXVnlXMXDKT6LJojr9+nP8+8d9UVFT4d0CNQ34U6GVuU4m5H0jIhbCep+u6z547yyeffMI/vvgHaweuJexEGOfeOkdpdanzMe4nNH3KU8hbQWLuYxJyIazjHvDw8HDnm3o2bNgAwL/85l849tMxjk85Dseg7v06qG4Y8EAIOUjMfUpCLoT/eQr4+fPn2bhxI19//TU7d+5k54mdcD1sK9gGNwJFoD5QcB40vnlTT5O8EHKQmPuMhFwI/3E/fGKsTqi1JjMzk3Xr1rFr1y52795NZmam42qUPvUbTgd+At4HXe3HgBu8FHIIoJgrpZ4GZgDJwDlgI/AfWuu9lg7MAwm5EP7hfns14631OTk5bNy4kezsbLZv3853331HZWWlc5uIiAhqjEtESoD3AT9dqNKAF0MOARRz4Brg78CPOMb9R+BrpdQwrfUZS0fmRkIuhG8Ze+FhYWGEhTnuSX/w4EG2b9/Ovn372LRpE19//TWnT592bhMREeG8cqXBdeJfERQhhwCKudZ6qvvvlVI/B4qAUUCGFWNqTEIuhG94Wp2wsLCQ3Nxc9u3bx/fff8/nn39OSUmJc5uIiAjq6uqoq6tr+o0+fnj/zwVMhNz4KaM1x+4DJuYexNX/WtLso/xEQi6EdzUOOMDJkyc5cuQIBw4cYP369Xz66accPnzYuU1YWJhzz90v79RsrRZC7r40Lji+nu7x3Tnx0wlaEpAxV0qFAX8FNmits5p5XDQQ7faptt06uwUSciG8zwh4WVkZxcXFFBQUkJGRwcKFC8nJybngce53uLclE3vkRsS7du1K9+7dGTNmDGk3pPH07KdbfPqAjDmOY+epwNUtPO5p4FlfDkRCLoT3nTlzhvLycoqKili3bh1vv/0227Zt8/hYv15G2FYmQt6hQwe6dOnC6NGj+fnPf056ejrdunVj3d51pl4i4GKulHoFuAmYqLUuaOHhLwIvu/0+BmhpG9Mk5EJ4h9aaqqoqqqqqOHnyJOvXr+ett95i48aNnD9/3urhtU8zIVdKERUVxRVXXMH999/P9ddfT8+ePQkPDyciIsL5qxkBE3Pl+Fnqb8DtwCStdX5L22itqwDnSvHGj2PeICEXon2ME5Naa8rKytiwYQPvvvsua9eu5fTp0/Y85t1azYT80ksv5b777mPq1Kn069ePDh060LFjR+cVOq0VMDHHcWjlHuBWoEIp1bv+82Va63P+HIiEXIjWM45pG5cVnj17lh9++IEFCxawevVqysrKOH36dGAcNjHDQ8gvvvhiZ8AHDhxIXFwcMTExbQ64u0CK+Zz6X9c2+vxs4G1/DUJCLoR57iclw8PD0VqzdetWFixYwJo1ayguLubUqVP+W17Wx5z3+oyoc4Y8bmkcD8x4gOuvv56LL76YPn36cNFFF3n1SAEEUMy11t79yttAQi5Ey4yAK6UICwsjPDycXbt28cknn5CRkcHhw4cpLCxs8K7MQHbBzZojNeE/DyesVxhP93+aKf8zhYSEBPr27dvgyhtj6V1vCZiYW01CLkTTjIAbb6sPDw9n//79LFu2jIyMDPLz88nLy2uwPnhb3hhjF8bXaBzz11rToUMHrp16LXtG7qFIFfHelPe4fcztHgPu7b1ykJibIiEX4kKeAl5QUMA333zDd999R15eHrt27eLECdcbXhrvxQaSxgE3TtBOnjyZK6+8koTkBF4te5WSMyWsud9xj2H3n1J8EXB3EvMWSMiFcPEU8OLiYtavX8/mzZvZt28fW7Zs4ciRI85tLjgMEUARbzx2I+BXXHEFo0aNIikpiauuuoohw4dw88c3c+DMAVbeu5LRfUb7dC/cE4l5MyTkQjRc2MoIeGlpKVu2bGHHjh3s3r2b7777jtzcXOc2gRxwcLyNvvHYk5OTufTSS0lKSuLaa6/lqquuokOHDlRUVTD1/alkFWfx9X1fM7b/WEvGLDFvgoRchLLGAVdKcfr0aXbv3k1OTg47d+5k9erV7Ny507lNsAQcXEvrDhw4kMTERFJSUpgyZQqTJ08mNjbW+Ziyc2Xc+MGNZBVnser+VYzpN8ay8UvMPbBDyLOKmlxyRgifcT80YNxiLT8/n/3797N7926+/PJL5y3XIPAD7s4IeM+ePenXrx/JyclMmzaNqVOnEh8fDzhuAG0cAz9z/gw3LrRHyEFifgE7hHzz0c089uVjlry2CD3ul8gppaiqqqKwsJCCggJ2797N559/zooVKxps4+kwRCCLi4sjPj6ehIQEbrnlFm677Tb69+8PNAx4eHg4gOPQyoKpZBXZI+QgMW/ALiGf8t4UBncfzM6fdra8gRDtpJSiurqakpISTpw4wZ49e1i0aBGff/55g3VR3G9ybOvVCVuhW7duXNL7Eu68807uuOMOBg8ejFLKGXDjOnl3dgw5SMyd7BTy1J6pvJT+EhPfnmjJOERoOH/+vHN1wuzsbD766CM+++wzSktLPT4+GPbAw8LCiI6OpseAHhzhCK+88gq3jb2NyMjIBj+dNLW4lV1DDhJzwH4hX3HvCnJLclveSIhWOn/+POfPn+fcuXPk5uby8ccf88knnzS4lDBY9e7dm1tvvZV77rmHmvga0j9OJzExkY4dO5q6fNDOIQeJuS1DHhPtk3toiBCktW5w78u8vDw+/fRTFi5cyMGDB6murg6aQyaedO7cmZtvvpl7772Xyy67jNjYWDp16kTWSccFBhEREUERcgjxmEvIRTAy7ntprMRXUFDAkiVLWLhwIfn5+VRUVATNuijQ8Fi+4aabbmLWrFmkpaURHx9Pt27diI6ObuIZmhcIIYcQjrmEXAQL44oSrTXh4eGEhYVRXFzMsmXLWLx4MXv37qWkpKTJY+GByP1en0bIr7nmGmbOnElaWhr9+/enT58+dOzY0blNWxa2CpSQQ4jG/N0d7zJ/z3wJuQhYjQOulKKsrIxVq1bxxRdfsGvXLo4dO8bx48cbbOdpLzZQGD9pGD95AIwYMYLbb7+dtLQ0Bg0aREJCAl26dHFu4+mm0GbZJeTv7njX1ONCMubzM+czb5qEXHjR6NFw/Dj07g1btvjkJTwF/Ny5c2RkZPDVV1+RlZVFfn4++fkNb8LlHvBAC7mngCclJTFt2jTS0tIYOnQow4YNo2vXrs5t3Be2auu6KHYJ+fMZzzM/c76px4ZkzB8e+bCEXHjX8eNw1MNdetvJ/TCC8db6mpoavvvuO1avXk12djbZ2dns2rWrwXaeDkMECk8BHzhwIJMnTyY1NZXhw4dzxRVX0KNHD+c27gFv71177BTyuWvm8vDIh5lPy0EPyZg/kPaAJa8rIRdmeAp4XV0d27ZtY8OGDezZs4ft27fz/fffN9jOPeCBdoWKp4D36tWLsWPHkpSUxKhRo5gwYQJ9+/Z1blNbW+v8+/HGbdfAfiGfN3keE3tPlJjbiYRctMSImBEogJycHDIzM9mzZw9btmxh7dq1VFU571FORESE89LDQAu4+7ouxtjj4uIYMWIEgwcP5sorryQ9PZ1BgwY5t6mtrW1wByNvsmPIn5n4DBn7MkxtJzH3Awm5aIr7CTpj7/LQoUPs2bOHvXv3snHjRlauXEl5eblzG/eAB9od7BvfdUdrTceOHUlKSiIhIYGxY8cyffp0UlNTndv4MuAGu4a8NSTmPiYhF415usLi+PHjHDhwgAMHDpCRkcGSJUsoLi52buN+CCXQAg6uk7DG1x4VFUW/fv0YMGAAY8eO5a677mLUqFHOx/kj4IZgCDlIzH1KQi7cNb7zTElJCYWFhRw+fJg1a9awcOFCjrqdRA3kY+CNaa2JiIjgoosuokePHowfP5577rmHq666iqioqAZvdHJfndDXzlSfCYqQg8TcZyTkojGlFOXl5ZSWllJQUMC3337LggULyMnJafCYYFuZMCYmhri4OK6++mruvfdeJkyYQFxcHDU1Nc6v11snMFvr8eWPc7D0YMCHHCTmPiEhF4a6ujoqKys5d+4cx44dY+3atbz33nts3rzZ4+MD7TLCpkRGRtKpUyfGjh3Lgw8+yOTJk+nevXuDve6mVib0p7ySPL598NuADzlIzL1OQi7q6uo4f/48NTU1lJSUsHbtWt5++202bNjQ4EqUYBQREcHIkSN56KGHuO6664iPjycqKoqoqCjL9r6b8+r0V4Mi5CAx9yoJeWhyntAENFBaWsr69et566232LhxI+Xl5UEf8ZSUFH7+859z3XXX0adPH2JiYujcubPf7kzfVqk9U1t+kA/4Ym0oibmXSMhDi/tJyfDwcGfQT506xZVXXklxcTHl5eVBc9zb/ZJCwyWXXMLdd9/Nddddx8UXX0yPHj0avK1eeOarRf4k5l4gIQ8NRsDdrwnfsmULH3/8Mf9eXEwv4OzZs+TmBseNRRrfrBkcNzueOXMm1157LYMHD6Zfv3707NnTuY37ZZfiQr5crVVi3k4S8uBmBNz9krmsrCyWLFlCRkYGR44c4dChQ/xLo3tlGtsGGuNrrKmpcV4W2aVLF26++WYmTZrEkCFDSExMZMCAAc5t2rMyYSjx9bLbEvN2kJAHJ2NP1D3geXl5fPnll3z//ffk5+eTk5NDWVmZx+0DLeKNA15TU0NUVBQ33HAD48aNY8iQIaSlpZGYmOjcxgi9+9IDomn+uH+CxLyNJOTBxdgDN27uADivBd+6datzjZQTJ044t3FfHCrQeAp4REQE48aNY/To0aSkpDB27FiGDx9+wdfZ3uVlQ42/boQjMW8DCXlwaLx3GR4eTlFREZs2bWLbtm3s2bOHDRs2UFBQ4NzG0+p+gaLxMXBjWYARI0aQlpZGamoq48ePZ/To0URGRgKur9ObKxOGEn/e0Uxi3koS8sDWOOBKKUpLS9mxYwc7d+5k9+7drFmzhn379jm38bS6X6BoHHDjEFBSUhJJSUkMGzaM9PR0xo8f77zFmgTcO/x9a0rTMVdK9dVaF/pyMCbH8Wvg34DewA7gCa2157fTeZmEPDB5Cvjp06fZu3cv+/btY9euXSxfvpzt27c7t2kqgoHCfWErY+z9+/cnISGBxMREbr75ZtLT04mNjQUcKxNKwL3HinsMt2bPfLdS6tda6w98NpoWKKVmAS8DjwKbgN8AK5VSSVrrIl++toQ88LgvbKWU4uzZsxw5coTDhw+zY8cOlixZwoYNG5yPD5aAg+skbPfu3enTpw9DhgxhxowZTJs2zXmHHveA+2thq1Bg1c3iWxPz3wH/o5S6HfiV1rrER2NqzpPAP7XWbwEopR4FpgMPAS/56kUl5IHD/Q7sSimqqqooKiri+PHj7Ny5k0WLFrFy5Urn4wM94O6MsXfp0oVu3bqRmJjIjBkzuPXWW52XEkrAfcuqkEMrYq61flUptRyYD+xRSv1Ca/2F74bWkFIqChgFvOg2pjql1GrgKl+9roQ8sCilqK6upry8nJMnT7J7924++ugjli5dSmVlZYPHBeo9Mj2Jjo6mQ4cOJCQkMHPmTO644w4SExMbfJ0ScN+yMuTQyhOgWut84Fql1OPAp0qpbKCm0WNGenF87noA4cBPjT7/E5DsaQOlVDQQ7fapVpVYQh44zp8/T2VlJadPnyY7O5sPP/yQRYsWUVpa6vHxwRBwpRQRERH07duXO++8k7vvvpuhQ4c6F7UKDw+Xywf9xOqQQxuuZlFKXQzMAE4Bn9Mo5jbzNPBsWzaUkNubcWldbW0t1dXV7N27l4ULF/LJJ59w7NixgLwbT2t069aNWbNmMWvWLJKTk+nSpQvR0dHOSwqF/9gh5NDKmCulfgH8BVgNDNdaF7ewiTedAGqBXo0+3ws43sQ2L+I4YWqIAQqaeKyTHUJ+pvqM31/T7txvJ1ZXV8f+/fv5+OOP+eyzz8jPz6eyspLq6mqrh+k17ic0ATp27Mitt97KrFmzGD58ON27dycmJoaoqCgLRxna7BJyaN2liSuAMcDjWut3fTckz7TW1UqprUA6sKR+TGH1v3+liW2qAOfao2Z+5LRDyCuqKnh8+eN+f127cT+mbRwyKCws5LPPPmPJkiXk5uZSWlrKmTPB8z++xjc8Vn5A3roAACAASURBVEoxdepU7rzzTi677DJ69epFfHw80dGuo4fuJ32F/9gp5NC6PfNw4DKtdYt7tj70MvCOUmoLsBnHpYmdgbe88eR2CfnUBVPJK8nz+2vbgaeAFxcX89VXX7F48WL279/P8ePHOXXqVIPtGu/FBhL3e30aX8OECRO47bbbSEtLY8CAAQwYMMD5ph648Kod4V92Czm07mqWKb4ciMkxfKSUigf+gONNQ9uBqVrrxidFW81OIc8qyuLV6a/y4JIH/T4GK3gKeHl5OatWrWLZsmXk5uZy8ODBBjc7Bs/XVQcKTzdrvvzyy5k+fTqpqakMHTqUIUOG0KVLF+c2sjqhPdgx5BCAb+fXWr9CE4dV2spuIV91/yoiwgJualrFfS/UeGfmuXPnWL9+PatXryY7O5u9e/desDZ4WFiYM37BEPCkpCSuvfZahg0bxmWXXUZaWhpxcXHObYxrwiXg9mDXkEMAxtzb7BjyMf3GkHks0+/j8DVPAT9//jw//PAD69evZ/fu3ezatavB2+qNxzaOYKDwtDDXgAEDuPrqqxk2bBgjRoxg7NixxMfHO7dxD7i8td4+7BxyCPGY2zXkwcRTwOvq6tixYwebNm0iJyeHLVu2sGHDhgahjoiIoLa2NmgCHh8fz6hRo0hOTmbUqFFMnjyZfv36Obepra11/v1IwO3H7iGHEI65hNy3PB0eyM3NZfv27eTm5rJx40a+/vprzrvdocc94IF8nbgR8K5du5KcnExCQgLjxo3jxhtvZNCgQUDDyyyNN/gIewqEkEOIxjy7OJt///TfJeRe5n6Czti7PHz4MLm5ueTm5pKRkcHSpUsbXEoYyAH3dHs4BQwfPpz+/fszceJEbrnlFlJTU52Pc98Dl4Dbnx1Cnl2cbepxIRnzJ1c+yYhLRkjIvcDTFRY//fQTBQUF5OXl8e233/Lpp59SXOx6f5n7MfBAC7g742uPiooivLYWamvp3KULb775JqNHj3aerJWAByY7hHzz0c08ufJJU48NyZgndEuQkHuJEfBTp05RXFzMoUOHWL16NR9//DEHDx684HGBeAzck7CwMOLi4ujZsyfjx4+n29KlcOIEXePiGDVqVIM11EXgsUvIp7w3hYRuCexmd4uPD8mY/3nKnyXk7aS15syZM5SXl1NYWMjq1at55513yMnJafLxwaBDhw7ExcUxbtw4Zs+ezYQJE+jSpQvh9cvqapC97wBnp5Cn9kxl7pi5TGNai9uEZMw7RXXy+2sGQ8jr6uqoqqqiurqaoqIiVq9ezZtvvklmZmZQ7G03p0OHDkyYMIEHHniASZMm0b17d8LDw4mMjGyw9y1Xggc2u4V8xb0r2HZom6ntQjLm/hbIIa+trXWeoDx58iTffvst77zzDps3b+bs2bMBfczbjCuvvJL777+fyZMn06tXLzp06ECHDh3k8EkQsmPIW3MEQWLuY4EWcuOYtvHW+oqKCjIyMliwYAHr16+noqKC06dPWz1Mr2q8rktycjIPPPCA81rwrl27EhMjSyAHs0APOUjMfSpQQm4E3LiksKamho0bN/LBBx/w3XffUVJSwokTJ4LmUIr7reLA8fUPGDCAn/3sZ0yePJmBAwfSu3dvunfv7txGViYMXsEQcpCY+4zdQ27sfbu/6/DHH3/ko48+YvPmzRQUFFBYWNhgffBAXpnQ070+e/bsye23387EiRNJSEhg0KBB9OrlWi5fFrYKfm9kvsFrW14L+JCDxNwn7Bpy98MnxjHfrKwsvvjiCzZt2kR+fj779u274F6Z0PBt+YHCuK67pqbGOf6uXbtyww03MG7cOJKSkkhJSWHgwIHObSTgoSVYQg4Sc6+zW8g9BTwvL4/Vq1ezadMm9u/fz86dOykrK3NuE0wBr6mpoUOHDkyaNIkxY8Y4F7dKSkpybmMcbjF+ShGhY87oOUERcpCYe5VdQm4cA3cP+NGjR8nIyGDLli3s27ePzZs3U1RU5NzGfXGoQAy4+yGUmpoalFKMHz+eESNGkJKSwrhx40hLS3PG2gi4LGwV2h4Z+Yglr+uLtaEk5l5idcjd33Fo7J0WFxfz448/smvXLrKysli3bh1HjhxxbuNpdb9A4ekYOEBaWhrDhg0jOTmZ6667jjFjxhAR4fhnbjxWAi6s5KtF/iTmXmBVyBsHXCnFqVOn2L17N/v27WP79u2sWLGiwQ0eAjng4Lo5hXvABw0axODBgxk+fDg33ngjEyZMoEOHDoDjOnn3K3V8pnfvhr8K4YEvV2uVmLeTv0Pe+ASdUoqKigry8/PJy8tj+/btLF26tMENHgI94O5X0Rjj7927N/379yc1NZWbbrqJKVOmEBsbCzQMuN/eWr9li39eRwQsXy+7LTFvB3+G3Dg8YBzzPXfuHIWFhRw5coQdO3awePFi1q9f73y8+2GIQAy4OyPkXbt2JT4+nqSkJGbMmMH06dPp2bMnYFHAhTDJH/dPkJi3kb/3yJVSVFZWcurUKYqKitixYwcffvghy5cvv+Bxje/0HsiMha0SEhL42c9+xm233caAAQOcJ3nr6urk5g7C1vx1IxyJeRv4M+Tnz5/n1KlTlJWVsXPnTt5//32WLVvGuXPnPD4+GAIeERFBZGQkgwYN4u6772bmzJkkJCQQHh7u/MkkLCxMTmIK2/PnHc0k5q3kj5CfP3+ec5WOWO/YsYM3lr7BF198wU8//eT117Kbiy++mJkzZ3LXXXcxdOhQoqOjiYyMJDIy0uqhCdEq/r41pcS8FXwVcuN2YlprqqurycnJ4fVFr0NHeOKJJ6g5UhPwx72b061bN2fAU1JSiImJoVOnThJwEbCsuMewxNwkb4fcON4bHh5OXV0deXl5LFq0iMWLF3P06FFKO5bCQzjWRgmCjjde1yUqKorbb7+dO+64g2HDhnHRRRfRrVs3oqKiLBylEO1n1c3iJeYmeCPk7icljXdmHj16lCVLlrBkyRLy8/M5efIk5eXljg36ePmLsIBxTTi4juVPmzaN2267jWHDhjFgwAB69+5NdHS0cxtZnVAEMqtCDhLzFrUn5I0DrpSiqKiI5cuX89VXX7Fv3z6OHj3a4GbHUL8XS2CeyHS/WbMR8quvvpqbbrqJ4cOHM3jwYBISEpxv6oGGAZeQi0BlZchBYt6stoTc/ZJA452ZZWVlrFmzhq+//pqcnBz279/f4G310PAwRKBdkeIp4CNGjOD6668nNTWV5ORkUlJS6NKli3Mb45pwWZ1QBAOrQw4S8ya1JuSeAn7u3Dk2btzIN998w969e9mzZ88FNzv2dBgiUHgK+NChQ5k4cSLDhg3jsssuY/To0cTFxTm3cQ+4XFYogoUdQg4Sc4/MhNxTwKurq8nMzGTjxo3k5OSwdetWMjMzG2znKYKBwtPYBw4cyOjRo0lKSmLMmDFcffXV9OjRA2h4ByMJuAhGdgk5SMwv0FzIPQW8traWnTt3sn37drKzs/n+++9Zt25dg+eMiIhwXnpoOuCXe+1LahdPAY+Pj+fSSy8lMTGR8ePHk56eTr9+/QDXZZbui38JEYzsFHKQmDfQVMgbL25VV1fH/v37ycnJIScnh3Xr1rFq1Sqqqqqcz+Ue8FbfwX4iYOE9LdxvTmEEPDY2lsTERAYNGsSECROYPn06gwcPdj7OPeDy1noR7OwWcpCYOzUO+RV9r2iwuJXWmoKCAvLz88nNzeXbb79l6dKlVFRUOJ/DfS+21QE3TASuBTZjWdCN/3l17NiR/v37079/f6655hpmzpxJamoq4Dj+LQEXociOIQeJOdD8HnlxcTHHjh0jPz+fVatWsXDhQkpKSpzbevUYuBHyb4FcLIl5VFQU3bt3p2fPnlx77bXcc889jBw5kvDwcGpra50Bl+PfIhTZNeQQIDFXSl0CzMWRut5AIfA+8ILWurrpLVvWOOSj+4ymrKyMU6dOcfjwYVasWMH777/f4FJCT+trt5t7yDPw65uGwsLC6NKlC127diU9PZ377ruPsWPH0qlTJ2prawGc18oLEarsHHIIkJgDyUAY8CtgP5AK/BPoDPy2rU/qHvKldyxlUPQgdu7cyYoVK1iwYAFZWVket/P6ZYSNQ+4nkZGRdOnShUmTJvHggw8ybtw4YmNjUUoRERHh/FWIUGf3kEOAxFxrvQJY4fapA0qpJGAObYx56dlSpn0wjT3Fe3g7/W2yV2fz9LtPs3XrVsd6KP5iQcijo6MZN24cs2fPZsKECXTv3p2oqCiio6Pl6hMhGgmEkEOAxLwJcUBJcw9QSkUD0W6figGoOFfB9e9dT1ZRFpdtv4xH/vwIZ8+epbKy0ofD9cDPIR85ciSzZ89m4sSJ9OzZk5iYGDp37uz7FxYiQNkh5Gerz5p6XEDGXCmVCDxBy3vlTwPPNv7kPW/fQ0WnCjov7symvZt8McSW+SDk7pcUGoYPH87dd9/NxIkT6devH/Hx8c57ZQohmmaHkFdUVfDbVeYOPlgac6XUS8B/tPCwFK21833wSql+OA65LNJa/7OFbV8EXnb7fQxQUB5WDu/A6aOn2zLs9vNiyN2vfTcifskllzBjxgwmTJhAQkIC/fv356KLLnJu437dvBDiQnYJ+dQFU8k/lW/q8Vbvmf8FeLuFxxww/kMp1RdYA2wEftnSk2utqwDnO3mc8VoJqlCBsmBNFC+E3Lgs0Ai41poePXpwyy23MH78eBITExkyZAh9+rguiWn8xichhGd2CnlWURYv3/Ayj857tMVtLI251roYKG7xgTj3yNcAW4HZWuu2XxN4wqKFrdoRcuONOTU1rrsOxcTEMHXqVMaMGcPQoUNJS0vj4osvdm4jKxMK0Tp2C/mq+1dRecbcuTyr98xNqQ/5WuAQjuPk8W7Hh49bN7JWaGPIw8PDqcW1LECHDh2YMGECl19+OSkpKVx11VUkJSU5H2+EXt7YI0Tr2DHkY/qNIWOfuWAERMyBKUBi/UdBoz+z/y5nK0LuPAZef6+42tpaIiMjGTVqlHNp2YkTJzJixAjn3rYRcFmZUIi2sWvIWyMgYq61fpuWj63bk4mQNz6J6X4IaNKkSUwcMpEpU6Zw5ZVXOt/EU1tbK0vLCuEFwRByCJCYB6wWQm4sC+Ae8EsuuYSLL76Y+Mvi+YRPeOGFFxiXMA5wBNw4Di5vrRei/bKKsnhi+RMBH3KQmPtOEyH3dHu4nj170rt3b1JSUrjzzjuZMmUK+8/s55PXPyEyMlICLoSPPPblY6T1Tgv4kIPE3Dea2SM3Ah4bG0vXrl0ZNmwYd911FzfeeCO9evVyrr5YV+E4Dh4eHi6HUYTwkcHdBwdFyEFi7n3NhDw6OpqOHTuSnJzMrFmzuPXWWxkwYIDzz4310yXgQvjHK9NeCYqQg8TcuzyE3Fh5cNCgQfzsZz9j5syZXHLJJURGRhIeHi6rEgphoc5R/l+byBchB4m593gIee/evbnnnnu44447GDx4MJ06daJDhw4ScCFClK9CDhLzdlNKoSdoZ8i77uzKHY/cwYwZMxgyZAjdu3cnNjZWAi5EiPNlyEFi3iZhYWHON+oYIR/+03D+81f/SUpKCr1796ZHjx5ERkY6tzGOhwshQo+vQw4Sc1Pcb+rsfq/PxIcS2T9wP7Mvns3v7vsdAwYMICoqyrmde8Al5EKEJn+EHCTmTWoccOOSwvHjx3PDDTewp8cePiz6kGcnPMtz1z7n3E5WJxRCGPwVcpCYXyAiIoLa2toGAR8xYgSTJk0iOTmZtLQ0vjr9FR9u+JB5k+fxuwm/k9UJhRAX8GfIQWIOOI6BG/GuqakBYMiQIVx11VUkJyczevRoxowZQ1xcHM9nPM+8DfP4w6Q/8LsJv5OACyEu4O+QQwjH3D3gxjHwfv36cfnll5OcnMzYsWO55ppriI+PBxyHT/6w9g88u+5Z5k2exzMTn7Fy+EIIm7Ii5BDCMTcCHh8fT1JSEomJiVx99dVMnTqVfv36AY6A19bWEhYWxgvrX5CQCyGaZVXIIURj3qlzJwZfOpj+/fszZcoUbr31VgYNGgQ4Im8E3Hhr/fMZzzN3zVwJuRCiSVaGHEI05nfddRf/eu+/kpqaCjQMeOM1USTkQoiWWB1yCNGYz549m5TBKU0G3CAhF0K0xA4hhxCNOdDi2uASciFES+wScgBZZ9UDO4Q8qyjLktcVQphjp5CDxPwCdgj55qObeezLxyx5bSFEy+wWcpCYN2CXkE95bwqDuw+25PWFEM2zY8hBYu5kp5Cn9kzllWmvWDIGIUTT7BpykJgD9gv5intXWHIHFCFE0+wccpCY2zLkVtyTUAjRNLuHHEI85hJyIURLAiHkEMIxl5ALIVoSKCGHEH3T0Ls73mX+nvkSciFEk+wS8nd3vGvqcSG5Zz4/U0IuhGiaXUL+fMbzzM+cb+qxIRnzh0c+LCEXQnhkp5DPXTOXh0c+bOrxIRnzB9IesOR1JeRC2JvdQj5v8jzTvQrJmFtBQi6Evdkx5K05giAx9wMJuRD2FughhwCMuVIqWim1XSmllVIjrB5PSyTkQthbMIQcAjDmwJ+AQqsHYYaEXAh7O1N9JihCDgEWc6XUNOB64LdWj6UlEnIh7O/x5Y8HRcghgN40pJTqBfwTuA04a3KbaCDa7VN+KaqEXIjAkFeSx7cPfhvwIYcA2TNXSingbeAfWustrdj0aaDM7aPA+6NrSEIuROB4dfqrQRFysDjmSqmX6k9kNveRDDyBY6/6xVa+xItAnNtHf+9+BQ1JyIUILKk9Uy15XV+sDWX1YZa/4Njjbs4B4FrgKqDKsZPutEUptUBr/aCnDbXWVUCV8ftG23qVhFwIYYavFvmzNOZa62KguKXHKaX+F+D+VfcFVgKzgE2+GZ15EnIhhBm+XK3V6j1zU7TWh91/r5Q6Xf+feVprnx8Hb46EXAhhhq+X3Q6IE6B2JSEXQpjhj/snBMSeeWNa64OA7w6AmyAhF0KY4a8b4cieeRtIyIUQZvjzjmYS81aSkAshzPD3rSkl5q0gIRdCmGHFPYYl5iZJyIUQZlh1s3iJuQkSciGEGVaFHCTmLZKQCyHMsDLkIDFvloRcCGGG1SEHiXmTJORCCDPsEHKQmHtkh5CfqT7j99cUQrSOXUIOEvML2CHkFVUVPL78cb+/rhDCPDuFHCTmDdgl5FMXTCWvJM/vry2EMMduIQeJuZOdQp5VlMWr01/1++sLIVpmx5BDgC605W12C/mq+1cRESZTI4Td2DXkIHvmtgy5VfckFEI0zc4hhxDdM997ci9djnUhqyiLx758jMHdB/NS+kvkluT6fSxnqs/w+PLHySvJ49XprxIRFkHmsUyyT2QDOH8VQnhPa7+/3sh8g9e2vMac0XO4cciNZB7L9OXwGth7cq+pxymttY+HYh9KqVigjKeADlaPRgghTKgEXgIgTmtd3tTDQnLP/KahN7Hs8DI6RXTi07s+Jb5LvN/HUHy6mBkfz+BszVmenfgstyTf0uDPs09kc9+n9/H+jPdJ6ZHis3G473E8MvIRn71Oc9x/Qnpl2it0jurs9zE0/gnJqru2y3w4+Ho+zH5/PbrsUX4s/JEr+l7BP276h1fHYNazK55lGctafqDWOmQ+gFhA8xQ65o8x+mjZUW2Fo2VHdcwfYzTPod/MfNPjY7YWbtU8h95auNVn45i3bp7mOfS8dfN89hot2VSwSce+GKvHzR+nyyvLLRlDeWW5Hjd/nI59MVZvKthkyRi0lvkw+GM+zHx/pb+TrnkOnf5Ouk/GYMabmW9qnkIDGojVzfWtuT8Mtg8j5tHPRNs65Fr7PuYSDgcJuUsozUdL31+2Cflzjl5JzJuI+Z+++VN7/57bxGzItfZtzCUcDhJyl1Cbj+a+v+wU8pg/xug/ffMnUzEPyUsTu3bs6vfXLCwvJPnvyVRUV/DmLW8y+/LZfh8D2OPyKrkc1EXmw8Eu83Hdu9fxTf43pCeks/qB1ZaM4a1tb/HQ0oeIiYoh59c5pnsVkjH3Nwm5i4TDRebDwS7zYceQ943ta3pbibmPSchdJBwuMh8OdpmPQA85SMx9SkLuIuFwkflwsMt8BEPIgdA8Afr6xtfbe36iRa052emJt06Aysk1BznZ6SLz4fr+GvP6GFud7PR0ld3rG1+XE6BWkT1yF9kDdJH5cLDLfABsLtwc+HvkhuZKH2wf+GHPvL175Ib27pnLHqCD1XuABpkPB7vMx5xlc5x75lZpaY/cYHbP3PLA+vPD1zH3Vsi1bl/MJRwOdgmHzIeD3ebD1++wbo7ZkGsth1n8Tg6tuMiP8i4yHw52m485o+dY8vrg5UMr7porfbB94KM9c2/ukRvasmcue4AOdtsDlPmw33z4Y+0jT1qzR26Qwyx+irkvQq5162Mu4XCwYzisIvPh0ng+rIh5W0KutcTcLzH3Vci1bt0/NgmHg13DYQWZDxdP8+HvmLc15FoHacyB6cAm4BxwCljSyu29FnNfhlxr8//YJBwOdg6Hv8l8uDQ1H/6MeXtCrnUQxhyYCZQAjwJDgWHAXa18Dq/E3Nch19rcPzYJh4Pdw+FPMh8uzc2Hv2Le3pBrHWQxx3FHpALg4XY+T7tj7o+Qa93yPzYJh0MghMNfZD5cWpoPf8TcGyHXOvhiPqb+i5kNbAOOAcuB1Ba2i64PuPHRrz0x91fItW7+H5uEwyFQwuEPMh8uZubD1zH3Vsi1Dr6Y313/xRyqP9wyCvgAOAF0b2a75+q3a/DRlpj7M+RaN/2PTcLhEEjh8DWZDxez8+HLmHsz5FoHSMxx3HP6gtg2+kgG7qn/71+6bRsNFAO/aub5vbJn7u+Qa+35H5uEwyHQwuFLMh8urZkPX8Xc2yHX2nzMIzy9kciP/gK83cJjDgB96v97j/FJrXWVUuoAMLCpDbXWVUCV8XulVKsHKO/sdJF3ErrIfDjIfLj47J2dZjVXert84NirrsTtBCgQCfyE2966yecxvWduxR65wX3PQfYAHQJxD9BXZD5c2jIf3t4z98UeuSEgDrO05gP4K44rWq4HkoA36mPerRXPYTrmVoZca9c/NmN1NwlH4IbD22Q+XNo6H96MuS9DrnVwxjwS+HN9wMuBVcDwVj6HqZhbHXKtXf/YJByBHw5vkvlwac98eCvmvg651kEYc298mIm5HUKutXbeAWXOsjmWjUHC4SIhdwiW+fBGzP0Rcq0l5m2KuV1Cnv5OuuXrLUs4XCTkDsE0H+2Nub9CrrWsZ95qdrlqxbi57Ji+Ft5KS66ScLLDVRIyHy52mA/Lr1ppgsQc+4U8PSGd1256zZIxSDhc7BAOmQ8XO8yHXUMOEnNbhtyqm8tKOFzsEA6ZDxc7zIedQw4hHnMJuYuEw8UO4ZD5cLHDfNg95BDCMZeQu0g4XOwQDpkPFzvMRyCEHEI05qXnSiXk9SQcLnYIh8yHix3mww4hLz1Xau6BzV3qEmwf1F+aGP1MtG0uP0x/J93jn/tjvWW53M1FLj90CJX5MPP95c/LD5tytOyoo1dynbnnmPOUvUOute9jLuFwkZA7hNJ8tPT9ZZeQx/wxRvMUEvMLvtj6mD/44YPt/GtuOzMh19q3MZdwuEjIHUJtPpr7/rJVyJ9z9MpMzEPymPn4geMteV05Ru4gx2RdZD5c7DAfdjhG3vjiDLO9CsmYW0FC7iDhcJH5cLHDfNgx5K25OENi7gcScgcJh4vMh4sd5iPQQw4Sc5+TkDtIOFxkPlzsMB/BEHLA8tvGWWL9vvV+eZ2XN7xMzskcki9KZtaQWfzz+3+a3vZQ6SGohK0Ht3K64nSbx5BdnM2TK58koVsCc8fMZduhbW1+rrY6W32W3676Lfmn8nn5hpepPFNJxr4Mv4/j3R3vMj9zPg+PfJiJvSdaMgaZDxcr52Pvyb1QCc+ueJZl+5YRHRHN3Alz+XL3l34bg6H0XClz18ylqqaKB0c8SE1lTYNWmO2V0o6rPEKCUqofjrsVCSFEoOmvtT7a1B+GWswV0Beo8OLTxuD4H0R/Lz+vLwTSWCGwxitj9Q0Zq+u5C3UzwQ6pwyz1fxFN/p+tLRz/fwCgQmtd7s3n9rZAGisE1nhlrL4hY3Vq8fnkBKgQQgQBibkQQgQBiXn7VQG/r//V7gJprBBY45Wx+oaM1aSQOgEqhBDBSvbMhRAiCEjMhRAiCEjMhRAiCEjMhRAiCEjMfUApNV0ptUkpdU4pdUoptcTqMTVHKRWtlNqulNJKqRFWj6cxpdQlSqn5Sqn8+r/TPKXU75VSUVaPDUAp9Wul1EGlVGX9vFuzalULlFJPK6V+VEpVKKWKlFJLlFJJVo+rJUqpp+r/bf7V6rE0RSnVTyn1vlLqZP2/0V1KqdH+HIPE3MuUUjOB94C3gDRgPPCBpYNq2Z+AQqsH0YxkHP9WfwUMB/4FeBT4o5WDAlBKzQJexnFJ2khgB7BSKdXT0oF5dg3wd+BKYAoQCXytlOps6aiaoZS6Ase877R6LE1RSnUDNgDngWnAMOBfgVN+HYdcmug9SqkI4CDwrNZ6vsXDMUUpNQ1HjGYCu4HLtdbbrR1Vy5RS/wbM0VoPsngcm4AftdaP1/8+DDgC/E1r/ZKVY2uJUioeKAKu0Vr7f9nEFiilugCZwGPAM8B2rfVvrB3VhZRSLwHjtdYTrByH7Jl710igH1CnlNqmlDqmlFqulEq1emCeKKV6Af8E7gfOWjyc1ooDSqwcQP1hnlGAc6F6rXVd/e+vsmpcrRBX/6ulf4/N+DvwpdbamhsBmHcLsEUptaj+8NU2pdQv/D0Iibl3GXuJzwHPAzfh+FFrrVKqu1WD8qR+Bcm3gX9orbdYPJxWUUolAk8A/2PxUHoA4cBPjT7/E9Db/8Mxr/4niL8CG7TWWVaPpzGl1N04do6etnosJgwC5gC5wA3Aa8D/U0o96M9BSMxNUEq9VH8CprkP47guwAta68Va663AbBx31r7TZmN9nFJU8wAAA6lJREFUAseymi/6Y1ztHKv7Nv2AFcAirbX5u32Ixv4OpAJ3Wz2QxpRSA4D/C9yrta60ejwmhAGZWuv/1Fpv01q/juMn3kf9OYiQWgK3Hf6CYy+2OQeAPvX/vcf4pNa6Sil1ABjom6FdwOxYr8VxKKDKbelOcPy4uEBr7Y+9CrNjBUAp1RdYA2wEfum7YZl2AqgFejX6fC/guP+HY45S6hUcPzVO1Frb8WYto4CeQKbbv81wYKJS6nEgWmtda9XgPDiG2/d8vWwc56H8RmJugta6GChu6XFKqa04FtlJAr6r/1wkcAlwyIdDdGrFWP8XjpNKhr7ASmAWsMk3o2vI7FjBuUe+BtgKzK4/Nm0prXV1/ZynA0vAefgiHXjFyrF5Un9o7W/A7cAkrXW+xUNqyjfApY0+9xaQA/y3zUIOjitZGl/iORQ/fc8bJOZepLUuV0r9A/i9UuoIjsn8t/o/XmTdyC6ktT7s/nullHGj0Ty77a3Vh3wtjr/P3wLxxh6b1trqPeCXgXeUUluAzcBvgM444mM3fwfuAW4FKpRSxnH9Mq31OeuG1ZDWugJocBxfKXUGOGnH4/vA/wE2KqX+E/gYGIPjJ0e//vQoMfe+fwNqcFxr3hHHXu61Wmu/XnMaZKYAifUfjf9Hoy58uP9orT+qv8TvDzhOem4HpmqtG58UtYM59b+ubfT52bR8uEs0QWv9o1Lqdhznn/4LyAd+o7Ve4M9xyHXmQggRBORqFiGECAIScyGECAIScyGECAIScyGECAIScyGECAIScyGECAIScyGECAIScyGECAIScyHaQCkVrpTaqJT6tNHn45RSR5RSL1g1NhGa5B2gQrSRUmoojrfv/8J467ZS6l0ctwu8QmtdbeX4RGiRmAvRDvWrTz6H496kY3AsqHaF1nqHleMSoUdiLkQ71C8r+y2Odc0vxXHvz+etHZUIRRJzIdqp/m5I2cAuYKTWusbiIYkQJCdAhWi/h3DcEDsB6G/xWESIkj1zIdpBKTUOWAdcj+vOTddp+cYSfiZ75kK0kVKqE46bOrymtV4DPIzjJKhfb+QrBEjMhWiPF3Hc6egpAK31QRy3tfuTUuoSy0YlQpIcZhGiDZRS1+C48fAkrfV3jf5sJY5bMsrhFuE3EnMhhAgCcphFCCGCgMRcCCGCgMRcCCGCgMRcCCGCgMRcCCGCgMRcCCGCgMRcCCGCgMRcCCGCgMRcCCGCgMRcCCGCgMRcCCGCgMRcCCGCwP8H92vmhYAWqBgAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "f = plt.figure(dpi=100)\n", "sim.plot2D(ax=f.gca())\n", @@ -152,22 +120,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 46.95/383.3333435058594 = 12.2% done in 4.0s, 28.7s to go\n", - "Meep progress: 127.0/383.3333435058594 = 33.1% done in 8.0s, 16.2s to go\n", - "Meep progress: 210.425/383.3333435058594 = 54.9% done in 12.0s, 9.9s to go\n", - "Meep progress: 306.75/383.3333435058594 = 80.0% done in 16.0s, 4.0s to go\n", - "Normalizing field data...\n", - "run 0 finished at t = 383.35 (15334 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "f = plt.figure(dpi=100)\n", "animate = mp.Animate2D(sim, mp.Ez, f=f, normalize=True)\n", @@ -184,32 +139,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating MP4...\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "filename = \"media/oblique-source-normal.mp4\"\n", "animate.to_mp4(10, filename)\n", @@ -229,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -274,28 +206,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1,1e+20)\n", - " axes (0.939693,0.34202,0), (-0.34202,0.939693,0), (0,0,1)\n", - "Meep progress: 51.475/383.3333435058594 = 13.4% done in 4.0s, 25.8s to go\n", - "Meep progress: 125.7/383.3333435058594 = 32.8% done in 8.0s, 16.4s to go\n", - "Meep progress: 196.625/383.3333435058594 = 51.3% done in 12.0s, 11.4s to go\n", - "Meep progress: 275.02500000000003/383.3333435058594 = 71.7% done in 16.0s, 6.3s to go\n", - "Meep progress: 351.475/383.3333435058594 = 91.7% done in 20.0s, 1.8s to go\n", - "Normalizing field data...\n", - "run 0 finished at t = 383.35 (15334 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "f = plt.figure(dpi=100)\n", "animate = mp.Animate2D(sim, mp.Ez, f=f, normalize=True)\n", @@ -305,32 +218,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating MP4...\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "filename = \"media/oblique-source-eig.mp4\"\n", "animate.to_mp4(10, filename)\n", @@ -355,94 +245,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Meep progress: 84.72500000000001/383.3333435058594 = 22.1% done in 4.0s, 14.1s to go\n", - "Meep progress: 180.65/383.3333435058594 = 47.1% done in 8.0s, 9.0s to go\n", - "Meep progress: 286.125/383.3333435058594 = 74.6% done in 12.0s, 4.1s to go\n", - "run 0 finished at t = 383.35 (15334 timesteps)\n", - "flux:, 1091.653061, 941.670245\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1,1e+20)\n", - " axes (0.939693,0.34202,0), (-0.34202,0.939693,0), (0,0,1)\n", - "Meep progress: 107.4/383.3333435058594 = 28.0% done in 4.0s, 10.3s to go\n", - "Meep progress: 214.875/383.3333435058594 = 56.1% done in 8.0s, 6.3s to go\n", - "Meep progress: 323.375/383.3333435058594 = 84.4% done in 12.0s, 2.2s to go\n", - "run 0 finished at t = 383.35 (15334 timesteps)\n", - "flux:, 1110.194293, 1111.953628\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1,1e+20)\n", - " axes (0.766044,0.642788,0), (-0.642788,0.766044,0), (0,0,1)\n", - "Meep progress: 107.575/383.3333435058594 = 28.1% done in 4.0s, 10.3s to go\n", - "Meep progress: 207.60000000000002/383.3333435058594 = 54.2% done in 8.0s, 6.8s to go\n", - "Meep progress: 309.625/383.3333435058594 = 80.8% done in 12.0s, 2.9s to go\n", - "run 0 finished at t = 383.35 (15334 timesteps)\n", - "flux:, 1087.626286, 1041.967491\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for rot_angle in np.radians([0, 20, 40]):\n", "\n", diff --git a/python/examples/oblique-source.py b/python/examples/oblique-source.py index 25458e449..7e5a28da0 100644 --- a/python/examples/oblique-source.py +++ b/python/examples/oblique-source.py @@ -1,6 +1,7 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np + +import meep as mp resolution = 50 # pixels/μm diff --git a/python/examples/parallel-wvgs-force.ipynb b/python/examples/parallel-wvgs-force.ipynb index db2954e22..39114272e 100644 --- a/python/examples/parallel-wvgs-force.ipynb +++ b/python/examples/parallel-wvgs-force.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -142,2204 +142,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00295496 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.55,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.55,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0137441 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7594150d888346fbb6eab2f3b74120a0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 239.38333333333333/366.6666717529297 = 65.3% done in 4.0s, 2.1s to go\n", - "on time step 14404 (time=240.067), 0.000277733 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.2337849185921515, -1.9695192598601073e-09, 59350757.150949866, 0.5581279688138194, -0.5553187433557634-0.055927836092732594i, 2.543717760177644e-13+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.1, 0.2337849185921515\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00183916 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.55,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.55,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0193331 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.770666) = 0.233421 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.771882) = 0.233785 after 7 iters\n", - "MPB solved for frequency_1(0,0,0.771882) = 0.233785 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.770666) = 0.233421 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.771882) = 0.233785 after 7 iters\n", - "MPB solved for frequency_1(0,0,0.771882) = 0.233785 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "25d74586424d475ba7fd3c46c7af0993", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 211.81666666666666/666.6666717529297 = 31.8% done in 4.0s, 8.6s to go\n", - "on time step 12744 (time=212.4), 0.000313893 s/step\n", - "Meep progress: 438.3333333333333/666.6666717529297 = 65.7% done in 8.0s, 4.2s to go\n", - "on time step 26339 (time=438.983), 0.000294248 s/step\n", - "Meep progress: 660.8666666666667/666.6666717529297 = 99.1% done in 12.0s, 0.1s to go\n", - "on time step 39695 (time=661.583), 0.000299507 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00133109 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.55,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.55,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0117002 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b4da8f0d9e514cd8973e7a86f36aa83a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 246.28333333333333/366.6666717529297 = 67.2% done in 4.0s, 2.0s to go\n", - "on time step 14810 (time=246.833), 0.000270096 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.21436005656524434, 2.1977416545924374e-08, -4876825.629557369, 1.0437686932994705, -0.6908642657795828-0.7824063211534797i, 3.332567959460228e-13+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.1, 0.21436005656524434\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00119805 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.55,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.55,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0203319 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.706409) = 0.214152 after 11 iters\n", - "MPB solved for frequency_1(0,0,0.707103) = 0.21436 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.707103) = 0.21436 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.706409) = 0.214152 after 11 iters\n", - "MPB solved for frequency_1(0,0,0.707103) = 0.21436 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.707103) = 0.21436 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "133c2f48e1d04fcc94f8ba14ae285e83", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 83.76666666666667/666.6666717529297 = 12.6% done in 4.0s, 27.8s to go\n", - "on time step 5044 (time=84.0667), 0.000793118 s/step\n", - "Meep progress: 168.71666666666667/666.6666717529297 = 25.3% done in 8.0s, 23.6s to go\n", - "on time step 10144 (time=169.067), 0.000784439 s/step\n", - "Meep progress: 255.01666666666665/666.6666717529297 = 38.3% done in 12.0s, 19.4s to go\n", - "on time step 15324 (time=255.4), 0.000772402 s/step\n", - "Meep progress: 340.3666666666667/666.6666717529297 = 51.1% done in 16.0s, 15.3s to go\n", - "on time step 20446 (time=340.767), 0.000780987 s/step\n", - "Meep progress: 424.3833333333333/666.6666717529297 = 63.7% done in 20.0s, 11.4s to go\n", - "on time step 25484 (time=424.733), 0.00079398 s/step\n", - "Meep progress: 509.8/666.6666717529297 = 76.5% done in 24.0s, 7.4s to go\n", - "on time step 30615 (time=510.25), 0.00077967 s/step\n", - "Meep progress: 594.2/666.6666717529297 = 89.1% done in 28.0s, 3.4s to go\n", - "on time step 35680 (time=594.667), 0.000789747 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00357318 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.6,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.6,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0155571 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c6edcf731c7e4156999f724734815579", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 97.31666666666666/366.6666717529297 = 26.5% done in 4.0s, 11.1s to go\n", - "on time step 5854 (time=97.5667), 0.000683346 s/step\n", - "Meep progress: 195.31666666666666/366.6666717529297 = 53.3% done in 8.0s, 7.0s to go\n", - "on time step 11733 (time=195.55), 0.000680444 s/step\n", - "Meep progress: 290.3666666666667/366.6666717529297 = 79.2% done in 12.0s, 3.2s to go\n", - "on time step 17439 (time=290.65), 0.000701091 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.2321007699342635, -6.589441383742643e-10, 176115664.75642273, 0.7050710049152503, -0.3914039769859229-0.5864537908239694i, 2.563795272483861e-13+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.2, 0.2321007699342635\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00154901 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.6,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.6,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0192392 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.763979) = 0.231754 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.765136) = 0.232101 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.765136) = 0.232101 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.763979) = 0.231754 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.765136) = 0.232101 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.765136) = 0.232101 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ae454de372884127a593b61a598bd379", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 83.15/666.6666717529297 = 12.5% done in 4.0s, 28.1s to go\n", - "on time step 5003 (time=83.3833), 0.000799551 s/step\n", - "Meep progress: 168.0/666.6666717529297 = 25.2% done in 8.0s, 23.7s to go\n", - "on time step 10096 (time=168.267), 0.000785392 s/step\n", - "Meep progress: 252.48333333333332/666.6666717529297 = 37.9% done in 12.0s, 19.7s to go\n", - "on time step 15165 (time=252.75), 0.00078922 s/step\n", - "Meep progress: 336.6166666666667/666.6666717529297 = 50.5% done in 16.0s, 15.7s to go\n", - "on time step 20213 (time=336.883), 0.000792426 s/step\n", - "Meep progress: 421.5/666.6666717529297 = 63.2% done in 20.0s, 11.6s to go\n", - "on time step 25308 (time=421.8), 0.000785113 s/step\n", - "Meep progress: 506.68333333333334/666.6666717529297 = 76.0% done in 24.0s, 7.6s to go\n", - "on time step 30420 (time=507), 0.000782534 s/step\n", - "Meep progress: 591.7833333333333/666.6666717529297 = 88.8% done in 28.0s, 3.5s to go\n", - "on time step 35528 (time=592.133), 0.000783211 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00184393 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.6,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.6,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.015429 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cf6496e54bc04252bd6312b4c1784ff9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 98.25/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", - "on time step 5906 (time=98.4333), 0.000677395 s/step\n", - "Meep progress: 196.18333333333334/366.6666717529297 = 53.5% done in 8.0s, 7.0s to go\n", - "on time step 11784 (time=196.4), 0.000680643 s/step\n", - "Meep progress: 289.8/366.6666717529297 = 79.0% done in 12.0s, 3.2s to go\n", - "on time step 17401 (time=290.017), 0.000712187 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.21796392210149537, 2.7101979244778688e-08, -4021180.8911241614, 1.2931472666535015, -0.6549272150232459+1.1150337197929565i, 1.1090356875112212e-13+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.2, 0.21796392210149537\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.0045712 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.6,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.6,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0223899 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.717283) = 0.217732 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.718056) = 0.217964 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.718056) = 0.217964 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.717283) = 0.217732 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.718056) = 0.217964 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.718056) = 0.217964 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d5cd0cc64a6c4b4c89c769e543a30d50", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 80.98333333333333/666.6666717529297 = 12.1% done in 4.0s, 28.9s to go\n", - "on time step 4873 (time=81.2167), 0.000821001 s/step\n", - "Meep progress: 167.45/666.6666717529297 = 25.1% done in 8.0s, 23.9s to go\n", - "on time step 10064 (time=167.733), 0.00077061 s/step\n", - "Meep progress: 249.98333333333332/666.6666717529297 = 37.5% done in 12.0s, 20.0s to go\n", - "on time step 15016 (time=250.267), 0.000807818 s/step\n", - "Meep progress: 332.5833333333333/666.6666717529297 = 49.9% done in 16.0s, 16.1s to go\n", - "on time step 19974 (time=332.9), 0.000806779 s/step\n", - "Meep progress: 416.75/666.6666717529297 = 62.5% done in 20.0s, 12.0s to go\n", - "on time step 25023 (time=417.05), 0.000792321 s/step\n", - "Meep progress: 500.4166666666667/666.6666717529297 = 75.1% done in 24.0s, 8.0s to go\n", - "on time step 30045 (time=500.75), 0.000796587 s/step\n", - "Meep progress: 584.0333333333333/666.6666717529297 = 87.6% done in 28.0s, 4.0s to go\n", - "on time step 35066 (time=584.433), 0.000796753 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00176692 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.65,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.65,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.022615 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3746edd53d114015b5824f25e125594b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 98.23333333333333/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", - "on time step 5908 (time=98.4667), 0.00067706 s/step\n", - "Meep progress: 195.71666666666667/366.6666717529297 = 53.4% done in 8.0s, 7.0s to go\n", - "on time step 11757 (time=195.95), 0.000683889 s/step\n", - "Meep progress: 289.85/366.6666717529297 = 79.0% done in 12.0s, 3.2s to go\n", - "on time step 17406 (time=290.1), 0.000708109 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.23050129868403818, 1.1111522223430954e-09, -103721746.69190612, 0.8510771099307869, 0.2097756989886373-0.824819012366937i, 2.532377183551288e-13+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.30000000000000004, 0.23050129868403818\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00193691 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.65,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.65,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0224741 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.759804) = 0.230167 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.760921) = 0.230501 after 7 iters\n", - "MPB solved for frequency_1(0,0,0.760921) = 0.230501 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.759804) = 0.230167 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.760921) = 0.230501 after 7 iters\n", - "MPB solved for frequency_1(0,0,0.760921) = 0.230501 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "11a3ef7cf85a4d2a9db9e4e40bd4b300", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 84.26666666666667/666.6666717529297 = 12.6% done in 4.0s, 27.6s to go\n", - "on time step 5068 (time=84.4667), 0.000789299 s/step\n", - "Meep progress: 168.88333333333333/666.6666717529297 = 25.3% done in 8.0s, 23.6s to go\n", - "on time step 10146 (time=169.1), 0.000787785 s/step\n", - "Meep progress: 253.36666666666667/666.6666717529297 = 38.0% done in 12.0s, 19.6s to go\n", - "on time step 15217 (time=253.617), 0.000788949 s/step\n", - "Meep progress: 337.76666666666665/666.6666717529297 = 50.7% done in 16.0s, 15.6s to go\n", - "on time step 20281 (time=338.017), 0.000789954 s/step\n", - "Meep progress: 422.93333333333334/666.6666717529297 = 63.4% done in 20.0s, 11.5s to go\n", - "on time step 25393 (time=423.217), 0.000782588 s/step\n", - "Meep progress: 507.96666666666664/666.6666717529297 = 76.2% done in 24.0s, 7.5s to go\n", - "on time step 30498 (time=508.3), 0.000783646 s/step\n", - "Meep progress: 592.5/666.6666717529297 = 88.9% done in 28.0s, 3.5s to go\n", - "on time step 35571 (time=592.85), 0.000788548 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00206399 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.65,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.65,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.020714 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "05665f9e0ca64c61919b04105aea010b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 97.15/366.6666717529297 = 26.5% done in 4.0s, 11.1s to go\n", - "on time step 5847 (time=97.45), 0.000684132 s/step\n", - "Meep progress: 194.53333333333333/366.6666717529297 = 53.1% done in 8.0s, 7.1s to go\n", - "on time step 11689 (time=194.817), 0.000684739 s/step\n", - "Meep progress: 288.7/366.6666717529297 = 78.7% done in 12.0s, 3.2s to go\n", - "on time step 17342 (time=289.033), 0.000707681 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.2202853119212703, 1.661718958542485e-08, -6628236.104211189, 1.3726411235812384, 0.8679324852926675+1.0634081319606588i, 4.943132075236113e-14+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.30000000000000004, 0.2202853119212703\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00254488 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.65,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.65,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0223539 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.726009) = 0.220034 after 11 iters\n", - "MPB solved for frequency_1(0,0,0.726846) = 0.220285 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.726846) = 0.220285 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.726009) = 0.220034 after 11 iters\n", - "MPB solved for frequency_1(0,0,0.726846) = 0.220285 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.726846) = 0.220285 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "32edc002b0594a85872a5f15e469b486", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 83.03333333333333/666.6666717529297 = 12.5% done in 4.0s, 28.1s to go\n", - "on time step 4991 (time=83.1833), 0.000801588 s/step\n", - "Meep progress: 166.5/666.6666717529297 = 25.0% done in 8.0s, 24.0s to go\n", - "on time step 10001 (time=166.683), 0.000798491 s/step\n", - "Meep progress: 250.11666666666667/666.6666717529297 = 37.5% done in 12.0s, 20.0s to go\n", - "on time step 15019 (time=250.317), 0.0007972 s/step\n", - "Meep progress: 334.71666666666664/666.6666717529297 = 50.2% done in 16.0s, 15.9s to go\n", - "on time step 20097 (time=334.95), 0.000787764 s/step\n", - "Meep progress: 418.81666666666666/666.6666717529297 = 62.8% done in 20.0s, 11.8s to go\n", - "on time step 25142 (time=419.033), 0.000792875 s/step\n", - "Meep progress: 503.55/666.6666717529297 = 75.5% done in 24.0s, 7.8s to go\n", - "on time step 30228 (time=503.8), 0.000786594 s/step\n", - "Meep progress: 587.45/666.6666717529297 = 88.1% done in 28.0s, 3.8s to go\n", - "on time step 35264 (time=587.733), 0.000794317 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00176716 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.7,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.7,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0131691 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1b075265f05142889634e50818f79e06", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 98.36666666666666/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", - "on time step 5915 (time=98.5833), 0.000676356 s/step\n", - "Meep progress: 196.03333333333333/366.6666717529297 = 53.5% done in 8.0s, 7.0s to go\n", - "on time step 11777 (time=196.283), 0.000682479 s/step\n", - "Meep progress: 289.5833333333333/366.6666717529297 = 79.0% done in 12.0s, 3.2s to go\n", - "on time step 17392 (time=289.867), 0.000712464 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.22938047554467897, 2.8259525192417854e-09, -40584630.13494343, 0.9554083501216192, 0.7088623048929175-0.6405617442401746i, 4.7858601707454104e-14+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.4, 0.22938047554467897\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00361514 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.7,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.7,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0198278 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.754994) = 0.229057 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.75607) = 0.22938 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.75607) = 0.22938 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.754994) = 0.229057 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.75607) = 0.22938 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.75607) = 0.22938 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "49e7f1011eb04db090ccea4009ce3a6a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 82.06666666666666/666.6666717529297 = 12.3% done in 4.0s, 28.5s to go\n", - "on time step 4936 (time=82.2667), 0.000810417 s/step\n", - "Meep progress: 166.41666666666666/666.6666717529297 = 25.0% done in 8.0s, 24.0s to go\n", - "on time step 9998 (time=166.633), 0.000790205 s/step\n", - "Meep progress: 249.71666666666667/666.6666717529297 = 37.5% done in 12.0s, 20.0s to go\n", - "on time step 14996 (time=249.933), 0.00080039 s/step\n", - "Meep progress: 333.55/666.6666717529297 = 50.0% done in 16.0s, 16.0s to go\n", - "on time step 20028 (time=333.8), 0.000795032 s/step\n", - "Meep progress: 416.65/666.6666717529297 = 62.5% done in 20.0s, 12.0s to go\n", - "on time step 25015 (time=416.917), 0.000802087 s/step\n", - "Meep progress: 500.7/666.6666717529297 = 75.1% done in 24.0s, 8.0s to go\n", - "on time step 30060 (time=501), 0.00079297 s/step\n", - "Meep progress: 584.4666666666667/666.6666717529297 = 87.7% done in 28.0s, 3.9s to go\n", - "on time step 35087 (time=584.783), 0.000795733 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00191903 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.7,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.7,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.029588 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "20771f63a2984401ad8c7b9ba6494fb8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 97.71666666666667/366.6666717529297 = 26.6% done in 4.0s, 11.0s to go\n", - "on time step 5876 (time=97.9333), 0.000680849 s/step\n", - "Meep progress: 194.2/366.6666717529297 = 53.0% done in 8.0s, 7.1s to go\n", - "on time step 11665 (time=194.417), 0.000690981 s/step\n", - "Meep progress: 286.93333333333334/366.6666717529297 = 78.3% done in 12.0s, 3.3s to go\n", - "on time step 17231 (time=287.183), 0.000718741 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.22197905508783583, 2.689453714201604e-09, -41268428.21567816, 1.380617017276195, 1.3806167115397914-0.0009188088320141777i, 5.815262450716548e-14+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.4, 0.22197905508783583\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00408697 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.7,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.7,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0230129 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.730545) = 0.221717 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.731419) = 0.221979 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.731419) = 0.221979 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.730545) = 0.221717 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.731419) = 0.221979 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.731419) = 0.221979 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e183edf238ee4fb9b268879c6cb80157", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 83.2/666.6666717529297 = 12.5% done in 4.0s, 28.1s to go\n", - "on time step 5007 (time=83.45), 0.000798937 s/step\n", - "Meep progress: 167.13333333333333/666.6666717529297 = 25.1% done in 8.0s, 23.9s to go\n", - "on time step 10044 (time=167.4), 0.000794127 s/step\n", - "Meep progress: 251.06666666666666/666.6666717529297 = 37.7% done in 12.0s, 19.9s to go\n", - "on time step 15081 (time=251.35), 0.000794205 s/step\n", - "Meep progress: 334.51666666666665/666.6666717529297 = 50.2% done in 16.0s, 15.9s to go\n", - "on time step 20090 (time=334.833), 0.000798598 s/step\n", - "Meep progress: 423.31666666666666/666.6666717529297 = 63.5% done in 20.0s, 11.5s to go\n", - "on time step 25419 (time=423.65), 0.000750669 s/step\n", - "Meep progress: 507.84999999999997/666.6666717529297 = 76.2% done in 24.0s, 7.5s to go\n", - "on time step 30493 (time=508.217), 0.000788413 s/step\n", - "Meep progress: 592.3333333333334/666.6666717529297 = 88.8% done in 28.0s, 3.5s to go\n", - "on time step 35564 (time=592.733), 0.000788837 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.0018878 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.75,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.75,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.016355 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b214004d80b44af9ab864af98dae656b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 93.36666666666666/366.6666717529297 = 25.5% done in 4.0s, 11.7s to go\n", - "on time step 5619 (time=93.65), 0.000711909 s/step\n", - "Meep progress: 186.51666666666665/366.6666717529297 = 50.9% done in 8.0s, 7.7s to go\n", - "on time step 11208 (time=186.8), 0.000715819 s/step\n", - "Meep progress: 275.8333333333333/366.6666717529297 = 75.2% done in 12.0s, 4.0s to go\n", - "on time step 16567 (time=276.117), 0.000746435 s/step\n", - "Meep progress: 364.9166666666667/366.6666717529297 = 99.5% done in 16.0s, 0.1s to go\n", - "on time step 21916 (time=365.267), 0.000747914 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.22839396754116606, 4.693442764536769e-09, -24331176.387926824, 1.0415890909263652, 1.0168824850407956-0.22551684184128717i, 4.249035856082629e-14+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.5, 0.22839396754116606\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00539398 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.75,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.75,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.023149 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.752833) = 0.228077 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.753888) = 0.228394 after 7 iters\n", - "MPB solved for frequency_1(0,0,0.753888) = 0.228394 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.752833) = 0.228077 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.753888) = 0.228394 after 7 iters\n", - "MPB solved for frequency_1(0,0,0.753888) = 0.228394 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "35aea70ba52b49b285dbd05619bd4a8e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 81.8/666.6666717529297 = 12.3% done in 4.0s, 28.6s to go\n", - "on time step 4924 (time=82.0667), 0.000812495 s/step\n", - "Meep progress: 164.1/666.6666717529297 = 24.6% done in 8.0s, 24.5s to go\n", - "on time step 9862 (time=164.367), 0.000810167 s/step\n", - "Meep progress: 247.63333333333333/666.6666717529297 = 37.1% done in 12.0s, 20.3s to go\n", - "on time step 14875 (time=247.917), 0.00079793 s/step\n", - "Meep progress: 330.1666666666667/666.6666717529297 = 49.5% done in 16.0s, 16.3s to go\n", - "on time step 19829 (time=330.483), 0.000807541 s/step\n", - "Meep progress: 413.0333333333333/666.6666717529297 = 62.0% done in 20.0s, 12.3s to go\n", - "on time step 24804 (time=413.4), 0.000804159 s/step\n", - "Meep progress: 496.95/666.6666717529297 = 74.5% done in 24.0s, 8.2s to go\n", - "on time step 29839 (time=497.317), 0.000794594 s/step\n", - "Meep progress: 580.2166666666667/666.6666717529297 = 87.0% done in 28.0s, 4.2s to go\n", - "on time step 34837 (time=580.617), 0.000800409 s/step\n", - "Meep progress: 664.0166666666667/666.6666717529297 = 99.6% done in 32.0s, 0.1s to go\n", - "on time step 39867 (time=664.45), 0.000795323 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00177789 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.75,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.75,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0188749 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6f23ec57cbd44ccd9c05b973705f2da3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 96.9/366.6666717529297 = 26.4% done in 4.0s, 11.1s to go\n", - "on time step 5834 (time=97.2333), 0.000685744 s/step\n", - "Meep progress: 194.31666666666666/366.6666717529297 = 53.0% done in 8.0s, 7.1s to go\n", - "on time step 11679 (time=194.65), 0.000684423 s/step\n", - "Meep progress: 288.7/366.6666717529297 = 78.7% done in 12.0s, 3.2s to go\n", - "on time step 17345 (time=289.083), 0.000705968 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.2230300941405038, -7.841965023994586e-09, 14220293.858623678, 1.3613884433238386, 1.1598737564442396-0.7127912476508345i, 7.609870353913999e-14+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.5, 0.2230300941405038\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00159287 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.75,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.75,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.02321 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.735089) = 0.222758 after 11 iters\n", - "MPB solved for frequency_1(0,0,0.735997) = 0.22303 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.735997) = 0.22303 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.735089) = 0.222758 after 11 iters\n", - "MPB solved for frequency_1(0,0,0.735997) = 0.22303 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.735997) = 0.22303 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e436a71259cd488398bd3aedff3a057c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 82.91666666666667/666.6666717529297 = 12.4% done in 4.0s, 28.2s to go\n", - "on time step 4986 (time=83.1), 0.000802322 s/step\n", - "Meep progress: 165.63333333333333/666.6666717529297 = 24.8% done in 8.0s, 24.2s to go\n", - "on time step 9950 (time=165.833), 0.000805971 s/step\n", - "Meep progress: 249.08333333333334/666.6666717529297 = 37.4% done in 12.0s, 20.1s to go\n", - "on time step 14957 (time=249.283), 0.000798944 s/step\n", - "Meep progress: 332.0333333333333/666.6666717529297 = 49.8% done in 16.0s, 16.1s to go\n", - "on time step 19937 (time=332.283), 0.00080335 s/step\n", - "Meep progress: 416.8333333333333/666.6666717529297 = 62.5% done in 20.0s, 12.0s to go\n", - "on time step 25028 (time=417.133), 0.000785816 s/step\n", - "Meep progress: 501.56666666666666/666.6666717529297 = 75.2% done in 24.0s, 7.9s to go\n", - "on time step 30113 (time=501.883), 0.000786698 s/step\n", - "Meep progress: 585.2666666666667/666.6666717529297 = 87.8% done in 28.0s, 3.9s to go\n", - "on time step 35137 (time=585.617), 0.000796204 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.0018599 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.8,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.8,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0129621 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5218926d24124ece9c74de7f1cc1effc", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 97.08333333333333/366.6666717529297 = 26.5% done in 4.0s, 11.1s to go\n", - "on time step 5839 (time=97.3167), 0.000685119 s/step\n", - "Meep progress: 193.31666666666666/366.6666717529297 = 52.7% done in 8.0s, 7.2s to go\n", - "on time step 11612 (time=193.533), 0.000692887 s/step\n", - "Meep progress: 287.93333333333334/366.6666717529297 = 78.5% done in 12.0s, 3.3s to go\n", - "on time step 17291 (time=288.183), 0.000704366 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.22779219587071284, 6.5733308116063705e-09, -17326999.233669, 1.0936986894641623, 1.0885703324807652+0.10578967141617628i, 1.6452384436270636e-13+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.6, 0.22779219587071284\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00145602 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.8,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.8,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0227098 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.749747) = 0.227483 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.750779) = 0.227792 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.750779) = 0.227792 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.749747) = 0.227483 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.750779) = 0.227792 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.750779) = 0.227792 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bb85e22b6bea4492b28f6bd247b5fe39", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 81.93333333333334/666.6666717529297 = 12.3% done in 4.0s, 28.6s to go\n", - "on time step 4932 (time=82.2), 0.000811153 s/step\n", - "Meep progress: 164.1/666.6666717529297 = 24.6% done in 8.0s, 24.5s to go\n", - "on time step 9863 (time=164.383), 0.000811203 s/step\n", - "Meep progress: 246.46666666666667/666.6666717529297 = 37.0% done in 12.0s, 20.5s to go\n", - "on time step 14805 (time=246.75), 0.000809507 s/step\n", - "Meep progress: 328.0/666.6666717529297 = 49.2% done in 16.0s, 16.5s to go\n", - "on time step 19699 (time=328.317), 0.000817414 s/step\n", - "Meep progress: 410.98333333333335/666.6666717529297 = 61.6% done in 20.0s, 12.4s to go\n", - "on time step 24678 (time=411.3), 0.000803526 s/step\n", - "Meep progress: 493.6333333333333/666.6666717529297 = 74.0% done in 24.0s, 8.4s to go\n", - "on time step 29641 (time=494.017), 0.000806037 s/step\n", - "Meep progress: 575.4833333333333/666.6666717529297 = 86.3% done in 28.0s, 4.4s to go\n", - "on time step 34552 (time=575.867), 0.000814608 s/step\n", - "Meep progress: 657.2666666666667/666.6666717529297 = 98.6% done in 32.0s, 0.5s to go\n", - "on time step 39463 (time=657.717), 0.000814686 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00235796 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.8,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.8,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0135491 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b671743684474e82b4692b977e92a73a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 98.13333333333333/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", - "on time step 5904 (time=98.4), 0.000677588 s/step\n", - "Meep progress: 195.1/366.6666717529297 = 53.2% done in 8.0s, 7.0s to go\n", - "on time step 11722 (time=195.367), 0.000687535 s/step\n", - "Meep progress: 289.7/366.6666717529297 = 79.0% done in 12.0s, 3.2s to go\n", - "on time step 17400 (time=290), 0.00070453 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.22389046766301668, -1.7192955887682313e-08, 6511110.396770701, 1.3350122958073318, 0.7195356752054972-1.124511557105276i, 1.7074960255719633e-13+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.6, 0.22389046766301668\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00139403 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.8,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.8,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0244219 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.736859) = 0.223613 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.737783) = 0.22389 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.737783) = 0.22389 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.736859) = 0.223613 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.737783) = 0.22389 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.737783) = 0.22389 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "291128db6c26474996b42b7bfffc01a6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 80.78333333333333/666.6666717529297 = 12.1% done in 4.0s, 29.0s to go\n", - "on time step 4859 (time=80.9833), 0.00082328 s/step\n", - "Meep progress: 163.13333333333333/666.6666717529297 = 24.5% done in 8.0s, 24.7s to go\n", - "on time step 9802 (time=163.367), 0.000809344 s/step\n", - "Meep progress: 246.68333333333334/666.6666717529297 = 37.0% done in 12.0s, 20.4s to go\n", - "on time step 14815 (time=246.917), 0.000798049 s/step\n", - "Meep progress: 328.95/666.6666717529297 = 49.3% done in 16.0s, 16.4s to go\n", - "on time step 19751 (time=329.183), 0.000810423 s/step\n", - "Meep progress: 411.5/666.6666717529297 = 61.7% done in 20.0s, 12.4s to go\n", - "on time step 24705 (time=411.75), 0.000807463 s/step\n", - "Meep progress: 494.3333333333333/666.6666717529297 = 74.1% done in 24.0s, 8.4s to go\n", - "on time step 29677 (time=494.617), 0.000804549 s/step\n", - "Meep progress: 576.9/666.6666717529297 = 86.5% done in 28.0s, 4.4s to go\n", - "on time step 34633 (time=577.217), 0.000807203 s/step\n", - "Meep progress: 659.4/666.6666717529297 = 98.9% done in 32.0s, 0.4s to go\n", - "on time step 39582 (time=659.7), 0.000808279 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.0023129 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.85,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.85,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.013726 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3132625bea184efaa79e72754b9a7edc", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 98.66666666666667/366.6666717529297 = 26.9% done in 4.0s, 10.9s to go\n", - "on time step 5932 (time=98.8667), 0.000674405 s/step\n", - "Meep progress: 196.48333333333332/366.6666717529297 = 53.6% done in 8.0s, 6.9s to go\n", - "on time step 11802 (time=196.7), 0.000681457 s/step\n", - "Meep progress: 289.5833333333333/366.6666717529297 = 79.0% done in 12.0s, 3.2s to go\n", - "on time step 17390 (time=289.833), 0.000715836 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.2272133016731052, 8.742503028543528e-09, -12994751.098814214, 1.1387362638662792, 1.0488380657857905+0.4434625016874079i, 6.376331244480289e-14+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.7000000000000001, 0.2272133016731052\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00217509 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.85,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.85,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0203781 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.748927) = 0.226907 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.749949) = 0.227213 after 7 iters\n", - "MPB solved for frequency_1(0,0,0.749949) = 0.227213 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.748927) = 0.226907 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.749949) = 0.227213 after 7 iters\n", - "MPB solved for frequency_1(0,0,0.749949) = 0.227213 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "00b16ae25da64c109934700b9b64b551", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 80.98333333333333/666.6666717529297 = 12.1% done in 4.0s, 28.9s to go\n", - "on time step 4872 (time=81.2), 0.000821152 s/step\n", - "Meep progress: 163.65/666.6666717529297 = 24.5% done in 8.0s, 24.6s to go\n", - "on time step 9835 (time=163.917), 0.000806157 s/step\n", - "Meep progress: 245.54999999999998/666.6666717529297 = 36.8% done in 12.0s, 20.6s to go\n", - "on time step 14749 (time=245.817), 0.000814041 s/step\n", - "Meep progress: 328.2/666.6666717529297 = 49.2% done in 16.0s, 16.5s to go\n", - "on time step 19710 (time=328.5), 0.000806343 s/step\n", - "Meep progress: 409.6666666666667/666.6666717529297 = 61.4% done in 20.0s, 12.5s to go\n", - "on time step 24599 (time=409.983), 0.000818225 s/step\n", - "Meep progress: 492.71666666666664/666.6666717529297 = 73.9% done in 24.0s, 8.5s to go\n", - "on time step 29585 (time=493.083), 0.000802381 s/step\n", - "Meep progress: 575.6666666666666/666.6666717529297 = 86.3% done in 28.0s, 4.4s to go\n", - "on time step 34560 (time=576), 0.000804049 s/step\n", - "Meep progress: 657.1666666666666/666.6666717529297 = 98.6% done in 32.0s, 0.5s to go\n", - "on time step 39453 (time=657.55), 0.000817607 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00278711 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.85,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.85,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0183229 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b09562e43e724c0db50a813c1b962259", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 97.63333333333333/366.6666717529297 = 26.6% done in 4.0s, 11.0s to go\n", - "on time step 5868 (time=97.8), 0.000681683 s/step\n", - "Meep progress: 194.08333333333334/366.6666717529297 = 52.9% done in 8.0s, 7.1s to go\n", - "on time step 11656 (time=194.267), 0.000691144 s/step\n", - "Meep progress: 287.7/366.6666717529297 = 78.5% done in 12.0s, 3.3s to go\n", - "on time step 17275 (time=287.917), 0.000711899 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.22437226062905724, -2.3099396489881195e-08, 4856669.3230999485, 1.313923864774931, 0.4094809417964661-1.2484875973475147i, 1.7003140322728657e-13+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.7000000000000001, 0.22437226062905724\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00296402 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.85,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.85,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0208819 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.739529) = 0.224089 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.740473) = 0.224372 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.740473) = 0.224372 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.739529) = 0.224089 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.740473) = 0.224372 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.740473) = 0.224372 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "be6d782d86854a299bd5634c042b1837", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 82.01666666666667/666.6666717529297 = 12.3% done in 4.0s, 28.5s to go\n", - "on time step 4933 (time=82.2167), 0.000811033 s/step\n", - "Meep progress: 163.2/666.6666717529297 = 24.5% done in 8.0s, 24.7s to go\n", - "on time step 9805 (time=163.417), 0.000821053 s/step\n", - "Meep progress: 246.13333333333333/666.6666717529297 = 36.9% done in 12.0s, 20.5s to go\n", - "on time step 14783 (time=246.383), 0.000803661 s/step\n", - "Meep progress: 328.68333333333334/666.6666717529297 = 49.3% done in 16.0s, 16.5s to go\n", - "on time step 19737 (time=328.95), 0.000807463 s/step\n", - "Meep progress: 410.4166666666667/666.6666717529297 = 61.6% done in 20.0s, 12.5s to go\n", - "on time step 24642 (time=410.7), 0.000815659 s/step\n", - "Meep progress: 492.2833333333333/666.6666717529297 = 73.8% done in 24.0s, 8.5s to go\n", - "on time step 29554 (time=492.567), 0.000814442 s/step\n", - "Meep progress: 575.4166666666666/666.6666717529297 = 86.3% done in 28.0s, 4.4s to go\n", - "on time step 34545 (time=575.75), 0.000801476 s/step\n", - "Meep progress: 658.3/666.6666717529297 = 98.7% done in 32.0s, 0.4s to go\n", - "on time step 39517 (time=658.617), 0.000804507 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00178504 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.9,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.9,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.012604 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c2b5a9f01c2b49eaa039716b0b7bd1da", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 97.18333333333334/366.6666717529297 = 26.5% done in 4.0s, 11.1s to go\n", - "on time step 5847 (time=97.45), 0.000684151 s/step\n", - "Meep progress: 192.4/366.6666717529297 = 52.5% done in 8.0s, 7.2s to go\n", - "on time step 11561 (time=192.683), 0.000700084 s/step\n", - "Meep progress: 284.7/366.6666717529297 = 77.6% done in 12.0s, 3.5s to go\n", - "on time step 17100 (time=285), 0.000722162 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.22692081083425694, 1.1307039395132144e-08, -10034492.801535204, 1.1623124097996433, 0.9889626651292537+0.6106742052475259i, 7.110203970811583e-14+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.8, 0.22692081083425694\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00310993 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.9,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.9,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0245988 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.746869) = 0.226619 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.747876) = 0.226921 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.747876) = 0.226921 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.746869) = 0.226619 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.747876) = 0.226921 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.747876) = 0.226921 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "26d380369f4047da9fb61042e8adcd81", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 82.35/666.6666717529297 = 12.4% done in 4.0s, 28.4s to go\n", - "on time step 4956 (time=82.6), 0.000807173 s/step\n", - "Meep progress: 165.3/666.6666717529297 = 24.8% done in 8.0s, 24.3s to go\n", - "on time step 9936 (time=165.6), 0.00080335 s/step\n", - "Meep progress: 248.29999999999998/666.6666717529297 = 37.2% done in 12.0s, 20.2s to go\n", - "on time step 14916 (time=248.6), 0.000803232 s/step\n", - "Meep progress: 331.3833333333333/666.6666717529297 = 49.7% done in 16.0s, 16.2s to go\n", - "on time step 19901 (time=331.683), 0.000802426 s/step\n", - "Meep progress: 414.55/666.6666717529297 = 62.2% done in 20.0s, 12.2s to go\n", - "on time step 24893 (time=414.883), 0.000801303 s/step\n", - "Meep progress: 497.6666666666667/666.6666717529297 = 74.6% done in 24.0s, 8.2s to go\n", - "on time step 29882 (time=498.033), 0.000801855 s/step\n", - "Meep progress: 580.8333333333334/666.6666717529297 = 87.1% done in 28.0s, 4.1s to go\n", - "on time step 34873 (time=581.217), 0.000801512 s/step\n", - "Meep progress: 663.0/666.6666717529297 = 99.4% done in 32.0s, 0.2s to go\n", - "on time step 39805 (time=663.417), 0.000811121 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00191712 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.9,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.9,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.013334 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9268b5878e46401383a15b709fc207e2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 95.85/366.6666717529297 = 26.1% done in 4.0s, 11.3s to go\n", - "on time step 5762 (time=96.0333), 0.000694243 s/step\n", - "Meep progress: 192.11666666666667/366.6666717529297 = 52.4% done in 8.0s, 7.3s to go\n", - "on time step 11538 (time=192.3), 0.000692638 s/step\n", - "Meep progress: 285.15/366.6666717529297 = 77.8% done in 12.0s, 3.4s to go\n", - "on time step 17122 (time=285.367), 0.00071637 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.2248450102131107, -2.925827242064695e-08, 3842417.7439547367, 1.2923701545067185, 0.08958085376950176-1.2892617604263483i, 1.389119717333207e-13+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.8, 0.2248450102131107\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00451803 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.9,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.9,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0211821 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.740012) = 0.22456 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.740962) = 0.224845 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.740962) = 0.224845 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.740012) = 0.22456 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.740962) = 0.224845 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.740962) = 0.224845 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0c31aca58273447eb9309328463cb41e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 81.1/666.6666717529297 = 12.2% done in 4.0s, 28.9s to go\n", - "on time step 4876 (time=81.2667), 0.000820368 s/step\n", - "Meep progress: 162.61666666666667/666.6666717529297 = 24.4% done in 8.0s, 24.8s to go\n", - "on time step 9769 (time=162.817), 0.000817623 s/step\n", - "Meep progress: 243.96666666666667/666.6666717529297 = 36.6% done in 12.0s, 20.8s to go\n", - "on time step 14650 (time=244.167), 0.000819636 s/step\n", - "Meep progress: 326.0833333333333/666.6666717529297 = 48.9% done in 16.0s, 16.7s to go\n", - "on time step 19578 (time=326.3), 0.00081184 s/step\n", - "Meep progress: 408.95/666.6666717529297 = 61.3% done in 20.0s, 12.6s to go\n", - "on time step 24552 (time=409.2), 0.000804271 s/step\n", - "Meep progress: 491.7/666.6666717529297 = 73.8% done in 24.0s, 8.5s to go\n", - "on time step 29519 (time=491.983), 0.00080546 s/step\n", - "Meep progress: 574.1666666666666/666.6666717529297 = 86.1% done in 28.0s, 4.5s to go\n", - "on time step 34468 (time=574.467), 0.000808346 s/step\n", - "Meep progress: 658.6333333333333/666.6666717529297 = 98.8% done in 32.0s, 0.4s to go\n", - "on time step 39536 (time=658.933), 0.000789417 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00183201 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.95,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.95,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.012459 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1c47ae4da9804255bbe8a11a807fefbd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 98.45/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", - "on time step 5917 (time=98.6167), 0.000676164 s/step\n", - "Meep progress: 195.05/366.6666717529297 = 53.2% done in 8.0s, 7.0s to go\n", - "on time step 11715 (time=195.25), 0.000689941 s/step\n", - "Meep progress: 288.35/366.6666717529297 = 78.6% done in 12.0s, 3.3s to go\n", - "on time step 17313 (time=288.55), 0.000714587 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.22657284275616438, 1.4388403173786857e-08, -7873453.364475506, 1.1864826851861325, 0.8798551388030453+0.7959874979975307i, 4.0990972663369745e-14+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.9, 0.22657284275616438\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00310707 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.95,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.95,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0196011 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.746808) = 0.226272 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.747813) = 0.226573 after 7 iters\n", - "MPB solved for frequency_1(0,0,0.747813) = 0.226573 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.746808) = 0.226272 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.747813) = 0.226573 after 7 iters\n", - "MPB solved for frequency_1(0,0,0.747813) = 0.226573 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "163cbc8758b949588f6454ca36a3f81b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 81.61666666666666/666.6666717529297 = 12.2% done in 4.0s, 28.7s to go\n", - "on time step 4905 (time=81.75), 0.00081566 s/step\n", - "Meep progress: 164.35/666.6666717529297 = 24.7% done in 8.0s, 24.5s to go\n", - "on time step 9871 (time=164.517), 0.000805577 s/step\n", - "Meep progress: 246.91666666666666/666.6666717529297 = 37.0% done in 12.0s, 20.4s to go\n", - "on time step 14826 (time=247.1), 0.000807378 s/step\n", - "Meep progress: 329.56666666666666/666.6666717529297 = 49.4% done in 16.0s, 16.4s to go\n", - "on time step 19787 (time=329.783), 0.000806447 s/step\n", - "Meep progress: 411.8666666666667/666.6666717529297 = 61.8% done in 20.0s, 12.4s to go\n", - "on time step 24725 (time=412.083), 0.000810105 s/step\n", - "Meep progress: 494.0333333333333/666.6666717529297 = 74.1% done in 24.0s, 8.4s to go\n", - "on time step 29663 (time=494.383), 0.000810117 s/step\n", - "Meep progress: 576.4333333333333/666.6666717529297 = 86.5% done in 28.0s, 4.4s to go\n", - "on time step 34601 (time=576.683), 0.000810185 s/step\n", - "Meep progress: 658.8166666666666/666.6666717529297 = 98.8% done in 32.0s, 0.4s to go\n", - "on time step 39546 (time=659.1), 0.000809041 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00218797 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.95,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.95,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0134099 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e0d410126c864bb1aa8b0d161bc590f8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 97.06666666666666/366.6666717529297 = 26.5% done in 4.0s, 11.1s to go\n", - "on time step 5840 (time=97.3333), 0.000685049 s/step\n", - "Meep progress: 194.68333333333334/366.6666717529297 = 53.1% done in 8.0s, 7.1s to go\n", - "on time step 11698 (time=194.967), 0.000682896 s/step\n", - "Meep progress: 288.45/366.6666717529297 = 78.7% done in 12.0s, 3.3s to go\n", - "on time step 17323 (time=288.717), 0.000711171 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.22505482918759098, -3.331421542112138e-08, 3377759.7092216844, 1.2802884326550177, -0.05183926206700584-1.2792385085270808i, 1.4238944309468102e-13+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 0.9, 0.22505482918759098\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00150323 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-0.95,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (0.95,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0196998 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.741787) = 0.224766 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.742749) = 0.225055 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.742749) = 0.225055 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", - "MPB solved for frequency_1(0,0,0.741787) = 0.224766 after 12 iters\n", - "MPB solved for frequency_1(0,0,0.742749) = 0.225055 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.742749) = 0.225055 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0b5fd46eedaa41c593621dbeed7aa53e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 81.55/666.6666717529297 = 12.2% done in 4.0s, 28.7s to go\n", - "on time step 4901 (time=81.6833), 0.000816171 s/step\n", - "Meep progress: 161.51666666666665/666.6666717529297 = 24.2% done in 8.0s, 25.0s to go\n", - "on time step 9700 (time=161.667), 0.000833518 s/step\n", - "Meep progress: 243.5/666.6666717529297 = 36.5% done in 12.0s, 20.9s to go\n", - "on time step 14621 (time=243.683), 0.000813012 s/step\n", - "Meep progress: 326.46666666666664/666.6666717529297 = 49.0% done in 16.0s, 16.7s to go\n", - "on time step 19599 (time=326.65), 0.000803559 s/step\n", - "Meep progress: 408.7/666.6666717529297 = 61.3% done in 20.0s, 12.6s to go\n", - "on time step 24535 (time=408.917), 0.000810512 s/step\n", - "Meep progress: 490.46666666666664/666.6666717529297 = 73.6% done in 24.0s, 8.6s to go\n", - "on time step 29442 (time=490.7), 0.000815343 s/step\n", - "Meep progress: 571.4333333333333/666.6666717529297 = 85.7% done in 28.0s, 4.7s to go\n", - "on time step 34304 (time=571.733), 0.000822786 s/step\n", - "Meep progress: 652.35/666.6666717529297 = 97.9% done in 32.0s, 0.7s to go\n", - "on time step 39159 (time=652.65), 0.000824059 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00182199 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-1,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (1,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.013756 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "acffad1129974b9c931a184a58126db1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 97.63333333333333/366.6666717529297 = 26.6% done in 4.0s, 11.0s to go\n", - "on time step 5871 (time=97.85), 0.000681371 s/step\n", - "Meep progress: 195.38333333333333/366.6666717529297 = 53.3% done in 8.0s, 7.0s to go\n", - "on time step 11737 (time=195.617), 0.000681959 s/step\n", - "Meep progress: 288.0/366.6666717529297 = 78.5% done in 12.0s, 3.3s to go\n", - "on time step 17297 (time=288.283), 0.000719542 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.2264506053061966, 1.848141445855612e-08, -6126441.399114868, 1.1963136036156947, 0.833990004289438+0.8576869539297383i, 9.41930743041389e-14+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 1.0, 0.2264506053061966\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00198913 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-1,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (1,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0247951 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.745316) = 0.226152 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.74631) = 0.226451 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.74631) = 0.226451 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.745316) = 0.226152 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.74631) = 0.226451 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.74631) = 0.226451 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a4348fa2e77c48249feaaf9a90659b10", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 80.21666666666667/666.6666717529297 = 12.0% done in 4.0s, 29.2s to go\n", - "on time step 4831 (time=80.5167), 0.000828124 s/step\n", - "Meep progress: 160.66666666666666/666.6666717529297 = 24.1% done in 8.0s, 25.2s to go\n", - "on time step 9662 (time=161.033), 0.00082815 s/step\n", - "Meep progress: 242.7/666.6666717529297 = 36.4% done in 12.0s, 21.0s to go\n", - "on time step 14584 (time=243.067), 0.000812847 s/step\n", - "Meep progress: 323.06666666666666/666.6666717529297 = 48.5% done in 16.0s, 17.0s to go\n", - "on time step 19408 (time=323.467), 0.000829324 s/step\n", - "Meep progress: 403.96666666666664/666.6666717529297 = 60.6% done in 20.0s, 13.0s to go\n", - "on time step 24267 (time=404.45), 0.000823243 s/step\n", - "Meep progress: 485.68333333333334/666.6666717529297 = 72.9% done in 24.0s, 8.9s to go\n", - "on time step 29170 (time=486.167), 0.000815931 s/step\n", - "Meep progress: 567.6833333333333/666.6666717529297 = 85.2% done in 28.0s, 4.9s to go\n", - "on time step 34092 (time=568.2), 0.00081282 s/step\n", - "Meep progress: 648.8666666666667/666.6666717529297 = 97.3% done in 32.0s, 0.9s to go\n", - "on time step 38962 (time=649.367), 0.000821455 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00247097 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-1,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (1,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0148768 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b3f6c97a008c43a3a8241dbd8b8d6e77", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 98.18333333333334/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", - "on time step 5906 (time=98.4333), 0.000677366 s/step\n", - "Meep progress: 195.21666666666667/366.6666717529297 = 53.2% done in 8.0s, 7.0s to go\n", - "on time step 11728 (time=195.467), 0.000687257 s/step\n", - "Meep progress: 288.5333333333333/366.6666717529297 = 78.7% done in 12.0s, 3.2s to go\n", - "on time step 17331 (time=288.85), 0.000714072 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.22533719120317752, -3.886614292556672e-08, 2898888.0069052, 1.265821467168656, -0.2370236086793528-1.2434322642080775i, 1.8277875767837685e-13+0.0i\n", - "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", - "freq:, 1.0, 0.22533719120317752\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "time for choose_chunkdivision = 0.00464702 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 5 x 0 with resolution 30\n", - " block, center = (-1,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - " block, center = (1,0,0)\n", - " size (1,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", - "time for set_epsilon = 0.0233421 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.741638) = 0.225048 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.742601) = 0.225337 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.742601) = 0.225337 after 1 iters\n", - "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", - "MPB solved for frequency_1(0,0,0.741638) = 0.225048 after 10 iters\n", - "MPB solved for frequency_1(0,0,0.742601) = 0.225337 after 6 iters\n", - "MPB solved for frequency_1(0,0,0.742601) = 0.225337 after 1 iters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6a022e55ec594e10bbff1ebc6c1a6333", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 80.03333333333333/666.6666717529297 = 12.0% done in 4.0s, 29.3s to go\n", - "on time step 4812 (time=80.2), 0.000831265 s/step\n", - "Meep progress: 158.71666666666667/666.6666717529297 = 23.8% done in 8.0s, 25.6s to go\n", - "on time step 9536 (time=158.933), 0.000846831 s/step\n", - "Meep progress: 239.76666666666665/666.6666717529297 = 36.0% done in 12.0s, 21.4s to go\n", - "on time step 14400 (time=240), 0.000822458 s/step\n", - "Meep progress: 321.46666666666664/666.6666717529297 = 48.2% done in 16.0s, 17.2s to go\n", - "on time step 19304 (time=321.733), 0.000815746 s/step\n", - "Meep progress: 402.9/666.6666717529297 = 60.4% done in 20.0s, 13.1s to go\n", - "on time step 24189 (time=403.15), 0.000818995 s/step\n", - "Meep progress: 484.1333333333333/666.6666717529297 = 72.6% done in 24.0s, 9.0s to go\n", - "on time step 29067 (time=484.45), 0.000820091 s/step\n", - "Meep progress: 565.6833333333333/666.6666717529297 = 84.9% done in 28.0s, 5.0s to go\n", - "on time step 33961 (time=566.017), 0.000817343 s/step\n", - "Meep progress: 646.4166666666666/666.6666717529297 = 97.0% done in 32.0s, 1.0s to go\n", - "on time step 38805 (time=646.75), 0.000825862 s/step\n", - "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "s = np.arange(0.1, 1.1, 0.1)\n", "fluxes_odd = np.zeros(s.size)\n", @@ -2354,22 +159,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(dpi=150)\n", "plt.plot(s, -forces_odd / fluxes_odd, \"rs\", label=\"anti-symmetric\")\n", @@ -2394,808 +186,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.55,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.55,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyodd.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3206050395965576\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 20 iterations.\n", - "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.233911\n", - "elapsed time for k point: 39.92626595497131\n", - "total elapsed time for run: 42.247997999191284\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.55,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.55,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyeven.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.2972378730773926\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 17 iterations.\n", - "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.214446\n", - "elapsed time for k point: 33.999393939971924\n", - "total elapsed time for run: 36.29679226875305\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.6,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.6,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyodd.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.31347393989563\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 18 iterations.\n", - "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.232136\n", - "elapsed time for k point: 35.45386290550232\n", - "total elapsed time for run: 37.767595291137695\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.6,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.6,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyeven.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3267719745635986\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 19 iterations.\n", - "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.218002\n", - "elapsed time for k point: 38.01395606994629\n", - "total elapsed time for run: 40.34090757369995\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.65,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.65,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyodd.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3398044109344482\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 17 iterations.\n", - "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.230609\n", - "elapsed time for k point: 34.236557722091675\n", - "total elapsed time for run: 36.57751989364624\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.65,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.65,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyeven.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3188600540161133\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 18 iterations.\n", - "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.220388\n", - "elapsed time for k point: 36.10376501083374\n", - "total elapsed time for run: 38.422903060913086\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.7,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.7,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyodd.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3080191612243652\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", - "Solving for bands 1 to 1...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finished solving for bands 1 to 1 after 19 iterations.\n", - "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.229408\n", - "elapsed time for k point: 38.052414655685425\n", - "total elapsed time for run: 40.36163353919983\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.7,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.7,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyeven.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.351583242416382\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 17 iterations.\n", - "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.222012\n", - "elapsed time for k point: 34.26416039466858\n", - "total elapsed time for run: 36.61595106124878\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.75,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.75,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyodd.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.34535813331604\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 18 iterations.\n", - "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.2285\n", - "elapsed time for k point: 36.24232602119446\n", - "total elapsed time for run: 38.587929487228394\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.75,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.75,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyeven.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.342602014541626\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 18 iterations.\n", - "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.223137\n", - "elapsed time for k point: 36.02590990066528\n", - "total elapsed time for run: 38.3686728477478\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.8,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.8,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyodd.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3229269981384277\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 19 iterations.\n", - "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.227819\n", - "elapsed time for k point: 37.89797854423523\n", - "total elapsed time for run: 40.22118377685547\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.8,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.8,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyeven.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3231875896453857\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 18 iterations.\n", - "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.223921\n", - "elapsed time for k point: 35.854113817214966\n", - "total elapsed time for run: 38.17753458023071\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.85,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.85,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyodd.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3750038146972656\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 18 iterations.\n", - "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.227319\n", - "elapsed time for k point: 36.51261854171753\n", - "total elapsed time for run: 38.88789224624634\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.85,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.85,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyeven.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3955740928649902\n", - "solve_kpoint (0.5,0,0):\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 17 iterations.\n", - "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.224479\n", - "elapsed time for k point: 34.263073205947876\n", - "total elapsed time for run: 36.65884232521057\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.9,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.9,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyodd.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3697447776794434\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 17 iterations.\n", - "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.226949\n", - "elapsed time for k point: 33.61285185813904\n", - "total elapsed time for run: 35.98284029960632\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.9,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.9,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyeven.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3623385429382324\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 19 iterations.\n", - "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.224876\n", - "elapsed time for k point: 37.994656801223755\n", - "total elapsed time for run: 40.35737872123718\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.95,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.95,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyodd.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3798489570617676\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 19 iterations.\n", - "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.226677\n", - "elapsed time for k point: 38.10903787612915\n", - "total elapsed time for run: 40.48903441429138\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-0.95,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,0.95,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyeven.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.361907482147217\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 19 iterations.\n", - "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.22516\n", - "elapsed time for k point: 37.75140643119812\n", - "total elapsed time for run: 40.113561391830444\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-1,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,1,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyodd.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.344741106033325\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 20 iterations.\n", - "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.22648\n", - "elapsed time for k point: 39.63300061225891\n", - "total elapsed time for run: 41.97804236412048\n", - "done\n", - "Initializing eigensolver data\n", - "Computing 1 bands with 1e-09 tolerance\n", - "Working in 3 dimensions.\n", - "Grid size is 1 x 1280 x 1280.\n", - "Solving for 1 bands at a time.\n", - "Creating Maxwell data...\n", - "Mesh size is 3.\n", - "Lattice vectors:\n", - " (1, 0, 0)\n", - " (0, 10, 0)\n", - " (0, 0, 10)\n", - "Cell volume = 100\n", - "Reciprocal lattice vectors (/ 2 pi):\n", - " (1, -0, 0)\n", - " (-0, 0.1, -0)\n", - " (0, -0, 0.1)\n", - "Geometric objects:\n", - " block, center = (0,-1,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " block, center = (0,1,0)\n", - " size (1e+20,1,1)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", - "Initializing epsilon function...\n", - "Allocating fields...\n", - "Solving for band polarization: zoddyeven.\n", - "Initializing fields to random numbers...\n", - "1 k-points\n", - " Vector3<0.5, 0.0, 0.0>\n", - "elapsed time for initialization: 2.3879048824310303\n", - "solve_kpoint (0.5,0,0):\n", - "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", - "Solving for bands 1 to 1...\n", - "Finished solving for bands 1 to 1 after 20 iterations.\n", - "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.225369\n", - "elapsed time for k point: 39.115745067596436\n", - "total elapsed time for run: 41.503973960876465\n", - "done\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "from meep import mpb\n", @@ -3278,22 +271,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(dpi=200)\n", "plt.plot(ss[:-1], force_odd, \"b-\", label=\"anti-symmetric\")\n", diff --git a/python/examples/parallel-wvgs-force.py b/python/examples/parallel-wvgs-force.py index d45500365..99200c32d 100644 --- a/python/examples/parallel-wvgs-force.py +++ b/python/examples/parallel-wvgs-force.py @@ -1,6 +1,7 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np + +import meep as mp resolution = 40 # pixels/μm diff --git a/python/examples/parallel-wvgs-mpb.py b/python/examples/parallel-wvgs-mpb.py index dbd421137..84420fe26 100644 --- a/python/examples/parallel-wvgs-mpb.py +++ b/python/examples/parallel-wvgs-mpb.py @@ -1,9 +1,8 @@ -# -*- coding: utf-8 -*- +import matplotlib.pyplot as plt +import numpy as np import meep as mp from meep import mpb -import numpy as np -import matplotlib.pyplot as plt resolution = 128 # pixels/μm diff --git a/python/examples/perturbation_theory.ipynb b/python/examples/perturbation_theory.ipynb index c78cb2cb9..0a32c2b97 100644 --- a/python/examples/perturbation_theory.ipynb +++ b/python/examples/perturbation_theory.ipynb @@ -24,147 +24,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000187874 s\n", - "Working in Cylindrical dimensions.\n", - "Computational cell is 8 x 0 x 0.01 with resolution 100\n", - " block, center = (1.5,0,0)\n", - " size (1,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.56,11.56,11.56)\n", - "time for set_epsilon = 0.00455093 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "Meep progress: 63.24/394.1176452636719 = 16.0% done in 4.0s, 20.9s to go\n", - "on time step 12648 (time=63.24), 0.000316286 s/step\n", - "Meep progress: 127.035/394.1176452636719 = 32.2% done in 8.0s, 16.8s to go\n", - "on time step 25410 (time=127.05), 0.00031345 s/step\n", - "Meep progress: 190.945/394.1176452636719 = 48.4% done in 12.0s, 12.8s to go\n", - "on time step 38195 (time=190.975), 0.00031288 s/step\n", - "Meep progress: 254.66/394.1176452636719 = 64.6% done in 16.0s, 8.8s to go\n", - "on time step 50941 (time=254.705), 0.000313832 s/step\n", - "Meep progress: 316.92/394.1176452636719 = 80.4% done in 20.0s, 4.9s to go\n", - "on time step 63396 (time=316.98), 0.000321182 s/step\n", - "Meep progress: 376.46/394.1176452636719 = 95.5% done in 24.0s, 1.1s to go\n", - "on time step 75307 (time=376.535), 0.000335853 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.17578499227611236, -4.816700722801396e-05, 1824.7448034707427, 0.24613200861807683, -0.14560581011759283-0.19844372937023874i, 1.1797259436409042e-10+0.0i\n", - "run 0 finished at t = 394.12 (78824 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000282049 s\n", - "Working in Cylindrical dimensions.\n", - "Computational cell is 8 x 0 x 0.01 with resolution 100\n", - " block, center = (1.5,0,0)\n", - " size (1,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.56,11.56,11.56)\n", - "time for set_epsilon = 0.00452399 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "Meep progress: 64.11/1237.7535400390625 = 5.2% done in 4.0s, 73.2s to go\n", - "on time step 12822 (time=64.11), 0.00031198 s/step\n", - "Meep progress: 129.395/1237.7535400390625 = 10.5% done in 8.0s, 68.5s to go\n", - "on time step 25882 (time=129.41), 0.0003063 s/step\n", - "Meep progress: 194.32/1237.7535400390625 = 15.7% done in 12.0s, 64.4s to go\n", - "on time step 38871 (time=194.355), 0.000307977 s/step\n", - "Meep progress: 260.045/1237.7535400390625 = 21.0% done in 16.0s, 60.2s to go\n", - "on time step 52019 (time=260.095), 0.00030423 s/step\n", - "Meep progress: 325.825/1237.7535400390625 = 26.3% done in 20.0s, 56.0s to go\n", - "on time step 65178 (time=325.89), 0.000303988 s/step\n", - "Meep progress: 390.665/1237.7535400390625 = 31.6% done in 24.0s, 52.0s to go\n", - "on time step 78150 (time=390.75), 0.00030838 s/step\n", - "Meep progress: 455.2/1237.7535400390625 = 36.8% done in 28.0s, 48.1s to go\n", - "on time step 91060 (time=455.3), 0.000309849 s/step\n", - "Meep progress: 519.66/1237.7535400390625 = 42.0% done in 32.0s, 44.2s to go\n", - "on time step 103958 (time=519.79), 0.000310148 s/step\n", - "Meep progress: 584.73/1237.7535400390625 = 47.2% done in 36.0s, 40.2s to go\n", - "on time step 116975 (time=584.875), 0.000307307 s/step\n", - "Meep progress: 649.64/1237.7535400390625 = 52.5% done in 40.0s, 36.2s to go\n", - "on time step 129957 (time=649.785), 0.000308119 s/step\n", - "Meep progress: 714.945/1237.7535400390625 = 57.8% done in 44.0s, 32.2s to go\n", - "on time step 143025 (time=715.125), 0.000306096 s/step\n", - "Meep progress: 780.855/1237.7535400390625 = 63.1% done in 48.0s, 28.1s to go\n", - "on time step 156209 (time=781.045), 0.000303408 s/step\n", - "Meep progress: 846.395/1237.7535400390625 = 68.4% done in 52.0s, 24.0s to go\n", - "on time step 169322 (time=846.61), 0.000305051 s/step\n", - "Meep progress: 911.86/1237.7535400390625 = 73.7% done in 56.0s, 20.0s to go\n", - "on time step 182419 (time=912.095), 0.000305427 s/step\n", - "Meep progress: 977.405/1237.7535400390625 = 79.0% done in 60.0s, 16.0s to go\n", - "on time step 195532 (time=977.66), 0.000305057 s/step\n", - "Meep progress: 1042.795/1237.7535400390625 = 84.2% done in 64.0s, 12.0s to go\n", - "on time step 208616 (time=1043.08), 0.000305738 s/step\n", - "Meep progress: 1107.3500000000001/1237.7535400390625 = 89.5% done in 68.0s, 8.0s to go\n", - "on time step 221526 (time=1107.63), 0.000309854 s/step\n", - "Meep progress: 1172.115/1237.7535400390625 = 94.7% done in 72.0s, 4.0s to go\n", - "on time step 234482 (time=1172.41), 0.000308753 s/step\n", - "Meep progress: 1237.6100000000001/1237.7535400390625 = 100.0% done in 76.0s, 0.0s to go\n", - "run 0 finished at t = 1237.755 (247551 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000117064 s\n", - "Working in Cylindrical dimensions.\n", - "Computational cell is 8 x 0 x 0.01 with resolution 100\n", - " block, center = (1.51,0,0)\n", - " size (1,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (11.56,11.56,11.56)\n", - "time for set_epsilon = 0.0026741 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "Meep progress: 64.245/1237.7535400390625 = 5.2% done in 4.0s, 73.1s to go\n", - "on time step 12849 (time=64.245), 0.000311334 s/step\n", - "Meep progress: 128.08/1237.7535400390625 = 10.3% done in 8.0s, 69.3s to go\n", - "on time step 25621 (time=128.105), 0.000313206 s/step\n", - "Meep progress: 192.4/1237.7535400390625 = 15.5% done in 12.0s, 65.2s to go\n", - "on time step 38487 (time=192.435), 0.0003109 s/step\n", - "Meep progress: 256.76/1237.7535400390625 = 20.7% done in 16.0s, 61.1s to go\n", - "on time step 51365 (time=256.825), 0.00031061 s/step\n", - "Meep progress: 321.26/1237.7535400390625 = 26.0% done in 20.0s, 57.1s to go\n", - "on time step 64271 (time=321.355), 0.000309956 s/step\n", - "Meep progress: 385.66/1237.7535400390625 = 31.2% done in 24.0s, 53.0s to go\n", - "on time step 77154 (time=385.77), 0.000310499 s/step\n", - "Meep progress: 449.995/1237.7535400390625 = 36.4% done in 28.0s, 49.0s to go\n", - "on time step 90026 (time=450.13), 0.000310771 s/step\n", - "Meep progress: 514.22/1237.7535400390625 = 41.5% done in 32.0s, 45.0s to go\n", - "on time step 102876 (time=514.38), 0.000311289 s/step\n", - "Meep progress: 578.95/1237.7535400390625 = 46.8% done in 36.0s, 41.0s to go\n", - "on time step 115826 (time=579.13), 0.000308899 s/step\n", - "Meep progress: 642.9250000000001/1237.7535400390625 = 51.9% done in 40.0s, 37.0s to go\n", - "on time step 128622 (time=643.11), 0.000312613 s/step\n", - "Meep progress: 706.915/1237.7535400390625 = 57.1% done in 44.0s, 33.0s to go\n", - "on time step 141424 (time=707.12), 0.00031246 s/step\n", - "Meep progress: 770.58/1237.7535400390625 = 62.3% done in 48.0s, 29.1s to go\n", - "on time step 154164 (time=770.82), 0.000313974 s/step\n", - "Meep progress: 834.445/1237.7535400390625 = 67.4% done in 52.0s, 25.1s to go\n", - "on time step 166942 (time=834.71), 0.00031304 s/step\n", - "Meep progress: 898.195/1237.7535400390625 = 72.6% done in 56.0s, 21.2s to go\n", - "on time step 179696 (time=898.48), 0.000313635 s/step\n", - "Meep progress: 961.405/1237.7535400390625 = 77.7% done in 60.0s, 17.2s to go\n", - "on time step 192338 (time=961.69), 0.000316413 s/step\n", - "Meep progress: 1025.53/1237.7535400390625 = 82.9% done in 64.0s, 13.2s to go\n", - "on time step 205173 (time=1025.87), 0.000311651 s/step\n", - "Meep progress: 1089.78/1237.7535400390625 = 88.0% done in 68.0s, 9.2s to go\n", - "on time step 218020 (time=1090.1), 0.000311357 s/step\n", - "Meep progress: 1151.805/1237.7535400390625 = 93.1% done in 72.0s, 5.4s to go\n", - "on time step 230424 (time=1152.12), 0.000322493 s/step\n", - "Meep progress: 1210.695/1237.7535400390625 = 97.8% done in 76.0s, 1.7s to go\n", - "on time step 242207 (time=1211.04), 0.000339497 s/step\n", - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.17493286736703903, -4.814897914072506e-05, 1816.5791932551942, 1.216470973025523, 1.2069307717323075-0.15205176901081904i, 2.316660865172475e-11+0.0i\n", - "run 0 finished at t = 1237.755 (247551 timesteps)\n", - "dwdR:, -0.08544696397218933 (pert. theory), -0.08521249090733263 (finite diff.)\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", diff --git a/python/examples/perturbation_theory.py b/python/examples/perturbation_theory.py index df9f35d42..5782b48ab 100644 --- a/python/examples/perturbation_theory.py +++ b/python/examples/perturbation_theory.py @@ -1,7 +1,9 @@ -import meep as mp -import numpy as np import argparse +import numpy as np + +import meep as mp + def main(args): if args.perpendicular: diff --git a/python/examples/perturbation_theory_2d.py b/python/examples/perturbation_theory_2d.py index 920efe9fb..08cb3a5ea 100644 --- a/python/examples/perturbation_theory_2d.py +++ b/python/examples/perturbation_theory_2d.py @@ -1,7 +1,9 @@ -import meep as mp -import numpy as np import argparse +import numpy as np + +import meep as mp + def main(args): if args.perpendicular: diff --git a/python/examples/planar_cavity_ldos.py b/python/examples/planar_cavity_ldos.py index e6b2e516a..f421c77d8 100644 --- a/python/examples/planar_cavity_ldos.py +++ b/python/examples/planar_cavity_ldos.py @@ -2,16 +2,14 @@ # dielectric cavity with lossless metallic walls. The result is computed in # cylindrical and 3D coordinates and compared with the analytic theory from: # I. Abram et al., IEEE J. Quantum Electronics, Vol. 34, pp. 71-76 (1998). - +import matplotlib +import numpy as np import meep as mp -import numpy as np -import matplotlib matplotlib.use("agg") import matplotlib.pyplot as plt - # important note: # Meep may round the cell dimensions to an integer number # of pixels which could modify the cavity structure. @@ -202,7 +200,7 @@ def cavity_ldos_3D(sz): plt.plot(cavity_thickness, pe_meep_cyl, "r-", label="Meep (cylin.)") plt.plot(cavity_thickness, pe_theory, "g-", label="theory") plt.plot(cavity_thickness, np.ones(len(cavity_thickness)), "k--") - plt.xlabel("cavity thickness, $nL/\lambda$") + plt.xlabel(r"cavity thickness, $nL/\lambda$") plt.ylabel("Purcell enhancement factor") plt.title( "planar point dipole at λ=1.0 μm in a planar cavity\n" diff --git a/python/examples/polarization_grating.ipynb b/python/examples/polarization_grating.ipynb index 65165d5c6..085165c17 100644 --- a/python/examples/polarization_grating.ipynb +++ b/python/examples/polarization_grating.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -166,3030 +166,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 5.79357e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.2 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.0339479 s\n", - "-----------\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 2.71797e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.2 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.1,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (-8.32667e-17,0,0)\n", - " size (0.2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "time for set_epsilon = 0.758735 s\n", - "-----------\n", - "Meep progress: 224.88333333333333/408.0 = 55.1% done in 4.0s, 3.3s to go\n", - "on time step 13493 (time=224.883), 0.000296471 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 65 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 37 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 47 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 42 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.89247\n", - "tran (uniaxial):, 1, 4.77, 0.01875\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 4.41074e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.4 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.0652409 s\n", - "-----------\n", - "Meep progress: 193.48333333333332/208.0 = 93.0% done in 4.0s, 0.3s to go\n", - "on time step 11609 (time=193.483), 0.000344592 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.00815e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.4 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.2,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (-1.66533e-16,0,0)\n", - " size (0.4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "time for set_epsilon = 3.35973 s\n", - "-----------\n", - "Meep progress: 100.36666666666666/408.0 = 24.6% done in 4.0s, 12.3s to go\n", - "on time step 6022 (time=100.367), 0.000664325 s/step\n", - "Meep progress: 200.35/408.0 = 49.1% done in 8.0s, 8.3s to go\n", - "on time step 12022 (time=200.367), 0.000666721 s/step\n", - "Meep progress: 301.4166666666667/408.0 = 73.9% done in 12.0s, 4.2s to go\n", - "on time step 18088 (time=301.467), 0.000659487 s/step\n", - "Meep progress: 400.9/408.0 = 98.3% done in 16.0s, 0.3s to go\n", - "on time step 24058 (time=400.967), 0.000670094 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 29 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 55 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.84732\n", - "tran (twisted):, 1, 4.77, 0.03707\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 8.70228e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.4 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.0973799 s\n", - "-----------\n", - "Meep progress: 104.83333333333333/208.0 = 50.4% done in 4.0s, 3.9s to go\n", - "on time step 6290 (time=104.833), 0.00063597 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 5.91278e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.4 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.2,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " block, center = (-1.66533e-16,0,0)\n", - " size (0.4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "time for set_epsilon = 3.38153 s\n", - "-----------\n", - "Meep progress: 100.73333333333333/408.0 = 24.7% done in 4.0s, 12.2s to go\n", - "on time step 6044 (time=100.733), 0.00066186 s/step\n", - "Meep progress: 201.35/408.0 = 49.4% done in 8.0s, 8.2s to go\n", - "on time step 12083 (time=201.383), 0.000662443 s/step\n", - "Meep progress: 301.6333333333333/408.0 = 73.9% done in 12.0s, 4.2s to go\n", - "on time step 18102 (time=301.7), 0.000664572 s/step\n", - "Meep progress: 403.3333333333333/408.0 = 98.9% done in 16.0s, 0.2s to go\n", - "on time step 24205 (time=403.417), 0.000655435 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 50 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 37 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 66 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.79050\n", - "tran (uniaxial):, 1, 4.77, 0.06529\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.79493e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.8 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.0874732 s\n", - "-----------\n", - "Meep progress: 102.21666666666667/208.0 = 49.1% done in 4.0s, 4.1s to go\n", - "on time step 6133 (time=102.217), 0.000652292 s/step\n", - "Meep progress: 205.86666666666667/208.0 = 99.0% done in 8.0s, 0.1s to go\n", - "on time step 12353 (time=205.883), 0.000643173 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.41346e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.8 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.4,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (0.8,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "time for set_epsilon = 6.61127 s\n", - "-----------\n", - "Meep progress: 97.56666666666666/408.0 = 23.9% done in 4.0s, 12.7s to go\n", - "on time step 5854 (time=97.5667), 0.000683333 s/step\n", - "Meep progress: 195.35/408.0 = 47.9% done in 8.0s, 8.7s to go\n", - "on time step 11722 (time=195.367), 0.000681668 s/step\n", - "Meep progress: 293.75/408.0 = 72.0% done in 12.0s, 4.7s to go\n", - "on time step 17627 (time=293.783), 0.000677395 s/step\n", - "Meep progress: 391.4/408.0 = 95.9% done in 16.0s, 0.7s to go\n", - "on time step 23487 (time=391.45), 0.000682659 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 38 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 36 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 34 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.64693\n", - "tran (twisted):, 1, 4.77, 0.14670\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.70092e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.6 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.0854559 s\n", - "-----------\n", - "Meep progress: 102.86666666666666/208.0 = 49.5% done in 4.0s, 4.1s to go\n", - "on time step 6172 (time=102.867), 0.000648176 s/step\n", - "Meep progress: 207.0/208.0 = 99.5% done in 8.0s, 0.0s to go\n", - "on time step 12421 (time=207.017), 0.000640153 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.69956e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.6 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.3,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " block, center = (2.22045e-16,0,0)\n", - " size (0.6,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "time for set_epsilon = 4.98919 s\n", - "-----------\n", - "Meep progress: 98.01666666666667/408.0 = 24.0% done in 4.0s, 12.7s to go\n", - "on time step 5881 (time=98.0167), 0.000680194 s/step\n", - "Meep progress: 197.98333333333332/408.0 = 48.5% done in 8.0s, 8.5s to go\n", - "on time step 11880 (time=198), 0.00066678 s/step\n", - "Meep progress: 297.0833333333333/408.0 = 72.8% done in 12.0s, 4.5s to go\n", - "on time step 17827 (time=297.117), 0.000672654 s/step\n", - "Meep progress: 395.51666666666665/408.0 = 96.9% done in 16.0s, 0.5s to go\n", - "on time step 23735 (time=395.583), 0.000677137 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 70 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 31 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 70 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 50 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 44 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 33 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.66119\n", - "tran (uniaxial):, 1, 4.77, 0.14065\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 2.69413e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5.2 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.090482 s\n", - "-----------\n", - "Meep progress: 97.23333333333333/208.0 = 46.7% done in 4.0s, 4.6s to go\n", - "on time step 5834 (time=97.2333), 0.000685701 s/step\n", - "Meep progress: 196.66666666666666/208.0 = 94.6% done in 8.0s, 0.5s to go\n", - "on time step 11801 (time=196.683), 0.000670365 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.22272e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5.2 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.6,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (1.2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "time for set_epsilon = 10.1716 s\n", - "-----------\n", - "Meep progress: 89.91666666666667/408.0 = 22.0% done in 4.0s, 14.2s to go\n", - "on time step 5395 (time=89.9167), 0.000741585 s/step\n", - "Meep progress: 183.15/408.0 = 44.9% done in 8.0s, 9.8s to go\n", - "on time step 10990 (time=183.167), 0.000715001 s/step\n", - "Meep progress: 274.8333333333333/408.0 = 67.4% done in 12.0s, 5.8s to go\n", - "on time step 16493 (time=274.883), 0.000726979 s/step\n", - "Meep progress: 367.5333333333333/408.0 = 90.1% done in 16.0s, 1.8s to go\n", - "on time step 22056 (time=367.6), 0.000719077 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 36 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 27 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 50 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 31 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.39057\n", - "tran (twisted):, 1, 4.77, 0.27182\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000102043 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.8 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.0766759 s\n", - "-----------\n", - "Meep progress: 102.18333333333334/208.0 = 49.1% done in 4.0s, 4.1s to go\n", - "on time step 6131 (time=102.183), 0.000652499 s/step\n", - "Meep progress: 205.46666666666667/208.0 = 98.8% done in 8.0s, 0.1s to go\n", - "on time step 12330 (time=205.5), 0.000645285 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.38962e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 4.8 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.4,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (0.8,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "time for set_epsilon = 6.60763 s\n", - "-----------\n", - "Meep progress: 93.03333333333333/408.0 = 22.8% done in 4.0s, 13.5s to go\n", - "on time step 5582 (time=93.0333), 0.00071674 s/step\n", - "Meep progress: 188.96666666666667/408.0 = 46.3% done in 8.0s, 9.3s to go\n", - "on time step 11340 (time=189), 0.000694738 s/step\n", - "Meep progress: 285.1666666666667/408.0 = 69.9% done in 12.0s, 5.2s to go\n", - "on time step 17113 (time=285.217), 0.0006929 s/step\n", - "Meep progress: 381.0/408.0 = 93.4% done in 16.0s, 1.1s to go\n", - "on time step 22865 (time=381.083), 0.000695525 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 58 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 54 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.47140\n", - "tran (uniaxial):, 1, 4.77, 0.23506\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 8.98838e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5.6 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.100205 s\n", - "-----------\n", - "Meep progress: 92.38333333333333/208.0 = 44.4% done in 4.0s, 5.0s to go\n", - "on time step 5543 (time=92.3833), 0.000721698 s/step\n", - "Meep progress: 186.2/208.0 = 89.5% done in 8.0s, 0.9s to go\n", - "on time step 11174 (time=186.233), 0.000710356 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.50883e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5.6 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.8,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (2.22045e-16,0,0)\n", - " size (1.6,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 71.5192% done, 1.70329 s remaining\n", - "subpixel-averaging is 71.5192% done, 1.68457 s remaining\n", - "subpixel-averaging is 71.5192% done, 1.68515 s remaining\n", - "time for set_epsilon = 13.1647 s\n", - "-----------\n", - "Meep progress: 87.73333333333333/408.0 = 21.5% done in 4.0s, 14.6s to go\n", - "on time step 5264 (time=87.7333), 0.00076 s/step\n", - "Meep progress: 176.35/408.0 = 43.2% done in 8.0s, 10.5s to go\n", - "on time step 10583 (time=176.383), 0.000752144 s/step\n", - "Meep progress: 265.5833333333333/408.0 = 65.1% done in 12.0s, 6.4s to go\n", - "on time step 15938 (time=265.633), 0.000747004 s/step\n", - "Meep progress: 353.1166666666667/408.0 = 86.5% done in 16.0s, 2.5s to go\n", - "on time step 21191 (time=353.183), 0.000761606 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 51 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 34 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 64 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 43 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 31 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 33 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.17500\n", - "tran (twisted):, 1, 4.77, 0.38032\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.10487e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.0600371 s\n", - "-----------\n", - "Meep progress: 100.0/208.0 = 48.1% done in 4.0s, 4.3s to go\n", - "on time step 6000 (time=100), 0.000666774 s/step\n", - "Meep progress: 201.83333333333334/208.0 = 97.0% done in 8.0s, 0.2s to go\n", - "on time step 12113 (time=201.883), 0.000654451 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.10352e-05 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working in 2D dimensions.\n", - "Computational cell is 5 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.5,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (1,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "time for set_epsilon = 8.54403 s\n", - "-----------\n", - "Meep progress: 93.58333333333333/408.0 = 22.9% done in 4.0s, 13.4s to go\n", - "on time step 5615 (time=93.5833), 0.000712491 s/step\n", - "Meep progress: 189.16666666666666/408.0 = 46.4% done in 8.0s, 9.3s to go\n", - "on time step 11352 (time=189.2), 0.000697323 s/step\n", - "Meep progress: 283.1666666666667/408.0 = 69.4% done in 12.0s, 5.3s to go\n", - "on time step 16993 (time=283.217), 0.000709178 s/step\n", - "Meep progress: 378.1166666666667/408.0 = 92.7% done in 16.0s, 1.3s to go\n", - "on time step 22691 (time=378.183), 0.000702061 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 30 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 35 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 61 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 44 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.27041\n", - "tran (uniaxial):, 1, 4.77, 0.32724\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 3.60012e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.131748 s\n", - "-----------\n", - "Meep progress: 87.66666666666667/208.0 = 42.1% done in 4.0s, 5.5s to go\n", - "on time step 5260 (time=87.6667), 0.000760564 s/step\n", - "Meep progress: 179.5/208.0 = 86.3% done in 8.0s, 1.3s to go\n", - "on time step 10772 (time=179.533), 0.000725698 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.29425e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6 x 6.5 x 0 with resolution 30\n", - " block, center = (-2,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 68.5946% done, 1.84034 s remaining\n", - "subpixel-averaging is 64.3072% done, 2.3664 s remaining\n", - "subpixel-averaging is 64.3072% done, 2.43912 s remaining\n", - "time for set_epsilon = 15.5353 s\n", - "-----------\n", - "Meep progress: 83.8/408.0 = 20.5% done in 4.0s, 15.5s to go\n", - "on time step 5028 (time=83.8), 0.000795612 s/step\n", - "Meep progress: 168.46666666666667/408.0 = 41.3% done in 8.0s, 11.4s to go\n", - "on time step 10110 (time=168.5), 0.000787236 s/step\n", - "Meep progress: 253.21666666666667/408.0 = 62.1% done in 12.0s, 7.3s to go\n", - "on time step 15197 (time=253.283), 0.000786469 s/step\n", - "Meep progress: 337.46666666666664/408.0 = 82.7% done in 16.0s, 3.3s to go\n", - "on time step 20253 (time=337.55), 0.00079122 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 38 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 46 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 28 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.04972\n", - "tran (twisted):, 1, 4.77, 0.44671\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000128031 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5.2 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.082103 s\n", - "-----------\n", - "Meep progress: 98.0/208.0 = 47.1% done in 4.0s, 4.5s to go\n", - "on time step 5880 (time=98), 0.000680371 s/step\n", - "Meep progress: 197.83333333333334/208.0 = 95.1% done in 8.0s, 0.4s to go\n", - "on time step 11871 (time=197.85), 0.000667708 s/step\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.10352e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5.2 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.6,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (-1.11022e-16,0,0)\n", - " size (1.2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "time for set_epsilon = 9.882 s\n", - "-----------\n", - "Meep progress: 92.15/408.0 = 22.6% done in 4.0s, 13.7s to go\n", - "on time step 5529 (time=92.15), 0.000723491 s/step\n", - "Meep progress: 185.61666666666667/408.0 = 45.5% done in 8.0s, 9.6s to go\n", - "on time step 11139 (time=185.65), 0.00071314 s/step\n", - "Meep progress: 278.5833333333333/408.0 = 68.3% done in 12.0s, 5.6s to go\n", - "on time step 16719 (time=278.65), 0.000716963 s/step\n", - "Meep progress: 371.01666666666665/408.0 = 90.9% done in 16.0s, 1.6s to go\n", - "on time step 22267 (time=371.117), 0.000721017 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 42 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 44 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 33 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 62 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 37 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.14816\n", - "tran (uniaxial):, 1, 4.77, 0.39120\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000138044 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.4 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.109352 s\n", - "-----------\n", - "Meep progress: 85.88333333333333/208.0 = 41.3% done in 4.0s, 5.7s to go\n", - "on time step 5153 (time=85.8833), 0.00077639 s/step\n", - "Meep progress: 172.66666666666666/208.0 = 83.0% done in 8.0s, 1.6s to go\n", - "on time step 10361 (time=172.683), 0.000768107 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.69956e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.4 x 6.5 x 0 with resolution 30\n", - " block, center = (-2.2,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (-2.22045e-16,0,0)\n", - " size (2.4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 58.4164% done, 3.10351 s remaining\n", - "subpixel-averaging is 58.4164% done, 3.12547 s remaining\n", - "subpixel-averaging is 58.4164% done, 3.0991 s remaining\n", - "time for set_epsilon = 19.9794 s\n", - "-----------\n", - "Meep progress: 80.01666666666667/408.0 = 19.6% done in 4.0s, 16.4s to go\n", - "on time step 4801 (time=80.0167), 0.000833327 s/step\n", - "Meep progress: 160.8/408.0 = 39.4% done in 8.0s, 12.3s to go\n", - "on time step 9649 (time=160.817), 0.000825088 s/step\n", - "Meep progress: 241.7/408.0 = 59.2% done in 12.0s, 8.3s to go\n", - "on time step 14504 (time=241.733), 0.000823907 s/step\n", - "Meep progress: 320.8333333333333/408.0 = 78.6% done in 16.0s, 4.3s to go\n", - "on time step 19253 (time=320.883), 0.000842394 s/step\n", - "Meep progress: 401.3/408.0 = 98.4% done in 20.0s, 0.3s to go\n", - "on time step 24084 (time=401.4), 0.000828145 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 65 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 44 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 29 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 42 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 27 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.00536\n", - "tran (twisted):, 1, 4.77, 0.46488\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.10352e-05 s\n", - "Working in 2D dimensions.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computational cell is 5.4 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.0821059 s\n", - "-----------\n", - "Meep progress: 93.78333333333333/208.0 = 45.1% done in 4.0s, 4.9s to go\n", - "on time step 5627 (time=93.7833), 0.000710922 s/step\n", - "Meep progress: 189.56666666666666/208.0 = 91.1% done in 8.0s, 0.8s to go\n", - "on time step 11376 (time=189.6), 0.000695889 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.29425e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5.4 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.7,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (-1.11022e-16,0,0)\n", - " size (1.4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "time for set_epsilon = 11.8445 s\n", - "-----------\n", - "Meep progress: 89.43333333333334/408.0 = 21.9% done in 4.0s, 14.2s to go\n", - "on time step 5366 (time=89.4333), 0.000745554 s/step\n", - "Meep progress: 178.88333333333333/408.0 = 43.8% done in 8.0s, 10.2s to go\n", - "on time step 10735 (time=178.917), 0.000745129 s/step\n", - "Meep progress: 269.46666666666664/408.0 = 66.0% done in 12.0s, 6.2s to go\n", - "on time step 16172 (time=269.533), 0.000735702 s/step\n", - "Meep progress: 359.71666666666664/408.0 = 88.2% done in 16.0s, 2.1s to go\n", - "on time step 21588 (time=359.8), 0.000738555 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 27 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 63 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 42 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.02627\n", - "tran (uniaxial):, 1, 4.77, 0.45435\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.79629e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.8 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.105287 s\n", - "-----------\n", - "Meep progress: 82.33333333333333/208.0 = 39.6% done in 4.0s, 6.1s to go\n", - "on time step 4940 (time=82.3333), 0.000809818 s/step\n", - "Meep progress: 165.56666666666666/208.0 = 79.6% done in 8.0s, 2.1s to go\n", - "on time step 9936 (time=165.6), 0.000800751 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.00951e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.8 x 6.5 x 0 with resolution 30\n", - " block, center = (-2.4,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (-2.22045e-16,0,0)\n", - " size (2.8,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 53.5143% done, 3.8263 s remaining\n", - "subpixel-averaging is 53.5143% done, 3.81257 s remaining\n", - "subpixel-averaging is 53.5143% done, 3.81274 s remaining\n", - "time for set_epsilon = 23.647 s\n", - "-----------\n", - "Meep progress: 74.46666666666667/408.0 = 18.3% done in 4.0s, 17.9s to go\n", - "on time step 4468 (time=74.4667), 0.000895362 s/step\n", - "Meep progress: 150.38333333333333/408.0 = 36.9% done in 8.0s, 13.7s to go\n", - "on time step 9024 (time=150.4), 0.000878078 s/step\n", - "Meep progress: 226.41666666666666/408.0 = 55.5% done in 12.0s, 9.6s to go\n", - "on time step 13587 (time=226.45), 0.000876645 s/step\n", - "Meep progress: 302.23333333333335/408.0 = 74.1% done in 16.0s, 5.6s to go\n", - "on time step 18137 (time=302.283), 0.000879289 s/step\n", - "Meep progress: 377.98333333333335/408.0 = 92.6% done in 20.0s, 1.6s to go\n", - "on time step 22683 (time=378.05), 0.000879927 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 65 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 40 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 37 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.00085\n", - "tran (twisted):, 1, 4.77, 0.46955\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000133991 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5.6 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.096647 s\n", - "-----------\n", - "Meep progress: 93.56666666666666/208.0 = 45.0% done in 4.0s, 4.9s to go\n", - "on time step 5614 (time=93.5667), 0.000712629 s/step\n", - "Meep progress: 188.45/208.0 = 90.6% done in 8.0s, 0.8s to go\n", - "on time step 11308 (time=188.467), 0.000702542 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.29425e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5.6 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.8,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (2.22045e-16,0,0)\n", - " size (1.6,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 71.5192% done, 1.72158 s remaining\n", - "subpixel-averaging is 71.5192% done, 1.70206 s remaining\n", - "subpixel-averaging is 71.5192% done, 1.70269 s remaining\n", - "time for set_epsilon = 13.3156 s\n", - "-----------\n", - "Meep progress: 87.53333333333333/408.0 = 21.5% done in 4.0s, 14.6s to go\n", - "on time step 5252 (time=87.5333), 0.00076165 s/step\n", - "Meep progress: 176.3/408.0 = 43.2% done in 8.0s, 10.5s to go\n", - "on time step 10580 (time=176.333), 0.000750865 s/step\n", - "Meep progress: 263.75/408.0 = 64.6% done in 12.0s, 6.6s to go\n", - "on time step 15829 (time=263.817), 0.000762183 s/step\n", - "Meep progress: 352.4/408.0 = 86.4% done in 16.0s, 2.5s to go\n", - "on time step 21149 (time=352.483), 0.000751941 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 40 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 42 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 37 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 46 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.00005\n", - "tran (uniaxial):, 1, 4.77, 0.46429\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.69956e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.2 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.0993609 s\n", - "-----------\n", - "Meep progress: 78.7/208.0 = 37.8% done in 4.0s, 6.6s to go\n", - "on time step 4722 (time=78.7), 0.000847187 s/step\n", - "Meep progress: 160.36666666666667/208.0 = 77.1% done in 8.0s, 2.4s to go\n", - "on time step 9624 (time=160.4), 0.000816089 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.81877e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.2 x 6.5 x 0 with resolution 30\n", - " block, center = (-2.6,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (3.2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 49.3713% done, 4.3752 s remaining\n", - "subpixel-averaging is 78.9961% done, 1.1067 s remaining\n", - "subpixel-averaging is 49.3713% done, 4.42276 s remaining\n", - "subpixel-averaging is 78.9961% done, 1.10731 s remaining\n", - "subpixel-averaging is 49.3713% done, 4.43155 s remaining\n", - "subpixel-averaging is 78.9961% done, 1.11122 s remaining\n", - "time for set_epsilon = 26.3941 s\n", - "-----------\n", - "Meep progress: 72.93333333333334/408.0 = 17.9% done in 4.0s, 18.4s to go\n", - "on time step 4376 (time=72.9333), 0.000914306 s/step\n", - "Meep progress: 148.9/408.0 = 36.5% done in 8.0s, 13.9s to go\n", - "on time step 8935 (time=148.917), 0.000877482 s/step\n", - "Meep progress: 224.29999999999998/408.0 = 55.0% done in 12.0s, 9.8s to go\n", - "on time step 13460 (time=224.333), 0.000884124 s/step\n", - "Meep progress: 297.3333333333333/408.0 = 72.9% done in 16.0s, 6.0s to go\n", - "on time step 17843 (time=297.383), 0.00091272 s/step\n", - "Meep progress: 372.43333333333334/408.0 = 91.3% done in 20.0s, 1.9s to go\n", - "on time step 22351 (time=372.517), 0.000887407 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 58 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 52 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 52 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 42 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 27 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.00061\n", - "tran (twisted):, 1, 4.77, 0.46431\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000139952 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5.8 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.103642 s\n", - "-----------\n", - "Meep progress: 92.38333333333333/208.0 = 44.4% done in 4.0s, 5.0s to go\n", - "on time step 5543 (time=92.3833), 0.000721725 s/step\n", - "Meep progress: 186.01666666666665/208.0 = 89.4% done in 8.0s, 0.9s to go\n", - "on time step 11163 (time=186.05), 0.000711761 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.69956e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 5.8 x 6.5 x 0 with resolution 30\n", - " block, center = (-1.9,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (1.11022e-16,0,0)\n", - " size (1.8,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 67.7217% done, 2.0545 s remaining\n", - "subpixel-averaging is 67.7217% done, 2.04229 s remaining\n", - "subpixel-averaging is 67.7217% done, 2.04423 s remaining\n", - "time for set_epsilon = 14.95 s\n", - "-----------\n", - "Meep progress: 86.31666666666666/408.0 = 21.2% done in 4.0s, 14.9s to go\n", - "on time step 5179 (time=86.3167), 0.00077244 s/step\n", - "Meep progress: 173.38333333333333/408.0 = 42.5% done in 8.0s, 10.8s to go\n", - "on time step 10404 (time=173.4), 0.000765589 s/step\n", - "Meep progress: 261.1/408.0 = 64.0% done in 12.0s, 6.8s to go\n", - "on time step 15668 (time=261.133), 0.000759921 s/step\n", - "Meep progress: 347.5333333333333/408.0 = 85.2% done in 16.0s, 2.8s to go\n", - "on time step 20857 (time=347.617), 0.000770998 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 52 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 40 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 62 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.02864\n", - "tran (uniaxial):, 1, 4.77, 0.44626\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 5.00679e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.6 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.137346 s\n", - "-----------\n", - "Meep progress: 78.08333333333333/208.0 = 37.5% done in 4.0s, 6.7s to go\n", - "on time step 4685 (time=78.0833), 0.000853827 s/step\n", - "Meep progress: 156.88333333333333/208.0 = 75.4% done in 8.0s, 2.6s to go\n", - "on time step 9414 (time=156.9), 0.000845896 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.69956e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.6 x 6.5 x 0 with resolution 30\n", - " block, center = (-2.8,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (2.22045e-16,0,0)\n", - " size (3.6,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 45.8237% done, 5.1262 s remaining\n", - "subpixel-averaging is 73.3197% done, 1.54776 s remaining\n", - "subpixel-averaging is 45.8237% done, 5.0838 s remaining\n", - "subpixel-averaging is 73.3197% done, 1.54977 s remaining\n", - "subpixel-averaging is 45.8237% done, 5.10381 s remaining\n", - "subpixel-averaging is 73.3197% done, 1.55772 s remaining\n", - "time for set_epsilon = 30.0956 s\n", - "-----------\n", - "Meep progress: 70.91666666666667/408.0 = 17.4% done in 4.0s, 19.0s to go\n", - "on time step 4255 (time=70.9167), 0.000940257 s/step\n", - "Meep progress: 142.71666666666667/408.0 = 35.0% done in 8.0s, 14.9s to go\n", - "on time step 8564 (time=142.733), 0.000928472 s/step\n", - "Meep progress: 213.79999999999998/408.0 = 52.4% done in 12.0s, 10.9s to go\n", - "on time step 12830 (time=213.833), 0.000937849 s/step\n", - "Meep progress: 284.56666666666666/408.0 = 69.7% done in 16.0s, 6.9s to go\n", - "on time step 17078 (time=284.633), 0.000941825 s/step\n", - "Meep progress: 355.2833333333333/408.0 = 87.1% done in 20.0s, 3.0s to go\n", - "on time step 21322 (time=355.367), 0.000942543 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 31 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 28 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.00230\n", - "tran (twisted):, 1, 4.77, 0.45960\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 5.38826e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.0905509 s\n", - "-----------\n", - "Meep progress: 90.25/208.0 = 43.4% done in 4.0s, 5.2s to go\n", - "on time step 5415 (time=90.25), 0.000738743 s/step\n", - "Meep progress: 181.95/208.0 = 87.5% done in 8.0s, 1.1s to go\n", - "on time step 10918 (time=181.967), 0.000726975 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.41346e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6 x 6.5 x 0 with resolution 30\n", - " block, center = (-2,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 64.3072% done, 2.38699 s remaining\n", - "subpixel-averaging is 64.3072% done, 2.40457 s remaining\n", - "subpixel-averaging is 64.3072% done, 2.40796 s remaining\n", - "time for set_epsilon = 16.6695 s\n", - "-----------\n", - "Meep progress: 82.75/408.0 = 20.3% done in 4.0s, 15.7s to go\n", - "on time step 4965 (time=82.75), 0.000805785 s/step\n", - "Meep progress: 175.51666666666665/408.0 = 43.0% done in 8.0s, 10.6s to go\n", - "on time step 10533 (time=175.55), 0.000718519 s/step\n", - "Meep progress: 259.15/408.0 = 63.5% done in 12.0s, 6.9s to go\n", - "on time step 15553 (time=259.217), 0.000796936 s/step\n", - "Meep progress: 342.56666666666666/408.0 = 84.0% done in 16.0s, 3.1s to go\n", - "on time step 20559 (time=342.65), 0.00079916 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 68 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 30 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 34 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 55 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 42 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 34 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.15199\n", - "tran (uniaxial):, 1, 4.77, 0.39654\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 8.79765e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.125251 s\n", - "-----------\n", - "Meep progress: 75.01666666666667/208.0 = 36.1% done in 4.0s, 7.1s to go\n", - "on time step 4501 (time=75.0167), 0.000888818 s/step\n", - "Meep progress: 150.11666666666667/208.0 = 72.2% done in 8.0s, 3.1s to go\n", - "on time step 9009 (time=150.15), 0.000887502 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.39098e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8 x 6.5 x 0 with resolution 30\n", - " block, center = (-3,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 42.7517% done, 5.69995 s remaining\n", - "subpixel-averaging is 68.4044% done, 1.92679 s remaining\n", - "subpixel-averaging is 42.7517% done, 5.7258 s remaining\n", - "subpixel-averaging is 68.4044% done, 1.91778 s remaining\n", - "subpixel-averaging is 42.7517% done, 5.72558 s remaining\n", - "subpixel-averaging is 68.4044% done, 1.9235 s remaining\n", - "time for set_epsilon = 32.8454 s\n", - "-----------\n", - "Meep progress: 67.6/408.0 = 16.6% done in 4.0s, 20.1s to go\n", - "on time step 4056 (time=67.6), 0.000986321 s/step\n", - "Meep progress: 136.23333333333332/408.0 = 33.4% done in 8.0s, 16.0s to go\n", - "on time step 8175 (time=136.25), 0.00097132 s/step\n", - "Meep progress: 204.93333333333334/408.0 = 50.2% done in 12.0s, 11.9s to go\n", - "on time step 12298 (time=204.967), 0.000970339 s/step\n", - "Meep progress: 273.5833333333333/408.0 = 67.1% done in 16.0s, 7.9s to go\n", - "on time step 16418 (time=273.633), 0.000971036 s/step\n", - "Meep progress: 341.8666666666667/408.0 = 83.8% done in 20.0s, 3.9s to go\n", - "on time step 20515 (time=341.917), 0.000976364 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 42 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 37 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 30 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.05159\n", - "tran (twisted):, 1, 4.77, 0.43938\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 5.29289e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.2 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.104505 s\n", - "-----------\n", - "Meep progress: 86.15/208.0 = 41.4% done in 4.0s, 5.7s to go\n", - "on time step 5169 (time=86.15), 0.000773898 s/step\n", - "Meep progress: 174.43333333333334/208.0 = 83.9% done in 8.0s, 1.5s to go\n", - "on time step 10468 (time=174.467), 0.000754955 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.50883e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.2 x 6.5 x 0 with resolution 30\n", - " block, center = (-2.1,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (2.2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 61.2204% done, 2.79341 s remaining\n", - "subpixel-averaging is 61.2204% done, 2.64891 s remaining\n", - "subpixel-averaging is 61.2204% done, 2.75618 s remaining\n", - "time for set_epsilon = 18.3206 s\n", - "-----------\n", - "Meep progress: 81.01666666666667/408.0 = 19.9% done in 4.0s, 16.1s to go\n", - "on time step 4861 (time=81.0167), 0.000823039 s/step\n", - "Meep progress: 163.05/408.0 = 40.0% done in 8.0s, 12.0s to go\n", - "on time step 9784 (time=163.067), 0.00081255 s/step\n", - "Meep progress: 246.23333333333332/408.0 = 60.4% done in 12.0s, 7.9s to go\n", - "on time step 14776 (time=246.267), 0.000801294 s/step\n", - "Meep progress: 327.5333333333333/408.0 = 80.3% done in 16.0s, 3.9s to go\n", - "on time step 19655 (time=327.583), 0.000819844 s/step\n", - "Meep progress: 407.8666666666667/408.0 = 100.0% done in 20.0s, 0.0s to go\n", - "on time step 24477 (time=407.95), 0.000829674 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 51 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 49 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 43 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 36 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 46 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.30148\n", - "tran (uniaxial):, 1, 4.77, 0.31081\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 9.58443e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.4 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.128375 s\n", - "-----------\n", - "Meep progress: 72.65/208.0 = 34.9% done in 4.0s, 7.5s to go\n", - "on time step 4359 (time=72.65), 0.000917816 s/step\n", - "Meep progress: 144.25/208.0 = 69.4% done in 8.0s, 3.5s to go\n", - "on time step 8656 (time=144.267), 0.000930968 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.69956e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.4 x 6.5 x 0 with resolution 30\n", - " block, center = (-3.2,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (4.4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 40.0657% done, 6.53195 s remaining\n", - "subpixel-averaging is 64.1067% done, 2.36242 s remaining\n", - "subpixel-averaging is 40.0657% done, 6.48991 s remaining\n", - "subpixel-averaging is 64.1067% done, 2.36408 s remaining\n", - "subpixel-averaging is 40.0657% done, 6.52898 s remaining\n", - "subpixel-averaging is 64.1067% done, 2.35834 s remaining\n", - "time for set_epsilon = 36.7265 s\n", - "-----------\n", - "Meep progress: 65.65/408.0 = 16.1% done in 4.0s, 20.9s to go\n", - "on time step 3939 (time=65.65), 0.00101563 s/step\n", - "Meep progress: 132.1/408.0 = 32.4% done in 8.0s, 16.7s to go\n", - "on time step 7927 (time=132.117), 0.00100305 s/step\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 198.91666666666666/408.0 = 48.8% done in 12.0s, 12.6s to go\n", - "on time step 11937 (time=198.95), 0.000997598 s/step\n", - "Meep progress: 265.51666666666665/408.0 = 65.1% done in 16.0s, 8.6s to go\n", - "on time step 15934 (time=265.567), 0.00100085 s/step\n", - "Meep progress: 331.93333333333334/408.0 = 81.4% done in 20.0s, 4.6s to go\n", - "on time step 19920 (time=332), 0.00100371 s/step\n", - "Meep progress: 397.23333333333335/408.0 = 97.4% done in 24.0s, 0.7s to go\n", - "on time step 23839 (time=397.317), 0.00102074 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 47 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 43 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 50 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.17455\n", - "tran (twisted):, 1, 4.77, 0.38170\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 4.50611e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.4 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.11933 s\n", - "-----------\n", - "Meep progress: 86.23333333333333/208.0 = 41.5% done in 4.0s, 5.6s to go\n", - "on time step 5174 (time=86.2333), 0.000773265 s/step\n", - "Meep progress: 173.26666666666665/208.0 = 83.3% done in 8.0s, 1.6s to go\n", - "on time step 10398 (time=173.3), 0.000765817 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.50883e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.4 x 6.5 x 0 with resolution 30\n", - " block, center = (-2.2,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (2.4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 58.4164% done, 3.09306 s remaining\n", - "subpixel-averaging is 58.4164% done, 2.89371 s remaining\n", - "subpixel-averaging is 58.4164% done, 3.09871 s remaining\n", - "time for set_epsilon = 19.6879 s\n", - "-----------\n", - "Meep progress: 80.91666666666667/408.0 = 19.8% done in 4.0s, 16.2s to go\n", - "on time step 4855 (time=80.9167), 0.000824082 s/step\n", - "Meep progress: 162.7/408.0 = 39.9% done in 8.0s, 12.1s to go\n", - "on time step 9763 (time=162.717), 0.000815005 s/step\n", - "Meep progress: 244.65/408.0 = 60.0% done in 12.0s, 8.0s to go\n", - "on time step 14681 (time=244.683), 0.000813452 s/step\n", - "Meep progress: 326.3833333333333/408.0 = 80.0% done in 16.0s, 4.0s to go\n", - "on time step 19586 (time=326.433), 0.00081554 s/step\n", - "Meep progress: 407.9166666666667/408.0 = 100.0% done in 20.0s, 0.0s to go\n", - "on time step 24479 (time=407.983), 0.000817639 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 69 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 49 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 37 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 51 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 36 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 28 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.46820\n", - "tran (uniaxial):, 1, 4.77, 0.22878\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000147104 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.8 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.139945 s\n", - "-----------\n", - "Meep progress: 69.91666666666667/208.0 = 33.6% done in 4.0s, 7.9s to go\n", - "on time step 4195 (time=69.9167), 0.000953672 s/step\n", - "Meep progress: 140.83333333333334/208.0 = 67.7% done in 8.0s, 3.8s to go\n", - "on time step 8450 (time=140.833), 0.000940074 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.10487e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 8.8 x 6.5 x 0 with resolution 30\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " block, center = (-3.4,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (4.8,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 37.6973% done, 7.13631 s remaining\n", - "subpixel-averaging is 60.3172% done, 2.75956 s remaining\n", - "subpixel-averaging is 82.9371% done, 0.865693 s remaining\n", - "subpixel-averaging is 37.6973% done, 7.11049 s remaining\n", - "subpixel-averaging is 60.3172% done, 2.75036 s remaining\n", - "subpixel-averaging is 82.9371% done, 0.858843 s remaining\n", - "subpixel-averaging is 37.6973% done, 7.12251 s remaining\n", - "subpixel-averaging is 60.3172% done, 2.75345 s remaining\n", - "subpixel-averaging is 82.9371% done, 0.866829 s remaining\n", - "time for set_epsilon = 39.6605 s\n", - "-----------\n", - "Meep progress: 61.016666666666666/408.0 = 15.0% done in 4.0s, 22.7s to go\n", - "on time step 3661 (time=61.0167), 0.00109264 s/step\n", - "Meep progress: 122.33333333333333/408.0 = 30.0% done in 8.0s, 18.7s to go\n", - "on time step 7342 (time=122.367), 0.00108692 s/step\n", - "Meep progress: 185.16666666666666/408.0 = 45.4% done in 12.0s, 14.4s to go\n", - "on time step 11113 (time=185.217), 0.00106086 s/step\n", - "Meep progress: 248.03333333333333/408.0 = 60.8% done in 16.0s, 10.3s to go\n", - "on time step 14886 (time=248.1), 0.00106035 s/step\n", - "Meep progress: 310.8/408.0 = 76.2% done in 20.0s, 6.3s to go\n", - "on time step 18653 (time=310.883), 0.00106216 s/step\n", - "Meep progress: 373.48333333333335/408.0 = 91.5% done in 24.0s, 2.2s to go\n", - "on time step 22416 (time=373.6), 0.0010633 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 37 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 27 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 51 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.40540\n", - "tran (twisted):, 1, 4.77, 0.26104\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.20024e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.6 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.098484 s\n", - "-----------\n", - "Meep progress: 84.7/208.0 = 40.7% done in 4.0s, 5.8s to go\n", - "on time step 5082 (time=84.7), 0.000787265 s/step\n", - "Meep progress: 168.76666666666665/208.0 = 81.1% done in 8.0s, 1.9s to go\n", - "on time step 10127 (time=168.783), 0.000792943 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.69956e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.6 x 6.5 x 0 with resolution 30\n", - " block, center = (-2.3,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (2.6,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 55.858% done, 3.41444 s remaining\n", - "subpixel-averaging is 55.858% done, 3.40405 s remaining\n", - "subpixel-averaging is 55.858% done, 3.39686 s remaining\n", - "time for set_epsilon = 21.5757 s\n", - "-----------\n", - "Meep progress: 78.68333333333334/408.0 = 19.3% done in 4.0s, 16.7s to go\n", - "on time step 4721 (time=78.6833), 0.000847361 s/step\n", - "Meep progress: 157.51666666666665/408.0 = 38.6% done in 8.0s, 12.7s to go\n", - "on time step 9452 (time=157.533), 0.000845538 s/step\n", - "Meep progress: 237.29999999999998/408.0 = 58.2% done in 12.0s, 8.6s to go\n", - "on time step 14240 (time=237.333), 0.000835482 s/step\n", - "Meep progress: 315.93333333333334/408.0 = 77.4% done in 16.0s, 4.7s to go\n", - "on time step 18959 (time=315.983), 0.000847783 s/step\n", - "Meep progress: 394.68333333333334/408.0 = 96.7% done in 20.0s, 0.7s to go\n", - "on time step 23686 (time=394.767), 0.000846355 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 53 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 38 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 70 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 47 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.66179\n", - "tran (uniaxial):, 1, 4.77, 0.14253\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000156164 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 9.2 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.172037 s\n", - "-----------\n", - "Meep progress: 68.06666666666666/208.0 = 32.7% done in 4.0s, 8.2s to go\n", - "on time step 4084 (time=68.0667), 0.000979654 s/step\n", - "Meep progress: 137.1/208.0 = 65.9% done in 8.0s, 4.1s to go\n", - "on time step 8226 (time=137.1), 0.000965725 s/step\n", - "Meep progress: 205.7/208.0 = 98.9% done in 12.0s, 0.1s to go\n", - "on time step 12343 (time=205.717), 0.000971618 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.98566e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 9.2 x 6.5 x 0 with resolution 30\n", - " block, center = (-3.6,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (5.2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 35.5933% done, 7.99426 s remaining\n", - "subpixel-averaging is 56.9506% done, 3.19121 s remaining\n", - "subpixel-averaging is 78.308% done, 1.1792 s remaining\n", - "subpixel-averaging is 35.5933% done, 7.82132 s remaining\n", - "subpixel-averaging is 56.9506% done, 3.19462 s remaining\n", - "subpixel-averaging is 78.308% done, 1.17005 s remaining\n", - "subpixel-averaging is 35.5933% done, 7.79931 s remaining\n", - "subpixel-averaging is 56.9506% done, 3.1716 s remaining\n", - "subpixel-averaging is 78.308% done, 1.18297 s remaining\n", - "time for set_epsilon = 43.3607 s\n", - "-----------\n", - "Meep progress: 61.21666666666667/408.0 = 15.0% done in 4.0s, 22.7s to go\n", - "on time step 3673 (time=61.2167), 0.00108931 s/step\n", - "Meep progress: 122.93333333333334/408.0 = 30.1% done in 8.0s, 18.6s to go\n", - "on time step 7377 (time=122.95), 0.00108006 s/step\n", - "Meep progress: 183.58333333333334/408.0 = 45.0% done in 12.0s, 14.7s to go\n", - "on time step 11017 (time=183.617), 0.00109908 s/step\n", - "Meep progress: 244.83333333333334/408.0 = 60.0% done in 16.0s, 10.7s to go\n", - "on time step 14693 (time=244.883), 0.00108818 s/step\n", - "Meep progress: 306.65/408.0 = 75.2% done in 20.0s, 6.6s to go\n", - "on time step 18403 (time=306.717), 0.00107845 s/step\n", - "Meep progress: 367.8/408.0 = 90.1% done in 24.0s, 2.6s to go\n", - "on time step 22073 (time=367.883), 0.00109002 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 47 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.67195\n", - "tran (twisted):, 1, 4.77, 0.13686\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 9.08375e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.8 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.099133 s\n", - "-----------\n", - "Meep progress: 83.68333333333334/208.0 = 40.2% done in 4.0s, 5.9s to go\n", - "on time step 5021 (time=83.6833), 0.000796734 s/step\n", - "Meep progress: 168.46666666666667/208.0 = 81.0% done in 8.0s, 1.9s to go\n", - "on time step 10110 (time=168.5), 0.000786127 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.69956e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 6.8 x 6.5 x 0 with resolution 30\n", - " block, center = (-2.4,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (-2.22045e-16,0,0)\n", - " size (2.8,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 53.5143% done, 3.79773 s remaining\n", - "subpixel-averaging is 53.5143% done, 3.81162 s remaining\n", - "subpixel-averaging is 53.5143% done, 3.8089 s remaining\n", - "time for set_epsilon = 23.541 s\n", - "-----------\n", - "Meep progress: 74.93333333333334/408.0 = 18.4% done in 4.0s, 17.8s to go\n", - "on time step 4496 (time=74.9333), 0.000889767 s/step\n", - "Meep progress: 151.51666666666665/408.0 = 37.1% done in 8.0s, 13.5s to go\n", - "on time step 9092 (time=151.533), 0.000870396 s/step\n", - "Meep progress: 229.31666666666666/408.0 = 56.2% done in 12.0s, 9.4s to go\n", - "on time step 13761 (time=229.35), 0.000856824 s/step\n", - "Meep progress: 307.1666666666667/408.0 = 75.3% done in 16.0s, 5.3s to go\n", - "on time step 18434 (time=307.233), 0.00085611 s/step\n", - "Meep progress: 383.71666666666664/408.0 = 94.0% done in 20.0s, 1.3s to go\n", - "on time step 23028 (time=383.8), 0.000870738 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 69 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 34 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.80825\n", - "tran (uniaxial):, 1, 4.77, 0.06307\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000152111 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 9.6 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.157447 s\n", - "-----------\n", - "Meep progress: 64.9/208.0 = 31.2% done in 4.0s, 8.8s to go\n", - "on time step 3894 (time=64.9), 0.00102741 s/step\n", - "Meep progress: 130.88333333333333/208.0 = 62.9% done in 8.0s, 4.7s to go\n", - "on time step 7854 (time=130.9), 0.00101027 s/step\n", - "Meep progress: 196.55/208.0 = 94.5% done in 12.0s, 0.7s to go\n", - "on time step 11795 (time=196.583), 0.00101506 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.20024e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 9.6 x 6.5 x 0 with resolution 30\n", - " block, center = (-3.8,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (-4.44089e-16,0,0)\n", - " size (5.6,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 33.7117% done, 8.53515 s remaining\n", - "subpixel-averaging is 53.94% done, 3.58281 s remaining\n", - "subpixel-averaging is 74.1684% done, 1.47227 s remaining\n", - "subpixel-averaging is 33.7117% done, 8.64079 s remaining\n", - "subpixel-averaging is 53.94% done, 3.58974 s remaining\n", - "subpixel-averaging is 74.1684% done, 1.46986 s remaining\n", - "subpixel-averaging is 33.7117% done, 8.56235 s remaining\n", - "subpixel-averaging is 53.94% done, 3.61465 s remaining\n", - "subpixel-averaging is 74.1684% done, 1.47245 s remaining\n", - "time for set_epsilon = 46.6436 s\n", - "-----------\n", - "Meep progress: 58.0/408.0 = 14.2% done in 4.0s, 24.1s to go\n", - "on time step 3480 (time=58), 0.0011497 s/step\n", - "Meep progress: 116.86666666666666/408.0 = 28.6% done in 8.0s, 19.9s to go\n", - "on time step 7013 (time=116.883), 0.00113224 s/step\n", - "Meep progress: 175.05/408.0 = 42.9% done in 12.0s, 16.0s to go\n", - "on time step 10505 (time=175.083), 0.00114558 s/step\n", - "Meep progress: 233.93333333333334/408.0 = 57.3% done in 16.0s, 11.9s to go\n", - "on time step 14039 (time=233.983), 0.00113194 s/step\n", - "Meep progress: 293.5/408.0 = 71.9% done in 20.0s, 7.8s to go\n", - "on time step 17613 (time=293.55), 0.00111922 s/step\n", - "Meep progress: 352.01666666666665/408.0 = 86.3% done in 24.0s, 3.8s to go\n", - "on time step 21125 (time=352.083), 0.00113908 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 47 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 51 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 31 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.86580\n", - "tran (twisted):, 1, 4.77, 0.02804\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000117064 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.103678 s\n", - "-----------\n", - "Meep progress: 80.11666666666666/208.0 = 38.5% done in 4.0s, 6.4s to go\n", - "on time step 4807 (time=80.1167), 0.000832301 s/step\n", - "Meep progress: 161.7/208.0 = 77.7% done in 8.0s, 2.3s to go\n", - "on time step 9704 (time=161.733), 0.000816961 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.39098e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7 x 6.5 x 0 with resolution 30\n", - " block, center = (-2.5,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (2.22045e-16,0,0)\n", - " size (3,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 51.3594% done, 4.1118 s remaining\n", - "subpixel-averaging is 51.3594% done, 4.09325 s remaining\n", - "subpixel-averaging is 51.3594% done, 4.09493 s remaining\n", - "time for set_epsilon = 24.965 s\n", - "-----------\n", - "Meep progress: 75.21666666666667/408.0 = 18.4% done in 4.0s, 17.7s to go\n", - "on time step 4513 (time=75.2167), 0.000886419 s/step\n", - "Meep progress: 150.08333333333334/408.0 = 36.8% done in 8.0s, 13.7s to go\n", - "on time step 9006 (time=150.1), 0.00089035 s/step\n", - "Meep progress: 222.83333333333334/408.0 = 54.6% done in 12.0s, 10.0s to go\n", - "on time step 13373 (time=222.883), 0.000916091 s/step\n", - "Meep progress: 297.3666666666667/408.0 = 72.9% done in 16.0s, 6.0s to go\n", - "on time step 17846 (time=297.433), 0.000894295 s/step\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 371.5/408.0 = 91.1% done in 20.0s, 2.0s to go\n", - "on time step 22295 (time=371.583), 0.000899168 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 64 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 45 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.88675\n", - "tran (uniaxial):, 1, 4.77, 0.01655\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.20024e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 10 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.155606 s\n", - "-----------\n", - "Meep progress: 63.06666666666666/208.0 = 30.3% done in 4.0s, 9.2s to go\n", - "on time step 3784 (time=63.0667), 0.0010573 s/step\n", - "Meep progress: 127.85/208.0 = 61.5% done in 8.0s, 5.0s to go\n", - "on time step 7672 (time=127.867), 0.001029 s/step\n", - "Meep progress: 191.65/208.0 = 92.1% done in 12.0s, 1.0s to go\n", - "on time step 11501 (time=191.683), 0.00104491 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.00951e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 10 x 6.5 x 0 with resolution 30\n", - " block, center = (-4,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (4.44089e-16,0,0)\n", - " size (6,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 32.019% done, 9.22186 s remaining\n", - "subpixel-averaging is 51.2317% done, 3.98466 s remaining\n", - "subpixel-averaging is 70.4445% done, 1.77788 s remaining\n", - "subpixel-averaging is 32.019% done, 9.30279 s remaining\n", - "subpixel-averaging is 51.2317% done, 4.03405 s remaining\n", - "subpixel-averaging is 70.4445% done, 1.79632 s remaining\n", - "subpixel-averaging is 32.019% done, 9.17258 s remaining\n", - "subpixel-averaging is 51.2317% done, 4.02283 s remaining\n", - "subpixel-averaging is 70.4445% done, 1.78612 s remaining\n", - "time for set_epsilon = 50.0774 s\n", - "-----------\n", - "Meep progress: 55.21666666666667/408.0 = 13.5% done in 4.0s, 25.6s to go\n", - "on time step 3313 (time=55.2167), 0.00120767 s/step\n", - "Meep progress: 112.33333333333333/408.0 = 27.5% done in 8.0s, 21.1s to go\n", - "on time step 6741 (time=112.35), 0.00116717 s/step\n", - "Meep progress: 168.81666666666666/408.0 = 41.4% done in 12.0s, 17.0s to go\n", - "on time step 10131 (time=168.85), 0.00118012 s/step\n", - "Meep progress: 225.75/408.0 = 55.3% done in 16.0s, 12.9s to go\n", - "on time step 13549 (time=225.817), 0.00117055 s/step\n", - "Meep progress: 281.48333333333335/408.0 = 69.0% done in 20.0s, 9.0s to go\n", - "on time step 16894 (time=281.567), 0.00119605 s/step\n", - "Meep progress: 338.5333333333333/408.0 = 83.0% done in 24.0s, 4.9s to go\n", - "on time step 20317 (time=338.617), 0.00116858 s/step\n", - "Meep progress: 395.55/408.0 = 96.9% done in 28.0s, 0.9s to go\n", - "on time step 23739 (time=395.65), 0.00116892 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 65 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 45 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.94211\n", - "tran (twisted):, 1, 4.77, 0.00255\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.00011301 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.2 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.111866 s\n", - "-----------\n", - "Meep progress: 80.61666666666666/208.0 = 38.8% done in 4.0s, 6.3s to go\n", - "on time step 4837 (time=80.6167), 0.000827066 s/step\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 162.21666666666667/208.0 = 78.0% done in 8.0s, 2.3s to go\n", - "on time step 9734 (time=162.233), 0.000816884 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.91414e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.2 x 6.5 x 0 with resolution 30\n", - " block, center = (-2.6,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (3.2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 49.3713% done, 4.4399 s remaining\n", - "subpixel-averaging is 78.9961% done, 1.12404 s remaining\n", - "subpixel-averaging is 49.3713% done, 4.44133 s remaining\n", - "subpixel-averaging is 78.9961% done, 1.12126 s remaining\n", - "subpixel-averaging is 49.3713% done, 4.42219 s remaining\n", - "subpixel-averaging is 78.9961% done, 1.12413 s remaining\n", - "time for set_epsilon = 26.6699 s\n", - "-----------\n", - "Meep progress: 73.56666666666666/408.0 = 18.0% done in 4.0s, 18.2s to go\n", - "on time step 4414 (time=73.5667), 0.00090629 s/step\n", - "Meep progress: 147.21666666666667/408.0 = 36.1% done in 8.0s, 14.2s to go\n", - "on time step 8834 (time=147.233), 0.000905054 s/step\n", - "Meep progress: 221.16666666666666/408.0 = 54.2% done in 12.0s, 10.1s to go\n", - "on time step 13272 (time=221.2), 0.000901458 s/step\n", - "Meep progress: 294.68333333333334/408.0 = 72.2% done in 16.0s, 6.2s to go\n", - "on time step 17684 (time=294.733), 0.000906649 s/step\n", - "Meep progress: 368.93333333333334/408.0 = 90.4% done in 20.0s, 2.1s to go\n", - "on time step 22140 (time=369), 0.000897794 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 45 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 28 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 65 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 37 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.93860\n", - "tran (uniaxial):, 1, 4.77, 0.00001\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000154018 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 10.4 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.161312 s\n", - "-----------\n", - "Meep progress: 60.9/208.0 = 29.3% done in 4.0s, 9.7s to go\n", - "on time step 3654 (time=60.9), 0.00109495 s/step\n", - "Meep progress: 122.28333333333333/208.0 = 58.8% done in 8.0s, 5.6s to go\n", - "on time step 7338 (time=122.3), 0.00108598 s/step\n", - "Meep progress: 183.23333333333332/208.0 = 88.1% done in 12.0s, 1.6s to go\n", - "on time step 10996 (time=183.267), 0.00109353 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.10487e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 10.4 x 6.5 x 0 with resolution 30\n", - " block, center = (-4.2,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (6.4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 30.4883% done, 9.98227 s remaining\n", - "subpixel-averaging is 48.7824% done, 4.42182 s remaining\n", - "subpixel-averaging is 67.0766% done, 2.07037 s remaining\n", - "subpixel-averaging is 85.3708% done, 0.733933 s remaining\n", - "subpixel-averaging is 30.4883% done, 9.99655 s remaining\n", - "subpixel-averaging is 48.7824% done, 4.44853 s remaining\n", - "subpixel-averaging is 67.0766% done, 2.11095 s remaining\n", - "subpixel-averaging is 85.3708% done, 0.733249 s remaining\n", - "subpixel-averaging is 30.4883% done, 9.9553 s remaining\n", - "subpixel-averaging is 48.7824% done, 4.42917 s remaining\n", - "subpixel-averaging is 67.0766% done, 2.08722 s remaining\n", - "subpixel-averaging is 85.3708% done, 0.729907 s remaining\n", - "time for set_epsilon = 53.5962 s\n", - "-----------\n", - "Meep progress: 54.233333333333334/408.0 = 13.3% done in 4.0s, 26.1s to go\n", - "on time step 3254 (time=54.2333), 0.00122939 s/step\n", - "Meep progress: 108.7/408.0 = 26.6% done in 8.0s, 22.0s to go\n", - "on time step 6523 (time=108.717), 0.0012238 s/step\n", - "Meep progress: 163.1/408.0 = 40.0% done in 12.0s, 18.0s to go\n", - "on time step 9787 (time=163.117), 0.00122558 s/step\n", - "Meep progress: 217.76666666666665/408.0 = 53.4% done in 16.0s, 14.0s to go\n", - "on time step 13068 (time=217.8), 0.00121939 s/step\n", - "Meep progress: 272.51666666666665/408.0 = 66.8% done in 20.0s, 9.9s to go\n", - "on time step 16354 (time=272.567), 0.0012174 s/step\n", - "Meep progress: 327.21666666666664/408.0 = 80.2% done in 24.0s, 5.9s to go\n", - "on time step 19637 (time=327.283), 0.00121851 s/step\n", - "Meep progress: 380.9166666666667/408.0 = 93.4% done in 28.0s, 2.0s to go\n", - "on time step 22860 (time=381), 0.00124152 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 62 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 46 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 62 iters\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 42 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.77362\n", - "tran (twisted):, 1, 4.77, 0.07215\n", - "tran (twisted):, 2, 9.56, 0.00000\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 3.60012e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.4 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.107628 s\n", - "-----------\n", - "Meep progress: 78.06666666666666/208.0 = 37.5% done in 4.0s, 6.7s to go\n", - "on time step 4684 (time=78.0667), 0.000854101 s/step\n", - "Meep progress: 157.66666666666666/208.0 = 75.8% done in 8.0s, 2.6s to go\n", - "on time step 9461 (time=157.683), 0.000837429 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.69956e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 7.4 x 6.5 x 0 with resolution 30\n", - " block, center = (-2.7,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (3.4,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 47.5314% done, 4.80929 s remaining\n", - "subpixel-averaging is 76.0521% done, 1.33452 s remaining\n", - "subpixel-averaging is 47.5314% done, 4.83031 s remaining\n", - "subpixel-averaging is 76.0521% done, 1.34121 s remaining\n", - "subpixel-averaging is 47.5314% done, 4.85467 s remaining\n", - "subpixel-averaging is 76.0521% done, 1.34843 s remaining\n", - "time for set_epsilon = 28.6162 s\n", - "-----------\n", - "Meep progress: 69.88333333333333/408.0 = 17.1% done in 4.0s, 19.4s to go\n", - "on time step 4193 (time=69.8833), 0.000954222 s/step\n", - "Meep progress: 141.2/408.0 = 34.6% done in 8.0s, 15.1s to go\n", - "on time step 8473 (time=141.217), 0.000934654 s/step\n", - "Meep progress: 212.45/408.0 = 52.1% done in 12.0s, 11.0s to go\n", - "on time step 12749 (time=212.483), 0.000935672 s/step\n", - "Meep progress: 283.9166666666667/408.0 = 69.6% done in 16.0s, 7.0s to go\n", - "on time step 17038 (time=283.967), 0.000932807 s/step\n", - "Meep progress: 355.21666666666664/408.0 = 87.1% done in 20.0s, 3.0s to go\n", - "on time step 21317 (time=355.283), 0.000934988 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 50 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 33 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (uniaxial):, 0, 0.00, 0.90581\n", - "tran (uniaxial):, 1, 4.77, 0.01950\n", - "tran (uniaxial):, 2, 9.56, 0.00000\n", - "tran (uniaxial):, 3, 14.43, 0.00000\n", - "tran (uniaxial):, 4, 19.41, 0.00000\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 6.91414e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 10.8 x 6.5 x 0 with resolution 30\n", - "time for set_epsilon = 0.160437 s\n", - "-----------\n", - "Meep progress: 59.15/208.0 = 28.4% done in 4.0s, 10.1s to go\n", - "on time step 3549 (time=59.15), 0.00112713 s/step\n", - "Meep progress: 119.0/208.0 = 57.2% done in 8.0s, 6.0s to go\n", - "on time step 7141 (time=119.017), 0.00111385 s/step\n", - "Meep progress: 179.25/208.0 = 86.2% done in 12.0s, 1.9s to go\n", - "on time step 10757 (time=179.283), 0.00110643 s/step\n", - "run 0 finished at t = 208.0 (12480 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 7.39098e-05 s\n", - "Working in 2D dimensions.\n", - "Computational cell is 10.8 x 6.5 x 0 with resolution 30\n", - " block, center = (-4.4,0,0)\n", - " size (2,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", - " block, center = (0,0,0)\n", - " size (6.8,1e+20,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "subpixel-averaging is 29.0972% done, 10.5851 s remaining\n", - "subpixel-averaging is 46.5566% done, 4.83 s remaining\n", - "subpixel-averaging is 64.0161% done, 2.40549 s remaining\n", - "subpixel-averaging is 81.4755% done, 0.964889 s remaining\n", - "subpixel-averaging is 29.0972% done, 10.5591 s remaining\n", - "subpixel-averaging is 46.5566% done, 4.83987 s remaining\n", - "subpixel-averaging is 64.0161% done, 2.39451 s remaining\n", - "subpixel-averaging is 81.4755% done, 0.972159 s remaining\n", - "subpixel-averaging is 29.0972% done, 10.6613 s remaining\n", - "subpixel-averaging is 46.5566% done, 4.82302 s remaining\n", - "subpixel-averaging is 64.0161% done, 2.39088 s remaining\n", - "subpixel-averaging is 81.4755% done, 0.967715 s remaining\n", - "time for set_epsilon = 56.7605 s\n", - "-----------\n", - "Meep progress: 51.55/408.0 = 12.6% done in 4.0s, 27.7s to go\n", - "on time step 3093 (time=51.55), 0.00129338 s/step\n", - "Meep progress: 103.8/408.0 = 25.4% done in 8.0s, 23.4s to go\n", - "on time step 6229 (time=103.817), 0.00127578 s/step\n", - "Meep progress: 157.21666666666667/408.0 = 38.5% done in 12.0s, 19.1s to go\n", - "on time step 9435 (time=157.25), 0.0012479 s/step\n", - "Meep progress: 210.46666666666667/408.0 = 51.6% done in 16.0s, 15.0s to go\n", - "on time step 12631 (time=210.517), 0.00125168 s/step\n", - "Meep progress: 263.68333333333334/408.0 = 64.6% done in 20.0s, 10.9s to go\n", - "on time step 15825 (time=263.75), 0.0012525 s/step\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meep progress: 316.7833333333333/408.0 = 77.6% done in 24.0s, 6.9s to go\n", - "on time step 19011 (time=316.85), 0.0012555 s/step\n", - "Meep progress: 370.1166666666667/408.0 = 90.7% done in 28.0s, 2.9s to go\n", - "on time step 22212 (time=370.2), 0.00124999 s/step\n", - "run 0 finished at t = 408.0 (24480 timesteps)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 43 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 56 iters\n", - "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", - "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 51 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", - "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", - "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", - "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", - "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 30 iters\n", - "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", - "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", - "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", - "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", - "tran (twisted):, 0, 0.00, 0.49695\n", - "tran (twisted):, 1, 4.77, 0.22184\n", - "tran (twisted):, 2, 9.56, 0.00001\n", - "tran (twisted):, 3, 14.43, 0.00000\n", - "tran (twisted):, 4, 19.41, 0.00000\n" - ] - } - ], + "outputs": [], "source": [ "ph_uniaxial = 0 # chiral layer twist angle for uniaxial grating\n", "ph_twisted = 70 # chiral layer twist angle for bilayer grating\n", @@ -3236,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3252,22 +231,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(dpi=150)\n", "plt.subplot(1, 2, 1)\n", diff --git a/python/examples/polarization_grating.py b/python/examples/polarization_grating.py index 25cb227e5..23fbee9a4 100644 --- a/python/examples/polarization_grating.py +++ b/python/examples/polarization_grating.py @@ -1,12 +1,11 @@ -# -*- coding: utf-8 -*- - # polarization grating from C. Oh and M.J. Escuti, Optics Letters, Vol. 33, No. 20, pp. 2287-9, 2008 # note: reference uses z as the propagation direction and y as the out-of-plane direction; this script uses x and z, respectively - -import meep as mp -import numpy as np import math + import matplotlib.pyplot as plt +import numpy as np + +import meep as mp resolution = 50 # pixels/μm @@ -157,7 +156,7 @@ def lc_mat(p): ) tran = (abs(coeffs1) ** 2 + abs(coeffs2) ** 2) / input_flux for m in range(nmode): - print("tran (uniaxial):, {}, {:.2f}, {:.5f}".format(m, angles[m], tran[m])) + print(f"tran (uniaxial):, {m}, {angles[m]:.2f}, {tran[m]:.5f}") m0_uniaxial[k] = tran[0] m1_uniaxial[k] = tran[1] ang_uniaxial[k] = angles[1] @@ -167,7 +166,7 @@ def lc_mat(p): ) tran = (abs(coeffs1) ** 2 + abs(coeffs2) ** 2) / input_flux for m in range(nmode): - print("tran (twisted):, {}, {:.2f}, {:.5f}".format(m, angles[m], tran[m])) + print(f"tran (twisted):, {m}, {angles[m]:.2f}, {tran[m]:.5f}") m0_twisted[k] = tran[0] m1_twisted[k] = tran[1] ang_twisted[k] = angles[1] diff --git a/python/examples/pw-source.py b/python/examples/pw-source.py index f9932bc8f..2a244dc99 100644 --- a/python/examples/pw-source.py +++ b/python/examples/pw-source.py @@ -1,12 +1,10 @@ # This example creates an approximate Ez-polarized planewave in vacuum # propagating at a 45-degree angle, by using a couple of current sources # with amplitude exp(ikx) corresponding to the desired planewave. -from __future__ import division - import cmath import math -import meep as mp +import meep as mp s = 11 # the size of the computational cell, not including PML dpml = 1 # thickness of PML layers diff --git a/python/examples/refl-angular-kz2d.py b/python/examples/refl-angular-kz2d.py index bf67ac337..1009e047d 100644 --- a/python/examples/refl-angular-kz2d.py +++ b/python/examples/refl-angular-kz2d.py @@ -1,6 +1,7 @@ -import meep as mp import math +import meep as mp + def refl_planar(theta, kz_2d): resolution = 100 diff --git a/python/examples/refl-angular.ipynb b/python/examples/refl-angular.ipynb index ae5c0b948..492cf0059 100644 --- a/python/examples/refl-angular.ipynb +++ b/python/examples/refl-angular.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -146,1356 +146,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000254154 s\n", - "Working in 1D dimensions.\n", - "Computational cell is 0 x 0 x 12 with resolution 50\n", - "time for set_epsilon = 0.000342131 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.25332329653323415 / 0.25332329653323415 = 1.0\n", - "field decay(t = 100.01): 6.806395978139867e-16 / 0.25332329653323415 = 2.6868417043700194e-15\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108004 s\n", - "Working in 1D dimensions.\n", - "Computational cell is 0 x 0 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.000371933 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.25332329652480207 / 0.25332329652480207 = 1.0\n", - "field decay(t = 100.01): 1.9736380723733672e-11 / 0.25332329652480207 = 7.790985272371642e-11\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 0.0, 0.8, 0.0, 0.29476330384323207\n", - "refl:, 0.0, 0.784, 0.0, 0.29416578611798405\n", - "refl:, 0.0, 0.7686274509803922, 0.0, 0.2935560666132851\n", - "refl:, 0.0, 0.7538461538461539, 0.0, 0.29293290546540196\n", - "refl:, 0.0, 0.739622641509434, 0.0, 0.2922951429790378\n", - "refl:, 0.0, 0.7259259259259259, 0.0, 0.29164324229177097\n", - "refl:, 0.0, 0.7127272727272727, 0.0, 0.29097731021514417\n", - "refl:, 0.0, 0.7, 0.0, 0.29029575759541565\n", - "refl:, 0.0, 0.6877192982456141, 0.0, 0.2895975033066778\n", - "refl:, 0.0, 0.6758620689655173, 0.0, 0.2888834125810835\n", - "refl:, 0.0, 0.664406779661017, 0.0, 0.2881539488987047\n", - "refl:, 0.0, 0.6533333333333333, 0.0, 0.2874077841401458\n", - "refl:, 0.0, 0.6426229508196721, 0.0, 0.2866443691325259\n", - "refl:, 0.0, 0.632258064516129, 0.0, 0.2858651486082286\n", - "refl:, 0.0, 0.6222222222222222, 0.0, 0.28507064287177303\n", - "refl:, 0.0, 0.6124999999999999, 0.0, 0.2842593661161744\n", - "refl:, 0.0, 0.6030769230769231, 0.0, 0.28343094611850356\n", - "refl:, 0.0, 0.593939393939394, 0.0, 0.28258688014584116\n", - "refl:, 0.0, 0.5850746268656717, 0.0, 0.2817272035905085\n", - "refl:, 0.0, 0.5764705882352942, 0.0, 0.2808500622387931\n", - "refl:, 0.0, 0.5681159420289855, 0.0, 0.27995515394321197\n", - "refl:, 0.0, 0.56, 0.0, 0.27904384994339343\n", - "refl:, 0.0, 0.552112676056338, 0.0, 0.27811574176551984\n", - "refl:, 0.0, 0.5444444444444444, 0.0, 0.2771688207349352\n", - "refl:, 0.0, 0.536986301369863, 0.0, 0.2762029589907331\n", - "refl:, 0.0, 0.5297297297297298, 0.0, 0.2752195125027274\n", - "refl:, 0.0, 0.5226666666666667, 0.0, 0.2742178237240486\n", - "refl:, 0.0, 0.5157894736842105, 0.0, 0.27319583895650085\n", - "refl:, 0.0, 0.509090909090909, 0.0, 0.27215367552975717\n", - "refl:, 0.0, 0.5025641025641026, 0.0, 0.27109276268116617\n", - "refl:, 0.0, 0.4962025316455696, 0.0, 0.2700121717612113\n", - "refl:, 0.0, 0.49, 0.0, 0.26890977948431494\n", - "refl:, 0.0, 0.4839506172839506, 0.0, 0.26778602305927346\n", - "refl:, 0.0, 0.47804878048780486, 0.0, 0.26664234270279846\n", - "refl:, 0.0, 0.47228915662650606, 0.0, 0.2654773959072893\n", - "refl:, 0.0, 0.4666666666666667, 0.0, 0.2642890605183494\n", - "refl:, 0.0, 0.4611764705882353, 0.0, 0.26307815170848525\n", - "refl:, 0.0, 0.4558139534883721, 0.0, 0.2618459695575972\n", - "refl:, 0.0, 0.4505747126436782, 0.0, 0.2605907641320623\n", - "refl:, 0.0, 0.4454545454545454, 0.0, 0.25931062280334477\n", - "refl:, 0.0, 0.44044943820224725, 0.0, 0.25800677969709146\n", - "refl:, 0.0, 0.43555555555555553, 0.0, 0.2566804027177118\n", - "refl:, 0.0, 0.4307692307692308, 0.0, 0.25532959438320507\n", - "refl:, 0.0, 0.4260869565217391, 0.0, 0.2539529088676918\n", - "refl:, 0.0, 0.421505376344086, 0.0, 0.2525520862693137\n", - "refl:, 0.0, 0.41702127659574467, 0.0, 0.2511282531123407\n", - "refl:, 0.0, 0.4126315789473684, 0.0, 0.24967934554669954\n", - "refl:, 0.0, 0.4083333333333333, 0.0, 0.24820404444956207\n", - "refl:, 0.0, 0.4041237113402062, 0.0, 0.24670386250022297\n", - "refl:, 0.0, 0.4, 0.0, 0.24517862401765392\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108957 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.00770998 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.25242167342001043 / 0.25242167342001043 = 1.0\n", - "field decay(t = 100.01): 1.8867425254108467e-14 / 0.25242167342001043 = 7.474566267815881e-14\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000110865 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.026345 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.2524216734361254 / 0.2524216734361254 = 1.0\n", - "field decay(t = 100.01): 2.0310851043564926e-11 / 0.2524216734361254 = 8.046397429777174e-11\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 0.1089446784345727, 0.8, 5.0, 0.2934227118859898\n", - "refl:, 0.1089446784345727, 0.784, 4.899752997934953, 0.29287850682357097\n", - "refl:, 0.1089446784345727, 0.7686274509803922, 4.803451415694315, 0.2923194417293526\n", - "refl:, 0.1089446784345727, 0.7538461538461539, 4.710866569098618, 0.29174404751628646\n", - "refl:, 0.1089446784345727, 0.739622641509434, 4.621787131270349, 0.2911512998642181\n", - "refl:, 0.1089446784345727, 0.7259259259259259, 4.536017514803643, 0.2905417123133207\n", - "refl:, 0.1089446784345727, 0.7127272727272727, 4.45337643175598, 0.2899156301931104\n", - "refl:, 0.1089446784345727, 0.7, 4.373695609047488, 0.2892720597047358\n", - "refl:, 0.1089446784345727, 0.6877192982456141, 4.296818640028281, 0.288609702000128\n", - "refl:, 0.1089446784345727, 0.6758620689655173, 4.222599955651541, 0.28792940285545326\n", - "refl:, 0.1089446784345727, 0.664406779661017, 4.150903900954991, 0.28723211458816683\n", - "refl:, 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0.2535831260087174\n", - "refl:, 0.1089446784345727, 0.421505376344086, 2.631991754497782, 0.25219031084070415\n", - "refl:, 0.1089446784345727, 0.41702127659574467, 2.603972444256211, 0.25077428012312447\n", - "refl:, 0.1089446784345727, 0.4126315789473684, 2.576543617889841, 0.24933314662117714\n", - "refl:, 0.1089446784345727, 0.4083333333333333, 2.549686797979975, 0.2478653741944476\n", - "refl:, 0.1089446784345727, 0.4041237113402062, 2.5233842703240543, 0.24637235281997996\n", - "refl:, 0.1089446784345727, 0.4, 2.4976190449198983, 0.2448541987123429\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.00011611 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.00750589 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.24974540035413878 / 0.24974540035413878 = 1.0\n", - "field decay(t = 100.01): 6.006906595531682e-14 / 0.24974540035413878 = 2.40521210281106e-13\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108957 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.0191441 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.24974540044608928 / 0.24974540044608928 = 1.0\n", - "field decay(t = 100.01): 2.1519308385946042e-11 / 0.24974540044608928 = 8.61649838095467e-11\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 0.2170602220836629, 0.8, 10.0, 0.2893781746253207\n", - "refl:, 0.2170602220836629, 0.784, 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"refl:, 0.2170602220836629, 0.41702127659574467, 5.19344999309547, 0.2497191778959394\n", - "refl:, 0.2170602220836629, 0.4126315789473684, 5.138634260422533, 0.2483012320567707\n", - "refl:, 0.2170602220836629, 0.4083333333333333, 5.08496509184317, 0.2468560224651321\n", - "refl:, 0.2170602220836629, 0.4041237113402062, 5.032406839989342, 0.24538435149961998\n", - "refl:, 0.2170602220836629, 0.4, 4.980925321928872, 0.2438873823711191\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108957 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.00942492 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.24537918139687384 / 0.24537918139687384 = 1.0\n", - "field decay(t = 100.01): 1.2755953357084837e-13 / 0.24537918139687384 = 5.198466016745522e-13\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000109911 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.015625 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.24537918161848085 / 0.24537918161848085 = 1.0\n", - "field decay(t = 100.01): 2.2796495621609012e-11 / 0.24537918161848085 = 9.290313657111033e-11\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 0.3235238063781509, 0.8, 14.999999999999998, 0.28256373042287475\n", - "refl:, 0.3235238063781509, 0.784, 14.693171512000124, 0.28247093802487827\n", - "refl:, 0.3235238063781509, 0.7686274509803922, 14.398780921441814, 0.28233355058613946\n", - "refl:, 0.3235238063781509, 0.7538461538461539, 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0.24660386238549814\n", - "refl:, 0.3235238063781509, 0.4083333333333333, 7.591281232641979, 0.2451961050843521\n", - "refl:, 0.3235238063781509, 0.4041237113402062, 7.512566605892175, 0.2437600099079589\n", - "refl:, 0.3235238063781509, 0.4, 7.435472226131853, 0.24229747168145319\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000112772 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.007622 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.23946072431081186 / 0.23946072431081186 = 1.0\n", - "field decay(t = 100.01): 2.143367304660103e-13 / 0.23946072431081186 = 8.950809410724429e-13\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000111818 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.0173719 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.23946072470582727 / 0.23946072470582727 = 1.0\n", - "field decay(t = 100.01): 2.2924410648414684e-11 / 0.23946072470582727 = 9.573348897434795e-11\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 0.4275251791570859, 0.8, 20.0, 0.27287026974445927\n", - "refl:, 0.4275251791570859, 0.784, 19.583468236198428, 0.2732024101218738\n", - "refl:, 0.4275251791570859, 0.7686274509803922, 19.184283570762837, 0.2734622444200681\n", - "refl:, 0.4275251791570859, 0.7538461538461539, 18.80136282780684, 0.27365329059595683\n", - "refl:, 0.4275251791570859, 0.739622641509434, 18.433712921276232, 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0.4275251791570859, 0.4558139534883721, 11.23724290704701, 0.2551740956437644\n", - "refl:, 0.4275251791570859, 0.4505747126436782, 11.106426741117268, 0.2540803028372474\n", - "refl:, 0.4275251791570859, 0.4454545454545454, 10.978640299154753, 0.25295873317085843\n", - "refl:, 0.4275251791570859, 0.44044943820224725, 10.853778883451254, 0.2518070732573948\n", - "refl:, 0.4275251791570859, 0.43555555555555553, 10.731742594690267, 0.2506247305646683\n", - "refl:, 0.4275251791570859, 0.4307692307692308, 10.61243605852877, 0.249413825082604\n", - "refl:, 0.4275251791570859, 0.4260869565217391, 10.495768170772996, 0.2481743775271508\n", - "refl:, 0.4275251791570859, 0.421505376344086, 10.381651859681224, 0.24690419443990608\n", - "refl:, 0.4275251791570859, 0.41702127659574467, 10.270003864057921, 0.2456037441531416\n", - "refl:, 0.4275251791570859, 0.4126315789473684, 10.160744525922071, 0.24427554305470142\n", - "refl:, 0.4275251791570859, 0.4083333333333333, 10.053797596639106, 0.2429193465329389\n", - "refl:, 0.4275251791570859, 0.4041237113402062, 9.949090055502005, 0.24153322642643202\n", - "refl:, 0.4275251791570859, 0.4, 9.846551939834079, 0.2401180039660742\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000110865 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.00934601 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.23217567515545764 / 0.23217567515545764 = 1.0\n", - "field decay(t = 100.01): 3.1895846890533654e-13 / 0.23217567515545764 = 1.3737807317315728e-12\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108957 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.016118 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.23217567572617967 / 0.23217567572617967 = 1.0\n", - "field decay(t = 100.01): 2.511386584143695e-11 / 0.23217567572617967 = 1.0816751480484724e-10\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 0.5282728271758743, 0.8, 25.0, 0.2601481976279196\n", - "refl:, 0.5282728271758743, 0.784, 24.46679999189225, 0.2610696372423531\n", - "refl:, 0.5282728271758743, 0.7686274509803922, 23.95662859236144, 0.2618770325163737\n", - "refl:, 0.5282728271758743, 0.7538461538461539, 23.467975961566616, 0.2625788276586943\n", - "refl:, 0.5282728271758743, 0.739622641509434, 22.99946566139384, 0.2631817618626755\n", - "refl:, 0.5282728271758743, 0.7259259259259259, 22.54983979533518, 0.2636926798419164\n", - "refl:, 0.5282728271758743, 0.7127272727272727, 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0.2373967326334257\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108004 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.00868416 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.22375100718842 / 0.22375100718842 = 1.0\n", - "field decay(t = 100.01): 4.4054832270861915e-13 / 0.22375100718842 = 1.9689221883038714e-12\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108957 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.0171471 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.22375100783149995 / 0.22375100783149995 = 1.0\n", - "field decay(t = 100.01): 2.604590852088861e-11 / 0.22375100783149995 = 1.1640577074183724e-10\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 0.6249999999999999, 0.8, 29.999999999999993, 0.2442050337697129\n", - "refl:, 0.6249999999999999, 0.784, 29.34058157502373, 0.24592026654412946\n", - "refl:, 0.6249999999999999, 0.7686274509803922, 28.711017527148794, 0.2474593095717218\n", - "refl:, 0.6249999999999999, 0.7538461538461539, 28.10922128260952, 0.24883736533494075\n", - "refl:, 0.6249999999999999, 0.739622641509434, 27.53330580109674, 0.25006920079221073\n", - "refl:, 0.6249999999999999, 0.7259259259259259, 26.98155921981659, 0.25116546178265714\n", - "refl:, 0.6249999999999999, 0.7127272727272727, 26.452424118557172, 0.2521360799590939\n", - "refl:, 0.6249999999999999, 0.7, 25.944479772370002, 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0.6249999999999999, 0.44044943820224725, 15.978754596053776, 0.24448044797494067\n", - "refl:, 0.6249999999999999, 0.43555555555555553, 15.796544910968182, 0.24348141406546195\n", - "refl:, 0.6249999999999999, 0.4307692307692308, 15.618498275648548, 0.24244508607372425\n", - "refl:, 0.6249999999999999, 0.4260869565217391, 15.44447176897603, 0.24137187009594935\n", - "refl:, 0.6249999999999999, 0.421505376344086, 15.274329022304034, 0.2402639528246127\n", - "refl:, 0.6249999999999999, 0.41702127659574467, 15.107939843449019, 0.23912065251888115\n", - "refl:, 0.6249999999999999, 0.4126315789473684, 14.94517986659885, 0.23794038016947253\n", - "refl:, 0.6249999999999999, 0.4083333333333333, 14.78593022605342, 0.23672496742401775\n", - "refl:, 0.6249999999999999, 0.4041237113402062, 14.630077251904048, 0.23547697979370008\n", - "refl:, 0.6249999999999999, 0.4, 14.477512185929921, 0.23419574038931623\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000112057 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.00802183 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.2144470639002981 / 0.2144470639002981 = 1.0\n", - "field decay(t = 100.01): 5.749510870152026e-13 / 0.2144470639002981 = 2.6810863089387505e-12\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000110865 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.016397 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.21444706445458073 / 0.21444706445458073 = 1.0\n", - "field decay(t = 100.01): 2.737725032991902e-11 / 0.21444706445458073 = 1.276643744205575e-10\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 0.7169705454388076, 0.8, 35.0, 0.22482605012853205\n", - "refl:, 0.7169705454388076, 0.784, 34.201491482224874, 0.2275879297659293\n", - "refl:, 0.7169705454388076, 0.7686274509803922, 33.4413597291606, 0.2300861771421993\n", - "refl:, 0.7169705454388076, 0.7538461538461539, 32.71669424141867, 0.23234663839124975\n", - "refl:, 0.7169705454388076, 0.739622641509434, 32.02489215108529, 0.2343911194485937\n", - "refl:, 0.7169705454388076, 0.7259259259259259, 31.363616172201723, 0.23623793864435802\n", - "refl:, 0.7169705454388076, 0.7127272727272727, 30.73075960358516, 0.23790584943527182\n", - "refl:, 0.7169705454388076, 0.7, 30.12441698853809, 0.2394088473062314\n", - "refl:, 0.7169705454388076, 0.6877192982456141, 29.542859352190995, 0.24075882698160853\n", - "refl:, 0.7169705454388076, 0.6758620689655173, 28.984513173223203, 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0.7169705454388076, 0.49, 20.56780221092012, 0.246087935342834\n", - "refl:, 0.7169705454388076, 0.4839506172839506, 20.302607744753697, 0.2455807741452668\n", - "refl:, 0.7169705454388076, 0.47804878048780486, 20.044318389931625, 0.24502832199568722\n", - "refl:, 0.7169705454388076, 0.47228915662650606, 19.79266184720379, 0.24443328421700983\n", - "refl:, 0.7169705454388076, 0.4666666666666667, 19.54738026749198, 0.24379556106465222\n", - "refl:, 0.7169705454388076, 0.4611764705882353, 19.308229285168313, 0.24311440973651832\n", - "refl:, 0.7169705454388076, 0.4558139534883721, 19.07497712952083, 0.2423917032614313\n", - "refl:, 0.7169705454388076, 0.4505747126436782, 18.847403806983923, 0.24162921270758458\n", - "refl:, 0.7169705454388076, 0.4454545454545454, 18.62530034751981, 0.24082577322829152\n", - "refl:, 0.7169705454388076, 0.44044943820224725, 18.408468109247096, 0.23998068018388435\n", - "refl:, 0.7169705454388076, 0.43555555555555553, 18.196718136035763, 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12 with resolution 50\n", - "time for set_epsilon = 0.00882411 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.20454876789491885 / 0.20454876789491885 = 1.0\n", - "field decay(t = 100.01): 7.178635175771892e-13 / 0.20454876789491885 = 3.509498125874663e-12\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108004 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.018625 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.20454876813558592 / 0.20454876813558592 = 1.0\n", - "field decay(t = 100.01): 2.8174572450318613e-11 / 0.20454876813558592 = 1.3774012284270024e-10\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 0.8034845121081741, 0.8, 39.99999999999999, 0.20179462162966672\n", - "refl:, 0.8034845121081741, 0.784, 39.04509528455022, 0.2059250719323383\n", - "refl:, 0.8034845121081741, 0.7686274509803922, 38.139646206191365, 0.20966397204966036\n", - "refl:, 0.8034845121081741, 0.7538461538461539, 37.27949726994566, 0.21305427397378518\n", - "refl:, 0.8034845121081741, 0.739622641509434, 36.46098976062103, 0.21613269723029083\n", - "refl:, 0.8034845121081741, 0.7259259259259259, 35.680884053707004, 0.21892741270212848\n", - "refl:, 0.8034845121081741, 0.7127272727272727, 34.93629688867566, 0.22146683922801672\n", - "refl:, 0.8034845121081741, 0.7, 34.22465020933728, 0.22377279980470224\n", - "refl:, 0.8034845121081741, 0.6877192982456141, 33.54362905484555, 0.22586624586940138\n", - "refl:, 0.8034845121081741, 0.6758620689655173, 32.89114661043291, 0.22776517254801126\n", - "refl:, 0.8034845121081741, 0.664406779661017, 32.265314978971055, 0.2294835019692795\n", - "refl:, 0.8034845121081741, 0.6533333333333333, 31.664420565727855, 0.2310373437439053\n", - "refl:, 0.8034845121081741, 0.6426229508196721, 31.086903214629274, 0.23243828166083527\n", - "refl:, 0.8034845121081741, 0.632258064516129, 30.531338419096144, 0.23369542692345546\n", - "refl:, 0.8034845121081741, 0.6222222222222222, 29.996422070856152, 0.2348212532271984\n", - "refl:, 0.8034845121081741, 0.6124999999999999, 29.480957317802616, 0.23582404434823864\n", - "refl:, 0.8034845121081741, 0.6030769230769231, 28.983843185365757, 0.23670970324155213\n", - "refl:, 0.8034845121081741, 0.593939393939394, 28.504064681021994, 0.2374874618810027\n", - "refl:, 0.8034845121081741, 0.5850746268656717, 28.0406841528977, 0.23816414677923553\n", - "refl:, 0.8034845121081741, 0.5764705882352942, 27.592833714170844, 0.2387438174110003\n", - "refl:, 0.8034845121081741, 0.5681159420289855, 27.159708577552564, 0.239232508084756\n", - "refl:, 0.8034845121081741, 0.56, 26.740561170354134, 0.23963612123153596\n", - "refl:, 0.8034845121081741, 0.552112676056338, 26.33469592188663, 0.23995828312103898\n", - "refl:, 0.8034845121081741, 0.5444444444444444, 25.94146463225064, 0.24020224568501714\n", - "refl:, 0.8034845121081741, 0.536986301369863, 25.560262345758233, 0.24037244343095185\n", - "refl:, 0.8034845121081741, 0.5297297297297298, 25.190523663916437, 0.2404730149043355\n", - "refl:, 0.8034845121081741, 0.5226666666666667, 24.831719442577725, 0.2405057199519815\n", - "refl:, 0.8034845121081741, 0.5157894736842105, 24.483353825914165, 0.24047273277761147\n", - "refl:, 0.8034845121081741, 0.509090909090909, 24.144961576600384, 0.2403784518025041\n", - "refl:, 0.8034845121081741, 0.5025641025641026, 23.816105667237974, 0.24022489861203003\n", - "refl:, 0.8034845121081741, 0.4962025316455696, 23.496375102814216, 0.24001212440412875\n", - "refl:, 0.8034845121081741, 0.49, 23.185382948015043, 0.2397434129524054\n", - "refl:, 0.8034845121081741, 0.4839506172839506, 22.882764536632674, 0.2394219863711472\n", - "refl:, 0.8034845121081741, 0.47804878048780486, 22.58817584322306, 0.23904730465890137\n", - "refl:, 0.8034845121081741, 0.47228915662650606, 22.301291999661412, 0.23862033792003395\n", - "refl:, 0.8034845121081741, 0.4666666666666667, 22.021805941382443, 0.23814466238694304\n", - "refl:, 0.8034845121081741, 0.4611764705882353, 21.749427169932794, 0.23762099874005177\n", - "refl:, 0.8034845121081741, 0.4558139534883721, 21.483880620051718, 0.23704845905896132\n", - "refl:, 0.8034845121081741, 0.4505747126436782, 21.224905620871482, 0.2364290006894749\n", - "refl:, 0.8034845121081741, 0.4454545454545454, 20.972254942022712, 0.23576489053070165\n", - "refl:, 0.8034845121081741, 0.44044943820224725, 20.725693916468796, 0.23505540516189377\n", - "refl:, 0.8034845121081741, 0.43555555555555553, 20.48499963280006, 0.23429986728741037\n", - "refl:, 0.8034845121081741, 0.4307692307692308, 20.249960190511292, 0.23350021386178307\n", - "refl:, 0.8034845121081741, 0.4260869565217391, 20.020374012481035, 0.23265749904183425\n", - "refl:, 0.8034845121081741, 0.421505376344086, 19.79604920948226, 0.23176986721335244\n", - "refl:, 0.8034845121081741, 0.41702127659574467, 19.576802992091316, 0.23083693098169072\n", - "refl:, 0.8034845121081741, 0.4126315789473684, 19.362461125837058, 0.22986107450928206\n", - "refl:, 0.8034845121081741, 0.4083333333333333, 19.15285742585141, 0.2288423958877941\n", - "refl:, 0.8034845121081741, 0.4041237113402062, 18.9478332876545, 0.22777913143869027\n", - "refl:, 0.8034845121081741, 0.4, 18.747237251037504, 0.22667336344348024\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000111103 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.006598 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.1943564114319847 / 0.1943564114319847 = 1.0\n", - "field decay(t = 100.01): 8.613369505850488e-13 / 0.1943564114319847 = 4.431739319731548e-12\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108004 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.0163691 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.1943564112167818 / 0.1943564112167818 = 1.0\n", - "field decay(t = 100.01): 2.929219089217826e-11 / 0.1943564112167818 = 1.5071378766870859e-10\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 0.8838834764831843, 0.8, 44.99999999999999, 0.1749506546010766\n", - "refl:, 0.8838834764831843, 0.784, 43.865246854678944, 0.18084614262894314\n", - "refl:, 0.8838834764831843, 0.7686274509803922, 42.79498686880298, 0.18616490888473136\n", - "refl:, 0.8838834764831843, 0.7538461538461539, 41.78306957363623, 0.1909834896778329\n", - "refl:, 0.8838834764831843, 0.739622641509434, 40.82419653278361, 0.1953532231998348\n", - "refl:, 0.8838834764831843, 0.7259259259259259, 39.913765803587594, 0.19932045676242707\n", - "refl:, 0.8838834764831843, 0.7127272727272727, 39.047751315763335, 0.20293092176670602\n", - "refl:, 0.8838834764831843, 0.7, 38.2226079814105, 0.20621465898807328\n", - "refl:, 0.8838834764831843, 0.6877192982456141, 37.435196089128915, 0.20920558346668153\n", - "refl:, 0.8838834764831843, 0.6758620689655173, 36.682720369951866, 0.21192936258982145\n", - "refl:, 0.8838834764831843, 0.664406779661017, 35.962680378531154, 0.2144091185242741\n", - "refl:, 0.8838834764831843, 0.6533333333333333, 35.272829708778914, 0.21666666414632094\n", - "refl:, 0.8838834764831843, 0.6426229508196721, 34.61114218453038, 0.2187182335956619\n", - "refl:, 0.8838834764831843, 0.632258064516129, 33.975783613579615, 0.22058228827610923\n", - "refl:, 0.8838834764831843, 0.6222222222222222, 33.36508802077569, 0.22227211810191635\n", - "refl:, 0.8838834764831843, 0.6124999999999999, 32.77753751829758, 0.22379917822267162\n", - "refl:, 0.8838834764831843, 0.6030769230769231, 32.21174515294742, 0.225178014297656\n", - "refl:, 0.8838834764831843, 0.593939393939394, 31.666440208036935, 0.22641701701757366\n", - "refl:, 0.8838834764831843, 0.5850746268656717, 31.14045554291693, 0.22752429105750807\n", - "refl:, 0.8838834764831843, 0.5764705882352942, 30.6327166347442, 0.22851103000945724\n", - "refl:, 0.8838834764831843, 0.5681159420289855, 30.142232050688204, 0.2293833309292736\n", - "refl:, 0.8838834764831843, 0.56, 29.66808512880701, 0.23014665030198314\n", - "refl:, 0.8838834764831843, 0.552112676056338, 29.20942668547734, 0.2308091072410071\n", - 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0.23266777547883646\n", - "refl:, 0.8838834764831843, 0.47228915662650606, 24.673610260104642, 0.2324286704369094\n", - "refl:, 0.8838834764831843, 0.4666666666666667, 24.360654339720863, 0.23213016162250638\n", - "refl:, 0.8838834764831843, 0.4611764705882353, 24.055807269832574, 0.23177609164136181\n", - "refl:, 0.8838834764831843, 0.4558139534883721, 23.75874710068368, 0.2313686559958002\n", - "refl:, 0.8838834764831843, 0.4505747126436782, 23.46916928052956, 0.23090689658521463\n", - "refl:, 0.8838834764831843, 0.4454545454545454, 23.18678545794031, 0.23039248392782377\n", - "refl:, 0.8838834764831843, 0.44044943820224725, 22.911322384688706, 0.22982847953509086\n", - "refl:, 0.8838834764831843, 0.43555555555555553, 22.64252090923418, 0.22921468760037644\n", - "refl:, 0.8838834764831843, 0.4307692307692308, 22.380135051959574, 0.2285503145110825\n", - "refl:, 0.8838834764831843, 0.4260869565217391, 22.123931154313652, 0.22783728199610298\n", - "refl:, 0.8838834764831843, 0.421505376344086, 21.873687094881706, 0.22707701516938034\n", - "refl:, 0.8838834764831843, 0.41702127659574467, 21.629191566166806, 0.226267933168201\n", - "refl:, 0.8838834764831843, 0.4126315789473684, 21.390243406530644, 0.22540929123705422\n", - "refl:, 0.8838834764831843, 0.4083333333333333, 21.156650982328358, 0.22450324859888127\n", - "refl:, 0.8838834764831843, 0.4041237113402062, 20.928231615787418, 0.2235503997974889\n", - "refl:, 0.8838834764831843, 0.4, 20.704811054635428, 0.22254911459777174\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000109911 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.006423 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.18417632037527257 / 0.18417632037527257 = 1.0\n", - "field decay(t = 100.01): 1.0178427595748204e-12 / 0.18417632037527257 = 5.5264583280895835e-12\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000110865 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.0184629 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.18417631975521828 / 0.18417631975521828 = 1.0\n", - "field decay(t = 100.01): 2.8713012611847576e-11 / 0.18417631975521828 = 1.5589958931750262e-10\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 0.9575555538987225, 0.8, 50.0, 0.14429067301668\n", - "refl:, 0.9575555538987225, 0.784, 48.6530933185261, 0.15241249489296654\n", - "refl:, 0.9575555538987225, 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23.535740239681235, 0.22157652716566317\n", - "refl:, 0.9575555538987225, 0.4126315789473684, 23.27331242373393, 0.22084351574148542\n", - "refl:, 0.9575555538987225, 0.4083333333333333, 23.016851878423925, 0.220057130518136\n", - "refl:, 0.9575555538987225, 0.4041237113402062, 22.76615102993319, 0.21921919579654955\n", - "refl:, 0.9575555538987225, 0.4, 22.521012118111, 0.21832995493951468\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108004 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.00824594 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.17431196343224772 / 0.17431196343224772 = 1.0\n", - "field decay(t = 100.01): 1.226599619152291e-12 / 0.17431196343224772 = 7.0368068547919865e-12\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108957 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.0162461 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.17431196264184795 / 0.17431196264184795 = 1.0\n", - "field decay(t = 100.01): 2.9177363356244976e-11 / 0.17431196264184795 = 1.6738589201817764e-10\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 1.0239400553612397, 0.8, 54.99999999999999, 0.11015750327021501\n", - "refl:, 1.0239400553612397, 0.784, 53.39534223243568, 0.12100519786278613\n", - "refl:, 1.0239400553612397, 0.7686274509803922, 51.90867481651075, 0.13067356321855755\n", - "refl:, 1.0239400553612397, 0.7538461538461539, 50.524208813748004, 0.1393743175816963\n", - "refl:, 1.0239400553612397, 0.739622641509434, 49.22931269112476, 0.14718461760941978\n", - "refl:, 1.0239400553612397, 0.7259259259259259, 48.013685667151286, 0.15425442212255983\n", - "refl:, 1.0239400553612397, 0.7127272727272727, 46.86879216202182, 0.16064116852684127\n", - "refl:, 1.0239400553612397, 0.7, 45.787462696319906, 0.1664466525245801\n", - "refl:, 1.0239400553612397, 0.6877192982456141, 44.7636047370949, 0.17171830451504444\n", - "refl:, 1.0239400553612397, 0.6758620689655173, 43.79198840588911, 0.17651889499651638\n", - "refl:, 1.0239400553612397, 0.664406779661017, 42.868084512177944, 0.1808955749197593\n", - "refl:, 1.0239400553612397, 0.6533333333333333, 41.98794000564178, 0.18488590864155194\n", - "refl:, 1.0239400553612397, 0.6426229508196721, 41.148080731197126, 0.1885334768453189\n", - "refl:, 1.0239400553612397, 0.632258064516129, 40.345434464399176, 0.19186399028554912\n", - "refl:, 1.0239400553612397, 0.6222222222222222, 39.57726925315509, 0.1949105801621511\n", - 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28.178307252549438, 0.21971428386147993\n", - "refl:, 1.0239400553612397, 0.4558139534883721, 27.821993787605123, 0.21965764862748693\n", - "refl:, 1.0239400553612397, 0.4505747126436782, 27.474997313821564, 0.21953193965864698\n", - "refl:, 1.0239400553612397, 0.4454545454545454, 27.13693982621647, 0.21934178652625613\n", - "refl:, 1.0239400553612397, 0.44044943820224725, 26.80746444237855, 0.21908700093353123\n", - "refl:, 1.0239400553612397, 0.43555555555555553, 26.486233883298294, 0.21876853077786598\n", - "refl:, 1.0239400553612397, 0.4307692307692308, 26.172929088582443, 0.2183905958409472\n", - "refl:, 1.0239400553612397, 0.4260869565217391, 25.867247951913267, 0.21795488284012238\n", - "refl:, 1.0239400553612397, 0.421505376344086, 25.56890416433822, 0.21746063743615796\n", - "refl:, 1.0239400553612397, 0.41702127659574467, 25.277626154460066, 0.21690850348145124\n", - "refl:, 1.0239400553612397, 0.4126315789473684, 24.993156115881433, 0.21630059246127992\n", - "refl:, 1.0239400553612397, 0.4083333333333333, 24.715249113370163, 0.21563737630418256\n", - "refl:, 1.0239400553612397, 0.4041237113402062, 24.443672260178424, 0.2149184652144797\n", - "refl:, 1.0239400553612397, 0.4, 24.178203959791162, 0.2141434469115452\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000112057 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.0063448 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.16505586379118362 / 0.16505586379118362 = 1.0\n", - "field decay(t = 100.01): 1.6796657955389861e-12 / 0.16505586379118362 = 1.0176347310289891e-11\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000111818 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.020278 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.16505586311279807 / 0.16505586311279807 = 1.0\n", - "field decay(t = 100.01): 3.3223715038456985e-11 / 0.16505586311279807 = 2.0128769988468766e-10\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 1.0825317547305482, 0.8, 59.99999999999999, 0.07360773441443474\n", - "refl:, 1.0825317547305482, 0.784, 58.07108488083593, 0.08756183349395384\n", - "refl:, 1.0825317547305482, 0.7686274509803922, 56.311309260669624, 0.09993186958234106\n", - "refl:, 1.0825317547305482, 0.7538461538461539, 54.69254540505164, 0.11102792920544496\n", - "refl:, 1.0825317547305482, 0.739622641509434, 53.19365179333026, 0.12095671757064261\n", - 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0.18439374819992094\n", - "refl:, 1.0825317547305482, 0.6030769230769231, 40.756819839298586, 0.1875864032458676\n", - "refl:, 1.0825317547305482, 0.593939393939394, 40.01276444425686, 0.1905057507798468\n", - "refl:, 1.0825317547305482, 0.5850746268656717, 39.298592486049785, 0.19318030905511865\n", - "refl:, 1.0825317547305482, 0.5764705882352942, 38.61232652957572, 0.1956239605857035\n", - "refl:, 1.0825317547305482, 0.5681159420289855, 37.9521780657052, 0.19785784772625742\n", - "refl:, 1.0825317547305482, 0.56, 37.31652360111115, 0.19989839276598212\n", - "refl:, 1.0825317547305482, 0.552112676056338, 36.70388451304266, 0.2017547526768604\n", - "refl:, 1.0825317547305482, 0.5444444444444444, 36.112909964600526, 0.20344360330889855\n", - "refl:, 1.0825317547305482, 0.536986301369863, 35.54236232752769, 0.20497480625666326\n", - "refl:, 1.0825317547305482, 0.5297297297297298, 34.991104674473426, 0.20635699484209216\n", - "refl:, 1.0825317547305482, 0.5226666666666667, 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0.4558139534883721, 29.566478763613983, 0.2140025648813409\n", - "refl:, 1.0825317547305482, 0.4505747126436782, 29.193553043244272, 0.21404664550288702\n", - "refl:, 1.0825317547305482, 0.4454545454545454, 28.830408737409662, 0.2140158741099825\n", - "refl:, 1.0825317547305482, 0.44044943820224725, 28.47664548610104, 0.2139163515040305\n", - "refl:, 1.0825317547305482, 0.43555555555555553, 28.131885619609587, 0.213747755347228\n", - "refl:, 1.0825317547305482, 0.4307692307692308, 27.79577249602797, 0.2135103221579689\n", - "refl:, 1.0825317547305482, 0.4260869565217391, 27.46796898905743, 0.21320790783224955\n", - "refl:, 1.0825317547305482, 0.421505376344086, 27.14815610992485, 0.2128440342182022\n", - "refl:, 1.0825317547305482, 0.41702127659574467, 26.836031749237822, 0.21242171734687695\n", - "refl:, 1.0825317547305482, 0.4126315789473684, 26.531309526343414, 0.21193780892543815\n", - "refl:, 1.0825317547305482, 0.4083333333333333, 26.23371773525153, 0.21138950860192895\n", - "refl:, 1.0825317547305482, 0.4041237113402062, 25.94299837747481, 0.21078013578978685\n", - "refl:, 1.0825317547305482, 0.4, 25.65890627325528, 0.2101171783272214\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000109911 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.00752401 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.15668265201002152 / 0.15668265201002152 = 1.0\n", - "field decay(t = 100.01): 2.2446823295144107e-12 / 0.15668265201002152 = 1.4326297779098349e-11\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000112057 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.0143809 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.1566826516265948 / 0.1566826516265948 = 1.0\n", - "field decay(t = 100.01): 3.452976998428892e-11 / 0.1566826516265948 = 2.2038030136597423e-10\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 1.1328847337958123, 0.8, 64.99999999999999, 0.037170886043112084\n", - "refl:, 1.1328847337958123, 0.784, 62.645633431180784, 0.05362783985573467\n", - "refl:, 1.1328847337958123, 0.7686274509803922, 60.547811567584965, 0.06875709351287543\n", - "refl:, 1.1328847337958123, 0.7538461538461539, 58.65172469781183, 0.082354616478198\n", - "refl:, 1.1328847337958123, 0.739622641509434, 56.919780417243786, 0.09462755396270182\n", - "refl:, 1.1328847337958123, 0.7259259259259259, 55.32480276439999, 0.10557645638030469\n", - "refl:, 1.1328847337958123, 0.7127272727272727, 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35.22166925577522, 0.20253568251243403\n", - "refl:, 1.1328847337958123, 0.5025641025641026, 34.70471652928195, 0.2036726801968963\n", - "refl:, 1.1328847337958123, 0.4962025316455696, 34.203941485939055, 0.20468320295652365\n", - "refl:, 1.1328847337958123, 0.49, 33.7185340819115, 0.20557517176695547\n", - "refl:, 1.1328847337958123, 0.4839506172839506, 33.247741642788455, 0.20635542059630924\n", - "refl:, 1.1328847337958123, 0.47804878048780486, 32.790863531764046, 0.2070277254966668\n", - "refl:, 1.1328847337958123, 0.47228915662650606, 32.347246434236666, 0.20759964094140226\n", - "refl:, 1.1328847337958123, 0.4666666666666667, 31.91628017368787, 0.20807496149136276\n", - "refl:, 1.1328847337958123, 0.4611764705882353, 31.497393987322308, 0.20845469225613245\n", - "refl:, 1.1328847337958123, 0.4558139534883721, 31.090053201105746, 0.20874562394291427\n", - "refl:, 1.1328847337958123, 0.4505747126436782, 30.693756253024553, 0.20895386380475825\n", - "refl:, 1.1328847337958123, 0.4454545454545454, 30.308032020994055, 0.20908023176898172\n", - "refl:, 1.1328847337958123, 0.44044943820224725, 29.9324374181665, 0.209125738596599\n", - "refl:, 1.1328847337958123, 0.43555555555555553, 29.5665552236732, 0.20909456295065154\n", - "refl:, 1.1328847337958123, 0.4307692307692308, 29.209992121269647, 0.20899049887482632\n", - "refl:, 1.1328847337958123, 0.4260869565217391, 28.86237692208816, 0.2088190433084917\n", - "refl:, 1.1328847337958123, 0.421505376344086, 28.523358950864424, 0.2085833129971406\n", - "refl:, 1.1328847337958123, 0.41702127659574467, 28.192606577687982, 0.2082742041204916\n", - "refl:, 1.1328847337958123, 0.4126315789473684, 27.86980587961545, 0.2078930131964556\n", - "refl:, 1.1328847337958123, 0.4083333333333333, 27.554659418441407, 0.20745662776433935\n", - "refl:, 1.1328847337958123, 0.4041237113402062, 27.246885122601544, 0.20697561960434535\n", - "refl:, 1.1328847337958123, 0.4, 26.946215262627685, 0.20643519600186538\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000110865 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.00959396 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.14944315649486126 / 0.14944315649486126 = 1.0\n", - "field decay(t = 100.01): 4.987937476864823e-12 / 0.14944315649486126 = 3.3376820952228335e-11\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108957 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.0218339 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.14944315644072245 / 0.14944315644072245 = 1.0\n", - "field decay(t = 100.01): 3.807851695141726e-11 / 0.14944315644072245 = 2.548026812222836e-10\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 1.1746157759823854, 0.8, 70.0, 0.008715934286147569\n", - "refl:, 1.1746157759823854, 0.784, 67.05783140972835, 0.024210799209089452\n", - "refl:, 1.1746157759823854, 0.7686274509803922, 64.53417775149677, 0.04029873111181387\n", - "refl:, 1.1746157759823854, 0.7538461538461539, 62.31059707934406, 0.0555678814867085\n", - "refl:, 1.1746157759823854, 0.739622641509434, 60.31629899672389, 0.06970125775811391\n", - "refl:, 1.1746157759823854, 0.7259259259259259, 58.50481213939229, 0.08255524971343331\n", - "refl:, 1.1746157759823854, 0.7127272727272727, 56.843594941553754, 0.09413263635827869\n", - "refl:, 1.1746157759823854, 0.7, 55.30875739675561, 0.10463698983404623\n", - "refl:, 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0.1722356163059057\n", - "refl:, 1.1746157759823854, 0.5764705882352942, 42.619888854495166, 0.17567897390885354\n", - "refl:, 1.1746157759823854, 0.5681159420289855, 41.860398715356425, 0.17883996497200733\n", - "refl:, 1.1746157759823854, 0.56, 41.131149701242414, 0.18173480153232927\n", - "refl:, 1.1746157759823854, 0.552112676056338, 40.430128463310105, 0.18439121641848574\n", - "refl:, 1.1746157759823854, 0.5444444444444444, 39.75551721886283, 0.18682378756973045\n", - "refl:, 1.1746157759823854, 0.536986301369863, 39.10566845306894, 0.18904674104030247\n", - "refl:, 1.1746157759823854, 0.5297297297297298, 38.479083699202135, 0.19107686788902303\n", - "refl:, 1.1746157759823854, 0.5226666666666667, 37.87439561623927, 0.19292625454975332\n", - "refl:, 1.1746157759823854, 0.5157894736842105, 37.29035275442276, 0.19460884663783543\n", - "refl:, 1.1746157759823854, 0.509090909090909, 36.72580652885898, 0.1961345571897698\n", - "refl:, 1.1746157759823854, 0.5025641025641026, 36.179700019842116, 0.1975122933988443\n", - "refl:, 1.1746157759823854, 0.4962025316455696, 35.651058294458764, 0.19875051751344916\n", - "refl:, 1.1746157759823854, 0.49, 35.138980002927255, 0.19985541870413245\n", - "refl:, 1.1746157759823854, 0.4839506172839506, 34.64263004924646, 0.20083789883449643\n", - "refl:, 1.1746157759823854, 0.47804878048780486, 34.161233172132036, 0.2017049645889793\n", - "refl:, 1.1746157759823854, 0.47228915662650606, 33.69406830116886, 0.20245751259203246\n", - "refl:, 1.1746157759823854, 0.4666666666666667, 33.24046357629389, 0.20310354230391978\n", - "refl:, 1.1746157759823854, 0.4611764705882353, 32.79979193741594, 0.2036476720462249\n", - "refl:, 1.1746157759823854, 0.4558139534883721, 32.37146720614281, 0.20409307625590972\n", - "refl:, 1.1746157759823854, 0.4505747126436782, 31.954940593960732, 0.20445096223605416\n", - "refl:, 1.1746157759823854, 0.4454545454545454, 31.549697581364857, 0.20471845410715214\n", - "refl:, 1.1746157759823854, 0.44044943820224725, 31.155255120816925, 0.20489444063169535\n", - "refl:, 1.1746157759823854, 0.43555555555555553, 30.771159123350074, 0.2049889592193817\n", - "refl:, 1.1746157759823854, 0.4307692307692308, 30.396982194426784, 0.20500542736591415\n", - "refl:, 1.1746157759823854, 0.4260869565217391, 30.032321589495837, 0.20495768982428397\n", - "refl:, 1.1746157759823854, 0.421505376344086, 29.67679736376329, 0.20483056853588974\n", - "refl:, 1.1746157759823854, 0.41702127659574467, 29.33005069412437, 0.20461216911883964\n", - "refl:, 1.1746157759823854, 0.4126315789473684, 28.991742354111988, 0.2043374679527374\n", - "refl:, 1.1746157759823854, 0.4083333333333333, 28.66155132518973, 0.20400220026620766\n", - "refl:, 1.1746157759823854, 0.4041237113402062, 28.339173529827534, 0.2036399273981529\n", - "refl:, 1.1746157759823854, 0.4, 28.024320673604695, 0.20318419566870832\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108957 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.00775695 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.14355855111904026 / 0.14355855111904026 = 1.0\n", - "field decay(t = 100.01): 9.667874474337293e-12 / 0.14355855111904026 = 6.734446954901761e-11\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108957 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.017272 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.1435585513271557 / 0.1435585513271557 = 1.0\n", - "field decay(t = 100.01): 5.780725980413138e-11 / 0.1435585513271557 = 4.0267374719040156e-10\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "refl:, 1.2074072828613354, 0.8, 75.0, 0.002279010064127535\n", - "refl:, 1.2074072828613354, 0.784, 71.19254305121969, 0.004052585814133056\n", - "refl:, 1.2074072828613354, 0.7686274509803922, 68.13230108184366, 0.017669454881364767\n", - "refl:, 1.2074072828613354, 0.7538461538461539, 65.5329128763873, 0.03336169435169541\n", - "refl:, 1.2074072828613354, 0.739622641509434, 63.25596068464814, 0.048654509225169895\n", - "refl:, 1.2074072828613354, 0.7259259259259259, 61.22160180379117, 0.06275216167717797\n", - "refl:, 1.2074072828613354, 0.7127272727272727, 59.378629248099124, 0.07576309243511313\n", - "refl:, 1.2074072828613354, 0.7, 57.691773022139905, 0.08758135787027703\n", - "refl:, 1.2074072828613354, 0.6877192982456141, 56.13545775731428, 0.098248929360899\n", - "refl:, 1.2074072828613354, 0.6758620689655173, 54.69040263962552, 0.10794664183105998\n", - "refl:, 1.2074072828613354, 0.664406779661017, 53.3416232015308, 0.11670121483117171\n", - "refl:, 1.2074072828613354, 0.6533333333333333, 52.0771858356392, 0.12465585169755833\n", - "refl:, 1.2074072828613354, 0.6426229508196721, 50.88739414232009, 0.13187389976516842\n", - "refl:, 1.2074072828613354, 0.632258064516129, 49.76423655717566, 0.13846172295217293\n", - "refl:, 1.2074072828613354, 0.6222222222222222, 48.70099913863975, 0.14446173011718028\n", - "refl:, 1.2074072828613354, 0.6124999999999999, 47.69198665804526, 0.14995965765204525\n", - "refl:, 1.2074072828613354, 0.6030769230769231, 46.73231696062462, 0.1549825384470223\n", - "refl:, 1.2074072828613354, 0.593939393939394, 45.817766249324485, 0.15958402411708034\n", - "refl:, 1.2074072828613354, 0.5850746268656717, 44.94465059915575, 0.16380217089082738\n", - "refl:, 1.2074072828613354, 0.5764705882352942, 44.109733785683, 0.1676654538139623\n", - "refl:, 1.2074072828613354, 0.5681159420289855, 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1.2074072828613354, 0.4307692307692308, 31.33989242755132, 0.20171671281423958\n", - "refl:, 1.2074072828613354, 0.4260869565217391, 30.96140049976025, 0.20176796051792995\n", - "refl:, 1.2074072828613354, 0.421505376344086, 30.592494717856905, 0.20173546160628877\n", - "refl:, 1.2074072828613354, 0.41702127659574467, 30.23279287960783, 0.2015919333831556\n", - "refl:, 1.2074072828613354, 0.4126315789473684, 29.88193405422147, 0.20140043913698885\n", - "refl:, 1.2074072828613354, 0.4083333333333333, 29.539577041619506, 0.20116952947891878\n", - "refl:, 1.2074072828613354, 0.4041237113402062, 29.2053989700849, 0.2008832766544487\n", - "refl:, 1.2074072828613354, 0.4, 28.879094017427605, 0.200502480384686\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000110865 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - "time for set_epsilon = 0.00754905 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.1392134275610875 / 0.1392134275610875 = 1.0\n", - "field decay(t = 100.01): 1.3144200424788948e-11 / 0.1392134275610875 = 9.441761944278838e-11\n", - "run 0 finished at t = 100.01 (10001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000108957 s\n", - "Working in 3D dimensions.\n", - "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", - "time for set_epsilon = 0.019413 s\n", - "-----------\n", - "Meep: using complex fields.\n", - "field decay(t = 50.01): 0.13921342793500532 / 0.13921342793500532 = 1.0\n", - "field decay(t = 100.01): 9.871965144118156e-11 / 0.13921342793500532 = 7.091244925544889e-10\n", - "run 0 finished at t = 100.01 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0.47804878048780486, 36.049408464086156, 0.19395530230339666\n", - "refl:, 1.23100969126526, 0.47228915662650606, 35.54854542021205, 0.19498186511670068\n", - "refl:, 1.23100969126526, 0.4666666666666667, 35.062607663064846, 0.19588322966927102\n", - "refl:, 1.23100969126526, 0.4611764705882353, 34.59087921692149, 0.19666472127374038\n", - "refl:, 1.23100969126526, 0.4558139534883721, 34.13269294834013, 0.19734835806450404\n", - "refl:, 1.23100969126526, 0.4505747126436782, 33.6874261758209, 0.197930496667696\n", - "refl:, 1.23100969126526, 0.4454545454545454, 33.25449677173539, 0.19840503576945626\n", - "refl:, 1.23100969126526, 0.44044943820224725, 32.83335969046958, 0.19877805012361263\n", - "refl:, 1.23100969126526, 0.43555555555555553, 32.42350386700291, 0.19904737688450852\n", - "refl:, 1.23100969126526, 0.4307692307692308, 32.0244494386133, 0.19925404758581897\n", - "refl:, 1.23100969126526, 0.4260869565217391, 31.63574524940934, 0.1993954429097289\n", - "refl:, 1.23100969126526, 0.421505376344086, 31.2569666032255, 0.19942422718572894\n", - "refl:, 1.23100969126526, 0.41702127659574467, 30.887713235292672, 0.1993498431053417\n", - "refl:, 1.23100969126526, 0.4126315789473684, 30.527607477190394, 0.19917542826890433\n", - "refl:, 1.23100969126526, 0.4083333333333333, 30.176292593038497, 0.19903411145233238\n", - "refl:, 1.23100969126526, 0.4041237113402062, 29.83343126780691, 0.19890224385206828\n", - "refl:, 1.23100969126526, 0.4, 29.498704231103652, 0.1984945592268268\n" - ] - } - ], + "outputs": [], "source": [ "theta_in = np.arange(0, 85, 5)\n", "wvl = np.empty(nfreq)\n", @@ -1523,22 +176,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n1 = 1\n", "n2 = 3.5\n", diff --git a/python/examples/refl-angular.py b/python/examples/refl-angular.py index 55230fb4c..1f20d3f05 100644 --- a/python/examples/refl-angular.py +++ b/python/examples/refl-angular.py @@ -1,7 +1,8 @@ -import meep as mp import argparse import math +import meep as mp + def main(args): resolution = args.res diff --git a/python/examples/refl-quartz.ipynb b/python/examples/refl-quartz.ipynb index e11604456..928294810 100644 --- a/python/examples/refl-quartz.ipynb +++ b/python/examples/refl-quartz.ipynb @@ -19,45 +19,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.000452042 s\n", - "Working in 1D dimensions.\n", - "Computational cell is 0 x 0 x 12 with resolution 200\n", - "time for set_epsilon = 0.00140619 s\n", - "-----------\n", - "field decay(t = 50.0025): 0.25018932773921454 / 0.25018932773921454 = 1.0\n", - "on time step 32117 (time=80.2925), 0.000124548 s/step\n", - "field decay(t = 100.0025): 4.358317201613301e-16 / 0.25018932773921454 = 1.742007639173244e-15\n", - "run 0 finished at t = 100.0025 (40001 timesteps)\n", - "-----------\n", - "Initializing structure...\n", - "time for choose_chunkdivision = 0.00031209 s\n", - "Working in 1D dimensions.\n", - "Computational cell is 0 x 0 x 12 with resolution 200\n", - " block, center = (0,0,3)\n", - " size (1e+20,1e+20,6)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - " dielectric constant epsilon diagonal = (1,1,1)\n", - "time for set_epsilon = 0.00108504 s\n", - "lorentzian susceptibility: frequency=0.101049, gamma=0\n", - "lorentzian susceptibility: frequency=8.60279, gamma=0\n", - "lorentzian susceptibility: frequency=14.619, gamma=0\n", - "-----------\n", - "field decay(t = 50.0025): 0.16530523240803463 / 0.16530523240803463 = 1.0\n", - "on time step 24293 (time=60.7325), 0.000164657 s/step\n", - "field decay(t = 100.0025): 1.4363078056736928e-16 / 0.16530523240803463 = 8.68882239691211e-16\n", - "run 0 finished at t = 100.0025 (40001 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "from meep.materials import fused_quartz\n", @@ -140,22 +104,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "freqs = mp.get_flux_freqs(refl)\n", "wvls = np.divide(1, freqs)\n", diff --git a/python/examples/refl-quartz.py b/python/examples/refl-quartz.py index 76ec061af..6427a9256 100644 --- a/python/examples/refl-quartz.py +++ b/python/examples/refl-quartz.py @@ -1,10 +1,10 @@ -# -*- coding: utf-8 -*- - -import meep as mp -from meep.materials import fused_quartz -import numpy as np import math + import matplotlib.pyplot as plt +import numpy as np +from meep.materials import fused_quartz + +import meep as mp resolution = 200 # pixels/μm diff --git a/python/examples/ring-cyl.py b/python/examples/ring-cyl.py index 843535a2a..2d87151ea 100644 --- a/python/examples/ring-cyl.py +++ b/python/examples/ring-cyl.py @@ -1,9 +1,8 @@ # Calculating 2d ring-resonator modes using cylindrical coordinates, # from the Meep tutorial. -from __future__ import division +import argparse import meep as mp -import argparse def main(args): diff --git a/python/examples/ring-mode-overlap.py b/python/examples/ring-mode-overlap.py index 4617e81b9..593392a88 100644 --- a/python/examples/ring-mode-overlap.py +++ b/python/examples/ring-mode-overlap.py @@ -1,7 +1,6 @@ # Calculating 2d ring-resonator modes, from the Meep tutorial. import meep as mp - n = 3.4 # index of waveguide w = 1 # width of waveguide r = 1 # inner radius of ring diff --git a/python/examples/ring.ipynb b/python/examples/ring.ipynb index e4a8090bf..53ec7058b 100644 --- a/python/examples/ring.ipynb +++ b/python/examples/ring.ipynb @@ -18,17 +18,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import matplotlib.pyplot as plt\n", @@ -54,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -91,34 +83,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " cylinder, center = (0,0,0)\n", - " radius 2, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (0,0,0)\n", - " radius 1, height 1e+20, axis (0, 0, 1)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sim = mp.Simulation(\n", " cell_size=mp.Vector3(sxy, sxy),\n", @@ -141,22 +108,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", - "harminv0:, 0.11806892700709783, -0.0007236871181289625, 81.57456727456747, 0.003389451986143974, -0.003074693510136736-0.0014264096834704844i, 6.038103449592875e-06+0.0i\n", - "harminv0:, 0.14716744717130087, -0.00020855489877134902, 352.82663710683005, 0.027968648426445402, 0.01873165284311301+0.02076946018959576i, 5.489828592169395e-07+0.0i\n", - "harminv0:, 0.1752390711833313, -4.340297760906741e-05, 2018.744805502949, 0.00716056650544672, -0.0008759244837260834-0.007106790342885785i, 1.35846494123861e-06+0.0i\n", - "harminv0:, 0.1979976562664712, 0.0010186615368048548, -97.18520289256865, 1.1722788570050586e-05, 4.797901958214431e-06-1.0695976283512917e-05i, 0.0010751369028298237+0.0i\n", - "run 0 finished at t = 400.0 (8000 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "sim.run(\n", " mp.at_beginning(mp.output_epsilon),\n", @@ -195,42 +149,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " cylinder, center = (0,0,0)\n", - " radius 2, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (0,0,0)\n", - " radius 1, height 1e+20, axis (0, 0, 1)\n", - "Meep progress: 596.1/600.0 = 99.4% done in 4.0s, 0.0s to go\n", - "run 1 finished at t = 600.0 (12000 timesteps)\n", - "Normalizing field data...\n", - "run 2 finished at t = 625.0 (12500 timesteps)\n", - "Generating MP4...\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sim.reset_meep()\n", "fcen = 0.118\n", @@ -265,41 +186,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " cylinder, center = (0,0,0)\n", - " radius 2, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (0,0,0)\n", - " radius 1, height 1e+20, axis (0, 0, 1)\n", - "run 3 finished at t = 500.0 (10000 timesteps)\n", - "Normalizing field data...\n", - "run 4 finished at t = 525.0 (10500 timesteps)\n", - "Generating MP4...\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sim.reset_meep()\n", "fcen = 0.147\n", @@ -335,41 +224,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " cylinder, center = (0,0,0)\n", - " radius 2, height 1e+20, axis (0, 0, 1)\n", - " cylinder, center = (0,0,0)\n", - " radius 1, height 1e+20, axis (0, 0, 1)\n", - "run 5 finished at t = 500.0 (10000 timesteps)\n", - "Normalizing field data...\n", - "run 6 finished at t = 525.0 (10500 timesteps)\n", - "Generating MP4...\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sim.reset_meep()\n", "fcen = 0.175\n", diff --git a/python/examples/ring.py b/python/examples/ring.py index ad3a589ef..4d6042488 100644 --- a/python/examples/ring.py +++ b/python/examples/ring.py @@ -1,6 +1,4 @@ # Calculating 2d ring-resonator modes, from the Meep tutorial. -from __future__ import division - import meep as mp diff --git a/python/examples/ring_gds.py b/python/examples/ring_gds.py index 7f44c98b9..6da93fe4d 100644 --- a/python/examples/ring_gds.py +++ b/python/examples/ring_gds.py @@ -1,7 +1,9 @@ -import numpy as np +import importlib + import gdspy +import numpy as np from matplotlib import pyplot as plt -import importlib + import meep as mp # core and cladding materials diff --git a/python/examples/solve-cw.ipynb b/python/examples/solve-cw.ipynb index a6823a4ab..a32bddf6f 100644 --- a/python/examples/solve-cw.ipynb +++ b/python/examples/solve-cw.ipynb @@ -20,17 +20,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -40,48 +32,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Field time usage:\n", - " outputting fields: 0.12903 s\n", - " everything else: 0.000952456 s\n", - "\n", - "-----------\n", - "Initializing structure...\n", - "Halving computational cell along direction x\n", - "Halving computational cell along direction y\n", - "Working in 2D dimensions.\n", - "Computational cell is 16 x 16 x 0 with resolution 10\n", - " cylinder, center = (0,0,0)\n", - " radius 2, height 1e+20, axis (0, 0, 1)\n", - " dielectric constant epsilon diagonal = (11.56,11.56,11.56)\n", - " cylinder, center = (0,0,0)\n", - " radius 1, height 1e+20, axis (0, 0, 1)\n", - " dielectric constant epsilon diagonal = (1,1,1)\n", - "time for set_epsilon = 0.0209911 s\n", - "-----------\n", - "Meep: using complex fields.\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "err_dat = np.zeros(num_tols - 1)\n", "for i in range(num_tols - 1):\n", @@ -191,29 +131,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PASSED solve_cw test: error in the fields is decreasing with increasing resolution\n" - ] - } - ], + "outputs": [], "source": [ "eps_data = sim.get_array(vol=nonpml_vol, component=mp.Dielectric)\n", "ez_data = np.real(ez_dat[:, :, num_tols - 1])\n", @@ -236,19 +156,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - "run 0 finished at t = 225.0 (4500 timesteps)\n" - ] - } - ], + "outputs": [], "source": [ "sim.reset_meep()\n", "\n", @@ -281,22 +191,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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VhEAZoMzl9zJbUamHRSesdTgsKh4bMJiPs1xf7r8PEulY2THy98uRvBvVonWVZdiufO+QQ3VRZZmW73c1HrEnAcRt1yLaPeqh3O/j6sG/L7fNt/PL1uEw71cpBLf+19pnDRJxnXHL+xHX1aqqFYdYPLsfkRWrcYlWw4m7BPVQ/Eou4eDfV1CI+7X+0jZLUOTHXoVEPLd0nPwLMp+iIlb3a71eG1vDvwDwcUg8CSDW7SPqYS32sPYZud1SD81ahxtwWAPDltWwqNVEDola1eSuRj2+RJHKzZdvhCVb7bnZ1TsuW5PBi8rJWT0UrgUwByTDSbf6STQh0LINUNyERHY9hauR7xusFYRtwbDdD6UO7j6qIj7HngYQP1s9FOtj5qLYxy2Os5Ds1U5NdbABh/x880PHzl1rf/V11mqihsTWsHZRRdTHrFWCq9bvstUHNLodpauxSG0Wu9gmHPzrPBaQ/7rbzb/82C2XYQ0Sxecs++wXb5sq4lZGo3Xcn6AiHrGnAMQaHO5VD9h6v/XBG/vlaU1/TjU0lgC4BYd5O7f4ZW9Zvl1LTeSfGZfnrkYrYNlyM1r3+2FzFRTWHvD4wbl6yOMO+YkBS9UALAGwdkotUFTHbXUpX63UXMRLqvdb6qo8seJts8AsrdupIj4JEk8BiD22RzztCU4u91kuu9U+1n5Vb1VH1nAoPtO61b/WPoWayDMYG+dQBytvuzc7SbFG8Oa2ZaPcTE26dt3BKhha2YtWFuPWL3B9Do1t667lTTejsr1uRnWy2+dXnddn29MAYo96SNtuuBc3P6dxwNYUeHvdC38Ot9XDGhxqCDTPOdumVhPxM2o3p6Ui8mub73c4Vnq/cg6PqIpaPWSqoh5fcqEesga4Bod8/5ujSdWByRYk9qqIe90Mv7B1UtvnvHWMZk3Jtop4xJ4CEHtPf497cSv+4JctrakuFlWIbfdi7XNaVsOhdW75X24tNVFnUraqM+tgpUuAie+X64B9308ZXyjDnsX6bF1dGLU84bLWYAGHFhiMKf8Wx1yBxJZtjTtxy80A9rsam3GI227Gtj0GiacARLT6EtIDfO9x9sQffqKtqYc1OKwBobWuVhPx+IsMRkNFzMdsKLCV9Q/brUKeeuCXWj3UcYcWHKJtAaG1rgWJNRXxV9nNOMQNsxHEN4Kdd9pTASLa1o/Kh3LyxYFax14ua6UHP/zRFRz2WpHFqSABLF2Nxf5ZsHI101HD9a7rvZ3aAzIJ3iqMKrZbcQtqOOy1FUg8alvjX84b7XU3HjmBjeN80mc8R1+MB60uJPp7zmEZfyjXx3X7XJ21rMY8kY43hocEMYV9aB6KzsL3FSDnQRjex+4Dxvlu4X6vGIegol8G8KDyWvsFy+MPwNJ1qNRDHXdYwKECw1och+pujMYAohxLyzkLornfRWHWpX4XqZ/GiqWu4J9tzhVjRRRdxH+yPY2CuPUw/k3t/9Nt7UG+lfJc1EQ0jrev6CoGJf3/dRwCwLLa8l5b9Dys4g9ZPcPNX+FWiXMGh1tB3s31f7Ub8bPsnszRnfY0gIhWZy9+XgJnaa3Oi4/2aLylaPLr2lML0do2h0Qdj0ixh0bxVO1mFHD41KDXMg5RuBfZCTXVQx53WIHDXiu2veWWPAiOpnr4C3/tF3GIT3Azng4Qf5k9OoAG1e8/UqfWtry8ui61BtqQqJe3LA9Wxm2LPdZ+aB1S9al12WYuFCul3pmtwI4t3Au/KAQCq7LqlhWZhjU4xDEd6r9Pttq9SN2/7z7Qv0+ze4oz/dnew54m3Bpxh++kSD48215w3BMEXavCtCiDlmsqYj7O0s1wVWbDwjV7xt4yCtBYLQCq+1EAq+qhiDuYmF1wSzis2aOQyJXAvUMxPaoifsIPTWEPqomnAMRn2D2/5PVFt+aL4MaDUX9Ga5uP2B7FvKUmasndqtuYC7nmZcWx7zvlbat6bC7ci6pPRJ218NvcUA03z+Exl2TV6iBmAwhUb+MXbr+vrBzp/SfDY8P+rQGx1j6Lmfyqxl9PAtM6Ruuwre+8WP+hLjH3W/2s1/GIuQ0uVUTLzfiMtrNqzrbdC2Cl7sGuxh3CTj/nPNe+5NqVWEBi+d27RqNeLquPs0dp/LVN9t8aEPfa4ntswWFHoJJpe7TnXFl8RGTELt31X1pfNfQ6HpG7Gvkx/b6lm5EsxBseymQUsYgsNpE+vHIvWurB2mbcIf36F1WNt/tdPGp1wPFW/GF3gLJa1gLJM9m/DSC27mO+jnYsB9oXvghAMi2UQcvNyJetxSHuzYZsFjzdgERLSucduVL/jMzNqOMQ0faDYrtoJ3cviuBkrR7S4cxtOKyeyn2QoLzRbsUfbrkXLJbuBfFt96J4UBuTSPmDL867uf8n29MDYhEviBOItLbNbtS9bkYehyBaHl8QLZ6PWkXscTNi4U481MNp1ExNrEGipSLq13uDpFuMWBvSvj0ZTlb8lABQqYctOHxAJaSiqagG9hQ17XAvavXgiHYohXX34lb8IR2rBs0eF+VOe1pA7G03t2II9XG4AAcWJNirIorjNFTEZ7kZt2wNEi2rVYTfH2k4vhS3wJzJeOCEFkPAk3PIh4xfUw9156kmHH6WZY2LWBRfGglRriexXz3U7/P5ZuuHc1U9bCxrmPvEOMXTAuJeawGlVhElOP4aFdFyMxblvztsqy6iBYk9KqI+RsvNuNlxcLUXJ1IMIu/+HN2LlnrwJ+LVwyYctsab3GtxqPo19wJoqoci9rBHPRBvqweidfXQ2v4vtqcDRLsewVtSVpWbUfyaN1yQOhaxpSKomsyWsFQRzJ+jIvbe/NVKYZf/KN+GhF9exyIaJd6urKjcVQtRbFMFJ9EAQVQPDddiFQ5bMNgLiVZfijvUg99eVA+RVw8FCOrYQ0s93Io9NNYv3Iv4fk9q9QF7CkCs/Z7e656ne7UjFsFUqQiqQdNwT0BFzCBCog5MljCpwbFUEXHZo67IGiSAMmhZdv+et63djNQ+955AMSCt3ysO3Jq7F0Vq09ZjPVRdsltweMTCLz5VDRoI6iE2LBalGiBRZi6iesi28TCpXIsmCMpgYwGSm7GH+5roqnvxICyeAhBACYmPqIh7MxpEVNyENVcjqoEIifyZiJAoFANoCY4YI8shs7zUXdYKLvrGPrsP+Ta5qwEgDWDr9w0qI8+OhP9bnbmirY5ZUAwKY5cjQhUBxzKt6awL6iJTHItBWh6ERdbgNzMXVIJh1bXI9rvpWhBj4VoU6xqv4/YfVQ8fUBJPA4g124ot7HU1KINH/HXPXY0ECZqPk7saNSSAOR7Rciv2uhqPqIi8xHpz5OsMEot4RKYi6i7qeRyipSRWvY3WILWLwWltqR5aroUzbdVQxxv2xB421IN/vcO14AokVdwhuRb5BNQ5HJKiyBp53rBT4+dlw4/7YwMOmX1mcDLaUwFiTUWk+9nap3GjtlyNNUik439iPCJu1wbHtoq4t4y7OVblKiSCysCsNnI3A0CiQp7JaPb0bI3tmMMidy+CeoiKYhccHglCttKXtXqoXYv0ayAWcEiuxc64wxIO4bZswWE+ufgB5X7xeItrbR3jc9RDeRZPaFuQWGSIqn1ySFC1TwsSETQREuk4ARJpX8yQSL/6GSRE+v5LSBTLMlejpSLWbOlWLKfui6BYg4Tfb1YP5f8ZMLJApXO3iqXyCV/m2bsL9yJTD0Wwcg0OjX4aizk147YbVquH2rVoxh1aKU2OsSOxhEMEwWpQkm/DgXgJh9V4QularMLhE+zpAFE3kc+IRzwDJNbiEYB/iOs4yC1bdMSqYDHHHEpImMrVaKqIrB7irkqIAAb/2m6oh+BaqKmKOZRgWIPCrvEi85iBqOoWmJtxhwIOeVDyDjgkd+OT4PChrMV/ahZjDRJlRqJc9jMhkcck7oVE/OwIiRTsTNuVrkbxfoUUy16albseQJGriRwSuatRqwgglFdncYib3b7zjAVQvm6oh+harMFhAYVGPGMVEkV8gNquRcxixMb/CXBwRH85HBxxeZzcFnC4z2WN9jRjUsbTdyvvmfzzFe+Rc/52WvhlzmXxiPBw5vs45xtletDTPgA7goVLkLDh09k5WPIn4Y9FYMqm7XOhEdvshNkh7RROwlo/BqQf+tGlZdEEEQxnlYPZudeva6vjeIVxvlM4CBMsAWwdLPnzsCIAhPz4lnGdcCieq9YpNMd9iO6F1k31kFKaFRxSo9/Kjqz9KhapxkaVYx53QAMO2XFyOCQXJIfDTwlIzsdsxhz2uhWfBIZoTwOIaFnTWrzPG0pq9JghAcygcG5u8Ajb5JCgcPAcEn47l4GFZkggfFaARDxmDgkL5xXATkjEA6RrYgIHou0Zhq6AQ9V8GTSvT6DwF6YAdEzQxiI9mMKCnYA2DkI6OEeeJ+H6BeL9aZxAHFkqiz/kaUqnJzitAa3gjPJxB6OXcNia8So27k1INNRDsyS6AYc8nXkLDlQ15uJXPG/gNJ9369eeCNirGrJ78FfBAXhCQABtNZFDAqiUQVhXq4kICYesPWJ2K6xz5S+kCw0rQMIf00MC8G0+QsKfi1tAIp1wAxLpJBuQAEooeFdjqTaipSB/BQaTKaR5Yw8t6xw65gISxjmwA4yjQkVIeDcjqqlVXKWYg0vqgZyDDQPSOqVK16KCg9NqPk4elAxFU6kBx3XM25AggbqUPXct9roVeZ3DAg634g3+Q9fh8IF4wz6X4uNgiPYUgKiBkC//K12OaNHlSPtULkcCUu5yOECwH0o+uRwVJEwOkjVIcJD/jfskNpRFvbx4T759MQgKFkyEDoDKtmdy0ORVhLWApdnNcIhxiHJY/HT4opIy9L2IUc/gWjijfVAywkHrWTXEoGRjMNkFKGpb68EY1MNH4BDB4IBFvAH5ss92KT5ZNdwVaK7sKQARrQWKrdjEpprY4XIA62qidjn863WXYzUukUEiuUg7IYFMRaDqXBXPDZhhYFYCFQYOwkdak5pQFugAGALYApocOLgdghjMFsKJufS6fu4aQ9un9Kae4Mw0qwet5qBkAw7OGDiznq4kwfOcFK25K2LgMaqHvXDIKyRX4NCMNzziUny2avjJYIj2VICIthcUt9QEMIMiHSFTE8AyNlF8GNpqQgB+lOfM5RAUJTmakJhjAnnkEQkSDxYPF3DIXZEICxEu1FoHwwTBBGHLtCoEAGN9VkWyz2hYwAl/Tb4OglI9RPngxQxD+D9lK4J60BOcUXBKA2bycDCqAENeam0DKFhkZ2gqSDxia3DYE29ouhQPqoZHwFBstw2Gz4BCbk8CiFofLN+1FMSWmgDaQcwWKFpqIsYTAR+gc25dTXCgVcvlYFBwGVw7LmH95/GGirjVmzKHQwRDAkeopRZEfjumBAqY7GETAFkHNg4MryK0tWDiMDdoVDDVL5WLMLApOBnVg9MazugFHJxSCQwRCM7U2JlB4WMXHhJbtqkebsHhlktxSzX8bHfiQTB8FBhPAohorShE+WoLFEAbFDGIvxafeNTt2FQTFBrUhssRT1jagLEKEjpTLmCC3Zq6G0hFUP51tS4cSzgHYQmWyX/7GSQEueBqOEgXaibCNcRP9tWV8d4tA5ROaw8M47MWUNMCDlZrWGPhjFufYTucl4Ut1UTLAgCitVyLvXC4pRo+7E78BWD4TBXxZICobelk1Kpi7X0eyK6zfba4x76Ayu0ARYpzBFAQhS7SMdNBSL5PrSZuQYINFfEI61xKe8bQHdNy/s+oHiIcjM1URCMmIZgg2IPCWAcjHXr4h5+tVw9MgCaCZoYhrzrswrWIN91nLSjWNlgT1EN0LUYPBzXCTgpG6wQG5092eUzBPn8iGDAcIIGgIpY9LYFSPfh1GRxkfzccNouefhYYVmMMfy0UcnsOQLR+RRbk3A+LVfcjLMvrh/IYxSOg8LUP/iHI6wY4P2ZQE5S5HIptCQnBvi4hQILd7GqIWC8B7yqsuRwRDoXLUd1baxkKHli9jA5YZpJB1kE6B2kB6SykI8gYh8g29YHJOf4QoQATXItpCQerDdykk0thq/Nj8onm+N1BIOi0DcsCjVE95EHJJhxkP7sUNzIUrTjDLTB8TvBxfsLvhcJnAeM5ANGyTWhsw2LN6oYPtFQgcu6kAAAgAElEQVTFDAoR4hBboCBXxSeIwqTavkEvMh1BTXSWYYWDMtn1ZJCwIYNhQhzFwO9njS+4snBFgLIOVhpnk4LIg5cKFswEEeoejOTFWBCxBLwLKsKKOZi7sFj7YI2POSgFN13hxhGYRjg1wowjzGRgtYabDGxQD3XmggTDwIIcz6AQDGccLCyEDIHKCABgVgxFwVOWsei6Ag4k5U7V0I4zfBQMt92IdSj8rDjDlj0HIGoYLNTD1nbLuMVafCLaPvdjjlFEUKRq5R2gSPEJBNfDzWrC2Nnl6IR3G3T8RQ3R0U4Ayvg+HVFF2KBuWqGIqB5yOCTXI+w7X39wM9hiMgRjJUz2JHg4WIxMkILDIDQEy7FfB0HErIUNcLA2uRRuugLqmuCgRwU3af9/CE7WgCDBIMept6UVABv2F5apiIWLgeBeJPUQIFDDQcqlS8GyCQYApTuxBYadWYlttbDuQnzEnVgrz7/HngMQtW3NI5jf4BvAoIrGm7CguRAoxuF8UNM3/ML92AGKOj5RBDF5djlSXEJ4SHSCvKpw4ccwnIxL4JlNEKXgIxCVhIeD0jaBQQdQAF5NxE5gggm9YFirYO38KDAIUhDIWAzGQjlCD4KLQZbkGdkUoHQ6ZC7UmP7MOEKdR5hRwU7aByeVToPYOuNAoVcbWQYZTqBgSFgBCENAHaQsGpkAROVaZBmLGg4QYulStNyJe8DwEBTCQxdsCwpb7fwzILBlzwmILVuDB/GmEimGkUP7pq/FKlbdjwiVAJFVUGRuR64mYgETx1LHChLC50qhjQUxeRXBgHDwv+bZ5eaKIcJhMhks3AwIwCsYQQTFFpPhoDQsjBVhvb8fo7EYNOMgkHp2FrfYOcDqGQzjFXYaYc8XqMsIc1XQlyus0rDKLNKbMUNBxOBegGyAAzQY0jcyYwFZ96mIWQoKLgWHYGTIWEhZwIFkNzdmlg+A4QE3YsOF2IJA69n82SBYs6cAxOrYhjssfRktOOTLKrclAiOHRXxduxRrqgJVnGIvKAxcCmJGl0MyLyDhU33WlzwTkoqouhqUQ84Fl2IyFkpbTMbO4Kh8EyavIjrDUJqhTIxlEJgJkggdGxwEQUlGxw6aHawVcFbDGgMYBVIKTk2w0xVuGuEuHg76PEKfr15BaA0z6VBNmRVGMYOYfCGUs2DpH0kPB5ueUGdc+bQyV+ohy1iswWHhUsj7wXA3FB5TCWtA2NGH71PtKQCxaXknnYa14OJaamKxY1tdzMt2wCJzP7wT4JeneEQDFKDZZYhqAsHlkOxTjRAW2nhXgA0lVyOqCI3QM7R6rFLsIaQwIySUcQkU8clLUGRCJxidIEzGQqcKTP9wCLKQzoKthusY6AAnCAc3gcY3dOMb6PwdeH+De/sBur4lOKi3M/RlhL76SkqjdNalPQBaAEJKDwgZ4wzk4xPSZzlElcWIAcpCPcRaiNytiJkKFr6Bh/8L1SDEOhjW3IhPgMIeINwDgzmQ/rn2HIDYM+bg3nEJmdvQWCywTd+Qqn9d9n8TFvH0KJYiz+5HDQoAEI4WakIkRDkgZDh8btMBiIE69tF/uJCFmGMgQFnzoINbEeFwVQZOWz+QjLHFrWQGFHmpfxkJ48iYJonxKqBPHfSBYXsBGhg0CLiJQsOdIMZ3qPEd9PYd9ONPuB9/wvz4AfXnO8z5CvV+hb1MsJOBmXy8Iq+YJEFgKWCEg+gJEj0MvLtBzHMRlZmVRKp94Fk9kOwBIUFdBxLdEg7RpWCJVXeiAkOZ7twPhT1AuAcG92rrx7V4254DEC17REsRrYKklRgpPuEGMPbCwoYHyzPBlaCo3hN5JWBsAEioi/DByxkSPmvgIJlgnA96cnhmhZtHtIoZC+9OzHAwysAaC6MdTKhgTJftHBxZGKNhYcCS8Nozfjt1GF96mC8D3EFC6g6969D1DA2CsBPsNMJcz3Bvb7D/+ifMv/4vpj/eMf7rB6YfZ9jzCHOxYMtgMNhRqmB1AFgyRG8hOoYzAsDkISE02Io50yEFSHg3BCEYiVjTILsUd8jhkIKRLGcFQWIfGPKAJLfGcWhDYS8QWo/2VsN+tCl8hj0PID7Dudo6hjGLu0awpevibBnTqICRwwLIZHoGC4qBvMIF2QYFOHgeocLSUTyAh4R1AAzDsEXnCM4SLANsl0+BdTFI6d0Kpy20MjDawmoHrQ20NjBKwxgDYwxsgIR1BqIjXA8SzvYQsBgkcGCHsSdYK2AcYCC8WgqVkCaAYnq/4vrjDeP3N4x//MD4Y4K5GJAin8J0BCkFhBCQnYToBbqDhJEC3cGBBINZg6Tw1ZaNKsuUquwkSMQYg4875HDIwZBiDbk7sQWGVSisq4Q1INSPZAsEPyOusAgmPwiM5wBE6w59IHC5bg0ZUYGjgMYdwChgEdWpK1UFBxfEgXwKMgOFQQwOOj8gDc+QsOzvhXUES34kbHZ+9KqoHkQVuVTGwWrrYaAttLJQo4aeNMZR4XodMV6v0Eb7LGvHkL2ANAwi4NwJXAeDSVso66CNg7IWzuX3MI4WpXzFpNYw1wn6MkKdFaYfE6a3CWYyMFevXKSQGI4DDocD+tMAqyzkqQPgU54sCNxV3b9jpiP2p0gVkQIQAQyiX8IhuhTMgJAlGEiuq4UNKNwDhPoJXgNBczqBD1o9HcSjH/EcgIi2AYXmFPP3Hn5NyDmUjT8OXEKUyn7TSEaogqDRFUkuCS1gIYDUlyGpCi5BAfgu2IbCxpgh0YWH1AgOvSq9itDku5HncNCpT4aD0d6t0Mpiuk64vE+4nK+4Xq6YrmdoreHg+yRILfyDShKymzMgKgQu/QC45W0j5+CsBYXSaqsMrDYwo4IeDfSooUeN8YeCHjWMAxxN6C4Tjv0Vx9MBxy9HHO0RhB6mF+DJQA6hmCpzF1nE2APP6kHGuEPvi6MqOLiQ5fABStkGww0o1EDYA4Pm793G8/sZeKgFQv55rblj9tpzAKIBhptAeEBhUA2C4nhh3sj8Zubbb0CjBkYOi3SYFLhcgsLHFXy6khyBOFRLurh3SHc6B2sZRgDKOkhBkNZnOnJI2NCgrfHuxXSdcP5xxY8f73j//ifUeIEJQ70RM1j2AA5gwWDhaxQm5dXDpE0YJTscexF+n+e+MMoXQpkQlNRX4/9GjatxUC6kUZXG2zjidB3xbVI+s0ExJiHg7FwnAcAXTxH7GIPoQ7WkVwwkOg+HrvdQEJ2HQK4aWMArCdEGQ4BCSyW0gLAFgxoEtxr/o/UNdaq7Pky++iMK5TkAEWwVCrtgsBMYN+7VAiI1OJI84AQNogYwMB+HiJOy4ACK+PAZF4qoAhyMdSAicJa3ovhC+uu0iqCZYKx3MXrBuLJJkHAA4hyd4zji7e0d7z8ueP/+HePlDVarUO48D8BCzLBGpGCmC7GMBBvnyq+hHoY+DP5iQkHUrCYMlHVQDhhtGHaP/DgV1inw+wWOHaywoJ7QvXSwdgkH7rtZPXRDyFoMoH7wcBB9AICEkzIoBu+O5GBwQq5CoVYJNRBcAYvbINjT+Pe23VoEtI7N1W9b2nffRzTtKQDRBEMTCnshsHXXZyWwvn/RnctvXtxxTuAAcnj4jQhZXCMLeFF4MCMsiJBGw4qgYPJZPQoBSyZAwUHCB9SMcBhcdDW8kmBNIR7BkOzpY7XBNHmX4u3HO97frtBq8r/Occi3YJYUWPYwxgYQuFRwZSzSnBqr9yn2xbA2dRTxfTO8ijEOmKz/P/4J8l3OL5MCfTeAAORRYnjt0akuCKfQaUuEzIPskmKgYfCByQgH0SWXwol+didYrIIh/uVA2ILBQjw1XYnGfWrYrUGAACCfqnFr87hZfj5rsLjXngIQhS3AsBY32HnZq3MsVO/XXA9kA6kmEJQASfDIKJJDIwEjy6VTckUoVUdGUMTXTACF2KgOcYrBjw8H4wSUcRiYoCXjajiVRzs4TGrC2/s73t/eobX2F9yaui5aPXvVB54qa1wxuI1ZeUR9RsSv01rj/e0N8lWi/9LhAAcWBAjyhVRRLcgO1B9A3QDuD3ARDiy9S5FcjBoMYgGFXCG4BIklDLayEn797Zu193bmP1tbx12DRw2L2hW5154HELfA8AH3Y1cp91pFZrGgPjCjAEh2zpSi/SbBx1kKCmIGRoSFIBG6WcdfWa8ziAiUDhuClxCw1sBKAescRmvRM6MXDIKDUQrX6xXjeIVxJjx1PhhJLEDCFqCIqcN8Ji+iuY+Un0Lw9i0EAMRMRDa1mGj0fonXKojAgkHsqyan6YrrdMVRKzgQuJPgXgKi81A4nEDdwcNBDgkOTvYAdyFAKTwYWCKqBYsZCjUQbgYg74wrPArXlC7f2mblnCIw4uLPAsVzAKJonDfA0AxoPgaAW7Z63LX+H8ihYrLtPBC8wOACGJRX6rEMysL3vTABDkwIDoavwgQAdB4O2jFGzeilxdAxGAZGXaCn0ZdmM0MIhuwYVncQslQKsWqRRec/V/i+ERwHuN2q3Y3XF7pQ++Nwei06Ae4EhLbogyyKD2pHfl6Ojggk/baiY0AQJqtx0QpHBkQvgW4ADV5BeEgcAxwGH28QMxyc7BIYYi2qzxQhVbrmQEgqInvO8iduq7F/ZnaS6PbxWgCpgXELFPfacwAiWdbgboCh2Xi3IHDvFPKbtpGOrfuMZCrERcURoJADw8PCpLQcsfTBSgEY6/MhOrgc0Y5BQahOYDK+vwS0AluNY88YJ0Y3CBhrIQxDmg4a8A1a+/Ni9hWILCVYMIQgCOl/1ZkJnRTomMFEVafEJC/88YTwAUVmcM/gToA6hugYg2JA23nYfwAdETomDAeJ7iAgDgLdqYc8SFAvYchCWQNNgOyOoOEEOpxAw2mGQxfdiw6Qwc0gAQuvwpyL/7ulW5GBoow31N/0J1Jgw+Iwhi2rQlzlsvg+/N8CxUeqKp8EEPvAsIBCCwhbINiB/I/WWyxTSnl6FHMRVuoIFAKXsarPRvdDh19iCWbhu4VbgOJqfzA4SGgLnK8jWE9gp3DsGS+D9L0zjUXnB4YIp0EwRsKKLpyG7zItO4FuEBDS/w2dwKET6AT7SYeDS1AEv4jAHBSQlBBSQhw6CCkgD4xuFHCqgzUOAzQ6O9dSdILAnUB/kpCnDt2xQ3eS6L8c0J0O4MMAI4ArGH3XQx5fQYdXYDjAygNcNwBigBPBvSABg1ktWFtCIQJhdi38/xEAa9/6R7tZ75H2hHUQMahZFVnDYgsUH3mknwQQwQqU3wGGFhQad2W78d+pMFbUyhywXIlf2FxRzH0T8v4BRAzYkJpjE36d+zSGwxTDChJwjjCQRe80JAy+9AL6ZfADS2cDxXCIhArB0MrAmrmHJAsPCNl7SHS9h8MgGYNkSGZIEVRE/cDHmgTRBzhIyNOAbtQwygcrDwBUxzDKogu7iY7RHSTEQaB/HTB86TB8O6J7OWL49or+6wvoeIKSAybZQ8gB3B9h5RGuP/rgZDcA3CUwxBnKrfPXHoEQy9vj11DGGtrPxK2nYcvzyi0CcatYqQWR1NAzcMTa3dp9yN+3QMEfkBDPA4g9cNia3HU3EO50TXbGLpafVWUFGl9SLKjyETrAuxs0BzBZgiwDRgCsQLJDJ3rvBjDAmmCtAVuNzim8CIIa8q/Un5Ngwhv7X3s1GYiOYUOtA5HvTi6kgOx9ufVhkDh0jNMgcOgYvWR0PJd1zxa6WUsJ6geIoYM4HSDOI7qTCgPE+GwEdX5syZieET1DDhLdi0R37DH8dkT/9cX//faK/ts38Nff4YYXjNxDsASJA3g4wckDnDzAEkHbWD06Q8HGMnZEBeHvgy1vS2G3lELdiFefirW26FwTKkS0+GxeiTXUsGiBInc9PgMSzwGIBhweAcMuIOyMXTxUtLVVKr66ou4URAEYDIKeOxmxBKwCWEHIDgcxgISFUgad0+jJ4fejXAwo4+MIfryH752GGDWMErDWFqNLMTO6QaLvBb4cJb4eO7wMHQYpMAjyY1XwXGvBJMAydLmWvtSZhwHdaYD5cvK1FsYPXa/OBDHMna9IsO/ePUh0pw7y9Yj+yxH911ccfn+FfP0K/vo7+Iv/U/KIK/VgcYDoTiA5QFk/sI62CIPu+pRpBIIFfGY3uhShcT3iMqyNA+q/13ZJY0sVNPrWpYmh0/EqYHDW4P3nhWOFJTUoajWxlvXYa88BiGj3wGETDCv7t9639t8DkTug4nYESFNwsxiHwLsbzhAo9C0g2cEZCYgJ5BjSGHQweJEEBkOwz0ZI9kHAXhB6IXDsBA6dwnsvcVUGSpn0y0pEYMk4dgKnQeDbscO3lx5fjz1eDx1eDwO+HgecDh1OA2PoBAZHYHsEOQWrRyh1BakJUAp21EAYfZr7EXIQMGruZk7Cw0EMvXdHvpzQvxzRfztBfP3Ng+Hr7xCvv8H2L7D9CSMfQdT7MSqdhQpFXAZhmH8X+7u4IhiZPLsdZOCVgEHra51/kKOvX643bgmPloKooZEDowWLFihy16NWEzkkHrHnAcQWHHaBYaMBLzIgN4Cw8b7eN2/8DigKjhZFSRsPabEmPG1zfYLvjkws4bQACQlLAmYyGK8KPXdAL31aUlsQJA7Cxw2kYAxS4OUgcegZ59HgMhk/TkSolhQMdNLXUbwMEv/4esR/fT3hf3054rfjgN9PPf7XqcepE3jpGC+9QO8GUEfgzpeAawGYXkAdegx9j/e+A4QAdxL2OMAonUooWbCHw9Chez1Bvh7RvZ7AL9+SaqCXrzD9CW54hetPuHKP9x9n9JogDwTrKMUcjMVcXo55CoA7xhjyFr6u3JXiupHz3BiBufGZRraAqfy+bZWp4Lo8hOanmFHGR3JY1KCo1cRnQuI5APGZcFgBAzXcmFv71Ps5m3mBayDIIZBvE0uz66d24yl2wNyDMZv0BSxhiKEvE+ykQd0RQ3dC1x/R8awepAAOgnGU/u+lF/hx1XgbNZQ2mLRL/qkUhKGX+PZyxO9fjvjH6wm/nw74dhrwdRA49RLHjnGQjL4jdA5gN4DdCcQGPQFWCCghfFBTdpCHHtfv79DXyfevMD6Ny1JADj340KF/PYGOL+DX38AvX8FffNzBDi+w/SswvOBqHC6jwWQ1OicxOAFHIvQRyQKT7rZSqFWC8C2snAqhcNFcti3NblnW5HJo5EevoZEDg9BwJbJWn4PCH9ulQGessgUqNyKoiTVIPGLPAYjaNuDwaWDYsf1dQAjLnMvXldfhYo/QBAvXOF6jFDqNwegBoY3DaAy0dhAsYfUEp0awmXDsjxB9D0mUhrXvhQ80HieDoxT4cpC4qrkrNxHh5dDjy8sJr6cDXoceL73E6yBxDNkMyb54q/yV9GMtkOh9EdPxBGkNiAjd4Yjjl1dcvr3j/OMN4+UKwAHMEJ13L2g4gU8voOMX8OtX0Ms3uP4FdniFG14wUY/rZHE1FpN2mIyDVhfw6CD7HixEAYWWn50H5+JcJLHI00d55m3SdABZQ458iJMVRbVh4JLSsNY1geG/3OyrLc6rDYstUOQuYa0mtiDxEXsaQCT1cC8cPgEMNRTmj2hAoQKCP71KHeRAaMHAztuncRfjMmOygV0x7x+fAhJQasJlnPyIUf0BfDhBHL/AmhFOn9D3r+iGF0gGeuHVxLFjfJcGp967FxftB8YFE7quwzAMeD0ecRx6D5NOBOXhISMZIc0ZG0OoCBUSzoT+EdaAAZCQ4OEAeXwBv5whX99wvZyhjYV15DtbhaInOr2Aj18WquGiHc7K4KotRu3wNhlctMHVOJBQOL28QPZ9uK1zE8nbpiBKv8DEBJiwLAKCfMUonB9HFMZDw2bAiB3L4qRF8WuqgVF2ophfRlgQygBi2cDD830HKGo1sQWJj8DiKQCxgENud8Lhw2DYgkK2vgWFlkKYj2GWQIgwcCatT8eyxp9nWGaNCQ+Zw3S54Pr+Dss+vegOJ9jjm/fhTyNgFJyZ8DK8oBNeTXTCByyvmnFRAhdtYB2Duw7dMOB0OEAK9iNcs69/6CXhIIRPcQYFwaD0JKaBYFn4vhLD0a9gBgsJ9EcMxxd0X36HvF4wBqhZFqDDETycgOEE25/g+pekGi6jVw1nZXGeLL5PCu/K4qIMRm1hMOHFMo7HUF/g5hG1GGEUcJrTsn78TkpTCjICMOBnOmdCBoUwHwngJ1SGX5fDIlx9+K7CNjdg0VIVd4GC5sPtgUT6qAwSj9hTAGJhsaF8Bhw+AwwbUACWSuFuKEQgGJ3GVYiTy1ht0uCtzlgYrXG+XPD+9gaSDB56dKcD6PgCN17A41fwlzi79jSrCYUss8F4tb4DVHc4QPY9hBAACJJ9IxoEQ4R4hmA/RwbnhV0A0uArQgCuB4QDDQCFHpawFmQUBADWBkIr0KShnO9l6bojXHeA7U7A4UtSDRdl8a4MfowG30eNH6PGuzJ4n3yAVVvgVTFORwchBEQ4116Gbu95X5KQ0eEwdgZTmE7AeRfBwc2woDgLWgaLqB4MBdXQVhb++43SxD83IqiM/LFlziZA3gBFzICkeMOdkMizGx+x5wFEy7VY2I5G31INDdej5Ur8FDDcgoKzfsq60PidcbBa+8FgAxys1rCThtYa17d3XC4XXK+j7+swDNDHAeJ0Rj9e4K5nuGn02QDr043OGbwMr5DsC54GCzgSkIcj+uHgfXnr0q8wwWc2YmPrgiwXsY4LmOFAEk64cE8GgAjEBiRFutfWGPRCgKyFG/21KHAomT5AiwOuyuKsLS7K4s+rwfdJ48+rh8OfF4W3UeP9qnFRBto4DBeL01Hj9XRA30l0wtd7CCZ0QqCXDMFAH/qU+OsIXeJvwCKVlFskIIBCQLfQ+4vf6/D9t0HByIKcARQt16OlJvZAorbPiEc8DyBqW6iHj8FhSzWsuRI/FQyZWjBaz0phMuG9b0hxyjo9TlDXET/+9SfO72fY0BVcDD2mQ4f+9QhzHtF/u0CGWbVZjeCvGs75cSCG/gjZHWDAEMMBXX+ECyNKGTfDAeQfPsEEkUn2EHmYjWMnM/L9Iaz2g2qyAVw3K6cOsCzBjtAfADMZqMnCiR6jJQ8H5eHwx1Xjz6vCn1eNPy4T/jgr/Lgo/HlRPmCp/BB49KbwctL4TQHHofP9RkJpeCdMSNsKTNKFYC3BSJemCfDFXmF+EYIfJZz8TAMfAQUnVUHzb12MdGbB0ThHahZmSA25zFK43ZDYcjUetecARK0emq5Fe5974PBR1VDEGOqgY4THGhziKE4riqEGQ5yuTl8nOOXHdbz8uODP//sdl/ervy5BkMMV8iChLyO68wg9ThiUQW+mcF4G4htgnQU5C9E5HL/8huF0BHcdjPWjUsFlchgIXcuDB0EU1EPZFyP1GyEJcHgwieGcKJVg7GfCHYSQ6A4OfJ7wdhlxVRZXbXHRDv+8KPzrqvDHReGfZ4X/92PEH+8Tflw13i8KWluYrMDLKgfBHbQBrp1FJwiH2I/EOChh0RkPCiMZJiiFTjKM84rCsm/MHTC3QCKwdX7mQ9DsRljMLTcOLgq/PLod2d3xlx5AwYz0+bmaSClSPAaJ2tZcjf+sGMTCKvXQijlE+2w4tFKUH1QNVus5vjBpGG3S7Ndm9DNR6XGCHRX0qKCvBuqscPlxxvv/O2O8jID1v3LcM7pjB3WecPg2heHkvOTvs9sivnm5yoIxOIVTx4BkKAsI67ML9TQUsXOWL9+mLECXH9iH9Mj/xMLFGb/z2fLiuJAsvNIwgB0dNBTO2uCsLP68avzzovDnVeH/vE347x8j/vttxB9nhfGsoCYNNZmiD4nTFwzyCHIM3QtM0o+ypbSFCoriaAErHYxjGOtdD8DCMgESENbHXZTFpppAHPQmVw0ho2NduVxkDTxXE2uQAJYuRw2J9OhWkNjjanzE/k0AccPqWMMWQPbA4bNdCqMAa2EnP1ismUwRZ/BQiIBQ0FcFO05QVwN9njC9a6jLhPd/nnH+7zOmy+SvT/gSaXVQ6MceRjkYlY0IzYwhzh/BPpg34IiBNFhdwkQ1Apo4Bd3qO0aYlUOERXJG4mjeRB4Azk+Qk/+6+UxHHOHJ97ycrIHlDoYkJqvwPQQjv48a/zwr/PePEf/n+xX/epswXhTGq4a6amjl+49E2FsNDMMVTAKddpC9BayDcT6mcnAifGV+9vJITGN9QBMADHvHiYEANSpbYaUmgNjgXYKEv1CUy4PtgURua5DIA5d/pT0PIFbdixuNfytFirjqRsxho8jpr4LD7FJ4OEzvCmbUmN41ph8jxvcJ1z9HvL9NOCsF67z07wgYzgwzWRgV7o0giE6CpYDoJKTw4zWKwxH912/oYUFWAdMF6AZ03KVUX5TJdU/BVgYvdU/ncL9IzOXBYcc4JqQjAe3CrOPOxyQsS7xPBufJByO/jxp/vHvl8OdZYbwoXN8njFeN6aphphHOKN+DlQVgNMbjCZ3swi9xB8BAOoflo83AZAIkBIwlTLDowTDsAAK0wUOQEOSlfA6J6DrcoyTW+oJEW3M1bsUiPpLReB5AfJatuRYfcSvy7eM+e92KELuIaUvnbIKDnXRSDjkc1NV4OJy9cpjOCpfvV/xxHvFdWbwrN/vTDBysw5fvI5x1YTxI331bHHy5szgegPEMmi4Q1kA6DafHNFiNkwidvOLDXCqJBInWM0bstxXlpMn5nBMOBO3ge15a/78jhgVDWYeLtrhoi+8XhT8vGt8vGuNZ4XpRuJ41pquCunyHUSOc8oAgFnBW4fKjhwzD5eXjYDIZXBcnyxB6PsdeMgyFE5OcuBAubBckABT1EXnX6jkVeh8k9rgaf5X95wHiAWv1k9gTkFy1PIUZMhVlTYNLYHDWhtmvNZzSMMrCjMpD4qqhzxrq7NN8P5TGd21w1iY9fB0RlPB+Nd4mP4XeIKGOygctXybYcQRPI6AUoJVXD7YHrEJ44uEkwCz9bxQBrRHQms/oYki9fDml4ZqZobEAACAASURBVN/iuA0qjN0QX08OGI3FqA2uk8F51JhCvGG6znBQ5zdYdYXRfuh+YgY5g3EYMAwHyE6C2Y/MRQxo9sVQinwHtpiynbQDs4Mg3wt0ghcVbB0QUqAx67A1hkKq4MwDlFVMoqyX+Psa+UfsKQHxsHvxoHoorCjxbhRPxfdb6iGeS5bZSDNqGwvrwtRyxvpBVZyFsw5Gh1mplIeJHg3UVWNSBm+Txo9J411bjMZhsr48uEsPIYPJQp415FmhH1VwW/zs3sIaEKxvss4CTns4sASc8NPnZUPxr343i3eugES+zvq1Wa/L2KnK+ZGe4Dew1vezmLTFVfl5RPWkoUcDM75Dnd9gxnfo6eon/bEGLDsoAqb3Aer0AqNOsL0MIPZuAwsLlj7+YJwPXkomKG3DEH4eGsY5iFAHYuEA61WYdVhVEXmPzzX3oLX8oyriXjfjo/YcgKjiD3/rOWxu0wBKwyJMcvXg39s0a7UzfqKZuN4pDWcAZ11QGH69VR4GozU4K42LNpiMDYCIMQOL3hAGhgeOsjBqHqCFBfn+EcJPv0POecCFP7LGw8FqP4oVzSmI+7+R4HuHfU1IakS3xTm/IkSaQiXknCExNky2E8bT1NMVVl2hpyvMdIUzKqgzAwMHra5Q1wusVjDKwHYMYywk2A8coy2sYD8REDlo69AJP4NZVBGC/Xu2DkLE2IErILBm9WhNLX+/LNH+e+zRwXefAxB/oxWBybhsLfYAzLGHlmXxi+Xn2Fk9RGDYGRwAYI2P0BtlYa2FCZBQ1v+66gCGyTpo+F8fYxwEMSbroBz5Wb1tNjgLE8TxCD6cwMPBKwYEleXg3aCoKiwhztSTZv+6eQeXWyT9F7J/LiqGAIp4SwUTDpJx7CROfTZ1oPNwsFr7wK6eYLWCVVe4MItX/Gw9jTBq9NuE/YTjkO3wf3Fszk5gHgPDOhi2YBumMAxMNEUMwd+Blj5K6yhf1o5FRPs73Ix93+G67R178z/PdqqBXftH96JYnxVObVh0L2ZYuOL/ODemtg46Tl8X94V/bRzSVHzWudmdAcCd9MPaH06A9PNb3rToqt3ecrlr9f+WMftOZIPw3cm7kHqMtQHOGq/CQkwnh0N872tKsqn/wodnHt482/kNhZq6jVdnX48x4eofjTvt0eHf/g77nwuIX/bLftlN+58LCBa3t9m7fxztqVjvby01A3jZruRnsopjUlLQqPH/ONS9ZD9wrKC5SJEA9DR3w44Vj5Q5vFZpXztwPQNa7Yu1xDk7bm+53LX6f8usdVDGYQwD1ygdsgex01ickId5njYw3qfw3k/4M8/sFT+ceBZLMYNxK6YQP3dtmLl0jR/0Ez4yDP1fbf9zAREsD8ilZSJbVj8MzOuNnnlVwhMxIOJs1fEhj1Dw/7OgMGUdg5khev/Qd0zopR9bsmdCzwQJD4cuduFmXzTVCT9CdYSEsw7mcoG9nmHHqy8/hK9wdBRTkzy3qGz2ayCEKbK/pS23oPDH5B+wuhIz3lJjHa7a4KI0zpNOc3jEKQBZ+qkIhezBsgN3B3B8LTuIrofsDxDd4LcJ+1EYv4HCnwh/ANJ0gnGE7np6wRwi+eAytbXW5YHIFgT+jjTnRz/yOYKUzKESj/6+TEY8h9xIFKlOn/u6HbsgFmHIdw8adgxjjAeBsCDnIUHG/8+OYTsJ0to/1ML3saCRwR2hnwgDCwxS4CgFGADTnOY8MOMQRo6ap7yjBB5rHJzRsMbPEe6IUhGT70QVZsJmmQqfHv8WIiQodRu3NkxKjFB0RfHB9UFFY+YZtwTTPAWgYMj+kFKbAGC1SGlOMRwguwHd4RiAIcBhP4DSaN1MSF2+ZQYKXoAjL7Ta17Tq7VoVi393BgNon9ceew5AVOaIQi1DKABKNf/+/7S+Wl6vJ2YfuAoN21ffmblcsNXgW6AA5n3je2LECbwJBs6KWY/ZcC4MP/w7sZ9RW8OrCOP8pLPGgrsAE/bT3LsevnZBCsjBwioJMxl86SWufYdxHCGcw0H4FtUR4SAYJ0F4EYz+JNGfOsihgxi6UG7t565wYLioFsiXX0f3KJVEB/XQAkSWdo/fVPiv4bYQ+7w+Qh+P0CM0RvqJ3Ny1PHTH7iXj0PmJhmUvIQcDrV4gQ3CSWMDoKXxNArI/oH/5gm44QXR9qCJlsPTziwrBhXvRST87WCc5qQcgqIHMvYgiMIKFqRyJqnZV1kqkW8vn8S+RPjt+br5PctVoPhe/nIr3cxfyuH21/oP2lID4q41I+HRnhAP7r8sZExqQr6YkFnPD2fLl4/4WhYoAQulXUBEcIdELCCfhrPM9K4cOnfEpOmktOm3xojQuo8AoBUSmsjoGDkz4IhnDa4/+S+9nqzp1fs6J0wE8DCApga6Dk36yW8e++7UTXepIFWsXrCuFXP6QLp67WE8BNEutOc47GkqUnfPZljS6FQFDGJr/0AucBom+l1C9QX+QsNoB+AoAMKqDUCp9B93phMPLN/SnVz9l4CAgOz+3qJQCHCYM6iSjCwPv9rIccaoXswviQYFiuLrVrzjEe+aBbrNGXi2P2/872n8eIB5UEQtIwDfuVBMRtwdSQJIs1lUEYiWczQZa8eM/OMEp0FinQTWADhOckb5fkQNc6OL8u9ZQlzNkqKRLnbUkow9wGL4dMHw7oP/2gu7lBHka/FiRwwmuP8KygCYJkgMgPDBiL0tf5RjPK1xDkA1Byy3lchhnYtHZLkxD7oSPI0jy98Y6QDpg0hYM6wfUDcPyfz12uE4aV9VBG+s7flkXPKFvi85a/eGIw+sXHE49hkOHrpeQvYDsGKIXOEiex4fIYZGNOCU4KgoKgV5K8YU19RDhEG0t9lDD4aPq4e+w5wFEFYd42M1o2N2QyM7HQwI+FsEEQtx3LyQ6OCgfuDMWInQ5ttABEvmoDd50WKqy6BlJgoHGy9iju4Rlobu3PIgAiAGHbwP6ryf0317Rf3uB/PLFzzlx+gIcTpggQWB03IH6Y+plGYePr10Lcl62xi4KLlcRORycC+/rwrJwr4T0vUYlw1oNDn8vvcCoJVTom6FeBkzal0XHegMS7Ccd7mXR3Xs4DDi+DjicevQHCSEZfS9W4XDoxWI4uhwOUtwHBx/oDI9LgMti+Z1w2LJ73Yu42UfGpnweQHzEbsQiCtsDiTwRkUMCANg8Bgky4H6u0iRBsNokSFCe3SCGZgKx8kM5dAKiZzi2OE0niItAHDBGHESaBPcQlMPw+1ccfn9F/5ufiIZfvkJ8+R1OnjBCAk5CdEeAhJ/j0lo4RymLkG5VyD5wLB8Ot4oo5DkyOFAchTsOGJOuHYANfT2EhpA9BgbOVkE4jQMDvx9kcD28UtLhPAQT/pAMFj4uoZWvMo39DQ6HAcfTgMOpg+y9a9F3fsLhOATdsRPoZakcolsR4ZCGn3sADrlbkS/3y1Ct8+9rOBSPcvx/JfbwV9u/CSBuqIjcNgKWAO53N+JhUcYkKDTt+OlkAQifESBrAENwKX5hQDoE+JhD6i9IcGJAaDAxjPCQsFKDewHTS1AnIAcDeZBAB3xxI/iHPy/Rk5+l6iDRfzmgezkGQHxB/9tXiG//Bf7yO8S3//JzTgwv0HwAIAHlO27pmEFwZQWhnx/CpSHnfA9Hgly4GBEO2l9njEk0hpyDESCjfE9HoyBhcZA+rfGPY1ccNo6m3QnGj040h5w7vRxw/NLjcOzRBTB0khcuRS88LOKo12lw2ziIbUgXbwUkH4053KMc9rgWf1VwMtpzACI26lU3Y32fhauRH68Bkl3uBkKDR1AGWeASCJlPDkogzqIQj7emJiRANl4fg4wGCfZjUwqCFQwyAm6SMNJ3BWcpIfoOVhnwOIF6hhEa4kUUg9aKMGht93rC8PsrxOs3Pzv2t3+AX3+DHb6EOS6PMOKAt1Hjggu6HnBCpP4S/pz9PwzfiUm4AAdLIHZwmY+RYBDhEEbRJmeK4GX6TiCgAEyTgZ0sjqIHdxwGg7X4B8K4DuSLwqJb8HpR+HMQ5aC1LPByOuLL66ExaG0JhhiMjK8/qhqApUsBYB7kFmUs4qNwWHMttqx2Lx6tzXoOQLTsViwCuBsSAJaBS8w308VeDlsuR9o+BDDZgFgC0X+PCoI21ITl4Jf7X1xSDCd9JzAjDdh1fqzKYR72Xk4DpD6Cjh26ywWjmgAmiGFAdxwgTgP6r6+LCXBt/wp3eIUbXjFawqgslLVw0xny4NAPxzTsPeBlPSHULzjA+tINfxtD2jKZDTEH50B6AlkL2Mlfs9W+16gxcABYCChrMYXxHgAGyQOG7gDZHXyFKAOC/cTDvfR/p17iz0Hiy7FLw947B3T9gOPxiNeXA3p557D3PwEMwH0uRR2QjI98fhy/fh0Ot2IPn2HPA4haRTRtJySAZUwiLLvlcgAZKNBQE/kph212gQJAPX4EhfEjSHSIg9qyDIPKDB1EHFxGazhj0RuL4esLussF75czQIA8DRDHE2g4lBPgHl5hhle43iuHs/KzVY1hPAllHHh06A52Y+Icn3GAILCNMju4VS4GJjXIeDiQGQEzAdrAqWvozq4AAJM2GJXCVWkYF7qfd0c4PQKdwuvhi69ZCH9+Vq954uH3g8T7MZs45/UFp+Np/8Q5N6AA4KeBIe6f7zs/P+WveyvmsAcOLfuoegCeCRC5bboaOyCRLVsAA/Ddm4ESFNnHLxRFBAVCYZUoU6LEDNgACjE/cLDefUlwuAmLLqzzvRdF5svnU+/x5QJ3OcGC/KS5hxPo6NUDnV7D5Lf+b4JMU9ldtf97mywmY6CsQndRzan3tHS+kUL4RiP8qM/+9tJ8T53zs3bbCTAT3HiBUyPcOAJawWo/0vb1csY4KUxawwo/lycfX0DDyU8VaDWOwwtE3/vyceEb9lEKHDsupt5zxDi+vuB4PBZT782NsFQJftljUAD2xRji58Zj5vvH49bPVwsMfv39cPjMzEVuTwEIR2E8w1aq8gFIAGirieq1ywTzQlEg+04YWRVlqSpyWKTOQjYoEWMKWPj/Q9dwIcO46nZeDvjGBsw1FyF9ImIJIwn0LxMO1eS9fPwC2x9h+5N3K4aXoBr8VHYXbfF99BPgnifjO0gZB1wmCHnF8TDi5XjEKU7e6wSOjuEHUvKQMBRGXHK+UZJzIKtB1oCM8nAYL3DXC+x4hhuvmK5njO+NyXv7Ae76BXQ4gV++zlMFdif0hy/o2AOiF37i4Th572gcuOtxOB43J++dG2n8fksg+G0bDX+HUoj7+m3Kz4vHSK83XIn6mE0wVDvvgUNucbN/+xjEAhK5q3ELEkDTlQD2gaLYPstctFRFgkUYeAXWtZVFOlY3jw1hDEjI1Oj9vjl8kEBRfJ8RWCwAJnTG4egcSPtqCtsdYLrDPAFuUA1xKru3yU9ld57C/Jba4KosVOhFSUQ49CO+vCp8eznideihegk7SAAdBFsIEpDs4Bz7gqk4yIzRYYTsK9x4gX3/AfP+HebyDn3+gfP3N5x/vGEaJ39HmSE6CTH0oPM7+PQCdz2DX7+CXyZQP8EGNdENUU1Y9IIwGQErBER3AHW9j524bByHKqC96CfRgEG+Xb3er0O1zbzuFhTq7/EzwOCPsw2H2rX4z3MxbkACQBa4BO4GxYblqgJinl8SMbCJ0FBjo66AQVEpAACFbQTgnPHlzkAZY7F2XQymTgHse52yAMkwdJx10FeNcdRw3QB0J9j+iKt2OOtyAtx/XRXeIyRGg7fx/2/vXNccR5lsvQBJdlZVV38z93+HM9On6sy0LQn2jyBQAIEkO53d7t6O58kqW0JHi1crggAmjNOMyxTSyEedO+PX1xN+/vqC//rpBf/5+oIQXmCtRW+B3tJsVHkd9BSYnC8IlzPC+xumP3/H+bf/w+n33/H66+84vb5jOl3gvSd15Fwajp/mE32DOb0hnN9gL2eKocTZybvhG74fvmKYDd6Nx9AFdIcXHF6+wLqOZgoPBAaZBVoO8JLf0uVuayCgz7XbQOWvAwKwDwrlumvAUB7zHnEHaY8BiOQeiKHTVyABQFcTvB3QBAWwAYvCzQkGy/cCGLRrn4KbALLOX8HLB2dRHNmsU5qxLI0T3hhr4wQ0HbWGuI7e6P0M24+YbI8LOpwu5Eq8jZ5mxi4mwP39/YK3MwX6TuO8DL9mEZsFR7y+jzhdRowj9f4cxwvmLwPsywDMFra3cKED/AnmdII5vcG8/cD0+y+Y//gF4x+/4PTrL/jzf37D+49XTG8n+HHCPE6pp5Z1Fu4woPuzx/jnO/pvbxi+vxNkLmfYaYKdLzE2MeI4fEF3GDDDYng5oDuSa3HobBwMl9Se9wEey8hRe4a+0Hrnt0BA5fPvLYUgfkZRdgUKxc7K07oVDklF4DZ7DEAA25AAlsAlsKImgKaiiBbyxjroow7W5wYgHw4+eECMHWFCyJ64OOMjwsaTmrk16Re16c9LOHQ9gu0QXA93tOgPM95e3/F6uuB98jjNAb++0wS4P84TzXX5OuKPd/p7PRMcRpFwxN2iX3oX38oB8zxjvJxx/jJgPh0Qvhzw/aWHP1j43uIQLrBvvwOn3+F//Irp1/+hv99/wemXH7j8eMXlz3dM72fMpwtlQPI4mc6gO5Cb0X050+jbpxHDz2e46YIwXWDHM9x3D+9n+HnE8eU7jt//C+54BCxNGTiHZTg5apKlGbpTZzOXu4lrygKoK//yOxbflWLaptcAIZXJjtPeXgNDeW4fhQPwSIAA1iEB1GoCyEABKLAo784OYMAIpSDPjbcvl/F+WuNPtkavkiM38WdjFjggKgfnaKwG6xBsT5Bwhzgi9hkjLnidAt7jHJe/nUb8dprw6/uIX1/P+PWVZsj+4zRhPE9pUFw5L6S1FvOhw2Wi5TY2D3KPy94E9GZG5x06b4FwgTm9w76/Yf7zB/yfv2N++4HL6zsubyec/3jF+OMd4/uI8e2C+bKMsm2chRsMusMZ/fsJ0/mC+XzGPE0YxhnDNAEzJV/Zn2jI/sNhwAsmOOdhuj6miAdM3mC2Ad4DMwJ8iD1G441NsV6a9grAsmyvrWUntlKg1W02gACsQ0HbxWfCAXgUQAjXQUICwBK4BGo1wdsCmeuRdlspC+y+Y0kpVK0qemXfinEE7bXD5dN2EQjGUMwjDuCSJr/tegQ3wAdDo1vPBiOod+YlWPz6fsZvJ5oA95c3gsP//rjgt7cR76cR43nGeJkxTfkkuNbSTFwEjS4mG404DhbfDg6j7zBFoMzBY/IG/TzDzrGpdrogTBP8+Yzx7YzxxxumtzMuf55x/jGmiYDCHGusNXADTfAznSf0pxlhmuEvc4LIAIDnGTn8t8UxXNDPJ9jRIQSPoTui60yckIem+FhcDWptCeCu69TKkRqeFEBsQWN3CvNKOe3p0PIYdDVS7msdDOU2tw5z9xiAAFRIAKjVBJCDAshdD7ZCWQANYKT9Nt7+rUpfgCNzPVaDoUIpAMt5x/4Ky5R1Dpw3AecQ3IAAg9GDej3GyXMus8FsO4ymw+sc8Md5IvXweklweH27YDxPOJ9IPUzjDC9HvnYGXR/gg4MxBic34dQ7vJ2pteMy+TgjVlmL4gxi04RwIVdhfjthejtjfLvQ1IFvF5odLB6bzfUW/XHGdJ4wfwvgOULoltA9GQC44YCD/xmDH9HNJ4RzfE78DNsfMNgenTUREjSrlRewoERPk/qZ8BUkZRGvSauUa47hdrg7t9WEph1AoGMWz3OpLu4MB+CRAAHUkABqNVGsA1DDgvdV2PpLooglbJ6r/ojoSkFGn3LVsADBAGBIGCwzYncpxXn0wDiHNG3deQo0yxYsLqbDJTj8eZmjW3HBj9OE1zeaIXs8TbhcaLYunnMinVLXJWBYQ0HE1/OEn146nCePyfuYwxDqEQH9nHIY5tOI6TxiPl8iGAgOpx8XzOcJo4BS7wym84xh7OKQeIv7YZwFrEHXO/Tff8YwXeCmM3B5py4zANBTmndwE2w3YLAujmmxTHe3KAjkE/dAgMLk36U5XO+OSNurOtaKqT0+d4KBzuFjTsaDAEI2V+YugwYKoHA92EplIU1TGQ1TK/leK48vzjFIlyIdQ0DBWFILxmZgmJO/Tf9feDToyeNtnPB6mTHCAv2A2fR4v7zi9TxRD8gLuRWXC03jN11G+JkyHOl0HYLvARziYLEG82QxzdQxapxJPXAzoqwwJsSxGUIApgnzNFGLxTRjOnmMpxmXN5ql/Dx5XGJzJAD0nvI50m1zBm5wsP0JpnPoXwZ0weKIGXa6ILz/ieBoQh8LijOE3gOhJ1ek6+Este44Q0ldIZ5zCDRhnRwpywdyQWhf4rcvQ0/mA4S4wtYyH7feOcA6GP5FeRCyubINCgAZLIAGMMpd38ua6qENhxIIVN4ssBAxBwaDD+Rf+0BAmD0wxs80K/ZMM2OPlBnpbQfTDRjR4X30mCaP8Uxzfc6jJzjEaew4oOqNhe1itqazNIDsRNPfeR8wTjNmHqtBau6UAepJRcRpBmkyYi+mAKT+H6cINt7FbAJmGOBEA/W6fsbYX+AODsNlhgsWvXHUF+TyjnAeaBxP42DlMNTBLzngdkboeljbwRoa5MYBmD3dT5tgATgJDCOSrEyuJkqAZL/3HdmxVYlb67cUw0fzIR4DEFnugVATQBsUbA1gZLvfgkfD1vapnkv2a9iqXAICl7V5zCEACQycIcgzYS9/FIN4HykZ6jx5XLzHZQo4j55yBQ4v6IYzwo8zZk8xh2mkjlPzNCIEDz+O8dTofGw3pCnvgg907Jgn0b5BS5o4z2zlec5RT2M3+JFmIr/4kEauSrcnzo95iOM8zKMD5oDBOrx0PZwH5suELvbtCP0ZoRvgTw72yCdB/VVC52FCH1XkhOA6WNsBoCAs31eKR0SemBoY6dLE53KmLf7Wqny3gmNPZW4V2QLD7iBrYY8BCEBpRmyAgq0FjNIK12T12JvnqIeOtP1k+Qy8LSdXiRwHwFRgCGFRDT629Us4nGfqk3CefZyzEwSJ2SPAwPU9jocXHA8XnN8mGDMBCEjT12m13s9ZnkAISHDwPuz3xecAH//SIsXD94GnDQwRKoA1DsNwxHE4out6GAT4cYK/THCHEWE8ExyshbdF9soE6tuCniOTgJnSmJgu9icJRWwCpgYG/UbLro34olX+MsW7CY6V25a23VEGaMcW7gUGtscBBFsTFEAWV97C7RZAdmuvVqtHPEwWIVJcClYJvF5Agf9KMMw+VpzYtj96+jxFOFymgMs803dPsYhx8qT2A2BgMPQ9vn79iukSMJ5nXDDFrEwHqk3lZebNtx8KwzgDK/KYqf9qXT1oljADwKDrOnz79hXfvn5FPwwwMASZOWCeJtjxDFx66hp/OQGW+oQYEzMRQhyKLngEO8MEB5gugsIkUJh4/yUsTHQzQgAlpSEHgZEtIOK+MDRl5uVe9bA1P+c1wcW9iVu32EMAIs+IjCYrmwqL0sp8haYY2z4hBSpV4LIBAypb5DcIKAA6GEIgd2KO40n4GG9gUFDLBfXAPE/RtYg9Mjm9mHIUAmAA2zkMhwEvX14wjzNMsHhDwGm60HD7wcbOYw6262EsDQxrDc1GReMpIM1CpSf+xOuzYuo7njowzu5lnYWbPAZrqWs7AnWvBlWsl6HHt59e8PXnrzh+fcFwHOB6SxXR82zogW7INNL4Ehdyyah/CgVbDQ/+GwLQcWVmYMTEtwgKRFDIwVgCCBip5UI0qGnQCCFkfTnSKgUgmjlln2u2hxctKHyEFQ8BCLY6yYm/KJW6ylq8reK3jt/cRmmy1IEAtKAQApKsZTCE6Erw/zKNePbA6ONgL1OgQCWPAh1di9ljmboOXKkNDocB4ds3eqPCIvgZ4/kd8zQCjsBguwHWDTTpjLNwHc1K1XdxNCZjIjja98U4RxmSfQfbO/rrHNzBoR8p0Am7xCB6a/Cl7/HT1xd8/+/vePn+guPLgWbCkqnnM4205S8jXH9AGKMSmieE8ZyuFwBM6BAcYCYA1gOuQzA+hnxtBgoZ/5GwCOInZs+CGzJS0yiQ8hpEbDN+X55bBsgqA8xtTalbCuFOAuJBAKGkMetJTso2V9hq8+VqwBFZ82UFhOxzDQVAB0MA+fncGzEEcim4b8EcFjhQywUwBcpJmGMg0XukgOJyqrFSO4uudxiOPQAaHKfvO5xPJ5zf3zBNEwIMjHXoDx3NJ9ETJIY+jgDdOcqNYJFUhc0t9VK1LsKhgxtoFO7u6OAnh3nscDxP6INBMAZ91+HLYcDx5YiXn454+ekFh68DupcO1pESkS0VDIkwjzDzBfAdwkRD7sFaYIqD8PRUMYL1MKCRso1z5H7Yjnqemo6egxBo4GAZKAYqWAQsORUogRGWZfzkluDgzcKKVLiHO7C2i7UkrS17DECwqW5F/PrR9ppy/9nyHTAotxdAALAJBUAHA7sT5CYAPPMUw2GOOQ+TD5hmn5o451jOI4dDZ5dh11xn4b2nvhNxaP3DscfpdMT5/Yjz+YxxGmnk6s6iGxyGg4OLM1ENcYSptM8yTmyoD0cwLg4hx6qhR3e4oDt01PXFGMwnmjms73oMhwOOLwccvx1popsvPdzBwcUJb4yLb3XxO/jZw/nYYhFVBCYHmDH9XkYM9BP4J5oCgrXR1ejI7TCmBkX83ddgwf+XwJAddBMHTO70GtNuLr1re6k43j3sMQAhMyjTsnsmLqDtxBWJTXuBAKxDAajVwpLVF0QaMHcyWgKTAQIOMa4wzuRaTJ6m5POBlAWnP0tIuDhk26Wz6LxbOmWZHtNkYTuLw6HDPL9gnmfy841HwAzbGfSHDl8ONOnMS0+zi3fOoLe2uI0Wphtguh7oOpiugzsO6F4O6L9c4OcA1znYry8wE03nS2NIOnRRafTHDqZzGL52cIcerrcwnUtzhMSLo/s8z6QCwryMzDVPwBRHFp9Mh9kXpgAAIABJREFUikVQ4DLEgYOjmoi/I0EggkImqglVkZ4FAQtEXAjhkAMjHjgFL1E81gU00uI1eNzJbuXFYwACqK/glju2dheUDMv1xKbM4abyvF3x/5LXr6uFFhgy1QBSEikOwXCIA8xOgTtLEShaXZd5GDUa6t3i1AV0wcFa6vVonEE/dOBZq0IICMZjnid4eNgO+DZY/OdLj5+/Dfh6PODL0OHQdbA2Tnobb7WJUUzH42J+fYUZZ9g5wBmL48sF89sM6y0sHGwQbzZjYRzIHektwWEwsH0X061NDgm+r57G70wqwjpgusTK72LlHZZjAAgWMLA0kJeJY4gKUAA0VypMSKCAMRS7YFUhcmmMeFlIYPAv0gIGoECDn6GVR/feVeEaexxAlHbrFTZSrdVciA0gALdBgcutgYHXzyGAuyUnt0HCIboinDDF6oHKEUhYRbjU8kATzpCqcDiB+1gEuNkoc9pQy0XnDL4MFt+OHf7z0uH7S4efDw7/OTh8GyxeDjTnRNcBHQY4f0AXvgDfvsGECcEZ+OEHxuMLpu8njK8n+PcLJUudKSU7iPwImjrQie7fA0zn4LoutoIss42F2F8jTfUXpxDANFK/lXkG7Cgq7ABjw1J5jSd1YEhN7AJF/FErF0Qm3pk0TEsGiSYwoKsI24AABUT1dX+FPQYgWv0nbrRNGFTfayDIzxoUgMWFoGUhK1eCAYjqIK1fxixIYFDgMM2eXIuZ3AlSGjEGIc7WWYNxpv87Sy0QBA4PwGF2FuPs4bjCpdtAMOFJar4eCAzfX3r8fOzw02Dx0+DwU2/xpbf42gNfnMEhdLB2Ruc8DCaqVNbA9A5jP2A8vGMc3jAdz5hOI0IcVSp4WYVAKqKLqqFzcEMHO8RmU/6NpJKIcYg0KrhQEFXl7IYFGMbGaQsmGFggWAKFY3fEK6CwQJiXDNh5XlwQLt+ARXYeWAcGP0ulxYafv9UeAxA32mqG5CoQgI9Agb+XaoGXewGLLdXA8YYAmiNzDoFyGyIsPELKZAxCLWiJNs5YjPA05Ht0MQDAWQpk9i5PJ7YGaY7KwVm8DA5fDg5fDz2+Hjp86Tu8DA7fDj1+Onb40ht87S2+9A6DP8N0AdaF+Kb2mP2E4Cf00yzG77TUW3SaYC8REIWEMZbcCeuoLKsHOseGmwFyk9LcIz6OIO4dJUrFqQ5pHM+oIICoDnyszF2cTpEqfarEAQkUgI2JVOx+oHJBWrAAWFdsW9mS4YOuHD47VlHaQwBiMxV6y5qtE7pKAGoQyM9rUKD/bwNDOdIRxxs0OHDcYQ7sYoSUjl2GHyjphhZS/CFgiO8eaw3GycPagB7IRpHi1g6a5JbmtDwODt8ODl96h0NvceyoubN3Fp2zcJZyI4xxsM7Buh6m7+H7HnY4wg9nmJcX9BIQ/QXzOSZocV5DDDxyc6YxNikH23cECjm7edmEwm7GbJYZzLxDYDXRgSDRAZgA0/UIfiIw2A50Nws1Id0O/oFNrKkhMsNY2jZ+xjxfAYvl3zULaIxP0YDGZ9pDAOIqu0Y1bKgE+VnrqKOBoXQj6HPuSmyBQY6bOPslE1LCIcUd4sjNrB6Se1FoUmcNZhvn0uwsMHkMsPCW5tgsZ+4GaNBXa+IsVn2H40CtFl/6Di+9w0vncHBx0ltLD20VSzYGxnYw/YH+DkfYeYwd5GIg0xAofE/dwYP31bi9nInJcYflTw9Wpl6kRsxgNl1guoFaO1BDgmb/AeAncl8yNbEXFIr7wY5AgmK8N/VZXw0LuZ8WND7THhMQuztPaeV0KMjvcvk1aoHL7wUDb1/CQRuFuYSDdC2kepBWjvDkrIH3lrIIOwsXszCtMYBb3BJyQRb3gueyZBVx6CwOMZbROYs+5gOkO5sOa2NzRmzu7HuY/gAcaag4JygWOod56uAvU0x68tkgMfy/MQscrLF5/CHtjOIq9FmqCCRXI8wEnWAXSKSMSxvBYDtq3UgdAkGVP4QVUFjEA4E6gwtQbKmK0orWEM0CaozIX30tyeoe8HgMQNwJCEAbCvLzXrXA21QQWXEleP9rqkHCIQEhILkTcvkcQhF70G8N1SNSEWBIILoSRWBwmbcyJkR1FK849g5DVAydszg4mhODBrWN1SEmL4LfopZSmK21MC4CYhoR+iOMUAqzsYCbYI2N/St81atUgsKaRT0Y8bvTbGXijW1dikUEC/p/AsUdRsD0/QIJG2g5IjzY5SjUBIyFCREKMUaxCQrpftBdXs4R+1QF/0ZrumILGNLukaH5GIDQbBUa9ZVvuRBAXdGBbTdCrmuBgY9TgoH3ocFhDNz7coHDNPuUG8HL5TmWM0g5YzCDXAjEnp8uPhUWDs5QBy4nnhRnltjE0FkMbglSDs7iwJ87ijl0zqTOVcYodz5WKBpUt4Ppe2A+UuZiIKlPeRMjZmfgnYW5TPRG7QBfZMwyGOhkGRhmiVXEaQZIKVjwjGaYZ3IhWLV4mk09g4QHwgSKXXQDQUJOv8hqIgi3AxEK3gNYAUX8zqDYcj/EI1LfT3FGpigr77+2bHXfN9hjAGKXgliHQvn9o25EVl4BA+2nHDkZu1QDxxCacBCuRYo9xGPWA8cuxipCmi28fcfqwRJgSjgc4lD3vTE0o5axcIY6b2nxh1SRopuBaUJwI00xOBzSG89m29nkZrhZebwLl2PTwgyizbyAwM8w0CEBuNiXgycmErEIBl7mdoDiE8ZE1YI0CFELFLRpGxSAoio4uJlWLgjYAkP5VNwrlvkYgGjadVCQ3691I+Q214CB9yPBQB/3w2FZx3CI5+XzGTs090KqAx5XjYKSxbpUHlnrRW9sTKM2MaXaij8qZ00MUNaHR0pTto7e7tZFFTFQZXAzQjfHHqYWZrTw0wQ/GwKF0zIAkMDA6oFjE5lxZWM3g2djd6QsCE4uJlVF5SEhgVn026jVBFVudjvK+IT5GCjE+W+7H3LN9bD4iD0YINrcu1YtADUY5LJrYgz8fzPOEHdUqoa0/w04pOZML+MRy4VwTsSWsasBoErN4/EHnF1SsSUcGAZdVA/OICoHUOWI+8k6AaVmPLP47l2HMFuavbyLQ8DFc+ctyV3xCM7DRwUhMywBJDfDakHK0vwc6x09ziH4rI4auNSyAeyABDdRRnW0/MpeiU9EUGA5XoKAAopFmRBswKpE3M4glUR2U3JVsfxbIiS3jwDjQQChg0G7sGvAAFzfKsHrSjDw8ltVAwBdOaR1ORxYPay5FGysFLxfYhB84VJFLMFJGhlawoFcCurk1cUenJ21Kf5AAUrld+Kh9DwHLR2MG4DeI3Wq6ufisQaM81RpDeVFsCdU5kfQIRT1oJhUEaF4kaeWDS6rQcLPafAbXU2YdBGG0jKz+AQnWy2tFjUoeKC8KpiZ3JdlHphQuhwN90PeV6CuNx9xNx4EELltgaH8vjf4KLe71p3gbdZUQ9oWbdUA5HBg1+JWc8ZgDqFyJ8p5JiUYrKWmTgkHdissSD3Y+FY0WOIP6S8/Unxg4//OLeM0GEdxifiGlI90MHPMe/BpRGyAup2XZooA5U3mA8UjNEh4INgAWFOrCQAyaNlUE0CKT6RrzO6RiQfi9Q1QJLdJAUWSGOKz4mRoquJWeyhA3AoGoB1noM/r7gSvu0Y18PGvdSmAGg5r6sGn9fW9saAZo1qQYGP3ooQDZ1Oa6Fo4g6QeekNduzn+UJnhhz5ed6SH4WClD0BPLRnsahg7IwQHcAITtRASiFzscNWwBAet306qVEDwrq0iOGgpIcFv/h1xCaBwE2iBoiaW60rL19yOmICV3I5sP3Esi1U1IXZ4Z1A8BCDuAQbgc1VD2v+GS8Hbt+INfNw9cChttQUjVtbUs1NUXgkGZziXgSDh7AKLztikHtI4lFExmBYoQG84Ayx+u7UIUUUkWLguVtL4e3SA8RZhGimoGHwukyMsNlVDlOX1zdoJiTghdCsuQZeluBwQb3/2PK5VE+DrLvdXxCe21ARvk65SnFT26Xp7CECUdqtqkMtuiTXQvhotFHEDVg1yn1I1ALgZDqWtBSd5onPuP8jHlmpBltXgYGN5a9itoPXOmORe8GPXfMiUQCWlXzvQpMM+yhAH4z2Cy8e4Nl1PFTTWl7Rb7YLLY2oWZlIRgmZa0LJUEoAel0jb3KombA6/ppowlmYqK9WECGI21QRQuB3LlX7UHg4Q/xSXgre7BxzSeV8RmAQWSAA5EOR6oA2H5E64RT10juIUrBhMjFukfZYHmAHOqISPcIjBSlgHhJlUBJZHNlMS0ShImG6ErgqAdTjEICPtQjR7yiH9i+bPMEeVwnGSEhLWLdvshARlVobK5SAIxCdFAqCITaguR6EmggaGT4DEwwBiDQzA5wQi+X8tGzLtX4FDK94A6C0VfGwt1wFYXAvtesvPpWWQKOoOtzpIOKSsSCuWwWTqQboX0rRHLZgiNVi4Geg4eclTwNIGBDsDgZssgqoaqha+SkYrJl0NqvVi3ZJAlYaTk4lUG5Dgc1uFBFdqcSHJ5RAJVhkkAPwdLsc1tqOR+fPtkeHgw+1wWM4/h4O8rizfAcX3lntRNDdam8PBJrfBVHDoY1Aycy3E/zxMq5wzwpj6mJlF90LOJpY+W0cPdGrdcElhGGuoIscy/GfSBD9I+yvhkK2X5pdAZ/AhxSIAcjXSYDMgSNCPOucBUh+Dq7w+laVxLtII68GnPxMCLedltKO4LKT98ro0U33w4sEWy4D8OHQBy374PoR8G3HxxY3Zp0pLewhASGvBIVvGZe8Ah4B1ONDH2+DQaspsuRZr11xaBYlY4SvVUMYWbAENoR4YMqlu48r3DschACC5GSZOcBObPRkYxgL90IQE7cLVsMAKHKQFUeHnOQOHhAR9F5BIfTmuhwSAvwUSyzVvQeJ6ezhASNsbcwBuVw68zWfCIR2niDuUrsVW7MFtQIHLMACAHA42bQdVPUj3guMPAFL+Q9vE9IIst6ObYexS+aWKSDRyhZKgk65kUQsWW8MVZq6bn8GzmqfxJCQoHh0S6TxzSBgJgjtAQdpDAULehq3WimVdXanKgKRc1gpIpv3L46J40yMPSAI5HJZlORxacYfsuIXtSa2WxmAA0ISDdC009bDMAoEkHWSAUp1rQUp/a7Nl3EdDVxGLq9GEBP9/7ZilstKzla6GKBsEACpIABUklu18XnkzKNTfNyFBB+MD1NegVf4tSHwQGA8FCM20l+qWa0Gfl3X3bK1gk60VyzINBHXcIV2Hoh62uCBBwJ+znIdNOCAmSC3qQRplSuagMcV61SQU+HsZVBQqInM1tiDBnyUs9oAjcKwhr9jLZwGBAgAJEj6HCXzIVEeCRKYEFHUBqS4akCjdCN4eK66GMHNn9QA8ECBa6iEt43IbcMjurdx/c3nt/wdRooSDTIKS21QgWIk7SNfi1p+0djdEvEHAQZZn1wJAUg+07eJepP3F/1M8QgFDc14RI10D4WYASUXQZwGJVF7GJJwOgltHQV9REcvnkMcveDavkJdhVwNQIEELa3UBTSmg2k4cKFt2czziA+B4CEC0Xpo70wGq8pprAbTjDnwSZdMiw0Fun/eybLdYpO8rrkW57EqvIplUDfI7sDRp0nKTqQcuK90LmVq9O0ApQRH3y3GIys0AchWRTpRUxLIfARG6uL1nk9uKikitGnGZGo9I24h4BBfx+fe4UHEr8kqfgUNrkaCTS+WvsaaKuBESDwEIaR9RD/Q5Xye34+XXxB2A6+IObHtci70/WSvFWVbmEg6aa0HLl7iCDE5mJgKUtM8dqMjKNJolZXDS5AHLytXgMkAdvFRvxs5HWevvscfVyMrfOR7B68vP17gae1o1brCHA8SttharAJD1r6B1+QYt10KaNpp02RPTo9zvunrYa8mFMDkYgH1wsCZ3KVIylNiHjD+wyXqvciLrsKW4HJw0lYqLFg3k4NgFCTrZ+u9akyqiWLZcUPG95WrIS95yNWhDsT60VYRW/i+2hwXE3rqkKY6WeijLl+qB1+cKMKiV/lb1cG9rwUGzUj3Q9rl7AdBDYUXZK08on4m9cDPSQYFFRTRsFRL3titUROlq0PbLZEFiR/X3UkW0ymug2KkI7hmsfFhAsG25F1nZHbEHuY9lOxT64j7qQbPSvbhVVXAwEsjhwLalHvizFuzUbA0VQZMWxlbLy2Dllorg66APMrB5GyhSHILdDK05tNqoVhH59x0qAgoMSlWwGYtQ9lWCQHMzPmgPD4g9dm3LBdDIPdiIPVCZUJcp1INW9loQlAO+tNaVcJBBSWl17EG2GOruBYDU1XvHGbdXSTdD5kQAbRWxBgle3zyV6wCy2qIhTYtFFN9VFbGV4XhNi4ZmH3Bdt+xfAYi9Vkn9UC/T7nVZucuWi+o4d3IvUpNl8ZfWK3Bg09QD75O2XZo7M4sByv1gkNvG4KQxSIHKLJGq3TJRqYh0IRuQ0P7uYKUqKAezCYWyKMvHhZvLamXxWPaPBkSr8l3jXqjba8faUG973It7WvnSTXkLtk61ZvUgmzaBBSxG2d9dTbgb3LSZNXmWKkK6GkDe/Ekbfc55tn7kHW5GaVrFv8bNaNonJEOt2T8aENK02a6bZYvvZfwByFVA6xjXpkNv2Z5KWrZglHGHvGzx3ZosOMnLsn1fd8rrVsQhpJuRTkJTEdlJO7FeBj/dPlCYxva32pabAdRuBi1c/17YVhzir7KHAMRnvryAdiKWND3/4rofphV/WLO9QUINDFrcoXQtSvVA+8rdizL+wC0Ym928FQtx8JgqcMluQ+lmIFcRqqtRxCN2g+JWpbEWh7hmW7Y9b/3PhsDaWBor9hCA+Fvsxt+jilt9wg+7lvMAlONN6ss1c2Jf0r1ItgInE5tPrRHFeCSpLPZQmIhDqG7GRg6DFo+gfZQAcvXfnW0rDrF/R39fXsO19nCASLEo0Sb/V9meAOVe23rzXlOxpWlw4LgDHVdXD3mgkv/PYxJAnkG5bnvKyRRsHuOhcDPiCbVUBJ2THo+4xmXIym4NhHtzM+qN6uFexqNlKX1ibt7lh/dwJ9v6qa+OqD+otR7qslfm1noJh7RswyUoO2sBAsQSEvjg/daGxOflQOZmmK03fdajU4fEGihW139m4tVfaVs/1gdA8TBjUt5i1iyS3xrzKXJ/+xyW4zpTN3/yOfLcFdk61AHTLTUhf+oy7pDKKOpBdusu3YvUvbs41k2MKKeNk2MmBp+5GcFPfMLpeGEGYGm8yACQTx/nrQjBU6XmGb2BlPS0W00o6mGJdSgVSex3awj+XSNd3WJFZupfaQ+jIKSt/db7JfCGae6ysmxvEPGqQ8u3/hXbteCQ1heuRLW9MWrrRV6mONerrje/GjXDEoWbsVKpylYNUyoQYNtdkCbL3qEyb6ofQK/Q96rka/u50zEeChDl43RrHELpnPiXWpWMVAQGgRoS/Fftq1gnJXMZd2DT1EOmMIoWC9qxvv5m0+ax0JKmIIKVRSwiKyvjEWmZcDn4rzRtnQqdHD6fpgZaVjy0Vce3LSvjD2m/H6viDwGI3WGxwk/eWg7oD7taEZWTKLfV8grWjqPZWt4Cn1sLGLJ8Obwcn085QlR+vsXxi/iDUdYB+34fqRbyhzRvwcgHk1nvrFWPGVFAAqiVgASCBgwNDltm6nNoHl8Dy14VUe5buY9AoczuGlRWTummrT7ByhdZ2ZqRlc1k9ZXHUXao9UEoJXj528mKWL695faliqAyOSS2/OdSNVRjT5atFIp6yPIgrGnGH1pncpNnV0JBNHeaIvioqgjeTTkiFahyq2qiZS13ReyzUg+t+EM9AUn21WixDBVGV1a/bGCeetuq9cJs/arb9jCA2LI9J1q+4cuHWlUJGoCK77uTmTZ+iBYkgAUC2p+2jQYHLSmqvA7VvWie784H65o3WvH2X5XywtXQIEGHK9TEjv4ZKhzWzkEpW8YfyuvQ4i+7llUuwo7f4NbRtnbYQwCiVA9rKmKPm6F+XzvwynZ73Yw9KoI+Q5Rbb9rUysnEKe2Y8rykeqjUa3bz8nO7SxxYG8S2WLecrNFVhFyfPgtIaGpi7ZTKckoT6qZ6yM57w73Y61psxh+23Yst9XBr+95DAAJoQ0JaFsVfcTO24hCcFZhvY6r97HUztFjEFiQ0ULT+yuuUc2Fk/SuUpKjs/ONAtXJtGX9I+RHF+l3WTNApYAHhZrQUROlqyHjEippY+5P73gUHTT1c615oo3sbJcK0Gn8ovm8poDvBAXggQLTsWhUhK6Ixt7kZxqy7GVbkHWixiDVIrIFCszLdWoJBg8Nyvrl60NykVv5DyzRlsRptb7VkqONVFiqC1ymQyEDBx9kz9JwGhg04aLEHI4fml9tG4wF7q2WFae5Fdj+NwU3q4Y72IIAgxn22iqiX1QU1FZH57VYv2xpnQVUZDVBof9o2MuYgj8XrymZNuc5Ady/kx9pd+4DP0UryUUacWjZZXI1lYV6BVTUB5LBojVtZBiy34NCKPRSuj6oeyu9ajOEe6mHDtfjHuxgtSAAfUxFlWd5f+dDLNypvk50DSrmfv62BfZDQQNEKBpbr5fYlHDTXQqoHtbnX6J/ZruJCejDZndDffPUwdBYpWKklTsl4hAKJChRNl6Ven2+/AQct5RusfpbvlXpQQaj3dv2Qesh3Ruu4jFJirz0QIHTjW5En+oj1GyrCZNvdriLYnAj4aa7GFiS4jFqZi79W+TU4rAUmAQZoCcMcjHezRjt+epOquQCKq7ECCdpd0Rqy1YpRld8Hh8y1KHI0Hks93M8eoy8G5+ojgB9d+pT/D9A9DYEeN2+WHphlGYuljwQ/9LIrhDWAh1lGmDJxn8GkAWRM3ClvZmHSGBHWLmOHWMP9LagvhrUG3gdwPw3ejis299dYS4vWTHUpFDjk2yxzcFZHMzlsy/3ebMaIm2aXQWLLfhpYgpXpdxY9Io1xCBZ0o62NfTVmAQkefDb+XtdmP8qbtQcO2nZ71EMByg+rBy1rcsO1+Oe7GOnBud7VSLNQo/jtjKYY8mV7WjQMhDIQroa1QinsUBJSTfDflpVlM9VgapcjrWu4Fqwe1mI2rc9bVg53v3zOl2tBPDq5hqtRKAlVTeyJ+GplxdgRm3AQmZ/StTDKUP+bLRct9aDdK2VdFpj8JDggP+O/0eTEp/QBQA6Ja12NMkZRtmhUMIkLS788/T6oXQ1gPyRKUGiw0P60bZrBSqO7FmpwUq7fU69WV24/Rq2h8bkSVBJduhryGFxpJSTkADFrEd8SCgIMV8MhXUJZiYs3v7qshEAJlOL7lmtBX2j/fJzi/1tHR3wMFwPIugRr7gawuBHkHlzvaqRlQbgKZt3VkMcCYqX0iC4DdeG28SR8CKq7ASC5HMAyCtW1naLKuARQw4FtzbWwZrnOMv6QxWyuO724k8WNCMYWk7jYhfTKaEyqq2Fd7Oo9L35d/J8rdRrp6cpRpJrp03vgULZayEq+x7WwdoFD2ofBbtdCiTvcGw7AIwECaEICWCp+CYBUrw2VCiEkdVCNFSHKc1xg2ZeBFTN90/Z6PGINEvFCsstqgYKtNY6FFgfQwCDLajkPfP5lcDLbb5VMphbbaRaAiDtYVHNiBmMgnbtQTGprrENAnOEqQgIAxSSkCVCkfWnzbwJVOTrVDTAAq3DYbLVouRalQrjWtUj7BqQjeE84AA8CCCPeOBokDEwVtGQAkCKgt7yEBLgMcnXB95LUB0EigwliPQ8LJKwJ6VhbkPB+AZJUE0AOCmAZFXtzeLriVb4HDlI9tHZvYitMrTDasMjWyKCjDEw2vgcAhqKOyslYgkRUEfBzBQn4mQKXpZpgi1HjrYFdFB9MnOaNcBDukvzOllwLK5ftcC14f9Zuxh1acPjIOEoPAQg2E3wDErqCqCo27ydKi72tGgk4ES4SErR+HRK8kxIS8SgAclAANSy2LA9Cys9tOEjLgpOmHVP4kHCodiYAUi5HVA18JnLOyh2QAAo1UcJiy4qyFRiAHA4iIAnocMiuT+SFNOMOy8FRuRY3xB3k53vAAXgUQKT2QrsCibz5E1iPRzAkMqURIREgwBzjES1IUDxiHRLeU1PmEkkN6bJKUAA1LKTNK+taYAB0OLB6WHMt2MpneFl+BTKMrWILwbDzsjJ4a+ZHk6uhQgIAECuwBAUritI2oJE3XTbAINZtwaEOShZwKOMOSSE0YhercYccDlJBlHD4yLC5jwEINm7vvhMkgNwdARb5n6mLRtByDyQAoQSEy0EralCw65FdtqB81Suz+r4s2AOHal9mCU6ye9EKlt6iJoIxMNpbK0qr5GawiitiD8nVSMEit0DCz8uPaWtQAMiHom+phOy8VsAQ12dgiOeY/t+CgzyXvXEHOtm/HQ7AowBCRhrvAAneFcqgZVxfBi0lJJZ91ZAAlpgEsOyPz0e6HFUAE6WiEK0ZGzWx7mK+fG7BYdlWuBY7jVVHfR5aYcWNMDY+sfvmjcgCliketTR9ZkqCIQHkoOAye1oyqnTu5cJWVQNfW9laUS6XcCiCkqmyp99Ui10s53MPOPxrYhCptl4BCQD5Z6MHLe8BCRm4lE2gdO70n1QT6eTYrHAzfF3x16zuar6AQR6X96i6Fg31wCqY7tEtuqFhUT4nmFuoKmIJdIqLjCNgZ+4G5CZzTqwSFlumQQFYB0Px/6fCwVoFDvHaxf+fCQfgUQAhH5ArIQHc3rLxUUjQ/haXo1ITQKUoktnrf7l8XgzpauhwSOuNWHmjrXGD4gwfELOlW8GVoYSE90sFlscvYbHrkKWKyMEA7HApimV3h8NysvzhJjjsmbC6ZY8BCLYEgc+BBLds7IUEsEACAOVJ8G+IPC4B1GpCAwV9DGquwh7bAgOfM1C7FqV62GNGuFfrJ2arXIe1nWYqQroVkL8vGWenpLe6AAW7HjdZo7PVmmpI67NlcvlSybfgIA6uw6Fozvyr4QConGEDAAAGT0lEQVQ8CCD4zRGMeThI5PsTyVSi4kg1AWyAIisgF+k/pNpNW8pj/n8NDsK1KPdduheLa1KX37TkUsTmywBQhlPtZuTbFYoByu9cqAkq7lG5Fdr0d4DqfpTp3bvAUC2/HQ5LYPNz4fARRDwEIPghMCHUkAA+BRJAGxJsNlZ8TqaizwQJAKqaCFlZcVLiY5mjkJpJG6blS5Rg4OsBdDjIMh/0Nu5jpkic2gMJCDUhQMGmAkMeUmnyVDuYKfK+Vg2A6lLE9XVTJpXf5VZcCQct3lDC4V/TFyOpCb5sBgVnyVlsQoJNVtRrApcBy+8mMy5lXALAqppQQUErUH7dkzBVlmiBQX7P4pOmdDcW9fBXWcqJECqCVEGEBJCrhdK5UUDBpgFDP4figlVff/m8qRqA+8HhhtaKParhX9AXg2RoljmpqYnocqSXDVC9YWRaNpdhCGxBAkDeuatQE1lcQrgcQK4mgAUUVD6WCXmFLATLppXPtqzwLTiUroVB7bZI9+L+ZpObkTWHcsppeu2JeEThUpS/cRmjkHN+7jJj9e8y7tFyN/a6FEByuXj7Cg478hwkIAB6hva6FBIMt4YiHgQQQAYJoO1ylHGJWLbVdwOoIQFwhV5kgpZxKT0SjkvQmeYuB4AVUMjKm/9Kt8T9S7FRgUFcloRD6Vq01MPN8YfspGQcIr/KpCJEHw5yNQJakKBLasOhqvA7zq/1fRcY4nlL1cDXtgoHue4fAAfgUQDBNVC8x1fjEhok2HZAYi0tG2BURRgUr3npcgDYDQraV17pSmDsNelMtcAA5HBI5ZErBVYP6nEMSpG/bpUqKHZWvABqV0NAQpoGirj8ajjwseXuV+MQChiA3S5F2vYDcFgLRspWilbwkpbd9qw9BiCA5WoSKJaHiR+KIAEC6MFLIINEaZk7EZaKzFIhlGVWXA4Au0EhzpqWB6jnt9c0MMhjlHAoXYvKFTe1OmmV1awe+yE7W0i9lJUVUMkggUVFlAFMAIvrcaO1R7/aDwa+lpZqSNvfEIzk/zU4XKMabgUD22MAQkuU2qsmtOAl20dbOBouhzTZHMpwAWpQAG1YlCZ/6NX45QoY5LYSDlvqoXW49fOoXQkAeWtEOngB8uBXICFOqAGK6lTkG3WNbK3gZAmGdN6oYw28rVQNcdlnt1S04KCpho8g4jEAwVY2b5ZqYsPlaAcvt1s4AD0ukcpIlyMW02ITfPwSFEAbFvk9aFRGLV5QFlFUA1DDwZh8d7WauF3ZLDspoSFUhIl5EbIlSowCzHepUhNA260QyVbVebTOLzs31NtvuROyzAddCv7/I/EGTTV8TD88CCCqAWMAXU2kuqm4HJvBS9wUl4AoQ0cSoFDUBJAnPcl+RdnbXZF+3GS6Zi0olMdbg8NStlYby37rY6unpqkHce+qMmJdcjVKSMQylZrQTkLrx6GdY2WNOER2gxR3gr9vqQbe1x2Dkbeqhn9+kDKaPrLUTjWhxSWynd8YlwAqNQG0YxO0j2VBGaNYlivnseOX1N7ueXOnWN6Ag3QtSpcj2+/m2SgmlAAAUcl5jz5zNVYhgfxBr2ChnbR+UtWSWmkUagHQwZDKNmINxb4+O96wpho+GH4A8CiAKN4CatBxj5pQ4hKiqDjGNS4HHUWqiVROcTuA/AWqwULuR5oGDc20bdfAID9rrkUGmDImcYW3UQUqq9+QQV4mRQlIAEt9FqBgRZGdmwYM9by0G6YoBT4nCCiIZU13gssUkLmnS/ER1fCvURB5opRffqQy3tDYptUUmq/L4xKbLgf4kEtilSwH1KAIWCAhnu+lXAxqpkvg/dzg+mtgALAKB821WItJ3GSZK2Gy4GG2voQEkKsJoAIF21XJUXxMaY2h3FbjDGL5PeHA1oo3yHJ/FRwAwHy0t9fTnva0f6/d5GY+7WlP+//DnoB42tOe1rQnIJ72tKc17QmIpz3taU17AuJpT3ta056AeNrTnta0JyCe9rSnNe0JiKc97WlNewLiaU97WtOegHja057WtCcgnva0pzXtCYinPe1pTXsC4mlPe1rTnoB42tOe1rQnIJ72tKc17QmIpz3taU17AuJpT3ta056AeNrTnta0JyCe9rSnNe0JiKc97WlNewLiaU97WtOegHja057WtCcgnva0pzXt/wHGuoAh/dkMLwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "eps_data = sim.get_array(vol=nonpml_vol, component=mp.Dielectric)\n", "ez_data = np.real(sim.get_dft_array(dft_obj, mp.Ez, 0))\n", diff --git a/python/examples/solve-cw.py b/python/examples/solve-cw.py index 95b1f0498..76f58dbb4 100644 --- a/python/examples/solve-cw.py +++ b/python/examples/solve-cw.py @@ -1,7 +1,8 @@ -import meep as mp +import matplotlib.pyplot as plt import numpy as np from numpy import linalg as LA -import matplotlib.pyplot as plt + +import meep as mp n = 3.4 w = 1 diff --git a/python/examples/stochastic_emitter.py b/python/examples/stochastic_emitter.py index fece7d6ca..c6cc377f9 100644 --- a/python/examples/stochastic_emitter.py +++ b/python/examples/stochastic_emitter.py @@ -1,8 +1,9 @@ -import meep as mp -from meep.materials import Ag +import argparse + import numpy as np +from meep.materials import Ag -import argparse +import meep as mp parser = argparse.ArgumentParser() parser.add_argument("-res", type=int, default=50, help="resolution (pixels/um)") diff --git a/python/examples/stochastic_emitter_line.py b/python/examples/stochastic_emitter_line.py index 54a44ffe0..5cae674ce 100644 --- a/python/examples/stochastic_emitter_line.py +++ b/python/examples/stochastic_emitter_line.py @@ -1,8 +1,9 @@ -import meep as mp -from meep.materials import Ag +import argparse + import numpy as np +from meep.materials import Ag -import argparse +import meep as mp parser = argparse.ArgumentParser() parser.add_argument("-res", type=int, default=50, help="resolution (pixels/um)") diff --git a/python/examples/straight-waveguide.ipynb b/python/examples/straight-waveguide.ipynb index 53de3462b..5ef4a9e74 100644 --- a/python/examples/straight-waveguide.ipynb +++ b/python/examples/straight-waveguide.ipynb @@ -12,17 +12,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import meep as mp" ] @@ -40,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -64,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -94,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -160,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -178,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -203,33 +195,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "\n", @@ -258,17 +226,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 0 finished at t = 200.0 (4000 timesteps)\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sim.run(until=200)" ] @@ -282,22 +242,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.figure(dpi=100)\n", "sim.plot2D(fields=mp.Ez)\n", @@ -329,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -348,23 +295,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------\n", - "Initializing structure...\n", - " block, center = (0,0,0)\n", - " size (1e+20,1,1e+20)\n", - " axes (1,0,0), (0,1,0), (0,0,1)\n", - "Normalizing field data...\n", - "run 1 finished at t = 100.0 (2000 timesteps)\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sim.run(mp.at_every(1, Animate), until=100)\n", "plt.close()" @@ -379,17 +312,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating MP4...\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "filename = \"media/straight_waveguide.mp4\"\n", "Animate.to_mp4(10, filename)" @@ -404,25 +329,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from IPython.display import Video\n", "\n", diff --git a/python/examples/straight-waveguide.py b/python/examples/straight-waveguide.py index c845e6497..86d3d9495 100644 --- a/python/examples/straight-waveguide.py +++ b/python/examples/straight-waveguide.py @@ -1,8 +1,4 @@ -# -*- coding: utf-8 -*- - # From the Meep tutorial: plotting permittivity and fields of a straight waveguide -from __future__ import division - import meep as mp cell = mp.Vector3(16, 8, 0) @@ -35,8 +31,8 @@ sim.run(until=200) -import numpy as np import matplotlib.pyplot as plt +import numpy as np eps_data = sim.get_array(center=mp.Vector3(), size=cell, component=mp.Dielectric) plt.figure() diff --git a/python/examples/wvg-src.py b/python/examples/wvg-src.py index 421022523..de0b7b0f6 100644 --- a/python/examples/wvg-src.py +++ b/python/examples/wvg-src.py @@ -1,8 +1,5 @@ -from __future__ import division - import meep as mp - # Example file illustrating an eigenmode source, generating a waveguide mode # (requires recent MPB version to be installed before Meep is compiled) diff --git a/python/examples/zone_plate.py b/python/examples/zone_plate.py index aec9b4991..17db0e0b9 100644 --- a/python/examples/zone_plate.py +++ b/python/examples/zone_plate.py @@ -1,7 +1,9 @@ -import meep as mp -import numpy as np import math + import matplotlib.pyplot as plt +import numpy as np + +import meep as mp resolution = 25 # pixels/μm diff --git a/python/geom.py b/python/geom.py index 323bb59ad..1bb871cc3 100755 --- a/python/geom.py +++ b/python/geom.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import functools import math import numbers @@ -9,8 +8,8 @@ from numbers import Number import numpy as np -import meep as mp +import meep as mp FreqRange = namedtuple("FreqRange", ["min", "max"]) @@ -27,7 +26,7 @@ def init_do_averaging(mat_func): mat_func.do_averaging = False -class Vector3(object): +class Vector3: """ Properties: @@ -258,7 +257,7 @@ def rotate_reciprocal(self, axis, theta, lat): return cartesian_to_reciprocal(v.rotate(a, theta), lat) -class Medium(object): +class Medium: """ This class is used to specify the materials that geometric objects are made of. It represents an electromagnetic medium which is possibly nonlinear and/or dispersive. @@ -571,7 +570,7 @@ def _get_epsmu( return np.squeeze(epsmu) -class MaterialGrid(object): +class MaterialGrid: """ This class is used to specify materials on a rectilinear grid. A class object is passed as the `material` argument of a [`Block`](#block) geometric object or the `default_material` @@ -697,7 +696,7 @@ def update_weights(self, x): self.weights[:] = self.check_weights(x).flatten().astype(np.float64) -class Susceptibility(object): +class Susceptibility: """ Parent class for various dispersive susceptibility terms, parameterized by an anisotropic amplitude $\\sigma$. See [Material Dispersion](Materials.md#material-dispersion). @@ -744,7 +743,7 @@ def __init__(self, frequency=0.0, gamma=0.0, **kwargs): different `sigma` will appear as a *single* Lorentzian susceptibility term in the preliminary simulation info output. """ - super(LorentzianSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.frequency = frequency self.gamma = gamma @@ -785,7 +784,7 @@ def __init__(self, frequency=0.0, gamma=0.0, **kwargs): + **`gamma` [`number`]** — The loss rate $\\gamma_n / 2\\pi$. """ - super(DrudeSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.frequency = frequency self.gamma = gamma @@ -831,7 +830,7 @@ def __init__(self, noise_amp=0.0, **kwargs): [here](http://doi.org/10.1103/PhysRevB.92.134202) or [here](http://doi.org/10.1103/PhysRevB.88.054305). """ - super(NoisyLorentzianSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.noise_amp = noise_amp @@ -862,7 +861,7 @@ def __init__(self, noise_amp=0.0, **kwargs): [here](http://doi.org/10.1103/PhysRevB.92.134202) or [here](http://doi.org/10.1103/PhysRevB.88.054305). """ - super(NoisyDrudeSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.noise_amp = noise_amp @@ -881,7 +880,7 @@ def __init__(self, bias=Vector3(), **kwargs): orientation of the gyrotropic response, and the magnitude is the precession frequency $|\\mathbf{b}_n|/2\\pi$. """ - super(GyrotropicLorentzianSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.bias = bias @@ -900,7 +899,7 @@ def __init__(self, bias=Vector3(), **kwargs): orientation of the gyrotropic response, and the magnitude is the precession frequency $|\\mathbf{b}_n|/2\\pi$. """ - super(GyrotropicDrudeSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.bias = bias @@ -939,7 +938,7 @@ def __init__(self, bias=Vector3(), frequency=0.0, gamma=0.0, alpha=0.0, **kwargs the magnitude is ignored; instead, the relevant precession frequencies are determined by the `sigma` and `frequency` parameters. """ - super(GyrotropicSaturatedSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.frequency = frequency self.gamma = gamma self.bias = bias @@ -957,12 +956,12 @@ class MultilevelAtom(Susceptibility): """ def __init__(self, initial_populations=None, transitions=None, **kwargs): - super(MultilevelAtom, self).__init__(**kwargs) + super().__init__(**kwargs) self.initial_populations = initial_populations or [] self.transitions = transitions or [] -class Transition(object): +class Transition: """ """ def __init__( @@ -1002,7 +1001,7 @@ def __init__( self.pumping_rate = pumping_rate -class GeometricObject(object): +class GeometricObject: """ This class, and its descendants, are used to specify the solid geometric objects that form the dielectric structure being simulated. @@ -1135,7 +1134,7 @@ class Sphere(GeometricObject): def __init__(self, radius, **kwargs): """Constructs a `Sphere`""" self.radius = float(radius) - super(Sphere, self).__init__(**kwargs) + super().__init__(**kwargs) @property def radius(self): @@ -1167,7 +1166,7 @@ def __init__(self, radius, axis=Vector3(0, 0, 1), height=1e20, **kwargs): self.axis = Vector3(*axis) self.radius = float(radius) self.height = float(height) - super(Cylinder, self).__init__(**kwargs) + super().__init__(**kwargs) @property def radius(self): @@ -1199,7 +1198,7 @@ def __init__( """ self.wedge_angle = wedge_angle self.wedge_start = Vector3(*wedge_start) - super(Wedge, self).__init__(radius, **kwargs) + super().__init__(radius, **kwargs) class Cone(Cylinder): @@ -1220,7 +1219,7 @@ def __init__(self, radius, radius2=0, **kwargs): Radius of the tip of the cone (i.e. the end of the cone pointed to by the `axis` vector). Defaults to zero (a "sharp" cone). """ self.radius2 = radius2 - super(Cone, self).__init__(radius, **kwargs) + super().__init__(radius, **kwargs) class Block(GeometricObject): @@ -1252,7 +1251,7 @@ def __init__( self.e1 = Vector3(*e1) self.e2 = Vector3(*e2) self.e3 = Vector3(*e3) - super(Block, self).__init__(**kwargs) + super().__init__(**kwargs) class Ellipsoid(Block): @@ -1265,7 +1264,7 @@ def __init__(self, **kwargs): """ Construct an `Ellipsoid`. """ - super(Ellipsoid, self).__init__(**kwargs) + super().__init__(**kwargs) class Prism(GeometricObject): @@ -1326,10 +1325,10 @@ def __init__( self.axis = axis self.sidewall_angle = sidewall_angle - super(Prism, self).__init__(center=center, **kwargs) + super().__init__(center=center, **kwargs) -class Matrix(object): +class Matrix: """ The `Matrix` class represents a 3x3 matrix with c1, c2, and c3 as its columns. @@ -1496,7 +1495,7 @@ def inverse(self): H = property(getH, None) -class Lattice(object): +class Lattice: def __init__( self, size=Vector3(1, 1, 1), diff --git a/python/materials.py b/python/materials.py index 31056b6e6..604571494 100644 --- a/python/materials.py +++ b/python/materials.py @@ -1,8 +1,7 @@ -# -*- coding: utf-8 -*- # Materials Library +import numpy as np import meep as mp -import numpy as np # default unit length is 1 μm um_scale = 1.0 diff --git a/python/meep.i b/python/meep.i index c629e67a8..66a4703cf 100644 --- a/python/meep.i +++ b/python/meep.i @@ -843,12 +843,12 @@ meep::volume_list *make_volume_list(const meep::volume &v, int c, //-------------------------------------------------- %inline %{ -void _get_gradient(PyObject *grad, double scalegrad, +void _get_gradient(PyObject *grad, double scalegrad, meep::dft_fields *fields_a_0, meep::dft_fields *fields_a_1, meep::dft_fields *fields_a_2, meep::dft_fields *fields_f_0, meep::dft_fields *fields_f_1, meep::dft_fields *fields_f_2, meep::grid_volume *grid_volume, meep::volume *where, PyObject *frequencies, meep_geom::geom_epsilon *geps, double fd_step) { - + // clean the gradient array PyArrayObject *pao_grad = (PyArrayObject *)grad; if (!PyArray_Check(pao_grad)) meep::abort("grad parameter must be numpy array."); @@ -873,7 +873,7 @@ void _get_gradient(PyObject *grad, double scalegrad, // calculate the gradient meep_geom::material_grids_addgradient(grad_c,ng,nf,adjoint_fields,forward_fields,frequencies_c,scalegrad,*grid_volume,*where,geps,fd_step); - + } %} diff --git a/python/mpb_data.py b/python/mpb_data.py index 2b7d987ee..608a5f0ee 100644 --- a/python/mpb_data.py +++ b/python/mpb_data.py @@ -1,12 +1,13 @@ -# -*- coding: utf-8 -*- import math + import numpy as np + import meep as mp -from . import map_data -from . import MPBArray + +from . import MPBArray, map_data -class MPBData(object): +class MPBData: TWOPI = 6.2831853071795864769252867665590057683943388 diff --git a/python/simulation.py b/python/simulation.py index ee3162745..accc0af3a 100644 --- a/python/simulation.py +++ b/python/simulation.py @@ -1,6 +1,3 @@ -# -*- coding: utf-8 -*- -from typing import Callable, List, Tuple, Union, Optional - import functools import math import numbers @@ -10,36 +7,36 @@ import subprocess import sys import warnings -from collections import namedtuple -from collections import OrderedDict +from collections import OrderedDict, namedtuple +from typing import Callable, List, Optional, Tuple, Union try: from collections.abc import Sequence except ImportError: from collections import Sequence +import meep.visualization as vis import numpy as np - -import meep as mp -from meep.geom import Vector3, init_do_averaging, GeometricObject, Medium +from meep.geom import GeometricObject, Medium, Vector3, init_do_averaging from meep.source import ( - Source, EigenModeSource, GaussianBeamSource, IndexedSource, + Source, check_positive, ) -import meep.visualization as vis from meep.verbosity_mgr import Verbosity +import meep as mp + try: basestring except NameError: basestring = str try: - from ipywidgets import FloatProgress from IPython.display import display + from ipywidgets import FloatProgress do_progress = True except ImportError: @@ -149,7 +146,7 @@ def bands_to_diffractedplanewave(where, bands): return mp.diffractedplanewave(*diffractedplanewave_args) -class DiffractedPlanewave(object): +class DiffractedPlanewave: """ For mode decomposition or eigenmode source, specify a diffracted planewave in homogeneous media. Should be passed as the `bands` argument of `get_eigenmode_coefficients`, `band_num` of `get_eigenmode`, or `eig_band` of `EigenModeSource`. """ @@ -158,7 +155,7 @@ def __init__(self, g=None, axis=None, s=None, p=None): """ Construct a `DiffractedPlanewave`. - + **`g` [ list of 3 `integer`s ]** — The diffraction order $(m_x,m_y,m_z)$ corresponding to the wavevector $(k_x+2\\pi m_x/\\Lambda_x,k_y+2\\pi m_y/\\Lambda_y,k_z+2\\pi m_z/\\Lambda_z)$. The diffraction order $m_{x,y,z}$ should be non-zero only in the $d$-1 periodic directions of a $d$ dimensional cell of size $(\Lambda_x,\Lambda_y,\Lambda_z)$ (e.g., a plane in 3d) in which the mode monitor or source extends the entire length of the cell. + + **`g` [ list of 3 `integer`s ]** — The diffraction order $(m_x,m_y,m_z)$ corresponding to the wavevector $(k_x+2\\pi m_x/\\Lambda_x,k_y+2\\pi m_y/\\Lambda_y,k_z+2\\pi m_z/\\Lambda_z)$. The diffraction order $m_{x,y,z}$ should be non-zero only in the $d$-1 periodic directions of a $d$ dimensional cell of size $(\\Lambda_x,\\Lambda_y,\\Lambda_z)$ (e.g., a plane in 3d) in which the mode monitor or source extends the entire length of the cell. + **`axis` [ `Vector3` ]** — The plane of incidence for each planewave (used to define the $\\mathcal{S}$ and $\\mathcal{P}$ polarizations below) is defined to be the plane that contains the `axis` vector and the planewave's wavevector. If `None`, `axis` defaults to the first direction that lies in the plane of the monitor or source (e.g., $y$ direction for a $yz$ plane in 3d, either $x$ or $y$ in 2d). @@ -192,7 +189,7 @@ def p(self): Vector3Type = Union[Vector3, Tuple[float, ...]] -class PML(object): +class PML: """ This class is used for specifying the PML absorbing boundary layers around the cell, if any, via the `boundary_layers` input variable. See also [Perfectly Matched @@ -315,7 +312,7 @@ class Absorber(PML): """ -class Symmetry(object): +class Symmetry: """ This class is used for the `symmetries` input variable to specify symmetries which must preserve both the structure *and* the sources. Any number of symmetries can be @@ -380,7 +377,7 @@ class Identity(Symmetry): """ """ -class Volume(object): +class Volume: """ Many Meep functions require you to specify a volume in space, corresponding to the C++ type `meep::volume`. This class creates such a volume object, given the `center` and @@ -488,7 +485,7 @@ def pt_in_volume(self, pt): ) -class FluxRegion(object): +class FluxRegion: """ A `FluxRegion` object is used with [`add_flux`](#flux-spectra) to specify a region in which Meep should accumulate the appropriate Fourier-transformed fields in order to @@ -591,7 +588,7 @@ class EnergyRegion(FluxRegion): """ -class FieldsRegion(object): +class FieldsRegion: def __init__(self, where=None, center=None, size=None): if where: self.center = where.center @@ -603,7 +600,7 @@ def __init__(self, where=None, center=None, size=None): self.where = where -class DftObj(object): +class DftObj: """Wrapper around DFT objects that allows delayed initialization of the structure. When splitting the structure into chunks for parallel simulations, we want to know all @@ -668,7 +665,7 @@ class DftFlux(DftObj): def __init__(self, func, args): """Construct a `DftFlux`.""" - super(DftFlux, self).__init__(func, args) + super().__init__(func, args) self.nfreqs = len(args[0]) self.regions = args[1] self.num_components = 4 @@ -707,7 +704,7 @@ class DftForce(DftObj): def __init__(self, func, args): """Construct a `DftForce`.""" - super(DftForce, self).__init__(func, args) + super().__init__(func, args) self.nfreqs = len(args[0]) self.regions = args[1] self.num_components = 6 @@ -738,7 +735,7 @@ class DftNear2Far(DftObj): def __init__(self, func, args): """Construct a `DftNear2Far`.""" - super(DftNear2Far, self).__init__(func, args) + super().__init__(func, args) self.nfreqs = len(args[0]) self.nperiods = args[1] self.regions = args[2] @@ -784,7 +781,7 @@ class DftEnergy(DftObj): def __init__(self, func, args): """Construct a `DftEnergy`.""" - super(DftEnergy, self).__init__(func, args) + super().__init__(func, args) self.nfreqs = len(args[0]) self.regions = args[1] self.num_components = 12 @@ -811,7 +808,7 @@ class DftFields(DftObj): def __init__(self, func, args): """Construct a `DftFields`.""" - super(DftFields, self).__init__(func, args) + super().__init__(func, args) self.nfreqs = len(args[4]) self.regions = [FieldsRegion(where=args[1], center=args[2], size=args[3])] self.num_components = len(args[0]) @@ -824,7 +821,7 @@ def chunks(self): Mode = namedtuple("Mode", ["freq", "decay", "Q", "amp", "err"]) -class EigenmodeData(object): +class EigenmodeData: def __init__(self, band_num, freq, group_velocity, k, swigobj, kdom): """Construct an `EigenmodeData`.""" self.band_num = band_num @@ -839,7 +836,7 @@ def amplitude(self, point, component): return mp.eigenmode_amplitude(self.swigobj, swig_point, component) -class Harminv(object): +class Harminv: """ Harminv is implemented as a class with a [`__call__`](#Harminv.__call__) method, which allows it to be used as a step function that collects field data from a given @@ -996,7 +993,7 @@ def _harm(sim): return _combine_step_funcs(at_end(_harm), f1(self.c, self.pt)) -class Simulation(object): +class Simulation: """ The `Simulation` [class](#classes) contains all the attributes that you can set to control various parameters of the Meep computation. @@ -1458,7 +1455,7 @@ def _create_grid_volume(self, k): self.dimensions = 2 self.is_cylindrical = True else: - raise ValueError("Unsupported dimentionality: {}".format(dims)) + raise ValueError(f"Unsupported dimentionality: {dims}") gv.center_origin() gv.shift_origin( @@ -1804,23 +1801,21 @@ def _init_structure(self, k=False): and verbosity.meep > 0 ): stats = self._compute_fragment_stats(gv) - print("FRAGMENT:, aniso_eps:, {}".format(stats.num_anisotropic_eps_pixels)) - print("FRAGMENT:, aniso_mu:, {}".format(stats.num_anisotropic_mu_pixels)) - print("FRAGMENT:, nonlinear:, {}".format(stats.num_nonlinear_pixels)) - print( - "FRAGMENT:, susceptibility:, {}".format(stats.num_susceptibility_pixels) - ) + print(f"FRAGMENT:, aniso_eps:, {stats.num_anisotropic_eps_pixels}") + print(f"FRAGMENT:, aniso_mu:, {stats.num_anisotropic_mu_pixels}") + print(f"FRAGMENT:, nonlinear:, {stats.num_nonlinear_pixels}") + print(f"FRAGMENT:, susceptibility:, {stats.num_susceptibility_pixels}") print( "FRAGMENT:, conductivity:, {}".format( stats.num_nonzero_conductivity_pixels ) ) - print("FRAGMENT:, pml_1d:, {}".format(stats.num_1d_pml_pixels)) - print("FRAGMENT:, pml_2d:, {}".format(stats.num_2d_pml_pixels)) - print("FRAGMENT:, pml_3d:, {}".format(stats.num_3d_pml_pixels)) - print("FRAGMENT:, dft:, {}".format(stats.num_dft_pixels)) - print("FRAGMENT:, total_pixels:, {}".format(stats.num_pixels_in_box)) - print("FRAGMENT:, procs:, {}".format(mp.count_processors())) + print(f"FRAGMENT:, pml_1d:, {stats.num_1d_pml_pixels}") + print(f"FRAGMENT:, pml_2d:, {stats.num_2d_pml_pixels}") + print(f"FRAGMENT:, pml_3d:, {stats.num_3d_pml_pixels}") + print(f"FRAGMENT:, dft:, {stats.num_dft_pixels}") + print(f"FRAGMENT:, total_pixels:, {stats.num_pixels_in_box}") + print(f"FRAGMENT:, procs:, {mp.count_processors()}") fragment_vols = self._make_fragment_lists(gv) self.dft_data_list = fragment_vols[0] @@ -2077,7 +2072,9 @@ def dump_structure(self, fname, single_parallel_file=True): self.structure.dump(fname, single_parallel_file) if verbosity.meep > 0: print( - "Dumped structure to file: %s (%s)" % (fname, str(single_parallel_file)) + "Dumped structure to file: {} ({})".format( + fname, str(single_parallel_file) + ) ) def load_structure(self, fname, single_parallel_file=True): @@ -2104,7 +2101,11 @@ def dump_fields(self, fname, single_parallel_file=True): raise ValueError("Fields must be initialized before calling dump_fields") self.fields.dump(fname, single_parallel_file) if verbosity.meep > 0: - print("Dumped fields to file: %s (%s)" % (fname, str(single_parallel_file))) + print( + "Dumped fields to file: {} ({})".format( + fname, str(single_parallel_file) + ) + ) def load_fields(self, fname, single_parallel_file=True): """ @@ -2119,7 +2120,9 @@ def load_fields(self, fname, single_parallel_file=True): self.fields.load(fname, single_parallel_file) if verbosity.meep > 0: print( - "Loaded fields from file: %s (%s)" % (fname, str(single_parallel_file)) + "Loaded fields from file: {} ({})".format( + fname, str(single_parallel_file) + ) ) def dump_chunk_layout(self, fname): @@ -2518,7 +2521,7 @@ def use_output_directory(self, dname=""): def hook(): if verbosity.meep > 0: - print("Meep: using output directory '{}'".format(dname)) + print(f"Meep: using output directory '{dname}'") self.fields.set_output_directory(dname) if not closure["trashed"]: mp.trash_output_directory(dname) @@ -2633,22 +2636,18 @@ def run_k_point(self, t, k): pts = [s.center for s in self.sources] src_freqs_min = min( - [ - s.src.frequency - 1 / s.src.width / 2 - if isinstance(s.src, mp.GaussianSource) - else mp.inf - for s in self.sources - ] + s.src.frequency - 1 / s.src.width / 2 + if isinstance(s.src, mp.GaussianSource) + else mp.inf + for s in self.sources ) fmin = max(0, src_freqs_min) fmax = max( - [ - s.src.frequency + 1 / s.src.width / 2 - if isinstance(s.src, mp.GaussianSource) - else 0 - for s in self.sources - ] + s.src.frequency + 1 / s.src.width / 2 + if isinstance(s.src, mp.GaussianSource) + else 0 + for s in self.sources ) if not components or fmin > fmax: @@ -2686,11 +2685,9 @@ def run_k_points(self, t, k_points): freqs = [complex(m.freq, m.decay) for m in harminv.modes] if verbosity.meep > 0: - print("freqs:, {}, {}, {}, {}, ".format(k_index, k.x, k.y, k.z), end="") + print(f"freqs:, {k_index}, {k.x}, {k.y}, {k.z}, ", end="") print(", ".join([str(f.real) for f in freqs])) - print( - "freqs-im:, {}, {}, {}, {}, ".format(k_index, k.x, k.y, k.z), end="" - ) + print(f"freqs-im:, {k_index}, {k.x}, {k.y}, {k.z}, ", end="") print(", ".join([str(f.imag) for f in freqs])) all_freqs.append(freqs) @@ -3006,7 +3003,7 @@ def _display_energy(self, name, func, energys): if verbosity.meep > 0: display_csv( self, - "{}-energy".format(name), + f"{name}-energy", zip(freqs, *[func(f) for f in energys]), ) @@ -3708,7 +3705,7 @@ def output_components(self, fname, *components): ) def h5topng(self, rm_h5, option, *step_funcs): - opts = "h5topng {}".format(option) + opts = f"h5topng {option}" cmd = re.sub(r"\$EPS", self.last_eps_filename, opts) return convert_h5(rm_h5, cmd, *step_funcs) @@ -3875,7 +3872,7 @@ def get_dft_array(self, dft_obj, component, num_freq): self.fields, dft_swigobj, component, num_freq ) else: - raise ValueError("Invalid type of dft object: {}".format(dft_swigobj)) + raise ValueError(f"Invalid type of dft object: {dft_swigobj}") def get_source(self, component, vol=None, center=None, size=None): """ @@ -4362,7 +4359,7 @@ def run(self, *step_funcs, **kwargs): self._check_material_frequencies() if kwargs: - raise ValueError("Unrecognized keyword arguments: {}".format(kwargs.keys())) + raise ValueError(f"Unrecognized keyword arguments: {kwargs.keys()}") if until_after_sources is not None: self._run_sources_until(until_after_sources, step_funcs) @@ -4789,9 +4786,7 @@ def _eval_step_func(sim, func, todo): num_args = get_num_args(func) if num_args != 1 and num_args != 2: - raise ValueError( - "Step function '{}'' requires 1 or 2 arguments".format(func.__name__) - ) + raise ValueError(f"Step function '{func.__name__}'' requires 1 or 2 arguments") elif num_args == 1: if todo == "step": func(sim) @@ -5271,7 +5266,7 @@ def _disp(sim): if do_progress: sim.progress.value = val1 - sim.progress.description = "{}% done ".format(int(val2)) + sim.progress.description = f"{int(val2)}% done " if verbosity.meep > 0: print(msg_fmt.format(val1, t, val2, val3, val4)) @@ -5283,7 +5278,7 @@ def _disp(sim): def data_to_str(d): if type(d) is complex: sign = "+" if d.imag >= 0 else "" - return "{}{}{}i".format(d.real, sign, d.imag) + return f"{d.real}{sign}{d.imag}i" else: return str(d) @@ -6091,7 +6086,7 @@ def merge_subgroup_data(data): return output -class BinaryPartition(object): +class BinaryPartition: """ Binary tree class used for specifying a cell partition of arbitrary sized chunks for use as the `chunk_layout` parameter of the `Simulation` class object. @@ -6153,7 +6148,7 @@ def __init__( def print(self): """Pretty-prints the tree structure of the BinaryPartition object.""" - print(str(self) + " with {} chunks:".format(self.numchunks())) + print(str(self) + f" with {self.numchunks()} chunks:") for line in self._print(is_root=True): print(line) @@ -6178,10 +6173,10 @@ def _print(self, prefix="", is_root=True): def _node_info(self) -> str: if self.proc_id is not None: - return "".format(self.proc_id) + return f"" else: split_dir_str = {mp.X: "X", mp.Y: "Y", mp.Z: "Z"}[self.split_dir] - return "".format(split_dir_str, self.split_pos) + return f"" def _numchunks(self, bp): if bp is None: diff --git a/python/solver.py b/python/solver.py index 62777c316..799af1c45 100644 --- a/python/solver.py +++ b/python/solver.py @@ -1,20 +1,21 @@ -# -*- coding: utf-8 -*- import functools import math -import os import numbers +import os import re import sys import time import h5py import numpy as np -import meep as mp -from . import mode_solver, with_hermitian_epsilon from meep.geom import init_do_averaging from meep.simulation import get_num_args from meep.verbosity_mgr import Verbosity +import meep as mp + +from . import mode_solver, with_hermitian_epsilon + try: basestring except NameError: @@ -70,7 +71,7 @@ def __array_finalize__(self, obj): self.bloch_phase = getattr(obj, "bloch_phase", False) -class ModeSolver(object): +class ModeSolver: def __init__( self, resolution=10, @@ -642,7 +643,7 @@ def _output_complex_scalar_field(self, fname_prefix, output_k): curfield_type = "C" kpoint_index = self.mode_solver.get_kpoint_index() curfield_band = self.mode_solver.curfield_band - fname = "{}.k{:02d}.b{:02d}".format(curfield_type, kpoint_index, curfield_band) + fname = f"{curfield_type}.k{kpoint_index:02d}.b{curfield_band:02d}" description = "{} field, kpoint {}, band {}, freq={:.6g}".format( curfield_type, kpoint_index, curfield_band, self.freqs[curfield_band - 1] ) @@ -668,7 +669,7 @@ def _output_vector_field(self, curfield_type, fname_prefix, output_k, component) components = ["x", "y", "z"] kpoint_index = self.mode_solver.get_kpoint_index() curfield_band = self.mode_solver.curfield_band - fname = "{}.k{:02d}.b{:02d}".format(curfield_type, kpoint_index, curfield_band) + fname = f"{curfield_type}.k{kpoint_index:02d}.b{curfield_band:02d}" if component >= 0: fname += f".{components[component]}" @@ -1099,15 +1100,15 @@ def bfunc(ms, b_prime): ks = list(reversed(ks)) if verbosity.mpb > 0: print( - "{}kvals:, {}, {}, {}".format(self.parity, omega, band_min, band_max), + f"{self.parity}kvals:, {omega}, {band_min}, {band_max}", end="", ) for k in korig: - print(", {}".format(k), end="") + print(f", {k}", end="") for k in kdir1: - print(", {}".format(k), end="") + print(f", {k}", end="") for k in ks: - print(", {}".format(k), end="") + print(f", {k}", end="") print() return ks diff --git a/python/source.py b/python/source.py index 007530399..bedede024 100644 --- a/python/source.py +++ b/python/source.py @@ -1,8 +1,9 @@ -# -*- coding: utf-8 -*- import warnings -import meep as mp + from meep.geom import Vector3, check_nonnegative +import meep as mp + def check_positive(prop, val): if val > 0: @@ -11,7 +12,7 @@ def check_positive(prop, val): raise ValueError(f"{prop} must be positive. Got {val}") -class Source(object): +class Source: """ The `Source` class is used to specify the current sources via the `Simulation.sources` attribute. Note that all sources in Meep are separable in time and space, i.e. of the @@ -126,7 +127,7 @@ def __init__( self.amp_data = amp_data -class SourceTime(object): +class SourceTime: """ This is the parent for classes describing the time dependence of sources; it should not be instantiated directly. @@ -196,7 +197,7 @@ def __init__( f"Must set either frequency or wavelength in {self.__class__.__name__}." ) - super(ContinuousSource, self).__init__(is_integrated=is_integrated, **kwargs) + super().__init__(is_integrated=is_integrated, **kwargs) self.frequency = 1 / wavelength if wavelength else float(frequency) self.start_time = start_time self.end_time = end_time @@ -276,7 +277,7 @@ def __init__( f"Must set either frequency or wavelength in {self.__class__.__name__}." ) - super(GaussianSource, self).__init__(is_integrated=is_integrated, **kwargs) + super().__init__(is_integrated=is_integrated, **kwargs) self.frequency = 1 / wavelength if wavelength else float(frequency) self.width = max(width, 1 / fwidth) self.start_time = start_time @@ -349,7 +350,7 @@ def __init__( automatically determine the decimation factor of the time-series updates of the DFT fields monitors (if any). """ - super(CustomSource, self).__init__(is_integrated=is_integrated, **kwargs) + super().__init__(is_integrated=is_integrated, **kwargs) self.src_func = src_func self.start_time = start_time self.end_time = end_time @@ -512,7 +513,7 @@ def __init__( simulation. """ - super(EigenModeSource, self).__init__(src, component, center, volume, **kwargs) + super().__init__(src, component, center, volume, **kwargs) self.eig_lattice_size = eig_lattice_size self.eig_lattice_center = eig_lattice_center self.component = component @@ -623,9 +624,7 @@ def __init__( + **`beam_E0` [`Vector3`]** — The polarization vector of the beam. Elements can be complex valued (i.e., for circular polarization). The polarization vector must be *parallel* to the source region in order to generate a transverse mode. """ - super(GaussianBeamSource, self).__init__( - src, component, center, volume, **kwargs - ) + super().__init__(src, component, center, volume, **kwargs) self._beam_x0 = beam_x0 self._beam_kdir = beam_kdir self._beam_w0 = beam_w0 diff --git a/python/tests/test_3rd_harm_1d.py b/python/tests/test_3rd_harm_1d.py index 9e9d645d4..99a4fe910 100644 --- a/python/tests/test_3rd_harm_1d.py +++ b/python/tests/test_3rd_harm_1d.py @@ -1,7 +1,9 @@ import unittest -import meep as mp + from utils import ApproxComparisonTestCase +import meep as mp + class Test3rdHarm1d(ApproxComparisonTestCase): def setUp(self): diff --git a/python/tests/test_absorber_1d.py b/python/tests/test_absorber_1d.py index 04fdc8c7e..5a408f1ad 100644 --- a/python/tests/test_absorber_1d.py +++ b/python/tests/test_absorber_1d.py @@ -1,7 +1,9 @@ import unittest -import meep as mp + from meep.materials import Al +import meep as mp + class TestAbsorber(unittest.TestCase): def setUp(self): diff --git a/python/tests/test_adjoint_cyl.py b/python/tests/test_adjoint_cyl.py index 67acf54f5..98bc01d33 100644 --- a/python/tests/test_adjoint_cyl.py +++ b/python/tests/test_adjoint_cyl.py @@ -4,11 +4,13 @@ import meep.adjoint as mpa except: import adjoint as mpa + +import unittest +from enum import Enum + import numpy as np from autograd import numpy as npa from autograd import tensor_jacobian_product -import unittest -from enum import Enum from utils import ApproxComparisonTestCase rng = np.random.RandomState(2) diff --git a/python/tests/test_adjoint_jax.py b/python/tests/test_adjoint_jax.py index 782c691e5..ea007ddc5 100644 --- a/python/tests/test_adjoint_jax.py +++ b/python/tests/test_adjoint_jax.py @@ -1,13 +1,13 @@ import unittest -import parameterized - -from utils import ApproxComparisonTestCase import jax import jax.numpy as jnp -import meep as mp import meep.adjoint as mpa import numpy as onp +import parameterized +from utils import ApproxComparisonTestCase + +import meep as mp # The calculation of finite difference gradients requires that JAX be operated with double precision jax.config.update("jax_enable_x64", True) diff --git a/python/tests/test_adjoint_solver.py b/python/tests/test_adjoint_solver.py index dd23d1448..56cee371a 100644 --- a/python/tests/test_adjoint_solver.py +++ b/python/tests/test_adjoint_solver.py @@ -4,14 +4,15 @@ import meep.adjoint as mpa except: import adjoint as mpa + +import unittest +from enum import Enum + import numpy as np from autograd import numpy as npa from autograd import tensor_jacobian_product -import unittest -from enum import Enum from utils import ApproxComparisonTestCase - MonitorObject = Enum("MonitorObject", "EIGENMODE DFT LDOS") diff --git a/python/tests/test_adjoint_utils.py b/python/tests/test_adjoint_utils.py index 267d32813..7bec9b774 100644 --- a/python/tests/test_adjoint_utils.py +++ b/python/tests/test_adjoint_utils.py @@ -5,20 +5,21 @@ (i.e. forward and adjoint runs). """ - import meep as mp try: import meep.adjoint as mpa except: import adjoint as mpa + +import unittest +from enum import Enum + import numpy as np +import parameterized from autograd import numpy as npa from autograd import tensor_jacobian_product -import unittest -from enum import Enum from utils import ApproxComparisonTestCase -import parameterized _TOL = 1e-6 if mp.is_single_precision() else 1e-14 diff --git a/python/tests/test_antenna_radiation.py b/python/tests/test_antenna_radiation.py index 89e3ee504..501f2869a 100644 --- a/python/tests/test_antenna_radiation.py +++ b/python/tests/test_antenna_radiation.py @@ -1,9 +1,11 @@ -import meep as mp import math -import numpy as np import unittest + +import numpy as np from utils import ApproxComparisonTestCase +import meep as mp + class TestAntennaRadiation(ApproxComparisonTestCase): @classmethod diff --git a/python/tests/test_array_metadata.py b/python/tests/test_array_metadata.py index b79b2b150..2bd98e5ab 100644 --- a/python/tests/test_array_metadata.py +++ b/python/tests/test_array_metadata.py @@ -1,8 +1,10 @@ -import meep as mp import unittest + import numpy as np from utils import ApproxComparisonTestCase +import meep as mp + class TestArrayMetadata(ApproxComparisonTestCase): def test_array_metadata(self): diff --git a/python/tests/test_bend_flux.py b/python/tests/test_bend_flux.py index 37d298dcd..83aa10688 100644 --- a/python/tests/test_bend_flux.py +++ b/python/tests/test_bend_flux.py @@ -1,9 +1,11 @@ import os import unittest + import numpy as np -import meep as mp from utils import ApproxComparisonTestCase +import meep as mp + class TestBendFlux(ApproxComparisonTestCase): def init(self, no_bend=False, gdsii=False): diff --git a/python/tests/test_binary_grating.py b/python/tests/test_binary_grating.py index eff359a9b..3f2961dad 100644 --- a/python/tests/test_binary_grating.py +++ b/python/tests/test_binary_grating.py @@ -1,11 +1,11 @@ -# -*- coding: utf-8 -*- - +import cmath +import math import unittest + +import numpy as np import parameterized + import meep as mp -import math -import cmath -import numpy as np class TestEigCoeffs(unittest.TestCase): @@ -176,9 +176,9 @@ def _pw_amp(x): Rflux = -r_flux[0] / input_flux[0] Tflux = t_flux[0] / input_flux[0] - print("refl:, {}, {}".format(Rsum, Rflux)) - print("tran:, {}, {}".format(Tsum, Tflux)) - print("sum:, {}, {}".format(Rsum + Tsum, Rflux + Tflux)) + print(f"refl:, {Rsum}, {Rflux}") + print(f"tran:, {Tsum}, {Tflux}") + print(f"sum:, {Rsum + Tsum}, {Rflux + Tflux}") self.assertAlmostEqual(Rsum, Rflux, places=2) self.assertAlmostEqual(Tsum, Tflux, places=2) @@ -325,9 +325,9 @@ def _pw_amp(x): Rflux = -r_flux[0] / input_flux[0] Tflux = t_flux[0] / input_flux[0] - print("refl:, {}, {}".format(Rsum, Rflux)) - print("tran:, {}, {}".format(Tsum, Tflux)) - print("sum:, {}, {}".format(Rsum + Tsum, Rflux + Tflux)) + print(f"refl:, {Rsum}, {Rflux}") + print(f"tran:, {Tsum}, {Tflux}") + print(f"sum:, {Rsum + Tsum}, {Rflux + Tflux}") self.assertAlmostEqual(Rsum, Rflux, places=2) self.assertAlmostEqual(Tsum, Tflux, places=2) diff --git a/python/tests/test_binary_partition_utils.py b/python/tests/test_binary_partition_utils.py index 1267fa233..31d1f7996 100644 --- a/python/tests/test_binary_partition_utils.py +++ b/python/tests/test_binary_partition_utils.py @@ -1,11 +1,11 @@ import copy import unittest +import meep.binary_partition_utils as bpu +import numpy as np import parameterized import meep as mp -import meep.binary_partition_utils as bpu -import numpy as np PARTITION_NO_DUPLICATE_PROC_ID = mp.BinaryPartition( data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] diff --git a/python/tests/test_cavity_arrayslice.py b/python/tests/test_cavity_arrayslice.py index f4945bf5a..b731a7c4c 100644 --- a/python/tests/test_cavity_arrayslice.py +++ b/python/tests/test_cavity_arrayslice.py @@ -1,8 +1,10 @@ -import meep as mp -from utils import ApproxComparisonTestCase import os import unittest + import numpy as np +from utils import ApproxComparisonTestCase + +import meep as mp class TestCavityArraySlice(ApproxComparisonTestCase): diff --git a/python/tests/test_cavity_farfield.py b/python/tests/test_cavity_farfield.py index c9b0284a5..9d2d8da5c 100644 --- a/python/tests/test_cavity_farfield.py +++ b/python/tests/test_cavity_farfield.py @@ -1,8 +1,10 @@ -import meep as mp -from utils import ApproxComparisonTestCase import os import unittest + import h5py +from utils import ApproxComparisonTestCase + +import meep as mp class TestCavityFarfield(ApproxComparisonTestCase): diff --git a/python/tests/test_chunk_balancer.py b/python/tests/test_chunk_balancer.py index dd9b4770e..4c25bd4f8 100644 --- a/python/tests/test_chunk_balancer.py +++ b/python/tests/test_chunk_balancer.py @@ -1,13 +1,12 @@ -import meep as mp -import numpy as np - -import parameterized import unittest import meep.binary_partition_utils as bpu - -from meep.timing_measurements import MeepTimingMeasurements, TIMING_MEASUREMENT_IDS +import numpy as np +import parameterized from meep.chunk_balancer import ChunkBalancer +from meep.timing_measurements import TIMING_MEASUREMENT_IDS, MeepTimingMeasurements + +import meep as mp class MockSimulation(mp.Simulation): diff --git a/python/tests/test_chunk_layout.py b/python/tests/test_chunk_layout.py index ea0227a6f..b437025bf 100644 --- a/python/tests/test_chunk_layout.py +++ b/python/tests/test_chunk_layout.py @@ -1,7 +1,8 @@ -import meep as mp import copy import unittest +import meep as mp + def traverse_tree(bp=None, min_corner=None, max_corner=None): diff --git a/python/tests/test_chunks.py b/python/tests/test_chunks.py index 97db1a712..a8cdd61ce 100644 --- a/python/tests/test_chunks.py +++ b/python/tests/test_chunks.py @@ -1,6 +1,7 @@ -import meep as mp import unittest +import meep as mp + class TestChunks(unittest.TestCase): @classmethod diff --git a/python/tests/test_conductivity.py b/python/tests/test_conductivity.py index 8f9176ad2..f5018eefc 100644 --- a/python/tests/test_conductivity.py +++ b/python/tests/test_conductivity.py @@ -1,5 +1,7 @@ import unittest + import numpy as np + import meep as mp dB_cm_to_dB_um = 1e-4 diff --git a/python/tests/test_cyl_ellipsoid.py b/python/tests/test_cyl_ellipsoid.py index 82e717f4b..18e6209a4 100644 --- a/python/tests/test_cyl_ellipsoid.py +++ b/python/tests/test_cyl_ellipsoid.py @@ -1,4 +1,5 @@ import unittest + import meep as mp diff --git a/python/tests/test_dft_energy.py b/python/tests/test_dft_energy.py index bf88c8633..8577c5f52 100644 --- a/python/tests/test_dft_energy.py +++ b/python/tests/test_dft_energy.py @@ -1,4 +1,5 @@ import unittest + import meep as mp # compute group velocity of a waveguide mode using two different methods diff --git a/python/tests/test_dft_fields.py b/python/tests/test_dft_fields.py index c90af5ab0..30de0f038 100644 --- a/python/tests/test_dft_fields.py +++ b/python/tests/test_dft_fields.py @@ -1,9 +1,11 @@ +import os import unittest + import h5py import numpy as np -import meep as mp from utils import ApproxComparisonTestCase -import os + +import meep as mp class TestDFTFields(ApproxComparisonTestCase): diff --git a/python/tests/test_diffracted_planewave.py b/python/tests/test_diffracted_planewave.py index 0096eae22..ee5c7ecdc 100644 --- a/python/tests/test_diffracted_planewave.py +++ b/python/tests/test_diffracted_planewave.py @@ -1,9 +1,10 @@ -import unittest -import meep as mp -import math import cmath +import math +import unittest + import numpy as np +import meep as mp # Computes the mode coefficient of the transmitted orders of # a binary grating given an incident planewave and verifies diff --git a/python/tests/test_dispersive_eigenmode.py b/python/tests/test_dispersive_eigenmode.py index ea5294f21..80b40ed93 100644 --- a/python/tests/test_dispersive_eigenmode.py +++ b/python/tests/test_dispersive_eigenmode.py @@ -4,13 +4,14 @@ # * check materials with off diagonal components # * check magnetic profiles # * once imaginary component is supported, check that - -import meep as mp -from utils import ApproxComparisonTestCase +import os import unittest -import numpy as np + import h5py -import os +import numpy as np +from utils import ApproxComparisonTestCase + +import meep as mp class TestDispersiveEigenmode(ApproxComparisonTestCase): @@ -86,7 +87,7 @@ def test_chi1_routine(self): # fields and structure classes by comparing their output to the # python epsilon output. - from meep.materials import Si, Ag, LiNbO3, Au + from meep.materials import Ag, Au, LiNbO3, Si # Check Silicon w0 = Si.valid_freq_range.min @@ -124,7 +125,7 @@ def test_get_with_dispersion(self): # Checks the get_array_slice and output_fields method # with dispersive materials. - from meep.materials import Si, Ag, LiNbO3, Au + from meep.materials import Ag, Au, LiNbO3, Si # Check Silicon w0 = Si.valid_freq_range.min diff --git a/python/tests/test_divide_mpi_processes.py b/python/tests/test_divide_mpi_processes.py index e0312fffa..43ec2ba6d 100644 --- a/python/tests/test_divide_mpi_processes.py +++ b/python/tests/test_divide_mpi_processes.py @@ -1,4 +1,5 @@ import unittest + import meep as mp diff --git a/python/tests/test_dump_load.py b/python/tests/test_dump_load.py index 3fd66c7f1..c97712733 100644 --- a/python/tests/test_dump_load.py +++ b/python/tests/test_dump_load.py @@ -4,11 +4,13 @@ import sys import unittest import warnings + import h5py import numpy as np -import meep as mp from utils import ApproxComparisonTestCase +import meep as mp + try: unicode except NameError: diff --git a/python/tests/test_eigfreq.py b/python/tests/test_eigfreq.py index b0f3a7041..3ceb6ee1d 100644 --- a/python/tests/test_eigfreq.py +++ b/python/tests/test_eigfreq.py @@ -1,6 +1,7 @@ -import meep as mp import unittest +import meep as mp + class TestEigfreq(unittest.TestCase): @unittest.skipIf( diff --git a/python/tests/test_faraday_rotation.py b/python/tests/test_faraday_rotation.py index d1c610a8a..0e9a33695 100644 --- a/python/tests/test_faraday_rotation.py +++ b/python/tests/test_faraday_rotation.py @@ -1,7 +1,10 @@ import unittest + import numpy as np + import meep as mp + ## Farady rotation rate for gyrotropic Lorentzian medium def kgyro_lorentzian(freq, epsn, f0, gamma, sigma, b0): dfsq = f0**2 - 1j * freq * gamma - freq**2 @@ -73,7 +76,7 @@ def record_ex_ey(sim): Ey_theory = np.abs(np.sin(kpred * (zout - zsrc)).real) expected = np.arctan2(Ey_theory, Ex_theory) * 180 / np.pi - print("Rotation angle (in degrees): {}, expected {}\n".format(result, expected)) + print(f"Rotation angle (in degrees): {result}, expected {expected}\n") np.testing.assert_allclose(expected, result, atol=tol) def test_faraday_rotation(self): diff --git a/python/tests/test_field_functions.py b/python/tests/test_field_functions.py index ce35758e1..00acbe3ae 100644 --- a/python/tests/test_field_functions.py +++ b/python/tests/test_field_functions.py @@ -1,6 +1,7 @@ -import meep as mp import unittest +import meep as mp + def f(r, ex, hz, eps): return (r.x * r.norm() + ex) - (eps * hz) diff --git a/python/tests/test_force.py b/python/tests/test_force.py index 7a728db5c..a38b4c9ee 100644 --- a/python/tests/test_force.py +++ b/python/tests/test_force.py @@ -1,4 +1,5 @@ import unittest + import meep as mp diff --git a/python/tests/test_fragment_stats.py b/python/tests/test_fragment_stats.py index 56c4c47fe..2bbfe0bb3 100644 --- a/python/tests/test_fragment_stats.py +++ b/python/tests/test_fragment_stats.py @@ -1,4 +1,5 @@ import unittest + import meep as mp diff --git a/python/tests/test_gaussianbeam.py b/python/tests/test_gaussianbeam.py index 32104cebe..b3bf83c63 100644 --- a/python/tests/test_gaussianbeam.py +++ b/python/tests/test_gaussianbeam.py @@ -1,8 +1,10 @@ -import unittest -import meep as mp import math +import unittest + import numpy as np +import meep as mp + class TestGaussianBeamSource(unittest.TestCase): def gaussian_beam(self, rot_angle): diff --git a/python/tests/test_geom.py b/python/tests/test_geom.py index 2fe9a9bdc..7851a1675 100644 --- a/python/tests/test_geom.py +++ b/python/tests/test_geom.py @@ -1,9 +1,11 @@ import math import unittest import warnings + +import meep.geom as gm import numpy as np + import meep as mp -import meep.geom as gm def zeros(): diff --git a/python/tests/test_get_epsilon_grid.py b/python/tests/test_get_epsilon_grid.py index 07876836a..d678d11a3 100644 --- a/python/tests/test_get_epsilon_grid.py +++ b/python/tests/test_get_epsilon_grid.py @@ -1,13 +1,16 @@ import unittest -import parameterized + import numpy as np +import parameterized + import meep as mp try: import meep.adjoint as mpa except: import adjoint as mpa -from meep.materials import SiN, Co + +from meep.materials import Co, SiN class TestGetEpsilonGrid(unittest.TestCase): diff --git a/python/tests/test_get_point.py b/python/tests/test_get_point.py index 146309b85..7884aa9f6 100644 --- a/python/tests/test_get_point.py +++ b/python/tests/test_get_point.py @@ -1,7 +1,9 @@ -import meep as mp +import math import unittest + import numpy as np -import math + +import meep as mp class TestGetPoint(unittest.TestCase): diff --git a/python/tests/test_holey_wvg_bands.py b/python/tests/test_holey_wvg_bands.py index 4f15f44d2..73c6474a9 100644 --- a/python/tests/test_holey_wvg_bands.py +++ b/python/tests/test_holey_wvg_bands.py @@ -1,6 +1,7 @@ -import meep as mp import unittest +import meep as mp + class TestHoleyWvgBands(unittest.TestCase): def setUp(self): diff --git a/python/tests/test_holey_wvg_cavity.py b/python/tests/test_holey_wvg_cavity.py index 44e7f5386..29333b579 100644 --- a/python/tests/test_holey_wvg_cavity.py +++ b/python/tests/test_holey_wvg_cavity.py @@ -1,7 +1,9 @@ -import meep as mp -from utils import ApproxComparisonTestCase import unittest +from utils import ApproxComparisonTestCase + +import meep as mp + class TestHoleyWvgCavity(ApproxComparisonTestCase): def setUp(self): diff --git a/python/tests/test_integrated_source.py b/python/tests/test_integrated_source.py index f380ceff4..67246bf07 100644 --- a/python/tests/test_integrated_source.py +++ b/python/tests/test_integrated_source.py @@ -1,7 +1,9 @@ import unittest -import meep as mp + import numpy as np +import meep as mp + # Test that is_integrated=True source correctly generates planewaves # for sources extending into the PML, as in this tutorial: # https://meep.readthedocs.io/en/latest/Perfectly_Matched_Layer/#planewave-sources-extending-into-pml diff --git a/python/tests/test_kdom.py b/python/tests/test_kdom.py index 87f430a9a..749915459 100644 --- a/python/tests/test_kdom.py +++ b/python/tests/test_kdom.py @@ -1,6 +1,7 @@ +import math import unittest + import meep as mp -import math class TestKdom(unittest.TestCase): diff --git a/python/tests/test_ldos.py b/python/tests/test_ldos.py index 2536c1a74..51e9224bc 100644 --- a/python/tests/test_ldos.py +++ b/python/tests/test_ldos.py @@ -1,5 +1,7 @@ import unittest + import numpy as np + import meep as mp # Computes the Purcell enhancement factor of a parallel dipole in a planar diff --git a/python/tests/test_material_dispersion.py b/python/tests/test_material_dispersion.py index 1ffcd5186..91d2f9c39 100644 --- a/python/tests/test_material_dispersion.py +++ b/python/tests/test_material_dispersion.py @@ -1,5 +1,7 @@ import unittest + import numpy as np + import meep as mp diff --git a/python/tests/test_material_grid.py b/python/tests/test_material_grid.py index a5d7acf30..284b5c4ed 100644 --- a/python/tests/test_material_grid.py +++ b/python/tests/test_material_grid.py @@ -4,9 +4,11 @@ import meep.adjoint as mpa except: import adjoint as mpa + +import unittest + import numpy as np from scipy.ndimage import gaussian_filter -import unittest def compute_transmittance(matgrid_symmetry=False): diff --git a/python/tests/test_materials_library.py b/python/tests/test_materials_library.py index 99d79d544..9bef924b7 100644 --- a/python/tests/test_materials_library.py +++ b/python/tests/test_materials_library.py @@ -1,5 +1,6 @@ import unittest -from meep.materials import InP, Ge, Si, LiNbO3, SiO2_aniso, Ag, Cr + +from meep.materials import Ag, Cr, Ge, InP, LiNbO3, Si, SiO2_aniso class TestMaterialsLibrary(unittest.TestCase): diff --git a/python/tests/test_medium_evaluations.py b/python/tests/test_medium_evaluations.py index ef680563a..e3ce7d8ce 100644 --- a/python/tests/test_medium_evaluations.py +++ b/python/tests/test_medium_evaluations.py @@ -5,15 +5,16 @@ # TODO: # * check materials with off diagonal components # * check magnetic profiles - import unittest -import meep as mp + import numpy as np +import meep as mp + class TestMediumEvaluations(unittest.TestCase): def test_medium_evaluations(self): - from meep.materials import Si, Ag, LiNbO3, fused_quartz + from meep.materials import Ag, LiNbO3, Si, fused_quartz # Check that scalars work w0 = LiNbO3.valid_freq_range.min diff --git a/python/tests/test_mode_coeffs.py b/python/tests/test_mode_coeffs.py index d826b7f80..42078cb04 100644 --- a/python/tests/test_mode_coeffs.py +++ b/python/tests/test_mode_coeffs.py @@ -1,7 +1,9 @@ -import meep as mp import unittest + import numpy as np +import meep as mp + class TestModeCoeffs(unittest.TestCase): def run_mode_coeffs(self, mode_num, kpoint_func, nf=1, resolution=15): diff --git a/python/tests/test_mode_decomposition.py b/python/tests/test_mode_decomposition.py index 127f6772c..54e10cd98 100644 --- a/python/tests/test_mode_decomposition.py +++ b/python/tests/test_mode_decomposition.py @@ -1,8 +1,10 @@ +import cmath +import math import unittest + import numpy as np + import meep as mp -import math -import cmath class TestModeDecomposition(unittest.TestCase): @@ -190,7 +192,7 @@ def test_oblique_waveguide_backward_mode(self): kpoint_func=lambda f, n: kpoint, ).alpha[0, 0, 0] - print("oblique-waveguide-flux:, {:.6f}, {:.6f}".format(-flux, abs(coeff) ** 2)) + print(f"oblique-waveguide-flux:, {-flux:.6f}, {abs(coeff) ** 2:.6f}") print( "oblique-waveguide-flux (decimated):, {:.6f}, {:.6f}".format( -flux_decimated, abs(coeff_decimated) ** 2 @@ -385,9 +387,9 @@ def test_grating_3d(self): Rflux = -r_flux[0] / input_flux[0] Tflux = t_flux[0] / input_flux[0] - print("refl:, {}, {}".format(Rsum, Rflux)) - print("tran:, {}, {}".format(Tsum, Tflux)) - print("sum:, {}, {}".format(Rsum + Tsum, Rflux + Tflux)) + print(f"refl:, {Rsum}, {Rflux}") + print(f"tran:, {Tsum}, {Tflux}") + print(f"sum:, {Rsum + Tsum}, {Rflux + Tflux}") ## to obtain agreement for two decimal digits, ## the resolution must be increased to 200 diff --git a/python/tests/test_mpb.py b/python/tests/test_mpb.py index b50c87608..a95e22d05 100644 --- a/python/tests/test_mpb.py +++ b/python/tests/test_mpb.py @@ -8,11 +8,11 @@ import h5py import numpy as np -from scipy.optimize import minimize_scalar -from scipy.optimize import ridder +from scipy.optimize import minimize_scalar, ridder +from utils import compare_arrays + import meep as mp from meep import mpb -from utils import compare_arrays @unittest.skipIf(os.getenv("MEEP_SKIP_LARGE_TESTS", False), "skipping large tests") @@ -40,7 +40,7 @@ def setUpClass(cls): def tearDown(self): end = time.time() - self.start - print("{}: {:.2f}s".format(self.filename_prefix, end)) + print(f"{self.filename_prefix}: {end:.2f}s") @classmethod def tearDownClass(cls): @@ -1050,7 +1050,7 @@ def test_honey_rods(self): self.check_band_range_data(expected_te_brd, ms.band_range_data) def test_line_defect(self): - from mpb_line_defect import ms, k_points + from mpb_line_defect import k_points, ms ms.deterministic = True ms.filename_prefix = self.filename_prefix diff --git a/python/tests/test_multilevel_atom.py b/python/tests/test_multilevel_atom.py index 17885d8e8..c0db13e22 100644 --- a/python/tests/test_multilevel_atom.py +++ b/python/tests/test_multilevel_atom.py @@ -1,7 +1,8 @@ -import meep as mp import math import unittest +import meep as mp + class TestMultiLevelAtom(unittest.TestCase): @unittest.skipIf( diff --git a/python/tests/test_n2f_periodic.py b/python/tests/test_n2f_periodic.py index 922415b4c..bca8b4384 100644 --- a/python/tests/test_n2f_periodic.py +++ b/python/tests/test_n2f_periodic.py @@ -1,9 +1,11 @@ -import unittest -import meep as mp import math +import unittest + import numpy as np from numpy import linalg as LA +import meep as mp + class TestNear2FarPeriodicBoundaries(unittest.TestCase): def test_nea2far_periodic(self): @@ -87,7 +89,7 @@ def test_nea2far_periodic(self): dft_Ez = sim.get_dft_array(dft_obj, mp.Ez, 0) norm[j] = LA.norm(n2f_Ez["Ez"] - dft_Ez[1:-1]) - print("norm:, {}, {:.5f}".format(res[j], norm[j])) + print(f"norm:, {res[j]}, {norm[j]:.5f}") sim.reset_meep() self.assertGreater(norm[0], norm[1]) diff --git a/python/tests/test_oblique_source.py b/python/tests/test_oblique_source.py index d5a9c463d..558f8551b 100644 --- a/python/tests/test_oblique_source.py +++ b/python/tests/test_oblique_source.py @@ -1,7 +1,8 @@ -import meep as mp import math import unittest +import meep as mp + class TestEigenmodeSource(unittest.TestCase): def test_waveguide_flux(self): @@ -63,8 +64,8 @@ def test_waveguide_flux(self): fluxes.append(mp.get_fluxes(tran)[0]) coeff_fluxes.append(abs(res.alpha[0, 0, 0]) ** 2) - print("flux:, {:.2f}, {:.6f}".format(t, fluxes[-1])) - print("coef_flux:, {:.2f}, {:.6f}".format(t, coeff_fluxes[-1])) + print(f"flux:, {t:.2f}, {fluxes[-1]:.6f}") + print(f"coef_flux:, {t:.2f}, {coeff_fluxes[-1]:.6f}") self.assertAlmostEqual(fluxes[0], fluxes[1], places=0) self.assertAlmostEqual(fluxes[1], fluxes[2], places=0) diff --git a/python/tests/test_physical.py b/python/tests/test_physical.py index ef75ea112..f19a98556 100644 --- a/python/tests/test_physical.py +++ b/python/tests/test_physical.py @@ -1,4 +1,5 @@ import unittest + import meep as mp diff --git a/python/tests/test_prism.py b/python/tests/test_prism.py index 1dbb0d48f..1abc472da 100644 --- a/python/tests/test_prism.py +++ b/python/tests/test_prism.py @@ -1,6 +1,8 @@ import os import unittest + import numpy as np + import meep as mp diff --git a/python/tests/test_pw_source.py b/python/tests/test_pw_source.py index 4e16c9952..e42d61804 100644 --- a/python/tests/test_pw_source.py +++ b/python/tests/test_pw_source.py @@ -1,9 +1,11 @@ -import meep as mp import cmath import math import unittest + from utils import ApproxComparisonTestCase +import meep as mp + class TestPwSource(ApproxComparisonTestCase): def setUp(self): diff --git a/python/tests/test_refl_angular.py b/python/tests/test_refl_angular.py index fb365238e..6e6bad0b6 100644 --- a/python/tests/test_refl_angular.py +++ b/python/tests/test_refl_angular.py @@ -1,12 +1,12 @@ -# -*- coding: utf-8 -*- - +import math import unittest -import parameterized + import numpy as np -import math -import meep as mp +import parameterized from utils import ApproxComparisonTestCase +import meep as mp + class TestReflAngular(ApproxComparisonTestCase): @classmethod diff --git a/python/tests/test_ring.py b/python/tests/test_ring.py index 46ed58c24..d4f5eaa9b 100644 --- a/python/tests/test_ring.py +++ b/python/tests/test_ring.py @@ -1,6 +1,7 @@ # Python port of meep/examples/ring.ctl # Calculating 2d ring-resonator modes, from the Meep tutorial. import unittest + import meep as mp diff --git a/python/tests/test_ring_cyl.py b/python/tests/test_ring_cyl.py index 6762b448b..525ac7c09 100644 --- a/python/tests/test_ring_cyl.py +++ b/python/tests/test_ring_cyl.py @@ -1,7 +1,9 @@ import unittest -import meep as mp + from utils import ApproxComparisonTestCase +import meep as mp + class TestRingCyl(ApproxComparisonTestCase): def setUp(self): diff --git a/python/tests/test_simulation.py b/python/tests/test_simulation.py index 2a72f9f1b..a0c3b3b7a 100644 --- a/python/tests/test_simulation.py +++ b/python/tests/test_simulation.py @@ -4,8 +4,10 @@ import sys import unittest import warnings + import h5py import numpy as np + import meep as mp try: diff --git a/python/tests/test_source.py b/python/tests/test_source.py index b009a24b6..adecb5a38 100644 --- a/python/tests/test_source.py +++ b/python/tests/test_source.py @@ -1,12 +1,12 @@ import math import os import unittest -import numpy as np -import meep as mp +import numpy as np from meep.geom import Cylinder, Vector3 -from meep.source import EigenModeSource, ContinuousSource, GaussianSource +from meep.source import ContinuousSource, EigenModeSource, GaussianSource +import meep as mp data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) diff --git a/python/tests/test_special_kz.py b/python/tests/test_special_kz.py index 067381d6b..08ae722d4 100644 --- a/python/tests/test_special_kz.py +++ b/python/tests/test_special_kz.py @@ -1,10 +1,12 @@ -import unittest -import parameterized -import meep as mp import cmath import math +import unittest from time import time +import parameterized + +import meep as mp + class TestSpecialKz(unittest.TestCase): def refl_planar(self, theta, kz_2d): diff --git a/python/tests/test_user_defined_material.py b/python/tests/test_user_defined_material.py index bed170591..54dc746f7 100644 --- a/python/tests/test_user_defined_material.py +++ b/python/tests/test_user_defined_material.py @@ -1,4 +1,5 @@ import unittest + import meep as mp diff --git a/python/tests/test_verbosity_mgr.py b/python/tests/test_verbosity_mgr.py index 097e71718..da4fd5b92 100644 --- a/python/tests/test_verbosity_mgr.py +++ b/python/tests/test_verbosity_mgr.py @@ -1,4 +1,5 @@ import unittest + from meep.verbosity_mgr import Verbosity diff --git a/python/tests/test_visualization.py b/python/tests/test_visualization.py index 4a7a5c46b..627686b0e 100644 --- a/python/tests/test_visualization.py +++ b/python/tests/test_visualization.py @@ -3,21 +3,22 @@ # boundary conditions. Checks for subdomain plots. # # Also tests the animation run function, mp4 output, jshtml output, and git output. - +import os import unittest from subprocess import call -import meep as mp +import matplotlib import numpy as np -import os + +import meep as mp # Make sure we have matplotlib installed -import matplotlib matplotlib.use("agg") # Set backend for consistency and to pull pixels quickly -from matplotlib import pyplot as plt import io +from matplotlib import pyplot as plt + def hash_figure(fig): buf = io.BytesIO() diff --git a/python/tests/test_wvg_src.py b/python/tests/test_wvg_src.py index 608ae2515..9fc616f80 100644 --- a/python/tests/test_wvg_src.py +++ b/python/tests/test_wvg_src.py @@ -1,5 +1,5 @@ -import unittest import sys +import unittest import meep as mp diff --git a/python/tests/utils.py b/python/tests/utils.py index 771aff7b0..877e0b384 100644 --- a/python/tests/utils.py +++ b/python/tests/utils.py @@ -1,4 +1,5 @@ import unittest + import numpy as np diff --git a/python/timing_measurements.py b/python/timing_measurements.py index de19a2e67..d9aa0c60d 100644 --- a/python/timing_measurements.py +++ b/python/timing_measurements.py @@ -1,7 +1,9 @@ from typing import Dict, List, Optional -import meep as mp + import numpy as np +import meep as mp + # Codes for different Meep time sinks, used by `mp.Simulation.time_spent_on()`. # See # https://meep.readthedocs.io/en/latest/Python_User_Interface/#simulation-time diff --git a/python/verbosity_mgr.py b/python/verbosity_mgr.py index e7bb3e57e..755f8f314 100644 --- a/python/verbosity_mgr.py +++ b/python/verbosity_mgr.py @@ -1,7 +1,4 @@ -# -*- coding: utf-8 -*- - - -class Verbosity(object): +class Verbosity: """ A class to help make accessing and setting the global verbosity level a bit more pythonic. It manages one or more verbosity flags that are located in @@ -53,7 +50,7 @@ def __new__(cls, *args, **kw): # Create the real instance only the first time, and return the same each # time Verbosity is called thereafter. if cls._instance is None: - cls._instance = super(Verbosity, cls).__new__(cls) + cls._instance = super().__new__(cls) cls._instance._init() return cls._instance @@ -96,7 +93,7 @@ def __init__(self): # If a name is not given then manufacture one if name is None: - name = "cvar_{}".format(len(self._cvars)) + name = f"cvar_{len(self._cvars)}" self._cvars[name] = cvar # And create a property for so it can be accessed like `verbosity.mpb` diff --git a/python/visualization.py b/python/visualization.py index b7f7620de..4f4879e8e 100644 --- a/python/visualization.py +++ b/python/visualization.py @@ -1,13 +1,11 @@ -# -*- coding: utf-8 -*- -from collections import namedtuple import warnings +from collections import namedtuple import numpy as np - -import meep as mp from meep.geom import Vector3, init_do_averaging from meep.source import EigenModeSource, check_positive +import meep as mp # ------------------------------------------------------- # # Visualization @@ -847,9 +845,9 @@ def visualize_chunks(sim): if sim.structure is None: sim.init_sim() - import matplotlib.pyplot as plt import matplotlib.cm import matplotlib.colors + import matplotlib.pyplot as plt if sim.structure.gv.dim == 2: from mpl_toolkits.mplot3d import Axes3D @@ -965,7 +963,7 @@ def plot_box(box, proc, fig, ax): # to pass to plot2D() -class Animate2D(object): +class Animate2D: """ A class used to record the fields during timestepping (i.e., a [`run`](#run-functions) function). The object is initialized prior to timestepping by specifying the @@ -1177,6 +1175,7 @@ def to_jshtml(self, fps): # Only works with Python3 and matplotlib > 3.1.0 from distutils.version import LooseVersion + import matplotlib if LooseVersion(matplotlib.__version__) < LooseVersion("3.1.0"): @@ -1188,6 +1187,7 @@ def to_jshtml(self, fps): return if mp.am_master(): from uuid import uuid4 + from matplotlib._animation_data import ( DISPLAY_TEMPLATE, INCLUDED_FRAMES, @@ -1227,8 +1227,8 @@ def to_gif(self, fps, filename): # requires ffmpeg to be installed # modified from the matplotlib library if mp.am_master(): - from subprocess import Popen, PIPE - from io import TextIOWrapper, BytesIO + from io import BytesIO, TextIOWrapper + from subprocess import PIPE, Popen FFMPEG_BIN = "ffmpeg" command = [ @@ -1273,8 +1273,8 @@ def to_mp4(self, fps, filename): # requires ffmpeg to be installed # modified from the matplotlib library if mp.am_master(): - from subprocess import Popen, PIPE - from io import TextIOWrapper, BytesIO + from io import BytesIO, TextIOWrapper + from subprocess import PIPE, Popen FFMPEG_BIN = "ffmpeg" command = [ diff --git a/scheme/casimir.scm b/scheme/casimir.scm index 1f01d183a..5c802e3ca 100644 --- a/scheme/casimir.scm +++ b/scheme/casimir.scm @@ -6,15 +6,15 @@ ; return a list (source-vol mx my mz) ; given the volume integration-vol and n, pick out the appropriate side and mx my mz to use ; sides are ordered by decreasing weight -; weights are w(side, m) = area(side)/total area * 1/(m+1)^4, a rough estimate of the -; contribution to the stress tensor from that side and that m +; weights are w(side, m) = area(side)/total area * 1/(m+1)^4, a rough estimate of the +; contribution to the stress tensor from that side and that m (define (casimir-source-info integration-vol n) (define (modround x n) (modulo (inexact->exact (round x)) n)) - (define (get-src-index n) ;given n, extract out the two values of m for 3-d + (define (get-src-index n) ;given n, extract out the two values of m for 3-d (let* ((s 0) ;sum of diagonals (r 0) ;row intersection (c 0));column intersection - (while (< (+ s r) n) + (while (< (+ s r) n) (set! r (+ r 1)) (set! s (+ s r))) (set! c (- n s)) @@ -43,8 +43,8 @@ (list (vector3- center-vec xshift) (vector3+ center-vec xshift) (vector3- center-vec yshift) (vector3+ center-vec yshift) (vector3- center-vec zshift) (vector3+ center-vec zshift))) - (m-list - (list (vector3 0 m1 m2) (vector3 0 m1 m2) (vector3 m1 0 m2) + (m-list + (list (vector3 0 m1 m2) (vector3 0 m1 m2) (vector3 m1 0 m2) (vector3 m1 0 m2) (vector3 m1 m2 0) (vector3 m1 m2 0))) (orientation-list (list -1 1 -1 1 -1 1)) (size-list @@ -75,7 +75,7 @@ (list (vector3- new-center-vec z-shift) (vector3+ new-center-vec z-shift) (vector3+ new-center-vec r-shift) (vector3- new-center-vec r-shift))) (m-list - (list (vector3 m-dct m-phi 0) (vector3 m-dct m-phi 0) + (list (vector3 m-dct m-phi 0) (vector3 m-dct m-phi 0) (vector3 0 m-phi m-dct) (vector3 0 m-phi m-dct))) (orientation-list (list -1 1 1 -1)) (size-list @@ -91,7 +91,7 @@ (m (/ (- n s) 4)) (x-const-size (vector3 0 sy )) ;sz may be non-zero for quasi-3d systems (y-const-size (vector3 sx 0 )) - (center-list + (center-list (list (vector3- center-vec xshift) (vector3+ center-vec xshift) (vector3- center-vec yshift) (vector3+ center-vec yshift))) (m-list @@ -105,7 +105,7 @@ (print "Casimir.scm: working in 2 dimensions\n") (print " Surface center: "(list-ref center-list s)"\n") (print " Surface size: "(list-ref size-list s)"\n") - (list surface-vol + (list surface-vol (vector3-x surface-m) (vector3-y surface-m) (vector3-z surface-m) (list-ref orientation-list s) 1)))))) @@ -113,7 +113,7 @@ ;n contains both the side number and the harmonic expansion index (define (casimir-force-contrib force-direction integration-vol n Sigma T source-component gt . step-funcs) (define (cos-func X mx my mz source-vol) - (let* + (let* ((min-corner (meep-volume-get-min-corner source-vol)) (max-corner (meep-volume-get-max-corner source-vol)) (size-vec (vector3- max-corner min-corner)) @@ -147,11 +147,11 @@ (begin (set! global-B-conductivity Sigma) (set! global-D-conductivity 0))) - (if (eq? dimensions -2) + (if (eq? dimensions -2) (begin (print "Cylindricals: m = "my" and (nr nz) = ("mx", "mz")\n") (print " surface center = "(meep-volume-center source-vol)"\n") - (print " source size = "(vector3- - (meep-volume-get-max-corner source-vol) + (print " source size = "(vector3- + (meep-volume-get-max-corner source-vol) (meep-volume-get-min-corner source-vol))) (set! m my))) ;set exp(i m phi) field dependence (set! sources @@ -173,13 +173,13 @@ (define (integrate-function) (let* ((f-temp (meep-fields-casimir-stress-dct-integral fields - force-direction + force-direction (meep-component-direction source-component) mx (if (eq? dimensions -2) 0 my) mz ft source-vol))) (set! force-integral (+ force-integral - (imag-part (* (list-ref gt counter) dt + (imag-part (* (list-ref gt counter) dt source-orientation (if (eq? dimensions -2) (* (if (eq? my 0) 1 2) @@ -190,8 +190,8 @@ force-integral))) ;%%%%%%%%%%%%%%%%%%%%% BLOCH PBCS %%%%%%%%%%%%%%%%%%%%%% -;here the source is specified in -;the form exp(i k x), k = pi/L (m + k_red), +;here the source is specified in +;the form exp(i k x), k = pi/L (m + k_red), ;m = (mx,my,mz) are integers (reciprocal lattice vectors ;k_red = (kx,ky,kz) is in the 1st BZ, m an integer ;source-vol is assumed to occupy one entire plane intersecting diff --git a/scheme/examples/3rd-harm-1d.ctl b/scheme/examples/3rd-harm-1d.ctl index 36fb77290..261c6812d 100644 --- a/scheme/examples/3rd-harm-1d.ctl +++ b/scheme/examples/3rd-harm-1d.ctl @@ -39,13 +39,13 @@ (make flux-region (center 0 0 (- (* 0.5 sz) dpml 0.5))))) ; also compute a ''single'' flux point at fcen and 3*fcen -(define trans1 (add-flux fcen 0 1 (make flux-region +(define trans1 (add-flux fcen 0 1 (make flux-region (center 0 0 (- (* 0.5 sz) dpml 0.5))))) -(define trans3 (add-flux (* 3 fcen) 0 1 (make flux-region +(define trans3 (add-flux (* 3 fcen) 0 1 (make flux-region (center 0 0 (- (* 0.5 sz) dpml 0.5))))) (run-sources+ - (stop-when-fields-decayed 50 Ex + (stop-when-fields-decayed 50 Ex (vector3 0 0 (- (* 0.5 sz) dpml 0.5)) 1e-6)) diff --git a/scheme/examples/bend-flux.ctl b/scheme/examples/bend-flux.ctl index 9fed645d6..35fda3e21 100644 --- a/scheme/examples/bend-flux.ctl +++ b/scheme/examples/bend-flux.ctl @@ -16,16 +16,16 @@ (set! geometry (if no-bend? (list - (make block + (make block (center 0 wvg-ycen) (size infinity w infinity) (material (make dielectric (epsilon 12))))) (list - (make block + (make block (center (* -0.5 pad) wvg-ycen) (size (- sx pad) w infinity) (material (make dielectric (epsilon 12)))) - (make block + (make block (center wvg-xcen (* 0.5 pad)) (size w (- sy pad) infinity) (material (make dielectric (epsilon 12))))))) @@ -33,7 +33,7 @@ (define-param fcen 0.15) ; pulse center frequency (define-param df 0.1) ; pulse width (in frequency) (set! sources (list - (make source + (make source (src (make gaussian-src (frequency fcen) (fwidth df))) (component Ez) (center (+ 1 (* -0.5 sx)) wvg-ycen) @@ -52,15 +52,15 @@ (center wvg-xcen (- (/ sy 2) 1.5)) (size (* w 2) 0))))) (define refl ; reflected flux (add-flux fcen df nfreq - (make flux-region + (make flux-region (center (+ (* -0.5 sx) 1.5) wvg-ycen) (size 0 (* w 2))))) ; for normal run, load negated fields to subtract incident from refl. fields (if (not no-bend?) (load-minus-flux "refl-flux" refl)) -(run-sources+ +(run-sources+ (stop-when-fields-decayed 50 Ez - (if no-bend? + (if no-bend? (vector3 (- (/ sx 2) 1.5) wvg-ycen) (vector3 wvg-xcen (- (/ sy 2) 1.5))) 1e-3) diff --git a/scheme/examples/holey-wvg-bands.ctl b/scheme/examples/holey-wvg-bands.ctl index 12a463dfc..8b9a82bd9 100644 --- a/scheme/examples/holey-wvg-bands.ctl +++ b/scheme/examples/holey-wvg-bands.ctl @@ -26,7 +26,7 @@ (set! pml-layers (list (make pml (direction Y) (thickness dpml)))) (set-param! resolution 20) -(define-param fcen 0.25) ; pulse center frequency +(define-param fcen 0.25) ; pulse center frequency (define-param df 1.5) ; pulse freq. width: large df = short impulse (set! sources (list @@ -42,7 +42,7 @@ (if kx (begin (set! k-point (vector3 kx)) - (run-sources+ + (run-sources+ 300 (at-beginning output-epsilon) (after-sources (harminv Hz (vector3 0.1234 0) fcen df))) (run-until (/ 1 fcen) (at-every (/ 1 fcen 20) output-hfield-z))) diff --git a/scheme/examples/holey-wvg-cavity.ctl b/scheme/examples/holey-wvg-cavity.ctl index fe0b09f33..3abc97728 100644 --- a/scheme/examples/holey-wvg-cavity.ctl +++ b/scheme/examples/holey-wvg-cavity.ctl @@ -36,8 +36,8 @@ (set! pml-layers (list (make pml (thickness dpml)))) (set-param! resolution 20) -(define-param fcen 0.25) ; pulse center frequency -(define-param df 0.2) ; pulse width (in frequency) +(define-param fcen 0.25) ; pulse center frequency +(define-param df 0.2) ; pulse width (in frequency) (define-param nfreq 500) ; number of frequencies at which to compute flux @@ -58,7 +58,7 @@ (run-sources+ 400 (at-beginning output-epsilon) (after-sources (harminv Hz (vector3 0) fcen df))) - (run-until (/ 1 fcen) (at-every (/ 1 fcen 20) output-hfield-z)) + (run-until (/ 1 fcen) (at-every (/ 1 fcen 20) output-hfield-z)) ) (begin (set! sources (list @@ -69,12 +69,12 @@ (size 0 w)))) (set! symmetries (list (make mirror-sym (direction Y) (phase -1)))) - + (let ((trans ; transmitted flux (add-flux fcen df nfreq (make flux-region (center (- (* 0.5 sx) dpml 0.5) 0) (size 0 (* w 2)))))) - + (run-sources+ (stop-when-fields-decayed 50 Ey (vector3 (- (* 0.5 sx) dpml 0.5) 0) @@ -83,6 +83,6 @@ (during-sources (in-volume (volume (center 0 0) (size sx 0)) (to-appended "hz-slice" (at-every 0.4 output-hfield-z))))) - + (display-fluxes trans) ; print out the flux spectrum ))) diff --git a/scheme/examples/material-dispersion.ctl b/scheme/examples/material-dispersion.ctl index 42d7fa38f..325662c63 100644 --- a/scheme/examples/material-dispersion.ctl +++ b/scheme/examples/material-dispersion.ctl @@ -14,7 +14,7 @@ (set! default-material (make dielectric (epsilon 2.25) - (polarizations + (polarizations (make polarizability (omega 1.1) (gamma 1e-5) (sigma 0.5)) (make polarizability @@ -35,8 +35,8 @@ (define all-freqs (run-k-points 200 kpts)) ; a list of lists of frequencies (map (lambda (kx fs) - (map (lambda (f) - (print "eps:, " (real-part f) ", " (imag-part f) + (map (lambda (f) + (print "eps:, " (real-part f) ", " (imag-part f) ", " (sqr (/ kx f)) "\n")) fs)) (map vector3-x kpts) all-freqs) diff --git a/scheme/examples/ring-cyl.ctl b/scheme/examples/ring-cyl.ctl index 336448128..658520497 100644 --- a/scheme/examples/ring-cyl.ctl +++ b/scheme/examples/ring-cyl.ctl @@ -27,13 +27,13 @@ ; put a single point source at some arbitrary place, pointing in some ; arbitrary direction. We will only look for Ez-polarized modes. -(define-param fcen 0.15) ; pulse center frequency -(define-param df 0.1) ; pulse width (in frequency) +(define-param fcen 0.15) ; pulse center frequency +(define-param df 0.1) ; pulse width (in frequency) (set! sources (list (make source (src (make gaussian-src (frequency fcen) (fwidth df))) (component Ez) (center (+ r 0.1) 0)))) - + ; note that the r -> -r mirror symmetry is exploited automatically @@ -44,8 +44,8 @@ ; almost zero and get a distorted view.) We'll append the fields ; to a file to get an r-by-t picture. We'll also output from -sr to -sr ; instead of from 0 to sr. -(run-until (/ 1 fcen) +(run-until (/ 1 fcen) (in-volume (volume (center 0) (size (* 2 sr))) (at-beginning output-epsilon) - (to-appended "ez" + (to-appended "ez" (at-every (/ 1 fcen 20) output-efield-z)))) diff --git a/scheme/materials.scm b/scheme/materials.scm index 7436801d9..9a79485fd 100644 --- a/scheme/materials.scm +++ b/scheme/materials.scm @@ -24,7 +24,7 @@ (define cSi-sig3 -0.107) (define cSi (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency cSi-frq1) (gamma cSi-gam1) (sigma cSi-sig1)) (make lorentzian-susceptibility @@ -42,7 +42,7 @@ (define aSi-sig1 14.571) (define aSi (make medium (epsilon 3.109) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency aSi-frq1) (gamma aSi-gam1) (sigma aSi-sig1))))) @@ -56,7 +56,7 @@ (define aSi-H-sig1 12.31) (define aSi-H (make medium (epsilon 3.22) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency aSi-H-frq1) (gamma aSi-H-gam1) (sigma aSi-H-sig1))))) @@ -70,7 +70,7 @@ (define ITO-sig1 2.5) (define ITO (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency ITO-frq1) (gamma ITO-gam1) (sigma ITO-sig1))))) @@ -84,7 +84,7 @@ (define Al2O3-sig1 1.52) (define Al2O3 (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Al2O3-frq1) (gamma Al2O3-gam1) (sigma Al2O3-sig1))))) @@ -98,7 +98,7 @@ (define AlN-sig1 3.306) (define AlN (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency AlN-frq1) (gamma AlN-gam1) (sigma AlN-sig1))))) @@ -116,7 +116,7 @@ (define AlAs-sig2 1.900) (define AlAs (make medium (epsilon 2.0792) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency AlAs-frq1) (gamma AlAs-gam1) (sigma AlAs-sig1)) (make lorentzian-susceptibility @@ -138,7 +138,7 @@ (define BK7-sig3 1.01046945) (define BK7 (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency BK7-frq1) (gamma BK7-gam1) (sigma BK7-sig1)) (make lorentzian-susceptibility @@ -162,7 +162,7 @@ (define fused-quartz-sig3 0.897479400) (define fused-quartz (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency fused-quartz-frq1) (gamma fused-quartz-gam1) (sigma fused-quartz-sig1)) (make lorentzian-susceptibility @@ -186,7 +186,7 @@ (define GaAs-sig3 1.957522) (define GaAs (make medium (epsilon 5.372514) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency GaAs-frq1) (gamma GaAs-gam1) (sigma GaAs-sig1)) (make lorentzian-susceptibility @@ -204,7 +204,7 @@ (define Si3N4-VISNIR-sig1 2.8939) (define Si3N4-VISNIR (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Si3N4-VISNIR-frq1) (gamma Si3N4-VISNIR-gam1) (sigma Si3N4-VISNIR-sig1))))) @@ -221,7 +221,7 @@ (define Si3N4-NIR-sig2 40314) (define Si3N4-NIR (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Si3N4-NIR-frq1) (gamma Si3N4-NIR-gam1) (sigma Si3N4-NIR-sig1)) (make lorentzian-susceptibility @@ -758,7 +758,7 @@ (make lorentzian-susceptibility (frequency Au-visible-frq1) (gamma Au-visible-gam1) (sigma Au-visible-sig1))))) -;------------------------------------------------------------------ +;------------------------------------------------------------------ ;; UNSTABLE: field divergence may occur ; silver (Au) @@ -936,7 +936,7 @@ (define SiN-sig1 1.2650) (define SiN (make medium (epsilon 2.320) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency SiN-frq1) (gamma SiN-gam1) (sigma SiN-sig1))))) @@ -950,7 +950,7 @@ (define Si3N4-sig1 4.377) (define Si3N4 (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Si3N4-frq1) (gamma Si3N4-gam1) (sigma Si3N4-sig1))))) @@ -964,7 +964,7 @@ (define SiO2-sig1 1.12) (define SiO2 (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency SiO2-frq1) (gamma SiO2-gam1) (sigma SiO2-sig1))))) @@ -982,7 +982,7 @@ (define InP-sig2 2.765) (define InP (make medium (epsilon 7.255) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency InP-frq1) (gamma InP-gam1) (sigma InP-sig1)) (make lorentzian-susceptibility @@ -1002,7 +1002,7 @@ (define Ge-sig2 0.21307) (define Ge (make medium (epsilon 9.28156) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Ge-frq1) (gamma Ge-gam1) (sigma Ge-sig1)) (make lorentzian-susceptibility @@ -1026,7 +1026,7 @@ (define Si-sig3 1.54133408) (define Si (make medium (epsilon 9.28156) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Si-frq1) (gamma Si-gam1) (sigma Si-sig1)) (make lorentzian-susceptibility @@ -1044,7 +1044,7 @@ (define PMMA-sig1 1.1819) (define PMMA (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency PMMA-frq1) (gamma PMMA-gam1) (sigma PMMA-sig1))))) @@ -1058,7 +1058,7 @@ (define PC-sig1 1.4182) (define PC (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency PC-frq1) (gamma PC-gam1) (sigma PC-sig1))))) @@ -1072,7 +1072,7 @@ (define PS-sig1 1.4435) (define PS (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency PS-frq1) (gamma PS-gam1) (sigma PS-sig1))))) @@ -1086,7 +1086,7 @@ (define CLS-sig1 1.124) (define CLS (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency CLS-frq1) (gamma CLS-gam1) (sigma CLS-sig1))))) diff --git a/scheme/meep_op_renames.i b/scheme/meep_op_renames.i index 6000c9dee..d644d11c9 100644 --- a/scheme/meep_op_renames.i +++ b/scheme/meep_op_renames.i @@ -12,4 +12,3 @@ %rename(meep_dft_chunk_subeq) meep::dft_chunk::operator-=; %rename(meep_dft_flux_subeq) meep::dft_flux::operator-=; - diff --git a/src/dft.cpp b/src/dft.cpp index d7780e090..a12953dd2 100644 --- a/src/dft.cpp +++ b/src/dft.cpp @@ -334,9 +334,9 @@ double dft_chunk::norm2(grid_volume fgv) const { grid_volume subgv = fgv.subvolume(is,ie,c); LOOP_OVER_IVECS(subgv, is_old, ie_old, idx) { for (size_t i = 0; i < Nomega; ++i) - sum += sqr(dft[Nomega * idx + i]); - } - } + sum += sqr(dft[Nomega * idx + i]); + } + } /* note we place the if outside of the loop to avoid branching. This routine gets called a lot, so let's try to stay efficient @@ -346,7 +346,7 @@ double dft_chunk::norm2(grid_volume fgv) const { LOOP_OVER_IVECS(fgv, is, ie, idx) { idx_dft = IVEC_LOOP_COUNTER; for (size_t i = 0; i < Nomega; ++i) - sum += sqr(dft[Nomega * idx_dft + i]); + sum += sqr(dft[Nomega * idx_dft + i]); } } @@ -1421,7 +1421,7 @@ void fields::get_mode_mode_overlap(void *mode1_data, void *mode2_data, dft_flux /* deregister all of the remaining dft monitors from the fields object. Note that this does not -delete the underlying dft_chunks! (useful for +delete the underlying dft_chunks! (useful for adjoint calculations, where we want to keep the chunk data around) */ void fields::clear_dft_monitors() { diff --git a/src/fix_boundary_sources.cpp b/src/fix_boundary_sources.cpp index f31ad4b28..39579fe55 100644 --- a/src/fix_boundary_sources.cpp +++ b/src/fix_boundary_sources.cpp @@ -174,4 +174,4 @@ for (int psrc = 0; psrc < P; ++psrc) finished_working(); } -} // namespace meep \ No newline at end of file +} // namespace meep diff --git a/src/loop_in_chunks.cpp b/src/loop_in_chunks.cpp index 4c86bd939..c0880888e 100644 --- a/src/loop_in_chunks.cpp +++ b/src/loop_in_chunks.cpp @@ -418,10 +418,10 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con ivec _iscoS(S.transform(gvu.little_owned_corner(cS), sn)); ivec _iecoS(S.transform(gvu.big_owned_corner(cS), sn)); ivec iscoS(max(user_volume.little_owned_corner(cgrid), min(_iscoS, _iecoS))), iecoS(max(_iscoS, _iecoS)); // fix ordering due to to transform - - //With symmetry, the upper half of the original chunk is kept and includes one extra pixel. - //When looped over all symmetries, pixels outside the lower boundary "user_volume.little_owned_corner(cgrid)" is excluded. - //isym finds the lower boundary of the upper half chunk. Then in each direction that chunk isn't the upper, + + //With symmetry, the upper half of the original chunk is kept and includes one extra pixel. + //When looped over all symmetries, pixels outside the lower boundary "user_volume.little_owned_corner(cgrid)" is excluded. + //isym finds the lower boundary of the upper half chunk. Then in each direction that chunk isn't the upper, //checked by ((S.transform(d, sn).d != d) != (S.transform(d, sn).flipped)), //the end point iecoS will shift to not go beyond isym ivec isym(gvu.dim, INT_MAX); @@ -433,10 +433,10 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con ivec iscS(max(is - shifti, iscoS)); ivec chunk_corner(gvu.little_owned_corner(cgrid)); LOOP_OVER_DIRECTIONS(gv.dim, d) { - if ((S.transform(d, sn).d != d) != (S.transform(d, sn).flipped)) iecoS.set_direction(d, min(isym, iecoS).in_direction(d)); - } + if ((S.transform(d, sn).d != d) != (S.transform(d, sn).flipped)) iecoS.set_direction(d, min(isym, iecoS).in_direction(d)); + } ivec iecS( min(ie - shifti, iecoS)); - + if (iscS <= iecS) { // non-empty intersection // Determine weights at chunk looping boundaries: ivec isc(S.transform(iscS, -sn)), iec(S.transform(iecS, -sn)); diff --git a/src/meep.hpp b/src/meep.hpp index 0dc5b5227..95025ed98 100644 --- a/src/meep.hpp +++ b/src/meep.hpp @@ -1417,7 +1417,7 @@ class dft_ldos { std::complex *J() const; // returns Jdft std::vector freq; double overall_scale() const { return saved_overall_scale; } - + private: std::complex *Fdft; // Nomega array of field * J*(x) DFT values std::complex *Jdft; // Nomega array of J(t) DFT values diff --git a/src/meepgeom.cpp b/src/meepgeom.cpp index 14ef07802..530e3a9f1 100644 --- a/src/meepgeom.cpp +++ b/src/meepgeom.cpp @@ -2909,12 +2909,12 @@ void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vectorc; meep::grid_volume gv_fwd = gv.subvolume(fwd_chunk->is,fwd_chunk->ie,forward_c); - + // loop over each point of interest LOOP_OVER_IVECS(gv_adj,adj_chunk->is_old,adj_chunk->ie_old,idx_adj){ - double cyl_scale; + double cyl_scale; IVEC_LOOP_ILOC(gv_adj, ip); IVEC_LOOP_LOC(gv_adj, p); std::complex adj = adj_chunk->dft[nf*idx_adj+f_i]; @@ -2941,11 +2941,11 @@ void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vectortrivial) adj *= cond_cmp(adjoint_c,p,frequencies[f_i], geps); - + /**************************************/ /* Main Routine */ /**************************************/ - + /********* compute -λᵀAᵤx *************/ /* trivial case, no interpolation/restriction needed */ @@ -2991,7 +2991,7 @@ void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector= fwd_chunk->N) || (fwd1_idx<0)) ? 0 : fwd_chunk->dft[nf*fwd1_idx+f_i]; fwd2_idx = gv_fwd.index(forward_c,fwd_pr[node]); - fwd2 = ((fwd2_idx >= fwd_chunk->N) || (fwd2_idx<0)) ? 0 : fwd_chunk->dft[nf*fwd2_idx+f_i]; + fwd2 = ((fwd2_idx >= fwd_chunk->N) || (fwd2_idx<0)) ? 0 : fwd_chunk->dft[nf*fwd2_idx+f_i]; fwd_avg = std::complex(0.5,0) * (fwd1 + fwd2); meep::vec eps1 = gv[ieps[node]]; cyl_scale = (gv.dim == meep::Dcyl) ? eps1.r() : 1; @@ -3019,7 +3019,7 @@ void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector> $GITHUB_PATH - - name: Installing the library - shell: bash -l {0} - run: | - pip install -e . - pip install -r requirements_dev.txt - sudo wget https://github.com/jgm/pandoc/releases/download/1.16.0.2/pandoc-1.16.0.2-1-amd64.deb - sudo dpkg -i pandoc-1.16.0.2-1-amd64.deb - #sudo apt install pandoc - - name: Running the Sphinx to gh-pages Action - uses: uibcdf/action-sphinx-docs-to-gh-pages@v1.0-beta.2 - with: - branch: master - dir_docs: docs - sphinxopts: "" diff --git a/.github/workflows/pythonpublish.yml b/.github/workflows/pythonpublish.yml deleted file mode 100644 index c96bda539..000000000 --- a/.github/workflows/pythonpublish.yml +++ /dev/null @@ -1,30 +0,0 @@ ---- -name: Upload Python Package - -on: - release: - types: [created, published] - push: - branches: [master] - tags: [v*] - -jobs: - deploy: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: 3.x - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install setuptools wheel twine - - name: Build and publish - env: - TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} - TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} - run: | - python setup.py sdist bdist_wheel - twine upload dist/* diff --git a/.github/workflows/test_code.yml b/.github/workflows/test_code.yml deleted file mode 100644 index 2a589bacb..000000000 --- a/.github/workflows/test_code.yml +++ /dev/null @@ -1,35 +0,0 @@ -name: Test code and lint - -on: - pull_request: - push: - schedule: - - cron: 0 2 * * * # run at 2 AM UTC - -jobs: - build: - runs-on: ${{ matrix.os }} - strategy: - max-parallel: 12 - matrix: - python-version: [3.8, 3.9] - os: [ubuntu-latest] - - steps: - - name: Cancel Workflow Action - uses: styfle/cancel-workflow-action@0.10.0 - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - cache: 'pip' - - name: Install dependencies - run: | - pip install -e . - pip install flake8 pytest - - name: Lint with flake8 - run: | - flake8 . - - name: Test with pytest - run: pytest diff --git a/.github/workflows/test_code_updated_requirements.yml b/.github/workflows/test_code_updated_requirements.yml deleted file mode 100644 index 4afe7afc8..000000000 --- a/.github/workflows/test_code_updated_requirements.yml +++ /dev/null @@ -1,35 +0,0 @@ ---- -name: test with updated requirements (bleeding edge versions) -# https://docs.github.com/en/free-pro-team@latest/actions/guides/building-and-testing-python - -on: - pull_request: - push: - schedule: - - cron: 0 2 * * * # run at 2 AM UTC - -jobs: - build: - runs-on: ${{ matrix.os }} - strategy: - max-parallel: 12 - matrix: - python-version: [3.9] - os: [ubuntu-latest] - - steps: - - name: Cancel Workflow Action - uses: styfle/cancel-workflow-action@0.9.1 - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install pur pytest - pur -r requirements.txt - pip install . - - name: Test with pytest - run: pytest diff --git a/.github/workflows/test_docs.yml b/.github/workflows/test_docs.yml deleted file mode 100644 index a1e85f3b4..000000000 --- a/.github/workflows/test_docs.yml +++ /dev/null @@ -1,30 +0,0 @@ -name: Test documentation - -on: - pull_request: - push: - schedule: - - cron: "0 2 * * *" # run at 2 AM UTC - - - -jobs: - build-linux: - runs-on: ubuntu-latest - - steps: - - name: Cancel Workflow Action - uses: styfle/cancel-workflow-action@0.10.0 - - uses: actions/checkout@v3 - - name: Set up Python 3.9 - uses: actions/setup-python@v4 - with: - python-version: 3.9 - - name: Install dependencies - run: | - pip install -r requirements_dev.txt - pip install . - sudo apt install pandoc - - name: Test documentation - run: | - cd docs - make html diff --git a/.gitignore b/.gitignore index a525caf09..b8aac4c1c 100644 --- a/.gitignore +++ b/.gitignore @@ -18,6 +18,7 @@ _build python/examples/.ipynb_checkpoints/ h5utils/ hdf5/ +extra/ # autotools stuff Makefile From 6470d14768550823362305dd5eba744872d20b41 Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 17:12:57 +0200 Subject: [PATCH 05/12] remove shellcheck --- .pre-commit-config.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6e666bc53..052e0e1db 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -46,10 +46,10 @@ repos: # - id: codespell # args: ["-L TE,TE/TM,te,ba,FPR,fpr_spacing,ro"] - - repo: https://github.com/shellcheck-py/shellcheck-py - rev: f87a493c6596a5338d69395905a4e13ed65584f6 - hooks: - - id: shellcheck + # - repo: https://github.com/shellcheck-py/shellcheck-py + # rev: f87a493c6596a5338d69395905a4e13ed65584f6 + # hooks: + # - id: shellcheck - repo: https://github.com/pre-commit/pygrep-hooks rev: 8e68d79b05355c9e107f81fc267f2bfc3e9e5a04 # Use the ref you want to point at From 314d390dbd5f46deafe3894a7977e9b987a30ea5 Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 17:28:59 +0200 Subject: [PATCH 06/12] remove nbstrip --- .pre-commit-config.yaml | 10 +- python/examples/3rd-harm-1d.ipynb | 754 +++- python/examples/absorbed_power_density.ipynb | 342 +- .../01-Introduction.ipynb | 305 +- .../02-Waveguide_Bend.ipynb | 336 +- .../03-Filtered_Waveguide_Bend.ipynb | 2026 +++++++++- .../adjoint_optimization/04-Splitter.ipynb | 2005 ++++++++-- .../adjoint_optimization/05-Near2Far.ipynb | 1892 +++++++++- .../06-Near2Far-Epigraph.ipynb | 1929 +++++++++- .../adjoint_optimization/Bend Minimax.ipynb | 2006 ++++++++-- .../ConnectivityConstraint.ipynb | 168 +- .../adjoint_optimization/Fourier-Bend.ipynb | 2381 +++++++++++- python/examples/antenna-radiation.ipynb | 205 +- python/examples/bend-flux.ipynb | 225 +- python/examples/bent-waveguide.ipynb | 245 +- python/examples/binary_grating.ipynb | 498 ++- python/examples/binary_grating_n2f.ipynb | 463 ++- python/examples/binary_grating_oblique.ipynb | 362 +- python/examples/binary_grating_phasemap.ipynb | 959 ++++- python/examples/cavity-farfield.ipynb | 235 +- python/examples/coupler.ipynb | 1899 +++++++++- python/examples/cylinder_cross_section.ipynb | 237 +- .../examples/differential_cross_section.ipynb | 474 ++- python/examples/finite_grating.ipynb | 294 +- python/examples/holey-wvg-bands.ipynb | 402 +- python/examples/holey-wvg-cavity.ipynb | 990 ++++- python/examples/metal-cavity-ldos.ipynb | 69 +- python/examples/metasurface_lens.ipynb | 1658 +++++++-- python/examples/mie_scattering.ipynb | 196 +- python/examples/mode-decomposition.ipynb | 552 ++- python/examples/oblique-planewave.ipynb | 186 +- python/examples/oblique-source.ipynb | 402 +- python/examples/parallel-wvgs-force.ipynb | 3228 +++++++++++++++- python/examples/perturbation_theory.ipynb | 344 +- python/examples/polarization_grating.ipynb | 3253 ++++++++++++++++- python/examples/refl-angular.ipynb | 1575 +++++++- python/examples/refl-quartz.ipynb | 142 +- python/examples/ring.ipynb | 248 +- python/examples/solve-cw.ipynb | 252 +- python/examples/stochastic_emitter.ipynb | 171 +- python/examples/straight-waveguide.ipynb | 187 +- python/examples/zone_plate.ipynb | 176 +- 42 files changed, 29462 insertions(+), 4819 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 052e0e1db..334af9508 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -28,11 +28,11 @@ repos: # hooks: # - id: flake8 - - repo: https://github.com/kynan/nbstripout - rev: 8cafdcc393232045208137698dbeb42d6e0dd9e8 - hooks: - - id: nbstripout - files: ".ipynb" + # - repo: https://github.com/kynan/nbstripout + # rev: 8cafdcc393232045208137698dbeb42d6e0dd9e8 + # hooks: + # - id: nbstripout + # files: ".ipynb" - repo: https://github.com/asottile/pyupgrade rev: 85f02f45f0d2889f3826f16b60f27188ea84c1ae diff --git a/python/examples/3rd-harm-1d.ipynb b/python/examples/3rd-harm-1d.ipynb index c4c4df04c..715a97e31 100644 --- a/python/examples/3rd-harm-1d.ipynb +++ b/python/examples/3rd-harm-1d.ipynb @@ -20,14 +20,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", - "\n", "%matplotlib notebook" ] }, @@ -40,16 +47,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "sz = 100 # size of cell in z direction\n", - "fcen = 1 / 3.0 # center frequency of source\n", - "df = fcen / 20.0 # frequency width of source\n", - "amp = 1 # amplitude of source\n", - "k = 10**-5 # Kerr susceptibility\n", - "dpml = 1.0 # PML thickness" + "sz = 100 # size of cell in z direction\n", + "fcen = 1 / 3.0 # center frequency of source\n", + "df = fcen / 20.0 # frequency width of source\n", + "amp = 1 # amplitude of source\n", + "k = 10**-5 # Kerr susceptibility\n", + "dpml = 1.0 # PML thickness" ] }, { @@ -61,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -98,16 +105,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "sources = mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ex,\n", - " center=mp.Vector3(0, 0, -0.5 * sz + dpml),\n", - " amplitude=amp,\n", - ")" + "sources = mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ex,\n", + " center=mp.Vector3(0, 0, -0.5*sz + dpml), amplitude=amp)" ] }, { @@ -119,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -127,22 +130,16 @@ "fmin = fcen / 2.0\n", "fmax = fcen * 4\n", "\n", - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=[],\n", - " sources=[sources],\n", - " boundary_layers=[pml_layers],\n", - " default_material=default_material,\n", - " resolution=resolution,\n", - " dimensions=dimensions,\n", - ")\n", + "sim = mp.Simulation(cell_size=cell,\n", + " geometry=[],\n", + " sources=[sources],\n", + " boundary_layers=[pml_layers],\n", + " default_material=default_material,\n", + " resolution=resolution,\n", + " dimensions=dimensions)\n", "\n", - "trans = sim.add_flux(\n", - " 0.5 * (fmin + fmax),\n", - " fmax - fmin,\n", - " nfreq,\n", - " mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5)),\n", - ")" + "trans = sim.add_flux(0.5 * (fmin + fmax), fmax - fmin, nfreq,\n", + " mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))" ] }, { @@ -154,15 +151,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.1691008357827005e-12 / 4.1691008357827005e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420168812386e-08 / 1.0165420168812386e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650785262405232e-06 / 4.650785262405232e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454866366283557 / 0.0005454866366283557 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017815669509667658 / 0.017815669509667658 = 1.0\n", + "field decay(t = 350.175): 0.13192124155368243 / 0.13192124155368243 = 1.0\n", + "field decay(t = 400.20000000000005): 0.2505510561658855 / 0.2505510561658855 = 1.0\n", + "field decay(t = 450.225): 0.2499503102652878 / 0.2505510561658855 = 0.9976023014638582\n", + "field decay(t = 500.25): 0.11167289094888443 / 0.2505510561658855 = 0.4457091207587955\n", + "field decay(t = 550.275): 0.012841517437730344 / 0.2505510561658855 = 0.05125309641172774\n", + "field decay(t = 600.3000000000001): 0.0003786349795018242 / 0.2505510561658855 = 0.0015112088741351644\n", + "field decay(t = 650.325): 2.4161065872412755e-06 / 0.2505510561658855 = 9.643170634417973e-06\n", + "field decay(t = 700.35): 4.490921803004586e-09 / 0.2505510561658855 = 1.7924178296144254e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n" + ] + } + ], "source": [ - "sim.run(\n", - " until_after_sources=mp.stop_when_fields_decayed(\n", - " 50, mp.Ex, mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5), 1e-6\n", - " )\n", - ")" + "sim.run(until_after_sources=mp.stop_when_fields_decayed(\n", + " 50, mp.Ex, mp.Vector3(0, 0, 0.5*sz - dpml - 0.5), 1e-6))" ] }, { @@ -174,18 +191,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "freqs = mp.get_flux_freqs(trans)\n", "spectra = mp.get_fluxes(trans)\n", "\n", "plt.figure(dpi=150)\n", - "plt.semilogy(freqs, spectra)\n", + "plt.semilogy(freqs,spectra)\n", "plt.grid(True)\n", - "plt.xlabel(\"Frequency\")\n", - "plt.ylabel(\"Transmitted Power (a.u.)\")\n", + "plt.xlabel('Frequency')\n", + "plt.ylabel('Transmitted Power (a.u.)')\n", "plt.show()" ] }, @@ -200,59 +230,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "def run_chi3(k_pow, amp=1):\n", - " k = 10**k_pow\n", + "def run_chi3(k_pow,amp=1):\n", + " k = 10**k_pow \n", " default_material = mp.Medium(index=1, chi3=k)\n", + " \n", + " sources = mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ex,\n", + " center=mp.Vector3(0, 0, -0.5*sz + dpml), amplitude=amp)\n", + " \n", + " sim = mp.Simulation(cell_size=cell,\n", + " geometry=[],\n", + " sources=[sources],\n", + " boundary_layers=[pml_layers],\n", + " default_material=default_material,\n", + " resolution=resolution,\n", + " dimensions=dimensions)\n", "\n", - " sources = mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ex,\n", - " center=mp.Vector3(0, 0, -0.5 * sz + dpml),\n", - " amplitude=amp,\n", - " )\n", - "\n", - " sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=[],\n", - " sources=[sources],\n", - " boundary_layers=[pml_layers],\n", - " default_material=default_material,\n", - " resolution=resolution,\n", - " dimensions=dimensions,\n", - " )\n", - "\n", - " trans = sim.add_flux(\n", - " 0.5 * (fmin + fmax),\n", - " fmax - fmin,\n", - " nfreq,\n", - " mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5)),\n", - " )\n", - "\n", + " trans = sim.add_flux(0.5 * (fmin + fmax), fmax - fmin, nfreq,\n", + " mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))\n", + " \n", " # Single frequency point at omega\n", - " trans1 = sim.add_flux(\n", - " fcen, 0, 1, mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5))\n", - " )\n", + " trans1 = sim.add_flux(fcen, 0, 1,\n", + " mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))\n", "\n", " # Singel frequency point at 3omega\n", - " trans3 = sim.add_flux(\n", - " 3 * fcen, 0, 1, mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5))\n", - " )\n", - "\n", - " sim.run(\n", - " until_after_sources=mp.stop_when_fields_decayed(\n", - " 50, mp.Ex, mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5), 1e-6\n", - " )\n", - " )\n", - "\n", + " trans3 = sim.add_flux(3 * fcen, 0, 1,\n", + " mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))\n", + " \n", + " sim.run(until_after_sources=mp.stop_when_fields_decayed(\n", + " 50, mp.Ex, mp.Vector3(0, 0, 0.5*sz - dpml - 0.5), 1e-6))\n", + " \n", " omega_flux = mp.get_fluxes(trans1)\n", " omega3_flux = mp.get_fluxes(trans3)\n", " freqs = mp.get_flux_freqs(trans)\n", " spectra = mp.get_fluxes(trans)\n", - "\n", + " \n", " return freqs, spectra, omega_flux, omega3_flux" ] }, @@ -265,30 +280,114 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.1691008357817126e-12 / 4.1691008357817126e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420158581846e-08 / 1.0165420158581846e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650783221205379e-06 / 4.650783221205379e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454598673598967 / 0.0005454598673598967 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017781404956060017 / 0.017781404956060017 = 1.0\n", + "field decay(t = 350.175): 0.13014055244440104 / 0.13014055244440104 = 1.0\n", + "field decay(t = 400.20000000000005): 0.2443206506136912 / 0.2443206506136912 = 1.0\n", + "field decay(t = 450.225): 0.2437445632391615 / 0.2443206506136912 = 0.9976420848050188\n", + "field decay(t = 500.25): 0.11044620451678894 / 0.2443206506136912 = 0.4520543156682302\n", + "field decay(t = 550.275): 0.012822890989720101 / 0.2443206506136912 = 0.05248386068681144\n", + "field decay(t = 600.3000000000001): 0.00037862010232358545 / 0.2443206506136912 = 0.0015496852246118257\n", + "field decay(t = 650.325): 2.41614906850782e-06 / 0.2443206506136912 = 9.8892543976077e-06\n", + "field decay(t = 700.35): 4.492194624953141e-09 / 0.2443206506136912 = 1.838647127727241e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835772216e-12 / 4.169100835772216e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420065576282e-08 / 1.0165420065576282e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650764664560235e-06 / 4.650764664560235e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005452160206480376 / 0.0005452160206480376 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017507545378757882 / 0.017507545378757882 = 1.0\n", + "field decay(t = 350.175): 0.11744036969268859 / 0.11744036969268859 = 1.0\n", + "field decay(t = 400.20000000000005): 0.20769602060422127 / 0.20769602060422127 = 1.0\n", + "field decay(t = 450.225): 0.2072148912173212 / 0.20769602060422127 = 0.9976834925122764\n", + "field decay(t = 500.25): 0.10101160503184105 / 0.20769602060422127 = 0.48634347802130234\n", + "field decay(t = 550.275): 0.012670991144119082 / 0.20769602060422127 = 0.06100738525108532\n", + "field decay(t = 600.3000000000001): 0.00037847776427704205 / 0.20769602060422127 = 0.001822267769868624\n", + "field decay(t = 650.325): 2.416057119618937e-06 / 0.20769602060422127 = 1.1632659656117805e-05\n", + "field decay(t = 700.35): 4.491338910111841e-09 / 0.20769602060422127 = 2.162457854053155e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835677209e-12 / 4.169100835677209e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165419135520952e-08 / 1.0165419135520952e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650579070094068e-06 / 4.650579070094068e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005427300142038431 / 0.0005427300142038431 = 1.0\n", + "field decay(t = 300.15000000000003): 0.015051531229532927 / 0.015051531229532927 = 1.0\n", + "field decay(t = 350.175): 0.10911319432715134 / 0.10911319432715134 = 1.0\n", + "field decay(t = 400.20000000000005): 0.22726682483704244 / 0.22726682483704244 = 1.0\n", + "field decay(t = 450.225): 0.22744269278874502 / 0.22744269278874502 = 1.0\n", + "field decay(t = 500.25): 0.10405323422921754 / 0.22744269278874502 = 0.45749209593585416\n", + "field decay(t = 550.275): 0.011388920462070745 / 0.22744269278874502 = 0.05007380242657031\n", + "field decay(t = 600.3000000000001): 0.00037704599306494387 / 0.22744269278874502 = 0.001657762614581575\n", + "field decay(t = 650.325): 2.4160426967236526e-06 / 0.22744269278874502 = 1.062264373983533e-05\n", + "field decay(t = 700.35): 4.491856927586553e-09 / 0.22744269278874502 = 1.9749400926055307e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.16910083472712e-12 / 4.16910083472712e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165409834939769e-08 / 1.0165409834939769e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.648720329217878e-06 / 4.648720329217878e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005182735319569587 / 0.0005182735319569587 = 1.0\n", + "field decay(t = 300.15000000000003): 0.013974697322011669 / 0.013974697322011669 = 1.0\n", + "field decay(t = 350.175): 0.07995289002032732 / 0.07995289002032732 = 1.0\n", + "field decay(t = 400.20000000000005): 0.17867229467477655 / 0.17867229467477655 = 1.0\n", + "field decay(t = 450.225): 0.23241654769547887 / 0.23241654769547887 = 1.0\n", + "field decay(t = 500.25): 0.18817882350661239 / 0.23241654769547887 = 0.8096618996043756\n", + "field decay(t = 550.275): 0.014319414613175686 / 0.23241654769547887 = 0.06161099437694745\n", + "field decay(t = 600.3000000000001): 0.0003754598154438489 / 0.23241654769547887 = 0.0016154607714756648\n", + "field decay(t = 650.325): 2.9312708100424914e-06 / 0.23241654769547887 = 1.261214332244172e-05\n", + "field decay(t = 700.35): 1.8599873539004834e-07 / 0.23241654769547887 = 8.002818096831515e-07\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n" + ] + } + ], "source": [ - "k_pow = [-3, -2, -1, 0]\n", + "k_pow = [-3,-2,-1,0]\n", "freqs = []\n", "spectra = []\n", "for k_iter in k_pow:\n", " freqs_iter, spectra_iter, omega_flux, omega3_flux = run_chi3(k_iter)\n", " spectra.append(spectra_iter)\n", - " freqs = freqs_iter # Each iteration will simulate over the same frequencies, so just remember the last set." + " freqs = freqs_iter # Each iteration will simulate over the same frequencies, so just remember the last set." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=150)\n", - "plt.semilogy(freqs, np.array(spectra).T)\n", + "plt.semilogy(freqs,np.array(spectra).T)\n", "plt.grid(True)\n", - "plt.xlabel(\"Frequency\")\n", - "plt.ylabel(\"Transmitted Power (a.u.)\")\n", + "plt.xlabel('Frequency')\n", + "plt.ylabel('Transmitted Power (a.u.)')\n", "plt.legend([\"$\\chi^{{(3)}} = 10^{{{}}}$\".format(i) for i in k_pow])\n", "plt.show()" ] @@ -304,12 +403,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.016542016891584e-08 / 1.016542016891584e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650785283023354e-06 / 4.650785283023354e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454869069704206 / 0.0005454869069704206 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017816014066636514 / 0.017816014066636514 = 1.0\n", + "field decay(t = 350.175): 0.13193941695549694 / 0.13193941695549694 = 1.0\n", + "field decay(t = 400.20000000000005): 0.2506140638300714 / 0.2506140638300714 = 1.0\n", + "field decay(t = 450.225): 0.2500128531691806 / 0.2506140638300714 = 0.9976010497906516\n", + "field decay(t = 500.25): 0.11168480654371428 / 0.2506140638300714 = 0.4456446092324733\n", + "field decay(t = 550.275): 0.012841704978003306 / 0.2506140638300714 = 0.05124095903376999\n", + "field decay(t = 600.3000000000001): 0.0003786351315011641 / 0.2506140638300714 = 0.001510829542901859\n", + "field decay(t = 650.325): 2.4161060812423408e-06 / 0.2506140638300714 = 9.640744195747047e-06\n", + "field decay(t = 700.35): 4.490904760943549e-09 / 0.2506140638300714 = 1.7919603921305078e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "Omega: 225.25726603587026, 3Omega: 5.026979907074879e-16\n" + ] + } + ], "source": [ "_, _, omega_flux_cal, omega3_flux_cal = run_chi3(-16)\n", - "print(\"Omega: {}, 3Omega: {}\".format(omega_flux_cal[0], omega3_flux_cal[0]))" + "print(\"Omega: {}, 3Omega: {}\".format(omega_flux_cal[0],omega3_flux_cal[0]))" ] }, { @@ -321,29 +444,381 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420168905492e-08 / 1.0165420168905492e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650785280961551e-06 / 4.650785280961551e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454868799362667 / 0.0005454868799362667 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017815979612345514 / 0.017815979612345514 = 1.0\n", + "field decay(t = 350.175): 0.13193760003745314 / 0.13193760003745314 = 1.0\n", + "field decay(t = 400.20000000000005): 0.2506077676780644 / 0.2506077676780644 = 1.0\n", + "field decay(t = 450.225): 0.250006603482365 / 0.2506077676780644 = 0.9976011749305723\n", + "field decay(t = 500.25): 0.1116836154292776 / 0.2506077676780644 = 0.4456510524955018\n", + "field decay(t = 550.275): 0.012841686224530456 / 0.2506077676780644 = 0.051242171555620476\n", + "field decay(t = 600.3000000000001): 0.00037863511629851986 / 0.2506077676780644 = 0.0015108674396115361\n", + "field decay(t = 650.325): 2.416106131823971e-06 / 0.2506077676780644 = 9.640986607118051e-06\n", + "field decay(t = 700.35): 4.4909064662334595e-09 / 0.2506077676780644 = 1.7920060929645904e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420168894454e-08 / 1.0165420168894454e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650785278757183e-06 / 4.650785278757183e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454868510330065 / 0.0005454868510330065 = 1.0\n", + "field decay(t = 300.15000000000003): 0.0178159427756053 / 0.0178159427756053 = 1.0\n", + "field decay(t = 350.175): 0.13193565734819723 / 0.13193565734819723 = 1.0\n", + "field decay(t = 400.20000000000005): 0.2506010350871025 / 0.2506010350871025 = 1.0\n", + "field decay(t = 450.225): 0.24999992057177803 / 0.2506010350871025 = 0.9976013087291696\n", + "field decay(t = 500.25): 0.11168234185404839 / 0.2506010350871025 = 0.4456579431734209\n", + "field decay(t = 550.275): 0.012841666174342072 / 0.2506010350871025 = 0.05124346820785731\n", + "field decay(t = 600.3000000000001): 0.00037863510004548367 / 0.2506010350871025 = 0.0015109079653796314\n", + "field decay(t = 650.325): 2.416106185906883e-06 / 0.2506010350871025 = 9.641245835505452e-06\n", + "field decay(t = 700.35): 4.490908289112879e-09 / 0.2506010350871025 = 1.7920549639996314e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.016542016887149e-08 / 1.016542016887149e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650785274196042e-06 / 4.650785274196042e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454867912281307 / 0.0005454867912281307 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017815866554169706 / 0.017815866554169706 = 1.0\n", + "field decay(t = 350.175): 0.1319316371539862 / 0.1319316371539862 = 1.0\n", + "field decay(t = 400.20000000000005): 0.250587100703692 / 0.250587100703692 = 1.0\n", + "field decay(t = 450.225): 0.24998608899268374 / 0.250587100703692 = 0.997601585599097\n", + "field decay(t = 500.25): 0.11167970629203668 / 0.250587100703692 = 0.4456722073020547\n", + "field decay(t = 550.275): 0.012841624687286904 / 0.250587100703692 = 0.05124615214121316\n", + "field decay(t = 600.3000000000001): 0.00037863506641780544 / 0.250587100703692 = 0.0015109918481619068\n", + "field decay(t = 650.325): 2.416106297824614e-06 / 0.250587100703692 = 9.64178240236536e-06\n", + "field decay(t = 700.35): 4.49091206005567e-09 / 0.250587100703692 = 1.7921561195466212e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835782728e-12 / 4.169100835782728e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.016542016882427e-08 / 1.016542016882427e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650785264758402e-06 / 4.650785264758402e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454866674834187 / 0.0005454866674834187 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017815708836639688 / 0.017815708836639688 = 1.0\n", + "field decay(t = 350.175): 0.13192331666924295 / 0.13192331666924295 = 1.0\n", + "field decay(t = 400.20000000000005): 0.2505582526127686 / 0.2505582526127686 = 1.0\n", + "field decay(t = 450.225): 0.24995745365544636 / 0.2505582526127686 = 0.9976021585756716\n", + "field decay(t = 500.25): 0.11167425141378004 / 0.2505582526127686 = 0.4457017489915599\n", + "field decay(t = 550.275): 0.012841538842926424 / 0.2505582526127686 = 0.05125170976815797\n", + "field decay(t = 600.3000000000001): 0.000378634996846925 / 0.2505582526127686 = 0.0015111655389459305\n", + "field decay(t = 650.325): 2.4161065294750964e-06 / 0.2505582526127686 = 9.642893436079026e-06\n", + "field decay(t = 700.35): 4.49091985908172e-09 / 0.2505582526127686 = 1.7923655725769776e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.1691008357828475e-12 / 4.1691008357828475e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420168726412e-08 / 1.0165420168726412e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650785245230697e-06 / 4.650785245230697e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454864114377906 / 0.0005454864114377906 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017815382476511977 / 0.017815382476511977 = 1.0\n", + "field decay(t = 350.175): 0.13190609125009525 / 0.13190609125009525 = 1.0\n", + "field decay(t = 400.20000000000005): 0.2504984938450849 / 0.2504984938450849 = 1.0\n", + "field decay(t = 450.225): 0.24989813526348434 / 0.2504984938450849 = 0.9976033445455691\n", + "field decay(t = 500.25): 0.11166295794325522 / 0.2504984938450849 = 0.4457629913428168\n", + "field decay(t = 550.275): 0.012841361210895873 / 0.2504984938450849 = 0.05126322723056899\n", + "field decay(t = 600.3000000000001): 0.00037863485293490557 / 0.2504984938450849 = 0.0015115254671713265\n", + "field decay(t = 650.325): 2.4161070090188778e-06 / 0.2504984938450849 = 9.645195753205065e-06\n", + "field decay(t = 700.35): 4.490935979334019e-09 / 0.2504984938450849 = 1.792799593482321e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835782678e-12 / 4.169100835782678e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420168523849e-08 / 1.0165420168523849e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650785204825125e-06 / 4.650785204825125e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454858816409209 / 0.0005454858816409209 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017814707103397807 / 0.017814707103397807 = 1.0\n", + "field decay(t = 350.175): 0.13187041021795567 / 0.13187041021795567 = 1.0\n", + "field decay(t = 400.20000000000005): 0.250374554159776 / 0.250374554159776 = 1.0\n", + "field decay(t = 450.225): 0.24977510744804346 / 0.250374554159776 = 0.9976058001830729\n", + "field decay(t = 500.25): 0.11163956208436386 / 0.250374554159776 = 0.4458902082083042\n", + "field decay(t = 550.275): 0.012840993630635223 / 0.250374554159776 = 0.051287135283087794\n", + "field decay(t = 600.3000000000001): 0.00037863455532979455 / 0.250374554159776 = 0.0015122725094826118\n", + "field decay(t = 650.325): 2.4161080020963407e-06 / 0.250374554159776 = 9.649974256387518e-06\n", + "field decay(t = 700.35): 4.490969255239367e-09 / 0.250374554159776 = 1.7937003503852332e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835782697e-12 / 4.169100835782697e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420168104837e-08 / 1.0165420168104837e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650785121220347e-06 / 4.650785121220347e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454847854047326 / 0.0005454847854047326 = 1.0\n", + "field decay(t = 300.15000000000003): 0.01781330928257498 / 0.01781330928257498 = 1.0\n", + "field decay(t = 350.175): 0.1317964137861031 / 0.1317964137861031 = 1.0\n", + "field decay(t = 400.20000000000005): 0.25011687030360397 / 0.25011687030360397 = 1.0\n", + "field decay(t = 450.225): 0.24951931317382292 / 0.25011687030360397 = 0.9976108883456933\n", + "field decay(t = 500.25): 0.11159103280876947 / 0.25011687030360397 = 0.4461555618911866\n", + "field decay(t = 550.275): 0.012840232906414287 / 0.25011687030360397 = 0.051336932574073034\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "field decay(t = 600.3000000000001): 0.0003786339402465762 / 0.25011687030360397 = 0.0015138280747994807\n", + "field decay(t = 650.325): 2.416110058754147e-06 / 0.25011687030360397 = 9.659924401826058e-06\n", + "field decay(t = 700.35): 4.491037714235971e-09 / 0.25011687030360397 = 1.795575687791284e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835782566e-12 / 4.169100835782566e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.016542016723775e-08 / 1.016542016723775e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650784948230582e-06 / 4.650784948230582e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454825170836912 / 0.0005454825170836912 = 1.0\n", + "field decay(t = 300.15000000000003): 0.01781041537070784 / 0.01781041537070784 = 1.0\n", + "field decay(t = 350.175): 0.1316425937154995 / 0.1316425937154995 = 1.0\n", + "field decay(t = 400.20000000000005): 0.24957847104378408 / 0.24957847104378408 = 1.0\n", + "field decay(t = 450.225): 0.2489848350078133 / 0.24957847104378408 = 0.99762144533746\n", + "field decay(t = 500.25): 0.11149010814744109 / 0.24957847104378408 = 0.4467136435333084\n", + "field decay(t = 550.275): 0.012838658222915229 / 0.24957847104378408 = 0.05144136899798106\n", + "field decay(t = 600.3000000000001): 0.0003786326703923277 / 0.24957847104378408 = 0.0015170886687814647\n", + "field decay(t = 650.325): 2.4161143069600103e-06 / 0.24957847104378408 = 9.680780144438605e-06\n", + "field decay(t = 700.35): 4.4911771950517e-09 / 0.24957847104378408 = 1.7995050519657217e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.1691008357823855e-12 / 4.1691008357823855e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420165443837e-08 / 1.0165420165443837e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650784590290659e-06 / 4.650784590290659e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454778233702294 / 0.0005454778233702294 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017804420515894783 / 0.017804420515894783 = 1.0\n", + "field decay(t = 350.175): 0.13132132371111363 / 0.13132132371111363 = 1.0\n", + "field decay(t = 400.20000000000005): 0.24844276804284895 / 0.24844276804284895 = 1.0\n", + "field decay(t = 450.225): 0.24785728763909465 / 0.24844276804284895 = 0.9976433992892346\n", + "field decay(t = 500.25): 0.11127911965592859 / 0.24844276804284895 = 0.4479064556096729\n", + "field decay(t = 550.275): 0.01283539726004091 / 0.24844276804284895 = 0.05166339660902179\n", + "field decay(t = 600.3000000000001): 0.0003786300533999004 / 0.24844276804284895 = 0.0015240131817183668\n", + "field decay(t = 650.325): 2.416122931610749e-06 / 0.24844276804284895 = 9.725068476108912e-06\n", + "field decay(t = 700.35): 4.491452301556477e-09 / 0.24844276804284895 = 1.8078418369504868e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.1691008357820155e-12 / 4.1691008357820155e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420161731733e-08 / 1.0165420161731733e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650783849662967e-06 / 4.650783849662967e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454681103843687 / 0.0005454681103843687 = 1.0\n", + "field decay(t = 300.15000000000003): 0.0177919867184528 / 0.0177919867184528 = 1.0\n", + "field decay(t = 350.175): 0.13064418737686334 / 0.13064418737686334 = 1.0\n", + "field decay(t = 400.20000000000005): 0.24608078239905923 / 0.24608078239905923 = 1.0\n", + "field decay(t = 450.225): 0.2454910539147474 / 0.24608078239905923 = 0.9976035167047076\n", + "field decay(t = 500.25): 0.11083352926803994 / 0.24608078239905923 = 0.45039489954280826\n", + "field decay(t = 550.275): 0.012828638329285625 / 0.24608078239905923 = 0.052131817057058695\n", + "field decay(t = 600.3000000000001): 0.00037862466733590196 / 0.24608078239905923 = 0.0015386194063781123\n", + "field decay(t = 650.325): 2.4161389249309716e-06 / 0.24608078239905923 = 9.818478718150437e-06\n", + "field decay(t = 700.35): 4.491929211746334e-09 / 0.24608078239905923 = 1.8253880566999964e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835781244e-12 / 4.169100835781244e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420154050911e-08 / 1.0165420154050911e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650782317199479e-06 / 4.650782317199479e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454480084043649 / 0.0005454480084043649 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017766133980041062 / 0.017766133980041062 = 1.0\n", + "field decay(t = 350.175): 0.12943453172485406 / 0.12943453172485406 = 1.0\n", + "field decay(t = 400.20000000000005): 0.24162814253773343 / 0.24162814253773343 = 1.0\n", + "field decay(t = 450.225): 0.2410719556831018 / 0.24162814253773343 = 0.9976981702181286\n", + "field decay(t = 500.25): 0.10987489072588702 / 0.24162814253773343 = 0.45472720839514214\n", + "field decay(t = 550.275): 0.012814604477285866 / 0.24162814253773343 = 0.05303440378549736\n", + "field decay(t = 600.3000000000001): 0.00037861349073161515 / 0.24162814253773343 = 0.0015669262973889296\n", + "field decay(t = 650.325): 2.4161558408009105e-06 / 0.24162814253773343 = 9.999480256831406e-06\n", + "field decay(t = 700.35): 4.492861541918341e-09 / 0.24162814253773343 = 1.8594115299366343e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835779622e-12 / 4.169100835779622e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420138158315e-08 / 1.0165420138158315e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650779146309852e-06 / 4.650779146309852e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454063955002492 / 0.0005454063955002492 = 1.0\n", + "field decay(t = 300.15000000000003): 0.01771662173687749 / 0.01771662173687749 = 1.0\n", + "field decay(t = 350.175): 0.12689706943678725 / 0.12689706943678725 = 1.0\n", + "field decay(t = 400.20000000000005): 0.23320693569541642 / 0.23320693569541642 = 1.0\n", + "field decay(t = 450.225): 0.23267453504759023 / 0.23320693569541642 = 0.9977170462523398\n", + "field decay(t = 500.25): 0.10799807195922452 / 0.23320693569541642 = 0.46309974288362116\n", + "field decay(t = 550.275): 0.012785358594898285 / 0.23320693569541642 = 0.05482409241720325\n", + "field decay(t = 600.3000000000001): 0.000378589249816587 / 0.23320693569541642 = 0.0016234047614735157\n", + "field decay(t = 650.325): 2.416107138602139e-06 / 0.23320693569541642 = 1.0360357128304853e-05\n", + "field decay(t = 700.35): 4.493125804229867e-09 / 0.23320693569541642 = 1.9266690293028782e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835776254e-12 / 4.169100835776254e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420105274407e-08 / 1.0165420105274407e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.6507725852541425e-06 / 4.6507725852541425e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005453202108380395 / 0.0005453202108380395 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017624098227291815 / 0.017624098227291815 = 1.0\n", + "field decay(t = 350.175): 0.12218039718656669 / 0.12218039718656669 = 1.0\n", + "field decay(t = 400.20000000000005): 0.21924792050420605 / 0.21924792050420605 = 1.0\n", + "field decay(t = 450.225): 0.21875381922070578 / 0.21924792050420605 = 0.9977463809811103\n", + "field decay(t = 500.25): 0.10436138938509092 / 0.21924792050420605 = 0.47599716861665214\n", + "field decay(t = 550.275): 0.012733297749038119 / 0.21924792050420605 = 0.05807716542877698\n", + "field decay(t = 600.3000000000001): 0.00037853854019597556 / 0.21924792050420605 = 0.0017265319521637774\n", + "field decay(t = 650.325): 2.4161208970651143e-06 / 0.21924792050420605 = 1.102004019700047e-05\n", + "field decay(t = 700.35): 4.491390315511021e-09 / 0.21924792050420605 = 2.0485440888935858e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835769312e-12 / 4.169100835769312e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420037232922e-08 / 1.0165420037232922e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650759009321587e-06 / 4.650759009321587e-06 = 1.0\n", + "field decay(t = 250.125): 0.000545141533396581 / 0.000545141533396581 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017421291008439022 / 0.017421291008439022 = 1.0\n", + "field decay(t = 350.175): 0.11463570073897522 / 0.11463570073897522 = 1.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "field decay(t = 400.20000000000005): 0.20163815113463796 / 0.20163815113463796 = 1.0\n", + "field decay(t = 450.225): 0.20120253259282825 / 0.20163815113463796 = 0.9978396025783889\n", + "field decay(t = 500.25): 0.10109030457587416 / 0.20163815113463796 = 0.5013451274326259\n", + "field decay(t = 550.275): 0.01262521101057978 / 0.20163815113463796 = 0.06261320558404479\n", + "field decay(t = 600.3000000000001): 0.0003784354270167391 / 0.20163815113463796 = 0.0018768046864506805\n", + "field decay(t = 650.325): 2.4161436765026146e-06 / 0.20163815113463796 = 1.1982572062413455e-05\n", + "field decay(t = 700.35): 4.492170330890932e-09 / 0.20163815113463796 = 2.2278374928618628e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835754938e-12 / 4.169100835754938e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.01654198964457e-08 / 1.01654198964457e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650730917976917e-06 / 4.650730917976917e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005447703360772811 / 0.0005447703360772811 = 1.0\n", + "field decay(t = 300.15000000000003): 0.01702522296927303 / 0.01702522296927303 = 1.0\n", + "field decay(t = 350.175): 0.10518227880576582 / 0.10518227880576582 = 1.0\n", + "field decay(t = 400.20000000000005): 0.1772727477817735 / 0.1772727477817735 = 1.0\n", + "field decay(t = 450.225): 0.17689224598144007 / 0.1772727477817735 = 0.9978535798361865\n", + "field decay(t = 500.25): 0.0941896635654982 / 0.1772727477817735 = 0.5313262458223283\n", + "field decay(t = 550.275): 0.01239882951385902 / 0.1772727477817735 = 0.06994210711463807\n", + "field decay(t = 600.3000000000001): 0.00037822000681681363 / 0.1772727477817735 = 0.0021335485095679255\n", + "field decay(t = 650.325): 2.4160847007280536e-06 / 0.1772727477817735 = 1.3629194170907224e-05\n", + "field decay(t = 700.35): 4.493256393452313e-09 / 0.1772727477817735 = 2.5346571594769923e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835725167e-12 / 4.169100835725167e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.016541960513757e-08 / 1.016541960513757e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650672789399802e-06 / 4.650672789399802e-06 = 1.0\n", + "field decay(t = 250.125): 0.000543995966140328 / 0.000543995966140328 = 1.0\n", + "field decay(t = 300.15000000000003): 0.016307143716002143 / 0.016307143716002143 = 1.0\n", + "field decay(t = 350.175): 0.09153211905255858 / 0.09153211905255858 = 1.0\n", + "field decay(t = 400.20000000000005): 0.22855260392168325 / 0.22855260392168325 = 1.0\n", + "field decay(t = 450.225): 0.22862376766772263 / 0.22862376766772263 = 1.0\n", + "field decay(t = 500.25): 0.0829714083179759 / 0.22862376766772263 = 0.36291680941311816\n", + "field decay(t = 550.275): 0.011966639931831521 / 0.22862376766772263 = 0.0523420642302755\n", + "field decay(t = 600.3000000000001): 0.0003777734295152949 / 0.22862376766772263 = 0.0016523803862087667\n", + "field decay(t = 650.325): 2.416110201923171e-06 / 0.22862376766772263 = 1.0568062221049122e-05\n", + "field decay(t = 700.35): 4.492316812744696e-09 / 0.22862376766772263 = 1.9649386669516103e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835663596e-12 / 4.169100835663596e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165419002380455e-08 / 1.0165419002380455e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650552497483136e-06 / 4.650552497483136e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005423672260522596 / 0.0005423672260522596 = 1.0\n", + "field decay(t = 300.15000000000003): 0.01462324952255367 / 0.01462324952255367 = 1.0\n", + "field decay(t = 350.175): 0.11286948557899079 / 0.11286948557899079 = 1.0\n", + "field decay(t = 400.20000000000005): 0.22340193165372965 / 0.22340193165372965 = 1.0\n", + "field decay(t = 450.225): 0.22423295751360775 / 0.22423295751360775 = 1.0\n", + "field decay(t = 500.25): 0.10875776760662008 / 0.22423295751360775 = 0.4850213314428591\n", + "field decay(t = 550.275): 0.011253173596244885 / 0.22423295751360775 = 0.050185190085458235\n", + "field decay(t = 600.3000000000001): 0.0003768391018113586 / 0.22423295751360775 = 0.0016805696450241478\n", + "field decay(t = 650.325): 2.416028682322177e-06 / 0.22423295751360775 = 1.0774636829091274e-05\n", + "field decay(t = 700.35): 4.491221029097323e-09 / 0.22423295751360775 = 2.002926366800817e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835536189e-12 / 4.169100835536189e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.016541775519228e-08 / 1.016541775519228e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650303529004109e-06 / 4.650303529004109e-06 = 1.0\n", + "field decay(t = 250.125): 0.000538888737006022 / 0.000538888737006022 = 1.0\n", + "field decay(t = 300.15000000000003): 0.012940856099568963 / 0.012940856099568963 = 1.0\n", + "field decay(t = 350.175): 0.10610514124453076 / 0.10610514124453076 = 1.0\n", + "field decay(t = 400.20000000000005): 0.21497310015816543 / 0.21497310015816543 = 1.0\n", + "field decay(t = 450.225): 0.22343131384615714 / 0.22343131384615714 = 1.0\n", + "field decay(t = 500.25): 0.12060305444368463 / 0.22343131384615714 = 0.5397768664007654\n", + "field decay(t = 550.275): 0.010356579521407651 / 0.22343131384615714 = 0.046352408456670664\n", + "field decay(t = 600.3000000000001): 0.0003748549630386882 / 0.22343131384615714 = 0.0016777190116547107\n", + "field decay(t = 650.325): 2.4161257892828187e-06 / 0.22343131384615714 = 1.0813729497855586e-05\n", + "field decay(t = 700.35): 4.502116718465644e-09 / 0.22343131384615714 = 2.0149891440756423e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835272573e-12 / 4.169100835272573e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165415174584912e-08 / 1.0165415174584912e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.649788088402476e-06 / 4.649788088402476e-06 = 1.0\n", + "field decay(t = 250.125): 0.00053174759709517 / 0.00053174759709517 = 1.0\n", + "field decay(t = 300.15000000000003): 0.01247454839135486 / 0.01247454839135486 = 1.0\n", + "field decay(t = 350.175): 0.08976065034162566 / 0.08976065034162566 = 1.0\n", + "field decay(t = 400.20000000000005): 0.18870963593996914 / 0.18870963593996914 = 1.0\n", + "field decay(t = 450.225): 0.22826836798863853 / 0.22826836798863853 = 1.0\n", + "field decay(t = 500.25): 0.12497876557323505 / 0.22826836798863853 = 0.5475080348384299\n", + "field decay(t = 550.275): 0.009665581444509555 / 0.22826836798863853 = 0.042343061063066935\n", + "field decay(t = 600.3000000000001): 0.0003923542204648252 / 0.22826836798863853 = 0.0017188286923940053\n", + "field decay(t = 650.325): 2.7457665901575746e-06 / 0.22826836798863853 = 1.2028677535795227e-05\n", + "field decay(t = 700.35): 5.739133736098904e-09 / 0.22826836798863853 = 2.5142045683633898e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.16910083472712e-12 / 4.16910083472712e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165409834939769e-08 / 1.0165409834939769e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.648720329217878e-06 / 4.648720329217878e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005182735319569587 / 0.0005182735319569587 = 1.0\n", + "field decay(t = 300.15000000000003): 0.013974697322011669 / 0.013974697322011669 = 1.0\n", + "field decay(t = 350.175): 0.07995289002032732 / 0.07995289002032732 = 1.0\n", + "field decay(t = 400.20000000000005): 0.17867229467477655 / 0.17867229467477655 = 1.0\n", + "field decay(t = 450.225): 0.23241654769547887 / 0.23241654769547887 = 1.0\n", + "field decay(t = 500.25): 0.18817882350661239 / 0.23241654769547887 = 0.8096618996043756\n", + "field decay(t = 550.275): 0.014319414613175686 / 0.23241654769547887 = 0.06161099437694745\n", + "field decay(t = 600.3000000000001): 0.0003754598154438489 / 0.23241654769547887 = 0.0016154607714756648\n", + "field decay(t = 650.325): 2.9312708100424914e-06 / 0.23241654769547887 = 1.261214332244172e-05\n", + "field decay(t = 700.35): 1.8599873539004834e-07 / 0.23241654769547887 = 8.002818096831515e-07\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n" + ] + } + ], "source": [ - "pts = np.linspace(-6, 0, 20)\n", + "pts = np.linspace(-6,0,20)\n", "_, _, omega_psd, omega3_psd = zip(*[run_chi3(k_iter) for k_iter in pts])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "quad = (10**pts) ** 2\n", + "quad = (10 ** pts) ** 2\n", "\n", "plt.figure(dpi=150)\n", - "plt.loglog(10**pts, np.array(omega_psd) / omega_flux_cal[0], \"o-\", label=\"$\\omega$\")\n", - "plt.loglog(10**pts, np.array(omega3_psd) / omega_flux_cal[0], \"o-\", label=\"$3\\omega$\")\n", - "plt.loglog(10**pts, quad, \"k\", label=\"quadratic line\")\n", + "plt.loglog(10 ** pts,np.array(omega_psd)/omega_flux_cal[0],'o-',label='$\\omega$')\n", + "plt.loglog(10 ** pts,np.array(omega3_psd)/omega_flux_cal[0],'o-',label='$3\\omega$')\n", + "plt.loglog(10**pts,quad,'k',label='quadratic line')\n", "plt.grid(True)\n", - "plt.xlabel(\"$\\chi^{(3)}$\")\n", - "plt.ylabel(\"Transmission/ Incident Power\")\n", + "plt.xlabel('$\\chi^{(3)}$')\n", + "plt.ylabel('Transmission/ Incident Power')\n", "plt.legend()\n", "plt.show()" ] @@ -359,19 +834,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.1691008357817126e-12 / 4.1691008357817126e-12 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420158581846e-08 / 1.0165420158581846e-08 = 1.0\n", + "field decay(t = 200.10000000000002): 4.650783221205379e-06 / 4.650783221205379e-06 = 1.0\n", + "field decay(t = 250.125): 0.0005454598673598967 / 0.0005454598673598967 = 1.0\n", + "field decay(t = 300.15000000000003): 0.017781404956060017 / 0.017781404956060017 = 1.0\n", + "field decay(t = 350.175): 0.13014055244440104 / 0.13014055244440104 = 1.0\n", + "field decay(t = 400.20000000000005): 0.2443206506136912 / 0.2443206506136912 = 1.0\n", + "field decay(t = 450.225): 0.2437445632391615 / 0.2443206506136912 = 0.9976420848050188\n", + "field decay(t = 500.25): 0.11044620451678894 / 0.2443206506136912 = 0.4520543156682302\n", + "field decay(t = 550.275): 0.012822890989720101 / 0.2443206506136912 = 0.05248386068681144\n", + "field decay(t = 600.3000000000001): 0.00037862010232358545 / 0.2443206506136912 = 0.0015496852246118257\n", + "field decay(t = 650.325): 2.41614906850782e-06 / 0.2443206506136912 = 9.8892543976077e-06\n", + "field decay(t = 700.35): 4.492194624953141e-09 / 0.2443206506136912 = 1.838647127727241e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "field decay(t = 100.05000000000001): 4.169100835782652e-10 / 4.169100835782652e-10 = 1.0\n", + "field decay(t = 150.07500000000002): 1.0165420167882405e-06 / 1.0165420167882405e-06 = 1.0\n", + "field decay(t = 200.10000000000002): 0.0004650785076841832 / 0.0004650785076841832 = 1.0\n", + "field decay(t = 250.125): 0.05454842035005856 / 0.05454842035005856 = 1.0\n", + "field decay(t = 300.15000000000003): 1.7812567092193932 / 1.7812567092193932 = 1.0\n", + "field decay(t = 350.175): 13.175704416847648 / 13.175704416847648 = 1.0\n", + "field decay(t = 400.20000000000005): 24.99794167845129 / 24.99794167845129 = 1.0\n", + "field decay(t = 450.225): 24.9382864152439 / 24.99794167845129 = 0.9976135929919857\n", + "field decay(t = 500.25): 11.156520728608672 / 24.99794167845129 = 0.44629757410090326\n", + "field decay(t = 550.275): 1.2839829021813205 / 24.99794167845129 = 0.051363544994912066\n", + "field decay(t = 600.3000000000001): 0.03786336141250103 / 24.99794167845129 = 0.0015146591627237845\n", + "field decay(t = 650.325): 0.0002416111150361613 / 24.99794167845129 = 9.665240368347396e-06\n", + "field decay(t = 700.35): 4.491073802270905e-07 / 24.99794167845129 = 1.7965774382705666e-08\n", + "run 0 finished at t = 700.35 (28014 timesteps)\n", + "-------------------------------\n", + "Difference between powers: 1.3663690082658835%\n" + ] + } + ], "source": [ - "_, _, omega_flux_1, omega3_flux_1 = run_chi3(-3, 1)\n", - "_, _, omega_flux_2, omega3_flux_2 = run_chi3(-6, 10)\n", + "_, _, omega_flux_1, omega3_flux_1 = run_chi3(-3,1)\n", + "_, _, omega_flux_2, omega3_flux_2 = run_chi3(-6,10)\n", "\n", - "print(\"-------------------------------\")\n", - "print(\n", - " \"Difference between powers: {}%\".format(\n", - " abs(omega3_flux_1[0] - omega3_flux_2[0]) / omega3_flux_1[0] * 100\n", - " )\n", - ")" + "print('-------------------------------')\n", + "print(\"Difference between powers: {}%\".format(abs(omega3_flux_1[0]-omega3_flux_2[0])/omega3_flux_1[0] * 100))" ] }, { diff --git a/python/examples/absorbed_power_density.ipynb b/python/examples/absorbed_power_density.ipynb index 191be8d9d..d6188fb16 100644 --- a/python/examples/absorbed_power_density.ipynb +++ b/python/examples/absorbed_power_density.ipynb @@ -13,9 +13,168 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00278592 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8 x 8 x 0 with resolution 100\n", + " cylinder, center = (0,0,0)\n", + " radius 1, height 1e+20, axis (0, 0, 1)\n", + " dielectric constant epsilon diagonal = (1,1,1)\n", + "time for set_epsilon = 0.94641 s\n", + "lorentzian susceptibility: frequency=9.67865, gamma=0.0806554\n", + "-----------\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d636f14778924d5ca3612af32c90cddd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=200.0)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 3.24/200.0 = 1.6% done in 4.0s, 243.3s to go\n", + 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"on time step 36288 (time=181.44), 0.00597098 s/step\n", + "Meep progress: 184.735/200.0 = 92.4% done in 220.2s, 18.2s to go\n", + "on time step 36961 (time=184.805), 0.0059467 s/step\n", + "Meep progress: 188.035/200.0 = 94.0% done in 224.2s, 14.3s to go\n", + "on time step 37621 (time=188.105), 0.00606458 s/step\n", + "Meep progress: 191.455/200.0 = 95.7% done in 228.2s, 10.2s to go\n", + "on time step 38307 (time=191.535), 0.00583756 s/step\n", + "Meep progress: 194.79/200.0 = 97.4% done in 232.2s, 6.2s to go\n", + "on time step 38973 (time=194.865), 0.00600972 s/step\n", + "Meep progress: 198.16/200.0 = 99.1% done in 236.2s, 2.2s to go\n", + "on time step 39649 (time=198.245), 0.00592657 s/step\n", + "run 0 finished at t = 200.0 (40000 timesteps)\n", + "flux:, 0.13120421825956843 (dft_fields), 0.13249534167200247 (dft_flux), 0.0097446702362583 (error)\n" + ] + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -28,75 +187,60 @@ "dpml = 1.0\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "r = 1.0 # radius of cylinder\n", + "r = 1.0 # radius of cylinder\n", "dair = 2.0 # air padding thickness\n", "\n", - "s = 2 * (dpml + dair + r)\n", - "cell_size = mp.Vector3(s, s)\n", + "s = 2*(dpml+dair+r)\n", + "cell_size = mp.Vector3(s,s)\n", "\n", "wvl = 1.0\n", - "fcen = 1 / wvl\n", + "fcen = 1/wvl\n", "\n", "# is_integrated=True necessary for any planewave source extending into PML\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=0.1 * fcen, is_integrated=True),\n", - " center=mp.Vector3(-0.5 * s + dpml),\n", - " size=mp.Vector3(0, s),\n", - " component=mp.Ez,\n", - " )\n", - "]\n", + "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.1*fcen,is_integrated=True),\n", + " center=mp.Vector3(-0.5*s+dpml),\n", + " size=mp.Vector3(0,s),\n", + " component=mp.Ez)]\n", "\n", "symmetries = [mp.Mirror(mp.Y)]\n", "\n", - "geometry = [mp.Cylinder(material=SiO2, center=mp.Vector3(), radius=r, height=mp.inf)]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=mp.Vector3(),\n", - " symmetries=symmetries,\n", - " geometry=geometry,\n", - ")\n", - "\n", - "dft_fields = sim.add_dft_fields(\n", - " [mp.Dz, mp.Ez],\n", - " fcen,\n", - " 0,\n", - " 1,\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(2 * r, 2 * r),\n", - " yee_grid=True,\n", - ")\n", + "geometry = [mp.Cylinder(material=SiO2,\n", + " center=mp.Vector3(),\n", + " radius=r,\n", + " height=mp.inf)]\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=mp.Vector3(),\n", + " symmetries=symmetries,\n", + " geometry=geometry)\n", + "\n", + "dft_fields = sim.add_dft_fields([mp.Dz,mp.Ez],\n", + " fcen,0,1,\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(2*r,2*r),\n", + " yee_grid=True)\n", "\n", "# closed box surrounding cylinder for computing total incoming flux\n", - "flux_box = sim.add_flux(\n", - " fcen,\n", - " 0,\n", - " 1,\n", - " mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r), weight=+1),\n", - " mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r), weight=-1),\n", - " mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0), weight=-1),\n", - " mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0), weight=+1),\n", - ")\n", + "flux_box = sim.add_flux(fcen, 0, 1,\n", + " mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r),weight=+1),\n", + " mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r),weight=-1),\n", + " mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0),weight=-1),\n", + " mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0),weight=+1))\n", "\n", "sim.run(until_after_sources=100)\n", "\n", - "Dz = sim.get_dft_array(dft_fields, mp.Dz, 0)\n", - "Ez = sim.get_dft_array(dft_fields, mp.Ez, 0)\n", - "absorbed_power_density = 2 * np.pi * fcen * np.imag(np.conj(Ez) * Dz)\n", + "Dz = sim.get_dft_array(dft_fields,mp.Dz,0)\n", + "Ez = sim.get_dft_array(dft_fields,mp.Ez,0)\n", + "absorbed_power_density = 2*np.pi*fcen * np.imag(np.conj(Ez)*Dz)\n", "\n", - "dxy = 1 / resolution**2\n", - "absorbed_power = np.sum(absorbed_power_density) * dxy\n", + "dxy = 1/resolution**2\n", + "absorbed_power = np.sum(absorbed_power_density)*dxy\n", "absorbed_flux = mp.get_fluxes(flux_box)[0]\n", - "err = abs(absorbed_power - absorbed_flux) / absorbed_flux\n", - "print(\n", - " \"flux:, {} (dft_fields), {} (dft_flux), {} (error)\".format(\n", - " absorbed_power, absorbed_flux, err\n", - " )\n", - ")" + "err = abs(absorbed_power-absorbed_flux)/absorbed_flux\n", + "print(\"flux:, {} (dft_fields), {} (dft_flux), {} (error)\".format(absorbed_power,absorbed_flux,err))" ] }, { @@ -112,9 +256,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "x = np.linspace(-r, r, Dz.shape[0])\n", - "y = np.linspace(-r, r, Dz.shape[1])\n", - "plt.pcolormesh(\n", - " x,\n", - " y,\n", - " np.transpose(absorbed_power_density),\n", - " cmap=\"inferno_r\",\n", - " shading=\"gouraud\",\n", - " vmin=0,\n", - " vmax=np.amax(absorbed_power_density),\n", - ")\n", + "x = np.linspace(-r,r,Dz.shape[0])\n", + "y = np.linspace(-r,r,Dz.shape[1])\n", + "plt.pcolormesh(x,\n", + " y,\n", + " np.transpose(absorbed_power_density),\n", + " cmap='inferno_r',\n", + " shading='gouraud',\n", + " vmin=0,\n", + " vmax=np.amax(absorbed_power_density))\n", "plt.xlabel(\"x (μm)\")\n", - "plt.xticks(np.linspace(-r, r, 5))\n", + "plt.xticks(np.linspace(-r,r,5))\n", "plt.ylabel(\"y (μm)\")\n", - "plt.yticks(np.linspace(-r, r, 5))\n", - "plt.gca().set_aspect(\"equal\")\n", - "plt.title(\n", - " \"absorbed power density\"\n", - " + \"\\n\"\n", - " + \"SiO2 Labs(λ={} μm) = {:.2f} μm\".format(\n", - " wvl, wvl / np.imag(np.sqrt(SiO2.epsilon(fcen)[0][0]))\n", - " )\n", - ")\n", + "plt.yticks(np.linspace(-r,r,5))\n", + "plt.gca().set_aspect('equal')\n", + "plt.title(\"absorbed power density\" + \"\\n\" +\"SiO2 Labs(λ={} μm) = {:.2f} μm\".format(wvl,wvl/np.imag(np.sqrt(SiO2.epsilon(fcen)[0][0]))))\n", "plt.colorbar()" ] } diff --git a/python/examples/adjoint_optimization/01-Introduction.ipynb b/python/examples/adjoint_optimization/01-Introduction.ipynb index 5c8b0a984..d40973f54 100644 --- a/python/examples/adjoint_optimization/01-Introduction.ipynb +++ b/python/examples/adjoint_optimization/01-Introduction.ipynb @@ -17,16 +17,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", "import numpy as np\n", "from autograd import numpy as npa\n", "from matplotlib import pyplot as plt\n", - "\n", "mp.verbosity(0)\n", "seed = 240\n", "np.random.seed(seed)\n", @@ -43,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -51,7 +58,7 @@ "\n", "Sx = 6\n", "Sy = 5\n", - "cell_size = mp.Vector3(Sx, Sy)\n", + "cell_size = mp.Vector3(Sx,Sy)\n", "\n", "pml_layers = [mp.PML(1.0)]" ] @@ -65,27 +72,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "fcen = 1 / 1.55\n", + "fcen = 1/1.55\n", "width = 0.1\n", "fwidth = width * fcen\n", - "source_center = [-1, 0, 0]\n", - "source_size = mp.Vector3(0, 2, 0)\n", - "kpoint = mp.Vector3(1, 0, 0)\n", - "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", - "source = [\n", - " mp.EigenModeSource(\n", - " src,\n", - " eig_band=1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size=source_size,\n", - " center=source_center,\n", - " )\n", - "]" + "source_center = [-1,0,0]\n", + "source_size = mp.Vector3(0,2,0)\n", + "kpoint = mp.Vector3(1,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]" ] }, { @@ -99,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -107,25 +110,17 @@ "Nx = design_region_resolution\n", "Ny = design_region_resolution\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", - "design_region = mpa.DesignRegion(\n", - " design_variables, volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0))\n", - ")\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0)))\n", "\n", "\n", "geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", - " ), # horizontal waveguide\n", - " mp.Block(\n", - " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", - " ), # vertical waveguide\n", - " mp.Block(\n", - " center=design_region.center, size=design_region.size, material=design_variables\n", - " ), # design region\n", - " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", + " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", - " #\n", + " # \n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; We give an alternative approach to impose symmetry in later tutorials.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", @@ -141,18 +136,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " eps_averaging=False,\n", - " resolution=resolution,\n", - ")" + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " eps_averaging=False,\n", + " resolution=resolution)" ] }, { @@ -166,13 +159,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "TE0 = mpa.EigenmodeCoefficient(\n", - " sim, mp.Volume(center=mp.Vector3(0, 1, 0), size=mp.Vector3(x=2)), mode=1\n", - ")\n", + "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,1,0),size=mp.Vector3(x=2)),mode=1)\n", "ob_list = [TE0]" ] }, @@ -185,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -202,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -212,8 +203,8 @@ " objective_arguments=ob_list,\n", " design_regions=[design_region],\n", " fcen=fcen,\n", - " df=0,\n", - " nf=1,\n", + " df = 0,\n", + " nf = 1,\n", ")" ] }, @@ -226,11 +217,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "x0 = np.random.rand(Nx * Ny)\n", + "x0 = np.random.rand(Nx*Ny)\n", "opt.update_design([x0])" ] }, @@ -243,11 +234,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "opt.plot2D(True, frequency=1 / 1.55)\n", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.9/site-packages/meep/visualization.py:357: UserWarning: The frequency parameter of plot2D has been deprecated. Use the frequency key of the eps_parameters dictionary instead.\n", + " warnings.warn(\"The frequency parameter of plot2D has been deprecated. \"\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.plot2D(True,frequency=1/1.55)\n", "plt.show()" ] }, @@ -262,9 +274,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + } + ], "source": [ "f0, dJ_du = opt()" ] @@ -278,12 +300,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "plt.imshow(np.rot90(dJ_du.reshape(Nx, Ny)))" + "plt.imshow(np.rot90(dJ_du.reshape(Nx,Ny)))" ] }, { @@ -297,13 +342,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "db = 1e-3\n", "choose = 10\n", - "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose, db=db)" + "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose,db=db)" ] }, { @@ -315,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -331,38 +376,47 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", "\n", - "fig = plt.figure(figsize=(12, 6))\n", + "fig = plt.figure(figsize=(12,6))\n", "\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot([min_g, max_g], [min_g, max_g], label=\"y=x comparison\")\n", - "plt.plot([min_g, max_g], [m * min_g + b, m * max_g + b], \"--\", label=\"Best fit\")\n", - "plt.plot(g_discrete, dJ_du[idx], \"o\", label=\"Adjoint comparison\")\n", - "plt.xlabel(\"Finite Difference Gradient\")\n", - "plt.ylabel(\"Adjoint Gradient\")\n", + "plt.subplot(1,2,1)\n", + "plt.plot([min_g, max_g],[min_g, max_g],label='y=x comparison')\n", + "plt.plot([min_g, max_g],[m*min_g+b, m*max_g+b],'--',label='Best fit')\n", + "plt.plot(g_discrete,dJ_du[idx],'o',label='Adjoint comparison')\n", + "plt.xlabel('Finite Difference Gradient')\n", + "plt.ylabel('Adjoint Gradient')\n", "plt.legend()\n", "plt.grid(True)\n", "plt.axis(\"square\")\n", "\n", - "plt.subplot(1, 2, 2)\n", - "rel_err = (\n", - " np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx]))\n", - " / np.abs(np.squeeze(g_discrete))\n", - " * 100\n", - ")\n", - "plt.semilogy(g_discrete, rel_err, \"o\")\n", + "plt.subplot(1,2,2)\n", + "rel_err = np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx])) / np.abs(np.squeeze(g_discrete)) * 100\n", + "plt.semilogy(g_discrete,rel_err,'o')\n", "plt.grid(True)\n", - "plt.xlabel(\"Finite Difference Gradient\")\n", - "plt.ylabel(\"Relative Error (%)\")\n", + "plt.xlabel('Finite Difference Gradient')\n", + "plt.ylabel('Relative Error (%)')\n", "\n", "fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - "fig.suptitle(\"Resolution: {} Seed: {} Nx: {} Ny: {}\".format(resolution, seed, Nx, Ny))\n", + "fig.suptitle('Resolution: {} Seed: {} Nx: {} Ny: {}'.format(resolution,seed,Nx,Ny))\n", "plt.show()" ] }, @@ -375,50 +429,69 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + } + ], "source": [ "resolution = 30\n", "opt.sim.resolution = resolution\n", "f0, dJ_du = opt()\n", - "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose, db=db)" + "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose,db=db)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kIHTo0IGjjz6aJUuWsHnzZjIyMjj66KMBWLp06b73VTQF9kUXXcSLL77I7bffzqeffnrAttesWUObNm0466yzAGjevDkA77//PnfccQcAp512GieeeOK+kDRgwACaNWtGs2bNSE9PZ8iQIQB0796dZcuW7dv2yJEjAbjgggv45ptv2LlzJwUFBdxwww2sXbsWM2PPnj3lam3ZsuUBNfbt25ff/OY3bNy4kWHDhtGpUyfef/99rrzySpo0aQLAsGHDWLBgAVdffTUnnXQSPXv2BODMM88sF7hEpObmrZvHxMUTyS3KpXWT1ozrNY7BJw8Ouqx6w93nAnN79+59y0HfLCK1Ys6SbB6ev4ZNO0O0bZHGXQM7MzSjXdBlSZKpVyGpqhafunTzzTczefJkcnNzGTNmDEC1WpJKS0tZtWoVjRs3ZseOHbRv3/6waznyyCP3PW/QoMG+1w0aNGDv3r371u0/E5yZcd999zFgwABmz57N+vXr6d+//771ZYFnf9deey1nn3028+bNY9CgQfz973+vdn0pKSnqbidyGOatm8cDCx6guCQ89X1OUQ4PLHgAQEFJRBLSnCXZjJ+1nNCeEgCyd4YYP2s5gIKSxJTGJNWCK6+8ktdee41FixYxcOBAAJo1a8bSpUv3PT744IN9z7t27QrAH//4R7p06cJzzz3HTTfdVK7lBqBz587k5OSwaNEiIBy89u7dS79+/Zg2bRoAn3/+OV9//TWdO3euUc0zZswAwq1S6enppKenk5+fT7t24f8BTZ48uVrbWbduHSeffDI//vGPueKKK1i2bBn9+vVjzpw57Nq1i6KiImbPns25555bo/pE5OAmLp64LyCVKS4pZuLig4+LFBGJRw/PX7MvIJUJ7Snh4flrAqpIklW9akkKSqNGjRgwYAAtWrQgJSWlWp9Zs2YNTzzxBAsXLqRZs2ZccMEFPPjgg/zyl78st90ZM2Zwxx13EAqFSEtL48033+SHP/wht912G927d+eII45g8uTJ5VpoqiM1NZWMjAz27NnDU089BcDdd9/NDTfcwIMPPsjgwdX7K/QLL7zA1KlTadiwIa1bt+aee+6hZcuW3HjjjfTp0wcIt7T16NGDvLy8GtUoIlXLLcqt0XIRkXi3aWfFPUwqWy5SV8zdg66h2nr37u0ff/xxuWWrVq2iS5cuAVUUVlpaSq9evXjxxRfp1KlThe+paExSUPr3788f/vAHevfuHbN9xur44+HnYX9ZWVnlui4mGx1/3R3/96adx+a9B05W06ZJG14f/nqNt2dmn7h77P7HkEAquv4ksmT/77IiOicVi/V5Oe+3b5FdQSBq1yKND3723ZjVURX9rFQsEc9LVdc9dbc7TJ999hkdO3bkwgsvrDQgiYjUth0LpzNu079pVFp+eWpKKuN6jQumKJEamrMkm/N++xYn/Wwe5/32LeYsyQ66JAnYXQM7k9awfK+ctIYp3DWwZsMKRA6Xutsdpq5du7Ju3bqgy6iRrKysoEsQkcOQs3MXa+Y/Q9u9JzDm/B/ycvazmt1OEo4G6EtFyr57zW4nQVNIEhFJIDk7Chj5xMfk7/0RT1/fg9tPac/tXB90WSI1VtUAff1CnNyGZrTTz4AETt3tREQSxI6Fz1PwpwsoLdzGk5nn0vOUw79tgEhQNEBfROKZQpKISALYsfB5mr96GwUlR/Lo9X3pdcJRQZckcljatkir0XIRkVhSSBIRiXNlAWmpdybl+plqQZJ6QQP0RSSeKSTVgpSUFHr27EmPHj3o1asXCxYsOKTtPPLII+zatavCde+99x6nn346PXv2JDs7m+HDhwOwdOlSXn311UOuXUTi2/bFL0cC0qkKSAEwsyFmNik/Pz/oUuqdoRnteGhYd9q1SMMIT/H80LDuGosiInFBEzfUgrS0NJYuXQrA/PnzGT9+PO+8806Nt/PII48wevRoGjdufMC6adOmMX78eEaPHg3AzJkzgXBI+vjjjxk0aNChH4CIxKWc/BA/emMv13s/Trz+LwpIAXD3ucDc3r173xJ0LfWRBuiLSLxSS1It++abbzjqqP+MFXj44Yc566yz6Nu3L7/4xS8AKCoqYvDgwfTo0YNu3boxY8YMHn30UTZt2sSAAQMYMGBAuW0+8cQTvPDCC9x3332MGjWK9evX061bN3bv3s3999/PjBkz6NmzJzNmzIjpsYpI3dm26j1G//0DPt/VlOMzpyggiYiIxFD9a0l6uoL7g5w+FPrcArt3wbSrDlzf81rIGAVFefDCflPp3jTvoLsMhUL07NmT4uJicnJyeOuttwB4/fXXWbt2LQsXLuSbb75h1KhRvPvuu2zdupW2bdsyb1542/n5+aSnpzNhwgTefvttjjnmmHLbv/nmm3n//fe57LLLGD58OOvXrwegUaNG/OpXv+Ljjz/mscceO2idIpIYdix6gaPm3coVfjXnZ/5WkzSIiIjEmFqSakFZd7vVq1fz2muvcf311+PuvP7667z++utkZGTQr18/Vq9ezdq1a+nevTtvvPEGP/3pT3nvvfdIT08P+hBEJE7sWPQCzefdylI/lQuu/7kCkoiISADqX0tSVS0/jRpXvb7J0dVqOapK37592bZtG1u3bsXdGT9+PLfeeisFBQU0a9Zs3/sWL17Mq6++ys9//nMuvPBC7r///sPar4gkvrKA9Kl3ikzScHzQJYmIiCQltSTVstWrV1NSUsLRRx/NwIEDeeqppygsLAQgOzubLVu2sGnTJho3bszo0aO56667WLx4MQDNmjWjoKCgRvs7lM+ISPzJ3ZzLEfPu5FPvRIPrX1JAEhERCVD9a0kKQNmYJAB3Z8qUKaSkpHDxxRezatUq+vbtS2lpKc2bN+fZZ5/liy++4K677qJBgwY0bNiQv/71rwCMHTuWSy65hLZt2/L2229Xa98DBgzgt7/9LT179mT8+PGMGDGirg5TROpITn6Ikc+sorXfy8+uv1wBSUREJGAKSbWgpKSk0nXjxo1j3Lhx5brbnXLKKQwcOPCA995xxx3ccccdFW5n8uTJ+5536NCBFStWANCyZUsWLVp0GNWLSJB2fPwiM15fSN6332NC5rX01BgkERGRwKm7nYhIQHZ8/CLNXrmVC759hyk39dIkDSIiInFCLUkiIgHY8fFMmr1yK8u9Iw2um0nPDscGXZKIiIhEqCVJRCTGwgFpLMu9I3bdTHp2PCHokkRERCSKQpKISAzl5Id49vUFCkgiIiJxTN3tRERiJHfLZkZO+Yy8bwdy3k0/I6PDcUGXJCIiklDmLMnm4flr2LQzRNsWadw1sDNDM9rV+n7UkiQiEgM7Pp5J2l/OpFXhaqZk9qGXAlJCMLMhZjYpPz8/6FJERJLenCXZjJ+1nOydIRzI3hli/KzlzFmSXev7CiwkmVmqmS00s0/NbKWZ/TKoWmrDnDlzMDNWr15d6Xv69+/Pxx9/DMCgQYPYuXNnlds899xzD7rfRx55hF27dtWo1iBt2rSJ4cOHB12GSExt/+Qlmr0ylvXemvHXDdYsdgnE3ee6+9j09PSgSxERSXoPz19DaE/5W++E9pTw8Pw1tb6vIFuSvgW+6+49gJ7AJWZ2Tl3vdN66eVw882LOmHIGF8+8mHnr5tXKdqdPn87555/P9OnTq/X+V199lRYtWlT5ngULFhx0O4kUkvbu3Uvbtm2ZOXNm0KWIxEzqpgU0n3sLK/1k/LpZGoMkIiJyiDbtDNVo+eEILCR5WGHkZcPIw+tyn/PWzeOBBQ+QU5SD4+QU5fDAggcOOygVFhby/vvv8+STT/L888/vWx4Khbjmmmvo0qUL1157LaHQf77ADh06sG3bNgAmTJhAt27d6NatG4888si+9zRt2hSArKws+vfvz/DhwznttNMYNWoU7s6jjz7Kpk2bGDBgAAMGDDigrkWLFnHuuefSo0cP+vTpQ0FBAcXFxdx00010796djIwM3n77bSB8s9qhQ4dy0UUX0aFDBx577DEmTJhARkYG55xzDtu3bwfCrWHjxo2jZ8+edOvWjYULFwKwcOFC+vbtS0ZGBueeey5r1qzZt93LL7+cyy67jAsvvJD169fTrVs3AFauXEmfPn3o2bMnZ5xxBmvXrq30fKxfv54uXbpwyy23cPrpp3PxxReXO58i8Wjbqvfo/fkfFJBERERqQdsWaTVafjgCHZNkZilmthTYArzh7h/V5f4mLp5IcUlxuWXFJcVMXDzxsLb78ssvc8kll3Dqqady9NFH88knnwDw17/+lcaNG7Nq1SruueeefcujffLJJzz99NN89NFHfPjhhzz++OMsWbLkgPctWbKERx55hM8++4x169bxwQcf8OMf/5i2bdvy9ttv7ws7ZXbv3s2IESOYOHEin376KW+++SZpaWn8+c9/xsxYvnw506dP54YbbqC4OHxOVqxYwaxZs1i0aBH33nsvjRs3ZsmSJfTt25dnnnlm37Z37drF0qVL+ctf/sKYMWMAOO2003jvvfdYsmQJv/rVr7jnnnv2vX/x4sU888wzvPPOO+Vq/Nvf/sa4ceNYunQpH3/8Me3bt6/yfKxdu5bbb7+dlStX0qJFC1566aVD+bpEYiInP8SIucX8rfQKBSQREZFacNfAzqQ1TCm3LK1hCncN7Fzr+wp0djt3LwF6mlkLYLaZdXP3FdHvMbOxwFiAVq1akZWVVW4b6enpFBQUVGt/uUW5lS6v7jYqMnXqVG677TYKCgoYOnQoU6ZM4dRTT+Wtt97iBz/4AQUFBXTp0oVu3bpRVFREQUEB7k5hYSFvvvkmgwYNorS0FIDBgwfzxhtv0LFjRwAKCgrYtWsXZ555Junp6RQVFXH66aezatUqevTosW87Rx55ZLmaVq5cyXHHHcdpp51GQUEBZkYoFCIrK4tbb72VgoIC2rVrR/v27VmyZAnFxcWcf/75AKSmptK8eXMGDBhAQUEBnTp1YsWKFRQUFFBSUsIVV1xBQUEBGRkZ5Ofns2HDBgoLC7n77rv58ssvMTP27Nmzr+Wqf//++76nwsJCSktLKSgooGfPnjz44IN8+eWXDBkyhI4dO1Z6PgYNGsSJJ57IKaecQkFBAd26dWPNmjUHfG/FxcUH/IwErbCwMO5qiqVkO/5FhYt4Je8ltnsR3rIFn7e5mG4b15G1cV3QpYmIiCS0slnsYjG7XVxMAe7uO83sbeASYMV+6yYBkwB69+7t/fv3L/fZVatW0axZs2rtp3WT1uQU5VS4vLrb2N/27dt59913WbVqFWZGSUkJZsbEiRM54ogjaNy4Mc2aNaOgoIAGDRrQpEkTmjVrhpnRtGlTUlNTOfLII/ft/8gjjyQ1NXXf62bNmtG4ceN924FwiGnYsGG57exff5MmTUhJSTlgeXRNACkpKTRp0oTU1NRy20lJSeHoo4/et/8GDRrQrFmzfe8ve5+Z0bx5c+677z4uuugi5s6dy/r16+nfvz/NmjUjNTWVFi1a7KuladOm+7aVmZlJ//79mTdvHldffTV///vfKz0fTZs2JS0tbd/yxo0bU1hYeMDxpaamkpGRcUjfZV0p6y6ZrJLp+Oetm8eM96fzLXvAwBru5H2fzcATejH45MFBlyciIpLwhma0q5NQtL8gZ7c7NtKChJmlARcBlU8NVwvG9RpHakpquWWpKamM6zXukLc5c+ZMrrvuOr766ivWr1/Phg0bOOmkk3jvvfe44IILeO655wD47LPPWLZs2QGf79evH3PmzGHXrl0UFRUxe/Zs+vXrV+39lwWw/XXu3JmcnBwWLVoEhFuk9u7dS79+/Zg2bRoAn3/+OV9//TWdO9esiXLGjBkAvP/++6Snp5Oenk5+fj7t2oV/YCdPnlyt7axbt46TTz6ZH//4x1xxxRUsW7bssM+HSJD++OFDfOt7yi3b43sOu0uviIiIxFaQLUltgClmlkI4rL3g7q/U5Q7L/pI7cfFEcotyad2kNeN6jTusv/BOnz6dn/70p+WWff/732f69OlMmDCBm266iS5dutCpUyfOPPPMcu8zM3r16sWNN95Inz59ALj55ptr1BIyduxYLrnkkn1jk8o0atSIGTNmcMcddxAKhUhLS+PNN9/khz/8Ibfddhvdu3fniCOOYPLkyQd01TuYstaaPXv28NRTTwFw9913c8MNN/Dggw8yeHD1zucLL7zA1KlTadiwIa1bt+aee+6hZcuWFZ6P9evX16hGkVjbvng2W3bvBLMD1lXW1VdERETik7nX6YRytap3795edp+hMqtWraJLly4BVVR9BQUF+7qGlZSUcNxxx5Gbm0vDhg0Drqxm+vfvzx/+8Ad69+5do89FH39disefh2TqblaRZDj+nB2FFP3pPG5uW0peBf9Jt2nShteHvx77wmrIzD5x95r9x50kKrr+JLJk+O+ypnROKqbzciCdk4ol4nmp6roX6Ox2yer000/n5ptvTriAJCIHyskPMfKJRdxccg9Xdf1/B3TpbWgND6tLr4iIiMReXEzckGxWr67ToVd1KplmKRM5mO2L57Bi3pPk7/0BT2ZeTK8TjqJD62PLdem9KPUiTdogIiKSYBSSREQOwfbFc2j2j0xaeQeevq47PU84CgiPfYwORfrDgoiISOKpF93tEmlcldQd/RxIrJQFpFXeAR/9km4UKyIiUs8kfEhKTU0lLy9PvyAnOXcnLy+P1NTUg79Z5DBsX/xyuYDUo1OHoEsSERGRWpbw3e3at2/Pxo0b2bp1a9ClVKm4uDipf4GPxfGnpqbSvn37Ot2HJLec/BC/fn0T13kXGo9+VgFJRESknkr4kNSwYUNOOumkoMs4qKysrBrd/6i+Sfbjl8S35evVjJyRQ17oZG4e8w96nNgy6JIkBsxsCDCkY8eOQZciIiIxlPDd7URE6tr2JS/T4qnz6Fv4JlMy+9BLASlpuPtcdx+bnp4edCkiIhJDCd+SJCJSl7YveZlmL49hjZ/INaNvoUdkFjsRERGpv9SSJCJSieiAVDJ6lsYgiYiIJAmFJBGRCmzesJamCkgiIiJJSSFJRGQ/OfkhRjy/kXv8RwpIIiIiSUhjkkREomxf8g9+O/8r8kKncm3mnRqDJCIikoQUkkREIrYv+QdNX76J67wTN4x5lV4KSCIiIklJIUlEhP8EpLV+Ag1HP6/7IImIiCQxjUkSkaQXHZD2jp6tMUgiIiJJTi1JIpLUcvJDLJ43mZP8eAUkERERARSSRCSJ5ewoZOQTi9ix5xamXNeVnh1PDLokERERiQPqbiciSWn70lcIPdqXlMJcns48RwFJRERE9lFIEpGks33pKzSdcwPFpSlMGH2OZrETERGRchSSRCSplAWkL/x49oyeTY9OJwVdkoiIiMQZjUkSkaSRt+INmikgiUgNzFmSzcPz17BpZ4i2LdK4a2Bnhma0C7osEaljakkSkaSQkx9izKu7eM3PVUASkWqZsySb8bOWk70zhAPZO0OMn7WcOUuygy5NROqYQpKI1Hvb1nzAdX9/j3VFqbTPfEYBSUSq5eH5awjtKSm3LLSnhIfnrwmoIhGJFXW3E5F6bfun82g++wZG+SX0yPyTJmkQkWrbtDNUo+UiUn+oJUlE6q28T1+l6ewb+MLb02v0rxWQRKRG2rZIq9FyEak/FJJEpF7K+/RVms2+ni+9ncYgySEzsyFmNik/Pz/oUiQAdw3sTFrDlHLL0hqmcNfAzgFVJCKxopAkIvVOzrY8mPNDvvR27B6lgCSHzt3nuvvY9PT0oEuRAAzNaMdDw7rTrkUaBrRrkcZDw7prdjuRJKAxSSJSr+Tkhxj59DKOKR3PfaMuosepJwddkogksKEZ7RSKRJKQQpKI1Bt5y/6Pl+e+St7uy5iQeTU9NAZJREREDoFCkojUC3nL/o9ms66jv7fl7BvuIUMBSURERA6RxiSJSMIrC0jrvC3fjppNxsltgi5JREREEphCkogktLxl/0fTWdfvC0g9Tj0l6JJEREQkwSkkiUjCyskP8cQr7/Olt1NAEhERkVqjMUkikpByt2xh5JSV5H17ARfdNI5eHY4LuiQREZGkMWdJNg/PX8OmnSHatkhj8Akl9A+6qFqkliQRSTh5y16j8V8y6FC4lCmZfRSQREREYmjOkmzGz1pO9s4QDmTvDDF5xW7mLMkOurRao5AkIgklb9l8ms66jlxvyU9GXU4vzWInIiISUw/PX0NoT0m5ZbtLw8vrC4UkEUkY4YA0mq+8NaFRczQGSUREJACbdoZqtDwRKSSJSELY+uViBSQREZE40LZFWo2WJyKFJBGJezn5Ia5+aQeP+1AFJBERkYDdNbAzaQ1Tyi1r1CC8vL7Q7HYiEtfyVv6TO1/ZxrZdLTg38/f00BgkERGRQA3NaAdwwOx2ZcvrA4UkEYlbectfp8lLo/iBn0F65kxN0iAiIhInhma0KxeKsrKygiumDigkiUhcKgtIX3trjr7275yhgCQiIiIxojFJIhJ3ogNS8bWzOaNzx6BLEhERkSSiliQRiSs5O3exZfYvaOytFJBEREQkEApJIhI3cvJDjHz8I/aU/Dd/vbaHApKIiIgEQt3tRCQu5K14g88fvZKCwkL+lPldBSQREREJjFqSRCRweSveoMnMa2nrrXhqVFdN8y0iIiKBUkuSiASqLCBt8FaErp2tG8WKiIhI4BSSRCQweSveLBeQzujcKeiSRERERBSSRCQYOfkh7n7la5Z5JwUkERERiSsakyQiMbfl6zWMnLGJvFA7bh/zCmec2DLokkRERET2UUuSiMRU3oo3afZUPy4q/AdTMvvQSwFJ4piZDTGzSfn5+UGXIiIiMaSQJCIxk7fiTRrPvJZsP5Yh1/6QXprFTuKcu89197Hp6elBlyIiIjGkkCQiMREdkHZdO0djkERERCRuBRaSzOx4M3vbzD4zs5VmNi6oWkSkbuXmbiR15uhIQNIkDSIiIhLfgmxJ2gv8P3fvCpwD3G5mXQOsR0TqwPbiUq6ZupZ7/PZIQDo16JJEREREqhRYSHL3HHdfHHleAKwC2gVVj4jUvryVb/HeR/8ir3A3N2T+SAFJREREEkJcTAFuZh2ADOCjgEsRkVqSt/ItGr94Dbd4G6676VZN0iAiIiIJI/CQZGZNgZeAO939mwrWjwXGArRq1YqsrKzYFlhLCgsLE7b22pDMx5+Mx25bltPns1+T7cew4JS76bh+BVnrg64qGMn4/YuIiCS6QEOSmTUkHJCmufusit7j7pOASQC9e/f2/v37x67AWpSVlUWi1l4bkvn4k+3Y81a+ReOsX7PJj2HXtXPomLMpqY5/f8n2/YuIiNQHQc5uZ8CTwCp3nxBUHSJSe3LyQ/xz9tNs8mMoGqlJGkRERCQxBTm73XnAdcB3zWxp5DEowHpE5DDk7Chk5KQPeXDPtRSOfpUzTuscdEkiIiIihySw7nbu/j5gQe1fRGpP3sq3+HbmHaSV/pTJmUPooUkaREREJIEF2ZIkIvXAtpVv0/jFkZSWOr8bebZmsRMREZGEp5AkIods28q3afLiNeT40RSOnK0udiIiIlIvKCSJyCHZtmaBApKIiIjUSwpJIlJjOfkhbnh5O2/6WQpIIiIiUu8EfjNZEUksW9cu5MbZ29lU1JB2mVM5Q2OQREREpJ5RSBKRatv2WRZNXxjBLX4eJ2c+qUkaREREpF5SSBKRatn2WRZNXhhBrh/FqSMfUguSiIiI1FsakyQiB7XtsywaRwJSwciXNQZJRERE6jW1JIlIlXK2f0Ppi2MpUEASERGRJKGWJBGpVE5+iJFPfsIPS3+qgCQiIiJJQy1JIlKhbZ+9w/zZ08jbPYwJmcM0BklERESShkKSiBxg22fv0PiFq+nvR9HzunvpqYAkIiIiSUTd7USknLKAtMWPomDkHHp2PDHokkRERERiSi1JIsK8dfOYuHgiuUU5tNpTwqjGLTnz8tl0P+20oEsTERERiTm1JIkkuXnr5vHAggfIKcrBgdyGKfypTWO+bvRl0KWJiIiIBEIhSSTJTVw8keKS4nLLdpfuZuLiiQFVJCIiIhIshSSRJJdblFPJ8twYVyIiIiISHxSSRJLYtlXv0WpPSYXrWjdpHeNqJBmZWRMzSwm6DhERkWgKSSJJatuq90ibcRWjtjuNGjQqty41JZVxvcYFVJnUZ2bWwMyuNbN5ZrYFWA3kmNlnZvawmXUMukYRERGFJJEktOWr1aTNuIptns6ZQ2bxq/N+RZsmbTCMNk3a8MC5DzD45MFBlyn109vAKcB4oLW7H+/uxwHnAx8CvzOz0UEWKCIictApwM3sd+7+04MtE5HEkJMfYuSMTQz1oVx4zTi6d+lCd7ooFEmsfM/d9+y/0N23Ay8BL5lZw9iXJSIi8h/VaUm6qIJll9Z2ISJS97at/oC7/vYSeUV7uCDzIbp36RJ0SZJk9g9IZpZqZjeb2R1mdnRF7xEREYm1SluSzOw24IfAyWa2LGpVM+CDui5MRGrXttXvk/r8VdzpJ9JgzKv0OuGooEsSAZhI+JpSDMwB+gVajYiICFV3t3sO+D/gIeBnUcsLIt0iRCRBlAWk7d6MI695iu4ntgy6JElSZjYd+Lm7l92tuCXwYuT5zyr+lIiISGxVGpLcPR/IB0ZGpmdtFXl/UzNr6u5fx6hGETkM0QEp/5o5dO/SNeiSJLndCzxoZjnAr4E/ALOBVOCBAOsSERHZpzoTN/yI8IVrM1AaWezAGXVXlojUhpz8EOtf/AXtFJAkTrj7OuBaMzsfmAHMAwa7e8U37AqYmQ0BhnTsqJnJRUSSSXUmbrgT6Ozup7t798hDAUkkzuXkhxg56UPu3HsH+de8rIAkccHMjjKz24GuwFXADmB+JIzEHXef6+5j09PTgy5FRERiqDohaQPhbncikiC2rfmAdY8OYVfhN/w18zuaxU7iyRxgJ+EeCVPdfSowBMgws7kB1iUiIrLPQbvbAeuALDObB3xbttDdJ9RZVSJySOatm8cfP/o9W77N49g2cFPnzZrFTuLN0cBMIA24FcDdQ8CvzKxNkIWJiIiUqU5I+jryaBR5iEgcmrduHr/44H6+Ld0NZmxpCE999TgntGunG8VKPPkF8BpQwn6z2bl7TiAViYiI7OegIcndfwlgZo3dfVfdlyQih+KPH/0+HJCiFJcUM3HxRIUkiRvu/hLwUtB1iIiIVOWgY5LMrK+ZfQasjrzuYWZ/qfPKRKTacvJDbP624tuX5RblxrgakcqZ2eNm1q2SdU3MbIyZjYp1XSIiItGq093uEWAg8A8Ad//UzC6oy6JEpPq2bPickdM3QssW0HDnAetbN2kd85pEqvBn4H4z6w6sALYSvkdSJ6A58BQwLbjyat+cJdk8PH8Nm3aGaNsijbsGdmZoRrugy4opnQMRSTTVCUm4+wYzi14Ul/ezEEk229YsoPH073OlX05K/zuY8vnDFJcU71ufmpLKuF7jAqxQpDx3XwpcbWZNgd5AGyAErHL3NUHWVhfmLMlm/KzlhPaEL5vZO0OMn7UcIGlCgs6BiCSi6oSkDWZ2LuBm1hAYB6yq27JE5GC2rVlA6vTvs9Ob8t0Rd9C9azdOOqYJExdPJLcol9ZNWjOu1ziNR5K45O6FQFbQddS1h+ev2RcOyoT2lPDw/DVJExB0DkQkEVUnJP0AmAi0A7KB14Hb67IoEaladEDaMWI23buGh3gMPnmwQpFIHNm0M1Sj5fWRzoGIJKLqzG63DdAgWpE4kbt1G0dOv4ad3pSdI2btC0giEn/atkgju4Iw0LZFWgDVBEPnQEQSUaWz25nZ3ZF//2Rmj+7/iF2JIlImJz/ENZOXc6/fxs4Rs+jWtXvQJYnUmJmlmNkfgq4jFu4a2Jm0hinllqU1TOGugZ0Dqij2dA5EJBFV1ZJUNu7o41gUIiJV27bmX/x11lvkFffm5swf0O2Eo4IuSeSQuHuJmZ0fdB2xUDbmJplndtM5EJFEVGlIcve5kX+nxK4cEanItjX/4sjp3yfTm3HljTeToYAkiW+Jmf0DeBEoKlvo7rOCK6luDM1ol/SBQOdARBJNpSHJzOYCXtl6d7+8TioSkXLKAlK+N6ZgxEwyTmoVdEkitSEVyAO+G7XMgXoXkkREJPFU1d2urL/4MKA18Gzk9Uhgc10WJSJh0QFp54jZGoMk9Ya73xR0DSIiIpWpqrvdOwBm9r/u3jtq1Vwz0zglkTqWkx/itZmTucgbs/NqTdIg9YuZtQf+BJwXWfQeMM7dNwZXlYiISFils9tFaWJmJ5e9MLOTgCZ1V5KI5OwoZOSkD5mw+0ryRr9Bt9PPCLokkdr2NPAPoG3kMTeyTEREJHDVCUk/AbLMLMvM3gHeBu6s06pEktjWzz9i96Nnk164jimZZ9Oj00lBlyRSF45196fdfW/kMRk4NuiiREREoHo3k33NzDoBp0UWrXb3b+u2LJHktPXzjzjyuWHs9jR+c/VZmuZb6rM8MxsNTI+8Hkl4IgcREZHAHTQkRXQCOhOejaiHmeHuz9RdWSLJpywgFXgaO66epS52Ut+NITwm6Y+EZ7VbAGgyBxERiQsHDUlm9gugP9AVeBW4FHgfUEgSqSVb1y1VQJKkYWYpwP/oVhIiIhKvqjMmaThwIZAbmbK1B5Bep1WJJJGc/BCjZ+bytvdSQJKk4O4lwIlm1ijoWkRERCpSne52IXcvNbO9ZtYc2AIcX8d1iSSFrV8uJvOlHDYVHcHxmc9oDJIkk3XAB2b2D6CobKG7TwiuJBERkbDqhKSPzawF8DjwCVAI/KsuixJJBlvXLuTIaVdyh59Bq8zn6KWAJMnly8ijAdAs4FpERETKqTIkmZkBD7n7TuBvZvYa0Nzdl8WiOJH6qiwgFXoqx1/9O7UgSVKJjEk61d1HBV2LiIhIRaock+TuTniyhrLX6xWQRA7P1rULaTRtGIWeyvarZ2sMkiQdjUkSEZF4V53udovN7Cx3X1Tn1YjUczk7iyiafguN/Ui2X6VJGiSpaUySiIjEreqEpLOBUWb2FeELmRFuZDrs3+7M7CngMmCLu3c73O2JxLOc/BAjH19IWsl/8fBVPenWrUfQJYkESWOSREQkblUnJA2sw/1PBh5D91ySem7r2kW8/cJfyNs9gimZl2sMkiQ9d//l/svMrLo3OBcREalTB71Pkrt/5e5fAXsJ3xXdgeza2Lm7vwtsr41ticSrkrwvaTTtSgbsfodp156iWewkqZnZ+1HPp+63emGMyxEREalQpX+1M7PxQEN3/1Vk0b+AnUAjYArwUJ1XJ5Lgtq5dRO/l97PLjyTvqtmc0blT0CWJBK1J1PP9u1lbLAsRERGpTFVdG64C+kW9znP3jMjUre8Qo5BkZmOBsQCtWrUiKysrFrutdYWFhQlbe21IxuMvyfuS3svvp8iP5L2uv6bVth1Jdw4gOb/7aMl+/BXwSp5X9FpERCQQVfb/dveiqJcTI8tKzCytTqsqX8MkYBJA7969vX///rHada3KysoiUWuvDcl2/Dn5IR75yyI6elM+7Ho/I0Yk7+1gku2731+yH38FWpjZlYS7e7cws2GR5QakB1eWiIjIf1QVkpqaWUN33wPg7pMBzOxIoHkMahNJSLlbtzJy8gryis9gxE3v0Wr9Z0GXJBJP3gEuj3o+JGrdu7EvR0RE5EBVhaSZwN/N7EfuvgvAzJoQno1uZm3s3MymA/2BY8xsI/ALd3+yNrYtEoStX3xCo2nD6FU6htGZP6bXCUeRpZAkso+73xR0DSIiIgdTVUi6D/gN8HXkHkkAJwBPRtYdNncfWRvbEYkHW7/4hEbPXkHIGzHmqis0zbeIiIhIgqo0JLl7CfAzM/sl0DGy+At3D8WkMpEEEh2Qtl01i27degZdkoiIiIgcooPeuC8SipbHoBaRhLR501ekKiCJiIiI1BsHvZmsiFQuJz/EiGe/ZJIPVUASqQEza2xm95nZ45HXnczssqDrEhERgWq0JIlIxbZ+uZh7Z35K3q62XJj5a41BEqmZp4FPgL6R19nAi8ArgVUkIiIScdCWJDP7Z3WWiSSTrV8upuHUK7hr1yNMGXMWvRSQRGrqFHf/PVB2m4ldhO+VJCIiErhKW5LMLBVoTHh67qP4z8WrOdAuBrWJxKWygPStH0HpVZPpdWLLoEsSSUS7IzcmdwAzOwX4NtiSREREwqrqbncrcCfQlnCXiLKQ9A3heyWJJJ3ogLTtqlmc3i0j6JJEEtUDwGvA8WY2DTgPuDHIgkRERMpUNQX4RGCimd3h7n+KYU0icSknP8SK5x7gDAUkkcPm7q+b2SfAOYT/CDfO3bcFXJaIiAhQvSnA/2Rm5wIdot/v7s/UYV0icSUnP8TISR9SsPcWpozoQLeu3YMuSSShmdlc4DngH+5eFHQ9IiIi0aozccNU4A/A+cBZkUfvOq5LJG5s/XIx6x8dzO7CHTyeeb4Ckkjt+APQD/jMzGaa2fDIWFgREZHAVWcK8N5AV3f3ui5GJN6UjUE62VOYdNXJmuZbpJa4+zvAO2aWAnwXuAV4ivDkQCIiIoGqTkhaAbQGcuq4FpG4svXLxRwxdSjfegrbhutGsSK1LTK73RBgBNALmBJsRSIiImHVCUnHEO4OsZCo6Vnd/fI6q0okYFu/XMIRU4ey2xuwbfgsTu/eK+iSROoVM3sB6EN4hrvHgHfcvTTYqkRERMKqE5IeqOsiROJJTn6In8xcw93emiOH/00BSaRuPAmMdPeSoAsRERHZX3Vmt3snFoWIxIPNG7/g2ue+Ytuuo2DMa5yuG8WK1Coz+667vwU0Aa4ws3Lr3X1WIIWJiIhEqTQkmdn77n6+mRUQuSN62SrA3V2Da6Ve2frlEhpNvYLR3p+MzIn00iQNInXhO8BbhMci7c8BhSQREQlcVTeTPT/yb7PYlSMSjPAYpCvY4w04Z/g4TldAEqkT7v6LyNNfufu/o9eZ2UkBlCQiInKAg94nCcDMepjZjyKPM+q6KJFYig5IW4e/xOndzwy6JJFk8FIFy2bGvAoREZEKHHRMkpmNI3z/irIuENPMbJK7/6lOKxOJgZy8ndizV+HegK3DZyogidQxMzsNOB1IN7NhUauaA7qZrIiIxIXqzG6XCZzt7kUAZvY74F+AQpIktJz8ECOfWkKn0lu5c/gATu/eO+iSRJJBZ+AyoAXlxyUVEP6DnIiISOCqE5IMiJ6itSSyTCRhbV23lCdnzCGvuC8TMm/WGCSRGHH3l4GXzayvu/8rqDrM7GTgXiDd3YcHVYeIiMSn6oSkp4GPzGx25PVQwve3EElIW9ctJeWZy7nFU7js+pvpqYAkEoQlZnY74a53+7rZufuYg33QzJ4i3Bq1xd27RS2/BJgIpABPuPtvK9uGu68DMs1M46BEROQAB524wd0nADcB2yOPm9z9kTquS6ROlAWkEje2DX+Rnqe0D7okkWQ1FWgNDATeAdoT7nJXHZOBS6IXmFkK8GfgUqArMNLMuppZdzN7Zb/HcbV1ECIiUj9VdZ+k5u7+jZm1BNZHHmXrjgK+0Z3SJZFEB6Qtw1/SGCSRYHV096vM7Ap3n2JmzwHvVeeD7v6umXXYb3Ef4ItICxFm9jxwhbs/RLjVSUREpNqq6m73HOELyycceDNZgKZm9ri731NXxYnUlpz8EC89P4URbmz5/kwFJJHg7Yn8u9PMugG5wOG08LQDNkS93gicXdmbzexo4DdAhpmNj4Sp/d8zFhgL0KpVK7Kysg6jvPhSWFhYr46nNuicVEzn5UA6JxWrb+elqpvJXhb5t8Kb+0W6NqwAFJIkruXsKGTkE4vI+/YS+o26gx6nnhx0SSICkyK9Eu4D/gE0Be6P1c7dPQ/4wUHeMwmYBNC7d2/v379/DCqLjaysLOrT8dQGnZOK6bwcSOekYvXtvFTV3a5XVR9098VAl1qvSKQWbV33Kd9OvZbjSn/EhMxr6KFJGkTigrs/EXn6DlAbf7nIBo6Pet0+skxERKTGqupu97+Rf1OB3sCnhLvanQF8DPSt29JEDs/WdZ+SMvVympY6D3y/N10VkEQCZ2b/VdX6yGRBh2IR0MnMTiIcjq4Brj3EbYmISJKrdHY7dx/g7gOAHKCXu/d29zOBDPTXOYlzW/69jJSpl1Na6mz5/kt0PeOsoEsSkbBmB3kclJlNJ3xT885mttHMMt19L/AjYD6wCnjB3VfWQf0iIpIEqnOfpM7uvrzshbuvMDN1s5O4teXr1RzxzBAFJJE45O6/rIVtjKxk+avAq4e7fRERkYPeJwlYZmZPmFn/yONxYFldFyZyKHLyQ4x6/mveKe2pgCQSx8zsVDP7p5mtiLw+w8x+HnRdIiIiUL2QdBOwEhgXeawEbqzDmkQOyZb1K7j176+TW+ScmDlZAUkkvj0OjCcyFbi7LyM8jkhERCRwB+1u5+7FwB8jD8ysHzABuL1uSxOpvi3/XkbKM5dzd+kJNM58mV6apEEk3jV294VmFr1sb1DFiIiIRKvOmCTMLAMYCVwN/BuYVZdFidREWUCitJSWw/6gWexEEsM2MzuFyM3KzWw44YmCREREAlfVfZJOJRyMRgLbgBmARWa8E4kLW/69fF9A2jxsJl179Am6JBGpntsJ36j1NDPLJvwHuFHBlnQgMxsCDOnYsWPQpYiISAxVNSZpNfBd4DJ3P9/d/wSUxKYskYPL2bmL3Km3KCCJJCB3X+fu3wOOBU4DvgOcH2xVB3L3ue4+Nj09PehSREQkhqoKScMId31428weN7MLCd9MViRwOfkhRj7+Ef9V8mMFJJEEYmbNzWy8mT1mZhcBu4AbgC8Id+kWEREJXKXd7dx9DjDHzJoAVwB3AseZ2V+B2e7+ekwqFNnPln8v571pv2fHnmt5OvNSjUESSSxTgR2EbwZ7C3Av4T/AXenuSwOsS0REZJ/qzG5XBDwHPGdmRwFXAT8FFJIk5rb8ezkNnhnChaUldB1xN90UkEQSzcnu3h3AzJ4g3GPhhMhMqiIiInGhOvdJ2sfdd7j7JHe/sK4KEqlMWUCy0hI2D5tJt67dgy5JRGpuT9kTdy8BNiogiYhIvKnWFOAiQduyfkW5gNS1x9lBlyQih6aHmX0TeW5AWuS1Ae7uzYMrTUREJEwhSeJeTn6I3z7/NneVHsE3w2YoIIkkMHdPCboGERGRg1FIkriWu3UbIycvJy90Kjfe+AEZJ7UKuiQRERERqedqNCZJJJa2rF9Jg7/0oV/ha0zJ7KOAJCIiIiIxoZYkiUtb1q/EplxGSukeRg67UtN8i4iIiEjMqCVJ4k50QModNpOuPc4JuiQRSVJmNsTMJuXn5wddioiIxJBCksSV3M25CkgiEjfcfa67j01PTw+6FBERiSGFJIkbOfkhrnlmFY+XDlVAEhEREZHAaEySxIUtX33GL6e/R16oA5dk3q8xSCIiIiISGIUkCcy8dfOYuHgiuUW5HLu3hOsblDD2pvfopYAkIiIiIgFSdzsJxLx183hgwQPkFOXgOFuOaMCjrZuQU7ow6NJEREREJMkpJEkgJi6eSHFJcbllu303ExdPDKgiEREREZEwhSQJRG5Rbo2Wi4iIiIjEikKSxFxOfggraVHhutZNWse2GBERERGR/SgkSUxt+WoVXz86iLRt36FRgyPLrUtNSWVcr3EBVSYiIiIiEhZoSDKzS8xsjZl9YWY/C7IWqXtbvloFkwdz6t61PHXxIH513i9p06QNhtGmSRseOPcBBp88OOgyRURERCTJBTYFuJmlAH8GLgI2AovM7B/u/llQNUnd+XZnNkweQ8PS3eQOnUHXnn3pCgpFIiIiIhJ3gmxJ6gN84e7r3H038DxwRYD1SB3Z8tVqei79+b6A1CXjvKBLEhGpFjMbYmaT8vPzgy5FRERiKMiQ1A7YEPV6Y2SZ1CM5+SF+8PxKNvoxCkgiknDcfa67j01PTw+6FBERiaHAuttVl5mNBcYCtGrViqysrGALOkSFhYUJW/uh2vXNNh5cciQ79qSyoMv9dM/fw+YkOweQnN99NB1/ch+/iIhIIgoyJGUDx0e9bh9ZVo67TwImAfTu3dv79+8fk+JqW1ZWFola+6HY8tVqfHImd5b2pOPYJ/hm3adJdfzRku2735+OP7mPX0REJBEF2d1uEdDJzE4ys0bANcA/AqxHakk4IA3myNJiug/9Cb1OOCrokkREREREqi2wliR332tmPwLmAynAU+6+Mqh6pHZEB6ScoTPoknF+0CWJiIiIiNRIoGOS3P1V4NUga5Dak7OjkOLJwzmqtJicoc8rIImIiIhIQor7iRskMeTkhxj5xCKOL72Fe4b2oktGv6BLEhERERE5JApJcti2fL2aqc8+Q96332FC5vV00RgkERE5BHOWZPPw/DVs2hmibYs07hrYmaEZujuIiMSeQpIcli1fr6b06csYWxpi4Kib6aGAJCIih2DOkmzGz1pOaE8JANk7Q4yftRxAQUlEYi7I2e0kwZUFpLTSXeQMfZ4ep54cdEkiIpKgHp6/Zl9AKhPaU8LD89cEVJGIJDOFJDkk0QFp09AZGoMkIiKHZdPOUI2Wi4jUJYUkqbGc/BBPPzuV1NIQm65QQBIRkcPXtkVajZaLiNQlhSSpkZwdhYyc9CHPfnsBX416ly69FJBEROTw3TWwM2kNU8otS2uYwl0DOwdUkYgkM03cINW25es1hJ4ezvGlNzMh83pN0iAi9Z6ZDQGGdOzYMehS6r2yyRk0u50cDs2QKLVFIUmqZcvXayh9ejBHl+7init6aZpvEUkK7j4XmNu7d+9bgq4lGQzNaKdfaOWQaYZEqU3qbicHteXrNZSUTdJwxQx1sRMREZG4oxkSpTapJUmqtDl7PaVPX0bj0iIFJBEREYlbmiFRapNakqRSOfkhrp32Be+XnqGAJCIiInFNMyRKbVJIkgpt2fA5t//tVbYUlXBK5hMKSCIiIhLXNEOi1CZ1t5MDbNnwOSVPDeYXpemUjHmdXpqkQUREROKcZkiU2qSQJOWUBaQmpUUcecUTnHZiy6BLEhEREakWzZAotUXd7WSfLRs+Z+9Tl9GktIhNV0zntF7fCbokEREREZGYU0gSIDxJw9rJP6RpaaECkoiIiIgkNXW3E3LyQ4yc9CG+dyx/G9qGLhmapEFEREREkpdakpLclg2fs+BPmeQX7uKPmRcpIImIiIhI0lNLUhKat24eExdPJLcol2P3lnJbowKevfwnnK5Z7ERERERE1JKUbOatm8cDCx4gpygHx9lyhPFQ66NZ33RL0KWJiIiIiMQFhaQkM3HxRIpLisst2+17mLh4YkAViYiIiIjEF3W3SwLR3escr/A9uUW5Ma5KRERERCQ+KSTVc2Xd6/ZvPdpf6yatY1SRiEjiMLMhwJCOHTsGXYqIiMSQutvVcxV1r9tfakoq43qNi1FFIiKJw93nuvvY9PT0oEsREZEYUktSPVdVNzrDaN2kNeN6jWPwyYNjWJWIiIiISPxSSKrnjk09hi3FWw9Y3qZJG14f/noAFYmIiIiIxDd1t6vHcvK2c+OGHI4sLT9Zg7rXiYiIiIhUTi1J9Uz0THZW0oJepWdy6wln8OKON8ktylX3OhERERGRg1BIqkf2n8nOU3awrM0uhp80ktcvvDvg6kREREREEoO629UT89bN45737znwRrGl3+pGsSIiIiIiNaCQVA+UtSCVemmF63WjWBERERGR6lNIqgcOdi8k3ShWRERERKT6FJLqgapaijSTnYiIiIhIzSgkJbic/BBW0qLCdQ2sAQ+c+4BmshMRERERqQGFpAS2OXsdXz06iKbb+tGowZHl1qWmpPI/5/+PApKIiIiISA0pJCWozdnr2PPEILrtXcWTF36PX533S9o0aYNhtGnSRi1IIiIiIiKHSPdJSkBlAalF6U6yhzzLaWcO4DRQKBIRERERqQVqSUow+wekzr2/F3RJIiIiIiL1ikJSAsnJD3HLtOVsLm1B9mUKSCIiIiIidUHd7RLE5k1fc/2zq8ktSsXGvErnE1sGXZKIiIiISL2kkJQANmf/m91PDOInpSfROnMavU44KuiSRERERETqLYWkOFcWkFqWbueUy/5EZwUkEREREZE6pTFJcSw6IG28bBqdz9IYJBGRWDKzIWY2KT8/P+hSREQkhhSS4lTOzl3kPXl1JCA9q4AkIhIAd5/r7mPT09ODLkVERGJI3e3iUE5+iJGPf8RxJWP49WUd6XzWRUGXJCIiIiKSNBSS4szm7PVMn/IX8r69kAmZ12gMkoiIiIhIjCkkxZHN2ev59olLubV0OxeNuIHuCkgiIiIiIjGnMUlxoiwgHV26nY2Dp9K96+lBlyQiIiIikpQUkuJAOCAN4pjSPDYOnkrnPhcHXZKIiIiISNJSSApYTn6IvzzzLC1Lt7Nh8LMKSCIiIiIiAdOYpADl7Cxi5OMLySs+i2GjPqDHqacEXZKIiIiISNJTS1JANmevp/DR8zi1cBFTMvsoIImIiIiIxAm1JAVg86avKH5iMO1Kt/Lfg8/gVM1iJyIiIiISNwJpSTKzq8xspZmVmlnvIGoIyuZNX1H8+CCOLd3KxsHPcGqfS4IuSUREREREogTV3W4FMAx4N6D9ByJ38yYFJBERERGROBdISHL3Ve6+Joh9B2V7cSkjp6zivdLuCkgiIiIiInFMY5JiYPOmr3jmow1sK21N18y/agySiIiIiEgcq7OQZGZvAq0rWHWvu79cg+2MBcYCtGrViqysrNopMEaKvsmj6+L7+L0b7/X8I9+s+5SsdUFXFXuFhYUJ993VlmQ+dtDxJ/vxi4iIJKI6C0nu/r1a2s4kYBJA7969vX///rWx2ZjYvOlrQo/fznG+jbc6/ZzMK5P3RrFZWVkk0ndXm5L52EHHn+zHLyIikojU3a6OhAPSpRxXupWNg6bQNJQWdEkiIiIiIlINQU0BfqWZbQT6AvPMbH4QddSVnPwQnz75o30B6dSzLw26JBERERERqaZAWpLcfTYwO4h917Wc/BAjJ33I7r038tSQcZzW+8KgSxIRERERkRoI6j5J9dLmnK/58E9jKCgs5LHMAQpIIiIiIiIJSGOSasnmnK/ZNWkQl5Ru5rQrbqOLpvkWEREREUlIakmqBWUBqXXpZjYMmkKXXhcEXZKIiIiIiBwihaTDtH9AOvXsQUGXJCIiIiIih0Eh6TDk5If42TNvcWRpSAFJRERERKSe0JikQ5S7bRsjn1pG3q42bL5xARkntQq6JBERERERqQVqSToEm3O+ZtefBzC8aDpTMvsoIImI1FNmNsTMJuXn5wddioiIxJBCUg1tzvmaokmDaVOay8WXDqWXZrETEam33H2uu49NT08PuhQREYkhhaQaiA5IGy6dzKnnDA66JBERERERqWUKSdWUs6OAgseHKCCJiIiIiNRzmrihGnLyQ4x84mP6lFzOzZeeo4AkIiIiIlKPKSQdxOacDTw85SXyQl24JvO/OFVjkERERERE6jWFpCpsztlA4aTBPFC6jRtGL6CHApKIiIiISL2nMUmVKAtI7Uo3sfnSSfTo1CHokkREREREJAYUkioQHZA2XDqZTudcFnRJIiIiIiISIwpJ+8nJDzHnqd8rIImIiIiIJCmNSYqSkx9i5KQPyds9mH7DrqNrjz5BlyQiIiIiIjGmlqSI3NyNrH/0MhoXfs2UzLMVkEREREREkpRakggHpMJJg8koyeaRQcdomm8RERERkSSW9C1JZQGpfUk2Gy55mlPP1o1iRURERESSWVKHpP0DUqe+Q4IuSUREREREApa0ISknP8SYZz5lW0lTNlzylAKSiIiIiIgASTomKTc3m5ueWUZ20RE0GjOXTie2DLokERERERGJE0kXknJzsymcNIh7So6i6ZhZ9FJAEhERERGRKEkRkuatm8fExRPJLcrlmL3OHak76NbvV2pBEhERqUNzlmTz8Pw1bNoZom2LNO4a2JmhGe2CLktE5KDq/Zikeevm8cCCB8gpysFxth4Bv2l9HJ+3Sop8KCIiEog5S7IZP2s52TtDOJC9M8T4WcuZsyQ76NJERA6q3oekiYsnUlxSXG7Zt76HiYsnBlSRiIhI/ffw/DWE9pSUWxbaU8LD89cEVJGISPXV+5CUW5Rbo+UiIiJy+DbtDNVouYhIPKn3Ial1k9Y1Wi4iIiKHr22LtBotFxGJJ/U+JI3rNY7UlNRyy1JTUhnXa1xAFYmIiNR/dw3sTFrDlHLL0hqmcNfAzgFVJCJSffV+9oLBJw8G2De7XesmrRnXa9y+5SIiIlL7ymax0+x2IpKI6n1IgnBQUigSERGJraEZ7RSKRCQh1fvudiIiIiIiIjWhkCQiIiIiIhJFIUlERERERCSKQpKIiIiIiEgUhSQREREREZEoCkkiIiIiIiJRFJJERERERESiKCSJiIiIiIhEUUgSERERERGJopAkIiIiIiISRSFJREREREQkirl70DVUm5ltBb4Kuo5DdAywLegiApTMx5/Mxw46/kQ6/hPd/digi4gnZjYEGAKMANYGXE5tSqSfy1jROamYzsuBdE4qlojnpdLrXkKFpERmZh+7e++g6whKMh9/Mh876PiT/fglPunn8kA6JxXTeTmQzknF6tt5UXc7ERERERGRKApJIiIiIiIiURSSYmdS0AUELJmPP5mPHXT8yX78Ep/0c3kgnZOK6bwcSOekYvXqvGhMkoiIiIiISBS1JImIiIiIiERRSIohM7vKzFaaWamZ1ZvZP6piZpeY2Roz+8LMfhZ0PbFkZk+Z2RYzWxF0LUEws+PN7G0z+yzycz8u6JpixcxSzWyhmX0aOfZfBl2TyP6S8ZpUmWS+VlUm2a9hFUnm61pl6vP1TiEptlYAw4B3gy4kFswsBfgzcCnQFRhpZl2DrSqmJgOXBF1EgPYC/8/duwLnALcn0ff/LfBdd+8B9AQuMbNzgi1J5ABJdU2qjK5VlZpMcl/DKpLM17XK1NvrnUJSDLn7KndfE3QdMdQH+MLd17n7buB54IqAa4oZd38X2B50HUFx9xx3Xxx5XgCsAtoFW1VseFhh5GXDyEMDQCWuJOE1qTJJfa2qTLJfwyqSzNe1ytTn651CktSldsCGqNcbSfL/mSQrM+sAZAAfBVxKzJhZipktBbYAb7h70hy7SILRtUpqLBmva5Wpr9e7I4IuoL4xszeB1hWsutfdX451PSJBM7OmwEvAne7+TdD1xIq7lwA9zawFMNvMurm7+vZLTOmaJFL7kvW6Vpn6er1TSKpl7v69oGuII9nA8VGv20eWSZIws4aELyTT3H1W0PUEwd13mtnbhPv2J/xFQxKLrknVomuVVJuua5Wrb9c7dbeTurQI6GRmJ5lZI+Aa4B8B1yQxYmYGPAmscvcJQdcTS2Z2bOQvaphZGnARsDrQokSkMrpWSbUk83WtMvX5eqeQFENmdqWZbQT6AvPMbH7QNdUld98L/AiYT3hw4wvuvjLYqmLHzKYD/wI6m9lGM8sMuqYYOw+4DviumS2NPAYFXVSMtAHeNrNlhH8Be8PdXwm4JpFyku2aVJlkv1ZVRtewCiXzda0y9fZ6Z+71YgIKERERERGRWqGWJBERERERkSgKSSIiIiIiIlEUkkRERERERKIoJImIiIiIiERRSBIREREREYmikCR1ysxKoqbJXGpmHcxsQTU+94SZdY08v+cQ9ptlZmvMbJmZrTazx8rm8Y+sXxD1/GEzWxn591gz+8jMlphZv5ruN1bM7L8ix7XczD41swmRG9wd6vY6mNmKyPPeZvboYWyrxt+XiEg80bWrbujaJYlEU4BLnTKzQndvGuttmFkW8N/u/nHk5oAPAb3d/TsVvDcfaOnuJWZ2DfA9d7+5BvtKcfeSmtR3OMzsB8BQ4JrI3a0bAf8F/MXdvzmU2sysA/CKu3erhfoO+zsXEQmSrl21T9cuSTRqSZKYM7PCyL/9I381mxn5y9K0yN2sy/6a1tvMfgukRf6SNy2ybrSZLYws+7uZpVS1P3ffDdwNnGBmPfar4R9AU+ATM/sp8Hvgisi208zsYjP7l5ktNrMXzaxp5HPrzex3ZrYYuOog7/tlZPlyMzstsrypmT0dWbbMzL4fWV7hdvZzL3Cbu+8sOz53/23ZRcbMCs3sf83sU6Cvmd1vZovMbIWZTYo6x2dG/pL3KXB71PfT38xeiTxvYmZPRc73EjO7IrL8RjObZWavmdlaM/t9ZPkB35eISH2ga5euXZJk3F0PPersAZQASyOP2ZFlhZF/+wP5QHvCgf1fwPmRdVmE/3q27/2R512AuUDDyOu/ANdXsN99n49aNgcYUcE2o5/fCDwWeX4M8C7QJPL6p8D9kefrgbur+b47Is9/CDwRef474JGo/R5V1Xai3tcc2HGQc+7A1VGvW0Y9nwoMiTxfBlwQef4wsCLqe3kl8vx/gNGR5y2Az4EmkfO0DkgHUoGvgOP3P5966KGHHon40LVL1y499DgCkboVcveeVaxf6O4bAcxsKdABeL+K918InAksivxRKQ3YUs1arJrvK3MO0BX4ILKvRoQvhmVmVPN9syL/fgIMizz/HnBN2RvcfYeZXXaQ7Rx4QGYDCV+0WgDXuvsCwhf3l6LeNsDM7gYaAy2BlWb2HtDC3d+NvGcqcGkFu7gYuNzM/jvyOhU4IfL8n+6eH6njM+BEYENV9YqIJAhdu3TtkiSnkCRB+zbqeQkH/5k0YIq7j6/JTiLdGroDq2ryMeANdx9Zyfqiar6v7BgPdnwH2w7u/k2kS8JJ7v5vd58PzI90MWgUeVuxR/pym1kq4b9Y9nb3DWb2AOGLRXUZ8H13X1NuodnZ1Py7ExGpL3Ttqv7+dO2ShKQxSZII9th/Zr/5JzDczI4DMLOWZnZiVR+OfPYhYIO7L6vBfj8EzjOzjpHtNDGzUw/jfdHeoHxf6qNqsJ2HgL9aZMajSD/tyi4eZcu3RfqIDwfwcJ/wnWZ2fmT9qEo+Px+4I6oveMZBjgvKf18iIslK167ydO2ShKKQJIlgErDMzKa5+2fAz4HXzWwZ4f9ht6nkc9Mi71lBuC/yFTXZqbtvJdx/eXpkO/8CTjvU9+3nQeCoyIDUT4EBNdjOXwlfcD+KvO8DYEnksX9tO4HHCZ+D+cCiqNU3AX+OdBWprDvHr4GGhM//ysjrg9n3fVXjvSIi9ZWuXeXp2iUJRVOAi4iIiIiIRFFLkoiIiIiISBSFJBERERERkSgKSSIiIiIiIlEUkkRERERERKIoJImIiIiIiERRSBIREREREYmikCQiIiIiIhJFIUlERERERCTK/wdVw8AcK6xpUQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", "\n", - "fig = plt.figure(figsize=(12, 6))\n", + "fig = plt.figure(figsize=(12,6))\n", "\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot([min_g, max_g], [min_g, max_g], label=\"y=x comparison\")\n", - "plt.plot([min_g, max_g], [m * min_g + b, m * max_g + b], \"--\", label=\"Best fit\")\n", - "plt.plot(g_discrete, dJ_du[idx], \"o\", label=\"Adjoint comparison\")\n", - "plt.xlabel(\"Finite Difference Gradient\")\n", - "plt.ylabel(\"Adjoint Gradient\")\n", + "plt.subplot(1,2,1)\n", + "plt.plot([min_g, max_g],[min_g, max_g],label='y=x comparison')\n", + "plt.plot([min_g, max_g],[m*min_g+b, m*max_g+b],'--',label='Best fit')\n", + "plt.plot(g_discrete,dJ_du[idx],'o',label='Adjoint comparison')\n", + "plt.xlabel('Finite Difference Gradient')\n", + "plt.ylabel('Adjoint Gradient')\n", "plt.legend()\n", "plt.grid(True)\n", "plt.axis(\"square\")\n", "\n", - "plt.subplot(1, 2, 2)\n", - "rel_err = (\n", - " np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx]))\n", - " / np.abs(np.squeeze(g_discrete))\n", - " * 100\n", - ")\n", - "plt.semilogy(g_discrete, rel_err, \"o\")\n", + "plt.subplot(1,2,2)\n", + "rel_err = np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx])) / np.abs(np.squeeze(g_discrete)) * 100\n", + "plt.semilogy(g_discrete,rel_err,'o')\n", "plt.grid(True)\n", - "plt.xlabel(\"Finite Difference Gradient\")\n", - "plt.ylabel(\"Relative Error (%)\")\n", + "plt.xlabel('Finite Difference Gradient')\n", + "plt.ylabel('Relative Error (%)')\n", "\n", "fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - "fig.suptitle(\"Resolution: {} Seed: {} Nx: {} Ny: {}\".format(resolution, seed, Nx, Ny))\n", + "fig.suptitle('Resolution: {} Seed: {} Nx: {} Ny: {}'.format(resolution,seed,Nx,Ny))\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb b/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb index c2e360d25..6ffc6cacd 100644 --- a/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb +++ b/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb @@ -13,9 +13,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -23,7 +31,6 @@ "from autograd import numpy as npa\n", "import nlopt\n", "from matplotlib import pyplot as plt\n", - "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -38,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -46,83 +53,66 @@ "\n", "Sx = 6\n", "Sy = 5\n", - "cell_size = mp.Vector3(Sx, Sy)\n", + "cell_size = mp.Vector3(Sx,Sy)\n", "\n", "pml_layers = [mp.PML(1.0)]\n", "\n", - "fcen = 1 / 1.55\n", + "fcen = 1/1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-1.5, 0, 0]\n", - "source_size = mp.Vector3(0, 2, 0)\n", - "kpoint = mp.Vector3(1, 0, 0)\n", - "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", - "source = [\n", - " mp.EigenModeSource(\n", - " src,\n", - " eig_band=1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size=source_size,\n", - " center=source_center,\n", - " )\n", - "]\n", + "source_center = [-1.5,0,0]\n", + "source_size = mp.Vector3(0,2,0)\n", + "kpoint = mp.Vector3(1,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]\n", "\n", "design_region_resolution = 10\n", "Nx = design_region_resolution\n", "Ny = design_region_resolution\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", - "design_region = mpa.DesignRegion(\n", - " design_variables, volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0))\n", - ")\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0)))\n", "\n", "\n", "geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", - " ), # horizontal waveguide\n", - " mp.Block(\n", - " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", - " ), # vertical waveguide\n", - " mp.Block(\n", - " center=design_region.center, size=design_region.size, material=design_variables\n", - " ), # design region\n", - " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", + " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", - " #\n", + " # \n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; We give an alternative approach to impose symmetry in later tutorials.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", + "\n", "]\n", "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " eps_averaging=False,\n", - " resolution=resolution,\n", - ")\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " eps_averaging=False,\n", + " resolution=resolution)\n", "\n", - "TE_top = mpa.EigenmodeCoefficient(\n", - " sim, mp.Volume(center=mp.Vector3(0, 1, 0), size=mp.Vector3(x=2)), mode=1\n", - ")\n", + "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,1,0),size=mp.Vector3(x=2)),mode=1)\n", "ob_list = [TE_top]\n", "\n", - "\n", "def J(alpha):\n", " return npa.abs(alpha) ** 2\n", "\n", - "\n", "opt = mpa.OptimizationProblem(\n", " simulation=sim,\n", " objective_functions=J,\n", " objective_arguments=ob_list,\n", " design_regions=[design_region],\n", " fcen=fcen,\n", - " df=0,\n", - " nf=1,\n", + " df = 0,\n", + " nf = 1\n", ")" ] }, @@ -135,11 +125,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "x0 = 0.5 * np.ones((Nx * Ny,))\n", + "x0 = 0.5*np.ones((Nx*Ny,))\n", "opt.update_design([x0])\n", "\n", "opt.plot2D(True)\n", @@ -157,17 +160,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "sensitivity = [0]\n", - "\n", - "\n", "def f(x, grad):\n", " f0, dJ_du = opt([x])\n", - " f0 = f0[0] # f0 is an array of length 1\n", + " f0 = f0[0] # f0 is an array of length 1 \n", " if grad.size > 0:\n", " grad[:] = np.squeeze(dJ_du)\n", " evaluation_history.append(np.real(f0))\n", @@ -188,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -205,9 +206,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + } + ], "source": [ "x = solver.optimize(x0);" ] @@ -221,15 +259,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "plt.plot(evaluation_history, \"o-\")\n", + "plt.plot(evaluation_history,'o-')\n", "plt.grid(True)\n", - "plt.xlabel(\"Iteration\")\n", - "plt.ylabel(\"FOM\")\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('FOM')\n", "plt.show()" ] }, @@ -242,16 +293,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "opt.update_design([x])\n", - "opt.plot2D(\n", - " True,\n", - " plot_monitors_flag=False,\n", - " output_plane=mp.Volume(center=(0, 0, 0), size=(2, 2, 0)),\n", - ")\n", + "opt.plot2D(True,plot_monitors_flag=False,output_plane=mp.Volume(center=(0,0,0),size=(2,2,0)))\n", "plt.axis(\"off\");" ] }, @@ -266,19 +326,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "TE0 = mpa.EigenmodeCoefficient(\n", - " sim, mp.Volume(center=mp.Vector3(-1, 0, 0), size=mp.Vector3(y=2)), mode=1\n", - ")\n", - "ob_list = [TE0, TE_top]\n", - "\n", - "\n", - "def J(source, top):\n", - " return npa.abs(top / source) ** 2\n", + "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(-1,0,0),size=mp.Vector3(y=2)),mode=1)\n", + "ob_list = [TE0,TE_top]\n", "\n", + "def J(source,top):\n", + " return npa.abs(top/source) ** 2\n", "\n", "opt.objective_functions = [J]\n", "opt.objective_arguments = ob_list\n", @@ -294,9 +350,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + } + ], "source": [ "evaluation_history = []\n", "solver = nlopt.opt(algorithm, n)\n", @@ -316,15 +409,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "plt.plot(np.array(evaluation_history) * 100, \"o-\")\n", + "plt.plot(np.array(evaluation_history)*100,'o-')\n", "plt.grid(True)\n", - "plt.xlabel(\"Iteration\")\n", - "plt.ylabel(\"Transmission (%)\")\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Transmission (%)')\n", "plt.show()" ] }, @@ -337,9 +443,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Achieved an improvement of 28.0x\n" + ] + } + ], "source": [ "improvement = max(evaluation_history) / min(evaluation_history)\n", "print(\"Achieved an improvement of {0:1.1f}x\".format(improvement))" @@ -354,16 +468,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "opt.update_design([x])\n", - "opt.plot2D(\n", - " True,\n", - " plot_monitors_flag=False,\n", - " output_plane=mp.Volume(center=(0, 0, 0), size=(2, 2, 0)),\n", - ")\n", + "opt.plot2D(True,plot_monitors_flag=False,output_plane=mp.Volume(center=(0,0,0),size=(2,2,0)))\n", "plt.axis(\"off\");" ] }, @@ -376,11 +499,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "plt.imshow(np.rot90(np.squeeze(np.abs(sensitivity[0].reshape(Nx, Ny)))));" + "plt.imshow(np.rot90(np.squeeze(np.abs(sensitivity[0].reshape(Nx,Ny)))));" ] } ], diff --git a/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb b/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb index f2008a8ce..b7f61e4f7 100644 --- a/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb +++ b/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb @@ -17,9 +17,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -30,7 +38,6 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", - "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -45,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +66,7 @@ "\n", "resolution = 20\n", "\n", - "frequencies = 1 / np.linspace(1.5, 1.6, 10)" + "frequencies = 1/np.linspace(1.5,1.6,10)" ] }, { @@ -73,19 +80,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.20124611797498096\n" + ] + } + ], "source": [ - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = (\n", - " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - ")\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", "print(filter_radius)\n", - "design_region_resolution = int(1 * resolution)" + "design_region_resolution = int(1*resolution)" ] }, { @@ -97,33 +110,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", - "Sy = 2 * pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx, Sy)\n", + "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1 / 1.55\n", + "fcen = 1/1.55\n", "width = 0.1\n", "fwidth = width * fcen\n", - "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", - "source_size = mp.Vector3(0, Sy, 0)\n", - "kpoint = mp.Vector3(1, 0, 0)\n", - "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", - "source = [\n", - " mp.EigenModeSource(\n", - " src,\n", - " eig_band=1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size=source_size,\n", - " center=source_center,\n", - " )\n", - "]" + "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", + "source_size = mp.Vector3(0,Sy,0)\n", + "kpoint = mp.Vector3(1,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]" ] }, { @@ -135,21 +144,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "Nx = int(design_region_resolution * design_region_width)\n", - "Ny = int(design_region_resolution * design_region_height)\n", + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", - "design_region = mpa.DesignRegion(\n", - " design_variables,\n", - " volume=mp.Volume(\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(design_region_width, design_region_height, 0),\n", - " ),\n", - ")" + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" ] }, { @@ -161,24 +164,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", - "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", - "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", + "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", + "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", + "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", "\n", - "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", - "top_wg_mask = (Y_g == design_region_width / 2) & (np.abs(X_g) <= waveguide_width / 2)\n", + "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", + "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", "Si_mask = left_wg_mask | top_wg_mask\n", "\n", - "border_mask = (\n", - " (X_g == -design_region_width / 2)\n", - " | (X_g == design_region_width / 2)\n", - " | (Y_g == -design_region_height / 2)\n", - " | (Y_g == design_region_height / 2)\n", - ")\n", + "border_mask = ((X_g == -design_region_width/2) | \n", + " (X_g == design_region_width/2) | \n", + " (Y_g == -design_region_height/2) | \n", + " (Y_g == design_region_height/2))\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False" ] @@ -192,29 +193,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "def mapping(x, eta, beta):\n", - " x = npa.where(Si_mask.flatten(), 1, npa.where(SiO2_mask.flatten(), 0, x))\n", - "\n", + "def mapping(x,eta,beta):\n", + " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", + " \n", " # filter\n", - " filtered_field = mpa.conic_filter(\n", - " x,\n", - " filter_radius,\n", - " design_region_width,\n", - " design_region_height,\n", - " design_region_resolution,\n", - " )\n", - "\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", - "\n", - " projected_field = (\n", - " npa.rot90(projected_field.T, 2) + projected_field\n", - " ) / 2 # 90-degree symmetry\n", - "\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " projected_field = (npa.rot90(projected_field.T, 2) + projected_field)/2 # 90-degree symmetry\n", + " \n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -230,35 +223,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", - " ), # horizontal waveguide\n", - " mp.Block(\n", - " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", - " ), # vertical waveguide\n", - " mp.Block(\n", - " center=design_region.center, size=design_region.size, material=design_variables\n", - " ), # design region\n", - " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", + " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", - " #\n", + " # \n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution,\n", - ")" + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution)" ] }, { @@ -270,47 +255,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(\n", - " sim,\n", - " mp.Volume(\n", - " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", - " size=mp.Vector3(y=Sy),\n", - " ),\n", - " mode,\n", - ")\n", - "TE_top = mpa.EigenmodeCoefficient(\n", - " sim,\n", - " mp.Volume(\n", - " center=mp.Vector3(0, Sx / 2 - pml_size - 2 * waveguide_length / 3, 0),\n", - " size=mp.Vector3(x=Sx),\n", - " ),\n", - " mode,\n", - ")\n", - "ob_list = [TE0, TE_top]\n", - "\n", - "\n", - "def J(source, top):\n", - " power = npa.abs(top / source) ** 2\n", + "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=Sy)),mode)\n", + "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,Sx/2 - pml_size - 2*waveguide_length/3,0),size=mp.Vector3(x=Sx)),mode)\n", + "ob_list = [TE0,TE_top]\n", + "def J(source,top):\n", + " power = npa.abs(top/source)**2\n", " return npa.mean(power)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation=sim,\n", - " objective_functions=J,\n", - " objective_arguments=ob_list,\n", - " design_regions=[design_region],\n", + " simulation = sim,\n", + " objective_functions = J,\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", " frequencies=frequencies,\n", ")" ] @@ -324,13 +293,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "rho_vector = np.random.rand(Nx * Ny)\n", - "opt.update_design([mapping(rho_vector, eta_i, 1e3)])\n", - "opt.plot2D(True, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", + "rho_vector = np.random.rand(Nx*Ny)\n", + "opt.update_design([mapping(rho_vector,eta_i,1e3)])\n", + "opt.plot2D(True,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", "plt.show()" ] }, @@ -343,26 +325,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "beta = 2048\n", "\n", "plt.figure()\n", - "ax1 = plt.subplot(1, 3, 1)\n", - "opt.update_design([mapping(rho_vector, 0.498, beta)])\n", - "opt.plot2D(True, ax=ax1, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", + "ax1 = plt.subplot(1,3,1)\n", + "opt.update_design([mapping(rho_vector,0.498,beta)])\n", + "opt.plot2D(True,ax=ax1,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", "plt.title(\"Dilation\")\n", "\n", - "ax2 = plt.subplot(1, 3, 2)\n", - "opt.update_design([mapping(rho_vector, 0.5, beta)])\n", - "opt.plot2D(True, ax=ax2, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", + "ax2 = plt.subplot(1,3,2)\n", + "opt.update_design([mapping(rho_vector,0.5,beta)])\n", + "opt.plot2D(True,ax=ax2,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", "plt.title(\"Intermediate\")\n", "\n", - "ax3 = plt.subplot(1, 3, 3)\n", - "opt.update_design([mapping(rho_vector, 0.502, beta)])\n", - "opt.plot2D(True, ax=ax3, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", + "ax3 = plt.subplot(1,3,3)\n", + "opt.update_design([mapping(rho_vector,0.502,beta)])\n", + "opt.plot2D(True,ax=ax3,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", "plt.title(\"Erosion\")\n", "\n", "plt.tight_layout()\n", @@ -378,42 +373,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", - "\n", - "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", - "\n", - " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)]) # compute objective and gradient\n", - "\n", + " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", + " \n", + " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)]) # compute objective and gradient\n", + " \n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", - " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", - " ) # backprop\n", - "\n", + " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1)) # backprop\n", + " \n", + " \n", " evaluation_history.append(np.real(f0))\n", - "\n", + " \n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - " )\n", - " circ = Circle((2, 2), minimum_length / 2)\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", " ax.add_patch(circ)\n", - " ax.axis(\"off\")\n", + " ax.axis('off')\n", " plt.show()\n", - "\n", + " \n", " cur_iter[0] = cur_iter[0] + 1\n", - "\n", + " \n", " return np.real(f0)" ] }, @@ -426,22 +412,1607 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 1\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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ED3iG6AHPED3gGaIHPEP0gGeIHvAM0QOeIXrAM0QPeIboAc/Ee/4+GGQrAAyGkR7wDNEDniF6wDNED3iG6AHPED3gmf8H5Xh9SXsZLCkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 3\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 13\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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jcOshucULWXu20EjUYcw1Rmtz6LDb7Z7cE/fTZ1kWttsysyf34gkfS4oWeQVCNYj0R8Bbb7ZoLD7hv9lut8Fis7KOxTaxF35f1DrLhMe2Vdi6ChtadDqdJwk2WGPE8H7nW6jvEIt7K9/r9UKCME3TEMuD4JyIxM8QGsSSelV0DrH+/yI9hFAMkf5AeLfYWzWO4zmG9/V3tuq+LOfn3vGwDC5xIZHW7/ft/Pw8bF11eXkZ9qsDITnWNvue+NxUMxqN7P7+3kajkY3H47BFFiw0vAWcjxV+fiNMjudxH8C+UV2+jId79pN0RfLjIdIfgCILz644i2ViPfGInbk85nvmecgl5wvMLDepFrvTgvCDwcAGg4FdXl7a2dlZICRnxWF1oa+HdR8OhzYcDu3+/j5YeiY9mncg+AHxUa5jInvS82JVpWmGSY/jFan+qsqdhUeI9BVRRnjUtUHcIm/At8n6oRg+N+AVaUx2bCN9enpqFxcXYa86bFCJLabZykMzj/NOp1O7v7+3u7s7e//+vd3e3gbSc788W/hY446Z5UIBPycPMmGf3MR9lYGtvNf3y+IfB5F+D3zyjsnOG0gg285iFBCZrTwTn+N5noDjS1Q8TJL3mkfSbjAYPNmckq08Ex5x/Gw2C3vb39zc2M3Njd3e3trDw0POykPhB8LjnKgKNJuP22dhkg4veDys0yvs9oFd/tj2VzH5c9Vj1xmlpI+1N9YB3gp5wvuZcXhxlp3ddN8x5zPb3CLLrrwfZgkXG1n6i4uL4NIPBgO7urqy8/NzOzk5CSU0M8sREIQfj8c2HA7t/fv3dnNzY+/fv7fhcGjj8dgWi0WoQoDwOB9Ij0UFCcosy5705vvFy8fjRaSNJfn4eRS59SJ7NZSS/rmqJ1ak8ff+5y95jtjvzQ67F092fLA58cXjo7gxhRNZsPIgG1t9frEbzICFhPgGMfzp6WlI2l1fX9vV1VWO8HDrzeyJSz+bzULS7vb2Nrj1w+HQJpNJkN1ikUnTNBdCIHRI0zSU4DAzz0/g8c801oIbI3xssSiz9MJhKCU9jyiuK3hnWEySwfbU3GsO9ZpvigHp+SsWEc4PFFlFVr+B8NhvHtb96urKzs7OgsuNmjwvQGiVBeHfv39vd3d3dnd3Zw8PDznCY5Fhr4IXln6/b0mSmNljLO/18+yhmFmO7FAPxoZtsHrRo0jMI/IfBrF6D5Dswn70t7e3IeE1Go1yM+OQnIuRm+Wo3p33H3C2ikx41OG9S392dhasLxZqJANZeIMsPcg+HA7t4eHBptNp8FIajUbQ0/d6PTs7O3uyuMCTgGeyXC5z2gFuygHQnMP71sc2xoy14frnUxbPC/tRSvo///nPH+o6Pingg4TmFyTtJpOJ3d3d2e3trd3d3YXSVpFGnq05v2LiFJbv+pKcr8MjS4/SHFx6tvCbzSbIasfjccjS44VFC3JbJnyr1QpuPc7Jrj2sPBKSRfE1z8ED4fnFDUA+Wcpgvb7wfJSS/ne/+92Huo5PAvhwYcfXr776yn75y1/aYDCwxWIR3GCO6YvcerbmTPSiWJUtFzfPIGOOOjyy9D6GZ8JzDX40GoWEHROeFyzU0SGy8YIfvLyV55wDd8XFZMLwVqDgY6UgnkXM0kOg45+fOu2ORynp//CHP3yo6/gkMRgMbDgc2tdff22r1comk0kgM5fhfOdbWecd16vxPZfkfLccXHqO4aG4Y8Kb5VV2sO63t7ehHAfCY7ItN9Pw7jdw6z3hY1Ye5/UeDKw79wZAwecXKrzfNyaZWU5nwAuCJz9ecvf3QzF9CYbDoX333Xd2f39vZhYSVtw3bvZosVl5hp+jjs3Exu+88IQ3pUBdHKQH+S4uLkLLLBOe4/fRaJSrv9/c3OQsPBJ2cKMRV8Ot55IgEx4Z+9g8ABYUIfNvZuGYsPI4jm/QYdLj2GaP+9mzxxQjP0PkL4dIT/DlvW63a+/evbPRaBRI6V3UWPkJ70e8y91jLDHFB5/JjuPDreeuOZAP/fGwgiDeYrGw8Xhsd3d39u7dO3v79q29ffs2JB5RaUCuwewxpOH2XMTyIPzp6WkuSRhTHLLHA88B1h7H5Dl9rCHgfgCv4mPC+/ApVt4T9kMlO4LPFG+322AZ4faaWXBxQdh2ux0+iFgA1ut1sP5ebMNSWrSpImHn++FZ9uoHW5o9ZumXy6VNJhO7v7+3m5sbe/v2rf3lL38JohuU5JbLZc4VhovNyTtYeXTqQW7LVt7LkLmxhu8T1t1P8GElH/cZMMFxrEaj8aQ/wYcUIn51lLLaZ1HrBm+5YWXMLNSxT09PAwGRQEuSJGTzufSEZBky2XDjeZYdCAKSoPbux11xI856vQ41eMTwb9++tZubm6Cyg4Vna8whB6S9sPIgPHsVZo/NNChR8g62HIfz7jo8wccLh7gVOTZeC9e6Wq2s2+0+mRvocwly7fejXqb8QOCDyEIXtopwubvdrplZbnQ0b+VsFt9bDuQGKWDR2ar70hYIAuteFMdz8wzaZGMJMoQZsMjIG6BLjwmP84LwrETkgRtY2NjKg/SoEPBAEH7W7MabPYYfzWYznMcvNGztRfj9EOn3AET3hOe4N01TM7Ngcf0UWR5pxf3oTHj8DPkCnjLrB2BwbAu3HlY+lqVnq2iW17xzZh0ufUzdh4QbD92ABBla/e12G6y8v08+Ho7FixgWlFiyjhcqKAs98eXeV4dIvwdcgkJiCuo4lNH6/b41Go1gdXkLKGT8/Tz62HZT+7aR5ky3J7wX3niXnnX9fC/I1mPiDqoDvV4vV1LbbrfBumPKDm9djfIdN+n4jTM7nU7ueN7KM/m99cZXeCV+q60qffrC9xDpK4L16Nzhdn19bf1+PyTvYOXRTQfwhpJI2sXEKl6XDrDF4265yWRiw+EwZ+HRDw9dPBPey3u9CMcP3+C8AW9djQk73IYLUkKIA8Lj/pAYhr7f7GmDDb5y7R7uPRaKbrf7ZNts/lu5+OUQ6SsAsS9EM76ODdLD+mKSDmftkbFHnO5j9qINI8zyAzYhDfbtsezS8+hqLwLiOXew8BD88PANkAyEwoaWDw8PYY4elwDNLLfRBhYz9l784EsvrPGCG5/Uwz3BkxiNRnZ5eZnrbkTtX8Qvhki/B15ow7VsLqehPMeZZVgmjulh8XmTCJCd3Xl26Vm0AsJDXou2WO/Sc6LMJ+24XRbeymAwCFae7wWJO1j4+/t7e3h4CM1GSOBxvwBPy+UdcpEQ5XvysluzfEszj9wyexRIIZE6mUyCiy9LXw0ifUX4RhjfQIIEVafTicbQLLHFK9Zb7gmPY/H20Yjh4dIPh0MbjUa5Cba84OCYuGZY+M8++8w+++yzMHHn5OQk54KjDs+TcrG4cGeemQVi43lgYSva0NIn7PyzNnsc/sGThuFVdLtdu7i4yCUrFddXg0h/ILiZhBcBn/Ti+jw3nzDJY668t4L40M/n80A8EB5uPXfL4bwYVuktPEZlDwaDQHpYebj1THicdzQaBQuP5B1Uc6y+g2VnxSDuy08U8sNDWMLMf48FD8nE7XZr3W43N65bWfzqEOkrwpORrYontu+k86ObY8MjYufgDzwPsXz37l2uFj+ZTJ40z7Brz4RHHgLdemjR7ff7YaHgKTsgPBJ3SJ6xheeGHSY8T7M1y3sP3ILMiJGe5w/CC0Iy0TcPSZ23HyJ9Bfgaue+b96o7fl/sBfg4ll1fJvxkMonOtONMvbe68Cg4k86NO9yPj0k4u90unBOJQlj4yWQSkoS4bh+ecOUB9wSXnOv8se26PGLafh68ybvxQBPhOxqFOET6A8DWhyfbZlkWLF7R4MeyjLWXn/K0XR6CwZl6yGvZwvPOsV75B8KjFu+Hb+x2uydTduDOc9KOlXLouoM34UVELM3lJCfvxsuLpn/WHNPjPfgd9BC8u65i+moQ6fcgllFniwVVGHegxeJ0fI2R3FszSE05nkbWnEtm+LAjScftsWmahg453uqKO/VglX0fPsZq+ZFgyJA3m80gPeaQBRaepwaZ5VWEficfbqsFOPbn3X/477g0yscQ6fdDpC8Bx8S8CSSsIogJi7fb7XLCGp4b5113b/V4EcFxZ7NZKJXhhRiWh1gihma9O9py0TyDoRvY0JK79CAoQt4ApUDMz4PYCHkCEJ7LkUhiYkFEuMHJOh8Sce6CS3c+ienbabFQlU0lEoqh1lqCT6xxgwwLadDqCVkqdnnZbDZPmmNwPN+dhvfyhhl4IRMPwvkx27B6iKNZ3stk5/ZYHl7BLbJclkNJjhOEs9kskJiHY2BB5OQlPAbukPNDNmLNMWzZvffjy3v4e3+MWM5EiEOttQTfT9/tdnO7v0KIw0mv2WyWayLxTTKx0hPiZlhyblzh3XL8HvVciwYBuX2Vp+ww4aH+Y3eePQ3kDUB6TtxxGZCfE4uJQG6WHsfKcyyp5XCA/56tu0+YguR48UhtrhYI5aiXKd8D7x6uViv78ssv7Uc/+lFwY5HtbrVaIfHFlh9hAGew8TtYbRAKL05K+V1svZXj0Vp+Ui6/MAyDJ/zABecNODhvwEk7npILosZ0CbyIeMLzQoVkI4uc8NW/h0MEvycAhxTwwviZy9Lvh0hfgsFgYD/72c/sm2++CcSFdUVNez6fBwKx5NQntTgTz/E5W3k/GYYXIZ5GgzIcRmn5MdWI3yGDZcJzhYDr8CytxeLDUl7O0jNp2f3GAseEj80UwD3AY0Lsz4lMTgby+7mphzX+XD0Q8ctRSvrf/va3H+o6PgnAVV0sFtZsNu3rr7+2X/3qV/bFF18EzTtvT7XdbgNB5vN5Tl7LCT+40J70IDx3xBWJfmDNePNKNMt4wvOOsjwAAxYZSbsi4Q3H5Xx+ltfyPWIEly+v+R5+eAnwHHBdZpZrVoKl9wsflyX9RB7W9wvlKCX973//+w91HZ8UWEnmR0NxWykWAc4qc6wKkiFpxzPzmfCwjpycYvlsbPwUYncmPEIPDLHk6wCpcB2I4bljjhtXsFiwyIdltkjecb8+z8vjn8NL4GEYaJzhDjr2EPBekB6E7nQ6oTqBEqQfFCqUo5T0v/71rz/UdXyyQI0c8TvKVbCY+Dn3lHNyi0nPiTu8lzvyzOIbPfpJNCjB+ew8t7CaWU4P4OP3+/v7wjo8CA+vhTsD8TOz/NZZXHbkPn5eBHlsuFl+eEashs95DCyC3CHIno0XBwnFUEy/B2ma2sXFha3Xa0uSxLIsC+RBAg9Zd85Qwwpy1t5n59nCA9y7z/E7puxAcIOvPIaKh1eyFWb9Pmfp4a2wtBbeBUqTrPBjF5olyTg+K+1wT9vtNkwPxvF9eY0XJ/9MWO8AK4+pRTz0A4lFxfT7oZLdHjQaDUvTNCStMO0GGXEW6iDOB/nxAYbLy+U3H7ubWc6yo0kGGXoercWTZf04bCw0bD29ug8him+eYaUdYvBYSYyz9Swo8pN6uJaPBYxzA5zs9CIdgEMmNA0hrAHpEc6I7NVQSno9xPw2VDz5hhV6XonGH2LvtnL5iq0e/s3DNXienp81h5idwwiIY7huzlae9+DD1BuOszHg04cYnHRj8QxPpkXGnSfYcGjACxhCBbPHnnmACc8ipyRJniQv4dornj8MpaT3MVgd4evjLMn1mXouiXklGgCr55N2PGSD5+KD+MjGs8AGCw28DXzofZceq/p4kizOz+48rgVf8Qw4XODEpg9VzB49Fh68yWOwQVJW8GFhYRcdNXwe7+X31+N5fnLtq0Ex/R6wzJMFKjyiOjYMg9+PMCBJEttut+EDjvd4t549Cpb/+tr/bDYLWXCfqUeszeTk5CEP2/Atub5bDlbdH7eI8D4XAWkwS4Ebje+nB2O2IN7HiysqIkmShFieuwRl5Y+DSF8RXDP3BMXLC1GQ6MN7YkITHBMLCM/NiyXP0F6aZZm1222bz+dRyS+LY3hoBffC49i86DDhESIgR8FJQW5p9YSHK89VBi4n4lksl8tAegifsDBynoOn9mIB8aVJJr0WgHKI9AfCk54TU3CxuQsMVh5k83V4Jp0fkomXmeWIt1wuc80ugG/b5YYVLDas7OOsPMiDa8cC4wdg8IvjcSY8qgtww6+urnLDOmDlF4uFmVnwJHiB4evkNmFULPw2X3Ltq0OkPwBMVt+Bl6ZptDss1uftG0aYhBxTc72fiVtm1fxwDjPLue0+Ueilw7ywgNC+O9Bn6ZnwnGy7vr4OWXZshIlFcLVa5RqVeCGBLBdhh69e+PyGcBhE+gPBpOfYO03T8GFGycw3y3ArqI+fi9px2cLjWLHecc49mD1mvnlRQWINTThchWCPItYtB6vvJ954C39xcWHX19f25s2bMIOPs+zsznOzDnsVUOohycgbgsT29pOFPwwi/QHwbrnPtIMEvmzHk3NwHLP8JpL8weUhEuw98AAKb8n5haw3XyPi7NgmFLgOrvPjOnx+wEtjEXOjAejq6srevHmTG63NtXSz7xeW2Kht3B8WBnbveZccT3QNzzgMIn1F8IeMycXEZz05rDQSU7H5bTErxSIVJlvZeCm26qh/I0eAJBjX+SHs8dOAcF4vI/Z6eFw7t/ii2w+bZ+CFrD3yBjgPEpNcDcA5WJPPHXneE9K0nOMg0h+ImLXnMhuTHu4qLwJmlvuw8lf+W9+A4vvrOTGH68L3rOhjFV9sV1xO3MFzYJ0Bl/hgfeHRcB2eSc+TdtnK473IHWRZlmuR9XkE7uP3kl3f0su6Bw6jhKcQ6Y9EjPyIi+GewmVm8Pc+6eYFPmxdOabn5hyANfM8Bddvh+2Td2aWW6A4tGBZLGfUsVhgtxx0vHF7L9flfU8/ngOXO+EtcT4Bz7CoFOnDHaEaRPpngOvsHEuz285DKDih50tpvtzmm3f4uCzuQUYepIH7DtJ7wvs4nhtjvKLQl/pYoek382Sic0mNS5D83GIVDH7x84PWgYeGcjjApJeV3w+R/gXghTsA/6yI8GzlvZuKY+CYXMZj6W5Zc05sK+yY4o7jd5834Pgd1pcn7/K5uDcgplT0Ftnfd8zTMHv0kKDOw6423hsR4fdDpK+IIveRrRbHrGb5eLuonl6U4GPXG+FCbKpOLDsPAoLofqoMcgfciccbd/he9iRJcqSDd8GLinfjcR5fsmRvgpOT7Lr7mfZmj4M3Go1GSBz6vemFahDpD0RRxti73mb2xMrHyO+tN7vcrObz9Xkv3wXJY3G7j4tjcXJsqyncD3TwWIj8YA3uC8CCAiLjHnzfAHcAYjAov9Cqywk9aPUxecir+ET8ahDpXwC+KQcA6VGXBtFgob2FYpeeUeYNeDkwYnZWq3GJzGfB2cJyrz+uAyVHM8ttjMmz8nA8iHigo8figrid9fvo+MMLk4Exustvu91sNm21Wlmz2bSTk5PQLRjrwRfKIdK/ALyVZ4u92+0CWViUgvKXT9KxWKcoFo4163CjDstpudzma9zezeYY2eyx/IfFwnsAZo9kXywW0d1qsyzLzdSDGg9baGESL79AaB6OiRAnSZLcuDFl7w+HSP8M+Cw0yILfxQCrCxL4GJ0JXyYzjVUOuNsOMTgy3z7O9roAXgD4PuCtcJYfx4HVLlITrtfrXLUA70F/P2b1Yd88HtLpp+lisUzTNJd/EOEPh0h/BJjs3uqyi14UwzebzSez8fA73z3ne8X9v72Xwdl4XzqMvUBi1gB44pctGD4Zx4nB5XKZS/AhcYhJPqPRyIbDYY70fgw3PAvkB2KKRGXsD4NI/wx4a+stIpOOyYkPcKy/3h+Tf14GHMfPNfRE5Wx60XGY+LFse6zDjjPuPBXXz99HS+1kMgkDOtnK+7Hi/Gx4EYh5RkI1iPQHwpPYfwABkLrINeeMPMfQ7NZX+UAXWW+QkonrxT58H4yiJB++Z7cb95okyZOyX5ZlufFYsPQY0um33uZR3LheFvb4xKXfN1DErwaR/gjErLfP3ANc6uL3sua9qCd8nwvr5btek87y3SKFH98HH5ObfkBmHpnly4cYD85yYZy/0+mE42OfAIziBtk5Oee9FYQ9LELi7aw0CfcwiPTPhLf4Xk3HZTpYu3a7HRJsZvnkmP/q3Xuu4zPZmWx+iAdc85ilxzFxPJ/VZ9J7vTveyxJjn5fgNtntdhsy97xrL9fk2a3n6gQkxtjhB7vxcsVA1r4aRPqKOPbDxJl6/jcrzODue9LHQgmGJzEr7Tzh2RPwSkD+3jf7cLcfz8PjBQlW3s8SwPtxDzyhlwU4PlOPv4cb3+v1clt5XV5ehsEcvCOvUA0i/QshZkUPVYlxfO9xSFLPLD5GK+b6cxYeP49l5H17MCw6C3aKmmbwHlbjcZ7Al91g3Vnfjy6+wWCQm7nnLX3s3oU8RPpnIFbrjv0+JqMt+rtY3A1PgGP/WBmPQwj/b19fZ+sNq84iHS79sZfA5+EeAywAfjw4awe4kcZ7HzgOdw7Cwl9eXoahHG/evHky816W/jCI9M9AUSKt6G+8+1523KK42+cQAB9CsALP7LGEyEo9Lq95F5633sKL8xYgGs8KhFXmQR1cquN7Y+uOhQECHB7OAcL7EVzYsDM2Alsoh0j/AvAfZJ8YA7yQhsEW3X+Avdscq097r4MtKmblo2Rmlt9Gm5ttYOW5Fg+gfIZ74Ok86Kv3HX7spWy32yf74/GcPdxrt9u109NTOz8/D4T//PPP7c2bN1HXXm79YRDpK4Cz8r4uz/PpvNjGS1fNnnoHTFQOA/z5/XRbHzfzsZn0IDZProHVxbXBtYcKzpfk+J55Lh7m2/MADYy65to89PZm9iS0MLPQfISyHDa2uLy8tDdv3jyx8qenp7mden11QyiHSH8gOH5Fp1mv1zMzyzWnxBAryfECAMtaRjhP/CJrz0RGmWw2m+VaXJFxZ88j5pk0Go9ddTwtBxtaYESW3zqbF5nFYvFkkk6SJLktvjEEk608xmgPBoNwHngSvEGHUB0i/R6wxQb5QPherxdcZ5StfLmNj+MRU9MVJfK8Z1FGem+9uTYOQYtvwuHzsZrQ18m5dIaNLPy2VX6OPryN2WwW3H+0x8Lio0TH5+Djn5+fPxnFJdf+OIj0FcHEQywLy9zpdIJajWN6vC/2lRGrt/P7PfHLVIDexUecDr37ZDLJTbzB18lkEnrYOTcB6wtRDOrkmHiLTDoP7+DBHZw0RH0ePfTIJeBcXKJD2IAZf36Kb2wcl4hfDSJ9RYB4cO2ReEISCh/emKX3WXb+ORO8ao2eSe+PH7P2sLQXFxc2nU5tPB4Ha31/fx8WAwycBOk5fuddY1kgwy6976fnMp3fRdePw4IHhdFbIDpcecwL4OSfVHjHQaSvAE7k4cNp9vhB9dJUvMf/u2gh8PCW3n8tS1yx1+CTeiAc5K+YVIPxUzy4wsxyYQySdEjaQQYLKSy72xw2+GvwTTs8JIObaXgUV4zonvAifnWI9BXBAhh8jw9q0Yy2srj+mA/pIe/3xGd5Lcp0WACw9TQr5GI1eH7xJpJFVje2APk+Aa7VI2fhSe6JjvsX4Y9DY49QRCNJCBxzx0Qm+0i/7+dVUeX9Rdfqe+O5kYbn45k9ymFZZecz8J6MVa7D5zBwT7HwJUbu5y6eNUL04Yj0RyBWevuhceiH219TjHDe8uJnOJ8vFRZVDfZdH19L0b8PTX6K7JUg0r8kYs+t6EP8sREjWswC+7/11ve5lrbq4vhDeUg1hEj/Q+E5ln6fW/ycD3rZde1btIrOfQwhj30+IvmzIdILQs0QJb30i4JQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAziPSCUDOI9IJQM4j0glAztPf8vvFBrkIQhA8GWXpBqBlEekGoGUR6QagZRHpBqBlEekGoGUR6QagZ/huJWk8mPy381gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 15\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 25\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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46Z8J2/GlL6bV0tNixSWilPQsy2k9XrvpHnPr0+k0CoUCqtVqIPz29jaq1WrYpkrLdDy/ym5brVYoDcZl6zWG5zw8jtpiX73ed9yiQbLrJ07Oq8/aif98OOmfgThRDaFyUibkJo2OVn16v99Hu90OSTRtY7UZe4VaX4pwNjc3sbOzg+3tbdRqNZRKpQexvJ5bZ/5R8cdGHj2PzrbL5XLhwxKgZuJVHqzJN5sP4AIQ93wBRJ6Vk/55cNLPAZts4wvOGJbZbf0Z3XW67lwENBNPcQpJf3l5iaurK3Q6ncjvP+bWF4vFMN56Z2cHm5ubKJfLyOVykU0iCHoYnU4nqP5Yl9dJt8B9eY4uPcdRU87Lerq2BDM0obXXxB6Pxw+fHxcOIi7xqV8dT4eTfk5oY4wOqwTu41a13pTU8mPHTfEY2ruu1p7JNIUmxmzyjhtZvHr1Cmtra0F9Z5OMGsuzffby8jK49nFEpYXnEA5ukMHBGKquUw9HBTm8ZuveEzwvF1Jb+nTMDyf9HFBLpHV0dp2pbFSJTFUdPxTb8Bi6oQT7523vupW/AggJMW48ubGxEbL11WoVxWIxEsfrPdCt5/z+er2Oi4uLUJvXVlh6EtzCmgo/duoBD0d3q2cDIDLyKs7as3zIn8clSvX/g2N2OOlnhBJet7bqdruRVlMAkZ9TO69JMk6fscSnhdTMPd3suLKZqu6q1Sq2trawvb2NjY0NlMvlkK2390Av5Pr6GhcXF/j48WPopuM8ey0x6hCO9fV1rK2toVKpIJfLIZVKhV14dKwXP3HlNyvf1VyDhVv5zwcn/Rwg6ZXw7Xb7AelpuTUjrv3wtPbWDdaPzoizCTUSJp1Oo1gsolqthjiebj3dbtvqqp109XodJycnODo6wunpKRqNBjqdTsja0wor4Wu1GtbX14OVVzIzrLFJS14zoTMGrKpRr5X361b+88BJPwOsW09Cs/uNW0Tz5dS6N7eB4le67SS93byCOQM9L8HwgTPv8vl8SNzt7e1hZ2cHGxsbKBaLyGQysYTv9/uB8MfHx3j//j0ODw9xdnYWtuhiT3sqlQoNO9VqNSj7mCtYXFwMOQqOzVJRERcE24bLe9GPPuvFxeigDfssnPjzwUk/I7S81W63wzSbVqsVLD0z0JS08ve0H17bY7WWbeW4Fpq041AMWvj9/X3s7u7i1atXkWy9xskkfLvdRr1ex4cPH8L+9kdHR7i8vIxsxqn6/Wq1ilevXmFrawsbGxtYXV1FOp0OjTXdbvfBAmMbgmy/gsbz+rdxAp24Z+PEnx1TSf/YLPWfKzRZZq3PpDFSrVYrkmlmi6pOkaVHoIIb3V1GyWF74zWRRcKzkWZrawv7+/vY39/Hzs5OkNramXd67efn5zg8PMTbt2/x/fff4+DgIFh5Dslgpp4bXb569Qo7OzshQUgrT0/FVgf0+vnV7menm1bGEV2/t12FTvj5MJX0z32oqvjSf9v//jnPEfdz4Gn3Yl9Wvlj879zEsdlsol6v4/T0FKenpzg7OwsxsGbudfINyW63eFaRziQtPaEWnoSnS//69Wvs7OygVqtF3HoeX7P09XodR0dHeP/+Pd69e4eDg4OwISUTcSRmNptFuVwOQh/mC8rlMjKZDO7u7sI9saV2UledduNpgw4XC15vnJegYY+LdJ6HqaS3iZWkgjF8t9sNFvL9+/c4ODjA4eEhGo1GsI580TXRx1n1OqNeB0Jo7D4JtJJ06ZXwtPAbGxth7h0JpJ1znIRzeHiIDx8+4ODgAMfHxzg7Ows5Bi3RpdPpSEWA6j5a+aWlpRD7a4eduuw68oqSXd0hV3e0BRAht3oo3BVH1X1O/PngrH4Et7e3wUo3Go3gEjMGrtfrIZ6nSwzcLxQsY3ECjs1oT9PS00rSQmoMv7W1FQi/u7sbYmwlvJbkLi8v8fHjx0B4ZuovLi7QbrdDbgFA0MUXCoXQpUfC8zyZTAYAgnuvbrruUstnwn9TwUdBD3e25UJp1Y52br4S3wk/H6aS/m9/+9sPdR0/OuLCBMbDg8EAzWYTHz9+xMHBAd6/f4+Tk5MQy6uIhe44X0ytv89DeK3Dq7x2f38/uPRaj2f2nF5Gs9nE+fk5Tk5OcHh4iIODgxCSUOKrI7BodbVLb2trKyTv1tbWIuehFdYEo25DzQVIt9MqFArI5/PI5/MPwhBLeC4Eaumd9M/DVNL/6U9/+qGu4ycBSkQ5nHF3dxe/+tWvUCqV0Gg0cHx8jOPjY9Tr9VDLpoW0GWcAkRfXuqaTRj7xGGo1VXizvb2N169fY39/P2J5manXqsEk635+fh6qBwwzeP9sqKGVJ+k3NjZQqVTCmC0AkRwG/95q86nH532olWeDDoAHm3XYZ8bOPXfvn4+ppP/LX/7yQ13HTxKlUgnffPMNarUa2u12xDrqVFgSPi4RqBNfrHWa1Dij7nChUMDq6mog397eHvb29iKdc7S84/E4kp0/Pj7Ghw8fQihC4Q1zC5MGY9gSHS08t7+i1dV71IWMpOfPGZqolWcbLnA/2prelgqTeI22scmWN12x93R4TD8FrVYL3333HQqFAm5vb4MrTEmswqrNAERIHmeZlGj8qlaxWCxibW0NGxsbIZHG7DnVcBrDDwaDsDix/s5kI8tx7JyzAy55DXTDK5UKarVaEOGwLVeTuyrC0YYjklyHZZLs7MhbXl4Ox6DHATzsYNRGnTiVn2N2OOljoGW+s7MznJ2dRX6mBI+blKOYJrLR77Ucl8/nUS6XI2IYNtDUarXgZms8TMJTcPP999+H+rvmHrTF1V4XycrJuZTaKuFtDz4TlLqJJgU9CwsLkQYddevpLfBvuC+euvUa0+viYpV+jtngJbsYqIzWwrqUVrwzzc20P1N3nq48NfRsjVWy07pTT6/ucK/XC3kHCm7ev38fqgssFU5LHGqnno7Ltvp9Via074AhD9V53B8vn89HWnDVW+DzZQ6FsKSn669NPPQsPK6fHVNZPWnbY8dDxImOtC2UUA/BluIqlUpoi1VXnmOrmURTaS078ZrNJk5PTx8IbprNZmR8ddx1c+HJZDLI5/OoVCqoVquoVCqR8VrAPeE5ZafZbIauwV6vF0hPwpdKpfDJ5XIhsaduvd1hh669JvQAhPKnNinFDRVxTEcyTflnhO35tj/Tr7arTPeXo8x1d3c3zLTb3NxEpVIJ7rXKXEkI6gjo1n/48AHHx8cPXPppFp6eBtV33LveJgnpYvd6vTDkg9tXU747HA4jiTtupcVYXpN3LPcB8aPHNKZnkpLehU1GejLv6XDSzwESPU4zrr9jO8iY2OIgSd0rnmOq9/b2gjuvO9Hojq6a6GJpjq2xR0dHIWn3GOEJXhN3tOW+9SwDAvckJeEvLy9Rr9dxfn4e9rljlx2PxxKdrcnzHmxoxDyBLdnpYE4dRMLhIh7XzwYn/RyI6xTT2W78HWbiqS+n9JRZbCbMNjY2QsKOGXNaWd3JFbhv+iEBr66ucHp6isPDQxweHuL09PRJMTxBK89YnsMxOG1nYWEhEFHVffV6HR8/fsT5+TmazWZIxLGkRy8mm80G1R3zEDpLkPej2Xj9t5bvxuNx8DDYrciFjcd2PA4n/YzQWJwbO1jS04pRlcZpsUxmcedYutG1Wg3VajWyzxwTXnamnXb6sR/+6OgIHz58eDAA4zHCq56fC9Da2lrYAUfLZGzHZWchp+xcXV0FzT4XNr13jsm222FbV97W+tXaa8Y+lUqFoaG6x57ODXBMh5N+Rmj3mU6BpQWjReKioNtDc8dY7g+v3zPuZROKDR2U8DoAg4Q/OTnBxcVF2Fn2sbZoZuuZRGS2nuXApaUlDIfD0FvfbrfRaDQC4VWVeHt7G+mHpxRXG3B4HxqrM0dgY3e18szYc/TY4uJiuI6Liwu0Wq3QAOR4Gpz0M0CJots+Z7NZAPeZbQAhe83EGLPhTMzpFFk2njB2t7PiLOG1H57im3q9PnFqbtx96BCOSqWC9fV1VKtVlEolpNPp0DI7HA5Dh169Xg9x/MXFRZiLD3xy6ykU4sKo1l07CnX3nrgWY/v7JDy9AF4LQwtWDRxPg5N+BpD0LGu9evUKtVotWEZapfF4HBpWSKi1tTWUy2WUSiXk8/mwDZTu8jppe2tmztWl1/be09PT0Av/WO1aXXoq/jY3N0OlIJvNYjweB8J3u93QkstJuRzLzXulC8/jk/isMminHEMTHRWm/Qt6zzpinKQfj8doNpshzKC34aR/Opz0M4AWLJ/Ph4k1Ozs7KJfLwR1m8oyuPS09rbvu+abW0FYBNLYl4a+vr3F2doaDgwO8ffsW7969w/HxMS4uLoKbbbX0WsqyuoD19XVsbm4GLUChUAjz7jqdDjqdTsSVZhytk3LVrdcEJ699MBgEYY1aeZbeVCVos/Baq9fMPl18bgbCkMbxNDjpZ4DOfecQi93dXaytrYVZcXz50ul0RI3GkpWdLmPLVvyqdXha+PPzcxwdHeHt27f4+9//HgQ4JLy29/JYtLpai6enQsJzEk46nQ7DQhjDcxY+M+Z06Zm3UC2CztVnKELCa3hCkQ0HjKgVtxNz7GRgWvRWqxWmCtMrcTwNTvoZwOkvWs9mB1omkwkvqfbA88M20km1fbXs2ofP8dnsiaeV//DhQ0RxRzLYvgDdWZYKOeYYNjY2UK1WQ16CcXyr1QplucvLy4j4hosLBTt069XC69Qg5jh0qAjjeJ3xr+65ba9Vaw8gspeAdjw6ngYn/QwggUgezbxzhxe606xV2zFSWrYC4mvStITcTPLi4gInJyc4Pj7G0dFRSNyxPk7CxEl8KZJhFYH5BSYWi8VimHDD6UDn5+c4OzsL8TsJTxKS8LT0diGjt8Akn5bdSHASWWP3OFFOXButyo+5EE2aaeB4CCf9E8EkXjabDV1jdNtpyak2s+IddXttHVo3etRYl2OzG40G6vU6zs7OcHp6GpJpOrWW12Ytey6XCxUEFd4wx0CJLT2KRqMR6u+swet21Rp+8J5UoDQejyOEJ6G17KYWWbP7tmU2runG1vZ1tj5/7qR/HE76J0LbXkulUqRGb+e2qwQ3juxKdG6Y0el0wq43HJlNbfvl5WXE6nY6ncjeeTpSWjXvqqMn2bVxB0DEo1DRDaW1tPBcXPQebbjCzDxjeOvOc+Fg+EOlIj2HuC46jfVtSc/HZs0Hb62NgSajaNV0/jvdYvaFq3Wx5TbbQMJ4lBpyJqS47ZXOx9fv2+12GDWtI6Ro2XVBUqLTjS+Xy2Gh4nw7ehRU2p2dnYU4noSP241XPRhLWl6bDgSlW09LTMJTlsz3jJn9SfME+Uzts46bWuSYDG+tFdjWWNWPF4vFiD6+VCqFKa4ktx6HUBeXrrtuCc0uNW1RZTOJ1rLVjQUQidlVx8/kIvvv6cprTzzr7xTeMISgNxFHeELHW6sEmb0AGm+zE06FNZTorqysBDJzio7uAGy9g7j/V6oJsBuTOCYjmaZ8AuzLNRwO8cUXX2Bvby90xHE+3erqanhZ7egpHouuvGbhte7Nr5y7R0Ud684ki1o628XGLr1arYbNzU1sbGyEigKtO9tyea0qvOH1sOatAzOV8DaM0LBmPB6HmJ2Lm07TYXxOJaCV3Npr46JhE3i8DoYIzF3ENSW55Z8MJ/0UbG5u4ptvvsGvf/3r4MrSbWbbKePYOJeecTuHTdjuNCbkaNl1I0urqtMYmp4HR2pxyo5OreUMfG1EUb27bn5BZZvG8NbC63ZUWpXgcRm6WLWdnbarpTc+M94vJwCpYCfOypPslP5yAXI8DVOf1B//+Mcf6jp+EuBEl263i1Qqha+++gq/+93v8Pr16+DC0rKo206rqB8lPOveLIWR8BSW0LIz7tfr0Q/FNXTlNzc3IzvPcIgllX9050kout7aPKMJQr0OQhcbEs1O71G3nHkHLmBaSuO1LC4uRibm6Ngs/u2kAZhaRWH/gg7ncDyOqU/qz3/+8w91HT8J6Ay4hYWF0ElH4Y0Smsk4Zr+pLKObq5tXshXUuvP8Gya5rISW3zMOZlaerjxHanH+vbrzWk2g1bSEZ9JO6/1actQZfhz8weSbWnkdjsmPeiwEFwo+R9bxdb8BfYbWymv3onYp5nK5iHvvmI6ppP/6669/qOt4EeCLynbT0WgU4mJ2ezGO1VIYt7Omso3WcNqMN0s4Jus4IXdnZyeM1apWq2EuPZV/WoHQEVcMMbRLjfej0l1+1VJg3DAMyoTVpaeV1jFWmiTlAppKpYKVV4/B/j3DAi5G7AykZ1MoFCKW3uP56XCfaAZYsQ2z37Ti3ATSimxYctPYPW6aq5afbN2dhKdLzxl6dmKtzpzTEqFKa634hglDkgrAg1o6Lb1uf63NM3ZY5VNGWPEaATyYnx+X0wAQZNC1Wi0kLPP5vFv6GeAluymwNWCbQWZCjGRiB5qSgSGAlq9sMkstk9a/qaorFotYX1/H+vp6eNl1yo6dVgvcW0618GdnZ7EDMKhFUKWdZul1Q8pphLfqOd4fv9oSm0qRdaCG9X7U61lZWQkJTO4B4O79bJhK+qS6SVryiRPeaNb55uYG7XY7uO/qurP0xI/GqTpcgrVu3b+OmWnVzHOklqrqLNl5TB1TzaETtPAkfL/fD3Gyaul1ZxolO88zifBxSUASVhcz9ZZsY431ELQtmHkNtgRz6Af1Eo6nYSrpffV8CJ2HZxV2/GhziW4BTRKo5pwvq+7nzhq8NvZwAIedHc9+dR02qYlErRywRZbXyPMCCMfUzTe0Bdj2CUyK4QmSnbV53cmW968E17p9XE2eGXsugBQesXTqpH86PKafEzrRxraJ2rhWyW5fTh0vRVLQyjNbHzeL7+bmJiS4+v1+xOKrB0JBEDUB2u5KohOqpediwHukdX8saaf3q9tea7mPoQSPacMnHkOPxe5Gqg6r1WqQQrsSbzY46eeAvqQ62kmz1fw9tew2mx0XOzNLTvmv3fuNZGe8TpGMhgskk+r7qbSjG89MPK9TY231GOipxAlvphFeBTQ6B5DnpPqO32sHng2pqNNfXV2NxPIcU+ZWfjY46eeExqu240x/TqumVkzdXu27p0XU8dHajEJCdzqdyDx9rclr2EE3XK07F4hsNhtJrmlHIP9eZcRai59WfbCjv0ulUpj0S5JSp68S3tvb27DwaIMPj6cbg1CAxBn7jtngpJ8TqkUnOXVkFrvgbGJqEuF1jj5jarrvtPB6Xq2ha6JMG4A0ScbMN4d98BgacqgUlvegjULTynJKeJUJM/bmdJ5UKhUm+pLgugstj6UJQA7xpCCHw0V9g4v54KSfA/pCqhubyWTCsEhawbhsttb7dbIOY2wuFiyn6QQZzRPYpJtmyHl9ujjYwR52RDWFR7TymrDjJ26IJe9L9+bTKbu1Wi1MFxqPx2GHXRX4aIOPhj608hQn6bxBd+3ng5N+Dlgrr/JUlevaWe5AVE+v5SsV1bBJha51nFKN18GFR8MD7VPX/x43chu4d+e1jKc7xKqWXjvkeA0aw3Os9tbWVlAM1mq1sE3WaDQK4QnvlfoF1eXrMTkBKJfLhYRmXFLU8TQ46ecEiW8TcNrcEpfo4t8C9/G3lgCBe6vL0htj6rjSGBcOJv+Ae407M94kDLPnShpm/xcXFwPhWeOPI3xcWY6hCffD29zcxO7uLvb397G9vR1RzQ2HQ+Ryuci5dd89LmRKet0TjwuWEt5baWeDk34O6MuuJSm694zFCSU+XX4KUzjUQstOOldOy4A6KprXMRqNAolpPXW4RrFYfLDBBklFgtO68txxO9A8Jhnmrj8Uzmirb7lcDqW14fDTVtYaTmhbMZ8XgHAfWuoDolUTx+xw0s8JG9fTgqp0WVtlafnjuuloudTik3xW5MOQQTPuVi/PcddMfnGmn1pK9Sz6/T6A6GJjzxnXAaiDLJip53ht3ZCzUCiEmXx8BoPBAKVSKYwD40aU7LajJJiejKr36AVNalZyTIeTfg5odlkTY7T8OkSDll03apxkMQmdukPS2wk6/Fs7zYYNOrrRRrFYDPV+bW8F7ttaLeH12nku+1U9HfYIcCy4npPEJeJq+Pl8PqIU5NBPnbKr/fYMd57S2OOIwkk/J5T0VlPO2FUtN4kfZ6Hi1Ho6F17Pp4uEJhNJoEKhEBnPzeQXyafZei3p6TBKPa8mHllX53/XczOnwO/jdt6Ne3ZcOHgsKxDS9l12LXIWARdDj+lng5N+TvAlU+LzJR6NRpGXnRZTs99qobTjzIpNSC4ek+fmAkPLTrJTBKMKOF4br0evRQdR6vAKXVg4KUjn+rNEx8WEVQElKz0VtfJcZKbNs9fqhyr1dHIwZ+nZsV6Ox+GkfwZsq6iq8jTmtlZIS3k2Ro77qglAVQDq3nRKeJa1bHectrJqsk5bfnleuu26CNjRV+rZaM2dFpljrMbj++2vKDTSvezsDrZW8bewsBASjqVSKTL73+P62eGknxM2rqcklhl0uvN2IQDi22rVY9BFQ8/Hr0ygWdLbPfPU4pI8Wv+n0k5dZeC+jTWTyUQm1qhqzvbF67AOLjraCcjsPUmvE4V0kw8dI6aEHg6HSKVSYS8AJv6c9LPDST8HrGuvGXy6oupCq7vKpJRm3dVd5/FoSeNieTuoko05OgyTVpffx83LY5yslt4SnvfJj4Ymat05rMM27PT7fZRKJaysrIQFgosDt7/WPe91Gy219JQ1dzqdyMwCT+TNDif9nIhLpmmdHrgX38Q1pegLrdY7Lilowwi19JS+assqyQZ8Ksdpm6zti9f59Izb2dVm1YQLCwthmKWGKOzeizt+t9vF6upq6Hu/u7vDzc1NZCQ4Z+6z9Ze5BR6f97W8vBwWKM/czw8n/YywbjZJQtIDiAyfsH/LhhNrMa1Gnk04Nl9gk4b0MmiF2UtPa85j81wkpZYDSVC17LwnjqrW+9FkG6+feQJ+dE7g9fV1aJDhIsHhHvxonG6nA3NxtBJkz9jPByf9nIhrumGZjqRXt1y72paXlx/sIBNXwuKxVQugsb9KVlUXQLdeBT9aQdDQQ8VAXAhoYZV0QLTMp2ELE22M+3kc3fGGlh5AZMQYLTyHhsZp++0z0pZi1+DPDif9HIgT59Ay0urZ/nqtdevedDZutsTXf6t7r8IV1QFYAthyWNy/9fd4XXa3WZXk6qx+663ogqC9/SQ9FXeM32nhSXjrQejzow6fVQpvvJkPTvo5MYn0dKWti28XCm2gUcTNz4t7qUk4Ja0SOs66699orE7iqhiGpTwmzXR0t9bYlaCMvXkf3W43XDsrGSQ9y3p2Gytbd+cz0w4+bsrpk3Pmg5P+GbBEpiVnQ02cVVW3lS6xVd4R6lZrPG2bc/hVCa5af9vmaxtWdDHQ2JxKOK2fk5g8ry4edPVt3oBJRYYO2qfP8+gCQnBBpQBpbW0t7MhbqVSCDsBJPxuc9HPCWnBbggPwwAuY1GPPmNzWm0kigtl1tcwktybX4j528VHS2lhf+/d1N9q43oFpz0PvTa+DyUMNA+zxlPCrq6vY2NiIbPKhk3gcs8FJ/5kQZ0HVE1BvQLPvcW42j8f/ZuW8mpzTJBwXAPvf7M91AVBvgH/Lr2qBbXswoQRXb4cLHb9Xz8cmAq2XYwlfq9Wwvb2N/f197O3tYWNjwyfhPgNO+jlhY2Z1aQFECGiz0Tazr1bRZszV5VXZriW91bRrIw2tq2rZ40gdl9GflAfQ69XFzU4T0kYfGxbYxUMlxkr43d1dvHnzBm/evAlDOXze/fxw0j8D6l6ry8oXWgmmdXm19rogxElKp9Wj9Xc1k26TdtrqG+eya+vuJKLbBiFVJWpfvbbLajsvS4kMWawXxHtl0k4J/8UXX+DLL7+MWHkmTZ30s8NJ/0woKdT9tYo77VYDomSxLjePq+Sw5FcyUz6r3XhKLr1GW5JTr8AuFHp/CiYTtWa+srISSmls66UsmMm9m5ubiHSX52LpkdLiQqGAarWK7e1tvHnzBl9++SVev36Nra2tEMu7lZ8fTvo5YN1ZWjlNvJH4KrKJi5tt1t269/piW9feimU0bqfnQZkt/zbOsuu5lfAWGl6QoOzh5wCNcrkcSM+59KwIdLvdEOer6o/NNNyRloM19/f38ebNG+zt7eHVq1fBrWe44KSfD076OaCWm+Wk0WgUrDbw0BKrJZ+USVfrOil+tse2WXuSX0tu3Guv1WpFBmmoVwLchxeTQgz+rYpkyuVy2GpqbW0tkJ79AEB0o89Wq4Wrqys0m83Q7ENrn8/nUS6XUavVsLOzE5mmy+24XZDzfDjpZ4S663RFx+NP/eLaeAJELXJcbTzu3wAmWtq4Y+qiYi09a+vUvzebTTQaDVxeXqLRaKDZbKLT6QD4tIPO4uLiA7EQE2tMznH+vJK9VquhWq2iUqlgdXU1tPhqAxB30L2+vg7tse12O+RAOLmXM/Y4L193s6Ek2a388+CknwM6ZAL41GCTy+ViO79ssm0apv2uLefpx3oPmp2ne9/pdEJn2/n5eWQX2+vr67DJBeW1QHSuPmN1bh5JkUytVgsDMO0sPl4rPQ+28tr2WJ5Hd+nVOXt2jJYT/nlYeORF9OkEExBXqosT2EzCYy9u3HFsvG/JH1dGpMXv9Xpot9uB+BcXFzg/P8fFxUVwtUlCuvskPJN0lUoljLje2NiIJbt1v22FQ8dzMbdA0mv2n3P2VM7sZJ8ZsQ/MSf8MTNOzT8LneHEneQRxYYWq7Ghp6eoztubMOR1MwRCG/fqMtyuVStC/M3a3c/hslSHuehiS8JlY0dK0cWOOJ8NJ/4/CU617HGZ5oWc5T9wCYIdncFQWm15ofQlV1nHSbaFQiEzYJUGfet3WWyHiypKOZ8NJn2RMs7hxI71sF6Ht6Xcr/CLgpHc8DiuNdbxoxP4P9Oy9IwIn+s8fTvqEI075F/dzhS8MLxvu3jscP1/Ers7ejOxwJAxOeocjYXDSOxwJg5Pe4UgYnPQOR8LgpHc4EgYnvcORMDjpHY6EwUnvcCQMTnqHI2Fw0jscCYOT3uFIGJz0DkfC4KR3OBIGJ73DkTA46R2OhMFJ73AkDE56hyNhcNI7HAmDk97hSBic9A5HwuCkdzgSBie9w5EwOOkdjoTBSe9wJAxOeocjYXDSOxwJg5Pe4UgYnPQOR8LgpHc4EgYnvcORMDjpHY6EwUnvcCQMTnqHI2Fw0jscCYOT3uFIGJz0DkfC4KR3OBIGJ73DkTA46R2OhMFJ73AkDE56hyNhcNI7HAmDk97hSBic9A5HwuCkdzgSBie9w5EwOOkdjoTBSe9wJAxOeocjYXDSOxwJg5Pe4UgYnPQOR8Kw9MjPF36Qq3A4HD8Y3NI7HAmDk97hSBic9A5HwuCkdzgSBie9w5EwOOkdjoTh/wOBr7TpMKyPyQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 27\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 37\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 39\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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22dpnEfhzXjfp9UwbrrHfZBXzQUn/ApCZb/vPCXspbtqHW7bYXl1doVQq4fj4GJ8/fzaz60zePUV4p9MJv9+PTCaDQqGAr776CoVCweLW2+fxaeVbrRYqlQrK5TKazaZltFUeXyruxONxxONxMw/P50hyyiYeTtjJOQX7ANG06zxtiEkxH5T0fwVMs/SzEr7f75tM/enpKQ4PDy1u/azttqurq6bV9s2bN3jz5g1yuZxpwpkGltdqtZqlD8Du1tMd93q9JpaPx+MIh8Pw+Xzm+PYhG9mmTAIzwSfn6JkoZGzPv5PPVdIvDiX9C0ASeBZrb0/ayZ56Ev7k5MQQnom0fr8/s7w1Y/nNzU3s7OyY6Tlm66dhPB6bPEKlUnlQbNPhcFgy9olEwlh5Cmly/v76+trSRQfAWHSSmGPIskdA3iT5N3w+wwV7E5RiNijpn4l5E0uPxfDSwn/+/BmfP3/G2dmZEb6cJY4HfrLyoVAIyWQSuVwOmUwGsVjMtMY+VOYaDoemzVf22EvS0yX3er1GSDORSJgZfLbW9no9XF1dmeEceih2HT2KZtiFMO2eEYU0peCmWvrFoKR/BuwEBqzad/YuN/vfShkqCk7Spf/8+TNOTk5M5pz18afgcDiwtraGWCyGTCaDTCaDeDxu4mz5+pI0d3d3pqZerVZRr9dN/mBaXZ6juel0GolEwlQDJpOJZSKv0Wjg6urKeCmyVZkTeSQ+XXrpwvOaSnESaemV+PNDSf9M2EkP3Mf009pqZQcaRSnq9bolS390dISzszNUKhW0Wq2Zu+6AnyxoMBhEMplENptFOp1GOBy2xPF2wk8mE9PmW6lUUCqVpuYPaOUDgQDW19eRTqfNTSUQCMDlcpmZ+3q9bm4e9qk8WmkO6XAqj0rCt7e3lrid6sJ20ivhF4OS/hmQjTT2DTfyq/25bK0lOc7Pz3F8fGyJ4TnYwmm2WcMHTtFlMhlks1kkEgn4fD5L/GsnC5txSqUSzs/PUSqVzM1G/o106zOZDPL5vCG91+u13DxqtZohfafTsfQVkLC03mtra1hbWzPz8/bS3ng8NuU/qd2npF8MSvoFIUlMIvPnhJ30Mn7nfDq31BweHuLk5MQM0jCGn3ewxOfzYX19HZlMxujdra2tPUgQuuPVahUnJyc4Pj42K7LkUA3d+kgkgnQ6jY2NDaOpFw6H4Xa70e/3zfYdJgKvrq4sfQUy/JGk9/v9cLvdFg1Aekyj0ch4A+raPx9K+mdAEpnWmPVvu4otFWB6vZ6Zljs7O8PJyYmlvZbddrPG8ITD4YDP50MsFjNu9/r6umVTjRwIAu7r8vV6HScnJ/j06ROOjo5QLpfR6/Usmv0ul8vE8Zubm9je3kYul8P6+jr8fr/JT/D9UdWn1+uZIZlpdXvG9JL09u48Tt1JDX0l/OJQ0i8A+zgoxz1JeqkZZ5ePbjQaZlru+PgYZ2dnOD8/N4mzed154CcCeb1exGIxM0XHEh0776YlEvv9Pmq1Go6OjvDhwwd8+PAB5+fnxrUn6am2E4/Hkc/nsbOzg52dHWSzWUQiEayurppEXbfbtWj3yfdDbTzg3spTypuWnM+x5z7s+vmKxaGkXxB2gUp2rckFi3LDLPfPUYHm7OwMFxcXxrrTIs6asCMcDodpt+Ve+d3dXWSzWYRCoS8acUimm5sbVKtVHB0d4bvvvsN3332Hw8NDVKtV9Pt9S2nN4/EgHA4btZ29vT1sbGwgHo/D5/PB4XAY0nPlFiWySXjpZciFF1Tbofw18yMUx5TWX+vyL4NHST/PBtS/JciE3LReeiaXqGTDOfPJZGLcVS5q6PV6FsKXSiUUi0UjSNFut407P+8QCd3jSCSCjY0N7O/v4+DgALu7u0gmk4aQPGeSqdvtolar4fj4GO/fv8e3335rrPzV1ZUhKktlnNLb3d3FmzdvsLW1hWQyaZpx+DmhR0NZbnuIIrvwaOFJ+LW1NbjdbhMqSUVd2Z7Lrzp0szgeJf1zL6rdrXysbv1SrzHt98Bs72VWt3EwGKDdbpv98KVSCd1uFwBMacnhcBgBiUajgWq1ikqlYsQlOWu+qAoMCR+NRpHP5/H27Vv85je/wf7+PvL5vKmbA/ckub29NVn6o6MjfP/993j//j0+ffqEs7MztFotC1E5NptMJo2I5t7eHjKZjHHr5fHlZl27Nj3DBNbmmbzz+XxmaaVsuZXxv5w+tLf1KubHo6R/qEd7WSE3t5ZKJZycnJiM+/X1tWlPZTKPXXatVguNRgPNZhPtdtuS3Frkg8vXiUaj2NjYwJs3bwzhNzc3LcsqSHZm1S8vL3F4eIgff/wRHz58wNHRkRHIuLm5Mcdn2BCLxZDP5y3iG8wVcBkGYF1oYSe7LNFxaWUoFEI4HEYoFDJCmmzflcfjjcTpdJobJIkvwwbF7FBWzwhOnzHrfnp6ipOTE5ydnRmBCbqulMTu9/tmnTRr1Yzb543dCRnD08J//fXXePfuHba3t82yCoYgdq/k48eP+PjxIz5//mz6AVhSk6DmXSqVws7ODvb29rC5uWnq/jQIsjlJWmGeq1xRzbXUkUjEjOFySIeuPXA/qCOFMwCYxRe67OJ5eJT033777c91Hr84pLXg2CfVXQGg1Wrh8vLSUmIrFoumL55DIqwv393d4fb2Fre3txYxx+d8WJmlZwxPl17OyVPzjvE1z/vw8BAfP37Ejz/+iOPjY5TLZbRaLfT7/S9Kg3ydeDyOjY0N7O7uYnt726Kcax8colWWtX0O39AzCQaDiMfjSKVSSCaTWF9fRyQSsVh5kn0wGJjrx1Fi/ow3TdXIWwyPkv6Pf/zjz3UevwrQHe71elhZWUGhUMDvfvc7xONx1Ot1s5P99PQU1WrVMpBir4HL7rvnkp1gHT6fz2N/fx9fffWV2U4jO++kku3Z2Zlx5T9+/PiFwOW0c2IrLxdisDwXCoVMvoLvkXV0aZVlFYNbb3juyWQSmUwGqVQKsVgMPp8PLpfLnAu7FeWDpOe/5QINxfx4lPT//M///HOdx68S//Zv/4ZSqYRcLodWq2WWPTSbTctWmYe6w2TcuQhkqypJk8vlUCgU8O7dO7x9+xabm5umQYYWvtPpoFwu4/DwEB8+fMD333+PT58+4fz8HM1m80kRDpkgZM2frbZyrgC4X31FgtJFZ22fHlMkEjEDOqlUyoziyi02FAKVFQBJev6O+RB5s1XMDo3pH0Gj0cC//Mu/IBwOmzXNjMvlXPtfw+Iwwx0MBo0yTSaTwdbWFvb29rCzs4NcLmcm6GjhO50OLi8v8fHjR7x//x7v37/H58+fUSqVzLTeY5ADNblcDrlcDolEAoFAwBLH8yubZ/r9vrk2wE/z/LTyHMFNpVJIpVJYX183XgMTnnTv7aSnS8/n8aFx/eJQ0k+BdF+LxSKKxeLP8rqyLZW6c6lUyrTVZrNZCxHD4bBJ2rH+Xi6X8fHjR/zlL3/Bn//8Z3z69AnlchndbnemmxNzBul02mzAkW49j8Hw5fb21vQicF6Ax/H7/QgGg0YLn8M54XDYzPYPBgMzzccqA4U35A2WpO/1embphT0ZqlZ/NmjJbgoY28+qUvMSoN5cLBazkCSbzSKbzSKVSiGRSCAWiyEUCsHn8xnFGS62rNVqODw8xPv37w3hS6USOp3OTOdAXb1EIoF8Po9sNotYLGY24BB2tR/22l9dXeH6+hoATBI0Ho8jnU5b5u5l7oGxOTsX5YPxO2v+7PaTPQ7cda+YHY+yetGykmJ2cFCG46obGxvY3NxEPp+3kCUajSIQCMDr9VpUY5jVbrVaOD09xQ8//IDvvvvOEJ5NQ7PA7XabBB5vNKFQyLLnjl8p0d1sNs0YLXv2nU6nyUGkUimTuKO6DvsY+JDSWt1u1yKxxRIdN9ZeXV0Zr2IWgVDFl1hOUz4H7BbuJeF0Ok38LAUst7e3kU6nTezL5hX7aij20LfbbVxcXODjx4/4/vvvcXh4iHK5PLOFJ7j6KpFIWLLrdreeu/Wo9nN5eWkSnOyX9/v9iEajZvlFNBq1hAl02dmvz14GaeVJepYCuTyj1WqZZqJlbRV/DpT0T+CvZUlk8ws3z/zmN79BoVBALpczXXVSMEJmzfnvfr+PSqWCw8ND/PDDDwtZeB6PHgfDiGAwCI/HY8IdSXhuz+WUILfuADAttuFwGJFIBOFwGIFAwJTw5H56xvG08IzXWf6T9f+7uztcXV2h0WiYsd3hcPiFFJjicSjpf0awYYVTa5lMBjs7O3j37h3evXuHQqGAbDaLaDRqEl0S8gbE5p9Go4GTkxP8+OOPpizXbrfnrijwnGiZmWxjvMy2WA4ZnZ+f4/DwEIeHhzg7O0O9XjdiF7T0wWAQwWDQ5B+YI2IML136Xq+HXq9nKcmxLCeFShhSSBmuYDCoI7dzQEn/M4D1do/HY9Y553I57O7uYn9/H2/fvsXu7q7peHtIplrG8ZS4uri4wKdPn0zjDYdm5gGbceLxOJLJpEXzjoMug8EA/X7frNmyi3f2+33ThMNxWb/fb0Zm5c2DHXckvBzFpVQ2E3y08Pxeincyj8DEpmI2KOn/iqDiK0dII5GIEazk1Nru7i42NjaMyg3JIZNmds09bpUtlUqmAef4+Bi1Ws0MzcwDt9uNcDhsNs5yoIYNMdyc22g0zF496vlVKhV0u11Lnz3FLpmH4Htitv7m5sZk4ZmYs++ep3WXCT8AJmlZLBZxcXFhBowYOiiehpL+BTDNrWRjSjAYtEhYUVuOnW6pVOoLV5rHnFZ3ZhxfrVYN4Y+OjlAqlUy5bB5IK59Op5FMJs3NhyWydrttWnpPT0+NeCfr/3Tr2WNvX1pJ686QhMo6LPMxnpfuvBynlVN7cqMuRTw56mu/Yaq7Px1K+mdCijwQlHYOh8NIpVLY2NjA9vY2tre3sbGxgUwmg2QyaUpY9gEWwv6hZa2aijc//PADfvzxRxPHS/XaWbCysmJ6A0j4UCgEl8tlGm46nQ6q1SrOz89xenqKs7MzXF5eolqtGqIydJFqOHTpGYrQpecSjHq9buJyLsiUXXbyIfMTdPErlYpRHup0OprFnwNK+gVBd9au2yZbT2XcXigUsLGxYWlQIVFmsUh2C88W26OjI0PAeUDCJxIJ0wAUj8fhdrvR6/VMTbxSqeDy8hLn5+dmbTUTaKPRyFwDvm8pUU3LTreesmHUFyDppXKQDGvsrj1BMRBqFNi38Cgeh5J+QdjXMtHic248k8lgb2/PDMbI0VQ+n7BP6EkwiXV9fW1ELP/yl7/g22+/xcePH42Ax6ylRarXMOxgfiGTycDv9+P29hbFYtEQ/uLiAsViEeVy2Uhz27feSAksrp1iDwHddQqDss7OWJ7xvFTskUS3C3Lw31yqQUESnbibHUr6BcAPuN/vN80zjF+pWZfP53FwcGAIz1hZNtg8RVS68yTg8fExPnz4gO+++85o2s0yRAPcz8hTxIIxPFdfRaNRTCYT1Go1U4Mn2ev1utHyY2wuJawZzlAKi2OwdM1lbqDVapkSHV16ZuztEliPTSjK2ftF5MaWGUr6OcHsNFc0J5NJRCIRs6DB6/VaZKxIeLafAtMtu0xcUfHGLs3FWvynT59QLBa/2EIz7VxJRJ/Ph3A4bFpj+aCi7WQyMSO55+fnuLi4QKVSMUq9dpFLejZSytrj8QC4VxmitaeFp2Vmlp4uOUuC/J7X4jEiy+ulIpnzQUk/J+gaMxPP7jnG53JKTY6+MnaflpGXdXA2qXQ6HdPmSo18Zs25N/4hC8+bDz0RuvIc5JHjrW63G7e3t6jVaiiXyzg+Psbp6alR1rGr2krNOxnHcyiH8lxcWslkoHTFOchkVxqyd989ReaHYn7F41DSzwEKRW5sbBihyFwuh0gkYtpVudE1Go1+QXjgPi6Va6ppDdlpVqvVzKNcLqNYLFqSaPYNsDyuDDvYE0CCU54qHo8jFovB7/ebLD1XaTFDf3l5aVmcOS1elq/HISAA5sZFwnOtFb0FDnGxO5G5EQCWiTp71v4hKOnnh47WTgGJJGNFSkFvbW3h7du3KBQKRv9duu6SeKy92914Ep0ZcmrnVyoVlMtlI5XN7DQTX91u16IYw3NlbTwYDCIajZpddrlczkzLxeNxk3+g6IbM0nMJh9TOs09ZymUVdOnlxlmKX0gLzxq8LMfRS+C6Krr10yz9LP9XWo+fDzpa+wicTqdJfsXjcWxvb+Pg4AAHBwfY2tpCIpEwCx+A+1idcS4HVeSH2L6PniId5XIZ5XLZbHqlJJdUiZFWUr6OXB2dz+eN0AYJL4dnWDuXlrhSqaBYLM60GpsVCt7YOBTE82NZjlLfUu5K3qjkEI8kPRNzs5D+oWUkKqbxOJbTlM+Iu7s7I021vr6Ozc1N7O/vY3d31yTAGJPKTjKW45iQA2DaT9nddnl5aUQ2Ly8vUavVDNHpDkv5Z+D+Q87tLxxfJdnZACTJLgU32Et/e3trsumyyWXaTnoJ6dKvra2ZJRVc7EHJb1p5HsvumZDwUgyTYQ/d+1lceyYTdYPtfFDSP4J0Oo2///u/xzfffINYLGaUaNPptMlU80PLDyktHkUq+Xtuc+UOeC7KYKwud9HLGwghrbvH4zFz71woKXXz6IFIwQ25GoqtsLz5FItF1Ov1qVl6QmbqSXhaeXuWnosrp6kA2y0+V1jxd9Peu/xbgltsOb2npJ8dj5L+H/7hH36u8/hVgF1k19fXcLlcODg4wO9//3u8efPGdNBFIhH4/X7zgZXEpisrB0YoKcXS28XFBU5OTox1bTQahvB2yCQdrTuVZbmdlglFNtjEYjHTACQHdJgYGwwG6HQ6qFQqOD8/t8zCP0V4Ts+xIiBLdLTwJPxTq7btq7z5lQnOp1x79huEQiGzbcd+3RTT8Sjp/+mf/unnOo9fBaRbLkUlmPhiPEv3VLqkjGWlYi7nxa+uroxVpYx2tVo13W2PafHZs/JUqd3d3TW75TY3N81CyWlyWvw6Ho9xfX2Ner1uvI2Liws0Go0vll48VBUg4XlNKIzJstwshLeTnNdxlvo8cJ9UpQwX5wUUs+FR0n/99dc/13m8Okh9NwpLcL6bHWeMm6kjV6lULL3rj22qtVt3jr5yr1yhUMDOzo5ZGc2EorTu8lwZYnAe/vT01NT8WRXg6/KrdOlp4QOBgClDUuqKWXq69LO2xN7d3Vl20c9afguFQkin09jc3DQlU3ulSa39w9CYfkGwoeTm5sZS8qpWq6b7jC4vNd1kvPvQ8kp77O73+03n39bWFnZ3d7Gzs2Pc+Wg0anbB2SW1pMQVLTzn4Y+Pjy31eLmpFrBm6dfW1oyF50YauYabTTeL7Oib1m//GJxOJ0KhkNkBQNLbx5IVD0NLdo9gWjlI/ns8Hhs1mcvLSxwfH5ulEiQDLb6UdJb73uRx5bQaicYlFxsbG4bs+XweyWTSzOFLK2d3nRl6UABDSlxxvZXspedXdtoxaUeXXhKeHo0sK856XSXmaa5hXoUzA1zEoZgdj5Je75hW2GvB0mVmCY7CEv1+36jBSM03fsBl4on5ApKMW21k7X1jY8NUDujOswuQ52OP3zm/3mw2USwWcXR0ZJG46vV6XyzelK21JLzU2Gc4Q0+GYcqsQy8PNdPMSny2FEtZL3kM/cw+jUdJr8mRhyHjeWq2sZOObbKyrRSAqZOTqIzZKanF9tloNGpaaGnRSHbO4lM7Xta0+f1oNLIQvlwu4/T01GzbpcQV5+GB+7ZYOaDj9XotngRr8czS2zfQPAbZzSfDENmc8xQo+MEEXjgcflBPUPEwNKZfEHSdOTZ6dXVlYnYm6OSudrruwP0svqx5Uy6aViyRSFg048PhMPx+vynFybXQPB/Zy8++gFqthouLC1xcXODs7MxCeOrXyfidvfRSDIPlSFp46dLPSviVlRXTkszSKK/RNNUgO6jJn81mkc/nkclkEA6H1TAtACX9gqBFpfWToo60YPywA/cuvOxZDwaDCIVChuzxeNyQnC489eLdbrep/wPWCTOeCxdAsnxID4Qtvo1GAzc3N3A4HEYrXtbg+eANSm6RpTvPMuMsLj2PTe9BiofQG5Etuo8da3V1FfF4HPl8Hpubm0aQROvz80NJvyBkI8lDHXR2y06ys7GE5OYjFouZxRCydXYymZjYmWEDbwDyK0kv22FbrZapHkjrzhsS43cSU7bqcr3UU62108AbnGzmYRMNtQLa7fajLr70ANbW1kzLMUMdinEq5oOSfgFIy2TvKZcuLHBv7SiDHQqFzCScdOEjkYiF7MD92io5Z89YWm6BYW2c/6Z1lquead25x14KWNKVB2DyFGwsmtZa+1RVhzcSquzyPfr9fgBAt9tFvV43eYnHbpycIgwEAojFYpa9fhrPLwYl/YKQ5TySnYm5yWRirLysdctd8yQ8P8Sym05ucaW1bjabRjLaXiZ7KBHGkIJLKCTRGbdLb4I9B8zKs/Q4a2stAEurMEuNLDH6fD6Mx2M0m01cXFwAuHfz5aQdry+vKVd3R6NRRCIRSwswoFn7eaGkXwD2FlW6xiSRfI78nUySMVPOJh/5wae1pWpstVo12nUkPS29rLHLSgCrAfwaCATg9/vNoAzPlUm1fr9vPBfq8kmXfhbCcxSZSsCFQgFv3rzB5uamcccHgwFqtRpWV1dNIpSz+9PCI1p5rshiMtMuO66YHUr6BSEtvExUeTyeL5Ri5YSbXNrIhY+DwcDITVE5tt1uo16vo1KpGBUdLm20k9AeQrhcLkPyWCxmsZAsxdkJz2y6FMDga83SWitVgzKZDAqFglECzufzJtN+e3trLD7blO0Ku7Tcsl+ANyy5Ikv+Xyhmh5J+QUjSM1Eny1LAfcMJXVj+nFlxTt/JNlq2zHIhRKPRsFj4fr9vEeWgRWRcbo+Bud+epLcTXkpi2SWuZNLuKaysrMDn85mBoEKhYHb0ra+vm7l76gt0Oh2kUimjECT7/6lJwLLm6uqqURvm71XyenEo6Z8BORRDd93tdlt03kgY1rpZ76ZMFt19Wi+pEU8CUn56WiOMXLjB9l1un2VjD7fQys46iliQ1MzSS4krSlPPUoenYGgikTBru3K5nBHgZN7A6XSaNdbsTaBXcnNzY5qNSHo5+cjqxDwtv4ovoaR/BmSHmSQfcN+xx3KatMrSO2B8b9/qylhXLnYk4WXFgOfA4Rx281HTPpVKmfVZUi6LVlUulJRJPPvGmceugVygwV4Ddg9SJ5Ceheztl7kOfs/qgOzak6PLcsCHoYxiPijpF4TM3pPIMrkk9ev5QbYnqhgOyL+XgzL2vyVx5N8zM09RzEQiYTTt2ehDt15uj5UVArsuvRSxnOU6MLxgTkHeYIAvNf1lf8FDbrq8Dgw7mNtgOMBEpcb080FJ/wzYk3WM8YH7gRzG81LskVZfjtHKnnT+vXyu1JqX9WuWszicQ+s+zaUH7t1khhksCTKOZ4wvvYnHZv7lZB5vWkxW0hXn7xi6sN9AtvRyIEkmDeUWG5fLhVqthsvLS+TzeWSzWYTDYZMAVcwOJf0zIK08XXbZYy/77+0rmAEYwozHY0sMS4JKb8Du0sshHenSy0UW1LBjWVBazk6nY+r/FP5gGVC+DonM9yPfu/05Uum3VquZzTnU2KdyDwVFqPxLl12GQxJOpxPdbhfVahWlUgmlUgnNZtOoBckEn94AnoaSfkFI116W7Fh/ZsxuryfLBBSHTXg8kh/A1BuAvAmwlBUOhy3tqdTI49itVJhlIozxO6sDtPKS8NPClYfAUiTHjM/Pz82sQK/XM5t0+H2lUsHZ2ZnR56OSEBOVdtUfotVqmWnGZrNpegsU80FJ/wzIshLjWa5NJtlkqyzJJIkv3XqZ5GK8Lj0Ifi9n3Un6ae2pMqRgwk52+XGbDnfb8xxJUFpvwLqZxw4+r9PpmPBjNBqh2+2i0WiYDUCszVerVcvUn1xXbY/zJamvr68tswS6rXYxKOkXhF1hhl1vjGFJbvuiC/6tvWefVlxm9WVvvNwZ5/V6TcNKKBQy5S9u2plMJpbyHmP46+try/ZYro3udDpmEaYc++U58rxZSrOTUur78wYjk28k/WQyMXqC1AtsNBpf5BKmWW858cfy5bIrOy0KJf0zQNJzci4cDuPu7s7U6yVIbnbSySy8dKdluEByr62tGU+CMtRsp/X7/SZbLpt7AGv5j4RnAo1JNE7S8bx485FDMMwpyJsVv8p5eBlCsNegXq+b6gEA020ok4cy0fkQ+Hpq2Z8PJf2CkJaewzTRaBROp9Oy301m9Ulwe+1edvbJ59O6c7UWyc4bgGzsoXWXW2Mo1cVsOTXtSHQ+nwm0WaSrpUS1XY9PJgx5vH6/j1arZQZk+DN5DrMq6HKYhzdBqf6rmB1K+mfATvrBYACXy2VIL0ksy1rD4dCyy03G9PYH8wayJVW23LLezgSYXUyDFp6juWy8kc0+Mu7n8eTM/LTHNNjVgmRowfOV5zeLhSd4XSk6wsYfHbyZH0r6BSEJSZebpLe31trR7/e/kLyy1+dJYLtQhlybRXdaxtWDwcDE79fX14bstK4yuTht5RSPz4d87iwNO3a3nz+TQ0fsFZi1AQj4ycoHg0Gk02nT3mtXzlHMBiX9MyB77xl/M/tNBRrZYCK3z8qavSS6tNYrKyu4ubmxZO7Z5CK9Bdlww0QXCc82Xrr6fK7daksFGxKTVnjWVVN2yPdF8P3xuLN2/fl8PiSTSTOfn0qlzHox+TzF01DSPwOyUYbxt2yXlT30jEVvb29NSYzZcCkgYXftpUAH3XmPx2N239k74Ph6tO52CW6ZT3iI9AAMKWfdIDvtusj38Fhn32PHcblchvDb29vY29vDxsYGYrGYtuAuCCX9M/FQHG4frJH99SQBcJ+NZiJMlqFke68cy2UYwefbJbLkgzcDPk9acuBLvXy797HItZBLO9hvwPc4a5mNVQS/349kMolCoYCDgwMUCgVkMhl17Z8BJf0zIK2jbMKhVZUDM/I5Mm6WLbrAvTa+jPVlbDwej78YOZWxvCS61N6Xgy72cEK+l+deD5JeKgnZQ51ZXs/lcsHv9yOdTqNQKODv/u7v8NVXX2F7exuxWEz18Z4BJf0zIeNfWltmp+WDxLPHtPZ42V73lvVyvgbbdWX8TwFM+XrSpZeEl7P+L9nGyiEg9i1IkU+Ox/LmJ0dnpx2HLn2hUMA333yDb775Bvv7+0ilUvD5fOrWPwNK+r8CZCmOZT0OyLAWTvdXCm7Y+/n5N1KTXirI0HrKOX77ecibhrzhvBSY05CyVuFwGKFQCH6/H2632wzasAPQ6XSaBqJpWgOBQACJRAKFQgFff/01vvnmGxwcHCCbzZo+fsXiUNI/A5KgJDRJYG+0oQX0+/2WBJusa/P5drFNSXg5dy9Hd2nh7Rl8quDYa+j29/EYHvMGuIqLyzqi0ShisZhlwy378uv1OsrlMur1OprNJjqdjvFEeH1CoRCSySQ2Nzfx7t07o7NHwttXUivmh17BBUErvrq6Cr/fb2JtxvHM3MuaOR9SCWdaZ57cKSc14mQsL+vdMpyQijsUuOTKLSm7JfXr53HxmWDzeDwIBAJYX19HNptFLpdDNps1Gv5UrQV+6sJjH36pVEKxWDSTcpTI8nq9CIfDSKVSyOfz2Nvbw97eHra2tpBOpxEMBtXCvxCU9M8AJ94mkwlWVlYQCAQsyTlJSGmN7bG2PJ7U3JNfZdluWk1fxvVSqIIuNXXzpX4+5a1nRSAQMNt55FZd1s4zmQxisZghvJTmkos+S6USqtUqms2mcfO9Xi+i0SgymQzy+bzZ0BuLxczCTsXLwPHEXV6HlR+BPdkmG07siTqZsbc/H7gfsZWiHA+V+ezZd3lsehB08eVUHafbLi8vjbXlLLscwyVk12EoFDJim7lcDul0Gslk0iLeIWf57RUHtgVL4Q5501ldXTWCnhwTlgs7FQth6oVT0r8A7LVu+8/lQ9bA5d9Nq/XbfzbtNe03AHkTkG4/FXg5y35+fo7Ly0s0Gg10Op0Hh20YwnBjzebmJra2tpDNZs3uPS6hWFtbe9AFlxN4MqfBEEPq9nNqUOvwz4aS/teAWdtOn3Pcac02LJmR+KVSyVh6quZM0/Bj9YEJNsbv7H1nNWHajemh87RP6/E9y5ud4kWgpF92cPyWM/XM7NtXbBMy5PB6vQgGg0aww+fzPfo6gPbC/wqgpFf8BBkGPBVuAPfkl+3FilcBJf0y4aFOt7/Wa8xz7KdCHPUQXgxKeoViyTCV9Fr8XFJMqzY8BSnXrXi9UEu/xFhk2EYJ/6qgll5hhRJ4OaHdDwrFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZlPQKxZJBSa9QLBmU9ArFkkFJr1AsGZT0CsWSQUmvUCwZVp74veNnOQuFQvGzQS29QrFkUNIrFEsGJb1CsWRQ0isUSwYlvUKxZFDSKxRLhv8PokYq5r3fTWkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 49\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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uFLzH44Hf74ff74fT6RRtseTw0+v1RHPNpLM6PRhIwJNqBeh7qDdf7tFn5odF/x0gi4DKbEnw7969w9HREVKpFGq12szXXF1dhcPhQCgUwsbGBgKBAKxW6y2hK3cYjUYD19fXiMfjwlJ70jl6ZWUFVqsVa2trCIfD8Pv9oi2XSmfr9Tqq1SqazeaYMw4wLnZawYHf6vGVryl765EjD2/xF4NF/zuiTJX1+31Uq1XE43G8f/8ev/zyCz58+DDXOZ4wGo1wOBzw+XzinK0cWDHpfprNJvL5PK6vr5HL5aYG8DQaDRwOB4LBICKRCPx+v6jfb7fbKJfLyOfzyOfzoj9AdrwhwSuddeTiHOVDQmm2Kd83PwBmh0X/OyILSfaf+/jxI37++We8e/cOl5eXczvdUq98IBBAIBCAw+EY86+flJcHvpznq9UqCoUC8vk8qtXq2N/LIiR/OyrndbvdwtWmVqshl8shlUohmUyiUCig3W6P1cuT8Mn6i4RP5bj0eb/fF/l42WyT5+otDov+GzJNbO12G4VCARcXF/j48SPevXuHDx8+4OLiAoVCYa6GGrVaDbvdjnA4jI2NDQSDQdhstqnnX9kZp1Ao4Pr6WqTo5Ly8LHhy3IlEIlhfXxeW2VQqWy6XkU6nEY/HkU6nxUqv3KloNBpotVrh80dC73a76HQ6ogS32+3e8sZn0S8Oi/6RmCZoGaV11HA4HKtvPzw8xNu3b0VqrlKpzN02q9Fo4HQ6RX19MBiExWKZeh/Al3N0uVxGIpEQjTzVanViJ5tcv7+1tYVIJAK32w2tVivMM6lcOJFIIJvNCi89ZaqODDKNRqN4aNDvhCzHZC89eZXnIN7isOgfiDLaPOl8OSl41m63xdCIs7MzfPr0CYeHhzg+PhZBu1m39HLue2VlBTabDX6/H6FQSKzCckOQ8gFFk2/Pz89xfHyMq6urqTPxLBYLAoEAdnd3RS++3W4XjTWlUgmZTAaJRAKZTGbqg4vO9Hq9HhaLBXa7HXq9Hv1+/9Z2n0p9tVrtmDcgsxgs+kfgvmIT+UFALjT5fF7U0lPhDbWuzhu0k/+tnEbzer1j5hh0H0qn2kqlgkQigc+fP+Ps7GxqpoCaara2thCLxRCNRuH1emEymcQqXyqVkE6ncX19jWKxeOeoLTLcsFqtcDqdIiZAHn0U1KN/K6/yLPrFYdHPybRtPKWYJs1Zk+e/ketNPB7H2dkZTk5OcHp6ikQigUKhMHMefhIGgwF+v1+Uw5LJ5V0/S6VSwdXVFY6OjkRd/yQfe61WC4fDgfX1dcRiMTHvTt5FtFotYZGdzWZFl90kyAyTTDccDgfMZjMGgwHUarXorJN98Tk//ziw6BdAXjmp5ZPe3BScopQS+bZXq1UR0SYXmrOzs7Fz76yVdpPQ6/UIBoPY3t5GLBZDKBS68yw/Go1QqVRweXmJDx8+4PDwEKenpyiVShMba8xmM0KhEGKxGGKxmNjW088pH1lKpZLw0ZskeorEa7VamEwm2O124XlPwbtWq4VWqyXcc2SLbeZh3Cn6ZbUkmuTqOmnb3mq1UC6XUS6X0W63oVKpxOQZjUYjilRI8BQoi8fjSCaTyGazQhwP6RjT6XTw+/04ODjAmzdvcHBwgGAwCKPReOtnGQ6HIsJ+dXWFjx8/4pdffsHh4SGSyeTEnQYJfn9/Xzjn+nw+GAyGsd8ZHV3I4WZaFyCZZ5pMJjHkwuFwwGAwoNPpoNVqCTtspdgphsIddotzp+gf+oud1FyhDCY9lPuupSwqmfVayoCc8utWq4VMJiOi3Y1GA2q1WoxmWllZEfbNcnCLutYWcbGdhF6vh8/nw97eHn744Qe8efMGu7u78Hq9t7b2w+EQlUoF19fXuLy8xNHREQ4PD/Hp0ydcXFygWCzeqoTT6/VYW1vD7u6u6NKjXYQyNkBbcvqYtMqrVCqxrbfZbHA6nXA6nbDZbFhdXRV+e0qhK1trWfSLc6fo5aon5jcjB2o7laPd9Xpd+MTp9XpRmVar1cSWl6rTpm1754UEub+/jz//+c949eoVYrHYxF55aqQhJ5zj42McHx/j/Pxc1NgrBa9SqUSX3rNnz3BwcICNjQ3Y7faJcYvhcCiq6abtEmlmnd1uh8fjgdfrhcvlgsViERWBJHIyy5Ddc5R1/Mz8sKpnRLaeTiaTOD8/x+npqXCVIT84qg0noTUaDbRaLXQ6HTGNZpFVSrmj0el08Pl82N/fx08//YQ3b95gf38fXq/3VvVdvV7H9fU1Tk5O8OHDB3z69Encd7FYRLPZnCgi6tLb3d3F/v4+Njc34Xa7xdgrpbsPiV1pcEH/RqPRiPLgtbU1BINB+P1+uFwu6PV6tNttMViDCnS63a6Im/T7ffEgWNaj52Nwp+jfv3//re7jd0feupMpo9lsFimvUqkkxE7OsFdXVyIX3e12x6a00BmXVqeHIotIr9fD7/djb28Pf/7zn/HmzRs8e/YMa2tr4pxNIqzX60gmk/j06RPevXuHw8NDYcShrLqTIQuszc1N7O3tYXNzc8xTTznRhrbdZGOlbJ+lFd7tdiMUCiEajWJjY0M06gAQppftdhvNZhPtdlsInwJ69BBQlvQys3On6P/2t799q/v4LqARzc1mEysrK9jb28NPP/0Et9uNfD6P09NTYR9FxpR0LpdTeST6r3HuJOsrCtq9evUK+/v7twRPQUaq5f/1119xeHgoSnvv23GYzWZhorm1tQW/3z8WuKOflV5PXo2poIY+KLjp8XgQDofFkI319XV4PB6srq6i2WyKTAeN6pJFT3PtqDz3MR6ky8qdov/Xf/3Xb3Uf3yVv375FLpdDMBhEqVQS7aY05WWSaB5b7HInmsViEUG1V69e4c2bN4jFYvB6vUKQFEHP5XLCgOPt27d4//49rq6uUCqVJr6OfHwgx51IJILNzU2Ew2E4nc6xGI9yZ9Rut8Uxhgw0dDodVldXhdFGKBTCzs6OELzP5xMDKan9ttFooFariQwArer0UOl0OmM22Bx3mh/+jd1BPp/Hv/3bv8FisYg3JK1A3wIqiHE4HLDZbKLmnZxwdnZ24PP5xBl+OByi2WyK8/vbt2/xyy+/4PPnz2JnMg1lQw2524bDYSFO+d+R6EejETqdDur1Omq1mphxp9FoYDabYTKZRM/91taWELzX6xWFPfQ9rVYLtVpNXEduuhkOh+L/gZwSZNHPD//GJiCnipLJ5Dd/bUppUeMMmVRQPT21s3o8HhGll5t3Tk5O8Pe//x0///wzPnz4gOvr65kbd7Ra7S3BW61W8fdK0dODplQqoVAoCKcdKrzxeDyIRqPY2dnB9vY2wuEw1tbWYDaboVar0ev1xIOjVquhWq2iWq2K8VUUFJSLnMrlMqrVKjwej8iU0L3x+f5+OGU3ATrbf8tzo0ajEdVptLoHAgFsbGwgGo0iFArB6/XC4XDAarXCaDSO5eFpqMTFxQXevn2Ln3/+GR8/fpxL8CqVSoyw3tzcRCQSgcvlEtH6SXQ6HdE4RPUH/X5flNZubGwgFothd3dXnOHJEpu27b1eD81mUwietvr0QAC+7Cjq9bro9S+VSmi1WrBardxmOyd3qvoxcsnM/eh0OjgcDvj9fqyvryMcDsPr9SIQCCAUCiEQCMDn88Fms41FzwlKyyUSCXz48EE47swjeOA3X71wOIxoNIpwOHxrmq3sZUdpzOvrayQSCaTTadGoQ9fZ3d1FLBbD5uYmfD4fTCaTyHCMRiNRcku1DJVKZWxIJYmeGnqKxSKy2awQPRfpzM9yLuVzoKzKe2wMBgPW1tYQjUbFirixsQGv1ysq1SwWC0wm00QXWaXgyURzVsErA3hkkBEIBOD1emGxWIRA5So58sZPpVJi6g5NwSHnHvLni0Qi4lqrq6vievK2vlQqoVQqjY2ilivvRqMRWq2WKHIql8u3fr5ZPA0YFv29fM2VRG5iefHihbCpDgQCor+cVtZpb+ROp4Pr62t8+vRJpOXmXeGJ1dVV0eZKD5xJr0uWWKlUCicnJ6ItuFAoYDAYiKAj2XW53W6YTCYx5YYyHBS1L5fLKBaLKJfLInUnF/jIbbZU4Vir1YQF17IeQxeFf1u/AzqdDjabTeTb//SnP4lBFF6vF1ar9VaZq5wKpBV3MBggn8/j8+fPovCGPPVmRX6o2e12uN1uuFwuWK3WsZgB3Q+t8LSzILfeeDyOZrMJo9EogpAejwdOpxNms3msR5629eTJJ6/yrVZLFPhMWrk7nQ6azaaI8FMOX/69MHfDov+GUBqLilQODg7w4sULHBwcIBqNwuVyTR3/pHxDU/PM1dWVaI29uLhAuVye+74ogEelsRRdVx5tqO8gkUjg48ePIv9/cXGBSqUi/PkoiGe322E2m4X/HV2H0m+1Wg3lchmFQkF0G1JeftoOi3YHpVIJxWIRlUoFJpOJ3XTmgEX/lSG3V6PRCLvdjrW1NUQiEezs7GB/fx87Ozui+EXZtabc3sp/3263kclkcHJyImbcFYvFme9LPsuTnXUkEhEe+TabTUTFB4MBGo0GyuUykskkjo+PcXh4iI8fP4oe/OFwKARuNpthtVphMpmE9ZWc1+/1eqL7MJfLiem27XZbVPTJyA8AiuKTN38oFILJZILD4WBzjRlh0X9FdDodrFarCIz5/X4hLKp0kz3sZO5atUajkUjPHR0d4eTkBNlsdq7UnPI+3W43wuEw1tfX4ff7hQEHWXNTOy6d4U9PTxGPx1EsFtHpdIRppdFohNVqFYMr5SEWlKKj1BvZauXzeeEJSNH6u2IpNBvg9PQUgUBAdOkpMxu88k+GRf8VUKlUY1Vt29vb2NraEkU1fr8fHo8Hdrt9rAV2UqOK8nNyro3H48LiKp1OzzzFFrjt6Uc1AZFIZKw0tlaroVgsikajz58/4/T0FJeXl7dML/V6/ZizrclkEoMraXAFVe4VCgUkk0lcXV0hmUyiVCqJAN4s1Go1XF9fi+/f3NxEKBS60xqM+Q0W/SNDjjA+n090qB0cHGBnZ0cMnqDCGmXUeZLhh/JcXalURE394eEhzs/Pp1pc3XVd+nN6MJFdtslkEm4/2WwWV1dXoo34/Pz8VqMRXYfsrE0mkxhNrVarxXZdHqRBW/OrqytcX1+LIZmz9sjf3NyICTqTWoO5Mu9uWPQPgNxcaUUjS2e3242NjQ0cHBzg2bNn2N3dRSgUGhstBdwW9yRLLuC3LjYK3L1//x5///vf8enTJ5Ebvwvl61CcgWbdUa+83W5Hp9PB5eWlaNg5Pz/H5eWl8PKjYJt8Pdra00qv1WoxGo3E6t3r9VCr1YTgaVufzWbHtvb3pUflOARV8VEcgIt0ZodFvyB6vR42m02UxlLAymg0jq3y29vbWFtbm5iGA6av6sRgMEC5XEY2m0UikcDx8THevXuHjx8/ivScvMrdZx9ms9lEai4YDIr4QjAYhEqlQiqVQiqVEoInLz/Ki09y16GOOvIGHI1GqNVqYnJttVpFsVhELpcTQi+Xy+KanU7nVm5+FmS/PBb97LDoF0Cv18Pj8YjW00AgINJGRqMRLpdLBMXIQFK5xVYabhL0xpctrhKJBM7OzvD58+cxi6tisXjrzT5p90COPna7HX6/XwydjEQiohBIpVKhWCzi4uICx8fHODs7QzqdFtvnaajVauEWRI5B9Xod6XRaRNqLxSIKhcJYao5WaMrJ04fyZ7gLeuBwfn4+WPT3oFw5V1dX4fF4sLOzgxcvXmBvbw/BYBBms1l4uZvNZtE0Qy2pwG+CnvYmpbN0o9FAs9lEpVJBJpPBxcWFGESRSCRmctDVarWw2Wyw2Wwwm82wWCyip319fR3r6+tYW1uD0WhEp9MRM+nfv3+P4+NjpNNp1Ov1e2256YFC9QWdTkc031A+vVwui5p6Kr5ROt8ssmJzim4xWPT3IL8JtVqtcJ59/fo1Xr16ha2tLWFiQed6ee6azLTtvTz1JpVKiTRWoVBANptFMpkUTrrUez4Nqp+X22PJfNLlcsHn8yEQCGBtbQ0mkwmtVks4AR0dHeH4+BjxeHyqSYjytegsr9VqRcEQ5dzpc3llpxVdOZ9+ka26fBzg7f3scGvtBEiccsBqdXUVgUAAz58/F4Lf3d0VqyU1ksjIuwTl33W7XeESQ6Wo6XRa+OJfX1+jUCigWq2iXq+L/06DZsI5HA54PB7RHhuNRuH3++F2u0UqjSrl+v2+MPuk4RvpdHomwcsBPPrZ2+22OKNTmazsciND6Um5cIeq9WZF3trz9n52uLV2Aso3EI2Lev78uXCe3dnZESv8tMKaaamyZrMpilNo0GMul0M6nUY6nUYqlRLz4SmqPU2EarVa2FGFw2FEIhGxwq+vr4s+fPKVJ6EBX/LddHw4Pz8XW/pZVk3KAOj1etEb32w2xWQass6iHP0kSOTKQRazMm8MgPnCci7l9yC/iWjk8/r6ujCj3N/fh9vtvlVYI6MsrJFz1ZT/lnPfxWIRpVJJrPyzFNsYDAY4nU4EAgHhTiNX+lGn3KSKv263i0KhgHg8jvPzcyQSCRSLxZlGa1HwjgpyVCqVmDtH53a5uu4uHhJ5pyq/eaP+yw6L/g6cTif+8R//Ea9evUIwGBTGjn6/f6yBRHbApT8jyGyiXq+Lue3n5+dicGU8Hhf5b6VxxF2QSebm5iZ2d3fF6Gg6w8u960rBUxowlUqJPHwul5tpeCYJXl7lyaueVvpZBf9QKCbAAb35uFP0//RP//St7uO7QK1WYzQaod1uQ61WY2dnBz/99BNisRhsNhusVitcLtfUgBzlpQeDgRjMQGf3fD4vpstQKWs6nRYTb5RMOxpotVpYrVaEQiHs7u7i2bNnorhmbW3tVsYAGJ+oe3Nzg0ajgXQ6LabmxuPxiaOpJ92TVquF0WgUzTTAl9gHnee/puDl34XZbIbL5YLdbhdBVPk+mencKfp/+Zd/+Vb38V1Abxw6ZxoMBthsNtE4QsMslJDYqdebfNvJ2bVUKomCl4uLC1xdXYn897S4yaSSWZPJBKfTiXA4jP39ffzpT3/Cs2fPEI1G4Xa7xQw9JbII+v0+SqUSrq6ucHx8jIuLC+RyuXsdfsm/nmrryZBStqWm6PzXxmAwCP9AqnRc1qDzItz5m3r+/Pm3uo8nhXKyCzWS0Lm8UCigWCyKD0q9pdNp4e+mLGWVr62EIvNerxcbGxvY3d3FixcvsL+/j42NjVvmlfI5mSLcwJdtfaVSQTKZxMnJibC4ajQad4qVtvQkeFpZaScjW1V/C2w2m2hPpsk78s/PK/3d8ONxAeQ3VbfbFek2eQR1LpcT4i+Xy6jX66jX62JFnMQkwcuDIra2thCLxbC3tzdmqzVtlZtkuhGPx3F8fIyjoyNcXV2hUqmMPYCUTBM8PewoJfc1nYOVBVI2m0346JP5CDvizg6n7O5gWg5YPm+3Wi1ks1kcHx/j06dPuLq6EnPnycut1WrdOUZqUkOMRqOB1WqFz+fD+vo6tre3EYvFsLOzI4wmyTtevobynmVvuUQigU+fPuHw8BAnJyfIZDJotVpTf3aNRiOCdiaTSWzpaca9bFX9rSCrcPIokFd57qOfjTtFz7+8uxmNRiIodnx8jLdv34qx1ST0eeauqdVqWCwWWK1WMdk1Eolga2sL29vbiEajWFtbg81mGwteKYdJynS7XVSrVTHE8pdffsHh4SESiYTwqJ90H1RLr9frx1Jz5G1HHW6zdMc9FPn6NNeeUpHKkdz8nr2fO0XPW6bbKK2bms0mstksLi8vRc6dWj3v8noDfutDpxXVarXC7XZjbW1trMCGCm5odVf+f1Gu7LS60yCKVCo1Zp5JzrVkgEHfr1arRf8ArfA0jw6AOL/Tx7dKzRFkrU1lxUajcarpCDMdPtM/ADmIR3XmlOueVIIr5/RpFTWbzTCbzbDb7fB4PGJ0VSQSQTAYFEU2drsdJpPp3vuRswhkc3V+fo5Pnz7h6OgIl5eXwrQC+G1Appx/J6FTnp/6+UnstIP5lgUxKtWXQRzyUE273X4rfcrCvx8W/QMgMVC9uXy2XVlZuWXjTKu6TqeDxWIRq5bP54PX64Xf78fa2pr4L9XLk1+8XJJLdQH0MRgMxL2Q6WQ2mxUVd1R1V61WMRwORf87rezUNEOCl8VOM+NpOz9v0I5+dlmgVE0364NDo9HA6XRiY2NDzMRTmpKw4GeDRf8A5DJQZSkobZfpc2pBpTOpz+dDMBgUY6zW1tbg8XiEdbTVah2LlLdaLTSbzbHgGbWoDodDDAYDdLtdMeSRxj+RU00+n0er1RK1+vKqrtfrhQHGysqKGD1Nk2Nlh5p5g7vybkav149NuaXI/yzCX1lZgc1mEzshv99/786HmQyL/pFQ2l3JX5OzDBls+P1+bGxsYGtrS2xVaciFXq8XnWdkNUX205QCJI866l4j0dMWnIZBVCoVVKtVMQmGrKL1er2oqjMajTAYDMIAg/r4qWlm3lp6GUo30u6FOvtKpRIymQyy2awYY3Uf5CxMLcLyJF1q1WVmg0X/QOT6b3rjyVtZakHV6/XiTSvbVMkReUqJ0VBH2Rs+k8mI4p5isShq9Un0cgCPHgAkUp1OJ2ICFosFFotFGGuQp91wOES9Xh9zuSXhzyt4tVoNq9UKv9+P7e1tbG9vIxKJwGKxoNvtIp1O4+TkROxi6J7vwmAwiHs2GAx3TtJl7oZF/wAo2i2bZtAWmVZr2trLxpEmk0kUupCnHLWhUg6cfPFSqRSSySSSySTS6TRyuZwIGJIY5dp6OW5gtVpht9vHRkzRqGsaiqnT6TAajcQwykwmg3a7LeoLFlnhrVarKBV++fKlcBcymUzodrtIJpPQ6XTC3PKucmTgS9SevADIi5DTc4vDon8AcnBOFj39udIai6L9FFnP5XIAgHq9Lma90XY+l8shlUqJKr9MJoNCoXCnGKk+nlZEmloTDofFIEky0KAtvVqtRqPREH3vtL2n8/wiW3q/34/9/X28efMGr169Ek67Op0Og8FAWHTR4MpqtTo1XiC38VJXHzUOTXMPZu6GRf8AaGtPFXT0oazio4YcEvvKyoqYHON0OmGxWMb60qvVqhC9vLrTbmDaqkhHCbPZLOr0Y7EYNjY24Pf7xTBJedQU1c+TTTXNiL9v9Z2EXq+Hy+XC1tYWXrx4gX/4h3/A3t6eqJqTH37BYBDhcBiJRALX19eo1+u3ovly7YD8++Xz+8Ng0T8AWtFp+766uipWeGDcoplmrtN5vVgsIp1Ow2w2i3M18KULrl6vo1wui2mu9xlUykcIGkQZjUaxv7+P3d1dRCIRuFyuMcFT8RCl5WiaDbX6LuIlT6v89va2aPf1eDzQ6/Vj/47MPzwejxhJpdfrx1Z7+cEppyTJPZdZHBb9A5DP7PI5Hvitv172fKOKtnq9PjYggh4WwO3c/6y17SsrKzCZTHC73aJWf3d3VwiP5sPLs+WokIcGUZAnX7vdnnuVp1Tg2tqaGN/lcDjG6uLlbAYFN41G49jAECU08LLZbKLRaIhjx3A45BV/QVj0D4Si9nKajlZ3OYdPqxOt2Mqov7KMdp7iFRocST55m5ubY405FotFHDsI2b4rn88jm82iUCjc67Y7DXmslbx7mXTuliP2FEuY9nPKrjw0yop2Iw6H49a1mfth0T8AeaWn8yZV4hHTxEvCVl5P/vv7oBXTYDDA4/FgfX0dOzs7YkvvdrthNBrFOVh+oMgWXtfX18jlciiXy2i1WgttnyeV6046IpCI6QhTrVZnyhK0Wi1hEZ5IJOD3+0U2hJkPFv0DkUVPW3zaGk8S9izXAzC2DZeR/eJJ8OSmI+fEqTmHAmjy7oFEl8/nhQMvrfKLnpkpbkGxikQiAaPRiMFgAJPJJHLy3W5XjOgiJ2AKUspHIeXq3+12USwWEY/HcXl5iVAoJGoblH6FzN2w6B+A3HMut6DKk2zozT4P8nGBxK/cVdCW3uFwiAo/2tLTGZ7MNeT6fIopkEkneeyXSiVRuUdegfMwHA7RaDSQzWbx+fNnaLVatNtthMNhOJ1OaLVa9Pt9NBoN0eZ7cnKCdDo90wDLfr+PcrmMZDKJeDyOdDqNYDA41bOQmQ6L/gGQ6MnOym63o9FoCNFOq8uf5bp01ieRU75aDoDRAE2q8KP58vLKSvfQ7/dF0U2xWBTz3cn0o1arjY2eVrrV3AcZiuZyOdEcRF1+Ho8HBoNBDNdIpVI4PT3F6ekpcrnczEcKMhjNZDLI5/O3RnuxicZssOgfgJwXpxRUt9u9dYaeZVyTvJLLo7Fku2mDwQCj0SgE73A44PP54Pf7xRQbGr7R7XaFjRX50VMengZrUDNOoVAQEXs5Nz5PFxwAkfobDAZoNBooFotIJpNwOp0wmUwYDAao1+uiESiXy4lVflbIeJQqEtnvfn5Y9A+A+uLNZjM8Hg9CoRBUKpVIQ8mluFRTP6v46dhAZbvkqEOltXa7HQ6HAy6XSwy10Gg06PV6YnWnVFetVhPVbzQymj6ncVndblestspMxKzc3NyIBw0N48xkMuLsTfZi9JoUwJv3d04PJl7RF4NFPyfKNxpNh/V6veh2u8IBR86Lq9VqNJtNseWnM77S147eyPSwoDy+yWSCzWaD0+kU9fPUfkuFLWTdVa/XRQ98o9FApVJBqVQShTfyBFm5aUeeD//Q1ZN6CXq9HlqtFiqVisjX085jkZp+jUYDm80mSompipGZDxb9A5C39x6PBzc3NzAajeJNqdVqx/LjVGZL4p90Pflz+iDxGwwGmEwm0Xii0WgwGAxE4YrsmiMPxiSx12o11Ot1kU6TZ8NTdH+R6bHToPQcHRuA3+bXzXttlUoFs9kMn88n+uktFsutIRf8ELgfFv0D0Wg0MBgMsNvtUKm+DMig9k9asWUByZ8rhS/n0alNliywSKS0itOZW06FUTtutVoVoqfPqS+erjVN1HJh0WOcl+nnWATl8cJqtSIYDCIajU61y2Luh0X/AGhLTmfvm5sbEV2nllk638qWWnS2n5Qak/viqdCl1WqJyD3FBihmQLPyaLWnqTq0qtP0WHlln3S8mBQF/57Q6XRwOp1ioGggEIDNZmNjzAVg0T8QOdJOhSKyxVW1WoXdbkepVILBYJhaqSZDoler1eJsTuf9fr8vHgJ0TKAHA53pyYqKTCyp7HXSgM1JXz828vFmnmOD/O9cLteYP57T6RQ1DFyUMx8s+kdCrqNX2kjTmZzO93dtSWl7Tw+OXq8nBE+reqvVEoExKrhpt9totVpjYu/3+2O5+t+DRc7Z8s5Do9HA7XZjZ2cH+/v72NnZgc/nu9W5x8wOi/6BkEgpAk6Ck/3h6Wt5ku0sFlG9Xk9YWdG/73Q60Gq1Ymsvz5Mjoctin2TD/a2QLcTkpqP7kAXv8XgQi8Xw+vVrvHr1CltbW3A4HDyl9gGw6B8IBb3kdthpW20SPol4lmtTHpt2CCR0Wg3l4N6krTyh3GI/hFkeIOS0S/dM9zfPa7tcLsRiMfz000/48ccf8fz5c2G7xSwOi/4RkD3oaUWmVZa2+pRrv7m5Ef5wk7bdcs5eLr2lMVNy3zk9FMg1lz7knYGcf3/Mn1eGmn+ooIjSihRspElAcmBxUlEO/exarRZutxvb29t4/fo1fvzxR7x8+RLBYHBsW89n+cVg0T8Q2bVGq9UKgdHqTzlq6nenNz5F0+nhIBfnUFGPXIore/DR6knpPHmlpzM/+eRTyepdPfKzCGfaQ0On04nKQLlgiERPtQQ0yrtQKCCfz4u6AXkrb7FYYLPZ4Ha7EYlEcHBwgJcvX4oVXnmOZ8EvhuqeFeD7y918ZyjP1LTy0lTXer0uUmiUK6eVTq5KU3bQyWab9BAgU0jaoss5fErv0YQbqsSjPnnyl6cHzaKBPblgyGAwCOOOjY0N0fVGnn8Ue6ACokKhILrk4vG4aJoBMDYAhOzBaf58IBAY29LzCj8zE39JLPoHQnlvedqMHNiTV2F5NZYn1ADjI6rlVV3u01dG/em16XXoYUMGFeSIQwMy6vU6Go0GqtWqGKM9j/hNJpNYzW02G6xWK7xeL6LRqEiluVwuMZlHfkCREWgqlcLl5SWurq6QTqeF6O12OwKBAKLRqCi+8Xq9sNvtY0YZ3Ek3Fyz6r42ywEWusJMfDvK5f9JKL/9XacelvL4ylkCVedRRR7X2tNsolUrCfSaZTKJcLs80YcZqtSIQCIy18NLwDpqwS382adrxcDhEs9lEsVgU/vqFQkEM/LRYLPB6vQiFQggEAnA6nVxb/3BY9N8Tk5pbJol7XmjlJ/HTvDhqryX3mdPTU1xdXSGXy6HdbosKQWB8+0xFQg6HAxsbG9jd3cX29raYJWc0GkXn3yzWVXQfyljD6urqWCeh8sHBW/qFYNEvE8oHCjW/VKtVpNNpXF1dIZlMCsec+0Rvs9kQCoUQjUaxsbEBj8cDnU4H4LeH1bz3NimtyE0zjwqL/vdgWr583u+7j1mvOxqNxDa/Wq3eiuzTGZyuNxqNoFKpoNfrYbfbRf8+WXFNuv60e5r3wbDI9zFjsOiZL1Dkn2IKszxgKLbAE2aeFCz6ZWKad9xjrZry9nze605r/JGrBplHgUXPPC6PEVzjFNxXhUXP/Ma01fYueCV+crDomXEWqcdnwT8pJv7P4tr7JYYFvJxwGJZhlgwWPcMsGSx6hlkyWPQMs2Sw6BlmyWDRM8ySwaJnmCWDRc8wSwaLnmGWDBY9wywZLHqGWTJY9AyzZLDoGWbJYNEzzJLBomeYJYNFzzBLBoueYZYMFj3DLBkseoZZMlj0DLNksOgZZslg0TPMksGiZ5glg0XPMEsGi55hlgwWPcMsGSx6hlkyWPQMs2Sw6BlmyWDRM8ySwaJnmCWDRc8wSwaLnmGWDBY9wywZLHqGWTJY9AyzZLDoGWbJYNEzzJLBomeYJYNFzzBLBoueYZYMFj3DLBkseoZZMlj0DLNksOgZZslg0TPMksGiZ5glg0XPMEsGi55hlgwWPcMsGSx6hlkyWPQMs2Ro7vl71Te5C4Zhvhm80jPMksGiZ5glg0XPMEsGi55hlgwWPcMsGSx6hlky/j/E/4mMGwSnCgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 51\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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nUa1Wp5a8yh16GxsbiEQiIjAoPx/l4JV19vT60zoE5Si+DJX4kuh5+MZisOi/AZTdbBREOz4+xq+//oq3b9/i8vLykz548tabRBmJRLCxsYFQKAS73f7JXHm9Xkc6ncbNzY3YVUzbbuv1erjdbvH8wWAQdrtdCJqKasg8U66Xl40yqQhH3n1Qmk7prkuCNxgMLPolYNH/gShX+OFwOHWFp8Ddp4pwlMMnnU4nfD4fvF4vHA7HhLBkt1n5RtBoNETFXzabnRnAM5vNWFtbQyQSQTgchtfrFe65coMOteKSEYZ8pCDbLPIEkK+N+ucJ8tmTV3pmMVj0fyDKFZ5MJ4+Pj/GXv/wFb968wfn5OYrF4lz19RTA8/v98Pl8wg1HZlq6q9vtolKp4O7uDnd3dyiVSlMzBEajER6PB+FwGNFoFD6fT8y6ozp7ep50Oo1cLiccc6nMl3zzqEGHRDwYDNBut9HpdNDr9URunhx2aJWXi3K4tXY+WPSfiWkFLo/9PjlK//79e7x+/Rpv377F+fk5CoXCXH72arUaTqcToVBIbOudTufM4JjcTUcBvFwuN3P6rVarFQ07m5ubiEajE2d5qpHP5XK4ubkRTTq1Wk3k2Gk7bzAYYLVa4XA4YDaboVar0el00Gw2Ua/X0W63RWmuXq8XD97aLweL/jMgr5qfWnWU/9bv90V9+9u3bye29MViEa1Wa64iFI1GA5fLhY2NDcRiMeGE89A1UMltKpVCIpFAKpWaGT+wWCwIhULY3t7G9va2eH65956e6+bmRpQLk4AJ8vuzWq3weDxwOBzCCLNSqUy46FCpL/ndc+R+OVj0SyL3hD/EtBtBu91GsVjE5eXlxAp/fX09V8usHMDTaDSw2WwIBoOIRqOi403pVSdf02AwQLFYxPX1Nc7OzpBIJFCpVO6d5cl8Y2trC3t7e9ja2oLP5xNn+eFwiFarhWKxiFQqNREXUJbfUu+97HtvMBhEGy0Zc9LOgHL9VODDol8cFv2SyF73wHRjTOX3kwdcPp8XlXavX7/G+/fv5+6co+ckjEYjvF4vAoEA/H4/XC7XvaAd2XUTtVpNzMU7Ozubamut0+ngcrmwvr6OnZ0dxONxhMNhOBwOaLVaMQyjVqshl8shlUohk8kIA01CNs40mUxwOBxYW1sTZcGdTmei1ZdWerPZLOyxeWu/HCz6OVGuktQBRv3gFGVWMhqNJs6rdOb9+PEj3r17h5OTE9ze3grn10XQ6/Xw+/2IRCKIRCJwu91iQARdg3ICjyz4k5MT3NzcTN3am0wm+P1+scpTww4F1Mj2qlQqIZvNIp1Oi+PJNKgd1+l0inFaZrMZnU4HKpVKdNTRsYAET9t+DtwtDot+AeQUG/nUdbtdqFSqCbso2nbTdJhyuYxsNotsNit61GmuPAW7FkWv1yMYDCIej2N3dxfRaPReXl65xa/VakgkEnj37h3evHmD09NT3N3d3TtWUHBQLvQJBAITDTvAXzsBS6WSsNWq1WpTb2A0XMNut8PlcsHj8cDj8cBkMqHVaqHX66Fer6NWq4kbgSx4ZjkeFP2qmg8q68Jn2TrX63Vks1nkcjm0Wi2o1WqxelGKjEwsq9WqyH9TgCuVSgnLq8fU0s9Cp9PB7/fj4OAAL168wNHRESKRCCwWy8TvQh1unU4H1WoVyWQSx8fH+OWXX/DmzRskEomp02ooWr+3t4fd3V2Ew2HY7fZ71Xf0vMViUeTmlZ8hStVZrVY4nU54PB643W44nU5hu202m4WvP23n5SOK/CczPw+Kftk3dpppJH0IP9f/tE89lyzSeZ7rU1F42hafnp7i+voa9XodWq0WNpsNDocDRqNR5KzJOSabzeL29hapVAp3d3ciLbboOCrgr4L3er3Y2dnBjz/+iJ9++gkHBwfw+/33VmJy3qGjxcnJCd6+fSuyBfl8/l7wzmKxIBwOY3d3FwcHB6J+Xzk1ls70jUZDrNKUZydojp0seK/XC5fLBavVKrb1lMeXn5u8/bmtdnkeFD27kkwyHA5FSun29hYnJyd4//49Li8vUa/XodPpYLVaYbVaodPpMBwO0el0hBDK5TIKhQLK5bIwjFwGWuF3dnbw8uVLvHjxQvTKU+MLAOFGWywWRY88neFPT0+RSqVQLBbvXQ/dULa3t3F4eIi9vT2EQiFYLJZ710Lls3QO73a794ZbaLVamM1mUTgUCoXg8/ngcDhgMpnEUYDq9QeDgbDLUqlU4msea7UcrOpHQi60NMDx/PwcJycn+PjxI1KpFNrttqgwMxgMopS02+1OeLuRv9u8H1rljoYEubu7i1evXuGnn37C4eEhAoHAveo7Kq29uLjA8fExTk5OcH5+jmQyiXw+P/UGpFar4XA4sL6+jv39fezv72N9fR1ut3tqOS811PR6vYkVmXZLlHZzu92icEiu5qPBlHRjbbVa4kEuuWSTxa45y/Gg6N++ffu1ruMPR1khp1KpxFZ9PB6jUCiI4NvZ2RkuLy9xfX2NbDYrUmx0XqWtKX2Il9m+E7LgKUpPK/xPP/2Eo6MjhEIhsU0mETYaDdze3uLDhw9iK0/OO9Py5wRZYG1vb2N/fx+xWGyiLVd506LfUx5VJafnjEYj3G43otEotra2sLW1hWg0CrfbLeyyR6MR2u22OB7UajU0Gg1RoEQ3TnodZjEeFP2f//znr3Ud3wSUv242m9BoNDg4OMCrV6/g8Xhwd3eH09NTYR9FATjZg12e5rIMD8UpKEp/cHCAH3/8ET/++CMODg7EtpsE3+l0xDFEbt65vLwUPfIPrZZWq1UYXW5tbSEQCEyk/+g6Cdra0zme6gH0ej3MZrMQ/N7eHg4PD8Vz2u12aDQaUW9Po7PK5TIqlQpqtRqazSZUKtXETomHXSzOg6L/13/91691Hd8kv/32G+7u7hAKhUTF2u3tLQqFwqP72hdBFrzcfmo0GhEIBBCPx/HixQu8fPkS+/v7CAQCYoWXq+Kur6/x7t070Z57dXWFQqEw9TXlGw017ESjUVHKK3fp0fcT1CRDwiXR01GHWnDj8biIDYTDYbhcLrGtp7RmpVJBsVhEoVBAtVpFq9VCt9sVE2zlZhxutFkMPtM/QD6fx7/927/BarWi0+mgVquJ3vCvgU6ng8PhgMvlgs1mg9frRTgcxv7+Pg4PD3FwcIBgMAij0ShW+Ha7jXw+j4uLC7x+/Rq//vorjo+PRdHNLJSOOFTkEwqFJhpq5NZY+jmaN0fb8X6/D71eD6fTCYfDgWg0ing8jr29Pezs7CASiYi0JrXidrtd1Gq1iVHYtVoN7XZ7IkCoHHihzCIwn4ZFPwX5A317e/vVX1s+A1P7ajAYRCAQQCQSQTQaRTQanUjL0Za+UCjg/Pwcv/76K/7jP/5DlPY+tg6AKu/oNbxe79RoPUElxaVSCYVCAZVKBf1+X5TYkl3X/v4+4vG4uImQ4GmVp+fI5/Miz0+rPAXvaCdANwQauMmr/Xxwym4KdLb/HOfzxyLnr+lB46disZgQudvtht1uh8VimYjSD4dDVCoVXF9f47fffsNf/vKXuQWvUqngcDgQDoeFlbXH43nwcyD3zlOcYzAYCP88CgTKK7zJZBIddJTWrFarKBQKyOfzYmtPI6mpqrFer6NYLCKfz6NUKsHn84k4BsB99Y/lQVXzkMCvg8FggMvlQiQSwfr6OsLhMHw+H3w+H0KhECKRiGieMRgMUwN95Ljz7t07/PLLL3j79u1cggd+Fz31ypNI5bO8/PderycqDROJBG5vb1EulzEajcTvs729ja2tLdELYDQaJ56j3++j0WigVCoJ913a1ssBO7IRy+VyyGazKBQKaDab8Hq9i77tK8tqLuVzMK0l9XNisVhEaozKXGmwpNPphN1uh81mg81mmxpIG4/HIi1HQbvj4+NHC17ZlkvTYf1+P7xe78TxQT7LkwhpCg5N3anX6zAajXC5XOKGFQgExBme0plUZddut1GpVCbcd8llRy7uoe19oVBALpdDqVS65zXwWCOTVYdF/wm+ZOUXpcUODg7w7NkzYVMdDAbhdDpFkQ8w+4Pc6XSQzWbx4cMHscInEolHr/Dy7yc3wTidzpnDLUejERqNBjKZjChSuri4QDabxWg0EsFH2q04HA7he083GTo+0ZadmnSoNFmZ76eAIUX36fu4SGd+WPR/AHq9XmyjadQUDaJYW1ub6VpLBSyUAx8OhygWizg/P5+ooX8oSj8LtVothOr1emG1WifO8nLlHR0lTk5OhB8/2XPbbDZRakvxBzLFoN+Bzui0yufzeRHAazQa6Ha7otRW+T5Q8U61WhXfSxF8eSfCzIZF/xVRq9Ww2Wxwu92IxWI4PDwUo6Y2NzfhdrtFS+40aOAD8PvoKeqUI8HPysM/BJ3l5e045f1lqGEnlUrhw4cPIv9/cXGBcrks6gmsVivsdrsQvGxvRdv6brcr+hFyuRwKhQJqtdpE8I6+X1kTQDGAYrGIarUKi8XCPfZzwKL/wpC/m8lkmojI7+7u4vDwEPv7+8JcUj6zKz3flZNfOp0O7u7ucHFxgZOTE1xeXiKfz891XbJHvsfjwfr6+kQAT77BtFot4X13enoqevAvLi6Qz+cxHA5hs9lgNBqF6MlXQBb8aDQS/fK0rae8fLPZRL/ff3DLTlH8TCaDZDKJaDQKi8Vyr3iImQ2L/gtC9fter1esohsbG6L2fGNjY+aqqrS0An5f9cbjsXDP/fjxI87Pz5HNZmfW0U+7Lhmy2IpGo+KayEyTrLmp0ejs7AzHx8f4+PHjxMx6Mqy0WCwi8CgH7qgXgM7xhUIB6XQaqVQKuVxO9AGQY+60AB1RrVaRSCRwenqKUCgEt9stbjDy9/PKPx0W/ReCnF6pOGV3d1fkvmmyKxWpEHJXGoCZf6cP/cnJCU5OTpBIJGZOopmGst1VnlRDNyFq1imVSshkMsI08+PHj7i6ukIqlRIFNOPxGEajUbgG2Ww2UUdAsQeqqqMV/vb2FtfX10ilUigUCqKS7zGB01qthnQ6jUQigWQyic3NTYRCIa7OeyQs+i+AXq8Xtes0Ivrw8BCxWEwEyfR6/b2iF2V6cNowRxpqeXx8jN9++0144z+0ys9q4NFqtfB6vYjFYiKXbrPZhDjv7u6QSCRwdXWF8/NzXFxcIJlMit4Dqn+nzkLa2suCp444KmOmFT6ZTIpeBnL+fexNS/bopxuG/LNcpPMwLPolUKlUQrz0IdNqtXA4HIhEItjb28Pz58/x7NkzxONx+Hy+exZT8nPNWtnJVIJWeCrA+fDhw1TnWiXK15HLfGOxGI6OjhCPx+HxeNDv94WzD4n96upKDK2gVBk9J7nhUNzCarWK3YtcRksVd5lMBul0GplMBtlsVhTj0MirT73f9LpU70+eemyq8XhY9AuiVqvFdBZyyqGpLW63W8yEp8i81+udOn9NKUglNIiiUCggmUzi5OQEb968wZs3b3B5eYlSqTQ16Dfrmp1OJ9xutzjDb21tYXt7G9FoFBqNRoyiuri4wNnZGa6vr5HJZFCpVNBqte6VJtMkWZPJJM7xKpUKzWZTROnp+mlcFqXn6vX6RPPMvMKl3dBjZw8wf4VFvwCUZ/f7/WJ4Iw1hoKDY5uYmDg4OplpFA/cNNwl5THO320WpVEI6ncbV1ZWwuPr48aNo8VWujsqbiDxCiqrkqJlmc3MT4XAYbrcbGo0GtVoNl5eXwkaLptM8FC+Qx1NRTX2r1UImk8FgMBAWYbQVL5VKYmSVbLpBj3nES/UKnJ+fDxb9J5hmU+XxeBCLxbC7u4tYLAa/3y9WOIPBAIfDgUAgIHrGZcGTeKZ541M6ipxjqDyVItXn5+dIJBLI5XL3zrFKqMvN6XTCbDbDbrdjbW1NiH19fR3BYBBWqxX9fh93d3e4vLzEmzdvcHx8jEQigVKp9GBlHx0VzGaziJ73+33k83mk02lxhpd9AWm3oCy+WWTV5hTdYrDoP8E0m6p4PI7vv/8eR0dH2NjYEEMlaNgFnW1tNttEsE4urpGfn1xzKVKeTqeFtTZtt29ubpBOp1Eulx8M2pE5pzz0ghxnPR4PgsEgQqGQiNL3ej3c3t4inU7j5OREuOvMmlirfC2TySQsq8lLv1gsij9LpZIww1C648orNQCRqnus6NkVdzG4tXYK01pr9Xo9wuEwDg8P8fz5c3z//fciOEcTV+lnaY66PMl1WnUbGU9QWSn54lNUm2yyadVvNBozh0cYjUZR7efz+RCNRrG9vS12Ik6nU6TTqK5+OByKKP3FxQVOT09xe3v7KMHLY6ZtNht0Op2w+ybBVyqViTO7EroJyqv9PN53vLVfDG6tnYLyQ0STWg8PD/Hy5Uu8fPkS29vb8Pl84hw7LRKvHJBBUONIJpPB7e0tMpmMCHKl02nxoFQWFaxMQ6fTiUo/OquT8cbGxoaIOVitViEw2m00Gg0RpafBlY8RPB1jyDiUOvGU/nbUHjtrNZZn1dHX86zccvyDeTyruZR/AvlDFIvFsLm5iY2NDTFB5uDgAF6vd8IoUv7ATlt9KFdN1WjUknpxcSHO6bIZ5KyRUIRarYbFYoHX6xVVfjs7O4jFYggGg1hbW4Pb7Z4QpcxgMECpVMLNzQ3Oz89xfX2Nu7u7T3bnkeDJ8MNut0Ov16PX66FSqaBUKgnBP8aEZJnIO1X58TZ/Plj0D+D1evHzzz/jxYsXYvRzPB5HMBi8F0RSBqHk1YuaRPL5vJgBf3V1JZx15emu9EF+6EOs1WrhdDrFnPj9/X0cHByIaLzL5RKdbdOCXZQGpEq7y8tLZDKZR83SoxWeuujMZjP6/b6Y4lOtVh8t+GWhmAAH9ObjQdH/4z/+49e6jm8CmvXWbrehVquxs7ODv//7v8fe3p5Y1eRhDyRyMqWUJ7LQ30kQxWIRyWQSFxcXuLy8xM3NDTKZjOgue8xRSq/Xw2QyiWq/nZ0dHB0dYX9/H5ubm/D5fPdmzAG/b4PpulutFnK5nPDwJ9PMTxXHUL+9x+PB2tqaqM9vNpuoVqsTRpZfAqV5p9frFfZbcoCUz/gP86Do/+Vf/uVrXcc3AYmCzpmU9pJXTWU0nr6/1+uJMdTkCkuParWKu7s7XF9f4+rqCslkUgy9fGw1mdFohNPpRCAQwObmppgtt7e3JybPyB1tMsrW1EqlIqb00OCLWSOlCVnw5IQDQAQjKUL/NVZ4k8mEcDiMWCwmfneeWf94HhT9d99997Wu40khR+NlyyeqOpMdXavVKsrlMvL5vAjYUYBOZlYlHXWuUY6dRlHv7Oxga2sLoVDono+dvGIrU2LUD392dobT01Mkk0lUq9WZYqUzvCx4j8cDnU43IXga1/01ztYOh0O0J5PxiLy74ZX+YfhMvwBKz/dSqYRkMjlRo57P54XoyeWl0WiIiLaSaWKRZ79tbW1hd3cXu7u72N7enmgpVa7us8p5SfCnp6c4Pj7G5eWlaI2d9XtOE7zBYJgYTEHOtV9K8MobIvU2xONxbG5uwuPx8Eo/B5yye4BZpbLy161WC9lsVhhLXF9fC+PGer0+IfTHiEKn08FgMIjIPH24aXXf2NiA3++/d3aXjSuV10uVfuR48+7dOzF4s16vz+zAoyi9y+USRp06nQ6tVkvsXorFIlqt1lf7rOh0Otjtdvj9fgSDQXi9XnHkUpp3MtN5UPT85j2MbBBJor+5uRG2T51ORwxz+BTUwEMNMVTXv7m5KVY02sqbTKaJ2MJDAThqk6WKO7K4ur6+RrlcvrfroAYaapOl66GgXb1eF0eZQqEg4hJfEvmmRC45VGDEPfTz86Doect0H/kDOBqN0Gw2RX389fU10um06BiT68uVqykVych96Gtra6J0NhwOY319HdFoVMxxp2GPSqalD2lMdrVaRTqdxvn5Od68eSN68PP5vDDAoLQXDZyULa/I545ucNVqVfjTUevs18JoNGJtbQ2BQABerxdms3lmOzIzGz7TLwGNkqLVjwJawO+lvCR6uTqPtvBms1nYRa+trSEcDmNjY2Ni4AWtaGaz+VE34cFggE6nI/LmVAR0cnKCDx8+4OrqCvl8XohVq9VCq9VOXJPFYhGvqdPpJlxvqHjoa6zwMiqVCk6nU5QXr6+v3wtg0vcxD8OiX4LhcIjBYIBerydGKBOU8wcmi0j0ej0sFgtcLpc4l9IjHA4jHA4jGAzC4/GIhh05+g5MFgKNRqOJGgG6CZVKJVHLf35+jvPzc9ze3op8PJleaDSaCQMMap7R6/UYj8fo9XoTUXqynZ7nDE+pTlmg9N49Nvin0+ngcrmwsbEh+v9dLhfn5xeARb8EtI2eVkFHqTLaPpO4HA6H2MZTiW8kEoHP54PH44HT6RTe8UqR0ORWeVwzxQyoKKjVaomW1mw2KzrostksGo0GNBoN7Ha7aJiRLa5kscuz4uXVfZ60nEqlEg05NPCCip/kIOdjbLI0Go1oWabjDpUXsz3WfLDol2DaOZ3+lLf0FAm32+0IBAKIxWIiIq8M0MlTWGkV7/f7woJa9nuXO9hoxyFv7akOno4cFBOgdlir1ToheJVKJaytSPDUGkuVdvP0ulOUnabuUu9+sVgUTUWf6tkn5NShHFik/w8s+sfDol8C2rZrNBpxNqaeernVlgpsXC4XAoEANjY2EI/HxTZVnhlH0FadgmfkPpPNZidmvpHoqWafnGflrAF57pvNZuFUa7fbhehlTzt58ITcLTdPpZ1OpxO59P39fTGfz263o9vtikwC3WR6vd4nn58Cizab7V72gpkPfueWQI7AkyMseeXRh5LO8ZR7t9lsEx9ejUYjIu10o6CVvVKpIJvNCn/4ZDIpCn9ohex2u6LLjG5CdEan3nlqjnG5XHA4HMKXnlxrx+Mxms2myDzI/fDzCl6tVsPhcGB9fR3Pnj3D3/3d3+Hg4ADBYBAWi0WYduh0OnFToyEXs6C+fbLk0mg0vLovAYt+CUj0Op1OCJu259MCTCRu8n43mUxiljvVzZOLjjwC+ubmBslkEplMZsJQUp75BkCk/2i+PZXuUibA5XJNzLanAZmtVksE1pYRvEqlgt1uRyQSwbNnz/Djjz/i+fPn2NragsPhgF6vx2AwgMFgQLvdRrFYFFZaykAoId/EKOZAmZFZfgXMw7Dol4BWVkp56XS6qdtOEjK51KhUKlG+S0MeSYDD4RDNZlMYYiYSCWFQWSqV0G63JyLf9OEngwwSPQ3Z2NraEkcI6q0nE0+VSjURCyATS9pFzJuSM5vN8Pv9wvr7+fPn2NnZEWWylK50uVyiVTmRSCCdTqNer2MwGNxz9iVjT9pJ8cy65WHRL8G0M73sokOipBJceY5bPp+H0+kU220aAUW99zTNlbbzVN8+C61WC4vFAo/HI5pRqMc+EAjA4XDAaDROrJR0gyEnH5oeu0gtPY3wCofD2NvbE6/t8Xju3Qgpi+HxeOD1emG322E0Gici+XIglIKZlK3g8dTLwaJfAjn/TiuZXH0n2zorI+v5fB5Go3EiL04rPZ11G42GCNY9VPlG6TeXy4VIJCKMNba3txGJRITgaaUEfhdSp9MRgUKaD79IiywN+SCL7VAoBIfDIY45yu24nDI0Go0Tk20Jej/Ie4+cgslCjM0zFoNFvySyoyuJilJtsuipI48+qPS9NPhRXg1py00rG5lFKst5adtrNpvF8IqdnR3s7+8jHo+LAhYaMUXQKk+Cz+fzuLu7E1H7RSrt6OwtV/PJY7eVI7uotoDeH+XqLa/ychyEmpkajQZcLte952Y+DYt+SeTVXj5vKivm5K+B3zsYu93uvbOq8mfl/067CTpS0JZ+fX0de3t7ODw8RDweFystxQrohqE8ZpBfH6UBKWawyPtAZiJys9G0hhh5Nn25XEatVhNZCEJ+ryi7kMvlkEqlcHt7i2AwCKPRONX/j3kYFv1ngLb3Sr82+uA+ZP4oC1u+gcjpQAD3tsaU+ycve1rhd3Z2EA6H4XQ6J1Z4ugbaLpPgU6kU0um0GKCxaD39eDxGq9USE2mDwaAY8UWDQGi3k8/ncXt7i0QiIfwBKRMhP59Mt9tFsVjEzc2NyEjIAVD6GV71Pw2LfgnklJ3BYBCBMrm7js6ln2JWF558Q6HUIOWtqdhnfX0d29vbwhjT6XTeEwNtqak2n4SXTCaF3fZjS2KnQR2H6XQap6en0Ov1aLVaotpQp9OJIOXt7S0+fPggfPYrlcq9lV75nvT7fVQqFaRSKdzc3IhKRo/Hw2f7OWHRL4EckKKJNhaLRfwbncdnWWEpn0te7eXON7qhmEwmUU3ndDrh9XoRCoVEO67f74fL5RLW3HTzoSq9drst8vDpdBrX19e4ubnB3d0d6vW6KLNVBiQfAxl1ZDIZqFQqdDodFIvFiYpDmm2XTCZxdnaGs7Mz4c/3mAaeer0uqhLz+TyazebENSqdiJnpsOiXgPLOZKDp9XrRbreh0+lQr9cnhlQ8xt+dtvYkeKqPpwo6KrpxuVzwer3wer3w+XzC495ms4kzNKW2qAmH0oDFYlEM1aBBGyQgukHR7mKegZKydVi/3xe7iWQyCY/HA7PZLEw5aXQXTfCZpye/1WqJ4iQeUb0YLPol0ev1YjhkIBDAaDQSjTNEu90GgJmTauRVXi6lpcGTZDlND6/XC7fbLfL88sRYEsJgMEC73Uaj0UC5XBaR73w+L2bklUolMViS0mDK65l34gxV8VFqMpvNwm63w2QyYTQaiZ58Sg3OG0NQNjUx88OinxOlUwuJ3ufzod1ui355Ej6tmpT7plQeMDvwJBf8kNGG1+tFIBCA3+8Xgiex63Q6jEYjsU1WdtqRyPP5vCh7JZtuKn+d1SK8CNTsQ7bghUIBOp1O7AbkzsB5oFoA6iGgoaHMfLDol4C292R1NRwOYTQaRQcbdd1RoEmuZVeWm04bxihv96n+nIpZKK9PHWqy2Gu1GkqlkljVaYUvlUoTffF0E6IMg/LPZW8AlIfvdDoTaceHZvM99F7bbDYEAgFEo1H4/X7YbLaJIB6v/o+DRb8ktBo7nU6Mx2MR1FOmy+QiHRLTtGg1iU6uiSfjDHrQYAo6C9NWXmlaSW2yhUJhwpu+1+vNHP4op/Y+x6pPN5VFmGZ9TWahs+yymE/Dol8CSqvRln48HouIOwXCZLcbWpVJULKl1mg0El/3+32o1eqJMzlt42mLTJ55ct6dAnVUQ08GGGS2QfPhp1XAAb/fAL7F4Bjl/Dc2NkS6zm63szHmArDol4TSdjRnjv4b5aRpBh4FzKjghoRHQlcW9cg5dTrfAxCVdBS4GwwGwiKrVCqJR6VSQb1eF8Eyiub/EcKWt93zHBvkFNza2tpMfzwuypkPFv2SKCvpZFMNKqahDjz6t2kfUBK+cnvfarXEeZ9GaFElGrXottttMUCyWq0Kscur++fari/z/lDl4WNqFuh7dDodPB4P4vE4jo6OsLOzA5/PNzEmnJkPFv2S0PlXdsVVnsO73a44S9Njmt+ccstNTSlyAw8V2FBKsNvtotVqiS60h8Q+bwpuWeRW43mCd7LgfT4fdnd38fLlS2HIoTzL8yo/Hyz6JSEx9no9tNttNJtNYRlNraDk+krif6zB5GAwEKs57RDozK/RaETxDUXtKSpP23n5NeT89rLCl7fqszCZTDAYDNBqtcIxaFqp7UOv4fF4sLu7i59//hmvXr3C4eEhQqGQqHpkFoNF/xmQO+Jo1afzM1lfWywWdLtdUaJKK59cl6/s2KMgIaXpKGUn997LffxarXbCGptW+3lcbB/7+8pQizCVJNPADIvFAq1WK44pNM56lrOu3MuwtraG7e1tvHz5Eq9evcKzZ88QDocntvV8ll8MFv2SKJtuZDHT1lyr1cJut4vzNrWdyh98Ejz11pP9ll6vnxC+TqcTASx5paeVVE7r0W6j0Wg86LrzGOHMumkYjUZhS01BS6pTIBNLKsulqsBcLie89Gnl1+l0wh9/bW0N6+vrODw8xA8//IDDw8N7gn/sdTP3UX1iBfj2cjffGOQ+Q+d4WXz1eh21Wk08Go2GOHPLxTHA71bZZKphMBiE4OlBgpcDfv1+X4ydotdV1tmT0SVVBS5rOUUru9lshs/nw/r6OmKxGILBoOgBkN2AKJORz+dFl1wikUA+n0e9XodKpRKDLKLRKKLRqLAIp/ScvKXnFf7RTH2TWPRLIm/p6aEM7FFwT37Ipa9KV135QZF/CorJASzla9NrNZtNMUqaGmqoGq9er6NarYqU3jylsBaLRTT92Gw2MZpre3sb29vbCIfDcLlcYoWnrAO535TLZaRSKdHdl8lkUKvVoFKphJnn1tYWYrEYotEofD4fHA4HzGazuAbupJsLFv2XRpkDp3O9PIRCfsgRbTnNJzvrKv335NdSOuzIVXy0nZbz9pTLJ5fdVCqFQqHw4NafIP+7WCyGjY0N+P1+0RMQiUSESK1W69QqueFwiFarJZx6MpkMCoUCGo2GKLH1+/2IRCIIhUJwuVwTdlvMQrDovwXk9JU8pEJuaV2mtJQKeyh4JqcOG40GSqUSEokELi4ucH19jbu7u4mR1fQcJLbBYAC1Wg23241YLIa9vT3E43EEAgHhhedwOIT55qeg6kFqj6VeBIPBMDEee9r4bb4BzA2LftWQq9+ofLdWqyGdTuPm5ga3t7dizvxDoqehl9FoVGy/vV6vWInnbXRR9iAQcnMS81lg0f8RLHMGnTfN9tgovDynrtFoTHXMkd1oVSqVmIdHkXp5go/83NNqA+a5Pvm55v0Z5h4seuavyHEGuR7/IeQSY16Nnwws+lVDGVj8nP3m8iQa+c9FrotY5LmYB2HRM5+XzxFc4238F4VFz/zOIi22vBI/OVj0zCSL1OOz4J8UU/9nce39CsMCXk04DMswKwaLnmFWDBY9w6wYLHqGWTFY9AyzYrDoGWbFYNEzzIrBomeYFYNFzzArBoueYVYMFj3DrBgseoZZMVj0DLNisOgZZsVg0TPMisGiZ5gVg0XPMCsGi55hVgwWPcOsGCx6hlkxWPQMs2Kw6BlmxWDRM8yKwaJnmBWDRc8wKwaLnmFWDBY9w6wYLHqGWTFY9AyzYrDoGWbFYNEzzIrBomeYFYNFzzArBoueYVYMFj3DrBgseoZZMVj0DLNisOgZZsVg0TPMisGiZ5gVg0XPMCsGi55hVgwWPcOsGCx6hlkxWPQMs2Kw6BlmxWDRM8yKwaJnmBWDRc8wKwaLnmFWDBY9w6wYLHqGWTG0n/h31Ve5CoZhvhq80jPMisGiZ5gVg0XPMCsGi55hVgwWPcOsGCx6hlkx/j/LnuMfWGbVXgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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+avZBqwjJ3N8PEf03wmSEp6O5w8NDvHnzBm/fvsXBwQEKhcKtj+icTicWFxexubmJra0txGIxrpSbhjo44/T0FKlUCpVKZWqicG5uDk6nE+FwGLFYDOFwGA6HY8xIczgccn9Ap9PhjLvajjxN9Nc1D2m1Wu7IE9HfHxH9N8JkhL+4uMDR0RHevn2LX3/9FXt7e8hms6jX67dKYOn1erjdbsRiMcTjcSwvL8Pv918xu5ysA2g0Gjg/P0cikUA6nb52G0EJvPn5eUSjUQSDQdhsNr5Wr9dDq9VCrVZDtVpFs9lkHz21Xp+O8Si6U9utWqJL74/qyEPtxMLdEdF/ZaZF+EKhgMPDQxb87u4uH5ndNmNNttYkSvVM/roeAaqzLxQKyGQyyOfzqNfrAMYfSsCnBJ7b7UYwGEQwGITX64XFYuGCGqrCy+fzuLi44MpBivRkm0UuOyT64XDI3njqCoNab0nw4pF3f0T0X5mbIvwvv/yCDx8+IJVKoVar3boIR6PRwOVyIRgM8hGaWnk3aZBBUBvrxcUFisUit7RO/pxWq4XT6UQgEEAoFILP54PNZmPnnna7jVKphPPzcySTSZyfn6NarXKkp6W96ohLD6Rer8etwtThNxgMOCGoGm2q9yUPgNsjov8KTBMdZenVCL+zs8NL7LsKfn5+HouLi4hEInA6ndcuhdUKPIrw1Emnnsur9+xwOBAKhRCLxTA/Pw+Xy8UJvOFwyI65p6enODk5QSaTYSMMqu0nEVssFjgcDna57fV6qNfrYy6/5IQrSbzHQUT/SFwXPaehfg8tqXO5HPb39/HmzRu8efMGu7u7SKVSV8T3OcgRJxaLYXl5GfPz87DZbGPRcPIeqbw3mUzi9PSUHzTTTDlMJhOCwSCWlpawsrKC+fn5seRgt9tlT/7j42Ocnp4in8+PtcNShp5676kVlxpsVDNN+hlK3onoH46I/oHctTJsUnCNRgPpdBp7e3ss+IODA6TT6Vsv6dWzc1p6RyIRLCwsIBwOw263X3nQqPeiTr7d29vD2dnZ1GIcjUbDybu1tTWsrKwgHA5zAo8stfL5PBKJBM7OzpDNZlGr1fjYTz2XNxgMfM7v9XphNBrRbrfH9vjUdmswGGAymVjwspy/PyL6R2CaOK5LlBGj0Yhr6T9+/IhffvkF7969w+HhIbLZLBqNxq2X9Op1qcU1HA4jHA7D4/Fc6ZWnc3KiWq0imUxif38fx8fHnEOYxGq1svnG2toaFhYW4PV6YTKZMBwO2eZK3ctPa/2lezCZTLDb7fD5fAgGgzCZTOh0OmzMSRZbAMYm31w3PUi4HSL6OzJtGa9G+5uiECWmOp0OyuUyUqkUL+k/fPiAw8NDXFxc3ClLr6LX6+H3+xGLxRCNRlmQhNorT6uDarWKRCKBvb097O3tIZVKccZexWAwwOv1cicdWWyZzWb+Hsr8n5+fI51OX1tTQJHbarXC5XLB5/MhEAjAbDaj3W6zvVan00Gr1cJwOOTlPS37hfsjor8jk/Xh5OxC589Ubz55Hk5GlpQdTyQSODg4wM7ODnZ3d5FIJFgk9xV8MBjE2toa4vE4YrHYlWX95IOqUqng9PQUHz58GKv2m5ZDUNty4/E4otHo2Lk8lQwXi0Uu363X61NXK2ScSft5KuM1Go1otVrodruo1+uo1+sc/WlZL4J/ODeK/rbuqn9vTCa9rluuk3dcuVxGt9vlNlObzcZHUFRsQkMbyACDZsGRBVWlUkGv17vX/Wq1Wvh8PmxubuK7777Ds2fPEIvFYLVax34XKmvtdDrcl7+zs4O//e1vePfuHdLp9NTWV5vNhvn5eWxsbODZs2dYXV2Fz+e7soqgI79yuYxKpcLTa1ToqM5sNsPpdMLj8cDr9cLtdnMzjsVi4eX8ZJOOdNc9nBtF/9A3d9IlRW20eKz/cZ+71mRRyW2vNXn2e13l2snJCVKpFFqtFgwGAxwOB1wuF8xmM68EWq0WGo0GKpUKcrkcUqkUl7mqQyPuw9zcHLxeL+LxOL7//nu8evUKm5ubCAaDY0tv4NNDvFarIZfLseDfvXuHnZ0dnJycoFAoXLkPk8mEUCiE9fV1/PGPf8TW1hYikQhsNtuVJpler4dGo8FGnZNDKWglZDab4XK54PV64ff74Xa72Ziz2+2ONefQtVUnXRH+w7hR9GoBhPDbB5vq0w8PD7G7u4vT01O0Wi12g6Vza1rykmstRcF8Po9SqYRqtXqrxpnrmJubg8/nQzwex8uXL/Hy5UtsbW0hHA5fKcbpdrsoFou8ytjf38fHjx9xeHiIVCqFUql0RfDUlruysoLt7W1sb2/zQIzJZTataGi70+12ryztdTodTCbTWOFQIBDg7rx+v8/99FSU0+v1eNoNmWeIgcbDEFXfkn6/z3PZz87OeHl+cHCATCbDR000a91gMGA4HLJVNUVA8nmbLDP9HJMrGq1WyxH+1atXeP36NZ49ezbWRUc0m01kMhkcHR1hZ2eHxX52doZisYh6vT5VROpAjO3t7SttuerqhzLtJNLJunny0lMNN2Kx2Fh3HjnjUBJPHeRBwyx7vR4Gg8HMbj0fgxtF/+7duy91H18ddelOZ8M2m43dZSjbTiaR5ChDJaYUjdR9KAmBPrwPiU6q4PV6/ViEf/XqFZ4/f87+9VQOS9VxqVQKu7u7ePfuHd69e4fj42M2xrhupUE22cvLy9jY2OBsPVXe0RJbPesnUU4u62levcPhQCQS4dl50WgUPp8PFouF75dyH9Vqle2u2+02NBrN2PVF9PfnRtH/5S9/+VL38U1A59fNZhM6nQ6bm5v46aef4Pf7uSb+6OiI9/HlchmtVmss263OcVMtnh4LvV6PUCiEjY0NfP/993j58iWePXt2RfCUVEulUnj//j3XAdDe/XMPIbvdjlgsxqcB01YQJPh+v49Op8OlszTVRo3wNpsN4XAYy8vLWF9f58IeKhFutVq8KqpUKqhUKlyO2+l0oNPpeJVE0X7yPoTbcaPo//Vf//VL3cc3yZs3b5DL5RCNRlEul5FIJHB+fo5cLndl4ANBSafHgrLdWq2WXWdXV1fx3Xff4dWrV7yHJ8FTkUyxWOTjuF9++QVv375lX71pSbDJqj6Px4OFhQUuwlGLfOj7gd/m3DUaDdRqNdTrdV49UNmsy+VCKBTC6uoqtra2sL6+ztc0m81sdtlsNlGpVFAsFtlJt9VqsQU2NeOos/ukHPfuyJ7+Bi4uLvBv//ZvsNvtPFWG9uVfIntMJbVUm+7z+RCNRrG+vo5nz55hc3OTI7Da4aaab/z888/Y2dnB2dkZyuXyta+l/j52ux2BQAALCwuIxWJjRTj0QFP736kSr1gsolQq8XLcbDZzhCfBb25uYmlpCX6/HxaLhZtsaGVSLBb5GJTm5dGSnk5BGo0Gr7BE9HdHRD8FNYqlUqkv/vpzc3Oc5Z6fn0csFuMRVDSogowrKEtPS/pisYjDw0P88ssv+Otf/8rn77cx0QQ+7eX9fj+i0SgWFxcRCoWuzLlToZFVFxcXyOVyKJfLaLfb0Ol0cDgc8Pv9WFlZwdbWFk/HDYVCsFqt3FBDo6lLpRK39dI5PyUFKcrTCUitVkOn04HRaJTl/R2RI7sp0N7+S89Lo4IVt9sNj8fDXvLLy8vcPOPxeNhSWj2HHw6HqFarOD09xZs3b/DXv/4V79+/RyqV+qzXPEENNbFYDKurq1hcXITX671SXTjpAUClt+fn5yiVSrwc93g8WFxcxNraGu/jg8EgT6QdDocYDAa8rL+4uMDFxcVYlKeEHSUlC4UCH3nSA0m16JIHwOe5UdV3OVIS7g/NiyeRU586RfdIJIJwOMydaNMKkhqNBpLJJD58+ICff/4Z7969u5Pggd9EH41GObvucrnGzuRVb/xWq4VisYh0Oo1EIjHWN2+z2bgFd3V1FUtLSwgGg3A4HDAYDNyHQP3zhUIB2WwWhUKBp+KqR3+DwYArGnO5HIrFIk/EFe7GbIbyOzCtJfUh15q8BkXESCSC9fV1bG5uYnV1FbFYDF6vF06nE3a7fSyiqfdFgzBI8H/729+ws7Nza8Gr90RlxD6fD+FwmPfdAMZsroBPy/pKpYJUKoWjoyM+Bmy1WmMDMKj5JxAIwOFwjD20KMqrtlqlUgmNRoMLcejeRqMRms0mbwFoG6FyF0+DWUZE/xkeM5JMXovmxC8vL/O4KUp0BQIB3vcC13+QO50Ostksdnd3x3zxbxvh1XuiwRUej2esNHYSKudNp9M4ODjA/v4+EokEj6WmLUowGEQ4HEYgEIDdbuc+eTrKVKN8LpfDxcUFarUaL+snvfYpf0DVjA+tfZhVRPRfCbKPjsfjPHlmfX0d0Wh0rAhGhRxiqR99OBxy4u7t27dceFOpVO58P1TDHwqFeBmu5nTUbH29Xmfjjw8fPnBVInXFWSwWuN1uBAIB+P1+OJ3OsQhPJbuTUb5cLnOUnxQ90W63uXiHTlLovbquMUoYR0T/hZmbm+Mz8M3NTfzhD3/A9vY2Z7UpyTUN1SeeTDiSySR2dnbw/v17nJycoFgs3vmeVF+969x2gE8Rvl6vj1X47ezs8MSd4XA4NvWGhlfSxFrgt/nzlIm/uLhAPp/ncmA1eTcN9WFBWX5q/hHB3w4R/e8MFdUYDAZYLBYWVzwex/Pnz7G9vY2lpaUxC2lgvIWUIqSaUFONNHd2dnB0dISLi4tbb0fUvTwZZCwsLGBlZQXRaHTMTJOsuWlJv7u7ywM4jo6OUCwW2dvOZDLB4XDA7XbD6XTCarWyXTUt66m1N5/Pj43Oomq+ySX7pONQvV7H+fk5zs7OEIvFYLPZriQchesR0f+O6PV6Hu4YDAb5GG5hYQGrq6ucsHO5XNcei02DjCzJ0+7o6Ai5XO7WHXuT16bxV0tLS3w2b7PZ+LXIESeZTGJvbw/v378fe9CQxRU1HNGeXt3Hq7X1JPh0Oo10Oo18Ps8+evSwu+nhVa1WuekpEomMDc0EJKH3OUT0vwNarRZWqxVutxvz8/NYXV3F8vIywuEwgsEg+8XTfpeY5rU3+WdyvEkkEtjd3cXHjx+RTCavLQuehvo6RqORq++WlpYQiUR4ad9sNrmzMJFIcDvuwcEBd+i1Wi2+jtlsvhLldTodF+DQaoE6FemYjzLx142zmqRWqyGTySCRSCCVSmF1dRWDwWBqHkS4ioj+EVAHLtKeNhAIYGlpaewYLhwOw+12w2Kx8KSWaUwaT6h/r1pc0fL6cwMtrzMaMRqNCIVCbHRJs+5GoxFKpRJyuRySyeRYZ6F6Hk+RmSbV0PbF5XJxboLO12lJf3FxgXQ6jWQyydeqVCrodrt3emiVy2Wu3ms0GmM/K0U6NyOivyfUPWaxWLiGnBxevV4vlpaWsLm5yYKfn5+/0rQCTJ8cOy2BRuYdiUQC79+/55r6ZDI51chy2msAnx5M5F5DzrYvXrxAPB6H3+/H5eUlcrkc8vk8jo+Px9qI0+k0V8tR4RY96Ggv73K5OPPf6XR46i0JPpvNcvVeoVBAqVTipprPiV59ePV6vbGBGFKkc3tE9PfEZrPxERcZS2g0n8Y3+/1+7kMn44nJ4hriuqhODAYDlMtlttna29vD27dv2eJKnR1P17hOADQIQ80tUOvswsICTCYTW1iTScj+/j7S6TRHVIruhGpySVHeaDSi3++jWCyi2+1yVM7lcsjlcigUCvzwaLfbLHh1LPVtRKzu/0X0t0dEfw/IUWZ1dRVra2uIRCJcB28ymXhwZCwWY5dXYjLJNM12ij7ENMySpsWQTfXx8TFbTE9+2CcfAOQgS/dFjTQLCwtYXl7mLjqj0Yhms4lEIoGPHz9iZ2cHh4eHHN2nmXbS9elcnrYuw+GQtxylUgn5fB75fB6FQgGVSoWX++SAQ1n9uwqY6hXkfP5uiOjviNlsRiQSwfb2Nr7//ntsbm4iFArBbDbz/tZiscDpdHJRCkGCvu5D2m63uSedmlCy2SxOTk6wv7+Po6Mj9rP7nDc+delRQs3pdI418NCcO4fDwXvks7Mz/Prrr7ceuqHu46lkWKPRcLUc5QWo8KZWq/GxHJ3Fq0U/d0WO6O6HiP4OmEwmRKNRbG9v46effsLLly+xsrICt9s9duRG+9zJ/fu0DykVq9CxWCqVQiaTQaFQ4CVxKpVCMpnkFtmbuv/m5uZgs9m4PZZWGz6fD36/n+vhqepuOBwil8vh/PwcOzs7vHXIZrM3+gaQjTWZXJIlthrd1QYatU12NBqNRejJ2oTbRvrbZvuFcaS1dgpUH642fJjNZiwsLODFixf48ccf8cMPP2B9fR0+n4+7xlTURhG6pgr1kFerVVSrVZRKJaTTaZyenuLs7IyFT15x9M/roESax+NBIBDg9tjl5WWEQiE24qBzdKqrJ4EeHh7i48ePODo6QjabvdLMoqLRaHg+fTgcRigUgsvlYi/BbDaLTCbDtfSdTufKg4rKiVWra1rm3xZZ2t8Paa29Afpw22w2hEIhjvCvX79GPB6Hz+cbKy+d9vPTklLT5rfncjmk02nObOfzeR5RrdahT0JtuarxxcLCAp+7z8/Pw+/3w26389AIegDRtNzj42Ps7u7i4OCAI/zn3hOPx4NwOIyFhQUEAgHo9Xo+g0+n0zzu+qasPIn8voMs6LoS7e/GbIbyW3J5eYmlpSWsra0hGo1iY2MDL1++xObmJrxe71hv+bSjN4K83GmsFRWnUEtqMpnkM2fyx7+N0w0Nq4xEInzWTlV+wWCQ99mThpbAb1V9Z2dnODg4GJuU+7lcgdfr5cm1kUgEJpMJtVoN5XIZmUyGH1g3PTzU9/i+oqUqP1nm3w0R/Q0Eg0H84z/+I16+fMlTYMkKWmXanlQVPi3lKRNPxS4HBwdIJBIcFcns8TZJLbvdzu6yGxsbXA+wsLDAkZ2OESchx998Po/T01McHR0hnU6jUqnc+NokeJppt7y8DJfLhUajgWaziYuLC87QP2SIx22hVYsk9O7GjaL/p3/6py91H98EVCNORhAbGxv405/+hI2NDbaocrvd/P2Tx2+UByB3Vxrc0Gq1eIbdwcEBDg8PcXJygnQ6zb3hk0wbx0XTXh0Ox9hsOdV7zu12XxlnNZlX6HQ6Y0M0T09P+Uz9Okjwi4uLWF9f52KewWDAR3K5XG7qaOrHQn0vbDYbfD4fjxCbZjAiTOdG0f/Lv/zLl7qPbwISBe0z6eiNasjJ056YFDt5v9PEVRpnValUkMlkOKpSdKeM9nVMCp4KgibbcqkAyGw2T02+qiIgLz2aR09R/qbtBAl+YWEBGxsb2N7eRiQSAQBks1nO/heLxRsTgI+F2WzG/Pw8lpaWEIvF4PF4xBX3Dtwo+ufPn3+p+3hSqMt32qs3Gg2Uy2U+aisUClxmSufVmUwGmUwGxWLxSjScTPipqwiLxQK73Y5QKITFxUUW3ubm5lTzSnWJrvaZUw8+GWDQHD6aujsJJe0owtPrLi4uwmg0Ip/P4/z8HKlUimcBfAkzUafTySuO1dVV+P3+sd9fIv3NyJ7+Hqgfql6vx5H87OwMyWQSmUwG2WyWfeArlQofuVGd+STTElE0CoqO4FZWVhCPx7G+vs5de06n89qj1ckIX6vVkEql8PHjR7x//x77+/vIZrNoNptTO/xI8LFYjOfZLS4uwmq1olKpIJlM4ujoiD31f69l/eQDkcw719bWsLy8DK/XK5H+DsiR3Q1MFo+ofw/85giby+W47fTk5ISLUqjklGrWb9NQotfruZ7d5XIhHA5jaWkJ8XicP+Tz8/Pw+Xyw2WxX9v7qkRz9fa/X4wj/8eNHvH37Fh8+fMDZ2RkfC6r3oNPpYDabuTU4Ho8jHo8jEonAaDRyL//+/j6Oj4+Rz+e5xfb3Rq/Xs3Mw9T3QQ2/SvFOYzo2ilzfvZsiJ9vz8HHt7e3jz5g03wbRaLW4muc2SlyrpqHSWjCUpwq+srHB1nc1mGxvyMO3EABifPqM63nz48AEnJyfcAUfQQ0cVPL02nVjk83nOB+zt7SGTydxYqvsYqFHearVyY49qnCHcnhtFL0umq0xaN1GBy+npKc9673Q6fIZMYryuEYZmttM0mHA4jEgkwk6yqu89zX6bZLKUVTWepJn0BwcHY156hUKBfePpPqi01uPxcLku1ecPBgNkMhmk02kcHx+PTb6dtl35PaDpOxThrVbrVKMR4WZkT/8AaFikOoONjt+olFdtsKFhlNSHT40w1LCi+sRTPTuVzFJ0vwn1iLBWq6FQKCCdTuPo6IhttRKJBEqlEu+/qUeA7onKeEOhEAKBAEwmE5/BZ7NZ7gOg04cvJXgy76SVz8LCwlRfPBH+5xHRPwCKqOoIZUKn03HUpQIStSstEAggGAyyfZbP5+N9aiAQgNfrhdVqHavrV9tPqXFlNBpxbQDZQ5Ot9Pn5ORKJBE5OTpBIJMYiMwldr9fDZDLBbrfD7XaPudhSaW2j0eDr0by6ZrN56844akBSBUojrW5bSafX6+F2u3lMViwWg9vtlvP5eyCifwBUOz4YDMY+wJRMu7y85GW80Wjkhhiqkafa+EAgwI4zFNXJjYegAY7NZpNnuVO+QI3wtOpQjwjpJIFspYxGI3vaWSwWdryhphxqE6ZhkdTtVyqVuIHmNmi1Wj5udDqdMJlMnGegOoZut3urfIA6NScSiSAQCPBWR+yx7oaI/gHQh432xBR1qHOMlvMUScmAktxqlpeXEYlE2HyCMvdqZCcH2Xq9PjbVtVqtsvi73S5H+nq9jkqlwvUBNON9MBjAZDJxoo5WHJQ0pBp9rVbLyb9sNjt1vtxt0Gq1cDgcvGUJh8OwWq0YDAZXBl7eRvT00PR6vfB4PGPTd0T0d0NE/wBUYev1ev6nTqeDXq/nJT1V0kUiEayurmJjYwPxeBzRaJSX8ZPQPHYSO1W9nZ+fI5vN8jQYcqChJX6320Wn0xmzoaKTAYrqDoeDxU4mlkajEcPhkEdEZ7NZtqe+a2mtXq/n/ffGxgY2NjYQi8Vgt9vR7XZxfn6O3d1daDQa9Pv9W51wWK1WXgmZTKaZbft+DOSdewCUhSexGwwG9nmnBJnBYLiyZ6Zx0yaTCRrNp+mtascemT4Wi0VkMhl2jqU2XOpiUw0l1T50en2j0chLa/W1ydrK6XTCZrNBr9ej2+1yGW0+n0cmkxlrBLotWq0WLpcLS0tLeP78Ob7//ntsbW0hEonAYrGg1+shmUxCr9ej0+lws85Noqf2ZpvNxoKX6H5/RPQPgESv1+t5n6xGeFoFqMUjnU6Hozfwqad9crBjq9XifvuTkxMcHx/j7OwM5+fnKJfLvNSe1qtuMBh4+U5Hb/Tl9/s5SUeRnwRPQytoZPTFxcWt22MJWtLHYjE8f/4cr169wnfffYelpSVODJI/PY25pu3KNKMNuiblH0wmE4xGI79X17kHCzcjon8AqugNBgMLHxif467aYdEHv1ar8RQYk8nE2X7quVc958/OzjjqTs5tV6GVhcPh4MKepaUlLCwsIBKJ8BEgnQrQIAoSHeUMCoUCT4+9CxaLBaFQCBsbG/j+++/x4sULrK2tcUMMFf+Q4878/DxCoRA3/Ey2Fav1DOoJgIj8YYjoH8Dk8p6iOi21aalOR2y9Xo/76s/Ozng/Td1xlNkmj/jz83O2zaJM93X3Qct5t9vNphrxeByrq6uIRqMseIrutK2gEmEaNaXu4e9iVqnRaGC32xGNRnnAx8rKylSvf6PRCKfTCZ/Px0YfhUJh7DXVgiPavtDeX8ZTPwwR/QOgD6UqfBohTR/UwWCAbrfLmXVylzGbzbzENhqNLHra55J33k1NOup9GAwG7sRbW1vD5uYmNjY2sLi4yKYaBoOBLbOoxkB9yOTzeZTLZbRarTt3y1FzUDgc5mz95BBMdTlODymTycT79GlmGOrpBdUMdDodDIdDMc+4JyL6B0JLz0mTxuFwiH6/P2b3DPz2gaemGhIiJacoA99ut/kM+zqzSFU8LpeLTwc2Nze5zz4YDHKNulolSNWE6uQZWlHcpz2W9t5Wq5UfZmphkbokpwdOr9fj0dTX2WZR63Kz2WQTT7IAJ0MTWe7fDRH9AyGhq7ZNJCqK9GodPv2MVqtFq9XiM366jur7Njn1ZfJ16WSAuvEowm9ubrLg1cmx9PoUPcmvjzL1NC76Pstn1QdQrR2YnMYLgJOZ5XIZlUqFty7XVeep9l7pdBqpVArhcBgmk2lqL4JwMyL6BzJZnDMpfFXw9KGmPb56DWC8qIfyBZNNT2rykHznQ6EQe+XF43EsLS2x4Cdnww8GA47wVFpLrjfULXcf0VObcbFYRDqdxtnZGQwGAzweDx9N0kOBOvXOzs6QzWY5h6AW6Uw+AOhIMZFIIBaLYX5+Hg6Hg7P59DMS9T+PiP4BTGbv6avb7V4xbLypxlyNwKrw6c9q9poq/KjFlKr8lpeXsby8zEk7i8Uytn+nRCIJPpfL4ezsbOwoUN0r31X4o9GI24x3d3cxNzeHVquFSCTCRh+DwQD1eh2pVAofPnzA/v4+UqkUG2neNKKr3++jUqnwA4WqGVVXYuF2iOgfCLXGUsUYOcqSbx4l9qb536tMO56iIzj6ogm5NLCCWnEp8oVCIXg8Hi6nVfMKVJtPET6dTiORSODs7AyFQmFsEu11fv03QYLOZDLQaDTodDooFov8EDKZTOj3+yiXy0gkEjg6OsLBwQFyuRxardatHjLU+EODNCYdfyaNSoXpiOgfAJ07Ux27x+NBq9W6ctQ0OS1n2nXU3ACdBNDDxGazwW63cwktVdb5/X4EAgEuuqGmFipvHQ6H6HQ63IijNs+oJb2lUgntdpujPD2AbhqyMQnVGJRKJfT7fdTrdXbcJR+A0WjENf3UsXeXegDaQtCsPzoOFe6GiP4BkOjV2vrRaMSRGfj0QSWHWBL+5z6oOp2OK+uofJfOtP1+P/9ZrZ03m83QaDTo9Xrodrvo9XpXxE6CpwIcashpNptjy2v1IXTXiTNUPNRsNvl4kkqOB4MBD+akRqC7FgBR/kSKdO6PiP6OTH7QSPQ+nw/tdptHNxuNxisfTo1GM3UiC0X4yYhPS3qn08kltdRrT9V8VGxDS/nJvvpCocCe9BcXF2zUWa/X+ViQCl5uO2jjc1ADEB21XVxcwGAw8GqA2oLv+lrUyEN9A7SqEe6GiP4BqJHe5/NhNBpxc43ZbL4yYUY1j7ztHDZaalOZL9Wfq2W7vV6PffepiIXq2qnK7uLigo06SeyU4KPTBHVCz0PGTREU2TudzthI6rtsG9T3gYqPotEowuEwHA7HWBJPTDFvh4j+gZCvnNPpxOXlJdtO0fKenG3oKGzSY2/yQ0oCpLJTtVW21Wqh0Whw1KRmGFXw5XKZBU/NLMVicWwffF1EV115HkP0dM37mmaq2wuNRsPW12QSOmmXJYK/HSL6B0DLcErmXV5echKOkmk0CENdSlM2Xz0aU0XY7/fZzKJer8NgMHBtP0V3OpKjs28afEkiJ899VexU3fe5gY/fYnLMaDTC5/PxNN5IJAK73S5Cvwci+gdC2XaDwTAWQXu9HpxOJ2fcy+Uyd7ZN+udNoo7JopJdqqjrdDqo1WowmUx8LNdut3l8FlW5VatVXlpP9tx/aVGrOY27rCDo+7RaLQKBABYXF3kqrxrlpSjnbojoHwm1oEa1lKYIPVmxp6IOmKSHwWAwgEajQbvd5g80PQiq1Sq38FKFHTXp0HANWlmopcBfg8l99m1OBNTvoQgfj8exvb2NtbU1+P3+zzoDC9cjon8gqkhpD95ut8e+aPAFJd0oyz754Z8UJn2vOgm33W6z1x0A/jva76uGmWqiTvXF/1JQRSCAO50MqIIPBoPY2NjADz/8gO+++w4rKytwuVzid/8ARPQPhARPYqdIS3tsOgdXRX/bs2kqnVVXEbSHpy2CasFNgp92HDa5xH4It3mAUMss5S263e6tRnsRWq0WPp8PGxsb+Omnn/D69WuelmuxWB50/7OOiP4RUDPutJymYylqObXZbOh2u9BoNFyoMnl0pdbZ05fqyEMRnhKF9FCgrYTBYGDR0+rgrv7yt/19Veg+qS+ABnnQsSUV61BBDhXwTLsOvV9+vx/xeBw//PADXr9+jRcvXiAcDo8t62Uvfz9E9A9k8hydlrG03B8Oh3yWHwgEOLmmnpMDv33gyU2XMvbkv0dVfurZv9qXTpGUjveazSaf2d/GhONzXPfQoMm2Ho+HvfsdDseYiWW/30etVuN+eHLnqdVq/PuTzZfL5YLP58Pi4iK2trbwxz/+Edvb21cEf9v7Fq4ion8gJFCLxQKNRjM2ssput8Pr9bIDDi39J/fddB3Va09ttCHxq774aqssRXdqqmk0GqhUKigUCvxF/nrTEnt3XQWo1YLU5be0tMTz9sj3T/XhazQauLi44EafVCqFfD6Per3OZ/CRSIQHgaysrGB1dZWP5yTCPx6az/wP//YObL8xVFss+qKSWLW4hiKwuvdWRU+ZflrKk+Cp246SYlSuq24pJj3v1dFWVH5bLpf5zL5SqVyJtLdBnRhrt9u5l391dRWrq6uYn5+H2+2G2WweK0GmPT1Nzz09PcXZ2RkymQxqtRo0Gg3cbjcX3tDkH7/fD4fDMbaHl066OzH1TRLRPyKqSYZa3UZ7fXWwA2XlqSpP3ZdTVFfP6KeVmKoz7dTr07k9Tbkpl8ucWCyVSkin00gmk0ilUtxH/zkoEi8tLWFxcRHBYJDNLaPRKGKxGHvxXed1RyYbNG6L2mM1Gg07+EajUUQiEbhcLhlD/XBE9N8Ck3ZYagHK5His+15f9dlrtVr8Z7LhPjs7w9HREU5PT5HP59FutznpCIwvn8kTwOPxYGlpCRsbG1hbW0MoFGIvPKfTyQ0wn4NMLmmr0+/3eVtEPgE2m+3Kg0OW9PdCRD9rqEt3WmJXq1WeZptOp3mqzU2i1+l0PMSClt8+n4+NL+/a6HJdQw9tX4RHQ0T/NXjIHvQ+nWifYzQa8dKfSnVV91vKF6gnBBqNhv34KFM/bTUyKeRpZp63Rfbuj4KIXviE6tb7ueYbYpr5p/DNI6KfNdTEIvC4/eaT1tz3ieLTlvd3vZZwIyJ64XF5jOSaLON/V0T0wm9cF21vQiLxk0NEL4xzn3p8EfyTYur/LCnDnWFEwLOJpGEFYcYQ0QvCjCGiF4QZQ0QvCDOGiF4QZgwRvSDMGCJ6QZgxRPSCMGOI6AVhxhDRC8KMIaIXhBlDRC8IM4aIXhBmDBG9IMwYInpBmDFE9IIwY4joBWHGENELwowhoheEGUNELwgzhoheEGYMEb0gzBgiekGYMUT0gjBjiOgFYcYQ0QvCjCGiF4QZQ0QvCDOGiF4QZgwRvSDMGCJ6QZgxRPSCMGOI6AVhxhDRC8KMIaIXhBlDRC8IM4aIXhBmDBG9IMwYInpBmDFE9IIwY4joBWHGENELwowhoheEGUNELwgzhoheEGYMEb0gzBgiekGYMUT0gjBjiOgFYcYQ0QvCjCGiF4QZQ0QvCDPG3Gf+u+aL3IUgCF8MifSCMGOI6AVhxhDRC8KMIaIXhBlDRC8IM4aIXhBmjP8PouwFiYFmK58AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 61\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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ovzPC87vwjcui9J8+feJBu1AohEwmM9GYKmFJ7OrqKjwez9C2Xvi97MHTbreRTCYRjUaRTCZRrVYHtvXCP+v1eng8Hvj9frjd7oEHCvB7qfDZ2RnOz88Rj8fH1usrFArue8+MMFlXntgcRGiuSYG76SHRf2dGvWHL5TIuLy/x4cMH/PLLLzg4OMDFxQVyudzEc+lkMhmsVisCgQDW19fh8/lgNBqHhlAI6Xa7yOVyfAhmNBpFuVwe2SevVqt5CnBjYwNer3egfh/4vXIwFArh/Pwc2Wx25M8hk8mgVqu5N55CocD19TVKpRL3xWNxAGGWgQQ/GyT6GRi3TR73+XGCD4VCeP/+PX799VcueHGq7CbE/nhGo3EogCdszWW7DZZe6/V6yOfzuLi4wPHx8Y0xBIPBgOXlZTx69AiPHj2C1+sdWOmr1epAK24ikeBxATFsyIbT6eTNPyymwO6NPSzYEYBEPzsk+hkY9+Yb93mxiEqlEq+0+/vf/86DdswH/q4Iv5f1tLvdbrjdbl4Sy75vlGgajQZSqRQuLi5wcXGBaDTKxSZc7VUqFWw2G/x+P9bX13nJrfA6rO03HA7zHcO435FKpeIZhpWVFZjNZlQqFajVam4HVq/XcX19PTQ0g4Q/PST6CZnF314ikXDHm2KxiKurK3z69Alv3ryZuNJu1D0B4M62S0tLsFgsQ5Fxdh/s891ul7fqnp+fI5lMotFojLy2yWTC8vIy1tbWhgp9AHBvO2Hbr/BaQpRKJfR6PWw2GzweD9bW1mCz2fhDotFooFqtolQqcdGT4O8HEv0UjDoTi8cujaJcLiOTySCRSCASieD09BSHh4c4PDxEOByeyUdeIpHA6XRifX0dGxsb8Hg8Q2dt8X21223EYjEcHx/zexi3MrPg3fr6OgKBAFwuF1QqFf96v99HtVpFIpHgq3yhUBiZ4xeu8k6nE16vFz6fD1arFaVSidtlpVIpqNVq1Gq1kQM4iOm4UfQsbTJvjJq8OioIVq1WUSwWuWUVmzzDprAII/XNZnNgWAQrfolEIkilUjML3u12Y2dnBy9fvsTTp0/h8/mg1WoHOtGkUik6nQ6vrU8mkzg5OcHbt2/x8ePHkXPpgK/BO4/Hg52dHe6aO2qqLUvVJZNJZLNZ/jOJ7bqAr51/bJX3er08z8+yDwaDAWq1GgqFgq/wxP1wo+hnrXQSR3/FwaT74LZrjXrD3eVaYrdV4bWAr6s2E3A8Hke9XodCoYBOp+MDG4RtoMzmORqN4vz8HJeXl0ilUqjVahM534y6Z6fTic3NTezv72N/f5/3yisUioGfm3nWZTIZxGIxhEIhHB4e4vPnz7xzT7wys2j97u4uXrx4gcePH2N5eXkgL89ot9uo1+uo1WpDDw/h71apVPLhlR6PB263GzabDTKZDNVqlVfcCev4u90uf4jeVMBD3M6Noh/lfz7PsOESxWIRl5eXXDDhcBi1Wo33jzM3WKG9U7PZRKVSQTabRSqVQj6fn9i6WoxEIoHL5cL29jZevXqFly9fYnt7Gy6XiwtGaHCZTqdxcXGBs7MznJ2d4cuXL3zUdTabHbkVt1gsCAaDePz4Mfb29rC2tgar1Xrj70fsbMO+xjAajVhcXMTKygqWl5d5sw4zwBReg32wXvrb7LSJ2yFV35Hr62t+Jr+6usLR0RE+ffqEo6MjRCIRNBoNKJVK3jfO0mitVosLn72Br6+vJ3Z+kUqlA8cFtsLv7Ozg559/HljhxS2szWYTiUQCx8fHODg4wOHhIc7PzxGLxVAoFMaWyOr1eiwtLWFzcxM7Ozs82MYQHh3Eq/CoMVbAV8G73W7uxLu8vAyLxQKlUsl/N41GA7VaDfV6Hc1mk1+PxU5GNSgRd+dG0X/69Ol73ccfjnCr2Ol0IJFIYDAYuNkEKyu9uLjA6ekpTk5OEAqFEIlEhhphWE552nFToxBeRyqVYnFxEZubm3j58iVevXqFnZ0dOJ3Ood1Zs9nkzTtv377Fu3fvcHZ2hnQ6jXK5PPb+WNssS8+trq4OCJ5tt4U1+eyD3aMYg8Ew8BAJBoNwuVy8B7/ZbKJcLiOfz/M+fpY6FIqdBD8bN4r+n//5n7/XffwQsNWURYs3Nzfxl7/8BQ6HA+l0GqFQiAfg4vE4crncyMDXrP5tt8Uh2Aq/v7/Pt/RiwbM4QiwWw8ePH/H3v/8d7969w5cvX+5Uy8+i9Wtra1hdXYXD4RgI3I3KBLDdzKjyWZ1Oxy272IANlvZj02krlQpyuRxSqRSy2ezA+Cz2kKEt/uzcKPp//dd//V738UNycHCAVCqFpaUl5PN5XF5eIhaLIZ1OD63u9xmcFAc/2XZdpVLB4XBgY2MDr169ws8//4zt7W0sLCwMjKPqdDrc2JJ1671+/RonJycoFAojX1N4/xKJBFarFT6fb2yvvHAl7/f7KJfLKBaLKJVKfEvOYGf4ra0tPH/+HE+ePEEgEIDdbodGoxkYW51MJpFIJJDJZAYeqOznYr35rVaL/8wU0JsMOtPfQCaTwd/+9jcYDAYeiKtWqxPXw8+CyWSC1WqF0WiExWKB1+vFxsYGnj59is3NzaEzfLfbHajl//XXX/H+/Xt8+fJlrOCBwQeNXq+Hw+HA8vIyD7SxLfio7Xu1WkU6nUYsFkMqlUKpVOLfZzAYuB/+06dP8eTJE57yEzrkNhoN7snHdlHC33O/3x/oz6/X6/yeSPSTQaIfgXB7HY1G7/Rv7jMFCXyNCzCX2UAggKWlJbjdbiwtLfFZ8rcJ/t/+7d/w7t27OxlwMFQqFex2O3w+H/x+PxYXF4dMNIV0u13uxc9KeFndvkajweLiItbX1/kKHwwGeW+/MLNQLpeRTqcRj8f5Tkp4BOn1enyCLdv+a7XaIU8+4nYoZTcCdraf1d5pUtRqNcxmMy9OcTqdWF1d5YE0t9sNi8UCnU4HnU43VBxTq9UQjUbx4cMHfoYfJfhxRxHmhuP3+7G2tga/3w+73T70PhD+W2HRUTgcRjweR7lchkwmw8LCAgKBAHZ3d7G7u8sn42q1Wn4PbGBlNptFIpHgffziI0K320WpVEIsFsPl5SU8Hg9MJhMvhGL3Rav+7dyoahoo8H2QSqXQ6/VwOp3w+Xzwer2884yVqHq93oHmGRbMYn9vNpuIx+P4+PEjfvnlF7x58wbn5+cjy2pv2pWwXvxAIDDSAku4rWerPKu3D4fDfMwWa6TZ2NjA1tYWAoEAnE7nwArP7jufzyMej/N4Sa1WG7rHXq83sKPw+/3weDwDDT/E3ZjPpXwCRjWszHIt8TV0Oh1sNhvPXQtXdTYdxmQyDdlWC8XXbDZxdXWFg4MDfoYfJ/ib7qnX60GlUsFqtcLlcg2c5VlJtvA4Ua1WEY/HcXZ2xqfg1Ot1KJVKLCws8HTfysoKHA4HNBrNwGuzev1kMomrqytcXV3dWLTUaDSQTqd5ma/44TBLM9Q8QaK/hW+ZGmJ562AwyE0pVldX+Uqv1+v5UWMc19fXiMfj+Pz5M/793/8dr1+/RigUupPgxSgUCmi1Wv6QETviiL384vE4Tk5OcHx8jIuLC2SzWXQ6HdjtdiwuLsLv98Pv9w/k4oWwGARrQEokEigWiyMHZLLfgbDfQXwEIO4Gif47IvaYW11dxe7uLvb29vDo0SPen242mwc62ITGF8IZd51OB+l0GsfHx3jz5g3evn17Y1rutntiYh3lYS/8c7vdRiKRwNHREd6/f4/Dw0PE43FUq9WBowqrq7dYLLwPQFjQc319jWKxyNN0rEnnJiGzkuZGo8FttWednDNvkOj/AJjgnz17hlevXmFvbw8+nw9ms5mX8IoRt5WyHoCLiwscHBzgw4cPt6blxiGVSmG1WrG6uspLY8dF7JngDw8P8ebNG7x//x5nZ2fI5/MAvlbyGY1G2O12OBwO2Gw26HS6oWYrFphj2/V0Oo1KpXJrBaNEIkGn00GtVkO5XEalUuEtxGSWeTdI9N8B4VQWk8kEj8eDra0t/PTTT3j+/DkCgcBQ15q4zl7setNut3mV4G+//cZdc4WMi9Kz6zCByWQy2Gw2rKyscEccq9U6ICLWQcfacZnxx+npKdLpNA/6ajQaWCwW2Gw2WCwWaDSaobbkXq+HSqWCZDKJSCTCewDGneXFPwOz5IpEIvB6vdBoNDAajXSWvyMk+m+MXC6HzWaDy+WC0+nE4uIifD4fgsEgtra2sLq6OrJNVfwGFqejmF02M59Mp9NDZ+GbvPqEvnrMTJOl6paXl/nqycqSWYT99PQUBwcH+PTpE2/HZYJnJpcOh4PbbovTiux6yWQS5+fn3IizWCzeKVvU7/d5l+Pp6SlcLhdMJhM0Gs3A2O1Rv0PiKyT6b4hCoYDT6RwQuNfr5ZFxp9M5IHgmQuGblf1ZeKZutVpIJBIIhUI4OztDIpGYyGZL2BgDgFf6sTQYu6dWq8VNMZi77eHhIU5PT7kdllCoWq0WdrsdTqcTTqcTFouFxyb6/T7vnkulUvxa7P5rtdqdTVvYGO2FhQX4fD74fD4sLi7S2f6OkOi/EXq9Hna7HRsbG3jx4gWePn2KtbU1OJ1OaLVaKBSKoTepMGAn/JyQTqeDaDSKk5MT/Pbbbzg/Px87ekp8jVErv9lsxurq6kBeHvgq+KurK953/+XLF5ydneHy8hLJZBKlUmlgZ8FGVttsNh4MNBqNkMlkvBcgk8nw6TnMKoxF/SeJxLMhm4lEYmQsgIp0boZEPyNyuXzAzkkYvQ4Gg9jb28Pz58+xvb0Nr9d7Y3XbOA841rVWr9d51Pzt27f4/PkzIpHIrVZbwtdgrrLCAZRPnz7F1tYWXC4XAHAfv6OjI+6qc35+zs0/RpldMqNLh8MBq9UKrVaLbrfLR1ql02letceMM6PRKC/GuUv1ozBGwWIM9XodrVbr3lqY5wES/QwIy2aVSiXviDOZTPD7/dxTbn19HYuLiyMFL9zSjxI822KzltOLiwscHh7iw4cPOD09RSaTGWpjvWnFtNlssFqtsNvtWFpaQiAQwNbWFtbX16HX6/nZ/fj4GB8/fsTR0dHA4I1x526NRgOz2Qyz2Qy1Wo1ms4lYLIZKpcKbcS4vL3F5ecmdethAi2ntwihHPx0k+imQy+V8NV9aWsLi4iK0Wi0f02QwGLCysoLNzU0EAgE4HI6B1leWqxa2zTKEM+PZys786Fmpazgc5h574hVOvKqzfL5CoYDdbkcgEMDKygr8fj9WVlYGCoFqtRofrcUCdZeXlzwdNw61Ws27ATUaDZrNJi4vL9FsNpFMJhGLxZBIJPiMvEKhwKfXzCJc5qNHLrmTQaK/BfH0GNbTzkY0r66u8oozuVwOuVwOnU6HhYUFeDwe2O32gYaQbrc79g3a7XZRKBRQLpdRrVaRz+cRjUYRCoVwenrKt9ilUgm1Wu3GLS0bemEymaBWqwc63piNNZtz1+/3kc/nEQ6H8fr1a7x+/Zq764iHWAKDuwmFQsHz8gaDAf1+H8lkEvV6nRtwsiaacrnMh1fcJyT4ySDR34JQWEqlEm63G48ePcLu7i62trawvLwMm80GtVoNmUwGmUwGpVIJrVY7VNUmkUjGdi62Wi1ks1mEw2FcXV3xIBWLnLMOttt6+SUSCT9b+3w+LC8vw2q1wmw28wcRyyKwMttMJoN4PM6HZ378+HEgFSeGCV4YvLPZbFCpVLysNpVKIR6PI5PJ8IEV970dZ/9vaJs/GdRaOwJWlCI8a0qlUiwtLeHJkyf4y1/+gsePHyMQCMBqtXLBCyewsAcAMDpv3G63UalU+OrHBkWwSPnV1RX3w69UKrzddBzCiTFsgEQwGOQ7EVZLbzQaeaCN/YzMNPPTp08IhUIDxTaMUXbgLEPBrt/pdLjgk8kkcrncrWW1s0ATb6aDWmtHIG5ykcvl8Hg8ePz4MX7++We8fPkS6+vrQ1v3m9584nbSbDaLaDTKg1psLvzl5SXC4TAikcjYIZJimPhYGy77YGd2h8MBg8HA71X4UGPOvh8/fsTJyQkymczISLr4PoT19WzaDWuzZRV239phiJxxp2M+l/JbEG7pXS4XD3rt7e3hxYsX2NraGnCGBW4+V7KvCRtMLi8vee6bdZcVi8UBJ9jbUKvVvC13dXWVxxiWl5fhcrl4a664pRX4KuJsNotQKITPnz/zsVbiM/yobIBGo4HT6eS5fbVazT3y7svTfxTie2Geecw0k7gbJPobMJvN2N/fx7Nnz/gqurGxMST4u1Aul5FKpXB5eckDc6FQiG/jK5UKfwPfJeesVqvh9XqxtraGR48e8bZcj8fDC2OYFfcohJVxbDz1qCj9qP5/VoPw6NEj2O12Xgufz+dRLBa/ieBH3QsrJ6bt/WTcKPp/+qd/+l738UPAIvWNRgMymQyBQAA//fQT1tfXYbFYYDabh4Y9CM+UzLyRubW22210Oh0+4y0cDuP4+BgnJyd8QmyhUBhZXCP0kxfDZsTv7Oxgb28POzs7vC131MouNsBotVrc3ZdNublLs45arcbS0hK2trawvb0Nj8eDXq+HbDaLdDqNXC43dkrtfTDKvJN18YkDpsR4bhT9v/zLv3yv+/ghYEJjaTXWvcXScaySTfz9wGBjSiaTQT6fR6VSQb1e506v4XCY++ZnMhnU6/Ubt6WjRMfMMpl/PLOTttlsQ80tQoRCYH74rHY/lUrdev5Wq9Xcxvqnn37C2toapFIpIpEI0uk0EokESqXSd4kD6fV6+Hw+7jIkrIMgbufG39TOzs73uo8HhXi45fX1Ne8Nj0QiuLy85DbO1WoVjUZjoHc8l8sNiEM8GgoYXOHVajWMRiOfPb+xsYHHjx9jd3eXZxCEiKfhiA0wkskkr31nxTdisQofOGxQBfOt39ragsFgQDwe5z9vOp2eafLuJFgsFgQCAWxvb2NjY4MP62TQSn8z9HicAvGqyebbsQk4LKfOVnO23Wdb/9vy30LY7Lfl5WXeGLO2tsaHUIi98266Xrvd5hZXBwcHOD4+RjweH9iSi7f1rKiHrfDb29uwWCzI5/M4OzvjD45JuvwmRXxPJpMJy8vLCAaDWFlZ4Q89arS5G5SyuwHxbHrh54Gvb7JKpYKrqyt8+PABHz58wNnZGTdurFQqd2okEa7Mwnns7M29vr6OR48eYX19nUfmbTbbgIcdO5Kw0lsxzNPu6OgIb968wadPnxAOh4fEKs7Ds6Dd9vY2AoEAr89ntfknJycje/m/Fazi0Waz8TP9qHsnxnOj6OmXN4x4NWGi//z5M96/f4/Ly0veRDJp5xcbNGG1WmEymeBwOOD3+3l03u/3c/884Xb2ptlu3W4XlUoF8XicW1y9e/cOJycnyOVyI8Uq7BRk2QGPxwOZTIZ4PI5QKISPHz/i8PAQyWTyTtH6WVyFxTsPrVbLTTPoPTo5N4pe3AxCDL4B+/3+wPZ+VJ77Jth5WyaTQa1Wc9ton8/HG3ncbjc8Hg+8Xu/YVOGo1Z1lDVjBzOnpKd6/f4/379/j5ORkZLMO8HWnodPp4HA44PF4eP9Ap9PB1dUVIpEIjo6OcHJygmQyeecCnPvIoyuVSthsNiwsLHCDjl6vx9+n9AC4G3Smn5Fut8tnqovTVWIzSOHn9Xo9b0XV6XQwGAzc+54VvbBKOq1WO2RHPQ525CgWi7zh5ezsDMfHxzg6OkIoFBpqx2WwbAXzvXe5XNBoNKhUKshmswP+9Ol0+pvl40chkUj49B3WO2A2m8cevYjxkOhnRGxgyRAKXVzSazab4XK5+OQam8020BDDzu16vf7G1xP+vd/vo9VqccPJaDTKDStYS24sFhtw2WE9AnK5nPvd2+122Gw27hFQKpW4PTWrq69UKhPHe2YdGiKXy7noNzY2eABPmKojwd8NEv0M3PTmFTeoSKVSPj2GNcM8evQIq6urcDqdMBgMvCHGaDRyY0oh9XodlUoFlUoFzWYT7Xabn+d7vR5PHcZiMd6Dzzr22EQYhlqthl6v57sIs9kMi8UCg8EApVLJLbZZaTCrthNe4y7odDq+o1EoFGi326hWq9xA46buO/H4bIPBAJfLxev9WdMQRe0ng0T/jRC/kdl5lDnqsAEXPp+Pz2kfF0NhW3a2XY/FYrz6rdPpcNG3222Uy2VuXMG+r16vo9vt8n5/drSw2+38w2w2Q6PRoNfr8dWdFd4Ui0XU6/WJA5N6vR4ej4cfV/R6PR+FxR5Gt3UPMqRSKd+NWCyWAUNREv1kkOjvgXFpPeEqpVarYbfbsba2hr29PTx79oxX0o2j2WyiVqvxRparqyu+VWfecq1Wa6Cgh+0GisUiqtUqWq0W32EYjUYYDAZul8XO7awXvtFoIJFIIB6PI5VKIZFITN08o9Pp4Pf7sbe3h729PQSDQRgMBlQqFZydneHTp0/8ZxwneuGDkxmBsKg9DbaYHhL9DAjz+DcJXyKR8MkvDoeDW2DfFJyr1WrI5XJ8VQyHw7i4uEA4HEY0GkU2m+WmkELYqt/v97nnPhtX5XQ6sbCwwE0vXC4XHx3daDRwdXXFB1okEokbB1DchE6nw/LyMp48eYKff/4ZT58+hd/vh06nQ71ex8LCAqRSKcrlMncKum0XoVKphlKVxHSQ6GdEKHjh9pw177CCGeB3B1dmPtnpdKBSqQauwcp6C4UCj7wLq/zS6TTK5fKtZ2thjz3zxGMdeGazGXq9np/hu90uotEo6vU6r6NnRpiTotfr4ff78eTJE+zv7+PFixdYX1/n1trsQcd+B2w3cdvATYVCwQuXgK9ZE0opTweJfkaYqIVOOaNotVooFAo4Pz+HRCJBPp/nHWJKpZIbPHY6HVSrVWSzWUQiEb6dTyaTvG31tug3K91dW1vjnngrKytYXFzkrjlyuRwqlQpSqZQ3B7FYQCaTmThgB3xd4T0eD3Z3d/Hzzz/jxYsXCAaDQ3PxTCYT3G43vF4vFhcXEY/HUa/Xx2YEhC20rCaBzvDTQ6KfEaHox70Re70eb69l5pdnZ2c8R6/RaHjqiUW3C4UCt51i01zFrysejiEs3WU+fpubm/D7/XzijDgrwB4yqVQKsVgM6XQapVJpqrSa2WzGysoKnj59iqdPnyIYDPLeAGEbslwu50cd1g6cz+fHNuz0+320223eIjyu1Ji4GyT6e4C9CYVvRKGVE8uhl0olVKtVZDIZqFQqHpxSq9V8lHOr1UK9XketVkO9Xkez2bxxsKN4/PXS0hK2t7fx/Plz3oXHsgPi83Cv10O5XOa+9JOe48VFR0ajET6fD4FAgE/hZa/T6/UGcupyuRxqtZqX1I5qjRVen/0eaLDF7JDoZ0QYwBOvPkJB9Ho9HnQTVu4pFAqeSgO+rvR3eWOLV2LhNNynT5/iyZMnCAaDWFhYgFqt5v9GmN5iff5XV1e4vLxEIpFAuVyeumRWqVRCo9FAr9cP7CjED0Q2MbfT6fAPMeJdEys8KhQKPIXI8vS01Z8MEv2MCIN402w52+32zB1qJpMJPp8P29vb3FhjbW0NdrudD5AEhn0AarUaH6YRDoeRSqVmcr65vr5GtVrlO5pR03gZ9XodxWIRuVwO5XJ5KGgozr23220+B+Dy8pKbcrJAKHF3SPQzIjzTi510vuVrsjQgC4oxwe/t7fGpOsLZ8MDvLr/dbpd75LFS3Wg0OnGKTvwzsofI+fk5FhYWAIBX4rH7BoBsNssNOFiAclSmQHj9druNbDaL8/NzuN1uLCwsQKvVwul0DpiQ0APgdkj0M8AEzybQKpVKyOVydDqdW2fKTft6LF/NZse53W4Eg0Hs7Oxge3sbq6urfIUfJYBOp8MHUpyenuLk5AThcHjqFJ2QSqWCSCSCg4MDAEChUIDb7YbJZIJcLufjqtlgjdPT0zuP2e73+zz7wToAFxcXYbfbhx62JPybIdHPCGtFNZlMMJvNfKt6V1fb25BKpZDL5fzBYjAYYLFYYLVa4Xa7sbKygmAwiLW1NT5th6XihJV63W4XzWaTl9ien5/j8PAQR0dHiEQiKJfLMx8zWEVfv9/n5bY+nw8OhwNqtRrdbhelUgmRSARfvnzB6ekpUqnUnXcX19fXSKVSiEajI+MPJPi7QaKfEPGbiplVLi4uwuv18nx8tVqdSfgstSUMjlksFl7Rt7S0xDvyWAOK2AmX9dSzSTqFQoH7+J2dnSEUCiEcDiOTyaDRaMy8M2EZCubvz+IFdrsdOp2ON/GwYZZ39dUTzhOs1WqoVCq8xJj87ieHRD8DrKZ+YWEBKysrqFarkMlkiEajyGQyvCttGuGz4KBKpeKtuMxMgwnd7XYP2F7L5XI+CZZFu9mIa1b9lkwmEY/HEYvFeA1AvV6/F7sroQV4qVRCLpdDLBaDyWSCVqvlKcJCoTDRIEtxazI7SlGufjpI9BMyaqW32WxYWVkZMMdgNfJSqXRqUbFhmAaDgT9Y1tbW4PP54HQ6YbPZoNfreY6/VqvxJp1yuYxMJoNEIsGNL1iLbaFQ4JNvb5o5Pyu1Wg2NRgPlcpnf4011B+MQFh+xnY7dboderyfhTwGJfkaUSiUsFguA362qrVbrQHkt64ibRlxM+CxuwIZusDf89fU171QrlUooFArIZDK8Sy4ajfLCG1Zeyxx5v8ccuF6vN1VJrxiJRAKbzcZHhPv9flgslqH8P53pb4dEPyPMdYaZZOj1ehiNRj7Jttvt8kk3k8xcY6m1druNZrPJ89/FYhF6vR69Xo+vntfX1yiXy9zSKhqNIplMco99oQHGQzoDi92HjEYjVlZWuPmIcIAo+37idkj0M8Ki60zkarUaarUaEokEzWaTD3Ysl8t81NVdYKJvNBo8+KVWq/m5mKXBut0u6vU6crkc75Bjs+3ZFn6S1/2REIpeoVDAarVy/3+v18ur/ihqPxkk+nuACZ/l7KVSKRqNxsDkWK1Wyx1s7hLYk0gkfKUvl8s8BVetVpFOp6HX63ngrlqtolgsIpvNDpzZv/Wo6EkQ24fdBeHvyWw2Y2lpCT6fD4uLiwOdeyT6ySDR3zMs6s7y6iqVip/tWdXeJG/8TqfD02mssCadTkOtVkMqlXLRs1SWcKLO9+Yuwr6taImdy8WCDwQCWF9fH2jkEb8ucTdI9PcAc6thzSP1eh3VapXnk1kUu9FoTJS+E1632WyiXq+jVCohm83ysywrumFGmX8kkwzjvOn7hN9rtVqxvr6OFy9e4OnTp1hZWRmq6SfRTwaJfkaEwmy1WjyKnsvlkMlkkM1mZ8rXC2m1Wmi1WvwowVb/uwhqVgvqUde67TqjVm0x7OcYdS2z2Yz19XXs7+9jf38fu7u7A2d5YjpI9PcAe9N2u10ufDbaCvjq72Y0GnngbVwUf1SLrtgthgmevS574Ai98VjcoNvt8jThfUbtxddisQx2fwqFAmq1mo+dYiYizWYTrVZroJ1WPGEX+Bq0Y4YcL168wP7+Pp49ewav1wulUjlwH7TKTw6J/h5hAmVpPJZXlsvlcLvdvBCGiVE4g05ouSUc08QceZjohQ49zJyCCV54FKjVarz3/LZS15uEI3ytUahUKlgsFtjtdhgMBuj1eqjVam5iyUpoWTlwLpdDNptFJpMZCDSyPLzZbOZ9BcKR3GLB33bfxHhI9DMiFDoTn8FggNPphEwmg8ViwerqKneu7XQ6vIeeDasQBv+YqQYL+rEtsvDPwu21WPitVot77EWjUUSjUZ6+ExYHCf88yVmcvb5CoYBGo4HdbudptMXFRe6fL/wZgK9Hk2KxiGg0ilAohIuLC940o1AosLCwAJ/Ph5WVFfh8PiwvL8Pn8/EpQLTC3x8k+hkRr8bMcJKNU2aRdObx1ul0eEUc+5xwW6xUKqFQKAauyV4HGNz6s9WXHS+Y6CuVCu+VZ444LIXHHgrMgXaSKkFWbcgqAnU6Hex2OwKBAILBILxeL6xWKzfuYOKUyWR8iMbV1RXcbjdcLhcikQhKpRLkcjlcLheCwSA2NjYQCAR4AxGbwsN4SMVFPyqSW36J9Bu+J5gohRV6bIsPYGiln8b8kU25qdVqyOfziMViiMfjyGazfBRWtVpFMplEOBxGJBJBNpu9Uz7faDTC7/cjEAjA7/dz3z2TyQSv1wufzweXy3WjW06r1UIymcTl5SV/GFWrVe7PLyy8uevATuJGRm6HSPR/MKOGX94HrNGlUqnwmXGsQjAajeLk5ATn5+dIJpO8PJf59Am3z9fX15BKpXA4HAgGg9je3kYwGITT6eQPKdYTIGzrHUe73eYz8iqVClqt1sDIKta3IIa29FNBop83hME3ZiPNzC3Y1l/ocS8UPUulsdy/1WrF6uoqgsEgVlZWYLPZeNWg8BhyF4QuwcKH3qTXIW6FRP89ETu6MNhqNWrVEjaXTMtdOs0qlQry+Tw3+2CpxVFec8xrnmUj2IScUdzWtXfXLjihay+t7jNBoie+wnL5LJ8vPmKIXXOB31OKcrmchPhwINHPG6N2DvexeopX9EmvOW5HQyv7vTPyl0kpuz8xYgHdl6CE15nmmqP+DYn9+0Er/ZwyTfyAVuIHB23viUEmDRiS4B8ctL0nBiERzyeUFCWIOYNETxBzBomeIOYMEj1BzBkkeoKYM0j0BDFnkOgJYs4g0RPEnEGiJ4g5g0RPEHMGiZ4g5gwSPUHMGSR6gpgzSPQEMWeQ6AliziDRE8ScQaIniDmDRE8QcwaJniDmDBI9QcwZJHqCmDNI9AQxZ5DoCWLOINETxJxBoieIOYNETxBzBomeIOYMEj1BzBkkeoKYM0j0BDFnkOgJYs4g0RPEnEGiJ4g5g0RPEHMGiZ4g5gwSPUHMGSR6gpgzSPQEMWeQ6AliziDRE8ScQaIniDmDRE8QcwaJniDmDBI9QcwZJHqCmDNI9AQxZ5DoCWLOINETxJxBoieIOYNETxBzBomeIOYMEj1BzBnyW74u+S53QRDEd4NWeoKYM0j0BDFnkOgJYs4g0RPEnEGiJ4g5g0RPEHPG/wcQRuRWl5uFdAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 65\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 66\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 67\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 68\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 69\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 70\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 71\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 72\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx * Ny,))\n", + "lb = np.zeros((Nx*Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx * Ny,))\n", + "ub = np.ones((Nx*Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "cur_beta = 4\n", @@ -452,10 +2023,10 @@ " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", + " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", " solver.set_maxeval(update_factor)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta * beta_scale" + " cur_beta = cur_beta*beta_scale" ] }, { @@ -467,15 +2038,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "plt.plot(10 * np.log10(evaluation_history), \"o-\")\n", + "plt.plot(10*np.log10(evaluation_history),'o-')\n", "plt.grid(True)\n", - "plt.xlabel(\"Iteration\")\n", - "plt.ylabel(\"Average Insertion Loss (dB)\")\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Average Insertion Loss (dB)')\n", "plt.show()" ] }, @@ -488,23 +2072,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "opt.update_design([mapping(x, eta_i, cur_beta)])\n", + "opt.update_design([mapping(x,eta_i,cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - ")\n", - "circ = Circle((2, 2), minimum_length / 2)\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", "ax.add_patch(circ)\n", - "ax.axis(\"off\")\n", + "ax.axis('off')\n", "plt.show()" ] }, @@ -517,37 +2108,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n" + ] + } + ], "source": [ - "f0, dJ_du = opt([mapping(x, eta_i, cur_beta // 2)], need_gradient=False)\n", + "f0, dJ_du = opt([mapping(x,eta_i,cur_beta//2)],need_gradient = False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef / source_coef) ** 2" + "top_profile = np.abs(top_coef/source_coef) ** 2" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n"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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", + "plt.plot(1/frequencies,top_profile*100,'-o')\n", "plt.grid(True)\n", - "plt.xlabel(\"Wavelength (microns)\")\n", - "plt.ylabel(\"Transmission (%)\")\n", - "# plt.ylim(98,100)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('Transmission (%)')\n", + "#plt.ylim(98,100)\n", "plt.show()\n", "\n", "plt.figure()\n", - "plt.plot(1 / frequencies, 10 * np.log10(top_profile), \"-o\")\n", + "plt.plot(1/frequencies,10*np.log10(top_profile),'-o')\n", "plt.grid(True)\n", - "plt.xlabel(\"Wavelength (microns)\")\n", - "plt.ylabel(\"Insertion Loss (dB)\")\n", - "# plt.ylim(-0.1,0)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('Insertion Loss (dB)')\n", + "#plt.ylim(-0.1,0)\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/04-Splitter.ipynb b/python/examples/adjoint_optimization/04-Splitter.ipynb index 5c82ed9a1..4518401a7 100644 --- a/python/examples/adjoint_optimization/04-Splitter.ipynb +++ b/python/examples/adjoint_optimization/04-Splitter.ipynb @@ -13,9 +13,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.9/site-packages/meep/simulation.py:5441: RuntimeWarning: quiet has been deprecated; use the Verbosity class instead\n", + " warnings.warn(\"quiet has been deprecated; use the Verbosity class instead\", RuntimeWarning)\n" + ] + } + ], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -25,7 +41,6 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "import nlopt\n", - "\n", "seed = 240\n", "np.random.seed(seed)\n", "mp.quiet(quietval=True)\n", @@ -42,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -56,13 +71,13 @@ "\n", "minimum_length = 0.09\n", "eta_e = 0.55\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", "eta_i = 0.5\n", - "eta_d = 1 - eta_e\n", - "design_region_resolution = int(5 * resolution)\n", + "eta_d = 1-eta_e\n", + "design_region_resolution = int(5*resolution)\n", "\n", - "frequencies = 1 / np.linspace(1.5, 1.6, 10)\n", - "# print(1/frequencies)" + "frequencies = 1/np.linspace(1.5,1.6,10)\n", + "#print(1/frequencies)" ] }, { @@ -74,33 +89,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", - "Sy = 2 * pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx, Sy)\n", + "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1 / 1.56\n", + "fcen = 1/1.56\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", - "source_size = mp.Vector3(0, 2, 0)\n", - "kpoint = mp.Vector3(1, 0, 0)\n", - "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", - "source = [\n", - " mp.EigenModeSource(\n", - " src,\n", - " eig_band=1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size=source_size,\n", - " center=source_center,\n", - " )\n", - "]" + "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", + "source_size = mp.Vector3(0,2,0)\n", + "kpoint = mp.Vector3(1,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]" ] }, { @@ -112,21 +123,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "Nx = int(design_region_resolution * design_region_width)\n", - "Ny = int(design_region_resolution * design_region_height)\n", + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", - "design_region = mpa.DesignRegion(\n", - " design_variables,\n", - " volume=mp.Volume(\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(design_region_width, design_region_height, 0),\n", - " ),\n", - ")" + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" ] }, { @@ -138,28 +143,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "def mapping(x, eta, beta):\n", - "\n", + "def mapping(x,eta,beta):\n", + " \n", " # filter\n", - " filtered_field = mpa.conic_filter(\n", - " x,\n", - " filter_radius,\n", - " design_region_width,\n", - " design_region_height,\n", - " design_region_resolution,\n", - " )\n", - "\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", - "\n", - " projected_field = (\n", - " npa.fliplr(projected_field) + projected_field\n", - " ) / 2 # up-down symmetry\n", - "\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " projected_field = (npa.fliplr(projected_field) + projected_field)/2 # up-down symmetry\n", + " \n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -173,32 +170,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Define spatial arrays used to generate bit masks\n", - "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", - "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", - "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", + "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", + "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", + "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", "\n", "# Define the core mask\n", - "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", - "top_right_wg_mask = (X_g == design_region_width / 2) & (\n", - " np.abs(Y_g + arm_separation / 2) <= waveguide_width / 2\n", - ")\n", - "bottom_right_wg_mask = (X_g == design_region_width / 2) & (\n", - " np.abs(Y_g - arm_separation / 2) <= waveguide_width / 2\n", - ")\n", + "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", + "top_right_wg_mask = (X_g == design_region_width/2) & (np.abs(Y_g + arm_separation/2) <= waveguide_width/2)\n", + "bottom_right_wg_mask = (X_g == design_region_width/2) & (np.abs(Y_g - arm_separation/2) <= waveguide_width/2)\n", "Si_mask = left_wg_mask | top_right_wg_mask | bottom_right_wg_mask\n", "\n", "# Define the cladding mask\n", - "border_mask = (\n", - " (X_g == -design_region_width / 2)\n", - " | (X_g == design_region_width / 2)\n", - " | (Y_g == -design_region_height / 2)\n", - " | (Y_g == design_region_height / 2)\n", - ")\n", + "border_mask = ((X_g == -design_region_width/2) | \n", + " (X_g == design_region_width/2) | \n", + " (Y_g == -design_region_height/2) | \n", + " (Y_g == design_region_height/2))\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False" ] @@ -212,45 +203,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(x=-Sx / 4),\n", - " material=Si,\n", - " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", - " ), # left waveguide\n", - " mp.Block(\n", - " center=mp.Vector3(x=Sx / 4, y=arm_separation / 2),\n", - " material=Si,\n", - " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", - " ), # top right waveguide\n", - " mp.Block(\n", - " center=mp.Vector3(x=Sx / 4, y=-arm_separation / 2),\n", - " material=Si,\n", - " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", - " ), # bottom right waveguide\n", - " mp.Block(\n", - " center=design_region.center, size=design_region.size, material=design_variables\n", - " ),\n", - " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e2=mp.Vector3(y=-1))\n", - " #\n", + " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # left waveguide\n", + " mp.Block(center=mp.Vector3(x=Sx/4, y=arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # top right waveguide\n", + " mp.Block(center=mp.Vector3(x=Sx/4, y=-arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # bottom right waveguide\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e2=mp.Vector3(y=-1))\n", + " # \n", " # The commented line above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " symmetries=[mp.Mirror(direction=mp.Y)],\n", - " default_material=SiO2,\n", - " resolution=resolution,\n", - ")" + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " symmetries=[mp.Mirror(direction=mp.Y)],\n", + " default_material=SiO2,\n", + " resolution=resolution)" ] }, { @@ -262,55 +237,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(\n", - " sim,\n", - " mp.Volume(\n", - " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", - " size=mp.Vector3(y=1.5),\n", - " ),\n", - " mode,\n", - ")\n", - "TE_top = mpa.EigenmodeCoefficient(\n", - " sim,\n", - " mp.Volume(\n", - " center=mp.Vector3(\n", - " Sx / 2 - pml_size - 2 * waveguide_length / 3, arm_separation / 2, 0\n", - " ),\n", - " size=mp.Vector3(y=arm_separation),\n", - " ),\n", - " mode,\n", - ")\n", - "TE_bottom = mpa.EigenmodeCoefficient(\n", - " sim,\n", - " mp.Volume(\n", - " center=mp.Vector3(\n", - " Sx / 2 - pml_size - 2 * waveguide_length / 3, -arm_separation / 2, 0\n", - " ),\n", - " size=mp.Vector3(y=arm_separation),\n", - " ),\n", - " mode,\n", - ")\n", - "ob_list = [TE0, TE_top, TE_bottom]\n", - "\n", + "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=1.5)),mode)\n", + "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,arm_separation/2,0),size=mp.Vector3(y=arm_separation)),mode)\n", + "TE_bottom = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,-arm_separation/2,0),size=mp.Vector3(y=arm_separation)),mode)\n", + "ob_list = [TE0,TE_top,TE_bottom]\n", "\n", - "def J(source, top, bottom):\n", - " power = npa.abs(top / source) ** 2 + npa.abs(bottom / source) ** 2\n", + "def J(source,top,bottom):\n", + " power = npa.abs(top/source) ** 2 + npa.abs(bottom/source) ** 2\n", " return npa.mean(power)\n", "\n", - "\n", "opt = mpa.OptimizationProblem(\n", - " simulation=sim,\n", - " objective_functions=J,\n", - " objective_arguments=ob_list,\n", - " design_regions=[design_region],\n", + " simulation = sim,\n", + " objective_functions = J,\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", " frequencies=frequencies,\n", - " decay_by=1e-5,\n", + " decay_by = 1e-5,\n", ")" ] }, @@ -323,18 +271,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "x0 = mapping(\n", - " np.random.rand(\n", - " Nx * Ny,\n", - " ),\n", - " eta_i,\n", - " 128,\n", - ")\n", + "x0 = mapping(np.random.rand(Nx*Ny,),eta_i,128)\n", "opt.update_design([x0])\n", "opt.plot2D(True)\n", "plt.show()" @@ -349,42 +304,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", - "\n", - "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", - "\n", - " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)])\n", - "\n", + " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", + " \n", + " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)])\n", + " \n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - " )\n", - " circ = Circle((2, 2), minimum_length / 2)\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", " ax.add_patch(circ)\n", - " ax.axis(\"off\")\n", + " ax.axis('off')\n", " plt.show()\n", - "\n", + " \n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", - " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", - " )\n", - "\n", + " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1))\n", + " \n", " evaluation_history.append(np.max(np.real(f0)))\n", - "\n", + " \n", " cur_iter[0] = cur_iter[0] + 1\n", - "\n", + " \n", " return np.real(f0)" ] }, @@ -397,22 +342,1613 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current beta: 4\n", + "Current iteration: 1\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 3\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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oEgsXPfYlz9qZI68havNGAbIlw3PwMtHCs0Xl43m5+FvksXj+giukqF2eKy5uUeBKDL0TbA2J+j/4/UTv4fe//31blhJ7xxRF/6Mf/ehZncdzgYvB51t77bXX7Hvf+569+uqr7XDOKOOddad18AHnd9tFCSyG4/9SzI7nhJa3r2eA1t6X7PabWdsFmUMQFjjux82G/t3vER6XcwQoeO7m7Mecz+c2GAw0yq6Douh/+tOfPqvzeC756le/ak3T2Le//W3b7XZtZcBxJC5x3ey0zTxrEUCiGDQKHRDOcmM5mTtfAiuIrCJg76OrmfBJLD2LH5vp/Pf79++3M+L+5S9/sffee892u51dXFychF81o5i+wM3NjX3wwQc2nU7beHgymbSvTOLutlnmPrNy7NZz0q3U8pBlwvnvh8MhfOkB7+tEYo+2ydrtuULAfTAPEFUCvh1XXFy54gzC4/HYptOpTadTm81mNpvN7NVXX7U333yzPSc13x0j0QPsBs9mM/v4449tOBy270fzB8vfmopup8NizRJW+ECyh4BDbLlcbCrMLH7pe1SxIH0TXlGsH4UfHB5kYUHUL4RHHuL4h8lkYtPp1C4uLuzi4sImk4nN53P72te+Zt/5znfs/v37va6jNtRkB/iD5W9M2Ww29tlnn5nZwxcm+MO1Xq9tOp2Gr0pGMlHg8TgkiKbK6qoYuKzs2rJPdN7ZveEmRb7ObP+oYsj64ZfwezQajWw6nbb/l/v379tsNrOLiwt78cUXJfgCRVXX9kYQxBN6Nzc37dtR8LPZbGwymRy5+JxV93JwaXYsINzfO7rwxBictOLsNZN5B1EO4hz4vDgUyGJm7s2HIo+aIVn8XDZaeve+5vO53bt3r3155WKxsIuLi7OurxbqMuVn4s123uEDLZP38fb3zvn2Zvk4dLN44I4npLz5ja09NgFiMxl7GFmcH2XCswk7/Jxx/6icyOIzWW6CrXtJ+OwdHA4Pmxx96nAPs1D8k8kkbBmoDb2f/jE4HA5tU52DFteFzx1Zutx5HLCDk3H4b27NuVsvd3ktucORWFHwUdOiXwOXUwoLWNBRhZPd21LWP2oZwB58OM7Bxc8fnEpcCb1HSPQFXNg4OaNPyoh/xwe/lNTiJifedjQa2Xa7PQoXvGx+gP14DAuyJHi29lkowhVQ1BmIz4GP79+jPAAPzY0y+97PnitBnlg0m8tAgn+ERF/AH0IXooseHyzvmYbbZ5Yeu6tmCT2zh4JwceNx0Lrj/vv9PmxBYPH5embpozxBJvrI2uN6V8IQrwMrQLbu3qehaZo2wWp23HYfLSXyHIm+g8z64LhtbpJiV9XsWGz+3X/DvuhuwTxm9yUKPuoVmIEPfzQKMLL0vG/WB55F3yV4zgP4BytCPwZ3KPLKAZv1JOzHQ6LvSdTcxGPq2VL5bw72D/f9uVx03/F7lNHm8s3iOLpLoLgdx75dgu6zTXZOfF143Vjh+L2Jzkucj8YcClEZsvQ94aQY9wc3O85gcxKP486o+27k+vOxsTxcOlEWPbKmbtHRsvM2+HdcZmFFKUPOf8ua47iTTtRlGc9RnI9E30EUz7po8eUWZsdvj2XXlSsMzjpj2X7cKHbmysDpI0R3kTO3msvyfbFPgFdkTzORx4KPEnnYT6LrmkUZib5A1J6OnUG8Gy4+0JzBLzXZ8Tz50TvtOFsdiQg9DbbaKPLMA8ksPf89y4r3bbJDSk12Uds8vrKqaZpwss3IUxCnSPQF0DKj6H1UF/e9z5rrImvPlQkPFUXR+4cnkMjE5H/jZr3sGqP98e/o3rvlx/14oExXJeHnU3Lts+Y7zPBnr7zKPAIl/x4i0Rfw7p4+ss4Hd8znc5vNZjadTo9EH/W3Z6sTufkuduyDn2XAMRQoiSqydNHot1LF4eeL5x1Z78yqstvP94VFHVWaURjg148dp3xGI5zhyAeMqRvuMRJ9gaZpbDab2eXlpc3nc5vP5+26D7XlATdmx01PJYvDAubvvm3UMaZPF1r8nd34UtItIxukg9fbh8iFz7wk3N7xHMpgMGhnK16tVnZ3d2fD4bCdQQd7MYpHaGgt4A+09/yazWZ27949u3//fmvh0dL7oI8nHVqLou47tLYrXuZ1dtNxEE+2L98b7HKM23YlBqN78SRDa32/wWBgy+Wy7Wtv9rDi8f+XiNHQWoAf6PF4bC+88IJ95StfaUXubr4P7cTEG5fh5XiyKqoAfHt210v9xzGDj8cpweLvkxfoouSGl7bjfvX4G5PdFzyOW3J/X/3FxYVdX19rTH1CXaa8A37Yl8ulvfzyy/baa6+1ve+y6bKyrqloxbbbbejS+vaZICMR9rHy+J3d+VIllZWFv/Mw4shFz8bQd02XhecVNZn6/2I8Htt+v29HQs7nc1utVnZ9fW2//vWv2wRjrQm8H/7wh+HvTalWb5qm6naPr3/96/b973/fvvWtb9l+v29jRo692eVGK80P+zkTY2KegAWEDzLnAHybc2NtJHLVI3c+S8ZxV+SoGY5dfH4WswRm18SYf/jDH+znP/+5/eY3v7H9fm/z+fyk334N/PGPfwxru6Kl/8EPfvD5nM1ziotstVpZ0zT2zW9+07773e/aK6+8kk6BzVaIKwB80J9kCmwWTrQNJxPZGvdx21nY/BuWGVnu6DyjZrcu0XMlimFUNgX2YrGwwWBgv/3tb+2Xv/yl3d7empm1U56JhxQt/TvvvFOlpfeJLO7u7uzBgwd2e3vb+bIL7lnHD+SzfNmF/8779xF/5lnwh99WG1n8z+NlF5js9HPzuQ48yffhhx/aO++8c/SWmxo5HA7nW/o33njj8zmbvyLee+89+9Of/mTr9dq2260tl8v2vXbYCSR6Yy2KPgoFfAhp5n7jvrgNCrpL8FHMXToe7ofr6JGULHfWQy4SOldifF5+r7bb7Uk45efhMb3/f9brtX322WdtT8nD4dBm9vt4OjWgRF4H8/nc7t+/b8vl0larVdjUFLn4PAbet/FJOMweDbX1/vpmpy0IWLGU4uyMzDpH+0YxOJcRxfF+T7zsLssfbZNZe1/y2AQzayve9Xpty+XS1uu1rdfr1jPzsvrG8v5/ehyifZ+kvM8TNdkF+IPliTtv893tdkc955xSAop/w/Hx/nvk3uN3ttwYu5eIxMQic9idj/ZHrwQFW3pVVWkb9iKiygiz+Jwgdbd+tVq1lbKv4/vrzklkPok3EO37PHoXRdE/j7XU5wnXzN4NdzKZtN06uZ89xvUeX3fFzdw8l91nFN85+3A4gH9jEeNxsu2iEIG9B65IsuPwveHKKLp2X/KUYThnIQp/vV4/VotFLRRFX3v3RW6eQ8FjYs5BMWIFEokG90EiS+/9zPuInveNvI1zrU8Ub2dlZNfD58B/xymzouOwZ+QJUX91uMf00YAbcYxi+gIsMnZz/SEzO42zo2ReFMfisXDJv7sH0SV6btuPcgZYpm+H15BVPF6e77vf749e0IH7+G/Zd9/W7wU3cSLsQeC991DBl94BSuRI9D1B4fK02GanQ0WjbrSc2GKRRRYwcusjEfK5cnkoPpzbjz0SFCFPCIKiRJHiueC+fq+y73wveD1KwHGlUQpFRIxE3wFbaR7O6aLHzjbRaLnowTTrdof5dxRQaaAPJgB9P5x1l+N+P6dosk6O0/l6cJJP9ma8gom2i/IEvC0nB7ECEo+HRN8Dtj78TjsUiwsqeulC5NKj5cbfkcjFzwTvoLDZEkeVTynxFq37MQ6HQ9sHnsXM7fne6Slqm8ffPCZHtx3vL3oc2ZgHkSPRF4hiSRa+i96nc/LkHyfeIrizSWnkXFRBoMXjyoXdZz+3khtcqhCiZZSVj9rnS010XKmgpUfPCr/j9Xu/Bwm/PxJ9D6KHmvvQu/XhbD8/iJwIY6GjNY88A0y++fdM+KWYGbfha8ysep97w9aa2+mz88AKxBN1vt9mszlJnvp98P8DjnrE+ytOkeh7wqJn4WOiCpv3IvfTxephAMfSnOX2/bw8LBfLZAsXCTkScSZ8/Ft0P7JjZNY+arnILD1befekvBJw6+4VwXq9ttFodPRCUREj0Z9B5uqjtW+aeI47s2NLjS+x5P4QbumjprOurL7/DcXKHkO0fo7geV+20pn4/VM6p6hidUvv99o7Svn1YuXrPfTk4udI9GeSWTV31aOONA66/ZzcQisfTZlldtqMFw3iedLr4nUkagLMvAl0683y5kosi8vzytStvAt7s9kcTVWGf9tutzYej2XtC0j0j0kWr3IPPbNTlxwngvCy/IPvUs/my4sm8OjKIfA6Xwuexzn3APcrWfwsns/KZGvPCdTJZGLj8fjEGzocDkdvtxWnSPQ9ySwpP/Bd+7uYMaPNbdWeDDSzo/wAu/JdM/iUYv5ILLjkc4/IrHzJ8nMIkcFeFIp/s9m0osfBT16ZbjYbu7q6suVyeXT951RoX2Yk+jPpkywzi7PdLkp27bH9ejweH1l83BfPgS0/W3v+cMXBZKIvWUy27lGIUPqUiBJ7mMF30Xtsj28f2mw29umnn9rV1ZWZ2dHYeiHRd5IlzKLmsWjEGT5ovh92NolmjsHebL7E/blMMzux+OwpZOeO11C6B9n2mVXne9MnZ4DH4nuBS2+iw7cDTSYTu7y8bMfVv//++22ZOMxWSPRF+GHvEr4vuXmKy0D33i07P9i+xHcPoNUuWWauCNj952vrK3iscErWmwffRBUBH7crhOBORm7Z/eMvuNhut+0kGiJGou9Jn6Yys0cPOGetsRxP1LmgWUhRd9XIY8AKADsIefnZ+eOy7zXz+fe9Z34uPM+Any9SmvoLOzN57I4u/W63s+l0ak3T2O3trb3++uv29ttv2+FwsHv37rVhlZDoe9El+Mxljjqj+MPHI9jQirnbj17AufGoH4crAlwiJXc72zaz9H08h0jg0T3EcQPswRwOh/YFJF6JDodDe+ONN+yVV15pZ3Ou7U1NXehu9IStK7vOpcw+u7dmdpSow5yACx2tPWau2fpHfetdEFGTYN+Yniu66LrOzcgjeD84HIi242Pg/wETesPh0L7xjW/YW2+9ZS+++OJRGX28mxqQ6HvA7m32wX7zuB8+uE40PbSLFDv7cKafewKORqN2X/x4sjBr1kMisfURfTZVVlRGCS+nq2XEKxk/J7fs7uK7u//CCy+0gjeLvYqakeg7wAcRrTrGljhmHF1R3DZK6mVZbhSsCxqbqrhPOg424am48Q0wkVdSSqploQtb+SxhWao4+N7yd/aC8Hh4/33d3Xx/ffh6vbbJZJL9W6tGoj8DjCkjNx/xBxQz7Zz9Nntk1XHAjcfjKH6cVAJdWk9oZaLnufj5PEvJLW7qw+vqSjT6/rhk8WfrfA+9IsR7il1w3a0fj8c2nU7bDjrYL6JG9H76J4Tbvdl1Ho1G7Sw6KAKcs43n08PKAHvnYabaLbkf00fxbbfb1LKz6N0VzgSM5+Kgl+LfnZLos7Ans/ylECDKW7joXeyTyaQ9Nv5fvA3ff49aNGpFou9B5O5y05H/vevFCjyNM4rNLRIOz8Us/HA4bN/2wsLuWkbxfJfoI7Hi9ujWs+ijsQFm5SHBpYQhVpjo8fhgHD7vrAejkOh7k2XvceBM1PYcJQHdpUcRmZ0m+rCN2isUFBJP2MHnhRUH9lFn8XLrQtaVl88zS97xPeIKpPRBolDCzFrR+/h5vFeiG4n+TPChRtcaLXL0IDdN075k0T8oNraYkQeA5eEIvCjBGAmvJCr/zlY4yvZzGXhv/Nyi84jEz9cSlY8tBWaP2t19rnu39hJ+PyT6M4hiVYznM3cVH3bPwmNcyuIvWVIvD4+H52ZmRYExkYsfXUcmfq4osiRn5PKXPAA8H6ycsHIdj8dH4+ijVgRxikR/JvxgovDdIrFgfHw3uugoWn+I/W9mpwNNzLpnsuFQIsuaOyh4tpJdLj5vi/tE4Uck8JL4o3P1a8F4Ht8enPUXEMdI9I8JWzMzO7FEKG637lgZeILK182OO6qwKCN33Il6uHk5JRFxPI9llEKV6F6weKP5/6OwJBJ/dBz8jWfJleD7I9H3JHLrMWmGLqlbIt/Whe/bedMb9qzz74PB4Mjim9nR90is/LDz/ngNUeIwi+n5us2O29yj+2L2KKbHjjS+5H703LLA63wsL5evWWLvj0TfQSR2f6i9n7w/iGbH2fDRaNR2lXVhj8fjkxleWfw48yt2ysFZdzkfgJ5BKX4vLXkdy0EvJmr35kogOz7/Dbsfd91/9CC4fwJ7FCJHou/Axe4PmI/fns1m7UPMbfM82wsKOrLwkdhZ+LgflsuxbFf8n4UEEZwTyFz9kmvP+2RhQxb7R+vT6bT9+P8DuxtL+GUk+gL4sHknnMlkYtPptLWu+D47BC0+ijVa8sSPKHSe/hnXsXzuIefnEJ0Xr0dJPLz+yIpnwkR3nRN8pYqDK4ks6+8V72w2s4uLC7u4uLDZbHY0Zx7mNMQpEn0HLHgXZdM0bVNdZDGjLqRsodlqZ9a/5ClEwvfj4xLPi9ejDja+PEfwLPosPs/Ez2MEWPxePor+3r17Np/PW6uPZYgYib4AP2g4tNNHcnGnEEyWsduNwsSZdSJvIBI65wBwX7b054g+u3a8B7jO4u/b/o6U3PzSEivg+XxuFxcXdnl5abPZ7MjNl+hzJPoO0NK7SDyJh23EZnHfcV9GHxQsewPs0meVg1nepl8SeJ9sd2bpu6x15sJny6wstvIufh8373H9bDZrXXy5991I9AUwjvTvaPkxkVYiEiIKnb9HLnv0nffpK3iz7iSeWdw8h/cB10vxPy95nY/V9XFhexw/mUzaSTRk6buR6Dvwh8zX3dJgp5AuMgFGQu3ziYboZi593wqJrzn7Hq1HAnciq5uV31W5+BKbTnEabF9K8GUk+gKcmGJLHIkMH7iS4EoVQbY8t509O945ZALqWzH0KaurDF5ndz/6SPg5TcfDUH03p5KwHldIfY73NP/2efK0xdW3vD65AmHhTZDohfjyEopeaU4hKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyJHohKkOiF6IyRh1/b57JWQghnhmy9EJUhkQvRGVI9EJUhkQvRGVI9EJUhkQvRGX8H+ZiaskfvzH4AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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u3Xa7PZokhEMlc0xV9K1PLrher+PTTz+Ni4uLuLy8PHgtdcliAn64WbBZrzOQVSRZTDwkycb7Z4LHeVWYfTE95xvwOUsElu4LyjqZTOLm5qYTO1tvvYclTwn3A/kVNP/t9/uDATeuAA5pW9UDuLy8jLOzs26mVe5Ioq4w3HuOOyNy0WN/tmDq3kfkcX3mepcqjJLo9Td1vbMEG5OJurQt7gnH3Sgz7lVEHHR20nNl14Vywxvb7W7b7CeTSdctF6FXrXytURX973//+y+qHK8VeMAwiuvi4qI4UARi0Y48mrmHC4yHlC030H70vL/Gx1pZZFnuiOMcAn9X+v4+9ymreCByTsppnkJzD7X/Q82LYQ8F5/3oo4/ivffe64672WwGX1MLVEX/85///Isqx2sBHtLLy8sYj8fxrW99K77//e/HkydPuoEdmkGvWVyNT9nSZzFzlgSMOG56U5c5GyOfiZ4/Z1ZfUbHxZ5wDFZS65ty8xmQxeu3+8TVyGfT+RNyGY7Dqb7/99tH1mFuqov/Vr371RZXjteSb3/xml52/ubmJ8/Pzo2Yljm1BloHPknkqttKQWj6+7sPt/dhfxaTC1rIzbK35vFlczBZby5EJmj9rnM7HyYSv9wa/8SjHhw8fxnK5jNlsFk+fPo0///nPcXNzE+v1+qB8reOYvsLZ2Vn87W9/696KutvtuhlsM5czS6xlsXNEpB4CW7Mstsd+vF3mUoPMuvNnRq0qflfrzWA7PjcqydrxVfD8u1575v3gO375x3K5jNVqFcvlMr7xjW/ED37wA8fwBSx6gi14RMRisYiPP/44xuNxLBaLg9cj4wHvi02zNuZMEJoPUHddj8lNXTj2EGtW8k74PKVYvXY8XbJ9sybF7Ny1/Ai+Q+W7WCxivV7Her2O+Xweq9Uq3njjjfjud78bDx8+rN6LVnGTHYGHa7PZxGg0is1mE59++mns9/tYLpfdwu+U49i9lMWuudG6ZK+eUivMi2a2lZIYcSxNquk5VXilLHotGVfLOZTuCcrHn1E+zF0AkZ+cnMTJyUmsVqtYr9fxta99zYKvUFV1a28EYZDQOz09jYg4EP1yuezGzvOEF5yJryXG8OByDz70IsOgnEx0XLmgwsk6otR6o2XCzyjF1tnxNXTQfbQiK7n6mbXPyj4ajTorv1gsOtf+4cOHcXJyEvP5PM7Pz2O9XqfX1jptmfJ7stvt4vLyMiaTydFkD3gzTTZ8tubCaxdeJKNYvOri84KKptTrjI8BK67CyoSm5ayJFvRl4LlS4/U+SvkSFj065ED8EP56vY7ZbHaUP2kRv5/+JUDb+tXV1VEWfrvddsNoh4ieH34eeosOKVwJaOcerigwhzvEy0JQUZWEj+11PRO7dnHNrLSWg0MVDUdK+YFa0k3DCB3tWHpTELwwfsmHseirQNyI8SMOs/LcjRTUklkQAkbooSbe7/cHD6bG0HjguVLgsEJFy2XRbfThzwSvIs2EnzULRhxXblrJ1dx2Lq+GD1x5cYXCf0vDmvk6jUVfhS0MhD+ZTA7emNI3JRbAvvhN37WWJbkgHli3iMMurZmAM3HXEme8nwp+iJVGObS5kcWuFr9P9NrCwNfC65oPKXkk5hCLvgeIHmJDrzo8wBF3cXQpxsU26gJz+7Z2goFQ4U3U2th1Py57LanHZckqAA0vaqLn45WSlENzA7zAs+H7ZHG/Ghb9PeC4EoIsiUCFAJc8G+yi7ivDMb6eR8lcWq5oagm8iOOOOCquISJTjwFCL1liCFvzAtwEiv1am9/h88JjDo1pDFv6e6CWSl9dlVktUGqOyyygWtQsDi5lwEuJvKzDkB6DE4TsiXBokp0zOzZ7J+yWa8tEKZ7XpdVmt88Di74HFqS+e44zxZpgy47D+2RuL1cgeu6+OFbFqVn1ElkbfhZT4/chMT2vj0ajg8FFL5vIK63XmvpMjkVfgcXGU1ujC6i200f0N9nxnPilV2Bpk50KP2s6w7mZUlKRf9djlMYKoFLR82ZNdlwBcRIOFV+f6LNy8NBiHAefdaJNVwZ1LPoK6DzDo7nQ+wv974d0zlF3XjvnlLLbmj3nSoHpEzeXDetZIg1AYDxiDiLOwge1zCg7xI6x9ty5KCtfX9m5c042V77OZqTHdqb/Fou+AkS5WCy6wR2LxSJWq9WBpQcl0Uf0d8Nl155nhdVFX2fN4q0NrVWyrD5EzW45yqOxOF+vnoP3z5rw+ihVClqx8KQk/AJLLBgw1mo+wN1wX4LxeBzL5TJOTk466w7h84AbbmIqWV2NdTWm1w4mvG0W15cEpO3+JZe/73PWrFYi8yJ4/1L7vMIhDa+XysbvxLu8vOwq4dVq1VWqOL+5w0NrCTxg6H23XC7j0aNH8fjx407sEP98Pj94dVXNyuN3PZcumXuvIulz9SPqlq0vhs+2544/maW/79DaWtk0rNF99JrhjWEMw83NTTx8+NAj7Cp4aC2hD/RsNouvfvWr8eTJk1gsFjGZTA6G1eKhrIk9Ip/qqhbzayIvK+cQy5ntnwk+i391u9J5sma1LJuvf0sVjC4qfHynswnv97dDoa+urmK9Xsfp6Wk8evSoem9apS1T3oM+iFdXV/H1r389vv3tb3eJKZ48o7a/ViCcaVbryJYNn/VhHyISPkZEbvFZdNpFt89bySoQtfZ9+5daG9S1z/o16L2ZzWax2Wzi6uoqLi4uYrVadVOWv/POOwdNjS3ys5/9LP1+VMuajkajpts93njjjfjxj38c3/nOd+Lm5iYuLi4i4rivPVs5/M4PGhJitZl0S66tNomVWgay8+q+2Zx5mVi5+YuvSeHjYnt9k00pZ9Dn8ZSErp9RAU8mk25izPfffz9++9vfxp/+9KfY7XaxWq26l2u0xHvvvZfWdlVL/5Of/OTzKc1rCkTGU2C/9dZb8eTJk9hsNnFxcXH0+uW+CS5ZSNkU2Pi9FsurEEuVDIuCm+K07bxGyVWvjTPAtWGdr0u9DRZ7n/DxGdfIn3kd+z9//jym02n89a9/jd/97ndxeXkZERFPnz6tXnNrVC3922+/3aSlR7YXrmLtZRd4yLM4lEXP7cjZ23EiIo3n+0SF7TLRRxwKs2TpuSLhJXPTVWhZ2XR/Fbi2cmjmX8+l64DvDUYj7na3L7v4wx/+EC9evHi5B+DvhP1+f39L/73vfe/zKc2/Id5999344IMPutdawQuAuLkjiGbW8aDiwZxOp7Hdbrsx+drLrGTlUWlg4S6/oLYvVw4lt13n5htC5rpnXkkmdK1osuPpdxru8Ess+bVWZ2dnMZvNuu15shLjRF4vy+UyHj582GWG4drrK6y1040m1LQy4F5loOba82/ZMFzehwe4qCVl0egxuDMNtlHU+nKvu1KFkfWZVw8BFZsm+rLEn1p4eF141+D5+Xk3xRmuawjqadyHbN9XOd7niZvsEvDQY1LM1WoVEXej0DJrDrHo1FCoEPSfz+6/fqehAc+LNxrd9XQrJfRwDWxlIVD2KvBblqQDKvysMuN1PSf20ZmCIXANczLvQGN/7X6LsAvreP1Y6RpqvIo30OepvC5URf861lKfJ1lMib73m83mQOxZvIp9dIEg0Jedu9lmSUCOmyEefQljrcLAvlhQUfAgFU3yaaXBHomeF+i+LDSN/SOOk4FcRn3dl+YHeB98x5Ye1p5zLSanKvrWuy/yYBi1jvzgAXa9SxWmiimL51kwfF72vFj0ffE8H4PzARyacEISn4e4xXwvuAy4ZxqawGvhc2bCV9c/yxdA3Cx47GvRl3FMfw84luT3zEfEUV/6iENXOMuC65INosE6z5OH/XU9s/QqZIhc42MsLMbaOfV+cBijOQUWMu8Lz0ldek3ycazPx+Pj9rU6mDss+oHow8uuZMRxH3YVjCaqWNAq+FKczJ5XNtRVXXEWMcqPc2tyT93pLKueiR6tErC86ASDSgPfa3Mk5xMyL4XL1rfoPqaORT8ATjhxDJmJnkWkDzm7p0xm7SOOJ6Rg8akQNQGIMmXnzf6qddWKogRvD5Gz643vUVFm90M9C55mPAuVSpVQ6TtziEXfAwuC3Xp00EGCDQ8sP8A8Rh7HypJvTOk7tuxZwo3Fz+cqCZ7LFHHXrJUJPhOZHl9dbYhe+ydwpbbb3Y5lKFUuLHwuF47BnsXQsfrGoq9SerDRTg/Rc5zMsTG312fWWROEpW0QH/NkFtgmSwCi7HoN+FzajsXO111Dk5oqfs1/qPD5/nAYgG35uFxOHGez2XQVB7bX+20OsegHUBI+4noWPXcJzdrzIw4H02QtA5oPwD4q2Cxbrk1lmXCz2J+vsxSG1O5JJvzsXul94YlCsR320WNy2LLdbg9mEUJ5StNxmTss+oHwQ87JPDycnPUuiZ7jdh4lxrC7Ckpt6TzpRsm9zbLvWSiAv9pEVroXEYe961Sg2X3CZBdoCp1Op12YNJvNDkSv7fZaRlQimDWHf4MX5ma7HIu+B83A84POrj56nUHwGtOz0LnnHqMuPdb5L7bjY6noawmuLGeg15uJPssF6P3YbrdpjM+tHTx/nYpdRyByHM9lwPEgeJ7fYL/fH7xw1Bxj0b8EKnqsc6cWFmO2Doun7crcW0/Fr1Y9G+CTNeFlsT9XArX4P7t2vgc1N19dfAh8s9nEfD4/qAR4H61QtNVit9sdTJ7x/Pnzg/wJhkGbHIu+h8xisAVCHI/4nAVfEig3bWFuN4Bj6oy4vI5YmI+ZCT8TejZmAOfl8/N3inoDLHRt7VCPiON7jd95Ag4tA/8fdrtdNyHm+fl5LBaLrhIdjUbdSDuTY9G/Aip6jiFVhJq4Go9vX3ahMTQvLGxu988qklL+IBN7X4cYUIuJM/eeKwC+L+zmawWglUUJDl9wnMvLyzg/P++mJMe05NvtNp49e9ZZe06UGot+EJmAQKlpC1YrS+BBzOoW4zOsP1v8rEtsJvySyLXCyGL7Pguv20DQEZGKH+FLKcHH145jZq0cfL3YjofRYqbi9Xod6/U6NptNfPzxx91x4AFY9LdY9APpE74mnbL9WfQQtlr6zPIz3G++VD4VOI8L6HPvdT2DywYxZb0HMw9AM/JaWWZ5h6w5Ehn61WoVy+UylstlPH78OL7yla/E5eVl/PGPf+yOqSMUW8eir1Bqasvc44i8qy3HpHwMbYvOLL6KfjS6G0vPzYRcEahlzJKImaVnS9iX+c4SfljnzkOoDLIKja17dq9LiUfsg8oDU5Ivl8sYj8dxfX0dz549GzxxRotY9AMoiZ+/0x5jnJTi40RE5/aytVd3N4uRs7Lg3FyGviHRmahr1j3zBvg3Lhs8IO1XwBVAdqyhCUfsC2uPOH69Xsd8Po/9fh+ffPJJvPnmm/HRRx9FRMTJyUlX0RqLvpfMcmpsju+1I0nm8rN1VrFrBYCHeoi7zX91neGOPkO2L21XCkVKyTitAPAdl0sFr9toGZDn4G64ERFvvfVW/PSnP40f/ehH3e+O6e+w6AfAD2BJ8GpJWMD8sME955cr9jV9ZSFAth+aAjlRyE2EtRdR8rq6/Uom9CGeQva5lGAshRhcPu34hGt7880344c//GH3hhtURH1hSytY9APIsuSckMNUWjzSTuHvRqNR1yEla+/nJi+dNls9Ae3ays18+A69BEuj0fpcbkVFnyUcs6x7yV3P4CQhn5PR/wO+e/To0cErrTjpaiz6waiV53fKc8867ZzD8AOsv+12d8NM0aSFNmf8BqHPZrNOyHjNFoueB7Dw67A55u4TFLapJSyzhJzuV+s7kHkAmReSnQ8VLJ8H8xmOx+Our785xnelh1ISD2KHwCB8nWN9v78b+aXiwgOMpB7WuS0bFhr91LHwhJ1aFq6UMBJNh7SCLBOvlpk/a0WRtTBkCc9aL0VORvZVMDgXro33RSU4Ho8PmulajeX9fvrPADyoLHSIb7fbxXw+j+vr69SCYqy4NsUhqceDVVjs/BcdUvAedohfxc6CZ49ERZyJSUWvSbVM9Hx/eB8daZg1eZZGI2rCUDvyTKfTmM/n3TrKrf8fLqtj+lss+gFk1h4PFVxtWPSI/L3qEXfCV8vK2XQIgcUPNx2j066vrzsLDmHDhc/CD53RF6joawLOEn9ZHM+C5zKwsLNxA/w7n0f78eMewbMajUZdf35uFeA4vpYYbBGLfiCZe8/C521qsSoeThUZr/M00TybjFptiDwTEn/ORK9uc5Z8rGXUS4Iv5T2yyoC3xW+lSolFj8oOXtJsNovlctnbimBusejvgVp6juUjDsfMZ5aS49dscogs5ldXO3OX+fgRkTYnZlZ0iKXHdZWspZZPPYys8rlvxaRNn6PRKBaLRUTcuvaLxaJLeJaSkuYOi/4e1Kw9fs+EyVZ7NBodjb3PcgB92XQWIAteK5iIwxl2ADcT6rn5OBFl0Wexf5ZbqAlet+kLQXA9sPIYl5+9X9DkWPT3hB9KPNhs6dH5JhMfVwDIzrPlB1kf9fugFQCXGbmDUlOYHifbP/ubCTwTti6luL/klSCsgZu/WCy699j1DXoyt1j0LwmPXMM6rE8mFMTzo9Ho4L14PM0zEoEQZmnEHWf++XMGVziMZuxrngV/5gqFt8lidJ64MvN+SpWDehlaJhwXVl5jfou+jkU/kMx6w9JHHI4n10QW2tKvr6+7cIBnh4Vrmll9db8zsfaFBSg/0JaDvkpDPQc+XpZLwF99s43+1cRe1sSnFSgqFB2Tb7EPx6LvQV1kPJw8x914fDiGHEklbsdH5xp+lzqa+3QWWF7n2B/rmm0vWWwOO7LvS60HfO2I+9XyshB5ABEqLgauuDap4Tt0J+YKE79pwpLPrR5Elu8wx1j0FWpin8/nBz3qIg7dbp0eqm9BrzuevlkrgtrAG/UI9vvDd98x6MJaChciDsXN9yL7LhOgWm1sn7VmaKhUSv6NRqOYzWYHU2NpN2SLvh+LvgcV/Gw2i8Vi0Vk17nfPSTLtTqvCVgvPr1rWvyx+dml1yunM/Vcy6z5U9PhbSuJl8ToLnPfXCiDrWDQeH3cjRhPdarWKBw8edDPncO9EC7+ORd8DHuCS4LmzDR40trw8Ii5bdJbYmhfAx9H1ksVXaqKPuHPV+fprwmexa6xeSsrV8iMsfE4IcvPofD7vRH9yctLNkYfuyZr8M4dY9BXUymNCCzyI8/m8E11GaTx8qSKAC68eQMna619tsrqP8HldM/Z9f7PsfCnOzrwEde1LHXzwd7FYHEyE+eDBgwNrzyGFOcair6Ci58SSWnlFhacVAKwy/tYqg6Fi72viy8pX+n2o8LNEZy2xVtq31IzHTYBq7TEhJpb5fN6J3pSx6HvAw6aZZ4ysY8vKD3epl53G/BHHr4fOBJ0JHK68xvP3EXz2vVrJvgqgtAANF3g9E34pVOBKAOEWknpI7HFYYXIs+gr8APIkFNPp9KDvfE30vJ6Jnz9rT7zsb6nTDrcgvIzg++5D6XMpwZdtkx2jVHlEHHYfVk+Crb5OImLB17Hoe4DId7u7V0hnFnUIfRWB/i15Cfy5doyhZSkxRDwlcfftW6pI+iqRrALIFgu/zKjnn998F6dMSH3x8KucZ8j399n2i+KzEFnpGEM9hexv46Q3waI35u+XVPROcxrTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY1h0RvTGBa9MY0x7fl99IWUwhjzhWFLb0xjWPTGNIZFb0xjWPTGNIZFb0xjWPTGNMb/B4y8u3/DfHhuAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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aP9DYvow900puaVRgcIBtCC5ytKZRMx9/9muJqiOeBltbzJsfi7EFT5+3eXUJ84aFFHpWnsZut7PpdNr9J23bmpnZ7e1t979guqJH9L/+9a8/rXx8rvAHbb1ed4M5NpvN0YIdQvyBx2BSJnoWfubeZwG9qErAx+LxkXWPCin/nS05/o4eBObH699+/R7b4FgBBt38nhwOzye0zIJ4uESDkdDiY57fe++97vNmsxn4z9dBUfQ/+tGPPq18fC7wh361Wtl4PLavf/3r9uabb9pXvvIV22w2dn9/f2KdkGw6KxeEP5woeBRwFhPgQFkkDv6O1rJk3TGPfD4+V+QFYEHkoo7EHhUWaPWxYOHqCHdX5u2eznK5tIuLC9vtdvarX/0q+ouF9Yj+pz/96aeVj88lX/va1zrruNvtuskXPdYRPZxRDzt8yLntvGTd8Hhfs1fAAsff+Hvm3roLnFn07JjMbecCg6sFpWbI7D7wyEUcmTgaPR/3cHV1ZbPZzMbjsX344Yf2zjvv2G63s+Vy2XliQnX6Ire3t/bee+/ZbDbrXFicgz6auKIUuc8Ccn4c1199e2bZoxF80X5s3UvNWZl1zwqhqHluiIfA1RXcj8/HFp47SM1mM5vNZrZYLGw+n9trr71m3/nOd1SHT5DoAW4yms/n9pe//MUmk0n3WmR/wKbTaffw9bUJY6AOvzvsrkbWngXshVA0Ms8LA2xBwGO5XhzV3yMi4Wdij9KJqgSlOAVWkzDvft+n02kn9svLS5vNZjafz+3111+3N954w77whS+k11IzarID/MHyTh+bzcaePHliZtZZkfl8bsvl8ug1yRmRuxyJHS1ZFBOIXFwPmDF4XJRGVAWJvAmuMnDBgvtlFh6P5XsRWf0h8Qq/rouLi07wV1dXdnV1ZfP53C4vL+2LX/yiBF+gqOra3giCeEDPm37W63X3UG2326M3o6L7zOKO6pEobP+MXXcji491d46Oc7pOJNpS7CEiKyTwGqPr7gtKRm4+p4HXxs2AaOm9IHbxN01jd3d3tlwui9dWK3WZ8jPxTh7YJOVu+mazsel0etLH3tcly+7CxT782NzFojezTuQ4wMSby5hIcOxFDBE+bscCCX8rufNZ+pngM6+B6/4euGuaxqbT6ZH4F4uFNU1z0jpQI3o//QvgzWs4nbLZw0O43W5P6vRRf3OHo884BfZutzuaeovF6RF2T4d7ufn2SJgcM+D27qjHGh7LQTT8na8xygsLv2Tpcckm58D7h6Mbvcrli98vnqa8diT6Am4tvI7PYnHhZw90FJ3HQNRut7OmaexweHizKj6gfixac08jC7iVRM+CRy+FBcFBv6HeQXRsydpHTX0c4MPmTvR0sOkOvSa8Ls6TkOh7wQfPe9J5IWBmJ3V6rpuy6LE+bvYgDCw83I33tQtjNBp1VYAs2s2fs3o8ViP4WD4uCzKWqgV98QO/N9hDL4ro+4IFLd43rCpF1SJxikTfA1of70LrVhrF6EQWC+vljrvo2G3XH258sN2d9f7tJSuPafM6EnFmhSPXnoOPfN2lY/tcfLxOFDkegwUhX5M4D4l+AJkFRyHiflG93q00FgButbk64MLHBz4STESfWxt5A9nnUmERpd13TKlej8Lnez+0WiGGoTGHnwF6cMVniSz9ANBi+XeuS/p2X7sFQ3hWnei9d0OCYCWLF3kF/D36LfMcuNlsSPXC08UqS8nSZ8G7rClPfDwk+h7QTY3mwfNmN7OHbq9DAnkcfY7qzbjN9/O0SmD0P8oPB8YiQeLvUZ2bOwJF96zkmpcEH4mfByipEHhxJPoesJkLB3l4pxDuqjy0ya5pmiPR43vwOEqO27jzDp+b4wBs2TEe4XmJ2rDZa8H9OF1miLeSNdlxIJSb7DDYiWlEx4sYib6AizDq+eWDbrhNuE/07DVgAcBvrc2sfZ97n8FdWdFqM1FVpWS1o+N5XyyQIivP2z2/Uecc9gC8SRVfkMH5UyzlORJ9AR7Y4d08/bP3+uJ6ceTem5W74fL48KgbLhcInqZTqpdjHRu3DbXaH6cbbpTPkuCzBQuB0Wh0NO0Yz2y02Ww6L0zdcI+R6AuMx2NbLBa2XC47oV9eXtpisfjEBtxgj7Os+Ys71jCZiN2l57p2Cd8H+71Hgo/EXxJ8SfjRdbCX4uu2ba1tW7u/v+/+j8Vi0RWgZsf9I4SG1h7hD6f3uJvP53Z1dWWvvvrqiaU/d2itr9nVjAJ3XF9ny5/VldlVjwQ4RPCZcKP9z42uD7HyDOcdWwXwdWJmzwu2q6srjbAroKG1AFukpmns8ePH9tprr3WWHSfRyPp5Y1qRVSu5/ZGguQAws/S8WOfNKLUCDHH1cb9MtJwObo+Cd1m3Ys6rC533PxyeD4Vu29aWy6VdX1/bo0eP0mupmbpMeQ/80K1WK/vSl75k3/jGNzohvch0WWbl+ev52ChYlxUSbAX9XNFxpfo37pO52Fj/z9z0TPzc3NYn+Ox+YEuKj4VYr9e2Xq9tsVhY27Z2c3Njv/3tb8Nu0jXxwx/+MNw+6inZq273eP311+373/++fetb37Ldbmd3d3dH77BjsUcLiiJ70YWTudJ9gcHoGNy/T+ylakjmMbDYs3ft+b4YQCx1vuFrY88Gm1DR02qaxi4vL20+n9v7779vP//5z+2dd96x/X5vi8Xi6E06tfD++++HpV3R0v/gBz/4ZHLzOcVFilNgv/HGG/blL3+5dwrsUp0cm5RQ9Cz8zFuIrCnnmylFwf13hL2DksXmfSIRs8jx+Kyg8P2jgjRqyuSA4nK5tMlkYu+++6699dZbdn9/b2ZmH3300cn9qZmipX/77bertPQ+d7u/3cZfdrHdbq1t2/RlF9zk5vgDzi+7QLE4+MCza8ziYlgEWYcX3Ac/R+569jsLHr0ZPHeUZsk74Otha88F6uFwsM1mcxTL+NOf/mS/+c1v7Pb29rw//q+Mw+FwvqV/8803P5nc/A/i97//vf3xj3/sXmu1Wq26+e/xzSrc7OYPpz/MKHj/nV9rZRZbeS4YzPrbnktCHlLvZq8iKjRQ6FwA8My/vD3b19elOAfGLrCd3tdPnz7tmlIPh4cJSkoGriYUyOthPp/bo0ePbLVa2Xq9PnLTUajc0SYKsvGD7O3fkYVD8WXNZed0Ohli9bPqQOTSs6XGHnJ8Pj5n5LVgwRbdj8il9/9hvV5b27adJ3Z7e2ubzaZLZ2hdHgubc4mO/TjpfZKoyS7AXXOfFHOxWJiZHb2nLWr2iprezI7F4uPnMRiIDzm7rvzdF2yvZtjN73Pf+bdIhJmrzoIvWfbIMyi5+dF9QSvvVay2bbsXi7Zta6vV6uj9decUjh/HG4iO/Tx6F0XRfx5LqU8SFpk3DU2nU2vb9mhEHVtBbs5iokh7qc97lB8/H47qi9Lgh7zvf4xE3fdb6WFGLwW/R9WFqKDICia/Fv/d31Trlt5Fj1ZenFIUfe3dF6M+8WYP4sMps8xOm814Rh22XC78qA5r9uDaj8en02XhZ8c/41BYTps9Cz4npuO/RYUPbsdrwYBa1tc/OiemHcUEot990lKcu9ALAok+R3X6AlEHGHRlPYjkvyG+L7fVR81ufB4/BgsNnCU32tc/+/6j0eho9lhvp8ZqCVtk9lY4Dcy3Fyj+O+aLt2GffwfTxU40WJhEHgZXE9DNx//mHJe+NiT6gaBo0bK46LmJiyP4UTArqwbwmhcUYZRPz0+UNhdEZsfv8OPf8bcobf/dxeZeEQbq3OK7t+KLzwDs+WJwTsHI2mPcIWpiFDESfQ9RsCoTvT94HOxzuM5qFveki4J7kTvv+fN9+DsKKupAw3EJ3p6NS+d9PV2fzjoK6qEFxgLCC1FsxsQBNZgHLIQ4WMr3T+RI9ANglxLrkh408kkxWPRZJN1By116WKOWARa84+KIxB61iUeLv3EnyjcfGzXD4Tbvj4CeEk4jjp4BvuKLvQasBmBBUSoAxCkSfYHIIrKld9FjDzvsoGN2/NoojLij4CMr7qCrj1YQ4XN4/rl9vq9tPrP6fF94HxY+d9jBcQfoEWCBgJ+5qzKmx3EGjK9krSLiAYl+IGzJ0NpjpNwtF1ohduFL7nppPDwXEFzv5/yi1cb0onVpie4FHhe12fPigkYRR14A/8bVArz3fs3+PRqdKE6R6AeS1U/9wTSzkx55PPiGXXSc3SWqwyP+PRpfHhUskaD5PNl+kTdQuiclF59bOyILX/IM8B67R+UdcvAVYVwg9M0pUDMS/QC4Phs9zGYPfcGjee5QlNj276JBdzWqn5ai+UOE33d9mdUfctwQ0UfWnuv+/ht6KFEA0DvkrNdru7u7O4m5tG0ra19Aoj+DyK3FtvrRaNS1h7MI0epz8MqDf2bH76EvNd9hAMvTj4TP9HWKGVpQoBWN4gYsxMh9x305D5EHgJZ+vV7barU6maPQRa96fY5EP4BIKJE7bPbQiw6Pjea38zn0o37nmfBZ7F7IRB5FVGBEa78WvCbe3nf9bO15JF22RF4FrrPCxAfW3N/fH01D7kLfbDZ2c3Mz8N+tD4n+DKJgGVqibF8WozeF7ff7bkJHFoC7/9gKgF4DCh6XkvD7Al19Lj03P/o1s4AjsUYFwdBAIaft1nw+n3cvHPFxEpPJxNq2tevra1utVkf3v897qQWJfiB9dWcevsmBOcfden9II4uH7r6nw+4qCj16NRbuM7Tuz5/5+nkfFjkHAXk7r6PzRQUre1IerPOXjvichV4ItG1rT548sadPn5qZHY2tFxJ9L1lzG1KyTJwWBvsi68ifcdBTNiovcv25EMi8gOgaMG0+V58VZledt/fdn5IX4muPo/jMxD5L8aNHj+yVV16xzWZj7777bncsDrMVEn2RkoseWUt0P/nBR1cbe53h/ujyR8JAce/3+7C5j/eLxM/XhtdQuge8L4s6KhR436gq5GDeSp6I3zN/zVjTNLZYLGw0et5TzyfREDES/QAisUf148zqsXD9ocX6vJmd1HV54Tx40NDFhoVLln//nl1nRNTbD+v32Tmj4Byuo3NH+WTR+/31dww2TWPL5dJms5mZmV1fX9s3v/lN+8UvfmFmZldXV93wZCHRn0UkeBY/12mjB80tMD7AUTDMj8d3kr1IvRQF6J8zS8rH4TUhJXc9q6dH5+bfs4KJvQa/f+7amz30f/j2t79tjx8/tu9973vdPqrTPyDRDySrN3N0HS0KWnD/7g9fFuRit79pmjQY5gv2Z8dear7mc3teHbakLLxIgH0eDd63vvuK97e0P3tMGMj0qs54PLavfvWr9t3vftdeffXVo/vfl5dakOh7iAJ5LHjsXZc9WByt5okpcPE6PY5Dx+6oXhB47zQXPDZbeSehyWTSjWqL6vRDrHMm+sg7cbL6OX/Ge4ZVGDyXrzF9j42MRqNuSjP//Morr3SCx7yI50j0ZxBFx9EDQOFHfb9RWFmkOxqN5uJumuboN1xc6NvtNnz9tQ9FLYkKGWJ1Wfh4LKaRWXHOT6mA4YLFCzEX/mQysel02nXW2Ww26auaa0eiHwC79f5wcq86M+ui7z7oAwNsuI9bH+7g4se4q+/VBbTqbrld7Cj6aIk675jlPfH8mjOxRrEHJIt5RAHF0nnwXFi4eBDTqzCellt8F30UCKwJvZ/+YxJZdVz4wcoeNBQ8FgL4gGJb/mg06tqlXeCR2NG15/yx6DmfQ0TP3kHWuuDHRs2FpUKAz8NuPYveWz7cG8Lz4n3C/KpO/xyJ/gw4eIQP2DlkkW73GFzoXhjgLDIlgbsYeN9M9FHkHa91qLXH4CCKOxsfUCoEonuFXXfNnnsSHpFnTytKq6+qUhsS/QD44Ufh+0OH+/KxmAaOMOOoudnxZJDYDh8FDiNh8XfuootkQTizUxcdryWqFvhvWZ6yHoJRfITzh02f7tL796Zpjt4rKPqR6HuIRBy5+Gy5vE7pUzj5d6+Postqdl79OhKLC5Mn8MBt7D73ib7PCns6XLCh2LkwKn3OPArur4/jF/zlFliYijIS/RlkrmvTNJ0LjgtO8exW3kWPs8Hig2122rwX5QNd9cgNj1xo35cLmBdx77P8RN4Iu/hYGLHwPe94HzCvZnZUn8c3CJdG7okHJPoXILJovh3XvL8vXhhwnZ0fcl9nMYBIiFwQ4DZ079kqRnVz/8yFRnZtZsezAkX3KBoRGMUcIuE7Xp9vmqaz8jhRh0RfRqI/ExYDPuD+HWfOwWPc+vs+/LIHLAB4EE7khkcxgSifGVlh4sex5xClywVaFlsYUgXJWhjQO8EZhtzCy8qfh0Q/kKg+zaI3e2iSwwcde8b5d9/m4sfoNFYHvDCIrDJ7AdnoNbN8imxMk68X11znZtH7715oueDxu69xqmrfD+exN7OjexrlJaoWiWFI9ANgsWM7sIucJ3rAnnRukXyufP4Np3rGl0CUpomO+u5jPlgI2QizzNL7dUcdivB3vGYc+4/pc6Hk+2HnIw/OmdnRtsijKLVilDwb8RyJvgd80FzsPqTTrTBH4VGgKGAsAKICwffHbVwA4Ph7FH82tz2T1ePNToXNLj6LKmuGK7XL4zZPM+pPwMdwd1sfR+8L9kuQ8MtI9AXwgfPOMP7AueBxyiuHm5lQtFwIZAUArtk7YIuP4o/c3agAiJoK8brx+nkbCpbFnbXL82IWj2WIYgKc9mw2s8ViYZeXl7ZcLo/myvPjRY5E34M/aBcXFzabzY7q2d4PPhpYwxY/GjZbKgRetvAxb32fs7p0VL/PIvElK49pRaLOFrT08/ncLi8v7fLy0haLRWf5Ze37kegLsFuPLr0/fFlPsKjzC7vjvs6qAlGVICo4uI6fdbjBvEWf+dpxbXYazIsEjSIuiT2y/n2C94LWJ85A4c/n826CTIm+jETfgz9oLiR/sL37Z2ZVsw4wURNctPSJnNulI9GXxF2K9DtRM10mWhY5utgswj7hYwFgduoNeBB1Pp8fLT7CTu59GYm+AD9oZg/uvvfC6xNPqfdbJNbIVR/iyrN1H2LN+5q6MvfeiZrx+qw7px8d62lHBYkXuu5peVDV6/Sy9P1I9D1gOzy6++xSm+XzuuHnaB1F/jPvwEXN+0RpMS/Snh1ZaPycue19xzPoVfQVJFHTKQZbJfgyo54HoepeD5nlzSxqKZ3StnMKhL79+877omRCKom6T3yl/SPvALdxNSCLA1ROeAMk+h4iQQ1xnT/Ouc757WWe/2XxssRWSqevhUGCNzOJXojqCEWvMKcQlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZEr0QlSHRC1EZFz2/jz6VXAghPjVk6YWoDIleiMqQ6IWoDIleiMqQ6IWoDIleiMr4b+qyOj+UpAwIAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current beta: 8\n", + "Current iteration: 13\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 15\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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MxiALymUXolpKoKKHkDg9lVl6PX4Vfcnt1v48i5cbHf1etp4KWhsLfY9RMQ8G+7fy4q4Sx0K4Ao89BpPTKPoP+8Ppxf63cMMObUvF25YsUswC5X6+NhJ8UQIViBadZBc2HiFejXgfEj03Dln+uxS4K7npbcScfYcbNZwjN6J6zurOl+5ew1Zeu04WfZlG0WPkWJfZbDZ1qqlkYeGacs6Y+54ld3u5XKbWnrerQbKmEWrs3rPVZfFlQTlt4LL3udtQCubxZxkQOLpD8JD4M+yP19HPgXZ5MgPADbJ5iVV9gPV6vXfziux209vtNs2783Zwkeq6JYsYsdt3LuXaI/JAnvalXzXarYVFmWue9bl5NhxuNLANfT/brk47xuLH+enNQ7kst+m4u0qj6H/7299+XMfxiZONZkMKrt/vx3K5jNvb25jP53F7e1s/R7WZuqt8sarF1vRZk6XPBJw1EmoFM+ub7YPPXa1pJkJ2qTkYyVac96UNTRa8axI8/5aaBsX2NHPw/Pnz+NOf/mSRF2gU/Q9/+MOP6zg+FQwGg9hsNnF9fR2DwSBee+21+M53vhNf/OIXY7lcxvX1dTqghPvYvC22gNqXb+ouAA5kaT9ZXWAIj9dVK8/fz9zp9Xq9d9wQGbaF3wiv9fg0+Ja5220Ez1N2l252qX193NfuD3/4Q7zzzjudmwizLY2i/8lPfvJxHcenkrfeeisGg0FcXl7GdruN29vb2uqU+tka5dcAnwbYMiuvgS3tWwN1q7l/rP1vbBd9XO5T4z08xzGpwDH/X+ZFNHUf+PyyOAFe82+GY8xEz1OJoS6iqqq4uLiIk5OT+PznPx/b7TbeeuutWK/XdeVkydPpGu7TN/D06dP49a9/Hc+ePYvxeFyPpBuPxzsWnNNonOZUEeL7JYFkfeOmoFhE7FhEtfa6fpZxUNe/KXinXQW11Nn5KKUAIq+jLr1ady6Oms1mMZvN4uzsLC4uLuLs7Cy+9rWvxTe/+c24vr6u/wf36e/pNf0QvV6vU7+SCm06ncbjx4/j4uIiTk9P4+zsLE5PT+tBNWwZ9XZWgIWv+1JvQF3r7NgyS9g0pp7PR4WcfSd7reuXIvzZcZZ+2yZLz7MN6flB9BD8+fl5PHz4MB49ehTn5+dxfn4ejx49is997nOO2kekP0Cj6EejUedEz78HprYaj8dxenoas9ksTk9P69lrcVFqTrtk8fii5otYRasDcNSF13W55FcbjibL3/Q7lNbHNkquvDZmTZN7lGIZ2qfX+AJED+v++PHjePz4cZyfn8fp6Wk8fPgwxuPxwfPsAKnoG917zzV2D1JDt7e39VTVsPYR9znjTBAaGONx9DxvngoX6+KRP0fFGh4hfOxDhVvyIHgf2uhwI8PfA9k5ZpaaP2PLXsrr6/FyNwQu/3Q6rRtheGBokLF9W/oc9+kPgIZPB4rM5/OoqmrnYtYAF6PuKk+qORwOY7Va7c20g/VUgNn2sT7QMuDM8mauuE7qyVHy0hBgbTSybWhQM+tqlLwQpOXw/eFwuHdDTzTC+A1tsHx/+lcGlXSDwaC+UeJ6vY6qquLu7m7nFlZNOXHtm2JgDufeN5v7iS6zyDyEVLKwWK/0uVpebQBYqOqN6Pq8/ewYOQhXcvGbov/8G3Jju92+LOrR23pn8xLa0udY9C1gISP3C+vDd09pE5WGGEoXPIuGF65kw/a0j8/7RgMCsmpBPiYWbDZisCSmrCuQ3TFX18uCgmzxOTvARU1IM/I8hKXbfZkci74FfEH2er094XNevSl9BWuJz1XYaBB4Hb6QkVvH+ywWiJz7vrxfFnd2cw4WLHc9eEIQrogreSJs4TVImf2uTalA9oI4XhERe9u22Ntj0bckS1vBAmdCz1JgekGy8LRQBwKGyFEoo/ED9jYOReZV/Cr6rAAms/jaUOF4s+7BIUvP56LvoUHlYbtZas8ch0V/BFz8ggtTLZg2AGp1eZ3Mvc/c+FK/N9svKumYpqBemyUTMG+35N63Eb3W7WdBSvyGTZkE0559n8t8pPjiNJ82bOmPQC1mloo6JPKS1c0i6iD7nsJutlLyLjTDwEFDXtDFADhPPl90RzI0zsDvZSW+7N7rGAX1hMzxWPQtKYmTXc5ShRmvr64vgmNZ0EsbgVKknBuRJjQWUfp+lpLbbF5O/ol+dhbIwyO+l3UJsuMpBfRY+NwA6LrmOCz6FuhFjTntEdwCpZQdW0NEtzk4xje9LKXstNHIGgDsq6nvzKLhoKCm/fgcIGAWPHiVlB3HH7AvnQ1XLb6m7NQDaIp3mF0s+hbwRc3DOrl0tpRzBhoc0xtY6lz5bBmbgmSl/LRa1lIQEu9jHyxwVPlxcY7uJ0vb8TlqI4b9Ny18jDh2Lc4ZDAbFeQmy7oS5x6I/AF+82b3kua+saTe29NodgLegjyourK/Cz6bazopnWEia8uLaA6yj5bcqZHxfyY6RBZ9lOdpaaE7Xbbf34wx0+jIuZfbYeZfhvjKwwhjZhVrvyWRSW/qI2LM2EfvTZkfsNiKc+85GymFdPPLnOmCH18+ED7JpzfnYVqtVMY6gYs8KkLL8f1OOPhN85k2w9UYBE/4THgTFdQa29DmeDZfQqDAsMg+tPTs727nA4CK3HWWngjg0tFatfWZN2wytPZTjz4J3TUJvE7vQRoO30RSI4/Pg3wLPMYMP/z/4PyJeTptlwZfx0FpCRT+dTuPRo0c7k2jMZrOdSTQiXlrPbBKNkjucCTlifxKNJjea3XltNA4JNYs5ZAUx/FwDe9n72TGWyCx9U9CSG7rRaLQ3dwGGPs/n84iI+OxnP5tmDIxnzmlkMpnEt771rfjKV74Sk8kkBoP76bLAoemy1MJxf18v+OwR31W0D97kgrNV1emwS1Y3++zY6bL0PHS7JdeeGzDuwrDrjumyTk5O6gb54uIiZrNZRERcXl7G9fV1bLfbvQrFrvCDH/zg+Jlzui76L33pS/H9738/vv71r8d2u62tSK93f0cVns6aBaAi5NSUzkev/Wy2UJmosliBusIqTB3AcqgByAa+6OdNQ2Nx7EypUVEvQQOTmq3gjAeyKFVVxYMHD+Lk5CSePHkSP//5z+PNN9+MzWYT0+m0zkh0iT//+c/Hz5zzve99729zNJ9S0D+/ubmJfr8fr732Wnz729+OL3zhCztTYK9Wq1gsFsUpsLlvDrjQRO8r11Rkkom3FDNQ7wCNE97XvLhum6e03mx2bx2tDYY2BGr1S+d0yMPA+WRxj9LoPZwrvLE//vGP8Ytf/KLunj579uzYS+EfmkZL/8Ybb3TK0kOkEC8maOj1enV/ETe6wM0uIHy2vtq/xgXO02VnHkLWr2aBZcLTYwdsjbP7w3P8QRsB/Q5/F0LS0tjM7edz0HPixgfvMyp8vOZpwbAeulfYnm928ZLtdnu8pX/99df/Nkfzd8Td3V28ePEilstlnZ/X6PpwOKytTRZYgwhK94tnwSkswmNua6XReBxbtg/uNqjIWfhZw6ANU+a2Z5b9GIuvmQAOoCKAxzchubu7qyP8/Nt0vREA3crJvQKDwaC+tVWv93LuNUyVhQuLU0rc92RLz6JnF7XkskfsihDrtbmBZcR9BR7W48q7Y1FLnX3eJHCNSzTFFLJ9aFQf8A1H9FbeGl9pe56vmurL1v0w2/tb4pTdAbbbbW3JISCNyuNiRJ8zu+eaznuH9fR2WAy71NplOHQ7LA3oqaXkRkCDgdpAaI0APud6fN43P+pxZd0JjVfoOjgGRb2f0l2DjrHwH8YbKJ3zp41G0X8aW6mPG7biLGSNYnNOXiPpWH+7va8bx8XALrf+3rztLNffJHruU6/X67rQisWwWq12BI3PMDtv9j1uQPBcj1+nEOv3+zuRc3XxD2UCOECq6/P9BriLYco0ir6r+U0m6xfyxYbgGFeQqWXNLkLum2qDwftmsbBIMtFz0JC7Heqec0OmYuFSXJ2qK4vwcwBNPQAeERdx3xioqPnc+PfV73CDpTEHjSmYMu7Tt0CFyPdCXywWsdls0pJlCIwvZLZSLPysz6p5d3QzImJnW1lfkkWv54Kpu1ksmKEX54Lt8/scbdfUHY/K0+fsIWgjxik3vNfkJrP115gAf27KWPQt4QscUWKk7VgcqNZTt7wU9Y7YtfrsXcEyQqAY9gqRsOXjBoO7BZrvRlxCuyf8Gvl5jdirJca+uE+NQBr3tXmorrr6eA/PuXuUlfvyawv+1bDoW8AuMwTPOXu1jqvVameoLNAcdsT+CLwswg6LyN6ERqe1+o8bCSxZyi8LpGXpOf4NeB8segg+e9TaBD5mfo3n2A+Ezx6T/m7mOCz6lnA/frFY7Ige7jFEhaG4GulXi4X+L54rHCNga83b0kYDsNuu1X+ZkLMGIEsnHhK91iKgYpGrF/E+ex7qCXC2g4NznALluIOtfHss+hawOHDRzufzmM/ncXNz0yj60hx2EDz66qVcNL7Lgkc/mD/T9bS/XRKyLuruq+vM4mevgAObLHjEPfT1aDTaaSQ4Co/gKB8/H4+WFmeejilj0bck69PD2sONxgXMN4qAuDXXDXdd3VWO/gMN+GWCz9J6pX57JuSs317qL2vfHmJVC49lPp/XwseYhcVisefy67bYg9CGF41Dr/eyRFrjAKaMRX8EfJHjwp3P57XlwcWe3UyRxYkCHrbAAOLPbljBj3jeNJa+TbArc9t1nex3yNz7zNIvFoviwqJngWObPFZBy4HRgCwWi9hut3XDYFf/MBZ9S/hCzy7wiKjf46msskkgeFJMDchFlOsjWPilpRQIVEo5/KYGggWlXgRb6sy9x8JWP7Py3LBqnACfYZu3t7dxc3MTt7e3xXMw+1j0R6KpLVzcsDY66QOPDcdr9PnH4/FegQ0u2KzcN0vB8SMLPyKfvz77DPvNHkvfbbL2JYs/n89rC82i11SmNiQ6OhCCv7q6isvLy3j+/PlOgE9/U7OLRf8KaJCMXUuknFjseEQ/HqLnIJVa2M1ms9NQROzeJDKbBlutvQa4Sg0Cn9exvwH/FhzzQJ8bXSG2/Jngs/PPApEQ/fX1dVxeXsYHH3ywM3UZWC6Xx/+xHcGib0nJ2nH0Gt9TYWXTPVVVtXdB88U+Go3qffAYco4LlLoREdHYADTluDOXvvQ9PWacy3g8Tl10HQnH9QK6P64J0BqC5XIZNzc3cXl5Gefn5/VEpe+99148e/Ysrq6uas+r6Ry6ikV/JKVCGPQ5S+uwG15V1U6QKouuQ0RVVUVE1I0AH0PWp+dugT5qFuCYc2ZKaT5ekLpsctObCoy0toEXpEyvrq7i4cOH8eDBgzg7O4uTk5N6Vtxnz57teEzmHov+CEo58ayqjS1VxK61V8GXrCZPwMlR/ZL4S7PjZlYf6zNNVl4Df3yOSDNm57HZ7JYoq2ej55c1TPw97kLM5/O4uLiI8/PzOD8/j0ePHsVnPvOZ+N3vfhcvXrzYmeXH3GPRt0Qvykz4bPG1n3p3d7eTn89uycRWMEvnIQ8N4Zci1VlNQHbcmt7LioL0uYqe10V9wXa73WkIMHYA7rkecxZ3KHVB2Au6u7ur70dwcXERX/7yl+Pq6ipms1m8+eabbf/azmHRH4FaVZ2nHhcyp5/4AucCkqxSTqvbmqwiC5kbB4iuZLVLVr7k7mff02PRlCPg+AI3AE2NVCnuoI3NdruN8Xgc0+k0ptNpPHz4MKqqqhugN998M376059GRMTp6akLdwiLvgVqOTlwlgk/S2cBrjPPxK4Ngkb4+ZgUnfyCBY51tdoPtA3gZeeY9bt5v/jdSunANoHGbF28nkwmERF1FP8b3/hGVFUV3/3ud2Oz2dR3vHEa7yUW/RGo8PUGkpnLr4KF6FCCqzEAzXfrdFDaFeBoP8YA8HZhWbmfj3MBmRgOpfaaRN/md9THpq4H75ff57Qon99Xv/rVeP311/duZuko/kss+iPAxcn3l+cFIlBBsThwYav7rxafi1s4r50NU8UNH7DgeND/Z8+EGycWaMnlxnNGA3ZNgs+20RQrUDK3XmMAOF+Ifjab7TUO5h6LviWZlef7yrPoecJIda0BT2SBAFcmei1jnU6nO++Nx+N6fn6+4wsfWzYGgNFMQ8R+QE27L1maMfu9siCdft60Lz4+Fj0XPXFjjKCh03RlLPojUNFDYCinjdi9CYRaeA5ocT9/uVzuNBoqei5jxci+yWRS361VhZ+JX6v7OOCXWekstw+0lkD78VnWQIWt2+dHwMfG++HuEc8khP1st9vGuomu4PvTf0gySw9xVVW149ZnbjMuQgg/YndWGB1sghr94XBYj9yD8CFyFrtaehU+rCCLmBuqLMugggUcnMzSiofGBkTE3ntZ10O7PjhOFDhFRN346n/k/nsZi/4IWPQ8aAYDbkr9YBUMP2fXH9vg2WTYbWXLrQLn97OF+7wsKvZMGM1OsLVX914bC44jcIOTNQRZg8ANI9c/wNoPBoOYTCax3W7rAOYx2YeuY9G3QKPMuJjhWqPGOxscow0BBuRA4BqgwvdwoWO7unBfnYOJpXiD1hWwy1wSr4qSaVqnFOjMziMbNMT7yEQ/HA7rCTSqqorJZLJX0mvKWPRHguARRDUej2shw5pqtBwXM6ry4MJH5Lei5v5+v38/Dz0Em42qw77xXFOKPCUX0PiD9ss1+NeU5tP9cixBA4p6fBpszDwRiD4i6rELg8EgptNpXdPPv6OFX8aib4kGqHBh4wLERc/po5ILi4k2dP63iEjz+pkFKwXEImJvOC9HuQEHyPg1b1u3URKSegXqYTSJnr+TzSGg2Y1er1cHTsfjcdze3u7ctdYcxqJ/BSAwXMy4nXVpbLsKU60391+5XFRz4KWBI+wSa1dEU2SARa4RfE2fZWk13V/W/eAGMHPvVfAcbFQ4iDcej6PX68V0Oo3z8/NYLpdp1aLJseiPRPu7uLAjYs9F1WUwGMRisYjBYFAX1/R6vdqClVx+nWhDuwNNjUEWWMS6mTeh56nnUPpcuxYsdH1eaiDwvSyGADBKcTgc7gg+OweTY9EfAV/g3G9FcAmppSyaXlVVPfUzzyCDqaDh8nNDwGk+dcPZndXH7LiVrPHQbkST4PFaPRq+sy8HDzV7wA2kWnqNh/C+8V0uZLLYj8Oib4mmo4bDYUwmk7reHVYahSFcWDOdTncmhuR54nhW2KzsFgvSWLBqaBg0eJU1ABzYymoIVDSZiDglydkGHItmItAd0cYAjVhE1GPtsX1e8FtH7BcKaa0EZyXMYSz6A2i/Fqm6k5OTug+O+djYCmdVdU0Ll9ziORoFHoCTjdDTtBvc/SYLeEjoh4KGLDKNY7BVzzIA/B67+aPRaMfys9XHY1VVMZvN4vz8PGazWUyn06iqKs1OmByLviW4WEejUZ0XRlAJeXqgaSYeJKOWncXNN4Ngj0BvCZU1ANkQ3EOWXIOD6t6XYhMsXvWASmJtEj0aCY76l9x+jKF/9OhRPHz4MM7OzmIymezNI2jKWPQt4IsVokc/dTqd7k3OwLXpLEjkk9lt5waBLT/fCQYNAM8kq+LXMfgReb+dn5e8Ak7/qfC19qDUP2+Tycgi+VlBDz+vqqqO2j948KCeGBNeggV/GIu+Jf1+vy77jIj6AoTQFO3b6sIuenZjBxZ5JvaS6LNIPws6E/kh114tNb8Ha82R+1JxUpYGLHkJGvXnZTKZxMnJSb2wpbfoD2PRH4AvInYfYfVLueHMxdaGIGsUdMYcFnVJ5DxYR7etx9cmkFeK3pdy96X+e1PKL0v3cUPCXoQ2MDr+IJvMxJTpHUh3OBcSzcLlzyP255DTbej2su1qw5AN19WlFMzL9t/0HpPl+LOCn0N9f92GbvvYJWtkmmoSOkz6Q1j0LVHRlqxnSfSHtsmvS40CP296Xdp+074zSkLV16XH0nqlz7L1S++1bVw6Tvpj2L1vyTEXcoljikgOfffvqSDloxJi039gsbfHlt6Yf1zSltAzBhrTMSx6YzqGRW9Mx7DojekYFr0xHcOiN6ZjWPTGdAyL3piOYdEb0zEsemM6hkVvTMew6I3pGBa9MR3DojemY1j0xnQMi96YjmHRG9MxLHpjOoZFb0zHsOiN6RgWvTEdw6I3pmNY9MZ0DIvemI5h0RvTMSx6YzqGRW9Mx7DojekYFr0xHcOiN6ZjWPTGdAyL3piOYdEb0zEsemM6hkVvTMew6I3pGBa9MR3DojemY1j0xnQMi96YjmHRG9MxLHpjOoZFb0zHsOiN6RgWvTEdw6I3pmNY9MZ0DIvemI5h0RvTMSx6YzqGRW9Mx7DojekYFr0xHWN44PPex3IUxpiPDVt6YzqGRW9Mx7DojekYFr0xHcOiN6ZjWPTGdIz/DxdIPSiXFujNAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current beta: 16\n", + "Current iteration: 25\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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rVWhQeB/tEsTy8bFjeBXdWH89K3DoYn9xuOhzgmAecu2w9AjIwa2PBeS4H45hnip+jr5rv5xTYjxhh3oKHFHn5a95dh+19mmix/s85VfM2vO1pnkH6BLge2t3x4X+cnDR54Qj+K1Wy8zMFouFVSqVxAg4XTOd6/Ahfg2SaY04KtPYfeaoN8+uy6LnASmcL+cFN7gMFsLmAhk0GhA1eycsYvydn+OatJFIs+L6vZ0XS6boudSzSMRSYvo+hL9YLDby5Rqt15ucXVsIBgU+fAxExAs2QsgsWrPkCjgQrs7uo8tq6RLYsSAe58xjVjsWq4g1Zpoi3GbV08ble5ru+ckU/fP+uLGhnLi5b+oft+1caTdP1r4x1Epx2o0Fyzc6ovZmyf45u9+xQSf8mbGUGETPKbU09149A+4qaL+eBR6L4Mei7WkBuW0pN/6/sfejv3esbJiP9QYgP5mi9xlFn6E3ZGxjz0jz5hx9Z6vNfXOGBcWWXvvksUYVx6UJXqP2LFyNrKurroJWUcesOIuUG0f+TXdZpSaW4ciqzONz6ucVGVf1DqiwNbCGPjHn3XUZKPSZeSAKp9Bi4lGvgBsKPUYbJc3paz88C67tZyusFX8xy6vC0oIhHKeeUZYgdTwBH4vrQ2CU4fkNnGdkiv6TTz55WdfxyuGbarFYWKlUsr29PTs+PrZWqxUi2yhCQcUZSkphcXh8Pc/OygNFcDwXraS5+DqYRYWMa4+V4OqCmexOL5fLxHHY0F2pVCqJ62FRLZfLRNdF+/F4Tx91RiE0Jmmi5JgKP9cRhdrF4fUDnU1KWa3ghx9+WKgmEjd6v9+3crlsH3zwgX344Yf2jW98wxaLhQ2HwyAkTYOhj222uUSVWnqtVIu5yWabA1u4uIX3ZTEALrbRCSx0GGushiCrG8AeQ6w/j2vHdej34df8CLgB098SVY5atlypVMIswY8fP7ZPP/30ue+H1531eh11nzIt/c9//vMXczWvCQ8ePLBWq2Xn5+dmZjYej8PNyCkx7mfDemkUH2JhAXIBCxMbYafiwn6wzrElqzidxm41z4euQ3Qhbn4eK67R9B2uNSvAFvMMdF8dm8ACx0QkOjwZoxuPjo6s2+3ao0ePrNfr2Z/+9CdbLpfWbrdDitTZYulLpVKhLL1Sq9Xs/ffft3fffTcU3aCuHhaci2DY2qeNbde8NsOz3jCx/xEvmKETbsSmnGJLztF4DsjxMRrM09exfHzMYvNjLKofO4a/Dy/NjfkIMPPQ4eGhHR0d2fHxsd2+fduOj4+t1+vZdDq1R48e2fn5ua3X6zACsWh9+48++ihq6V30ETTNB6vC89i1Wi2rVqsJ4cN9jp0vFqnmfqrm+GOBLfYgeGy+LiuttQWxaDuLji21EhOqWv2YcNOm5+bz6DgE9kTwfXgEIgY7NZtNOzg4sDt37tjbb79t7777rt25c8cODg5CGbRjZmb5RV+r1QopelhxFTCWi+blpOA2cj859pvGAlGlUnIuvdhCk7EGgqfg4sUqdex+3roDPOqWdpzGIdQ1Z2+Ey4vZ+4idI7Z+PTcI7Xbbbt26ZXfu3LF33nnH7t69a0dHR9bpdHb7BxeH/H36WBqkyCwWizCvW2zYbJrLarYpJo7ws5XWWXVxrFb9YVAPrCWWJV6tVlatVkO/XF1+bjzw2iwZDFTBxcbr6/fUa+RyZJ26W7sdmnrka+HGAvGSVqtlBwcHdnR0ZIeHh9bpdEIMYpd8f9HxPP0W1C2dzWZWKpVC+W2saAfP9TxsOSuVSjjHLqJnCwhRp+XGOfVmloyAa0OgXQz1IPA8LViIz2PBww1nj0S7HBoIzAqycd3Ber22RqMRulpw5xGc1CKiIuPr01+TtH4uBtmYJYtTsm5etrawdIgL4DkXwqh11j4076d5dz6WYwYsYO1msNgxvx82XUqLz82i165HbFZf/E4qfm002SNAzGS5XIaAamyJL2c7LvrnAEID2ywL9ucqN8QAYJm5D8z16Gzp8Rpi50E/MSvHOW6IkT2M2Nz8unHsgAUc6xawW582lXdagFHf02HCi8UiTEyqcxY4u+GivyZskTTavw2IH0JnKw1PIWZN2cJzDh3nicUVVJA6J19aDpxn6I2JXiPz2v3ICk7it0pL4bGF50bN7Iv6CO0SpWU7nDgu+hsgT99RGwm8xs0OLwDvw3pzSS/n0TEoKq2uXiPq2gCwZVaXHg1A2oo7MfdexY59tWvD3z9N+Ih7oJFD10aDfE4+XPSvCL3x2ZXnv6MhiOXKNWiY1vioNY4F9rRB4L55rBYgS/Sx59xNSRO9RunNLDEWYFsdg7MbHvlwnILhlv4VocUwGt1Oi8Jrmk3PFYOtKWrsMVIO8QR+rRNpsHWHRVYXP5a358+/jnvPpcOc0+dzOPlx0d8AeQJ5MWHjuVbs8T6xRoIj11qEAzQGAMHjuFjgL9ZnXq1WOwfyuCuA7snzBvJ4WDHHPVz8+XHRX5Nt1jXrOBWMFsyoFcVxsQo5Xrs+JlYWfLlcDqXFCAKiEeChtGplMdtvbBUeM0u8x/1/DvyppU+LUeh7sZQdin94mK+Lf3dc9M/B8xTncFEMV+OxaDUfrvl2DbqlBbogYggegTE0BLiO+XweHjFTDxfnxCL3+D44R2xLy9OnDQDi3xLCx1BmeBy1Wi2Md3DB58NFv4WY667lrID7rnqMCoUtYJrw9VgVuYoRx7Lbz315XA8XCfGjRu05p6+Niv5GOA+PjuNqOb2m2Jz4sZgAXz+X4VYqFet0OsHl55V+YwVKRcTLcK+Jip1vanZzY6k0Jpbi4gE3LI6Y6DUNxqWx/FzdfVybmSUKXWJBwFiuPdaH50AcD3JJS/lxrp7H4HPuPS2mwAFEeCbNZtPMLCw4grkOdGCPE8dnw42AG2c2myXer1arYWgtbrRSafvQWnXRtwmMBZvWJcgztBaCAXx9ad6Iuu+6f8wdj30v7pbgeLbwED6IxQfYU6hUvhhaO5/PrVKphLkOWq2WC35HfGjtFkqlUhhHj5FdvV4vlKau1+vE4o2xaalVwPy+BvOyik840Mcegpa9aiBvWx86zTuJFRCxW67dBoateyxqz2LnRsPMNoKB/B2xstBoNEp4Ccvl0g4PD8NcB046xTTlO1Kr1ezb3/62ffWrX7VGoxEsfbPZDOkoXigSgSYuFWU0/xxza/Gcuw04Fqj41UvQLoeuRMNz3sX61TGB47U+19QbE2u4Yik6/Q04wJk2XdbV1ZVdXV3Z+fm5/fOf/7Tj4+PEdFmff/554afL+vGPfxx936fLyuC9996zH/3oR/ad73zHzL6YWhnoxJiYchmCh/XmfjVPg80TY+qNz6gAsaUFBRl0O/DIS1nF0nP8Wfw3biR0eSu29NsCaGmeRSyAp+XBPEIQIwF5Ysx2u21HR0fW6/Xs0aNH9pvf/Mb++Mc/2mq1slarFbIVReL09DT/zDnf//73X8zVfElBoKnf71u1WrXvfve79r3vfc++/vWv23K5tMFgEHLGmPd+NBoFwcemwMZNzIKPrVQTmxRDRchz2HNqMFaYw4Kcz+eJ/Djn39OmwObpttNEz16ECj6WwtQGIa2B0C4Rd310WDBnNeCFPX782H73u9+F811cXFz/pngDybT0n376aWEsPQsGI7t6vZ4dHR1Zu90OEznAwvO891jswuyZ6x1b7EIXlYytcMMj7cwsIXjsy+vFs9XXxoKPQfeD575PW2Ka17SLCZ8/W7sIICZwfT8tlgC02CjWuKn3Y/ZssYuis77OvPff/OY3X8zVvGbghuJcOED6DGKNReI5z5xnWavYcbrMdKxbsF6vQxCWU12awlMLrf3/LMFnWfo0geM9vk59T+G5BoAWQ8EDUZCnLlpffhseyNsBzk9zVBkTU3I/NDYVNcSB/ij3T3ddwBJlqJPJJFTNwRXn68RxqBbEc00XZvVvcQ4cj/fSXPg0l53Pk7WP/j3r/6DXmVWRp9mB2Dn0fFl/zyJ27POc70XiKbsMtDgkFmXWtBt7A+oRqMVCH1XFywNKePFKrbnnWACfV0tuuQ9frVYTkXeersssOX59vX62QGS5XA7vc+wCnxGLR4Aswcf6/noM/z/071mNRawkelvj8jxeQezYL6OXkSn6L2Mr9TKItdAQfizIxlVp2v/k47hwBYVPEBP3zfn8PLSVrwXnUQGaWRhNp/EE1NazldfrjVEqlRIz2KAhYNdb+/mAGzD9jUHMG4jt92UU0OtIpujR+jtfoBVu6GtDZBAiNwJp5+HqOhU8C0XTfmxRuVvADQwsPefr2Zsol8uhok29ALzPkXGeh4/Phdl7+T32Dvl6Yx5Jlohd4C8O79PnhAU/Ho9tPB4HwZglvSMNnmlBC8TNXgCLH5ZUxYZjYLXVSvOwWb0eFLrw6rUcsUcjNp/PrV6vhyAjdyViQsdrNHqc3uMGiWMgEDZnK5wXj4s+J7hxJ5OJDYdD6/f7tlgsrF6vJ9xbtv5myQEmLF4zS7j9WplnZkHcvKINewOasoPgdPQeilo09adpOk7p8fsseK0fSHvU4h50Bfj7sffiFv7F46LPAW762Wxmo9HIrq6u7PLy0ubzuTUajYRAeFipWmsOYKXFAiBsuOAQPhZnhEXFsTrABg0RZrLFtNZcEITGh4UP0ceq97hRg5C5kUCWAYFHPheeQ/iaFeDfSH9z52Zx0eeART8cDu3i4sKePn0aRI8bHkstYbklFiPfxBA1njNs7SFw7v+rledsAbvX8/ncms1mYlXdWCUe9mfx636ay8c+qB/QwiO8RrpRuxUaAFTx82vn5nDR5wT93sFgYBcXF/bkyRObzWZWr9dD1dt4PLZmsxnmio+Nj0ewDAUk3L/XfbnKz+yZlVfBx8pwWcDqqrO4YlafXXlNr2FfLTgaj8c2HA4TZcpavciVghxMTBN/DG8Iro+LPgfq3l9eXtr5+blNp1OrVqtB8BjfzUsvsTCRn8c8b2bxCTABewRs+Th1p0HAWPBPBaxbbP9YEQ1bfNQRwJpD9MPh0AaDgfX7/fA4HA4T4ofwY41MVqWflvs6+XDR54Aj99ynH4/HVqlUbDKZ2Gg0CstB8YwuupoM/t5qtcL5OXAXqxPQCSY5AMiWnhsGtubbCmZ43225c24k4LKztR+NRjYYDGwwGIRhsP1+PzQCEH8saMgNgaYCcV1ZlXhONi76nKAvO51OgzUbjUZWKpWCpUfgDNacy3Y5qNZqtTZuXg7wae4eqIVXSx8rB1XSKue0Yk735/20Pj8mfmQ4+v2+XV5eBvHDG9BYAzcCPM4Aw5a5keGRgN4A7I6LPidamAOrtl6vg+gxRTOErivCNptNa7fb1u12E7l7nB9FOTExcxchNiVVrHuw7fl1qt5i3QJO4bH42erzpsOLNZDImQD2AMbjcWhEBoOBDYdDF30OXPTXANaeh6wiwDeZTELhjM5/ByuPSR+Q1tI+92q1Cnl/nSNOR/JliV4bgDTx8/dKI63uXa2+BgN1sBAKmljwaRkCtfzL5TJ4WI8fP7bT01P7+9//bo8fP7Z+v59IYXojkI6L/prgxoTYUZuuLjaLk/vynU5nawqNZ7nlEXKxIbzqEVxH9PhezLbGIS0giK3ZbCbEmzYvgJYuc9+df5/5fG6DwcCePHliDx8+tPv379v9+/ft9PTUzs7ObDKZJAKd3gBs4qLPCQtKU1eA+8VaZ4/8vbq27B7zaz4PR/DV1Y+JnvfFc/0ufM3b9tF9+TmLngcRrVar0K3R76YDcWIxCQ1Ecsr0a1/7mr333nv22Wef2b179+wPf/iD/e1vfwuzGPNsxc4zXPQ50Ko5Hloaq1rTY5Gqq9Vqieo1toJaE895a1j22HXx9e1i6WOv0867bR/Oq2sjgAYAxUWYQVgHD8WuPwaOXSwW9s4779hXvvIVOzk5saOjIzs6OrK7d+/a/fv37R//+MfGFObOF7joc6JpMq6O476opr0g+tlsZrVaLVGaGqt9x99jlXcQPwssTSTbhK/77YIGAFn4uh9nIzQ7oPvGvJPYucy+cP/b7XaYGPP4+Ni+9a1v2dnZmf3qV7+yn/3sZ3Z2drbzdyoSLvoc4GbkGXIgfu2Pq3sOMFRVrTreYw+Ai1d0cA42VOaxR/Ayf49dha+1A7FzxR7TPg8NLkqej4+Prd1u23q9tpOTE/vLX/5iH3/8sZmZdbvd8Ls7LvrccEUd5sKH8Nkd5wAVCzKtSo6FzzXrOh9eWt+b4a4HXqvwdiHrM7IKffKcb5fUIp831jig8Anvvf/++/bRRx/ZBx98EDwCH777DBd9TnCTodAGUzKjhpxdXxY03oOnwEUlLHrOTSPFhbSgBv1arVYIlPFCllq1h+d5PYFtDUTMcu/SqOwqdP4ts7ol2pgdHx/bD3/4Q/vBD34QPW/RcdHngPvUSL2h5HYymQTRsfgBCwTpPS1ugehh7ZHPRgGQ5ri73W5YV4/Teyx+BNO29emzvrOiYs9y27M+c1v3YFtjoueFJecsh7OJiz4nSL01Go2wuEKz2Qz5YUSWeXopoDcur3un4ofoUcqq1WxYV6/b7YZgFsbOq+WPDchR0vLzWfvrpsdn9dPzeB6xRiV2/qwpypxnuOhzAvccooe1nU6nYR/ux+toNbPkzQvXX2ei0VFr/X7frq6uwnDeg4MD29/ft16vZ51OJxHJhveBRiBtfflYjh3fUdOTjH4vTb/FUpuxtFzaa/2dYg0LBI7GVY/zenxfn/5G4Oh9vV63Tqdj3W7Xer1ecNnNbMN6m1l0umrst1gsEvlsnpwCFh/DUy8uLsLn4rPxut1upzYAsP4qehWv2WZaUo9hr4RjFjhWy4Rj8+7zY1p+Hr+PDvXluQXw/1Avwid1TcdFnxPcUCil7fV6tre3F6a0wrBYtlzwAnj2Ws2xc4OAuABmrUW8YDQabYzS440Xc+RH7vPz1NjsXbB4uWyYhYvjYEU5C8HxDg5yaqwhNiYhNrBIPSXOYKB7ZZaca0D/T04cF/01gKVvt9vW6/Vsf3/f1ut1YqYcDSZhEUmILAbf6DEXGavb4PPxiM+FO4/lnOHiY2NLz2KKLZHFIwN5AQ9cpw5pxTE8kpADnUhv4jpQksyjENkbUMGjpmG9Xluj0bBut2tmFo592TUKrzMu+pxAgPV63VqtlvV6PRuPx1YqlRICZMuGY2C5kTOO9Ts1aMWZAO5ejEajIBC2xPhsbBzY48YIllonvsTnsEWOHRurNsTnsSfC4ueJOvU5fjedQVhTmSjIuXXrVvC4cH3ObrjorwFucLj4e3t7Vi6XE7PlaPoMqTZUhqEfH5uDblsKDF5DLDrODYxOtMH7sleBBoC/X+w8/DkaVOOGgmfg5ecQd61WS8wuxJ5IvV5PWHsdo18ul0N3Cha/2Wze5L/3jcdFfw04mAfhl0qlxM3LVovHwpfLZZtOp1YqPVsqCm6/Rvi5IchTTcYFOXjMqhvYVhYbC7LpPhys4ynCYot2Qvja/eDfjhsn9kaq1aodHBxYqfRsKXENkDrZuOivAW5y9FExzx2nyFjw2AaDQRAPrBbcVzQCsbx+2nx1aa81ZpAm+LTXepxGxnUfDsLFZvXh3wOuOPfr2QOApdeAKIRdq9VsOp1aq9Wyk5MTm06nGylRJxsXfU40bYd57mq1WpjkAX3YtIg6im54vnhE63Vsvlm6Vd6l/FVd8dh504D11KIYPT8Cj3jN2QczS3Qz2CvgiUK5EeBuEaffSqVS+G1RluyCz4+LPics+Ha7HVxOVNfxtFmj0SgxMSQmhcS8bjwnPHLyWJJ6NpttVPQxaRV0u1w/B+x2PWYb3EDwWAOOW/DIwHK5nBhOvFwuQ2OB3xcewXqdnDaMsxOxdQWcbFz0OWAr32g0ws2LgS9mm/PncVUdl9FiRljMpottOBwmVohB5JrH6XOefNfgH9jV6qeJKM3l1+Cf9vM1MMiBv1h/n7MOnNbrdrt2cnJiJycndnh4aK1Wa6M4x8nGRZ8TtvRmFibEYEunOfDY5JA8SaSKnheL4ME2aAh4nTieBlpH9KU1BNtiBfieOjyXt5iAY5tW5OmcgRC1Rvz1NeInnU7Hjo+P7e7du/bWW29Zu9326rucuOhzwjlpuKoQGLvOWlHGOWeeNQej6tAgYEkoeANXV1cJLyHWAPDc8PxZWf3/XaL3LHCzeN+crTUPOeYpwHm/WAPBaTsdN4D8PjbURhwcHFiv17NWq+XufU5KW9xBj5CkkGVJY/vyMdog6Mw5XHOvm4odVp8r62JLQul1ZI3+M3vmvnO6kbs3aVZb5/pXSx+z+OrGc6Uen4sr/rjC0AtzUom2hC76GyAW3c7TEOBRPQNdRCJtS5s7P6vgRwOEseBebEBMLD0XG6CTNcKOn8cG4WwbmMP7uIXPxEX/JsPVdWleyC55/lhOPibWtNfOlwoXveMUjKjoPZD3JeS6xSavwtq+6MIY9yBuHrf0rxF5i2qcwuOW/nXHxe7cBJ7rcJyC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yC4aJ3nILhonecguGid5yCUd3y99JLuQrHcV4abukdp2C46B2nYLjoHadguOgdp2C46B2nYLjoHadg/H9AM7/dw0JAtgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 27\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current beta: 32\n", + "Current iteration: 37\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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er4d4PG5KZmmuSxFK01qK3z5H4hUgo5hkAZAsEAJghOmWJmP6i0J2EzwPDhpsEELfm9fDVBwPDhI83221m4r5YVHRrwELbdLpNEKhEIbDISKRiPHt5/nmbnvJS2x/2zanZbEKc9F2gdB0OjUDBP1s9uZvtVomRSZNeAbUpEVgC5ivR9HLW9lRd5P34PMDKvo14XLYYDCI8Xg8U4jjZqpTjGxKaYvbjkJLn5yi5KzN3Db30JPr8oE3C4J4br1eN5tkcodcWguyG65Ml/GQXWjtrkB8LSlsu8W38vhQ0a+BFFk0GjW+6rySWfvxMv9ui95Oh3GG56zdaDRMYIzFKDKQxxr1TqdjAmvVahWNRmOmF79MlUm/Xh4ygCivzRa65sk3h7mi92t1k1xIM0/AwWDQ1Tfnc3ilz+wadHu25H2KstfrodVqmUUm7NjDBh68Flv07Xbb5MwpeO65x1ldCl6a67bY7bSd/Ttlc5gr+rf9Z7o1kbBrxd+WRc+1SrTYa0HLosd4Cd7tNW2z3d65lgOAjLxTvFwyStHTr+d10LyX5apMpfHodrszZr1M67k19rRTe26fpYp+s5gret0c8EvkbC5/JnY5rL2U1D6fopKpNApR7uYi68xl/rvRaMxslwXgVtkuI/cM3Mn6djnL2wJfRfCPXex2ulQr975EVb0EbjObnUPn72U03X6M7aMzlcY0mjS7Zf0689/05RmIkwE16Ypw9pbRfrmSzY7CA2/Wr9v++qYKHnjTGHITrvVdMlf0dlOC9xkp0tFohEAgYNa82y2VA4GAEYmdk3YruJHmOnvrUcQUsr3SjAcj9nK2ZprNbtQh93aT9fC2Dy9ndMDdT5cw4/DY4dZZsqpQuU1g3ij4wx/+0FdDZDgcxng8Rr1eRzgcxne/+138+Mc/xosXL2bOY2NMmt6y4IVmpFweyxlV1qczKCcj8VLQNPdlVZwUrjRXZamv3KBSuhAyHScr8WyrhQMU3+cqgTu3388zqVeZgaXLJM12vudYLIZ8Po90Oo1ms4nz8/Oln/t9ZTqdun74c2f63/72t/dzNRvC9fU19vf30ev1AACdTsfM3oyqyxJUBtZs0bNYhqJnUI2mupzhaT3YUXW3mnu5EaRc1CM3qJSP9+oFT0HxcaFQCOPx2GQnePDvi2Ibb+sGSIHzM5SLlWTLMS5lzmQyZt1DrVbDP/7xD5yfn2MymSAej5vBV1Gffi6ffvopfvWrX+EPf/iD+R2bYjDISb/ZzpvbMxLFLAtt7G2iZYyAM65b5Z4tAlkgJOv9pejt1CIFZf+NwpYDh3x9maYE3IOU88qKvbBXDEqBs+gpHA6bSsjt7W2cnp7i2bNnODw8xNbWFnK5HBKJBHq9Hq6vr1Gr1TCdTo3bo779l8w17wOBgC8/pXlpPna/SSaTZgtlaYbL2djtOef9Tn4xvYpdpGjlVtIUh73wRqYHbYvBHkx4vh25lz97fS5u12cfsnmo/RnIc/h+KHIuXmK3onw+j5OTE3z961/HBx98gKdPnyKXy2l0/jarm/d+TdlxpnTr3sJoeCAQmNlWat4uN27Imc3edFJipwt5n331pcnKc+ztjOT50uWwz5G3boJ3E70taloa7BPAvgFuPQPk4CT7DcjH8uD/JBaLmVn+xYsXODs7Qy6XW/pzVxaI3m/bAK3CdDp9qx1TKBz62sBsrl2eZwveS7Rym2spKPl7t0Yccj2+9KH5dy/LhedRzJyV6QKxh6DcOpuDgrRIpLtCi0UOEvwc2Hw0n8/j4OAABwcHiMfja/8P/Io/p/IVsMtb3UzidZEzJyPu80xU+tMUCc+ntSGvTQbB+Bq2mS59Z9ndV/rQMjgog4fSDKfYpdCTyaTpD5hIJEwHIbmdtj0wAXAdFCj4fr+P6XRqfPpUKoVwOOzqqtzl/2lT0f3p10QW39wny3xBpUUgRUzT3Z697Y49Eil4Cp0NPO2OvhS2HAikCc7HsN8fb/l7L9PeC2ld8POXwU8+r9eKRmU+KvpHxrwgIGd5Rsi9SmWlye4mDHuGpznOZp08ZCdfztT2QXNetvrm+bLZp1fDT/t925WAUvSRSASj0ci4AJu8r99DoqLfAOQMTuF7BdaI9LXlDCvNdM7gFDxz3jxkC2/Z0Vea//YAYPvjtu++SPTyvoxB8PfSvdi0rb4eCyr6DcIO6vF39izvFmSTQuF9tuTmDM8il2w2a4RPU52i5+OkT2/ft+MA9vUueo8SabXQjbH3BVBWQ0X/HiL9ZikYOwhnm+o00d1uZSDObfsuN8Hb/vsyArWzBXRn3Lb4UsGvh9pHPmXTItubdr2PGZ3p30Ps4J6d9+dBS2AwGCAajZpOuLFYDL1eb6bCj8/LugLO7l71/ESmGZfBLe0m3wtnfftcZXlU9BuEVymvLKaRImEen34wF9EwkDevRTWXAvd6vRmfXu7CuyiQJ69v3nvga/LvMkApxW6vUeA5auavhor+keNWn7+MryzXy3O2l+fR/5addGQf+3a7PZNvt7fgslN2Ml1np/jmBfaILXQpeNmLgCk7WhnK6qjoHxmLilbkfXsAkLO93ara7bmk8Lmev91u38q1U9wyiCdFL3fllQU6XuJfNhAnhc/VjOPx2BTmxGIxNfHXQEW/gPssw5UsIwSZprKFLmdy6cu7mcpeZbidTmdmJpd/s8twmQ5cVIZLS8Euw3V7L/J65DkAjKsBfLklOCsBbavALjv2M1qGuyb3WYZrC32Z3LMtEK/0GP1y3peCn7fgRtbse+XYbaG6Lbhh/t9twY0948u0otuCG6btRqMRQqEQ8vm86R/A55QDoDIfXVrrAotA5q2iCwQefmmtrJ2XP0vhA28GLrntlF2r7/XaboOH2/twuya5Dl7O8m5VetJ6sOsH5Gq7aDSKQqGAVquFQCAAx3GQSCSW/twVXVrryrxNPmRdeiQSmVkBxpbUi8TvFYUntklun2fPsLLphFsTDQrX7oDrtjKNt25dfu3z7SXB9vW5CdttgJHXK4t8aDlw8IhEIqaJRrVaxXA4NCnBXC6nM/2S+HMqXwC/2PF4HN/+9rdxfHxs/GP6rhS8bGdt7zjDWwrG3nlWtsuyZ2E7+s7rkiax9H9laazdLouPpfjdTH1bzFL4dp2/20AhH+v2t2UHQtngU1b6ycBhKpVCqVRCuVzG559/btplZbNZz3ZZfuSnP/2p6++1XdYcPvjgA/zsZz/Dd77zHQAwHXMAzGwm0W63zTZTMhcue7Mt0xhT7hsvb+1NKCh+u6zWqzEmU3Fy7zo7um/7/V5713mJWnIXATTpMkgrgYMbYwaZTMYsFNrd3UU+n0etVsMnn3yC8/NzE+3nZ+InLi4uVm+X9eGHH97P1TxS2CiyVqshFArhe9/7Hr7//e/j+fPnM+fJFtjsR88W2KwYk/41m2Kyey53q6nX66YFtr3ZBfvu0TKQ+8R7tcDm6wKY2YeOQS+7Oo94Cf4uWmADiwtyvP4mBxk+R7/fRzAYRLPZNOeGQiHE43Hkcjmk02m0Wi1tgT2HuTP9J5984puZXvqDbGOdy+Wwv7/vmvqgkClEzpp2YAqYbU7pttkF95jr9XpmiyuKvdfrmcGFVoHc7EK+ngziyc0u+LxeHXhtwbvN7Has4jGmw+zNLvzO1KPv/VzRA3h8/9kHwJ5t7GoxGdCyA1aE58sW2O122xwUpqyMkxtqyE0o7W2t5GvSjB2Px0botETka9gbXni13rYDevL9PHY4WG/Ctd4Hw+FwdfNe+RKvlJYtcK/0m1t+mzMzN22wN7CUgwOtAroE9XrddQNL+XqMOTCm4BYXIIxDALf71suBRBb7yPuPFRmXIPNqIKTLtCpuj32b57tPNGU3B69Z2/7C858rzeJ5oqDgo9GoibzL1lBu7kA6nTapwkQiMbPPPK9J+vOj0QidTsf4+nxOuRkHfw4GgzNbXvM5ZPBLCl++Z95/jLgF7hZd69u8F7fHPsbPZq7oH+Mo9S5wK4iZd65XkYtb7l1Gpfkzi4Hk81Gkg8HgVgMLbvpAH306ne0mw1r1RCJhCogIswoyKCi3sOLKPK7Kk9cvG3Lydbw+O+XxMlf09qYJyizSZLYtAjfrQN6XZaN2UYn0p936zNFKYK6fz8Fr4GDR6XRmFrxw0JBbV9vpQfvgEly5TJeHvSZB2QzUp18DGcTjLrBy5paH/Th5yPN53z6XQrbFRtFztpWNImXRkOxoywaYcltsGc2XQUQedq2And+3ff1NDfr5CRX9mjBQ1ul0MBwOEYlEkEwmAcwKkEhR2IG0eYMEb6PR6Ex+nu2gZZqQxUDMEvR6PaRSqZndXbm1NlN/9tbYrBaUkX4580vh81ZaCjJoqBbA40RFvwb0mRuNBqrVqukuw+IQt7Xj9izvFTewf5YVd9FodEb0cuaXryfjAexwm8/nZwQv6ws427OAiFYCBwDO/qxNkKlHuRbfHihk4FCiA8HDoqJfg8lkgna7jXK5jMvLS7RaLSSTSSOYZDJpdoZxW/xCZBGP12zP4Bm3bwJgUn0ypSafR7ofMucvC3+kKGW5rlwbIEVvrxOQhT+sNWi1WqaOgHUBHBjk7M/PQnkYVPRrMBqN0Gg08Pr1a7x8+RK1Wg3JZBKNRgP7+/vIZrNIpVImiGbn7mVAjrO4nQOXt1L4drRfFuXYLoXsKyfNcNv8luk86dvbQT05mMjtuWUBUb1eR61WQ61WQ6VSQa1WM6XGvV5vbjcfm3XKd5XFqOiXROalR6MRarUaXr16hc8++wzlchnxeBzVahXVahXb29vI5XJwHMesyJNlsnLFWDQaBeDt1/NvssGGHTT0shRsd0L62G63bv64HBjs57KLiNrttnF5yuUyisUiLi4uUCwWUSqVUKvVjAUgYwPyOuzPXLl7VPRrQNFfXFzgiy++QKVSQTgcRrPZRLVaRT6fN7vEyE6ysomkXJcPeK8vl7+zZ3Nb9PbzAKsLZ9mouy1+uhHdbhfNZhO1Wg3lchmlUgnFYhGXl5colUoolUqoVqtmhaFsyrnuKrhNqA58TKjo12A8HqPZbOL6+hrlchmVSgUA0G63cXNzMxMxl7u3JhIJOI5jAmv5fB65XO6Wr++2bZOd2rNndi8rwY1FpairYFsI6XQa+Xwe+/v7ZmFRtVrF9fU1Li8v8fr1a7x+/RqlUgk3NzdoNpvodDoz8QMey1yLXwvI3gYV/RLYX77xeIxOp2OWxrbbbYzHY3S7XTQajZkZXW75nEwmkU6nkcvlUCgUsLu76+rnSvOdLBK8ff66rPocMu4AwLgrk8kE+XzeZAQajQYqlYox9TnjNxoNI3pZH8B4gWzRzawJ1yO0Wq1b/5vHXhr8GFDRL4lda05Tlj4qo9u9Xm+mF5y9V1wymUQ2m8XOzg7q9bp5rFwIw9ScjMoD7q2o+HvyUDPfvKwDuw1lMhns7e2ZaD/Th3Z1oMwiMEjITECr1UKxWMT5+TnOz89RrVZnXpcDpt/XjcxDRb8GjHZLU1SWqhIG3Shctm2uVquo1+umeQYLZcbjsQkAMvJP3DIAvC9vHxq3mEAoFDJuTjabvZVVkDvt0FWQAUJZW0DRv3z5Ep999hlevnyJ169fm3oJKXZ715/H8hk9NCr6NZAzvt1tRubJgdleecFgcKbbDg+Zzx4MBtje3kYqlcJkMplp8Wx/cR+b4IHZz2aZ67PjGfJ3dqqRA2273TapwVevXuGjjz7CH//4R/ztb3+beR4uNpJ9BxQV/VrY5rVMPRG3lWiBQGAmvy3LXeXPFH4mk3Gt0d8E3K51XmbALXuxTENL1kaEw2FkMhkUi0VUKhVUq1X0+/23eAfvLyr6NZC7u0gTktiltkTWxcscN6vbZFddmvwyty6FL/38TZzF7ioOkclk8M1vfhOO4+AHP/gBer0e/vKXv+CXv/wlisXiXVzqe4eKfg3on9PvtqvppLlvl93y8TyHqS4GBlnOKktZC4UCMpkMACAWi21sf/e7GpjswXR3dxf7+/vm569+9av47LPP8Otf/xoAkEqlEAqFNLj3/1HRr0EwGDSR+Gg06loFx/sSafJzrTzr0uXOrN1u17TIajab6PV65kttt+fiLO+2gGfTZn8br7Sb/Hzdyo+/8pWv4Cc/+Qm+8Y1vYDweI5VK+bIFthcq+jUIBoNIJBJIp9NIJpMm0OaGV3mpDDDxYLqK+X7WsMsClu3tbaTTabNzq72jzaYLXeL1Xha9x2g0ig8//BA/+tGPljrfb6jo1yAUCpnKOsdxbol+mS+ZW9MJRqjl6jWKv1KpoFKp4MmTJzg4OMDW1pYR/7wgn5fV4ca65ayriOo+BCij/axz2LTA57tERb8EboUnqVQKhUIB2WzWLKqh2b7sl81ewMLVc1zVxhmfi1cuLy9xdnaGs7MzHB0dYWdnx2zlFI1Gb23xvI6pf19CuU8ByjUIWom3GBX9GoRCIaTTaRQKBTPjcvEIZxv2lgOW78Bq96CXFWmNRgM3NzcolUp49eoVDg8Psbe3h0KhgFwuh0wmY9wNrud/7EG/t11i63Wu/Az9jO5Pf4cEg0E4joOtrS3s7Owgn8+b9eL9fn8mmLeO8O0vLst7m80mSqUSzs/Pkc/nsb29jUKhgJ2dHezu7qJQKGB7e9t1lZ/0/+3XlcVEwO3GnfOu2W7/5VYmPO/xq7hCXv0GbNxWJCpvUNEvifyCBYNBJJNJbG9vY29vD/v7+xgMBmZTSu6zxp70qwqfj2Gwj33pu90u6vW6Wb/vOA6y2axZsZfP583urWzdxTX9dEFYmy6Xxcotrvj+5BbY9r738zbUZJ8ALjjikmK5z7x0Q5adxeXMvcyApP68Nyr6NQgEAojFYshms9jd3cWTJ08wmUxwc3NjgkjtdhuBQOCW8IHlxe8VhGMxD5etXlxcGHFxZV8ikUAikTCbY1D0NPnsFllyKavsycdeAGwEwuuRjTEBmP3juaQ4m82aQ27UQbdD7jsv+/vJYKK0dmT3Hg4ssjhKWR4V/RqwRNRxHOzu7uL09NT4+WyTdXNzg0ajYRpMshrPbhm9jF9rm7WcmVnQ43Z9i4QrawOkoGQJbDgcnnmsm5Vgz/JS9LlcDtlsdibeIAci2WtAbuhhX6MsXQ4EAshms9ja2kIqlbq1mYeyGBX9mgSDQcTjcWxtbeHo6AiRSMQ0xWCv+Ugkgnq9jmAwOLPTrJsfvYr4lzmPQuZ+dm7dee1mlRJ7hSBNantBkRQ9y5NpcchDCpw/s9kI95hPpVLGJeDryNV23W4XsVgMh4eHeP78uXEVdFOW1VDRrwlLcZm6i0QipiGm28zVbDZdV+W9ixQTxXPfj5G4LSvmzjwcFGQnIfblpwvA9BtNe1pMiUQCz58/RzgcNs8hLRCd9Rejol8TmsCJRALZbNYMAEyZcbbnJpXAlyvC5NbWfB55C7zZLJLMW532WLEHDVocMphH31ya+Mw0yIInPle/30cqlcJoNILjOKb7ELv1KMuhol8D+r2hUAjxeBzpdBqxWAyj0chsLpHJZIxfm8/nUSwWUS6XTXsoNnywd4cB3DeG3GTkgMb3C8D46HQdGIegmyDNd7pEvV4PW1tbZk3C+/ZZvQtU9GsSCATMVlYsxOGMxPz9wcEBnj59iuvra9MXrlwuo1wu4+bmxuw3zxV165jTXpbCvHPfFtvasDew8FpoZP/sJVh+ttIakO3DM5mMWYOg/vzqqOjXhKZqIBAwPe0AzPihXDXHTi+VSgWlUglXV1coFoummy7bZ1H89q4wMh9u4ya0xzT72YOSvUpQ+v4Uucwa0PxngHBvbw9f+9rX8OLFC+zs7HhWnSneBBb4iJvhQD4gdjUaf8dbDgD9fh+dTgfNZhONRsN00m02m2YrKK6lZ+voRqNhzmX3V9le67Fiz9Q026W5LtOJXKbMNB5z+jKtx/Py+TxOT0/x7NkzFAoFJBIJDd554/rBqOjfAfbCGjmDyw6wcmBgr3haBldXV8YtYOUfN41021H2PgJ+0v+Wpa4UtBQxZ2reUuRyJmfO3nGcmVgIi3kcx5kRvYz6JxKJuUuaFQAq+s2BXV+5rLZarRr/n1113HaIddsi2svPXlQjYJvgbjX1MiXHiLsUNw9Zzstbu1zXrWjHLuFVVkZFv0nI9fV2q2jZMtprrzm3GMC8n+d13pG39n2vw21/Pftnu+jH3n1X18S/NSr6h2ZR6e372P3mLpGfm35GS6Gif0zMM6kV5Y5w/UKpo/RALLPW3L6/yFxf5nnvEvv137Ztlv07HQTvB53pHznLCH2TWEbIKvY7Q817RfEZrqLXDgSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jPCC/4eeCdXoSjKO0NnekXxGSp6RfEZKnpF8RkqekXxGSp6RfEZKnpF8Rn/AYFKpVEQpJU9AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 39\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current beta: 64\n", + "Current iteration: 49\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 51\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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EQm4sEhv5twF01bGTreeUDUjtrpPJJLLZrKjWS4KnmZ4GgVqtJgYOrXVc3hqweC8vLPpzoF8RiUGP1z5Gy3TK7qO9c7VaFUk4NOjI0WzpdBoHBwfY399HPB5HJpMRy3AaNGipT5Vxh0UbkMODwsVykujZkDdGRpnlTxI83Wipns/nkUwmkUgkcHR0hFKphFarJZb4wH8q+xSLRWQyGcTjcdE8M5/Po1wui1mcYvvlzxmFiyjRxQxOX9GPsyLMVYIuWioBree1Mv0s9r3+fZLrjpbqtKSnYpzb29s4ODgQsecUDUjH1ut1MdMfHR0hk8mIfbw2RFXbcVcPLOqrTV/Rj6O2mnbJS/vfcV04p72XvNTU817j/vyT0BaB6PU8zfCUWba/v4+3b9/izZs3iMViyGazQsjy+TebzS53WrlcRrVaPdbYote5MNefvqLXliEyKrTi6ReiOqh7TpuNR/tq2luTdbxYLCKbzSIej2N3dxfb29vY3d0VjTFrtZoYNGgAok6vtVpNGOdkg5zMVRd8v0H9OtQfOAtY1TogUcmx3uMKnaV88XK53OUuU1VVGOLi8TgODg5weHiIVCqFXC4nqvbIszadBxn9SPy0fycPAH2Hqw5taagxqHarIrfhYr6nr/X+2bNnhvm1ZOE2Gg2YTCZMTk4iEonoFrVcgVUO0NG63ORkFAqCyefzIrKNWl9ns1mk02mkUikcHR2J9NJ6vd7zIpc/Tw6TpftGcLOZzeZr/x31MpTL7osvvjDUL2e1WkWpJavVih//+Mf41a9+hXA43Pe4drstcsObzaZwn5HvnGZcsr5TiGoymRTJJ4VCQey/KSiGBga6UVQdcLIRUNsOm+7TTH+RYhg1huG0c1cUBeFwGI1GA4lEYqTPug4M5bL7y1/+cjZnc0VQVRV3797FD3/4Q7Tbbaiq2iUssqrT7EzCNJvNXU0j2+22sKhns1kkk0nE43HhN5ddaeROazQaXUEw/ZATeHolz1yWWW+c52G1WuF2u+HxeKAoClwuF6ampjA/P496vY7NzU3s7++j0+mIHAhO0PkPfWd6k8l0Oa6WC8LtduPTTz/FwsKCELk28o4KO1Bsuuwzl2c2Ej7N4DRIUHSdHP2mB60BURuGS8jRdGfhR+81e5/1IGOz2TA7O4tHjx7hk08+wfz8PKxWK1wuFzwej1itqaqKTqcj/r8uy+B3XvziF78YfHlvVNHrdfNdlgtJNmhpY9zprxxCS5zXuWvPjx7Tvkb7V26VTY8pioJAIID3338fn3/+OT7//HNMT0+fy/e4ggy+vDeqy85isYj49X5cBsEDJ5+HLCwyLPYLxhn3gGC32+F0OsXN5XLBbrfDbreLGgPyQEW1BC0WS9d9Wpo3Gg04HA5EIhGsrKzgo48+YsEPAc/01wQSDonkpBn/pGO1yLYA7XHa2RiAEKjNZoPNZoPT6YTX6xWlwwKBALxeLzweD5xOJ2w2W5fw5WPtdrt4nmZ5Kn9tt9uxsLCAO3fuIBQKwW63j+HXu7Zw7P0wWK3WY4E0xLCGobOOUdeKvpeo5Rm237Jbfk8Sod1uF+W/nE4nPB5P140MbNq/VDbMbrcL74a8jJcNoHK1IeD70GKz2YxwOIzp6WmxvdL29AM42Qfg/vRDc5WKQchCJs9Br4Ie9LxcuZeEphUi3bdarWIG93g88Pl8CAQCmJiYwOTkJEKhECYmJkRhUJfLJQQuezL6DUb9chbIYNrp/KdRSLvdFudq1G3osPCvdc2QBS0LmwQnP04ztqIo4i8trWkgoH/TzO7xeLqW7cFgEIFAQIidZvRxQ7YAbU4/h9oODov+mkEzs7xHJpHTjKsoihC62+3uWn673W44nU5hhCMfOLnD5CU7Le9psKBl+1l/N3lbwgwOi/4aQkterWGM7stCptr8NEvTIKC1urtcrmNCl41x2piEQVKO9bzmJGMiMzgs+muEHKhDs71sVacZnoRMYpebcsiipyW9/Jdq9dO2odfna+8Pev7jeh3TG14fMZcGo1vbzwue6a8RcpKNXMvebDaLarXaeHw5HVfOAqzVanA6naJoJmXr0TGym037+dr78ueM8t16nTczOCz6awi5uCiSjazcVFHHbreLmH9KGHK73XC5XGLpT4Y+WtqTIY/2/bTHp9fIATVnYWAzmUxd/nhtTQNGPyz6awa5skggdJ9me63LTnbXaV12ZAy0Wq3HAnGoQafssvP7/WfmspO/G/B9WDFb8AeHRX/N0FbHkZfkRK/gHDlIR+saI4Mg+et7BefQjcJtaRVwUnDOIAE6cnBOu90WxkgW/HCw6E/hKoXhUqtrEn6/89MKsFfknrx/lgcK2QugDcOV/f70V94SkPW/V/Sf3jDcqakpuN1ucY5ywxEOw/0eDsMdkqsUhkucZFCT7/eKV9e+rt/gJA8Y8oBgtVpFYI824cbn83WtAOTVhnwsxRPICTdUi8BmsyEajaLVaiEcDovXMfrh1Noe6E2tvWxoLenaKjpA/9XJWaTWOhwOMcPL0Xu9Em7kgYNmevpLnghFUTA/P4/V1VV88sknuHfv3sjnaTT6qvoqznLjQG+Tj8tShLFXxRzg4otokLuPyowBOBa9Bxy3N8j7fXlwAACHw4FAIIB3796h1WohEAhgZmbmTL/HdcOYU/kpkBg8Hg8+++wzRKNRUe5K7hkHQNSXp86tsnVZbihJnV5rtVpXP3cqs0U178dZLktb2eeiymXpXWnohdp5AUAikcDc3BxsNhtcLhe8Xi9arZbo2QfgTPMBLjO//OUvez7ORTT68OGHH+LXv/41fvSjH+kqjFkqlYR/nJaoJLx6vY5SqYRsNotEIoF4PI5kMilKW9OgIQ8AevvMaffXwPHCmNet/DXVxJO3DjMzM5ibmxOFMWOxGFqtFhRFEf9nRuLg4GDwGnk/+9nPrscVohOLxXKsBPZXX32FUCjU97h2uy1KV5NPXF6WdjqdniWwqQkllcCmUtdUPJN6xNNjNCDIM7Z2ZpcvblnsRiiBbbfbRQnsZDI50mddB04qgd1X9M+fPzeM6GUjGFW9nZiYwPz8vO6LlQSm3ZeS2MgY1avZBd1OanaRTqdFZxvaRmjr3WlnebkzLTe7MB5DiR7cnx4ARIOJXm2ttMvqQaG2VtrGFnJbq8PDQ9HaKpVKIZvNHmtrpU1tpdZWRm5rRd6n6/Bdh6HRaLDoh2VcDSxPgiLO5Bs1sCyVSqLP/O7uLt69e4e9vT3RpppKSGnPV25gSeLXzvj02VcZbfFPGXmQ1r6+F6PE8/c69qLzA1qt1uCFMY3qsiO0dddPQruc1FMIQnvfbDaL6jPyezabTUxPT2N6ehqhUEiEuR4cHCCbzaJcLh9zMVLIKnkJSqWSsCv0cpddZeH3O/eTWoAP+37DnMtl/G37it6oWUyD/EcN+58qi022J2h/c20deKvVCofDgWAwiHQ6DVVVhejpfShkNZfL4ejoCEdHR+J95ew74PvQ3UG+z2W8kBn99BW9Uf2bgzDIwNgvKKXXcptme7PZLDq7AP+JqfZ6vTg6OkKxWBTCpf1to9FAqVRCOp1GMpnE4eEh0um06JlXr9fRbDaFkU82/LGgrz8cnDNG9O4de9FrmS3bCyi9lQYBmu1pTy8HA7VaLZTLZRQKBaRSKcRiMezv7yORSIiGmZVKRez1yY4AjLal09o32Ip+OWFD3ghoM7zkSL1xfw59FhnoejXMlCMAqSNMLpdDMplELBbDwcEBkskkstmsiAakUFl6X/o3vbds+b/OwT7XEXbZnQGtVkv4zR0OB7xe77HXnCYKPYOEHMIqC1EecLSlqyhikDrk5nI5ZLNZ5HI5qKqKSqUixC17CygoiAKDKpUKqtVq16qASmrJ1Xeq1eqp35NXAOcLi37MtFotFAoFxGIx5HI5+Hw+zM/Pw+/398xNPwm9KwNZ+HICjfw+2veSa+WRUGlWpyW9HDBEOQQUIEShxdRSm1YC8uBQKBSQz+eRy+VQLBa7IgeZi4VFP0Y6nQ7y+Ty2t7fx3XffYWdnB16vF/fu3UM0GkUoFILX64XD4ejrvqPfXm9wTz/f+knH9/Idy0tzbRFNEj8JW94C0PPaqEJKHqKBQg41jsfjSKfTunIH9Hz3k74/c5yTRM+GPJ3IgRatVgupVArPnz/H3/72N6yvr8PtdmNnZwcrKytYWlrC3Nwc/H4/HA5HVxUY2couV6zRQ7/goEHeQ+uVkQcT7Z5du3c/abCQtxKpVAq7u7vY2NjA8+fPsbW1hXg8fuIW4Cx958xxWPQ6oAtd9nXv7+/j2bNnePbsGTY3N2G1WpHP55FKpfDmzRvMzs4iGAzC4/F0ZYLJveAcDsexqLFxbgm03+GkY8cRVSiTz+exuLgoWkrv7OwgkUggnU6LXALyIowSGSj/djww6IdFrxP5omo2m4jFYnj16hVisRjK5bIwnKVSKayvr4tqsdpmj1NTU4hEIpibm8PU1BS8Xq8oTEmchQfgPAOtaECbmZnB6uoqSqUSVFXF4eEhtra2sL6+jo2NDbx9+xZHR0enGgGZ8cKi10Gv2PZsNouDgwPkcjlhFGs0GlBVVVSWpXrxbrdbVI8Nh8OIRCJYXFzE0tISotEowuEw3G63WHYPMuuf9Xc96THtc9oEJCqe6ff7xWuXl5cRjUZx8+ZNLC0tYXt7G5lMBuVyGbVaTeQH0JZBtjGQC7FWq6FQKKBarR4zZvLMrw8WvU60hqRqtQpVVVGtVrsy18hqTRerqqqi+ivVi/P7/bhx4wZWVlbw6NEjvPfee5ibm4PH4wHQHRp73sIfNGmkX5ShFpfLhVu3biEcDosVAIld9iRQ7oAcSkyDQzKZxObmJl6/fn2shiHFSQxSgciIsOiHQJsVB3TPdHTByRcwYTKZYLfbcXh4KMJiyR9+48YNsdyX8/AvMgdilKwzrfAoktDhcCAcDvc9ngqT5HI5kU5cLBaRTCaxsbGBhYUFkWl4dHSEeDwuUqDlz5PPxai5JFpY9EMi56bTv3vd73VcrVZDJpPBixcvUCgUkMlkkMlk8PDhQ9y5cwcTExOiig8wes7+RSEPgIOeu9lsFkZQCjcmD8GjR4+Qz+cBANlsFl9//TX++Mc/IpVKdb0HDZ69ag4YGRb9kJyUcqtnWWkymYTRTxsAU61Wcfv2bUxMTEBRlGPNNq4K/bYnp+255WPNZrNooEFEo9Gu13s8HhweHuKf//yn2CZks9muFRbzPSz6IaAEGNqrDxp9Jl/w1WoVe3t7aDQaKBQKSCaTePDgAVZWVhCNRru6lJDPvFcZ6atCvxWRHKY7yPdbW1vDV199hS+//BJWqxVPnjzB7373O+zv74/npK8ZLHqdaAtQKooCt9sNm802cshppVLB/v4+VFUVFXIpnDUSiYjovuvQu21clYbor8lkQjgcxk9+8hPx/AcffIDXr1/jD3/4A4D/rASsVuuxPb9RYdHrQJv2SkvOQCAAp9OJSqUy8mc0Gg2k02nhpspms9jb28Pq6iru37+PhYWFrm6w2vh7bTHO60KvlYEcOSjXwiNu3bqFn//857h79y6azSb8fn9X4RCjw6IfArPZDL/fj3A4DJ/Ph1wu12V0G8VdRD7oRCKBvb09xONxqKqKWq2GaDQKr9crRG2EIifaFZbeY7744gv89Kc/Heg4o8Ci14l84VgsFoRCIdy8eVNE5Y1rFmm328KgR1ltmUwGb968wb1797C8vIzFxUUEg8FT32eUEFctw8T49/p7FtCqh/zzlOjEYu8Ni14n8gVktVoxPT2NW7duYXp6Gi9evBD7xXEWmiwWi3j16hUODg7w9OlT3L17F48fP8Znn32G5eVlTE5Onjjba/PXR2VYAZ2H8CiJ6DrYPM4DFr0OtEK2WCyYmpoSIbTynnJczRboMykENZ1OI5vNQlVVxONxLC4uIhwOIxgMdt38fj8URRlqWXzWaH+Xk3z4J7n55OdOyzik+n9GhvvTj4hWRMFgEJFIBNPT0/B4PCgWi8deNwq9Bo1UKoV//etfePnypYjln5mZwcLCAm7fvo07d+5gYWEBMzMzCAQCl0bsMqcF7PSrCzDIQKqtJMR8D4t+QMhibDKZMDk5iRs3biAajXYljYw77psueGqiSRlrABAIBDA7O4vNzU1sbGwgEokgHA7D7/fD6XSKWAJty2dtXrx8znLOv1z7XxaktqMMvcZms4kcA7o5nc6u489jq3AZB7zLAot+QOTAEbfbjWg0irW1NbRaLZF1V6lUxrq07DeI5HI5VKtVHB4e4unTpyK7zW63C8HbbDbYbDbY7XZhAyDDl7YUNtDdF177XnQ+NLjR97RYLHA6nfB6vQiHw5idncX8/Dzm5+cRDocRCAS6MgmZi4NFPyDyDGK323Hjxg08evQIdrsdW1tb2N3dRTKZRKFQOLMwUK2Bjqz9uVxu4GP1QKKn2Vq2lMuidzgc8Pl8mJycFILXit7lcolOPvJgRIMLrUrINiLX+Gs0GrBYLKI+ATMcXCNvBFqtFpLJJHZ3d7G3t4ednR28ePECL168wPb2dlf8Ny2Tr1MfuV5QCrHT6RQx87T6UBSlK82YKggHAgFMTk5iYmICgUAALpcLVqsV7XZbFOKkjr5utxurq6tYW1uDoigALr5n3CWGa+SNG7PZDJ/Ph4WFBQSDQczMzGBiYgI+nw9erxfv3r1DIpEQXWjO2prca8982gAzSEFKPTSbTVEoU5v1pkVRFHi9XkxMTCAcDmNqagrBYFCkF9MMTym2hUIBgUAA1WoVfr8fi4uLsNvtLPgBYdGPiN1uh9frhaIocLlc8Hg8mJycxMzMDJ48eYL19XW8fftWWPd7IRer1FqpR20qQe+nx/o9Tr++HsgdWSwWkU6nsbu7C6fTCUVRhOWdthJUmptKcQUCAVgsFkSj0S5bAw8Ap8OiHwGyclO1W5vNBo/Hg1AohHA4LJarwWAQOzs7KBQKXWWkKVFHrrgzCsNsGy5DaWmySWQymVNfWywWcXBwIApo6PH7M92w6EdE68oymUzCTRUIBHDr1i08fvxYVINNJBKIxWIirl6P8U2L7FKTH7tMaFcr9NioA0un00E4HMbi4iKmp6e7vAEceqsPNuSdEfS70vK0VCohlUphZ2cHL1++xIsXL/D69WscHByI+m9yYUj5PYxCr/gAcjkSS0tL+PLLL/Hll19iYWHhAs/2SsAdbi4D6XQah4eHiMViSCQSovabvOQn41WhUBB14tPp9JUuF03bIArgsdlsoiYBVQv2eDzw+XwiqEd25dEgEA6H8fDhQ7be64NFfxmQo+DkGy19m82m8LkfHh7izZs32NzcxNbWFnZ2dpBMJpHP51EqlS5tbDm55RRFgcPhEMKlGxk9KZQ4FAphampK3MgDQk1CKEaAov7In8+cCov+PNG2gtLuwfVA4baxWAyxWKyrKmy9XheRdL3cgWe1NdDOqHKzTjlkl4RPAtVGBiqKIjr+kPjpRi670+AZ/lRY9FcRikSTS273c+Odpx2gX8KMXMFXW82XBgg5JZbCfrWGUWYkWPSXAbnUE/1biywKo9Lvd+IMOt2w6C8TJ/nHr2Odu2HRUyab6QuL/qqhLQQpP3YVOS1/nge8scOiv4qcFjF3WQaBYYQ6SBENZihY9AxjMHqKnq0hDGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwrKc8bzqXs2AY5tzgmZ5hDAaLnmEMBoueYQwGi55hDAaLnmEMBoueYQzG/wfBc8/34qQ2LAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current beta: 128\n", + "Current iteration: 61\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 65\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 66\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 67\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 68\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 69\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 70\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 71\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 72\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx * Ny,))\n", + "lb = np.zeros((Nx*Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx * Ny,))\n", + "ub = np.ones((Nx*Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "cur_beta = 4\n", @@ -420,15 +1956,15 @@ "num_betas = 6\n", "update_factor = 12\n", "for iters in range(num_betas):\n", - " print(\"current beta: \", cur_beta)\n", - "\n", + " print(\"current beta: \",cur_beta)\n", + " \n", " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", + " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", " solver.set_maxeval(update_factor)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta * beta_scale" + " cur_beta = cur_beta*beta_scale" ] }, { @@ -440,15 +1976,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "plt.plot(10 * np.log10(0.5 * np.array(evaluation_history)), \"o-\")\n", + "plt.plot(10*np.log10(0.5*np.array(evaluation_history)),'o-')\n", "plt.grid(True)\n", - "plt.xlabel(\"Iteration\")\n", - "plt.ylabel(\"Mean Splitting Ratio (dB)\")\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Mean Splitting Ratio (dB)')\n", "plt.show()" ] }, @@ -461,32 +2010,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n" + ] + } + ], "source": [ - "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", + "f0, dJ_du = opt([mapping(x,eta_i,cur_beta)],need_gradient = False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef, bottom_ceof = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef / source_coef) ** 2\n", - "bottom_profile = np.abs(bottom_ceof / source_coef) ** 2" + "top_profile = np.abs(top_coef/source_coef) ** 2\n", + "bottom_profile = np.abs(bottom_ceof/source_coef) ** 2" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "plt.plot(1 / frequencies, top_profile * 100, \"-o\", label=\"Top Arm\")\n", - "plt.plot(1 / frequencies, bottom_profile * 100, \"--o\", label=\"Bottom Arm\")\n", + "plt.plot(1/frequencies,top_profile*100,'-o' ,label = 'Top Arm')\n", + "plt.plot(1/frequencies,bottom_profile*100,'--o',label = 'Bottom Arm')\n", "plt.legend()\n", "plt.grid(True)\n", - "plt.xlabel(\"Wavelength (microns)\")\n", - "plt.ylabel(\"Splitting Ratio (%)\")\n", - "# plt.ylim(46.5,50)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('Splitting Ratio (%)')\n", + "#plt.ylim(46.5,50)\n", "plt.show()" ] }, @@ -499,23 +2069,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "opt.update_design([mapping(x, eta_i, cur_beta)])\n", + "opt.update_design([mapping(x,eta_i,cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - ")\n", - "circ = Circle((2, 2), minimum_length / 2)\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", "ax.add_patch(circ)\n", - "ax.axis(\"off\")\n", + "ax.axis('off')\n", "plt.show()" ] } diff --git a/python/examples/adjoint_optimization/05-Near2Far.ipynb b/python/examples/adjoint_optimization/05-Near2Far.ipynb index f2a0df62d..b1396e9b6 100644 --- a/python/examples/adjoint_optimization/05-Near2Far.ipynb +++ b/python/examples/adjoint_optimization/05-Near2Far.ipynb @@ -11,9 +11,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -24,7 +32,6 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", - "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "Air = mp.Medium(index=1.0)" @@ -39,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -50,87 +57,68 @@ "\n", "resolution = 30\n", "\n", - "Sx = 2 * pml_size + design_region_width\n", - "Sy = 2 * pml_size + design_region_height + 5\n", - "cell_size = mp.Vector3(Sx, Sy)\n", + "Sx = 2*pml_size + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", "\n", "nf = 3\n", - "frequencies = np.array([1 / 1.5, 1 / 1.55, 1 / 1.6])\n", + "frequencies = np.array([1/1.5, 1/1.55, 1/1.6])\n", "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = (\n", - " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - ")\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", "design_region_resolution = int(resolution)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1 / 1.55\n", + "fcen = 1/1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [0, -(design_region_height / 2 + 1.5), 0]\n", - "source_size = mp.Vector3(design_region_width, 0, 0)\n", - "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", - "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", + "source_center = [0,-(design_region_height/2 + 1.5),0]\n", + "source_size = mp.Vector3(design_region_width,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.Source(src,\n", + " component=mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", "\n", - "Nx = int(design_region_resolution * design_region_width)\n", - "Ny = int(design_region_resolution * design_region_height)\n", + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), Air, Si, grid_type=\"U_MEAN\")\n", - "design_region = mpa.DesignRegion(\n", - " design_variables,\n", - " volume=mp.Volume(\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(design_region_width, design_region_height, 0),\n", - " ),\n", - ")\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),Air,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", "\n", - "\n", - "def mapping(x, eta, beta):\n", + "def mapping(x,eta,beta):\n", "\n", " # filter\n", - " filtered_field = mpa.conic_filter(\n", - " x,\n", - " filter_radius,\n", - " design_region_width,\n", - " design_region_height,\n", - " design_region_resolution,\n", - " )\n", - "\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", - "\n", - " projected_field = (\n", - " npa.flipud(projected_field) + projected_field\n", - " ) / 2 # left-right symmetry\n", - "\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", + " \n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", - "\n", "geometry = [\n", - " mp.Block(\n", - " center=design_region.center, size=design_region.size, material=design_variables\n", - " ),\n", - " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", - " #\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " # \n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "kpoint = mp.Vector3()\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " symmetries=[mp.Mirror(direction=mp.X)],\n", - " resolution=resolution,\n", - ")" + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution)" ] }, { @@ -146,81 +134,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "far_x = [mp.Vector3(0, 15, 0)]\n", - "NearRegions = [\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(0, design_region_height / 2 + 1.5),\n", - " size=mp.Vector3(design_region_width, 0),\n", - " weight=+1,\n", - " )\n", - "]\n", - "FarFields = mpa.Near2FarFields(sim, NearRegions, far_x)\n", + "far_x = [mp.Vector3(0,15,0)]\n", + "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", "ob_list = [FarFields]\n", - "\n", - "\n", "def J1(FF):\n", - " return npa.mean(npa.abs(FF[0, :, 2]) ** 2)" + " return npa.mean(npa.abs(FF[0,:,2])**2)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation=sim,\n", - " objective_functions=[J1],\n", - " objective_arguments=ob_list,\n", - " design_regions=[design_region],\n", + " simulation = sim,\n", + " objective_functions = [J1],\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", " frequencies=frequencies,\n", - " maximum_run_time=2000,\n", + " maximum_run_time = 2000\n", ")\n", "opt.plot2D(True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", - "\n", - "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", - "\n", - " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)]) # compute objective and gradient\n", - "\n", + " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", + " \n", + " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)]) # compute objective and gradient\n", + " \n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", - " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", - " ) # backprop\n", - "\n", + " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1)) # backprop\n", + " \n", + " \n", " evaluation_history.append(np.real(f0))\n", - "\n", + " \n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - " )\n", - " circ = Circle((2, 2), minimum_length / 2)\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", " ax.add_patch(circ)\n", - " ax.axis(\"off\")\n", + " ax.axis('off')\n", " plt.show()\n", - "\n", + " \n", " cur_iter[0] = cur_iter[0] + 1\n", - "\n", + " \n", " return np.real(f0)" ] }, @@ -233,19 +217,1604 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 1\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 3\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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Nl8EyavC6EdtmBCBlo0KTGFqRiZTQDYiGyzSS96s//z/TNh+7k5ru8yiT14m8OG6Og98ZJbFNZhFMGQU6U64tjZLG6aUMXwetgfp242ul0Fwn3cvIX0as3z0l1z1qi0GG5qvx+nr475SHO24PZAQ6IhIvSVey8CNbbMP1xAmXdsiaLo9meRalMUt3JBOVAvkUHcuMaofBkPpmxEzd8g26yWRy9gCHj+810I10D4fD8NQIidHPV5II/SxeVT0zNKZdTNmolB6ZuBdn30phWGbwSMRreW4MNCw/ztSKGHi/1zN9h9Y3Nz4Vnp62wGidhOtk4amdH/dqRQ6tmuengJE29YUpsDZBSHy691PgmUurf86f97Q2arxM8NK8L5UmHLKFVtZBYhu7TjrFuUmv6JT1payIZQlHK2vTuVmdjffSwpgdKutsbRCqf9o/75ccRLr6G50jdUIyWq/XZxzz2uga6ZJgnRwZqdGgqdx6+oX1XIIRjUfPVTV4ST5tREKRB1V5wQmXR3WccD3V93mO7d6SlFrH29S2KykVSd8JKh2v900EzUv96bsMRH27nMcIjYbp4+Za+lh8Y4jteb+t763rvU83ePbp8mVU14I7Sfbt8yMBt0o7vq5OGFV1ts6tNJ/RM4mTZQRmAJSf+tRxTs6JEaNIjmeFmRWoH5GuMkc+Ts77/cEKjVF7AQKP0zHa3W63w+P6rI2rLZIvHbLLm2eJe+CL1HQJkpQrODc7lGKsVqu6vr6u1Wo1HOdSbUcE6i+LYXtMJ0i4+l278owmSVI6rkbDcCfCcoHX8BiJUXlJugJJ19NgEgcdAP+vCIZGznSY50zVH4nWox1mA3RIHvW5Y3BiEzRmRi9OQpwTU0vJpCU370N65A6o5RDGxsk6NcsIvnatNSUZOPn6OH0Mmv+l/5N4SaLUI9dLRrsat2TKiJh65fNlmcIdJrNVnkbg02d8OIP2qz4YGKn/5XI59CGbq3p6D4n0iPNW25Tnl0Q30p1MJoOgSWgeLepaCV7HwSaTyfBGodvb27q9vR0IUovKQ9mtsoBHMV43bBGXG7Nv4PkTLooePAITmM7ReEkgHpUwIqfSeHTEsb+0FiyR8HHd1rWaJ4lHP5NgWgTYmj/h93u0wuvcabQInzLzKJfRK7/GZFT1VOckEWje3gc3qkiSjOYk59lsdhbNed8t0nXi1lyoF3TqGotHuLrWa+++TixJcBw8qy5ydbm12uFxNWWtzLIYnGiduQa0P96n7NTJ37Nc1yeNg0+b9kA30hWB6vlpLYLXl6rOn+ihAul1eXo5jbwya36qIXHjik+Z6W+6x6OYVgrHiFkGRefhhCmj5MZEy8A9InTCcqLlOD2KVzuX0mG/lqTRcgQyWI8+fylaUT/hETLH6g7MU0OuEcffck6SH8fV2sj8pfBIueUUXN6OSxFYi8i8Zsssi85P4yEZe/BB5+ulEToPOmpFwm4jdFB+moN/14uqGLyoZMDI9nT6+f0n4gm2e3Nz8yxSlxzpaPid6yPC5bHTHnh10nXSZBrjxsPfqQT0jHoggkQmL8k3iUnoeoql6sm7qRxABWKfrF8xgm3VmkWqqi+TED0iUR+XNtSqnjb2vFYnMGJzObOvsf/zZ0bI/v9WP/r9pU05Xtv62fsbi7aYOfjvvIZr5anupT4+dYytcbX6aJVIeB8dzNiajK2fE5y+eDpAUJmIZSG1o/SeBCmd9z0L/U+Bi2zG5e+BCYmc2QL/rv+xBCA7rnpyBtxQV7+y/+VyOay57w25o+EXx6H3Hfs6vBa6lxdYB/J0hx5RC6KaDx/9VQTGCJebWDwpMJbmsy8phXvyqnNF8N1rjlV98MSELyBroR5xMXLwefn1TIc8KqHCca5McT3ddrnwO+/33915tCLnltIzVfcoS6Ax+8kWryfz7wL79syktYnFa/l39uNzoIy8rODyJLwc4+PV/DkmHydTex6tJFn7U1wMPnwckhHJkBFh6zH7qvNXgpLwPDImwbvDYbnDOYEb4/piBO0Bj0ia7blOqX1d/7stL3hU50equJOqxeanIuir6mlX/HA4DMdRSIaeKojE6PXdcEny6r/qPAV1j+spk/p3Yq56Sm354IZHLL4BwCNAfuzNjbx1KNzlrTG2jEcg8WlMkgEdFOfO9WHmIEOT8Uu+7Kfl6EgSLbL1er3G5qUIjoURDtfHX+7NeakNj+y9PZYVuC6XbIE6I3mQ6NxunPS0JnwQweuvJG53SCxBcPOP+k/y5TVcIw9InHT9jWEan65lecTlS53QXPUGQEbbXDuWFaS73Dxkpuv99cIXebUjvSMfEZQRVD0JksdLpGxVzw/MVz29NYjpGRdZC6b7BUWqTKE8qmBkTU9cdR71ciF5CkBzFklIFvzeinQ1PyquRwxSpJbyUPnVr5MjxzAGJxo3LhqxR8Ecg+7hhhyJhkf9PMJ18mM5waNY9SNS0bxp7C2iaTlK3cc1YPufYrQeMVOGXBN3hp4duCP1SNcDDpIw22Pq33KcLC+MRad0gF5eYB869+4OnU+g0ZHSGfB0A52Mxic9YtBD2dBmySWyh08pJ31udH+JuRMua7BaVE+DKfiqetYGi+x80TE9Kd9kLxKUNxR4BthTDhq81xWrzo3jJZCQ1Db7oWNqKaS/O6GFluG2iMEJzMfCMbQ2nBjp+/xbJQ45OG74MKvQd2Y/rRIRo7iq86eS6HBZehGZcMPJyczHLUL3VJ3ryExAa9Sau7fNdfKIvHWt/uaZon/KB3XR19z7+U8I1+2BfeiFVDpHK0g2PLfuJTTPdOmQdfaWpUoSNc/mkiPUvgjao/Oe5Nv9MeDpdDooiD8WzFqfFIJ1XE9pqGy8h7VTeuGWAlG5/CU6WkiWGEhCNHDCFdsNyetWvE/X6LuXM7gRqWuoOC8pj0eNjEy9P5ZV+Ii1+mAkSEen7y1llkF4BKd7pCciFHcCLiuXk/7Hg/zsV9ePOUnKxf/Oeakvbla5M+H89PdW2eRSlMy1Yqbkukin05JTy7FTXy7pbOs+d8bcR5Ht6ANoqROCy6HlMBj5swzBNfMTF1oTRrzMnsg/nr32wquTLhWBdVWPmJiyadH8peRMO0W8ipRJTGqH5KGx0Mh1HT2zTljQwzJa5mc6EYwEW+RHEmsZyqdGpmyLxtgiuJbRMDprRSwtYxLh8uOBqs7PTzKy0lqPES/nqJ/paHi/y0dG3ZojSYy65teOydVlQmdPOcj5VJ2/tU5fLEcwaxuTeauG6zLTWriTbKFFnGpTc+GatcoSY85Ha09baL2QSnarT9RWdMt1VnuuUyRQEq4+547OjCUSXU/Z8+8+D5E4SfclJ/g50PXdC5vNZlhkESDTzdlsNhCfnjhTnVWLVvUUNfOxYtZax7w0CYdKyd3Z1hNnWnBPpRnlufJQwUmM/kIedxQCvbeuadVhPWqtap88cEX269Qe01YqJv8uwmGtdDabnT1A0YroHJ+q3E64TlL8+ddGLLxPsvT5tEpaKvd45uNrOTYfpvAsBzj5tmSpa1UfVr9s10s3updnbEVULfLX+Fk/PRwOZ+9WYIlQ/bA8INtiVOm662TH+eu+qho2iyV39kV7Ue2XG9GyXclB67nZbJ6Vr14TXUlXHz/O4y18AEKP+d7e3tb19fXwQZBaaD5I4TWtMcPm37UYNF4tFD/6h2NiSqOP4eYpCv8QTBrEWCrmL/Jh9KJx6u/qTwrju8i612ulrVIFr+cGiubiMnPZcS4C0139LHlcinJ5P//mpOMZEOfC6zmGl9oZI2Z3gjzpwDWk49F1LqeWPpLUfGOUJwW4dpqT11elX9rD8BSd9/NnRqMiMJb2dC0J352dotz7+/uBeOmkOS/ZFfWGWRVldMmGZTcai5e5/Igp21dJQXLXnDWPx8fHs/V8bXQj3ePxWOv1uubz+dlTJ4w09THninJZWuCCkWy9CC942uneVMqsg9EsZzAtZLRaVcMJiNbmD5Wahs4xMnKh4rBN9SmFoeG1jhW5cXD+rXTOU3Rv56VygN/DyFYZgOZ3Cb5G/E4nRCfxEniP2nMyHUvfW2DpYKyEc0k2/E74ZvEY6fp1Xh6jbuhnLyHoOq2HOyCWOjh2d9TqV0e39Mi9p+ian04ILBaLIcutev5JvlqbsQxGY5FTp+PjOHkySZ/+Ip1kdqJrVK5Yr9e/X9LdbDaDMLgZxRouFc8VgjUYpu0e+dBgdR9LE1JGvYVexM+P/mG060YnL8moyY2SSuR/F7xN/s/LFV4e8BT1Ejwy5ndPWy+VBbRWrRSXNUcvL7xE2PqZtWk3grE2+H+C6+8E5CToUepY+cblx5LA2D2UJdeY6+blhVb5xyNd/p+ZjUe2Pt9WGYPyp3N2Xda1rYDHgw3pgT4pRh8lr+v1rgTKm0+Pep8cI4nXjxiKN8grygIZsJHI+dqAHuh+esE3kTRxJ1Y+VVL1lNK0Xi7eSuNaURQ3rvxwtUhf5wp9E0Xt+pxcSTkWRg7sk/UtzXcsrVK/NCLWb50UWoTiRqe/swTCujPXwqMJGpU/ZEHDbTkMJ0aXH0swbpC/BJwPnfsl5+TkQbl4Kae1keflFf1P4/AItTW3li6zHa6X+tR3/o364e3wrLSfgWebLV1m9kAwyGHApPta2SN1xe2Rsm7Jx0uL/JifqmqebHI7Ydlo7MVDr4WupDtGUFJKCUBhPwWil9nwOfMxQtDv2uDwtMojBNasPIU/nc5TPL7F3jdBSDQCo12Pnlm2aBnLpehJ8KhyjLxJsmzbr2kRrhOoP7nV2qxxA/IIjddQFp65uCxaZRHPEjgntutj8HsoH8qMJQuSFYlTMiLxentjfbnza83ddYF9cu2ckHWP9Frrx5Ie9Vv3sk/qOdvS/byXUa6u9Uh+LOtSm3xSb0xXxAE8SUSu4EaaHxETxgKV10bXJ9I8sq16igBZTtDuKAnT36+g9rx9gVGJdjb5qCSJk0rCe0mS+jufTffjKi3lF7wUQeVpPeTg5ECj8wjHSxHsk+1J9t4H+/GfffysGStyGtvkEimRqDgHbrqJrHy+HpG1shhtlMzn87M6nrdDg+faawx8IpBOhf1xx9z1xdevJd+WU+XPLdJkf4y+W2k9nYGPzzdkvZznjo1j9khYQQQjcK4JZdkah94cpvXjfopnT2OkKB0S2V7iCvGM2qSsLvXxGuha0/WnZriD7iQo5WItlveKAPnZSi5ALaZ+ZhpEo/J0p6rOxqT+3Vu3Nra8rdaX2pDyaTyMrgg6prE0kAaoeygrytRrg62ohCmft+Hyc0L1aIZP0FWdnzRwJ+IfvUOSaDlCGbgfWdMYZMiuX5Inx6Q2fI4ekY05aZef/72lJ74eHtXRfugQ3BZaTo/roXXgk1xjY2abrifc4PWASdfpGtVtvZTostC68vQB5+xr0RqXcwXteDJ5ypo9I71U3nstdD0ydn9/X3d3d1V1Tmp6AswVvKWAWiAKicL2griELnL2Y11Mpf2cId/DQEL0qIapm8ahxb0UmTuxXVp4knsrDfTIR+07YfL6saiL96pvzdPJxq/3uVCOjMJowK2+WUfnJqu/jex4fHo3AuVAmakkRIJplYcYebv8LsmV0bPLjvd4RtCKyFvgeuk6ykCnBF5qo3U6wudEQmxd68cFnbC1nn7aSHagOVNW6s8fSFIbJFnfd2kRtM9XstbJKZ5+OBwO9fDw8PvcSNvv98O5vun06aiWXmbsBXiv1wpcACqEbwRVndeZnNy0SExRPEqWkWs87lkVHTkJtYjEN3bcG9OIPGJnVEJDULtjxNuKevxnRYr64iv6vH8qNg2I777g+B2tv6ltOapWdM0D9pSBDFPlBTlXOkg6c3c0Y+Nq/c5MzTOW1txb6zdG4v6zb8aNRc/6P69ln07m1E/1w/+3Ngj9Ht3HaNGdjv7GYIWb36x/e1St8dPxqW3pwGq1OtMHXk+ZkdgpVz0MoaOrHz9+7LqZ1j3SXa/XA9FWnQtIHlKK7amePJen9CQD1vRIUIw2uXPJ6Ff9tqKY1uafE7+UiKTskbS/PcyNpBUNaxytGjN/doN3xaW8uS5+7rJFuIRvSqi2545DsnADd6cwFkV61MVygTCd/nzIfzabDeUJr+VxTJy3j8mJ1Z2b/s92eTZU3112nrFx/J6djGU71DX/maDDaZF8Kxts3c9SAGXjn97N8dERsF/qAHXHAxmuBcsYV1dXgz3rKdXW5xRS7tzs5TFH9qHNtvv7+98X6dJzubcbUwQqP1PFqude7HA4nJERoz2mnB59cVyMQFsbUmrXUyaOtxUJU+F0jxf5qSiXoiRuIvk1HulzA9KjPI8mpHh8jp5pv0eL3qe+0+DcUSoj8Lovr/cIUmNnecIfIGiRd+slOe6AeD3XzDMRv55kzLn4GnikzXF4Wszx+Vh5zErwzIpffqKnlRHqPn6nTlNXGFEzWm2RrjtmyatlXy0CbMmB7YpI+ag+14Q2ycyHmZivlWe5vOY10e3jevQzUzISkXt/Xe+enIRb9RSpXV1dnZGYkyLbc2VTEd5f4qGHJ6R8TsZSotls9qyuPLZ4Y6Suv/lmHsscLjf9zCjCzzDT8D3y5wtcdLymVdZxI5ch+HEcXcPr9T+Nn/L3+qb3w5TSd9zVT8vYfdxq/5L8RCqtqMkjutbRJneY1EOPUBkg0ImS8Pl3f1es4OUNEhfXgwELIZnQSROeLbpuc16e5bkt+Utx3GFoPC05KzMW2Xq2406k1Z7WwsfvtXKX0WugW3lhsVjUmzdvarVanUU9PO7BqMrTeioAd19bkeFmsxk8XKvGyaiVyvnw8DBs7OkxZKUxTFNYl+Ic6NVZ72KkKI/tkSLrXPouktFYuPOqnWEnE48UJC9uJpFgSHAar0eCJBkZviJkzb+1A6xrdZSL6+ZKTvLUmGRcNDbOQ46DGzpsk5FzK9KnvNxpteYiGUg/SP6SATcLdR/LZ+5QRFTUoVbkR7moPX2wo2Qxm81qs9kMY2YG4rKnvKiHLd2lvbqusD3dr5fhPDw81MePH+vh4aE2m83Z29m45tRh2ojKCcvl8tnrAVj2aJ0w4vqyfMj9B7Xz5s2bZxnFa6Ib6V5dXdXXX39db9++PYtQ5BWphDw6QsFpkRnt0pioBHoJh6cPni6SKLVo8qw3NzfD73w7kafyvhHFV9ipJsUzka7w/qRe1dOz63rjmgxM8lItczqdDuQn8EXOmgtfzH46nc7e5E9HILixe4Qhw5pOp8Pjna3NyJZTVH+sGfoaSh9ItP6uY8lLc9HfeVJFBu3EW3X+3lX/8uxI41mtVsOX9iUOh8Ozdafx63euqeai/vW2q9lsdraedDwiWsnEnbEeFHCC4z7I2HlpPlDAubuz40kU12Xawf39fX38+HH40vsNPHN0PWMfkrMIV6TLzW1urnI8shPppDtTPnW62WyGteyBrpHu+/fv6+7ubnjWWYutxeUiK5Koeioh0APTOClwKd/h8PNbzdbr9ZDaMOKg55PR670Q0+nPpyvevXs3/H21Wg0EofHwY4Zk4P6+Vb7+UHJgisw0n59iLKW7vr6um5ubwUBpXOv1eiAFlTeqzqNHRgkyUD1I4KkpIyBGfzIGOh8R3eFwGNZTBicoWj8ef34Gn/22amge7bCOS+dBshJBkSxZYyfJen/UAeoNdU1r6M5Ya1JVZ08/iVRczyTfxWIxrKsIRHqzWCyGN/GxvuqRvl4OTmd8Op3OXsYknZQu8Q161GOeVuFj9tRZzYXvJuGTmdLLx8fHenx8HDanvv/++/r+++/rp59+qs1mM8jA9yfUv+aq0wnT6bRWq1Xd3NycyUv9MdLleMQZWhs5ZjlF6pRs/ndJuqvVqv7yl7/UarUayFBvHpKBLZfLQaFFElU1KIKUmt6w6ullOiJuKdDj4+PwfzduGaIiJoHeWG89u7u7q5ubmyEalGGqZsU3KDHKU/siCs3r+vr6LEVm/UskrqhKCifjEsmJAHTqgKQtwmAbkqcIWm9805NBGquU2iNvka5kvtvtzqJ/RjCSAcsSrUipFXVJdiQYfZfhMerfbrdnZ3glh8ViMThbtenZxXw+P3MWKkmx/ESyub29rbu7u3r79m3d3t4O+imjphz1xShrbE0U6a7X61oul0OWpnGTNEUUjPx0cmO73Z45J74KkXVRlmZE8rQT2ob6kl3y46xIuiof0Q4lG9qdlw+91CMZLZfLQd5v3rwZ1l73+bpRz1r6yaxGAY2XLXph4rUrw2fbyjscDsP7aFtRTtX42UK/fqzY3UoTmdqyzbH+q56/Rs/H5PeyD+/P64yttloy+ZTrW3K8FDm+dP+lcV0aw9hY/B5f109BS35jYxzTqxb8mjEZet8tHfX7PuV/n7om3t6v0YuXZHdJfuynZZuttrhHISIdm88lDnhJ3mNzbNnmpTWtqmGv6TNiVMG7kW4QBMEfCKOk2/0j2KsuRyKfGgH9mrZ/Kf7TsfxR8Gtk/luQ7X/ruP8b8Fuyw7GxfKm1TKQbBEHw+THK6C9//kkQBEHw2RDSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHTF/4f+TLqMIgiD4gyCRbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI/4/gpfE0gNg++YAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 13\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 15\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 25\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 27\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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lkHo2m6UI6Pb2NnmdpI8mk4lev36dcrf1el2tVivpUrvdTjKNF3xwcJDSKUQt5PbJ75OqOz8/183NTUq9eNqAlBZpMHdgKpWKJO0UdZEJcttuYDFy9/f3yQARlZKCoYbE94h8cvn53CiMdOv1ul69eqXj4+O0mAggngoKxwTiceVWyCu7eAAuKAcHB2o0Goks9nUD+DU9n1ur1RIBUNSjCNHtdtXr9XRwcKBWq5VI33OJpBFYZApc5IaHw6Hu7+/Ts6F0/gyQV6/X0/HxsU5PT9XtdlNqYblcplQDaQWPFCBbCA5lxrBBtnhobjzyyi+eCV4YAlupVFJhqt1uq9frpeIeniMK73l4DCbE5PPHvTAKTk6El3nl3TsO3Ptx+eC5+DzEiWz52CjotVqt5JmTauJzEHGe+sHg7JtDjB1rQFoF5wGDKz11m3h9gGfHsJNK6/V6aSykVyqVihqNhmazWfKiuT6e4mKx0HQ61Wg0UqfT2ck3V6vVZPg9ipCU/u1dAqQy8HiR9aurK11dXe04Bfs6CfIIlnFC9Mydy4V3NrhOey6fKMINIXPN85TLZb169Sp5xUXgs5MuE9vv9/WHf/iHOj09TSE0oYiHEPtyK96mw79RWCYR4shbRlxpva3FFwxyfnh4SLlHcm4ePu9rKcuRe2GudHklHOEmtdDr9RKpE8YdHR2p1+up0+kkolsul8kgkEMGOdlISgpG+IeSViqVVCzBo3FjIT1FAqSBWq1WCsuWy6XK5XJKgRweHqrT6Wi73aacMsbFc3OQGx5kHgFxT+YSZZGelN69FS+M4Umxbu4JuRHJWwcJ0SlKkb/n/5EfikoYm4ODg0S6uZeWkwDhMREDREQ1vdPppPnnedAPDF2z2VSv10vy4sU45pA5w/NmHfDyFotFGmceuTAX++Q510nXQZcb0hqj0Ujj8XinuyX/ft5S6XrrJNlsNnciDYjXOWHfuL1bhJTgwcGB6vW6BoOB5vO52u22ut1uus7nRmGe7mAw0J/+6Z+myuFyuUxVYvp38149V1LCLw+B86o+Xh9KLWkn90il23sGUUS6AvDOEBBXcgT08fFxRzg894jhyKvM5KQhOoi/0WgkJSJlgZBRac29UPJd/X4/5dxQVASVzzMGil8oFgUYUjN4du4V8izu3bRarZSyISeLl07Bh7mdz+cfdRTg3XkI7vlQL0h5KOqeCd91Y+1kKin9nu97XtjTGFwbeSFlgDFyY99sNneeudlsph8vJLoRJJpxGXp4eNjJkzNOoh6fb8aLoca7zUke4+BjQs8w0pvNJuWoiYiYJ2TUCTj3Jn2ec7l2Qyp9iAa8drAvqvC1xzi4blFDQD8hd3fOGKcTdx7Zch+iGJwa5vqLL77QYDD4/2C3Xw2Febrtdltv375NXpAXDFggCiwodN4i4oUT93AhXDyH6XSa0gQIlOcInTBZIATUC0kQGNVZyEt6UmQnXYyFe7az2SzluvB0Kbah4OTSuIaHv57jdOJxBXOD5UUkcpSuIJDMcwpFrs2LHr6WpD7wXt04MX6vNu8L610ZmIc8j5fnnfm8e+DuwfGMEIB7tnyeaz43Bkk7GxqcICAo2qx8DSELCDkPyT18hgw8jwypEBLn68gYIF5qDblxzGXGjRw5d0mpX9pbDb1Vr9VqJRlgnNwHXUVP6fUmgkMOkA8n8jyt4LqIwyQpGSW+x1hwEpjTfUAm5vN5kilIF7mVnvreiWaoq/xOebp4p0wghaXb21tdX1+nfjo8TYpeeKruwSB8eLqETHi5PtEsHgqZ54By0uE6EC9WWFL6Pdd26+2khzJ5CxdFQe8M8Dz2YrFISuBk454Tz+phoCs2ZOUtV3nhzJUd5cHb4f4eEjup5k39eNd8jlyn98J6NOH9o5ABaQDG7WkDFBWjyjpiUFnbvAeUeeMefj3SFu7hOgExl3R1MF7mIS+cIq/75spDboy/f9ZDZO4JgXkeOPfakDN/Hq8beFhPp4l71e6t5utMvtg93VxffC2QZ8ZGBOeGPvdyJX2kL3i4/OkRiefoKZJ5NJkTMOvKdZz4PU1ExOnF7yJQGOliyWazme7u7nR5eal3796lXUneWeBJec+9uYV0b8O9LoTYvSx+v2/xc0WjKnt/f58E2ntHvYjnIQ3XQojoiHDD4gU091pyMvSWH/8hf8fvHAghOUPmzHOcnt/250XpEVI8dC9+kFrA08VbQmmZd/dY8xAVovD/zyME1hPFBuQhKQ55vtw9Y0CRMb+eGywv3jihuJfmHjLzxN+RU2+bQjZ9YwGEjjfsEZy3mTFnkLQTc55b9Tw50R2y5j3QkB8dNE7E5NPr9XrqXSfdlRtNlyFPDXlul3XHAOR94Lmn63PObjrWPG8NRF/cefNI1Z0fuja4HrzjHRToMGv1nOf8OVAY6S6Xy0Sul5eXaXPAzc1N2qaXW+I895qTLh4XYYmTLnmwXKn3FQo8dGfBaDGDdMl5urfnqQcnTDwa0ijsPBuPx8njZvydTmcnJ5aH2B7mrdcftkq7IrhX6wrsBStSMTyvt4gBz6f7M0AQ5MKZY7wGzwEydpRhnyA7wTt4Hp7V197XaV/BJM/hYTyeKwj5/OzzcJjr556P+3mPOSSGN4ZCuxHiu3lKhfSF537zMXpqzT1jOktGo5Gur691d3eXjLt797R34QH7eGlFrNVqOjg42Cm6sqGCefPiFrJElIYR8JRa3nGQy5yvnTtFXthzzxVjj0fsBM7a8dxuWEiFOEcwp61WK5F+EfjspOvWh0Zxwh8XAg9TmGxJewWQnj5yVHgR/J3rIFQoIyEnC+G5KpS9VColL4+F8G2bEC9FJw9fc48C4qZXl1DUc3hcH+FyIve2IhTYPWxCWN9Pzv8zPs8749FRjPOCnW9vJirxLg0vVC2XyxRleBHPUzu5Z0Nkwr89x5cbAOCkmYej+f/nufoc+4yM9xjneXHmi+f0dI+kHTLmh+s6MTG3KLoTCrLjFXb6bXMv13P43tZIqo52RDYS8Xyk3iSlnYj0vpNW2Ww+dGMgr77xwecF+cPjZE0fHx9VqVSSd0kEgJfraw2QrVzG8py3rxfziW5DpG48fQ29q8ULq/V6PRXT2PVG5Jg7ZZ8DhZ6n6/DcJZaJSZGelIK/S0qFjH6/n3pXEU6avn3BvEAiKV3Prek+S8lCeTGNcNGLGHkuzr2JyWSys9slzx1LSsrqhQ76XD0PlR9Og0fLuFBGLxJQYPP5cyWniks7mjf5554mSotHDQGwXdQVhecj+nBPRVJKf6AETnCuPJ4z35e7Bk6mnrLwdiTP8+Yhsf/kpAy5uLdJ6P/w8JAUP8+7Y8hoKfPzGXxNmAPuxTpK2iFdSR91SXh6hTrEvl2CyN10Ok1j98jJc8SsNVvfyXl6WsENvzsCGAbPv7tMuPHnWt6Gmeuupxa4F1vvIXtPQXm+NneGXA5z8v9NoDDSrVaraZ87FoYNBh62SU8kgSK4d3Z0dKRXr17p5cuXarfbaZvjPi8CRfE2HK/6ehXb4USBoLHPPU8reA7XD6thXAhOo9HYaa9xxcg3RpTL5eQ15OkW8sHSU8EA4cWT5Hl9a7KHbhRL2EjiGy3I6zUajY8OHnEDRG8xxT0P6Xh293CY81zJPdrAe+Pz3q3gKQvP8Xu7mRs2T5fksuWK7LvI+LwTl7fkETkQTrPmyJhHH8whZArJeEgtKeUm9/VIsyb8fZ9HnstGnlPm+e/v73d62XFKfKxeHMQjzruFnNjcW18ulx9tpthHcJ4apGtH2j1bw8naUyrNZlODwSDJrnecsOU+Txn5GDwCIVL281SKQmEtY7VaTYeHhymM8a20rjh8J89tERK8ePFCb9++1enp6Y5XBOHm1VSujfATMqI8npD3MXv7i/SkyJ4/zZUby8t1IW9yt06+Tn6+C43wyb0FjwY8VPI0jIdp7nn6M7mH4WdI4OXmFes8tYMCsC/fT7/y/mUUwPOwzBkpH8bppO7ywrq6XHiOzz1+91I9P8i/PX3kz+Xky//x/JASEQHGlrTPer1OHiHpFb+2jwVZJI3kcuJz5ON1rz1fX6/ce9HVDRFkyud9vTebTSJQ98zx7CFR5DifO18Txu7ebK47LoPoJPPK3OXpCK6Lk7HdfqitdDodHR8fJy5hbshje47fi83e9eS7+eh55lqfO7UgFezpsuBelMpDjLzQwJ8c9PHy5Uudnp6q3+9rvV5rNBpJehJE/xPvBQHAoiOo5FidQN3D3Ze/c3LIQyYPQZ3g2AHDgRueukAZ3PhI+ojcqcIStj/XLuPk7+NB6cvlcrLypEogQ+blU83sefiMkSDM43AX4KkFFAlP0hXE75GnB9zLdWVyAs+9XeYozxH6Z/B8fN49N8g48Ix8m+58Pk+59H2edk6K7lkjB8yDRz8eOeUFPObQ14i0ght5lx3kn0jq8fFRjUZjJ3XmLW5e3HOZdrlmflzevGOG+7sx87n1ThgOhsKQudeO7DjpwwWumxAyHSTMEcbZUyNeB/EUkM/x50ZhpEsIBYl4bgnrCIn5zhz+3ul01Ov11O12k0XOe/lyL8EVWtolABTN82p5vm+zeToZbTab7ezacs/Er53vCnJLzeddQBEi7pd7mdLT2cBs+kAAIZ/c83Dj4FVa6UngiTR8h0/ex5obEhdW5gIi8vQDP35tyMXbsPLiRa7Uvh4oYt7fStVdetqF5qGtk2e+vn7SF4SHYXHZ8xCbOSaXjvPAvDl55l04zCHPTFcIBivPTe+TB+SHwjGpAE8F+We5XqVSSafUeWubyzKOSb7Tbp+8swZ5i6F3uzDnpC18HZjPPJf+KT32SAxj2O12085WUj6kOrzP2TuZXP/cuSsKhZIuXg5hGSdQeYEIZfATt/AOvOcRa+/E4wTjCiA97VRBAJyY+H6u+BRMqOri2eHd+LPlXoxXSr0Q5eTjaQZX1NybcC+GkAsiYuxOKm4AMFB8DtLlWUgNeEju12Rs5NRywvXKPooEOTshMu/0BTNn3l+bG0ngygdhu1HAsOVefx7p+BwQ3pI6YHyS0u89CnPj43lB6goQq5MNz+SpHm8LhOR8DjCwePGeBuGa6NB6vU7HfNJTzXrl8+aRDuSPHHuaBxnmND3kfR/xshZsZqLVLCd/ngH4Onoazb+TFzD5PnrPenkkUiqVdnbB+XxRcKTu4nNWNAp/GzCN3L5zxtuoEEqKS15sohJNuwvFGifrfQrshS6sJUUuabe6T2iEgXCBOjg42EkN5N/z+6OctMNhnSFN70jIvQcPdRgDhobPoKx5x0cexnqLkfR05KMXdTwl4x6Jh3Z4RX6uA8qAJ+FrhZfvu9e2223aUVav11OzuuervQ2LOZN2W9ZyTxcvx8kgr3znxVTC2263u1MM5HueAnPizXPBHpUxDuYHA+tjxlgQBfAMyCxk4fUJT9dgsJlLfx1NTm4uU6y9G0XP4eZdNBRZPdfrpOvE6T3pyLT0lCJzEmZM8/k8zSkk6flvyNcdMOYl1wdyxBhyNtEwrz7/ktJhNxjLfYb+c6Jw0vVGbdqqvGfQlRjPCkXx6jGFG0IlFJ2JZUEQfMIxwh8/r9S9IBYNcvSNEt62hXLyfbxC6SlXyPjzLgDvQJCUDE6eN8M6+9moEA+VacaVF9Wc/EhJeKuaF3G8Pc1/fHOGpEQE7KwiLGfsOfEyJm8toseXsbFDi11DEK4rKl6Nk64/C4Y3LwS6AfLDg/CMqFwzP75VNK83uEfoc+CbELyA6zlp5s0jDp/X/PMQquezyd/6y13peffDyakTMHf5+lAc5KByJ1P3Gv1ddZ7nRYZZA7xH70X3yAdj5MVCT4+wbn5uBuN1OcEwwh+eUiRnjdyT24Vv0D/kyD1dPlckCiNdt4zeV+hhtvTU4kOuk+PuPC/pRQSICEVxy8WEYsW9T3JfZd9zkVSqfYcarVCLxWLnOp5383YuyJftzb65IC82eC7UhRMvAg8RYUOQxuOx7u/vkwB5isUJy8O4PEfpO9D8wCHfO+9FGQh0s9mkIiHP7UUcjCTeCs/pZIBXwlxLu50bXqTyuc7Dd99R5N9jzH5ymLcMoawQBevIc+StZGxxZcvtaDRK88/c4lWxVp7LdKfAC0YgLyS6DHJfIi1I9/7+XqvVKnmEOBN+T/8/l708Z53ncvMuBE+DoMeegmPdXI9YT4ePyaMb5iCvDXhqkgiMtWGsHP8oaadvn/sjdy7feYG8CBR69gJbJvO3N7iniSXudrvppY8otJM2O73G4/HOGQHuMeahGwSMonJPxoCl991cvMbFPVeEANLy0BfBweoC91g81PGQyAtT0u6uG9/SWK1Wk3fKZgy8RPdqXbC9/Qdj4CE68+qFGT/PAs/fPZz5fK7BYLDzmiGfS8+pel6U3CMtVIwzb4vydIt7Y/zeDTFr7OkWCM/z2/zpSg1R5orvnh3FovF4nA4wuru702g02tkE4/OJjHuXAGPP12O9Xn90mAvz7QaIz5IP96NIt9vtR8eSuiw5sbgM5pGR97KzLv496YPRwAjc3d2lw/kJ/SFB0mCeW+Y6z3Wx5Plc0pLeGbNer1MhGI+d8VLYnE6nO7UOX888dehy87lR2DZgXlLIiWJ4ny4YvC3h6OhIZ2dn6fXThGgcRINVxcoj9F6VzIHQ5gssPZF9vsV3s9nsbFLgsxAQROOkK31cPXbC9VDaD79xEsAwOBminK6EeD9437kAu4fhXqJ/xivFeLzuBfj3CAHJ4/m25n6/v7PdNU+3uLLlqRBPSdTr9Z02Pp/PPI/sBReQG3DSBB4mMweQmof2+ZghyMfHRw2HQ11fX6fXnvPm2n3FQM+jMi/75t2/456+/w6CJOoh5+xGEBn0rhA3SlTz8dbzeWU+WSMnSuaD9ARjub291fn5uc7Pz3V1dZUiQWTa9cl1yJ/ZnzEHOuCpHb+GR8N0AJF6w1B62oLvkKbgZbWVSmXnGAGXp8+BwjzdyWSif/zHf9T9/X16I6+knRCi2+3q5OQk7Tg7OTlJ22IpfNEEzY4ZBN/DhOdIlwXMwz8U2b1NiJ6mbcJ3iH2xWKTmbgQey+2enSutpykwFJISITC+nGx9jE76eS4R7yCvMu9r3fHw3JXWr4kAevjriusnZkG8kGDeSsZn3HP3qIBuB78m8+OFqdybfe7/815M94bIIfqzuWfuaRDGxIEyFxcX6W3L3kXhZM5Y8vn0kBh5Y229yOrGhvX0MJnrecHTyTfv7GG9vMjFLkX3dhkbRsSdGGQaveDFqLy23g9zwpChT6QQ/TnQSdfHHJAx+u2yTu66VCqlyJjDoPz8CsZO3hvwJuJms6nr62v96Ec/0qtXrz4aw+dAYZ7uaDTST3/6U5VKJd3e3qY8FLnYg4OD9BJG3rXlRwhijb1a6mfuPke0+/5078GVzns33aKv1+tEkgj7drtN22DxDBAIz2dJSsl/EvicHewH3qAwnPjE0Xx4P7lX5ykE6ckD9/YaT12g0JABc+keQX4GquddJe0YHH/TgJMXrTvkdJlnD+e8gOEkzdz7c/OZ3Ftm7fz/8Nzy6/l5BUQbeDsoMtFNbqAY8+3trS4vL3V5eZkMP2uN7LDWTpBet4Bk88Itz8eacO08VZavO3PncuE1CMZFioyDbqSnVkY32O5t+4409IO02mKx0Gg00vn5ud6/f6/b29sUonvUwNx7HtYjDHcQgOe9WX9PQWGkarVa6iZifNzb31rsLWx++tpkMkme9Hb74Y3dzlefE4V5utPpVD/72c/U6XR2tuy55UewSCVQ+dxutymdQLXWuwkI3VzpuKaHYE7MfJ6WE9ILFBJo6fEcLMUm8kD7UgGSkpKhiN4fSIsPaQGKDpPJZMdaQ9A8n3umkAV5Q88P5/D8qhMvniak47lB/t8JyJXSuxsYvzefo5ye/yUHz3N7bjnPfefryFq6rPh6AwyFe4hupFgLz6VC8FTsfewQJl4ux5CiyO79QZrcC0XH2GCAPEz3YpVHTPvW0FNAEKJvuuG+i8UinZlAGxmH6XNIT7lcTpEF84gM+1kIuSPhBenc6fENHqzVZrNJxMtY0FUcrlxO3cB4xMY46dGl1591gytKpVLqpvA0ixst5pBnRDaLQqHn6Q6HQ223250eOiaEw8NHo9GO8ND+Mp/PdXd3lyrGfsi5k47nqRCY53Jp+QKzcQDi9fC6VCrt9G3m3Qf820MniA3SoXncj3/EU4R4KWKQ2vDcmoeXePzeRoVQOyHT0eBRAl40SuehpBdwPDXgIbz/lEol3d/f77w6KI9MeFY/yxVjh5ePQnkeeZ/HgyK6wu7Lj7qHjee43T690QDy8RSOXw8DhXIzdg/RPR3A/dx78sjC25aIQAiL3dnAKLDOXNONOzlvPGdPqzHf2+021U687XC73aYiqhf4nHjzQpYbCv+M5+IZJ2vK+uR94sxVnq/nd/yfRza53vKMnsZbLBbphDYcHHfQPJJDrtzLzbsrPicKI12I1EmCSUQop9OpJCWSor1HUgqRmEjfZ517Pbl391y4kJM0wkUfI/lVSIdTjqjY93q9nV1yKHmeV/NdMhgKz7+tVqtUjaboIz0VHvw5mDu/po8RkvVQ2YXV39eWF5Go+nox0nPhOTkBiAWhRiFRUH6Xe+dOWDnpevHjV4F/x0kXOWHePD3BZ0jtMNd5emBfhdtzyT5uDCnE5NV1yNe/x3z5llo/AIY1cFkl1M5z+RgL6SnqYm4hKZc/95zztkl0wmsJRIFu1Ola8LQSBO8560+taa6LGIKcsPNOhtVqlU5Rk7Qzh96Ng2xh5N0g7sspfy4USro8tLR/iyCuv/RExHhaeMLecrZvovyaWHSAd/0pbwllIafM2Mrlcir2HR4e7rwC3fduI8i8VQCvwK+PIHv6xMcg7eaY/bk8F+ufz70CmvEpKEBqvhkFA4OHsFw+Hc/nrUdeSGP8eMG+cWVfEY/PexeBrzPP40UsnjuPhvJnzA2t5wO9x9SLTJ4DJ+/LuDw3mkdDfIaQnvTCvhQI80Ju0U+x8jcYUMzbbrc7J9/lnSOuI77uzFWp9LTDzeU5Lyz72HzDA5/BWDLm/JXyrBvPguHiIHCvnfhLAHwMzJP/m+dAlvKx5cBY0d8NCbP7Ee8+T7/l+XHG6rxUBAoppEm74bakHeLxHTBeqUYw+dP7VaWPX/2yTxilXZL1fBvXkJ7OrUUBer2eBoNBagtz8uBISj8bws8h8BAQAeC5vAld0k7Xgodr+S4g5pBre745L5whrBSMeF6EkPv6IeakeFiH/ExcvDgUjrUjKmB3l+cpWWdf62q1mgwn64i3TQqH6zPu3IvLyShvA3Mlzg9scdnjIPZ87P5ZNlJ4/YCxY6A9HIaYmReMMw4AaZb7+/skG9KTQ4DXjdfo12dekBFSYRg817m8kAf5+Pd9SzcGiJcEUF9gPrgeeslny+VyOj0QOZnP5+ktFsitryVr5GvPuJg/nC2exw2sp2MoDGIQPMfOdb0fm/F7Csg93X0k/12j0M0RCBTChGD66WF4f56z9N1rHhbmLSdurVwZpad8nrTbs8rvXGGbzWZ6O0W73d65BwrtOTvpqd8URUS48NDxpBgXn/XddnnjPgLP/f16LoDuwecGJg+rSClwriidInjC7nFWKpWddh0PGXnOnLRcuN1L9Hw4z+bC7kqIN5yHlsiPt7PxeSdLJ2QnbJSwXH4645izjPM0kXt+EIy3LBFt5N6t737rdrvpzFZy175Di9oGhOGF39z7Q16d0P1MamTL88qQrrcf5sU8l0eMkL+aKk9PuXOCw0REyP2n06m2261ub2938t45obnRxOFgXOip691z3RzuUeOdu1GBnNnk5O+A82JyUSg0vSBph3Sr1Q9n7B4eHurw8DB5OfP5XOPxOBGlexkoGhOPt4CyeN7RPcB97SmMy0NJwqt+v6/BYKB2uy3pyQNhgbyI5d/Hw2OcTjLSbtjnlpidcL7f371+yJXXw3jhJq/2ejjtc8+/XYkhAEkfeUVejMqf1efVQ3/uy//xfF40c08jD+2Yw5wwUS5y3xAr8+Unv7lnnl/b0wr5llfPQT/33VyO3Fgjc3yXyAJvzNfAjYj08Zs1ICN/Jmn3LNput7vjkXrRzTtMSFnkGzlcfvZ5hD5OunbYjMQc5Skm5GuxWKTzcj2F8VxKMF9rdxJ8PnBe3LPGGeFzrCdzIyl1kPhn87koCoWevSA9vUob78zDHa9ko2Tu5aJknkNEAT3sdE9F2v8qcB+T57RIH/jbHCB/925ZOC92UGTwEMatvBdyvEld2u3JzPPSLmguOBCwPxPPyv2dQCAq0hlc8/7+XtvtNvU/0z6WP5uTbqVS2VFoChvME88ESXgDv3upbhCc0D0VwxpBCr4tNu+Jlp7SKLlB3NcCh9HBeOfExHy5HLrH55/xOeFa5PY5BWuxWOzsqnQD4oYKz91TC27IPTWCp+kOSC5Hec1Bejpn1+fpueKjb0bKN614bpx7Mi6vd+T5aP+3F7gYp0dw7pyQqsGD9RoHKSqPFpg/Nwxe8HYuKAKFk657IU4enBxE5dW3o3qOi/CK3Sh5fpOF4v+kj0nXx5OP0QnBc5PS7uEZngJBETl5C6+Ujgw+w48f6ehhO90L3rvKvzmngPYY31fu3jOKl3c2uMeAl0k3AwqAAOdVc58X6alVyotz/N29FbwO+p5ZC19PvCr3TPNiV37ua07cfg1CRiddSMdJ19NNfgId5Oq5cgySb5DJQ2bveqhWP7zFgHUbDoepjcqdCfqV/Xnd69xsNikqcaOBc4DHC6G7UYDQfZMD9yQ3ixFHXmi1Yk4wlI+Pj+ndf+ibb1v3uob08WuWnuOD3AN20nV5c8Ilj73dblO/NEaSKBrewBEgTeY7Id0ReY4PPhcKPcTcgdBziMh2u00eHWE832Px8UK9+DOfz3faUtidxo8nzPPQODcE5Nko9KB4+9rPnIAxGCia9JSOoGUFgXUlcJLxzgGek+cih4cVJ70Bobvn5cSOYQBOONyf1/+41+cVXQ9B85CX53TSYm7cEyWK8TnkWTh3F1LAa5F2j8jk+3nRiLFiwJ1MPVrwSryH4B4p5SG45yn5vRfQXA489+hzR5uWtzjlabA8lUHeF3hkR+RAEZTDhrg3rZTIcd6qhzFlDr3ThshxOp3ueIUYYoyRH49J4dU7ByBq332Y11H2pWjwunlWvH3PkdNVsdk8tdkh917HITphrun7J5e7L79cFAp/BTsTCiHiJa1Wq/SuMISLfCpKTZ/sycmJ+v2+arVassL07+bhsRNMLtzAyfP29lYXFxd68+aNXrx4oUrlaY93q9VKu5darVY60o9QjK2Q7jH5jx+/6F6Xk5wrRKPRUL/f1/HxcXqPE03gEFi1Wk0nXfnRejwTHoOH4XnRCUBOeV6Q33nByglaejqTQfr4BCsMjbeNebFGeurNZg4xNu12OykbXh3k6WSNYWNefQMAB6LgbXv7YZ4O8A4bD8uZJ5dP71bBkCLjno/P59nz+553Zb3cE2WNma9+v5/OJzk7O0tb0dfrdTLInL6HHOZGwQu0kOxm8+FsZqISfxURc8q//c3V/kZvvM/FYqHhcKjLy0vd3t6med5XrGJMpVIpRXoUs/khf93v99ObPpbLpUajUZpnnAeMgbfoEXGS0sERg9iLRmF3RHixTtLTywzTYKxvtN1uJ4+OsLXVaun09DQdhkO+lXNFb29vdX19rcvLS11cXKSFxhPaF054rnU6nerm5kbv37/X9fW1vv/97yfyz8NjFJ0ff/U6b8TwTQiAvDTKK+0W8yC3Xq+nk5MTvXjxQicnJ+p2u6pUPuxU81en93o9XV9fp+P1pCcjBbzLgPOJPaRn7gkXIdU898xZGMw5z5uH3J6P4/qQP2s8GAx0dHSU2qlQft8ejQLh0fHmVrxe9/iZ806nk3qRIW6em/tMJhPd3t4mT989OSeHvCiKwaDrwV/K6X2p3veLbLkBy1McPLcfgsTnWq1W2pBzeHiok5OTdAIfnSfk+XlhI14ehCs9dYXskzk2EXBPOjt83l1++MmPgeQ5Hx8fdX19nfQIGXE98PSCp2og+E6no7OzM52dnenk5ERHR0fq9/vJAZrNZrq+vk4G1Q/Q8rNTSFWipzg+PGtewCwChZOuez6SdrZkdjqdHc/EC1LlclndbldnZ2d69eqVXrx4kT6Pdb+9vdU333yjf/qnf9JoNEreK97Lp/I23trCEZT0I/qefA/DDw4O0jZRioO3t7caDoe6urrSaDTaOV/UPYQ8z4nXWKt9eFX92dmZXr9+rZcvX+rw8DD1C282m51WJ7osLi8vdzwd+kCZ+1arpaOjIx0dHaW96gi6e5UILB4k0QLeT73+4aD48Xisy8tLXV9fp+f04p1Xt/EsiBrq9br6/b7Ozs50fHycwkV/SwcRgb/u3UmXuSDSwRPsdDo7Z1r40X/UAG5ubrRer3fyl05ShLrMj7eZ4WmenZ2ls549T+9pE9IE5IMZF54dkR7j4SB0SWnOu92uXrx4oVevXun09FSnp6fpFeR+wP96vd7pYoAA8SDdO5e04zgQsfF5WiaRtWazqaOjIx0eHqYNQXl7IPOEkwHp3t3d7RQMP6V/ECVOyZdffqkf/vCHevv2rQ4PD5NRYe0Gg0EyAHjrRBnb7XYnvYKesgbcE8P6O0W6hFe1Wi0pTS7c3t/Y7XbTS/GYED7X6/WSAB4dHSUrL31QnJOTEzUaDY3HY/385z9PnhuL8W1AufCMyZ/5KVWSkmDku5Rms1kiUi+WbLfbpIjVajV5qBCB91E2Go1Euqenp0mwvPXGixje9kQxhVwawFvEc+71ejvVfs8TQroIKkrEvSCZ29vbnTCZcBUP03Pm3gIHuQ8GAx0fH6eIRXrappwfe8n48x2AeMO+8aLVaqXvkgtk/iSleXl4eNDd3V0yJL7TCiKBVFi7g4MDnZyc6PXr13r9+rUGg0EyQnjMkDMdG5Cuv90DLJdLjcfjVIREViSlHObx8bFev36tL774IhEuNQ3fRQfpkZ6A6EhJ4ekSaZKC8BQcO+SQGeb/4OAgedqkAFkDT79A2PTG4iy5h/0peMdKr9fTmzdv9Pu///t6+/ater3ezmE8dIRQMxiPx4kn+AxrQxqCa/O5SqWS3jbB2heR2y19S9XuOyvp5ZXynUHsaW/Z9/Dez5f3puZV0Lxd65cF1/b+0m+rwvq991Xh943fi3JcB3jva/65/POfujefyed13zX9//Iio3/G77/vXt/mzTAW34WW5z39z3xcz81FPh/+Hf8zl5O8q+W5cefzuK9fd9/n83HtG7fP5b41e04ePjUX+2QwH69/Nh/bp+79bfpAnto7c35ZcP18A8c+OeHaz90jn9d9a8AauQP3HeHZSSqMdAOBQOC/EZ4l3cLfBvxt+K7c+1/Fun6ucewbQ1GtKUW3xPyy811ka84vi9+Wsf8m25g+x71/G3Twlx1HkXMdnm4gEAh893iWxYsr2QUCgUAgSDcQCASKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIgg3UAgECgQQbqBQCBQIIJ0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgWi+i2/LxUyikAgEPhvgvB0A4FAoEAE6QYCgUCBCNINBAKBAhGkGwgEAgUiSDcQCAQKRJBuIBAIFIj/B8mADxmWxVGTAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 37\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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4FEEHIAKSSCQQjUZRKBSwv7+PW7duYWtrC7FYDLPZDMPhEOPxGKenpzg8PBT3iW1BEYxEIhLGmE6n4uLR0tfJQ/7QygiHw4jFYvIeWkE6dqvjX4FAwPEZFKdut4vBYIByuSzxND2RdKJHJ+jcgq7dYJ3g06KsE0scc9oC14lJtpMWRLYNAMd9jMdjh/jyvmmtsQ+5GEejUUeymMlg/nDhZxhGv8d9LWwnABJ+W61WuHXrFuLxONLpNEKhkCN0Q9Hm2Of463Q6mM/nMt9arRbq9To+/vhj7O3tSew3kUiI2NOgoEXL+9ex5Xa7jfl8jlarhVarhV6vJ7HhJ0+eoFKpXGo4LRYLh3cXj8fF+ueix7biuLkMekKtVkvCYJFIRMY0NYjfSzFmvN0r3rro8mZisRhu376N27dvY3d3F7lcTmJwtBLoIrmTPNd9vhaZRCIhqz/wyh12VxW4YYf2+32Uy2UAwGAwkGvNZDKIRqMyGN3VFzpUAcAhxjrO+uzZM0lScHLTRea10+LN5/O4c+cOHj58iN3dXaTTaQSDQUkWbWxsSLiAExmADCK6x5zwdC8Zy6ao0qpIp9NIp9OIx+Mijpz8tJTq9TparZbcK0WT7m4ulxPrYjQaodfrSWx1NBq9MbDdGXptjXJy8fe6CsXdbzrrve697kqZdVYT/01vKZVKifXX6XTkNdPp1CHYvO+NjQ3xVGj5UkCHwyG63a645PQ0uFCGQiEZX8wlMB/AOCvwesGJxWLY2NhALpcTz4jiMpvNREwajQaCwSDG47HEx3nvDPUdHR0hk8k42llXP+iqFY5zJvX4/4xjL5dLtFotvHz5EicnJzg+Pka5XEa/378yCa6TaxyXTPoyPs/7uq6igWOC10RtYbiKyXq9UHc6HcTjcbmGt41nolsqlfDJJ5+ISxQKhTAYDODz+SQ2SSvn+9w4Bwgbl66mjgHTGroqK8mVu9/vo9FoIJFIIJvNolQqOSwqd/bcLbz6h8m8breLZrMpCRpm92mFJRIJbGxsoFgsYnNzE8ViETs7O7h7966IPicVk3l7e3vY39+XxA9FlxOHyQ9auExW0i2niCWTSWxsbIglT2uHFiAtpYuLC7x8+RIvX75Eo9EQa8rn84nwbG9vI5fLYblcotlsSgkW/3T3m7ucSpdBaUuX18JJyb7QFQs6s++2dPWYWvcdhFZdIpFAoVBwxHt5rQy70NUvFovY3t7G7u4uisUiUqmUJKgosJ1OB81mE7VaTapheK1cLNgvFEda0brdQqEQstks9vf3xTLluKBY7u7uSsiGlqq+Xy7cnC+0DJvNJlKpFOLx+BvliuvGOf+uy8a4UHe7XUmgUXCvslC14LvHhxZNd4nidfAzotEokskkstksNjY2sLW1hUQigb29PRwcHGB3dxelUslxn28Tz0R3Z2cHf/7nf45oNCoTpV6vYzAYiHWly5m0m3gZtHAppgxbBAIBTCYTmSScdNqNXQcnAF2d5XIpwqiz1hzk2gLQaMGlldhsNmWRofu0WCykvI3WbSqVQiaTQS6XQy6XQyKRcCwknEzFYhF37tzBN998IwkMWp5cfPTE5bXre6dwZLNZx/dzQVgulyICtEAymYwkJCkekUgE+XweGxsbyGazsngytMI20ROG18a/rxvsWki5sGqrjqEo3Xe6soH9oD/PHVPk9+swCdskl8vJGFssFggGg5hMJpLdz+VyKJVKKBaL0na6JGuxWEjlBfDKIuz1eo4kHa+XVird7VAo5Ih3czHY3t7GnTt3HBURbD+Gpnjt2ruhqPd6PUynU6km8fv9koBNpVJIJBLy52WhON0v/GHIheOGeQEdx16Hnpf02vg94/FYxpj2aK+zdgFn3TTbMx6PS/KbCcRAIIAPP/wQ29vbcj1vG89iuslkEvfv35eyKAb5tevFKgG6Xjqe5cbt+nACssPdE/MmZSfusiMKGMMe4/HYUUBNQdbv1wNyNBpJidz5+TlarRYmk4m4pYFAQOLQtI64ILkLxCkkHHy0sljRwElOoaGFpLP4+rP0hKclyooJDnS6dbPZTKykQqGAaDSKXC6HXq8nk4RlUhR2t3ehY6Lr0J6Ebkstnky08vPZVvoe3ZsE+Fm0itehrSwdE9ftz3sGXsV7WcbH7D6TSYzL6/FH8dHtrftDtw9DCgCkvSgcjPMXi0WpdHG3lx5/9Gy4ULOKQns8jPnTO2LlzmUbFHTcnWEjlnjRMuW407Hr6+C8pXhz/LFNdMiIfb/uc3UFy2AwcFjRqVRKysMYH2f/JRKJa6/xh8LTRFogEMBsNsNoNEKtVsPz58/x5MkTnJycSKaX8UmdSFj3WTrBQleOVh2zp5xAejW+Dm3t0lrWg4AWOUVCx3KJjhFXKhUcHx/j5ORERJcWNK1RChcHMq1HJgTXFbNzElK8tbuqxVknh9zXqOO1AGSQzudzyUqztpLxMFpX4XAY0WhUJle/35f4JScz+08LzLpJqGPiur3dVjGFRC902pLXE9H9OW532T2W3AsS3WIaBrSoddJrsVig1Wo5KkZ8Pp/E51lBQw+s2WxKnHtdTJmJIL5H17ZygWdFgV7M2A7j8VhK/liJwVCQtjy5mNKq7vf7GI1GWK1WUp2gPQT3dbKNGG/VXpbP55OQFtvvOjhf2IY0tnQiUc8z9v864eX7tAfE/tHXyTwK589VoccfGs9El6Ur3W4XR0dH+Oqrr/DrX/8aL168cBQo8+e6DtPxJD3wtOjo+k63ZXEZtPqYBdYxUHfHXOYWA693XZ2fn+Pk5ASnp6ciTLRcKFxMHHAjAwc841kcZDqjzolI4eY9cgGazWbyPneVBfA6KdTv9xGJRABAdpb1+330ej2p412tVlJGlMlkkEgkJITDCc12YgUDBcQteFp8Ca9Nl4a5LXxtweh+WCeq7jCSFnv39/L3+jXT6dQhVFxAONZoaTGE0ul0ZFJTdGkJc1Jzp2C/33dU5OiFRlv1eg7w2nTuQt8LBbrZbOL09BTfffcdzs7OUK1W0el0ZAFlAo+bZShArLP1+XwoFAq4c+fOpV7JujHPcBvjroPBQMb6dYaOtsx17Flb+bqv3DH6dfD3tJQZ5uMP8xHUl3Q6LTv/vMAz0WXNZqPRwJMnT/DrX/8ah4eHqNfrUjPqnpRXCSQHIBt/3SBhoucyK+cyOHCYbWZmmdll1i7qya8tAFrzrVZLahdZOsXO1mVurEmkiE4mE3Q6HZyenjqy9tlsVtx4AGJ96moKPYkp7Drc4C7T4v/xhy4jFx0dsqGlxGw4Fyhamu5FScds+edVMXV3W64TUv25697j5roYnbsttBUHwFHipWO6/P9+vy+lWexXvWjqRVELrU4Q6rGgt/zq6+fYYJKT7UAPjJuOXrx4gbOzM0musr/6/T663a4kren50aCgd8WaedZQ6xp0Xguvl2OR/8/5wrHuTp5e1986POSeszQyOO+uGkP8LOB1uIFJPh1j5uuYxGYFw9vGk23AdKHPz89Rr9dxenoqO290/JZctzqy8elmaRd6XWfozrvJpKd7wjInBuB1sbjbJXELLjPWTDit21SgExA8R4EbInw+HwaDASqViljBeispLSqWekWjUcdkpwvMAQdAVn66VwxP8DPC4bBYQYytu++NIRYmtVjixO9m4shdgUAL3W3VsU+0Je7+nW7jq9Cv1UKqQw1aQNw/2vIFIEk7wlgt74996i5v1GWAjNdzjPM9tKhXq5WMZZYksqpAGxKMybJigW20WCzkHIaXL1+iUqlImIjJMHpPtNr1fKOHxI1E7XZbNlLkcjlHOE23qS4toxB2u10AkPF+EyNHL3a6n9ctuGwnHX64LAwCvK4BJrzndruNSqUiJY7T6RSPHj2S737byTRPY7rsPH34yzp3U7/H3XG0cJnlXywWssJelnjS7yVXCbv+DD0Z3MXi2oqmdTQYDMTCZbxvXfyO18CJxcNFWM7CAcVDcOjqM+NM4aRrxPcAkN1ErBThdTLZRpFPp9NSpsYzBIbDobjxtPBo9TGuqNskEAg4kh10DQeDgaMf9CEo7v52Cx/F0b1Y6rZzjxP9u3Wfp1/Hv+ski7vM0OfziQDytXTtKXRcYDmuAchixhKlQqGAeDyO5XIpfef3+9HpdBwVMlwAWdnCccMxy6qEVCrlKOZn/zB0x910TJolk0nM53M0m03pG+1FutuTJWT1el0+g2NNtyn7lfezWq2QSCQc505cJ7pazN3zTr+X44cJzEAgILvr1iXbdT+7x5nuX73l/G0LrcazkrFoNIq9vT1EIhG0Wi1UKhVcXFyg1+s5Gl27MLSM3JZHPp/H7u4ustksOp2OJHDcYQk9sfSfFLvrLGqWmdBq1JPT3bG0fJiYYo3iarVy1BC7hYQDLxaLoVgsYmtrS/aMs9yKiQktbHw/S7kSiYT8nmVeeqebrs1lkq5UKmF7e1uEgdYaLfpIJOLYSUThicViUvOoDwbitlDuLtSJULfwXiaGuu/5O/f4cE9I/ae7bdeNRX6HW3DZl7w+lo6xPZiZp2XJmLZO7NEa0wf4cPMBvRl+J7els210P9FL4QLExJwWXLd3xeQRxYlnmIzHY1QqlTcqRNgO3EzDMEOv10O9Xkcul3Och+GuVdf9oT2nm8RGtYXMe9Fx93ULKEslM5kM2u02Xr58iWaz6fB03fqhxw37m5tLtre3sbOzI+V368bi28AzSzcajWJnZweRSEQSTGdnZ7LVVzcSO1ZPBg6OfD6Pu3fv4v79+wiFQjg+Pka1Wl1rMfP/9GrtLuO5bDVmWRetObcr6oZWBIWHcWpdH8j7oDvK1ZaWYzKZlDpZuoWMQzGGyAy5rkvVggq8XgRYGsT75MRJpVKyAYMnba1WK9lAQaFlH+jCem7syGQyctIaq1J4QhpLznSZlu5HWtxst3XwXvQEv6zsS4uBOw6ox4G2vPW1uOPFvHaWFeXzeTmdignh+XyOTqcjbUIDQVvMOobIhdt9diy/U79eZ+61d8ixy77XY47hCMC5w43WLq1QbsPneOBrk8mkVFvQkmeY4KpSO91HNB503Pmy97nL6jjm3BUJtPZ5vYVCAbdv38ZsNkM8HseLFy9km7sWWfavDmlRQ9LptByTure3h0Kh4AgjvW08E91gMCjhgFwu56hx1IMWeNNCpYWVyWRwcHCAd955B/v7+xiPxyiXy1dOSL1istyHA/uqDC3FhcmOm8QTea26g+kWssSLta0cZLSIGLpwD+TVauU4WIQCyYHKGK3bUptMJo5aUz0pcrkctra25LxWlg1x55o+Z0EPYFrJtKyZoQ8Gg1Kqx5Ix3WZaEPm6yyYyFxF3nFf3Jf+u28pdL60rWi5DW7tui5qTnFYjz96lsHW7XUcSU4dDuOANh0P0+31ZvBnf5f2zjZk7YPvo6gXdHrwfiiwTejrp6Q6rUeD0+Rr8Pa1TbobQh+fovMVNrD+2OS1Jd7hGw2vipqh1Xpx7TPB3oVBIasU5znw+n3i8+rVuTaHWMI+Ry+WQzWaRyWSuXCR+aDyN6XK15kBzu656wNAioGtD95tngfJgZbc4rENbDOys8XjssK7d18rXszbUvdtp3es54JLJJPL5PHq9nmSA0+k0BoOBVEXo811pGXAi6rIwTrJ2u41GoyHlWJygwOvEIu+VljAtW3196XRaBLdYLCIWi0nsVif72Cf8YW0uY8FcNHVybzKZOE5e43u0BcLXua0SHS9c92/9et0POoSgX8sJ7+5jbZVxEdGLnb5XWmNMiumYLtuAmX6WkOkEq062LZdLx/GVbO/lcik5CZ2Z1xY38PokOPY7a7v1CWI6UcvKElqAFBttdVN8GIqIx+PIZDIoFotyroM+3OkyD48LPeeJ9iDWzUuORRoa+nTBdejFjJ5DKBRCPp/H5uamnLXNkJbeeu72blarlYTrdBXR/6iYrhse5VYul9FsNh2P4tGxLR1H1ZM9EolgPp/L0XWspQWurkzgAGanXRfP9fv9khCi283KhcuEl6t3oVCQa4pGoxLjZIkRT3jimbvaraNI8z64sYOCGwqFZNDpQ270DjCKCWORJBgMSrKOoQGGOwBnWQ6FRi9YnKS0Dii6/F59VCF/CK1unj3hjvleh3Y9OdG1QGlLV/fJutiujqMzzqqtb32AN61ULZbA69pOii49LS3UOuTD7+UYYjXBarVynB0LQOK8XFxofHBxYCx5Npuh0WigUqnIJgh3e3G8B4NBOcSeMWJaeslkUjwYjo+dnR05x1cvSu4+cVv2XIDW9ce6/gTgWFzWoa18PgiBIS3u6OT8pPDrPIiu/WU9c7lcRqPRkHCdl3j+NGA++ub8/BztdltWHMD5zCW6PbSkOOgZS/P5Xh1EwrImt4u37rvpfunVcB1cpbnBgaVtdCfXwWuPRqPisjN+xInJIu1Wq4VEIiHXT6tKu3O0UBeLhSNGTOFKpVLiZmrLlG3F8xw40XRyiLE+DkRdKK5FV8d39S40hkOYMKIXs1qt5HD6VCol26Z1PF2HSljCpq0lipc7oaL/z+0+6gnMvtaWsE7a8Fr1rjEuLrxXxjgZZ+frdMyYgs2FkNYS20mLLq+PVR58XSKRkCc40BpdLpfyxAfeM611XiMXrF6vh3K5jNPTU9Trdcznc7lOd4iGBgG9sXw+77BoGQNmgpQhQH2+w2VzWlfZ8BE7/O51c4z3wSTkVedla68TgOzs43cvFgtJDLNah/PMXVUDvKq75uOsKpWKLJhe4qno0nKs1WoiOO4Jry3ETCYjhxpzIPGoxPl87jiDl+4i8DqOpy1SunocyFeFJDhRuYuIFjUfI6RdYKInnBY/lg5xh1e32xWLSbuUtBD1zjSf7/VjhBaLhexmmk6nYk2zNI3F7Ix78ZT/bDYLAGJpc+FjG/J3DHfQHdWxRVoL+nlutCJ0LHq1WskmEt6PDktEIhG5D2495Xfq82bdIkor1B2S0FbvulI0LmJcLOg98Xq4QAQCAceZwfQS3JYuXVO69/qIRr3DS092/o5njPA6eVYDBZyW5mq1kuSyPrBGP0ONY73RaOD09FSewKErDJi/ACD9wU0t9MZKpRI2NjYklssyzHU16W7Rdbv8LJXkSXqcQ+vQYTO2l9tb5ffxnugp8akpXKQYLqF3SC+Yu0o5brQm8EwUfYi5l3gmuu56Qu1echLpvzNzvLGx4Yh3cgBz+6VeqbQryeoA4PrJqdHxz9VqJUc9tlotWfkpquusah0jYqiErjrvg1tvaeEwSZjNZuWQdF4HQxIURi4W3GZ5fn6OcrksCxDvXx9Ozhi63kU1Go0kkTmbvX62FgWx3++Lu8hkHWNgOkmyXC6lYJ/3q0vkAEjSjfWqLMPTT7Hg5GPf6bitO57rHle8Fr5fT1rGQnmOqg6BpNNpx0lbOp+g74WL+XQ6lbMNTk9PcXJyglqtJh4bRdltjWuDgLsSeQIbLTVdmsXn0gFweGTsU3oQzWYTZ2dnuLi4kBruVCoFn88nf2fMlAs2F0qdEOW4o/hSxHR8mujxzvk0HA7RarUcZZLas7jM8+S4Ylvp+D7w5iFIy+VSxH25XMrz21imxhyM3+93nIHs1hjOB/3IKi+tXU8fwV6r1XB+fo5ms3lpuRYHPZ8LtrOzI4mvTqeDRqMhz4G6qtHcnc3JeV3sSCdRlsslOp0OXr58Ka6kPnxGDwYdbwRe17TqpAZFUb+eCQ4W0vPJwVw0GIeiVxAOh+WBkMPhEJVKBZVKRYrFeRALwwa0WJnlpsBxAaNrzf3ynU5HEn2ssqAloK08igafTsxjEHVsnpOG9ZvxeNwx+NmGDOXoRYNtqQV0ncvqntiXLYK8Bn3OBf/k52trlQsnrTJO9rOzMxwfH+Ply5dytgENCOC1t8a4Ij0jihAXUp6opy1dVsvo6gV+jnbZ+YQFJmUZ3+V40fF3LnQ8c4BxX12S5n48j976qysA2J7sF57dUa1W5cByjkMuWO6DiIju33WLqduyZuJRV/XwPrmzjAZGOByWM30vi0XPZjO0Wi2Uy2Xs7OzIUY9e4Nk24Ha7jV/84hd4/PgxqtWqrHJ6JWUCrVgs4uDgAA8ePMDW1hYCgYA8vZMrNg9l0VUQl4UL9LWQy6oWaBlRvNrtNo6Pjx1VEru7uxL24GfpzRZ6dXZb30yCMKYJvD7DlfFDv98v8S63Fc/Bx2vj87D0Z9Hl02ckuC18Tmp+B91rWrj6wZM6JEOPhSEKPll3f38fiUTijRgwwx3u8wL0awBIXFDXT7uFmfFN7YLqH92/FDqGFvRTPygcXPx0n+uYK4WFHsDJyQmeP3+Ok5MTOR+Zi5peHPR4pKhR8Blf1y41rT4mgXQCiH0JvCpTo0HAz6BwuoWRIZTlcin5CO2ic5GlweKuONAxdN2eHMt048/OzvDNN9/g8PBQji+l8NHK1pamnn/aE71u3rrFm/XmkUhENjqkUiksFgskEgmxZHl6GtuQ9zifz1GtVvHll19iNpvJTlDdj28Lz0T37OwM//AP/4DhcIhqtfrGM70Y1yyVSrh37x7effdd3L17F4VCAQDkDAPGWvUxkOs61f397tXULdI6iaeTdvqZT61WC91uF48ePcLe3p4kqXTc051p1nE9Wkv8LBbX60e38170pHQLCN0oXY5EEXNb37wmWjP8nY5/84g+tqeum9RJMh3iYbiD4jybzbC5uSntwYnPNuf7GP/jFlhawhQ43acavfHD3W/rQkq6Cka7zQAcgsaKA6K3eTMUxIdWPnv2zHEqnluQdNtTvFmHyr7iIsN+57hnQpKLIe+F96cXvPF4LMlQhhvYllxgtUjpeLx+dA7bhkkzCjPfpzdrsD0p3Hya7uPHj/H48WO8fPlSPpuLOasJ3F6gTo7q+ajn67p5ysoNn+/102ZYkcOt7MCr6hMaZpPJBLVaTXI/ZDqd4vz8HP/xH/+Br776Cr/3e7+H3d3d/xmiSy4uLvDv//7vyGQyjkw88Hp7bzablUef8Mm7jHty0ulJq4uhr+Iy11PDwc2JytIsxt2azabEornlldsHKUYcbIylMhvNVZeJkGq1ilqtJnFExjn5XLdAICDfx8lLwaWlRgHTbagnv7a0eC1cWFgxwsoPWnN0eXVIQScxAOd5EZxgZDqdIpVKyYLK1zNBRmuWgkXh0VuZmfDRiTNdDucOO/CHC4S+bvfnMqzCa2E1h3sHFUWcScxer4fj42O8ePEC1WpVYutckPSOMS3mNA70YknRZSiICT1WFrD+lmOSMXfirlXlZ/P+GZ9vNptiVTMEValU0G63sVqtRLxYm8snlHDB1Afk0Btg3HQ8HqNWq+HJkyf48ssvcXR0hHa7LfOUhgvj9utOG7uJZ8rXuf/O+ae9RbaF3++XJPLu7i5arZZ4HvqMbsbn+Wej0bj2Wn4oPLF0AYj75H5IHVcVCp7O2MdiMYmL1et1nJ+fo1qtSlkKG3CdW/+bQIuQ9ae0vvS2WMal8/m81D4Cr5/QwAHL82zn87nEvXj99XpdqjeYTWUhe71eRzQaRbfbRbValfaikNL11lamdsPZ5loQKVr6uEJ9zCSTIVzMdCxSJzNoZborQOr1upwJsLGxIROTFjR/dPiAViA3WVBQdbzc/fd1YQQdxnG/h33K9qCFxk01wOuQFq1N/uiD5XkyHh8vRRHhIqaTixRceho+n09CRePx2CHSHGcUNF4z+849R3RuYDgcymspYBxrlUoFfr8f9XpdTvc7OTlBtVqVmD6vhWVxtLT1dmeWJHKB5Hjq9/uo1Wool8uo1Wrixvt8Pkmo8vX6nOGbiKxGj2dtWPHaOEfK5TLS6bS0N09KYwWQjlHrGLJeHHWt+tvGM0vXnT3UA4pCNRwOJfg9Ho/lpCNO7BcvXuD09FRimFdZuLrY/SZoK5elQjqrulqtZPXW2XmurvpJqxxgXI2bzSaq1Sqq1arEYHUhOcVoOBzi4uJCnvLL5AdFSg9exoXdZ0hQ8Jl8Y0gGgMRheQ16xw6tBx3i0MLhtjLZ9jxsqN1uy2YIFvuzMkELun5mnT6TlxsmdPbfnTDjeHEvrHpsaYtcx58pSjqezpAMXXQm+3RIZDQaoV6vS6WMXuTcbaIFV7cfK0Z0IohhB7YDLW0uply0uDDyczlOWPLHccG+HAwGuLi4kNPGeP1cMOiNrVYrdLtdNBoNx84zVgUwnKBPWWP4gv/P+cANSz6fTxKWHKfconxVXfxl8/EymIPo9Xo4PT2VI0k3Njbg873aElypVFAulx33fZn28DO9wnPRdf8deL2C8zjA6XSKbreLcrmMRCIhVQT6gOXLrFy3u7nu+9bB9+kDXYLBoFhpwKtdSHt7e7h9+za2trYcB6HouDEnOzubu9A4cbkpgANBd/h8PpfaUb3NV7u8/Eyd7NKiqycm3dPlcol2uy2PUaeVrUujdMac16QFRV+ntjD5vbSMGBfU8UkdgtGlXex/egWXTc7rYm3r+lhbhmwnbW3zfdyOra/XXWfLa14n+pzM+jp0uIpJJbr/i8XC8YQQv98vCytLIfXmEbafrvJguI1iTcEEIAYJY9KsM9ebENhfo9EI7XZbzurlWRosx3In2LhjkmOQFjPP0qWnwOvW778Jeg5zUWMbuecKjTSObZ7mRm+GlR3uxfKqMeMFnm8DBt4s53K7xWykfr8vbgoPD9G7mC77bHdy5aaxI4pmOBxGoVBANpuVeCZjRZubm9jf35cj5mgh6NpVXfjd6/XE6nUnp5jd1e4zALG+mH12W07uk6h0G/KeaV2x7pPZZp7julqtZAsltwPTYuWhPHrh4nfQlaYIcNur2wtwHyCiLULdLww7cSKzbXSsmG2jrS39O90GOg7MxZCf5a404ILDjRC6bIrbp+n98P/Y/quVsxaV6Pgzd1XmcjnpS1ZCMLbMTQ1MCrGCRMfVdf+6+929k1HHvhkicf8/+0cn7mj5c0xwy717XGtLmX/P5/Po9/syT5gD0U+CvqnA6ZDNVUaT1goupoyLc0zp/tZt6G5Tr/FcdHUDsHH1KUgcRJws7oy62+JaV7ztFl7gZu4DPzscDssjztPptFgVnKCsCdTxOV1OQ8uN5StMFjEpxeQHRYIiy8euFwoF2bhA0eXEoDByYHHwsT311lN6DxyA+uGJ8Xgc+Xwee3t7KJVKCIVC4l2w4F9nfN2WHCctnwfGa+ezplarlQgNv5vWhy5VYtvput51wqsn4LpEmr42Xh8L57kAcOHi2OCOQX3oC+N/vD9WPOjdjLoemdeuN1HQ9c5msyiVStjZ2UE6nZZa9bOzMzkHlosjE2OsGKAXoh/nTsFkJYo+wJ7bjimuDKXwzA695Z73rk9Ic+/S45kL7nrd1Wol8zWfzyMQCKBUKonnsFi8eqjAd999h2q16vACroL9eJXguuc6P5uJ4Nls9saTXWjcMEnJOecez17ieXiBE4aTjJOWB2/ow0D0Uwx0csHt2rob7jcRXoqbz+dDKpXC3t4e7t69K0X/uqMohnSlaf0BzmJ9vevHbQVyMrkPBd/c3BTRZSkTY1h87pqOH3OC0erkgsXBpR+RTWs7GHx12tju7i4ePHiA7e1tBINBtNttSej4/X6JnetaUd4j+5CHdNMzSKfTDutTW8b9fl/CG9wSC8AhLrTwddiE96o3e9Cq0pNKewzumKQWW/Yhy6VYKcPr1Neuy8z43bQQdQiB18ExnU6nUSqVsL+/j9u3byObzWI+n0uOgp/FmDgrBHi/bFudFKXoJpNJ2ca7tbUllrR+SjA9ReYT6vW6LKQcA/zhGLzswH6dQGXCl4sEd7NxHiwWC9kK/OLFC8fcuorrBNc95/Vc17FtjiXeC/WEYRb9NGbtBXspvj+apQtABhbLO3jQd6/Xw8XFhWProk4W6aSKDgtcZgHdFFoAhUIBu7u72NzclG2VTDRRuBizpQvJR6XrDRPaWqGrpl0txo/T6bQUeJdKpTesF+CVBTEcDiUUwAnLCaytXF1Ur0MS+kAXCjYFlPenvQltZfN+2O5uweOio+PUFC1OasZtdVKI16FddVrKevFgcpCeD3+vtzvrA8L14qwTfxRftjtPTGPyj5YTr59jje1La1SPZX4+J79uc92uHGP8TO3J8dlzXCS09a9jzaHQq0dV5fN57OzsYG9vT04D030wnU5leyzbRM8Rff00Amj56tyE/jzGSnUyVIeXuAMzEomg3+9jY2PDsRDcFO3NuMccx4TbiKMHN51OZTzwJD3g1cYSCvC6vIKX/CgxXT0J6C4xtqjjNFrk3JaTTl6x/MftMrg75jo4ibkdl5OR4snv4gBkLDKVSkkHM2zAZImOQXNV5n1xcmmR06LEsAMtKHoCPCmJCTFeu46/UaSYBNLxZCYm+QiXer2OxWKBVqslVpFO5OgB6m5LZvi73S58Pp+UuNGNpRscjUYdGz4oKDrG67YeKUJMNGnXf7F4/ch3fUQj25D37V4s2EZ6cwitv/F4LLXYbH8mIvVDVPV9cEFmG+nkK6s3+JDHQCAgD0VkUpULIc9T0JtgtNXOxYkLGBfqzc1NeawOhZ5HhuqkKC07XQIJvD7bQcfX6Ybze9kuuuqFsV+KNS1kztFCoSAnqN3E+NHGk/4/wsWKnpjbA+DcpPAy3qyrMlgrDby5ycZLfhTRBZwnDdEN4gDR23t1o3KV0xsYdDmV7jTdoDctB9GWNIWOA09bdQDkYPHpdIpYLIZer4d8Pi+xWFox+pQybdWsO01LlwTl83mxGvQBJUzO8QGFug5VW7j8HrqCOjRD0V0sFuh0OnKMo05WaiuZuMM17D8mC1mryWugKLIED3ht3TE2yHbXNcM6hs4JDQDJZFKe3kpLlJOen8VQCgWEbrteqPmdrOBgwkffP61K9j9/RyvPbeXy33Rx+XqKLK1Ova1Yl/sxh6G9gfl87tgFpnMgtHbz+bw8QJJ9zqw+dz42Gg05jU6XrvF6WdrYbrflySraNWfZY6PRkKNIdThQezs6tKNrY28C+9QNBVcna7lI6jIwbdQwh8A2Yy5B75Djd3rNjya6wGuRabVaAF65ACxM1yU6/JOTjImbaDSKyWSCTqcjWXmdwV7nUl0GBZDJB2ZjAchg0q/jBgMmtljmxiJz1qn2+315uipPBtMrtE5UUcBoLbJektl14FVJzmw2k3pbvf+fVgzFVi9a7ntlIksfgEKh1Qsd0SEAfgavnwse2197JrTM9G46fpZ2SSkW+gwJ9rXeMbVuUuqSQ9av8skgnKysSuACrV1/xudZ26xDAbw+HZ5xV5voEAwACTPomK1OtOkKCN0n7EddbqcTXhwLGxsbchhUqVSSBY3nKQyHQ9m8wFiuPlODHg+vjw+iDAaD6Pf7SCaTYiGybzjWA4GAHN/JsJruW4ogt87z6MSbzD+2pW5bjpVwOOx4BD11Q49l4M0Dz/W84oYJbUi4q2G84EcXXa78HIg8SEbvRuNKzyMQb926hTt37iCXy2E8HsuulGq1KjEnxiZvClfJRqOBw8NDHB0dYX9/X54ZpeNXdGmTyaSUxtBi5xmd3NjAgc54GC1AusJMPtDS5KAGIAmTfD4vVi4P+WASJBqNSja82+2KAOldNgAcBe06NKNFmffpjptxsePk4kDXhw3pTQEUDb6O4qbbLZ/PO8ryuHhxYdLZ/2KxKOe+MozAWCfFlpP84uJCqi8YJ+WZxnxcDQ8KajabUu+svRCKEq1OtoXO+uvzEijYHK+AM9+grWIdh9aWmTtGz8/gvfJQ+r29Pbz77rt49OgR9vf3kc1mRRxZH8strbS29dZuutoM0zF0cnp6ilarJQk1iipL6UKhEIrFosPCZthIx9IZbjo6OsLh4SEajcaVJZ6XoauaUqkUNjc3sbe3h2KxiEgkgna7je+++w4nJydSzcNxyiSaz+eTseEO5/yYePqMNP6pLQQOWiZduJrxuDY21mr16qkE9+7dw4cffoj3338f+XweANBqtXB8fIzDw0M8efIER0dHsqp9H5isevr0Kb7++mt8+OGH2NraciSKlsulCEcul8Pm5iY6nQ6azaZs3iiXy/I4Ih77py1DvZNNV1noR6bs7u7KJgx9kPtqtXJ8//b2Nr799ls8f/4cx8fHYmHrgcUKEU4WffoUS8qYwKKVo7elMmPOScbtyi9fvnQkPGnlaNGl8PJzKCC3b9/GvXv3sLW1JZ/Z6XQkw87XFYvFtc/scseuKboU3m63i8ViIe2ZzWZlF9j5+TlevHghbaXj7Fw89HjldbN9mPgtFovymdzQoOPfFCC2D4VRnzjGA7X14s33M+FXKpVw69Yt3Lt3Dw8fPsTdu3exs7PjMAj42az5bTabqNfrcgocxV6PQ4o+N+5woeGTJXZ2dnDr1i1sbGygVCpJKINjhGKra+Ln8zm63S6+/vprPH36VPIz3weKZz6fx8HBAd5//3288847uHXrFnK5HACg0WjgyZMn+NWvfoXnz5+j3+9LKWQmk5Gdha1WS8J5+ugAtxZ5ieclY+viYRyoFLTNzU3ZS63dmu3tbXzwwQf4nd/5Hezu7sp2w/l8jo8++ghnZ2f49NNP8bOf/UyeDvp9r5GnEj19+hS9Xs9R8we8rpyg1cinqPJR5PV6Hb1eTwSJokvrlhYED/XWyaRQ6NWTTu/fvy9PPE6n0w4LlBYXvQEKf7FYRDgcdjyhgN8biUSwvb2N999/H/fv30cmk5HJwR1+wWAQ+XwepVJJQjdsE7/fLy4lE0Xn5+d4/Pgxvv76axwdHaHVajniqdq64YJFUSgUCrh79y7effddbG9vy3cxZMPkoE6w6icZuMsAeX4AH6q4t7cn4qVL9vgd2WwWq9VKwj5u0QWcG3gouNlsFgcHB3j06BEePXqEra0tsS65vVgLNl1gPillPp87zn8NBALodDp49uwZvv76awkN8Rr8fj8ymQwePHiAjz/+WMZENpt1PEZHj4t0Oo39/X3ZZs45pa1nGhc85IghKo6XWCyG0WgkXtX29jb29vbkgH232Gq4O+3p06eoVqvSDzedf1ys4vE47ty5g08++QR/+Id/iL29PTE+GGPf39/H9vY2vvzyS1QqFSwWC8fjqLrdroQTdNWL5vsuCD8Enj6CHXj1FAG3GHIVZsH+7u4uCoWCuD+TyatT8ff29vDOO+/IsYo6McLY33K5lCP43I2s/86BqieXjiNxAq3bdQQ4dx7x75PJRNw9rvo61kcXP5VKYWtrC4VCQawPZoSLxSLu3bvnEFw9uLX7z8J/lpYxlscYs66lvH37Nj766CO899570k5M9IxGI6naYM2qTnLR2tM7vCjy3PhAseHv9X1T+LlIFQoFbG9vY3t7GxsbG2JVM8zCmDDFTpczucsFdR/oLdysnqBXoatPAMg5yYlEwrEDTQueDuHwKcoPHz7Exx9/jIcPHyKXy4kIrKsjZ60sk1ocx0xC+f2vnnCQSCTkCEktfvzOd955Bx988AH29/eRyWQcXpJ7XPAQof39fVl8U6mUVH/4fK8PlAIgC5z2TrhgB4Ovjk0sFouSJHbX0mrYZsvlUhZyLnbu+bcO7V2kUik8ePAAv//7v48PPvjgjU0uDO/Q+zg7O5NxzC3drLZg2FHPdRIKhSTh5hW+axriBwt+MGDP2M8bF+LzOQTssiymXt3fuFiV2Pm+B2y4r0WXm9wEnT11l7Loz9VJGL2RQv/+qja47Lt1beg6F0rv/NJJQXdlyE1dLu2l6HvVXoxG37uuKdbfd9lY/D4u4GVjS/+ecWddnrcu2eoOielrv0x41l3PZe3M3+t+09fB73Nv9b0JLEF0fybHG+953b27xwwX25t+N8M2N0mgXQYXTNb5XjbfdT7hqja4rESMiXc+2ugH5NLG8kx0DcMw/hdxqeh6/gj26/ihAts/RIbyv3ItN3Wl3gbXWXtefedl/BjJi8v4bbpur/vtbY7RH3v+abzUnZtglq5hGMYPz6Uq/v02RRuGYRj/JUx0DcMwPMRE1zAMw0NMdA3DMDzERNcwDMNDTHQNwzA8xETXMAzDQ0x0DcMwPMRE1zAMw0NMdA3DMDzERNcwDMNDTHQNwzA8xETXMAzDQ0x0DcMwPMRE1zAMw0NMdA3DMDzERNcwDMNDTHQNwzA8xETXMAzDQ0x0DcMwPMRE1zAMw0NMdA3DMDzERNcwDMNDTHQNwzA8xETXMAzDQ0x0DcMwPMRE1zAMw0NMdA3DMDzERNcwDMNDTHQNwzA8xETXMAzDQ0x0DcMwPMRE1zAMw0NMdA3DMDzERNcwDMNDTHQNwzA8xETXMAzDQ0x0DcMwPMRE1zAMw0NMdA3DMDzERNcwDMNDTHQNwzA8xETXMAzDQ0x0DcMwPMRE1zAMw0NMdA3DMDzERNcwDMNDTHQNwzA8xETXMAzDQ0x0DcMwPMRE1zAMw0NMdA3DMDzERNcwDMNDTHQNwzA8xETXMAzDQ0x0DcMwPMRE1zAMw0NMdA3DMDzERNcwDMNDTHQNwzA8xETXMAzDQ0x0DcMwPMRE1zAMw0NMdA3DMDzERNcwDMNDgtf83ufJVRiGYfwvwSxdwzAMDzHRNQzD8BATXcMwDA8x0TUMw/AQE13DMAwPMdE1DMPwkP8PPsE6cuXGhM8AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 39\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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e4mZGz61er4t8RQ/IK3hKugDENePi54LSC4vESLf+qs4GIJM9Go0ilUphaWkJy8vLSCaTMtkqlYpYb+5uNhwO0W63cXBwgPF4jEKhgN3dXWSzWaRSKczPz2NhYUHcQQYoGCTRFh6vHQwG5bm63a5sMPfu3cOnn36Kg4MDlEolTCYTxGIxZDIZpNNpcfWXl5eRSqWwu7uLW7duYXd3VyyKi4sL0QAfPHiAWq12ZR9RbtHWIvCUsGjN0GLk4tcBG35+VhCNY6A3H07iXq8n1urp6SkikciUm8vP0H2mFUfpgkSl+3YW2P8ks0gkgng8Lp4A5Rc+D6069k2n05H7kXC5OfBv9TPrDYeWKy12d+Ph/fil56C20jmvLkMwGMT8/Dz29/exu7uLra0trKyswO/34+bNm9jf38cXX3whshLJpVgsolqtinzh8/nwy1/+El999RV+8zd/Ezdv3sTy8rJozJRd3GBeIBAQC5ReUK/XE7mqXC6jVqvJPWnlHhwcoN1uPzNHOb8oJzWbTWSzWdHM6/W6kC4Nr6vIkfMnn8+LMcONkRsp12MwGJxaC17CM9JloCOTySCbzUq0sN1uizXAiaizBJ5n7utIMUl3cXERi4uLSKfTojPp6KgLbYU/fPhQiHpnZwebm5uiC3Jh6IU1K7Cjf85N5OLiQoJbjx49QqlUkkAWLZlIJCKWWSaTwdbWFvb393Ht2jWkUikhDrpJX3/9tbj91PNIoiRSEj/7WU989gvd8lQqhVQqJcE/AKLfVioVCRZp4qGFp6Ud6pfMwKAlxMmuF7TOLqE1yC+dRaCtSB3o4ELUC5IbRLvdFktaW/HaatZkyLFj5J86N7VvXp/Pzb/jnFtYWJBNme1otVqo1+uo1+toNptTsg0AaQuflZkwtAr5e7/fL4HCTCaDa9euYWtrS6SHbDaLbDaLlZUV3L9/HycnJ2Jo1Go1mZvcPEqlEh4+fCib7Hg8xtramshMel7r7zo7g+PAuEW/30epVMLjx49xeHgo7ajX65dmH7FPmX0Qj8eRSCQwGo2EyJvNJnq93lTG02XQm6Q2gKijz83NYWFhAalUCpFIBP1+/8pA93eB75x0OWBzc3N49dVX8eabb0rgiXpXKBRCpVKRgdGT8EWuzwVJN1UHrTQJ0TKddV1aJLVaTQgkHo8jmUxicXFxZnvcKLCWLPjFABmlklqtJsTJNqfTadkoaKVvbm5ie3sbq6urSCaT8kzcsXd2drC/v4/Dw0MJonDCuSlKJDA9EblouJCXl5exurqK1dVVyVLw+XzodruoVCo4PT3FyckJzs7OUK1WRdcEnkxqTuhkMgm/3y8WFS0cV+PmWLiTnUTjBub0GGspR6fy8TOasHg/9x5sjyZrWvvchGKxmGxk/BuSJY0IuvpbW1vY2NhAJpMRHZJZDblcDrlcTjZh9huvSeuaLjDHh5sBEQ6HsbKyguvXr2Nvbw8LCwtTmTaRSATRaBTJZBILCws4PT1FPp8XEidhkiRJbPl8Hul0WjZcl9h0UI39pH9P2anT6aBareLi4gKPHz/G+fm5aKiXrWWOq96IeQ96Qtw43YDtZeC9tDzCsdra2sLu7i4WFhbQ7XanAmu873cNz0h3fX0df/Znf4bd3V15QGYtAE86nwGTbrf7Qp3r5ktSkri4uEAgEJDUl0ajMbWwLgOtslarhUqlIl/VahXVahXxeFzaGw6Hpd2zoCdiuVyWSClTsJgKFI/HkUqlkE6nRWJYWloS8mOATk/GSCSC5eVl3LhxAw8fPsTFxQW63e6Uq0RrTJOSziNl30UiEczNzSGTyQjxcjFTI6b1o7ME6vW63DMQCCAWiyEej4s2zUWiNWBaixyrWdYUoRc+/47XZYBxNBrJZqODXroPdOR71qboEjufL5VKIRaLTVmiJE0tZS0vL2N9fR3r6+sSuKPnwr6jvttut0VWc9ukMweCweAzJBUKhZDJZLC7u4vf+I3fQDablX7QfRePx5HNZiUfnV7GeDxGMBhEIpGQjZAudrFYlLmXTCYl1e6y9ccNix5Uo9EQfZQyRqVSEWngKhdeyzDNZhMXFxfw+/2SnUMdGnhqKLxI7i7nmQ6wbmxs4Pr167h+/TqWlpYAADdv3sTe3h7W19en2vNdwjPSXV5exu///u/LYuaBB7rTkUhEtFxNEpftkDqAAkCiyYVCAd1uF9VqVSxEunfPGyguXmYXlMtlLCwsoFQqTREurzOLeNlmXqdareL8/BzHx8cyiQCI1jg3Nzc1yWe5066VwdxaWsTpdBrFYlHur9NftBbpRuHdYCI3HE7uwWCAdruNVqsllmsymZTAIt0+TfDMT6b7rRPV9eKbZT1pApmlu9Mi0s9Di5aWrp4v+lquVOXeX0sX3OToOdC9pyvOjSqVSiGZTEpq2HA4RL1el/Ss8Xg8RTzuxuCOATdqtksH/Kjnbm5uSp6pexhBewRaTuFcpRfCdL7hcIhGowGfzyfEND8/j2QyKVa+Hj9eT88VWsr5fB7FYhHlclmCoNyUL1t3enzq9TqOj48lMEyduNFoiBHGcQJw6XU5B8gj9BCp8c/Pz4tnSSlsd3dXSPjXgnSJcDiMTCYjE5ERY7pAlUoF9Xp9KsDmRjsJTbhaD+QXA1d0vRgoe5FTbXpClUolsQAYZKCmSxmDk2DWdZrNJs7Pz3FwcID79+/j4uICvV5PJrmOhPd6PSEwarrpdFr0JjfPV08kHSQiOWmL8rKNi2l8zAsFgHq9LonlDGi5QSQSA4lKZxUwMMiFqrX0WRYmn0WnErnuK4CpTIJutyuWrrbk3UCNSxaz7ssvvdlNJhPRhRnYY6qZDi4NBgOx7vgMPBXFrJZ+v49msynpXJpAZo0Hn4HPqi3fUCg0deCDf8fvDGw1m03Jcy4UChJAY8Cw0WhIWyaTCWq1mmRhkJCSyeTMNrKd3JBpnJRKJVQqFfl3q9USy/0ycDz6/b54o/l8XuYlg+g68KvXwCzi1ZsCx58bVzQalYMz8Xgc4XAYiURCske8gucpY+12G7lcDg8ePMCXX36Jg4MDnJ+fo16vS+CFg3XVgFHfo0ZLy5idzURyQru5z2snSZqRWK1JPY+09TUajQaOj49xcHCAw8NDSY0jYdZqNdEOY7EYEomE5D4y8s6Jo4/SagJ0j0/q9CZNvgCe+U6yp3VLjY/BOW6Ao9FISFb3Od1XbmY6q0EHg3S/aAuV4PVIXLqvtQ45S6PlItPXdhenaxnr+7rQXgrngc5q4OforZGA2AZKD5Rb/H6/BCOZxqdJxP3O+7t6M5+Bz6afhfONHl4ul8PZ2RlyuZzIWvRWmINNEp5MJgiFQpJzTO9pYWHhhec6wRzrarUqlvSLyAD0UvWmqYOb7Bt6TtpYmQXOV31YhCTOTbTb7WJvbw+rq6uIx+O/niljg8FAdt2DgwPcuXMH9+7dQy6Xk+OTmjCe55YweMPjlXqBcxJqK2dWmtNl0Gltg8FAdLJsNotMJiPWxqxTPXoSFYtFCT6VSqWptBm67a1WC4lEAslkUsiVhwOYoD8YDOR0FSUON2WJ99ffSZS0kmYtAvaVPh+vSVeftdcZB9zYdL/TyqaO6t5vlgXr4jLdVZONe61Z1vM3gTuGDEbqVC9en4TM+akzIYCnqYLsFy27uP2h83JJKLMkFt0HepPVrnSn00GlUsHFxQXOz8/leH2r1RLLl8Tvpg5yg0wkEjg5OUGxWJQDP67ExTlFKzGTyQjhn52dSRzjRdOxtETE/tL6P+/JPuJxdcpos8ZcS1n8O85vEi417+FwiEwmg42NjW8wY341eEa61LtKpRLOzs7w+PFj5PN5CcjMcv8uQygUktSPQCAgCe2uZaBxlevqQlttkUgEKysr2NzcxNramiSr6yi6/gwnYL1el6g19S09EbV2TdeHkyoUCqHX66FQKEg7uOD0AuDkZ0BFHzrQ2RokQj2hgaeBCZIpLVlGt11C15uKvjb7VGcXuGPJn+nTeYQO7OmxmjUuz/u3i1nygf6dPtShr6efh4tYB275M20g6Odg//DzWg7T80b/PTdydw5zLJkL7Eo2roVbKpXQ7/cliMR81Xa7PaXDa6PE7/ejVqshl8vh4uIC165dEw3YnXtsq/Z+fD4fCoWCnL57UQPHnV86s0OvWda8mJ+fx2g0QqlUEjnlMuhx09KN3+8XCY8pd887C/Ay4RnpBoNBJJNJtFotOaYLYGpXc4lyFkGy83d2drCzs4N+v49Hjx5NHSvlZ9jh+novGv1km7PZLDY2NrC6ujplbc4KnjE3lQchmDLjVoKaZZEmEgksLS1hfX0dmUxGdDwGFFKpFObm5mRhAk/TlqLR6FT9BC5SvTDYH24+qtYgqQ/zOtql1Z/h37i5z7MsMU3wtKhciWRWsNCdB1dBE5gmv+ddUxOuJnxNdtwIZ8k53DT1ZqdPK9JS5FwnodDCcjdIBuook9E74e+oPeoNgu2gNUttlJol+5y51nxGbT3z3zrwm8/np0hXB0Vdy5wb6ubmJhYXF6884HHZGPBZ2D5+5/0ikQiy2Sx2dnYQDodxeHiIBw8eyPPPmg+ulKQ3X2ap0Mt80Ta/DHh2p0gkgvX1dYTDYTQaDeRyOSGlWZiVT8uUl/39fbz33nvY3d1FqVRCp9PBxcUFgGnXQg+qtlDoGj/PQorFYshms1haWkIymRQrdJZmSBmAx3g5cdvttrSdViTbwAWZTCaxtraG/f19bG5uIh6Pi5sGPLWKZ3kD7mGIWQcItAVGstSEoo8z8zrhcHjKEueYRCIRORNPq0tvOrT03UCKbpM+tjyr3105wXX/3b/XcP/+qvuw//TC5zjSg6ClyH7jwtWBNq3582/Zl1oD5+8ZIKJLrcfNbSN/PqtQjfY+eD3OWxo2CwsLCIfDcurLPYxEK5KkwxNd5+fncjzYtXYJziNmPnCtMBZxFVwi15u83pC5wYTDYayuruKNN97A4uKipHjdu3dPsp70uM7iD/ZnMpnE+vo6dnZ2cO3aNSwtLf16BtLC4TCy2aycQsvlcjg+PpaCE7NcTkasOeDRaBQ7Ozt455138O677yKTyeDo6Ah3796VTtZasHYTObl8Pp8QwVWVhfx+PxKJxFQGgd7pZ+lu1NZYrITHc2mlMC9SL8D5+Xmsr69L/uDKygqCwaBURqPFAzy1HjXRuUSsgwzsN22xkjh5+IOLk1aLzoJw3WYepGBecSKREM2SUfNGo4FOpzN1HT2mrjY7S/O9zEJ1LbNZfzeLhPVc0J+bZRHTch0MBrKxJJNJzM3NiZU5Hj/JKWVuKgAJSnHT16lmjD9EIhHZTNvttmiLrnxxmffH+e0GBvV8YrxB5xnHYjGRvPQhGs5zekncaHUxJkb69RxziZdzh3VPGEC8CvoIuA6Eu7EcSlkAkEwmsbW1hWvXrmFlZUXm3eHh4ZREyTkyy/BgfGZnZwe7u7siGTL90wt4RrokzdFoJAcBmEjOnEY3OKQt1FAohKWlJdy8eROvvvqqWM3UkHQEW8PVhZjGo13ty9pLsnUDRleBKU20KHgAIhqNyu95X0aLb9y4gZs3b2JnZwepVAo+35OTXIFAQI6y8rNcnDo44x4+4ITTJMOJS8uaqTOcbAwe8oCIrndLySIUCsmk5cENACKp6ACam8J1GSm6Y6UtFXdzcwlHj9VlFqCeA7Pgku6s9jNopI/48hg7dUVd4pIGg64xoXN3efKR7j43f72huFkZvLYecz0n2E7qlel0WsgyGo3KpjgcDhGNRqdqTHBucIOg/svNcxbZarCtlEKed6yW81EbIjpVTkP3BQA5Kh+LxXDr1i1cXFyg3W6jWCyKNzgrW4Ucwg2JtVSYCvq8TeJlwtN3pHFSMXWMuxMnPgffLSZD/WVhYQFLS0tSCo/XcSfHLLDT9bn4q1xP16Jpt9uycGbl5upn0AVXeLSSGp8uhBMMBrG8vIybN2/i2rVrkrDNPtBZAUx7CYfDU1HrWRFiblZc+HxOTnSePqP7yLxkFgt3Fznb4/f75UAHA4qanLTFoi1yrZlSXtEpWNri1G6s/tIEpMlU5/fqf/P/szZKrUtqzZVt19amznOmNUci7fV6U3WUORb0GLiJ8DAFrdB+vy9zgV4NN1P2jf4/oWUc/o3OFiDhsoaGnkvb29tot9vw+/3I5/PigXE+MquBVjnjBHosZq0VjrkuWqO9y8vWF5+dRtNVwSzOR6a8cXOPx+NYWlrCwsLCVLaRJmp3459MJmIUMdbyPCnkZcPTPF123MXFBR49eoTz8/OpNCoOBqO01Be52LnIWTVsOByiUCig1Wo9t/NIVM8jZ41er4d8Po+zszMsLS1JIEvrUBq0jlOpFFZXV9FqtaRINQv9sEITFxr1pWw2O3UyjRYKo81ah+NimlU1jJsLj6lyo2D7SLo8surz+STflMRO70KPHT+vi8HMCppxgbpBPY4lg40kUjerYBYRzyIeYpb8oMEgD9vJe+nMD7ZVb2Jau3fdeL2Ba3JxLXW67tyAedJrMpmINRiJRKZKl9LK5AlKvTnRG6GWDEBOhTG/WgfyWCvX5/Mhm81id3cXkUgE9Xp9KuWSNSLK5TLG4zEWFhakDOcsq1VLNvTsdBzDLVQ+C64Ff5kXqceg2WyiUChI+YBms4nRaCQBMXezpAfCzZSbSy6Xw8OHD7G/vy9F4l+UE14GPCVdlvk7ODjAvXv3pAQbJ7q2bGn2M3BB7bBareL4+FjIJpfLTRGFa91ol1Mfx3xeHjAAqdJ/enqKtbU1LCwsPFOsXLtedK0WFhak4HOr1ZLTeNSHSbo8WcX6v3RHufOzDcViEb1eT86zM82FRy51yUpdiGVtbU10dJ3qxUCLq4O72rD+Ga0GnWiuXV1N9pSRuFj1e7D057Ul4mqU9ICIWW3Un3Mtfq1hu1IFNwK2ld4DczcBTD2LDoZRRmDA0JV3eG9tjbsbPqU2PR/0plQsFuV+vC7nDE99cdOqVCqyjriBAJja6CaTiWTHMPebwdNAICBZCyw6lUgksLKyIsV7XOnGlXyazSZyuRweP34s9UW4JmatMU3WOj9/1mf0GHa7XeRyOSHYi4sLMb7ogdLboRftHtxhfYevv/4ae3t72N7extzcnHjPXsAz0mXaytnZGQ4ODnB2diYak3bLSLCsluTz+URfpJU8Ho9RLpcxmUzkvVIcHJKVSyBcMDpYdBm4SIfDJ+8bOz8/x/n5uaRzzcpg4OeoObFAyXA4nHLZuHnQWqFFS8tebxr9fh+VSgXHx8eoVCqIRqNYXl7GysoKwuEwisWiFLyhtUR3lqUh19fXEQqF5Lgvj342m01xx/g7Hg/lKSz9ckx+plKpyMIlcU8mEznbrwmw2+1Ke/TbLUi62k1mYRb3NTmEa21y8Wtydj/jyiy6spsmVQbOdPoTPQXWIgiHw+JljcdPDo9w06M3wiAcN2Dq8mwbn11XzGIbmB0zGAwk8KWPDXc6HTnJGYvFpNJbPp+X4+UkyZWVlWeenxIH5y7nI9cD31nI+RqPxyXtbJbeyfGgPq2LwNM6v0zeIR/o9ehau1quY592Oh2cnp7K9avVqtRa4TNFIpGpDZCbnT4swdrO9+/fx6uvviqlHr1KG/OMdFlJ/vj4GOfn51LN3c024AAEAgHZ0WjJkYSY1sIOrNfr8hkdUCJm/f8qK1eTt3adCoWCpJfovE59PVqaweCTt0HQsnHrRHD3Ho1GU3UcdACMBXyY5REIBOQ1NoFAAMViUWo6tFotjMdj2fFZl3Rubk5IhaShtSy6YTy5xOOijKxzfPQ48YgzK1Lxi8/N/qbVQmJhMJHZHDpgx4pw2uK9SjKalaWg5xHHkf3P1+OwbgYj9tT4uRnx2jxtRe9mMpnICULq37VaTU596ULZfA7qrywC3+l0pvrI5/NNjT1JgzEAbgBcJxcXF/jqq68wHA6RzWYxGo3klOdo9KSa3sLCAtrt9jN9wNQunWmgc36pBfMZ+HeuEaP7mFpusVjE+fm51Ih2yfIya9eVF3T/awuX67per+Px48dS60JXJGTch4V6mKrpBtipg/P9jCcnJ1JO1au0MU9eTOnzPanC9MUXX+D+/fsoFouXFp+hDsYIO60S5qoy3YaDzgntapsuOJlmkbIGLSEuBr5i5+joSI4BU/7ghGQwQRMFLQq2x42s64g7J7fWMWmZM8pdqVTg8/kkCj0ajWSy6xfr0bpg35CMK5UKisWiuJB8VhIDC2wzwOkWkuff0kKp1+syRsvLy0LwtDR0JgPHUBftYSAlHA5LTq9b8tB1M/XzaVLRn9Hf3TlFQqPlq0+ccY6xbaxJyzfpNhoNNJtNKc5ET0G/3cI9+EELSwc9KVXojANayHxOEri20kiqjx8/xnj8pAh5IBCQND22m4dxdIBaEyi9KU1o7tzU/3azRTR5saDTgwcPcHR0JLVFOK7MX54VINOB1svWoysNMkOGGyb/hjEgBhDJE1dZ6Sxp+fXXXyMUConMcNVG/7LgmaWbz+fx/vvvy+J38xJ1PmMmk8HKygrW19enzlvrIIJOadIa7VWC+CyXVYOTkK4nXZ9arYajoyMhYwDY2NgQzVnnZTIYqJPK9eTRUVWdjgXMrgHgVtDizs63HXMiMl0HeFo9jDmkw+FQXsJHguMCZNSYAS6SiCZcYPo0FYmXpMOEc2rTlIL4d1zg7vjwuWOxmBTwDgQCU8Ej9osOqOjvrsyjfw483UQpcZDsCR0cIyFRImLmibboWIieXoD2BHh/LV1xU2WWiB5/kjB/zheeVqvVqddU8Zo0XkqlkpANx556MGWaWRuW60m69R50QJCbMecon4Hj2Ol0cHZ2hs8//xyff/45jo6OpEA/vT29ebiepraAn7dm2c/0ykjq9JjC4bBs/pT0JpOJeCEcD/IM12ypVMKdO3dwdnaGt99+G2tra5e242XCM0s3n8/j3//93xGPx6fe6cVBp460uLiIzc1NqXVAIubEp1WnRXjtmvCes9rhDrwGF4c+Fst0GrrfdGfa7TZef/11LC4uTm0IDIjwSK3OwNATWVd7AiDHRemSkQR5TRIBLWjg2SpM2nIBIJYBia9Wq00dXNB5pJoEXLJ1rUkuALZT67msL8vXrtBy1fqfvr6O8MfjcdlI6QLOcjfdsXZ/rj+jU/eoT5KcaAly7gHTr3XhoZHxeCxlPvmuMcpdbjYEMH2U1e1X3ldvpiQmbjaRSETSmbQs5br1w+FwKvOCxKj1cZ3KxzncarUAQCpr6cMxnKec80zH1EeRA4GABHe/+OIL/OIXv8D9+/dRLpeFcLmW3SCju0b15nTZmuWc43No78Hn80nhfH2EnpsQYxP8t14vw+FQXoLbbrfxl3/5l3LP/+8tXT4kddhUKjVVi4CWYSwWQzqdxsrKirwQkpFjRlT1ySx9jHLWru7e3/23C519wHKLbDcj7Qw88Vo8zABALJ9QKIRUKoXhcCgaIhcNT6w1Gg15id9kMpHX5VC6GA6H8rZSamRcGPrV4Gw3SdHV4Wi96re2ckEy24CE4/antgr4XWtvmjzL5bIQm+5jkiqJ1CUDkiI9COq+nB/8OxKP1tHd8dbuODBdN4EZCgBEjuI4ukE27ZIy0MdgFbNH9FFurT1qC07LWexbRvU1AdO67/V6oimybXojZn9qyUrLNSR7vg26Xq/Lpk+JLJ/PS9FyvmyVmQy8FjfHVqsl16FnxPbV63UcHh7i008/xd27d5HP59Hv90UrpsFCI8ON3XAML1uPl61hrSuzrcDTwyusjTsYDJBKpZDNZsXwoHWu08dI4Kzj7c6t7wqeyQs6Is/FrdN5tGDearVQKBQkgZtFz6k36mtoC1brYlo3exFoeWNubg5zc3MiZ3Dh+Hw+NJtNef1OOp2Wya9lAD7LZPI0H5PWBgtan52dyWt24vE41tbWsLa2hmQyifH4STHn09PTqVdEu5Fw1xoleegi63w2foakA0ynFV0m0WhSd2USWo2sN8G0m0gkIp4ISZ/jxig/MJ2WpUlLE4tugyvV6H/rz+u2kwR1qhrbBkAi+yR9yj0MrPV6PVxcXKBcLgvhct7pNrnuOdvFfnfzRbUcwZQ1QrvPurynfl5ewyXLarWK09NTzM3NodPpwO/3o9FoSH3ddrstr1Hn5kMtXs9TBlZ1RgPXAQmcb3nWNTooe9Cr0OmFl8VRLluPeiz1GtcxC1rk9Xod5XJZ4jvcILlutCXPDVp7cN+kbb8qPH8Fu3Yr+F0HfRhIqFQqMhn6/b5Uwefx2lkCvLsovwm0xT0/P4+5ubkpCYNEqt8FlkgkJNVEW1m0yPXxRkoiLJ93cnKC4+Nj1Ot1AJC82sXFRQQCgalILQMSJEWSmQ5GutFe/cW+1rotCUqTswudgTDL5dKkwpoTJFLKQbpotJsRQQuP+cq0Bl13lHqmPgnotkf3D9tGAqG7TvJjG3hN1lnQXpMmXy0pXGWhzZqTun2aLHSfUqICpktgagvfDSrpN43wi/m2R0dHGI1GePz4sWiX5+fnovGn02mRLzg36VWyrWwX+1xLX/1+X+Z/LBaTk26UiZj+5vf7JdPj27jslwXEucFywy8UCmIQMYDHAKN+jTuJX/OPNmS8gqfHgIFnH067q8w1ZRI4XT6eJGE606y3QOhdS+t8s+7pQg8uU0/4aqFYLCaBk2g0itXVVdy4cQN7e3vY3NyUnFwdteY9NdFRC67X6zIZ6vW6FPxhKhjfxDsej6VMH0ldWzSuns0Nx61GpXUwrakCTxcSU4eYcQA8TbVxNzHXonTlBx2xJslqDVPrtfw/i/roLBCXuF6E7Nx5xevR4tPeiH4Gto9He/lzejCuV+Y+u+4f3R90yRnsmeWdcaNxtUqOo0u4fC667RxnnePNLAemW5ZKpak5DACpVAqNRkNyrSkz0ErVlcL4TCQ6blCMD1C/1wFIXlNvlC8C9pubbqbXs54bzGTiQSbGKLSUpr2MWfPKa3hOum4UWssLAGTi8TikFvb1wr3MHXAj166W9CKIxWJy7Je7ab/fRywWw9raGvb29rCzs4OFhQWpQasnF9vM2hBcuDrxX6eJMdtAB2RYq4Gatg6u0dXUmqJO+tfunbaMNOHo03N8sywDRrNem60XAP9PSYPWv66TrIM7dJfdQAj7SV/bzWoBnnU1Lxv3WZ9hEAuYrg3h9pvOl9Ukrp+Pm5ImYpcgtA5LEmJxoF6vh2KxKPmsOouFJEGy08fhucGyb/i8+lSd7mfq16yHoA/F8HN8ThIUr8Hqc7rMJ8H26qPw8XgcuVxOUrQ4Z5nz7RpDL2IA6S/OJRduUJwBajdwrUlbzxFXyvIS35ulq3NUWY+TxxI1wfDfJNxZZOvmarIzNRFepdm4An0qlcLW1hZWV1fFQmG+aTKZxOLi4tQbJNwCPRxwbQFrDYqv+M5ms5hMJmJ98G0YPCHDOgzUI1mo2u/3T7nJJA6+7ZRn/P1+v0gkWuogIWSzWVy/fh27u7tIJBKSj3xyciKyhhv8cGUUlvNjn9A6ZACQn+GioE6oMzP0IqOGqRfoZdKGS0AkEv6MQSq9ydNr0JkqnHs6ik+NNxQKyWGFcDgswU0d7HI3Beqa6XQaW1tb2N7eRiaTQavVwqNHj3BwcIB8Pj8VWKNRQYuTBzK0x0HLlsSYTqelhgaPzXOz7fefvK+MfcA5lkwmJVBNC1wXXU8kElKvw53P/D+PME8mT44X7+7uiqfAqmG5XE5S2/T6ugpuIJjjrH/vrmOuLf1/bj46O4dzT7+h2A3Ce4XvRdMFnmqoiUQC2WwWy8vLEkSiKD4ajabOt+uBc90u9zt/903F8mAwiIWFBWxvb2Nra0smq7YQudPTVdbtmRVp1mTFTAxOWrpmPIG3uLg4RepcQO12W96BBWDqNM5k8iS5n+RHwtY6pg5mkHSXlpZw/fp13LhxA+FwGKVSSSboeDyWI6960ej+Z6rY0tISFhcXJbmc2rx7Hr7VaslipruvCZP9NR6Pp7I0tFaq0340kbquvxv5J4FxHtGC5Su5E4nElBxDfZ8/JykHAgHJjeZ8cq0zv98vRb03Nzexv7+PxcVFifDzzde8hpZiuIkyA4AERNJlW1KpFBYXF7G1tSV1Emih0t2u1WoolUryYkoSOush0zth2UoSrvv+P+2V0Pjx+/1S6lLnp3NuRiIRnJycvPDRWu01ELPI2tVgdVxBB/Li8bgYLwwm8jXxuui5yx1e4HuxdPmAPCq7vr6Ovb09JJNJdLtdqT5GTVNrYZe5Hm4A5du4Dj6fD8lkEhsbG9jY2JAjv1yI2s3jiyW587Nohna9mBFBXY9BHW0hsiatPoXHQB5zc3k/pmTxzLkuM0nrh8dxfT6fBLOYY6zzUwlaoaPRSE5dMdNAp4i5/a4PHdBSolVBcIPS1a601MG2aw2abdKBMTdvV3slmui0ZTsrzUxLDUwnY1CUG4YuegM8JQNduUsfXHDnNZ+B1j4DSYlEYqZOzv6n5s9x5v2onWqvQ8+f5eVlbGxsSCyA1n6v1xN9dWFhYareMTccfWiEtRhcHZmyGN+vpmUKXalMnzikNLW+vi71U74JZm3yXJ/u2nc3ZL0elpaWsLa2hmg0ikajITnGWuP9tbZ0NfTD6p2JBESLQ0sK2qqYZUW6Irn7Ny/SuX6/X6xNWj88J8/dnW49XzXd7XYRi8WwvLws1rrOzeV1tdXC9uocUS7+ZDKJZDIphyt4HVp+1WpVrBldd5WbgS6byPdi6YIsJEUGHu7fvy8WbaFQwOnpKYrF4lS03rXk6DKSLFjhijIQSZ7tYB/o++tqalpLdXOQdWL8VTq+lnhIvNpd1fqu1kb5M24ymuA491gwiKTDftcL15Wo9BtnG40GlpaW4Pf7p2oU6GuwEDx1efaNHjfOG25ALMidTqeRTqenTkhqPZabn64hy/7kBkWy1WuKhMu3U7O4FOUFelSMIfCewWBwSsvW1utl0OSp2+CuZ3ddux6wGyilbEIr/5tywneB74V0gacpTJ1OB6VSCXNzc3LaTOtmbiCMC4kD7J7M0uDPXjQHTxO4tqA0OdBa5LnzYrEoRzf7/T7W1takmhPvrXVpne7DwxTu/XUb+Jw+n08sMlap4vVIEMCTQxo8YNFut6UAuyY8yhyFQgH9fh+Hh4fyTK1WayqtC3g2HY2bou5XBlgoTzAlSR+pZv5nv98X+YGLFoAQn14Q2uUkAepFpev1aqLRBEiNj/1DfVunKTISrz0C5ogzs6Ner0/lrrKtJEStEZJ4W60WisWiFB6i269rhZB8mbXDDATON1qZDDpGIhGJMeg6vfp0mjuX9Bzj87EvODcplbFdrMlxfn4u9a+HwyfFduil6k3T1ee/qW5K6WsWtOeoDSG9UfPfPIbeaDRQKBQkmMj3KZJXeE+v8b2RLvDkgdvtNi4uLjAYDOSwgT4vDUwnyXMBUWultXUV6br/vqwtw+FQDi7UajV5S6i2ACjQM+DHxQA8fV1Kv9+fSjbXC0e7pjoNSZMoU3s0qXBBZbNZZLNZmfA6fWg0elKEhFkbTJfRQR/2I3U/LmR9Qsfd6NxglLYStWXNxaAPQHC8qIFzk/D7/UIYiURC5gKLt+jPc+NjPuss0nWDlvwsyybyvV1092ndk2zZlzrgyDxd9o/WXfW81CSn5y0NAub6arJgME/3M91/Sj0cR91v1FsZEGNQLB6PP2PNa1LVmifnM4m+2WyKNOTWDq5UKjg9PZVXu7NkKa1nt0Ie52OtVsPp6SkKhYKQ/FXQsgv7VgfUSLg6n1gfM3c/zz4k6VerVZRKJbTb7WcsaK+J93sn3X7/ST1OLpZUKiVVvLi4OGm4s7MgeDAYlFeU03Vy05Ius4JntaXX6+H09BQ///nPsbW1hfn5eTlKSSlAF/2ghkT3ejgcTh2bZDtIplwATEHSbh0XAy0/rZVykuljncxZplWkDz1oK4cuJDU3N1DB9rnpNcB0TVNudCRQTW4cI5KJmxrHojgAhJxZ83dtbQ3pdBoAJPDDFyjq+/K4sNZ+3WfQR55Ho5FUnmJgEoC8YpwbobZ6OGdIUDrwyPtQrtB5scwuoXXvuq5a/uIm5OrqnKck/Vl5wXxJ5rVr1/Dmm2/izTffxM7OztQrxLnBcuPn8WJu2rwXx2cwGKBcLsvBAvYx5y83JaaCzc3NSV0UBuP0qTiuga+++gqffPIJzs7OpoKOV0H3kZaGuC4SiYRk5wyHQym8znHk5stsjlAoNCUFcp18H9athmekq61VLdLTpWMmA9+FNh6PpTIWX13CPFm+qjwSiUy9woP1CkqlkhAxLawXAQfys88+w/r6OnZ3d7G4uDilx+o6AXNzc1heXpZINJ+LByCq1apESnWgbHFxcerUjqsf0gJ0I8m09hgAisfj+Oijj6Rcpg6Aafec+aLU4fx+v5wg47lzEo6WEEjWXOzMUAAgBz3oagPTtUs5trymtg6ptS0uLmJ3dxerq6sIBoOo1+u4uLgQN5AZHel0GgsLC/IyU/3WC741mW9U4Hu6xuMnB1uYGTM/P4/BYIDz83ORCrhpuboxrVCty3PjZfCI1iUANJtNlEolWdTUXPX1tKU2Pz8vb83V2js3G/YnN2JuwNlsFvv7+3jnnXfw3nvvYW9vD6lUamp+6KAkP8taG5ocWbieFiADbZQNdPH2xcVFeZU7NzHWRtGbIK3ri4sLfPjhh/jss89QrVafCTpeBW42TInjhplOp6e8vF6vh8ePH+P+/fs4Pz+XFxkwFXNhYQF+vx/lclnmE7N93KwTr+F5yph2XfXi5KmktbU1/OAHP0A0GpUjs0wfW1xclLcBb2xsSLoKazPwLQuffvopPvroIzQajW804LR2z8/P8dlnn+FHP/oRbt269UzuIC0dLiB91FUf83348KGUseSCYY2FTCYzJUHoAI8OTmgC5L0zmQxeeeUVIedwOIzbt28jl8tNWZx+/9M3cFy/fh2vvfYaNjY2EAqFUCqV8ODBAzx48EDePAFAFjjdRhYi2tjYwObmJtLpNEajkRRX5/FSunp8Dq0h64mtpRrWANje3kY8Hken08HS0hKKxaLIJCwGtLq6imw2O7VZafIoFovI5XJS1GU0GslGsbS0JMdVx+MxTk5ORAbhQnTnKd1VWuvUpnlce3t7G9lsFoFAANVqFY8fP5ZaGdyIaAEzH5fP+4Mf/AD7+/tYWFjAYDDA48ePcefOHdy7d29q49TZL+l0Gq+++ip+9KMf4e2335Y+06RH2YZftEx1TMKVDnq9Hh4+fCgbdyAQQDabxd7enrSRb99lMRv9lhOXtHw+H87OzvDZZ58hl8vJs78IaHyQPN9++2388Ic/xNbWFjKZjGxU9J5OT0/x5Zdf4quvvpL6wgsLC1hdXcX8/Dy63a4U+afU4lq5L2qQvUx4Rrp0fxKJhLhABFNyMpkMtre3cePGDSQSCbTbbayvr8ureVZWVuRV5SxwDjxNe2K6WSgUwtHREcrl8qWJ1lqj1MEPtqfRaMjrVmYlv2thn9kXfP0Oo/+sv8oFNBwO5QBEMpkUHdLVA3VwYtakpgW4vb2N4fBJARK+oFOnVzH4try8jNdeew3vvfcerl27hmAwiHK5LO/MCofDUgOCbxBg2hLfl7W9vY3NzU1x7S4uLuRFnfw8PQu2kZau235ajDzMQQt6MBjIRsIShKlUSir7M3dZnzrS1aU4DnyWRCIhpMFC5OVyeaquLnVWDVr79GgoB6VSKfG0rl+/juXlZZG41tfXcXR0JG/x0Me+WcIzlUphf38fb731Fm7cuCFlCI+PjwFAXGXOaRJnMpnE2toa3njjDfzWb/0Wtre3JStg1rzUshCDlO7hHf68WCxiOHxSbzmfz8umSO+D1ch4kGJWTrTbb4xf6EMZ7vpzoa8ViUSwurqKt99+Gz/60Y8k7YvSHvBERqHFnU6nkcvl4Pf7p94YzgJFx8fHsg5dkuW80DzxXcP3HL3zpeVUDAZPXpGhXbaphvieFnvW7pJOHXETqPUOLw3+fzqV1hW/DThhGPV+EbC91HK1nqzJZhaZfhvwfpeVz+N9uXjdoOCswJletPrzOufV/byWM2al4ribHTCdhaLHG3g2ouxGxS/rBzfNiJ/lfdlm9yi5u+m6/ef2hXuYg212swV0u/h5ftbNS2aGy6xov14bmnh+Vej+mDVXdTu/yXylhf9trUgdR9BvYOHv2HbgqUSpOUJnT+j8+st4ZzweixH0EnFph3lGugaDwfDfCJeSrqfZCy9idb4sYfvbWrgaL8safVnXepH7XIar7j/LsnqZ9/42bfqu8W3a/G3a+0369lcZw5eBlzlX/6usP8Bb3nkRmKVrMBgMLx+XsvjLEYcMBoPB8EIw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cw0jUYDAYPYaRrMBgMHsJI12AwGDyEka7BYDB4CCNdg8Fg8BBGugaDweAhjHQNBoPBQxjpGgwGg4cIPuf3Pk9aYTAYDP9NYJauwWAweAgjXYPBYPAQRroGg8HgIYx0DQaDwUMY6RoMBoOHMNI1GAwGD/F/AcLJdDNXOWCOAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 49\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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kFxYW0kaj5fT29uI//uM/MDc3h71796KwsBB6vR65ubmrZkSkg15GeqmuXr2K9957D++//77wia1mZdLvFAoFbDYbtm3bhqamJuTk5ODatWu4fPky3G532u8lny7tby3hUKlUUKvVsFgsKCoqQlFREcxmsxDIhYUFTExMYHp6esXoOUX1rVar8F/SZ8lnOz8/n5RZkM6KlPvk1/sMECR6dN7prPdQKASv17ssQ0KhUIiMEL1ej3g8Dp/Ph6WlpbTTY7VaDYPBgJKSEhQXF4vpbCwWw8LCAlwuF6anp+Hz+RAOh9MKKN2veDwuBiy6jytdg7y8PLS2tmLr1q2Ix+O4evUqrl27Bo/Hk/TsyK9r6jWYnJzE0aNH4fV68fTTT6OpqQkOhwN6vX7F2dtKxGIxLC4uYmlpCcFgENPT0zh9+jSOHj2Kvr6+VbelaxyJRJLeXYPBgMbGRpSUlIgZUaZI/5NpFAgEEA6HxaCYjvVm/nxaPhOODoPBgPr6etTV1WFwcFBYPOuBUpQofUU+xSHLai2i0Sh6enrwy1/+EqOjo2hubobT6YTT6URpaanw/2UCWTK3bt3CiRMncPr0ady6dQtutzvppVAoFMLKMRgMwr8YjUah0WhQUVGBgwcP4tChQ6ipqYFSqcTo6CjeffddHD16FN3d3cvSlOSClWqBpEIpVKWlpWhtbcWuXbtQW1sLi8UCpVKJpaUlzMzMoLe3F5cvX0ZPTw+mpqZEUIhEymq1ora2FnV1dVAoFBgaGkJvby88Hk/a71/LHbMaq4lJ6jVIJ0ArbUvnYjab0dDQgKqqKiQSCdy+fRsDAwNi9iVJElQqFfR6PYqLi7Fp0yZs374dDQ0NcDgcyM3NRSKRgM/nE66yixcvYmJiQvinU5ELbKplLj8+rVaLLVu24Mtf/jIOHDiAsrIyJBIJDA4O4tixY3j33XcxMjKCSCQiZjyJRAKLi4titiN/NlwuF95++20MDw/jsccewxNPPIFNmzbBYDBkLLo0UxgbG8Pk5CQmJiZw5coVnD59Gnfu3FlzxkoCSc+x/P3V6XTIzc1FTk7OuvzZ5Fs3GAzYsGEDNmzYsMwYe1h8JkRXqVTCbrejoqIiaeqyXqanp3H+/HmUlZWhtLQUarUakUgEt2/fxsjIyJqWLnBXeEdGRnDixAmMjIygvLwc1dXVaG5uxpYtW2C329fMsZQkCZFIBL29vXj55Zdx4sQJEbSiF45GXqPRiIKCApSXl8PhcAifpiRJKC4uxq5du7Br166kSLvVakVVVRW2bNmCX/ziFzh16hQ8Hk/SdJxe2nQ+Rjl0DBs2bMBjjz2G9vZ2OBwOERwJh8MoLCxEUVERqqurMTAwgMHBQUxOTmJ+fh6RSAR6vR5lZWXYvn076urqkEgkcO3aNfh8PmHtruXnzAT5y0jTcJoxrCa+mX4fiW5xcTHa29uxdetWKJVK1NTUoKurC2NjYwgGg9BoNMjPz4fT6URNTQ1qamrE/TMajWLgj0ajKC4uhkajgc/ng9frRSQSWTH7gO5X6jGRqyMvLw+PPvoonn/+eezevTvpXbFarSgrK8O2bdtw9uxZuFwucT6xWAwzMzMiMElZBBTcm52dRWdnJ0ZGRjA1NYXnn38eW7ZsgUajWfM5j0ajmJmZwY0bN3D16lUMDAxgfHwc/f39mJ6ezshFKEkShoeHcfv2bTGjjEQiGB8fx/nz5z9VoNloNKKioiJtpsrD4jMhugBEOk8gEFjV97gS8XgcU1NTeOWVVzA2Noa2tjYUFhbC6/Xio48+wo0bNzLeZygUwvT0tEivosh0NBpFa2urCHqs9kAuLCzgww8/xLFjxzA+Pr4seV+j0cBgMKCwsBDV1dWoqqoS7gwSwqqqKjQ0NCA/P3+ZdWcymfDYY49BqVTC7/fj3LlzWFpaSvqM/OdaKJVKMbUlf2ooFILP58PCwoIQkNLSUnR0dGBhYQGzs7OYmZlBKBSCxWJBeXk5CgsLkUgkUFpaivz8fAwNDWX0/Zkgt1pTB5f7hUqlEoJaWFgoXlSNRoNNmzZBp9PB4XDA4XDAZDJBrVYjHo8jHA5jenoaZrMZFosFOp1OHFu6oo7VzlF+PnRf9Ho9Wlpa8M1vfhN79+4V+5cHrwoKCrBz507k5eVhaGhIiGswGEReXp5IQZMkSbxn8sDwnTt38Pvf/x7l5eUoLy+H3W5f9Tij0Sjm5+fR1dWFDz74ADdu3MDU1BQ8Hg98Pl/GQfBYLIYbN27gF7/4BYaHh2GxWDA9PY2LFy/i7NmzmJ6ezshgkkMum0AgIALHn5UUsocuuqFQCKOjozh+/DhOnjyJqampe8pDpCnKwMAAJiYm8P7778NisSAcDmNubg4+n29d+6NADIklReLLyspgMplE1HYlXC4XPvnkE0xPTydNi+QBFqry8Xg8yM3NhUKhQF5eHmw2GwwGA/R6vbB+5Kk9ZN2pVCqUl5fD6XQmRdBTXQ2rkUgksLS0hNHRUVy7dk2ITiAQEFNFv9+P3NxcVFVVob6+HsXFxSgrKxP+6snJSZEH6/f7xb1Y74uSCfdbZNNBA04gEIBCoUA4HIbJZEJhYSFKSkqE3zMSiWBqagp9fX0YGhrC0tISTCaTcEkZDAbMz8/j+vXrGB0dXdEvnHp+qVVflCHhdDpRVlaGnJyctMEf2o6m5IFAQOQpU9ZKMBgUwTm5EULiS2J38ODBNUU3Eolgenoat27dQl9fn5j9kB91PbhcLvz2t7/F2bNnodVq4fP5MD8/L9xY9wLdn5MnTyI/Px9PPfUUysvLodPp7ml/94usiS7dVCrt83g8mJiYQG9vLzo7O3Hx4kWMjY2JooN7JRaLwe/3w+/3A/iDQN3r8cZiMWg0GthsNlit1qQUmZW2SyQSmJycxNDQ0IqjPVkZFGxbWFiA3+9HVVUVdDodgsGgmJJSWpo8uEHHQA8/vUjyoFAmkJU2MzOD69evY2lpCVqtFlNTUxgZGcHc3BxisRgsFgs2btyInJwcWK1W6PV6GAwGWCwWLCwsIBQKYWlpCQqFArFYTBQk3Etp88OEBiG32435+XmoVCpxXgaDAWazWfg7l5aWcPv2beGvX1hYgEqlEq6yoqIihMNh3L59G7Ozs2lLhFdD7nuNRqPimUl9/uTPkcfjSbofLpcLw8PDGBsbE6ln4XB4xWl/KBQS7qONGzeuaKHLg44WiwV5eXmYmJhIKrleD+TmmJubu2+DaiKRwMLCAi5fviyMoJ07d6K+vh5OpxM2m02kFa5lRN1Psia6lJQ+PT2Nvr4+9Pf3Y2hoCNPT08LX9SBe0E97A81mM3bs2IH9+/dj8+bNKC0thVarXfUGLS0tob+/f81Kumg0ing8LlKOaMpaUFAAg8GAYDCIqakp8YBTjwW5H5Nerlgstmq0ey1isZh4SePxOCYmJjAzMyPq5aPRKCYnJ4V4KJVKqFQqWK1WxONx5ObmCouE3BHkw37Qlun9Qp75EYvFRFDNYrGIc7VarSI7IRKJYG5uTlh4wWAQCoVCbB8MBpGTk5ORhbvWMVHaoTy/mp4D+t309LQIchoMBtjtdlHx53a7M37PZmZm0N/fj127dsFgMKT9DAX1SktLsWfPHlitVuh0OiwsLHyqnOL7/ayQK6W/vx/Dw8M4ceIEioqKUFVVhQ0bNoiZW1tbG/Ly8u7rd6/EAxddejgmJyfxyiuvYGpqCgMDA0mVRvcKpYB8miqdtfa/detWfOUrX0FLS4sYFVdyyNMLMDIygvPnz6dN6UqFXiidTofGxkbs2rUL5eXlAACfz4fFxUXMz8+LAI1Go0ny18rzaeUNaYDkJPWVoNxLk8mE/Px85OXlieIEKmlNJBLIzc1Ffn4+HA4HrFYrjEajKEKw2WxCaGhKd+fOnaSKwP8Nwis/TpPJhLKyMhEIU6vV4g9lmFitVjgcDuTl5WFqagrxeBxKpRJ6vR4mk0kUqZDl7/f70wYVU48htayWBtrU504eLA0EAnC73YhEIrDZbLBYLAAAu92OYDCI4eHhjItM5ufn0dnZiccee2xVa1elUsFsNqOurg7FxcXIy8vDzMwMXC7XA30nMz0POZSrPj09jZmZGQwMDODKlSuora0VsYq8vLx1zRLvlaxZunq9HqWlpZCku2WZlLi+1kO4EmazGVarVeRb3osfeC0sFgueeOIJ7NixQ1SprXZDEokEpqen8fbbb+PChQsZ+7V0Oh1aW1vxxS9+EY2NjdDpdCJ30e12iwh2KgrF3S5PZrMZGo1GdOwC/pB7KA84pdterVbDZDKhoqIC9fX1cDgcCIVCQmi8Xi8AwOl0YteuXaKyjBLpCbmfWqFQwGq1Jn3//xaonNVqtaKkpARFRUVCaOX3XqvVQqlUoqmpCT6fTxgWwN1MAgpG6XQ6WK1WBINBMYjJ07bkyAdQOhYAoiDHbDYv6+hFf8/JyYHRaITBYEB+fr7Is3Y6nTCZTHC5XHC5XBm9J+FwGOfPn8e7774Lm82GoqKiFcuF6dhsNhva2trwxBNP4OLFi+vql5IpGo1GWNRerxcLCwvr3gcZGZTmSIK7UqezB8EDF116KEpKSvCNb3wDU1NTuHTpEj744ANcvHgRXq93XaKrUChQXl6OvXv3orS0FFevXsX58+cfiOg6nU60tLRkJLiSJGFxcREff/wx3nzzTUxMTGRcurphwwY899xz2LFjh3BdUPDOaDQKS5gse7klpNPpYLFYhAiks8JXur60fWFhIaqqqlBRUQGTySQqsyRJQl5eHgwGAzZu3IidO3eiqqpKVJal5kNL0h9aJ5JL4n+DhSuHfOTA3cFQq9WmPVelUgmTyYTq6mqRr9zb24vFxUUYDAYUFxcjPz8fBoMBOp0Ofr9fuCBWui6pVhbdS+o5YbFYoNfrk/z6lB1hNBpFNSC1nlQo7rZObGtrg9vtRn9/P65evbrmc5lIJDAxMYE333wTFRUVOHDgAIxG44rPP/2/xWJBS0sLnE7nAxHd3NxctLS0oKmpCePj4/joo48wNja2bv0wm81oaWnBk08+idbWVhQVFaGkpCTpXB4kWbN0c3NzUVdXh8rKStTX14tE/zNnzmTsA1KpVKirq8Of/umf4umnn4ZarUZubi66u7uFRXY/obShTFN9PB4POjs7MTAwkPEgUFBQgM9//vPYvXu3yFaQp5VRpJoENTWQRhVRlL0gnyKvNQ2j3hF5eXlJlWQU1IvFYtBqtXA4HKiurkZpaSmMRuOyUlG5u4Ms9MnJSfj9/v+Vli5V4Pn9fhQUFCT50em6030wGo0oLS1FTU0N3G43JicnEYvFEAgEsLCwIFK+bDYb8vLyMD09vapbLXWmQt9DKYby+0w/KaWMBuzUajK9Xo89e/bgyJEjGBsbw+zs7JrXIRKJoL+/H52dnWhra8sof16hUKCwsBBOpzNtY6lPi8lkwq5du3DkyBFEIhE0NDTg5ZdfxsDAQMbuDIPBgPb2dnzrW99Ce3s78vLyoFarV238c7/JasoYvayFhYXo6OjA4OAgenp6MvLtqlQqlJaW4mtf+xq++tWvoqioCKFQaNWpTzpWqvZJB5VDZurnCQaDIkKcyf5VKhW2b9+Ow4cPi5rw1KmjSqVa9fup/DY1uJdJAIuCIZQCt7S0BL/fj8nJSRFE0+l0sNvtMBgM4qWXCy4JO/UK8Pl86OnpQW9vb9J1+Kz7deVWbCAQQG9vL27dugWTyQSLxZLUy0JugdKgl5ubKwJroVAIHo8Hfr8fJSUl4vqaTKY1g7B0DOTmoGPTaDTIzc1NW8pKQpvO90r/zs/Px+HDh3H+/HkcP358zfdNkiRRjZhJNSdtQwNAJtDglenArFKpUFxcDKfTCZ1OB5vNhng8jhdffBHj4+NrCq9SqUR+fj727duHXbt2JQ2o2STrebp0gmq1Gnq9XrzEqzViUalUMBgMaG5uRkdHR1KxQCgUyniUo4dzrSotwu/3w+PxoKysbM1u9PRi6PX6jCtf8vPz8cQTT6Curm7Nfa8EiS4lvq8HEl0qj/T7/Ziensb4+LgoejCbzVhcXBRdw+RCQJbt0tKSyKscGRnBuXPnMDIyIvy78vv7WRReuS9VobhbSTY8PIxz584hJycHlZWVyMvLg8ViEaXadP7k06Qshfn5eSwsLECn0yESiSQZBHq9fs0qr3TQtScjYLXzWI0NGzbg8ccfR1dXV0bTf3JPZNpzJBaLwePxZDxzpfcxUyMoGo2KAUChuNstrqOjA11dXfB6vVhcXFyz5F2uO7SfbPNQ6uIkScLk5CS6urowOzubtvSROjbRw2a321FWVobc3FzRyISmsetp/7ie5PqpqSn09vbC6/VmJNRmsxm1tbUwm81r7lutVqOxsREdHR0rpuWshvxY0lk5mQgwiS4li5N4Uq7n4uIiFhYWRKVbqm+TjiMQCGBkZARXrlxBZ2cn+vr64Pf7xUtFgQvq+PUwHvR0kNDKj4/O0e/3ixzyK1euYHR0dNnSMXQtyPqk60U/vV4vvF6vuH6ZLm0DLL9/dJzEvQxeBoMBHR0daGxszGhJG4vFIvpwrAbltHu9XvT29mJqaiqj41lvoQtlH/h8PlE1aTAYUFZWhoKCAuFi0Wg0afNu4/E4ZmdncenSJUxPTz80A+ChVKT5/X588MEHOHnyJHw+37KTp2YiVBRAqUmhUAgTExMwm81Qq9WiEUmm/lN6ODJldnYWFy5cQGVlJbZs2SL8mSthNBrR3t6OpqYm0Zx7JQoKCrB//37RICZTUv2nZF3J1ypLDbSkg/zBZHlRGTZZavLc0rm5uRWbENF3BYNBuN1uzM7OiuwHSnMjF0RqEYdcwKkxz4PwAZNgpXY3I9Gl60CuAxrwqZqxoKBAZN6ku1fkB6a+BtS7d2FhAfPz8yJ9jKxitVq9aqFE6sBGebhzc3MIBoNJ/Z7p82tBn6mrq8P+/fvR09Ozak8DrVaLpqYmtLe3r2kUUEFJf38/Pvnkk4ybjq/3faQ+Kr29vairq0MkEsHExISoGKQCEqoopKCl/Pt8Ph9OnjyJpqYmFBcXZ7SCxv0m6/10JUlCT08Pfve732F8fDztg6dSqVBQUIDi4mIxbYvH4xgaGsKHH36I4eFhqFQq9Pb23vPyJZkQCARw9epVlJeXw2KxoKamJil6nIparcbmzZvx1a9+FR6PB1euXEkrvGazGQcOHMDhw4fXtCIIunb0R/6gd3V1icoxEt5UN0Aq8tlELBbD3NycSK6nctF4PC5q8q9du4bm5uak86djoYi50WgU1UlklZF1F4/H4fV6RaWaJEliWk6VVH6/f8V0qntFnhZns9lEu0ZqfanX60UgMScnRxSbUNqY2WyG0WgU/tRU6yyRSGB2dhbXrl3D0NCQCEDKYwGUq0vnTFbYSveFrq18AJqdnUVXVxd27twJk8mE3NzcpL7Ha2XXEFarFYcPH8bt27fx+uuvp0270ul0aGpqwle+8pU1rWJJutvFb3R0FB9//DGuXr2a1APkfhKJRHDr1i0cO3YMGzZsQDQaxeDgoFiYwG63i5nw5ORk2oY7kiRhfHwcb731Ftra2tDW1pbxtbtfZN3SXVxcxIULF3Dr1q0VRzlq9bh582YEg0H09/djdHQUAwMDYg0rpVIpSjUfVCI2dTo6e/YsLBYLzGYzSktLVwxYKBQKmEwm7Nu3D+FwGK+88gquXLmCQCCAeDwOhUIBh8OBp59+Gt/5zndQV1eX0TIiZBHQKE7/HhoawnvvvZf0HXJLWP4zFfIF01pjFLEna406llEl2okTJ1BSUoLPfe5zIngpbzhOFVulpaUiR7SgoAAlJSUiZ9ftdmNsbAwul0sIWzQaxfj4OG7duoVgMPhABlDqJ7Fp0ybRp4IKPoqKipIS471eL6ampjA7O4tEIiHywXNychCNRoWgKpVK0WSJ+iRPTk6KKjw6N3lAjPJsc3NzxaoY6e41gGX3cnFxEVeuXMF7770Hi8WCqqoqId40G8yklFWlUmHDhg349re/jXg8jvfee08su0SpZ83Nzfja176Gxx57DCaTaUVRooFhZmYGH3/8Mc6ePYvx8fEHZgTFYjGMjY3hnXfewYULF4ThQcsblZeXo76+HjqdDt3d3fD5fGmDgLFYDN3d3bhw4QIaGxs/VWfDeyGrohuLxdDX14czZ86smOKl0WjQ0NCAw4cPo66uTpRY9vf3w+PxiBtKN/xBTUnJEvH5fGJNKLVajQMHDsDhcCxzM8ij9FarFfv27YNer8cHH3yA7u5uLC4uIj8/H48++iieffZZbNy4MaPWeZJ0ty8vTftp+uv3+3H69Gl8+OGHy5rqyI9npXPTaDQwmUwwGAzCH+d2u0UrRrKYSXx7enrws5/9DJOTk2hraxNtKMPhMAKBAJaWlpCTkyP87na7HUVFRbDZbGJ1g2g0Cp/Pl2RRz8/Pi8DVg2q9R1ViDocDtbW1yM/PF4nxRUVFsFgsYkn1SCQCj8cjhJdeWspKMRqNogx4ZmYGFy5cwPHjx9Hb2ytmB/LrFggEROYCNcM3mUzw+Xyr5uum/jsajWJ6ehonT55EcXGxGODp+TebzcjPzxczkdVyajUaDZqbm/GXf/mXcDqdOH36tHCDNDY2Yv/+/Whra4PVak2yvFP3SYL74Ycf4q233kJPT49wFz6IbBXqqxIMBjE5OZmUTmez2WC329Ha2ori4mJUVlZiYWEBFy9eTDsIeL1enD17Frt378bWrVuzumx71kSXLsCxY8dw+fLltBdCpVKhoaEBX/va13DgwAFRetrT04POzk6Ew2Ex/XzQTnAS1Ugkgvn5eVy6dAlLS0uIRqM4dOgQiouLl6VOUYu8aDQKnU6HLVu2QK/Xo6GhAeFwGJWVldixYwcqKyuFEK1GIpHA3Nwcbt68KVok2u12UWF05coV0WRkPVB+M1WyUYkkrQuWGjQk33F3dzcmJibw7rvvIi8vT5Ql09pslZWVqKqqSuotK7fkyXdKAkeWLWU5UBDrfrsXaL/k85N3jJM3MQIgAotk4Y6NjWFoaAhDQ0NwuVyiJSKtXkytDOWzEPl1UyqVool+IpEQlWV6vX7VxjPpiMViYn056nbm9/sxMzMDhUKB6upqbNq0CQUFBWumUVKrSr1ej/r6eoyMjECr1aK2tha1tbXQ6XTivkiSlOQTByDW+3v77bfx6quvoqenJ6kj2HqbjmcKxQaA5SXTpaWl2Lx5M5xOJ2prawHcjR+lm1VHIhFcvHgR//3f/w2v1ytW0s4GD1x0aXp1/fp1/NVf/ZVYaC/VOlWpVNi0aRO+853v4POf/zzsdrv4THV1NcxmM6amprIiuGRNytfICoVC6OrqEkuSHD58WJSIUocnr9cLj8cjxIR+FhQUiP64+fn5y3okyKGHSL7qxMWLF+H3++F0OrF161YUFxcLn5W89DcTKFJPVjaJ7VrNaagbGS0/Iw9CGY1GVFZWYu/evaiurhapVemWfKFzn5+fx8DAAIaHh5MWp1SpVPd1ekp+awDiO6lrWllZ2bIFGWkbWnpofHwcAwMDOH36NEZGRsRMQB4MTPW/yqHfUdtLytOlKPt6gkny/gHj4+NC+K5fv46JiQmYTCa0tbVh//792Lhxo7B601nOwN17QWuIabVa4fIYGxvD3NycSIGkDnuUskZW91tvvYVXX30V/f39SalcJPj30iNhPcjPy2w2o6amBk6nExaLBTabDUeOHEEikRBL18uvM3UC/PWvf433338f//Ef/4Fdu3YluYMeFFlpeANAdJenFU/lUNLy008/jQMHDoiRmgIdDocja6MQvRAGg0G8LPSSLS4u4tatW3jjjTeg0+mwe/du5OXlib6it2/fxp07d4TlQ1avUqkUvkSabstzXukaAHdHYJfLhXPnzuE3v/kNLl++DJ/PJ1YfMBqNYpkV6veb6XnRQEKWJk1x6WVeLTsB+EPBhfxhp/XPaHBpaWlZNbBDgblTp07hypUrIh0vGo0mpZTdj4GVBICEgtZ5GxkZgd/vh9lsFv7n1O3oONxuN27duoX+/n4RJEv9bGomQeqx0/V1u91iCSRaTZd8/Zm2Q6SKP0p5Gh4exq1bt8QSPV1dXbh8+TK++MUvYufOnSgsLBSDjvz+0rPt8/kwNjaGmzdvitJ1auxDqxVXV1djw4YNYl9utxtnzpzBG2+8gZ6eHrGmIRkr9GzTEkEP2khSKBSwWCyw2+1iYKAB5cCBAxgZGcHMzIzw0xPRaBQul0vkpQPZySPPaj9d+c9UqM8AACHKJHT0sGejqkmr1cLpdKKkpAThcBiTk5OiexO9HLOzs7h9+zZqa2tF0565uTmMjIzg5s2bGBsbE078WCwGpVKJvLw8DA8PY3Z2Fg0NDdBqtQgEApibmxPnR534e3t7cfXqVYyOjiISiYiHeXZ2FuPj48jJyRGiu5KVRGJAokOuAKoAok5i4XBYWFyppcZyPzX9O931p7LXwcFB9Pf3Y+vWrTCbzUnVW/Jg0KuvvoqTJ0/C4/GIKTcFpuRTxk8LHTet4UWDIa0zRy0om5qaktYEo+vjdrvR19eHwcFBkXifivwayY9bXnBB1j3NfMgVQcJGvmRq/L7awpzkf6eA1fj4OGZnZ7GwsJC0GOb169fR3NyMhoYGUepKvmWaeYVCIRFjuXnzJtxuNxKJhEjZpNlATk4O8vPzRU8HMjBmZ2eTFv5Uq9XIy8tDSUkJtFotxsfH151Hv17kgcdAIIDFxUVRcEU+c/LDr7Z9NnN2s569kM6aoqhxZ2cnCgsL0djYCKvVimg0itHRUZH79yBWIpBDI2Zrayva2trEMiJdXV2YmJhAPB6HXq9HYWGhWKjSaDSKnNnFxUXRpd/v94sVYCXpbme12dlZTExMoKamRjQVGRkZEV3SaAqfbqFJEqVIJAKv14vZ2VmxSoP8s6lCm5+fL46VlpSZn58XqWxGo1GsEDA1NYXJyUlR1kv7ywRqX9jd3Y3GxkYxLSVrzuv14uLFi/jVr36Fs2fPYm5uTkzlaL04+t779QLQfaFVLeQNtqlL1fz8PJ577jm0tbXBYrEIq9jj8aCnpwfd3d1icc1MkOdI0z0oLi5GcXExgLupX9Tyk9LVioqKoNVq4fF4MD4+jvn5+WUCTOdDhghN/8kPL792oVAI/f39uH37tphVUYaD1WpFRUUFnE4nlEolBgcHRQEQCSg113E4HCgtLUVdXR2cTqcIrNlsNpSWlqKoqEi403JyclBaWort27dj8+bNUKlUuHDhAk6dOrXu5u3rhbJILly4ALPZLCpIvV4vuru70dnZKVZ+TuVh9AZ56Mv1AH+o8+7q6oLH40FFRQUsFkuSVTkzM7OuoMO9oFAoYLfb0dHRgcceewwajQaPPPIILl26JHJhTSYTduzYgY6ODlRUVIhldmh643Q64XA4cOzYMXR3d4sXn/yYPT09IrWIsgXWuvE5OTmwWCxoaGjA1q1boVKp4PF4ljVFIReCVqtFfn4+Ghsb0d7ejvr6euj1eoyMjODixYvo6+tDPB5HeXk52traUF9fD5VKJUpfL1y4gNnZ2aSg5VrHSJYF5WsuLi6iuLhYWPQ9PT344IMPcOnSpaRVdcnypmt0P5ue0/5oliIXMYVCgbm5OZw8eVJU1DU0NAgLkHylY2NjwkWUyTUgC1ej0cBut6O9vR179uxBeXk5otGoKCAYHh6GRqNBfX092traxPJHfX19uHDhAm7evCkGRxoo5Pc5NzdX9PuVxxRSl4YKhUJJlubMzAympqaEm0u+YCYAMWBXVVXh4MGD6OjoEJ3lyC2l1Wqxe/duxONxEcwj11JLSwsKCwtFCXR3d7dISXtQ0OKY77//PkZGRmC326FSqeDz+TAyMoI7d+48sNzhe+EzIbrA3RHH5/Ohr68Pw8PDYqpGkd/7nTS/ElT6WFFRAY1Gg7KyMtTV1WHPnj2YmZkRjXdKSkrE0tByPzBNr/R6vWicQpYI+faAP/hC13qRFQoFioqK8NRTT+HQoUOorKyE3++HRqPB2NiYGMEpwpybm4uKigq0t7dj37592LZtm1jdoaCgANFoVPSK7ejowMaNG2E2m0VV2p49e/Dee+/h7bffRl9fn2jzmC6rQX6MJPbhcBg9PT1wuVxJfX6Hh4fR19eXlE+casE9KFILS+RTUvLTx+Nx9Pf3i54J1EsiFApBq9VCpVKtev4kVtQAp76+HocOHcKBAwdQU1Mj3AgLCwvYtWsXzp07h6mpKVRVVaG5uRkVFRXIycnBtm3bUF9fj5MnT6KzszNpbTUqtCgoKEBrayv27NkDo9GI1tZWvPPOOzh+/DgmJiZWfU8SiQSCwaBoF5n6DJLgfulLX8Lhw4dFE3e5i4lyfY1GI7Zt24ZYLAaHw4GysjLhVopEIqirq8tKLIaMtuHhYbhcrqT2qDTofJa63X0mRJfKI6nsl6ZM9LI/qFzcVCRJgl6vF3XcZEna7XZYrVZxXBT9T+1FQOJbXl6OL3zhC4hGo/iv//ovDA0NIR6PQ6PRiN638pr81aioqMALL7yAL3/5yygrK4NKpUIoFIJOp8P09LTwVebk5MBut2Pz5s145JFHsGPHDlRVVYmXIBQKQa/Xo7i4GCUlJdi2bRuqq6uTmpmoVCps3rwZDocDJSUleOutt3D9+vWkPrCp1qK8PaTD4YDBYBDTdmpURA11jEYjFhYWhOuC7in5PolMrMpMIH+q3K8s97WS79JkMgGAaLtI/ltarof6R3g8HjHtl58/ia1er0d+fj62bt2KZ555Bk8++STsdntSoC4/Px/t7e2w2+24evUqAIieAdRnhPrx1tbWorOzE93d3Zibm0M8HofNZsPOnTtFhoJOp8PGjRuxefNmVFZW4sUXX8Tw8PCq14WeU3qmSSRVKhWqqqrwJ3/yJzhy5IhwQaS6mKiTWFlZmSiU0Wq1SStkU3EMtSt90NCMhgwbateoUqlEb5EHtSTYennookvNoO12O4xGI6LRKLxeL/x+f1YFF4BIOKfczdQpHVk79O/U39NPhUKBkpISPPvsszAYDDh//jx8Pp/IjQ0Gg6Kl5WoWvMViwbPPPosXXngBJSUlSTmTtbW12LlzJyYmJjA5OQmbzYb29nbs3r0bdXV1YsltCv4tLi5iamoKCwsLqK6uFhZMKkqlEna7HY888ohYYoamt/IgEfmZtVot7HY7ampqUFhYKFKa/H6/SCmj1YMtFguUSqXoVZtIJMT1knebI//1vSxwKL9XJIZyEaf7RM+d0+lEQ0MDHA6HaFLj8/mQSCREwn1ZWRlsNptYZorS6+TiTc3ka2pqsHv3buzcuVO0DkxFo9GguLgY4+PjuHPnTtJ0n4JRRqMRTqcTmzdvxtmzZ3HhwgV4PB6UlJRg165dqKurE2vmabVa1NfX45vf/CY8Hg9++tOfrriqgkJxt3F9WVmZEG3qtWGxWLBr1y489dRTKCkpWZbyJ5+ZkAslXctLekatVmvGZe73A8qVp2OmtD+1Wo1AICDiIA9beB+66Gq1WpSVlWHLli2wWq1wuVy4ffu2WBAxmxcoJycnKT0tNRWIotBk7dH/y7tTyT9fVFSEZ555Bu3t7VhaWhLBoqmpKRiNRjF9XSkvta6uDgcPHkRRUVGSlQb8YTUBKlHdvHkzHn/8cWzcuFHkyFJUfGZmBpcvX0ZnZyd8Ph/UajWamppWvA4UCZYv2S0/X7m1qNPpREOY/Px80TUuGo2K4E1hYSEqKytRWloKlUolekTQgCMvYyWXEqUapaappQvspWYMyAOJNNWkl5EyJOi4Nm/ejG3btsFoNGJ4eFg0Iad7TAs8qtVq+Hw+kacrv890zaLRKJaWlpKMhpWKFMj/ff78ebGS8Pbt2+FwOERWA80cKPJ+8+ZNmM1mcU7y50GSJBQXF+PgwYP46KOP0NXVlfZ71Wo18vPz0draiieffBJFRUVQKpWIxWLIzc0Va77JBZR+pg6E1KA91Rqmv1OcgzJ8sgG58ShjqL6+Hna7HV6vFzdu3MDAwMADzabIhIcuukqlUozq+fn5UKlUosRvNSsnNa3pfkDLi9N0JPX76LtoCkMpYeTPlS/tQvvLy8tL6v8biURQUlKCYDCIwcFBuN3utOWgKpUKNTU1wteXWuk0NjaGy5cvY2hoSOQmUpNxCk4tLS1hfHwc586dwwcffIC+vj5hhTQ1NcFmsy0TBerk9Pvf/x4nTpzA0NCQSK1KNwjS/1HmRTAYFNkClLlAUXOj0Qi3242JiQmxOgUdDy1HRPuTCz1df/nPTO6lWq0WrRQpwETiS1kb8ikyNaah/Gq6x7Toplxw5H5h+UDc398vBhzyfabOKOLxOEZHR3Hu3Dl0dnZCoVBgZGQE8/Pz2LlzJ0pLS0WAlqoHVSqVSDG8fPmy8KnSvmkQrKioQE1NDa5fv542p1iv16Ourg67du1CS0sLbDab2AcNbvS80b2me0urEad2AVzpvpBRcL+LDdZ698lAITdNSUkJDAYDhoaGsrpCxEo8dNGlHNeJiQkhGPQCrvSC0bQVgBh97weUjkOjstx9QJD40ioBlHBtMpnEygD0Oco91ul0whqgB3Hjxo1ob29PCpTIHyJaOI8CMHQ8kiRhamoKx44dw7vvvovp6WkYjUaYzWZUV1dDr9cjNzcXS0tLGBoawoULF3D+/HkMDg7C7/dDrVajt7cXly9fRmFhoShEoXPv7u7GG2+8gffffx+jo6PCj53uWgBI6mvs9/tFRgY1l/d6vZiZmRHVeyqVSiygKEmSGLTIr0liGwqF0voSybqiakF5upT8HtFMgFZwJrGgnwaDQbTqpEFidnZWWPdU5+9yuZatjpzuuSHhjUQiCAQCIhD3xS9+EZs3bxbNz+PxuBDOvr4+eDweYSEHAgFMTU2hvb0dlZWV4j729fWhu7sbg4ODYhkgqroqLS1NEiFaUYEGEPk1UalUKCkpQXt7OzZu3CieL/kMjnppyNO8qJqOXEZ2ux2FhYUrroJBz0o0Gl3Wg/jTQrNKAKKwJxU6V4rLyJs5ZcviXo3PhOjOzMygt7cXWq0WeXl5KC4uxvT09LKpHHC3AxmtXRYKhTAyMnLfUlIikQiuXr2K27dvIy8vb9nvaTpHpbQKxd1m1xMTEwgEAmLF18XFRSgUd9eLampqQnNzs2gQIw+2HTp0CIlEAm+88QYGBgaSzpWWyZEndZNI9ff34+TJkxgeHhbJ9n19fTh79qxIJXO5XOjr68Pt27fhdrtFII38lIuLi6KUVK1WY2lpCYODg3jnnXdw4sQJjI6OiqgvPdipgSm59U5FHgsLC+Klpf8fGhpCbm4u8vLyEI/HYbVa4XQ6k5rBkA/a5/OJ85JfD7IcHQ4HKisrYbFY4PP5MDQ0JNb8kre11Ov1oh+EyWRCJBJJ8lEbjUaR1eFyueB2uzE0NAS3242lpSUhHLSCBp0PnXdqqat8ZYxEIoGRkRGcOHECwF3Rqq6uRm5urujatri4CLvdLsSZuunNzc2hr69P+JkTiQRu3ryJvr4+cR9DoRBOnjyJHTt2iMGMoHRBnU6X1GGL4gB/9Ed/hEOHDqG8vFz4/Om5crlcuHr1Kq5evQqXywVJkkSDHr1eD7PZjJKSEhQWFooBVO7ikCNJklgI834JHbkMqHeJy+XCxMQEFhcXkz5HrWFpBjM5OYne3l7MzMw8sA5o6+Ghiy5V6lD9PeWi1tTU4OLFixgcHEQwGIRerxcNY5qbm1FYWIhAICC6PN28eRN+v/9TH8/ly5fxn//5nzAajWhoaEhyF5AFRf1ZnU6nSLUaGBgQ031Ki9LpdKisrMS+fftw+PBh0UaOrNimpiaUlpZi27ZtePXVV3H27Fl4vV4olUrU19ejtbV1mX9ZqVTCbDaL3qE0ZVSr1ZiZmRGZA9RhPxqNwmg0YuPGjdi2bRs2btyY1JCG8mupWMLhcKCmpkZYfhTdJt8m+RnJiiH/mEKhEL1oqXRaoVAgEAhgYmJCZDBYrVbU1taiurpaBFvsdjtsNhtUKhXcbjeuXbuGU6dOJa2zptVqUV1djcOHD2P37t2w2WzweDw4c+YMjh07hjt37iAcDgvXwYYNG/DYY48JNwoVb8zNzYl8Vho8SHBp8JTnEAeDQREwstvtsNvt0Ol0wpWzuLgoSkzJqqPgYnV1NRwOB4C7AxMF9RwOB5544gls3boVo6OjuHXrlujHSwNAX18fCgsLkZ+fL6LxlAkgL41P7ZtsNpvR1taGkydPipV/rVYr9u7di+eee04E+Oi5Jv/9zZs38dZbb+HDDz8UA25OTg5MJhMqKyvR0tKCqqoq1NTUJK37liq6ZDH39PTgJz/5yYq+5fVCHdCefvppsVAmNX26ePEihoeHhU7U1taitbUVtbW1iEQiohgn07XeHjRZF9106UA0TSwvL8fOnTtFC8RDhw5hYmJC1PVXVlbC6XQKsUkkEti5cyeeeOIJvPvuuzh16hT6+vpEWtK9WL/BYBCvvfYaVCoVvve976G6ujrp9/Lkd2oCQqtYUNUZvbTkgxsYGMDIyAj+4i/+Aq2trcLCUKvVKC0txZe+9CW0tLTgvffew8cff4x4PI79+/ejo6MjaXkXsrIbGxvx7W9/G9XV1XC5XDAajcjLy0MwGERvb6+w/snt0draiq9//evCh0edv+RWms1mQ2FhociKuHr1Km7cuIG5uTnRKW3Lli2idWM4HMb8/DzGxsYwODiIgYEBDA0Nif2R1Un1/W63G+Xl5WK5a2qvaLFYxBpvNIA1NDTAZrPht7/9LQYHBxGLxVBdXY0//uM/xpEjR1BSUiKeo40bN8LhcODVV1/FnTt3hEV35MgR7N+/H6WlpUmltvKldObn58W0c35+XlQQ0rSczoMEv6amBjU1NSgrK0N+fj60Wi2WlpYwNjaG69evo6+vD8FgEAUFBdiyZQuamppEA3wKFKbOFmhdsUuXLuGXv/wlzpw5A5/PJ0q0acBsbm4W648VFRXh8ccfx6ZNm5K6uJFrrqOjA9/5zndw4sQJqFQqdHR04Omnn0ZlZeUygYxGo7h58yb+/d//HcePH09quUqDjtFoREFBAbZt24by8nIRu0gH+af/9V//Fa+99to9Cx3NJqxWK+rr67F3714cOHAAW7duFStnSJKEgwcPYnJyEsPDw6JKr7S0VGTn0Lp98gb/cu5XeuJ6yHrvhZV8YtRqrqamRkzdy8rKRGpSOme8UqmEzWbDnj17sGXLFjzzzDN455138Otf/xp9fX33fKyBQACvvfYaWlpa8Od//ucrBgIoX7GiokIsLrlS787jx4+jubkZ27dvT+snrq6uxre+9S187nOfQzAYRGFh4bJlWQij0Yi9e/di27ZtWFpaEp+ZmprC73//e9GEBLhruX7hC1/Avn37lgUIyTcKQAQDbTYbysrK0NTUJGr65ZaV/CWnngvDw8M4e/as8J3JW3BSBD8QCIgXora2VuRwkvVH50DtBYG7a7bpdDpIkoSnnnoKn//850XuKB2/0+nEkSNHEA6Hcfz4cSiVSjz66KPYv38/amtrkxL7dTpd0gA1NzcHAOjv70cgEEjyRdM2iURCLIC4Z88eVFRULGtZ2dTUhEcffVSsnEszEcoiWQ21Wg2Hw4HHH39crMs2Pz8P4O7zvWXLFlGkANx9j3Jzc2GxWFZcGbigoADPPvssOjo6hJslXXogWbrnzp1bJrj0XfQ819XVoaKiYs013uLxOI4fP4433nhj2bR/PUiShNraWnz5y1/GoUOH0NDQkHaZIprVbNiwQQz4ch+3xWJBY2MjtFptWnElPcpGLjGRNdGlB4SaZgNIerDJkqUpVKZRatoPJY0XFxdjdnYWLpcLwWBwzSyIdPsC7q5wQZHo1Xrf0vS+tLRUNMOmF01+HtQOb7VRVaPRoLy8PKPjpNUQaOl2ALDZbJiamsKZM2cwMzODnJwcbNiwAdu2bRPilcl1zcnJER245Lm06Y7BYrGgrq4Ofr8f169fx8DAABYXF0UmAgW0qI+s3W5Hfn6+EK7UAY183qWlpWhtbRV+3127donlm1KPtaioCB0dHaIBUEtLi7Bw06X9UTBOkiQxuJELgQJzNKugctumpibU1taKWVbqdcjLy4PNZhPnvB4oANbU1IS6ujqMj48jHo+jqKgIra2taGxsFO6ZTFAo7jbSpxU71vruqakphMNhMSjLn5N4PA6LxQKn05nR0upU6ry4uJi0v/UcO7lQ9u3bhxdeeAGVlZUZbZtqnNEAVVFRgYKCAkxOTi5LhVOpVOJ5zRaKNS7IfZN/igCv9NLTA/5pUzqo9O9+NNmQL0++FtToe7WuXxqNZl0vz71AvX/pOMgX+KB7hFLkm1wrqXm1lJKVatmuhDwvlM5jtWeD8mSBP0S4M/kOSk+Tp6il5v1S3uyDTjeiEl35eVAmz4NkrSKd9b6b5Nf/NJCrZKUMifWw1rspSZLoLXEfWfGgsya6DMMw/4dYUXSzvhrwanzaEW0935UJ6z2ebJ5fpseRre9M992pZPtY1sNn6bgfxv2738/uw3j/VuOz8m4CbOkyDMM8CFZU8Qfr6GMYhmGSYNFlGIbJIiy6DMMwWYRFl2EYJouw6DIMw2QRFl2GYZgswqLLMAyTRVh0GYZhsgiLLsMwTBZh0WUYhskiLLoMwzBZhEWXYRgmi7DoMgzDZBEWXYZhmCzCosswDJNFWHQZhmGyCIsuwzBMFmHRZRiGySIsugzDMFmERZdhGCaLsOgyDMNkERZdhmGYLMKiyzAMk0VYdBmGYbIIiy7DMEwWYdFlGIbJIiy6DMMwWYRFl2EYJouw6DIMw2QRFl2GYZgswqLLMAyTRVh0GYZhsgiLLsMwTBZh0WUYhskiLLoMwzBZhEWXYRgmi7DoMgzDZBEWXYZhmCzCosswDJNFWHQZhmGyCIsuwzBMFmHRZRiGySIsugzDMFmERZdhGCaLsOgyDMNkERZdhmGYLMKiyzAMk0VYdBmGYbIIiy7DMEwWYdFlGIbJIiy6DMMwWYRFl2EYJouw6DIMw2QRFl2GYZgswqLLMAyTRVh0GYZhsgiLLsMwTBZh0WUYhskiLLoMwzBZhEWXYRgmi7DoMgzDZBEWXYZhmCzCosswDJNFWHQZhmGyCIsuwzBMFlGt8XtFVo6CYRjm/whs6TIMw2QRFl2GYZgswqLLMAyTRVh0GYZhsgiLLsMwTBZh0WUYhski/x9ZGxUdpitclwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 51\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 61\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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IXV1dsNvtY85sR2K1WtHT0+PTb9PT07F8+XKUlJQE7TOhwnEcli5diqeeegr33HMP7rrrLnR2duL3v//9pPUff0y66PILTGlpaXjxxRfxne98BykpKQCA9evXw+Vy4fnnnw/bXugPhUIBvV7v4ybj8XhgNpvHXPQJRmJiIjIyMoQFq3AF1+FwoLS0FMeOHQvaIZVKJTIzM7F9+3bBFMOXH9/A+X83btyIhx9+GK+99tptPZs/eFenlJQU5OXlITk5GSaTCRcuXEB9fX1QAfNeoPlbJ5RnkcvlyMzMxKJFixATE4PW1lZcvnwZra2tfl3vbgfGGGQyGR566CFs3LjRpz3w/2ZnZ2PmzJlYs2YNPv30U+zevRs3btwI2O4cDgeOHDmCpUuX4tFHH4VCoQg5P3z5yGQyZGRkIDExEXV1deN6NpfLBbPZDMaY0OZ5tzyFQhH2uo4/5s2bh+eeew7r1q2DTCaDwWDAT3/6U8TGxgoDTqgL4rfDpIsuXzH5+fmYN2+ejyDKZDKsXr0aW7duRXNzM/r7+28rLZvNhkOHDmHevHnIz8+HUqlEbW0tSktLQ/Z8GIlcLkdMTAzUavW4ZriMMdTW1mLfvn3CCq4/eD/hu+++Gw888ACys7N9OhOfLv83lUqFLVu24PPPP0d1dfW4ni1YXvR6PbKzs7F+/XrcfffdAIDKykrs2bMHp0+fRn9//yhB4ThOqF+n0/k3LbxjPQvHcTAYDFi6dCm2bduG/Px8MMbw1VdfwWq1or+/f8LfDgBg1qxZ2LJli7A6790uAPiI7/3334+6ujp0dnYGnXi0t7dj3759mD9/PmbPnj2uNh4REYGYmBjI5fJxzfTtdjtKSkqwcuVKZGdnw2azoby8fJSnxHiJjIzE1q1bsWrVKh9vn5SUFLzwwguC2IqxW00084JUKvVroFYoFIiIiJiwEaa4uBitra3Iy8uDwWBAbW0tLl68OC4B4GcWCoUCTqcTbrd7lHvWWJjNZnzyyScoKSkJKPwcx0EikUAikcDpdAqdY6wGkJWVhVmzZk246LrdbgwNDeHKlSuQSqXo7u5GVlYW4uLisGbNGrjdblRUVMBkMvmIircATKbJQwy8n2Hks/AD8fz587FmzRrExcXhxo0bqKurw4ULF3D16tVRbosTBT+TDZZvHrfbDZfLJbSvQHVit9tx4sQJ7NmzB9OnT0dUVFRYeeJNKryJZTwTHMYYvvzyS/T39yM7OxtmsxkVFRXjnjmPRCKRICIiwu9MfiI3kITClO5I419t9uzZE7YNKBAejwfXr1/H9evXJ+x+/Gq70+kMW3RPnDiBjz/+OKgnBvuLj+7AwADKyspw9OhRJCYmIiUlxa/weu+Ei4yMDO+BQsDj8WB4eBjDw8MwGo04efIkEhISkJSUBL1eD5fLBa1Wi8HBQbjdbsHe5vF44HQ6/+YFF7hVJ/wM1/tZJBIJlEoltFotnE4nSktLceDAAbS3t6OzsxNDQ0OTaqeOjIwU3N+C7VgzGo04evQoysrKhN1gweqkt7cXH330EfLy8vDggw+GnB++nHgf9tsZaIaGhnDixAmcOHFi3PcIhNlsxp49e5Cbm4sNGzaEZUaZaKZMdHnn7V//+tcTPlObSBhj0Gq10Ov1kEgkYZkYOjs7UVhYiBs3boz5W4/HA7vdjubmZuzdu1d4HUpKSgrowiKVSie98TgcDvT29qK3txc1NTXQaDSIiIiAx+MZtSUXwCiR+lsl0HPwA6TZbMbly5dhsVj8biufLJRKZVCXJrfbDaPRiP379+Ozzz5Dc3Mz7HZ7SPmrra1FYWEh7r33XiQkJIScJ4lEAr1eD41Gc8fWPWMM58+fx7/8y7/g5s2b2LFjx6RMWEJhSkI7dnZ24s0338TLL798RwsucKuRz5kzB2lpaZDL5WHZfM6ePYtTp06F/HvGGDweD27evIlDhw7h888/R11dXcDXNZlMhtjY2LBn3+OF/SUuwcDAAKxW6x3bwSYb9pcYFf39/bBYLKKVg1wuD1rfdrsddXV1+Pzzz3Hw4EEYjUa/A2MwTp06JezICwXe9p2WloY5c+YEjKNwp3D16lW8/PLL+M1vfoOurq4pyYOoossYg9vtxrlz5/DHP/4RnZ2dYiY/LpKSkrB8+XLo9fqwBLenpwf79+8PuBMnEFKpFDqdTjCTXLlyBWaz2a/LjFKpxKxZs8LeuROMUELtyWQyREVFITY2NmjQnf9t8BtN4uLiEBUVBblcPqagTWTZ8Jtk/L3deDwe9PX1oaqqCrW1tWCMQafThe3o39bWhv3798NkMoV8Dcdx0Ov1WL58ueBWeSfT0dGBXbt24dy5c2EPShOB6OYFfl/17bo5RUZGIj09HSaTaVJ3ES1atAi5ubkAQu9ATqcTBw4cQFFRUVhpKRQKZGVlYdWqVcjKyoLBYEBMTEzQdDMzMxEXF3fbNnF+QZPjONhsNr/uThKJBPHx8bjnnntw9913Q6VSoaGhAV999RXq6uowPDwMl8s1IT6VdwoSiQQymQxarRaZmZlYuHAhMjIyYLPZ8NVXX+H8+fPo6uoa9cy8251KpRLeEG7X1hsfHx90EU0ikSA2NhYLFy7EzJkzcePGDRw/fhw1NTVheVEUFRXhwIED+MY3vhHSIhPfPu+66y4sXLjQ7y7TiSIlJQUxMTFoaGgY1041Ht7+PBUTBlFFl18RzsvLw5w5c9DY2Diu+yQlJeGZZ57Bxo0bcfjwYfzyl79EX1/fBOf21nbJFStWhGX78Xg8OHPmDN566y20tbWFlV5ycjJ27tyJ+++/HwaDQVi08Q4hOJKEhAQkJCSEZDcOhEKhQExMjE/8hr6+Ph9boFwux/Tp0/HII49g+/btgt+y2WzGpUuXsH//fpSUlKCjoyNkG+KdDh+jITExEatWrcKWLVuwYMECGAwGOJ1ONDY2orCwEJ9++ilu3rwpCBvHccIWXt422t7eDpPJdFvCm5CQgNjY2IB51ev1mD17NrKysgT/9KSkJLz99tth9bXW1la89dZbyMjIwNKlS0P2LDIYDFixYgU++eSTcbtoBiM6Oho/+MEPsGHDBhw5cgRvvPEGOjo6xnWvuXPnYt68eaL45Y5kShbSZsyYgVWrVuH48eMYGhoK61qdTodnnnkG//RP/yT4zhYWFk6K6BoMBqSnpwMIfRdad3c3CgsLUVFREVZaOp0O9913H7Zt24YZM2b4+A16R4UaiUqlgk6nCyutkdcnJiYKgWFMJpPPjjz+36ioKHz961/Hk08+iaysLCF/Go0GarUaQ0NDqKurQ3d397jzcicilUqRnJyMtWvXYtmyZYiKihKe3WAwQKvVwm63o7CwUNhRxZebVCpFZGQkYmJioNfr0dTUhI6OjnH7ner1+oBxYPkBIioqShjw4uPjoVQq0dbWhg8++CCsvlZeXo7du3cjOzs7pEU1/rkzMjJgMBgmxXSYmpqKjRs3Ij8/H7NmzQIA/PznP8fg4GBY99HpdFi1atW4Yk5MBFOykMYb3sdji1y6dCl27NghND6lUjlpC0l6vT5sQevq6sK1a9fC6lgcx2HhwoV49NFHhQU7mUwGmUwGqVQ6ZjDt8QZk1uv1wnbje+65B3FxcXC5XLBarfB4PELnlUgkSEtLw6ZNm5CZmSnkif/wC4y8j/H/hlku8FdPBYfDAalUKjwn/5FKpcjMzMSmTZt8Bkp+QdRqtcLlciEhIQH33nsvNmzYgLy8vHEPkmq1OujrPp8nvu3w/Wz79u0oKCgI61XaZrPh6tWrYQ+iOp0Oer0+rGtCRS6XCwt1ERER2LFjB5YsWRL2fTQaDdLT00X3z+WZEtF1u91obW0NO4qURCIRbJ28Da2zs1OYmU00drs97FFUpVIFjD6mUCgQHR2NuLg4aLVaKJVK6PV6LF68GE8++STy8vJ84p16d5JAQiaTycZ1OoNGo8GiRYvw2GOPYdu2bSgoKEBCQoLgd+ktury4ZGdnj2qoHMfBbrejo6MDPT09k7IhYCpxu93o7e2F0WiE3W4fJVxyuRyzZs0SBiPgr6LLmxvi4+OxaNEiPPTQQ3j88cdRUFAwrjqLiIgIOMHwbh982+Ht0fPnz8e3v/1tLFq0CHq9XjBZxcXFITo6OqDboUqlCtsbYXBwcFJMC8CtxWne48Dj8SAqKsrnrStUhoeH0draOmVtVVTzAv8K0tbWhiNHjoQtaBKJBB0dHejo6BCc069evRrWSms4mEwmVFZWYunSpSE3vmnTpmHNmjUoKysTXrE4jkNmZibWr1+PgoICKJVKdHZ2or29HSqVCgUFBViyZIkQ4s8bvgMD8Jn1+tspFSocxyElJQXr1q3D8uXLodPphKDy3g2YT0sulyMpKSngTiWn0zlpO7CmGn7hd3h4OKA9NioqCklJSZDJZHA4HD51KJFIoNVqkZiYiPj4eCQkJGBoaAg3b94UvAzCyQvfFrzNGLxXEJ+ev/Q3b96MyMhIXLx4EQ6HA4mJiUhISIDdbsfFixdx5MgRNDY2CvlJSEjAunXrQvZG4AffysrKcYVkDYWenh5cvXoVS5YsgUQiEQ4ekEgkYS3eDg4O4siRI9iyZQtSU1NFCVzuzZTYdE+fPo2zZ8+GfR0fNf/jjz/GAw88gOHhYRQXF09afNihoSEcO3YM69atw9y5c0O6RqPRYMeOHeA4DkeOHMHAwAAyMjKEfd+8NwK/i8flckGtVkOlUvkVXJvNhpaWFnR2diImJgZpaWk+s6SbN2+ipaUlYH5GCjO/yGMwGCCXywWf2+bmZjQ0NKC3t9dnpsuLztDQkDBz8xZ89pcg5LGxsYiJiUFXV9eUuOFMBhzHCb7Q0dHRkEqlwnN5bxF2Op3C7jzAd6bLB2VPTk4GAGG7rMFggFKpHLXoGGwgbW5uhtFoRFxcnJDO8PAwmpqaYDKZkJCQgNTU1FGnTfA2+bVr12LJkiWQyWSIiIgQXN4eeOABrFq1Cvv27UNDQwMiIyOxceNGPProo2HNyK9fv45jx46FvU4TKsPDw/jyyy8xe/ZsaDQafP7557hw4cK4PKHOnDmDM2fOTIldV3TvBZvNhrNnz47bxamtrQ3//d//jQsXLsDj8aCiomJSXZT4bblpaWkh2+KSk5Px/e9/H1u3boXFYkFMTAzi4+N9fCb5kwv4vPsbaT0eD2pra/H++++jvLwciYmJ2Lx5syDe7e3t+PDDD1FZWTnqWolEArVaDYVCAZfLBbvd7pNWR0cHjhw5gitXroDjOBiNRjQ0NAjeB94zKpvNhtOnT6O0tBRbtmwRXkc5jhPiUWRlZaGgoAB9fX24efOmKCEbJxt+hn/33XcjKysLMpkMHo/HJ9qcw+FASUkJTp8+7WPH53cYtrS04NixY2hoaEBSUhIYY7h58yY6OzsFGyzw1+3F/GyZt6t7U1FRgQ8//BARERFITEyEyWTC8ePHcejQIXR2dgpmhDlz5ozyz+Xbg1qtHvU2ExcXh4ceegjLly+HyWRCREQEkpKSwlor4GePZWVlYZdzqHg8Hpw+fRrDw8OQSCSoqqqC0Wgc1736+vpw9uxZPPjgg1CpVBOc0+CIGk+X4zj09PSgqqpq3PfxeDxobW1FR0eHMMuYTHp7e/HFF19g4cKFWLFiRcivIWq1GhkZGUF/4z1r8ndfu92OU6dOYf/+/TAajVAoFGhoaEBDQwNSUlJw9uxZHDhwwO8Axm+y0Ov1sFqtMJvNgpi6XC7BBssLicPhCOo/XV1djV/84hfo7e3F2rVrER0dDZlMBqfTCavVivj4eKxYsQIDAwMoLi5GV1dX0NluKPbqySDUdDmOQ3R0NO69916sWLEC8fHxsFqtkEgkkMvlcLlcMJlMKC4uxttvv+031gcfT8NisaClpQUKhQISiUQoZ2+TAP/2oVar0d/fD4fDMUp0zWYzPvjgA/T09ODee+9Fa2srvvjiC9TW1sLhcKClpQXZ2dnIysoata7gbYrw9zotlUqRlJQU1tFT3uV46dIlHDx4cFK8iLzp6urC8ePHhbWH26GqqgomkwnTp08X1cQgmujyD1ReXo7a2tqwrvHXOSZbbHlcLhfKy8vxu9/9DhqNBgsXLpyQ+3o/k7f4ejM0NITr16+ju7tb8Aqoq6vD3r17IZVK0djYGNBB3HvlnTcXeMdLCLf8PB4PysrK8Pzzz+Ojjz5CRkYGoqKiYDAYkJaWhoyMDGRnZ2Pz5s0wm804depUwGPk+UGGnzGKtaGCX1jytoEGyp9Op8P8+fOxadMmZGdnY3h4GFVVVWhqaoLZbEZfXx8aGhpw7dq1MRdyeZENdDQT8NdgQVKpNKh5pqurC3v27EFZWRncbjfa2tqEo+m7u7tRU1OD4eHhUaI7MpbERIrMpUuX8Pvf/x7l5eUTHtt5JIyxoG9RwTRjJLW1tSgvL8f06dP/d9p0+/v78eWXX+Ktt94KyaFZLpf7vBpPFYwx9Pf3489//jMGBwfxox/9CCtXrrxtNzXeHufxeAT7mvd3HMcJM1S32+3zSmgymWC1WoPazrxdlvi4rhNha+3p6UFpaSlKS0uFnVo5OTnYvn07Vq9ejfz8fPT396Ovrw8VFRV+hca7w0skEuFwyMlGKpX6LLoEKguVSoW77roLDzzwAPLy8uB2u3HhwgV88sknqK6uFnbeTQT8AMAvRtpstjGjdQ0NDaG5uVnwUwcgHPhpNpuFc9JGxtt1uVywWCzC+XC3KzQulwsnTpzAa6+9huPHj98Rm2IUCoVgohlrYmE0GvHmm2/C6XRi7dq1k+bqNhJRTgOWSCQ4d+4cdu7cGVIUKn4XUHR0NPr6+tDZ2TkhgYzHC2/XPHz4MFpaWvDMM8/g0UcfHVcluVwudHd349KlS4I9NT8/H4sXL4bBYPBJs7u722ejglQqhVKphFwuHzOylfdsbrIWtvhoW5cuXYJarUZ6ejrmz5+PpUuXYmhoCHa7HbW1tX7tk/zM29u9aTKF1zueLJ+2v9+o1WpkZ2dj69atWLJkCfR6PcrKyvD555/j0qVLk/aGxb+ZABjTC4R/BrlcDpVKJcx0gVuDYnd3t3A6C09fXx8uXLiA8vJycByHuXPnYsGCBeMOmDQwMIA//elPeP3111FTUzPlYgvcGiwTEhIQFRUFk8mEzs7OoLNixhiKi4tx4sQJ/PnPf8aGDRsEvZpMJl10+coYGBiAw+EY84E4jkNERAQSEhKQmJgIpVKJgYGBO2IUZYzh6tWr+Nd//Vc4nU7s2LFDEEpvkeNnVCPthw6HA1euXEFhYSGOHDmClpYWcByHGTNmYNu2bdi6dSsyMzMhl8vR2dmJ4uJi1NXVCeLEO7wD8PGj9Qe/aUGj0QhBySdLfJ1OJ8rKylBaWoqMjAzExMRgyZIlMBqNMJvNaG9v91t/vFlh5Ax0MuDDcgYyZfA7uhISErB8+XLce++9iI6OhtlsRmlpKcrLyydNcPm61Wq1kEqlgntaoLrydiPkN9LwA8mNGzdQXFwsuIQ5HA40NDTgs88+w969e9Hc3AzGGFJTU7F582Zs374ds2fPhkKhGNVevWMle7srms1mfPTRR/jlL38p+unJgeA4Dmq1WnDNk8vlGBwcHPMEE++3DECc9QXRzAu82IbSuex2O4xGIwYHB2Gz2SbdTuQPvpP6q4S2tjYcPXoUy5YtEzwavDs0v9jCPzMfFPzKlSt47733cODAAR87YF9fH+rr6/Hll19iyZIlSEhIQF1dHb788ku0tbUJzy+RSGCz2Xwc7wOh0Whw1113IS8vD4wxVFVV4dq1a7cVJCQY/f39KCkpwbx587BgwQJw3K0DPXnbYqDGPNbgMVEEs+F6/12tViMyMhIcxwmz+NLS0ts+SioYOp0Oubm5glvi5cuXce3ataB+7N5C4b0A2tbWhj/84Q9obW1FZmYmOjs7cfr0aZSXl/vcz2w2w2g0oqurC08++STmzp3rc4IL38a8Z3686La1teHYsWMBI+jxby9iBz5yuVzo7OxEf39/yJs0eD36X2nT5RmrIvhYpUajURhdxd5aOpY9OSIiAhkZGYLvJs9IeyF/XIrVakVdXR3279+P48eP+93MMTg4iJKSEpw9exYRERFwOp3CayNvm3O73YJZIZjoSiQSzJgxAzt37sTq1avBcRxOnjyJt99+G5cvX560smxqasKJEycEe/W1a9dgMpnGHDTFqNtQ0uA9EqqrqxEfHw+NRoOSkhI0NTVNWr44jkN6ejq++c1vYtmyZcIr77vvvovq6uqA/YX38+bbBT9BsNvtuHHjBlpbWyGTyYJGN+NdzuLi4iCXyzFz5kyo1WrB3MD/yx8lxRMVFYXMzExERET4XVfg7ap2u120CRNjTLCJ830vlDqfioh4U3pcTzC8bVxio1KpkJ6eDoVCgfb2dvT09AhbQPnANI8//jgSExN9GqP3f/OvgLzwDg8PC4G/gznA88eSe+/j5wnV3SkyMhL33Xcfvv71r2P69OkAbg0kp06dwrVr13wEW6fTITExEcCtLdW3MxN2OBy4fv26sKBz9epVYSHwbwE+32VlZbDZbDAYDKirq7ttn2O9Xo/ExER4PB50dnb6zDjlcjlmz56NNWvWCMGVtFotjEYjjEZjQBesYO3C4/HAYrGMKSj8bNRisQgbdfi1g2BBlpKSkvDYY4/h5s2bOHz4MAYHB8HYrcNS+Wh1DocDjY2NYe86vR0mwo1MDO5Y0Z1K+B05CxcuRF9fH6qrq4VdX7Nnz8aDDz6IefPmBQ0Q7b3wZTAYkJubi/vvvx+9vb04dOhQSJtDlEolFAoF5HI5IiIioNfrIZVKhXgT/gYlmUyGdevW4bHHHkNqaqrwqhcTE4OEhARhBi+Xy1FQUIDHH38c8+fPB3BrI8j777+PCxcuhF1mvA3ZZrMJbm78LHeqbfGhwg/03d3duHLlCuLj42G328cdGIXjOCxatAjf+ta3fMp4165dwqKcXC5HfHw8oqOjhXRSU1Px2GOPobGxEfv27fNbz1KpFAaDAQkJCXC73YI/sNPphMPhCOnVmg88fv/99yMnJ0fYpTjWuotUKkVeXh6ee+45ZGdn4+rVqwBuRQ/Mzc2FwWDAxYsX8fHHH4squn8rkOj6ISkpCZs3b8bixYsFOyq/zVOj0UCn0/ldKPM3M+Ad3xMSErBmzRokJiZCp9Nh9+7dfoWX4zhMmzYNixcvxty5cxEVFYWIiAjodDpotVq4XC5cunQJe/fuxfXr1306pEqlwrp16/Dss89i9uzZPra4oaEh9Pb2wuPxQKlUYv369XjppZeE49UBYPHixZgzZw5eeuklnDp1KqxZg1arRUJCAlQqFcxmMwYGBoRDKoPZx+8UvKOHOZ1ODAwMQKVSCcLmbUMNBblcjmXLluH//b//h2XLlgl/X7x4MQoKCvDKK68IQe57e3sxPDyMuLg4YVv1nDlz8Mwzz8BqtaKoqMhHRGUyGXJycrB161YsWLAAMpkMQ0NDGBwchMViQW9vL6qqqnDhwgUYjUa/5W4wGLBjxw78/d//Pe66664xxXak+5lEIsHs2bORmpqK4eFhYSFQpVLB4/EgNjYWJ06cCPvklP8T8J0hwOe2cblcjDHGPv30UwaASSQSBmDUh+M4plAomFwu9/u9mJ+/+7u/Yz09PQGfx2azsf7+ftbR0cH6+vrCLpNr166x7du3M4VC4ZOuRCJh+fn57Le//S2rr69nVquVud1un2vdbjczGo3s1VdfZTNmzGAcxzGJRMLkcjlbt24dO3ny5KhrXC4X2717N5s9ezaTSCQsPj6evffee8L3Ho+HeTwe4f7/9V//xRISEkIuL6VSyWbMmMGWLVvGFi9ezJKTk5lGo2FKpZKpVCqm1WqZXq9nKpUqYP1P1UcikTCVSsX0ej3TarVMpVIxpVLJNBoNS05OZosXL2bLli1jqampTKlUhnzfxMRE9tZbbwl14V3GjDH2zjvvsPj4eCaRSNjs2bPZp59+KvQV73o7efIkW7NmDZPL5UwikTCO41haWhr7z//8T9be3u63fVitVlZfX89+85vfsLy8vFFlrlAo2KOPPspqamrCbrt9fX2so6OD9ff3M7vdPirPPN3d3eyxxx6b8vqVy+VMoVAwjuMC1j8A9tlnnwllPkEE1NU7YqarUCgwY8YMJCUlobu7G01NTYKD91TkZd68eYiJifH5O19g/Nlle/bsQVVVlfD7rVu3Csf6BIMxhuzsbDz44IOoqKhAY2Mj3G63ECLw6aefxiOPPOIT54GNsN/Fx8dj5cqVKCoqQmdnJxhjSEhIwNatW4WZK38Nx3G4fPky3nvvPdTW1gqbJiwWi8/vmNeGjO7u7rA3pDgcDnR3d8NiscBsNgt2UKlUKpymbLFYYDKZ/B4FFM5OonAIZgfng//ExMQgIiICAwMDwnl0DocDZrNZcGEM165rt9vR1dUFq9UqnJLrHSTHYrEI/su1tbV47733kJmZifz8fJ+6u/vuu7F161bU1dUJ8Rpyc3OxcuVKxMfHj1ofkEgkUKlUyMjIwBNPPAG1Wo033ngDtbW1wo639PR0PPjgg8jKygp5Z1p1dbXQ5p1OJ+bNm4dt27YhNzfX5y2BJzY2FvPmzcPu3bunzM6qVquRlpaGuLg4GI1GtLS03BExQe4I0ZVKpZg2bRpyc3NRX1+P9vb2KRPdyMhI5OTkAMCoVyqO4zA4OIhPPvkEv/rVr4ToZoWFhTh8+DBeeuklrF27dsw0pFIpZs6cifT0dMGB22AwYPny5VixYgV0Ot2otL3hO5ZSqRS+87b5er8m3rhxA7/61a9QXFwsNP7BwUEcPHgQmzdvRmZmppCG0+nEZ599hv/5n/8JKyARf0x7X1+f4GrE559fXed9UWUymY9QeP+deXl8TIT4et8b+Ktrlb+0AQh+qfxv+G3UvHkknA7b19eHDz/8EGlpaXj00Ud97MJ1dXU4ePCgYO90Op0oKipCXFwcXnjhBeEcNH5dgD8xgv9/hUIhRKULJJiM3TqYcsWKFbh06RK6u7vR398PhUKBjIwMzJw5U3iusSgqKsIrr7zic7L10aNHwRjDj370o1GbhPi6z83NRWRk5KTFux4LpVKJ5ORkoY3fKaaOO0J0+aOj+eO9p3Lbr3dMAH9YLBZcvXpViHTEz4BPnjyJ559/HlqtFosXLw64uuy9/ZXvTBKJRLDZAvDxjQyE2WxGd3e3IGgmkwmlpaXIzMxEWloaPB4Prl69ij/84Q84cODAqNnGiRMn8PLLL+OJJ55Aamoqurq6cOTIEXz00UdoaGgIq8zYX1yV/P3d4XD4OKnzwsWXM3/AaHx8PEwmE2pra2E2mydMdHU6HbKzs4Wwk42Njejv7xds4Q6HA319fUIEPG9hDVdoR1JTU4OXXnoJtbW12LBhA+Lj49HS0oIPPvgApaWlPr/lBzyn04lvfetbgk2+sbERJSUlMJlMwup8T0/PmIOit58sH/jIbrcLAcxH+t56P7P3Pc6dO4cXXnhBOJadF3re73x4eDjgzsyxTj2ZbPgob7xP8lTqijd3hOh6PB4YjUaYTCbIZLIpcxUDbi1qVFZWYtOmTX4bjE6nE7ZYjnSqPnfuHF5//XW89NJLwmx5JPxsjuM4TJ8+HSkpKTCZTFAqlejr60NXVxdSU1MDRvPn0Wq1iI6OFqJWDQ4OoqioCB0dHYiLi8PAwACuXLkimBRGYrVaUVhYiDNnzginCTc3N0/KdmtezLz9mGUyGaZNm4b77rsPmzZtQmJiIlpaWvDRRx/h6NGjExIjWa1WY+XKlfjGN76B1NRUdHR04NChQzh48CDa29uF3Uj8DHcyfDYbGhrw6quvYvfu3TAYDOjq6kJbW5vfV+6hoSEUFhaivLwcc+fOhU6nQ3d3N6qqqoQdhTKZDNHR0X4D3o+E3yzQ19cHpVKJqKgoxMTECAFeXC7XKGH0/u/q6mq8/vrrguDy8OWUmprqV3D5e1RWVk561LFguFwu3Lx5E83NzXfEjlaeO0J0Ad/ZkkKh8NmCOBKZTIaoqChhy+REuqW4XC4UFhZi/fr1WLBgwajvtVotnnzySTQ1NeHQoUOjOk9JSQl+9atfYf369cjIyEBCQgL0ej0iIiKEwC4OhwNxcXHYunUrZs+ejdraWrS3t0Mulwv20GCiy3EccnJy8M///M+YNWsWqqurBX/ikydPwmazhdTInE6nECqSx/t1eyIbqnesBalUiqioKGzbtg3f+973kJaWJrgh8ScrFBUV3VbaHMdh6dKl+OEPf4iCggIhHGNOTg5UKhU+/PBD9PX1CeaMiYS3FQMQoov5C/3oD4/Hg5qaGly/fh1KpRIqlUoIKpSUlITZs2dj8+bNwsGMwXA4HBgYGIBMJkNeXh6mTZsmhH6MjY2Fw+GAUqkUIpvxb5qdnZ2or69HUVERSkpKfO7JGINcLsd9992Hb3/72wGDnF+6dAm7d++e8LLV6XTC1va+vr6A95dIJD4bnO4UwQWmQHSDbQP2dpdyu924du0a6uvrBfuuVqtFXl4e1q5di4KCAqjVahiNRly7dg1tbW3o7+9Ha2srqqurb+tVory8HO+88w5ee+01v4Gc8/Ly8Lvf/Q4ff/wxjh49isbGRuF8MJvNhv379+PYsWOIjIxEbGwsUlJSkJWVhVmzZmHOnDlISkpCfHw8EhMTsXDhQvT29uL69etoa2sTjl4fC7VajbVr1+Lee+9FV1cXGhoa8Mknn6CwsNDvbFWlUiE/Px/z5s2DXq+H2WxGbW0t6uvrMTQ0JNiy58yZI/j3mkwmdHV1CaYUpVIJxhiMRiMqKyvDPmKeh7f3Pfzww8jKyhL+rlQqsWzZMvzgBz9AU1OT32PlZ8yYgVmzZkGj0WB4eBjXr19Hc3PzqN9lZWXhBz/4AZYuXSrMvBQKBbKzs/HII4/g0qVLo2Zw4ZCcnCwIGW+aYIxBo9EgPj4eMTExQuznqqoq1NTUYGBgAFqtFjNnzkR2djYiIyMxMDCAy5cvj4rIxk9CIiIi8LWvfQ2PPPKIYIYJdAbfSHgT1uLFi5GcnIxZs2YJ/sD84F9XVye8EfE72Xp6etDf3y/sZtPpdMLpIOnp6di4cSN27twZ8Cgfq9WKd955J+wTsUeiVCqRm5uLlJQUREZGIiUlBbm5uZg2bRosFgsuXryIoqIi4U0AuNUvMjMzBfPM+fPn0dTUFFB0Jzvmhz9EDWIOIOjIp1Qq8fDDD+PJJ5+EWq1GY2MjysrKUF9fD7lcjjlz5mDFihXIyMjw2ZjA27ocDgfa2trw4Ycf4o033ritGfDRo0dRX1+POXPm+F3hnT59On784x/jW9/6liBe5eXlOHz4sLDS7I1EIsG0adPw9NNP46mnnhJmQvzGh+joaHR1dQkbIkIlIiICaWlpSEtLQ3x8PGpra1FcXOzzG6VSiW9+85t4+umnMXPmTEilUtjtdjQ1NaG8vBzt7e2YMWMGFi5ciJSUFCF9fjHJO/YDYwx9fX0oKSnBm2++ibNnz4bVaPl7pqamCkHeRy4arl27Fg8//DBef/11YcDV6/W4//778cQTTyA/Px9qtRpWqxXl5eX44IMPcPDgQWE3nVqtxkMPPYQ1a9b49aVOT09HamoqTp06FfYMSCKRYMmSJfjHf/xHrFy5EgaDwcd+6r1YCNx6W2htbcVXX32F1tZWJCUlIT8/H2lpaVAqlXC73airq8Mbb7yBXbt2+UwWGGNYsGABnn766ZA8Y0bCL5olJyf7FWu73Y59+/bhzTffhNFoHFWPCoVCOO14/vz5wuGksbGxftPjy7e+vh5Hjx4NO7/e6PV6PPvss9i5cyeSk5OFTULe9bl582Y8/vjjOHnyJK5cuQKXy4XMzEwsWLAAaWlpsFqteO+99/Cb3/wmoNmMb9uizoSD+ZONzz3NF97vbe/evQyA4L/p/VGpVEwmk7Hs7Gx2/Phxn+udTiczm81seHg45DTb2trYqlWrGADB7zLUj0qlYnK5nCUlJbFDhw4xxpiPf2Uwamtr2Y4dO5hGo2EqlWrURyKRsO9+97usv79/1LUej4fZbDZms9lCTs/7WsZulfXPfvYzptVqfco1NzeXXbx4MeC1NpstrPT46959913Bf5V/xrHKVqVSMY1Gw773ve8xs9kc8FmKiopYXl4e02g0TKPRsM2bN7PKykq/eamoqGCbNm0Sfpufn8++/PJLn/t509fXx5566imfegol30qlkqWmpvr4OIdDsLo9f/48y83NZTKZTEhLq9Wyf/u3f/Px9Q2HsdqU2Wxm3/nOdxjHcX7bq1arZTt37mQ3btwIOT3GGDt48CBLTExkcrl8XP0PAFu9ejUzGo0hP+vw8DDr7+9nTqfT5+/FxcUsKyvLp1y9PxqNhgFg+/fvZ4yJ46fLseAKP2Hyzx/eF8z4L5VKodFofI6y9udnGcxNhv/XarXe1sozx3HQaDRjbgEdWX5Wq3WUacM7vwqFYtTBgROJ3W4XYjzw8Me0jzRb+CvfUPLF/46PKTGe1zOlUhm0HPhAQXzcBr7ceEb6p3rXt1QqFWzogfLvr55Cgb837+IWTnkF+n/gli135InKfLjCcI9BDxV//WRke+bryZuxntnpdI4Z73ksFAqFz8x8rD4fqHz5IFHB4n+wv7jXjXe7dwACFpJooksQBPF/iICiK+pCWigj30TNAG9nlOUZb16CpS2G36K/9Ccr3fGWc6gzxFB/PzIf4d4/HMQsy6lqL7eb/lT2v5GIqTuhQDNdgiCIiSegik/uYUAEQRCEDyS6BEEQIkKiSxAEISIkugRBECJCoksQBCEiJLoEQRAiQqJLEAQhIiS6BEEQIkKiSxAEISIkugRBECJCoksQBCEiJLoEQRAiQqJLEAQhIiS6BEEQIkKiSxAEISIkugRBECJCoksQBCEiJLoEQRAiQqJLEAQhIiS6BEEQIkKiSxAEISIkugRBECJCoksQBCEiJLoEQRAiQqJLEAQhIiS6BEEQIkKiSxAEISIkugRBECJCoksQBCEiJLoEQRAiQqJLEAQhIiS6BEEQIkKiSxAEISIkugRBECJCoksQBCEiJLoEQRAiQqJLEAQhIiS6BEEQIkKiSxAEISIkugRBECJCoksQBCEiJLoEQRAiQqJLEAQhIiS6BEEQIkKiSxAEISIkugRBECJCoksQBCEiJLoEQRAiQqJLEAQhIiS6BEEQIkKiSxAEISIkugRBECJCoksQBCEiJLoEQRAiQqJLEAQhIiS6BEEQIkKiSxAEISIkugRBECJCoksQBCEiJLoEQRAiQqJLEAQhIiS6BEEQIkKiSxAEISIkugRBECJCoksQBCEisjG+50TJBUEQxP8RaKZLEAQhIiS6BEEQIkKiSxAEISIkugRBECJCoksQBCEiJLoEQRAi8v8BEaiwbLsU3NMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 65\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 66\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 67\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 68\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 69\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 70\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 71\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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dE2jjCbjsRj98+DBefvnlaQMuCxAGgwErV67E9u3b8eCDD0Kn0/HfKSkpwfbt23HhwgUcO3aMD4PMFDb2x443HA7PagtwtgOhEJXJMZsUCgVkMhkkEgnPRIi35RkP9j0vWbIE27dvR3FxMYAbDY1Vq1ahvLwc69atw86dO1FXVwe73T7tg6yjowP//d//jczMTGzZsmXarAm2HwwbI09LS4NKpUJDQwMaGxthtVqnnJeIhbUyZTIZcnNzsXHjRhw7duymW7uxZGRk4Itf/CIeeOABNDc3QyKRoKamRpRhBWbWgy77gjIzM7F9+3Z861vfQlVVFdRqNYqKigAAr776akJjhVNRq9V8eEImkyEQCNzUDefz+TAyMgK3281TpW4m7YZNkuzbty+ugJucnIyioiJs3boV27Zt4zfW+FZ1VVUVnnrqKTQ0NEzbdYuXXC6HVqtFfn4+5s+fj3nz5iESiWBgYADXrl1Dd3f3tK2W8elEf8ziPRaVSoW8vDyUlpYiMzMTUqkUVqsVbW1t6O7uxujo6Iw8WARBQFpaGrZt24bKysoJP9PpdHj88cdRWVmJd955h8+ZTJVaBtwIvPv27cOKFSuQl5eX0D6xc6RUKpGUlASPx4ORkZGEAy4Afq/KZDJIpVJ+L88Uo9GIZ555Bs8//zxycnKwYsUKlJeX43//93+RlZU15nhm06wHXTZTvGLFClRVVfHZWEEQkJ+fj+3bt6O5uRn79u2bkdZIb28vdu7cCbvdjpycHNTX1+PKlSs39VnBYJDnEUdP3iQiEAjg1KlTOH369JTHJ5FIoFKpkJaWhvvuuw9PPfUUiouLJ2Q7AH8IwBs3bsSHH37IMzJuhUQigUKhQGZmJioqKlBVVYXMzEwIggCbzYZ58+bh7NmzfPIkVgtOIpHw3OhAIPBHHXhZax+Y/FjYuLDZbMZdd92FxYsXw2Qy8QetTqdDMBhEV1cXwuHwjJyP5cuXY+PGjQAQs9clCAJKSkrwla98BQ6HA06nE8FgcMrvIxAI4PTp06ipqUFGRkbM/PapsN4Qy1e+2fv4ypUr2L17N5YsWYLe3l7s2bMHvb29N/VZ4ykUCtxzzz346le/yh8sycnJWLt2LSorK3lwnyxPfyaJNrygUCj4RcxIJBKkpqbCaDTO2MG6XC7s3bsXtbW10Ov1sNvtY9LHEsEm0np7e+FyuW6qtdvZ2Yn33nsvrlYuo1Qqp7zwo3sPixYtwqFDh2akJRWJRGC323H16lUMDQ1BpVLxG8rtdkMmk8FgMEAikfCbK/pGjg4AiQ7r3G6kUumYtKfxx6lQKJCUlAS9Xg+ZTIb29nYMDAzw4QW/34/BwUHY7fYZG2KQy+UoLy9HRkYG34/JqFQqfg2xh/ZUQb+9vR3vv/8+qqqqUFJSktB+CYKA0dFR9Pb2YmRk5Ka/9/b2dvz85z+HwWCAw+GA1WqdsXMnlUphNBr5QzH6XGi12hnZRrzmrPYCy4vcvXs3jh8/fkuTZ+OxLnG82QGTCYVCsNlsaGtrw9DQEFJSUhLq7oRCIRw8eBDHjh2b9unPxn6HhoZw8uRJ3mqa6oKQyWQxV/HdDJaXOjQ0hOHhYX5hsouTrZJiE4kymQyRSGTMDRaJRBAIBGZ8jHkuhMNh3rsZfyxs3FEQBNjtdthsNoRCIR4gooPc+HN0K6RS6bTft0QiwejoKE6dOoXq6moMDQ3FNUEZDAZx9OhRHDhwAIWFhQll5gSDQQwNDaGtrY3n696MSCQCi8Uyo3M8jN/vx/Hjx/Hhhx9i27Ztk+aNi2HOgm5fXx9+85vf4De/+c2MdSFmw+joKNxuNwCMafnEo7e3Fx9//HFcY64s6Ho8Hly6dAlvvPEGZDIZ7r//fqSmpk76dywwzJSpbhj2YGSTLbFaIbGC1B+jqY6DBdJgMChqa54F8akMDw/j4MGD2LFjBy5duhRzSfdkhoaG8Mknn+DRRx9Ffn5+3PvE7geXywWPxxPX382F9vZ2/PSnP4XNZsM3v/lNPo4rtjkJul1dXfjFL36B119/fczKltuRXC5Hfn4+srOz45rZjVZfX4+LFy8mtD1BEODz+VBfX8/zR7/0pS8hPz9/wrbZ+nqZTCZqfmkoFBJl7Ot2xlrCYpqqZxOJRNDd3Y33338fH3zwAZqamuDz+RJ+AF64cAH19fVxB12WbZOdnZ1wC3ku9PT04KWXXsLIyAj+6q/+Ku7jnEmi3jksdefkyZN4//33b/uACwBpaWlYtWoVX9UUL4fDgUOHDiXcVWJjgqFQCJcvX8b//d//Yf/+/TEXO7AHglqtTmgbk4m1HHey32PDDX9uwTf6uOM9VzM1I56UlIS8vLyYyfujo6PYt28f3nrrLVy9ehWhUIiPrSfCYrHg0KFDcDqdcf+NRCKBXq/HypUrkZaWltD25oLdbscHH3yA6urqCfMSYhD9scQuhFu9EJVKJUwmE9xud0IXSKIWLVqE8vLyhP5GEAScOHECn332WUJfqFQqRUpKCgwGA6/AxpbtejwepKSkTPibvLw8pKamxlULYSpsOS1rNU81Y5+SkoKCggLMmzcPwWAQFosFNpsNbrcbgUBgxhY5zDXWilMqlUhOTobJZEJGRgYUCgUsFguuX78Op9M5ZSaHQqHgec63OhRhNBonTelyu93o7u6Gy+XitRukUilfqhxvq1wQBBw9ehQnTpzAww8/nNB9Wl5ejoULF85oXu14KSkpSE5OxtDQ0C317lgcmotGg6hBl13Ey5cvR0VFBa5fv35Tn5OSkoKnnnoKGzZswP79+/Hb3/72pvICpyORSLBixQr+9I73Arx27RreeOMNtLa2JrQ9g8GA1atXo7i4GMPDw7h+/TpUKhXMZvOkrVmj0QiDwZDQdsaTy+W8cAxw4wZmyf3RFAoFsrKysGHDBmzcuBFmsxnhcBjd3d2or69HXV0dmpqaeFL+HzuZTAa9Xs+XYFdVVfGWZmdnJw4dOoQjR45gYGBgwrmSy+VQqVS8poTb7YbX672l85Kamjrpd61Wq2E2m1FSUgK/34/8/HykpqaipaUFNTU1CeVyt7a24vXXX0dxcTFKS0un/X12X6Snp2PFihX49NNPZ+Whq1ar8aUvfQmbNm3CkSNH8M4779z0cvfKykosW7ZMvHKOUeZkAMZsNmPjxo04fvx4wq1UtVqNZ599Fj/+8Y+RkZEBlUqFQ4cOoaenZ8b3U6PRoLi4OKEvxu12Y//+/aiurk5ozE+lUmHp0qV48sknUVxcjMHBQbS3t0MqlWLp0qWTFiZhLbGbxZLadTodlEol/H5/zEkkuVwOk8mEhx9+GF//+texYMECnlK2cOFC5OXlIRAIoLu7+5Zb3beT5ORkVFVVYcuWLSgtLYVWq4VEIsGiRYtQXFyMpKQkfPjhhzxLgImuaKZSqfiQhNfrvemlp7HSLhmNRoMlS5bwnPKioiKkpaWhtbUVfr8fJ06ciHu7oVAI1dXV2L9/P/Ly8sZUOpuKTCZDUVERNBoNn3yeSenp6di6dSu+8IUvYN26dUhKSsKvf/3rhDOf9Ho97r//fhQUFMz4PsZjToKuXC5HUVERUlJSEg66y5cvxze/+U2eq2g0GmdsTHM8hUKR8Ge7XC5cunQp4cCTlZWFRx55BPfccw90Oh3MZjOKi4sRDoeRkZExZeC/2ZVyWq2WFyhhxeKdTicCgcCEgtMsR3Tr1q248847oVAoeAaDUqmEXC6Hz+eDz+ebtrj5H4twOAyv18uLXLPjZOO6ixcvxmOPPYampiacPn16TNBliwX8fj+Sk5NhMBiQmZnJCyjdTDGfqbrCMpmMj+/LZDIYjUYkJSUhOzsbNpsNra2tCXX77XY7rly5gtHR0biDLnAj+E/2YLhVrMIecGM577PPPotz586huro6oc9JSUlBcXHxnE36zclWI5EIenp6MDo6mtDfSaVSlJeXIzMzk9dTZTVWZ0MoFOIJ2vGO/ahUKuj1+oTGitjKmHvuuQdpaWmQy+VQq9VISkpCIBDgdUZjpavFqg87HYlEApPJhKqqKpSXl0OhUKCjowPXrl3j+abjA0JSUhIqKyuxcOHCCYtEvF4v2tra0NLSMu2S0z8mbFigubkZ7e3tKCwshEajGbP0taysDJWVlTh//vyYIS6W3hUOh6FSqVBcXAyz2YxgMIjGxkZcuHABNpstoXM1XZqeSqXiK8pUKhVkMhmUSiXuvfdenD17FhaLJe7KfTKZjPd+4hWJRGC1WmdtaMnlcvFyAZFIBFlZWSgvL8fp06cT6lW6XC50d3fP2Bs1EiVq0GUH2dvbi3379iXcGpTL5XC5XBgeHoZer4ff70dra+usZUF4PB7U1dVhy5YtyMzMjOtvUlJSsHHjRtTU1KC+vp7PIrPi1ampqXC5XOjp6YHL5YJMJsPChQvx4IMPorCwEEqlkteNYDeZz+fjdRlYi5edS5/Pl9CEglwuR0pKClatWoWHHnoIhYWFcDgcfLnqZBehVqvF/Pnz+Yo0hq1ia21txcDAwJ/EWG60UCgEi8WC1tZWLFu2DEajccxEsMFgQFFREbRabczrWSKRICkpCTk5ObyYe0lJCdRqNU6fPp3QJFcwGORd6eiAEYlE4PF44PP5oFQqoVareU9EKpVi/vz52LRpE1paWnDt2jWEw2HodDrk5uZCp9NheHgYXV1dfP8VCgUqKiqwcePGmJO3k7FYLKirq5uVoQXgRkYQGy6Ry+UYHh7m91AiQddut2Pfvn3YvHkzcnJyZmVfpzInLd3q6mqcOHEi4b8LhUKor6/Hxx9/jPvvvx92ux01NTUzupotWiQSQU1NDc6ePYuHH344rhalTCbDunXr8A//8A/45JNP0NnZiZSUFFRWVmLp0qVITU2FzWZDTU0Nampq4PP5sHr1al48PToVia0uYt28wsJCFBcX8/dA+f1+XL58edKVd2q1Gnq9HgqFAqFQCOFwGGq1GvPnz8eSJUt4qUpWVJxNoMVqfQWDQf7z6NVq7J1iLpeLp0f9KaxIA8ame7HaseztCOxn7I0fsR580UuoXS4XvF4vTCYTcnNzsXTpUrhcLrS3t8Pn80Emk0EulyMYDMLhcMScGO7v78eVK1dQVlYGpVLJh4RaW1vR0dEBrVaLsrIyJCcn8/1jb9eorKzE+vXreQ9q5cqVWL16Nb8ez58/j88//xwulwtmsxmPPPII1q1bF/d8RiQSwZkzZ1BbWztr373f78fp06exYsUKpKSk4PDhw6ivr7+p4azjx4+juroaTz75pOgtXtGzFzweD06cOBFXVbHxVZ4ikQg6Ojrw6quv4syZM/D5fKipqZnVJPXOzk4cOHAAy5cvj7u1q9VqsXnzZqxevRoOhwNKpRIGg4G/kSIYDPKUq66uLtx5551IS0ubkPsZDodx7do1vPnmm2hra0N2djY2bNiANWvWwGg0orGxETt37oxZK1ilUmHBggW4++67YTKZMDw8jP7+fni9Xuh0OrhcLly8eBEejwcdHR24cuUKent7J51hHx4exoEDB1BRUYE1a9ZApVIhHA7zYKTRaJCeng6bzRazFGR0ALtdVq1Nt09yuRw6nQ7p6elQq9VwOp0YHR3ly6H9fj9qa2vx6aefxryeQ6EQvF4vent7cebMGbjdbpjNZmg0GrhcLmRlZUGr1fKx19TUVAwODqK6uhpXr16dMPHV39+PN998EzqdDuXl5RgZGUF1dTWOHj2Kvr4+lJSU4Gtf+xrS09PHHKNUKoXJZEJ5eTkEQUBBQQE2bNiAwsJCPv66du1ajIyMIBgMQq/Xw2AwJDRsZbFYcODAgVlNF4tEIjh16hRvPDQ0NKCzs3PM/R9vZbiRkREcP34cjzzyyIy8VDYRogVd9jSxWCwTijJPhnWRouuS+nw+NDU1oa2tDcCtFTyPh9/vx6lTpxJq7QI3xp9TU1PHLOFlF4JcLofRaERWVhZcLheSk5P5sTKsqExdXR2OHTuG4eFhNDY24vLlyzh9+jT0ej3Onz+PhoaGmK0shUKB+fPnY+3atcjMzITFYkFDQwOuXr0Ki8WCnp4e3kq12WxwOBx85juWQCCA6upq+Hw+bN68GQsWLIBSqeTvB1MqlSgqKoLL5YLf75+wYovVK4iuScBagmIG4Og88eiiNuxt1QxrIebk5KCoqAgKhQLXrl2D1WpFcnIyAoEAmpqa8Mknn6C+vn7SzACWt9zS0gKr1QqTycTfkKxQKGAymbBgwQJUVFRg3rx5GBgYQG9vL1pbWyd8ZjAYxPHjx2G321FVVQW73Y7z58+jp6cHkUgEvb29KCsrw9KlS8dMALMCPexdctnZ2TAajWNWWCYnJyc0YRYtEong7NmzOH369KzfjwMDA7yqHnvNF8MqwwmCEFemRkNDA6xWK8xms6itXdHfHFFbW8sD5lSUSiVSU1MhkUgm1Occf7JnkyAIaG1txY4dO5CXl4fKysqb/oKiu+Wjo6Po7u5Gb28vsrKyYnaRnE4nmpqaMDw8zANrd3c3n4CZ6sV9oVAIIyMj6O/vh1Qq5YH2+vXrsFqtvEqY3+/n53O64MdeN3ThwgUYjUYkJydDrVYjMzMTJSUlyM/P58uYOzs7+QKL8SvY2DlgRatnutj3ZNhNGR142RABm5Bk+6tSqZCdnY2Kigrk5+fDarXi9OnTGBgYgM/ng9vtjnsSl43Lh0IhOBwOqFQqXqWMPXRZNk5fXx9GRkYm7TL7/X6cP38eLS0tvOfIAszQ0BCuXbsGp9M54S0QkUgEDocDvb290Gg0vHcF3HwNWXauLl68iB07dqCtrW3WH6Ds+opFqVTCaDRCEAQMDw9PG3hbWlpQV1cHs9n8pzm8YLPZsHfvXvzmN7+ZttSiRCKB2WzG2rVrEQ6HUVNTg46Ojjl7f5Xb7caBAwcgk8nwt3/7t1i2bNktf96ZM2dw+PBhDA4OIikpCWvWrMG8efP4RcuqsFmtVr5UkQXt6EA5mVAohPb2dhw7doynKrW0tGBgYIAX1WZvN060tTk6Ojom2BgMBvh8Ptxzzz1YuHAhL3zC3i3Hgu74XoJMJuOBV4ygywLt+HHK6IlLQRD4u77KysqwcOFCeL1eNDY24uLFizedg8zGv9nDRiqVwufz8Zawx+NBamoqBgYG0NHRMWXAYL/PXkHEPj8cDsNqtfI0zOhryel04vLlyzhz5gw6OzuRnp6OtLS0hCbKxpNIJDh79iz+4z/+AwcPHpzTYjcKhQL5+flYvXo1JBIJTp06hebm5in/xmq14uWXX4bX68Wjjz46ZWGpmTTrQZelW9XV1eEb3/hGXLlxubm5eOKJJ/Dggw/C5/NBo9Fg9+7dsFqtczIWyFKHPvroI/T39+NHP/oRNm3aNCGdJvoij/UZwI10lSNHjuCNN97A2bNn+SvdKyoqkJubyz8zHA6jq6uLT5JFtxbjKXATDod5bVw2Vmuz2eByucYsSZ2J8VWW05mTk4Nly5ZhwYIFsNvtvDXOWpIswI8vgcgC8mwG3uigH92qZwGXdbWlUil0Oh2Ki4v5ApCLFy/i6tWrt7zogwVGdpzsoQfcGDpISkqC0+nk5266z2IPD/aZrKRpV1cXysrK+PGysedDhw6hvb0dzc3N8Pv9UKvV2LBhA38V1FTX7fifBQIB7N+/Hy+88ALOnDkzpy/0ZCmQGzduxNatW6FUKpGdnY2dO3eiu7t7yr+tq6vDqVOnsH//fmzatCmh9NCbNetBl31p8T4FlUolSkpKsGTJEuTl5SEYDGLhwoUwGo0YHByc0wmYYDCI6upqnti+ceNGPnbGls0Kwo2X9o0vBsMC0OHDh/HLX/4StbW18Hq9kEgkuHz5Ml599VWkpKRg7dq1UKvV6OzsxMGDB3k+IfCHABHvRcEKDLEkfZZeNhtFPoaGhnDhwgWYTCY+Xs2GRdgqLDZuGn0js+MRI+iyoBfdsmdL02UyGa9ZYDKZkJWVBaVSifb2djQ0NGBoaGhG9iP6IccCld/vRyAQ4A/TeL8bdh1EZ4t0d3fj4MGDKCkpgdlshtfrxcmTJ/Haa6/h8uXL/PyfPHmS93buu+8+6HS6CamArDfFxoTZ9rxeLw4fPox//ud/jnt+ZjZJJBIYDAaUlpaioKAAcrkcS5cuxenTp2GxWKbsNbBrjsUnMeKL6GO68dxYvb29+PTTT9Hb2wuZTIazZ8/OaAX+eEw1C9rU1IQjR45gyZIlyMrK4q246Hc8seR0FvgsFgtOnDiBt956C7W1tXxcig36V1dXw2az4b777kNWVhYuXbqEzz77DC6Xa0z2BrtRpzsX0e870+v16Ovr48W2Z+PCYqUF6+vrYTabYbfbefeZBZXx+8y63GKMp40PttH7EAwG+RgqS8VjOaHt7e3o7u6elWuPHT9wY2lqdnY2TxebLv+a9ZDY57Djcjqd+Oijj+B0OrFo0SL09fXhyJEjvPIY4/V6+QsZHQ4H7r77bl7MRyKRIBwO8wnR6JeUSiQS2Gw2HD58GE1NTTH3Ld4MgpnCcsXPnj3Lh6waGhrQ19cX9z78SY7pxisQCKClpQV9fX349NNPIZPJYLfbRS0DKZVKoVQqJ30LbnRCPHv6S6VSaDSaMWOlwWAQTqcTnZ2dOH78OPbt24dLly7FnAgIBoNoaGjAtWvXkJSUBL/fz+sgMNGTK9NNJMpkMmRlZfGEfEEQ0N7ePqsPLq/Xi87OTj65Mzw8DI/HM+E4xhPj5pxuG9FBzGaz8WvAarXO6lhlJBKBTCZDdnY2lixZwt9rxtK3JjN+yXH0f+/p6cGuXbugUqmmrPXg9Xpx7tw5BAIB9Pb24t5774XZbIZOp+Or2WKVstRqtSgqKoJer4+5wo31HGI9aGeL1WrF73//e5w8eRLhcJi/VFbsmsfxuO2CLnDjQmQJ93NBqVQiMzMTKpUKIyMjcDgcCAQCkMvlmDdvHp566ik89thjE5b7ymQynrLC1t4Hg0G0t7fjzJkz006QADceOuyVN+PF21uQSCTQ6XQoKyvDXXfdBblcPmX+JGvJzES6TyAQ4NkmrNTj7ZCTGw/W63C73fxttH6/f0ZaQSqVKmblNuDG92kymbBo0SIEg0FYrVb09PRM+7Bi44+xFqOw4aTpBAIBtLe34+zZs8jPz0dOTg5/R99kNYMNBgO2bt2K3t5evP3227zYj1KphF6vh9FohN/v55keYohEIhgZGZmxt4rPptsy6M41tVqNVatWYdWqVQgEArh+/ToGBweh1WqxfPlybNq0acpXVUePE6anp2P+/PkoKChAc3Nz3C326FQrYOxE0HRBV6FQ8DfUFhcX823GKmJTVVWFe++9F1qtFp9//jmOHz9+0xcuK2Uol8t5d/6PEdt3NkST6FLraKmpqbjnnntQWVkJl8uF48eP4/PPP4/ZUjUajdDr9bjrrrv4tTLdQzp6PJxdF4lMjrL6yPn5+Zg/fz7S0tKgVqunfNBIpVIUFBTgL/7iL1BSUoJz585hdHQU6enpyM/Ph0KhQE1NDT799FPRgu4fEwq6MaSlpWHz5s3YvHkzlEolz2dlr9xWq9UTLurJWqZarRaVlZWQy+UwGAz4+OOP0dTUNGXhkaSkJBQVFaGkpAQmk4nfVF6vF/39/Tz1a7JFESUlJdi8eTOqqqqg0+nQ3d2Nnp6eMTeAUqnEF77wBfz93/89ysvLIZVKMTg4iDfeeAO/+tWvYq5ym0pKSgrmz5+PzMxM2O123tKKNXl1O2KTeqy3olarkZaWBoPBgP7+fnR0dCRcES8rKwvPP/88vv71ryM9PR2RSASNjY346U9/io8//njMRFpvby+cTidyc3OxZMkS2Gw2vsQ31vesVCqRkZGBkpISZGVl8SXkkUgENpsNLS0taGtrm/Y6Ky0txRe+8AVs3rwZ5eXlfAlxLOO/v9zcXDz99NN47LHHEIlEeP5xIBBAamoq6urq/qTKfM6U2ybostZh9OTCXCkuLubr0sebKi0sFtbVX7ZsGbKzs5GWloYdO3bg2rVrMVsBBoMBGzduxFNPPYXFixdDp9NBKpUiFArB7Xajq6sLH330ET788EP09vaOafWyalZPPvkkHnroIcybNw8OhwMXL15EU1PTmFZTdnY2nn322TE5xzk5Odi2bRvOnz+PTz75JO76q2q1Gvn5+aioqIBGo0FbWxusViufTGQLEGKNU881thBCpVLxySK2Wkyv16OwsBAZGRkIhUJoa2uLu+WmVCqxZs0aPP3002OKqixfvhzf+MY3UF9fz4v4+/1+XL16FQ0NDSguLkZ+fj4eeeQRSKVSvPfee7zICyOVSpGZmYmtW7diy5YtKCgo4D0MNjR38eJFvPPOOzh8+HDMwKdWq1FaWoqvfe1r2LJlC7Kzs6ctyRjrmk9KSoq5jHbVqlW444470NHREdf5mi0sFfB2epvJbRF0FQoFsrOz+dr9/v7+Oe2WsDcEAH/o5kd/YWy1V09PD8LhMPLy8qatyyCXy5Gbm4tNmzahvb0d/f39YyYa2ETc2rVr8fzzz2PlypUTavmycnaBQACNjY2wWq38ZpRIJEhNTcW9996L9evXIysrC263G7W1tdi/fz/6+/snXHTjK5YBN5ZZ2my2hCYg2N+yCaD+/n643W4+zseWOTscDtjt9ttmnFcikfBxSL1ez4v6sHHd/v5+/uBgvx+vSCSCwcFB9Pf3w2w2A/jDeR6/OEMQBPT392P//v3IycnBunXr+Bs62KuQLBYLP2cKhQJFRUXYvHkzVq5cyR8YTHp6OrKzs2EwGOD1enHs2DF4vd4x15per8e6deuwadMm5ObmTpuGKAgCLBYLuru7IZPJkJuby5cSRzdE2DHm5+ejsrISBw4ciPuczTS1Wj2mpkVfX9+c5hMzt0XQjR77lMlk065Ym006nQ4LFy7kE2LRFb+AGxef1WrFW2+9hT179sDhcGDBggXYvn07Nm3axIvajMduNpPJhJycHCQlJY0JenK5HHq9HhUVFbjjjjt4Dd3x9RjUajXS09Oh0WjGBC7WU4hEIhgeHkYoFMKFCxewa9cuXLx4cULvoa+vD7t27cL8+fNhNpvhdrtx/vx5vPzyy6irq0uot+H3+/nyZFZ1i7WSQ6HQmCW3kw3DsPHrmR6GYOedLdCINSzEWkI+n49nXng8HrjdblgsFkilUrjd7oQmGkOhEOrq6vDCCy/g+9//Pn/7R0dHB957770Jwzfs+3r99dfhdrtRUVHBx+JZay169VlycjIvxDOeIAhQKpVYsGABFi9ejAsXLvD8bHY+kpKSkJubC5PJNG0lMa/Xi/3792Pnzp1obm6GXq/HY489hq9+9avIzs6ecD6BGw+GhQsX8uJKc4FNfLM3/losFgq6TCAQQFdXF0ZHRyctkycWlioUCwuCPT092LNnD2prawEAV69exaVLl2C1WvHUU0/xFT7Rf8d4vd4x6UDRiwJCoRBsNhucTicyMjJitj7YRe3xeHiOK8sTHh4extGjR3mmQmtrK7q6umIOEwQCAbz//vvo6+tDWVkZbDYb6urq0NLSknCaDVvXz3KKx/cKQqEQRkdH+Yo0dhzsQcFakyxTYKa6guzz2TZY7mn054fDYbhcLn7dRfc+WIuXDY8kyufzYc+ePbhy5QpWrlyJ1NRUXL58GTU1NTGvcb/fj/r6egwNDaG4uBgA0NHRMabnwc4PS2OLlbnA9tfhcIyp2xE9MRsMBjE8PAyv1zvh4R3N6XTinXfewc9+9rMx7/yTSCRYv349srOzJzQO2L+zDIi5EgwG0dvbC7vdDpvNNuvFeOJ1WwRd1hVzOBy8qthccTgcuHTpEh5//PFJLxhW+i5aa2srXn75ZSgUCjzwwAMwmUxQKpVjCl57PB60traira2Np4VFX/AejwdNTU3o6OhAYWHhhBYI+5y0tDTk5uYCwIS/Zwn9LF94Ki6XCwcOHJixLmCs7bFiLyygsv2VyWTQaDQoKipCaWkpX4V36dKlhN+oMBm2Uqm8vBxmsxk+nw9Xr15Fe3s7PB4PXyHHWtextnmry6QjkQiampomXUgwHstTb2tr4z2X6CwQFtByc3OnLFgTCoXQ0dGBpqamCXnGEokEgUAAbW1taG1thclkGtNDY+mONpsNBw4cwH/9139NeMmqwWCAXq+PeQws6Dc2NoqaXz8eS79jqYC3i9si6DLs1TRyuXzKk8S62DKZjK9Vn8kk6D179uCRRx7BXXfdNea/s4u7uLgYP/zhDyGRSFBTU8Mr5Q8ODuJ3v/sdmpub+YWcnJwMrVYLmUyGkZERNDQ0oL+/H3q9HiqVis/ys+68x+OZ9pUqOTk5eO655+Dz+XD06FEMDw/z7vPNTEKKsYKIBS/WvTUYDNiwYQO+/OUvY9GiRVAoFOjp6cHBgwexd+9eNDU1xWyZsIkvuVyOUCg06cScSqVCaWkpHn30UWzcuBG5ubkIBoO4dOkS3n33XRw9ehR2u31CCcrZkug5Zisco7G6uOvXr8dzzz03oWs/ntfrhcfj4edcpVJBrVbz/+/r68NHH32E9vZ2GAwGXh+ZvbnYZrPx1wqxSbbk5GSsWbMGf/3Xf42ioqIxx8aOjxXC2bt3b9znJx5SqRQGgwEpKSkIh8MYHByccu6HjTffTgEXmIOgO9Uae4lEAqPRiIqKCqhUKnR2duL69et8TCg9PR1LlizBAw88gKVLl0KlUmFgYAANDQ1oamqCxWJBU1PTTb/anWlsbMSOHTuwePHimGNmcrkc999/PxYsWICamhpcuHABg4ODcLlc6O/vxwcffMBfgcOChEKh4Kt8MjMzsXHjRuTn5yMYDKKzsxNNTU1wOBxYuHDhmOLSschkMqxevRoFBQU4deoUjh07xqsqTXYRssptixcvhl6vh8vl4q1i9tBgOZtGo5G/GTgYDPL6vxkZGdBoNHA4HGhubkZvb29CgZoNKajVaqxYsQLf+973sHr1al4EqbCwEOXl5Vi2bBnefvtt1NTUwG6384ImixYtwqJFi2A2m5GcnAy3243Ozk40Njbi6tWrvIVsNBqxatUqbNu2DevWrRvTK5k/fz5MJhNGR0dx8uTJm1pzL5FIkJOTgzvuuAN6vR4ejwcWiwUjIyMIhUJQKBRQqVR8oYjT6eTXvFar5W8A0el0cDgcaGhomHKWX61W44477sCaNWuwfv16rFmzZtqAq1AoUFhYiIULF8LtdsNgMGDBggW8B9Xd3Y3Lly9j//792LNnD199yR5iLHsjOzsb69ev58XcKysrsXr1at7TinVuvF4vduzYgcuXL8d9TidTUFCAO+64AxkZGXyMOjMzkw/FHDx4kA/JADfmZPLz82E2m+H3+3Hx4sUpC2XNds2PWEQtYg5MvXxVLpfjoYcewne/+12YTCb09/ejubkZfX19/E0Iy5cvR1ZW1pi/e/TRRxEIBOB0OtHY2Ihf//rXeP/992+p5Xb06FH09PSguLh4wpgVk5eXh7y8PGzduhWjo6Po6OjAm2++icbGxkkXGLAFFs888wxKS0sB3Khif+nSJbS1teGOO+5AcXFxXGNhOTk5eOKJJ/DQQw/h7NmzePHFF3mB5/Huu+8+/PCHP8Rdd90FtVrNhyJOnDjBi3AXFxejoqICeXl5Y1rhCoUC6enpSE9Ph1KphMvlwtmzZ/Haa6/h2LFjcWeasNZuUlIS1q1bh6qqKt4aYefXaDTi4Ycfxvz583Hw4EFcvnwZKSkpWL16NVasWMEL0TCBQAD9/f2ora1FTU0NnE4nysvL8cADD6C0tHRMVTs2YVlVVYV169bxpP5EqNVqrF+/Ht/4xjewfPly6HQ6BAIBDA4OYnBwEMFgkLcm/X4/rl+/joaGBrS2tkKpVGLZsmVYt24df1W51+vFmTNn8MILL+Do0aMxt7l+/Xr86Ec/wrJlyybMF0xGKpWipKQETz75JKqqqlBUVITy8nJeZ/fatWv4xS9+gfr6+knfaRYOh/Hwww/jmWee4cuDp6oSGD3nMdmxxEsikeCJJ57Ac889hzvvvDPmSzLvvvtuPPnkkzhz5gyvnJaTk8Nzl202G371q1/h7bffnrLuNNt3sYgWdNmXlZycPOEEsMmBzMxMPProo7xbX1RUxGvqxpphjb5ZlUol0tLSsH79eigUCtTW1qK/v39MSks82HiX3+/H8PBwXH+jUCj4aqKGhgbs3r2bd+vG769cLkdhYSHKysr4DaTX65GRkYGqqipoNBpotdq49xe48XTfsGED+vr68Pnnn2NkZIRvOxwOw2Qy4dlnn8WmTZv432g0GqSlpWHJkiXo6+tDKBRCRkZGXDd1amoqCgoKoFarMTAwgGvXrk04zvGiz4XRaOSvrRn/M+DGtbJo0SKYTCb09fVBr9fDbDbHbP0rlUoUFBTwOhMOhwM5OTkxJyLZdpKTk2E2m2EwGMbMrE92nUTvX2lpKb7//e9j8+bNY35nqpRBl8uFgYEBnhoZHTw0Gg0eeughjIyM8NZ6dFaL0WjE008/jfXr10/6+ZPRarVYuXIlKioq+CvZmbKyMn5OVSpVzGs1OTkZVVVVqKysTChdbnh4mC8mYlk48WKxIDs7Gz/4wQ+watWqCb8Tfd9nZWVhy5YtADAhThQVFaGnpwdHjhyBxWLhBaiiyeVyuN1uUV/HLpnmhMxY+A8Gg/zlhZORSqU8yTvWjOp0CxOiU2pYHdebFb0viQgEAmNyIsdj3evJLvRbWefPFlCM3/b48xp9PmPtw1SiczHD4TDPokgEy0meLhl//DUw2fmJlUs93Xmc7nuajFwuh0aj4TdwrOyB8ab6ntk/x/Pd3azJvufoVYOxsFcWJfIadmDy6zARMpkMWq12QsrmeONjwvhrIJ59EQQBOp1u2usxQZNegKIFXUII+TMyadAVdSItnm7GTFR0indb07nZfUm05TOT4ukii7G96czmOYjX7bbvYn93U23zVrc9l/ffeGLGnXhQS5cQQmbepFF87paLEELInyEKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiL5ND+XiLIXhBDyZ4JauoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIiIKuoQQIqL/BwGql5fm7YmBAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 72\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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7D+np6ZBKpUN8YPH4woQ/rkgfPXoUr776Kpqamsb8vkQigVwuh9FoxLJly/Dcc89h48aN0Ov1/Dtz5szBc889h9raWhw7dgwSiWRCfVFSqRQKhQIKhQLAbf9gvP70OyERv92dIAiCKBasSqUa0mfBYHBCp63sPt9333147rnnMHv2bH7eZcuWobi4GCtXrsSuXbtQXV0Nu92OUCg05thoamrCz3/+c0ybNg2PPPLIuFETrB0MQRAglUqRnp4OhUKBuro6NDc3w2azxe26YjArUy6XIzs7G+vXr8exY8fQ3t6e0HHGIi0tDVu2bMHGjRvR3NyM3/zmN6ipqRHFl8uYdNFlNygjIwPPPPMMvv71r6O8vBxqtRpz5swBAOzatQt2u31CzsdCQqRSKaRSKQKBwB0JRiAQgNPphN/v56E3dxJ2I5FIuJ+1paVl3O9qNBrk5+fjiSeewNNPP837KFrsAaCiogLbtm3D5cuXE/KzjoVcLodOp8PMmTMxd+5cZGZmQiqVwmq14vLly2hqaoLX6x33Glh7/9yJ91q0Wi1mz56NkpISpKenIxKJ4NatW2hqakJ7eztcLteEvFgEQUBaWhq2bduG8vLyEZ/p9Xr81V/9FUpLS/Huu+/io48+wo0bN8YMPwRu+/w//fRTLFy4EJmZmQm1ifWRSqWCVquF1+uF0+lMWHAB8GeVPbvsWZ4oTCYTtm3bhm9961vIycnBypUrUVZWhjfeeAMZGRlDrmcymXTRZc7wxYsXo6ysDDqdDsDtQTJr1iy88MILfKFnIgZmV1cXdu3ahcHBQZjNZpw7d25c63I0wuEwAoEAjy4YLnzxEAgEcOrUKZw8eXJM8ZdIJFAqlUhLS8O6deuwbds2zJkzZ0S0A/AnAd6wYQM++ugjHDx48I6ub/j5FQoFMjIyUFFRgcWLFyM7OxtKpRJOpxP5+fk4evQoGhoaYLPZYt4rdg0AJtzKExtm7QO372Es0ZLL5TCZTCgqKsKqVatQUlICvV6PQCCAjo4OaLVaBAIBtLW18fFztyxatAjr168HgJizLkEQUFBQgG3btsHhcMDhcKCnp2fUawBuRxScOHECp0+fxubNmxP27wp/jAkPBoMIhUIJzSyjuXr1Kn73u9/xxdP9+/fzhcW7RS6XY9myZXjhhReQn58PANDpdHjggQdQUVHBI10SXby7o7ZM+hn+iFKpHHEzJRIJUlNTkZycPGEX63K58Nlnn6GmpgYGgwE2m+2OLcFQKMQHrcPhgFqtTtjJ3traivfffx/Xr18f83vRwsrCesb6LnB79lBYWDhhL6xIJAKbzcaFNTqxwefz8ZdCJBLhkQTRD3K0AEil0j970WXT+eHuG/Zy0Wq1fNGIWbXAnxIsurq6YLPZJqwf5HI5iouL47LKol0dbGyNJfo3btzA3r17UVpaymdX8RKJROB0Ovlzcqdjsb29Ha+99hqMRiMcDgf6+vomrO+kUilSUlJ4iF50XzBDUCymrPaCRCLB4OAgPvzwQ5w+ffqOpiOjwUKn2Irq3RzHZrOhtbUV/f39SElJSUh0g8EgDh06hMrKynF9isz329/fj9OnT6OiogJpaWnQarWj/kYmk0Gv10/IC0v4Y1xqX18fBgcHUVdXN+RzlrHD/NpyuRyRSGTIdUUiEQQCgQn3MU8FLCyNLX5Ew/pCEAT09vaip6eHZ1xGwyzAifJZS6VSaLXaMe83C9Oqrq7G6dOn0d/fP65fF7htzR85cgSHDh1CXl5eQpE5oVAIfX19aG1tvauXTCQSgdVq5TG9EwmbcX744Yd4/vnnYTKZJvwc8TJlonvr1i28/vrreOONN8YN5ZpKfD7fkNC1RFwMt27dwqeffhqXpc1E1+PxoL6+Hm+//TZUKhVWr149amIBAC4ME0Us8YiGLfRJJJKY08hYIvXnyFjXwabSgUAgLkGbyDaNJ2h2ux2VlZXYtWsX6uvr4fF44hbBvr4+fPbZZ3jssceQk5MTd5sA8PT1Ow3zFIPW1lb853/+JwYHB/H1r389Yf/1RDElotvW1oaf/exn2Llz54QtoE0Wcrkc+fn5yM7OhkKhSMine/78+REW43gIggCfz4cLFy7gtddeg8ViwebNm5GRkREzk4rlmosVX8peDvdCtaaphImtmC8YFlscy9IVBAE9PT3Yv38/9u7di/r6evj9/oTbV1tbi/Pnz8ctumwtICsrC/n5+eLVL7hDuru78bOf/QwOhwMvv/wyZs6cKXobRK2ny0J3Tpw4gQ8++OCeF1zgdojJsmXLeFZTvNhsNvzhD38YNRNnNGQyGWQyGfx+P+rr6/Gb3/wGx44dixk1wEJrkpKSEjrHaAxfsBsLlg0mxsLDvUSi151In45HUlISsrOzY7q4PB4Pjh49infffRcNDQ0IBAJ8LCWCxWLB4cOHE3o2JRIJTCYTli1bllB25lRht9vxwQcf4Pjx4wgGg6LPzER/LbFFlrt9WBUKBVJSUuB2u+FyuSaodSMpKipCcXFxQr8RBAHHjh3DkSNHErqhcrkcKSkpPOzI7/fzBRmfzxezGlN2djaSk5PHzfiJ59wqlYqH2Y222i2VSmEwGDBz5kykpqYiGAzCarWiv78fbrebuyf+ElwMzJUil8uh1WqRmprK41H7+vrQ0dEBh8MRc/oukUj4Yha7l3fr201NTUV2dnbMz1g2n8vlgkaj4YvTVqsVAwMDcZ9bEARUVlbi+PHjePTRRxN6YRQXF2P+/PljprHfLXq9HhqNhvuq7xSpVDqi+p5YiCq6bBAvWrQIpaWldxz0rNPp8OSTT2L16tU4dOgQ9u7dO6ELcQyJRIIlS5bwt3e8N+jKlSvYuXPnuHG5w0lJScEDDzyAwsJCOBwOdHZ2QqPRIDc3d9RohuTk5LteFGBlNQ0GAyQSCS9tONxloVAoYDabsWbNGqxduxY5OTkIhUJob2/HhQsXUFNTg+bm5rtawb6XYAuVBQUFWLJkCSoqKnjSTVtbGw4fPowjR46gu7t7RF/J5XKo1Wro9XoIggCHw8HLC94pJpNp1HutVCqRm5uL4uJieL1eZGVlwWAwoKGhAceOHUtocaq5uRk7duzArFmzUFhYOO732XORnp6OJUuW4ODBg5Py0lWpVHj88cexYcMGHDlyBHv37r1jg6u0tJQX1hKbKXHA5ObmYt26dTh27NiQakXxoFKp8MILL+Cf//mfYTabkZSUhMOHD0/KYpxGo8Hs2bMTmqK5XC4cPHgQp06dSmgVV61WY/HixXjqqacwa9Ys2O12dHZ2QiaToaioaNTYSblcnnBcZTRKpRIajQZ6vR5JSUnw+XyjxqSmpaXhsccew/PPP4+CggKo1WpEIhEUFxcjJycHgUAAXV1dCd/TexmdTofy8nJs3boVhYWF0Ol0kEqlWLBgAebOnQudTocPP/wQVqs1ZvSCRCJBUlISn+F5PJ47NhCiswSHo1KpUFxczONkWaGf0tJSuN1uVFZWxp0kFIlEcPLkSRw4cAA5OTljRtBEI5PJMGvWLGg0Grjd7rivK15SUlLw+OOP44knnsCaNWug0Wjwv//7vwn3p9FoxLp165CbmzvhbYyHKRFdhUKB/Px86PX6hB/QiooKbN++HWazGcBtS28is1aiUSgUCftLXS4Xrly5Elfpw2hmzpyJzZs3Y9myZbz4S05ODiKRyLhxzHeaKafT6TB9+nQkJycDuF0S0+fzwe/3j4hMkMvlKCkpwdatW1FSUsIXFSORCFQqFVQqFUKhUMzf/rkSDofh8/l48WvmLmBT05KSEjz++ONoamrCiRMnhohuOByG3+/nbqGMjAxkZGRgcHAQFovljor5sHC90T6bPn06Fi9ezIvHK5VKTJs2DX19fbh+/XpCSUI2mw1XrlyB0+mMW3SB24bKaC+Gu0WlUvGxmpmZie3bt+PcuXOorq5O6Dh6vR6zZ8+eskW/KTkrS5NM9G0okUhQXFyMzMxMXtpvYGBg0ny60XV04/VBK5VKGI1GyGSyuKaSEokEBoMBq1evxgMPPMBjgZVKJRcyFhMaK1yN5b4ngkQiQVpaGsrLy1FcXAylUonW1lZcvXqVW0rDH26NRoPy8nLMnz8fSqVySDv8fj/a29tx/fr1MXcz+HNDEAR4PB5cv34d7e3t3Ipji2NKpRLz589HeXk5zp07NyK0MBKJ8N1SZs2ahdzcXAQCAdTX1+PixYvo6+tLqK/G2v+LxU4nJyfz2Q/LqnvggQdw/vx59PT0wOl0xnVOmUwGk8k0ZpLOcFh8/GS5ltxuNw+/jEQiyMzMRGFhYcK1E1wuFzo6Ou4ow3QiEFV02UXeunULv//97xO2BuVyOZxOJ/r6+mAymeDz+dDU1DRpouvxeFBdXc1DtuKBTV2qqqpw4cIFPgBNJhNyc3ORnp4Ol8uFrq4u2O12KBQKlJWVYdOmTUPC0piQRhdNT0pK4q4O1peJLtCwB3PFihV4+OGHkZOTwwujtLa2jirger0e+fn5I6I4BEGA0+lEW1sb+vr6/mKsXAYL/G9ra4PL5RriU5VIJDAajXzWNnwxk70Q1Wo1srOzUVFRAaPRiLlz50Kj0eDUqVMYHByM+/6xmQQ7NrsPkUiEz1AUCgWUSiVfJJJKpZg5cyY2bdqEGzduoLa2FqFQCEajEZmZmdDpdLBarTyxAbg9RkpLS7Fu3ToYDIa4+6q3txdnzpyZFNcCcHsm1tTUBI/HA6VSyct/yuXyhEImbTYbfv/73+ORRx5BVlaW6OI7JZbuiRMncPz48YR/Fw6HceHCBezbtw/r16/n2VuTVQErEomgqqoKZ8+exaZNm+KyKGUyGa9C/8knn6C1tRUGgwHl5eVYtGgR0tPT4XA4cOHCBZw5cwY+nw8rVqxAUVERr+gfnU7rcrn4iyUnJwd5eXnQarV8N4yrV6+O6s9mBc8VCgW3YFlxlkWLFvFFIa/Xy4uKsxCa4ZYD25MrEonwjDOWceXxeOD3+3mI0l9CRhoAvleZVCrl188qYUWn1rKU6GhY/wSDQV483ev18giERYsWweVyobm5GR6Ph1fXCgaDoxYm7+npwdWrV1FYWAiFQsHPfePGDbS3t0On06GgoID7kKOL0RQXF+Phhx+GXq+HWq3mC4N6vZ7XZz5//jxcLhfy8vLw6KOP4oEHHoh7PSMSieDMmTOoqqqatHvv9/tx+vRpLFq0CCkpKfj8889RW1t7Ry/648eP4+TJk/jqV786CS0dG9GjF9xuN44fPx6XlTu8ylMkEkFbWxt27NiBmpoaeL1enD9/flIf8NbWVhw6dAiLFi2K29rV6XR45JFHsGzZMtjtdiiVSiQnJ/OQr1AohLy8PGRnZ6Orqwvz5s2DyWTi+f6McDiMpqYm7Nq1C83NzZgxYwbWrFmD5cuXw2Qyob6+Hrt27YpZFESlUmHu3LlYsWIF0tLSMDAwAIvFAr/fD6PRCKfTibq6Orjdbly/fh319fXo7OwcdcPJwcFBHDx4EAsWLMDy5cuhUqm44Ho8Hmi1WpjNZlgsFp6tFU20CNwrWWvjtUmhUMBgMMBsNvMKWkwgWZH9mpoaHDx4MGbWYTgchtfrRWdnJ2pqauB2uzFr1ixotVq4XC5kZmbyDUYzMjKQnJyMvr4+nDx5Eo2NjSP6sKurC2+//TYvsjM4OIjTp0/zLa/mzJmD559/HqmpqUOuUSqVwmQyobi4GAqFAjNmzMCiRYtgNpv59jr3338/BgcHEQgEYDQaE66HYrFYcPDgwUkNFxMEAWfOnMFPf/pTJCUl4cqVK2hraxuyYB1vZbjBwUEcP34cmzdvnpBNZRNBNNFlJnxvby8uXboU12+YQz4UCvGO9fv9aGlpQXt7OwRBmJRQsWj8fj9OnTqVkLUL3F7YSE1NHfIAsIHAQpGmT58Or9eLpKSkEU595jo4d+4cvvjiC/T29qK2thZ1dXWoqqqC0Wjk1kmsqRVbrFy5ciWmT58Oi8WC+vp6NDU1obu7G+3t7QgGg3A4HLBarbDb7fD7/aNGXPj9fhw/fhw+nw+bNm3CvHnzoFareSyxWq3GvHnz4HQ6+R5m7FjMYoy2gln68kRV34oX1hYmRqw94XB4SFukUik0Gg1mzZqF+fPnQ6VSoaWlBT09PVCpVPD5fGhsbMRnn32Gs2fPxhyHbHyGQiE0NTXBYrGgrq4OBoOBRyIkJyejoKAAJSUlmDZtGiwWC7q6unD9+vURx2T1ERwOBxYuXAibzYYzZ86go6MDoVAIHR0dKCoqQnl5+QhfLAsLTE5OxrRp04bU7GCZjYksmEUTiURw9uzZSZ11MqxWK44cOcJnetHjlfmw49WF2tpaWCwW5OXliepiEE102QVVVVXFFb+qVCqRkpLCC+NET7eYD0sMBEFAS0sLdu7ciaysLJSXl9/xDYqelns8HnR2dqKjowMmk4lP26NhrgW2l1kgEMDNmzcxMDDAi/GM5ssKhUKw2Wzo6emBVCqFxWJBR0cHbt68CavVykOXEqkh4PP5cPz4cVy8eBEpKSl82yK20WJeXh5vd0tLC3w+H9+ynk2f2Qo8EzqpVDpEoCcTFnXAssqYpcvOzf5XJpNBrVYjLy8PixYtQm5uLiwWC44ePYqOjg6+Tc3AwEBc2yKx8cqqjzG/q0ajQXp6OpKSkjBt2jRIJBL09PSMWjoTuH0Pzpw5g6amJkgkEjgcDi4wbMNKl8s1xBfLrtFms6Gjo2NI9NDdZMyx5+DSpUv49a9/jevXr0/6C5QVZooFm1EKwu39/MYT3paWFlRXVyMvL+8v06fb39+Pjz/+GG+++WZcO0Xk5OTg/vvvRzgcRnV1NVpbW6ds/yq3243PP/8cMpkM3/3ud7Fw4cK7Oh6rrXDgwAH09vZCKpWivLwcJpNpSAERl8sFq9U6YrofLZijEQqFcPPmTRw7dgwZGRno7+9Hc3Mzuru7ue+W7W6cqLXpdDq52EgkErS0tMDr9WL9+vUoKiqC0+mEx+NBd3c3rzoWK3WW+QtlMpkoosvaMNxPGZ2dJAgClEolMjMzcd9996GoqAhutxsXLlxAdXU1+vv770hYWM0K5uf1+Xzwer1cjJm/t6enBzdv3hxzcS0YDHLxjiYUCsFqtfKF5eFjqb6+HseOHcPVq1eh1WqxevXqu5paSyQSnD17Fv/xH/+Bzz//HB6P546PdbcoFArMnDkTS5cuhVQqxcmTJ8c17qxWK37xi1/A6/XyzXDFYNJFly081NTUYPv27XHFxmVlZeHLX/4yHnroIfh8Pmi1Wnz00Ufo7e2dEl8gW7DYt28furu78d3vfhcbN268o2LPbrcbJ0+exFtvvYWqqioEAgG4XC7MmzcP06dP54IQiUT41vTRbglmqY1HOByGzWZDY2Mjuru74fF40NvbC5fLxS1b1qa76VNBENDX14eLFy8iOzsby5cvR1FREfdxshRUJvDD3Q7RaeGTKbzRqefRVj0TXLY9OUvFLikp4Yub58+f5yFedwOz7qMta0G4vWNwOByGRqPhOwyPtzjEIiPkcvkQ10h3dze6urowe/Zsbr2FQiGcP38en376KRoaGnhheoVCgfvvv58vzCZCIBDAgQMH8Morr6CmpmZKMxAlEglSUlKwdu1abN26FUqlEmazGe+88w46OzvH/O3Zs2dRVVWFzMxMPPTQQwmFh94polm68b4FlUolZs+ezVMug8Eg5s2bh+TkZFit1ildgAkGgzh16hT+9V//FQCwdu1anpjBBIXFSw637CKRCLxeL06cOIFf/OIXOHnyJLdIzp07h9dffx16vR5LliyBSqVCV1cXvvjiC7S2tvKHlD1k8cIENRQKcfcE27trovvRYrHg7NmzfMfkmTNnoq+vD4FAAHa7ne++EX3uaAtYDNEdzXcbiUT4DtQ6nQ4ZGRnIysqCUqnE9evXcfbs2YQLF41G9EsuGAxCIpHw+6NUKhMq1cnS6lnfRiIRtLa2orKyErNmzYLZbOaLff/zP/+Dc+fO8Sn34cOH+WzngQce4PHHDDYDYhXlol/2Pp8Phw8fxr/9278N2d58qpBIJEhOTsa8efOQk5MDhUKBiooKVFVV8YXd8WD6JIa+iO7THe/BEgQBXV1dOHDgALq6uiCVSnH27FkMDg6Kuzf9GKugTU1NqKysRHl5OY9oiN6uhFlNzGoNh8Po7+9HdXU1duzYgRMnTgyJLfb5fPjiiy/Q19eHjRs3YsaMGWhoaMDBgweHTGeZsLNjjoVcLofBYEBubi6Sk5PR2dmJ/v7+SVu4CoVC3EdWUFAAm83G66v6fD7eN9GIWSZyuNgyWOF1ZomzRbLBwUFcu3YN165dQ0tLy6RYcuwlwJJVsrKyMDg4yKMkxhILJojRi5LAbTfee++9B7vdjvnz56OzsxOHDh1CXV3dEB+n0+nE0aNH+frCkiVLkJqaOmTMMjEfXhhmYGAAlZWVuHbtWsy2xRtBMFFEIhEMDg7izJkz/CV66dKluLb6iZ51icU9V/wyGAyipaUF3d3d+MMf/gCZTAabzSZqPr9UKoVSqeQDbzgGgwH5+fk8D5/9hu08zKyZQCDAfZtVVVXYt28fqqurYyZzBAIBnD17FpcvX4Zer4fP5+NxsQy2Zxt7WMdCqVQiKysLS5Ys4QH9zc3Nk5q84PF4cPXqVR63yxJAxoqKAMR5OMc7B3N72O12dHV18Sph7e3tkxbsD/zJ/ZadnY3FixdjcHCQb1Uznuj6/X5eTD7aer5+/Tp27twJtVrNU7tj4XK5eI0Qq9WKZcuWwWw2Q6PR8EgApVI5okSkVqtFfn4+TCbTqCVHZTLZiOiCycRqteKzzz7DqVOnEAqFeNz5vbhl1D0nusCf9lyKZ2V4MmA563K5nG/uFwwGedGXZ599Flu2bBmRnRUdssKsUlaF68yZM7h27dq4URfMMowO54mOlmCCPpaIsPKLZWVlWLJkCeRyOW7evMl/Pxxmld9tRIgg3C7AbrFYeMLFn9MGlayymt1uh1Qq5QWAJgK1Wh3zJc7uR3p6OgoLCxEIBHiG2Fgvq+FjYPg4cbvdcT0/Pp8PV69e5S6VadOmcd/28LhxhtFoxOOPP47Ozk7s3r0bfX19CIVCPK7ZYDDwkp9iRRmx6IxEs1yngntSdKcalUqF++67D8uXL0cwGERHRwf6+/uh1WqxdOlSPPzww6PWNQWG+ipNJhOys7N51Ser1RpXxlYkEuEDP7pw9lghMwxWE2D58uXIz8/ns4RY5QcrKiqwatUq6PV6XLx4EcePH7/jBSO1Wg2dTsdjKMUKBZtI2MsyEAhAo9FAp9PxaI87IT09HQ8++CBKS0vhcDhw9OhRXLx4cYi7gk37TSYTDAYDli1bhqtXr44IlRwOs8aZX5i97KPdUGPBdn0YPkaH19YYDkstfumllzBr1iye+MGy7eRyOaqqqlBZWXlPb98zVZDoxiA5ORmPPvooHnvsMajVagQCAZ7mytIoh1sZsWBl/QoKCvClL30JWq0WH3/8Merr68csDKPVajFnzhwUFxcjPT2dP1g+nw/d3d2ora1Fa2trzAGtVqtRVlaGrVu3orS0FFqtFrdu3UJ7e/uQxUylUonNmzfje9/7HoqLiyGRSNDf34/du3fjl7/8JS8IEi+sBsGMGTNgs9ng9Xp5Ae/oRcZ7lejFIrYbc1paGoxGI/R6Pa5fv56Qi0sikSA7Oxvf+ta38MwzzyAlJQWRSAT19fX493//d3zyySdcyD0eDzo6OuB0OmE2m/n983g8qK2tjfmSVavVyM3NRVlZGTIzM6FSqfgL2Wq1or6+Hs3NzaO6RlhCRFFREbZu3YqNGzfycp1jFdaJJisrC88++ywef/xxhMNhqFQqKJVK+Hw+pKSk4MKFC38Wu8OIzT0jumzQs8WVqWT27NlYsWIF0tPTR3wWvfIeDxKJBBqNBkVFRUhPT0dycjLeeust1NXVxRRNk8mEhx56CE8//TTKysqg1+t5HCsT3U8++QTvvPMObty4MaSvmOA+99xzWLduHYxGI1wuFy5duoS6urohD29mZiZeeOEF3HffffzfzGYztm3bhrq6OnzwwQdxWylqtRo5OTkoLy+HVqtFc3MzFy7mDwyFQrwoy70kvsx/r1arefiVXC6HQqHgRX6mTZuGUCiE5ubmuPtEpVLhwQcfxLZt24akjy9cuBAvvPACzp8/j7a2NgC3s/3YPcrLy8P06dPx0EMP8QSO2traIeeVy+XIycnB008/jc2bN8NsNvNdP8LhMJxOJ2pra7F7924cOnQo5pRbpVKhqKgIf/3Xf41NmzZxd9pYxBrzSUlJI2J99Xo9VqxYgfz8fH6NUwVbBLyXXvr3hOiyHQlSU1MxODiInp6eKZ2WlJWV8QLHzE8WfcNCoRAGBwdx69YtRCIRZGVlYfr06WMeUyaTISMjA2vXrkVLSws6OjpgsVi4b46lnT744IP4xje+gcWLF49I5dTpdEhOTobP58O5c+fQ3t7OV/8lEgnMZjM2btyIFStWICUlBR6PB2fOnMH+/fvR2dk5YtANr1gG3A79ipWQMd61AYDD4cDAwAC6urrgdDp5HVpW29Vms6G/v/+eEV5WnjE1NRUmkwmBQIBneLFtklQqFX9wEylmHw6HYbVaYbFY+K6zrJ+Hi5sgCOjo6MC+ffuQmZmJ+++/HykpKVi5ciUsFgt6enp42jsLSZw/fz7Wr1+P+fPnjwhPNBqNSE9Ph8FggNfrRWVlJd8VmI2V5ORkPPjgg1i7dm3MTU9jYbFY0NnZCalUiqysLJhMJm4osf5k18is8CNHjsTdZxONWq0eUtOip6dnyhKsorknRFcmkyE1NRV5eXmQy+V3HYR+N2i1Wl4zNlqMogdlX18fdu/ejY8++ggOhwNz587F888/jw0bNoxaUD16sM+YMYPv4stChlhQfnl5OebMmcMX5KLPy3xwRqNxxHmiA+UHBga49fTuu++ipqZmxGDr6urC3r17eZ1Xr9eLS5cu4bXXXuMb9sWL3+9HW1sbT4RgCRjMwmALdawPol9i0QkSACY8pC1aLFlo1fBzswgAn8/Hs/3cbjfcbjcsFgsvKZpIXYFgMIhjx47hlVdewTe+8Q2UlpYiKSkJN2/exN69e0eEMwWDQdTU1EAmk8HlcqGsrAwul4tb3syKZURXkBveX4IgQKFQoKCgAOXl5bh48eKQhBi2q3BWVlZchW18Ph8OHTqEt99+G01NTTAYDHjiiSfw9NNP880EovsbuG1JFxYWQqvVTmr0x1jI5XKkp6fz9Rer1Uqiy/D7/ejo6IDH4+EP7FTBppuxYCLY2dmJ/fv3o6qqCsDtPdEaGhrQ39+PJ598ckThkOiHwufz8bzwaLFhD77NZoPL5UJ6evqoD0MkEoHL5eJWLgsh6+npwaeffopr165BEAQ0NjaipaUlplgEAgG8//776O7u5hlkNTU1uHbtWsLuHRaiwwpkR18vS8ZwOBxDYmWZ2LG9xKRSaUJ1IOKBHV+pVHL/Mku7ja7/YLPZuKhGhzmxkD9g/PjyWHi9XnzwwQe4fPkyFi9ejJSUFNTX1+PUqVMxH36fz4fTp0/DYrFg/vz5kEgkaG5uhsViGdJvoVBozDUBVmvB5XLxOg7Dx1ogEMDAwAC8Xu+IGsHRuN1u7NmzBz/+8Y+HxOXK5XKsXr0aZrN5hHHA/pu5PKaK6O2j2AzrXuCeEF2WSspCdabSp+t0OtHQ0BAzHZANLKPRCKPROOSzq1ev4tVXX4VarcaaNWtgMpn41i7sd16vFy0tLWhubuYxltG43W40Nzejs7MTM2fOHDGdZd9n1nK0xSgIAt8qqLGxkQfPj3etBw4cwIEDBxLspdjEEiYmdNEvUiYAOp0Oc+bMGVLFq76+/o7rGwyHzSyKi4sxa9Ys+P1+NDY2orm5GS6Xa0hY31jtvxuCwSDq6+tRX18f1/d9Ph+uXLmCa9eucV9+9EuICVpmZibfuiaWayAcDqOjowNNTU0jYmmZ6DY3N6OlpQXJyclDtqVioXM2mw1ffPEFfvGLX4xIhDAajbyozvDzMwOCbfczVbAC9L29vfeEhcu4J0SXEQwG+VR0rMGuVquRnp4OmUwGh8MBm802oaFJ+/fvx+bNm7F06dKYn+fn5+Mf/uEfIJVKceLECXg8HkgkElitVrz//vu4fv06D71hCw0SiQQ2mw319fXo6uqCyWTipRGjq3F5vd5x38jTp0/H9u3b4XQ6UVlZOWQ76jsZXGJkEEVn/rDdK9auXYunnnoK8+fPh0KhQEdHBw4dOoR9+/ahqakpZj+wreKZa8br9ca896ye8JYtW7BhwwaeUt7Q0ID33nsPX3zxBd+1QQz/cqJ9HP0yYL+Vy+VITU3FunXrsH37dkybNm3MY7CxBYAnOajVaqhUKqjVanR2duKjjz5CS0sLjEYjz07zer0IBAKw2Wy4ePEirFYrn/1pNBqsXLkS3/72t3lVuVicOXMG+/bti+ta44WFYBoMBu4zH2vth+nIvSS4wBSI7ng59kajke9+297ejs7OTu4TSktLw8KFC7FhwwYsXLgQarUaPT09uHTpEpqamtDT04Ompqa7XjFlW6gzP9xw5HI51qxZg7lz56K6uhp1dXW8XGJPTw/ee+89Pp1hizXM6lWr1cjMzMTGjRv5xpPMIrHb7SgtLeX542P14aJFi/Bf//VfOH36NI4cOYITJ06MmXzBFjcWLFjAi5jfvHkTN27c4G4BtvFfWloa5HI5/H4//H4/9zezMoR2ux2NjY0Jh5Uxl4Jarcby5cvx0ksvYcmSJdyiz8nJQVFRESoqKvDOO++guroaDocDUqkU6enpKCkpQUlJCfLz87mv8MaNG6irq0N9fT2sVisikQgMBgOWLl2K5557Dg8++CC3CAEgLy8PKSkpcLvdOHLkCB9biV7HzJkzMW/ePBiNRni9XlitVu7Tjt6os6+vD4ODg3wssGxGltFot9tRV1fHk1diwV4gK1euxOrVq7F8+fJxC+orFArk5OSgrKyMuxDmzp2LrKwsSKVStLa24vLlyzhw4AA++ugjLk4s41GlUiE1NRVZWVlYt24dL0O5YMECLF26FDNmzBj13B6PBzt37sTVq1fj7tPRyMnJwdy5czF9+nQUFBSgtLQUGRkZfDH50KFDOH/+PF8HYr7qnJwc+P1+XLlyZcw1osmu+RELUYuYAxhzyiuXy7F+/Xq8+OKLSEtLQ1dXFxobG9HT0wO1Wo3CwkIsWbJkxA1/7LHH+Irz5cuX8atf/Qrvv//+XVkwrHZqQUHBCJ8VY8aMGfjSl76ELVu2wO12o6OjA++88w4uXryI3t7emMfV6XRYvHgxnn32WcydOxcSye2aqM3Nzejo6EB+fj5ycnLiWk3OyMjAE088gQ0bNuDMmTN45ZVXcPDgwZjfXbt2Lf7xH/8Rixcvhlqthtfrxc2bN1FVVYWLFy9CEATMmjULJSUlmDFjBq9B4PP5uIWVkpIChUIBp9OJc+fO4a233sLhw4fjjjRhflRmLS1YsIDvXBDtOnn00UeRl5eHgwcPorGxESaTCStXrsTSpUuRkZExZPU/FAqhp6cH1dXVOHHiBGw2G+bPn4+HHnoIRUVFQ15egiBAJpOhrKwMK1euRE1NTcLTX7VajfXr1+NrX/saFi5cCJ1Oh2AwiIGBAT7jUKvVfBbT2dmJ+vp6tLS08HMvW7YM+fn5/D6wezfaSv/q1avxne98B4sWLYJOp4urnewl++Uvf5kXj5ozZw4MBgMEQcC1a9fw85//HOfOnRt1j8FIJIJHH30UzzzzDLKzs6HVascMK4te8zh69Ghc7Ryr/U8++ST+9m//FiUlJTwCJpqVK1fiySefRHV1NRobG+H3+2E2mzF37lxkZmair68Pv/rVr7Bnz55RhTW62p5YiCa67GZptdoRwst8QGlpadiyZQtWrFgBiUSC+fPnY82aNTwFd7iPNfphZaE/q1atglKpRHV1Nbq6uoaEtMRDdNWnvr4+FBQUxHVtzMdVUlKCpKQkntAwvL1yuRz5+fmYN28ef4B0Oh3S0tJQUlIClUqV8LbvOp0Oa9asQWdnJ2prazE4OMjPHQ6HkZqaiq997Wt46KGH+G80Gg1SU1NRXl6Ovr4+RCIRpKamxlWu0mg0IisrC0lJSbh16xYaGxtHXOdwovsiOTkZOTk5PL5zeD8pFAqUlJQgJSUFt27dQkpKCvLz82Na/3K5HFlZWdiyZQtKSkowMDCArKwsZGZmjuoTT0pKQm5uLkwm05Dg/bEWphiFhYV46aWXsGHDhiHfSUtLi/nbhQsX4pFHHkF/fz+kUinS0tKGtEuj0eDhhx/G4OAgGhoaMDAwMCSULzk5Gdu2bcPq1atjHn8s1Go1SktLMW/ePOj1+iH9N2/ePB4tFGusRiIRaDQaLFiwAEVFRQkVhGE+VLlcfsfPX2ZmJl5++eWYLr7o554ZPsz3zTZ2Zd+7desWjh49ir6+Pv6Cj0Yul8Ptdou6HbtknA6ZMPlnhZfHSy9kb9NYSQjjJSZELzY4nc67mjZEtyURgsEgj4mMBZtejxZadjewVe3h5x7er9GDMtaLYSyiYzEjkQjcbnfCC58sJnksFwprS3Skw3gk8v3x7tNoKBQKXhAmVgx3LGL18fB7EM+9m2jYTGa09sd7n4Yz2rUkAiuzGStkM5rxdCKetgiCMOKlNAGMOgBFE12CIIj/R4wquqIupMUzzUhkGnO35xqPO21LopbPRBLPFFmM843HZPZBvNxrbRf73o11zrs991Q+f8MRU3figSxdgiCIiWdUFZ+6dBGCIIj/h5DoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIkOgSBEGICIkuQRCEiJDoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIkOgSBEGICIkuQRCEiJDoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIkOgSBEGICIkuQRCEiJDoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIkOgSBEGICIkuQRCEiJDoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIkOgSBEGICIkuQRCEiJDoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIkOgSBEGICIkuQRCEiJDoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIkOgSBEGICIkuQRCEiJDoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIkOgSBEGICIkuQRCEiJDoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIkOgSBEGICIkuQRCEiJDoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIkOgSBEGICIkuQRCEiJDoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIyMf5XCJKKwiCIP6fQJYuQRCEiJDoEgRBiAiJLkEQhIiQ6BIEQYgIiS5BEISIkOgSBEGIyP8B5MSUiDR7AywAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx * Ny,))\n", - "ub = np.ones((Nx * Ny,))\n", + "lb = np.zeros((Nx*Ny,))\n", + "ub = np.ones((Nx*Ny,))\n", "\n", "\n", "cur_beta = 4\n", @@ -257,25 +1826,42 @@ " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", + " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", " solver.set_maxeval(update_factor)\n", " solver.set_ftol_rel(ftol)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta * beta_scale" + " cur_beta = cur_beta*beta_scale" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ + "\n", + "\n", "plt.figure()\n", "\n", - "plt.plot(evaluation_history, \"o-\")\n", + "plt.plot(evaluation_history,'o-')\n", "plt.grid(True)\n", - "plt.xlabel(\"Iteration\")\n", - "plt.ylabel(\"FOM\")\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('FOM')\n", "plt.show()" ] }, @@ -288,23 +1874,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "opt.update_design([mapping(x, eta_i, cur_beta)])\n", + "opt.update_design([mapping(x,eta_i,cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - ")\n", - "circ = Circle((2, 2), minimum_length / 2)\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", "ax.add_patch(circ)\n", - "ax.axis(\"off\")\n", + "ax.axis('off')\n", "plt.show()" ] }, @@ -317,28 +1910,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n" + ] + } + ], "source": [ - "f0, dJ_du = opt([mapping(x, eta_i, cur_beta // 2)], need_gradient=False)\n", + "f0, dJ_du = opt([mapping(x,eta_i,cur_beta//2)],need_gradient = False)\n", "frequencies = opt.frequencies\n", "\n", "\n", - "intensities = np.abs(opt.get_objective_arguments()[0][0, :, 2]) ** 2" + "intensities = np.abs(opt.get_objective_arguments()[0][0,:,2])**2" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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CHX1XlgkV7/y0kVe+WcPVSU14/JIOltDpsLNOr8t9f2zDJ4t3MOnXzU6XYxzkcfMXkTOBAcBn7mmRvinJhIoPM7bx909XcGGHBJ67ujMRHiZ0Gt+6rc/pnNeuPv+cuYJFWw46XY5xiKfN/x7gYeAjVV0uIi1xRTMbU6yvlu/igQ+X0LvVabx+Y3eqRNq5BYEiIkJ4+bquJNSsxu1TFnLAAuDCkkf/I1X1e1W9DHjD/XqDqt7l08pM0Pp5/T7umLqITo1r8fZNKVSLsj8SA018rCsAbl92Dve8l0m+BcCFHU/P9jlTRFYAq9yvu4rIaJ9WZoJS5tYs/m9iOomnxTJhSA/iqlo0c6Dq0iSev17Wgblr9vLGd2udLsf4mad/i78KXATsB1DXLRjP8VFNJkit3X2EIe/Mp05cNJOG9aJ29WinSzJl6N+zGVclNea1b9fy/RoLgAsnHh+IVdWtRSble7kWE8S2HjjGwHHziIp0RTMn1LRo5mAgIjx1RWfaJtTgnrRFbLcAuLDhafPfKiK9ARWRKBH5C2BBIQaAPe5o5hO5BUwa1pPmp1V3uiRTDjHRkYwekERuvnL7FAuACxeeNv9bgduBxsB2oJv7tQlzh47lctP4+ew9cpJ3hvagXYOaTpdkKqBlvThevLYLmVuzeOqzFU6XY/zA07N99qnqAFVNcN+Dd6Cq7i9rOREZLyJ7RGRZoWkviMgqEVkiIh+JSHwl6jcOOpaTx9AJ89mw9yhvD0ohqVltp0syldC3U0NuObsFE3/ZzCcWABfySm3+IvKA++cbIvJ60YcH658A9C0y7Wugk6p2Adbgun7ABJmTefn8eVIGmVuzeP3Gbpzd2qKZQ8GDf2pHj0RXANza3UecLsf4UFl7/qeO66cDGcU8SqWqc4EDRaZ9paqnUqV+BSzeMcjk5RdwT1omP6zdx7NXd6Fvp4ZOl2S8JCoygjf7JxEbHcmtkzPItgC4kCWehDuJyLWq+n5Z00pYNhGYqaqdipn3KfCeqk4uZt5wYDhAQkJCclpaWpl1liQ7O5u4uLgKLx+MfDVmVWX8shx+2J7Hje2iuSgxMMJd7TP2rpX783l+wQl6NIhkRNeqAZPJZJ9z+aSmpmaoakqxM1W1zAew0JNpJSybCCwrZvqjwEe4fwGV9khOTtbKmD17dqWWD0a+GHNBQYE+OXO5Nn9wpr44a5XX118Z9hl735vfrdXmD87Ud37c4NPtlId9zuUDpGsJfbXUyy9F5E/AxUDjIsf4awIV/ntQRIYA/YDz3QWaIDBq9jr+/cNGBp/ZnPv+2MbpcoyPjTi3FQs3H+Spz1fSpWm8faEfYso65r8D1/H+E/z2WP8nuK74LTcR6Qs8AFymrphoEwQm/bKJF79aw5XdG/PXSzsGzGEA4zuuALhuNKjlCoDbn33S6ZKMF5Xa/FV1sapOBFqp6sRCj+mqWmYWrIhMBX4B2orINhEZBrwJ1AC+FpFMEXnLGwMxvjMjcztPfLKcC9rX5/lrulg0cxipFRvFmAHJ7D9qAXChpqzDPtNU9TpgkYgU/tQFUHWdrlkiVb2xmMnjyl+mccq3K3dz37TF9Eysw5v9k4iyaOaw06lxLf5xWUcemr6U175da4f8QkRZkYt3u3/283UhJvD8umE/t01ZSMdGNRk72KKZw9n1PZqSvvkgb3y3lqRm8fRpW9/pkkwllXXYZ6f76T5gq6puBqoCXXF9H2BC1NJth7hlYjpNascwYWhPalQLjFM6jTNEhH9e3skVAPdeJtsO2td1wc5u4G5+Z92ebAa/M59aMVFMvqUXdSya2eAKgHtrYDL57gC4k3kW7BvM7Abu5je2HTzGoHHziBBh8i29aFgrxumSTABJrFudF67tyuJth3hypgX7BjO7gbv5r71HTjJo3HyyT+bxn5t70qKuRTOb3+vbqQHDz2nJpF83MyNzu9PlmArytPnfjd3APaQdOu6KZt556DjvDOlBh0YWzWxK9sBFbemZWIeHPlzKGguAC0qeRjrPVdXLVPU592u7gXsIOZ6Tz7AJC1i35wj/GpRCSmIdp0syAa5KZARv9u9O9apVLAAuSHl6A/c2IvK2iHwlIt+devi6OON7OXkF3Do5g4wtB3n1+u6c26ae0yWZIFG/ZjXeuLE7m/Yd5cEPlmBJLcGlrPP8T3kfeAsYi927N2TkFyj3Tsvk+zV7eeaqzlzSxaKZTfmc2eo0Rl7Ujue+XEXyT7W5+ewWTpdkPORp889T1TE+rcT4lary2MfL+GzJTh7+Uztu7NnM6ZJMkLr13JZkbD7I05+vpGvTWiQ3t8OGwcDTL3w/FZHbRKShiNQ59fBpZcannvtyNVPnb+G2Pq3487mtnC7HBDER4aXrutIoPobbpyxinwXABQVPm/9gYCTwM/9L9kz3VVHGt8bMWc9b369nQK9mjLyordPlmBBQKyaKMQOTOHgsh7vTFlkAXBDw9GyfFsU8Wvq6OON9787bwnNfruLSro34x+WdLJrZeE3HRrX45+Wd+Gndfl79Zo3T5ZgyeHq2T6yIPCYib7tftxYRC3sLMp8u3sGjHy8ltW09Xr6uK5EWzWy87LoeTbkupQlvfLeO2av2OF2OKYWnh33eAXKA3u7X24EnfVKR8YnZq/dw73uZ9Gheh9EDki2a2fjMPy7vRIeGNbnnvUy2HrAAuEDlaQdoparPA7kA7pwf220MEgs2HWDE5AzaNqjB2CEpxERbMofxnWpRkYwZmESBKre/awFwgcrT5p8jIjGAAohIK6DMr/RFZLyI7BGRZYWm1RGRr0Vkrfun3RjUh5bvOMTNExbQqFYME2/uSU2LZjZ+0Py06rx0bVeWbDvEPz5d4XQ5phieNv+/AV8CTUVkCvAt8KAHy00A+haZ9hDwraq2dq/nIQ9rMOW0YW82N42bT42qVZh0Sy/qxlV1uiQTRi7s2IA/n9uSKfO28NGibU6XY4rw9Gyfr3DFOQ8BpgIpqlpmsJuqzgUOFJl8OTDR/XwicIWHtZpy2H+8gEHj5gMw6ZZeNI63aGbjfyMvbEuvFnV4ePpSVu067HQ5phDxJI9DRL5V1fPLmlbCsonATFXt5H6dparx7ucCHDz1ushyw4HhAAkJCclpaWll1lmS7Oxs4uLiKrx8sDmcozz1y1EO5woP9qhGYq3QP8Yfbp8xBM+Ys04W8NefT1AtEv7WO4aYKhX/ujBYxuxNlRlzampqhqqmFDevrBu4VwNigbruY/OnPrWaQOMKVVOIqmqRG8MXnvc28DZASkqK9unTp8LbmTNnDpVZPpgcPpFL/3//yoGTwuRbzqBXy9OcLskvwukzPiWYxtyw9X76j53HJ7tqMnpAUoWvLwmmMXuLr8Zc1mGfP+O6mrcd/7uyNwOYAbxZwW3uFpGGAO6fdjKwl5zIzeeWiems2nmEO7pXDZvGbwJfr5an8cBFbfli2S7G/bjR6XIMZd/A/TVVbQH8RVVbFrq6t6uqVrT5f4IrLgL3zxkVXI8pJDe/gNumLGTBpgO8dF1XutbzNLPPGP8Yfk5LLuyQwLNfrCJ9U9GvAo2/efqF7xsi0ltE+ovITaceZS0nIlOBX4C2IrJNRIYBzwJ/FJG1wAXu16YSCgqUv7y/mO9W7eGfl3fi8m6VPiJnjNeJCC9e15UmtWO4/d2FFgDnMI92D0VkEtAKyOR/ef4K/Ke05VT1xhJmlflFsfGMqvLXT5YzI3MHIy9qy8AzmjtdkjElqlktitEDkrly9E/cNXURk4b1spgRh3h6bCAF6KB2q56A89JXa5j062b+fE5Lbutj0cwm8HVoVJMnr+jEyA+W8PLXqxl5UTunSwpLnl7ktQxo4MtCTPn9e+4G3py9jht6NOWhP7WzhE4TNK5NacoNPZoyavZ6vl252+lywpKnzb8usEJEZonIJ6cevizMlO69BVt46vOVXNK5IU9d2dkavwk6f7usIx0b1eReC4BzhKeHff7myyJM+Xy+dCcPT1/KH1rX5ZXru9kxUxOUqkVFMmZAMv3e+IERUzL44NbeVIsK/QsSA4WnZ/t8X9zD18WZ35u7Zi93py2ie7Pa/GtQMtFVLJrZBK9mp8Xy8nXdWLb9MH+3ADi/KrVziMgRETlczOOIiFhQh59lbD7Inydl0KpeHOMH9yA22s7lN8Hvgg4JjOjTiqnzt/BBhgXA+Uup3UNVa/irEFO6lTsPM/Sd+STUrMqkYb2oFWvRzCZ03P/HNmRuyeLRj5bSsVFN2jes6XRJIc+OGQSBTfuOMmjcfGKjqzBpWC/q1bBoZhNaqkRG8PqN3akVE8WIyRkcPpHrdEkhz5p/gNt16AQDx80jv6CAybf0pGmdWKdLMsYn6tWoypv9k9h68DgPvL8Eu6zIt6z5B7CDR3MYNG4eWcdymXhzT06vb0fhTGjr2aIOD/Vtx5fLdzH2BwuA8yVr/gEq+2QeQ96Zz+YDx/j3TSl0aRLvdEnG+MUtf2hB344NePbLVczfaAFwvlLW2T5NRSRNRH4QkUdEJKrQvI99Xl2YOpGbz/9NTGfZjsOM7p/Ema0smtmEDxHhhWu70KxOLHe8u5A9R044XVJIKmvPfzwwB7gTaAh8LyKnOpEliPlAXn4Bd05dxC8b9vPitV24oEOC0yUZ43c1qkUxZmASh0/kctfUReTlFzhdUsgpq/nXU9W3VDVTVe8ERgNzRaQVrlRP40UFBcoDHyzh6xW7+ftlHbmyexOnSzLGMe0a1OSpKzrz64YDvPT1GqfLCTllXSUUJSLVVPUEgKpOFpFdwCygus+rCyOqyj9mrmD6ou3c98c2DO6d6HRJxjju6uQmpG8+yJg560lqVhu7usV7ytrzHwv0KjxBVb8BrsWV9Gm85NVv1jLh500MO7sFd553utPlGBMw/nppBzo1rsl90zLZc8wO/3hLWbdxfKW4DB9VXaSqf6zMhkXkXhFZLiLLRGSq+2bxYWn8jxt57du1XJvchMcuaW8JncYUcioALkKEUZknOZGbX/ZCpkweneopIl69L6B7fXcBKaraCYgEbvDmNoLFBxnb+MfMFfTt2IBnrrJoZmOK07ROLK9c35XNhwv42yfLnS4nJJTZ/EWkM/CBD7ZdBYgRkSpALLDDB9sIaLOW7+LBD5dw9ul1ee3GblSJtMsujCnJee0S6NcyirQFW5mWvtXpcoKelHYJtYikAm8Cl6vqOq9uWORu4CngOPCVqg4oMn84MBwgISEhOS0trcLbys7OJi4urhLVet+K/fm8nH6CZjUjeKBHNapV8e4efyCO2ZfCbbwQnmM+fCSbMSsjWZdVwGNnVKN5zdDP/6/M55yampqhqinFzlTVEh/AEVz37i31feV9ALWB74B6QBTwMTCwpPcnJydrZcyePbtSy3vbws0HtP3jX+iFL3+vB4+e9Mk2Am3MvhZu41UN3zHvPXJCez71tZ7z/HeadSzH6ZJ8rjKfM5CuJfTVso4zvAs8Lt4/EH0BsFFV96pqLjAd6O3lbQSk1buOMHTCAurGVWXSsJ7Ex0Y7XZIxQaVuXFVG9U9i+8HjjHx/sQXAVVBZZ/v8GVgOTPbydrcAZ4hIrPsXy/nASi9vI+Bs2X+MQePmER0ZweRhvahfM2xPcDKmUlIS6/Dwxe35asVu3p67welyglKZ3zCq6pO4LuryGlWdh+tL5IXAUncdb3tzG4Fmz2FXNPPJvAImDetFs9MsmtmYyrj5rEQu6dyQ52etZt6G/U6XE3Q8vYfvf7y9YVX9q6q2U9VOqjpIVU96exuBIutYDoPGzWdf9kkmDO1B2wYWzWxMZYkIz17dmeZ1Yrlj6iL2HLYAuPKwcwt97OjJPIa8s4CN+47y75tS6N6sttMlGRMyXAFwyWSfyOMOC4Arl7IinTeKyIZCP4s+Tk2/y18FB5OTefn8eVIGS7Zl8fqN3Tnr9LpOl2RMyGnboAZPX9WJ+RsP8MJXq50uJ2iUdQP3Fv4qJNTk5Rdw99RMfly3jxev7UrfTg2cLsmYkHVl9yakbzrIv77fQHKz2lzY0f6/lcXTeIdBIhJXZFo/35QU/AoKlIenL+XL5bt4ol8Hrkm2aGZjfO2JSzvQpUkt7n9/MZv3H3W6nIDn6TH/N4AfRaR9oWn/8EE9QU9VeerzlbyfsY27zm/NzWfbH0/G+EPVKpGM6p9EhAi3Tl5oAXBl8LT5bwRuBj4QkWvd0yyBrBhvfreOcT9uZEjvRO69oLXT5RgTVprWieXV67uxcudhnphhqfOl8bT5q6ouBM4FhovIi7iSOE0hE3/exEtfr+Gq7o15ol8HS+g0xgGp7epz53mnMy19G+8t2OJ0OQHL0+a/E0BV9wEX4bqFYydfFRWMPl60nb9+spwL2ifw3DVdiIiwxm+MU+65oA1nn16Xx2csZ9n2Q06XE5A8vcjrkkLPC1R1JFDVZ1UFmW9W7Ob+9xdzZsvTeLN/d6IsmtkYR0VGCK/d0I06sdHcNmUhh47nOl1SwCnrPP8fCz2fVGT2PJ9UFGR+Wb+f295dSKdGNfn34BSqRdnRMGMCwWlxVRk1IIkdWce5f9piCgosAK6wsnZRC9+kvWOReWF/XGPJtixumbiA5nVieWdoT+KqlnrZhDHGz5Kb1+bRS9rzzcrd/MsC4H6jrOZf2q/KsP41um7PEQaPn0/t6tFMGtaLOtUtmtmYQDSkdyKXdGnIC7NW8ct6C4A7pazmHy8iV4rI1e7nV7kfVwO1/FBfQNp64BgDx84nMsIVzdyglkUzGxOoRITnru5Ci7rVudMC4P6rrOb/PXAZ0M/9/FL3ox8w17elBaY9R04waNw8juXkMWlYTxLrVi97IWOMo+KqVmHMwGSOnszjjncXkWsBcGVm+wz1VyHB4NDxXG4aN5/dh08y+ZZetG9Y0+mSjDEeapNQg2ev7szdaZm8MGs1j1zcvuyFQlipzV9Emnm4nixVPeyFegLWsZw8bp6wgPV7sxk/pAfJzS2a2Zhgc3m3xqRvOsjbczeQ1Kx2WAculnV6ykQP1qHABKBcN3wRkXhgLK6LxRS4WVV/Kc86/CUnr4BbJy9k0ZaDvNk/iT+0rud0ScaYCnqsX3uWbD/EyPcX07ZBDVqE6aHbsg77pPpw268BX6rqNSISDQTkfQ3zC5R738tk7pq9PHd1Zy7u3NDpkowxleAKgOtOvzd+ZMTkDD667SxiosPv+hxHLkUVkVrAOcA4AFXNUdUsJ2opjary6EdL+WzpTh69uD3X9/D0KJgxJpA1qe0KgFu9+wiPfbwM1fA7c12cGLSIdMN1w/YVQFcgA7hbVY8Wes9wYDhAQkJCclpaWoW3l52dTVxcXNlvLERVmbYmly825nJpyyiubhNc5/FXZMzBLNzGCzZmb/hobQ4z1ucypGM0fZpGeW293lSZMaempmaoakqxM1XV7w8gBcgDerlfvwb8s6T3Jycna2XMnj273MuMmr1Wmz84Ux/7aKkWFBRUavtOqMiYg1m4jVfVxuwNefkFOnDsr9r60c916bYsr67bWyozZiBdS+irTiWQbQO2qeqpfKAPgCSHavmdyb9u5vkvV3N5t0b8/bKOFs1sTIhyBcB1p271aG6dnMGhY+ETAOdI81fVXcBWEWnrnnQ+rkNAjpuRuZ3HZyzjvHb1efHarhbNbEyIq1M9mlEDkth9+AT3TcsMmwA4J7OH7wSmiMgSoBvwtIO1ADB71R7un7aYHol1GD0gyaKZjQkT3ZvV5rFLOvDtqj2M+X690+X4hWMxlKqaievYf0CYv/EAt07OoF3DGoyzaGZjws5NZzYnffNBXvpqNd2bxdO7VV2nS/Ip27UFlm0/xLAJC2hcO4aJQ3tSo1pgfutvjPEdEeHZqzrTsl4cd01dxK5DoR0AF/bNf/3ebAaPn0/NmCgmD+vFaXF2gzJjwlX1qlV4a2ASx3LyuePdhSEdABfWzX971nEGjZ2HCEwa1pNG8TFOl2SMcdjp9Wvw7NVdSN98kOe+WOV0OT4Tts1/X/ZJBo2dx5ETeUy8uSct64XXxTLGmJJd1rURg89sztgfN/LF0p1Ol+MTYdn8D5/IZfD4+ew4dJzxQ3vQsVHY3pfGGFOCRy/pQLem8Yz8YAkb9mY7XY7XhV3zP56Tzy0T0lm96whjBibTI7GO0yUZYwJQdJUIRg1IIipSGDF5Icdy8pwuyavCqvnn5BVw25QMFmw+wCvXdyO1bX2nSzLGBLDG8TG8dkN31uw5wmMfhVYAXNg0//wC5f73FzN79V6euqIzl3Zt5HRJxpggcE6betx9fmumL9rOu/O3OF2O1zh2kZc/fLxoOy/MWs32rOPEfjeLYzn5PNi3Hf17WTSzMcZzd53XmkVbsvj7Jyvo3LgWXZrEO11SpYXsnv/Hi7bz8PSlbM86DsCxnHyqRAgNa1VzuDJjTLCJiBBevb4b9WpUZcTkhWQdy3G6pEoL2eb/wqzVHM/N/820vALlhVmrHarIGBPMarsD4PYcOcG97wV/AFzINv8d7j1+T6cbY0xZujWN54l+HZi9ei+j56xzupxKCdnmX9LVunYVrzGmMgae0ZzLuzXi5a/X8NO6fU6XU2Eh2/xHXtSWmCLJnDFRkYy8qG0JSxhjTNlEhGeu6kyrIA+AC9nmf0X3xjxzVWcau/f0G8fH8MxVnbmie2OHKzPGBLvY6CqMGZjMidx8bg/SALiQbf7g+gXw00PnMaFvdX566Dxr/MYYrzm9fhzPXdOFjM0Heebz4AuAc7T5i0ikiCwSkZlO1mGMMRXRr0sjhvROZPxPG/lsSXAFwDm95383sNLhGowxpsIeubg9Sc3ieeCDxawPogA4x5q/iDQBLgHGOlWDMcZU1qkAuKpRkYyYnBE0AXDiVFCRiHwAPAPUAP6iqv2KzB8ODAdISEhITktLq/C2srOziYsLr7z+cBtzuI0XbMyBZtm+fF5KP8EZDSMZ3qUqIuKV9VZmzKmpqRmqWvy90lXV7w+gHzDa/bwPMLO09ycnJ2tlzJ49u1LLB6NwG3O4jVfVxhyIXvtmjTZ/cKb+55dNXltnZcYMpGsJfdWpwz5nAZeJyCYgDThPRCY7VIsxxnjFHamn06dtPf756QoWb81yupxSOdL8VfVhVW2iqonADcB3qjrQiVqMMcZbCgfA3TZlIQePBm4AnNNn+xhjTEiJj41mzMAk9h45yb3TAjcAzvHmr6pztMiXvcYYE8y6NInniUs7MGf1Xt6cHZgBcI43f2OMCUUDejXjyu6NeeWbNfywdq/T5fyONX9jjPEBEeGpKzvRun4cd6dlBlycvDV/Y4zxkVMBcDl5Bdz+7kJy8gInAM6avzHG+FCrenE8f00XFm3J4unPAyfNxpq/Mcb42MWdG3LzWS2Y8PMmPl28w+lyAGv+xhjjFw9f3I7k5rV56MMlrNtzxOlyrPkbY4w/REVGMKp/EtWiIrl18kKOnnQ2AM6avzHG+EmDWtV4/cbubNibzcPTl57KOnOENX9jjPGjs06vy/0XtuWTxTuY9Otmx+qw5m+MMX424txWnN+uPv+cuYJFWw46UoM1f2OM8bOICOHl67qRULMat09ZyAEHAuCs+RtjjANqxUYxZkAy+7JzuOe9TPL9HABnzd8YYxzSuUkt/nZZR+au2csb363167at+RtjjINu7NmUq5Ia89q3a/l+jf8C4Kz5G2OMg0SEp67oTNuEGtyTtojtfgqAs+ZvjDEOi4mOZPSAJHLzldun+CcAzpHmLyJNRWS2iKwQkeUicrcTdRhjTKBoWS+OF6/tQubWLJ76bIXPt+fUnn8ecL+qdgDOAG4XkQ4O1WKMMQGhb6eG3HJ2Cyb+splPfBwAV8Wnay+Bqu4EdrqfHxGRlUBjwPe/7owxJoA9+Kd2LN6Wxf3TMnly5gr2HDlJ41+/Y+RFbbmie2OvbUeczJYAEJFEYC7QSVUPF5o+HBgOkJCQkJyWllbhbWRnZxMXF1fJSoNLuI053MYLNuZQ9vXmHKaszP3NtOgIGNIpmt6NojxeT2pqaoaqphQ3z9HmLyJxwPfAU6o6vaT3paSkaHp6eoW3M2fOHPr06VPh5YNRuI053MYLNuZQdtaz3xV71k/j+Bh+eug8j9cjIiU2f8fO9hGRKOBDYEppjd8YY8JNSff79eZ9gJ0620eAccBKVX3ZiRqMMSZQNYqPKdf0inBqz/8sYBBwnohkuh8XO1SLMcYElJEXtSUmKvI302KiIhl5UVuvbcOps31+BMSJbRtjTKA7dVbPC7NWsz3rOI3jY7x+to8jzd8YY0zprujemCu6N/bZl9wW72CMMWHImr8xxoQha/7GGBOGrPkbY0wYsuZvjDFhyPFsH0+IyF5gcyVWURfY56VygkW4jTncxgs25nBRmTE3V9V6xc0IiuZfWSKSXlK+RagKtzGH23jBxhwufDVmO+xjjDFhyJq/McaEoXBp/m87XYADwm3M4TZesDGHC5+MOSyO+RtjjPmtcNnzN8YYU4g1f2OMCUNB3fxFZLyI7BGRZSXM7yMihwrdM+CJQvP6ishqEVknIg/5r+qKq+h4RaSpiMwWkRUislxE7vZv5RVXmc/YPT9SRBaJyEz/VFx5lfx3HS8iH4jIKhFZKSJn+q/yiqvkmO91/7teJiJTRaSa/yqvuLLG7H5PH/d4l4vI94WmV75/qWrQPoBzgCRgWQnz+wAzi5keCawHWgLRwGKgg9Pj8eF4GwJJ7uc1gDXBMN7KjLnQ/PuAd0t7T6A9KjNmYCJwi/t5NBDv9Hh8OWagMbARiHG/ngYMcXo8XhpzPLACaOZ+Xd/90yv9K6j3/FV1LnCgAov2BNap6gZVzQHSgMu9WpwPVHS8qrpTVRe6nx8BVuL6TxPwKvEZIyJNgEuAsV4tyscqOmYRqYWroYxzrydHVbO8W51vVOZzxnVfkhgRqQLEAju8VpgPeTDm/sB0Vd3ifv8e93Sv9K+gbv4eOlNEFovIFyLS0T2tMbC10Hu2ESTN0APFjfe/RCQR6A7M83tlvlPSmF8FHgAKnCnLp4obcwtgL/CO+1DXWBGp7mCN3va7MavqduBFYAuwEzikql85WaQXtQFqi8gcEckQkZvc073Sv0K9+S/ElW3RFXgD+NjZcnyu1PGKSBzwIXCPqh72f3k+UeyYRaQfsEdVMxyszVdK+pyr4DqMMEZVuwNHgaD4PssDJX3OtXHt9bYAGgHVRWSgU0V6WRUgGddfrxcBj4tIG2+tPKSbv6oeVtVs9/PPgSgRqQtsB5oWemsT97SgVsp4EZEoXI1/iqpOd7BMryplzGcBl4nIJlx/Fp8nIpOdq9R7ShnzNmCbqp76q+4DXL8Mgl4pY74A2Kiqe1U1F5gO9HawVG/aBsxS1aOqug+YC3TFS/0rpJu/iDQQEXE/74lrvPuBBUBrEWkhItHADcAnzlXqHSWN1z1tHLBSVV92skZvK2nMqvqwqjZR1URcn+93qhoSe4SljHkXsFVE2rrfej6uLwyDXin/l7cAZ4hIrHv++bi+0woFM4CzRaSKiMQCvXCNzSv9K6hv4C4iU3GdBVBXRLYBfwWiAFT1LeAaYISI5AHHgRvU9XV5nojcAczC9c35eFVd7sAQyqWi4xWRs4FBwFIRyXSv7hH3HlRAq8RnHLQqOeY7gSnuprABGOrn8iukEmOeJyIf4DoslAcsIkgiIMoas6quFJEvgSW4vrcaq6rL3MtWun9ZvIMxxoShkD7sY4wxpnjW/I0xJgxZ8zfGmDBkzd8YY8KQNX9jjAlD1vyNX4jIKyJyT6HXs0RkbKHXL4nIfV7c3gQRucZb6yu03kcKPU8sLZGxyHL3FLo839Nt/Vze+ipLROq5Ty80Ic6av/GXn3BfeSkiEUBdoHAOT2/A782uAh4p+y2/5Q4cuxlXuqjHVNXjK1Xd26g0Vd0L7BSRs7yxPhO4rPkbf/kZOJUt3xFYBhwRkdoiUhVoDywUkSdEZIG4stnfFpd2IjL/1Irce9xL3c+TReR7d/DVLBFpWHTDJb3HHZj1nIjMF5E1IvIH9/RYEZkmrvsffCQi80QkRUSexZUemSkiU9yrjxSRf4srb/0rEYkpZuznAQtVNa/Qdl8RkXRxZe73EJHpIrJWRJ4sVHd2oecPishScQWbPVtoPa+KSDpwt4icL65At6Xiyoqv6n7fJhH5u4gsdM9r555+rvwvH3+RiNRwb+5jYIDnH60JRtb8jV+o6g5cV1Y3w7WX/wuuZNEzgRRgqTue9k1V7aGqnYAYoJ+qrgKiRaSFe3XXA++JK6/oDeAaVU0GxgNPFd6uB++poqo9gXtwXWEJcBtwUFU7AI/jCtdCVR8CjqtqN1U91RxbA6NUtSOQBVxdzPDPAooGzOWoagrwFq7L+G8HOgFDROS0ImP4E67wsl7uYLPnC82Odq9nFDABuF5VO+O6en9EofftU9UkYAzwF/e0vwC3q2o34A+4rpwFSHe/NiHMmr/xp59xNf5Tzf+XQq9/cr8n1b2nvRTXHvOpQ0PTcDV93D/fA9riaphfu2MrHsMVclVYWe85FXKXASS6n5+NKwwO9+X0S0oZ00ZVzSxmHYU1xBW1XNipLJalwHL3PRdO4opkaFrkvRcA76jqMXdNhTPg33P/bOuuZY379URc2f6nFDfOn4CXReQuXDd9yXNP34MrIdOEsKDO9jFB59Rx/864DvtsBe4HDuPKoK8GjAZSVHWriPwNOHVLvveA90VkOqCqulZEOuNqnKXdqlDKeM9J9898Kvb/4WSh5/m4/lop6jj/G0fR5QqKrKOgnHUc9fB9vxunqj4rIp8BFwM/ichF7r+yqvG/vwJMiLI9f+NPPwP9gAOqmu/eg43HdejnZ/7XIPeJ694D/z1bR1XX42pcj/O/vd3VQD1x36dWRKLk9zew8eQ9Rf0EXOd+fwdcv6xOyXUfSiqPlcDp5VymsK+BoeJKdkRE6hTzntVAooic2s4g4Pti3vdfItJKVZeq6nO4kiLbuWe1wfXL2YQwa/7Gn5biOsvn1yLTDqnqPvctB/+Nq/HMwtWQCnsPGIjrEBDu7wiuAZ4TkcVAJkWy3D15TzFG4/qFsQJ4ElgOHHLPextYUugLX098wW8PwZSLqn6J6zBRuvvQ1V+Kec8JXAme77sPmRXg+j6hNPe4v1hfAuS66wRIBT6raL0mOFiqpzFFiEgkEKWqJ0SkFfAN0Nb9i6Si6/wIeEBV13qrTl8RkbnA5ap60OlajO/YMX9jfi8WmO0+vCPAbZVp/G4P4friN6Cbv4jUA162xh/6bM/fGGPCkB3zN8aYMGTN3xhjwpA1f2OMCUPW/I0xJgxZ8zfGmDD0/8jTrI6zcWU9AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ + "\n", + "\n", "plt.figure()\n", - "plt.plot(1 / frequencies, intensities, \"-o\")\n", + "plt.plot(1/frequencies,intensities,'-o')\n", "plt.grid(True)\n", - "plt.xlabel(\"Wavelength (microns)\")\n", - "plt.ylabel(\"|Ez|^2 Intensities\")\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('|Ez|^2 Intensities')\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb b/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb index 407781c03..3e552be49 100644 --- a/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb +++ b/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb @@ -11,9 +11,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -24,7 +32,6 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", - "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "Air = mp.Medium(index=1.0)" @@ -39,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -50,87 +57,68 @@ "\n", "resolution = 30\n", "\n", - "Sx = 2 * pml_size + design_region_width\n", - "Sy = 2 * pml_size + design_region_height + 5\n", - "cell_size = mp.Vector3(Sx, Sy)\n", + "Sx = 2*pml_size + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", "\n", "nf = 3\n", - "frequencies = np.array([1 / 1.5, 1 / 1.55, 1 / 1.6])\n", + "frequencies = np.array([1/1.5, 1/1.55, 1/1.6])\n", "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = (\n", - " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - ")\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", "design_region_resolution = int(resolution)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1 / 1.55\n", + "fcen = 1/1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [0, -(design_region_height / 2 + 1.5), 0]\n", - "source_size = mp.Vector3(design_region_width, 0, 0)\n", - "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", - "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", - "\n", - "Nx = int(design_region_resolution * design_region_width)\n", - "Ny = int(design_region_resolution * design_region_height)\n", - "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), Air, Si, grid_type=\"U_MEAN\")\n", - "design_region = mpa.DesignRegion(\n", - " design_variables,\n", - " volume=mp.Volume(\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(design_region_width, design_region_height, 0),\n", - " ),\n", - ")\n", + "source_center = [0,-(design_region_height/2 + 1.5),0]\n", + "source_size = mp.Vector3(design_region_width,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.Source(src,\n", + " component=mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", "\n", + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", "\n", - "def mapping(x, eta, beta):\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),Air,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", "\n", - " # filter\n", - " filtered_field = mpa.conic_filter(\n", - " x,\n", - " filter_radius,\n", - " design_region_width,\n", - " design_region_height,\n", - " design_region_resolution,\n", - " )\n", + "def mapping(x,eta,beta):\n", "\n", + " # filter\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", - "\n", - " projected_field = (\n", - " npa.flipud(projected_field) + projected_field\n", - " ) / 2 # left-right symmetry\n", - "\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", + " \n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", - "\n", "geometry = [\n", - " mp.Block(\n", - " center=design_region.center, size=design_region.size, material=design_variables\n", - " ),\n", - " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", - " #\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " # \n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "kpoint = mp.Vector3()\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " symmetries=[mp.Mirror(direction=mp.X)],\n", - " resolution=resolution,\n", - ")" + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution)" ] }, { @@ -146,39 +134,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "far_x = [mp.Vector3(0, 15, 0)]\n", - "NearRegions = [\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(0, design_region_height / 2 + 1.5),\n", - " size=mp.Vector3(design_region_width, 0),\n", - " weight=+1,\n", - " )\n", - "]\n", - "FarFields = mpa.Near2FarFields(sim, NearRegions, far_x)\n", + "far_x = [mp.Vector3(0,15,0)]\n", + "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", "ob_list = [FarFields]\n", - "\n", - "\n", "def J1(FF):\n", - " return -npa.abs(FF[0, :, 2]) ** 2" + " return -npa.abs(FF[0,:,2])**2" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation=sim,\n", - " objective_functions=[J1],\n", - " objective_arguments=ob_list,\n", - " design_regions=[design_region],\n", + " simulation = sim,\n", + " objective_functions = [J1],\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", " frequencies=frequencies,\n", - " maximum_run_time=2000,\n", + " maximum_run_time = 2000\n", ")\n", "opt.plot2D(True)" ] @@ -192,17 +185,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", - "\n", - "\n", "def f(x, grad):\n", - " t = x[0] # \"dummy\" parameter\n", - " v = x[1:] # design parameters\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", " if grad.size > 0:\n", " grad[0] = 1\n", " grad[1:] = 0\n", @@ -228,54 +219,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "def c(result, x, gradient, eta, beta):\n", - " print(\n", - " \"Current iteration: {}; current eta: {}, current beta: {}\".format(\n", - " cur_iter[0], eta, beta\n", - " )\n", - " )\n", - "\n", - " t = x[0] # dummy parameter\n", - " v = x[1:] # design parameters\n", + "def c(result,x,gradient,eta,beta):\n", + " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", + " \n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", "\n", - " f0, dJ_du = opt([mapping(v, eta, beta)])\n", + " f0, dJ_du = opt([mapping(v,eta,beta)])\n", "\n", " # Backprop the gradients through our mapping function\n", " my_grad = np.zeros(dJ_du.shape)\n", - " for k in range(opt.nf):\n", - " my_grad[:, k] = tensor_jacobian_product(mapping, 0)(v, eta, beta, dJ_du[:, k])\n", - " # Note that we now backpropogate the gradients at individual frequencies\n", + " for k in range(opt.nf): \n", + " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k]) \n", + " #Note that we now backpropogate the gradients at individual frequencies\n", "\n", " # Assign gradients\n", " if gradient.size > 0:\n", - " gradient[:, 0] = -1 # gradient w.r.t. \"t\"\n", - " gradient[:, 1:] = my_grad.T # gradient w.r.t. each frequency objective\n", - "\n", + " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + " \n", " result[:] = np.real(f0) - t\n", - "\n", + " \n", " # store results\n", " evaluation_history.append(np.real(f0))\n", - "\n", + " \n", " # visualize\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - " )\n", - " circ = Circle((2, 2), minimum_length / 2)\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", " ax.add_patch(circ)\n", - " ax.axis(\"off\")\n", + " ax.axis('off')\n", " plt.show()\n", - "\n", - " cur_iter[0] = cur_iter[0] + 1" + " \n", + " cur_iter[0] = cur_iter[0] + 1\n" ] }, { @@ -287,24 +268,1455 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 0; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAC/CAYAAABdcw+KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAD1klEQVR4nO3dQU4jRwBA0WrTICQEB5hL5CZBYpFVLhZpDsGGO8xcB4TkgE1nEXlWAWY0nm8rvLctqau8+S6XSu1pWZYBQGN16AUAfCSiCxASXYCQ6AKERBcgJLoAofmdcffJAH7c9NqAnS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkurBnf2+242nzcuhlcKREF/bsj7++jD8/fz30MjhS86EXAP83v//2acyr6dDL4EhNy7K8Nf7mIAD/6dVvXccLACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AUPo+3XdeIwlwENPUvf84jW75wQCOURbdp6encX9/L7zAUVmWZVxdXY2zs7Nkvl8e3e12O05OTsbd3d24ubkZ5+fn4+XFn/YBh7darcZ6vR63t7fj+vr6W69+pWynu91uxxhjrNfrakqA77LrUyG7vbA7Vphn/4UJHIddj8pjz/zKmKMF4Fgcokfu6QKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKE8uiuVjoPHIdD9CibcVmWMcYYm82mmhLgTbse7fpUmLOJ5n+nuri4EF7gKMzzPB4fH7/1qTC9U/i95f/5+Xk8PDyMaZr29UiAn7Ysy7i8vBynp6f7fOyrocuiC/CBvBrdbk892nMTgO9V/gJPo+toAfjo3N8CCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCM3vjE/JKgA+CDtdgJDoAoREFyAkugAh0QUIiS5A6B+NOmaIgLcIiwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 1; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": "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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 3; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 13; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 15; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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oMgO2oYdn8kuiS8dWxkt7VF+RrCZ8NSl62SXr5R63XxBc6mPVnp74+XaQYKDh8d4n2ed8Pk+3KPHm+6pO2oNiXdm+t4px/+rZvrKREgGeW+FCU12YrUR3zq9Vp/enup+XbWM90gCV419V1vYCr8/wLgaVob95d5Jfn+nZgivAf8tMV8hQ/Lpma/3lH69EckIyWmrScfNd4qSB0oDwQRocGL9DYi5DpXBdm614ZOVV32sGnP33Owe8be8VtupzlUV7S3D9djweX+0p6nzC4yoxr8aDWbDvhzNDYh0UR9qnZwOJu2fZbjeVoa0a1sc6elQBwe3f+1355dJKiGX41o0HT9bBLa7Kx/2c6lyfI6pTe7hKmPisiPV6PSVRz8/Pr76WX40zVyTsc0906dej+dAH3vSWJK29vbHZRdmXInqsoC6g8Qomy+bg9PZTq4zDhcR/6FQUE++fiwpFt8oivBwuvRzPbGnn3lJqLkPiObT1XLZd2UVtYP0UVE4A3iyv931PmHuhzHB54VTirC9KSBxZX8/u3EenPfSbwchXDZUAVpkiy1U/aC/aiPWzTAYHr8vLYF8Z3ObaNOfb/EzHsw0+/l6GEiJfoVBQ+awF3/Ou6nRRn9OXj2Toox25J9Pa6+0GRlPPYjyS6hyJwcvLy5tnuVZXS10sekLk+BKpcqqqjeqj18HMyyOtHLKyg473Petq4lTL7Wo5rfOrrIp29vPn7Oa28Tbot/rKQOKZNI/pwfH0Nq5WqylAcSk5NxErYfb6fAXjwrmUCTLLZHZcjZv3UW3Ueb5VVwU6H1+JnguX28Pfm1sF9ras3G6+ouFvtk+rKz6e1besqrbofJ/3nq2rP3/09s73MlR0lWHSwFpSyEDae6Uj6Vx9zkcUcgmicisx5BaE37vLwWmtLe43E5+clWP5coj18pw526kuDyKt9fcDe/WpTE7OSnjnBFdlLAUsTta5JV31YB7vj9owtxdXjauEtxpXz6DcTnN98nbJHtzW8nFxv7ymvsqPJEB675pnwrKNvkqgDdjeyieuoTrefYBbggyytBEDcmtt+lqx3w9frYY9QVH5+rq3Z/GjGLq94J1jNJMRdHMz9+88SrX2fU9I2wn+bF3ft6qEkA7lSzlOJA0Wmcv6WC6/nKB2iN7r3l5fa98vJvr2wpLjVJkdswef0HOZo4t1tUWi19XkrZ4t4VstOt+fbiZf4ErBMz6OpT737QJvn7e9sl1vuUyb8W9fEfUyXx7fwwOtbK8vBFwbKKrA7AJZBSf3b6/P/cfP8TlU+ZaPEZMNf/au2unCyQtzshcfZO938vRWZL+SoZkuHZBGoej6kprOqv05QaP6xKdRe89D6AmdT6Rr+8e6uQycyw7mstMlEVyaqMIngTt2NaGqC1Pe5t4yvLc8ZSbLO1F0Pv+m2Pb2hf087nHKr4QHJQV1LrOrAEF7M1v18niO/JtZZWUbzzzfM569JEJBsPLna8r2bJeJzDXtcnt5O1z8NT/9m2Ssk9ku28RyOL+rbJpB8L0rxJ/Nh15IExyoueWlZxb81ktvqcmnX+mJV9WDcujE3jYXJraN+9BermdcpCfGc/txbpP3Cu41QYTZue+zVcs3Fzi1f26LRsf79gX757bluQ5tJiH1fjPTosDxdq/KFswudW5v5cPP+QWU6jj+XbVrjl7W2hPens1cUGlvv3Dpq81ev3rzpBI33VEkf9AWUO/imvo1l2B58LzW70fyoaLLAfTlWGtvJ2CVgXIQqmWw3yrmWS9FtSd01YC5KKgu3rZSiZT6zd892/hkcZHgpKkmqmfFlY30mdfl5+m1Z6xcqbT29hGAPYHg+Y5nQb1M0d/TCoPC5e3XcVW2VAmJ3x2hNmlfuReQXTwlWp5N9/C2+Bygv1U2oW/1/Ix2qJIHtkP9ndsDrUSumr+09fl8fiW8DJIsV8dyrrvweps5rtSX0dsJznDR9ahHY/mgXpPVcQCqh5Bw6cFvpdH4frsPs146x5z4+tdFda6EQK99CcW+VULBMjx77GVpLjJzgss6lE3wIT8uKr0Hh1TZsDs/x5aZYBUsdYxnXeyX19fa62yXIszjKebub1Wm6QFYZXsQZfkeMFyo2G9vX9VeP1fl85+hVnW4yLPsapntc0/tqb6Y4jZxe3k5rX1fAWmcdK5v/6kc3gK45MM9f6v24XuBdgQfkulWxnFD+QUUN5Qbq1qSeJ3XLFV7E4/lVsuV3gDS2avMtRdU6PDVvqNEi45ZZedzTur1ERf4Xvbf23rwfkgAuU9bZdNVllK1p6L6RtTcZPSJ6YJX2caz/qqfvtVUBY4eDPrVePlVe4quzyW3pycUgndb9PpfBaLesr03D1arVTkHKjH3cqsvU/U0QfZndl4d9xGC29oHZbrCJ5o7Ds+pJoiXwyvZvQnk51TOUy1TqmyrWrr6Z5VjzjlBVZ6yKrZLAiPBrbLGawVXx7o4KvNluXzfbV0FtopKEFiHt8+XplXb1WZf7bB8vt8LknxPk1d+xcyVPutl0ofn2t7z0TnB9VWVBxAv38en8ivasOez1XzxsjQGveMru8+NJ89jkGbfetsdtAd91un5wa9kqOheGy3nMkl3LmV61a0hS7fTqB7fG+VEqrY6eL7/XnLOXp8qG81lHdVE7tU5l7l7PVUmwt+V4/t2RJVxVe2rxrhqVyX+FdXdDb1yl7YfPDPviYaX7595Frk0DpWdWa4Hkl5blrK6ntj3/Kf67UmJ7MStON+D7V3vYP18FrZsst1up/ksluam25vz/T334v9sPuwh5hycnlDRaBrY3kNP/MsRrb2976/XFv3mAPZuL6uy1GsjpWdFbAMnmzu975e5zXrZCvu31HfV0xNGvmaGVGV+nuHxfL93kufSRvQPTWa2k8d6nzjp2VZ9zrsz2Gf3Ne7dVuLlWR2DAtvLpfFcEKuEQqxWb59058fzYp0HEq/LM/VrqPxe9uGeq+YdV18cx6XAQ+HV8c/Pz2+2F+b0YM6PqwvvI/mwf0y5RHXhqlpKVcZ0ASNVZumOUAkHtzZ8WTcnvPysyhiWMtaeoFZXcqtj3H6sX3b2enkL2DVBwO1WXQBjuX7nQm8/UsdIcLzO3jcLXcwlDG4TiYP7mtrde16Di5zbabV6/ZQyt1mVDPTGyvvsduPYuDBX51a/de41fqxx4pjxHltmu73gvJSBu39X7fI9eS+jul7jvEePfiZDn6crQ9JpfK/Sz6HRaWSJX5UhMqpWcCJw2cpMioLrV7rnBHcpkvfumXxPxjxHJdatfZ+QS+dVNuvZkZPXBfc9ffGVRdUuHfee9nNsKYSkZxMGdom772VXbZh7j2Ld64OOm5sX78Ez757/LZ2vdnAe+tadbOz9ZL94Po/hcVWAZD2+p+2Bukq0fMtQ9fh9vyMYKrr+LaRqGer3HfaWRL0Larz9y/9lyFzbOOBV1lstqziALKfKJKo6hWzC7+s7nOwSKZXjN5R7duxCQbsxm2TbdFGKS0i2eU6kPRhV2XC14uA+Wy8A9uzDvrAuQb/Q7ypLWqqnWqWwPNq4skVley9X53HvkYGJCUcvY/T2Vne4zJ3T8+NqVeL9Y5+uFTTPcLna8uew+NzvCa77lN5jv7l3PIrhossOVhGWglplOb60cbHgZOVyluLrF1GYfbsjVo5FGJW5/7eUOVbRuMqgqiVaZTdm7VUdnrXzuF4W6DZwkagm5lLGXgmFTyIXXPatCsr+sBwP7KzDfe/atro9ZTcGhaWMdM72sm9lSwZ6fgGnqre3z131oeq/Jx+cI71MuRpT1ukPhPeMVuVKHzz7dD92O7Bv+vH9X8JzXJNG8CH/gt0jJ+k5AV971Bd8nmpr32+o5lOMNEl4jyuvklZOWU0oFxyWvZTp+qSjwy6JgLfLL/jMUWWZ3o9qGVpNPN9zq/aLvUwKpONZuQuELw+rIOCPhNTxldjMXTR0UfdzWKYHWp3rvuArFR+XubFjn3srr7lz/XWvHd5n94drxLuXfXJ8GeTpS/Qvn8v6l11VoFlq11wgqub8r2b43Qs0vuNpf2+DvCrDBVBZiI737/rrWH/guQs7l6CVuLizeD+8b565ibl9tYoqu6jqq97zCeFLuiqY8DaeaqnqF5V8bNxOnCBzKw+1t8r+5vriNnXhUL28UFatnPxcDyDKuAj3grlFwL5w9VAJ49L4XSu0bFPP73pJhfedttXn1UpM9zbzfCY7fi2Gfue+6PPNEwb3uypp6AlrJdIjGH6f7twA65gl3KBLUfta4/pgsT3MMqqyPbvyicTJdU1W6su0Xp/mln1eFs9v7fWDZ87n74/P82WfjtOzjyv7L+FZjNvMM+trAlj1mZ+r174tQgFYsp+XXwVuZWKe0a3X6+nfzZAqyM5lXb6NVWXlXtYSLrhev/tANZe42uoF22vn3txcrbLbHxmzarV4ja1+JsMfYu4XS3yQdWxvKezwKqbfpN9rh36rTT54FBp/SLK/Zj+YYXk2U/X3Gpu11kphYFn+m1lNhR/v77k4+oOmq6yjl3nTZr3+Veh4Lqu5h8fjNIY6bsnGvUywCpbeN9pIr9fr9RSQ+LhI+kElZkttq1Z6PP+ajHUJjmXlpy64tIfvkdNfKrHVfOtl+V5+7xt4VR+qDN7bzXZwbvUeGvSr+JB/18MBFW44zyx7wrVaraZHNvqyVGJxOp3afr+f/sNE79souqChfw8i9vv9VA8v9AmvV33hpGMflrJSFynPOPh+da47kAcMHk+Blnio7LmLGxxPfv/fnw0gdC+nvlXEh857270O/SNRL1sTXHdvKNv0MjgGXG2oTD32U+1X/5kJ6/3eakCf6VjaXP+htrrFSWVVgbUnorT93EW1nujwcw9S9FH3O7aN20H0j9Pp9Op/m8l/qgByubz+zy9PT0/Tf/9VHRp//2eyLMPtVa2UqC+cB34RdwQfsr2gAav2gxjhliK3Jsxut3sz8B5tPaJxOdjadyfTfxLm0sj3dfnEMn7GJTP3CecyLxdEFy85kZxbPxJHwv8m4RlblamzzZoA6/W6HY/HyQ5+dZdluHCp7bILswk++b/K0qsJQTuzHl5I0/JdQXW1WpX7ztUKZ7vdtv1+3/b7ffkfolW2248P03b/UR0a+5ubm1dlV7aXj3B/kuNW2Xmz2ZSPKnV/4RaSCxKhj/b23tVObpWwXIqtxJcXzDywPD09tcPh0B4fH9vj42M7HA7tcDi8ulVMgqsxUp+dKtNtrX+rIP1tpOC2NlB0GQU56NVyleIluMxlVPRJ9fLy0g6Hw/RvfFpr7ebmph0Oh3Y8HtvxeGwPDw9tv99P3+fWfiZFS2J+e3s7taW6aKI+CPaBE0iv/SKPZy0UGgYJTR61U5NftvEgxvbIyTwrYl+4Z3s8Htv5fG7H47GdTqdXk44BwdvtgsusUePHcdKk5LaQBzJODH+alkSK2wue6WlsGbTUrt1u9yrY0d7KzpSVu519+c3kYb/fT+3fbrft9vb2zf/98wtHvKDnQdrtLBHXbwY8tZP2VSDiBS223X2TffKthorz+dyenp5eiafmp9BK5+npadqKOJ1O7du3b+23335rv/32W3t8fGzH47G11l4FFdlVdSk5oC/yGI6Vv65WgafT6er94Z/BMNFdr9dTtHJj0CDVN5M46Vv7flVY79Gh+X/TGGUpWLwVRZNK0bu6pUkOLhGmo7uzu0j4MxyICyIz9znRVTah1/ph/9gWZs++P0abnk6naamuOiRoFFRNBv9vHBRM3wagjZnt9ITb7UGBoeiybNlBfZC9NMkZKPb7fbu5uWm3t7ft/v5+CsLctqAoyq5uZ60IlEyoL/KVh4eHqXz5KIXWs1HVq/FzO9MWzNB7osslO/vEeeVzkceyLfRDzRv1icGMZbMPGlt95nOSwrfZbKZVyN3dXbu/v283Nzdv5ohnv5XwVis1CjWD5AhWc0vf1tpPu5/i5eWlPT4+Xp3K+3HVvlP12oWc5bmxr22HL03n2nlNeX/kc+HLc3/9o2XP2bAq5z32qDKPa9o5Vx/L9dfV317uNf4wV+acjd6zfH3P2L3Xh5fKXvp86fheVnlNOXPn9Wx4zeuqnXPtuLu7617r+UG6gzNMdEMI4S9EV3SHfyPtZ21avzc6/xFGb7T/maiyj19R7s8uf2Rdv8pG/xP5nzgvf6YuXUMy3RBC+Pl0Vfxj/y1mCCH8xYjohhDCQCK6IYQwkIhuCCEMJKIbQggDieiGEMJAIrohhDCQiG4IIQwkohtCCAOJ6IYQwkAiuiGEMJCIbgghDCSiG0IIA4nohhDCQCK6IYQwkIhuCCEMJKIbQggDieiGEMJAIrohhDCQiG4IIQwkohtCCAOJ6IYQwkAiuiGEMJCIbgghDCSiG0IIA4nohhDCQCK6IYQwkIhuCCEMJKIbQggDieiGEMJAIrohhDCQiG4IIQwkohtCCAOJ6IYQwkAiuiGEMJCIbgghDCSiG0IIA4nohhDCQCK6IYQwkIhuCCEMJKIbQggDieiGEMJAIrohhDCQiG4IIQwkohtCCAOJ6IYQwkAiuiGEMJCIbgghDCSiG0IIA4nohhDCQCK6IYQwkIhuCCEMJKIbQggDieiGEMJAIrohhDCQiG4IIQwkohtCCAOJ6IYQwkAiuiGEMJCIbgghDCSiG0IIA4nohhDCQCK6IYQwkIhuCCEMJKIbQggD2S58vhrSihBC+IuQTDeEEAYS0Q0hhIFEdEMIYSAR3RBCGEhEN4QQBhLRDSGEgfw3XMboPpgAycAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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ssww/gda95xjQLlhHCrZnqexbbjvnVyzXRY9LDbrRw5MrzpZ0yR19hJm2j48Lr7et6x+dzKNNslzWi7dR0yZ/KYbeBqxrRwXX9joR6qZ2x/Do203J+Kp9uuNwiqSrBzjtmTOMri4uwIJ39/g659wJqLdEVq8UCd7cwSzGp7I01G6dT2PCqXSXoXtQ7ZYZ2A4Jhp/44JjzOO6g3bayHTresYBF8fQM25eRPBD7n/db1ctUnoLLGzo8G+6mw2+NO23GxVPjxvrMHYdBg2Ph49f5Cf+f8y0vz7+nv3RLTHN94OPkS10af46Nrkr6TWW6Yr9/uUaVzion7aZObvAUPD8Z1WWU7Eg6nl9TyWtStY1Hcl/T1Hcu5HT+uYxKztdlyT7tdZGfCyZz09/uoSG+nMCxcHHp+tunqJ3w6tUFscsk6BAaJ2Ylfizva2aVbAcv+/NAI7pxZ/96n2gf7wvPxLv2eoasz7it/MTr533Osv24FPWuPpxFvZUxugATXybols6UWHC2xQf+MEjzuL5u7icUPVvnOM8F527mqQz4rYTuWzI00+XyAp3r9PT04GywT3tcBNlpzLr0na8du+Aqw6D4qS4q2y8BUtkefX3AtZ2+EzSALgvhGfK56zP9tRMkOhHLoIF7G45NqWjkygr8Tjcvt8tMuvr6yQ5mosf28TGZm31ov66Mbhxd0H18GRRdrGXHc0sp7GfVmWVQuHnrcdVLoPCA4OLJAMHZDY/hJ/zYdh8z/+7s7KxdGpibMahv5E968hlvje/uyuRaq56p0dmvX942NzOm0EoXZM+np6cHywsjGH71gkcVZV5dRqTvacjaVmtIFDr+VR1euiRDYPYj8aexavA9O2SdWHdm4i4GfM9oq31Zjl/T2mWUNMhjUy7P0vVZt9TBAEdB4P58voKeOiUUwFy0PCNjW9Rn3p8MnLz6xNcXXQBZV8/8tGThY8PtdUyurWr/rl0SHt2G7XBGwf71qa36ohvvzua4r+rHYE079jVNJjs8qev2xmMzkZD9dHbezQr0v/qDokv/U8DibcOdTei77iqJuXHi8hmXPNy+KPojGCq6ypCq6sAQedmNvnMxoJPx1tpuKuZZDYWXU0U5vj90QxzLflg3Px4Fh+tdbtjcnnVWds5+kTN2997zDDT7wdvjmaTa4hlC1eEzK/RMBT7zWPt5v3TjzmxZMGPxMrUt+8yFhXbCvuY4cgbVletjzuN2dkCYXXuA8RM4PC7XEnnWvRsr78eufVU1PWWM48j33scuNJzdeT9wpuTXwXb+wf91HM4w5bf06bmlMbZddfUnoTEoe0BmEPclEPbjbzbTZYSi4PpF4nPZpDuOnNLFwjMiGrRnw12mxejvUzGvS2eAPJYcyx/oXVUHa10SWBk3285XP2FBwWQdmTGxnZ4VMOtnBsOpmD+YnA7vV2w43r+c2qt/KC6+3tYxl9U7c2Uw43HxYr3nylHmTGfmpYVuGxpn2gNnK132KjvwQMpjKDCr/p0PeD+wvhItwZOYnq3yb87+VUdO4T0JYv+yTe5fXVu0nT/ng3XslgM7MWWfKBn8zWa6jExVr2/X7OBalZ5NW/U6K6LwKKr6mpNnjqwXBVJOp1s8T09fHgEpXNy69lK4/JZZ7SOHZZ94JNZ7P5YcQycmeJ0lIzozKv3xoSS+nqw+4WVAXO/upq8UeV9C0SvHnk6k49LpOYvxZR6H9qG2KpvyulFwPaPrlqpoVy7O3g6fTXE5S/VQn56cnEzOzrH2KyT0GddkOf5qB7fhuMsWePcYH1REn1FG7JdGuk13QUl1o6BzO9WbV1G4farNblsu5lxWmOuzzkYozlxq+M1dp0u6NTfvqG7awjVPLp5rII4dw6duXMNjtuKPfpQIUMB0bJZFgaRw+HfMUBhsJBQKJt00kE6i9yqHU0IucbjjUGhk2DyxwSmZ+okGyWUaf7awZ37sK46hZg8UZi6v+Boin63KMfPx1OfaxzMxF1uvI69M4HHZDwxoPL4CAwMjT/xwOs1lEs8+KVCsE7Ne4ZmeRJzLCiqDfuIzJdq6bMiTCAZS/67LcmXLtHVtS8FVuzxAd4mY9qHw0r6oDz5rI77UwnqPZPgDb7op0LFttEa22+0Onu7vTtFlSxQefn5sOkwhr6pXQsL9mU36VGYug2fbmC2rXAoLHyQiY+a6OB3RRVf98VZ2yHrSwSj0FHnfz/tIr55FUrTpRBojBgsKp09B5xykE0UKgQdAwfVFio4HA+3n27PfNGvRs5SZIHggODYuDEAUI+GzCQoabZdJQxds+Orl++ccWy/b+5rr8d2DpliWJ0sUz6qXJMlnf11W7D8JpHrNld21fwTDRNejNw2Da41dllT18gR/Cqz292gpfAB4XDqY6sS1VdFFds/KurbSuXxbXw/1trhAeGbi0ZqO4s5NgfGshWVIDFmeP76R/eZr5f6YRw9AnKbze2Y+3m+C9eyciLblDseg68d4Swg9CHWzJg80nEKzHxVcvO89WMs+fEzdHzi74LjJrugTHoS5zqztKPLuox3H+ovjorGhH+iPNsXjdmPIfq6qg2NSeHmOw/d1++H+Ixkmuqenp9NP33iWV/VyMbxHeXWIOrN7khVFiJmBIq1PRzyT5rSM60g0rE7k/H23HdfbWDe/gN339QzaT7b4GpZnhN6XPGuu+jArUp9S2OnQ6kMaaLee+xYU7q7PCAVe40FRZ3+yL/z4Xo7vd6wOKsunoR4MtZ36mn3IgOPXkHNcPbtl2T7T8jZKUFyc1DbtwzvuvA+7tfMuEfJ+8yDBKb5P/d3XuvbNCa8vDao99CfapycuDHTsi5E/v171HUSXt74yM+ouV9HA8sTYnHP7Z3MG0mWxLvSsg2fncwLLY84JYJfN8f9jKHtifeZOFnVl+bTf6+lO5E7I5RxvlzvlXDbqGa/3e5ddUXjn+on7HZvZaBu1uRO5Y8emoPGyJY4R++2YgFJkNNPo+sEFX+POpOBLsjX1u/cHPz9WT+8T9o2LrTJu9cmcUHt5fkyvi4+JRNZvcydcpuNYqW5VNf0k1yiGiq4eYOwnH6oO1588qktsPEJy3y4Tq3ottFwrYhY5l8m5AAk3BJ/KzK2jdcKifTxz1D7KTP32SRoZMwF3ApbTOZ1nF7vdbnruKa8L7s46+7W6/p7/y8G7QNatyR4bxy9B+/BcgIJC1xdz+7sI+Dp/1eGvHvtPr3d3QHZTedp4J/Sqk8/G/HkZ3K+zqblx6my3C6Ru+x5oeBUJkwKel9F++m4ugamqA7ujLVEPmFlrjGinfi6EiYtu2BjFUNHlQrdO0Ljo8qRZVR10pIyaU03PAo6V79HTLzvzKY6iKJ/QJQOcW5vjQHdZoUPDpnF1Rq1j0HgoInJqbTcXvXmRuq+DqU673a6Wy+WB6LrTdlmy6tMJGqfE+mxuJnEs8+B33TICy1K7GNS5bt/V8dgySTc2agfvuqLwMqDs9y9LXpz16RjHRFdtYPt9+u719EB5bNmF27lAztk8g7v/Jhp9WwFayy20Z0+iql4/stMTMQq8X7XAtvOyR65f81JJzhhHMPTqBRmer7VU1SSqXA/zCOhT0arDO4yq+rPY/irmBFKDKAfiZVXHpj3ddIxOwkzHncrr54ZGI6V4eXY9t1zg9TmWtXObboZB/EQdx1l1Z1u5LbP2L93Hy2Sd/b36yQMTy+/2kTB4H/v0tJtVvLW0ICgaLii+Tze2LMdnYi5g/MwzZgbrt+rMz3ysldEvl8uD75gYHbukkOLu5fjVOvTxzpY9OLjWSKhpeyP5Lr+RVnX4y6YypKenpyla8gRE54CCUwlu42ez5zq3WwKg4CpjUWT8mkGi4LEeXr7qPWeA2t8FldvxBFlnzF02qvUuHvNYlj5X966uzDwoej5ecvwuqKkMd6S5AMHslnhbOfVlIOE0n23UPt5+71Mvz5czdBzafJcI8BgUDR6Hgt9luZ60eNChb7jtf419S3Cfn59rtVodCLI/p9Yz7S4xUp/7cpZeu7oxGLOfeaml3nO24zONUQz/NWA6TeeI7CxG3y7q8hhzhkzj7D73MigYfseWZ9pzAjxnxN0Sx9yUr2OuLAkNndyFx+tLAeCDg1Qff8aDjtGNHb/vMmTfXkavQKIlJc/i2T4KppepYKNx6vbXfjqO1jt9OaFb1ngrALlg8HpZtVPfsS9UHvuG5Xig6ZZ4Oo7ZUzdbpBh3gY+47Xf+oj7guqr7mtuj14XBSX49t/TDMlxDqCtdNv2lffotGXob8FvGSxGSE3Z3S/manh9bA0vHmjOm7jNfpO+mXnPZ1LF1L88uaAhd3/jxXfDoKBImZrG+/sU2+JRVx3GR6oIdAyX7g/38JRmTxscF2cWdzuvZno5zevpyjSwd2ffT9g6PR/FXezw4Mov3fmHfMKDr2J5Ru+2wzhpX/q/juj3oPesjOCachag/1EaOgQdHoiDPY3d+49kqZ3/63/vcZzfsN46BL6e46Haz2DnNGSm830V02Ql69elR917bsuM0sC5CnjF0Zyd9gJgpc9r2lnjQyeQkfrbUxYjTVQqdOBYo2Hfc/+TkZBIeX//ys9u8bpFtZ1ku2MzU5m4ymOs371fvDwrQXGaifpkLmm5bfrKGbdJs4NgsSAHB+7rq5Xpn2qEHNtpkF3z9YTgMCr6s8CVB2V+7ING1jwGPWaaO48sQc7igM+jJ5n32RHyG4kGGr3MC2gUi7Ud96PYZxfBFjZ/TwLkpXVeGO2H3XsbOa/g4uJ69eQZS9XoNdLfbTY8/1DY8q8o1R4q+P3Sjywy+xFi7/uH0j+u+/nQlCoPqrX09SLhDzAmuG7tPM309ck5oOifrxpt9xuua1R4en2PDvtIY7ff7A+GdyzQpNj47cqfWtrwihgGbmSGvrOmWoTzT9z6bsyfPsJlRq3zZpS6n8vZ7Ru5j6zausn3ZoAsW/l3Hl2rBMb60rG/Nd1lJPtbILrtx6NDCs7W3kDHzeQY8Lq+79F+R6DIDTX34c9SsLzMjzyh4l5K2Z4Yx52zsJ88gKO4UBYoQ28zlCE71OsHlmt2xLIjTWQm9Z5weVDxT0/+cvjIIUrBc3F0ItJ3E1wW9qy/7mRmo6tH1MY/FffmdbIv979urn996aD1txG2T/adXHzfaIkW3qmqz2Uw/GuvH9aCjzN1nW6wHLyv7GnxZYS4AO8d05q0+/aUY+uyFrqOYCYm5KYJHJR+IuUxQ5Tt+bDrf+fn5dNuyL03QsLkkQBGlQXblUrjozGqLypC4zBkFHYACIJFnO12MjmWuc/3Genei9/z8fHDiUahNPsXlmr0LD8eF9eE6Keswt73K175++7MLcyd+FMGql8eEvhUQj4lUV55eaUt6z9lER5cteiBWvT3D746jftIDfFgvb4+Pi8rp2tYlXW+JXpdgdcfqsue547DuI/kuD7xhVNQg6b3gJVAeMd+KlC4snpVIoHhhuZxVF3nzbCz36wSwG3Rtw4x5LkPpDJDGwOyLZbgQyDGZzbEf9TnL8ODhmSdP4jD79ODXZQ50QpXv4kt8aq9x6gKE97vK4z6+Duh9zH7gksecI/tYuaCqv2jTnlF3bXdR5xTegzhtmXVR3zF5cFyE2Rd8JoMHMdWl28/HhzMkv6nIb+RwW/NLHo+JIYX9LY1g2czKffY0iqHLCy5AFIOqemVMXQbMtSRBYa06XI91o/DB162unUDTIVU+jboTXxfQbq1PhqXB76aANNSuDDopBaoTiC4r6ZYaukDW3ZHmbfcptouYtlFA4AlGz8rY304nvCyX0LF1XPYzlzvc8TjO3oauDxi45pxXwubLPF0G2AU31ZdLWQxgagu31XYqX77nts1gwDXmuayU7e+CIj+Xj+33+ymJof+xbB7Ty+U4VB3+KCfbST3RfnwguvuWn2AewdBMV69aJ3WjYibHzIuDM3cpCAWEA8gy3PF4bzyjvH6IkU8Ac0N1Z2VWorpxHYxC1E1x1C6Kljs131Nwff2RfcC2LpfL6YlKyix4PN0q6ZnX3G+VsS85i2Fw9UuT/PpNBlE6GvuJwsR+cEGnnQnty+muhKALPrQ9n46rLJWjOnIf9ZcHft1sc3FxUev1enrICvfTw8jnsjzZR5fheT94gqI2UPh4c4AHHNmU2sLj+yyH5T09PU2/qcff01Mf8BzJ3NgyiPgSDLf1GRozXL0y2NGH5+xlBMMvGat6cVIKowTLH7Dil+2ww5mtVM2fYJBouFG64+ikGgd0t9vVer0++OloirULFx/6TWNVeX5FgF596upCVfXyE/E+rfXslWXI0S8vL+vq6qrW6/V0BxEDi8rTT/OwLzox9CyEGYUcWnf0MVtXe3R8/myMZyjuaJ6BqZ98pkM78WAhwfVnagieBFVg4u/Z+QxLZfF36yQwEpnValUXFxd1fX1dV1dXdXV1NYnudrut+/v7VwmCTkZ5guFJCtvH4KdtWVcKL2ddPKZEU75Fge5Ei2O62Wzq/v6+bm9v6+7ubvoh06qXn5SicHM8fWmlm+GpHcxuPTP3y9K6k+FMsFT/Lpv/pRj6w5RyaGZDEi111m63q8fHxwMD5rMPXNiqXi8veBaobSj2Mmz9yu3d3V09Pj5O9Vsul3V1dVUPDw/17t27ury8rMvLywPjUf01yE9PT3V/f18PDw+12/3nNkMJl0SJ0ysZA0WXTq33zO4pVozmbqQU3cvLy3r37l3d3NzU5eXlgej6z/WovspWlLm7oLsDcoqv9xLergz1udq22WwODN+zZLcRb7cHVGboqp+E7vz8vC4vL+vi4mIaT9mD6sGTbWxvtyTCeqnd6jOVe3NzM42Bi65mH6y37Ep49uYzPdmYCy/7hjbrfSXBlFgqIbi4uJj6jLMPladE6e7uru7u7urjx4/16dOnur29nWZOi8WiLi4uDuoi/+bslYHdM135gbbhdeYaE42fjnV2djadDJev6Tjcnw/DGcHQ5QVGWjpj1ctDzNkhNCpO1TSQjPB++Q2foUlnpjNwYJlxK5IqSDDjWa1WU92Xy+UUaff7fT08PFTVS+TebrfTD1oqw+J+qo8HCUZiTu8k5Kyj+ofPitDrer2u6+vrev/+ff3hD3+om5ubV5muP8xH5T48PBxkM13QoNiyL1erVa1Wq1qv17Ver+vi4mJq836/r81m86oP5DQqixmZ2n56ejrdskzR5dO8tK3qJmeSzSnjvL6+nmYw2m6z2dTDw8OrZapj46PM2JfEFLivr6/r3bt39f79+/rw4cMU+Fx0eaOE+4v6lYmGLxfQNv3JZj4+7HctAbDvuNSlsrqlKfqNPtcTvTSDYZt8hqs6UCO0HNAtQ3S/YsJkzGexsg3ZIrVGGTj9axQnb6TV3yzn3u120zTq2Pobp45TJX/iustc27geyXJ9n7ksbq5Ox+p/bK3uS+rf1Y8OyrLmyu3KZ52ZvXl5c2U43j9fMtZfO7XrlmG+lLf64tjrW3XxJY+qw3MBXzMG3XG9DLZp7rMv4dhYHLNb7x8GpG6M3PeP9eu38Pmu3K7u+/2+1uv1q9niz2S2AcNEN4QQfkfMiu7wp4x9aQTrIvpPLZPMRbxj/Nw6hPB74af407ea2frxvkZrRvp4Mt0QQvj2zKr42BXkEEL4nRPRDSGEgUR0QwhhIBHdEEIYSEQ3hBAGEtENIYSBRHRDCGEgEd0QQhhIRDeEEAYS0Q0hhIFEdEMIYSAR3RBCGEhEN4QQBhLRDSGEgUR0QwhhIBHdEEIYSEQ3hBAGEtENIYSBRHRDCGEgEd0QQhhIRDeEEAYS0Q0hhIFEdEMIYSAR3RBCGEhEN4QQBhLRDSGEgUR0QwhhIBHdEEIYSEQ3hBAGEtENIYSBRHRDCGEgEd0QQhhIRDeEEAYS0Q0hhIFEdEMIYSAR3RBCGEhEN4QQBhLRDSGEgUR0QwhhIBHdEEIYSEQ3hBAGEtENIYSBRHRDCGEgEd0QQhhIRDeEEAYS0Q0hhIFEdEMIYSAR3RBCGEhEN4QQBhLRDSGEgUR0QwhhIBHdEEIYSEQ3hBAGEtENIYSBRHRDCGEgEd0QQhhIRDeEEAYS0Q0hhIFEdEMIYSAR3RBCGEhEN4QQBhLRDSGEgUR0QwhhIBHdEEIYSEQ3hBAGsnjj+5MhtQghhN8JyXRDCGEgEd0QQhhIRDeEEAYS0Q0hhIFEdEMIYSAR3RBCGMj/B0+8ZNxuuN7XAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 25; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 27; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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TFiKQhEOQEYJOvpgwkFwhISZFEbx6t9AIhhefKJTRgQE5uvBxXwQQ5USRfS4Z82PeENcmh97v9xM54+nhpRKCE6kgtITr3I95z/Pa/OsKxedRHsaKYXHFZb295QjSca8J0svnFuRKzlgZt8uPpEJkwbNiEHu9nqS3UVev11O/30/jYI4ZHzKDHLgBRzZZf0iR/PlsNntHF7g288T8UfTDqCJn6AJGIC/skSqhOIrhQcbRFz6Pc0SEQpSSrxvE7GTKOjLvrpusj3/foz4vgPFZ5IbPIvcUQTEK/J65/9gpBUeppEuOU1KBMJkgwhLvQ2UB3DMkzEFBUQCsNUKJteM7Ts7SMVfoRORhoucVyb95DtG9dUJPPB/p6AGTB+SeGBhIBjBm/huBxpJjAJg/9/AolLgn6gTInDkBohT8+LVJrzDnGAuIg/wnQo/x4PpECnnLHPPvRRxXOp4LJcfQuUfjypjn/FhLlIqiFOkHPO48d8dYWEfWlGciheVrylojr6SUOp1O6hRhbt3D9cIo3hkyhiHE0HlaAs/Y140xuceMM4NX6nqH3HFtLxqSWkA+SSWgu9KxgwICnkwmyRD4HHpayvPUeRcL92btXcYwTnmR0HXfIybvaCGthwx2u91U7KOmgZEtI4/rKN3TRdm83xFBpYUs98TcK2BCO51Oqm669+e5tLxZ2xUcIXDyytMdwHNJ5NcQRumYU3YBpS0J0vS2FfdUPPRzi+5CkBsevBsPEcknQ6YoDmRAEcRTIv5DHhrB93xcnu+FCD3dwzqgIBC3K/up8NG9S57V7+PKR5cLZOikTWFmvV4nA8Uzu0ftrYZ5zpH5Imd76lkwalTRCVPxnvI5I7rykNe9auSSdXPvbb1eJ7nhXkRTHp67fOSGxKMDjzZYT9aP8XqfbL/fTwYIvWNcyACkm+dF3dD6WuMJs4ZOqj5XrD1FQgjXayPIoPME8oAxo3d5OBxqOBwWOndY23zePjZKz+nibSJ07vb3er3UM0k1GUEjfGm32+r3+xoMBhoMBgWLScXZc2coredt8N5IGxCWe+jpHgeeHQn43MvBeAAP4SjIeIjqYb2Hy06qnjOUlCy7tw/lXhNK5Vafwo/3M7qH5HlOzytCdF749H5iL2pivMiRgVO5dZ4TpXQDA+ESwnsRkPvg2fpa8rxO1pvNRp1OR5IS8ea5RJdHV0KPingOj8qYF78OwPsibId4kUOXfW9d4xoUfb2Q6evKeDCk/kyMOe9M8WKaF1S9Pc9lE28ZWcuB40DXBmvluuYODGPz1A2GxMfG87tse0oEHct1AMeBucHo4pj1ej0NBgN1u90C2efyXxZK7V6Yz+dJoAmVqXyenZ0l8rq/v0/hYj6hbBigJ5BiA+GjW/PcG3Qv0fPGeUECRaJ1ZjKZpNYc2lg8P40X5ATj9/biT16kycNqfk+oijfkqQEnwrwv1p/JC1I03EOMCJ4LHJFEnofLnyk3Dl7Q8rBXKlasWROu5+kcv48rA2sPyfBMrDOpFM9pIm/b7Talb07dh/Gwbp5r9CKUk5/nkJkPJ6fcs3Tvmr9jPGi/w/gz93lxzceLDkCWPiekpjz/nxctXSZPpQD4yQ3cqdSdd5PMZjNNJpNUWHW58mtDlqxRXkTM5ccdEOSXlIu3+nEd5rrVahV4otfrveMI0XqYO2UfG6WR7nb7drMD4QJtV3i7CAmeBFtPc+Vki2an00lVZFd+4ATgVo3F9XA/n3AKd4vFQpPJpLB1EtJ1qy0dLbl09BjcCrt3C2mhNJ6r9dQLQpSnQlAaD/m5HnNIKOY7qTqdTvLE3EMjR+3pAM/Fojxe8fcQjs+gNKfab3KPxgsjnn/1MNplApJicwN5fPeKkReu6Z0QuVHLw3DkIQdjy58TeWQNz87O1O/31e/31ev1Ct0DpEa4jqfV3GnIIylPTyC7rIunsng2z6vjqZIPhmy9RZNn4Dk83eO/5xmQAU8xQbjj8Ti1s+H1Onn6d5lDT5e4t+06IhU7W4jWdrtdOjYgj6a8GHx+fp566w+HYxEY4yCpUJsoA6WRLpsjut2uJL2z5ZIwGA83J2LAxOY5UA/RWUwnVPfOEORTVXBfQPK4ED2E6593QfXOCC/sncqfohzSMdz01i3Glefw8u4LF04nAdqYIIFut5uMlBcoeV73mMjr0mfsuV7CUveIvOgJmbtH695VXkTyDhHG49fmdxT2qD5Dut5c7zIFQbNueT7XQ2BkkOfwkJj/5/k98nKnwb0q2seYE3KzeHhEdzw3z+kpnMPhUMiTIjOnogknTI+SWL+8EOqFSZdflyMiR49ivMccz9Zby8bjccqrOwGe0i2XW0+dMMdeRMuv48bPuYCx42h0u93CDkQ/T4JdfZJSTroslEq6t7e3SWHJW3lo40lw3xJISEOxBI8Zb8ZbWbzIQ7jpoZRXc71YAtwTcmGjR9I3O7DAeKVO6tIxNHIv0AVGOpIPB5BIx1QCgsJzPFZY5Nm8gd1bl6imuycM0eQ5My+25TuPaJRnPbgexkg6Ko2Hs6fCNyddPPBTaR9+5+2FTroYCcgDTxxP0outkJ53iQDmIG9p8iKfF9A891mr1VJf9vn5eXoe5iPPG7v37HnYTqeTWvWq1WqhkOjnUbghgxw93cBGBZcpPE/IxWXUPV03OjmxoQu+ocNTCq5v/myu06yXR3EecXnR0Xd78oMxYuu17zzz6AYdJwrYbI5bqO/v7/Xw8JBqQJPJJMlGGSh1G/CbN2/SRLfb7XfyiYQAHiJ6HtMP7JDeEmS3200C6iGPhzhO6JCk74DxHWV4aQgigkNoRgjYbrfV6/UKXpKnCIAbFZTFvWQP/wkZaYhnXk6FdK6Evj0ToYPM/GQuT0tAjJ4qOPUcFAGZJzZE7Ha7gpcKWbuX4pVp9y6ZD5TEe6HdG/L5Y0s23guki1EjP5gb0TxkxljkEY/nxX0emBcvuCBrkFSv1yukizxNA6lQwIGUIH6Xf56z0+mklAo563q9XujIoS+b5/B1IMKB/Mlj8j3mTjp6+b5Jxv/fU3bICnNAwds9cvQmd3T8jAz0zh0kZBmj7nrMfLNZaDqdphP07u7u0tZyl2HG4EZrNBrp9vZW9/f3abfm4XBI54s4X31MlObprlYrvXnzRtJbRSAUBPv9PjVz+040Jysm3gsml5eXqYEfAeXHld7zwf1+P21aoO9yOp2mzQgYBhdEBMLDS7xDSNNbbzwt4mG3V7aZCydNzw1D9OSeCes4S8JDQA/V3cpD2L4F14thrmSe1/TCCbnth4cHPTw8aDqdFtbNw1TGzd98zphbT0VIKpBmblg8/1qv11NFGuKAUKTj5oU8t5fnphkbRMr46WRBZpyAkSGXQ3B2dqbJZKLz8/PC5gw3rnjyeP38N2Nzo5HXCvi7pwi8V5d1xOh6zYP7QpRed8DgILf8d+4tIn+QmP8OHcuf02sn9MmS60bvZrNZej7foo/c+Q8eOhGzH+EqHVM2yAqpJ9Z6Pp+nMy44UwQdevPmzQ83vXBzc5MWkgqkK9V6vS603LiSeA+h56MkqdfrFQQAIfPvI8h4qMPhMPXSTiYTScUDQ6TiBg7/l2eAxPnxVhbGjEWH1Enau+Dm9+K73tS+Xq/TYTQcFCId26EwWH4Nrw7Tx5t72b6DyMmJ3N90Ok0H4PgRiO7JecGFeT9VSHTj4+SBwLMLyw8swVva7/eFOUdxIS4nQy/mucGUjptT3HP1tE2e6/RiDmNhbjGunHXR7/dTSF+pVJL36TKD1waZ5AXIvA3Q1xMDyHzlGxkYJ86C1wg82iFiwZCfnZ0V5gsjTnoJOd3v9+l0M/coT+kJ8oExQOf6/b5qtVpKM55q+8pTGoyTFMHNzY1ub281Go3S+Q2eKvOUINcnLcnJfsjydrvVzc3NO+mmj4lSj3Ycj8dpeyTeLgd6sLic1erhmpMPRMLuMG8JOiWsebHN29Q4UxMC80Xmmp6j8lA172DwYoSHrFhYP4Bmv3+7uWK73abcK0Tingljcm8cwSFMJIT0Z8douGfkYeSp4/O80u1EPZ1O07GLruB4VCigRyO+a8rH5WE84/L2InKmhKsYJ59/7/bAcHiYzfqRcqILhjU91bedk2mtVkvP6rKD58zaoaiMG/n2Dou8GwHCk5Ranbxo5EVIz4syt5y9QHqHtT2VrnNDg0HCcJPT5LvD4bBwLc+Te6Th8o9eusHF6XGnijE5kRPR5t0hbsTda4UrHh4eEnH6jlM3bJ5aIFr0rc2eriA//IPqXvD8Kw+M5fRigFdkIVjOkmXiPO+C1Z1Op4lQTvV0enrBiRcvAIFnUb0FizAJj9jDTU834Cnxe/dWZ7OZbm9v9erVK41Go/T8KAkFFO8awDvmmfE47+/vdXt7m4QEQ0VO0RXFCchzpZAW7U1eaUdQPRyl+R3PhHwj3gv5VU9FQJoesnqBjf/G2LGuecjL87iBg/xyBeW58HzdQ6YYKR2P42R8p1qlGIeTGGNAJuv1esoJVqvVwmYB3+mVH0+IwfC8JvLnsurG04tBkDt5f5630+kUzgPGG8abxHjiANze3qYUSavV0nw+T7lhzpHwMXg0gF7kuuB1B0iX9fS8L+uVF3ZZB09vMH6MlvcBkwIknYJMo0+kiryo67zjdSDvtvnYKH1HWl4RBe7JIKR+NCKL76HJdvu2n5fJl467nQidXZDzkAhFcML1zQaucHh/+fc4gyDfIMFYVqvjGbUeDm232+Rtcs4rBC+poMQcnUg+ihPE8Na9oOanc3n4CknhbdB+5a153kaFguMh7Pf7QqHC23G4l59uxlq5UnrrmG/f9DCYaIeeU8JTL3y6EqLUFJBINbjn5CkFiME/48Uvdwj83AdODWMrLKRDOgIngPmC5JkD1oQw3+UH8mYdpWOHA4TFs2PYKB6xLpwKR09q7t3TJjUej1PHAZ0Y6Fun09HV1VWhiOkdBci6zx/P4/l4P/eDZ+bv/j33nNFx78X1Aq7LJB45ctLv9/XkyRNdXV3p4uKiUEDECDkPOfd4jaMsfC8H3nieKq/eQkRMmjeaQ8x+bCEEzHUJN1g89wDJX0JWnhf20MhzSyhtnu/FUqIALLS3MHmOFE8Qr5P8HcK4WCwKaQK8TPogCQkpAEhKljw3WF5kckvP+FHmvNiHcnkul3QG+a/ccEGcjJXPQe6e6/OiqOdiITw8HI9EvA1KOnqqfBeF4qBxz9cSxXhhlX8he+TKPWOKMHiTrpCe8+T6GAFCc1r1OLuAe6AH+fx7VwPX5vndKHl0hScLabLVFdL1DgUPpfMcKs/mXUF5eM8zeoHTU3C+pj6//N3TTBgh5gqdlJRkwfvhGRcGm/VGznknHe+VI32J3kpKHr07ee5Ze5cGz/wx8dFJN688+2tLpGOOlhwg5EhhCI9QOuYoK5XjAeIeEiMokK6nB6rVaupcoGGeqqkXkUCezHdv0UOuU+1Xef7XF/dUrtoNEFYXMvaeSEJXDIuTKNfxMNmr5S64/uOdIB6+ksqgxQavyL07npNCBakI7nWqwAZpuDHgWsxh/vodyJcx8sxuIHNj40U1lB5SgACcHKRjRwMpHe7l7U8YRtaFwmi1Wk2GBOL1lyh6326+BjnhORG4LDlJM5cYZ2SEnDgRlRcR/dreCuetYi6zrk+eqnJCRRdcTzy64nNEn97Xvl6vU18yxs27MNwIepTkhb7hcKiLi4vk6bLdl44FUgce7UjHFAcePvJVBkrzdJvNZpoUFgEBp/fVd8ogGJAshMBnpKPSeKiJsCLkkFO9/nbnEMqA5+QtVnku0IUm//Hfe+GO+3ne0fOG0rGi6yG+h1SeBz+lmH5dLwLxN37vxRTIHG8BYyMdDxDxlAY7jGgTo3hTq9UKR/uRm4bQvJWJcZAq8LnyroC8WIXhxVP252Ve/F8U2jsnJBW6H/wafm+/pofJeHOQKZ6eRwL+BgeMT6Px9nQuTt/ynmJSF56vZ12J1giZIQT3sn1+csJzOXGS5bN5pw0pEAgvLzz7vLl8Pyb/7rCcWk8v4voxmThMdC3hJKHHdNK444OukMflDdxEFqwF803x0l9eW6vV0jGtV1dXSRfKQKmk++zZMw0Gg4L3RdGBUMFzqijF4XBIu4t6vV76DvkjJyS8MYSNPJ0vEukKr1g6oXqo4d6Gp0Wk4rZbv6d7Vrl1pVjhpMvuITy7SqWSPBUPsfEUEJpcOXMy8R+EUHr3OMA8n8UYUGaIhjF5fpt5cCJE8T2f6Z6ipDQnXg3PdxLloV+e/3MPlLUkSnCvH4Xid17Q8UjDc76QgRcl+TvRAO8P2++Pr7NxEs2J0a/j6+VtZfl7/3y9PP/sZwTnnQROdPwwL8hau91OBsajTvdm8+KsF79cB3IH4BRp56SME0QKgKKgr2mlUjlZZPdzOHyLOwZLOkbFGEh/4wZrz7xh5MtCaaTbbrf15MkTtdvtdDIQngMT3+12C2dz+qJ70zeeFp4yC+ML656l79Cisgnhs6uGBXchwUvzxTklPHmemnF54SFXGgSfYpTvYvIKPtXk4XBYID0sNSTmXo4XErkuREkY7ooOcbvHe3FxkV40OBqNCrk7nm+9XheOtAQ5qUvHzSnMQZ6/PEW+zKl3MuS5cjeUEL4bKU+JeHrBK/AUknJDlFey+T3P7oVHSamgxVujmT/OvGC8jJF14hkgEy8CebTDPeggIQc6GAzSvbydztM7TlSdTqcQevNs6ANdEB6tOInnOuDdQp7W87QRHifv8+PZu91uIm3vMuE7rJl3wmCcaHnMC+6e9+bVQxQPSaf4pib3vstAqTldjmJkAfCg+AyVVfJSfgALoQlkRa+j52ukYxjt22GZYC8yEEqzw8o9bFcCD8sYiz+b51CdmHzvuIdNtVotWWjG432mnhKAWL2HktYgFIK+XicC5ts9OVI1kEzeb+lpESrxo9FId3d36XCQ3Lh5KJ93ong47/lsJ3cPOd0rO5Xr9u4X98ydIPO8KKGm56+9L9bHxzPloXseHXgbmXt45HEvLy91fX2tq6urdGg2xoa5Iw0BEboBpPPAHQrWFgPi27upU1xeXmo4HBY26eQ5c4x4t9stvIqH1Id3VBBBMjceOTjp4pigYzgY3jGAx8muRvdKSb9wD9cZPHGuBbmzTp77R15IR3ghmlQP9QZJSZ9xwriuP9vHwvf25gi3ZPzdwzZeqeGhfq6QLiT5LjBJ6bOcAuUbCfBwPezwEMYLIuSMvP/PLTv3cW/JCw5Ul6XjO7TIQREOMW4vpjGG8/Pz9N80hvtxdHlO1T1eD+mdiHPFYZ7xAvCuIQ68BBSxVju2pZH3hMhcEb3dy4krvzdziAGA/H0+8nYf5CJPC3mE5J/JoyHu78TCXDpxQABOZHhqfB+jjpfLwTzeDpfrA0YPnfBUSp5/h4ArlUoiau6Jp0tO04uoPDOyjWfMWuNw8DfkyeXRPcxc5iGu4XD4TleDpwZwcpBd30Tiz41D5q1x7lDhtPiYct3LO3/QddYz7/rJI5qPjdJIl4lnMfyHhWWzw8PDQyJdVwafnNybdAWRjsUhmqcJQ7Cmnr/zHDOGAXK8uLjQ5eWlLi8v09t9PTQ51UvswkqFtd/vp9DQT4CCvHxXnOfwICwXeO5FccuVk8N73LPM86/MH0bPCcpb41Aqwi+viOOxsSaspadFPHyTju8Agww8/wnRcn+u6x0PELuP0T1pjzhYB368PQ9vzJ8bw4QMOVntdrvk/fF393J5Xi/KetSDwfE2Npd71xHmNJdtvodsQ3SkMZzkSWG4lwvwCvk+HmC+G43xSO8eJE6Kz6/vxhEQPXr0x/Oga8wZ8kTPsreoeb6eKNUNt19/v9+nQjCnkPF8rFvOP97BUwZKO2XMNzLkD0jBBgvlnq6k5J0irE4YHjZyLVphpOM7r/C4EBQXEve+IBu8vYuLi9R0TajovZj5K9q9UIIQEzpSCOR3Xi13QfKcsverSkdPgO2tHnJDvvSx0meaF9XwhJxEyCcTCuK1OMHlhOZpHU8nQLi+vZn7OfmwXvyLJ5MTEd/1Nfe8t3td7rXzfTfQPBty5dFN7mFXKpVEHC5jPm6fF9aBz+Y77rxnGALxPDnznRcveeZT6RPSVPS2u8eYp6o8zw/5UGjisKd8owzzTPTCulKY9ZSDG0ccGebSDaDnwtENCswuhzy7y1hufF3u+awfqO5vT/Y2NmSDTT2eJvvYKYZSX9czm80KGwDy/Bm7XggN8JDoqWXyvGiUh56+GG6hIR8Xcm8S5/tOdnjIXvH1c1r9QG2eEU8KIiUc9AM/2LSB8lHUQJDdW4KE8NqdhPNWHsaAR0qhBa/a88dS8SWQXAOvG88ur7T7euZzRthJwcb7LcknM2ZkgPGiUJ5DBW7MPOx1RUbx8iIP3+fveJ7IHwTmc+oRmHvKTsa5Yp4yfCizG2SuAVm5w+HbeLkGKYV8vb2w5cVI5tTJG9nhO6TJMCrIxXQ6TUSEDOOFMgYKWJ6Oc7kh+vNDmXxuvDfcn81TLcgm38mjDr+WrzsdTX6gOqlHXzfXV0jXO5k+Nkp9Xc9isVCn0ynkfTyn4lVO3thAOxD/fpdQ4LGcn1tiOgv80JF8YU6FrNKxbcWFhOtymhEH3Gy320LVnjDUvWvv3cQD8WoqAuX/790ZpB7y3BbCSlqD4yd9rnMv6hTB5OTuHgthon8OEsBrYo7wzjBqPt+eB3cPLW9JOlVl/q7fZT25r8+bRxM+fjd6kFlen8jnByLFQ/O6RC5XrD8FP8J2JzT36CFsz1czB6wZ9/ZQfr/fp7nzqEBSKmb7d6TiG33zjpNTG4p87b3DBKL0jh7IEN12PT1leP1Zfd2JCCBnCnbe05+nSbgOhE2RuCyUevYC5Jbnfvg7uRwmYjabJWEkRPW+V1co/v/Uv9JRMX2XFyGIdxigkL4HHCIkXHcvj98fDm/fv/Tw8KCbmxu9efNGk8lE+/0+hdkuAMBzy4vFInl8EC/K6TlQ93Q8xMtbqlAQb6/xwp0fqeg582/Ll+IJ5s8CmbMe/LgSQbooMPfyOYAwPNXiRs67LTyU94q7h9QQQE4GLj+MnbH6+D1V4XLrxJ2nOZxw3PixTn46m6TkqXkeNzdizWaz0NPuaSLmz71AP+2NufbaBc/A5zmBjAOkkA0+xzsNPap0b9oNvKcwuMfhcEjnkKB3g8GgUPgDj+lxTpp4qsyFO2y5M5Vf2+ftB5fTzeFeSV404XcI32KxSKEoiurHBkpFRSH/lId/bm29sumLQxiYpwMGg0HyEAjD/XAcen6327cnIN3d3SXSnc1mSVC73W7y9B8LA1EqT3vkYaMTYl4c87n0FiTSIv76HSIMSMu9qzxf6YTuikTEwhq59+55aeBE4uNH6UnP5EU27omnS7qJcJLPIjt5CgCy97cru6fuspKPO89Deq3AC47enueeWj4fXpxkLBAG6RjfneVrmqcXfP7ce3OPFU+SVs38OTD2nEI2Ho8lHb1fb8Wq1+vvFCTRR+n4Bl5CdkJ8z8t7+nA2m6nX6yXdPhWhPqbXGNz8NLX8zTHOM77u6N0PunvBvYL891LR4/FKvOe9fKJdmBH4/X7/jsfnhORegC8KQuE5r6urKz19+lQXFxfJu0VwWFhypSygt6lQnEDJXAkYu1tX9xw8L42XgOAjiC58WHjvTfbP+g9hKdeUjl6vV4lz4+ahswstn4c4WFNfAxd45hkCJZLxinUuI6fCzDzlcupzPK9vDGAtkSHP/edGIU8V5MbeldlJGhl18vaUgN9DOr5Hjd/7fSn+kfNn3fIw3OXTyXC/3xciC8boqS3PIdMtsN/vC7l5vHTG5MQmHVsN6RBarVbpkCYcD+aMMXrBjWfxufE5yg0Na0iNqFqtpnnPuz7cuOYpCi9Wl4VStwHTmwd8MrDITr4uvJBmXs2FzPkM1XNydXk+ViqeV+q5OIjAW8WGw6EqlUryCHIlRBF8wf29T77YThancqAYmNzi46nk4RXzgaLzdz9YxD0bHw9AiUizQIyr1SqdueBGzsNLL6Q5+biHjaJCyKwvZOyhOflu5MLzp9yb8eJd5+kE5tA9/VwevAjrXr17q0RXTmzIYO6BIsu+4UY6vtUDuapWq+lt2MBTEL5W7hVixPP2Pnc4MF65rENceY6ae/PsLpvuXDiB5bLPmlar1ZQyQR6m06n6/X56h1kuv7lhzWXdnQfkwL3uPDp00nUyd+cCw4E80P6Gs1YWStuR1mq19Pz586SMFMrciqOM0jHXmXtUvvOKVIAXZSAdPxj8VNhNvyyWnVA+z+Ox2K4UedHEvRsvztE9wJi884Drsj3Zd/IQytF6hcLy7HgO7qHmW439DQfe7cEzuTGiAMjh2BAbeW/fLuqRhHeh8IzseMrfQ9doNNK8oAhu7JrNpjabTTqBiuv73zHOeNaeL80P73EP1/PA7v1Dqr4tm/FKSmEyJO/eUB5J7ffHXXzk97leo9FIm2vyZ/cIDLKA8Jh/GvwhB+SR5/Xtt94hAgnjSNAz7Z083v2AXOYpPhwdj1pywsdp8C4AdyoYq7+pmEhHOho11prtwWdnZ++k1XyMvrGFCDHfmcn38Ma9A4k2Tj8n+WOjNHrvdDr6yU9+omq1Wnh1BvkkJys8R3K6CIekQnHAixJ+Sld6OCtUsFgQdb/f12AwSDlBckPr9Vp3d3f6+9//rt1up2fPnqnf7ydh5p5+ZgJKM5/P02Hj5MY8tHKPw/NV/L3VaqX8mHtpGA4a2T2MxGCh/M1ms3DQtHTs0XTiR2lXq1Xa4bPZbFKRw3freWO5dyDkG1D6/X46au/y8jLtuHPPCSPhxEl4CIGwtp6KyAsgwMNR7wrx6zkR8Tnf1YSBwkiv1+ukhD5ecpqem0WpiTioGUByzEm1Wn3ncCM8arxqz91zHXZT5QfJkzqo1Y5tXKfCd2TJ3xDM+rF2XpSD7JnrVquVahsYKNIOzEVeK3n16pW++uor3d7eFtIPp3Zjkos9HA7ps/4aLuYDp8b13yNh6Ri1eSqGZ0dnvf2TnuPdbleIwD82SiXdn/3sZ9rv9+lQG0hjMpmk14jgWXluiSKDVDxTlzTA06dPdX19nSaOEG8ymaSQjw4IiI+DSTiL1MOS9frtSftv3rzRs2fP9KMf/UhPnjzRcDgsHCfHBg4UczQa6eXLl/r66681mUyS5e/3+4WCBt4vHhmKz9miuVJ7wQgh9fmDZGq1WiESIOwbDAYaDAapewEPC8LY7XbJ+PE6oNvb2zR/fJYx4TEwVjzF8/PzdJj09fV1OguAZyV/6GkYjpLk2Tw95Lno3BuEtPMWLIxYnqvF8Hh/NRtmnDyZC4p1nrpwp8CNAWTFM5BKu7q6SsrOQduXl5dpO7qkdC1Ih+4BIjzv28WgNBqNdwpYfng33yMFx5xCNnwOgsZL5JVQ9/f36f8rlYqGw6Gur68LrWLSMdXEm1Fev36tb775Rq9evdLd3V3acCEdt+Sjd2zmYAyM+XA4pAPg0TdJ6bVXvHbdN60A5Mnf58eO0MvLS11cXKSt0jg03W43GcSyUFp6od1u68WLF8kj9EMtxuNxCj9ZrHxjAaGqC16n09Gnn36qzz//XC9evFC329Vut9PDw4P+8Y9/6Msvv0xeW7V6PBzbCxHuVbOQ1WpVDw8PqRNhNBrp5z//uVqtlq6urhKJ9Xq9gofw6tUr/e1vf9PXX3+txWKRvPpWq6UXL16k1ATernTssSUVQIjlLWF4spPJJM2fv9GU65J7xgOBCBE2Vxjp2N4lvTVm0+lUr1690mw2S8/NOnBIj7/t1kmSMyrYMp3v4vOinb+Zgjex5jlnT8lAJHnx040BoXzekeEtS5w7cH19XTgfwTsaVqtVgfg87eUHIxEOU/wjWuM+q9Uqpb6ur6/14sULXV9fp2q9p7DcsCyXS93f3xeMK06Ip9+azaaGw2FyJgiXSbV5MdaNuMsAcoXD8M033+if//yn7u/vtd/vU5vjxcWFfvzjH6cis7d5YTy/+uor/fWvf9VXX32l0WhU6Dd2Q+o1jrwOst/v1el0dH5+rk8//VSffvqphsOharW3b4v5+uuv9Ze//KVw3oo/Aw4UW4l53k6no6dPn+r58+caDAbJyBLxID/OVx8TpR9ivl6/PQ4QYSaPt9lsdHt7WwixWJj1el1o5mYREYaf/vSnev78eSIuPEssIxYUQmMXGTklFNcVFELmEB5CLk9N0LuIVcVbhBzq9XrqGyR94cKPl0To5TlXCBHF3u/3BS+VENsLQJA5Xs1gMEjkkp8PwXeJGK6urjQajdIrst2rxNi5V+seJgoPwROO+g48DAipBozoZnN8FxsFF+lYDccrY469yEZoTXjtbYWkS6RjcQ8j9OTJE11eXhYOVYKoVqtVoUjnUUOr1UoRGPKbv+MMb5d2pMFgcPJ+j60Dc+cH23vqge85ofruSYxUXigkyvJilBehcTzw2InMSAVKSiTlhSvSaP7W6DwS8Hu73knH18rTokb64ZNPPtGnn36qwWCgWu3tsY+t1tsXaN7c3KQeeOnYMXKqW6FWe3tO99XVVUoVeu82TgROUBmovKdH7YM2sEEUfk+vCrug5BV8fhynWsO4phe+vMru1/D75vfkvvyb38uvBQkgrH6tXOjyQp2P5dS9H5sjxszf+byH5Iz11P1O3d9bwbjXqTXICcP/7nNzqnPksWfyboj8uj4/j8nGY5/Nx+5FrG8bm48pl49T8puPi/vkPc7vQ97JwP25lz9rvsan5Ntx6v7cz4ut+bw5Yfoc53rmJH/q3vn853Po0VOey2eseZ/xqXXI7/ltc8R/f4SWsUcXu1TSDQQCgf8meJR0S9+R9j68xwgkfKjcy3e933e952PX+xi5olP3KmNeysh7/f+CMubpY67z++7zXe/1ofXou6BsrvhQCE83EAgEPjweZfr3J/sCgUAg8MEQpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRIRpBsIBAIlIkg3EAgESkSQbiAQCJSIIN1AIBAoEUG6gUAgUCKCdAOBQKBEBOkGAoFAiQjSDQQCgRJRf8/fK6WMIhAIBP6bIDzdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJeL/AeMwyKNJeJOSAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 37; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 39; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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+vGyUgPE5kTL/zGO73U75S9I47jFst9uU78MjZD55HtbQr+ueD+93JXbj4tGHe0BOIt764xGCp2e4v7c0uUy6sfHx+trkhS8q4ni/brjwBOlagHQhOdbfPVL3jrkf9+J10mTuhbuTAnkfDodCUctz0J6CY77wwPH43dN3Eve18GiH93Md1xsvpLpu5a2XGErvpODv/sOa5O2Srn+eXsnnwO+BrhHJvK/w97FRKumSMHel6Pf76YHJB6K8npfKPRnCfboLvOUkt4Asgi80Y8I79TDRPVlJKZyfTCYpZKbwh7dEiEvuE2+VsB4F9gq//x0v0O+JdyW9zWHjeXqqQTrlgjEsbulzw5YXTSAAFAXh5vmYE/JvXjkmH4ZQt9vtQmqCnHSuINzDQ/Lco+EZCHe9es6zE4ZCbBj1RqOR8pUonxcq3QPKizx4a6wBz8L1IBpkkw4awmEvAHmawb1aHA6u7c6AF7MgQn4z/xhVj5xIVbk8IPvMrTshyL57rNvtNkVn0qkYRdrOyR/HYjKZpCKiy1sewaG3kHKui3zOPVjmwmWIdJKnwtwxcyPPs5Evvri4SG2cueH5SZKuh5TSqeCFd9Fut7VYLAp5V1cUhAhl9qS+L5aH+nkYL6mg8CgVi8vrLiyQGIUYvNZut5sWDiWlqu+hMeEZhgEF8YV2b+1c3ssFEUGCeBg7+UBJhXlzYsrblXLFpfeUe/o48pwnIS5CTe6aa+Z5U+Yyjz748TDZCR7PEKNC4dTXyucaw+5E6BVzJ7Fc0bgW88B73QMnCkHm/HkwoDwrxIHX6oYQuUc2mDfkgufPi7Csm8+nk5TXO3IHwlMnGDPWinFiSOv1eiIo5sULejgio9Eo5ZhxJs7pkV8jJz03gG6g3NlCjphjL5LywzOyXuShqc/w4wVEntsj4E+N0kgXZcoXkSR8v99PRRJXbrdo7lH1+/1U5JKUBFp614tzrxchzPOy3m7i18Crm8/nyatBWai28ndXQq6HIckX2XuOscw+Rrx5iBcPgXujZBCAew4eRruX4A3tnoZwoSMPyBi9c8BDTz7roSHpITesTho8P88onaKQvFBH6sK9bfLs7hE74UDWkCtjeCwP6UYPeP4TY+Vr5IUaJxUvAFKPwJBS1POahLeP8czkbfNNPS6TyIynQ3gexgOpedTkMs3as64um+gRGy1YK34gfzoc+CG3z5qdSyvkEWROusyt662kxBWe7nHv3g0V60a/MvxCbQanwuXe02dloFTS9aq5E4sT53K5TG1O3ua12+0S4XU6HQ0GA11eXibv0vNK0slqerEJged3nj8CCBbkP5vNUk8wBRNCZ35c+L1ggvJ73tHTEiiqdzgQzmGpJaXQzAWT3B4Gye8hqWDR8f6oEqO8CN45r8/nhLHxQ/RAOoXXz+Xf83ExT/66e46e73QC95SSh8gon6eIPI3A9T2sd88Gbx2F9Ly5G2iQ5zh9nB+aOz6P7NdqtUIB0sN3npv583FSOHUCR9aYI+6LvDm585z8cA9/xnzHmn+eYhQOiW92Yvx+PZ8PL2Yiv36Pc+lESBRvl3TSfD6XpMKmnEqlktr86CByDxlPnWeZz+c/rUIaQr/bvW0Ed2uTKxK5UkmpZ5RrcB2E1XNnLJ4XbDzX4wUWz/14XtRBGM+i+v281QvvUzoVhTyfhgC5cnpYj2B7iMrfPIdLCJiHiJ6qyHNoKBJhVq/XS5siXOiZX99xh9edeyR4vZ6zc+/D00Lu2fr6ea7R83xemGw2m4W0knsvvrnBFa9arRZ6UP15kDkUk2dg7b0w4wSbe2juJfM37sUc9/v9Qn+2FyD5nMsTXjlOxmKxKBgpXvMIinll/vlNOsSf2wt5eLa+cxMSd2/ee3hzPcEbzvP70+k0ebx4jm5kXbd4HtaYeclTDegI43EPnqjZnR1+SHddXFxoOBwm54UaCw4LqZTZbFbQhdxgfmyU2jI2nU6Tt8XkQFpe+GDLsIfGHmbl+SomKs8Du1cmvf+AFvfIpJNwodQoCUTvHRc8D3Byz/PS7tXkVX1vC2PcnlqRTkLr13MvyIm72+1qMBhoMBgkMmB+mRueOT9TQHrrPbgXjvdPSkM6eeB5t4GTrXsv3nlAyEg+GMLkGTAQPA9r4DlX7t1oNAp9m0QlECDpj8VikQx8no/1HDSylacVfOs4Y+x2u2nn4mAwSHlD5J455jrMM+kpFJ6Qn+vzPD6fPJ+TlXusODL5+Qq8H0Pt0RDz7vPvRI9Mu4fLpiB+fMvwua6Fc0TmROnefJ5icCPnzoXni50n3ED3+/203hQy0Wmi48lk8tPydAFEigVGmTwn6rlBFBJlQACxVmwI8Ov7xgNffM+7ocDeGO/wsMY9PN/QgRfF4uYChmB4fhDS4X7uuUOQGCDfzlqpVFKezXOeLtjuMTJv7KYbDAbJO/T97BAM5JB7hswNnra3vkE8kJKHb54XZLzc058pz725IfYw0cNFvHU8Hrxir1ZDbm4k2SrOnHlO1hU6b//yrgVkDBn08TEH/X4/7Vhk3pw4fa28Na1arabncyPLOvMb2XFv0zc3oC9s6un1eoUWPo/wPAeMt+tjcjkG7oigC2xw8kKhGwOe2fPxzI1HJ7zP70FUyfN7Nwsc4Jt5PGpCDzA4ni6cz+ep35l+6J8k6e73e00mk3RugLeXIHjsg/YCjRdZaIlhUiuVSuonZZFYkPz0KxYAwfTWI4jac7Dcx6vqhGcIA3lR9/ak4nZirDWLz7OiAHglEGOlUknhr1t+lMyf08NpruXFPQoJ7j1Lp84FSQUD4T8YKMZOrhHPZr/fFzxIukhIdbgX7pEHqRvW3XPjrHse4rNe7un6fHraxncWOakwB61WS6vVqpCG8tRBnidlfIfDIbX/4S2y8cVb2pBNH5//YNTx5vIiHs/p+Vv3AjG6rI/n1Vn3Xq+XjC3pK8boss4aeNTB/53AkBOMEdehsJrvYnRSRY/IyyIzFLBIr3gaxvPFLu/cezKZ6M2bN+kEPi9q+pxxLUnpYCG2NvtuWM7mKAulphfG43HyajynhEIgTH6IDGCh3cvw0JjQLN/Hn3syXgzzvCDbk907dkHjXk5YnuAnRKYIh0IwXpQGa41AejjEmBB0FAyF8YoxoRH3gSj4wVhBRF4V9+4JJxnvNPCcMT3KfiIXY5eUPGufI/fsWY+8w8SRGxYMJ/B0FK2GeNOQGV6Qv5/58Otwf6+Qk3ZxefTPc33mj1ZASep0OhoOh7q4uEipBWTBr+P1BQq7+Zq4w+FElHvZLuPIoqcWaKdE1yS904IG8TI290gxdB4RIoNe9PVCWJ4Sgnjfp3d5SiI3Lt4+SOTAsaOQ7vF4atvk2bwlDoMJ2c5mM02n09RZNB6PC8b6U6P0sxf6/X6h+OKtLYSzKF+e6/GwlQUmtMuLZ56Tcs+T3CBeoAsIn8fqQbjcx/+NUDrp4nVxPRSDDgisK72kCHOem5NUCPUhGDo7xuNxOnsh332UzyeCh1fm+TAIxVM5/lnGzRGRo9EohXQetuZhqFfLPTeHtysVe3/5LK1inDjmvce9Xq9QSGVOeA+eOM/rnjzzyT3y4iaevBdkub7LEPMrnRSbXtX7+/u0C817fUllsUOLUJnxet4bckEm3JtF/pn/SqWibrebjA5FOZcbyB9S5D6crUBOHEeFYys9RYVMMFfL5fKsV+wFOXTY015EKf1+X7VaLa271zlYa6+JsJaQ72Kx0P39vR4eHjSdTpMR8E1GyDu6ttvtCqRLDpozm39yni5Cy4N783Wz2dRqtSrshsIye87TyYCwxPfBS6ctvdIpRERJvCXFCceb07m2KxoKCNwrdKFz4XNi2W636ZhFmsgJ7di9RZWVUJBx5Dm13W6XTkh6eHhI/ZW0suHZeUoEcoHkvTcaQ4Hx8fyXRw0cfIKAenXcc8VScTsqwp4XQTzFwUlw/M2r6543Zt187vNCGoAcSNMsl8tC7t6JzeUhj2Y8UvF1J3xnLsgVc/gPcwLZeqohzyv6UZW+8cGjQMbsJM+ZHKTbms2mLi4ukoz6s3ir2GKxSKfI3d/fJ8KiBXM4HOrm5qZgUCFSl38vILu8Se9uNvAcr5Mznq7rpY/fHRDk6nA4aDqdJi8V79e7HDwdg3Ej0vM8MemL7Xb702sZA5AlVto9IPeEpFOPYLvdLrSneBMzEzmbzdKC5nmlvCeVcfiYWFB+vGCBwJHqYFyeZ85TGX5dzlq9u7vT3d1d4fxgioAosR8OzfMhsDzrdDpNHieEhULjmXj45l7EdrtN4aJX3PMtq57WwbjhMRBVVCoV9Xo9XV5eJg+L53XD6hEJxogfD/8gJu85xgjjvXm04d66p45oNdvv92luGBfj8RSUh7++WcELYJJSIQ+PkuIgp9iRmvDijqTk4eU9oqRiiF78tDmXX19H5pCDhjBayNBwOCzoFGNkTTGefBsEx0MSml9cXKRNHX6GM/Ls8uRGAWONnLrBdR1C95lH10F0Hq/d11s6OWy0d+UbSDx9QQrDtxp7Kokfxog8sNbOD58KpZIuv8+lAKTi1tDj8Viwsp5/4zeHwkhKOR0+572ufn0vXHiIS2Eqz8FWKpWCQqD0fA4Lyv3znNlisUjHH04mk5SHpfI+mUw0mUwSeXml3Y+x83NuOW4PkqlWqymX5wUtz7lRcJNUKLTlbUU8M2GoH2rD2jSbzXfacShUOOk4UXonA2uC8HvnCnPg3Qz0ckNUeFJuUHl+8ruQAnONkkG4rCcGx3c8cQ+vDxyPx6TMg8EgGXqe1XOU+dfIeBThpM65GhCVF8+8gHc4HNLzsy44I6QZhsNhyo96gYoiGnLmec3FYpEMNve4vLwsGF7k2L1DIgX3xj0F4LsVkafVaqV2u13oiHGd9IIeP7wP44ITkMsXXSPD4VDD4bDwNTwYCuehnIvKRmmk6zkxPEXp3ZCecAevwvdpY40oLLEgTC65TUJg9264NoSDJwVciZ2oUVAvWkinwh4VXG97ci/MvQQPHbmft8BAYJAmvZB51RUP0Qs+3sfKXOfeiefEfYw8vxshwmVIHkXyYh1KtdlskkLT2eB5TUkpd+jPjXd7OBwKxRvW3PPcGDXmC+8ahYIwvLeT1yFP93IhGydoT+VgaCAvgJfKHDC/5Em9e4DjHV3WfM7dA+Me3l3D1nI6RVwPINxqtZrWaTweazAYFAgHY5CfHOZ5U+6fOxbIqUdbkK7rbN494g4SEaiTG8aQLxvwrd152yKRChEpMoSDxs4zvrliOBwWoi8KnjnP5J1TZeKTk67navKNBdKpaov1JIRHcNzD8bDQvSXCA/I5LAhnF/B3Qicax/m89zo63OM9l5bwQpGnRjzcxWJ7vsktOn9zgsTT9X5ED6uYJ2+RcTL0YlieF/NqMMU1z7+RxhiNRnr9+nVqzcHQeRGU9YWk2QbqJEHONVdynzMUHJkgTeJdBx5q4snlZOXrx/whL3m/K0Yr93ScRLkPhhCP+ng8JgIi103+lzw7PdJEEZ6+8XSWnxrmuWPfpelr6zlLN9p4rnxHHwYFXcHQEqlwRoRHl+fkyItoLvN594nrib/m+XIcHuowXBPj65ENkU/eBQOZMxekyfgqLb4DD4cMXvGoRSq2nbJT0/nqU6I0T7fRaKQdO5AFFnu5XKper6vX66WwxZXWiwh4IBCxdLK0rrh8zsM6Fonwg+t6n60XIdziAw9NpHd7Qd37hBAxIhgHr+Z6IQtvy0md0DMvSDjhOtl4SItykTLxnKb3PjKPnpeERPFgKd4QUnMdqvK+PRrl83SEF9QgTOndrxtH0b0IBbxAynuZA5QaefDqv6+T388LlXyGZ4GwuQZFJ9Yj/+YTSLfRaBSq5H5eAHOcN/6fI0Q3VsC7Z5gH77zxfCrRhsuD9zqzTjwjnvW5rgTSHnlk5L/zOSa9kOfl6fjx4irRhOupG9w80uQzbCriK7UuLy/TF7fiZHga0LdAU58gH+73+9QojXRbrZaePHmii4uLRCCQAArC1zKjoN7ywqliFHWcUD009jYb7zPEgjLRFD88xHBScNJ15XSL7gKNgObvg1xQKgpHWHzGg1AisO4xeLGA1AvFOIxMLpikaSBblJPf3jKF0jNOVy4iCXLeEA9zwI4894Y85+7z4gTsf8sLWXmBzBU09/Y8Z+zE6ddA2V2hj8djIiIUDgV1j5gClofOpBKm02nypDAQbhxzY53PuxfyKNb1er2UZ+eZPPT3CA5Z8u4TqfgtKW7AXN4oUvuaMz6X7zya8pqF38f1xA2dr6evuef7ST0xd65HPL+PiXH4eRecOAih835y57SJ8cw4JRgV7/P91CiNdNvttp49e6ZOp1OwOlQQpbdN5v7dR1Jxl06/3y984yy5HhdISYWQjgX2g3VckfA4uIZ0OkXKw2MXOCfTvFWF10mZ4BW6p+edBt5+ds6jprB0cXGRwldyuvl2U99C6uQkKXVyuCflfbp8RnrbonR5eanBYJAIgL5gCI1WP1dCnj3PJZOz9WM9MQS+RrkBc0/IX/eIgPd55d4/4wf45OkgUllOmKzdYx0puRHyIg15dU7A4xwGDlxBpr19DweAdabXF6fAC1REGv5tzvlpWsxH7oEyd566QCYwCGxM8WKj59bdKHp6KNcTJ2Lg80Z7pPcyM5+kEzwy87QAz+dnavf7/cLuS/8shEvkwRx6IRluKAul5XTr9bouLy/TuQukCahMSlK3200WCcFCIbxQ5LuVCCPI+yBEnp7IDxghJ/fYHm73kNwrzfOGbrXdG/YCjoePbqFZbN/J5YU8J4V+v18oGPlXlaOcfrao9O6XStbrp75WPGsnXffuD4eDrq+vdXt7q/v7e41Go5RPRtE8BYKS++dzT5MKuYf8Hi572Jynerife/8+ZznBePjJvHuxyCMCP7uB8eXePvdHmZ1weA66J4bDoW5vb3V9fV3ov4ao3MOmV5sUF14oxo/19Nykh8WNRiMV7a6vrwu93hgP5g2j7x4vr3ungRcbXR88febz4lEY3rPLiOsoukYvs5M/1/ZiX77DFH3AaNEmhi4hh14P8UN5fBchc0EHDnP+k8rpuoKxwBQVEAh2I3FGg+e0PDzNK+dYSG+T8dDVPQhybxA/1VgnXZQWReKULgqBecHBhTHvVmBxOTPY81AYFrwMhJW5cc8GgYMwvXWGZ3HvCzJx78NzZk4sHhLiHTJGiAOid2JCMSim+fzlHhWki3eap12cbL1f1r3PD4H59+o245He/XJLD28hXW815J7uHDAml5Fq9XRgzcXFRaH/2ftN864Rbw3LvXi8aj9vw4tIyBFeLl41UcS5bcbk2Xu9Xvp3u91OcuTGIQ/pvT7h6SCXFd9Nh1wyXxCoH3Tu9Q5PkyHDnlvn/T5O5sE/6+mfyWSSjpxEz72e5M9VBtmC0s7T9aS9ewxY20qlkrbq+Vc8nyNTgHCwSAiMpCR8hG0oAULA531bqJM1nkev10v9fx4u+jGP3uKE0kunr6d2hcgPQSeshfg9TEcxyN26wULAXKC9QIMSAw/Xc6MHwXkujDnMQ38Ih/mjhceb5vPowtudiEgIFyFB8pRe2HMjxGfytcqr757X9Ny4p3wgIubXc/7Mp4/hcDikwtNjMpanRrg+Y+e5kVVX+jxK8kjJC5ReBEKuKCDRscA8I9fIIWOBpNgJ6d91x3XzYhnzDEEi+56Sy/WZ5+PZz3UgeDRCioguGSdd9CKXD38299rn83naREQR2DnC8+WM2Y3xp0bpX8Ge54NYEBSYra5e3KD44t6g59hYyEqlkoo93krklVkWzL2ec/kvckZ4Lu5NkLfz4/NypSEf5gUcJ2qExwkzT294bhJjRV+jd1nwLCgqW4QhSPesuPZ+v087kkg78Hf3mN1zYWxS8Us9PXzF+8Bg5a1fCLp76W5o8qZ795S8Ou9eu2+59fDXPS3vsPDKuhcr8VjdwCNzHrm4zLgT4M9GVw6eva8Pa42X7R69e/b+1fYYGjc2nvpCpnhuL4hJp7yrn+eBPBF6o2u85jly7udeLdclWvPWT9/A4imOPEWEwZJU8Hi5BgaQ5/CIiXVgrqXT1ns2EnkNyD/jcuXbgHPH7lOgdNJ14mPSPddGz6Enxfm3E4ArTZ638jDXi0p4Jm7lcgH1HHR+RgEC7ukBzyUhJK4seLuEnnzmcDgkq+4nLaGkjFcq7keHgD1fm1d6OS/gcDj1eFK0yL0w7ushvXsYXmnnc9JpV6ArtbceeVcG3pcbEy/2uDeYk5q/7gbW1xrvj0gK+LU8j5uPw6vseHMuDyite47g3DgheP+GXH8GxuthsROWb+TIx80PZOVFR7+WFw29nkB6AY92s9mo1Wql3YSeE10ulwWHAuLt9XoFIoP4vFbh+VOPNvIoDWeAXDfyCWfwmXyuc5lnrjkvhLQCtSP3rr0egNFx7/lTo9TzdJfLZdq7Lr3bo4lgs93PvS8snHspXpSRzvfQOkFJ7279JWzzMMhzPd4FkBeGnKggA3aQPTw8pCPjKG54F4UrMcYGEnavBOJEUJg3V9pzYTXhItV5iid8xr1G/s018t1JKDjetisb7/H8nKdP+Kzn8KRTH7Xnk72I6NGQkyNRD3DZ8TCRcXstgfnwNJI34rOWzLsrO2uTe2l5VMI1iAQ8RcLn8lwkpOFydG7TDU5D3i2DUXFvk8gATxUDKJ3CesZPrtM3kHjqA0fAHYK8xY/nz9snPbXgZ6hA2rkhdHlwvc6Ld+ijpwgxFhTjMVru5Pn13UlCF8tAqWcvuCD4RDg8rPCzO/OcY14IOodcMTykoLLpe8n5DAIOcXjeyDskPE/Fgk8mE71+/VqvX7/WZDLRfr9PKQUPE3kmL5YsFouCZ8IBIXl7jxO/KwfXzAsgrVYrFQJ9swnejJ83wVxCShAEBtC9hDx9QVThbWn+uiu7dPI63BC64nvuNA+N3QDkVW6uzWeYg7xAx/sYjz9DnjP358YwSqedcHkrHvBIjGt79wFkh2Hygl1u2HJS8wiLNc3bviBK6W1LZh5meyqD07uIGjwSYReZyz7GwQuszPm5uZZU2F3p3nJuuB7TZ/9h/O6s+bdVe5rDr8FzYKi9llIGSiNdqbhhwAXKX0MIsNhukfF6nbgcThjnQkgPKfLDO/AMpbeK5N8v5luR3VqTu5OU0gQcm3d/f58OWN7v3x7hiDB4KOhGxi1urVZLgoOy8Ax5esEVAMKiaOielW8EwHPxHW/SqUeZe+QHkKBgXgzi+Zl7b6THaLLGeKve+UEe+Xg8psKcF9uk4rcP5Adh89s7Do7HU5GM4o8ffs5zuyx5qJu3RznBIKueinJjg8xxbeaG+SAaYC0oIuW1AQjBDb0bMCdd3pufj+Bz5w4Pc8tn/BzjSuV0lonXE+j8cW+V+5BaGgwGBafGdVtSYXeYk26ur7k+v49LWEffKZmvb566Qme8Q6oslHrgzbk+1zwn6eTRarVSfpJFOecJ+TU833WOdN26n7OEKPbl5aVubm50fX1dCGnzTgFe2+/3hUZswhtJaZ87P3wmz0czrnMeZ54H5fW8AODhn9/H54b3M69esGCuvIDhoR9KjrAzDrxyX2uMlXtMkJV7h4zP0ywuEzkpOllTeOOZIAdkwjeicAKbP5sTooe3FJkkFUgar6jRaKTCkxOGRy/ujfn1PRfra8izuhF2ecKg5KkFL775ebEuE7l3ixHPU0w4EdVqVdPptPBtDzgC7rQwJp9nSaltC7L3yNC943OpQE8J5brt6+Ebj5x0mT9P0TwWGeOdPxYtfwqURrok8FkUqVj19oKDh3aegHeyfF/OL28hyifcQxkPZ1CGdrutwWCQDtCA7BEw93r833jnPjYvivm9PI2B5wcBQX6unLkAupUntPJ7cj0PuxFynxPIZTabJZKBmPwrTc4ZiJxM3ZPFm8OL83xtnibysJn58Bx73g6GrOR5Z7xUyNllId895zLFZ323Il63E6fPgf+bOdhut6nt8Xg8FgwKBdecAM5Fd3iDnPTGOpDPpb7hcpSTubez5T3lvN91x2XUOyk85UPaJe9q8XswL5PJ5J3irZPpuSKmE6+vmx8VwPq53rlOnOMPnx9kE/mkv9ojsk+N0g+86ff7Wi6X6cFbrVahUp6Tiltx6fQ11VhPLBWLSVsLC+5KI50qsLR+0SKEMrjwOTFKpwKdkzrwUD0P7VjgPOdHrqzb7SYPw3duuWDU6/WCYULgSHPwdz6Xz60Xmdzw4OniUTEXhIjj8Th1OLhS0B3BnHoenC3bHLFHzrJarRaI3eeUuWdjAmTLHPrhN57H5Fp41cyttwrm68dnfFss8gCx4NXlhTYP792IUgwbjUY6Ho+az+dpzPV6PW1V9dSAy7oXd5fLZWE3lR/niQy5zDIWrttoNJIRpojm3wrtO7B2u12h+EkXgJOr53dZj7zDBh30lj+e3cnVD91hftyYs4aQIXKB7GO43NN9jDN8rHCD9497cZkiYxkobRtwu93WZ599lk7b8p02hE8UdaTTLiBv+Mb78MZvr0C78FB0IV8onU6np2d2MBikEIn3bLdvvzPpxYsXyePjiD4Wjv3eHE4CqRNSjUaj9K0CkD7j8zMIPOyr1WqFbZQIKMLM9cn75t0XkIH/DY/Zuya8FY80Qr/f12g00na7Ta1DeaHRQ1tyoygUpDIYDHRxcaGrq6u0LRUjAol7p4jno30e8jFCJB7C54TbaDQKHrl7VVKxtY0vbXSP2EnAU0CeI5RORtF3ZUlvnQEM1HQ6Tffg6EHmyHc2UqPY7/eFzTLMF/rBmrrn77u/IBOiCowxcsTzea+59wd7Wx99q6wNW2UHg0HBg3ZvF2eDvPDd3Z1evHih6XSa5s1z6/nuOcZK/3Kz2UxfZe+pPC/AezcEsu/5Zq+N+BECfk4Duoh+O199SpR64M3nn3+eDrfwgtZsNtPDw0MqPiFwHrqxQ43X8Lw4qpHtkAifh/7sSnHPlIVot9sp4U9bG/m00WikJ0+e6Pnz53r27Jmurq7U7XZ1dXWlq6srXVxcpPCH3sAXL17o+++/13w+T6RNIQyCIIxFabHsubDgVTJXeDcIlnuxHsZDVigM4/XzXclL4iGzDpy1wFqg7E7eGDPCV+7DeQ23t7e6ublJ6+FFOycTyJ3+UOkUiTBOD0XzkF46FbF4HW/HvXDplMOGCImG3PMjZbFardI2dM8JUmPw9A/zR/jP+5gPCO/29lZPnz5NxogojDnEoZjP5xqNRoUIDTnmmfBk6Trx3ts8PcAc4dVhXPBK8er3+31h/Q+Ht191fjgcNBgMdHNz805XCmvBAeqvXr3SixcvdHd3lxwPDKoXQjE6kgo74jCakDMGjq4gNjt4t5BHXMyfb5F32STS9l2SbIPmfO0yUBrpNptNPXnyJIWbXnEkJONAaCa2Xq9rPp8XPBPaWwhZe72enjx5op///Od6+vRpUgy+pnk0GqXCjCuz5xkhMG8mJ5/28PCQSBkreXl5qeFwmM7uZNx3d3f65ptv9O2332qxWKharWo2m6nRaOjZs2dJwfwbFfCqSDG4d+cFHfKE5F+pNBMOItSuaE6El5eXSdCAh7nb7dudPK9evUreysPDQypkssOMU8c89UL4SuERT5evTnFPimLZdDpNa8Tc+/Zb7wZw0vbip6eDnDQ9BcX/8cr7/b6ePHmi6+vr9M0OECDvnc/nBY/Ox8RYMQB4lb4WrVYrHabdaDR0e3urzz//XE+ePEnfhu0FZW+Bw9iTn/Z2KOYIOeKAHeltzhjv0A0QBpO59A4Wn/PpdKqXL1/qm2++0cPDgw6Hg7rdrrbbrYbDob744ou0yWe32xW6R16/fq2XL1/qb3/7m7777juNx+NCZ4KnIrwIy6lfXhDjfNunT5/q6uoqOWl3d3f6+9//nvLm3vmD1050xhohE81mM11zOBwmDvA89E8yvdBsNnV9fZ0U3AtBtVotfeWNV2a9Fca3J3LdZrOpy8tLffbZZ/r1r3+t58+fq9FopF5DtgP6Vk6fYBcG9xK4PiSHp1yr1QoHJmMIEELIhB1m1Wo15eTc8nr/L8rg4RqExhzU6/VEHJLe8eQgP/KTHPDDt7uijD63PGOlUlG/39fNzY1Go5EGg0FSDk8FYIwwEF7oYFs0hDscDtMcuQfmhTuOO8SIQIrk3/B2GDfK6WdUeNHOjSlzD5GiYHSl4I33+/3CjkIiCgjPi6WsnR9Jutvt0lpDAswTMjEYDPTkyRPd3NykE608n+s6cnFxoZubG93f3xdO4oIc/DO5J+tGETnxYiXy4ek6DD6hOYaDNjxJKZ9cqVSSlwrp73ZvvyiyVqsl2Sdn76kd17O8a8MjV/LPnU4nGSqIv9/vp9qDnzLo0Q2G1nvu6Ytmbjmw3PPfpFF8LT4lKh/oUftoDWwIsf/f/+1hYT6mvM3Diy/nihp5ddqT9HxOKn4dTz4mf9+5YoV7Kt7u5YTIZ89VbfO5+dDc+TN5n3M+Fz4nHp5/CJCOh7L5c+QtN/477w7J38v7vHUpXyefi/w+ubz4Nd93fX8Pc+/je998510Kfs38Pnnaw41h3vP7vjVgHc6t87m1zjsBPnSP/HWXKffg/TnoTsiNxblugXx+8vt6US2fR5cffybu5ffI19bnL7+v68E5YyfpHYfkI+DRi5VGuoFAIPAfhEdJt9Qdaf8MPuT15fhnrNNjntGnvte/+tl/BZ/6Pu+bmzJCsP8qKGOe/ivI1P+PLj2mn5/iXj8GhKcbCAQCHx+PMn25X/geCAQC/+EI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQImof+D1SimjCAQCgf8QhKcbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRPw/r0ioF84a7+EAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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Wqys90x8bqSndwWCgFy9eaHNz05I0w+FwZdyVxYGFz2QyS8mvu7s7U0B8+Sx1Mjv5+4JgfjLIzueyqLk/Yl+3t7cW59zc3FypdFkUWHeIA+WMyzqbzSzjulgsbNHNZjPd3Nyo0+mo3W6bAp9Op2o2m7q+vrYFJb0hgX6/r2azaZuyWCyaCoXUUZk7Ozs6ODgwJcA9c+1ut6urqysj22TlCMqCxQ5B+jHgC8UF4ebzeYtdJw2Wz0574mWdEG7xc0XowI879+ddd29wPAlAnsPhULe3t8rn85Kkra0tra+vW/y9UCioWq1+J/nrva3JZKLRaKThcKhOp2OJSp9nGA6Hur6+1sbGhgaDgYWzCM2Q+FtfX1e5XFaxWFQmkzGFR1KtUCgYOTLnjJd/zuS8TCYTu7dut2uejh/fZBwWgZLP5zUYDP7gfcc+5lreQDOH/r/z+dwEDqSfFFsYe+ZRkvr9vsbjsX71q1/9Qff8Q5Aq6Z6cnGhnZ8csPcqBL78hkpMqaWmCyaajPLDoEFiyLOmHxHH5fYiA++90OqrVakvKkPv1WVjU6Gg0smA98JsZV1iSJWZINrbbbdXrdVOXuIok26bTqSksNkmhUDBFdHt7a/Eu3DTpPnwBGTAmPo7qK0f4DBKYg8FAw+HQkmg+hseY+XngK6ke+DnGkU1GfJ/1wFqBILlHT9aoVUIEEEOpVLK15UMGq9ZB8n59QoZ7Z35arZZ5FYVCwQxFsVjUo0ePtL+/b2Ev1h0qdDQaWZgEYyXJDCvuOqRAeG04HFriDzd7Y2ND3W7XiJ89VS6XtbGxob29PRtbfod1J2kl6ZJrGAwGS/fmvRQfK2UtQLS+VO2H7jfA3/v/cm0/TsxT8nO8iOF7rAmImT3jBWBaSC28MBqNdHV1ZYTq3cFyuWxqiMWOamVDAb5PJhV3H0vuww7ebUUNr7q/h8AEjkYjXV9f6/z8XMViUTs7O9rY2LCEV7FY1O7urk3q1taWKQ/itWxgX1/oE0V+saB2SRgyVhCjj5l1Oh1ls1kNh0NtbGxYXJYEHZuOcWPsIVM2kU9cSlpSnZAW4YzBYKB+v29xZ68uvdL1asWrVZ7V12Ama4+Hw6GNH4aI+/Kfw7+pipnNZhY/5pm5Dq5+smSQ+/EK2a875gniwkWlqqRcLqtcLlusHKPhVRiEPRwOLd7MGoDUmHfGt9PpmOEggdbpdGw8vEHCqLA+9vb2zJNifCHYpHJkzr3hymQy2t7e1uHhoWazmZXISTKPjvzM+fm5rq+vbU29yz1/6OfJMJc3Er7WmrnxyU7gDTzGeW1tzRKM8AXVVORH/H74kwkvTCYTKy0pFArmjlQqFYtDEf/zD54sgMbl39rasnpKEm0saO9egKQlle4J4iHixR3p9/u6vLzUixcvzNUrl8uS3kzyzs6OkfPa2pq2t7fN1WeBehfPB/qT6pDrrlJ5LDiIRpJVG1B+x6ZlERHvrtVqtrkJh2Ck2LCEQiA64uIk/NrttpEt4wxZcV9sbP6N8vGqxCfBIGWMD6RGaZknSa+Kk+RNyAPjg6uLi0+CC/Xk64AxdsRBvfHzhOzHDoL3DQr5fF7D4XAp4ch4MJY8kx8Dxm0wGKjZbJpqJLbNvePlSFoyED4ZyO+xZ7xB92TF77M2vcplLRwdHalYLGo+nyufz1ulEIneXq+ni4sLvXjxQpeXlzYm30fIJPdgct8R0pHeVIkUCgXzfvAO/Rr24SZvxBhjxBIeiPSGkwg1/NBStz8EqTZHkFzKZDJGvGT4y+WyueQkM3ys0AfMi8Wi9vf3dXR0pK2tLc3nc1uog8FgSWGssobeNU26Jx5sFupd8/m8xTf39/ft37u7u6pUKvZZlKxA2NPp1BQcpMtm9S62jyn6GKcklUolbW1t2b8hRq8EKUEjoUXWnRI63HcUMcRGuII5IQTR7/fV7XbVbrfV7XaN1HxJFveOeuBLuu+Q8nFXH4pIhnAwGjyDT05BtihvVIxPVpEII+PNPeLms2l9FYEkC0ehAL0r7p+B+/UE5ePS+Xxe3W7XStB8kpikKZvb1+FSndLr9XR9fa1Op2PzVywWjXghaOaRyghKw3yugedDgPiEIcaFUrDk2pxOp8rn83r06JF2d3dtjLgGxuPq6konJydGuu1225pT3gZPuMl96T1B1hexcoQO3h3Gza9H1iJhHx/6oXGHecZwUN+fFlKt0yXrCBiQUqlkv4Pr1mg0TBH6ukzc94ODAx0fH2t7e9uSSpTUAF8NkVwIqyysh99YnU7HFjoTRykZNX5sXJ+E8S4f1/TKUtISEXqrzWKDBCqVinZ2dixuS30rVQX9ft9cf99cAYEUCgVtb29re3vbNg5qZzwemzrg2X0sLdl+6ZWsJ81VyRnvseDRSPcbj+skK1L44lrEvzE+KFNIzhtXQlWeeH3MH3jiT7aZJn/Oz7yK9OPhn4Vn87FcXxZHGSBVL7PZzJoxyB2Mx2Nls1n1ej0LYZRKJVUqFRUKBe3s7JiXh1rzpYzMO4aIfeQbjbynkSRCKk94JsiQHAJln2dnZ7q4uND19bXVyr+rZMx7mMnfS+5XDM/Ozo52d3e1vr6uTqej+Xxu4TfmHI6gc3Vvb0/VatVUMusQj4FwGQo9LaRKutSy+kH38TDipFdXV7ahpOVJooaxWq1qd3dX5XLZFjQJDv7mIcJNZjQfAqqITHE+n7f7RWn45I8vh4JYUeoQHOqS0p2trS1tb29bOZckK+mh642ytd3dXRUKBU2nU62vr5tB8LE4rL4kG6tarabDw0NL8HANn6yACNiAXv34+B9zgTFIZpiTJMRnoEr4WxQJYQBiu34D+TknpESDBrXYNNpQpUAoBlIrFApWZA+JJ6sf+F1CEX5deIXrCRRi96GuJ0+e6OnTpzo8PDQDyTW8EYFEiZHSVk6tqE++kRgkaYeXWK1WLbcwHA4tkTsajVQoFJZKwny5FDWurCHGn5g188C/fV4FL4dSt6urK9XrdauhpYb6Xd7jQ3vP79dksrlcLlvd89ramlqt1nfyEPxNLpfTzs6Onj17poODA2uYwWtD4fJFG3FaSL0NGFXCQ7LRqf1DCXC2gXclQVKJ8PnJttq3qdnvEzD3Cx9lWSwWrX4QN4bN49VCMmtNHMwrMywwpUds+EqlolqtZi4+1QSUUWFkkpuFTQJpVioVHR8f64MPPtDTp0+1t7dnccZkfBQXDPeLcxR8TJFn8S4qbhuurs8YY4QwDmwmXGeuSQzcq7RcLmfKkNIo6qWJT0+nU7t2Pp83xQKRlkqlJaVDzDN5H/yurzhI5hHIeFM7TUKKUNf777+vTz75RE+ePDHyRI2TcIXAIV1IFFf/gw8+0NXVlYXKKI30XzyrH3cqKRi/YrGoWq1mZ1VQ6wpxQjzZbNY8B4ja5w4I5/n9NRgMdHNzo4uLC52dnVmTE4nbH9IY8RAw3t7A+XXla3L95+B1FYtFM4KHh4fKZrNLCUha031+4l2e74+JVGO6xLm8C10sFpcSaZAFblSyTo+SnaurK5XL5SV3p9Vq2SEsvrU2CU/WkOPbkml+wTWbTVuglUrFssQsBv4GEmLhsmlZTCQnCE/QrAAR8Ly3t7dL7nGyVpHSObppCFNsbm7q8PBQz58/19OnT015+ZpbEgqEIvgiSePDExTmE+cmK7y2tmbq36sOn7CiZnk4HC6V7xSLxaWwyXz+pi0Y1Y0y5lyB3d1dHRwcqFKpWNyWcwcKhcJSW2g+n9f29raq1arlDbg+c8D8401sbW3ZPHi15ZNndLl5AucMhMePH1snoi/Rw/hiiFDV/tS63d1dPX36VPV63c7yoCzKNwp4EvQ16t4A1mo1uxcOOSLEQKs1YS+f3ScO6pVu0uMZjUYWWoBwUbjJGO3vs+/Ys+Px2Co42u22bm9v7d7a7baurq5svhFwvrIJDimXy1YRBeF6hUtu40+KdBlUFE+/37cF6WtCqTMlqcBiS3ZMDQYDXV1dWeJmb29PktRsNnV7e2tWPFm9IC3X8PlawmTpzCrgomElad/1brJ351AldGNJbxIePqnkT6xis0OAPinS7/fNfWYMb29v1ev1LBmHQoXsUWD7+/tGiMyDTxJBwn6T+aYUEj6LxcKevdfrmaFZLBbWqcTc8fk+POMTJNJ9SRrhlc3NTfNSvLLLZrMqFouqVqt69OiRDg4OzDOYzWYWd4QIuT5/T+iAMAg11Chfn3RlPaJSpfuaVt+lxhxAdiRvfbszhtaHYXwCd1Xsslwua29vTwcHB2bkJS2FcKjPZk7Ih5CMLpVKOjw81PHxsZ3/wXMRB/VtwaxfCJs9h2FMNs1AXpSvccrcu9xzr5xZN6wJxiHpteIZNptNi39LUqPR0MuXL3V1daVer7dk0JLjj1cIeRPi7HQ6JgCT52781EhV6Uoyie/LoLD88/lcxWJxqQbUT4SvvyX2h/tNpxbKl/iZn0yvsiBzVA+JhuSgs0j43WRoZHt721xFn4Vng2Qy94fuYL0peEcRe+XDNSGDxWJhp2BhoZvNpprNpp11wKbALURNQaiEKVACXnkxDr5NV7oPrXDU5MXFhRXmU5DPZxGDZmP4eDbxMxJ8vk0VsDkgM1RIMsHnE2mQLipckrVQM5cQRTJByHpkjfjWa+bRdzdClDwvlTU8w/n5uS4uLvT8+XPt7OxYiMJXZ/iSOJQz94MCY81jRCEmX7tM4hTyqNVqtv62t7e1t7enw8ND1Wq1JcWdyWTM+GA47u7urNHACwVI3gsewjm+gsZXCCFiVjUsQLY+/MW+Y+/5tcDexjviPBDWSL1etxZ3X/YGWPOUgtGyfn19rdvbWzMWnmz/pJQugMx8LzqxRzY79a9Yc1xA/h4S8kptsVioUql8J4mzCn7zsADn8/tyMyZ7Vfaae+W/JNU4sSnp+nG/GIarqyu9evVKp6enarfbluTxBeUsXEnmIlE+x3kLZIvpOINgCR144iccUq/XtbGxYUcP+sYBr2h8YTmhnNvbW52enurly5e6vr62HnjmzStJYouEkajv9aVmKHMyyIPBYIlUfJhoMpnYvfn6VAwKqlWSGTTWDeTiQwCSjPwx3rieqCrKyrwhXdUth/EdDAa6vr7Wy5cv7SAYf0KWJ3AfE8cwQAq47HSbJVUxBMTPaNU+OjrS8fGxxcZ9LNcrVeaVbjcOMqLJg7H1ZXmedEnKsubxrLznyL36cjxveBiXbDZrZMn4P6SU2UeUEBJaoFICI8Z9wjG9Xk9XV1fa2Niwc52psOj3+1a+l0zCpYHUSdfXXbJRmGxJVufHpEDILBpIlw3nVZcvyPcF4cmkmf89ryQeyqYCCBs3kg3lazn953D84bfffqsvvvhCX331lXXu4PIfHx/ryZMn1rrJ/XnjAGFeX1/r1atXuri4sGMxOXIPUmCToWYajcaSq8XBPRC1P6fC13aiFjhJ7Pr6WqPRyCodKOOp1WoWNyTR5A/jIYHojRmhAFowUb+sB197yYb0dZgYPtS6T1jyu5A/bdQks/yG8y4l7iweCnPE2qHSpFarWfvzYDCwz766utLp6akODw8tIee9D58bgEBxe29ubuyIzEajYffK32IAUbh0jOHp5HI5K6fCAPr55PkIzZ2cnOjly5fq9Xra3Nw0MmLPEVP3ZMbYY2xRxLlczlpokyGT5N73Ast7sax5xpovv5cxUrQc+5buVTXfEHQmk1Gv11tSuBjdNMvEPFInXR/v862MFOn72GS5XLaicgLmfuN60sYd8y2DvjQoeS9+E/jNmvxdn4zzVh1S8gsgGSfu9/t6/fq1PvvsM3322Wd6/fq1dY5BlE+ePDHSZVH6+CeB/pubG71+/VpnZ2e6ubmx+LCPxRFX5W8WizcF94ByI37PJyo9MfBfFDoZdbL1NIQ8e/ZMR0dHKpVKurt7c+aDPwSHOfMNEYwphIPiZbNIMlKnrM4fBO5L2Pzc+KSsz/RLWirTg3R9Gy73BTH5TjwUNsX5R0dHpgxvbm5MuXsXljOQmU9v/DHUJA0hQk5rw5jikZAEIwnLmiOpRRUM59z6RCuVHDwXBvTVq1c6Pz+3N0E0Go0lAt/a2rKGGvaLFzpcP5kb8esnue/9fifhmiRn71XihSEYUPmQJgaTPemPFKDVV5LFbDHkyf3uxVxa+FneBpzc3J5MSShIMrXA73EOgSdwFACqSJKVG/mklbRcGM91fQnUqoFnY/uQhiQ7R7XZbFr9rFe7Pul3cnJip9yzibPZrHW6XV1dWRmUjxv2ej21Wi2dn59baOLm5saSF7is1CXiZmLlKbIn5rm5uand3d2lRJMvv/FuIX31vFmBrqdcLqdqtWrZ+mq1amqiXq/r8vLyO+fqek/DezHezaZeFJJlnilf4/U5GBTmjtgtb/bgbGVfW+vPPcBIYgiIT/LskBvG3KtOjjWE8KkXZR02Gg2dn5/r6dOn2t/ftzXkx5c15Yn3+vrajnakqmRra8uMIyR3c3NjzwNxSLJmiVqtZiVyfD4xY9zyer2uVqtlxoe5zmazqlQqevbsmd57770lLwDC7fV6urm5MaUvaclLfKhayIfa8Gr873lF7T0Z7/1icHylAcZsY2PD5v/w8FB7e3tWxomn40XVKiGWJn4W0vWD7OOfKDdKiAjcE3tlIfgkGS4t5OMzsigqgPvhkwSeDB6ydqtIl0XIAq5Wq0sHu3j15GOI/MxvctxGn+jg9TEccP3y5UtLqCV7zUulkg4ODvTs2TPzDq6vr428+RsUKmqNZ/JxOO7p9vZWL1++1Ndff62LiwtT6FSY+LNOeSXOxcWF6vW6uZuEYny23nebQQj8Oxk3RIFT5UHm3Cto/g5S9iVavkuPUAeKx6tO7xbjQeHCU5nAemGOGAfi2JxDwDv8Dg4OlsrovEFjHPAkLi4urKOSXAHNLIVCwRQ3r6xhPaI8r66u9PLlS1WrVat5lmTHMVK251+r5Ncgn0Wi069f/3MSWlTN+P3wtrp4v7dQphhQv5f5PMIWPKtvd2fu2LvUue/v7+vZs2dWJ03TCLzilTjX8SIsTaRGujygt2jeFSeOyCDu7OyY9SWm6c8Y8FlHQhQ+DoYK5PO5JoSBJUy6mA/BLwoWDdf26huD4utnyeyTbPJhARQyyk2SleT4+BvlMZ4sWXS8SoeXTxJ7JWZKwkp603hxcHBgB04nnwcCef36tT7//HN9++23qtfrpmxwDyEdFFO9XrcEh6+7ZpMlq0Q84fMsVLBQFULShfn0Rs2vK5SOL1mjioHn9+dMeBVHDJfDloif+twCcexM5k1jAwlG3g/H2OINoLiq1ap5B2x84s3dbleXl5c6PT3V5eWldVPSeXVwcGDdVPl8Xs1m004c4965P0+8VC1wv/4cCupxaXpBCBALZl8k4/xcz6tFT57fd/+zJ6gP53O98kXh+k441owvA/UJ7Wq1qsePH+v999/XkydPrMmKElI+Az7wYQx/f2khddL1ZVHSfXwPFYJipXaTUINv4YMcAKSN9Ua9oLL8IkFJ0Ou+Kjb1tmeA6MjEPrRIWTx0CEG+d3dvOrnoOtvZ2bEyHDYmpVa0uPrn9vdB7Mv38nMw+d3d/fkMZHrb7bYlbB49emSk5hc4rbXn5+d6/fr1d5IsqEDIgzZs2kBRS76EyHdEebWXjAUm1RbjzGf4Mwh8Wy/hBO4Ld9QfhMO1/fX5r/dkfDs136e6gPsgM47nwdhMJhPlcjmdnp7q4uJC77333lLYibnt9/u6ubnR+fm5HRQzmUysOWNvb88SnihpSgqJwfvQGUnAdrtt6wUXm3VJjoQwBJ4LMXAIl+oM5m9V9QEG15cYvmvvJ6t+2Ed8tk+U+niu92i8eJKWXxjLa4SOjo6sRRwPmGQu68LzjyQTKWkhNdLFQheLxSWVygbDFUzWKtJgcHR0ZAuKv/GuCRvP1wQysUw6ypP6RZJRfgIegreOvnWUpEOyAsAreBYTv+PbTknQ+HpSxoaFxr2jBHlWT/YsWu5NekOQKDFimIRjfAbfqyfp3gtJqgyfoOEZCd/4+mhcfjYNBOs3mY/H4QFwTYgSJYraq1Qq2t7eXiJdr7ay2azFYPEcvMH1yRU+22e8/ZyhqiBzxnI+n9vr0old+/tY1ZTjDQvGivWOgSoUCkYenN2AGmUeGEPvxdFEhJr2XiNjQlhka2vLQhCEofzvcH8+v+FJlzdRkKhiPr+v4uU63C+GhHJCDCOfidFLqlTGgfvxL8vc2tqy5CFxYFrvfZ24z2l0u93vxQE/Fn5y0mUycrnckvtLnJOBpQSGrhEO7sAl2tnZ0f7+vp2sxSR4AuZ6DCoulleCZMLZLP5804cAiUPoLEKUpQ8bSMvn8PI8uKv+bzn0xKsGfx+EDqrVqp3OhruIkkdFcizmYrGw4nbGhe+Nx2MrF0s+j98QvIDw8ePHevHihVqtln0Om58EJiTg1WQyWeGVHm4uZJ+sgU22xzL2/oAh3zDhDx2C7L0XgIFbX1+3ty14ouR3qHaRtEQ8q1Q4ryX3MUnW2fb2to6Pj3V8fGyJMG9kfHUNDQ3M5ZMnT/T8+XOrfoDE8QTn87kJBx92QcFWq1UTAIzBKu/Mn5THWqIcixwFyU3mD3L353N4IcJzvS2uS2hwOp1ayK1QKCyFfpJdgt6Qs7+950NrOOddYHhGo5F5x4g5vwf9uru7uz+jNw3Fm5rS3dzc1P7+vr0e2p9DADjkptFoqFqtGuEuFoululBa+HyBdbISgkXlmxhYkKs6pR5aLH5R+Ww/E59sVZbuT+DvdDr2KhA/2Sx8Sr18hYZ3dXO5nCqVio6OjqxOlEVEC3I2m7UYoW9c8K2QjD/HBNZqtaXi+WR8a2trS0+ePNEnn3yiFy9e2DGb3j335WyoSn6eJBiIEiXrDTGGEJeVcyj8KVkkUxgn/p9wCAaH+DyE5g9QYmP78iHGhS9fvsbPeSZPGEklCakVi0U9fvxYn3zyiY6Pjy0J7McWwmUeINhyuWw127VazdqiKfei6YN8AEaoXC6rWq3aYUDVatWSsb75g/WHESKf4OubaZYg4YbHyXNCVL4tGQPIvmJekvBz1e12rSKF//oXTmLQfAw+GUf2ddN0AUqyKpWbmxvjEkScP9KSua9UKkveYRpINbyAW+9Vj4+lcYLRycmJHf5C4T199pCnb2f1xO2zxShB1CK1hxy27OPIq0rGvFUlLOHfieVjW6uyvSwY1Ig/kOTw8NBKnCBpngeDQpkSCcW9vT1bSLRF4v761lm/wSqViqk6Gkloo/VW3bvo6+tvDj/H1X358qUZDp+I8BsDMuLnvrsJBYKx9V1SuIl+fDg601creGPk79uHRPwX7rR032mFcvQqEI8I48n1vEfhjQv/5joYGJJ/lNKxmfn5qv2wtbWlR48eaTabWQYedezHq9vtWgVDJnPfzlutVpfiv+Vy2c4a9t16/Jsk5d7enhkuXowJqUK+Ptnp9wLziWfC3EGUydg5cwT5Q4qj0chCa3gJkuy+SX4+1MSAkSNpSJ34bDaztvWTkxPd3NxYKM17Mj40w/pJC6ke7egLo5OqTpJZ2vPzc62tvTkbdHd31xI+1KZ64uYzkoTp46GeMIlB+lrHVaEFH/fhxCq+OMGIdstV8VxUF8fMod44jJnYGjFuDlAhKYJqpTSL62HRWVwQtn9vGQkdX91A/SehBjaXP1zH3z8bIZk0IW7MnBLS8GVguOz+GExfGgiZZTL351J4JeVdRLq3eD6+x9+gliFrDC0/pyoBBeaTa4yPD/uwRtjorFsOR/GZ9o2Nje88M8+bXAu+coHPhzipivDVCfwuz019MWTB4ThPnjxZKk8jdEM4CuPN9VHjlKXd3Nzo5uZG4/HYRA3zzJfPZRASIb7OfKDKk2WdSQ7wtfHSfbKUkBX73Ndwe2Pr475wwWw2s3OleeP12dmZzs/PrabYc40Pf/kcRVpIjXQp0JaWT9+XvtvxcnNzs6TgUKlMlA/cJy0rk+4nlgliM7PpHqot9K5xMukFae7v71s9sQ/Ce0OAQn369KlyuZxqtZodJI4LSP89WXA2ZTKRxDNMp1O1Wi1z2VmUuMa8ZdYbJkg7eah2r9ezMfHhCNRe8nAiT86MtY+HJz0DCNsnyHwnma/R9U0MzBvEy+HbxOaSpIvaIsnmz8FIfr7/N8/umx2Yb59E415804V/Xj/3yQ4ojIsnUZp5GB+eA+/Nd5/596pxT6hzYrn+aEzWAXvHn3xGHJSYPSGw6+trNZtNTSYTe7NIstpEuleXrGMSgZRs+nULyb+NeFkPrG1f0eTDOP4z+DfjIMnWDCWSHLB+c3OzVGbJ33F/rLc/2df1EKinnEn6btCaBUeCRlo+XQxS8NUJwMfYkgojmY3n4BUfC35baMHXMJIB3t3dNfL0BoDNReB+bW3NiPro6MgC/pJswnllOqVdKCpCIySc5vP5d2p+GQ+ezxM3v4PS53c8AZGB5zNRn76N14+J/28yy02pD+EX79L70APKyZdAUQkBIbIpfYs4X/6zIAfUqD/Gkc/27iRj6Z8pWXJITJg1gOLy6o+/TYY6KFmDeLm2PwjIh8p8+RfGyCti3wzg8wqoan8GLuMD0SwWCwvxcG4xMWDc6uFwqEqlosvLS3sDB+uXA4f8vNMmXKvV7HjRfr+/tC59t5wfm6S3Q/WMj/P73/P7WLov84Jw/brh7N1ms2nht4delOn3AQk3H6L8qZHq63r6/b62trZWbmRp+cBwf1KVzzRK9y8SfFscZlVcySsB38/+UHjBZ5qTVQsk6nx8miQPh6xfXV2p0+lY/Mu7wP66w+FQjUZDV1dXSy+yJP7rNxvKDtd9Npt951UzvsSG+/VHR3piYIOxCKmZJE7pX/3iVa2//+S4eKPgy36IR3KvPqFI3J4Qia+r9NchsZpcP5A08Ul/777KJJ/Pm7vOcyZjvXgJEDchlKSr62PDEDXXYR5QVKw5SZYIYxz876DafJ7Bh1Hm87m5+X6cfSKMNzn4fAKG1YeNWOMYaPZdJpOxscPTQiH6RCX3nzQID5WQJfMdKGMvjpKGbRW4Hvuf+6brjsRqUiknjST7gLFKC6kdYi7du3qrahn972MJPTFgdf0Avgs+i84EkUX1B2CssoSeSJLdMT4s4d1TOsl4XfvJyYkGg4G5Y17p+THxRzD6LPX29rbm87ltfr4gK4gJA8DnefXgS9swEvP5/Vsrkm/Z8CGNnZ0dO5uVsjHUib8GoQ5iq/7MDB+XQwVz6A6vaCJ+32q17EAVkoL8XZLUIBpcdv/maEh3sViY6q5Wq1YeRGkUTSd+/lDrXNOHXHyy0JdJ+SL9R48eWTadv2Vs+V3yC5C7N0r81681SJ2Y/KqEMqECjDc1xWT519fXVavVltp8vQfoCZuXPXpvyieu2UcYar8O8vm8nXsCUfu97Y2LrxrySdDvs6+TfOE9vKQnuwqML6G2P8mYLkgmz4CPA3kXiwGEhN+VAAOeOMlOQ3pssIdULsqCQ6GJh5JN9+4uBCnJSlXOzs50enqq8/Nz22y+FMY/I8/plT1ZamKtvFkCQoDceCODdJ/17fV6pqZ9Gy5E4suHpO++wZeFT+UDb2toNBpLr1/3m23VxuP7bFLIPNkEgGG5vb3VfD43cvcNEpKMrDkvFmXM62f8cX0+LIDxOjo6stZyDCNHKfoDySE4T5gQY7J+V5KtL4r0Hz16ZBUIKH0fg/RVLz4sJN3nKlBuvowKssT4QNwYW/ICrVbLyr4YN2qU/dz5SgxCEre3t9bBmM1mrXOTeeM6kCaGLpvNWgLOK/dV4sqrXb9mvPGGsN/GIT5E4ROkPqnH7zFfyc98iIt+aqTWHCFpSXFKssXsA9zSfVsv/4WUpPu3A7xNpXrXj6wyKgcX+6FYLvdZKpV0fHysp0+f2oJj8ig3Q2nyHD6QT3yW52GCvaLk2t4184F+Pw5+Y0K+EBvk5NUXBqdYLC6NsXexfJOBzyaj7Kl88F130vL7uTBEGEdCGpQf+YXtlV6tVtPBwYGq1aqRY6fTWQqT+DHCEJLUzOVytgY4opC/4fmIffIW4f39fVPxnMTmD4Hx8WjKrFCR3vjz2cwV40wij/HzSp/KG98JlawawQvjIKXr62s7hY4svC9143OYE58kTCY3V62vVeuRsUD1ttttlctlM8aEo3yi0J9lsru7a404vgoE+PCBr/og9OQbY5LhAL9v8XAGg4GFgHwFRdLg+7i0H3f2zPfxnH8spKZ0sdC4SH6hScu98BAOXxAvMdNk3Z20fHweizLplrMJfSwq6QJhcWu1mp4/f673339fuVzOypak+9fpcP/cp0+gkPhDBaE+uS4KmHK03d1de3Ei5T5sZLrWfFKIDUpNo39pIvckaSm2mXxOFhqxVFwtf3oY8cxVLh3/9cko79LiovI5jK2fE9SNj1MTf2bDQla+Jz8Zc08myHwogzMyqBRIrgu8IDwI4p246N74eWHgKxcYA858Zb35mDk1qQ8l4KjS4FyG8/NznZ2dWbjg7u7OjIKPLfuqi0qloul0unTS2O7urh027xU2hONb2jkHwudV/DnXEDQeADF85g4R4pssVq0731zEvfoORV/jnAw5wAd4Avl8/jut6qsUbtL7xSsrFotLc/lTIzXSzeVy9ibXfD6/VCTtVRLwxOtbMP270LwK8vFSiDXZj+7VX6lUsoXsrbEnbUps2LCUeaH8fGkS1rff75s7mMlk7KWKlUrFypl8rXKpVNLe3p6R3c7OjjUiEIvkPViZTMbGAaVN4km6P3eUzeSVofcKvBJEvbK5IIvpdGqb37/SxROeHzcWOF7C7u6udVX5g695Rq+cvetNstHXl9JI4FtcATFuSJ7wwkOvlpHuS592dnZ0d3dnDSj+ACPG0nf1efhxkGSlfOfn53b6GmqfJBYH1fO3XnVSfdFut9VoNFSv1+1AnGazaXM8m82sddZXKDCWGxtvDlPyCbGdnR09evRI+/v7drgSatw3D1UqFTWbzaWkpI/5+xg26wpvB2M5GAxsvFeJGv/3zKdX3d47JfTBZ/gKBwQHSWlCDJ5w/X5GVftqJN+4A/GngdRIt1Ao6L333rMNhSLgPVr+NRrScpspCiaTydgGJibEoodAfGspm/729tbcu+FwaIsEC+kniQm9vLzU//zP/2gymVhLJ5uSN7aSnMlk3hR2dzodvX792o5ilO475Lwh4Hmw4D7WiYqXtNR2KcnKjXgXmG+blGTVBygevu+VvvcIUJiVSsXGlXdINZtNXVxc6Pz83KoqfEIHRcNz+GTXzs6OvS+MUjc/XySe2JyU1hEK2NnZsTGVZI0WuO/eWNBWDmlSIYDB2trasut4VVwoFLS3t7dkqHBtp9Opms2mZrOZrq+vl8ICfs48efGWkMlkopubm6WXQ9JtR27Afw7EzTXw5vxba/0bLEiC+jfxMu4cCYmIke7fwkFjD006jB/ji6DgHGfpjSFhvfvD0SuVil1jbW3NYv3kM7766ivV6/UlYeSfFXWZzWbtvGJKSieTiXUTrgobsEcxChj/xWJhVQvJKhO8HgyFN8Y0qBAeSQOpxXRLpZI+/vhjK1+Zz+d2fOHFxYUuLy/tNS8+hindD7KkpfCCdO9G8lK+7e1tc02JvZ6fnxvZZbPZpU3O9bw7wgLinNj3339fv/jFL/Thhx9qb29Px8fHlgTiWtPpVBcXF/rss8/029/+1lpuR6ORDg4O7JrJmKkvdEfx+gXjVRBJDM508AevkJDxRfzETjEOydiVr1LgOs1mUy9evNDXX3+t09NTO4+WvyPuhhLhWig5Nv7z58/1+PFjGyMfc8WLQbHTG49XQD0y3gklTlwbtcs9Y2R84wTqGLLAlfXnOnjFKd0X5Xc6HZ2dnWk4HKper1ubK9f0Bh4lzGFN9Xpdp6enevr0qT7++GN9/PHHVgXCSyuT7rKfBzowfSLSr08OGuLlnayr7e1tI1OvLpPJ5GR3Ic8zmUzsTN6LiwvNZjPV63UtFgsdHh7a+R++qgOcnp7q7OxMv/vd7/TixQvd3NzYvloVWybEMRqN7Bo+KYYXQvMP9bc8r/cqJRkXEAL0RseP7dHRkZ1zjMLd2NiwM0k8X/2USFXpPnv2zB4ORdFqtZTP542AfdaaYDztqpnM/TF7ZD4p/P6Lv/gL/fmf/7n29vY0mUx0cnKir776yrpSUFksOl8CtCrYT4IGNSnJXk9DNxqlUbj8X3/9tT799FOdnJzYZuh2uyqXy/qzP/szffLJJ0s1hn4Dk+hbFWJB8c3nc3W7Xesgarfb5raT6PCEe3BwoKOjIyO+VSAcU6vVNJvNTNl+/vnn9sZYlDrn2eJJsFGYX36+t7dnb6nl2n6TEtIhhrlYLKyxgfMSWCc+/pZUiIwVXpHPXEuyz+N+6eCCoLwx4L6m06kdlHN9fW0ZfDwaSUsHvqC4iK1nMhk7cH57e1t//dd/bap3FeHyLMTBDw8PdXl5qW+//XYpUeoVufRGnTYaDR0fH5uRInSXNK4+/u3dfj5vNpuZZ/fll1+q1WpZ/Hg2m+n58+f6y7/8SzO2vmSv3+/rq6++0u9+9zv99re/VaPRWBmOYU9B8IQtEEeEKcrlsh4/fqyPPvpIz549Uy6XU6PR0Oeff67//u//Vr1etzgx841K9+ViPlGHl/H8+XN98MEH9mIExM1gMLBkeRpI9ZSxg4MDc1EIym9vb2s8Huvi4kJXV1d2ylOyfg8Qh2LB0Ev+N3/zN/rVr36lSqViDRWXl5c6OztbImkWk3TvIvpYkb8OmEwmVruIm+ZVBQqz0WjYeQgs/OFwaC9r5Pv+mSBhn0FOVjDgauNK+bMDqJ/0bhvkQvnSQ4QLUJPValXVatVctWQSBLect+RyH15ZUn+La+/HCRDDQyVSoF4qlWxjepWbJFw+w39mkni5J9+AQbKIpGayNIn48WKxsKMSOWMAgvCJuEwms+T68xnEsSXZeCRPG1sF5uDRo0emeCF81hjK3JdQ+qThQ6RLviOZxGPt3d7e6urqypK5fJ9qHBKkjE8mkzFXnd+bTCZLp3Wt2k++acY3KbGGSTh+9NFH+qu/+iuVy2W1220VCgU1m0170Sr73z8Lqj4Z9iLk9fjxYz179szO3MWDGA6HqZ4ylllllRx+tIphNumqEpBVGcelm3yH5CdO6EtiUAhJ9ZjcZP6/D4EF60MUyXuiesG3i/I7njy+T/F3EsmKjmSnTfJaLDZfUvR94dukV83DQ2U8PiuNel/1uw89mzc6P3T+k5/30D2vIp233VOyMyxJ9P53V13XZ+J/CJhj5tlX+KzKxCeTcz8UuOu+IsjPKQo3uZaYN9bLQyWYHv7++Yzkz4lxJ/czdcZvw0Pz75X+qjn07c4/Eh78sNRINxAIBP4X4UHS/VneBrwK77KQ78JDVuoP/dzvc43vc70fO0D/tuf6Ma71+45bGomItPFzjsVPPc/f51p/yLr/ffBT7eU/lrUZSjcQCAR+fDzI8Okdlx4IBAKBIN1AIBBIE0G6gUAgkCKCdAOBQCBFBOkGAoFAigjSDQQCgRQRpBsIBAIpIkg3EAgEUkSQbiAQCKSIIN1AIBBIEUG6gUAgkCKCdAOBQCBFBOkGAoFAigjSDQQCgRQRpBsIBAIpIkg3EAgEUkSQbiAQCKSIIN1AIBBIEUG6gUAgkCKCdAOBQCBFBOkGAoFAigjSDQQCgRQRpBsIBAIpIkg3EAgEUkSQbiAQCKSIIN1AIBBIEUG6gUAgkCKCdAOBQCBFBOkGAoFAigjSDQQCgRQRpBsIBAIpIkg3EAgEUkSQbiAQCKSIIN1AIBBIEUG6gUAgkCKCdAOBQCBFBOkGAoFAigjSDQQCgRQRpBsIBAIpIkg3EAgEUkSQbiAQCKSIIN1AIBBIEUG6gUAgkCKCdAOBQCBFBOkGAoFAigjSDQQCgRQRpBsIBAIpIkg3EAgEUkSQbiAQCKSIIN1AIBBIEUG6gUAgkCKCdAOBQCBFBOkGAoFAigjSDQQCgRQRpBsIBAIpIkg3EAgEUkSQbiAQCKSIIN1AIBBIEUG6gUAgkCKCdAOBQCBFBOkGAoFAigjSDQQCgRQRpBsIBAIpIkg3EAgEUkSQbiAQCKSI9Xf8PJPKXQQCgcD/EoTSDQQCgRQRpBsIBAIpIkg3EAgEUkSQbiAQCKSIIN1AIBBIEUG6gUAgkCL+f/Lplvch9+D2AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 49; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 51; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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LZRSLRQAQU7zX4uPimp6eRjweF2HZbreRTqfFlbO/v49CoXCmjJFeArOXsDWzQMxA1cu+n/PA4XDA4/HI++J1ThLwp91f9/fX63UUCgXs7u6i2WyKIKLP3+VyYWJiQiwdcxGb85kCvlarSTCSAbRCoYBUKoXFxUXk83kMDQ3Bbrcjn89jfX0dT548wfb2Nmq1mgg6l8sl1lE+n4fP50Mul8PAwAAGBwdlrgIQ4XtaXjLf87Nnz7C3t4disQi32y1WRfecoQA0LY2vi+leAL56h72EKe/BzKY4ab40Gg0UCgUkk0n09/ej1WrB5XIhGAzC4/F87fs+C9+40OXLLRaLWF9fh9PpRC6Xk4lG84mTweS0nZjaU3da2Xn7Zex2OyqVClKpFCqVSkfaESeC6RNqt9vI5/PI5XIdQvqk7zZ/3mw2YbPZUCgUsLW1hdXVVRQKBbhcLvj9ftGqqfm1221ZiCMjI+jv74fT6US1WhULIhQKwefzwePxiOBg0JKTutvdYLPZEAwGMT4+jgsXLqBWq4k2m8lkUCqVkM1mkcvlzrS5mYueUMiZwRj6UelWoKXDv0+Dv1etVmVeeTwecTN0C15qfOa7OItwbzQayOVyYtJybCkAL1y4gMnJSfj9/g7lgH9TQDEVkBuWx+OB3+9HKpVCJpNBNpvF5uamxCfS6TQSiQQODw9RqVRESHs8HtGCqcQEg0GEw2Hk83kZC3MDeJn7jWY4A8Tmd3QrQAxwHx0doVqtnrtrr3ttc46ctkmeJEv4HAAkrkTXEoOfp21G54Vlmm4+n8fy8jIGBgZkEVCQZDKZDtMT+GqAzQHkYHBhcqJyIKkpnrbTvSoM9jx9+hSzs7OYnp4WH2q9XofT6cTExATm5+exv7+PUCiEyclJMYtOc9Bz4zDhJlSpVEQLoklVqVQkql2r1SQomcvlsL29DY/HI5uCmWERjUZlzE2XhmnCUes18175c16PvjRq9aZG02uiA3hBM+LvmMLWXEwcD94jBdTLsjWYHcH75vdzPLmhnZTLa95TrwXd7TPP5/PidqCvnL5+m832Qv6w+Sx8Hv7NeVssFnFwcID9/X0JEnGjrFar8nwARADSBVQsFmUz5fzh/Zjjaj5LL7gxOJ1OTE1N4dKlS8jn8xgeHpbgXr1el+vXajVsb2/j6dOnSKfTp76jV8Fcw3yfLpdLXGzmvXa/J1PjJXQ9hcNh+P1+tFotZLNZFAoFHB0d4Y033sDo6Oi53f9pWKbp5nI5LC0tYXp6WnwpXq8X0WgU2WxWTO1ei8t0mHNxer1e9Pf3IxgMSlSyVCqJb/G8hG6r1UIqlcL9+/cxMjIiQRJqIH6/H9evX0cwGEQmk4HP58Pc3BxCoVDHfXT7Ts37M3dymscej0cWivn7XEQ2mw3lcln8rYwam0LN6/Uin8/D5XJhbGwMo6OjaDQasNvtHUUdbrdbkuqZgpVKpbC3t4ednR1J+yoWixLQMn1qvTQgPg+FrjkXzHs0hS2FFX2hZkDkLO4Lao5mEYW5oZj3TMFqjn33O+p+Dv6cmTRMk6Pga7fbiEQiGB8fx9DQEDwej+TcUrM1x53/Xq1WkUqlsL6+jufPn+Pw8FACReY9crw4P6jl8tocX3MO8Tq9tO5urc501wWDQbz11lsYGBhAqVRCJBLB7Oys5KlT4B4eHuL+/fu4f/8+jo6Ozi2OYt5fX1+faPX1eh0ejweZTKZjQzPdS93KDMeDsRGv14tarYZUKiXBz1wuJ9f9H6XpLi0toV6vIxKJoK+vT0yhkZER9PX1SQCCJnB3FJID4na7EY1GMTs7i5GREdFGmYJWLBbPTegyALi4uAifzwcAeO211xCLxeDxeETITk5OikDr6+uDz+cTAdKtzZr+KXNBcMK63W4Eg0EEAgHR/oPBIIaHh6WoIJfLYX9/X4I3ZgSeUEgGAgEsLCxIUMssbmDKXiAQEC26UCggkUhgY2MD8XgcBwcHEgTt9q2Z1ojdbn9hMfeq7AI6qw3NogtTC+xewL1cSN1CxPTxU0tnpJ0/63Znme/AtEzM99PtxjIXOoU9APj9fgwPD4vQDQQCACABtnq9DrfbLcUVFLxHR0cSVc/n8x1WBDcPp9MJv9+PUCiEoaEhhEIhNJtN0Y6pAQYCAYRCIfFRmkFL8/2YG6L5e+12G36/HwsLC5iZmZGx5MZMrfzw8BBLS0t4//338fjxYxGE5wHv0ePxSMomU/0SiQTW1tZERnTPAW66rJzr6+tDIBBAJBJBMBiUYHOlUpGgdKFQOJf7PguWCd1SqYSNjQ04nU55ePruRkdHMTIygkqlgmw2i2QyiWQy2WFiAV8tjr6+PoyPj+ONN97A1NQUAGB3dxdut1uKLs5rt6IJt7Ozg/v370t6js1mQywWg8/ng8/ne8GENhek+V3Utmju0mQyNTG3242BgQGZZC6XC6Ojo7h8+TImJydhs9mwvb0tvjeaoKY5S+FaKpVQLpdlwVLzYiFBoVAQgQSgw8dsum1MIWP6+Uwh1B01Nk098/Pmz6nlmgL7JN+q+fsUmieVAPN7TPeF+W74faYW232v3dft/g5TiHcHc/m73AxZQNFutxEMBkVjbbfbCAQCcDqd4roxhQmv3W63xd0Ti8Vw9epVjI+Po91uIx6P4+nTp0gkEqjX6zJ3XC5Xx8ZPFx41VY5lLxcYA5nmnOZ8LpVKODo6wrNnz/DJJ5/g/v372NnZOdcyYL4PpqldvnwZY2NjAIDNzU1Uq1VRBHh/fHfMv43FYojFYgiHw1I8YbPZRMbk83lkMhlsbGygXC6fy32fBUvcCwAk7ePw8BCFQgGBQADhcBjhcBgjIyOSb0iNmBUovQI1Ho9HKnCmp6cBQDTl3d3dc6+r5s6+u7uLR48eiVbIXZSaSLcAMnNAqb1TCLZaLfExBYNBuN1uAMdaDbWl0dFROJ1O+Hw+XLp0CTdu3MDk5CRarRZCoRCy2SwODw/FNOqGG9Tk5CTm5+cxMjKCYDDY05Xj9/vh9/vhdrtFCHAxOp1OrKysIJ1Od6Ru8bkpME1/Oj/Hn3GxA+j4udvths/ng8PheKGKqDsliIuJY1+v18XX3e335e/TxKcpzlxu8z4YIzD9sWa6ornBmO4PjkFfXx/6+/tx8eJFvPXWW7h9+7aUK9tstg7/NACEw2EJcPL7FxYWMDk5ibW1tRMrKlkWy4yV6elp2Gw2KTRxOBwolUoYHBzE8PCwpDny3dRqNRH+lUpFXFBMzaMS1O3n51jxubPZLDY2NvDw4UMsLi5iZ2fnBR//eeByuaQKbm5uToSuw+HAxsYGnj9/3vNzDocD/f39eP3113H16lUEg0GUSiXJtc5ms+LPLZfLHVlU57VpnIalxREA5EEZFPB6vQiFQpibm8PIyAiazaYE1zKZjPgsTbjA6GrgxKfgOku0+1Wp1+tSDUMfqNfrRTAYlF2021QztbFKpYJcLodkMilR04GBATgcDvj9fjH1vF4vhoaGMDc3h0QiIYKG1kAsFhN/Gk2udDot2oupxfX19WF2dha/8zu/g+vXr0t2A7Ucjlur1RKNnUHCYDCIaDSK4eFhDA8P49e//jW++OILKdgAIEKNn6G2x/dCv3S1WkU+n0exWJR3yecKhUKIRqNSSZVOp5HP50WQAhBTMRQKob+/XxLaGdxLpVLI5/OiIZrfHwwG0d/fD6/XK77TXC4n309rw+/3S9oQg3KMD/DdcNw5x4BjBSAWi+HGjRt47733cOvWLUxOTspGws9wrLip+v1+EfJOpxPXr1/He++9h3g8juXl5Q6tkUEkxjFGR0cxOjoq6WBMM+RzTU1NYW5uDoODg2KZ8d0UCgWpwnM6nYhGo4jFYgiFQmKWm64HU/un5bS9vY0HDx7g7t27WF1dRT6fP7cUTXP9dOdfc64yMNrLlUELYnp6Gu+88w6uXbsGh8MhueWcA5lMRgpR+HxWYWkZMABJ6aHTm5pHIBDAwMAAXC4XarUanj9/jvX1dRQKBRFe3G2Zfra8vCzCj34tpjadl2/JhClT6+vrCIfDmJiYkCR0099nCl9OYE5YfgeDcHTy08VA83F+fh65XE6CXm63uyNfttVqic8wEAiIMKjX6+ILm5iYwA9/+EN8//vfRywW6/BtttudTXDMYJrp9qDgPDo6wvr6Og4PDzs0GvoY6WLhZmj6tdPptASczKyCQCCAsbExjI+Pw+fzSaHBwcEBstms+Mh9Ph+Gh4cxNTWF8fFxKRSp1+tIpVLY2dnB1tZWR9Sf1UbcNPx+P0qlkixcU5NlMJHNlex2e0dwjKZ5qVSSMmSOAS2D119/HW+//bYEis0gKOcGLTC+bzPlMRaL4Qc/+AF2d3dRLBaxvb0tHcQocPmuzblAAcQ4h8fjwWuvvYb5+XnEYjFRCFqtluRm0zSnL7/bgjC1XNOPTlfb5uYmFhcXpSz3pL4pXwcqKul0GgcHBzg4OIDD4UC5XMby8jLW1tZEuzYVDbvdjlAohEuXLmF+fh7j4+MS9ASO3Zz5fF6CoKY/2CosF7pmNJrVVUdHR5LzySADd18Gi8zPsyyQWQuDg4M4OjrC1tYWjo6OelZHndczsBEMd07TxDYDNGamBTUeBjbMrl00fU33hM/nw8TEhCyqnZ0d2Gw2pFIpcVPs7e3h4OBAxoymKhdNOBzGjRs38M4772BsbOyFtoZmlBtAz4UGQN4HcGylcOMwxyQYDGJ0dFSCoxR6NttxfjYXvelnZdFGLBaTz1LLpcBvt9vo6+vD8PAwrl27Jj78SCQiAR2WFT948ADAcbUc86k5Lv39/ejv7xdXxNHRkQS1zBxebhbcSMx7ZXEPha6ZnsUgDC2FbrOc7gszWGfC9zY2Nobvfve72NjYkMYs7CXg9XolY+bg4ADr6+uoVCpwu92Sj8u0rhs3bmBiYkI2QuArNwg3WjP9jC6Yvr6+F+ayGSTlHCkUCtjf35c1+02ttVqtJus6FAohnU5jf38fDx48wPr6umyw5jiysGdgYEBypSl02YGsWCxKVVt3VocVWN7a0Qy4MDVpY2MDIyMjYqpRM+KL7vb1MTCxuroKu92OyclJKdM0zdKXYUawz4IZPKF/jQHBbmFlfjcnPv1K8Xhc8nvNQIapKXu9XkxMTKDRaMDlcuHg4AB7e3tYWVlBMpkUjZAmEwUGNRpqhv39/SL0TIuBC6mXKWk+a71ex87ODhYXF7G7uyudxMzPhcNhjI2NwefziY+MuaN0E7ELWHfwi6Y8tVaa/2Z7zkajgUAggKmpKczOzkqns2azKa0aV1ZWJJODPT5sNptoiDSxzetS6LB1YyaTkQyOQCAgloTX60WpVEKpVBJz3vRfJxIJPH78GN/73vcwNDQkG4b5PinMzPfA/+a6sNvtiEajmJmZQTwel5xqvic2a2F2Cd1NgUAAPp8P4+PjEmylcDXnJOdapVLBwcGBjOvExITcZ3detSl4qRkzI6M7k+WsnHXd8X2mUilsb2/j4OAAm5ubWFtbO7GalRsD51O7fdzYiGX16XQa5XJZXJRWlf523KPlV0Rn34FcLod4PI6+vj7U63WMjY2J34WTkpPVhD7WnZ0dMY9exS9DLZT3cpZJYC4iv9+PaDT6QuYCf8/UzM0GMh999BE2NzclheXGjRs9r+NwHHcyGx8fF3/ws2fP8OzZM8TjcdGCAEhAilqW1+uVtDwG78wsCVPjMqP3vd7R4eEh7ty5gy+++KLDnOM90gc9PDwMu90uwa2joyOk02npSWE2LaLgKpVKODw8lGulUins7+9Ly08Kpmw2i1qtJv7/QCDQMSf4nNlsVjRxc6MBIAKTWTHdRTRMHwKOq5X6+/slC4DBzmw2K75ajhEVAHZkGx8fx8TERIemyDE2/ZTdGSEsJa/VavB4PAiHwxL/oEZObQ0ANjY2EA6HJUh6/fp1TE9PY3x8XNxW3e8UOE4jXF1dxaNHj1AsFsWSdDgcmJqa6th8u+c0XT0DAwOSgWEK5bOsH97Xq/RGabVaKBaLODo6wu7u7omVkLyfVqsljYg8Hg+2t7exvLyMeDwu7pCzrvlvgm9F0wW+mrBMUmbXLDbsYKnlaRVdLMnMZrMiRClYXvZCueubUeWzYAY1GEA66f7oCnn+/Dn+4z/+A//1X/+F1dVVFItFSd+6efMmpqamepqcDocDPp9PouDb29t49uyZuFB4Pz6fT4JGwWAQfr+/o31hIBAQrYYBx24z0oTCqFKp4NmzZ/j000+xv78v48TgTzAYxOTkJCYmJhAMBpHL5ZBKpbC7u4ujoyPk8/mOBiWmkGMNPFv0AV+5L0yBSIHEGAAj63zH9I2yYMHUvEzBzpQgs1zW1PrpQ+QGwWAvfb2hUAgTExPY3d2VwCUXfrN53Ebzzp07uH79unQY41iZ79R0P3H+M4eXqZLtdhvRaFSEDVOb6Bfn3Ob4+v1+3LhxA5FIRDaFXnOyXq9jY2MDn3/+OZaWllAsFrG5uYmdnR0cHh7i93//93Hx4kVp29gNBS8DW2ansbPSHZx72e/SXcZNtTuHufv++OxsudloNLC9vS29SMxTWM5yD98E3+pxPabGy4YdjGi73W7RMOgy6N6ZaJpmMhm43e6O3MOXuRjMBXfWHc/UWijwK5VKh+/M/P5Go4F4PI5//dd/xc9//nPxw9G1srS0hIcPH+LatWsYGhrqyJE1gwOt1vEpBxsbGzg4OJCMDgo/4LgT2vz8PIaHh8VH7HA4RJMpFosYHR2VpP1ud0L3cwLHG+DS0hLW1tY6eho4HMcnOczMzODKlSsYGhpCo9HA3t4e1tfXsbOzI123emk0/H+mzlUqFcmq4EKjFcJ0rFAodGIfC/qwI5GIBMBM9wczEWiudmvd5n2xOIepfe32cTMbpvFduXJFno0nVQCQdptffvklXnvttY7eC73mEP+bZvDe3p50GmOXsImJCVSrVezv72N5eVnaoHb3pNjY2MDh4eELTV/M6zebxz2SHz58iKWlJSSTSXkGWiPNZhN//Md/jIsXL/a0flqtlpSds/LvVTTdV11zZjyErirTF909rk6nE6FQSLJb2COEjYC+bQ2XfOtCl4LX1BzYVYt5hs1msyNVyfw8A1sAJDpsVji97PqvmuXAxVwqlUSjY1vF7klqnk6wubnZcQaYzWbr6OTEqLnp2Kf2FY/HJR/STKEzsxAuXbqE9957DxMTE2g2j1sPHhwc4PDwEAcHB1KiSfP8JO2cNJtN7O3t4fHjxzg4OOgoNGDWwfz8PEZHR9FsNrG1tYWlpSWsr69LdgbfRfeY07TmH/qtefZbKBQSd0g4HMb09DSmpqYkat/dz4CZGtevX0c4HEY2m5X5RE2SfmMzeNZ9f+ZcBNDhG3a73ZiamsLo6Cjm5+dRLpcRj8c7WkgeHh7iyy+/xO7u7gubaK95RD/t9vY2lpaWcHh4CJvNhv7+fszMzEgXs+3tbbRaLQlemVpauVzG9vY2FhcX8eabb3Y02zefq1arYXd3VzrgUXjbbMfl5BsbG7hz5w7efPNNGevu98ZKLvPEhpfNo17z6iyfMZUCBj35XnsJTWZvXLhwQdqqmhsE3/urKFnfFN/6Eew0B7lrmzmprVYLw8PDcLvd+OKLLyQdzIQ9Nuno525/mtDtzkd8FRPD1KTT6TTS6TQmJiY6GokTarTd5qzp62MAh9oeJ1qz2RTz78MPP8SDBw+Qy+V63msoFMK1a9dw8+ZNxGIxCbjkcjlJt6GWHY1Gz9RgPZfL4d69e3j48KFodmYUm2lRPDXjyZMnWF1dxdHRkSwMjrOpfZmVW6bgZZMVj8eDsbExTE1NYXBwEP39/XIEDqP31LL4ObfbjZmZGTkPLZ1O4/DwEFtbW9jd3e3ob2sKXFoR3ffH3+GRSebcoulN9xK19UajgWw2iwcPHuD+/fuYm5t7aatAnr7x4MEDPHr0SI7dGRkZEQHvdrsxNDQkTen39/c7vqPVaiGXy+HBgweYnZ3F4OCg9Eigi4BxhUwmI24WU0Pl+qN7pxdmNgHzzE+zlk7CDNSZc+Gk32OKJWMFvSxYt9uN/v5+XL16Fd/5zncwMzMDu90uJ0SY8uXbcCd0860LXaCzPLavrw9DQ0OYnZ2Fx+ORnbtQKGB5eVkmuPlZCl5zMZ5k9jAnkyWOjGSeFX6ePq3TTCvmogaDwQ6ByhQhBuNYDmoKI6YoffLJJ/j444+lF3EvmLLFAEdfX5+U/vKcs3q9jmKxKAEi5t+eFEBbX1/HBx98ICWXpnZdKpVwcHCAvr4+Sffb3NxEKpV6oRMWOUnLMAUgK7eCwSDm5uZw8eJF9Pf3S6oTMxDY5YrC0el0imU0NTWFdDotFXTUXDm/zEXHezEtHrNMm/eXSqXw/Plz1Go1qQRkTrBZCddsHh/k+MEHH+D27duiqZ40xslkEvfu3cPHH3+Mzc3NDl8p/cjMhBgdHUUwGOz5/uv1OnZ3d/HRRx/J+WXj4+NSLUnFhn0MAoFAR4k9N1L2+zgtos9Cld+28TeD3lQ+zFxZwrXLd82MmV6uAVZsXrhwAd///vdx69YtDAwMyJw123t+2xousVzodmuWpl+X5jeFkd/vl9MXUqkU0uk04vE4AHQMoukiMDMMuDD57/QXBgIBae/WarXOnNfL3Zfd0UZHR6XGvXthmdFeBrCYBmez2STF58KFCxgYGJC8Vo5HoVDAysoK7t27J4Kv1z2aWoO5gJhryhzTXC6HarWKSCSCiYkJhEIhSb0yabePj8bZ2NjA2traC8ei8zr0LQeDQdFGTmpAchY3DzdPpnxxjNi4xSzTNT/DDYxCmYcP2mw2cSuctd2nmUNuzi0+HwDJi2UpbbcCUC6Xsbq6io2NDbz22mtSnNB9HSoRH3/8MZ49e4ZSqSTvKxgMyqGYZtYD51Wv8atWq9jc3MT9+/c7jouiAHM6nRgYGJBy2u6Tk9kUplc2Dq/LzWBkZAQDAwNIJBJnduXxO1hZaLfbJYe7+8gqsxdHd5qh+V3MMx8dHcV3vvMdvPXWW3KMEoPVdNGd5M99VUv3PLBc6J6069NPms/nxdRklczMzAzefvttaeB8eHj4wiCaPjpOGtO0ZXJ4IBCQ9m65XE6yJM66C9rtx2WGLJENh8MdhQ3mczGdiO4PM+Lu9/sxPj6OmZmZjgXCBcZjew4PD0WIdN+nmQ5Ffx8FqSnUt7a2pLR2a2sLKysruHLlipQfd987Nxb69brHhsKRJicXjim0+E5M7f6kZzD/UMtnhzT6AM3Pdvu0zZ9zkdGlYAorU2D3uj5/1p17SuuKp1KXSiUR5t3vHPiq2OAkAdlsHp+LtrKygq2tLemDQJ8kK/TMgO3+/r40/j9pDJl/Sy08HA7LzykwmVa2s7MjQUMKOLrCTgtUsVdKf39/x6GXZ4GKCDVq5umf9q5MFxShQuV2uzE4OIg33ngDb7/9thxbxU2IygsVh15C91V90ueBZUKXL6dXbix3NHYvYp9LljxGIhFcvXpVFuL9+/dF8Han/hBzh6fZFolE5Lwz5gL3aiF4EmZEnInzzATopt0+Pr322bNnUtJpVjiFQiGpnzfPnOKip/OfR4mwCYppzvIZ8/k8VlZWsLe3J+lKPp8PU1NT+O53vyul1LlcDh6PpyMY1esZPR4PFhYWcOvWLTx//rynls1ADLX3k5LkueCZw8tnIPSRU0s1m9Ow6xYAKZIxG/GYgS8WLLDxO4tEvF6vCCu73f6CxsT5wY5cveaCaY3x3ZwkmEKhEG7duoWFhYVTT4Smq4PzMhQK4ebNmyI82Ea0Wq1id3dXehyYAonjZ/aZYHl09+naVDoGBwcxOjqKUCgkATX6fNmM/ObNmz0PajQVF2bIvEowjZsXc64jkQj8fr9UinETMF1R3XOVyhQF7s2bN/GjH/0IV65cEXeO2QmNedndATh+DwspXtU3/XWwTOhSk6O/sXvScnfiGWOMoEYiEbhcLgwODuLWrVuyyD7//PMXOtVTuzQrgNipaGBgQDRTRveZhH9WzAAchclpWu7u7i6+/PJLSc8BIFro0NAQxsfHxdTi58yJ1tfXh5GRETGVWGzAvFcA4peOx+OIx+OSZ+lyuSS4wO+Kx+MIh8PShP0kM9Jms2FiYgI/+tGP8Nlnn0kgw5y05uZgunHM3+F7MDcVU0s1TURWjrEdH90xR0dHkpdspn7x8xTWbLwDHGeNUMDQhKWmT6vBvD4/z3E3fb+mm4GadC9XBefD3NwcfvSjH2F8fLznQqYAdLvdiMVimJmZwcDAACYnJ/G9730PV69elcZEVETi8Ti2trakwIPC0OFwSKe6/v5+DA0NSUm1qdnxXu12u3TtGhwcRDKZlGdtNBpIJpNYWlrC3t6enM1mzg9e25zzrxpMY3nzwMAAYrEYIpEIwuEw9vf3JT5gZrX02gSpLV+7dg0//vGPcevWLQwODooAZRzj+fPn2NvbO9E1x/GrVqvn3pnwNL5xocuXw6CSx+M5sXUdD618+PChJPTPzc1hYGBAavDffPNNJBIJSU3qBV8aJzfLVJnGw4YqxWLxzN2RzAAcU5vM0x26YWPqdDotwR5qNRcuXMDt27cxPz8vJZWc/DSPWcxw8eJFxGIxqXdnY3EKQuB4Ih8eHmJvb6/jGHozqutyubC5uQkAGB4elrxYCp9u3G43Ll26hIWFBSwtLb2QJ21q5b1MQFOgmpF00w1h/o7Z1SwSicBut0vaWyqVkuPfzeO36QbhRjIyMoJoNCobSiQSwfDwsFyLm2Gv6wcCAdHeu4M23dqW+X3m83q9XszPz+PSpUsvpFwRjrnT6cTw8DCuX78Om82GmZkZXLx4Ue6fv1sqlbC3t4dkMimZOxR8kUgEk5OTmJmZkVaOkUhE1g4zYmhdAsdB14WFBaRSKdhsto4Uv3b7+EBKZqD0EkRmII1/GMQ+i8XIgG4mkxE3BQs6TJ/+aZkNNttxg6Bbt27hjTfekNJrVkKurq7i7t27uH//PpLJ5IlrnFYdy8T5b980lja8OckEJazAWVlZkchmo9GQBHxqB+xVyu/tBf3CDGaxE1apVEIul5Nk6bPAAAADewMDA9K5/6TqH/pww+EwBgcH0Wq1MDQ0hGvXruHGjRu4ePEiRkZG4PP5pMiDPtJcLodisQiPx4PJyUnpm5pOp/H48WMxB800GKaedR/zQsF78eJFhMNhVCoVaebC2vOTJhqf2awA635f5n+bApwaKF0dvFduLqZGz+/hvdA3yWPF9/b2sL+/LyaoGQBi+hZ9jQzycBGamqGpxXabrhS+TEkzs2S63WEnaV8s3T5NazLHIBwOY35+XrRyMyjL+2Nj/+5sA2YofOc738Hrr78uFYh0qxSLRemzbLbe9Pl8uHz5MsLhMC5evCinTh8eHkqzGPp4e60DCvvh4WEMDAxIAJVZBmeJjbABTTabFaFrdqrjuzpp/ABILrN5dFY6ncaTJ0/w6aef4u7du9KJ7LTN4Cxy6byxrIk5/W30nZ0E8x1XVlZkgrEiiBOa0d3TdiXTrDKbYGQyGaRSKZRKpTMXRrhcLulWNTo6ipmZGVy4cEHMwJMIhUK4ePGiRI6vX7+O1157TUxAm80muzsbqrDtHCeLz+dDOByWjlz1eh2bm5vY3d19Id3NjPKaASaPx4OBgQH4fL6OTA2zqVB3MIkBHDM32DTLSfcYmiY7O1cB6DgfjBOdgpw5mDzsMZVKodlsyniYPRW6NW5qpfTjJZNJmR/077GrlKnlmtfnvdEa8Xq9Mq6miW66dHqNB3NmWaraXezCcaVLiyY2x8rM9e61KZnj29fXh7GxMSwsLODSpUvw+XwdApqNcZiyyH7JHo8HQ0NDGBwcxMWLF/HGG2/g8ePHePToEdLpNGZmZhAKhU6c006nUwQeXR50/XAsXwbLs9PptLgPzaD3y4QgrQqm1Jk57Z9//jk+++wzrK+vd/Qn6QXv17R4rRC+lmm6nLAvS9+htpfP5xGPx6XmnYniXCxnyQ80ne8ApJn2q7Sk4yRnD4SRkRGJMLPUs5dPlxrHxMQEotEoLl26hCtXrkhfW45JsVhEOp3GxsYGdnZ2pAuSzWZDOByW/rHMixwZGZH+sDyqx/SLdm8C9Llx46AGR2HCxWLmETOwmc/nO7p9mWNpji/hwqH5yU5XrArrJaD5fdTwmb3ChdytefaaO1xYXMzJZFJ8vGYj8e5rErp0OAZer1e0zO5TH7qf2/yuVqvVMb+6TxPhs7Tbx6XFZv72SePKPNS+vr6OoKPf78fQ0BBGRkYkgEQf8M7OjhwNxMbnphuC38XTW2KxGIaHh/H8+XNprt7L9cSxMzNvzK5wtCxetq44v/i+KeTN+XcaZtENBSdzs5eXl7G9vS1Nkk6TM9wE+X6swvJ+ur16KPT6XS76vb09bG5u4tKlSyLk6EM6C9QKKHDMaPdZhC61NgqSYDAofjMzt9bUUKilZbNZtFotMceYGkZBwOdMJBK4d+8elpaWpF8ri0TY24F5m5FIBGNjY4jFYuLTZulsOBzuyKZg0KY7LcoMVNCPyWc1n4MNut1ud0+hyd8zAywUtl6vVxav6QqhicrAE4UjeySwdJcLxlwMp220ppZvt9tRLpflvTE7gQ2ReB3TR0sNlH5K/i4b9gDosCC64WbD0yf4vaaWzLJbM/LfHUQ1n5nuIeZUs6iAVs/Y2BjC4bD4Q1OpFFZXV/H06VMcHBxIX+H+/n5cuXJFzt4z0yqBY4tseHhYUjLp3jLPFTPfNXOJWWhBhYCZE2fpe8INqNlsyjxgxsTL/Kqcu+VyWdIx9/f3sbW1JRr+WYoh/tcI3e6FdNLv0uxkw/CDgwNEo1HYbDYcHR2d6cRfmiGDg4MYGxtDvV6XMsqzuBa6o9tmsrl5r+buzlQnBvt2dnakIQy/0/ws2z4uLy/jyZMnyOVy4o9OJBJymi+PtWG7x4mJCYn20jzt1lD4p1uTopbAPNDulBzg2KUyNDQkVWF0d/TKUgC+yl9m4xkej8MTh7lJMuODQR9qOfl8Xtw+dCN0B7woCOkuoiDrdSinufGwaQ4runK5HDKZTEdPVmZhcIPmRpNOpztMffPZOVa8t2g0iosXL0rA1tzk6Crj5tQrM4DX6RY6LP5gFkgoFMLk5CTGx8clEJvL5bC2toZHjx7h2bNnUo1HP63NZsOFCxdw6dKlFzR1urlyuZz0f2DbUuZym3O2+/Ms3/Z4PJIf+7K1yd8JBAIYHh6Gy+VCPp/H2traS4UuA4zJZFLS3vb39+X8s+5ucydhtS+XfGv9dF+GuZsdHh5iZ2dHEp8ZvX+Z38dut6O/vx9zc3OYn5+XworFxcUz3QNT1oaGhsThz6AVcwq7T0yt1+tIp9NYW1vDs2fPkEwmAUD8fOZ1+fulUgnFYlE6W5kFB6y4mZqaEk2Wm0g8HheTjtkNyWRSjoc/LYeS42P2hjV/5nA4REOanp7G0dGR+BdPymW12+2yiOj+6PaV0aydmJjA5cuXMTIyIlVcpl/XXBD8m+l/kUhEzkjLZDKyUXVv7BTAPp8Pk5OTmJubg9frRSKRwNOnT7GzsyPuDH6W2mAwGJT+wXTjnPTMdElMT09jYWEB/f39HbnE5ubHDe9l8Qhu3slkUo5Xp+YbjUYl7Ys5zTwMgB3H2IiHbTw5v3ql/vEZDw4OEI/HUS6XEY1Gxd9q+pq7tcxmsykpkNxM2A3sNNrttpyWwWyPRqMhaV6nuSna7TYymQzi8TiGh4dhs9mwu7srLTzPmnv/bQhc4L9J74VecOGwUUc8HofH40GtVuvolHQazHbgqcG1Wg3b29uiHZyG0+nE0NAQrl69irGxMWkdybLaQqEgVXFcVPQNJpNJ6XTP6iUzat4d5Osu2jBzJ1klZDb1YRYF76lSqWB/fx9ffPGFHFNCE/A0/zcFQa/cUy7w4eFhjI2N4cmTJ6c2Q+E9U1gPDg7K0ShsgWkKXh6tfeHCBdlouGl0CyRTYDG9jEUP5XK5I3e6lwYeCoUwNTWF+fl5CVhRM+L983mZBREOh+WEjtOKSXgNv9+PsbExadDUbdGYGvjLBG69XkehUEA8Hsf9+/exuLgoFWl0x4TDYXFxVavVjio8011EF9HAwIBUT/I6/JtzrVqtig8/Ho9jenpaMnTMoiYGn9hvmE162Mnsyy+/RCKRONXNYLPZpFcGmxWVy2UMDg6KlXES3GSeP38uJ2nH43E5cebb0mDPimVC14z40jQ8bWBM/xajscBxm7eNjY0XzkfqhhOO5m4wGESz2cTo6Cii0aicsXUSXq8XCwsL+OEPf4jh4WE5qbbRaIgWaQpQTkg2wmaja7MfbLd7gprh7Owsbt++Dbvdjo2NDWkc43a7MTIygqGhIWlEws8zgFCpVET4PHnyRExwAHJcz2n5xBxrM8+SAQq6HmgSn4YpeP1+PyYmJjAwMCBalJnyxIXbaDQ6gpT0h1KQclM1/dJmUMxMUeruAUCNkkKaDb7pUjHNYLNKkBox809fJnDN+cYNzMxxNSvYTsrdJXynPPftk08+wa9//Ws8efJELLvud0XznucKjoyMyBixXH1mZgY3b97E3NycnLphup/YQpNzhVk+6XS6I9OG75ljwe9nRoPX68X+/j5arZY0HD9tvKLRqOTq0iplL9zTemJTJqyvr0uFHJvhn1S80g3lkCmXrMLyMmAGGbhATpvQZlDq4OBAuujT13nS50xfrM1me+HMLHauOm03ZcCD0d1m87gRT61Ww+DgoDRaNwMhTHdjS8FSqYTBwUHJaew2+R2O4+PXZ2ZmJCc3Ho9jf39fDr2kX5VnyFGwc1Kzyopj9eWXX3acLkwf6EkaFv+NwoJClsUdW1tbLz06yXxfXEwzMzNS78/P0UXBTYCJ8rw3BuHMXhX82zyskgKT7hq32y0CmvfJ9+f3+yVtjVpQtVoVrZmHMcZiMbz++ut46623MDU1JW0MT8pZJWalXTqdxtbWFqampkQYclxNH/NJY8deAdvb27h37x4++ugjLC8vS0Uen4snSXD+0gV18+ZNhMNh6X9MVw9zvUdHRzuO8uF7MXuJrK6uijsvHo+L39hshUq3G49oonLjcDhEG3+ZEKMywmO2eE4dABG6J8kGzoFEIiEppCz/ftkGSdlglvOz94VVWCp0Wb7n8/lkNzZTg7oHixPWPLqHuZu9fp8TwIyKHx4e4sGDB8jn8/B6vdjd3UW73e5oe9eLQqGAu3fvotFo4NatW9K5iabU0NBQR7SVGt2DBw/wi1/8AsvLyx3uD6ZzmcKPJi0DT6Ojo7h+/bqY20w7o0ncbh/3c2CFFjuHcaKZZunq6ipmZmYwOTkp1+oFNR1qWfQxc9x++ctfYmVlRZpW808vdwQLFUZHRzE7O4twOIzLly9jYWGh42w3h8OBSCSCWCwmY03XBTMfTFcB/YoMaHJuABAtzOVydQTsKMT5+3TT+Hw+cRddvHgRzWZTXB2XLl3CxMQEPB4P0uk0tre3O46/6RWA4niUy2WsrKzgl7/8JWw2G958800MDg5K1gTn5WnvATjO7jk8PMTKyoqc6cXN0GazSRkte8VOTk7K0U/9/f1YWFiQwyx5GjLHtFe/BGr57PVwcHCA1dVVrK+vI5/Pd5zkwnHguXgsL89ms9Kz+cGDB3j48KGckNwL9nBot9vSoJ1FUclkUrJA6CrhWiCmHzoej0uBkTlWvcaXAT/OJc5Z9rSwCsvKgBlAYVSUgjeTyWBvb08qW7oH1zyHymaziVnK/6fGMjExgUuXLiEajYp/8OjoCDs7Ozg6OsLdu3cRDAbRarWQTCY70od60Wq1kEgk8Ktf/QorKyt455138Lu/+7u4fPkyxsbGOtwFdIM8f/4cP/vZz/Dhhx+iUChIcv7Nmzclv7jXuJgTPxqNdvjbTJ8b/cOJRAK7u7sdGRzmomWgj4vuZa4BRu2dTqdEhe/cuYN/+Zd/weLiovjPuUk4HI6OYBffg8/nk1OIx8fHxYc4NzeHd955RwpA6BZhSiD7YFQqFdH+KSDMEyXoIjGva1YwmSdENBoNOU+NgmpoaAgTExN47bXXJAWLqUrMWKD7gQ2DhoaGsLGx0dHVzhS2vCf2v+DhkbVaDe+++y6Gh4c72jSeBucAq+m6O29xThSLRezt7SGRSGBhYaHjPZuuA37O9CX3Ei5UPuLxOJaXl7Gzs4NmsymnP4+NjUnmkLnxcu4nEgl88MEHuHPnDuLx+EuDaNyI9vf38eGHH8Jut3do7zwKiv5x+m+3trY6MlWocDBo2Kt4hs9H5WZ0dFSsL87HXC73P7MMmK4EmkKsfGHX+4cPH/Y092kOnvQi7XY7bt++jT/90z/FrVu34PP5kM1m8cEHH+BnP/sZlpaWpFEIF7DdbheXw8uoVqtyvPfVq1fx7rvvSmWcuYhqtRo+/PBD/OpXv+poxLO+vo579+7hJz/5CcbHx3teg0LrJFOKApuCamNjA7u7u8jn8y+4WSgUQqEQRkZGxIf3MmjC05f5ySef4De/+Y2cGkGBS03B9FUCXwkMWgKhUKij0Q1LdYGv/Jy0YJjlkU6nJRuDlVQMAFHTZwctjonZZ5eHF/JQTB4wmUql4PP5MDs7K1Vc3BC6x4bB0nA4jNHRUQwPD8Pr9XZobtywTf8w84qLxSKKxSIGBwfx1ltvdTS7P8s7CAQCEsijUKPFB0Dexc7ODjY3N2Vz7z5stNccO+26qVQKd+/exfr6uqyLcrmMX/3qV7h16xZu3LghbgOuJb4jNlDf2Nh46TMCEItsfX0dq6urHXnTPp8PV69exR/+4R/i3XffRTgcRqlUwt27d/EP//APuHPnTkcxDCs6T6PVOu7mtrCwgDfffFMa0W9sbGB7e/uFqr9vGkvdCxR6kUgEU1NTGBsbkxLYzc3NjkXcrcH1ot0+PjX1D/7gD/BHf/RHklJGU/n999+X7zADEMyhpFlymm+YFAoF5HK5nqeg0uxbX19HuVwWHyLvcXd3F5lM5tQ0mJOe1dROHA6HaG1MJ6PWaWrLFLixWOylWm73tRjsoR+R2iLwVS8G5rvSNURBNTAwgMHBQcmsOEkAmDm9LIOllsIFHQwGMTIygomJiY7UKEbq+cxm46FqtYqDgwMJYtEMZeDO5XIhFouJED9pDEzNKBaLYWBgQLRn+or5eQaLzOdk4xWO2atoTzwFgxsXzX6mQfE9M4fdLOU+S2HBSc+cyWSwu7srbhlSLpdFEJvfbW7SrMLrnvcnXYv32X0iBL9/aGgIb7/9Nq5fvy5zaHJyEolEQjKXTqNbdjDOMD8/jzfeeAN9fX3Y3d1FNptFMpn8rU41/jrYXhLlO7e8CwZNTHOavkGz1PNVYY5k9yKiuckdtNdkfJW0Ei62XicB8LtoOndD0/tVBGAvOFbdfYTNezSFxqsueMIc0V6dxUyz1XSDmBrQST2Ge2H6ks3UKv4xgz68lnlN3gv/jZqhmVVi3turNN6msO6+N/P5zfsxx8k8R+5VMeMdZpqh+f28BjXcr2sW033Vyx9K98VJ894M5p6Vk76L78l0JxGeLvPbygmzFStddrTAWf58jpz4QiwTuoqiKP+LOFHoWloccZrL4OskM7/MBXFenEWbOIur4jw4Sx7iN/n9533dXtf7Os9wnt9n9Vic9bpWzamvM+9/G76p9Xya3LEye0E1XUVRlPPnRClunfdYURRFUaGrKIpiJSp0FUVRLESFrqIoioWo0FUURbEQFbqKoigWokJXURTFQlToKoqiWIgKXUVRFAtRoasoimIhKnQVRVEsRIWuoiiKhajQVRRFsRAVuoqiKBaiQldRFMVCVOgqiqJYiApdRVEUC1GhqyiKYiEqdBVFUSxEha6iKIqFqNBVFEWxEBW6iqIoFqJCV1EUxUJU6CqKoliICl1FURQLUaGrKIpiISp0FUVRLESFrqIoioWo0FUURbEQFbqKoigWokJXURTFQlToKoqiWIgKXUVRFAtRoasoimIhKnQVRVEsRIWuoiiKhajQVRRFsRAVuoqiKBaiQldRFMVCVOgqiqJYiApdRVEUC1GhqyiKYiEqdBVFUSxEha6iKIqFqNBVFEWxEBW6iqIoFqJCV1EUxUJU6CqKoliICl1FURQLUaGrKIpiISp0FUVRLESFrqIoioWo0FUURbEQFbqKoigWokJXURTFQlToKoqiWIgKXUVRFAtRoasoimIhKnQVRVEsRIWuoiiKhajQVRRFsRAVuoqiKBaiQldRFMVCVOgqiqJYiApdRVEUC3G+5Oc2S+5CURTlfwmq6SqKoliICl1FURQLUaGrKIpiISp0FUVRLESFrqIoioWo0FUURbGQ/x8lNqDlq0kSRwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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dbG5uolQqdVggvZ7VXCDm8/cSuB6PR7S89/HR891T0FMb4nUodLjRkV5ZEyYc+62tLQBANBqFw+FAuVxGX18fQqEQ4vG4+DxNS8e6AfBdU3MeHh7G5OSk3NPS0hJ8Ph+mp6flva6treHx48dYXFxEoVCAx+NBIBCA2+2WzbhSqSAQCODixYsIhUJyj9agmunntUJf+6tXr7C7u4tqtSoaMk1y64ZFi4zxht8VmvymVm4K027ygfTKXqDFVC6XUS6XZaNrNBoIhULvtEyPC1s1XdOMZMBiYGAAfX19qFarh076bouTfl/TDOVkNoMsvwtOpxO1Wg3ZbBYHBwfyYqit9PX1IRAIYGBgQJ6hWCyiUCig2WweGlDjM5hj5HQ6USwWsbKyglevXiGfz8PpdMLr9cLv98Pj8ch4uN1upFIprK2tYXx8HIFAQHbzbDYLh8OB4eFhxONx0ZTr9Tqq1arcW7dNyu12Y3h4GHNzc9ja2hKXR6VSkdSgbDYrPrV3jTEXvfXnKAA5FlxofJ/ckI/in+fPMdOC84Hjamq7vK55PzRND3sWaku5XE7M3oGBAQCvN6rBwUFMT09jbm4Ow8PD8m6tAVIG/GjpuFwuDA0NIR6Po1KpIJ/PY3NzE+VyGQsLCxKZ39jYQDKZRDablQArALGyzKBlKBRCsVjsSNUzn/Uwms0m8vm8+Ni5Vjn/TO293W7LfKjX68ei7JjWJBUpZqpwzvL5re/0sO9k6hlTxkxXhFUp+CqxVeiWSiUJdHCSjoyMiIPc9AmaDn/r4nC5XOjv70cwGEQoFBJNlIEna3Dqd8HhcKBQKGBxcRGnT58WIc9n8vv9mJubQyqVQjqdRjgcRiKRgMvlQrPZPNRBTy3XfEYuVJr13Kjq9Trq9boIeAqNXC6HSqWC9fV18fvS9+n1egFAhKzpF+X4UqOk5muaoF6vV4QKhS0jw7Ra+By9xtoauLG+S37G90rhwA2Mi+IoQpcZDNTEuHAdDodott0WJ+/NDOb2ehbgtcabz+dRr9fR398Pj8cjQmlgYAA+n08WtelC4YbH9DM+H/3StVoN+Xwe29vbEp9ggIhKC8eF309fbqVSQa1Wk8wS/n5fX5+MK9/TYW4FWiVutxtTU1M4ffo0crkcRkZGMDc3B5/PJ3MJQIeflJvR74rpOmSgjq6XarWKXC73VvDZfEemFcx/o7sqGo1iaGioI67wv1bo0vT2er0yGX0+H0ZHRzE6OoparSZ+OHPSW80ILqRgMIh4PI6xsTH09fWhUqlgb29PdvvjzNNNp9N4+PAhEomE+HKZMhQOh/Hhhx9ieHgYuVwOfr8fs7OzCAaDHZpTN9+pNZWHQpi+LPN3mLfMHFGXy4VyuYxSqYRSqSRpdFzQDocD/f39yOVy6Ovrw8jICAYHB0V74GKnph4MBsWcp/9yb28Pu7u7SKfT2NvbkyR5U2MDIILSGrgwBa654LttrBSOpmZLYXuUDZRCzdQeqfHyGswEMV0J1lQqblzd3g+fk8/NOAU1KABIp9PIZDIoFosIBoPyPdVqFYVCQQKO3LybzSZyuZxkcbx48QLpdLpDuDITgYLd7/eL8Nvb25NUKXOuMLBoLSjh+Fufz5yXrVYLwWAQ165dw/DwMMrlMgYHBzEzM4NwOCwbAK2qx48f49GjR9jb2zvWdUdrLhgMSl40A8m0wMzr0YI2XWacY16vV2SN1+sVfzoViMnJyWO576PwlQtdvthsNot79+4hFouhr69P/FkMFjgcDokOU+vlArdGkj0eD2KxGC5cuIDp6WlJmF5eXpbI/3HRaDSQzWbx6NEj2SFPnTolAiwYDGJubg5TU1Ni8jBBngKkW9DG6h8G3kw0l8sFv98vk8zlciESieDMmTO4cOECxsbG0Gg0sLa2hhcvXiCfz6NYLEqeLMfL6XSiUqmgr68P8Xgc4+Pj8Pv9HdH4VquFcDiMsbExeaZqtYq9vT1sbm5ie3sb+/v7KBQKYolwopuCylzMVreBdWGb79L8fY6XWSFmpVfwz/w7Bbbb7Ua73RbB63K5RPBa76ObO4rPYW4g/HlqRzR76dLa3t5GMpnE3t6ebGScQ6lUCrlcDk6nE4ODgyKUc7kcVlZWsLCwIKl35hgz5a6vrw/Dw8P44IMPRAFIpVJ4/Pgxnj9/jr29PTSbTXi9XglCck6Z88+0dHrNTZ/PhzNnzmB6elqEHy2iVqslwdWXL1/i1q1bePTokQTFjwuH43VRzNjYGGZmZhCNRtFoNLCysiIZJNaMGW5UVFz6+/sxMDCAwcFBxGIx+Hw+VKtV7O7uiktke3tbvv9dlsBxYJvQTaVS+PnPf465uTkEAgF4PB6JeMfjcUQiEZTLZeRyOezs7CCVSnW4CoA3i8Pv92NqagqXL1+WJHIGeDKZDHZ2do71/kulEtbW1nDv3j2Ew2HJT2QCPSuTCO/Zahbz3yhYqIlRI2FBhMPhQDAY7BDsFy9exPe+9z1cuHABwWAQlUoFz58/R6lUwubmppitptB1uVwoFovI5XKoVquikfFzBjTr9Tq8Xm9HmTM3D9MvaQpcqzCl9mgKXKuA5PeYn1ndD+bPWDFdELzPwzJVrILVFKBWjdtqcXAMzGc1/aKmgDL9w7RU+G/NZlMKRdLpNAqFAvx+PwYHB8VkZkwjl8uJJkxzl9fk905OTuLDDz/EBx98AK/Xi0KhgNnZWYRCIQm8BoNBKYQx0+BMa4CbOy0f6/jT7WSd12bhC9fE3bt3sba21pExchz09fUhHA7jxIkTOHfuHOLxOABgaGgI2WwWyWRS8pVNV4Pb7RZhHYvFJGOH8ZCtrS2k02mx6F69eoXvfve7mJub+98hdEmhUMC9e/eQzWYRDAbh8/nEVIpEIhgbG4PX60WxWMTTp09x584d8VO9ddNuNyKRiLgXOEipVAp+v//Y83Tr9ToKhQKWlpYQDofFj2wmZZt16NS0qAGxjLRarYq/jalug4ODCIVC4mNyOBxS/RSLxVAul5FIJPDtb38b169fl4R7+q+XlpbwxRdfAOgurDhWp0+fxokTJxCJRMTX3Gq1kMvlJEhHH67X68Xk5CSuXr2KUqmEWq2G5eVl5HI5WbBWYWlqVUBn3wwAHRuK6UOl6UyNEehM8SIUEGbwldYSs2BMvxyvz3dEDY1+Qaugpe/TvL5pMfCPufmY1wiHwzh58iS++c1v4urVq0gkEggGg/L9fr9fIuThcBijo6MYHByU8Tl9+jTu3LmDzc3NjvxT8z58Ph+mpqYwNzeHRCIhkXemyTUaDWxubsLv9yMajUqqGL+DPmOWTNPs9vl8MqbmH7MHBYOI1HKTySSePHmC+/fvSwHFcVejOZ1OBAIBRKNRxONxxGIxCdyNjIz0zDbgnL98+TLOnz8Pv9+PSqWCVCqFTCaDVColCkexWMTS0hJyudyx3vth2KLpAq99utlsVpLjA4GAVHqdOHECMzMzGBsbQ7PZlDzQdDotKTXm9zHtg8UWZhUSHePHueMCkKT0p0+fdkxQr9crvlCrq8CcrOVyGXt7e+IfbbfbGBkZEVcCNSmfz4d4PI5Tp05JhsD09DROnDiB4eFhCaLRROXi7e/vl4XEoJHb7cbIyAi+9a1vdVQRtVotEXaFQkH81LRAuBgDgQBCoRDC4TB+9atfSc4mN0KavlZhBbxJwqcvlX0RzCohms3BYBB9fX2ieTCAaAq9gYEByQVlCWe9Xsfe3h729vY6Chd4b3Tz8Pu5eXI+8j5obXi9XnFJmEUr1s2DBQh8hsHBQZw7dw4/+MEP8O1vfxszMzNSPEDNixpxo9Ho8FFyrG/cuIHFxUVx6VBQUuj19/djcHBQ3EAsUWamyYkTJzA7OysW2NzcnJjTvH6z2UShUMD6+joymQwcjtfZLZxDTD8zLRBTgaFvent7G48ePcJnn32Gp0+fIp1OH7vANdez6YOm5VAul7u6oDie09PT+OY3v4nz58/D5XIhlUpJ9eDW1hay2aykXppz+rjlRjdszdMFIJoeHzIUConWx1LXmZkZTE9P48WLF5J6xUFn5PjFixeIx+Not9vw+/3Y3t7G+vo60um0lIseJ5xwu7u7ePr0qWgTsVisQ6MB3pjBhIKHwa9MJiP3zXQcmnJ9fX0YHx/H/Pw86vU61tfXJXBGbY5Rb0arqaFSWzaF8ocffog/+IM/wPT0tCzydrstWpC58M2yUZrxJ0+elLLg1dVV8RtScFMbYQMb023CCDv7a5jjQRMwEokgGo1iYGAABwcH2Nvbw/7+viwq5kLH43GcPHlStHUK0Uwmg9XVVSwuLiKZTIq7xO12w+fzYWhoSDarSqWC3d3dDjcPMTNieN+mGQ5AgmEcf/5cIBDAuXPncOPGDZw8eRKBQKDDTw1A/q3Vaok1wWCp0/m6N8YPf/hDrK2t4Te/+Q2y2axs3Ayi0f1jlofzPpl25nA4MDU1hYsXL4oP38ykYCk5hS7fP7+fFlyvTmuVSgVra2u4f/8+njx5gt3d3a+ksx8DXel0Gmtra4hEIqjX61LezliGKXipuAQCAczMzGB2dhZjY2NSkNVqvemPwpzmbhkQXzW2dxkzgzFcsOl0GtlsFqOjo2LqsNLGmjZG8+bVq1dSoTU2Nob9/X0sLS2Jr+ar2LG4y+7u7mJjY0OCIuYENaP0pvBqNpvo7++X1Dl+zjQjM1vB5XJhdnYWwOvFymTuTCYjWizdHWtra6hUKh05jQDg8XgwNTWF69evY25uTjRkq2lPAU2N1eoy6O/vl2hvoVDoSBUDIK4Jdg7jxkAtpVAodPgSzbHp7+9HOBzGyMgIBgYGpEEOtW8zkHLp0iVcu3YNs7OzCIfDkquZy+WwtLSEQCCAZrOJ7e1tuXcKduatMkhLbRrorAzknAwGgzI2/B6+d24g5hjk83kJYLGyydQWOY4UkmZGhRldn5ubw/Xr17G6uio+evNZmBo4OjqKdrstgTi6C8LhMOLxOM6cOSMZNGZmCTdbCl5uPrQ4uBGYATbTv83nyufz2NjYEEv0q0i3MpWclZUVeL1epFIpbG9v48GDB3j16pW0ByAcS8ZDPB6PpBJms1nJxMnlcrI5f9X+227Y2vAGeOOMb7fbHT0Y2CVpeHhYdiFqTNaOQawMYtUVMwf4e0eNoFqj1UfBTKimP8xcROZ3E1Pj29zcxNraGgYGBjAzM9O1Dyp36xMnTqDdbmN9fR2lUgkvX75Eu92WnhOsR0+lUqjVah3fFQgEMDo6img0+lYUm6ar2SymWxVfq/W6inB5eRmPHz8WM5KCn4twcHBQmtgwUZ4d5CjgzEwUa6YD3Q+FQgGFQkFcRxSCzE+enJzE5ORkhzYaDAZRq9Wk2xsFK5+lUChI5J/Cwbw274elwwwg+f1+hMNhMeXz+TxqtZr0ITCtr3Q6jcePH2NpaQkTExNv9TYwff4UEqZfmH9n97qxsTFsb2+LVu1yuVCr1bC1tYVPPvkE6+vrmJ6eRjweRygUkmyKkZERJBIJnDhxokPbJnRH0L95cHAAr9eLRCLRYSFZU654j263W5QE+ui/jIZ41HXHeZHJZLCxsYHt7W2sra1haWlJmhNZ4VqkVdNoNMQtuLS09FY3PGsBix18bf10WT3EdBkmdcdiMWkraAYrrC+30WigUCggmUx2pGgdFdP8fR8fME1jNhQx+xiYP8PFTMG1urqKmzdv4tatW0gmk5Kmde3atZ7XCAaDGB8fx8HBARYWFvDq1SssLy+LG4UluGbaFH2pXBzVahX5fF7+39wgzJzGblkGdG/cvHkTz54969pk3O/3IxaLSbe3TCYjzXrok+ZGaL4fvn9qtUydYg8DWiutVku+jz5vs48tTWtejwKeY89npB+2UChIjqnpKzQbJjGwFgqFEAgEEIlEpI/y5uZmR3tJmsFPnz7FzZs3MTMzI8UR1gwNc/Mz85CZEcAsE2qedCEBr4tlUqkUdnZ28OzZM7mvRCKBmZkZnDp1ColEAvF4HMFgsGdTo1KphOXlZTx58kQKMajtnjlzpqNAxTqn6QpjI53DCn8OWzt0tx11vdKtwQBeoVDoKnA5l5nF9PLlS3g8HiSTSbx48QIrKyvi++/W2MgubBe65k7PxZHJZCQAtL+/D7fbLY2xD2uN12w2peSWVSvUMg57oeZC4PccBf4O/YxmB6hez1oqlfD06VP89Kc/xS9/+UupvqPZe/36dck1tkIh4/f7USwW8cUXX+Dx48dSCGHmJDKqzwh/OByG0+lEOp3G6uoq2u02hoeHEQwG5ee6Nb0B3myMpVIJjx8/xt27d7G/vy/jZAqlRCKB6elpSfkrFovY3d3F7u7uWxPc+v00+zgPWPFmFidQo6Iw5WZm3jcLa6zdpUwXB3+XPT+sC46Cl8KPzzg0NIRoNIpIJILp6WnJtTV9wpy3d+/exdWrV0Xb5Vh1m3d8LlaU7e3tYW1tDbu7u5INAUA2CCbxU7tk7wv2v4jH41I00atxS71ex+rqKu7evYuFhQVUKhUkk0nJWf3Rj36E8+fPd7glrNAtxJJgq/vvXVg3oMOgAuHxeMS9VSwWe65X02JjQ/NGo9HRTN86H+305ZKv9bgeLqparSapS0xkZvUVzbpuuZisjMnlchLIYarLYdHUbgLgqHAxApCAIMttrdfgJP/pT3+Kn/70p1hfXxdhyfaLjx49wqVLlzA6OtqzT8PBwQHW19fx8uVLbG9vd1SdUXtgsIlBJrOH7YsXL1CpVDAzMwOPxyPaea/UOi4immUbGxsddfUulwvBYBAzMzM4f/48xsfHAUD8b1tbW9jf339rgpuLk/5xZgqYbg5z42T5JtPkaBbST85/4wZF7d/0p5v9JlhKa00v4zszMy0AoL+/X1w9ExMTuHDhgvS/ZZAXeC3QNjY28PTpU1y/fl0apXcbW/47y7K3t7elGfze3h78fj9mZmak3WQmk0EymZT5xnXAdMSBgQF88MEHXRt3E24MDx8+xIsXL6R/AzdK9mkIBAI4c+ZMT+uH7TgBdD3+6DDed60xN7ndbqNQKIgl0E3oMi7B1gIDAwOSmcDslq8qs+l9+dqFLgUvNQen04mhoSGEQiGMjY1JMn8ymXyrKQ4FG4/6MXMxu7kkiDUi+z4wvYjpSsViUU5TsE7AWq2GV69e4fbt29ja2uqYME6nUzo5bW9vi0/bTDlj1HVhYQEPHz7sELi8f0a3Z2ZmcOPGDUxNTQEAstksdnZ2pN0fq/RYRvyuxcKcT0aJTZ8f/atnz57F1NQUHA4Htra28OzZMywuLnb4fs0xN/2ovH+a14zQ+3w+DA4OyoL2eDyIRqNyLFOpVOroZlUqldBqtRCNRqVIgBsbzXM26jGrHE2t3TSTzbRD/pfaXSwWQyKRkGbtGxsbUj1GX/vz58+xubmJRCJxaNcqCl+a+w8ePMDOzo74yE+fPi2tQdfW1vDJJ5+8lUvKe00mk3jw4AEuXLiA8fFxscC4SVJYJpNJvHz5UioRTR/31tYWbt++jWvXrmFmZkaUGMJ1ym5m9Id2i7kchrnmDtOQuRZarZbkAO/v73fNlDDnyblz56Tfbz6flwwHs8rx/7TQBd4kfzMFxu/3Y3JyEidOnIDT6cSJEyfg8/nw6aefSvNuE+7WTGGi4D5M6JpVTe9jXlg1MSZYN5vNrguMwUJWGVkjrQ7H605VNMPNEzQo1B8+fIhf/vKXePz4sQgYK6FQCFeuXMEPf/hDxONxEQg0rWiK1mo1aTZ92FEy9K/evXsXz5496yhLpfbBxjtM63nx4gUWFhaws7MjTXqsrgvTh8p3Zw0keTweTE5OykkE9E+PjIyIxkOXCvNu2+225CCzmCOfz2NrawsbGxsdftx3dUUzFYFGoyHVjRTyzK5hi0PTT1ssFvH8+XPcvXsXZ8+e7WoBmdepVqtYX1/H7du38fjxY9RqNTkG6vz585icnJRTUQ4ODsQnaWK6sH75y19ieHgYV65ckVxmznFahNbCEH5HvV6XoGev8THbInIjooVy1HXEzYDvvVfQm2uYwpbN5rv9vMfjwejoKK5du4ZvfetbmJiYQLPZxNraGtLptNw7n/Xr5msXukBnSaXX60U8HsfMzIxURrndbimRtOZXmr44M3fRjLqamD5ZACIQjwqDaOFwWIIJvWDhA3vumilT9IlGIpG3+vWyVv/x48f42c9+hk8//bRnXTstA1acDQ4Oioa2uLgokep6/fVpu9FoVI6r6WYe8vqvXr3CrVu3sL29LZoCf7ZSqWBnZwdLS0uSqbCysoLd3d2O7BFrChq/33o9fjdNf7fbjXg8LhsuF2q73ZbTNExNl/7qwcFB2ehWV1dlA6CF0c207ObbNbVA+k8XFhaksGFvbw87OzuiPfO9ulwuJJNJ3Lp1Cx999JEUv/Qa42QyiU8//RS3b99GMpmURuFmMyf6NKn5bmxsdA0qZzIZfPLJJ1IIcvHiRSkj5zPyjLNgMCgbuDkfOVd7ubloFbCM+bCf7QV/H4Dcg/V5zE2B1m2vDZMB57Nnz+J73/serly5ImmWtVpNMi1MC+vrxnaha90VOclZJttut6VajaWTzWYTqVQKyWRS0sTMBWSaSdZosdWMZG7o4OBgR0u9o+yA9J+GQiHEYjGMjY31DDrwHmgyM3DFe/T5fBgfH8fJkycRiURk8lIr2dzcxK1bt/DZZ59JSliv8WQurXkUTzgclpNhmelRr9dx69YtzMzMIBKJvOXS4BhWq1WsrKxgZWWlQ4jSTKVQqlarCIfDopVbfbimoOZ77oY5B9gxbX9/X05NoF+Pgtl8t9TcqLnTpWCeVGwG57rB1DTeizm3uKHv7e0BeO3CyuVy0tHO3AjpD6Z/dn5+XjYN67Pm83k8ePAAt27dkrxc9vJgtgQ1SVbLHXZ0OAtYPvnkE4yMjCASiUiWB+ctXTDj4+MdhQX83FpdaYW+/FgshlgsJvnSR/XrMh+Z850bNjdFMwPHmllkbb9orueJiQlcv34d8/PzmJiYEGuUpzkz57nbpvs+WvpxYbvQ7bXr079FVwEnm8fjQTwex9WrV7GysiJJzkBn1oFp/vI6pqZFAchm0YFAoMPXedR7Z2BneHgYkUik5xlYpr+Z2pj1O8bHxzE1NSXHilMzZ3Dl5cuXUuzR636cTqcUCVQqFYRCIcnz5amyPPSRwnxhYQFXrlyRI4ismEUb3aBw2dvbk1MFuGFafXb8L9+HVbO0ZiKwBeja2posGroZeG+VSkWELjUgBqXYiIUnFJtxAHMDMK9vBhVNf7P5Lpl6R79yr3xwh8PRtQmSCVsyvnz5EltbW5LOxvaJPH6J40UXlNmxzio8uGml02ksLCxge3sbiURCqtjM5jETExPSoMbcUMvlsmSTUBhbx4lzn+mS7yt02UebPUdSqZQEXU3Nm89IgWt1zVE+jIyMYH5+HleuXJFDOmkJsZCqXC73tHTeJxB4XNgmdM3It1WbMKPGOzs7oqFQe/P5fJidncVHH32E/f19PHjwAJlMBsAbjddczGbuKbW/QCCA4eFhxGIxDA0NoVqtio/4qGaHKSC4m/aadDRLnzx5gq2trY6JTKFI/ypLgDnR6Ks0TWlz8lmFSLFYxPLyMnZ3dzE0NCSLfmpqCjdu3BDXBINQh2mdFBrnzp3DN77xDbx69aprhR8tBE7+bmYiv48bCt04fGemRWIGtOr1OtLpNNbX18V1wAXeTTM3Cyx4aGMmk5FUNLMZkTmGvCaDo9zwrJiaODXpbhF0h+N1d7gPP/wQ586dw8DAQM9FbZZSB4NBBAIBXLp0Cd/85jcxNTUlwrJarWJnZwcrKysoFosd751/Nyvn2u22HNpqzjlaRJFIRE4aNs8nrNfr2NrawtOnT3Hjxg0JDlufjy0e+S66BZB7YWaPsNlTIBBAKpWSoDQ3UOBNLw9TVpjvLBKJ4OLFi/joo48wOzsrJ11zrqfTaXEDWV0T/B5mt/TaIL8KbBO6DDQx+GKdtK3W6y74W1tbeP78OSYmJuB0OsUvNTQ0hEuXLkmlEwNUJhTezO+lacquTuPj4xgaGgIAySU104PeBScZF2EvM4yTeGVlBU+ePJFG08CbSKupcZtaFoMa7B/Ac9HMP2ZQjhbCysoK1tfXRbth79WLFy/i4OAALpcLa2tr0jSll0DgMyUSCXz/+9/H559/LlpWN+3KXLTdJjVT15xOJw4ODiTdiD9jLUdmcIpaW6PRwPb2tuS2moUNphBgQIsVcdwQuKFxAdPVwPfHhP+BgQEZUzNPmP9PAd6rjSTvZWZmBt///vcxOTl5aErewMAAYrEYpqenMTQ0hBMnTuA73/kOLl68KEEwbiQbGxsidHkdzkcqFfxDP2u3ftQskY3FYhgeHsbOzo5sqKzofPLkCVZWVsRMt84RM8uDbpmjCixq7el0Wpr0+Hw+CU7u7OzIqRBmC8pu40dF7OOPP8alS5ckI4e536urq3j+/LlkDXX7Hr578xguO/jKhS5fEh3oHo+n50GDLLO8c+cOPB4PisUiZmdnxTc5OjqK+fl5rKysYHV1tcNsNzGrfjwej6SfjY6OwuPxSA32+7Sj4+I+rClIt2dhOhs1g2AwiEQigatXr+L06dNiQnMTqlarEmAYGhrC1NQUarWanFSRy+WkftysVkomk9ja2kK5XBZfrcfjEfPL5XJheXkZTqezI3m/F2zWfvLkSTx79qxnEIobganpUnBTCDDRn4upm6bJ4hYKklarhWw2K9VHbOHIAgr+PlPNzOIQCkYKXBYimL5D8x77+/sRCoXk3unrN+cTBT6fw4rD8boT2OzsLObm5o40vhMTE7hy5QparRZmZ2dx7ty5jswSBg43NzclZRJ4UzTAvhVsFMXUOx5CWiqVxOdNmIebTCal4pD9MVj9R2FsdS9xk6SAN9cDheS7qNfrMoeZCsj3zU2ZroZugpLvjsGzixcvSqk74wFLS0u4f/8+7ty5c2g8hPPHDIjb4W6wtffCuxKT6YNZWFgQf2g+n5cGxmxVyOj7YTSbTTErzXOrisUidnZ2xB95FC2Xplk4HJbqpOHhYfnOXs/CDAVG1UdGRnD27FlcuXIF58+fRyKRgN/vl3FhTwmmyNBnzGixw+HA5uYmHj16JIdWUvMtlUrY29vraAJCwTs6Ogrg9em11WpVjspmW8xeE81Mj+sVcDBNQavfmvcdDAZlozXdQWYpLE09anEHBwfSlIQmqfUMPFMIsGeCWW1HAe5wODpMVfNeKVwpvCl0qd3yXmlBHTZHqFkfltHCdcD3cfHiRfT398tRMtx4OD4M4plZNnSXzczMSPCo3W5L/IAlxPv7+wgEApKO12q14Pf7cfbsWfj9fkxPT+PBgwd49uwZ0uk0HA4HQqGQuCl6PafX68Xw8DBGRkYQjUY7emu8KyhF/zgzQHw+X4fwNt1Ah+H1ejExMYGRkRFpSMQev3fv3sWjR4/kJJnD7ukocum4sa2fLrUV04zrBgsClpaWZKGZQo/O/HdpEpy0pllZKBSQzWaRTqd7NszoBvNSx8bGMDU1hdnZWUxMTHT1exGXyyVClsGH+fl5zM/Pi7ClScwy1nw+Lx3MMpkM3G43xsfHMTExgbGxMTidTkSjUamZNyeUKcisQsnr9Uq0meNJ36lZam0GmlgJRb+fNZABdJZ0898pPNlWkcfQm6lVvE8KF1OoMThG85h/NwMhZizAFN7M5TRdSxQgprC2Xp/XoFbOn7UGYMxsDD6v6YJggGxvb0+OWTLH1LyW0+mUhkRmX2arq8madUHhzorAK1euYHJyEq1WC6lUCpubmygUCkin03j16hUODg4QjUYRDoelfSdjCWfOnMG1a9fw4MEDPH78GHt7ezhx4gRGRkZ6zmsWx0xMTGBmZgaFQqGjkfxRCiWohafTaSmGMbsPHiW9y6ysZOXZ8+fP8cknn+Dhw4cyDoetcW6sZv6vHcLX9n6676oKYTCDzWxYDjw2Nia+KmtktRem+csWdJlMBrlc7sg9QKktBoNBRCIRTExMIJFISA/YXuWSABAOh/HBBx9gamoKJ0+exNmzZ0WbAd70DGWd+NLSkhQzVCoV6TFMM5LBHhYO8CBA+k3pvjHvh0KQvWIpHChIOA5mBJ9pa3t7exKx7zW+hKY6zbWhoSFprs6uYd2CGWakmv5WBqyolZoLsZuLg2Np+sRrtZpkv1C48l1ZA3Hm/PD5fKI9sdGOdcPoBVPzaEWZ74Ljzu9hc/XDcplp/gYCAemUZlpQ8XhcNmRuODyiPZlMii84kUhgdnYWU1NTYj05nU6ZVxMTE5iensby8jJ8Pp+4WrplMDALKBqNIpFIIJPJIJPJiJV1lM5j3CDz+Tz29vYkmGWO87vgfbGoY3t7G8+ePcPz58+lKc5hLV7NTZDvxy6+NqF7GNQqrG3dgsGg1OwfdZDMLAYuPpqpR3m5Zp5kIBCQ0xR6pQSZke5GowG/34/h4WGZ8DTX+dKbzSZ2d3fx4MED3L9/H1tbWxIwYRBxYmJChCt7po6MjEjqnGnumX0VTCFj1WQpeK35j/y83W5L9zazsKMbFLhMyYvFYlIYQE3IPN2W99Lf3y9/GASh8DCFqHkdLnxifm4Gd0xTl/5eXss8ypwCn/8WDAYxOjqKSCSCdDotB3N267DGe+I844ZjZojwc3OcOQe6acKmYHe5XJLXOjQ0hFQqBQDSQD8ejyMcDsPj8UhK28bGBhYXF5HNZtFut0UrzefzkjFgzg36gKenpzv83tb+HtY1wYZNgUBAjnqiW+coLgZqxXxfZh/fo6xLul7YwGh7e1ua2vR6V73ug99nF19La8ejvBSzuxSPuYlGo2i1WpLofxTcbrecBOpwOESgHWWQzdxETiren3mv5uKlwDUPIqSZa0Z6OZGZk/v8+XM8ffpUtCQAkpDPzItAICBHSU9MTCCZTAKAbAgswuB3U0BZF44pvGjSm89BgTg5OYnTp0/jiy++eCsSbtVYnc7Xx8RMT0/Lacksbzb7MFD7DAQCciw8nzWdTksZard74hiabTnpCjA1UVPgcbOKRqNyLdPFxCALBY3b7ZbDDLPZrBSXdEuuN8eZRTNzc3NSvmsVqPw5qzuH32Heu/m91IpZaMFjrniuYLv9ujx6dXUVCwsL0m8WeN2wJ5fLSf9m9mg2750bAHsr7O7uyhlr1nTGbu+fSonP5+uantUN+vGDwSDGxsbkGZLJ5JGsWKZV8iABusJYJXgUoXsUN8ZXwddSBnyUBzUFGE02NlI5aqoXBQFr2R2O10edLC0tHek++/v7EY1GxYTz+/1otVoSYWW02JyITJ43jw9yOBwdu685gWke5fN5OcqIQrfZbGJrawvr6+vY29sTs5ebyNDQkDR7Pzg4kKPS2TDmsMCPVWM0TVpaBrFYDJcuXcLnn38uJdjdUqb4uyxkOXXqFEKhEJLJpPQu4O9YO5TFYjEUi0W8ePFC/NrdFjY3iIGBAUmLYj7mwcGBbB78PY6x0+mEz+eTzSAQCCCZTEpTazZ/4f35fD7EYjHE43Hk83k5U8x6P/x/+lgHBgZw4sQJXL58GbFYrMOyMv3rvdppWr+XbhI2mOF77u/vx9DQkJyVRpdbJpPB2toatra2OjJb6JsuFAodR5abgpfWYzablf66kUhEgmrWzYN+UAZt2VOZwpknSrwL5pKfP39evpf9ig+j3W5LTn8qlYLb7RYF513Vh9bv+Tr4vei90A3zBfOECdZUb2xsSJCjF9QSIpEIzpw5g7Nnz8LhcMgZSxsbG4denxrPlStXMD09Lf5bdt5nbbcZceX9FgoFbG9vY3t7G8ViEUNDQ28FgfiMrP4Kh8Pw+XyifVGAc9PhCcIMjPEQSaYybWxs4OHDhxgdHcXAwEBHLm6vaPphi5+5kNPT05ienpaS4G5jTiHBbAj2oG2321hdXe2oz6cWzcYuiURCCkHW1tbeKZAouOiiedfGwvSi8fFxnDp1SvoJ7+zsYHNzs+Na7GPLMle+bzMI1+uemA1w4sQJKWw47J56Qa2dxwM9ffoUjx49kr65pqXAOWnGBsxOdnQf+Hw+mV8cN2L6NiuVivRNHhsbE03a9DnTAqXi4Xa7MTY2hmg0ipMnT0rHNDZcOuzdhEIhnD59GmfPnhVB+uzZM2nO1Gu8m80myuUyNjc3sbKyAp/PJydCWC2y30dsE7qm74umzFF8Lu326yOX2eg8l8tJb9HDfp/aGk/MHR4ehsvlwsTEhOT1HYbP55MmGvF4XE6qpXZr3h/vgwGoXC4n52kBkLp5a3oWtbBEIiEHUS4uLmJ3d1cStkdHR0XL5u+b2gbbFnI8+Pn8/DxGR0cRDAYPFbx8DrOoxExZYnrSuxq2U1CwAxUPHOVJBix0AN6Y8lwg9JubvSP4Ds33yd8tl8syh0wB0+13aM6bPXrNAhPT7A+Hw2K2h0IhWci9uluZ12GKHFOzTFcBhfa7msOY2m0qlcLDhw/x3//937h37x6SySQajQa8Xu9b+cZmhePY2BgASJ/d0dFRzM7O4tKlS1I4Y25U/H2v1ytpmMyiyeVyGBoa6rCazPnOHPjh4WFRAqamplCtVpHJZGT+d8PlcokVOTIyIoKe1zMzQ6xQQG9tbSEYDCIcDkuD8qP6ZvncVnefHdgmdLnouTszAf0w4Utt7+DgQEqD6Y87zKdrRtKpqTBNh03SvV5vR3VUr+9g8KHdbktOLSeZ1YRnni1Pp93Z2UE0GpWDETmZ+II52ROJBJxOJ2KxmDQBZwvD0dFRnDlzRvqkMoBguiQofM1MhGaziQsXLiCRSBxaxGEKM/riqD3Rv0f/4Lva+DECTReCz+fD+fPnxU+dyWTQarUk6b5UKonpTFOV92nVyEwfuKltWxsaEev38HQGr9eLUqkEANKi0el0IhKJ4Pz58zh37pxUQzId6bC5ZroMOF6FQkG0cQpSzqdei9t0TW1ubuLx48f49a9/jdu3b2Nzc1Pmarvdlj6xDAYODAxgfHwc3/jGNxAIBLCzsyNBtPHxcUxPT2NmZgYTExMdx0uZrqRgMCgbJdMVx8bGpAue2fuCKZSRSESen/5mnvzyLiHGdcWgo1mFyXV7WIYRS8XZuNwsqniXMmbmhHs8HunlYRe2VaRR64xGo+LoNitQujnfacrk83kAkKoTs67cei2WkTJRPpvN4uHDh6hUKlLnTbfAYQKkUCjg9u3bqNVquHbtGk6ePCnJ4CyQ4AQG3uQePnz4EP/xH/+Bhw8folar4eTJk7h27VpHvTonOzMEeFJGPB7HpUuXOo6U4dEo1EIobHd2diSgSMFDP9fz58/laPNYLNbxHrq9H6aiMWWHlW8894v5npyw1vQtMyjHjY15ocPDwxgbG8PFixel6TcXClsM8pRXVhgelpxPN4Z5/+b1Tah18hosBx8cHMTly5cxPT2NWq0mTdmZ3scNmXOk22bD61KAVSoVvHr1Cjdv3pTeFcwrp6A67KQF/nu9XkcqlcKTJ0/w/PlziV/wmakJs2S21WpJ86ShoSGcO3dOqtDo/+YxPjx12tyAad309/ejVqthfX0di4uLePHiBfb39+FyuXDjxg35PfqvWfXW398vKWps2P/o0aO3SvRN6JuuVqtYXFyUI7cWFhYkeOnz+USjt651ztV8Pg+HwyHHJzEVtFdfjL6+PinEoOXJuc8yYDs0Xts03f7+foyNjSGRSMjLbzQaUgzAblrmxDZ9utQWzFw+c6emGTU8PCxpWDw94X/+539w//59qc7hwYI8AqYbrVYL29vb2Nvbw+LiIj7++GP84R/+IU6ePIloNIpAINDRSKXVamFpaQn/8i//gl/84hcSVa1Wq7h+/bpocSYUFh6PR8xTNuu2/qHfjpo+/cVmUne73Rb3RjabhcPh6Mi66AUXATW7YrGIx48f49///d/x29/+Fru7u2IpMO/VWn1ErZ3mXjgcljzUyclJXLt2TTZYnuaQTqfx/PlzeR4ms/OdmsE+aozW61LYmlaH6TZoNBpyenIkEhHhyjxrVqKxcMCM4ofDYckYYdaL9bqcA2zpWC6XpWjl6tWrktJ1FO2PlpXD4ehoe2gGvmhNbW9vI5PJoNFoiCCJRCIdGqy5IVn/zfr+2+02NjY28OTJE+nZu76+joODA4yOjuLSpUuywZhno7VaLWxsbOBXv/oVbt68iY2NjZ5rynzOdruNzc1N/Nd//RecTqcoE7VaDX6/v8MNuLe3h6WlJezs7HQEAq1d/KgtWzdsztuRkRFMTk4iGo2KJk1l4l3FVseJrT5dr9eLkZERxONxxGIxDAwMIJPJ4PPPP8fnn3/eNeJJP2mvSheHw4EzZ87gz/7sz/Cd73wHgUAA+XweN2/exL/927/J+WDAm5xbCu+jpJ3xyJ1Wq4X5+Xl8+9vfltxVc/LWajXcvHkT//mf/yn5swAksPAnf/IniMfjXbVzU+swMX3F3PHZFCiZTHb0iiX0lzKB/bCqOet9UDjXajXcvn0bv/jFL5BMJqX1oHk8OTVijiGFF5twM1+Vi5OVR3weZqEw6ry9vY1sNivv2Vzc3Lz4bFYo1Ex/Khcf3REOh0OKWih0u2V4mEFBuoUonHltWlNcuMwvZtpTqVTC+Pg4Ll68eGSBy/cfCARkQzAr8TjOTqcTpVJJem0wmGWe4sDxs77fw65bKBRw//59LC8vy3NWKhX8/Oc/x+XLl3Hu3Dkp9uCm6PP5EAgEsLW1hV//+tdYXl5+5zMCbwoaFhcXZS5QO/V6vThz5gz++I//GB9//DFCoRAKhQJ+85vf4J//+Z/x+PFjWRdm4dNh0Bq4ePEirl+/juHhYcnt3drawu7u7pHWyHFha2tHVkXFYjGcPHkSg4ODKJVK0prQjLyaputheL1e/OAHP8Cf/umfSg06AxeffPIJXrx4Idoov5uTk+ev9TJlzWsXCgU5ddgqcB0Oh5hKhUKho1FIu/36fLfDzK1u1zP/3/QBM02K/i+fzyf3T02J/jammB0V0+9IIcj3RiHIAhE+MxcM3UfUDq1mrAm/kz2JgTeNxHk2F//Oa/OddfP1siSU75ONzrkZ8e8OhwORSET8lL3GwCx0oNbOZ+XGTV+k2W6R2ihTr/hO3sdkZX+RkZER6cBFDZfXYQUX0/joozysj8a7MHNkzfmbz+exuLiIarUqzZl4HxwHZp9Y5303TO3bjOuYn8ViMXz00Ue4du2ajF8ikcDGxgbW19cPjcWY1zbXTTQaxdmzZ3H16lVpawlA7ttOoet4RwbBseVdNBqvz1eiX9OMxJv19e8LJ4k1uk5z3PQVWyfk+6SVOBwO8RN3m9jtdlvSyKxQOP6uznozjcw0s8x7BN6YU+/KOOgFg3XWtB2redrLp04z/SiwMqlXQr31Wof9TK/74mdmL9ijYB3rXvfV7T1Q8/8yi5kbh/UQUvP76drgWP+uvkhmhXTL0qD7ote8NysOj0qvtcg5RJ+r+Xm1WpVYx/tiZsjw+5hz3mg0pLDoGOn5QmwTuoqiKP+H6Cl0bS2OoPnV67Mvy1fxne9znaNc87ijou96tuO43pcZvy97XTuS2e28NzvG36459bvM+y/DYdf7quSEHVkLch+q6SqKohw7PaW4fd5jRVEURYWuoiiKnajQVRRFsREVuoqiKDaiQldRFMVGVOgqiqLYiApdRVEUG1GhqyiKYiMqdBVFUWxEha6iKIqNqNBVFEWxERW6iqIoNqJCV1EUxUZU6CqKotiICl1FURQbUaGrKIpiIyp0FUVRbESFrqIoio2o0FUURbERFbqKoig2okJXURTFRlToKoqi2IgKXUVRFBtRoasoimIjKnQVRVFsRIWuoiiKjajQVRRFsREVuoqiKDaiQldRFMVGVOgqiqLYiApdRVEUG1GhqyiKYiMqdBVFUWxEha6iKIqNqNBVFEWxERW6iqIoNqJCV1EUxUZU6CqKotiICl1FURQbUaGrKIpiIyp0FUVRbESFrqIoio2o0FUURbERFbqKoig2okJXURTFRlToKoqi2IgKXUVRFBtRoasoimIjKnQVRVFsRIWuoiiKjajQVRRFsREVuoqiKDaiQldRFMVGVOgqiqLYiApdRVEUG1GhqyiKYiMqdBVFUWxEha6iKIqNqNBVFEWxERW6iqIoNqJCV1EUxUZU6CqKotiICl1FURQbUaGrKIpiIyp0FUVRbMT9js8dttyFoijK/xFU01UURbERFbqKoig2okJXURTFRlToKoqi2IgKXUVRFBtRoasoimIj/z94PXiqHC8bZAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 61; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx * Ny,))\n", - "ub = np.ones((Nx * Ny,))\n", + "lb = np.zeros((Nx*Ny,))\n", + "ub = np.ones((Nx*Ny,))\n", "\n", "# insert dummy parameter bounds and variable\n", - "x = np.insert(x, 0, -1) # our initial guess for the worst error\n", - "lb = np.insert(lb, 0, -np.inf)\n", - "ub = np.insert(ub, 0, 0)\n", + "x = np.insert(x,0,-1) # our initial guess for the worst error\n", + "lb = np.insert(lb,0,-np.inf)\n", + "ub = np.insert(ub,0,0)\n", "\n", "cur_beta = 4\n", "beta_scale = 2\n", @@ -312,37 +1724,50 @@ "update_factor = 12\n", "ftol = 1e-5\n", "for iters in range(num_betas):\n", - " solver = nlopt.opt(algorithm, n + 1)\n", + " solver = nlopt.opt(algorithm, n+1)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", " solver.set_min_objective(f)\n", " solver.set_maxeval(update_factor)\n", " solver.set_ftol_rel(ftol)\n", - " solver.add_inequality_mconstraint(\n", - " lambda r, x, g: c(r, x, g, eta_i, cur_beta), np.array([1e-3] * nf)\n", - " )\n", + " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta * beta_scale" + " cur_beta = cur_beta*beta_scale" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "lb = -np.min(evaluation_history, axis=1)\n", - "ub = -np.max(evaluation_history, axis=1)\n", - "mean = -np.mean(evaluation_history, axis=1)\n", + "lb = -np.min(evaluation_history,axis=1)\n", + "ub = -np.max(evaluation_history,axis=1)\n", + "mean = -np.mean(evaluation_history,axis=1)\n", "\n", "num_iters = lb.size\n", "\n", "plt.figure()\n", - "plt.fill_between(np.arange(num_iters), ub, lb, alpha=0.3)\n", - "plt.plot(mean, \"o-\")\n", + "plt.fill_between(np.arange(num_iters),ub,lb,alpha=0.3)\n", + "plt.plot(mean,'o-')\n", "plt.grid(True)\n", - "plt.xlabel(\"Iteration\")\n", - "plt.ylabel(\"FOM\")\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('FOM')\n", "plt.show()" ] }, @@ -355,23 +1780,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", + "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - ")\n", - "circ = Circle((2, 2), minimum_length / 2)\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", "ax.add_patch(circ)\n", - "ax.axis(\"off\")\n", + "ax.axis('off')\n", "plt.show()" ] }, @@ -384,27 +1816,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n" + ] + } + ], "source": [ - "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta // 2)], need_gradient=False)\n", + "f0, dJ_du = opt([mapping(x[1:],eta_i,cur_beta//2)],need_gradient = False)\n", "frequencies = opt.frequencies" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "intensities = np.abs(opt.get_objective_arguments()[0][0, :, 2]) ** 2\n", + "\n", + "intensities = np.abs(opt.get_objective_arguments()[0][0,:,2])**2\n", "\n", "plt.figure()\n", - "plt.plot(1 / frequencies, intensities, \"-o\")\n", + "plt.plot(1/frequencies,intensities,'-o')\n", "plt.grid(True)\n", - "plt.xlabel(\"Wavelength (microns)\")\n", - "plt.ylabel(\"|Ez|^2 Intensities\")\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('|Ez|^2 Intensities')\n", "plt.show()" ] }, @@ -417,73 +1871,142 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "opt.sim = mp.Simulation(\n", - " cell_size=mp.Vector3(Sx, 40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution,\n", - ")\n", - "src = mp.ContinuousSource(frequency=1 / 1.5, fwidth=fwidth)\n", - "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", + "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution)\n", + "src = mp.ContinuousSource(frequency=1/1.5,fwidth=fwidth)\n", + "source = [mp.Source(src, component = mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10, 20))\n", + "plt.figure(figsize=(10,20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "opt.sim = mp.Simulation(\n", - " cell_size=mp.Vector3(Sx, 40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution,\n", - ")\n", - "src = mp.ContinuousSource(frequency=1 / 1.55, fwidth=fwidth)\n", - "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", + "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution)\n", + "src = mp.ContinuousSource(frequency=1/1.55,fwidth=fwidth)\n", + "source = [mp.Source(src, component = mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10, 20))\n", + "plt.figure(figsize=(10,20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "opt.sim = mp.Simulation(\n", - " cell_size=mp.Vector3(Sx, 40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution,\n", - ")\n", - "src = mp.ContinuousSource(frequency=1 / 1.6, fwidth=fwidth)\n", - "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", + "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution)\n", + "src = mp.ContinuousSource(frequency=1/1.6,fwidth=fwidth)\n", + "source = [mp.Source(src, component = mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10, 20))\n", + "plt.figure(figsize=(10,20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, diff --git a/python/examples/adjoint_optimization/Bend Minimax.ipynb b/python/examples/adjoint_optimization/Bend Minimax.ipynb index 8e9837dcd..1253d83df 100644 --- a/python/examples/adjoint_optimization/Bend Minimax.ipynb +++ b/python/examples/adjoint_optimization/Bend Minimax.ipynb @@ -17,9 +17,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -30,7 +38,6 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", - "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -45,9 +52,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.20124611797498096\n" + ] + } + ], "source": [ "waveguide_width = 0.5\n", "design_region_width = 2.5\n", @@ -60,121 +75,89 @@ "resolution = 20\n", "\n", "nf = 10\n", - "frequencies = 1 / np.linspace(1.5, 1.6, nf)\n", - "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = (\n", - " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - ")\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", + "frequencies = 1/np.linspace(1.5,1.6,nf)\n", + "\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", "print(filter_radius)\n", - "design_region_resolution = int(1 * resolution)\n", + "design_region_resolution = int(1*resolution)\n", "\n", - "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", - "Sy = 2 * pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx, Sy)\n", + "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1 / 1.55\n", + "fcen = 1/1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", - "source_size = mp.Vector3(0, Sy, 0)\n", - "kpoint = mp.Vector3(1, 0, 0)\n", - "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", - "source = [\n", - " mp.EigenModeSource(\n", - " src,\n", - " eig_band=1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size=source_size,\n", - " center=source_center,\n", - " )\n", - "]\n", + "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", + "source_size = mp.Vector3(0,Sy,0)\n", + "kpoint = mp.Vector3(1,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]\n", "\n", - "Nx = int(design_region_resolution * design_region_width)\n", - "Ny = int(design_region_resolution * design_region_height)\n", + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", - "design_region = mpa.DesignRegion(\n", - " design_variables,\n", - " volume=mp.Volume(\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(design_region_width, design_region_height, 0),\n", - " ),\n", - ")\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", "\n", - "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", - "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", - "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", + "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", + "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", + "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", "\n", - "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", - "top_wg_mask = (Y_g == design_region_width / 2) & (np.abs(X_g) <= waveguide_width / 2)\n", + "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", + "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", "Si_mask = left_wg_mask | top_wg_mask\n", "\n", - "border_mask = (\n", - " (X_g == -design_region_width / 2)\n", - " | (X_g == design_region_width / 2)\n", - " | (Y_g == -design_region_height / 2)\n", - " | (Y_g == design_region_height / 2)\n", - ")\n", + "border_mask = ((X_g == -design_region_width/2) | \n", + " (X_g == design_region_width/2) | \n", + " (Y_g == -design_region_height/2) | \n", + " (Y_g == design_region_height/2))\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False\n", "\n", - "\n", - "def mapping(x, eta, beta):\n", - " x = npa.where(Si_mask.flatten(), 1, npa.where(SiO2_mask.flatten(), 0, x))\n", - "\n", + "def mapping(x,eta,beta):\n", + " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", + " \n", " # filter\n", - " filtered_field = mpa.conic_filter(\n", - " x,\n", - " filter_radius,\n", - " design_region_width,\n", - " design_region_height,\n", - " design_region_resolution,\n", - " )\n", - "\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", - "\n", - " projected_field = (\n", - " npa.rot90(projected_field.T, 2) + projected_field\n", - " ) / 2 # 90-degree symmetry\n", - "\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " projected_field = (npa.rot90(projected_field.T, 2) + projected_field)/2 # 90-degree symmetry\n", + " \n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", - "\n", "geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", - " ), # horizontal waveguide\n", - " mp.Block(\n", - " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", - " ), # vertical waveguide\n", - " mp.Block(\n", - " center=design_region.center, size=design_region.size, material=design_variables\n", - " ), # design region\n", - " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", + " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", - " #\n", + " # \n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution,\n", - ")" + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution)" ] }, { @@ -186,47 +169,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(\n", - " sim,\n", - " mp.Volume(\n", - " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", - " size=mp.Vector3(y=Sy),\n", - " ),\n", - " mode,\n", - ")\n", - "TE_top = mpa.EigenmodeCoefficient(\n", - " sim,\n", - " mp.Volume(\n", - " center=mp.Vector3(0, Sx / 2 - pml_size - 2 * waveguide_length / 3, 0),\n", - " size=mp.Vector3(x=Sx),\n", - " ),\n", - " mode,\n", - ")\n", - "ob_list = [TE0, TE_top]\n", - "\n", - "\n", - "def J(source, top):\n", - " power = npa.abs(top / source) ** 2\n", - " return 1 - power" + "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=Sy)),mode)\n", + "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,Sx/2 - pml_size - 2*waveguide_length/3,0),size=mp.Vector3(x=Sx)),mode)\n", + "ob_list = [TE0,TE_top]\n", + "\n", + "def J(source,top):\n", + " power = npa.abs(top/source)**2\n", + " return 1-power" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation=sim,\n", - " objective_functions=J,\n", - " objective_arguments=ob_list,\n", - " design_regions=[design_region],\n", + " simulation = sim,\n", + " objective_functions = J,\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", " frequencies=frequencies,\n", ")" ] @@ -240,17 +208,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", - "\n", - "\n", "def f(x, grad):\n", - " t = x[0] # \"dummy\" parameter\n", - " v = x[1:] # design parameters\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", " if grad.size > 0:\n", " grad[0] = 1\n", " grad[1:] = 0\n", @@ -276,52 +242,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "def c(result, x, gradient, eta, beta):\n", - " print(\n", - " \"Current iteration: {}; current eta: {}, current beta: {}\".format(\n", - " cur_iter[0], eta, beta\n", - " )\n", - " )\n", - "\n", - " t = x[0] # dummy parameter\n", - " v = x[1:] # design parameters\n", - "\n", - " f0, dJ_du = opt([mapping(v, eta, beta)])\n", + "def c(result,x,gradient,eta,beta):\n", + " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", + " \n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", "\n", + " f0, dJ_du = opt([mapping(v,eta,beta)])\n", + " \n", " # Backprop the gradients through our mapping function\n", " my_grad = np.zeros(dJ_du.shape)\n", - " for k in range(opt.nf):\n", - " my_grad[:, k] = tensor_jacobian_product(mapping, 0)(v, eta, beta, dJ_du[:, k])\n", + " for k in range(opt.nf): \n", + " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k])\n", "\n", " # Assign gradients\n", " if gradient.size > 0:\n", - " gradient[:, 0] = -1 # gradient w.r.t. \"t\"\n", - " gradient[:, 1:] = my_grad.T # gradient w.r.t. each frequency objective\n", - "\n", + " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + " \n", " result[:] = np.real(f0) - t\n", - "\n", + " \n", " # store results\n", " evaluation_history.append(np.real(f0))\n", - "\n", + " \n", " # visualize\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - " )\n", - " circ = Circle((2, 2), minimum_length / 2)\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", " ax.add_patch(circ)\n", - " ax.axis(\"off\")\n", + " ax.axis('off')\n", " plt.show()\n", - "\n", + " \n", " cur_iter[0] = cur_iter[0] + 1" ] }, @@ -334,44 +290,1627 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 0; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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ED3iG6AHPED3gGaIHPEP0gGeIHvAM0QOeIXrAM0QPeIboAc/Ee/4+GGQrAAyGkR7wDNEDniF6wDNED3iG6AHPED3gmf8H5Xh9SXsZLCkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 1; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 3; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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azcfrQpcRsWfhMXU2m0XHz6rDb7EvTheWLH2WqYCgcU25Go9hS45/c/BPO19zPBZ9hZLQebmo0gMd1f2vCR6CygpveBXbtpr60rp3auFrguciHBU8exEQvK6+o50kP/KKl9bCfvV6M2rp24TedbJO36mKnudA9wG+ySMiXR9Ot2yhyFJKScfGWkev69JrlF7r6kuTadTdznLxqKvHLDp+3LUueBkRe50KxxXY0uvQhyP/OG+17rUJSjoUyrId5niqon9X90kLQXjq6XO5Zm370mKPY/a53W6bWWdak473WsDCqarMMmWuPI/dS/X0XFOvll6Xv8pWw2ELj/bf3t7GYrGI29vbg8dds1BZ6FlZL0+80UdwadFNlpIsTbJhF7/2SG/er2mnKno816yPDAaDOD8/jy+//DKurq4a8S8Wi8ZCck4bC0XqmJT3x5um1FjssPIcoMN7nTlXW+iSBc8z5yB2bJhYw1YebYT3oZkC9iqARtxV9Oza8zXJUOvOWQ3+rRYG1fZpvqG/qu7Izc1NvHnzpnGNr6+v4/r6unGJdaHIiMPptfpcdl3iSoNjnG+HqDU1xx2CCp5dY7QbngkEf3NzEzc3N7FYLJrIPQueA3dohwYMebIMW3p9MEdpXX4VqwqdO05d8Vc9B9Odquh///vfv692fFTgSTFXV1fx5ZdfxvX1dZPeglgwBoal10dVl25OWDwN0mU5eBY+i5/Lb0uBO3bpkZa7vb1t2n99fb0neC7E0aCidjY8RZcDbZq50MIaUKo6RNv5lVN92bUs7ceUqYr+V7/61ftqx0cBxqaPj48xGAzis88+ix/+8Ifx6tWrWC6XzbJREJKO5dWl51lt2D9beRVxbdJMadZczaWHhX94eGisOzwVuPacptvtdnuVdjpHX916DbLxmJ4LabT+AO/bgnilWgZb+nejKvrf/OY376sdHyWvX7+On//853F5eRmr1SoWi0Uj8C4VdjyRBDdn9ripzGUvzZZrK75RwcOlv7m5iaurqz3R89RZCBQdlbYN/+ZxPIYPeK+pSu0Q+NpkwTv9bg3tQEx3PKavcHt7G3/+85/3IvkRcRCQUnTMymN5zsFnS1xlz5zLHjipxTc1wUPoLHiOR/A4nsfw2iZ9qAULFcfWWoWSRS659Jrn19/U9mO6YdETmt47OzuLr7/+uhFFRBw867303DbeOC2nz5fD02f4lQN0nI7D8bIJNJng1aXHBpefH02lUXptE84ZufbVapVWyLWN5bu49FqQY2E/L07ZEbi58LimzWYTi8WiqSSDhUbN+263awSpItdgmBa16JJWnIrLHjRZcue58q0k+K+//roJ4HF6DuLkgB0Kf7ChE+ICHI5hZBH7kpUGpSWxNADIbn5pvsJz1Xv0iaqqkYLqIwjo3d3dRcRbS4j3EDVEzoLkohu26pr60vp5tqo6H740k40r7doED5den1CDdk6n0zg/P4/z8/NG9Jyei4i9aj10AKWpw5lrX3Lrs6g/kxVZaU2EO4Bu9MuUHwlu5uVy2bip6/U6xuPxXsmuWuNs/ntmRXnMnLn26s5rDj4TPIJ2meBRQchz14fDYdMZnZ+fx2w2a4SPtuDcYcn52qDj4VeGA5il1XuyoYFWcuK7/LvSq8Vfx6KvwAUuEfvzubm4hpenKq1wo5Zdha9LXWn+ncXSRfDX19ep4OG9sSfCFh7CR2cE70YDdzys0CxGRBx4PFnZLefiNQvC+1Gx1/5t2rHoK+BmRLEOwA0NUbNA2CXPFrvIHijJY3iefFMav9cEz2m5TPAIrtUEj7bhXODGA47U80QjraDTGEfm2vN7tfqom9BOAp/zq8XfHYu+A2x1eCYc3PLz8/OYTqd7aTUN3HH9fJaeK61nx22AZcWQA7Pl1MJjy2rq2cJz+2ezWSP4s7Oz5lwi9he31GnGOg7nWAdPE84CkXxeWeQ+K+EtRfg9ru+ORd8BtliZ4LGxtdcy22zFWg3YZZaQLRpbeMyHRy09LLuW15aCdhjD8znAwqNNWQyBpxuzW4/rhFeNbZSsfW0srh0Ef6+08bUzORZ9C7A0sFKc2oJrfHFx0Yiex+Q6Bx2C405B6+a5JiDibQ4+mx57d3fXlNOyO59F6XUMD4s+n8+b4B0LHq51JnZeQ0BX1wFs4bOxvXZqXQTP14Q7o0z4poxFX0Fnx6lwZrNZzOfzRvRw8dXNzwSgkfmSe5stgMFTZDFTjmvps8KbzMJfXFzsjeF1uiw6HHQ2vHaA1tZj7M8BvGzIosOWtuuO/fPkHo30a02AqWPRt6BFNhyJ57EwIt4afdeyWR7XZkE6dadV8LDymAfPYmeXngtvhsPhXvWfWnjk4zk9h/hBtmpQSfQRsefW88KZNdderzdeszx/m3tvF78di74FLr5BRJ5z7hz11sdBZ5H4WhCLZ6dlD6Lgde1g6fkZ8vqQSa0izKL0WmYbcfgUW14PXx9xFfHWrdeVgEqBu9J11vdt7n3J2lvwdSz6CpngVfT6HHiuk8+snLrxWsKK6DyvzacPlWSh60MmdfIMu/RceMNjeLQ5Ihe8PvWGrTxXKbJlx/7QlmzWXalUt1Sjz3GALFVnsXfDoq+AG7aUb9fpsLosNK9XB3CTwm3np8xA5Nljplj07MZnD6XQgCNbd47S47x4DJ9ZeBa+LpoJgerYnbMRu92uWWFH0foHfs1Qz8jCPx6LvgLmles681qXXpryqjcvu+3quqvI9akzLELuHDhXrrEHjjto4Q0PQ9C2kuB1STAE7EqpudK6efg3rgOTCR4eAv+OO03+Gx/L1LHoK4xGoyZKj1dYSp1jXnJH2SUtBeZKmz5Qkh9EwYt5aIYBQxCNOWRpObStJnisn8dr2LN15+FMROztF686Bufro4IveUd4r/t1qu44PLWWwE2HWvvJZBKvXr2KTz75pBE9rCVPN43YnykWsZ9v51y7PjsuW1abLTrG68vlsvm9VsCxtdWJPVx4ky2GURI8YgU1wauHo9eDz52vj+bmWfgZ2nmWXHvTDU+tJVCQAsbjcXz66afxve99rxnPX1xc7Ik+4q1wIt6OX7UzyHLtHJRDdZ268Biz68QWwPl+navPrrxW/kEstTE8C15z/lqDkHWA/IphTSZUHgqxldfx+2Aw2MtwZF6DaadfprwFjSY/PDzE69ev4/Lysrkx5/N5nJ+fN3PMcUMjN65jWh7Ha65dRc+FNVr1prPYeMUeXgSDLb2ui8+dFLcbVX4ll57z8Ryh1+KbiMN0Gj/vT61zKUrP/xfcaeL4WadiumPRV7i4uIjPP/88Li8vY7v9Zo08BMggeo7C48bkklq28nDrWez6LDleUnu1WqU3NgTP5b48mScTO3dQWmnHlr0meK620yyF1uljSjJPzOFUH59HCU3taVwgC+C5A2inKvpf/vKX76sdHwW4cR8eHmI4HMbl5WX85Cc/ic8++6yxivgeblbOjeMzULLybOlV8PpcPL65uW5AV9TlJar1MVdoC0fS+VHVmi0oCR77RCGPzhvgzoTTkezal657xGEAUC062s4diIV/PFXR//rXv35f7fiogBt8f38fNzc3TR27Fs5kbjfI6ubZoqrYWfB8Q5fSY1wopBuv28fWHe3iNmnKMAvaqeB5qix7NVpopILnfbahXgPHFHQcb5EfR1X0P/3pT99XOz5a/vCHP8RXX33V3HhwyXFT69NqIw4fHMkCYxeaBZcF6nQBCl4+G2N4nqfPU2LRDn6oZkTseR6c7+dz4biBdjQlwWsNgq6dl6XneMtgUcObylx8i/84PKZv4ezsLF69erXnsi6Xy4h4+7AHTGHVaDOvLsNWFdF5Lq5RK8jWnYXH03KzRTg0FccpxMxbQdvVXdYqu0zweq66ISZRitaXovYR+dp5+J6F/m44ZZeAG/Dh4SFGo1HMZrO9B13gRs0Wlcii11piyyIDHCRjl16LYPjhFzx+1ymxOE7E4VBDg48sKj4m9pmt84/jaOfG6UUNQrLIeW49dyK1MlvuaEqBPNNOVfRdxl5/S2iRCG543MDr9TotXWWLqaLXdeTY1Wa3nS0YPlMrj/awtefoPPbPIuCgF7dFxY7z1cKb0jJeKnhNL2YLZWoGQGfh6Ww8FT9/xvTtPn1XqqLHzd1X2KXebDbFohu1bnyz1ixpxNv6co5Q60QWde/5fVYQo+3jdnLAjcG+YNF5U3GWBM+ix/fUfef9ZTMQ8Vqy4pnXACz+bnhMX4Fdz9rCF2rd8Lla0Yj9m340Gh2k5XBcdX1LC1NE7C/NzVF6HtfruF2DdThu25x43q9G6DVSr/EJrtxj9569Byb7m8YDNDZQCwyab7DoO5BFmTVXXAssQVQQ0Ha7jZOTkwMBsqXn42rHo242twlZAxW9pr4iolniCsdkDyLrXLIqOx0uZAFJ3ldm5bNUnlZHlq4H78di74ZF35FapDiz1BFvRcXFO/x9dWOz/et4Vz0NHfdqcC7rmCAYvFfrntXS875LgtchAwtdRc/nkoHOUc+bha6vbfs032DRPxNZ/hlobETHqzwUyD7LfgcRatCQrbFGwLmdEbEnQp0L30Xw/Dft9HifbQtsoLPgzoivJToA/I4zCdnqRBZ+HYu+IyrkzLWsCb9UZ66CZ6GqhY44nJuulp3Fru58xH7KMRMkCx/H5biAjuE1W6DReV0BGMfg/UfE3rwFvZaaVdBFO0q/MzkW/ZG0BZEi9sfwOg7l70Xk5aaDwWCv1hzf45ScjtdZ+Npp4Jgs7Oxhm+yRZGlHzVRo9SDOGZs+26+UBeDrAMuPtjGZ95Dt19Sx6FvIBJ6NKbWiTD0BdUEZtcY6NlZ3vjRu18IWtF/bqQE7delZiCx4LOChlXYcw8gq+FScfF7aAcCiZ9SWFbeF745FfwQ14evNlrmn7NoC3Oj6+2zMnll7/oyPzcLhNqhwsplyfPzS5BkcU8+TXfpScQ/2rzEKBD41joEIf5ZK5Otp4XfDou+IjtdLBSYRh3XkbWgUniPhmm7L8voc6c7y2hGHa9JnY3kdY2M8z659qa5AvYhs42vEbcV5q0ufpTKztJ9d++Ow6DuQCb5k9bP8MltE3Nyl4F3WAail53ZlQcPscw6GZW4xt4uHEzxbToOKul8tIFLBZ+N57iDZi8hc/My7stiPx6I/gtLYXv/GaOqNg3SZ667uu1ru4XDYRLoj8lJptaiZWLgiDm1R74Kj9pl1Vy+C3fvMGmeuODwUtfLr9XovfYcOQjtgczwWfQs6Tuw6plexZmP3Uo49K7phUL6bDSkyUfD7zEry/rN0oXov/PvSev+lAFttGMTnDcGXOtPsGptuWPRHUOoAeMuKaRgOTKm7XLqJWbD4jlrNrOPRjkDbnrU1E70eB2T5ffUg2tBAIFt+9mi0nW3XzJSx6DtQEk0XV7MkfLyWrDn2y0IotUW9jS5tycST5c35WDxEyUpstV3YD36r7eL2l7IY2AdfA+2Y9Pq5I6hj0VcoiUctq4pfb7osop79Td3w2vf1N9oeFUFJFBohz75f8irUpefzz4KS2Xny9dP3aAsXKmFokwU2td0mx6J/R9iFLo2TlSw4x5YONzYElUXNs2MyWTt4H6Usg/4mE6jGNdSyq2Xmc2wbm2sb0E6uVMzSlxZ7dyz6J9LmTqs7rJ8pLAq2mBjXlix99r40fFD3vXZubccpBQRL1YQIzvH54rxKcw00q8Gi17Lj0hDJHGLRvyOZFax9rzQ2x5iXJ55wwU3tpu4ieE7H8feytoBSkZEWw2i71uv1XnUd8u4sfnXLVcz6ul6vGy9IP9NztuWvY9E/A1nArRS4yjoJDnbht7D6mUveFjNgsZUm4GTnUPNcsvQaDz14Hxxw0yAf/5Z/z3UB2SuOkVl4W/njsOgrHDMuVzLx16w94Py0LqJZqvjj9pRc45IFrLnytXLiUjvgBXCnxVYe1lo9BYhZH4XFVXo6ps8ChhZ/Oxb9O1ATUhZ0y171PcNlsaWsQBaoU0ufRdGxfxy/lDkAKvIs6IfvcUCSvZTM6mMfPLlH1/PTMX3p/Ew3LPoOtLnTGU+19LVjZhZb30fsz8GvCb7NpY84TOV13S/Ejd+w1ddhkE4Z5hV5auP2tmtmciz6I8jEximljJL7XBN812Nnlj4TKSjl2zOXPst/88Ydi7YzYn/RTd63RvdxLK311/UCuG0lL8N0w6J/Apm15de233LwLttnFojjziVzaUvud8Rhzr7W4WRiL9Xjl4Y2pViABvBwPF0NiMf4vDqPBuy6pELNIRZ9C12ErdY2otsNWBqPl4JUKviSd8HHL9ULaIGPtouPwek+7QSUTPDZcdVL4uW32MJzx6KeSe0cTBmL/hkoBeW6uvZs9dmqgVqkvA2tjGsjE3zmeZSsfO14XJGnnkuWbdB947dc78/P17P4u2HRt6DWJJtVxnPBu1h6FqsGtRheIJIfq6WCa4sTtHkr/HfuYLTarhb4w795IQ1+zl62cg4XIiHQx+N2LkzCfk9PT/eW4uJjlOIUZh+LvoIKHjcnbjp+cmzE4ZhT96PvIw4Fx9FsWFpEvGuR65IgNfin77PIOLeDJ7jg37qWnXaIpWWqI/bTkHjKT3aeGvAbDAYxHo9jOp3G+fl5TKfTmE6nzVN7sRCnBd+ORd9CJnY8E346nTaP88ZDLruQFdmUgndZ1VnNytdy7CVxlwJ22cKcmg7ka1RanjrLz/P+dAyvwwe+/mdnZzGfz2M+nzfiPz09TR+0aXIs+hbUlYfop9NpI6bxeNwsD12LLpcoBetK1We6/1LaLaIejc+EngULa4Ln46vI2fXORN+lg+P/g5OTk5hOpzGbzeLi4qIR/mQyaR7drZ6FOcSir8BWHjedCn40GsVkMtlbKRa/7ULJ+qoI+bvcPqA3OkfteaxeO072Wssm8DVia6zizybo1LyarFMZDr9ZS38ymTTW/uLiorH2cO8t+HYs+hb4Zt7tdnF6enogeNSLd0mhAb6pM0GVRKadSpe0le6nJOiSuLNOg8+Lg3ga4GQrz0HBkneRufYRcdDpnp2dxWw227P0ftpNNyz6Fnhduoj9stLxeBzr9XrviS/HomLORN5lLF+7ybN9a6691MGU2sKoR6Tvs7a1xRV0/6WYymQyaSw9p+9MGYu+At/M+nekj7io5CmiB5n4s1duQ/a+bd+ZoPG3UlVfqQ18fBZ3ljevZRS486mdKz+Oi7MnPJ7nLILJsehbKAkfdeRtqbSuZN5E6d/cttLfSvsoeRZPbUdpmPHU2oHaMEbjBtwJ8HDCgq9j0XcgS4fh5npXwWe/e+q+anED/ncm7HdtR224UXLvu7RL94NhQxY7sOC7YdFXUIu12+32qsdKQbbn4CmC67qfrp5El33psdv+/S5tyKruOIbQJahpIgYtF9tzF/8/T3G/P3a+zXYfm7J8yn67ehU9Jr0YFr0xf7ukoj/umcrGmBePRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZFr0xPeOk5fPBe2mFMea9YUtvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnvGv2wYXnj4q9DMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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j74GHEBp/4HOlwu1a5FSKe2SdrzkMi75CSehwfREAw3u27FlgryR2LrphUXHQTivuMGtOLX1pCawsaAcLn82i07vl8BRetfSlG16qe68r6/BrFXAm7Mw7MIdTFX2pTvr7Ci4mXJy8GCTnrvnBK8PyGBiWNYsqsysfEXvjY13uSuvqVfA8nmfry/vloB3EjvvaX19fNzX2mCvPQstiCmzptaqOYxx8DXG8gs+DvtZzhiCidpylwKepUxX9m/aoWk/NEyqeqrdu29Yx1oFzxHCB2TLqKjI8Di4JXbfPaS8VPK9Lz1F5ranHgwWvK9vC2qrgcZttvt02L5CBdrPguSPiefnawWTj+dL/IYv667YO+f+5A2inKnpe/qhvDAaDmM1m8erVq7i8vIy7u7u4vb2N6+vrWCwWe2Pg1WpVLbrRCrZsiSutstNIPYs+my7L43i2ujpPHseQWXkcg07s4fX2uJPJPArOWGinj2e9q62i4levoPZbU6e/qu7I5eVlvHr1qhHO69ev4+rqKq6vr+P6+rqZlMLjeYXH2Do+1mWuSpV27N5rfl7d7IjYE/xyuWwWxIDg8bi9vW2GK2g/Zw8gco4foP28L4icaxJ0yKTFR3yOtMMsdZy8Hf7cHUA3qqL/7W9/+7ba8V6BlWBev34df/nLX+Lq6qqJeF9dXcXV1VUsFotG8BzEY3AhogCFx8cqcIi8VlpbmkjTZVEMFvzV1VVcXl7ueCwQPIpg+C63PLzgKbpcgcf71cyFzshTkWYz9kruvP7WQj+cquh/8YtfvK12vBegMuzu7i4Gg0H88Ic/jC+++CI++OCDWK1WjSusE1M4cAW0VBWfZWLKymjb/lZLzSG2gHZC8BA7Oi2M5RGQhBuNbWbFP+PxuDkGHbtr7QIsdxfhM20xEf5/2cIfTlX0v/rVr95WO95LPvroo/jZz34Wn332Wdzd3cX19fXezLRahR0vlAna1rWDoLW8VifQlMbwLHgE7VjwKnp0Xix47pR0HM/3sOfhDKfn2MqrKEsC1bx8LQBq3gyP6StcX1/HH/7wh+YCXq1We7eQql2EOmbl4J3m3bP0G9+NRsfuWvNeEjzG7Sz4q6urJnh3f3+/M+7m4YXWAfD0XB6vZ/MPtHyXz4lO/tGyWs0CqJeQ5ftNdyx6QtNKZ2dn8de//nUnesxBN71Ro441eRyvbn1295mspBYPLtjh/WmKTAUPC//69eumA+B17zg6nnVEOo7H+cGyWRpDUM+nFHgDbNH5d7X8vnkznLIjcDGu1+sYDL6d271YLBprAwvNZa8ajWaxczltloYrLWKZBep4tpyKJlvUMhP85eXlXuAObedg3dnZ2d7NMTRwpyvvaADvUPecYwJ9Kwp721RVzYsg9g0E9JbLZUQ8FqngtbrZXBRTuglFbcVaFr3OlDtk8szNzc2e4NWl19Qcu/O4BdZsNtux8twh8jheq+8yK8+doJ5jLoTSGXn4O3s0XJZ7aOGO+ZZ+mfIDQSUbhDwej3fKbsfjcZN31xln7BnULHvJymfBuqwQJkvLlQR/e3u7U1fPQTuU985ms0b4bOUjYsfCq9gzC595P2rts+BdyVPI/j+lCj5TxqKvwK5zxO5FBgtcWnyyFKFX91nFngle7+GG8XNJ8FdXV6ngucQW280EP5/PGysPTyYTWLaOAAfvWPAR+/n4UiAwE69+XkvrWfh1LPoKuKD1fm0alINwtbS2NEOObypZmjCTLXHFLnBp8gyi9HDxs1w8eyFoB8Q+n8+bdiE9p1WGOutQp+FG7NYk4DmL3GtRTzY8UPR7tvKHYdF3gK0Xj9V5HKxprWyWXKmctlRoA3iqrlp4zsNzLp6r7eDSRzymDrkTYpceHgjaD9Sd52GOCpUtvAYgS6599j77P2QpvcwDMGUs+g7oRQwhq2Bg7bOJM7osdcmVBxqwqgkeVp1LhHWqLAQJwcPCz+fzdBzPbj2vDoSgIQTPLnlWeaj1CZmlr603oNQsvAXfDYu+BR6PspWHiz6bzeL8/Lyx9jp5pvaMDkSFgGWgNUquaTlM+mGxq+C5nj5z6XkMP51Od2ITmoPnBUR4Db2Ix3Qnr6GHmEFN8JlgNbevnk9pSMB/N2Us+gqcamLrBbd4Pp/H+fl5nJ+fx3w+31uySleX0am0KgQOhPFMNV3EEoJHeo4ffCsqCJJr6Tloh3bDrYcXwivnQuy6LDbX20c8rq7D6UtsqxSQzEB6Lsvpl9z6kvhNjkXfAoudo/Xs2iMABkupFXS8Kq0GtLjgBXChi1p4jtRD9DwnHstdZUFHWHO49HjmSD0LnlOCaAOeue5e03G1ji0iFzynI7NpuHpuspy9Bd8Ni74FrqzjnLa6yBCPRuA1z14K0mmBSiY4XfUGYudVfCB4WEsOJGLcDrFz1R2X2fK0XL4BBq+sw4FBlMnyeeKIfSb4TKRaxlyiZO11+ybHoq+QCT6rmVfxlAQPWOBa4MKBMrjz9/f3jduORSx5AQ92uznTwKnCrIMqufQ8LVdvgKGLZur8A16yS5fSyhYYyVJ9JUrufZa7N2Us+goQvebaOTdfKqzJLF3JmmtwTG8gycLDdFjk3vF9jqDzXHgehpSi9Gzh0eHoLavxHuP5iHyNfg7i6QQmkI2/Vew8/teOgVOYXav3zCMWfQWIJ1uQsrTOfCkvzRdptsouu8/qUkN8+j1e0w6C16IhFrsW3qiFV8HzsGG1Wu2tkhuxv5JvKWCXFd9klh/nPesw2MKX5t+7A2jHoq8wHA6bwFeWk+fZZ1ltPBaRwHu17GpN+aHjaLj5egcaDqbVaukh+LagnbZHLTzvjwOc2SxALCKiq+jUZtJlxUk4p20Rewu+G55aS+BiwySbyWQSFxcX8cEHHzSiRxBMhcPpNQ6kseCzSHx2wwm47mzRIXQWH7vzXEuvU2QxhmeXXtNy2iYIfrlcNvtluOgmqyZUkeMc6Ew6Pu+1akT2GErW3nTDU2sJFKREfHtBjkaj+PDDD+Ply5dNyuv8/LwJgLHgYdU4x8zTRjPBIxLPEfnsdtF8I41sUovO9c/Wxc8WtCwF7TLB8z6z21uVXHKOX2iniO2V4P8FvKbSEmWmO/0y5S3oGHO5XMbHH38cn332WRPsQvUdRL/dbptFN7DYhs4B1znvyLWz6PFgwbNlZ+FF7C68ma1ply11xdYd7eZ0YBfB8/idBc8dIHs2vKZg1mm1/T/UovOCnIeU75pHLPoKFxcX8emnn8aPf/zjxiKOx+M4OztrFpbgcSuPaXnsqbPi2Mpz6o2La7IJLYALYLhgKFtgk1No6nJnbj2P4SF4FMxw3IADeNkxY/t45iEJvCE+Hn6O2M/nY0jAotcOwXSjKvqf//znb6sd7wW4cJfLZZycnMTnn38eP/nJT+KTTz5pBBKxm06CRWP3lqvscNGX7hTLd8phwXORjbrz2aQeXapa70Cj42oE7bhdmeBxvDqBht9rhqI09ZaDdzp+17n2LGaeMFSz8BZ+N6qi/+Uvf/m22vFeAVf+9vY2rq6uYrlcNu4wxK9BtYg8vaQz47iyjq0rj9/ZwmqMgCfq8J1nOCrP1j0i9sbAnEHA1FtYfL1pB8cMsF9dwitid+yOY+ZzVIrWlyL1fA7xmjutLF1nulEV/VdfffW22vHe8rvf/S5evXrVWJibm5uI2B0Xa3Rbx/LqRuO+eBqZLwXqtJ5dp+rqYprsznMxDUSkhUB8621Or/F+ecZcJnhO+6nYuYKvNI6vpek0NdmWp3cnUMdj+hbOzs7ixYsXO+Wxq9UqIh4LWngCSjauZYHBsur0VBUaXqul5RV5eHEOFNpoKi4bx2sVIIuT4VRctkgnH6tadxa9juO5M8Px4fOSoNX6W9zH45RdAi7C5XIZw+Ew5vN5M57XslUWkLrRbPnYmuqYOeJxSAFLxhVvXATD8/HZsuPB7VL3XttTssSlwpssNcfehIpdj5HPL++nbRot2q4pVQv/OKqib0upfN/QqDJcWljL9Xq9FxwriR6vWRAsMJ6dhn1xMIun32Z5cZ2zz8FDzZOz683i5DEz2oDnbB2AkoVHdkKPk+FhCh+r1jTULHoWO8mu0b5dt4dSFT0uyL7Slp7KxrERu2W4ujQ0rBWEBSGo1WILWHKzdRabBhV1co8+0MnxcfHxah6+ZOEh+FIBEZ9PnZyjUXumZM217DlL/5kyHtNXYGubuaAcZML4mS2VRsEhKhaPrrSb7Tubq64i5H3xqjZcCVey7lxjwGLnjkWDdip4Lr7RIic9Bp2Oi2M4JBiXCd+C74ZF34FM9F0ixywobAcCPT09rQaksmCXdkL4HXsYLDwWvApSMwMsxNJaADXBa5kt9qHbrY3j20TL28J7PiemGxb9EWg+XoWrLi270RGPE5myTkMpubE61s3ceRZ65nJjeMFCL7nz6Kw0YFdy6TVYxwFJ/g7aXyI7fi0Q0g7S1LHo3xC9IEsi1fiICl0LUdq+yyLnMTaP7bOJKZwVYC8ii6jrvjQQyHl9rbbLxF5y6yMep+GWHtmcfd2eXf1uWPQHkom6JvyI+v3UNTKtOWqevYfvs6BrYmfrq+3XoF0Wodf9ZTl47ngyy84WubZm3sPDw9655aDndrvdi21oGXDpHJtdLPoOZKLOxpPZ3zPXsxQb0Mg7vqvfYbGp4LU2nS08W/ZM6OicNBiJIiQuwtH8fkTsCZIr+dQV5+3jbxA4t4NjIprBKAUFTR2LvoWSFc9ElEXrsw5A0ag/rF5m9dvG7Roc1Haoy80PDtgBze/zuF7Pk+6jlOfnjgz7UOHjXOCziNgTvG7T4/puWPQHkIk9i0RnhSf8GwYCz4KDWkKrkfnMS8i8CXXn26wvnjkVWbPwmdi1piCzxhrA04AnipfwvVLdhK38YVj0HWkTuwr60IuQxavPOk7nCTQA6cGsA+I2sxizqbF41qKeUi29nhMIX2/4kQmTzxe2eXp62ngReM0eQO38m25Y9B3IrGY2rofF1guQP8usWyb0LBiXBeUidstn+fPSMEQDa7xtHiLUcvBMNlzI8vKcwdDxOr/n70Hw+FvmPVn4h2HRH0Cb4EEmDE2Z4bPSmDwiXy4q4tGq6/a0rfiutjOzvBxEzNrVVkvP2+QSW/2MYe+Exc4ix2vepkX+Zlj0LXQJ5PFDx+Ygc701SKevdRv822xeRKmtoBSD0DZrRV+tDerWQ5xZek5/jzZl52M4HDYzBfW8Zu023bHoD6AmfP5OSfgKCwzvtQCnFJjL2hSR1wRknUHWPk0LatqPt5cVyGD/2XlRss6g5rrredPzlLXT5Fj0HegiOLVI6s7z59n3sn2ykDNPAX87dGxb6pTY09Bj0CClxghqXox2hDU3X49BRc3eR1Z8ZNqx6CvUxst6gWaWKRN6m+AzsWeR77Z9M5ll1PbhWUuBS54GexRa3KP71aCdHofWFGTt5ABolt3IjsvkWPRvSC1H3CXQpBc9hAFrm82159+WvJDSGDhzhUsTXmodTVaRyPvRwB93YNmxc/u4/Tq/oFSIZMF3x6I/klKQSt35tm0g/xzxGOzD33i839axsJhKVpGtbpe2Za+xr+z4ebsnJyd7cQFuIyLz+J12SNxWPA8Gg7QS0a7+YVj0b0gmxjaBZoE/CEmHBJyeK4lVBa9ucS1gWBN3tv22YCAEibE+9s3TifmYuJ2cMchKjCH6UoqT2+IOoIxF/wSwOLOxO76TPfPfOfCVza5jj4BRAeEzjsTjb6VovJJlAbLvZ1kGPgcQPyrr0BHw99BOLvll4WNSEbans/s0DmHB17HoK9Quntrf+KLnz7Jn/j6jlWh6YZfaoG6xutjaEbEAQVste5dAJDrC7Xa7V4mXBTzVwutaflln5iDecVj0R5BFwoEKXq1g9qy/zwReakPWHjwywbNbr+PsiLrgWXiltnCMQbevXgF/rpZeOwH2nmoP045F34G2MWOb1a896/f44i6JI+t09DOOnre59SzUTPAa/GsTmpbPAi6n1W1lFp4X8kQHYpG/ORb9AehFxmLoEhE/dn/qsutFX3rW1zqkqOX28VsN/vGjFDHn8lxdY0C9oIjdqcM6ySfLPFjsb4ZFfwQ10R17QdZE1dWlzdzvQzIJ2Ta0LaWOR7cLq86Li6gnwdtW0fPn+H3JOzKHYdG3ULOi+p1jtpt9rh2K1sKri69ks/BK2QQmc+E5IIjvaBu46IafkYFgi68Vd7w9dud1v0zJY2nzXMy3WPRPQC0wp98pfa/WCZS2p+P+7DULs6sgMsFnVpnhTgb7zMputTNTkbd5ETp3P1s5x8KvY9G3oPlnXb1F13NvIxOC5vdL8QGOYtfaqrR5K1nsgLenQUYtGsJxZdNsdUkrjsDzsbOV1so9tAEr8fCqPLpIZpcOuO9Y9BVU8LjY+eLDfeJRUFNzoWuiZMHBpeXqNh1Pl9pZQocMmcXV97DAHI3fbDZ78wH4/JSW1NZVerJ8e1Zlh44F53w6ncZ0Oo2zs7Od23SXrL7Zx6JvIRP7eDyO8Xgc0+m0WegB96ivUbLyEfvCz0SQ5e+7FNW0ReHx4Mo9DaKpC65Wnr2fk5N8Hb5sPI+OUgtxuBPCtiH6+Xwe8/k8ZrNZTKfTGI/H1bX4zC4WfQvqysOyT6fT5sIfjUbNuvBs/bJtMWp5VfRq+dU9z/Lsup8sGq/izYJqGrArCR5kN6JQ68vjfd2X5uS1Y1PRX1xcxHw+j7OzM1v7A7HoK7CVx0U3Go12BD8cDmMymezdtbZN9HxRZ6LPhFiibSyrYi1tv9Yh6DYy175tPJ+1J/NsMtEPBoMYjUYxmUxiNpvFbDaLFy9exHw+j+l0uiN6C76ORd8Cu/bb7TbG4/Ge4HH31pIwDwmwlQTH3+NttlX6ZdtWN74m8kzwGdkdZ2pWt9ThZMU3bOnR6cLiw8WH6C38diz6Fnjed8SjiE5Ovr1hxHq9jvv7+zTA1pVMmPyev8NkQu86hChZ7y5tyKywZjf0dWbpdd9Z/QEPXXDO4c4jmDeZTGI0GjU31bDg61j0Ffji1c+Hw2GMx+N0JlhpWyD7XklYbR1JLUOQDSH4dSZsteZZh5e1gcWpQq/VJXT1JDgLAIvPGRQE8rizMTkWfQsl4aPKLHPDS9uJOHzmXOn9IYFCft2lc2nbd2m/mdDb0oj6XOtYsjqA7C46Fnwdi74DWdGIFpoc69pHPE1pb82aZu+7iL1re2pCP0T0tf1kAUMujsqGESbHoq+glmu7fcwZl8a/tW21fUfp8t0uF3mXTuVNOq1ae7qIvksbSoVSGjvg75icQcs/23MY/503cX/fV77Ldh8iuq7tKHUoXT2LHpKeDIvemO8vqej360KNMd9rLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RkWvTE9w6I3pmdY9Mb0DIvemJ5h0RvTMyx6Y3qGRW9Mz7DojekZFr0xPcOiN6ZnWPTG9AyL3pieYdEb0zMsemN6hkVvTM+w6I3pGRa9MT3DojemZ1j0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6RmnLX8fvJVWGGPeGrb0xvQMi96YnmHRG9MzLHpjeoZFb0zPsOiN6Rn/Blk2nrqv2B5WAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 13; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 15; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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LMg6HSb8n1Grz14h8S2kqUactqvrSv4H0sOzNZvNJpQDJOy7N7UN4zidoLM9WnmN46PZ5Rxyemb8tgac5gVTWn49RlPQrWjCMYpj0ByCVRS4qO4FU3C0HpRtbVMTOiOF5Cg7vXMOLierrmfTYqgqk17HSuDZ267WTDqEFPhP+HwlDfiF0qNUeB2GoAg9gK68/a76jzLKb8IfBpN8BRSWjovHX/F7uVkNyDKo6fOWtsXimPZ8bcXREfi95nl/P+9NBeZfS1+O42ivPcl/u3EMiDYk6JjsvKgg5WD+go79TpU5dAFL3m1GmAjS2w6TfE5p841lxyFjzrLgUqaBlZ4IhU6+hAuJ4JMyQqefBGFqX533pVF+PzwBCcq8+rgeLEfT1ICeug3e47XQ6mRfBajuebqOlPnxlF19RZOFV6WjsD5N+R3DmnZNe3FfO1gsLACfH0CyDjSdHo1HOqqay9K1WK3vA2coinsYLmfuUW69tsxGRK8+hbRfXNR6Pc001KR0A9+Mz4bENlm4EEvF0fh5baWgOikQ3RdqIIpWjUQyTfgvUnWfCcy1bSR/xSCyU4LDhJDrWtFuOm0s4Sw/isivPe8nznvL4XgmPz8KbVPA0nru7u7i9vc1advGZIh51ALy4QN2HkGO9XsfDw0PuvuHzpyogTF4NPYqgykaT/TCUkj41hrgKUFdUY3PtekOGXRtvUv3wsKjD4fBJLT6ltMNXbpcF8VLE5z3pmEw8dQbXNZlMYjgcxs3NTVxfX8f19XUMh8OYTCbZ9cClR5suT95B6AALj/56znewy48FQkubu5BXLX2RuMfYjlLSP7cmqmUW/FxUfvkQ50j9PWK/z6JkT7nCt7e3cXt7m7OKcME5ntetpblrDXGzil8i8i237F5zDA83HkTnkdacqWc1HTwVWPjb29u4urqK6+vrnGsPAiOkANmxlz22tY6IbIHgch0WSW6w0ZId369dn4ki8hu7o5T0PNW0qoBrPp/P4/7+PkkSJODYem02m9wEWZAMXgIm3qj4BcTRJJ6Or1bLrrVykEgtPEpz8Dyur69znwefFdcC0Y+25yKWRwiyWCxysl5WHEZEMpZni6+E39ZQY7IfDrN6C0ajUVxdXcVsNovxeBzX19fx/v37uLm5yUiMZhh2NVVLr3V53e0Vbjx3qyFDryUyJTzcebjbuAYk0LiKAMIPh8O4vb2N6+vruLm5ycKNh4eH7FrQooshHIPBIBu11Wq1IiKyz84LDQtyGBzH65z9IhlumSAnJeE1tqOU9H/84x8/1nV8VsBDNJ/Ps+w6yMIkQRJPy2ycxGJlHW8Qgfo3/kdr73Dj2YXXGB4WPqV7Z826DsUA4W9vb3NTdXkEVrvdzkqA5+fncX5+nrn2nLGv1Woxn89z4UiKsDpMRLPvKsqJeCz7qeuv8l5Lc/dDKel/85vffKzr+CwAN3M+n0etVosf//jH8dOf/jROT09jOp3mCIK4V/vLI/KZa90Civeai3icgQ+lG+vokTiDlh7lOJAfLj3X4tnSlk3Tvb29zZUM4dJz4u7k5CTOz8/j4uIizs/P4/T0NBfLI2bXwZopwnNegtWBmj/RJCDvbqMewDbFnpFGKel/97vffazr+CxxdnYW33zzTXzxxRcxm81iNBpl1hCuc8RjvApwWQxuddkOryynRXaeE2eDwSCntOM6PFt4kIFdeSY7LDuXC6EErNVq2bE6nU6cnp7GYDCIi4uLuLi4iMFgkMXyrP1nt14Jz9JdeDLwZoo219AuRCygkPfyeyLchHMIHNOX4O7uLv70pz/F1dVVRmBOQHGbqvamc/aayR7xmMxidz7VD4/X2dlZ1irLSjgty+GcUP+B7Dc3N9kLyUck7PCZkBPAxhjs1mOoJs4ZEbn7kbK8/BmhMcCLd8iNiByZWdasCyRLlL3xxeEw6QkaO7Zarbi6uorhcJhzwUFUuOMsyOGMOT/IfA5O1KHMxrV3bo1l6w5BTEpPj3Ni1BXIfnl5mdXgIb6BNgDWmreqhlsPKw+3vtfrZepAjbXZMuugDV5QEI7Azcf9Yp0+SK39DFz/TxHf2B0u2ZVgs9lkyTo8vHiAAZBGE1E6x47Vetoog2aZ1ItltboNFVx65BWg/huNRnFzcxPv37+Pd+/eZSU5TT5GPOYUoLpjK4/FR9V33AyUssx8XCyUXFLEQhnxWGHQngYewImFrdls5v6O99vS74dSVnPcWlVwJxhUdxpz8oOZ+n8mvLryPPiCv+qYK7aSnADjNlZoCe7u7uLy8jLevXsX7969i8vLy6wkhz6BiO8Wok6nky1oIDxyCGdnZ9mC02w2c5UJJjyTlBt08II3Aw+Fpbta5kup+fB5ucGJKyDar2+Uo9qmfAfg4Y54dDHVeiMzHRG59/J7OHZnsnM7rI6q1j3kNePNCw/celj5q6uruLy8jMvLy7i9vc1N4UHGHS42b3+l03M5lIiIjGhIFuKlqkI+NpOeW3BZR8Ck1+48kFknDfFGHyb87jDpt4Dd9qJEHOrWEZF7UPE+LsV1u93o9/u5sVaamWfrnipxaQgBuS+sPLT0LLrhph58hojIzcnn5CGPy8Z5uRTIGgTeWosXQG7UUSsPrykleU6FR7i3WGzUzbel3x0m/R5gsnPXGSxxxGNsGvE47ILfu2tmnq17apClludYS48sPWS1PNGWlX/cwMOLELv0vNDoWC1IjHlibypRyZ+HJ/9wok7ddK3HR0TOvWfdg5bwrNArh0m/J5gsSMB1u93MgnE4wEIXdelTsTvX3tm6sw6A3V7V0qMfHrV4HqHNSTFOrvGYLW2mUWktTwDSsVo8vJNjec5HwBvi7H9Zow2XAbk3n0mvO/0Y22HS7wi18L1eL9Oj93q9JyUoZPWbzWZm4UF6CG94m6mUddcuMpanssVlaS3q8Nznr4Mt8RlS4h/O1MOlh0ehW12pFxGRH/5RZOVR38f90vvMLyAlbeb+BY7rbe3LYdLvAHXr4Q4PBoN4/fp1pkdn95utqQ6fwCLAUlq27rr7TER6ci3Kc2zhWWlXJPXVWjxekNky4eFZqMIP5+EJO1jomPBq5XkRUrAmH/edPzsnD1P5BLzXhC+HSb8FmoVHQu709DRevXoVr1+/ztRq/LAx6RGnF7XCMtmLxkGDLNw8A8UdhDd3d3e5xhlcN7wQXA8s/OvXr+PNmzfx+vXrzGNB4g4kQ+KOCQ8ZL1x7kJjn9nF/P0/xVfksrDOXNnkQCf8fEx85Bd59x+79bjDpdwDLbhEHn52dZc0og8EgOp1OTiGHRBbKdEr0VNyu2WwlPB52jN5C0g4dc9wIBM+k0+lERORi+LOzs7i4uIg3b97ED37wg3j16lWcnJzkBDgs+GFZLwjPjTqctdcSHcKVlHhJ6+vaY59qxqnX60/2CeAMvrEdJv2OYNIjNkdMPxgM4vj4OOeWcz88y3bVqhe1l6bceZAPI66urq4ytd1oNIrpdBqLxSK7Vi0rImkHTf3r16/j1atXcX5+Hv1+P1qtVtbYgnKYTvFVwqP0potcaqCHqu5YURfxuLiibRfg5CVvFsL7BFigsztM+i1IyWfR+oq2V87gq2BHiZ5y4SPiidvL2Wq28OzS48X98HDncU7E8Fio4KHwUAzukWdioRQIwutMfCYrb2apykFevFhNB8IzwfXeaANTROTiehzT7v3uMOm3gK0Pym869x0vngbDC0XKqkc8nRCjnWY8dVen1iKG1xFXvIssko4sBkLHnO5Xz4q71Jx+ntzLc/65XTYlJMLnUQuNV6r7MJW1hzXnxVCFOSb9bjDpt0CHUoL0qWScEnxbvK5ac95AQ7fA0tn0+B6luc1mk52ftf0sBFKyw7pzKQ3n5Cw9rDxn6iMic9/x2fHSgZy8571uwa3imhT4XnFYgO9t5feDSV8CHT3NFl7nyutrm1XnbDyrzEAsqN1AQJAQvfBI2s3n89hsHnevZUkt8g5Q/WHLavZKIvJtuSz0SSXtYOE5Z8AeDbvzqicA2XVkWBlp2YKjLOik3fPg1loCN7LAwvPoKkhlj4+Pc+2hsNY8Ny5luTiZpdact4hmwmOgJlxr3jOeVXAoDSJuR8wOwrO0FoRFCUzzBvAmMLJb6/48v161BUWE54w7wgMdNabfsyKvaGEoEvMYxXBrLYEz6Ov1OrrdbjZI4vj4OEuAoZ7NmW7ExI1GIystqUvPiTkmOSw3iA2SITkH0iAOZkktxlRDMIRrRCmR3XnOpoOUKf0+rDw2vlCZLec4lPB6TOQkeNtt7YFPiZD470p2vga9z8Z2VMuUb4E+XIvFIn7yk5/E27dvs/ozatoYpIGHO+KxC0yVZdydxltC8y45yMDzZpapYZpsbeGJ8NRaLFJI2DHhtVsO15RK2oHw8CrW63Vu3l0qYceEx3GZ9PBOVBZcpMHX5JyWQzWPYmu/G0z6Erx69Sq+/vrr+NnPfpZZapCs3W5HRGS17OVymbnN+Brx6NLrDHxOkqUIDxcY8St7D3jgWV3HhMfUG+QfuB+ecwpFhNdNNbHQcDONxvDwBLTqUDb2m0t+fK9SmXi8h0uRnES1td8dpaT/9a9//bGu47MAHsTpdBr1ej2++uqr+OUvfxlffPFFrFbf7f+G9wHQfsPKgPAsSFELr1lxbpDhybmqVONMOfTzKMPxEEtu0y3qlmMtfRHhQVKuRqRm7CvhOUuvLbD8efheRaTn2KtKD4udztxL9SsYaZSS/re//e3Huo7PEiou4RgVDzRPb9GEkhIMmXEmO7enIl5nySmX/lA6VHEQ2mI5Yae73XACEYlCJbw20EREzqXntl/O/LNLz+48y2S19VYViPiacuexmEY86hDKRogZ5Sgl/c9//vOPdR2fLSB7BWHG43HUarXMrcfDncpE80LBCTstuzHh2bqj4USHcXAcj5ZYtOlyKRHXwY0zvIsul+V4ug63yaYkxdoXn1oQUzE8SMnzARSaqcfnB3h/AJP+MDim34JWqxX9fj8TrsDNh6AFWW/u9oqIHMk4U8/DJ0AUlZFywop1AtDPcz8+b3yhhORJsymXHqSHS8978ql2X5NmEflJNmrhca/YwmvLrGbtt8XzKE1yU0+K9CZ/OVyy24LNZpMbAMHxI/eZT6fTnCxUY3mtwyM0wDbPGr9zsg6WjXvxEbPzgAouIbKWn8tn8DQ4zNAWWbjS+KxK9ojIFryUhcdnw+diC8+JQJ0KlCrX4f/Y42DS6/x/E347SknvGxi5GD2VtWaXmWNyLABKCnZ5ObEFC82E540sVeuvU2qxyIDkXEZjeS1vlc0uPciJUdfIISg52WvQBQ1eC2f82WtJyXXRu88jwHQR5IWQvZ6iKcFGOUpJz7FUVaEZZ364mABwaZX0WBS4YQRQC8UZet2mOjVGOuJx3j2sPCcf2dvgxh0eaMmjq7UbsEhpxx5MSiWo9wyfhZuBQFTustOeBOQ0WIjErbuauTd2g2P6LWBSptphdcMHbhfVoY1s0VWqy+24THpOWmnMjlIhXO1U7zq3yYL4sMYgMSfs2Aor4XUhK/Jg8HmUpPBOODnHi2CRe48cA98XXTxM+t1h0u+BIrkod8qldOJKdmj0OUONBxfuKotP2I2FFYRXgRIcP/ggM7fo8ldW9mn9X8UuurDx4gErry49l/g4JwGi8g43CEm42qDuPZcsmfgcz7tGvztM+mdALZMmrpjobMlTliyldoOl1EGVUACiisBiIFbc8WKknWnaB68lOW3I2VVpx4RHaILZgFi8ON8Q8dg9B2izElcyUpWEshKg8RQm/QdGKhyAJWs2m1slpizIYdeVk1wppBYfnTrLQhtcT9l4K9XSa1muyKXnagPv2JMa1oHjF7norMZj0mvOwVZ+d5j0z4AS/OjoKCdEYbcVLm1Ze2hEflw1u+up2BchAv7GCTE+LjwMHL9ogCW33KrSTgdg6OhpJjzEQ1ANgvRc+kQ+AjkC9ljY1cc1lzX7mPD7waTfE0xI1YVr7J56GIssNR+ftegp680VAs52MzicwAhsrXOnVG0RjzkBJbz2xBe59CA8KwV56y9UGTgByXkNLg9yx2LZ9GBjd5j0B4CJqcRHfKrqsxSKNOdssVmlpqO1eAFQcU+9Xs/afyMeCc9lQN19BpY3NQBDu/80CakuPQiPoaFw7aFtgLXnkVv4ysdmT0UJbxwGk35HqOILVp3VZuoF4Pf8fxGPZNbkm2b+tW7NhOefGZo8ZFdea/4ax7MHwdoD7ZaDh4Fz8PHh0oPw3M/PZUq0IqsmQD0m/iyapbeVPwwm/QHgh08fyKKYPQUldETkLLuSHoTU/wHYCnK5T0tnqZZUhA5FQyx5Uwm+B3wOKAdZJswDOGu12pMhI6lXo9HIeRNF4pttoZKRhkm/BzRxp9/zexgp152Jq8Tm95UJVuAucyJR1Xy6xZSOqeZcgWbotYuQY/iIx/3nufuPwwbdkJPLmXrPuGKBhY+VfXwvOaRJVUOMcpj0z0DK1eQHO+JpzZmtN9xkjc35IWZXH+fhrjO+DhBaLbzursOJMnxNzbTjhB1eqjngoRZMdG2EUbIWddSpB6Xv52sx8Q+DSb8nUnV4te7baumw8JqFT6nR+LxazuK8AcfFSj6dR68KQp2Iy9Y9ZeEj8pUAXmx0cId+Vt5eumw7Ki5FYuw2C41YfGTsB5P+AKSSemXE18Qc6/FTbnzq2Hp8LRuqTBUJvBTZIyKXQ+ANNlRpx5l0TbQV6fSRmUfdnxcq7gVgebASObWJBasDOYmp3pBRDpP+QGjWHt+XuecpkuvxUgtJqjLAYh4mJEiowzl1oAZ+z6RHTM9lObj0mlFXZRwfG2KbiO8mCmuTEKYO8dZWqZ1rdCjJ0dHRk51tijwFoxgm/QHQcpySUbP4GufjGOwuq/48JQBi8qk+gK0tkxNEhHuswh/8HYk87Rbka0xpATjzjxAB58J+ANq8k9rXjnf5YWLzoqkLgvbeG7vBpN8T6lKzhWV3kxN4vAioVBdf4SWopedMuSrWVIrKHgAsYFFlAdfDMbe61bowMVggA4Xdw8NDdpxms5lT2/HigBkArAXgxGFqohBIrx6ACb8/TPo9oCTVBBrHruzqqxCHCaA686LzKNnVCwA0Z4DfpUIO/l6z43wtTFoGL27cfDOfz3OVAq0YpKYKqdXXyULwHpyxfz5M+j2RsvTc9snxshK+iPx4f+o8qRi+LIzg6oCWxlSuG/F0Mq0mz8p0Byk1IGL4Wq2WE/+kBnLwMA7evlpde17wdCEw9odJvyeY9NrjjZiZs/T8s1r/iO2ZZ324yyy2lgO3lbZ0AdHzqKdQVG3AC51zGvpwDgKLClcM+JWy8nrNvFCZ/PvDpD8AKeKD/BGPhIBLmiIIW2lN4qXcaI7VU+CkHEi+TaPPktiISIYKfD79DKnPpglHJT7+j2v2WsLjnALfa64W6IQfk393mPR7QB9CrVmn6u8Rj3E2Mth8PM6Qs3VTD6DIE1ASaheeJr5wfB6iCWJqdp+Pz9enlh/nBnBvsOBgJ18Oc3gaj04IZrceYQJeLDzyUMzDYNIfALU+3CwC7Tg/iPzAl1l0/n1ZIo6hMbvWvPGVz8f5AW4HLlpsUp4HvhZJh9frdaYVQOlOM/gpYQ6Og/vJQqOiDkH1UIxymPQHosjqc+yK9ynUJY4obrDRBFwKnHVPSV9TSbzUIpS6PvzMFQV9b+reKNbrdeZNqAyYz8X3EITnxiHM20O7rre02h8m/Z7Y5eEqSoIx+bQ0pu9hkpZZevydM/ZFlljDjojILVCpBYcJr7MCtALAf0sRkc+TKr3xQsqE73Q6T1p2Ye09Nmt/mPR/YaQy3kz4MsuupCgDrCPH5fidut58Xh25lXLZVRPAmnrkKThDrxNuVJyj1428ArwB5Eh48EeqT9/72B0Gk35H8MOUqqGzW6/vBRn5f1PvBTQ8UNIXPdggTVEZTrXqHApoXM3n5IQasv6YhMO/V8IrIdlrSIUQOBe364LovV4vTk5Osvl7RaQ3tsOk3xNaruPONm4B5Qw2J/iKSngR25ty+PyAZv65VKc77yChxtp6Pa+GFmzR2fVG2Yy/pja81MQltPfI6vNiwyVQtvAYwYVtuTFWu8i99wJQDpN+B3DSC64obzl1fHwcm80ml6hKJdTU0qbi7CILiOtQz4KvTy03SmHoZIPajfXtENSAKKjds1UGqWFZOXvOPfsYsMl99bwoKel5QAfuH+4tJ+2wPffJyUkMBoM4PT3N4noe6mnsBpN+C9i95jJdq9XK5rujPNXpdJ7Exkr6IuuO7/V3fB1KeCYrLyBcDgPpeadZNLZA/qobV3BowVN0uVympTOelsNlTL4+1RGgqQbnA+l50CaIjym7JycnmbW3e38YTPodwS49LBFc9UajkW01pZr3IuuuxE4Rn8+9D+nX68fNM1nuylp3fummlsg/8MYYqJNjsWPS88AOHaqBRROkL8ohRETu/qbOx4M38btUp6FRDpN+R6hrD+uETR5UQlqUlQdShC/6WR/o1EOu52GLyi4997EXdbfBtYerzlZerb2OytKNK/jaNMzRsiUnAHV7a939VsU5xu4w6XcAx/R4KCMi+77dbufc+Ii0q15G9KLfaXJKya6JMvYydKZcytXnARaqe9eZexrPszyWM/cpi6vXVlQWjIicxl6Tgzqxx1Z+f9S21IDdsEwoKnWpC8/vT32vxyyDlv+KfsfHKrOsbP15Eo1uYR2R39mWyaakU0UiX2dZvqJokeP5AakX/82EL0Xyppj0e0Af3JTF2kbi1PHKkHqYyx7wIpJtc7FTUt9diFfmeRR91rLPXebRpPQHJnwpkjfG7v0eUAvLZa2IjzO6aZcHvChHkFoI+Gd+zy5k2+eaDoEe92Oe+/sMW/oDsS359pdG6mHfdg1l11yWTyj7/i+NonOZ7DvB7v1fCh+b8B8Ch1zzpySaSX4QTHrDqBiSpHeB0zAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCoGk94wKgaT3jAqBpPeMCqGxpa/1z7KVRiG8dFgS28YFYNJbxgVg0lvGBWDSW8YFYNJbxgVg0lvGBXDfwNujeWWV1HX5AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 25; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 27; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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gSVmbZ1hBbT975mP3zo49mxxU4o8rtdmyJfDYvVdZshN/ejjpnwm19CS17ZqzVl0TVzGX/uzsLDTSXF5ehjia5yPJWTJTwnP+HWvyq6uraDabIXmX5tbz+thF1+v1wnVwFJh167nQ6Vw7rfvbmroV7qTlOKyrb7P59nmOyeCkfyaUAIT+bImu2Wy685w+c3V1FRpqTk5OQhxNeau60/yZ59byHLP1a2trCbltWomO4OLDxhqKcKyXQeuuQy3ZnqtjrvSe1VIr2a1ewQpzuDBq+KTP83h+cjjpPxBiGXpr4VR0Q5Lz4Fjpi4uLQDgtkQEPpTi+F4DgWlcqFTQajWDhtSbP5F2aWw88kIyDPulp9Pv9xCYXfA9Ori2Xy+Eg8bWmnjb0Iq3lWK/FLpauxPswcNJ/YNgvcaw9lpZUD2bnlfwcdT0cDhOCG+YOaG3n5uaCtn5rawtbW1th8wrbL29hBUEc6X12dha69zSWV5eeZK9UKoH0WrMfp5G3icBYolMVedbSO6aHk35KjKsf04Lpc2zzzMnJSehY0xnyTNrprHl2nXFnF+Bh8wfOsaeV39zcDPvP2eQdXxe7l7QE3mAwCKEF5cZcaObn51Gr1TA/Px8WFy50DEk0e0/YASM2J6Kfr4p2FOP0Do7xcNJPiFiSyQ6GVCuvI6a0eebw8BCHh4ehJEaVnd0tRq2hzrPjAlCpVMKGkysrK1hdXcXq6mpw65+SvKMYyC5ILNNxIWMoQevOfe5IeJbVqNjTvAUXR+1CjA0OBR48BGvp+Xk44Z8HJ/0U0My7ik4AJKS4dJlJ+G63i3a7jf39fezt7QU9O91oTZhp3Z3lMFrXSqWCSqUSLCxJv7S0FAZkvC95pxZV3frj42N0Oh2cnp6GfnkAIUHHCgEHctRqNVQqlaDBp5eighx+VjHtQqy2Pw5PVTc60uGknwBq4bXMRuGMdtzlcrmEy9ztdnF0dBQ65fb29kLzDN16LU/RstONLxaLYd/4er2e2FGWP+u/zc3NJZJ3eg96L8wznJ+fBw9E+/QpjOFQzVqthnq9jsXFRTQajbArDoDQ4quE52w99RaAh40u1NXnNekEHqtLiLUmOyaDk35CqDtMbTx3i6V1ZjJrOByGunun08H+/j7evn2L/f39RD88X29degCBUIVCIbjUy8vLWFxcTJB+YWEhTLitVCrBrU9L3nHhIuGPjo5wcHCAg4MDdDqdkEAEEEpylPbyYLfezMxMqDBcXV0lFIbanENYJaPuqhOr5fNRQwM7pMTxdIwlvf3Pygo0U21dSJvlZjzO6S4kPWNbjpniPvL7+/shlmd7rHaeAQ/NKxoHsyxHK7u8vIxGo4FqtRqIzmx6GuE1H8HE4vn5eViQdnd3sb+/j5OTk3BPtPDVahWLi4tYWVlBq9XC8vIyqtUqZmdnMRwOw1ANlus0Can5Di392SSeJTxh8wCxASSOp2Ms6Z9bHtFsrP5u//4hzxH7d+Bp9xKLe/Vv1l1n91mv10uQnsMgaEU5BENbVJkZ1354no9iFF4TR1fbTSfpXnN2/TgprJbmlPDtdhu7u7t48+YN3r59i3a7jfPz81AxKBaLoVuPsl6q/Obm5pDL5UJooqIc3e1W/8+VvHbXWStqikFDBCf8dBhLeq25ZhlaY6erzuz78fHxI0vP17BTrdvt4vj4GN1uN5Gpj3WdAUn3l641W2XpzquFt5s+askQeCCQVhJo4d+9e4cffvgBr1+/xv7+PrrdbnDRi8ViqP9vbGxga2sL6+vrIVHIMVkMcXjvqrVXb0XvTTP3XOC0z95eOxBvVXZMDmf1e3B3dxescq/Xw8HBQYjJj46O0O12w4BIleOyJ53im/Pz88So6hjhLSFYHmM9/KmEJ2zikbPrT09PA+F3d3fx9u1b7O3todPp4OLiIrS7Uta7tbWF7e1tbG1tBZUfu/WYvFMlHhdJ68FogtLuABTT1GvpzvHhMJb0//jHPz7WdfzkiIUJJPHt7W2YFXdwcJDIvFOmqlNfACS2f7q8vHzULRfbhUabZ7QG32g0wgYVdOkt4W0tXuWr3AKLo7c6nQ729vZCDM+kIvvlZ2dnQ9JuY2MDOzs72N7exvr6eujJ145CXoM9t+rjVWdAvb4uVCq75SLFR+DxSC3H9BhL+j/96U8f6zo+CdDiDAYDzMzMYGdnB19++SUajQbOzs5weHiYGA55eXmZ2JedYO3bbues6jQlBMmitXDdmELHVnNXGibs+JqY3p8hCVV2LMlRJ3BwcBAGZDAu5xCORqOBtbU17OzsYGdnJ6j82K2Xy+VCWU7785Wcen+8TuYd+EjX3mofdCcbAIlFQEU7jskxlvR/+ctfPtZ1fJKo1+v4+uuvsba2Fqykkt1+uQmVodKNt2TX16g7Pzc3h4WFhZAp39raSnTMcf+5crn8KGmnmXm68sxB0EPZ29sLM/OpEeDCVSgUwvkp6d3Z2QluPYdwqDuu8lklpSbt2ChEfb7q9HXjSi4iscx/Pp8PXpJva/U8eEw/BmdnZ/jb3/6G7777LrjrtrxG19VaOnWv+ahQC0jryok3zWYzZMpJek6/4Yw7Wnkbw+sWWAxH3r59i93dXbx79y6oAO1gDEp7K5UKlpeXg1u/ubkZdPy6G45Kj5XwvF9+Lird5T1ywZqdnQ3XbDP+ulElgITnFNu51pN6T4eTPgIlMAUr/LsdABnbjZVHbBa8vo92q1FtR3eewyzX1tbQarWC5JWEj+nVKYPt9XrodDrY3d3F69ev8f3332N3dzchCKI7DyDcR6lUCtNzNzc3sbm5Gd0Yg/fJR+YN0noGuJhQPMRWXy4efA+SnVJe28PPMIkhk57P8XR4yS4CTcYpaJX4hSa5x8W0FiQCk3XUsS8vL6PVamFlZSW4861WC0tLS4k43u4jr4sMRUMnJyfY3d3FP//5T3z77bd48+ZNIDz19LTSuVwu9MEzeWf78VXSy3vUUIKWV6frqISYPQNUDs7NzYXPj6GFkl4tvX6OJLttTHIXfzKMZbW6sY4ktN2T+nT+nGbd6cpr3b3ZbAbrTrJzg0kq7qw7b5V2Krg5OzvDwcEBXr9+jW+//Rbfffcd9vb2cHp6GrLzGgtzweJsPe6EQ8LbuXoAHhGe1pek10w95bvUGLDqkMvlwmt5/XTv6eIzP8BQiLMIBoNB4nxO+smQTVP+AZAmDtGpMcDj1lh2qekOstx9hsm6xcXFBEGsdSesle/3+2i323j79i2+//57vHnzJtTfdVa+FcrQ4+DiExuXbc+pQh8tRwIIc/d1/j4P/l2bi7RBR5Oetg9fx4rZrkSP658OJ/0zoMk4/cIxu61TcnXCDTPzJDs3lWR2Xl15zc6ntcdSCESN/+7ubiKGV8KTsFpGY4mu1WphdXU16OrtuOyYso9TfrTpaHZ2FgDCYkJREfv7AQQXnu+rZUYt3fGcfD5JT2vvcf3kcNJPCZKQxLT1ZgDRRN3i4mIiUccafLPZjGbm0zwKJSC3oFLRTYzwCr4322VbrVbCradFti49ycgpQDrxh14E+/i5yLHMyPfUBhwNi2zFQw8AIZ6n4Mm6+G7pnwYn/RTQfd9ZcyY5NXvNmrQOuSDhSTAKbsa58mn98NQBXFxc4OTkJNTiDw8PHyXtVAoLPAzUXFhYCDvhrK2thX3rudcdz6WWmKO6KS/mdtmM57nQMYzhSC1aecbsT2mG0sXGTg1myTGr3aDTwkk/IRij6wQZValpgswm61ZWVkJrKvvRrdCGxBzXNqqJNN1Zlq277Idn0k5lrtrlxpr82toaNjc3w5gtdetj3Xkc0316ehrm4nOvei507BdQMRGtPBOfukjGJMn2ftnlyOGhuieAJ/OeDif9hKAlm5+fD33ti4uLwXUl+Bz2v3OUFS073V115Z9K9lh7rE7j0f3jNaGo2gIm7jY2NvDZZ59hY2MDy8vLiUk4aYTXOXpnZ2dhwo7ulqsNQhzqAfxo5d+n0WeiTz0BLha09JwerHG9fv6OdDjpJwStfK1WCxZybW0NCwsLCUtN15kNMxTXxMg+yRQYEkDbYymvpVtPEhJM2ukwjuXlZWxvb+PVq1f4/PPPsbGxgUajgXK5/Kj8aAnPOXo6FwBAIDzLc3Tr+Z5M9CmJNfOuk3TYz6+eAOv6HA2uLr5b+qfDST8BmIknaTY3N/HFF19gc3MT9Xo9QWKSS0dYaZPMuLhdYWN4ZurVwtvWWN0Ci266ioFo4V+9eoVf/OIX2NnZCc00Og+A57OEPzo6Cm3F/X4/DP3g/Wq2nko+Et1q7K2KTysejNU1yceQhsQn6T2D/3Q46ScAN5cgcTY3N/HZZ59ha2srDJXgl5bCFBKdCTptgZ3UuusADE68+eGHH/D27VscHh7i/Pw8xNbMzNuEIttlt7e38fnnn2NnZwfr6+uo1+uJZhrVunPHm06ng3a7jU6nEwjP4SEUHLHtl4sclXkqvrFSWs3mU63ISoBtJNJSIVuWOXjT8TQ46ScAXXvKZldXV4NctlarBfdepbZ2ms24eruFxu/smiPh9/b28ObNG7x+/Rp7e3vodrsYDAZBFmyHcHAzDGoDeLCZRqWxusD0+32cnJzg+Pg4EN7OBeQCR7KzEsHPwwp5NAnHrD/wUMKz6j8AIT9BbT4J77X6yeGknwCsQetwStbX5+fnEy57bIgjMN6qa6LOjtmme802WQpw9vb2cHx8nJh4w40wWDJbWloKhGepkE088/PzIVsPIFhTluROTk7QbrdxdHQURn6xFMjPg4sLO+jYEMT34+Tgfr8fJgjZkps21ujCqLV6Jvesx+DZ+8ngpJ8AqqrjjHkdaKGE18enEF1jdt3JVneQ7XQ6YXb+wcFBYnoPJ9fS0rJjj6IbioDYwMMyGr0QTdixD5/xO9txLeFpzTnHT3UGwMMCwrFhnB58fn4etuHWxpk0L0gTfioQSpsx6BgPJ/0TQd08a8+07lrTjg1utBbeJuaU6Coz1U0umUCji02y68x9WlvG7SoCorQ2RnZeC6/h8vIyTPo9PDxMzALkDrokPF17kt6OvmLSjRaehNdymw7RtNbaqvKsJ6TlPvs6V+elw1trI+AXRru8WHdvNBpoNpsJbTqhX7a0LyOAhLacO+RcXl4GMuggTW4meXp6+mizS7q1hUIhbGLZarVCLzzbY3m92otP6Ix6WngV+ujEXzbU0KuxFp7NRtzgQre95sGdefkcW5LTRVFJzkfbLORTcSeHt9aOATP1TIZpJ1y1Wk3sFa9S0Nj8dv0Cq0VXt5cKN24TzYNbWdvR2ezco3XnpJ3t7e0E4ZlYs334OrmWgzM5o59z9I6Pjx/V4llS4z57mrCjRoD79/HeLOF5btXf60Kpn5dCB5CMa0ZypCObpvyJGA6HePXqFTY2NjA3N4dGo4HPP/8c6+vrIVtP0pBINp4HHtxnWnaSnco2uu7cwVZJzo4yWmNt5ikWiyFRt7m5ie3t7TDiamVlJWj6Nd/A6wEeb3xxcnIStreihbdDM+nS60TbmZmZ4HVYwnMR473oNlc22Wlj91isTs0BFxwtC+pzHOlw0o/B6uoqfvvb3+Krr74K2njKaavVaohf+UWObbuszSI6mVbj83a7jePj41AKY7yrO8RodptuNS381tZWmFqrhOfEm1h7rM7Ct9NymamnhWdpzrr0GsOz9KaE59Hv9xNJO+BhA0sAQYOvrryFCne0FEnS28EijnSMJf0f//jHj3UdnwRobS4vL5HP5/GrX/0Kv/vd7/DZZ58B+JEonDXPuJgWWL+U2kxCQlBF1+12QxaeWfHj4+NADlr1mFJN+99J+DQLb0dcAXHtvi5CR0dHCeENLTzPrZNsGS5QD68LCEMWeixq4ekxMP/B92AFwcb2+nmqJoBKR+YpnPRPx1jS//nPf/5Y1/FJgF8casTZSVcqlRLTXrVOTEuoBKWrz+fRVedGE6x7c0toJYZadlpnWlT2qHOWHbX/3G6KHkhsX/oY4dktR69D6/A6BWdmZgZzc3OJg1aa8bn215PwWotXaTDDIHpBOm/QZuQ1VGIOg8o/kt66947xGEv6X//61x/rOj55qJvObawYb3OIhH5h+VyN39mowvidyS1dOGI6dNujvrS0hFarlZi6wzZdO+2G185rshbeSmtZhwceknYqvtEcASW0fD9aeR2swUXBNtVozG51Cny+LYHymrjwsXnJST8ZPKZ/Iqy8lsow7g3H/nV+aZkR197zbrebKLtRRmrr1CrXZcKM8l/bm68jtmz8rjPoYtfEjTh14wuGKqzDq8JPS372/WjhLy4uAuHtAA8tFZL8acq7tC5BXofu5WfHlTnGw0t2Y2C18vzyqvtO0rOvnF90KtFYp6aqjmS3VlDPp0073PWGE2WbzWboz9f6O7/4tJZWwaZyXs3Uq4XX5hklPONoWlTW4nl/tO5U2dFzUcLz+rTUpjvWxtptVaWn12NJbz0bx3iMJX1WP0iNp5Xk+juAoDhjfVtFJyQFLZ+W4NS663vy0H3fyuUyKpUK6vV6Yl96kp1E5CIEINGSqpUDNuzoEAwlPPMYFCPxoAfBc+h9cyHT8qKdyacNQHw/3daK18z3txl8nptJTCbxdB+ArH5Xp8FY0vskkgco+ZVkOtSh1+sFQtuustgkG9b51f1V4QnjaA6k0CEcFAbRxc7lckETDzxszGE1/Fo7V2Ufm2dYmtQdcVnVoLegSjstybHMqPeoG3vooTJgLn7aM6/JTFp6DTN0eyzP3E8Gj+mngCaddEKrWnG775qWoFSyahNcahFp5TVTrTvGUkU3HA5xcXERXGgSx5JUpb66CSez4lqDt0o7vo9m6em9cFFT8RDvhz0L2oWn8mV+hvq52nq9luv4PrFRY46nwUk/JRiD2j3Y6NoqoWnRASQaUixBmCmn4o2kpyvL92EZkJoCHaqp6j9tP9VZ8cwl8Nwkuk73VcLTS2GWPhbDK+Fp4e30IHXJASREPSxVKukJ1SioVoBJRSf8ZHDSTwlNkmnyTPeIYywKPJBdXVc+z+rJaXVpeTVjTg389fU1er1edCNLLjx2XzhNzCqBKpVKsJ5WaUeBzs3NTQhjWIenZ6Oz5zUBybKadiXqVFyGJfSK7GfLzwdAwgOyoYdjMjjpnwGVtBJKdM0628y0xqtWcWcPlbrSrY9di7bq8mdrfbmXXrFYTIQOOsOP59PmGVuDp4W3XgMJT7I3m000m81QViyVSgAQ8gz39/ehXVcJbLX0XAxJeA0/3NJPBif9MxDrndef03rBLVS+a5tQACR6zoFkaKHk1lKdXp/W+mnddbspTQ7qbHrV0lulnZ03r+epVqthNx+7826hUAjZ/3w+H3IiOl9QBTzAg04/NqzDLf3kcNJPCWul+UUl+HPMCsUaSvS56qrHwgi66nqkkZ3xM912JXy9Xk/MpefUWp7PNs9YwttuOVr4RqMRBnmsrq5iaWkJtVotlAO52eZoNAp74dl9+2zfPO+FhyfwpoeTfgpYsuvBbLgmmGJfTrX61u3X3IBq/nUfeB66GAAPNXGeP23LaMbZjOe1gUgn3mh/v1Xa8bPQDUA4P5CTe7h1F7e1ur+/D3mCwWAQ8giawNT70XwHya4jxB2Tw0k/JdJcezscU7vECI3pNZOvP2tsrtadVQIuAra0BSTLZapg000oaOFV4KPCGx1iqc0zPLe63pof0N1vVSbM6gOTidfX14mEnBKaQiFCy5mxLbsdk8FJPyGsek7Jbb+IaV9MJbmS18bp+rPKe/n4vvidRFRxDwU+TNxpaY7NRCS8inh0UwkVEzFkoGqQEmHKhDmXj/oCNuooyVmdIPm1hKcJSE/afRg46Z8BS34Lto1SQw4kCW/jclvSswk6tepMuGnbqZXucospWnhad1WyqXKPhNeZdtr2y2tjNp2VAJsnYK6Ao8Y0X6CLo+0z0OQcn8/nEfq52DZcx9PgpJ8StqQEPJ7rRguqUPGMxulW1GP156rcA5JdaqrXp8BmYWEhkaHXUd2qhuO5Y0MsYyOuuMBQn8/wIbaFl47Dtt6NEpfvq2685jv4qNebNkHX8X446Z+BmIXnFBglPmGz8JbwWgLTph8ACbLba7ANOiQhm3IsAVX6SuENtfk6plotPL0VzVOQ9Haji5g6kPdNhaBua6ULgO2y0wQl8xn83GyJ0vE0OOmnhLqoMeJbqJCH1k6PWNmNj5ostBUBjYU1cUdrG9O5c0fY+/v7oM/XTTXOz88TFp4Ly/39fXgvFcvYBhrtSWCJLpfLJSbs6LZUvCYuAPw8dDHI5XK4ubkJ0mK7M47j6XDSTwElXlqGHkgf6aylOb6fegf8m90Wy/5NM9raCqviFU3SqfoNQCAaSa/tv6q0s3kJXm+sPZZeAxeJ6+vrxKhw9SpU2Ud1n+1hoKVnqU8Hhjrpp4OTfkqkiXM0AWVbafULqkS3rrstB+rQCY3lrVyXRNeBGiSvDu/kQkCdflqnIK+N57OSXmvd2ZDD3weDQcILUPee03u0Sy9GatUraFigpHfiTwYn/RTQpJMSjhp7S3TGtkpqfS+NXYEH191adGa1+XdaWRWr0G0naRm763k11NAtpOnqW8LzubHef+CB8LwXzhLQGF+HcJDg9CysxbdNPLxmFSZ5Im96OOmnREwpViwWEy68WimWx7go0PKR8HYLsZgeP0Z8K1bR/ICW+4D41lF6DRpT6zWQzNaq8vXaHsuJQTpmS0MAXRQowaW11zFiauX5eSvZvWQ3PZz0U0AtvQ6fYHsoiaaWX4lka80aEug50uJ4Ld0BSCwm6rbbzLm1jBqj26YgPk9DkFjzEO+LU3sGg0GimqBaeZ5HrT33qtd+fxUe0bNII7mLdSaHk34KxEhfLpdDtloJSLIwGca/6xhotbz2PDZrr+GC5glI6nFyXZso1AqArUKkJR5jHX38m4YlGvZogwyvnXkE5hKU8NbKa/OSDX1cpTc5nPQTIqZv1/FWAMKEG1uWs1bTKvVij/yZFk/FPwq16poFV1fZJuI0NxAjkLXsSnS+p/27eic238H3povPKoES3g7V1KSmliV9IOb0cNJPAfvFVuIrIWzCjgMobbnOEtnG4ECy9q+uvYpa1MJz4AYtp+191xyB7lWniLnzaRZfk3xamdBww9bxteauFl5DC81jpA3FdOJPBif9lLAuPq2ZKtfUreffrBWLJaJiljUtBFAC6jBMOybLSl7pediBHRrf67XouS35LemV3Fzs9HUagujgUFvBiG1wQXmxbrzhmAxO+ilg6/NavlO1m7V6RMxq2uSYtahpbj3wOJ7XRJ4eNoln43lLdJ5PFwtF2kJkkZYATOsz0HKk7lBbq9XQaDTCHABuyuGWfjI46T8QbELOxr5pmXQlvJb47DGuPGXPowlD+3pLfLWuae48nxuTA/Pf9DHtZ70mLRHyfDqERLX93LCSG31ou64P0pgcTvoPCLXMGvPGNPYxQivRNFa2mXfgcVxvkeZhWPIrsWPeh76XJb6GBXxUaTCttXoTad4KwyG+pzbysE+fBy29b3QxHZz0U8I2v2hsbN1+ftH5O2NdEkhLcEoMjZGVgHpeggtMPp8PIYaeQ7v57KKimf1YCdGSXuW3MXWgPVSYw+frwqOLEz8jxvB06TmNh5N4OPHH3fvJ4aSfAhoP60YRFKiQYIVCISSrZmdnE+69NpJYS2+tvyV8rEbN1/F8qlPno5bwACS8DutlKNSK89y6/RUTmSrG0T3wVC3Ia9PMvR3OUSqVUKlUAuHtzD2S3nernQ5O+ilAwnNH2VqthtFohGKxGEpl/ILbnvk0GWlaLB1LvsWmxsZIT/GLHlwMtKtNr5EuNqEk1zo5h3Xorjg6k56kV29Gqws87HbW/Ew5AER36W21Wmi1WqjVaqFl2N37yeGknxBKPO4Zf39/j9nZWdRqtcRgiDQpbCyxNu5nTZ6lae41mUeLroo3HpxomyZ/5XVqSdJuHFmpVBKHkl8Jr2GAkl5r9HaUNpN3HPPFqb185BQgT+JNj9x7yi3ezRABCaYtqbFJMBovp7n0/F3f2/7Nkj5NOqtZcbWovD4SnU0u3MSCHW5KQiU8S2acxmM31VTCU3PPWF7DD12UVDik8bwdBMJz6OaXvnHlkxH9cNzSTwGVheZyP06gtXPbbDkuFqMD6TVuWxu3OvxY6UxjciW/tfwkP4nP8dYkvY2vmVSjOMbufafbTKnSzzYFxSoatrmH1l5VjrF590746eGW/hmI1drTSP0UoqdhXE08pt23IhgesXif7j03saCnohN5dHtojd+VjFqx0MUoJvqJHbEQxr6nEt0J/yREPyQn/QfAcwj9VEz6JbdEs1bWymCtq63lOe2W0+k8luRp4pzYZ5L2OaWR2kk+FZz0WcY4a5um+LN5hFhY4WT8pOGkdzgyhijpvebhcGQMnr13BLwvznb8POCkdwQ4ubMBd+8djozBSe9wZAxOeocjY3DSOxwZg5Pe4cgYnPQOR8bgpHc4MgYnvcORMTjpHY6MwUnvcGQMTnqHI2Nw0jscGYOT3uHIGJz0DkfG4KR3ODIGJ73DkTE46R2OjMFJ73BkDE56hyNjcNI7HBmDk97hyBic9A5HxuCkdzgyBie9w5ExOOkdjozBSe9wZAxOeocjY3DSOxwZg5Pe4cgYnPQOR8bgpHc4MgYnvcORMTjpHY6MwUnvcGQMTnqHI2Nw0jscGYOT3uHIGJz0DkfG4KR3ODIGJ73DkTE46R2OjMFJ73BkDE56hyNjcNI7HBmDk97hyBic9A5HxuCkdzgyBie9w5ExOOkdjozBSe9wZAxOeocjY3DSOxwZg5Pe4cgYCu/599xHuQqHw/HR4Jbe4cgYnPQOR8bgpHc4MgYnvcORMTjpHY6MwUnvcGQM/x/HmrpjMR+ltgAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 37; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 39; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 49; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 51; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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NRrG9vY2NjQ243e6JCTFq9Pt9NJtNVCoVUVQjP1fNtKd+d2TWt9tt1Ot1lMtlUSRDRTfKdcgOOuqJN8n8n2be864/Gyz6r8CkzLFWqyW89YeHh3j79i1OT09RKBRmPstbrVYsLy8jGo0iGo1ieXlZjK+ehryedruNRqMxVtii/FmlA492eSqHrdVqKJVKKJVKqFQqomhGeZ4n8ZKg6TXVQnv0M/JzmPlh0X8FZM+33DGGzvDHx8ciCefq6grVanWmslmz2YxgMDhWUOPxeMS/T0tTpb/v9XooFovI5/MolUqqMXlZjNTV1mq1wmKxQKfTCdM+n8+jWCyO5cqrnefpxkE796RMO/pZ9t4/DBb9V6TVaqFcLiOXy+Hm5kac4U9OTnB2diYq2WYJ0ZnNZtHOmrz1Ho/nztJTZe56tVpFKpUScflyuTw13ZVabzkcDlitVmg0GrTbbbHTU3cdpddezVxXW5t8k5CPAMruRMzssOi/AoPBAM1mE5lMBolEQsyhu7q6wvX1NW5ubpDP51GtVm+dg2VINDRldmdnBwcHB2LSrDxPnn5+GsPhEJVKBclkEufn57i8vES5XB6zMuRIg8lkgsvlgs/ng8/ng8PhgF6vR7vdRrlcRqlUQrVaFTX3ctSAPP6yNx7AraOEmujZvH8YLPrPxKRc/F6vh1qthlQqhU+fPuHw8BAfP35EIpFANpsVTq9ppaPkxabMN6/Xi42NDezu7uLFixfY3t4eK6iZtkZl1KBSqSCdTuPq6ko051DDZDLB7XZjeXkZ6+vrWF5eht1uFyWwlUoFlUpFxOflM7q8y5PXX3bKTbIs5FFYHKe/Pyz6z4RaDjlNhkkmk6J45s2bNyLTjoZFTHtN6krjdruxtLSE5eVlrKysCLOezvJOp/POnVD5741GA/l8Hul0GplMBuVyWdWZZjab4fP5sLa2hkgkgvX1dXi9XgwGA9FNh7rlUmyednl6T7pxUUIPOe8AqPovZGcfh+weBot+Tu5bTddut0XnmQ8fPuDNmzc4PDzE6ekpstnsTN55o9EIh8MBv9+PjY0NRKNRIbrV1VUsLy8LM3va2miHl01kapJxeXmJy8tL1fJZGjRB77+zsyNGWZtMJhSLRdGgU26PLd/IyAFHDkCLxSJGVJF3X61sl5x4NP+ORX9/WPRzcp9zJPW0Ozs7w+HhIV6/fo0PHz7g4uJiahMMGcpr9/v92N7eFvn00WgUoVAIHo9HDJ6cpW+c/Dn6/b6o5Ds+PkYikbi1y+v1etjtduEsfP78OeLxODY3N+F2u0VTC7lDjtzoQn5PMutNJhOsVqsYX0VWgVq2HfkA5AQdFv39mCr6RW1SoOyAM60bDs2xo+fQxUodYGkSzMXFBT58+IDDw0N8+PABl5eXt6rl1BJfCIPBALfbjc3NTezt7eHly5eIx+NYWVmB0+kUnWfv+lzKzzEajZDP53F0dITXr1/j7du3uLy8RLVaHYvFO51OrK6uYnt7GwcHB9jb20M0GkUgEIDJZBIjszUajehyQ7u8mnip2Ybb7YbD4cBoNBLhPgrJyc/lnf7xmCr6h9YsqzWXIHE8Vj30Xa+lrAab9bVmab1EoSlqB0U7GO12pVIJ6XQaiUQCnz59wqdPn0R/ORroIDPNS2+z2RAMBhGJRLC7u4t4PI5wOAyXyzVTyys1SPCfPn3CL7/8gp9//hlHR0fIZrPCxNbr9XA6nVhfX8fz58/x8uVL7O3tYXt7G4FAQJTpdjodsQ55Zr0Med/lwZl+vx92u110yG02m2JohtrNQo7TM/dj6tVy34vp90yn0xFdWcvlMorFokieoaITrVYrQnIXFxe4uLjA5eUlrq+vRbLKPF1fdDod7Ha7yLSLRCJYXV0dE7zaLq5E/rdut4tcLicE/+rVK7x//x7pdFr4F7RaLdxuN9bW1vD8+XP84Q9/wIsXL7C1tYVgMCji8jRiSil6NSegwWCA1WqF2+1GIBDA0tISbDabCOvR96e0rugmoOa5595488GqnoNarYZsNiu825lMBvl8HrVaTUxkoTN1o9EQ89vT6TTy+bxq/vksmM1mUTVH4TGn0zl2U55URKP2b7S24+Nj/Prrr/j555/x4cMHpFIpkYGn0WiESb+3t4cffvgB3333HWKxGPx+/9h8eHLMyVbVpBRaaqcVCAQQCoVEF59arYZarTY1006t/JaZn6mif/v27Zdax1dHvpDIl2G32+FyucZETC2gLy8vkU6nhbeapq2QaUrZdsViUeSe3wdKvNnY2EAkEsHKygo8Ho+YJT9Lai3w1zBYp9NBpVLBzc0Nzs7O8ObNG/z666/48OHDWOkuCT4UCmFnZwffffcdXr58KeL/d7XaUopeTiJyOp3w+/1YWVkR03XID0BTduR+eMoW2dwq6+FMFf0f//jHL7WObwIq62w0GtDpdIjH4/jxxx8RCoVQqVSQSCRwfn4uzuVUCCM7PMnh1O/3RdOI+/Znp9TaZ8+eiXP08vKyMKsnobwRULjw5uZGtLE+OTkRVXzK5hxGoxEejwdbW1s4ODgQCT/ktFN7H6Uo1fLzKdy3traGtbU1rKyswG63o9lsQqfTiaaX5ASUh17QjYA67yi/c2Z2por+X//1X7/UOr5JXr9+jUwmg3A4jHq9juvra6RSKTGPfdbKt3mg9Far1YqlpSXEYjEhvEgkInrJyz+v9hpEq9US2X9HR0c4OTkRefU3Nze3QoY00XZjYwN7e3vY399HLBZTbaYpC36SIIG/it5ms8Hv9yMcDiMSiWBjYwNLS0vC6UmZfK1WS7Wt1nA4FNaKnNbLHvz54TP9FPL5PP7t3/4N7969E+WirVbrs01N1ev1YkjE8vIyNjc3sb29jXg8ju3tbaytrcHhcEy80JW98pvNJtLpNA4PD/Hq1Su8e/cO5+fnyGazIj1WmSNgt9uxurqK58+f4+DgQNWkV1oSsuDJqpGbYlgslrFOPuSItNvtaLVaGA6HYua9nIIsi57CgPT901irWbv6Mr/BoldB3sGSyeRnfz8Shs/nw/r6uqiFpx1xZWUFS0tLd6bWyoKvVqtIJpN4//49fv75Z7x69QrHx8fIZrNjNyw5TGk0GscKd3Z3dxEOh8cKd9TO03KIjrrd0jGHdviNjQ3EYjHEYjFsbGzA7/cD+KslIs+7J2cnvY78vuQnoUIesnrYvJ8PDtmpQGf7x97N1fIWKEElGAwiHA4jFothZ2cH0WhUOO2ou+yki1ut1VUymcTr16/x5z//Ga9fv8anT5+QzWYnhgopPLexsYF4PI5nz55hfX0dLpdr6meingBUQ087vdFoFP3vKWU3Ho+Lxh5WqxWtVguDwUD00SPRq02voZ8rlUrI5XIoFosIBoOw2+1jN2m+AdzNVFXzgMDHRblLUgw8Eolga2sL0WgUm5ubYnd3u91zJ6G0Wi2k02m8f/8ef/rTn/Dv//7vODk5QalUmromu92OlZUVcZzY3NxUbcChJioyvSkcaTAYREhxbW0N8Xgce3t72NnZwerqqmjfRceLer0u6u7JbFd+V4PBAPV6Hfl8XvhUms0me/LvwWJu5XOgTFl9DLRaLXw+H6LRKHZ3d7G/vy/O7H6/X6SmzrtGctodHh7i559/xuvXr1UFTz4BufLN5XIhHA5je3sbW1tb8Pv9M91waJAF7fI6nQ4OhwM6nQ4ul0vk6e/s7IiuvEajUVTeUVaj3DxTzRoh875QKCCXy6FUKqHZbIoJOvLP8W4/HRb9HTz2TmI0GuHz+RCLxfDy5Uv88MMP2Nvbw9ramoi/38cj3ev1xlJqX716NXGHpzO3ck2bm5uIRCJYXl6euQEHedVpdya/gE6nQygUwvb2triheb1emEwmcaOQx09Ti+xpR6pOpyMyIavVKjqdztj/Dwt+Nlj0Xwiz2Szi1JFIBAcHB/juu++wv78vcujVavCB2bLt2u02bm5ucHR0hLdv3wqnnYyytoCgY8bm5qYQp5zeO01IlI/Q7XaFxWA2m8dek3r1WSwWaLVaEW6TvfEUEVG7ycrrVnrv2byfHxb9F8DpdGJpaUk0u3j27Bl2d3fFGVceNSUzz65VqVRwdXWFT58+ibDcLAU9TqcTKysr2NzcxPr6ukixnYXBYDAWWzcYDPD5fDCZTOLzhkIhuN1umEymMTOcdvlGoyHM+lmqOpUVfCz6+WHRfwYo/95kMsHpdIqYOwl+a2sL4XAYS0tLYwIjs/uubDtg/IZQr9dxc3OD8/NzXFxcIJfLjZnJclcaGeqRH4vF8OzZM1EbP8vNhkZWUavsTqcjuuC43W6EQiEEAgFR9isX0VAXoWazKWLzlMp8F5T8Qx5+Hlc9Pyz6R4L6t5lMJthsNrhcLvj9ftFDbmtrS8SoA4EA7Hb7WGYdvcZdKM3tTqeDm5sbnJ6e4uTkBNfX12Pjrwi1EmS73S5CdHdZHco1UK8AqjLs9/uwWCyis4/f74fT6RStsGTIQqjVahMHYUxCzsyjYwWV93Ixzmyw6O8JpctS62cyX6l/XTAYxMbGBtbX17G2tibaWZEzS2YWB5TcpEP+u1wuh+PjY7x9+xZHR0e4ubm5Vdyj3A0pD35ra0sInvrcKc/yynWRx51ad+dyOdFww+l0wuv1wuv1wuFwiB1e+TnkCTrVahW1Wm1m0VM7rWazKZ7rdDq5O+4csOjvid1uFwk1VFtuNBqFebu8vCzi7dS3zmKxqCY8zWLOK816ZQOMX375BaenpyKXflJzEfIvhMNhxOPxsXJZZaqtcm3D4RDNZhOFQgE3NzeiypBe12KxwOl0wm63i5ugsiCn1+uh0WigXC6jUCigWCyKMz29n3Ldyj+3220Ui0XkcjkUCgW43e6xJB1mOiz6e2AymRAMBhGPx7G/v4+trS2x29Au6vP5sLS0dO+dnVBr1SWH5169eoVffvkFHz9+HJtdr8z8o5bV5EykHT4Wi2Ftbe2WWa+0KChclsvlkEqlkEwmkcvl0O/34XK54PF4YLfbx2bTK9fe6/XEJJ90Oi0Kfur1unDK3dUJaTQaodlsIpfLIZ1OI5vNCt/IrBGHRYdFPydarRYejwebm5t4+fIl/vZv/xY7Oztj8+Jox5+0s6uhtqOrXbjtdhuZTAZHR0f45Zdf8Oc//xnv378fa3Elr5V6Avj9fqyuriISiWBnZwc7OzvY2NiAz+cTO7Maw+EQjUYDhUIBqVQKiURCjM5utVqw2+3i+RaLZcxLTx13gd8KgHK5HC4vL8Vwj1wuJ4puaM131cxTks7NzQ2y2SxqtRp8Pt/Yd83CnwyLfk6oIyw1iNzd3UUkEpmrTmFaOaxarJ6q2Or1OjKZDE5OTvDrr7/i1atX+PDhA66vr2/NnKOaeHIkbm5uihRf8jN4PJ6pVWr9fn9szNXZ2RlOT0+RSqXQaDRgMpmwuroqfBkWi0VELuTiH9rhqSPwx48f8enTJ1xdXaFUKt3qSXAXNEwjn8+jXC6LzDxmNlj0U6DdVr6gXC6XmP0ejUYRDAbvFPykLrTy+6jR6/XEpBi6yC8vL3F0dCQm48g97YDfWlUHAgFsbm4iFouNDcEIBAJwu92w2WxT102NPa+urvDx40dRi59MJlGpVKDT6UQ9PFk1RqNRCJ664ZDTj7oOHR8f4+joSDQioVZjSgtn2k7f6/XQbrdFjgAn6cwHi34KSjPTYrGIvu+xWAwrKysiXDSNuxpdKN+TTOFSqYTr62skk0mk02mkUilcXV2JJhiySU/ndpfLhdXVVezs7Ije9FTZRllxd5m9NHGWRma/efMGR0dHuLq6EgMtKZ5vsVhgs9nEUYa881QVVywWkUwmcXZ2huPjY5ycnODq6grZbBaVSkW1ou4u6P+F2mmx4OeDS2tVoHOossFEKBRCLBZDPB7HxsYGPB6PuNDVdu5Zd3aab1er1USGGlWUkVlNgy1pikytVhOC12q1cDgcYqYdNcCIx+Miy85ut98qoKF1y047EvzJyclYD73r62tUKhV0u11Rw242m2Gz2WCz2UR4rtfrodlsiiail5eXODs7w8nJCc7Pz0VH4Elpt7MImDriGo3GW8cJ5m64tFYF5QWk1WqxsrKCeDyOg4MDPHv2DKFQaCwpRO15016T6HQ6yOfzSCaTuL6+RiaTEWWmxWIR19fXuLy8xM3NDcrlsohnkzj0ej0cDoewQPb29vDixQs8f/4c6+vrU8tzlWvq9/solUq4uLjAmzdv8Kc//QmHh4dIJpOoVqtjzT8tFgusViusVisMBoPoftPr9VAul8f6/Z+fnyORSCCXy4kmobS7y9/drDs2fWbZiciin53F3MrvQL74QqGQyEvf3d3Fd999h2g0CrfbPdNrTKo/r9frqFarKBQKuLq6wtnZGc7OzpBMJoVzizrFUFWZUhQ09WZ1dRXPnj3D/v4+Dg4OsLOzg5WVlVsNMNTaUtP6hsMhqtUqLi8vcXh4iF9//RXv3r3D1dWVGJmt1WpFqi0Jzmg0Ct9DtVpFuVwWXv6zszNcXFwgnU6L+ne5Mo665E77/tWg9luhUGiiFcM3gcmw6Kfg8XjwD//wD/j++++xvLyMcDiMnZ0dBIPBWym0yots0kXX7/eRy+WQTCZxdXUlzuiJRAKJREI4t6g/nNx3ToZ2u7W1Nezt7eH7778XgyiWlpZUfQ3T/AiNRgOpVEpM0yWTnlJs5ZFSVqtVxONHoxGq1SparZZoE06fhUJyVAarVgBEzS3nOZeTo3J5eRnBYBBOp3NM9Cz46UwV/T//8z9/qXV8E1BhSqvVgk6nw9bWFn788UfEYjE4nU643e5b3WhniQdTrnir1UKxWEQikcDR0RE+ffokdsJ8Po9SqTRxHryMwWCAy+XC2toa9vf38cMPP+Dly5eIxWKqTSyB6ULodDooFAq4uLjA0dERjo6OhOBph6c58jabDXa7XbThJt9Cq9VCPp8XHYMzmcxM03xk4QO3U4Zp7fLnsFqt8Pl8oqhnWrNQ5jZTRf8v//IvX2od3wRyIgl5pp1OJ6xWq5ifpnRu3iX4TqeDUqmEfD6PTCaDq6sr0XP+5OQE6XQalUpl4o6uRK/Xw2az3Zo8s729Lcpa51nfcDhErVYTHnY5LDcYDFQF73Q6YTQa0el0RM986miTzWaF2JVNLh4LilJ4PB7RiQfghJxZmSr6/f39L7WOJwvtVPSg0s/BYCAmytAZ9+LiQji1kskkMpkMarXardec1kzDZrNheXkZOzs7ePnyJQ4ODrC1tYVAIHDLAlF7LRnKtqMsudPTU+FToOmxJpMJBoNBCJ7CfqPRCOVyeSxRplAoCFN/3mQZ+vm7QnAajUaM7bbZbGPHmFk+M8Nn+gdD4qZWzuSAa7VaqFarYqoMhaxoMk69Xp84LGNSDrrVakUgEBDZgCR4tSPHXYxGI1G4QhEC2eowGo0inZjO8FRUYzQaxRlebkmtdNR9DuSptRS6Y+aDQ3ZTIO+2vHMoY+/tdhulUklUjFG5Z6VSQbFYxM3NjXDapdNpsYsq32davJoSb6iP3fPnz7G7u4toNHqrEYdyjWpQAwzKlLu+vkY2m0W9XodOp4PH4xHHGyqioSpCKq2lWnr5BjZvy/BJFs00ZNHzjn4/poqev9TbyOfG4XCIer2OdDqN8/NzXF5eChOXhldSj/ZyuSx6us+LyWSCx+PB6uqqaFEdjUYRCARmyrCTIcukWq2KCby5XA7tdhtmsxnBYFCE5ijxhnruU0otVcoVCgXhyJt3g5BvpnJnHzXhy39HEQQajc3X6PxMFT2bTreRL0AqSEkmk/jw4QOOj4+RyWTQbDaF6UujsKY1iVD7e9rRjEYjHA4HVldXb3XfmWWQpZyySk0s6/W6GBpRLpfR7/dhs9mwtrYmpu1Qaq3JZBIxfEr7pRvaQwR/352aqvnMZjOL/p7wmf4B9Ho9cW4/OzvD0dER8vm8yCenM+6suzs5qSjrjQZZ+nw+hMNhhMNhLC8vw+12iyw4ijQQJHCKBtCj1+uN9aWjYwgA0WefquVI8NSfnsZx005Pu/19BE/RALmwhh535eBTMZHT6RSpvyz6+WHRz4l8kdF0lkKhgGw2K2LTWq0WWq12LORFYgR+Cw1S+yvqr0c7Oz3IvA4EAgiFQiLXv9Vqiaw9g8EgWkWRcOQxU/KD+tPTYApqcUWhPiqPNZvNYoenUVNUw57P51GpVNBut+cSPN3EzGazyJeX22fPInpqReb1ekWrbaW/hW8Cd8Oivydy3/ZGoyEKZTqdjjhvAuOppvR7esgCJ1HID/KcU575YDBAoVBAr9fDzc3NrWITOYOPRE6tpSkBxmAwiFJY2jFpZ6d6eNqJW62WqANQjpOax3oxm81wuVxwu91wu92wWCwiP4AsB7JYJh2B6NhBU31nrRhkbsOivyfUlVUe3CjHiWn3lXdzebezWq1CaNRY02QyiZp02vnpub1eT4hOrQkkmcg07ZXM+tFoBJ1OB7PZDKfTKfraOxwO+Hy+MVOZrAYA4qZBHn4q5aWU2lmgqbU+nw+rq6tYW1sT0YZWq4VsNotkMjm23mkDNq1WKzweD/x+/9jwDGY+WPT3RJmMozTdSZjUN492V4fDAafTKR6U0moymYRjStnmmWL/1ECSBEnvK89xl+vMyUdAzTDNZjPMZjM8Hg8CgQC8Xu9YE0uyGsgHIBfPpFIpFItFtFqtmUJrBoNBNA+NRCKIxWKIRqMIhUIwGAyoVqtIJBLQaDRiwo3SRyDv/NSsgzLxqL32pHAqMxkW/QOY1MiBdngy88mUpgvW6/WKXdZutwtPNDWgpDh4t9sd65xDCTCUBCTv5sodko4PTqdTrMPj8WBpaUlUpzkcDmFZyOseDAao1WqiPPbi4gKZTObOPHrCYDCIYiAq96VhGn6/HxqNBqVSCQaDAc1mE8ViEaVSSQy9oLXIkOjtdjscDgdsNtutoidmNlj0nwk6vxuNRpHR5nK54PV6EQgE4PF44HA4xDAISphRJr6QIKrVqpjsSoKfdq7WarViXDRNnKH++2qTZ2QPeqPRQCaTEQ08kskkisXiTGY9NeOkEdUvX77E3t4eIpEIgsEgbDYbhsMhLBYL2u22qDh0OBwol8viu5D9FPR9yscjsoyY+eFv7YFMMinl5hAUgtPr9WIqLZnRzWZzbK4bJfaUSiWUSiVRS0/mLznmpnnOSXh+v19M16GdNhgMjg2ikBNkaPIM1fifnJyIMVn1ev1Os55qA4LBILa2tnBwcIDvv/8esVhM5BXo9XqRF0BOOeUxQzbp5WMTRTm4W87DYNHfEzk8JF98ZO7TmZ+cayTYRqMhwlV6vV5kyNFMOPKWUxydJrRStxk6v6vV75MD0GKxiMaY8Xgcu7u72NrawurqKlwu19jkGbldFp3jk8kkTk9PcXZ2JvLxZzHrzWYzfD4fIpEI9vb2sL+/L3oJktON3o8sINm3oRS92nfOPBwW/QNRJsbIDjUSO2XXAX/1itdqNXEepc6uFPqTZ7VTYg+Ndp4mBrIiaNDG+vr62HTctbU1MT1WbdTUYDAQKcVUYptIJFAoFG7VCqhBNf7Ly8tiIKacKiw3ypCtHznDjkKXFK9Xfl7y8stOTPbezw+L/gFMCpvJHnW6cOUpr3q9Hnq9XlzEJHDZM08dZSc5CoHfTF5yctHwSDLpnz9/ju3tbdHj3mKx3DKLR6PfRk3R5Nvj42OcnZ0hl8uh0WjMtMsbjUa43W6Ew2FEo1FxlLBaraoDLORMPPmzTGqBTXkRckVjv99nZ949YNHfE7kCTyl+WfQk2m63i3a7Db1eP2Zay6mydFaXBaEUO1kMJBCj0QiLxQKXy4VAIIBwOIxIJCJ63a+srIgdXm2dNIgil8uJvvQnJydIpVIignAXVKDjdruxtLSEpaUl+Hw+2Gw2Ya7Ln4Pel6IQdMOTby5qVYfypNt6vY5utysSiZjZYdHfE4rBUwKN2rmezFRKx+31erdyzukGAdw+Hij9BvR7Styh3Hy3241AIIC1tTVsbm4iEolgY2MDoVAILpdLpAGT84/WRrn48hCN4+NjMXlmltp4+WhBeQAGg0F8buqvJ4cDaS6eXJIsz6hXC0EOBgORfkxVi61Wa6zoiDvnzAaL/p7Qrkse5WlZcpRwM6l+XK1bjFLs9F4UBqSe87S7rqysiHFVa2tr8Hq9wrSmc7CcRUjRAkrAubi4wMnJCc7OzpDNZtFoNO7MhVeui8J9lUoFhUJBHDkoF4BuPLVaDZlMRvQZuLm5QalUQqvVEhaP8vuQB4DQOKtZE4WYcVj090QWIIWR5Gw8mVkuzEmFI7KoZMcXhbyoGGd1dRUrKytjnXDJMSiX1VJXn3q9LsZFp9PpMQFSB1xai9r6lccbSuihY4LBYEC73YbX64XNZhPWRqfTEd16qCd+KpUam2mn5kOghqXlclmIvt1us+jvAYv+ASiTRSgWr+Yso1/VzE+10J8sdgpxkeCpvNTv9yMYDIqUWhI7RQDkVF4SO02fofAgmcvUjbder4vJM7STT4sc0OeifoDJZFKsIZvNisxDs9kMACIPIJlM4vz8XGT7zVKbT70A6vU6Go2Gao8CNvHvhkU/J/JFpdfrxbmaHs1mE8BvyS5yvftdjRvl4hw6LpCnn0QvV94pG13I+evtdntMIHIlINXUyyO05NRepVkvJ8ko/57O31RrT2dvErbX6xW99TQajegOnMlkkEqlkM1mx0z7adDnarfboshp0v8NMxkW/QPQ6/UiH5ySTNrtttgZe72eEMykfu4AxhxdcqGO3ABSGdIiByCl7XY6HZHt1mw2Ua1Wxx5y4045/k8ZfnJ4ULk+OYFH/hxKbzsJt9lsolwuI5vNioIis9kskpLoaFEqlVCr1UR/vbtMddn5eZf1wUyGRf8A5JJVKqIhk5q81DTZZVq8XVmKKzvIZGShyyWw1WpV7Lq0w1erVZHd12g0bhXoyEeOScz6b3KUQtmSi8qF6ehD8+rltGK5LHka8pQduiEy88OifwCUFON0OoVDbTQaiU4zJGq5VlwWnFqsn24AwG/mqhxqk8tuqckFXfwU+6bCHErhpYQfNWQLYxpqjSvVztPy7k9rJGtF/j7oc8zTH59Kk91utygY4s4588OifwCUHCOLfjgcolwuo1KpCFGQyQ+oi0dG2VaLfk+xfjLjW62WCBfKNfhK8/0uYU1KApq0xnlMagrRUV4Ava48HGRWqCFHIBBAMBiE3++/szEoow6Lfk6UFxnlu1P4jDLYKG2UdlnygsuoxeaVqbuT1qA0/+XuM5SbrhbvnsS0HXxe1KIR9LpyluIsryOn6dpsNiwtLWF1dVVUC8rfAd8AZoNF/wCUNfPUy4484sqmlcD4GCxgvEmm2kWrPHvLJrIyLEhm87y76Jdg3huJMgdfr9fD5XIhFAqJfAS73c5Cvwcs+gcih9kI2nWpQIQcVnK13KQMPEIpdlnE8o1C/vlvRezT1j7Pa8jfEY30WllZQSgUgtvtFk00OFQ3Hyz6ByKLjfLZ5d52cvWcbHI/xIR+iLAn3Vzmfd48z30oJpMJS0tLovOP1+sVyT7M/LDoHwiZ+HKaLDXBtNvtwvNOMWoS/qTXUtvF5X+f5PFXFvDIj2nVa7Mya0hNrkOgzER5fXIzz0nJOPIcAOoAFI/HRV8Al8vFrbIeAH9zD4Ri9VTLThNfqRiGpsFQFZnSoy7H5UkkcuWeHFKTe+bLcWpZVBTOo2w76r4zaULuY2AwGEQrb+oHSD3s5OiCXOgjd8Cl70NOdnI4HHC5XPD7/VhbW0MsFsPe3h7C4TAcDsetUB0zOyz6B6DRaMTs9kAgINpNh0KhWxlxdKFT3Jx2e6WlQDucfANQjmamZBf55iCXy8qVbvl8XkzTpXx82a8wy3FDmQ1ID1nsbrdb9Lxzu91jXX6B30pj6/W6aPZJuf6UwKTX68XNMxQKCafd8vLyWFNPNu0fhuYOs43zHO+AhCanuNLv5Tx3SpiRRU/prSRouekjPWRTl/59Ulaa3G+PBlRmMhkxiqper4+F9DqdjlgfFeRQOizV68vjqim7Tq4JoD53VNNPgygcDgcsFosQfb/fR6vVEp1+8/m8mHpL1XXUcisYDGJlZUWE5ih/X+6jx7v7TKh+SSz6R0KZF07ikodVKLvEyKKXd3K6qOWce/ln6CGfmQGIFFcSF1XQUY67fMOhVFkap53L5VCpVESNOlkwPp8PS0tL8Pv9YiSVsvKP2nvT2Cp5ag7dlOQbUqVSEeOsqFpuMBiIAZX0nktLS3C73SJvn5kbFv3X4K6kGfnMLhfbKE1pufpOrWEHQed6mrOnbLIp58dXq1Xkcjmk02lkMhkxp244HMJgMIjJOBQmo/JduhnRTUgu+6UyY2qcIWf3Ka0iZbWc3CWXZuyxw+5BsOi/BWZpP/WY76UsriFrRJ64Szu93I2Gzte06wYCgbGdXpl1p4wm3LUueT3Kz8859I8Gi/5LcFfN/Od6v/u852g0GvM5KM/0FIWgTEPqffclecjnY1j0jArK+nSlyMiMZ8E9SVj0X5uHZL895P3U3vc+JvS00N48cfNpxT0cf39UWPSLzmMdPdjkfjKw6Bl1vqRzkfmiqP7H8SAwZqqpz4L//cFBUEbAAl8MeKdnmAWDRc8wCwaLnmEWDBY9wywYLHqGWTBY9AyzYLDoGWbBYNEzzILBomeYBYNFzzALBoueYRYMFj3DLBgseoZZMFj0DLNgsOgZZsFg0TPMgsGiZ5gFg0XPMAsGi55hFgwWPcMsGCx6hlkwWPQMs2Cw6BlmwWDRM8yCwaJnmAWDRc8wCwaLnmEWDBY9wywYLHqGWTBY9AyzYLDoGWbBYNEzzILBomeYBYNFzzALBoueYRYMFj3DLBgseoZZMFj0DLNgsOgZZsFg0TPMgsGiZ5gFg0XPMAsGi55hFgwWPcMsGCx6hlkwWPQMs2Cw6BlmwWDRM8yCwaJnmAWDRc8wCwaLnmEWDBY9wywY+jv+XfNFVsEwzBeDd3qGWTBY9AyzYLDoGWbBYNEzzILBomeYBYNFzzALxv8H8Lzs9n9B3p0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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aWsLc3BwcDocIzim7DKlaj+yy6HkU0KObntzAI0OFP9TFyKIfHxb9D6ZfJ1ssFsPh4SH29/dxdHSERCIxNB8vC8NkMmF2dhYLCwsIBoNwuVzCbELpd6f8HXL0nM7zsVgMiUQC+Xz+xs+YTCbR3z4/Pw+/3w+73Q6dTifO5XLDjGyPLZtmAl9XdfLPo+Id2gHI6HS6nmMCi358WPQ/gH4BNDKNSCQSoupuZ2cHkUgE6XR6pLO8Xq+H1+vF3NycyJHLufhRi4fo5lMsFlEoFPrWA2i1WpGTn5+fRzAYhNPpFFt2+j3NZhO1Wg3ValUYdwIQqzzRbreFiOkmIVffAb9nCaxWK8xmM3Q6HYt+Alj0PwB5RSULaxpYQem5nZ0dfPnyZeTuOa1WC5fLhXA4jMXFRQQCgR4La/l1h10TAFEuWygUUC6XB57l3W43wuGwWOUtFotYqam5plKpoFAoIJ/Po1gsipvXzMyMcBcCvoqefAnIc1A+05PgHQ4HnE4n7Hb7DeccPuOPBov+B0GFN5lMBvF4XJyfj46ORLT++vp65Jy82WxGKBQShTFkijEqJJZ6vY5UKiW29cVi8cYWW6PRwGKxYHZ2FuFwGMFgEG63GyaTSfTCk+Cz2awYuZXNZsUNjHL1JHwyFe10OqjX6yK6T2f+mZkZcZzwer1iF8MiHx8W/XdGtoSi8tajoyMcHR3h7OwMFxcXSCQSyGazPdH6ftDZ2GQyIRgMipTZysoKfD7frT3ydD3yzqNYLOLq6gqRSARXV1fI5XI9otdoNDCbzULwCwsL8Pv9sNlsIu/ebDZRLpdFjv/i4kKk/SqVCrrdLnQ6HUwmE4xGI7RarRC97PdHZ3zg687AbDbD7XbD5/PB6XSKmwwzHiz6b4jyDE0Bslwuh6urKxGs+/Lli1jZC4WCOP8Og0Rjs9kwOzuL1dVVPHv2DOvr65ibm4PVar01j93PmltZgacUPW3rFxYWsLq6ioWFBbjdbuj1epFyox3M5eWluKFdXFwgm82i2Wz2WF5brVbodDqRGSBLLmV0Xq1Ww2g0wuFwCE985UrPN4DRYNF/Q5SR+XK5jGQyiUgkgi9fvmB3dxd7e3s4PT1FMplEuVy+tfyUPvxWqxWzs7MIhUIiR76+vo6FhQU4nc6B3XODrg/4urVPp9O4vLzExcUF4vE48vl8TzrQ4XBgYWGhx3XHZrOJbXij0UA+n8fV1RWOj49xcHCA09NTYbpBW3p6D263GzqdTszAK5fLIqovi576861WK+x2OywWCwfyJoRFPyG3iVO5wpdKJUSjURwdHWF3d1d0y52fnyOTyYwUnddoNDAajSKAtrKygrW1NSwvL2N+fr5vxH7YtcuBr263i3Q6jfPzc5yenoqmHqXgw+EwNjc3sbW11XOMUKlUaDQaKJVKuL6+xunpKQ4ODoR/X6VSgVqthtlshkajEUE52iVUq1XhjKOcfwdApPhMJlPPsYAZHxb9hAxaYZQ3A6quky2uPn361NMtN0otPfD71nppaUkI79GjRyKQRmmscctTO52OaOw5ODjAyclJTzGOSqWCzWZDKBTCxsYGnj17hidPniAYDIqJOM1mE/V6HdlsFldXV2JbH4lEkM1m0el0xLw6vV4vVnmPxwO9Xo9yuYx6vS6+L6/09JDn31H5LjM+Q0V/X4MMHhrKlXCcc6PSYiqXy+Hi4kJYXH348AHHx8eIxWI3IvODSmuBr4K32WwIh8PY2trC8+fPsbm5KSruDAbDSGf4fu8hk8ng8PAQ7969w8ePH4XoKVVHgn/27BlevXqFra0tLCwswOFw9Ky21WoVqVQKFxcXIihJwTvanut0OpjNZjG+irb3Op0OpVJJiJ4m08q19nIJ7zDnXmY4Q0V/135l5YdYNpu4r17o237XOD3a93ld1LBydnaG3d1dfPjwAbu7uzg5OUEqleobmR/02iqVCkajUQTstra2sLm5iaWlpZHP7/T7leLIZrM4OTnBb7/9hl9//RW7u7s9Bh1WqxWhUAhPnjzBq1ev8Pz5cywvL9943U6ng0qlglQqhWg0KswzaXAFNcgYjUbY7Xa43W7xoCi91WqF0WiEXq+/IXj6b3kSMDMZQ0XPs8L6Q5Nqm81mT+UYiYrOpvIKv7+/LxpXBvnLDWJmZgZ2u70nDz8/P98jvFEMPGShUK/+yckJ3r9/jzdv3mBnZwfn5+ei5Jb88Tc2NvDq1Stsb28PtNnqdr8Ot8zlckilUkilUigUCmg0GkK0BoMBNpsNHo9HzKNzOp0ijUmCp629/H5I/ASv7JPDqh4Tik6TDTONd5JLRqnAJRKJ4ODgAAcHB7i6ukI+n781YNdvt0H19EtLS1heXkYoFLqxtR6Ug+/3vUajgUQigYODA7x79w5v377F/v6+SNEBX3vkZ2dn8eTJE7x+/Vr46rnd7hu1/EDvBJx8Pt8zAYdm0NtsNrjdbszOzopcu9VqFWk+eRyY8j3IcKPN3Rgq+k+fPn2v6/jhyB8iavM0mUzivExuruT+Eo/HkUwmxfw4aiihstpMJoOrqyvEYjHE43EUCoWRbKrlD7lWqxWrLUXq5+fn4XK5oNfrxfMHCUAZX6CimUQigZOTE3z8+BFv377F3t6euEZ6Xa/Xi5WVFWxtbWF7extra2vweDw9gpdfm3Y/VFhTr9dFTECn08FiscDtdiMQCCAQCMDj8cBms8FoNIoKPOD3en15Cq/cvEPPYausyRkq+n/8x3/8Xtfxh4BmyZfLZWg0Gjx69Ag///wzQqEQms0mcrkccrkcEokEYrEYUqkUisWiMHlst9uiwaRUKonadfKDGweDwQC3241gMIi1tTVsb2+Lwhuz2Tz0TKu8ETQaDWQyGSQSCUSjUbED2d/fx+HhIa6ursSIKpXq68z6YDCIJ0+eYGtrC6urq/D5fD3NNMDNmwp52suNNSqVCgaDAU6nE36/XwyfdDqdIu3WarXEFr9Wq4mbBomdJt8oG3GYyRgq+n/+53/+Xtfxh+Tt27e4vr7G0tKS2NbT9jWVSiGfz6NarfZ8IGnFoxvBONDEW5rBPj8/j0ePHuHx48dYX1/H4uLijU62Ye43wFfBk7El1fWfnp7i4uIC0WgUiUSiJ8ZAXnfkj0+Cl3cWyteg1bnRaIgtPaUhqfLO5/OJOn2v1ysq8eg4Qw475XIZlUrlxihsauAhJ11lMw4zOnymH0Imk8G//Mu/YH9/H+12G/V6Xaxo8mokmzlOuu20Wq2iWMXn8yEYDIox0svLyz2r46BVfpB99f7+Pn777Tfs7OwIF558Pi9WVeV1kPUVueDc5pzbbrdF+2ytVhP5fSrC8Xg8CAaDCIfD8Pv9Pe+Dbpa1Wg35fF48KpWK2ObTa9DfncqU+zkAMbfDou+DnOYjj7hvicPhECJfXFxEOBwWZ99gMAifzyeKYEa5bgDCK39vbw9v3rzBmzdv8Pnz5xutunLg0GAwiDl3FD+gYRX9nk/QKl2pVMRKTzEREvzi4iJCoRA8Hg9MJhO0Wq0QfL1eR6lUQi6X6xsgBSC2/uVyGcViEeVyGY1GAwaD4c5//2mDU3Z9oLP9oFlt94VK9XUQxfLyco+LLDne2Gw2kbse5wxfrVYRjUbx4cMH/Prrr3j79i0ODg5wfX194z2RgLVabU8jzeLiIrxerxDVoIBht9tFo9FApVLpidhT8C4QCIibGfX401BLOhbQ9JtMJoNcLodKpXJjC091APLz5EYfZnSGqnrU8lBmfFQqFTweD5aXl/H8+XO8evUKm5ubCIfDYvsrr7Cj0mg0EI/Hsbe3h3/7t3/Dv/7rv+Lg4ADJZHJo8Q8F71ZXV7G6uio69W6DRF8ul1Eul1Gr1UShjcvlEmnGcDgMj8cj3hftGBqNBgqFAtLpdM/ce+VwSprYm0qlkEgkkMlkEAgEYLfbe3ZmfAO4nelcysdgWP77Lrjdbjx69AivXr3C69ev8fTpU1FSO67YZQOMeDyO3d1dvHnzBm/fvsWXL1+QTCZ7ni+Pjqaft9vtmJ+fx9raGpaWluB2u0c6L1NnHW3vW60WDAYDTCaTKCaimITNZuvpjJNbjeWCHsokyFDKlLIQmUwG5XKZz/UTwKK/hW+RD/Z4PFhdXcWrV6/wl7/8Bc+ePUM4HIbFYpn4d7ZaLWQyGRwfH+Pdu3d48+YNDg4OkEqlbjxXHiEFfM2jU7aAtuHKaxm0gtJZm87zKpUKDocDdrsdq6urYiS20+m80R/QbreFVVg6nRbGIf16PuQbBD1XeXO477Fif1ZY9N8RnU4Ht9uNtbU1vHjxAr/88gu2t7exsLBww8BS6YWvRPkBbzQaYpT1zs4OPn/+jEQiMdAqW/53h8OBQCAghk7KW+Z+K6l8PfV6XWzt5VWenHwWFxfh8/lgMpl6Kulkh51cLodMJtOTAu0HzcIjs07qwWfGg0X/nTCbzfB6vVhbW8PLly/x+vVrbG1tIRwO97W1Gne1omg95eGHBe3k16HOvaWlpRvVfvScfpBo5SIkALDb7WKk1tLSEvx+v8g8yIKncuVisYh8Po9CoSACeMr6BvlmRUcJqvhj0Y8Pi/4bQnlqu90uauc3Njbw4sULbGxsIBgM9uTAads9TPD9trCNRgPX19dinLQyLSfPf5fR6XTw+/1YXV3F+vp638EYg2i32yiVSiKaTrXzPp8Pc3NzWFxcFLsGZYSdAnhU6ERpOor8D6PZbIrHbTPvmP6w6L8RarVa1JrT9JfV1VUR2AoEAjdyzJMEpFqtlpiGc3BwgIuLC1FDL9Mvv26327GwsIAnT55gfX0doVCob1xBGRXvdDqiv+D6+hrpdBq1Wk241YbD4RvFRMrehkqlIgJ46XRadOTdJmKqzCOL7Gn1fLgLLPp7hFZpGjqxsrKCx48f4/Hjx6I7zufzweVy9Qh+lFTToOk0qVQKBwcH+PjxIz5//oxoNHqjyk5e5VUqFXQ6Hex2O5aXl0WJL6UKKXMw6PVIsMlkEpeXl7i6ukImk0G32xU7Gr/fD5fLBZPJdKOwR/bRu76+Fj0MgwJ4Skj0co0+jd5m3/vRYNHfE2QNTe2jS0tL2NrawtbWVs9QRxrTLDOK4Ptt67PZLI6OjvDu3Tv89ttv+PLli6ilH2QIQsKkFX57extLS0s3euT7vR5t6ROJhPDSi8fjaDabooyYPOlNJtONsVMk+GKxiOvra2GNTd2KzWbzViMTGnlFAznK5bKYnceCHw0W/R2QG2TIHMLv94va9cePH2N1dRXBYPBWs8phKFewTqcj0nPv37/Hb7/9hv39fcRiMbHKKy2/yNhyfn4eKysrYku/tLR0I7ZA701+vXq9jkKhgHg8jvPzc5ycnODq6gqVSgUWiwVerxcOhwMOh6PHq0/OAtAKH41GxXBO2VKr2+2KashhwqdryWazyOVy4ibDoh8NFv2EkIi8Xq8whSBL6oWFBTFA0u12j1wfrkyn9duudjodsaV/9+4d/v3f/130wyvHX2m1WlgsFtjtdni9XgSDQSwtLQm77HA4LAQjR+yVr1cqlcR2nkZnX1xcCOdacqi12Ww9M+boQTcN2TTz8+fPoq03l8sNjcQrc/H1er0nX18ul2EwGCaqYJxGWPQTQHXq5DdPFWcej0ecaT0eDywWi1gxRykc6ZeXl28EtVpNWFy9e/cOv/76K3Z2dvoG7wwGg2h2oaIbmjsnD5wc1sRDtl+0Mh8cHIjR2blcDiqVCl6vVxxtzGYzDAaDSM9RByIV4NDwi8+fP+Pg4ABnZ2dIpVLC/nrUSLy80lNun0Zf0d+KV/3BsOhvoZ+5J5XQbm9v4+nTp6Ln3GKx9Hz4hyGLeVgAinLZxWJROM1++fIFnz59wu7ubo+nnUqlgtlsFnPmFhYWhOMOzY6nRh6LxTJ0ZSTjjUgkgv39fSFUEny73YbNZoPP54PBYIDRaOwRPK3INIn34uICx8fHODw8FDeOVColcvPjtCWTOxH13ffbJbDwB8OivwX5w6TRaODxeERF3c8//4yNjQ2Ew2HYbLahXYnKD+Cgajt6PXkiTiwWw9XVlbCWPjk5EQMpaIWnrjYyq6D2WOqYo+YUpftNP8jj7+TkBDs7O/jw4QO+fPmCy8tLZDIZYWJJtfRkbQ383mZLW/B4PC4Gcx4dHeH8/ByxWAy5XK6n+m6cfDvdIMg2i3P148GttX2gLTmZQdC/zc3NYX19HS9evMDLly9FV5zD4ej7e/rNihu0qlNUu1gsCjOKQqGAWCyGSCSCs7MzRCIRxONxpNNpZDIZYVNNffDUJffo0SMh9rm5OTEIo98NhoJn8nVQzOD9+/d4//499vf3cXFxIazBKC2pUqmEBz0dP+imkMvlEIvFcHZ2huPjYxH4S6VSwleQCnEo4Cf74A2Dpt0YDIaBgy94lR8Mt9b2QfmB0Wg0mJubw8bGBn766Se8evVK+NUNa5JRCnzQB5H84smwI51Oi2q1RCIhVvl4PC4EQyW2FosFfr9fROQ3NjawtraGcDgMr9fbE1e47X1S0w7ZYv/666/Y29vDxcUFSqUS2u021Gq18J0nsctpuE6nI6L8kUgE5+fnPas7uQ8p22bHKUzS6XQiRWiz2W408rDghzOdS/ktyB/Iubk5LCwsYH5+Xgx82Nzc7GkiUW4xRymlrVQqItdMAyLkbXupVBIuMblcTthIyVDd/Pr6Op49e4bNzU3RC+9wOG5E5JXddfJ1djod5HI5nJ2dYWdnB+/fvxcxg2KxKFZgedQUReUzmQy0Wi3i8XjPkSQajSIejyOTyaBYLPa1/x7W2DMIg8EgptfSyGpurx0dFv0QnE4n/v7v/x7Pnz9HIBAQ/eHBYHBoU8ptKw2ddUkYdGanbTxNsCUfOLKVkn8/Db/Y3NwUk2fkgZL9gnSD/OQpLReLxcS8vf39fVxeXvYInsROv6fVaqFQKPTMsafS2mQy2SP2Qdt2Oasx6DlyMJXcde12OzweD8+pn4Chov+Hf/iH73UdfwioMaVarWJmZgYrKyv4+eefsba2BrvdLqrt+uW0+33oZJdYOsMWi0UR3Do5OcHZ2RlisZhwjslkMn1r5wl5pt3m5iZevnyJ58+fY21tDV6vtydQp9x9DLreer2OZDKJk5MT7O/v48uXL2JLL6/w8gP4OkueIvAqlUrEIfL5PIrFoii4uU8onmCxWEQWot/wDWYwQ0X/T//0T9/rOv4Q0IeZhl1QhJpqyGdmZgbmtWmLTx7tVB9eLBaRzWZFJxkNwYhEIiJ1lU6nRauoHDxUotfrxXz4zc1N/PTTT3j69ClWVlbg8XjGLu+l91ooFHBxcSFGS9MKL3vXy/Pk5JsjpfbInloedDFuRJ5W9Nt+Tq1Wi0Cesodh1Pc9zQwV/dbW1ve6jgeLbIktf+Bp4EU6nUY8Hkc8HhfbXvJ5o0j8bQFTjUYDvV4Pl8uF+fl5bG5u4sWLF2KYpMvl6lntgNEFL+886CaUyWR6ZtCR0Clar1KphFOt7PUv+91Pwig3CZpeK0+wZcaD/2J3gCycMpmMEDOl3CqVCgqFAlKplDi7p9Np5HI5YSA5Sv84jbbyer1YXFzExsYGtre3sbGxIRpl+n3w+wlIWb9fqVSQSCQQiURwenoqCm/IwpoMLOVtPf0OmuZD7a3jVNT1u1Y5rTkMyhrQtfGqPj6cshuCXD+u/Hfg95Xy4uICh4eHODs7E80jtNJTYIvsoEaZWKtSqaDVasX5nQT/+PFjbG5u4smTJ2Jq7bgrPB0/qD2WuuUuLy+Rz+fRbrdhMplEGkzpdkOruTxv7r5GTI1y06AjlnJGPTM6Q0XPd9HhNJtNZDIZnJ2d4ePHj9jb28P19bXIRdNqPuqqDnzdyhuNRjHtxu/3i8442YDDbrcPHCY5DLKrzmQyosKPJuoCEAFLOQ9Px5VKpdKTTbgvwY/STkvQOV4u+WXGY6jo+U46HNra00pPZpQUABtnWIZWq4XBYIDFYoHH40EgEEA4HEY4HBZ1AjQHzmKx3BhTPajajlZoGh1FFleJRAKXl5eIx+OoVCrQ6/Xw+XxQq9XQarXQarWiyo586cm/jlb7+xL8qGg0GlgsFlit1p5uPmY8+Ew/IWQKST7syWRS2DKPg1qtFj561I8fDAaxsLCAcDiMUCiE2dlZ0ShDk14HIdtJUaqQAo3lchn5fB6ZTAaZTAbZbBbNZhMWi0Wkvqi0VaPRiMg89c3T0YAGdt4VulnJkfthUH6e/hYGg2FgsREzGBb9hJCxI42vLpfLPSv7bR9iOrfThNpQKCS89Obn5xEIBDA7Owu32w2r1Qq9Xi9SipQOo4YTEiJF0ev1es8EWHqQVTU1xHS7XdhsNpH3NpvNMBqN0Ol06Ha7KBQK0Gq1yGazANAzxHNS5DJeAD07kWFQCtXpdMLtdsPhcMBoNLLIJ4BFPwHy/DaybFJWnQ2LRlOemVpTl5aWsL6+Lnzi/X6/cKChlV32l6PJrdRaSjPhaVUnwdOjUqkIsZKLjslkEk43VMMutwTXajWo1WrE4/Ge+MRdovR6vR56vV646tAxgWoThrnbzszMwGQyCdG7XC4W/YSw6EdEmVaiMzLl5pViGFRkQjl3h8OBubk5LC8vY2NjQ8yC9/v9PUMeAYjgGfWnZ7NZMQYqm82iVCr1iJ9WetkffmZmBgaDQZSt0lFCLmU1GAzQaDRotVpIp9OivTeXy6FYLI4cjOz3ng0GA6xWK+x2u6iVpxiDbIQxaDiHWq2G0Wi8dXvP3A6LfkLkQNkggcvnVQBCeHa7HYFAAKurq9jY2MDm5iZWVlbEVFcqpaWbS7PZFJbR8XhcdN1Fo9Ge+W/UfUfneWqwIbFbLBYxzYZmxbtcLhEU02g0UKvVYiYddfnRYMl+DTO3odVqYbVa4fP54Pf7MTs7C6vVKtKd19fX0Gg0SKfTwniD/nYyarVaBDrtdrs48rDQx4dFPyFUvKIsWun3PHou2Up5vV5hnvnkyRMsLy9jdnYWZrMZarVanG/lUc7pdFp41B0dHeHs7EyIno4XckSdatQp0u1wOBAMBrG8vCyOEE6nE2azuWdXIXvQUSVhMplEsVgce3Q3WW2TN9/S0hJCoRCsVitarRZSqRROT08xMzMjgoMUiFQej+hYQs5ERqORq/EmhP9qE0BValQKSuIftNWkCjKDwSCKbUKhkBgKQeaUVNNOwS2l4I+OjnBwcICjoyNhN9Vv200BQoPBALfbLabRyhNkKRBGqzt52tVqNeRyOUSjUUQiEeFrP27zjE6ng9PpxPz8PB49eiT6/GdnZ2EymcTsPXIJpso+pZMO3cTob00xAc7RTw6LfkSUHzBaubVaLXQ6nSgLJeQ2Vsp9GwwGmM1mMe/NbDZDo9GIAB0dBWTBk6HkyckJPn/+jKOjox7baHkbT6uhnOsn887V1VXMz8/D7/fDbrcL91i56pC23LJbTzweR7FYHEvwWq0Wdrsd4XAYGxsbotd/YWEBDocDWq0WjUYDNptNrPIUoKTdBP0vBfco6k9193INCXfXjQeLfkLklX5YSahye09baepSy+fz0Gg0qFQqPS6yVBQjm0pSG242mxXnbroO8qqj9t9QKISlpSUsLy8Lb3sq7JHTfwRdTyKRwNnZmbi5pFKpsc7yGo0GVqsVwWAQ6+vrwjx0eXkZXq9XRNxbrZa44dHkW5pES30BrVYLMzMzYrsvG3gwk8OinxD6EI7T/EElrbRlNxqN6HQ6yOfzoqwU+H1uOwXSotGo6H6jbTbdaMgvzmazwel0YnZ2FsFgEIuLi8L2enZ2VsyVoxuLsvmGHHCouvDk5EQ44YxTiGMwGOD1esWwzo2NDSF42eGG4htut1sEKak/Qa55kPP3ck7/vur9pxEW/QTIq7cczBuGHIVPp9PQaDRiAITFYhHpMnmlp5JZSs3V63UxCZcGQ5pMJlitVni9Xvj9fhErCIVCwoufIt3K3YhseUXHiOPjYxwfHws3nHFWeSry8fv9YmDn/Px8j+CV7sJmsxkulws+n09MyaExV3JAD/i9jVn2CKT3wYwOi35ClO2mgz54cncaDWkAfreaSiQSogqORE+pOjKlIG93mlgDfA2UUWOO1+vF3NwcgsEggsEg/H6/qOSj1b3fTUkeNSUHCqlbcBzBU8GRw+EQu43Z2Vk4HA4RP1AWL1F/vl6vh9FoFJN2aOckp0Lp70GVhfLfhBkPFv2EyCs9RcBl+m0/qfS10+mIMztZOJPo5RJVGRI5iYQq6misViAQgN/vF6ulyWQSYpdvSLK7D92EotGoaBiibT2ttKMyMzMj4go0cIOuV1mSLH8tZyrkXgHq1ae/I/npkx0Xnf9Z9OPDop8QOs/LKbt+KL3cKVBF0Wo5EEjCp8AcdbuRmGg1pOi/2+0W22KqrKOjwiDBU6ScztHX19c4PT3F/v5+zwSbUWbFy8hZACpTptJg5Tad6FdlmMvlRFZCTuFRdWAmkxFmJLIjMb0/3urfDot+QuQWVHmVlr9PYlcKn6ymSPx042i1WtDpdCK6rtPpREGK1WqFzWYT1s8ej0c8nE7nja28UjAUU5DNPeLxOC4uLnBycoKjoyNEIhGk0+mxy23llF+tVkM+nxeWYNT3T118cmkxtSZHo1Gcn5+LsdU0VEMubW61WqhUKuLmkMlkUK1WebrNBLDoJ4RWebkVdVgwTxa+vMXWaDTodrtim0opONpJ0CpvtVpFs4nb7RZz4B0OR99AIIlc2XFHPvrpdFpYb19eXorqvkqlcsNu+7ZuQXpPlJmIx+OiDZjy8DTQk/r0KVsQiUSEIef5+bm4BqWxpnwkyuVyYnvPoh8fFv2EUCEMBaFIdPS9fttMEj7tAqjTjH5GDnZRwY1yR0GdarSik9hUKpXoVqOgYalUEiLJZDKiDZjceWV/+kKhINxtgd6dSr/3QddM0Lm8UCiIWIfsulMul8XY7k6nI4qADg8Psbu7i6OjIzGbb9DRgjoI5cGV/a6Nt/jDYdGPifyhopWeUmgkRgBCkMo+8X7Cp3FRcl+8bIZBKzZZVtGZnURFEW/6ulqt9qzoJG4SfalUEi3BpVJJVMJR4IzeI11jvzp45df0Pmu1GrLZLLrdrkixVSoV5PN5cQZvNpvI5XKiJuD4+Bjn5+fIZrOi264fdGORDTl5pR8fFv0dID87k8kEm80Gm83WszWVa+mVH07y1idotVar1ajX6z3z4ugGQjEA2qabzWYRpSdbavpeNpsVgk+n08J3n5pzlMJRdgvKLa1yE4/y+/J/y00zyhhCNpsV+XryFry6uhJz7m4TPF0L7XoohsKr+viw6O+A3CrrdDrhdDrF2ZlWOblrjpAFI3+PotzKvn05P53L5WAymUSnGcUTKBIuu/nQtr1YLIozMIlduWrflnLsJ3IldNOgfgASP0XoY7EYdDodms0m8vn8DdvwYYKnFmFqraVy4n7PY4bDor8DsgOO0+mEy+VCqVSCSqVCuVy+1TxSXiHlrjK5j54CcTqdDvl8/saIZmUVX7lcRqFQQKFQEFbclP7q53pDW/nb7Krk6x0FSg3SpByq69doND07Eooj3FZWS3ZZHo8HXq8XbrebB1dOCIv+Dsg96x6PBz6fD8Vi8YY3/LAIuNK1ls7mJFIqQJG7y+QUoezBLzvmkJhGsbe673OxnK9vtVqoVqtotVool8viGELFOIPGXynjBjMzM7BarXC73Zibm4PX64XZbGbnnAlg0d8BMreU22WtVqsYMy0XjdyG8lxNgTzK31PJr1zrT8U38k2CxEQBwe8d6OpnxU0ZCrqeUafiyDfLmZkZWCwWzM7OCpsvpeiZ0WDR3xEKLsmpNOB3U4hJusFIsBTYA26OiZb/m35Gdsb9UVFtZXkt8DXgKe94Rvm7KIOfFotFNBV5vV4xkIOey+IfHRb9mCg/XCQ+5aorD3ec5EMpr/j9rkEpegqcjXv98uvd58/I3HU8GrXrBgIBBAIBOJ3OvkE8ZjRY9HeEKuio0YRSd6VSSWyzxx3bTFAXGhW7yFt7pUWXfCSQ8/3KbfQk1zHKzyi9BaiqkP4d+L0cWN7eK3clsvegPLzz0aNHWF9fRygUgt1u5+lLd4BFf0fIj93lciEQCKDRaIhgm16vRyKRQKFQGHu1o155mj5DBUByU44cyJPP9WTNTWm+UqkkmljuG7lAiR5yoZI8E4+ujcqCqTCIymkpMErvmeyuQ6GQcA4OhUKwWCwcwLsDLPo7QIE8CjCpVCoRyfd4PHC73Tg/P8f19bUwpOhXqEO/i1Y58rmjTjqXywWXywWr1QqTySQ68GTzDhIVVeTRyC0ao53NZsWIbCpzlXsA6HcoV12lTRU9lJ5/VKdAgzPk3niqv6dUXT6fF8VDVBlIVYlk9+X1eoVttuwV4PV6xUAOZjJUt2zduMbxFigfTTXhVA0Xj8cRiUQQiUQQjUaF882giLXcny832FDrLA14IDHJKz054FDFHpXhptNpJJNJUQRDuXs5nShPzKFWWGoAopoA6t6jgZG00yBPPqfTKW5yLpdLDLSg66Q6AGrIocIhapGl3v2ZmRnY7XYRsKOgHXURUkEOV+KNTN8/Eov+npDNIKrVKrLZLK6vrxGLxZBMJlEoFEQv/bAVlVZ65QpKVWhUdz/sTE/jqGlYZTab7elMo/w9GVLm83nkcjnRdNPtdsU2m8ZekeUWndXppkBxDHqefJ00vkp2+JWn2tDkHOrsm5mZEfZZbrcbHo9HzNrjM/xEsOi/J/Thpke9XhdR/UGiJ+GTay6tsnRGli21hiELjAp1aFsvB9FqtdoNA4tyuYxutyvKi6mVl1pl5bZfOXZBnYayL30/6KZEhURUoEPNR1qtFkajUfQVcMXdnWDRf28GNbIMEz19Lf+7/G93uQb5a9ra00qfzWbFTLlutwudTie27sqV/j6uc1g2YZjnIDMWLPo/E4P+f5ukHoAi/dRi2+12e7z4yAzjrtc5iZDv+vNTDoueGUy/YwcPl3jwsOi/N3cpiAF6V7a7rpJ33UYPSjUqr23c7b38Nefe7x0W/Y9E+XdWdpEp+VYfemUf/aQ/f5ffwXw3WPTMcIa1uDIPkr7/53FFHiNggU8HnARlmCmDRc8wUwaLnmGmDBY9w0wZLHqGmTJY9AwzZbDoGWbKYNEzzJTBomeYKYNFzzBTBoueYaYMFj3DTBkseoaZMlj0DDNlsOgZZspg0TPMlMGiZ5gpg0XPMFMGi55hpgwWPcNMGSx6hpkyWPQMM2Ww6BlmymDRM8yUwaJnmCmDRc8wUwaLnmGmDBY9w0wZLHqGmTJY9AwzZbDoGWbKYNEzzJTBomeYKYNFzzBTBoueYaYMFj3DTBkseoaZMlj0DDNlsOgZZspg0TPMlMGiZ5gpg0XPMFMGi55hpgwWPcNMGSx6hpkyWPQMM2Ww6BlmymDRM8yUwaJnmCmDRc8wUwaLnmGmDBY9w0wZLHqGmTI0t3xf9V2ugmGY7wav9AwzZbDoGWbKYNEzzJTBomeYKYNFzzBTBoueYaaM/w/p9LjAUeAjPAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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Vnlb4UXt76razsLDATTSmhEX/lZGFdnV1hVarNXIfP2kFnU6ng8fjQTgcxvLyMjwezzXH3W2r49XVFZrNpkh5lU17OZQIfBK92WyG0+mE2+2G3W6HXq/H3NwcWq2WqL2n+nta7WXTnlb8+fl5IXKyCJQttXU6HXQ6neifxyv93WHRfwVGOdA6nQ6q1SoymQwODw/x97//Hbu7u4jH4yiVShPt5efm5ob21l6vFxaLZciMnkQk3W4XrVZLtLUed2yVSgW9Xg+r1Sr62lGNe6/XE/X+l5eXolsu5d3LTUZIvHQzoDJdpUNP7qVHTTWU58PcDov+KyCvltQggvLaj4+P8fHjR+zu7mJvbw/ZbHZoLz+Oubk52O12BINBhMNhhEIhOByOW6vn5NcTslCbzeaQiS1vKxYXF2GxWOB0OuFyuWCxWKDVaoXFUiqVkMvlRIFMrVYTTTJI8GSmy6Y8ldMqE3eoy4/ZbIbRaMTCwsKDZ0LOAiz6rwQl3hSLRaTTaSQSCRwfH4t9PI2oqtVqE72f0WhEIBDA+vo6NjY2RGOMu55TrVYT55NKpVAul8c61EwmExwOB7xeL9xut+hp1263US6XkclkcHFxMZSvT34ByuCjZB1qmEE3Qrl9+NXVlWjtZbFYYLVaYTabsbi4yCKfAhb9F4b2qSSuk5MTHBwc4OjoCCcnJ2I0ValUEj3ixiEXuPj9fkSjUTx69EjUyCuz7m5z4FHN+vn5OY6PjxGPx3F5eTnSgafX6+FyubC8vIylpSXhO6DPls1mkUwmcXZ2hkQigWKxiEajgW63K86bhl1QB95utyty65VxeJVKhcXFRdHE02QyQavVsuingEX/GRm1h2632yiVSri4uMD+/j52d3fx8eNHnJ6eIpPJiJTVm/rdkVlMQyE8Hg8ikQgeP36MWCyGpaUlWK3WWyvoyEtO50ihQvLaJxKJkSv94uIiHA4HwuEw1tfXsbKyApfLBa1Wi06ng3w+j/Pzc5yenuL09BSpVAqlUgmtVgtXV1ci7La4uAi9Xi+2IFQ512q1RLhP/sxarVaY97Sn/5ylzH9UWPSfEWWLq2aziUKhgLOzM3z8+BE7Ozt49+4djo+Pkc/nJ6qYW1hYgE6ng81mg8fjgd/vx/LyMiKRiBDgpHt5pUjo/JLJ5Nix1zTZdmVlBVtbW9ja2sLy8jKsVivm5uZQr9eRzWYRj8dF6m6hUEC9XheOOQq70cQanU4nQnTUG29UC/GFhQUYDAYYDAbo9XqR5cfcDRb9FEyS9668GOv1OjKZDI6Pj0V5LK3wk9bE00Xv8XiwvLyMtbU1RCIRLC8vIxgMwuPxwGaz3ZpbL4fA5Dz/XC6Hs7MzYXUow4Qk+LW1NTx9+hRPnjzB+vo6vF4vtFqt6Fd/cXGBeDyO8/Nz5HI5VCoVsU+nFuHkiSenHA0KoWYbzWZz6Duk+XeydcCdcKeDRT8Fyli1ckWXkZ1je3t72NnZEYKnarlJculpMIXH48Ha2hq2t7exvb0t9u9Wq1WsfnfNSe/3+8hmszg6OsL+/j5OT0+v7eVlwb948QLPnz8XDkOj0SgScC4vL5HNZnFxcYFsNotyuSysBTLraSyVxWIR7bfpWLVaTUywVRbkyPPvqHiHuTs3iv6mSqc/MsqVcFxHnFGmpbLLC618h4eHePfunWiAkUwmrxWw3FSXr9VqYbPZsLy8jEePHuHZs2eIRqMIBAIwm83XYta3fS6ZQqGA/f19vHnzBjs7Ozg7O0OpVBJ7ea1WC4/Hg/X1dVGTv7W1BZ/PB7PZDI1GI8ZWl8tlEaa7vLwcmmILQOzljUYj7HY7nE4njEaj2M9Tvb1S9BTP12q1Q+W5zN25UfT3rVtWXsT080M2nbjtvZSr8n3eaxxyYQglmTSbTeTzecTj8SGH3fHxsXDYjXqfUVB+u9frFat8NBpFKBSCxWKZesUbDAYoFos4PDzEr7/+in//93/Hhw8fkEqlxOqs1WrhcrkQiUTw8uVLvHjxAltbW6JBhryvpgQjZTIOACFi6p9nt9vhcrng8Xig1+vRaDTQarWGmmkqb7i0NaCbAjMdN4qeZ4Bfh1o+0aPT6Qy1faKMsnK5jGQyif39fXz48AH7+/s4Pz8XTq273Fzm5+dhNpvh9/sRiUSwtraGQCAwJPhJGnjI/9btdpHL5XB0dITXr1/jl19+wfv375FMJsVefmFhQZj0z549w4sXLxCLxRAMBmEyma4Jj7LwKO2WQnDypFqdTicE7/P54PF4oNVqUS6XUS6Xh8z2Ua3F5WEiBPe7vxus6jtAcWwyXymWLs9zoyoxmux6fHyMk5MTpFIpVCqVO42eIqigZXl5GeFwGF6vF2azeaIimlH/Rl119/b28ObNG7x+/Rrv3r1DMpkUyUAajQZ2ux0rKytDbbaoRFcpeKqkU94Mgd/n/Wm1WpjNZrhcLtFDz+VyiRsl5dMrG2XKn3HcVouZnBtFv7Oz86XO46sjX0S0B9Xr9SK1tNfroVwuI5VKIR6PIx6PI51OixVNXrl7vZ7oHptOp8XstbsyNzcnMu3kFZ6q2ICbVzn59yRIyu+nrrqvX7/Ghw8fkE6nxTnOzc2JgRhbW1t49uwZtre3xQo/KpefMumoqk52ApJZbzKZ4Ha7EQgEEAgE4PF4YLVaxbZAHmR5dXV1bQqvvI3illnTc6Po/+mf/ulLncc3AV109XodarUaGxsbePXqFYLBINrttmgBTaLPZDJiyov8IIE1m01RaTbNuVAu/ebmJh4/foy1tTV4PJ5b+9spbwStVguFQgGZTAbJZBInJyc4PDzE3t4e9vf3kU6nh85Rp9PB5XJhbW0Njx49QjQavSZ45fGoXp461cqilNtiy402HA4HdDqdsC5oIAZZCiR8OgZNu1F2wuVV/27cKPp/+Zd/+VLn8U3y5s0bZDIZrKysoN1uixlu2WxWmPftdnvowrzPCkSJK7TvDQaDWF9fx6NHjxCLxbCysnKtbfUkEQQq1d3f3xddeCgBJ5/PD205FhYWYLPZEA6HEYvFRBMOq9V6zaSXM/lIsLTSy98DRR4okYjSdmlopkqlEsMxqbKPau/lUttutytuCnQMFvzd4T39DeTzefzrv/4rPnz4gH6/P7RyUx75Q0FptTQ+OhAIIBwOi3lzS0tLogXVuFCV0tymEVjv37/Hr7/+ip2dHZycnIg5cKPSfY1GI4LBIKLRKGKxGMLh8JDgR/kJyLKh95NXetrHe71eLC0tIRwOIxAIwG63Y3FxUQi4Xq+jUqmgUqmI71Ze6ekYjUZDePp7vR4n6EwBi34EsnCSySSSyeRnPZ7cbiocDiMcDmNpaQnBYBChUEjsfW9LrZVXXuqV//79e/zyyy/45ZdfRKmuLHQ5TCl767e2trC+vi686+Mg077VaombIhXMaDQasY9fXl7G6uoqQqEQXC4XTCaTuDE1Gg2Uy+Whcl4y7+XjtFotVKtVlMtlVCoV2O12Fv0UcMhuBLS3f8iVfBQqlQoWiwXBYBCRSESs6svLy/D5fLDb7WIe3E0Xt9LMbTabSCaT+O233/Dzzz/jzZs3QvDj5sOTD2F5eRmbm5tYX1+H3++HyWS68TMMBgN0Op2hFZhKYY1GI1wuF5aWlhCJRLCysiIiD4uLi8IyoPr9YrGIarUqVnF5i9Dv90XfgXw+j1KphGazOWT5sLk/GTeqelRJJfNwUKVaLBbD48ePxax4t9sNq9V64wo7DtrDv3//Hj///DP+7d/+DYeHhygUCjf6G0wmE3w+H9bW1rC5uSn28QQJalQCE+3H6/W6sCKMRiP0er3YoqyvryMUCsFut0On02F+fh6DwUCIvlAoCNErV3ngk+jr9Try+TwymYwo1bXb7Xf+jmad2VzK74By5XioUJE8W46SXkKhEGw224379pvOkfrr7e7u4m9/+xtev36N/f19FIvFoecr4+BkcSwtLYlKPafTOVHW29XV1dBK3+12odVqYbfb4fV6EY1GEY1GxfALo9EoLMirqys0Gg2USiVh2jcajZHp31dXV6jX67i8vEQ+nxei59Dd3WHR38LnmFHncDiwtraGH374AT/99BOePn2KUCgkOs9MQ7fbRaFQwMHBAX799Vf8+uuv2N/fHzmRhkpcCa1WK6yOlZUV+P3+iZtp0n6eRkerVCqYTCYYjUaEw2FsbW0hEomIPH2qgadOudRHj9J22+32WAuz2WwO3SBoK0E89FixPyos+i+IVquF0+lEJBLB8+fP8erVKzx79gzLy8vXRKasWVCivMDb7baYeffu3Tuxhx9V+6B8f6vVimAwiOXlZQQCgaGW2bdNwWm326jVaqjVauj1etDpdNBqtfB6vcKsp4QirVYr2l9RgU6tVkOlUkG1WkWz2bzVj9JoNFCr1YRVwSv93WHRfyFMJhNcLhc2NjZEWuv29jZCodDI+ve7rlY0nOLw8BAnJyc3Ou3kY5hMJhE1oLDgJA04yIFXrVZRKpVEkpLZbIbFYkE4HMba2hqCwSBsNpsQPL222+2i0WigWq2iWq0KS2FUPz75ZtXpdNBqtUSaL4v+7rDoPyM0CMJiscDr9WJlZQWxWAxPnz5FLBaD3+8Xo5+A383umwQ/yoSlXPqTkxPRAEMOy40bAS2H6DY2NkRMfpQfQ/m7Xq+HarWKfD6PfD6PWq2Gubk5uN1ueL1erK6uIhgMij74ZDlQFSKt8lRoM24vr4Qy8nq93rXMP2YyWPSfCY1GA4/Hg0AggKWlJayuriISiSASiWB1dRVer/faijrNfr7T6SCdTuPo6AiHh4dIJBIjW2aP8rqbTCaEQiFEo1Fsbm4iEAhMNNGWPOm5XA4XFxeiBoHSd5eWluDz+eBwOITgZc8/hd8uLy9RKBRE+G2SaBFZCeNy/JnbYdE/AFTjTU4qjUYDh8OB1dVVbGxsYHNzEysrKwgEAnC5XMLcJSaJL8shM/l3uVwO+/v7ePv2Lfb29q6t8gCu5alT8cvq6ipisZgI0dntduFZH3U84JPDkDoBnZ6e4uzsDIVCAb1eDw6HAx6PBz6fD06nEwaDQQyykBOeKEyXy+WQz+dFQs4kqzb5AyhE2Gw2h3IJWPi3w6K/J9SL3WazwWazibFLbrcb6+vriEajwntN4Splos0kgh9l1hcKBRweHuL169d4/fo1Dg4ORC79uIYgZrMZbrcbwWBQbDUikQicTudQ951Rx+t2uyiVSmIox/HxMdLpNLrdLiwWCywWC1wuF+x2u5h0I+/jZT9ALpdDOp0WNQwk+tsamVBmnjyQgyohWfCTwaK/B1TnvrS0JLLoLBaLqChbWlrC0tLSSFP+LihX3F6vJ8Jz1ADj48ePSKfTYpVXeu21Wi2sViv8fj/W19exubmJaDQq9t5Ks17ealA8/fLyEufn5zg6OsLx8bHYStCNxGq1wmq1iukzcjdbiufTTSMejyORSCCdTovGmeNEryyvbbVaKBaLyOVyKBQKcDgcU/UGnFVY9FNC46Cj0Si2t7exvr4On88nGlSSA4+GMkyCskpv1EXc7XaRzWaxt7eH169f429/+xvev3+PTCZzrYR3bm4OJpMJVqsVDocDgUAAKysr2NzcxMbGBpaWlkRjynHnSF2AMpkMTk9PcXh4iKOjI8TjcdHpxmQyQa/Xi862sqeeHHck+GQyKSr+Tk9PkcvlroXfblvtKR2XhmtSujCn404Gi34KdDodAoEAtre38cMPP+Dp06cIh8Ow2+3iop9mouqoPTTdCGgfS11rZcHLHW8IuZyVqtuWl5dFaStVut3UULPT6YgOQDRF9+DgQDT1HAwGcDqd0Gg0MBqNMBqNok6AhEshNhL84eEhPnz4gL29PcTjcdFbX64BuM2LTw048/k8Li8v0Wg0RoYnWfijYdHfgjLcpdFo4PV6EYvF8Oc//xkvXrzA+vq62BOPS11V7pFvS74hqtUqKpUKSqUSCoUC4vE49vb28O7dO3z48AHJZHLIW0/95N1ut4iVU2qt3++H0+kU02VvKqhqtVrI5/M4OTnB7u4udnd3cXBwINp293o9mM1mUf9vMBiE4KlgqdPpoFariYk31GKbogw0nFNuNzYJlMlHj7u+ftZh0d+CUvA+nw+xWAw//vgjfvzxR0Sj0aE89XErzCTts+n1JJhKpYJUKoVkMomLiwucn5+LYRRnZ2dIp9OiiSX1xbfb7WKQZTQaxcbGhhA8lefetgKSRXF4eIi3b9/it99+w8ePH3FxcSH69Ot0OjFJloZP0CpNyTOVSgWZTAZnZ2diXh+NuaLc+WkTbKjWXtlSi7kdLq0dAe0N5akzKpUKPp8Pjx8/xk8//YSXL1+KFV65ut92EY4SHaWj1mo1sYLJzTXj8TiSyaTwdtNsOODTzchqtYrVfXNzE7FYTFS2ORwOGI3Gaz4CusHIv2+1Wshms/j48SPevHmDN2/eCMFTBRxVyFEPe9rDUx9B8qqn02mcnZ0Jxx+Z8zSvTzbjaTswKolIyfz8/LWhF2zKTw6X1o5AeQGp1Wph0r969Qo//fTTtRV+3GtvYzAYCI82jYe+vLxEvV4XbbTPz89F+2yaF0/iWFhYgN1uFxVysVgMsVgMa2tr8Pv9N/bFV54rDZ88OjrCmzdv8PPPP+PDhw+4uLgQabbUhlqj0Yj+8zSPXi6PJcHTIMxkMimGX1A2HZ0DbQcmhZpz0MhqvV4/sSXFsHk/EvkC9Pl8WF5eRigUQiwWww8//IBoNAqPxzPUkVZp1t900fV6PTQaDSHsVCqFs7MzHB4e4uzsDLlcTqz25XIZpVIJ5XL5WtINjZpaXV3F1taWaGIZDofhdrthNBrHfi46RzpPatt9fHyMv//973jz5s1IJyH1nKc/qeSVTPZms4lcLodUKoXz83PRh49W91HilnvjT4LcO5+iD7e1A2d+h0V/AzabDX/+85/x7Nkz+Hw+hEIhMbBR9npPKnbg0wVeLBZFGy5a4ROJBE5PTxGPx1EsFtFut4XXXtn9Ffh98gwNk3zx4oXoWjuuAYcym08+p0qlgkQigd3dXbGHT6VSQ4KnmwSt8NTuKpPJoF6vo9froVQqiUYXci++m6rnJjXr6RxI9B6PR4zF4ok3k3Oj6P/xH//xS53HNwGtXM1mE/Pz84hEInj16hXW1tZE9ZjD4RgZ5rqp3lxu61wqlXB+fi7i3aenp0in06IxRLFYvLW8lAZZ0mw5efIMjYwmxtWYyz83Gg2k02kcHByI8Vtk0svfjTx1lpx25XIZnU4HKpVqqN69UqmIG8FNTBJaU8btdTqdyD2gpiPM5Nwo+n/+53/+UufxTUBioWEXOp1O7Bnn5+fFBX8btEJ3u11Uq1Uh5mKxKLzZVAIrT66l3vE3QSb95uYmXrx4IVZ4mjwzahTUTXS7XRSLRZyenoo4PDnt5BuGUvAAhpxx1D2nXq8L7/2kq7fyu7tpf0+1DQaDAUajEQaDYSgMyqb97dx4BT969OhLncd3C4m70+mIGm9a3RuNBgqFAi4uLkTYLZ1OI51OI5VKIZ1OX5tcC4wfujlqhd/e3kYgEIDJZLpzDgDl0lPSDCXeyKs3ecbl4ZFqtVpYRDThh1pgTxszv8trqH6BJtgq34OFfzO8p78HVDFWLpdFiSitdNQGihJTEokELi4uUCwWUalURLvoSSHBb2xsiL560WhUtLa6Ke5PyM/p9/uoVqu4uLgQITUSfLfbHXLYyYInM58sE+qAQze/z43yvDjf/u5wyO4GyHE1bj9M/eUvLi5wcnKCRCKBYrEoSj7JtKeBl8Vi8Vq67ChkoWo0GlHAE4lE8PTpUzx79mxoXPRdwobUp57KY09OTkQufalUQr/fF2OzyGFHYieHmzzNhkZZTWPK3/bZRyH7FuSCHmZybhQ9f6E3Q97qs7Mz7Ozs4OPHj6LwhZpF0j6XRjVNytzcnJgOQ0k3sVgMjx49wubmJrxe71CoalLTloZY5nI5HB8f4+joSPgVAAiHpTIOT5l21OFGbmQxbUbcpJ2G5d8vLCyIGfbssZ+OG0XPX+rNtNttkTW3t7eHnZ0d5HI5dDod9Pt9sRpOAu2bKduMqvRcLhdCoRA2NjYQjUbFEAqz2XzjTDtyiMmrMwk+n88jmUyKIZz1el007dRoNCLLjgRPE2godCiv8F86BZb6FciFPczd4D39lMg15plMBul0WnSCuSskNIPBAJPJJLLNnE4nfD6fqJKjwY/UjGOU0Kl/nGyCU6VbrVZDqVQS2418Pi8aYFB7ap1OB71eD61Wi8FgIEZbk4OOnHb3FTzd5Oi7nOS9tFotjEbjUM3+XXIkmE+w6KeEhi7KGXNU/AJ82g/f1Lhxbm5OtNgyGo2w2WxwuVxwu91wOp1wOBxwOp1wu93idzSXnkQHYGg1J7ObxmTTQ95m1Go10W663++LRB45BEarfLPZFP3vAIh8g1HJQpNA2wXakwOftkhkidwmfOpSZLfbYbVaodPpOP12Clj0U0ArXrPZRK1WEx57ZR34uGo7Cjfp9XpRKOP3+8WD+uiZzWaYTCYYDAaxqlWrVbTbbZHsI8f3aXWn6EG9Xr82+hn4tG2jY1ssFpHDTvXw8/Pz6HQ6IpdeLmWV3+cuaLVa6HQ6sUIDwyWy8nZkFFRFaLPZ4HQ6RWsy9t7fHRb9lNAemYSgXNWVzSjJ6yy3xXY6nQgEAmI6bTAYhMfjgc1mGxK6PBiCqthKpZJYsUmIsujpxkDnBnxaKekm43Q64fV64XQ6RU872if3+32USiWRS0+WDE2guSs6nQ4WiwVut1vkyvf7/aEZdmSpjBM93ajsdjscDsfYlZ65HRb9hCizvcikHlfTLa9AJHjakzocDrFXp/56tMJTjz1KgCGPOeWzp1IpUcRCQybIyqDQWbfbFTn71B+PMgupT14oFBKTcWn1pQYYNC6qWq0im80il8uJCrm77OPn5ubEGG76nBR1aLVayGQySCQSwnIi5ychp9/SSm82m4VlouwNwDeAyWDRT8i4C+q235PDivbN1KuOutqEw2HRukp2TvV6PbHKZrNZ0UAjHo/j4uIC2WxW5LfTZBj55kOFMXSTMZvN8Pl8WFlZEU01aNUlscstrqgePpVKIZPJiCy9u3xfJpNJDL6gyIPX64VWq0W9Xsf5+Tk0Go2wTshakYd+yJ+H/B/Uk49n008Hi35K5BAbreTKVYce5J03mUxwOBzwer0IBoPw+/2iiyztTyn2TdNcM5kMzs/PcXp6itPTU5yfnyOXy4nsv3GjoEgger1eTNdZX18Xo6Zo8oyyTTVlGFK57/n5OfL5POr1+sTOO2rI6fP5EI1G8fjxY8RiMaysrIg5ebVaDTqdTiQKVSoV0SBzlM+AQpmLi4sipDirTV7uC39rU6LMDJPNeeUqTxcsObJk5xwAYU7LK3y5XEYul0MymcTZ2dmQAOVhj0ohkjlvMpng8XjEfHhqrCELXh5EoVKpRE+7bDaLeDwuWltVq9U7ZWcaDAYxpvrFixd48uQJIpEIPB4PdDodBoMBjEYjVCqV6GxbLBZFzwDZoSeP05aLfjiHZHpY9FMiC3rUiiMLny5Y+h15ranYplQqYX5+XuzLaQ57NpsVNfe0jyfvPTnn6IZDgqCwFmXxUY/7SCQipseSh162TMh/kMvlcHp6KlJz8/n8nWsEnE4nVldX8ejRIzx58gTRaHQog7Df70OtVqPX68Hr9SIQCCCTySCfz4uMP2UIT/6cvHe/Hyz6KZHFrLwQ5U46ynx1ysdXq9VoNBpDXWkpIkBeeqqxLxQK14Y8yhVw5CSkHH2v1yvaZ5HfwOv1ikkwytly1JeehllQrT81wpx0lddqtbBYLFhaWkI0GkUsFkMkErmWMkw5ClQX73K5xGQcchjKn1Ne9cc1FWEmh0U/BUrBK817gjLk5ufnhZipUUej0RCZb/Q72s9SjJ0SaRqNhvCck2AADK3uZrNZOAlDoRBWV1fF9Bq32y0y7kbdoNrttiixPT4+Fi2qLy8vJw7RkXedBliura1hZWUFHo9HCF6+GZKvQ6/Xw2QywWKxDIUNycwn5FRiCknSd8vcDRb9lChX+XG577RXJqhwpVwuDxWNyCm0JH45+Ybei/bhlM2n1+uFOU+CD4fDCIVC8Hq9IuavzFOXV3jq5nNwcIC9vT2cnp4Ks37SEB0NvCBLg/INqDU2HVP+k75HKtsdNftO/t7oxklOv06nIyoCmclh0U+J0qk0qr20/Cd1kul2u2IAo9yFhsJto2L+JAyqfFOr1cJZZ7PZhOCDwaCYXuN0OkUsm24UZBIrC3BSqZSYPHN0dDSV844Sj+TcfbkcVw4JyufQ6XREVh414aCbnNKhJzcKrVQqaLfbE/XxZ4Zh0U8Jmdlyc4lRFx8JjS5gSpcdtyUgq0GOCtCNhfq8k0lst9uvpfC63W7YbDYxQ4/MahIZ5eZT3UAul0M8HherfDwev7avvg2yegCI8Vu0NTGZTCJ6IfchkMOS+XxeJBtRJx7avxP9fh+1Wg3FYlGMs2o2mzCbzdwu646w6KeEVjYaujAqjCSvrLRvp7AcgCGHmiwcWexkxpPYDQYDrFYrbDYbPB4PvF4vfD4fPB4P7HY7zGYztFqtEJ/cr6/dbqPRaKBarYpqu3Q6jXg8jrOzMyQSCTFQchLBk8Doc3e7XTHVJpFIiDTZTqczNKueWoBT0hEN8qDohLylIejzkGNTHm/N3A0W/ZTIySJarfbaqGQSsVL49HfKqZd9A/LryJynY8gOL+r57vF4RBtoMuUBiIESsulcr9dRr9eF4GnFzGazItVWHjVF50LcJq5+vy9i7vF4HPPz8yL86PP5RIEMFfOQH2F/fx8HBwdIJBJimMeost3BYCDKg8vlsghdjnoer/Y3w6KfEjnTTqfTiW4uAG409UeZ9MrfyxEB2Zqg/bLBYBjydJMHXvYfNBoNVCoVlMtl8ahUKqhWq0P7YhJQo9EQlXvyJJtJQmOUAtxut1EsFkUqr5xv4HQ6YbFYoFarxXDMRCKBw8NDHB8fI51Oi7FZ447Z6/VEBSHd2GTRs+Ang0V/R+QLi0RPQiTxE7JZr3wPQtnBVl7xZfOf3o9MdVr1aEWtVCoAIMRWLpeHWm+T4KnUVi65JT/DqKET8rZEee7y56HXUwSCzo/aflM/AI1GI5KAaAoOdQUel1Isf5/0/hyrnx4W/T2gAY6UWms0GlGr1USJKJmpSjMfwJCpL3u5KfZMjjcyh+VGkEpHGHnK5cjA5eUlCoWCKMOV+9GTcOTzGWUm34ZylZUn8sjOwnw+L/oDqNVqkRdADjlKK75NxBQtoSgG19JPB4v+HlBiDHnS7Xa7MD3lHPJRK75s0pNYaFXvdrtDK6scYiPPeKVSEd58tVotTGoy68l0p64541pcyZaEfCMahVLko/5dPl8qlaWUYwof0g2LxD7pYAwKU1osFhiNRmi12pFFTszNsOjvgdyx1m63w+l0ol6vC1ObLn76O3B9hhxBXm1ZOBQRaLfb4u9U9y7H7Om96KZAQqfknptaVMtJRLd57O/iKZctHVr5yU8hd/mhUtrbkGvzXS4XnE7nyGm1zO2w6O+BXONts9lgt9tRqVSEmd5ut4dWn1EZacAnzzeZ+CQSaqIhh/Gobx1592WHobzXJ6FPKqhR53Qf5GIj4PdiHkrOka2Bm46rbKJhMBhEXoLb7YbJZBpZ3cjcDIv+nlDhiGx2Ut487cPlFXSUiaws0JH/lCv0lNV68gU/ysn1LcSw5aw6OcvuptZYwO+mOj1HrVbDbDbD4/EgEAjA7XbfONmHGQ+L/h7INd5KR5vcinqSTq/073J1mRLZs6/cv8qOwK8tdlnkcj28/G+TvIf8XCrmoUQkCv/Rc1n8k8OifyDkFUwWu3LFneTiVF7wxEOEqEYdfxIhTvu6aZ6rRKvVihXe7/fDbrcPhUaZu8GivycUT6fMOeofT62f5Jz7u74vFdqQBaHs0iP/XS7YoRsP7Zvlm8W04rvtdcrzG9VGTL4p3lQXT595fn5eOO82Nzexvr6OYDA4tMozd4e/uTuiXPHUarVoeBkMBnF1dSX2+JlMBsVicaiQZBIoKkD94HQ6nQjPUcov5bFTAo8sKDm0R+m3jUbjs5n9dJ5ypiCNkZbPj4Zl0OAN6vFH0QX63DTph9qE+/1+rK+vY3t7G6FQSLTaIti0vxss+ntA3nuz2Qy/3w+1Wg2bzYZAIIBsNouLiwsxh/7y8lL0jVfu8ZW99EhAZrNZDLygvnokMHnAJPB7KizF6qvVKgqFgnhQ33pqNU2WgVzOO+qmIFf9yT/LNyZqS22z2WCz2YYaYshlvXJXIEocoixBEj01A6FiIq/XC7/fj0AgIBx4bNrfD9Utd/+v7/79xqGVVR4dRS2x0uk0ksmkmHNXrVZFyapS9BSGoxx7k8kkxESz5mgCjTy1Va7sozAhDZzM5/PI5XLI5XIoFApDSTr0XErblevZB4OBqO4zGAzQ6/XQ6XSi8w5lxslNPGjyjMPhgM1mE+dKoqfmIbVaTWQL5nI5MQ6MMhGpIYjP54Pf7x+qHqSbnrK/HzOWkV8Si/4BUMaeqRfe5eWlGGp5eXmJer0u0mDlvay8D6biGvINUHqvUvCjmnfITTpIXCQwWlEpIYYq4MgioOdSZ17KP3A4HGKijDznbm5uTlglRqNRjMeiQRTUSEO2ROSMwVKpJKb0yK2vjEajqCJ0uVywWq1C6MydYdF/SUh8tPpTyaosePruZZNZNvPpBkB/l036m1Y6ZVmtcvSV3HWXhmlks1kUi0XR357MdrfbDY/HA4fDIRpzyDcpZRWgXGo8qr8A1fW3Wi2RrkznRJ+bCpioTTczNSz6r4Gy7dNtCSnKvyv/vOuxR50DWSTNZlPMk6PGFOTwI18FpRfL8/WUWXDyOU56nuO+k1E5CMzUsOg/N18jSeSuOQBKaMWnfT2FGamYSK/Xi63F1+C+n2/GYdEzoyF/hHIWnrKrD/PdwaL/Gkxi1o9Cuardx7wfd/y7mtHjwnrK87vtPW+q3ef4+4PCov/aTJvu+jmOP+1xRtXjM98sLHpmPJM6GJnvipH/cRwPYQCMroJjsf8xYdEzQ7DQ//iwW5ZhZgwWPcPMGCx6hpkxWPQMM2Ow6BlmxmDRM8yMwaJnmBmDRc8wMwaLnmFmDBY9w8wYLHqGmTFY9AwzY7DoGWbGYNEzzIzBomeYGYNFzzAzBoueYWYMFj3DzBgseoaZMVj0DDNjsOgZZsZg0TPMjMGiZ5gZg0XPMDMGi55hZgwWPcPMGCx6hpkxWPQMM2Ow6BlmxmDRM8yMwaJnmBmDRc8wMwaLnmFmDBY9w8wYLHqGmTFY9AwzY7DoGWbGYNEzzIzBomeYGYNFzzAzBoueYWYMFj3DzBgseoaZMVj0DDNjsOgZZsZg0TPMjMGiZ5gZg0XPMDMGi55hZgwWPcPMGCx6hpkxWPQMM2Oob/l31Rc5C4Zhvhi80jPMjMGiZ5gZg0XPMDMGi55hZgwWPcPMGCx6hpkx/j8zpE7Htbw4vAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 61; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 65; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 66; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 67; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 68; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 69; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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58V9eXuL09JT3/NdqtYH7Fa/uEolkbJBPpVLxHYXJZIJarZ56EAfxd0j0XxCx6FiPeDabRTKZRCQSwfn5OQ4ODvDhw4c7BS/GYrEgEAggFArB4/EMpM2Au00q+/0+arUaotEoLi8vefvquKi9UqmEzWaD0+mE3W6HXq/H0tISer0ef4AdHBzg4OAA19fXyOfzvAefCZ6ZYgIY2AGIryWRSKDT6bhzr81mG2ntRcK/GxL9F0S8wqfTaZyfn+Ps7AwXFxe4urrigyhvb29nFnw4HMb29jY2NjbgdrvHdtAJEQfwCoUCLi8vcXh4iPPzc6RSqZFpQrlcDrvdjtXVVYRCIbjdbhgMBnS7XRSLRVxfX2N/fx/v37/H0dER/37a7TYXutAXnxX6tFotSKVS7p0vDBjqdDo4nU5uj63T6aiybw5I9J+RcWf3ZrPJS2t/++03fPjwAefn50gkEqhUKrzvXIi4TZatfFqtFiaTCV6vF1tbW3j69Ck2Nja4s+1d9yf0tRfW2rMH0bj6fovFglAohN3dXTx69IgbcVQqFdzc3ODg4AAfP37krj6lUmng4SGMBbA4APt3NmBD/P1qtVosLy/D4XBgeXkZGo1mbGERMR4S/WdE/EvY6/VQrVaRSqVwcnKCX3/9FW/fvuXBuknmEuLAFiuI8fl88Hq9fMVlaTrh1n7as3y73UYulxsIvInP18x5JxgMYnd3F3t7e1hfX+dTcbLZLM7Pz/Hx40ccHh7i5uYGhUJhSPDMaMNgMECj0aDf76Ner3P3WzFswo3BYIDJZILBYKApN3NCop+DcYMdhIgDVO12G8VikTevvHv3Du/evcPh4SGi0ejUFWoSiQQmkwmBQIDbXbEzvN1u5+OgxF8z6nsQ32Mmk0E0GkUkEkEulxsSn1wux/LyMsLhMPb29vD8+XNut6VWq5HP53F7e4uTkxOcnJyMFDy7H4VCAaPRCKvVCqPRiG63i0KhgE6nw11wxUFDhULBx1oJjy7kmDMbJPo5ELegjnsNo9lsIpvN4ubmBoeHh3j37h3++OMP3i03S0mqyWRCMBjE3t4eXr58ie3tbfj9fp4fn2YC7KhqPDaq6ujoCDc3NyiVSgOvUSgUWF5exvr6Op49e8ZbdL1eL0wmEzqdDsrlMm5vb3Fzc4NIJIJMJjNy9yKVSvlW3efzwWKxoNlsQiaToV6vo1gsjgzQsSm4LEcvvH8S/fRMFP2osstFQHgWF+bBhSvjNL9kLKiVSCRwfX2N4+Nj7O/vY39/HxcXF0in0wPnZWZcMWoHIZH8OY0mEAjg2bNnePXqFZ4+fYrV1VWYTKaJFXbCvxvlgZdOp7G/v4+3b9/i999/x8XFxUBzjVwuh8PhwMbGBp4+fYoXL15ga2sLKysrA7uKer2OXC6HdDqNVCo1VEXIvg82vsrlcmF1dRU2m4373eXzeW6gKT7XC2fj0aCL+Zko+vu6k4wKxgjNGj4Fd73XqPTPvO81C/1+H+l0GpeXlzg+Psbh4SHPV8diMWSz2aEA2aTOM51OB7fbjd3dXXz33Xd49uwZQqHQVNbVwnsSk8lkcHR0hJ9++gmvX7/mjjxsa8989TY2NvDdd9/hu+++w9bWFlwuF++NZ7Aae1ZuOwqFQgG9Xg+73Q6/34+1tTVYrVbk83mUSiXEYrGxrrksxUc1+/djouiF/0MXHeHDo9FooNFoDJw72cNMJpMNpL5Yzv3k5ASxWAz5fB7lcnkqAwyhSLVaLY/Q7+zsIBgMDgievd+krb3w3/r9PlKpFI6OjvDmzRv89NNPeP/+PeLxOD9usLTc2toanj9/zo8TKysr0Gq1Q+YbbOQVG3s16ntiFYM+nw/hcJh77svlcl5wc9dcPhL9/SBVzwg7t97e3vJgl1CgYtEfHR3h6OgI8XgctVptYJTzOMRbWxa88/l8CIVC8Pl8A1Nb2WvEjEsZAkAqlcL+/j5++ukn/PjjjzyDIBQ8C9q9ePECL168wKNHjwby/+IcPxO+uHSXwYTt8XgQCoV4AFKhUKBSqfCdwyy7M2J2Jor+w4cPX+o+vjriUlO2KhmNRmi1WgBAqVRCNBrF2dkZTk9Pufe78JeUGU1WKhVeZTftIAqG+FzPSmtZ66pQ8CynPak3vt/vo9PpoNvtotFo8LTar7/+ih9//BHv379HIpEYiLIL/e729vbw6NGjoTp+8ez6u0Sq1WrhdDp5etHn88FqtaLb7fIiHeHDYxS0wt+fiaL/13/91y91H98E7JeuWq1CJpMhHA7j1atX8Pv93JwyEong9PSUb9eZ6MXxCna+Zf7s897P8vIyQqEQHj16hHA4DLvdPmSGIabX6w1s5VkxUCaTQTKZRDQa5bl0doYXZhBYj3wgEMDm5iY2Njbg8Xj4w48xqodg1Cq/tLQEtVoNu92OQCCAcDiMQCDAq+rYlFqhuw7ruRczTbqUmMxE0f/7v//7l7qPb5Jff/0V6XQaoVCI18mzCSvRaBSZTOaTX5PlsNVqNSwWC5859/TpU4RCoSGv+lFneOHfdbtdJBIJHB0d4eTkBJeXl4hGo4jFYri9vUUikRhKGer1erjdbqyvr2NtbW2gjn/ckYEJdVR+nW3r3W43wuEwwuEwXC4XL6Ntt9uo1Wq8GnHcEAv2YGHe98R80Jl+ArlcDj/++COOjo741phNWCmXy5/8euwcbbVaYbPZ4PV6EQqFsLGxgY2NDfj9fhgMhrFbXPEK3+12cXt7i48fP/JA3dXVFTKZDLetEh87VCoVVlZWsL6+jq2tLfj9/qkyBOxn02g0hkTPGnNWV1extrYGv9+P5eVlyOVyNJtNlMtl5HI5ZLNZbpcthh1Rms0mv29Wvktb/tkg0Y9AeBaOxWKIxWKf9XpLS0vQaDR85BQrpfX7/fB6vXC5XHA6nXeW1goF3263cXt7i/39fbx58wavX7/GwcEBksnkwNeIg5BOpxNbW1vY3d3lNfzCHvlRtFotVCoVFItFVCoVNBqNoZhEIBDA+vo6VldXecsvK0vOZrNIJBJIJpN8EIYYVqZbKBSQzWZRLBah0WjISGMOKGU3Ana2n8aEctIvnHiLOqrYxGAw8HZRv9/Pt79MHKw2Xdw8I06XiQORt7e3eP/+PV6/fo2ff/6ZW2ePukeWarTb7djc3MTe3h6fgmMymSamAVk7bj6fRy6XQ6FQQKVS4T87lnXY2NjA+vo6XC7XwFGBBTxjsRji8fhY0fd6PRSLRcTjcUQiEXi9Xmi1WpjN5oGHND0A7maiqu/jWLoozBJQEr9W6GW3traGYDDIG2jEZpbTwgS/v7+P169f4/Xr1zg6OuIed+PQ6/Xwer08frC2tgabzTY0+1384GIrcD6fRzqdRjabRa1W462wwWAQ29vb2NnZQSgUwvLyMl9M2AMjnU7j9vYWqVQKpVJp5Hm90+mgUCjwXn+fzwebzTbgx0dMx2Iu5TMwqpT1U2AymRAOh/Hdd9/hyZMnWFtb42WtbBKMmHErGRNis9kc2NK/efMGh4eHQ4JnohM+1I1GI4LBIDY3N7G2tgaHwzF1r3qz2UShUEAmk+FGGTqdDj6fj4/SYjl+YQag1+vxWvtsNotCoTAx01EqlXB7e4tIJIJ4PI5wODyQNpxUl0D8HRL9HXyO1BAT/IsXL/BP//RPePLkCdxuN1/ZJzndjoONvxKW1B4eHiKdTg+9dlT3GiuLDQaDcDqdEz3yhQ8fJlxmolmtVqFUKmG327G7u4sXL17g8ePHPCAofJB0Oh00Gg1UKpWJpbsMVpvPHhBC6y1iekj0n4FxNfwKhYI73Dx9+hQ//PAD9vb2EAgEBoJl7OsnecOLy26Z6JmL7qSgnXjHYLfb4fV6B3LnwuuIy3fFHYQsiFer1SCXy7GysoKNjQ08f/58Ymyg0WigWq2iWq3ysuZRP0Phn8UDM0j0s0Oi/wLIZDLodDpuL8V60Xd3d+H1ekdGx2etL280Gkgmk7i6usLV1dXYoJ3wzzKZDGazGcFgkKfSLBbLQAB3kgcdC65lMhnupWexWLCysoLnz5/zUVpms3no3M0stguFAorFIur1+sBZfpKYqUDnfpDoPwNCt1cWnXe5XLzCbXd3d6BTjcHKf++KlrP3FsIMMG5uboaKhmQy2cipNktLS7Db7QiHw3j06BFfkcddc5Tg2Rk7m81CKpXC6/VifX0du7u7PHAnfqixAB4zBM1kMqhUKlOLWNjyTOf32SHRf0aEefdwOIxQKIRAIAC/3w+HwzF0bp5ncku/30cymeQGm/F4fOo6f7VaDZfLNVB5JzbTHEe1WkUsFsPZ2RkuLy+Rz+eh1WoRCASwvb2NYDAIq9XKv0dxHIA1LsViMSQSCZTL5amzRbTC3w8S/SeEFYrIZDLYbDaEw2Hs7u5iZ2eHl54yOyvhoIZJZ3fha4Dh7XYymcT+/j5++eUX7O/vj/TaE4qJzYZXq9UIBoMIhUIj592xs7z4ep1Ohw+3PDw8xMHBASKRCJrNJj++BINB2Gw2qNVqSCQS3hTEaLVayOfzPOfODDemETOrzGu1Wmi1Wmg2m3cWDxGDkOg/EWw+u9FohNlsht/v55VtbCuv0WgGfN6nhXWdiR8MzFH3xx9/5BV3iUQCzWZzbDDRYrHAbrdjZWUF4XAYOzs7cLlcQ800owJ+rVaLG2ceHh7i/fv3ODs7Q61Wg8VigdPphNfrhcPhGOq3F1Iul5FIJHjqjY27mpZ2u82zBaVSifvfkx32dJDo74FMJuNBOqvVipWVFXg8Hng8noFV1O12D6xG4lX7ri0984cTfr3YAEPcDy8WLKt/DwaDWF9fRzgcRjAYhMvlgsvlGjpqiK9XqVSQSqVwfX2Nk5MT7mefy+VgMBgQCATgdDp5FaEwGCjMMJRKJdzc3OD8/ByXl5e4vb29s49BHIBkjsLxeByJRAI6nW5iTwIxCIl+SsQrp1qths1mg91uh9PpxMrKCp/c6vf74XK5sLy8DKPROLT9HLcKi/vox235U6kUPn78iDdv3uDHH3/E/v7+SLtquVwOg8EAg8EAm83G3WoePXrEi4G0Wi2USiW3kxY/kFhunE2wPTo6wvHxMZ+mC/xZ2GMymWCz2WA2m0daUzO326urK3z8+JH7BAqn3kwLK9K5urqCz+fjHvjsyETluJMh0U+JUIwKhQJut5vXkweDQT6AweFw8Mo6sWMrMHl1HyVy4Ta71WrxXPybN2/wH//xH0MrPINZXbH8O/vw+/0IBAIj++OF98Gunc1mcXZ2hg8fPuDdu3c4Pj5GLBZDMplEtVqFyWSCSqWC2WyG2WyGXq8f2iU0m03kcjk+9ebdu3f4+PEjotEoisXizG2y1WoViUQC0WgU8Xic7zJGxUmIYUj0d8Bmswk/X1lZwc7ODl69eoWdnR0EAgG+oms0mpEz1sS/hMJVfVIQjzWzlMtlZLNZfp7+/fffcXh4OFLwarUaDoeD++ltbm5yYbC4gzhKL34o9ft9bpr5888/45dffsHh4SHi8TjK5TIvl1UqldDpdNDr9dBqtQOrfLfbRalUQiKR4NZh+/v7ODk5wfX1NTKZzFwGI81mk9cH5PP5gUm4xN2Q6O9A+Mskl8vhdDrx6NEjvHz5Ej/88AO2trZgs9nuXFXEQaZxQhc+HCqVCiKRCB8mycw7rq6ucHl5OdLxRq/Xw+VyYXNzE8+ePcPe3h7W1tZ4O+ukIKJwS892FG/fvsV//ud/4sOHD9wpiKHVavl5mo2Y6nQ6fBYdcxpi1t9HR0e4urpCIpFAoVCYyUJMDPPiY8YalMabHmqtHYFw3LLw79xuNx4/fozvv/8eL1++xObmJux2+8j3mGT1NCrKXKlUkM/nUSwWuZBLpRIfJnl6eopYLIZcLodyuTzQgiqXy6HX62E0GuHxeLC2tobHjx9jd3cXa2trYy22mLWV0H2WWXcfHBzg7du3fGR2PB4fWJUlEgnkcjmfpyeTydBqtZBKpSCRSJDL5XjA7uDgYGDEVb1en/kcL0Qmk0Gr1cJgMAyNyCbuhlprR8D66YWfezwe7O7u4i9/+Qu+//57bGxswGq1jn2Pu4wqhf/Ohj5eXFwgEomgVCrxqjVmxMnmu4t7/JnbjsfjQSAQwNraGra2trCxsYFAIACr1TpWEOLvk23p2QrPYgZCH3zh98KyF8CfNfGJRIK738TjcVxfX/Mo/c3NDfL5/CdZkbVaLWw2GzcXofba2VjMpfwOhFt6Zm4RDAbx+PFjPuxBKCa2zRRv2Sdt4cvlMhqNBq9MOz09xeHhIc7OzpDL5biJB2tZHTUcQ6FQwOl0cuNM1hbr9/tht9sHDCbYfY67P2b8eXR0hLdv3+Knn37iW/pRD3/2vbMg3c3NDXK5HDfhZKYYyWQSuVxuaEzWfWAefiwgKa7tpwDeZEj0EzCZTLwTzuv1wu/3Y2NjY+AMz1btWVaaQqGAm5sbxONxXpXG8t+spLXf7/Mg4ihnWBadZ7Pl2Ihql8sFo9EIpVI51k5L/F79fh/5fB7n5+f4+eef8fr1a/zxxx8jg4RCOp0OKpUKL/2VSCQ8n59Op1EsFtFsNqdyIJqEuONOp9PB4XBwKzFa6Wdjouj/5V/+5UvdxzcBE1mj0cDS0hKCwSBevXqFtbU1mM1mmEwmLC8vz2zPxM6wbMgjm2t3fHyMy8tLpFIpZLNZZDKZqRx2lUolHA4HNjc38fz584FBFHq9fuC10xhLsOPFhw8f8Ouvv+LDhw9DW/pRtQWsMq7b7SKVSnHPu2Kx+FmMQxkqlYr/v7BYLJSfn5GJov+3f/u3L3Uf3wRsJRQOu2CRaZlMBqlUOrHtdBQsp8y2uel0mq/qp6enfEIsOyLcBZstt7m5iZcvXw7MlhvVLDMptgCAB99OTk746OxYLDaUShMLnu1ASqUSqtUqAPCBGvdd2SfBym2VSiXUavVAgJKcc6Zjouh3dna+1H08KMQdY6wBhHm+s6h4q9VCJpPBxcUFLi4uEI1G+QMgmUwiHo8PnXWFRwVxKkomk2F5eRlra2t49uwZXrx4wWfLCSfPiN9P+PdCQXQ6HeRyOVxdXeHw8BDHx8d8as80PwM2mOJLp8tY/8I8fQwEnennQigcZsucTqe5XRQTPxshdXl5ibOzM9zc3CCdTqNWq6HVas2Up5ZIJLBYLFhfX+ez5ba2tuB0OgdW+FHGleOoVCqIRqM4OTnhE3um2ZYLHXiIhwel7CYwzqhB+DlrMz05OcHFxQUfatntdtFqtfh8+lgshlQqNTL1BQy6wYh/7kKbLWY0yTzpxSv8NMYS7OwdjUZxeHiIDx8+4OzsDMlkknfosW208CHCdjDfgtjZfXwL9/LQmCh6OhsNI9zas9Tbzc0Nfv/9dx7xZttyZvzYaDT46i5mXPMNQ6VSweFwIBQK4cmTJ3j27Bk2NzexsrLC+9WFrbfsPcfBHG9isRgODg7w/v17HB4eIhKJ8LO5RqOBQqHg8Yt2u33njLkvCbnm3I+Joqfz0jDCX/hut8vz7GdnZ9jf37/TX16MMHjHDC5Y4YtGo4HVakUoFMLu7i729vawubkJj8czkKYalzIUDpPs9Xq8Zj0ej+P8/Bz7+/s4Pz9HPp+HVCqF2WyGUqmEwWCAVqvF0tISzzgUCgVeNPQ1USgUUCgUkMvld86xJ0ZDZ/oZEUe+K5UKCoUCL6Gd5utHBdtYe6rBYOBRaa1Wy80uWJUdy0vfRbPZRK1WG5hZVy6XeeEMq5Kr1Wrc+EOlUkGv10On03EXnXw+j1gshm63+9Usp4XXFDb3kGPOfJDoZ0QcxKvVanzSqvh1k+rv2WuY447b7YbH48HKygpMJhPUajV0Oh0fZOlyuWCz2aBSqXhKTBgHYFt8dqQol8vI5/PI5/PccbZYLPJZcIVCAZ1OB1arFQaDASaTCRaLhQ/akMlkqNfruL6+Rq1Wm3kH8zlQqVS8fXl5eZnvRhi05Z8OEv2csJWvUqmgVqsNnXWlUunYwBdbUfV6PbeMZqW+zBSCGVuw17GWVVbbXq/X0W63B67BcuTVapW34rKCn1wuh0ajgV6vxxtlmPnHysoKN8DQaDSQSqVoNBq8lp5t8+8b2FWpVDxewIqgGo3GVM03bDfkcrn42C+9Xk+NNnNAop+TdrvNA3T1en1o8MKodk+JRMKn0zJbLY/Hw1d5j8cDl8sFk8kEhULBz/ftdhuVSgWZTAbxeBy3t7fcV449bNhKz+6LHTuy2Szy+Tzq9TpvzvH5fAgGg1hdXYXX64XT6YTJZOIPl06ng1Qqxfvdy+UyqtXq3KIXWoHbbDbodDo0Gg1erDRuaKUQqVTKRe/z+fggTHEvAXE3JPo5YX3cbKUVj4oSI5fLodFoYLfbEQqFsLW1hfX1dfh8PtjtdphMJn6mF55VWbDw5uYGp6enODs74zPm2XRY9nBhDxpWI8BGRbVaLWg0GqysrMDtdmNrawubm5tYXV2F3W6HwWCAUqnkwUB2/UqlgnQ6jWQyiWKxOFelnVKphNVqhdfrRTAY5I49LGV4fn6O6+tr5HK5ofcXHpHYOG+r1QqHwwGr1TrRfJMYD4l+ToR5bPb5OJjltM1mQygUwt7eHp4+fcpHN+t0Osjl8qEofLPZRCaT4T31Hz9+xMnJCSKRCDKZzMCxQtyU0u120el0IJPJoNfreenu8+fPuduPzWbjJcZC2u02d72NRqNIp9Nz1dKrVCo4nU6sr69je3sbGxsbcLvdUKlUqFQquLq64juLVqvFp+SM+nlKJBJ+3GG7EvFEXWI6SPRzwkpAWU/5qJQZEyJLv7EZb8wL3+/3j/Spa7fbfITz5eUlz6cfHx/zaTIsp34XGo0GPp8PT5484aOmVldXsby8PHIybrfbRSaTwc3NDXfnmSYrIUalUsHj8WB7extPnz7F48ePsbq6CrPZzIOEJpOJD74ol8uo1WpDRh3Ch4BMJoNCoYBSqRxyFybRTw+Jfk6kUinkcjnkcvlAfl0MW701Gg2Wl5d54Gx5eXnINZaZSJbLZT6Xbn9/Hx8+fMDh4SE3kpzWV85isWB1dRWPHz/Gy5cvsbu7i0AgMHLUFLt+qVRCJBIZMO6Y1cdOqVRyW7EffvgBz58/5/Pu2XU7nQ4UCgVvxU0kEkin0xOvJSzKIZHPD4l+TthKLxT9KNgxQNjB12g0BlZP1mHXarVQrVa5KcXp6SkODg5wenrKHXWEwTRxWnBpaQkKhYK3ngaDQTx69AjPnj3j46JNJhNvRRVPvmXz7Y+Pj3FwcMDLimc5yy8tLcFqtfI+/+fPn2N7ext2u31gN8Sah9xuN3w+H66urnizT7PZnFgPQKK/HyT6OWEr+F2dXiyw1mw2uX+8VqtFo9GAXq/nZpKtVovPeU+n07i5uRmYQFupVIai52JhaDQamEwm2O12BAIBbG1tYXt7G1tbW/D5fANVfOJRWt1uF9lsFhcXF9zEkrX9zoLBYIDX68X29vaAR9+on5FSqYTJZOKpQ5vNhkKhMJCKFH+/LEtB7rfzQ6KfE7aCs1LQcbZYwJ8raqVSQSKRwNLSEmq1GmKxGC8uabfbqNfrqFarqFQqyOVy3H2Gde6NS5dJJBIoFApotVrY7Xb4fD6srq5ibW0N6+vrWF1d5VV849JbnU4H+Xwe19fXODo64oIvFAoz/UxUKhWsVisCgQC/ts1mG2oVZoFDFpwzGo2wWCwwmUzQaDSoVCpDKTxmH8YKohqNBj8q0Ko/GyT6e8C27VKpdOJqz35Zk8kkz08bDAZ+pme59Xq9zj+q1SrPw48SPKvmU6vVMJvNfBR2OBzG2toaH1nFCm4mCaNYLOLy8hIfP37EwcEBrq6u5jKxZCk1NirLbDYPDaBg/2X3I5VKh+rpR9Hv93kbM6soVKvVPOtBTA+JfkaEv7BCM4dJ58x+v49Go8FLZAuFwkBenLXhNptN7j4jPquz/7IPlUrFC15Ysc3a2hpCoRAfhS2eNsPuRXhNNluOTbA5OjpCIpGYaaAkQ6FQQK1W86EX4lSg+PNRq3er1Rr5sOn1enxwBpuww9J3s9qXLTok+nsgTNdNUw7KSllbrRZqtdrAL6u4oo8h3EmwlJVOp4PZbIbD4YDP50MoFEI4HIbP58PKygosFsvYkVXs2qVSCblcDolEgnfcsbHT8xbiCIuChFV8wtVeSLvdRrFYRDKZRCKRQCaT4dWNo352rEPw+voafr8fNpsNBoOBukFnhER/D5gQZ91ijgtEiQt9hCs7G5llNBq553sgEEAoFEIoFILX64XNZuODJ4TVgsJGnFKphEwmw1dMYcAwGo0ik8nMPXmm0Wggn88jGo3i4uICBoMBcrkcNpsNSqVy4MHI5vIxb/zr62uk02lUq9WxxwoWF2G2Y+VyGb1ebyA4Kfz5EaMh0c+IcAspXOk/VW+3sNKP/Zltm9n0Wa/Xi3A4zMdNezwe3nUmkUjQbDZ50w2LDTQaDVSrVeTzeaRSKUSjUT4Akpl2CmfUzUOj0UAmk8H5+TmUSiW63S4qlQr3pler1ZBKpXyFZ8cKNv3mrh0G+zo2l77RaHz1/v6HCIn+HtxVkTcrwmg/e4iwBwoTPuvMY/bP7NzOLLZbrRbK5TIymQySySRvaKlUKnzbzVb6dDqNXC6HarU69iw9C0yUkUiE9wykUin4/X7e1KNQKNBoNJBMJnFxcYGjoyOcnp4imUyiVquNfF9hPQJ7eLEYCYl+dkj090Aoyrsi+Pe5BoNt01k+P5PJYGlpCaVSCXK5HN1ulxf3JBIJRCIRJBIJ5PN53gLMdgHsIXCfIZKjYFN5mD02qyx0uVywWCwDor+5ucHNzQ1SqRTfqt8Fi4sw81Fidkj094CJnvW+K5XKqWvi70IYZWcrOduey2Qynltnjjcs38/y/IlEAre3t0ilUnxoJDvnf26DSyb8Wq2GXC6H29tbXF9f88k7bBRWKpVCqVRCvV6futiG7awmpfeIyZDo7wErjNHpdLw1tlarodPpjKwomwbhFp+t8qxQhVX3VatVZLNZnhpTKBQ8/cUi88wtZ1Shy5ei1WrxbEGxWIRKpYJcLh8oRpqmR1/YXsucc1iMgEw0ZodEPyPiYJ1KpYLZbIbdbofdbketVkO5XEar1Zq7VJT9knc6Hb6bYKW8TEDZbHbAHJIZaLDZcWz4xrdw5hVPwmEPr1nujTnnuN1u+P1+bg5KdlmzQ6K/J2q1GsvLy7wKrVwuo9vt8o/7Ch/4u/jZn4G/R/kZ7Lw/Tkhif/3PjVB889TLMwELv8ZoNMLn8yEcDsPv98NisQyJnrgbEv09ENaO22w2OBwOfk6t1Wp8IOZ9EItUGLwaNShj2vf5nIwywJjn+sKfn1QqhclkGhhRPY0rMDEMiX5GxL/QUqkUKpUKarUaKpUKUqmUp84+5YSgu5x1vyU+xX2JJ/2o1WpYLBa4XC6srKzAbDbzf+v1enS2nwES/T0RniPZFpullEa9dho+l5gn9QbM83XTfv1diI8CoxBagRuNxqE6fmJ66Cf3CVhaWuLGlwaDAWazmXfOCa2j593iipt62KomFqN45hzzyZtm+z+Jab9uaWlpwEVIvPpOe1/C8mPmLbi5uYn19XV4PB7uQ8Cgs/xskOjvCZuVbjab4fF4uDlGKpVCNptFLpdDNpudqwiGld6yiS5qtRoKhWJgnJU4mMe2xdVqlTfViB13PgdqtZpP6WFW2uLVmN0XG5NVLBaH0olLS0s8/WkymXifwfr6Oh4/fgyfzwe9Xj/wNST62SDR3xOZTAatVgun04l+v4/l5WVeeBKLxXB9fY3r62veS8+EOe4XVdhcw1xl7HY7lpeX+eQbuVw+YMElDOixTrdCoYB4PI6bmxskEgk+7EI8gPOuyDpLGYqbgdj7sMEZFosFbrcbbrebd78x7372etaQk0wmEY1GcXt7i0KhgEajgXa7za22WFqOvR+z7vZ4PHA4HEPegsRskOjviUwmg06n48MYWC6d+dxdXl5iZWUFiUQC1WqVR9/HCZ/V2bMae5fLBafTyQtStFotz8+L01Vsla/X6wPXj8ViSKfTqNfrA9dlU2bYroA1sQB/+vRrtVoYjUY+6koofvY+SqWSW2z7fD74fD44nU4YjUaoVKqB1FutVuOz8a6vrxGNRpHNZrlZCPPN8/v93AiEvZdOp+PFSBS0ux+SO85r32Z4+BuHjbBOJpOIRCK8ZZUNgJzUAsqq/NhKb7PZYLPZYLFY+HBLdm4eJXpWxFMsFnkbajKZRD6f54aTS0tL6Pf73EQjlUohHo8jkUjwGXc6nQ52u52vtCaTaUBwwpVeq9XCbDZzp1+r1QqdTgelUjnwkGCGoOx6bIgGi32wtJzL5eIFOGazmcpt52fkdpJE/xmp1+t8cCQrzxWLXvwAELbTstlvWq2Wz4CbFtblViqV+KQbdq5nom82m/wYwPrps9ksOp0O9Ho9z4l7vV5YrVaekgT+HnwTdwCyFXnc8YW125bLZd7ww8qWhY5AbNoPcS9I9F8DJvJZGlyED4BpJuhMc232ufC92EovPmN3u13odDo4HA643W7udyde6Ufd6yxpSfG9iR98xL0h0X9uxKv454gqT/PwmPXa5XKZj7FmwUZ2Vmcr7qx58VH3Oc/PhAUYqa5+Lkj0xGhYBJ81wbBVlwULadV9sJDovwb36Vu/bwHKNEU50wr6ru9jlmPIXfd1nyMNMQCJ/msyq/A/9S/8qOvPeo27RDovwvcloX9SSPTEeMYF+0iEDxoSPTGZUYE34kEz8n8gVeQRHBL5YkBhWYJYMEj0BLFgkOgJYsEg0RPEgkGiJ4gFg0RPEAsGiZ4gFgwSPUEsGCR6glgwSPQEsWCQ6AliwSDRE8SCQaIniAWDRE8QCwaJniAWDBI9QSwYJHqCWDBI9ASxYJDoCWLBINETxIJBoieIBYNETxALBomeIBYMEj1BLBgkeoJYMEj0BLFgkOgJYsEg0RPEgkGiJ4gFg0RPEAsGiZ4gFgwSPUEsGCR6glgwSPQEsWCQ6AliwSDRE8SCQaIniAWDRE8QCwaJniAWDBI9QSwYJHqCWDBI9ASxYJDoCWLBINETxIJBoieIBYNETxALBomeIBYMEj1BLBgkeoJYMEj0BLFgkOgJYsEg0RPEgiG7498lX+QuCIL4YtBKTxALBomeIBYMEj1BLBgkeoJYMEj0BLFgkOgJYsH433f8wFvf6pguAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 70; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 71; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx * Ny,))\n", + "lb = np.zeros((Nx*Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx * Ny,))\n", + "ub = np.ones((Nx*Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "# insert dummy parameter bounds and variable\n", - "x = np.insert(x, 0, 0.5) # our initial guess for the worst error\n", - "lb = np.insert(lb, 0, 0) # we can't get less than 0 error!\n", - "ub = np.insert(ub, 0, 1) # we can't get more than 1 error!\n", + "x = np.insert(x,0,0.5) # our initial guess for the worst error\n", + "lb = np.insert(lb,0,0) # we can't get less than 0 error!\n", + "ub = np.insert(ub,0,1) # we can't get more than 1 error!\n", "\n", "cur_beta = 4\n", "beta_scale = 2\n", "num_betas = 6\n", "update_factor = 12\n", "for iters in range(num_betas):\n", - " solver = nlopt.opt(algorithm, n + 1)\n", + " solver = nlopt.opt(algorithm, n+1)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", " solver.set_min_objective(f)\n", " solver.set_maxeval(update_factor)\n", - " solver.add_inequality_mconstraint(\n", - " lambda r, x, g: c(r, x, g, eta_i, cur_beta), np.array([1e-6] * nf)\n", - " )\n", + " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-6]*nf))\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta * beta_scale" + " cur_beta = cur_beta*beta_scale" ] }, { @@ -383,24 +1922,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "lb = 1 - np.min(evaluation_history, axis=1)\n", - "ub = 1 - np.max(evaluation_history, axis=1)\n", - "mean = 1 - np.mean(evaluation_history, axis=1)\n", + "lb = 1-np.min(evaluation_history,axis=1)\n", + "ub = 1-np.max(evaluation_history,axis=1)\n", + "mean = 1-np.mean(evaluation_history,axis=1)\n", "\n", "num_iters = lb.size\n", "\n", "plt.figure()\n", - "plt.fill_between(\n", - " np.arange(1, num_iters + 1), 10 * np.log10(lb), 10 * np.log10(ub), alpha=0.3\n", - ")\n", - "plt.plot(10 * np.log10(mean), \"o-\")\n", + "plt.fill_between(np.arange(1,num_iters+1),10*np.log10(lb),10*np.log10(ub),alpha=0.3)\n", + "plt.plot(10*np.log10(mean),'o-')\n", "plt.grid(True)\n", - "plt.xlabel(\"Iteration\")\n", - "plt.ylabel(\"Average Insertion Loss (dB)\")\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Average Insertion Loss (dB)')\n", "plt.show()" ] }, @@ -413,23 +1965,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", + "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - ")\n", - "circ = Circle((2, 2), minimum_length / 2)\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", "ax.add_patch(circ)\n", - "ax.axis(\"off\")\n", + "ax.axis('off')\n", "plt.show()" ] }, @@ -442,37 +2001,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n" + ] + } + ], "source": [ - "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta // 2)], need_gradient=False)\n", + "f0, dJ_du = opt([mapping(x[1:],eta_i,cur_beta//2)],need_gradient = False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef / source_coef) ** 2" + "top_profile = np.abs(top_coef/source_coef) ** 2" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", + "plt.plot(1/frequencies,top_profile*100,'-o')\n", "plt.grid(True)\n", - "plt.xlabel(\"Wavelength (microns)\")\n", - "plt.ylabel(\"Transmission (%)\")\n", - "# plt.ylim(70,100)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('Transmission (%)')\n", + "#plt.ylim(70,100)\n", "plt.show()\n", "\n", "plt.figure()\n", - "plt.plot(1 / frequencies, 10 * np.log10(top_profile), \"-o\")\n", + "plt.plot(1/frequencies,10*np.log10(top_profile),'-o')\n", "plt.grid(True)\n", - "plt.xlabel(\"Wavelength (microns)\")\n", - "plt.ylabel(\"Insertion Loss (dB)\")\n", - "# plt.ylim(-0.1,0)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('Insertion Loss (dB)')\n", + "#plt.ylim(-0.1,0)\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb b/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb index 076ab1998..56279b124 100644 --- a/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb +++ b/python/examples/adjoint_optimization/ConnectivityConstraint.ipynb @@ -11,9 +11,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep.adjoint as mpa\n", "import numpy as np\n", @@ -37,29 +45,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "foward value 0.6369810999723368\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAL6klEQVR4nO3dX4ilhXnH8e+v69bgP9gl7TJai22wAQl10w6mECkWm2i9UW+kexE2NDBeRDA0FxVvIpSAlMT2pgRWlGzBWELU6kWoMSLdBkrIrqy6um02hA11XXcRA64UbNSnF/PamSw7O3P+zcw+8/3AMue857x7Hl9evp59z3veSVUhSerlNzZ6AEnS9Bl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIZWjXuSq5O8kOS1JK8muXdY/kCSE0kOD39um/24kqS1yGrnuSeZA+aq6sUklwOHgDuAu4B3q+obM59SkjSSi1Z7QlWdBE4Ot88kOQpcNevBJEnjW/Wd+689ObkGOAB8Cvhr4IvAO8BB4KtV9ctzrLMALABsY9sfX8IVEw8taWP8wR/+z8jr/PTlS2YwydZyhl++VVW/Nco6a457ksuAfwO+XlVPJtkFvAUU8LcsHrr5q/P9HVdkZ30mN48yn6RN5Nk3Xhp5nVuuvH4Gk2wtP6zvHaqq+VHWWdPZMkm2A08Aj1XVkwBVdaqqPqiqD4GHgRtGHViSNBtrOVsmwCPA0ap6aNnyuWVPuxM4Mv3xJEnjWPUDVeCzwBeAV5IcHpbdD+xJspvFwzLHgbtnMJ8kaQxrOVvmR0DO8dD3pz+OJGka/IaqJDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDa0a9yRXJ3khyWtJXk1y77B8Z5Lnkhwbfu6Y/biSpLVYyzv394GvVtV1wJ8AX05yHXAf8HxVXQs8P9yXJG0Cq8a9qk5W1YvD7TPAUeAq4HZg//C0/cAdM5pRkjSii0Z5cpJrgE8DPwZ2VdXJ4aE3gV0rrLMALAB8jEvGHlSStHZr/kA1yWXAE8BXquqd5Y9VVQF1rvWqal9VzVfV/HYunmhYSdLarCnuSbazGPbHqurJYfGpJHPD43PA6dmMKEka1VrOlgnwCHC0qh5a9tAzwN7h9l7g6emPJ0kax1qOuX8W+ALwSpLDw7L7gQeB7yb5EvAL4K6ZTChJGtmqca+qHwFZ4eGbpzuOJGka/IaqJDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWpopN/EJJ3Ps2+8NPI6t1x5/QwmkeQ7d0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakhz3PX1HjOurR5+M5dkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpoVXjnuTRJKeTHFm27IEkJ5IcHv7cNtsxJUmjWMs7928Dt55j+d9X1e7hz/enO5YkaRKrxr2qDgBvr8MskqQpmeSY+z1JXh4O2+xY6UlJFpIcTHLwV7w3wctJktZq3Lh/C/gEsBs4CXxzpSdW1b6qmq+q+e1cPObLSZJGMVbcq+pUVX1QVR8CDwM3THcsSdIkxop7krlld+8Ejqz0XEnS+lv1F2QneRy4Cfh4kteBrwE3JdkNFHAcuHt2I0qSRrVq3KtqzzkWPzKDWSRJU+I3VCWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWpo1bgneTTJ6SRHli3bmeS5JMeGnztmO6YkaRRreef+beDWs5bdBzxfVdcCzw/3JUmbxKpxr6oDwNtnLb4d2D/c3g/cMd2xJEmTuGjM9XZV1cnh9pvArpWemGQBWAD4GJeM+XJab8++8dLI69xy5fUzmETSOCb+QLWqCqjzPL6vquaran47F0/6cpKkNRg37qeSzAEMP09PbyRJ0qTGjfszwN7h9l7g6emMI0mahrWcCvk48B/AJ5O8nuRLwIPA55IcA/58uC9J2iRW/UC1qvas8NDNU55FkjQlfkNVkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDU07m9i0gTG+S1HF4L1/O/ytz4t2ez7k7/Va2P4zl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyPPcN8CFcA6v5yZfONZzu7tfXDh85y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktTQRFeFTHIcOAN8ALxfVfPTGEqSNJlpXPL3z6rqrSn8PZKkKfGwjCQ1NGncC/hBkkNJFs71hCQLSQ4mOfgr3pvw5SRJazHpYZkbq+pEkt8Gnkvyn1V1YPkTqmofsA/giuysCV9P0gbytypdOCZ6515VJ4afp4GngBumMZQkaTJjxz3JpUku/+g28HngyLQGkySNb5LDMruAp5J89Pd8p6r+dSpTSZImMnbcq+rngAfgJGkT8lRISWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGpoo7kluTfJfSX6W5L5pDSVJmszYcU+yDfhH4C+A64A9Sa6b1mCSpPFN8s79BuBnVfXzqvpf4J+B26czliRpEhdNsO5VwH8vu/868Jmzn5RkAVgY7r73w/rekQles5OPA29t9BAr2TY3zlrHxn25Tb0t1pnbYonbYsknR11hkrivSVXtA/YBJDlYVfOzfs0LgdtiidtiidtiidtiSZKDo64zyWGZE8DVy+7/zrBMkrTBJon7T4Brk/xekt8E/hJ4ZjpjSZImMfZhmap6P8k9wLPANuDRqnp1ldX2jft6Dbktlrgtlrgtlrgtloy8LVJVsxhEkrSB/IaqJDVk3CWpoXWJu5cp+HVJjid5JcnhcU5xupAleTTJ6SRHli3bmeS5JMeGnzs2csb1ssK2eCDJiWHfOJzkto2ccT0kuTrJC0leS/JqknuH5VtuvzjPthh5v5j5MffhMgU/BT7H4hedfgLsqarXZvrCm1iS48B8VW25L2gk+VPgXeCfqupTw7K/A96uqgeH//nvqKq/2cg518MK2+IB4N2q+sZGzraekswBc1X1YpLLgUPAHcAX2WL7xXm2xV2MuF+sxzt3L1Og/1dVB4C3z1p8O7B/uL2fxZ25vRW2xZZTVSer6sXh9hngKIvfgN9y+8V5tsXI1iPu57pMwVjDNlLAD5IcGi7PsNXtqqqTw+03gV0bOcwmcE+Sl4fDNu0PRSyX5Brg08CP2eL7xVnbAkbcL/xAdWPcWFV/xOIVNb88/PNcQC0eJ9zK5+d+C/gEsBs4CXxzQ6dZR0kuA54AvlJV7yx/bKvtF+fYFiPvF+sRdy9TcJaqOjH8PA08xeKhq63s1HCs8aNjjqc3eJ4NU1WnquqDqvoQeJgtsm8k2c5izB6rqieHxVtyvzjXthhnv1iPuHuZgmWSXDp8UEKSS4HPA1v9SpnPAHuH23uBpzdwlg31UcwGd7IF9o0kAR4BjlbVQ8se2nL7xUrbYpz9Yl2+oTqctvMPLF2m4Oszf9FNKsnvs/huHRYv//CdrbQ9kjwO3MTi5VxPAV8D/gX4LvC7wC+Au6qq/QeNK2yLm1j8p3cBx4G7lx13binJjcC/A68AHw6L72fxWPOW2i/Osy32MOJ+4eUHJKkhP1CVpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGvo/PL0GIHoV1ZcAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "nz, ny, nx = 25, 1, 25\n", - "c = mpa.ConnectivityConstraint(nx, ny, nz, sp_solver=cg)\n", + "nz, ny, nx =25, 1, 25\n", + "c = mpa.ConnectivityConstraint(nx, ny, nz,sp_solver=cg)\n", "\n", - "r = np.zeros((nz, ny, nx)) + 0.0001\n", - "r[:10, 0, 8] = 0.999\n", - "r[7, 0, 5:20] = 0.999\n", - "r[5:, 0, 18] = 0.999\n", - "r[7, 0, 11:16] = 0.0001\n", - "r[17:18, 0, 9:10] = 0.999\n", + "r = np.zeros((nz, ny,nx))+0.0001\n", + "r[:10,0, 8]=0.999\n", + "r[7,0,5:20]=0.999\n", + "r[5:,0, 18]=0.999\n", + "r[7,0,11:16]=0.0001\n", + "r[17:18,0,9:10]=0.999\n", "\n", - "r = r.flatten() # flatten the structure before pass in\n", + "r=r.flatten()#flatten the structure before pass in\n", "f = c.forward(r)\n", "ag = c.calculate_grad()\n", "\n", - "print(\"foward value\", f)\n", + "print(\"foward value\", f) \n", "\n", "plt.figure()\n", "plt.pcolormesh(r.reshape((nz, nx)))\n", - "plt.show()" + "plt.show()\n" ] }, { @@ -71,16 +99,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# padding to show the side with Dirichlet BC\n", - "fullT = np.pad(c.T, (0, c.nx * c.ny), \"constant\", constant_values=1).reshape(\n", - " (nz, ny, nx)\n", - ")\n", + "#padding to show the side with Dirichlet BC\n", + "fullT = np.pad(c.T, (0, c.nx*c.ny), 'constant', constant_values=1).reshape((nz, ny, nx)) \n", "plt.figure()\n", - "plt.contourf(fullT[:, 0, :], 100, cmap=\"RdBu\")\n", + "plt.contourf(fullT[:,0,:], 100, cmap=\"RdBu\")\n", "plt.colorbar()\n", "plt.show()" ] @@ -94,9 +133,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "r = np.zeros((nz, ny, nx)) + 0.0001\n", - "r[:10, 0, 8] = 0.999\n", - "r[7, 0, 5:20] = 0.999\n", - "r[5:, 0, 18] = 0.999\n", + "r = np.zeros((nz, ny,nx))+0.0001\n", + "r[:10,0, 8]=0.999\n", + "r[7,0,5:20]=0.999\n", + "r[5:,0, 18]=0.999\n", "\n", - "r = r.flatten() # flatten the structure before pass in\n", + "r=r.flatten()#flatten the structure before pass in\n", "f = c.forward(r)\n", "ag = c.calculate_grad()\n", "\n", - "print(\"foward value\", f)\n", + "print(\"foward value\", f) \n", "\n", "plt.figure()\n", "plt.pcolormesh(r.reshape((nz, nx)))\n", "plt.show()\n", "\n", - "fullT = np.pad(c.T, (0, c.nx * c.ny), \"constant\", constant_values=1).reshape(\n", - " (nz, ny, nx)\n", - ")\n", + "fullT = np.pad(c.T, (0, c.nx*c.ny), 'constant', constant_values=1).reshape((nz, ny, nx)) \n", "plt.figure()\n", - "plt.contourf(fullT[:, 0, :], 100, cmap=\"RdBu\")\n", + "plt.contourf(fullT[:,0,:], 100,cmap=\"RdBu\")\n", "plt.colorbar()\n", "plt.show()\n", "\n", "plt.figure()\n", - "plt.contourf(ag.reshape((nz, nx)), 100, cmap=\"RdBu\")\n", + "plt.contourf(ag.reshape((nz, nx)), 100,cmap=\"RdBu\")\n", "plt.colorbar()\n", "plt.show()" ] diff --git a/python/examples/adjoint_optimization/Fourier-Bend.ipynb b/python/examples/adjoint_optimization/Fourier-Bend.ipynb index d681f2ad1..c1b1cc45d 100644 --- a/python/examples/adjoint_optimization/Fourier-Bend.ipynb +++ b/python/examples/adjoint_optimization/Fourier-Bend.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,6 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", - "\n", "mp.quiet(quietval=True)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -40,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -54,120 +53,108 @@ "\n", "resolution = 40\n", "\n", - "frequencies = 1 / np.linspace(1.5, 1.6, 3)" + "frequencies = 1/np.linspace(1.5,1.6,3)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.20124611797498096\n" + ] + } + ], "source": [ - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = (\n", - " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - ")\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", "print(filter_radius)\n", - "design_region_resolution = int(1 * resolution)" + "design_region_resolution = int(1*resolution)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width + 2\n", - "Sy = 2 * pml_size + design_region_height + 2\n", - "cell_size = mp.Vector3(Sx, Sy)\n", + "Sx = 2*pml_size + 2*waveguide_length + design_region_width + 2\n", + "Sy = 2*pml_size + design_region_height + 2\n", + "cell_size = mp.Vector3(Sx,Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1 / 1.55\n", + "fcen = 1/1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", - "source_size = mp.Vector3(0, Sy, 0)\n", - "kpoint = mp.Vector3(1, 0, 0)\n", - "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", - "source = [\n", - " mp.EigenModeSource(\n", - " src,\n", - " eig_band=1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size=source_size,\n", - " center=source_center,\n", - " )\n", - "]" + "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", + "source_size = mp.Vector3(0,Sy,0)\n", + "kpoint = mp.Vector3(1,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "Nx = int(design_region_resolution * design_region_width)\n", - "Ny = int(design_region_resolution * design_region_height)\n", + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si)\n", - "design_region = mpa.DesignRegion(\n", - " design_variables,\n", - " volume=mp.Volume(\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(design_region_width, design_region_height, 0),\n", - " ),\n", - ")" + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si)\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", - "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", - "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", + "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", + "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", + "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", "\n", - "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", - "top_wg_mask = (Y_g == design_region_width / 2) & (np.abs(X_g) <= waveguide_width / 2)\n", + "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", + "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", "Si_mask = left_wg_mask | top_wg_mask\n", "\n", - "border_mask = (\n", - " (X_g == -design_region_width / 2)\n", - " | (X_g == design_region_width / 2)\n", - " | (Y_g == -design_region_height / 2)\n", - " | (Y_g == design_region_height / 2)\n", - ")\n", + "border_mask = ((X_g == -design_region_width/2) | \n", + " (X_g == design_region_width/2) | \n", + " (Y_g == -design_region_height/2) | \n", + " (Y_g == design_region_height/2))\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "def mapping(x, eta, beta):\n", - " x = npa.where(Si_mask.flatten(), 1, npa.where(SiO2_mask.flatten(), 0, x))\n", + "def mapping(x,eta,beta):\n", + " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", " # filter\n", - " filtered_field = mpa.conic_filter(\n", - " x,\n", - " filter_radius,\n", - " design_region_width,\n", - " design_region_height,\n", - " design_region_resolution,\n", - " )\n", - "\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", - "\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -181,30 +168,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", - " ), # horizontal waveguide\n", - " mp.Block(\n", - " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", - " ), # vertical waveguide\n", - " mp.Block(\n", - " center=design_region.center, size=design_region.size, material=design_variables\n", - " ), # design region\n", + " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", + " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables) # design region\n", "]\n", "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution,\n", - ")" + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution)" ] }, { @@ -222,24 +201,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "Ez_top = mpa.FourierFields(\n", - " sim,\n", - " mp.Volume(\n", - " center=mp.Vector3(0, Sy / 2 - pml_size - 0.1, 0),\n", - " size=mp.Vector3(x=waveguide_width),\n", - " ),\n", - " mp.Ez,\n", - ")\n", + "Ez_top = mpa.FourierFields(sim,mp.Volume(center=mp.Vector3(0,Sy/2 - pml_size - 0.1 ,0),size=mp.Vector3(x=waveguide_width)),mp.Ez)\n", "ob_list = [Ez_top]\n", - "\n", - "\n", "def J(top):\n", - " power = npa.abs(top[1, 7]) ** 2\n", - " return power" + " power = npa.abs(top[1,7])**2\n", + " return power\n" ] }, { @@ -251,93 +221,2094 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation=sim,\n", - " objective_functions=J,\n", - " objective_arguments=ob_list,\n", - " design_regions=[design_region],\n", + " simulation = sim,\n", + " objective_functions = J,\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", " frequencies=frequencies,\n", - " decay_by=1e-6,\n", - " decay_fields=[mp.Ez],\n", + " decay_by = 1e-6,\n", + " decay_fields=[mp.Ez]\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "rho_vector = np.random.rand(Nx * Ny)\n", - "opt.update_design([mapping(rho_vector, eta_i, 1e3)])\n", + "rho_vector = np.random.rand(Nx*Ny)\n", + "opt.update_design([mapping(rho_vector,eta_i,1e3)])\n", "opt.plot2D(True)\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", - "\n", - "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", - "\n", - " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)]) # compute objective and gradient\n", - "\n", + " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", + " \n", + " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)]) # compute objective and gradient\n", + " \n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", - " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", - " ) # backprop\n", - "\n", + " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1)) # backprop\n", + " \n", + " \n", " evaluation_history.append(np.real(f0))\n", - "\n", + " \n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - " )\n", - " circ = Circle((2, 2), minimum_length / 2)\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", " ax.add_patch(circ)\n", - " ax.axis(\"off\")\n", - " # plt.savefig('media/bend_{:03d}.png'.format(cur_iter[0]),dpi=300)\n", + " ax.axis('off')\n", + " #plt.savefig('media/bend_{:03d}.png'.format(cur_iter[0]),dpi=300)\n", " plt.show()\n", - "\n", + " \n", " cur_iter[0] = cur_iter[0] + 1\n", - "\n", + " \n", " return np.real(f0)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 1\n", + "Starting forward run...\n", + "Starting adjoint run...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/site-packages/meep/adjoint/filter_source.py:91: RuntimeWarning: invalid value encountered in true_divide\n", + " l2_err = np.sum(np.abs(H-H_hat.T)**2/np.abs(H)**2)\n", + "/usr/local/lib/python3.8/site-packages/meep/adjoint/filter_source.py:91: RuntimeWarning: divide by zero encountered in true_divide\n", + " l2_err = np.sum(np.abs(H-H_hat.T)**2/np.abs(H)**2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 3\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 13\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 15\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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ZWuc2xMN0VSg0PqGiBSB0aK7X60JNxO3tbYgP8HztdjukUIfDIbrdLo6OjrBer8OcCU7opgVAN0W7O1muTWHi3yQunNIybgtF9SRFwqbd22O1WhWGxpZVLOqrjHi0G10KuhG6c1dsPdBy0JH4tCLUoqDZ//DwgPF4HLIZdGs6nQ4GgwFGoxFOT08LcQgOtQFQsHq0Z0RrKjRIqruSsdRbS7fjDIypjqRIWLHfHovFAp999llIKepmNloApelELmxmKuIR97prl3ZysgdDYxEqEACCBaHpV5Zes65hPp+/sgXgYDDA+fk5zs/PcXp6im63i0ajERY5F7+Kg/aMPLVXqWZstHw8yzIcHx9jMBiEAbv+/7JakiLxySefYDqd+j/KW+DFixf429/+Fp6WtCB0G7z1el1oeMqyLCw8rYuIJ1tzn0zd3JepTd07U2sxtKOUcQRmUmgR0NKhq8JKzZOTk2BFMIipnaJc8Nohys5TFTLOwtQZF4yn8G/T6/WC1UJBsstRLUmR+PWvf43f//73YfCIeTNqtRp+/OMf40c/+lGoduz3+2i322FBcNDscrkMW+RRIDhjkguOcQFaC9ooxZoFTX1SZCgIOuuSLy3cYqCR90UroKzGotFoFMbl6+QrpmR5HzyWAgYgHKM7qvN6nU4Ho9EIl5eXODs7C6P0TLUkReL+/h5ffPFFVffyg4ZP2KurKzSbTQwGg7BYWD+w3+9DcFPNfKYCGRNgcxbFIN5ER92U2J3gaH2+WJPAWgy1dFhnQYGiW0NLh5mJuJycIsEyb+06HQwG6HQ6Ye7lbDYL/RoUQ1oug8EAFxcXuLq6wsnJCbIss7txAJIioU1CtiTenFqthslkgq+++iqMc+t2u8HP3u12YSQ9RYJWRLfbDQFKzp/USdZaeq0TsOPiLG3WGo/HITBZJhC8ZwoFgFfiCYxv8N4pOqxxUFFhGfjJyQn6/X6wIvI8D26W7koOfFODMRqNcHFxgfPzcxwfHxeEyVRHUiT0fzrz5mjT03a7DcG44XCILMtCelDjA9zDgiXWFAd2brIpi4tYi6G0DoH/7eKZEA8PD69s98dFz6ChujhlwUaK2mQyCaIzm83C56FADIdDnJ6e4vT0FL1eL0yrYlcoXRy6LM1mE71eD2dnZ7i4uMDJyUmwPhyPqB7XSVQEi6Tom7NKsdVqhUKpxWIR4gq73Q5ZloUCJgYMT05OgsnO0mgABXFYLBYhrUmhoEgwMKkb9aiLwfiFBh21zoIWBBd1nue4v78PqV2ej5WZw+EwZEO42DlIhhO0GSQFvnkw0Wo6PT3FcDhEr9crTLky1WKRqAg+0Rnhj6dRM6bABclOSUb4B4NBEIher1cYD6eDbzWTwSpJrarUKkhaD1q8pLUMFCwGRbV5i+djSvf29rbQyMXahuFwiNFohLOzs2A57ff7YFVRuOii8Fidts3PaZE4DBaJCuEC1F26498zmwE8ZgfoasTTrOPMBSsZ4yEw8cxLHXhDeC9avKSNYFyom80G0+k0LPTxeBysCAYh6a7QAtLpV0dHR4VWdF6DgsZ70O0Fgce+F35vwagOi0SF8OmuL/rnrDwEHofKMg3IhcrFGovMU9WacYepLjQu5Lj8melVTXXqLl55ngNAIb7BACiHyrB5TNOxWtjFeAvrPJjuZTqVVhVFKd5CwFSLRaIiaPLPZrPQWcmFwqyD7vRd1neh1oEu+HgHMN3URisZdfBMq9UKAUaNQeh2fxQJWjY6/IYdnsxosBybi1s7UMvuXz9ju90OWRWtBNXrMNbhYbjVY5GoCBZK3d3d4a9//StOTk6w2WxCVaVmNbiAdFYEz8FeCx1+q52jOhNTG8hoJXQ6nVBJqQ1X/D1H7fPFUmjNmHAGJV/a1s37Ltt0h+6QWjN8H/8OwGOBFeMTdGWWyyW63W6hVd58/1gkKoI++O3tLT7//HNkWYbpdIp+v19outLxczyOAgA8zoJU8dCJVlo9yWlWwKMLw+sxIKoujlZyaj0E753njvcWpbAxhsBFT/cCeIyL6M7pwKPbQ6tCS8UpflmWhXSoXY7qsUhUBGsAptMpvvzySzQaDcxms5AG1X0oWO6sgUkKAwVEOynLNvvRITUqCowVHB8fB5dE3RAubO3SpOXAxi8KBNOrtVotxCHorjAOwVSstoarRaENYMDjpO3JZBLcsGazifPzc9zc3Hg61QGwSFQIp0c/PDyEAS1M87FsmRaFLlI1q+NWcRLHIzRgyVoLlnXHw2u0q1RTpow/sJybMzdZ16CuEQWCborWcTBgG7tIOmyHP2McgrEOWhhMsTouUT1JkSjb8MW8GTomjmPmtVdDBUILoYBHd0L32tDz8hjg1UE02s8Rb6JD9HstDddp1josh8Vfcfu3dqIyI6Kfg/EU/h3ijlTWTGhfCeMWd3d3hYnZjklUR1Ik6K9aIN4OWiHJhae1EgzisUSbgcW4eapsDoW6CtopysYwzTJovwfRad28vqYgtcOTFkS8jwe7W2lFAI/WAT+PWkmxAKm1QteGFgotC93rw1RDUiROT0/x4YcfulX8H4RuA0fKx0HH1WqFLMtCPIGLlE91Lh7tcQAQgoWaIWDgkTUVAAodpIwdxJv5UiBYKs2FHE/2pjjw2pouVYFgLIW1EySOP3AaFkfv86U9JTrazsNwqycpEr/61a/wi1/8wmbdP0ij0cAf/vAH/O53vwuTorU2QZ+ujOzzyc06AWYU6NsDCLEABgl1Lw5OsopjBvGcCQCFOIbei47Wo9nPn+s4/diCoAUSx0YUxir4GVm5ySYx7QFxIdVhSYrExx9/XNV9/OBpNBr4/PPPMR6P8eLFi1DCDCBUVzLtqSnNeEcrLhg+qQEEF4HCUBa30NgEC5vidKRaDvyqcQeWfAMoVGYym8HOUd1FPN46AMAr7gbjEFq9SddG42FxubmpBg/CrYBarYbz83P8/Oc/x2effYa//OUv+OqrrzCZTAp7WTBWQItCR+pr4DI+t7oAcWAyDlDGQUouVp5bdwjTOZqMb2hFqDZ/UXC05Vs3Ddap4Ly2BknVYqIlpeLAv4/H11WPB+FWxHA4xE9+8hP0+/1g+n/99ddh8ApdAPZI6NxH4NU0pVY16kv7HoCidUAhoBABj1YL6yp0IxzWV3DTHR3Gq12ruoGOno+1FHG6U2s7dH8OzZxQpFgqztSqfjZTDa6TqIh2u42zs7NCNWGj0cB4PAaAQo2CLjaKgVYr0n3QYKXu1KXn4nm4cCkQscXCRcqnP6+jWRLGPWK3QTf2UXHQ8mrdb4Nf+bfQJi7NXtCK6Xa7YX8Pi0T1WCQqgh2dJycnuLi4wHQ6DSXHOktBezPW63VYmCzd5qQqWiNxelN35tJFq1WU8V4b+jTngtd+DlouWrClrgmP1+7WssAlBSae1k0h0YngtJra7XaYzs2KUYtEtVgkKoJ+vI6lz/McjUYj+N9c1LqA+dTWtm5aF2XioENoOP1JXY/NZlNIf6qlEbesq0gwNarCotO246YyDVTGLoZeU7tVtUxb/1aj0Qij0cgj9Q+ERaJC+ETWfTS5+HS8HfDY8cnjmH7UFmu6HOpe8GmsNQXaSr5arQoDZXUgTby4NQ7Cn2svh9ZvxDMf4iCqZim0fJz3qX8jilO/38f5+Tmur69xdnaGTqdTSN2aarBIVEhcpdjr9UI7NBcMUNxEh+jio6+upda0RDRAyXoFXZQUibgFPR5Kw8XKn/Ppz5JybfaiBaHHxuXgPAc/S1nlZGxtjUYjXF9f4/r6OozU974b1WORqAhNSWpmgsNu41F0avpz4WtaMo4/aAaDwgAUn9r8Xqdbve6prDGH2WyGyWSC8XiMyWQSejk0a1JWuEUriJOw9b5430CxnZ0C8cEHH+Dy8hL9ft+zLg+EReIAUBR0QIsGFXUvUL6Px8Wt1XHptAoCRSWueOS/dSCMVn7yvDwX3QsdV6duRhxLYKk2p3pz+O1iscB4PA7BRy3ooktFgbi5ucEHH3yAm5sbjEYjuxoHxCJRIWV+Orf344LR11NPTS5g/l5dDP0+nmmpcQ+9DhefdmjyHCx2mk6noaeCo/a0RJxixgnZZ2dnODs7w/HxccjQcPz+y5cvgwhqOjjLMgyHQ1xfX+P58+d4/vw5zs/PgxXhasvDYJGoEC5aXbwkbvGOBUJ9ei5y9es1rqDH6HU1kKnXjK0Tvo/pU81iaEVknAVhN+j5+TmePXuGq6urMEZ/t9thPp8XrIt2ux1G63NDHroZNzc3QSAYi7BAHAaLxIEoE4vYytB2bn0vrQGdqRAHHPXfsRCUWS2a7VBXQ+daajWkZjBYoj0YDIJAfPjhh7i6uipYEvP5vLBnSKfTwXw+D67P8fFx2NqPVggLuczhsEgcgJRAaGBSrQoSxxY0u8Hz6AAaAIUNhPUVnz8OfupLG770uHq9HqZqsVDs+voaV1dXBVdht9sVtupjBWdsSXCXMt21K2WBme8fi0RFxKa//kyDfsw+MPvBvTnilGh8DgDBgtAYg2ZT4lgEr83zaDwjHi3HwCIXvAoQy6ZPT08xGo3C9nwczael4BpYrdVqmM/nISZBsdF5FFqZqWlaux7VYZE4IBq85L/jp7kWKbFUW4VBrQkAofBJ6wl0PH+8cxgXfFxnQYtF4w0apGQ8otlshkwGdzvnFoa0YFgtqhO0ODBXy8R1r1FuWcjBNtpJaqrFInEAYteCT9l4QnbcK8FFquXauuBVKJgxoQg95V7oiEK9Js/NfUnVImFvBvDNXAnOttTht5qWpRUQZ13UMtDJWGpB1Gq1UDthkTgMFomK0aIqPj2Bx8VOMzwukOJXEk96ihePxgzK0psax4gzH9ovAjymNxls1ApLLmK6COwZ4aRrWhCcNcFybo7lZ0BUG8rU2gCAbrcbUqUWieqxSFSE+tBqRcQ1CnG5dBxEJPFo+dctnlgQ4vRpWSCVwcX9fl+IkWh7N/DYfLZarTCbzYI1sd1uw9Rs/f14PMbDw0Oou1DB0e0NOadTp2R/m89q3i4WiQNAEVDzX1u5tfqybFK1NkppNWUcaNRUqIpDXH8R107EAlZW5anXZFGVuhcs42alJIBgSbC0m5OxdX6FlnTXarVXYjD69zPVYJE4EFowpWKh8Qkdj8/JUyoUDDrSzI8Lqfj7OG2qAdK4CIv3wylUdDkoWrpLGMuyGSjlU58j8fv9fhg7B6Aw2Jf7auiYfAYqAYRj2OkaN6WZ6rBIvCOUmdJx3QQFg9kO7p3J4zQIWVZfEAcpNaCogVBOqI6H83JmBMuydR8QHpdlWbAQuEkP6x00XsGXbvTDtnfgMbXKnc05aNeFVdVjkTgQ+gRXi0Cf8FrVqJ2jcdBSTXIuOL0O8CgcGgjVSdYaFNVCLh7LYKWWaNMF0uxHq9UKv8/zPHSC6rStp6Zg8drtdhvD4RCj0QhnZ2cYDochtuEu0OqxSBwALmgu2DgWoWnP2P3QlGS9Xg8b6WhgUwVGr8nz6SRufl/W08Fz6fvpZmglJo/R9KiOqKOFQPHTa1Mg+dnYBXpxcYHLy8tCebZH1x0Gi8SB0NZuAK+kKDX2oIuI742rJykWzCrEwT51ORhb0CnZ6nbE9QtPDaxVgWC9B4AwN0JndzK2UebeMLXK7tGrqytcX1/j8vIyzLZ0F+jhsEgcAM0o6Ii6uOJSFxKfolpirWPsuIBigeH11O2In+jxvh6xUDFuoFkMihsFDEChyjMumNJSbn4mvtg9OhqNcHl5GQRiNBqFLlD9jBaKarFIHIg4qBiLBPD4hOZC1AXaarVCjEDLn7no45iHpiw1lUn3I67HoIDpz+MZmAAK9xcHWBlDUSuAvSX8zK1WC71eD6enp8GCuLi4wMnJSciOOGB5WCwSFaJBSC4mrREgsYAoamnofhmcWK2zHrSOQmdUxmZ7nDotu67euw7JYUaDvRh8dbvdwoZBKoI8H0fms8Wc8ywZrGQxlQOWh8UiUSHqLnS7Xex2u0JXpT7h4+N0kVEodNMdCgRjB9oURsGgSLDQKc/zUKvAdKS6HxrYjKdas5aC4nB8fIxerxcavJjV0BF5Co9nLILBytPTU5ycnASRsZtxeCwSFUKR4BAW1iPEIhEfo7+L06SaRdCgYmxJ6H4XmqKMeyn0VRazAB4LnDjL8vj4OHSAsqZBty1U4maxdrsdzkGhYW1F2UwNC0X11F5TB+8i+bdIvLB1qrUS93nEPwNeHYCrFkPcrKXvo1DE+3XqDlyxOGg2Q0un2f3Z6/VCe7gGGWMXSqGrolPDGb+IxSEWSfO98OQf1iJRIfGCLfP9Y163KOL4RRxT0K8pYYlb0vWrBlO1r4OLOl7YTwUZNR7Bc2lsIx4CrO+1OHzvWCTeFeJF+114KtgY/7vsvGW//zYCk4qPxE/6N4kblB1bZkmZ7x2LxPvGdxWgNz3mbSxiC8E7gUXCGJPkSZFwhYoxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiTxCJhjElikTDGJLFIGGOSWCSMMUksEsaYJBYJY0wSi4QxJknzNb+vVXIXxph3FlsSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYk+T//u/ZdAZqqOQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 25\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 27\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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fYo8izknovIYmInmH1kSmHvLD3gedzoyXyejr0wugF8G1dACCQPAIQM5oZFmGer0ecic6N6J7NA+HL873oDAwccmRduYiKCrxtKtX2ZWPRaIkmKXnjkndw1DUFKV5ingRTdHdVBfa6NAWvQZNampJlKPlvFMzN3B3dxfyCbxG9kOwfHp+fo7hcBjCjP1+j/l8nhNE5hW4+4L9EMxbcEHuarV6NKVK6CHpYh1THkmRsGv3/liv13j9+jVevXqFly9fhiP+dCFs0d933AcRN0exvZlJRE0k6oNGptuotZeByUOuumPykOeVAgjncp6dnWE8HmM4HGIwGISj/5hH4WtrclavXcfC+XP0HlQg4s3irVbLR/ydgKRI+B/j/bFYLPDXv/4Vr1+/Dsf7xYfuxsYfVykA5NxuLqqlELD3Qac5+T3tuNRwhr/XqVEtQdIDoAD1+32Mx2NcXl5iOByGMEO9BU3IAg/t4HpKmR4gpGISl1XZFcqxcx/xVz5JkfjjH/+I+XxusfiSVCoV/O1vf8Of/vSn0AdAg2LikP/5taTZbDYftTJTIOgZMKRgxYINUtr/wG3UFAcmT7VZi66+5ko4vKWHEHNfBA8R5hCXVm5o8PE+Tw50aQs4P5vOlzD52m63gyhRkPQYAVMOSZH41a9+hd/97ne5jLn557i6usLV1RUA5CYm9RQv5iZ4OE673Q6ehp70pQanIQW9Bz3vQqsXbFxiWMBHXG5Vr4B5CAC5oTDdf6ldowwZgIcSrHo82p+hw1vapk2vJ8syXFxc4OrqCsPh0OHGiUiKxGQywSeffFLWtXyjubu7w3K5RK/XC8k+3lXZokzj0rkN3skPh0MwIo53Uwy0pKlTmFwYo7H/ZrMJ27NZTSiqtAAPY+WdTufRQFZ8KherFRz6AhAatyqVCtrtdvBu+HqHwyE0WdFrYmK20Wjg/Pwcl5eXuLy8xGAwyAmWKY+kSGhfvj2JL8dms8F0Og2xeJZlIfHHu/F6vQ53fRoql7RwMIvCojMXvKPHRwMyBGBicrfbhVKjdkKqcWu5UTs4dRiL/y/o+WgrtYZS9Jbo5XCgjKLITVU6Is9wot1uYzQa4eLiAqPRKOQ+7EWUT1IkUll3827QEAAgyzKMx2M8e/YMvV4PlUolbG4CHjZFVyoVdDodAF/kLphzOD8/DyKhOQcAud4CzTscDg+nirP9WTdoa+cnXf+nBIKhEfs34kN/drtdrkej3++Hfgp6EWzY4owHr5HP63a7GA6HGA6HIffhfMRpcJ9ESVBom80mzs7OQo4iy7IwxKReBF1uzmfQ2Njd2O/30el0guHGLd8cpmKFgSEBpyzjRieGCCRecceluCyfMixZr9eYTqeYTCZh+IvNXa1WC4PBAKPRCKPRKFcuZWWFZVc2T1EouOeCQugeidNhkSiRSqWCLMswGo1Cxr7VagUD1ZkO4ItEISsZdNf5iA/O0W1XNGTmNDRpqe3gumAXeCivxv0JeugvBYLXylFyniqu4+Bs2x6NRiG0Yn6FYdT9/T06nU6uHMuZEOZZLBCnxSJRIjS8eJ8kgFxHJMWBd9VWqxXyECxv8rnaWq0bobSvQmdG4p4EAOGAHK0wMBlKkWBIQ6+HXsRsNsN0Og07JrhURj8rrzvLsiAOrOIwz6ILbJgkpYcUT8tSzEw5WCRKQtum9ZyJ+KAatiBrP4T2QOh5GPQi+Hw1nKK9FUrcpKXTma1WK3gu/X4/TIoySclNUcvlMnRnLpfL0OJND0cTlxqu8HNpl2iWZQAQxErzHrrQ1/mx8rFIlAQNlslDPjgmrWGGGnC8r4FVDH1dFYTUijud6WDIwJX1wIM3owtyOZcBIEykcg5F8xpMyrI0yvfR8zu0JZyCSaGIFwDz74r5E90nYaEoF4tESXA2YjabhS1PLGuyNMkuR+Ch85AGpl6DhhZA/sRxfeiCGj3KL8uy8Gc0buDxmn2Kk5Ypea1FA1kME+hFUCCYU9BlvcCDaPF9dZ0+r73RaIT1d/z7YXOfQ45ysEiUBO+Mk8kEL1++RL/fx3a7DQarR+bFPQ98PjdCaT9BvJWaFQy66OqdMEcAfJEUpVdAwWArOI1W5yvoRejGLF0Ew+uhZ6AbrzR5qjswNEkaiwnX9R+PR3S73VzlxJ5EuVgkSoJGPp/P8fLly7C+rdvthhyE3lVZSeBdlZ4I43UVj7gnQjs2tWsTeBALehs6Zaq7MWmsLKdyB4bue6AAxQlPbQuPE6sqGCoUfPA9WZrdbDbIsgyTyQSr1coicQIsEiXCkt9kMgltyOwdYMmQbcx6OI62Isd7JeLN20VJSh0nb7fbyZX8usZOjwnUFm6GGAByy230PFHdvq1r8XRGJTZ4fV++T6PRyK3A857L8kmKhK5m9z/Ml4PhA9uvp9NpcOM5MMVwQrP78XLaeIWd/rvE4+aa01B3Pq6C8CvDHt7J6S0wtND2bfUCKHAUCJZomWDVzdp8r3gZsJ5fymGz5XIZxIcr+e1JlE9SJJhE8z/K+4EGy5h7sVjkDFanJgGErkZdtV90+C9DBE12coSbeQXmGnT5DKHRamjBnICu+qdoxDs0434ILrnR9fwxWu2ht6Ir9fUwH/5eh89MeSRFYjgc4uOPP/ao+JeEd2i9wzOU2Gw2wdg0+UhjUPe7aL0bS6WacGy32yHHoUlJPb1buxjpqbB5SSspmtsA8qeCsRKi+ytigYiPI9S/E35/tVrlVunr0JkKiZZAXd0oj6RI/PKXv8TPf/5z/2N8SWq1Gn7/+9/jt7/9Ler1epjK1BO2gfwdnZ4C3W8OYqmx6MLbuKwYVxC0ekGPJi6p8iuvRfMVfD5hGEBxYBu1ekHAQ3k2zoHo9ygSk8kEs9ksN3TGnyPxCjzz1ZMUiR/96EdlXcc3nkajgc8++wzz+RyvX78O50poVSGuWOixd7yzsukIeMgZ0QMAHu/C5K/1GED1JIoSnXqnVnFRL0O3THEIi/kTekLMM2i1hZ+P78EwI55MpZBSdPSgIVMuXoRbApVKBePxGD/96U/xySef4O9//ztevXqF+Xweeh5YDYj7InQQS/MQWjLVszV0u9Pb7rjqumvFQ0MLhhVslqIHpMtvGF6wgqE7M2ORiLtDOQWq538yQclr189poSgfL8ItibOzM/zwhz9Et9sNd+fr6+uweIWzDTqYRbTpiDDMYNOSbqZisjKuJhTNceheSlZQNDFJgeA1apikQ1hMPmpvhTZ0aW5Cp1LjBjD9WYodW8U9Mn4a3CdREq1WC6PRKMw9MGbn4hVtvY6TktqGTG9DBUIHvhhKFO225Cng2oyl1Qs9KYuhhlZJ6GkA+dPAGV7w+VoJYZJVPSF9T/15fX2KHJfvcn+GRaJ8LBIlwZBiMBjg4uIiHH3HQ21oFNp4xLFq3r25DFc7M+Pj+jQPES+h0TbrWCT0yEEKEhOVvJtrpUM9D12AG4sN8w/a7KWdobH3wM9AMex0OuGsUYqEKReLREnwzqwH3PAsTGbxaUhsl9bKA0MN5hzoQbCioJUKzS2wlVvbnilKQH44TKsqOkaungfDCYYS9Iric02L4PXERxhqp6h6QdzGNRqNcHZ2Fhq0XN0oF4tEiegIdZZl6PV6wej0Lgsgdxdm3B8f36dTlnqnLtojofkJXUpT1M2plQ19Dq81XoGnU6dxKZZio+3l6oXw17oPQ4fRxuMxLi4ucH5+7pX6J8IiUSKagKRQ0HPQNXKaR9Alt5qs1DCDz6EgaMgStzHHIqGVhniWI262ihfNsJ9BBUKX6jCnoeLAr3FruHaOMlE5HA7DLlBu2fZK/fKxSJSE5grisWwgb0Cc4WBTVBxqsNQZ91Zo7K8nf8U9FPqapGgWRF+LZ4RyHT/X1XFcXIWH4RCbrPheDH20YWy/34fv68Lf0WiEy8tLPH/+HOPxGN1u91HexZSDRaJktLGJngE9AK6a18YhFRf9qv0GmouIHxSJOK/A+J/E3obmR9jxqQLBkXGKEXMe9HR46rgeBbharUJeQT0czbv0ej2MRiM8e/YM3/72t3F1dRXOAXWocRosEicintjU5qdYCLQDkp6G7pVgriCO8bWdmbMZushGqxz6fhpesC2cIUYcZmjOg4lZGjoPFeYa/eVyidvb29y5pyytMlF5fn6Oq6srvHjxAldXV+FgHm0QM+VikTghKgpPzSTQSwBQmITUikXc1aivEY9n62voe8aTnzq2HQ+ZMVFK8Wk2myFUePbsGV68eIHhcIhOp4Pj8YjlchkO56Hn0G63sd1uQx5iNBrh+fPnePbsGS4uLsKOTeciTodF4oQU7YJ4ypuI8wtMVMZt3E+NUheJRDznoTMjLHNqN2Tc0wAgl6DMsgxnZ2cYj8d4/vw5nj9/HqoSwBeehG7MbjabmM1mYW9lt9vFaDQKHoTOhJjTYZE4AW9bFhOHIHwO8w9083kXp3jE1QldGsTXAB4EJvZMtKqiZVHtnwAQSprAw9YrPc7v4uIiHD40GAxCO7eens5EJ5ftMB/B4wB1JkSTqp4nKh+LREnwP3f8nzxOKLLqsd1ucxUMbWlWg9H+AjUivfuqCOiD4YJ6I3EPA0MaJiU1h8CSJwVCz/zUA411H8l2u0Wv1wu7IthVSsHgTgr2hqhgqVg4N1EeFokTod5DfFiPhgVqGNrqTGJvIn6PGG20Ah52WvK19MHrY0mTuZFGo5HbcambqRgicPhLvaH49ZiroEjowBgTpzr4Fe+jMOVgkTgBNJp4R4KGGDQe7bxUb4TGzt/HglCUmFQPhNUOzXWop6K5Bg0zGo3Go1X6PIZQDzDW3AaQnzbV0CUOp5gL4Z/Rw9BuUFMuFomSiQVBy5I6q6Goccf5AYrAP2o8NEaGMEXfp0joaVzMO7A3gtehA2f0SlgVYaMY91CwY1PP7aCXoOVdVleY0OR7Fl2v+eqxSJSEGj5DDbYhAwhf4/Kkuv9xNyTRu3LsbcSo9xE/R6+Txq9dnhwq43CWdnXSC1gsFmHvw/F4DGvxAQSR4AYqbsBmOzrFZrvdot1u43g8ot1u56o29iTKxyJxIlQoADyqaKj7rWdl0EiKzsUsEo4Y7bso+lkVL82ZxC3getcHHtrKKWYMG7RKsd1uw2AYhYKhC0Upzn3E+yucsCwfi8QJiPsTaJg6Bcm7OM8K3Ww2IT+g3Zc0qKeqJ0rsZcRJTL43RUCrEtpkpSv+deemhhFswqJI1Gq13OZvXexLb0RnVRgS6Xo+d1yeBovEiYlzC3ElgILB5TQ6ys3H2zyD+H20/KlCw59n6ZUDWUx0MofAXZQ8io/hQqVSQavVCgKxWq3C2rn4CD8KCkMJrdBwVR4P+mHFxANep8EicQKK8gZ6R9c/1/Fyut8UiWq1GjZux7MapKhzU5e/xOvuebdWz4Y/pwfo6HmgfN96vf4oIbler3OneXFoTJfUAPl1dVmWYTAYYDgc5obE4kOFTDlYJE4EcwM0sPiU8Hjfo4oEjZh3ehoxvYqikENLnBoy6Kh3XJYE8pur4oW1umSGosXV+4SlT4YL8Zg4n8t+i263+6hrk/Mb3m95GiwSJ0BbqDWnEDdTafek7rhkjwMfFAvd1xD3Uej7aqOSLqTVXIWKFTdP6YJbnd+gaHHUnfkHvg9DDeY79HNVKpUw3DUYDDAej3F5eYmLiwuMRqPcsYFxm7kpB4tESRQlDfUOXtRxqcbEnAR3MbDrkQtuVTD4XBWi+LV1iEvDBm3Z1rCEAqH5DAqbLo0BHhuxhlREKyhcEEyBuLy8xHg8DjMc6kVYIMrHIlEScUWDxHdv/hl/lnF4XI2gATcaDWw2m1yZUsMVhifMXfD1+TO6gyLeRUFxoKDwz+IQQ5Od7KnQDVp8xNUJ5iB6vR6Gw2EQidFohMFgYIH4mmCRKBGd16DhNJvN3JAVUFzOLNr5oHd4JgvjE7/UI6Drz9d+atYjXm8fl2oJQ4xGoxH2bnY6nXCyF8MEGroae7zFajwehx0S5+fnuYqGcxGnxSJRIjQyZvAPh0OuT6DIEOImIg0bNO4vyhkUiYRuumapkslIPQ9Dk5X653EIRGHgcFev1wsCwUW9zCfoNi16HXzuaDQKiUrmIfj8oq1dpjwsEiVCkWBTVL1ef1QhiH/+KZEoyi/EY9XaC6E5CJ63qf0KOpPBlmutZlB82NOgBwYPBgMMBgP0+/2cQNBboDDw+lUkWNFQgdGjAlQgzGmovKUX3o3y7xHd/KR3e+Wpac5YJIDHsx3x1Kh+jXdFUAwoCOpB6LIZndPQfghuoiryHuLQIkaH2/QwYD2qMPYe4r8H89558i/WIlEicfLxqSEs5W1GESc+ixq14u8XiUrRQxOgrJjobEd8mvlTd/6i3IfmJlQ0NBmqn9/i8JVjkfi68LYpzRQpw3uX379NTIquU0MiNfCnjPpdPk/KW7A4lIZF4kPkXUXon3nOlzVii8DXBouEMSbJkyLhaRljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTBKLhDEmiUXCGJPEImGMSWKRMMYksUgYY5JYJIwxSSwSxpgkFgljTJL6W75fKeUqjDFfW+xJGGOSWCSMMUksEsaYJBYJY0wSi4QxJolFwhiT5L8APBUrHBsTgeIAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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+SCTitl+DwQCdTse1qi8WC5cDURk0GCQSAWCbpezMR4qFHe3mLQMyWbhtO8Fw306XthOm2Y6dz+ext7eHWq2GWq2GcrnsogjglUDM53P0+31cXFzgyZMn+OKLL9But1+bWMXrsoQ5mUxwfX2N8XiMZrOJFy9eoN1uu+3DfD5HJpN5bRJVr9dDq9XamHK1WCxwc3ODXq+nk8MCQiIRALZhy0YGFIvxeOws1AzBbS+Ht2Xc2zm5bQwev0+n084TYQWCjVa2zXw0GuHq6gpnZ2f44osv0Gg0MBqNnEB5E5YcLsOqRLvdxsuXL9FoNNyIvFgshsVigWw26+Z18rk43er6+toN512tVq4pTRWOYJBI7BhbTrSNWrbEaT0O1n5tzVDEJhkBuC0F7/CpVMoZmDgyr1QqoVqtolQqIZfLuW2G3QZxm3F+fo6zszO8fPnSjf/nc4TDYWQyGVQqFVdCTaVSWCwW6HQ6uLi4wIsXL1xUwaQlANzc3CCXy7kGN0YszWbTdaky58F8jSKJYJBI7BhGEcPh0FU1rJ/Ba5piRcDbCckcAxctgA3LtW1F55h+fk1vAg1M7M2w/RKc5v3s2TOcn5+j0+m4cidFJZ1Oo1wu4/DwEMfHx6hUKohEIq634+rqamOrwPkRoVAIvV4P7XbbRT2cnNXpdDambtvuV+UkgkEisUN4p+YgmHa7jU6ng36//9owGZuc9M6YtNezlQjCXATH9HPOZCqV2vie4+844o55j36/j1ar5drBm80mRqOR81fQOLW/v4/j42McHx9jb28P8XjcGcSGw6H7bLtOKRTD4RCdTscJXL/fdwlK67Dkewd01F9QSCR2CKsFrVYLL168wOXl5YYpyet9sFsLrxBY7Lg3bjPopqQo5HI5F03QMGW7RZmMHI1GuL6+xosXL/Ds2TO8ePHCRRHxeBzpdBrFYhH1eh23b9/GrVu3NrYZzWYTq9UK4/H4taYywmQtJ3Mvl0sMBoONcXp2+0R3qEbZBYOvSChJ9N1Bv8Hl5SWePHmCJ0+eoNFooNvtbvRG2MQkf/6mMNt7Bqidpk3no91a0BbNhWkP9+H3dtRds9l0HoX1eo1UKoVyuYyTkxPcu3cPp6enqNVqyGQyCIVC6Pf7uLm5cY5J2+JtLeHApqGM4mRH+PH90ZnJgcFi9/iKhFT7u2M8HuNvf/sbHj9+jE8++QRPnz51d2hbqrRDbe10J69Q2O5LGqR41kWxWESpVEKlUnHTqpPJ5IbZiclARjCj0cjlEew2iCd+8do8du/999/H0dGRc0uyMsPBNN5+CzvolkLlzYXYORf8/UQigb29PdTrdbc9ErvFVyQ+/fRTDAYD/cN8B/z973/Hn//8ZzdKjof02hIlAHfOhJ2zYHMPwKZAsMzJrQVNUvV6HXt7eyiVSm4EHQ/ToXHLnqTFuROdTsdtPzgDM5lMuuvXajUcHx+jXq+jVCohmUwCgIsY7FmeADYEkJEOzVTJZNLZvvm+WGXhe8vn87h9+zbq9brzYYjd4isSv/rVr/D73//e7V/FPwct0naxxONxZ2TifpwlUDuliZGEPeWK1YtsNuvG81cqFVSrVVQqFTddis/LDtNOp4Pr62tnWup2u+j1eu7POUyGz8eKBMudnKTNmROMAmgxpwDyddrp4Nls1h1AHI1GN8795OukCKZSKZycnODhw4fY29vTIT0B4SsSnU4H5+fnu3ot33vYHp3NZt3oenZecro1Lcr0BHBeJT0GsVjMCUOhUECxWEShUHDJyUKh4IxNTPYxkWiP5OPcS9qfR6ORSyjaiMWOx2cEYCsjtu2d0QjH1NGvEYlEXMcpxYtzLzudzkYOA4AbjlupVHD//n3cu3fP2bjF7vEVCfr4bagr/nEoAuFwGMViEScnJzg8PHTH9vX7fRe18U7MxWSPBrTmJXtXTyaTGwcGh0KhjaYwRhHNZhMXFxfO2UhfhvVh8G7unRfBygjwqjzJcibdkjx/I5VKIRqNujkTBwcH2N/fRy6XAwD3fmksWy6XLlrIZrN477338ODBA/d3pCgiGHxFwjsLQPxzcMHlcjncuXMHP/rRj1Cr1RAOhzEcDnFzc+Pu0DRDcfR8KpVCqVRCvV7H0dGRm/+wbaK1HahrHZ2sPrA/otfruf4Jb8nV3iDo3EylUq7CwE7PyWTipmdfXFyg3W5jOp26Ywjz+TwODg5w69YtZ7iKx+OYTqdot9tYr9fOeUpPSDweR7lcdiXWQqGgg4MDRD6JHRONRnFwcIBHjx7h0aNHKBaLWCwW6PV67vwN3rmz2aw7SbxYLOLg4ACHh4c4ODjYGFZrOzft7Al7IK+3w3I0GjmXpzVree/WjGAYRbBBizbrwWCARqOBZ8+e4eXLl+h0OpjP526cHashd+7cQb1eRzabRSgUwmg0cjMker0eRqORew+pVAr7+/s4PDxEpVLZqMyI3SOR2CFM/t2+fRsPHjzA8fExUqmUW1RMUjIHwIN8OTuyXq+7OZQ252CP+WMSEIArX/Kuz0oGe0K8A2uI9V3YxCgnTbGvgsaw58+fu1PFWTZl5HNycoLT01O89957KJfLSCQS7rxQmqhoxea2lgnSYrGIVColgQgYicQOCYfDyGazODw8dHvzeDzutgOZTAaFQsEtIibwmIfg0XsUCFYO7IwKfqZQ2P4HNpBtM2cxgrDVEyZZaeMOh8Ou8YtJx0ajgefPn+Pi4gI3Nzeu05PRD2de8nXzcGSKAU9Wp+17sVi4ciuFU2duBItEYodwf88EI5OLzEHQEGUP6mWykmPeKBD28VxIXkfjNqMSXwc/27u0HVRjy5WFQgGJRALz+RydTsd5KVqtFhqNBq6urpy9HPja68HxeEysplKpjQnctgxcKBTcdoOREKs8zKmouSs4JBI7hL0StvEJwMZRdiwXUgjS6TQymQwymYzzJdiFbS3c9gxROyDXHgWYSCScddvrwfBGEMVi0W1t1us1+v2+80F0Oh202+2Npqz1eu0iHJZ4bd7EiqIVTD7XarVyp4LN53M3mbvf76NYLMpxGRASiR3CDsuzszN89dVXrtlqPp+7kfoUCruYbeRhBcH2dmw7HtAmLunKzOfzG2Yne0gvRYTbAHovmGCkE7PX66Hb7aLf77sEKK/HCohtKrMmKNvHwQoOhZClWk7lms/nLolZrVaVnwgIicQOWa/XGAwGePLkCT755BOsViuUy2UAcJEEF46d/8jEJM1G/D1ek9sKe+o4J2/bEmqxWMRsNkM4HEY6nX5tgXPMPisZjADYgMUBtYyE6JYEvo5CONiGJi87fdtuiWwlhcLE6Immsm636yIhHhuYz+fVCRoAEokdM51OcX5+jj/96U8YDoc4PDx0d1uWGjmxabFYOOvyYrF4LWT3eiLscYD2IB9OkALgDsTx9mfQzWnzHRyOY63cdGfaQ3/sY+ki5XGB9FVQyCgS3oOGufhtNyr7S0qlEu7evYtaraYtRwBIJHYMnZVPnz7FdDpFo9FAsVhELpdDuVxGpVJxFQYuGuDVwBo7pcqeRM67rjVGMaSPRCLIZrNuFsS2fIU9m4Mt3OwEHQwGuL6+docIcVoW8Kpdnb4IDti1I/rZxMVIgs/hHaZD8xcbzWgZL5fLaDabuHfvnhM7sTt8RcK2L6sE9d0xn8/R7XYBfN1CXiqVUCqVMJ/PNxqqbGLTbiHs3MttA3HtXAk6Je0BwG9KfHJ03WAwwM3NjTsUx07v9p5/wW0Gz/HgQT/sz+Dr95ZluX1i+ZcDgCkQNzc3Lmq5uLjA1dWVDugJCF+RsHcx8d3BBck2fP79clHTtBSNRt2i5eJhspALj6LCfADNT+yPYNIznU67rlPvYcCMRjilipUM5jnsAcF2GA7zJ5lMBuVyGfV6HbVazbWn2+Y0vm/C55xMJu481Ovra9edShdmLBZz3aq2+UzsDl+RoGNOreL/HOv1Gjc3NxgOhxuH56xWK5dDsDMeOPMSgOvp8JYbmQ/gndzOfGCjFENz5jt4WhZnUFiRYKjPPIidnL1tC8OBMHaGBedXsKvVHkfID/t3YpvOGo0GGo0Gms3ma9saRjI6DzQYfEXil7/8JX72s58pUfRPQIv17373O/z61792J2ezQuE9Uo/hPcPwbre7cUweW7q5v2d1gFFFPB53+Yb1eu1yEhxdx/H6tmrCCMEbXdgyK69DWFKlq5Ij+mm7puixh8T2iRDmPjgy7/z8HFdXV+j3+xsHANmhwPq/uHt8ReLDDz/c1ev43pNIJDAajXB2doazszMXPnOqtT1ol3dvtmDTk+CdPM2cAOFCpinK+7V34hUXnN0O2MQor0lxoZDxNbOSwWazSCTiTh9nlMDoyOucZPQyGo1c5aTVarn3SWHg++NrF7tHg3B3xK1bt/Dzn/8cf/3rX/HHP/4Rn332GdrtNubz+cYgGisUDNe98yKtfZpbjWw26/wNyWTSOR+t9dp7F/Y6Nb0VEnaAptNpLBYL13dB+3g+n0ehUEAqlQKAjVwJp1TZQ5B5XesK5VRtJinZY2L/7zFq4RAbsVs0CHdHZDIZnJ6eOnt1Op3GkydP0Ov13CzHbDbrhqvYXgXe/a1HwOYZ2BjGqVE0MdHCbUul/Ay8ygvYQ4FsvoODZmazmUuiWuMVE6F0kvJoPu95pvRjWJGwxxzaA5G9UQw7Z1kt0f/J3SOfxI5giF6pVPDgwQOMx2OEw2E0m00AcBEAHY7MZTDUZ5jPPAOjBzZh0Qdhj/QD4O7YNFbZQ4Ft2dPaue2EbFZJUqnURgmS+RD2WLA6QscnKyLes0TswUOMOuwhyPb6wNfNYjyWUCP1g0EisUMoFJy6NB6PXShv51FyStNsNnPNVSxj8i7OFm4KBH0QFl5jPB67Kdw0aHEqNe/k9GBwUdvnYe8G/Rl2qI0VGPs7Nipg5ELXJbcbdp6Ft8uT0VMqlXKt9XJbBoNEYsdwn88zNJnsY1VhPp+7EqStUHDfzy0AW7h5poYdmc9ogHt+JjgpEByx7xUJLnrbrMUWdrt1YbTAnAPHz3kdn4TvzYqDLYt6c1/WxUlBrVarmpYdEBKJAGAirlKpAIAriXJ+AoCNOy0FwLZgs4GKCT16Ljiqzt7xbQ6Cz2OFiFGBPYOTfw7AiQ+fo9frbYzB8w7StTMy7GAcXoufrduTWCt5JpPBrVu3cPfuXRQKBXWABoREYsdwETCUt9Osp9Opm840m802JmVzvL1NUnJKNi3cjDwoNhwCY4/bY+mRd2V7kjgfz9fIxCHFgXZtGrsYfXj9DyxXMqfCbRATojxbY9vfDaOeZDKJvb09fPDBB7h9+7YzaIndI5EIANvzYO3LANxdnr6GaDS6MWOCfRi0b1vvgLdlnFGJt1rAidS8wzNqsbMeALjtiO0CbbVaaLVaG12k3hkRdGNyfgW3SpysbYfd2IVvzWGVSgXvv/8+Hj58iFqtpvJngEgkAsCG4twqANgwO9kFYY1PxBqbvFUK+wG8mj9hDUm0bvNaNo/A79nLQYG4urpCq9XaOorfbjHY8MXjBnO5HEKhEHq9njvmkH0bjFRshFUul3Hnzh18+OGH7mAezZEIDolEQNhFZUN7uxBsyZDRh60sWA+ErS5YzwEXvjVmWfu1d2IUxYeHCHOEXLvddv0nXk+DNWuxZfzo6AgPHjzA7du3USqVAADX19c4OztzpcxOp+Ps19yeFAoFnJ6e4oc//CF+8IMfoFaryR8RMBKJtwxv7wQXLICNkB6AS2baBi3mN2xiEMBGZYGVD+DVITzeATasXnhnO3hH8dvXzaayWq2GDz74AB999JE7XGe9XqPb7br+jkgkgvPzcwwGA7fFymazOD4+xqNHj/Dhhx/i5OQEmUxGUUTASCTeErz2adt85S0X2gQkj9yzCVArJtvas/nhPbHcTriiXbrX66Hf77tReFaAvK+dJ2/dvXsXjx49wgcffIBarYZ0Oo31er1xwjnNWq1Wy1m+9/f3cXp6iocPH+K9995DoVCQN+ItQCIRADa031YytB82uWgnUjHCYAKSOQTvpCkArx3gw1F4NtHIvAYFgs1Z1hzlnUbFx9rBM/V6HXfv3sXdu3dRr9dRLBadkHGbwfJsNBpFuVzGbDZzA295HKB3OrY6QINDIhEw3olSdnhMKpV6bRIV7+LcOthj/rx9D/Q6WH+B3VLw+Ynto2DewV6PDV52DB3zEGwbPzo6wvHxMarV6mvHEAJwA2oODw+xXC5dGZgGs/39fefytCP7RHBIJALERg9czN5JUWyCYjWA2wwrFiwP8rH22t7kpBUIKyCMMKw4UADYSGav7W0l54zOarXqDvOxA3a84+7saWWMiOypXUzOcg4GfR2KJnaPRCIAtjkSgVelym35CFuWtDkJPs4mIK2xiVjxsXMkbD6C17QzIzKZzEYJlSeCeztCc7kcCoWC6y+xfRp8fjt8187EsNsddpDy9bLb1esJEbtDIhEQ1n5ss/feE7q807EBOAOTt+Xb4k0u8mc0bXFR8ms7oQqA693wnsfB+RB2pD7FhF2ho9EIg8Fgo9+E0cFwOHRnd/R6PfR6PcxmM0QiERc5TSYTpNNpTCYTVzFJpVLKSwSERCIgrEjYyoadZm3vvl7fAxcoF741XG3rh7Bfe+/w1rINvMpncKtBgchms27EvnVccltDR+XFxYWbZMXZFuwp6Xa7uLq6cvMsu92uOz2d7e88HnCxWCCTyWgAbsBIJALEKxJ2r0+ByGazzj1p+yvscJhtrdb2OXg9Gz3wZ94ohXZwTtRmQpUH9ljrN6/Nyd/Wk8Ep3+Vy2fVdUESazSYajYab2+mdX0GDVSqVUtLyLUAiETDck9s8hZ1ExVCf4be34uAdWe+NIJhPsCVXmy/wnt9hz+2gONiWcnuUoPeov9Fo5EbR9Xo9tNttVCoV5PN5hMPhjcG+rVbLTcXmSeIUBTovvfkIbTWCQSIRMG8Ko61g2JH5XNQ2+mA51Hs976KyPgk7Oo7XBLBRirVVE3aB8rBgHv9ncxORSMQNvh0MBuh0Ok4kWD4dDofodrvuGqzY2K5UWrtLpRIKhQKSyeTW/hWxGyQSAWLv/LbSYH9OuzM7Rr2j8jmin2LB6/Kx3qnYFAgapuy4e1umtCP07dxKDpnZZs+2yUf2iQyHQzc5C8DWA4352EgkgnQ6jWq1isPDQxweHrrZlpolERwSiYDwliStPfpNY+2TyaQTCN7tx+OxO1T4Tc5Ie10ueN7t6aq0j+NjbbMXFz2/9vaGAHitQsKfzedzt9Dt9ay4JBIJ1zl669Yt3Lp1yxmr1CYeLBKJgLBiALxqB3+TSNgZl8wZ8IN3ZdtfYaMJm6CkF+FNZ2J4u0GZt7Cj6fhhn8NCYeB2hqPyrMWc74sCUSwWcXh4iLt37+L09BT1et31bmirESwSiQCwCUQuaD+R4NwHe84Gbduj0cjlDziw1joyvRUM22putxwUF9tEZgfZeudRekWIi9j72b5X/szOzYxGo8jn804g7t27h6Ojo9dcmyI4JBIB47VS2xDeOjJZZbC9F9Pp1B3EQ/syj9ajUDB6oDsS2JxazTu+d7S9FTD79bZEq52LwRIqhcu+Nu8g23A4jEwmg2q1ijt37uDevXuuuYvDfSUQwSORCAAmB7mYeJd9kxmKjyFeoeDWgYfh2PwEP08mEwwGAxeV2PkSrF4A2KiSULS2vQZ+b8UhkUhsHFycyWScW9J6Lfj7yWQShUIBR0dHOD09xZ07d7C3t4d0Ou3EUSIRPKFvcLLJ5vYvwBqS7EnZb1oQVkBstMFFbj0MtoPTRgscm0//AkfRXV9fo9frYTAYuOQkf982fNkOVOvpoI+jUCigWCyiUqm40iWPLuRpYvYcUts5ure3t3EiOcVEArFT3viXrUgiAFjWtA7LN/3etq+J3Rp4cwf257arlKVM+h1ubm7cYJnBYLCR1Oz3+y5CsUNveYJYoVBAuVxGvV7HwcEB9vf3US6XkcvlNgb12m5QG0nY/IrXNCWBeHtQJBEgfj0W3+Ya3s9v+pmtWDAC8Z4BasuddviM9TTYVu9SqYRiseiiBtq5t0UCXtGz8z23zfgUO+WNf/ESie8pb/p39RMQ74c3GvEmVBklvOnu//+74CUMbwUSCfHN/KOdllrk3wuUkxDfjBa72IYM8UIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF8kEkIIXyQSQghfJBJCCF+i3/DnoZ28CiHEW4siCSGELxIJIYQvEgkhhC8SCSGELxIJIYQvEgkhhC//D/mr4re9HpYyAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 37\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 39\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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tNpsYDodYr9fI5XLI5/MA4IKjzGYwRtPtdl2HKudUvEkQxb8WiURI3FRJedNAFhu4Y5TfNnHZqk2WeTNz4W+7Zpoyl8u5Sdi1Wg2VSuW1Zi5eV7/fx+npKR4/fownT57g6uoKo9HotSIuxiUWiwUGg4ETwVar5crBp9Op+xoKFWdZTiYTdLtdXF5eOqtjPB5juVyi3W6j3+8reBkSEokQ8A+QpWDwbmpHu9n4g405WKy1AFy3m8fjcaRSqY05FJ7nIZ/PuynY1WoV+XzeWREUCFZstlotPHv2DE+fPsXFxQVGo5GzYGi12ClUth6i2+3i4uJiY25FMpnEcrl08zTS6TQmk4mLeZydneHy8hL9ft9lZtrtNrrdrtYLhoREYstYV4MTr2kp2GYoWhOscWAg019UxK5LW7PAAS+2f4IzG2xlJTs9rUDwOuhmvHr1amPqFHtFOGuCosOUZyqVcpmQ8/NzXFxcuANv50Sk02lkMhln3fR6PZycnODi4sIFRfmZer2esyQUvNw+EoktY9OfDFbaiklbMGXTof6sBaG5D2CjOhPAhuXgeR48z3MzLMrlshMIBh5tcLTX6+H09BQvXrzA6empawXnuDymPCk4u7u7KBaLiEaj7lDTAhiNRu6aePBZZck28W63i7OzM7TbbSeedGeYHVHgMhwkEluEB5GDYK6urtDv911U37ZG+0fI+eseLHamhN/l4Jj+bDaLTCazsZuDAkHLhoI0GAzQarXw/PlzPHv2zC3wYY8HezRYOLW/v49isYhYLOamb3P0v0198lqj0Sj6/T5SqZRrV+/1emi1WhtuBoA3ulhie0gktgjN+MvLS2dad7tddyhsfMI/W/K/Op2J8QlWU+bzeRSLxQ1rwr/Pw06tZuco3YyTkxN0Oh2sViukUik3lKZWq+Hw8BD1et11ek4mE7x69QoAXNDVpmmBa/eItRf8vjAGc1OfBmddaNZlOASKhPy/bw92T15cXODzzz/H48ePcXZ2hm63u9Eb8U3ggWEdBIOVdCtY0VgsFpHL5VxsIpFIYL1eb8yVsALBqspms+myEgCc9XD79m0cHx/j8PDQLc1ZLpfOOgKwUeR1E8zsMOZCobJfz8xNJpNBsVhEMpn8Zt908a0QKBJS7W+PyWSCzz77DI8ePcInn3yCJ0+eoNPpuPoF+vp2crSdVnXTz4Jfy94JbuUqFouoVCqoVqtOJDj8he3YLE5ir8dgMMDl5SWazabLSHBQLg9qPp/HrVu3cP/+fdy9exe1Wg3ZbBbRaNQFYBnfuCnAaq+X6VkKCes+bqr92N3dxd7enrN+xHYJFIm//vWvGAwG+sF8Czx+/Bh/+tOf3L4LliCz4MmmKZlatHMXAGwcIFufwAAlN4HX63Xs7+9vzIagCNl1gWwlpwXQarXQbrfR6XTcHZ7ZB+4p3dvbw61bt7C3t4dSqeSyFQwy2uG9drEQPyvTpVxByGsiVhTj8TiKxSJu376Ner3uPoPYLoEi8ctf/hK/+93vnHkq/jnsTEemI9nHwAYnbuhiZgO4Nt2Ba+uBB46pRI7nZ7PW3t4eqtUqcrmcO4ws1up2u04UWDLNICrLrW2GhDMuPc9DpVJx/RYMfDLuQKvELtuxIsiYBpciR6PRjVF2tgmN6dX9/X08ePAAu7u7Gq8fEoEi0el08PLly21dy3cepv14wDKZjEtFRqPRjb2dFAb/waHAMLXJBcF2XFy5XHbr+Xj3pTvQ7/fRbDZxcnKyMRy33++7A85Mii3RZsqTG8/puvib1eiisBXdjupn6rVQKCCZTGI+n6Pb7braENZ3AF/VeJTLZdy7dw/37t1z3yOxfQJFgv9J7JJY8Y8zm82cKZ3P53FwcID9/X1kMhksl0uMRiNntdnFNRQLmuqlUgk7OzvY2dlxcQfenT3Pc4tu+POzE7BYIEWR4OzLNzWMsUmMsyttfwfTk+PxGL1eD1dXV+h0Om6XJ7s7M5kMdnZ2UKvVsLu7i0wmA+CrIqmzszNXlk4x4nMajQbef/993Lp1y2U3xPYJFAk7N1H889BPz2azODo6wsOHD7G/v49YLOY6H3n3pSgws5BIJFAsFlGtVl3MYWdnxw1ssSv+7DBc2zXK4OTFxQXOz89d4xSDmDcVK9kFx+l02sUYGGjk2H6WVHc6HcxmM7eGMJ/PY39/H4eHh7h16xbK5TISiQSm0ymazaYbNMPiqfl8jkQigVKphKOjIxwdHaFcLjuxEttH3/ktE4/Hsbe3hw8++AAPHz5EuVzGarVCr9fbqAVgwRJjGAwa2tZu9j7YITH+tnJaBuyw5PZvVkKyXNqKhL1j01XIZDLOHWAr+Hq9dj0XL168wKtXr5xIUCDq9Tru3r2LO3fuuGxIJBLBaDRyLgddHeCrIGY6ncbOzg729/dRqVRkRYSMRGKLMPh3+/ZtfO9738PR0REymYy7e9reCUb2I5GIi13UajXs7e1tbNeyQ15s4xjTiwA2Ygb9ft8Nt7HuhX94DK0Hz/PcpCkGV4fDoZuMdXV1hZOTEzx79gwnJyfodrtYrVYuyHlwcOBqKsrlsivFTiaTWK1WbqL3ZDJxcZlUKoVqtYpKpeIqPEV4SCS2iPW1WcpMl4JxA06m4iFKJBJuQ3e1WnVLczzP20iTUmBsGhK4tizsTEz/1CteG7CZPbF7QXO5nOuj4GLgXq+Hi4sLt4SYhVdsWWdl5u7uroubMAgLfBWjKZfLqFarGI1GAOAEk6IEQI1dISOR2DKsabAxBD54MJm2pKnvr5y04+6tm2GLruykKJuxsO9lYxhvmqJdLpedmC0Wi42mrVar5YqvaA3w+Zx+ZUvCec0UP4pQqVRyLejj8dhldDilyr+ESGwXicSWmc1mbp4lG5/svAiOl2P5NM19llUz9WgPi93lYedd+jeP8/Wy2ayzWOyyYXvA2RhWqVScFcE6CltrYTeDAXDZD6ZoKXhWkOjOsIS8UCi4vg279o8xlF6vh2q1upEiFdtDIrFFmOZ8+fIlXrx44Q6sFQ5WOfKwMqV5U+zBdlaytoJuBTMGdDu4MbxSqbiMSTKZ3GjBpiXDishCoeDMfrtvg52enB7FzAQFjNYP4yZ24pWtzuTOUL6fna/B33ueh3q9jlqtpvhESEgktgizAV988QX+/Oc/Y7lcolKpuEW8nBlpF//aWRE8jOz3sK6GfzGP3cdht2ut12skk0kUi0XXym3f1y9M7BTt9/tOHBj8tF2eViAKhYIrmrKZCbvr1K4HZICULtZ0OkWv13Ovvbu7i8PDQ5RKpY19IGI7SCS2zHQ6xenpKf74xz9iOBxif3/fCQIDdvl83vVxsDSbPRfWZAeuDx5FxA7L5YFkbQZ3bTBQyLu13UzOOAeFazgcYjAYoN1uu6U8HKtnN4mzjJpDaLjohyXhfA8bZPXP6GRGZjKZoNPpuGusVqt48OABGo2Gm+EptodEYsssl0v0+308ffrUCQarJZnFAK47cCkSADZiDrbTkocOwI3Dce18CTsq3z78o+vYNs7R9p1O57XqTOC6QY1l1xxCs7Ozg2w26wKVdrmwvQabguXELjadccbEixcv0Gw2X9tlKrZDoEjYtJh+ON8OrCrs9XoAgOFw6LIX1WoVAJxJTVfBv1jYv2mbgmBNd8YFuB3cH9uwroqtr+DqQQY77QIhO/PBZkrovtRqNVcNynWBvH6bPfFbP2w8Y1dqt9t1QdJEIoHz83M0m82NCVdiewSKhL07iW8HHk4OeeHvueSG+zIZCGQasNvtusNjN23bmoZMJuOyEvTfPc9DLBZzDWWpVGojQ2LTpHRn5vO5G31PcaIw+eMJtCD29vbQaDTQaDTciH4WR7GBy7+2kMNmuOqQ1aCdTgeDwcCN9eMeDmtVie0RKBLlchmHh4dqFf8nWa/X7j8+fX76/RzUwkKq8XiMwWDgvt+ch8k5DxQJm5Gw8yRofTCbwZgEg5LMNjBLYLduUZiA6w3idqu3XR3I1m9Oyt7f30etVkOpVHLLfznM147m42tYkWi32zg7O3Pj9LvdrhMmuj+cnq3/h9snUCR+8Ytf4Kc//akCRf8EPHi//e1v8atf/coJAg+vv96B1ZEA3FBa3mH7/b7bX2E3crF+gEJgsw7A5gQr/0Abfo21Gikadvwcg5qcDsUuz0ql4gKV3ALG4COtEwZI7TVRJOzIvJcvX26M4Ldl5faziO0SKBIffvjhtq7jO08qlcJ4PMbf//53tyqPreP2Dk8XgHfhwWDg6hOYrqR7Ymc+AHgtQ+HH/p1fnPwxAmYvGHPgBCqKG8fps12dGRm6JIxvcMaEnQgOwFlRo9EI7Xbbbe6iFWHdKbuyUGwfDcLdEkdHR/jZz36Gv/3tb/jDH/6AR48eodVqYT6fO7Od5cvWFfDPi7Tj3diOzfJmbtGyNQ62NsHWKFgLguLA2grexem2cNBtKpVysROWU7NVfb1eu9oJTsJmbQUrMik8zGpQJJhmHQ6H7vn2/x7fz3a7iu2hQbhbIpvN4s6dO8hkMi7I+MUXX6Db7TrTvVAouFV5NPlt1oIlz9xVYbs08/k8CoUC8vm8q5KMRCLudeygWrofdC1YoWmzJ+xE9TwPxWJxo2bD9l2wp4NFVqyWZMfpTZvS/ddkYx9WwOgSUZA0CDccVCexJRjo29nZwfvvv+9aoy8uLlwLOQ835z/Yvg66FrY+gf0RrLFg9oJiwrs1g6J2WjZFgm7NaDRyxUsUA1o4LJSytQ6McfgXGlNobLGW3U5mR/Pxefx8fosJuB5jV61WNVI/JCQSW4T+fbVaxdHREcbjMXK5nGsNtxOtOU2bd9VYLOayFTblyf4KzpYgzC5wJJ7dMM4ybOB6gAxXDjLg6N8pyjs9KzpZ+ESRoRDZnhG7bIiZCSsY/mVEViDs8F0O9pUlEQ4SiS3DmoVKpYJGo+GCfTy0vPvbuAQDhRyUm0ql3Ih7FkuxJsG/Q9S/HIcbva0lwUIm1l/wOvl1iUTCWT7MrjAgSQvEuhRvimW9aY2hTYtSBBgPKZVKuH37NqrVqqZlh4REYsswUp/JZFwJNlOeNMEnk8lGmTRbrxmsZNdkNpt1DVS2nNqa+TYQaFOsnFXJr7GBReC66tNaHLRwut2umyvBzeespSBMz/p3b9jMjH1YWIfBKV737993u0bF9pFIhADdjlwu5w4p7+o88JzQZEXAuhl8MIZhA4O2G5RdnMB1eT2nWQPYCCLapTrE9nOw0Ytj+Jm1sJvMmYLlr4yRAHCfk5kTP8za0IrZ29vDgwcPXMBXVkQ4SCRCwLZIUwR4cJi5sIeNsQaKBhf/JpNJtxzHjq2jy8G5EbbqkjMp2JZN0bDLf2yHqS0Lb7Var60AtFaKHcPPQbjFYtGN0B+NRuh0OhsNXv7aDRaHlctl3L17Fw8fPkStVlP6M0QkEiFg75iJRGIjOGmrMK05bsfcWTfAP6aONQ8co8/3o0vBkmxbiHVTVoHWCTtAue2LnaH+jeF8Hq2kQqGARqOBer2OUqkE4KtlT6enp866YROZ/fycjXl8fIwf/OAHuHv3LgqFguIRISKRCAlbFPWm8mz7oDXAuz57NPzFUHaWhA0IWiGxgVJ/h6+NW7Bt2+4JtRaEfww/rZ5sNot6vY4HDx7gvffec92tV1dXKJVKzn3izlG7NJmByo8++ggPHz5EvV53gVkRDhKJtwD/IbUj6nio+W/WiuDhonth7+w8tAA2An72te1r2VQlA5Rs27ZzJez73DSG3/M87Ozs4O7du/joo49wfHzsLIlut+usAlZu9vt9l3Kl9fHBBx/gww8/xO3bt92EbREe+u6HxJsi+34YZ7hpSIsNdNoGKjZyEVZt3lSm7e/KZNDTbh3n8Bcb3LTYrtBCoYCDgwN8//vfx/3791Gv15HL5QDATd2mtZJKpXB1dYXZbOYW8hwfH+ODDz7AnTt3UC6XXW2EXI3wkEiEwE3CYJuz7DZufj2FgAebBVj8GsYjALhYh7Uk7PwIWgx8XwBOhGxhFNObLNO+SRz4KzMvlUrFreer1WpuIQ8AF3/he8TjcbRaLUynU2SzWdRqNRwdHeHg4OC1Mmwb9xDbRSIRMtbk5wBcdoXaZTp2PL6NQ7AWwY6Io9gQioSNSwDXuzr8g2d4iO2Yf2YeWPPA5/H6mc3gKsJqteq2pzPdul6vkc1mUS6XUa/XsVgs3LTwbDbrlgoXCoWNxcnWrRHbRyIRInb/hL8+wYqBdTfsn9kAZofX3rTP029B0CLxT7GmSNhZkkxJep638dp2kC2Dlfl83k3JtouP/Z+X8zaLxaJ7X47bY3+GDcYy1Su3IxwkEiFiexR4kG2PhX/RjrUmGB+wIkH87eC0NOxgGVoR/HrbncnDzyU9TJeyB4TWjW0nZ5m4Lf6yQVcAG30f/qwIPy+bxCh6dGOsayW2i0QiBGydhN+K8A+qtZOi7LxIOznbLxI2GGlfw4qBxZr1tBDoIthdHNls1g3EZUUnhY2dp5wl0ev1kM/nndgAcEN0uL+DMyRs1SnFha+fSCSQzWbhed6/9oci3ohEIiRsoBK4TlNaS8JfQWk7MW18wsYNgOvMCblpqTC/ju9jx8PZACoAN2B3OBwik8mg2+26HhFrKXAK+Pn5OXK5HKLRqMtcsKCr2+26/aGtVsulQOPxuGsvn0wmrjuWpev+qk6xPSQSIeEvpKI14L/7z2Yz16fBgTA3tVnzOTe9D016G3C8ybqw4sDWdRtUpQvAak67w5Rbt/he/Ltut+tmUkynU3Q6HTSbTZydnaHdbjtLgo1rbFUHvhInXrPiEeEhkQgR+5/fHgL/YWW2w/O8jXkN/noHvxsBwBUuAdgQE1uoRbFgEJKpSroSADaEwU6ysoea3af89+FwiFqt5iZbzWYz9Pt9N8+S28D4PmxIY68He1sYBBXhIJF4i7HZD3ZTep7nSqKJbdDyt2szi2EtFMY36MLYAbVMc9rR+6xt4JKebre7sSzYuioUCE7J7vV6KJVKSKVSWK1WGI1G7jWGw6GLa9ihOyzKqlQqbgK3ejfCQyIRIjbLYDMcN01qYgeoFQjb7GXTmjZz4U9D2rJrWyxlR9NRHGyhFkfSce8HLQhaEbxOO0iGWZjhcOjmYVA87NBcBjftHo9Go+FqJjSRKlwkEiHhLxSyQTlbgg1cV1CyVoFuAUfTjcdjFwuwOzlsrAO4rr2wa/XsYbVVmBQbdoP6N5XfJGQ2pmK7VtfrtdsOxt/bVCsH+pbLZTQaDRwdHaHRaKBarSKTyWwEc8X2kUiEhC1uYgrUX/TEQ8jJ2Pw6xin4oKnOmZN2AIxdwsMHXQL2Z9jqSltfwX6Or5tJCVwLi51rYSs42eXJoCmtFIrfzs4ODg4OcHx8jMPDQ+zu7iKXy20ET0U4SCRCwHZ62lJqv0gwXck5CxSL+XzugpjpdNqVPnMWpd8q4B3bzpe0g2vtcygU9vr8NRT8DISWij/4elNKllYQ/41zLA8ODvDee+/h6OgIe3t7TiAUiwgfiUSI2MMIXB9oHihbQ+FvIWftBDeI06q4qVvTVmj6hYh3fOtCWEHwWw7+NKtN5dqsDIuw7MNaBRS/XC6HWq2G4+Nj3LlzxwkEKywlEOEjkQgBO5XKDpTx10n4p0X5axzY1m1H4tsYAw83zf7hcOhMfgqD3RRuC6Ns4ZW//+Kmz0Jrh/tDCoUCisWi2wvied6GZcAMio1DHB4euq5RCcTbQ+Rr5hloz9+/AHtwbQDPX1p9kxnvFwq7v5P+v3+dHr+G6UfOqry4uECz2USv13OxCWY67HDcNy3PYXwkk8m45q5qtYqdnR3X6JXL5TbmcdLaYCyiWCyiWq2iUqm4DWZq5gqFN36zJRIh4K94fFO5cdAdnK9j3QNbqm2bvGz1JrMaHIt/dXWFTqfjhMJu4GJgk3/P96AVkM/nUa1WUavVUK/XUavVUK1WUSgU3D4QuxjIpmwpFCwUs+6IApWhIJF4G/H7+P/InfOmZq6b/g54vduTFggtCBZW2apJisVoNHJxDu4uLZVKKJfLKJVKbtmxtRj8FpD9nP5YhkqvQ0ci8a4R9HP1C8lND2uB2OClrQJlm7q9+98kCkFIFN4aJBLi6/ma/wtvRAf9O8Ebf4jKbgiHDru4CUWIhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgUgkhBCBSCSEEIFIJIQQgcS/5t8jW7kKIcRbiywJIUQgEgkhRCASCSFEIBIJIUQgEgkhRCASCSFEIP8PnEiGq1CBzb8AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 49\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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37Ha7KBQKuHr1Kq5cuYLDw0MRwciLnMxwms2mqFrQ9yrPiFCrt1zylGdB0uk0bt68idXVVbhcLpw7dw6tVgtut/v7/8UwT8AioROyI5N6MlIe81abxWpVKSgHMRwORVOVbPlmNBqhKAomJiYwPz+PS5cu4dy5c6J1elwLNs2LbG5u4uuvv8bOzs4TTlH0HBpJLxaLQgxoKK3T6cBoNMLhcMDlckFRlJHnUv/FwcEBrl27huvXr2Nvbw92ux0PHjxArVZDKBTihiodYJHQATlSkDsP5a/JlYunZfbVkQVFC/RBJi52u110Qy4vL2N5eRnxeBxOp3OkokGvQ9dYLBaxtrY20n4tb21oBsVms6FWq+Hg4EC0gMsGNjabDVNTU1hYWEAikRCj6MCjbtJ8Po+rV6/i888/x+bmpjhxbGNjQ4zSMycPi4ROdDodNBoN0dcgo44ingW665O7VL/fh9lsFlZ3Ho8Hfr8f0WhUNE3F43G43e6xTthyFJHL5cRwmOyURaPcfr8fMzMziMViAIC9vT1Uq1Wk02lsbGxgf38fjUYDFosFyWQSP/7xj3H58mWkUim4XC4Mh0OUSiVcvXoVH3/8Me7evSts/nu9Hg4ODlAqlcRUKHOysEjoAHUt5vN50dasjhq+S1+APP9BdnQulwvhcBiRSATRaBSJRAKTk5MIh8PweDxjz7Sg16GO0Ewmg3K5LEqm9ByPxyNEh/wwTSYT8vk8VldXhV0/LXiDwYC9vT1kMhkcHR3hzTffxOTkJPr9PtLpNP72t7/h6tWrKJVKI7kZakPnXgl9YJE4YagdO5PJYGdnB6VSaWTwihbG87w+LSbaBkQiEUxPTyORSCASiSAUCokIQt0OTe8vC0Q2m0W320UoFILD4YDNZhOvOTMzIyKS4XCIXC4nej+2t7dRqVRG3K96vR7W1tbQ6XRweHgoyptra2v4z3/+g0wm80TOQ96WMScPi8QJMxgMUCwWcevWLayvr6NSqYh+A+pefBF3TPJq8Pl8iEajiMfjiEaj8Pv9cDqdIxGEusLQbrdRLpext7eH9fV1HBwcwGw2Y2ZmBsFgEMlkUmwvvF4vbDYbAAi/TMpjyPZ49Po0pLa7u4ter4ft7W30+30hRuPs861WKx/OoyOaIsHh3YuDqg7lchlXrlzBp59+inQ6LfwjgPGW9N/lfcxmM+x2O/x+P2KxGKLRKCYmJuD1euFwOJ7wnJT9HhqNBvL5PDY2NvDgwQNsb2+jXq/D4XBgZmYGKysrmJ2dRTAYhKIoIxb+5GwFPDq/Y1xehd6r1Wrh6OhINJCVy+Wx7ddGoxGhUAihUIiP+tMJTZFg5X5x9Pt9bG9v41//+hc++ugjfP311yiXyyNeCc8rEiQQVOacnJwU+QfZlJbei8SJypTFYhHpdBp37twR+YRGowGn04mFhQXEYjHMzMyMDG/JJUyr1Srcvo+z4pebvmjrRcNp47YUTqcTZ8+eRSQS4f+POqEpErdv30atVuNfzgtgbW0Nn332Ga5fv450Oo1WqyUW0ov4+VInpdPpRDQaxdzcHBYXFzE5OQmfz/fEXZ+2N+12G5VKBbu7u1hdXcX169eFWS6NaodCIWFn53a7xVSqHI2oB7Zk12u56YuG1pxOJ1wul2g1V9vV0WukUim88847CAQCz/0zYr4bmiLxhz/8AX/5y19E5x7z3alUKsIngpDnLOTFoe5X0IKalmw2Gzwejxj5Pn/+PM6cOYNoNCoqEnSHJ0+KcrmMbDYrkoa3bt3C9vb2SMs1jXLXajUAeML7ga5Zdtei0qjVahX/Tv/m8XgQCoWE10Sz2UQ+nxePo9c1m80Ih8P44IMP8MYbb4i8B3PyaIoEHejCvHhkh2pZGOhzmrU4rsOSWrAVRYHP5xs5bPjMmTNIJpMiDyFPkJJPQy6Xw/r6Om7evImbN2/i4cOHKBQKI7kEusuTZZ48VyJDC7zb7YrjAl0ul4g6zGYzAoEApqamMDc3h8nJSXg8HvR6PWQyGWxubiKdTiOXy6HT6cBsNmNiYgKXL1/Ghx9+iFgsxvkIHdEUCWpckUNG5sUgJxjVMxbUUyB7QsoO0iQMkUgEMzMzmJ6exuTkJBKJBEKhEPx+vzgblHIQ1D1JW4vbt2/jq6++Gsk9qBu7ZPMaOmVMbg1XVy3IpLfX64lzPenw4ZWVFSwtLWFychJutxtGo1GY7sRiMQSDQezv76PX6yEQCGB5eRmXL1/G0tISRxE6oykS8hgw82JRFAWRSAThcBgGg0G0INOhO/I5GrQ/d7lcCAaDmJmZweLiIhYWFjA9PY2JiQm43W4hCrRFkHMGlJw8PDzEvXv3cOXKFdy4cQMHBwfC/m4c8hyJ7F9Bw1302lQVOTg4QKvVgt/vRzwex9zcHM6fP4+FhQWEQiHY7XaYTCYR1fj9fng8HkxMTCCfz8NgMCAWiyGVSmFqagoul4ujCJ3hPgkdII+En/zkJ5idnUW73cb+/r7wuKxUKuKubTQaRTmT5h5WVlYwMzODUCgkeh7kPIEc+cmnZZFPw8OHD7G+vo5cLjfiYTEO2c262+0KoaD3orJpJpPB/fv3sbGxgWq1Cp/Ph/n5eVy8eBGLi4sIBAIj7d80hCafZVqv14VTViAQEDMljL7wb0AHAoEA3n33XXz44YeIRCKiuWh7ext7e3vCgAYA7HY7gsEgEokEUqkUkskkwuEwXC7XiDiMs4CjSJAikm63i1qtJsx2yQZPCzr6z2aziW0HNUnRaPfBwQHu37+P1dVV7OzsYDAYYHJyEtPT05ienkYgEIDdbh+pXlCERHb7FotFnF6mKIo4HZ3RH/4tnDBGoxFTU1N4++23sbKyAqfTiXa7DZ/Ph4mJCSSTSdHKbDabxQlfExMTCAaD8Hg8wqthnHGLuuSoTohSLoRKlDRiPm5LaTabYbVaxYnltF2h96lWq9jf38e9e/ewtraGvb09dDod0ZMRCATgdrvFtaorIsBjmz5K1pJg8PF+pwcWiRPGbDYjFAohkUiI+QlaRDabDcFgUHQeUvWCFqmccxjnZn1cgplyGjabDYFAAIlEAtFoFNVqVUQb6mP/SCBcLhf8fj/sdjsqlQo2Njawt7eHer2O/f19pNNpbG1t4fDwEO12GxaLBYqiiFbqcQNk6muTXbPl74NzYacDFokTRq5iyEa1VqsVbrdbnJtJd3nyjaS7v3p7oX7tcR8UPTidTsTjcZw/fx7D4RBOpxNbW1soFArCpRuAuB6HwwGv14uJiQkYDAbhx1mv14VPxNHRkdi6GI1G0SBFXhbPEhHI/SJyv4Xsj8FRhX6wSJwwcm9AKpUaCcNJBKgyIX+uNrgdNxMhz2Go7e3MZjMcDgcikYg4zXxhYQFbW1vCr0GuctDdnRZoq9VCqVQShxVXKhWRmyBxoYjI6XSKLs9nqUzIWyFKtNJULJ3yxT4S+sEiccIMh0Ps7Ozg888/F8fq0SIglydZLOSoQe1iRa9HX5Ndrehxcps07f8dDgeCwSBSqdTIkYGNRkMcC1iv11Eul5HL5ZDJZHB4eIhcLodCoYBGo/FEiZa2Sy6XC9FoFNFo9AljXXk7M24rIZdq6ZxTp9MpRtQ5mtAHFgkdqFar+Oc//wlFUfD222+L+QqPxwOXy/VEeC3PWqh9MOXogf5OUYjFYhnbN0FC4fF4xPPIhLfdbqPRaKBQKGBnZwfdbhdbW1vI5/PI5XIjVRG5h4PKmHRATzweH4kk1ElV+jf6Ux5TL5VK2NnZQTabhc/nw8rKCuLxuDDWZU4WFgkd6PV62N3dxaeffopCoYCzZ89icXERs7OzI63a1EpNi5fao2u1mjCXlfftcjWCBqjkpKB6gEq9SKlz0uFwwGw2i+E+2mLQ7Ik6yUneFbFYDGfPnsXS0hJCodDIOaDjUPtYdLtdVKtV7O3t4ebNm3jw4AF8Pp9IuLJI6IOmSKizzcyLo91uC+fpo6MjlMtlABC9D9Q/0O12hdXd9va2yCGQWQ31GjidTgQCAYRCIeEhQYNfwOjWQ17cwOhipd81nSper9eFQMgRBEFJzlAohJWVFVy8eBHT09Nwu91C7NT5EUKOIDqdDqrVKg4ODnDnzh18/fXXePjwoXDWWl5eFklR5mTRFIkX6ZTEjELNTYeHhwAelRzJsJbMW4bDIWq1muhFoEGsw8ND1Ot1YfTicDjELMf09DQMBoNIHj7L707dv0A3B5oUJYFQvxYlRN1uN+bm5nDp0iXMz8/D5/ONzIzQ9zvuWmimhL7P1dVVMVOSz+dht9tx//59VCoV9pTQCU2RoFZgHhV/PobDIQ4PD8W4NUF30HK5jEwmg+3tbcTjcTgcDhHW5/N53Lt3D9euXcPq6ioymYxws6KTx+12O9rtNoxGI3w+n2jpBp4UgGdZZHR3pwQmTYbKyANqiUQC586dG2m/JoGQTz8noZEjGDr3M5vN4u7du7hy5Qpu3rwpZkparZZw3+b/g/qgKRK//e1v8etf/5rV+zkYDoewWq346KOP8Pvf/x4Wi0UcCqwekCoWizg6OkIwGBQCsr+/j42NDaTTaeTzeZEXkAesTCaTOLiXDr+RG5meVRzoeunu3mq1hK8EvYfctWm328VBPwsLC+IsUXKcokRko9FAvV5Hs9kc2a5QW3c+n8fDhw9x584d3L9/H5lMRvRtDAYDMcvC6IOmSJw7d+6kruMHD41Hf/nll+IsTVrotJBpcdLxf61WC7VaTSww2VEbeHw3J8PbWCyGZDKJeDwOn88nSqvq3MPTGrFIoHq93sg4e7/fF/0KVqsVPp8PiUQCyWQSPp8PRqNRiAPNeRQKBeRyORwdHYk8CjWTycY3u7u7yGazwutSTo7KRj3MycNGuCfE7Owsfve73+HGjRv485//jL///e/I5XIYDoew2+3iEB2a6qROS9nqzWazod1uA4DYajidTkQiESwtLeHixYt45ZVXREmVbOaA0R6F46AogrYHJpMJHo8HgUAAzWYTZrNZCIeiKAiHwwiHw3A6nSIBSw1WtPj39vawv7+PYrGIarUqpkjJHater6PRaAinbTpsWMZisYhKCXPysBHuCWE2m+H3+/Hmm2/C4XDAbrfjs88+Q7lchtvtxtTUFJLJJCKRCPx+PxRFEXflarWKSqUiXJuoqkEngy8tLeHChQtYWlpCLBaDx+MRU5TqwSpgNKJQT43KJ5GToW4ikcBgMBC2dtR2TQnKcrksTuoiY5tcLod8Po/Dw0NUKpUREaB8C/VmyG3qagwGA9xuN5xOJ/tK6AT3SZwgVK5cXl7GBx98gMFggAcPHsDj8WBxcXHEuNZms2EwGIgRa4oaKKFHZceFhQUsLS0Jfwl1LgJ48qxQuha1QMgNW9QcFYvF0Gq1YLPZxFka5JwFAIVCAdVqFcDjowur1arYIrXbbXECuSxAz3qUocViQTweh8fj+T5+JcwzwCJxwtBsw/LyMqrVKmKxGBRFQTKZRCKRgN/vF81MtBWx2+2iX+Do6AjdblfMYSQSCeFMRdsLLW8JdRJT3bFJH+SEFYlEhF2+0+kUuQVq56ZtA4kB5RPGiYD6PZ4Ft9uNxcVFFgkdYZHQAZPJJNqN/X4/TCaTsHGjszEotKYTwRVFQTAYHDGjcbvdcLlcwi5fDsfHDXlRy7bRaHyiPVq9eKlS4vV6xdkYVIFpNps4OjoSEQOdPape/HJSVuZZc13kvbGysgJFUb7DT5p5EbBI6AC1MYfDYdjtdgCPFr3sLSEvamq5djgcYoycxIMGwsYJhHw3l2cs6O9yN6Q8+wFAzH6QKxVNghaLRVGtUHdhqs1uKKoh/wuq3jzNDYtwu9147bXXsLi4yC5VOsI/eZ0wmUxiAIp6HcbZ6xPk2ARgRDjkwS3gyWP7yAFbjh7o71oiIb9eq9VCoVDA3t4ednZ2kMvlnhgTVz+HRMJut4tzP9rtNqrVqkhiamGxWLCwsIB3332XOy11hkVCJ+gOKxvDjFvs8uPpMbLXxHH5BRIIGgKTj0eQXxMY3ZrIr0Ht0tlsFul0Gpubm8J0ZtzBvjKU00gmk5ibm4PX6xXOVul0GuVy+dj+B7PZjHg8jp/97Ge4dOmSiLYYfWCR0BG62467e8tbBnUT1LiFTshVBMoV0PPlSVCt6gY1OlWrVWQyGTx8+BAPHjzAwcGBSFZqCYTRaITD4cCZM2fw3nvv4cKFC/D5fKhWq1hdXcUXX3yBGzduoFAojFwfRUvhcBg//elP8Ytf/AKxWIwNZ3SGRUIn5InMcUlEdYVAjjSOc6iSk49yuVH9OHoPOZKQHaHa7TZqtRry+Tw2NzextraG3d1dlMvlpwoE8GirMDU1hffffx8///nPkUwmhdv23NwcYrEYHA4Hrl27Jqo1AGCz2RCNRvHOO+/gV7/6FZaXl/lgnlMAi8QpQhYIajSSh6Joe6LOW5DQjCsxytsU9XuNe08SiEKhgN3dXWxsbGB3dxelUmnsJKgao9EIj8eDH/3oR3jvvfewsLAgGqGGw6GYLfF4PIhGo7hx4waKxaLYYtDzzp49C4/Hww1UpwAWCZ1QG77Q5/KClduUDQbDE30H46oaskDIfiDqE73U70cDXeQfkc1msbOzg93dXRSLxbGToOOwWCxIJBK4fPky5ufn4XK5RioTRqMR0WgUb731FsLhMC5evIhsNgubzYbp6WnRUOZ2u0VVhNEXFgkd0borq+coAIyUGofD4YhRLfBYJOTtiRxJjOvAJIGgOYpyuSycsLPZLEqlkrD4fxYURcGZM2ewtLQ0YjxDkElNIBCAzWZDIpFAo9EQZ4zQcYUsEKcHFolTxHEVDLlRSb2tOK5HQn49+lzORcgCIbdTl0olFAoFFItF1Go1IVDj8idq6Ii+VCqFSCQijGfU3yPwKOKgobV+vz9S1mUL/dMFi8QpQN0qPc5/koa95I9utzty2pXc9zCuYiK/HpU4O50Oms0marUayuUySqWScKoGICztyR1bffK4fM0kEuFw+KlTm3KzFQnXuDIwPZbRDxYJHTnOd1JtFDMuoUkTofKH+rXk95GhbQwJRL1eR6VSQblcFj0QZKEXDAbF5CeZv6i9LmnBW61WsVVQb32OQ0vMAIxt62ZOFhYJnVDfOdVNUWqRoMiBDGHI74Fs8+lzdS+EWjjUicparSZG0avVKprNJobDoZgVobM0PB4PcrmcmASla6A2cfogJ6lKpYJgMDh26zCuTDvuWgEce6Qhc3KwSOjMuAYp4HFlwmKxCIMZqnDIdvokDmSnL89yqE8AAx5HEZSorFarKJfLIoqgKobdboeiKPD7/QgGgwgEAvB6vdjd3UU+nxcuU3K0QALx8OFDbG5uCrcq+TxQdS6ExEaeLVHPfnCOQl9YJE4BxwmFOolJC5ESjdSERC5WJBLyoTyUDKTXkp9fq9VQKpVQLBZRLpeFwS4JDk2X0p/kxUleETR/QduhTqeDQqGAu3fvIhKJwOVyAQC8Xq+YO6FIiM4Qoe+DTgBTFAU2mw1Wq1UMlzH6wiKhI8e1V6tFQ92dKftgypUBEgr5gyY5KaoYDAZot9toNpui3Hl4eChGwIfDoag89Ho9WK1WYVhLH2SQS1GJnEeoVCpIp9P48ssvoSgK+v0+pqenxUFBJBD0vjTDYbPZ4PV6EQwGEQwGhcBxTkJ/WCROCXKCb1x1gqCIgMJ22nZQWE5RAN2VaTiK8hW0XanVamLs++DgQJwsTv0XZHZjMpnQ6XREg1U+n0e5XBZCIV8nlWrL5TIePnwoDhhqtVoIh8OwWCziGL9MJoNMJoNyuYxerwePx4NEIiGOObTb7U+Y6DD6wCKhI+NMYcZ9Xc70yyJAVQayh6OwXfZsoCiDPicXazoMeHd3F/v7+0IkyDafxtAp8pCjCHq/cdB1FItFbGxsiEVfqVTgcrnQ6XRQLBaRyWSQzWZRr9dH+iOooYps+Hi4S39YJHTmuIhBbpiSZzfIkYr+brFYhJckbQG63a44J0N2vpYX/NHREfb397G7uyvu6FTeVLdty8a1z2I7R4nJcrmM/f19BAIBmEwmeL1eEWlQObXf74utxsTExIgVH89tnA5YJHTkuG2F3PAkRxFWqxUAxFkbDodD3OGpAUr2aJBfh0qNzWYTlUoF+XweBwcHyGazODw8FEYw8vuqP/823xclMilqKZVKYuvQ6/VEotJmsyEUCiGZTGJ6ehqBQECY//I243TAIqET6g7IcR2T6m0G2daRQLTbbTgcDjgcDthsNuH6RKd5ye5TFAk0Gg2USiXkcrmRvgfZwPbbioIauYQJPO7N6PV6ouricDhgsVigKAqmpqYwPz+PSCQCh8PBeYhTBouEzqg7DGXk7ksyp5EHsxRFgaIo4hwPu92OWq0mDvCReyfoPbrd7ojtvVypeBECATz2x3Q4HPB4POLYQVkYKFfh8/kwNTXFk5+nGBYJnZDvturW5WdZpBTSkz+D2+2G3+8X/QvUxSgPgLVaLZhMJjSbTeTzeTHlSbmBF4HdbofX60U0GkUqlcL8/DxmZ2cRDofhcrnE0JfB8MgM2O12IxQKCR9MzkOcPgxP+Q/J5/x9z8jbje/6fHWbs9p3grYcvV4PzWYTxWIRW1tbuHv3Lu7cuYP19XVks1nUajURWajnM7Sw2+3w+XyIx+NIpVKYm5tDKpXC1NQUwuHw2BPFaAtFvR3qaVbmxDn2PyCLxA8Ira0C/Ts1QLVarSfO68zn86L7kjoxaSq02WyKvgjKKQSDQUxPT2N+fh6pVAozMzOYnJxEIBAYKWGOG2KjP3ku49TAIsGMoq580CwFnWZOh/hWq9WR9mng0YniLpcLPp8P0WgUoVAITqdzxIGKF/5/O1gkmOeH/R1+0Bz7S+XEJaOJOrdx3L8xP1w4kmAYBtCIJDidzDCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaMIiwTCMJiwSDMNowiLBMIwmLBIMw2jCIsEwjCYsEgzDaGJ+ytcNJ3IVDMOcWjiSYBhGExYJhmE0YZFgGEYTFgmGYTRhkWAYRhMWCYZhNPn/iEOWy33IY9IAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 51\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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00UeoVCqcX6DPATBRDaHjDVVUNE2DaZrwer0IBoMTnZ005EZHEUpa0tFJfS3hYhGRsAk6OtAZvFQqoVqt8vlbHXRSjWcoMUnJSrXrkpKdDocDXq/3iTKqmhQ1TRNLS0u4ceMGrl27hmQyee5RgyoaOzs7+POf/4zDw8MnEqHD4RCNRgPlchm1Wo2PFIPBgI9UhUIB7XYbhmGwj2YwGITT6eTJ0X6/j1qthmq1imazCU3TkMvlOHkp06AXj4iEjZAJTLvdZrFQS37TfQpq5yJFDQA4aiBRIR8IihyoSkJPa5/Ph0QigevXr+P111/H+vo6AoHAzLInNUCdnZ3hgw8+wK1bt7jDklATmplMBg8fPuScR6PRQDabxcOHD1EoFOB2u7GwsIB4PM4zJ/S63W4XxWKRKzLtdhuj0QjHx8efa6mQ8PkQkbAJNQyno4Jazuz3+xMfVw1kVJs4VQioEUnTNAQCAfj9fq6SUPLP5XIhHA7j0qVLePXVV3HlyhXE4/GZZrP0Os1mEw8ePMA//vEPTipOVxuogSuTyeDu3bvsdnV6eor9/X2cnp6i0+kgGAyiXq8jGAxywpIilbOzM3zyySfIZrPchzEcDnFycoJqtSqRhE2ISNiA2hhFA0yq1wLNLailS3WGYxpN07gZC3g0E6HrOrdFB4NBHqIyDAOpVAobGxt4+eWXMT8/D8MwJsbC6TWoxFooFLC9vc3HjOnXVo1xSqUS7t69i52dHZTLZWSzWRSLRbRaLQCA3+9Ho9FgTwzgkafE2dkZ7t69i+3tbeTzeY6UhsMh8vk8isUiJ12Fi0VEwiaoYYiODqprtDoINT0XMQuKSgCg1WrB4/HAMAwAgGEYXELVdZ0dsS9dunRuCzZ9TzKVOT4+xsHBAYf81LOhCgS9TrfbZQfuYrGIWq02cXxqNBro9XrcbVmtVuHxeLC/v48PPvgABwcHnIil66jValxyFTPci0d+4zZA+QXK3NOg1qy3Z2kiopwDRSKj0QherxfhcBiBQACBQADxeBwLCwtPuHHPEgiypctkMigWiwAe2e4Bj57+lFwNhUKIRqNspFsul5HL5WZ2epIB8M7ODobDIU5PT+F0OnF4eIjt7e0nqibAo/UD1WpVeiVsQkTCBkajEWq1Go6Pj3FycsKNVKonxGc1WlEHwQDwsBVZyZHt/rRAqK+nCsTBwQEODw/RarW41wF4dGyIxWKYn59HMpmEaZrodDo4PDxEPp/n/Mj0jU1l3EKhgNFohGKxCE3TUCqVUCgUnjjOAJj4eYSLR0TigqEkXSaTYZeoer3ORw51iOuzfn/gsakNJQhpExdFFWqiUnW+ojJrsVjE3t4ebt++jf39ffR6PUQiEaRSKczNzWF1dRVra2uYm5tDIBDAcDhENpvFYDDAgwcP+Kg0C7UaQlvO6Yg1SxjJw1OSlvZgKRLSL//F0+l0sL+/j7/+9a+4desWTk9P+cZ+1uPFLKikaBgGW8MlEgnE43F24ybHKdWvgpKU9Xqdre5IICqVCgAgmUxiY2MDN27cwMbGBhKJBAzDYD8L0zRxeHjIFnRWPwslaulnnnazUjEMA8lkUvIRNmH5Wxfl/uIYj8fI5/O4d+8e/vCHP+Avf/kLstksNxHRUJXqkP2s0QR1UpIJLdnix+NxhEIh+Hw+trensmqn0+FBMypB3rp1C/fu3cPJyQmazSYcDgcfWa5du4YbN25wVYSiEV3X0el0EIvFOG9B0YnV70TdbXoeyWQS6XRaTHFtwlIkbt++jUajIWLxORmPx8hkMvjoo49w69Yt7O7uct2fjGLoKUl/p36HTxtZUCIxGo1iZWUFm5ubuHz5MhYXFxGJRCbW5JEPBb0vFos4PDzEvXv3uNRJPpgAeGGOYRg8kEX7PdR8BpVdqZpyHiRmqsnuefh8Ply7do0Ng4WLx1IkfvnLX+J3v/sdeyYKnx2aRaAEHE1gUpckNRVR5YN6JKwSdmoJ0jRNxONxrK2t4fr163jllVewurqKaDTKT3yaF6EBsZOTE2QyGezu7vI2r3K5POGBSfs4aPCK7Oior2Lac4LmL0j4KM9Cn0tiRu3j1HVKZrzqvzOHw4GlpSW8+eabiMfjX97/HMESS5GoVCo4Ojq6qGt5YaA5C7KMi8fjvAvU7XazeaxabVAhYVAHpdLpNNbX1/ltbm6OF/3Qzaja6+/u7mJ7exsPHjzA0dERSqXSTNNZmguh61HLpdPDXNQ2Tj0ZnU6HowVKPpqmyca/lLykdnS1k5TmS9544w289tprvLdDuHgsRYLOgM8S9gqfDp/Ph5WVFVy7dg1zc3MAHo1Kn56ecgclzTOoQ1q6rsM0TSQSCaysrGBtbQ1ra2tsPRcOh2GaJo9qq3b73W4XuVwO9+7dw4cffoh79+4hm82y2eysHIjqt6kuA1bf1KRnvV6Hw+FAIBDg76Fe79LSEkKhEOdo9vb2sLe3x8NftJBH13W8/PLLePvtt7G8vCxHDRuxFIlZa+SFz4/b7UY6nWYHJ7/fj1arhUKhMDFopes6u1GTD2UqlcLly5exubmJ9fV1LC4uIhwOc1KS7PinnaxoBuPk5AT379/H/fv3cXx8PDFJ+jTUioT6b0P11Dw7O8NgMODmrUQigStXruDq1au4dOkSYrEYz5mcnp7izp078Pl8ePDgAUqlEvr9PrxeL+bn5/Hmm2/i5s2b8Pv9IhI2IjUlGwiFQvjmN7+J73znO0in09A0DfV6fWIhDvk/ttttOJ1OhMNhrKys4KWXXsLGxgYWFhb4OEERw3TXJEUANCZOFYyjoyMUCgWubDxNINR9pLQVnF6TjgyU+MxkMuj3+2xAs7W1hddffx1ra2sIh8MT5jnRaBRer5dzFUdHRxgMBggGg7h06RJee+01zM3NSVXDZkQkLhhN07C8vIw333wTL730EvsuqJ4R4/GYn6YAEAwGkUqlsLKygsXFRUSjUY4cZonDdARB77vdLk+ZTjtYWV0v5T8AoNFooFKpcLNUtVrFw4cPsb29je3tbb7R4/E4Njc38dprr2Fzc5NX/VGE4/F4OClKVZRQKIRut8tTqqlUikVEsA8RiQvG4/FgeXkZly5d4i1ZdLP4/X5EIhH2tez3+9B1HdFolBOcar5hurrwNKj6QJUHtSX7vM93u93sVTEcDpHL5bh6UavVcHR0hAcPHuCTTz7B2dkZer0e/H4//H4/FhcXkUqlEAwGWRTo9eg4FQ6HkU6n0e12oes6ms0mDMPgfovP2p4ufHGISFwwqoXbdKWAEnZqVULduk3dkpR3oK8DnswbTf+dzGbi8TiSySRyuRy7YNGRQ7WgUyMIauOmLeLkDXF2dsbzJzSY5XK5WFTIeWo64qH36s+bSqUwGo1QqVS4uYq2eFGeRrAHEYkLhm4EWpZDlQwqHwJgb0rVE8LqeKH+ebrqQB/3eDzsaVmr1TAajaDrOgqFAjdWEWo/g9/vRyAQgMPhQKFQYLv7SqWCSqXClns0/6GKm2maTywdVqFSsM/nQzAYRKPRQLfbRa1WQ6lUwng8ZoFU/S6Ei0VE4oLp9/v45JNP8NFHHyEWiyEUCvH2b8oV0HCWruvQdX2ibRuYbTpL79UNX6oBrsfjQSQSgaZp8Pl8mJ+fx97eHjKZDHK5HK/TU5uo6EjidDrR7XZxcnLCS3Pa7fbE5jAAfCOr4+lPu7np+ENbvMbjMarVKvL5PA4PDwHgiY5R4WKR3/oFQy3a77zzDjRNw/r6OjRNY08J6p6cXu1HVYrphib6nurnzXKxIqHxer2IRCJYWVnBzZs3US6XUSwWUalUOKnZbDbRaDTYkLZcLqNaraJer/NGremBLLomr9eLeDyO+fl5Fgm6xvPEgnIl4/EYrVYLp6en2Nvb40XCyWRywnpfuFhEJGygXq/jgw8+QL1enzChpdZqqmpQok9tXT7PzXpaIOgJra4KpK81TRPRaBQLCwsTbli047NUKuHk5AS7u7t48OABzs7OeGsXOWapAkTHE13XkUgksLa2xtvAVLu56aOR2phFHhbFYhGZTAYHBwe8jSydTuPq1avSL2ETIhI2MB6PUSqV8M9//hMHBwcwTZO7EtPpNLdADwYDNBoNnmugXZrU+TjtO0Fj4l6vF36/H6Zp8tOcPnZeFEIdle12G9FoFLquo1Kp4MGDB7wNjARCfU1KuNJw2cbGBl555RWkUin4fD6OEOjYQ6jCNhgM0O12eZ6ENoTRJrJ//vOf+N73voeFhQVJYNqApUioGXQpQ32x0Mq8Xq+HcrkMwzA4mQk8GuWuVCq8k6Jer3OikBKNtDfU7XazsWw4HEYymcT8/DwWFhbg8Xi414Caomb5WaoiMhwOeVGPumpvWiCAxyIRDAaxvr6Ora0tXL58GZFIhDd4qUcTdWqU5kna7TYqlQpb7+dyOf7d9Pt97O/v4/j4GFtbWyISNmApEjQzIALx5UAzD2pE4PF4eEmNYRjo9/uoVCooFAooFouo1+s8iAU83j7u9/tZIFZWVjAYDOD3+3lOAnhc2lRLkWqugESEbsROp8NHjFn9CnTMMAwD6XQaN27cwMsvv4xkMsnCNCtHoopEu91GoVDA4eEhT6JWKhX0ej327KxUKsjlcrLqzyYsRSISiSCdTsuo+OdkPB7zGr9paBxc0zR0Oh12hqZ1f81mE6VSaWJD+PSuT7fbPbHRixqwaH/HrMGtWUJB/43yA7Vajb0qzxMIr9eLZDKJzc1Ntuj3+XwAwHMeFBGouQyaA6nVashkMvjXv/6F7e1t7sGg1xyPx+h0OqjX62KEaxOWIvGzn/0MP/nJTyRZ9DmgFuTf/va3+MUvfsFRwizRpTwE7aggO/lKpfJEVYFubNXevtvtsk0/3Zjq5z/tOilHQPtGaTqUBIQ6Q4HHjVCRSARra2vY2NjA3NwcdF3nORFqA6fKCFVwNE1jV6xCoYD9/X3s7Oxgb2+P+zDUqIOiEcEeLEXi+vXrF3Ud//EEAgFomoa//e1v7PhF//DVrd9001O4rfYj0LFEvYGAx8cIaqGmtmkATzhwE7MqDfTUp9IjVVTILUu9VjLZTaVSSKfTiEajcDgcnL8gM93T01OcnJygVCqx6Q6JYafT4YXJpVIJtVqNj18qtNtUHlb2IEa4F8Ta2hp+/vOf48MPP8Svf/1rvPvuu8jn85z1p3V8Pp8Puq5PPLVnQaJCNzF1LUYiER4dp5wADWOpm8VV1MpJq9XiowsNmhmGwccDSnLSfEUikYDf70ev10M+n2cxyOVyyGQyODk54aPW9NoAcsimyOe8iVSa8ZCkpT2IEe4F4XK5EI1G8fWvf53Hu9977z2USiU4HA5exxcKhWAYBjcWdTqdiZFuNYdAT9hAIMA7MFKpFE+JAo+t6mmuYparFAkEbSyn3IfH40E4HObhK5rWJFEKh8OcDzk4OMDBwQFHEPl8HoVCgY8ZFCGoUcv0CsPz8Pv94pZtI/Jbv0CoJfr69ev40Y9+hG63izt37mA8HnMUQGv3BoMBarUagMcRneqgrdrmJxIJ3odB7dAej4ePK81mc2LPhioWFCFQJaNWq6HVavFUaiKR4O3g1CdBr69pGmq1Gmq1Gr9Wo9FAo9HgMq26qlAViU873elwOJBMJjE3Nyd7QG1CROKCcTgcME0T169fR7FYRCgUQqfTQSAQ4IEoALxBi/IBXq93wqXKMAxe2bewsMBty3RMGY/HbIevNlFRIpVyH5SHaDabqNfrLAYulwuhUIg7NOnGp7ZtEgSKdNSIZ9rmDsDMXMqnQdd1bGxsIB6PS2RrEyISNuB0OhGLxXDz5k0YhoFms8lJRwCcPDQMg3MVwWAQnU6HHavj8ThSqRRSqRRisRh8Ph+XC2u1Gs9fUKiv3qx03FETiHTMoIlOSkzS2Dft0mi329z8RBu41KrLdIJ0+sZ+1jxXNBrF1tYWrxcULh4RCRugQaiFhQW4XC5edUdVDbrRqZ2a9nnSzRuJRDA3N8fGt5R/oBIjHVWazSY0TWMnauCRSKgRC+UiqMRKDUwUbVBkQh2QlUoFxWIR5XKZ+zLOSziqTVr02s8iEi6XCy+99BK2trb4ZxQuHhEJm3A6nXzmp83b1IFIfRL0dKZlPTQhGo1GJ7ZykdckNVo1m02Uy2XOaVAEQqPfFE1Q9aPT6UxUF6hiAoB7L2iu4uTkhBcCqxHKLNTXpN6I85q7ZhGNRvHNb34Tly9flsqGjYhI2AT1NZBPAt1AaqMRzV3QE5hcnKjM6ff7uRuW8gC0fIeOD5QoNAwDgUAAhmHw61GUQE1XwKTpLQ2Y5fN5ZDIZHB8fI5/Pc5PV0wSCfj4yrWm329x/8TSh8Hq92Nrawre+9S1Eo9Ev7hcvPDMiEjZC49zA42XB9ORVB7GokuD1ejlHoXpGqo1QlDOg3gPg0ZFC7UmgJil1AEttzKKZklqthtPTUxweHuLg4ABnZ2dPuFidh9vt5m7MlZUV6LqOs7Mz7O7u4vT0lPswZqHrOi5fvozvf//7eOWVVziqEexBRMJm1NZqdYxbHaOmaU/V+VodnCJPCDo2qMt81L2b6ki4uj5QtbwjsanX69wQdXh4yMtzVCeq83A4HAgGg7hx4wbeeustbG5uwu124/T0FH//+9/xt7/9DYeHh+yxqf4uPB4PFhcX8Z3vfAff+ta3EIlEpPRpMyISNnNeWY8GvyjXQBEFlRrpmOFwOLhHQR0f93g8PO6tLvFV/R1IdEhwqNJRr9dRLBaRzWbZ26HRaDz1iEHouo719XV897vfxVtvvYVUKgWn04l6vc7zHX/84x9xdHTElRGHwwFd15FKpfD222/j+9//PlZXVyWKeA4QkbAZq3Ih8LjZSW2oUpOP9GcaqKI+CNVZm8x0vV4vJwDVTkfquCT/ykqlgnw+j9PTUxaIWV4Ss3C5XAiHw3j99dfxX//1X1hdXWVHKbomEsAPPvgAuVwOvV4Puq5jfn4eX/va1/C9730Pm5ubLHKCvYhIPCeo+YdZg1o0aq3u9fR6vRxRqDkI2hc6HA7ZjVrdBK7mIdSOS7Kvq1QqXB2hsuqnLV26XC4sLS3h5s2bSKfTnFwFHglcNBrF1atX0Ww24XK5sLu7i3a7jVAohCtXruCNN97A1atXEQwGpaLxnCAi8RygDmtR85JpmnzjUq6BhICanyiBSdEElRfJb5LKmeoyH3XxL7lQqYNdtPSXOi/V/MbTHMooubq4uIjl5WX4/X5OrqqTqpFIBFeuXMFgMEAymWTLvEuXLmFjYwOhUEjmNJ4j5P+EzahTmWrrtFqxIIGgqkWj0eAlOGS5T9+DeivU7wk8znGo+Q36fCqbkqcklSjJGo+auihJeh4ul4uX8hiGMdMEl3ongsEgFhcX4XA40Ov1EAwGMT8/z0cM+vlVpyzBHkQkbGTaNEZ1iKK/041NRwGKJKjPgY4lakkUeGxhTzfYLE9LSl6S/4Nqi0d5DWqYcjqdaLfbnCg9z+uSOjkp6qG8Cb2uauBLUQ59HX1vEjJKzJ633Ee4GEQkbEId2Vat49SbQp2tqNVqKJfL3JVJSUqKKFTr/Onw3u12Q9d1bsqiXAUA/v5qdUTd/UFNWNVqFZVKhec71OlOABORCZnarqyssGio/RfNZpMdt6rVKh+BpjtCDcNgg16JJuxDRMJGpg1gpo1paVw7EAggEAjwU5mapsgbkxYA07GAhIaOC7quw+fzPfEUB8BJy2nhMU0Tfr8f0WgU7XYb1WoVhUIB+Xye7fSmTXzJAyOTyeDWrVuIxWIAgHA4DJfLxS3jtJ0rk8mgVCphOBzCMIwJ/4xAIMBiQcNlgj2ISDwnTJvBqOvvZu0DpXwFJRanxYG+Xm3tpu9PYTy1cdNRQ81FqEcY0zTZ6Urt3lSPHfS+3W7j6OgI//jHP9ioJp1OwzAMXhNwdHSEvb09HB0doVqtYjwew+/3T7Rs00i9+jsR7EFEwkam//FP5yXUigeF3z6fj29eABMJTvX7qFEEiQJFGnRDU0RC/hD0PahsSqJEQ2AkJCQQatcn8Dg5Wa1Wsbu7yxOoV65cQSQSwWAwQLlcxtHREe/XoGE2ihz8fj/m5ua4yuPxeEQkbEZEwmbU48UsKCFIW7kCgQB7S6hOT+pWLwAcbVBewuv1otfrwel0TtjV0fo+aphSy6aUTOz3+6jX6ygUCpyXmOVJqVY/KpUK9vb2MBqNUKvVkEgk4HA4eBkwrQ6kawKAWCzGDtzxeJz9LKShyl5EJGxEfQKrPQjT1m4U/pOXpeo16XK50Gq12FKfvn7aGYr+mzoEVqlUnjCPUasidE30NRRJPK09m3Il1WoVR0dH0DQN9Xodbrebx87VBCglXiORCBYXF7G0tIR4PM59FoK9iEjYzHn7N9QbneYaQqEQ+0pQVEEbwev1Oic1KU9ByUzVz4GSlGQeQ9vEp9f4qSY16qDZp+28pCNQq9VCpVLhSgmVOMm5m8bJE4kENjY2cOXKFe6XUL04BfsQkbCR88xgp23gKEcwHo/hdrt552csFkMkEkGhUEChUGC3KIooKJdBvQh0g1arVRSLRRQKBdRqNe7oPM+X8rOsVpieKlUdrCgyIpFIJBJ4+eWXcf36daTTaZimKQLxHCEiYRPTR4vpZif1v5MRLnU0BoNBRKNRNBoNRKNRdtk2DAOVSoWX4FB1gxKd1H5NPQq0pYsE4ovc/arOolDCVJ3opGOEaZq4fPkytra2cOnSJYRCIbjdbhGI5wgRCZuZlZdQJz7pPRnEqJ4QoVAIwWAQ4XAYoVAIoVAI+Xwe1Wp1YqEwJTEbjQab5VJ+gY4Yz3KUeBokbNSfYZomQqEQu3lTTsPj8WBubg7Xrl3D5uYmYrGYVDOeQ0QkbEJ90qpzDcCkcMyCxIJuvlgshrm5OaTT6YmkoLrNu9vtolqtIpvNTuQmut3uRI/G5xUKh8PBi3sWFhaQTqexvLyMWCzGIkFHDtM0MT8/j5WVFaRSKe6wFJ4vtKf8o5A9f18ys44bz/q11K9ABjU0Vq52WNJRo1gsIpPJ4OOPP8bOzg4ODg5QLBZRr9efGEf/tIJBScloNIqFhQWsra1hfX0dq6urWFhYQDQahWEYfIxQ5zz8fj8Mw5jo/RBs4dx/gCIS/yGoeQz17+qfSSyounF6eorj42Nks1lut6YxcfKVoB4KGhtXE4+BQACpVArr6+tYW1vD6uoqlpeXkUqlEA6HWRhUc93pmRXVy1OwFREJ4RHTfROqPyZ1U1LOgrZ6qSPkqvNVOBxGPB5HMplENBqdcL4CpJ363wwRCeHzMd3NKV2Q/3GISAjPznRC9dN+TPi3RERCEARLzhUJiRkFQbBEREIQBEtEJARBsEREQhAES0QkBEGwRERCEARLRCQEQbBEREIQBEtEJARBsEREQhAES0QkBEGwRERCEARLRCQEQbBEREIQBEtEJARBsEREQhAES0QkBEGwRERCEARLRCQEQbBEREIQBEtEJARBsEREQhAES0QkBEGwRERCEARLRCQEQbBEREIQBEtEJARBsEREQhAES0QkBEGwRERCEARLRCQEQbBEREIQBEtEJARBsEREQhAES0QkBEGwRERCEARLRCQEQbBEREIQBEtEJARBsEREQhAES0QkBEGwRERCEARLRCQEQbBEREIQBEtEJARBsEREQhAES0QkBEGwRERCEARLRCQEQbBEREIQBEtEJARBsEREQhAES0QkBEGwRERCEARLRCQEQbBEREIQBEtEJARBsEREQhAES0QkBEGwRERCEARLRCQEQbBEREIQBEtEJARBsEREQhAES0QkBEGwRERCEARLXE/5uHYhVyEIwnOLRBKCIFgiIiEIgiUiEoIgWCIiIQiCJSISgiBYIiIhCIIl/x+IHyHB9MqXEAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 61\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 65\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 66\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 67\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 68\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 69\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 70\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 71\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 72\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 73\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 74\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 75\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 76\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 77\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 78\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 79\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 80\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 81\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 82\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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NTz/9NOUYDElfXx/efPNNnD59GoIgoLGxEQ0NDRgcHJS3OXjwIG655RZUVFTAarXKraNz587h4MGDV3WK9vX1jVqMRaYOhUQaxePxKQ0JqaRbuudnGAYZGRmorq7Gs88+iw0bNoxpSHgwGMR//vMfuFyuUbdNJBKor6/HmTNnhlWODtXT04P3338f5eXlWL9+PXQ6Hbq7u7F9+3YcP378qu2lmaxIelBIpFE4HIbH45nS4dBarRY6nQ46nQ5WqxXV1dX44Q9/iHXr1o16jy817Y8dO4YDBw6MeZ/SlHRKTp8+jb/85S/o7e1Fbm4uDhw4gO3bt4/4JEMq2Qao8zIdKCTSYOhEuNJcjlNBajkUFBSgoqICCxYskJ8q2Gy2UZ9mMAwDv9+Pr776atRWhHTLMtaCrmQyiW+//RYtLS3IyMiAx+NJOaI0EonA7XZjyZIlNNArDSgk0kAaSVlfX4/29vYp249KpYLNZsPy5cuxbt06ed5Kk8k06skmdSQeOHAAu3fvvmoMCMMwyMzMhNPpRE1NDQoLC9HR0YFDhw6hs7NzTMc31gWMpRnCach4elBITDOpFdHa2orPPvtsTEO4J0qj0eCmm27Chg0bsGTJEmRnZyMjIyPliTb0yUEgEMD+/fvxyiuvXLUcoMPhwJo1a3DvvfeiqqoKDocDRqMRfX19+MMf/oD33ntvUvtZdDod8vPzKSDSRDEkprvX/ftO6kjs7u7G3/72Nxw+fHhK9+d0OnHXXXfhlltugcPhkGeuupIUDgzDIBwO49KlS9ixYwc++ugjXLp0aVifSVVVFX7+859j48aNyMvLG3biZmVloaamBrt27UpZtj0Rer0epaWl8qA26peYXoohQb+MycVxHBobG/Haa6/hvffem9JyY4vFgh/84AdYv349cnNzodVqU/ZBMAwDn8+HS5cuYe/evdi1axfOnTt31ZgJu92OZ599Fg8//LA84GzohYRhGDgcDphMpkkNCYZhxr0GCJk8iiHR0NAgr8lIJk4URfj9ftTV1eHjjz9GQ0PDlHVWApcns7333nvx2GOPobS0dMSAkIand3d3o6GhAYcPH8aePXtw8eLFlOFVWFiIO+64AyaTaVjr48p9T/YJvXDhQnlqffpbnH6KIfHqq69i9+7d0Gg0dOtxjfx+P/x+/5Q+7tRoNCgqKsKWLVvwyCOPYO7cuVetgCWFQ2dnJw4dOoQvvvgCR44cQVdX16iPLc1mM+x2u+KJOtlrh2ZmZuKhhx5Cbm7upH0mGR/FkPD5fClnEyKTR6VSQaVSQRCEcYexRqNBdnY28vLysHr1atx9991YtGgRcnJyrurokwLi3LlzeOutt/DJJ5+MOsX+UCzLynNYpBIOh0etjszIyJCfnoympqYG8+fPl4+fWhLTT/E3Lv2RDV1lmkwevV6Pqqoq5ObmIhKJoLW1Fd3d3WM6efLy8lBWVoZFixbhrrvuQmVlJRwOB6xWa8oTSRRFdHR04PXXX8f27dtTDtRKRZrRO9Vni6KInp4eBIPBq17PyclBeXk5SkpKUFRUhFgshtOnT+PEiRMp6yNMJhMeeughVFZWAqBbjXRRDAmpaUwBMfmsViuefPJJbN26FdnZ2ejr68Phw4exa9cu1NfXj9gvwLIsZs+ejVtvvRUbNmzAbbfdBpvNBr1eP6ZVtzmOw8GDB7F79+5xBwSAlOuMSlf4trY2nDx5cthJb7FYsHLlSjzwwANYt26dvKgxz/NwuVx49dVXU07Nf9ttt2HdunUUDmlGdRJpoNFo8Mgjj+Dll1+Wl78rKipCSUkJHA4H3nnnHflkE0URer0e5eXlWLNmDe655x4sXbpUHmY9VlLn6ddffz3mYqcrSTNfj8Tr9WLHjh34+uuv5U5Zu92ORx99FNu2bcOsWbOuaonMmzcPL7zwAnw+Hz777LNhLSiHw4F7770Xs2fPntCxkslDIZEGZWVleOKJJ4Z1Amq1WuTm5uKuu+6CXq/Hvn370NHRIY+3WL58ORYtWjThORVEUYTX60VLS8uEOk9VKpW8ypj0ecDlMuzm5ma8//77ePfdd+UA0mg0uOOOO/CTn/wEs2fPHrE1oFarMWfOHDzzzDPo6+tDbW0tBEGARqPBypUrsWrVqnEFIZkaFBJpkJeXh/Ly8qtOHLVaDbvdjrVr16KyshLRaBRWq1WeCPdam93hcHhcU+wPpVKpEIlE0NPTA7PZDFEU0dfXh4MHD+LTTz/FwYMHh1WPZmVl4fbbb0dpaanicWu1WixduhRPP/00AKC5uRnZ2dm49dZbkZOTM6FjJZOLQiINjEZjyhNHmkE6MzNz2PJ/k3FfbjabYbVaJ/z+S5cuYfv27SgsLERXVxdOnz6N+vr6q1YTBy4HYXV19ZhKqU0mE+68806IooivvvoKHMfBYDDQOqAzBIVEGkSj0ZR/+FIwDH2yNBkYhkFubi7Wrl2LhoaGMQ2sGiqZTKK1tRVvv/02GIZBIBBQrBh1OBwoLi4e0/GrVCpkZ2dj1apV4HketbW1qK+vR05OjhyYJH0oJNKgo6MD586dw/Lly+V7+6En01RcOaVRmw8++CDUajV2794tV9SOpfozmUyOeeZqjUaD8vJy2Gy2MX8vLMsiKysLOTk5GBwcRF1dHS5evIiMjAxs3LhxTDN5k6lBIZEGzc3N+P3vf4/f/OY3WLx4sVxIlUwm5cKqqQgKaf2L559/HuvWrcOZM2fQ09MDj8eDwcFB+P1+RKNRhEIheL1e9Pb2IhQKjXvquLy8PKxdu3Zc5dnS+hzhcBgtLS3o7OxEa2sr9Ho9KioqUF1dPd5vl0wSCok0EEURX3zxBfx+P9asWYOsrCzYbDbMnj0bFRUVyMrKSlmTMNbPT/VelUoFi8WCW265BbfccgtEUUQ4HEY4HEY0GgXHcQgGg2hpacHHH3+MvXv3jisktFot7rjjDqxYsWLU6swrjzmRSKC5uRmtra3yPr/55hvs3LkTpaWlY1qCkEw+Cok04Xkehw8fRm1tLQwGAywWC6qqqvDAAw9gw4YNyMvLG7aGhSAI8noU0tyR0gpe0i2LWq2WlwDMyMhIuQaGRHqfyWS66gSsqKhAb2/vVU8tRlNTU4NHH30UeXl54/yJAB6PB6dOnRo2pX4sFsNnn32GJ554YtjAMjJ9FENi6ASqVHU5NXieRyAQQCAQQG9vL/r7+8FxHNavX4+srCzwPI/+/n64XC589913OH/+PDweD+LxuBwQUlPdZDKhqKgIK1aswKpVq1BUVASdTpdy36mqJ4HLLYKMjIxx1VQUFxfj2WefxbJly8bdiuB5HnV1dairq7tqn4ODg5M69JyMj+JvcjLWmSRjl0gkcPbsWbz55ptobm6G0+lEb28vTp06hebmZni93pTjHCQZGRmoq6tDLBbD/fffP+qozStJ2453sV6TyYStW7di48aNMBgMY96fNObD5XLh888/H3HAWSwWQ3t7O6qrq6kVkQaKIWGz2VBUVERDxa+RVO04lquhNDFNT08PNBqN3MoY688/Fovh4sWLqK2tlcdKTOTE4jgObrd7zIv1rlixAg8++CAcDse49idVbL7++uvYs2fPiE9aIpEILly4gDvvvJOecqSBYkhs27YNjz/+OKX3NRBFEVqtFjt37sQf//jHMb2H4zj09fVNeEm/RCKBgYGBa1rTIxQKobOzc9Q5JoDLk9Fs3rwZVVVV8q2p9M9kMol4PI5QKIRAIACO4+R1NPr7+9HY2IivvvoK+/fvT9n3IT1+pQtVeiiGxLx586brOL73Zs2aBYvFgh07duDcuXOKJ9+1LuunUqmg0+kmPO5BEAR4vV4MDAyMegzSamCLFi2Sn8hIc0X4fD40NTXhf//7HxobG9Hf3y+f7IIgwO/3o6urC16vV3E/Go2GJsJNI5oIdxqIogir1YoXX3wRDzzwAP7617/iH//4R8pHi9fScmMYBmazGTk5ORO+TeR5Hj09PXJlqNJnmEwmZGdnIxwOo6enB6IoYmBgAKdPn8ZXX32FU6dOobW1dVy3TCPtY86cOePqDCWThybCnQbSz1Gn06GiogK//e1voVKp8MYbb1wVFCzLyrNCj3Whm6H7MZlMKCwsRGFhIVQqFZLJ5LgeG0pN+4GBATAMA61Wm3JQmHTSNjU14Z133oHJZEJfX5+8Unp/f/+kzOUpVWMCNDtVOlA0TzNRFJGTk4Nf/vKX8Pv9+PDDDxGPx2EwGGA2m2GxWOSVs3w+36grjkv0ej3sdjsKCgpQVVWF7OxscBwHjuNGrZcYiud5BINBcBwHu90Om80Gv98v12UMPZZkMolIJIIzZ87g/Pnz4Hle/ppMKpWKbjXSiEJimkkna3FxMX7xi18gJycHnZ2dyM/Ph9PphMFgQCQSQWNjI06ePImWlhaEQqER6xU0Gg2sVitKSkpQWVmJ4uJimM1maDQaCIKAtrY2GAwGOJ3OMc1eJQiCXHGp0+lw0003gWVZdHd3o7+/H4FAALFYDIlEYlin5FSrqamRp/CnVsT0Y0a5SlGnxBTieR4ejwfBYBAGg0Ge+SkUCsHlcuGbb77B/v37ceHCBQQCAQiCAIZhoNPpkJOTI09GU11dDafTCZZl4fV6cfHiRbjdbgCXKyeXLFmCsrIyGI3GlEEhnfB+vx89PT3o6uqCx+NBX18fWltbcfbsWVy6dAkejwccx01bf5XRaMRrr72GRx99dFr2dwNLmb7UkkgjlmXhdDrhdDrlx52iKMJiscil0mazGUePHkVnZyfi8ThsNhsqKyuxfPlyzJ8/HwUFBTAYDGAYBpFIBPF4HAMDAzh16hT6+vpw4sQJtLW14Z577kFVVRWMRuNVTfdkMgme5+XbE2nyG7PZjKysLIiiiObmZsTj8XH3k1yrsrIy3HzzzQCoPyJdKCRmCGliGamuwmKxoLS0FKIowm63o7+/HwaDASUlJSgvL0deXh4MBoN8wieTSbAsi2g0ipaWFjQ1NWFwcBButxter1deBau8vBwZGRlyi0K6beB5Xr6N0Ol0UKvVYFkWPp8PPT09cLvd8Pl8U7qo0Eg/k4cfflgeAUoBkR4UEjOMdCKwLAuDwSAP9OJ5HpmZmfIV/spVuRiGQTKZhNfrhdvtxuDgoFxS3dbWhm+//RZz5sxBVlYWsrOzh9U0SAPFpP1KxU6hUAjnz5/Ht99+C7fbPeGp7yZqyZIlWL9+PTQazbTulwxHITEDSa0KtVoNnU6HzMxMqNVq+TZECggpUKTbFKlS0+v1guM4uQ8jkUjA6/Wis7MTPp9PnmlbetQqPSaV9p1MJuH3+9HQ0IB9+/ahoaFhwuuWWiwWlJWVQafTob29fcSp7kZitVrx4x//GEuWLJnQfsnkoZCYwaSTXxrlKdVQpBq9GY1G4fP55Cs+wzDy40OWZeXaC2mCmyv3I73u9/vR2NiIffv24eTJkwgEAhM6/tmzZ+P555/HmjVroNVq5TEa+/fvV3wqotFosHXrVtx3330T2i+ZXBQSM5h0lRcEYVidwtAOvKEnN8dx4HkeOp1OLstWq9WwWq1wOp2w2+3yAKmhwZBMJuWnG9Lq4gcPHkRdXR0GBgYmdOy5ubl48cUX8dhjj8lzVcyfPx8lJSX405/+hF27do1Yms6yLB588EG89NJLKCgomOBPjkwmCokZbOgtQSKRAMdxw8ZkDJ2QJpFIIB6PQ6/XIycnB4FAQP7vwsJCzJ07F/n5+dDr9cMGX0kBE4vF4Pf74Xa7UVtbi2PHjqGvr2/Ca3Rs2rQJDz/8sDxRjGTRokV4+eWXkUgksHv37mFBYTKZcP/99+PXv/41ysrK6GnGDEEhMUOpVCqwLAu9Xo94PC5/SX0DGo1GDhBBEOQiJ7PZjKKiIgBAPB5HZmYmZs2ahcrKSrnDEvj/SzhKLRC/34+2tjbU19fj5MmT8iPXiSgsLMSPfvQjZGZmjniiL1iwAC+99BIMBgO+/PJLhMNhFBQUYMuWLXjqqadQXl5OATGDUEjMUAzDQKPRwGAwyAVW8XhcLpnWarXy489EIoFQKIRIJAKdToeCggLodDokk0lYrVYUFhYiJycHWq0WyWQSiURCvo2RAqKrqwtnz55FfX093G73NQ3Nzs/Px5w5cxTn2Vy8eDF+9atfYcGCBfB4PFi4cCHWrl2L3NxcCocZhkJiBpOW1pP4fD45JKTXgcutgWg0isHBQcRiMWRkZCArKwsMw8hPRFQqFRKJBKLRKJLJJBiGkUOnt7cXFy5cwJkzZ+B2uxEMBid0mwFcDrf8/HxkZGQobqdWq+XWDcdxyMrKmpRVysjko5CY4aSgkAZThUIheYRlPB6Xr/bJZBIcxyEWi8lBwLIsNBoNQqEQ/H6/vJq31MqIRqPweDxwu91obGyE2+2G3++Xp/afyNym0sjRsW6r1+uh0WhoGPgMRr+Z64BKpYJGo4FWq0UikUBrayuampowMDAgd/xdWTchvSczMxPBYBDRaBSRSASZmZnQarUQBAGBQACdnZ1wuVxoaWmB1+tFIpEAy7JyB+nQ25OxkFb68nq9yM7OTtm3INViuN1uRCIR5OXlwel0yuNLqEUxc1BIXCek6fIZhoHH44HL5UJfX588lFw6qYYuE6jVamE0GpGdnY2BgQE4nU7YbDbodDokEgl4PB50dnaira1NXohnaEhInaPxeByBQACRSGRMZdnnz5/Hp59+imeeeWbEtUd5nkd7ezsOHDiAb775BqFQCGVlZVixYgVqamrgcDiuaWYtMrkoJK4TUkemVKIdCATk+SaunOdBKqLSaDTIyMiA3+9HJBJBNBqF3W6HRqNBJBJBb28vOjo64PF45IAAIL8vJycHVqsViUQC3d3d6OrqQigUGvUWxOfz4bXXXoPBYMD9998vd0aKoohYLIampibs2LEDu3fvRnt7OwRBgNFoxPHjx7F582bcc889KCwslEORpBeFxHVCaiGYzWZ5VXLpVkCqoLwyKDiOk9fnYFkWarUaiUQCDMNgcHBQXuJPCoihi/xIfRrSZ0cikWHbjKalpQV//vOf4XK5cN9996GkpASxWAwNDQ3YtWsX9u3bN2xuS2kFsezsbCxfvlx+jEvSj0LiOsKyLKxWK8rLy1FQUCA/zZBaEkNbFFKoxONxeXRoMBiUC6f6+/vh9Xrl4eVDT35phbBIJCIHhTRKdDza29vx97//HceOHcOcOXPA8zzOnj2LCxcujLi8AM/zsNlscmuHWhEzA4XEdUSlUsFms2HhwoXw+/1gGAZnzpzBwMCA3DcxdEGlobNuS49JpcrKwcFBuXUwksmahi4YDOLIkSOor6+HSqWSWzcjKS4uxoYNG5Cfn08BMYNQSFxHVCoVDAYDysvLodfrkZ+fj0OHDqGurk6ub2AYBoIgyCXdarV6WAdkLBaDz+dTDIjJJooiIpGI4iNVk8mEzZs3Y9WqVbQAzwxDIXEdkUaDms1mzJo1Czk5OSgvL0dVVZU8ff3AwIB8pZaGmmdkZECj0ciL5ESj0WkLCInSQkMsy2L9+vV45JFH4HA4pvW4yOgoJK5D0uI7Go0GJpMJ+fn5KC0txX//+18cO3YMXV1diMfjcstDWpszFouB47hpDwjJ0DoOCcuyWLlyJX7605+iqqoqLcdFlNFEuNc5aTRnMBiEy+XCiRMncPbsWXi9XgiCINcb+P1+uFwuXLp0KW0rdA+dLEdaBb2mpgbPPfcc7rzzThiNxrQcFwGgMBEuhcT3xNAh31feUgiCAJ/Ph/Pnz+PQoUM4cuQImpubp3xCW51Oh6KiIhQVFcFut8NqtUKv18uPcqWZvEtKSkYd60GmHIXEjejKhXQEQcDg4CBqa2tx6NAhNDQ04MSJExOeWOZKLMuisrISNTU1sFqtyMvLw5IlS3DTTTchMzNTfqwpfUnD4cmMQCFBhksmk2hubkZtbS36+vrA87w8M1VHRweamprQ3t6uOGScYRiUl5djw4YNyMnJgU6nw/z587FkyRJ5NiqApsK/TlBIkLGJxWLypLkej+eqQquhVCoV8vLysGjRIuh0umk+UjLJKCTIyK78/U/0ij+00pNclygkyPgM/btQut0Y+k9yXaOQIIQoShkSNGCfEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKKIQoIQoohCghCiiB3ldWZajoIQMmNRS4IQoohCghCiiEKCEKKIQoIQoohCghCiiEKCEKLo/wGgmezdsylAyQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 83\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 84\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 85\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 86\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 87\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 88\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 89\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 90\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx * Ny,))\n", + "lb = np.zeros((Nx*Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx * Ny,))\n", + "ub = np.ones((Nx*Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "cur_beta = 4\n", @@ -348,11 +2319,11 @@ " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", + " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", " solver.set_maxeval(update_factor)\n", " solver.set_xtol_rel(1e-4)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta * beta_scale" + " cur_beta = cur_beta*beta_scale" ] }, { @@ -364,37 +2335,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "plt.plot((evaluation_history), \"o-\")\n", + "plt.plot((evaluation_history),'o-')\n", "plt.grid(True)\n", - "plt.xlabel(\"Iteration\")\n", - "plt.ylabel(\"FOM\")\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('FOM')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "opt.update_design([mapping(x, eta_i, cur_beta)])\n", + "opt.update_design([mapping(x,eta_i,cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(\n", - " False,\n", - " ax=ax,\n", - " plot_sources_flag=False,\n", - " plot_monitors_flag=False,\n", - " plot_boundaries_flag=False,\n", - ")\n", - "circ = Circle((2, 2), minimum_length / 2)\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", "ax.add_patch(circ)\n", - "ax.axis(\"off\")\n", + "ax.axis('off')\n", "plt.show()" ] }, @@ -407,37 +2398,69 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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61dTytLq+Uft1+NCklN707xnJ8i1FVoeiVJdoAreBxVm5VNU1nHdM+3X4jsslfGV8Eh/vLeX4mRqrw1Gq0zSB24D26/C/2WlJNDQa3tZNj5WDeZTARSRPRLaLyBYRyXYf6y0iK0Vkn/uyl29DDVzar8P/UvvFMKJfDK/rNJVysI6MwK81xow3xjRv7LAAWGWMGQasosVGx8pz358+7IJjUWEh2q/Dx2anJZFzpJy8skqrQ1GqU7oyhTILWOq+vhTI6HI0QSrE1bTaMq57OAIkxUbx6Jwx2q/Dx74yvj8i6MlM5VierhQxwAoRMcCTxpglQIIxpnkCsRhIaO2JIjIPmAcwcODALoYbmF7aeIQhcdGs+sHVunTejxJ7RjF5cB+WbSnke9OH6nuvHMfTEfiVxpgJwI3AgyLypXPvNMYYmpL8BYwxS4wx6caY9Pj4+K5FG4D2lVSwMe8kd04coAnEAhlp/TlUVsm2glNWh6JUh3mUwI0xhe7LY8DrwCSgREQSAdyXx3wVZCB7aWM+YSHCrZclWx1KULphdCLhoS49makcqd0ELiLRIhLTfB24HtgBvAHMdT9sLrDcV0EGqpr6BjI3F3D9yH7EdY+wOpyg1DMqjOkj+vKWdihUDuTJCDwB+EREtgKfAW8bY94DFgEzRGQfcJ37tuqArJ0lnDxbx12TBlgdSlDLSEui7Ewtn+wvszoUpTqk3ZOYxpiDwLhWjh8HpvsiqGDx4oYjDOgdxdRL4qwOJahdkxpPz6gwlm8p4prUvlaHo5THdCWmRfLKKll/8Dh3pg/A5dKTl1aKCG3qUPjejmIqa7RDoXIOTeAWeWljPiEu4fZ0nT6xg9lpSVTVaYdC5SyawC1QW9/Iq5vymTaiLwk9Iq0ORwHpg3qRFBvFsi1ajaKcQxO4BVbtLqHsTC1368lL23C5hFnj+7NmXxll2qFQOYQmcAu8uDGfxJ6RXD1cT5jZSYa7Q+FbW3VpvXIGTeB+ln/iLGv2lXJ7+oDPe6AoexieEMPIxB68rr1RlENoAvezV7LzAbhzok6f2FFGWn+25pdzSDsUKgfQBO5H9Q2NvJKdz9XD40nSXt+29JVxSYig29kpR9AE7kcf5ZZScrqGuyZqV0a76tczkilDmjoUNvVoU8q+NIH70UsbjxDXPYLpl+rJSzvLSEvi8PGzbMkvtzoUpS5KE7ifHD1VxQd7jnFHejJhIfq229kNo/sREerSaRRle5pJ/OTf2QU0Gj156QQ9IsO47tIE3tp2lDrtUKhsTBO4HzQ2Gl7emM/UoX0Y1Cfa6nCUBzLSkjheWcsn+7RDobIvTeB+sGZ/GYXlVXry0kGuHh5PbLcwXVqvbE0TuB+8uOEIvbqFcf2oVrcNVTYUHuri5jGJZO0s5ox2KFQ2pQncx0oranh/dwm3XZZMRGiI1eGoDujTPZzqukZG/zSLqYs+0JOaynY8TuAiEiIiOSLylvv2YBHZICL7ReRlEQn3XZjO9eqmAuobDXfq9ImjLMsp5KnVBz+/XVhexcLM7ZrEla10ZAT+fWD3Obd/A/zeGDMUOAnc783AAkHTycsjTErpzdC+3a0OR3XA4qxcqurOr0CpqmtgcVauRREpdSGPEriIJAM3A3933xZgGvCq+yFLgQwfxOdonx48Tt7xs7rnpQMVlVd16LhSVvB0BP4H4IdA85CkD1BujGk+u1MAJLX2RBGZJyLZIpJdWlralVgd58WN+fSIDOWmMYlWh6I6qH8bvWraOq6UFdpN4CJyC3DMGLOpMy9gjFlijEk3xqTHx8d35kc40onKWrJ2FDNnQjKRYXry0mnmz0wlqsXvLSLUxfyZqRZFpNSF2t2VHpgKfEVEbgIigR7AE0CsiIS6R+HJgJ7dOUfm5gJqGxp1+sShMtKa/qBcnJVLUXkVBrhyaNznx5Wyg3ZH4MaYhcaYZGNMCnAX8IEx5qvAh8Bt7ofNBZb7LEqHMcbw4mdHGD8glhH9elgdjuqkjLQk1i6YxqFFNzNtRF92HT1NQ6N2KFT20ZU68B8Bj4jIfprmxJ/2TkjOtiynkIm/ep8DpZXklVVq2VmAmJ2WxNFT1Xx68LjVoSj1OU+mUD5njPkI+Mh9/SAwyfshOdeynEIWZm6nqq4BgPKqOhZmbgfQP70dbsbIBGIiQsncXMjUoXFWh6MUoCsxvaqpdrjhvGNaOxwYIsNCuGlMIu/tOEpVbUP7T1DKDzSBe5HWDge22ROSqKxtYMWuYqtDUQrQBO5VibGRrR7X2uHAMCmlN0mxUWRu1vMayh40gXvRza0s2IkKC9Ha4QDhcgkZaf1Zs6+UY6errQ5HKU3g3rSnuIIekSH0j41EgKTYKB6dM0ZPYAaQ2WnJNBp4Y2uR1aEo1bEqFNW2A6VnWLOvjEdmDOd704dZHY7ykaF9uzMuuSeZmwt54KohVoejgpyOwL3kuXV5hIe4uHuSto0NdLPTkth19DS5xRVWh6KCnCZwL6ioruPVTQXcPDaR+JgIq8NRPvblcf0JdQmZOQVWh6KCnCZwL3h1UwGVtQ3cd0WK1aEoP+jTPYKrh8ezPKdIl9YrS2kC76LGRsNz6w8zfkAs4wbEWh2O8pPZE5IoPl3N+gO6tF5ZRxN4F63eV8qhskodfQeZ6y51L63XaRRlIU3gXbR0XR7xMRG6aUOQiQwL4eaxiby3o5iztbprvbKGJvAuOFRWyYe5pdwzaSDhofpWBpvZaUmcrW1gxc4Sq0NRQUqzThc8tz6PUJfw1cu1dDAYTWxeWq8tg5VFNIF30pmael7NLuCmMYn07dF6DxQV2FwuYXZaEp/o0nplEU/2xIwUkc9EZKuI7BSR/3EfHywiG0Rkv4i8LCLhvg/XPjI3F1BRU899U1OsDkVZaPaEJBoNLN+iS+uV/3kyAq8BphljxgHjgRtEZDLwG+D3xpihwEngfp9FaTPGGJauy2Nsck/StHQwqF0S351xA2J1GkVZwpM9MY0x5oz7Zpj7nwGmAa+6jy8FMnwRoB19sr+MA6WVzJ2SgohYHY6y2Jy0JHYfPc2e4tNWh6KCjEdz4CISIiJbgGPASuAAUO7ekR6gAGi15Z6IzBORbBHJLi0t9ULI1lu6Lo+47uHcMk5LB9UXS+tf1z7hys88SuDGmAZjzHggmaZ9MEd4+gLGmCXGmHRjTHp8fHznorSRI8fPsmrPMe6eNJCI0BCrw1E20Ds6nGtS41m2pVCX1iu/6lAVijGmHPgQmALEikhzO9pkICiGH8+tzyNEhK9ePsjqUJSNzE5LpuR0DesOlFkdigoinlShxItIrPt6FDAD2E1TIr/N/bC5wHIfxWgblTX1vJydz8zR/ejXU0sH1RemX9qXmMhQnUZRfuXJCDwR+FBEtgEbgZXGmLeAHwGPiMh+oA/wtO/CtIfXcwqpqK7nG9r3RLUQGRbCLWMTeW+nLq1X/tPujjzGmG1AWivHD9I0Hx4UjDE8tz6PUf17cNmgXlaHo2xodloyL36WT9bOYmanJVsdjgoCuhLTQ+sPHGdvyRnmXqGlg6p16YN6kdxLd61X/qMJ3EPPrsujd3Q4XxnX3+pQlE01L61fu7+MEl1ar/xAE7gH8k+c5f3dJdw1cQCRYVo6qNo2O615ab2OwpXvaQL3wPOfHkZE+NpkLR1UFzckvjvjB8TqNIryC03g7aiqbeCljfnMHJVA/9goq8NRDjBnQhJ7iivYVaRL65VvaQJvx/IthZyqqmPulBSrQ1EOcctY99J63W5N+Vi7ZYTBzBjDs+vyGNEvhkmDe1sdjnKI3tHhpPaL4elPDvH3NYfoHxvF/JmpZKS12i5IqU7TEfhFbDh0gj3FFdynpYOqA5blFLKvpIJG09S2s7C8ioWZ21mmLWeVl+kIvBXLcgpZnJVLYXkVIuDS5K06YHFWLrUN5ze1qqprYHFWro7ClVdpAm9hWU4hCzO3U1XXAIAx8NM3dhIe6tIvn/JIUXlVh44r1Vk6hdLC4qzcz5N3s+bRk1KeaKtaSauYlLdpAm9BR0+qq+bPTCWqxYKvyDAX82emWhSRClSawFvQ0ZPqqoy0JB6dM4ak2Ciaz57cOKqfTsEpr9ME3sL9V6ZccCwqLERHT6pDMtKSWLtgGocW3czElF5sPHyS+oZGq8NSAUYTeAu7jlYQIpDQIwIBkmKjeHTOGB09qU574KohFJys4r2dxVaHogKMVqGcY29JBZmbC/jm1MH89y0jrQ5HBYjrLk1gcFw0T60+yM1jEnVNgfIaT7ZUGyAiH4rILhHZKSLfdx/vLSIrRWSf+9Lxuxw8lpVLt/BQvnPtUKtDUQEkxCV888rBbC04xca8k1aHowKIJ1Mo9cAPjDEjgcnAgyIyElgArDLGDANWuW871uYjJ1mxq4R5XxpC7+hwq8NRAea2Ccn06hbGU2sOWh2KCiDtJnBjzFFjzGb39QqaNjROAmYBS90PWwpk+ChGnzPG8Jt399AnOpz7rxxsdTgqAEWFh3Dv5EG8v7uEg6VnrA5HBYgOncQUkRSa9sfcACQYY4667yoGEtp4zjwRyRaR7NLS0q7E6jOr95Wx4dAJvjttKNERelpA+ca9U1IIC3Hx9CeHrA5FBQiPE7iIdAdeAx42xpzX6NgYY2jq23MBY8wSY0y6MSY9Pj6+S8H6QmOj4bfv7SG5VxR3Xz7Q6nBUAIuPiWD2+CRe3VTAicpaq8NRAcCjBC4iYTQl7xeMMZnuwyUikui+PxE45psQfevt7UfZWXSaR2YMJyJUt0tTvvXAVYOpqW/k+U8PWx2KCgCeVKEI8DSw2xjzu3PuegOY674+F1ju/fB8q66hkcdX5JKaEMOs8VrnrXxvWEIM16bG89z6PKpb9NxRqqM8GYFPBe4FponIFve/m4BFwAwR2Qdc577tKK9k55N3/CzzZ6YS4tLaXOUf37pqCGVnarU/uOqyds/YGWM+AdrKbtO9G47/VNU28MT7+7hsUC+mX9rX6nBUEJlySR9G9e/BU2sOckf6AFw6eFCdFLRL6Z9dl8exihp+dMMIXRmn/EpE+NZVQzhQWslHex156kjZRFAm8FNn6/jrR/u5NjVe97pUlrh5bCKJPSN5arWWFKrOC8qi57+tPsDp6nrmzxxhdSgqSIWFuPjG1BR+/c4edhSeYnRST6tDCnjNWyUWlVd1eKPprjzXl4JuBF5yuppn1h5i1vj+jOzfw+pwVBC7a9JAukeE6vJ6P2jeKrGwvKrDG0135bm+FnQj8P9dtY/6BsMjM4ZbHYoKcj0iw7hr4gCeWZfHj24YoZuG+FBbWyX+6LVtvLa5ABH5vFJDpKlqo/nY2v1lVNc3XvBcO2xSHVQj8ENllby0MZ+7Jw1kUJ9oq8NRim+4e+88s1bnwn3FGENhG1si1tQ3UlFdz6mqOsqr6ig/W8uJylrKztRyrKKakorqC5J3s8LyKj7YU2JpPX9QjcB/t3Iv4SEuvjtN28Uqe0iKjeKmMYm89Fk+35s+jJjIMKtDChjGGD7eW8rjK/a2+Zik2CiWPTj1oj9n6qIPWv0PQIBvPptNt/AQrkmN5/qR/bg2tS89u/nvdxg0CXxH4Sne3FrEg9deQt8ekVaHo9TnvnXVYN7cWsTLG/N54KohVocTED49eJzHV+SyMe9kU5+jSQN4PaeQ6rovRtOebpU4f2YqCzO3nzcFExUWwi9mjSIuJoIVu0pYuauEd7YXE+oSJg/pw/WjEpgxMoENB0/49OSnNPWh8o/09HSTnZ3tt9c719x/fMaW/HJW//BaekbpKEfZy51Prif/xFk+/uG1hIUE1cymV23JL+fxFbms2VdG35gIvjttKHdOHEh4qMunVSiNjYYtBeWs2FnCip3FHCyrBJrm089NsVFhIZ3aolFENhlj0i84HgwJfP2B49z91KcsvHEE3776Er+/vlLteX9XCQ88l80Td43XvjydsPvoaR5fsZf3d5fQOzqc/3P1Jdw7ZRCRYdY0qNt/7Axz/rKW09X1F9yXFBvF2gXTOvTz2krgAT2F0vS/5h4Ky6txCbrTjrKtaSP6MiQ+mqfWHOQr4/rr6uCLOHc0HB8TQVJsFFsKyukeEcoPZgznG1cOprvFff2H9u1ORSvJG6CojROqnRGwf6t9UbtZDUCjgZ8s32mL2k2lWnK5hAeuHMKOwtN8evCE1eHYVsua7GMVNeTklzN9RF/W/PBavjt9mOXJu1lbZaHeLBcN2ATeVt3n4qxciyJS6uLmTEiiT3Q4f9eFPW1q7XsNsPtoBbHd7PUX9vyZqUS1mMLx9MSpp+zxX5UPtPVnijf/fFHKmyLDQpiY0ov3dpYweMHbtlqybRdt1XPb8Xvd/HvzZRVKwCbwnt3CKD9bd8FxXe2m7GpZTiEf7W3aN/bcJdtA0CfxhkbDr97e3eb9dv1eZ6Ql+fR358mOPP8QkWMisuOcY71FZKWI7HNf9vJZhJ2w4eBxTlfV0bLNsrf/fFHKmxZn5Z5Xpww67Qdwpqaebz2XzT/WHuLqYXFEhZ2ftoL5e+3JHPizwA0tji0AVhljhgGr3Ldt4fDxSv7j+U2k9InmlxmjSYqNQmgq3elM/aVS/qLTfhcqLK/itr+u4+O9pfwyYzRL77+cR+eM1e+1myc78qwWkZQWh2cB17ivLwU+An7kzcA643R1HfcvzabRwNP3TWRwXDT3XD7I6rCU8kj/2KhW53jtOj3gazlHTvKt5zZRU9fAM/dN5EvD4wHfT0s4SWerUBKMMUfd14uBhLYeKCLzRCRbRLJLS0s7+XLtq29o5MEXNpNXVsnfvnYZg+O0WZVyltaqFgBuvSz4ktVb24q4a8mnRIW7yPzOFZ8nb3W+LpcRmqalnG0u5zTGLDHGpBtj0uPjffdL+MVbu1izr4xfZoxmyiV9fPY6SvlKRloSj84Z8/n0QGLPSPpEh/HapkJOtXJCPhAZY/jjqn089K8cxib3ZNl3pjIsIcbqsGyrs1UoJSKSaIw5KiKJgKUb+/1zfR5L1x/mW1cN5q5JA60MRakuaTk9sK2gnFv/uo4fvbaNv35tQkCv0Kypb2DBa9t5PaeQ2WlJLLp1DBGh1iyFd4rOjsDfAOa6r88FlnsnnI5bvbeUn725i+kj+rLgxkutCkMpnxibHMsPZ47gvZ3FPL/hiNXh+MzxMzV89akNvJ5TyH9dP5zf3TFOk7cH2h2Bi8iLNJ2wjBORAuCnwCLgFRG5HzgM3OHLINuy/1gFD76wmWF9u/PE3WmEtKwbVCoA3H/lYNYeKOMXb+0ifVAvLk10/laALfuZ1Dc2UlnTwJ/vmcDNYxOtDs8xHNuN8ERlLRl/XsvZ2nqWPTiV5F7dvPJzlbKjsjM13PjEGnpGhfHGQ1PpFu7cNXjN/UxaLon/zxnD+P503eqwNW11I3RkL5Ta+kb+4/lNFJ+uZsnX0zV5q4AX1z2CP9w5ngOlZ/j5m7usDqdL2upn8srGAguicTbHJXBjDP/39e18dugEi28by4SBtloEqpTPTB0ax3euuYSXNubz5tYiq8PpNF2w5D2OS+BLVh/k35sK+N70Ydr4XgWdh68bzoSBsfw4czv5J85aHU6HHauobnPHoWBdsNQVjkrgK3eVsOi9Pdw8NpGHpw+zOhyl/C4sxMUTd6WBwEMv5lDX0PqO6Xa0raCcWX9aizGGsJDzCw6CuZ9JV9g+gS/LKWTqog8YvOBt5j2XTXJsFI/fPg6XVpyoIDWgdzd+c+tYtuaX89gKZzS6WpZTyO1/W49LhGUPTWXxbeO0n4kX2PpUdmtnq0sranhvR7H+slVQu2lMIvdcPpAnPz7I1EvibLvUvKHR8Nv39vDk6oNcPrg3f/nqBPp0j2BU/576HfYCW4/AWztbXV3fGPTtNZUC+MktI0lNiOGRV7ZwrKLa6nAucKqqjvuXbuTJ1Qf52uSBPP/A5fTpHmF1WAHF1glcz1Yr1bbIsBD+eE8aZ2rq+cErW2ls9N+ajvYcKD3D7D+v5ZN9Zfxq9mh+mTGmzZOXqvNs/Y76Y1NQpZxseEIMP7llFGv2lbHEJntpfrjnGBl/Wsupqjr+9a3JfFVbOvuMrefA589MvWAOXM9WK3W+uycNYO3+Mn7z7h6eXnOIsjM1luynaYzhbx8f5LdZexiZ2IMlX08nSQdbPmXrBO6PTUGVcjoRYeqwPryz/SilZ2oA/+2neW5Pk8iwEKrqGrhlbCKLbxtHVLg2o/I1Wydw0N03lPLEnz84cEFT/ub9NH31/WlZJVZV10CoS5g+oq8mbz+x9Ry4UsozbZ3YLyyv8slJ/9r6Rn7+1q4LqsTqGw2Prdjr9ddTrbP9CFwp1b629tMEuOq3H3L9yAS+PiWFyUN6d3pTCGMMWwtOkbm5gDe3FnGyjV2CtErMfzSBKxUA2jrh/18zh1NaUctLG4/w7o5ihid05+tTUpidlkR0hGdf/8LyKpblFPLa5gIOllYSEeri+lH9WLe/jOOVtRc8XqvE/KdL/cBF5AbgCSAE+LsxZtHFHu/NfuBKqfOde0Kx5Qn/6roG3thaxHPr89hReJqYiFBuS0/m3smD2FZw6oLnXTcygXe3HyVzcyHrDx4HYNLg3tw6IYkbxyTSIzKs1ZXSUWEhuizeB9rqB97pBC4iIcBeYAZQAGwE7jbGtNmsWBO4UtYyxpCTX85z6/J4e/tR6hoMLoFz1wCFiCBiqG+ElD7dmDMhmdlpSQzofWHf/Yv9p6G8xxcJfArwM2PMTPfthQDGmEfbeo4mcKXso7SihmmPf0RFdf0F93ULD+Gf91/OhIGxAb2RslP4YkeeJCD/nNsF7mNKKQeIj4ngTCvJG6CqtoHLBvXS5G1zPi8jFJF5IpItItmlpaW+fjmlVAdouwpn60oCLwQGnHM72X3sPMaYJcaYdGNMeny8PVteKhWs5s9MJSrs/EU32q7CObpSRrgRGCYig2lK3HcB93glKqWUX2i7CmfrdAI3xtSLyENAFk1lhP8wxuz0WmRKKb/QdhXO1aWFPMaYd4B3vBSLUkqpDtBeKEop5VCawJVSyqE0gSullENpAldKKYfqUjOrDr+YSClwuJNPjwPKvBhOINL36OL0/bk4fX/aZ9V7NMgYc8FCGr8m8K4QkezWegGoL+h7dHH6/lycvj/ts9t7pFMoSinlUJrAlVLKoZyUwJdYHYAD6Ht0cfr+XJy+P+2z1XvkmDlwpZRS53PSCFwppdQ5NIErpZRDOSKBi8gNIpIrIvtFZIHV8diNiOSJyHYR2SIiumcdICL/EJFjIrLjnGO9RWSliOxzX/ayMkYrtfH+/ExECt2foy0icpOVMVpJRAaIyIcisktEdorI993HbfUZsn0Cd2+e/GfgRmAkcLeIjLQ2Klu61hgz3k41qhZ7FrihxbEFwCpjzDBglft2sHqWC98fgN+7P0fj3d1Gg1U98ANjzEhgMvCgO+/Y6jNk+wQOTAL2G2MOGmNqgZeAWRbHpGzOGLMaONHi8Cxgqfv6UiDDnzHZSRvvj3Izxhw1xmx2X68AdtO056+tPkNOSOC6eXL7DLBCRDaJyDyrg7GxBGPMUff1YiDBymBs6iER2eaeYgnaKaZziUgKkAZswGafISckcNW+K40xE2iaZnpQRL5kdUB2Z5rqZ7WG9nx/BS4BxgNHgcctjcYGRKQ78BrwsDHm9Ln32eEz5IQE7tHmycHMGFPovjwGvE7TtJO6UImIJAK4L49ZHI+tGGNKjDENxphG4CmC/HMkImE0Je8XjDGZ7sO2+gw5IYF/vnmyiITTtHnyGxbHZBsiEi0iMc3XgeuBHRd/VtB6A5jrvj4XWG5hLLbTnJjcZhPEnyMREeBpYLcx5nfn3GWrz5AjVmK6y5n+wBebJ//K2ojsQ0SG0DTqhqY9Tv+l7w+IyIvANTS1/ywBfgosA14BBtLU1vgOY0xQnshr4/25hqbpEwPkAd8+Z743qIjIlcAaYDvQ6D78Y5rmwW3zGXJEAldKKXUhJ0yhKKWUaoUmcKWUcihN4Eop5VCawJVSyqE0gSullENpAldKKYfSBK6UUg71/wEqw80nnvlCswAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", + "f0, dJ_du = opt([mapping(x,eta_i,cur_beta)],need_gradient = False)\n", "Ez_coef = opt.get_objective_arguments()[0]\n", "plt.figure()\n", - "plt.plot(np.abs(Ez_coef[1, :]) ** 2, \"-o\")\n", + "plt.plot(np.abs(Ez_coef[1,:])**2,'-o')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "src = mp.ContinuousSource(frequency=1 / 1.55, fwidth=fwidth)\n", - "source = [\n", - " mp.EigenModeSource(\n", - " src,\n", - " eig_band=1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size=source_size,\n", - " center=source_center,\n", - " )\n", - "]\n", + "src = mp.ContinuousSource(frequency=1/1.55,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]\n", "opt.sim.change_sources(source)\n", "opt.sim.run(until=500)\n", - "opt.sim.plot2D(fields=mp.Ez)" + "opt.sim.plot2D(fields=mp.Ez)\n" ] }, { diff --git a/python/examples/antenna-radiation.ipynb b/python/examples/antenna-radiation.ipynb index 6bf5e69fa..bf2498020 100644 --- a/python/examples/antenna-radiation.ipynb +++ b/python/examples/antenna-radiation.ipynb @@ -32,9 +32,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import math\n", @@ -45,53 +53,44 @@ "\n", "sxy = 4\n", "dpml = 1\n", - "cell = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml)\n", + "cell = mp.Vector3(sxy+2*dpml,sxy+2*dpml)\n", "\n", "pml_layers = [mp.PML(dpml)]\n", "\n", "fcen = 1.0\n", "df = 0.4\n", "src_cmpt = mp.Ey\n", - "sources = [\n", - " mp.Source(\n", - " src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), component=src_cmpt\n", - " )\n", - "]\n", + "sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df),\n", + " center=mp.Vector3(),\n", + " component=src_cmpt)]\n", "\n", "if src_cmpt == mp.Ex:\n", - " symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=+1)]\n", + " symmetries = [mp.Mirror(mp.X,phase=-1),\n", + " mp.Mirror(mp.Y,phase=+1)]\n", "elif src_cmpt == mp.Ey:\n", - " symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=-1)]\n", + " symmetries = [mp.Mirror(mp.X,phase=+1),\n", + " mp.Mirror(mp.Y,phase=-1)]\n", "elif src_cmpt == mp.Ez:\n", - " symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=+1)]\n", + " symmetries = [mp.Mirror(mp.X,phase=+1),\n", + " mp.Mirror(mp.Y,phase=+1)]\n", "\n", - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - " boundary_layers=pml_layers,\n", - ")\n", + "sim = mp.Simulation(cell_size=cell,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + " boundary_layers=pml_layers)\n", "\n", - "nearfield_box = sim.add_near2far(\n", - " fcen,\n", - " 0,\n", - " 1,\n", - " mp.Near2FarRegion(mp.Vector3(y=0.5 * sxy), size=mp.Vector3(sxy)),\n", - " mp.Near2FarRegion(mp.Vector3(y=-0.5 * sxy), size=mp.Vector3(sxy), weight=-1),\n", - " mp.Near2FarRegion(mp.Vector3(0.5 * sxy), size=mp.Vector3(y=sxy)),\n", - " mp.Near2FarRegion(mp.Vector3(-0.5 * sxy), size=mp.Vector3(y=sxy), weight=-1),\n", - ")\n", + "nearfield_box = sim.add_near2far(fcen, 0, 1,\n", + " mp.Near2FarRegion(mp.Vector3(y=0.5*sxy), size=mp.Vector3(sxy)),\n", + " mp.Near2FarRegion(mp.Vector3(y=-0.5*sxy), size=mp.Vector3(sxy), weight=-1),\n", + " mp.Near2FarRegion(mp.Vector3(0.5*sxy), size=mp.Vector3(y=sxy)),\n", + " mp.Near2FarRegion(mp.Vector3(-0.5*sxy), size=mp.Vector3(y=sxy), weight=-1))\n", "\n", - "flux_box = sim.add_flux(\n", - " fcen,\n", - " 0,\n", - " 1,\n", - " mp.FluxRegion(mp.Vector3(y=0.5 * sxy), size=mp.Vector3(sxy)),\n", - " mp.FluxRegion(mp.Vector3(y=-0.5 * sxy), size=mp.Vector3(sxy), weight=-1),\n", - " mp.FluxRegion(mp.Vector3(0.5 * sxy), size=mp.Vector3(y=sxy)),\n", - " mp.FluxRegion(mp.Vector3(-0.5 * sxy), size=mp.Vector3(y=sxy), weight=-1),\n", - ")" + "flux_box = sim.add_flux(fcen, 0, 1,\n", + " mp.FluxRegion(mp.Vector3(y=0.5*sxy), size=mp.Vector3(sxy)),\n", + " mp.FluxRegion(mp.Vector3(y=-0.5*sxy), size=mp.Vector3(sxy), weight=-1),\n", + " mp.FluxRegion(mp.Vector3(0.5*sxy), size=mp.Vector3(y=sxy)),\n", + " mp.FluxRegion(mp.Vector3(-0.5*sxy), size=mp.Vector3(y=sxy), weight=-1))" ] }, { @@ -103,9 +102,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "f = plt.figure(dpi=150)\n", "sim.plot2D(ax=f.gca())\n", @@ -121,13 +141,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "field decay(t = 50.01): 4938.653280565278 / 4938.653280565278 = 1.0\n", + "field decay(t = 100.01): 7.375043707445242e-11 / 4938.653280565278 = 1.4933309322336344e-14\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n" + ] + } + ], "source": [ - "sim.run(\n", - " until_after_sources=mp.stop_when_fields_decayed(50, src_cmpt, mp.Vector3(), 1e-8)\n", - ")" + "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, src_cmpt, mp.Vector3(), 1e-8))" ] }, { @@ -139,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -155,28 +183,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "r = (\n", - " 1000 / fcen\n", - ") # half side length of far-field square box OR radius of far-field circle\n", - "res_ff = 1 # resolution of far fields (points/μm)\n", - "far_flux_box = (\n", - " nearfield_box.flux(\n", - " mp.Y, mp.Volume(center=mp.Vector3(y=r), size=mp.Vector3(2 * r)), res_ff\n", - " )[0]\n", - " - nearfield_box.flux(\n", - " mp.Y, mp.Volume(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r)), res_ff\n", - " )[0]\n", - " + nearfield_box.flux(\n", - " mp.X, mp.Volume(center=mp.Vector3(r), size=mp.Vector3(y=2 * r)), res_ff\n", - " )[0]\n", - " - nearfield_box.flux(\n", - " mp.X, mp.Volume(center=mp.Vector3(-r), size=mp.Vector3(y=2 * r)), res_ff\n", - " )[0]\n", - ")" + "r = 1000/fcen # half side length of far-field square box OR radius of far-field circle\n", + "res_ff = 1 # resolution of far fields (points/μm)\n", + "far_flux_box = (nearfield_box.flux(mp.Y, mp.Volume(center=mp.Vector3(y=r), size=mp.Vector3(2*r)), res_ff)[0]\n", + " - nearfield_box.flux(mp.Y, mp.Volume(center=mp.Vector3(y=-r), size=mp.Vector3(2*r)), res_ff)[0]\n", + " + nearfield_box.flux(mp.X, mp.Volume(center=mp.Vector3(r), size=mp.Vector3(y=2*r)), res_ff)[0]\n", + " - nearfield_box.flux(mp.X, mp.Volume(center=mp.Vector3(-r), size=mp.Vector3(y=2*r)), res_ff)[0])" ] }, { @@ -188,29 +204,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flux:, 1.227787, 1.227651, 1.227260\n" + ] + } + ], "source": [ - "npts = 100 # number of points in [0,2*pi) range of angles\n", - "angles = 2 * math.pi / npts * np.arange(npts)\n", + "npts = 100 # number of points in [0,2*pi) range of angles\n", + "angles = 2*math.pi/npts*np.arange(npts)\n", "\n", - "E = np.zeros((npts, 3), dtype=np.complex128)\n", - "H = np.zeros((npts, 3), dtype=np.complex128)\n", + "E = np.zeros((npts,3),dtype=np.complex128)\n", + "H = np.zeros((npts,3),dtype=np.complex128)\n", "for n in range(npts):\n", - " ff = sim.get_farfield(\n", - " nearfield_box, mp.Vector3(r * math.cos(angles[n]), r * math.sin(angles[n]))\n", - " )\n", - " E[n, :] = [np.conj(ff[j]) for j in range(3)]\n", - " H[n, :] = [ff[j + 3] for j in range(3)]\n", + " ff = sim.get_farfield(nearfield_box,\n", + " mp.Vector3(r*math.cos(angles[n]),\n", + " r*math.sin(angles[n])))\n", + " E[n,:] = [np.conj(ff[j]) for j in range(3)]\n", + " H[n,:] = [ff[j+3] for j in range(3)]\n", "\n", - "Px = np.real(np.multiply(E[:, 1], H[:, 2]) - np.multiply(E[:, 2], H[:, 1]))\n", - "Py = np.real(np.multiply(E[:, 2], H[:, 0]) - np.multiply(E[:, 0], H[:, 2]))\n", - "Pr = np.sqrt(np.square(Px) + np.square(Py))\n", + "Px = np.real(np.multiply(E[:,1],H[:,2])-np.multiply(E[:,2],H[:,1]))\n", + "Py = np.real(np.multiply(E[:,2],H[:,0])-np.multiply(E[:,0],H[:,2]))\n", + "Pr = np.sqrt(np.square(Px)+np.square(Py))\n", "\n", - "far_flux_circle = np.sum(Pr) * 2 * np.pi * r / len(Pr)\n", + "far_flux_circle = np.sum(Pr)*2*np.pi*r/len(Pr)\n", "\n", - "print(\"flux:, {:.6f}, {:.6f}, {:.6f}\".format(near_flux, far_flux_box, far_flux_circle))" + "print(\"flux:, {:.6f}, {:.6f}, {:.6f}\".format(near_flux,far_flux_box,far_flux_circle))" ] }, { @@ -224,15 +248,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=200)\n", - "ax = plt.subplot(111, projection=\"polar\")\n", - "ax.plot(angles, Pr / max(Pr), \"b-\")\n", + "ax = plt.subplot(111, projection='polar')\n", + "ax.plot(angles,Pr/max(Pr),'b-')\n", "ax.set_rmax(1)\n", - "ax.set_rticks([0, 0.5, 1])\n", + "ax.set_rticks([0,0.5,1])\n", "ax.grid(True)\n", "ax.set_rlabel_position(22)" ] diff --git a/python/examples/bend-flux.ipynb b/python/examples/bend-flux.ipynb index 8a451fe8b..df16d007f 100644 --- a/python/examples/bend-flux.ipynb +++ b/python/examples/bend-flux.ipynb @@ -18,19 +18,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 10 # pixels/um\n", + "resolution = 10 # pixels/um\n", "\n", "sx = 16 # size of cell in X direction\n", "sy = 32 # size of cell in Y direction\n", - "cell = mp.Vector3(sx, sy, 0)\n", + "cell = mp.Vector3(sx,sy,0)\n", "\n", "dpml = 1.0\n", "pml_layers = [mp.PML(dpml)]" @@ -45,12 +53,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "pad = 4 # padding distance between waveguide and cell edge\n", - "w = 1 # width of waveguide" + "w = 1 # width of waveguide" ] }, { @@ -62,12 +70,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "wvg_xcen = 0.5 * (sx - w - 2 * pad) # x center of horiz. wvg\n", - "wvg_ycen = -0.5 * (sy - w - 2 * pad) # y center of vert. wvg" + "wvg_xcen = 0.5*(sx-w-2*pad) # x center of horiz. wvg\n", + "wvg_ycen = -0.5*(sy-w-2*pad) # y center of vert. wvg" ] }, { @@ -79,17 +87,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "geometry = [\n", - " mp.Block(\n", - " size=mp.Vector3(mp.inf, w, mp.inf),\n", - " center=mp.Vector3(0, wvg_ycen, 0),\n", - " material=mp.Medium(epsilon=12),\n", - " )\n", - "]" + "geometry = [mp.Block(size=mp.Vector3(mp.inf,w,mp.inf),\n", + " center=mp.Vector3(0,wvg_ycen,0),\n", + " material=mp.Medium(epsilon=12))]" ] }, { @@ -101,20 +105,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "fcen = 0.15 # pulse center frequency\n", - "df = 0.1 # pulse width (in frequency)\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(-0.5 * sx + dpml, wvg_ycen, 0),\n", - " size=mp.Vector3(0, w, 0),\n", - " )\n", - "]" + "df = 0.1 # pulse width (in frequency)\n", + "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(-0.5*sx+dpml,wvg_ycen,0),\n", + " size=mp.Vector3(0,w,0))]" ] }, { @@ -128,35 +128,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,-11.5,0)\n", + " size (1e+20,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources,\n", - " resolution=resolution,\n", - ")\n", + "sim = mp.Simulation(cell_size=cell,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources,\n", + " resolution=resolution)\n", "\n", "nfreq = 100 # number of frequencies at which to compute flux\n", "\n", "# reflected flux\n", - "refl_fr = mp.FluxRegion(\n", - " center=mp.Vector3(-0.5 * sx + dpml + 0.5, wvg_ycen, 0), size=mp.Vector3(0, 2 * w, 0)\n", - ")\n", + "refl_fr = mp.FluxRegion(center=mp.Vector3(-0.5*sx+dpml+0.5,wvg_ycen,0), size=mp.Vector3(0,2*w,0)) \n", "refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", "\n", "# transmitted flux\n", - "tran_fr = mp.FluxRegion(\n", - " center=mp.Vector3(0.5 * sx - dpml, wvg_ycen, 0), size=mp.Vector3(0, 2 * w, 0)\n", - ")\n", + "tran_fr = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml,wvg_ycen,0), size=mp.Vector3(0,2*w,0))\n", "tran = sim.add_flux(fcen, df, nfreq, tran_fr)\n", "\n", "plt.figure(dpi=150)\n", "sim.plot2D()\n", - "plt.show()" + "plt.show()\n" ] }, { @@ -172,13 +190,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "field decay(t = 50.050000000000004): 4.825189380557789e-09 / 4.825189380557789e-09 = 1.0\n", + "field decay(t = 100.05000000000001): 0.02880180987942578 / 0.02880180987942578 = 1.0\n", + "field decay(t = 150.1): 0.0268934650933857 / 0.02880180987942578 = 0.9337421921042788\n", + "field decay(t = 200.15): 2.3158397338096397e-13 / 0.02880180987942578 = 8.040604890819488e-12\n", + "run 0 finished at t = 200.15 (4003 timesteps)\n" + ] + } + ], "source": [ - "pt = mp.Vector3(0.5 * sx - dpml - 0.5, wvg_ycen)\n", + "pt = mp.Vector3(0.5*sx-dpml-0.5,wvg_ycen)\n", "\n", - "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, pt, 1e-3))\n", + "sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,pt,1e-3))\n", "\n", "# for normalization run, save flux fields data for reflection plane\n", "straight_refl_data = sim.get_flux_data(refl)" @@ -197,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -214,45 +244,65 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (-2,-11.5,0)\n", + " size (12,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (3.5,2,0)\n", + " size (1,28,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "field decay(t = 50.050000000000004): 1.697652269444903e-10 / 1.697652269444903e-10 = 1.0\n", + "field decay(t = 100.05000000000001): 4.6910710639105265e-07 / 4.6910710639105265e-07 = 1.0\n", + "field decay(t = 150.1): 2.992872733686264e-07 / 4.6910710639105265e-07 = 0.6379934758846679\n", + "field decay(t = 200.15): 0.0039278135652722765 / 0.0039278135652722765 = 1.0\n", + "field decay(t = 250.20000000000002): 0.00015009081939073738 / 0.0039278135652722765 = 0.038212307406279115\n", + "field decay(t = 300.2): 8.806226395655623e-11 / 0.0039278135652722765 = 2.2420174097660296e-08\n", + "run 0 finished at t = 300.2 (6004 timesteps)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sim.reset_meep()\n", "\n", - "geometry = [\n", - " mp.Block(\n", - " mp.Vector3(sx - pad, w, mp.inf),\n", - " center=mp.Vector3(-0.5 * pad, wvg_ycen),\n", - " material=mp.Medium(epsilon=12),\n", - " ),\n", - " mp.Block(\n", - " mp.Vector3(w, sy - pad, mp.inf),\n", - " center=mp.Vector3(wvg_xcen, 0.5 * pad),\n", - " material=mp.Medium(epsilon=12),\n", - " ),\n", - "]\n", + "geometry = [mp.Block(mp.Vector3(sx-pad,w,mp.inf), center=mp.Vector3(-0.5*pad,wvg_ycen), material=mp.Medium(epsilon=12)),\n", + " mp.Block(mp.Vector3(w,sy-pad,mp.inf), center=mp.Vector3(wvg_xcen,0.5*pad), material=mp.Medium(epsilon=12))]\n", "\n", - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources,\n", - " resolution=resolution,\n", - ")\n", + "sim = mp.Simulation(cell_size=cell,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources,\n", + " resolution=resolution)\n", "\n", "# reflected flux\n", "refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", "\n", - "tran_fr = mp.FluxRegion(\n", - " center=mp.Vector3(wvg_xcen, 0.5 * sy - dpml - 0.5, 0), size=mp.Vector3(2 * w, 0, 0)\n", - ")\n", + "tran_fr = mp.FluxRegion(center=mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5,0), size=mp.Vector3(2*w,0,0))\n", "tran = sim.add_flux(fcen, df, nfreq, tran_fr)\n", "\n", "# for normal run, load negated fields to subtract incident from refl. fields\n", "sim.load_minus_flux_data(refl, straight_refl_data)\n", "\n", - "pt = mp.Vector3(wvg_xcen, 0.5 * sy - dpml - 0.5)\n", + "pt = mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5)\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, pt, 1e-3))\n", "\n", @@ -275,23 +325,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "wl = []\n", "Rs = []\n", "Ts = []\n", "for i in range(nfreq):\n", - " wl = np.append(wl, 1 / flux_freqs[i])\n", - " Rs = np.append(Rs, -bend_refl_flux[i] / straight_tran_flux[i])\n", - " Ts = np.append(Ts, bend_tran_flux[i] / straight_tran_flux[i])\n", + " wl = np.append(wl, 1/flux_freqs[i])\n", + " Rs = np.append(Rs,-bend_refl_flux[i]/straight_tran_flux[i])\n", + " Ts = np.append(Ts,bend_tran_flux[i]/straight_tran_flux[i]) \n", "\n", "if mp.am_master():\n", " plt.figure(dpi=150)\n", - " plt.plot(wl, Rs, \"bo-\", label=\"reflectance\")\n", - " plt.plot(wl, Ts, \"ro-\", label=\"transmittance\")\n", - " plt.plot(wl, 1 - Rs - Ts, \"go-\", label=\"loss\")\n", + " plt.plot(wl,Rs,'bo-',label='reflectance')\n", + " plt.plot(wl,Ts,'ro-',label='transmittance')\n", + " plt.plot(wl,1-Rs-Ts,'go-',label='loss')\n", " plt.axis([5.0, 10.0, 0, 1])\n", " plt.xlabel(\"wavelength (μm)\")\n", " plt.legend(loc=\"upper right\")\n", diff --git a/python/examples/bent-waveguide.ipynb b/python/examples/bent-waveguide.ipynb index aed11b6f0..ca9d4e5d0 100644 --- a/python/examples/bent-waveguide.ipynb +++ b/python/examples/bent-waveguide.ipynb @@ -18,15 +18,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "from IPython.display import Video\n", - "\n", "%matplotlib inline" ] }, @@ -39,23 +46,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "cell = mp.Vector3(16, 16, 0)\n", - "geometry = [\n", - " mp.Block(\n", - " mp.Vector3(12, 1, mp.inf),\n", - " center=mp.Vector3(-2.5, -3.5),\n", - " material=mp.Medium(epsilon=12),\n", - " ),\n", - " mp.Block(\n", - " mp.Vector3(1, 12, mp.inf),\n", - " center=mp.Vector3(3.5, 2),\n", - " material=mp.Medium(epsilon=12),\n", - " ),\n", - "]\n", + "cell = mp.Vector3(16,16,0)\n", + "geometry = [mp.Block(mp.Vector3(12,1,mp.inf),\n", + " center=mp.Vector3(-2.5,-3.5),\n", + " material=mp.Medium(epsilon=12)),\n", + " mp.Block(mp.Vector3(1,12,mp.inf),\n", + " center=mp.Vector3(3.5,2),\n", + " material=mp.Medium(epsilon=12))]\n", "pml_layers = [mp.PML(1.0)]\n", "resolution = 10" ] @@ -76,18 +77,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "sources = [\n", - " mp.Source(\n", - " mp.ContinuousSource(wavelength=2 * (11**0.5), width=20),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(-7, -3.5),\n", - " size=mp.Vector3(0, 1),\n", - " )\n", - "]" + "sources = [mp.Source(mp.ContinuousSource(wavelength=2*(11**0.5), width=20),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(-7,-3.5),\n", + " size=mp.Vector3(0,1))]" ] }, { @@ -99,27 +96,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources,\n", - " resolution=resolution,\n", - ")" + "sim = mp.Simulation(cell_size=cell,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources,\n", + " resolution=resolution)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (-2.5,-3.5,0)\n", + " size (12,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (3.5,2,0)\n", + " size (1,12,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "f = plt.figure(dpi=150)\n", - "sim.plot2D(ax=f.gca())\n", + "sim.plot2D(ax = f.gca())\n", "plt.show()" ] }, @@ -132,25 +154,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalizing field data...\n", + "run 0 finished at t = 100.0 (2000 timesteps)\n" + ] + } + ], "source": [ "f = plt.figure(dpi=150)\n", "Animate = mp.Animate2D(sim, fields=mp.Ez, f=f, realtime=False, normalize=True)\n", - "sim.run(mp.at_every(0.5, Animate), until=100)\n", + "sim.run(mp.at_every(0.5,Animate),until=100)\n", "plt.close()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating MP4...\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "filename = \"media/bent_waveguide.mp4\"\n", "fps = 10\n", - "Animate.to_mp4(fps, filename)\n", + "Animate.to_mp4(fps,filename)\n", "Video(filename)" ] }, @@ -165,27 +219,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (-2.5,-3.5,0)\n", + " size (12,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (3.5,17,0)\n", + " size (1,42,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Meep progress: 307.35/400.0 = 76.8% done in 4.0s, 1.2s to go\n", + "run 1 finished at t = 400.0 (8000 timesteps)\n" + ] + }, + { + "data": { + "image/png": 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fGHjse4D3TFmeyny2ncFQbQMcZw1GjcOp823iO++pppCuYX0bYy2EDoHGYPynqxsl7Ptq1iIRrI0uXUWWx9XfOobN2Ft7JoBzWhSeoa2+jlO+7FOldVoBVDH1RGG9V16FVdfbz6aIPULWR6hNszTEg0eMAUnc/DkLg9EViVpM1B6ukj8vIbvMhzLg12sIrLL8ItiMvZMzcE6bjhsCo/M/Le0Uth40VT44VVwgPBd5kzQFu3bf4+OyTjouKJ6sE2TtwwKHaUqNQWKM+WJkOUqeZavKsKqQKX0vtrxERe+QXZtm4OxIbc/G4c1SV6Apestuc39gMDxOg8WjVWOpvJ0QOYqjA3LHsXorJ12BTdEQfRNwYX5dbHHRBbJegl0hdu0HgSaE116MK0OAj1B6jlouYxgt0SopfY/+jh3YU7SEzcA5ATq4ccjDH3pzvqLPC7mtffvAZAhATgN0YvmpQgEHnsaxb4poiqjFro4cdHzscWcqGDfBeqIONukaSVeoWSAqbuypqKsHedPCXS0pQGcDQGV1vMTlztPlW1NoJu+WZK1m4OxQGz84IhzcOOTy+1/HtRvP1BxV3L+TOr/MHaAyeZ6FgwYcsyPVFS2AR2xpXcWu3p+jNnVVrLBexFtBFuzaLxZh7Xw94Pscp64gaojrNH3GX3VRVWplo6duv76agbMjVT2SATZPP3+NB+5+VTV0urzIrfu0gGWSPKDvI3liW+6Ajde6fGqaQ0f9kIVq8yCAAwcYcGMeQqdPa13rlq+SZSM/46sjtljFk/LXcSioq1FtQqdUrgE6LR68M6eDG4dcft838/RzV3n1PQ/yxKMfzjc29Xpt7RlrKpaOx7f2vpWWJX8kuyx99z/eJTh748ip/rtx8xq7lic/x7F3CiPiYlMliQONqZpIP4JNCB1cWOd7MqtWL80l7nDXokegtL54Z+noqa7XbOEcgw5uHHD5fa/NYHPl236e+87dW39Ao3XR8JvROOimKc36bT0aRjrpJFs5dT7X3JpR34SUOuhYdXDxE6pjEljuQbpGUucoztLw4JIwU+BG5prfo6yVKzitS7MPVkFAWj088dlEf5VLMw1ogmbg7FgHNw546L0P5bB528e4eNf93CrPFVsLhAGAqU1rOFjGbi/se4Kpo0TvPbmjNnPixj31xNsmxrqomsmeA5IkkKSIXZLNRpi1ViXeOtq0fAT1LU014AEqW9O6KoNSqbWtfPfi8X6zhXN6dHDjgNe+9zJXY9jcXTElT2U1qUITAabpERq6DXo+mycZOpGpE97/AnzEuHUCogngAuHpYt85fzVFbBpVmXxaIrkl5KtlUjuvc4CNFL9Xdukz7Rc/TqdtPu3s71IU2AGagbMjHd445I0feCSHzduf5OJdF8hu4BjI9ABM3WM4GDoTmjonYKr0evnzMETvaWg1ylqPEveui0EXe7DYRyVBrHXDHULTuEYtVsnCLb4Xct4xMLp3ldWrmqoUFC2g2vOJnq8uE/hPdHdm4OxIj/z0w1x//joP3vMgV97xlINN1rejfDMrHL1lVUKmuwXTFzyNYOkAlRMNkx4qnIeWPlGMClaVNYbULFASZIG3cvL46RIuaLIHxoX+dWAxPqXSvYyhE/4uqwylKmVpxGdS/l5zt+Yq1elRDpsrrhpVuHE1rUlldYTMWMAMhcvUUBkSU3tXqitZimJ9cLvbqfvu5rAxGDFuZj/BWzzqql4+FDB+YvXeE+p3AU3Yr2qftuMmvA8zcHakS+cv8eQ7rkTTN2pLC0PF93zlxpqukOkFmJr1U4BlCEyOEz9t1y3Mc6O47jYrH13TWuv77LkZAMOSiCERcVWvZOEtHLuZU5fr1AU0Tft1qVINmCiuSjNwdqSPvPWJwlyxGWwyB0AJIhugaYfMSQNM59CyA9Mfe2znPCrfP83yV//FoqgKa6sc+SB4YTrRMJNfIkoigjVCKsrSKhYXLjjM35V1FKwdld6x3F1GeVYNUN2iZTkDZ0fqFZO58BBU9opoXNcZMhMCpg0ux9lgNcX7E59fwXWj+fWy1kEntco6VV5MLTZ1W12UBsEKpAILlIVIFrfKDZsyZO1fI2HTdkwBRp2cxtH2EZqBc5yqrToVu19tGzJ9ATMFXIY8ttt26TSdl1Z997Bx76ibxS/FTZK+sq5KtV5HwBE3t/EizGhl8pn/QqhfEfEtSCmVGnMR4o6H0RltP8ZZrhk4J1hDqkzbgEzXF3HMPi6fjju2pjNNQrXX338J11C9hRKqVKkqa6usfWhfcFbMMppuwngjxmoeZ7xOGRA2zqvtPCuqS61H6FYBNANnR9r0/db7aDK/QPQ3TX+fIMhsEy7bbrXq5NragE1UxYoAYgOEfJkNrrnc3dvw2TS8pFyIQkWu60E16yvGcsXfy03whSPnKtWZ0CTWzJYg0/aIdbJyej6nU8NlaGpNsClsarNSss6B8T/36ptsW0goH6iZDeLcLE23i1oJjg4PTpz2hHMXzcDZobrc+vK6Tr6ZEaDZFmT68GJbVaBdphWmqzFZ87fz2UiSjbpyMaoSH2lTXJA88fPfGCF32DbBpj+5NwvafEB9GhOAZwbOMWrqatO2rZmTBJltVq56wyb7FN/07QLbLY2QYrIInAtjSAwsfYtVYpwDORHBoGg2PantBhutueN147Gq0mi1gKazbmAGzrGpqUZeANEMml5l2ar8aM14MIp7X50HWFzDE4mRLLKmGgeiJIDFAyeE91142LgJ11OfTYBNBWjqIBOrvE8bgFq7lnfsydxBM3B2pPh3ogo2J9E/MyVomsrQ+fhRR3dX40wyEXQgmnY47jVuHGD2EkHVeKvHAads1Ri7gvTITUGqNp/sqi9kmhRNidG+73av8gycHSo4CXtVn04gaFy6PQrRUI4+imae2bqa2nHijXGsKBHxU1QoSyMeOGQ+nRg0YlfOqkn23STqPkpn+ewmDdvcFTx1rVSzD+f0qmzlnASLpst2l3aPgniJyGTO4UYYbEltr1r8LlpcNM1l4ibOyRzEpB4uLmYVaiFRSI9cSBnVLDRMZcKV5XJn3xs8Q6EzUjNwdqQm11wbbHbVSa/ri9s0oLn5OF9hmNhsH9DuMkn6xZjckQWrsPAxww1k/hkXp8r7ajQFFElA0iMHIMR1OS7Ph1PMdOTZHK9m4OxQldBpeBumHHIwtRUwFDru2Jp5e7bkP5jyFa2CTJxH4lcuBBZYdH0UgcYFxgthYfwBSBoC4YVJ1s3mDZsSNF18OTXqZUlVaAbOSdEIqwZ2C5ugtvmg+qfX/2HeZu/jqvLkHfjyz7BfmHoCazF2jaxvw/rIhfG1a0hXkK5hvXJN4ODqW+naReoUzW9WmAGwXLWpqOp0gkBfyLTOXjBMM3B2pEbrZouw6aPG1pm2Yzs8j9tgwxBIDcsn+k4EHPHVJgnTqKkLfBcC3KWr3II5uo2mKx933Me0Wi7RtQdRskDUuKlLVX0mzS9+I2yGWjI1sxWMtW5gBs6Olfdp0HjVmBS36A/ZRjVs25rG0qpZH31mVo1fYUL1ykfbdLHF1xl4MtisVg5IwXGbpqhqdB/Vb0say1j58k8BmLabNPImzsA5IdrGHDRBQy2XoY/WcXbOm9TVUfN32arKfDlhHFSARuhPE+YyTtMcNtm+NPSzkdDJJ8+IEmy6QmbQhcnzncK6gRk4O1T9a7hN2AQdZx+W065CBSN6cU15YwYQ/2fFMAIR4zd7C8c7iSV7qYWqGFUbL30VaCajbY3FI8LhZz81KuUZOLvUlvo29NFZg8GuVLZq4te9iKAwT2gEDuOmECVxQHJDGHzo3yRBFgs3r7GIm0w9Dhnjlww2MWg6P0utvYga91VfloMbhzz8wTd1zLNaM3B2rnhsyvBUpuxId1K0LQdwn+vUqwyV3WUCLIL1snADrJa4Tn0mhYVF1boAeos9ZLHn5jSWxE8zKlkM8g3QdJjrerQqrsHBjUMuP/56rj1/fVTSM3B2peihnwoT8cuxDfjsqgVo25r6PGLrRqPfD5ePZNE01SzRRD2YjIvMsHDhYcLsxeztg1mgZumtGQcqDdByCYczqTq5Cc6ouVXq4OazXH789Tz93FUeOH+JawyHzojAxLOm0FSxnCTMrTLhMiuXiZY6OfgY1CRo4qpJWQTO5Tm3LM7B4mXoYh8W+5CcwyZLHyomQZMlKj78b4g5HlfPygsODGOX7BxK6w5uPsvl97+Op33E2Cfe8pFR13G2cHapE+DD2ZXO/C9ZqS9eMHTcgAWDSuKC3MkCDVE3URf2N2vFAl3uw+Kc2zcaS9XaKrTtjk/eZ3P5/a/j6eevufDUb3uS+1523/A0mYGzO51if8soeJx1vka31aKID/WbKqzVDY0SWbgxU+DD/vomcVV0sY8u99DF0rWih05/g8sTHdv1x63cDyfA5vHXF2Bz8fxFbh3dGl42zghwROQO4CHgYeB/AC7hfmx+D/gQ8AOq+tnSMe8E/k5Dst+vqn9tKwU+QRoEkwEQ2aZh1zXpschvG/cWDNhU8zAx7pj8wDB/sRHjLCKzxMrS+XD8JF7FuXDiTLcUob0EnNhn42DzMReeurzvAJ0J4ABvB/6F//4fgY8CdwNfD/xd4G0i8mdU9f+rOPbf4sBU1se3UdCy4tnjtplHZ/VppOnXoDOpRnRjy9QHQErxfLVg2eT7WFXX2VghtbC2dmPCTpF87uMk9VE6fWu6EcGI5GUVjTJMNjOvPMmeT1R0Yoef/RSPfPDNJdi8KvIZzcABWAH/HPhBVf2PYaWIfD7wc8BXAD+IA1NZP6qq79l+EbeNlR5gGWBpj0yq977bOL4uvS7Tdkjp79p9FVedsspaLWsLihYY4YCiGAML62JXpaqIqnPYBp8wEKI7eNPHF1yaoaN97dZ8/4c/8EauPX89r0bdfWEy2MAZAY6q/jjw4xXrPy0i3wX8O+BNIrKnqkc7L2Cdomeoj6a0WKaAylTdz+q1Xf+XVHyrUwYfKdd8fBRNH3vKqpJaH8bXQhoNXzDiphqVFNZpsIRcU7moYhGM9+fE8+xAbPkMfHiqFD0EBdicv7ixfWyOZwI4LfpN/7kPfB7w6WMsS6WaALIruIyFSjeYDHhcd+psr25FlLqzE39GMXjwFg3OprW+WpVqHmHTiJJYB51UNdvm/D9uxDnioRPlExATA7KyZH3IXrq+D5y/xJW3P8nFu8uwEX9u4/RSAM6r/ecK+MOK7d8gIl8OnAMOgY+p6lb9N1W/Tab0vRU0W4LL0G2dHsWxMZV2ocquC/m68iuueD+chFAv5SNDJE4/jlMVFcF634yzhsI2EOO2qypWHGlMoEzXqnAfN3o8OBR44rGPcvH8qwqphXJOcTdeCsD5Hv/5C6p6u2L7t5f+fpeIfAj4jnLLVpNE5BM1m76g+UC63cktAKbveqcm30HLifQGyPb9XpVq8oFI+HBfjPhSZu6VPKqm8fsZcc3kZdkwkBw3PYWK73gXNY+LCBZyaycqw1jlYMoTvHDXxY0MtOb7EJ1p4IjI64A/j7Nu/lZp8+8B3wt8DLgO3Av8aeAfAG/GNQm8cWeFLWtHgKlPZgBYWoHSAyDH2m8p3bzAAULBApIidGKFwHcikBgIbZHOMZwfUz4OvCXhraCQXwydsE+mgfCJf+fKQClf+ar9hurMAkdEvhj4Sdy1/T5V/c14u6r+ZOmQW8BPici/An4beIOIfK2q/ocu+anql9aU4xPAazbW+/+0ylyueIiOBTCDwNICla4gOe6OkhtVqwhCarLtwaLJbigOMokPdqcqiCjGGFKrrkrlkzbiAuN1rQKFK2uiS1N5ldp+rKi3WuqqTg6E42/LmQSOiNwP/ALOavkBVf2hrsf6lq1346yfbwI6AWcbqoPMTgFT+4Q1gKWxybbHE3vSoJP97eGjOPiEvjPeqkGFhbgIm6JC6pvKE3G9kMNZCT5OlYAxgqj/7SndePVWTlAGHti8jS0t5qHYhfRL3yuBM9GtOHPAEZH7gKdwvY0DOPrqk/7z86cqV52qoFLy47l1dcd3XDcOMDVwGQqWPtNF7GTKsAb5vjHx34V+MOJNVBfTl0QM4mNS7SXO+kn8cIfYeRwUrCEDbnxmbTF0A0Tlu1IJoFg1MOp8q52LswgAACAASURBVCbwHJ8p4IjIy3E+mdcAHwb+gg6bt+Fe/zlu4EiLYtO2EjwNx7WvmxgwQ6pXLZe+F0yO0drJyhn5VYJUgxfZZh30RMFoykIUk0gGGquQqmR9dVSdFZRE/hxjQmvYpqqgE6upMtsIo7iKVldtmi2cokRkH/gI8NXAk8DbVDUdkI6QO4t/bboSlvKh2LeivK38WG0XMh0BMwA6rVAZBJJjgk9FWbN7oBLtZpHVbWT1IqJKIgnGz/qnCJbc4ll4X09iHIjaWr/j388+U4h0dtc3WDG2Tzo1OhPAEZEEeB/wDcAvA29q6lEsIq8E3gr8hKrejNa/HPiHwNcAv4+zkrYuAZ69eVi5vsu6jSdkozPIQCumJ2Aa4dIJLCNAcix9dkrXUP1MMmqRdQCOm7tYTAKSoCZBTEIiCSoSWThFx3MXw7xun75zGdnS9212RjgTwAG+m9wq+QzwIzUX/XtV9TPAncA/Af6+iPwqrvfxK4GvxPVGfg54VFVfmLSUfjxK2bo5uHHAtz7+cL4brY1WtEKmct3mC9J+TPW64XA5Ia1UtZES2iXlOoj/VB+NQWyKpEfI+kUkdCEWA5K4OYxNgpoFYhYY9r2F4/rkCO4uNYVFrrKIC9ubLM5jno/prADn3uh7U9+Zd+KA9AfA9wNfC/xx3KjyFLgKvAf4R6r67LRFrO5MdnjjgIfee5nrFXPFDqoy7RoyY+DSu+fxbjsCtp5v5vBwn6IWUYvaNayP0KMXo5C+CzAG0SXIwk2onqgDk127ceC+BclotZXRwQVTcx5xkSsAtkPr8EwAR1XfiYNJ1/1vAsc6140Az9w44PJ7L3P1uatcOn8pg85mPb5vlWlgdam0rtaKqX1AJ2iZmgAqk7dsVV27bJ3/riGapnXB7la34ehFNPVN6MYgyRIWFhKL4KatEJtmcatCfx71yYKzSLqGca5t4apY19QaFvLbhjV05meCPKk68JbNVT/vyM8+9kTFXiUPXrkJYaNJoVQDD9sjs782jfCQkc9qWyxKxbFZ+So8jfH+tb4gu7k0qOsMvaNVV/aNdTlsJEAndVE3Wa/QsKyO4OjIQWh1G1G/j127Y0JVjBwE5Rez7qqX96lbNk6xZbs7XS0AaAqdCQvn1Mj/Yhx4yyab5OgdV7jvXDxXbAVoYvWtNnWwbrpXl0ZYMR2tl631vRn64pSh475k1SkJnwC+SqWpg4+uV4gxqFgkFdQYF0dcEteFRwMkLZW2banMQ69MVysnfvLaqmJDNANnV2qAzcW7L3LrqGKc6JZB088n07d6xW4Bsw0/RO31iwFkC9sqfSTWIknSoYybvYnj3ApWTo/TrasZVaVbtX3KitUMnB3KweahCDZP+blim3wEHCNoBlozHUAzCjK7cHJ2zUOMqxaFP8Pb7QZLuSgMoZXKt1BJskCNcV13RFDfgpVZF1kNOC9DBocBpx4f0+aWCWWQ0rqpoDMDZ0c6vHnIIx94Ywk2F/MdpmjW3jZoJqgynXjQDMhH/QBOVRAxiFkgPs6UJH6cSpIgiyUs913cKVlA4lusQpRN/ztTtm7GwKbp1JrgMyVkYs3A2ZEe/ulH8ukbA2xqnallR+tcddoZbKA4Vqpyve9BFUZCinFn5kZjgi5hsUSW++jChfTFJMhyDzULMEsXFsYsXcA8WWS9j3O4aCNs2nwqXVqYWn8/mB46M3B2pBw2V1w1qtMLdAKcwSfFEVwxjmmrqssvWx/+9t8lcU1LKpAoZrmH7J3DiD9zs/AROZfue+Jgo8mSFMkiPUA9aPo4btvGXRX37ZzsaM3A2ZEeOH8pcxC3+myqmrfL+5e1a6sGOsPG5SDTOIerXqJtvjF1+UVz/cb0UT963CZ7LrSvqvPPJCHe+CKDTqqwsi6G1VrdXDniq1Z5VsPPrQ90atMgt3Km6JczA2dHeuKxJ4o+m6DWl3qbVg0Mhs0AhWkeJm/2HvoiDD3HjblD/DAEPH6MgeWe89cokXWzAElcWJjUT5yeKqlV1mGG9cr7ObSY4wExV6lOqS7cdWGzTtzHT1O1bluw6SoJ02726x1cFzd753PfjHghN85BDKCoGNRaNDnnwvhinFWDOCvGw8VNV6EYq6ytm6BLguEUJV03m0D7qR3vmKk6zcDZkQoPzdjWp4p1kw5D6KuB4CmrDkR9NQW4GssiUT/gQtVK3FzEyQJdLNHFOVIf5G5tNfue+hq0oiwsrNVtE/yocc17GmfT7XQsd1/QVE32tk3NwDkudTXnh1o1ffKo0hAnrbSMlNnR4MupwJWpfF4FyJD3pUH8nDdgJWHtfTRrG4DjAuMpkPoq1CLNLR4R1yZujJ80vU8RO1KjD1wKKPUHjh0LNQPn2NVjbuBdNicElef0HZXWKR66V3hTI9Ag2eBMFYNIgrWaO4Q9UJxTGI7WeVA8F1vc+snVfZUqn589j9QgGzlHxWomyFDrZVtGzwycHSk0ddbeyAHOYRhj3VR5B9oOGfEYHgcsh6j2HKWwj0bQcR33EhCDxVkxqffNrNXBZp3CUWq9NeNicwrCnjrLJ342urQuVW0fcnuq5l6qQGvDin6agXOsKo7DydTx5Wz0VdR1XiulEGXaKc/BOqFOzHpVlDdrjYocH8bHSfV/h0nSnZ9Gs/41IdxvqjbbR6QqUqf34+CrL1Ks2rhi5GVru6xjrno5vyns0xk4p0E7sQ5OGxB2oHK4ltiqyT4DbCQMHvcz3eRWbXZ89IcfarWZZV1RyF/8PhPud1GjhVPKbOzvxgycHWkwMnbt+N22Tpmls9n8HcFGSgCqUG4l+Cic1kXhBBdSxoWJ8ckRwFK0bgJs4mw6VPxGK4ZNKM9YzcB5KeiEv+STtypNrcKbXmXdlHZ3WzD4IHfA0tHGDbfylRNB2EtMFpcqBMQzRACKrJpG30pt0dv3LFbRpPGYsXdqBs5xK1gfA6yQyYYLDNT0zc8nGDxV0KncDx+F07l3jMLCCikgiUGTfJpjY/IwMQsjvh8OWX+cjG0UP10RdnetMmtrtnBmxS/9GPhMAo8pnsjTAp3YharqX0hnnSguztRCAGOwohhVVJ2fx0aHLhPJojYIkoEm9qUcV6/hrJgR+GYL55RoJ3NGbatq0veBH/OCnGTgZCrDJm8RNN4Bk4gL4SsoVoUFQj5cSrNm8D3jYlMtjEvDSD1gttWLyZS+F/Ip+8lH5jUDZ4c6Qa7bXNuCySBwnNKOgdmvie8fbC1GLQtRbKgnEYYz5D8MqspeIiwNLEXQBthsVU3NVLBhcY3RDJyTrjEtTYN6gk0JlB4AORWWjVfNWDg3KfoasYqkKxd90+Y9ySUMf3B/oCIsxLIUWPgexvFb3XTHp/zxKrunNgbDEwNnXM4zcE6Lpn4hu6TXuk8LULZqDbUmWrFu5GsaQFPVqTLMaaTqvMJ2DekKSdOKfN1bLWIwCwPr25Ae4eJSmeytl9J5aDGFKc6oUdu4KzNwdqjKG9ipR/CYTMeCpQEqrd1cuzyywx/r/letOq9uJVAK1mZ236Le4moRVdSuEbtC0pULCePjTuUZentBElgaF30zXbmqlkkiMyNqdq8AUPhr6NMTjmuqURVbyHT0szoD56Ro7K/8Ni2WxmbgpjSb8xvy6G4DzVUvXlkS2xQBNiKgedQGUbdd1DorJ11DukY0RW0x0J8kCzf5erpw1a90hUricgmjzyXUsaIRnURWVqHc/Z6fuPbWBThTwAZm4ByPsl/KvNdp72NH79sTLl0GNZbU9nj2eXx34XDvBR6huooVrB61LsKmpi7ypk3RYOmIwHrlJlbf33eWkE0R8YMhTOKT95ZOmJlLod7i1B7PhtRApc45nF/9wxuHHfOo1gyck6gqz13X42rVAzA94VIHgymcnscyKqOLsRiPqtzwIef+HE3XDjY2hAG2fioLgcR6GK3dYhaIijOEjHMyb5y/2CjPAa16sZWUSUvfqwcTHzx/wMMfeKR/npFm4JxmbRUwTb9z49ZDR5AcUz+CcmsRVPxNBB111R8Vv85U/GBYX81SdeAJXZH9hXC+Hwti/ChyZyll0NEKC0ZK1rF2cOK3zR65MaG/08HzB1x+32u59vz15jxaNAPnNOkEAKbrOmiBSgeY7GZ+wBpp8Ypmxd1oMg5wSVzVR9UHujNgjPPVWAuJP5vUR+k0UerW5jlkL7x1TmXV7j0jygAqyNQn0AKcgxuHXH7fa3n6uas8cP4S1xgOnVPa0+r0qdm32uKUra1iGSr6hlYfs7FOSktuTMdGdXld5aLVS9NBIRBO03LcKpTHlzu4YUL7lAJ5U7ZBTYDNAjVL1CyQ5R6SLH3kzSUkiVvC/TBR/5xY4SIGK4j44lK62DTciI2zqVjis86/H9x4JoPNq+95kCce/fCoazpbOCdFbb2vMo2xZIp/d7FWKvfpaOaMhcaYmExTKe75m52P5lN/Wl+rctE1xbVQGesibCYLdw6hX42qa7kKVSsRSBYOVI0/LGT7i58rcKOZve5adel2UdOR0Vk235yHp/62n+e+c/c2p9WiGTg7VOFR6t1SVQGaCSDTCToddhoClzagHD9u2Dj5ACALGXiyxiQECdE1F3voYh8wqF14x3AKZgGaRsBZeqsosng27qGnWwSdfEsNULrWw8p5eavo4MYhl9//ugJsLt59gVu3P9t2xRo1A+dYVNVSUKWO1kwLZCjlNsiKGQGYJrCcYN9xlnd8NcO5hHU2NFerq/JY36HPmgW62MdKgtgUsSlqfSdAmzpLCJyFYxauwx+AhInZa+593BQPGzMEZAN4m0ATPy9ln42qcxC//3U8/fw1Xn3+Aa489nNcvOv+jp7+Zs3A2ZGaW1rLD1eNX2Yj0XHWTAFCHSjUBTJ1cBnpP/Zpd9xxYikVhkD2pThGX1SxallZWKmALDDJAkkU8cARm6LeytFkz8UY9+FlMtDUWTlRvgXVAKiYQg2Mor8PbxzyyIffksPGWzZTaQbOidF2fDOt0GnYeShg6h75Nl4MAcqu/DxdswlO9JVVjlLX1C0iJAJGFm7Kigg+JHtuMYu8mpWlVJU61DuYSyo9J3Uwitc//KE3c+3GM/WWzTy04ZSrzqnXEzRTWjNtoOkKmUarptWPeSI8OEBH60zD5OlCmipHqeV26kM2iJvnJhE35ejCQGKWGAG7XGYWTiFQ4EamTT8jQRX+mDrVOKcDbH7xsZ/l4t33Q3BST6QZODtS3vjcZefuPppGsJS3jbBmygDoA5im574PWI7bl1O1otCIbN3ruVZYW1il+fkZ3x8wET8hl2vUYp8ExPtx1GbDH3JDp89ZdwTRRrr59wfufpWHTV6NChbQFOCZgXNsCr1UtfbX5rhB08WS6QuZLoDZFlhGpxslEF83VVxwO3XrU+ssnBfXKdZaBDeF6DIxWMn9tIkRF1fcjxJXq6553Vr3WEx2JarSqX7mfvZNH9isRk04fcgMnJOiRqumm49mW6Dpas0MgcyQV2rnlk4DaMJ0oYqrUlkV1tZmPhzrJ+BKrCG1lr3EQKLhAKyHlcX35VHNeiwD9VXuLpZPIyiqH44Ld99fnZdMM2H/mehpLCJ3iMgbROTHROR3ReRFEbklIr8pIn9bRF7ecOx3iMiviMhnReQPReTnReTrJy9jr52rYRMe7PJ3yt+j57W8Y1MvXlUtwKEqj411WsqvlFadlVROp05asexEFRnWXjeClePgkVpYp0qqLrb4KlVW1oX5XVsXedPaAKg4m2Lnz9oqTGcPtm4u7QexzSt9JoADvB34GeB/BlLgo8AvAw8Cfxf4VRH5I+WDROQHgXcD/x3wi8CvAJeBfyMib9hJyRtHZldXoaogABXP1MSgKR5T98NbDZm6dOr22SlcOmRcvnaxdRMOd+s1i7yZ2tz6CdWuABuN0qhTNpShnPEY9YbPRPl6nRXgrIB/DrxGVV+jqm9V1W8Cvgj4deCLgR+MDxCRbwS+B/gD4L9X1Tf4Y/40DlrvFpF7pitiwyu00bt0mFVTddBpA81OtGWzyfj7F2JMuaEP0XaTr8sC3WUFC2WsuNO1dVabL33UCTzTXpwzARxV/XFV/U5V/Y+l9Z8Gvsv/+SYR2Ys2/1X/+fdU9ZPRMf8e+KfAPcCf30p5s2/NFa3eVk3HKoA7rvggbYCl8pjqdMaApi6vrWoCH2iARUgqB4uLK5UYIREXnyp8XyZucRE288icmS2ripu8q2uVqQIyQ8GzI50J4LToN/3nPvB5ACLyMuAb/PoPVhwT1j283aLVqwk22fcTUH2qU9dHeOewCZKapUZ1L0qATjBSA2QWRlgkhr3EcG7hlv0kcYHvxC2Jkax/joQr7y92Xp2qsG66QKUvdHakl0Ir1av95wr4Q//9i3AA+i+qWjVn4q/5zz8xWSlqX04pfY6HTX0RprFqqtIaqqZ+tceiqhEFXgE64RqHapJBCKPCFVgaFzP8XGLc3Fy+WdzBKA/vm4jb16Bg1z7CA+2w2aYaf0SqBpb200sBON/jP39BVW/776/yn5UTtKrqLRF5DrhXRO5S1ZttmYjIJ2o2fUGXQh7eLBalL2hgd7DZhgoDJHeXbbsq3i9TAnzmiwkXzIfu3UsEVeMtn9j68b2OPWhIj9zEXGqLvpwxoKkb/Nl6XNc+YcN0poEjIq/D+WFWwN+KNoVm8hcaDr+F8+PcBbQCp5NqOvkd3DjgWx7Pa28nATZjNcZyma6b2bTKziV2AEfbBR8XXJU9Ix44edjfbHgDitgV2DUmUSRdI3adWUmjrJghoJmwY1+bzixwROSLgZ/EPR7fp6q/2XLIKKnql9aU4xPAa+rMhYMbB3zjey9zvWKu2F3AZqhEpFO1qqGGcuq0Wfnd3EFEMNY5h/cSAQ0OZEBTDxcXzQFrYSFgV34wpyJmExitQwr6QqYXYNqG2fTTmQSOiNwP/AJwL/ADqvpDpV3CLEJ3NCRzp/+cxrqpUIDN1eeucun8pQJ0dgWbMQAITbp9/Dkn1XrpqnLs7/BnWGvEhfBdmhw42DWyXjnQ+Im4JIyZWoOsXRgZYNNBFM/yt1GYjqAZDIlpYQNnsJVKRO4DngIu4Tr1fW/Fbs/4z8qJPkTkTlx16r928d8MUQybB+95kI8+9kS2bRuwGaKuz5drCi4up01V51BejJF8GuKwEEaBhyko3N8LURK7Rlafw6w+5+KMrz6HrF5E1i+6v9MjFw44hImxKfmkyUU/zsawgibYxDMH9r4XFU12ca/n2cLJ5YcwfAx4DfBh4C9o9Zv3u8Bt4JUicr+qPlva/pX+87e2Uc7DEmyuvOMK9567b2O/obDpI9lMurhdKsrSJd1TCJ2gWr9p6Xs4R+NXhH411qYuuN36CFkfIekavM/GReP0QfEAEuOic9rURXtQFyomuynBiiwAoMPkbK1n0GV9n7S76cxYOCKyD3wE+GrgSeBtqj4Ga0mq+jngX/o/31Kxy6P+84mKbQMVJqY+4HIJNhfuvlhRxraU6tXXuunySJV/NJuWKdM6jiVck7rFRFZcBptwbIBNuvYxw49cK9T6CI5uo0cvumV1hK5XaLrKph6V6se1dPGiV7bygjeVvIs2L4bG60Yi40wAR0QS4H24zny/DLxJVY9aDvsB//k3ReQLo7S+DvhO4Dngx6YsZ4DN0xFsLnaBTcd+NmPU97FsTGskkJrKtqulMt8SaAzu/Q9VK3xoX1clSl0VyaaQrmC9Qle3YbVyTeDxYmuav/0Fy6szprC+upTli9eFsPU3ZcrJt+DsVKm+G3ij//4Z4EdqTPrvVdXPAKjqL4rID+H66fyGiFwB9nCDNwX4c6r63FQFPLz5LI988M3ZLPhPedjEoxIqNSFspCmf0n4nWbsqX121MFg1IvFrnvcSRlMfV9x6AFlvxbjtoTollZE4auY0DrGv8hXlwvY6txgk5e8bkBn7ixHprAAnDpbzxtq94J04IAGgqn9FRH4DB6zLwBFu1Pi7VPXfTVnAhz/wRq49f92F3KipRm1oC23I4XHZRfP0tsBwXP6hDA8VVS9UXdypsNii0zeWxFAxJq+PZdZG/ncWwaFg0VD54k9tjWzmMb5CdCaAo6rvxMFkyLHvAd4zXWmqlcPmqYJlAzUv/wgncZc+Mrt+ZU+jE3nj9Sq967FdUNxPHEQQXLwpi5gFuvDbQgc/Y3xEzj1cMLzEwciYCtgUM2+ES99rHe9ftmKkfNKzhXMq9MD5S1x5+5OVPhtobpEaoq4d84amvWsdi7OxxiUSNlV6P4JlIom3Xly8KTEG0sTFGA/3JUmQvX03vahZEOKRq5giaKog09U73+k8G4CTbS9X6YZpBs6O9MRbfqYWNm0a6rs5iVZFb3Ac0ynUXbqm4qgIklklDjaaqIe/yUPBZHfUoMkSDWFiRDx4ctBU9n8ZY8G0ba/00xQHcIzRDJwd6cJdm3PFVk0FcVrVCSQ9ntXjZGWXrKX8l4S1CYiCWTiQYFATrBrNO/aBA8pi30FnsedqWsYUR2WPAc0oVfT1mS2cs6ExfW52pSmA0tnC77bb1tPoIs3yCv4aRTXJISJJETY4HgGo4ICz2HeWDaAbY6l2UZk0pe+lvj7Z5/hp1GfgzCqo9fEe4atsg8AQSOwSLF22KYKVBCsGK8kGQERK+y/PwWIfFnuoar9q8FQ+ui5O44k0A+ekqOLZ2aZ1sw2wND2a08HmOCqhUlm+ch8qVT9WU5VUXTC8NOrUF76aKDGTKkdWWVtnPRgpTltKxfd8ZXntwGvT6sMJTuvxV38GzrEoN8R3oanhUrd73/VOHR/hXc4GVtZmU9TGLuFltAqpKmurrKySpiFygxZOwXUaFBZWWVtYh06BoTugxPsVr1L99awGY7vKaKs/vxk4swoa42vpY7l0XedU85i2QeQ4IVMnCa+/FECQh3wRH5/KWTdr1SwG1SZwYJk6MK2s+uERGqXuZhfUkv+46edqyE9ZuUfG1NOYxJqBszNN6/zrlVoPwIyDS8VjWT8pcvX6TDuMPNDmp9CqIQjZf/luOOiE2FOpuqB4qYdOqjZuoCIRw8o6aygExMOqmwZDXYO49U4fE04xhs5ERnIX4JT3G6oZOMcsbbjDgxE10oLpBpiOcKkFQQ1Q+oBjKgsopkCVJCqr+g5wqrXQcZ8aReL0iwdQyCoRy77Nt7sRDgLWOY+NuLKJSN5zRzfz2ixv1xPfTKet2jT2ks/AOSua2IppBUxnuFSAZbDV03O/vqqZc7ogsTl0yK9TVtMqJ0lwIgdrJzSNC5g8OqdzKKtbL2BUseTQcXlI4Wqaqssg/S/PLmu2M3B2pW102jpuyGw8ieWgbD3B0jr+a/s+nU4vl4RYvW4ogu9fjImmrsgmVCduecoTd07k3N9TsCy832aDYaVm8zLKDeUDupxL8c+6GGRRwUdpBs5p1AjQ9ILMWMD0qGJ1gskOnMhChZWjpTmFgyUk1l2CCDpAGBJFIkIqLmSM9c5grMGq+km8ik3kZQUnNDF8ImunrC5er41qerkONUVTVINm4OxSpYek933tCJrtQWY8YGrB0gkmO2q1qsgmayEqb/PQEWOchSOg6qpCCyNYA2IFEoMRJRXF+mYnI3n4mHJjdNzaFHEnWx8PzO3TWbAKSrb0vWqfqZo8ZuDsSmOqVFsBTQMshkKmau6XPi1XbUDZVTN51fUOfuVgAamrEjlLxzdLiSGRJIvasBBQI4goxjprxxrvs8mmNJZsQvbMRpL6IsTVrBxIFdd94iq8LX0O1Qyck66qatJUoGmEhe22X5sFUwmJEVbOtkPdiqkvc8HMCfHAJQOPoGAsRh1slomzWxIVUnG9j1Pvs8G1dmdxxo0/vs2SKFg+5aLH+3W4lscxm8AMnJOsMli2DpoayDQe0wUyfawcOkNlKidy0T/TkLdCPol5bmcIkPXOU9+HZn2bxK691zghNeKbxgWrod+NskjA+EB5GjmaoR4IZQunvL6sOqwEKMVwUtUNWE0Jphk4O1OPm1au0NMMm62BpitkhgKmBSy7aJXqnY+mJUBV7aMuUsPqRQwuMN7CLEjMAkUiS0dYGsPSuJjjdYM36973Jgunab/e2/sOKm3QDJxjUYupUt472t4El22DptmS6QCZGsCclBaqrqq7Doq6qXBs6uJRrW+7+pMRMAs3zahJkGTBwiRYhYVxSyLubsTDF/qqr4VTl0YVyKaCzgycnWqjZ0Xz3h2tmp2Cps2a6QCZRsAMAcuQY/q+PI0tcO7The+1YC2S3kbWL6LWIpKAWYEsIFkgqZsvxyQLZ92IW1yRilPDToHZpjS6XIXYb/TszcNRZZmBsyO1PjgDq1D9YLMl0IyBTCdH8Rasm6Fplq069TZBiBWuFtI1enQbPbrtIm2KgSRxsNElahaIWsRaZG+PBMUYl4QQxk+1Q6d8CkMMkLJFE/9dTu7wxgFv/MDD/TOJNAPnBGoUbPpYNccFml32pZ9Cddcggo2oouoC2+l6BekKvf05NE19L8DERWjY88cp6AIXBljXGNTNAOiTjyfBr7KLu/apHFMLisFzeOOA173/Ia4/f314gszAOTZlN1Oa368qwByPVbNl0OyqKhXU9CZ26dtfgk38qekKXXnopCmkIOkCXfhxVHugImAFF6HTW0e+A47R4M/x/X1GnPYUVlCAzbXnrnLp/CWuMxw6ZyLU72nQkFcjBkxn2Kg2wybeHn138QGq0igZ3QVQ2Q3YFNKJj6n7Sa7bVqV4/7FWUDmtTulWWIbRerXWWSVWfYjfFA2LXbu/7RpN187PY/OqmAOOv79b7B7T99LFsHnwngf5mbc8MSr/2cLZoZzzrbrZs64aNboKtSOrZqsWzUmrYo3RCTkX1XZr5/DGAa/3sHngngf52NuucO+5+0blO1s4x6C2R24cbKLRMFXbx1o1sFvYbEuB8lVL/UH5sRXrJUyYbsQFwEsSxBgkSQgjOsU3i2MMaor5Zld9B5enL2x+/tueL6JVfwAAIABJREFU6haeukWzhbMjVT1DTY3kw2FTXkcRNlmaHayajW10g83Uqhi93Wv/SfPzdy2EXxCDm7lGnNVgEsQsHFSSJRJ+05MEWSxhue9Cx8jCB79b5KF9PfNtlu2m/2bK06pTFWxCEMexFsoMnBOi8S1RU7RC9YNNrbZtwex6DFCcn7qK8SZ08CMwvU9muYfs7aPpGvFROGW55+JPmSWa+E/j4KO4IQ+SZZNfw6FXc8hlevbmIW/50Bsy2PzcRJZN0AycY1YHA95r27ApaWhLFHRoeiu/wMekITGgsmMkcoQECFnX+pQostzD7J1zrUwiIAlqXIe/EEs8BMuzfrhDqFLFlk2lZVxxeadi8Js/+K088/z1DDZDw1PXaQbOCdXoalTFut7jn8aoDTrxfqdBlbObhXMUz58s1gK62McuzqFqUfFxxU0MnQVWYe0jO6TWD5rsOLRhqssWt4ACGWxqfTYj852Bs0M1PUQ79dm0layj1eF6jpwCSybW1IArnKNx4BGFZA9d7nseBdAkIIkbvOnjVYn1AfO8/6bR4u1Z9CFn+qrzlzZgIzAP3jxtaqxh1B+1eXBX2AwtTE8Fr0Or8/gEWzLa59WUkttUij8VCj5m+B4s9rFIZs24cDHOmgnzGIu1rH1EB8FVp7JKW49rNtXV/fCjH81gU05ziibtGTjHoHKbR+NeQ2HT2brpKDGNTuPyS7urqSV6waKLykApbKuwQ/06JW7eFnSxT5rsuWB4qWaB8VLVvCVKIfHhgK1z3mBEsgnU0eYR2m1n3vXKxPtduOvCxnFxGcb+bszAOUYN79RXsa6rldPVr1K1X/wytrRYTQ6CrmoCRuc0qsouG9tjyATQiEmwFtYqrFIX5C5YMCE4Xnxl3TYHndDL2GieXZgWommKkrp19acntX+7vLZ372bgnABV3t62HsR1+26kPdDSaG1pOuF9Rke9NDUOYq8MNGG9JP7TuG6Xar0zGI5SmwElxKUKwfCMwDLASMFITppw5au7GNaWcmuwCPHOx6Y+A2fH6vT6d4FNHQwG+WdquiDu0uk7+YsyML2KcmiFdZP3SvYxYXzHv+CrSX0LVGrJWqFWaV6lCknE/pzyFQ7O2izLijPbxbzE4adliqxm4ByTNh+elirTGCdx5xJ1bHHaqkbmM7KclVVBKQEn+zuHTRhQYnGDv62NI25qVr1y8ancdU4wPuJmRZYVfw9xJvtSdt5u6vYvQW+oZuDsUFHVvGJLedUA2JT+rqxONVaVyqUbai2N0EBgjPYZNeVbBk54JcVhoNBhzzuEQ2xxhQJkwmeIzJmHiImuXPhb8knVm5qmO1du606xXE+rYe4UPzkzcI5RtTewbzWqLpm6fjJdO+XtGB6DoDGF5dUVNgXQ5J++gWmjWhRe0hDqV0RJfBoiLjJnYsI2t5+zl9phswGZLRqgbQ7rPpqBsyPVP9PuET28cVCxrSlkyciqVFWBug5H6KHOEOmT/tSDMxtV0+8msm4Km7PPPN54EsZvanGEhPHASfy8xiZan1k9UVlNOZOhp9RwbKHGWDqvojt7mM4McETkTwKXga/2y/0Aqlp5K0TkncDfaUjy+1X1r01czEodPP8MD//0I807tQGmZntrb+BY27JI2tLtku/gN2pka1o533gsVXlXv91IHltcxd0Bk7ihD3Hrk4gLB7zwUBLILB5TBk2F37qQ7wh1qFHVru+rMwMc4G8BLW9tpf4t8HsV6z8+rjjddHDjgMs/9VqubcwV2zEoXQfFYOgCn95Vm85VkgHb+wBjm8MWihuAzepTaKkKfpnECKhg0QJsQgoisGfEh4pxAfQCaGLIlFupNksyTl2BA4y2rM8ScP498FvAr/rlGrDf4bgfVdX3bK9YRcWO44Mbz3D5vQ/x9HNXeeD8pQg6w/027fkPfESHQqURAl179Y7YZ5Da062rShlPkoWPLW4SwVYb2a4fTiIskzxMTLa0lGbKM+8CHKfxz+KZAY6qfn/893HETe6jgxsHPORh8+p7HuQjj/4MX/ajX17cqa13cazOjuAGjbFGarfVQKVu/9b7Nu6+bqM3UVbJEp++uNC9y0SwtnrfoP1EWBphz4c0rz79bQ8T0dL3hj5es4VzOhQ/Rwc3Dnjtey9nsLnytie579y9E2Tic5nC+dsbCBVg6ZXGNL+r23w1mytYLm83FZfFqMVoitjUb6x+kWUtyPoI0iNC1IZaR/W2VO5qscVOnjNw4BtE5MuBc8Ah8DFV3Zr/JsDmaoDN25/k4l0XuHV0s7hjH+umrL4PaG/LpSNcOoKl7qy6nO1uhoiy0S2y8izUgoLYFElXHiRrv00roSMJLkpnuvJDJgyIN4tqHdQTDytpA07Beu4462ONZuDAt5f+fpeIfAj4DlX9bNdEROQTNZu+IHzZgM07nuLiXRdGO4V7ayxgOsGlG1i6rits3xVlvLKqUvib3BeXuY/Di6oW7Dpf0iMfDiYlCwkTJSwLA+sjBxxxk6y74eJC1sBa6vcDaenvsbKl7/HfZtIL/lIGzu8B3wt8DLgO3Av8aeAfAG8GEuCNU2V2ePOQN/z0I1z18X2uvOMKF++639/MDr8aY276NiyYFsCUS9sZNk2n2fMSdPkt7mIrlFuj6nqMSwwdtUi6chaOTRFNs3hUmZIFpAuM30/EIKizdEziq1jOulBk08lT7/TppyYLR8ZZNGW9ZIGjqj9ZWnUL+CkR+VfAbwNvEJGvVdX/0DG9L61a7y2f13zr4w9z/fnrOWzuvkjnScn7apDzt8WK6QGYTrDpYdps6Sp1TtvE72P4EveLiXf2L6yEiJo2dRE41ysXDE/VrTcG0hUsF2DXiF27aA6AGOMeDcn6HXdyzw1WHXCqGiJmp/G0UtVPi8i7cdbPNwGdgNOmAJunAmz6TOvZdQ6b3tuHQ6YJKhvbOhBoKFR04jewqnWzUMHw5o36T7d3ycMTho3jq1Lp2sFmvc7C+gKQJGiwghxhCFUY13sH97eQZVgu3iSnXwWcMmwmqr7NwKnWJ/3n50+V4KXzlyLY9NDYFqeNfVqqSx0h0wicho1dwdIGkq25cSryjSFkKU2QVbFP431RRb1lK1aycMCodWlk4AGRsK/xaepG/a6tI+fgfldl2EwE9hk41Qpt1LemSvBnH3uiBjY9f9sHd4jrY820Q6bRiukBmCqwTOjGmUZRGUUkmx7UuHZwtx5BjfPBYJ0PxplACSQLxKY+XIzUn0SWT25GuXfdxhmRfamclbF4X7v0LI/3KceG1zifCcAzA6ckcT9VwVn8a1Ole+GuC3ke4Uu4eV2awLcBmgHWTK0l0xEyZcD0baXaegtV22XWfJ5hCxjPAmeX+OqPj9JAsnA+HCzoMm/dCiGBRSBJ/PwU0mIZWefTyRzF5XazvHz9VbqREWACfKaaMvYlCRwReSXwVuAnVPVmtP7lwD8Evgb4feDDk+ZLx1/oSWDTFTTbhUwbYPq2VE3ts9lUVZP/ZhmE3NoxFlTA4OY0dlE1faA7VSR1vhkXUzwBm6JqXatUskTMAmdbGKQi/6zlKi9A6R7WXZORkCiBZ4pLf2aAIyKvxw3gDNrz62On77tU9eeAO4F/Avx9EflV4NPAK4GvBD4PeA54VFVfmK6E7m5tWDdtGusM7lB16gWanpBpA0y1cbd7/024KtV5VzdJBfBYX01SXJRN8bAhWYKKC4Qnxlk7koCmGECtRZZ7kCycRZT5asotgMFh5K0cl3lUprpnpOuVKt/1kvU0VfM7Zwg4OGB8TcX6ryntA/AHwPcDXwv8ceDrcb2prgLvAf6Rqj67tZJOpQmtmirQ9LFmukKmK2DaXpUpoNPVRiicmzrLRsndMRJvt26nNQab7LnOfGmCmCVqV74JXFFNIVFnBRlnEbkMTX6vur7kHXw5nY+PW6ncCkZbSpHODHD8iO/3dNz3JrCTuW5qStBtt14d9mqsmh7VpyEWTR1omiDTxY/TtL5VWdNRt93qVFcFDpZN0cgI1qILdre2ylHqvMpJkmAWIOnC90AOvY6tA46PN46qnyM5lG/Eiz62/hM7in3r2BRGzpkBzolXeEgbtnfWCNgcF2jGOosH0afvMWVfTZ+kVTNjwFpYpXDkrZ1sfhxZsFguUU2RNAVNYbHvwgKbhbtGJ22Wg4nLNANnx+p86zqPtG6DzfDqU1fQdLVmujiMmzcU1bezYOswhoFGQSiHqoKKi9KglqPUoqoYUQ8cIVUwYlgkiXPEJgu3mAV5z/NSFWfbinu8e8sLYBuxx2bgnCZtGzYNVk2bRTMaNDXv1ZTDGurS6vNaVUPYna/b5kLDrFM4ShW1ijH5LICpKgsRrIFEyJ3LSQJWfHVNoypNlMlU6jqkJnZST6QZOMehbLyKrX6QOo3GHg6bpipUk1XTt+rUCpodQKaLxuQXYKP+uwXWqbKyzsKx1iKpm7N4mbg4VNbAQsEaYWUVi3HN6KSIWg+d8Gz4ivgUg6mGjt2bW6lOoVQZ4QYtqRtsmvw1fatQbVbNWNDsGjJTKIaN1RB/SkhVWXmnsfWjwxNrSK1lLzGAuhZwzSN1ZnPhhI526sdNxdCBcb19xQyHzkStVTNwToMKvy4TwqZnFaqvVXNSLZopVLBsosWqjyFuQ9XKTYxuxXVJNmIxxmAtpBIFy8N1HHQksrj5cNgOdGAAeKaxcE54RPpZZxk25amezoIyi1Dz71Z9RE51gLGWrAqmfl12GbtWXcZWcUJnxL4+mpH5zhbOLjWV46/FZ5NlV/48QbA5qSq/fnVAdJfdWRrB/gjDHcKwqNA3x0gI6+v/NsW7FYZS1fc+DBZNaaepJslpg07Wx2iuUp1ODRrW0DStRLuD+NhgU6OTYNl0+W2P96kqs4iLJxXmxjFIFpMqEUESd28SMSyMCwkjhFYryaCU3cEh8OhidXR+5gZYPT00A2dnannFGh+aiqpUxf6tsGkozRSw6SNTUYZta+xrVIZPuAUGwaobeonAwhgX+iURrLgBmcavTwxZeN+FceFkQuxx51epabnM1Nh9tOGwk9GhcAbOLtWnebFT0ziU/TZVsMjzdx+N00d0Ktw0Ci/w1ODpBZaB76GJrqX4Tn3W+t7kRthLDOcWCWEidCPe+jH40L4ONgE6ohYN/W/8Uj+XTZ9Cd7ij5Sp6qMLFFnTf8V01moGza3UxbXtWpZpS7NPPprCuKo2afVvV8qO8k5aLDu9Jr1dJ3CllZc/+cC/rwsAyAWsl6/RnyEETPg2K2DWE4Q7+ZS/AZpSPpstZdfxxm8BKmoFzWtThF6aL32bjmAmqR7Fq2RIXextmVI93oW3XrklJ6Q8/3x8mcRbOfmJQcVOH5uOp3Hc0RdYpshAXsSFd+w5/PR3BYyBQmUdwf5d29esOb356eH7MwDnhimaG21CPqpRX5/mEO+4XSlHeP5S2Np0tuxMGAaO8reZFrvzhL323qpkPRyWPG45dI6n10TjVWzWC2JWfGTBObTsWRnV65TOo3u/g5rM8/IFHRmU5A2enCq983/YRrxFVqTZ1hYyIbFSr6qya8qM71rDp+7q1WjINL3AXsJTTENz0o4kalgaWolhcVUmsBQ1RG1JXbVJgYZyFY9c+kdA/ppRTZSPBNADaSKeUlyIc3HyWy4+/nmvPXx+V1wycY1E0KXabxlSlSjluHDfQN1AHnXJ5No4blNvwdJqA4rZ3z6MAm+jA4LYRAVFXXbI2xdg1sr6NWXvrxa6RMBI7CoYnaeIC5tkUcM3oxUgNRVVCZnQnwLhbhWzkcXDjkMuPv56nn7/GA+cvcY3h0Jl7Gh+byvGjYzXdlv6tUn218bJVPuNSX+3Y8hLn37zQvNSkb8p/l9LF72PEGSNGXJhWF7Fh5QCyPkLWt2F9G1m/iIS/jz6XL+vbiF27oHluqsDq1qmqjnfxiZT/HrJUpSu5ZfP089d49T0P8sRjH6285101WzgnRU2tAkOqUpGmaHbOOrturK+GThfrqc0CaSpL6z4DtzeVKfsZqAAW6n0zwZqxR7B28BG7RldHqJ88PTQ5izU+/G8AjtR2nchg02TxDrV0qoBD0bJ59T0PcuVtT3LfHZ83LA+vwRaOiPwjEbljVO4vKTVc6k6w2dQ2rJss+6p1NT+Klcd3sEDq0m1aTINlUmehbGzvUKYgQ27N4C2aKtjgrRQJ4ElTRH0I36PbqA/3y3oNK/epfj+gMC9y4S6IFGHTZNUUS9xzKd6Mg5vPcvn9r+Pp56562HyMi+cvhjMerDFVqu8BfltEvnFUCWZ1VNG6qXo8x1g3lYCpWZ9tH2nFt1VthoCkCSZN1UCoeA2jxOOyxuUgWCyq3jEcegs7X42GML5pmi9V/Z4iiGjIEPLvldWgCnCMrVJhOLjxKS6/75sj2DzJxbtf1fAkdNcY4LwfeBB4UkTeLSL3th0wK1YHi6dDVWqbvpt4/XEuk4OkvEgRLgVLphaKmsMmG47g12XDv9VBJ0wFYSqAkCTkLVNhvUdaG2Q2zL/xwDn87LNcft9rS7C5kO03tqVxMHBU9e3Aw8CzwP8E/I6IvHVkeV7CagjxUlLbWKeuqnpJ45dqjPrAZJcg2YBK3Tsal3HjusQXO7dwihfA750sXKC7JHHLcgnLJbJYIskyj0nlC6OS+E/j41mFAkVxq+rgMgrrTg//9CM5bN7xlKtGFdIfp1GtVD6o3JcAP4yL+fQ+EfmIiNw/umQvKfVvlYpVNXtfVzX2RRmxhLS3ApMGkMTTvNT9mIfjKwFIc9Utu8BVkAGyZisxDgrGwGKJLPeRxZ5b9s4h+y+DvX03n7FxETnVLDLYZMfGoIkhs2FvycZkYH2WoGvPX/ewueKqUSXYjLVwRrdSqeot4C+JyHuBH8NZPX9GRP4ZcKvhuP9jbN6nSqV6cvX38r71N7nLje/sv4nKts3WpVi1iK1JuinLLqUZt0/FuKYqi0bdy5lZKWbpZko3pRHgEqJxJllcKk2WgDhrx0RVppB2VLryHRoLgfj4S+cv8eQ7rnDh7ot+np/6fIdosmZxVf0PIvIVwL8Bvhr43ppdBVf2lxZwKlVqHSho8/FvrUpN8ERMAZOgPlCpy7auNG2l7H4WHS5aGS7Z30Wkq4Dgq0ahulQ1j3Bsgi3OocleBpyCadbFum0vfaviND7y2BMZbLaR12TAEZFXA/8C+Cpc2NyfocHCmdVNPUcoHIvGgqUKDsNAMwAeXdR2THZSiZtiwiQONskebvyCTyYzXIO/RrDL/TwYXtQHp+2+Txo1Jkrr/pdfyCcXrNp3ZF6jgSPuJ/F/B94J3AH8JvC/qOrHx6b9klGNdVNp0cTfJ7Zu2rQLsHSq1hRWt5z4JG9mh8ppVP1Rb+HoYg+rSlw9Ui36TdZqWCms/TzHchy/LOXnKLinpvETFzQKOCLyZTi/zZ8EjoC/AfwDVU0bD3wpqrbOIJXbe8FmC2psTegAmDaQVF+NipOqOtHWk9/lXIL5lVIDIsa9sJJgJSFVdZOo2zycDOS+MlkoKwsr74Q22eMw8ZveIFv6ng0x1vyOhOKMfe4GA0dE/h7wfcAS+GXgL6jqfxpXnJeoxsKm5iEwdHv1WpsqyzDpYL1Iw7aNAneGSs3Z9H0Lxs41s0HXQAnjoaKk3mpZpZpFZrC4z/j0jXUxrNY2jz0v4spYbDqIsxsOo41Btw1pWchmNpzq922MhfPXgRvAX1bVfzZRec64SgiQ+tdyKstmUL+HkRZMI2BqHbBBFVCpO+kuF2NqU1CEyvFO/m9LiE3lILL28amsjYATKUn9PtY1bqtx1SoBwgDUsktFdaqJKVxahUnYor9FpGDtTKExwHkC+Iuq+qlpinLWFR6R9g5+vWAz1fvUEzLNVaSugLEN2yr+rltXKMd0gNnIKsAm/jvO2VdBXBROXEA81ejTLQE6RmAZgcmIggoG3+Ll58wR38N3W7WsCheO/0MzCyibUOW4qlSqOm7qr5eyKpzEbSzZCmx2AZk6wLTBpeKEa2GyJYdW+Vpk2cQOjfJFUVDU+0KcZZOqskptZslYn5ARYZlaByF/rFgFI85/EtLOM44KN9VZNgDH5x1DZ6zm6Sl2qfIvZKTDm4eFv7cGmw5uie7VpTbIdATMxmReHSydcv7bUJy8SFauIngsbjaccsWnmIRmFo/NhlolJoQFjlqv/CNihSJ0oozdBGgTnmbpVmzwLYLOWM3A2bUKb7Zrd3zmxgEPP/5wtjp+SDc0IWi6+mUmgUzN93a47NaqKajwsod1DjzF7J1PJ/Md+wPLrcrivcLh2FC1cr4d10KleB+N958E6GQOZYqO364gaOpBXuXDyaA2MXRm4OxIdbf74MYBl997meuluWJ3CZs6C6YVNFXVpb6QaepMVOss3kGzt5iSvybk7f+MjVWx3smRZGF73aeP1oALC2MVP04qmmJU8NE6haYbnMGGEgcnAG9llSoyc6b0Hc3A2aXip1SEgxuHfON7L3P1uatcOn8pg07XH/n2/KqLULdL9fcm0FRYM22Q6QOYCrBM6RSukyIVeZtQgLBTfjutdZsVjCQkIn4Ba8QNKDfO8jFiSdVk1k0iQmKK1mSbJVHh0em0fx8VjJvo+1jUjxotPmuA/DiZYNlcfe4qD97zIB957InNfTc8eH3yqc463iyN36PMQ8U+q+BH3cPK26IljGGWaJKqPN2qY2xhyY6Plqb8JllgM0+IyhWdR5jhz0+2JambYtSF9eX/Z+/9Yy1Jrvu+76nqvu+9+bnc5ZJc7gz3hwAHkGDZkRIrdmAIibhiFGpFSaRESQQcO4YhxDHixMk/ASQjsAQBQRLDAhLYCAxbECzIJMWIP0TL1JKC7MCWgySyJFtIIiXLXc7sLrnk7s6b9/Pe7qqTP05VdVV1dd++P94bknPP4M29t7u6qrq6+9PnnPpxUGsJ5+tjiteaMKsU9rTGTEsYmVpLcLwwmz26TsseTp74NyWP0m9v3gHL85kqOw3nksTfygA72HwvXnSw+dxHXsDb9h5NE2/5Rb7MhCqaT1NMp0KaUW0m0YDS9+UkZ/FF+G7ixsnzj5zFsjsL9SNOFRCzrNpnJZidYouKAGiCIUAzoSUZEGgJsO74WnfaEIGg3FDjABtKPjaWqa2XaDhBm9u87XfAuWTJYfMbH3kBT16/jZPF8DzXkhq7ytSD6bCZYD6tBJpSfrafPk8z9HvqvikyBpl4f+LHieDDxplefp+SeVBkxelqFlDtHJoUtK5gWFaqMEwwJN3gzBJbvFai5YA5hJ0JRW52lt0prpo4PjWkANpEvmmAQ0TfCeA5yNIYfwrAkwDAzKPtRER/HsBfBvCtkPlg/wLAzzLzP992HWPYPOthc+N2Z2k4ieezDMlgZKsJsBn11ayh1WwEmmWQmQyWUrqRSz8GmdL+wpCGFJgcNAGyDGoXgFnICF3SIF1BqwoMQqsAy+JEninx4WhnS63yUF+ArpdmHpjb9VBt6oP5pgEOgJ8GsNJgRCL6W5DF4M8A/AaAfQi0vpeIPsTMn9xW5e4e3cUPfuwD+GJYvtHBBlj7zlkWTm9sbE26bQg2q4KmlNcE0EyCzDqNNHZMoUUybWZ5nSKxNpQnURsWoGYurh5FICtLjbLSqLWs8Gc49fMky1MkzVMuO+s4W08ulFp9+WYCzm8D+H0A/7v7ewnA3lBiF23irwJ4A8CfZuY/ctv/NIDfAvD3iei3mPneNir3Ax99Hi9Hyzf6RY78vXSRHb2TzKiNYbMiaJZCZsKTMAaBpX258bFL4DMlH3bOcbCE7jULkJmDrQWRBlQDUCVrHNsarDS0rlA7k6oigo2UKKa4hpmHradp9c9oK8IIY4C2Jd80wGHm/zb+PWGQ0l9znz/rYePy+W0i+jsA/jMAfxHA/7CN+r18+HLw2dz2sHH7tgabWKMp9VL1kl0sbCZpNFNBs6rPpmQSDSfG6o9VfM4M34MlPVYGaBrwYt6F9dWyKBdxDbYtSNcga0GzGTQYyi0MqPyEyUTbSk+JghY0HTyDo3y2SZMJ8lB2ixPRAYB/3/38lUISv+35wr615KmbTxVhc9EqbQ6ZTWDT65rOu7eBPmySfQPfQ30KWk8v3ZqyNJ9VyujDhpjhg+Fx24DbOdDMwYvz8IeFbCPTgNpGzC7TAqaBAktPlXKLuJMLjYNuqEJYedTJ0EL08TCHKdsvU75pNJwV5d+AmFtfZea7hf2/4z6/fVsF+rViY9lqD29Buyl3eQPrwiZNO8GEmqTVFBrhIrq+L1wc0Kwbq2OMgMdH1jQGXFl56JWsmyNqhwuUZ1sxvUBgkikNFk6bYQ46GKPfgTa0CH583XMDclILZ+bUbmrD+uLDCJZgA2Y+IaJ7AN5GRNeZ+WhZhkT0BwO7vgUAbl2/JXkjvYj+MR2azzImq6qnxdxW6PbeGDbLzKdJoBkyGCbIaHtu6d3vBgiyj8JpBTisHHRUAxgtPVKsZcBgCKjHQYthIJxqDh2/q9xzPw4fHvg9WTZspofSpAJwzX2ejqTxA2Oub7vwYDwsudrrDLQa0m4GTam0wOJncTpB4filmk2awdL8lh7jSp0kg7AZMTTC8N98/0iZpNyHHxhYON7GcM/buss9fzhz53+4tlSuajGO+0g+4xu3Iw+rhrN1YeZvK213ms+3Spryfb9sJu/W17cdMqUK+0bH2ABln02eV/59bcfVGu0wCpoV0vfG6FBk40QmkoJEbSAFKA3yw8CIAK1BLqJDt7A69fMvFi/d5kOaSrItryr6ms+UluyAttr4oDF5WIFz7D6vjKS56j6XmlOrSLDFOe2d2shrkd2zS7UbYNiUSrKd5rdZqgE9CJ/MKqApdulNeMS8XcNWAEMMQAlQXLRNpkbSKuVC+7qQv0qHoHchpC/690G8KG3cO1UEzUD1km08Dp+SBhTLpu++hxU4X3Kft0o7iegqgEcAvDXFf7OqJO96Z5sP7d96qSuYUqsXsUFDRzS+AAAgAElEQVTNRxYnm3z88M5p6VcFT6iznyoOMBOgGap2IX2r2qX14XurDja6BqhysNKhnstaYYq2kvsKw7ZM+1lmcvnT3Gk4m8n/A2AO4HEiepKZX8n2f4f7/P0Lq8GGVBlyvg1rN3HZBVMq27dKr9TqMtBPsmx+U55maRlLjp0ImN4j7dIQIhuZGczOEQyCrfaAeiarbHnTiTRYkYNOBSYCaxkMCFLh5bMKd0vm9uRF1gvWXJ5f/HsbDt+HEjjMfEZEvwng+wD8CIC/lSX5kPssrBmxScFlNXcjmeQCWEG7uQgpai8D0ImPmV7A9DyWQKf4qFL5UUtqT5DeKUBMpKoGV/vOP0cuXriLHU4arLrY4hYKPpBbyVU2NdxPd0oD7TFwjRMTitLjuyUzqJd2HXkogePkb0KA81NE9NlsasNPArgHCfK3dZnybJdU4qmytnYTjlviKJ5UiQwyg9DpVXCwVpPKXGNbApkcLst6qBKtTAMEMLHApt53p0wBMCAK/jtmF7GBZW1jLyW8raJdDF3ZHoiinrGQJjulxMTawovymwY4RPR+yAROLzO3/V9E236GmT8LAMz8eSL6ech8qt8lohfcMc9BmvYvbGseVUnC+Jvs08vYte2tlZI5i1O5BN9NqM8SyEwxmdYFy9i+ZVpMDJkhwMR+k17DR4gnBVYMrvbA1T6s5QAXawFGGptKu0XUrRsJrRQlwOiBZkLzDIVzyUEUtJYlPpwVix+VbxrgAHgcwHcVtn9XliYIM//nRPS7AP4KBDQLAJ+HgGnry1Pkso0FjXIZ1W6CXOBU0SHI5Oe6qV05pfvayShgesdQcXsRMmG/9DKxy9daRsuEhUWIRcUQdw6jW4CdCKh8oDy/bWgYxAT3U54+b/ISiEoQUq4LPtk2pcwJ8k0DHGb+BQC/cFnHrS0rMmbZGJxx7caXeQF+GlLBcSxr3C2BzNh5TE3Xq8MEuPi6Dh63BDDx9uRTRdChDigMNNaiMRLCtzFWom46/53vkyTI2BZjZRVAwxwC4CGHTsm52z/L/mlT4XbLQKRQNteG7rudhvMNItvyE+fm1HhZJcBMmMIwVTLoSNkj8Fil52kCeFaHi9RwaP9yyAAl0HjYWKeptFb+FsaiNQihfuMBEATxIxtGiDful6SISkpgM4DJUfHp4pbPQRQ3WQygZDFV2s49vAPO14msdTNl2s2ok3giUMpREQjhFu2ZTB10gPSh7eW1ok4+2rlb6jkq5l/WYHr50whswvrF8Xan1UCaowvv66DDFsYCjWEY9z0GjiZCBQXr5s35cHrFU6X+NV714c/BE/9O7j3VbVBRgm2NxdkB5+tYcrV2c+1mufTMo3LF+tAJGZThs7IMdEWH8vsbR9MMjaUZ/T4CmvDH3pQS0FjEZhLQWhvii/sQv1qRQ/hAN3X0Jgnd0r0zzI4tvVAK7UTR//5b/rLzAfnipJveV152wHkAspHLNtf0082pDM2Zmmo+JWCJtJy4Aj1n8BaGh41qQsvNr1G4LP1dgkxXbgk2QNfN7cdaAZ22482tsTPyikWsU/VhU9BWx67loG+Mk23xCyauxyo9p1NlB5zLlNJVHEo6pN0MZBt/Ti5kZb9NwSOw1VGMcRn55v72sg9nDbiU9hVA4z89bLrnn8O+brmRNGsVuqC74HdaSYgYRV1cKuV7hFQHogQ0ScYrvLrCYbE2yn1tFX7to4HrsGHHww44lySrPJbLTKmVxt1sY2xNL5+85CllTGiBAXhtDhYvI+NtBnquctAAHWzisVSlXn/tIjIoF6EBEHNFu7AwmkimVzn4eIdtWPXP556AZomWurTHz6T7GACbbrcvK/hsCprRBrIDzgOWyU7AJabU8sd5+tuw6McZMqEmlr5ME5oEldK2ZWCZdExZNyx978HGj9YlgNj18pADCwG1TusiPhIJfKcUwgLqmqTNlYq0mgCagZ7FULkJ8MnPOx4MlJhoJoIOZbxh3L1fXLNusuyA83UuxTESA5+TtJuxruloX7Gb26dbQwYdyFO7xLcMlq5e5e+9fTyQKCnOQcMBRIeBNVK0714WM0rAUytCpSQ0MBhQOWhi7Sa7divHWQ+mXwae3CfExt0PqgMPJK7a8x9fKRJTT3bAuSQhFMzlJTJkSuX5Lpf13dQlUPgbfaVeqLXBMqH7e0WwTPo9kiBuTYouKkMgwsTS7U0KSvs1g0TrIQcb7YAz04SaAMUWHMcuz0AwGjxwRQl5eb9SPPPfRnUgDuC5c/8unvvl9+Glw5c3KnsHnAco6azc1fw2g4/URgP5xsymuKQVtJWh7RcAllKtp2wrnm62bQzZHjrKV0cBWgGVRtBwvEbj5owDbFyETgLaOdAuQGy7+2CMeCVZdr1L14C9czgFG7FNrvGdoy/huV/+Prx474t4+uZTeAnrQ2cHnK9DmQqbQVMqkcLyoctkk56ndeCyIVgmg2ZqQqyuE3pzCpBJCz7AHVhgI4Bx0Rm82cIMUtYFzVtIVVYZVrDKmkT+PHv5c5aPi6/lFLc79+/iuY++P4Sn/tSHPoE//nPfgXVlB5xLE16lV3wz2Kw1zmZFmazNbMeBO+U3cLFQGRPngnGnI07hihjWtiBjJPyvbQU4lkPPEGmA2gXIyFKkFC03uv0hBwjlpn6c7KXEFgDh7v1X8NzHnseLhy9JxNgf+0d49ODRjYrfAeeyZQp1tgabCdrNFOiM3firAGaLcJlicawLlLFZ/KVJjd4ZrEDdNWMDahdQRiAD0zjoGHnoXeQGqpXTcBq3trF30tKFgiderTD1D0l4mztHr+G9H/sBgc3Np/HChz+L2zdu4aQZC3SyXHbAuUQZum1KM3a3CpulFdvE+buKBlMGzGhP0RKwrAKVqcuB5KnSnmHfDV5yprvV+WwrETVt66JrNhJvnA24WQDWgH28Kk3g5twFwlMgUrIqYDzGOOop2qoUBv4RW9w9uov3fvyHAmw+/+Ffw63rT/Z7tNaQHXAegPglI5NHNVMESs7htWGzrcF/QaZqMasBZmrP0JCMhtuZcPzUY8mVVVqKE2DnoxHThIzTakwDbubgtgGM6Xq26hrctuC2AelKJqNbCHTYlR6gcAHQAZKGv3v/VTz/6Y/gxcOXBTY/+mncvv7kFob8ieyAc1mSPQxDsIl/Dmo1cX69h2xLsFllpO6akOGBBMvgkoNl7Ay39aAA3ZmNPvoxbKwBs/PdmFa0mrYF27YDTts4IBkwKRdAj+VYpaMCc1t8GXwmnjmnSd//yR/HS0d38OzNp/CFH/lV3Lr2hNQHvNowiAHZAecShZBpsCPuja2YUJt0jQdZTZspgmWoSu77GGCSEMil/YPHjWS6hhBFHT2+3EzLGRVmwHZjbThE37RpJE4/lJkIId6V35eUta0TTInz0tEdPHvjKXzhhz+B29feDYDdNcjItKbsgPMgZMyPii3AZiugAZZrNH1tZqomMwSZIcBM7pHK8tiGSNRL/72v4VgAir0i4py9vrcp/lPOR0M2CgU8pRvclRhXYjDpGuceHfP09dv4wgd/Bbevv/tCfEc74FyqdLOUkucXfV9NT18omlAXZT5NN51y0BS1mRHITAFMeYZG+VwvxIRKyqJ8pxv4B7diH4VImqwqsGpBXIFq0V64siBDwWksoX+1H4bc/Y1JDILJ13ya4fnZH/gHTrNBpHHZrYFnB5zLkrgnILtn8+/ugPS4i3IKT4HNlkFTgswYYC7TZzNmtMTO4sSHywxLJM5iIpDSEuBOVYCuu3xmTlsyjTiTAaCuQcrFHFfdajTstaRereIKLjvbqX6c7grdui4+m6DGMYOI3c+dSfWNJdGbqazRhITTzKeL7H3qwWbYfOqZTktAswwy6/ptlu/MpPAclw6nfF/khPPaKvlzswAxYF2QO2aIVuu6vEEaimdg0wLWgqoZqN6TwHkgZ3J52CyzvWnkHhiyN5e45N04HFk6FoOsW1d2wLlkKWk1vWu6TKsp2xgjhU65a5bDZtugmQKZ4llt019akqy5coNqCDw+NbOV5UWhoKoZWCkZi6M0oFyvlK7FWTzbF9ioSnqB8kF/y2SV0eITpkJQuPcsQDq9F7cAnx1wHpAs7fION9EIaCbfaAXnX/J7Q9isCZpJkCmc4ranJPSkZEsVdnVWR3QeLEuKti5MDJihSaOqK7BtQaoS4LgRx1zNwHoGqDrKcYpENSn6c/Ju9LIQcweZaBvH3fBbgg2wA87lCnPRf9MzoQAMgmbb/b2xrACbMa1mHdCMQeYCw/aNR7n0MtLkOYCkbXxMKrmuhmRBdU0auqpAMCDTymTOqnbQqZxWUYBH8UXTM/RW03Ymy3bz2wHngUrJdAKKy0iuYkYNLUUw1vV9gbCZBJpLhMyQJHGYVjwG8OF6fbQGxsIBRxFAllEpWQunUgqV3gNpBleVaDi6lrE5zlHrcw5znhLTJoZSBp7iQC+/Lw3pMyai5azQEBNlB5xLF+kaT2CTmFBIf6+j3SwbrzHRP7AObJZpNWOgmTZ9YUKiSC5iChJQMh+jCA7MaC3QWAZbWTaUIDHEtWJYJlgli3CxrgHtNBwYZ1TZAA4GALb98xgDT09Liu63FaBTlA0bdAecSxU/g6oPk7tHd9Kkl2JGjWs3wPZgM2Y6jY80Hqv/clnG3nWkDBvRbiQulYT7XRgLa2XcjVaAJoUKHZwqRWgsw4AA5Xqp2Ipl1RloABTYQWJl8CyBDl8UkQdkB5zLFuaeinzn/pfw/Md/KE2TO/Im2tK99WpXvqF6Qw6XQmIINptoNUOgWTaKeGiqwdSmWDWqVn6ezHCB7xitZRgj0FCWUClZSY9Vd96tdekBKHKzxJXkSbAIfmTXY7QyeKKxQimM/JQJOxk6u7lU36gSAeXOfVm+MVkrNnqoVh1stfYku8JNl0BnABJFv8wE2KwCmlWmKuSzuL1sBJusw8fP9vf5lqpn4UL/hnZgwCqXHYGIXVxxCZbHFrAEaOVpZMBOGybfqUBdLQfBE0uATAlGkbZTOvSCNJ8thEncyUpSgM2L976Ip2+8JyQhd5MF2MR+ntJfJitBahlokJpSQw7ii4ANM68Em3Wltx4RZX/5tiz9sgmc3oSyzGBIrPFuG0KI4FBQNgerNz4nzNVS4U5JpkTkc7jiihe3dWfDREtgsxkydsC5dJFbK4bNszefxmc++Ik02QhQejKWbvD4sQA0KUTChlL2A0VtAptNQLOqdlMETfazxx/3JUw8oGi/296FgpEF1f0i6kD3mYtl10xEUrMh6IyAh0uTRqeCJ2kL1U+/BdmZVA9Acti88OHP4rH9R6IUgx6UTLKbYIqjYmiI/EBpq5hS+fbBTEfK2ERWgc3g4melfAfKYw8dhhDFdVP5yJqVIiio0HsVh/j1ERzy/MXgcTBgtxqX38dyQmJiZQdHo4KXmlvxWyCfLOoAljbA9qCzA85liTP27xy90sHmkWfwwoc/i/dcfzdOFsdR4p5+MSD5XRcVt7IvZxg6Y9pNbErlyfPDyrPF89/b0WwmaTUDoMkP7eUN79T1GoksTwEFEItJUivCfqXRmvg4Ce2rqYu8qaPnXdEw3JKXQgweRE6kkCT184S03uEUHE9UoH1JCyrXYx3ZAecS5c79u3juH/6HCWxuX38Sw0/0wPZ+FwXCTbJKz9RIujFYDC4NcQn+llw21WpKoMmXDl1WrvSIu6VH3OZaEWoNaImlGUwx5fL0JpdWnenViQdIX8tJUkXJwhmE68/TtZ5Ew4khQ642kRm3oeyAc0ly9+guPvDxH0phc+OWRD3kTKNZ5rtZZ3DJhEGAY+ZUrwpL0k313fSrQ2t3fW8LNMnzV0jXL7jr1WEAigm1JtRKgWN3sPf1ONgQugicyjNi8AwKa9KEwYGU3BOJ5pPBhwH0BhJSfpbU37fz4XxjyfMf+wBecgtTe9gk3T+JTeKWCMglt60TbSbScpbKcF/BMnNqo6UjClLqVp68bCcmmk5ADzTp85aCZizNWBleyzFaQvjuVwRrI23J5RM/0pUiVOSHg7qGiBskNFA8wbZ0b3QNyRFsivBxWleYu5UTtqfxDCzItobsgHNJ8tLhy4lmEw/8myzBJt9252J6E03xveSyTCuJx670Si+5EkZk2T2/LdCsHn5Zvikm1MSoiWE9HNwJkk1PlIx2AfIMPAx60jvhaNmIsMumDZlrP7Gzuaf1RPkrlZTne8e6XrLNZAecS5Knbz6FF37812X5Rn/zebcrc6LRJGNwAPScv6OLa08UGofMZbtjtqGxTwFNObTLNMjEgMm9HH4UMIGBtgW1c9DiDCqABJkpLJoDmQowLcAGyRy7ZaaM3x4ulINQCUAxfMK9Fe3z0SGABCwMr934TDd/0e2Ac0nymQ9/WjQbY/o7l0ym8/DphWedounk6vEWZCUWRc/QmJazrizr3h7yz0wCTfTMx+ljyMgD7+JxWyOzrE0r4XvbcweTns0o42WUBmzlInK6ltHRWVFey4LEJnUCoRhAKXzY1zs2uUJ+Ckw6zb/gSF5XdsC5JLl1/Vbym5IbtoPKmGwrNlCX4biWs9qAZUq6iiPObBU6PcQWIBN/XwU0JZNpCmhgnWZjjYT4NQ3QzkHtPCwlGrJWsoQokZaXhnXajZ/TlGgpUnJP+yycr/9F4WTj7u8B+DithxP/kMrui8JI5g3koQYOEf0WgO8eSfJ9zPyPt1Ra9C2z43tO4+j3mhc5AVOSRy+o8Fr5T5Ex6MQyBKDRuU39r2v7Z9YGjfsj7+S3bp1i04LMAmgWQDOHj0UVHLBKg+qZgNkap+HmmoaUHPdfDoEnPq8ORv78/Ul5sy6CGrl6qa6lGW7gX3z/bQk2wEMOnEg+AeC4sP2Viy029d2Uk6Q+mq1oOSuMv1kreyB5CyfQKRQyahgWqroqZPz3qf6ZkD6ubAE2YUgDW5B1kTXZAtxKhM1m7mKJd8BhrUEVRPMpXYfwcKcLVPR8apmak8CHUi3T6z5B+4nhg/T+CuZenCMR/FSLTe+PHXBE/itmfunii1EASj6cJZdxG4P5tgiZ3Gzq3APdGJocOkk5K/CylLQEGWCaNgNMBY2r8ZBWEzn7iU0AkPfHsGlCHHF2GhCpKnIuJycRldw92AlsXOP1Xk8FeHPMk2K7ZPCJfTa+lyp2OG9JuwF2wHlwEt+4/ne8b2UZeFsOJh8B0AavsRw6wJDPYWJ+hfy77/10QzAC+o5g/7N/bOpfG4MNIi3Hf3LU68j5mCqSUDBwIWPY9wL53iIlEzEtOtCMxfXK2yZJ603YCECx9hO8Psk4G+0AFA809H6dnQ/nG0ZWfoYHtJqeObUszUT/zbqMybUcKdIZIxl41so/O7+1IBMlmgQaINVq/O8YNs4cTv1vXg2JRxdHkyG1i7JZ1S7appZgeaoCqw4+Fs4l5HMNA/o6KfpvCu2dQIj78AEyoJESLYeTC7o1LWcHHJG/SESPQdr+DwF8kpm/9IDrNC6lLvGhLvAtdo3nvVGl4nPweBl7M5e3Z7+Hvq8AmdJnETT+e7Q9hU3u24kqrRSgKpCuujZSSqJr1nsSk0pHi6d76Hi/jYONX0kx4GzgrTB0HQh9CNlwkRx8qFuL2eclAfkiX4/LIa7LurIDjshPZb//eyL6GWb+makZENEfDOz6lihRqgp4WWbDLIXEFIiUh6cPvTWnSOqUjEynqDrpszgNLHH+xe+FA4qD/kZBk5myOWj8Z8/sjXw3/Udd/jxcKgnjC8BpN1UHGF3LOsa6BqoZDClYy67IDjT+t5exa9RpOv78KbRD8Lkxp/DhFDi+v8znGF/bTWED7IDzTwH8XQD/HMBrAG4D+BAEQH+DiO4z889fWOljPVRjppLXbgqay7A5lf9eX9NZ5hwGyvAZzG/Ctu1AJqrZBNDIcQXY9Crrn2g/ZkUBdQ1Vz2SJUSIxnaiS6AwOOqxrWFXLQuoWMJYBQgKdqMadlDiXbU60mwKAcviErK0AKB4aUKzDmvJQA4eZ/3q26Q8B/BwR/R8APgfgvyGi/5mZzybk9W2l7U7z+Vb3o3TgeMYraDecP3Xh+/iI5LVc1CPO4bRWS/IZyHtIimvZrAUZoAga99l35sdmVHnyJMPFD2eAqYLVM4AZ7H01MWhAaC2DLKOxQGOs02g4FOdl6UDJKK3KNxW0G9+j6Dlpo3vQr8WsuPPzlK7vuvJQA2dImPk3HHT+LQDfBeC3tpa5v8rL0uR1mqDdJN9zRx/1YbTpDZQvJbGqzjRlVviYFhOX2YcMsDXQJGmiAHXdiUSlU2cyVTO5bt4prGcBND6yA4xFYxit02rydXamStEB7KofRtXk8HH5JyYVy/Kulkig409rS7IDzrD8EQQ4T2w7Y7dUk/yY0HU9DJuCdtPLs+S7We0OGp/pvb27cXTaAvU2bQ0ykscAaJK0A8tCBNHyUJMC6xqsZ+57BYYW04kZjbWwViJ0wjAWVqADcM+USYoa2B5XsdSrF/dSAd3wAA+fWMMJvhoHndi5HGWxtuyAMyxvc58n28gsNT4myFTYeL+B31fQZMoa08Dntoz1JTIGF2Bci0m/l8CQQaa0bwPQ5M7i0P7KjdJlFgdxNQOTDqF/GyOxqlpmtAYwbIFWYON4I8Nz8vMeedh7PpjczOUUQgFAEXzifK3l4MNRTuOySKGzieyAUxAiehzAn3U/f2fLmS/fXnIAJ2kGYNNLpwoAWu+u2WTSZdGDNMLDlbWY5PtyyEh+m4NGjspfCG49YeevaY1FYyPgMGPRWucoZlDFOG8tFq2VpUYhKwASnA8lqk7pyjlfdXl7OB0uAsjDJz5L8eE4c0tRgM625KEFDhH9GQDvAPAZZjbR9qcB/AMAVwF8mpnvXlwlFMCFqQ6Yptl0+ZT8NsOm1JB2s0yGJl0uXSRjRHvJd0/WYpLvI5CJvhdHdRfhla5NtFTCDGv3AlAy2K9lMZuMZTROszlvJARwaxmWGao1aC1jYSy0AipSYGZopUBTfShDVaQ0SfjpBxJm2k+ym+TTOk3H+3Q2lYcWOAD+GIC/D+DLRPQ7AO4BeArAdwLYB/AHAP7SNguU6+gWxQ6dEeTu1YI/ZglsklXYSrBJtpVhMyo0nnCZ5hI2rQwYoAyCRPkf3l/SZJLtQwAbAU0+uK8o0t5M3RJqxoHGWNFsFiYGDlBZ8ekYZoBJVgRUJLPOScLJJHMt+yUOXiLmrE0j0yyGj428xl7jUQxY6XcLmtY25GEGzv8G4G9DeqH+bYjP5gTA7wL4OIC/PaU7fGWJATH21E/12fjPFWATS/KuH3ISjNZzYHPBB5P/LgImrkgJMEP7Lxoy+fZR05i6QXuuaEYX+te4Tx9101j/kDOYSaoU0TwAwlcn01yGJL90MYC8KcaFNEGzgfMJ7UyqzYWZ/y8Af/mBFB4NFAMUmG3Wy1SATc+JvD5sVjWlprzeNtJigHHIDGk5uanUO35oO9YDTZ5maFzVCGx7yQeuggeQhfh0Uq1kJMOJMBpK4LvFyd2j7KHkzKq7R5t5GB5a4DwIEcaU9OIs2mEARsGESvYjhc2Ag7gEmPxeW6d3agwy8e/pkNlUixnIP/xeETJjjULi5JAPp5KQqAcE65y/cAPsOIT8lSB4LKv+oYvGKfGqKLSp/72yLIFRabfNmwLSWwVF3XIXBNy9fwcf+Ojzq9cpkh1wLklSW5rcWBxnHA/aMhj214RPVdjWHX8RsNmK03ddh2++fwwcE3qZ+lCa2Bhew0mgI5DwC3EpkrhTlSIxU7SCcVVSJGNyKkXQpCQwHgAFSt49Pg75Voya/FSpvzmYgYguBxh3ju7i/f/we/Hy4csbVWEHnEuXgh5AyJ7iNUyo8DndhPq6AM2mkBnRYnp5DB0ztG1su4NN+tM6G4RApoFiK7Bx2VgA+7WCIoRu8ZlWqBWh0k7jUQjOYqn/ajLlko6ZZr6u0kslZtSr9+/g+Y+9Dy/d+yKeuvkUXsb60Nl2gKOdjEjnoFMZOCLzKfyOtJr8L88jM8FiwBQ1HL4Y2KQ14K6g+M/3g5T2sVs1z/fxhO02yjM/1iZ//tgk1E6vDuhvz2VZI+V5Wle+bQHbAFbWNVbcYqYkMN6eD5BXKxxUGge1wn6lsFc56CiCVqLthHfQ2N+awtFf2JDvc5rOK/fv4PmPCmyeeeQZfOrDn1m/YOw0nAcqoUscQKoXZIHHVtRqhgyOdfw0cVF5TUe1mqkaTa7NrNCrlBwXyzJQjMnkRuL+V7YACMQMsg1gGigYsLKYVTMXQ1x8OBUxDGvMKoWZJlRagXr92EtKpnwDlnYqlvLibIP//crRXXzw4+/DS4dfxNOPPINf//EX8PYrj66Qe192wLlkYThnoDeZuPDQ5NrMGr6apaAZuiuX3PA5bDYBzVYG4q1qCo3JpGNKgIsndJKsa9w2oGYuW3QNsgZVVUPrCg0BLcmgwJkm1FqhFktssKSiEua/ZOBZBp147pTK3ibs/nvl+C5+5Ffeh5cPv4inbz6Dz/7Yb+DWjds4bzeb6bMDzoMWIqe+UrJt3YF8gzfpKi/urCrh+8DnKGzGNJoxbWYIMqv2Jk2VdWCT15+tBMKzBmQbkFnIoD1rwLqF4hasZ5jpGbRSaIMPB6g0gS11jlwG4qVFk2FbeTX8l4g0BeUHQAob/zvf9srRXfzoJwQ2T918Bp/+8Odw68btJW0zTXbAuWRhIAOHEyoAZwXzaWPQrC0loKywoFWcDkBx+YdVepJWgU/e5kvziJ7oGDYeNO6cyBqgacDNXHw7ugJZLVE4KyMROnUNrWvUzq9TKYJFuqSojbTgZFnXAZqU9uXajiLqASaWV+7fxZ//zA/jSw42v/ojn8OT17cDG2AHnAckDiY+EFkJQAPjaiaBJrufpky6XNZ7MKzdYBg2o+ZTDqoMNIVeq0S2odUMSX4tkrLcI5z1Usn5yPXktgG3c6BZSPwprUGqAs0ETuwidIIt9F6Nis5RfxEAACAASURBVAg1EVo3PsdaBpO7JiyjfslpwlgDPKtA58996gdx9+hlPHXzGXz8Q5/Dk1nE2E1lB5xLknD9vclCBLCDDpDd5KVerBQy4fuANrPqzO5kNH1mVuWSmFIrw2ZN0GwbMKsMqvNpQx0GvCS+t8oaiUfVLmTeQtuCdQu2LWh2IM5hr4G0CyjIxE3fWUBKBuMxO/CwtCytCx7uJ/G+mxw8d49exntuPIOPf7AMmw06xwDsgPNAJDzPHjoAerO7J5hPubKw7vIRU6XgXswqgumwGQLNUsisAp6Bx2MINmMQ8ppoXK+eloNwXuzAw6YN2+XZPwPN3LEtpBvdNCC20ORDukhKr+0g1nawBngybSdOkjuNb11/Ch/94D/Gu7es2XjZAecBCAP9Yes9f8Kw+bQqaJJlQEceqmzOYHfMwPf0YYvGx7h9gwuQL4PNRTqhxtp9aHsMm6Ra7tXhF90i9z0Tti7SpnGaa9tIZAdSgDUuvrgBkRYAyA0i182NUg7ajit2m+CJofOLH/jk1hzEJdkB54EJdR+9G7ocxH4KaIZiP5X2r7+wUq6FZDVZApu1QTN2blPOZchJPHJ8OlYqSx/OzYK9tqoYIAVSPromue9pJk6HQRjIaK2DkBymLNwaNAxL7gjfa0VdQLttgcfLkzeGNZttjBLeAeeyhRFm4MrFJm9fxUnKfpoB0OSQGUNOfIPli3avJKWeo1jDCWni31NgU8h3an3GzmUINhS3ew4hldaIO39bOh5IdR+WJbJmVYOqWrrF/TloHWJ3p2dlQ1v5WXbBd+OgIzMmUm2HsR3wDLXapv6akuyA84DEX3uK/o/3JbCZAJqphkdabh86U8yqqAKFWiEaY9P32XTHFT7HusJXliWA8V3OSbpysMBum46q586dAPJzqCyBtZhXVO9JlE2vCZGLKa5cQDzS6FYJkHLJnXPoU5AKyvUJfiP5j5CBx6VdFTzJdl92ti28E7cgO+A8APEv4yEDIn4p+psoPZ57x5R+lyS+51a9iQbTl7SbZF/ks1kGm0mO4hIMlvhiekMMItiUQu8MASuuE8kS5MxKzpGUwEczUM1As32oqhYHMgBSSkwvBxumzuyKy4gf+q5rvA8ZijWePD2mgYcwfh/0okCMpJ0iO+BcooQLzlg+jL2g1SwLah+OLZVNaXpfF38TTzetBsAS8s20m3h7XsmlsBkqZxkuByCTazVTlnBNss1UAa+REAPQADOYLSwpcL0HrvfBplumXFIrufbKaTiqcuChsEZOVBNx3SDuGo/A46oUm1oEJM5lIB3HEx8buxGT86JOy8rrsqnsgHNpkvlZMi2Hs6RTQbOKiwNIwRNDZ2XxJsWQZuPT5KbUJNisaE6VNJMx2KyyfGspr0xCba0DLWmw3gdX+2CdtQHgyhc/DSstvp3IXMvOpIOOKywHj4cM51DJ/TxRhjm04jKTvyV+nlVlB5wHIH6R6vhix8/bGGxKoFnWMwV0qvGQb7Wn5axKIt8zNaTd9JzJwPqwGarYMGzKWs0AaHqAWRLt1JXNKnrY633w7CBZoLzk72Lvz1EqaYf8ZRBeTtQBxJtaIb2DDICygzkcE5XCqR6nIu2mp21tQXbAuUTpXTjuHq9X7qdrxS4DzdSeqWCuB/WfEu1qmzdTWqHCm31jR3BBBsyhQdjE6UdBM23CbO8FwLJA+sICc5Mu30lQ4UEOGgq0ROcsPN7xlkQjzeETNJhhrafo53GmVilap69jt21geMCKsgPOA5LQG8RurdiPd2vFxrAZA82Uxzf22fjjY+j4NPG9NNRTtY4MLyexBe1mKmxyrSYxrwqgSX53kOldj8j09Q+0RGFgLAxjbhg2mfUuQFCuSEVAzRLFIb9OfS2xyyPey66KwXRyRLLITKsIPD5Df//lJpW/9kT9tZU3Zc4OOA9QLIBX79/B+375uWSt2OLNjXFfzpAE0KB8s1yolhMK2ZZmMxE2g/6aAa1mADTJNcAwZBiy6LhP0xh28adkGVEvMWi0+5NQMS68bkg5YIKS13E6CMQAcqubSlm+rnnvVgae4GD2dVSZxhPzGJvLDjiXLd09g7v37+D7fvk5fNGvFZtDZ1XQ9F+To71SQ/6cyRJ1hw/5b5K0G8masCmZUAlogHSy7Pqg8bGmWmY0Fjg3Fm0rR0nUBpI/IoAYUNSLmOAKiNordrrHaVK/kphoffhMAU9+C/hmUa6FY61nU9mtaXyJEl/YGDbPPPIM/pcPfTrsG4NN8khz9lfaHm1CliyXKc5nlxCjM7hK/ptJUqIfYTPYZJpNT6vxo39dzxG6prPuNKyDDbMsVmXRmU4WLpwvu4nhDLSGMW8N5q3F3BjMjcF5a0MM8S4IHrs84zPirn3ZuNnnpb/W7W+jdLLkBYHhhxMq1xQ+brlfoN3vU4qSuVQq+gvd42tcySHZaTgPQHLY/KMf+w08st+tFRvDpgiKAhfixzt5iwzYTHmv1GTT6iIcv4mM1GId2JT8NbEJlWk1yzSa2AHLLo114LFgGAssDLs/i9ZIe2mvNZBbbz3qBfemVjcNwkHFO/oHXhPMcTvY7pzZzeGCN70oaDwU6t1pPCpqchU3KwRI/sfOpPoGlLtHfdg8eeM2ThbpWrFF2GT33ZAO4bfn69sEkwrpzXNRfpyhB2X1jOLarQab8mL068EmmE/o0gTYMKJQvu7PihYk5o6/nhT+lyp0v0UrFNgQd8AZ0kujnFxd/TnZ7vw8fJzJ5c0tIqe9UabhOI3Hv5A8tLbRQwXsgHOpcuf+l/C+X/reBDa3btzuW0RLYDPVWNlmb1OvEtsSonGtaQJs+t3eYybUhloNuq5u5mg/QqSY4AS2vZ4m8d8o5R3H1H3C229GYONNq8KSq735Xwyn0VD4HuBD/m1TAI/MzEidxpC6+VHP234R7YBzSXL36C4+8LEfHIRN0imRffov63hF4u73WMsJWTO29vZaW3wFkt6YJEGaNgLOg4SN5VTjieOEi89EpitoAmpNsn5xpVCRrGFcKUKtCBoMmKaDDbNbgjQqLG+N0Ga2A4xvh9ABT65e5LzHffCQUwq75u00nPAbiR5VuIDTZQecS5LnP/oDeOnwZTzj4vssW1FtG7DZpqzDJA7v0qEcs31F8hW0Gvd5EbAJEMlAkwIp9ePkZ6icCSIxwyV2+Ewr7Gkt6xc7+NSKoGAAswCZVtY6zoMEDrUfp23TQYhT+HjNx7JrF+tGNXfgiSdVeOeyH8EcO4+3ITvgXJK8dPgynn3kGXzuIy/giWu3w1qy8c3dkwuwYJLsM8fxhYo3nRITqgCdsD07NvscHWMzFTZ2mlYzBJt+rQmkGNoSaqUw07IglyaJtulhM9OEmSIo20A1LOFkzEJsMhRAU5yLFbUV+/Z1Xp1wvg4+mdbDhlONJ8o/9Gr5KkQK5TZuyB1wLkmevvkUXvgJie/T2OFHDYXty5cQlc8hdmzfl7Oh9KAzki7+njuHQ5rpsJHubUw2oQY1G/87qq5SAFjmVFWKMNMKCgztzKc9p9XMNIHauUCGNKhdOJMqWqBs7OGOR5uHNvKGj2tT58OhIfB4jUcxwKY7B/cXDyLcpi9nB5xLks98+NO47Xw2k2SCKZUrRRflj1krS4renCXtZsxZnIMm+pw+oO9iYNOrqt/v3iCaCHDazZ5WAKzARzkzigC1OAPMHNQuQKhBzZnAZ7LzvCs5LGgRRg67cw8+HJdO+bV7UvBIx1g8uNBDj7YGmVh2wLkkueV8Nv528O+UMRV9TLYxHObCzKllPU9xuin7iv4aYCpsnKGyFdgMeVUoqrJW8ldpgJi66JpkBTatwAbtHOAaPD8DL86lfoTEZEqnGUR6qgoznpJP6aniCDxun3UQIdFqPHgI7HrF/MnKIEIBVHx9tmPf74Czk1HZFEnBcRxrOcBK2k3ROez2PyjYxNWn6BuR1FeC2ykoxZhpguYWanEu2kw7F8g0C3Bbg8/PwPNzqbPSkVanpLww+K4rNEDCQ8gv1M5eO+mg39N6DKcaT2RSSd++iZcM7J/wBrIDzqVJUL7djbFhbgUlYkhhGPPfEA1AhYbzW1IzDJ5cblKNVcpJ0V/jvn89aDb5qfheHU3SO1URoGGg2gWomYPaOezZMXh+Dm7msGZPtJvFuUBDV+iiPrgSgzPF/XahY8RsNaK1GJvCJ/K89LUeCkAhIumC9+fCbpSzPxFW694IRdkB5wGIPBPdWIdN8pny/CbHjGz36vvWHMzOj5N0j0/03QwumJX4csZ9Nhet2SRVd39KyctEOcdxRQA1C6A9B8w57PkJ+PxUgGNaMBi2mQNtI6v/Mcs5ax3OPYScYSPnZ3y7mHH4DGg9TP6loARmNjWpiK34dpSChKNOJ4puIjvgfJ3Iupdy8j1Axa/blRGncA86A9IbRZv5a0KaS+qNAsZhk5yK++6Xn1AEwLYga0CmBRZzYD4HL+ai0VgDJhbYtI0svu5Ohw1ASrRhsZ6cXyWCy1L4cAQe38ftncVxpaMT8zHPCUihs6XX0A44ly3Z2Bf5WngA3UtI+jnWl6HbZNsO48FBflFv1dhAwGK4lpJz2G+/BM0GGIdNcpruM3blKgIomsUNa8C2lfC/1sA2rSwxao2DD4EMhYyEF90dkLttQmlD8EEBPO6+8gMMJYBfdIdZC2IT8FTUdDaQHXAegKz6qK8LnXB7UPLR1ePiVB0kHo8MOsOHJWPs0fPX+O3LYCMFbd2MyplDbt+kdmSJ6OBHELO1YMvuz8o+axGG/bLoaQE6HgykorAzcQH+hzsbm2s9nGk8nbZDNnIaM7teKrj5VLKExrZulq+r8WAPQojogIj+BhH9IRGdE9GrRPT3iOjJCymQOXSNj/lT0i+rX6hBzabwe+NbaehmHOrS7aVTqVazzDm8BDaxNrIKbMIxWA6bfJv/bvN9UZ0pikFFSoEUgVwPFAUQLJFIGxFo2W57BLSgVRkTFvZha8DWAKaV7eGYdBwOPPys6fLbUi/VQw0cItoH8JsAfhrANQCfAnAHwF8A8C+J6NmLK7z78E7kwu4edKZcsCRNQbspmVOJw3gTAsVmUPIbHVjyP58u99fEsEnSLIENpw/+Mth4ycdDjcEmPS4qzJcF95wrBahKururGqRrkK4AXUPVFUgr+e3bIopJHnqregXa7Kft4BNDJMAiAw9zB57SNAo/gdS1VjGm2JryUAMHwE8B+HcA/DaAP8bMH2bm7wLwXwJ4HMDfu5hi5aIN+VGyR7anhqglf3lGvfyA4e7wbP/WNKAxLSiBVME5nKRbATYTH44hv00v3cAf0K0KKOvjcFgRkKHBqgLrGqj2QHv7oL0DUD0D6j0XEnjmYKS7cTgxbEptN7Ci4qjW48HjtBtm7rQdJxQDy2s5vpG2IA+tD4eIZgD+ivv5nzLzsd/HzH+TiP4jAN9NRN/JzP/nlgoNF5/cWByKdiVJgc5xF28Ehp+IPF30tSsnXUyJMAy+VJbfcH2ncO7LWWpEdlqNTx8DZ0tLTIyZUsi2c2Ffes7pD0sALNBaRuvKnVUzkDXgyghsPCzaBlTXUPWeAAgEKNX5bIBxUzSU2/Mmd9BJNkd+HufjYUWi7UR5EbuVfFhFPpzl1ZgiDy1wAPy7AG4C+P+Y+V8W9v8KgG8H8DyA7QDHC3P0HPXH48SrwHnoAAXwLJESbMayWHprD/ULl0Yghm0ZdIo1RKrV+N8XBJv+aS3XbvoHRV+py4cgTlrDjMYwrBusN6v3oeAjXRJIVeDFOVRdg2b7oHomwHb+ncFibUGz8U75ktZTdDJH3epWpccZ3y1Ozsntu9SBbSyS8jAD50+4z98Z2O+3f/t2i5UxDd5xbFE2WzyIYtjEaSY8wsnvALFl2o13nwxVf6pq3YPOSDqf9YqwKVePR/cDy7WbpZInjrUhFrOqNaLldKv+EWb1QViUi0iDqhpUadD+AWh2AK8jSmbZw+0qGys8CXxIlYETaT9F8MAiCR0RljZ147SDqrczqTaV97jPuwP7/fanpmRGRH8wsOtb+pssCDq8/P3asV1e/nlzb33mHmyWKTklJ3F4hgvHl96p62jRm40qLn2msEnL6ms3yf4JjuJNpfPhiKeYYWGZ0ViJS6WIu0GIIMxmB+5UNEhpoK7EzNrbB4wJbRe0Lh82gtx30j34BPAMQcfnk32V41XnpwE6Z3JVp/6fIdt/RXmYgXPNfZ4O7Perml+/yEqUxtj0r230cPLoSJY0n9iEyhzI+X6/s+RLmvT+H4LLkht0Omx8+tVNqdHyl7y5B31p0WFduBeZDOkjb54bC8WMmVZg3dV7rz6AD0/DtQZX++JU9lMbwCA3HgZMMhqZLQDttBF/xgIYUmocOvk4Gj8myk+/4uRk5B5zfxyl34Y8zMDZqjDzt5W2O83nW7stzn6GC0YHBC0nHIPUb+N3MRcggfShKcWJjvMN+Uc7cu1mOdDGb8DxpUW7NL1KlmAT1WoqbPplDewYkZifRegMiIVEbBANRxywhhk+UpTUUqHWMyilgEqDqxmgZ855yzIQj9jTAGSMgIdZBgYyReDx863cuVo7DJ3eCVrn+hkYh6OGWnR9eZiB43ulrgzsv+o+jy6kdN9TRe66ZlJ86LON4YEojqspH1b027gdPf9OqbCJMgSdnn62DDYjTuLBsge0G5ulSaqBPlSGoAMgLLjVL1e6w42VgHjgNL54p9Mq1KqCrWtwtSfQMQ1kuoHTXByAoMn9FhgwFKBcT5M3tXJtZ8y8ShrApi3DDBgDJgXiamu+Gy8PM3C+5D6HVjP321/eXpGxWtv1VOXxnaXrOlJrChJrPhNKK/ZUAf2BfgX9KanzKjI+jWFIw+lPbxjiy5ApldZhuHivMfZ6CaNjk+aKnMMEuW4SsZdgojys60q24BDLyteWwj+/VYt2o2t3AgbEBFgCK5Lf4M60ZOXAE5latoNYz8SKT7YkvQZz1PShatANAGRI9JFN5GEGzu+5z+8Y2O+3//7FFG8h0Yio5A8N4gGxiu8mOb6Ql5cUcukxa2k3Yzd2ni7/HsNmBSdxSZPpbZta/eyQXntHfrDg03XQkZeEn7bir5m3fGR7SxYLQ1BKQVmgJaCxDAOS0ciaBTRsQWRkUA/77mnb+dO0A49pU20nM7GEGRP8L/GwAD9aOdsOAHfu38XzH/2B5fmNyMMMnH8G4BDAtxDRn2Tm3832f8h9fmbrJUddjaXu6njEsL9dembQCtpGyYSKJ3YO916N6RWDhY3XbRA20baJTuK8plO1m36VOqiHbaV0UZ5EXZlMgAYBpMAsi6ZrJUHvZC3zeGl0hrWAIUAxh2idTEqcxgCIqXPoWl+yX2nYt4YFdJVpOxSmLwAqhU52XYrjeQCE1rXeaS2/79y/i+c+9jxeOtxM4X9opzYw8wLA/+h+/k9E5H02IKK/Bhl/80+2Nsq4y9xXQICDwoMebRyaP+XNrsT8GtifyxBs4uKDDI3DmAqVeNsobMadxCUpOYpXWdSsXyVa+qfiP8gSFP5P1jHugtz575pkBUAKOor8k/DAHKZFSM9VFeDDpGVRLj+/SlVgpWW//3MLdYk/UCHEeXG/w+JdSVtn7Zg4jTnVciCwee9Hvx8v3vsinr751OT2LcnDrOEAwM8CeC+APwPgj4jof4WMu/kuAF8F8B9fbPFeBR4ah9M9TLFjufRumjI9IZ9nlcMmBd+GzsKx+pRgk+zru5tLplRvv/++xaqOH9eZTuzc5EQAtERt2NcKLQQqitySo077yWMisEXQhqBU1GUdaTvRQMpE29EIvh1RblITq6Tp9Pw8vcqIpnTn6FV8zyc/jBddXLVP/egn8cd/7k+Uj5sgDzVwmPmciP49AP81gJ8A8IMA3gTwCwB+mpk385ANCZFTVzmYVbmGQfEP9+nvt7xXa5mVXpzQWYBNJ5lWk/RobChDsMnVDfRNqVw20W6mSE+zHAGTJQIxQzFAirBfKRzUGm3MUnRxxclpReF4n31Yl9jxwX9n5Vbjc/cOFXw7rtMKTGCYYeiMSAyhO8ev4Xt+7c/hxftfwrM3n8YLP/7rePTK46PHL5OHGjgAwMxnAP66+7s0CZPimMXrGD06ovMUDwKQPXu8gl28BDaTTKmhbZPKH3hiB0ypXIYcxSXtpnR87hcb2u+q0fsaPvPzIKdksJSrCBJdUwOaVFqZyARTENMsFvb5swrQEVRZWZ3PmhFtB+JQNs5pPAQdt9rfoJbDjDvHr+J7PveX8OLRHTx78yl8/sO/hls33oOT9myg9abJQw+cS5f4BvNzVrqx735HcpP3Hp44i3xfviF/NpbChgdgs6F2U/Lf9EypVKY4isP3FSA4COgByMSO/TH4+G52QwZ7WrQc60buli6L+ITQxfNm3wxuAqcfL8HstB0uazsAmFiWHmYrfp2J0CnJnZMv472f/0/w4vFdPHvjPfjCh34Vt27c2soQwB1wHrQwQ9aL5XRbdHlHYyDm/JqgRIzCpijZwLBVZbKTZNn45HHtZiRbl3hkX/o1ACHvwcsXKkshJN8qUgIcTdKz7bU2V9m4Ry1vmeSqezPTWkCT00ZK2g4BbCWii3W5aJShk2hb/Znpd09exwf+6X+BF49fwbPXb+M3f/CjuHX9ya2NN94B50GIf2N6swpA76F2drpL6D4K6nf8fcJzvRQ2Y36boad7qCt8qLcKQOK7yST33SzrBo8f5FEZgnH2PddoEtBQtt1rhMxuEqR8quYcqjkDWZZRya5XiZUCSLq/LQNaObPKqbL+pcEhf5JJnmGEsgVbkjV1lBuz4wPXAdFgQZShQ9zz58TQef6f/FW8dPIanr12C1/4/l/E7evv3hpsgB1wHoDkKsmQ+WIRVslnr2vL5zLwLCt5FDZDZtQyVWLdrp4Vjp2y9ETIckm6vMTYRCqCBo4Z8Kh0LwUfGteBRkb+tqDmFDQ/cU5dJSBwM8Slq7uCUhU0ZXWJLdoEbg48TNKL5cslAjMB8COUrc+mDB1oIJn5kOJcYPMkvvAf/F3cvvZEVK0Nrm8kO+A8EPFvFHZaDvomFTPkTnEXOg7RMVHjyZIn38dhs6EJNSSjYFnthp6i3SzLMWmXCVqNOPO9RuMeeAcZYgM2rWge7QJqIcBh16dNYW3jWqJrRtEZ4muWdwiETgFfB1Lu3UMAGbC1XU9WMliwDx2Zh+H632N/TnRdnr76BD7/3r+D21fftaT11pMdcB60+DlV8YNtCw49CjpztE3+82/z+K0+UWcIdejBZstdzFNrM2RObTrIz0tPs4lA4/ePwibWaljiSZH1ny3ItuD5OezZCezpfbBxpnFdg6oZMNsDYV98MCqlg5xOYaoG3FXmTutRfrAfrNN2JAIE23YQOgQPGNdZUVgc5TPf/fN92GyivWayA86DkOQCslhK0YUnRADw5pT/zAOSjUCnV2z2GfIPUoDNmN/mAYnwsavXKqYT0MFEvqfpembUCGzI+nArBmQaCd3bLGDPT8FnJ7Cnx27mNoHaGTBzvpU9DbCWaQmu/hZuES8iWJuekYzTEk3I94QxyaRR5c0sMi5aZyV+HbR96Cju7g8DwEfzjPrtbl19x0hrbi474DwgYchAMQDubuB0rx8VisIzn2s7K0AnLqP76m95pKBbR4IJuOz4bqJhV58yxNaZDY4st1XXCQpmjN8fhjCUYQPTghdnQLMAL85h5yfg+SnYGJkjZZ15XFUhnC47W6hbwMudK2ctxAx48JFM+fWeZVZO2wEADXEoW4BVlUBH8pCxPcRWfErBidxdh7H1lLchO+A8CMlH1facte7xYq9My5uvCJ4MOitJKNdmv1EGxqSBe4VtubaW18Glz0E5NPJn2Up+Q6ApjNdLvg93ebt2sdaFUfHajnXgEc0GrXzy4gy8mMOcz8GWQdpAkZIlRMP5Sm+V7zJv3Ro6TKkGZ+E1HA8gAZOPX05WtB0dTKxWcGQha9q4AT6sdHhPwW1nmM5GSxom8vBtWZPdAeeSpfceD0sBZI7afKBGZFnJ9i2bNL7MCDRhfV1fiRIwJvXFUx9k1D1CSVL0tZZYu4lhM+QY74rt+2fysvK0+f5Eu3GOJa+hwGsq1gCmAbeNAKdtYeYL2EacugpVd32JIJM0NVhrtCzRHVoGDFswUxixHOrh/HziK5ZeNHadDdrTEd7EqgC0YGYxr2wLcpEjmCQagx8YKOtq5L5C31gCsG178nbAeSDiX5+dWRVfWmLjAsqTI5RC5zokl9prNhZdl4fI0APZh130IEWwyW+zHniKUlLFR0YnB3jFWlo//9ALNQIb/zsHyNCoamTpStLbzIxkzA07s9cFlmPTglsxrdA2YGPBxggoQ7QEBdI1oGqwnsFCgy3QWhfHyjWXzbRLReTW3PETQdFNxLLsms+frIcOnE9HS+tYOI1KAAalOtOq9xJJr2USkHBD2QHnQcmYSeVv6KDWOPj42XkMhEcudihHsuyBTHdy+kAN1Dd04VN3cxenJ4Q8+r0giYbTq3e5ljFkON8Yyu4+htaHXnlGfV5nAF0YFWkrdj1UbFy0A2vAlsMfFEFVLsxvNQPqGbiegas9zFsGKYuFi/DQWuv8ONFpkcy5IiVLXGindbBlaJKuBm05WbpRYKtllq/lzp/j6+21GlLo1k4ebYGtyQ44D0zcFM2CKdPzpXg12q9dS0jH78Ts8n8lblCUNDNxOtiUTB+3eZkPZ8g/U/qdPFVOyyEKgMg1maDduA0xxkqPSGk8zRTxRl7CQqIUcMwIS3CyBZs2gMjP4AYActqDjx9Oe/tAdQCu9jE3jLmxUIpx2licLgxaa3s+E99TpllBE4eFvbSSpVG113Yy6GhSYlL55iUN8gCKTCs/Yjm0Wxz5M4797tthQ9kB55IlmDbZxbt7/9XuR7ih3REBOnI0w6nJA1pNzI3YS8KA+AiBvl8lemun/iQgDLGNyea1m+AAj/fFlcq0nAhwHJ0bGrtROgAAIABJREFU/JygolnVKXUlI83CLdkRcS+GTXhkemZSd3xJfDOOaoY2bkPb/XZlU12B6j3Q3gGwdwCu99CqGvPG4LxhtKbF8aLFSWv63eEQaGkiaLaykJcDT82EyvcoeS9yBB0iN1OdtEzsJCtOZGJ0sa1IBiFOCSe8JdkB50GKe1jvHN7B+z/1E932ROPhDg5uWLuMTfcmTl/DyR9M/125LLqHyHamFFgGr+VmFVGnWfm6xBoNRbHAu4M6PmVQi/NPet+sDa/zeAS1aGuizdmQTfZQx0pYqEEEG+8yi9soaqvc8LPRdmGgCudJpYfTaTVJvRSBKg1Va1A9A+0fANUB7OwA563Fecs4nDewGjiaG5ws2kSrIsgcK82MFrJqYKUVNFvUAcq2Bx2yMldKWYJVkFA0UE6DZFF3iFyvlbRY8vKLNZsL6CLfAedBibuB7x69gu/5xAfx0tGdaCdDYld58Ree0nkwKraRvG3vH8y065ggi0R56CSmFTvYxJEWo3qG3opE++gewsS3gvShj82Rzulqk9TkneC+7LhblsPMMqdQZOfFDAWZle3HqcSwcUNPUqXLfVrqoNOV52BIFJQV4aoOD2vqfyu0ma+HVhLOd28fav8qbL2HhVU4by0OzxscnrcwinDvrMHhWesupyzSReSXJ43WR4aFYQkVbEFgpz166JBrK2LAQC60tIcK2k3QcryGA+qbTcl1zvZvKDvgPDAh3Dl6Bc99/Ifw4v2X8fT12x102HbjNeAfUOrsewuQFqMkvtH92rh+ndz4jasUyaLdoOR+Ipc/+eBnITxIOFJuXO1inRMH+DDEaekh51J3w+8RxabiDjbSrdw9zQyZmCinaYIWIZoNB0vPOE0nqR3B1St1FCvICzpMtIx74dxbXMFBJ1SxG2BILHqbX0+v8m98Wzb7/HULMFUEpZ2zuN6TXqnZAeYN43jR4vC8xVtnDQwx7p82uH/WgCBajPLajCLUmlBr5YCjUDG6uVGwrq0sKq1gfDu4T9+zpZ2WI+2k+lpOrMmEFQdHznMD2QHnAcmd+3fw3EffjxcPX8KzN57CZ57/RXzbL303ADjYRH4VJ+RikrCq3I3fmVPBlGKGsRzMgjBjxvVqaEg42rhHidxwfbDpgq85TYOdHwBWnIxSEXlgLSCD1bLuaoLTpNybGhyDzSCOBtCp9GISsCUZgu/qx5BzMAGibhvLOsECP3TDSchpFx42VkYD97p+3Mxt71OTOFKdyebf/8rZZIYATRpMJgDLC1sHa8vJCnqktURWmO2Dqz20rDA3BvfnBofnLe6dLNDA4q3TBY7OWrcIu3ILsFtopWAqhcYwZpVy5+1w6qCj4CHKICVhhqFkeoRSALv4Vp2GpvpaTq7B5M5i32ZbkB1wHoDcuX8Hz/3Sc/jivS/i2ZtP4ws//Ct4dO9GlII7VT1+ON0q/dLrQPLwQoe3smg3cKFHuvEcFqLhBBx4s8oDzcPGNFKWaUN5pLVAzpUnDmvVmW/cRR8AuodUAJgtoenHrbCfYS3HsJK5RR4+bK0MjPPnZRmGXURLB1TpKhaIQokmpZ0/J8wOskaWivAQ9UJKfNnc+WR8Ob4NvSi/ng1ksSqlNdjqzs/ktDY2xsGNU+evroCqBnSNxgLnjcXxQmDz1skCpBhnZw1OzhqQJswqhUop1BVhpkXDq6u821peI0oTGmYQSyA+xVIrxfJ+sAhGk2snZ4vHWk4OEpX93rKWswPOJUsCm0eewed/5NO4feVxnMyjiMJe/TetG+fB3Y1iNaiqO+eeW1Hdv/WNZRlElrkVlO9C1RJvjf0j480c0wCtjJKVhaQgZVQ1qJ4JECIzyDDQspRlIquJSB5STRHYAITQtTHY/IA4reEXpWJSgLYgdItUedgsjEVr2JkOgDISimUGBaWi6a8ENyivCTO4Q5sq94b3Woyede0H0dhiP5GCTDkS81RhpiqAmkhDtA5sMvjPGusG/cXmlQKrCq1lnLUGJ/MWh2cNzs9baCIs5gbNonWzvTWMZlhWoRPMZhAjKLTEUCQmVQsLBQUFC9IyN4u4oOUEX47zRXntJrGxlQA87nncInR2wLlEiWHzzCPP4IWf+BxuX3kH0M77F9VaefjdLGQvpN1sYCIwue5xOOXBwaaxwMJaGMsBArUiVFqBrARh8xeePADaBXh+JsPzIw1HzfZcN748NLKdYKPRsa034eDfpgCU66n16PCOadOAmznQNm5CowKU68kB5OE0sm4Mw/tugHlrce6B40BVaYWaCQriWPXlKwJgWpBZgEwL2EYABICtjE8BXPA5LVqftaKptUGbkvRaKShmQEsPkAS56zQctqK1cdvANi3sooVpWvluDJQvVymYlnHeMk4XBidzg8W8RaUIzaJF21gZt4NY0/Kg0QCs01L9FAYLwwpkBDaGGIYkFI0fJhBrOeJAV+4N4CO+EtxyhOm9d4Hd5DvgXJLcPbqLD3zsA3jRw+YjL+D2jVvgZi4Oyq+9ibefSFp6403g0bcD1sAu5iHIPQDn6BNth0iLBgS5sVoGGgbOWoN5K6NXrTM1akU4qACqFbSC+HGYxcSxLXh+JssqzM87DUdpWNOArJUh8kEbEBW+tcDCaR4ebprELABEC6kcSP0Ma27m4PNT8OK8m0GtNNTeAWhPTCnSs9CjbpixaC3OGoOz1oaylAIqw5hpMaMq7d/iYvCRaeSvnUuZxp2TFrgx77n2q2Ghpf3cuZw7qMn5WMmbNVABlSVoVXWOVutgs1jAnC9gHHDMooFtDJQx4XEWoFnMGwPTGLQLA1YK7cKiWRjoymsWFoJ8DUUMZSwUKSiyEsfcWhir0YBBWrRNw+z8XM635YZReDeNuHaoc9IPmFXB+R40n51J9Q0p3//R5/Hy4csRbG6H3hX75j1c/7Y/ha/6xP/dn4X5vX8GvrInD8viHNw28lZVGtQs5I1V1WJ2QR6Q1jLmjcHxvMVJY9AYuQG1Isy0EociaVRKd/N12IDnZzCnx+DTI9j5HKaR211pBX1wIA5ZLQPY4NT81sjDedoYnDYGrQUYjIoIe0bhal0JCJyfhd0CVXx6DHt2DDs/BVoph6oavJhDWQvSVQCbdSbbaSPnc7xocbowMEbabb9WuDLT8l1bWA0A2s1nmoOaM9izY/Bc2k/KqkB7B6AD1zuja0CpoB2eLAxOW2k7f31mWqGtZcClJqCqlDywVkYZ22aO5uQci5MzcNOiXTSwZwIg3SzcSoBOk2JgYRjGWJjWgpQDVCvmkVXyScSwrUUDN5eKRMPRxqJRBK1YXhyQl4p1PYaGGZqp026cj03BhR1GB5rErPISZp1fjOyAc0nysotc+MJHXsCtG7cB+OvtehByIQK3C/D5CVp3I7NlUKVQHeyJSVXVoGoffnkmYxnHC4P7ixaHZy3OFgbGSm/HwUyjPahdkDYtjlFmkG1h52fgk/to7h9jcXIGO5eHU+3VmC1a1Mzgqoa6ch1MYuI0VobkH563OG0N5q3AbaYVDmpxKmtdY49drxEz+PxUYHN8iOb4HGbhyqkr1FfnAABd1aD6QCYWQso5c+NW3jpZ4GRu0BqZwHgw07h5ZQZFhINa4QoEPmRaqHYOe3wIe3JfNCoPnNkMysGbdO18MwKCk8biaBGBzYq2cDDTaJyJVSvCvlYydYAt0CxgzuZoT8/RHp+hPW9hW4s3Xnkd8/MTVNcOxIS0LUAOpC50jJhxDdjKtbXGwioFUiwD+IidJmlRsUZrGJUSP53XcqT3jGGIwc65z9GQJj/Q0/dWUgSaYFaVtJgLWhdnB5xLkif2n8DP/8mfxxsvvoE38EYY6wHToHrzTfybWfp//a//FebNKZp7b2F+7wjmfAHbWuhao7p6gP1Hr0O/7XHox56AufZ2HBmNN89afOV4jtcOz/Hm8QInixbWMJQmXJ1VePv1Pbzrxh7edW0fj12pcJXPoY6+ivYrdzB//as4e+MQi6MztGfiwNR7Gns3r2D/sZvYe/xxVO+4BXv9cZzSPu7NDV4/nuONswb3ThY4XYjJMtMKN67UeOxKjXdc28Pb9ivcqCzUyRuwb7wG87UvSzn3T9CeCwT0rEJ9/QoOHruJ+vF3Qr/93TDXHsdhq/C1kwavHc9x981TfO14jsV5C2MslFKoZxo3rszw5NsO8M7re3ji+gyP7GnMFvdBh6/DvPk6mrfexOLoFLaV+um9GfZuXkX1yKPQjz0Be/UxzOsruHdu8PrJAq+fLPC1+3McnTdYtIxKC3AeuzbDO67t4Ynrck5X7Bno/pfRfuUuzl57Haevv4n54QLNWYP79w7xyt1XUN2o0D4yRz2fg6yBlg418cV4BdNasBulbFlGdbMlmfyp3CRQp1Fqxa5XkF0vvP9Nwfy0GDarZAgBRX8KbkBPJ3EsckRa0JZkB5xLkp+89pN45f9+Fa8gnjMlPTf79+/3gPOvfu/3cXz8Bk6//FWcf+0Q86NzcMtQM4W96/s4ePwR7L/rHaje9RTsjXfhntH48vECX7p3hi+9cYrDw3Ms5gbWWCitMNvTuHlzH+957AqeetsB3n1tD9cxB735JbSvvoSjO6/j+LVDzA/naM4EBPVBjYPH9nH1HTdx7T3vRP3uZ2EfeTeOsIevnCzw6tEcd948xZvHC7SLFpaButa4caXGu992gCdv7MsDOgP00ZdhXnsZZ6++hpMvv4GzN87QnDpPxZ7G/s09XHnnTVy99a5Qzlutxt2jOV564xQvfe0Ep0dzLM4EOKQIs70KV67P8MTbDvD026/iqZsHePuVCvXx67Cv38H8K6/i7Kv3cP7WCYzrBaqv1Nh75BquvusxVO+8DTz6JM5m1/HGmcGdwzPcefMMr907w/ysgTUy+K/e07h+bQ/veewK3vPIAZ64PsNNWkC99Sqau/+vtN0r93D65jmaswbNokF9pcI1dRXN8Rn2FmdQtoXWMv+pUhQcxGMr7PlBj9ayBFvwQxAsd+OS3D9vVnXjiKjTbPyAyWj8Veil7I0svljZAeeS5Jq9hqZp0o1sQNZCFcKtNk2D+fERTt88xMnr9zE/WsA0BrrW2DtbyOqQBzPwjcdgDxZYmArHZ3O8cXiKN++d4OTwHPPTOWy7gKpmmB3swViDqxXw6J7C+YxwQAvg7Aznh8c4+dohjl4/wtHhHKdGnJNXjs/Rtg1oplA9chX8mPRinVnC4ekcX37rGF9+4xSnx3M0cwMwUM80zs9raFhcq4C37REapcGLBZqTI5y8KeWcvH6Ks7mBYcaBVjg42wdrQF87AD9yBL5yjsbMcHo+x73jM5wcneL43hzzk/uwzQKkNWZXb8Jag7dqwjuv12jaCqZl4PwE7eGbOP7KGzj+8j2cv3mO9twACphdm+HKfAGuNa5cvQm6+igaOsDp+QKHJ3N85a0THN07x/npAu2iAZFCvV9hsWiwpxjXa8IjNeHKjKGaBovzOc6PTnDijjt1YwSunbaYHS9g5gvwYg5qF9C1OO/rSrrxlZJpHcX5WZH4HiuvafgBkKLpiL/GL4nkp4EMzTqVzU6zoYIG48frXBCEdsB5UJLMvelfXLYGZtGgOT3H4niB88M55tZiz70RZ9fOYM4XsrQlW4AhTtyFweK8xfx0jsXRmzCmgdY1wI9gvqdxPG+xMLabSmMM2vMFFqcN5keL/7+9M4+1Jbnr++dX1ctZ7vLefbN4Zt7zzJiAMwxbxoBHJCQOZsbBMDYweE8QyERIELaQICVxpASEHGIgEuAggeJIUQzYeJsBDPZ4IzEKq53YsZFwbI/nPo+X2d7d7z2nuyt/VHV3dZ8+55773rvnzp1XX+nec04vVdXdVd/+bfUrNrKCnaxACWRGke6MyfbHViUx1uicF7A3ztnYHXOwN2Z/Z8x4zy6JkqVDULCxO2YvK8hyrD2qKChGI7LdAw42R2zvZ2xmBbkx7BWG6zYOGK1Y42s6GlVBgXlh3cjjUc54f5fRzoY1oGsbKZz17czrvHBTSIsMDvbItrcZbe6y98Q+25f22XN2n+WDHB0r0tVdiv1dosJ6kfIC9kY5+6PMXtP2Jvn+rjUoZ0sorXhqZ8TuOGeUu2hvsapPPs7IRjl7uWHHZdESoLefke3ZcAOKMZGyKmeiFUrbwD1FhKjIEpCz6dXhL84NPk93KqwNx8/FZrxPU34pu5o3x21qmdIioquAQDgnAeOE3Yp0Oh5mkWPGOflBzmg3YysrrAtaFejdMdluRrY/soPP1BGyB5n1fmQHO2QHu+TjfQqdoKKEbNRnb5QzdjEngDVY5jn5KOOgKNjL7Z8SIZKC8bigGNdpF4yyc3b2spztfTs4RzubZHvbFMbGDul4jf2hNbxW5GZsMFwxzsn2M3Zzw3ZWkBkoTMHOKKe/O3bu5AOUm7xalBHG44L8YJ9idECRHSA6phiPyLOiOcmyyDEH+2R7B+xvHnCwecDGuLwme1y8aYmP8agKASiMYZTnZOOcbJSR7e0w3tuqvFGjtM/+QVYTNr5r3P7lxjB2z3RsDPnIXm8xtlkBIxESbb1rUaSIIo2g0ZGuCcgF3UkZfOfarOYc8JZ4ZDZLVWV1lXm86lUgnBNEY4JmG3lu3aUH9u2+XxjGRUGB4mCUkx3kFOOsmitU9q/CeTtMnlNkIwrnei6yEUVeejjKSNoyeM3YVJdl4BtUxsjKfa7cyo+iGedj9scFWVaQjQvygz3G+3aVSSWKbDQmG+dW8nBkWBoii7ygKMnT1XVQ2PihYuSuKXeRwV0qgSns4m+qVkOV1EvmSj7GZCOy/ZEltqxgJ7dSWyRCrAwr+xn5QUYxGlm11tVT2keKfGyJemw9Z6I1+eiAPOtxMLYkmhtDpCNEq87BbZwXsMgLijyHbIxgiJQijTX9RLOXKKQQdKRsDI4SSzxKPI+1VNJPSTpXMq+yrWnZ9KGtg44h/qZEIJyTQum3tD8md7u0lUXupioUhlFhnZm5qUmljPFwU4qItHtDdk3A88unXvNaVD3ZMVKCdu5gLUIkdqa4HVjaud8hz91gyjLyfIzJrX2qKOx0jMK1OXcuYFRZT20w7bol/qxypXCRve48rVHaJiRXUYJojY4USaSIlc0ZYyXDkYv6zRk7F35JouPCEp5tu51IqkTcX7s99ZyNIreu69xNrcgNoCJUHKPjCBXbNsRiZ/HrMuShLNQlXY+0VavSWBPFGjHaEY62M7t1k3jK9BRK1W1sRhyXjiXx/rqfeekaB+ZnrDCX6hkGZzieQFE4l6jdp6SeV9dY4tdN7lTKekBirdCxQsUJOkntuTpCRQlKSy0JUHpJtB24SURfK1JVULgUCX0txL0InWp0HCFx4s3crklS+fNxWkRncOqbaFQcoWNt3e37GWluo2RLsqiIrSwXa2RNnPqhkz5ROiDXGh2lROmQKLLSQuJSOFCUUp9pmsko8+K0WcUmoNAKIqXQWqF0jI4S8sgOj4rocPOsXPAjSkOcoHsJ8TCiv6WqOWZ9LUSpttck1t0tRYESTaptXFQcW5UqSjVRrNyzUpVUE2nlPFv2uUVa7FQL9zIo8+ecJgTCOVEYK50U+eSecmKjKBJlB15hINVC4t6AlQ3BpZ9MtDBINHGiiftDivEIpa0kEPWXiBJNGutq3hGiII6JeglxPyIeRKzkBbGya0KsxJpkKSYe9FC9PkZFFdEo9/bWcYyKU3TSB0DHKSqK0C7Ngr1KbFvjlGiQkgxilrbH5LFV4VItDLUi7keoKLJpMIy1uSRaMUw1cU+T9FNMsYLOxug4JemnJP2IYRrRj23+GPEy74kWYoHU3Tst9v6pWLuB7Qa5WMmun1ipI0ojdG9A5PLbROkQnaRWEvEI0SiNJD3iQY9kmNBbGqO2x2TGkCaaeBgR9RJUHLn8wsZdkzBIIgZpRIYlHh3bVBmldFORjJKKdMqXjhK7rZTM7LRMqWfJT8GRQ/mOYU5VIJyThinAZJPbCxs3EqUanWiGhU0tmSiIexFR30oddjqwnb7QizRL/ZheP2E8yoGz5OMxSmniXkTai1lKdTX/CBdtq3sJyXKf/tkRFDA4sHUnSwm9sz2S5YFNkaniykCbREIca+JUk/WXKteuTnskaUQUa+LIRuQCIBGSpMTDPslKwnB3jNoZkxtDrBTpckKylKD7iZsNr6ppBcu9mKVBQjbOERliClBaSAcxy4OY1UFMqjV2CpedjqCSmLgfMYgUB4UhVnbaxZJWJEsxuh+jYl0Nqlgr+rG9f/u9mHzpDErHGFMQJX2SXkSURPQT7eaiGatSJT2iYY9kpUf/IEdHiqIwRL2IdNler0p7LpeQNEh0KdXsF4o4iUhi7TzSNZFEuiadUrop8+VYoqknq/rhNC6Fz2yTcFv8WxAC4SwIkdZE4qV4cNY7ISfq6BlaNFGSkC6lDM/mRJuaIjc2SG4lob8yIBn2idKejUmJI5Z6hnNLPXYO7BzhJBm7eUc2PmZ1OeXscp+lNCGOInQs6P4As7xC/+wekhfEaUJ+kFnpYBDTX1ticG6VeGkV0hTt6hr0ElaHPbKxQSlFdjCw7Y41/aWEM0s9lvsJSazRWqPSFDVcpndmmXz3AC2a3uaBvaZYkawkDNaW6S0NiXoD0Jo40iz1Ys4OU3ZGNgn4QW9s0y5oxbAfc8NKytlBj6V+YqU3rZG0RzrsMzg7wIwM0caILC/QCtKlhP65Pr2VJaL+gCi20z3SOGKln3Ddil1RQUea8SDBFBDFirQfc26lx+ogJU0StNZolSKDIb3VFcxBRhzFHGztkY0zdKzt9ZxdIR4uodM+eRwR55p+CkuZYXVoGErEyiBm2IuquVvipJiSdKxR3NqqtCfxWNuOIylchkVxU0nwPOB0k08njjkIMBDOgnDnnXfS1333yxpG7ZrUI+LHH5s4/o4772B3b5fdxy6xf2mXbC/D5AUq1qQrCb21ZQY3nCW64Rbk3C1kgzWe3Ms5vzPiK3dGfHlzn629jIMsr+YDrS2lXL+U8KyllHP9mOW4QG0/gXn2TeRPfpn9J7cYb+9Zz5YIUT8hPbNMvHaO6LqbKZavZ0/3uWUv56btfS5c2uNLGwfs7I3Jx27qQKwZ9mNuXE25abXPLcs91vqafr6LbN5C/uULHDz2BAeXthnv7lFkBSpSxIM+6Zkl0uvPoa+7CbN6I6NkmY39nGfvjnhid8yTOwds79uYmyQSlnoxa8OE6wYJ1w9izqQKtf0Y5pZzjM7fzN6XnmLvyW1G23YVTKUV0TCmd2bA4MY10htvRN9wgXz5BjbGwi07Y27fPuCLbmrI/tjmIlKRYrlXTw25aanHmZ5myAFq5zzFE7eSbzzBaHOX0c4eW5ub9AcDhufOkJ5ZQVavQ87cQDFcYydXbB7kPLmXcWl/zO7YcOPN17Pci6q0FFCrTuJsNWXKUZugy6ahKNepssnInLGZWvoBb/4U1KxzQtINBMJZGL7xed/IMBm6X84Tk49R2T588QsTx9/1d+4i04bRxo6dCzSqZzvHg5RkdUhy9ix67UZYvo5iuMbWqGBjlPPk3pjNfRszMs5tF+5FmuVexGoasTZIWIqFYQRq9xLsPkWx+STF9ibZ3j4mz619JrETNvXqGiyvUfTPsC8Jm6OCx3ZGPLU35ontA7b2Mw7Gtp40toPz3FLK2X7M9cOElUTRKw7Qe09RbDxOsfEE4+0dsl2bNsLamGKipSFqeQ199nqK3irjZImdccHmfs7GgV1OZT8rKIyxNhd3TWd7McNYWIpA7T4Fm4+TP/klRpcuMdrYYVzWoxS6l5CuDEnWVtBnrveIQLM5Knhid8yl/TEbe2P2yukazsh7ph9zph+z1o9ZThR9xqi9Tdi5RLH9FMXuFmY0stNJIo3qDZDBMnplDdNfoeivspvB9rhgaz9ja5SxM8o5yAtGmU270bXqppKmLccSjaq8V1qsfUphjd9V/GAriFAm1KxyAoS/6XjJKBDOglBFe5Y/bASEFaM7jMYYg05ion4KIjY+BTuzOuolxEtDpDcAHdkZwSa3s8K1YjWJ0MAgtvlwAWJtB+hSoolcZzSAcTl3ZbBsbT3pvk0lIQJxWg2aIkpdKlAbzNqPFKNYY4YJ/UQzymw9SST0Y80w1vQjVQe+Kk2hE2SwjOQ5sY6IBqM6CVecoPpDVH/oonhtknMt0IuFwlgbzciljdBKSLVimGgSZY2+FJnVVKMY6Q+J88ySzCDFZAUSKXRijeDSX0aSHkhkr0lZY/xyYpOaxUoY9exCclqENFIMY80wUsRu4BpR9v71+ojJrc1nbNNsoK19R3oDTJxiVFQ+cTTWcJxqRRGbyk6TVbPI625QruBQkY7CkQ2OaNyqDkq5FKs1sfifzZ5YNIllgQJPIJwFoaFH295qvxqDnF1l66/ez3N+54UA/L+XvIvByipkI6J+jkS6SiCl4gidpkhvYLPkxQkgSJETKV15qpRIFaAGLjGWVqSxsgbI0sioNOgU1Xfh+mnfZvwT5RZw62GiHujYZspzUwR6kSJLrKEzjTTj3MXNiCLVlnR6zuYglPXESNxDLa1iIjs4q5UUotgSX2+AUTaFqjjvUaIVJrErGIxbib5iZcnUEo5dlUDiBNUbQJETK0XUH9v7J4JK0qoeSfsufN9UUcD9RINTYTKXwExh96WRjZ8pDbsiLp+OTlF9S95kTm0Wsc/GrdhgylUwq3gpRRq72JgysZYpqhSnpXG+Uq2gur+VOqXc/fWkG+0bk71+V36/bEwJxDwqAuGcCGwnr/PsCmZtlcedxmXWzmCnB+dIkhDp3EXXuiTqUYokqU2IVb6+jCHSkGvBUpA1MuaVe915O5z4Xa3iKdqmLSW1AyjPbJ4XUVZ60jFEcTVggCrmJ43slkgZcm3boZWqBmeklRdFbweniVKbiUVpzDilyvsbxTYRl7Ju8XI2s405MSTKqhGxnUBdST+RljrozxjKDILEiV3tUgTJ3dpvgpZUAAAb9ElEQVTfStkEXEnPLrurdOXlE+2kHAPEmljV5CZClcQsdvfP3dT6PmGQRCAuvH3akk2UuGuq7582EIu4+2YTo+dGkUuZU7kWO8Szy+hShVKqUqV86abMKKFU+b1elFBcP8F4CxIu2J5zzRKOiLwA+OCMQ/7MGHP3sbahVLSqSDJ/p9gsewAqqlMJaBuAJ1FsXa3OrSumQFXpRG0RujAU5SJpuMHronerrSIYHTsKFPvGrjL7u8GkYlBRcz1v594FmwCqXL9JYT0rvUhXdgUoY3G0K99YCUpH1PK8XScJFdtczY4QFU56wSYG96GlHIBSDyCsqiM6hqRvr6GwEcWiXP7kKLbJt7yAxTLKupyVrVBEZcpo8Qiu9A6V1yXKPh/jciT7SzQrjVHaXY9uJrXHkaVRlCub2kT3Lr8NVrUqQ62EOspYIRXZaqWIpTYi+7E4vi1HWoRTRnN7T2cSpiMg9QpxzRKOh08DH56y/XhRPviuSGOxb+OGAu4PmCixc5tKOdeVE3nbcidIVSHtUnfK2o2hwNgE6SJSt0WkGkxG23gf05IuCmVXTNDKNFeyKUPwvXoKrKsfpa3dQwQj/vItpbTgSNRdd7lsbaSwicGNcQOpnl9kVz8uKJcOtuJKVCVOr5akcRKJRJFbU9tKUuUbv7STgHHRwfX0hsr13LquUqIyUK+55e6rcddbXVO5y5Wjndu7wK6kkOWQK6HA3U9v5nfpgSqlrYaUo+z3Ul3z516VBGSb22W/OWzK+NWVgALhwIeNMT+w8FpNaUaefKDlm7jMsF/vEERHVrpxg6ZOIWDcJE7lIonFDVCbgKmK76B+41m4pUMULkGTP/VaVQvh2YFZtsOWobHqoDJCIaYORnOivlK1e9YYHGnpyuAqTp0p9RbjVnDwJQ8rmdlvJSlAPZHRzp1sDSRxq0pGsVUTXfRxtYCdjqw05d/bokAp7UgHxK1HVa6bXkqISlr3z7VbXHrPifvnJrxWeWrqW+juk42GVm7NeG3cUjWV3lY+pZJ0nJ3GkU7sDMYV2Xj3zfdUTahTvoeqQSrBS/XMRUOs9SDiwu5VPcXBJUUSHVky8ucv+ZKSFGilKdexVeKPw3KwevY/q/C7Zvg7cC4S+5b2B0zZmZVTN6zhs9nRy7drVZzBLqYnGiOmJji/s5eE4F2XT5QgDWlNhHoJmlKdws0TK1cM1aU65iQcf1/VZpszT2GjiCnsBMj266Ak68b9Q0G5Zje1YdWUDRT7rBrD2BGBwtTTTHJ7vwtApKgea+MxIQ3SaUg2VfuYlG7a6pT//YgSzMWti0c6vo1AOCeGohb1oXyNA1hvkLLziaRaXlfVko9LPlUNHrDHYjAmx654qUCV/cljEWlqaYiAscdO9HBUNQi6JkJSeYYawk/tii3fsA4GR5xEtp2FIGK8/eUgFWqKqbjTqoStekrJrqvtRoGU6e9qhnVSR1lXef8s+Vf3rRC6LBjVNAJ/Y0naE+Rp/0z71panOBW1ZE4xuCVwbJsaNrPyHMRTWz0PValKUatspXSjqrtfdNhvyn3+z5Kcm1jfXOe+t7yk467Mj0A48JUi8nrgHPA41p7zR8Ycg8WshUbQVVt1Ur75j/rNX5JNKw2kJZuyM+XuDWqXBmnZWpsuelefHcqTA6YSvD17hlWJ7Da7mJqpymuobm6b6+qVYVqUKo06dZtb9bbbq5gkNWug8gaRX4Zb4M044m7e59Yd6LpvyrW3w5bf6WJuD9BKupkIrauvx0k5BtDOE6UoLMEXDRNO1T2s+1t5kqQjGEc2IrUqWxEPVPepoU5Vav3hWN+8yD2/82Ie3vjcXMdPQyAc+Bb35+PjInK/MeZT8xYiIp+YsusrJje1xFnVWgvIGSKbFSjXe5SndrS7vXGzpaXyykjtC6uLahaMxxaN7SVRGEP32x4reXSuY9SxyRhnlkKcsbpkrXbpPplavW3yasv71zKCiqOmSkoyzTNr0at5vZ5a2rhvU8naJzlrf6nLK11Lk632BC1bljWDVfc6Us4iJZNEUE1ZEF/iaZFNqcqKlYSsKlXQlG7aapV3/6trqutf31znnt+1izjetnorD3P5pKMOP+QZiw3gDcDdWOnmHPBC4E+BrwXeKyKrx1Z7l+2iROWpcX9aV2RTSTeUKybKZJnlQCwKSrNnx3vdgzT+6vfg9Jeg3/Hb8N2yzWuuB1Zj0IumtkJ4ZNMgndZfa/BMyBEVofj1TCGb8nz/vjkV1WtVy9vTddWeVCrdR1rJxJNAWt6vsmmlu7+OJK7TU+jqUybJxr0ASsnGj7exXjtHMHPabta3Ps+3v/UlfObSZ3nOmdv5vVc8ONd503BqJRwReSdwxxFP+35jzJ8DGGM+Cny0tf8DIvL3sPE53wr8CPD6eQo2xtw5pZ2fAL664wTvobs3fnWSI5lmOU7K8SWczoZQDUgpSpHC1TLlHJoDo002h0RrTJKOJzT5KNe8Lg3NdsWCSjfrsMN0NbRFqv62qp3SPL0h3nWV6xpVGt4VYFS9r31Oow0d8Mlt2m5XpTKAEhfIa5r3vi2YUb8W6qx/7re0XOHly6BBoqZWqzwvqUy5jvWtR3nhu17BZ8pFHF/9Htb657qveU6cWsIBbgeee8RzBocdYIzJReQXsITzIuYknCPB1C5JI6VXpe5d1g7i97ZSgVetN7X/2zu8tOfYGspCaasX7W42KUz7Ta63ikcOlcrhG1imnC/OumxaxwneaJqFNtnM42XpVPfsPTN+hcbUnGcM1m3k5JCrFItiVVC7CqlSYlfgdKRTqqZV7E9ns2siLT11vuesUqdKVaokljJGyZduOo3HNda3HuWFD7yaz2w+wnNWb+OhV76bCysX2BnvXtE9OLWEY4z5hmMsvrTd3HSMdVhI2aG9HqZdYFrjuHK/E5SnvkG9t7UIdmD6Yr7pPK9NNm3p5vDraH6dxh2llNMmHftzmlgCh0oVVSGWJIwp3OV2SCiHoSFpzXsHZlsnfAGuepIidpG6ShqZYRPza/GkGvFJB6rc1FIZ1PMGwXQbi5v3dH37Ub7tgdc4srmV9738Qc6vnJ/zPszGqSWcY8ZZ97lzLKW33symLaV0SC2V7aEkG3851ln1tA2jHZ35MLKxKzl2Sz7S2j6NAufZ16TCdsk+juBAbKtq5bZGI0xVXdfhM89ttMkjHVem1LJGpUIVrhIl9tmXUk1FPNUJZZ2Nj0qlKom9k2w8KbBTunGVtNWpi9tf4Lve/UOWbFaezfvvfwfnV87P6cs6HIFwunG/+/zI8VXhDbXKVUu9rUuBb5CNR0qz3uCVLWeG3YdJsplVXBvTSm2/qUu1ypdynJZVlTM36cyh5pjS1zTz/hQ0DPalakUH80xjo6p8RzodxC7un6FJOv5OK5DV29qyaCP1BPX9LWODGmTjplmIy8s8Id1MUade/Ac/yMNbn7dk8z1v5cLyLVeNbOAa9lKJyE+KyIXWNhGRHwZ+Cvu8f/14avfjQpzUohoTZ5p/vhrVIJuadMzUYT8bDcHa+zGPDHF5NXrley9bn+euuIN7EuC0+9K9vVbbKl9dpxt5iro3o6aaJGjZXjwjr1AtAayqFKLedylja6SSaqrzmCSbCVtNS7qpPr32l2TzgZf+FheWb55xXZeHa1nC+UngF0XkI8BngR7WHX47djz8uDHmr46/GVK//hyMlDYa/zBPhep093rlHRFm6o9anToKprXA2AKbko8n6ZTHNCWdWarVtNpLKUGBKSpyqQz1XS2spJKmDcx6jlqSTZdHzbeZ+YZmqa/Cb2Il6UDlJjdm+mvDv2eVHceXdnyy6XCDT0g30Omdum35Fj7w0jdzYfnmqyrZlLiWCeeXgHuBO7Fu6xj4AvDfgV8xxvzF8VTrvXVKsiiKJml0LmLnq08d3XKWq7y7Fc3fR5Rujoq2elDWUzmf3QGTatUUTLPN+APdmIp0bBvmvT+TpNM4v0Um1bOcSjr2XyfplNXh1KwpqHrDBNG4AgovuK/hlerKfdOtTgG8+8Vv4sLS1ZdsSlyzhGOM+VXgV0+0ETKFRErCaXds+6N5Xtu9ewRMvMFm2W6mbG/LH/O0xHRIOdVY9spoEM8h1tzKXnMlaNhePNJx9c8knssgncp+5U6bMBhL89hyU4NoKiKpY23EMxCL76WakG687Q7nh8+arPwq4polnKcLqrlFqkUqPrH4kCbhmNLo7KlZjUmQzk08vf4mGlP65ow/OYrS05Ze2lJOW7XqrkExYQdrtNUd70s5MPWt3mxgB+m0tncSz9ykY2nHl+RKlbK6Ap9oWldVEU1Zb1uNanij2nackmRm3Ac1va/MY6g/DIFwTgLG67DiMr5Jq5dNIxvvsyKbedDpap+jqXMeN6vkuUJhpkg5kxXNaU/p+t3IyDcvWqRTlgm1fafcNw/pQIN4Gpc2pQX19hbRtOZHtcmmqUpdBlmElTefAWh0WlV9NtQiNcUe0/ZIVatatqSbmZjcfxVeXHOhoS55atU0Kad9zmHk0lSrpshd8wyiCYZsUeA0accnm0ad5RSMVt3S+GDy2Xjl+J6ydpR1w0DcIpuyHE+6aapTHbiMF9O8CIRzkqiIgsbscIPUCZ28bfV5zQmCE+rXIepUZzdbEOmUVXV26fa4pj0Y51Srqu8t1epIjewinbIdTJd2DrXrUBPP1Dpa7ajgTbpsSDUegbQlm8t9m7Sk6auFQDgnBv+B1tq53WaNxjO7SgfZmDbx+Me2tl8pv9h5P8fHUg1piBZBzZB0GsF+bdKBow3ATl1wtrTTqWK14U+XaEs9DXSkjfBIZD6y6ZJuZqAzxOLqkU4gnJNAOTga4n2HhDITXfYcb1vn+bNJ53IMxleCedWqBmbaaKZJMp50dDnSzmTLmVvFOqyN0+Zq+cc01CmfaFxbDiWbrvbPQusFeBURCGfR8DueN6rWtx71DmpPdejAhD2ni2wmbUHtrrYo+83cuFy1aqaU0zr3cqSdQxvaZMkJaafVxtlFm87fzaTnvg3nMDXKEtJ0zOhrQaV6BkDESfv2Ya5vXuS+t99f7e6y4bRh2hKR/wk005NePXWq+bKerlbNmvE8L+ZWqzq+H0o65bFwBcTTQTpeuVPjdrquoV1G1eK2pNOUdia9UV7b2mXNU3/pfLhMr+ZhCIRzUnAPtMoVu/nIxL6Z46BtJG5saxuV4bhE5JJYJvLlXCHmV6s6pJyjkE553uW3lIl72yHt1Ed3EFQLkyTj10WlQlXHTqhfHedMQ/vaj9FDBdfw5M0Thwjrm49yz29/R5Ur1t+HSDPhVvvPP678DkyQjdeBpnbDLrtm9Zam8dkqsnH8ROKwGcd3YZrQ337ndxbcvt5pKmdVxvEOrGkeonJS6Ky/SdXINwxPIZt2fY3v7Tt7GMkI5SThqY6Iy0QgnIXCUA6r9a3Pc89vv6jOFfvyB+rDppHMrD9gOtl0k87TFqbza7PtXerjlIHRIJ1jIZ4Zd9V0EciM/W2SaRONU6Gmk8280s1U8bFO7nYM0k9QqRaEnfEOYKDIuLh5kfve9r08vPE5blu9lQde/i7O9s56x+5RvwsOi4xtB5N1k027O1afngTerqlUk2Z14aN4WbsUvLZE1J6k2DiWFnW2vDcTcSqtBspcg/FyKflqSQGzpZSJ1TKrT+/cNvl4Ek6XW9z2N4vtbM+lSrESttEx5CO37nvETnZlKUZlEe7Paxki8gmu56v50ZNuSUDAVcIb4Y7r7uCTn/zkkVk2qFQBAQELQ1CpFoS3fu9beNU7X01ucrRo/vCV7+aum7+pEsR3xztc+BWbgPDij68zjId0ivedkatdL5pJu02XQtGWvrsUOF8Kvrh1ke9920t4ZONzPHv1Vt7xfQ9y83J3gu3Zrer2ZqnWgYeqVf5F+KpV4/fk94tbn+e+t9/Pw5uPcNvKs/n9+9/G+eVbOq/jeC1fhzxjXx2aUJng4uajfOcDr+LhzXVuW77AH7z0zZwvM/V1xeN0YGe8x83/5esBePSHPmb7nlCpUUbKlV41j+09zle98W8f+SpLBMJZEEqyiVTEB1/zfr75/N3V4G4Pu0E8ZJgMvS2X0+Fne6ca2zo2dhHP+sYjvOzt380jG5/j9jO38+5XvpdbVi50HNnVCm/bFOPjPGQzWe50m820davWN9Z56TtexsPlEiiv+AMuHLIqwRXn2unCVHPG4d4mwbC+9Xnue/A1PLy5znNWb+X997+DCyVpzkk2trB6Ht8wGTJMlrCxOBqUthHxKgKRK7bhBMJZEHyyef755x/Bf3C5Hb2OD/EjT6Sxx32XVk1m0mR9cXOdF//OvXz20me5/czt/OGrHuL8IWQzDxo6fZeXu+OcmYaDiRgd8KcPrG9e5J63fCef2XjYLu72ynfXZDPDnnm5Sc4mC/Lq6AohmIjr8Y9X1e9HNi/y7b/7PXaRutXbeN/L3sV5l/B8ghwPs9P6uZiUsgRTzee7um7xQDgLghbNB//xB3j+Ld9cbet+Y/s4YseBVueoaaZBMFPPrT/LqhRwcWOd7/jteyqyec+rHuLmI5JNp7Fwhtd12r2ZSsbtaGOYIJ71zYtV3NNzztzOQ6/6w6Zkc8xBbxPoeJ6mva/xOO229c2L3PPWl1jSXL2N973i9+11THgVp/SXVr1Gx/V3FVXSjHGeKkR3hBRcHgLhLAjvfNk7PLJpBtW1v9cxGOXPI0g5nW/QSeIpt04lIvdjfXOdF/lk8+orl2w6AweP+Hsuyc+fq7a53oh7euhV7zlUjZra2KsFYfqzraptKreWbL6rIaE11o2aFuvTWbbbjW58NzIl4LTr5CMiEM6CcNez7sJ/WHMNoKsesuArVjPIxmF9c50Xvbkmm/e+5srIZnqo2VG2z7JFSOc9W99c557f8sjm1e/hwqzrWKSkM6suY8AjA0ua3zGbNCeKmyPDob+Ovdbe76Ml5p8HgXCeFpj2luseQFNxmZ2jSwm7uLnOvR7ZPPSah7iwcuGKTKfztG72MUdVKTvI5jXvnU02l9myY4HU1re5rqOzr+iObe16dPN74/fhiv9REAjnxHGEUN3LxvxlCLZzd5HN0Uq6mq3qOuNw8lnfXOeeN997OsnGq3vyOh5qXUfLdnXkKlrW+kPCLK4EgXAWhMd3H5+x17A7rt2NdhrEPLj6A+Hi1kXue8t91bSLB1/+AOd6a+yOjmeZ9eNC+zoeePkDrPXOsTPXdZwkwTTRfR1rc16Hj+kE7fe3x3Yf6+h/9f2Y3Y8PR5jacMwQkU00y6yddEsCAq4SnoSl/hJbW1tHZuZAOMcMEfkiMADWT7gpX+E+P32irTg+PNOvD54+13gB2DXGPOvQI1sIhHONQEQ+AWCMufOk23IceKZfHzwzrjFM3gwICFgYAuEEBAQsDIFwAgICFoZAOAEBAQtDIJyAgICFIXipAgICFoYg4QQEBCwMgXACAgIWhkA4AQEBC0MgnICAgIUhEE5AQMDCEAgnICBgYQiEExAQsDAEwgkICFgYAuFcgxCRF4iImfH3pyfdxnkhIn0R+VkR+RsR2ReRR0XkTSIybRnNUwUR+dAhz+ofnXQbj4KQYvTaxqeBD0/Z/rSHiPSADwB3A18AHgBuA34Q+C4RudsY85mTa+FVxduB7Y7tn190Q64EgXCubXzYGPMDJ92IK8DrsGTzv4B7jTHbACLyz4FfAt4EvODEWnd18S+MMQ+fdCOuFEGlCjiVEJEE+Gfu54+WZANgjPll4GPAPxCR551E+wK6EQgn4LTi7wKrwKeNMR/t2P8293nf4poUcBiCSnVt4ytF5PXAOeBxrD3nj4wxcyzXeOL4evf5kSn7y+1ft4C2LAKvFZFz2KU0/wZ4lzHmkRNu05ERCOfaxre4Px8fF5H7jTGfOokGHQHPdp8Xp+wvt9+6gLYsAq9r/f5FEfk5Y8zPnUhrLhNBpbo2sQG8AWtwPef+Xgj8KfC1wHtFZPXkmjcXltzn7pT95Wpuywtoy3HifwD/BLtEzAB4LvBvgAz4WRH5iRNs25EREnCdQojIO4E7jnja9xtj/vyQcjXwQeBbgX9tjHn9ZTbx2CEivwH8U+DnjTHttz8i8reATwGfMsZ81aLbd9wQkXuB9wCXgJuNMXsn3KS5EFSq04nbsW+6o2Bw2AHGmFxEfgFLOC8CnraEQx2TMu26hu5zawFtWTiMMe8Vkb8EvhF4PvChk23RfAiEcwphjPmGYyy+tN3cdIx1XA2UBtPzU/aX2z+3gLacFD6FJZyn+7OqEGw4AW2cdZ/tFe2fbvg/7vOuKfvL7R9bQFtOCqflWVUIhBPQxv3uc5q7+emCP8Eav79CRLokvu9zn7+3uCYtDiJyPVb1haf/s6oQCOcahIj8pIhcaG0TEflh4KcAA/z6iTRuThhjRsCvuZ9vFJHSZlNObfg64I+NMX91Eu27GhCRbxGR73bGfH/7bcA7sXaqB40x00IDnnYIXqprECLyMNbG8RHgs0AP6w6/HRtY9hPGmF+bWsDTBG7y5oewRtMvAP8TG3fzfOAx4FRP3hSRHwD+K/BF7LO6hL2+52Gf2SeAbzPGfPmk2nhUBMK5BiEiPwbcC9wJ3ADE1AP2V4wxf3GCzTsSRKQP/Cvg1cAF4Engj4B/e5re/F0QkTuAH8MS6AWszWYH+Gvgd4FfPy3u8BKBcAICAhaGYMMJCAhYGALhBAQELAyBcAICAhaGQDgBAQELQyCcgICAhSEQTkBAwMIQCCcgIGBhCIQTEBCwMATCCQgIWBgC4QQEBCwMgXACAgIWhkA4AacSIvL9bm3tj4tIPOWYu0UkF5HHXf6YgBNGIJyAUwljzH8D3gd8DfAz7f2OhH4T28d/2hjz2GJbGNCFMFs84NRCRJ4D/F9AgK/z19ISkdcBPwe8zxhzzwk1MaCFQDgBpxoi8i+B/wh8yBjzD92252JzHhfA1xpjPn2CTQzwEFSqgNOO/wR8FHiBiLxWRAT4DSAF/l0gm6cXgoQTcOohIs8D/gzYxBLQzwL/G/gmY0x2km0LaCIQTsAzAiLyi8BPu585Np/xX55gkwI6EAgn4BkBEbkZuIg1IL/JGPPaE25SQAeCDSfgmYJ/jyUbgBeJyPJJNiagG4FwAk49ROTvA6/FrjzxLuAW4OdPtFEBnQgqVcCphoikWBf4c7GrbX4Yu4zKKtaOc2qWvLkWECScgNOO12HJ5kFjzNuNMV/CRh4r4DdFJDrR1gU0ECScgFMLEfka7IqU+8BXlwvfuVicP8auvf0zxpg3nFwrA3wEwgk4lRARBfwJcDfw48aYX23tvwMbi5MBdxpjHl54IwMmEFSqgNOKH8GSzZ8Bb2zvNMb8NfAfgAHwnxfbtIBpCBJOwKmDiJwHPgn0gbuMMR+fclwKfAz4KuCVxpi3LK6VAV0IhBMQELAwBJUqICBgYQiEExAQsDAEwgkICFgYAuEEBAQsDIFwAgICFoZAOAEBAQtDIJyAgICFIRBOQEDAwhAIJyAgYGEIhBMQELAwBMIJCAhYGALhBAQELAyBcAICAhaGQDgBAQELQyCcgICAhSEQTkBAwMIQCCcgIGBh+P+3xMnUu5xusQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sim.reset_meep()\n", - "cell = mp.Vector3(16, 40, 0)\n", - "geometry = [\n", - " mp.Block(\n", - " mp.Vector3(12, 1, mp.inf),\n", - " center=mp.Vector3(-2.5, -3.5),\n", - " material=mp.Medium(epsilon=12),\n", - " ),\n", - " mp.Block(\n", - " mp.Vector3(1, 42, mp.inf),\n", - " center=mp.Vector3(3.5, 17),\n", - " material=mp.Medium(epsilon=12),\n", - " ),\n", - "]\n", + "cell = mp.Vector3(16,40,0)\n", + "geometry = [mp.Block(mp.Vector3(12,1,mp.inf),\n", + " center=mp.Vector3(-2.5,-3.5),\n", + " material=mp.Medium(epsilon=12)),\n", + " mp.Block(mp.Vector3(1,42,mp.inf),\n", + " center=mp.Vector3(3.5,17),\n", + " material=mp.Medium(epsilon=12))]\n", "sim.cell_size = cell\n", "sim.geometry = geometry\n", - "sim.geometry_center = mp.Vector3(0, 12, 0)\n", + "sim.geometry_center = mp.Vector3(0,12,0)\n", "\n", "sim.run(until=400)\n", "\n", @@ -203,28 +280,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (-2.5,-3.5,0)\n", + " size (12,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (3.5,17,0)\n", + " size (1,42,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "run 2 finished at t = 200.0 (4000 timesteps)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "vals = []\n", "\n", - "\n", "def get_slice(sim):\n", - " vals.append(\n", - " sim.get_array(\n", - " center=mp.Vector3(0, -3.5), size=mp.Vector3(16, 0), component=mp.Ez\n", - " )\n", - " )\n", - "\n", + " vals.append(sim.get_array(center=mp.Vector3(0,-3.5), size=mp.Vector3(16,0), component=mp.Ez))\n", "\n", "sim.reset_meep()\n", - "sim.run(mp.at_beginning(mp.output_epsilon), mp.at_every(0.6, get_slice), until=200)\n", + "sim.run(mp.at_beginning(mp.output_epsilon),\n", + " mp.at_every(0.6, get_slice),\n", + " until=200)\n", "\n", "plt.figure(dpi=150)\n", - "plt.imshow(vals, interpolation=\"spline36\", cmap=\"RdBu\")\n", - "plt.axis(\"off\")\n", - "plt.show()" + "plt.imshow(vals, interpolation='spline36', cmap='RdBu')\n", + "plt.axis('off')\n", + "plt.show()\n" ] } ], diff --git a/python/examples/binary_grating.ipynb b/python/examples/binary_grating.ipynb index ce4a3a291..670528df6 100644 --- a/python/examples/binary_grating.ipynb +++ b/python/examples/binary_grating.ipynb @@ -26,9 +26,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import math\n", @@ -45,63 +53,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n" + ] + }, + { + "data": { + "image/png": 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OhbQlpfSliLga+E8RcRZwE/By1l7g9x/afF+lN0/ScNUwX3b6XUy1+nesfevty4CfBr4GPC+l9Nm23kENmydpmGqZL4MMxPorp9+4/qd1tWyepOGpab4M8qe55lTT5kkaltrmi4FooLbNkzQcNc4XAzGmGjdP0jDUOl8G+TWIHA5ed5C5pbmqNk9S/9UaB/AMYmxzh42DpHbVHAcwEGM7sO9AdZsnqb9qjwMYiLHN7p+tbvMk9VMf4gAGYmw1bp6k/ulLHMBA9E5KqfQSJE2oT3EAA9ErK6srLCwtlF6GpAn0LQ5gIHpj4861eHzrr9qWVLs+xgEMRC9svnPNTM2UXo6kBlJKvYwD+EK56m39zGP+6HzpJUlqYGFpgUPHDvUuDuAZRNVGnZY2+R3ZkspbPL7YyziAgahWX5+zlHRPM1MzvX38GogKGQdpOPbu3tvbx6+BqIxxkIalz08LG4iKGAdJuaysrnBk+UijYwxEJYyDpFw25svyieVGxxmIChgHSblsni/Tu6YbHWsgCjMOknLZOl/2TO9pdLyBKMg4SMpl1HxpykAUYhwk5dLWfDEQBRgHSbm0OV8MRMeMg6Rc2p4vBqJDxkFSLjnmi4HoiHGQlEuu+WIgOmAcJOWSc74YiMyMg6Rccs8XA5GRcZCUSxfzxUBkYhwk5dLVfDEQGRgHSbl0OV8MRMuMg6Rcup4vBqJFxkFSLiXmi4FoiXGQlEup+WIgWmAcJOVScr4YiB0yDpJyKT1fDMQOlN48ScNVw3wxEBOqYfMkDVMt88VATKCWzZM0PDXNFwPRUE2bJ2lYapsvBqKB2jZP0nDUOF8MxJhq3DxJw1DrfDm99AL64uB1B5lbmqtq8yT1X61xAM8gxjZ32DhIalfNcQADMbYD+w5Ut3mS+qv2OICBGNvs/tnqNk9SP/UhDmAgxlbj5knqn77EAQxE76SUSi9B0oT6FAcwEL2ysrrCwtJC6WVImkDf4gAGojc27lyLxxdLL0VSQ32MAxiIXth855qZmim9HEkNpJR6GQfwhXLV2/qZx/zR+dJLktTAwtICh44d6l0cwDOIqo06LY2I0suS1MDi8cVexgEMRLX6+pylpHuamZrp7ePXQFTIOEjDsXf33t4+fg1EZYyDNCx9flrYQFTEOEjKZWV1hSPLRxodYyAqYRwk5bIxX5ZPLDc6blCBiIhnR8T7I+KbEXFnRNwcEe+LiIeUXtvJGAdJuWyeL9O7phsdO6hAALPAM4A/A14H/AnwS8BXIuLsguu6V8ZBUi5b58ue6T2Njh/aC+V+FfhcSulHGxdExP8APgO8FvjNUgsbxThIymXUfDn/q+c3uo1BBSKl9NlRl0XEHcAFBZZ0r4yDpFzami+DCsQoEXF/4P7AsTGuexZw5paL97W9JuMgKZc258vgAwFcCuwCPjLGdV8DXJ5zMcZBUi5tz5dqAxER92FtsI/jeBrxm3Qi4kLWBv5VKaVPjXE7VwBXb7lsH3DtmOs4KeMgKZcc86XaQAAXAp8e87oXAN/YfEFEnM/adzPNA788zo2klI4CR7fczphLODnjICmXXPOl5kB8A3jlmNe9ffN/RMQ5wBzwA+C5KaWlltfWiHGQlEvO+VJtIFJK3wM+0PS4iHgQa3GYAp6dUrp9m0OyMg6Scsk9X6oNxCQiYhr4BLAXeGZK6Vsl12McJOXSxXwZVCCADwFPAN4PXBARm1/78MOU0jVdLcQ4SMqlq/kytEA8bv3vf7/+Z7NbgE4CYRwk5dLlfBlUIFJK55Zeg3GQlEvX82VoP6yvKOMgKZcS88VAtMQ4SMql1HwxEC0wDpJyKTlfDMQOGQdJuZSeLwZiB0pvnqThqmG+GIgJ1bB5koaplvliICZQy+ZJGp6a5ouBaKimzZM0LLXNFwPRQG2bJ2k4apwvBmJMNW6epGGodb4M6kdt5HTwuoPMLc1VtXmS+q/WOIBnEGObO2wcJLWr5jiAgRjbgX0Hqts8Sf1VexzAQIxtdv9sdZsnqZ/6EAcwEGOrcfMk9U9f4gAGondSSqWXIGlCfYoDGIheWVldYWFpofQyJE2gb3EAA9EbG3euxeOLpZciqaE+xgEMRC9svnPNTM2UXo6kBlJKvYwD+EK56m39zGP+6HzpJUlqYGFpgUPHDvUuDuAZRNVGnZZGROllSWpg8fhiL+MABqJafX3OUtI9zUzN9PbxayAqZByk4di7e29vH78GojLGQRqWPj8tbCAqYhwk5bKyusKR5SONjjEQlTAOknLZmC/LJ5YbHWcgKmAcJOWyeb5M75pudKyBKMw4SMpl63zZM72n0fEGoiDjICmXUfOlKQNRiHGQlEtb88VAFGAcJOXS5nwxEB0zDpJyaXu+GIgOGQdJueSYLwaiI8ZBUi655ouB6IBxkJRLzvliIDIzDpJyyT1fDERGxkFSLl3MFwORiXGQlEtX88VAZGAcJOXS5XwxEC0zDpJy6Xq+GIgWGQdJuZSYLwaiJcZBUi6l5ouBaIFxkJRLyfliIHbIOEjKpfR8MRA7UHrzJA1XDfPFQEyohs2TNEy1zBcDMYFaNk/S8NQ0XwxEQzVtnqRhqW2+GIgGats8ScNR43wxEGOqcfMkDUOt8+X00gvoi4PXHWRuaa6qzZPUf7XGATyDGNvcYeMgqV01xwEMxNgO7DtQ3eZJ6q/a4wAGYmyz+2er2zxJ/dSHOICBGFuNmyepf/oSBzAQvZNSKr0ESRPqUxzAQPTKyuoKC0sLpZchaQJ9iwMYiN7YuHMtHl8svRRJDfUxDnAKBCIi/jAiUkT8Rem1TGrznWtmaqb0ciQ1kFLqZRxg4C+Ui4ifB14B3F14KRPb+pnH/NH50kuS1MDC0gKHjh3qXRxgwGcQERHAu4D/BhwpvJyJjDotXfvfktQXi8cXexkHGHAggJcBjwUuK72QSfT1OUtJ9zQzNdPbx+8gn2KKiN3ALPD2lNL3xv2sOyLOAs7ccvG+lpe3LeMgDcfe3Xt7+/gdZCCANwN3AX/Q8LjXAJe3v5zxGQdpWPr8tHDVgYiI+wC7xrz68ZRSiohHAa8HXpxSOt7wXV4BXL3lsn3AtQ1vZyLGQVIuK6srHFlu9uXYqgMBXAh8eszrXgB8A3gn8PmU0p82fWcppaPA0c2XdVV/4yApl435snxiudFxtQfiG8Arx7zu7RHxLOAi4EURce6mt50O3G/9sjtSSlW92sw4SMpl83yZ3jXNMuNHoupApJS+B3xg3OtHxMPX//mxEW/eC3wbeAPwjh0vriXGQVIuW+fL9V+9npu5eezjqw7EBD4FvHDE5VcCtwBvA27odEUnYRwk5TJqvpz/1fMb3cagApFS+g7wna2XR8Q7gCMppWu6X9VoxkFSLm3NlyG/UK5axkFSLm3Ol0GdQdyblNK5pdewwThIyqXt+eIZRIeMg6RccswXA9ER4yApl1zzxUB0wDhIyiXnfDEQmRkHSbnkni8GIiPjICmXLuaLgcjEOEjKpav5YiAyMA6SculyvhiIlhkHSbl0PV8MRIuMg6RcSswXA9ES4yApl1LzxUC0wDhIyqXkfDEQO2QcJOVSer4YiB0ovXmShquG+WIgJlTD5kkaplrmi4GYQC2bJ2l4apovBqKhmjZP0rDUNl8MRAO1bZ6k4ahxvhiIMdW4eZKGodb5ckr8ytE2HLzuIHNLc1VtnqT+qzUO4BnE2OYOGwdJ7ao5DmAgxnZg34HqNk9Sf9UeBzAQY5vdP1vd5knqpz7EAQzE2GrcPEn905c4gIHonZRS6SVImlCf4gAGoldWVldYWFoovQxJE+hbHMBA9MbGnWvx+GLppUhqqI9xAAPRC5vvXDNTM6WXI6mBlFIv4wC+UG4cuwAuetdF7DpzV5EFHFk+wvKJZaZ3TXPnP93J3T+8mxtvvLHIWrK46aaT/7cGa+hbf/dtd/Pt//ttDn39ENO7prn+q9dz/lfPL7aeW26+ZeOfYw2z8IueJxcRzweuLb0OSWrRC1JKH9/uSgZiGxHxAODpwHeBE/dytX2sReQFwOGOltYnfny258fo5Pz4bG+cj9Eu4BzgMymlH2x3gz7FtI31D+JJSxsRG/88nFIa0HM/7fDjsz0/Rifnx2d7DT5GXxn3Nv0itSRpJAMhSRrJQEiSRjIQ7fg+8Nvrf+sn+fHZnh+jk/Pjs73WP0Z+F5MkaSTPICRJIxkISdJIBkKSNJKBkCSNZCAkSSMZiMwi4g8jIkXEX5ReSy0i4tkR8f6I+GZE3BkRN0fE+yLiIaXX1rWImIqI2Yi4LSLuiogvRcRzSq+rBhHx+Ih4T0TcGBHLEfGdiLgqIh5Vem21iojL1ufNfCu357e55hMRPw98Afgn4H+mlJ5XeElViIi/AR4IXA18C3gk8FrgTuBxKaXvFVxepyLiw8DFwDtY+1i8Ang88MyU0ucKLq24iPgo8Aus3U++BpzN2v3k/sCTUkqtDMGhiIiHAX8PJOAfUkqP3fFtGog8Yu0nZ/0f4BDwbGDeQKyJiAuBz6WUfrTlss8Ab0sp/WaxxXUoIp4AfAl4Y0rp99YvOwOYB46mlJ5Scn2lRcRTgL9JKZ3YdNnPADcAH00pvbTY4ioUEX8CnAmcBjy4jUD4FFM+LwMeC1xWeiG1SSl9dnMcNi4D7gAuKLOqIi4GVoErNy5IKd0N/BHw5Ig4p9TCapBS+vzmOKxf9i3gRk6t+8m21j/Buhi4tM3bNRAZRMRuYBZ4+6n0dMlORMT9WXvq4FjptXToZ4FvppS2/qLxL6///biO11O99TPzPZxa95OTiojTgHcD70sp3dDmbfv7IPJ4M3AX8AelF9Ijl7L2y0w+UnohHXoIcPuIyzcue2iHa+mLlwB7WXuMac2rgUcA+9u+YQNxEhFxH8b83a3A8ZRSWv8Oi9cDL04pHc+3ujpM8jEacRsXApcDV6WUPtXm+ip3P2DUfeTuTW/Xuog4H3gva9/48ceFl1OFiHgQ8DvAW1JKrf8gQ59iOrkLWTsTGOfPo9ePeSfw+ZTSn3a+2jIm+Rj92PqD/s9Y+8LsL3ez5GrcBUyNuPyMTW8XEBFnA38J/AC4OKW0WnhJtXgra1+7e3eOG/cM4uS+AbxyzOveHhHPAi4CXhQR52562+nA/dYvu2PEc8591uhjtPk/1r8IO8fag/65KaWlltdWu9tZe7pkq43Xg9zW4Vqqtf574f8K+CngaSklPy78+Du6XsXa07MP3fQrR88A7rs+bxZTSndM/D78Ntf2RMQrgP+6zdXekFJ6RwfLqdr6qfHnWHs9xFPXvzvllBIRvwu8AXjg5k8aIuI3gLcBD08pfbfU+mqw/m2/c8DPAftTSl8ovKRqRMQzgE9vc7V3ppQm/s4mA9GiiHg48C9HvOlK4BbWHvQ3pJQOd7qwykTENPAp1r5V8ZkppesLL6mIiHgi8EXu+TqIKdaebvvHlNKTSq6vtPXvzvkY8FzgBSmlTxReUlUi4sHAU0e86a3Abta+Fnp4J9/ZZCA6EBH/gC+U+7GIuAZ4AfB+fvIzoB+mlK7pflVlRMRVwAtZ+463m4CXA08Anr3+2pBTVkS8g7Uh9+fAVVvfnlL6YOeL6oGI+F+09EI5A9EBA3FP6x+PR9zLm29JKZ3b3WrKWn8K5S3AS4GfZu1HSvxWSumTRRdWgfVB9/R7e3tKKe7tbacyAyFJys5vc5UkjWQgJEkjGQhJ0kgGQpI0koGQJI1kICRJIxkISdJIBkKSNJKBkCSNZCAkSSMZCEnSSAZCKiQiPhgRd6//mtqtb3tTRKSI8Ac8qhh/WJ9USEScxdpv5Pu7lNKzNl3+z4AbgU+klC4utT7JMwipkJTSUeAg8MyIePmmN10BrLD2uxCkYjyDkAqKtV8k/L+BRwPnA88BPgy8LqWU5RfRS+MyEFJhEfEY4CvANcDTgFuBJ6aUflR0YTrlGQipAhHxduDXgVXgCSmlvy28JMmvQUiVOLb+923AfMmFSBsMhFRYRJwD/DZrYTgH+LWyK5LWGAipvPes//2vgKuByyLikQXXIwEGQioqIl4IPB/4rZTSrcClwAngvUUXJuEXqaViImI38HXg+8DjU0qr65e/Dngn8EsppasLLlGnOAMhFRIR7wReCzwppfTXm3CFIkkAAAB7SURBVC4/DfgycDZwfkppqdASdYrzKSapgIj4OeBXgCs2xwFg/Uzi1awF4q0FlicBnkFIku6FZxCSpJEMhCRpJAMhSRrJQEiSRjIQkqSRDIQkaSQDIUkayUBIkkYyEJKkkQyEJGkkAyFJGslASJJGMhCSpJEMhCRppP8HnDSqQdS4hYAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fused_quartz = mp.Medium(index=1.5)\n", "\n", - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 3.0 # substrate thickness\n", - "dpad = 3.0 # padding between grating and PML\n", - "gp = 10.0 # grating period\n", - "gh = 0.5 # grating height\n", - "gdc = 0.5 # grating duty cycle\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 3.0 # substrate thickness\n", + "dpad = 3.0 # padding between grating and PML\n", + "gp = 10.0 # grating period\n", + "gh = 0.5 # grating height\n", + "gdc = 0.5 # grating duty cycle\n", "\n", - "sx = dpml + dsub + gh + dpad + dpml\n", + "sx = dpml+dsub+gh+dpad+dpml\n", "sy = gp\n", "\n", - "cell_size = mp.Vector3(sx, sy, 0)\n", - "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", - "\n", - "wvl_min = 0.4 # min wavelength\n", - "wvl_max = 0.6 # max wavelength\n", - "fmin = 1 / wvl_max # min frequency\n", - "fmax = 1 / wvl_min # max frequency\n", - "fcen = 0.5 * (fmin + fmax) # center frequency\n", - "df = fmax - fmin # frequency width\n", - "\n", - "src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub)\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy),\n", - " )\n", - "]\n", - "\n", - "k_point = mp.Vector3(0, 0, 0)\n", - "\n", - "symmetries = [mp.Mirror(mp.Y)]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " default_material=fused_quartz,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - ")\n", + "cell_size = mp.Vector3(sx,sy,0)\n", + "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", + "\n", + "wvl_min = 0.4 # min wavelength\n", + "wvl_max = 0.6 # max wavelength\n", + "fmin = 1/wvl_max # min frequency\n", + "fmax = 1/wvl_min # max frequency\n", + "fcen = 0.5*(fmin+fmax) # center frequency\n", + "df = fmax-fmin # frequency width\n", + "\n", + "src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub)\n", + "sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(y=sy))]\n", + "\n", + "k_point = mp.Vector3(0,0,0)\n", + "\n", + "symmetries=[mp.Mirror(mp.Y)]\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " default_material=fused_quartz,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", "\n", "nfreq = 21\n", - "mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad)\n", - "flux_mon = sim.add_flux(\n", - " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", - ")\n", + "mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad)\n", + "flux_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", "\n", "f = plt.figure(dpi=120)\n", "sim.plot2D(ax=f.gca())\n", @@ -117,16 +135,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "field decay(t = 50.01): 0.10609306658233127 / 0.10609306658233127 = 1.0\n", + "field decay(t = 100.01): 8.493199823356459e-20 / 0.10609306658233127 = 8.005424008330923e-19\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n" + ] + } + ], "source": [ "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -142,38 +170,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (-2.25,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sim.reset_meep()\n", "\n", - "geometry = [\n", - " mp.Block(\n", - " material=fused_quartz,\n", - " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", - " ),\n", - " mp.Block(\n", - " material=fused_quartz,\n", - " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh),\n", - " ),\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - ")\n", - "\n", - "mode_mon = sim.add_flux(\n", - " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", - ")\n", + "geometry = [mp.Block(material=fused_quartz, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub))),\n", + " mp.Block(material=fused_quartz, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))]\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + "mode_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", "\n", "f2 = plt.figure(dpi=120)\n", "sim.plot2D(ax=f2.gca())\n", @@ -182,9 +223,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "field decay(t = 50.01): 0.1031398354415896 / 0.1031398354415896 = 1.0\n", + "field decay(t = 100.01): 8.275841626038753e-06 / 0.1031398354415896 = 8.023904237006029e-05\n", + "field decay(t = 150.02): 7.578862246277466e-06 / 0.1031398354415896 = 7.348142658778572e-05\n", + "field decay(t = 200.03): 2.633198313201328e-06 / 0.1031398354415896 = 2.5530371479917354e-05\n", + "field decay(t = 250.04): 1.0595609940381944e-06 / 0.1031398354415896 = 1.0273052981922272e-05\n", + "field decay(t = 300.04): 4.182093600425728e-07 / 0.1031398354415896 = 4.054780175399971e-06\n", + "field decay(t = 350.05): 1.7897453529966721e-07 / 0.1031398354415896 = 1.7352610127153491e-06\n", + "field decay(t = 400.06): 7.323581231104047e-08 / 0.1031398354415896 = 7.100633038387535e-07\n", + "field decay(t = 450.07): 2.934107857572782e-08 / 0.1031398354415896 = 2.844786250637789e-07\n", + "field decay(t = 500.08): 1.1841535133169714e-08 / 0.1031398354415896 = 1.1481049084934639e-07\n", + "field decay(t = 550.08): 4.9984066703627664e-09 / 0.1031398354415896 = 4.8462426267816536e-08\n", + "field decay(t = 600.09): 2.35073055720754e-09 / 0.1031398354415896 = 2.2791684194016495e-08\n", + "field decay(t = 650.1): 1.1816026525464885e-09 / 0.1031398354415896 = 1.1456317023267471e-08\n", + "field decay(t = 700.11): 3.9576427413983696e-10 / 0.1031398354415896 = 3.837162163827255e-09\n", + "field decay(t = 750.12): 1.4213834548284144e-10 / 0.1031398354415896 = 1.3781129752076983e-09\n", + "field decay(t = 800.13): 8.16132061584665e-11 / 0.1031398354415896 = 7.912869533778332e-10\n", + "run 0 finished at t = 800.13 (80013 timesteps)\n" + ] + } + ], "source": [ "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))" ] @@ -198,16 +263,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "freqs = mp.get_eigenmode_freqs(mode_mon)\n", "\n", "nmode = 10\n", - "res = sim.get_eigenmode_coefficients(\n", - " mode_mon, range(1, nmode + 1), eig_parity=mp.ODD_Z + mp.EVEN_Y\n", - ")\n", + "res = sim.get_eigenmode_coefficients(mode_mon, range(1,nmode+1), eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", "coeffs = res.alpha\n", "kdom = res.kdom\n", "\n", @@ -216,13 +279,13 @@ "mode_tran = []\n", "\n", "for nm in range(nmode):\n", - " for nf in range(nfreq):\n", - " mode_wvl.append(1 / freqs[nf])\n", - " mode_angle.append(math.degrees(math.acos(kdom[nm * nfreq + nf].x / freqs[nf])))\n", - " tran = abs(coeffs[nm, nf, 0]) ** 2 / input_flux[nf]\n", - " mode_tran.append(0.5 * tran if nm != 0 else tran)\n", + " for nf in range(nfreq):\n", + " mode_wvl.append(1/freqs[nf])\n", + " mode_angle.append(math.degrees(math.acos(kdom[nm*nfreq+nf].x/freqs[nf])))\n", + " tran = abs(coeffs[nm,nf,0])**2/input_flux[nf]\n", + " mode_tran.append(0.5*tran if nm != 0 else tran)\n", "\n", - "tran_max = round(max(mode_tran), 1)" + "tran_max = round(max(mode_tran),1)" ] }, { @@ -236,29 +299,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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to36Nioj1KH047055LTZj8Xf1ZrX5ewy42p6vPaVf+dWUPsi3iojNMrN9LIpH1uYPzh79nvt4KKU/McAXc3G/925OpLTUbULpt79U92fhc3QxZYyArjLzxoj4OHBg9dCDlrHtYdW39ePM/Hmvwpl5ZkScQBksc5p8JzMvGXahiNiGMm7FHSlnm2xCaYFu2a02fw9u/l12P0qfdiit5ocOW4cGPLg2f3hmXt2rcGaeFBGnUfpeQ3kP9Tp48dvM/L8+dTilNr+6T9l+6u/pn2TmKV1LApl5ZUQczsKYIA8CPtxnG4P8v70jC//nrgc+3ace10XEp1k8VkYnj67N91xnzVFA6yoRDwR6vR6DflbOAXYGtoyIp2TmZwasi6QZY9CXxu/HwxSOiGdRWjL6Xnqnsh7lVNV+PwDaf+h28qfafKfB9D7Lwo/Pt0fEwyiDmv1vZp47wPo7GSSE1/ft9K6lFvy5Nj/ooIArKsplu15HOYCy1YCLDVLuL5l5Tq8C1UGeSyhBHxZOGa6rX2ru6AHr10k9iN4tIt435PJbRsQmmXnlErZdbzE7qVtLZpvj68tHRFQtneNWr+sPB1zmh0xf0B/2+/FOlAOMj2Kh9bSfTp+T+9XmT+gXoldI/T0w6MCbx7MQ9O/VqyCj+Z8wjKXuTyvo99uf6xhsn+r1OCMzLx1gmZ6fwerShq3/2ddSLkk5wGoXnW2yU9dSxaCflc8Cr63mD68GH/0McPQAB8klzRCDvjR+A53iXp1O+DHg2UvYxmb0D/q9TlFuqV8FYL0Oz3+U0rL7uOr+Q6qJiDgbOI4SAr+SmRd3WH6p9aoHsmHLd9qPiRIRW1JadgZtoW/ZrH+Rgf5e0OO1j4jNWTgIAGXE86XaoTb/QJbWQr8lpYV/WFvX5gc5FRfKOBct61P+5kOPnr4E9bqePeAyg5abJAN3AYqIR1AuBdd+dlM/nT4n29bml/N+Hqflvl/7HQgcxf+EYYx7fy4Z8ODdOD5b9as2rM/ibiGD2rLP84N+Vv6dMhbH/ShnuTy+mqiuXHMcZdyTIzqcuSVphthHXxqzIVqKns/ikP8t4FmU1pktKSMGR2ti8Q+lQT7Ly26FrPoIP4FyXeH2U4n/itJ/+qPA+RHx0Yi45RjqNY39kPt5Pwsh/1rK3/CxlBaizSiDZbVe99vWlmvkdefmQWk5/T5vsZyKVJZ6kHrT2vygBwrayw1ycGUU6nW9asBllnLwY6UN9P1Yje3wGRZC/lmUU6kfSDl4tDFl1PHW5+Rfa4t3+pzUX8dJ7ce83Pdrv/dq09+l496fQf/XjuOz1cT32kD7V53ttDfwKhYfKIHS9e45lDPxLoyId1Znk0maQbboS5PjlbX5N2bmm/uUbypwLFKdtvwx4GMRsSvlB8UDKH3od66KrQc8F9inuvb5KAfumykRcWvgqdXd1pURep0avxKve3urz6YsPRzVfzC/PDPfs8T1LEW9zpsMuEx7uaZawOp1HfRa14Pu0zR6Pgth6lTKlUx6nVnR73NSfx037VpqZV3Bwj4v5f06aa21y/38jWp/xvHZqn+vXZaZowj+S1YNcvjuiDiQcsnJvShjlOwJ3LoqtjHlYMBeEfGgCe2+ImkZbNGXJkBE7MTCIHeXUq6V3Kv85vQ/zW/sMvPMzDw4M9dk5i7A7YH/oFyKDMo1y9+4YhWcDg9mYRCxb/YJ+VAuN9ioKlDVfwTetlvZAfyhNr9d11LjUT/g9FcDLrO6Nn8tzYWnpdS1Xx/fafaQ2vy/9wn50P9zUn8fLuf9PE7Lfb8O2n2qKZOyP+P4bNXfT5tHxKAHEMYqi1Mz878y82mZuSNlrIP61RTuy9K6GkiacAZ9qb8mTm+s91s+IzOv61qyeCCLR5ieCFXwfwWLw/3frFR9RmApr/2wy9Rf+0EGktqrf5GxOLE2/+CupYZbzwOWsZ6lqI/yvXtEDDKYW/0Sd6c0NBAfLK7r/bqWWmzaBuIbxsCfk+p17ffeOqE2v8eEnr5cfw/cv2upxerl+o2o37RJ2Z96Pe4QEYO0vvf8bGXmBSxc1hUG37/GZeYpmfkcShexlmn+Py2pC4O+1F/98l/jGtitfmm8QVoCXjSmeozKV2vz23YtNfmW8toPu8zAr33VSvTMAesxat+szT+/ug75UnybhcES7x8Rd19etYbyA6B1WcCtgX17FY6IVSweN+OoMdWrk/qZHbtFxB16FY6I2zHbQX+Y78jH0f9skRNYGMB0M5b/ubrpc19dInMU6u+3p0bEhr0KR8RulNO0W5ZzdYxxqO/PPatLyHZVfd89tfbQqD5/ZwAXVvPrAk/rU4++ZSpfq82/eGlVa9Ss/J+W1IVBX+qvfnmhW3cttTy/Y6El+C4RsXO3gtWlcvYbUz16iohBL/tWP81xmi/ns5TXfthl6iN+P7pPK/OBrNwPsoNZ6Nt6G+C9S1lJZp4HfKq6G8BhVVeUviJiVTUo25JUl9GqX1P6XRHRqy/3P7BwqbIbgY8sddvDysxfsPgSZAdVBx5upnr8P5nAs3xGqP456dr6WL0/+o77kJnXAB+oPfSOiLj90qs3lv8Tn2bhM7c9PbpBRcT6wH/VHjo6M385onqMRGaeARxbe+h9fQ6K/DuwTTV/GYNfm75fPW4EPl576I19vldeyWDdOw5kodva4yNizaB1ioiRdGOKiA0iYtAxJ2bl/7SkLgz6Un/167w/eRwbqC5F1zqVdBXw+fYfnVXI+Xvgk5QfE2tp3tkR8eGI2LtH6NiNxT84v9mp3JSov/b3jYhB+nMOu8xRLIz8fDvg0IjYol4gIjaPiI8AL2SFRlbPzEuA19QeemFEfCYiduxUPiLuHBEHRcTDOzz9euCCav5uwEldyrXWtWNEvAz4JfCUpe3BTd7MQnjaFfh2+4G16rN2AGW8iZb3Z+bvl7ntYb2ehQOADwc+3em9QflOeBRlDIFZdURt/p8i4u/aC0TEvYDvUQLMIJ+TdwK/qeZvAXw/Ip4aHS6AHhEbR8TTIuLj7c9VRv5/ohqH4N9qD702Iv6tCvX1um1Luexgq4vH9ZQrEkyif2IhDO8JfCEitqkXiIj1I+JtwMtqD/9rZo7y6gjvYaHP/3bA/7afNVN9D7wCeAsDfLYy8zeUgxMtH4+Id3c7QB4R60bEwyPikyzuTrAc2wPnVNvdrVuhiHgYi69MMc3/pyV14aj7Un9fAF5Qzb84Iu5N6StYvyzPB6t/8svxL8B3KEH/nsBpEXE8pSVrU8qPota1el8P7E/zA7NtVG13f+DyiPgJ5TJXV1KucXwH4M618hcBb2q4jiOTmRdGxA8o/S03BE6NiG9RQmrrVOLfZOYHl7pMZl4SEe8G3lA99wzgURFxInAe5TXfhzLq8/WUU0IPHdMu95SZH4iIu7DQdeRvgSdGxMnAmZSDT1tT3r+rqzI3O304M8+PiMcC36C8b25PCdznASdR3jfrVc/dhREOlpaZv4mI51EuL7UO5XT3X0bEcZTQ1/qs1VtlTwBePao6DFHXY6r3xquqh54C7BcRR1FOPd6WMl7CppTT0A9iij9vfRwKvIJycGYD4JMR8TrKCPxrKe+TVrA5ldJFpOdrlpmXRcQTgP+ltBxvBRwOvLf6DF9E+QzvQhnAbKNq3Z18AXhENf+OiHgUcDoLXUUA3lIdMBvGuyljsjymuv/PwIsi4mjKa74T8CAWLjsI8KrMPJEJlJk/iIjXAu+qHnoM5QDy0ZQ+7ltS9udWtcW+xABnaQxZj4sj4rnAFynfA3cHTo+I71O+yzaljIfSGhviVZTPVz//SvnuexblDJtXAC+JiB9Rvl+uAjavytyNhdH8/9S+omXYotruKyLiz5SDCOdRPifbVNutH9w8k8H2TdK0yUwnJ6c+E+WUwewx7dNW/qbnhtzOC4HremznBsoPiaBcH7f1+Oou6+tbpq38IbXyazo8f3mfv0N9+glwhy7bWVMrd8gA9XpTrfyblrsfg5apyu1GOW20234es9xlKD80D+3z97yE0vd4de2x33epc98yy3mvAAcAfxngPXAj8PAe67kNcOQQ76kLgUeM6DO9X7W+ftv8NLBxn3UN9X5eQl3fRvnsd6vjeZQDFn3rQTlo1PW9u4zP3DG18vss9/PZZbldKWGp1+v1fcpBmoHrX70Pvzfge/D7Xdax3gDrWN22zE3P9anfupSzpK7vs/5L+/09h32vsoTvkgFfy+fS/zvkekoXoXXGVT/gSdXfrVsd1lIObA+1HeAlwJ8HfE/dCHxlFJ+V6r2/dsDtJuVA7Hajel2dnJwma7JFXxrMMygD7TwNuAel5afnwEhLkZkfqlrxX0Zp1diBclmz8yineH88M08B6HB2aRNuRWnl2Bu4D+WSgNtS/hZXAecCP6a0bn01S1/IqZaZP6oGjXoJ5TXZmdLa07Uv/bDLZOYNwLMi4nOUH5X3pbRsXQKcTTkt9+NZWsJXj2bPli4zD4qIT1FCwyOAO1E+E1BOh/0FJfR8JjN/1WM9ZwEPjYg9KKc770VpodyS8iP/T8CvgB9RznY5JjOv77K6Yffha9UAds+hhP47V/twNXA+5QfwYTkBLaOZ+U8R8XnK2RwPppzlcQXl4MwXgY9kaaFcTh/ziZeZZ0bEPSmXAnsC5UyQ9SkHbE6jHJT5bGbeMMz3Y/U+3DsiHkJ5H7bOntqccrbSWZTvta+zeACz+jqui4iHUgLsEylnGNyyqt+yVO/5l0TEhyjv14dQPiebUcLkmZSzYw7OzFG2DI9NZn4sIr4CPJ/S7WRXyt/rckrL/pGU77yfj7ken6/O3ngJ5eyC21AC8LlVHT6Ymb8Y9ns3M/8rIg4B/h/wMMoZA1tT/ldeXq3/dMoBsm9k5jmd1zSczDwvIm5F+Z7YE7g3pUvY1pT34uWU9/PJlO/nI0exXUmTKTJzpesgSZIkSZJGxMH4JEmSJEmaIQZ9SZIkSZJmiEFfkiRJkqQZYtCXJEmSJGmGGPQlSZIkSZohBn1JkiRJkmaIQV+SJEmSNBci4jYRcWBEnBERV0bEnyPi5Ih4VURsPKZtbhwRv42IrKbfj2M7i7aZmePehiRJkiRJKyoiHgN8Cti8S5EzgX0z89cj3u67gVfUHjorM1ePchs326ZBX5IkSZI0yyLinsDxwEbAFcDbgKOr+08Fnl8VPRPYLTMvH+F2Twauq6bNaCDoe+q+JEmSJGnWHUQJ9dcDD8/Mt2bmDzPzqMzcH3h1VW5XFre+L1lErAMcDKwDvBX48yjWOwiDviRJkiRpZkXE7sCe1d2PZeYPOxQ7EPhFNX9ARKw3gk0fANwb+CXwjhGsb2AGfUmSJEnSLHtcbf4TnQpk5o3AYdXdLYAHLWeDEXEb4M3V3Rdm5rXLWd+wDPqSJEmSpFn2wOr2SuDHPcp9rzb/gGVu8wPAJsAnM/OYZa5raOs2vUGNVkRsANy1unsRcMMKVkeSJEmad+sAW1fzp2XmNStZmWFExLrAditYhe0YINNk5rlDrveO1e2vM/P6HuXO6LDM0CLiqcCjgUsYUX//YRn0p99dKaM4SpIkSZos9wF+tNKVGMJ2wDkrXYkBxMAFIzYEtqru9jxAkJmXRMSVlJb4nZZUsYgtgfdWd1+bmRctZT3L5an7kiRJkqRZtVlt/ooByl9Z3W66xO29C9gW+CFlxP0VYYv+9LvpCNFJJ53E9ttvv5J1kSRJkubO2trJ4BdccAF73X/31t0Vac0dhfV3fRKx7iaNbCuvv5Jrz/x86+59gAtHuPoNa/ODDIjX6mqx0bAbioi9gOdQLuH3wszMYdcxKgb96XdT/5Xtt9+eHXfccSXrIkmSJM28tb16eS82teNnxbqbEOsvtVF7WS5cQh/8XtbW5tcfoPwG1e3Vw2ykGjvtI5RuBQdl5k+HWX7UDPqSJEmS1McQ4X42xKoyNbWt8bm8Nj/IkYvWaQyDnOZf93rg9pQxDt445LIjZ9CXJEmSpA7mLtzPoMxcGxF/Am4F9Dz9uRpIrxX0hx2U8DXV7ZHAYyI6jhfYWvcm1cj8AH/MzKOG3FZfBn1JkiRJwmA/w34O7AncLiLW7XGJvTvU5n8x5DZa3QKeXU29bAUcXs1/Dxh50HfUfUmSJElza0Y5rVgAACAASURBVO31C5NqAohoaBr73ny/ut0EuHePcnvX5o8fX3XGz6AvSZIkaa4Y7ufOl2vzHVvbI2IV8Mzq7qXA0cNsIDOj3wScVRU/q/b4PkPuy0AM+pIkSZJmWj3YG+4H1BqMr6lpjDLzJOC46u5zI2KPDsVeAdyxmj8oM6+rPxkR+0REVtMh46vtaNhHX5IkSdLMMdCrzQGU0/E3Ar4TEW+ltNpvBDwV2L8qdyZw4IrUcIQM+pIkSZJmguF+hFr955va1phl5ikR8RTgU8DmwFs7FDsT2DczL+/w3FTx1H1JkiRJU8tT8jWozDwCuBvwHkqov4rSH/9HlMvj3TMzf71yNRwdW/QlSZIkTQ0DvZYjM88CXl5Nwyx3DMu8PkBmrl7O8sMw6EuSJEmaaIb7FdDAIHmLtqWRMuhLkiRJmjiGe2npDPqSJEmSVpzBfsLM2GB888agL0mSJGlFGO6l8TDoS5IkSWqM4X5aNNhH34vBjZxBX5IkSdLYGOyl5hn0JUmSJI2U4V5aWQZ9SZIkSctmuJ8xQYOD8TWzmXli0JckSZI0NIO9NLkM+pIkSZIGYrifI9HgYHyNDfo3Pwz6kiRJkroy3EvTx6AvSZIk6SYGe2n6GfQlSZKkOWe4181ENDgYn6PxjZpBX5IkSZpDhntpdhn0JUmSpDlgsNdQHIxvqhn0JUmSpBlluJfmk0FfkiRJmiGGe42EffSnmkFfkiRJmmIGe0ntDPqSJEnSlDHcS+rFoC9JkiRNAcO9GuVgfFPNoC9JkiRNIIO9pKUy6EuSJEkTwnCviRHRYIu+g/GNmkFfkiRJWkGGe0mjZtCXJEmSGmSw11SIgFVeXm9aGfQlSZKkMTPcS2qSQV+SJEkaA8O9pJVi0JckSZJGwGCvmeLl9abaXAb9iNgceDRwH2A34NbA1sBGwKXAz4FvAB/LzD8NsL77Ay8G9gS2rdZxKnBIZh4+jn2QJEnSyjPcS5pEcxn0gd2BbgF8a2DvanpVRPxdZn6724oi4k3AvwD1w1DbAg8HHh4RzwCelJlrR1FxSZIkrRyDveZGRHOD5DkY38jNa9AHOAc4GvhxNX8BJazvCDwJeAKwFfDViNg9M09tX0FEvAB4Y3X3N8BbgdOAHYADgAcB+wIfB54+zp2RJEnSeBjuJU2beQ36R2fmX/V4/rMR8TjgS8D6lDD/hHqBiLgl8I7q7tnA/TLz4trzX6uWfwzwtIj4SGYeM7pdkCRJ0rgY7jX37KM/1ebyL5qZNwxQ5svAL6u7e3Yo8jzgFtX8a+ohv7aNFwOtbb1qabWVJEnSuK29fvEkSdNsLoP+EC6vbjfs8NzjqtvLgC92WjgzzwWOrO4+JCI2G231JEmStFQGe0mzyqDfRUTcHrhHdfeMtufWpwzoB/DDzLy2x6q+V91uQBnhX5IkSSvEcC8NqDUYX1OTRsqgXxMRG0fEX0fEyykBvTWGwXvbiu4KrFPNn0Fv9efvuPxaSpIkaVCeki9pHs3rYHw3iYg1wCd6FHk78Om2x3aszZ/bZxPn1OZ3GrxmRUTs2KfIdsOuU5IkaZYZ6KURiGhwMD5b9Edt7oN+Dz8B9s/Mkzs8V+9rf0Wf9VxZm990CfU4p38RSZKk+WWwl6TFDPrwZeBH1fxGwC7A3wKPBw6PiJdm5tfalqkPzterfz7ANbX5jZZTUUmSJBWGe2nMmuw7b4v+yM190M/MS4FLaw+dDPxPRPw/4FDgKxHx3Mw8pFZmbW1+/T6b2KA2f/USqtjvdP/tKHWWJEmaaYZ7SRrM3Af9bjLzkxGxH6V1/30R8dXM/HP19OW1ov1Ox9+kNt/vNP9O9eg5BkB49EuSJM0og70kLY2j7vf2lep2E+CRtcfr4bvfYHn1Fnn720uSJPXgCPnShIhVzU4aKVv0e7uoNn+b2vyZwA2US+zdoc866s//YkT1kiRJmhmGekkaLYN+b7euzd902n1mXhsRJwF7AHtExPqZ2W1Qvr2r22tYGPRPkiRpbhnspSngYHxTzXMkentybf60tue+XN1uDjyh08IRsSPw0OrudzPz8k7lJEmSZp2n5EtSc+Yy6EfEmojYsE+ZlwGPru7+DjiurchHgb9U82+PiFu1Lb8O8AHK6f0A71pWpSVJkqZIPdgb7iWpWfN66v6bgAMj4gvA94HfUE7N3wy4K/AM4AFV2WuB/TPzhvoKMvPPEfEa4EOU/vsnRsRbKC3/OwAvBR5UFT88M48Z5w5JkiStNAO9NEuaHCRvLtufx2pegz7ALYHnV1M35wLPycwjOz2ZmR+OiB2AfwF2AT7eodg3gOcss66SJEkTyXAvSZNnXoP+I4B9Ka32twO2BW4FXA38EfgJ8DXgs5l5Va8VZeYbI+LbwN8De1bruhQ4FfhEZh4+rp2QJElqmsFemhMOxjfV5jLoZ+YvgV8C/zGi9f0A+MEo1iVJkjRpDPeSNF3mMuhLkiSpO4O9pNKi31DfeVv0R86gL0mSJMO9JM0Qg74kSdKcMtxL0mwy6EuSJM0Jg72kgUWDl9dr7DJ+88OgL0mSNMMM95I0fwz6kiRJM8RgL2kkvLzeVDPoS5IkTTnDvSSpzqAvSZI0ZQz2ksbOPvpTzaAvSZI0BQz3kqRBGfQlSZImlOFekrQUBn1JkqQJYbCXNDEcjG+qGfQlSZJWkOFekjRqBn1JkqQGGewlTYWIBgfjs0V/1Az6kiRJY2a4l+bHjTfmSldBMuhLkiSNg+Femg83zGqwt4/+VDPoS5IkjYDBXpoPMxvsNVMM+pIkSUtkuJdmn8Fe08igL0mSNCCDvTT7DPZFEERjp9R76v6oGfQlSZJ6MNxLs81gr1lk0JckSaox2Euzz3A/gGiuRT8djG/kDPqSJGnuGe6l2Waw17wx6EuSpLlkuJdml8Fe886gL0mS5oLBXppdBvsxCJobI88z90fOoC9JkmaW4V6aTQZ7qTeDviRJmhkGe2k2GeybFw0OxtfcZfzmh0FfkiRNNcO9NHsM9tLyGPQlSdJUMdhLs8lwP1ls0Z9uBn1JkjTxDPfS7DHYS+Nj0JckSRPHYC/NHoO91ByDviRJmgiGe2m2GOynm6fuTzeDviRJWhEGe2m2GOylyWHQlyRJjTHcS7PDYD/bbNGfbgZ9SZI0VoZ7aTYY7KXpYdCXJEkjZbCXZoPBfs5FNTW1LY2UQV+SJC2b4V6afgZ7aXYY9CVJ0tAM9tJsMNxLs8mgL0mSBmK4l6afwV6DcjC+6WbQlyRJHRnspelnsJfmk0FfkiTdxHAvTTeDvUYlormWdhv0R8+gL0nSHDPYS9PNYC+pE4O+JElzxnAvTS+DvZoSNNhH3+vrjZxBX5KkGWewl6aXwV7SUhj0JUmaQYZ7aToZ7CWNgkFfkqQZYLCXppPBXpPKy+tNN4O+JElTynAvTR+DvaQmGPQlSZoSBntp+hjsNbWimpralkbKoC9J0gQz3EvTx3AvaaUZ9CVJmiAGe2n6GOw1kxrso4999EfOoC9J0goz3EvTxWAvadIZ9CVJapjBXpouBntJ08agL0lSAwz30vQw2EteXm/aGfQlSRoDg700PQz2kmaNQV+SpBEx3EvTwWAv9WeL/nQz6EuStAyGe2nyGewlzRuDviRJQzDYS5PPYC9p3hn0JUnqw3AvTTaDvTQGUU1NbUsjZdCXJKmNwV6abAZ7SerNoC9JEoZ7aZIZ7KXmORjfdDPoS5LmksFemlwGe0lansaCfkTsAuwN3BvYGdgO2AS4DrgUOBs4HTgRODYzr2mqbpKk+WC4lyaTwV6aPLboT7exBv2IWA08G3gqcLv2p2vzCdyvdn9tRHwb+BTwlcy8YYzVlCTNKIO9NJkM9pI0XmMJ+hGxG/DPwL7AKhaH+uuAP1fTJcBGwC2BLYHNqzIbAY+tpvMj4j+B92fmVeOoryRpNhjspclluJek5ow06EfE7YB3A49hIdz/CTgCOAE4CTitWwt9RGwD3AfYHXgIsAdwa+DtwCsj4g3ARzLT/xSSJMBwL00qg7003YIGT933+nojN+oW/Z8B61Na7b8AfBr4VmYO9DMsM/8IfL2a3hgRtwGeDjwL2BX4AKXl/+0jrrckaUoY7KXJZLCXpMkx6qAfwIeBt2Xm2ctdWWaeBbwtIt5O6ef/OsqBBEnSHDHcS5PHYC/NNgfjm26jDvo7Z+Z5I14n1an6hwOHR8T2o16/JGmyGOylyWOwl6TpMdKgP46Q32EbF4x7G5Kk5hnupclisJfmXEBjXedt0B+5sV5eT5Kkbgz20mQx2EvS7JiooB8RGwBbABdl5o0rXR9J0mgZ7qXJYbCXpNm1qomNRMSmEfHoatq0w/NbRcQXgMuA84FLIuLAKvhLkqbU2usXT5JWzg035qJJknqJWBiQb/zTSu/t7GmqRf+JwCeAc4HV9SciYhXwTeBeLPTO2Ax4aVX2iQ3VUZI0AgZ6aTIY5iVpfjXSog88orr9UodT8p8C3Lua/z/gPdVtAI+LiEeOo0IRsVtEvCEivhMR50bENRFxRUScGRGfiIgHDrCONRGRA05rxrEfkrTSbLWXJoMt9pJGqbnW/OYu4zdPmmrRvwuQwA86PPfM6vbHwP0z8/qIWA84DrgP8CzgW6OsTEQcC+zZ4an1gb+upjURcRjw/My8dpTbl6RpZ6CXVp5hXpLUTVNBf5vq9nf1B6tAvxflIMD7M/N6gMy8LiI+BOxeTaO2Q3V7PvA5ykGFs4F1gD2AVwC3phyEWA94+gDrfES1vm7OXWplJWmlGeyllWewl9SoJlvabdEfuaaC/i2r2/aW8fsAG1GCfnur/ZnV7XZjqM8ZwOuAL2TmDW3PnRARnwSOB3YFnhYRH8rMY/us88zM/P3oqypJK8NwL60sg70kaama6qN/VXW7Tdvje1W3v87MP7Q9d/W4KpOZ+2XmZzuE/NbzF1Na9VueNK66SNKksK+9tLLsYy9JGpWmgv5vqtt92h5/PKU1v1Nr+dbV7R/HVKd+jq7N77JCdZCksTLYSyvHYC9pokXDk0aqqaD/v5SX78UR8aiI2DQiXkI5dR/giA7L3K267dXvfZw2qM13bPmXpGljq720cgz2kqSmNNVH/yDghcBmwNfanvsFnYP+vpTW/lPGW7Wu9q7N/2KA8p+IiNsDWwGXAb8GjgQ+mJnnLbUSEbFjnyLjGMNA0gwx0EsrwzAvaZo1edk7L683eo0E/cy8ICIeA/wPsH3tqd8CT8rMRf8JI2IXFi5/d2QTdWzb/irgtbWHPjvAYvvU5m9VTfcFXhERL83MDy+xOucscTlJc8pgL60Mg70kaVI01aJPZh4XEbcFHkBphb4A+H7rknpttgf+rZr/TkNVrHsZC5f1+2Jm/rhH2d8CXwR+yEIo3xl4ImUQvw2BD0VEZuZHxlRfSXPOcC81z2AvSdMnIm4D/CPlDPKdgGsoY8p9lnLJ96t6LN5v3XcEHkLpon5XymD0W1G6gv8BOBn4NPDV9sbuUYsxr3/qRMTelLMI1qUMBHjXzOw4IGBE3AK4rNuLFBH7UQ4CrEe58sAumXnhkPUZ5NT9kwHOOeccdtyxX3FJs8BgLzXPYC9pEOedey633+WvWnd3ysxzV7I+w6iyxzkAOzz746y76VaNbPf6Ky7m/E88p3V3bH+z6izzTwGbdylyJrBvZv56iev/FPCMAYp+D3hiZv5pKdsZRGMt+tMgIu4MfInyd1kLPLlbyAfIzL/0Wl9mfi0i3kw5O2Fj4LnAW4apU783uf1ZpPlgsJeaZ7CXpNkREfcEPgNsBFwBvI1ypbWNgKcCzwd2Bb4eEbtl5uVL2Mz1wInA8cBpwIXARcCWwB2AFwB3oYwHd0REPDAzb1zOfnWzIkG/6oO/B6U1emPgA9W161dM1a3gO5QX4QbgqZnZ6bJ/w/oI8GbKVQf2ZsigL2l+Ge6lZhnsJWlB0OBgfM1cX+8gSqi/Hnh4Zv6w9txREfEr4J2UsP8K4E1L2MbzunRNBzgyIj5I6SLwBEoe3g/46hK201dTl9cDICLuFRHHUk6JOBR4B/BGSt+Ferm/j4g/RsSvImK9Buq1A+V0/R0oI/0/JzO/Mop1V2cEtE7JuPUo1ilpNnnpO6lZXu5O0jisWuUZt5MmInZnYbD3j7WF/JYDWbja2gFLyaE9Qn7r+RuAd9Ue2rNb2eVqLOhX/dWPpwzGF7Wpk8MoR1t2phzlGGe9tgL+t9oWwEsy87ARb8ZfD5I6MthLzTHYSxq1dVbFzaZZ0bq8XlPTmD2uNv+JTgWqU+hbOXAL4EFjqku9S8CGY9pGM0E/IrYHDgc2AH4OPArYrFv5qj9E6xSGR42xXrcAvg3cqXrotZn5/hFvY2vKSIsA549y3ZKmj632UnMM9pJGbVZD/Rx4YHV7JdDrimrfq80/YEx1eWpt/owxbaOxPvovAzYBzgL2zMxLoe9AcscATwPuPY4KRcTGwNeBe1UPvSUz3zGGTe3PwpkL3+tVUNJsMtBLzTDMSxolg/yK2a5fC/8SRuW/Y3X76z6n19eD9x27lhpSdRb5XwPPA55dPXwx8N+j2ka7poL+Iymnrx/YCvkDaP2RbzvqykTE+pTR9VtHaQ7KzH8ech2rgS0z85QeZfYD3lDdvZoup4lImi0Ge6kZBntJo2Swb9Oro/U4trXg5KGX6FUwYkMWzrDueYAgMy+JiCspjdQ7DbqNLts9hjIYeycXA48fIhsPramgf5vq9qQhlrmsut10xHWB0o3g4dX8UcDHIuIuPcpfm5lntj22Gjg6In4IHAGcCrQuxbcz8KRqar0JX5mZ542g7pImkOFeGj+DvaRRMdTPlXqX8SsGKN8K+uPIoQD/CfzbuK8611TQb21nmDEBblHdDvJiDOsJtfkHAz/tU/4sSrDvZI9q6uYq4GWZ+ZGBaydp4hnspfEz2EsaFYP98BoaJO+mbdXch3L9+VGpD3h37QDlr6luN1rmdp9NOWAQlMH9dgNeBPwDsHNEPC8z/7DMbXTVVNC/kBKUdwZOGHCZ3avbs8dRoRH4MfB3lJC/G7A95ZSQdYFLgNOB7wIfrS6xJ2nKGe6l8TLYSxoFQ/3Uu3AJffB7WVubX3+A8htUt1cvZ6OZ+bu2h46LiA8Cn6NcWe7kiLj/iPf1Jk0F/eMofe2fDHy6X+GqD/0LKP36jxl1ZTJz2Z/+6soA/80YB1CQtLIM9tJ4GewljYLBfjxWsEV/1OqXsxvkdPxNqtuRn1memWsj4tmUM8Z3At4JPH3U24GGLq8HHFLd/k1EPKxXwSrkHwbsQgn6B4+3apJUeOk7aby83J2k5Zrl69ZrPDJzLfCn6u6OvcpGxJYsBP1zxlSfi4Hjq7uPjYj1xrGdRoJ+Zh4DfIbSP+GIiHhHROxeK7I6Iu4fEa+inPL+ZErI/1Bmnt5EHSXNJ4O9ND4Ge0nLYajXCP28ur1dRPQ6q/0OtflfjLE+F1W3G7NwRYCRaurUfYA1lBEPHw28sppa//WPqJVrfYK/CBzQVOUkzQcDvTQ+hnlJy2GQnywRZWpqW2P2fWBPSmv9vYETu5SrXw7v+C5lRuHWtflxDD7f2Kn7ZOY1mbkfpe/9b1m4MmP7dC7w4sx8Umbe0FT9JM0uW+2l8bDFXtJS2Vqvhn25Nv/sTgUiYhXwzOrupcDR46hIROzIwlXbzqrGfhu5Jlv0AcjMg4GDI+JOlNHqtwHWofSbOAX4v8z014KkJTPQS+NhmJe0VAb56VNa9JsajG+868/MkyLiOEqr/nMj4tDM/GFbsVcAd6zmD8rM6xbXMfZhIfwfmplr2p7fFdgxM4/qVo+IuAVlcPrW6P+HLWF3BtJ40G/JzJ+z0FdCkpbFcC+NnsFe0lIY6jWhDqCcjr8R8J2IeCsluG8EPBXYvyp3JnDgEta/A/DdiDiVcgbBjymXmb8e2A54APDcah7gZ8Dbl7QnA1ixoC9Jy2Gwl0bPYC9pWIb6GdZgH30a2E5mnhIRTwE+BWwOvLVDsTOBfZd5Ov3dq6mXrwPPzsyrlrGdnlYk6EfELpR+CdtRRhr8QHWZAUnqyGAvjZ7BXtKwDPaaZpl5RETcjdK6vy/lcnvXAr8GPge8bxnh+3jgEcBDKV3UdwS2peTdy4DfAScAh2fmOAf6AxoO+hFxL+C9lNMW6j4PXFwr9/fAG4G/AHdq7x8haT4Y7qXRMthLGoahXrMoM88CXl5Nwyx3DD3OPagy63eqacU1Nup+ROxHOcrxABaPst/JYZS+EjsD+zVSQUkrrj46viFfWj5HxZc0DEfBV11ENDpptBoJ+hGxPXA4sAFlAL5HAZt1K1/1ifhqdfdRY6+gpBVjsJdGx2AvaVBe3k6abU2duv8yYBPgLGDPzLwU+l6u4RjgacC9x105Sc0x0EujY5iXNCiDvIYVNDcYn+/O0Wsq6D8SSODAVsgfwBnV7W3HUyVJTTHcS6NhsJc0CEO9pKaC/m2q25OGWOay6nbTEddF0pgZ7KXRMNhL6sdQr3FZtSpY1dD7q6ntzJOmgn5rO8OMCXCL6vaKEddF0ogZ7KXRMNhL6sdgL2kQTY26f2F1u/MQy+xe3Z494rpIGgEH0ZOWz8HzJPXigHmSlqqpoH8cZYyFJw9SOCLWB15A6dd/zPiqJWlQXvpOWj6DvaReDPWaJBHNThqtpoL+IdXt30TEw3oVrEL+YcAulKB/8HirJqkbg720PAZ7Sd3YWi9pnBrpo5+Zx0TEZ4CnAEdExEHAF2pFVkfEFsADgP0pp/gn8KHMPL2JOkoy0EvLZZiX1IkhXtMoIvpdDn2k29JoNTUYH8AaYDPg0cArq6n1i+iIWrnWq/xF4ICmKifNK8O9tHQGe0mdGOwlrbSmTt0nM6/JzP0ofe9/Swn0naZzgRdn5pMy84am6ifNC/vaS0vnqfiS2nkKvqRJ1GSLPgCZeTBwcETcCdgN2AZYB/gTcArwf5nprydpRAzz0tIZ5iXVGeI1T5ocJM8z90ev8aDfkpk/B36+UtuXZpnhXloag72kOoO9pGnVSNCPiN9Ws/+Rme9rYpvSPDHYS0tjsJfUYqiXFnMwvunWVIv+jpTT83/S0PakmWe4l4ZnsJfUYrCXNMuaCvoXArcGrm5oe9LMMdhLwzPYSwJDvbQkDbbo20l/9Joadf/E6vbODW1PmnqOji8Nz1HxJTkKviQ1F/Q/SLl03ssiYr2GtilNHYO9NByDvSRDvSTdXCNBPzOPAt4G3B34WkTs1MR2pUlnq700HIO9NN9srZea07q8XlOTRqupUfffAFwDnAY8DPhtRBwP/BS4BLih1/KZ+eaxV1JqiIFeGpxhXppvBnlJWpqmBuN7E9D6tZaUEfj3rKZBGPQ1tQz20uAM9tL8MtRLkyVo8PJ6+PkftaaCPnCzV89XUzPJYC8NzmAvzSdDvSSNVyNBPzObGvRPWhGGe6k/Q700vwz20vRpsu+8ffRHr8kWfWlmGOyl/gz20nwy1EvSyjPoSwMy3Eu9Geyl+WOol6TJZNCXujDYS70Z7KX5Y7CX5kdEg4Pxee7+yBn0pYrBXurNYC/NF0O9JE2vRoJ+RNywxEXXAn8BfgWcAByWmaePrGKae4Z7qTuDvTQ/DPWS2jkY33RrqkV/qS/dRtW0HfBA4JUR8VHgHzPzmlFVTvPDYC91Z7CX5ofBXpJmW1NB/1+r20cC963mTwV+BFxU3d8a2A24O5DAycC3gc2BuwB7AesBzwNuCTy5iYpr+hnupc4M9tJ8MNRLWgr76E+3RoJ+Zv5rRPwTJeSfBOyfmT/tVDYi7g58BLgP8PXMfFn1+A7AIcBDgSdExCMz81tN1F/TxWAvdWawl2afoV6SBLCqiY1ExD7AvwOnA/t0C/kAmXkqsDdwBvCmiHho9fj5wN8Av66KPmucddb0WHv94klSccONuWiSNHvWWRWLJkmSoKGgDxxQ3b4rM9f2K1yVeSelb/9L2h7/QPX4/cZQT00Jg710cwZ7aba1h3qDvaRxag3G19Sk0Wqqj/7u1e3PhljmtOr2Pm2P/6i63WZZNdJUMdBLN2eYl2abQV6StFRNBf1bVrebD7FMq+yWbY9fXt36C3eGGeylmzPYS7PLUC9p0pSW9qYG42tkM3OlqVP3L6huHzvEMo9vW7alddDgIjRTPB1fWsxT8aXZ5Cn4kqRxayrof5vSr/7FEfGYfoUj4m+AF1Fa7dtH1r93dXvuSGuoxjmInrSYwV6aTYZ6SVOpyf75fjWOXFNB/63AFZSuAl+OiP+JiP0iYvuIWLeatq8e+wzwparslcDb29b1t5QDAEc1VHeNkMFeKtpDvcFemg221kuSJkEjffQz85yIeBzwFWAT4MnV1E0AVwFPyMyzb3owYhdK+D+WcjBAE85ALxUGeWn2GOIlSZOqqcH4yMyjIuJuwH8A+wHrdCl6I/A14OWZ+Zu2dfwGeNBYK6plMdhLhcFemj0Ge0nzJCIaHIzP79dRayzoA2Tm74DHR8T2lMB+FxZG1b8EOB04JjPPa7JeWh7DvWSwl2aNoV6SNM0aDfotmXkB8OmV2LaWz2AvGeylWWKol6SbC5q77J3fwqO3IkFf08VgLxnspVlisJckzboVCfoRsRHlMnnbARsDX87My1aiLurMcK95Z7CXZoOhXpI0jxoN+hGxE+VSe08G1qs99SPg57VyzwVeAPwFeHhm+ot7zAz2mncGe2n6GeolaXQcjG+6NRb0I+K+wNcpg+/VX8lOv66PAN5PORjwcODbY6/gnDHYa94Z7KXpZ7CXJKmzVU1sJCK2AL4C3BK4EHgxcNdu5TPzj8A3q7v7jr2Cc2Lt9QuTNG9uuDEXTZKmyzqr4maTJGl8IpqdNFpNtej/I7ANcDGwR2aeDX1P0TgSeCyw+9hrN6MM9JpnhnlpehniJUlanqaC/mMop+j/RyvkD+D06naX8VRpNhnuNa8M9tL0MthL0uSxj/50ayroAb1LwAAAIABJREFU3666PXaIZS6pbjcfcV1mliFf88JQL00vQ70kSePXVNDfsLq9bohlNqlurx5xXSRNGYO9NJ0M9ZLm0bXX3bjSVZAaC/p/BHYEbgucPOAy96huzx9LjSRNLIO9NJ0M9pLmyTUzHug9dX+6NTLqPnBidfuoQQpHeaWfT+nXf9y4KiVpMjgivjR9HAVf0jy55robbzZJk6ypoP/fQADPiIh79CsMHAjcvZo/dGy1krQiDPbSdDHUS5oXnQL9vIZ6L6833RoJ+pn5FeBoSleB70bEiyJim1qRdSNih4h4ckQcBxxAac3/Ymb+oIk6Shofg700PQz1kuaFgV6zrKk++gBPBL4L3BN4XzW1fvGf0lY2gBOANU1VTtLoGOal6WGQlzTrDPBLYx/96dbUqftk5qXAHsDbgMsoYb7TdDXwTmCfzLyyqfpJWjpb7KXpYGu9pFlnK71UNNmiT2ZeC7w+It4K7A3sBmwDrAP8idKyf2Rm/qXJekkajmFemnyGeEmzzAAv9dZo0G+pWuq/UU2SJpzBXpp8BntJs8pQv0KaHCTPf2EjtyJBX9LkMtRLk89QL2kWGeil0ZnboB8RuwGPBh4I3AnYGrgOOB84HvhYZn5/iPU9CtgfuE+1rouAk4GPZOY3R1t7aXQM9tJkM9RLmkWG+snnYHzTbaRBPyKeOcr1tWTmYaNcX0QcC+zZ4an1gb+upjURcRjw/GpsgW7rWgV8BHhu21O3rqbHRcRHgRdkpt9oWnEGe2myGewlzRIDvbQyRt2ifwgLl8wblQRGGvSBHarb84HPAccBZ1MGBdwDeAUlpD8TWA94eo91vYWFkH8K5YoBvwF2AV5NuZzg8ygt/K8b5U5IgzDYS5PLUC9plhjqZ0vQXB99/xuO3jhO3Z+G1+kMSuj+Qmbe0PbcCRHxScrp+7sCT4uID2Xmse0riYhdgVdWd38E7JWZV1f3T46IrwLfo1xd4FUR8fHM/PUY9ke6icFemkyGekmzwkAvTb5RB/3b9nhuS+DDlD7sPwMOBU4C/lA9v2313LOAu1L6t78AuGTEdSQz9+vz/MUR8QrgiOqhJwE3C/rAS1n4G76kFvJb67kqIl4C/LAq9zLg75dTd6mdwV6aPIZ6SbPCUC9Np5EG/cw8q9PjEbE+8AXKaexvAN6Sme3p5EzguIh4D6W1/d+Ag4EHjLKOQzi6Nr9L+5NRRox4bHX3jMw8odNKMvOEiPgl/5+9+47TrKzv///6zNJbWEBEQUVBBTUmRJqKgI2oiBA1QROjIERjoiEGjTEm+aKJxhI0/NQYC1LERqIUg4XYUBB1NXZEiiBNOigssIX9/P44Z9x7hpm7zbmvu72e8ziP065zrmvYZXbe93Wd68AjgUMj4pULfO9S1wz20ugx2EsadwZ6zTcTwUyhsful6pkmM4XqeRXwe8B/Zea/tAu6WXkzcHp9zTGF2jjfxi3b84f3QzV6YfZZ//M63Gv2/I7AzktrlqbNvetyziJpuJbNxH0WSRonq9asu88iabKUCvp/TDWp3sk9XHMS1fP+LxhEg7pwQMv2Txc4/6iW7Ys73Kv1/O59t0gTb36oN9hLw2WolzTOFgr0hnp1K6LsomYNYjK+hcwOfb+hbam5bpx3bTH1K/P+ruXQ6QsU26ll+5oOt7y6ZftBPbZlpw5FdujlfhotBnlptBjkJY0rA7ykVqWC/uxvTg+negVdNx4+79qSXg3sXW9/OjO/u0CZLVu27+xwv5Ut21v02JarOxfRuDDYS6PDUC9pHBnoJXWjVND/KdWM+n8dEf+dmW1/QtU96q9uubaYiDgAeGu9eyPwikWKbtKyvbrDbVe1bG/aZ9M0hgz20mgw1EsaR4Z6DVNEEIXG1JeqZ5qUCvqnUvWQ7wOcGREvy8zrFyoYEfeneg3fPlTP9Z9aqI1ExKOBM6j+u9wD/GFm3rhI8XtatjfqcOvWif3uXrTUwjoN9d+B6lWEGgEGe2n4DPWSxo2BXlLTSgX9/6SaVG8/4GDg5xFxLlVAvZEq0N+fqtf/INYH4wvqawcuIh4KnAssp5pl/wWZ+bU2l9zRst1pOP7mLdudhvnPkZltn//306/hMthLw2ewlzRODPUaFzNRLaXqUrOKBP3MXBcRzwQ+CjyHatj7IfUy3+wf82eAP+k0zL8JEfFA4ItUr8tL4KWZeVaHy1oDeKcJ81p75X3mfowZ7KXhMtRLGhcGeknDVKpHn8xcCRwWEQdTPfd+ILDZvGJ3A18F3peZ/1OiXRGxHfC/wMPqQ6/KzG4eF7ioZXu3DmVbzxedc0BLY7CXhsdQL2lcGOo1kaLg6GH/yW9csaA/KzPPAc6pJ9zbBdimPnUbcHlm3luqLRHxW8AXgEfVh/4uM9/b5eVXANdRjQI4oEPZ/ev1tcCVPTZThRjqpeEy2EsadQZ6SeOieNCfVQ/Jv3RY9UfEZsA5wO/Vh96cmW/r9vrMzIg4i2p0wm4RsW9mfnOBevZlfY/+WZlpmhwRBntpeAz1kkadoV7SOJsZdgOGISI2oppd/4n1oRMy8x/6uNW/U03cB/DuiJjz6rx6/9317tq6vIbk3nU5Z5FUxrKZuM8iSaNi1Zp1Cy7StIsou6hZQ+vRH7KPU83uD/Bl4MSIeEyb8qsz85L5BzPzkoh4B/B3wJ7ABRHxNuByqscSXgfsURd/R2YObQTDNDLMS+UZ4iWNMgO8pGnRaNCPiB8Ab8zMTzd53/reOwKvB67KzLcv8XbPbdl+CvDDDuV/Aey8yLk3ANsDL6UK9Z9YoMyJQD8jBtQDg71UnsFe0igy0EtLF/VXqbrUrKaH7v828F8R8cOIODIitlrqDSNi74h4P3AZ1fPwmyz1nk3KzHWZeRRwMHAW1QR9q+v1WcCzMvPoEq8JnDYOxZfKcgi+pFHksHtJuq+mh+4fDbwFeAzwIeC9EfE/wKeBb2bmlZ1uEBGbUw2DfyrwQta/9i6Bj1L1ji9JZjb+22lmfhb4bNP31XqGeakcQ7ykUWOAl8qaiWopVZea1WjQz8wPR8QngWOAVwPbAs+rFyLiFuD7wI1Ur9O7DdiU6hV7y4FHAI9k/UiD2T/yzwGvz8xOQ+w1IQz1UjmGekmjxlAvSUvT+GR8mbkSeEtEvBP4U6pn1/epT29H1VO/mNbfNm8CTgU+uNBEeJosBnupHIO9pFFhoJekwRjYrPuZeQ/wQeCDEfFg4EDgScATgJ2ALVuKrwVuBn4EfL1evpGZawbVPg2XwV4qw1AvaVQY6qXxEhFEoffelapnmhR5vV5mXkXVO3/q7LH6XfbbAPdk5u0l2qHhMdhLg2eolzQKDPSSNHxFgv5CMnM1cP2w6tdgGeylwTPYSxo2Q700uQIo1dHubzTNG1rQ12Qx2EuDZaiXNEwGekkaLwZ99cVgLw2OoV7SMBnqJQHMRDBTqEu/VD3TxKCvjgz10uAY6iUNi4FekiaXQV/3YbCXBsdgL2kYDPWSNF0M+jLYSwNiqJdUmoFeUmOi3GR8zsbXPIP+FDLYS80z1EsqyUAvSWrHoD8FDPZSswz1kkoy1EsahoggCnXpl6pnmhj0J5DBXmqWwV5SCQZ6SVJTDPoTZN26NORLS2Sol1SCoV7SqIuCz+jbod88g76kqWWolzRoBnpJ0jAMLehHxAywDbAZcG1m3justkiafIZ6SYNmqJckjYqiQT8ilgFH1MtewIZAAo8FLmop92xgf+BXmfnmkm2UNBkM9pIGxUAvaRrMRDBTaEx9qXqmSbGgHxHbA2cC+9D5TYlXAmcDGRHnZOb3B9w8SWPMUC9pUAz1kqRxNFOikron/zPAvlQ9+KcDr1ysfGb+GPhWvfsHA2+gpLGxbCbus0jSUq1as27BRZKmVRRe1KxSPfovoRqqvwZ4TmZ+ASAi3tPmmrOpev/3G3zzJI0iQ7ykQTDAS5ImXamg/0Kqnvz3z4b8LnyvXj9yME2SNEoM9ZKaZqCXJE2rUkH/sfX67B6uubFeb9twWySNAIO9pKYY6CWpeRFBFJokr1Q906RU0N+6Xt/SwzXL6rWv3ZPGnKFeUlMM9ZIkdVYq6N8KbA88iPVD8jt5eL2+aSAtkjQQhnpJTTDQS9JwzUS1lKpLzSoy6z7wk3q9Vw/XHE71XP+K5psjqQnOgC+pCc52L0lSs0oF/TOp3prwyohY3qlwRDwfOKTe/dQgGyape4Z6SUvhK+wkaXzMPqNfalGzSgX9DwJXAVsB50bEoxYqFBHbR8SbgY9R9eb/GDi9UBsltbC3XtJSGOglSRqeIs/oZ+aqiDgU+CrwOOBHEfGzliKnRcQWwMOoev6DauK+52VmlmijNM0M8ZL6ZYCXJGn0lJqMj8z8QUTsBZwCPB7YreX071CF+1nfBv44M39eqn3StDDUS+qHgV6Spo8j6sdXsaAPkJmXAU+MiP2A5wB7Us3Gv4yqB/97wNmZ+b8l2yVNMoO9pF4Z6iVJGm9Fg/6szDwfOH8YdUuTzFAvqRcGeknSYkpOkudkfM0bStCXtHSGekm9MNRLkjQ9DPrSGDDUS+qWgV6S1ISZqJZSdalZBn1pxBjqJXXLUC9JkhbSaNCPiHubvF8tM9MPJDSxDPaSOjHQS5KkXjQdoE0sUhuGekntGOglSaMiotwkec7F17ymg/4bG76fNLYM9ZLaMdRL0njbeMOZBY9vtMhxqaRGg35mGvQ1lQz1khZjoJek8bZYoJ90Qbnh2v4m3TyffZd6ZKiXtBhDvSSNp2kN85pcBn2pA4O9pPkM9JI0fgzzvZmJYKbQw/Ol6pkmBn2phaFeUisDvSSNHwO9VCjoR8SL+7gsgXuAXwGXZuYVzbZK085QL6mVoV6SxodhXmqvVI/+yVTBvW8RcRNwCvDWzLytiUZpehjqJc0y0EvSeDDMD1f1er1ydalZJYfuL/WPb3vgNcCLIuKZmfnDBtqkCWSolzTLUC9Jo80wLw1GqaD/UGBr4D+BfYD/Az4CfAe4qS5zP2BP4E+B3wO+BfwFsA54DPBC4FnAA4BzImK3zFxZqP0aYQZ7SQZ6SRptBvoxFEHYpT+2SgX964BPAnsBx2bmuxYocwlwAXBCRBwLvAP4APDEzPwB8NGIOLo+9kDg5cA7SzReo8NQL003A70kjS7DvDQ6Sv3f+JfA3sBHFwn5c2Tm8cBHqXr2j2k5/iHgLKrHAJ4zmKZqVCybifsskqbHqjXr7rNIkoZr4w1nFl2kcRARD4mI4yPi4ohYGRG3RsSKiHhtRGy2xHtvFhHPjYj31fe8LSLWRMQtEXFhRBwXETs09b20U6pH/0+oJuM7rYdrPlJf9wKq3v1ZnwAOBXZvrHUaOkO8NL0M8JI0egzumsTJ+CLiEKpMulXL4c2oHiHfEzg6Ig7OzMv6uPdjqUaob7HA6W2Afevl1RHxssz8ZK919KJU0N+1Xt/UttRcs2V3mXf88nq99ZJapKEx1EvTy1AvSaPDMK9pEhF7UD1OvilwJ/CvwFfq/RcAfwY8gmo+uD0z844eq9iK9SH/AuB/qOaku4VqPrrn1nVsRfVY+q8z83NL+qbaKBX0Z3+K7Ap8r8trZj8cmJ8KZ39L7PU/vIbEYC9NHwO9JI0Gw7z6NRPBTKGu9kL1nEAV6tcCB2XmhS3nvhwRlwJvpwr7xwLH9Xj/dcDpwBsz86IFzp8bEZ8DzgCWAe+OiIdn5pJeQ7+YUv/nX1yvX9lN4aimd/yrevdn804/pF73MjpAhfhcvTRdFnqO3pAvSWX53LzUXkTsDTyp3j1xXsifdTzw03r7mIjYsJc6MvMbmXn4IiF/tsxZwKfr3V2APXqpoxel/u//KFXP/H4RcXpEbLtYwfrc6cATWfi5/gPr9aL/AVWGoV6aLgZ6SRouw7xKmn1Gv9QyYIe1bJ+0UIHMXAecWu9uDTx5QG35Ssv2/MfUG1Nq6P57gRcC+wDPA54VEZ8Hvsv6nvn7AY8DnkE1pALg2/W1AETEJlTPTyTwhSItF+Dwe2maGOAlaXgM7tJA7FevV1Jl0MWc17L9RODcAbRl45btewdwf6BQ0M/MeyPiIKrJD55BNbPhH9TLfLOJ8lzg8Mxs/ea3Af623v7MgJo79Qz10vQw1EtSeYZ5qa0dokMXf2Ze0+M9Z9/Ydllmrm1T7uKW7UG95e2Alu2fLlpqiUr16FPPWvisiDgUeDnVN7jpvGL3AF8D3p+ZZyxwj+uAUwbd1mljsJcmn4FeksoyzGvcBUGnwN1kXS1WdHVJt/euRoVvV++2/YAgM2+LiJXA5sCDuq2jh7b8DnBwvfujzBz/oD+rnoDgrIhYRvVMwvL61G3A5fN68NUwQ7002Qz0klSWgV4aeVu2bN/ZRfnZoL9Fp4K9iIiNgQ9RzbgP8IYm7z9f8aA/qw70lwyr/mlgqJcmm6FeksowzGsazVBu5vZ59ewFXN/g7Tdp2V7dRflV9Xr+6POleg+wZ719SmYO9FH0oQV9NW/Gme+liWSgl6TBM8xLI+P6Pp7Bb+eelu2Nuig/O1ne3U01ICJeDxxd764A/rKpey/GoC9JI8JAL0mDZZiXuhdR8Bn9wdZzR8t2N8PxN6/X3Qzz7ygiXg68pd69GHhWZq5s4t7tFA36EbEt8CLgScDDqJ6XWNb2IsjMHNj7BSVpGAz1kjQ4BnpJszLznoi4BdgW2Kld2YhYzvqgf/VS646IFwL/Ue/+Anh6Zt681Pt2o1jQj4g/BD4AbDV7qMtLczAtkqTBM9BL0mAY5iX14CKqzuZdI2KDNq/Y261le0kz4kfEc4BTqaYg+CXw1IYfSWirSNCPiH2Aj1F9kwFcB3wPuBXwt2BJE8FQL0nNMsxLwxMBpab/KvCEwPlUQX9z4HHAtxYp1/qO+wv6rSwingqcTpW3b6Hqyb+83/v1o1SP/uuohujfDfxZZn6sUL2S1DgDvSQ1xzAvqYAzgdfX20eyQNCPiBngxfXu7cBX+qkoIp4AnEU1qd+vgN/PzJ/0c6+lKPWT9QlUQ/DfasiXNC5WrVm34CJJ6t3GG84suEgaTTNRdhmkzPw28PV696iIePwCxY4Fdq+3T8jMNa0nI+LAiMh6OXmheiLid4FzqEYOrAQOzszvNvE99KpUj/7W9foLheqTpJ4Y4CVp6QzukkbYMVTD8TcFzo2It1D12m8KvAB4WV3uEuD4Xm8eEbtQ5d3Z7PsPwK8i4jFtLrsxM2/sta5ulAr6vwQejBPrSRoyA70kLY1hXpoOE/R6PQAy83sRcThwGtUE8W9ZoNglVL3wdyxwrpMnAdu37L+ri2veCBzXR10dlfpJ/cV6/bhC9Umacg67l6T+LTbM3pAvaZxl5meAx1KF8EuAu6iex/8O1bxye2TmZcNrYXNK9ej/G/DHwGsi4rTMvLNQvZKmgAFekvpjcJc0bTLzF8Df1Esv132VNq+Iz8yTgZOX0LRGFQn6mfmziPgTqlfsfSkiXjqMmQcljTcDvST1zjAvqR8lJslrrUvNKhL0I+LD9eZFwF7ADyPiR8DFVMMl2snMPGqQ7ZM0Wgz0ktQbw7wkqVWpoftHsH4ivqQa8vDb9dJO1OUbD/oRsT2wd73sVS/b1qdPycwjurjHEcBJXVZ5ZD2cQ1ILQ70kdccwL6mkiGopVZeaVSroX8Xozbh/w7AbIE0TA70kdcdAL0laqlLP6O9cop4luIrqMYKDlnCP3weua3P+miXcWxorhnpJas8wL2nURQQzE/R6vWlTqkd/FL0JWAGsyMwbImJn4Iol3O+SzLyygXZJY8NAL0mLM8xLkoZlaoN+Zv6/YbdBGhcGeklamGFekjSKpjboS1qYoV6S5jLMS5pGM/VSqi41a2hBPyKWAcuBTalm119UZl5VpFHSFDHQS9JcBnpJ0qQoGvQjYjvgVcBhwKPo7sObZDxGHpwUEY8EtgN+DVwGfBF4X2ZeO9SWaaqtXruOHLV3XkjSkBjmJak7vl5vvBUL0BHxBODTwP3o0IM/pg5s2d62XvYBjo2Iv87M9/dz04jYqUORHfq5rybT6rX20kuSYV6SNO2KBP2I2BY4iyr83gl8CLgdOI6qx/5oYBtgT+A5wCbABcCJJdq3RD+n+gDjQuDq+tjDgOcBz6f6Xv4zIjIzP9DH/a/uXETTxkAvadoZ5iVJWlypHv1XUoX8VcDjM/MnEfFoqqBPZp40WzAiHgB8DNgfuDAzX1eojf04Azgl8z4Do1cAn4yIZ1N9CLAh8K6IODszry/dSI0vA72kaWegl6ThmCGYKTSmfmYiB3wPV6l/PZ9J1XP/4cz8SbuCmflL4FnA5cBrIuIpBdrXl8z81QIhv/X8/wBvqnc3A47qo5oHdVj26uOeGkGr1667zyJJ02DjDWcWXSRJUu9K9ejvWq+/2HLsNwE5IpZl5r2/OZF5d0S8C3gv8OfAl4u0cjA+QBX2AzgAeHMvF2fmNe3OhzNXjB0DvKRpZGiXpPHiZHzjrVTQ36pe/6Ll2D0t21tSPbPf6jv1ep9BNaqEzLwxIm6hmo1/x2G3R+UY6CVNG8O8JEmjoVTQvxP4rXn13dqyvTPw/XnXbFKvtx9cs4rx5WYTzlAvaVoY5iVpOsxEtZSqS80q9a/1ZfX6wbMHMvN2YHZiuicvcM1+9XrlANs1cBFxP6refIDrhtkWLd1Cz9Eb8iVNIp+ZlyRpfJX6F/tb9Xr+xHGfp3p2/W8j4uGzByNiX+C1VD3hK4q0cHBeBr+ZRvK8YTZE3TPQS5oGToInSdJkKvUv+Reowu5z5x1/J7CWanj+TyJiRURcBHwd2Louc0KhNvYkInaOiD06lHk28E/17t3ASW2Ka0gM9JImmWFektSPCJiJKLI4GV/zSj2j/wXgVGBZRDw0M68AyMwfR8QrgPfVbXncvOuOy8zPD6JBEbEf698GAOuH1wPsGhFHtJbPzJPn3WJn4CsRcSHwGeAHwI31uYcBz6+X2b+2r8nMa5tou/pjgJc0qQztkiSpVZGgn5lrgCMWOXdiRJxfn3903aZLgY9k5ncWuqYhRwMvWeTcE+ul1cmLlH18vSzmLuDVmfmBnlqnvhnoJU0iw7wkqSRfrzfeSvXot5WZPwNeP+x29Oi7wIuoQv6ewAOoRgVsANwG/AT4EvChzLxxsZtoaQz1kiaNgV6SJC3VSAT9YcjMI1hklEGX198BfLReNGAGekmTxDAvSRp1vl5vvE1t0NdoMtBLmhSGeUmSNCwGfQ2NoV7SuDPMS5KkUWTQ18AZ6CWNM8O8JGkaRf1Vqi41y6CvxhjoJY0zA70kSZoUBn31xVAvaRwZ5iVJ6o6T8Y03g77aMtBLGjeGeUmSNO0M+gIM9JLGi2FekqTBioI9+mGPfuMM+lPIUC9pHBjmJUmS+mPQn2AGeknjwEAvSZLULIP+BFmzdp3hXtJIMsxLkjReIoIoNKa+VD3TpHjQj4gZ4FHAw4AtgWWdrsnMUwfdLknS0hjmJUnT4OY7Vrc9f9ud7c9LJRQL+hGxKfAPwJ8B2/ZwaQIGfUkaAYZ5SdKk6xTkp8UMBV+vV6aaqVIk6Nch/8vA3oDjMiRphBnmJUmTyhCvaVGqR//VwD719o+B9wDfBW4FfKhckgozzEuSJpFBvjkR5V575yP6zSsV9A+v198AnpKZ/h8oSQUY6CVJk8IQL3WvVNDfhepZ+7cb8iWpWYZ5SdIkMMhLzSkV9FcDmwJXFapPkiaKYV6SNM5uvmNVm7OO2x5FMxHMFBpTX6qeaVIq6F9M9Yz+DoXqk6SxY5iXJI2j9iFe0jCUCvonA/sCfwh8vlCdkjRyDPOSpHFjkJ9OM1Hw9Xp26DeuVND/IPBHwIsj4ouZ+fFC9UpScYZ5SdK4MMRLk6lU0H8Q8CqqwH9aRPwB8DGqIf13dbo4M322X9LIMdBLkkadQV6aTqWC/pVUs+5DNdvG8+qlG0m5dkrSHIZ5SdKoMsRroKLg++0dut+4kgE6FtmWpKEyzEuSRpFBXlK/SgX9IwvVI0kLMsxLkkaJIV6jboZgplD/bKl6pkmRoJ+Zp5SoR9J0M8xLkkaFQV7SMPnsu6SxYpiXJA2bIV7TIAo+o19sLoApYtCXNHIM85KkYTHES5oEQwn6EXF/4EDgMcA29eFbgR8DX83MG4bRLkllGeglSaUZ5CVNg6JBPyIeALwTeG6butdGxKeAYzPzl8UaJ2kgDPOSpFIM8VJzZqJaStWlZhUL+hHxO8AXqXrw2/1RbggcDjwtIp6amT8q0T5J/TPMS5JKMMhLUneKBP2I2Bw4B9i2PvRF4IPAt4Dr62M7AHsDRwMHAdsB50TEbpl5V4l2SlqcYV6SNEiGeGm0zEQwU2iWvFL1TJNSPfqvBB4IrANenpknLlDmqnr574h4KdUHATsCfwm8o1A7palmmJckDYpBXpLKKRX0DwUSOHmRkD9HZn44Ip4AvBT4Awz6UmMM85Kkphnipcnj6/XGW6mg/4h6/Ykervk4VdB/RKeCku5row0M9JKkZtz067lB3l/KJWm0lQr6W9TrW3u45rZ6vXnDbZEmhmFekrRU80O8JGn8lQr6N1E9o7878H9dXrNbvb55IC2SxoRhXpLUL0O8pH4F5Sbji7YvZVM/SgX9bwLPA/4mIj6ZmWvbFY6IDYC/oXqu/5sF2icNlWFektQPg7wkaSGlgv6pVEH/d6lemXdkZl63UMGIeCBwIvB71BP4FWqjNFCGeUlSLwzxkobJyfjGW5Ggn5mfiYgzgcOApwE/j4hzgW8BN1IF+vsD+wBPBzaqLz0jM88p0UapCYZ5SVK3DPKSpEEp1aMP8EKqnv0/pAryB9fLfLOf5/wX8OIyTZO6Z5iXJHViiJc07mbqpVRdalaxoJ+Zq4DDI+JU4C+AA4DN5hW7CzgPeG9mfrZU26T5DPOSpMUY4iVJo65kjz4A9VD8cyJiGfAwYJv61K3AzzPz3tJt0vQy0EuS5jPIS5LGXfGgP6sZub+KAAAgAElEQVQO9JcOq35ND8O8JGmWIV6SuhMRRKnX6zkbX+OGFvSlJhnmJUlgkJckCQz6GiOGeUmaboZ4SSonWD9Leom61KxGg35EfLjezMw8aoHj/ZhzL002w7wkTS+DvCRJzWi6R/8IIOvtoxY53ouorzPoTxDDvCRNH0O8JEnlNB30r2LhQL/YcU0ow7wkTQ9DvCRNnpkIZgpNkleqnmnSaNDPzJ17Oa7xZpiXpOlgkJckabw4GZ86MtBL0uQyxEuSFmM/+/gy6AswzEvSpDLIS5I0fYoE/Yi4AlgH/H5mXtblNQ8Gvko16/4uA2ze1DDMS9JkMcRLkgYlolpK1aVmlerRfwjVZHwb9XDNhsDOOIlfTwzzkjQZDPGSJKlfDt2fIBsa8iVpbBjkJUnSoIxy0P+ten3XUFshSVKPDPGSpHFXDd0vM6beofvNG+Wg/6J6/YuhtkKSpHkM8pIkaZQNJOhHxJcXOXVSRKzscPnGwMOA7amezz+3ybZJktSOIV6SJJipl1J1qVmD6tE/kCqktw7CCGCvHu/zc+BfG2qTJGnKGeIlSdI0GFTQ/xpzZ8s/oN7/LtCuRz+Be4BfAt8APpGZnUYASJIEGOQlSWpKRBR8Rt+H9Js2kKCfmQe27kfEunrziMy8aBB1SpImmyFekiSpO6Um4zuVqrf+tkL1SZLGiCFekiSpOUWCfmYeUaIeSdJoMshLkjRegrkTrg26LjVrlF+vJ0kaA4Z4SZKk0VIk6EfEbwNnAfcCB2bmtR3K7wicR/XhzjMz85LBt1KStBCDvCRJ08fJ+MZbqR79FwE7A1/oFPIBMvPaiLgE+P362n8abPMkaToZ4iVJkiZPqaA/+3q9s3u45izgGcBTMehLUs9uvmMVmZ3LSZIkzTdTL6XqUrNKBf1H1Osf9nDNj+v1IxtuiyRNhJvvsDdekiRJ91Uq6G9Rr+/s4ZrZsls13BZJGnmGeEmSJPWrVNC/DdgO2AH4QZfX7FCv7xhIiyRpSAzxkiRp5BWcjA8n42tcqaB/KVXQfwbwhS6veWa9vnwgLZKkATHIS5IkaZhKBf0vAE8AXhYRH8jMn7YrHBGPBv6MagK/zxdonyR1xRAvSZKmQdRLqbrUrFITHL4PWAlsAnw5Ip69WMGIeA7wRWBT4G7gvYNoUERsHxHPjog3RcTnIuLmiMh6ObmP+z0zIs6IiGsiYlW9PiMintn5akmj4uY7VrVdJEmSpFFXpEc/M2+OiD8HPgJsD5wVET8Hzgd+WRd7APAk4KFUH+ok8IrMvGFAzWrkvhExA3wAOGreqR3r5bCI+BDw8sxc10SdkvpjUJckSepOUO7ReXv0m1dq6D6Z+dE6FL8P2AzYBXjYvGKzf8YrqUL+aYWadxVwMXBQH9e+mfUh/3vA26nmFdgF+FtgD+Bo4Cbg75fcUkkLMsRLkiRJlWJBHyAzPxIR/wv8FXAw8BjWh/t1wI+AzwDvGWBP/qw3ASuAFZl5Q0TsDFzRyw0i4hHAa+rd7wD7Z+bd9f6KiDgbOA/YE3htRHw4My9rovHStDHIS5KkcbB8i42G3QSpbNAHyMzrqXq2/z4iNgC2qU/dmplrC7bj/zVwm79m/X/DV7WE/Nk67oqIVwEX1uVeDfxlA/VKE8UQL0mSRtV2W/YW3K/51YAaUtgMwUyhQfWl6pkmxYN+qzrY3zjMNvQrqpdKHlrvXpyZ31yoXGZ+MyJ+BjwSODQiXpmZWaqd0rAZ4iVJ0qjpNbxL42aoQX/MPRR4YL19Xoey51EF/R2BnenxEQFplN18x+o2Z/1MS5IkDZ7BvXkRBSfjs0O/cQb9/j2qZfviDmVbz++OQV9jon2IlyRJGhzDu9S/4kE/Ip4MHAb8DrAdsCnt36iQmblLibb1aKeW7Ws6lL26ZftBvVQSETt1KLJDL/eTZhniJUlSSdttufGwmyBNjWJBPyK2Bz4BHDB7aJGiOe/cqI793bJl+84OZVe2bG/RYz1Xdy4i3ZdBXpIkDZrhfXJF/VWqLjWrSNCPiA2BzwG/SxXivw9cS/WKvQROo5p9//eAB9TH/g/4cYn29WmTlu1Oiap1NrJNB9AWTRlDvCRJGgSDuzQZSvXoHwHsQRXgj8zMUyLi0VRBn8x8yWzBiDgMeA/VM/BvzcxPFWpjr+5p2e70AFHrT8y7Fy21sE5D/XcAVvR4T40wQ7wkSWqS4V39cDK+8VYq6D+vXn8+M09pVzAzz4yIHwHfAU6OiB9m5qUDb2Hv7mjZ7jQcf/OW7U7D/OfIzLbP/4f/V4wdg7wkSVoKg7ukTkoF/d9h/RD9+4iIaH23fGZeHhEnAP8EHAO8skgre9MawDtNmNfaK+8z9xPMEC9JkvpheNeoCYIZn9EfW6WC/jb1uvW1cq2JaDPmTlgH8CWqoP/0AbZrKS5q2d6tQ9nW8z8dQFtUiEFekiR1w+AuaZhKBf3VdV2tKenXLds7ApfMu+aelnOj6ArgOuCBrH+TwGL2r9fXAlcOsE1aAkO8JElqx/AuaVyUCvpXUfVq33/2QGbeEBF3UD3fvg/3DfqPmS1apIU9ysyMiLOAVwC7RcS+mfnN+eUiYl/W9+if1fqIgsq55c7FQ7x/IpIkTSeDu7Q4J+Mbb6WC/v9Rhd09qF6zN+trVDPvHxMRp2fmKoCI2Bp4HVXIv4jR9e/Ay4BlwLsjYv/M/M2s+hGxKfDuendtXV4D0C7IS5Kk6WF4l6RyQf9LwJ9Qhfq3tBz/z/rYHsAPI+JsqhnqD6Easp/AqYNoUETsB+zacmi7lu1dI+KI1vKZefL8e2TmJRHxDuDvgD2BCyLibcDlwC5UH1bsURd/x4i+PWDkGeIlSZpeBndpOOzRH2+lgv6ZwHHAThGxS2ZeDpCZ50TEh4GXAg8H/qYuP/tHfS7wvgG16WjgJYuce2K9tDp5kbJvALan+h72AD6xQJkTgX/ovYmT79ZFQryj6SVJmkwGd0kavCJBPzNvB3Ze5NzREXEhVfB+dN2mS6l68k/IzHUl2tivun1HRcSnqIbx70U1OuBmYAXw/sz8XJtbTLTFgrwkSZochndp8kT9VaouNatUj35bmXkiVa93yTqPAI5o8H6fBT7b1P3GgSFekqTJdL+tDO6SNM6KBP2ImH293C99Tn08GOIlSZoshndJmh6levS/SvXY9VFUw/I1ZAZ5SZLGm8Fd0iDNRLWUqkvNKhX076SaTf9HheqbaoZ4SZLGk+FdktSEUkH/KmB3YLNC9U2l21euZjNDviRJI8PgLmlcORnfeCsV9M+hCvpPA75eqE5JkqTGGd4lSaOuVNB/F9V75v86Iv4rM39cqF5JkqS2DO6SdF8R1VKqLjWrSNDPzOsj4tnAp4ALIuJtwMcy88oS9UuSpOlhcJckTbtSr9f7eb25EbAl8M/AP0fEncDtwL1tLs/M3GXATZQkSSPM8C5JUvdKDd3fed7+7OCMLeulnWy8NZIkaagM7pI02oJyk+Q5cr95pYL+KYXqkSRJQ2J4lyRpNJR6Rv/IEvVIkqTmGNwlaXpFwIyT8Y2tRoN+RPxVvfmRzLytyXtLkqSlM7xLkjT5mu7R/3eqZ+q/CPwm6EfEl+vjL83MXzRcpyRJU8vgLklS9yLiIcBfAQcDDwJWAZcDpwPvzcy7lnDvGWA3YO962Qt4LNWk9ABPzsyv9t34HpR6Rv9AqqC/eaH6JEkaSwZ3SdIoiPqrVF1F6ok4BDgN2Krl8GbAnvVydEQcnJmX9VnFnwInL6mRDWk66N8DbAxs3fB9JUkaW4Z3SZKGKyL2AD4JbArcCfwr8JV6/wXAnwGPAM6JiD0z845+qmnZXgP8CNgQ+O0lNL0vTQf9K4FHAs8GvtHwvSVJGgkGd0nSpIsoN0leoXpOoAr1a4GDMvPClnNfjohLgbdThf1jgeP6qOMiqscCVgDfz8x7IuI4JiDof5bqmYTXRcRTgUuoPsmY9S8RcXuP98zMPKqpBkqStBDDuyRJkyki9gaeVO+eOC/kzzoeOBLYHTgmIt6cmWsWKLeozPw28O0lNbYhTQf9twDPAXalmnhgz5ZzARza4/2C6tl+g74kqScGd0mS+hdQ6Mn5IvUc1rJ90kIFMnNdRJxKNaR/a+DJwLmDb9pgNBr0M/PWiNgTeCXwVGBHqmf2H0IV2H/J3B5+SZK6ZniXJEl92K9erwS+26bceS3bT8Sgv15m/pqqZ/8ts8ciYl29eVBmXtR0nZKk8WRwlyRJ8+wQHR7az8xrerzn7vX6ssxc26bcxQtcM5ZKvV5PkjQFDO6SJE2GGYKZQrPkzcwdvL+ii0u6blhEbAJsV++2/YAgM2+LiJVUr4V/ULd1jKJGg35EfJpqiP4x8z5leXJ9/Iom65MkDZ7hXZIkjbEtW7bv7KL8bNDfYjDNKaPpHv3DqAL9P847/hVgHfBYqlcOSJKGxOAuSZI6GeJkfHsB1zd4+01atld3UX5Vvd60wTYUN6ih+wv9nSj190SSps52WxreJUnSRLi+j2fw27mnZXujLsrP/lJ1d4NtKK7poH8H1RCH+wM/afjekjQ1DO6SJGmoJuf9ene0bHczHH/zet3NMP+R1XTQvxjYEzgmIr6dmfP/42TD9UnSWDC4S5IklZeZ90TELcC2wE7tykbEctYH/asH3bZBajrof4zqmYpnA7dGxA3Ampbz50bEmgWvXFxm5i5NNVCSmmJ4lyRJGgsXAU8Cdo2IDdq8Ym+3lu2fDr5Zg9N00H838ETg+fW9d2w5F/P2u+UoAElFGNwlSZIqUX+VqmvAzqcK+psDjwO+tUi5A1q2Lxh0owap0aCfmeuAP4qIxwNPowr2GwMvoQrsZwO3N1mnJLVjeJckSZp6ZwKvr7ePZIGgHxEzwIvr3dup3hw3tgYy635mXghcOLsfES+pN9+Qmb5eT1LfDO6SJEkFBMRkTMZHZn47Ir5O1at/VEScUmfWVscCu9fbJ2TmnEfOI+JA1of/UzLziAE2eckG9Xo9SeqKwV2SJEkFHEM1HH9Tqrnj3kIV3DcFXgC8rC53CXB8v5VExBHzDv1uy/YzImLnlv3LMvP8futqp0jQz8yZEvVIGg3bbdnNK0olSZI0qibn7XqVzPxeRBwOnAZsBbxlgWKXAAdn5h0LnOvWSW3OvW7e/ilU8wc0zh59SR0Z3CVJkjTuMvMzEfFYqt79g6let7cauAz4L+A9mXnXEJvYGIO+NKUM75IkSZo2mfkL4G/qpZfrvkoXgw8ys9RAiLYaDfoR8eV6MzPzqQsc78ece0lamMFdkiRJjZm0sftTpuke/QPrdS5wPOntj3C2/Px7SVPB4C5JkiSpH00H/a+xcDBf7Lg0VQzvkiRJGgdRf5WqS81qNOhn5oG9HJfGncFdkiRJ0qhxMj6phcFdkiRJgohqKVWXmmXQ18TbdgvDuyRJkqTpYdDX2DG4S5IkSdLimn693oObvN+szLxqEPfV6NjG8C5JkiSNDN+uN96a7tG/ouH7QTVbvyMPxozBXZIkSZKGo+kA7YcxE8rgLkmSJE0Ru/THWtNB/8gO5/8C2AtYA5wLfBu4oT53//rcQcCGwHeA/2i4fWpheJckSZKkydNo0M/MUxY7FxEnAntSBfyjMvPaRcrtCHwQ+H3gSZl5dJNtnGRbb76R4V2SJEmSplyRZ98j4vlUvf0rgIMz897FymbmtRFxCHAhcGREnJuZp5dopyRJkiQBLH/2O/u6Lu+5veGWDEfUX6XqUrNKTXL3cqpJ9d7ZLuTPysx7I+J44OPAywCDviRJkqSu9BvSpUlRKug/tl5f0sM1s2V/u+G2SJIkSRpBBvTREVEtpepSs0oF/S3r9fY9XDNbdsu2pSRJkiQN3fJnvq2/C5dt2GxDJBUL+r8AHgG8GPhCl9e8uF5fNZAWSZIkSeo/oGui+Xa98VYq6J8F/C3wgoj4QWa+vV3hiHgN8EKq5/rPKNA+SZIkaewY0iUtpFTQfyvwp8AOwL9GxAuBU6hm4b+RKtDfH9irLve79XXXA/70kiRJ0kRZ/vR/7v/iDXydsqT2igT9zLw9Ip5GNWx/J6rJ+Y5vc0kA1wDPyMzJeD+FJEmSJsKSQro0Lhy7P9ZK9eiTmT+NiEcD/wS8FFi+SNHbgJOAN2Xmr0u1T5IkSZNt+VOOg2XLht0MSRq4YkEfIDPvAF4bEX8PPI7q1Xnb1KdvA34EfDczV5dslyRJkkbb8qccN+wmSFMl6q9SdalZRYP+rMxcA3yzXiRJkjTBDOmSVNZQgr4kSZJG3/ID3rDwCd97Lk2+gPAZ/bFl0JckSZpAi4Z0SdLEM+hLkiSNEAO6JGmpDPqSJEkNWb7/3/d+UbGxsZLUPd+uN94M+pIkaer1FdAlSRpRBn1JkjTWlj/p7+YeiJnhNESSJo1d7WPLoC9JkobiPgFdkiQ1wqAvSZJ6ZkiXpMkW9VeputQsg74kSVNk+X6vW9oNnDhOkqSRZ9CXJGlMLDmkS5KkqWDQlyRpwAzokqRxE1FuEJeDxZpn0JckqQ1DuiRJGjcGfUnSROpqsrjMwTdEkqQxFJR7u54d+s0z6C9RRHT7W+J5mXngINsiSZPCGd0lSZL6Z9CXJDVm+f5/v/jJXFeuIZIkaWns0h9rBv3mvA/4jzbnV5ZqiCT1o21IlyRJ0tgw6Dfnxsz88bAbIWn6LD/wHxc/ue7ecg2RJEnSSDDoS9IQtQ3pkiRJQxL1V6m61CyDviT1YflTjmtfwJ50SZIkDYlBX9LU6RjSJUmSplxEtZSqS80y6DfnDyPij4CdgXuB64FvACdn5leG2TBpkiw/6M3dFVy7ZrANkSRJkkaUQb85j5q3v2u9vDgizgSOyMxf9XrTiNipQ5Eder2nNAxdB3RJkiRJS2LQX7q7gLOBLwEXA3cC9wMOAP4c2BY4DDgrIp6emb12M17dYFulvix/1jv6u3Dt6mYbIkmSpCKCcq+3d+R+8wz6S7djZt6+wPH/jYh3A58D9qAK/q8A/r+SjdN0W/6cE6oNA7ckSZI0NQz6S7RIyJ89d0NEPJ+qp39D4FX0HvQf1OH8DsCKHu+pEfebgC5JkiQNg136Y82gP2CZ+fOI+F/gWcCuEfHAzLyuh+uvaXc+nKJy5Cx/7vvaF7B3XZIkSdIAGfTLuIgq6APsCHQd9FVOx4AuSZIkTYmqQ79Mp6Jdl80z6JeRw27ApNvm8A/3fW2uWdVgSyRJkiRpuAz6ZbS+es/e/Bbbv+jUBY+vXbO2cEskSZIkaTIY9AcsIh4KPL3evTwzrx1me5q0WEiXJEmSNOYCik0H5tj9xhn0lyAiDgE+l5kLdj9HxP2BTwEb1Yf+o1Tb2tnxpR/v+9o1q9c02BJJkiRJUtMM+kvzbmDDiPgUcCFwJXA3sB1wIPDyehvgfOC9g2zMXseeyczm2w6yCkmSJElTwLfrjTeD/tI9EHhVvSzmU8DRmemsb5IkSZKkgTLoL81LgAOAxwMPo+q93wq4E7ga+AZwSmZeOLQWSpIkSVKv7NIfawb9JcjM84Dzht0OSZIkSZJmzQy7AZIkSZIkqTn26EuSJEmS5oj6q1RdapY9+pIkSZIkTRB79CVJkiRJc0RUS6m61Cx79CVJkiRJmiD26EuSJEmS5vDteuPNHn1JkiRJkiaIQV+SJEmSpAni0H1JkiRJ0lyO3R9r9uhLkiRJkjRB7NGXJEmSJM0R9VeputQse/QlSZIkSZogBn1JkiRJkiaIQ/clSZIkSXMEEIVG1Dtwv3n26EuSJEmSNEHs0ZckSZIkzeHb9cabPfqSJEmSJE0Qe/QlSZIkaYn+76SXA3D9ddfylD3/ecitWbqIgs/o26XfOIO+JEmSpLE0G64lzWXQlyRJktS1iz5+TKP3u2fNvY3eT5JBX5IkSRppl53xujn7q9euG1JLNF2cjm+cGfQlSZKkeeaHa0kaJwZ9SZIkjYSrPvsPA6/D3nCpSwUn47NDv3kGfUmSpCn2yy++qedr1tybA2iJJKkpBn1JkqTCbvzKv/xm+951hmZJo8cn9MebQV+SJE2F1nAtSdIkM+hLkqSBueVr/1q8TnvIJUnTzqAvSdKEue38tw3s3uvSEC1J0yAKTsZXbNK/KWLQlySpAYMM15IkSb0w6EuSxtatX3vrkq63B0GSpIVF/VWqLjXLoC9J6sktX31zX9eFqVqSJKkIg74kjbgbv/TGvq9dZriWJEn98P16Y82gL0kLuO7z/7ik6zdY5r9YkiRJGg6DvqSRcdVZr2vsXgZtSZIkTSuDvjTlLv3kMQO798YbzAzs3pIkSRocR+6PN4O+NAQ/PuVlRevbeINlReuTJEmSNDwGfU2N849//oLHN9mobAjeeEN7uSVJkjTaIsq9hta5g5tn0NdAnfOmQ/q6bvPC4VuSJEmSJoVBfwJ9+LVPHti9t9l444HdW5IkSdJoiPqrVF1qlkF/gvzbnz+B7XZ44LCbIUmSJEkaIh8WliRJkiRpgtijL0mSJEmay/frjTV79CVJkiRJmiD26EuSJEmS5rBDf7zZoy9JkiRJ0gQx6EuSJEmSNEEcui9JkiRJmiOiWkrVpWbZoy9JkiRJ0gSxR1+SJEmSNE8QTsc3tuzRlyRJkiRpgtijL0mSJEmaw2f0x5s9+pIkSZIkTRCDviRJkiRJE8SgL0mSJEnSBDHoS5IkSZI0QZyMT5IkSZI0R1BwMr4y1UwVe/QlSZIkSZog9uhLkiRJkuaI+qtUXWqWPfqSJEmSJE0Qg74kSZIkSRPEofuSJEmSpDkiCk7G58j9xtmjL0mSJEnSBLFHX5IkSZI0R1DutXd26DfPHn1JkiRJkiaIPfqSJEmSpLns0h9r9uhLkiRJkjRBDPqSJEmSJE0Qh+5LkiRJkuaI+qtUXWqWPfqSJEmSJE0Qe/QlSZIkSXNEVEuputQse/QlSZIkSZogBv0GRcRDIuL4iLg4IlZGxK0RsSIiXhsRmw27fZIkSZI0zUpltoh4ZkScERHXRMSqen1GRDyzqTraceh+QyLiEOA0YKuWw5sBe9bL0RFxcGZeNoz2SZIkSVK3gnKvty9WT4HMFhEzwAeAo+ad2rFeDouIDwEvz8x1/dbTiT36DYiIPYBPUv2FuRN4A/AE4KnAB+tijwDOiYgth9JISZIkSZpSBTPbm1kf8r8HvBDYu15/rz5+NPAvS6ijI3v0m3ECsCmwFjgoMy9sOffliLgUeDvVX5xjgeOKt1CSJEmSujV5XfoDz2wR8QjgNfXud4D9M/Puen9FRJwNnEc1euC1EfHhQY34tkd/iSJib+BJ9e6J8/7CzDoe+Gm9fUxEbFikcZIkSZI05Qpmtr9mfWf6q1pCPgCZeRfwqnp3A+DVfdTRFYP+0h3Wsn3SQgXqZy9OrXe3Bp486EZJkiRJUv+i2FeBLv2BZ7aICODQevfizPzmIvV8E/hZvXtofV3jDPpLt1+9Xgl8t02581q2nzi45kiSJEmSWpTIbA8FHrjAfdrVsyOwc4/1dMVn9Jdu93p9WWaubVPu4gWu6SgidupQZMfZjVtvuqHb2/Zt7cYbDbwOgM02XFakHoCNC9YFsNGGpR52Wm/jZWW/x1kbbTC8zxKXLSv/37nVBjPDrX/WssF8SNyfUWpLbQSbNPIyc9hN6OjedaPfxlbj1l6AtWPY5lmr1w5skuti1tw7/t9Dq1VrJuv7uemG61t3h/NLWAOuv/6Xw6prh06d3Jl5TY9VDDSz1R61yH26qeeKHuvqyKC/BBGxCbBdvdv2L1tm3hYRK4HNgQf1UM3V3Rb82z95Vg+3lSRJkjRg9wN+MexG9GP/J+w9rKpXdFGm64/qC2U2gNYO2k4fRLRmvF7r6YpD95em9bULd3ZRfmW93mIAbZEkSZI0WrYfdgNULLP1Us/Klu2BZEN79Jdmk5bt1V2UX1WvN+2hjk6f8DwYuKDe3he4tod7S5JGxw6s78XYC7i+TVlJ0ujaEZidiK3TEO5Rcz0D6mHu0g7ATcC9Dd6zRGbrtZ5VLdu91tMVg/7S3NOy3c3D6xvX67vblmrR6fmTec+vXNvH8yqSpBEw7+f59f48l6TxNO/neTfBcmTUz68P89+fQdQ98MzWRz0bt2z3Wk9XHLq/NHe0bHcz5GLzet3NkBFJkiRJ0tKUymy91LN5y/ZAsqFBfwky8x7glnq37ez4EbGc9X+gXU+wJ0mSJEnqT8HM1joaodOb01ofjxhINjToL91F9XrXiGj3KMRuLds/HWB7JEmSJEnrlchsF7Vs77ZoqaXX0xWD/tKdX683Bx7XptwBLdsXLFpKkiRJktSkEpntCuC6Be6zkP3r9bXAlT3W0xWD/tKd2bJ95EIFImIGeHG9ezvwlUE3SpIkSZIEFMhsmZnAWfXubhGx7yL17Mv6Hv2z6usaZ9Bfosz8NvD1eveoiHj8AsWOBXavt0/IzDVFGidJkiRJU66JzBYRB0ZE1svJi1T176x/NeC7I2LOq/Pq/XfXu2vr8gNh0G/GMVSvRdgAODciXh8R+0bEkyPi/cDb63KXAMcPq5GSJEmSNKUGntky8xLgHfXunsAFEXF4ROwZEYdTPQ6wZ33+HZl5ab/fTCcxoJECUyciDgFOA7ZapMglwMGZeVm5VkmSJEmSYGmZLSIOZP1w/lMy84hF6pgBPgi8tE1TTgRelpnrumt57+zRb0hmfgZ4LPAuqr8gd1E92/Ed4HXAHoZ8SZIkSRqOEpktM9dl5lHAwVTP7F8HrK7XZwHPysyjBxnywR59SZIkSZImij36kiRJkiRNEIO+JEmSJEkTxKAvSZIkSdIEMehLkiRJkjRBDPqSJEmSJE0Qg74kSZIkSRPEoC9JkiRJ0gQx6EuSJEmSNEEM+iMiIh4SEcdHxMURsTIibo2IFRHx2ojYbEB1bhYRP4+IrJcrB1GPJE2TQf48j4gjWn5md1qOaOhbkqSpVPL384h4WkScHBGX1XX9KgReTUgAABYsSURBVCIuiYj/johXRMQWTdanyReZOew2TL2IOAQ4DdhqkSKXAAdn5mUN1/tvwLEth36RmTs3WYckTZNB/zyvw/tJXRY/MjNP7qceSZp2pX4/j4jlVD/XD+1QdI/M/P5S6tJ0+f/bu/dwSYryAOPvx/2iqAQE44VFiMQLKgIqgYUVg4pXokHRgBKRCIGIRBPvSIwaBI0XvEUxoAR5jCgEozFKZDEgREUwCAiiAgIaWIEVhOXmlz+qmtM7OzNn5lxmdue8v+fpp7unarqq9xyK83V1Va0z7gosdBGxA/AFYEPgduAfgLPr+X7AwcBjgK9GxE6Zedsclvt6YAVwD/DAubiuJC1UY2jPnw3c0Cf9ulleX5IWpFG15xHxIOCbwI71o9OB04CfAvcBjwT2AF4y45vRgmWgP34fpjQa9wLPyszzW2nfioifAMdSGpM3AEfPtsCIWBv4NLA28HfAQRjoS9Jsjbo9vzIzr57lNSRJqxpVe348Jci/C3hpZp7Zkf594PSIOJLyd7s0MMfoj1FEPBVYXE8/09GIND4AXF6Pj4iIdeeg6CMojcoVwPvm4HqStKCNsT2XJM2hUbXnEbEbcEA9fXuXIP9+Wdw7bBla2Az0x2uf1nHXMZeZ+Tvgc/X0wcAzZlNgRGwFvKueHpKZd8/mepIkYAztuSRpXoyqPT+87pcDH53B96W+DPTHa7e6/y1wYZ9857SOd51lmR8HNgZOzsyls7yWJKkYR3suSZp7896eR8R6TE2+983MXFE/XzsiHhkRiyJig2GuKXUy0B+vx9b9VdO8jvPjLt8ZWkTsBzwXuIWVZ9uXJM3OSNvz6sSIuCEi7o6IZRFxQUS8OyIePsvrStJCNor2/ElAE8hfEhGbRMSHgGXAtcDPgeUR8c2IWDLktSXAQH9s6lO6zepp35mRM/MWylNFKLNvzqS8hwAfqqdvzsybZnIdSdLKRt2etywBHgasC/we8DTgbcBVEfHaWV5bkhacEbbnj2sdr0WZdO8IyjCAxnrAH1Mm/3vTkNeXDPTHqD3L/e0D5G8akgfMsLzjgC2A8ykz7kuS5sao2/OfAe+nLLf01LrtB3wRSEov0Scj4i9meH1JWqhG1Z5v2jp+E/AHwNcp7fkGwEOBQynj9wM4JiJe1HkRqR+X1xuf9ribQSbEu6vuNxy2oIjYHXg1ZYmQQzIzh72GJKmnkbXnlDWWP9ulHf8e8IWIeD7wZUov/wcj4szM/NUMypGkhWhU7fnGHWV+E3h+Zt5XP7uJ8sD2R5S5ANYC/qG26f4dr4HYoz8+K1rH6w2Qf/26v3OYQiJifeBTlKeBH87M/x3m+5KkaY2kPQfIzOX9/sjLzH9namWVjYCDhi1DkhawUbXnKzrO39QK8u+XmedSHt5CmQdg+yHL0QJmoD8+t7WOB3ndp3nyN8hrRG1vA7YDfgG8c8jvSpKmN6r2fFCforzCD7DHPJUhSZNoVO15u5ybMvOiPnn/s3W885DlaAHz1f0xycwVEfFrygRKj+iXt06k1zQkvxiyqGbyjrOAF0REtzzNtTeuM/MD3JiZ3xqyLElacEbYng9anxtrfTYDnIFfkgY0wva8nb/vpH8deTcfshwtYAb643UZsBjYNiLW6bOExx+2ji8fsozmtaM/r1s/mwGn1uNzAAN9SRrMKNrzYTiGU5JmZhTt+aWt47WnydtO77fcn7QSX90fr3PrfmNgxz752q9enjd/1ZEkzdBq055HxOZMLQ91w3yUIUkTbN7b88y8Bri2ni6KHq/cVtu0jq8fphwtbAb643VG67hrb3tErAW8sp7eCpw9TAGZGdNtwDU1+zWtz5cMeS+StJDNe3s+hL+gTMAK5e0sSdLgRtWef6nuNwGe2Sffi1vH5/bMJXUw0B+jzPwu8N/19KCI2KVLtjdQZtmEMmv+Pe3EiFgSEVm3k+avtpKkXkbRnkfEoojYoV896vJ6R9XTO4ETh7gNSVrwRvj3+YeYmn3/HyNik84MEbE/sKSefjUz52VuF00mx+iP3xGU1302BL4REe+lPBXcENiP0jMDcCXwgbHUUJI0iPluzxcBZ0fE+cBXgB8CN9a0RwN/WremN/+NmelrnpI0vHn/+zwzr42Io4BjKcvmfTci3gf8L6WX/8XAoTX7b4AjZ3YrWqgM9McsMy+KiJcB/0L5j/q9XbJdCTwvM2/rkiZJWg2MsD3fpW693AEcmZmfmkUZkrRgjao9z8zjImJTyipZ2wH/3CXbjcA+mfmTmZajhclAfzWQmV+JiCdSnh4+j7Kcx93AVcAXgY9m5h1jrKIkaQDz3J5fCOxPCfJ3Ah5GmXRvHeAWyizO/wWckJk39rqIJGl6o/r7PDPfEhFnUnrvF1Pa9hWUBwlnAsdn5vLZlqOFJzJdgUeSJEmSpEnhZHySJEmSJE0QA31JkiRJkiaIgb4kSZIkSRPEQF+SJEmSpAlioC9JkiRJ0gQx0JckSZIkaYIY6EuSJEmSNEEM9CVJkiRJmiAG+pIkSZIkTRADfUmSJEmSJoiBviRJkiRJE8RAX5IkSZKkCWKgL0mSJEnSBDHQlyRJkiRpghjoS5IkSZI0QQz0JUmSJEmaIAb6kqSJFxFLIiLrtmTc9VmdRMTRzb/NiMt9dS33koiIUZY9HyLiY/V+PjvuukiSZKAvSZJGKiIeALy3nr4rM0f6kGGevA+4GzggInYcd2UkSQubgb4kSRNmDXiD4XXAFsBlwGljrsucyMxrgc8CAfz9mKsjSVrgDPQlSdLIRMSGwF/X0w9OSG9+4wN1v7e9+pKkcTLQlyRJo7Q/8HvAXUxIb34jM68AflBP/2qcdZEkLWwG+pIkaZQOqvuvZuatY63J/Dil7veNiAeOtSaSpAXLQF+S1mAR8e91HPYFPdLbY7VvjohV2v2I2LKV55COtLUiYs+IeH9EnBcRyyLinoi4NSIurp8/qkfZu7eue/AA9/KWVv7H9cjzlIj4ZERcERG3R8Rv6/EnIuIx05UxiIjYJyK+GBHXRsSKeq/fj4h3RsRD+nzvpFr3q+v5gyPiXRFxaa3nrRHx7Yj4swHr8YKI+HpE3BQRd0TElRFxXERsWdOvruWd1PrOojp7/tmtS53d+ndttgP7lLtBRPxNRPwgIm6r23cj4vCIWGeQuve59lbA0+rpl/rkO7BV10V98i3qd09dfiZb1t/ZK+u/6fUR8a8R8fgu1/1IzXdnRPxfRJwSEdsMcJvNfW0EvGiA/JIkzblZ/Q9bkjR25wDPA3aMiAdk5u0d6Xu0jh8CPBG4uE+epR1pRwHv7FLug4An1e3QiNg/M0/vyPPfwLXAo4BXAJ/ufyu8ou4vzszL2gn1AcX7gddTJjtre0zdXhMRh2Xmp6Ypp6saxJ8G7NmRtD6wY93+MiJelJldH6y0rrUd8HVgUUfSYmBxROySmYf3+f7HgL/s+PgPgDcC+0fEc6e5nRmJiC0o9X5yR9LOdXtWROyTmb+bYRFLWsd9/w3nWkQ8iXJvW7Y+3hDYF3huRDwnM8+NiD2BL1N+xxsbUH4/946IxZl5aa9yMvOaiPhVLWdv4F/m+FYkSZqWPfqStGZbWvfrALt1SV8yzXn7s//LzB93pK0D/BL4OHAAsCsl4N0HOBa4ndJz+fmIeGz7i3WStVPr6e4R8fBeNxERTwSeUE9P6ZLleOBISpD/beDVtd5PBQ4GLq11/aeIeGGvcvqUvz5wFiXIvw84GXg58HRKcP424NfAQ4Gv1Z7pXjYCvkIZh/7uWs+daj2vq3kOi4hn96jL3zIV5P8COIzSC7478B5KAHpaLafT9cD2lH+fxqvrZ+3tjB51/zLwOOAjwF6Un/UrgMtr+gvqfczU4rr/dWb+bBbXGdZGwOnAesBbKb/HTweOpiyJtzFwckRsS/m3uQ04oubZDfggkJSHZZ8ZoLzv1v0efXNJkjRfMtPNzc3NbQ3dgLWB31CCkGM60tYH7qxpZ9b9GV2ucVlN+0KXtEXAun3KfwQleE3g5C7p29e0BN7Y5zrH1Dz3AQ/vSNurdY2Denx/A+C/ap6rgXU60pe0rrGky/ffU9NuAXbsUcZWwA013yld0k9qlXEr8PguebZt/Uz+rUv6lq30nwCbdcnzR5SJ7JqyTuqSp+/9duQ9upX37h7/PpsCv6p5fjiL39fmd+2safId2KrToj75FrXyHTjNz+QmYJsueQ5r5bkRuBLYvEu+Y1v5dpim/ke18m4x038vNzc3Nze3mW726EvSGiwz7wPOradLOpKfRgmAl1N6JKH0rN/f9kfEQ4GmJ/6cLte/OjPv6VP+dcBx9fSFEREd6ZcAl9TTrmPT63de3tQhM6/vyPLmuv9SZnbtTc3MFUDzKvxWwDN61blL+Q+gBHsA78jMC3uUcQ1T66PvGxEb97nsO7LL692ZeRVTvend3sB4FeVnBvD6zFzW5RrfAT7Wp+zZOD4zl3Yp82bgxHq6fUQ8qDPPgB5R9zfO8Puz8Y7M/GmXz/8ZWFGPNwdel5k3dcn3idbx4i7pbe37e/TgVZQkaW4Y6EvSmq8J0HesQWujeW34XOA7lJ7iZpx+Zx5YdXz+KiJik4jYOiIeHxFPiIgnAHfU5E2Arbt8rXkV/8mdr/dXu1HG8bfz3l8eUw8w+i7FlpmXA01gvEvfG1nZHkyNx55uubdv1/26lNfau1YF+HyfazQPEjaNiAd3pP1x3S8D/qPPNT7Xr5Kz0G3YRKOpd9D959xXHR7RzEJ/y7Dfn6UE/rVrQuadlLcnoNTrP3vk+znllX6YPni/uXW8Zc9ckiTNEwN9SVrzLa37znH6S5r0zLyLqcnPlnTJc1N2TIDXiIitIuL4OnP5cuBnwI8oPfWXAO3J7zbrcolTKYEWdO/Vbybhu4tVZ2Lfgan/V53aZfb4lbZW+cMEVzu1jn85zfV/1Mrbq4xlmfnrPuW1g8DO5deaeQouzv4T3l1Cec1+rnXO0dDWr96D2LR1POpAf1l9K6GXZpm/qzIzB8g33f2376/fmx+SJM0LA31JWvNdSJkUD2rgHhHrMdWrvbRjv6T13aZHf5XX9ut19qaMqz6c8kr8dDbs/CAzr6XMwA9TQX1z/XUps55D93XVHzpAmd10m6iul7ku444enzfaAfzaHWnN8n3dXh2/Xx2y0S9wnZHM7Ff3fvUexIrW8Sq/J/Ns0J/JoPmmu//2/fUc+iJJ0nxxeT1JWsNl5r0RcR7wbKaC+J0pwcZy4KL6WRPMN+P0N6XMsN5Ou19EbEZ5BX0jyoOE91Nea/4psDwz76759qRMhAerLn3XOIUya/zWdWm58+vnz6bMTt/k6dQOqF5LGYIwiGF6jNtlPIXBA7Prps+illuBeyl/e2w6Td41Xfv+Oh9eSZI07wz0JWkynEMJmptx+kvq5+fW3l8or+6vYGqc/jZMBeZLu1zzT4FmDPmfZOZZPcoeJGj7ImWJvPUor+83gX7Tw78c+GqX77Vfgb8jM3/UJc9stcu4qU4wOC63UIYEbN4vU0SszVTv/xohMzMillHub5i693p4BOX3aXXUvr9rx1YLSdKC5av7kjQZltZ9M05/j47P6TJOv8mzjLIOfafH1/3NfYJ8WHmMe1eZeQtTk8u9NCLWqbPWv6h+dlqtX6eLmRrfv+t05czQRa3j+SpjUM3P4cnt1RG62J6yfGIv/caZj1OzAsNjhvhOv/kWHjaLusyn5v7uAq4aZ0UkSQuTgb4kTYbvA7+tx3tR1lqHVXvqm/MlTPX6f7vHBGTNW18b9Ao6I2Ij4IAB69i8mr95reM+TI1z7zrbe13mrHk48YqI6NvTPUNnMTU2+3WdSwSOWDMEYjNg7z75XjnNddrj4fs9EBi1Zq6G7SJi0An9ntgnbc9Z1me+7Fz3F/VbnlKSpPlioC9JE6AGE8349YMoM323x+c3mrH4ezI1w3vXifiYWnJsI+ClnYn19fETgN8fsJpfAX5Tj/+Mqdf2r+9TB4B31/0mwGldlqRr12n9iDgsIjboladTnQDwo/X0j4AP9utNj4gtIuI1g15/SJ+l9AIDfKjOk9BZ/i7AYdNc55et423mqG5zoQn012KAN0GqN9cHSiuJiK2B17U+Wi0eaNRlBJuHE98YZ10kSQuXgb4kTY4mWG7WhG+Pz29cQAkkH0j/8flQ1h1vgs4TI+KYiHhmROwUEa8C/gd4OXDeIJXLzBXAl+vpPpRefYBT+y0ll5lfAz5cT3cHLo+Id9a6PDkido2IV0XECZQA96MMPwfNUfV+AI4AflAfGOxay3hGRBweEWdQxlwfMuT1B5KZNwB/V0+3BS6MiEMjYueI2C0i/p7S638DUzPzr/I2Rl3poJlr4I0R8cKI2C4itq3bTJbHmwvfYarezxzwO1sB50fEARHxlIjYJSL+hvK7/CCm7v8lEfGsiNh2bqs8tN2Bdevx6eOsiCRp4XIyPkmaHEunOSczV0TEBUyNz7+ZqXHTnXmvi4hDKb32GwBvqlvbF4BPU15/H8QpwIGsvLZ419f2OxxZ6/oOypjto/vk/S3Q+YCjr8y8KyL2Ak4CXgw8iale/m5+0ydtto6hBLevBR4FfLwjfRllScLmockKuntv/e7WwL91pP055V5HKjPvjojPAW+gPCR6+wBfOw74W+BzXdLeDjwfeDrlwdFewDMY77j45k2VSzPz4jHWQ5K0gNmjL0mT43usvA740h752p/3Gp8PQGaeCCwGzqD0xN5D6TX/OvCyzNyP4YLqb7Hya+WXDRIMZfEuyiRnx1LmJLi5ln0bcBnlgcGrgIdl5p1D1Kkp47bMfAnlfk8ArqjXvreW9T3gY8BzmXobYc7Vez2EMlHhN2rZKyjB60eAHTLz+5ShDFCGaHS7zieAl9Rr3FjvY3Xw6bp/dEQ8fYD8n6A8fDmP8vO4nbJqw76Z+R7gr4Af18+/xhiD/Dpk5MX1tPMBjSRJIxN9/r6TJEmroYh4BPCLevqazPzMOOszrIj4GmWywRMy8+Au6QcCJ9bTrTPz6tHVbuYiYn/gZMqSjYsy8/YxV0mStEDZoy9J0prn5a3jC3rmWn29Bfgd8MqIeOS4KzMX6gSOb62nxxnkS5LGyUBfkqTVSERsHBE914ePiB0ocxUAXJiZl46mZnMnM38IfB5YjxL0T4J9gcdSJmv8yJjrIkla4JyMT5Kk1cvmlJUFzqDMhXAFZfWD3weeQ1k+cUPKbPN/Pa5KzoG3Aj8FVkRE9JsrYg2xNmXFhG/NZI4ISZLmkmP0JUlajUTEIuDn02S7Gzg4M7vNRL/GW1PH6EuStLqwR1+SpNXL9cDLKL33O1N6+DelrKhwNWUpw+Mz85pxVVCSJK3e7NGXJEmSJGmCOBmfJEmSJEkTxEBfkiRJkqQJYqAvSZIkSdIEMdCXJEmSJGmCGOhLkiRJkjRBDPQlSZIkSZogBvqSJEmSJE0QA31JkiRJkiaIgb4kSZIkSRPEQF+SJEmSpAlioC9JkiRJ0gQx0JckSZIkaYIY6EuSJEmSNEEM9CVJkiRJmiAG+pIkSZIkTRADfUmSJEmSJoiBviRJkiRJE8RAX5IkSZKkCWKgL0mSJEnSBPl/J2NHSJ1xfjUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=200)\n", - "plt.pcolormesh(\n", - " np.reshape(mode_wvl, (nmode, nfreq)),\n", - " np.reshape(mode_angle, (nmode, nfreq)),\n", - " np.reshape(mode_tran, (nmode, nfreq)),\n", - " cmap=\"Blues\",\n", - " shading=\"flat\",\n", - " vmin=0,\n", - " vmax=tran_max,\n", - ")\n", + "plt.pcolormesh(np.reshape(mode_wvl,(nmode,nfreq)),\n", + " np.reshape(mode_angle,(nmode,nfreq)),\n", + " np.reshape(mode_tran,(nmode,nfreq)),\n", + " cmap='Blues',\n", + " shading='flat',\n", + " vmin=0,\n", + " vmax=tran_max)\n", "plt.axis([min(mode_wvl), max(mode_wvl), min(mode_angle), max(mode_angle)])\n", "plt.xlabel(\"wavelength (μm)\")\n", "plt.ylabel(\"diffraction angle (degrees)\")\n", - "plt.xticks([t for t in np.linspace(wvl_min, wvl_max, 3)])\n", - "plt.yticks([t for t in range(0, 35, 5)])\n", + "plt.xticks([t for t in np.linspace(wvl_min,wvl_max,3)])\n", + "plt.yticks([t for t in range(0,35,5)])\n", "plt.title(\"transmittance of diffraction orders\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.arange(0, tran_max + 0.1, 0.1)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.arange(0, tran_max + 0.1, 0.1)])" + "cbar.set_ticks([t for t in np.arange(0,tran_max+0.1,0.1)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.arange(0,tran_max+0.1,0.1)])" ] }, { @@ -284,63 +358,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "field decay(t = 50.01): 0.11139427530016409 / 0.11139427530016409 = 1.0\n", + "field decay(t = 100.01): 1.824305440068526e-15 / 0.11139427530016409 = 1.637701250941967e-14\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + " block, center = (-2.25,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "field decay(t = 50.01): 0.1082498119209954 / 0.1082498119209954 = 1.0\n", + "field decay(t = 100.01): 7.512926070352665e-06 / 0.1082498119209954 = 6.940359467632024e-05\n", + "field decay(t = 150.02): 6.732414916367269e-06 / 0.1082498119209954 = 6.219331744687766e-05\n", + "field decay(t = 200.03): 2.3712635292765586e-06 / 0.1082498119209954 = 2.1905474819736332e-05\n", + "field decay(t = 250.04): 9.494875348809632e-07 / 0.1082498119209954 = 8.771262675023706e-06\n", + "field decay(t = 300.04): 3.8220269083178343e-07 / 0.1082498119209954 = 3.530746927400933e-06\n", + "field decay(t = 350.05): 1.5689447663324306e-07 / 0.1082498119209954 = 1.4493741268368232e-06\n", + "field decay(t = 400.06): 6.492618040375044e-08 / 0.1082498119209954 = 5.997809996301509e-07\n", + "field decay(t = 450.07): 2.9338161615346314e-08 / 0.1082498119209954 = 2.710227490903943e-07\n", + "field decay(t = 500.08): 1.115652431764312e-08 / 0.1082498119209954 = 1.0306275936798441e-07\n", + "field decay(t = 550.08): 4.421515879496221e-09 / 0.1082498119209954 = 4.084548324872104e-08\n", + "field decay(t = 600.09): 1.6989786783603636e-09 / 0.1082498119209954 = 1.5694980418075362e-08\n", + "field decay(t = 650.1): 8.779199940057175e-10 / 0.1082498119209954 = 8.110129509014344e-09\n", + "field decay(t = 700.11): 2.64630974046326e-10 / 0.1082498119209954 = 2.444632183189964e-09\n", + "field decay(t = 750.12): 1.5056115102443512e-10 / 0.1082498119209954 = 1.39086755304776e-09\n", + "field decay(t = 800.13): 6.39132960275918e-11 / 0.1082498119209954 = 5.904240838241642e-10\n", + "run 0 finished at t = 800.13 (80013 timesteps)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from meep.materials import fused_quartz\n", "\n", - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 3.0 # substrate thickness\n", - "dpad = 3.0 # padding between grating and PML\n", - "gp = 10.0 # grating period\n", - "gh = 0.5 # grating height\n", - "gdc = 0.5 # grating duty cycle\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 3.0 # substrate thickness\n", + "dpad = 3.0 # padding between grating and PML\n", + "gp = 10.0 # grating period\n", + "gh = 0.5 # grating height\n", + "gdc = 0.5 # grating duty cycle\n", "\n", - "sx = dpml + dsub + gh + dpad + dpml\n", + "sx = dpml+dsub+gh+dpad+dpml\n", "sy = gp\n", "\n", - "cell_size = mp.Vector3(sx, sy, 0)\n", - "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", - "\n", - "wvl_min = 0.4 # min wavelength\n", - "wvl_max = 0.6 # max wavelength\n", - "fmin = 1 / wvl_max # min frequency\n", - "fmax = 1 / wvl_min # max frequency\n", - "fcen = 0.5 * (fmin + fmax) # center frequency\n", - "df = fmax - fmin # frequency width\n", - "\n", - "src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub)\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy),\n", - " )\n", - "]\n", - "\n", - "k_point = mp.Vector3(0, 0, 0)\n", - "\n", - "symmetries = [mp.Mirror(mp.Y)]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " default_material=fused_quartz,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - ")\n", + "cell_size = mp.Vector3(sx,sy,0)\n", + "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", + "\n", + "wvl_min = 0.4 # min wavelength\n", + "wvl_max = 0.6 # max wavelength\n", + "fmin = 1/wvl_max # min frequency\n", + "fmax = 1/wvl_min # max frequency\n", + "fcen = 0.5*(fmin+fmax) # center frequency\n", + "df = fmax-fmin # frequency width\n", + "\n", + "src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub)\n", + "sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(y=sy))]\n", + "\n", + "k_point = mp.Vector3(0,0,0)\n", + "\n", + "symmetries=[mp.Mirror(mp.Y)]\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " default_material=fused_quartz,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", "\n", "nfreq = 21\n", - "mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad)\n", - "flux_mon = sim.add_flux(\n", - " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", - ")\n", + "mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad)\n", + "flux_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))\n", "\n", @@ -348,41 +460,25 @@ "\n", "sim.reset_meep()\n", "\n", - "geometry = [\n", - " mp.Block(\n", - " material=fused_quartz,\n", - " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", - " ),\n", - " mp.Block(\n", - " material=fused_quartz,\n", - " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh),\n", - " ),\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - ")\n", - "\n", - "mode_mon = sim.add_flux(\n", - " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", - ")\n", + "geometry = [mp.Block(material=fused_quartz, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub))),\n", + " mp.Block(material=fused_quartz, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))]\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + "mode_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))\n", "\n", "freqs = mp.get_eigenmode_freqs(mode_mon)\n", "\n", "nmode = 10\n", - "res = sim.get_eigenmode_coefficients(\n", - " mode_mon, range(1, nmode + 1), eig_parity=mp.ODD_Z + mp.EVEN_Y\n", - ")\n", + "res = sim.get_eigenmode_coefficients(mode_mon, range(1,nmode+1), eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", "coeffs = res.alpha\n", "kdom = res.kdom\n", "\n", @@ -391,33 +487,31 @@ "mode_tran = []\n", "\n", "for nm in range(nmode):\n", - " for nf in range(nfreq):\n", - " mode_wvl.append(1 / freqs[nf])\n", - " mode_angle.append(math.degrees(math.acos(kdom[nm * nfreq + nf].x / freqs[nf])))\n", - " tran = abs(coeffs[nm, nf, 0]) ** 2 / input_flux[nf]\n", - " mode_tran.append(0.5 * tran if nm != 0 else tran)\n", + " for nf in range(nfreq):\n", + " mode_wvl.append(1/freqs[nf])\n", + " mode_angle.append(math.degrees(math.acos(kdom[nm*nfreq+nf].x/freqs[nf])))\n", + " tran = abs(coeffs[nm,nf,0])**2/input_flux[nf]\n", + " mode_tran.append(0.5*tran if nm != 0 else tran)\n", "\n", - "tran_max = round(max(mode_tran), 1)\n", + "tran_max = round(max(mode_tran),1)\n", "\n", "plt.figure(dpi=200)\n", - "plt.pcolormesh(\n", - " np.reshape(mode_wvl, (nmode, nfreq)),\n", - " np.reshape(mode_angle, (nmode, nfreq)),\n", - " np.reshape(mode_tran, (nmode, nfreq)),\n", - " cmap=\"Blues\",\n", - " shading=\"flat\",\n", - " vmin=0,\n", - " vmax=tran_max,\n", - ")\n", + "plt.pcolormesh(np.reshape(mode_wvl,(nmode,nfreq)),\n", + " np.reshape(mode_angle,(nmode,nfreq)),\n", + " np.reshape(mode_tran,(nmode,nfreq)),\n", + " cmap='Blues',\n", + " shading='flat',\n", + " vmin=0,\n", + " vmax=tran_max)\n", "plt.axis([min(mode_wvl), max(mode_wvl), min(mode_angle), max(mode_angle)])\n", "plt.xlabel(\"wavelength (μm)\")\n", "plt.ylabel(\"diffraction angle (degrees)\")\n", - "plt.xticks([t for t in np.linspace(wvl_min, wvl_max, 3)])\n", - "plt.yticks([t for t in range(0, 35, 5)])\n", + "plt.xticks([t for t in np.linspace(wvl_min,wvl_max,3)])\n", + "plt.yticks([t for t in range(0,35,5)])\n", "plt.title(\"transmittance of diffraction orders\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.arange(0, tran_max + 0.1, 0.1)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.arange(0, tran_max + 0.1, 0.1)])" + "cbar.set_ticks([t for t in np.arange(0,tran_max+0.1,0.1)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.arange(0,tran_max+0.1,0.1)])" ] }, { diff --git a/python/examples/binary_grating_n2f.ipynb b/python/examples/binary_grating_n2f.ipynb index c3992260d..771b5c682 100644 --- a/python/examples/binary_grating_n2f.ipynb +++ b/python/examples/binary_grating_n2f.ipynb @@ -20,9 +20,208 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.52 x 0.04 x 0 with resolution 25\n", + "time for set_epsilon = 0.00124812 s\n", + "-----------\n", + "field decay(t = 50.02): 0.10083813086471559 / 0.10083813086471559 = 1.0\n", + "field decay(t = 100.04): 3.8807013552778427e-16 / 0.10083813086471559 = 3.848446338701171e-15\n", + "run 0 finished at t = 100.04 (5002 timesteps)\n", + "\n", + "Field time usage:\n", + " connecting chunks: 0.00231314 s\n", + " time stepping: 0.169821 s\n", + " communicating: 0.0808802 s\n", + " Fourier transforming: 0.0359166 s\n", + " everything else: 0.156131 s\n", + "\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.52 x 10 x 0 with resolution 25\n", + " block, center = (-2.25,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,0,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.105644 s\n", + "-----------\n", + "field decay(t = 50.02): 0.09814324428153094 / 0.09814324428153094 = 1.0\n", + "field decay(t = 100.04): 7.939880373487791e-06 / 0.09814324428153094 = 8.090093649962981e-05\n", + "field decay(t = 150.06): 6.627624476069797e-06 / 0.09814324428153094 = 6.753011401434805e-05\n", + "field decay(t = 200.08): 2.146203858444386e-06 / 0.09814324428153094 = 2.1868075323532677e-05\n", + "on time step 11243 (time=224.86), 0.00035578 s/step\n", + "field decay(t = 250.1): 8.032368775670381e-07 / 0.09814324428153094 = 8.184331824846705e-06\n", + "field decay(t = 300.12): 2.965177550201506e-07 / 0.09814324428153094 = 3.0212752511988307e-06\n", + "field decay(t = 350.14): 1.3086054960500633e-07 / 0.09814324428153094 = 1.3333627858237849e-06\n", + "field decay(t = 400.16): 5.285162741753926e-08 / 0.09814324428153094 = 5.385151856803365e-07\n", + "field decay(t = 450.18): 1.8336367759809585e-08 / 0.09814324428153094 = 1.8683270452330267e-07\n", + "on time step 22548 (time=450.96), 0.000353856 s/step\n", + "field decay(t = 500.2): 7.33127235003134e-09 / 0.09814324428153094 = 7.469971472515275e-08\n", + "field decay(t = 550.22): 2.8980535977675605e-09 / 0.09814324428153094 = 2.952881391870729e-08\n", + "field decay(t = 600.24): 9.12195167093687e-10 / 0.09814324428153094 = 9.294528357723631e-09\n", + "field decay(t = 650.26): 3.6982320415205274e-10 / 0.09814324428153094 = 3.768198278540582e-09\n", + "on time step 33740 (time=674.8), 0.000357411 s/step\n", + "field decay(t = 700.28): 1.7964764774382362e-10 / 0.09814324428153094 = 1.830463717181505e-09\n", + "field decay(t = 750.3000000000001): 1.1056722890352689e-10 / 0.09814324428153094 = 1.1265903192109368e-09\n", + "field decay(t = 800.32): 3.1235531072986875e-11 / 0.09814324428153094 = 3.182647089124699e-10\n", + "run 0 finished at t = 800.32 (40016 timesteps)\n", + "\n", + "Field time usage:\n", + " connecting chunks: 0.00979877 s\n", + " time stepping: 9.10302 s\n", + " communicating: 1.25385 s\n", + " Fourier transforming: 2.74567 s\n", + " everything else: 1.15844 s\n", + "\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.52 x 212 x 0 with resolution 25\n", + " block, center = (-2.25,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,-100,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,-90,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,-80,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,-70,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,-60,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,-50,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,-40,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,-30,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,-20,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,-10,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,0,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,10,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,20,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,30,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,40,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,50,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,60,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,70,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,80,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,90,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (0,100,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 3.14788 s\n", + "-----------\n", + "on time step 597 (time=11.94), 0.00670513 s/step\n", + "on time step 1213 (time=24.26), 0.00650265 s/step\n", + "on time step 1848 (time=36.96), 0.00630352 s/step\n", + "on time step 2483 (time=49.66), 0.00630129 s/step\n", + "field decay(t = 50.02): 0.09814324428153119 / 0.09814324428153119 = 1.0\n", + "on time step 3124 (time=62.48), 0.00624995 s/step\n", + "on time step 3763 (time=75.26), 0.00626324 s/step\n", + "on time step 4390 (time=87.8), 0.00638796 s/step\n", + "field decay(t = 100.04): 7.939880373488582e-06 / 0.09814324428153119 = 8.090093649963766e-05\n", + "on time step 5016 (time=100.32), 0.00639027 s/step\n", + "on time step 5655 (time=113.1), 0.00626925 s/step\n", + "on time step 6286 (time=125.72), 0.00634788 s/step\n", + "on time step 6913 (time=138.26), 0.00638669 s/step\n", + "field decay(t = 150.06): 6.627624476069556e-06 / 0.09814324428153119 = 6.753011401434542e-05\n", + "on time step 7542 (time=150.84), 0.00636212 s/step\n", + "on time step 8176 (time=163.52), 0.00631072 s/step\n", + "on time step 8807 (time=176.14), 0.00633942 s/step\n", + "on time step 9430 (time=188.6), 0.00642652 s/step\n", + "field decay(t = 200.08): 3.77258555623287e-08 / 0.09814324428153119 = 3.8439584750336235e-07\n", + "on time step 10053 (time=201.06), 0.00642324 s/step\n", + "on time step 10693 (time=213.86), 0.006254 s/step\n", + "on time step 11327 (time=226.54), 0.00630954 s/step\n", + "on time step 11966 (time=239.32), 0.00626743 s/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "field decay(t = 250.1): 3.5244910064367445e-10 / 0.09814324428153119 = 3.591170265705177e-09\n", + "on time step 12599 (time=251.98), 0.00632365 s/step\n", + "on time step 13244 (time=264.88), 0.00620512 s/step\n", + "on time step 13874 (time=277.48), 0.00634975 s/step\n", + "on time step 14516 (time=290.32), 0.00623725 s/step\n", + "field decay(t = 300.12): 1.0847045885564748e-11 / 0.09814324428153119 = 1.1052259342934691e-10\n", + "run 0 finished at t = 300.12 (15006 timesteps)\n", + "error:, 10, 5.981324591508142e-05\n" + ] + } + ], "source": [ "import meep as mp\n", "import math\n", @@ -30,190 +229,123 @@ "from numpy import linalg as LA\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 25 # pixels/μm\n", + "resolution = 25 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 3.0 # substrate thickness\n", - "dpad = 3.0 # padding between grating and PML\n", - "gp = 10.0 # grating period\n", - "gh = 0.5 # grating height\n", - "gdc = 0.5 # grating duty cycle\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 3.0 # substrate thickness\n", + "dpad = 3.0 # padding between grating and PML\n", + "gp = 10.0 # grating period\n", + "gh = 0.5 # grating height\n", + "gdc = 0.5 # grating duty cycle\n", "\n", - "nperiods = 10 # number of unit cells in finite periodic grating\n", + "nperiods = 10 # number of unit cells in finite periodic grating\n", "\n", - "ff_distance = 1e8 # far-field distance from near-field monitor\n", - "ff_angle = 20 # far-field cone angle\n", - "ff_npts = 500 # number of far-field points\n", + "ff_distance = 1e8 # far-field distance from near-field monitor\n", + "ff_angle = 20 # far-field cone angle\n", + "ff_npts = 500 # number of far-field points\n", "\n", - "ff_length = ff_distance * math.tan(math.radians(ff_angle))\n", - "ff_res = ff_npts / ff_length\n", + "ff_length = ff_distance*math.tan(math.radians(ff_angle))\n", + "ff_res = ff_npts/ff_length\n", "\n", - "sx = dpml + dsub + gh + dpad + dpml\n", + "sx = dpml+dsub+gh+dpad+dpml\n", "cell_size = mp.Vector3(sx)\n", "\n", - "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", + "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", "\n", "symmetries = [mp.Mirror(mp.Y)]\n", "\n", - "wvl_min = 0.4 # min wavelength\n", - "wvl_max = 0.6 # max wavelength\n", - "fmin = 1 / wvl_max # min frequency\n", - "fmax = 1 / wvl_min # max frequency\n", - "fcen = 0.5 * (fmin + fmax) # center frequency\n", - "df = fmax - fmin # frequency width\n", + "wvl_min = 0.4 # min wavelength\n", + "wvl_max = 0.6 # max wavelength\n", + "fmin = 1/wvl_max # min frequency\n", + "fmax = 1/wvl_min # max frequency\n", + "fcen = 0.5*(fmin+fmax) # center frequency\n", + "df = fmax-fmin # frequency width\n", "\n", - "src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub)\n", - "sources = [\n", - " mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt)\n", - "]\n", + "src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub)\n", + "sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt)]\n", "\n", "k_point = mp.Vector3()\n", "\n", "glass = mp.Medium(index=1.5)\n", "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " default_material=glass,\n", - " sources=sources,\n", - ")\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " default_material=glass,\n", + " sources=sources)\n", "\n", "nfreq = 21\n", - "n2f_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad)\n", + "n2f_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad)\n", "n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt))\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9))\n", "\n", - "ff_source = sim.get_farfields(\n", - " n2f_obj,\n", - " ff_res,\n", - " center=mp.Vector3(ff_distance, 0.5 * ff_length),\n", - " size=mp.Vector3(y=ff_length),\n", - ")\n", + "ff_source = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length))\n", "\n", "sim.reset_meep()\n", "\n", "### unit cell with periodic boundaries\n", "\n", "sy = gp\n", - "cell_size = mp.Vector3(sx, sy)\n", - "\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy),\n", - " )\n", - "]\n", - "\n", - "geometry = [\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", - " ),\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh),\n", - " ),\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " split_chunks_evenly=True,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - ")\n", - "\n", - "n2f_obj = sim.add_near2far(\n", - " fcen,\n", - " df,\n", - " nfreq,\n", - " mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)),\n", - " nperiods=nperiods,\n", - ")\n", + "cell_size = mp.Vector3(sx,sy)\n", + "\n", + "sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(y=sy))]\n", + "\n", + "geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub))),\n", + " mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))]\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " split_chunks_evenly=True,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + "n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)), nperiods=nperiods)\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9))\n", "\n", - "ff_unitcell = sim.get_farfields(\n", - " n2f_obj,\n", - " ff_res,\n", - " center=mp.Vector3(ff_distance, 0.5 * ff_length),\n", - " size=mp.Vector3(y=ff_length),\n", - ")\n", + "ff_unitcell = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length))\n", "\n", "sim.reset_meep()\n", "\n", "### finite periodic grating with flat surface termination extending into PML\n", "\n", - "num_cells = 2 * nperiods + 1\n", - "sy = dpml + num_cells * gp + dpml\n", - "cell_size = mp.Vector3(sx, sy)\n", + "num_cells = 2*nperiods+1\n", + "sy = dpml+num_cells*gp+dpml\n", + "cell_size = mp.Vector3(sx,sy)\n", "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy - 2 * dpml),\n", - " )\n", - "]\n", - "\n", - "geometry = [\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", - " )\n", - "]\n", + "sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(y=sy-2*dpml))]\n", + "\n", + "geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))]\n", "\n", "for j in range(num_cells):\n", - " geometry.append(\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", - " center=mp.Vector3(\n", - " -0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + dpml + (j + 0.5) * gp\n", - " ),\n", - " )\n", - " )\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " split_chunks_evenly=True,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - ")\n", - "\n", - "n2f_obj = sim.add_near2far(\n", - " fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy - 2 * dpml))\n", - ")\n", + " geometry.append(mp.Block(material=glass,\n", + " size=mp.Vector3(gh,gdc*gp,mp.inf),\n", + " center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+dpml+(j+0.5)*gp)))\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " split_chunks_evenly=True, \n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + "n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy-2*dpml)))\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9))\n", "\n", - "ff_supercell = sim.get_farfields(\n", - " n2f_obj,\n", - " ff_res,\n", - " center=mp.Vector3(ff_distance, 0.5 * ff_length),\n", - " size=mp.Vector3(y=ff_length),\n", - ")\n", + "ff_supercell = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length))\n", "\n", - "norm_err = LA.norm(ff_unitcell[\"Ez\"] - ff_supercell[\"Ez\"]) / nperiods\n", - "print(\"error:, {}, {}\".format(nperiods, norm_err))" + "norm_err = LA.norm(ff_unitcell['Ez']-ff_supercell['Ez'])/nperiods\n", + "print(\"error:, {}, {}\".format(nperiods,norm_err))" ] }, { @@ -225,40 +357,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "freqs = mp.get_near2far_freqs(n2f_obj)\n", - "wvl = np.divide(1, freqs)\n", - "ff_lengths = np.linspace(0, ff_length, ff_npts)\n", - "angles = [math.degrees(math.atan(f)) for f in ff_lengths / ff_distance]\n", + "wvl = np.divide(1,freqs)\n", + "ff_lengths = np.linspace(0,ff_length,ff_npts)\n", + "angles = [math.degrees(math.atan(f)) for f in ff_lengths/ff_distance]\n", "\n", "wvl_slice = 0.5\n", - "idx_slice = np.where(np.asarray(freqs) == 1 / wvl_slice)[0][0]\n", + "idx_slice = np.where(np.asarray(freqs) == 1/wvl_slice)[0][0]\n", "\n", - "rel_enh = np.absolute(ff_unitcell[\"Ez\"]) ** 2 / np.absolute(ff_source[\"Ez\"]) ** 2\n", + "rel_enh = np.absolute(ff_unitcell['Ez'])**2/np.absolute(ff_source['Ez'])**2\n", "\n", "plt.figure(dpi=150)\n", "\n", - "plt.subplot(1, 2, 1)\n", - "plt.pcolormesh(wvl, angles, rel_enh, cmap=\"Blues\", shading=\"flat\")\n", - "plt.axis([wvl_min, wvl_max, 0, ff_angle])\n", + "plt.subplot(1,2,1)\n", + "plt.pcolormesh(wvl,angles,rel_enh,cmap='Blues',shading='flat')\n", + "plt.axis([wvl_min,wvl_max,0,ff_angle])\n", "plt.xlabel(\"wavelength (μm)\")\n", "plt.ylabel(\"angle (degrees)\")\n", - "plt.grid(linewidth=0.5, linestyle=\"--\")\n", - "plt.xticks([t for t in np.arange(wvl_min, wvl_max + 0.1, 0.1)])\n", - "plt.yticks([t for t in range(0, ff_angle + 1, 10)])\n", + "plt.grid(linewidth=0.5,linestyle='--')\n", + "plt.xticks([t for t in np.arange(wvl_min,wvl_max+0.1,0.1)])\n", + "plt.yticks([t for t in range(0,ff_angle+1,10)])\n", "plt.title(\"far-field spectra\")\n", "\n", - "plt.subplot(1, 2, 2)\n", - "plt.plot(angles, rel_enh[:, idx_slice], \"bo-\")\n", - "plt.xlim(0, ff_angle)\n", + "plt.subplot(1,2,2)\n", + "plt.plot(angles,rel_enh[:,idx_slice],'bo-')\n", + "plt.xlim(0,ff_angle)\n", "plt.ylim(0)\n", - "plt.xticks([t for t in range(0, ff_angle + 1, 10)])\n", + "plt.xticks([t for t in range(0,ff_angle+1,10)])\n", "plt.xlabel(\"angle (degrees)\")\n", "plt.ylabel(\"relative enhancement\")\n", - "plt.grid(axis=\"x\", linewidth=0.5, linestyle=\"--\")\n", + "plt.grid(axis='x',linewidth=0.5,linestyle='--')\n", "plt.title(\"f.-f. spectra @ λ = {:.1} μm\".format(wvl_slice))\n", "\n", "plt.tight_layout(pad=0.5)\n", diff --git a/python/examples/binary_grating_oblique.ipynb b/python/examples/binary_grating_oblique.ipynb index 7a155ebbb..494b44cb5 100644 --- a/python/examples/binary_grating_oblique.ipynb +++ b/python/examples/binary_grating_oblique.ipynb @@ -29,9 +29,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import math\n", @@ -49,24 +57,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 3.0 # substrate thickness\n", - "dpad = 3.0 # length of padding between grating and PML\n", - "gp = 10.0 # grating period\n", - "gh = 0.5 # grating height\n", - "gdc = 0.5 # grating duty cycle\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 3.0 # substrate thickness\n", + "dpad = 3.0 # length of padding between grating and PML\n", + "gp = 10.0 # grating period\n", + "gh = 0.5 # grating height\n", + "gdc = 0.5 # grating duty cycle\n", "\n", - "sx = dpml + dsub + gh + dpad + dpml\n", + "sx = dpml+dsub+gh+dpad+dpml\n", "sy = gp\n", "\n", - "cell_size = mp.Vector3(sx, sy, 0)\n", - "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]" + "cell_size = mp.Vector3(sx,sy,0)\n", + "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] " ] }, { @@ -78,48 +86,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "ng = 1.5\n", "glass = mp.Medium(index=ng)\n", "\n", - "wvl = 0.5 # center wavelength\n", - "fcen = 1 / wvl # center frequency\n", - "df = 0.05 * fcen # frequency width\n", + "wvl = 0.5 # center wavelength\n", + "fcen = 1/wvl # center frequency\n", + "df = 0.05*fcen # frequency width\n", "\n", "# rotation angle of incident planewave; counter clockwise (CCW) about Z axis, 0 degrees along +X axis\n", "theta_in = math.radians(10.7)\n", "\n", "# k (in source medium) with correct length (plane of incidence: XY)\n", - "k = mp.Vector3(fcen * ng).rotate(mp.Vector3(z=1), theta_in)\n", + "k = mp.Vector3(fcen*ng).rotate(mp.Vector3(z=1), theta_in)\n", "\n", "symmetries = []\n", "eig_parity = mp.ODD_Z\n", "if theta_in == 0:\n", - " k = mp.Vector3(0, 0, 0)\n", - " symmetries = [mp.Mirror(mp.Y)]\n", - " eig_parity += mp.EVEN_Y\n", - "\n", - "\n", - "def pw_amp(k, x0):\n", - " def _pw_amp(x):\n", - " return cmath.exp(1j * 2 * math.pi * k.dot(x + x0))\n", + " k = mp.Vector3(0,0,0)\n", + " symmetries = [mp.Mirror(mp.Y)]\n", + " eig_parity += mp.EVEN_Y\n", "\n", - " return _pw_amp\n", + "def pw_amp(k,x0):\n", + " def _pw_amp(x):\n", + " return cmath.exp(1j*2*math.pi*k.dot(x+x0))\n", + " return _pw_amp\n", "\n", - "\n", - "src_pt = mp.Vector3(-0.5 * sx + dpml + 0.3 * dsub, 0, 0)\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(0, sy, 0),\n", - " amp_func=pw_amp(k, src_pt),\n", - " )\n", - "]" + "src_pt = mp.Vector3(-0.5*sx+dpml+0.3*dsub,0,0)\n", + "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(0,sy,0),\n", + " amp_func=pw_amp(k,src_pt))]" ] }, { @@ -131,24 +132,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k,\n", - " default_material=glass,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - ")\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k,\n", + " default_material=glass,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", "\n", - "refl_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub, 0, 0)\n", - "refl_flux = sim.add_flux(\n", - " fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, sy, 0))\n", - ")" + "refl_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub,0,0)\n", + "refl_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0,sy,0)))" ] }, { @@ -160,9 +157,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "Meep: using complex fields.\n", + "Meep progress: 9.32/200.0 = 4.7% done in 4.0s, 81.9s to go\n", + "Meep progress: 17.79/200.0 = 8.9% done in 8.0s, 82.0s to go\n", + "Meep progress: 26.91/200.0 = 13.5% done in 12.0s, 77.2s to go\n", + "Meep progress: 36.730000000000004/200.0 = 18.4% done in 16.0s, 71.2s to go\n", + "Meep progress: 46.550000000000004/200.0 = 23.3% done in 20.0s, 66.0s to go\n", + "Meep progress: 56.34/200.0 = 28.2% done in 24.0s, 61.2s to go\n", + "Meep progress: 66.34/200.0 = 33.2% done in 28.0s, 56.4s to go\n", + "Meep progress: 76.15/200.0 = 38.1% done in 32.0s, 52.1s to go\n", + "Meep progress: 86.03/200.0 = 43.0% done in 36.0s, 47.7s to go\n", + "Meep progress: 95.34/200.0 = 47.7% done in 40.0s, 43.9s to go\n", + "Meep progress: 104.71000000000001/200.0 = 52.4% done in 44.0s, 40.1s to go\n", + "Meep progress: 114.54/200.0 = 57.3% done in 48.0s, 35.8s to go\n", + "Meep progress: 124.45/200.0 = 62.2% done in 52.0s, 31.6s to go\n", + "Meep progress: 134.25/200.0 = 67.1% done in 56.0s, 27.4s to go\n", + "Meep progress: 144.12/200.0 = 72.1% done in 60.0s, 23.3s to go\n", + "Meep progress: 154.06/200.0 = 77.0% done in 64.0s, 19.1s to go\n", + "Meep progress: 163.6/200.0 = 81.8% done in 68.0s, 15.1s to go\n", + "Meep progress: 173.31/200.0 = 86.7% done in 72.0s, 11.1s to go\n", + "Meep progress: 182.85/200.0 = 91.4% done in 76.1s, 7.1s to go\n", + "Meep progress: 192.52/200.0 = 96.3% done in 80.1s, 3.1s to go\n", + "run 0 finished at t = 200.0 (20000 timesteps)\n" + ] + } + ], "source": [ "sim.run(until_after_sources=100)\n", "\n", @@ -179,44 +207,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (-2.25,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0,0)\n", + " size (0.5,5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Meep: using complex fields.\n" + ] + } + ], "source": [ "sim.reset_meep()\n", "\n", - "geometry = [\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0),\n", - " ),\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0),\n", - " ),\n", - "]\n", + "geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)),\n", + " mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))]\n", "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - ")\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", "\n", - "refl_flux = sim.add_flux(\n", - " fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, sy, 0))\n", - ")\n", - "sim.load_minus_flux_data(refl_flux, input_flux_data)\n", + "refl_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0,sy,0)))\n", + "sim.load_minus_flux_data(refl_flux,input_flux_data)\n", "\n", - "tran_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0)\n", - "tran_flux = sim.add_flux(\n", - " fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0))\n", - ")" + "tran_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0)\n", + "tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0)))" ] }, { @@ -228,9 +256,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 9.58/300.0 = 3.2% done in 4.0s, 121.4s to go\n", + "Meep progress: 18.59/300.0 = 6.2% done in 8.0s, 121.2s to go\n", + "Meep progress: 28.060000000000002/300.0 = 9.4% done in 12.0s, 116.4s to go\n", + "Meep progress: 37.59/300.0 = 12.5% done in 16.0s, 111.8s to go\n", + "Meep progress: 47.0/300.0 = 15.7% done in 20.0s, 107.7s to go\n", + "Meep progress: 56.58/300.0 = 18.9% done in 24.0s, 103.3s to go\n", + "Meep progress: 65.96000000000001/300.0 = 22.0% done in 28.0s, 99.4s to go\n", + "Meep progress: 75.53/300.0 = 25.2% done in 32.0s, 95.2s to go\n", + "Meep progress: 85.19/300.0 = 28.4% done in 36.0s, 90.8s to go\n", + "Meep progress: 94.8/300.0 = 31.6% done in 40.0s, 86.6s to go\n", + "Meep progress: 103.02/300.0 = 34.3% done in 44.0s, 84.2s to go\n", + "Meep progress: 108.78/300.0 = 36.3% done in 48.0s, 84.4s to go\n", + "Meep progress: 117.64/300.0 = 39.2% done in 52.0s, 80.7s to go\n", + "Meep progress: 126.28/300.0 = 42.1% done in 56.0s, 77.1s to go\n", + "Meep progress: 134.91/300.0 = 45.0% done in 60.0s, 73.5s to go\n", + "Meep progress: 144.69/300.0 = 48.2% done in 64.1s, 68.8s to go\n", + "Meep progress: 154.22/300.0 = 51.4% done in 68.1s, 64.3s to go\n", + "Meep progress: 163.89000000000001/300.0 = 54.6% done in 72.1s, 59.8s to go\n", + "Meep progress: 173.19/300.0 = 57.7% done in 76.1s, 55.7s to go\n", + "Meep progress: 182.75/300.0 = 60.9% done in 80.1s, 51.4s to go\n", + "Meep progress: 192.55/300.0 = 64.2% done in 84.1s, 46.9s to go\n", + "Meep progress: 202.20000000000002/300.0 = 67.4% done in 88.1s, 42.6s to go\n", + "Meep progress: 212.06/300.0 = 70.7% done in 92.1s, 38.2s to go\n", + "Meep progress: 221.95000000000002/300.0 = 74.0% done in 96.1s, 33.8s to go\n", + "Meep progress: 231.65/300.0 = 77.2% done in 100.1s, 29.5s to go\n", + "Meep progress: 241.43/300.0 = 80.5% done in 104.1s, 25.2s to go\n", + "Meep progress: 251.13/300.0 = 83.7% done in 108.1s, 21.0s to go\n", + "Meep progress: 260.92/300.0 = 87.0% done in 112.1s, 16.8s to go\n", + "Meep progress: 270.72/300.0 = 90.2% done in 116.1s, 12.6s to go\n", + "Meep progress: 280.64/300.0 = 93.5% done in 120.1s, 8.3s to go\n", + "Meep progress: 290.5/300.0 = 96.8% done in 124.1s, 4.1s to go\n", + "run 0 finished at t = 300.0 (30000 timesteps)\n" + ] + } + ], "source": [ "sim.run(until_after_sources=200)" ] @@ -244,36 +311,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " iteration 60: trace = 166.7770726644721 (1.83568e-09% change)\n", + " iteration 55: trace = 177.9876051266832 (7.99025e-10% change)\n", + " iteration 51: trace = 189.5448069917852 (5.38133e-07% change)\n", + " iteration 48: trace = 201.4664481059231 (1.8864e-08% change)\n", + " iteration 46: trace = 207.5342915143063 (3.85266e-10% change)\n", + " iteration 43: trace = 213.7703137192983 (4.51554e-09% change)\n", + " iteration 41: trace = 220.034335071049 (1.36799e-09% change)\n", + " iteration 41: trace = 226.4741893511782 (4.77871e-07% change)\n", + " iteration 41: trace = 232.9432676387035 (5.52227e-08% change)\n", + " iteration 39: trace = 239.595834286208 (3.53174e-06% change)\n", + " iteration 38: trace = 246.2788666241614 (2.25977e-09% change)\n", + " iteration 37: trace = 253.1530499209222 (9.10884e-06% change)\n", + " iteration 37: trace = 260.0589101770202 (1.34992e-09% change)\n", + " iteration 35: trace = 267.1635561942985 (5.91227e-07% change)\n", + " iteration 35: trace = 274.3011759549739 (1.72579e-09% change)\n", + " iteration 32: trace = 281.6452308165144 (1.0722e-05% change)\n", + " iteration 31: trace = 289.0234421086738 (1.50935e-07% change)\n", + " iteration 30: trace = 296.6161748479355 (0.000100081% change)\n", + " iteration 59: trace = 296.6157305576143 (5.87185e-11% change)\n", + " iteration 29: trace = 304.2434860777851 (2.93797e-07% change)\n", + " iteration 28: trace = 312.0938090067969 (0.000189191% change)\n", + " iteration 58: trace = 312.0929296806977 (1.19518e-10% change)\n" + ] + } + ], "source": [ "# Calculate the number of reflected orders\n", - "nm_r = np.floor((fcen * ng - k.y) * gp) - np.ceil(\n", - " (-fcen * ng - k.y) * gp\n", - ") # number of reflected orders\n", + "nm_r = np.floor((fcen*ng-k.y)*gp)-np.ceil((-fcen*ng-k.y)*gp) # number of reflected orders\n", "if theta_in == 0:\n", - " nm_r = nm_r / 2 # since eig_parity removes degeneracy in y-direction\n", + " nm_r = nm_r/2 # since eig_parity removes degeneracy in y-direction\n", "nm_r = int(nm_r)\n", "\n", "# Extract the coefficients for the reflected orders\n", - "res = sim.get_eigenmode_coefficients(\n", - " refl_flux, range(1, nm_r + 1), eig_parity=eig_parity\n", - ")\n", + "res = sim.get_eigenmode_coefficients(refl_flux, range(1,nm_r+1), eig_parity=eig_parity)\n", "r_coeffs = res.alpha\n", "\n", "# Calculate the number of transmitted orders\n", - "nm_t = np.floor((fcen - k.y) * gp) - np.ceil(\n", - " (-fcen - k.y) * gp\n", - ") # number of transmitted orders\n", + "nm_t = np.floor((fcen-k.y)*gp)-np.ceil((-fcen-k.y)*gp) # number of transmitted orders\n", "if theta_in == 0:\n", - " nm_t = nm_t / 2 # since eig_parity removes degeneracy in y-direction\n", + " nm_t = nm_t/2 # since eig_parity removes degeneracy in y-direction\n", "nm_t = int(nm_t)\n", "\n", "# Extract the coefficients for the transmitted orders\n", - "res = sim.get_eigenmode_coefficients(\n", - " tran_flux, range(1, nm_t + 1), eig_parity=eig_parity\n", - ")\n", + "res = sim.get_eigenmode_coefficients(tran_flux, range(1,nm_t+1), eig_parity=eig_parity)\n", "t_coeffs = res.alpha" ] }, @@ -286,34 +374,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "r_angle = np.squeeze(\n", - " [\n", - " math.degrees(np.sign(r_kdom.y) * math.acos(r_kdom.x / (ng * fcen)))\n", - " for r_kdom in res.kdom\n", - " ]\n", - ")\n", - "Rmode = abs(r_coeffs[:, 0, 1]) ** 2 / input_flux[0]\n", + "r_angle = np.squeeze([math.degrees(np.sign(r_kdom.y)*math.acos(r_kdom.x/(ng*fcen))) for r_kdom in res.kdom])\n", + "Rmode = abs(r_coeffs[:,0,1])**2/input_flux[0]\n", "idx_r = np.argsort(r_angle)\n", "\n", - "t_angle = np.squeeze(\n", - " [\n", - " math.degrees(np.sign(t_kdom.y) * math.acos(t_kdom.x / fcen))\n", - " for t_kdom in res.kdom\n", - " ]\n", - ")\n", - "Tmode = abs(t_coeffs[:, 0, 0]) ** 2 / input_flux[0]\n", + "t_angle = np.squeeze([math.degrees(np.sign(t_kdom.y)*math.acos(t_kdom.x/fcen)) for t_kdom in res.kdom])\n", + "Tmode = abs(t_coeffs[:,0,0])**2/input_flux[0]\n", "idx_t = np.argsort(t_angle)\n", "\n", "plt.figure(dpi=150)\n", - "plt.plot(r_angle[idx_r], Rmode[idx_r], \"o-\", color=\"blue\", label=\"Reflection\")\n", - "plt.plot(t_angle[idx_t], Tmode[idx_t], \"o-\", color=\"red\", label=\"Transmission\")\n", + "plt.plot(r_angle[idx_r],Rmode[idx_r], 'o-',color='blue',label='Reflection')\n", + "plt.plot(t_angle[idx_t],Tmode[idx_t], 'o-',color='red',label='Transmission')\n", "plt.grid(True)\n", - "plt.xlabel(\"Diffraction Angle (degrees)\")\n", - "plt.ylabel(\"Relative Power (au)\")\n", + "plt.xlabel('Diffraction Angle (degrees)')\n", + "plt.ylabel('Relative Power (au)')\n", "plt.legend()\n", "plt.show()" ] @@ -327,20 +418,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mode-coeff:, 0.061047, 0.937862, 0.998909\n", + "poynting-flux:, 0.061102, 0.938344, 0.999447\n" + ] + } + ], "source": [ - "print(\n", - " \"mode-coeff:, {:.6f}, {:.6f}, {:.6f}\".format(\n", - " np.sum(Rmode), np.sum(Tmode), np.sum(Rmode) + np.sum(Tmode)\n", - " )\n", - ")\n", + "print(\"mode-coeff:, {:.6f}, {:.6f}, {:.6f}\".format(np.sum(Rmode),np.sum(Tmode),np.sum(Rmode)+np.sum(Tmode)))\n", "r_flux = mp.get_fluxes(refl_flux)\n", "t_flux = mp.get_fluxes(tran_flux)\n", - "Rflux = -r_flux[0] / input_flux[0]\n", - "Tflux = t_flux[0] / input_flux[0]\n", - "print(\"poynting-flux:, {:.6f}, {:.6f}, {:.6f}\".format(Rflux, Tflux, Rflux + Tflux))" + "Rflux = -r_flux[0]/input_flux[0]\n", + "Tflux = t_flux[0]/input_flux[0]\n", + "print(\"poynting-flux:, {:.6f}, {:.6f}, {:.6f}\".format(Rflux,Tflux,Rflux+Tflux))" ] } ], diff --git a/python/examples/binary_grating_phasemap.ipynb b/python/examples/binary_grating_phasemap.ipynb index b0db5216a..f160cb571 100644 --- a/python/examples/binary_grating_phasemap.ipynb +++ b/python/examples/binary_grating_phasemap.ipynb @@ -16,123 +16,825 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00244403 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + "time for set_epsilon = 0.0124252 s\n", + "-----------\n", + "run 0 finished at t = 112.0 (11200 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00123405 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + " block, center = (-2.3,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.66533e-16,0,0)\n", + " size (0.6,0.035,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.014374 s\n", + "-----------\n", + "Meep progress: 266.34000000000003/312.0 = 85.4% done in 4.0s, 0.7s to go\n", + "on time step 26634 (time=266.34), 0.000150194 s/step\n", + "run 0 finished at t = 312.0 (31200 timesteps)\n", + "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", + "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", + "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", + "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", + "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 7 iters\n", + "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.875,0,0) = 1.875 after 7 iters\n", + "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", + "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", + "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n", + "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n", + "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n", + "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 8 iters\n", + "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", + "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", + "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", + "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", + "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n", + "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", + "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", + "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", + "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00058198 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + "time for set_epsilon = 0.00720787 s\n", + "-----------\n", + "run 0 finished at t = 112.0 (11200 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00100899 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + " block, center = (-2.3,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.66533e-16,0,0)\n", + " size (0.6,0.07,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.016 s\n", + "-----------\n", + "Meep progress: 302.72/312.0 = 97.0% done in 4.0s, 0.1s to go\n", + "on time step 30272 (time=302.72), 0.000132144 s/step\n", + "run 0 finished at t = 312.0 (31200 timesteps)\n", + "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", + "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n", + "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", + "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", + "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", + "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", + "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", + "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", + "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n", + "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", + "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", + "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", + "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", + "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", + "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", + "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 10 iters\n", + "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.375,0,0) = 2.375 after 10 iters\n", + "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", + "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", + "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", + "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000844955 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + "time for set_epsilon = 0.0124938 s\n", + "-----------\n", + "run 0 finished at t = 112.0 (11200 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00101209 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + " block, center = (-2.3,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.66533e-16,0,0)\n", + " size (0.6,0.105,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0164981 s\n", + "-----------\n", + "Meep progress: 285.47/312.0 = 91.5% done in 4.0s, 0.4s to go\n", + "on time step 28547 (time=285.47), 0.000140126 s/step\n", + "run 0 finished at t = 312.0 (31200 timesteps)\n", + "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 7 iters\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n", + "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", + "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", + "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", + "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", + "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 9 iters\n", + "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", + "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n", + "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n", + "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", + "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", + "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n", + "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.25,0,0) = 2.25 after 8 iters\n", + "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", + "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", + "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n", + "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", + "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", + "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", + "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00107813 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + "time for set_epsilon = 0.011832 s\n", + "-----------\n", + "run 0 finished at t = 112.0 (11200 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000978947 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + " block, center = (-2.3,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.66533e-16,0,0)\n", + " size (0.6,0.14,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0274839 s\n", + "-----------\n", + "Meep progress: 281.47/312.0 = 90.2% done in 4.0s, 0.4s to go\n", + "on time step 28147 (time=281.47), 0.000142121 s/step\n", + "run 0 finished at t = 312.0 (31200 timesteps)\n", + "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", + "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", + "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", + "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", + "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", + "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", + "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 9 iters\n", + "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", + "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n", + "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", + "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n", + "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 8 iters\n", + "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", + "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", + "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", + "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", + "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.375,0,0) = 2.375 after 10 iters\n", + "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 10 iters\n", + "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", + "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", + "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00116396 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + "time for set_epsilon = 0.0128059 s\n", + "-----------\n", + "run 0 finished at t = 112.0 (11200 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00101495 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + " block, center = (-2.3,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.66533e-16,0,0)\n", + " size (0.6,0.175,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0251269 s\n", + "-----------\n", + "Meep progress: 266.41/312.0 = 85.4% done in 4.0s, 0.7s to go\n", + "on time step 26641 (time=266.41), 0.000150151 s/step\n", + "run 0 finished at t = 312.0 (31200 timesteps)\n", + "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 7 iters\n", + "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", + "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", + "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 7 iters\n", + "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", + "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", + "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", + "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 9 iters\n", + "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", + "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", + "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", + "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n", + "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", + "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", + "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 10 iters\n", + "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n", + "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", + "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 10 iters\n", + "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.5,0,0) = 2.5 after 10 iters\n", + "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000806808 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + "time for set_epsilon = 0.0111492 s\n", + "-----------\n", + "run 0 finished at t = 112.0 (11200 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.0011282 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + " block, center = (-2.3,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.66533e-16,0,0)\n", + " size (0.6,0.21,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0204818 s\n", + "-----------\n", + "Meep progress: 266.28000000000003/312.0 = 85.3% done in 4.0s, 0.7s to go\n", + "on time step 26628 (time=266.28), 0.000150227 s/step\n", + "run 0 finished at t = 312.0 (31200 timesteps)\n", + "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 7 iters\n", + "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", + "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", + "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", + "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", + "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", + "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", + "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", + "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n", + "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", + "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n", + "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", + "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", + "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", + "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", + "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", + "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.375,0,0) = 2.375 after 8 iters\n", + "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", + "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", + "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.5,0,0) = 2.5 after 10 iters\n", + "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00195003 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + "time for set_epsilon = 0.01247 s\n", + "-----------\n", + "run 0 finished at t = 112.0 (11200 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000983 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + " block, center = (-2.3,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.66533e-16,0,0)\n", + " size (0.6,0.245,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.02367 s\n", + "-----------\n", + "Meep progress: 270.11/312.0 = 86.6% done in 4.0s, 0.6s to go\n", + "on time step 27011 (time=270.11), 0.000148096 s/step\n", + "run 0 finished at t = 312.0 (31200 timesteps)\n", + "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", + "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n", + "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", + "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 9 iters\n", + "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", + "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", + "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", + "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", + "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n", + "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", + "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", + "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", + "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", + "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", + "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", + "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", + "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n", + "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", + "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", + "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000772953 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + "time for set_epsilon = 0.0116861 s\n", + "-----------\n", + "run 0 finished at t = 112.0 (11200 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00118494 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + " block, center = (-2.3,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.66533e-16,0,0)\n", + " size (0.6,0.28,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0194418 s\n", + "-----------\n", + "Meep progress: 266.44/312.0 = 85.4% done in 4.0s, 0.7s to go\n", + "on time step 26644 (time=266.44), 0.000150135 s/step\n", + "run 0 finished at t = 312.0 (31200 timesteps)\n", + "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", + "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", + "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", + "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", + "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", + "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", + "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 9 iters\n", + "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", + "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n", + "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n", + "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n", + "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", + "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n", + "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", + "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", + "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", + "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n", + "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", + "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 10 iters\n", + "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", + "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.0010891 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + "time for set_epsilon = 0.00840378 s\n", + "-----------\n", + "run 0 finished at t = 112.0 (11200 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00111604 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + " block, center = (-2.3,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.66533e-16,0,0)\n", + " size (0.6,0.315,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.021673 s\n", + "-----------\n", + "Meep progress: 266.16/312.0 = 85.3% done in 4.0s, 0.7s to go\n", + "on time step 26616 (time=266.16), 0.000150297 s/step\n", + "run 0 finished at t = 312.0 (31200 timesteps)\n", + "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", + "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n", + "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n", + "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", + "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 7 iters\n", + "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", + "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", + "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 9 iters\n", + "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n", + "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n", + "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", + "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", + "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n", + "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", + "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n", + "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", + "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.375,0,0) = 2.375 after 10 iters\n", + "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", + "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", + "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", + "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00112605 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + "time for set_epsilon = 0.00722408 s\n", + "-----------\n", + "run 0 finished at t = 112.0 (11200 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000966787 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n", + " block, center = (-2.3,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.66533e-16,0,0)\n", + " size (0.6,0.35,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0155971 s\n", + "-----------\n", + "Meep progress: 266.89/312.0 = 85.5% done in 4.0s, 0.7s to go\n", + "on time step 26689 (time=266.89), 0.000149884 s/step\n", + "run 0 finished at t = 312.0 (31200 timesteps)\n", + "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n", + "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n", + "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.75,0,0) = 1.75 after 7 iters\n", + "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n", + "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n", + "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n", + "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n", + "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n", + "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n", + "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n", + "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n", + "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n", + "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n", + "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n", + "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n", + "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 8 iters\n", + "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n", + "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.375,0,0) = 2.375 after 8 iters\n", + "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n", + "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n", + "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n", + "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n", + "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", "import numpy.matlib\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 3.0 # substrate thickness\n", - "dpad = 3.0 # padding between grating and PML\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 3.0 # substrate thickness\n", + "dpad = 3.0 # padding between grating and PML\n", "\n", - "wvl_min = 0.4 # min wavelength\n", - "wvl_max = 0.6 # max wavelength\n", - "fmin = 1 / wvl_max # min frequency\n", - "fmax = 1 / wvl_min # max frequency\n", - "fcen = 0.5 * (fmin + fmax) # center frequency\n", - "df = fmax - fmin # frequency width\n", - "nfreq = 21 # number of frequency bins\n", + "wvl_min = 0.4 # min wavelength\n", + "wvl_max = 0.6 # max wavelength\n", + "fmin = 1/wvl_max # min frequency\n", + "fmax = 1/wvl_min # max frequency\n", + "fcen = 0.5*(fmin+fmax) # center frequency\n", + "df = fmax-fmin # frequency width\n", + "nfreq = 21 # number of frequency bins\n", "\n", - "k_point = mp.Vector3(0, 0, 0)\n", + "k_point = mp.Vector3(0,0,0)\n", "\n", "glass = mp.Medium(index=1.5)\n", "\n", + "def grating(gp,gh,gdc,oddz):\n", + " sx = dpml+dsub+gh+dpad+dpml\n", + " sy = gp\n", "\n", - "def grating(gp, gh, gdc, oddz):\n", - " sx = dpml + dsub + gh + dpad + dpml\n", - " sy = gp\n", - "\n", - " cell_size = mp.Vector3(sx, sy, 0)\n", - " pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", - "\n", - " src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub, 0, 0)\n", - " sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez if oddz else mp.Hz,\n", - " center=src_pt,\n", - " size=mp.Vector3(0, sy, 0),\n", - " )\n", - " ]\n", - "\n", - " symmetries = [mp.Mirror(mp.Y, phase=+1 if oddz else -1)]\n", - "\n", - " sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " default_material=glass,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - " )\n", - "\n", - " mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0)\n", - " flux_mon = sim.add_flux(\n", - " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0))\n", - " )\n", - "\n", - " sim.run(until_after_sources=100)\n", - "\n", - " input_flux = mp.get_fluxes(flux_mon)\n", - "\n", - " sim.reset_meep()\n", - "\n", - " geometry = [\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0),\n", - " ),\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0),\n", - " ),\n", - " ]\n", - "\n", - " sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - " )\n", - "\n", - " mode_mon = sim.add_flux(\n", - " fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0))\n", - " )\n", - "\n", - " sim.run(until_after_sources=300)\n", - "\n", - " freqs = mp.get_eigenmode_freqs(mode_mon)\n", - " res = sim.get_eigenmode_coefficients(\n", - " mode_mon, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y if oddz else mp.EVEN_Z + mp.ODD_Y\n", - " )\n", - " coeffs = res.alpha\n", - "\n", - " mode_wvl = [1 / freqs[nf] for nf in range(nfreq)]\n", - " mode_tran = [abs(coeffs[0, nf, 0]) ** 2 / input_flux[nf] for nf in range(nfreq)]\n", - " mode_phase = [np.angle(coeffs[0, nf, 0]) for nf in range(nfreq)]\n", - "\n", - " return mode_wvl, mode_tran, mode_phase\n", + " cell_size = mp.Vector3(sx,sy,0)\n", + " pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", "\n", + " src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub,0,0)\n", + " sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez if oddz else mp.Hz, center=src_pt, size=mp.Vector3(0,sy,0))]\n", + "\n", + " symmetries=[mp.Mirror(mp.Y, phase=+1 if oddz else -1)]\n", + " \n", + " sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " default_material=glass,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + " mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0)\n", + " flux_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0)))\n", + "\n", + " sim.run(until_after_sources=100)\n", + "\n", + " input_flux = mp.get_fluxes(flux_mon)\n", + "\n", + " sim.reset_meep()\n", + "\n", + " geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)),\n", + " mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))]\n", + "\n", + " sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + " mode_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0)))\n", + "\n", + " sim.run(until_after_sources=300)\n", + "\n", + " freqs = mp.get_eigenmode_freqs(mode_mon)\n", + " res = sim.get_eigenmode_coefficients(mode_mon, [1], eig_parity=mp.ODD_Z+mp.EVEN_Y if oddz else mp.EVEN_Z+mp.ODD_Y)\n", + " coeffs = res.alpha\n", + "\n", + " mode_wvl = [1/freqs[nf] for nf in range(nfreq)]\n", + " mode_tran = [abs(coeffs[0,nf,0])**2/input_flux[nf] for nf in range(nfreq)]\n", + " mode_phase = [np.angle(coeffs[0,nf,0]) for nf in range(nfreq)]\n", + "\n", + " return mode_wvl, mode_tran, mode_phase\n", "\n", "gp = 0.35\n", - "gh = 0.6\n", - "gdc = np.linspace(0.1, 1.0, 10)\n", - "mode_tran = np.empty((gdc.size, nfreq))\n", - "mode_phase = np.empty((gdc.size, nfreq))\n", + "gh = 0.6 \n", + "gdc = np.linspace(0.1,1.0,10)\n", + "mode_tran = np.empty((gdc.size,nfreq))\n", + "mode_phase = np.empty((gdc.size,nfreq))\n", "for n in range(gdc.size):\n", - " mode_wvl, mode_tran[n, :], mode_phase[n, :] = grating(gp, gh, gdc[n], True)" + " mode_wvl, mode_tran[n,:], mode_phase[n,:] = grating(gp,gh,gdc[n],True)" ] }, { @@ -146,50 +848,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=150)\n", - "plt.subplot(1, 2, 1)\n", - "plt.pcolormesh(\n", - " mode_wvl,\n", - " gdc,\n", - " mode_tran,\n", - " cmap=\"hot_r\",\n", - " shading=\"gouraud\",\n", - " vmin=0,\n", - " vmax=mode_tran.max(),\n", - ")\n", + "plt.subplot(1,2,1)\n", + "plt.pcolormesh(mode_wvl, gdc, mode_tran, cmap='hot_r', shading='gouraud', vmin=0, vmax=mode_tran.max())\n", "plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]])\n", "plt.xlabel(\"wavelength (μm)\")\n", - "plt.xticks([t for t in np.linspace(wvl_min, wvl_max, 3)])\n", + "plt.xticks([t for t in np.linspace(wvl_min,wvl_max,3)])\n", "plt.ylabel(\"grating duty cycle\")\n", - "plt.yticks([t for t in np.arange(gdc[0], gdc[-1] + 0.1, 0.1)])\n", + "plt.yticks([t for t in np.arange(gdc[0],gdc[-1]+0.1,0.1)])\n", "plt.title(\"transmittance\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.arange(0, 1.2, 0.2)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0, 1, 6)])\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.pcolormesh(\n", - " mode_wvl,\n", - " gdc,\n", - " mode_phase,\n", - " cmap=\"RdBu\",\n", - " shading=\"gouraud\",\n", - " vmin=mode_phase.min(),\n", - " vmax=mode_phase.max(),\n", - ")\n", + "cbar.set_ticks([t for t in np.arange(0,1.2,0.2)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0,1,6)])\n", + "\n", + "plt.subplot(1,2,2)\n", + "plt.pcolormesh(mode_wvl, gdc, mode_phase, cmap='RdBu', shading='gouraud', vmin=mode_phase.min(), vmax=mode_phase.max())\n", "plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]])\n", "plt.xlabel(\"wavelength (μm)\")\n", - "plt.xticks([t for t in np.linspace(wvl_min, wvl_max, 3)])\n", + "plt.xticks([t for t in np.linspace(wvl_min,wvl_max,3)])\n", "plt.ylabel(\"grating duty cycle\")\n", - "plt.yticks([t for t in np.arange(gdc[0], gdc[-1] + 0.1, 0.1)])\n", + "plt.yticks([t for t in np.arange(gdc[0],gdc[-1]+0.1,0.1)])\n", "plt.title(\"phase (radians)\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in range(-3, 4)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in range(-3, 4)])\n", + "cbar.set_ticks([t for t in range(-3,4)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in range(-3,4)])\n", "\n", "plt.subplots_adjust(wspace=0.5)" ] diff --git a/python/examples/cavity-farfield.ipynb b/python/examples/cavity-farfield.ipynb index 2c4b502b1..08ea67ee1 100644 --- a/python/examples/cavity-farfield.ipynb +++ b/python/examples/cavity-farfield.ipynb @@ -60,103 +60,210 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "field decay(t = 50.025000000000006): 61.714437239634854 / 61.714437239634854 = 1.0\n", + "field decay(t = 100.05000000000001): 47.39669513065221 / 61.714437239634854 = 0.7680001187827836\n", + "field decay(t = 150.07500000000002): 38.753487484300784 / 61.714437239634854 = 0.6279484868965497\n", + "field decay(t = 200.10000000000002): 31.933885594488228 / 61.714437239634854 = 0.517445949810579\n", + "field decay(t = 250.125): 26.076764607720868 / 61.714437239634854 = 0.42253912980630065\n", + "field decay(t = 300.15000000000003): 21.47655605320123 / 61.714437239634854 = 0.34799889643015885\n", + "field decay(t = 350.175): 17.53631126728562 / 61.714437239634854 = 0.2841524941593938\n", + "field decay(t = 400.20000000000005): 14.443563832839763 / 61.714437239634854 = 0.2340386541443446\n", + "field decay(t = 450.225): 11.896373926780527 / 61.714437239634854 = 0.19276484496791815\n", + "field decay(t = 500.25): 9.718573927428102 / 61.714437239634854 = 0.15747650569495178\n", + "field decay(t = 550.275): 8.004609807436562 / 61.714437239634854 = 0.12970400712486385\n", + "field decay(t = 600.3000000000001): 6.537545155581314 / 61.714437239634854 = 0.10593218455831122\n", + "field decay(t = 650.325): 5.384562849608928 / 61.714437239634854 = 0.08724964676743095\n", + "field decay(t = 700.35): 4.396534866374258 / 61.714437239634854 = 0.071239973384229\n", + "field decay(t = 750.375): 3.621135834360022 / 61.714437239634854 = 0.05867566806611696\n", + "field decay(t = 800.4000000000001): 2.9581397060851486 / 61.714437239634854 = 0.047932701623751385\n", + "field decay(t = 850.4250000000001): 2.4364448081077583 / 61.714437239634854 = 0.039479332828510356\n", + "field decay(t = 900.45): 1.9900002176534468 / 61.714437239634854 = 0.03224529472619754\n", + "field decay(t = 950.475): 1.6390396477862001 / 61.714437239634854 = 0.026558447603141393\n", + "field decay(t = 1000.5): 1.3499743126254105 / 61.714437239634854 = 0.021874530061475092\n", + "field decay(t = 1050.525): 1.1023166078985005 / 61.714437239634854 = 0.017861567847054106\n", + "field decay(t = 1100.55): 0.9079059458801253 / 61.714437239634854 = 0.014711402817379026\n", + "field decay(t = 1150.575): 0.7415977350408866 / 61.714437239634854 = 0.012016600461919312\n", + "field decay(t = 1200.6000000000001): 0.6108108814904085 / 61.714437239634854 = 0.009897374241924183\n", + "field decay(t = 1250.625): 0.4989128271302527 / 61.714437239634854 = 0.008084215775848247\n", + "field decay(t = 1300.65): 0.41092341533267496 / 61.714437239634854 = 0.0066584649186230875\n", + "field decay(t = 1350.6750000000002): 0.335555319091018 / 61.714437239634854 = 0.0054372256168855465\n", + "field decay(t = 1400.7): 0.27637506434209563 / 61.714437239634854 = 0.004478288658275236\n", + "field decay(t = 1450.7250000000001): 0.22572291785727622 / 61.714437239634854 = 0.003657538299843885\n", + "field decay(t = 1500.75): 0.18591483342900764 / 61.714437239634854 = 0.003012501478496957\n", + "field decay(t = 1550.775): 0.15312742040508032 / 61.714437239634854 = 0.0024812252570738393\n", + "field decay(t = 1600.8000000000002): 0.1250809574092981 / 61.714437239634854 = 0.0020267697965649983\n", + "field decay(t = 1650.825): 0.1030217208117877 / 61.714437239634854 = 0.001669329340422537\n", + "field decay(t = 1700.8500000000001): 0.08413043895503002 / 61.714437239634854 = 0.001363221358210797\n", + "field decay(t = 1750.875): 0.06929274173262323 / 61.714437239634854 = 0.0011227962990825323\n", + "field decay(t = 1800.9): 0.05658712763643502 / 61.714437239634854 = 0.0009169187983795317\n", + "field decay(t = 1850.9250000000002): 0.04660765052217251 / 61.714437239634854 = 0.0007552147051296399\n", + "field decay(t = 1900.95): 0.03807289009086582 / 61.714437239634854 = 0.0006169203154689755\n", + "field decay(t = 1950.9750000000001): 0.03135838502348845 / 61.714437239634854 = 0.0005081207319727313\n", + "field decay(t = 2001.0): 0.02560950778551407 / 61.714437239634854 = 0.00041496785729525993\n", + "field decay(t = 2051.025): 0.021092935013554397 / 61.714437239634854 = 0.00034178283003134775\n", + "field decay(t = 2101.05): 0.017223354025599415 / 61.714437239634854 = 0.00027908144019399826\n", + "field decay(t = 2151.0750000000003): 0.014185939167263606 / 61.714437239634854 = 0.00022986419064602556\n", + "field decay(t = 2201.1): 0.01168408783845677 / 61.714437239634854 = 0.0001893250325379764\n", + "field decay(t = 2251.125): 0.009544955140265965 / 61.714437239634854 = 0.0001546632452176994\n", + "field decay(t = 2301.15): 0.007861650796955479 / 61.714437239634854 = 0.00012738754736479216\n", + "field decay(t = 2351.175): 0.00642066767842841 / 61.714437239634854 = 0.00010403834119878947\n", + "field decay(t = 2401.2000000000003): 0.005288325829931077 / 61.714437239634854 = 8.569025444397566e-05\n", + "field decay(t = 2451.225): 0.004317908714773744 / 61.714437239634854 = 6.996594164842606e-05\n", + "field decay(t = 2501.25): 0.0035563445246973805 / 61.714437239634854 = 5.7625811459451985e-05\n", + "field decay(t = 2551.275): 0.0029053004098988015 / 61.714437239634854 = 4.707651142661528e-05\n", + "field decay(t = 2601.3): 0.0023929347699027303 / 61.714437239634854 = 3.877431079232002e-05\n", + "field decay(t = 2651.3250000000003): 0.0019544194427344327 / 61.714437239634854 = 3.1668755807421446e-05\n", + "field decay(t = 2701.3500000000004): 0.0016097324307071902 / 61.714437239634854 = 2.608356330718304e-05\n", + "field decay(t = 2751.375): 0.0013258509849590956 / 61.714437239634854 = 2.1483643767354885e-05\n", + "field decay(t = 2801.4): 0.0010826046664947212 / 61.714437239634854 = 1.7542162173350262e-05\n", + "field decay(t = 2851.425): 0.0008916728224396508 / 61.714437239634854 = 1.4448366740789007e-05\n", + "field decay(t = 2901.4500000000003): 0.0007283649985088117 / 61.714437239634854 = 1.1802181646420878e-05\n", + "field decay(t = 2951.4750000000004): 0.0005998993426679592 / 61.714437239634854 = 9.720567334002778e-06\n", + "field decay(t = 3001.5): 0.0004899838939467707 / 61.714437239634854 = 7.939534343385189e-06\n", + "field decay(t = 3051.525): 0.00040357507547706214 / 61.714437239634854 = 6.539394889237914e-06\n", + "field decay(t = 3101.55): 0.00032955488895076876 / 61.714437239634854 = 5.339996663521688e-06\n", + "field decay(t = 3151.5750000000003): 0.0002714323244167271 / 61.714437239634854 = 4.39819816168404e-06\n", + "field decay(t = 3201.6000000000004): 0.00022170111231405412 / 61.714437239634854 = 3.5923703144727217e-06\n", + "field decay(t = 3251.625): 0.00018260060353425382 / 61.714437239634854 = 2.9587988111310567e-06\n", + "field decay(t = 3301.65): 0.0001503954748659794 / 61.714437239634854 = 2.4369577297124076e-06\n", + "field decay(t = 3351.675): 0.00012284487711379126 / 61.714437239634854 = 1.9905371029600284e-06\n", + "field decay(t = 3401.7000000000003): 0.00010117728681778103 / 61.714437239634854 = 1.639442751862314e-06\n", + "field decay(t = 3451.7250000000004): 8.26234434523444e-05 / 61.714437239634854 = 1.3388025095573772e-06\n", + "field decay(t = 3501.75): 6.805354196313139e-05 / 61.714437239634854 = 1.102716722488808e-06\n", + "field decay(t = 3551.775): 5.557995527325353e-05 / 61.714437239634854 = 9.005989158977281e-07\n", + "field decay(t = 3601.8): 4.5777693507503735e-05 / 61.714437239634854 = 7.417663605964786e-07\n", + "field decay(t = 3651.8250000000003): 3.7393438370402545e-05 / 61.714437239634854 = 6.059107081411959e-07\n", + "field decay(t = 3701.8500000000004): 3.0797909234401674e-05 / 61.714437239634854 = 4.990389706514627e-07\n", + "field decay(t = 3751.875): 2.5150148826942176e-05 / 61.714437239634854 = 4.075245591122396e-07\n", + "field decay(t = 3801.9): 2.071514647935486e-05 / 61.714437239634854 = 3.356612715906121e-07\n", + "field decay(t = 3851.925): 1.6916336856910708e-05 / 61.714437239634854 = 2.7410663717511034e-07\n", + "field decay(t = 3901.9500000000003): 1.3933210839234033e-05 / 61.714437239634854 = 2.2576906575574684e-07\n", + "field decay(t = 3951.9750000000004): 1.1476555260458193e-05 / 61.714437239634854 = 1.8596224439178076e-07\n", + "field decay(t = 4002.0): 9.375123704517074e-06 / 61.714437239634854 = 1.5191135370989156e-07\n", + "field decay(t = 4052.025): 7.721326456512098e-06 / 61.714437239634854 = 1.2511377891254968e-07\n", + "field decay(t = 4102.05): 6.30564330583996e-06 / 61.714437239634854 = 1.0217452492283747e-07\n", + "field decay(t = 4152.075): 5.193524218131245e-06 / 61.714437239634854 = 8.415412098736288e-08\n", + "field decay(t = 4202.1): 4.240401634668664e-06 / 61.714437239634854 = 6.871004297103679e-08\n", + "field decay(t = 4252.125): 3.492891885354038e-06 / 61.714437239634854 = 5.659764621673968e-08\n", + "field decay(t = 4302.150000000001): 2.8538219922205117e-06 / 61.714437239634854 = 4.624237244745873e-08\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "field decay(t = 4352.175): 2.3504340518958436e-06 / 61.714437239634854 = 3.8085643441407335e-08\n", + "field decay(t = 4402.2): 1.919486550538664e-06 / 61.714437239634854 = 3.110271496255195e-08\n", + "field decay(t = 4452.225): 1.5810178663207582e-06 / 61.714437239634854 = 2.5618282156276092e-08\n", + "field decay(t = 4502.25): 1.3020179000714368e-06 / 61.714437239634854 = 2.1097460469675026e-08\n", + "field decay(t = 4552.275000000001): 1.0629751847546947e-06 / 61.714437239634854 = 1.7224092648324764e-08\n", + "field decay(t = 4602.3): 8.756223798546496e-07 / 61.714437239634854 = 1.418829076338557e-08\n", + "field decay(t = 4652.325): 7.15412237809199e-07 / 61.714437239634854 = 1.1592299465217191e-08\n", + "field decay(t = 4702.35): 5.892719179602837e-07 / 61.714437239634854 = 9.548364115711901e-09\n", + "run 0 finished at t = 4702.35 (188094 timesteps)\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 20 # pixels/μm\n", + "resolution = 20 # pixels/μm\n", "\n", - "eps = 13 # dielectric constant of waveguide\n", - "w = 1.2 # width of waveguide\n", - "r = 0.36 # radius of holes\n", - "d = 1.4 # defect spacing (ordinary spacing = 1)\n", - "N = 3 # number of holes on either side of defect\n", + "eps = 13 # dielectric constant of waveguide\n", + "w = 1.2 # width of waveguide\n", + "r = 0.36 # radius of holes\n", + "d = 1.4 # defect spacing (ordinary spacing = 1)\n", + "N = 3 # number of holes on either side of defect\n", "\n", - "sy = 6 # size of cell in y direction (perpendicular to wvg.)\n", - "pad = 2 # padding between last hole and PML edge\n", - "dpml = 1 # PML thickness\n", - "sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction\n", + "sy = 6 # size of cell in y direction (perpendicular to wvg.)\n", + "pad = 2 # padding between last hole and PML edge\n", + "dpml = 1 # PML thickness\n", + "sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction\n", "\n", "cell = mp.Vector3(sx, sy, 0)\n", "pml_layers = mp.PML(dpml)\n", "\n", - "geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(mp.inf, w, mp.inf),\n", - " material=mp.Medium(epsilon=eps),\n", - " )\n", - "]\n", + "geometry = [mp.Block(center=mp.Vector3(),\n", + " size=mp.Vector3(mp.inf, w, mp.inf),\n", + " material=mp.Medium(epsilon=eps))]\n", "\n", "for i in range(N):\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(0.5 * d + i)))\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(-0.5 * d - i)))\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(0.5*d+i)))\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(-0.5*d-i)))\n", "\n", - "fcen = 0.25 # pulse center frequency\n", - "df = 0.2 # pulse width (in frequency)\n", + "fcen = 0.25 # pulse center frequency\n", + "df = 0.2 # pulse width (in frequency)\n", "\n", - "sources = mp.Source(\n", - " src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3()\n", - ")\n", + "sources = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3())\n", "\n", - "symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)]\n", + "symmetries = [mp.Mirror(mp.X, phase=-1),\n", + " mp.Mirror(mp.Y, phase=-1)]\n", "\n", - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " sources=[sources],\n", - " symmetries=symmetries,\n", - " boundary_layers=[pml_layers],\n", - " resolution=resolution,\n", - ")\n", + "sim = mp.Simulation(cell_size=cell,\n", + " geometry=geometry,\n", + " sources=[sources],\n", + " symmetries=symmetries,\n", + " boundary_layers=[pml_layers],\n", + " resolution=resolution)\n", "\n", "d1 = 0.2\n", "\n", - "nearfield = sim.add_near2far(\n", - " fcen,\n", - " 0,\n", - " 1,\n", - " mp.Near2FarRegion(mp.Vector3(y=0.5 * w + d1), size=mp.Vector3(sx - 2 * dpml)),\n", - " mp.Near2FarRegion(\n", - " mp.Vector3(-0.5 * sx + dpml, 0.5 * w + 0.5 * d1),\n", - " size=mp.Vector3(y=d1),\n", - " weight=-1.0,\n", - " ),\n", - " mp.Near2FarRegion(\n", - " mp.Vector3(0.5 * sx - dpml, 0.5 * w + 0.5 * d1), size=mp.Vector3(y=d1)\n", - " ),\n", - ")\n", + "nearfield = sim.add_near2far(fcen, 0, 1,\n", + " mp.Near2FarRegion(mp.Vector3(y=0.5*w+d1), size=mp.Vector3(sx-2*dpml)),\n", + " mp.Near2FarRegion(mp.Vector3(-0.5*sx+dpml,0.5*w+0.5*d1), size=mp.Vector3(y=d1), weight=-1.0),\n", + " mp.Near2FarRegion(mp.Vector3(0.5*sx-dpml,0.5*w+0.5*d1), size=mp.Vector3(y=d1)))\n", "\n", - "sim.run(\n", - " until_after_sources=mp.stop_when_fields_decayed(\n", - " 50, mp.Hz, mp.Vector3(0.12, -0.37), 1e-8\n", - " )\n", - ")" + "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Hz, mp.Vector3(0.12,-0.37), 1e-8))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.5, 207.5, 79.5, -0.5)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "d2 = 20\n", "h = 4\n", "\n", - "ff = sim.get_farfields(\n", - " nearfield,\n", - " resolution,\n", - " center=mp.Vector3(y=0.5 * w + d2 + 0.5 * h),\n", - " size=mp.Vector3(sx - 2 * dpml, h),\n", - ")\n", + "ff = sim.get_farfields(nearfield, resolution, center=mp.Vector3(y=0.5*w+d2+0.5*h), size=mp.Vector3(sx-2*dpml,h))\n", "\n", "plt.figure(dpi=200)\n", - "plt.imshow(np.rot90(np.real(ff[\"Hz\"]), 1), cmap=\"RdBu\")\n", - "plt.axis(\"off\")" + "plt.imshow(np.rot90(np.real(ff['Hz']),1),cmap='RdBu')\n", + "plt.axis('off')" ] } ], diff --git a/python/examples/coupler.ipynb b/python/examples/coupler.ipynb index df8feaebb..8e4df35df 100644 --- a/python/examples/coupler.ipynb +++ b/python/examples/coupler.ipynb @@ -25,19 +25,864 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000570059 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 34.4 x 7.84 x 0 with resolution 25\n", + " prism, center = (-9.09425,1.32149,0)\n", + " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", + " (-4,0.06,-10)\n", + " (-4.108,0.061,-10)\n", + " (-4.215,0.062,-10)\n", + " (-4.322,0.065,-10)\n", + " (-4.429,0.07,-10)\n", + " (-4.535,0.075,-10)\n", + " (-4.641,0.081,-10)\n", + " (-4.747,0.089,-10)\n", + " (-4.852,0.097,-10)\n", + " (-5.062,0.117,-10)\n", + " (-5.167,0.129,-10)\n", + " (-5.271,0.141,-10)\n", + " (-5.479,0.169,-10)\n", + " (-5.582,0.184,-10)\n", + " (-5.685,0.2,-10)\n", + " (-5.788,0.217,-10)\n", + " (-5.891,0.235,-10)\n", + " (-5.993,0.253,-10)\n", + " (-6.095,0.272,-10)\n", + " (-6.197,0.292,-10)\n", + " (-6.299,0.313,-10)\n", + " (-6.4,0.334,-10)\n", + " (-6.501,0.356,-10)\n", + " (-6.703,0.402,-10)\n", + " (-6.803,0.425,-10)\n", + " (-6.904,0.45,-10)\n", + " (-7.204,0.525,-10)\n", + " (-7.303,0.552,-10)\n", + " (-7.403,0.578,-10)\n", + " (-7.502,0.605,-10)\n", + " (-7.601,0.633,-10)\n", + " (-7.7,0.66,-10)\n", + " (-7.799,0.688,-10)\n", + " (-7.898,0.717,-10)\n", + " (-7.996,0.745,-10)\n", + " (-8.095,0.774,-10)\n", + " (-8.193,0.803,-10)\n", + " (-8.291,0.833,-10)\n", + " (-8.389,0.862,-10)\n", + " (-8.487,0.892,-10)\n", + " (-8.585,0.921,-10)\n", + " (-8.781,0.981,-10)\n", + " (-8.878,1.011,-10)\n", + " (-9.074,1.071,-10)\n", + " (-9.268,1.131,-10)\n", + " (-9.366,1.161,-10)\n", + " (-9.463,1.191,-10)\n", + " (-9.56,1.22,-10)\n", + " (-9.658,1.25,-10)\n", + " (-9.949,1.337,-10)\n", + " (-10.047,1.366,-10)\n", + " (-10.144,1.395,-10)\n", + " (-10.338,1.451,-10)\n", + " (-10.436,1.478,-10)\n", + " (-10.533,1.506,-10)\n", + " (-10.63,1.533,-10)\n", + " (-10.728,1.559,-10)\n", + " (-10.922,1.611,-10)\n", + " (-11.02,1.636,-10)\n", + " (-11.117,1.66,-10)\n", + " (-11.215,1.685,-10)\n", + " (-11.313,1.708,-10)\n", + " (-11.41,1.731,-10)\n", + " (-11.508,1.754,-10)\n", + " (-11.606,1.776,-10)\n", + " (-11.704,1.797,-10)\n", + " (-11.802,1.817,-10)\n", + " (-11.901,1.837,-10)\n", + " (-11.999,1.856,-10)\n", + " (-12.098,1.875,-10)\n", + " (-12.196,1.893,-10)\n", + " (-12.295,1.91,-10)\n", + " (-12.394,1.926,-10)\n", + " (-12.592,1.956,-10)\n", + " (-12.691,1.97,-10)\n", + " (-12.791,1.982,-10)\n", + " (-12.89,1.994,-10)\n", + " (-12.99,2.005,-10)\n", + " (-13.19,2.025,-10)\n", + " (-13.291,2.033,-10)\n", + " (-13.392,2.04,-10)\n", + " (-13.492,2.046,-10)\n", + " (-13.593,2.051,-10)\n", + " (-13.695,2.055,-10)\n", + " (-13.796,2.058,-10)\n", + " (-14,2.06,-10)\n", + " (-17.2,2.06,-10)\n", + " (-17.2,2.56,-10)\n", + " (-14,2.56,-10)\n", + " (-13.892,2.559,-10)\n", + " (-13.785,2.558,-10)\n", + " (-13.678,2.555,-10)\n", + " (-13.571,2.55,-10)\n", + " (-13.465,2.545,-10)\n", + " (-13.359,2.539,-10)\n", + " (-13.253,2.531,-10)\n", + " (-13.148,2.523,-10)\n", + " (-12.938,2.503,-10)\n", + " (-12.833,2.491,-10)\n", + " (-12.729,2.479,-10)\n", + " (-12.521,2.451,-10)\n", + " (-12.418,2.436,-10)\n", + " (-12.315,2.42,-10)\n", + " (-12.212,2.403,-10)\n", + " (-12.109,2.385,-10)\n", + " (-12.007,2.367,-10)\n", + " (-11.905,2.348,-10)\n", + " (-11.803,2.328,-10)\n", + " (-11.701,2.307,-10)\n", + " (-11.6,2.286,-10)\n", + " (-11.499,2.264,-10)\n", + " (-11.297,2.218,-10)\n", + " (-11.197,2.195,-10)\n", + " (-11.096,2.17,-10)\n", + " (-10.796,2.095,-10)\n", + " (-10.697,2.068,-10)\n", + " (-10.597,2.042,-10)\n", + " (-10.498,2.015,-10)\n", + " (-10.399,1.987,-10)\n", + " (-10.3,1.96,-10)\n", + " (-10.201,1.932,-10)\n", + " (-10.102,1.903,-10)\n", + " (-10.004,1.875,-10)\n", + " (-9.905,1.846,-10)\n", + " (-9.807,1.817,-10)\n", + " (-9.709,1.787,-10)\n", + " (-9.611,1.758,-10)\n", + " (-9.513,1.728,-10)\n", + " (-9.415,1.699,-10)\n", + " (-9.219,1.639,-10)\n", + " (-9.122,1.609,-10)\n", + " (-8.926,1.549,-10)\n", + " (-8.732,1.489,-10)\n", + " (-8.634,1.459,-10)\n", + " (-8.537,1.429,-10)\n", + " (-8.44,1.4,-10)\n", + " (-8.342,1.37,-10)\n", + " (-8.051,1.283,-10)\n", + " (-7.953,1.254,-10)\n", + " (-7.856,1.225,-10)\n", + " (-7.662,1.169,-10)\n", + " (-7.564,1.142,-10)\n", + " (-7.467,1.114,-10)\n", + " (-7.37,1.087,-10)\n", + " (-7.272,1.061,-10)\n", + " (-7.078,1.009,-10)\n", + " (-6.98,0.984,-10)\n", + " (-6.883,0.96,-10)\n", + " (-6.785,0.935,-10)\n", + " (-6.687,0.912,-10)\n", + " (-6.59,0.889,-10)\n", + " (-6.492,0.866,-10)\n", + " (-6.394,0.844,-10)\n", + " (-6.296,0.823,-10)\n", + " (-6.198,0.803,-10)\n", + " (-6.099,0.783,-10)\n", + " (-6.001,0.764,-10)\n", + " (-5.902,0.745,-10)\n", + " (-5.804,0.727,-10)\n", + " (-5.705,0.71,-10)\n", + " (-5.606,0.694,-10)\n", + " (-5.408,0.664,-10)\n", + " (-5.309,0.65,-10)\n", + " (-5.209,0.638,-10)\n", + " (-5.11,0.626,-10)\n", + " (-5.01,0.615,-10)\n", + " (-4.81,0.595,-10)\n", + " (-4.709,0.587,-10)\n", + " (-4.608,0.58,-10)\n", + " (-4.508,0.574,-10)\n", + " (-4.407,0.569,-10)\n", + " (-4.305,0.565,-10)\n", + " (-4.204,0.562,-10)\n", + " (-4,0.56,-10)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + " prism, center = (9.09425,1.32149,0)\n", + " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", + " (4,0.06,-10)\n", + " (4,0.56,-10)\n", + " (4.204,0.562,-10)\n", + " (4.305,0.565,-10)\n", + " (4.407,0.569,-10)\n", + " (4.508,0.574,-10)\n", + " (4.608,0.58,-10)\n", + " (4.709,0.587,-10)\n", + " (4.81,0.595,-10)\n", + " (5.01,0.615,-10)\n", + " (5.11,0.626,-10)\n", + " (5.209,0.638,-10)\n", + " (5.309,0.65,-10)\n", + " (5.408,0.664,-10)\n", + " (5.606,0.694,-10)\n", + " (5.705,0.71,-10)\n", + " (5.804,0.727,-10)\n", + " (5.902,0.745,-10)\n", + " (6.001,0.764,-10)\n", + " (6.099,0.783,-10)\n", + " (6.198,0.803,-10)\n", + " (6.296,0.823,-10)\n", + " (6.394,0.844,-10)\n", + " (6.492,0.866,-10)\n", + " (6.59,0.889,-10)\n", + " (6.687,0.912,-10)\n", + " (6.785,0.935,-10)\n", + " (6.883,0.96,-10)\n", + " (6.98,0.984,-10)\n", + " (7.078,1.009,-10)\n", + " (7.272,1.061,-10)\n", + " (7.37,1.087,-10)\n", + " (7.467,1.114,-10)\n", + " (7.564,1.142,-10)\n", + " (7.662,1.169,-10)\n", + " (7.856,1.225,-10)\n", + " (7.953,1.254,-10)\n", + " (8.051,1.283,-10)\n", + " (8.342,1.37,-10)\n", + " (8.44,1.4,-10)\n", + " (8.537,1.429,-10)\n", + " (8.634,1.459,-10)\n", + " (8.732,1.489,-10)\n", + " (8.926,1.549,-10)\n", + " (9.122,1.609,-10)\n", + " (9.219,1.639,-10)\n", + " (9.415,1.699,-10)\n", + " (9.513,1.728,-10)\n", + " (9.611,1.758,-10)\n", + " (9.709,1.787,-10)\n", + " (9.807,1.817,-10)\n", + " (9.905,1.846,-10)\n", + " (10.004,1.875,-10)\n", + " (10.102,1.903,-10)\n", + " (10.201,1.932,-10)\n", + " (10.3,1.96,-10)\n", + " (10.399,1.987,-10)\n", + " (10.498,2.015,-10)\n", + " (10.597,2.042,-10)\n", + " (10.697,2.068,-10)\n", + " (10.796,2.095,-10)\n", + " (11.096,2.17,-10)\n", + " (11.197,2.195,-10)\n", + " (11.297,2.218,-10)\n", + " (11.499,2.264,-10)\n", + " (11.6,2.286,-10)\n", + " (11.701,2.307,-10)\n", + " (11.803,2.328,-10)\n", + " (11.905,2.348,-10)\n", + " (12.007,2.367,-10)\n", + " (12.109,2.385,-10)\n", + " (12.212,2.403,-10)\n", + " (12.315,2.42,-10)\n", + " (12.418,2.436,-10)\n", + " (12.521,2.451,-10)\n", + " (12.729,2.479,-10)\n", + " (12.833,2.491,-10)\n", + " (12.938,2.503,-10)\n", + " (13.148,2.523,-10)\n", + " (13.253,2.531,-10)\n", + " (13.359,2.539,-10)\n", + " (13.465,2.545,-10)\n", + " (13.571,2.55,-10)\n", + " (13.678,2.555,-10)\n", + " (13.785,2.558,-10)\n", + " (13.892,2.559,-10)\n", + " (14,2.56,-10)\n", + " (17.2,2.56,-10)\n", + " (17.2,2.06,-10)\n", + " (14,2.06,-10)\n", + " (13.796,2.058,-10)\n", + " (13.695,2.055,-10)\n", + " (13.593,2.051,-10)\n", + " (13.492,2.046,-10)\n", + " (13.392,2.04,-10)\n", + " (13.291,2.033,-10)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (13.19,2.025,-10)\n", + " (12.99,2.005,-10)\n", + " (12.89,1.994,-10)\n", + " (12.791,1.982,-10)\n", + " (12.691,1.97,-10)\n", + " (12.592,1.956,-10)\n", + " (12.394,1.926,-10)\n", + " (12.295,1.91,-10)\n", + " (12.196,1.893,-10)\n", + " (12.098,1.875,-10)\n", + " (11.999,1.856,-10)\n", + " (11.901,1.837,-10)\n", + " (11.802,1.817,-10)\n", + " (11.704,1.797,-10)\n", + " (11.606,1.776,-10)\n", + " (11.508,1.754,-10)\n", + " (11.41,1.731,-10)\n", + " (11.313,1.708,-10)\n", + " (11.215,1.685,-10)\n", + " (11.117,1.66,-10)\n", + " (11.02,1.636,-10)\n", + " (10.922,1.611,-10)\n", + " (10.728,1.559,-10)\n", + " (10.63,1.533,-10)\n", + " (10.533,1.506,-10)\n", + " (10.436,1.478,-10)\n", + " (10.338,1.451,-10)\n", + " (10.144,1.395,-10)\n", + " (10.047,1.366,-10)\n", + " (9.949,1.337,-10)\n", + " (9.658,1.25,-10)\n", + " (9.56,1.22,-10)\n", + " (9.463,1.191,-10)\n", + " (9.366,1.161,-10)\n", + " (9.268,1.131,-10)\n", + " (9.074,1.071,-10)\n", + " (8.878,1.011,-10)\n", + " (8.781,0.981,-10)\n", + " (8.585,0.921,-10)\n", + " (8.487,0.892,-10)\n", + " (8.389,0.862,-10)\n", + " (8.291,0.833,-10)\n", + " (8.193,0.803,-10)\n", + " (8.095,0.774,-10)\n", + " (7.996,0.745,-10)\n", + " (7.898,0.717,-10)\n", + " (7.799,0.688,-10)\n", + " (7.7,0.66,-10)\n", + " (7.601,0.633,-10)\n", + " (7.502,0.605,-10)\n", + " (7.403,0.578,-10)\n", + " (7.303,0.552,-10)\n", + " (7.204,0.525,-10)\n", + " (6.904,0.45,-10)\n", + " (6.803,0.425,-10)\n", + " (6.703,0.402,-10)\n", + " (6.501,0.356,-10)\n", + " (6.4,0.334,-10)\n", + " (6.299,0.313,-10)\n", + " (6.197,0.292,-10)\n", + " (6.095,0.272,-10)\n", + " (5.993,0.253,-10)\n", + " (5.891,0.235,-10)\n", + " (5.788,0.217,-10)\n", + " (5.685,0.2,-10)\n", + " (5.582,0.184,-10)\n", + " (5.479,0.169,-10)\n", + " (5.271,0.141,-10)\n", + " (5.167,0.129,-10)\n", + " (5.062,0.117,-10)\n", + " (4.852,0.097,-10)\n", + " (4.747,0.089,-10)\n", + " (4.641,0.081,-10)\n", + " (4.535,0.075,-10)\n", + " (4.429,0.07,-10)\n", + " (4.322,0.065,-10)\n", + " (4.215,0.062,-10)\n", + " (4.108,0.061,-10)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + " prism, center = (0,0.31,0)\n", + " height 20, axis (0,0,1), sidewall angle: 0 radians, 4 vertices:\n", + " (-4,0.06,-10)\n", + " (-4,0.56,-10)\n", + " (4,0.56,-10)\n", + " (4,0.06,-10)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + " prism, center = (-9.09425,-1.32149,0)\n", + " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", + " (-17.2,-2.56,-10)\n", + " (-17.2,-2.06,-10)\n", + " (-14,-2.06,-10)\n", + " (-13.796,-2.058,-10)\n", + " (-13.695,-2.055,-10)\n", + " (-13.593,-2.051,-10)\n", + " (-13.492,-2.046,-10)\n", + " (-13.392,-2.04,-10)\n", + " (-13.291,-2.033,-10)\n", + " (-13.19,-2.025,-10)\n", + " (-12.99,-2.005,-10)\n", + " (-12.89,-1.994,-10)\n", + " (-12.791,-1.982,-10)\n", + " (-12.691,-1.97,-10)\n", + " (-12.592,-1.956,-10)\n", + " (-12.394,-1.926,-10)\n", + " (-12.295,-1.91,-10)\n", + " (-12.196,-1.893,-10)\n", + " (-12.098,-1.875,-10)\n", + " (-11.999,-1.856,-10)\n", + " (-11.901,-1.837,-10)\n", + " (-11.802,-1.817,-10)\n", + " (-11.704,-1.797,-10)\n", + " (-11.606,-1.776,-10)\n", + " (-11.508,-1.754,-10)\n", + " (-11.41,-1.731,-10)\n", + " (-11.313,-1.708,-10)\n", + " (-11.215,-1.685,-10)\n", + " (-11.117,-1.66,-10)\n", + " (-11.02,-1.636,-10)\n", + " (-10.922,-1.611,-10)\n", + " (-10.728,-1.559,-10)\n", + " (-10.63,-1.533,-10)\n", + " (-10.533,-1.506,-10)\n", + " (-10.436,-1.478,-10)\n", + " (-10.338,-1.451,-10)\n", + " (-10.144,-1.395,-10)\n", + " (-10.047,-1.366,-10)\n", + " (-9.949,-1.337,-10)\n", + " (-9.658,-1.25,-10)\n", + " (-9.56,-1.22,-10)\n", + " (-9.463,-1.191,-10)\n", + " (-9.366,-1.161,-10)\n", + " (-9.268,-1.131,-10)\n", + " (-9.074,-1.071,-10)\n", + " (-8.878,-1.011,-10)\n", + " (-8.781,-0.981,-10)\n", + " (-8.585,-0.921,-10)\n", + " (-8.487,-0.892,-10)\n", + " (-8.389,-0.862,-10)\n", + " (-8.291,-0.833,-10)\n", + " (-8.193,-0.803,-10)\n", + " (-8.095,-0.774,-10)\n", + " (-7.996,-0.745,-10)\n", + " (-7.898,-0.717,-10)\n", + " (-7.799,-0.688,-10)\n", + " (-7.7,-0.66,-10)\n", + " (-7.601,-0.633,-10)\n", + " 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(-4.709,-0.587,-10)\n", + " (-4.81,-0.595,-10)\n", + " (-5.01,-0.615,-10)\n", + " (-5.11,-0.626,-10)\n", + " (-5.209,-0.638,-10)\n", + " (-5.309,-0.65,-10)\n", + " (-5.408,-0.664,-10)\n", + " (-5.606,-0.694,-10)\n", + " (-5.705,-0.71,-10)\n", + " (-5.804,-0.727,-10)\n", + " (-5.902,-0.745,-10)\n", + " (-6.001,-0.764,-10)\n", + " (-6.099,-0.783,-10)\n", + " (-6.198,-0.803,-10)\n", + " (-6.296,-0.823,-10)\n", + " (-6.394,-0.844,-10)\n", + " (-6.492,-0.866,-10)\n", + " (-6.59,-0.889,-10)\n", + " (-6.687,-0.912,-10)\n", + " (-6.785,-0.935,-10)\n", + " (-6.883,-0.96,-10)\n", + " (-6.98,-0.984,-10)\n", + " (-7.078,-1.009,-10)\n", + " (-7.272,-1.061,-10)\n", + " (-7.37,-1.087,-10)\n", + " (-7.467,-1.114,-10)\n", + " (-7.564,-1.142,-10)\n", + " (-7.662,-1.169,-10)\n", + " (-7.856,-1.225,-10)\n", + " (-7.953,-1.254,-10)\n", + " (-8.051,-1.283,-10)\n", + " (-8.342,-1.37,-10)\n", + " (-8.44,-1.4,-10)\n", + " (-8.537,-1.429,-10)\n", + " (-8.634,-1.459,-10)\n", + " (-8.732,-1.489,-10)\n", + " (-8.926,-1.549,-10)\n", + " (-9.122,-1.609,-10)\n", + " (-9.219,-1.639,-10)\n", + " (-9.415,-1.699,-10)\n", + " (-9.513,-1.728,-10)\n", + " (-9.611,-1.758,-10)\n", + " (-9.709,-1.787,-10)\n", + " (-9.807,-1.817,-10)\n", + " (-9.905,-1.846,-10)\n", + " (-10.004,-1.875,-10)\n", + " (-10.102,-1.903,-10)\n", + " (-10.201,-1.932,-10)\n", + " (-10.3,-1.96,-10)\n", + " (-10.399,-1.987,-10)\n", + " (-10.498,-2.015,-10)\n", + " (-10.597,-2.042,-10)\n", + " (-10.697,-2.068,-10)\n", + " (-10.796,-2.095,-10)\n", + " (-11.096,-2.17,-10)\n", + " (-11.197,-2.195,-10)\n", + " (-11.297,-2.218,-10)\n", + " (-11.499,-2.264,-10)\n", + " (-11.6,-2.286,-10)\n", + " (-11.701,-2.307,-10)\n", + " (-11.803,-2.328,-10)\n", + " (-11.905,-2.348,-10)\n", + " (-12.007,-2.367,-10)\n", + " (-12.109,-2.385,-10)\n", + " (-12.212,-2.403,-10)\n", + " (-12.315,-2.42,-10)\n", + " (-12.418,-2.436,-10)\n", + " (-12.521,-2.451,-10)\n", + " (-12.729,-2.479,-10)\n", + " (-12.833,-2.491,-10)\n", + " (-12.938,-2.503,-10)\n", + " (-13.148,-2.523,-10)\n", + " (-13.253,-2.531,-10)\n", + " (-13.359,-2.539,-10)\n", + " (-13.465,-2.545,-10)\n", + " (-13.571,-2.55,-10)\n", + " (-13.678,-2.555,-10)\n", + " (-13.785,-2.558,-10)\n", + " (-13.892,-2.559,-10)\n", + " (-14,-2.56,-10)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + " prism, center = (9.09425,-1.32149,0)\n", + " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", + " (14,-2.56,-10)\n", + " (13.892,-2.559,-10)\n", + " (13.785,-2.558,-10)\n", + " (13.678,-2.555,-10)\n", + " (13.571,-2.55,-10)\n", + " (13.465,-2.545,-10)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (13.359,-2.539,-10)\n", + " (13.253,-2.531,-10)\n", + " (13.148,-2.523,-10)\n", + " (12.938,-2.503,-10)\n", + " (12.833,-2.491,-10)\n", + " (12.729,-2.479,-10)\n", + " (12.521,-2.451,-10)\n", + " (12.418,-2.436,-10)\n", + " (12.315,-2.42,-10)\n", + " (12.212,-2.403,-10)\n", + " (12.109,-2.385,-10)\n", + " (12.007,-2.367,-10)\n", + " 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(9.074,-1.071,-10)\n", + " (9.268,-1.131,-10)\n", + " (9.366,-1.161,-10)\n", + " (9.463,-1.191,-10)\n", + " (9.56,-1.22,-10)\n", + " (9.658,-1.25,-10)\n", + " (9.949,-1.337,-10)\n", + " (10.047,-1.366,-10)\n", + " (10.144,-1.395,-10)\n", + " (10.338,-1.451,-10)\n", + " (10.436,-1.478,-10)\n", + " (10.533,-1.506,-10)\n", + " (10.63,-1.533,-10)\n", + " (10.728,-1.559,-10)\n", + " (10.922,-1.611,-10)\n", + " (11.02,-1.636,-10)\n", + " (11.117,-1.66,-10)\n", + " (11.215,-1.685,-10)\n", + " (11.313,-1.708,-10)\n", + " (11.41,-1.731,-10)\n", + " (11.508,-1.754,-10)\n", + " (11.606,-1.776,-10)\n", + " (11.704,-1.797,-10)\n", + " (11.802,-1.817,-10)\n", + " (11.901,-1.837,-10)\n", + " (11.999,-1.856,-10)\n", + " (12.098,-1.875,-10)\n", + " (12.196,-1.893,-10)\n", + " (12.295,-1.91,-10)\n", + " (12.394,-1.926,-10)\n", + " (12.592,-1.956,-10)\n", + " (12.691,-1.97,-10)\n", + " (12.791,-1.982,-10)\n", + " (12.89,-1.994,-10)\n", + " (12.99,-2.005,-10)\n", + " (13.19,-2.025,-10)\n", + " (13.291,-2.033,-10)\n", + " (13.392,-2.04,-10)\n", + " (13.492,-2.046,-10)\n", + " (13.593,-2.051,-10)\n", + " (13.695,-2.055,-10)\n", + " (13.796,-2.058,-10)\n", + " (14,-2.06,-10)\n", + " (17.2,-2.06,-10)\n", + " (17.2,-2.56,-10)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + " prism, center = (0,-0.31,0)\n", + " height 20, axis (0,0,1), sidewall angle: 0 radians, 4 vertices:\n", + " (-4,-0.56,-10)\n", + " (-4,-0.06,-10)\n", + " (4,-0.06,-10)\n", + " (4,-0.56,-10)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + "subpixel-averaging is 6.81898% done, 58.0995 s remaining\n", + "subpixel-averaging is 10.2289% done, 37.5564 s remaining\n", + "subpixel-averaging is 13.6388% done, 27.3374 s remaining\n", + "subpixel-averaging is 17.0487% done, 21.2318 s remaining\n", + "subpixel-averaging is 20.4586% done, 16.7154 s remaining\n", + "subpixel-averaging is 23.8685% done, 13.4232 s remaining\n", + "subpixel-averaging is 27.2785% done, 11.4875 s remaining\n", + "subpixel-averaging is 30.6884% done, 9.75372 s remaining\n", + "subpixel-averaging is 34.0983% done, 8.27854 s remaining\n", + "subpixel-averaging is 37.5082% done, 6.93392 s remaining\n", + "subpixel-averaging is 65.64% done, 2.22818 s remaining\n", + "subpixel-averaging is 69.0499% done, 1.90412 s remaining\n", + "subpixel-averaging is 72.4598% done, 1.64002 s remaining\n", + "subpixel-averaging is 75.8697% done, 1.32216 s remaining\n", + "subpixel-averaging is 79.2797% done, 1.09069 s remaining\n", + "subpixel-averaging is 82.6896% done, 0.886299 s remaining\n", + "subpixel-averaging is 86.0995% done, 0.703019 s remaining\n", + "subpixel-averaging is 89.5094% done, 0.498196 s remaining\n", + "subpixel-averaging is 92.9193% done, 0.325508 s remaining\n", + "subpixel-averaging is 98.8867% done, 0.0483677 s remaining\n", + "subpixel-averaging is 6.81898% done, 59.2108 s remaining\n", + "subpixel-averaging is 10.2289% done, 37.546 s remaining\n", + "subpixel-averaging is 13.6388% done, 26.994 s remaining\n", + "subpixel-averaging is 17.0487% done, 21.1849 s remaining\n", + "subpixel-averaging is 20.4586% done, 16.5219 s remaining\n", + "subpixel-averaging is 23.8685% done, 13.1793 s remaining\n", + "subpixel-averaging is 27.2785% done, 11.4351 s remaining\n", + "subpixel-averaging is 30.6884% done, 9.73853 s remaining\n", + "subpixel-averaging is 34.0983% done, 8.23782 s remaining\n", + "subpixel-averaging is 37.5082% done, 7.17582 s remaining\n", + "subpixel-averaging is 65.64% done, 2.2864 s remaining\n", + "subpixel-averaging is 69.0499% done, 1.91744 s remaining\n", + "subpixel-averaging is 72.4598% done, 1.63739 s remaining\n", + "subpixel-averaging is 75.8697% done, 1.36288 s remaining\n", + "subpixel-averaging is 79.2797% done, 1.06936 s remaining\n", + "subpixel-averaging is 82.6896% done, 0.882767 s remaining\n", + "subpixel-averaging is 86.0995% done, 0.708544 s remaining\n", + "subpixel-averaging is 89.5094% done, 0.495993 s remaining\n", + "subpixel-averaging is 92.9193% done, 0.328165 s remaining\n", + "subpixel-averaging is 98.8867% done, 0.0471172 s remaining\n", + "subpixel-averaging is 6.81898% done, 58.5489 s remaining\n", + "subpixel-averaging is 10.2289% done, 38.1212 s remaining\n", + "subpixel-averaging is 13.6388% done, 27.0431 s remaining\n", + "subpixel-averaging is 17.0487% done, 21.2724 s remaining\n", + "subpixel-averaging is 20.4586% done, 16.6743 s remaining\n", + "subpixel-averaging is 23.8685% done, 13.2109 s remaining\n", + "subpixel-averaging is 27.2785% done, 11.3869 s remaining\n", + "subpixel-averaging is 30.6884% done, 9.75682 s remaining\n", + "subpixel-averaging is 34.0983% done, 8.26105 s remaining\n", + "subpixel-averaging is 37.5082% done, 7.09754 s remaining\n", + "subpixel-averaging is 65.64% done, 2.24059 s remaining\n", + "subpixel-averaging is 69.0499% done, 1.91605 s remaining\n", + "subpixel-averaging is 72.4598% done, 1.63231 s remaining\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "subpixel-averaging is 75.8697% done, 1.34937 s remaining\n", + "subpixel-averaging is 79.2797% done, 1.08291 s remaining\n", + "subpixel-averaging is 82.6896% done, 0.888016 s remaining\n", + "subpixel-averaging is 86.0995% done, 0.710327 s remaining\n", + "subpixel-averaging is 89.5094% done, 0.493508 s remaining\n", + "subpixel-averaging is 92.9193% done, 0.332601 s remaining\n", + "subpixel-averaging is 98.8867% done, 0.0488103 s remaining\n", + "time for set_epsilon = 271.702 s\n", + "-----------\n", + "MPB solved for frequency_1(2.2349,0,0) = 0.687238 after 22 iters\n", + "MPB solved for frequency_1(2.08443,0,0) = 0.645247 after 8 iters\n", + "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 3 iters\n", + "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1eb3cf6322754e07ba3761f3426e295a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=177.5)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 44.68/177.5 = 25.2% done in 4.0s, 11.9s to go\n", + "on time step 2239 (time=44.78), 0.00178698 s/step\n", + "Meep progress: 93.64/177.5 = 52.8% done in 8.0s, 7.2s to go\n", + "on time step 4687 (time=93.74), 0.00163464 s/step\n", + "Meep progress: 142.64000000000001/177.5 = 80.4% done in 12.0s, 2.9s to go\n", + "on time step 7139 (time=142.78), 0.00163171 s/step\n", + "run 0 finished at t = 177.5 (8875 timesteps)\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "\n", - "res = 25 # pixels/μm\n", - "three_d = False # 3d calculation?\n", - "d = 0.12 # branch separation\n", + "res = 25 # pixels/μm\n", + "three_d = False # 3d calculation?\n", + "d = 0.12 # branch separation\n", "\n", - "gdsII_file = \"coupler.gds\"\n", + "gdsII_file = 'coupler.gds'\n", "CELL_LAYER = 0\n", "PORT1_LAYER = 1\n", "PORT2_LAYER = 2\n", @@ -53,28 +898,24 @@ "t_air = 0.78\n", "\n", "dpml = 1\n", - "cell_thickness = dpml + t_oxide + t_Si + t_air + dpml\n", + "cell_thickness = dpml+t_oxide+t_Si+t_air+dpml\n", "\n", "oxide = mp.Medium(epsilon=2.25)\n", - "silicon = mp.Medium(epsilon=12)\n", + "silicon=mp.Medium(epsilon=12)\n", "\n", "lcen = 1.55\n", - "fcen = 1 / lcen\n", - "df = 0.2 * fcen\n", + "fcen = 1/lcen\n", + "df = 0.2*fcen\n", "\n", - "cell_zmax = 0.5 * cell_thickness if three_d else 0\n", - "cell_zmin = -0.5 * cell_thickness if three_d else 0\n", - "si_zmax = 0.5 * t_Si if three_d else 10\n", - "si_zmin = -0.5 * t_Si if three_d else -10\n", + "cell_zmax = 0.5*cell_thickness if three_d else 0\n", + "cell_zmin = -0.5*cell_thickness if three_d else 0\n", + "si_zmax = 0.5*t_Si if three_d else 10\n", + "si_zmin = -0.5*t_Si if three_d else -10\n", "\n", "# read cell size, volumes for source region and flux monitors,\n", "# and coupler geometry from GDSII file\n", - "upper_branch = mp.get_GDSII_prisms(\n", - " silicon, gdsII_file, UPPER_BRANCH_LAYER, si_zmin, si_zmax\n", - ")\n", - "lower_branch = mp.get_GDSII_prisms(\n", - " silicon, gdsII_file, LOWER_BRANCH_LAYER, si_zmin, si_zmax\n", - ")\n", + "upper_branch = mp.get_GDSII_prisms(silicon, gdsII_file, UPPER_BRANCH_LAYER, si_zmin, si_zmax)\n", + "lower_branch = mp.get_GDSII_prisms(silicon, gdsII_file, LOWER_BRANCH_LAYER, si_zmin, si_zmax)\n", "\n", "cell = mp.GDSII_vol(gdsII_file, CELL_LAYER, cell_zmin, cell_zmax)\n", "p1 = mp.GDSII_vol(gdsII_file, PORT1_LAYER, si_zmin, si_zmax)\n", @@ -85,14 +926,14 @@ "\n", "# displace upper and lower branches of coupler (as well as source and flux regions)\n", "if d != default_d:\n", - " delta_y = 0.5 * (d - default_d)\n", + " delta_y = 0.5*(d-default_d)\n", " delta = mp.Vector3(y=delta_y)\n", " p1.center += delta\n", " p2.center -= delta\n", " p3.center += delta\n", " p4.center -= delta\n", " src_vol.center += delta\n", - " cell.size += 2 * delta\n", + " cell.size += 2*delta\n", " for np in range(len(lower_branch)):\n", " lower_branch[np].center -= delta\n", " for nv in range(len(lower_branch[np].vertices)):\n", @@ -102,32 +943,26 @@ " for nv in range(len(upper_branch[np].vertices)):\n", " upper_branch[np].vertices[nv] += delta\n", "\n", - "geometry = upper_branch + lower_branch\n", + "geometry = upper_branch+lower_branch\n", "\n", "if three_d:\n", - " oxide_center = mp.Vector3(z=-0.5 * t_oxide)\n", - " oxide_size = mp.Vector3(cell.size.x, cell.size.y, t_oxide)\n", + " oxide_center = mp.Vector3(z=-0.5*t_oxide)\n", + " oxide_size = mp.Vector3(cell.size.x,cell.size.y,t_oxide)\n", " oxide_layer = [mp.Block(material=oxide, center=oxide_center, size=oxide_size)]\n", - " geometry = geometry + oxide_layer\n", - "\n", - "sources = [\n", - " mp.EigenModeSource(\n", - " src=mp.GaussianSource(fcen, fwidth=df),\n", - " size=src_vol.size,\n", - " center=src_vol.center,\n", - " eig_band=1,\n", - " eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z,\n", - " eig_match_freq=True,\n", - " )\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=res,\n", - " cell_size=cell.size,\n", - " boundary_layers=[mp.PML(dpml)],\n", - " sources=sources,\n", - " geometry=geometry,\n", - ")\n", + " geometry = geometry+oxide_layer\n", + "\n", + "sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df),\n", + " size=src_vol.size,\n", + " center=src_vol.center,\n", + " eig_band=1,\n", + " eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z,\n", + " eig_match_freq=True)]\n", + "\n", + "sim = mp.Simulation(resolution=res,\n", + " cell_size=cell.size,\n", + " boundary_layers=[mp.PML(dpml)],\n", + " sources=sources,\n", + " geometry=geometry)\n", "\n", "mode1 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p1))\n", "mode2 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p2))\n", @@ -139,30 +974,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MPB solved for frequency_1(2.2349,0,0) = 0.687243 after 22 iters\n", + "MPB solved for frequency_1(2.08451,0,0) = 0.64527 after 5 iters\n", + "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 4 iters\n", + "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n", + "Dominant planewave for band 1: (2.084124,-0.000000,0.000000)\n", + "MPB solved for frequency_1(2.2349,0,0) = 0.687243 after 12 iters\n", + "MPB solved for frequency_1(2.08452,0,0) = 0.645271 after 5 iters\n", + "MPB solved for frequency_1(2.08413,0,0) = 0.645161 after 3 iters\n", + "MPB solved for frequency_1(2.08413,0,0) = 0.645161 after 1 iters\n", + "Dominant planewave for band 1: (2.084127,-0.000000,0.000000)\n", + "MPB solved for frequency_1(2.2349,0,0) = 0.687236 after 8 iters\n", + "MPB solved for frequency_1(2.08441,0,0) = 0.645241 after 8 iters\n", + "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 3 iters\n", + "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n", + "Dominant planewave for band 1: (2.084118,-0.000000,0.000000)\n", + "MPB solved for frequency_1(2.2349,0,0) = 0.687238 after 27 iters\n", + "MPB solved for frequency_1(2.08443,0,0) = 0.645249 after 7 iters\n", + "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 3 iters\n", + "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n", + "Dominant planewave for band 1: (2.084119,-0.000000,0.000000)\n", + "trans:, 0.12, 0.000001, 0.406761, 0.587083\n" + ] + } + ], "source": [ "# S parameters\n", - "p1_coeff = sim.get_eigenmode_coefficients(\n", - " mode1, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", - ").alpha[0, 0, 0]\n", - "p2_coeff = sim.get_eigenmode_coefficients(\n", - " mode2, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", - ").alpha[0, 0, 1]\n", - "p3_coeff = sim.get_eigenmode_coefficients(\n", - " mode3, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", - ").alpha[0, 0, 0]\n", - "p4_coeff = sim.get_eigenmode_coefficients(\n", - " mode4, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", - ").alpha[0, 0, 0]\n", + "p1_coeff = sim.get_eigenmode_coefficients(mode1, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0]\n", + "p2_coeff = sim.get_eigenmode_coefficients(mode2, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,1]\n", + "p3_coeff = sim.get_eigenmode_coefficients(mode3, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0]\n", + "p4_coeff = sim.get_eigenmode_coefficients(mode4, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0]\n", "\n", "# transmittance\n", - "p2_trans = abs(p2_coeff) ** 2 / abs(p1_coeff) ** 2\n", - "p3_trans = abs(p3_coeff) ** 2 / abs(p1_coeff) ** 2\n", - "p4_trans = abs(p4_coeff) ** 2 / abs(p1_coeff) ** 2\n", + "p2_trans = abs(p2_coeff)**2/abs(p1_coeff)**2\n", + "p3_trans = abs(p3_coeff)**2/abs(p1_coeff)**2\n", + "p4_trans = abs(p4_coeff)**2/abs(p1_coeff)**2\n", "\n", - "print(\"trans:, {:.2f}, {:.6f}, {:.6f}, {:.6f}\".format(d, p2_trans, p3_trans, p4_trans))" + "print(\"trans:, {:.2f}, {:.6f}, {:.6f}, {:.6f}\".format(d,p2_trans,p3_trans,p4_trans))" ] }, { @@ -185,47 +1040,901 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000232935 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 34.4 x 7.84 x 0 with resolution 25\n", + " prism, center = (-9.09425,1.32149,0)\n", + " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", + " (-4,0.06,-10)\n", + " (-4.108,0.061,-10)\n", + " (-4.215,0.062,-10)\n", + " (-4.322,0.065,-10)\n", + " (-4.429,0.07,-10)\n", + " (-4.535,0.075,-10)\n", + " (-4.641,0.081,-10)\n", + " (-4.747,0.089,-10)\n", + " (-4.852,0.097,-10)\n", + " (-5.062,0.117,-10)\n", + " (-5.167,0.129,-10)\n", + " (-5.271,0.141,-10)\n", + " (-5.479,0.169,-10)\n", + " (-5.582,0.184,-10)\n", + " (-5.685,0.2,-10)\n", + " (-5.788,0.217,-10)\n", + " (-5.891,0.235,-10)\n", + " (-5.993,0.253,-10)\n", + " (-6.095,0.272,-10)\n", + " (-6.197,0.292,-10)\n", + " (-6.299,0.313,-10)\n", + " (-6.4,0.334,-10)\n", + " (-6.501,0.356,-10)\n", + " (-6.703,0.402,-10)\n", + " (-6.803,0.425,-10)\n", + " (-6.904,0.45,-10)\n", + " (-7.204,0.525,-10)\n", + " (-7.303,0.552,-10)\n", + " (-7.403,0.578,-10)\n", + " (-7.502,0.605,-10)\n", + " (-7.601,0.633,-10)\n", + " (-7.7,0.66,-10)\n", + " (-7.799,0.688,-10)\n", + " (-7.898,0.717,-10)\n", + " (-7.996,0.745,-10)\n", + " (-8.095,0.774,-10)\n", + " (-8.193,0.803,-10)\n", + " (-8.291,0.833,-10)\n", + " (-8.389,0.862,-10)\n", + " (-8.487,0.892,-10)\n", + " (-8.585,0.921,-10)\n", + " (-8.781,0.981,-10)\n", + " (-8.878,1.011,-10)\n", + " (-9.074,1.071,-10)\n", + " (-9.268,1.131,-10)\n", + " (-9.366,1.161,-10)\n", + " (-9.463,1.191,-10)\n", + " (-9.56,1.22,-10)\n", + " (-9.658,1.25,-10)\n", + " (-9.949,1.337,-10)\n", + " (-10.047,1.366,-10)\n", + " (-10.144,1.395,-10)\n", + " (-10.338,1.451,-10)\n", + " (-10.436,1.478,-10)\n", + " (-10.533,1.506,-10)\n", + " (-10.63,1.533,-10)\n", + " (-10.728,1.559,-10)\n", + " (-10.922,1.611,-10)\n", + " (-11.02,1.636,-10)\n", + " (-11.117,1.66,-10)\n", + " (-11.215,1.685,-10)\n", + " (-11.313,1.708,-10)\n", + " (-11.41,1.731,-10)\n", + " (-11.508,1.754,-10)\n", + " (-11.606,1.776,-10)\n", + " (-11.704,1.797,-10)\n", + " (-11.802,1.817,-10)\n", + " (-11.901,1.837,-10)\n", + " (-11.999,1.856,-10)\n", + " (-12.098,1.875,-10)\n", + " (-12.196,1.893,-10)\n", + " (-12.295,1.91,-10)\n", + " (-12.394,1.926,-10)\n", + " (-12.592,1.956,-10)\n", + " (-12.691,1.97,-10)\n", + " (-12.791,1.982,-10)\n", + " (-12.89,1.994,-10)\n", + " (-12.99,2.005,-10)\n", + " (-13.19,2.025,-10)\n", + " (-13.291,2.033,-10)\n", + " (-13.392,2.04,-10)\n", + " (-13.492,2.046,-10)\n", + " (-13.593,2.051,-10)\n", + " (-13.695,2.055,-10)\n", + " (-13.796,2.058,-10)\n", + " (-14,2.06,-10)\n", + " (-17.2,2.06,-10)\n", + " (-17.2,2.56,-10)\n", + " (-14,2.56,-10)\n", + " (-13.892,2.559,-10)\n", + " (-13.785,2.558,-10)\n", + " (-13.678,2.555,-10)\n", + " (-13.571,2.55,-10)\n", + " (-13.465,2.545,-10)\n", + " (-13.359,2.539,-10)\n", + " (-13.253,2.531,-10)\n", + " (-13.148,2.523,-10)\n", + " (-12.938,2.503,-10)\n", + " (-12.833,2.491,-10)\n", + " (-12.729,2.479,-10)\n", + " (-12.521,2.451,-10)\n", + " (-12.418,2.436,-10)\n", + " (-12.315,2.42,-10)\n", + " (-12.212,2.403,-10)\n", + " (-12.109,2.385,-10)\n", + " (-12.007,2.367,-10)\n", + " (-11.905,2.348,-10)\n", + " (-11.803,2.328,-10)\n", + " (-11.701,2.307,-10)\n", + " (-11.6,2.286,-10)\n", + " (-11.499,2.264,-10)\n", + " (-11.297,2.218,-10)\n", + " (-11.197,2.195,-10)\n", + " (-11.096,2.17,-10)\n", + " (-10.796,2.095,-10)\n", + " (-10.697,2.068,-10)\n", + " (-10.597,2.042,-10)\n", + " (-10.498,2.015,-10)\n", + " (-10.399,1.987,-10)\n", + " (-10.3,1.96,-10)\n", + " (-10.201,1.932,-10)\n", + " (-10.102,1.903,-10)\n", + " (-10.004,1.875,-10)\n", + " (-9.905,1.846,-10)\n", + " (-9.807,1.817,-10)\n", + " (-9.709,1.787,-10)\n", + " (-9.611,1.758,-10)\n", + " (-9.513,1.728,-10)\n", + " (-9.415,1.699,-10)\n", + " (-9.219,1.639,-10)\n", + " (-9.122,1.609,-10)\n", + " (-8.926,1.549,-10)\n", + " (-8.732,1.489,-10)\n", + " (-8.634,1.459,-10)\n", + " (-8.537,1.429,-10)\n", + " (-8.44,1.4,-10)\n", + " (-8.342,1.37,-10)\n", + " (-8.051,1.283,-10)\n", + " (-7.953,1.254,-10)\n", + " (-7.856,1.225,-10)\n", + " (-7.662,1.169,-10)\n", + " (-7.564,1.142,-10)\n", + " (-7.467,1.114,-10)\n", + " (-7.37,1.087,-10)\n", + " (-7.272,1.061,-10)\n", + " (-7.078,1.009,-10)\n", + " (-6.98,0.984,-10)\n", + " (-6.883,0.96,-10)\n", + " (-6.785,0.935,-10)\n", + " (-6.687,0.912,-10)\n", + " (-6.59,0.889,-10)\n", + " (-6.492,0.866,-10)\n", + " (-6.394,0.844,-10)\n", + " (-6.296,0.823,-10)\n", + " (-6.198,0.803,-10)\n", + " (-6.099,0.783,-10)\n", + " (-6.001,0.764,-10)\n", + " (-5.902,0.745,-10)\n", + " (-5.804,0.727,-10)\n", + " (-5.705,0.71,-10)\n", + " (-5.606,0.694,-10)\n", + " (-5.408,0.664,-10)\n", + " (-5.309,0.65,-10)\n", + " (-5.209,0.638,-10)\n", + " (-5.11,0.626,-10)\n", + " (-5.01,0.615,-10)\n", + " (-4.81,0.595,-10)\n", + " (-4.709,0.587,-10)\n", + " (-4.608,0.58,-10)\n", + " (-4.508,0.574,-10)\n", + " (-4.407,0.569,-10)\n", + " (-4.305,0.565,-10)\n", + " (-4.204,0.562,-10)\n", + " (-4,0.56,-10)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + " prism, center = (9.09425,1.32149,0)\n", + " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", + " (4,0.06,-10)\n", + " (4,0.56,-10)\n", + " (4.204,0.562,-10)\n", + " (4.305,0.565,-10)\n", + " (4.407,0.569,-10)\n", + " (4.508,0.574,-10)\n", + " (4.608,0.58,-10)\n", + " (4.709,0.587,-10)\n", + " (4.81,0.595,-10)\n", + " (5.01,0.615,-10)\n", + " (5.11,0.626,-10)\n", + " (5.209,0.638,-10)\n", + " (5.309,0.65,-10)\n", + " (5.408,0.664,-10)\n", + " (5.606,0.694,-10)\n", + " (5.705,0.71,-10)\n", + " (5.804,0.727,-10)\n", + " (5.902,0.745,-10)\n", + " (6.001,0.764,-10)\n", + " (6.099,0.783,-10)\n", + " (6.198,0.803,-10)\n", + " (6.296,0.823,-10)\n", + " (6.394,0.844,-10)\n", + " (6.492,0.866,-10)\n", + " (6.59,0.889,-10)\n", + " (6.687,0.912,-10)\n", + " (6.785,0.935,-10)\n", + " (6.883,0.96,-10)\n", + " (6.98,0.984,-10)\n", + " (7.078,1.009,-10)\n", + " (7.272,1.061,-10)\n", + " (7.37,1.087,-10)\n", + " (7.467,1.114,-10)\n", + " (7.564,1.142,-10)\n", + " (7.662,1.169,-10)\n", + " (7.856,1.225,-10)\n", + " (7.953,1.254,-10)\n", + " (8.051,1.283,-10)\n", + " (8.342,1.37,-10)\n", + " (8.44,1.4,-10)\n", + " (8.537,1.429,-10)\n", + " (8.634,1.459,-10)\n", + " (8.732,1.489,-10)\n", + " (8.926,1.549,-10)\n", + " 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(5.804,-0.727,-10)\n", + " (5.705,-0.71,-10)\n", + " (5.606,-0.694,-10)\n", + " (5.408,-0.664,-10)\n", + " (5.309,-0.65,-10)\n", + " (5.209,-0.638,-10)\n", + " (5.11,-0.626,-10)\n", + " (5.01,-0.615,-10)\n", + " (4.81,-0.595,-10)\n", + " (4.709,-0.587,-10)\n", + " (4.608,-0.58,-10)\n", + " (4.508,-0.574,-10)\n", + " (4.407,-0.569,-10)\n", + " (4.305,-0.565,-10)\n", + " (4.204,-0.562,-10)\n", + " (4,-0.56,-10)\n", + " (4,-0.06,-10)\n", + " (4.108,-0.061,-10)\n", + " (4.215,-0.062,-10)\n", + " (4.322,-0.065,-10)\n", + " (4.429,-0.07,-10)\n", + " (4.535,-0.075,-10)\n", + " (4.641,-0.081,-10)\n", + " (4.747,-0.089,-10)\n", + " (4.852,-0.097,-10)\n", + " (5.062,-0.117,-10)\n", + " (5.167,-0.129,-10)\n", + " (5.271,-0.141,-10)\n", + " (5.479,-0.169,-10)\n", + " (5.582,-0.184,-10)\n", + " (5.685,-0.2,-10)\n", + " (5.788,-0.217,-10)\n", + " (5.891,-0.235,-10)\n", + " (5.993,-0.253,-10)\n", + " (6.095,-0.272,-10)\n", + " (6.197,-0.292,-10)\n", + " (6.299,-0.313,-10)\n", + " (6.4,-0.334,-10)\n", + " (6.501,-0.356,-10)\n", + " (6.703,-0.402,-10)\n", + " (6.803,-0.425,-10)\n", + " (6.904,-0.45,-10)\n", + " (7.204,-0.525,-10)\n", + " (7.303,-0.552,-10)\n", + " (7.403,-0.578,-10)\n", + " (7.502,-0.605,-10)\n", + " (7.601,-0.633,-10)\n", + " (7.7,-0.66,-10)\n", + " (7.799,-0.688,-10)\n", + " (7.898,-0.717,-10)\n", + " (7.996,-0.745,-10)\n", + " (8.095,-0.774,-10)\n", + " (8.193,-0.803,-10)\n", + " (8.291,-0.833,-10)\n", + " (8.389,-0.862,-10)\n", + " (8.487,-0.892,-10)\n", + " (8.585,-0.921,-10)\n", + " (8.781,-0.981,-10)\n", + " (8.878,-1.011,-10)\n", + " (9.074,-1.071,-10)\n", + " (9.268,-1.131,-10)\n", + " (9.366,-1.161,-10)\n", + " (9.463,-1.191,-10)\n", + " (9.56,-1.22,-10)\n", + " (9.658,-1.25,-10)\n", + " (9.949,-1.337,-10)\n", + " (10.047,-1.366,-10)\n", + " (10.144,-1.395,-10)\n", + " (10.338,-1.451,-10)\n", + " (10.436,-1.478,-10)\n", + " (10.533,-1.506,-10)\n", + " (10.63,-1.533,-10)\n", + " (10.728,-1.559,-10)\n", + " (10.922,-1.611,-10)\n", + " (11.02,-1.636,-10)\n", + " (11.117,-1.66,-10)\n", + " (11.215,-1.685,-10)\n", + " (11.313,-1.708,-10)\n", + " (11.41,-1.731,-10)\n", + " (11.508,-1.754,-10)\n", + " (11.606,-1.776,-10)\n", + " (11.704,-1.797,-10)\n", + " (11.802,-1.817,-10)\n", + " (11.901,-1.837,-10)\n", + " (11.999,-1.856,-10)\n", + " (12.098,-1.875,-10)\n", + " (12.196,-1.893,-10)\n", + " (12.295,-1.91,-10)\n", + " (12.394,-1.926,-10)\n", + " (12.592,-1.956,-10)\n", + " (12.691,-1.97,-10)\n", + " (12.791,-1.982,-10)\n", + " (12.89,-1.994,-10)\n", + " (12.99,-2.005,-10)\n", + " (13.19,-2.025,-10)\n", + " (13.291,-2.033,-10)\n", + " (13.392,-2.04,-10)\n", + " (13.492,-2.046,-10)\n", + " (13.593,-2.051,-10)\n", + " (13.695,-2.055,-10)\n", + " (13.796,-2.058,-10)\n", + " (14,-2.06,-10)\n", + " (17.2,-2.06,-10)\n", + " (17.2,-2.56,-10)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + " prism, center = (0,-0.31,0)\n", + " height 20, axis (0,0,1), sidewall angle: 0 radians, 4 vertices:\n", + " (-4,-0.56,-10)\n", + " (-4,-0.06,-10)\n", + " (4,-0.06,-10)\n", + " (4,-0.56,-10)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + "subpixel-averaging is 6.81898% done, 58.5777 s remaining\n", + "subpixel-averaging is 10.2289% done, 37.5198 s remaining\n", + "subpixel-averaging is 13.6388% done, 27.1639 s remaining\n", + "subpixel-averaging is 17.0487% done, 21.1306 s remaining\n", + "subpixel-averaging is 20.4586% done, 16.5868 s remaining\n", + "subpixel-averaging is 23.8685% done, 13.3518 s remaining\n", + "subpixel-averaging is 27.2785% done, 11.5065 s remaining\n", + "subpixel-averaging is 30.6884% done, 9.72629 s remaining\n", + "subpixel-averaging is 34.0983% done, 7.75935 s remaining\n", + "subpixel-averaging is 37.5082% done, 6.95236 s remaining\n", + "subpixel-averaging is 65.64% done, 2.22683 s remaining\n", + "subpixel-averaging is 69.0499% done, 1.91287 s remaining\n", + "subpixel-averaging is 72.4598% done, 1.63956 s remaining\n", + "subpixel-averaging is 75.8697% done, 1.36389 s remaining\n", + "subpixel-averaging is 79.2797% done, 1.08924 s remaining\n", + "subpixel-averaging is 82.6896% done, 0.888155 s remaining\n", + "subpixel-averaging is 86.0995% done, 0.678355 s remaining\n", + "subpixel-averaging is 89.5094% done, 0.499442 s remaining\n", + "subpixel-averaging is 92.9193% done, 0.326281 s remaining\n", + "subpixel-averaging is 98.8867% done, 0.0483846 s remaining\n", + "subpixel-averaging is 6.81898% done, 59.1477 s remaining\n", + "subpixel-averaging is 10.2289% done, 37.4525 s remaining\n", + "subpixel-averaging is 13.6388% done, 26.9655 s remaining\n", + "subpixel-averaging is 17.0487% done, 21.0934 s remaining\n", + "subpixel-averaging is 20.4586% done, 16.5002 s remaining\n", + "subpixel-averaging is 23.8685% done, 13.1996 s remaining\n", + "subpixel-averaging is 27.2785% done, 11.4347 s remaining\n", + "subpixel-averaging is 30.6884% done, 9.76244 s remaining\n", + "subpixel-averaging is 34.0983% done, 8.23291 s remaining\n", + "subpixel-averaging is 37.5082% done, 7.1923 s remaining\n", + "subpixel-averaging is 65.64% done, 2.28558 s remaining\n", + "subpixel-averaging is 69.0499% done, 1.93198 s remaining\n", + "subpixel-averaging is 72.4598% done, 1.63656 s remaining\n", + "subpixel-averaging is 75.8697% done, 1.36626 s remaining\n", + "subpixel-averaging is 79.2797% done, 1.08377 s remaining\n", + "subpixel-averaging is 82.6896% done, 0.8822 s remaining\n", + "subpixel-averaging is 86.0995% done, 0.711633 s remaining\n", + "subpixel-averaging is 89.5094% done, 0.493514 s remaining\n", + "subpixel-averaging is 92.9193% done, 0.328464 s remaining\n", + "subpixel-averaging is 98.8867% done, 0.0470705 s remaining\n", + "subpixel-averaging is 6.81898% done, 58.5464 s remaining\n", + "subpixel-averaging is 10.2289% done, 38.1292 s remaining\n", + "subpixel-averaging is 13.6388% done, 26.9734 s remaining\n", + "subpixel-averaging is 17.0487% done, 21.2453 s remaining\n", + "subpixel-averaging is 20.4586% done, 16.6093 s remaining\n", + "subpixel-averaging is 23.8685% done, 13.2456 s remaining\n", + "subpixel-averaging is 27.2785% done, 11.379 s remaining\n", + "subpixel-averaging is 30.6884% done, 9.74246 s remaining\n", + "subpixel-averaging is 34.0983% done, 8.25901 s remaining\n", + "subpixel-averaging is 37.5082% done, 7.07729 s remaining\n", + "subpixel-averaging is 65.64% done, 2.23432 s remaining\n", + "subpixel-averaging is 69.0499% done, 1.91417 s remaining\n", + "subpixel-averaging is 72.4598% done, 1.63028 s remaining\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "subpixel-averaging is 75.8697% done, 1.34979 s remaining\n", + "subpixel-averaging is 79.2797% done, 1.08361 s remaining\n", + "subpixel-averaging is 82.6896% done, 0.888317 s remaining\n", + "subpixel-averaging is 86.0995% done, 0.711677 s remaining\n", + "subpixel-averaging is 89.5094% done, 0.492528 s remaining\n", + "subpixel-averaging is 92.9193% done, 0.332357 s remaining\n", + "subpixel-averaging is 98.8867% done, 0.0488405 s remaining\n", + "time for set_epsilon = 271.364 s\n", + "-----------\n", + "MPB solved for frequency_1(2.2349,0,0) = 0.687243 after 9 iters\n", + "MPB solved for frequency_1(2.08451,0,0) = 0.64527 after 4 iters\n", + "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 4 iters\n", + "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "19ca7d2433564cacb893306433113d88", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=400.0)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 44.980000000000004/400.0 = 11.2% done in 4.0s, 31.6s to go\n", + "on time step 2255 (time=45.1), 0.00177414 s/step\n", + "Meep progress: 94.72/400.0 = 23.7% done in 8.0s, 25.8s to go\n", + "on time step 4743 (time=94.86), 0.00160783 s/step\n", + "Meep progress: 144.34/400.0 = 36.1% done in 12.0s, 21.3s to go\n", + "on time step 7224 (time=144.48), 0.00161273 s/step\n", + "Meep progress: 194.36/400.0 = 48.6% done in 16.0s, 16.9s to go\n", + "on time step 9725 (time=194.5), 0.00159943 s/step\n", + "Meep progress: 243.5/400.0 = 60.9% done in 20.0s, 12.9s to go\n", + "on time step 12184 (time=243.68), 0.00162705 s/step\n", + "Meep progress: 293.78000000000003/400.0 = 73.4% done in 24.0s, 8.7s to go\n", + "on time step 14699 (time=293.98), 0.00159097 s/step\n", + "Meep progress: 343.84000000000003/400.0 = 86.0% done in 28.0s, 4.6s to go\n", + "on time step 17201 (time=344.02), 0.00159902 s/step\n", + "Meep progress: 393.40000000000003/400.0 = 98.4% done in 32.0s, 0.5s to go\n", + "on time step 19681 (time=393.62), 0.00161343 s/step\n", + "run 0 finished at t = 400.0 (20000 timesteps)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sim.reset_meep()\n", "\n", - "sources = [\n", - " mp.EigenModeSource(\n", - " src=mp.ContinuousSource(fcen, fwidth=df),\n", - " size=src_vol.size,\n", - " center=src_vol.center,\n", - " eig_band=1,\n", - " eig_parity=mp.EVEN_Y + mp.ODD_Z,\n", - " eig_match_freq=True,\n", - " )\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=res,\n", - " cell_size=cell.size,\n", - " boundary_layers=[mp.PML(dpml)],\n", - " sources=sources,\n", - " geometry=geometry,\n", - ")\n", - "\n", - "sim.run(\n", - " until=400\n", - ") # arbitrary long run time to ensure that fields have reached steady state\n", + "sources = [mp.EigenModeSource(src=mp.ContinuousSource(fcen,fwidth=df),\n", + " size=src_vol.size,\n", + " center=src_vol.center,\n", + " eig_band=1,\n", + " eig_parity=mp.EVEN_Y+mp.ODD_Z,\n", + " eig_match_freq=True)]\n", + "\n", + "sim = mp.Simulation(resolution=res,\n", + " cell_size=cell.size,\n", + " boundary_layers=[mp.PML(dpml)],\n", + " sources=sources,\n", + " geometry=geometry)\n", + "\n", + "sim.run(until=400) # arbitrary long run time to ensure that fields have reached steady state\n", "\n", "eps_data = sim.get_epsilon()\n", "ez_data = numpy.real(sim.get_efield_z())\n", "\n", "plt.figure(dpi=200)\n", - "plt.imshow(numpy.transpose(eps_data), interpolation=\"spline36\", cmap=\"binary\")\n", - "plt.imshow(\n", - " numpy.flipud(numpy.transpose(ez_data)),\n", - " interpolation=\"spline36\",\n", - " cmap=\"RdBu\",\n", - " alpha=0.9,\n", - ")\n", - "plt.axis(\"off\")\n", + "plt.imshow(numpy.transpose(eps_data), interpolation='spline36', cmap='binary')\n", + "plt.imshow(numpy.flipud(numpy.transpose(ez_data)), interpolation='spline36', cmap='RdBu', alpha=0.9)\n", + "plt.axis('off')\n", "plt.show()" ] }, diff --git a/python/examples/cylinder_cross_section.ipynb b/python/examples/cylinder_cross_section.ipynb index 62ab123ea..e0dad84e6 100644 --- a/python/examples/cylinder_cross_section.ipynb +++ b/python/examples/cylinder_cross_section.ipynb @@ -19,9 +19,94 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000299931 s\n", + "Working in Cylindrical dimensions.\n", + "Computational cell is 4 x 0 x 8.88 with resolution 25\n", + "time for set_epsilon = 0.052002 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "06bb84a8b26649f389aeca44c7d1e125", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=15.49778699874878)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run 0 finished at t = 15.5 (775 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000163078 s\n", + "Working in Cylindrical dimensions.\n", + "Computational cell is 4 x 0 x 8.88 with resolution 25\n", + " block, center = (0.35,0,0)\n", + " size (0.7,0,2.3)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (4,4,4)\n", + "time for set_epsilon = 0.053905 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "dd6ce618a7f24cafa0fee3b7c304ff6d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=105.49778699874878)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 35.94/105.49778699874878 = 34.1% done in 4.0s, 7.7s to go\n", + "on time step 1803 (time=36.06), 0.00221964 s/step\n", + "Meep progress: 72.2/105.49778699874878 = 68.4% done in 8.0s, 3.7s to go\n", + "on time step 3616 (time=72.32), 0.00220671 s/step\n", + "run 0 finished at t = 105.5 (5275 timesteps)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -30,67 +115,47 @@ "r = 0.7 # radius of cylinder\n", "h = 2.3 # height of cylinder\n", "\n", - "wvl_min = 2 * np.pi * r / 10\n", - "wvl_max = 2 * np.pi * r / 2\n", + "wvl_min = 2*np.pi*r/10\n", + "wvl_max = 2*np.pi*r/2\n", "\n", - "frq_min = 1 / wvl_max\n", - "frq_max = 1 / wvl_min\n", - "frq_cen = 0.5 * (frq_min + frq_max)\n", - "dfrq = frq_max - frq_min\n", + "frq_min = 1/wvl_max\n", + "frq_max = 1/wvl_min\n", + "frq_cen = 0.5*(frq_min+frq_max)\n", + "dfrq = frq_max-frq_min\n", "nfrq = 100\n", "\n", "## at least 8 pixels per smallest wavelength, i.e. np.floor(8/wvl_min)\n", "resolution = 25\n", "\n", - "dpml = 0.5 * wvl_max\n", - "dair = 1.0 * wvl_max\n", + "dpml = 0.5*wvl_max\n", + "dair = 1.0*wvl_max\n", "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "sr = r + dair + dpml\n", - "sz = dpml + dair + h + dair + dpml\n", - "cell_size = mp.Vector3(sr, 0, sz)\n", - "\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True),\n", - " component=mp.Er,\n", - " center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml),\n", - " size=mp.Vector3(sr),\n", - " ),\n", - " mp.Source(\n", - " mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True),\n", - " component=mp.Ep,\n", - " center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml),\n", - " size=mp.Vector3(sr),\n", - " amplitude=-1j,\n", - " ),\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " dimensions=mp.CYLINDRICAL,\n", - " m=-1,\n", - ")\n", - "\n", - "box_z1 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, -0.5 * h), size=mp.Vector3(r)),\n", - ")\n", - "box_z2 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, +0.5 * h), size=mp.Vector3(r)),\n", - ")\n", - "box_r = sim.add_flux(\n", - " frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r), size=mp.Vector3(z=h))\n", - ")\n", + "sr = r+dair+dpml\n", + "sz = dpml+dair+h+dair+dpml\n", + "cell_size = mp.Vector3(sr,0,sz)\n", + "\n", + "sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n", + " component=mp.Er,\n", + " center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml),\n", + " size=mp.Vector3(sr)),\n", + " mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n", + " component=mp.Ep,\n", + " center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml),\n", + " size=mp.Vector3(sr),\n", + " amplitude=-1j)]\n", + "\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " dimensions=mp.CYLINDRICAL,\n", + " m=-1)\n", + "\n", + "box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,-0.5*h),size=mp.Vector3(r)))\n", + "box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,+0.5*h),size=mp.Vector3(r)))\n", + "box_r = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r),size=mp.Vector3(z=h)))\n", "\n", "sim.run(until_after_sources=10)\n", "\n", @@ -104,39 +169,21 @@ "sim.reset_meep()\n", "\n", "n_cyl = 2.0\n", - "geometry = [\n", - " mp.Block(\n", - " material=mp.Medium(index=n_cyl),\n", - " center=mp.Vector3(0.5 * r),\n", - " size=mp.Vector3(r, 0, h),\n", - " )\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " dimensions=mp.CYLINDRICAL,\n", - " m=-1,\n", - ")\n", - "\n", - "box_z1 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, -0.5 * h), size=mp.Vector3(r)),\n", - ")\n", - "box_z2 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, +0.5 * h), size=mp.Vector3(r)),\n", - ")\n", - "box_r = sim.add_flux(\n", - " frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r), size=mp.Vector3(z=h))\n", - ")\n", + "geometry = [mp.Block(material=mp.Medium(index=n_cyl),\n", + " center=mp.Vector3(0.5*r),\n", + " size=mp.Vector3(r,0,h))]\n", + "\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " dimensions=mp.CYLINDRICAL,\n", + " m=-1)\n", + "\n", + "box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,-0.5*h),size=mp.Vector3(r)))\n", + "box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,+0.5*h),size=mp.Vector3(r)))\n", + "box_r = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r),size=mp.Vector3(z=h)))\n", "\n", "sim.load_minus_flux_data(box_z1, box_z1_data)\n", "sim.load_minus_flux_data(box_z2, box_z2_data)\n", @@ -148,16 +195,16 @@ "box_z2_flux = mp.get_fluxes(box_z2)\n", "box_r_flux = mp.get_fluxes(box_r)\n", "\n", - "scatt_flux = np.asarray(box_z1_flux) - np.asarray(box_z2_flux) - np.asarray(box_r_flux)\n", - "intensity = np.asarray(box_z1_flux0) / (np.pi * r**2)\n", - "scatt_cross_section = np.divide(-scatt_flux, intensity)\n", + "scatt_flux = np.asarray(box_z1_flux)-np.asarray(box_z2_flux)-np.asarray(box_r_flux)\n", + "intensity = np.asarray(box_z1_flux0)/(np.pi*r**2)\n", + "scatt_cross_section = np.divide(-scatt_flux,intensity)\n", "\n", "plt.figure(dpi=150)\n", - "plt.loglog(2 * np.pi * r * np.asarray(freqs), scatt_cross_section, \"bo-\")\n", - "plt.grid(True, which=\"both\", ls=\"-\")\n", - "plt.xlabel(\"(cylinder circumference)/wavelength, 2πr/λ\")\n", - "plt.ylabel(\"scattering cross section, σ\")\n", - "plt.title(\"Scattering Cross Section of a Lossless Dielectric Cylinder\")\n", + "plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_cross_section,'bo-')\n", + "plt.grid(True,which=\"both\",ls=\"-\")\n", + "plt.xlabel('(cylinder circumference)/wavelength, 2πr/λ')\n", + "plt.ylabel('scattering cross section, σ')\n", + "plt.title('Scattering Cross Section of a Lossless Dielectric Cylinder')\n", "plt.tight_layout()\n", "plt.show()" ] diff --git a/python/examples/differential_cross_section.ipynb b/python/examples/differential_cross_section.ipynb index 88d22e2d8..12f091350 100644 --- a/python/examples/differential_cross_section.ipynb +++ b/python/examples/differential_cross_section.ipynb @@ -19,9 +19,290 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.00184703 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 6 x 6 x 6 with resolution 20\n", + "time for set_epsilon = 7.05405 s\n", + "-----------\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c12404a3f62e47c2b85ddfd51a7bf713", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=60.0)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 0.025/60.0 = 0.0% done in 8.0s, 19114.4s to go\n", + "on time step 1 (time=0.025), 7.95255 s/step\n", + "Meep progress: 1.425/60.0 = 2.4% done in 12.0s, 492.3s to go\n", + "on time step 57 (time=1.425), 0.0715737 s/step\n", + "Meep progress: 2.85/60.0 = 4.8% done in 16.0s, 320.9s to go\n", + "on time step 114 (time=2.85), 0.0706395 s/step\n", + "Meep progress: 4.3/60.0 = 7.2% done in 20.1s, 260.0s to go\n", + "on time step 172 (time=4.3), 0.0700726 s/step\n", + "Meep progress: 5.7250000000000005/60.0 = 9.5% done in 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"Meep progress: 131.6/150.0 = 87.7% done in 249.5s, 34.9s to go\n", + "on time step 5266 (time=131.65), 0.0460716 s/step\n", + "Meep progress: 133.8/150.0 = 89.2% done in 253.5s, 30.7s to go\n", + "on time step 5354 (time=133.85), 0.0458212 s/step\n", + "Meep progress: 135.975/150.0 = 90.6% done in 257.5s, 26.6s to go\n", + "on time step 5441 (time=136.025), 0.0461223 s/step\n", + "Meep progress: 138.125/150.0 = 92.1% done in 261.6s, 22.5s to go\n", + "on time step 5527 (time=138.175), 0.0469644 s/step\n", + "Meep progress: 140.3/150.0 = 93.5% done in 265.6s, 18.4s to go\n", + "on time step 5614 (time=140.35), 0.0463842 s/step\n", + "Meep progress: 142.5/150.0 = 95.0% done in 269.6s, 14.2s to go\n", + "on time step 5702 (time=142.55), 0.0459045 s/step\n", + "Meep progress: 144.55/150.0 = 96.4% done in 273.7s, 10.3s to go\n", + "on time step 5784 (time=144.6), 0.0489192 s/step\n", + "Meep progress: 146.65/150.0 = 97.8% done in 277.7s, 6.3s to go\n", + "on time step 5868 (time=146.7), 0.0480892 s/step\n", + "Meep progress: 148.775/150.0 = 99.2% done in 281.7s, 2.3s to go\n", + "on time step 5954 (time=148.85), 0.0470749 s/step\n", + "run 0 finished at t = 150.0 (6000 timesteps)\n", + "scatt:, 8.1554468215885674, 8.3429545590438750\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -31,72 +312,44 @@ "\n", "frq_cen = 1.0\n", "\n", - "resolution = 20 # pixels/um\n", + "resolution = 20 # pixels/um\n", "\n", "dpml = 0.5\n", - "dair = 1.5 # at least 0.5/frq_cen padding between source and near-field monitor\n", + "dair = 1.5 # at least 0.5/frq_cen padding between source and near-field monitor\n", "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "s = 2 * (dpml + dair + r)\n", - "cell_size = mp.Vector3(s, s, s)\n", + "s = 2*(dpml+dair+r)\n", + "cell_size = mp.Vector3(s,s,s)\n", "\n", "# circularly-polarized source with propagation axis along x\n", "# is_integrated=True necessary for any planewave source extending into PML\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(frq_cen, fwidth=0.2 * frq_cen, is_integrated=True),\n", - " center=mp.Vector3(-0.5 * s + dpml),\n", - " size=mp.Vector3(0, s, s),\n", - " component=mp.Ez,\n", - " ),\n", - " mp.Source(\n", - " mp.GaussianSource(frq_cen, fwidth=0.2 * frq_cen, is_integrated=True),\n", - " center=mp.Vector3(-0.5 * s + dpml),\n", - " size=mp.Vector3(0, s, s),\n", - " component=mp.Ey,\n", - " amplitude=1j,\n", - " ),\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=mp.Vector3(),\n", - ")\n", - "\n", - "box_flux = sim.add_flux(\n", - " frq_cen,\n", - " 0,\n", - " 1,\n", - " mp.FluxRegion(center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r)),\n", - ")\n", - "\n", - "nearfield_box = sim.add_near2far(\n", - " frq_cen,\n", - " 0,\n", - " 1,\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=+1\n", - " ),\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(x=+2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=-1\n", - " ),\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(y=-2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=+1\n", - " ),\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(y=+2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=-1\n", - " ),\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(z=-2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=+1\n", - " ),\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(z=+2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=-1\n", - " ),\n", - ")\n", + "sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=0.2*frq_cen,is_integrated=True),\n", + " center=mp.Vector3(-0.5*s+dpml),\n", + " size=mp.Vector3(0,s,s),\n", + " component=mp.Ez),\n", + " mp.Source(mp.GaussianSource(frq_cen,fwidth=0.2*frq_cen,is_integrated=True),\n", + " center=mp.Vector3(-0.5*s+dpml),\n", + " size=mp.Vector3(0,s,s),\n", + " component=mp.Ey,\n", + " amplitude=1j)]\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=mp.Vector3())\n", + "\n", + "box_flux = sim.add_flux(frq_cen, 0, 1,\n", + " mp.FluxRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r)))\n", + "\n", + "nearfield_box = sim.add_near2far(frq_cen, 0, 1,\n", + " mp.Near2FarRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r),weight=+1),\n", + " mp.Near2FarRegion(center=mp.Vector3(x=+2*r),size=mp.Vector3(0,4*r,4*r),weight=-1),\n", + " mp.Near2FarRegion(center=mp.Vector3(y=-2*r),size=mp.Vector3(4*r,0,4*r),weight=+1),\n", + " mp.Near2FarRegion(center=mp.Vector3(y=+2*r),size=mp.Vector3(4*r,0,4*r),weight=-1),\n", + " mp.Near2FarRegion(center=mp.Vector3(z=-2*r),size=mp.Vector3(4*r,4*r,0),weight=+1),\n", + " mp.Near2FarRegion(center=mp.Vector3(z=+2*r),size=mp.Vector3(4*r,4*r,0),weight=-1))\n", "\n", "sim.run(until_after_sources=10)\n", "\n", @@ -106,85 +359,54 @@ "sim.reset_meep()\n", "\n", "n_sphere = 2.0\n", - "geometry = [\n", - " mp.Sphere(material=mp.Medium(index=n_sphere), center=mp.Vector3(), radius=r)\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=mp.Vector3(),\n", - " geometry=geometry,\n", - ")\n", - "\n", - "nearfield_box = sim.add_near2far(\n", - " frq_cen,\n", - " 0,\n", - " 1,\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=+1\n", - " ),\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(x=+2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=-1\n", - " ),\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(y=-2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=+1\n", - " ),\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(y=+2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=-1\n", - " ),\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(z=-2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=+1\n", - " ),\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(z=+2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=-1\n", - " ),\n", - ")\n", + "geometry = [mp.Sphere(material=mp.Medium(index=n_sphere),\n", + " center=mp.Vector3(),\n", + " radius=r)]\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=mp.Vector3(),\n", + " geometry=geometry)\n", + "\n", + "nearfield_box = sim.add_near2far(frq_cen, 0, 1,\n", + " mp.Near2FarRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r),weight=+1),\n", + " mp.Near2FarRegion(center=mp.Vector3(x=+2*r),size=mp.Vector3(0,4*r,4*r),weight=-1),\n", + " mp.Near2FarRegion(center=mp.Vector3(y=-2*r),size=mp.Vector3(4*r,0,4*r),weight=+1),\n", + " mp.Near2FarRegion(center=mp.Vector3(y=+2*r),size=mp.Vector3(4*r,0,4*r),weight=-1),\n", + " mp.Near2FarRegion(center=mp.Vector3(z=-2*r),size=mp.Vector3(4*r,4*r,0),weight=+1),\n", + " mp.Near2FarRegion(center=mp.Vector3(z=+2*r),size=mp.Vector3(4*r,4*r,0),weight=-1))\n", "\n", "sim.load_minus_near2far_data(nearfield_box, nearfield_box_data)\n", "\n", "sim.run(until_after_sources=100)\n", "\n", - "npts = 100 # number of points in [0,pi) range of polar angles to sample far fields along semi-circle\n", - "angles = np.pi / npts * np.arange(npts)\n", + "npts = 100 # number of points in [0,pi) range of polar angles to sample far fields along semi-circle\n", + "angles = np.pi/npts*np.arange(npts)\n", "\n", - "ff_r = 10000 * r # radius of far-field semi-circle\n", + "ff_r = 10000*r # radius of far-field semi-circle\n", "\n", - "E = np.zeros((npts, 3), dtype=np.complex128)\n", - "H = np.zeros((npts, 3), dtype=np.complex128)\n", + "E = np.zeros((npts,3),dtype=np.complex128)\n", + "H = np.zeros((npts,3),dtype=np.complex128)\n", "for n in range(npts):\n", - " ff = sim.get_farfield(\n", - " nearfield_box, ff_r * mp.Vector3(np.cos(angles[n]), 0, np.sin(angles[n]))\n", - " )\n", - " E[n, :] = [np.conj(ff[j]) for j in range(3)]\n", - " H[n, :] = [ff[j + 3] for j in range(3)]\n", + " ff = sim.get_farfield(nearfield_box, ff_r*mp.Vector3(np.cos(angles[n]),0,np.sin(angles[n])))\n", + " E[n,:] = [np.conj(ff[j]) for j in range(3)]\n", + " H[n,:] = [ff[j+3] for j in range(3)]\n", "\n", "# compute Poynting flux Pr in the radial direction. At large r,\n", "# all of the flux is radial so we can simply compute the magnitude of the Poynting vector.\n", - "Px = np.real(np.multiply(E[:, 1], H[:, 2]) - np.multiply(E[:, 2], H[:, 1]))\n", - "Py = np.real(np.multiply(E[:, 2], H[:, 0]) - np.multiply(E[:, 0], H[:, 2]))\n", - "Pz = np.real(np.multiply(E[:, 0], H[:, 1]) - np.multiply(E[:, 1], H[:, 0]))\n", - "Pr = np.sqrt(np.square(Px) + np.square(Py) + np.square(Pz))\n", + "Px = np.real(np.multiply(E[:,1],H[:,2])-np.multiply(E[:,2],H[:,1]))\n", + "Py = np.real(np.multiply(E[:,2],H[:,0])-np.multiply(E[:,0],H[:,2]))\n", + "Pz = np.real(np.multiply(E[:,0],H[:,1])-np.multiply(E[:,1],H[:,0]))\n", + "Pr = np.sqrt(np.square(Px)+np.square(Py)+np.square(Pz))\n", "\n", - "intensity = input_flux / (4 * r) ** 2\n", + "intensity = input_flux/(4*r)**2\n", "diff_cross_section = ff_r**2 * Pr / intensity\n", - "scatt_cross_section_meep = (\n", - " 2 * np.pi * np.sum(diff_cross_section * np.sin(angles)) * np.pi / npts\n", - ") # trapezoidal rule integration\n", - "scatt_cross_section_theory = (\n", - " ps.MieQ(n_sphere, 1000 / frq_cen, 2 * r * 1000, asDict=True, asCrossSection=True)[\n", - " \"Csca\"\n", - " ]\n", - " * 1e-6\n", - ") # units of um^2\n", - "\n", - "print(\n", - " \"scatt:, {:.16f}, {:.16f}\".format(\n", - " scatt_cross_section_meep, scatt_cross_section_theory\n", - " )\n", - ")" + "scatt_cross_section_meep = 2*np.pi * np.sum(diff_cross_section * np.sin(angles)) * np.pi/npts # trapezoidal rule integration\n", + "scatt_cross_section_theory = ps.MieQ(n_sphere,1000/frq_cen,2*r*1000,asDict=True,asCrossSection=True)['Csca']*1e-6 # units of um^2\n", + "\n", + "print(\"scatt:, {:.16f}, {:.16f}\".format(scatt_cross_section_meep,scatt_cross_section_theory))" ] }, { diff --git a/python/examples/finite_grating.ipynb b/python/examples/finite_grating.ipynb index fb961a2d2..e60714631 100644 --- a/python/examples/finite_grating.ipynb +++ b/python/examples/finite_grating.ipynb @@ -20,9 +20,114 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00181007 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 14.5 x 9 x 0 with resolution 50\n", + " block, center = (-5.75,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.610417 s\n", + "-----------\n", + "Meep progress: 9.18/125.0 = 7.3% done in 4.0s, 50.5s to go\n", + "on time step 918 (time=9.18), 0.00435765 s/step\n", + "Meep progress: 18.490000000000002/125.0 = 14.8% done in 8.0s, 46.1s to go\n", + "on time step 1849 (time=18.49), 0.00429697 s/step\n", + "Meep progress: 27.810000000000002/125.0 = 22.2% done in 12.0s, 42.0s to go\n", + "on time step 2781 (time=27.81), 0.00429364 s/step\n", + "Meep progress: 37.160000000000004/125.0 = 29.7% done in 16.0s, 37.8s to go\n", + "on time step 3716 (time=37.16), 0.0042805 s/step\n", + "Meep progress: 46.44/125.0 = 37.2% done in 20.0s, 33.9s to go\n", + "on time step 4644 (time=46.44), 0.0043122 s/step\n", + "Meep progress: 55.75/125.0 = 44.6% done in 24.0s, 29.8s to go\n", + "on time step 5575 (time=55.75), 0.004299 s/step\n", + "Meep progress: 65.19/125.0 = 52.2% done in 28.0s, 25.7s to go\n", + "on time step 6519 (time=65.19), 0.00423808 s/step\n", + "Meep progress: 74.61/125.0 = 59.7% done in 32.0s, 21.6s to go\n", + "on time step 7461 (time=74.61), 0.00424806 s/step\n", + "Meep progress: 83.91/125.0 = 67.1% done in 36.0s, 17.6s to go\n", + "on time step 8392 (time=83.92), 0.00430039 s/step\n", + "Meep progress: 93.23/125.0 = 74.6% done in 40.0s, 13.6s to go\n", + "on time step 9324 (time=93.24), 0.00429399 s/step\n", + "Meep progress: 102.51/125.0 = 82.0% done in 44.0s, 9.7s to go\n", + "on time step 10253 (time=102.53), 0.00430959 s/step\n", + "Meep progress: 111.78/125.0 = 89.4% done in 48.0s, 5.7s to go\n", + "on time step 11180 (time=111.8), 0.00431786 s/step\n", + "Meep progress: 121.06/125.0 = 96.8% done in 52.0s, 1.7s to go\n", + "on time step 12108 (time=121.08), 0.00431251 s/step\n", + "run 0 finished at t = 125.0 (12500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00184488 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 14.5 x 9 x 0 with resolution 50\n", + " block, center = (-5.75,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (-4,-2,0)\n", + " size (0.5,0.5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (-4,-1,0)\n", + " size (0.5,0.5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (-4,0,0)\n", + " size (0.5,0.5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (-4,1,0)\n", + " size (0.5,0.5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (-4,2,0)\n", + " size (0.5,0.5,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.655086 s\n", + "-----------\n", + "Meep progress: 9.15/125.0 = 7.3% done in 4.0s, 50.7s to go\n", + "on time step 915 (time=9.15), 0.00437325 s/step\n", + "Meep progress: 18.43/125.0 = 14.7% done in 8.0s, 46.3s to go\n", + "on time step 1844 (time=18.44), 0.00430983 s/step\n", + "Meep progress: 27.73/125.0 = 22.2% done in 12.0s, 42.1s to go\n", + "on time step 2774 (time=27.74), 0.00430151 s/step\n", + "Meep progress: 36.980000000000004/125.0 = 29.6% done in 16.0s, 38.1s to go\n", + "on time step 3700 (time=37), 0.00432353 s/step\n", + "Meep progress: 46.13/125.0 = 36.9% done in 20.0s, 34.2s to go\n", + "on time step 4615 (time=46.15), 0.00437322 s/step\n", + "Meep progress: 55.32/125.0 = 44.3% done in 24.0s, 30.2s to go\n", + "on time step 5535 (time=55.35), 0.00435253 s/step\n", + "Meep progress: 64.53/125.0 = 51.6% done in 28.0s, 26.2s to go\n", + "on time step 6457 (time=64.57), 0.00434152 s/step\n", + "Meep progress: 73.79/125.0 = 59.0% done in 32.0s, 22.2s to go\n", + "on time step 7383 (time=73.83), 0.00432269 s/step\n", + "Meep progress: 83.04/125.0 = 66.4% done in 36.0s, 18.2s to go\n", + "on time step 8308 (time=83.08), 0.00432687 s/step\n", + "Meep progress: 92.32000000000001/125.0 = 73.9% done in 40.0s, 14.2s to go\n", + "on time step 9237 (time=92.37), 0.00431051 s/step\n", + "Meep progress: 101.59/125.0 = 81.3% done in 44.0s, 10.1s to go\n", + "on time step 10164 (time=101.64), 0.00431663 s/step\n", + "Meep progress: 110.92/125.0 = 88.7% done in 48.0s, 6.1s to go\n", + "on time step 11097 (time=110.97), 0.00428761 s/step\n", + "Meep progress: 120.25/125.0 = 96.2% done in 52.0s, 2.1s to go\n", + "on time step 12030 (time=120.3), 0.00428792 s/step\n", + "run 0 finished at t = 125.0 (12500 timesteps)\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -33,21 +138,21 @@ "# False: plot the diffraction spectra based on a 1d cross section of the scattered fields\n", "field_profile = True\n", "\n", - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 2.0 # substrate thickness\n", - "dpad = 1.0 # flat-surface padding\n", - "gp = 1.0 # grating periodicity\n", - "gh = 0.5 # grating height\n", - "gdc = 0.5 # grating duty cycle\n", - "num_cells = 5 # number of grating unit cells\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 2.0 # substrate thickness\n", + "dpad = 1.0 # flat-surface padding\n", + "gp = 1.0 # grating periodicity\n", + "gh = 0.5 # grating height\n", + "gdc = 0.5 # grating duty cycle\n", + "num_cells = 5 # number of grating unit cells\n", "\n", "# air region thickness adjacent to grating\n", "dair = 10 if field_profile else dpad\n", "\n", - "wvl = 0.5 # center wavelength\n", - "fcen = 1 / wvl # center frequency\n", + "wvl = 0.5 # center wavelength\n", + "fcen = 1/wvl # center frequency\n", "\n", "k_point = mp.Vector3()\n", "\n", @@ -55,49 +160,32 @@ "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "symmetries = [mp.Mirror(mp.Y)]\n", - "\n", - "sx = dpml + dsub + gh + dair + dpml\n", - "sy = dpml + dpad + num_cells * gp + dpad + dpml\n", - "cell_size = mp.Vector3(sx, sy)\n", - "\n", - "src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub)\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=0.2 * fcen),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy),\n", - " )\n", - "]\n", - "\n", - "geometry = [\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", - " )\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - ")\n", - "\n", - "mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dair)\n", - "near_fields = sim.add_dft_fields(\n", - " [mp.Ez],\n", - " fcen,\n", - " 0,\n", - " 1,\n", - " center=mon_pt,\n", - " size=mp.Vector3(dair if field_profile else 0, sy - 2 * dpml),\n", - ")\n", + "symmetries=[mp.Mirror(mp.Y)]\n", + "\n", + "sx = dpml+dsub+gh+dair+dpml\n", + "sy = dpml+dpad+num_cells*gp+dpad+dpml\n", + "cell_size = mp.Vector3(sx,sy)\n", + "\n", + "src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub)\n", + "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.2*fcen),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy))]\n", + "\n", + "geometry = [mp.Block(material=glass,\n", + " size=mp.Vector3(dpml+dsub,mp.inf,mp.inf),\n", + " center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))]\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + "mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dair)\n", + "near_fields = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=mon_pt, size=mp.Vector3(dair if field_profile else 0,sy-2*dpml))\n", "\n", "sim.run(until_after_sources=100)\n", "\n", @@ -106,89 +194,77 @@ "sim.reset_meep()\n", "\n", "for j in range(num_cells):\n", - " geometry.append(\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(gh, gdc * gp, mp.inf),\n", - " center=mp.Vector3(\n", - " -0.5 * sx + dpml + dsub + 0.5 * gh,\n", - " -0.5 * sy + dpml + dpad + (j + 0.5) * gp,\n", - " ),\n", - " )\n", - " )\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - ")\n", - "\n", - "near_fields = sim.add_dft_fields(\n", - " [mp.Ez],\n", - " fcen,\n", - " 0,\n", - " 1,\n", - " center=mon_pt,\n", - " size=mp.Vector3(dair if field_profile else 0, sy - 2 * dpml),\n", - ")\n", + " geometry.append(mp.Block(material=glass,\n", + " size=mp.Vector3(gh,gdc*gp,mp.inf),\n", + " center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+dpml+dpad+(j+0.5)*gp)))\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + "near_fields = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=mon_pt, size=mp.Vector3(dair if field_profile else 0,sy-2*dpml))\n", "\n", "sim.run(until_after_sources=100)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "grating_dft = sim.get_dft_array(near_fields, mp.Ez, 0)\n", "\n", - "scattered_field = grating_dft - flat_dft\n", - "scattered_amplitude = np.abs(scattered_field) ** 2\n", + "scattered_field = grating_dft-flat_dft\n", + "scattered_amplitude = np.abs(scattered_field)**2\n", "\n", - "[x, y, z, w] = sim.get_array_metadata(dft_cell=near_fields)\n", + "[x,y,z,w] = sim.get_array_metadata(dft_cell=near_fields)\n", "\n", "if field_profile:\n", " plt.figure(dpi=150)\n", - " plt.pcolormesh(\n", - " x,\n", - " y,\n", - " np.rot90(scattered_amplitude),\n", - " cmap=\"inferno\",\n", - " shading=\"gouraud\",\n", - " vmin=0,\n", - " vmax=scattered_amplitude.max(),\n", - " )\n", - " plt.gca().set_aspect(\"equal\")\n", - " plt.xlabel(\"x (μm)\")\n", - " plt.ylabel(\"y (μm)\")\n", + " plt.pcolormesh(x,y,np.rot90(scattered_amplitude),cmap='inferno',shading='gouraud',vmin=0,vmax=scattered_amplitude.max())\n", + " plt.gca().set_aspect('equal')\n", + " plt.xlabel('x (μm)')\n", + " plt.ylabel('y (μm)')\n", " # ensure that the height of the colobar matches that of the plot\n", " from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - "\n", " divider = make_axes_locatable(plt.gca())\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " plt.colorbar(cax=cax)\n", " plt.tight_layout()\n", " plt.show()\n", "else:\n", - " ky = np.fft.fftshift(np.fft.fftfreq(len(scattered_field), 1 / resolution))\n", + " ky = np.fft.fftshift(np.fft.fftfreq(len(scattered_field), 1/resolution))\n", " FT_scattered_field = np.fft.fftshift(np.fft.fft(scattered_field))\n", " plt.figure(dpi=150)\n", " plt.subplots_adjust(hspace=0.3)\n", "\n", - " plt.subplot(2, 1, 1)\n", - " plt.plot(y, scattered_amplitude, \"bo-\")\n", + " plt.subplot(2,1,1)\n", + " plt.plot(y,scattered_amplitude,'bo-')\n", " plt.xlabel(\"y (μm)\")\n", " plt.ylabel(\"field amplitude\")\n", "\n", - " plt.subplot(2, 1, 2)\n", - " plt.plot(ky, np.abs(FT_scattered_field) ** 2, \"ro-\")\n", - " plt.gca().ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0, 0))\n", - " plt.xlabel(r\"wavevector k$_y$, 2π (μm)$^{-1}$\")\n", + " plt.subplot(2,1,2)\n", + " plt.plot(ky,np.abs(FT_scattered_field)**2,'ro-')\n", + " plt.gca().ticklabel_format(axis='y',style='sci',scilimits=(0,0))\n", + " plt.xlabel(r'wavevector k$_y$, 2π (μm)$^{-1}$')\n", " plt.ylabel(\"Fourier transform\")\n", " plt.gca().set_xlim([-3, 3])\n", "\n", diff --git a/python/examples/holey-wvg-bands.ipynb b/python/examples/holey-wvg-bands.ipynb index 1ab325f21..8af8e0a94 100644 --- a/python/examples/holey-wvg-bands.ipynb +++ b/python/examples/holey-wvg-bands.ipynb @@ -17,15 +17,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from IPython.display import Video\n", - "\n", "%matplotlib notebook" ] }, @@ -38,26 +45,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Some parameters to describe the geometry:\n", - "eps = 13 # dielectric constant of waveguide\n", - "w = 1.2 # width of waveguide\n", - "r = 0.36 # radius of holes\n", + "eps = 13 # dielectric constant of waveguide\n", + "w = 1.2 # width of waveguide\n", + "r = 0.36 # radius of holes\n", "\n", "# The cell dimensions\n", - "sy = 12 # size of cell in y direction (perpendicular to wvg.)\n", - "dpml = 1 # PML thickness (y direction only!)\n", + "sy = 12 # size of cell in y direction (perpendicular to wvg.)\n", + "dpml = 1 # PML thickness (y direction only!)\n", "cell = mp.Vector3(1, sy)\n", "\n", "b = mp.Block(size=mp.Vector3(1e20, w, 1e20), material=mp.Medium(epsilon=eps))\n", "c = mp.Cylinder(radius=r)\n", "\n", - "geometry = [b, c]\n", + "geometry = [b,c]\n", "\n", - "resolution = 20" + "resolution=20" ] }, { @@ -69,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -87,20 +94,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "fcen = 0.25 # pulse center frequency\n", - "df = 1.5 # pulse freq. width: large df = short impulse\n", + "df = 1.5 # pulse freq. width: large df = short impulse\n", "\n", - "s = [\n", - " mp.Source(\n", - " src=mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Hz,\n", - " center=mp.Vector3(0.1234, 0),\n", - " )\n", - "]" + "s = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz,\n", + " center=mp.Vector3(0.1234,0))]" ] }, { @@ -112,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -130,18 +132,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=s,\n", - " symmetries=sym,\n", - " resolution=resolution,\n", - ")\n", + "sim = mp.Simulation(cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=s,\n", + " symmetries=sym,\n", + " resolution=resolution)\n", "f = plt.figure(dpi=150)\n", "sim.plot2D(ax=f.gca())\n", "plt.show()" @@ -156,17 +182,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.3050132495988499, 3.480691775115153e-06, -43815.03294539187, 0.011878313829683136, -0.01179628264240186-0.0013935764266843182i, 5.295036558380241e-07+0.0i\n", + "harminv0:, 0.43960436531181446, -2.7363657356159734e-06, 80326.31741985629, 0.01745119452862526, 0.017126214985327293-0.0033521561348289074i, 9.312403773661967e-08+0.0i\n", + "harminv0:, 0.5005873843994147, -0.0009565526575684324, 261.6622202858719, 0.00018410949058675333, -0.00011025456283318074-0.00014744570491736274i, 1.242711680938466e-05+0.0i\n", + "harminv0:, 0.6404729374549927, -0.005167317620050075, 61.97344391699944, 0.0031051734641799815, 0.0007710721023072853-0.0030079145692141806i, 3.514904299909929e-05+0.0i\n", + "harminv0:, 0.700226243241067, -0.0037510897788062153, 93.3363748313049, 0.004369347983945716, -5.4869994286284585e-05+0.0043690034434110386i, 1.3205854700252928e-05+0.0i\n", + "harminv0:, 0.762852889565266, -0.006722784235381431, 56.73638055721298, 0.0316955396820757, 0.02965548904826291-0.011187457488026218i, 4.404983351452081e-06+0.0i\n", + "harminv0:, 0.8234265979478261, -0.006957003127114023, 59.179691521096714, 0.000615608025293912, 0.0006152489526665892-2.102301236585398e-05i, 0.000399758756541917+0.0i\n", + "harminv0:, 0.8243479880387249, -0.0005179204557115729, 795.8248983486759, 0.030110588821435868, -0.005890629837883119-0.029528766301466433i, 5.666365518267213e-08+0.0i\n", + "harminv0:, 0.8878738611231001, -0.0002512055373818404, 1767.2258947331713, 0.0032343510475491863, -0.003225648132430831-0.00023710930923945473i, 8.018541351827511e-07+0.0i\n", + "harminv0:, 0.9495092084271758, -0.005235806245342398, 90.67459374301981, 0.005382664822832947, -0.0016788910104550295+0.005114137812962859i, 1.7544167778335838e-05+0.0i\n", + "harminv0:, 0.9758414410168742, 0.0009253350005759116, -527.2908948702515, 9.453933579227094e-06, 3.226576270516566e-06+8.886285258249198e-06i, 0.00012202208460776741+0.0i\n", + "harminv0:, 0.9858676174323542, -0.0037464233316342127, 131.5745085596497, 0.0012188508341391812, -0.0004042226659787743+0.0011498701631883445i, 2.708082943685142e-05+0.0i\n", + "run 0 finished at t = 306.675 (12267 timesteps)\n" + ] + } + ], "source": [ "kx = 0.4\n", "sim.k_point = mp.Vector3(kx)\n", "\n", - "sim.run(\n", - " mp.after_sources(mp.Harminv(mp.Hz, mp.Vector3(0.1234), fcen, df)),\n", - " until_after_sources=300,\n", - ")" + "sim.run(mp.after_sources(mp.Harminv(mp.Hz, mp.Vector3(0.1234), fcen, df)),\n", + " until_after_sources=300)" ] }, { @@ -178,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -200,23 +245,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "kx = [k.x for k in kpts]\n", - "fig = plt.figure(dpi=100, figsize=(5, 5))\n", + "fig = plt.figure(dpi=100,figsize=(5,5))\n", "ax = plt.subplot(111)\n", "for i in range(len(all_freqs)):\n", " for ii in range(len(all_freqs[i])):\n", - " plt.scatter(kx[i], np.real(all_freqs[i][ii]), color=\"b\")\n", + " plt.scatter(kx[i],np.real(all_freqs[i][ii]),color='b')\n", "\n", - "ax.fill_between(kx, kx, 1.0, interpolate=True, color=\"gray\", alpha=0.3)\n", - "plt.xlim(0, 0.5)\n", - "plt.ylim(0, 1)\n", + "ax.fill_between(kx, kx, 1.0, interpolate=True, color='gray', alpha = 0.3)\n", + "plt.xlim(0,0.5)\n", + "plt.ylim(0,1)\n", "plt.grid(True)\n", - "plt.xlabel(\"$k_x(2\\pi)$\")\n", - "plt.ylabel(\"$\\omega(2\\pi c)$\")\n", + "plt.xlabel('$k_x(2\\pi)$')\n", + "plt.ylabel('$\\omega(2\\pi c)$')\n", "plt.tight_layout()\n", "plt.show()" ] @@ -251,57 +309,241 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def run_sim(kx, omega, filename):\n", - " s = [\n", - " mp.Source(\n", - " src=mp.GaussianSource(omega, fwidth=0.01),\n", - " component=mp.Hz,\n", - " center=mp.Vector3(0.1234, 0),\n", - " )\n", - " ]\n", - " sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=s,\n", - " symmetries=sym,\n", - " k_point=mp.Vector3(kx),\n", - " resolution=resolution,\n", - " )\n", + " s = [mp.Source(src=mp.GaussianSource(omega, fwidth=0.01), component=mp.Hz,\n", + " center=mp.Vector3(0.1234,0))]\n", + " sim = mp.Simulation(cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=s,\n", + " symmetries=sym,\n", + " k_point = mp.Vector3(kx),\n", + " resolution=resolution)\n", " f = plt.figure(dpi=100)\n", - " animate = mp.Animate2D(sim, fields=mp.Hz, f=f, normalize=True, realtime=False)\n", - " sim.run(mp.at_every(5, animate), until_after_sources=1)\n", - " animate.to_mp4(10, filename)\n", + " animate = mp.Animate2D(sim,fields=mp.Hz,f=f,normalize=True,realtime=False)\n", + " sim.run(mp.at_every(5,animate),until_after_sources=1)\n", + " animate.to_mp4(10,filename)\n", " plt.close()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "Meep: using complex fields.\n", + "Normalizing field data...\n", + "run 0 finished at t = 1001.0 (40040 timesteps)\n", + "Generating MP4...\n", + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "Meep: using complex fields.\n", + "Normalizing field data...\n", + "run 0 finished at t = 1001.0 (40040 timesteps)\n", + "Generating MP4...\n", + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "Meep: using complex fields.\n", + "Meep progress: 929.575/1001.0 = 92.9% done in 4.0s, 0.3s to go\n", + "Normalizing field data...\n", + "run 0 finished at t = 1001.0 (40040 timesteps)\n", + "Generating MP4...\n", + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "Meep: using complex fields.\n", + "Normalizing field data...\n", + "run 0 finished at t = 1001.0 (40040 timesteps)\n", + "Generating MP4...\n", + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "Meep: using complex fields.\n", + "Normalizing field data...\n", + "run 0 finished at t = 1001.0 (40040 timesteps)\n", + "Generating MP4...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "kx = [0.4, 0.4, 0.1, 0.3, 0.25]\n", - "omega = [0.1896, 0.3175, 0.4811, 0.8838, 0.2506]\n", - "filename = [\n", - " \"media/holey-wvg-bands-{}-{}.mp4\".format(k, om) for (k, om) in zip(kx, omega)\n", - "]\n", - "for (k, om, fn) in zip(kx, omega, filename):\n", - " run_sim(*[k, om, fn])" + "kx = [0.4,0.4,0.1,0.3,0.25]\n", + "omega = [0.1896,0.3175,0.4811,0.8838,0.2506]\n", + "filename = ['media/holey-wvg-bands-{}-{}.mp4'.format(k,om) for (k,om) in zip (kx,omega)]\n", + "for (k,om,fn) in zip(kx,omega,filename):\n", + " run_sim(*[k,om,fn])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import IPython\n", - "\n", "for i in range(len(filename)):\n", " IPython.display.display(IPython.display.Video(filename[i]))" ] diff --git a/python/examples/holey-wvg-cavity.ipynb b/python/examples/holey-wvg-cavity.ipynb index fdbbf007d..1e58e242b 100644 --- a/python/examples/holey-wvg-cavity.ipynb +++ b/python/examples/holey-wvg-cavity.ipynb @@ -18,15 +18,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from IPython.display import Video\n", - "\n", "%matplotlib notebook" ] }, @@ -39,21 +46,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "resolution = 20 # pixels/um\n", + "resolution = 20 # pixels/um\n", "\n", - "eps = 13 # dielectric constant of waveguide\n", - "w = 1.2 # width of waveguide\n", - "r = 0.36 # radius of holes\n", - "d = 1.4 # defect spacing (ordinary spacing = 1)\n", - "N = 3 # number of holes on either side of defect\n", + "eps = 13 # dielectric constant of waveguide\n", + "w = 1.2 # width of waveguide\n", + "r = 0.36 # radius of holes\n", + "d = 1.4 # defect spacing (ordinary spacing = 1)\n", + "N = 3 # number of holes on either side of defect\n", "\n", - "sy = 6 # size of cell in y direction (perpendicular to wvg.)\n", - "pad = 2 # padding between last hole and PML edge\n", - "dpml = 1 # PML thickness" + "sy = 6 # size of cell in y direction (perpendicular to wvg.)\n", + "pad = 2 # padding between last hole and PML edge\n", + "dpml = 1 # PML thickness" ] }, { @@ -65,11 +72,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction" + "sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction" ] }, { @@ -81,11 +88,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "cell = mp.Vector3(sx, sy, 0)" + "cell = mp.Vector3(sx,sy,0)" ] }, { @@ -97,16 +104,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=eps))\n", + "blk = mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), material=mp.Medium(epsilon=eps))\n", "geometry = [blk]\n", "\n", "for i in range(N):\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)))\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d / 2 + i))))" + " geometry.append(mp.Cylinder(r, center=mp.Vector3(d/2+i)))\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d/2+i))))" ] }, { @@ -120,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -136,12 +143,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "fcen = 0.25 # pulse center frequency\n", - "df = 0.2 # pulse frequency width" + "fcen = 0.25 # pulse center frequency\n", + "df = 0.2 # pulse frequency width" ] }, { @@ -153,18 +160,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "src = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ey,\n", - " center=mp.Vector3(-0.5 * sx + dpml),\n", - " size=mp.Vector3(0, w),\n", - " )\n", - "]" + "src = [mp.Source(mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ey,\n", + " center=mp.Vector3(-0.5*sx+dpml),\n", + " size=mp.Vector3(0,w))]" ] }, { @@ -176,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -192,18 +195,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=src,\n", - " symmetries=sym,\n", - " resolution=resolution,\n", - ")" + "sim = mp.Simulation(cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=src,\n", + " symmetries=sym,\n", + " resolution=resolution)" ] }, { @@ -215,15 +216,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "freg = mp.FluxRegion(\n", - " center=mp.Vector3(0.5 * sx - dpml - 0.5), size=mp.Vector3(0, 2 * w)\n", - ")\n", + "freg = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml-0.5),\n", + " size=mp.Vector3(0,2*w))\n", "\n", - "nfreq = 500 # number of frequencies at which to compute flux\n", + "nfreq = 500 # number of frequencies at which to compute flux\n", "\n", "# transmitted flux\n", "trans = sim.add_flux(fcen, df, nfreq, freg)" @@ -238,9 +238,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "f = plt.figure(dpi=150)\n", "sim.plot2D(ax=f.gca())\n", @@ -258,30 +294,86 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalizing field data...\n", + "field decay(t = 50.025000000000006): 3.95790820471984e-05 / 3.95790820471984e-05 = 1.0\n", + "field decay(t = 100.05000000000001): 6.062401894821014e-05 / 6.062401894821014e-05 = 1.0\n", + "field decay(t = 150.07500000000002): 1.7734570553698603e-05 / 6.062401894821014e-05 = 0.2925337326918046\n", + "field decay(t = 200.10000000000002): 7.468025297259177e-06 / 6.062401894821014e-05 = 0.12318591586016357\n", + "field decay(t = 250.125): 5.439633091350728e-06 / 6.062401894821014e-05 = 0.08972735865627277\n", + "field decay(t = 300.15000000000003): 4.23291165174129e-06 / 6.062401894821014e-05 = 0.06982235300759884\n", + "field decay(t = 350.175): 3.476628845824414e-06 / 6.062401894821014e-05 = 0.05734738320127583\n", + "field decay(t = 400.20000000000005): 2.847134687922034e-06 / 6.062401894821014e-05 = 0.04696380638100359\n", + "field decay(t = 450.225): 2.3289750979705154e-06 / 6.062401894821014e-05 = 0.03841670576079938\n", + "field decay(t = 500.25): 1.9177580551625897e-06 / 6.062401894821014e-05 = 0.031633634464268874\n", + "field decay(t = 550.275): 1.5662051283234017e-06 / 6.062401894821014e-05 = 0.025834729460304812\n", + "field decay(t = 600.3000000000001): 1.2900383339035116e-06 / 6.062401894821014e-05 = 0.02127932717567875\n", + "field decay(t = 650.325): 1.0534058195581604e-06 / 6.062401894821014e-05 = 0.017376047280172294\n", + "field decay(t = 700.35): 8.676272594305365e-07 / 6.062401894821014e-05 = 0.014311609069859468\n", + "field decay(t = 750.375): 7.087820739725053e-07 / 6.062401894821014e-05 = 0.011691439899060524\n", + "field decay(t = 800.4000000000001): 5.837770400453704e-07 / 6.062401894821014e-05 = 0.009629467827662155\n", + "field decay(t = 850.4250000000001): 4.808205960298484e-07 / 6.062401894821014e-05 = 0.007931189722684065\n", + "field decay(t = 900.45): 3.92681229771858e-07 / 6.062401894821014e-05 = 0.006477320979120792\n", + "field decay(t = 950.475): 3.234259653563735e-07 / 6.062401894821014e-05 = 0.005334947615938655\n", + "field decay(t = 1000.5): 2.6407774313354585e-07 / 6.062401894821014e-05 = 0.004355992026182594\n", + "field decay(t = 1050.525): 2.1750627194785985e-07 / 6.062401894821014e-05 = 0.0035877903794809614\n", + "field decay(t = 1100.55): 1.7768960210318136e-07 / 6.062401894821014e-05 = 0.0029310099393934598\n", + "field decay(t = 1150.575): 1.4635346961931978e-07 / 6.062401894821014e-05 = 0.002414116915349122\n", + "field decay(t = 1200.6000000000001): 1.1953143759707475e-07 / 6.062401894821014e-05 = 0.0019716844853058654\n", + "field decay(t = 1250.625): 9.845036591681545e-08 / 6.062401894821014e-05 = 0.001623949840754035\n", + "field decay(t = 1300.65): 8.038713471789712e-08 / 6.062401894821014e-05 = 0.0013259948138141452\n", + "field decay(t = 1350.6750000000002): 6.621009853390941e-08 / 6.062401894821014e-05 = 0.0010921430100249761\n", + "field decay(t = 1400.7): 5.408528044424668e-08 / 6.062401894821014e-05 = 0.0008921427741445288\n", + "run 0 finished at t = 1400.7 (56028 timesteps)\n" + ] + } + ], "source": [ "f = plt.figure(dpi=150)\n", - "animate = mp.Animate2D(sim, f=f, fields=mp.Hz, realtime=False, normalize=True)\n", + "animate = mp.Animate2D(sim,f=f,fields=mp.Hz,realtime=False,normalize=True)\n", "\n", - "sim.run(\n", - " mp.during_sources(mp.at_every(0.4, animate)),\n", - " until_after_sources=mp.stop_when_fields_decayed(\n", - " 50, mp.Ey, mp.Vector3(0.5 * sx - dpml - 0.5), 1e-3\n", - " ),\n", - ")\n", + "sim.run(mp.during_sources(mp.at_every(0.4, animate)),\n", + " until_after_sources=mp.stop_when_fields_decayed(50, mp.Ey, mp.Vector3(0.5*sx-dpml-0.5), 1e-3))\n", "plt.close()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating MP4...\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "filename = \"media/hole-wvg-cavity.mp4\"\n", - "animate.to_mp4(10, filename)\n", + "filename = 'media/hole-wvg-cavity.mp4'\n", + "animate.to_mp4(10,filename)\n", "Video(filename)" ] }, @@ -296,50 +388,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "def sim_cavity(N=3, sy=6):\n", - " sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction\n", - " cell = mp.Vector3(sx, sy, 0)\n", - " blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=eps))\n", + "def sim_cavity(N=3,sy=6):\n", + " sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction\n", + " cell = mp.Vector3(sx,sy,0)\n", + " blk = mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), material=mp.Medium(epsilon=eps))\n", " geometry = [blk]\n", "\n", " for i in range(N):\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)))\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d / 2 + i))))\n", - "\n", - " src = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ey,\n", - " center=mp.Vector3(-0.5 * sx + dpml),\n", - " size=mp.Vector3(0, w),\n", - " )\n", - " ]\n", - "\n", - " sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=src,\n", - " symmetries=sym,\n", - " resolution=resolution,\n", - " )\n", - "\n", - " freg = mp.FluxRegion(\n", - " center=mp.Vector3(0.5 * sx - dpml - 0.5), size=mp.Vector3(0, 2 * w)\n", - " )\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(d/2+i)))\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d/2+i))))\n", + " \n", + " src = [mp.Source(mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ey,\n", + " center=mp.Vector3(-0.5*sx+dpml),\n", + " size=mp.Vector3(0,w))]\n", + " \n", + " sim = mp.Simulation(cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=src,\n", + " symmetries=sym,\n", + " resolution=resolution)\n", + " \n", + " freg = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml-0.5),\n", + " size=mp.Vector3(0,2*w))\n", " nfreq = 500\n", " trans = sim.add_flux(fcen, df, nfreq, freg)\n", - "\n", - " sim.run(\n", - " until_after_sources=mp.stop_when_fields_decayed(\n", - " 50, mp.Ey, mp.Vector3(0.5 * sx - dpml - 0.5), 1e-3\n", - " )\n", - " )\n", - "\n", + " \n", + " sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ey, mp.Vector3(0.5*sx-dpml-0.5), 1e-3))\n", + " \n", " freqs = mp.get_flux_freqs(trans)\n", " psd = mp.get_fluxes(trans)\n", "\n", @@ -355,31 +436,106 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "field decay(t = 50.025000000000006): 0.024304124551377593 / 0.024304124551377593 = 1.0\n", + "field decay(t = 100.05000000000001): 0.00029478773644369007 / 0.024304124551377593 = 0.012129123837418002\n", + "field decay(t = 150.07500000000002): 8.914669657976724e-14 / 0.024304124551377593 = 3.667965755825354e-12\n", + "run 0 finished at t = 150.07500000000002 (6003 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "field decay(t = 50.025000000000006): 3.95790820471984e-05 / 3.95790820471984e-05 = 1.0\n", + "field decay(t = 100.05000000000001): 6.062401894821014e-05 / 6.062401894821014e-05 = 1.0\n", + "field decay(t = 150.07500000000002): 1.7734570553698603e-05 / 6.062401894821014e-05 = 0.2925337326918046\n", + "field decay(t = 200.10000000000002): 7.468025297259177e-06 / 6.062401894821014e-05 = 0.12318591586016357\n", + "field decay(t = 250.125): 5.439633091350728e-06 / 6.062401894821014e-05 = 0.08972735865627277\n", + "field decay(t = 300.15000000000003): 4.23291165174129e-06 / 6.062401894821014e-05 = 0.06982235300759884\n", + "field decay(t = 350.175): 3.476628845824414e-06 / 6.062401894821014e-05 = 0.05734738320127583\n", + "field decay(t = 400.20000000000005): 2.847134687922034e-06 / 6.062401894821014e-05 = 0.04696380638100359\n", + "field decay(t = 450.225): 2.3289750979705154e-06 / 6.062401894821014e-05 = 0.03841670576079938\n", + "field decay(t = 500.25): 1.9177580551625897e-06 / 6.062401894821014e-05 = 0.031633634464268874\n", + "field decay(t = 550.275): 1.5662051283234017e-06 / 6.062401894821014e-05 = 0.025834729460304812\n", + "field decay(t = 600.3000000000001): 1.2900383339035116e-06 / 6.062401894821014e-05 = 0.02127932717567875\n", + "field decay(t = 650.325): 1.0534058195581604e-06 / 6.062401894821014e-05 = 0.017376047280172294\n", + "field decay(t = 700.35): 8.676272594305365e-07 / 6.062401894821014e-05 = 0.014311609069859468\n", + "field decay(t = 750.375): 7.087820739725053e-07 / 6.062401894821014e-05 = 0.011691439899060524\n", + "field decay(t = 800.4000000000001): 5.837770400453704e-07 / 6.062401894821014e-05 = 0.009629467827662155\n", + "field decay(t = 850.4250000000001): 4.808205960298484e-07 / 6.062401894821014e-05 = 0.007931189722684065\n", + "field decay(t = 900.45): 3.92681229771858e-07 / 6.062401894821014e-05 = 0.006477320979120792\n", + "field decay(t = 950.475): 3.234259653563735e-07 / 6.062401894821014e-05 = 0.005334947615938655\n", + "field decay(t = 1000.5): 2.6407774313354585e-07 / 6.062401894821014e-05 = 0.004355992026182594\n", + "field decay(t = 1050.525): 2.1750627194785985e-07 / 6.062401894821014e-05 = 0.0035877903794809614\n", + "field decay(t = 1100.55): 1.7768960210318136e-07 / 6.062401894821014e-05 = 0.0029310099393934598\n", + "field decay(t = 1150.575): 1.4635346961931978e-07 / 6.062401894821014e-05 = 0.002414116915349122\n", + "field decay(t = 1200.6000000000001): 1.1953143759707475e-07 / 6.062401894821014e-05 = 0.0019716844853058654\n", + "field decay(t = 1250.625): 9.845036591681545e-08 / 6.062401894821014e-05 = 0.001623949840754035\n", + "field decay(t = 1300.65): 8.038713471789712e-08 / 6.062401894821014e-05 = 0.0013259948138141452\n", + "field decay(t = 1350.6750000000002): 6.621009853390941e-08 / 6.062401894821014e-05 = 0.0010921430100249761\n", + "field decay(t = 1400.7): 5.408528044424668e-08 / 6.062401894821014e-05 = 0.0008921427741445288\n", + "run 0 finished at t = 1400.7 (56028 timesteps)\n" + ] + } + ], "source": [ - "freqs_wg, psd_wg = sim_cavity(N=0) # simple waveguide\n", - "freqs_cav, psd_cav = sim_cavity() # cavity" + "freqs_wg, psd_wg = sim_cavity(N=0) # simple waveguide\n", + "freqs_cav, psd_cav = sim_cavity() # cavity" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "fig = plt.figure(figsize=(11, 6), dpi=100)\n", + "fig = plt.figure(figsize=(11,6),dpi=100)\n", "ax = fig.add_subplot(111)\n", - "plt.plot(freqs_cav, np.array(psd_cav) / np.array(psd_wg), \"o-\")\n", + "plt.plot(freqs_cav,np.array(psd_cav)/np.array(psd_wg),'o-')\n", "plt.grid(True)\n", - "plt.xlabel(\"Frequency\")\n", - "plt.ylabel(\"Transmission\")\n", + "plt.xlabel('Frequency')\n", + "plt.ylabel('Transmission')\n", "\n", "ax2 = fig.add_axes([0.52, 0.6, 0.2, 0.25])\n", - "plt.plot(freqs_cav, np.array(psd_cav) / np.array(psd_wg), \"o-\")\n", - "plt.xlim(0.23, 0.24)\n", - "plt.ylim(0, 0.8)\n", + "plt.plot(freqs_cav,np.array(psd_cav)/np.array(psd_wg),'o-')\n", + "plt.xlim(0.23,0.24)\n", + "plt.ylim(0,0.8)\n", "plt.grid(True)" ] }, @@ -407,33 +563,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "def sim_cavity(N=3, sy=6, fcen=0.25, df=0.2):\n", - " sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction\n", - " cell = mp.Vector3(sx, sy, 0)\n", - " blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=eps))\n", + "def sim_cavity(N=3,sy=6,fcen=0.25,df=0.2):\n", + " sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction\n", + " cell = mp.Vector3(sx,sy,0)\n", + " blk = mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), material=mp.Medium(epsilon=eps))\n", " geometry = [blk]\n", "\n", " for i in range(N):\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)))\n", - " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d / 2 + i))))\n", - "\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(d/2+i)))\n", + " geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d/2+i))))\n", + " \n", " src = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Hz, mp.Vector3(0))]\n", - "\n", + " \n", " sym = [mp.Mirror(mp.Y, phase=-1), mp.Mirror(mp.X, phase=-1)]\n", - "\n", - " sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=src,\n", - " symmetries=sym,\n", - " resolution=resolution,\n", - " )\n", - "\n", + " \n", + " sim = mp.Simulation(cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=src,\n", + " symmetries=sym,\n", + " resolution=resolution)\n", + " \n", " return sim" ] }, @@ -448,9 +602,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sim = sim_cavity()\n", "f = plt.figure(dpi=100)\n", @@ -469,28 +659,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.23445415345698725, -0.0003147812363599428, 372.4080827818086, 5.812143030902256, -3.763107501450195-4.429450140163555i, 4.306293489743804e-09+0.0i\n", + "run 0 finished at t = 450.0 (18000 timesteps)\n", + "Normalizing field data...\n", + "run 1 finished at t = 454.0 (18160 timesteps)\n" + ] + } + ], "source": [ "h = mp.Harminv(mp.Hz, mp.Vector3(), fcen, df)\n", - "sim.run(mp.after_sources(h), until_after_sources=400)\n", + "sim.run(mp.after_sources(h),\n", + " until_after_sources=400)\n", "\n", "f = plt.figure(dpi=150)\n", - "animate = mp.Animate2D(sim, f=f, fields=mp.Hz, realtime=False, normalize=True)\n", + "animate = mp.Animate2D(sim,f=f,fields=mp.Hz,realtime=False,normalize=True)\n", "\n", - "sim.run(mp.at_every(1 / fcen / 20, animate), until=1 / fcen)\n", + "sim.run(mp.at_every(1/fcen/20, animate), until=1/fcen)\n", "plt.close()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating MP4...\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "filename = \"media/hole-wvg-cavity-res.mp4\"\n", - "animate.to_mp4(10, filename)\n", + "filename = 'media/hole-wvg-cavity-res.mp4'\n", + "animate.to_mp4(10,filename)\n", "Video(filename)" ] }, @@ -503,15 +729,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resonant frequency: 0.23445415345698725, Q: 372.4080827818086\n" + ] + } + ], "source": [ "f = [m.freq for m in h.modes]\n", "Q = [m.Q for m in h.modes]\n", "\n", "for fiter, qiter in zip(f, Q):\n", - " print(f\"Resonant frequency: {fiter}, Q: {qiter}\")" + " print(f'Resonant frequency: {fiter}, Q: {qiter}') " ] }, { @@ -527,29 +761,351 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.2340278559161547, -0.0018734141261924503, 62.4602570900316, 4.626071402369734, -2.8205983802960652-3.6667098872005526i, 4.878861173642782e-09+0.0i\n", + "run 0 finished at t = 450.0 (18000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.23453222952425626, -7.30923087629776e-05, 1604.356419256047, 6.010010961462772, -3.965258312230556-4.516299178994806i, 2.5784755758812747e-09+0.0i\n", + "harminv0:, 0.3203380792800332, -0.0028107745362353324, 56.98395142519764, 0.08939729348630548, -0.07611073206285417+0.04689384338623845i, 1.5030156708333963e-06+0.0i\n", + "run 0 finished at t = 450.0 (18000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.23454147739266504, -2.0401831532648347e-05, 5748.049556662018, 6.042373374596647, -3.9957221571700208+4.532601950396154i, 2.948523805875737e-09+0.0i\n", + "harminv0:, 0.32588515566889864, -0.0018520908802034915, 87.97763628993552, 0.08796924097874716, -0.05533533077673982+0.06838558712335283i, 1.0559310146633343e-06+0.0i\n", + "run 0 finished at t = 450.0 (18000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (6.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-6.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (7.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-7.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.19490287264916165, -0.0007098826592777302, 137.27823190347084, 0.021882941703098496, -0.012928672972631956-0.017655383109633823i, 2.500625566886593e-06+0.0i\n", + "harminv0:, 0.23454251746066898, -1.9496236307475725e-05, 6015.071672339521, 6.045187754860183, -3.9989015117156104-4.5335506715057345i, 3.135084966927066e-09+0.0i\n", + "harminv0:, 0.325392318417585, -0.0028756252341051265, 56.57766432120712, 0.10346319679053909, -0.06988052223995311+0.07629774375162895i, 1.7085893425281652e-06+0.0i\n", + "run 0 finished at t = 450.0 (18000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (6.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-6.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (7.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-7.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (8.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-8.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (9.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-9.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "Meep progress: 397.32500000000005/450.0 = 88.3% done in 4.0s, 0.5s to go\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.19654202912381852, -0.0001319711534889467, 744.6401123571296, 0.010139002924627917, -0.009080829279710545-0.0045097582971114i, 3.34141550460465e-06+0.0i\n", + "harminv0:, 0.23454237393497965, -1.928288781118331e-05, 6081.619522749968, 6.044742625704984, -3.998564131543015-4.533254713442919i, 2.0904183509113547e-09+0.0i\n", + "harminv0:, 0.32477010784223226, -0.0009459772540947283, 171.65851844557693, 0.10284612856999271, -0.05368440033289738+0.0877229235874676i, 2.3129729952416795e-07+0.0i\n", + "run 0 finished at t = 450.0 (18000 timesteps)\n", + "-----------\n", + "Initializing structure...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (6.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-6.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (7.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-7.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (8.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-8.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (9.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-9.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (10.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-10.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (11.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-11.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "Meep progress: 366.1/450.0 = 81.4% done in 4.0s, 0.9s to go\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.1689088120846072, -0.0008445701020748057, 99.9969165790139, 0.00035136553844277774, -7.403109585265841e-05-0.00034347800286486707i, 4.7246283165373895e-05+0.0i\n", + "harminv0:, 0.2345423978640612, -1.9291522826319817e-05, 6078.897969217604, 6.044913362697281, -3.9983665938157515-4.5336565974910785i, 1.6528012731386283e-09+0.0i\n", + "harminv0:, 0.3112255725190105, -0.000515974557591914, 301.5900376672834, 0.014525642975353973, -0.013494086961301489-0.005376236688268417i, 3.824912357074424e-07+0.0i\n", + "harminv0:, 0.3253120677284504, -0.0007471267911785511, 217.7087420565448, 0.10327042908975875, -0.0574294536238316+0.08582912897642073i, 1.5876314759103416e-07+0.0i\n", + "run 0 finished at t = 450.0 (18000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (6.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-6.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (7.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-7.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (8.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-8.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (9.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-9.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (10.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-10.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (11.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-11.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (12.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-12.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (13.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-13.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "Meep progress: 331.45000000000005/450.0 = 73.7% done in 4.0s, 1.4s to go\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.18634339022479032, -0.0005848109413962261, 159.31934325638528, 0.008396135658688452, -0.0015504592340709706-0.008251737402667624i, 9.722082433583947e-06+0.0i\n", + "harminv0:, 0.23454234757429138, -1.930720606601693e-05, 6073.9587792330785, 6.0453078999997, -3.9988080338829404-4.533793325015162i, 1.6246192346348785e-09+0.0i\n", + "harminv0:, 0.3251536244537306, -0.0007666444458027871, 212.06285797404294, 0.10960733464915637, -0.0601241424854098+0.09164526883198224i, 1.4407296748106877e-07+0.0i\n", + "run 0 finished at t = 450.0 (18000 timesteps)\n" + ] + } + ], "source": [ - "N_vec = np.arange(2, 16, 2)\n", + "N_vec = np.arange(2,16,2)\n", "f = []\n", "Q = []\n", "for N in N_vec:\n", " sim = sim_cavity(N=N)\n", " h = mp.Harminv(mp.Hz, mp.Vector3(), fcen, df)\n", - " sim.run(mp.after_sources(h), until_after_sources=400)\n", + " sim.run(mp.after_sources(h),\n", + " until_after_sources=400)\n", " f.append([m.freq for m in h.modes])\n", " Q.append([m.Q for m in h.modes])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resonant frequency: [0.2340278559161547], Q: [62.4602570900316]\n", + "Resonant frequency: [0.23453222952425626, 0.3203380792800332], Q: [1604.356419256047, 56.98395142519764]\n", + "Resonant frequency: [0.23454147739266504, 0.32588515566889864], Q: [5748.049556662018, 87.97763628993552]\n", + "Resonant frequency: [0.19490287264916165, 0.23454251746066898, 0.325392318417585], Q: [137.27823190347084, 6015.071672339521, 56.57766432120712]\n", + "Resonant frequency: [0.19654202912381852, 0.23454237393497965, 0.32477010784223226], Q: [744.6401123571296, 6081.619522749968, 171.65851844557693]\n", + "Resonant frequency: [0.1689088120846072, 0.2345423978640612, 0.3112255725190105, 0.3253120677284504], Q: [99.9969165790139, 6078.897969217604, 301.5900376672834, 217.7087420565448]\n", + "Resonant frequency: [0.18634339022479032, 0.23454234757429138, 0.3251536244537306], Q: [159.31934325638528, 6073.9587792330785, 212.06285797404294]\n" + ] + } + ], "source": [ "for fiter, qiter in zip(f, Q):\n", - " print(f\"Resonant frequency: {fiter}, Q: {qiter}\")" + " print(f'Resonant frequency: {fiter}, Q: {qiter}') " ] }, { @@ -561,9 +1117,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "idx = np.zeros(N_vec.shape)\n", "Q_fund = np.zeros(N_vec.shape)\n", @@ -572,10 +1141,10 @@ " Q_fund[i] = Q[i][int(idx[i])]\n", "\n", "plt.figure(dpi=150)\n", - "plt.semilogy(N_vec, Q_fund, \"o-\")\n", + "plt.semilogy(N_vec,Q_fund,'o-')\n", "plt.grid(True)\n", - "plt.xlabel(\"N (# of periods)\")\n", - "plt.ylabel(\"Q\")\n", + "plt.xlabel('N (# of periods)')\n", + "plt.ylabel('Q')\n", "plt.show()" ] }, @@ -592,22 +1161,115 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1.2,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " cylinder, center = (0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-0.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-1.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-2.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-3.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-4.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-5.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (6.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-6.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (7.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-7.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (8.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-8.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (9.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-9.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (10.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-10.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (11.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-11.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (12.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-12.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (13.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-13.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (14.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-14.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (15.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (-15.7,0,0)\n", + " radius 0.36, height 1e+20, axis (0, 0, 1)\n", + "Meep progress: 184.5/700.0 = 26.4% done in 4.0s, 11.2s to go\n", + "Meep progress: 363.225/700.0 = 51.9% done in 8.0s, 7.4s to go\n", + "Meep progress: 547.7/700.0 = 78.2% done in 12.0s, 3.3s to go\n", + "run 0 finished at t = 700.0 (28000 timesteps)\n" + ] + } + ], "source": [ - "sim = sim_cavity(N=16, sy=12, fcen=0.328227374843021, df=0.1)\n", + "sim = sim_cavity(N=16,sy=12,fcen=0.328227374843021,df=0.1)\n", "sim.run(until_after_sources=600)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "f = plt.figure(dpi=150)\n", - "sim.plot2D(ax=f.gca(), fields=mp.Hz)\n", + "sim.plot2D(ax=f.gca(),fields=mp.Hz)\n", "plt.show()" ] }, diff --git a/python/examples/metal-cavity-ldos.ipynb b/python/examples/metal-cavity-ldos.ipynb index 9f488cc65..6de91e4da 100644 --- a/python/examples/metal-cavity-ldos.ipynb +++ b/python/examples/metal-cavity-ldos.ipynb @@ -34,46 +34,37 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "\n", "def metal_cavity(w):\n", " resolution = 50\n", " sxy = 2\n", " dpml = 1\n", - " sxy = sxy + 2 * dpml\n", - " cell = mp.Vector3(sxy, sxy)\n", + " sxy = sxy+2*dpml\n", + " cell = mp.Vector3(sxy,sxy)\n", "\n", " pml_layers = [mp.PML(dpml)]\n", " a = 1\n", " t = 0.1\n", - " geometry = [\n", - " mp.Block(mp.Vector3(a + 2 * t, a + 2 * t, mp.inf), material=mp.metal),\n", - " mp.Block(mp.Vector3(a, a, mp.inf), material=mp.air),\n", - " ]\n", - "\n", - " geometry.append(\n", - " mp.Block(\n", - " center=mp.Vector3(a / 2), size=mp.Vector3(2 * t, w, mp.inf), material=mp.air\n", - " )\n", - " )\n", - "\n", - " fcen = math.sqrt(0.5) / a\n", + " geometry = [mp.Block(mp.Vector3(a+2*t,a+2*t,mp.inf), material=mp.metal),\n", + " mp.Block(mp.Vector3(a,a,mp.inf), material=mp.air)]\n", + "\n", + " geometry.append(mp.Block(center=mp.Vector3(a/2),\n", + " size=mp.Vector3(2*t,w,mp.inf),\n", + " material=mp.air))\n", + "\n", + " fcen = math.sqrt(0.5)/a\n", " df = 0.2\n", - " sources = [\n", - " mp.Source(\n", - " src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3()\n", - " )\n", - " ]\n", + " sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df),\n", + " component=mp.Ez,\n", + " center=mp.Vector3())]\n", "\n", " symmetries = [mp.Mirror(mp.Y)]\n", "\n", - " sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - " resolution=resolution,\n", - " )\n", + " sim = mp.Simulation(cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " symmetries=symmetries,\n", + " resolution=resolution)\n", "\n", " h = mp.Harminv(mp.Ez, mp.Vector3(), fcen, df)\n", " sim.run(mp.after_sources(h), until_after_sources=500)\n", @@ -81,13 +72,13 @@ " m = h.modes[0]\n", " f = m.freq\n", " Q = m.Q\n", - " Vmode = 0.25 * a * a\n", + " Vmode = 0.25*a*a\n", " ldos_1 = Q / Vmode / (2 * math.pi * f * math.pi * 0.5)\n", - "\n", + " \n", " sim.reset_meep()\n", - "\n", - " T = 2 * Q * (1 / f)\n", - " sim.run(mp.dft_ldos(f, 0, 1), until_after_sources=T)\n", + " \n", + " T = 2*Q*(1/f)\n", + " sim.run(mp.dft_ldos(f,0,1), until_after_sources=T)\n", " ldos_2 = sim.ldos_data[0]\n", "\n", " return ldos_1, ldos_2" @@ -108,13 +99,13 @@ "metadata": {}, "outputs": [], "source": [ - "ws = np.arange(0.2, 0.5, 0.1)\n", + "ws = np.arange(0.2,0.5,0.1)\n", "ldos_1 = np.zeros(len(ws))\n", "ldos_2 = np.zeros(len(ws))\n", "\n", "for j in range(len(ws)):\n", " ldos_1[j], ldos_2[j] = metal_cavity(ws[j])\n", - " print(\"ldos:, {}, {}\".format(ldos_1[j], ldos_2[2]))" + " print(\"ldos:, {}, {}\".format(ldos_1[j],ldos_2[2]))" ] }, { @@ -124,10 +115,10 @@ "outputs": [], "source": [ "plt.figure(dpi=150)\n", - "plt.semilogy(1 / ws, ldos_1, \"bo-\", label=\"2Q/(πωV)\")\n", - "plt.semilogy(1 / ws, ldos_2, \"rs-\", label=\"LDOS\")\n", - "plt.xlabel(\"a/w\")\n", - "plt.ylabel(\"2Q/(πωW) or LDOS\")\n", + "plt.semilogy(1/ws,ldos_1,'bo-',label=\"2Q/(πωV)\")\n", + "plt.semilogy(1/ws,ldos_2,'rs-',label=\"LDOS\")\n", + "plt.xlabel('a/w')\n", + "plt.ylabel('2Q/(πωW) or LDOS')\n", "plt.show()" ] }, diff --git a/python/examples/metasurface_lens.ipynb b/python/examples/metasurface_lens.ipynb index e42fc948f..a855f3b82 100644 --- a/python/examples/metasurface_lens.ipynb +++ b/python/examples/metasurface_lens.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -26,163 +26,114 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 50 # pixels/μm\n", + "resolution = 50 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 2.0 # substrate thickness\n", - "dpad = 2.0 # padding between grating and PML\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 2.0 # substrate thickness\n", + "dpad = 2.0 # padding between grating and PML\n", "\n", - "lcen = 0.5 # center wavelength\n", - "fcen = 1 / lcen # center frequency\n", - "df = 0.2 * fcen # frequency width\n", + "lcen = 0.5 # center wavelength\n", + "fcen = 1/lcen # center frequency\n", + "df = 0.2*fcen # frequency width\n", "\n", - "focal_length = 200 # focal length of metalens\n", - "spot_length = 100 # far field line length\n", - "ff_res = 10 # far field resolution (points/μm)\n", + "focal_length = 200 # focal length of metalens\n", + "spot_length = 100 # far field line length\n", + "ff_res = 10 # far field resolution (points/μm)\n", "\n", - "k_point = mp.Vector3(0, 0, 0)\n", + "k_point = mp.Vector3(0,0,0)\n", "\n", "glass = mp.Medium(index=1.5)\n", "\n", - "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", - "\n", - "symmetries = [mp.Mirror(mp.Y)]\n", - "\n", - "\n", - "def grating(gp, gh, gdc_list):\n", - " sx = dpml + dsub + gh + dpad + dpml\n", - " src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub)\n", - " mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad)\n", - " geometry = [\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", - " )\n", - " ]\n", - "\n", - " num_cells = len(gdc_list)\n", - " if num_cells == 1:\n", - " sy = gp\n", - " cell_size = mp.Vector3(sx, sy)\n", - "\n", - " sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy),\n", - " )\n", - " ]\n", - "\n", - " sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " default_material=glass,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - " )\n", - "\n", - " flux_obj = sim.add_flux(\n", - " fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", - " )\n", - "\n", - " sim.run(until_after_sources=50)\n", - "\n", - " input_flux = mp.get_fluxes(flux_obj)\n", - "\n", - " sim.reset_meep()\n", - "\n", - " geometry.append(\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(gh, gdc_list[0] * gp, mp.inf),\n", - " center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh),\n", - " )\n", - " )\n", - "\n", - " sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - " )\n", - "\n", - " flux_obj = sim.add_flux(\n", - " fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", - " )\n", - "\n", - " sim.run(until_after_sources=200)\n", - "\n", - " freqs = mp.get_eigenmode_freqs(flux_obj)\n", - " res = sim.get_eigenmode_coefficients(\n", - " flux_obj, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y\n", - " )\n", - " coeffs = res.alpha\n", - "\n", - " mode_tran = abs(coeffs[0, 0, 0]) ** 2 / input_flux[0]\n", - " mode_phase = np.angle(coeffs[0, 0, 0])\n", - " if mode_phase > 0:\n", - " mode_phase -= 2 * np.pi\n", - "\n", - " return mode_tran, mode_phase\n", - "\n", - " else:\n", - " sy = num_cells * gp\n", - " cell_size = mp.Vector3(sx, sy)\n", - "\n", - " sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy),\n", - " )\n", - " ]\n", - "\n", - " for j in range(num_cells):\n", - " geometry.append(\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(gh, gdc_list[j] * gp, mp.inf),\n", - " center=mp.Vector3(\n", - " -0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + (j + 0.5) * gp\n", - " ),\n", - " )\n", - " )\n", - "\n", - " sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - " )\n", - "\n", - " n2f_obj = sim.add_near2far(\n", - " fcen, 0, 1, mp.Near2FarRegion(center=mon_pt, size=mp.Vector3(y=sy))\n", - " )\n", - "\n", - " sim.run(until_after_sources=100)\n", - "\n", - " return (\n", - " abs(\n", - " sim.get_farfields(\n", - " n2f_obj,\n", - " ff_res,\n", - " center=mp.Vector3(-0.5 * sx + dpml + dsub + gh + focal_length),\n", - " size=mp.Vector3(spot_length),\n", - " )[\"Ez\"]\n", - " )\n", - " ** 2\n", - " )" + "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", + "\n", + "symmetries=[mp.Mirror(mp.Y)]\n", + "\n", + "def grating(gp,gh,gdc_list):\n", + " sx = dpml+dsub+gh+dpad+dpml\n", + " src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub)\n", + " mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad)\n", + " geometry = [mp.Block(material=glass,\n", + " size=mp.Vector3(dpml+dsub,mp.inf,mp.inf),\n", + " center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))]\n", + "\n", + " num_cells = len(gdc_list)\n", + " if num_cells == 1:\n", + " sy = gp\n", + " cell_size = mp.Vector3(sx,sy)\n", + "\n", + " sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy))]\n", + "\n", + " sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " default_material=glass,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + " flux_obj = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", + "\n", + " sim.run(until_after_sources=50)\n", + " \n", + " input_flux = mp.get_fluxes(flux_obj)\n", + " \n", + " sim.reset_meep()\n", + "\n", + " geometry.append(mp.Block(material=glass, size=mp.Vector3(gh,gdc_list[0]*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh)))\n", + "\n", + " sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + " flux_obj = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", + "\n", + " sim.run(until_after_sources=200)\n", + " \n", + " freqs = mp.get_eigenmode_freqs(flux_obj)\n", + " res = sim.get_eigenmode_coefficients(flux_obj, [1], eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", + " coeffs = res.alpha\n", + "\n", + " mode_tran = abs(coeffs[0,0,0])**2/input_flux[0]\n", + " mode_phase = np.angle(coeffs[0,0,0])\n", + " if mode_phase > 0:\n", + " mode_phase -= 2*np.pi\n", + " \n", + " return mode_tran, mode_phase\n", + " \n", + " else: \n", + " sy = num_cells*gp\n", + " cell_size = mp.Vector3(sx,sy)\n", + "\n", + " sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df),\n", + " component=mp.Ez,\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy))]\n", + "\n", + " for j in range(num_cells):\n", + " geometry.append(mp.Block(material=glass,\n", + " size=mp.Vector3(gh,gdc_list[j]*gp,mp.inf),\n", + " center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+(j+0.5)*gp)))\n", + "\n", + " sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + " n2f_obj = sim.add_near2far(fcen, 0, 1, mp.Near2FarRegion(center=mon_pt, size=mp.Vector3(y=sy)))\n", + "\n", + " sim.run(until_after_sources=100)\n", + " \n", + " return abs(sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(-0.5*sx+dpml+dsub+gh+focal_length), size=mp.Vector3(spot_length))['Ez'])**2" ] }, { @@ -194,44 +145,988 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00247622 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00647688 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00136495 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.03,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0103281 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000896931 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.0044961 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00125909 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.0382759,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0100789 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00088501 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00477791 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.0013721 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.0465517,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.00952506 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00219703 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00522304 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00136685 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.0548276,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0103021 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00116801 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00504303 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00156713 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.0631034,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.010608 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00124407 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00682497 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000928879 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.0713793,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.012069 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000815868 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00467396 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000994921 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.0796552,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.00999308 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000801086 s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00456905 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00162697 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.087931,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0103731 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.0009408 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00609112 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00112414 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.0962069,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0113389 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00133109 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.004987 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000950098 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.104483,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.00973797 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00119305 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00534606 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00095892 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.112759,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0165179 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000835896 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00442696 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.001477 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.121034,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0124168 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00136709 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00486684 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000990152 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.12931,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0103049 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.001513 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.005023 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000887156 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.137586,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0100081 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000860929 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.004879 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000782967 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.145862,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0097518 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000844002 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00591779 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00101209 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.154138,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0102739 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000947952 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00474691 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000870943 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.162414,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.00949287 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000854969 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00593901 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00108814 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.17069,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0101011 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000795126 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00502586 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00118899 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.178966,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0093739 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00110197 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00501704 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00141692 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.187241,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.013217 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00104403 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00458407 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000869989 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.195517,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.00961399 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00112414 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.0046351 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000927925 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.203793,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.009727 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00181007 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00469708 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00114608 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.212069,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0103791 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00141215 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00565505 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00124907 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.220345,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.011498 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00082016 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00524902 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000906944 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.228621,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0162721 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00110412 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00537992 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00147796 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.236897,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.010345 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00085187 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00499296 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000822783 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.245172,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0116842 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000720978 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00519085 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000974894 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.253448,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.012033 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000780106 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00697684 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00137496 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.261724,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0110409 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000945091 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + "time for set_epsilon = 0.00621819 s\n", + "-----------\n", + "run 0 finished at t = 75.0 (7500 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000971079 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,0.27,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + "time for set_epsilon = 0.0130391 s\n", + "-----------\n", + "run 0 finished at t = 225.0 (22500 timesteps)\n", + "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n", + "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n" + ] + } + ], "source": [ - "gp = 0.3 # grating periodicity\n", - "gh = 1.8 # grating height\n", - "gdc = np.linspace(0.1, 0.9, 30) # grating duty cycle\n", + "gp = 0.3 # grating periodicity\n", + "gh = 1.8 # grating height\n", + "gdc = np.linspace(0.1,0.9,30) # grating duty cycle\n", "\n", "mode_tran = np.empty((gdc.size))\n", "mode_phase = np.empty((gdc.size))\n", "for n in range(gdc.size):\n", - " mode_tran[n], mode_phase[n] = grating(gp, gh, [gdc[n]])" + " mode_tran[n], mode_phase[n] = grating(gp,gh,[gdc[n]])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=200)\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot(gdc, mode_tran, \"bo-\")\n", - "plt.xlim(gdc[0], gdc[-1])\n", - "plt.xticks([t for t in np.linspace(0.1, 0.9, 5)])\n", + "plt.subplot(1,2,1)\n", + "plt.plot(gdc, mode_tran, 'bo-')\n", + "plt.xlim(gdc[0],gdc[-1])\n", + "plt.xticks([t for t in np.linspace(0.1,0.9,5)])\n", "plt.xlabel(\"grating duty cycle\")\n", - "plt.ylim(0.96, 1.00)\n", - "plt.yticks([t for t in np.linspace(0.96, 1.00, 5)])\n", + "plt.ylim(0.96,1.00)\n", + "plt.yticks([t for t in np.linspace(0.96,1.00,5)])\n", "plt.title(\"transmittance\")\n", "\n", - "plt.subplot(1, 2, 2)\n", - "plt.plot(gdc, mode_phase, \"rs-\")\n", + "plt.subplot(1,2,2)\n", + "plt.plot(gdc, mode_phase, 'rs-')\n", "plt.grid(True)\n", - "plt.xlim(gdc[0], gdc[-1])\n", - "plt.xticks([t for t in np.linspace(0.1, 0.9, 5)])\n", + "plt.xlim(gdc[0],gdc[-1])\n", + "plt.xticks([t for t in np.linspace(0.1,0.9,5)])\n", "plt.xlabel(\"grating duty cycle\")\n", - "plt.ylim(-2 * np.pi, 0)\n", - "plt.yticks([t for t in np.linspace(-6, 0, 7)])\n", + "plt.ylim(-2*np.pi,0)\n", + "plt.yticks([t for t in np.linspace(-6,0,7)])\n", "plt.title(\"phase (radians)\")\n", "\n", "plt.tight_layout(pad=0.5)\n", @@ -249,43 +1144,361 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "phase-range:, 6.086174\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00169206 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 60.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-30,0)\n", + " size (1.8,0.109864,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-29.7,0)\n", + " size (1.8,0.123415,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-29.4,0)\n", + " size (1.8,0.136671,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-29.1,0)\n", + " size (1.8,0.152579,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-28.8,0)\n", + " size (1.8,0.169076,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-28.5,0)\n", + " size (1.8,0.186752,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-28.2,0)\n", + " size (1.8,0.0500621,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-27.9,0)\n", + " size (1.8,0.059489,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-27.6,0)\n", + " size (1.8,0.0692104,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " ...(+ 192 objects not shown)...\n", + "time for set_epsilon = 1.79243 s\n", + "-----------\n", + "Meep progress: 12.530000000000001/125.0 = 10.0% done in 4.0s, 35.9s to go\n", + "on time step 1253 (time=12.53), 0.00319322 s/step\n", + "Meep progress: 24.92/125.0 = 19.9% done in 8.0s, 32.1s to go\n", + "on time step 2492 (time=24.92), 0.00322858 s/step\n", + "Meep progress: 37.88/125.0 = 30.3% done in 12.0s, 27.6s to go\n", + "on time step 3788 (time=37.88), 0.00308826 s/step\n", + "Meep progress: 50.44/125.0 = 40.4% done in 16.0s, 23.7s to go\n", + "on time step 5045 (time=50.45), 0.0031845 s/step\n", + "Meep progress: 62.02/125.0 = 49.6% done in 20.0s, 20.3s to go\n", + "on time step 6203 (time=62.03), 0.00345439 s/step\n", + "Meep progress: 73.87/125.0 = 59.1% done in 24.0s, 16.6s to go\n", + "on time step 7388 (time=73.88), 0.00337615 s/step\n", + "Meep progress: 84.91/125.0 = 67.9% done in 28.0s, 13.2s to go\n", + "on time step 8492 (time=84.92), 0.0036253 s/step\n", + "Meep progress: 94.47/125.0 = 75.6% done in 32.0s, 10.3s to go\n", + "on time step 9449 (time=94.49), 0.00418097 s/step\n", + "Meep progress: 105.32000000000001/125.0 = 84.3% done in 36.0s, 6.7s to go\n", + "on time step 10534 (time=105.34), 0.00368885 s/step\n", + "Meep progress: 117.42/125.0 = 93.9% done in 40.0s, 2.6s to go\n", + "on time step 11744 (time=117.44), 0.00330608 s/step\n", + "run 0 finished at t = 125.0 (12500 timesteps)\n", + "get_farfields_array working on point 996 of 1000 (99% done), 0.00401804 s/point\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00165105 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 120.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-60,0)\n", + " size (1.8,0.09101,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-59.7,0)\n", + " size (1.8,0.115166,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-59.4,0)\n", + " size (1.8,0.141974,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-59.1,0)\n", + " size (1.8,0.174379,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-58.8,0)\n", + " size (1.8,0.0533026,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-58.5,0)\n", + " size (1.8,0.0730401,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-58.2,0)\n", + " size (1.8,0.0933667,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-57.9,0)\n", + " size (1.8,0.117523,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-57.6,0)\n", + " size (1.8,0.143741,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " ...(+ 392 objects not shown)...\n", + "time for set_epsilon = 3.96254 s\n", + "-----------\n", + "Meep progress: 5.39/125.0 = 4.3% done in 4.0s, 88.8s to go\n", + "on time step 539 (time=5.39), 0.00742406 s/step\n", + "Meep progress: 11.540000000000001/125.0 = 9.2% done in 8.0s, 78.7s to go\n", + "on time step 1154 (time=11.54), 0.00651044 s/step\n", + "Meep progress: 17.45/125.0 = 14.0% done in 12.0s, 74.0s to go\n", + "on time step 1745 (time=17.45), 0.00677692 s/step\n", + "Meep progress: 23.16/125.0 = 18.5% done in 16.0s, 70.4s to go\n", + "on time step 2316 (time=23.16), 0.00700984 s/step\n", + "Meep progress: 29.25/125.0 = 23.4% done in 20.0s, 65.5s to go\n", + "on time step 2925 (time=29.25), 0.00657387 s/step\n", + "Meep progress: 35.27/125.0 = 28.2% done in 24.0s, 61.1s to go\n", + "on time step 3527 (time=35.27), 0.00664574 s/step\n", + "Meep progress: 40.980000000000004/125.0 = 32.8% done in 28.0s, 57.5s to go\n", + "on time step 4098 (time=40.98), 0.00700592 s/step\n", + "Meep progress: 46.58/125.0 = 37.3% done in 32.0s, 53.9s to go\n", + "on time step 4658 (time=46.58), 0.00714546 s/step\n", + "Meep progress: 52.46/125.0 = 42.0% done in 36.0s, 49.8s to go\n", + "on time step 5246 (time=52.46), 0.00680792 s/step\n", + "Meep progress: 58.76/125.0 = 47.0% done in 40.0s, 45.1s to go\n", + "on time step 5876 (time=58.76), 0.00635169 s/step\n", + "Meep progress: 64.7/125.0 = 51.8% done in 44.0s, 41.0s to go\n", + "on time step 6470 (time=64.7), 0.00673928 s/step\n", + "Meep progress: 69.12/125.0 = 55.3% done in 48.0s, 38.8s to go\n", + "on time step 6912 (time=69.12), 0.00905871 s/step\n", + "Meep progress: 73.92/125.0 = 59.1% done in 52.0s, 36.0s to go\n", + "on time step 7392 (time=73.92), 0.00833534 s/step\n", + "Meep progress: 79.69/125.0 = 63.8% done in 56.0s, 31.9s to go\n", + "on time step 7969 (time=79.69), 0.00694212 s/step\n", + "Meep progress: 85.4/125.0 = 68.3% done in 60.0s, 27.8s to go\n", + "on time step 8540 (time=85.4), 0.00700793 s/step\n", + "Meep progress: 91.31/125.0 = 73.0% done in 64.0s, 23.6s to go\n", + "on time step 9131 (time=91.31), 0.0067687 s/step\n", + "Meep progress: 96.35000000000001/125.0 = 77.1% done in 68.1s, 20.2s to go\n", + "on time step 9635 (time=96.35), 0.00797192 s/step\n", + "Meep progress: 99.73/125.0 = 79.8% done in 72.1s, 18.3s to go\n", + "on time step 9973 (time=99.73), 0.0118546 s/step\n", + "Meep progress: 104.79/125.0 = 83.8% done in 76.1s, 14.7s to go\n", + "on time step 10479 (time=104.79), 0.00790892 s/step\n", + "Meep progress: 109.58/125.0 = 87.7% done in 80.1s, 11.3s to go\n", + "on time step 10958 (time=109.58), 0.00836883 s/step\n", + "Meep progress: 114.53/125.0 = 91.6% done in 84.1s, 7.7s to go\n", + "on time step 11453 (time=114.53), 0.00808508 s/step\n", + "Meep progress: 120.01/125.0 = 96.0% done in 88.1s, 3.7s to go\n", + "on time step 12001 (time=120.01), 0.0073038 s/step\n", + "run 0 finished at t = 125.0 (12500 timesteps)\n", + "get_farfields_array working on point 446 of 1000 (44% done), 0.00898336 s/point\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "get_farfields_array working on point 897 of 1000 (89% done), 0.00887155 s/point\n", + "-----------\n", + "Initializing structure...\n", + "Padding y to even number of grid points.\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000861883 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.8 x 240.3 x 0 with resolution 50\n", + " block, center = (-2.4,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-120,0)\n", + " size (1.8,0.109569,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-119.7,0)\n", + " size (1.8,0.161122,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-119.4,0)\n", + " size (1.8,0.0609619,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-119.1,0)\n", + " size (1.8,0.0983747,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-118.8,0)\n", + " size (1.8,0.145804,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-118.5,0)\n", + " size (1.8,0.0518297,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-118.2,0)\n", + " size (1.8,0.0883587,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-117.9,0)\n", + " size (1.8,0.132253,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " block, center = (1.11022e-16,-117.6,0)\n", + " size (1.8,0.190581,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n", + " ...(+ 792 objects not shown)...\n", + "time for set_epsilon = 10.2552 s\n", + "-----------\n", + "Meep progress: 2.2800000000000002/125.0 = 1.8% done in 4.0s, 215.4s to go\n", + "on time step 228 (time=2.28), 0.0175505 s/step\n", + "Meep progress: 5.0600000000000005/125.0 = 4.0% done in 8.0s, 189.8s to go\n", + "on time step 506 (time=5.06), 0.0144061 s/step\n", + "Meep progress: 7.79/125.0 = 6.2% done in 12.0s, 180.8s to go\n", + "on time step 779 (time=7.79), 0.0146795 s/step\n", + "Meep progress: 10.61/125.0 = 8.5% done in 16.0s, 172.7s to go\n", + "on time step 1061 (time=10.61), 0.0142034 s/step\n", + "Meep progress: 13.370000000000001/125.0 = 10.7% done in 20.0s, 167.2s to go\n", + "on time step 1337 (time=13.37), 0.0145284 s/step\n", + "Meep progress: 16.12/125.0 = 12.9% done in 24.0s, 162.4s to go\n", + "on time step 1612 (time=16.12), 0.014564 s/step\n", + "Meep progress: 17.88/125.0 = 14.3% done in 28.1s, 168.1s to go\n", + "on time step 1788 (time=17.88), 0.0228108 s/step\n", + "Meep progress: 20.5/125.0 = 16.4% done in 32.1s, 163.4s to go\n", + "on time step 2050 (time=20.5), 0.015286 s/step\n", + "Meep progress: 23.17/125.0 = 18.5% done in 36.1s, 158.5s to go\n", + "on time step 2317 (time=23.17), 0.015032 s/step\n", + "Meep progress: 25.310000000000002/125.0 = 20.2% done in 40.1s, 157.8s to go\n", + "on time step 2531 (time=25.31), 0.0186941 s/step\n", + "Meep progress: 28.01/125.0 = 22.4% done in 44.1s, 152.7s to go\n", + "on time step 2801 (time=28.01), 0.0148529 s/step\n", + "Meep progress: 30.72/125.0 = 24.6% done in 48.1s, 147.6s to go\n", + "on time step 3072 (time=30.72), 0.0147973 s/step\n", + "Meep progress: 33.410000000000004/125.0 = 26.7% done in 52.1s, 142.8s to go\n", + "on time step 3341 (time=33.41), 0.0148998 s/step\n", + "Meep progress: 36.11/125.0 = 28.9% done in 56.1s, 138.1s to go\n", + "on time step 3611 (time=36.11), 0.0148395 s/step\n", + "Meep progress: 38.31/125.0 = 30.6% done in 60.1s, 136.1s to go\n", + "on time step 3831 (time=38.31), 0.0182697 s/step\n", + "Meep progress: 40.82/125.0 = 32.7% done in 64.1s, 132.3s to go\n", + "on time step 4082 (time=40.82), 0.0159877 s/step\n", + "Meep progress: 43.5/125.0 = 34.8% done in 68.2s, 127.7s to go\n", + "on time step 4350 (time=43.5), 0.0149743 s/step\n", + "Meep progress: 46.17/125.0 = 36.9% done in 72.2s, 123.2s to go\n", + "on time step 4617 (time=46.17), 0.0150016 s/step\n", + "Meep progress: 48.86/125.0 = 39.1% done in 76.2s, 118.7s to go\n", + "on time step 4886 (time=48.86), 0.0149015 s/step\n", + "Meep progress: 51.57/125.0 = 41.3% done in 80.2s, 114.2s to go\n", + "on time step 5157 (time=51.57), 0.0148085 s/step\n", + "Meep progress: 54.26/125.0 = 43.4% done in 84.2s, 109.8s to go\n", + "on time step 5426 (time=54.26), 0.0148808 s/step\n", + "Meep progress: 56.97/125.0 = 45.6% done in 88.2s, 105.3s to go\n", + "on time step 5697 (time=56.97), 0.014799 s/step\n", + "Meep progress: 59.660000000000004/125.0 = 47.7% done in 92.2s, 101.0s to go\n", + "on time step 5966 (time=59.66), 0.0149044 s/step\n", + "Meep progress: 62.36/125.0 = 49.9% done in 96.2s, 96.7s to go\n", + "on time step 6236 (time=62.36), 0.0148605 s/step\n", + "Meep progress: 65.07000000000001/125.0 = 52.1% done in 100.2s, 92.3s to go\n", + "on time step 6507 (time=65.07), 0.014803 s/step\n", + "Meep progress: 67.77/125.0 = 54.2% done in 104.3s, 88.0s to go\n", + "on time step 6777 (time=67.77), 0.0148711 s/step\n", + "Meep progress: 69.79/125.0 = 55.8% done in 108.3s, 85.6s to go\n", + "on time step 6979 (time=69.79), 0.0198233 s/step\n", + "Meep progress: 72.57000000000001/125.0 = 58.1% done in 112.3s, 81.1s to go\n", + "on time step 7257 (time=72.57), 0.0144427 s/step\n", + "Meep progress: 75.16/125.0 = 60.1% done in 116.3s, 77.1s to go\n", + "on time step 7516 (time=75.16), 0.0154471 s/step\n", + "Meep progress: 77.78/125.0 = 62.2% done in 120.3s, 73.0s to go\n", + "on time step 7778 (time=77.78), 0.0152936 s/step\n", + "Meep progress: 80.57000000000001/125.0 = 64.5% done in 124.3s, 68.5s to go\n", + "on time step 8057 (time=80.57), 0.0143637 s/step\n", + "Meep progress: 83.37/125.0 = 66.7% done in 128.3s, 64.1s to go\n", + "on time step 8337 (time=83.37), 0.0143218 s/step\n", + "Meep progress: 86.07000000000001/125.0 = 68.9% done in 132.3s, 59.8s to go\n", + "on time step 8607 (time=86.07), 0.0148534 s/step\n", + "Meep progress: 88.82000000000001/125.0 = 71.1% done in 136.3s, 55.5s to go\n", + "on time step 8882 (time=88.82), 0.0145593 s/step\n", + "Meep progress: 91.74/125.0 = 73.4% done in 140.3s, 50.9s to go\n", + "on time step 9174 (time=91.74), 0.0137382 s/step\n", + "Meep progress: 94.64/125.0 = 75.7% done in 144.3s, 46.3s to go\n", + "on time step 9464 (time=94.64), 0.0138319 s/step\n", + "Meep progress: 97.55/125.0 = 78.0% done in 148.3s, 41.7s to go\n", + "on time step 9755 (time=97.55), 0.0137471 s/step\n", + "Meep progress: 100.47/125.0 = 80.4% done in 152.4s, 37.2s to go\n", + "on time step 10047 (time=100.47), 0.0137389 s/step\n", + "Meep progress: 103.35000000000001/125.0 = 82.7% done in 156.4s, 32.8s to go\n", + "on time step 10335 (time=103.35), 0.01392 s/step\n", + "Meep progress: 106.25/125.0 = 85.0% done in 160.4s, 28.3s to go\n", + "on time step 10625 (time=106.25), 0.0138042 s/step\n", + "Meep progress: 109.14/125.0 = 87.3% done in 164.4s, 23.9s to go\n", + "on time step 10914 (time=109.14), 0.0138697 s/step\n", + "Meep progress: 112.01/125.0 = 89.6% done in 168.4s, 19.5s to go\n", + "on time step 11201 (time=112.01), 0.0139446 s/step\n", + "Meep progress: 114.66/125.0 = 91.7% done in 172.4s, 15.5s to go\n", + "on time step 11466 (time=114.66), 0.01511 s/step\n", + "Meep progress: 117.28/125.0 = 93.8% done in 176.4s, 11.6s to go\n", + "on time step 11728 (time=117.28), 0.0153079 s/step\n", + "Meep progress: 119.9/125.0 = 95.9% done in 180.4s, 7.7s to go\n", + "on time step 11990 (time=119.9), 0.0152988 s/step\n", + "Meep progress: 122.79/125.0 = 98.2% done in 184.4s, 3.3s to go\n", + "on time step 12279 (time=122.79), 0.0138704 s/step\n", + "run 0 finished at t = 125.0 (12500 timesteps)\n", + "get_farfields_array working on point 236 of 1000 (23% done), 0.0170414 s/point\n", + "get_farfields_array working on point 472 of 1000 (47% done), 0.0170053 s/point\n", + "get_farfields_array working on point 704 of 1000 (70% done), 0.0172601 s/point\n", + "get_farfields_array working on point 937 of 1000 (93% done), 0.0171973 s/point\n" + ] + } + ], "source": [ - "gdc_new = np.linspace(0.16, 0.65, 500)\n", + "gdc_new = np.linspace(0.16,0.65,500)\n", "mode_phase_interp = np.interp(gdc_new, gdc, mode_phase)\n", - "print(\"phase-range:, {:.6f}\".format(mode_phase_interp.max() - mode_phase_interp.min()))\n", + "print(\"phase-range:, {:.6f}\".format(mode_phase_interp.max()-mode_phase_interp.min()))\n", "\n", "phase_tol = 1e-2\n", - "num_cells = [100, 200, 400]\n", - "ff_nc = np.empty((spot_length * ff_res, len(num_cells)))\n", + "num_cells = [100,200,400]\n", + "ff_nc = np.empty((spot_length*ff_res,len(num_cells)))\n", "\n", "for k in range(len(num_cells)):\n", - " gdc_list = []\n", - " for j in range(-num_cells[k], num_cells[k] + 1):\n", - " phase_local = (\n", - " 2\n", - " * np.pi\n", - " / lcen\n", - " * (focal_length - ((j * gp) ** 2 + focal_length**2) ** 0.5)\n", - " ) # local phase at the center of the j'th unit cell\n", - " phase_mod = phase_local % (-2 * np.pi) # restrict phase to [-2*pi,0]\n", - " if phase_mod > mode_phase_interp.max():\n", - " phase_mod = mode_phase_interp.max()\n", - " if phase_mod < mode_phase_interp.min():\n", - " phase_mod = mode_phase_interp.min()\n", - " idx = np.transpose(\n", - " np.nonzero(\n", - " np.logical_and(\n", - " mode_phase_interp > phase_mod - phase_tol,\n", - " mode_phase_interp < phase_mod + phase_tol,\n", - " )\n", - " )\n", - " ).ravel()\n", - " gdc_list.append(gdc_new[idx[0]])\n", - "\n", - " ff_nc[:, k] = grating(gp, gh, gdc_list)" + " gdc_list = []\n", + " for j in range(-num_cells[k],num_cells[k]+1):\n", + " phase_local = 2*np.pi/lcen * (focal_length-((j*gp)**2 + focal_length**2)**0.5) # local phase at the center of the j'th unit cell\n", + " phase_mod = phase_local % (-2*np.pi) # restrict phase to [-2*pi,0]\n", + " if phase_mod > mode_phase_interp.max():\n", + " phase_mod = mode_phase_interp.max()\n", + " if phase_mod < mode_phase_interp.min():\n", + " phase_mod = mode_phase_interp.min()\n", + " idx = np.transpose(np.nonzero(np.logical_and(mode_phase_interp > phase_mod-phase_tol, mode_phase_interp < phase_mod+phase_tol))).ravel()\n", + " gdc_list.append(gdc_new[idx[0]])\n", + "\n", + " ff_nc[:,k] = grating(gp,gh,gdc_list)" ] }, { @@ -301,29 +1514,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "x = np.linspace(\n", - " focal_length - 0.5 * spot_length,\n", - " focal_length + 0.5 * spot_length,\n", - " ff_res * spot_length,\n", - ")\n", + "x = np.linspace(focal_length-0.5*spot_length,focal_length+0.5*spot_length,ff_res*spot_length)\n", "plt.figure(dpi=200)\n", - "plt.semilogy(\n", - " x, abs(ff_nc[:, 0]) ** 2, \"bo-\", label=\"num_cells = {}\".format(2 * num_cells[0] + 1)\n", - ")\n", - "plt.semilogy(\n", - " x, abs(ff_nc[:, 1]) ** 2, \"ro-\", label=\"num_cells = {}\".format(2 * num_cells[1] + 1)\n", - ")\n", - "plt.semilogy(\n", - " x, abs(ff_nc[:, 2]) ** 2, \"go-\", label=\"num_cells = {}\".format(2 * num_cells[2] + 1)\n", - ")\n", - "plt.xlabel(\"x coordinate (μm)\")\n", - "plt.ylabel(r\"energy density of far-field electric fields, |E$_z$|$^2$\")\n", - "plt.title(\"focusing properties of a binary-grating metasurface lens\")\n", - "plt.legend(loc=\"upper right\")\n", + "plt.semilogy(x,abs(ff_nc[:,0])**2,'bo-',label='num_cells = {}'.format(2*num_cells[0]+1))\n", + "plt.semilogy(x,abs(ff_nc[:,1])**2,'ro-',label='num_cells = {}'.format(2*num_cells[1]+1))\n", + "plt.semilogy(x,abs(ff_nc[:,2])**2,'go-',label='num_cells = {}'.format(2*num_cells[2]+1))\n", + "plt.xlabel('x coordinate (μm)')\n", + "plt.ylabel(r'energy density of far-field electric fields, |E$_z$|$^2$')\n", + "plt.title('focusing properties of a binary-grating metasurface lens')\n", + "plt.legend(loc='upper right')\n", "plt.tight_layout()\n", "plt.show()" ] diff --git a/python/examples/mie_scattering.ipynb b/python/examples/mie_scattering.ipynb index 65050ed7a..d6b2790a2 100644 --- a/python/examples/mie_scattering.ipynb +++ b/python/examples/mie_scattering.ipynb @@ -24,83 +24,48 @@ "\n", "r = 1.0 # radius of sphere\n", "\n", - "wvl_min = 2 * np.pi * r / 10\n", - "wvl_max = 2 * np.pi * r / 2\n", + "wvl_min = 2*np.pi*r/10\n", + "wvl_max = 2*np.pi*r/2\n", "\n", - "frq_min = 1 / wvl_max\n", - "frq_max = 1 / wvl_min\n", - "frq_cen = 0.5 * (frq_min + frq_max)\n", - "dfrq = frq_max - frq_min\n", + "frq_min = 1/wvl_max\n", + "frq_max = 1/wvl_min\n", + "frq_cen = 0.5*(frq_min+frq_max)\n", + "dfrq = frq_max-frq_min\n", "nfrq = 100\n", "\n", "## at least 8 pixels per smallest wavelength, i.e. np.floor(8/wvl_min)\n", "resolution = 25\n", "\n", - "dpml = 0.5 * wvl_max\n", - "dair = 0.5 * wvl_max\n", + "dpml = 0.5*wvl_max\n", + "dair = 0.5*wvl_max\n", "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", - "symmetries = [mp.Mirror(mp.Y), mp.Mirror(mp.Z, phase=-1)]\n", + "symmetries = [mp.Mirror(mp.Y),\n", + " mp.Mirror(mp.Z,phase=-1)]\n", "\n", - "s = 2 * (dpml + dair + r)\n", - "cell_size = mp.Vector3(s, s, s)\n", + "s = 2*(dpml+dair+r)\n", + "cell_size = mp.Vector3(s,s,s)\n", "\n", "# is_integrated=True necessary for any planewave source extending into PML\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True),\n", - " center=mp.Vector3(-0.5 * s + dpml),\n", - " size=mp.Vector3(0, s, s),\n", - " component=mp.Ez,\n", - " )\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=mp.Vector3(),\n", - " symmetries=symmetries,\n", - ")\n", - "\n", - "box_x1 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r, 2 * r)),\n", - ")\n", - "box_x2 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r, 2 * r)),\n", - ")\n", - "box_y1 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0, 2 * r)),\n", - ")\n", - "box_y2 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0, 2 * r)),\n", - ")\n", - "box_z1 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(z=-r), size=mp.Vector3(2 * r, 2 * r, 0)),\n", - ")\n", - "box_z2 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(z=+r), size=mp.Vector3(2 * r, 2 * r, 0)),\n", - ")\n", + "sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n", + " center=mp.Vector3(-0.5*s+dpml),\n", + " size=mp.Vector3(0,s,s),\n", + " component=mp.Ez)]\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=mp.Vector3(),\n", + " symmetries=symmetries)\n", + "\n", + "box_x1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r,2*r)))\n", + "box_x2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r,2*r)))\n", + "box_y1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0,2*r)))\n", + "box_y2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0,2*r)))\n", + "box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=-r),size=mp.Vector3(2*r,2*r,0)))\n", + "box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=+r),size=mp.Vector3(2*r,2*r,0)))\n", "\n", "sim.run(until_after_sources=10)\n", "\n", @@ -117,56 +82,24 @@ "sim.reset_meep()\n", "\n", "n_sphere = 2.0\n", - "geometry = [\n", - " mp.Sphere(material=mp.Medium(index=n_sphere), center=mp.Vector3(), radius=r)\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=mp.Vector3(),\n", - " symmetries=symmetries,\n", - " geometry=geometry,\n", - ")\n", - "\n", - "box_x1 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r, 2 * r)),\n", - ")\n", - "box_x2 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r, 2 * r)),\n", - ")\n", - "box_y1 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0, 2 * r)),\n", - ")\n", - "box_y2 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0, 2 * r)),\n", - ")\n", - "box_z1 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(z=-r), size=mp.Vector3(2 * r, 2 * r, 0)),\n", - ")\n", - "box_z2 = sim.add_flux(\n", - " frq_cen,\n", - " dfrq,\n", - " nfrq,\n", - " mp.FluxRegion(center=mp.Vector3(z=+r), size=mp.Vector3(2 * r, 2 * r, 0)),\n", - ")\n", + "geometry = [mp.Sphere(material=mp.Medium(index=n_sphere),\n", + " center=mp.Vector3(),\n", + " radius=r)]\n", + "\n", + "sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=mp.Vector3(),\n", + " symmetries=symmetries,\n", + " geometry=geometry)\n", + "\n", + "box_x1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r,2*r)))\n", + "box_x2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r,2*r)))\n", + "box_y1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0,2*r)))\n", + "box_y2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0,2*r)))\n", + "box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=-r),size=mp.Vector3(2*r,2*r,0)))\n", + "box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=+r),size=mp.Vector3(2*r,2*r,0)))\n", "\n", "sim.load_minus_flux_data(box_x1, box_x1_data)\n", "sim.load_minus_flux_data(box_x2, box_x2_data)\n", @@ -184,20 +117,11 @@ "box_z1_flux = mp.get_fluxes(box_z1)\n", "box_z2_flux = mp.get_fluxes(box_z2)\n", "\n", - "scatt_flux = (\n", - " np.asarray(box_x1_flux)\n", - " - np.asarray(box_x2_flux)\n", - " + np.asarray(box_y1_flux)\n", - " - np.asarray(box_y2_flux)\n", - " + np.asarray(box_z1_flux)\n", - " - np.asarray(box_z2_flux)\n", - ")\n", - "intensity = np.asarray(box_x1_flux0) / (2 * r) ** 2\n", - "scatt_cross_section = np.divide(scatt_flux, intensity)\n", - "scatt_eff_meep = scatt_cross_section * -1 / (np.pi * r**2)\n", - "scatt_eff_theory = [\n", - " ps.MieQ(n_sphere, 1000 / f, 2 * r * 1000, asDict=True)[\"Qsca\"] for f in freqs\n", - "]" + "scatt_flux = np.asarray(box_x1_flux)-np.asarray(box_x2_flux)+np.asarray(box_y1_flux)-np.asarray(box_y2_flux)+np.asarray(box_z1_flux)-np.asarray(box_z2_flux)\n", + "intensity = np.asarray(box_x1_flux0)/(2*r)**2\n", + "scatt_cross_section = np.divide(scatt_flux,intensity)\n", + "scatt_eff_meep = scatt_cross_section*-1/(np.pi*r**2)\n", + "scatt_eff_theory = [ps.MieQ(n_sphere,1000/f,2*r*1000,asDict=True)['Qsca'] for f in freqs]" ] }, { @@ -216,13 +140,13 @@ "outputs": [], "source": [ "plt.figure(dpi=150)\n", - "plt.loglog(2 * np.pi * r * np.asarray(freqs), scatt_eff_meep, \"bo-\", label=\"Meep\")\n", - "plt.loglog(2 * np.pi * r * np.asarray(freqs), scatt_eff_theory, \"ro-\", label=\"theory\")\n", - "plt.grid(True, which=\"both\", ls=\"-\")\n", - "plt.xlabel(\"(sphere circumference)/wavelength, 2πr/λ\")\n", - "plt.ylabel(\"scattering efficiency, σ/πr$^{2}$\")\n", - "plt.legend(loc=\"upper right\")\n", - "plt.title(\"Mie Scattering of a Lossless Dielectric Sphere\")\n", + "plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_eff_meep,'bo-',label='Meep')\n", + "plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_eff_theory,'ro-',label='theory')\n", + "plt.grid(True,which=\"both\",ls=\"-\")\n", + "plt.xlabel('(sphere circumference)/wavelength, 2πr/λ')\n", + "plt.ylabel('scattering efficiency, σ/πr$^{2}$')\n", + "plt.legend(loc='upper right')\n", + "plt.title('Mie Scattering of a Lossless Dielectric Sphere')\n", "plt.tight_layout()\n", "plt.show()" ] diff --git a/python/examples/mode-decomposition.ipynb b/python/examples/mode-decomposition.ipynb index 5fb11dfc0..3c97872ea 100644 --- a/python/examples/mode-decomposition.ipynb +++ b/python/examples/mode-decomposition.ipynb @@ -17,34 +17,407 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00183296 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 33 x 12 x 0 with resolution 25\n", + " prism, center = (0,0,5e+19)\n", + " height 1e+20, axis (0,0,1), 4 vertices:\n", + " (-17.5,0.5,0)\n", + " (17.5,0.5,0)\n", + " (17.5,-0.5,0)\n", + " (-17.5,-0.5,0)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + "time for set_epsilon = 0.447863 s\n", + "-----------\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "on time step 2379 (time=47.58), 0.00168153 s/step\n", + "field decay(t = 50.02): 7.54740931333862e-08 / 7.54740931333862e-08 = 1.0\n", + "on time step 4877 (time=97.54), 0.00160183 s/step\n", + "field decay(t = 100.04): 0.001735614282175414 / 0.001735614282175414 = 1.0\n", + "on time step 7353 (time=147.06), 0.00161556 s/step\n", + "field decay(t = 150.06): 0.45397875691800166 / 0.45397875691800166 = 1.0\n", + "on time step 9819 (time=196.38), 0.00162256 s/step\n", + "field decay(t = 200.08): 1.5637205312092566 / 1.5637205312092566 = 1.0\n", + "on time step 12317 (time=246.34), 0.0016016 s/step\n", + "field decay(t = 250.1): 1.2378300815914483 / 1.5637205312092566 = 0.7915929073555166\n", + "on time step 14830 (time=296.6), 0.0015922 s/step\n", + "field decay(t = 300.12): 0.031049012038060042 / 1.5637205312092566 = 0.01985585750034836\n", + "on time step 17280 (time=345.6), 0.00163305 s/step\n", + "field decay(t = 350.14): 8.860450697395457e-06 / 1.5637205312092566 = 5.66626230234599e-06\n", + "on time step 19780 (time=395.6), 0.00160038 s/step\n", + "field decay(t = 400.16): 2.7848020268498908e-11 / 1.5637205312092566 = 1.7808821789251224e-11\n", + "run 0 finished at t = 400.16 (20008 timesteps)\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00132203 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 33 x 12 x 0 with resolution 25\n", + " prism, center = (0,0,5e+19)\n", + " height 1e+20, axis (0,0,1), 8 vertices:\n", + " (-17.5,0.5,0)\n", + " (-0.5,0.5,0)\n", + " (0.5,1,0)\n", + " (17.5,1,0)\n", + " (17.5,-1,0)\n", + " (0.5,-1,0)\n", + " (-0.5,-0.5,0)\n", + " (-17.5,-0.5,0)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + "time for set_epsilon = 0.664247 s\n", + "-----------\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "on time step 2337 (time=46.74), 0.00171202 s/step\n", + "field decay(t = 50.02): 7.55116423679346e-08 / 7.55116423679346e-08 = 1.0\n", + "on time step 4808 (time=96.16), 0.00161879 s/step\n", + "field decay(t = 100.04): 0.001737568234822459 / 0.001737568234822459 = 1.0\n", + "on time step 7266 (time=145.32), 0.00162736 s/step\n", + "field decay(t = 150.06): 0.4546391533256169 / 0.4546391533256169 = 1.0\n", + "on time step 9726 (time=194.52), 0.00162628 s/step\n", + "field decay(t = 200.08): 1.5661544172637174 / 1.5661544172637174 = 1.0\n", + "on time step 12196 (time=243.92), 0.00161985 s/step\n", + "field decay(t = 250.1): 1.2398082542830435 / 1.5661544172637174 = 0.7916258068914784\n", + "on time step 14700 (time=294), 0.00159784 s/step\n", + "field decay(t = 300.12): 0.0313140484926616 / 1.5661544172637174 = 0.01999422799405148\n", + "on time step 17203 (time=344.06), 0.00159832 s/step\n", + "field decay(t = 350.14): 9.723724333805377e-06 / 1.5661544172637174 = 6.208662585643395e-06\n", + "on time step 19681 (time=393.62), 0.0016146 s/step\n", + "field decay(t = 400.16): 5.154329670663798e-11 / 1.5661544172637174 = 3.291073736949327e-11\n", + "run 0 finished at t = 400.16 (20008 timesteps)\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 9 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", + "refl:, 1, 0.00033425, 0.00041190\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00108004 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 34 x 12 x 0 with resolution 25\n", + " prism, center = (0,0,5e+19)\n", + " height 1e+20, axis (0,0,1), 4 vertices:\n", + " (-18,0.5,0)\n", + " (18,0.5,0)\n", + " (18,-0.5,0)\n", + " (-18,-0.5,0)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + "time for set_epsilon = 0.462233 s\n", + "-----------\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "on time step 2327 (time=46.54), 0.00171948 s/step\n", + "field decay(t = 50.02): 7.571674211765607e-08 / 7.571674211765607e-08 = 1.0\n", + "on time step 4775 (time=95.5), 0.0016345 s/step\n", + "field decay(t = 100.04): 0.0017406599773709985 / 0.0017406599773709985 = 1.0\n", + "on time step 7173 (time=143.46), 0.00166846 s/step\n", + "field decay(t = 150.06): 0.4552933938871408 / 0.4552933938871408 = 1.0\n", + "on time step 9613 (time=192.26), 0.0016394 s/step\n", + "field decay(t = 200.08): 1.56824628067331 / 1.56824628067331 = 1.0\n", + "on time step 12052 (time=241.04), 0.00164018 s/step\n", + "field decay(t = 250.1): 1.2414144426139568 / 1.56824628067331 = 0.7915940614129616\n", + "on time step 14512 (time=290.24), 0.00162657 s/step\n", + "field decay(t = 300.12): 0.031139198594396233 / 1.56824628067331 = 0.019856064049472478\n", + "on time step 16935 (time=338.7), 0.00165142 s/step\n", + "field decay(t = 350.14): 8.886312411745595e-06 / 1.56824628067331 = 5.666401075684586e-06\n", + "on time step 19391 (time=387.82), 0.00162891 s/step\n", + "field decay(t = 400.16): 2.7609192234474572e-11 / 1.56824628067331 = 1.7605138028843823e-11\n", + "run 0 finished at t = 400.16 (20008 timesteps)\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00079608 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 34 x 12 x 0 with resolution 25\n", + " prism, center = (0,0,5e+19)\n", + " height 1e+20, axis (0,0,1), 8 vertices:\n", + " (-18,0.5,0)\n", + " (-1,0.5,0)\n", + " (1,1,0)\n", + " (18,1,0)\n", + " (18,-1,0)\n", + " (1,-1,0)\n", + " (-1,-0.5,0)\n", + " (-18,-0.5,0)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + "time for set_epsilon = 0.693091 s\n", + "-----------\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "on time step 2308 (time=46.16), 0.00173349 s/step\n", + "field decay(t = 50.02): 7.571396797724319e-08 / 7.571396797724319e-08 = 1.0\n", + "on time step 4692 (time=93.84), 0.00167819 s/step\n", + "field decay(t = 100.04): 0.0017395257849605922 / 0.0017395257849605922 = 1.0\n", + "on time step 7113 (time=142.26), 0.00165247 s/step\n", + "field decay(t = 150.06): 0.45384932485125673 / 0.45384932485125673 = 1.0\n", + "on time step 9546 (time=190.92), 0.00164416 s/step\n", + "field decay(t = 200.08): 1.5552337783783405 / 1.5552337783783405 = 1.0\n", + "on time step 11961 (time=239.22), 0.00165646 s/step\n", + "field decay(t = 250.1): 1.2260556872108188 / 1.5552337783783405 = 0.7883417298775754\n", + "on time step 14416 (time=288.32), 0.00162956 s/step\n", + "field decay(t = 300.12): 0.030083252819861385 / 1.5552337783783405 = 0.019343235234531443\n", + "on time step 16863 (time=337.26), 0.00163533 s/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "field decay(t = 350.14): 8.582297003142968e-06 / 1.5552337783783405 = 5.518332434942239e-06\n", + "on time step 19312 (time=386.24), 0.00163389 s/step\n", + "field decay(t = 400.16): 5.083050063922431e-11 / 1.5552337783783405 = 3.268351121606029e-11\n", + "run 0 finished at t = 400.16 (20008 timesteps)\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", + "refl:, 2, 0.00007150, 0.00008424\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00330091 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 36 x 12 x 0 with resolution 25\n", + " prism, center = (0,0,5e+19)\n", + " height 1e+20, axis (0,0,1), 4 vertices:\n", + " (-19,0.5,0)\n", + " (19,0.5,0)\n", + " (19,-0.5,0)\n", + " (-19,-0.5,0)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + "time for set_epsilon = 0.496261 s\n", + "-----------\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "on time step 2138 (time=42.76), 0.00187101 s/step\n", + "field decay(t = 50.02): 7.571674212930823e-08 / 7.571674212930823e-08 = 1.0\n", + "on time step 4454 (time=89.08), 0.00172765 s/step\n", + "field decay(t = 100.04): 0.0017406599772782168 / 0.0017406599772782168 = 1.0\n", + "on time step 6777 (time=135.54), 0.0017222 s/step\n", + "field decay(t = 150.06): 0.45529339380861855 / 0.45529339380861855 = 1.0\n", + "on time step 9069 (time=181.38), 0.0017456 s/step\n", + "field decay(t = 200.08): 1.568246280713512 / 1.568246280713512 = 1.0\n", + "on time step 11341 (time=226.82), 0.00176058 s/step\n", + "field decay(t = 250.1): 1.2414144439515642 / 1.568246280713512 = 0.791594062245601\n", + "on time step 13493 (time=269.86), 0.00185906 s/step\n", + "field decay(t = 300.12): 0.031139203524192197 / 1.568246280713512 = 0.01985606719247225\n", + "on time step 15734 (time=314.68), 0.00178549 s/step\n", + "field decay(t = 350.14): 8.886463070035761e-06 / 1.568246280713512 = 5.666497143543454e-06\n", + "on time step 18069 (time=361.38), 0.00171313 s/step\n", + "field decay(t = 400.16): 2.7650862223635545e-11 / 1.568246280713512 = 1.7631709103148714e-11\n", + "run 0 finished at t = 400.16 (20008 timesteps)\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00128293 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 36 x 12 x 0 with resolution 25\n", + " prism, center = (0,0,5e+19)\n", + " height 1e+20, axis (0,0,1), 8 vertices:\n", + " (-19,0.5,0)\n", + " (-2,0.5,0)\n", + " (2,1,0)\n", + " (19,1,0)\n", + " (19,-1,0)\n", + " (2,-1,0)\n", + " (-2,-0.5,0)\n", + " (-19,-0.5,0)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + "time for set_epsilon = 0.755732 s\n", + "-----------\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "on time step 1883 (time=37.66), 0.00212453 s/step\n", + "field decay(t = 50.02): 7.569671091769381e-08 / 7.569671091769381e-08 = 1.0\n", + "on time step 4139 (time=82.78), 0.00177353 s/step\n", + "field decay(t = 100.04): 0.0017391263700697627 / 0.0017391263700697627 = 1.0\n", + "on time step 6309 (time=126.18), 0.00184352 s/step\n", + "field decay(t = 150.06): 0.453841743379235 / 0.453841743379235 = 1.0\n", + "on time step 8560 (time=171.2), 0.0017771 s/step\n", + "field decay(t = 200.08): 1.5555871736720326 / 1.5555871736720326 = 1.0\n", + "on time step 10769 (time=215.38), 0.0018108 s/step\n", + "field decay(t = 250.1): 1.2260941054634609 / 1.5555871736720326 = 0.7881873328700771\n", + "on time step 13056 (time=261.12), 0.00174983 s/step\n", + "field decay(t = 300.12): 0.02968036484241766 / 1.5555871736720326 = 0.01907984672588669\n", + "on time step 15299 (time=305.98), 0.00178368 s/step\n", + "field decay(t = 350.14): 7.2347918961150245e-06 / 1.5555871736720326 = 4.650843114781525e-06\n", + "on time step 17653 (time=353.06), 0.00169945 s/step\n", + "on time step 19969 (time=399.38), 0.00172778 s/step\n", + "field decay(t = 400.16): 8.407569561060486e-11 / 1.5555871736720326 = 5.4047562896870926e-11\n", + "run 0 finished at t = 400.16 (20008 timesteps)\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 9 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", + "refl:, 4, 0.00004024, 0.00004337\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00212097 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 40 x 12 x 0 with resolution 25\n", + " prism, center = (0,0,5e+19)\n", + " height 1e+20, axis (0,0,1), 4 vertices:\n", + " (-21,0.5,0)\n", + " (21,0.5,0)\n", + " (21,-0.5,0)\n", + " (-21,-0.5,0)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + "time for set_epsilon = 0.565803 s\n", + "-----------\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "on time step 1905 (time=38.1), 0.00210044 s/step\n", + "field decay(t = 50.02): 7.571674211765524e-08 / 7.571674211765524e-08 = 1.0\n", + "on time step 4047 (time=80.94), 0.00186782 s/step\n", + "field decay(t = 100.04): 0.0017406599773711756 / 0.0017406599773711756 = 1.0\n", + "on time step 6216 (time=124.32), 0.0018447 s/step\n", + "field decay(t = 150.06): 0.4552933938912802 / 0.4552933938912802 = 1.0\n", + "on time step 8357 (time=167.14), 0.00186832 s/step\n", + "field decay(t = 200.08): 1.5682462811448037 / 1.5682462811448037 = 1.0\n", + "on time step 10518 (time=210.36), 0.00185132 s/step\n", + "field decay(t = 250.1): 1.2414144444964543 / 1.5682462811448037 = 0.7915940623753525\n", + "on time step 12633 (time=252.66), 0.00189134 s/step\n", + "on time step 14831 (time=296.62), 0.00182053 s/step\n", + "field decay(t = 300.12): 0.03113920465258198 / 1.5682462811448037 = 0.01985606790653486\n", + "on time step 17006 (time=340.12), 0.00183974 s/step\n", + "field decay(t = 350.14): 8.886516913937694e-06 / 1.5682462811448037 = 5.666531475815538e-06\n", + "on time step 19110 (time=382.2), 0.0019017 s/step\n", + "field decay(t = 400.16): 2.7625870947574233e-11 / 1.5682462811448037 = 1.7615773287476016e-11\n", + "run 0 finished at t = 400.16 (20008 timesteps)\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.000883102 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 40 x 12 x 0 with resolution 25\n", + " prism, center = (0,0,5e+19)\n", + " height 1e+20, axis (0,0,1), 8 vertices:\n", + " (-21,0.5,0)\n", + " (-4,0.5,0)\n", + " (4,1,0)\n", + " (21,1,0)\n", + " (21,-1,0)\n", + " (4,-1,0)\n", + " (-4,-0.5,0)\n", + " (-21,-0.5,0)\n", + " dielectric constant epsilon diagonal = (12,12,12)\n", + "time for set_epsilon = 0.863971 s\n", + "-----------\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "on time step 1966 (time=39.32), 0.00203556 s/step\n", + "field decay(t = 50.02): 7.568972576123606e-08 / 7.568972576123606e-08 = 1.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "on time step 4042 (time=80.84), 0.00192733 s/step\n", + "field decay(t = 100.04): 0.0017392532865220022 / 0.0017392532865220022 = 1.0\n", + "on time step 6101 (time=122.02), 0.00194315 s/step\n", + "field decay(t = 150.06): 0.4541781140774447 / 0.4541781140774447 = 1.0\n", + "on time step 8223 (time=164.46), 0.00188516 s/step\n", + "field decay(t = 200.08): 1.5597082712382018 / 1.5597082712382018 = 1.0\n", + "on time step 10355 (time=207.1), 0.00187692 s/step\n", + "on time step 12479 (time=249.58), 0.00188403 s/step\n", + "field decay(t = 250.1): 1.231655150453248 / 1.5597082712382018 = 0.7896702051053925\n", + "on time step 14552 (time=291.04), 0.00193036 s/step\n", + "field decay(t = 300.12): 0.030221199579030786 / 1.5597082712382018 = 0.019376187288562084\n", + "on time step 16622 (time=332.44), 0.00193299 s/step\n", + "field decay(t = 350.14): 6.594515383229233e-06 / 1.5597082712382018 = 4.228044118785151e-06\n", + "on time step 18688 (time=373.76), 0.00193665 s/step\n", + "field decay(t = 400.16): 3.0985772121881132e-09 / 1.5597082712382018 = 1.986638956353199e-09\n", + "on time step 20781 (time=415.62), 0.00191162 s/step\n", + "field decay(t = 450.18): 2.4580489315863303e-13 / 1.5597082712382018 = 1.5759671067429584e-13\n", + "run 0 finished at t = 450.18 (22509 timesteps)\n", + "MPB solved for omega_1(0.519356,0,0) = 0.176186 after 10 iters\n", + "MPB solved for omega_1(0.424206,0,0) = 0.149878 after 7 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 5 iters\n", + "MPB solved for omega_1(0.424377,0,0) = 0.149925 after 1 iters\n", + "Dominant planewave for band 1: (0.424377,-0.000000,0.000000)\n", + "refl:, 8, 0.00001847, 0.00001902\n" + ] + } + ], "source": [ "import meep as mp\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 25 # pixels/μm\n", + "resolution = 25 # pixels/μm\n", "\n", - "w1 = 1.0 # width of waveguide 1\n", - "w2 = 2.0 # width of waveguide 2\n", - "Lw = 10.0 # length of waveguides 1 and 2\n", + "w1 = 1.0 # width of waveguide 1\n", + "w2 = 2.0 # width of waveguide 2\n", + "Lw = 10.0 # length of waveguides 1 and 2\n", "\n", "# lengths of waveguide taper\n", "Lts = [2**m for m in range(4)]\n", "\n", - "dair = 3.0 # length of air region\n", - "dpml_x = 6.0 # length of PML in x direction\n", - "dpml_y = 2.0 # length of PML in y direction\n", + "dair = 3.0 # length of air region\n", + "dpml_x = 6.0 # length of PML in x direction\n", + "dpml_y = 2.0 # length of PML in y direction\n", "\n", - "sy = dpml_y + dair + w2 + dair + dpml_y\n", + "sy = dpml_y+dair+w2+dair+dpml_y\n", "\n", "Si = mp.Medium(epsilon=12.0)\n", "\n", - "boundary_layers = [mp.PML(dpml_x, direction=mp.X), mp.PML(dpml_y, direction=mp.Y)]\n", + "boundary_layers = [mp.PML(dpml_x,direction=mp.X),\n", + " mp.PML(dpml_y,direction=mp.Y)]\n", "\n", - "lcen = 6.67 # mode wavelength\n", - "fcen = 1 / lcen # mode frequency\n", + "lcen = 6.67 # mode wavelength\n", + "fcen = 1/lcen # mode frequency\n", "\n", "symmetries = [mp.Mirror(mp.Y)]\n", "\n", @@ -52,45 +425,35 @@ "R_flux = []\n", "\n", "for Lt in Lts:\n", - " sx = dpml_x + Lw + Lt + Lw + dpml_x\n", - " cell_size = mp.Vector3(sx, sy, 0)\n", - "\n", - " src_pt = mp.Vector3(-0.5 * sx + dpml_x + 0.2 * Lw)\n", - " sources = [\n", - " mp.EigenModeSource(\n", - " src=mp.GaussianSource(fcen, fwidth=0.2 * fcen),\n", - " center=src_pt,\n", - " size=mp.Vector3(y=sy - 2 * dpml_y),\n", - " eig_match_freq=True,\n", - " eig_parity=mp.ODD_Z + mp.EVEN_Y,\n", - " )\n", - " ]\n", + " sx = dpml_x+Lw+Lt+Lw+dpml_x\n", + " cell_size = mp.Vector3(sx,sy,0)\n", + "\n", + " src_pt = mp.Vector3(-0.5*sx+dpml_x+0.2*Lw)\n", + " sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=0.2*fcen),\n", + " center=src_pt,\n", + " size=mp.Vector3(y=sy-2*dpml_y),\n", + " eig_match_freq=True,\n", + " eig_parity=mp.ODD_Z+mp.EVEN_Y)]\n", "\n", " # straight waveguide\n", - " vertices = [\n", - " mp.Vector3(-0.5 * sx - 1, 0.5 * w1),\n", - " mp.Vector3(0.5 * sx + 1, 0.5 * w1),\n", - " mp.Vector3(0.5 * sx + 1, -0.5 * w1),\n", - " mp.Vector3(-0.5 * sx - 1, -0.5 * w1),\n", - " ]\n", - "\n", - " sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=boundary_layers,\n", - " geometry=[mp.Prism(vertices, height=mp.inf, material=Si)],\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - " )\n", - "\n", - " mon_pt = mp.Vector3(-0.5 * sx + dpml_x + 0.7 * Lw)\n", - " flux = sim.add_flux(\n", - " fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy - 2 * dpml_y))\n", - " )\n", - "\n", - " sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))\n", - "\n", - " res = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y)\n", + " vertices = [mp.Vector3(-0.5*sx-1,0.5*w1),\n", + " mp.Vector3(0.5*sx+1,0.5*w1),\n", + " mp.Vector3(0.5*sx+1,-0.5*w1),\n", + " mp.Vector3(-0.5*sx-1,-0.5*w1)]\n", + "\n", + " sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=boundary_layers,\n", + " geometry=[mp.Prism(vertices,height=mp.inf,material=Si)],\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + " mon_pt = mp.Vector3(-0.5*sx+dpml_x+0.7*Lw)\n", + " flux = sim.add_flux(fcen,0,1,mp.FluxRegion(center=mon_pt,size=mp.Vector3(y=sy-2*dpml_y)))\n", + "\n", + " sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mon_pt,1e-9))\n", + "\n", + " res = sim.get_eigenmode_coefficients(flux,[1],eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", " incident_coeffs = res.alpha\n", " incident_flux = mp.get_fluxes(flux)\n", " incident_flux_data = sim.get_flux_data(flux)\n", @@ -98,42 +461,34 @@ " sim.reset_meep()\n", "\n", " # linear taper\n", - " vertices = [\n", - " mp.Vector3(-0.5 * sx - 1, 0.5 * w1),\n", - " mp.Vector3(-0.5 * Lt, 0.5 * w1),\n", - " mp.Vector3(0.5 * Lt, 0.5 * w2),\n", - " mp.Vector3(0.5 * sx + 1, 0.5 * w2),\n", - " mp.Vector3(0.5 * sx + 1, -0.5 * w2),\n", - " mp.Vector3(0.5 * Lt, -0.5 * w2),\n", - " mp.Vector3(-0.5 * Lt, -0.5 * w1),\n", - " mp.Vector3(-0.5 * sx - 1, -0.5 * w1),\n", - " ]\n", - "\n", - " sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=boundary_layers,\n", - " geometry=[mp.Prism(vertices, height=mp.inf, material=Si)],\n", - " sources=sources,\n", - " symmetries=symmetries,\n", - " )\n", - "\n", - " flux = sim.add_flux(\n", - " fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy - 2 * dpml_y))\n", - " )\n", - " sim.load_minus_flux_data(flux, incident_flux_data)\n", - "\n", - " sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9))\n", - "\n", - " res2 = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y)\n", + " vertices = [mp.Vector3(-0.5*sx-1,0.5*w1),\n", + " mp.Vector3(-0.5*Lt,0.5*w1),\n", + " mp.Vector3(0.5*Lt,0.5*w2),\n", + " mp.Vector3(0.5*sx+1,0.5*w2),\n", + " mp.Vector3(0.5*sx+1,-0.5*w2),\n", + " mp.Vector3(0.5*Lt,-0.5*w2),\n", + " mp.Vector3(-0.5*Lt,-0.5*w1),\n", + " mp.Vector3(-0.5*sx-1,-0.5*w1)]\n", + "\n", + " sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=boundary_layers,\n", + " geometry=[mp.Prism(vertices,height=mp.inf,material=Si)],\n", + " sources=sources,\n", + " symmetries=symmetries)\n", + "\n", + " flux = sim.add_flux(fcen,0,1,mp.FluxRegion(center=mon_pt,size=mp.Vector3(y=sy-2*dpml_y)))\n", + " sim.load_minus_flux_data(flux,incident_flux_data)\n", + "\n", + " sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mon_pt,1e-9))\n", + "\n", + " res2 = sim.get_eigenmode_coefficients(flux,[1],eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", " taper_coeffs = res2.alpha\n", " taper_flux = mp.get_fluxes(flux)\n", "\n", - " R_coeffs.append(\n", - " abs(taper_coeffs[0, 0, 1]) ** 2 / abs(incident_coeffs[0, 0, 0]) ** 2\n", - " )\n", - " R_flux.append(-taper_flux[0] / incident_flux[0])\n", - " print(\"refl:, {}, {:.8f}, {:.8f}\".format(Lt, R_coeffs[-1], R_flux[-1]))" + " R_coeffs.append(abs(taper_coeffs[0,0,1])**2/abs(incident_coeffs[0,0,0])**2)\n", + " R_flux.append(-taper_flux[0]/incident_flux[0])\n", + " print(\"refl:, {}, {:.8f}, {:.8f}\".format(Lt,R_coeffs[-1],R_flux[-1]))" ] }, { @@ -145,19 +500,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=200)\n", - "plt.loglog(Lts, R_coeffs, \"bo-\", label=\"mode decomposition\")\n", - "plt.loglog(Lts, R_flux, \"ro-\", label=\"Poynting flux\")\n", - "plt.loglog(\n", - " Lts, [0.005 / Lt**2 for Lt in Lts], \"k-\", label=r\"quadratic reference (1/Lt$^2$)\"\n", - ")\n", - "plt.legend(loc=\"upper right\")\n", - "plt.xlabel(\"taper length Lt (μm)\")\n", - "plt.ylabel(\"reflectance\")\n", + "plt.loglog(Lts,R_coeffs,'bo-',label='mode decomposition')\n", + "plt.loglog(Lts,R_flux,'ro-',label='Poynting flux')\n", + "plt.loglog(Lts,[0.005/Lt**2 for Lt in Lts],'k-',label=r'quadratic reference (1/Lt$^2$)')\n", + "plt.legend(loc='upper right')\n", + "plt.xlabel('taper length Lt (μm)')\n", + "plt.ylabel('reflectance')\n", "plt.show()" ] }, diff --git a/python/examples/oblique-planewave.ipynb b/python/examples/oblique-planewave.ipynb index b889a4151..75efcdad6 100644 --- a/python/examples/oblique-planewave.ipynb +++ b/python/examples/oblique-planewave.ipynb @@ -23,9 +23,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -41,50 +49,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "def run_sim(rot_angle=0):\n", + "def run_sim(rot_angle = 0):\n", "\n", - " resolution = 50 # pixels/μm\n", + " resolution = 50 # pixels/μm\n", "\n", - " cell_size = mp.Vector3(14, 10, 0)\n", + " cell_size = mp.Vector3(14,10,0)\n", "\n", - " pml_layers = [mp.PML(thickness=2, direction=mp.X)]\n", + " pml_layers = [mp.PML(thickness=2,direction=mp.X)]\n", "\n", - " fsrc = 1.0 # frequency of planewave (wavelength = 1/fsrc)\n", + " fsrc = 1.0 # frequency of planewave (wavelength = 1/fsrc)\n", "\n", - " n = 1.5 # refractive index of homogeneous material\n", + " n = 1.5 # refractive index of homogeneous material\n", " default_material = mp.Medium(index=n)\n", "\n", - " k_point = mp.Vector3(fsrc * n).rotate(mp.Vector3(z=1), rot_angle)\n", + " k_point = mp.Vector3(fsrc*n).rotate(mp.Vector3(z=1), rot_angle)\n", "\n", - " sources = [\n", - " mp.EigenModeSource(\n", - " src=mp.ContinuousSource(fsrc),\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(y=10),\n", - " direction=mp.AUTOMATIC if rot_angle == 0 else mp.NO_DIRECTION,\n", - " eig_kpoint=k_point,\n", - " eig_band=1,\n", - " eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", - " eig_match_freq=True,\n", - " )\n", - " ]\n", + " sources = [mp.EigenModeSource(src=mp.ContinuousSource(fsrc),\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(y=10),\n", + " direction=mp.AUTOMATIC if rot_angle == 0 else mp.NO_DIRECTION,\n", + " eig_kpoint=k_point,\n", + " eig_band=1,\n", + " eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", + " eig_match_freq=True)]\n", "\n", - " sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " resolution=resolution,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=k_point,\n", - " default_material=default_material,\n", - " symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [],\n", - " )\n", + " sim = mp.Simulation(cell_size=cell_size,\n", + " resolution=resolution,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=k_point,\n", + " default_material=default_material,\n", + " symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [])\n", "\n", " sim.run(until=100)\n", - "\n", + " \n", " plt.figure(dpi=100)\n", " sim.plot2D(fields=mp.Ez)\n", " plt.show()" @@ -99,11 +101,125 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "Meep: using complex fields.\n", + "Meep progress: 11.120000000000001/100.0 = 11.1% done in 4.0s, 32.0s to go\n", + "Meep progress: 22.75/100.0 = 22.8% done in 8.0s, 27.2s to go\n", + "Meep progress: 32.68/100.0 = 32.7% done in 12.0s, 24.7s to go\n", + "Meep progress: 41.97/100.0 = 42.0% done in 16.0s, 22.1s to go\n", + "Meep progress: 51.26/100.0 = 51.3% done in 20.0s, 19.0s to go\n", + "Meep progress: 60.370000000000005/100.0 = 60.4% done in 24.0s, 15.8s to go\n", + "Meep progress: 69.64/100.0 = 69.6% done in 28.0s, 12.2s to go\n", + "Meep progress: 78.49/100.0 = 78.5% done in 32.0s, 8.8s to go\n", + "Meep progress: 86.27/100.0 = 86.3% done in 36.0s, 5.7s to go\n", + "Meep progress: 94.66/100.0 = 94.7% done in 40.0s, 2.3s to go\n", + "run 0 finished at t = 100.0 (10000 timesteps)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "Meep: using complex fields.\n", + "Meep progress: 3.87/100.0 = 3.9% done in 4.0s, 99.5s to go\n", + "Meep progress: 8.57/100.0 = 8.6% done in 8.0s, 85.4s to go\n", + "Meep progress: 14.43/100.0 = 14.4% done in 12.0s, 71.2s to go\n", + "Meep progress: 19.23/100.0 = 19.2% done in 16.0s, 67.3s to go\n", + "Meep progress: 24.62/100.0 = 24.6% done in 20.0s, 61.3s to go\n", + "Meep progress: 30.2/100.0 = 30.2% done in 24.0s, 55.5s to go\n", + "Meep progress: 36.06/100.0 = 36.1% done in 28.0s, 49.7s to go\n", + "Meep progress: 41.77/100.0 = 41.8% done in 32.0s, 44.7s to go\n", + "Meep progress: 46.78/100.0 = 46.8% done in 36.0s, 41.0s to go\n", + "Meep progress: 52.68/100.0 = 52.7% done in 40.0s, 36.0s to go\n", + "Meep progress: 58.67/100.0 = 58.7% done in 44.1s, 31.0s to go\n", + "Meep progress: 64.54/100.0 = 64.5% done in 48.1s, 26.4s to go\n", + "Meep progress: 70.48/100.0 = 70.5% done in 52.1s, 21.8s to go\n", + "Meep progress: 76.5/100.0 = 76.5% done in 56.1s, 17.2s to go\n", + "Meep progress: 81.32000000000001/100.0 = 81.3% done in 60.1s, 13.8s to go\n", + "Meep progress: 86.11/100.0 = 86.1% done in 64.1s, 10.3s to go\n", + "Meep progress: 90.9/100.0 = 90.9% done in 68.1s, 6.8s to go\n", + "Meep progress: 96.07000000000001/100.0 = 96.1% done in 72.1s, 2.9s to go\n", + "run 0 finished at t = 100.0 (10000 timesteps)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "Meep: using complex fields.\n", + "Meep progress: 5.2700000000000005/100.0 = 5.3% done in 4.0s, 72.0s to go\n", + "Meep progress: 10.35/100.0 = 10.3% done in 8.0s, 69.4s to go\n", + "Meep progress: 15.610000000000001/100.0 = 15.6% done in 12.0s, 64.9s to go\n", + "Meep progress: 21.490000000000002/100.0 = 21.5% done in 16.0s, 58.5s to go\n", + "Meep progress: 27.37/100.0 = 27.4% done in 20.0s, 53.1s to go\n", + "Meep progress: 33.26/100.0 = 33.3% done in 24.0s, 48.2s to go\n", + "Meep progress: 38.52/100.0 = 38.5% done in 28.0s, 44.7s to go\n", + "Meep progress: 43.32/100.0 = 43.3% done in 32.0s, 41.9s to go\n", + "Meep progress: 47.58/100.0 = 47.6% done in 36.0s, 39.7s to go\n", + "Meep progress: 51.89/100.0 = 51.9% done in 40.0s, 37.1s to go\n", + "Meep progress: 57.2/100.0 = 57.2% done in 44.0s, 33.0s to go\n", + "Meep progress: 63.230000000000004/100.0 = 63.2% done in 48.0s, 27.9s to go\n", + "Meep progress: 67.82000000000001/100.0 = 67.8% done in 52.0s, 24.7s to go\n", + "Meep progress: 72.65/100.0 = 72.7% done in 56.0s, 21.1s to go\n", + "Meep progress: 78.24/100.0 = 78.2% done in 60.1s, 16.7s to go\n", + "Meep progress: 83.16/100.0 = 83.2% done in 64.1s, 13.0s to go\n", + "Meep progress: 87.56/100.0 = 87.6% done in 68.1s, 9.7s to go\n", + "Meep progress: 92.73/100.0 = 92.7% done in 72.1s, 5.7s to go\n", + "Meep progress: 98.45/100.0 = 98.5% done in 76.1s, 1.2s to go\n", + "run 0 finished at t = 100.0 (10000 timesteps)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "for rot_angle in np.radians([0, 20, 40]):\n", + "for rot_angle in np.radians([0,20,40]):\n", " run_sim(rot_angle)" ] }, diff --git a/python/examples/oblique-source.ipynb b/python/examples/oblique-source.ipynb index 3974ac34e..96dec7d27 100644 --- a/python/examples/oblique-source.ipynb +++ b/python/examples/oblique-source.ipynb @@ -27,9 +27,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -50,47 +58,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "resolution = 20 # pixels/μm\n", + "resolution = 20 # pixels/μm\n", "\n", - "cell_size = mp.Vector3(14, 14)\n", + "cell_size = mp.Vector3(14,14)\n", "\n", "pml_layers = [mp.PML(thickness=2)]\n", "\n", "# rotation angle (in degrees) of waveguide, counter clockwise (CCW) around z-axis\n", "rot_angle = np.radians(20)\n", "\n", - "geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(mp.inf, 1, mp.inf),\n", - " e1=mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle),\n", - " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle),\n", - " material=mp.Medium(epsilon=12),\n", - " )\n", - "]\n", + "geometry = [mp.Block(center=mp.Vector3(),\n", + " size=mp.Vector3(mp.inf,1,mp.inf),\n", + " e1 = mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle),\n", + " e2 = mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle),\n", + " material=mp.Medium(epsilon=12))]\n", "\n", - "fsrc = 0.15 # frequency of eigenmode or constant-amplitude source\n", + "fsrc = 0.15 # frequency of eigenmode or constant-amplitude source\n", "\n", - "sources = [\n", - " mp.Source(\n", - " src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc),\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(y=2),\n", - " component=mp.Ez,\n", - " )\n", - "]\n", + "sources = [mp.Source(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc),\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(y=2),\n", + " component=mp.Ez)]\n", "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " resolution=resolution,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " geometry=geometry,\n", - ")" + "sim = mp.Simulation(cell_size=cell_size,\n", + " resolution=resolution,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " geometry=geometry)" ] }, { @@ -102,9 +100,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1,1e+20)\n", + " axes (0.939693,0.34202,0), (-0.34202,0.939693,0), (0,0,1)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "f = plt.figure(dpi=100)\n", "sim.plot2D(ax=f.gca())\n", @@ -120,13 +142,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 46.95/383.3333435058594 = 12.2% done in 4.0s, 28.7s to go\n", + "Meep progress: 127.0/383.3333435058594 = 33.1% done in 8.0s, 16.2s to go\n", + "Meep progress: 210.425/383.3333435058594 = 54.9% done in 12.0s, 9.9s to go\n", + "Meep progress: 306.75/383.3333435058594 = 80.0% done in 16.0s, 4.0s to go\n", + "Normalizing field data...\n", + "run 0 finished at t = 383.35 (15334 timesteps)\n" + ] + } + ], "source": [ "f = plt.figure(dpi=100)\n", - "animate = mp.Animate2D(sim, mp.Ez, f=f, normalize=True)\n", - "sim.run(mp.at_every(1, animate), until_after_sources=50)\n", + "animate = mp.Animate2D(sim,mp.Ez,f=f,normalize=True)\n", + "sim.run(mp.at_every(1,animate),until_after_sources=50)\n", "plt.close()" ] }, @@ -139,12 +174,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating MP4...\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "filename = \"media/oblique-source-normal.mp4\"\n", - "animate.to_mp4(10, filename)\n", + "filename = 'media/oblique-source-normal.mp4'\n", + "animate.to_mp4(10,filename)\n", "Video(filename)" ] }, @@ -161,38 +219,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "kx = 0.4 # initial guess for wavevector in x-direction of eigenmode\n", - "kpoint = mp.Vector3(kx).rotate(\n", - " mp.Vector3(z=1), rot_angle\n", - ") # Rotate the vector by the specified amount\n", + "kx = 0.4 # initial guess for wavevector in x-direction of eigenmode\n", + "kpoint = mp.Vector3(kx).rotate(mp.Vector3(z=1), rot_angle) # Rotate the vector by the specified amount\n", "\n", - "bnum = 1 # band number of eigenmode\n", + "bnum = 1 # band number of eigenmode\n", "\n", - "sources = [\n", - " mp.EigenModeSource(\n", - " src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc),\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(y=14),\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " eig_band=bnum,\n", - " eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", - " eig_match_freq=True,\n", - " )\n", - "]\n", + "sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc),\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(y=14),\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " eig_band=bnum,\n", + " eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", + " eig_match_freq=True)]\n", "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " resolution=resolution,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " geometry=geometry,\n", - " symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [],\n", - ")" + "sim = mp.Simulation(cell_size=cell_size,\n", + " resolution=resolution,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " geometry=geometry,\n", + " symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [])" ] }, { @@ -206,24 +256,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1,1e+20)\n", + " axes (0.939693,0.34202,0), (-0.34202,0.939693,0), (0,0,1)\n", + "Meep progress: 51.475/383.3333435058594 = 13.4% done in 4.0s, 25.8s to go\n", + "Meep progress: 125.7/383.3333435058594 = 32.8% done in 8.0s, 16.4s to go\n", + "Meep progress: 196.625/383.3333435058594 = 51.3% done in 12.0s, 11.4s to go\n", + "Meep progress: 275.02500000000003/383.3333435058594 = 71.7% done in 16.0s, 6.3s to go\n", + "Meep progress: 351.475/383.3333435058594 = 91.7% done in 20.0s, 1.8s to go\n", + "Normalizing field data...\n", + "run 0 finished at t = 383.35 (15334 timesteps)\n" + ] + } + ], "source": [ "f = plt.figure(dpi=100)\n", - "animate = mp.Animate2D(sim, mp.Ez, f=f, normalize=True)\n", - "sim.run(mp.at_every(1, animate), until_after_sources=50)\n", + "animate = mp.Animate2D(sim,mp.Ez,f=f,normalize=True)\n", + "sim.run(mp.at_every(1,animate),until_after_sources=50)\n", "plt.close()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating MP4...\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "filename = \"media/oblique-source-eig.mp4\"\n", - "animate.to_mp4(10, filename)\n", + "filename = 'media/oblique-source-eig.mp4'\n", + "animate.to_mp4(10,filename)\n", "Video(filename)" ] }, @@ -245,62 +337,130 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Meep progress: 84.72500000000001/383.3333435058594 = 22.1% done in 4.0s, 14.1s to go\n", + "Meep progress: 180.65/383.3333435058594 = 47.1% done in 8.0s, 9.0s to go\n", + "Meep progress: 286.125/383.3333435058594 = 74.6% done in 12.0s, 4.1s to go\n", + "run 0 finished at t = 383.35 (15334 timesteps)\n", + "flux:, 1091.653061, 941.670245\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1,1e+20)\n", + " axes (0.939693,0.34202,0), (-0.34202,0.939693,0), (0,0,1)\n", + "Meep progress: 107.4/383.3333435058594 = 28.0% done in 4.0s, 10.3s to go\n", + "Meep progress: 214.875/383.3333435058594 = 56.1% done in 8.0s, 6.3s to go\n", + "Meep progress: 323.375/383.3333435058594 = 84.4% done in 12.0s, 2.2s to go\n", + "run 0 finished at t = 383.35 (15334 timesteps)\n", + "flux:, 1110.194293, 1111.953628\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1,1e+20)\n", + " axes (0.766044,0.642788,0), (-0.642788,0.766044,0), (0,0,1)\n", + "Meep progress: 107.575/383.3333435058594 = 28.1% done in 4.0s, 10.3s to go\n", + "Meep progress: 207.60000000000002/383.3333435058594 = 54.2% done in 8.0s, 6.8s to go\n", + "Meep progress: 309.625/383.3333435058594 = 80.8% done in 12.0s, 2.9s to go\n", + "run 0 finished at t = 383.35 (15334 timesteps)\n", + "flux:, 1087.626286, 1041.967491\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "for rot_angle in np.radians([0, 20, 40]):\n", - "\n", + " \n", " resolution = 20\n", "\n", - " geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(mp.inf, 1, mp.inf),\n", - " e1=mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle),\n", - " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle),\n", - " material=mp.Medium(epsilon=12),\n", - " )\n", - " ]\n", + " geometry = [mp.Block(center=mp.Vector3(),\n", + " size=mp.Vector3(mp.inf,1,mp.inf),\n", + " e1=mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle),\n", + " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle),\n", + " material=mp.Medium(epsilon=12))]\n", "\n", - " sources = [\n", - " mp.EigenModeSource(\n", - " src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc),\n", - " center=mp.Vector3(),\n", - " size=mp.Vector3(y=14),\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " eig_band=bnum,\n", - " eig_parity=mp.ODD_Z,\n", - " eig_match_freq=True,\n", - " )\n", - " ]\n", + " sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc),\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(y=14),\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " eig_band=bnum,\n", + " eig_parity=mp.ODD_Z,\n", + " eig_match_freq=True)]\n", "\n", - " sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " resolution=resolution,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " geometry=geometry,\n", - " )\n", + " sim = mp.Simulation(cell_size=cell_size,\n", + " resolution=resolution,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " geometry=geometry)\n", "\n", - " tran = sim.add_flux(\n", - " fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5), size=mp.Vector3(y=14))\n", - " )\n", - " sim.run(until_after_sources=50)\n", - " res = sim.get_eigenmode_coefficients(\n", - " tran,\n", - " [1],\n", - " eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", - " direction=mp.NO_DIRECTION,\n", - " kpoint_func=lambda f, n: kpoint,\n", - " )\n", - " print(\n", - " \"flux:, {:.6f}, {:.6f}\".format(\n", - " mp.get_fluxes(tran)[0], abs(res.alpha[0, 0, 0]) ** 2\n", - " )\n", - " )\n", "\n", + " tran = sim.add_flux(fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5), size=mp.Vector3(y=14)))\n", + " sim.run(until_after_sources=50)\n", + " res = sim.get_eigenmode_coefficients(tran,\n", + " [1],\n", + " eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z,\n", + " direction=mp.NO_DIRECTION,\n", + " kpoint_func=lambda f,n: kpoint)\n", + " print(\"flux:, {:.6f}, {:.6f}\".format(mp.get_fluxes(tran)[0],abs(res.alpha[0,0,0])**2))\n", + " \n", " sim.plot2D(fields=mp.Ez)\n", " plt.show()" ] diff --git a/python/examples/parallel-wvgs-force.ipynb b/python/examples/parallel-wvgs-force.ipynb index 39114272e..32e9eb0d8 100644 --- a/python/examples/parallel-wvgs-force.ipynb +++ b/python/examples/parallel-wvgs-force.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 40 # pixels/μm\n", + "resolution = 40 # pixels/μm\n", "\n", "Si = mp.Medium(index=3.45)\n", "\n", @@ -42,89 +42,67 @@ "\n", "sx = 5\n", "sy = 3\n", - "cell = mp.Vector3(sx + 2 * dpml, sy + 2 * dpml, 0)\n", + "cell = mp.Vector3(sx+2*dpml,sy+2*dpml,0)\n", "\n", - "a = 1.0 # waveguide width/height\n", + "a = 1.0 # waveguide width/height\n", "\n", "k_point = mp.Vector3(z=0.5)\n", "\n", + "def parallel_waveguide(s,xodd):\n", + " geometry = [mp.Block(center=mp.Vector3(-0.5*(s+a)),\n", + " size=mp.Vector3(a,a,mp.inf),\n", + " material=Si),\n", + " mp.Block(center=mp.Vector3(0.5*(s+a)),\n", + " size=mp.Vector3(a,a,mp.inf),\n", + " material=Si)]\n", "\n", - "def parallel_waveguide(s, xodd):\n", - " geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(-0.5 * (s + a)),\n", - " size=mp.Vector3(a, a, mp.inf),\n", - " material=Si,\n", - " ),\n", - " mp.Block(\n", - " center=mp.Vector3(0.5 * (s + a)), size=mp.Vector3(a, a, mp.inf), material=Si\n", - " ),\n", - " ]\n", - "\n", - " symmetries = [mp.Mirror(mp.X, phase=-1 if xodd else 1), mp.Mirror(mp.Y, phase=-1)]\n", - "\n", - " sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " symmetries=symmetries,\n", - " k_point=k_point,\n", - " )\n", + " symmetries = [mp.Mirror(mp.X, phase=-1 if xodd else 1),\n", + " mp.Mirror(mp.Y, phase=-1)]\n", "\n", + " sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " symmetries=symmetries,\n", + " k_point=k_point)\n", + " \n", " sim.init_sim()\n", - " EigenmodeData = sim.get_eigenmode(\n", - " 0.22,\n", - " mp.Z,\n", - " mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx, sy)),\n", - " 2 if xodd else 1,\n", - " k_point,\n", - " match_frequency=False,\n", - " parity=mp.ODD_Y,\n", - " )\n", + " EigenmodeData = sim.get_eigenmode(0.22,\n", + " mp.Z,\n", + " mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx,sy)),\n", + " 2 if xodd else 1,\n", + " k_point,\n", + " match_frequency=False,\n", + " parity=mp.ODD_Y)\n", "\n", " fcen = EigenmodeData.freq\n", " print(\"freq:, {}, {}, {}\".format(\"xodd\" if xodd else \"xeven\", s, fcen))\n", "\n", " sim.reset_meep()\n", "\n", - " eig_sources = [\n", - " mp.EigenModeSource(\n", - " src=mp.GaussianSource(fcen, fwidth=0.1 * fcen),\n", - " size=mp.Vector3(sx, sy),\n", - " center=mp.Vector3(),\n", - " eig_band=2 if xodd else 1,\n", - " eig_kpoint=k_point,\n", - " eig_match_freq=False,\n", - " eig_parity=mp.ODD_Y,\n", - " )\n", - " ]\n", + " eig_sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen, fwidth=0.1*fcen),\n", + " size=mp.Vector3(sx,sy),\n", + " center=mp.Vector3(),\n", + " eig_band=2 if xodd else 1,\n", + " eig_kpoint=k_point,\n", + " eig_match_freq=False,\n", + " eig_parity=mp.ODD_Y)]\n", "\n", " sim.change_sources(eig_sources)\n", "\n", - " flux_reg = mp.FluxRegion(\n", - " direction=mp.Z, center=mp.Vector3(), size=mp.Vector3(sx, sy)\n", - " )\n", + " flux_reg = mp.FluxRegion(direction=mp.Z, center=mp.Vector3(), size=mp.Vector3(sx,sy))\n", " wvg_flux = sim.add_flux(fcen, 0, 1, flux_reg)\n", "\n", - " force_reg1 = mp.ForceRegion(\n", - " mp.Vector3(0.49 * s), direction=mp.X, weight=1, size=mp.Vector3(y=sy)\n", - " )\n", - " force_reg2 = mp.ForceRegion(\n", - " mp.Vector3(0.5 * s + 1.01 * a), direction=mp.X, weight=-1, size=mp.Vector3(y=sy)\n", - " )\n", + " force_reg1 = mp.ForceRegion(mp.Vector3(0.49*s), direction=mp.X, weight=1, size=mp.Vector3(y=sy))\n", + " force_reg2 = mp.ForceRegion(mp.Vector3(0.5*s+1.01*a), direction=mp.X, weight=-1, size=mp.Vector3(y=sy))\n", " wvg_force = sim.add_force(fcen, 0, 1, force_reg1, force_reg2)\n", "\n", " sim.run(until_after_sources=1500)\n", "\n", " flux = mp.get_fluxes(wvg_flux)[0]\n", " force = mp.get_forces(wvg_force)[0]\n", - " print(\n", - " \"data:, {}, {}, {}, {}, {}\".format(\n", - " \"xodd\" if xodd else \"xeven\", s, flux, force, -force / flux\n", - " )\n", - " )\n", - "\n", + " print(\"data:, {}, {}, {}, {}, {}\".format(\"xodd\" if xodd else \"xeven\", s, flux, force, -force/flux))\n", + " \n", " sim.reset_meep()\n", " return flux, force" ] @@ -142,34 +120,2242 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00295496 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.55,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.55,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0137441 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7594150d888346fbb6eab2f3b74120a0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 239.38333333333333/366.6666717529297 = 65.3% done in 4.0s, 2.1s to go\n", + "on time step 14404 (time=240.067), 0.000277733 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.2337849185921515, -1.9695192598601073e-09, 59350757.150949866, 0.5581279688138194, -0.5553187433557634-0.055927836092732594i, 2.543717760177644e-13+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.1, 0.2337849185921515\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00183916 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.55,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.55,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0193331 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.770666) = 0.233421 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.771882) = 0.233785 after 7 iters\n", + "MPB solved for frequency_1(0,0,0.771882) = 0.233785 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.770666) = 0.233421 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.771882) = 0.233785 after 7 iters\n", + "MPB solved for frequency_1(0,0,0.771882) = 0.233785 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "25d74586424d475ba7fd3c46c7af0993", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 211.81666666666666/666.6666717529297 = 31.8% done in 4.0s, 8.6s to go\n", + "on time step 12744 (time=212.4), 0.000313893 s/step\n", + "Meep progress: 438.3333333333333/666.6666717529297 = 65.7% done in 8.0s, 4.2s to go\n", + "on time step 26339 (time=438.983), 0.000294248 s/step\n", + "Meep progress: 660.8666666666667/666.6666717529297 = 99.1% done in 12.0s, 0.1s to go\n", + "on time step 39695 (time=661.583), 0.000299507 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00133109 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.55,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.55,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0117002 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b4da8f0d9e514cd8973e7a86f36aa83a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 246.28333333333333/366.6666717529297 = 67.2% done in 4.0s, 2.0s to go\n", + "on time step 14810 (time=246.833), 0.000270096 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.21436005656524434, 2.1977416545924374e-08, -4876825.629557369, 1.0437686932994705, -0.6908642657795828-0.7824063211534797i, 3.332567959460228e-13+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.1, 0.21436005656524434\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00119805 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.55,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.55,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0203319 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.706409) = 0.214152 after 11 iters\n", + "MPB solved for frequency_1(0,0,0.707103) = 0.21436 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.707103) = 0.21436 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.706409) = 0.214152 after 11 iters\n", + "MPB solved for frequency_1(0,0,0.707103) = 0.21436 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.707103) = 0.21436 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "133c2f48e1d04fcc94f8ba14ae285e83", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 83.76666666666667/666.6666717529297 = 12.6% done in 4.0s, 27.8s to go\n", + "on time step 5044 (time=84.0667), 0.000793118 s/step\n", + "Meep progress: 168.71666666666667/666.6666717529297 = 25.3% done in 8.0s, 23.6s to go\n", + "on time step 10144 (time=169.067), 0.000784439 s/step\n", + "Meep progress: 255.01666666666665/666.6666717529297 = 38.3% done in 12.0s, 19.4s to go\n", + "on time step 15324 (time=255.4), 0.000772402 s/step\n", + "Meep progress: 340.3666666666667/666.6666717529297 = 51.1% done in 16.0s, 15.3s to go\n", + "on time step 20446 (time=340.767), 0.000780987 s/step\n", + "Meep progress: 424.3833333333333/666.6666717529297 = 63.7% done in 20.0s, 11.4s to go\n", + "on time step 25484 (time=424.733), 0.00079398 s/step\n", + "Meep progress: 509.8/666.6666717529297 = 76.5% done in 24.0s, 7.4s to go\n", + "on time step 30615 (time=510.25), 0.00077967 s/step\n", + "Meep progress: 594.2/666.6666717529297 = 89.1% done in 28.0s, 3.4s to go\n", + "on time step 35680 (time=594.667), 0.000789747 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00357318 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.6,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.6,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0155571 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c6edcf731c7e4156999f724734815579", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 97.31666666666666/366.6666717529297 = 26.5% done in 4.0s, 11.1s to go\n", + "on time step 5854 (time=97.5667), 0.000683346 s/step\n", + "Meep progress: 195.31666666666666/366.6666717529297 = 53.3% done in 8.0s, 7.0s to go\n", + "on time step 11733 (time=195.55), 0.000680444 s/step\n", + "Meep progress: 290.3666666666667/366.6666717529297 = 79.2% done in 12.0s, 3.2s to go\n", + "on time step 17439 (time=290.65), 0.000701091 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.2321007699342635, -6.589441383742643e-10, 176115664.75642273, 0.7050710049152503, -0.3914039769859229-0.5864537908239694i, 2.563795272483861e-13+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.2, 0.2321007699342635\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00154901 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.6,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.6,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0192392 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.763979) = 0.231754 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.765136) = 0.232101 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.765136) = 0.232101 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.763979) = 0.231754 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.765136) = 0.232101 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.765136) = 0.232101 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ae454de372884127a593b61a598bd379", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 83.15/666.6666717529297 = 12.5% done in 4.0s, 28.1s to go\n", + "on time step 5003 (time=83.3833), 0.000799551 s/step\n", + "Meep progress: 168.0/666.6666717529297 = 25.2% done in 8.0s, 23.7s to go\n", + "on time step 10096 (time=168.267), 0.000785392 s/step\n", + "Meep progress: 252.48333333333332/666.6666717529297 = 37.9% done in 12.0s, 19.7s to go\n", + "on time step 15165 (time=252.75), 0.00078922 s/step\n", + "Meep progress: 336.6166666666667/666.6666717529297 = 50.5% done in 16.0s, 15.7s to go\n", + "on time step 20213 (time=336.883), 0.000792426 s/step\n", + "Meep progress: 421.5/666.6666717529297 = 63.2% done in 20.0s, 11.6s to go\n", + "on time step 25308 (time=421.8), 0.000785113 s/step\n", + "Meep progress: 506.68333333333334/666.6666717529297 = 76.0% done in 24.0s, 7.6s to go\n", + "on time step 30420 (time=507), 0.000782534 s/step\n", + "Meep progress: 591.7833333333333/666.6666717529297 = 88.8% done in 28.0s, 3.5s to go\n", + "on time step 35528 (time=592.133), 0.000783211 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00184393 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.6,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.6,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.015429 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cf6496e54bc04252bd6312b4c1784ff9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 98.25/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", + "on time step 5906 (time=98.4333), 0.000677395 s/step\n", + "Meep progress: 196.18333333333334/366.6666717529297 = 53.5% done in 8.0s, 7.0s to go\n", + "on time step 11784 (time=196.4), 0.000680643 s/step\n", + "Meep progress: 289.8/366.6666717529297 = 79.0% done in 12.0s, 3.2s to go\n", + "on time step 17401 (time=290.017), 0.000712187 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.21796392210149537, 2.7101979244778688e-08, -4021180.8911241614, 1.2931472666535015, -0.6549272150232459+1.1150337197929565i, 1.1090356875112212e-13+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.2, 0.21796392210149537\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.0045712 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.6,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.6,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0223899 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.717283) = 0.217732 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.718056) = 0.217964 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.718056) = 0.217964 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.717283) = 0.217732 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.718056) = 0.217964 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.718056) = 0.217964 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d5cd0cc64a6c4b4c89c769e543a30d50", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 80.98333333333333/666.6666717529297 = 12.1% done in 4.0s, 28.9s to go\n", + "on time step 4873 (time=81.2167), 0.000821001 s/step\n", + "Meep progress: 167.45/666.6666717529297 = 25.1% done in 8.0s, 23.9s to go\n", + "on time step 10064 (time=167.733), 0.00077061 s/step\n", + "Meep progress: 249.98333333333332/666.6666717529297 = 37.5% done in 12.0s, 20.0s to go\n", + "on time step 15016 (time=250.267), 0.000807818 s/step\n", + "Meep progress: 332.5833333333333/666.6666717529297 = 49.9% done in 16.0s, 16.1s to go\n", + "on time step 19974 (time=332.9), 0.000806779 s/step\n", + "Meep progress: 416.75/666.6666717529297 = 62.5% done in 20.0s, 12.0s to go\n", + "on time step 25023 (time=417.05), 0.000792321 s/step\n", + "Meep progress: 500.4166666666667/666.6666717529297 = 75.1% done in 24.0s, 8.0s to go\n", + "on time step 30045 (time=500.75), 0.000796587 s/step\n", + "Meep progress: 584.0333333333333/666.6666717529297 = 87.6% done in 28.0s, 4.0s to go\n", + "on time step 35066 (time=584.433), 0.000796753 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00176692 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.65,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.65,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.022615 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3746edd53d114015b5824f25e125594b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 98.23333333333333/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", + "on time step 5908 (time=98.4667), 0.00067706 s/step\n", + "Meep progress: 195.71666666666667/366.6666717529297 = 53.4% done in 8.0s, 7.0s to go\n", + "on time step 11757 (time=195.95), 0.000683889 s/step\n", + "Meep progress: 289.85/366.6666717529297 = 79.0% done in 12.0s, 3.2s to go\n", + "on time step 17406 (time=290.1), 0.000708109 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.23050129868403818, 1.1111522223430954e-09, -103721746.69190612, 0.8510771099307869, 0.2097756989886373-0.824819012366937i, 2.532377183551288e-13+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.30000000000000004, 0.23050129868403818\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00193691 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.65,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.65,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0224741 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.759804) = 0.230167 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.760921) = 0.230501 after 7 iters\n", + "MPB solved for frequency_1(0,0,0.760921) = 0.230501 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.759804) = 0.230167 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.760921) = 0.230501 after 7 iters\n", + "MPB solved for frequency_1(0,0,0.760921) = 0.230501 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "11a3ef7cf85a4d2a9db9e4e40bd4b300", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 84.26666666666667/666.6666717529297 = 12.6% done in 4.0s, 27.6s to go\n", + "on time step 5068 (time=84.4667), 0.000789299 s/step\n", + "Meep progress: 168.88333333333333/666.6666717529297 = 25.3% done in 8.0s, 23.6s to go\n", + "on time step 10146 (time=169.1), 0.000787785 s/step\n", + "Meep progress: 253.36666666666667/666.6666717529297 = 38.0% done in 12.0s, 19.6s to go\n", + "on time step 15217 (time=253.617), 0.000788949 s/step\n", + "Meep progress: 337.76666666666665/666.6666717529297 = 50.7% done in 16.0s, 15.6s to go\n", + "on time step 20281 (time=338.017), 0.000789954 s/step\n", + "Meep progress: 422.93333333333334/666.6666717529297 = 63.4% done in 20.0s, 11.5s to go\n", + "on time step 25393 (time=423.217), 0.000782588 s/step\n", + "Meep progress: 507.96666666666664/666.6666717529297 = 76.2% done in 24.0s, 7.5s to go\n", + "on time step 30498 (time=508.3), 0.000783646 s/step\n", + "Meep progress: 592.5/666.6666717529297 = 88.9% done in 28.0s, 3.5s to go\n", + "on time step 35571 (time=592.85), 0.000788548 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00206399 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.65,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.65,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.020714 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "05665f9e0ca64c61919b04105aea010b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 97.15/366.6666717529297 = 26.5% done in 4.0s, 11.1s to go\n", + "on time step 5847 (time=97.45), 0.000684132 s/step\n", + "Meep progress: 194.53333333333333/366.6666717529297 = 53.1% done in 8.0s, 7.1s to go\n", + "on time step 11689 (time=194.817), 0.000684739 s/step\n", + "Meep progress: 288.7/366.6666717529297 = 78.7% done in 12.0s, 3.2s to go\n", + "on time step 17342 (time=289.033), 0.000707681 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.2202853119212703, 1.661718958542485e-08, -6628236.104211189, 1.3726411235812384, 0.8679324852926675+1.0634081319606588i, 4.943132075236113e-14+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.30000000000000004, 0.2202853119212703\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00254488 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.65,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.65,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0223539 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.726009) = 0.220034 after 11 iters\n", + "MPB solved for frequency_1(0,0,0.726846) = 0.220285 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.726846) = 0.220285 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.726009) = 0.220034 after 11 iters\n", + "MPB solved for frequency_1(0,0,0.726846) = 0.220285 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.726846) = 0.220285 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "32edc002b0594a85872a5f15e469b486", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 83.03333333333333/666.6666717529297 = 12.5% done in 4.0s, 28.1s to go\n", + "on time step 4991 (time=83.1833), 0.000801588 s/step\n", + "Meep progress: 166.5/666.6666717529297 = 25.0% done in 8.0s, 24.0s to go\n", + "on time step 10001 (time=166.683), 0.000798491 s/step\n", + "Meep progress: 250.11666666666667/666.6666717529297 = 37.5% done in 12.0s, 20.0s to go\n", + "on time step 15019 (time=250.317), 0.0007972 s/step\n", + "Meep progress: 334.71666666666664/666.6666717529297 = 50.2% done in 16.0s, 15.9s to go\n", + "on time step 20097 (time=334.95), 0.000787764 s/step\n", + "Meep progress: 418.81666666666666/666.6666717529297 = 62.8% done in 20.0s, 11.8s to go\n", + "on time step 25142 (time=419.033), 0.000792875 s/step\n", + "Meep progress: 503.55/666.6666717529297 = 75.5% done in 24.0s, 7.8s to go\n", + "on time step 30228 (time=503.8), 0.000786594 s/step\n", + "Meep progress: 587.45/666.6666717529297 = 88.1% done in 28.0s, 3.8s to go\n", + "on time step 35264 (time=587.733), 0.000794317 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00176716 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.7,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.7,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0131691 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1b075265f05142889634e50818f79e06", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 98.36666666666666/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", + "on time step 5915 (time=98.5833), 0.000676356 s/step\n", + "Meep progress: 196.03333333333333/366.6666717529297 = 53.5% done in 8.0s, 7.0s to go\n", + "on time step 11777 (time=196.283), 0.000682479 s/step\n", + "Meep progress: 289.5833333333333/366.6666717529297 = 79.0% done in 12.0s, 3.2s to go\n", + "on time step 17392 (time=289.867), 0.000712464 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.22938047554467897, 2.8259525192417854e-09, -40584630.13494343, 0.9554083501216192, 0.7088623048929175-0.6405617442401746i, 4.7858601707454104e-14+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.4, 0.22938047554467897\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00361514 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.7,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.7,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0198278 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.754994) = 0.229057 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.75607) = 0.22938 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.75607) = 0.22938 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.754994) = 0.229057 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.75607) = 0.22938 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.75607) = 0.22938 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "49e7f1011eb04db090ccea4009ce3a6a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 82.06666666666666/666.6666717529297 = 12.3% done in 4.0s, 28.5s to go\n", + "on time step 4936 (time=82.2667), 0.000810417 s/step\n", + "Meep progress: 166.41666666666666/666.6666717529297 = 25.0% done in 8.0s, 24.0s to go\n", + "on time step 9998 (time=166.633), 0.000790205 s/step\n", + "Meep progress: 249.71666666666667/666.6666717529297 = 37.5% done in 12.0s, 20.0s to go\n", + "on time step 14996 (time=249.933), 0.00080039 s/step\n", + "Meep progress: 333.55/666.6666717529297 = 50.0% done in 16.0s, 16.0s to go\n", + "on time step 20028 (time=333.8), 0.000795032 s/step\n", + "Meep progress: 416.65/666.6666717529297 = 62.5% done in 20.0s, 12.0s to go\n", + "on time step 25015 (time=416.917), 0.000802087 s/step\n", + "Meep progress: 500.7/666.6666717529297 = 75.1% done in 24.0s, 8.0s to go\n", + "on time step 30060 (time=501), 0.00079297 s/step\n", + "Meep progress: 584.4666666666667/666.6666717529297 = 87.7% done in 28.0s, 3.9s to go\n", + "on time step 35087 (time=584.783), 0.000795733 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00191903 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.7,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.7,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.029588 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "20771f63a2984401ad8c7b9ba6494fb8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 97.71666666666667/366.6666717529297 = 26.6% done in 4.0s, 11.0s to go\n", + "on time step 5876 (time=97.9333), 0.000680849 s/step\n", + "Meep progress: 194.2/366.6666717529297 = 53.0% done in 8.0s, 7.1s to go\n", + "on time step 11665 (time=194.417), 0.000690981 s/step\n", + "Meep progress: 286.93333333333334/366.6666717529297 = 78.3% done in 12.0s, 3.3s to go\n", + "on time step 17231 (time=287.183), 0.000718741 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.22197905508783583, 2.689453714201604e-09, -41268428.21567816, 1.380617017276195, 1.3806167115397914-0.0009188088320141777i, 5.815262450716548e-14+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.4, 0.22197905508783583\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00408697 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.7,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.7,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0230129 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.730545) = 0.221717 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.731419) = 0.221979 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.731419) = 0.221979 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.730545) = 0.221717 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.731419) = 0.221979 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.731419) = 0.221979 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e183edf238ee4fb9b268879c6cb80157", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 83.2/666.6666717529297 = 12.5% done in 4.0s, 28.1s to go\n", + "on time step 5007 (time=83.45), 0.000798937 s/step\n", + "Meep progress: 167.13333333333333/666.6666717529297 = 25.1% done in 8.0s, 23.9s to go\n", + "on time step 10044 (time=167.4), 0.000794127 s/step\n", + "Meep progress: 251.06666666666666/666.6666717529297 = 37.7% done in 12.0s, 19.9s to go\n", + "on time step 15081 (time=251.35), 0.000794205 s/step\n", + "Meep progress: 334.51666666666665/666.6666717529297 = 50.2% done in 16.0s, 15.9s to go\n", + "on time step 20090 (time=334.833), 0.000798598 s/step\n", + "Meep progress: 423.31666666666666/666.6666717529297 = 63.5% done in 20.0s, 11.5s to go\n", + "on time step 25419 (time=423.65), 0.000750669 s/step\n", + "Meep progress: 507.84999999999997/666.6666717529297 = 76.2% done in 24.0s, 7.5s to go\n", + "on time step 30493 (time=508.217), 0.000788413 s/step\n", + "Meep progress: 592.3333333333334/666.6666717529297 = 88.8% done in 28.0s, 3.5s to go\n", + "on time step 35564 (time=592.733), 0.000788837 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.0018878 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.75,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.75,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.016355 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b214004d80b44af9ab864af98dae656b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 93.36666666666666/366.6666717529297 = 25.5% done in 4.0s, 11.7s to go\n", + "on time step 5619 (time=93.65), 0.000711909 s/step\n", + "Meep progress: 186.51666666666665/366.6666717529297 = 50.9% done in 8.0s, 7.7s to go\n", + "on time step 11208 (time=186.8), 0.000715819 s/step\n", + "Meep progress: 275.8333333333333/366.6666717529297 = 75.2% done in 12.0s, 4.0s to go\n", + "on time step 16567 (time=276.117), 0.000746435 s/step\n", + "Meep progress: 364.9166666666667/366.6666717529297 = 99.5% done in 16.0s, 0.1s to go\n", + "on time step 21916 (time=365.267), 0.000747914 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.22839396754116606, 4.693442764536769e-09, -24331176.387926824, 1.0415890909263652, 1.0168824850407956-0.22551684184128717i, 4.249035856082629e-14+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.5, 0.22839396754116606\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00539398 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.75,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.75,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.023149 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.752833) = 0.228077 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.753888) = 0.228394 after 7 iters\n", + "MPB solved for frequency_1(0,0,0.753888) = 0.228394 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.752833) = 0.228077 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.753888) = 0.228394 after 7 iters\n", + "MPB solved for frequency_1(0,0,0.753888) = 0.228394 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "35aea70ba52b49b285dbd05619bd4a8e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 81.8/666.6666717529297 = 12.3% done in 4.0s, 28.6s to go\n", + "on time step 4924 (time=82.0667), 0.000812495 s/step\n", + "Meep progress: 164.1/666.6666717529297 = 24.6% done in 8.0s, 24.5s to go\n", + "on time step 9862 (time=164.367), 0.000810167 s/step\n", + "Meep progress: 247.63333333333333/666.6666717529297 = 37.1% done in 12.0s, 20.3s to go\n", + "on time step 14875 (time=247.917), 0.00079793 s/step\n", + "Meep progress: 330.1666666666667/666.6666717529297 = 49.5% done in 16.0s, 16.3s to go\n", + "on time step 19829 (time=330.483), 0.000807541 s/step\n", + "Meep progress: 413.0333333333333/666.6666717529297 = 62.0% done in 20.0s, 12.3s to go\n", + "on time step 24804 (time=413.4), 0.000804159 s/step\n", + "Meep progress: 496.95/666.6666717529297 = 74.5% done in 24.0s, 8.2s to go\n", + "on time step 29839 (time=497.317), 0.000794594 s/step\n", + "Meep progress: 580.2166666666667/666.6666717529297 = 87.0% done in 28.0s, 4.2s to go\n", + "on time step 34837 (time=580.617), 0.000800409 s/step\n", + "Meep progress: 664.0166666666667/666.6666717529297 = 99.6% done in 32.0s, 0.1s to go\n", + "on time step 39867 (time=664.45), 0.000795323 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00177789 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.75,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.75,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0188749 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6f23ec57cbd44ccd9c05b973705f2da3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 96.9/366.6666717529297 = 26.4% done in 4.0s, 11.1s to go\n", + "on time step 5834 (time=97.2333), 0.000685744 s/step\n", + "Meep progress: 194.31666666666666/366.6666717529297 = 53.0% done in 8.0s, 7.1s to go\n", + "on time step 11679 (time=194.65), 0.000684423 s/step\n", + "Meep progress: 288.7/366.6666717529297 = 78.7% done in 12.0s, 3.2s to go\n", + "on time step 17345 (time=289.083), 0.000705968 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.2230300941405038, -7.841965023994586e-09, 14220293.858623678, 1.3613884433238386, 1.1598737564442396-0.7127912476508345i, 7.609870353913999e-14+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.5, 0.2230300941405038\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00159287 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.75,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.75,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.02321 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.735089) = 0.222758 after 11 iters\n", + "MPB solved for frequency_1(0,0,0.735997) = 0.22303 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.735997) = 0.22303 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.735089) = 0.222758 after 11 iters\n", + "MPB solved for frequency_1(0,0,0.735997) = 0.22303 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.735997) = 0.22303 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e436a71259cd488398bd3aedff3a057c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 82.91666666666667/666.6666717529297 = 12.4% done in 4.0s, 28.2s to go\n", + "on time step 4986 (time=83.1), 0.000802322 s/step\n", + "Meep progress: 165.63333333333333/666.6666717529297 = 24.8% done in 8.0s, 24.2s to go\n", + "on time step 9950 (time=165.833), 0.000805971 s/step\n", + "Meep progress: 249.08333333333334/666.6666717529297 = 37.4% done in 12.0s, 20.1s to go\n", + "on time step 14957 (time=249.283), 0.000798944 s/step\n", + "Meep progress: 332.0333333333333/666.6666717529297 = 49.8% done in 16.0s, 16.1s to go\n", + "on time step 19937 (time=332.283), 0.00080335 s/step\n", + "Meep progress: 416.8333333333333/666.6666717529297 = 62.5% done in 20.0s, 12.0s to go\n", + "on time step 25028 (time=417.133), 0.000785816 s/step\n", + "Meep progress: 501.56666666666666/666.6666717529297 = 75.2% done in 24.0s, 7.9s to go\n", + "on time step 30113 (time=501.883), 0.000786698 s/step\n", + "Meep progress: 585.2666666666667/666.6666717529297 = 87.8% done in 28.0s, 3.9s to go\n", + "on time step 35137 (time=585.617), 0.000796204 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.0018599 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.8,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.8,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0129621 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5218926d24124ece9c74de7f1cc1effc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 97.08333333333333/366.6666717529297 = 26.5% done in 4.0s, 11.1s to go\n", + "on time step 5839 (time=97.3167), 0.000685119 s/step\n", + "Meep progress: 193.31666666666666/366.6666717529297 = 52.7% done in 8.0s, 7.2s to go\n", + "on time step 11612 (time=193.533), 0.000692887 s/step\n", + "Meep progress: 287.93333333333334/366.6666717529297 = 78.5% done in 12.0s, 3.3s to go\n", + "on time step 17291 (time=288.183), 0.000704366 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.22779219587071284, 6.5733308116063705e-09, -17326999.233669, 1.0936986894641623, 1.0885703324807652+0.10578967141617628i, 1.6452384436270636e-13+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.6, 0.22779219587071284\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00145602 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.8,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.8,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0227098 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.749747) = 0.227483 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.750779) = 0.227792 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.750779) = 0.227792 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.749747) = 0.227483 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.750779) = 0.227792 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.750779) = 0.227792 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bb85e22b6bea4492b28f6bd247b5fe39", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 81.93333333333334/666.6666717529297 = 12.3% done in 4.0s, 28.6s to go\n", + "on time step 4932 (time=82.2), 0.000811153 s/step\n", + "Meep progress: 164.1/666.6666717529297 = 24.6% done in 8.0s, 24.5s to go\n", + "on time step 9863 (time=164.383), 0.000811203 s/step\n", + "Meep progress: 246.46666666666667/666.6666717529297 = 37.0% done in 12.0s, 20.5s to go\n", + "on time step 14805 (time=246.75), 0.000809507 s/step\n", + "Meep progress: 328.0/666.6666717529297 = 49.2% done in 16.0s, 16.5s to go\n", + "on time step 19699 (time=328.317), 0.000817414 s/step\n", + "Meep progress: 410.98333333333335/666.6666717529297 = 61.6% done in 20.0s, 12.4s to go\n", + "on time step 24678 (time=411.3), 0.000803526 s/step\n", + "Meep progress: 493.6333333333333/666.6666717529297 = 74.0% done in 24.0s, 8.4s to go\n", + "on time step 29641 (time=494.017), 0.000806037 s/step\n", + "Meep progress: 575.4833333333333/666.6666717529297 = 86.3% done in 28.0s, 4.4s to go\n", + "on time step 34552 (time=575.867), 0.000814608 s/step\n", + "Meep progress: 657.2666666666667/666.6666717529297 = 98.6% done in 32.0s, 0.5s to go\n", + "on time step 39463 (time=657.717), 0.000814686 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00235796 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.8,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.8,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0135491 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b671743684474e82b4692b977e92a73a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 98.13333333333333/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", + "on time step 5904 (time=98.4), 0.000677588 s/step\n", + "Meep progress: 195.1/366.6666717529297 = 53.2% done in 8.0s, 7.0s to go\n", + "on time step 11722 (time=195.367), 0.000687535 s/step\n", + "Meep progress: 289.7/366.6666717529297 = 79.0% done in 12.0s, 3.2s to go\n", + "on time step 17400 (time=290), 0.00070453 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.22389046766301668, -1.7192955887682313e-08, 6511110.396770701, 1.3350122958073318, 0.7195356752054972-1.124511557105276i, 1.7074960255719633e-13+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.6, 0.22389046766301668\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00139403 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.8,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.8,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0244219 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.736859) = 0.223613 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.737783) = 0.22389 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.737783) = 0.22389 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.736859) = 0.223613 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.737783) = 0.22389 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.737783) = 0.22389 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "291128db6c26474996b42b7bfffc01a6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 80.78333333333333/666.6666717529297 = 12.1% done in 4.0s, 29.0s to go\n", + "on time step 4859 (time=80.9833), 0.00082328 s/step\n", + "Meep progress: 163.13333333333333/666.6666717529297 = 24.5% done in 8.0s, 24.7s to go\n", + "on time step 9802 (time=163.367), 0.000809344 s/step\n", + "Meep progress: 246.68333333333334/666.6666717529297 = 37.0% done in 12.0s, 20.4s to go\n", + "on time step 14815 (time=246.917), 0.000798049 s/step\n", + "Meep progress: 328.95/666.6666717529297 = 49.3% done in 16.0s, 16.4s to go\n", + "on time step 19751 (time=329.183), 0.000810423 s/step\n", + "Meep progress: 411.5/666.6666717529297 = 61.7% done in 20.0s, 12.4s to go\n", + "on time step 24705 (time=411.75), 0.000807463 s/step\n", + "Meep progress: 494.3333333333333/666.6666717529297 = 74.1% done in 24.0s, 8.4s to go\n", + "on time step 29677 (time=494.617), 0.000804549 s/step\n", + "Meep progress: 576.9/666.6666717529297 = 86.5% done in 28.0s, 4.4s to go\n", + "on time step 34633 (time=577.217), 0.000807203 s/step\n", + "Meep progress: 659.4/666.6666717529297 = 98.9% done in 32.0s, 0.4s to go\n", + "on time step 39582 (time=659.7), 0.000808279 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.0023129 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.85,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.85,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.013726 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3132625bea184efaa79e72754b9a7edc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 98.66666666666667/366.6666717529297 = 26.9% done in 4.0s, 10.9s to go\n", + "on time step 5932 (time=98.8667), 0.000674405 s/step\n", + "Meep progress: 196.48333333333332/366.6666717529297 = 53.6% done in 8.0s, 6.9s to go\n", + "on time step 11802 (time=196.7), 0.000681457 s/step\n", + "Meep progress: 289.5833333333333/366.6666717529297 = 79.0% done in 12.0s, 3.2s to go\n", + "on time step 17390 (time=289.833), 0.000715836 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.2272133016731052, 8.742503028543528e-09, -12994751.098814214, 1.1387362638662792, 1.0488380657857905+0.4434625016874079i, 6.376331244480289e-14+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.7000000000000001, 0.2272133016731052\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00217509 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.85,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.85,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0203781 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.748927) = 0.226907 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.749949) = 0.227213 after 7 iters\n", + "MPB solved for frequency_1(0,0,0.749949) = 0.227213 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.748927) = 0.226907 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.749949) = 0.227213 after 7 iters\n", + "MPB solved for frequency_1(0,0,0.749949) = 0.227213 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "00b16ae25da64c109934700b9b64b551", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 80.98333333333333/666.6666717529297 = 12.1% done in 4.0s, 28.9s to go\n", + "on time step 4872 (time=81.2), 0.000821152 s/step\n", + "Meep progress: 163.65/666.6666717529297 = 24.5% done in 8.0s, 24.6s to go\n", + "on time step 9835 (time=163.917), 0.000806157 s/step\n", + "Meep progress: 245.54999999999998/666.6666717529297 = 36.8% done in 12.0s, 20.6s to go\n", + "on time step 14749 (time=245.817), 0.000814041 s/step\n", + "Meep progress: 328.2/666.6666717529297 = 49.2% done in 16.0s, 16.5s to go\n", + "on time step 19710 (time=328.5), 0.000806343 s/step\n", + "Meep progress: 409.6666666666667/666.6666717529297 = 61.4% done in 20.0s, 12.5s to go\n", + "on time step 24599 (time=409.983), 0.000818225 s/step\n", + "Meep progress: 492.71666666666664/666.6666717529297 = 73.9% done in 24.0s, 8.5s to go\n", + "on time step 29585 (time=493.083), 0.000802381 s/step\n", + "Meep progress: 575.6666666666666/666.6666717529297 = 86.3% done in 28.0s, 4.4s to go\n", + "on time step 34560 (time=576), 0.000804049 s/step\n", + "Meep progress: 657.1666666666666/666.6666717529297 = 98.6% done in 32.0s, 0.5s to go\n", + "on time step 39453 (time=657.55), 0.000817607 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00278711 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.85,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.85,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0183229 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b09562e43e724c0db50a813c1b962259", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 97.63333333333333/366.6666717529297 = 26.6% done in 4.0s, 11.0s to go\n", + "on time step 5868 (time=97.8), 0.000681683 s/step\n", + "Meep progress: 194.08333333333334/366.6666717529297 = 52.9% done in 8.0s, 7.1s to go\n", + "on time step 11656 (time=194.267), 0.000691144 s/step\n", + "Meep progress: 287.7/366.6666717529297 = 78.5% done in 12.0s, 3.3s to go\n", + "on time step 17275 (time=287.917), 0.000711899 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.22437226062905724, -2.3099396489881195e-08, 4856669.3230999485, 1.313923864774931, 0.4094809417964661-1.2484875973475147i, 1.7003140322728657e-13+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.7000000000000001, 0.22437226062905724\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00296402 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.85,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.85,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0208819 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.739529) = 0.224089 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.740473) = 0.224372 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.740473) = 0.224372 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.739529) = 0.224089 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.740473) = 0.224372 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.740473) = 0.224372 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "be6d782d86854a299bd5634c042b1837", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 82.01666666666667/666.6666717529297 = 12.3% done in 4.0s, 28.5s to go\n", + "on time step 4933 (time=82.2167), 0.000811033 s/step\n", + "Meep progress: 163.2/666.6666717529297 = 24.5% done in 8.0s, 24.7s to go\n", + "on time step 9805 (time=163.417), 0.000821053 s/step\n", + "Meep progress: 246.13333333333333/666.6666717529297 = 36.9% done in 12.0s, 20.5s to go\n", + "on time step 14783 (time=246.383), 0.000803661 s/step\n", + "Meep progress: 328.68333333333334/666.6666717529297 = 49.3% done in 16.0s, 16.5s to go\n", + "on time step 19737 (time=328.95), 0.000807463 s/step\n", + "Meep progress: 410.4166666666667/666.6666717529297 = 61.6% done in 20.0s, 12.5s to go\n", + "on time step 24642 (time=410.7), 0.000815659 s/step\n", + "Meep progress: 492.2833333333333/666.6666717529297 = 73.8% done in 24.0s, 8.5s to go\n", + "on time step 29554 (time=492.567), 0.000814442 s/step\n", + "Meep progress: 575.4166666666666/666.6666717529297 = 86.3% done in 28.0s, 4.4s to go\n", + "on time step 34545 (time=575.75), 0.000801476 s/step\n", + "Meep progress: 658.3/666.6666717529297 = 98.7% done in 32.0s, 0.4s to go\n", + "on time step 39517 (time=658.617), 0.000804507 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00178504 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.9,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.9,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.012604 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c2b5a9f01c2b49eaa039716b0b7bd1da", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 97.18333333333334/366.6666717529297 = 26.5% done in 4.0s, 11.1s to go\n", + "on time step 5847 (time=97.45), 0.000684151 s/step\n", + "Meep progress: 192.4/366.6666717529297 = 52.5% done in 8.0s, 7.2s to go\n", + "on time step 11561 (time=192.683), 0.000700084 s/step\n", + "Meep progress: 284.7/366.6666717529297 = 77.6% done in 12.0s, 3.5s to go\n", + "on time step 17100 (time=285), 0.000722162 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.22692081083425694, 1.1307039395132144e-08, -10034492.801535204, 1.1623124097996433, 0.9889626651292537+0.6106742052475259i, 7.110203970811583e-14+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.8, 0.22692081083425694\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00310993 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.9,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.9,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0245988 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.746869) = 0.226619 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.747876) = 0.226921 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.747876) = 0.226921 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.746869) = 0.226619 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.747876) = 0.226921 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.747876) = 0.226921 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "26d380369f4047da9fb61042e8adcd81", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 82.35/666.6666717529297 = 12.4% done in 4.0s, 28.4s to go\n", + "on time step 4956 (time=82.6), 0.000807173 s/step\n", + "Meep progress: 165.3/666.6666717529297 = 24.8% done in 8.0s, 24.3s to go\n", + "on time step 9936 (time=165.6), 0.00080335 s/step\n", + "Meep progress: 248.29999999999998/666.6666717529297 = 37.2% done in 12.0s, 20.2s to go\n", + "on time step 14916 (time=248.6), 0.000803232 s/step\n", + "Meep progress: 331.3833333333333/666.6666717529297 = 49.7% done in 16.0s, 16.2s to go\n", + "on time step 19901 (time=331.683), 0.000802426 s/step\n", + "Meep progress: 414.55/666.6666717529297 = 62.2% done in 20.0s, 12.2s to go\n", + "on time step 24893 (time=414.883), 0.000801303 s/step\n", + "Meep progress: 497.6666666666667/666.6666717529297 = 74.6% done in 24.0s, 8.2s to go\n", + "on time step 29882 (time=498.033), 0.000801855 s/step\n", + "Meep progress: 580.8333333333334/666.6666717529297 = 87.1% done in 28.0s, 4.1s to go\n", + "on time step 34873 (time=581.217), 0.000801512 s/step\n", + "Meep progress: 663.0/666.6666717529297 = 99.4% done in 32.0s, 0.2s to go\n", + "on time step 39805 (time=663.417), 0.000811121 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00191712 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.9,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.9,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.013334 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9268b5878e46401383a15b709fc207e2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 95.85/366.6666717529297 = 26.1% done in 4.0s, 11.3s to go\n", + "on time step 5762 (time=96.0333), 0.000694243 s/step\n", + "Meep progress: 192.11666666666667/366.6666717529297 = 52.4% done in 8.0s, 7.3s to go\n", + "on time step 11538 (time=192.3), 0.000692638 s/step\n", + "Meep progress: 285.15/366.6666717529297 = 77.8% done in 12.0s, 3.4s to go\n", + "on time step 17122 (time=285.367), 0.00071637 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.2248450102131107, -2.925827242064695e-08, 3842417.7439547367, 1.2923701545067185, 0.08958085376950176-1.2892617604263483i, 1.389119717333207e-13+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.8, 0.2248450102131107\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00451803 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.9,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.9,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0211821 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.740012) = 0.22456 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.740962) = 0.224845 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.740962) = 0.224845 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.740012) = 0.22456 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.740962) = 0.224845 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.740962) = 0.224845 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0c31aca58273447eb9309328463cb41e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 81.1/666.6666717529297 = 12.2% done in 4.0s, 28.9s to go\n", + "on time step 4876 (time=81.2667), 0.000820368 s/step\n", + "Meep progress: 162.61666666666667/666.6666717529297 = 24.4% done in 8.0s, 24.8s to go\n", + "on time step 9769 (time=162.817), 0.000817623 s/step\n", + "Meep progress: 243.96666666666667/666.6666717529297 = 36.6% done in 12.0s, 20.8s to go\n", + "on time step 14650 (time=244.167), 0.000819636 s/step\n", + "Meep progress: 326.0833333333333/666.6666717529297 = 48.9% done in 16.0s, 16.7s to go\n", + "on time step 19578 (time=326.3), 0.00081184 s/step\n", + "Meep progress: 408.95/666.6666717529297 = 61.3% done in 20.0s, 12.6s to go\n", + "on time step 24552 (time=409.2), 0.000804271 s/step\n", + "Meep progress: 491.7/666.6666717529297 = 73.8% done in 24.0s, 8.5s to go\n", + "on time step 29519 (time=491.983), 0.00080546 s/step\n", + "Meep progress: 574.1666666666666/666.6666717529297 = 86.1% done in 28.0s, 4.5s to go\n", + "on time step 34468 (time=574.467), 0.000808346 s/step\n", + "Meep progress: 658.6333333333333/666.6666717529297 = 98.8% done in 32.0s, 0.4s to go\n", + "on time step 39536 (time=658.933), 0.000789417 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00183201 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.95,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.95,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.012459 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1c47ae4da9804255bbe8a11a807fefbd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 98.45/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", + "on time step 5917 (time=98.6167), 0.000676164 s/step\n", + "Meep progress: 195.05/366.6666717529297 = 53.2% done in 8.0s, 7.0s to go\n", + "on time step 11715 (time=195.25), 0.000689941 s/step\n", + "Meep progress: 288.35/366.6666717529297 = 78.6% done in 12.0s, 3.3s to go\n", + "on time step 17313 (time=288.55), 0.000714587 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.22657284275616438, 1.4388403173786857e-08, -7873453.364475506, 1.1864826851861325, 0.8798551388030453+0.7959874979975307i, 4.0990972663369745e-14+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.9, 0.22657284275616438\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00310707 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.95,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.95,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0196011 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.746808) = 0.226272 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.747813) = 0.226573 after 7 iters\n", + "MPB solved for frequency_1(0,0,0.747813) = 0.226573 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.746808) = 0.226272 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.747813) = 0.226573 after 7 iters\n", + "MPB solved for frequency_1(0,0,0.747813) = 0.226573 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "163cbc8758b949588f6454ca36a3f81b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 81.61666666666666/666.6666717529297 = 12.2% done in 4.0s, 28.7s to go\n", + "on time step 4905 (time=81.75), 0.00081566 s/step\n", + "Meep progress: 164.35/666.6666717529297 = 24.7% done in 8.0s, 24.5s to go\n", + "on time step 9871 (time=164.517), 0.000805577 s/step\n", + "Meep progress: 246.91666666666666/666.6666717529297 = 37.0% done in 12.0s, 20.4s to go\n", + "on time step 14826 (time=247.1), 0.000807378 s/step\n", + "Meep progress: 329.56666666666666/666.6666717529297 = 49.4% done in 16.0s, 16.4s to go\n", + "on time step 19787 (time=329.783), 0.000806447 s/step\n", + "Meep progress: 411.8666666666667/666.6666717529297 = 61.8% done in 20.0s, 12.4s to go\n", + "on time step 24725 (time=412.083), 0.000810105 s/step\n", + "Meep progress: 494.0333333333333/666.6666717529297 = 74.1% done in 24.0s, 8.4s to go\n", + "on time step 29663 (time=494.383), 0.000810117 s/step\n", + "Meep progress: 576.4333333333333/666.6666717529297 = 86.5% done in 28.0s, 4.4s to go\n", + "on time step 34601 (time=576.683), 0.000810185 s/step\n", + "Meep progress: 658.8166666666666/666.6666717529297 = 98.8% done in 32.0s, 0.4s to go\n", + "on time step 39546 (time=659.1), 0.000809041 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00218797 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.95,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.95,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0134099 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e0d410126c864bb1aa8b0d161bc590f8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 97.06666666666666/366.6666717529297 = 26.5% done in 4.0s, 11.1s to go\n", + "on time step 5840 (time=97.3333), 0.000685049 s/step\n", + "Meep progress: 194.68333333333334/366.6666717529297 = 53.1% done in 8.0s, 7.1s to go\n", + "on time step 11698 (time=194.967), 0.000682896 s/step\n", + "Meep progress: 288.45/366.6666717529297 = 78.7% done in 12.0s, 3.3s to go\n", + "on time step 17323 (time=288.717), 0.000711171 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.22505482918759098, -3.331421542112138e-08, 3377759.7092216844, 1.2802884326550177, -0.05183926206700584-1.2792385085270808i, 1.4238944309468102e-13+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 0.9, 0.22505482918759098\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00150323 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-0.95,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (0.95,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0196998 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.741787) = 0.224766 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.742749) = 0.225055 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.742749) = 0.225055 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.151963 after 15 iters\n", + "MPB solved for frequency_1(0,0,0.741787) = 0.224766 after 12 iters\n", + "MPB solved for frequency_1(0,0,0.742749) = 0.225055 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.742749) = 0.225055 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0b5fd46eedaa41c593621dbeed7aa53e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 81.55/666.6666717529297 = 12.2% done in 4.0s, 28.7s to go\n", + "on time step 4901 (time=81.6833), 0.000816171 s/step\n", + "Meep progress: 161.51666666666665/666.6666717529297 = 24.2% done in 8.0s, 25.0s to go\n", + "on time step 9700 (time=161.667), 0.000833518 s/step\n", + "Meep progress: 243.5/666.6666717529297 = 36.5% done in 12.0s, 20.9s to go\n", + "on time step 14621 (time=243.683), 0.000813012 s/step\n", + "Meep progress: 326.46666666666664/666.6666717529297 = 49.0% done in 16.0s, 16.7s to go\n", + "on time step 19599 (time=326.65), 0.000803559 s/step\n", + "Meep progress: 408.7/666.6666717529297 = 61.3% done in 20.0s, 12.6s to go\n", + "on time step 24535 (time=408.917), 0.000810512 s/step\n", + "Meep progress: 490.46666666666664/666.6666717529297 = 73.6% done in 24.0s, 8.6s to go\n", + "on time step 29442 (time=490.7), 0.000815343 s/step\n", + "Meep progress: 571.4333333333333/666.6666717529297 = 85.7% done in 28.0s, 4.7s to go\n", + "on time step 34304 (time=571.733), 0.000822786 s/step\n", + "Meep progress: 652.35/666.6666717529297 = 97.9% done in 32.0s, 0.7s to go\n", + "on time step 39159 (time=652.65), 0.000824059 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00182199 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-1,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (1,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.013756 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "acffad1129974b9c931a184a58126db1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 97.63333333333333/366.6666717529297 = 26.6% done in 4.0s, 11.0s to go\n", + "on time step 5871 (time=97.85), 0.000681371 s/step\n", + "Meep progress: 195.38333333333333/366.6666717529297 = 53.3% done in 8.0s, 7.0s to go\n", + "on time step 11737 (time=195.617), 0.000681959 s/step\n", + "Meep progress: 288.0/366.6666717529297 = 78.5% done in 12.0s, 3.3s to go\n", + "on time step 17297 (time=288.283), 0.000719542 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.2264506053061966, 1.848141445855612e-08, -6126441.399114868, 1.1963136036156947, 0.833990004289438+0.8576869539297383i, 9.41930743041389e-14+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 1.0, 0.2264506053061966\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00198913 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-1,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (1,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0247951 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.745316) = 0.226152 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.74631) = 0.226451 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.74631) = 0.226451 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.745316) = 0.226152 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.74631) = 0.226451 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.74631) = 0.226451 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a4348fa2e77c48249feaaf9a90659b10", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 80.21666666666667/666.6666717529297 = 12.0% done in 4.0s, 29.2s to go\n", + "on time step 4831 (time=80.5167), 0.000828124 s/step\n", + "Meep progress: 160.66666666666666/666.6666717529297 = 24.1% done in 8.0s, 25.2s to go\n", + "on time step 9662 (time=161.033), 0.00082815 s/step\n", + "Meep progress: 242.7/666.6666717529297 = 36.4% done in 12.0s, 21.0s to go\n", + "on time step 14584 (time=243.067), 0.000812847 s/step\n", + "Meep progress: 323.06666666666666/666.6666717529297 = 48.5% done in 16.0s, 17.0s to go\n", + "on time step 19408 (time=323.467), 0.000829324 s/step\n", + "Meep progress: 403.96666666666664/666.6666717529297 = 60.6% done in 20.0s, 13.0s to go\n", + "on time step 24267 (time=404.45), 0.000823243 s/step\n", + "Meep progress: 485.68333333333334/666.6666717529297 = 72.9% done in 24.0s, 8.9s to go\n", + "on time step 29170 (time=486.167), 0.000815931 s/step\n", + "Meep progress: 567.6833333333333/666.6666717529297 = 85.2% done in 28.0s, 4.9s to go\n", + "on time step 34092 (time=568.2), 0.00081282 s/step\n", + "Meep progress: 648.8666666666667/666.6666717529297 = 97.3% done in 32.0s, 0.9s to go\n", + "on time step 38962 (time=649.367), 0.000821455 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00247097 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-1,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (1,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0148768 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b3f6c97a008c43a3a8241dbd8b8d6e77", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=366.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 98.18333333333334/366.6666717529297 = 26.8% done in 4.0s, 10.9s to go\n", + "on time step 5906 (time=98.4333), 0.000677366 s/step\n", + "Meep progress: 195.21666666666667/366.6666717529297 = 53.2% done in 8.0s, 7.0s to go\n", + "on time step 11728 (time=195.467), 0.000687257 s/step\n", + "Meep progress: 288.5333333333333/366.6666717529297 = 78.7% done in 12.0s, 3.2s to go\n", + "on time step 17331 (time=288.85), 0.000714072 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.22533719120317752, -3.886614292556672e-08, 2898888.0069052, 1.265821467168656, -0.2370236086793528-1.2434322642080775i, 1.8277875767837685e-13+0.0i\n", + "run 0 finished at t = 366.68333333333334 (22001 timesteps)\n", + "freq:, 1.0, 0.22533719120317752\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "time for choose_chunkdivision = 0.00464702 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 5 x 0 with resolution 30\n", + " block, center = (-1,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + " block, center = (1,0,0)\n", + " size (1,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.9025,11.9025,11.9025)\n", + "time for set_epsilon = 0.0233421 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.741638) = 0.225048 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.742601) = 0.225337 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.742601) = 0.225337 after 1 iters\n", + "MPB solved for frequency_1(0,0,0.5) = 0.152184 after 14 iters\n", + "MPB solved for frequency_1(0,0,0.741638) = 0.225048 after 10 iters\n", + "MPB solved for frequency_1(0,0,0.742601) = 0.225337 after 6 iters\n", + "MPB solved for frequency_1(0,0,0.742601) = 0.225337 after 1 iters\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6a022e55ec594e10bbff1ebc6c1a6333", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, description='0% done ', max=666.6666717529297)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 80.03333333333333/666.6666717529297 = 12.0% done in 4.0s, 29.3s to go\n", + "on time step 4812 (time=80.2), 0.000831265 s/step\n", + "Meep progress: 158.71666666666667/666.6666717529297 = 23.8% done in 8.0s, 25.6s to go\n", + "on time step 9536 (time=158.933), 0.000846831 s/step\n", + "Meep progress: 239.76666666666665/666.6666717529297 = 36.0% done in 12.0s, 21.4s to go\n", + "on time step 14400 (time=240), 0.000822458 s/step\n", + "Meep progress: 321.46666666666664/666.6666717529297 = 48.2% done in 16.0s, 17.2s to go\n", + "on time step 19304 (time=321.733), 0.000815746 s/step\n", + "Meep progress: 402.9/666.6666717529297 = 60.4% done in 20.0s, 13.1s to go\n", + "on time step 24189 (time=403.15), 0.000818995 s/step\n", + "Meep progress: 484.1333333333333/666.6666717529297 = 72.6% done in 24.0s, 9.0s to go\n", + "on time step 29067 (time=484.45), 0.000820091 s/step\n", + "Meep progress: 565.6833333333333/666.6666717529297 = 84.9% done in 28.0s, 5.0s to go\n", + "on time step 33961 (time=566.017), 0.000817343 s/step\n", + "Meep progress: 646.4166666666666/666.6666717529297 = 97.0% done in 32.0s, 1.0s to go\n", + "on time step 38805 (time=646.75), 0.000825862 s/step\n", + "run 1 finished at t = 666.6666666666666 (40000 timesteps)\n" + ] + } + ], "source": [ - "s = np.arange(0.1, 1.1, 0.1)\n", + "s = np.arange(0.1,1.1,0.1)\n", "fluxes_odd = np.zeros(s.size)\n", "forces_odd = np.zeros(s.size)\n", "fluxes_even = np.zeros(s.size)\n", "forces_even = np.zeros(s.size)\n", "\n", "for k in range(len(s)):\n", - " fluxes_odd[k], forces_odd[k] = parallel_waveguide(s[k], True)\n", - " fluxes_even[k], forces_even[k] = parallel_waveguide(s[k], False)" + " fluxes_odd[k], forces_odd[k] = parallel_waveguide(s[k],True)\n", + " fluxes_even[k], forces_even[k] = parallel_waveguide(s[k],False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=150)\n", - "plt.plot(s, -forces_odd / fluxes_odd, \"rs\", label=\"anti-symmetric\")\n", - "plt.plot(s, -forces_even / fluxes_even, \"bo\", label=\"symmetric\")\n", + "plt.plot(s,-forces_odd/fluxes_odd,'rs',label='anti-symmetric')\n", + "plt.plot(s,-forces_even/fluxes_even,'bo',label='symmetric')\n", "plt.grid(True)\n", - "plt.xlabel(\"waveguide separation s/a\")\n", - "plt.ylabel(\"optical force (F/L)(ac/P)\")\n", - "plt.legend(loc=\"upper right\")\n", + "plt.xlabel('waveguide separation s/a')\n", + "plt.ylabel('optical force (F/L)(ac/P)')\n", + "plt.legend(loc='upper right')\n", "plt.show()" ] }, @@ -186,9 +2372,808 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.55,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.55,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyodd.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3206050395965576\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 20 iterations.\n", + "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.233911\n", + "elapsed time for k point: 39.92626595497131\n", + "total elapsed time for run: 42.247997999191284\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.55,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.55,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyeven.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.2972378730773926\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 17 iterations.\n", + "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.214446\n", + "elapsed time for k point: 33.999393939971924\n", + "total elapsed time for run: 36.29679226875305\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.6,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.6,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyodd.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.31347393989563\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 18 iterations.\n", + "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.232136\n", + "elapsed time for k point: 35.45386290550232\n", + "total elapsed time for run: 37.767595291137695\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.6,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.6,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyeven.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3267719745635986\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 19 iterations.\n", + "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.218002\n", + "elapsed time for k point: 38.01395606994629\n", + "total elapsed time for run: 40.34090757369995\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.65,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.65,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyodd.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3398044109344482\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 17 iterations.\n", + "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.230609\n", + "elapsed time for k point: 34.236557722091675\n", + "total elapsed time for run: 36.57751989364624\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.65,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.65,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyeven.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3188600540161133\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 18 iterations.\n", + "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.220388\n", + "elapsed time for k point: 36.10376501083374\n", + "total elapsed time for run: 38.422903060913086\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.7,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.7,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyodd.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3080191612243652\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", + "Solving for bands 1 to 1...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finished solving for bands 1 to 1 after 19 iterations.\n", + "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.229408\n", + "elapsed time for k point: 38.052414655685425\n", + "total elapsed time for run: 40.36163353919983\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.7,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.7,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyeven.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.351583242416382\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 17 iterations.\n", + "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.222012\n", + "elapsed time for k point: 34.26416039466858\n", + "total elapsed time for run: 36.61595106124878\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.75,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.75,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyodd.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.34535813331604\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 18 iterations.\n", + "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.2285\n", + "elapsed time for k point: 36.24232602119446\n", + "total elapsed time for run: 38.587929487228394\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.75,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.75,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyeven.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.342602014541626\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 18 iterations.\n", + "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.223137\n", + "elapsed time for k point: 36.02590990066528\n", + "total elapsed time for run: 38.3686728477478\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.8,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.8,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyodd.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3229269981384277\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 19 iterations.\n", + "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.227819\n", + "elapsed time for k point: 37.89797854423523\n", + "total elapsed time for run: 40.22118377685547\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.8,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.8,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyeven.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3231875896453857\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 18 iterations.\n", + "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.223921\n", + "elapsed time for k point: 35.854113817214966\n", + "total elapsed time for run: 38.17753458023071\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.85,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.85,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyodd.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3750038146972656\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 18 iterations.\n", + "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.227319\n", + "elapsed time for k point: 36.51261854171753\n", + "total elapsed time for run: 38.88789224624634\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.85,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.85,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyeven.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3955740928649902\n", + "solve_kpoint (0.5,0,0):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 17 iterations.\n", + "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.224479\n", + "elapsed time for k point: 34.263073205947876\n", + "total elapsed time for run: 36.65884232521057\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.9,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.9,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyodd.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3697447776794434\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 17 iterations.\n", + "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.226949\n", + "elapsed time for k point: 33.61285185813904\n", + "total elapsed time for run: 35.98284029960632\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.9,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.9,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyeven.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3623385429382324\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 19 iterations.\n", + "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.224876\n", + "elapsed time for k point: 37.994656801223755\n", + "total elapsed time for run: 40.35737872123718\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.95,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.95,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyodd.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3798489570617676\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 19 iterations.\n", + "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.226677\n", + "elapsed time for k point: 38.10903787612915\n", + "total elapsed time for run: 40.48903441429138\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-0.95,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,0.95,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyeven.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.361907482147217\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 19 iterations.\n", + "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.22516\n", + "elapsed time for k point: 37.75140643119812\n", + "total elapsed time for run: 40.113561391830444\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-1,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,1,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyodd.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.344741106033325\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyoddfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyodd band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 20 iterations.\n", + "zoddyoddfreqs:, 1, 0.5, 0, 0, 0.5, 0.22648\n", + "elapsed time for k point: 39.63300061225891\n", + "total elapsed time for run: 41.97804236412048\n", + "done\n", + "Initializing eigensolver data\n", + "Computing 1 bands with 1e-09 tolerance\n", + "Working in 3 dimensions.\n", + "Grid size is 1 x 1280 x 1280.\n", + "Solving for 1 bands at a time.\n", + "Creating Maxwell data...\n", + "Mesh size is 3.\n", + "Lattice vectors:\n", + " (1, 0, 0)\n", + " (0, 10, 0)\n", + " (0, 0, 10)\n", + "Cell volume = 100\n", + "Reciprocal lattice vectors (/ 2 pi):\n", + " (1, -0, 0)\n", + " (-0, 0.1, -0)\n", + " (0, -0, 0.1)\n", + "Geometric objects:\n", + " block, center = (0,-1,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " block, center = (0,1,0)\n", + " size (1e+20,1,1)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Geometric object tree has depth 1 and 2 object nodes (vs. 2 actual objects)\n", + "Initializing epsilon function...\n", + "Allocating fields...\n", + "Solving for band polarization: zoddyeven.\n", + "Initializing fields to random numbers...\n", + "1 k-points\n", + " Vector3<0.5, 0.0, 0.0>\n", + "elapsed time for initialization: 2.3879048824310303\n", + "solve_kpoint (0.5,0,0):\n", + "zoddyevenfreqs:, k index, k1, k2, k3, kmag/2pi, zoddyeven band 1\n", + "Solving for bands 1 to 1...\n", + "Finished solving for bands 1 to 1 after 20 iterations.\n", + "zoddyevenfreqs:, 1, 0.5, 0, 0, 0.5, 0.225369\n", + "elapsed time for k point: 39.115745067596436\n", + "total elapsed time for run: 41.503973960876465\n", + "done\n" + ] + } + ], "source": [ "import meep as mp\n", "from meep import mpb\n", @@ -200,7 +3185,7 @@ "Si = mp.Medium(index=3.45)\n", "\n", "syz = 10\n", - "geometry_lattice = mp.Lattice(size=mp.Vector3(0, syz, syz))\n", + "geometry_lattice = mp.Lattice(size=mp.Vector3(0,syz,syz))\n", "\n", "k_points = [mp.Vector3(0.5)]\n", "\n", @@ -209,29 +3194,20 @@ "\n", "a = 1.0 # waveguide width\n", "\n", + "def parallel_waveguide(s,yodd):\n", + " geometry = [mp.Block(center=mp.Vector3(0,-0.5*(s+a),0),\n", + " size=mp.Vector3(mp.inf,a,a),\n", + " material=Si),\n", + " mp.Block(center=mp.Vector3(0,0.5*(s+a),0),\n", + " size=mp.Vector3(mp.inf,a,a),\n", + " material=Si)]\n", "\n", - "def parallel_waveguide(s, yodd):\n", - " geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(0, -0.5 * (s + a), 0),\n", - " size=mp.Vector3(mp.inf, a, a),\n", - " material=Si,\n", - " ),\n", - " mp.Block(\n", - " center=mp.Vector3(0, 0.5 * (s + a), 0),\n", - " size=mp.Vector3(mp.inf, a, a),\n", - " material=Si,\n", - " ),\n", - " ]\n", - "\n", - " ms = mpb.ModeSolver(\n", - " resolution=resolution,\n", - " k_points=k_points,\n", - " geometry_lattice=geometry_lattice,\n", - " geometry=geometry,\n", - " num_bands=num_bands,\n", - " tolerance=tolerance,\n", - " )\n", + " ms = mpb.ModeSolver(resolution=resolution,\n", + " k_points=k_points,\n", + " geometry_lattice=geometry_lattice,\n", + " geometry=geometry,\n", + " num_bands=num_bands,\n", + " tolerance=tolerance)\n", "\n", " if yodd:\n", " ms.run_yodd_zodd()\n", @@ -239,12 +3215,11 @@ " ms.run_yeven_zodd()\n", "\n", " f = ms.get_freqs()[0]\n", - " vg = ms.compute_group_velocity_component(mp.Vector3(1, 0, 0))[0]\n", - "\n", - " return f, vg\n", + " vg = ms.compute_group_velocity_component(mp.Vector3(1,0,0))[0]\n", "\n", + " return f,vg\n", "\n", - "ss = np.arange(0.05, 1.15, 0.1)\n", + "ss = np.arange(0.05,1.15,0.1)\n", "\n", "f_odd = np.zeros(len(ss))\n", "vg_odd = np.zeros(len(ss))\n", @@ -252,37 +3227,48 @@ "vg_even = np.zeros(len(ss))\n", "\n", "for j in range(len(ss)):\n", - " f_odd[j], vg_odd[j] = parallel_waveguide(ss[j], True)\n", - " f_even[j], vg_even[j] = parallel_waveguide(ss[j], False)\n", - "\n", - "ds = ss[1] - ss[0]\n", + " f_odd[j], vg_odd[j] = parallel_waveguide(ss[j],True)\n", + " f_even[j], vg_even[j] = parallel_waveguide(ss[j],False)\n", "\n", + "ds = ss[1]-ss[0]\n", "\n", - "def compute_force(f, vg):\n", - " f_avg = 0.5 * (f[:-1] + f[1:])\n", - " df = f[1:] - f[:-1]\n", - " vg_avg = 0.5 * (vg[:-1] + vg[1:])\n", - " return -1 / f_avg * df / ds * 1 / vg_avg\n", + "def compute_force(f,vg):\n", + " f_avg = 0.5*(f[:-1]+f[1:])\n", + " df = f[1:]-f[:-1]\n", + " vg_avg = 0.5*(vg[:-1]+vg[1:])\n", + " return -1/f_avg * df/ds * 1/vg_avg\n", "\n", - "\n", - "force_odd = compute_force(f_odd, vg_odd)\n", - "force_even = compute_force(f_even, vg_even)" + "force_odd = compute_force(f_odd,vg_odd)\n", + "force_even = compute_force(f_even,vg_even)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=200)\n", - "plt.plot(ss[:-1], force_odd, \"b-\", label=\"anti-symmetric\")\n", - "plt.plot(ss[:-1], force_even, \"r-\", label=\"symmetric\")\n", + "plt.plot(ss[:-1],force_odd,'b-',label='anti-symmetric')\n", + "plt.plot(ss[:-1],force_even,'r-',label='symmetric')\n", "plt.xlabel(\"waveguide separation s/a\")\n", "plt.ylabel(\"optical force (F/L)(ac/P)\")\n", - "plt.legend(loc=\"upper right\")\n", - "plt.xticks(np.arange(0, 1.2, 0.2))\n", - "plt.yticks(np.arange(-1.5, 1.0, 0.5))\n", + "plt.legend(loc='upper right')\n", + "plt.xticks(np.arange(0,1.2,0.2))\n", + "plt.yticks(np.arange(-1.5,1.0,0.5))\n", "plt.show()" ] } diff --git a/python/examples/perturbation_theory.ipynb b/python/examples/perturbation_theory.ipynb index 0a32c2b97..e4cfa98b4 100644 --- a/python/examples/perturbation_theory.ipynb +++ b/python/examples/perturbation_theory.ipynb @@ -24,69 +24,198 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000187874 s\n", + "Working in Cylindrical dimensions.\n", + "Computational cell is 8 x 0 x 0.01 with resolution 100\n", + " block, center = (1.5,0,0)\n", + " size (1,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.56,11.56,11.56)\n", + "time for set_epsilon = 0.00455093 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "Meep progress: 63.24/394.1176452636719 = 16.0% done in 4.0s, 20.9s to go\n", + "on time step 12648 (time=63.24), 0.000316286 s/step\n", + "Meep progress: 127.035/394.1176452636719 = 32.2% done in 8.0s, 16.8s to go\n", + "on time step 25410 (time=127.05), 0.00031345 s/step\n", + "Meep progress: 190.945/394.1176452636719 = 48.4% done in 12.0s, 12.8s to go\n", + "on time step 38195 (time=190.975), 0.00031288 s/step\n", + "Meep progress: 254.66/394.1176452636719 = 64.6% done in 16.0s, 8.8s to go\n", + "on time step 50941 (time=254.705), 0.000313832 s/step\n", + "Meep progress: 316.92/394.1176452636719 = 80.4% done in 20.0s, 4.9s to go\n", + "on time step 63396 (time=316.98), 0.000321182 s/step\n", + "Meep progress: 376.46/394.1176452636719 = 95.5% done in 24.0s, 1.1s to go\n", + "on time step 75307 (time=376.535), 0.000335853 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.17578499227611236, -4.816700722801396e-05, 1824.7448034707427, 0.24613200861807683, -0.14560581011759283-0.19844372937023874i, 1.1797259436409042e-10+0.0i\n", + "run 0 finished at t = 394.12 (78824 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000282049 s\n", + "Working in Cylindrical dimensions.\n", + "Computational cell is 8 x 0 x 0.01 with resolution 100\n", + " block, center = (1.5,0,0)\n", + " size (1,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.56,11.56,11.56)\n", + "time for set_epsilon = 0.00452399 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "Meep progress: 64.11/1237.7535400390625 = 5.2% done in 4.0s, 73.2s to go\n", + "on time step 12822 (time=64.11), 0.00031198 s/step\n", + "Meep progress: 129.395/1237.7535400390625 = 10.5% done in 8.0s, 68.5s to go\n", + "on time step 25882 (time=129.41), 0.0003063 s/step\n", + "Meep progress: 194.32/1237.7535400390625 = 15.7% done in 12.0s, 64.4s to go\n", + "on time step 38871 (time=194.355), 0.000307977 s/step\n", + "Meep progress: 260.045/1237.7535400390625 = 21.0% done in 16.0s, 60.2s to go\n", + "on time step 52019 (time=260.095), 0.00030423 s/step\n", + "Meep progress: 325.825/1237.7535400390625 = 26.3% done in 20.0s, 56.0s to go\n", + "on time step 65178 (time=325.89), 0.000303988 s/step\n", + "Meep progress: 390.665/1237.7535400390625 = 31.6% done in 24.0s, 52.0s to go\n", + "on time step 78150 (time=390.75), 0.00030838 s/step\n", + "Meep progress: 455.2/1237.7535400390625 = 36.8% done in 28.0s, 48.1s to go\n", + "on time step 91060 (time=455.3), 0.000309849 s/step\n", + "Meep progress: 519.66/1237.7535400390625 = 42.0% done in 32.0s, 44.2s to go\n", + "on time step 103958 (time=519.79), 0.000310148 s/step\n", + "Meep progress: 584.73/1237.7535400390625 = 47.2% done in 36.0s, 40.2s to go\n", + "on time step 116975 (time=584.875), 0.000307307 s/step\n", + "Meep progress: 649.64/1237.7535400390625 = 52.5% done in 40.0s, 36.2s to go\n", + "on time step 129957 (time=649.785), 0.000308119 s/step\n", + "Meep progress: 714.945/1237.7535400390625 = 57.8% done in 44.0s, 32.2s to go\n", + "on time step 143025 (time=715.125), 0.000306096 s/step\n", + "Meep progress: 780.855/1237.7535400390625 = 63.1% done in 48.0s, 28.1s to go\n", + "on time step 156209 (time=781.045), 0.000303408 s/step\n", + "Meep progress: 846.395/1237.7535400390625 = 68.4% done in 52.0s, 24.0s to go\n", + "on time step 169322 (time=846.61), 0.000305051 s/step\n", + "Meep progress: 911.86/1237.7535400390625 = 73.7% done in 56.0s, 20.0s to go\n", + "on time step 182419 (time=912.095), 0.000305427 s/step\n", + "Meep progress: 977.405/1237.7535400390625 = 79.0% done in 60.0s, 16.0s to go\n", + "on time step 195532 (time=977.66), 0.000305057 s/step\n", + "Meep progress: 1042.795/1237.7535400390625 = 84.2% done in 64.0s, 12.0s to go\n", + "on time step 208616 (time=1043.08), 0.000305738 s/step\n", + "Meep progress: 1107.3500000000001/1237.7535400390625 = 89.5% done in 68.0s, 8.0s to go\n", + "on time step 221526 (time=1107.63), 0.000309854 s/step\n", + "Meep progress: 1172.115/1237.7535400390625 = 94.7% done in 72.0s, 4.0s to go\n", + "on time step 234482 (time=1172.41), 0.000308753 s/step\n", + "Meep progress: 1237.6100000000001/1237.7535400390625 = 100.0% done in 76.0s, 0.0s to go\n", + "run 0 finished at t = 1237.755 (247551 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000117064 s\n", + "Working in Cylindrical dimensions.\n", + "Computational cell is 8 x 0 x 0.01 with resolution 100\n", + " block, center = (1.51,0,0)\n", + " size (1,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (11.56,11.56,11.56)\n", + "time for set_epsilon = 0.0026741 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "Meep progress: 64.245/1237.7535400390625 = 5.2% done in 4.0s, 73.1s to go\n", + "on time step 12849 (time=64.245), 0.000311334 s/step\n", + "Meep progress: 128.08/1237.7535400390625 = 10.3% done in 8.0s, 69.3s to go\n", + "on time step 25621 (time=128.105), 0.000313206 s/step\n", + "Meep progress: 192.4/1237.7535400390625 = 15.5% done in 12.0s, 65.2s to go\n", + "on time step 38487 (time=192.435), 0.0003109 s/step\n", + "Meep progress: 256.76/1237.7535400390625 = 20.7% done in 16.0s, 61.1s to go\n", + "on time step 51365 (time=256.825), 0.00031061 s/step\n", + "Meep progress: 321.26/1237.7535400390625 = 26.0% done in 20.0s, 57.1s to go\n", + "on time step 64271 (time=321.355), 0.000309956 s/step\n", + "Meep progress: 385.66/1237.7535400390625 = 31.2% done in 24.0s, 53.0s to go\n", + "on time step 77154 (time=385.77), 0.000310499 s/step\n", + "Meep progress: 449.995/1237.7535400390625 = 36.4% done in 28.0s, 49.0s to go\n", + "on time step 90026 (time=450.13), 0.000310771 s/step\n", + "Meep progress: 514.22/1237.7535400390625 = 41.5% done in 32.0s, 45.0s to go\n", + "on time step 102876 (time=514.38), 0.000311289 s/step\n", + "Meep progress: 578.95/1237.7535400390625 = 46.8% done in 36.0s, 41.0s to go\n", + "on time step 115826 (time=579.13), 0.000308899 s/step\n", + "Meep progress: 642.9250000000001/1237.7535400390625 = 51.9% done in 40.0s, 37.0s to go\n", + "on time step 128622 (time=643.11), 0.000312613 s/step\n", + "Meep progress: 706.915/1237.7535400390625 = 57.1% done in 44.0s, 33.0s to go\n", + "on time step 141424 (time=707.12), 0.00031246 s/step\n", + "Meep progress: 770.58/1237.7535400390625 = 62.3% done in 48.0s, 29.1s to go\n", + "on time step 154164 (time=770.82), 0.000313974 s/step\n", + "Meep progress: 834.445/1237.7535400390625 = 67.4% done in 52.0s, 25.1s to go\n", + "on time step 166942 (time=834.71), 0.00031304 s/step\n", + "Meep progress: 898.195/1237.7535400390625 = 72.6% done in 56.0s, 21.2s to go\n", + "on time step 179696 (time=898.48), 0.000313635 s/step\n", + "Meep progress: 961.405/1237.7535400390625 = 77.7% done in 60.0s, 17.2s to go\n", + "on time step 192338 (time=961.69), 0.000316413 s/step\n", + "Meep progress: 1025.53/1237.7535400390625 = 82.9% done in 64.0s, 13.2s to go\n", + "on time step 205173 (time=1025.87), 0.000311651 s/step\n", + "Meep progress: 1089.78/1237.7535400390625 = 88.0% done in 68.0s, 9.2s to go\n", + "on time step 218020 (time=1090.1), 0.000311357 s/step\n", + "Meep progress: 1151.805/1237.7535400390625 = 93.1% done in 72.0s, 5.4s to go\n", + "on time step 230424 (time=1152.12), 0.000322493 s/step\n", + "Meep progress: 1210.695/1237.7535400390625 = 97.8% done in 76.0s, 1.7s to go\n", + "on time step 242207 (time=1211.04), 0.000339497 s/step\n", + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.17493286736703903, -4.814897914072506e-05, 1816.5791932551942, 1.216470973025523, 1.2069307717323075-0.15205176901081904i, 2.316660865172475e-11+0.0i\n", + "run 0 finished at t = 1237.755 (247551 timesteps)\n", + "dwdR:, -0.08544696397218933 (pert. theory), -0.08521249090733263 (finite diff.)\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", "\n", - "resolution = 100 # pixels/um\n", + "resolution = 100 # pixels/um\n", "\n", - "perpendicular = False # perpendicular (Hz) or parallel (Ez) source?\n", + "perpendicular = False # perpendicular (Hz) or parallel (Ez) source?\n", "\n", "if perpendicular:\n", " src_cmpt = mp.Hz\n", - " fcen = 0.21 # pulse center frequency\n", + " fcen = 0.21 # pulse center frequency\n", "else:\n", " src_cmpt = mp.Ez\n", - " fcen = 0.17 # pulse center frequency\n", + " fcen = 0.17 # pulse center frequency\n", "\n", - "n = 3.4 # index of waveguide\n", - "w = 1 # ring width\n", - "r = 1 # inner radius of ring\n", - "pad = 4 # padding between waveguide and edge of PML\n", - "dpml = 2 # thickness of PML\n", - "m = 5 # angular dependence\n", + "n = 3.4 # index of waveguide\n", + "w = 1 # ring width\n", + "r = 1 # inner radius of ring\n", + "pad = 4 # padding between waveguide and edge of PML\n", + "dpml = 2 # thickness of PML\n", + "m = 5 # angular dependence\n", "\n", "pml_layers = [mp.PML(dpml)]\n", "\n", - "sr = r + w + pad + dpml # radial size (cell is from 0 to sr)\n", - "dimensions = mp.CYLINDRICAL # coordinate system is (r,phi,z) instead of (x,y,z)\n", + "sr = r + w + pad + dpml # radial size (cell is from 0 to sr)\n", + "dimensions = mp.CYLINDRICAL # coordinate system is (r,phi,z) instead of (x,y,z)\n", "cell = mp.Vector3(sr)\n", "\n", - "geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(r + (w / 2)),\n", - " size=mp.Vector3(w, mp.inf, mp.inf),\n", - " material=mp.Medium(index=n),\n", - " )\n", - "]\n", + "geometry = [mp.Block(center=mp.Vector3(r + (w / 2)),\n", + " size=mp.Vector3(w, mp.inf, mp.inf),\n", + " material=mp.Medium(index=n))]\n", "\n", "# find resonant frequency of unperturbed geometry using broadband source\n", "\n", - "df = 0.2 * fcen # pulse width (in frequency)\n", - "\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=src_cmpt,\n", - " center=mp.Vector3(r + 0.1),\n", - " )\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " dimensions=dimensions,\n", - " m=m,\n", - ")\n", - "\n", - "h = mp.Harminv(src_cmpt, mp.Vector3(r + 0.1), fcen, df)\n", - "sim.run(mp.after_sources(h), until_after_sources=100)\n", + "df = 0.2*fcen # pulse width (in frequency)\n", + "\n", + "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", + " component=src_cmpt,\n", + " center=mp.Vector3(r+0.1))]\n", + "\n", + "sim = mp.Simulation(cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " dimensions=dimensions,\n", + " m=m)\n", + "\n", + "h = mp.Harminv(src_cmpt, mp.Vector3(r+0.1), fcen, df)\n", + "sim.run(mp.after_sources(h),\n", + " until_after_sources=100)\n", "\n", "frq_unperturbed = h.modes[0].freq\n", "\n", @@ -95,69 +224,35 @@ "# unperturbed geometry with narrowband source centered at resonant frequency\n", "\n", "fcen = frq_unperturbed\n", - "df = 0.05 * fcen\n", - "\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=src_cmpt,\n", - " center=mp.Vector3(r + 0.1),\n", - " )\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " dimensions=dimensions,\n", - " m=m,\n", - ")\n", + "df = 0.05*fcen\n", + "\n", + "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", + " component=src_cmpt,\n", + " center=mp.Vector3(r+0.1))]\n", + "\n", + "sim = mp.Simulation(cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " dimensions=dimensions,\n", + " m=m)\n", "\n", "sim.run(until_after_sources=100)\n", "\n", "deps = 1 - n**2\n", - "deps_inv = 1 - 1 / n**2\n", + "deps_inv = 1 - 1/n**2\n", "\n", "if perpendicular:\n", - " para_integral = (\n", - " deps\n", - " * 2\n", - " * np.pi\n", - " * (\n", - " r * abs(sim.get_field_point(mp.Ep, mp.Vector3(r))) ** 2\n", - " - (r + w) * abs(sim.get_field_point(mp.Ep, mp.Vector3(r + w))) ** 2\n", - " )\n", - " )\n", - " perp_integral = (\n", - " deps_inv\n", - " * 2\n", - " * np.pi\n", - " * (\n", - " -r * abs(sim.get_field_point(mp.Dr, mp.Vector3(r))) ** 2\n", - " + (r + w) * abs(sim.get_field_point(mp.Dr, mp.Vector3(r + w))) ** 2\n", - " )\n", - " )\n", + " para_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ep, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ep, mp.Vector3(r+w)))**2)\n", + " perp_integral = deps_inv*2*np.pi*(-r*abs(sim.get_field_point(mp.Dr, mp.Vector3(r)))**2 + (r+w)*abs(sim.get_field_point(mp.Dr, mp.Vector3(r+w)))**2)\n", " numerator_integral = para_integral + perp_integral\n", "else:\n", - " numerator_integral = (\n", - " deps\n", - " * 2\n", - " * np.pi\n", - " * (\n", - " r * abs(sim.get_field_point(mp.Ez, mp.Vector3(r))) ** 2\n", - " - (r + w) * abs(sim.get_field_point(mp.Ez, mp.Vector3(r + w))) ** 2\n", - " )\n", - " )\n", - "\n", - "denominator_integral = sim.electric_energy_in_box(\n", - " center=mp.Vector3(0.5 * sr), size=mp.Vector3(sr)\n", - ")\n", - "\n", - "perturb_theory_dw_dR = (\n", - " -frq_unperturbed * numerator_integral / (4 * denominator_integral)\n", - ")\n", + " numerator_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ez, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ez, mp.Vector3(r+w)))**2)\n", + "\n", + "denominator_integral = sim.electric_energy_in_box(center=mp.Vector3(0.5*sr), size=mp.Vector3(sr))\n", + "\n", + "perturb_theory_dw_dR = -frq_unperturbed * numerator_integral / (4 * denominator_integral)\n", "\n", "sim.reset_meep()\n", "\n", @@ -165,44 +260,31 @@ "\n", "dr = 0.01\n", "\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=src_cmpt,\n", - " center=mp.Vector3(r + dr + 0.1),\n", - " )\n", - "]\n", - "\n", - "geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(r + dr + (w / 2)),\n", - " size=mp.Vector3(w, mp.inf, mp.inf),\n", - " material=mp.Medium(index=n),\n", - " )\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " dimensions=dimensions,\n", - " m=m,\n", - ")\n", - "\n", - "h = mp.Harminv(src_cmpt, mp.Vector3(r + dr + 0.1), fcen, df)\n", - "sim.run(mp.after_sources(h), until_after_sources=100)\n", + "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", + " component=src_cmpt,\n", + " center=mp.Vector3(r + dr + 0.1))]\n", + "\n", + "geometry = [mp.Block(center=mp.Vector3(r + dr + (w / 2)),\n", + " size=mp.Vector3(w, mp.inf, mp.inf),\n", + " material=mp.Medium(index=n))]\n", + "\n", + "sim = mp.Simulation(cell_size=cell,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " dimensions=dimensions,\n", + " m=m)\n", + "\n", + "h = mp.Harminv(src_cmpt, mp.Vector3(r+dr+0.1), fcen, df)\n", + "sim.run(mp.after_sources(h),\n", + " until_after_sources=100)\n", "\n", "frq_perturbed = h.modes[0].freq\n", "\n", "finite_diff_dw_dR = (frq_perturbed - frq_unperturbed) / dr\n", "\n", - "print(\n", - " \"dwdR:, {} (pert. theory), {} (finite diff.)\".format(\n", - " perturb_theory_dw_dR, finite_diff_dw_dR\n", - " )\n", - ")" + "print(\"dwdR:, {} (pert. theory), {} (finite diff.)\".format(perturb_theory_dw_dR,finite_diff_dw_dR))" ] }, { diff --git a/python/examples/polarization_grating.ipynb b/python/examples/polarization_grating.ipynb index 085165c17..c7e49ba03 100644 --- a/python/examples/polarization_grating.ipynb +++ b/python/examples/polarization_grating.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,102 +28,63 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 30 # pixels/μm\n", + "resolution = 30 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 1.0 # substrate thickness\n", - "dpad = 1.0 # padding thickness\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 1.0 # substrate thickness\n", + "dpad = 1.0 # padding thickness\n", "\n", - "k_point = mp.Vector3(0, 0, 0)\n", + "k_point = mp.Vector3(0,0,0)\n", "\n", - "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n", + "pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n", "\n", "n_0 = 1.55\n", "delta_n = 0.159\n", - "epsilon_diag = mp.Matrix(\n", - " mp.Vector3(n_0**2, 0, 0),\n", - " mp.Vector3(0, n_0**2, 0),\n", - " mp.Vector3(0, 0, (n_0 + delta_n) ** 2),\n", - ")\n", + "epsilon_diag = mp.Matrix(mp.Vector3(n_0**2,0,0),mp.Vector3(0,n_0**2,0),mp.Vector3(0,0,(n_0+delta_n)**2))\n", "\n", - "wvl = 0.54 # center wavelength\n", - "fcen = 1 / wvl # center frequency\n", + "wvl = 0.54 # center wavelength\n", + "fcen = 1/wvl # center frequency\n", "\n", - "\n", - "def pol_grating(d, ph, gp, nmode):\n", - " sx = dpml + dsub + d + d + dpad + dpml\n", + "def pol_grating(d,ph,gp,nmode):\n", + " sx = dpml+dsub+d+d+dpad+dpml\n", " sy = gp\n", "\n", - " cell_size = mp.Vector3(sx, sy, 0)\n", + " cell_size = mp.Vector3(sx,sy,0)\n", "\n", " # twist angle of nematic director; from equation 1b\n", " def phi(p):\n", - " xx = p.x - (-0.5 * sx + dpml + dsub)\n", + " xx = p.x-(-0.5*sx+dpml+dsub)\n", " if (xx >= 0) and (xx <= d):\n", - " return math.pi * p.y / gp + ph * xx / d\n", + " return math.pi*p.y/gp + ph*xx/d\n", " else:\n", - " return math.pi * p.y / gp - ph * xx / d + 2 * ph\n", + " return math.pi*p.y/gp - ph*xx/d + 2*ph\n", "\n", " # return the anisotropic permittivity tensor for a uniaxial, twisted nematic liquid crystal\n", " def lc_mat(p):\n", " # rotation matrix for rotation around x axis\n", - " Rx = mp.Matrix(\n", - " mp.Vector3(1, 0, 0),\n", - " mp.Vector3(0, math.cos(phi(p)), math.sin(phi(p))),\n", - " mp.Vector3(0, -math.sin(phi(p)), math.cos(phi(p))),\n", - " )\n", + " Rx = mp.Matrix(mp.Vector3(1,0,0),mp.Vector3(0,math.cos(phi(p)),math.sin(phi(p))),mp.Vector3(0,-math.sin(phi(p)),math.cos(phi(p))))\n", " lc_epsilon = Rx * epsilon_diag * Rx.transpose()\n", - " lc_epsilon_diag = mp.Vector3(lc_epsilon[0].x, lc_epsilon[1].y, lc_epsilon[2].z)\n", - " lc_epsilon_offdiag = mp.Vector3(\n", - " lc_epsilon[1].x, lc_epsilon[2].x, lc_epsilon[2].y\n", - " )\n", - " return mp.Medium(\n", - " epsilon_diag=lc_epsilon_diag, epsilon_offdiag=lc_epsilon_offdiag\n", - " )\n", - "\n", - " geometry = [\n", - " mp.Block(\n", - " center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n", - " size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n", - " material=mp.Medium(index=n_0),\n", - " ),\n", - " mp.Block(\n", - " center=mp.Vector3(-0.5 * sx + dpml + dsub + d),\n", - " size=mp.Vector3(2 * d, mp.inf, mp.inf),\n", - " material=lc_mat,\n", - " ),\n", - " ]\n", + " lc_epsilon_diag = mp.Vector3(lc_epsilon[0].x,lc_epsilon[1].y,lc_epsilon[2].z)\n", + " lc_epsilon_offdiag = mp.Vector3(lc_epsilon[1].x,lc_epsilon[2].x,lc_epsilon[2].y)\n", + " return mp.Medium(epsilon_diag=lc_epsilon_diag,epsilon_offdiag=lc_epsilon_offdiag)\n", + "\n", + " geometry = [mp.Block(center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)),size=mp.Vector3(dpml+dsub,mp.inf,mp.inf),material=mp.Medium(index=n_0)),\n", + " mp.Block(center=mp.Vector3(-0.5*sx+dpml+dsub+d),size=mp.Vector3(2*d,mp.inf,mp.inf),material=lc_mat)]\n", "\n", " # linear-polarized planewave pulse source\n", - " src_pt = mp.Vector3(-0.5 * sx + dpml + 0.3 * dsub, 0, 0)\n", - " sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=0.05 * fcen),\n", - " component=mp.Ez,\n", - " center=src_pt,\n", - " size=mp.Vector3(0, sy, 0),\n", - " ),\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=0.05 * fcen),\n", - " component=mp.Ey,\n", - " center=src_pt,\n", - " size=mp.Vector3(0, sy, 0),\n", - " ),\n", - " ]\n", - "\n", - " sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " default_material=mp.Medium(index=n_0),\n", - " )\n", - "\n", - " tran_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0)\n", - " tran_flux = sim.add_flux(\n", - " fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0))\n", - " )\n", + " src_pt = mp.Vector3(-0.5*sx+dpml+0.3*dsub,0,0)\n", + " sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.05*fcen), component=mp.Ez, center=src_pt, size=mp.Vector3(0,sy,0)),\n", + " mp.Source(mp.GaussianSource(fcen,fwidth=0.05*fcen), component=mp.Ey, center=src_pt, size=mp.Vector3(0,sy,0))]\n", + "\n", + " sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " default_material=mp.Medium(index=n_0))\n", + "\n", + " tran_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0)\n", + " tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0)))\n", "\n", " sim.run(until_after_sources=100)\n", "\n", @@ -131,30 +92,22 @@ "\n", " sim.reset_meep()\n", "\n", - " sim = mp.Simulation(\n", - " resolution=resolution,\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=k_point,\n", - " sources=sources,\n", - " geometry=geometry,\n", - " )\n", + " sim = mp.Simulation(resolution=resolution,\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=k_point,\n", + " sources=sources,\n", + " geometry=geometry)\n", "\n", - " tran_flux = sim.add_flux(\n", - " fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0))\n", - " )\n", + " tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0)))\n", "\n", " sim.run(until_after_sources=300)\n", "\n", - " res1 = sim.get_eigenmode_coefficients(\n", - " tran_flux, range(1, nmode + 1), eig_parity=mp.ODD_Z + mp.EVEN_Y\n", - " )\n", - " res2 = sim.get_eigenmode_coefficients(\n", - " tran_flux, range(1, nmode + 1), eig_parity=mp.EVEN_Z + mp.ODD_Y\n", - " )\n", - " angles = [math.degrees(math.acos(kdom.x / fcen)) for kdom in res1.kdom]\n", + " res1 = sim.get_eigenmode_coefficients(tran_flux, range(1,nmode+1), eig_parity=mp.ODD_Z+mp.EVEN_Y)\n", + " res2 = sim.get_eigenmode_coefficients(tran_flux, range(1,nmode+1), eig_parity=mp.EVEN_Z+mp.ODD_Y)\n", + " angles = [math.degrees(math.acos(kdom.x/fcen)) for kdom in res1.kdom]\n", "\n", - " return input_flux[0], angles, res1.alpha[:, 0, 0], res2.alpha[:, 0, 0];" + " return input_flux[0], angles, res1.alpha[:,0,0], res2.alpha[:,0,0];" ] }, { @@ -166,15 +119,3036 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 5.79357e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.2 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.0339479 s\n", + "-----------\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 2.71797e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.2 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.1,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (-8.32667e-17,0,0)\n", + " size (0.2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "time for set_epsilon = 0.758735 s\n", + "-----------\n", + "Meep progress: 224.88333333333333/408.0 = 55.1% done in 4.0s, 3.3s to go\n", + "on time step 13493 (time=224.883), 0.000296471 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 65 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 37 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 47 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 42 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.89247\n", + "tran (uniaxial):, 1, 4.77, 0.01875\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 4.41074e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.4 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.0652409 s\n", + "-----------\n", + "Meep progress: 193.48333333333332/208.0 = 93.0% done in 4.0s, 0.3s to go\n", + "on time step 11609 (time=193.483), 0.000344592 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.00815e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.4 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.2,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (-1.66533e-16,0,0)\n", + " size (0.4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "time for set_epsilon = 3.35973 s\n", + "-----------\n", + "Meep progress: 100.36666666666666/408.0 = 24.6% done in 4.0s, 12.3s to go\n", + "on time step 6022 (time=100.367), 0.000664325 s/step\n", + "Meep progress: 200.35/408.0 = 49.1% done in 8.0s, 8.3s to go\n", + "on time step 12022 (time=200.367), 0.000666721 s/step\n", + "Meep progress: 301.4166666666667/408.0 = 73.9% done in 12.0s, 4.2s to go\n", + "on time step 18088 (time=301.467), 0.000659487 s/step\n", + "Meep progress: 400.9/408.0 = 98.3% done in 16.0s, 0.3s to go\n", + "on time step 24058 (time=400.967), 0.000670094 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 29 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 55 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.84732\n", + "tran (twisted):, 1, 4.77, 0.03707\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 8.70228e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.4 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.0973799 s\n", + "-----------\n", + "Meep progress: 104.83333333333333/208.0 = 50.4% done in 4.0s, 3.9s to go\n", + "on time step 6290 (time=104.833), 0.00063597 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 5.91278e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.4 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.2,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " block, center = (-1.66533e-16,0,0)\n", + " size (0.4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "time for set_epsilon = 3.38153 s\n", + "-----------\n", + "Meep progress: 100.73333333333333/408.0 = 24.7% done in 4.0s, 12.2s to go\n", + "on time step 6044 (time=100.733), 0.00066186 s/step\n", + "Meep progress: 201.35/408.0 = 49.4% done in 8.0s, 8.2s to go\n", + "on time step 12083 (time=201.383), 0.000662443 s/step\n", + "Meep progress: 301.6333333333333/408.0 = 73.9% done in 12.0s, 4.2s to go\n", + "on time step 18102 (time=301.7), 0.000664572 s/step\n", + "Meep progress: 403.3333333333333/408.0 = 98.9% done in 16.0s, 0.2s to go\n", + "on time step 24205 (time=403.417), 0.000655435 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 50 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 37 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 66 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.79050\n", + "tran (uniaxial):, 1, 4.77, 0.06529\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.79493e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.8 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.0874732 s\n", + "-----------\n", + "Meep progress: 102.21666666666667/208.0 = 49.1% done in 4.0s, 4.1s to go\n", + "on time step 6133 (time=102.217), 0.000652292 s/step\n", + "Meep progress: 205.86666666666667/208.0 = 99.0% done in 8.0s, 0.1s to go\n", + "on time step 12353 (time=205.883), 0.000643173 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.41346e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.8 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.4,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (0.8,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "time for set_epsilon = 6.61127 s\n", + "-----------\n", + "Meep progress: 97.56666666666666/408.0 = 23.9% done in 4.0s, 12.7s to go\n", + "on time step 5854 (time=97.5667), 0.000683333 s/step\n", + "Meep progress: 195.35/408.0 = 47.9% done in 8.0s, 8.7s to go\n", + "on time step 11722 (time=195.367), 0.000681668 s/step\n", + "Meep progress: 293.75/408.0 = 72.0% done in 12.0s, 4.7s to go\n", + "on time step 17627 (time=293.783), 0.000677395 s/step\n", + "Meep progress: 391.4/408.0 = 95.9% done in 16.0s, 0.7s to go\n", + "on time step 23487 (time=391.45), 0.000682659 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 38 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 36 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 34 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.64693\n", + "tran (twisted):, 1, 4.77, 0.14670\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.70092e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.6 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.0854559 s\n", + "-----------\n", + "Meep progress: 102.86666666666666/208.0 = 49.5% done in 4.0s, 4.1s to go\n", + "on time step 6172 (time=102.867), 0.000648176 s/step\n", + "Meep progress: 207.0/208.0 = 99.5% done in 8.0s, 0.0s to go\n", + "on time step 12421 (time=207.017), 0.000640153 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.69956e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.6 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.3,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " block, center = (2.22045e-16,0,0)\n", + " size (0.6,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "time for set_epsilon = 4.98919 s\n", + "-----------\n", + "Meep progress: 98.01666666666667/408.0 = 24.0% done in 4.0s, 12.7s to go\n", + "on time step 5881 (time=98.0167), 0.000680194 s/step\n", + "Meep progress: 197.98333333333332/408.0 = 48.5% done in 8.0s, 8.5s to go\n", + "on time step 11880 (time=198), 0.00066678 s/step\n", + "Meep progress: 297.0833333333333/408.0 = 72.8% done in 12.0s, 4.5s to go\n", + "on time step 17827 (time=297.117), 0.000672654 s/step\n", + "Meep progress: 395.51666666666665/408.0 = 96.9% done in 16.0s, 0.5s to go\n", + "on time step 23735 (time=395.583), 0.000677137 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 70 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 31 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 70 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 50 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 44 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 33 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.66119\n", + "tran (uniaxial):, 1, 4.77, 0.14065\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 2.69413e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5.2 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.090482 s\n", + "-----------\n", + "Meep progress: 97.23333333333333/208.0 = 46.7% done in 4.0s, 4.6s to go\n", + "on time step 5834 (time=97.2333), 0.000685701 s/step\n", + "Meep progress: 196.66666666666666/208.0 = 94.6% done in 8.0s, 0.5s to go\n", + "on time step 11801 (time=196.683), 0.000670365 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.22272e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5.2 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.6,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (1.2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "time for set_epsilon = 10.1716 s\n", + "-----------\n", + "Meep progress: 89.91666666666667/408.0 = 22.0% done in 4.0s, 14.2s to go\n", + "on time step 5395 (time=89.9167), 0.000741585 s/step\n", + "Meep progress: 183.15/408.0 = 44.9% done in 8.0s, 9.8s to go\n", + "on time step 10990 (time=183.167), 0.000715001 s/step\n", + "Meep progress: 274.8333333333333/408.0 = 67.4% done in 12.0s, 5.8s to go\n", + "on time step 16493 (time=274.883), 0.000726979 s/step\n", + "Meep progress: 367.5333333333333/408.0 = 90.1% done in 16.0s, 1.8s to go\n", + "on time step 22056 (time=367.6), 0.000719077 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 36 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 27 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 50 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 31 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.39057\n", + "tran (twisted):, 1, 4.77, 0.27182\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000102043 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.8 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.0766759 s\n", + "-----------\n", + "Meep progress: 102.18333333333334/208.0 = 49.1% done in 4.0s, 4.1s to go\n", + "on time step 6131 (time=102.183), 0.000652499 s/step\n", + "Meep progress: 205.46666666666667/208.0 = 98.8% done in 8.0s, 0.1s to go\n", + "on time step 12330 (time=205.5), 0.000645285 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.38962e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 4.8 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.4,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (0.8,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "time for set_epsilon = 6.60763 s\n", + "-----------\n", + "Meep progress: 93.03333333333333/408.0 = 22.8% done in 4.0s, 13.5s to go\n", + "on time step 5582 (time=93.0333), 0.00071674 s/step\n", + "Meep progress: 188.96666666666667/408.0 = 46.3% done in 8.0s, 9.3s to go\n", + "on time step 11340 (time=189), 0.000694738 s/step\n", + "Meep progress: 285.1666666666667/408.0 = 69.9% done in 12.0s, 5.2s to go\n", + "on time step 17113 (time=285.217), 0.0006929 s/step\n", + "Meep progress: 381.0/408.0 = 93.4% done in 16.0s, 1.1s to go\n", + "on time step 22865 (time=381.083), 0.000695525 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 58 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 54 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.47140\n", + "tran (uniaxial):, 1, 4.77, 0.23506\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 8.98838e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5.6 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.100205 s\n", + "-----------\n", + "Meep progress: 92.38333333333333/208.0 = 44.4% done in 4.0s, 5.0s to go\n", + "on time step 5543 (time=92.3833), 0.000721698 s/step\n", + "Meep progress: 186.2/208.0 = 89.5% done in 8.0s, 0.9s to go\n", + "on time step 11174 (time=186.233), 0.000710356 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.50883e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5.6 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.8,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (2.22045e-16,0,0)\n", + " size (1.6,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 71.5192% done, 1.70329 s remaining\n", + "subpixel-averaging is 71.5192% done, 1.68457 s remaining\n", + "subpixel-averaging is 71.5192% done, 1.68515 s remaining\n", + "time for set_epsilon = 13.1647 s\n", + "-----------\n", + "Meep progress: 87.73333333333333/408.0 = 21.5% done in 4.0s, 14.6s to go\n", + "on time step 5264 (time=87.7333), 0.00076 s/step\n", + "Meep progress: 176.35/408.0 = 43.2% done in 8.0s, 10.5s to go\n", + "on time step 10583 (time=176.383), 0.000752144 s/step\n", + "Meep progress: 265.5833333333333/408.0 = 65.1% done in 12.0s, 6.4s to go\n", + "on time step 15938 (time=265.633), 0.000747004 s/step\n", + "Meep progress: 353.1166666666667/408.0 = 86.5% done in 16.0s, 2.5s to go\n", + "on time step 21191 (time=353.183), 0.000761606 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 51 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 34 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 64 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 43 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 31 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 33 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.17500\n", + "tran (twisted):, 1, 4.77, 0.38032\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.10487e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.0600371 s\n", + "-----------\n", + "Meep progress: 100.0/208.0 = 48.1% done in 4.0s, 4.3s to go\n", + "on time step 6000 (time=100), 0.000666774 s/step\n", + "Meep progress: 201.83333333333334/208.0 = 97.0% done in 8.0s, 0.2s to go\n", + "on time step 12113 (time=201.883), 0.000654451 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.10352e-05 s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working in 2D dimensions.\n", + "Computational cell is 5 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.5,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (1,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "time for set_epsilon = 8.54403 s\n", + "-----------\n", + "Meep progress: 93.58333333333333/408.0 = 22.9% done in 4.0s, 13.4s to go\n", + "on time step 5615 (time=93.5833), 0.000712491 s/step\n", + "Meep progress: 189.16666666666666/408.0 = 46.4% done in 8.0s, 9.3s to go\n", + "on time step 11352 (time=189.2), 0.000697323 s/step\n", + "Meep progress: 283.1666666666667/408.0 = 69.4% done in 12.0s, 5.3s to go\n", + "on time step 16993 (time=283.217), 0.000709178 s/step\n", + "Meep progress: 378.1166666666667/408.0 = 92.7% done in 16.0s, 1.3s to go\n", + "on time step 22691 (time=378.183), 0.000702061 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 30 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 35 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 61 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 44 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.27041\n", + "tran (uniaxial):, 1, 4.77, 0.32724\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 3.60012e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.131748 s\n", + "-----------\n", + "Meep progress: 87.66666666666667/208.0 = 42.1% done in 4.0s, 5.5s to go\n", + "on time step 5260 (time=87.6667), 0.000760564 s/step\n", + "Meep progress: 179.5/208.0 = 86.3% done in 8.0s, 1.3s to go\n", + "on time step 10772 (time=179.533), 0.000725698 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.29425e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6 x 6.5 x 0 with resolution 30\n", + " block, center = (-2,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 68.5946% done, 1.84034 s remaining\n", + "subpixel-averaging is 64.3072% done, 2.3664 s remaining\n", + "subpixel-averaging is 64.3072% done, 2.43912 s remaining\n", + "time for set_epsilon = 15.5353 s\n", + "-----------\n", + "Meep progress: 83.8/408.0 = 20.5% done in 4.0s, 15.5s to go\n", + "on time step 5028 (time=83.8), 0.000795612 s/step\n", + "Meep progress: 168.46666666666667/408.0 = 41.3% done in 8.0s, 11.4s to go\n", + "on time step 10110 (time=168.5), 0.000787236 s/step\n", + "Meep progress: 253.21666666666667/408.0 = 62.1% done in 12.0s, 7.3s to go\n", + "on time step 15197 (time=253.283), 0.000786469 s/step\n", + "Meep progress: 337.46666666666664/408.0 = 82.7% done in 16.0s, 3.3s to go\n", + "on time step 20253 (time=337.55), 0.00079122 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 38 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 46 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 28 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.04972\n", + "tran (twisted):, 1, 4.77, 0.44671\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000128031 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5.2 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.082103 s\n", + "-----------\n", + "Meep progress: 98.0/208.0 = 47.1% done in 4.0s, 4.5s to go\n", + "on time step 5880 (time=98), 0.000680371 s/step\n", + "Meep progress: 197.83333333333334/208.0 = 95.1% done in 8.0s, 0.4s to go\n", + "on time step 11871 (time=197.85), 0.000667708 s/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.10352e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5.2 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.6,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (-1.11022e-16,0,0)\n", + " size (1.2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "time for set_epsilon = 9.882 s\n", + "-----------\n", + "Meep progress: 92.15/408.0 = 22.6% done in 4.0s, 13.7s to go\n", + "on time step 5529 (time=92.15), 0.000723491 s/step\n", + "Meep progress: 185.61666666666667/408.0 = 45.5% done in 8.0s, 9.6s to go\n", + "on time step 11139 (time=185.65), 0.00071314 s/step\n", + "Meep progress: 278.5833333333333/408.0 = 68.3% done in 12.0s, 5.6s to go\n", + "on time step 16719 (time=278.65), 0.000716963 s/step\n", + "Meep progress: 371.01666666666665/408.0 = 90.9% done in 16.0s, 1.6s to go\n", + "on time step 22267 (time=371.117), 0.000721017 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 42 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 44 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 33 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 62 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 37 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.14816\n", + "tran (uniaxial):, 1, 4.77, 0.39120\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000138044 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.4 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.109352 s\n", + "-----------\n", + "Meep progress: 85.88333333333333/208.0 = 41.3% done in 4.0s, 5.7s to go\n", + "on time step 5153 (time=85.8833), 0.00077639 s/step\n", + "Meep progress: 172.66666666666666/208.0 = 83.0% done in 8.0s, 1.6s to go\n", + "on time step 10361 (time=172.683), 0.000768107 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.69956e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.4 x 6.5 x 0 with resolution 30\n", + " block, center = (-2.2,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (-2.22045e-16,0,0)\n", + " size (2.4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 58.4164% done, 3.10351 s remaining\n", + "subpixel-averaging is 58.4164% done, 3.12547 s remaining\n", + "subpixel-averaging is 58.4164% done, 3.0991 s remaining\n", + "time for set_epsilon = 19.9794 s\n", + "-----------\n", + "Meep progress: 80.01666666666667/408.0 = 19.6% done in 4.0s, 16.4s to go\n", + "on time step 4801 (time=80.0167), 0.000833327 s/step\n", + "Meep progress: 160.8/408.0 = 39.4% done in 8.0s, 12.3s to go\n", + "on time step 9649 (time=160.817), 0.000825088 s/step\n", + "Meep progress: 241.7/408.0 = 59.2% done in 12.0s, 8.3s to go\n", + "on time step 14504 (time=241.733), 0.000823907 s/step\n", + "Meep progress: 320.8333333333333/408.0 = 78.6% done in 16.0s, 4.3s to go\n", + "on time step 19253 (time=320.883), 0.000842394 s/step\n", + "Meep progress: 401.3/408.0 = 98.4% done in 20.0s, 0.3s to go\n", + "on time step 24084 (time=401.4), 0.000828145 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 65 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 44 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 29 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 42 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 27 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.00536\n", + "tran (twisted):, 1, 4.77, 0.46488\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.10352e-05 s\n", + "Working in 2D dimensions.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computational cell is 5.4 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.0821059 s\n", + "-----------\n", + "Meep progress: 93.78333333333333/208.0 = 45.1% done in 4.0s, 4.9s to go\n", + "on time step 5627 (time=93.7833), 0.000710922 s/step\n", + "Meep progress: 189.56666666666666/208.0 = 91.1% done in 8.0s, 0.8s to go\n", + "on time step 11376 (time=189.6), 0.000695889 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.29425e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5.4 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.7,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (-1.11022e-16,0,0)\n", + " size (1.4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "time for set_epsilon = 11.8445 s\n", + "-----------\n", + "Meep progress: 89.43333333333334/408.0 = 21.9% done in 4.0s, 14.2s to go\n", + "on time step 5366 (time=89.4333), 0.000745554 s/step\n", + "Meep progress: 178.88333333333333/408.0 = 43.8% done in 8.0s, 10.2s to go\n", + "on time step 10735 (time=178.917), 0.000745129 s/step\n", + "Meep progress: 269.46666666666664/408.0 = 66.0% done in 12.0s, 6.2s to go\n", + "on time step 16172 (time=269.533), 0.000735702 s/step\n", + "Meep progress: 359.71666666666664/408.0 = 88.2% done in 16.0s, 2.1s to go\n", + "on time step 21588 (time=359.8), 0.000738555 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 27 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 63 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 42 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.02627\n", + "tran (uniaxial):, 1, 4.77, 0.45435\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.79629e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.8 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.105287 s\n", + "-----------\n", + "Meep progress: 82.33333333333333/208.0 = 39.6% done in 4.0s, 6.1s to go\n", + "on time step 4940 (time=82.3333), 0.000809818 s/step\n", + "Meep progress: 165.56666666666666/208.0 = 79.6% done in 8.0s, 2.1s to go\n", + "on time step 9936 (time=165.6), 0.000800751 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.00951e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.8 x 6.5 x 0 with resolution 30\n", + " block, center = (-2.4,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (-2.22045e-16,0,0)\n", + " size (2.8,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 53.5143% done, 3.8263 s remaining\n", + "subpixel-averaging is 53.5143% done, 3.81257 s remaining\n", + "subpixel-averaging is 53.5143% done, 3.81274 s remaining\n", + "time for set_epsilon = 23.647 s\n", + "-----------\n", + "Meep progress: 74.46666666666667/408.0 = 18.3% done in 4.0s, 17.9s to go\n", + "on time step 4468 (time=74.4667), 0.000895362 s/step\n", + "Meep progress: 150.38333333333333/408.0 = 36.9% done in 8.0s, 13.7s to go\n", + "on time step 9024 (time=150.4), 0.000878078 s/step\n", + "Meep progress: 226.41666666666666/408.0 = 55.5% done in 12.0s, 9.6s to go\n", + "on time step 13587 (time=226.45), 0.000876645 s/step\n", + "Meep progress: 302.23333333333335/408.0 = 74.1% done in 16.0s, 5.6s to go\n", + "on time step 18137 (time=302.283), 0.000879289 s/step\n", + "Meep progress: 377.98333333333335/408.0 = 92.6% done in 20.0s, 1.6s to go\n", + "on time step 22683 (time=378.05), 0.000879927 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 65 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 40 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 37 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.00085\n", + "tran (twisted):, 1, 4.77, 0.46955\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000133991 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5.6 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.096647 s\n", + "-----------\n", + "Meep progress: 93.56666666666666/208.0 = 45.0% done in 4.0s, 4.9s to go\n", + "on time step 5614 (time=93.5667), 0.000712629 s/step\n", + "Meep progress: 188.45/208.0 = 90.6% done in 8.0s, 0.8s to go\n", + "on time step 11308 (time=188.467), 0.000702542 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.29425e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5.6 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.8,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (2.22045e-16,0,0)\n", + " size (1.6,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 71.5192% done, 1.72158 s remaining\n", + "subpixel-averaging is 71.5192% done, 1.70206 s remaining\n", + "subpixel-averaging is 71.5192% done, 1.70269 s remaining\n", + "time for set_epsilon = 13.3156 s\n", + "-----------\n", + "Meep progress: 87.53333333333333/408.0 = 21.5% done in 4.0s, 14.6s to go\n", + "on time step 5252 (time=87.5333), 0.00076165 s/step\n", + "Meep progress: 176.3/408.0 = 43.2% done in 8.0s, 10.5s to go\n", + "on time step 10580 (time=176.333), 0.000750865 s/step\n", + "Meep progress: 263.75/408.0 = 64.6% done in 12.0s, 6.6s to go\n", + "on time step 15829 (time=263.817), 0.000762183 s/step\n", + "Meep progress: 352.4/408.0 = 86.4% done in 16.0s, 2.5s to go\n", + "on time step 21149 (time=352.483), 0.000751941 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 40 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 42 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 37 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 46 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.00005\n", + "tran (uniaxial):, 1, 4.77, 0.46429\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.69956e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.2 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.0993609 s\n", + "-----------\n", + "Meep progress: 78.7/208.0 = 37.8% done in 4.0s, 6.6s to go\n", + "on time step 4722 (time=78.7), 0.000847187 s/step\n", + "Meep progress: 160.36666666666667/208.0 = 77.1% done in 8.0s, 2.4s to go\n", + "on time step 9624 (time=160.4), 0.000816089 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.81877e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.2 x 6.5 x 0 with resolution 30\n", + " block, center = (-2.6,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (3.2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 49.3713% done, 4.3752 s remaining\n", + "subpixel-averaging is 78.9961% done, 1.1067 s remaining\n", + "subpixel-averaging is 49.3713% done, 4.42276 s remaining\n", + "subpixel-averaging is 78.9961% done, 1.10731 s remaining\n", + "subpixel-averaging is 49.3713% done, 4.43155 s remaining\n", + "subpixel-averaging is 78.9961% done, 1.11122 s remaining\n", + "time for set_epsilon = 26.3941 s\n", + "-----------\n", + "Meep progress: 72.93333333333334/408.0 = 17.9% done in 4.0s, 18.4s to go\n", + "on time step 4376 (time=72.9333), 0.000914306 s/step\n", + "Meep progress: 148.9/408.0 = 36.5% done in 8.0s, 13.9s to go\n", + "on time step 8935 (time=148.917), 0.000877482 s/step\n", + "Meep progress: 224.29999999999998/408.0 = 55.0% done in 12.0s, 9.8s to go\n", + "on time step 13460 (time=224.333), 0.000884124 s/step\n", + "Meep progress: 297.3333333333333/408.0 = 72.9% done in 16.0s, 6.0s to go\n", + "on time step 17843 (time=297.383), 0.00091272 s/step\n", + "Meep progress: 372.43333333333334/408.0 = 91.3% done in 20.0s, 1.9s to go\n", + "on time step 22351 (time=372.517), 0.000887407 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 58 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 52 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 52 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 42 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 27 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.00061\n", + "tran (twisted):, 1, 4.77, 0.46431\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000139952 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5.8 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.103642 s\n", + "-----------\n", + "Meep progress: 92.38333333333333/208.0 = 44.4% done in 4.0s, 5.0s to go\n", + "on time step 5543 (time=92.3833), 0.000721725 s/step\n", + "Meep progress: 186.01666666666665/208.0 = 89.4% done in 8.0s, 0.9s to go\n", + "on time step 11163 (time=186.05), 0.000711761 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.69956e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 5.8 x 6.5 x 0 with resolution 30\n", + " block, center = (-1.9,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (1.11022e-16,0,0)\n", + " size (1.8,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 67.7217% done, 2.0545 s remaining\n", + "subpixel-averaging is 67.7217% done, 2.04229 s remaining\n", + "subpixel-averaging is 67.7217% done, 2.04423 s remaining\n", + "time for set_epsilon = 14.95 s\n", + "-----------\n", + "Meep progress: 86.31666666666666/408.0 = 21.2% done in 4.0s, 14.9s to go\n", + "on time step 5179 (time=86.3167), 0.00077244 s/step\n", + "Meep progress: 173.38333333333333/408.0 = 42.5% done in 8.0s, 10.8s to go\n", + "on time step 10404 (time=173.4), 0.000765589 s/step\n", + "Meep progress: 261.1/408.0 = 64.0% done in 12.0s, 6.8s to go\n", + "on time step 15668 (time=261.133), 0.000759921 s/step\n", + "Meep progress: 347.5333333333333/408.0 = 85.2% done in 16.0s, 2.8s to go\n", + "on time step 20857 (time=347.617), 0.000770998 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 52 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 40 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 62 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.02864\n", + "tran (uniaxial):, 1, 4.77, 0.44626\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 5.00679e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.6 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.137346 s\n", + "-----------\n", + "Meep progress: 78.08333333333333/208.0 = 37.5% done in 4.0s, 6.7s to go\n", + "on time step 4685 (time=78.0833), 0.000853827 s/step\n", + "Meep progress: 156.88333333333333/208.0 = 75.4% done in 8.0s, 2.6s to go\n", + "on time step 9414 (time=156.9), 0.000845896 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.69956e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.6 x 6.5 x 0 with resolution 30\n", + " block, center = (-2.8,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (2.22045e-16,0,0)\n", + " size (3.6,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 45.8237% done, 5.1262 s remaining\n", + "subpixel-averaging is 73.3197% done, 1.54776 s remaining\n", + "subpixel-averaging is 45.8237% done, 5.0838 s remaining\n", + "subpixel-averaging is 73.3197% done, 1.54977 s remaining\n", + "subpixel-averaging is 45.8237% done, 5.10381 s remaining\n", + "subpixel-averaging is 73.3197% done, 1.55772 s remaining\n", + "time for set_epsilon = 30.0956 s\n", + "-----------\n", + "Meep progress: 70.91666666666667/408.0 = 17.4% done in 4.0s, 19.0s to go\n", + "on time step 4255 (time=70.9167), 0.000940257 s/step\n", + "Meep progress: 142.71666666666667/408.0 = 35.0% done in 8.0s, 14.9s to go\n", + "on time step 8564 (time=142.733), 0.000928472 s/step\n", + "Meep progress: 213.79999999999998/408.0 = 52.4% done in 12.0s, 10.9s to go\n", + "on time step 12830 (time=213.833), 0.000937849 s/step\n", + "Meep progress: 284.56666666666666/408.0 = 69.7% done in 16.0s, 6.9s to go\n", + "on time step 17078 (time=284.633), 0.000941825 s/step\n", + "Meep progress: 355.2833333333333/408.0 = 87.1% done in 20.0s, 3.0s to go\n", + "on time step 21322 (time=355.367), 0.000942543 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 31 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 28 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.00230\n", + "tran (twisted):, 1, 4.77, 0.45960\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 5.38826e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.0905509 s\n", + "-----------\n", + "Meep progress: 90.25/208.0 = 43.4% done in 4.0s, 5.2s to go\n", + "on time step 5415 (time=90.25), 0.000738743 s/step\n", + "Meep progress: 181.95/208.0 = 87.5% done in 8.0s, 1.1s to go\n", + "on time step 10918 (time=181.967), 0.000726975 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.41346e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6 x 6.5 x 0 with resolution 30\n", + " block, center = (-2,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 64.3072% done, 2.38699 s remaining\n", + "subpixel-averaging is 64.3072% done, 2.40457 s remaining\n", + "subpixel-averaging is 64.3072% done, 2.40796 s remaining\n", + "time for set_epsilon = 16.6695 s\n", + "-----------\n", + "Meep progress: 82.75/408.0 = 20.3% done in 4.0s, 15.7s to go\n", + "on time step 4965 (time=82.75), 0.000805785 s/step\n", + "Meep progress: 175.51666666666665/408.0 = 43.0% done in 8.0s, 10.6s to go\n", + "on time step 10533 (time=175.55), 0.000718519 s/step\n", + "Meep progress: 259.15/408.0 = 63.5% done in 12.0s, 6.9s to go\n", + "on time step 15553 (time=259.217), 0.000796936 s/step\n", + "Meep progress: 342.56666666666666/408.0 = 84.0% done in 16.0s, 3.1s to go\n", + "on time step 20559 (time=342.65), 0.00079916 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 68 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 30 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 34 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 55 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 42 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 34 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.15199\n", + "tran (uniaxial):, 1, 4.77, 0.39654\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 8.79765e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.125251 s\n", + "-----------\n", + "Meep progress: 75.01666666666667/208.0 = 36.1% done in 4.0s, 7.1s to go\n", + "on time step 4501 (time=75.0167), 0.000888818 s/step\n", + "Meep progress: 150.11666666666667/208.0 = 72.2% done in 8.0s, 3.1s to go\n", + "on time step 9009 (time=150.15), 0.000887502 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.39098e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8 x 6.5 x 0 with resolution 30\n", + " block, center = (-3,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 42.7517% done, 5.69995 s remaining\n", + "subpixel-averaging is 68.4044% done, 1.92679 s remaining\n", + "subpixel-averaging is 42.7517% done, 5.7258 s remaining\n", + "subpixel-averaging is 68.4044% done, 1.91778 s remaining\n", + "subpixel-averaging is 42.7517% done, 5.72558 s remaining\n", + "subpixel-averaging is 68.4044% done, 1.9235 s remaining\n", + "time for set_epsilon = 32.8454 s\n", + "-----------\n", + "Meep progress: 67.6/408.0 = 16.6% done in 4.0s, 20.1s to go\n", + "on time step 4056 (time=67.6), 0.000986321 s/step\n", + "Meep progress: 136.23333333333332/408.0 = 33.4% done in 8.0s, 16.0s to go\n", + "on time step 8175 (time=136.25), 0.00097132 s/step\n", + "Meep progress: 204.93333333333334/408.0 = 50.2% done in 12.0s, 11.9s to go\n", + "on time step 12298 (time=204.967), 0.000970339 s/step\n", + "Meep progress: 273.5833333333333/408.0 = 67.1% done in 16.0s, 7.9s to go\n", + "on time step 16418 (time=273.633), 0.000971036 s/step\n", + "Meep progress: 341.8666666666667/408.0 = 83.8% done in 20.0s, 3.9s to go\n", + "on time step 20515 (time=341.917), 0.000976364 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 42 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 37 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 30 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.05159\n", + "tran (twisted):, 1, 4.77, 0.43938\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 5.29289e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.2 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.104505 s\n", + "-----------\n", + "Meep progress: 86.15/208.0 = 41.4% done in 4.0s, 5.7s to go\n", + "on time step 5169 (time=86.15), 0.000773898 s/step\n", + "Meep progress: 174.43333333333334/208.0 = 83.9% done in 8.0s, 1.5s to go\n", + "on time step 10468 (time=174.467), 0.000754955 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.50883e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.2 x 6.5 x 0 with resolution 30\n", + " block, center = (-2.1,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (2.2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 61.2204% done, 2.79341 s remaining\n", + "subpixel-averaging is 61.2204% done, 2.64891 s remaining\n", + "subpixel-averaging is 61.2204% done, 2.75618 s remaining\n", + "time for set_epsilon = 18.3206 s\n", + "-----------\n", + "Meep progress: 81.01666666666667/408.0 = 19.9% done in 4.0s, 16.1s to go\n", + "on time step 4861 (time=81.0167), 0.000823039 s/step\n", + "Meep progress: 163.05/408.0 = 40.0% done in 8.0s, 12.0s to go\n", + "on time step 9784 (time=163.067), 0.00081255 s/step\n", + "Meep progress: 246.23333333333332/408.0 = 60.4% done in 12.0s, 7.9s to go\n", + "on time step 14776 (time=246.267), 0.000801294 s/step\n", + "Meep progress: 327.5333333333333/408.0 = 80.3% done in 16.0s, 3.9s to go\n", + "on time step 19655 (time=327.583), 0.000819844 s/step\n", + "Meep progress: 407.8666666666667/408.0 = 100.0% done in 20.0s, 0.0s to go\n", + "on time step 24477 (time=407.95), 0.000829674 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 51 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 49 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 43 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 36 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 46 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.30148\n", + "tran (uniaxial):, 1, 4.77, 0.31081\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 9.58443e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.4 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.128375 s\n", + "-----------\n", + "Meep progress: 72.65/208.0 = 34.9% done in 4.0s, 7.5s to go\n", + "on time step 4359 (time=72.65), 0.000917816 s/step\n", + "Meep progress: 144.25/208.0 = 69.4% done in 8.0s, 3.5s to go\n", + "on time step 8656 (time=144.267), 0.000930968 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.69956e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.4 x 6.5 x 0 with resolution 30\n", + " block, center = (-3.2,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (4.4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 40.0657% done, 6.53195 s remaining\n", + "subpixel-averaging is 64.1067% done, 2.36242 s remaining\n", + "subpixel-averaging is 40.0657% done, 6.48991 s remaining\n", + "subpixel-averaging is 64.1067% done, 2.36408 s remaining\n", + "subpixel-averaging is 40.0657% done, 6.52898 s remaining\n", + "subpixel-averaging is 64.1067% done, 2.35834 s remaining\n", + "time for set_epsilon = 36.7265 s\n", + "-----------\n", + "Meep progress: 65.65/408.0 = 16.1% done in 4.0s, 20.9s to go\n", + "on time step 3939 (time=65.65), 0.00101563 s/step\n", + "Meep progress: 132.1/408.0 = 32.4% done in 8.0s, 16.7s to go\n", + "on time step 7927 (time=132.117), 0.00100305 s/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 198.91666666666666/408.0 = 48.8% done in 12.0s, 12.6s to go\n", + "on time step 11937 (time=198.95), 0.000997598 s/step\n", + "Meep progress: 265.51666666666665/408.0 = 65.1% done in 16.0s, 8.6s to go\n", + "on time step 15934 (time=265.567), 0.00100085 s/step\n", + "Meep progress: 331.93333333333334/408.0 = 81.4% done in 20.0s, 4.6s to go\n", + "on time step 19920 (time=332), 0.00100371 s/step\n", + "Meep progress: 397.23333333333335/408.0 = 97.4% done in 24.0s, 0.7s to go\n", + "on time step 23839 (time=397.317), 0.00102074 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 47 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 43 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 50 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.17455\n", + "tran (twisted):, 1, 4.77, 0.38170\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 4.50611e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.4 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.11933 s\n", + "-----------\n", + "Meep progress: 86.23333333333333/208.0 = 41.5% done in 4.0s, 5.6s to go\n", + "on time step 5174 (time=86.2333), 0.000773265 s/step\n", + "Meep progress: 173.26666666666665/208.0 = 83.3% done in 8.0s, 1.6s to go\n", + "on time step 10398 (time=173.3), 0.000765817 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.50883e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.4 x 6.5 x 0 with resolution 30\n", + " block, center = (-2.2,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (2.4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 58.4164% done, 3.09306 s remaining\n", + "subpixel-averaging is 58.4164% done, 2.89371 s remaining\n", + "subpixel-averaging is 58.4164% done, 3.09871 s remaining\n", + "time for set_epsilon = 19.6879 s\n", + "-----------\n", + "Meep progress: 80.91666666666667/408.0 = 19.8% done in 4.0s, 16.2s to go\n", + "on time step 4855 (time=80.9167), 0.000824082 s/step\n", + "Meep progress: 162.7/408.0 = 39.9% done in 8.0s, 12.1s to go\n", + "on time step 9763 (time=162.717), 0.000815005 s/step\n", + "Meep progress: 244.65/408.0 = 60.0% done in 12.0s, 8.0s to go\n", + "on time step 14681 (time=244.683), 0.000813452 s/step\n", + "Meep progress: 326.3833333333333/408.0 = 80.0% done in 16.0s, 4.0s to go\n", + "on time step 19586 (time=326.433), 0.00081554 s/step\n", + "Meep progress: 407.9166666666667/408.0 = 100.0% done in 20.0s, 0.0s to go\n", + "on time step 24479 (time=407.983), 0.000817639 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 69 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 49 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 37 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 51 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 36 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 28 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.46820\n", + "tran (uniaxial):, 1, 4.77, 0.22878\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000147104 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.8 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.139945 s\n", + "-----------\n", + "Meep progress: 69.91666666666667/208.0 = 33.6% done in 4.0s, 7.9s to go\n", + "on time step 4195 (time=69.9167), 0.000953672 s/step\n", + "Meep progress: 140.83333333333334/208.0 = 67.7% done in 8.0s, 3.8s to go\n", + "on time step 8450 (time=140.833), 0.000940074 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.10487e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 8.8 x 6.5 x 0 with resolution 30\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " block, center = (-3.4,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (4.8,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 37.6973% done, 7.13631 s remaining\n", + "subpixel-averaging is 60.3172% done, 2.75956 s remaining\n", + "subpixel-averaging is 82.9371% done, 0.865693 s remaining\n", + "subpixel-averaging is 37.6973% done, 7.11049 s remaining\n", + "subpixel-averaging is 60.3172% done, 2.75036 s remaining\n", + "subpixel-averaging is 82.9371% done, 0.858843 s remaining\n", + "subpixel-averaging is 37.6973% done, 7.12251 s remaining\n", + "subpixel-averaging is 60.3172% done, 2.75345 s remaining\n", + "subpixel-averaging is 82.9371% done, 0.866829 s remaining\n", + "time for set_epsilon = 39.6605 s\n", + "-----------\n", + "Meep progress: 61.016666666666666/408.0 = 15.0% done in 4.0s, 22.7s to go\n", + "on time step 3661 (time=61.0167), 0.00109264 s/step\n", + "Meep progress: 122.33333333333333/408.0 = 30.0% done in 8.0s, 18.7s to go\n", + "on time step 7342 (time=122.367), 0.00108692 s/step\n", + "Meep progress: 185.16666666666666/408.0 = 45.4% done in 12.0s, 14.4s to go\n", + "on time step 11113 (time=185.217), 0.00106086 s/step\n", + "Meep progress: 248.03333333333333/408.0 = 60.8% done in 16.0s, 10.3s to go\n", + "on time step 14886 (time=248.1), 0.00106035 s/step\n", + "Meep progress: 310.8/408.0 = 76.2% done in 20.0s, 6.3s to go\n", + "on time step 18653 (time=310.883), 0.00106216 s/step\n", + "Meep progress: 373.48333333333335/408.0 = 91.5% done in 24.0s, 2.2s to go\n", + "on time step 22416 (time=373.6), 0.0010633 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 37 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 27 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 51 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.40540\n", + "tran (twisted):, 1, 4.77, 0.26104\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.20024e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.6 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.098484 s\n", + "-----------\n", + "Meep progress: 84.7/208.0 = 40.7% done in 4.0s, 5.8s to go\n", + "on time step 5082 (time=84.7), 0.000787265 s/step\n", + "Meep progress: 168.76666666666665/208.0 = 81.1% done in 8.0s, 1.9s to go\n", + "on time step 10127 (time=168.783), 0.000792943 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.69956e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.6 x 6.5 x 0 with resolution 30\n", + " block, center = (-2.3,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (2.6,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 55.858% done, 3.41444 s remaining\n", + "subpixel-averaging is 55.858% done, 3.40405 s remaining\n", + "subpixel-averaging is 55.858% done, 3.39686 s remaining\n", + "time for set_epsilon = 21.5757 s\n", + "-----------\n", + "Meep progress: 78.68333333333334/408.0 = 19.3% done in 4.0s, 16.7s to go\n", + "on time step 4721 (time=78.6833), 0.000847361 s/step\n", + "Meep progress: 157.51666666666665/408.0 = 38.6% done in 8.0s, 12.7s to go\n", + "on time step 9452 (time=157.533), 0.000845538 s/step\n", + "Meep progress: 237.29999999999998/408.0 = 58.2% done in 12.0s, 8.6s to go\n", + "on time step 14240 (time=237.333), 0.000835482 s/step\n", + "Meep progress: 315.93333333333334/408.0 = 77.4% done in 16.0s, 4.7s to go\n", + "on time step 18959 (time=315.983), 0.000847783 s/step\n", + "Meep progress: 394.68333333333334/408.0 = 96.7% done in 20.0s, 0.7s to go\n", + "on time step 23686 (time=394.767), 0.000846355 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 53 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 38 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 70 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 47 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.66179\n", + "tran (uniaxial):, 1, 4.77, 0.14253\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000156164 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 9.2 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.172037 s\n", + "-----------\n", + "Meep progress: 68.06666666666666/208.0 = 32.7% done in 4.0s, 8.2s to go\n", + "on time step 4084 (time=68.0667), 0.000979654 s/step\n", + "Meep progress: 137.1/208.0 = 65.9% done in 8.0s, 4.1s to go\n", + "on time step 8226 (time=137.1), 0.000965725 s/step\n", + "Meep progress: 205.7/208.0 = 98.9% done in 12.0s, 0.1s to go\n", + "on time step 12343 (time=205.717), 0.000971618 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.98566e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 9.2 x 6.5 x 0 with resolution 30\n", + " block, center = (-3.6,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (5.2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 35.5933% done, 7.99426 s remaining\n", + "subpixel-averaging is 56.9506% done, 3.19121 s remaining\n", + "subpixel-averaging is 78.308% done, 1.1792 s remaining\n", + "subpixel-averaging is 35.5933% done, 7.82132 s remaining\n", + "subpixel-averaging is 56.9506% done, 3.19462 s remaining\n", + "subpixel-averaging is 78.308% done, 1.17005 s remaining\n", + "subpixel-averaging is 35.5933% done, 7.79931 s remaining\n", + "subpixel-averaging is 56.9506% done, 3.1716 s remaining\n", + "subpixel-averaging is 78.308% done, 1.18297 s remaining\n", + "time for set_epsilon = 43.3607 s\n", + "-----------\n", + "Meep progress: 61.21666666666667/408.0 = 15.0% done in 4.0s, 22.7s to go\n", + "on time step 3673 (time=61.2167), 0.00108931 s/step\n", + "Meep progress: 122.93333333333334/408.0 = 30.1% done in 8.0s, 18.6s to go\n", + "on time step 7377 (time=122.95), 0.00108006 s/step\n", + "Meep progress: 183.58333333333334/408.0 = 45.0% done in 12.0s, 14.7s to go\n", + "on time step 11017 (time=183.617), 0.00109908 s/step\n", + "Meep progress: 244.83333333333334/408.0 = 60.0% done in 16.0s, 10.7s to go\n", + "on time step 14693 (time=244.883), 0.00108818 s/step\n", + "Meep progress: 306.65/408.0 = 75.2% done in 20.0s, 6.6s to go\n", + "on time step 18403 (time=306.717), 0.00107845 s/step\n", + "Meep progress: 367.8/408.0 = 90.1% done in 24.0s, 2.6s to go\n", + "on time step 22073 (time=367.883), 0.00109002 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 47 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.67195\n", + "tran (twisted):, 1, 4.77, 0.13686\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 9.08375e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.8 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.099133 s\n", + "-----------\n", + "Meep progress: 83.68333333333334/208.0 = 40.2% done in 4.0s, 5.9s to go\n", + "on time step 5021 (time=83.6833), 0.000796734 s/step\n", + "Meep progress: 168.46666666666667/208.0 = 81.0% done in 8.0s, 1.9s to go\n", + "on time step 10110 (time=168.5), 0.000786127 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.69956e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 6.8 x 6.5 x 0 with resolution 30\n", + " block, center = (-2.4,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (-2.22045e-16,0,0)\n", + " size (2.8,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 53.5143% done, 3.79773 s remaining\n", + "subpixel-averaging is 53.5143% done, 3.81162 s remaining\n", + "subpixel-averaging is 53.5143% done, 3.8089 s remaining\n", + "time for set_epsilon = 23.541 s\n", + "-----------\n", + "Meep progress: 74.93333333333334/408.0 = 18.4% done in 4.0s, 17.8s to go\n", + "on time step 4496 (time=74.9333), 0.000889767 s/step\n", + "Meep progress: 151.51666666666665/408.0 = 37.1% done in 8.0s, 13.5s to go\n", + "on time step 9092 (time=151.533), 0.000870396 s/step\n", + "Meep progress: 229.31666666666666/408.0 = 56.2% done in 12.0s, 9.4s to go\n", + "on time step 13761 (time=229.35), 0.000856824 s/step\n", + "Meep progress: 307.1666666666667/408.0 = 75.3% done in 16.0s, 5.3s to go\n", + "on time step 18434 (time=307.233), 0.00085611 s/step\n", + "Meep progress: 383.71666666666664/408.0 = 94.0% done in 20.0s, 1.3s to go\n", + "on time step 23028 (time=383.8), 0.000870738 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 69 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 34 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.80825\n", + "tran (uniaxial):, 1, 4.77, 0.06307\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000152111 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 9.6 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.157447 s\n", + "-----------\n", + "Meep progress: 64.9/208.0 = 31.2% done in 4.0s, 8.8s to go\n", + "on time step 3894 (time=64.9), 0.00102741 s/step\n", + "Meep progress: 130.88333333333333/208.0 = 62.9% done in 8.0s, 4.7s to go\n", + "on time step 7854 (time=130.9), 0.00101027 s/step\n", + "Meep progress: 196.55/208.0 = 94.5% done in 12.0s, 0.7s to go\n", + "on time step 11795 (time=196.583), 0.00101506 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.20024e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 9.6 x 6.5 x 0 with resolution 30\n", + " block, center = (-3.8,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (-4.44089e-16,0,0)\n", + " size (5.6,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 33.7117% done, 8.53515 s remaining\n", + "subpixel-averaging is 53.94% done, 3.58281 s remaining\n", + "subpixel-averaging is 74.1684% done, 1.47227 s remaining\n", + "subpixel-averaging is 33.7117% done, 8.64079 s remaining\n", + "subpixel-averaging is 53.94% done, 3.58974 s remaining\n", + "subpixel-averaging is 74.1684% done, 1.46986 s remaining\n", + "subpixel-averaging is 33.7117% done, 8.56235 s remaining\n", + "subpixel-averaging is 53.94% done, 3.61465 s remaining\n", + "subpixel-averaging is 74.1684% done, 1.47245 s remaining\n", + "time for set_epsilon = 46.6436 s\n", + "-----------\n", + "Meep progress: 58.0/408.0 = 14.2% done in 4.0s, 24.1s to go\n", + "on time step 3480 (time=58), 0.0011497 s/step\n", + "Meep progress: 116.86666666666666/408.0 = 28.6% done in 8.0s, 19.9s to go\n", + "on time step 7013 (time=116.883), 0.00113224 s/step\n", + "Meep progress: 175.05/408.0 = 42.9% done in 12.0s, 16.0s to go\n", + "on time step 10505 (time=175.083), 0.00114558 s/step\n", + "Meep progress: 233.93333333333334/408.0 = 57.3% done in 16.0s, 11.9s to go\n", + "on time step 14039 (time=233.983), 0.00113194 s/step\n", + "Meep progress: 293.5/408.0 = 71.9% done in 20.0s, 7.8s to go\n", + "on time step 17613 (time=293.55), 0.00111922 s/step\n", + "Meep progress: 352.01666666666665/408.0 = 86.3% done in 24.0s, 3.8s to go\n", + "on time step 21125 (time=352.083), 0.00113908 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 47 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 35 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 54 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 51 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 38 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 31 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.86580\n", + "tran (twisted):, 1, 4.77, 0.02804\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000117064 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.103678 s\n", + "-----------\n", + "Meep progress: 80.11666666666666/208.0 = 38.5% done in 4.0s, 6.4s to go\n", + "on time step 4807 (time=80.1167), 0.000832301 s/step\n", + "Meep progress: 161.7/208.0 = 77.7% done in 8.0s, 2.3s to go\n", + "on time step 9704 (time=161.733), 0.000816961 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.39098e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7 x 6.5 x 0 with resolution 30\n", + " block, center = (-2.5,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (2.22045e-16,0,0)\n", + " size (3,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 51.3594% done, 4.1118 s remaining\n", + "subpixel-averaging is 51.3594% done, 4.09325 s remaining\n", + "subpixel-averaging is 51.3594% done, 4.09493 s remaining\n", + "time for set_epsilon = 24.965 s\n", + "-----------\n", + "Meep progress: 75.21666666666667/408.0 = 18.4% done in 4.0s, 17.7s to go\n", + "on time step 4513 (time=75.2167), 0.000886419 s/step\n", + "Meep progress: 150.08333333333334/408.0 = 36.8% done in 8.0s, 13.7s to go\n", + "on time step 9006 (time=150.1), 0.00089035 s/step\n", + "Meep progress: 222.83333333333334/408.0 = 54.6% done in 12.0s, 10.0s to go\n", + "on time step 13373 (time=222.883), 0.000916091 s/step\n", + "Meep progress: 297.3666666666667/408.0 = 72.9% done in 16.0s, 6.0s to go\n", + "on time step 17846 (time=297.433), 0.000894295 s/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 371.5/408.0 = 91.1% done in 20.0s, 2.0s to go\n", + "on time step 22295 (time=371.583), 0.000899168 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 64 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 45 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.88675\n", + "tran (uniaxial):, 1, 4.77, 0.01655\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.20024e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 10 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.155606 s\n", + "-----------\n", + "Meep progress: 63.06666666666666/208.0 = 30.3% done in 4.0s, 9.2s to go\n", + "on time step 3784 (time=63.0667), 0.0010573 s/step\n", + "Meep progress: 127.85/208.0 = 61.5% done in 8.0s, 5.0s to go\n", + "on time step 7672 (time=127.867), 0.001029 s/step\n", + "Meep progress: 191.65/208.0 = 92.1% done in 12.0s, 1.0s to go\n", + "on time step 11501 (time=191.683), 0.00104491 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.00951e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 10 x 6.5 x 0 with resolution 30\n", + " block, center = (-4,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (4.44089e-16,0,0)\n", + " size (6,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 32.019% done, 9.22186 s remaining\n", + "subpixel-averaging is 51.2317% done, 3.98466 s remaining\n", + "subpixel-averaging is 70.4445% done, 1.77788 s remaining\n", + "subpixel-averaging is 32.019% done, 9.30279 s remaining\n", + "subpixel-averaging is 51.2317% done, 4.03405 s remaining\n", + "subpixel-averaging is 70.4445% done, 1.79632 s remaining\n", + "subpixel-averaging is 32.019% done, 9.17258 s remaining\n", + "subpixel-averaging is 51.2317% done, 4.02283 s remaining\n", + "subpixel-averaging is 70.4445% done, 1.78612 s remaining\n", + "time for set_epsilon = 50.0774 s\n", + "-----------\n", + "Meep progress: 55.21666666666667/408.0 = 13.5% done in 4.0s, 25.6s to go\n", + "on time step 3313 (time=55.2167), 0.00120767 s/step\n", + "Meep progress: 112.33333333333333/408.0 = 27.5% done in 8.0s, 21.1s to go\n", + "on time step 6741 (time=112.35), 0.00116717 s/step\n", + "Meep progress: 168.81666666666666/408.0 = 41.4% done in 12.0s, 17.0s to go\n", + "on time step 10131 (time=168.85), 0.00118012 s/step\n", + "Meep progress: 225.75/408.0 = 55.3% done in 16.0s, 12.9s to go\n", + "on time step 13549 (time=225.817), 0.00117055 s/step\n", + "Meep progress: 281.48333333333335/408.0 = 69.0% done in 20.0s, 9.0s to go\n", + "on time step 16894 (time=281.567), 0.00119605 s/step\n", + "Meep progress: 338.5333333333333/408.0 = 83.0% done in 24.0s, 4.9s to go\n", + "on time step 20317 (time=338.617), 0.00116858 s/step\n", + "Meep progress: 395.55/408.0 = 96.9% done in 28.0s, 0.9s to go\n", + "on time step 23739 (time=395.65), 0.00116892 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 48 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 65 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 45 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 32 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.94211\n", + "tran (twisted):, 1, 4.77, 0.00255\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.00011301 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.2 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.111866 s\n", + "-----------\n", + "Meep progress: 80.61666666666666/208.0 = 38.8% done in 4.0s, 6.3s to go\n", + "on time step 4837 (time=80.6167), 0.000827066 s/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 162.21666666666667/208.0 = 78.0% done in 8.0s, 2.3s to go\n", + "on time step 9734 (time=162.233), 0.000816884 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.91414e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.2 x 6.5 x 0 with resolution 30\n", + " block, center = (-2.6,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (3.2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 49.3713% done, 4.4399 s remaining\n", + "subpixel-averaging is 78.9961% done, 1.12404 s remaining\n", + "subpixel-averaging is 49.3713% done, 4.44133 s remaining\n", + "subpixel-averaging is 78.9961% done, 1.12126 s remaining\n", + "subpixel-averaging is 49.3713% done, 4.42219 s remaining\n", + "subpixel-averaging is 78.9961% done, 1.12413 s remaining\n", + "time for set_epsilon = 26.6699 s\n", + "-----------\n", + "Meep progress: 73.56666666666666/408.0 = 18.0% done in 4.0s, 18.2s to go\n", + "on time step 4414 (time=73.5667), 0.00090629 s/step\n", + "Meep progress: 147.21666666666667/408.0 = 36.1% done in 8.0s, 14.2s to go\n", + "on time step 8834 (time=147.233), 0.000905054 s/step\n", + "Meep progress: 221.16666666666666/408.0 = 54.2% done in 12.0s, 10.1s to go\n", + "on time step 13272 (time=221.2), 0.000901458 s/step\n", + "Meep progress: 294.68333333333334/408.0 = 72.2% done in 16.0s, 6.2s to go\n", + "on time step 17684 (time=294.733), 0.000906649 s/step\n", + "Meep progress: 368.93333333333334/408.0 = 90.4% done in 20.0s, 2.1s to go\n", + "on time step 22140 (time=369), 0.000897794 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 60 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 44 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 45 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 28 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 65 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 43 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 37 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.93860\n", + "tran (uniaxial):, 1, 4.77, 0.00001\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000154018 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 10.4 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.161312 s\n", + "-----------\n", + "Meep progress: 60.9/208.0 = 29.3% done in 4.0s, 9.7s to go\n", + "on time step 3654 (time=60.9), 0.00109495 s/step\n", + "Meep progress: 122.28333333333333/208.0 = 58.8% done in 8.0s, 5.6s to go\n", + "on time step 7338 (time=122.3), 0.00108598 s/step\n", + "Meep progress: 183.23333333333332/208.0 = 88.1% done in 12.0s, 1.6s to go\n", + "on time step 10996 (time=183.267), 0.00109353 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.10487e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 10.4 x 6.5 x 0 with resolution 30\n", + " block, center = (-4.2,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (6.4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 30.4883% done, 9.98227 s remaining\n", + "subpixel-averaging is 48.7824% done, 4.42182 s remaining\n", + "subpixel-averaging is 67.0766% done, 2.07037 s remaining\n", + "subpixel-averaging is 85.3708% done, 0.733933 s remaining\n", + "subpixel-averaging is 30.4883% done, 9.99655 s remaining\n", + "subpixel-averaging is 48.7824% done, 4.44853 s remaining\n", + "subpixel-averaging is 67.0766% done, 2.11095 s remaining\n", + "subpixel-averaging is 85.3708% done, 0.733249 s remaining\n", + "subpixel-averaging is 30.4883% done, 9.9553 s remaining\n", + "subpixel-averaging is 48.7824% done, 4.42917 s remaining\n", + "subpixel-averaging is 67.0766% done, 2.08722 s remaining\n", + "subpixel-averaging is 85.3708% done, 0.729907 s remaining\n", + "time for set_epsilon = 53.5962 s\n", + "-----------\n", + "Meep progress: 54.233333333333334/408.0 = 13.3% done in 4.0s, 26.1s to go\n", + "on time step 3254 (time=54.2333), 0.00122939 s/step\n", + "Meep progress: 108.7/408.0 = 26.6% done in 8.0s, 22.0s to go\n", + "on time step 6523 (time=108.717), 0.0012238 s/step\n", + "Meep progress: 163.1/408.0 = 40.0% done in 12.0s, 18.0s to go\n", + "on time step 9787 (time=163.117), 0.00122558 s/step\n", + "Meep progress: 217.76666666666665/408.0 = 53.4% done in 16.0s, 14.0s to go\n", + "on time step 13068 (time=217.8), 0.00121939 s/step\n", + "Meep progress: 272.51666666666665/408.0 = 66.8% done in 20.0s, 9.9s to go\n", + "on time step 16354 (time=272.567), 0.0012174 s/step\n", + "Meep progress: 327.21666666666664/408.0 = 80.2% done in 24.0s, 5.9s to go\n", + "on time step 19637 (time=327.283), 0.00121851 s/step\n", + "Meep progress: 380.9166666666667/408.0 = 93.4% done in 28.0s, 2.0s to go\n", + "on time step 22860 (time=381), 0.00124152 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 62 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 46 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 39 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 29 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 62 iters\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 42 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 30 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.77362\n", + "tran (twisted):, 1, 4.77, 0.07215\n", + "tran (twisted):, 2, 9.56, 0.00000\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 3.60012e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.4 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.107628 s\n", + "-----------\n", + "Meep progress: 78.06666666666666/208.0 = 37.5% done in 4.0s, 6.7s to go\n", + "on time step 4684 (time=78.0667), 0.000854101 s/step\n", + "Meep progress: 157.66666666666666/208.0 = 75.8% done in 8.0s, 2.6s to go\n", + "on time step 9461 (time=157.683), 0.000837429 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.69956e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 7.4 x 6.5 x 0 with resolution 30\n", + " block, center = (-2.7,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (3.4,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 47.5314% done, 4.80929 s remaining\n", + "subpixel-averaging is 76.0521% done, 1.33452 s remaining\n", + "subpixel-averaging is 47.5314% done, 4.83031 s remaining\n", + "subpixel-averaging is 76.0521% done, 1.34121 s remaining\n", + "subpixel-averaging is 47.5314% done, 4.85467 s remaining\n", + "subpixel-averaging is 76.0521% done, 1.34843 s remaining\n", + "time for set_epsilon = 28.6162 s\n", + "-----------\n", + "Meep progress: 69.88333333333333/408.0 = 17.1% done in 4.0s, 19.4s to go\n", + "on time step 4193 (time=69.8833), 0.000954222 s/step\n", + "Meep progress: 141.2/408.0 = 34.6% done in 8.0s, 15.1s to go\n", + "on time step 8473 (time=141.217), 0.000934654 s/step\n", + "Meep progress: 212.45/408.0 = 52.1% done in 12.0s, 11.0s to go\n", + "on time step 12749 (time=212.483), 0.000935672 s/step\n", + "Meep progress: 283.9166666666667/408.0 = 69.6% done in 16.0s, 7.0s to go\n", + "on time step 17038 (time=283.967), 0.000932807 s/step\n", + "Meep progress: 355.21666666666664/408.0 = 87.1% done in 20.0s, 3.0s to go\n", + "on time step 21317 (time=355.283), 0.000934988 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 50 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 34 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 31 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 53 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 40 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 33 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 33 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (uniaxial):, 0, 0.00, 0.90581\n", + "tran (uniaxial):, 1, 4.77, 0.01950\n", + "tran (uniaxial):, 2, 9.56, 0.00000\n", + "tran (uniaxial):, 3, 14.43, 0.00000\n", + "tran (uniaxial):, 4, 19.41, 0.00000\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 6.91414e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 10.8 x 6.5 x 0 with resolution 30\n", + "time for set_epsilon = 0.160437 s\n", + "-----------\n", + "Meep progress: 59.15/208.0 = 28.4% done in 4.0s, 10.1s to go\n", + "on time step 3549 (time=59.15), 0.00112713 s/step\n", + "Meep progress: 119.0/208.0 = 57.2% done in 8.0s, 6.0s to go\n", + "on time step 7141 (time=119.017), 0.00111385 s/step\n", + "Meep progress: 179.25/208.0 = 86.2% done in 12.0s, 1.9s to go\n", + "on time step 10757 (time=179.283), 0.00110643 s/step\n", + "run 0 finished at t = 208.0 (12480 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 7.39098e-05 s\n", + "Working in 2D dimensions.\n", + "Computational cell is 10.8 x 6.5 x 0 with resolution 30\n", + " block, center = (-4.4,0,0)\n", + " size (2,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (2.4025,2.4025,2.4025)\n", + " block, center = (0,0,0)\n", + " size (6.8,1e+20,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "subpixel-averaging is 29.0972% done, 10.5851 s remaining\n", + "subpixel-averaging is 46.5566% done, 4.83 s remaining\n", + "subpixel-averaging is 64.0161% done, 2.40549 s remaining\n", + "subpixel-averaging is 81.4755% done, 0.964889 s remaining\n", + "subpixel-averaging is 29.0972% done, 10.5591 s remaining\n", + "subpixel-averaging is 46.5566% done, 4.83987 s remaining\n", + "subpixel-averaging is 64.0161% done, 2.39451 s remaining\n", + "subpixel-averaging is 81.4755% done, 0.972159 s remaining\n", + "subpixel-averaging is 29.0972% done, 10.6613 s remaining\n", + "subpixel-averaging is 46.5566% done, 4.82302 s remaining\n", + "subpixel-averaging is 64.0161% done, 2.39088 s remaining\n", + "subpixel-averaging is 81.4755% done, 0.967715 s remaining\n", + "time for set_epsilon = 56.7605 s\n", + "-----------\n", + "Meep progress: 51.55/408.0 = 12.6% done in 4.0s, 27.7s to go\n", + "on time step 3093 (time=51.55), 0.00129338 s/step\n", + "Meep progress: 103.8/408.0 = 25.4% done in 8.0s, 23.4s to go\n", + "on time step 6229 (time=103.817), 0.00127578 s/step\n", + "Meep progress: 157.21666666666667/408.0 = 38.5% done in 12.0s, 19.1s to go\n", + "on time step 9435 (time=157.25), 0.0012479 s/step\n", + "Meep progress: 210.46666666666667/408.0 = 51.6% done in 16.0s, 15.0s to go\n", + "on time step 12631 (time=210.517), 0.00125168 s/step\n", + "Meep progress: 263.68333333333334/408.0 = 64.6% done in 20.0s, 10.9s to go\n", + "on time step 15825 (time=263.75), 0.0012525 s/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meep progress: 316.7833333333333/408.0 = 77.6% done in 24.0s, 6.9s to go\n", + "on time step 19011 (time=316.85), 0.0012555 s/step\n", + "Meep progress: 370.1166666666667/408.0 = 90.7% done in 28.0s, 2.9s to go\n", + "on time step 22212 (time=370.2), 0.00124999 s/step\n", + "run 0 finished at t = 408.0 (24480 timesteps)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 61 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 45 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 43 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 35 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "MPB solved for omega_1(1.85185,0,0) = 1.85185 after 56 iters\n", + "Dominant planewave for band 1: (1.851852,-0.000000,0.000000)\n", + "MPB solved for omega_2(1.85185,0,0) = 1.85823 after 51 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_2(1.84545,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 2: (1.845450,-0.153846,0.000000)\n", + "MPB solved for omega_3(1.85185,0,0) = 1.87724 after 41 iters\n", + "MPB solved for omega_3(1.82612,0,0) = 1.85186 after 1 iters\n", + "MPB solved for omega_3(1.82611,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 3: (1.826111,-0.307692,0.000000)\n", + "MPB solved for omega_4(1.85185,0,0) = 1.9085 after 30 iters\n", + "MPB solved for omega_4(1.79347,0,0) = 1.85191 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_4(1.79342,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 4: (1.793415,-0.461538,0.000000)\n", + "MPB solved for omega_5(1.85185,0,0) = 1.95142 after 32 iters\n", + "MPB solved for omega_5(1.74693,0,0) = 1.85215 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "MPB solved for omega_5(1.74661,0,0) = 1.85185 after 1 iters\n", + "Dominant planewave for band 5: (1.746613,-0.615385,0.000000)\n", + "tran (twisted):, 0, 0.00, 0.49695\n", + "tran (twisted):, 1, 4.77, 0.22184\n", + "tran (twisted):, 2, 9.56, 0.00001\n", + "tran (twisted):, 3, 14.43, 0.00000\n", + "tran (twisted):, 4, 19.41, 0.00000\n" + ] + } + ], "source": [ - "ph_uniaxial = 0 # chiral layer twist angle for uniaxial grating\n", - "ph_twisted = 70 # chiral layer twist angle for bilayer grating\n", - "gp = 6.5 # grating period\n", - "nmode = 5 # number of mode coefficients to compute\n", - "dd = np.arange(0.2, 3.5, 0.2) # chiral layer thickness\n", + "ph_uniaxial = 0 # chiral layer twist angle for uniaxial grating\n", + "ph_twisted = 70 # chiral layer twist angle for bilayer grating\n", + "gp = 6.5 # grating period\n", + "nmode = 5 # number of mode coefficients to compute\n", + "dd = np.arange(0.2,3.5,0.2) # chiral layer thickness\n", "\n", "m0_uniaxial = np.zeros(dd.size)\n", "m1_uniaxial = np.zeros(dd.size)\n", @@ -185,22 +3159,18 @@ "ang_twisted = np.zeros(dd.size)\n", "\n", "for k in range(len(dd)):\n", - " input_flux, angles, coeffs1, coeffs2 = pol_grating(\n", - " 0.5 * dd[k], math.radians(ph_uniaxial), gp, nmode\n", - " )\n", - " tran = (abs(coeffs1) ** 2 + abs(coeffs2) ** 2) / input_flux\n", + " input_flux, angles, coeffs1, coeffs2 = pol_grating(0.5*dd[k],math.radians(ph_uniaxial),gp,nmode)\n", + " tran = (abs(coeffs1)**2+abs(coeffs2)**2)/input_flux\n", " for m in range(nmode):\n", - " print(\"tran (uniaxial):, {}, {:.2f}, {:.5f}\".format(m, angles[m], tran[m]))\n", + " print(\"tran (uniaxial):, {}, {:.2f}, {:.5f}\".format(m,angles[m],tran[m]))\n", " m0_uniaxial[k] = tran[0]\n", " m1_uniaxial[k] = tran[1]\n", " ang_uniaxial[k] = angles[1]\n", "\n", - " input_flux, angles, coeffs1, coeffs2 = pol_grating(\n", - " dd[k], math.radians(ph_twisted), gp, nmode\n", - " )\n", - " tran = (abs(coeffs1) ** 2 + abs(coeffs2) ** 2) / input_flux\n", + " input_flux, angles, coeffs1, coeffs2 = pol_grating(dd[k],math.radians(ph_twisted),gp,nmode)\n", + " tran = (abs(coeffs1)**2+abs(coeffs2)**2)/input_flux\n", " for m in range(nmode):\n", - " print(\"tran (twisted):, {}, {:.2f}, {:.5f}\".format(m, angles[m], tran[m]))\n", + " print(\"tran (twisted):, {}, {:.2f}, {:.5f}\".format(m,angles[m],tran[m]))\n", " m0_twisted[k] = tran[0]\n", " m1_twisted[k] = tran[1]\n", " ang_twisted[k] = angles[1]" @@ -215,52 +3185,65 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "cos_angles = [math.cos(math.radians(t)) for t in ang_uniaxial]\n", - "tran = m0_uniaxial + 2 * m1_uniaxial\n", - "eff_m0 = m0_uniaxial / tran\n", - "eff_m1 = (2 * m1_uniaxial / tran) / cos_angles\n", + "tran = m0_uniaxial+2*m1_uniaxial\n", + "eff_m0 = m0_uniaxial/tran\n", + "eff_m1 = (2*m1_uniaxial/tran)/cos_angles\n", "\n", - "phase = delta_n * dd / wvl\n", - "eff_m0_analytic = [math.cos(math.pi * p) ** 2 for p in phase]\n", - "eff_m1_analytic = [math.sin(math.pi * p) ** 2 for p in phase]" + "phase = delta_n*dd/wvl\n", + "eff_m0_analytic = [math.cos(math.pi*p)**2 for p in phase]\n", + "eff_m1_analytic = [math.sin(math.pi*p)**2 for p in phase]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=150)\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot(phase, eff_m0, \"bo-\", clip_on=False, label=\"0th order (meep)\")\n", - "plt.plot(phase, eff_m0_analytic, \"b--\", clip_on=False, label=\"0th order (analytic)\")\n", - "plt.plot(phase, eff_m1, \"ro-\", clip_on=False, label=\"±1 orders (meep)\")\n", - "plt.plot(phase, eff_m1_analytic, \"r--\", clip_on=False, label=\"±1 orders (analytic)\")\n", + "plt.subplot(1,2,1)\n", + "plt.plot(phase,eff_m0,'bo-',clip_on=False,label='0th order (meep)')\n", + "plt.plot(phase,eff_m0_analytic,'b--',clip_on=False,label='0th order (analytic)')\n", + "plt.plot(phase,eff_m1,'ro-',clip_on=False,label='±1 orders (meep)')\n", + "plt.plot(phase,eff_m1_analytic,'r--',clip_on=False,label='±1 orders (analytic)')\n", "plt.axis([0, 1.0, 0, 1])\n", - "plt.xticks([t for t in np.arange(0, 1.2, 0.2)])\n", + "plt.xticks([t for t in np.arange(0,1.2,0.2)])\n", "plt.xlabel(\"phase delay Δnd/λ\")\n", "plt.ylabel(\"diffraction efficiency @ λ = 0.54 μm\")\n", - "plt.legend(loc=\"center\")\n", + "plt.legend(loc='center')\n", "plt.title(\"homogeneous uniaxial grating\")\n", "\n", "cos_angles = [math.cos(math.radians(t)) for t in ang_twisted]\n", - "tran = m0_twisted + 2 * m1_twisted\n", - "eff_m0 = m0_twisted / tran\n", - "eff_m1 = (2 * m1_twisted / tran) / cos_angles\n", + "tran = m0_twisted+2*m1_twisted\n", + "eff_m0 = m0_twisted/tran\n", + "eff_m1 = (2*m1_twisted/tran)/cos_angles\n", "\n", - "plt.subplot(1, 2, 2)\n", - "plt.plot(phase, eff_m0, \"bo-\", clip_on=False, label=\"0th order (meep)\")\n", - "plt.plot(phase, eff_m1, \"ro-\", clip_on=False, label=\"±1 orders (meep)\")\n", + "plt.subplot(1,2,2)\n", + "plt.plot(phase,eff_m0,'bo-',clip_on=False,label='0th order (meep)')\n", + "plt.plot(phase,eff_m1,'ro-',clip_on=False,label='±1 orders (meep)')\n", "plt.axis([0, 1.0, 0, 1])\n", - "plt.xticks([t for t in np.arange(0, 1.2, 0.2)])\n", + "plt.xticks([t for t in np.arange(0,1.2,0.2)])\n", "plt.xlabel(\"phase delay Δnd/λ\")\n", "plt.ylabel(\"diffraction efficiency @ λ = 0.54 μm\")\n", - "plt.legend(loc=\"center\")\n", + "plt.legend(loc='center')\n", "plt.title(\"bilayer twisted-nematic grating\")\n", "\n", "plt.tight_layout()\n", diff --git a/python/examples/refl-angular.ipynb b/python/examples/refl-angular.ipynb index 492cf0059..b0dd4e27a 100644 --- a/python/examples/refl-angular.ipynb +++ b/python/examples/refl-angular.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -32,60 +32,47 @@ "import numpy.matlib\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 50 # pixels/um\n", + "resolution = 50 # pixels/um\n", "\n", - "dpml = 1.0 # PML thickness\n", - "sz = 10 + 2 * dpml\n", + "dpml = 1.0 # PML thickness\n", + "sz = 10 + 2*dpml\n", "cell_size = mp.Vector3(z=sz)\n", "pml_layers = [mp.PML(dpml)]\n", "\n", - "wvl_min = 0.4 # min wavelength\n", - "wvl_max = 0.8 # max wavelength\n", - "fmin = 1 / wvl_max # min frequency\n", - "fmax = 1 / wvl_min # max frequency\n", - "fcen = 0.5 * (fmin + fmax) # center frequency\n", - "df = fmax - fmin # frequency width\n", - "nfreq = 50 # number of frequency bins\n", + "wvl_min = 0.4 # min wavelength\n", + "wvl_max = 0.8 # max wavelength\n", + "fmin = 1/wvl_max # min frequency\n", + "fmax = 1/wvl_min # max frequency\n", + "fcen = 0.5*(fmin+fmax) # center frequency\n", + "df = fmax-fmin # frequency width\n", + "nfreq = 50 # number of frequency bins\n", "\n", - "\n", - "def planar_reflectance(theta):\n", + "def planar_reflectance(theta): \n", " # rotation angle (in degrees) of source: CCW around Y axis, 0 degrees along +Z axis\n", " theta_r = math.radians(theta)\n", "\n", " # plane of incidence is XZ; rotate counter clockwise (CCW) about y-axis\n", " k = mp.Vector3(z=fmin).rotate(mp.Vector3(y=1), theta_r)\n", - "\n", + " \n", " # if normal incidence, force number of dimensions to be 1\n", " if theta_r == 0:\n", " dimensions = 1\n", " else:\n", " dimensions = 3\n", + " \n", + " sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), component=mp.Ex, center=mp.Vector3(z=-0.5*sz+dpml))]\n", "\n", - " sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ex,\n", - " center=mp.Vector3(z=-0.5 * sz + dpml),\n", - " )\n", - " ]\n", - "\n", - " sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=k,\n", - " dimensions=dimensions,\n", - " resolution=resolution,\n", - " )\n", + " sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=k,\n", + " dimensions=dimensions,\n", + " resolution=resolution)\n", "\n", - " refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25 * sz))\n", + " refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25*sz))\n", " refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", - "\n", - " sim.run(\n", - " until_after_sources=mp.stop_when_fields_decayed(\n", - " 50, mp.Ex, mp.Vector3(z=-0.5 * sz + dpml), 1e-9\n", - " )\n", - " )\n", + " \n", + " sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(z=-0.5*sz+dpml), 1e-9))\n", "\n", " empty_flux = mp.get_fluxes(refl)\n", " empty_data = sim.get_flux_data(refl)\n", @@ -93,32 +80,20 @@ " sim.reset_meep()\n", "\n", " # add a block with n=3.5 for the air-dielectric interface\n", - " geometry = [\n", - " mp.Block(\n", - " mp.Vector3(mp.inf, mp.inf, 0.5 * sz),\n", - " center=mp.Vector3(z=0.25 * sz),\n", - " material=mp.Medium(index=3.5),\n", - " )\n", - " ]\n", + " geometry = [mp.Block(mp.Vector3(mp.inf,mp.inf,0.5*sz), center=mp.Vector3(z=0.25*sz), material=mp.Medium(index=3.5))]\n", "\n", - " sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " geometry=geometry,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " k_point=k,\n", - " dimensions=dimensions,\n", - " resolution=resolution,\n", - " )\n", + " sim = mp.Simulation(cell_size=cell_size,\n", + " geometry=geometry,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " k_point=k,\n", + " dimensions=dimensions,\n", + " resolution=resolution)\n", "\n", " refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", " sim.load_minus_flux_data(refl, empty_data)\n", "\n", - " sim.run(\n", - " until_after_sources=mp.stop_when_fields_decayed(\n", - " 50, mp.Ex, mp.Vector3(z=-0.5 * sz + dpml), 1e-9\n", - " )\n", - " )\n", + " sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(z=-0.5*sz+dpml), 1e-9))\n", "\n", " refl_flux = mp.get_fluxes(refl)\n", " freqs = mp.get_flux_freqs(refl)\n", @@ -127,12 +102,12 @@ " theta_out = np.empty(nfreq)\n", " R = np.empty(nfreq)\n", " for i in range(nfreq):\n", - " wvls[i] = 1 / freqs[i]\n", - " theta_out[i] = math.degrees(math.asin(k.x / freqs[i]))\n", - " R[i] = -refl_flux[i] / empty_flux[i]\n", - " print(\"refl:, {}, {}, {}, {}\".format(k.x, wvls[i], theta_out[i], R[i]))\n", - "\n", - " return k.x * np.ones(nfreq), wvls, theta_out, R" + " wvls[i] = 1/freqs[i]\n", + " theta_out[i] = math.degrees(math.asin(k.x/freqs[i]))\n", + " R[i] = -refl_flux[i]/empty_flux[i]\n", + " print(\"refl:, {}, {}, {}, {}\".format(k.x,wvls[i],theta_out[i],R[i]))\n", + " \n", + " return k.x*np.ones(nfreq), wvls, theta_out, R " ] }, { @@ -146,21 +121,1368 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000254154 s\n", + "Working in 1D dimensions.\n", + "Computational cell is 0 x 0 x 12 with resolution 50\n", + "time for set_epsilon = 0.000342131 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.25332329653323415 / 0.25332329653323415 = 1.0\n", + "field decay(t = 100.01): 6.806395978139867e-16 / 0.25332329653323415 = 2.6868417043700194e-15\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108004 s\n", + "Working in 1D dimensions.\n", + "Computational cell is 0 x 0 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.000371933 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.25332329652480207 / 0.25332329652480207 = 1.0\n", + "field decay(t = 100.01): 1.9736380723733672e-11 / 0.25332329652480207 = 7.790985272371642e-11\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 0.0, 0.8, 0.0, 0.29476330384323207\n", + "refl:, 0.0, 0.784, 0.0, 0.29416578611798405\n", + "refl:, 0.0, 0.7686274509803922, 0.0, 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structure...\n", + "time for choose_chunkdivision = 0.000108957 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00770998 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.25242167342001043 / 0.25242167342001043 = 1.0\n", + "field decay(t = 100.01): 1.8867425254108467e-14 / 0.25242167342001043 = 7.474566267815881e-14\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000110865 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.026345 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.2524216734361254 / 0.2524216734361254 = 1.0\n", + "field decay(t = 100.01): 2.0310851043564926e-11 / 0.2524216734361254 = 8.046397429777174e-11\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 0.1089446784345727, 0.8, 5.0, 0.2934227118859898\n", + "refl:, 0.1089446784345727, 0.784, 4.899752997934953, 0.29287850682357097\n", + "refl:, 0.1089446784345727, 0.7686274509803922, 4.803451415694315, 0.2923194417293526\n", + "refl:, 0.1089446784345727, 0.7538461538461539, 4.710866569098618, 0.29174404751628646\n", + "refl:, 0.1089446784345727, 0.739622641509434, 4.621787131270349, 0.2911512998642181\n", + "refl:, 0.1089446784345727, 0.7259259259259259, 4.536017514803643, 0.2905417123133207\n", + "refl:, 0.1089446784345727, 0.7127272727272727, 4.45337643175598, 0.2899156301931104\n", + "refl:, 0.1089446784345727, 0.7, 4.373695609047488, 0.2892720597047358\n", + "refl:, 0.1089446784345727, 0.6877192982456141, 4.296818640028281, 0.288609702000128\n", + "refl:, 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0.43555555555555553, 2.719789703245832, 0.256293125487968\n", + "refl:, 0.1089446784345727, 0.4307692307692308, 2.689879804508449, 0.2549512279458179\n", + "refl:, 0.1089446784345727, 0.4260869565217391, 2.660620830815881, 0.2535831260087174\n", + "refl:, 0.1089446784345727, 0.421505376344086, 2.631991754497782, 0.25219031084070415\n", + "refl:, 0.1089446784345727, 0.41702127659574467, 2.603972444256211, 0.25077428012312447\n", + "refl:, 0.1089446784345727, 0.4126315789473684, 2.576543617889841, 0.24933314662117714\n", + "refl:, 0.1089446784345727, 0.4083333333333333, 2.549686797979975, 0.2478653741944476\n", + "refl:, 0.1089446784345727, 0.4041237113402062, 2.5233842703240543, 0.24637235281997996\n", + "refl:, 0.1089446784345727, 0.4, 2.4976190449198983, 0.2448541987123429\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.00011611 s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00750589 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.24974540035413878 / 0.24974540035413878 = 1.0\n", + "field decay(t = 100.01): 6.006906595531682e-14 / 0.24974540035413878 = 2.40521210281106e-13\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108957 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.0191441 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.24974540044608928 / 0.24974540044608928 = 1.0\n", + "field decay(t = 100.01): 2.1519308385946042e-11 / 0.24974540044608928 = 8.61649838095467e-11\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 0.2170602220836629, 0.8, 10.0, 0.2893781746253207\n", + "refl:, 0.2170602220836629, 0.784, 9.798006528153513, 0.288998732433702\n", + "refl:, 0.2170602220836629, 0.7686274509803922, 9.604050171837292, 0.2885938951899122\n", + "refl:, 0.2170602220836629, 0.7538461538461539, 9.417658416993296, 0.28816419107846264\n", + "refl:, 0.2170602220836629, 0.739622641509434, 9.238395240840497, 0.28770900366948954\n", + "refl:, 0.2170602220836629, 0.7259259259259259, 9.06585764090149, 0.28722834374816003\n", + "refl:, 0.2170602220836629, 0.7127272727272727, 8.899672554443574, 0.2867235913729682\n", + "refl:, 0.2170602220836629, 0.7, 8.739494117841588, 0.28619525258422535\n", + "refl:, 0.2170602220836629, 0.6877192982456141, 8.585001222725978, 0.28564218452120266\n", + "refl:, 0.2170602220836629, 0.6758620689655173, 8.435895331947279, 0.2850647891546287\n", + "refl:, 0.2170602220836629, 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0.25382296989852066\n", + "refl:, 0.2170602220836629, 0.4260869565217391, 5.306671543905553, 0.25248068938520546\n", + "refl:, 0.2170602220836629, 0.421505376344086, 5.249449481049872, 0.2511120329000034\n", + "refl:, 0.2170602220836629, 0.41702127659574467, 5.19344999309547, 0.2497191778959394\n", + "refl:, 0.2170602220836629, 0.4126315789473684, 5.138634260422533, 0.2483012320567707\n", + "refl:, 0.2170602220836629, 0.4083333333333333, 5.08496509184317, 0.2468560224651321\n", + "refl:, 0.2170602220836629, 0.4041237113402062, 5.032406839989342, 0.24538435149961998\n", + "refl:, 0.2170602220836629, 0.4, 4.980925321928872, 0.2438873823711191\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108957 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00942492 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.24537918139687384 / 0.24537918139687384 = 1.0\n", + "field decay(t = 100.01): 1.2755953357084837e-13 / 0.24537918139687384 = 5.198466016745522e-13\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000109911 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.015625 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.24537918161848085 / 0.24537918161848085 = 1.0\n", + "field decay(t = 100.01): 2.2796495621609012e-11 / 0.24537918161848085 = 9.290313657111033e-11\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 0.3235238063781509, 0.8, 14.999999999999998, 0.28256373042287475\n", + "refl:, 0.3235238063781509, 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0.3235238063781509, 0.47228915662650606, 8.789039332825531, 0.26139274110511584\n", + "refl:, 0.3235238063781509, 0.4666666666666667, 8.683594366947904, 0.26030735036150837\n", + "refl:, 0.3235238063781509, 0.4611764705882353, 8.5806590541555, 0.2591947457863499\n", + "refl:, 0.3235238063781509, 0.4558139534883721, 8.480144519487721, 0.25805589551088276\n", + "refl:, 0.3235238063781509, 0.4505747126436782, 8.381966050745921, 0.256892047194308\n", + "refl:, 0.3235238063781509, 0.4454545454545454, 8.286042856724524, 0.2557015480946698\n", + "refl:, 0.3235238063781509, 0.44044943820224725, 8.192297842157469, 0.2544827165503608\n", + "refl:, 0.3235238063781509, 0.43555555555555553, 8.100657398042404, 0.25323697315876975\n", + "refl:, 0.3235238063781509, 0.4307692307692308, 8.011051206126568, 0.25196549708688976\n", + "refl:, 0.3235238063781509, 0.4260869565217391, 7.923412056447124, 0.2506663282693837\n", + "refl:, 0.3235238063781509, 0.421505376344086, 7.837675676917115, 0.2493383700330171\n", + "refl:, 0.3235238063781509, 0.41702127659574467, 7.753780574036344, 0.24798388324916928\n", + "refl:, 0.3235238063781509, 0.4126315789473684, 7.671667883886533, 0.24660386238549814\n", + "refl:, 0.3235238063781509, 0.4083333333333333, 7.591281232641979, 0.2451961050843521\n", + "refl:, 0.3235238063781509, 0.4041237113402062, 7.512566605892175, 0.2437600099079589\n", + "refl:, 0.3235238063781509, 0.4, 7.435472226131853, 0.24229747168145319\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000112772 s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.007622 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.23946072431081186 / 0.23946072431081186 = 1.0\n", + "field decay(t = 100.01): 2.143367304660103e-13 / 0.23946072431081186 = 8.950809410724429e-13\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000111818 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.0173719 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.23946072470582727 / 0.23946072470582727 = 1.0\n", + "field decay(t = 100.01): 2.2924410648414684e-11 / 0.23946072470582727 = 9.573348897434795e-11\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 0.4275251791570859, 0.8, 20.0, 0.27287026974445927\n", + "refl:, 0.4275251791570859, 0.784, 19.583468236198428, 0.2732024101218738\n", + "refl:, 0.4275251791570859, 0.7686274509803922, 19.184283570762837, 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0.4275251791570859, 0.4666666666666667, 11.508408977339421, 0.25727477704208235\n", + "refl:, 0.4275251791570859, 0.4611764705882353, 11.37119858666983, 0.2562395153461572\n", + "refl:, 0.4275251791570859, 0.4558139534883721, 11.23724290704701, 0.2551740956437644\n", + "refl:, 0.4275251791570859, 0.4505747126436782, 11.106426741117268, 0.2540803028372474\n", + "refl:, 0.4275251791570859, 0.4454545454545454, 10.978640299154753, 0.25295873317085843\n", + "refl:, 0.4275251791570859, 0.44044943820224725, 10.853778883451254, 0.2518070732573948\n", + "refl:, 0.4275251791570859, 0.43555555555555553, 10.731742594690267, 0.2506247305646683\n", + "refl:, 0.4275251791570859, 0.4307692307692308, 10.61243605852877, 0.249413825082604\n", + "refl:, 0.4275251791570859, 0.4260869565217391, 10.495768170772996, 0.2481743775271508\n", + "refl:, 0.4275251791570859, 0.421505376344086, 10.381651859681224, 0.24690419443990608\n", + "refl:, 0.4275251791570859, 0.41702127659574467, 10.270003864057921, 0.2456037441531416\n", + "refl:, 0.4275251791570859, 0.4126315789473684, 10.160744525922071, 0.24427554305470142\n", + "refl:, 0.4275251791570859, 0.4083333333333333, 10.053797596639106, 0.2429193465329389\n", + "refl:, 0.4275251791570859, 0.4041237113402062, 9.949090055502005, 0.24153322642643202\n", + "refl:, 0.4275251791570859, 0.4, 9.846551939834079, 0.2401180039660742\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000110865 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00934601 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.23217567515545764 / 0.23217567515545764 = 1.0\n", + "field decay(t = 100.01): 3.1895846890533654e-13 / 0.23217567515545764 = 1.3737807317315728e-12\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108957 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.016118 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.23217567572617967 / 0.23217567572617967 = 1.0\n", + "field decay(t = 100.01): 2.511386584143695e-11 / 0.23217567572617967 = 1.0816751480484724e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 0.5282728271758743, 0.8, 25.0, 0.2601481976279196\n", + "refl:, 0.5282728271758743, 0.784, 24.46679999189225, 0.2610696372423531\n", + "refl:, 0.5282728271758743, 0.7686274509803922, 23.95662859236144, 0.2618770325163737\n", + "refl:, 0.5282728271758743, 0.7538461538461539, 23.467975961566616, 0.2625788276586943\n", + "refl:, 0.5282728271758743, 0.739622641509434, 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12.457267175263851, 0.24007405600657153\n", + "refl:, 0.5282728271758743, 0.4041237113402062, 12.326811800951434, 0.238751417728497\n", + "refl:, 0.5282728271758743, 0.4, 12.199081690448809, 0.2373967326334257\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108004 s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00868416 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.22375100718842 / 0.22375100718842 = 1.0\n", + "field decay(t = 100.01): 4.4054832270861915e-13 / 0.22375100718842 = 1.9689221883038714e-12\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108957 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.0171471 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.22375100783149995 / 0.22375100783149995 = 1.0\n", + "field decay(t = 100.01): 2.604590852088861e-11 / 0.22375100783149995 = 1.1640577074183724e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 0.6249999999999999, 0.8, 29.999999999999993, 0.2442050337697129\n", + "refl:, 0.6249999999999999, 0.784, 29.34058157502373, 0.24592026654412946\n", + "refl:, 0.6249999999999999, 0.7686274509803922, 28.711017527148794, 0.2474593095717218\n", + "refl:, 0.6249999999999999, 0.7538461538461539, 28.10922128260952, 0.24883736533494075\n", + "refl:, 0.6249999999999999, 0.739622641509434, 27.53330580109674, 0.25006920079221073\n", + "refl:, 0.6249999999999999, 0.7259259259259259, 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0.4041237113402062, 14.630077251904048, 0.23547697979370008\n", + "refl:, 0.6249999999999999, 0.4, 14.477512185929921, 0.23419574038931623\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000112057 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00802183 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.2144470639002981 / 0.2144470639002981 = 1.0\n", + "field decay(t = 100.01): 5.749510870152026e-13 / 0.2144470639002981 = 2.6810863089387505e-12\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000110865 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.016397 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.21444706445458073 / 0.21444706445458073 = 1.0\n", + "field decay(t = 100.01): 2.737725032991902e-11 / 0.21444706445458073 = 1.276643744205575e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 0.7169705454388076, 0.8, 35.0, 0.22482605012853205\n", + "refl:, 0.7169705454388076, 0.784, 34.201491482224874, 0.2275879297659293\n", + "refl:, 0.7169705454388076, 0.7686274509803922, 33.4413597291606, 0.2300861771421993\n", + "refl:, 0.7169705454388076, 0.7538461538461539, 32.71669424141867, 0.23234663839124975\n", + "refl:, 0.7169705454388076, 0.739622641509434, 32.02489215108529, 0.2343911194485937\n", + "refl:, 0.7169705454388076, 0.7259259259259259, 31.363616172201723, 0.23623793864435802\n", + "refl:, 0.7169705454388076, 0.7127272727272727, 30.73075960358516, 0.23790584943527182\n", + "refl:, 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18.62530034751981, 0.24082577322829152\n", + "refl:, 0.7169705454388076, 0.44044943820224725, 18.408468109247096, 0.23998068018388435\n", + "refl:, 0.7169705454388076, 0.43555555555555553, 18.196718136035763, 0.23909605307915388\n", + "refl:, 0.7169705454388076, 0.4307692307692308, 17.98987056333767, 0.23817255818097147\n", + "refl:, 0.7169705454388076, 0.4260869565217391, 17.787754068005896, 0.23720797538105634\n", + "refl:, 0.7169705454388076, 0.421505376344086, 17.590205358285754, 0.236202366418755\n", + "refl:, 0.7169705454388076, 0.41702127659574467, 17.39706870053973, 0.23515820136339463\n", + "refl:, 0.7169705454388076, 0.4126315789473684, 17.20819547960629, 0.2340750107196232\n", + "refl:, 0.7169705454388076, 0.4083333333333333, 17.02344378999232, 0.2329510888033509\n", + "refl:, 0.7169705454388076, 0.4041237113402062, 16.842678055366356, 0.23178864386805378\n", + "refl:, 0.7169705454388076, 0.4, 16.665768674058118, 0.2305906700422077\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108957 s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00882411 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.20454876789491885 / 0.20454876789491885 = 1.0\n", + "field decay(t = 100.01): 7.178635175771892e-13 / 0.20454876789491885 = 3.509498125874663e-12\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108004 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.018625 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.20454876813558592 / 0.20454876813558592 = 1.0\n", + "field decay(t = 100.01): 2.8174572450318613e-11 / 0.20454876813558592 = 1.3774012284270024e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 0.8034845121081741, 0.8, 39.99999999999999, 0.20179462162966672\n", + "refl:, 0.8034845121081741, 0.784, 39.04509528455022, 0.2059250719323383\n", + "refl:, 0.8034845121081741, 0.7686274509803922, 38.139646206191365, 0.20966397204966036\n", + "refl:, 0.8034845121081741, 0.7538461538461539, 37.27949726994566, 0.21305427397378518\n", + "refl:, 0.8034845121081741, 0.739622641509434, 36.46098976062103, 0.21613269723029083\n", + "refl:, 0.8034845121081741, 0.7259259259259259, 35.680884053707004, 0.21892741270212848\n", + "refl:, 0.8034845121081741, 0.7127272727272727, 34.93629688867566, 0.22146683922801672\n", + "refl:, 0.8034845121081741, 0.7, 34.22465020933728, 0.22377279980470224\n", + "refl:, 0.8034845121081741, 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0.24022489861203003\n", + "refl:, 0.8034845121081741, 0.4962025316455696, 23.496375102814216, 0.24001212440412875\n", + "refl:, 0.8034845121081741, 0.49, 23.185382948015043, 0.2397434129524054\n", + "refl:, 0.8034845121081741, 0.4839506172839506, 22.882764536632674, 0.2394219863711472\n", + "refl:, 0.8034845121081741, 0.47804878048780486, 22.58817584322306, 0.23904730465890137\n", + "refl:, 0.8034845121081741, 0.47228915662650606, 22.301291999661412, 0.23862033792003395\n", + "refl:, 0.8034845121081741, 0.4666666666666667, 22.021805941382443, 0.23814466238694304\n", + "refl:, 0.8034845121081741, 0.4611764705882353, 21.749427169932794, 0.23762099874005177\n", + "refl:, 0.8034845121081741, 0.4558139534883721, 21.483880620051718, 0.23704845905896132\n", + "refl:, 0.8034845121081741, 0.4505747126436782, 21.224905620871482, 0.2364290006894749\n", + "refl:, 0.8034845121081741, 0.4454545454545454, 20.972254942022712, 0.23576489053070165\n", + "refl:, 0.8034845121081741, 0.44044943820224725, 20.725693916468796, 0.23505540516189377\n", + "refl:, 0.8034845121081741, 0.43555555555555553, 20.48499963280006, 0.23429986728741037\n", + "refl:, 0.8034845121081741, 0.4307692307692308, 20.249960190511292, 0.23350021386178307\n", + "refl:, 0.8034845121081741, 0.4260869565217391, 20.020374012481035, 0.23265749904183425\n", + "refl:, 0.8034845121081741, 0.421505376344086, 19.79604920948226, 0.23176986721335244\n", + "refl:, 0.8034845121081741, 0.41702127659574467, 19.576802992091316, 0.23083693098169072\n", + "refl:, 0.8034845121081741, 0.4126315789473684, 19.362461125837058, 0.22986107450928206\n", + "refl:, 0.8034845121081741, 0.4083333333333333, 19.15285742585141, 0.2288423958877941\n", + "refl:, 0.8034845121081741, 0.4041237113402062, 18.9478332876545, 0.22777913143869027\n", + "refl:, 0.8034845121081741, 0.4, 18.747237251037504, 0.22667336344348024\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000111103 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.006598 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.1943564114319847 / 0.1943564114319847 = 1.0\n", + "field decay(t = 100.01): 8.613369505850488e-13 / 0.1943564114319847 = 4.431739319731548e-12\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108004 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.0163691 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.1943564112167818 / 0.1943564112167818 = 1.0\n", + "field decay(t = 100.01): 2.929219089217826e-11 / 0.1943564112167818 = 1.5071378766870859e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 0.8838834764831843, 0.8, 44.99999999999999, 0.1749506546010766\n", + "refl:, 0.8838834764831843, 0.784, 43.865246854678944, 0.18084614262894314\n", + "refl:, 0.8838834764831843, 0.7686274509803922, 42.79498686880298, 0.18616490888473136\n", + "refl:, 0.8838834764831843, 0.7538461538461539, 41.78306957363623, 0.1909834896778329\n", + "refl:, 0.8838834764831843, 0.739622641509434, 40.82419653278361, 0.1953532231998348\n", + "refl:, 0.8838834764831843, 0.7259259259259259, 39.913765803587594, 0.19932045676242707\n", + "refl:, 0.8838834764831843, 0.7127272727272727, 39.047751315763335, 0.20293092176670602\n", + "refl:, 0.8838834764831843, 0.7, 38.2226079814105, 0.20621465898807328\n", + "refl:, 0.8838834764831843, 0.6877192982456141, 37.435196089128915, 0.20920558346668153\n", + "refl:, 0.8838834764831843, 0.6758620689655173, 36.682720369951866, 0.21192936258982145\n", + "refl:, 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0.2293833309292736\n", + "refl:, 0.8838834764831843, 0.56, 29.66808512880701, 0.23014665030198314\n", + "refl:, 0.8838834764831843, 0.552112676056338, 29.20942668547734, 0.2308091072410071\n", + "refl:, 0.8838834764831843, 0.5444444444444444, 28.765468598924116, 0.23137604508998047\n", + "refl:, 0.8838834764831843, 0.536986301369863, 28.33547814384704, 0.23185120425684555\n", + "refl:, 0.8838834764831843, 0.5297297297297298, 27.91877297273401, 0.23223967284122596\n", + "refl:, 0.8838834764831843, 0.5226666666666667, 27.514716656212954, 0.23254649427327514\n", + "refl:, 0.8838834764831843, 0.5157894736842105, 27.12271470851516, 0.23277497605931805\n", + "refl:, 0.8838834764831843, 0.509090909090909, 26.742211035417114, 0.2329274877105852\n", + "refl:, 0.8838834764831843, 0.5025641025641026, 26.372684751370738, 0.23300817835325607\n", + "refl:, 0.8838834764831843, 0.4962025316455696, 26.013647320299032, 0.23302096475520206\n", + "refl:, 0.8838834764831843, 0.49, 25.66463998102006, 0.23296661309174865\n", + "refl:, 0.8838834764831843, 0.4839506172839506, 25.32523142370283, 0.2328473819749458\n", + "refl:, 0.8838834764831843, 0.47804878048780486, 24.99501568834094, 0.23266777547883646\n", + "refl:, 0.8838834764831843, 0.47228915662650606, 24.673610260104642, 0.2324286704369094\n", + "refl:, 0.8838834764831843, 0.4666666666666667, 24.360654339720863, 0.23213016162250638\n", + "refl:, 0.8838834764831843, 0.4611764705882353, 24.055807269832574, 0.23177609164136181\n", + "refl:, 0.8838834764831843, 0.4558139534883721, 23.75874710068368, 0.2313686559958002\n", + "refl:, 0.8838834764831843, 0.4505747126436782, 23.46916928052956, 0.23090689658521463\n", + "refl:, 0.8838834764831843, 0.4454545454545454, 23.18678545794031, 0.23039248392782377\n", + "refl:, 0.8838834764831843, 0.44044943820224725, 22.911322384688706, 0.22982847953509086\n", + "refl:, 0.8838834764831843, 0.43555555555555553, 22.64252090923418, 0.22921468760037644\n", + "refl:, 0.8838834764831843, 0.4307692307692308, 22.380135051959574, 0.2285503145110825\n", + "refl:, 0.8838834764831843, 0.4260869565217391, 22.123931154313652, 0.22783728199610298\n", + "refl:, 0.8838834764831843, 0.421505376344086, 21.873687094881706, 0.22707701516938034\n", + "refl:, 0.8838834764831843, 0.41702127659574467, 21.629191566166806, 0.226267933168201\n", + "refl:, 0.8838834764831843, 0.4126315789473684, 21.390243406530644, 0.22540929123705422\n", + "refl:, 0.8838834764831843, 0.4083333333333333, 21.156650982328358, 0.22450324859888127\n", + "refl:, 0.8838834764831843, 0.4041237113402062, 20.928231615787418, 0.2235503997974889\n", + "refl:, 0.8838834764831843, 0.4, 20.704811054635428, 0.22254911459777174\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000109911 s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.006423 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.18417632037527257 / 0.18417632037527257 = 1.0\n", + "field decay(t = 100.01): 1.0178427595748204e-12 / 0.18417632037527257 = 5.5264583280895835e-12\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000110865 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.0184629 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.18417631975521828 / 0.18417631975521828 = 1.0\n", + "field decay(t = 100.01): 2.8713012611847576e-11 / 0.18417631975521828 = 1.5589958931750262e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 0.9575555538987225, 0.8, 50.0, 0.14429067301668\n", + "refl:, 0.9575555538987225, 0.784, 48.6530933185261, 0.15241249489296654\n", + "refl:, 0.9575555538987225, 0.7686274509803922, 47.39207738436693, 0.15970237410745958\n", + "refl:, 0.9575555538987225, 0.7538461538461539, 46.207396358038395, 0.16628917742963503\n", + "refl:, 0.9575555538987225, 0.739622641509434, 45.091066324150106, 0.1722363131532337\n", + "refl:, 0.9575555538987225, 0.7259259259259259, 44.036334278275795, 0.1776288135399773\n", + "refl:, 0.9575555538987225, 0.7127272727272727, 43.037427456538545, 0.1825236470768417\n", + "refl:, 0.9575555538987225, 0.7, 42.089365210987516, 0.1869718164862598\n", + "refl:, 0.9575555538987225, 0.6877192982456141, 41.18781524255363, 0.19102552097133876\n", + "refl:, 0.9575555538987225, 0.6758620689655173, 40.32898196453324, 0.19471651743120957\n", + "refl:, 0.9575555538987225, 0.664406779661017, 39.50951857909891, 0.1980846376246996\n", + "refl:, 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"refl:, 0.9575555538987225, 0.552112676056338, 31.91621381391884, 0.22119898455404\n", + "refl:, 0.9575555538987225, 0.5444444444444444, 31.42189714930561, 0.22211935917204448\n", + "refl:, 0.9575555538987225, 0.536986301369863, 30.943610409083956, 0.2229298821236265\n", + "refl:, 0.9575555538987225, 0.5297297297297298, 30.480537056602333, 0.22363493657078803\n", + "refl:, 0.9575555538987225, 0.5226666666666667, 30.031918444163345, 0.2242400133418431\n", + "refl:, 0.9575555538987225, 0.5157894736842105, 29.597048491686717, 0.22475115535497284\n", + "refl:, 0.9575555538987225, 0.509090909090909, 29.17526896927061, 0.2251729335780506\n", + "refl:, 0.9575555538987225, 0.5025641025641026, 28.76596530209651, 0.22550858491249842\n", + "refl:, 0.9575555538987225, 0.4962025316455696, 28.36856282886028, 0.22576196217784258\n", + "refl:, 0.9575555538987225, 0.49, 27.982523455399697, 0.22593807040959396\n", + "refl:, 0.9575555538987225, 0.4839506172839506, 27.60734265386835, 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0.4260869565217391, 24.079380926958596, 0.22288585592315466\n", + "refl:, 0.9575555538987225, 0.421505376344086, 23.804353351700417, 0.22225690920257898\n", + "refl:, 0.9575555538987225, 0.41702127659574467, 23.535740239681235, 0.22157652716566317\n", + "refl:, 0.9575555538987225, 0.4126315789473684, 23.27331242373393, 0.22084351574148542\n", + "refl:, 0.9575555538987225, 0.4083333333333333, 23.016851878423925, 0.220057130518136\n", + "refl:, 0.9575555538987225, 0.4041237113402062, 22.76615102993319, 0.21921919579654955\n", + "refl:, 0.9575555538987225, 0.4, 22.521012118111, 0.21832995493951468\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108004 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00824594 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.17431196343224772 / 0.17431196343224772 = 1.0\n", + "field decay(t = 100.01): 1.226599619152291e-12 / 0.17431196343224772 = 7.0368068547919865e-12\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108957 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.0162461 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.17431196264184795 / 0.17431196264184795 = 1.0\n", + "field decay(t = 100.01): 2.9177363356244976e-11 / 0.17431196264184795 = 1.6738589201817764e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 1.0239400553612397, 0.8, 54.99999999999999, 0.11015750327021501\n", + "refl:, 1.0239400553612397, 0.784, 53.39534223243568, 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1.0239400553612397, 0.47228915662650606, 28.920512975908146, 0.21961022544356806\n", + "refl:, 1.0239400553612397, 0.4666666666666667, 28.544338505905493, 0.2196984674330614\n", + "refl:, 1.0239400553612397, 0.4611764705882353, 28.178307252549438, 0.21971428386147993\n", + "refl:, 1.0239400553612397, 0.4558139534883721, 27.821993787605123, 0.21965764862748693\n", + "refl:, 1.0239400553612397, 0.4505747126436782, 27.474997313821564, 0.21953193965864698\n", + "refl:, 1.0239400553612397, 0.4454545454545454, 27.13693982621647, 0.21934178652625613\n", + "refl:, 1.0239400553612397, 0.44044943820224725, 26.80746444237855, 0.21908700093353123\n", + "refl:, 1.0239400553612397, 0.43555555555555553, 26.486233883298294, 0.21876853077786598\n", + "refl:, 1.0239400553612397, 0.4307692307692308, 26.172929088582443, 0.2183905958409472\n", + "refl:, 1.0239400553612397, 0.4260869565217391, 25.867247951913267, 0.21795488284012238\n", + "refl:, 1.0239400553612397, 0.421505376344086, 25.56890416433822, 0.21746063743615796\n", + "refl:, 1.0239400553612397, 0.41702127659574467, 25.277626154460066, 0.21690850348145124\n", + "refl:, 1.0239400553612397, 0.4126315789473684, 24.993156115881433, 0.21630059246127992\n", + "refl:, 1.0239400553612397, 0.4083333333333333, 24.715249113370163, 0.21563737630418256\n", + "refl:, 1.0239400553612397, 0.4041237113402062, 24.443672260178424, 0.2149184652144797\n", + "refl:, 1.0239400553612397, 0.4, 24.178203959791162, 0.2141434469115452\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000112057 s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.0063448 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.16505586379118362 / 0.16505586379118362 = 1.0\n", + "field decay(t = 100.01): 1.6796657955389861e-12 / 0.16505586379118362 = 1.0176347310289891e-11\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000111818 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.020278 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.16505586311279807 / 0.16505586311279807 = 1.0\n", + "field decay(t = 100.01): 3.3223715038456985e-11 / 0.16505586311279807 = 2.0128769988468766e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 1.0825317547305482, 0.8, 59.99999999999999, 0.07360773441443474\n", + "refl:, 1.0825317547305482, 0.784, 58.07108488083593, 0.08756183349395384\n", + "refl:, 1.0825317547305482, 0.7686274509803922, 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0.21242171734687695\n", + "refl:, 1.0825317547305482, 0.4126315789473684, 26.531309526343414, 0.21193780892543815\n", + "refl:, 1.0825317547305482, 0.4083333333333333, 26.23371773525153, 0.21138950860192895\n", + "refl:, 1.0825317547305482, 0.4041237113402062, 25.94299837747481, 0.21078013578978685\n", + "refl:, 1.0825317547305482, 0.4, 25.65890627325528, 0.2101171783272214\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000109911 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00752401 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.15668265201002152 / 0.15668265201002152 = 1.0\n", + "field decay(t = 100.01): 2.2446823295144107e-12 / 0.15668265201002152 = 1.4326297779098349e-11\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000112057 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.0143809 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.1566826516265948 / 0.1566826516265948 = 1.0\n", + "field decay(t = 100.01): 3.452976998428892e-11 / 0.1566826516265948 = 2.2038030136597423e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 1.1328847337958123, 0.8, 64.99999999999999, 0.037170886043112084\n", + "refl:, 1.1328847337958123, 0.784, 62.645633431180784, 0.05362783985573467\n", + "refl:, 1.1328847337958123, 0.7686274509803922, 60.547811567584965, 0.06875709351287543\n", + "refl:, 1.1328847337958123, 0.7538461538461539, 58.65172469781183, 0.082354616478198\n", + "refl:, 1.1328847337958123, 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1.1328847337958123, 0.4558139534883721, 31.090053201105746, 0.20874562394291427\n", + "refl:, 1.1328847337958123, 0.4505747126436782, 30.693756253024553, 0.20895386380475825\n", + "refl:, 1.1328847337958123, 0.4454545454545454, 30.308032020994055, 0.20908023176898172\n", + "refl:, 1.1328847337958123, 0.44044943820224725, 29.9324374181665, 0.209125738596599\n", + "refl:, 1.1328847337958123, 0.43555555555555553, 29.5665552236732, 0.20909456295065154\n", + "refl:, 1.1328847337958123, 0.4307692307692308, 29.209992121269647, 0.20899049887482632\n", + "refl:, 1.1328847337958123, 0.4260869565217391, 28.86237692208816, 0.2088190433084917\n", + "refl:, 1.1328847337958123, 0.421505376344086, 28.523358950864424, 0.2085833129971406\n", + "refl:, 1.1328847337958123, 0.41702127659574467, 28.192606577687982, 0.2082742041204916\n", + "refl:, 1.1328847337958123, 0.4126315789473684, 27.86980587961545, 0.2078930131964556\n", + "refl:, 1.1328847337958123, 0.4083333333333333, 27.554659418441407, 0.20745662776433935\n", + "refl:, 1.1328847337958123, 0.4041237113402062, 27.246885122601544, 0.20697561960434535\n", + "refl:, 1.1328847337958123, 0.4, 26.946215262627685, 0.20643519600186538\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000110865 s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00959396 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.14944315649486126 / 0.14944315649486126 = 1.0\n", + "field decay(t = 100.01): 4.987937476864823e-12 / 0.14944315649486126 = 3.3376820952228335e-11\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108957 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.0218339 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.14944315644072245 / 0.14944315644072245 = 1.0\n", + "field decay(t = 100.01): 3.807851695141726e-11 / 0.14944315644072245 = 2.548026812222836e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 1.1746157759823854, 0.8, 70.0, 0.008715934286147569\n", + "refl:, 1.1746157759823854, 0.784, 67.05783140972835, 0.024210799209089452\n", + "refl:, 1.1746157759823854, 0.7686274509803922, 64.53417775149677, 0.04029873111181387\n", + "refl:, 1.1746157759823854, 0.7538461538461539, 62.31059707934406, 0.0555678814867085\n", + "refl:, 1.1746157759823854, 0.739622641509434, 60.31629899672389, 0.06970125775811391\n", + "refl:, 1.1746157759823854, 0.7259259259259259, 58.50481213939229, 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1.1746157759823854, 0.4505747126436782, 31.954940593960732, 0.20445096223605416\n", + "refl:, 1.1746157759823854, 0.4454545454545454, 31.549697581364857, 0.20471845410715214\n", + "refl:, 1.1746157759823854, 0.44044943820224725, 31.155255120816925, 0.20489444063169535\n", + "refl:, 1.1746157759823854, 0.43555555555555553, 30.771159123350074, 0.2049889592193817\n", + "refl:, 1.1746157759823854, 0.4307692307692308, 30.396982194426784, 0.20500542736591415\n", + "refl:, 1.1746157759823854, 0.4260869565217391, 30.032321589495837, 0.20495768982428397\n", + "refl:, 1.1746157759823854, 0.421505376344086, 29.67679736376329, 0.20483056853588974\n", + "refl:, 1.1746157759823854, 0.41702127659574467, 29.33005069412437, 0.20461216911883964\n", + "refl:, 1.1746157759823854, 0.4126315789473684, 28.991742354111988, 0.2043374679527374\n", + "refl:, 1.1746157759823854, 0.4083333333333333, 28.66155132518973, 0.20400220026620766\n", + "refl:, 1.1746157759823854, 0.4041237113402062, 28.339173529827534, 0.2036399273981529\n", + "refl:, 1.1746157759823854, 0.4, 28.024320673604695, 0.20318419566870832\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108957 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00775695 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.14355855111904026 / 0.14355855111904026 = 1.0\n", + "field decay(t = 100.01): 9.667874474337293e-12 / 0.14355855111904026 = 6.734446954901761e-11\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108957 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.017272 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.1435585513271557 / 0.1435585513271557 = 1.0\n", + "field decay(t = 100.01): 5.780725980413138e-11 / 0.1435585513271557 = 4.0267374719040156e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 1.2074072828613354, 0.8, 75.0, 0.002279010064127535\n", + "refl:, 1.2074072828613354, 0.784, 71.19254305121969, 0.004052585814133056\n", + "refl:, 1.2074072828613354, 0.7686274509803922, 68.13230108184366, 0.017669454881364767\n", + "refl:, 1.2074072828613354, 0.7538461538461539, 65.5329128763873, 0.03336169435169541\n", + "refl:, 1.2074072828613354, 0.739622641509434, 63.25596068464814, 0.048654509225169895\n", + "refl:, 1.2074072828613354, 0.7259259259259259, 61.22160180379117, 0.06275216167717797\n", + "refl:, 1.2074072828613354, 0.7127272727272727, 59.378629248099124, 0.07576309243511313\n", + "refl:, 1.2074072828613354, 0.7, 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1.2074072828613354, 0.5025641025641026, 37.35844816099257, 0.192373003931677\n", + "refl:, 1.2074072828613354, 0.4962025316455696, 36.806794523773725, 0.19380521484150348\n", + "refl:, 1.2074072828613354, 0.49, 36.272732803336254, 0.19509368153538312\n", + "refl:, 1.2074072828613354, 0.4839506172839506, 35.755353950220794, 0.19624859910356954\n", + "refl:, 1.2074072828613354, 0.47804878048780486, 35.253816041990945, 0.19727952544337504\n", + "refl:, 1.2074072828613354, 0.47228915662650606, 34.76733773550362, 0.19818701041137332\n", + "refl:, 1.2074072828613354, 0.4666666666666667, 34.295192516152845, 0.19897646054575882\n", + "refl:, 1.2074072828613354, 0.4611764705882353, 33.83670362811667, 0.19965889435875267\n", + "refl:, 1.2074072828613354, 0.4558139534883721, 33.391239589169516, 0.20023796589376308\n", + "refl:, 1.2074072828613354, 0.4505747126436782, 32.95821020943854, 0.20072106469864034\n", + "refl:, 1.2074072828613354, 0.4454545454545454, 32.537063046367535, 0.2011073384968122\n", + "refl:, 1.2074072828613354, 0.44044943820224725, 32.12728023870626, 0.2013932866552523\n", + "refl:, 1.2074072828613354, 0.43555555555555553, 31.728375671037586, 0.20159478768644698\n", + "refl:, 1.2074072828613354, 0.4307692307692308, 31.33989242755132, 0.20171671281423958\n", + "refl:, 1.2074072828613354, 0.4260869565217391, 30.96140049976025, 0.20176796051792995\n", + "refl:, 1.2074072828613354, 0.421505376344086, 30.592494717856905, 0.20173546160628877\n", + "refl:, 1.2074072828613354, 0.41702127659574467, 30.23279287960783, 0.2015919333831556\n", + "refl:, 1.2074072828613354, 0.4126315789473684, 29.88193405422147, 0.20140043913698885\n", + "refl:, 1.2074072828613354, 0.4083333333333333, 29.539577041619506, 0.20116952947891878\n", + "refl:, 1.2074072828613354, 0.4041237113402062, 29.2053989700849, 0.2008832766544487\n", + "refl:, 1.2074072828613354, 0.4, 28.879094017427605, 0.200502480384686\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000110865 s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + "time for set_epsilon = 0.00754905 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.1392134275610875 / 0.1392134275610875 = 1.0\n", + "field decay(t = 100.01): 1.3144200424788948e-11 / 0.1392134275610875 = 9.441761944278838e-11\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000108957 s\n", + "Working in 3D dimensions.\n", + "Computational cell is 0.02 x 0.02 x 12 with resolution 50\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (12.25,12.25,12.25)\n", + "time for set_epsilon = 0.019413 s\n", + "-----------\n", + "Meep: using complex fields.\n", + "field decay(t = 50.01): 0.13921342793500532 / 0.13921342793500532 = 1.0\n", + "field decay(t = 100.01): 9.871965144118156e-11 / 0.13921342793500532 = 7.091244925544889e-10\n", + "run 0 finished at t = 100.01 (10001 timesteps)\n", + "refl:, 1.23100969126526, 0.8, 79.99999999999994, 0.028667884842985224\n", + "refl:, 1.23100969126526, 0.784, 74.82079670263325, 0.0021347453734021486\n", + "refl:, 1.23100969126526, 0.7686274509803922, 71.11813585396203, 0.0041164349187047915\n", + "refl:, 1.23100969126526, 0.7538461538461539, 68.12392493650243, 0.017504855468406976\n", + "refl:, 1.23100969126526, 0.739622641509434, 65.57213417097068, 0.03282494395652445\n", + "refl:, 1.23100969126526, 0.7259259259259259, 63.331955818023665, 0.04792423816150501\n", + "refl:, 1.23100969126526, 0.7127272727272727, 61.32721657609733, 0.06193140028783135\n", + "refl:, 1.23100969126526, 0.7, 59.508764013939604, 0.07458578833476998\n", + "refl:, 1.23100969126526, 0.6877192982456141, 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32.42350386700291, 0.19904737688450852\n", + "refl:, 1.23100969126526, 0.4307692307692308, 32.0244494386133, 0.19925404758581897\n", + "refl:, 1.23100969126526, 0.4260869565217391, 31.63574524940934, 0.1993954429097289\n", + "refl:, 1.23100969126526, 0.421505376344086, 31.2569666032255, 0.19942422718572894\n", + "refl:, 1.23100969126526, 0.41702127659574467, 30.887713235292672, 0.1993498431053417\n", + "refl:, 1.23100969126526, 0.4126315789473684, 30.527607477190394, 0.19917542826890433\n", + "refl:, 1.23100969126526, 0.4083333333333333, 30.176292593038497, 0.19903411145233238\n", + "refl:, 1.23100969126526, 0.4041237113402062, 29.83343126780691, 0.19890224385206828\n", + "refl:, 1.23100969126526, 0.4, 29.498704231103652, 0.1984945592268268\n" + ] + } + ], "source": [ - "theta_in = np.arange(0, 85, 5)\n", + "theta_in = np.arange(0,85,5)\n", "wvl = np.empty(nfreq)\n", - "kxs = np.empty((nfreq, theta_in.size))\n", - "thetas = np.empty((nfreq, theta_in.size))\n", - "Rmeep = np.empty((nfreq, theta_in.size))\n", + "kxs = np.empty((nfreq,theta_in.size))\n", + "thetas = np.empty((nfreq,theta_in.size))\n", + "Rmeep = np.empty((nfreq,theta_in.size))\n", "\n", "for j in range(theta_in.size):\n", - " kxs[:, j], wvl, thetas[:, j], Rmeep[:, j] = planar_reflectance(theta_in[j])\n", + " kxs[:,j], wvl, thetas[:,j], Rmeep[:,j] = planar_reflectance(theta_in[j])\n", "\n", "# create a 2d matrix for the wavelength by repeating the column vector for each angle\n", - "wvls = np.transpose(np.matlib.repmat(wvl, theta_in.size, 1))" + "wvls = np.transpose(np.matlib.repmat(wvl,theta_in.size,1))" ] }, { @@ -176,92 +1498,113 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=200)\n", - "plt.pcolormesh(\n", - " kxs, wvls, Rmeep, cmap=\"inferno\", shading=\"gouraud\", vmin=0, vmax=Rmeep.max()\n", - ")\n", - "plt.axis([kxs[0, 0], kxs[0, -1], wvl_min, wvl_max])\n", - "plt.yticks([t for t in np.linspace(0.4, 0.8, 5)])\n", + "plt.pcolormesh(kxs, wvls, Rmeep, cmap='inferno', shading='gouraud', vmin=0, vmax=Rmeep.max())\n", + "plt.axis([kxs[0,0], kxs[0,-1], wvl_min, wvl_max])\n", + "plt.yticks([t for t in np.linspace(0.4,0.8,5)])\n", "plt.xlabel(r\"Bloch-periodic wavevector (k$_x$/2π)\")\n", "plt.ylabel(\"wavelength (μm)\")\n", "plt.title(\"reflectance (meep)\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.linspace(0, 0.4, 5)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0, 0.4, 5)])" + "cbar.set_ticks([t for t in np.linspace(0,0.4,5)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0,0.4,5)])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=200)\n", - "plt.pcolormesh(\n", - " thetas, wvls, Rmeep, cmap=\"inferno\", shading=\"gouraud\", vmin=0, vmax=Rmeep.max()\n", - ")\n", + "plt.pcolormesh(thetas, wvls, Rmeep, cmap='inferno', shading='gouraud', vmin=0, vmax=Rmeep.max())\n", "plt.axis([thetas.min(), thetas.max(), wvl_min, wvl_max])\n", - "plt.xticks([t for t in range(0, 100, 20)])\n", - "plt.yticks([t for t in np.linspace(0.4, 0.8, 5)])\n", + "plt.xticks([t for t in range(0,100,20)])\n", + "plt.yticks([t for t in np.linspace(0.4,0.8,5)])\n", "plt.xlabel(\"angle of incident planewave (degrees)\")\n", "plt.ylabel(\"wavelength (μm)\")\n", "plt.title(\"reflectance (meep)\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.linspace(0, 0.4, 5)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0, 0.4, 5)])" + "cbar.set_ticks([t for t in np.linspace(0,0.4,5)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0,0.4,5)])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "n1 = 1\n", - "n2 = 3.5\n", + "n1=1\n", + "n2=3.5\n", "\n", "# compute angle of refracted planewave in medium n2\n", "# for incident planewave in medium n1 at angle theta_in\n", - "theta_out = lambda theta_in: math.asin(n1 * math.sin(theta_in) / n2)\n", + "theta_out = lambda theta_in: math.asin(n1*math.sin(theta_in)/n2)\n", "\n", "# compute Fresnel reflectance for P-polarization in medium n2\n", "# for incident planewave in medium n1 at angle theta_in\n", - "Rfresnel = (\n", - " lambda theta_in: math.fabs(\n", - " (n1 * math.cos(theta_out(theta_in)) - n2 * math.cos(theta_in))\n", - " / (n1 * math.cos(theta_out(theta_in)) + n2 * math.cos(theta_in))\n", - " )\n", - " ** 2\n", - ")\n", + "Rfresnel = lambda theta_in: math.fabs((n1*math.cos(theta_out(theta_in))-n2*math.cos(theta_in))/(n1*math.cos(theta_out(theta_in))+n2*math.cos(theta_in)))**2\n", "\n", "Ranalytic = np.empty((nfreq, theta_in.size))\n", "for m in range(wvl.size):\n", " for n in range(theta_in.size):\n", - " Ranalytic[m, n] = Rfresnel(math.radians(thetas[m, n]))\n", + " Ranalytic[m,n] = Rfresnel(math.radians(thetas[m,n]))\n", "\n", "plt.figure(dpi=200)\n", - "plt.pcolormesh(\n", - " thetas,\n", - " wvls,\n", - " Ranalytic,\n", - " cmap=\"inferno\",\n", - " shading=\"gouraud\",\n", - " vmin=0,\n", - " vmax=Ranalytic.max(),\n", - ")\n", + "plt.pcolormesh(thetas, wvls, Ranalytic, cmap='inferno', shading='gouraud', vmin=0, vmax=Ranalytic.max())\n", "plt.axis([thetas.min(), thetas.max(), wvl_min, wvl_max])\n", - "plt.xticks([t for t in range(0, 100, 20)])\n", - "plt.yticks([t for t in np.linspace(0.4, 0.8, 5)])\n", + "plt.xticks([t for t in range(0,100,20)])\n", + "plt.yticks([t for t in np.linspace(0.4,0.8,5)])\n", "plt.xlabel(\"angle of incident planewave (degrees)\")\n", "plt.ylabel(\"wavelength (μm)\")\n", "plt.title(\"reflectance (analytic)\")\n", "cbar = plt.colorbar()\n", - "cbar.set_ticks([t for t in np.linspace(0, 0.4, 5)])\n", - "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0, 0.4, 5)])" + "cbar.set_ticks([t for t in np.linspace(0,0.4,5)])\n", + "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0,0.4,5)])" ] } ], diff --git a/python/examples/refl-quartz.ipynb b/python/examples/refl-quartz.ipynb index 928294810..fcf26bf51 100644 --- a/python/examples/refl-quartz.ipynb +++ b/python/examples/refl-quartz.ipynb @@ -19,9 +19,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.000452042 s\n", + "Working in 1D dimensions.\n", + "Computational cell is 0 x 0 x 12 with resolution 200\n", + "time for set_epsilon = 0.00140619 s\n", + "-----------\n", + "field decay(t = 50.0025): 0.25018932773921454 / 0.25018932773921454 = 1.0\n", + "on time step 32117 (time=80.2925), 0.000124548 s/step\n", + "field decay(t = 100.0025): 4.358317201613301e-16 / 0.25018932773921454 = 1.742007639173244e-15\n", + "run 0 finished at t = 100.0025 (40001 timesteps)\n", + "-----------\n", + "Initializing structure...\n", + "time for choose_chunkdivision = 0.00031209 s\n", + "Working in 1D dimensions.\n", + "Computational cell is 0 x 0 x 12 with resolution 200\n", + " block, center = (0,0,3)\n", + " size (1e+20,1e+20,6)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + " dielectric constant epsilon diagonal = (1,1,1)\n", + "time for set_epsilon = 0.00108504 s\n", + "lorentzian susceptibility: frequency=0.101049, gamma=0\n", + "lorentzian susceptibility: frequency=8.60279, gamma=0\n", + "lorentzian susceptibility: frequency=14.619, gamma=0\n", + "-----------\n", + "field decay(t = 50.0025): 0.16530523240803463 / 0.16530523240803463 = 1.0\n", + "on time step 24293 (time=60.7325), 0.000164657 s/step\n", + "field decay(t = 100.0025): 1.4363078056736928e-16 / 0.16530523240803463 = 8.68882239691211e-16\n", + "run 0 finished at t = 100.0025 (40001 timesteps)\n" + ] + } + ], "source": [ "import meep as mp\n", "from meep.materials import fused_quartz\n", @@ -32,35 +68,27 @@ "resolution = 200 # pixels/μm\n", "\n", "dpml = 1.0\n", - "sz = 10 + 2 * dpml\n", + "sz = 10+2*dpml\n", "cell_size = mp.Vector3(z=sz)\n", "pml_layers = [mp.PML(dpml)]\n", "\n", "wvl_min = 0.4\n", "wvl_max = 0.8\n", - "fmin = 1 / wvl_max\n", - "fmax = 1 / wvl_min\n", - "fcen = 0.5 * (fmax + fmin)\n", - "df = fmax - fmin\n", + "fmin = 1/wvl_max\n", + "fmax = 1/wvl_min\n", + "fcen = 0.5*(fmax+fmin)\n", + "df = fmax-fmin\n", "nfreq = 50\n", "\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ex,\n", - " center=mp.Vector3(z=-0.5 * sz + dpml),\n", - " )\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " sources=sources,\n", - " dimensions=1,\n", - " resolution=resolution,\n", - ")\n", - "\n", - "refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25 * sz))\n", + "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), component=mp.Ex, center=mp.Vector3(z=-0.5*sz+dpml))]\n", + "\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " sources=sources,\n", + " dimensions=1,\n", + " resolution=resolution)\n", + "\n", + "refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25*sz))\n", "refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", "\n", "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-9))\n", @@ -69,22 +97,14 @@ "empty_data = sim.get_flux_data(refl)\n", "sim.reset_meep()\n", "\n", - "geometry = [\n", - " mp.Block(\n", - " mp.Vector3(mp.inf, mp.inf, 0.5 * sz),\n", - " center=mp.Vector3(z=0.25 * sz),\n", - " material=fused_quartz,\n", - " )\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources,\n", - " dimensions=1,\n", - " resolution=resolution,\n", - ")\n", + "geometry = [mp.Block(mp.Vector3(mp.inf,mp.inf,0.5*sz), center=mp.Vector3(z=0.25*sz), material=fused_quartz)]\n", + "\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources,\n", + " dimensions=1,\n", + " resolution=resolution)\n", "\n", "refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n", "sim.load_minus_flux_data(refl, empty_data)\n", @@ -92,7 +112,7 @@ "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-9))\n", "\n", "refl_flux = mp.get_fluxes(refl)\n", - "R_meep = -1 * np.divide(refl_flux, empty_flux)" + "R_meep = -1*np.divide(refl_flux,empty_flux)" ] }, { @@ -104,32 +124,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "freqs = mp.get_flux_freqs(refl)\n", - "wvls = np.divide(1, freqs)\n", - "\n", - "eps_quartz = (\n", - " lambda l: 1\n", - " + 0.6961663 * math.pow(l, 2) / (pow(l, 2) - pow(0.0684043, 2))\n", - " + 0.4079426 * pow(l, 2) / (pow(l, 2) - pow(0.1162414, 2))\n", - " + 0.8974794 * pow(l, 2) / (pow(l, 2) - pow(9.896161, 2))\n", - ")\n", - "R_fresnel = lambda l: math.pow(\n", - " math.fabs(1 - math.sqrt(eps_quartz(l))) / (1 + math.sqrt(eps_quartz(l))), 2\n", - ")\n", + "wvls = np.divide(1,freqs)\n", + "\n", + "eps_quartz = lambda l: 1+0.6961663*math.pow(l,2)/(pow(l,2)-pow(0.0684043,2))+0.4079426*pow(l,2)/(pow(l,2)-pow(0.1162414,2))+0.8974794*pow(l,2)/(pow(l,2)-pow(9.896161,2))\n", + "R_fresnel = lambda l: math.pow(math.fabs(1-math.sqrt(eps_quartz(l)))/(1+math.sqrt(eps_quartz(l))),2)\n", "R_analytic = [R_fresnel(i) for i in wvls]\n", "\n", "plt.figure()\n", - "plt.plot(wvls, R_meep, \"bo-\", label=\"meep\")\n", - "plt.plot(wvls, R_analytic, \"rs-\", label=\"analytic\")\n", + "plt.plot(wvls,R_meep,'bo-',label='meep')\n", + "plt.plot(wvls,R_analytic,'rs-',label='analytic')\n", "plt.xlabel(\"wavelength (μm)\")\n", "plt.ylabel(\"reflectance\")\n", "plt.axis([0.4, 0.8, 0.0340, 0.0365])\n", - "plt.xticks([t for t in np.arange(0.4, 0.9, 0.1)])\n", - "plt.legend(loc=\"upper right\")\n", + "plt.xticks([t for t in np.arange(0.4,0.9,0.1)])\n", + "plt.legend(loc='upper right')\n", "plt.show()" ] } diff --git a/python/examples/ring.ipynb b/python/examples/ring.ipynb index 53ec7058b..18b1fd4e7 100644 --- a/python/examples/ring.ipynb +++ b/python/examples/ring.ipynb @@ -18,23 +18,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import matplotlib.pyplot as plt\n", - "\n", "%matplotlib inline\n", "import numpy as np\n", "from IPython.display import Video\n", "\n", - "n = 3.4 # index of waveguide\n", - "w = 1 # width of waveguide\n", - "r = 1 # inner radius of ring\n", - "pad = 4 # padding between waveguide and edge of PML\n", - "dpml = 2 # thickness of PML\n", - "sxy = 2 * (r + w + pad + dpml) # cell size" + "n = 3.4 # index of waveguide\n", + "w = 1 # width of waveguide\n", + "r = 1 # inner radius of ring\n", + "pad = 4 # padding between waveguide and edge of PML\n", + "dpml = 2 # thickness of PML\n", + "sxy = 2*(r+w+pad+dpml) # cell size" ] }, { @@ -46,11 +53,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "c1 = mp.Cylinder(radius=r + w, material=mp.Medium(index=n))\n", + "c1 = mp.Cylinder(radius=r+w, material=mp.Medium(index=n))\n", "c2 = mp.Cylinder(radius=r)" ] }, @@ -65,13 +72,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "fcen = 0.15 # pulse center frequency\n", - "df = 0.1 # pulse frequency width\n", - "src = mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1))" + "fcen = 0.15 # pulse center frequency\n", + "df = 0.1 # pulse frequency width\n", + "src = mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r+0.1))" ] }, { @@ -83,17 +90,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " cylinder, center = (0,0,0)\n", + " radius 2, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (0,0,0)\n", + " radius 1, height 1e+20, axis (0, 0, 1)\n" + ] + }, + { + "data": { + "image/png": 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dhUdERM6kQv9geHEhFQqPiIicR5X+wfDiMqoUHhEROYtK/YPhxUVUKjwiInIO1foHw4tLqFZ4RETkDCr2D4YXF1Cx8IiISH2q9g+GF82pWnhERKQ2lfsHw0sDIUSvEGJZCCGFEG/bPZ6jUrnwiIhIXar3D4aX3X4FQJ/dg7CC6oVHRERqckL/YHjZIYT4bgAfBfAZu8dyVE4oPCIiUo9T+gfDCwAhRBjApwFcA/BJm4dzJE4pPCIiUouT+ofP7gEo4p8BmATwnQAqNo/l0JxUeEREpA6n9Q/Xr7wIIc4D+ASAz0op/8ru8RyW0wqPiIjU4MT+4eqVFyGEB8C/BZAB8HNHfKzpfb504iiP+zCcWHhERGQ/p/YPV4cXAP8AwAUAH5NSrtk9mMNwauEREZG9nNw/XBtehBDHAfwygL+UUv72UR9PSvnEPs8zDeDsUR+/FScXHhER2cfp/cPNe14+BSAA4KfsHshhOL3wiIjIHjr0D9euvAD4Ptzf6/KbQojGfw/tfD4mhHhx588fllIudnBsD6RD4RERUefp0j/cHF4AIIn7p0e3Emr4Wmif7+k4XQqPiIg6S6f+4drDRlJK0eoDwMTOt9xq+PeUjUM16VR4RETUObr1D9eGF6fRrfCIiKgzdOwfDC8OoGPhERFR++naPxheFKdr4RERUXvp3D/cvmF3j539LeKg7+sEnQuPiIjaR/f+wZUXReleeERE1B5u6B8MLwpyQ+EREZH13NI/GF4U45bCIyIia7mpfzC8KMRNhUdERNZxW/9geFGE2wqPiIis4cb+wfCiADcWHhERHZ1b+wfDi83cWnhERHQ0bu4fDC82cnPhERHR4bm9fzC82MTthUdERIfD/sHwYgtdCi+dS9s9BCIiV9GlfxwVw0uH6VJ4c9k5XHzhot3DICJyDV36hxUYXjpIl8Iz5pHKpOweChGRK+jSP6zC8NIh6Vxai8Jr/AUaT47bPRwiIu3pElys3GrA8NIhF1+46PjCa/4FuvT8JbuHRESkNV2Ci9VbDXyWPRI9UCqTcnzhNf8C9YR77B4WEZG2dAouVm814MpLh4wnxx1feE7/BSIicgpd3nfbtdWA4aVDLj1/yfGF5+RfICIip9Bxj6TVWw142KhDRhOjdg/hkTG4EBF13sUXLnKrwQG48kItMbgQEdlDx+Bi9TwYXmgPBhciIvtwj+TBGF5oFwYXIiJ7cY/kwRheyMTgQkRkP+6RPBjDCwFgcCEiosOxo38wvBCDCxERHYpd/YPhxeUYXIiI6DDs7B8MLy7G4EJERIdhd/9geHEpuwuPiIicSYX+wfDiQioUHhEROY8q/YPhxWVUKTwiInIWlfoHw4uLqFR4RETkHKr1D4YXl1Ct8IiIyBlU7B8MLy6gYuEREZH6VO0fDC+aU7XwiIhIbSr3D4YXjalceEREpC7V+wfDi6ZULzwiIlKTE/oHw4uGnFB4RESkHqf0D4YXzTil8IiISC1O6h8MLxpxUuEREZE6nNY/GF404bTCIyIiNTixfzC8aMCJhUdERPZzav9wbXgRQkSEED8ohPh3QogbQoiSEKIghLgqhPinQoiY3WN8GE4tPKJ2k1Ie+EHkZk7uH64NLwA+AuALAH4cQA3AnwD4KwATAH4JwKtCiAH7hncwJxceUTs8TGBhkCFyfv/w2T0AG1UA/BaAX5VSftP4RyHEMID/BOBbAPwq7occ5Ti98IjaQQjR8s9E9A4d+odrw4uU8nMAPtfi3xeEEB8H8HUAPyyECEgpyx0f4APoUHhEj+JBKyPtCCmdfj6iTtGlf7g2vBzg6s7nIIBeAAs2jmUXXQqP6GEYIaIxTDQHCyGE+WHF87U6jNTqsRliyGl06h8ML61N7nyuAFi3cyCNdCo8oodhBITmoGAEjHq9jlqttid0tAo9Bz12YwjyeDzweDyWhSIiu+nWPxheWvtHO58vSSm3H+YHhBDT+3zphBUD0q3wiJo1Bo6DQoPx9Xq9jkqlglKphO3t7V0f5XIZ29vbqNVqZsARQsDr9cLr9SIYDCIQCCAYDO76CIVCCAaD8HgOPp/BCEz7hSwiFejYPxhemgghngPw93B/1eUXbB4OAD0Lj6iVxhUUYwVkP5VKBfl8HrlcDpubm3s+b25uolAoYGtrC+VyGfV6HR6PB4FAAOFwGNFoFPF4HPF4HIlEAolEwvxzPB5HLBaD3+/f9/nr9fqulR0GF1KRrv2D4aWBEOIMgN8BIAD8rJTy6gE/YpJSPrHPY04DOHvYMelaeETNGldbarUaqtUqKpUKqtXqno/t7W0Ui0Vsbm4im80ik8kgl8shk8kgk8kgm80im80in8+jUChge3vbDETBYNAMLolEAl1dXUgmk0gmk0gkEkgmk+jq6kI8HkckEkEwGITP59vz4ff7zVUcIhXp3D8YXnYIIY4BuASgG8C/llL+XzYPSevCI3drXF1ptWLh8XhQKBSwvr6OtbU1rK6uYn19HblcDltbWyiVSiiVSigWi8jn89ja2kKhUEA+n0exWEShUDBXXYxDR8bKi9frRSgUQigUQiwWQyQSQSQSQSwWQzQaRTgcNv/d+L5wOIxEIoGenh709fWhr68P3d3diMfjh5ofUbvp3j8YXgAIIXoA/DmAxwB8FsDP2Dsi/QuP3K358FDzqsvm5iaWl5cxNzeH2dlZ3Lp1C3fu3MHKygpyuRxKpRLK5fKeFRljxcbY52KElnq9bj63sRnXWDXxer3w+Xzm58aPQCCAUCiERCKB/v5+jI+PY3JyEpOTkxgdHUW9Xkc8HjdXX5o3DjO4kB3c0D9cH152bgPwZ7h/aOc/AvjvpM2X23RD4ZG7GXtZarUatre3USqVsLW1Za6krK+vY3FxEXfv3sXt27fx9ttvI5VKYXl5GYVCoePjjUajGBgYwOLiItbW1rC2tobFxUUMDw+bKzDhcBjhcBihUAiBQICHk8gWbukfrg4vQogggD8G8G4A/xnAj0gpa3aOyS2FR+7xoEMoUkpsbm7i3r17SKfTuHfvHhYXF7G8vGweMlpZWcHKygpWV1dRKpVsmUOhUMDCwgIqlQoymQzu3LmDnp4e9Pb2or+/H0NDQxgZGcHY2BiGh4fR09Oz5zF4KInazU39w7XhRQjhBfC7AP4m7t/T6IftvpKumwqP3Mc4dGM08Wq1imw2i7m5Ody4cQPXrl3DjRs3kEqlsLKygnw+j3q9bm7cLZftvdB1qVTC8vIyMpkMbt++DY/Hg1gsZh5OOn36NM6ePYtKpQIpJRKJBHw+366wwtBC7eK2/uHa8ALg7wP4oZ0/rwL49X3eWH5GSrna7sG4rfBIf837WYxDRIVCAblcDuvr61hYWEAqlcLMzAxu3LiBmZkZpNNpVKvVfR93vxWcB/39QZofa78L4gFAuVzeFaI2NjawsLCA9fV15PN5bG5umv82NDSE3t5exONxRKNRhEKhXad+N14fhugo3Ng/3Bxeuhv+/EP7fhfwi7gfbtrGjYVHejOugWJcpRa4v78ll8vh3r17uHXrFmZmZvD2229jfn4ey8vL5iGiBwWX/Qgh9lxz5WECzEHB5WFUq1UsLS2hXq9jeXkZN2/exLFjx3Dy5ElMTU1hcnISIyMj5gZgAObVgQ+6lg3RQdzaP1wbXqSUv4j7wcRWbi080p+U0ryy7fb2NjKZDObn53Hz5k1cvnwZly9fxvXr17G+vo5qtQqPx4Na7eAtZ63uPXSUMT7o7w+rVCqZ+3b8fj96enpw5swZrK2toVQqmWdDJZNJBINBHkYiS7i5f7g2vKjAzYVH+mk8PdhYcdna2kImk8Hi4iLm5uaQSqVw48YNTE9P49q1a1hdfWdR09gT03h/IWMFx+YTAHfZb3zGitH29ra5ubder2N7exvr6+sYHx/H6OgohoaGkEwmEQ6HzfDCU6vpUbm9fzC82MTthUf6ab6sf71eRy6Xw61bt/Dmm2/i+vXrmJ2dNc8qagwuzY9j3LdIteACvBM0Gjcgt7K6uorr168jm83i7t27mJiYwJkzZ3Du3DlMTU0hGAzC6/Wa8+RZSPSw2D8YXmyhS+Glc2m7h0CKMe7ybOxvmZ2dxZUrV/Dyyy9jenoa6XQa2WwWlUrlwMdR2cOGqqWlJayvryOdTmNubg7r6+uo1Wrw+XyQUpoXuONtBuhh6dI/jorhpcN0Kby57BwuvnDR7mGQzVpdJbdUKmFhYQG3bt3C9PQ0rl69iunpaaRSKeRyuV0/L4QwG/lhNuqqxLhjtRAC1WrVDDeVSgWrq6vmzSFrtRqKxSLW1tZw8uRJDA0NIRaLmZuMuQpD+9Glf1iB4aWDdCk8Yx6pTMruoZDNjLNmgPvN22jU09PT+PrXv44rV64glUphbW1tT3Axft7Y1Ot0xlz2O9Mpl8vhzp07yOfzWFxcxNLSEkqlEvx+v7mJt/EMJIYXaqRL/7AKw0uHpHNpPPf55xxfeI2/QOPJcQYYlzMabLlcxvb2NtbW1nD9+nW88soreOmll3Dt2jXk8/kHPobqh4gexUGHk4y7XS8vL6NYLMLn8yEUCkFKid7eXgSDQQQCAQYX2kWX4GLlVgOGlw65+MJFpDIpRxde8y/Qlz7yJZz51Bm7h0U2aD4zaHt7G3fv3sW1a9fwjW98A6+99hpmZmb2BJfGpqzDastB9ptvPp/HzMyMGVzy+TzOnj2LsbExBINBAO+8xrwOjLvpElys3mrA8NIhugWXFz/6InrCe+/fQvozVheMQxzlchlra2u4du0aXnzxRbzyyitIpVLIZDItf9ZNHjTf9fV1XL16FdlsFrlcDlJKRKNRc/XFuMgfr8TrXjoFF6u3GjC8dMh4ctzxhdf8C1Qod/7uvmS/xsv9l0ol85Tgb3zjG3jllVdw9erVPdctcVtoeZDG1ySTyeCNN94AAMTjccTjcUgp0dfXh1AoZG4AJvfRLbiYWw2QsuRxuR7ZIZeev+T4wnPyLxAdXb1e33Uoo1arYWFhAVeuXMFXv/pVvPbaa0ilUnsu088G/I5Wr0e9XkcqlcKrr76Kr33ta7hy5QoWFxdRq9XMQ0aNrz3pL51La/G+29w/Lj1/ybLH5spLh4wmRu0ewiNjcCGDcaio8QJyxgXovv71r+Oll17CzMzMnkNFql5ozi77nV1lrMCUSiWUy2VEIhEkk0nzZo7GITpyBx33SFq91YDhhVpicKFGxum7tVoNW1tbyOVySKVSmJ6expUrV3adVWQcVjroCrRu1fh6GMEEuH+H6unpaUSjUQwPD6O7uxtCCMTjcQQCAW7cdREdg4vVWw0YXmgPBhcyNO5dMa5fkslkcOvWLVy5cgVXr15FKpXac1aRsdG0ncGl+UJuxnMd5jlb3SixE+Nvfp3y+TxSqRQuX76McDiMarWKEydOoL+/n/dBchEd90hajeGFdmFwoUbNjXJ7exuLi4t488038corr2B6ehpra2t7fqZTF56z6q7QxuGwTq4S7fc6ra2tYXp6Gl6vFz6fD7FYDF1dXfD7/ebPAQwvOuMeyYMxvJCJwYWaGYd/arUaqtUqMpkM0uk0rl+/jrfeeguzs7PY3Nzc83N2BJcHeZjry9hxeKvVc2azWaRSKXg8HvT09ODYsWPo7++H3+8373/EQ0h64x7JgzG8EAAGF2qtccUll8thfn4es7Oz5t2hG4NLO1cuGs+6eVjGPhGfz7frsJdxH6VarXbgDSINjYeo2nXWT+PrZ7zWqVQKs7OzGBgYgM/nQyKRMG8lQKQKO/oHwwsxuNADSSlRKBRw79493Lx5EzMzM+bdoTvlYQKDEAKhUMj8CAaDCAaD8Pl85r2CjIvrVatVbG9vY3t7G6VSyfx40KpMp1dmMpkM5ubmcPPmTfT09JgrL4FAoKPjIHoQu/oHw4vLMbhQM6NRG6sdUkrztOjLly/jrbfewr179x561cJqHo8HsVgMkUgE4XAY0WjU/HMkEtkVXPx+v7nyYpzZY6y8VCoVM8BsbW2ZH8ViEYVCwfxzPp+35RorlUoF9+7dw1tvvYVAIIBQKIRkMonu7m7ze4xTqLkSQ3aws38wvLgYgwu10rzKUK1Wsb6+jpmZGVy+fBnT09NYXV1t+XNWedCVeROJBI4fP46xsSuC1QoAACAASURBVDGMjIxgYGAAyWQSsVgMoVAIgUDADC1er9cMYY2nJRurL8ahI2MFplAoYH19Haurq7h37x7m5uZw9+7dlrc6sPrqwa0eZ2lpCfV6HR6PB/39/RgfH8fIyAh8Pt+un2F4oU6zu38wvLiU3YVH6jIOrxhnwmSzWSwsLODtt9/G9evXdwWXdu1z2e8xk8kkJicncfbsWTz++OM4ceKEuaE1Go0iEAjs2uPS+NH42M0f1WoV5XIZhUIBKysrmJ+fx8zMDLq7u+H1ejE7O7snwLT7NGrj8VdWVnD9+nVMTEzg3LlzGBsbM+dofC9RJ6nQPxheXEiFwiN1Nd4penNzE3Nzc0ilUpifn8f6+vqu7/X5fKjVam07rOLxeBCNRpFMJtHf34+xsTFMTk5icnISJ06cwNjYGIaHh5FMJs0zcY6iVquhr68PyWQSiUQCyWQSfX19OH78OObm5rCysoJMJoNCodDWOXu93l2H5dbX180NvMPDw+bF67h5lzpNlf7B8OIyqhQeqU0IgVKphIWFBdy4cQMzMzNYXl5GtVrd9X1WbWTdbwUnHo9jdHQUjz/+uLnSMjw8jN7eXnR3d5uHi6wILgDg9XoRj8chhEAkEkF/fz9OnTqFhYUF3L59G9PT07hx4wbm5uaQy+Ueeh6PotVrWqlUsLy8jJs3b6K7uxt+vx9+vx/hcPhIz0X0KFTqHwwvLqJS4ZFamvdOCCGwtbWFubk5XLt2DTdu3MDa2tqufSMA9oSZoz6/wePxoKurC2NjYzh79iwuXLiAp59+GhMTE+jq6jL3sxg3OazVauam3MMyNvMKIRCNRhEOhzEwMIBqtYpsNovHHnsMXV1d5inYxhlXja+HFUHOOIzVyOPxYG1tDTdv3kRXVxeSySQGBgb2/BzAw0jUHqr1D4YXl1Ct8Eg9RuM2FIvFXSsvKysrqNVqbR9HKBTCwMAAHnvsMZw5cwbnzp3D+fPncerUKQwODpqbVQ2NN388yqEco/l7vV7zaraGWCxmPq/f7zevenvnzh2srKygVCod+nkfRq1Ww8rKCjweDxKJBB577DFsbW3tGT+DC7WDiv2D4cUFVCw8UkvzoYparYZ8Po/FxUXMzs7i7t27ZjBo58XowuEwhoaGMDU1hSeffBJPPfUUTp8+jZGRkX33tRj3BzqqB22A9Xq9SCaTmJqaQjQaNQ9ZRSIR+Hw+LCwstC3AGK/31tYW0uk0+vr6sLS0hHw+j1qtZr4mXHmhdlC1fzC8aE7VwiO1NJ5h5PF4sLm5ifX1dSwvL2N1dXXXikbzXaOP+pwG43TgU6dO4V3veheefvppnDt3DseOHUMkEgFw/zCVx+MxLzrX+FjtYKzm1Ot1BAIBDAwMmNeYCQaD5mGqWq2GdDq953U6aqhqvNYOcH/+y8vLWF5exvr6OvL5PBKJxK5gSWQVlfsHw4vGVC48UosRSKrVKgqFApaXl7G4uIi1tbWWd4xux5k2XV1deOyxx3D+/Hk888wzePzxx3Hs2DEkEold4crQ7sMkjatRQghzf00ikcDo6Dv3nqnX69ja2sLm5iY2NjYsH0fz610sFrG6uoqFhQUcO3bMvLJw4+nTREelev9geNGU6oVHamgMAB6PB5VKBevr6+bF2dbX1/cEFWOPiRXPbTxvLBbD2NgYzpw5g6eeegpPPPEERkZGzBUX4J0AYfy53Rrvh9T8nNFo1Aww29vbyGQyyGQy5uE2K1+j5te/Wq1iY2MDc3NzGBwcRCAQQH9//67bBnD/Cx2FE/oHw4uGnFB4pKZqtYq1tTXMzs7i9u3bWFlZadvp0YZoNGqeVXTu3DlMTU2ZKy4AzDOJjJWPTmu8kq4RSnw+H7q6uiCEQLFYxMbGBjY3N1Gv13H37t2Wd9o+jFavs3Ho6NatW+jt7TXPPiKyglP6B8OLZpxSeGQ/I4Q0BoJqtYrV1VXcvn0bb7/9NpaXl/fcw+gowaXVIadkMonTp0/jwoULOH/+vLnHpXEDrQqrCI3jMD5HIhEcO3YMTz75JCqVCsrlMrLZ7J7wcpRDbc2vd7lcxvLyMmZmZtDb24vR0VGMj4+3/BkVXjdyDif1D4YXjTip8EhNxn2M7t69i1QqhdXVVZTL5bY+Z39/P86ePYunn34ap06dMlcRjGBl1QXorNB4HRkjIBhnIQH37wQ9OzuLdDrdtjGUy2Wsrq4iEAhgZGQEGxsbHTmFnfTmtP7B8KIJpxUeqalarSKXy2FpaQnLy8uWn/7batXl+PHjOHHiBCYmJjA4OAiv12veNNGuQ0UHadw8bJx5VKlUMDk5iePHjyOVSu26F5LVG5xLpZL53yiXy1l2sUByJyf2j8NfjpKU4cTCIzU0H5KoVqvmmTOFQsH8d6sP3Xg8HvMmixMTExgeHjavnGtcKVfF0NKocZzGHpjh4WFMTExgYmICyWTySFf8bfV8ja9JoVDA5uYmtra2Wu5LInoYTu0fXHlxOKcWHqmjcd/L9vY2SqXSnhWXxk2rVojFYjh+/DjOnj2LyclJ9Pb27rpyrtH0VQ0wrW5F4Pf70dfXh5MnT2J1dRW1Wg2pVKrlPZAO+5zA7v8Gxn+rxkN7DC70sJzcPxheHMzJhUfqMMJLpVJBsVhEqVTC9va2ZY/fqulGIhGMjY3h9OnTOHHiBHp6enbtbWnnVXyt1BiuvF4venp6MDk5aZ46bRzWaf5+q+ZmhM1isYhKpQK/3++I143s5/T+wfDiUE4vPFKHcQXZfD6Pzc1NFItFSzeAtgoi4XAYIyMjOHnyJMbGxlpe+l/VVRdD8/iMm0keP34cuVwOd+7cQSgUavlzVgWMarWKYrGIXC6HQqGAWCzWcmxEjXToHwwvDqRD4ZH9jD0UtVoNlUoFm5ubyGazyOfze06Ptlo0GsXAwABGR0cxPDyMWCxm3nLAyn0inWKMOx6PA7h/1lF/fz/C4XBbn7dSqaBQKCCTySCXy8Hv98Pv9/Nqu7QvXfqH894lXE6XwiM1GKsApVIJuVwOmUwGW1tbe1ZerD4UEYlEkEwm0dfXh2QyiUAgYN5g0WmHPYwxezweBAIBdHV1oa+vD93d3YhGo5Y/V6N6vY5CoYBcLodcLoft7W1eXZf2pVP/YHhxEJ0Kj9RghJft7W1sbm4ik8mgUCi0XHk5bKho/jkhBMLhMGKxGGKxWEcv+d9Oxvh9Ph+i0ah5A8dmVr2OAMx9SsaF8RheaD+69Q+GF4fQrfDIPs1N0Agvxv+9G/fmaddzhkIhRCIRhEKhXffjccLp0ftpPvsoEAggFAohHA7vOnRk9cpSrVYzDxs1hpdGTlvJIuvp2D9cH16EEGEhxD8XQtwUQpSEEPeEEP9eCHHM7rEZdCw8UosRXjKZjHnmilWam2coFDKDi0pXz7WS1+uF3+8359ouxl3AjT0vVp4lRnrQtX8cOrwIIf6NEGLvmqiDCCFCAP4LgF8AEAPwxwDmAHwMwGUhxKSNwwOgb+GROhoPG2WzWRQKhT0rL1b+33soFEIwGITf73fk5tyHYex/sTq8NP93MFZeGg8bERl07h9Heef4RwDeFEK836rB2OB/AfCtAP4/AFNSyr8jpXwPgE8A6Afw7+0cnM6FR/bY77BFuVxGPp83w0ury81bEWACgQCCwSCCwaDWZ8QIIeDzevEtq6v4lc1N/KkQ+DSAo7xZ7neHaeNU6c3NTZTL5ZaHjXjoyH107x9HCS+/B2ACwH8WQnxWCNFt0Zg6QggRAPD3d/76cSll3vialPJfA3gDwHcKIZ6xY3y6Fx6pZXt7G/l8HrlcruXZRlbxeDzmqouq9y2ygiebxXt/6ZfwP/7FX+BH8nn8LSnx3wP4MoCvAEha9Dy1Wg3FYtG8nUO7b6JJzuCG/nHo8CKl/AiADwKYB/BRANeEEH/bqoF1wLcD6AJwS0p5ucXX/2jn8wc7N6T73FB4pJZarYatrS1sbW1he3vb8g27Bp/PZ344eYPuA0mJ6Ec/iv4332z55e/GO28uR1Wv11Eul1EoFFre44jcxy3940gHnKWU/wnA4wA+hfuHWX5XCPHHKm12fYCndj6/vs/XjX8/34GxmNxSeKQO4w7JlUoF29vbqNVqbQsvjTcz1HW/C77yFfi++tUHfst373wcVb1eR6VSQblcRrlcbtuKGTmDm/rHka+wK6UsAPgHQogXAPw73F+p+E4hxKcBFB7wc//8qM99RMd3Pqf3+brx7489zIMJIab3+dKJhx2QmwqP1CKlRL1eNz+3g3FFX62DCwD84R8+1Lf9bQB/YcHT1et11Go17m1xObf1D8tuDyCl/GshxLcA+CqAdwP4mX2+VQCQAOwOL7Gdz8V9vm4Er3gHxuK6wiO1dGJFxGiuRkBqV0iy3cLCQ33bkEVP5/F44PV69T0MRwdyY/+wLLzsnFb8GQAXANQAfAEPWHnRjZTyiVb/vrMic/ZBP+vGwiN1CCHg9XrNM4GMRtgOjSs82hoefqhvW7TgqTweD/x+PwKBgLkJmtzFrf3jyOFF3I/6nwDwiwAiAK4C+Akp5WtHfew2M84u2u9aNcZNSTbbOQi3Fh6pxev1IhwOIxqNIhQKtS28VKtV80PbEPOhDwGf+cyB3/YHFjyVcT2ZaDSKcDgMn4/32nUTN/ePI71DCSGeBPAygH8BwAvg5wE864DgAgB3dz6P7vN149/vtGsAbi48UkswGEQ0GkU8HkckEmnb/8HXajVsb2+jUqmgWq3qGV7e/35U3ve+B37LV2DNfhefz4dIJIJ4PI5oNLrrdgukN7f3j6NcYfeXAXwDwLMAvgbgKSnl/yGldMp296s7n5/e5+vGv7/Rjid3e+GRPYwNs80CgQBisRgSiQQikUjL/4O3Yj+FcUaTcVaTluFFCOQ/+1ksnTvX8stfAfChQz3s3tff6/UiEokgkUggHo8jEAjs+b79/puTc7F/HO2w0f8MIAfgH0opP23ReDrpJQBZACeEEO+SUl5p+vp/s/P5i1Y/sS6Fl87td6IWOYkQAsFgEIlEAslkEpFIZM9hIyubX6lUMldfdN20W0sk8OWf+znc/I3fwInXXkN3uYxF3D9UdJQVF+Mu4Aav14toNIpkMol4PI5gMHjUoZPidOkfR3WU8PJFAD8tpbxn1WA6SUpZFkL837h/qOtTQojv2TntG0KIf4L713f5S6sPgelSeHPZOVx84aLdwyCLBINBxONxdHV1IRKJwO/3W/bYzQ23VCqZF8PT9aJqtVoNlWoVryQS+FceD0oNX2t+PY7C5/MhGo2iq6sLiUSC4UVzuvQPKxw6vEgpf8DKgdjkl3H/diPfBmBGCPFXuH9dl/cAWAHw41Y+mS6FZ8wjlUnZPRQ6hFaHFRpXXmKxmOUbdhsbthFeSqXSrsvZGxt4nXiIwziLynjdyuUytra2UCwWUSq9E12snlvjyosRXlr99yXn06V/WEXjK0UdTEpZAvA3APyvuH+9lx/E/fDy2wCellLetuq50rm0FoXX+As0nhy3ezh0REZYMFZeEokEotFoy5WXwzbB5p+TUmJrawuFQgGFQsG8KqzT978Y469Wq8jn8+Yl+5tZ9ToCgN/vN8NLLBYzw4vTX0vaTZfgYuVWA1eHFwCQUm5JKf+plPKklDIopRyWUn5MSmnpho6LL1x0fOE1/wJdev6S3UOiIzLCSygUMldewuHwnrONrP6/92KxiPX1dayuriKTyaBcLqNerztyc6kxZuM+Q5lMBmtra9jY2EChYO2lrppfG4/Hg0gksuuwEcOLXnQJLlZvNeBFAToklUk5vvCaf4F6wj12D4uOwLjarXGn50Qiga6uLsRiMUv3vLRSKBSwurqKdDqNZPL+PZY78bzt4vF4UKlUkM/nsbi4iLm5OaysrLRcebGSsfKSSCSQSCQQCoV2XcnYaUGQdtMpuFi91cD1Ky+dMp4cd3zhOf0XiFrzeDzmxs92XOel1SrA1tYW7t27h7fffhtzc3PIZrN7zjxSffWgeXz1eh2ZTAZ3797FrVu3MD8/v2u/y34/dxTGdV6Mw33tvDoydZYu77vt2mrAKu+QS89fcnzhOfkXiPZn/N+53+9HJBJBKBSy9KyVVjcMLBaLmJubw/Xr13Hr1i2sr6/vuiOyE24y2DzGWq2G9fV13Lp1C9evX0c6nd5z2MjqeQWDQYRCoV1niHG1xfl03CNp9VYDHjbqkNHEfhfyVReDizs0NjujGYZCoV3fY3WQyOfzuHv3LrxeL/r7+3Hy5Mldp00bqzCqHvpoPDRjrFJVKhWsra3h1q1bmJ6extzcHPL5/AGP9GjP2cz4b9V4ZV0VXy96NBdfuMitBgfgygu1xODiDs2NzufzIRwOm4chDFatGBjPZxximZ2dxezsLBYWFpDNZs1bBjjhvkeN46xWq8hms1hYWMDt27eRSqWQyWQsnUPzfwPjisit7mnEAONsOgYXq+fB8EJ7MLi4l8/nQyKRwMDAAAYGBvaswBxVc1M19ojcvn0bt2/fxuLiIorFIoD71zAxzpxRJcg0boQ1VlyKxSIWFxdx+/Zt3Lp1C3Nzc8hkMrt+zup9KKFQyPxvlEgkeENGzXCP5MFY8bQLg4u7+Xw+9PT04Pjx47h37x6q1SqWlpZ2XUzOaqurq5ienjbPOjp16hQGBwd3rdKocAp186EiYzwbGxu4efMmXn/9dVy7dg0rKyttHUcwGER/fz/Gx8cxNjaGnp6ett1Ik+zBPZIHY3ghE4OLu7QKAz6fD319fThx4gTW1taQzWaxsbGxK7wc5Toire5llMlkcP36dQQCgV13SU4kEgDUOevIuJZL40pQoVDA/Pw83nzzTbz66qu4fv06stnsnp89yj2cml9vv9+PwcFBnDp1CidOnEBfX9+eU8xVCHt0eNwjeTCGFwLA4EL3GeFlYmICq6ur5mm/jYymaFWoMAKAz+dDPB5HT08PotEoRkdHEY1G4fP59jxnJxtz43N6vV5zj0uhUEA6ncaNGzfwxhtv4Nq1a5ifn7f0wnRGCGl8rY3/RpOTk5icnERvby8PG5Gt7OgfrHhicHGxxhBQr9fh9/vR3d2NsbExLC4uoru7e89+DY/HY8mGWqMp1+t15HI5pNPpXVeKlVJibGwMXV1dAO6HiFqtZjb0dq8uNB4maj5UVCgUMDc3h2vXruHq1au4efMm0uk0crncnvkdhRDCfL0NPp8Pvb29OH78OEZHR9Hd3Q2fz7fr3kpcdaFOsat/MLy4HIMLAdjVoOPxOKSUGB4eRl9fH2KxGDY2NszvPcohkAfJZrO4c+eOefaMsTFWCIFIJGJ+XyeCi/E8wDuvjXEqd7FYRDqdxrVr1/Daa6/hzTffxJ07d1oeLrJC8+sdjUbR29uLoaEhDAwMIB6P7zqcxeBCnWJn/2B4cTEGFzIYKwTGxs9YLIbu7m709/ejv78fCwsLZvO26uyfVleoXVlZMVc4qtUqyuUyisUijh07hmQyiUgk0rI5W7kvpvHxjUNFHo8HxWIRGxsbmJ+fx40bN3D16lW8+eabmJmZwcrKyp4xWDEmY7Oywefzmf9Nenp6EIvFzDHWajWGF+oYu/sHw4tL2V14pJbmhmeswAwNDWF8fBwbGxtYXFxEqVRq6wbaUqmExcVF1Go1FItFZDIZrK+v4/z585iamkIwGNyzv6PxENZRGnfjYzQfKqvVauZZRW+88QbeeOMN3Lx5E3fv3sXKykrL2wBYpfHmmUNDQ5iYmMDQ0BBisdius4y4SZc6RYX+wfDiQioUHqmnufGFw2EMDw/j9OnTyOfzqNVq5unTjT9jdZgplUqYn59HPp9HNptFoVAwn7NSqaCrqwt+v99cETE+WoWOR2GEoFqthkqlgnq9bv45m83i9u3beP311/GNb3wD09PTSKfTyGazHTkbyuv1YmBgAFNTUzh9+jRGRkYQDod3fQ+DC3WCKv2D4cVlVCk8Uktz45NSIhwOY2xsDGfPnsXm5iaWl5eRTqd3fV/j4YqjPn/jY9TrdWxsbKBWq6Fer6NcLiOTyWByctLci9PT04Ouri7E43FLrnNiBJ9arYZ8Pm+u+qytrWFhYQG3bt3CtWvXcP36dczPz+/anLvfPA7DOAzUfLuEnp4eTE1N4fHHH8fo6CjDC3WcSv2D4cVFVCo8UpuUEqFQCCMjI6hUKuYhE7/fj+3tbfP7Wp3Ke9jna8W4B1I2m8Xs7CyOHz+OiYkJnDx5EpOTkzh+/DgAoKury5LThavVKnK5HBYXF83TxG/duoXbt29jbm4OKysr5mrQo8zjUbQ6/NN4bZfTp09jeHgYwWBw1xlGRO2kWv9geHEJ1QqP1GVsyA0EAuju7ka9XsfCwgKOHTuGnp4eLCwsmN9r3IuoXer1OjY3N7G5uYl0Oo1UKoW5uTmsrq4ik8kgl8shk8mgr68P0WgUgUDAPJy03ynVzadAG4eHyuUy8vk81tbWMDc3Z94denp62rxXUSe0Og29p6cHIyMjGB8fN0+Pbrx1AlddqJ1U7B8MLy6gYuGRuprPPEokEhgeHsbJkydx5swZVKtVrK6utvWeQ/tdCM+4mWO1WsX6+jru3LmD/v5+JJNJxGIx8w7Lfr8fPp9v174YQ71eNwNLtVpFpVJBuVxGqVQyDxctLS1hYWEB6XTaXPl52DFaoXHzcF9fH86cOYNTp05heHh41yoTzzCidlO1fzC8aE7VwiN1tbrTtLHfYm1tDVJKfPOb38TS0tKen7OqkT/ocXK5HFKpFFZWVvDNb34T4XAY0WgUkUgE4XAYoVDIDDE+nw9+v99ciTFWWiqVinkqdqlUQqlUQrFYxNbWFgqFAra2tlAqlVAoFJDP5x95jIfR6vUbHBzEmTNn8Mwzz+DUqVPo6enZdXiMZxhRO6ncPxheNKZy4ZG6mhuiEALxeByTk5MolUrm5tn19XVUKpWOj6/xUFIzI7g0Bhiv17vrDtW1Ws08TGQEF+NDJYFAAMPDwzh37hyeeuopTE5Omvd7MnC/C7WL6v2D4UVTqhceOYcQAtFoFCMjI7sO18zPz7f9DsoGo0kfdHXfxhBinE5tHDoyGIeMjNOgrR6DVbq6ujA2NoapqSlMTU1hZGRk34v0EVnJCf2D4UVDTig8cgbjMEYoFILP50O1WsX4+DgmJiYwNzeH7e1t85Thdm/cfVSVSmVXODnqYa12h5bGfS7xeBzHjh3DxMQExsfHcezYMfMeRtykS+3klP7B8KIZpxQeOYPRJD0eDwKBAJLJJMbGxnDmzBlsbGygXq+3vK9POy5e1+xRbxNw0HjafduB/Z6z+TkSiQTGx8dx/vx585ouyWQSgUAAwDtnIzG8kNWc1D8YXjTipMIjZ2hukMFgEIODgzh37px5to5xJdzGn7Hq4nWPMr6jPJfxOFY93sM+Z6vXqbe3F+fOncOFCxfwxBNPYHBw0AwuzWMksorT+gfDiyacVnjkDM3NXAiB7u5uTE1Nwe/3o1gsYmlpCcvLy+ZZOcYZPe1u/laeqm08Ticu9d/4nM2vUzwex/j4ON71rnfh2WefxcTEBBKJhLlCw7OLqB2c2D8YXjTgxMIjZzEaLQDzLB4pJVZXV7G0tIRCoYCZmRmsr68D2L0/pBOHkJyk8fVofJ2M09GffvppPPHEExgfH0dfXx88Ho95mwTjlG8iqzi1fzC8OJxTC4+cpflqtcZdp0+ePInt7W34/X6Ew2FcuXJl15VojQvEdeIQkhMYr51xoTxDMpnE+fPn8a3f+q149tlnMTk5ueueTcaZTgwuZCUn9w+GFwdzcuGR8zSfbuz1ejE0NAS/32/eZyebzeLq1atmYzYOizC43GdcZ6bx9fB4PJiYmMCFCxfw3ve+F6dPn0Zvb68ZcpqvEExkBaf3D4YXh3J64ZFzGWHE6/UiFoshGAxCSol8Po/NzU1IKXfdC4jBZbfG1yOZTGJiYgLvec978Mwzz+D06dMYGRmB3+/fdf8lrriQlXToHwwvDqRD4ZFzNR46Mv7c29uLs2fPmtcoefXVV3H16lVsbGzs+VmDG0LNg+bb09OD8+fP493vfjeefvppnD17Fj09PQgEAvB4PLuu+0JkFV36B8OLw+hSeORsxmEMYyNvMBjE2NgYotEootEoPB4Ptra2MD09veveQG4ILI32m288HsfU1BTe85734H3vex9Onz6Nnp4e8/AbDxVRO+jUPxheHESnwiM9GIc0AoEAgsEgQqEQAGB7exuVSgXRaBSpVApra2v73pnZOPtGh2BjzKfV1XiFEEgkEujv7zdPh75w4QLOnDmD4eFh8wwuADxURJbTrX8wvDiEboVHejCuvms0Wr/fj97eXjzxxBMIh8MYGhrC5cuXMT09jVQqZd5KoJHX64WUEtVqtdPDt1zjDSCbw5hxDRfjRovnzp3D5OQkenp6zDtfAwwuZD0d+wfDiwPoWHikh8aLphlnFoVCIYyNjSGZTKK7uxvhcNi8OeL8/DwymYx5zyEppS13pm6XVgEsEAigq6sLx44dw/nz53HhwgU8++yzGB8fN0+HNsIOr+NCVtO1fzC8KE7XwiM9eTweM6iEQiEIIVCr1eDz+dDb24vZ2VnMzc3h3r17WFpaeuDjANZeRddKzaFtv+8ZHBzE8PAwxsbGMDExgccffxxPPPEEJiYm0NfXZ54OreIcyfl07h8MLwrTufBIP8YZMkZT93g8iMfjmJycRDQaxbFjxzA7O4ubN2/irbfeQr1ex8rKyp7Hafx5Ixio1NyN4GKMb78rCPf19eHMmTM4d+4cpqamMD4+jtHRUQwODiIej5sBrfl1I7KC7v2D4UVRuhce6alxNaJeryMQCKC/vx9dXV0YGBjA4OCgeVaNx+PBzZs3sbq6ilqtBiEEqtWqudqy34qG3VqNzzh1HIC5yjQ1NYVnnnkGTz31lHn9FuPu0MaKlHFWEYMLWckN/YPhRUFuKDzSX+NmXr/fRlZChQAAIABJREFUD7/fD6/XC5/Ph1AohL6+PkxMTGB+fh7Ly8tYW1vDysoKtra2DnzcVs2+efXjYVZrmh9nv8c96LFCoRAGBgbQ09ODwcFBjIyM4NSpUzh16hQmJycxMjKC7u5u8+7QKoczcja39A+GF8W4pfBIb62uUeL1epFIJODz+ZBMJjE+Po4nn3wSs7OzmJmZwY0bN+D1ejE3N/fIZx4dJrgY39fqztmPwufzYWhoCFNTUzh9+jROnTqF8fFxDA8Po7u7G4lEApFIBD7fO2+3jSs1RFZxU/9wbXgRQpwB8AMALgJ4EkAXgDUAXwfwb6SUf9XpMbmp8Mg9GlcugsEgAoEAkskkRkZGMDY2hqGhIfT29iKRSCCRSKC3txfLy8soFouoVquoVqsol8sol8t7Hs+q8T3o742CwSD8fj98Ph98Ph+i0ah53ZYzZ87g8ccfx+nTpzE6Ooquri4zsBj7Yow9MjxMRFZzW/9wbXgB8BUAxwDkAfw1gHUAZwH8EIAfFEL8Eynlr3ZqMG4rPHKf5r0dRgAwDislEgk89thjWFpawvLyMlZXV7GxsYHl5WXz76VSybbxh0Ih9Pf3Y3BwEH19fejt7UVvby/6+/sxNDSEkZERjI6OmisujSstgLpnTpHzubF/uDm8XAfwPwH4Qyml+Y4ohPhJAL8J4JNCiD+XUl5r90DcWHjkHg9aaTDuheT3+zEwMICtrS3k83msr69jcXERd+/exa1btzAzM4NAIIDl5eVdtxvolFgshoGBAYyPj5v7WI4fP46hoSH09PQgFoshHA4jHA4jGAy2nC9XXKgd3No/XBtepJTv3+ffPy2E+GEA3wPgQwB+qZ3jcGvhkbs1XtskGAwiHA6bX6vVasjn81heXsbAwIC5wjEyMoLl5WVsbm6iVCphe3sb1WoVlUoF9XodlUoFtVoN1Wp112fjzCeDcYaPsXm48bNxpVvj0JBxy4NEIoGBgQGMjY3hxIkTmJycxOjoKAYGBhCLxXbtXzHmZpxBxXsUUbu4uX+4Nrwc4Cruh5eRdj6JmwuP3M1YgWi1GmFs7DXumZRMJjE2Nob19XVsbm5ia2sLpVIJpVIJxWIRhUKh5UexWESxWES5XEalUjE34/r9fgQCAUQiEUSjUUQiEcRiMfPPxs0lI5EIQqEQQqEQwuEwEokEuru7zUNG3d3diMfje8ZvXLeF126hdnJ7/2B4aW1y5/Niu57A7YVH7nbQIZR6vW7uMUkmk3jsscfM1RRjA2+xWEQul0Mmk0Eul0M2m0Umk0Emk0E2m0Uul8Pm5iYKhQLK5TJqtRq8Xi8CgQCi0Sji8TgSiQSSySS6urp2fTY2D0ciEQQCAXN/jrFKY6zM1Ov1lmcN8RARtRP7B8PLHkKIEwC+b+evf/IIPze9z5dONP+DLoWXzqXtHgJpxFitMA4n+Xw+87oorVQqFRQKBeRyOTOobG5u7vpzoVDA1taWeWjJOCQUDofNAGOEmEQigVgsZv45Go3C7/fv+/zNh4cYWKgTdOkfR8Xw0kAI4QPw2wCCAH5fSvma1c+hS+HNZedw8YWLdg+DNPOgw0nN/H4/YrGY+Xl7e3vXh3F6tbH3xTiMY6yeBAIBBAIBBIPBlh/NZws14+Eh6jRd+ocVHBtehBBfAPD4I/7Y35VSvvKAr/8agO8AcBvA//AoDyylfKLVv++syJwF9Ck8Yx6pTMruoZBGGoPLQRpXaIxDOJFIZNepyI96hd3m1ZOHuSYLV1uoU3TpH1ZxbHgBMAHg9CP+TGS/Lwghfh7ATwNYAvBfSSnXjzC2PdK5NJ77/HOOL7zGX6Dx5DgDDLWVEUD2CyVCCPNaMVaEiMZDV40BqTnkNP8bUTvpElys3Grg2PAipXyXVY8lhPgpAL8MIAvgopTybase23DxhYtIZVKOLrzmX6AvfeRLOPOpM3YPizS23/VSDvqeozzffneJbsfzER1El+Bi9VYDx4YXqwghPgzgUwCKAP6WlPJKO55Ht+Dy4kdfRE+4x+5hkUt0OjAwoJAKdAouVm81cPXVk4QQzwH4fwBUAfyQlPKldj3XeHLc8YXn9F8g0l/j4R7j4nQP+mg+PESkCl3ed5u3GljFteFFCPHtAP4IgADwd6SUf97O57v0/CXHF56Tf4HIHRo33T7qB5Eq0rm0Fu+7zf3j0vOXLHtsNx82+lMAYQCzuH8jxh9s8T1fk1L+WyuebDQxasXDdBSDCzkZAwk5lY57JK3eauDm8JLc+Tyx87EfS8KL0zC4EBHZQ8fgMtY1hkK5YNlzuDa8SCn5v2X7YHAhIrIP90gezLV7Xqg1BhciIntxj+TBGF7IxOBCRGQ/7pE8GMMLAWBwISKiw7GjfzC8EIMLEREdil39g+HF5RhciIjoMOzsHwwvLsbgQkREh2F3/2B4cSm7C4+IiJxJhf7B8OJCKhQeERE5jyr9g+HFZVQpPCIichaV+gfDi4uoVHhEROQcqvUPhheXUK3wiIjIGVTsHwwvLqBi4RERkfpU7R8ML5pTtfCIiEhtKvcPhheNqVx4RESkLtX7B8OLplQvPCIiUpMT+gfDi4acUHhERKQep/QPhhfNOKXwiIhILU7qHwwvGnFS4RERkTqc1j8YXjThtMIjIiI1OLF/MLxowImFR0RE9nNq/2B4cTinFh4REdnLyf2D4cXBnFx4RERkH6f3D4YXh3J64RERkT106B8MLw6kQ+EREVHn6dI/GF4cRpfCIyKiztKpfzC8OIhOhUdERJ2jW/9geHEI3QqPiIg6Q8f+wfDiADoWHhERtZ+u/YPhRXG6Fh4REbWXzv2D4UVhOhceERG1j+79g+FFUboXHhERtYcb+gfDi4LcUHhERGQ9t/QPhhfFuKXwiIjIWm7qHwwvCnFT4RERkXXc1j8YXhThtsIjIiJruLF/MLwowI2FR0RER+fW/sHwYjO3Fh4RER2Nm/sHw4uN3Fx4RER0eG7vHwwvNnF74RER0eGwfzC82EKXwkvn0nYPgYjIVXTpH0fF8NJACPELQgi58/Gj7XgOXQpvLjuHiy9ctHsYRESuoUv/sALDyw4hxGkAPw9Atus5dCk8Yx6pTMruoRARuYIu/cMqDC8AhBACwG8ByAD4k3Y8RzqX1qLwGn+BxpPjdg+HiEh7ugQXK7caMLzc9xMA3gfgE7gfYCx38YWLji+85l+gS89fsntIRERa0yW4WL3VwPXhRQgxBOBfAvgLKeUL7XqeVCbl+MJr/gUaTYzaPSwiIm3pFFys3mrg+vAC4NcAhAH8dDufZDw57vjCc/ovEBGRU+jyvtuurQY+yx7JgYQQ3wfgQwD+mZRy5oiPNb3Pl04AwKXnLzm+8Jz8C0RE5BTpXBrPff45x7/vNvePL33kSzjzv52x5LFdG16EEDEAvw7gJoB/0e7nc+IhFgYXIqLOu/jCRS23GvSEeyx7fMeGFyHEFwA8/og/9nellK/s/Pl/BzAG4LullNtHHY+U8olW/76zInP2qI/faQwuRET20DG4jHWNoVAuWPYcjg0vACYAnH7En4kAgBDi3QA+DuA/SCn/i9UDczoGFyIi+3CP5MEcG16klO86wo8/h/ublZ8UQrzY9DXjgNzPCyF+AsAlKeX/eYTnchQGFyIie3GP5MEcG14s8qAAdGbnI9WZodiPwYWIyH7cI3kwV54qLaX8RSmlaPUB4HM73/bf7vzbj9k41I5hcCEiosOwo3+4MrzQbgwuRER0GHb1D4YXl2NwISKiw7CzfzC8uBiDCxERHYbd/cPtG3b32Nnj8mM2D6Pt7C48IiJyJhX6B1deXEiFwiMiIudRpX8wvLiMKoVHRETOolL/YHhxEZUKj4iInEO1/sHw4hKqFR4RETmDiv2D4cUFVCw8IiJSn6r9g+FFc6oWHhERqU3l/sHwojGVC4+IiNSlev9geNGU6oVHRERqckL/YHjRkBMKj4iI1OOU/sHwohmnFB4REanFSf2D4UUjTio8IiJSh9P6B8OLJpxWeEREpAYn9g+GFw04sfCIiMh+Tu0fDC8O59TCIyIiezm5fzC8OJiTC4+IiOzj9P7B8OJQTi88IiKyhw79g+HFgXQoPCIi6jxd+gfDi8PoUnhERNRZOvUPhhcH0anwiIioc3TrHwwvDqFb4RERUWfo2D8YXhxAx8IjIqL207V/MLwoTtfCIyKi9tK5fzC8KEznwiMiovbRvX8wvChK98IjIqL2cEP/YHhRkBsKj4iIrOeW/sHwohi3FB4REVnLTf2D4UUhbio8IiKyjtv6B8OLItxWeEREZA039g+GFwW4sfCI/v/27jxMrqrM4/j3JUCAaEgii0CQhKCRRQeJrLKpIAkYDeIMEBAXXGbQQUZEh0dFBPclCIqOouKSODgKSKIQlhEQUEMIyL4JCaRZEyFEgiFJ8/rHuZWURVd3Lbfqnnvq93mefqr7Vtfp895++5y37z33loi0r1fnDxUvBevVxBMRkfb08vyh4qVAvZx4IiLSul6fP1S8FKTXE09ERFqj+UPFSyFSSby+5X1Fd0FEpKekMn+0S8VLl6WSeIufWczkWZOL7oaISM9IZf7Ig4qXLkol8SpxLFq2qOiuiIj0hFTmj7yoeOmSvuV9SSRe9R/QuFHjiu6OiEjyUilc8lxqoOKlSybPmlz6xKv9A5p7zNyiuyQikrRUCpe8lxqsn1tLMqhFyxaVPvFq/4DGbDym6G6JiCQrpcIl76UGOvLSJeNGjSt94pX9D0hEpCxSGXc7tdRAxQtgZtPMbK6ZLTGzlWa22MwuNrN98/oZc4+ZW/rEK/MfkIhIWaS4RjLvpQY9fdrIzNYDzgPeB6wArgeWAa8ADgUWZNvaNnbk2Dya6SoVLiIi3Td51mQtNRhCTxcvwGmEwmUO8B53f6ryhJmNBjYrqmNFU+EiIlKMFAuXbTfdlhWrVuT2M3q2eDGzscCpwMPAke7+9+rn3f1p4Oki+lY0FS4iIsXRGsmh9fKal3cDGwI/qC1cepkKFxGRYmmN5NB69sgL8Kbs8Q9mthVwDLAD8AxwNXC5u3tePyzPw2Wd0re8b+251nGjxnHp9EsZs/GYun2v3l6G+EREYlU9ho7eaHTpxtRG5o88Y7Ic5+dSMbPHgJcDJwJnApvWfMs1wOHuvqzB9u6s89QENmc4H261pyIiIok4F1jCXe6+czvN9PJpo9HZ4wzgNmA3YCRwELAQOJBwJZKIiIhEpLRHXszsYmDHJl92nLvfmL1+FbABsAQY7+5rj2eZ2S6EgsaAie5+Xxv9vJPN2enBex9kixFbtNpMx8x/ZD4H/ewg+r2fYTaMq951Fbtvs3tDr12xagVbfmNLAJ44+QlGbDiik10dVO0hy7nHzC3l5emKIy6KIy4pxzF6o9HRjKeNanb+eHLFk2w/cftcjryUec3LeGBik6/ZpOrzZwlHX35ZXbgAuPsdZjYf2APYH2i5eKkYscGI6JJxXt88Dp55MP3ez/rrrc/1772ePcfu2VJbIzYsLr7Fzyzm0J8fmsTlhYojHoojLqnHUb0epMjxtFGtzB8jVuUXU2mLF3fftc0mHiIUL4vqPL+IULzEd7gkB/P65rHv+fuy5oU1bRcuRUrl6ijFERfFERfFEZcY5o9eXvNyS/Y4us7zlVsBPtuFvnRVDImXh1QGAsURF8URF8URl1jmj14uXmZnjwfUPmFmLyEs4IV1RU4SYkm8dqUyECiOuCiOuCiOuMQ0f/Ry8TIHuBvYx8xOqGw0s2GEK5DGAHeQ03sbxSCmxGtHKgOB4oiL4oiL4ohLbPNHade8tMvd+81sOnAtcK6ZfRD4C/A6YHvgr8D0PG9UV6TYEq9VqQwEiiMuiiMuiiMuMc4fvXzkBXf/M7Ar8FNgS+BtZG8ZAExy99sL7F5uYky8VqQyECiOuCiOuCiOuMQ6f/TskZcKd19IeJ+jJMWaeM1KZSBQHHFRHHFRHHGJef7o6SMvqYs58ZqRykCgOOKiOOKiOOIS+/yh4iVRsSdeo1IZCBRHXBRHXBRHXMowf6h4SVAZEq8RqQwEiiMuiiMuiiMuZZk/VLwkpiyJN5RUBgLFERfFERfFEZcyzR8qXhJSpsQbTCoDgeKIi+KIi+KIS9nmDxUviShb4tWTykCgOOKiOOKiOOJSxvlDxUsCyph4A0llIFAccVEccVEccSnr/KHipeTKmni1UhkIFEdcFEdcFEdcyjx/qHgpsTInXrVUBgLFERfFERfFEZeyzx8qXkqq7IlXkcpAoDjiojjiojjiksL8oeKlhFJIPEhnIFAccVEccVEccUll/lDxUjKpJF4qA4HiiIviiIviiEsq8weoeCmVVBIvlYFAccRFccRFccQllfmjQsVLSaSSeKkMBIojLoojLoojLqnMH9VUvJRAKomXykCgOOKiOOKiOOKSyvxRS8VL5FJJvFQGAsURF8URF8URl1Tmj4GoeIlYKomXykCgOOKiOOKiOOKSyvxRj4qXSKWSeKkMBIojLoojLoojLqnMH4NR8RKhVBIvlYFAccRFccRFccQllfljKCpeIpNK4qUyECiOuCiOuCiOuKQyfzRCxUtEUkm8VAYCxREXxREXxRGXVOaPRql4iUQqiZfKQKA44qI44qI44pLK/NEMFS8RSCXxUhkIFEdcFEdcFEdcUpk/mqXipWCpJF7f8r4kBoJUBjTFERfFEZdU4khl/mjF+kV3oJellHhTZk1h4bKFpR4IUhnQFEdcFEdcUokjpfmjFTryUpDUEk+FSxwUR1wUR1xSiSO1+aMVKl4KkErizX9k/trPx48aX9qBIJUBTXHERXHEJZU4Upk/2qXipctSSbx5ffM4eObBa7++7JjLSjkQpDKgKY64KI64pBJHKvNHHlS8dFEqiVcdR8XYkWML7FFrUhnQFEdcFEdcUokjlfkjLypeumTBowuSSLzaP6CySmVAUxxxURxxSSWOVAqXBY8uyK0tFS9dMvWCqaVPvNo/oCuPvbLoLrUklQFNccRFccQllThSKVzm9c1j6gVTc2uvvP86l0y/95c+8Wr/gHbZYpeiu9W0VAY0xREXxRGXVOJIqXDZ9/x96ff+3NrUkZcuGWbDSp94Zf8DSmVAUxxxURxxSSWOVMbd6jiG2bDc2lXx0iVzjppT+sQr8x9QKgOa4oiL4ohLKnHMf2R+EuNu7fwx56g5ubWt00ZdMmnrSUV3oWkqXOKiOOKiOOKSShwAB888uPTj7kDzx/hR43NrX0deZEAqXOKiOOKiOOKSShwVZR93uzF/qHiRF1HhEhfFERfFEZdU4uhb3rf28zKPu92aP1S8yD9R4RIXxREXxRGXlOKYMmvK2q+vPPbKUo673Zw/VLzIWipc4qI44qI44pJaHAuXLVy7bfdtdi+wR63p9vyh4kUAFS6xURxxURxxSTGOPBezdlsR80dPFy9mNtzMPmlmN5vZs2b2vJktNLPzzGz7ovvXLSpc4qI44qI44pJqHJcdc1nRXWpJUfNHz14qbWYbAVcDewHLgGuBlcBuwPuBI83sje6e35sxREiFS1wUR1wUR1xSjmPMxmOK7lbTipw/evnIywcJhct8YJy7H+buRwA7AN8GXgrMKLB/HafCJS6KIy6KIy6KIy5Fzx+9XLzsnz3OcPdnKhvdvR84LfuyfKumGlR04uUllYFAccRFccRFccQlhvmjl4uX5xv4nr92vBcFiCHx8pDKQKA44qI44qI44hLL/NGza16AK4DpwMfM7LLK0RczGwackX3PD/P6YUueW5JXU21Z8OgCpl4wlX7vZ5gNY/aRsxk/ajxPPvtk022tWL1i3eerVgzynfnrW97H5FmTWbRsEeNGjePS6ZcyZuMxXe9HuxRHXBRHXHopjurPn1zxJCNWjSiiq4Nqd/7Icx40d8+tsTLJipSZwFGEBbs3EBbsTgK2JKx7OTU7jdRIe3fWeerVDGM9yrcWS0REJF9PAf38zd1HttNMzx55cfd+MzsWeBj4BHBY1dM3A//faOEyhBfoZwVLWNzk6yZkjw/k0IfUaN/Up30zMO2X+rRv6tO+qa/VfbMt8Fy7P7y0R17M7GJgxyZfdpy735i9fjRwMWFR7qnAhYQduj/wLWAbYLq7/yK3TjehciTH3Xcu4ufHTPumPu2bgWm/1Kd9U5/2TX1F75syH3kZD0xs8jWbVH1+FnAA8F/ufk7V9kvM7BHgRuAbZnaRu69ur6siIiKSl9IWL+6+a6uvzda7HJ19+asB2r7JzBYC22cf97b6s0RERCRfvXqp9BbAhtnnz9T5nsr20Z3vjoiIiDSqV4uXp4BV2eevr33SzEay7pTUQ93qlIiIiAytJ4sXd38emJt9OcPMtqo8l73n0XcI62NucPfHCuiiiIiI1FHaq43aZWYTCPd22RL4G/BH4O+Eq4+2JhydOcDd7yiskyIiIvIiPVu8AJjZlsAngSnAOMCAxcDlwJfdva+43omIiMhAerp4ERERkfLpyTUvIiIiUl4qXkRERKRUVLyIiIhIqah4ERERkVJR8SIiIiKlouJFRERESkXFS8HM7Boz8yE+XmiivfcM0dYFnYwnT2Z24BCx/KnFdqea2bVmtjz7uMbMDsu7/51iZq82s0+a2dVmttTMVpvZ42Z2kZnt10J7pcsZM9vYzM4ws/vMbKWZPWpmPzKzbVpoa7SZnW1mD5nZ89njN81sVCf63ilmtomZTTOzH5rZvdl+WWFmt5rZaWb2kibbWzREXry6U7F0QgNj7eQm20slb4YaZysfpzXYXlfyprTvKp2QucCiOs9NAnYBrmuh3VuBPw+wfV4LbRXtAeD6OtubYmYnAWcBa4CrgOeBtwC/MbP/dPdvt9PRLrkK2AZ4FvgT4W7QOwGHA9PM7GPu/s0W2i1Fzlh4C4/fAXsBjwGXEG4y+V7grWa2l7s/2GBbmxHurr0D8CDwa2Bn4KPAFDPb292fyj2IzpgOnJd9fjcwGxgJ7AN8DjjazA5w9yebbPcndbbXe1Pb2F1I+Nup9UijDSSWN49T/3c8DDg2+7zZeaizeePu+oj0gzBpOPD+Jl7znuw1pxfd/xziPzCL5cc5tTeRULSsBPau2v4qYCmwGtih6LgbiOMq4F3ARjXbP5TtrzXATqnmDPD5rL9/AF5Stf1j2fZrmmhrZvaaC4H1q7afk2fudWm/vBv4HrBjzfatgJuzeH7eRHuLwhRRfGw57Z9rsn0wLoe2ksmbIeKcksXzMNlNbWPJG502ipSZvRLYgzDR/rLg7qTio4T/JP7H3f9Y2eju9wFfIByJ/GhBfWuYux/k7j9z95U1278HXEGI8V8L6VyHmdmGwEeyLz/s7mv/g3b3GcBtwAFmNqmBtrYCjia8w/wJ7r6m6ulTgCXAsWa2RV797yR3/4m7f8jd767Z/hjw4ezLd2T7UFqUWt4MoXLUZZZnlUksVLzEq5I0c9y9rIdnY1NZ1/KrAZ6rbJvapb50yq3Z49aF9qJz3gBsCjzg7rcM8Hwzv8fJhDHwOnd/ovoJD+88P4dQCB7aenejUcmL4cDLiuxIAnoib8xsBPD27MufFdmXgWjNS7yOyR5ntvj6SWb2NcI578eB37n7tbn0rPteaWZfIgy6SwnrX+a6ezMLmUcBr8i+fNGk5+6LzWwpsJ2ZjXT35Tn0uwjbZ4+Pt/DaMuTMv2SPN9d5vrL9tTm19b4G24pdJS9WE9ZINczMTgEmENaH3Qlc7O5L8u1eVx1vZi8DXgDuA37t7g838fpeyZt3ACOAW9z9rmZf3Om8UfESITPbm/BL/ytwWYvNvDX7qDjNzK4Fjqz9b6EE9sk+qt1uZke4+/0NtlEpXJ529xV1vqcP2AzYDri9+W4Wy8wmsO53PruFJsqQM5XfY713fK9s367LbcWucjp0bnZ0oBlfrfn6rGxx+49y6FcRPl3z9dfN7Ex3P7PB1/dK3lSO/rd61KWjeaPTRnF6V/Z4gbuvbvK1jwGnA68jHF5/OfA24B7gAMJVNcNy6menPQN8jXBVycuyjzcTrrB5DXCFmW3aYFuVy0SfG+R7KkXNS5vvarHMbH3gx4TTAr9w9wVNvLxMOTPU77GZ32GebUXLzA4FjiccdflMEy+dTfjveztgE8KVjzMIOfYDM3v7IK+N0e8JY+sEQjwTgU8RFrifYWaNrndLPm+ydT1vBvqB/23y5V3JGx15aZOZXQzs2OTLjnP3G+u0twHwb9mXTVe87n45cHnVpuXAHDO7GlgAvD5rv9mEbFq7+yZb01B7iud3ZrYvcDWwH3AC8KV2+9pNeedM5hxgX8Jlmyc003BMOSP5yu6pMRMw4BR3v3WIl6zl7ifWbLoTONnM7gG+D3yFcJl6Kbh77X1K7gO+aGY3EfL/dDP7vrv/vfu9i87RhHU7c929qVPQ3cobFS/tG0+o4JuxySDPTSEcYbjf3XO7v4a7P2tm5wDfBg6hOxNR3vsGAHfvN7OvEIqXQ2iseKlclTJY+yOyx7810F47ct0vZvYp4D+AJ4BDPKf7SxSUM0MZ6vfYzO8wz7aik92wby4wGpjh7mfn1PQPCZerTzSzce6+KKd2C+HuV2QFzOuBPQmXVA8m6bzJtHvKaCC55o2Klza5+645N1lJmlYX6g6msj5kqw60/SId2DfVmo2lsiBvtJmNqLPuZWz2+FBbPRtCnvvFzP6dMCA8A0x297/k1XamqznTgMrvcWyd55v5HebZVlTMbAzhsvntgPOBj+fVtru/YGYPAFsQ8mJRXm0X6H5C8dJIniebNwBmtiPhFPKzhJvv5SLvvNGal4iY2UjWXeLZieJldPZYb8FqmTQVi7svY92g87ra581sW8Ji3YfKcqWRmR0FnEs4936Yuw90d9x2xZYzldMeu9V5vrL9ti63FY3sbQAuI9x1+SLgAx24R0dsedGuZuJJMm+qVNZcXuTug60RbEVueaPiJS7vBDYCbvAGb2/epCP9y16uAAAFs0lEQVSyx3qX+JVJK7H8Nnt85wDPVbbNablHXZQtwvwpYbHh4e5+Q4d+VGw5cwPhKNMEMxvoCFYzv8e5hMtl96u9oZiZDSf8I9EPXNp6d7sr6/clhBtcXg4c7e79Of+MnQmnPZ8jLOouNTPbnHAKGhrL8+TypsLMjPA2E5DzvV1yz5tO38JXH41/EN6vxYEPNfC992Qf29RsPxXYrGbbBsBns7afq31NrB/AScC2NduMcBv81YQBZFIT+6b67QH2qtr+Ssr19gBvyH6Pq4FpTbwuiZxh3dsD3ACMqNo+4NsDEO7Iew/wpQHaqtzm/Vf8823ez6Zkt3knLLC8KOv374FNGnjNgPuGcIO1Nw3w/a8F7sp+xtlFx9zEvtkHmAYMq9k+jnDfKAcu6cW8qYlr/6z/fcB6MeeN1rxEwszGEi5LXQX8XwMvqSz43KBm+xeBz2YL0BYTbji2K+GOqyuBY9294TcgK9hJhHsw3AwsJByVeg1hwesLwIk+8CXBA+4bd783u3HSDOA6M7uSsL/fAmyctZf3mpFO+A2hvwsJb8Q4bYDvud7df1CzLZWc+TxwEGFCut/MriOs7diTcGv299V8/2aE2Adaz3AS4VL8I4B7sn2wM+HyzvsJBVFZfITw5pwQivHvhH+kX+Tj7r40+7zevtmDkBMPEU6TPEe40d1uhLWS1wD/nWfnO+xVhLU/j2fjyTJCzkwijCt3Ah+oeU2v5E21yprLn/vgNwEtPG9UvMRjOuE03m/d/ek22jkD2JuQWLsRjlT0Ed6w7Sx3v7fdjnbRNwiFxc6E8/cbEO5JMhM4x93nN9ugu59lZn8hvAdJ5VDxTcBX3f03ufS680Zlj+Ozj3pqi5d6SpUz7r7SzN5IOGI0nfAf9VOE+9x8xt3r3TxsoLaWmtkehPvcTCNM/k8QLj3/rIe1UmUxuurzw+t+V4h16SDPQzjltC2wO+vekmE54SjFLOB8z/l0VIfNA75LKHB3J+yrFYR3Uf8l8F1v4hLpxPIGWHvKq3LatdU1l13LG8sO6YiIiIiUghbsioiISKmoeBEREZFSUfEiIiIipaLiRUREREpFxYuIiIiUiooXERERKRUVLyIiIlIqKl5ERESkVFS8iIiISKmoeBEREZFSUfEiIiIipaLiRUSSY2bHmZmb2e1mVvsu2pXv2cvM+s1sqZlt3u0+ikjrVLyISHLc/afAVcAuwCdqn88KmvMIY+DJ7r6kuz0UkXboXaVFJElmtj1wB2DAa939/qrnPg2cCVzl7gcX1EURaZGKFxFJlpmdAnwVuMbd35htmwjcCrwAvMbdHyiwiyLSAp02EpGUnQXcAhxoZsebmQHfB4YDp6twESknHXkRkaSZ2SRgHrCcUMycAfwZ2N3d1xTZNxFpjYoXEUmemX0dODn7sh/Yy91vKrBLItIGFS8ikjwz2xroIyze/ZG7H19wl0SkDVrzIiK94HOEwgXgEDN7aZGdEZH2qHgRkaSZ2f7A8cBjwK+BbYAvFNopEWmLThuJSLLMbDjhsuiJwDuB64G7gU0J617mF9g9EWmRjryISMo+TShcZrv7he7+BOGOu+sB55nZ+oX2TkRaoiMvIpIkM9sFuBlYCezk7n3ZdgOuBfYDPuHuXyuulyLSChUvIpIcM1sPuAHYCzjR3b9V8/yOhHu9rAF2dvdFXe+kiLRMp41EJEUnEAqXecC5tU+6+93Al4FNgO90t2si0i4deRGRpJjZWOAuYGNgN3e/vc73DQduA14FHOXuv+heL0WkHSpeREREpFR02khERERKRcWLiIiIlIqKFxERESkVFS8iIiJSKipeREREpFRUvIiIiEipqHgRERGRUlHxIiIiIqWi4kVERERKRcWLiIiIlIqKFxERESkVFS8iIiJSKipeREREpFRUvIiIiEipqHgRERGRUlHxIiIiIqWi4kVERERK5R/QHAoDc2WJnQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "sim = mp.Simulation(\n", - " cell_size=mp.Vector3(sxy, sxy),\n", - " geometry=[c1, c2],\n", - " sources=[src],\n", - " resolution=10,\n", - " boundary_layers=[mp.PML(dpml)],\n", - ")\n", + "sim = mp.Simulation(cell_size=mp.Vector3(sxy, sxy),\n", + " geometry=[c1, c2],\n", + " sources=[src],\n", + " resolution=10, \n", + " boundary_layers=[mp.PML(dpml)])\n", "plt.figure(dpi=150)\n", "sim.plot2D()\n", "plt.show()" @@ -108,15 +138,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "harminv0:, frequency, imag. freq., Q, |amp|, amplitude, error\n", + "harminv0:, 0.11806892700709783, -0.0007236871181289625, 81.57456727456747, 0.003389451986143974, -0.003074693510136736-0.0014264096834704844i, 6.038103449592875e-06+0.0i\n", + "harminv0:, 0.14716744717130087, -0.00020855489877134902, 352.82663710683005, 0.027968648426445402, 0.01873165284311301+0.02076946018959576i, 5.489828592169395e-07+0.0i\n", + "harminv0:, 0.1752390711833313, -4.340297760906741e-05, 2018.744805502949, 0.00716056650544672, -0.0008759244837260834-0.007106790342885785i, 1.35846494123861e-06+0.0i\n", + "harminv0:, 0.1979976562664712, 0.0010186615368048548, -97.18520289256865, 1.1722788570050586e-05, 4.797901958214431e-06-1.0695976283512917e-05i, 0.0010751369028298237+0.0i\n", + "run 0 finished at t = 400.0 (8000 timesteps)\n" + ] + } + ], "source": [ - "sim.run(\n", - " mp.at_beginning(mp.output_epsilon),\n", - " mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df)),\n", - " until_after_sources=300,\n", - ")" + "sim.run(mp.at_beginning(mp.output_epsilon),\n", + " mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r+0.1), fcen, df)),\n", + " until_after_sources=300)" ] }, { @@ -149,31 +190,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " cylinder, center = (0,0,0)\n", + " radius 2, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (0,0,0)\n", + " radius 1, height 1e+20, axis (0, 0, 1)\n", + "Meep progress: 596.1/600.0 = 99.4% done in 4.0s, 0.0s to go\n", + "run 1 finished at t = 600.0 (12000 timesteps)\n", + "Normalizing field data...\n", + "run 2 finished at t = 625.0 (12500 timesteps)\n", + "Generating MP4...\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sim.reset_meep()\n", - "fcen = 0.118\n", + "fcen=0.118\n", "df = 0.1\n", - "sim.sources = [\n", - " mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1))\n", - "]\n", + "sim.sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r+0.1))]\n", "\n", "# Start the simulation and get into steady state\n", - "sim.run(until=600)\n", + "sim.run(until=600) \n", "\n", "# Prepare the animator and record the steady state response\n", "f = plt.figure(dpi=150)\n", "Animate = mp.Animate2D(sim, fields=mp.Ez, f=f, realtime=False, normalize=True)\n", - "sim.run(mp.at_every(0.5, Animate), until=25)\n", + "sim.run(mp.at_every(0.5,Animate),until=25)\n", "\n", "# Close the animator's working frame\n", "plt.close()\n", "\n", "# Process the animation and view it\n", "filename = \"media/ring_simple.mp4\"\n", - "Animate.to_mp4(5, filename)\n", + "Animate.to_mp4(5,filename)\n", "Video(filename)" ] }, @@ -186,32 +258,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " cylinder, center = (0,0,0)\n", + " radius 2, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (0,0,0)\n", + " radius 1, height 1e+20, axis (0, 0, 1)\n", + "run 3 finished at t = 500.0 (10000 timesteps)\n", + "Normalizing field data...\n", + "run 4 finished at t = 525.0 (10500 timesteps)\n", + "Generating MP4...\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sim.reset_meep()\n", - "fcen = 0.147\n", + "fcen=0.147\n", "df = 0.1\n", - "sim.sources = [\n", - " mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1))\n", - "]\n", + "sim.sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r+0.1))]\n", "sim.init_sim()\n", "\n", "# Start the simulation and get into steady state\n", - "sim.run(until=500)\n", + "sim.run(until=500) \n", "\n", "# Prepare the animator and record the steady state response\n", "f = plt.figure(dpi=150)\n", "Animate = mp.Animate2D(sim, fields=mp.Ez, f=f, realtime=False, normalize=True)\n", - "sim.run(mp.at_every(0.5, Animate), until=25)\n", + "sim.run(mp.at_every(0.5,Animate),until=25)\n", "\n", "# Close the animator's working frame\n", "plt.close()\n", "\n", "# Process the animation and view it\n", "filename = \"media/ring_mid.mp4\"\n", - "Animate.to_mp4(5, filename)\n", + "Animate.to_mp4(5,filename)\n", "Video(filename)" ] }, @@ -224,31 +326,61 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " cylinder, center = (0,0,0)\n", + " radius 2, height 1e+20, axis (0, 0, 1)\n", + " cylinder, center = (0,0,0)\n", + " radius 1, height 1e+20, axis (0, 0, 1)\n", + "run 5 finished at t = 500.0 (10000 timesteps)\n", + "Normalizing field data...\n", + "run 6 finished at t = 525.0 (10500 timesteps)\n", + "Generating MP4...\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sim.reset_meep()\n", - "fcen = 0.175\n", + "fcen=0.175\n", "df = 0.1\n", - "sim.sources = [\n", - " mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1))\n", - "]\n", + "sim.sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r+0.1))]\n", "\n", "# Start the simulation and get into steady state\n", - "sim.run(until=500)\n", + "sim.run(until=500) \n", "\n", "# Prepare the animator and record the steady state response\n", "f = plt.figure(dpi=150)\n", "Animate = mp.Animate2D(sim, fields=mp.Ez, f=f, realtime=False, normalize=True)\n", - "sim.run(mp.at_every(0.5, Animate), until=25)\n", + "sim.run(mp.at_every(0.5,Animate),until=25)\n", "\n", "# Close the animator's working frame\n", "plt.close()\n", "\n", "# Process the animation and view it\n", "filename = \"media/ring_large.mp4\"\n", - "Animate.to_mp4(5, filename)\n", + "Animate.to_mp4(5,filename)\n", "Video(filename)" ] } diff --git a/python/examples/solve-cw.ipynb b/python/examples/solve-cw.ipynb index a32bddf6f..dc1799b6b 100644 --- a/python/examples/solve-cw.ipynb +++ b/python/examples/solve-cw.ipynb @@ -20,9 +20,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import numpy as np\n", @@ -32,9 +40,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Field time usage:\n", + " outputting fields: 0.12903 s\n", + " everything else: 0.000952456 s\n", + "\n", + "-----------\n", + "Initializing structure...\n", + "Halving computational cell along direction x\n", + "Halving computational cell along direction y\n", + "Working in 2D dimensions.\n", + "Computational cell is 16 x 16 x 0 with resolution 10\n", + " cylinder, center = (0,0,0)\n", + " radius 2, height 1e+20, axis (0, 0, 1)\n", + " dielectric constant epsilon diagonal = (11.56,11.56,11.56)\n", + " cylinder, center = (0,0,0)\n", + " radius 1, height 1e+20, axis (0, 0, 1)\n", + " dielectric constant epsilon diagonal = (1,1,1)\n", + "time for set_epsilon = 0.0209911 s\n", + "-----------\n", + "Meep: using complex fields.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "n = 3.4\n", "w = 1\n", @@ -42,41 +89,36 @@ "pad = 4\n", "dpml = 2\n", "\n", - "sxy = 2 * (r + w + pad + dpml)\n", - "cell_size = mp.Vector3(sxy, sxy)\n", + "sxy = 2*(r+w+pad+dpml)\n", + "cell_size = mp.Vector3(sxy,sxy)\n", "\n", "pml_layers = [mp.PML(dpml)]\n", "\n", - "nonpml_vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sxy - 2 * dpml, sxy - 2 * dpml))\n", + "nonpml_vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sxy-2*dpml,sxy-2*dpml))\n", "\n", - "geometry = [\n", - " mp.Cylinder(radius=r + w, material=mp.Medium(index=n)),\n", - " mp.Cylinder(radius=r),\n", - "]\n", + "geometry = [mp.Cylinder(radius=r+w, material=mp.Medium(index=n)),\n", + " mp.Cylinder(radius=r)]\n", "\n", "fcen = 0.118\n", "\n", - "src = [\n", - " mp.Source(mp.ContinuousSource(fcen), component=mp.Ez, center=mp.Vector3(r + 0.1)),\n", - " mp.Source(\n", - " mp.ContinuousSource(fcen),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(-(r + 0.1)),\n", - " amplitude=-1,\n", - " ),\n", - "]\n", - "\n", - "symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=+1)]\n", - "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " geometry=geometry,\n", - " sources=src,\n", - " resolution=10,\n", - " force_complex_fields=True,\n", - " symmetries=symmetries,\n", - " boundary_layers=pml_layers,\n", - ")\n", + "src = [mp.Source(mp.ContinuousSource(fcen),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(r+0.1)),\n", + " mp.Source(mp.ContinuousSource(fcen),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(-(r+0.1)),\n", + " amplitude=-1)]\n", + "\n", + "symmetries = [mp.Mirror(mp.X,phase=-1),\n", + " mp.Mirror(mp.Y,phase=+1)]\n", + "\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " geometry=geometry,\n", + " sources=src,\n", + " resolution=10,\n", + " force_complex_fields=True,\n", + " symmetries=symmetries,\n", + " boundary_layers=pml_layers)\n", "f = plt.figure(dpi=120)\n", "sim.plot2D(ax=f.gca())\n", "plt.show()" @@ -96,13 +138,13 @@ "outputs": [], "source": [ "num_tols = 5\n", - "tols = np.power(10, np.arange(-8.0, -8.0 - num_tols, -1.0))\n", - "ez_dat = np.zeros((122, 122, num_tols), dtype=np.complex_)\n", + "tols = np.power(10, np.arange(-8.0,-8.0-num_tols,-1.0))\n", + "ez_dat = np.zeros((122,122,num_tols), dtype=np.complex_)\n", "\n", "for i in range(num_tols):\n", " sim.init_sim()\n", " sim.solve_cw(tols[i], 10000, 10)\n", - " ez_dat[:, :, i] = sim.get_array(vol=nonpml_vol, component=mp.Ez)" + " ez_dat[:,:,i] = sim.get_array(vol=nonpml_vol, component=mp.Ez)" ] }, { @@ -114,75 +156,108 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "err_dat = np.zeros(num_tols - 1)\n", - "for i in range(num_tols - 1):\n", - " err_dat[i] = LA.norm(ez_dat[:, :, i] - ez_dat[:, :, num_tols - 1])\n", + "err_dat = np.zeros(num_tols-1)\n", + "for i in range(num_tols-1):\n", + " err_dat[i] = LA.norm(ez_dat[:,:,i]-ez_dat[:,:,num_tols-1])\n", "\n", "plt.figure(dpi=150)\n", - "plt.loglog(tols[: num_tols - 1], err_dat, \"bo-\")\n", - "plt.xlabel(\"frequency-domain solver tolerance\")\n", - "plt.ylabel(\"L2 norm of error in fields\")\n", + "plt.loglog(tols[:num_tols-1], err_dat, 'bo-');\n", + "plt.xlabel(\"frequency-domain solver tolerance\");\n", + "plt.ylabel(\"L2 norm of error in fields\");\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PASSED solve_cw test: error in the fields is decreasing with increasing resolution\n" + ] + } + ], "source": [ "eps_data = sim.get_array(vol=nonpml_vol, component=mp.Dielectric)\n", - "ez_data = np.real(ez_dat[:, :, num_tols - 1])\n", + "ez_data = np.real(ez_dat[:,:,num_tols-1])\n", "\n", "plt.figure()\n", - "plt.imshow(eps_data.transpose(), interpolation=\"spline36\", cmap=\"binary\")\n", - "plt.imshow(ez_data.transpose(), interpolation=\"spline36\", cmap=\"RdBu\", alpha=0.9)\n", - "plt.axis(\"off\")\n", + "plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary')\n", + "plt.imshow(ez_data.transpose(), interpolation='spline36', cmap='RdBu', alpha=0.9)\n", + "plt.axis('off')\n", "plt.show()\n", "\n", "if np.all(np.diff(err_dat) < 0):\n", - " print(\n", - " \"PASSED solve_cw test: error in the fields is decreasing with increasing resolution\"\n", - " )\n", + " print(\"PASSED solve_cw test: error in the fields is decreasing with increasing resolution\")\n", "else:\n", - " print(\n", - " \"FAILED solve_cw test: error in the fields is NOT decreasing with increasing resolution\"\n", - " )" + " print(\"FAILED solve_cw test: error in the fields is NOT decreasing with increasing resolution\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + "run 0 finished at t = 225.0 (4500 timesteps)\n" + ] + } + ], "source": [ "sim.reset_meep()\n", "\n", "df = 0.08\n", - "src = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3(r + 0.1)\n", - " ),\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(-(r + 0.1)),\n", - " amplitude=-1,\n", - " ),\n", - "]\n", - "\n", - "sim = mp.Simulation(\n", - " cell_size=mp.Vector3(sxy, sxy),\n", - " geometry=geometry,\n", - " sources=src,\n", - " resolution=10,\n", - " symmetries=symmetries,\n", - " boundary_layers=pml_layers,\n", - ")\n", + "src = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(r+0.1)),\n", + " mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(-(r+0.1)),\n", + " amplitude=-1)]\n", + "\n", + "sim = mp.Simulation(cell_size=mp.Vector3(sxy,sxy),\n", + " geometry=geometry,\n", + " sources=src,\n", + " resolution=10,\n", + " symmetries=symmetries,\n", + " boundary_layers=pml_layers)\n", "\n", "dft_obj = sim.add_dft_fields([mp.Ez], fcen, 0, 1, where=nonpml_vol)\n", "\n", @@ -191,17 +266,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "eps_data = sim.get_array(vol=nonpml_vol, component=mp.Dielectric)\n", "ez_data = np.real(sim.get_dft_array(dft_obj, mp.Ez, 0))\n", "\n", "plt.figure()\n", - "plt.imshow(eps_data.transpose(), interpolation=\"spline36\", cmap=\"binary\")\n", - "plt.imshow(ez_data.transpose(), interpolation=\"spline36\", cmap=\"RdBu\", alpha=0.9)\n", - "plt.axis(\"off\")\n", + "plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary')\n", + "plt.imshow(ez_data.transpose(), interpolation='spline36', cmap='RdBu', alpha=0.9)\n", + "plt.axis('off')\n", "plt.show()" ] } diff --git a/python/examples/stochastic_emitter.ipynb b/python/examples/stochastic_emitter.ipynb index a18bf3022..59f33cec8 100644 --- a/python/examples/stochastic_emitter.ipynb +++ b/python/examples/stochastic_emitter.ipynb @@ -28,27 +28,13 @@ "import numpy as np\n", "\n", "import argparse\n", - "\n", "parser = argparse.ArgumentParser()\n", - "parser.add_argument(\"-res\", type=int, default=50, help=\"resolution (pixels/um)\")\n", - "parser.add_argument(\n", - " \"-nr\", type=int, default=20, help=\"number of random trials (method 1)\"\n", - ")\n", - "parser.add_argument(\"-nd\", type=int, default=10, help=\"number of dipoles\")\n", - "parser.add_argument(\"-nf\", type=int, default=500, help=\"number of frequencies\")\n", - "parser.add_argument(\n", - " \"-textured\",\n", - " action=\"store_true\",\n", - " default=False,\n", - " help=\"flat (default) or textured surface\",\n", - ")\n", - "parser.add_argument(\n", - " \"-method\",\n", - " type=int,\n", - " choices=[1, 2],\n", - " default=1,\n", - " help=\"type of method: (1) random dipoles with nr trials or (2) single dipole with 1 run per dipole\",\n", - ")\n", + "parser.add_argument('-res', type=int, default=50, help='resolution (pixels/um)')\n", + "parser.add_argument('-nr', type=int, default=20, help='number of random trials (method 1)')\n", + "parser.add_argument('-nd', type=int, default=10, help='number of dipoles')\n", + "parser.add_argument('-nf', type=int, default=500, help='number of frequencies')\n", + "parser.add_argument('-textured', action='store_true', default=False, help='flat (default) or textured surface')\n", + "parser.add_argument('-method', type=int, choices=[1,2], default=1, help='type of method: (1) random dipoles with nr trials or (2) single dipole with 1 run per dipole')\n", "args = parser.parse_args()\n", "\n", "resolution = args.res\n", @@ -61,81 +47,56 @@ "dAg = 0.5\n", "\n", "sx = 1.1\n", - "sy = dpml + dair + hrod + dsub + dAg\n", + "sy = dpml+dair+hrod+dsub+dAg\n", "\n", - "cell_size = mp.Vector3(sx, sy)\n", + "cell_size = mp.Vector3(sx,sy)\n", "\n", - "pml_layers = [mp.PML(direction=mp.Y, thickness=dpml, side=mp.High)]\n", + "pml_layers = [mp.PML(direction=mp.Y,\n", + " thickness=dpml,\n", + " side=mp.High)]\n", "\n", "fcen = 1.0\n", "df = 0.2\n", "nfreq = args.nf\n", "ndipole = args.nd\n", "ntrial = args.nr\n", - "run_time = 2 * nfreq / df\n", - "\n", - "geometry = [\n", - " mp.Block(\n", - " material=mp.Medium(index=3.45),\n", - " center=mp.Vector3(0, 0.5 * sy - dpml - dair - hrod - 0.5 * dsub),\n", - " size=mp.Vector3(mp.inf, dsub, mp.inf),\n", - " ),\n", - " mp.Block(\n", - " material=Ag,\n", - " center=mp.Vector3(0, -0.5 * sy + 0.5 * dAg),\n", - " size=mp.Vector3(mp.inf, dAg, mp.inf),\n", - " ),\n", - "]\n", + "run_time = 2*nfreq/df\n", "\n", - "if args.textured:\n", - " geometry.append(\n", - " mp.Block(\n", - " material=mp.Medium(index=3.45),\n", - " center=mp.Vector3(0, 0.5 * sy - dpml - dair - 0.5 * hrod),\n", - " size=mp.Vector3(wrod, hrod, mp.inf),\n", - " )\n", - " )\n", + "geometry = [mp.Block(material=mp.Medium(index=3.45),\n", + " center=mp.Vector3(0,0.5*sy-dpml-dair-hrod-0.5*dsub),\n", + " size=mp.Vector3(mp.inf,dsub,mp.inf)),\n", + " mp.Block(material=Ag,\n", + " center=mp.Vector3(0,-0.5*sy+0.5*dAg),\n", + " size=mp.Vector3(mp.inf,dAg,mp.inf))]\n", "\n", + "if args.textured:\n", + " geometry.append(mp.Block(material=mp.Medium(index=3.45),\n", + " center=mp.Vector3(0,0.5*sy-dpml-dair-0.5*hrod),\n", + " size=mp.Vector3(wrod,hrod,mp.inf)))\n", "\n", - "def compute_flux(m=1, n=0):\n", + "def compute_flux(m=1,n=0):\n", " if m == 1:\n", " sources = []\n", " for n in range(ndipole):\n", - " sources.append(\n", - " mp.Source(\n", - " mp.CustomSource(src_func=lambda t: np.random.randn()),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(\n", - " sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub\n", - " ),\n", - " )\n", - " )\n", + " sources.append(mp.Source(mp.CustomSource(src_func=lambda t: np.random.randn()),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(sx*(-0.5+n/ndipole),-0.5*sy+dAg+0.5*dsub)))\n", " else:\n", - " sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(fcen, fwidth=df),\n", - " component=mp.Ez,\n", - " center=mp.Vector3(\n", - " sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub\n", - " ),\n", - " )\n", - " ]\n", - "\n", - " sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " resolution=resolution,\n", - " k_point=mp.Vector3(),\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources,\n", - " )\n", - "\n", - " flux_mon = sim.add_flux(\n", - " fcen,\n", - " df,\n", - " nfreq,\n", - " mp.FluxRegion(center=mp.Vector3(0, 0.5 * sy - dpml), size=mp.Vector3(sx)),\n", - " )\n", + " sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(sx*(-0.5+n/ndipole),-0.5*sy+dAg+0.5*dsub))]\n", + "\n", + " sim = mp.Simulation(cell_size=cell_size,\n", + " resolution=resolution,\n", + " k_point=mp.Vector3(),\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources)\n", + "\n", + " flux_mon = sim.add_flux(fcen,\n", + " df,\n", + " nfreq,\n", + " mp.FluxRegion(center=mp.Vector3(0,0.5*sy-dpml),size=mp.Vector3(sx)))\n", "\n", " sim.run(until=run_time)\n", "\n", @@ -144,29 +105,19 @@ "\n", " return freqs, flux\n", "\n", - "\n", "if args.method == 1:\n", - " fluxes = np.zeros((nfreq, ntrial))\n", + " fluxes = np.zeros((nfreq,ntrial))\n", " for t in range(ntrial):\n", - " freqs, fluxes[:, t] = compute_flux(m=1)\n", + " freqs, fluxes[:,t] = compute_flux(m=1)\n", "else:\n", - " fluxes = np.zeros((nfreq, ndipole))\n", + " fluxes = np.zeros((nfreq,ndipole))\n", " for d in range(ndipole):\n", - " freqs, fluxes[:, d] = compute_flux(m=2, n=d)\n", + " freqs, fluxes[:,d] = compute_flux(m=2,n=d)\n", "\n", "\n", "if mp.am_master():\n", - " with open(\n", - " \"method{}_{}_res{}_nfreq{}_ndipole{}.npz\".format(\n", - " args.method,\n", - " \"textured\" if args.textured else \"flat\",\n", - " resolution,\n", - " nfreq,\n", - " ndipole,\n", - " ),\n", - " \"wb\",\n", - " ) as f:\n", - " np.savez(f, freqs=freqs, fluxes=fluxes)" + " with open('method{}_{}_res{}_nfreq{}_ndipole{}.npz'.format(args.method,\"textured\" if args.textured else \"flat\",resolution,nfreq,ndipole),'wb') as f:\n", + " np.savez(f,freqs=freqs,fluxes=fluxes)" ] }, { @@ -204,24 +155,24 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "method1_f0 = np.load(\"method1_flat_res50_nfreq500_ndipole10.npz\")\n", - "method1_f1 = np.load(\"method1_textured_res50_nfreq500_ndipole10.npz\")\n", + "method1_f0 = np.load('method1_flat_res50_nfreq500_ndipole10.npz')\n", + "method1_f1 = np.load('method1_textured_res50_nfreq500_ndipole10.npz')\n", "\n", - "method1_freqs = method1_f0[\"freqs\"]\n", - "method1_f0_mean = np.mean(method1_f0[\"fluxes\"], axis=1)\n", - "method1_f1_mean = np.mean(method1_f1[\"fluxes\"], axis=1)\n", + "method1_freqs = method1_f0['freqs']\n", + "method1_f0_mean = np.mean(method1_f0['fluxes'],axis=1)\n", + "method1_f1_mean = np.mean(method1_f1['fluxes'],axis=1)\n", "\n", - "method2_f0 = np.load(\"method2_flat_res50_nfreq500_ndipole10.npz\")\n", - "method2_f1 = np.load(\"method2_textured_res50_nfreq500_ndipole10.npz\")\n", + "method2_f0 = np.load('method2_flat_res50_nfreq500_ndipole10.npz')\n", + "method2_f1 = np.load('method2_textured_res50_nfreq500_ndipole10.npz')\n", "\n", - "method2_freqs = method2_f0[\"freqs\"]\n", - "method2_f0_mean = np.mean(method2_f0[\"fluxes\"], axis=1)\n", - "method2_f1_mean = np.mean(method2_f1[\"fluxes\"], axis=1)\n", + "method2_freqs = method2_f0['freqs']\n", + "method2_f0_mean = np.mean(method2_f0['fluxes'],axis=1)\n", + "method2_f1_mean = np.mean(method2_f1['fluxes'],axis=1)\n", "\n", - "plt.semilogy(method1_freqs, method1_f1_mean / method1_f0_mean, \"b-\", label=\"Method 1\")\n", - "plt.semilogy(method2_freqs, method2_f1_mean / method2_f0_mean, \"r-\", label=\"Method 2\")\n", - "plt.xlabel(\"frequency\")\n", - "plt.ylabel(\"normalized flux\")\n", + "plt.semilogy(method1_freqs,method1_f1_mean/method1_f0_mean,'b-',label='Method 1')\n", + "plt.semilogy(method2_freqs,method2_f1_mean/method2_f0_mean,'r-',label='Method 2')\n", + "plt.xlabel('frequency')\n", + "plt.ylabel('normalized flux')\n", "plt.legend()\n", "plt.show()" ] diff --git a/python/examples/straight-waveguide.ipynb b/python/examples/straight-waveguide.ipynb index 5ef4a9e74..91d703bf3 100644 --- a/python/examples/straight-waveguide.ipynb +++ b/python/examples/straight-waveguide.ipynb @@ -12,9 +12,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp" ] @@ -32,11 +40,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "cell = mp.Vector3(16, 8, 0)" + "cell = mp.Vector3(16,8,0)" ] }, { @@ -56,17 +64,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "geometry = [\n", - " mp.Block(\n", - " mp.Vector3(mp.inf, 1, mp.inf),\n", - " center=mp.Vector3(),\n", - " material=mp.Medium(epsilon=12),\n", - " )\n", - "]" + "geometry = [mp.Block(mp.Vector3(mp.inf,1,mp.inf),\n", + " center=mp.Vector3(),\n", + " material=mp.Medium(epsilon=12))]" ] }, { @@ -86,15 +90,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "sources = [\n", - " mp.Source(\n", - " mp.ContinuousSource(frequency=0.15), component=mp.Ez, center=mp.Vector3(-7, 0)\n", - " )\n", - "]" + "sources = [mp.Source(mp.ContinuousSource(frequency=0.15),\n", + " component=mp.Ez,\n", + " center=mp.Vector3(-7,0))]" ] }, { @@ -122,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -152,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -170,17 +172,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(\n", - " cell_size=cell,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=sources,\n", - " resolution=resolution,\n", - ")" + "sim = mp.Simulation(cell_size=cell,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=sources,\n", + " resolution=resolution)" ] }, { @@ -195,12 +195,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from matplotlib import pyplot as plt\n", - "\n", "%matplotlib inline\n", "plt.figure(dpi=100)\n", "sim.plot2D()\n", @@ -226,9 +249,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run 0 finished at t = 200.0 (4000 timesteps)\n" + ] + } + ], "source": [ "sim.run(until=200)" ] @@ -242,9 +273,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(dpi=100)\n", "sim.plot2D(fields=mp.Ez)\n", @@ -276,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -295,11 +339,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sim.run(mp.at_every(1, Animate), until=100)\n", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------\n", + "Initializing structure...\n", + " block, center = (0,0,0)\n", + " size (1e+20,1,1e+20)\n", + " axes (1,0,0), (0,1,0), (0,0,1)\n", + "Normalizing field data...\n", + "run 1 finished at t = 100.0 (2000 timesteps)\n" + ] + } + ], + "source": [ + "sim.run(mp.at_every(1,Animate),until=100)\n", "plt.close()" ] }, @@ -312,12 +370,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating MP4...\n" + ] + } + ], "source": [ "filename = \"media/straight_waveguide.mp4\"\n", - "Animate.to_mp4(10, filename)" + "Animate.to_mp4(10,filename)" ] }, { @@ -329,12 +395,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from IPython.display import Video\n", - "\n", "Video(filename)" ] }, diff --git a/python/examples/zone_plate.ipynb b/python/examples/zone_plate.ipynb index 5796367a2..e97e055e6 100644 --- a/python/examples/zone_plate.ipynb +++ b/python/examples/zone_plate.ipynb @@ -30,139 +30,85 @@ "import math\n", "import matplotlib.pyplot as plt\n", "\n", - "resolution = 25 # pixels/μm\n", + "resolution = 25 # pixels/μm\n", "\n", - "dpml = 1.0 # PML thickness\n", - "dsub = 2.0 # substrate thickness\n", - "dpad = 2.0 # padding betweeen zone plate and PML\n", - "zh = 0.5 # zone-plate height\n", - "zN = 25 # number of zones (odd zones: π phase shift, even zones: none)\n", - "focal_length = 200 # focal length of zone plate\n", - "spot_length = 100 # far-field line length\n", - "ff_res = 10 # far-field resolution\n", + "dpml = 1.0 # PML thickness\n", + "dsub = 2.0 # substrate thickness\n", + "dpad = 2.0 # padding betweeen zone plate and PML\n", + "zh = 0.5 # zone-plate height\n", + "zN = 25 # number of zones (odd zones: π phase shift, even zones: none)\n", + "focal_length = 200 # focal length of zone plate\n", + "spot_length = 100 # far-field line length\n", + "ff_res = 10 # far-field resolution\n", "\n", "pml_layers = [mp.PML(thickness=dpml)]\n", "\n", "wvl_cen = 0.5\n", - "frq_cen = 1 / wvl_cen\n", - "dfrq = 0.2 * frq_cen\n", + "frq_cen = 1/wvl_cen\n", + "dfrq = 0.2*frq_cen\n", "\n", "## radii of zones\n", "## ref: eq. 7 of http://zoneplate.lbl.gov/theory\n", - "r = [\n", - " math.sqrt(n * wvl_cen * (focal_length + n * wvl_cen / 4)) for n in range(1, zN + 1)\n", - "]\n", - "\n", - "sr = r[-1] + dpad + dpml\n", - "sz = dpml + dsub + zh + dpad + dpml\n", - "cell_size = mp.Vector3(sr, 0, sz)\n", - "\n", - "sources = [\n", - " mp.Source(\n", - " mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True),\n", - " component=mp.Er,\n", - " center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml),\n", - " size=mp.Vector3(sr),\n", - " ),\n", - " mp.Source(\n", - " mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True),\n", - " component=mp.Ep,\n", - " center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml),\n", - " size=mp.Vector3(sr),\n", - " amplitude=-1j,\n", - " ),\n", - "]\n", + "r = [math.sqrt(n*wvl_cen*(focal_length+n*wvl_cen/4)) for n in range(1,zN+1)]\n", + "\n", + "sr = r[-1]+dpad+dpml\n", + "sz = dpml+dsub+zh+dpad+dpml\n", + "cell_size = mp.Vector3(sr,0,sz)\n", + "\n", + "sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n", + " component=mp.Er,\n", + " center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml),\n", + " size=mp.Vector3(sr)),\n", + " mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n", + " component=mp.Ep,\n", + " center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml),\n", + " size=mp.Vector3(sr),\n", + " amplitude=-1j)]\n", "\n", "glass = mp.Medium(index=1.5)\n", "\n", - "geometry = [\n", - " mp.Block(\n", - " material=glass,\n", - " size=mp.Vector3(sr, 0, dpml + dsub),\n", - " center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + 0.5 * (dpml + dsub)),\n", - " )\n", - "]\n", - "\n", - "for n in range(zN - 1, -1, -1):\n", - " geometry.append(\n", - " mp.Block(\n", - " material=glass if n % 2 == 0 else mp.vacuum,\n", - " size=mp.Vector3(r[n], 0, zh),\n", - " center=mp.Vector3(0.5 * r[n], 0, -0.5 * sz + dpml + dsub + 0.5 * zh),\n", - " )\n", - " )\n", - "\n", - "sim = mp.Simulation(\n", - " cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " resolution=resolution,\n", - " sources=sources,\n", - " geometry=geometry,\n", - " dimensions=mp.CYLINDRICAL,\n", - " m=-1,\n", - ")\n", + "geometry = [mp.Block(material=glass,\n", + " size=mp.Vector3(sr,0,dpml+dsub),\n", + " center=mp.Vector3(0.5*sr,0,-0.5*sz+0.5*(dpml+dsub)))]\n", + "\n", + "for n in range(zN-1,-1,-1):\n", + " geometry.append(mp.Block(material=glass if n % 2 == 0 else mp.vacuum,\n", + " size=mp.Vector3(r[n],0,zh),\n", + " center=mp.Vector3(0.5*r[n],0,-0.5*sz+dpml+dsub+0.5*zh)))\n", + " \n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " resolution=resolution,\n", + " sources=sources,\n", + " geometry=geometry,\n", + " dimensions=mp.CYLINDRICAL,\n", + " m=-1)\n", "\n", "## near-field monitor\n", - "n2f_obj = sim.add_near2far(\n", - " frq_cen,\n", - " 0,\n", - " 1,\n", - " mp.Near2FarRegion(\n", - " center=mp.Vector3(0.5 * (sr - dpml), 0, 0.5 * sz - dpml),\n", - " size=mp.Vector3(sr - dpml),\n", - " ),\n", - ")\n", + "n2f_obj = sim.add_near2far(frq_cen, 0, 1, mp.Near2FarRegion(center=mp.Vector3(0.5*(sr-dpml),0,0.5*sz-dpml),size=mp.Vector3(sr-dpml)))\n", "\n", "sim.run(until_after_sources=100)\n", "\n", - "ff_r = sim.get_farfields(\n", - " n2f_obj,\n", - " ff_res,\n", - " center=mp.Vector3(\n", - " 0.5 * (sr - dpml), 0, -0.5 * sz + dpml + dsub + zh + focal_length\n", - " ),\n", - " size=mp.Vector3(sr - dpml),\n", - ")\n", - "ff_z = sim.get_farfields(\n", - " n2f_obj,\n", - " ff_res,\n", - " center=mp.Vector3(z=-0.5 * sz + dpml + dsub + zh + focal_length),\n", - " size=mp.Vector3(z=spot_length),\n", - ")\n", - "\n", - "E2_r = (\n", - " np.absolute(ff_r[\"Ex\"]) ** 2\n", - " + np.absolute(ff_r[\"Ey\"]) ** 2\n", - " + np.absolute(ff_r[\"Ez\"]) ** 2\n", - ")\n", - "E2_z = (\n", - " np.absolute(ff_z[\"Ex\"]) ** 2\n", - " + np.absolute(ff_z[\"Ey\"]) ** 2\n", - " + np.absolute(ff_z[\"Ez\"]) ** 2\n", - ")\n", + "ff_r = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(0.5*(sr-dpml),0,-0.5*sz+dpml+dsub+zh+focal_length),size=mp.Vector3(sr-dpml))\n", + "ff_z = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(z=-0.5*sz+dpml+dsub+zh+focal_length),size=mp.Vector3(z=spot_length))\n", + "\n", + "E2_r = np.absolute(ff_r['Ex'])**2+np.absolute(ff_r['Ey'])**2+np.absolute(ff_r['Ez'])**2\n", + "E2_z = np.absolute(ff_z['Ex'])**2+np.absolute(ff_z['Ey'])**2+np.absolute(ff_z['Ez'])**2\n", "\n", "plt.figure(dpi=200)\n", - "plt.subplot(1, 2, 1)\n", - "plt.semilogy(np.linspace(0, sr - dpml, len(E2_r)), E2_r, \"bo-\")\n", - "plt.xlim(-2, 20)\n", - "plt.xticks([t for t in np.arange(0, 25, 5)])\n", - "plt.grid(True, axis=\"y\", which=\"both\", ls=\"-\")\n", - "plt.xlabel(r\"$r$ coordinate (μm)\")\n", - "plt.ylabel(r\"energy density of far fields, |E|$^2$\")\n", - "plt.subplot(1, 2, 2)\n", - "plt.semilogy(\n", - " np.linspace(\n", - " focal_length - 0.5 * spot_length, focal_length + 0.5 * spot_length, len(E2_z)\n", - " ),\n", - " E2_z,\n", - " \"bo-\",\n", - ")\n", - "plt.grid(True, axis=\"y\", which=\"both\", ls=\"-\")\n", - "plt.xlabel(r\"$z$ coordinate (μm)\")\n", - "plt.ylabel(r\"energy density of far fields, |E|$^2$\")\n", - "plt.suptitle(\n", - " r\"binary-phase zone plate with focal length $z$ = {} μm\".format(focal_length)\n", - ")\n", + "plt.subplot(1,2,1)\n", + "plt.semilogy(np.linspace(0,sr-dpml,len(E2_r)),E2_r,'bo-')\n", + "plt.xlim(-2,20)\n", + "plt.xticks([t for t in np.arange(0,25,5)])\n", + "plt.grid(True,axis=\"y\",which=\"both\",ls=\"-\")\n", + "plt.xlabel(r'$r$ coordinate (μm)')\n", + "plt.ylabel(r'energy density of far fields, |E|$^2$')\n", + "plt.subplot(1,2,2)\n", + "plt.semilogy(np.linspace(focal_length-0.5*spot_length,focal_length+0.5*spot_length,len(E2_z)),E2_z,'bo-')\n", + "plt.grid(True,axis=\"y\",which=\"both\",ls=\"-\")\n", + "plt.xlabel(r'$z$ coordinate (μm)')\n", + "plt.ylabel(r'energy density of far fields, |E|$^2$')\n", + "plt.suptitle(r\"binary-phase zone plate with focal length $z$ = {} μm\".format(focal_length))\n", "plt.tight_layout()\n", "plt.savefig(\"zone_plate_farfields.png\")" ] From f1c83bf63aa45163de0231c83bee83b2f644a667 Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 17:37:33 +0200 Subject: [PATCH 07/12] make sure sourcery min python version is 3.7 --- .sourcery.yaml | 2 ++ python/examples/holey-wvg-bands.py | 6 +++++- python/geom.py | 3 ++- python/solver.py | 4 +++- 4 files changed, 12 insertions(+), 3 deletions(-) create mode 100644 .sourcery.yaml diff --git a/.sourcery.yaml b/.sourcery.yaml new file mode 100644 index 000000000..f300aba4e --- /dev/null +++ b/.sourcery.yaml @@ -0,0 +1,2 @@ +refactor: + python_version: '3.7' diff --git a/python/examples/holey-wvg-bands.py b/python/examples/holey-wvg-bands.py index 55a2e19d2..3d8f7b447 100644 --- a/python/examples/holey-wvg-bands.py +++ b/python/examples/holey-wvg-bands.py @@ -1,10 +1,13 @@ # Meep Tutorial: Hz-polarized transmission and reflection through a cavity # formed by a periodic sequence of holes in a dielectric waveguide, # with a defect formed by a larger spacing between one pair of holes. + # This structure is based on one analyzed in: # S. Fan, J. N. Winn, A. Devenyi, J. C. Chen, R. D. Meade, and # J. D. Joannopoulos, "Guided and defect modes in periodic dielectric # waveguides," J. Opt. Soc. Am. B, 12 (7), 1267-1272 (1995). + + import meep as mp @@ -43,7 +46,8 @@ def main(): resolution=20, ) - if kx := False: + kx = False # if true, do run at specified kx and get fields + if kx: sim.k_point = mp.Vector3(kx) sim.run( diff --git a/python/geom.py b/python/geom.py index 1bb871cc3..0f676e3dc 100755 --- a/python/geom.py +++ b/python/geom.py @@ -1662,7 +1662,8 @@ def memoize(f): f_memo_tab = {} def _mem(y=None): - if tab_val := f_memo_tab.get(y, None): + tab_val = f_memo_tab.get(y, None) + if tab_val: return tab_val fy = f(y) diff --git a/python/solver.py b/python/solver.py index 799af1c45..106efd456 100644 --- a/python/solver.py +++ b/python/solver.py @@ -1041,7 +1041,9 @@ def find_k( def rootfun(b): def _rootfun(k): - if tab_val := bktab.get((b, k), None): + # First, look in the cached table + tab_val = bktab.get((b, k), None) + if tab_val: if verbosity.mpb > 0: print(f"find-k {b} at {k}: {tab_val[0]} (cached)") return tab_val From 7e69a3a95e6ad4bcc61ff1de090f8a7e59696ba3 Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 17:51:55 +0200 Subject: [PATCH 08/12] include clang formatter in pre-commit --- .pre-commit-config.yaml | 14 +- codemeta.json | 15 +- doc/docs/Developer_Codes/WriteChunkInfo.cpp | 5 +- doc/docs/mathjaxhelper.js | 6 +- libpympb/pympb.cpp | 228 +++---- libpympb/pympb.hpp | 1 - python/meep-python.hpp | 2 +- python/simulation.py | 2 +- python/typemap_utils.cpp | 97 +-- src/GDSIIgeom.cpp | 3 +- src/adjust_verbosity.hpp | 30 +- src/array_slice.cpp | 67 +- src/boundaries.cpp | 14 +- src/casimir.cpp | 6 +- src/dft.cpp | 232 ++++--- src/dft_ldos.cpp | 14 +- src/fields.cpp | 68 +- src/fields_dump.cpp | 71 +- src/fix_boundary_sources.cpp | 128 ++-- src/h5file.cpp | 29 +- src/integrate.cpp | 2 +- src/loop_in_chunks.cpp | 63 +- src/material_data.cpp | 24 +- src/material_data.hpp | 4 +- src/meep.hpp | 163 ++--- src/meep/vec.hpp | 94 +-- src/meep_internals.hpp | 113 +-- src/meepgeom.cpp | 718 ++++++++++---------- src/meepgeom.hpp | 57 +- src/monitor.cpp | 4 +- src/mpb.cpp | 66 +- src/multilevel-atom.cpp | 3 +- src/mympi.cpp | 36 +- src/near2far.cpp | 90 +-- src/output_directory.cpp | 4 +- src/random.cpp | 4 +- src/sources.cpp | 182 ++--- src/step_db.cpp | 22 +- src/step_generic.cpp | 42 +- src/stress.cpp | 3 +- src/structure.cpp | 55 +- src/structure_dump.cpp | 41 +- src/support/mt19937ar.c | 178 +++-- src/susceptibility.cpp | 9 +- src/update_eh.cpp | 7 +- src/vec.cpp | 99 ++- tests/aniso_disp.cpp | 2 +- tests/array-slice-ll.cpp | 25 +- tests/bragg_transmission.cpp | 8 +- tests/convergence_cyl_waveguide.cpp | 16 +- tests/cyl-ellipsoid-ll.cpp | 8 +- tests/cylindrical.cpp | 4 +- tests/dft-fields.cpp | 26 +- tests/dump_load.cpp | 82 +-- tests/h5test.cpp | 15 +- tests/harmonics.cpp | 18 +- tests/integrate.cpp | 23 +- tests/known_results.cpp | 4 +- tests/near2far.cpp | 2 +- tests/one_dimensional.cpp | 4 +- tests/physical.cpp | 10 +- tests/pml.cpp | 9 +- tests/ring-ll.cpp | 18 +- tests/symmetry.cpp | 10 +- tests/three_d.cpp | 4 +- tests/two_dimensional.cpp | 16 +- tests/user-defined-material.cpp | 6 +- 67 files changed, 1668 insertions(+), 1757 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 334af9508..dc0a0d360 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: "b0f06dc9f2260909f3423243de180edfc823ec5a" + rev: "08b4be84be2d74173246b7c9b6c22212a6c15a2d" hooks: - id: check-yaml - id: end-of-file-fixer @@ -19,7 +19,7 @@ repos: args: ["--profile", "black", "--filter-files"] - repo: https://github.com/psf/black - rev: "6debce63bc2429b1680f8838592f2e56e3df6b27" + rev: "4f1772e2aed8356e57b923eacf45f813ec3324a0" hooks: - id: black @@ -35,7 +35,7 @@ repos: # files: ".ipynb" - repo: https://github.com/asottile/pyupgrade - rev: 85f02f45f0d2889f3826f16b60f27188ea84c1ae + rev: v2.37.3 hooks: - id: pyupgrade args: [--py37-plus, --keep-runtime-typing] @@ -52,17 +52,21 @@ repos: # - id: shellcheck - repo: https://github.com/pre-commit/pygrep-hooks - rev: 8e68d79b05355c9e107f81fc267f2bfc3e9e5a04 # Use the ref you want to point at + rev: dc89c436469616a7c48293ec60c8e21e9d95be08 # Use the ref you want to point at hooks: - id: python-use-type-annotations - repo: https://github.com/PyCQA/bandit - rev: 44c05fcac53de3a39589173177c931b38de5bb0f + rev: 97501817e621db9975dfa6a2a1d36370942d1812 hooks: - id: bandit args: [--exit-zero] # ignore all tests, not just tests data exclude: ^tests/ + - repo: https://github.com/pre-commit/mirrors-clang-format + rev: 'v14.0.6' # Use the sha / tag you want to point at + hooks: + - id: clang-format # - repo: https://github.com/pre-commit/mirrors-mypy # rev: "214c33306afe17f1cc7d2d55e4da705b6ebe0627" # hooks: diff --git a/codemeta.json b/codemeta.json index a6282fb4d..9cef3da74 100644 --- a/codemeta.json +++ b/codemeta.json @@ -2,7 +2,8 @@ "@context": "https://doi.org/10.5063/schema/codemeta-2.0", "@type": "SoftwareSourceCode", "name": "Meep", - "description": "Meep is a free and open-source software package for electromagnetics simulation via the finite-difference time-domain (FDTD) method spanning a broad range of applications.", + "description": + "Meep is a free and open-source software package for electromagnetics simulation via the finite-difference time-domain (FDTD) method spanning a broad range of applications.", "url": "https://github.com/NanoComp/meep", "codeRepository": "https://github.com/NanoComp/meep", "issueTracker": "https://github.com/NanoComp/issues", @@ -87,8 +88,12 @@ ], "developmentStatus": "active", "downloadUrl": "https://github.com/NanoComp/releases", - "version":"1.11", - "dateCreated":"2003-02-12", - "datePublished":"2006-04-01", - "programmingLanguage": ["C++", "Python", "Scheme"] + "version": "1.11", + "dateCreated": "2003-02-12", + "datePublished": "2006-04-01", + "programmingLanguage": [ + "C++", + "Python", + "Scheme" + ] } diff --git a/doc/docs/Developer_Codes/WriteChunkInfo.cpp b/doc/docs/Developer_Codes/WriteChunkInfo.cpp index 43bc40d5b..ad96843d7 100644 --- a/doc/docs/Developer_Codes/WriteChunkInfo.cpp +++ b/doc/docs/Developer_Codes/WriteChunkInfo.cpp @@ -123,8 +123,9 @@ structure create_structure(double resolution, int symmetries) { geometry_lattice.size.z = 0.0; grid_volume gv = voltwo(sx, sy, resolution); gv.center_origin(); - symmetry S = (symmetries == 1 ? mirror(Y, gv) - : symmetries == 2 ? mirror(X, gv) + mirror(Y, gv) : symmetry()); + symmetry S = (symmetries == 1 ? mirror(Y, gv) + : symmetries == 2 ? mirror(X, gv) + mirror(Y, gv) + : symmetry()); structure the_structure(gv, dummy_eps, pml(dpml), S); vector3 e1 = v3(1.0, 0.0, 0.0); diff --git a/doc/docs/mathjaxhelper.js b/doc/docs/mathjaxhelper.js index bec7b177c..96f40a91d 100644 --- a/doc/docs/mathjaxhelper.js +++ b/doc/docs/mathjaxhelper.js @@ -1,5 +1,5 @@ MathJax.Hub.Config({ - config: ["MMLorHTML.js"], - jax: ["input/TeX", "output/HTML-CSS", "output/NativeMML"], - extensions: ["MathMenu.js", "MathZoom.js"] + config : [ "MMLorHTML.js" ], + jax : [ "input/TeX", "output/HTML-CSS", "output/NativeMML" ], + extensions : [ "MathMenu.js", "MathZoom.js" ] }); diff --git a/libpympb/pympb.cpp b/libpympb/pympb.cpp index 639aab4e5..c6033cb99 100644 --- a/libpympb/pympb.cpp +++ b/libpympb/pympb.cpp @@ -27,27 +27,27 @@ extern "C" int mpb_verbosity = 2; int xyz_index = ((i1 * n2 + i2) * n3 + i3); // #else /* HAVE_MPI */ // /* first two dimensions are transposed in MPI output: */ -// #define LOOP_XYZ(md) \ -// { \ -// int n1 = md->nx, n2 = md->ny, n3 = md->nz, i1, i2_, i3; \ -// int local_n2 = md->local_ny, local_y_start = md->local_y_start; \ -// for (i2_ = 0; i2_ < local_n2; ++i2_) \ -// for (i1 = 0; i1 < n1; ++i1) \ -// for (i3 = 0; i3 < n3; ++i3) { \ -// int i2 = i2_ + local_y_start; \ -// int xyz_index = ((i2_ * n1 + i1) * n3 + i3); +// #define LOOP_XYZ(md) \ +// { \ +// int n1 = md->nx, n2 = md->ny, n3 = md->nz, i1, i2_, i3; \ +// int local_n2 = md->local_ny, local_y_start = md->local_y_start; \ +// for (i2_ = 0; i2_ < local_n2; ++i2_) \ +// for (i1 = 0; i1 < n1; ++i1) \ +// for (i3 = 0; i3 < n3; ++i3) { \ +// int i2 = i2_ + local_y_start; \ int xyz_index = ((i2_ * n1 + i1) * n3 + i3); // #endif /* HAVE_MPI */ typedef mpb_real real; // needed for the CASSIGN macros below // support version < 1.12 of MPB #ifndef CASSIGN_CONJ_MULT -#define CASSIGN_CONJ_MULT(a, b, c) { \ - real bbbb_re = (b).re, bbbb_im = (b).im; \ - real cccc_re = (c).re, cccc_im = (c).im; \ - CASSIGN_SCALAR(a, bbbb_re * cccc_re + bbbb_im * cccc_im, \ - bbbb_re * cccc_im - bbbb_im * cccc_re); \ -} +#define CASSIGN_CONJ_MULT(a, b, c) \ + { \ + real bbbb_re = (b).re, bbbb_im = (b).im; \ + real cccc_re = (c).re, cccc_im = (c).im; \ + CASSIGN_SCALAR(a, bbbb_re *cccc_re + bbbb_im * cccc_im, \ + bbbb_re * cccc_im - bbbb_im * cccc_re); \ + } #endif // TODO: Support MPI @@ -73,8 +73,6 @@ typedef mpb_real real; // needed for the CASSIGN macros below namespace py_mpb { - - // TODO: Placeholder int mpb_comm; @@ -127,13 +125,12 @@ void matrix3x3_to_arr(mpb_real arr[3][3], matrix3x3 m) { cnumber cscalar2cnumber(scalar_complex cs) { return make_cnumber(CSCALAR_RE(cs), CSCALAR_IM(cs)); } -cvector3 cscalar32cvector3(const scalar_complex *cs) -{ - cvector3 v; - v.x = cscalar2cnumber(cs[0]); - v.y = cscalar2cnumber(cs[1]); - v.z = cscalar2cnumber(cs[2]); - return v; +cvector3 cscalar32cvector3(const scalar_complex *cs) { + cvector3 v; + v.x = cscalar2cnumber(cs[0]); + v.y = cscalar2cnumber(cs[1]); + v.z = cscalar2cnumber(cs[2]); + return v; } // Return a string describing the current parity, used for frequency and filename @@ -142,13 +139,9 @@ const char *parity_string(maxwell_data *d) { static char s[128]; strcpy(s, ""); if (d->parity & EVEN_Z_PARITY) { strcat(s, (d->nz == 1) ? "te" : "zeven"); } - else if (d->parity & ODD_Z_PARITY) { - strcat(s, (d->nz == 1) ? "tm" : "zodd"); - } + else if (d->parity & ODD_Z_PARITY) { strcat(s, (d->nz == 1) ? "tm" : "zodd"); } if (d->parity & EVEN_Y_PARITY) { strcat(s, "yeven"); } - else if (d->parity & ODD_Y_PARITY) { - strcat(s, "yodd"); - } + else if (d->parity & ODD_Y_PARITY) { strcat(s, "yodd"); } return s; } @@ -225,7 +218,7 @@ mode_solver::mode_solver(int num_bands, double resolution[3], lattice lat, doubl use_simple_preconditioner(use_simple_preconditioner), grid_size(grid_size), nwork_alloc(0), eigensolver_nwork(eigensolver_nwork), eigensolver_block_size(eigensolver_block_size), last_parity(-2), iterations(0), eigensolver_flops(flops), geometry_list{}, - geometry_tree(NULL), vol(0), R{}, G{}, mdata(NULL), mtdata(NULL), + geometry_tree(NULL), vol(0), R{}, G{}, mdata(NULL), mtdata(NULL), curfield_band(0), H{}, Hblock{}, muinvH{}, W{}, freqs(num_bands), verbose(verbose), deterministic(deterministic), kpoint_index(0), curfield(NULL), curfield_type('-'), eps(true) { @@ -394,9 +387,7 @@ int mode_solver::mean_epsilon(symmetric_matrix *meps, symmetric_matrix *meps_inv } if (id1 > id2) { normal = normal_to_fixed_object(vector3_minus(p, shiftby1), *o1); } - else { - normal = normal_to_fixed_object(vector3_minus(p, shiftby2), *o2); - } + else { normal = normal_to_fixed_object(vector3_minus(p, shiftby2), *o2); } n[0] = normal.x / (geometry_lattice.size.x == 0 ? 1e-20 : geometry_lattice.size.x); n[1] = normal.y / (geometry_lattice.size.y == 0 ? 1e-20 : geometry_lattice.size.y); @@ -667,9 +658,7 @@ void mode_solver::get_material_pt(meep_geom::material_type &material, vector3 p) // material read from file: interpolate to get properties at r case meep_geom::material_data::MATERIAL_FILE: if (md->epsilon_data) { meep_geom::epsilon_file_material(md, p); } - else { - material = (meep_geom::material_type)default_material; - } + else { material = (meep_geom::material_type)default_material; } return; // material specified by user-supplied function: call user @@ -701,7 +690,9 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry n[1] = grid_size.y; n[2] = grid_size.z; - if (target_freq != 0.0 && mpb_verbosity > 0) { meep::master_printf("Target frequency is %g\n", target_freq); } + if (target_freq != 0.0 && mpb_verbosity > 0) { + meep::master_printf("Target frequency is %g\n", target_freq); + } int true_rank = n[2] > 1 ? 3 : (n[1] > 1 ? 2 : 1); if (true_rank < dimensions) { dimensions = true_rank; } @@ -728,12 +719,9 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry block_size = (num_bands - block_size - 1) / (-block_size); block_size = (num_bands + block_size - 1) / block_size; } - if (mpb_verbosity > 0) - meep::master_printf("Solving for %d bands at a time.\n", block_size); - } - else { - block_size = num_bands; + if (mpb_verbosity > 0) meep::master_printf("Solving for %d bands at a time.\n", block_size); } + else { block_size = num_bands; } if (mdata) { if (n[0] == mdata->nx && n[1] == mdata->ny && n[2] == mdata->nz && @@ -757,9 +745,7 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry mdata = NULL; curfield_reset(); } - else { - srand(time(NULL)); - } + else { srand(time(NULL)); } if (deterministic) { // seed should be the same for each run, although @@ -769,8 +755,7 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry srand(314159); // * (rank + 1)); } - if (mpb_verbosity > 0) - meep::master_printf("Creating Maxwell data...\n"); + if (mpb_verbosity > 0) meep::master_printf("Creating Maxwell data...\n"); mdata = create_maxwell_data(n[0], n[1], n[2], &local_N, &N_start, &alloc_N, block_size, NUM_FFT_BANDS); @@ -781,8 +766,7 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry if (check_maxwell_dielectric(mdata, 0)) { meep::abort("invalid dielectric function for MPB"); } if (!have_old_fields) { - if (mpb_verbosity > 0) - meep::master_printf("Allocating fields...\n"); + if (mpb_verbosity > 0) meep::master_printf("Allocating fields...\n"); int N = n[0] * n[1] * n[2]; int c = 2; @@ -797,16 +781,12 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry if (block_size < num_bands) { Hblock = create_evectmatrix(N, c, block_size, local_N, N_start, alloc_N); } - else { - Hblock = H; - } + else { Hblock = H; } if (using_mu() && block_size < num_bands) { muinvH = create_evectmatrix(N, c, num_bands, local_N, N_start, alloc_N); } - else { - muinvH = H; - } + else { muinvH = H; } } set_parity(p); @@ -829,8 +809,7 @@ void mode_solver::init_epsilon(geometric_object_list *geometry_in) { mpb_real no_size_y = geometry_lattice.size.y == 0 ? 1 : geometry_lattice.size.y; mpb_real no_size_z = geometry_lattice.size.z == 0 ? 1 : geometry_lattice.size.z; - if (mpb_verbosity > 0) - meep::master_printf("Mesh size is %d.\n", mesh_size); + if (mpb_verbosity > 0) meep::master_printf("Mesh size is %d.\n", mesh_size); Rm.c0 = vector3_scale(no_size_x, geometry_lattice.basis.c0); Rm.c1 = vector3_scale(no_size_y, geometry_lattice.basis.c1); @@ -844,8 +823,7 @@ void mode_solver::init_epsilon(geometric_object_list *geometry_in) { } vol = fabs(matrix3x3_determinant(Rm)); - if (mpb_verbosity > 0) - meep::master_printf("Cell volume = %g\n", vol); + if (mpb_verbosity > 0) meep::master_printf("Cell volume = %g\n", vol); Gm = matrix3x3_inverse(matrix3x3_transpose(Rm)); if (mpb_verbosity > 0) { @@ -860,18 +838,18 @@ void mode_solver::init_epsilon(geometric_object_list *geometry_in) { geom_fix_object_list(geometry_list); - if (mpb_verbosity > 0) - meep::master_printf("Geometric objects:\n"); + if (mpb_verbosity > 0) meep::master_printf("Geometric objects:\n"); if (meep::am_master()) { for (int i = 0; i < geometry_list.num_items; ++i) { #ifndef WITH_HERMITIAN_EPSILON meep_geom::medium_struct *mm; - if (meep_geom::is_medium(geometry_list.items[i].material, &mm)) { mm->check_offdiag_im_zero_or_abort(); } + if (meep_geom::is_medium(geometry_list.items[i].material, &mm)) { + mm->check_offdiag_im_zero_or_abort(); + } #endif - if (mpb_verbosity > 0) - display_geometric_object_info(5, geometry_list.items[i]); + if (mpb_verbosity > 0) display_geometric_object_info(5, geometry_list.items[i]); // meep_geom::medium_struct *mm; // if (meep_geom::is_medium(geometry.items[i].material, &mm)) { @@ -903,8 +881,7 @@ void mode_solver::init_epsilon(geometric_object_list *geometry_in) { } if (verbose && meep::am_master()) { - if (mpb_verbosity > 0) - meep::master_printf("Geometry object bounding box tree:\n"); + if (mpb_verbosity > 0) meep::master_printf("Geometry object bounding box tree:\n"); display_geom_box_tree(5, geometry_tree); } @@ -938,14 +915,12 @@ void mode_solver::reset_epsilon(geometric_object_list *geometry) { // if (!mu_input_file.empty()) { // } - if (mpb_verbosity > 0) - meep::master_printf("Initializing epsilon function...\n"); + if (mpb_verbosity > 0) meep::master_printf("Initializing epsilon function...\n"); set_maxwell_dielectric(mdata, mesh, R, G, dielectric_function, mean_epsilon_func, static_cast(this)); if (has_mu(geometry)) { - if (mpb_verbosity > 0) - meep::master_printf("Initializing mu function...\n"); + if (mpb_verbosity > 0) meep::master_printf("Initializing mu function...\n"); eps = false; set_maxwell_mu(mdata, mesh, R, G, dielectric_function, mean_epsilon_func, static_cast(this)); @@ -1020,8 +995,7 @@ void mode_solver::set_kpoint_index(int i) { kpoint_index = i; } void mode_solver::randomize_fields() { if (!mdata) { return; } - if (mpb_verbosity > 0) - meep::master_printf("Initializing fields to random numbers...\n"); + if (mpb_verbosity > 0) meep::master_printf("Initializing fields to random numbers...\n"); for (int i = 0; i < H.n * H.p; ++i) { ASSIGN_SCALAR(H.data[i], rand() * 1.0 / RAND_MAX, rand() * 1.0 / RAND_MAX); @@ -1039,8 +1013,7 @@ void mode_solver::solve_kpoint(vector3 kvector) { curfield_reset(); if (num_bands == 0) { - if (mpb_verbosity > 0) - meep::master_printf(" num-bands is zero, not solving for any bands\n"); + if (mpb_verbosity > 0) meep::master_printf(" num-bands is zero, not solving for any bands\n"); return; } @@ -1087,9 +1060,7 @@ void mode_solver::solve_kpoint(vector3 kvector) { } evectmatrix_resize(&H, H.p - ib0, 1); } - else { - ib0 = 0; /* solve for all bands */ - } + else { ib0 = 0; /* solve for all bands */ } // Set up deflation data. deflation_data deflation; @@ -1228,11 +1199,9 @@ void mode_solver::solve_kpoint(vector3 kvector) { for (int i = 0; i < num_bands; ++i) { freqs[i] = negative_epsilon_ok ? eigvals[i] : sqrt(eigvals[i]); - if (mpb_verbosity > 0) - meep::master_printf(", %g", freqs[i]); + if (mpb_verbosity > 0) meep::master_printf(", %g", freqs[i]); } - if (mpb_verbosity > 0) - meep::master_printf("\n"); + if (mpb_verbosity > 0) meep::master_printf("\n"); eigensolver_flops = evectmatrix_flops; } @@ -1265,9 +1234,7 @@ void mode_solver::get_epsilon() { for (int i = 0; i < N; ++i) { if (mdata->eps_inv == NULL) { epsilon[i] = 1.0; } - else { - epsilon[i] = mean_medium_from_matrix(mdata->eps_inv + i); - } + else { epsilon[i] = mean_medium_from_matrix(mdata->eps_inv + i); } if (epsilon[i] < eps_low) { eps_low = epsilon[i]; } if (epsilon[i] > eps_high) { eps_high = epsilon[i]; } eps_mean += epsilon[i]; @@ -1286,11 +1253,11 @@ void mode_solver::get_epsilon() { eps_inv_mean = N / eps_inv_mean; if (mpb_verbosity > 0) - meep::master_printf("epsilon: %g-%g, mean %g, harm. mean %g, %g%% > 1, %g%% \"fill\"\n", eps_low, - eps_high, eps_mean, eps_inv_mean, (100.0 * fill_count) / N, + meep::master_printf("epsilon: %g-%g, mean %g, harm. mean %g, %g%% > 1, %g%% \"fill\"\n", + eps_low, eps_high, eps_mean, eps_inv_mean, (100.0 * fill_count) / N, eps_high == eps_low ? 100.0 : 100.0 * (eps_mean - eps_low) / (eps_high - eps_low)); - } +} /* get the mu function, and compute some statistics */ void mode_solver::get_mu() { @@ -1319,9 +1286,7 @@ void mode_solver::get_mu() { for (int i = 0; i < N; ++i) { if (mdata->mu_inv == NULL) { mu[i] = 1.0; } - else { - mu[i] = mean_medium_from_matrix(mdata->mu_inv + i); - } + else { mu[i] = mean_medium_from_matrix(mdata->mu_inv + i); } if (mu[i] < eps_low) { eps_low = mu[i]; } if (mu[i] > eps_high) { eps_high = mu[i]; } @@ -1651,7 +1616,7 @@ double mode_solver::compute_field_energy_internal(mpb_real comp_sum[6]) { void mode_solver::clear_geometry_list() { if (geometry_list.num_items && geometry_list.items) { - for(int i = 0; i < geometry_list.num_items; ++i) { + for (int i = 0; i < geometry_list.num_items; ++i) { material_free((meep_geom::material_data *)geometry_list.items[i].material); geometric_object_destroy(geometry_list.items[i]); } @@ -1681,15 +1646,15 @@ std::vector mode_solver::compute_field_energy() { mpb_real energy_sum = compute_field_energy_internal(comp_sum); if (mpb_verbosity > 0) - meep::master_printf("%c-energy-components:, %d, %d", curfield_type, kpoint_index, curfield_band); + meep::master_printf("%c-energy-components:, %d, %d", curfield_type, kpoint_index, + curfield_band); for (int i = 0; i < 6; ++i) { comp_sum[i] /= (energy_sum == 0 ? 1 : energy_sum); if (i % 2 == 1 && mpb_verbosity > 0) { meep::master_printf(", %g", comp_sum[i] + comp_sum[i - 1]); - } + } } - if (mpb_verbosity > 0) - meep::master_printf("\n"); + if (mpb_verbosity > 0) meep::master_printf("\n"); /* The return value is a list of 7 items: the total energy, followed by the 6 elements of the comp_sum array (the fraction @@ -2250,9 +2215,7 @@ std::vector mode_solver::compute_group_velocity_component(vector3 d) { if (freqs[i] == 0) { /* v is undefined in this case */ group_v[i] = 0.0; /* just set to zero */ } - else { - group_v[i] /= negative_epsilon_ok ? sqrt(fabs(freqs[i])) : freqs[i]; - } + else { group_v[i] /= negative_epsilon_ok ? sqrt(fabs(freqs[i])) : freqs[i]; } } return group_v; @@ -2298,9 +2261,7 @@ mpb_real mode_solver::compute_1_group_velocity_component(vector3 d, int b) { if (freqs[ib] == 0) { /* v is undefined in this case */ group_v = 0.0; /* just set to zero */ } - else { - group_v /= negative_epsilon_ok ? sqrt(fabs(freqs[ib])) : freqs[ib]; - } + else { group_v /= negative_epsilon_ok ? sqrt(fabs(freqs[ib])) : freqs[ib]; } return group_v; } @@ -2702,7 +2663,6 @@ bool with_hermitian_epsilon() { #endif } - /* --- port of mpb's mpb/transform.c --- */ /* If `curfield` is the real-space D-field (of band-index i), computes the overlap @@ -2719,11 +2679,10 @@ bool with_hermitian_epsilon() { * the Bloch included) or the `get_bfield` and `get_dfield` C functions. * Usually, it will be more convenient to use the wrapper `compute_symmetry(i, W, w)` * which defaults to the B-field (since μ = 1 usually) and takes a band-index `i`. */ -cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) -{ +cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) { int n1, n2, n3; mpb_real s1, s2, s3, c1, c2, c3; - cnumber integral = {0,0}, integral_sum; + cnumber integral = {0, 0}, integral_sum; number detW; vector3 kvector = cur_kvector; @@ -2757,7 +2716,9 @@ cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) // #endif /* prepare before looping ... */ - n1 = mdata->nx; n2 = mdata->ny; n3 = mdata->nz; + n1 = mdata->nx; + n2 = mdata->ny; + n3 = mdata->nz; s1 = geometry_lattice.size.x / n1; /* pixel spacings */ s2 = geometry_lattice.size.y / n2; @@ -2781,9 +2742,9 @@ cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) matrix3x3_inverse(geometry_lattice.basis)); /* hoist rescalings outside the loop (maybe licm takes care of it, but do it anyway) */ - kvector.x *= TWOPI/(geometry_lattice.size.x == 0 ? 1e-20 : geometry_lattice.size.x); - kvector.y *= TWOPI/(geometry_lattice.size.y == 0 ? 1e-20 : geometry_lattice.size.y); - kvector.z *= TWOPI/(geometry_lattice.size.z == 0 ? 1e-20 : geometry_lattice.size.z); + kvector.x *= TWOPI / (geometry_lattice.size.x == 0 ? 1e-20 : geometry_lattice.size.x); + kvector.y *= TWOPI / (geometry_lattice.size.y == 0 ? 1e-20 : geometry_lattice.size.y); + kvector.z *= TWOPI / (geometry_lattice.size.z == 0 ? 1e-20 : geometry_lattice.size.z); /* loop over coordinates (introduces int vars `i1`, `i2`, `i3`, `xyz_index`) */ LOOP_XYZ(mdata) { /* implies two opening braces `{{` */ @@ -2805,12 +2766,12 @@ cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) /* define `Ft` as the vector components of `Ftemp` transformed by `Wc`; we just * write out the matrix-product manually here, for both real & imag parts */ - Ft[0].re = Wc.c0.x*Ftemp[0].re + Wc.c1.x*Ftemp[1].re + Wc.c2.x*Ftemp[2].re; - Ft[0].im = Wc.c0.x*Ftemp[0].im + Wc.c1.x*Ftemp[1].im + Wc.c2.x*Ftemp[2].im; - Ft[1].re = Wc.c0.y*Ftemp[0].re + Wc.c1.y*Ftemp[1].re + Wc.c2.y*Ftemp[2].re; - Ft[1].im = Wc.c0.y*Ftemp[0].im + Wc.c1.y*Ftemp[1].im + Wc.c2.y*Ftemp[2].im; - Ft[2].re = Wc.c0.z*Ftemp[0].re + Wc.c1.z*Ftemp[1].re + Wc.c2.z*Ftemp[2].re; - Ft[2].im = Wc.c0.z*Ftemp[0].im + Wc.c1.z*Ftemp[1].im + Wc.c2.z*Ftemp[2].im; + Ft[0].re = Wc.c0.x * Ftemp[0].re + Wc.c1.x * Ftemp[1].re + Wc.c2.x * Ftemp[2].re; + Ft[0].im = Wc.c0.x * Ftemp[0].im + Wc.c1.x * Ftemp[1].im + Wc.c2.x * Ftemp[2].im; + Ft[1].re = Wc.c0.y * Ftemp[0].re + Wc.c1.y * Ftemp[1].re + Wc.c2.y * Ftemp[2].re; + Ft[1].im = Wc.c0.y * Ftemp[0].im + Wc.c1.y * Ftemp[1].im + Wc.c2.y * Ftemp[2].im; + Ft[2].re = Wc.c0.z * Ftemp[0].re + Wc.c1.z * Ftemp[1].re + Wc.c2.z * Ftemp[2].re; + Ft[2].im = Wc.c0.z * Ftemp[0].im + Wc.c1.z * Ftemp[1].im + Wc.c2.z * Ftemp[2].im; /* get the Bloch field value at current point `p` (without eⁱᵏʳ factor). * We multiply the input field `F` (either B or D-field) with μ⁻¹ or ε⁻¹ to get @@ -2818,44 +2779,45 @@ cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) * t-postscript denoting a field transformed by {W|w}. Here, we essentially * adapt some boiler-plate code from compute_field_energy_internal in fields.c */ if (curfield_type == 'd') { - assign_symmatrix_vector(F, mdata->eps_inv[xyz_index], curfield+3*xyz_index); + assign_symmatrix_vector(F, mdata->eps_inv[xyz_index], curfield + 3 * xyz_index); } else if (curfield_type == 'b' && mdata->mu_inv != NULL) { - assign_symmatrix_vector(F, mdata->mu_inv[xyz_index], curfield+3*xyz_index); + assign_symmatrix_vector(F, mdata->mu_inv[xyz_index], curfield + 3 * xyz_index); } else { - F[0] = curfield[3*xyz_index]; - F[1] = curfield[3*xyz_index+1]; - F[2] = curfield[3*xyz_index+2]; + F[0] = curfield[3 * xyz_index]; + F[1] = curfield[3 * xyz_index + 1]; + F[2] = curfield[3 * xyz_index + 2]; } /* inner product of F and Ft={W|w}F in Bloch form */ - CASSIGN_CONJ_MULT(integrand, F[0], Ft[0]); /* add adjoint(F)*Ft to integrand */ + CASSIGN_CONJ_MULT(integrand, F[0], Ft[0]); /* add adjoint(F)*Ft to integrand */ CACCUMULATE_SUM_CONJ_MULT(integrand, F[1], Ft[1]); CACCUMULATE_SUM_CONJ_MULT(integrand, F[2], Ft[2]); /* include Bloch phases */ - deltaphi = kvector.x*(pt.x-p.x) + kvector.y*(pt.y-p.y) + kvector.z*(pt.z-p.z); + deltaphi = kvector.x * (pt.x - p.x) + kvector.y * (pt.y - p.y) + kvector.z * (pt.z - p.z); CASSIGN_SCALAR(phase, cos(deltaphi), sin(deltaphi)); /* add integrand-contribution to integral */ integral.re += CSCALAR_MULT_RE(integrand, phase); integral.im += CSCALAR_MULT_IM(integrand, phase); - }}} - - integral.re *= vol / H.N; - integral.im *= vol / H.N; + } +} +} - mpi_allreduce(&integral, &integral_sum, 2, number, MPI_DOUBLE, MPI_SUM, mpb_comm); +integral.re *= vol / H.N; +integral.im *= vol / H.N; - if (curfield_type == 'b') { /* H & B are pseudovectors => transform includes det(W) */ - integral_sum.re *= detW; - integral_sum.im *= detW; - } +mpi_allreduce(&integral, &integral_sum, 2, number, MPI_DOUBLE, MPI_SUM, mpb_comm); - return integral_sum; +if (curfield_type == 'b') { /* H & B are pseudovectors => transform includes det(W) */ + integral_sum.re *= detW; + integral_sum.im *= detW; } +return integral_sum; +} cnumber mode_solver::compute_symmetry(int which_band, matrix3x3 W, vector3 w) { cnumber symval; diff --git a/libpympb/pympb.hpp b/libpympb/pympb.hpp index 3b5da8cfb..2222015b3 100644 --- a/libpympb/pympb.hpp +++ b/libpympb/pympb.hpp @@ -19,7 +19,6 @@ namespace py_mpb { // This is redeclared here so SWIG will see it. extern "C" int mpb_verbosity; - #define TWOPI 6.2831853071795864769252867665590057683943388 void map_data(mpb_real *d_in_re, int size_in_re, mpb_real *d_in_im, int size_in_im, int n_in[3], diff --git a/python/meep-python.hpp b/python/meep-python.hpp index 0e6c6e278..ab8b952af 100644 --- a/python/meep-python.hpp +++ b/python/meep-python.hpp @@ -1,7 +1,7 @@ namespace meep { #ifndef SWIG_PYTHON_THREAD_SCOPED_BLOCK -#define SWIG_PYTHON_THREAD_SCOPED_BLOCK SWIG_PYTHON_THREAD_BEGIN_BLOCK +#define SWIG_PYTHON_THREAD_SCOPED_BLOCK SWIG_PYTHON_THREAD_BEGIN_BLOCK #endif // like custom_src_time, but using Python function object, with proper reference counting diff --git a/python/simulation.py b/python/simulation.py index accc0af3a..0a4453c0a 100644 --- a/python/simulation.py +++ b/python/simulation.py @@ -13,7 +13,7 @@ try: from collections.abc import Sequence except ImportError: - from collections import Sequence + from collections.abc import Sequence import meep.visualization as vis import numpy as np diff --git a/python/typemap_utils.cpp b/python/typemap_utils.cpp index 3c1fa1b66..ab8c8a294 100644 --- a/python/typemap_utils.cpp +++ b/python/typemap_utils.cpp @@ -32,7 +32,7 @@ #endif #ifndef SWIG_PYTHON_THREAD_SCOPED_BLOCK -#define SWIG_PYTHON_THREAD_SCOPED_BLOCK SWIG_PYTHON_THREAD_BEGIN_BLOCK +#define SWIG_PYTHON_THREAD_SCOPED_BLOCK SWIG_PYTHON_THREAD_BEGIN_BLOCK #endif PyObject *py_callback = NULL; @@ -57,9 +57,7 @@ static char *py2_string_as_utf8(PyObject *po) { Py_DECREF(s); return result; } - else { - return NULL; - } + else { return NULL; } } #endif @@ -180,8 +178,7 @@ static PyObject *vec2py(const meep::vec &v, bool newobj = false) { Py_INCREF(py_callback_v3); return py_callback_v3; - } - ; + }; } static void py_user_material_func_wrap(vector3 x, void *user_data, medium_struct *medium) { @@ -463,9 +460,7 @@ static int py_susceptibility_to_susceptibility(PyObject *po, susceptibility_stru std::string class_name = py_class_name_as_string(po); if (class_name.find(std::string("Drude")) != std::string::npos) { s->drude = true; } - else { - s->drude = false; - } + else { s->drude = false; } s->is_file = false; @@ -495,7 +490,7 @@ static int pymaterial_grid_to_material_grid(PyObject *po, material_data *md) { int rval = 1; // specify the type of material grid - PyObject * type = PyObject_GetAttrString(po, "grid_type"); + PyObject *type = PyObject_GetAttrString(po, "grid_type"); long gt_enum = PyInt_AsLong(type); Py_DECREF(type); @@ -504,7 +499,7 @@ static int pymaterial_grid_to_material_grid(PyObject *po, material_data *md) { case 1: md->material_grid_kinds = material_data::U_PROD; break; case 2: md->material_grid_kinds = material_data::U_MEAN; break; case 3: md->material_grid_kinds = material_data::U_DEFAULT; break; - default: meep::abort("Invalid material grid enumeration code: %d.\n", (int) gt_enum); + default: meep::abort("Invalid material grid enumeration code: %d.\n", (int)gt_enum); } // initialize grid size @@ -526,9 +521,7 @@ static int pymaterial_grid_to_material_grid(PyObject *po, material_data *md) { PyObject *po_dp = PyObject_GetAttrString(po, "weights"); PyArrayObject *pao = (PyArrayObject *)po_dp; if (!PyArray_Check(pao)) { meep::abort("MaterialGrid weights failed to init."); } - if (!PyArray_ISCARRAY(pao)) { - meep::abort("Numpy array weights must be C-style contiguous."); - } + if (!PyArray_ISCARRAY(pao)) { meep::abort("Numpy array weights must be C-style contiguous."); } md->weights = new double[PyArray_SIZE(pao)]; memcpy(md->weights, (double *)PyArray_DATA(pao), PyArray_SIZE(pao) * sizeof(double)); @@ -584,7 +577,7 @@ static int pymaterial_to_material(PyObject *po, material_type *mt) { PyObject *py_damping = PyObject_GetAttrString(po, "damping"); double damping = 0; if (py_damping) { damping = PyFloat_AsDouble(py_damping); } - md = make_material_grid(do_averaging,beta,eta,damping); + md = make_material_grid(do_averaging, beta, eta, damping); if (!pymaterial_grid_to_material_grid(po, md)) { return 0; } Py_XDECREF(py_do_averaging); Py_XDECREF(py_beta); @@ -597,10 +590,10 @@ static int pymaterial_to_material(PyObject *po, material_type *mt) { PyErr_Clear(); // clear errors from attributes not present bool do_averaging = false; if (py_do_averaging) { do_averaging = PyObject_IsTrue(py_do_averaging); } - if (eps && PyObject_IsTrue(eps)) { md = make_user_material(py_epsilon_func_wrap, po, do_averaging); } - else { - md = make_user_material(py_user_material_func_wrap, po, do_averaging); + if (eps && PyObject_IsTrue(eps)) { + md = make_user_material(py_epsilon_func_wrap, po, do_averaging); } + else { md = make_user_material(py_user_material_func_wrap, po, do_averaging); } Py_XDECREF(eps); Py_XDECREF(py_do_averaging); } @@ -625,9 +618,7 @@ static int pymaterial_to_material(PyObject *po, material_type *mt) { master_printf("read in %zdx%zdx%zd numpy array for epsilon\n", md->epsilon_dims[0], md->epsilon_dims[1], md->epsilon_dims[2]); } - else { - meep::abort("Expected a Medium, a Material Grid, a function, or a filename"); - } + else { meep::abort("Expected a Medium, a Material Grid, a function, or a filename"); } *mt = md; @@ -775,15 +766,14 @@ static PyObject *material_to_py_material(material_type mat) { return py_mat; } case meep_geom::material_data::MATERIAL_USER: { - PyObject *py_mat = (PyObject *) mat->user_data; + PyObject *py_mat = (PyObject *)mat->user_data; Py_INCREF(py_mat); return py_mat; } default: // Only Medium is supported at this time. meep::abort("Can only convert C++ medium_struct subtype %d to Python", mat->which_subclass); - } - ; + }; } static int pymedium_to_medium(PyObject *po, medium_struct *m) { @@ -1015,24 +1005,12 @@ static int py_gobj_to_gobj(PyObject *po, geometric_object *o) { std::string go_type = py_class_name_as_string(po); if (go_type == "Sphere") { success = pysphere_to_sphere(po, o); } - else if (go_type == "Cylinder") { - success = pycylinder_to_cylinder(po, o); - } - else if (go_type == "Wedge") { - success = pywedge_to_wedge(po, o); - } - else if (go_type == "Cone") { - success = pycone_to_cone(po, o); - } - else if (go_type == "Block") { - success = pyblock_to_block(po, o); - } - else if (go_type == "Ellipsoid") { - success = pyellipsoid_to_ellipsoid(po, o); - } - else if (go_type == "Prism") { - success = pyprism_to_prism(po, o); - } + else if (go_type == "Cylinder") { success = pycylinder_to_cylinder(po, o); } + else if (go_type == "Wedge") { success = pywedge_to_wedge(po, o); } + else if (go_type == "Cone") { success = pycone_to_cone(po, o); } + else if (go_type == "Block") { success = pyblock_to_block(po, o); } + else if (go_type == "Ellipsoid") { success = pyellipsoid_to_ellipsoid(po, o); } + else if (go_type == "Prism") { success = pyprism_to_prism(po, o); } else { meep::abort("Error: %s is not a valid GeometricObject type\n", go_type.c_str()); return 0; @@ -1103,8 +1081,7 @@ static PyObject *gobj_to_py_obj(geometric_object *gobj) { // We currently only have the need to create python Prisms from C++. // Other geometry can be added as needed. meep::abort("Conversion of non-prism geometric_object to Python is not supported"); - } - ; + }; } static PyObject *gobj_list_to_py_list(geometric_object_list *objs) { @@ -1123,13 +1100,13 @@ static PyObject *gobj_list_to_py_list(geometric_object_list *objs) { ; } -void gobj_list_freearg(geometric_object_list* objs) { +void gobj_list_freearg(geometric_object_list *objs) { SWIG_PYTHON_THREAD_SCOPED_BLOCK; - for(int i = 0; i < objs->num_items; ++i) { - material_free((material_data *)objs->items[i].material); - geometric_object_destroy(objs->items[i]); - } - delete[] objs->items; + for (int i = 0; i < objs->num_items; ++i) { + material_free((material_data *)objs->items[i].material); + geometric_object_destroy(objs->items[i]); + } + delete[] objs->items; ; } @@ -1152,8 +1129,7 @@ static std::unique_ptr py_bp_to_bp(PyObject *pybp) { else { bp.reset(new meep::binary_partition( meep::split_plane{direction(PyLong_AsLong(split_dir)), PyFloat_AsDouble(split_pos)}, - py_bp_to_bp(left), - py_bp_to_bp(right))); + py_bp_to_bp(left), py_bp_to_bp(right))); } Py_XDECREF(id); @@ -1169,9 +1145,7 @@ static PyObject *py_binary_partition_object() { SWIG_PYTHON_THREAD_SCOPED_BLOCK; // Return value: Borrowed reference static PyObject *bp_type = NULL; - if (bp_type == NULL) { - bp_type = PyObject_GetAttrString(get_meep_mod(), "BinaryPartition"); - } + if (bp_type == NULL) { bp_type = PyObject_GetAttrString(get_meep_mod(), "BinaryPartition"); } return bp_type; ; } @@ -1180,7 +1154,7 @@ static PyObject *py_binary_partition_object() { static PyObject *bp_to_py_bp(const meep::binary_partition *bp) { SWIG_PYTHON_THREAD_SCOPED_BLOCK; PyObject *bp_class = py_binary_partition_object(); - PyObject *args = PyTuple_New(0); // no numbered arguments to pass + PyObject *args = PyTuple_New(0); // no numbered arguments to pass if (bp->is_leaf()) { // leaf nodes will have proc_id and no other properties int proc_id = bp->get_proc_id(); @@ -1189,19 +1163,18 @@ static PyObject *bp_to_py_bp(const meep::binary_partition *bp) { Py_DECREF(args); Py_DECREF(kwargs); return py_bp; - } else { + } + else { // other nodes will have left, right, split_dir, split_pos PyObject *left = bp_to_py_bp(bp->left_tree()); PyObject *right = bp_to_py_bp(bp->right_tree()); meep::direction split_dir = bp->get_plane().dir; double split_pos = bp->get_plane().pos; - PyObject *kwargs = - Py_BuildValue("{s:O,s:O,s:i,s:d}", "left", left, "right", right, - "split_dir", split_dir, "split_pos", split_pos); + PyObject *kwargs = Py_BuildValue("{s:O,s:O,s:i,s:d}", "left", left, "right", right, "split_dir", + split_dir, "split_pos", split_pos); PyObject *py_bp = PyObject_Call(bp_class, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); return py_bp; - } - ; + }; } diff --git a/src/GDSIIgeom.cpp b/src/GDSIIgeom.cpp index 172965c14..180d95e03 100644 --- a/src/GDSIIgeom.cpp +++ b/src/GDSIIgeom.cpp @@ -152,8 +152,7 @@ geometric_object_list get_GDSII_prisms(material_type material, const char *GDSII } double height = zmax - zmin; vector3 zaxis = {0, 0, 1}; - prisms.items[np] = - make_prism(material, vertices.get(), num_vertices, height, zaxis); + prisms.items[np] = make_prism(material, vertices.get(), num_vertices, height, zaxis); } return prisms; } diff --git a/src/adjust_verbosity.hpp b/src/adjust_verbosity.hpp index 3ca7df896..f4014b367 100644 --- a/src/adjust_verbosity.hpp +++ b/src/adjust_verbosity.hpp @@ -27,27 +27,29 @@ namespace meep { class adjust_mpb_verbosity { public: adjust_mpb_verbosity() { - #if defined(HAVE_MPB) && (MPB_VERSION_MAJOR > 1 || (MPB_VERSION_MAJOR == 1 && MPB_VERSION_MINOR >= 11)) - old_level = mpb_verbosity; - mpb_verbosity = verbosity - 1; - if (mpb_verbosity < 0) mpb_verbosity = 0; - if (mpb_verbosity > 3) mpb_verbosity = 3; - #else - // avoid warnings - (void)old_level; - #endif +#if defined(HAVE_MPB) && \ + (MPB_VERSION_MAJOR > 1 || (MPB_VERSION_MAJOR == 1 && MPB_VERSION_MINOR >= 11)) + old_level = mpb_verbosity; + mpb_verbosity = verbosity - 1; + if (mpb_verbosity < 0) mpb_verbosity = 0; + if (mpb_verbosity > 3) mpb_verbosity = 3; +#else + // avoid warnings + (void)old_level; +#endif } ~adjust_mpb_verbosity() { - #if defined(HAVE_MPB) && (MPB_VERSION_MAJOR > 1 || (MPB_VERSION_MAJOR == 1 && MPB_VERSION_MINOR >= 11)) - mpb_verbosity = old_level; - #endif +#if defined(HAVE_MPB) && \ + (MPB_VERSION_MAJOR > 1 || (MPB_VERSION_MAJOR == 1 && MPB_VERSION_MINOR >= 11)) + mpb_verbosity = old_level; +#endif } private: int old_level; }; -} +} // namespace meep -#endif // ADJUST_VERBOSITY_H +#endif // ADJUST_VERBOSITY_H diff --git a/src/array_slice.cpp b/src/array_slice.cpp index e131cdd75..fe053011f 100644 --- a/src/array_slice.cpp +++ b/src/array_slice.cpp @@ -76,9 +76,7 @@ std::complex cdouble(std::complex z) { return std::complex(real(z), imag(z)); } -std::complex cdouble(std::complex z) { - return z; -} +std::complex cdouble(std::complex z) { return z; } typedef struct { @@ -127,7 +125,8 @@ typedef struct { } array_slice_data; /* passthrough field function equivalent to component_fun in h5fields.cpp */ -static complex default_field_func(const complex *fields, const vec &loc, void *data_) { +static complex default_field_func(const complex *fields, const vec &loc, + void *data_) { (void)loc; // unused (void)data_; // unused return cdouble(fields[0]); @@ -523,8 +522,11 @@ bool increment(size_t *n, size_t *nMax, int rank) { return true; } -// get the dimensions of reduced arrays (i.e., arrays collapsed along empty dimensions of original volume) -void reduce_array_dimensions(volume where, int rank, size_t dims[3], direction dirs[3], size_t stride[3], int &reduced_rank, size_t reduced_dims[3], direction reduced_dirs[3], size_t reduced_stride[3]) { +// get the dimensions of reduced arrays (i.e., arrays collapsed along empty dimensions of original +// volume) +void reduce_array_dimensions(volume where, int rank, size_t dims[3], direction dirs[3], + size_t stride[3], int &reduced_rank, size_t reduced_dims[3], + direction reduced_dirs[3], size_t reduced_stride[3]) { reduced_rank = 0; reduced_dims[0] = reduced_dims[1] = reduced_dims[2] = stride[0] = stride[1] = stride[2] = @@ -534,7 +536,8 @@ void reduce_array_dimensions(volume where, int rank, size_t dims[3], direction d reduced_stride[r] = 0; // degenerate dimension, to be collapsed else { reduced_dirs[reduced_rank] = dirs[r]; - reduced_dims[reduced_rank++] = dims[r]; // reduced_dims is the size of the array after collapsing + reduced_dims[reduced_rank++] = + dims[r]; // reduced_dims is the size of the array after collapsing } } /*--------------------------------------------------------------*/ @@ -545,8 +548,7 @@ void reduce_array_dimensions(volume where, int rank, size_t dims[3], direction d else if (rank == 3) { stride[0] = dims[1] * dims[2]; stride[1] = dims[2]; - if (reduced_rank == 2) - reduced_stride[reduced_stride[0] != 0 ? 0 : 1] = reduced_dims[1]; + if (reduced_rank == 2) reduced_stride[reduced_stride[0] != 0 ? 0 : 1] = reduced_dims[1]; } } @@ -563,10 +565,11 @@ realnum *collapse_array(realnum *array, int *rank, size_t dims[3], direction dir direction reduced_dirs[3]; size_t reduced_grid_size; int reduced_rank; - reduce_array_dimensions(where, full_rank, dims, dirs, stride, reduced_rank, reduced_dims, reduced_dirs, reduced_stride); + reduce_array_dimensions(where, full_rank, dims, dirs, stride, reduced_rank, reduced_dims, + reduced_dirs, reduced_stride); if (full_rank == 0) return array; - if (reduced_rank==0) { + if (reduced_rank == 0) { *rank = 0; return array; // return array as is for singleton use case } @@ -575,10 +578,11 @@ realnum *collapse_array(realnum *array, int *rank, size_t dims[3], direction dir /*--------------------------------------------------------------*/ /*- allocate reduced array and compress full array into it -*/ /*--------------------------------------------------------------*/ - reduced_grid_size = reduced_dims[0]*reduced_dims[1]*reduced_dims[2]; + reduced_grid_size = reduced_dims[0] * reduced_dims[1] * reduced_dims[2]; size_t reduced_array_size = data_size * reduced_grid_size; realnum *reduced_array = new realnum[reduced_array_size]; - if (!reduced_array) meep::abort("%s:%i: out of memory (%zu)", __FILE__, __LINE__, reduced_array_size); + if (!reduced_array) + meep::abort("%s:%i: out of memory (%zu)", __FILE__, __LINE__, reduced_array_size); memset(reduced_array, 0, reduced_array_size * sizeof(realnum)); size_t n[3] = {0, 0, 0}; @@ -598,8 +602,8 @@ realnum *collapse_array(realnum *array, int *rank, size_t dims[3], direction dir return reduced_array; } -complex *collapse_array(complex *array, int *rank, size_t dims[3], direction dirs[3], - volume where) { +complex *collapse_array(complex *array, int *rank, size_t dims[3], + direction dirs[3], volume where) { return (complex *)collapse_array((realnum *)array, rank, dims, dirs, where, 2); } @@ -624,7 +628,8 @@ void *fields::do_get_array_slice(const volume &where, std::vector com int elem_size = complex_data ? 2 : 1; void *vslice_uncollapsed; - vslice_uncollapsed = memset(new realnum[slice_size * elem_size], 0, slice_size * elem_size * sizeof(realnum)); + vslice_uncollapsed = + memset(new realnum[slice_size * elem_size], 0, slice_size * elem_size * sizeof(realnum)); data.vslice = vslice_uncollapsed; data.snap = snap; @@ -676,14 +681,15 @@ void *fields::do_get_array_slice(const volume &where, std::vector com loop_in_chunks(get_array_slice_chunkloop, (void *)&data, where, Centered, true, snap); if (!snap) { - realnum *slice = collapse_array((realnum *)vslice_uncollapsed, &rank, dims, dirs, where, elem_size); + realnum *slice = + collapse_array((realnum *)vslice_uncollapsed, &rank, dims, dirs, where, elem_size); rank = get_array_slice_dimensions(where, dims, dirs, true, false, 0, &data); slice_size = data.slice_size; - vslice_uncollapsed = (realnum*) slice; + vslice_uncollapsed = (realnum *)slice; } if (vslice) { memcpy(vslice, vslice_uncollapsed, slice_size * elem_size * sizeof(realnum)); - delete[] (realnum*) vslice_uncollapsed; + delete[](realnum *) vslice_uncollapsed; } else vslice = vslice_uncollapsed; @@ -709,9 +715,11 @@ realnum *fields::get_array_slice(const volume &where, std::vector com frequency, snap); } -complex *fields::get_complex_array_slice(const volume &where, std::vector components, - field_function fun, void *fun_data, complex *slice, - double frequency, bool snap) { +complex *fields::get_complex_array_slice(const volume &where, + std::vector components, + field_function fun, void *fun_data, + complex *slice, double frequency, + bool snap) { return (complex *)do_get_array_slice(where, components, fun, 0, fun_data, (void *)slice, frequency, snap); } @@ -730,16 +738,17 @@ realnum *fields::get_array_slice(const volume &where, derived_component c, realn component carray[12]; field_rfunction rfun = derived_component_func(c, gv, nfields, carray); std::vector cs(carray, carray + nfields); - return (realnum *)do_get_array_slice(where, cs, 0, rfun, &nfields, (void *)slice, - frequency, snap); + return (realnum *)do_get_array_slice(where, cs, 0, rfun, &nfields, (void *)slice, frequency, + snap); } -complex *fields::get_complex_array_slice(const volume &where, component c, complex *slice, - double frequency, bool snap) { +complex *fields::get_complex_array_slice(const volume &where, component c, + complex *slice, double frequency, + bool snap) { std::vector components(1); components[0] = c; - return (complex *)do_get_array_slice(where, components, default_field_func, 0, 0, (void *)slice, - frequency, snap); + return (complex *)do_get_array_slice(where, components, default_field_func, 0, 0, + (void *)slice, frequency, snap); } complex *fields::get_source_slice(const volume &where, component source_slice_component, @@ -765,7 +774,7 @@ complex *fields::get_source_slice(const volume &where, component source if (slice) { memcpy(slice, slice_collapsed, 2 * slice_size * sizeof(realnum)); - delete[] (complex*) slice_collapsed; + delete[](complex *) slice_collapsed; } else slice = slice_collapsed; diff --git a/src/boundaries.cpp b/src/boundaries.cpp index dc136c2f1..be4917090 100644 --- a/src/boundaries.cpp +++ b/src/boundaries.cpp @@ -68,13 +68,13 @@ comms_sequence optimize_comms_operations(const std::vector &ope // Assemble send operations. for (const auto &size_pair : send_op_sizes) { int my_chunk_idx = size_pair.first; - const auto& ops_vector = send_ops_by_my_chunk_idx[my_chunk_idx]; + const auto &ops_vector = send_ops_by_my_chunk_idx[my_chunk_idx]; ret.send_ops.insert(std::end(ret.send_ops), std::begin(ops_vector), std::end(ops_vector)); } return ret; } -} // namespace +} // namespace void fields::set_boundary(boundary_side b, direction d, boundary_condition cond) { if (boundaries[b][d] != cond) { @@ -120,9 +120,9 @@ ivec fields::ilattice_vector(direction d) const { switch (d) { case X: return ivec(user_volume.nx() * 2, 0); case Y: return ivec(0, user_volume.ny() * 2); - case Z: // fall-thru - case R: // fall-thru - case P: // fall-thru + case Z: // fall-thru + case R: // fall-thru + case P: // fall-thru case NO_DIRECTION: break; } case D3: @@ -130,8 +130,8 @@ ivec fields::ilattice_vector(direction d) const { case X: return ivec(user_volume.nx() * 2, 0, 0); case Y: return ivec(0, user_volume.ny() * 2, 0); case Z: return ivec(0, 0, user_volume.nz() * 2); - case R: // fall-thru - case P: // fall-thru + case R: // fall-thru + case P: // fall-thru case NO_DIRECTION: break; } } diff --git a/src/casimir.cpp b/src/casimir.cpp index 10756d595..ebc42cf56 100644 --- a/src/casimir.cpp +++ b/src/casimir.cpp @@ -214,8 +214,10 @@ complex fields::casimir_stress_dct_integral(direction dforce, direction if (where.dim != gv.dim) meep::abort("invalid dimesionality in casimir_stress_dct_integral"); if (coordinate_mismatch(gv.dim, dforce) || coordinate_mismatch(gv.dim, dsource)) meep::abort("invalid directions in casimir_stress_dct_integral"); - if (dnormal == NO_DIRECTION) meep::abort("invalid integration surface in casimir_stress_dct_integral"); - if (ft != E_stuff && ft != H_stuff) meep::abort("invalid field type in casimir_stress_dct_integral"); + if (dnormal == NO_DIRECTION) + meep::abort("invalid integration surface in casimir_stress_dct_integral"); + if (ft != E_stuff && ft != H_stuff) + meep::abort("invalid field type in casimir_stress_dct_integral"); if (dforce != dnormal && dsource != dnormal) return 0.0; diff --git a/src/dft.cpp b/src/dft.cpp index a12953dd2..802687df9 100644 --- a/src/dft.cpp +++ b/src/dft.cpp @@ -81,11 +81,11 @@ dft_chunk::dft_chunk(fields_chunk *fc_, ivec is_, ivec ie_, vec s0_, vec s1_, ve by 1 pixel in each dimension, while ensuring we don't step outside of the chunk loop itself */ - if(persist){ + if (persist) { is_old = is_; ie_old = ie_; - is = max(is-one_ivec(fc->gv.dim)*2,fc->gv.little_corner()); - ie = min(ie+one_ivec(fc->gv.dim)*2,fc->gv.big_corner()); + is = max(is - one_ivec(fc->gv.dim) * 2, fc->gv.little_corner()); + ie = min(ie + one_ivec(fc->gv.dim) * 2, fc->gv.big_corner()); } if (use_centered_grid) @@ -174,8 +174,8 @@ static void add_dft_chunkloop(fields_chunk *fc, int ichunk, component cgrid, ive dft_chunk *fields::add_dft(component c, const volume &where, const double *freq, size_t Nfreq, bool include_dV_and_interp_weights, complex stored_weight, dft_chunk *chunk_next, bool sqrt_dV_and_interp_weights, - complex extra_weight, bool use_centered_grid, - int vc, int decimation_factor, bool persist) { + complex extra_weight, bool use_centered_grid, int vc, + int decimation_factor, bool persist) { if (coordinate_mismatch(gv.dim, c)) return NULL; /* If you call add_dft before adding sources, it will do nothing @@ -185,7 +185,7 @@ dft_chunk *fields::add_dft(component c, const volume &where, const double *freq, meep::abort("allocate field components (by adding sources) before adding dft objects"); if (!include_dV_and_interp_weights && sqrt_dV_and_interp_weights) meep::abort("include_dV_and_interp_weights must be true for sqrt_dV_and_interp_weights=true in " - "add_dft"); + "add_dft"); dft_chunk_data data; data.persist = persist; @@ -198,13 +198,14 @@ dft_chunk *fields::add_dft(component c, const volume &where, const double *freq, if (s->get_fwidth() == 0) decimation_factor = 1; else - src_freq_max = std::max(src_freq_max, std::abs(s->frequency().real())+0.5*s->get_fwidth()); + src_freq_max = + std::max(src_freq_max, std::abs(s->frequency().real()) + 0.5 * s->get_fwidth()); } double freq_max = 0; for (size_t i = 0; i < Nfreq; ++i) freq_max = std::max(freq_max, std::abs(freq[i])); if ((freq_max > 0) && (src_freq_max > 0)) - decimation_factor = std::max(1, int(std::floor(1/(dt*(freq_max + src_freq_max))))); + decimation_factor = std::max(1, int(std::floor(1 / (dt * (freq_max + src_freq_max))))); else decimation_factor = 1; } @@ -294,7 +295,8 @@ void dft_chunk::update_dft(double time) { else { realnum fr = f[0]; for (int i = 0; i < Nomega; ++i) - dft[Nomega * idx_dft + i] += std::complex{fr * dft_phase[i].real(), fr * dft_phase[i].imag()}; + dft[Nomega * idx_dft + i] += + std::complex{fr * dft_phase[i].real(), fr * dft_phase[i].imag()}; } } } @@ -318,7 +320,7 @@ double fields_chunk::dft_norm2(grid_volume fgv) const { return sum; } -static double sqr(std::complex x) { return (x*std::conj(x)).real(); } +static double sqr(std::complex x) { return (x * std::conj(x)).real(); } double dft_chunk::norm2(grid_volume fgv) const { if (!fc->f[c][0]) return 0.0; @@ -331,7 +333,7 @@ double dft_chunk::norm2(grid_volume fgv) const { and can replicate results with different chunk combinations. */ if (persist) { - grid_volume subgv = fgv.subvolume(is,ie,c); + grid_volume subgv = fgv.subvolume(is, ie, c); LOOP_OVER_IVECS(subgv, is_old, ie_old, idx) { for (size_t i = 0; i < Nomega; ++i) sum += sqr(dft[Nomega * idx + i]); @@ -342,10 +344,10 @@ double dft_chunk::norm2(grid_volume fgv) const { called a lot, so let's try to stay efficient (at the expense of uglier code). */ - else{ + else { LOOP_OVER_IVECS(fgv, is, ie, idx) { - idx_dft = IVEC_LOOP_COUNTER; - for (size_t i = 0; i < Nomega; ++i) + idx_dft = IVEC_LOOP_COUNTER; + for (size_t i = 0; i < Nomega; ++i) sum += sqr(dft[Nomega * idx_dft + i]); } } @@ -358,8 +360,7 @@ double dft_chunk::norm2(grid_volume fgv) const { int fields::max_decimation() const { int maxdec = 1; for (int i = 0; i < num_chunks; i++) - if (chunks[i]->is_mine()) - maxdec = std::max(maxdec, chunks[i]->max_decimation()); + if (chunks[i]->is_mine()) maxdec = std::max(maxdec, chunks[i]->max_decimation()); return max_to_all(maxdec); } @@ -374,8 +375,7 @@ int fields_chunk::max_decimation() const { double fields::dft_maxfreq() const { double maxfreq = 0; for (int i = 0; i < num_chunks; i++) - if (chunks[i]->is_mine()) - maxfreq = std::max(maxfreq, chunks[i]->dft_maxfreq()); + if (chunks[i]->is_mine()) maxfreq = std::max(maxfreq, chunks[i]->dft_maxfreq()); return max_to_all(maxfreq); } @@ -383,12 +383,12 @@ double fields_chunk::dft_maxfreq() const { double maxomega = 0; for (dft_chunk *cur = dft_chunks; cur; cur = cur->next_in_chunk) maxomega = std::max(maxomega, cur->maxomega()); - return maxomega / (2*meep::pi); + return maxomega / (2 * meep::pi); } double dft_chunk::maxomega() const { double maxomega = 0; - for (const auto& o : omega) + for (const auto &o : omega) maxomega = std::max(maxomega, std::abs(o)); return maxomega; } @@ -414,7 +414,7 @@ void dft_chunk::operator-=(const dft_chunk &chunk) { size_t my_dft_chunks_Ntotal(dft_chunk *dft_chunks, size_t *my_start) { // When writing to a sharded file, we write out only the chunks we own. -size_t n = 0; + size_t n = 0; for (dft_chunk *cur = dft_chunks; cur; cur = cur->next_in_dft) n += cur->N * cur->omega.size() * 2; @@ -431,12 +431,13 @@ size_t dft_chunks_Ntotal(dft_chunk *dft_chunks, size_t *my_start) { } size_t dft_chunks_Ntotal(dft_chunk *dft_chunks, size_t *my_start, bool single_parallel_file) { - return single_parallel_file ? dft_chunks_Ntotal(dft_chunks, my_start) : - my_dft_chunks_Ntotal(dft_chunks, my_start); + return single_parallel_file ? dft_chunks_Ntotal(dft_chunks, my_start) + : my_dft_chunks_Ntotal(dft_chunks, my_start); } // Note: the file must have been created in parallel mode, typically via fields::open_h5file. -void save_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix, bool single_parallel_file) { +void save_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix, + bool single_parallel_file) { size_t istart; size_t n = dft_chunks_Ntotal(dft_chunks, &istart, single_parallel_file); @@ -455,11 +456,13 @@ void save_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const file->done_writing_chunks(); } -void save_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix, bool single_parallel_file) { +void save_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix, + bool single_parallel_file) { save_dft_hdf5(dft_chunks, component_name(c), file, dprefix, single_parallel_file); } -void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix, bool single_parallel_file) { +void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix, + bool single_parallel_file) { size_t istart; size_t n = dft_chunks_Ntotal(dft_chunks, &istart, single_parallel_file); @@ -473,7 +476,7 @@ void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const file->read_size(dataname, &file_rank, &file_dims, 1); if (file_rank != 1 || file_dims != n) meep::abort("incorrect dataset size (%zd vs. %zd) in load_dft_hdf5 %s:%s", file_dims, n, - file->file_name(), dataname); + file->file_name(), dataname); for (dft_chunk *cur = dft_chunks; cur; cur = cur->next_in_dft) { size_t Nchunk = cur->N * cur->omega.size() * 2; @@ -482,7 +485,8 @@ void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const } } -void load_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix, bool single_parallel_file) { +void load_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix, + bool single_parallel_file) { load_dft_hdf5(dft_chunks, component_name(c), file, dprefix, single_parallel_file); } @@ -602,10 +606,9 @@ dft_flux fields::add_dft_flux(const volume_list *where_, const double *freq, siz } for (int i = 0; i < 2; ++i) { - E = add_dft(cE[i], where->v, freq, Nfreq, true, - where->weight * double(1 - 2 * i), E, false, std::complex(1.0,0), - centered_grid, 0, decimation_factor); - H = add_dft(cH[i], where->v, freq, Nfreq, false, 1.0, H, false, std::complex(1.0,0), + E = add_dft(cE[i], where->v, freq, Nfreq, true, where->weight * double(1 - 2 * i), E, false, + std::complex(1.0, 0), centered_grid, 0, decimation_factor); + H = add_dft(cH[i], where->v, freq, Nfreq, false, 1.0, H, false, std::complex(1.0, 0), centered_grid, 0, decimation_factor); } @@ -702,14 +705,14 @@ dft_energy fields::add_dft_energy(const volume_list *where_, const double *freq, volume_list *where_save = where; while (where) { LOOP_OVER_FIELD_DIRECTIONS(gv.dim, d) { - E = add_dft(direction_component(Ex, d), where->v, freq, Nfreq, true, 1.0, E, - false, 1.0, true, 0, decimation_factor); - D = add_dft(direction_component(Dx, d), where->v, freq, Nfreq, false, 1.0, D, - false, 1.0, true, 0, decimation_factor); - H = add_dft(direction_component(Hx, d), where->v, freq, Nfreq, true, 1.0, H, - false, 1.0, true, 0, decimation_factor); - B = add_dft(direction_component(Bx, d), where->v, freq, Nfreq, false, 1.0, B, - false, 1.0, true, 0, decimation_factor); + E = add_dft(direction_component(Ex, d), where->v, freq, Nfreq, true, 1.0, E, false, 1.0, true, + 0, decimation_factor); + D = add_dft(direction_component(Dx, d), where->v, freq, Nfreq, false, 1.0, D, false, 1.0, + true, 0, decimation_factor); + H = add_dft(direction_component(Hx, d), where->v, freq, Nfreq, true, 1.0, H, false, 1.0, true, + 0, decimation_factor); + B = add_dft(direction_component(Bx, d), where->v, freq, Nfreq, false, 1.0, B, false, 1.0, + true, 0, decimation_factor); } where = where->next; } @@ -794,7 +797,8 @@ direction fields::normal_direction(const volume &where) const { if (d == NO_DIRECTION && gv.dim == D2 && beta != 0 && where_pad.in_direction(X) > 0 && where_pad.in_direction(Y) > 0) d = Z; - if (d == NO_DIRECTION) meep::abort("Could not determine normal direction for given grid_volume."); + if (d == NO_DIRECTION) + meep::abort("Could not determine normal direction for given grid_volume."); } return d; } @@ -808,10 +812,10 @@ dft_flux fields::add_dft_flux(direction d, const volume &where, const double *fr return flux; } - dft_flux fields::add_mode_monitor(direction d, const volume &where, const double *freq, size_t Nfreq, bool centered_grid, int decimation_factor) { - return add_dft_flux(d, where, freq, Nfreq, /*use_symmetry=*/false, centered_grid, decimation_factor); + return add_dft_flux(d, where, freq, Nfreq, /*use_symmetry=*/false, centered_grid, + decimation_factor); } dft_flux fields::add_dft_flux_box(const volume &where, double freq_min, double freq_max, @@ -896,14 +900,17 @@ dft_fields fields::add_dft_fields(component *components, int num_components, con /***************************************************************/ /* chunk-level processing for fields::process_dft_component. */ /***************************************************************/ -complex dft_chunk::process_dft_component(int rank, direction *ds, ivec min_corner, ivec max_corner, - int num_freq, h5file *file, realnum *buffer, int reim, - complex *field_array, void *mode1_data, void *mode2_data, - int ic_conjugate, bool retain_interp_weights, - fields *parent) { - - if ((num_freq < 0) || (num_freq > static_cast(omega.size())-1)) - meep::abort("process_dft_component: frequency index %d is outside the range of the frequency array of size %lu",num_freq,omega.size()); +complex dft_chunk::process_dft_component(int rank, direction *ds, ivec min_corner, + ivec max_corner, int num_freq, h5file *file, + realnum *buffer, int reim, + complex *field_array, void *mode1_data, + void *mode2_data, int ic_conjugate, + bool retain_interp_weights, fields *parent) { + + if ((num_freq < 0) || (num_freq > static_cast(omega.size()) - 1)) + meep::abort("process_dft_component: frequency index %d is outside the range of the frequency " + "array of size %lu", + num_freq, omega.size()); /*****************************************************************/ /* compute the size of the chunk we own and its strides etc. */ @@ -969,14 +976,13 @@ complex dft_chunk::process_dft_component(int rank, direction *ds, ivec m double interp_w = retain_interp_weights ? IVEC_LOOP_WEIGHT(s0i, s1i, e0i, e1i, 1.0) : 1.0; complex dft_val = - (c_conjugate == NO_COMPONENT - ? w - : c_conjugate == Dielectric - ? parent->get_eps(loc) - : c_conjugate == Permeability - ? parent->get_mu(loc) - : complex(dft[omega.size() * (chunk_idx++) + num_freq]) / stored_weight); - if (include_dV_and_interp_weights && dft_val!=0.0) dft_val /= (sqrt_dV_and_interp_weights ? sqrt(w) : w); + (c_conjugate == NO_COMPONENT ? w + : c_conjugate == Dielectric ? parent->get_eps(loc) + : c_conjugate == Permeability + ? parent->get_mu(loc) + : complex(dft[omega.size() * (chunk_idx++) + num_freq]) / stored_weight); + if (include_dV_and_interp_weights && dft_val != 0.0) + dft_val /= (sqrt_dV_and_interp_weights ? sqrt(w) : w); complex mode1val = 0.0, mode2val = 0.0; if (mode1_data) mode1val = eigenmode_amplitude(mode1_data, loc, S.transform(c_conjugate, sn)); @@ -1023,7 +1029,10 @@ complex dft_chunk::process_dft_component(int rank, direction *ds, ivec m } // get variables that are needed by complex fields::process_dft_component -void fields::get_dft_component_dims(dft_chunk **chunklists, int num_chunklists, component c, ivec &min_corner, ivec &max_corner, size_t &array_size, size_t &bufsz, int &rank, direction *ds, size_t *dims, int *array_rank, size_t *array_dims, direction *array_dirs) { +void fields::get_dft_component_dims(dft_chunk **chunklists, int num_chunklists, component c, + ivec &min_corner, ivec &max_corner, size_t &array_size, + size_t &bufsz, int &rank, direction *ds, size_t *dims, + int *array_rank, size_t *array_dims, direction *array_dirs) { /***************************************************************/ /* get statistics on the volume slice **************************/ /***************************************************************/ @@ -1114,11 +1123,13 @@ void fields::get_dft_component_dims(dft_chunk **chunklists, int num_chunklists, /* if where is non-null, only field components inside *where */ /* are processed. */ /***************************************************************/ -complex fields::process_dft_component(dft_chunk **chunklists, int num_chunklists, int num_freq, - component c, const char *HDF5FileName, complex **pfield_array, - int *array_rank, size_t *array_dims, direction *array_dirs, - void *mode1_data, void *mode2_data, component c_conjugate, - bool *first_component, bool retain_interp_weights) { +complex fields::process_dft_component(dft_chunk **chunklists, int num_chunklists, + int num_freq, component c, const char *HDF5FileName, + complex **pfield_array, int *array_rank, + size_t *array_dims, direction *array_dirs, + void *mode1_data, void *mode2_data, + component c_conjugate, bool *first_component, + bool retain_interp_weights) { /***************************************************************/ /***************************************************************/ @@ -1134,7 +1145,8 @@ complex fields::process_dft_component(dft_chunk **chunklists, int num_ch int rank; direction ds[3]; size_t array_size, bufsz, dims[3]; - get_dft_component_dims(chunklists, num_chunklists, c, min_corner, max_corner, array_size, bufsz, rank, ds, dims, array_rank, array_dims, array_dirs); + get_dft_component_dims(chunklists, num_chunklists, c, min_corner, max_corner, array_size, bufsz, + rank, ds, dims, array_rank, array_dims, array_dirs); if (rank == 0) { if (pfield_array) *pfield_array = 0; @@ -1184,7 +1196,7 @@ complex fields::process_dft_component(dft_chunk **chunklists, int num_ch /* repeatedly call sum_to_all to consolidate full field array */ /* on all cores */ /***************************************************************/ -#define BUFSIZE 1 << 20 // use 1M element (16 MB) buffer +#define BUFSIZE 1 << 20 // use 1M element (16 MB) buffer complex *buf = new complex[BUFSIZE]; ptrdiff_t offset = 0; size_t remaining = array_size; @@ -1397,14 +1409,14 @@ void fields::get_overlap(void *mode1_data, void *mode2_data, dft_flux flux, int dft_chunk *chunklists[2]; chunklists[0] = flux.E; chunklists[1] = flux.H; - complex ExHy = process_dft_component(chunklists, 2, num_freq, cE[0], 0, 0, 0, 0, 0, mode1_data, - mode2_data, cH[0]); - complex EyHx = process_dft_component(chunklists, 2, num_freq, cE[1], 0, 0, 0, 0, 0, mode1_data, - mode2_data, cH[1]); - complex HyEx = process_dft_component(chunklists, 2, num_freq, cH[0], 0, 0, 0, 0, 0, mode1_data, - mode2_data, cE[0]); - complex HxEy = process_dft_component(chunklists, 2, num_freq, cH[1], 0, 0, 0, 0, 0, mode1_data, - mode2_data, cE[1]); + complex ExHy = process_dft_component(chunklists, 2, num_freq, cE[0], 0, 0, 0, 0, 0, + mode1_data, mode2_data, cH[0]); + complex EyHx = process_dft_component(chunklists, 2, num_freq, cE[1], 0, 0, 0, 0, 0, + mode1_data, mode2_data, cH[1]); + complex HyEx = process_dft_component(chunklists, 2, num_freq, cH[0], 0, 0, 0, 0, 0, + mode1_data, mode2_data, cE[0]); + complex HxEy = process_dft_component(chunklists, 2, num_freq, cH[1], 0, 0, 0, 0, 0, + mode1_data, mode2_data, cE[1]); overlaps[0] = ExHy - EyHx; overlaps[1] = HyEx - HxEy; } @@ -1425,12 +1437,12 @@ delete the underlying dft_chunks! (useful for adjoint calculations, where we want to keep the chunk data around) */ void fields::clear_dft_monitors() { -for (int i = 0; i < num_chunks; i++) - if (chunks[i]->is_mine() && chunks[i]->dft_chunks) chunks[i]->dft_chunks = NULL; + for (int i = 0; i < num_chunks; i++) + if (chunks[i]->is_mine() && chunks[i]->dft_chunks) chunks[i]->dft_chunks = NULL; } // return the size of the dft monitor -std::vector fields::dft_monitor_size(dft_fields fdft, const volume &where, component c){ +std::vector fields::dft_monitor_size(dft_fields fdft, const volume &where, component c) { ivec min_corner, max_corner; int rank, reduced_rank; direction dirs[3], reduced_dirs[3]; @@ -1438,14 +1450,18 @@ std::vector fields::dft_monitor_size(dft_fields fdft, const volume &wher dft_chunk *chunklists[1]; chunklists[0] = fdft.chunks; - get_dft_component_dims(chunklists, 1, c, min_corner, max_corner, array_size, bufsz, rank, dirs, dims); - reduce_array_dimensions(where, rank, dims, dirs, stride, reduced_rank, reduced_dims, reduced_dirs, reduced_stride); + get_dft_component_dims(chunklists, 1, c, min_corner, max_corner, array_size, bufsz, rank, dirs, + dims); + reduce_array_dimensions(where, rank, dims, dirs, stride, reduced_rank, reduced_dims, reduced_dirs, + reduced_stride); std::vector reduced_dims_vec = {reduced_dims[0], reduced_dims[1], reduced_dims[2]}; return reduced_dims_vec; } -std::vector dft_fields::fourier_sourcedata(const volume &where, component c, fields &f, const std::complex* dJ){ +std::vector dft_fields::fourier_sourcedata(const volume &where, component c, + fields &f, + const std::complex *dJ) { const size_t Nfreq = freq.size(); ivec min_corner, max_corner; @@ -1455,9 +1471,12 @@ std::vector dft_fields::fourier_sourcedata(const volume &wher dft_chunk *chunklists[1]; chunklists[0] = chunks; - f.get_dft_component_dims(chunklists, 1, c, min_corner, max_corner, array_size, bufsz, rank, dirs, dims); - reduce_array_dimensions(where, rank, dims, dirs, stride, reduced_rank, reduced_dims, reduced_dirs, reduced_stride); - size_t reduced_grid_size = reduced_dims[0]*reduced_dims[1]*reduced_dims[2]; // total number of points in the monitor + f.get_dft_component_dims(chunklists, 1, c, min_corner, max_corner, array_size, bufsz, rank, dirs, + dims); + reduce_array_dimensions(where, rank, dims, dirs, stride, reduced_rank, reduced_dims, reduced_dirs, + reduced_stride); + size_t reduced_grid_size = + reduced_dims[0] * reduced_dims[1] * reduced_dims[2]; // total number of points in the monitor std::vector temp; @@ -1467,12 +1486,13 @@ std::vector dft_fields::fourier_sourcedata(const volume &wher std::vector idx_arr; std::vector > amp_arr; - std::complex EH0 = std::complex(0,0); + std::complex EH0 = std::complex(0, 0); component c = component(f->c); direction cd = component_direction(c); sourcedata temp_struct = {c, idx_arr, f->fc->chunk_idx, amp_arr}; - int position_array[3] = {0, 0, 0}; // array indicating the position of a point relative to the minimum corner of the monitor + int position_array[3] = {0, 0, 0}; // array indicating the position of a point relative to the + // minimum corner of the monitor LOOP_OVER_IVECS(f->fc->gv, f->is, f->ie, idx) { IVEC_LOOP_LOC(f->fc->gv, x0); @@ -1482,38 +1502,48 @@ std::vector dft_fields::fourier_sourcedata(const volume &wher double dJ_weight = 1; // weight for linear interpolation int nd = 0; - LOOP_OVER_DIRECTIONS(f->fc->gv.dim, d){ - if (where.in_direction(d) > 0) position_array[nd++] = int((ix0.in_direction(d)-min_corner.in_direction(d))/2); - else dJ_weight *= (1-abs(x0.in_direction(d)-where.in_direction_min(d))/(f->fc->gv.inva)); // based on distances + LOOP_OVER_DIRECTIONS(f->fc->gv.dim, d) { + if (where.in_direction(d) > 0) + position_array[nd++] = int((ix0.in_direction(d) - min_corner.in_direction(d)) / 2); + else + dJ_weight *= (1 - abs(x0.in_direction(d) - where.in_direction_min(d)) / + (f->fc->gv.inva)); // based on distances } // index when dJ is flattened to a one-dimenional array - size_t idx_1d = (position_array[0]*reduced_dims[1]+position_array[1])*reduced_dims[2]+position_array[2]; + size_t idx_1d = (position_array[0] * reduced_dims[1] + position_array[1]) * reduced_dims[2] + + position_array[2]; - if (f->avg1==0 && f->avg2==0){ // yee_grid = true + if (f->avg1 == 0 && f->avg2 == 0) { // yee_grid = true temp_struct.idx_arr.push_back(idx); for (size_t i = 0; i < Nfreq; ++i) { - EH0 = dJ_weight*dJ[reduced_grid_size*i+idx_1d]; + EH0 = dJ_weight * dJ[reduced_grid_size * i + idx_1d]; if (is_electric(c)) EH0 *= -1; - if (is_D(c) && f->fc->s->chi1inv[c - Dx + Ex][cd]) EH0 /= -f->fc->s->chi1inv[c - Dx + Ex][cd][idx]; - if (is_B(c) && f->fc->s->chi1inv[c - Bx + Hx][cd]) EH0 /= f->fc->s->chi1inv[c - Bx + Hx][cd][idx]; + if (is_D(c) && f->fc->s->chi1inv[c - Dx + Ex][cd]) + EH0 /= -f->fc->s->chi1inv[c - Dx + Ex][cd][idx]; + if (is_B(c) && f->fc->s->chi1inv[c - Bx + Hx][cd]) + EH0 /= f->fc->s->chi1inv[c - Bx + Hx][cd][idx]; EH0 /= f->S.multiplicity(ix0); temp_struct.amp_arr.push_back(EH0); } } - else{ // yee_grid = false - // four or two neighbouring points in the yee lattice are involved in calculating the value at the center of a voxel - ptrdiff_t site_ind[4] = {idx,idx + f->avg1,idx + f->avg2,idx + f->avg1 + f->avg2}; - for (size_t j = 0; j < 4; ++j){ + else { // yee_grid = false + // four or two neighbouring points in the yee lattice are involved in calculating the value + // at the center of a voxel + ptrdiff_t site_ind[4] = {idx, idx + f->avg1, idx + f->avg2, idx + f->avg1 + f->avg2}; + for (size_t j = 0; j < 4; ++j) { temp_struct.idx_arr.push_back(site_ind[j]); for (size_t i = 0; i < Nfreq; ++i) { - EH0 = dJ_weight*dJ[reduced_grid_size*i+idx_1d]*0.25; // split the amplitude of the adjoint source into four parts + EH0 = dJ_weight * dJ[reduced_grid_size * i + idx_1d] * + 0.25; // split the amplitude of the adjoint source into four parts if (is_electric(c)) EH0 *= -1; - if (is_D(c) && f->fc->s->chi1inv[c - Dx + Ex][cd]) EH0 /= -f->fc->s->chi1inv[c - Dx + Ex][cd][idx]; - if (is_B(c) && f->fc->s->chi1inv[c - Bx + Hx][cd]) EH0 /= f->fc->s->chi1inv[c - Bx + Hx][cd][idx]; + if (is_D(c) && f->fc->s->chi1inv[c - Dx + Ex][cd]) + EH0 /= -f->fc->s->chi1inv[c - Dx + Ex][cd][idx]; + if (is_B(c) && f->fc->s->chi1inv[c - Bx + Hx][cd]) + EH0 /= f->fc->s->chi1inv[c - Bx + Hx][cd][idx]; EH0 /= f->S.multiplicity(ix0); temp_struct.amp_arr.push_back(EH0); diff --git a/src/dft_ldos.cpp b/src/dft_ldos.cpp index f55363e45..e6f994b0f 100644 --- a/src/dft_ldos.cpp +++ b/src/dft_ldos.cpp @@ -66,8 +66,8 @@ double *dft_ldos::ldos() { // overall scale factor double Jsum_all = sum_to_all(Jsum); saved_overall_scale = 4.0 / pi // from definition of LDOS comparison to power - * -0.5 // power = -1/2 Re[E* J] - / (Jsum_all * Jsum_all); // normalize to unit-integral current + * -0.5 // power = -1/2 Re[E* J] + / (Jsum_all * Jsum_all); // normalize to unit-integral current const size_t Nfreq = freq.size(); double *sum = new double[Nfreq]; @@ -113,34 +113,34 @@ void dft_ldos::update(fields &f) { if (fr && fi) // complex E for (size_t j = 0; j < sv.num_points(); j++) { const ptrdiff_t idx = sv.index_at(j); - const complex& A = sv.amplitude_at(j); + const complex &A = sv.amplitude_at(j); EJ += complex(fr[idx], fi[idx]) * conj(A); Jsum += abs(A); } else if (fr) { // E is purely real for (size_t j = 0; j < sv.num_points(); j++) { const ptrdiff_t idx = sv.index_at(j); - const complex& A = sv.amplitude_at(j); + const complex &A = sv.amplitude_at(j); EJ += double(fr[idx]) * conj(A); Jsum += abs(A); } } } - for (const src_vol& sv : f.chunks[ic]->get_sources(B_stuff)) { + for (const src_vol &sv : f.chunks[ic]->get_sources(B_stuff)) { component c = direction_component(Hx, component_direction(sv.c)); realnum *fr = f.chunks[ic]->f[c][0]; realnum *fi = f.chunks[ic]->f[c][1]; if (fr && fi) // complex H for (size_t j = 0; j < sv.num_points(); j++) { const ptrdiff_t idx = sv.index_at(j); - const complex& A = sv.amplitude_at(j); + const complex &A = sv.amplitude_at(j); HJ += complex(fr[idx], fi[idx]) * conj(A); Jsum += abs(A); } else if (fr) { // H is purely real for (size_t j = 0; j < sv.num_points(); j++) { const ptrdiff_t idx = sv.index_at(j); - const complex& A = sv.amplitude_at(j); + const complex &A = sv.amplitude_at(j); HJ += double(fr[idx]) * conj(A); Jsum += abs(A); } diff --git a/src/fields.cpp b/src/fields.cpp index 966d5e5b1..b9f43f338 100644 --- a/src/fields.cpp +++ b/src/fields.cpp @@ -59,8 +59,8 @@ fields::fields(structure *s, double m, double beta, bool zero_fields_near_cylori typedef fields_chunk *fields_chunk_ptr; chunks = new fields_chunk_ptr[num_chunks]; for (int i = 0; i < num_chunks; i++) - chunks[i] = new fields_chunk(s->chunks[i], outdir, m, beta, - zero_fields_near_cylorigin, i, loop_tile_base_db); + chunks[i] = new fields_chunk(s->chunks[i], outdir, m, beta, zero_fields_near_cylorigin, i, + loop_tile_base_db); FOR_FIELD_TYPES(ft) { typedef realnum *realnum_ptr; comm_blocks[ft] = new realnum_ptr[num_chunks * num_chunks]; @@ -188,9 +188,7 @@ fields_chunk::~fields_chunk() { delete dft_chunks; dft_chunks = nxt; } - FOR_FIELD_TYPES(ft) { - delete[] zeroes[ft]; - } + FOR_FIELD_TYPES(ft) { delete[] zeroes[ft]; } FOR_FIELD_TYPES(ft) { for (polarization_state *cur = pol[ft]; cur;) { polarization_state *p = cur; @@ -229,10 +227,13 @@ void check_tiles(grid_volume gv, const std::vector &gvs) { if (gvs[i].intersect_with(gvs[j], &vol_intersection)) meep::abort("gvs[%zu] intersects with gvs[%zu]\n", i, j); size_t sum = 0; - for (const auto& sub_gv : gvs) { sum += sub_gv.nowned_min(); } + for (const auto &sub_gv : gvs) { + sum += sub_gv.nowned_min(); + } size_t v_grid_points = 1; LOOP_OVER_DIRECTIONS(gv.dim, d) { v_grid_points *= gv.num_direction(d); } - if (sum != v_grid_points) meep::abort("v_grid_points = %zu, sum(tiles) = %zu\n", v_grid_points, sum); + if (sum != v_grid_points) + meep::abort("v_grid_points = %zu, sum(tiles) = %zu\n", v_grid_points, sum); } fields_chunk::fields_chunk(structure_chunk *the_s, const char *od, double m, double beta, @@ -252,9 +253,8 @@ fields_chunk::fields_chunk(structure_chunk *the_s, const char *od, double m, dou if (loop_tile_base_db > 0) { split_into_tiles(gv, &gvs_tiled, loop_tile_base_db); check_tiles(gv, gvs_tiled); - } else { - gvs_tiled.push_back(gv); } + else { gvs_tiled.push_back(gv); } FOR_FIELD_TYPES(ft) { polarization_state *cur = NULL; pol[ft] = NULL; @@ -268,9 +268,7 @@ fields_chunk::fields_chunk(structure_chunk *the_s, const char *od, double m, dou cur->next = p; cur = p; } - else { - pol[ft] = cur = p; - } + else { pol[ft] = cur = p; } } } doing_solve_cw = false; @@ -311,9 +309,7 @@ fields_chunk::fields_chunk(const fields_chunk &thef, int chunkidx) : gv(thef.gv) dt = thef.dt; dft_chunks = NULL; gvs_tiled = thef.gvs_tiled; - FOR_FIELD_TYPES(ft) { - gvs_eh[ft] = thef.gvs_eh[ft]; - } + FOR_FIELD_TYPES(ft) { gvs_eh[ft] = thef.gvs_eh[ft]; } FOR_FIELD_TYPES(ft) { polarization_state *cur = NULL; for (polarization_state *ocur = thef.pol[ft]; ocur; ocur = ocur->next) { @@ -327,9 +323,7 @@ fields_chunk::fields_chunk(const fields_chunk &thef, int chunkidx) : gv(thef.gv) cur->next = p; cur = p; } - else { - pol[ft] = cur = p; - } + else { pol[ft] = cur = p; } } } doing_solve_cw = thef.doing_solve_cw; @@ -505,7 +499,7 @@ void fields::require_source_components() { memset(needed, 0, sizeof(needed)); for (int i = 0; i < num_chunks; i++) { FOR_FIELD_TYPES(ft) { - for (const auto& src : chunks[i]->get_sources(ft)) { + for (const auto &src : chunks[i]->get_sources(ft)) { needed[src.c] = 1; } } @@ -517,8 +511,7 @@ void fields::require_source_components() { bool aniso2d = is_aniso2d(); for (int c = 0; c < NUM_FIELD_COMPONENTS; ++c) - if (allneeded[c]) - _require_component(component(c), aniso2d); + if (allneeded[c]) _require_component(component(c), aniso2d); sync_chunk_connections(); } @@ -546,7 +539,8 @@ bool fields::is_aniso2d() { void fields::_require_component(component c, bool aniso2d) { if (!gv.has_field(c)) - meep::abort("cannot require a %s component in a %s grid", component_name(c), dimension_name(gv.dim)); + meep::abort("cannot require a %s component in a %s grid", component_name(c), + dimension_name(gv.dim)); components_allocated = true; @@ -567,9 +561,8 @@ void fields::_require_component(component c, bool aniso2d) { } void fields_chunk::add_source(field_type ft, src_vol &&src) { - auto it = std::find_if(sources[ft].begin(), sources[ft].end(), [&src](const src_vol &other) { - return src_vol::combinable(src, other); - }); + auto it = std::find_if(sources[ft].begin(), sources[ft].end(), + [&src](const src_vol &other) { return src_vol::combinable(src, other); }); if (it != sources[ft].end()) { it->add_amplitudes_from(src); @@ -580,9 +573,7 @@ void fields_chunk::add_source(field_type ft, src_vol &&src) { } void fields_chunk::remove_sources() { - FOR_FIELD_TYPES(ft) { - sources[ft].clear(); - } + FOR_FIELD_TYPES(ft) { sources[ft].clear(); } } void fields::remove_sources() { @@ -674,7 +665,8 @@ void fields_chunk::use_real_fields() { int fields::phase_in_material(const structure *snew, double time) { if (snew->num_chunks != num_chunks) - meep::abort("Can only phase in similar sets of chunks: %d vs %d\n", snew->num_chunks, num_chunks); + meep::abort("Can only phase in similar sets of chunks: %d vs %d\n", snew->num_chunks, + num_chunks); for (int i = 0; i < num_chunks; i++) if (chunks[i]->is_mine()) chunks[i]->phase_in_material(snew->chunks[i]); phasein_time = (int)(time / dt); @@ -728,7 +720,7 @@ void fields::unset_solve_cw_omega() { chunks[i]->unset_solve_cw_omega(); } -void fields::log(const char* prefix) { +void fields::log(const char *prefix) { master_printf("%sFields State:\n", prefix); master_printf("%s a = %g, dt = %g\n", prefix, a, dt); master_printf("%s m = %g, beta = %g\n", prefix, m, beta); @@ -742,11 +734,8 @@ int mirrorindex(int i, int n) { return i >= n ? 2 * n - 1 - i : (i < 0 ? -1 - i /* map the cell coordinates into the range [0,1]. anything outside [0,1] is *mirror* reflected into [0,1] */ -void map_coordinates(double rx, double ry, double rz, - int nx, int ny, int nz, - int &x1, int &y1, int &z1, - int &x2, int &y2, int &z2, - double &dx, double &dy, double &dz, +void map_coordinates(double rx, double ry, double rz, int nx, int ny, int nz, int &x1, int &y1, + int &z1, int &x2, int &y2, int &z2, double &dx, double &dy, double &dz, bool do_fabs) { /* mirror boundary conditions for r just beyond the boundary */ @@ -775,19 +764,16 @@ void map_coordinates(double rx, double ry, double rz, dy = fabs(dy); dz = fabs(dz); } - } /* linearly interpolate a given point in a 3d grid of data. */ -double linear_interpolate(double rx, double ry, double rz, double *data, - int nx, int ny, int nz, int stride) { +double linear_interpolate(double rx, double ry, double rz, double *data, int nx, int ny, int nz, + int stride) { int x1, y1, z1, x2, y2, z2; double dx, dy, dz; - map_coordinates(rx, ry, rz, nx, ny, nz, - x1, y1, z1, x2, y2, z2, - dx, dy, dz); + map_coordinates(rx, ry, rz, nx, ny, nz, x1, y1, z1, x2, y2, z2, dx, dy, dz); /* define a macro to give us data(x,y,z) on the grid, in row-major order (the order used by HDF5): */ diff --git a/src/fields_dump.cpp b/src/fields_dump.cpp index fe704a393..34bac631a 100644 --- a/src/fields_dump.cpp +++ b/src/fields_dump.cpp @@ -58,9 +58,7 @@ void fields::dump_fields_chunk_field(h5file *h5f, bool single_parallel_file, for (int c = 0; c < NUM_FIELD_COMPONENTS; ++c) { for (int d = 0; d < 2; ++d) { realnum **f = field_ptr_getter(chunks[i], c, d); - if (*f) { - my_ntot += (num_f_[(chunk_i * NUM_FIELD_COMPONENTS + c) * 2 + d] = ntot); - } + if (*f) { my_ntot += (num_f_[(chunk_i * NUM_FIELD_COMPONENTS + c) * 2 + d] = ntot); } } } } @@ -71,9 +69,8 @@ void fields::dump_fields_chunk_field(h5file *h5f, bool single_parallel_file, if (single_parallel_file) { num_f.resize(num_f_size); sum_to_master(num_f_.data(), num_f.data(), num_f_size); - } else { - num_f = std::move(num_f_); } + else { num_f = std::move(num_f_); } /* determine total dataset size and offset of this process's data */ size_t my_start = 0; @@ -87,12 +84,11 @@ void fields::dump_fields_chunk_field(h5file *h5f, bool single_parallel_file, size_t start[3] = {0, 0, 0}; std::string num_f_name = std::string("num_") + field_name; h5f->create_data(num_f_name.c_str(), 3, dims); - if (am_master() || !single_parallel_file) { - h5f->write_chunk(3, start, dims, num_f.data()); - } + if (am_master() || !single_parallel_file) { h5f->write_chunk(3, start, dims, num_f.data()); } /* write the data */ - h5f->create_data(field_name.c_str(), 1, &ntotal, false /* append_data */, false /* single_precision */); + h5f->create_data(field_name.c_str(), 1, &ntotal, false /* append_data */, + false /* single_precision */); for (int i = 0; i < num_chunks; i++) { if (chunks[i]->is_mine()) { size_t ntot = chunks[i]->gv.ntot(); @@ -123,18 +119,14 @@ void fields::dump(const char *filename, bool single_parallel_file) { file.create_data("t", 1, dims); if (am_master() || !single_parallel_file) file.write_chunk(1, start, dims, _t); - dump_fields_chunk_field( - &file, single_parallel_file, "f", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f[c][d]); }); - dump_fields_chunk_field( - &file, single_parallel_file, "f_u", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_u[c][d]); }); - dump_fields_chunk_field( - &file, single_parallel_file, "f_w", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w[c][d]); }); - dump_fields_chunk_field( - &file, single_parallel_file, "f_cond", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_cond[c][d]); }); + dump_fields_chunk_field(&file, single_parallel_file, "f", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f[c][d]); }); + dump_fields_chunk_field(&file, single_parallel_file, "f_u", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_u[c][d]); }); + dump_fields_chunk_field(&file, single_parallel_file, "f_w", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w[c][d]); }); + dump_fields_chunk_field(&file, single_parallel_file, "f_cond", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_cond[c][d]); }); dump_fields_chunk_field( &file, single_parallel_file, "f_w_prev", [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w_prev[c][d]); }); @@ -184,11 +176,11 @@ void fields::load_fields_chunk_field(h5file *h5f, bool single_parallel_file, size_t n = num_f[(chunk_i * NUM_FIELD_COMPONENTS + c) * 2 + d]; realnum **f = field_ptr_getter(chunks[i], c, d); if (n == 0) { - delete[] *f; + delete[] * f; *f = NULL; - } else { - if (n != ntot) - meep::abort("grid size mismatch %zd vs %zd in fields::load", n, ntot); + } + else { + if (n != ntot) meep::abort("grid size mismatch %zd vs %zd in fields::load", n, ntot); // here we need to allocate the fields array for H in the PML region // because of H = B in fields_chunk::alloc_f whereby H is lazily // allocated in fields_chunk::update_eh during the first timestep @@ -214,10 +206,9 @@ void fields::load_fields_chunk_field(h5file *h5f, bool single_parallel_file, /* read the data */ h5f->read_size(field_name.c_str(), &rank, dims, 1); if (rank != 1 || dims[0] != ntotal) { - meep::abort( - "inconsistent data size for '%s' in fields::load (rank, dims[0]): " - "(%d, %zu) != (1, %zu)", - field_name.c_str(), rank, dims[0], ntotal); + meep::abort("inconsistent data size for '%s' in fields::load (rank, dims[0]): " + "(%d, %zu) != (1, %zu)", + field_name.c_str(), rank, dims[0], ntotal); } for (int i = 0; i < num_chunks; i++) { if (chunks[i]->is_mine()) { @@ -258,18 +249,14 @@ void fields::load(const char *filename, bool single_parallel_file) { t = static_cast(_t[0]); calc_sources(time()); - load_fields_chunk_field( - &file, single_parallel_file, "f", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f[c][d]); }); - load_fields_chunk_field( - &file, single_parallel_file, "f_u", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_u[c][d]); }); - load_fields_chunk_field( - &file, single_parallel_file, "f_w", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w[c][d]); }); - load_fields_chunk_field( - &file, single_parallel_file, "f_cond", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_cond[c][d]); }); + load_fields_chunk_field(&file, single_parallel_file, "f", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f[c][d]); }); + load_fields_chunk_field(&file, single_parallel_file, "f_u", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_u[c][d]); }); + load_fields_chunk_field(&file, single_parallel_file, "f_w", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w[c][d]); }); + load_fields_chunk_field(&file, single_parallel_file, "f_cond", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_cond[c][d]); }); load_fields_chunk_field( &file, single_parallel_file, "f_w_prev", [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w_prev[c][d]); }); @@ -284,4 +271,4 @@ void fields::load(const char *filename, bool single_parallel_file) { } } -} // namespace meep +} // namespace meep diff --git a/src/fix_boundary_sources.cpp b/src/fix_boundary_sources.cpp index 39579fe55..4676d3362 100644 --- a/src/fix_boundary_sources.cpp +++ b/src/fix_boundary_sources.cpp @@ -22,7 +22,7 @@ static MPI_Comm mycomm = MPI_COMM_WORLD; // data structure for sending source information from one chunk to another struct srcpt_info { std::complex A; // amplitude - ptrdiff_t index; // index in chunk's fields array + ptrdiff_t index; // index in chunk's fields array size_t src_time_id; int chunk_idx; int c; // component @@ -32,13 +32,14 @@ struct srcpt_info { // by (processor, src_time_id, chunk_idx, c) struct srcpt_info_compare { fields_chunk **chunks; - bool operator() (srcpt_info a, srcpt_info b) { + bool operator()(srcpt_info a, srcpt_info b) { int aproc = chunks[a.chunk_idx]->n_proc(); int bproc = chunks[b.chunk_idx]->n_proc(); - return (aproc != bproc ? aproc < bproc : - (a.src_time_id != b.src_time_id ? a.src_time_id < b.src_time_id : - (a.chunk_idx != b.chunk_idx ? a.chunk_idx < b.chunk_idx : - a.c < b.c))); + return (aproc != bproc + ? aproc < bproc + : (a.src_time_id != b.src_time_id + ? a.src_time_id < b.src_time_id + : (a.chunk_idx != b.chunk_idx ? a.chunk_idx < b.chunk_idx : a.c < b.c))); } }; @@ -57,14 +58,17 @@ void fields::fix_boundary_sources() { component c = src.c; ivec here = chunks[i]->gv.iloc(c, src.index_at(ipt)); if (!chunks[i]->gv.owns(here) && src.amplitude(ipt) != 0.0) { - if (src.t()->id == 0) abort("bug: fix_boundary_sources called for non-registered source"); + if (src.t()->id == 0) + abort("bug: fix_boundary_sources called for non-registered source"); // find the chunk that owns this point, similar to logic in boundaries.cpp std::complex thephase; if (locate_component_point(&c, &here, &thephase) && !on_metal_boundary(here)) { for (int j = 0; j < num_chunks; j++) if (chunks[j]->gv.owns(here)) { - srcpt_info s = { src.amplitude(ipt)*conj(thephase), chunks[j]->gv.index(c, here), src.t()->id, chunks[j]->chunk_idx, c }; + srcpt_info s = {src.amplitude(ipt) * conj(thephase), + chunks[j]->gv.index(c, here), src.t()->id, chunks[j]->chunk_idx, + c}; boundarysources.push_back(s); break; } @@ -94,78 +98,84 @@ void fields::fix_boundary_sources() { for (size_t idx = 0; idx < boundarysources.size(); ++idx) { int pidx = chunks[boundarysources[idx].chunk_idx]->n_proc(); if (pidx != p0) { - offsets[p*P + p0] = idx0; - numcomm_[p*P + p0] = idx - idx0; + offsets[p * P + p0] = idx0; + numcomm_[p * P + p0] = idx - idx0; p0 = pidx; idx0 = idx; } } - offsets[p*P + p0] = idx0; - numcomm_[p*P + p0] = boundarysources.size() - idx0; + offsets[p * P + p0] = idx0; + numcomm_[p * P + p0] = boundarysources.size() - idx0; // collect the numcomm data from all processes std::vector numcomm(P * P, size_t(0)); - sum_to_all(numcomm_.data(), numcomm.data(), P*P); + sum_to_all(numcomm_.data(), numcomm.data(), P * P); #ifdef HAVE_MPI // declare an MPI datatype mirroring srcpt_info, so that we can send/receive srcpt_info arrays - int srcpt_info_blocklengths[5] = {2,1,1,1,1}; - MPI_Datatype srcpt_info_types[5] = {MPI_DOUBLE, sizeof(ptrdiff_t) == sizeof(int) ? MPI_INT : MPI_LONG_LONG, sizeof(size_t) == sizeof(unsigned) ? MPI_UNSIGNED : MPI_UNSIGNED_LONG_LONG, MPI_INT, MPI_INT}; - MPI_Aint srcpt_info_offsets[5] = { offsetof(srcpt_info,A), offsetof(srcpt_info,index), offsetof(srcpt_info,src_time_id), offsetof(srcpt_info,chunk_idx), offsetof(srcpt_info,c) }; + int srcpt_info_blocklengths[5] = {2, 1, 1, 1, 1}; + MPI_Datatype srcpt_info_types[5] = { + MPI_DOUBLE, sizeof(ptrdiff_t) == sizeof(int) ? MPI_INT : MPI_LONG_LONG, + sizeof(size_t) == sizeof(unsigned) ? MPI_UNSIGNED : MPI_UNSIGNED_LONG_LONG, MPI_INT, MPI_INT}; + MPI_Aint srcpt_info_offsets[5] = {offsetof(srcpt_info, A), offsetof(srcpt_info, index), + offsetof(srcpt_info, src_time_id), + offsetof(srcpt_info, chunk_idx), offsetof(srcpt_info, c)}; MPI_Datatype mpi_srcpt_info; - MPI_Type_create_struct(5, srcpt_info_blocklengths, srcpt_info_offsets, srcpt_info_types, &mpi_srcpt_info); + MPI_Type_create_struct(5, srcpt_info_blocklengths, srcpt_info_offsets, srcpt_info_types, + &mpi_srcpt_info); MPI_Type_commit(&mpi_srcpt_info); #endif -for (int psrc = 0; psrc < P; ++psrc) - for (int pdest = 0; pdest < P; ++pdest) { - size_t N = numcomm[psrc*P + pdest]; - if (N == 0) continue; - if (pdest == p) { - srcpt_info *srcpts; + for (int psrc = 0; psrc < P; ++psrc) + for (int pdest = 0; pdest < P; ++pdest) { + size_t N = numcomm[psrc * P + pdest]; + if (N == 0) continue; + if (pdest == p) { + srcpt_info *srcpts; #ifdef HAVE_MPI - if (psrc != p) { - srcpts = new srcpt_info[N]; - MPI_Status status; - MPI_Recv(srcpts, N, mpi_srcpt_info, psrc, psrc*P + pdest, mycomm, &status); - } - else + if (psrc != p) { + srcpts = new srcpt_info[N]; + MPI_Status status; + MPI_Recv(srcpts, N, mpi_srcpt_info, psrc, psrc * P + pdest, mycomm, &status); + } + else #endif - srcpts = boundarysources.data() + offsets[psrc*P + pdest]; - int chunk_idx = srcpts[0].chunk_idx; - size_t src_time_id = srcpts[0].src_time_id; - int c = srcpts[0].c; - size_t idx0 = 0; - for (size_t idx = 0; idx <= N; ++idx) { - if (idx == N || srcpts[idx].chunk_idx != chunk_idx || srcpts[idx].src_time_id != src_time_id || srcpts[idx].c != c) { - std::vector idx_arr(idx - idx0); - std::vector > amp_arr(idx - idx0); - for (size_t i = idx0; i < idx; ++i) { - idx_arr[i-idx0] = srcpts[i].index; - amp_arr[i-idx0] = srcpts[i].A; - } - sourcedata srcdata = { (component) c, idx_arr, chunk_idx, amp_arr }; - src_time *srctime = lookup_src_time(src_time_id); - if (srctime == NULL) abort("bug: unknown src_time_id (missing registration?)"); - add_srcdata(srcdata, srctime, size_t(0), NULL, false); - if (idx < N) { - chunk_idx = srcpts[idx].chunk_idx; - src_time_id = srcpts[idx].src_time_id; - c = srcpts[idx].c; - idx0 = idx; + srcpts = boundarysources.data() + offsets[psrc * P + pdest]; + int chunk_idx = srcpts[0].chunk_idx; + size_t src_time_id = srcpts[0].src_time_id; + int c = srcpts[0].c; + size_t idx0 = 0; + for (size_t idx = 0; idx <= N; ++idx) { + if (idx == N || srcpts[idx].chunk_idx != chunk_idx || + srcpts[idx].src_time_id != src_time_id || srcpts[idx].c != c) { + std::vector idx_arr(idx - idx0); + std::vector > amp_arr(idx - idx0); + for (size_t i = idx0; i < idx; ++i) { + idx_arr[i - idx0] = srcpts[i].index; + amp_arr[i - idx0] = srcpts[i].A; + } + sourcedata srcdata = {(component)c, idx_arr, chunk_idx, amp_arr}; + src_time *srctime = lookup_src_time(src_time_id); + if (srctime == NULL) abort("bug: unknown src_time_id (missing registration?)"); + add_srcdata(srcdata, srctime, size_t(0), NULL, false); + if (idx < N) { + chunk_idx = srcpts[idx].chunk_idx; + src_time_id = srcpts[idx].src_time_id; + c = srcpts[idx].c; + idx0 = idx; + } } } - } - if (psrc != p) delete[] srcpts; - } + if (psrc != p) delete[] srcpts; + } #ifdef HAVE_MPI - else if (psrc == p) { - srcpt_info *srcpts = boundarysources.data() + offsets[psrc*P + pdest]; - MPI_Send(srcpts, N, mpi_srcpt_info, pdest, psrc*P + pdest, mycomm); - } + else if (psrc == p) { + srcpt_info *srcpts = boundarysources.data() + offsets[psrc * P + pdest]; + MPI_Send(srcpts, N, mpi_srcpt_info, pdest, psrc * P + pdest, mycomm); + } #endif - } + } #ifdef HAVE_MPI MPI_Type_free(&mpi_srcpt_info); diff --git a/src/h5file.cpp b/src/h5file.cpp index 0271d064d..3987462d9 100644 --- a/src/h5file.cpp +++ b/src/h5file.cpp @@ -23,7 +23,9 @@ #define CHECK(condition, message) \ do { \ - if (!(condition)) { meep::abort("error on line %d of " __FILE__ ": " message "\n", __LINE__); } \ + if (!(condition)) { \ + meep::abort("error on line %d of " __FILE__ ": " message "\n", __LINE__); \ + } \ } while (0) #include "config.h" @@ -311,7 +313,9 @@ void *h5file::read(const char *dataname, int *rank, size_t *dims, int maxrank, close_data_id = false; } else { - if (!dataset_exists(dataname)) { meep::abort("missing dataset in HDF5 file: %s", dataname); } + if (!dataset_exists(dataname)) { + meep::abort("missing dataset in HDF5 file: %s", dataname); + } data_id = H5Dopen(file_id, dataname); } } @@ -323,9 +327,7 @@ void *h5file::read(const char *dataname, int *rank, size_t *dims, int maxrank, data_id = HID(cur_id); close_data_id = false; } - else { - data_id = H5Dopen(file_id, dname); - } + else { data_id = H5Dopen(file_id, dname); } delete[] dname; } space_id = H5Dget_space(data_id); @@ -346,8 +348,8 @@ void *h5file::read(const char *dataname, int *rank, size_t *dims, int maxrank, data = new float[N]; else data = new double[N]; - H5Dread(data_id, single_precision ? H5T_NATIVE_FLOAT : H5T_NATIVE_DOUBLE, - H5S_ALL, H5S_ALL, H5P_DEFAULT, (void *)data); + H5Dread(data_id, single_precision ? H5T_NATIVE_FLOAT : H5T_NATIVE_DOUBLE, H5S_ALL, H5S_ALL, + H5P_DEFAULT, (void *)data); if (close_data_id) H5Dclose(data_id); } @@ -619,8 +621,8 @@ void h5file::create_or_extend_data(const char *dataname, int rank, const size_t that have no data can be skipped). */ static void _write_chunk(hid_t data_id, h5file::extending_s *cur, int rank, - const size_t *chunk_start, const size_t *chunk_dims, - hid_t datatype, void *data) { + const size_t *chunk_start, const size_t *chunk_dims, hid_t datatype, + void *data) { #ifdef HAVE_HDF5 int i; bool do_write = true; @@ -715,7 +717,8 @@ void h5file::done_writing_chunks() { I'm assuming(?) that non-extensible datasets will use different files, etcetera, for different timesteps. All of this hackery goes away if we just use an MPI-compiled version of HDF5. */ - if (parallel && !local && cur_dataname && get_extending(cur_dataname)) prevent_deadlock(); // closes id + if (parallel && !local && cur_dataname && get_extending(cur_dataname)) + prevent_deadlock(); // closes id } void h5file::write(const char *dataname, int rank, const size_t *dims, void *data, @@ -832,14 +835,12 @@ static void _read_chunk(hid_t data_id, int rank, const size_t *chunk_start, void h5file::read_chunk(int rank, const size_t *chunk_start, const size_t *chunk_dims, float *data) { - _read_chunk(HID(cur_id), rank, chunk_start, chunk_dims, - H5T_NATIVE_FLOAT, data); + _read_chunk(HID(cur_id), rank, chunk_start, chunk_dims, H5T_NATIVE_FLOAT, data); } void h5file::read_chunk(int rank, const size_t *chunk_start, const size_t *chunk_dims, double *data) { - _read_chunk(HID(cur_id), rank, chunk_start, chunk_dims, - H5T_NATIVE_DOUBLE, data); + _read_chunk(HID(cur_id), rank, chunk_start, chunk_dims, H5T_NATIVE_DOUBLE, data); } void h5file::read_chunk(int rank, const size_t *chunk_start, const size_t *chunk_dims, diff --git a/src/integrate.cpp b/src/integrate.cpp index 23fe3bb9e..f9efaaf2e 100644 --- a/src/integrate.cpp +++ b/src/integrate.cpp @@ -234,7 +234,7 @@ double fields::max_abs(int num_fvals, const component *components, field_rfuncti } static complex return_the_field(const complex *fields, const vec &loc, - void *integrand_data_) { + void *integrand_data_) { (void)integrand_data_; (void)loc; // unused return cdouble(fields[0]); diff --git a/src/loop_in_chunks.cpp b/src/loop_in_chunks.cpp index c0880888e..a831713b7 100644 --- a/src/loop_in_chunks.cpp +++ b/src/loop_in_chunks.cpp @@ -339,8 +339,8 @@ void compute_boundary_weights(grid_volume gv, const volume &where, ivec &is, ive void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, const volume &where, component cgrid, bool use_symmetry, bool snap_empty_dimensions) { if (coordinate_mismatch(gv.dim, cgrid)) - meep::abort("Invalid fields::loop_in_chunks grid type %s for dimensions %s\n", component_name(cgrid), - dimension_name(gv.dim)); + meep::abort("Invalid fields::loop_in_chunks grid type %s for dimensions %s\n", + component_name(cgrid), dimension_name(gv.dim)); if (where.dim != gv.dim) meep::abort("Invalid dimensions %d for WHERE in fields::loop_in_chunks", where.dim); @@ -359,11 +359,15 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con compute_boundary_weights(gv, where, is, ie, snap_empty_dimensions, s0, e0, s1, e1); int original_vol = 1; - LOOP_OVER_DIRECTIONS(gv.dim, d){ + LOOP_OVER_DIRECTIONS(gv.dim, d) { if (boundaries[High][d] == Periodic && boundaries[Low][d] == Periodic) { - original_vol *= (ie.in_direction(d) - is.in_direction(d))/2+1; - } else { - int vol_size_d = (min(user_volume.big_owned_corner(cgrid), ie).in_direction(d) - max(user_volume.little_owned_corner(cgrid),is).in_direction(d))/2+1; + original_vol *= (ie.in_direction(d) - is.in_direction(d)) / 2 + 1; + } + else { + int vol_size_d = (min(user_volume.big_owned_corner(cgrid), ie).in_direction(d) - + max(user_volume.little_owned_corner(cgrid), is).in_direction(d)) / + 2 + + 1; original_vol *= (vol_size_d > 0 ? vol_size_d : 0); } } @@ -390,9 +394,8 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con min_ishift.set_direction( d, int(floor((where.in_direction_min(d) - gvS.in_direction_max(d)) / L.in_direction(d)))); - max_ishift.set_direction( - d, - int(ceil((where.in_direction_max(d) - gvS.in_direction_min(d)) / L.in_direction(d)))); + max_ishift.set_direction(d, int(ceil((where.in_direction_max(d) - gvS.in_direction_min(d)) / + L.in_direction(d)))); } else { min_ishift.set_direction(d, 0); @@ -417,25 +420,30 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con grid_volume gvu(chunks[i]->gv); ivec _iscoS(S.transform(gvu.little_owned_corner(cS), sn)); ivec _iecoS(S.transform(gvu.big_owned_corner(cS), sn)); - ivec iscoS(max(user_volume.little_owned_corner(cgrid), min(_iscoS, _iecoS))), iecoS(max(_iscoS, _iecoS)); // fix ordering due to to transform - - //With symmetry, the upper half of the original chunk is kept and includes one extra pixel. - //When looped over all symmetries, pixels outside the lower boundary "user_volume.little_owned_corner(cgrid)" is excluded. - //isym finds the lower boundary of the upper half chunk. Then in each direction that chunk isn't the upper, - //checked by ((S.transform(d, sn).d != d) != (S.transform(d, sn).flipped)), - //the end point iecoS will shift to not go beyond isym + ivec iscoS(max(user_volume.little_owned_corner(cgrid), min(_iscoS, _iecoS))), + iecoS(max(_iscoS, _iecoS)); // fix ordering due to to transform + + // With symmetry, the upper half of the original chunk is kept and includes one extra pixel. + // When looped over all symmetries, pixels outside the lower boundary + // "user_volume.little_owned_corner(cgrid)" is excluded. isym finds the lower boundary of + // the upper half chunk. Then in each direction that chunk isn't the upper, checked by + // ((S.transform(d, sn).d != d) != (S.transform(d, sn).flipped)), the end point iecoS will + // shift to not go beyond isym ivec isym(gvu.dim, INT_MAX); - LOOP_OVER_DIRECTIONS(gvu.dim, d){ - int off_sym_shift = ((gv.iyee_shift(cgrid).in_direction(d) != 0) ? gv.iyee_shift(cgrid).in_direction(d)+2 : 2); + LOOP_OVER_DIRECTIONS(gvu.dim, d) { + int off_sym_shift = ((gv.iyee_shift(cgrid).in_direction(d) != 0) + ? gv.iyee_shift(cgrid).in_direction(d) + 2 + : 2); isym.set_direction(d, S.i_symmetry_point.in_direction(d) - off_sym_shift); } ivec iscS(max(is - shifti, iscoS)); ivec chunk_corner(gvu.little_owned_corner(cgrid)); LOOP_OVER_DIRECTIONS(gv.dim, d) { - if ((S.transform(d, sn).d != d) != (S.transform(d, sn).flipped)) iecoS.set_direction(d, min(isym, iecoS).in_direction(d)); + if ((S.transform(d, sn).d != d) != (S.transform(d, sn).flipped)) + iecoS.set_direction(d, min(isym, iecoS).in_direction(d)); } - ivec iecS( min(ie - shifti, iecoS)); + ivec iecS(min(ie - shifti, iecoS)); if (iscS <= iecS) { // non-empty intersection // Determine weights at chunk looping boundaries: @@ -501,15 +509,14 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con } if (gv.dim == Dcyl) { dV1 = dV0 * 2 * pi * gv.inva; - dV0 *= 2 * pi * - fabs((S.transform(chunks[i]->gv[isc], sn) + shift).in_direction(R)); + dV0 *= 2 * pi * fabs((S.transform(chunks[i]->gv[isc], sn) + shift).in_direction(R)); } - chunkloop(chunks[i], i, cS, isc, iec, s0c, s1c, e0c, e1c, dV0, dV1, - shifti, ph, S, sn, chunkloop_data); + chunkloop(chunks[i], i, cS, isc, iec, s0c, s1c, e0c, e1c, dV0, dV1, shifti, ph, S, sn, + chunkloop_data); int loop_vol = 1; - LOOP_OVER_DIRECTIONS(gv.dim, d){ - loop_vol *= (iec.in_direction(d) - isc.in_direction(d))/2+1; + LOOP_OVER_DIRECTIONS(gv.dim, d) { + loop_vol *= (iec.in_direction(d) - isc.in_direction(d)) / 2 + 1; } vol_sum += loop_vol; } @@ -525,7 +532,9 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con } while (ishift != min_ishift); } int vol_sum_all = sum_to_all(vol_sum); - if (use_symmetry && vol_sum_all != original_vol) master_printf("WARNING vol mismatch:, original_vol %i, looped vol_sum %i \n", original_vol, vol_sum_all); + if (use_symmetry && vol_sum_all != original_vol) + master_printf("WARNING vol mismatch:, original_vol %i, looped vol_sum %i \n", original_vol, + vol_sum_all); } } // namespace meep diff --git a/src/material_data.cpp b/src/material_data.cpp index 774f2a3d3..a24d87cee 100644 --- a/src/material_data.cpp +++ b/src/material_data.cpp @@ -32,24 +32,14 @@ bool transition::operator==(const transition &other) const { bool transition::operator!=(const transition &other) const { return !(*this == other); } -medium_struct::medium_struct(double epsilon) : - epsilon_diag{epsilon, epsilon, epsilon}, - epsilon_offdiag{}, - mu_diag{1, 1, 1}, - mu_offdiag{}, - E_susceptibilities(), H_susceptibilities(), - E_chi2_diag{}, - E_chi3_diag{}, - H_chi2_diag{}, - H_chi3_diag{}, - D_conductivity_diag{}, - B_conductivity_diag{} - {} +medium_struct::medium_struct(double epsilon) + : epsilon_diag{epsilon, epsilon, epsilon}, epsilon_offdiag{}, mu_diag{1, 1, 1}, mu_offdiag{}, + E_susceptibilities(), H_susceptibilities(), E_chi2_diag{}, E_chi3_diag{}, H_chi2_diag{}, + H_chi3_diag{}, D_conductivity_diag{}, B_conductivity_diag{} {} void medium_struct::check_offdiag_im_zero_or_abort() const { - if (epsilon_offdiag.x.im != 0 || epsilon_offdiag.y.im != 0 || - epsilon_offdiag.z.im != 0 || mu_offdiag.x.im != 0 || mu_offdiag.y.im != 0 || - mu_offdiag.z.im != 0) { + if (epsilon_offdiag.x.im != 0 || epsilon_offdiag.y.im != 0 || epsilon_offdiag.z.im != 0 || + mu_offdiag.x.im != 0 || mu_offdiag.y.im != 0 || mu_offdiag.z.im != 0) { meep::abort("Found non-zero imaginary part of epsilon or mu offdiag.\n"); } } @@ -92,4 +82,4 @@ void material_data::copy_from(const material_data &from) { material_type_list::material_type_list() : items(NULL), num_items(0) {} -} // namespace meep_geom +} // namespace meep_geom diff --git a/src/material_data.hpp b/src/material_data.hpp index f81496c29..4e1df20d5 100644 --- a/src/material_data.hpp +++ b/src/material_data.hpp @@ -136,7 +136,7 @@ struct material_data { double beta; double eta; double damping; - bool trivial=true; + bool trivial = true; /* There are several possible scenarios when material grids overlap -- these different scenarios enable different applications. @@ -163,7 +163,7 @@ struct material_data { material_data(); - void copy_from(const material_data& from); + void copy_from(const material_data &from); }; typedef material_data *material_type; diff --git a/src/meep.hpp b/src/meep.hpp index 95025ed98..f5a721e81 100644 --- a/src/meep.hpp +++ b/src/meep.hpp @@ -394,7 +394,8 @@ class h5file { // If 'local_' is true, then 'parallel_' *must* be false and assumes that // each process is writing to a local non-shared file and the filename is // unique to the process. - h5file(const char *filename_, access_mode m = READWRITE, bool parallel_ = true, bool local_ = false); + h5file(const char *filename_, access_mode m = READWRITE, bool parallel_ = true, + bool local_ = false); ~h5file(); // closes the files (and any open dataset) bool ok(); @@ -729,8 +730,8 @@ enum in_or_out { Incoming = 0, Outgoing }; const std::initializer_list all_in_or_out{Incoming, Outgoing}; enum connect_phase { CONNECT_PHASE = 0, CONNECT_NEGATE = 1, CONNECT_COPY = 2 }; -const std::initializer_list all_connect_phases{ - CONNECT_PHASE, CONNECT_NEGATE, CONNECT_COPY}; +const std::initializer_list all_connect_phases{CONNECT_PHASE, CONNECT_NEGATE, + CONNECT_COPY}; constexpr int NUM_CONNECT_PHASE_TYPES = 3; // Communication pair(i, j) implies data being sent from i to j. @@ -768,7 +769,7 @@ struct comms_operation { ptrdiff_t my_chunk_idx; ptrdiff_t other_chunk_idx; int other_proc_id; - int pair_idx; // The numeric pair index used in many communications related arrays. + int pair_idx; // The numeric pair index used in many communications related arrays. size_t transfer_size; in_or_out comm_direction; int tag; @@ -787,7 +788,7 @@ struct comms_sequence { // RAII based comms_manager that allows asynchronous send and receive functions to be initiated. // Upon destruction, the comms_manager waits for completion of all enqueued operations. class comms_manager { - public: +public: using receive_callback = std::function; virtual ~comms_manager() {} virtual void send_real_async(const void *buf, size_t count, int dest, int tag) = 0; @@ -799,7 +800,6 @@ class comms_manager { // Factory function for `comms_manager`. std::unique_ptr create_comms_manager(); - class structure { public: structure_chunk **chunks; @@ -867,13 +867,12 @@ class structure { // file after computing their respective offsets into this file. When set to // 'false', each process writes data for the chunks it owns to a separate // (process unique) file. - void dump(const char *filename, bool single_parallel_file=true); - void load(const char *filename, bool single_parallel_file=true); + void dump(const char *filename, bool single_parallel_file = true); + void load(const char *filename, bool single_parallel_file = true); void dump_chunk_layout(const char *filename); void load_chunk_layout(const char *filename, boundary_region &br); - void load_chunk_layout(const std::vector &gvs, - const std::vector &ids, + void load_chunk_layout(const std::vector &gvs, const std::vector &ids, boundary_region &br); // monitor.cpp @@ -908,7 +907,8 @@ class structure { void changing_chunks(); // Helper methods for dumping and loading susceptibilities void set_chiP_from_file(h5file *file, const char *dataset, field_type ft); - void write_susceptibility_params(h5file *file, bool single_parallel_file, const char *dname, int EorH); + void write_susceptibility_params(h5file *file, bool single_parallel_file, const char *dname, + int EorH); std::unique_ptr bp; }; @@ -918,10 +918,8 @@ std::unique_ptr choose_chunkdivision(grid_volume &gv, volume & const symmetry &s); // defined in structure_dump.cpp -void split_by_binarytree(grid_volume gvol, - std::vector &result_gvs, - std::vector &result_ids, - const binary_partition *bp); +void split_by_binarytree(grid_volume gvol, std::vector &result_gvs, + std::vector &result_ids, const binary_partition *bp); class src_vol; class fields; class fields_chunk; @@ -939,8 +937,8 @@ class src_time { // sources are not, but this may change. bool is_integrated; - // a unique ID > 0 can be assigned to a src_time object by fields::register_src_time, - // in order to communicate it from one process to another; otherwise defaults to 0. + // a unique ID > 0 can be assigned to a src_time object by fields::register_src_time, + // in order to communicate it from one process to another; otherwise defaults to 0. size_t id; src_time() { @@ -1212,14 +1210,18 @@ class dft_chunk { int vc; // component descriptor from the original volume - private: - int decimation_factor; +private: + int decimation_factor; }; -void save_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix = 0, bool single_parallel_file=true); -void load_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix = 0, bool single_parallel_file=true); -void save_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix = 0, bool single_parallel_file=true); -void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix = 0, bool single_parallel_file=true); +void save_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix = 0, + bool single_parallel_file = true); +void load_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix = 0, + bool single_parallel_file = true); +void save_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix = 0, + bool single_parallel_file = true); +void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix = 0, + bool single_parallel_file = true); // dft.cpp (normally created with fields::add_dft_flux) class dft_flux { @@ -1327,7 +1329,6 @@ class dft_force { volume where; }; - struct sourcedata { component near_fd_comp; std::vector idx_arr; @@ -1335,7 +1336,6 @@ struct sourcedata { std::vector > amp_arr; }; - // near2far.cpp (normally created with fields::add_dft_near2far) class dft_near2far { public: @@ -1392,7 +1392,8 @@ class dft_near2far { int periodic_n[2]; double periodic_k[2], period[2]; - std::vector near_sourcedata(const vec &x_0, double* farpt_list, size_t nfar_pts, const std::complex* dJ); + std::vector near_sourcedata(const vec &x_0, double *farpt_list, size_t nfar_pts, + const std::complex *dJ); }; /* Class to compute local-density-of-states spectra: the power spectrum @@ -1412,17 +1413,17 @@ class dft_ldos { } void update(fields &f); // to be called after each timestep - double *ldos(); // returns array of Nomega values (after last timestep) + double *ldos(); // returns array of Nomega values (after last timestep) std::complex *F() const; // returns Fdft std::complex *J() const; // returns Jdft std::vector freq; double overall_scale() const { return saved_overall_scale; } private: - std::complex *Fdft; // Nomega array of field * J*(x) DFT values - std::complex *Jdft; // Nomega array of J(t) DFT values - double Jsum; // sum of |J| over all points - double saved_overall_scale; // saved overall scale for adjoint calculation + std::complex *Fdft; // Nomega array of field * J*(x) DFT values + std::complex *Jdft; // Nomega array of J(t) DFT values + double Jsum; // sum of |J| over all points + double saved_overall_scale; // saved overall scale for adjoint calculation }; // dft.cpp (normally created with fields::add_dft_fields) @@ -1431,7 +1432,8 @@ class dft_fields { dft_fields(dft_chunk *chunks, double freq_min, double freq_max, int Nfreq, const volume &where); dft_fields(dft_chunk *chunks, const std::vector &freq_, const volume &where); dft_fields(dft_chunk *chunks, const double *freq_, size_t Nfreq, const volume &where); - std::vector fourier_sourcedata(const volume &where, component c, fields &f, const std::complex* dJ); + std::vector fourier_sourcedata(const volume &where, component c, fields &f, + const std::complex *dJ); void scale_dfts(std::complex scale); void remove(); @@ -1441,7 +1443,6 @@ class dft_fields { volume where; }; - // data for each susceptibility typedef struct polarization_state_s { void *data; // internal polarization data for the susceptibility @@ -1618,7 +1619,7 @@ enum time_sink { BoundarySteppingPE, BoundarySteppingE }; -using time_sink_to_duration_map = std::unordered_map>; +using time_sink_to_duration_map = std::unordered_map >; // RAII-based profiling timer that accumulates wall time from creation until it // is destroyed or the `exit` method is invoked. Not thread-safe. @@ -1633,7 +1634,7 @@ class timing_scope { void exit(); private: - time_sink_to_duration_map *timers; // Not owned by us. + time_sink_to_duration_map *timers; // Not owned by us. time_sink sink; bool active; double t_start; @@ -1685,26 +1686,26 @@ class diffractedplanewave { std::complex get_p() const { return p; }; private: - int g[3]; // diffraction order - double axis[3]; // axis vector - std::complex s; // s polarization amplitude - std::complex p; // p polarization ampiltude + int g[3]; // diffraction order + double axis[3]; // axis vector + std::complex s; // s polarization amplitude + std::complex p; // p polarization ampiltude }; class gaussianbeam { public: - gaussianbeam(const vec &x0, const vec &kdir, double w0, double freq, - double eps, double mu, std::complex E0[3]); + gaussianbeam(const vec &x0, const vec &kdir, double w0, double freq, double eps, double mu, + std::complex E0[3]); void get_fields(std::complex *EH, const vec &x) const; std::complex get_E0(int n) const { return E0[n]; }; private: - vec x0; // beam center - vec kdir; // beam propagation direction - double w0; // beam waist radius - double freq; // beam frequency - double eps, mu; // permittivity/permeability of homogeneous medium + vec x0; // beam center + vec kdir; // beam propagation direction + double w0; // beam waist radius + double freq; // beam frequency + double eps, mu; // permittivity/permeability of homogeneous medium std::complex E0[3]; // polarization vector }; @@ -1749,7 +1750,7 @@ class fields { void remove_susceptibilities(); void remove_fluxes(); void reset(); - void log(const char* prefix = ""); + void log(const char *prefix = ""); // time.cpp std::vector time_spent_on(time_sink sink); @@ -1772,8 +1773,8 @@ class fields { // file after computing their respective offsets into this file. When set to // 'false', each process writes data for the chunks it owns to a separate // (process unique) file. - void dump(const char *filename, bool single_parallel_file=true); - void load(const char *filename, bool single_parallel_file=true); + void dump(const char *filename, bool single_parallel_file = true); + void load(const char *filename, bool single_parallel_file = true); // h5fields.cpp: // low-level function: @@ -1830,15 +1831,13 @@ class fields { // must eventually be caller-deallocated via delete[]. realnum *get_array_slice(const volume &where, std::vector components, field_rfunction rfun, void *fun_data, realnum *slice = 0, - double frequency = 0, - bool snap = false); + double frequency = 0, bool snap = false); std::complex *get_complex_array_slice(const volume &where, std::vector components, field_function fun, void *fun_data, std::complex *slice = 0, - double frequency = 0, - bool snap = false); + double frequency = 0, bool snap = false); // alternative entry points for when you have no field // function, i.e. you want just a single component or @@ -1849,8 +1848,7 @@ class fields { double frequency = 0, bool snap = false); std::complex *get_complex_array_slice(const volume &where, component c, std::complex *slice = 0, - double frequency = 0, - bool snap = false); + double frequency = 0, bool snap = false); // like get_array_slice, but for *sources* instead of fields std::complex *get_source_slice(const volume &where, component source_slice_component, @@ -1861,7 +1859,8 @@ class fields { field_function fun, field_rfunction rfun, void *fun_data, void *vslice, double frequency = 0, bool snap = false); - /* fetch and return coordinates and integration weights of grid points covered by an array slice, */ + /* fetch and return coordinates and integration weights of grid points covered by an array slice, + */ /* packed into a vector with format [NX, xtics[:], NY, ytics[:], NZ, ztics[:], weights[:] ] */ std::vector get_array_metadata(const volume &where); @@ -1903,8 +1902,12 @@ class fields { bool is_aniso2d(); void require_source_components(); void _require_component(component c, bool aniso2d); - void require_component(component c) { _require_component(c, is_aniso2d()); sync_chunk_connections(); } - void add_srcdata(struct sourcedata cur_data, src_time *src, size_t n, std::complex* amp_arr, bool needs_boundary_fix); + void require_component(component c) { + _require_component(c, is_aniso2d()); + sync_chunk_connections(); + } + void add_srcdata(struct sourcedata cur_data, src_time *src, size_t n, + std::complex *amp_arr, bool needs_boundary_fix); void register_src_time(src_time *src); src_time *lookup_src_time(size_t id); @@ -1923,8 +1926,7 @@ class fields { const volume &eig_vol, int band_num, const vec &kpoint, bool match_frequency, int parity, double eig_resolution, double eigensolver_tol, std::complex amp, - std::complex A(const vec &) = 0, - diffractedplanewave *dp = 0); + std::complex A(const vec &) = 0, diffractedplanewave *dp = 0); void get_eigenmode_coefficients(dft_flux flux, const volume &eig_vol, int *bands, int num_bands, int parity, double eig_resolution, double eigensolver_tol, @@ -1994,7 +1996,7 @@ class fields { bool sqrt_dV_and_interp_weights = false, std::complex extra_weight = 1.0, bool use_centered_grid = true, int vc = 0, int decimation_factor = 0, bool persist = false); - dft_chunk *add_dft(component c, const volume &where, const std::vector& freq, + dft_chunk *add_dft(component c, const volume &where, const std::vector &freq, bool include_dV_and_interp_weights = true, std::complex stored_weight = 1.0, dft_chunk *chunk_next = 0, bool sqrt_dV_and_interp_weights = false, @@ -2066,15 +2068,15 @@ class fields { } dft_flux add_mode_monitor(direction d, const volume &where, const std::vector &freq, bool centered_grid = true, int decimation_factor = 0) { - return add_mode_monitor(d, where, freq.data(), freq.size(), centered_grid, - decimation_factor); + return add_mode_monitor(d, where, freq.data(), freq.size(), centered_grid, decimation_factor); } dft_flux add_mode_monitor(direction d, const volume &where, const double *freq, size_t Nfreq, bool centered_grid = true, int decimation_factor = 0); dft_fields add_dft_fields(component *components, int num_components, const volume where, double freq_min, double freq_max, int Nfreq, - bool use_centered_grid = true, int decimation_factor = 0, bool persist = false) { + bool use_centered_grid = true, int decimation_factor = 0, + bool persist = false) { return add_dft_fields(components, num_components, where, linspace(freq_min, freq_max, Nfreq), use_centered_grid, decimation_factor, persist); } @@ -2162,8 +2164,7 @@ class fields { } dft_near2far add_dft_near2far(const volume_list *where, const std::vector &freq, int decimation_factor = 0, int Nperiods = 1) { - return add_dft_near2far(where, freq.data(), freq.size(), decimation_factor, - Nperiods); + return add_dft_near2far(where, freq.data(), freq.size(), decimation_factor, Nperiods); } dft_near2far add_dft_near2far(const volume_list *where, const double *freq, size_t Nfreq, int decimation_factor = 0, int Nperiods = 1); @@ -2183,7 +2184,10 @@ class fields { std::complex get_field(component c, const vec &loc, bool parallel = true) const; double get_field(derived_component c, const vec &loc, bool parallel = true) const; std::vector dft_monitor_size(dft_fields fdft, const volume &where, component c); - void get_dft_component_dims(dft_chunk **chunklists, int num_chunklists, component c, ivec &min_corner, ivec &max_corner, size_t &array_size, size_t &bufsz, int &rank, direction *ds, size_t *dims, int *array_rank=0, size_t *array_dims=0, direction *array_dirs=0); + void get_dft_component_dims(dft_chunk **chunklists, int num_chunklists, component c, + ivec &min_corner, ivec &max_corner, size_t &array_size, size_t &bufsz, + int &rank, direction *ds, size_t *dims, int *array_rank = 0, + size_t *array_dims = 0, direction *array_dirs = 0); // energy_and_flux.cpp void synchronize_magnetic_fields(); @@ -2237,7 +2241,8 @@ class fields { void figure_out_step_plan(); // boundaries.cpp bool chunk_connections_valid; - bool changed_materials; // keep track of whether materials have changed (in case field chunk connections need sync'ing) + bool changed_materials; // keep track of whether materials have changed (in case field chunk + // connections need sync'ing) void find_metals(); void disconnect_chunks(); void connect_chunks(); @@ -2265,9 +2270,9 @@ class fields { // Helper methods for dumping field chunks. using FieldPtrGetter = std::function; void dump_fields_chunk_field(h5file *h5f, bool single_parallel_file, - const std::string& field_name, FieldPtrGetter field_ptr_getter); + const std::string &field_name, FieldPtrGetter field_ptr_getter); void load_fields_chunk_field(h5file *h5f, bool single_parallel_file, - const std::string& field_name, FieldPtrGetter field_ptr_getter); + const std::string &field_name, FieldPtrGetter field_ptr_getter); public: // monitor.cpp @@ -2297,19 +2302,19 @@ class fields { // The sequence of send and receive operations for each field type. comms_sequence comms_sequence_for_field[NUM_FIELD_TYPES]; - size_t get_comm_size(const comms_key& key) const { + size_t get_comm_size(const comms_key &key) const { auto it = comm_sizes.find(key); return (it != comm_sizes.end()) ? it->second : 0; } - size_t comm_size_tot(field_type ft, const chunk_pair& pair) const { + size_t comm_size_tot(field_type ft, const chunk_pair &pair) const { size_t sum = 0; for (auto ip : all_connect_phases) sum += get_comm_size({ft, ip, pair}); return sum; } - int chunk_pair_to_index(const chunk_pair& pair) const { + int chunk_pair_to_index(const chunk_pair &pair) const { return pair.first + num_chunks * pair.second; } }; @@ -2393,8 +2398,8 @@ std::complex eigenmode_amplitude(void *vedata, const vec &p, component c double get_group_velocity(void *vedata); vec get_k(void *vedata); -double linear_interpolate(double rx, double ry, double rz, double *data, - int nx, int ny, int nz, int stride); +double linear_interpolate(double rx, double ry, double rz, double *data, int nx, int ny, int nz, + int stride); // Value class that combines split direction and position. struct split_plane { @@ -2413,17 +2418,17 @@ class binary_partition { // Takes ownership of `left_tree` and `right_tree`. binary_partition(const split_plane &_split_plane, std::unique_ptr &&left_tree, std::unique_ptr &&right_tree); - binary_partition(const binary_partition& other); + binary_partition(const binary_partition &other); bool is_leaf() const; // Returns the leaf node ID iff is_leaf() == true. int get_proc_id() const; // Returns the split plane iff is_leaf() == false. - const split_plane& get_plane() const; + const split_plane &get_plane() const; // Returns a pointer to the left subtree node iff is_leaf() == false. - const binary_partition* left_tree() const; + const binary_partition *left_tree() const; // Returns a pointer to the right subtree node iff is_leaf() == false. - const binary_partition* right_tree() const; + const binary_partition *right_tree() const; private: int proc_id; diff --git a/src/meep/vec.hpp b/src/meep/vec.hpp index 4996b2dc7..461611166 100644 --- a/src/meep/vec.hpp +++ b/src/meep/vec.hpp @@ -148,7 +148,6 @@ component first_field_component(field_type ft); for (meep::direction d = dim == meep::Dcyl ? meep::Z : meep::X; \ d < (dim == meep::Dcyl ? meep::NO_DIRECTION : meep::R); d = meep::direction(d + 1)) - #define LOOP_OVER_IVECS(gv, is, ie, idx) \ for (ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), \ loop_is3 = (is).yucky_val(2), loop_n1 = ((ie).yucky_val(0) - loop_is1) / 2 + 1, \ @@ -200,7 +199,7 @@ component first_field_component(field_type ft); // the most generic use case where the user // can specify a custom clause #define PLOOP_OVER_IVECS_C(gv, is, ie, idx, clause) \ -for(ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), \ + for (ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), \ loop_is3 = (is).yucky_val(2), loop_n1 = ((ie).yucky_val(0) - loop_is1) / 2 + 1, \ loop_n2 = ((ie).yucky_val(1) - loop_is2) / 2 + 1, \ loop_n3 = ((ie).yucky_val(2) - loop_is3) / 2 + 1, \ @@ -212,13 +211,18 @@ for(ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), idx0 = (is - (gv).little_corner()).yucky_val(0) / 2 * loop_s1 + \ (is - (gv).little_corner()).yucky_val(1) / 2 * loop_s2 + \ (is - (gv).little_corner()).yucky_val(2) / 2 * loop_s3, \ - dummy_first=0;dummy_first<1;dummy_first++) \ -_Pragma(clause) \ - for (ptrdiff_t loop_i1 = 0; loop_i1 < loop_n1; loop_i1++) \ - for (ptrdiff_t loop_i2 = 0; loop_i2 < loop_n2; loop_i2++) \ - for (ptrdiff_t loop_i3 = 0; loop_i3 < loop_n3; loop_i3++) \ - for (ptrdiff_t idx = idx0 + loop_i1*loop_s1 + loop_i2*loop_s2 + \ - loop_i3*loop_s3, dummy_last=0;dummy_last<1;dummy_last++) + dummy_first = 0; \ + dummy_first < 1; dummy_first++) \ + _Pragma( \ + clause) for (ptrdiff_t loop_i1 = 0; loop_i1 < loop_n1; \ + loop_i1++) for (ptrdiff_t loop_i2 = 0; loop_i2 < loop_n2; \ + loop_i2++) for (ptrdiff_t loop_i3 = 0; loop_i3 < loop_n3; \ + loop_i3++) for (ptrdiff_t idx = \ + idx0 + loop_i1 * loop_s1 + \ + loop_i2 * loop_s2 + \ + loop_i3 * loop_s3, \ + dummy_last = 0; \ + dummy_last < 1; dummy_last++) // For the main timestepping events, we know // we want to do a simple collapse @@ -227,7 +231,7 @@ _Pragma(clause) #define PLOOP_OVER_VOL(gv, c, idx) \ PLOOP_OVER_IVECS(gv, (gv).little_corner() + (gv).iyee_shift(c), \ - (gv).big_corner() + (gv).iyee_shift(c), idx) + (gv).big_corner() + (gv).iyee_shift(c), idx) #define PLOOP_OVER_VOL_OWNED(gv, c, idx) \ PLOOP_OVER_IVECS(gv, (gv).little_owned_corner(c), (gv).big_corner(), idx) @@ -256,7 +260,8 @@ _Pragma(clause) // should only use these macros where that is true! (Basically, // all of this is here to support performance hacks of step_generic.) -#if !defined(__INTEL_COMPILER) && !defined(__clang__) && !defined(_OPENMP) && (defined(__GNUC__) || defined(__GNUG__)) +#if !defined(__INTEL_COMPILER) && !defined(__clang__) && !defined(_OPENMP) && \ + (defined(__GNUC__) || defined(__GNUG__)) #define IVDEP _Pragma("GCC ivdep") #elif defined(_OPENMP) #define IVDEP _Pragma("omp simd") @@ -307,7 +312,7 @@ _Pragma(clause) We can use simd vectorization in addition to the usual par for optimization */ // loop over indices idx from is to ie (inclusive) in gv #define PS1LOOP_OVER_IVECS(gv, is, ie, idx) \ -for(ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), \ + for (ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), \ loop_is3 = (is).yucky_val(2), loop_n1 = ((ie).yucky_val(0) - loop_is1) / 2 + 1, \ loop_n2 = ((ie).yucky_val(1) - loop_is2) / 2 + 1, \ loop_n3 = ((ie).yucky_val(2) - loop_is3) / 2 + 1, \ @@ -317,22 +322,25 @@ for(ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), idx0 = (is - (gv).little_corner()).yucky_val(0) / 2 * loop_s1 + \ (is - (gv).little_corner()).yucky_val(1) / 2 * loop_s2 + \ (is - (gv).little_corner()).yucky_val(2) / 2 * loop_s3, \ - dummy_first=0;dummy_first<1;dummy_first++) \ -_Pragma("omp parallel for collapse(2)") \ - for (ptrdiff_t loop_i1 = 0; loop_i1 < loop_n1; loop_i1++) \ - for (ptrdiff_t loop_i2 = 0; loop_i2 < loop_n2; loop_i2++) \ - _Pragma("omp simd") \ - for (ptrdiff_t loop_i3 = 0; loop_i3 < loop_n3; loop_i3++) \ - for (ptrdiff_t idx = idx0 + loop_i1 * loop_s1 + loop_i2 * loop_s2 + loop_i3, dummy_last=0;dummy_last<1;dummy_last++) - -#define PS1LOOP_OVER_VOL(gv, c, idx) \ - PS1LOOP_OVER_IVECS(gv, (gv).little_corner() + (gv).iyee_shift(c), \ - (gv).big_corner() + (gv).iyee_shift(c), idx) - -#define PS1LOOP_OVER_VOL_OWNED(gv, c, idx) \ + dummy_first = 0; \ + dummy_first < 1; dummy_first++) \ + _Pragma("omp parallel for collapse(2)") for (ptrdiff_t loop_i1 = 0; loop_i1 < loop_n1; \ + loop_i1++) for (ptrdiff_t loop_i2 = 0; \ + loop_i2 < loop_n2; loop_i2++) \ + _Pragma("omp simd") for (ptrdiff_t loop_i3 = 0; loop_i3 < loop_n3; \ + loop_i3++) for (ptrdiff_t idx = idx0 + loop_i1 * loop_s1 + \ + loop_i2 * loop_s2 + loop_i3, \ + dummy_last = 0; \ + dummy_last < 1; dummy_last++) + +#define PS1LOOP_OVER_VOL(gv, c, idx) \ + PS1LOOP_OVER_IVECS(gv, (gv).little_corner() + (gv).iyee_shift(c), \ + (gv).big_corner() + (gv).iyee_shift(c), idx) + +#define PS1LOOP_OVER_VOL_OWNED(gv, c, idx) \ PS1LOOP_OVER_IVECS(gv, (gv).little_owned_corner(c), (gv).big_corner(), idx) -#define PS1LOOP_OVER_VOL_OWNED0(gv, c, idx) \ +#define PS1LOOP_OVER_VOL_OWNED0(gv, c, idx) \ PS1LOOP_OVER_IVECS(gv, (gv).little_owned_corner0(c), (gv).big_corner(), idx) #define PS1LOOP_OVER_VOL_NOTOWNED(gv, c, idx) \ @@ -349,15 +357,15 @@ _Pragma("omp parallel for collapse(2)") (loop_s3 != 0 && (loop_i3 == 0 || loop_i3 == loop_n3 - 1))) #define IVEC_LOOP_ILOC(gv, iloc) \ - meep::ivec iloc((gv).dim); \ - iloc.set_direction(meep::direction(loop_d1), loop_is1 + 2 * loop_i1); \ - iloc.set_direction(meep::direction(loop_d2), loop_is2 + 2 * loop_i2); \ + meep::ivec iloc((gv).dim); \ + iloc.set_direction(meep::direction(loop_d1), loop_is1 + 2 * loop_i1); \ + iloc.set_direction(meep::direction(loop_d2), loop_is2 + 2 * loop_i2); \ iloc.set_direction(meep::direction(loop_d3), loop_is3 + 2 * loop_i3) #define IVEC_LOOP_LOC(gv, loc) \ - meep::vec loc((gv).dim); \ - loc.set_direction(meep::direction(loop_d1), (0.5 * loop_is1 + loop_i1) * (gv).inva); \ - loc.set_direction(meep::direction(loop_d2), (0.5 * loop_is2 + loop_i2) * (gv).inva); \ + meep::vec loc((gv).dim); \ + loc.set_direction(meep::direction(loop_d1), (0.5 * loop_is1 + loop_i1) * (gv).inva); \ + loc.set_direction(meep::direction(loop_d2), (0.5 * loop_is2 + loop_i2) * (gv).inva); \ loc.set_direction(meep::direction(loop_d3), (0.5 * loop_is3 + loop_i3) * (gv).inva) // integration weight for using LOOP_OVER_IVECS with field::integrate @@ -365,9 +373,8 @@ _Pragma("omp parallel for collapse(2)") ((i > 1 && i < n - 2) \ ? 1.0 \ : (i == 0 ? (s0).in_direction(meep::direction(dir)) \ - : (i == 1 ? (s1).in_direction(meep::direction(dir)) \ - : i == n - 1 \ - ? (e0).in_direction(meep::direction(dir)) \ + : (i == 1 ? (s1).in_direction(meep::direction(dir)) \ + : i == n - 1 ? (e0).in_direction(meep::direction(dir)) \ : (i == n - 2 ? (e1).in_direction(meep::direction(dir)) : 1.0)))) #define IVEC_LOOP_WEIGHT1(s0, s1, e0, e1, k) \ IVEC_LOOP_WEIGHT1x(s0, s1, e0, e1, loop_i##k, loop_n##k, loop_d##k) @@ -412,8 +419,8 @@ inline bool is_electric(component c) { return c < Hx; } inline bool is_magnetic(component c) { return c >= Hx && c < Dx; } inline bool is_D(component c) { return c >= Dx && c < Bx; } inline bool is_B(component c) { return c >= Bx && c < Dielectric; } -inline bool is_E_or_D(component c) {return is_electric(c) || is_D(c); } -inline bool is_H_or_B(component c) {return is_magnetic(c) || is_B(c); } +inline bool is_E_or_D(component c) { return is_electric(c) || is_D(c); } +inline bool is_H_or_B(component c) { return is_magnetic(c) || is_B(c); } inline bool is_derived(int c) { return c >= Sx; } inline bool is_poynting(derived_component c) { return c < EnergyDensity; } inline bool is_energydensity(derived_component c) { return c >= EnergyDensity; } @@ -1149,13 +1156,10 @@ class grid_volume { const char *str(char *buffer = 0, size_t buflen = 0); - std::complex get_split_costs(direction d, int split_point, - bool frag_cost) const; - void tile_split(int &best_split_point, - direction &best_split_direction) const; - void find_best_split(int desired_chunks, bool frag_cost, - int &best_split_point, direction &best_split_direction, - double &left_effort_fraction) const; + std::complex get_split_costs(direction d, int split_point, bool frag_cost) const; + void tile_split(int &best_split_point, direction &best_split_direction) const; + void find_best_split(int desired_chunks, bool frag_cost, int &best_split_point, + direction &best_split_direction, double &left_effort_fraction) const; private: grid_volume(ndim d, double ta, int na, int nb, int nc); @@ -1209,7 +1213,7 @@ class symmetry { volume_list *reduce(const volume_list *gl) const; symmetry operator+(const symmetry &) const; - symmetry operator*(std::complex)const; + symmetry operator*(std::complex) const; symmetry operator-(const symmetry &b) const { return *this + b * (-1.0); } symmetry operator-(void) const { return *this * (-1.0); } void operator=(const symmetry &); diff --git a/src/meep_internals.hpp b/src/meep_internals.hpp index c3461b3b1..b543ed41c 100644 --- a/src/meep_internals.hpp +++ b/src/meep_internals.hpp @@ -33,11 +33,8 @@ static inline int my_round(double x) { return int(floor(fabs(x) + 0.5) * (x < 0 int mirrorindex(int i, int n); /* map the cell coordinates into the range [0,1] */ -void map_coordinates(double rx, double ry, double rz, - int nx, int ny, int nz, - int &x1, int &y1, int &z1, - int &x2, int &y2, int &z2, - double &dx, double &dy, double &dz, +void map_coordinates(double rx, double ry, double rz, int nx, int ny, int nz, int &x1, int &y1, + int &z1, int &x2, int &y2, int &z2, double &dx, double &dy, double &dz, bool do_fabs = true); inline int small_r_metal(int m) { return m - 1; } @@ -55,7 +52,7 @@ class src_vol { // Constructs a new source volume. Takes ownership of `ind` and `amps`. // Requirement: ind.size() == amps.size() src_vol(component cc, src_time *st, std::vector &&ind, - std::vector > &&s, bool fix_boundaries=false); + std::vector > &&s, bool fix_boundaries = false); // Checks whether `a` and `b` are combinable, i.e. have the same indices and point to the same // `src_time` instance, but have potentially different amplitudes. @@ -77,7 +74,7 @@ class src_vol { // It is recommended to use `combinable` before calling this method. void add_amplitudes_from(const src_vol &other); - const component c; // field component the source applies to + const component c; // field component the source applies to bool needs_boundary_fix; // whether fix_boundary_sources needs calling private: src_time *src_t; // Not owned by us. @@ -91,78 +88,79 @@ symmetry r_to_minus_r_symmetry(int m); // functions in step_generic.cpp: -void step_curl(realnum *f, component c, const realnum *g1, const realnum *g2, - ptrdiff_t s1, ptrdiff_t s2, // strides for g1/g2 shift - const grid_volume &gv, const ivec is, const ivec ie, - realnum dtdx, direction dsig, const realnum *sig, - const realnum *kap, const realnum *siginv, realnum *fu, direction dsigu, - const realnum *sigu, const realnum *kapu, const realnum *siginvu, realnum dt, - const realnum *cnd, const realnum *cndinv, realnum *fcnd); - -void step_update_EDHB(realnum *f, component fc, const grid_volume &gv, const ivec is, const ivec ie, const realnum *g, - const realnum *g1, const realnum *g2, const realnum *u, const realnum *u1, - const realnum *u2, ptrdiff_t s, ptrdiff_t s1, ptrdiff_t s2, +void step_curl(realnum *f, component c, const realnum *g1, const realnum *g2, ptrdiff_t s1, + ptrdiff_t s2, // strides for g1/g2 shift + const grid_volume &gv, const ivec is, const ivec ie, realnum dtdx, direction dsig, + const realnum *sig, const realnum *kap, const realnum *siginv, realnum *fu, + direction dsigu, const realnum *sigu, const realnum *kapu, const realnum *siginvu, + realnum dt, const realnum *cnd, const realnum *cndinv, realnum *fcnd); + +void step_update_EDHB(realnum *f, component fc, const grid_volume &gv, const ivec is, const ivec ie, + const realnum *g, const realnum *g1, const realnum *g2, const realnum *u, + const realnum *u1, const realnum *u2, ptrdiff_t s, ptrdiff_t s1, ptrdiff_t s2, const realnum *chi2, const realnum *chi3, realnum *fw, direction dsigw, const realnum *sigw, const realnum *kapw); -void step_beta(realnum *f, component c, const realnum *g, const grid_volume &gv, const ivec is, const ivec ie, realnum betadt, - direction dsig, const realnum *siginv, realnum *fu, direction dsigu, - const realnum *siginvu, const realnum *cndinv, realnum *fcnd); +void step_beta(realnum *f, component c, const realnum *g, const grid_volume &gv, const ivec is, + const ivec ie, realnum betadt, direction dsig, const realnum *siginv, realnum *fu, + direction dsigu, const realnum *siginvu, const realnum *cndinv, realnum *fcnd); // functions in step_generic_stride1.cpp, generated from step_generic.cpp: -void step_curl_stride1(realnum *f, component c, const realnum *g1, const realnum *g2, - ptrdiff_t s1, ptrdiff_t s2, // strides for g1/g2 shift - const grid_volume &gv, const ivec is, const ivec ie, - realnum dtdx, direction dsig, const realnum *sig, - const realnum *kap, const realnum *siginv, realnum *fu, direction dsigu, - const realnum *sigu, const realnum *kapu, const realnum *siginvu, realnum dt, - const realnum *cnd, const realnum *cndinv, realnum *fcnd); - -void step_update_EDHB_stride1(realnum *f, component fc, const grid_volume &gv, const ivec is, const ivec ie, const realnum *g, - const realnum *g1, const realnum *g2, const realnum *u, - const realnum *u1, const realnum *u2, ptrdiff_t s, ptrdiff_t s1, - ptrdiff_t s2, const realnum *chi2, const realnum *chi3, realnum *fw, - direction dsigw, const realnum *sigw, const realnum *kapw); - -void step_beta_stride1(realnum *f, component c, const realnum *g, const grid_volume &gv, const ivec is, const ivec ie, - realnum betadt, direction dsig, const realnum *siginv, realnum *fu, - direction dsigu, const realnum *siginvu, const realnum *cndinv, - realnum *fcnd); +void step_curl_stride1(realnum *f, component c, const realnum *g1, const realnum *g2, ptrdiff_t s1, + ptrdiff_t s2, // strides for g1/g2 shift + const grid_volume &gv, const ivec is, const ivec ie, realnum dtdx, + direction dsig, const realnum *sig, const realnum *kap, + const realnum *siginv, realnum *fu, direction dsigu, const realnum *sigu, + const realnum *kapu, const realnum *siginvu, realnum dt, const realnum *cnd, + const realnum *cndinv, realnum *fcnd); + +void step_update_EDHB_stride1(realnum *f, component fc, const grid_volume &gv, const ivec is, + const ivec ie, const realnum *g, const realnum *g1, const realnum *g2, + const realnum *u, const realnum *u1, const realnum *u2, ptrdiff_t s, + ptrdiff_t s1, ptrdiff_t s2, const realnum *chi2, const realnum *chi3, + realnum *fw, direction dsigw, const realnum *sigw, + const realnum *kapw); + +void step_beta_stride1(realnum *f, component c, const realnum *g, const grid_volume &gv, + const ivec is, const ivec ie, realnum betadt, direction dsig, + const realnum *siginv, realnum *fu, direction dsigu, const realnum *siginvu, + const realnum *cndinv, realnum *fcnd); /* macro wrappers around time-stepping functions: for performance reasons, if the inner loop is stride-1 then we use the stride-1 versions, which allow gcc (and possibly other compilers) to do additional optimizations, especially loop vectorization */ -#define STEP_CURL(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, sigu, kapu, \ - siginvu, dt, cnd, cndinv, fcnd) \ +#define STEP_CURL(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, sigu, \ + kapu, siginvu, dt, cnd, cndinv, fcnd) \ do { \ if (LOOPS_ARE_STRIDE1(gv)) \ - step_curl_stride1(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, sigu, \ - kapu, siginvu, dt, cnd, cndinv, fcnd); \ + step_curl_stride1(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, \ + sigu, kapu, siginvu, dt, cnd, cndinv, fcnd); \ else \ - step_curl(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, sigu, kapu, \ - siginvu, dt, cnd, cndinv, fcnd); \ + step_curl(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, sigu, \ + kapu, siginvu, dt, cnd, cndinv, fcnd); \ } while (0) -#define STEP_UPDATE_EDHB(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, dsigw, sigw, \ - kapw) \ +#define STEP_UPDATE_EDHB(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, \ + dsigw, sigw, kapw) \ do { \ if (LOOPS_ARE_STRIDE1(gv)) \ - step_update_EDHB_stride1(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, dsigw, \ - sigw, kapw); \ + step_update_EDHB_stride1(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, \ + dsigw, sigw, kapw); \ else \ - step_update_EDHB(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, dsigw, sigw, \ - kapw); \ + step_update_EDHB(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, dsigw, \ + sigw, kapw); \ } while (0) -#define STEP_BETA(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, fcnd) \ +#define STEP_BETA(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, fcnd) \ do { \ if (LOOPS_ARE_STRIDE1(gv)) \ - step_beta_stride1(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, fcnd); \ + step_beta_stride1(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, \ + fcnd); \ else \ - step_beta(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, fcnd); \ + step_beta(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, fcnd); \ } while (0) // analytical Green's functions from near2far.cpp, which we might want to expose someday @@ -175,8 +173,11 @@ void greencyl(std::complex *EH, const vec &x, double freq, double eps, d // functions in array_slice.cpp: -complex *collapse_array(complex *array, int *rank, size_t dims[3], direction dirs[3], volume where); +complex *collapse_array(complex *array, int *rank, size_t dims[3], + direction dirs[3], volume where); -void reduce_array_dimensions(volume where, int full_rank, size_t dims[3], direction dirs[3], size_t stride[3], int &reduced_rank, size_t reduced_dims[3], direction reduced_dirs[3], size_t reduced_stride[3]); +void reduce_array_dimensions(volume where, int full_rank, size_t dims[3], direction dirs[3], + size_t stride[3], int &reduced_rank, size_t reduced_dims[3], + direction reduced_dirs[3], size_t reduced_stride[3]); } // namespace meep diff --git a/src/meepgeom.cpp b/src/meepgeom.cpp index 530e3a9f1..6b83d109b 100644 --- a/src/meepgeom.cpp +++ b/src/meepgeom.cpp @@ -232,9 +232,7 @@ int sym_matrix_positive_definite(symm_matrix *V) { static meep::ndim dim = meep::D3; void set_dimensions(int dims) { if (dims == CYLINDRICAL) { dim = meep::Dcyl; } - else { - dim = meep::ndim(dims - 1); - } + else { dim = meep::ndim(dims - 1); } } vector3 vec_to_vector3(const meep::vec &pt) { @@ -328,12 +326,10 @@ bool is_metal(meep::field_type ft, const material_type *material) { } bool has_offdiag(const medium_struct *material) { - if ((material->epsilon_offdiag.x.re != 0) || /* account for offdiagonal components */ - (material->epsilon_offdiag.y.re != 0) || - (material->epsilon_offdiag.z.re != 0) || - (material->epsilon_offdiag.x.im != 0) || - (material->epsilon_offdiag.y.im != 0) || - (material->epsilon_offdiag.z.im != 0)) + if ((material->epsilon_offdiag.x.re != 0) || /* account for offdiagonal components */ + (material->epsilon_offdiag.y.re != 0) || (material->epsilon_offdiag.z.re != 0) || + (material->epsilon_offdiag.x.im != 0) || (material->epsilon_offdiag.y.im != 0) || + (material->epsilon_offdiag.z.im != 0)) return true; else return false; @@ -362,8 +358,8 @@ vector3 to_geom_object_coords_VJP(vector3 v, const geometric_object *o) { if (size.z != 0.0) v.z /= size.z; return matrix3x3_transpose_vector3_mult(o->subclass.block_data->projection_matrix, v); } - /* case geometric_object::PRISM: - NOT YET IMPLEMENTED */ + /* case geometric_object::PRISM: + NOT YET IMPLEMENTED */ } } @@ -383,9 +379,7 @@ meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_objec double dx, dy, dz; bool signflip_dx = false, signflip_dy = false, signflip_dz = false; - meep::map_coordinates(rx, ry, rz, nx, ny, nz, - x1, y1, z1, x2, y2, z2, - dx, dy, dz, + meep::map_coordinates(rx, ry, rz, nx, ny, nz, x1, y1, z1, x2, y2, z2, dx, dy, dz, false /* do_fabs */); if (dx != fabs(dx)) { @@ -405,21 +399,23 @@ meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_objec in row-major order: */ #define D(x, y, z) (data[(((x)*ny + (y)) * nz + (z)) * stride]) - double du_dx = (signflip_dx ? -1.0 : 1.0) * - (((-D(x1, y1, z1) + D(x2, y1, z1)) * (1.0 - dy) + - (-D(x1, y2, z1) + D(x2, y2, z1)) * dy) * (1.0 - dz) + - ((-D(x1, y1, z2) + D(x2, y1, z2)) * (1.0 - dy) + - (-D(x1, y2, z2) + D(x2, y2, z2)) * dy) * dz); - double du_dy = (signflip_dy ? -1.0 : 1.0) * - ((-(D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) + - (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx)) * (1.0 - dz) + - (-(D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) + - (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx)) * dz); + double du_dx = + (signflip_dx ? -1.0 : 1.0) * + (((-D(x1, y1, z1) + D(x2, y1, z1)) * (1.0 - dy) + (-D(x1, y2, z1) + D(x2, y2, z1)) * dy) * + (1.0 - dz) + + ((-D(x1, y1, z2) + D(x2, y1, z2)) * (1.0 - dy) + (-D(x1, y2, z2) + D(x2, y2, z2)) * dy) * + dz); + double du_dy = (signflip_dy ? -1.0 : 1.0) * ((-(D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) + + (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx)) * + (1.0 - dz) + + (-(D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) + + (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx)) * + dz); double du_dz = (signflip_dz ? -1.0 : 1.0) * - (-((D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) * (1.0 - dy) + - (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx) * dy) + - ((D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) * (1.0 - dy) + - (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx) * dy)); + (-((D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) * (1.0 - dy) + + (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx) * dy) + + ((D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) * (1.0 - dy) + + (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx) * dy)); #undef D @@ -439,27 +435,28 @@ meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_objec gradient.set_direction(meep::Z, grad_u_J.z); } else { - gradient.set_direction(meep::X, geometry_lattice.size.x == 0 ? 0 : grad_u.x / geometry_lattice.size.x); - gradient.set_direction(meep::Y, geometry_lattice.size.y == 0 ? 0 : grad_u.y / geometry_lattice.size.y); - gradient.set_direction(meep::Z, geometry_lattice.size.z == 0 ? 0 : grad_u.z / geometry_lattice.size.z); + gradient.set_direction(meep::X, + geometry_lattice.size.x == 0 ? 0 : grad_u.x / geometry_lattice.size.x); + gradient.set_direction(meep::Y, + geometry_lattice.size.y == 0 ? 0 : grad_u.y / geometry_lattice.size.y); + gradient.set_direction(meep::Z, + geometry_lattice.size.z == 0 ? 0 : grad_u.z / geometry_lattice.size.z); } return gradient; } void map_lattice_coordinates(double &px, double &py, double &pz) { - px = geometry_lattice.size.x == 0 ? 0 - : 0.5 + (px - geometry_center.x) / geometry_lattice.size.x; - py = geometry_lattice.size.y == 0 ? 0 - : 0.5 + (py - geometry_center.y) / geometry_lattice.size.y; - pz = geometry_lattice.size.z == 0 ? 0 - : 0.5 + (pz - geometry_center.z) / geometry_lattice.size.z; + px = geometry_lattice.size.x == 0 ? 0 : 0.5 + (px - geometry_center.x) / geometry_lattice.size.x; + py = geometry_lattice.size.y == 0 ? 0 : 0.5 + (py - geometry_center.y) / geometry_lattice.size.y; + pz = geometry_lattice.size.z == 0 ? 0 : 0.5 + (pz - geometry_center.z) / geometry_lattice.size.z; } meep::vec matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { if (md->material_grid_kinds == material_data::U_MIN || md->material_grid_kinds == material_data::U_PROD) - meep::abort("%s:%i:matgrid_grad does not support overlapping grids with U_MIN or U_PROD\n",__FILE__,__LINE__); + meep::abort("%s:%i:matgrid_grad does not support overlapping grids with U_MIN or U_PROD\n", + __FILE__, __LINE__); meep::vec gradient(zero_vec(dim)); int matgrid_val_count = 0; @@ -467,9 +464,9 @@ meep::vec matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { // iterate through object tree at current point if (tp) { do { - gradient += material_grid_grad(to_geom_box_coords(p, &tp->objects[oi]), - (material_data *)tp->objects[oi].o->material, - tp->objects[oi].o); + gradient += + material_grid_grad(to_geom_box_coords(p, &tp->objects[oi]), + (material_data *)tp->objects[oi].o->material, tp->objects[oi].o); if (md->material_grid_kinds == material_data::U_DEFAULT) break; ++matgrid_val_count; tp = geom_tree_search_next(p, tp, &oi); @@ -477,13 +474,14 @@ meep::vec matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { } // perhaps there is no object tree and the default material is a material grid if (!tp && is_material_grid(default_material)) { - map_lattice_coordinates(p.x,p.y,p.z); - gradient = material_grid_grad(p, (material_data *)default_material, NULL /* geometric_object *o */); + map_lattice_coordinates(p.x, p.y, p.z); + gradient = + material_grid_grad(p, (material_data *)default_material, NULL /* geometric_object *o */); ++matgrid_val_count; } if (md->material_grid_kinds == material_data::U_MEAN) - gradient = gradient * 1.0/matgrid_val_count; + gradient = gradient * 1.0 / matgrid_val_count; return gradient; } @@ -492,17 +490,15 @@ double material_grid_val(vector3 p, material_data *md) { // given the relative location, p, interpolate the material grid point. if (!is_material_grid(md)) { meep::abort("Invalid material grid detected.\n"); } - return meep::linear_interpolate(p.x, p.y, p.z, md->weights, md->grid_size.x, - md->grid_size.y, md->grid_size.z, 1); - + return meep::linear_interpolate(p.x, p.y, p.z, md->weights, md->grid_size.x, md->grid_size.y, + md->grid_size.z, 1); } static double tanh_projection(double u, double beta, double eta) { if (beta == 0) return u; if (u == eta) return 0.5; // avoid NaN when beta is Inf - double tanh_beta_eta = tanh(beta*eta); - return (tanh_beta_eta + tanh(beta*(u-eta))) / - (tanh_beta_eta + tanh(beta*(1-eta))); + double tanh_beta_eta = tanh(beta * eta); + return (tanh_beta_eta + tanh(beta * (u - eta))) / (tanh_beta_eta + tanh(beta * (1 - eta))); } double matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md) { @@ -527,7 +523,7 @@ double matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md) { } // perhaps there is no object tree and the default material is a material grid if (!tp && is_material_grid(default_material)) { - map_lattice_coordinates(p.x,p.y,p.z); + map_lattice_coordinates(p.x, p.y, p.z); u = material_grid_val(p, (material_data *)default_material); if (matgrid_val_count == 0) udefault = u; if (u < umin) umin = u; @@ -537,11 +533,11 @@ double matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md) { } return (md->material_grid_kinds == material_data::U_MIN - ? umin - : (md->material_grid_kinds == material_data::U_PROD - ? uprod - : (md->material_grid_kinds == material_data::U_MEAN ? usum / matgrid_val_count - : udefault))); + ? umin + : (md->material_grid_kinds == material_data::U_PROD + ? uprod + : (md->material_grid_kinds == material_data::U_MEAN ? usum / matgrid_val_count + : udefault))); } static void cinterp_tensors(vector3 diag_in_1, cvector3 offdiag_in_1, vector3 diag_in_2, cvector3 offdiag_in_2, vector3 *diag_out, cvector3 *offdiag_out, @@ -589,16 +585,14 @@ void epsilon_material_grid(material_data *md, double u) { for (size_t i = 0; i < m1->E_susceptibilities.size(); i++) { // iterate through medium1 sus list first interp_tensors(zero_vec, zero_vec, m1->E_susceptibilities[i].sigma_diag, - m1->E_susceptibilities[i].sigma_offdiag, - &mm->E_susceptibilities[i].sigma_diag, + m1->E_susceptibilities[i].sigma_offdiag, &mm->E_susceptibilities[i].sigma_diag, &mm->E_susceptibilities[i].sigma_offdiag, (1 - u)); } for (size_t i = 0; i < m2->E_susceptibilities.size(); i++) { // iterate through medium2 sus list next size_t j = i + m1->E_susceptibilities.size(); interp_tensors(zero_vec, zero_vec, m2->E_susceptibilities[i].sigma_diag, - m2->E_susceptibilities[i].sigma_offdiag, - &mm->E_susceptibilities[j].sigma_diag, + m2->E_susceptibilities[i].sigma_offdiag, &mm->E_susceptibilities[j].sigma_diag, &mm->E_susceptibilities[j].sigma_offdiag, u); } @@ -624,16 +618,15 @@ void epsilon_material_grid(material_data *md, double u) { // TODO: dampen the lorentzians to improve stability // mm->D_conductivity_diag.x = mm->D_conductivity_diag.y = mm->D_conductivity_diag.z = u*(1-u) * // omega_mean; - md->trivial=false; + md->trivial = false; } - double fake_damping = u*(1-u)*(md->damping); + double fake_damping = u * (1 - u) * (md->damping); mm->D_conductivity_diag.x += fake_damping; mm->D_conductivity_diag.y += fake_damping; mm->D_conductivity_diag.z += fake_damping; // set the trivial flag - if (md->damping != 0) - md->trivial=false; + if (md->damping != 0) md->trivial = false; } // return material of the point p from the file (assumed already read) @@ -665,10 +658,11 @@ geom_epsilon::geom_epsilon(geometric_object_list g, material_type_list mlist, int length = g.num_items; geometry.num_items = length; geometry.items = new geometric_object[length]; - for (int i = 0; i < length; i++){ - geometric_object_copy(&g.items[i],&geometry.items[i]); + for (int i = 0; i < length; i++) { + geometric_object_copy(&g.items[i], &geometry.items[i]); geometry.items[i].material = new material_data(); - static_cast(geometry.items[i].material)->copy_from(*(material_data *)(g.items[i].material)); + static_cast(geometry.items[i].material) + ->copy_from(*(material_data *)(g.items[i].material)); } extra_materials = mlist; @@ -719,10 +713,11 @@ geom_epsilon::geom_epsilon(const geom_epsilon &geps1) { int length = geps1.geometry.num_items; geometry.num_items = length; geometry.items = new geometric_object[length]; - for (int i = 0; i < length; i++){ - geometric_object_copy(&geps1.geometry.items[i],&geometry.items[i]); + for (int i = 0; i < length; i++) { + geometric_object_copy(&geps1.geometry.items[i], &geometry.items[i]); geometry.items[i].material = new material_data(); - static_cast(geometry.items[i].material)->copy_from(*(material_data *)(geps1.geometry.items[i].material)); + static_cast(geometry.items[i].material) + ->copy_from(*(material_data *)(geps1.geometry.items[i].material)); } geometry_tree = geps1.geometry_tree; @@ -731,11 +726,10 @@ geom_epsilon::geom_epsilon(const geom_epsilon &geps1) { current_pol = NULL; FOR_DIRECTIONS(d) FOR_SIDES(b) { cond[d][b].prof = geps1.cond[d][b].prof; } - } geom_epsilon::~geom_epsilon() { int length = geometry.num_items; - for (int i = 0; i < length; i++){ + for (int i = 0; i < length; i++) { material_free((material_type)geometry.items[i].material); geometric_object_destroy(geometry.items[i]); } @@ -780,8 +774,7 @@ void geom_epsilon::set_volume(const meep::volume &v) { unset_volume(); geom_box box = gv2box(v); - if (!restricted_tree) - restricted_tree = create_geom_box_tree0(geometry, box); + if (!restricted_tree) restricted_tree = create_geom_box_tree0(geometry, box); } static void material_epsmu(meep::field_type ft, material_type material, symm_matrix *epsmu, @@ -865,7 +858,7 @@ void geom_epsilon::get_material_pt(material_type &material, const meep::vec &r) tp = geom_tree_search(p, restricted_tree, &oi); // interpolate and project onto material grid - u = tanh_projection(matgrid_val(p, tp, oi, md)+this->u_p, md->beta, md->eta); + u = tanh_projection(matgrid_val(p, tp, oi, md) + this->u_p, md->beta, md->eta); // interpolate material from material grid point epsilon_material_grid(md, u); @@ -1084,15 +1077,14 @@ void geom_epsilon::eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_ma if (!get_front_object(v, geometry_tree, p, &o, shiftby, mat, mat_behind)) { get_material_pt(mat, v.center()); - if (mat && (mat->which_subclass == material_data::MATERIAL_USER || - mat->which_subclass == material_data::MATERIAL_GRID) - && mat->do_averaging) { + if (mat && + (mat->which_subclass == material_data::MATERIAL_USER || + mat->which_subclass == material_data::MATERIAL_GRID) && + mat->do_averaging) { fallback = true; return; } - else { - goto trivial; - } + else { goto trivial; } } /* check for trivial case of only one object/material */ @@ -1203,25 +1195,26 @@ static int eps_ever_negative = 0; static meep::field_type func_ft = meep::E_stuff; struct matgrid_volavg { - meep::ndim dim; // dimensionality of voxel - double rad; // (spherical) voxel radius - double uval; // bilinearly-interpolated weight at voxel center - double ugrad_abs; // magnitude of gradient of uval - double beta; // thresholding bias - double eta; // thresholding erosion/dilation - double eps1; // trace of epsilon tensor from medium 1 - double eps2; // trace of epsilon tensor from medium 2 + meep::ndim dim; // dimensionality of voxel + double rad; // (spherical) voxel radius + double uval; // bilinearly-interpolated weight at voxel center + double ugrad_abs; // magnitude of gradient of uval + double beta; // thresholding bias + double eta; // thresholding erosion/dilation + double eps1; // trace of epsilon tensor from medium 1 + double eps2; // trace of epsilon tensor from medium 2 }; static void get_uproj_w(const matgrid_volavg *mgva, double x0, double &u_proj, double &w) { // use a linear approximation for the material grid weights around the Yee grid point - u_proj = tanh_projection(mgva->uval + mgva->ugrad_abs*x0, mgva->beta, mgva->eta); + u_proj = tanh_projection(mgva->uval + mgva->ugrad_abs * x0, mgva->beta, mgva->eta); if (mgva->dim == meep::D1) - w = 1/(2*mgva->rad); + w = 1 / (2 * mgva->rad); else if (mgva->dim == meep::D2 || mgva->dim == meep::Dcyl) - w = 2*sqrt(mgva->rad*mgva->rad - x0*x0)/(meep::pi * mgva->rad*mgva->rad); + w = 2 * sqrt(mgva->rad * mgva->rad - x0 * x0) / (meep::pi * mgva->rad * mgva->rad); else if (mgva->dim == meep::D3) - w = meep::pi*(mgva->rad*mgva->rad - x0*x0)/(4/3 * meep::pi * mgva->rad*mgva->rad*mgva->rad); + w = meep::pi * (mgva->rad * mgva->rad - x0 * x0) / + (4 / 3 * meep::pi * mgva->rad * mgva->rad * mgva->rad); } #ifdef CTL_HAS_COMPLEX_INTEGRATION @@ -1231,8 +1224,8 @@ static cnumber matgrid_ceps_func(int n, number *x, void *mgva_) { matgrid_volavg *mgva = (matgrid_volavg *)mgva_; get_uproj_w(mgva, x[0], u_proj, w); cnumber ret; - ret.re = (1-u_proj)*mgva->eps1 + u_proj*mgva->eps2; - ret.im = (1-u_proj)/mgva->eps1 + u_proj/mgva->eps2; + ret.re = (1 - u_proj) * mgva->eps1 + u_proj * mgva->eps2; + ret.im = (1 - u_proj) / mgva->eps1 + u_proj / mgva->eps2; return ret * w; } #else @@ -1241,14 +1234,14 @@ static number matgrid_eps_func(int n, number *x, void *mgva_) { double u_proj = 0, w = 0; matgrid_volavg *mgva = (matgrid_volavg *)mgva_; get_uproj_w(mgva, x[0], u_proj, w); - return w * ((1-u_proj)*mgva->eps1 + u_proj*mgva->eps2); + return w * ((1 - u_proj) * mgva->eps1 + u_proj * mgva->eps2); } static number matgrid_inveps_func(int n, number *x, void *mgva_) { (void)n; // unused double u_proj = 0, w = 0; matgrid_volavg *mgva = (matgrid_volavg *)mgva_; get_uproj_w(mgva, x[0], u_proj, w); - return w * ((1-u_proj)/mgva->eps1 + u_proj/mgva->eps2); + return w * ((1 - u_proj) / mgva->eps1 + u_proj / mgva->eps2); } #endif @@ -1329,11 +1322,9 @@ void geom_epsilon::fallback_chi1inv_row(meep::component c, double chi1inv_row[3] int oi; tp = geom_tree_search(p, restricted_tree, &oi); gradient = matgrid_grad(p, tp, oi, md); - uval = matgrid_val(p, tp, oi, md)+this->u_p; - } - else { - gradient = normal_vector(meep::type(c), v); + uval = matgrid_val(p, tp, oi, md) + this->u_p; } + else { gradient = normal_vector(meep::type(c), v); } get_material_pt(material, v.center()); material_epsmu(meep::type(c), material, &chi1p1, &chi1p1_inv); @@ -1368,23 +1359,27 @@ void geom_epsilon::fallback_chi1inv_row(meep::component c, double chi1inv_row[3] mgva.dim = v.dim; mgva.ugrad_abs = meep::abs(gradient); mgva.uval = uval; - mgva.rad = v.diameter()/2; + mgva.rad = v.diameter() / 2; mgva.beta = md->beta; mgva.eta = md->eta; - mgva.eps1 = (md->medium_1.epsilon_diag.x+md->medium_1.epsilon_diag.y+md->medium_1.epsilon_diag.z)/3; - mgva.eps2 = (md->medium_2.epsilon_diag.x+md->medium_2.epsilon_diag.y+md->medium_2.epsilon_diag.z)/3; - xmin[0] = -v.diameter()/2; - xmax[0] = v.diameter()/2; + mgva.eps1 = + (md->medium_1.epsilon_diag.x + md->medium_1.epsilon_diag.y + md->medium_1.epsilon_diag.z) / + 3; + mgva.eps2 = + (md->medium_2.epsilon_diag.x + md->medium_2.epsilon_diag.y + md->medium_2.epsilon_diag.z) / + 3; + xmin[0] = -v.diameter() / 2; + xmax[0] = v.diameter() / 2; #ifdef CTL_HAS_COMPLEX_INTEGRATION - cnumber ret = cadaptive_integration(matgrid_ceps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, maxeval, - &esterr, &errflag); + cnumber ret = cadaptive_integration(matgrid_ceps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, + maxeval, &esterr, &errflag); meps = ret.re; minveps = ret.im; #else - meps = adaptive_integration(matgrid_eps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, maxeval, &esterr, - &errflag); - minveps = adaptive_integration(matgrid_inveps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, maxeval, &esterr, - &errflag); + meps = adaptive_integration(matgrid_eps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, maxeval, + &esterr, &errflag); + minveps = adaptive_integration(matgrid_inveps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, + maxeval, &esterr, &errflag); #endif } else { @@ -1424,9 +1419,11 @@ void geom_epsilon::fallback_chi1inv_row(meep::component c, double chi1inv_row[3] minveps = ret.im / vol; #else meps = adaptive_integration(eps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, &esterr, - &errflag) / vol; - minveps = adaptive_integration(inveps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, &esterr, - &errflag) / vol; + &errflag) / + vol; + minveps = adaptive_integration(inveps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, + &esterr, &errflag) / + vol; #endif } if (eps_ever_negative) // averaging negative eps causes instability @@ -1562,12 +1559,12 @@ static double get_cnd(meep::component c, const medium_struct *m) { } static bool has_conductivity(const material_type &md, meep::component c) { - medium_struct *mm; - if (is_medium(md, &mm) && get_cnd(c, mm)) return true; - if (md->which_subclass == material_data::MATERIAL_GRID && - (get_cnd(c, &md->medium_1) || get_cnd(c, &md->medium_2) || - md->damping != 0)) return true; - return false; + medium_struct *mm; + if (is_medium(md, &mm) && get_cnd(c, mm)) return true; + if (md->which_subclass == material_data::MATERIAL_GRID && + (get_cnd(c, &md->medium_1) || get_cnd(c, &md->medium_2) || md->damping != 0)) + return true; + return false; } bool geom_epsilon::has_conductivity(meep::component c) { @@ -1575,10 +1572,10 @@ bool geom_epsilon::has_conductivity(meep::component c) { if (cond[d][b].prof) return true; } for (int i = 0; i < geometry.num_items; ++i) - if (meep_geom::has_conductivity((material_type) geometry.items[i].material, c)) return true; + if (meep_geom::has_conductivity((material_type)geometry.items[i].material, c)) return true; for (int i = 0; i < extra_materials.num_items; ++i) - if (meep_geom::has_conductivity((material_type) extra_materials.items[i], c)) return true; - return meep_geom::has_conductivity((material_type) default_material, c); + if (meep_geom::has_conductivity((material_type)extra_materials.items[i], c)) return true; + return meep_geom::has_conductivity((material_type)default_material, c); } static meep::vec geometry_edge; // geometry_lattice.size / 2 @@ -1670,8 +1667,8 @@ void geom_epsilon::sigma_row(meep::component c, double sigrow[3], const meep::ve tp = geom_tree_search(p, restricted_tree, &oi); // interpolate and project onto material grid - u = tanh_projection(matgrid_val(p, tp, oi, mat)+this->u_p, mat->beta, mat->eta); - epsilon_material_grid(mat, u); // interpolate material from material grid point + u = tanh_projection(matgrid_val(p, tp, oi, mat) + this->u_p, mat->beta, mat->eta); + epsilon_material_grid(mat, u); // interpolate material from material grid point mat->medium.check_offdiag_im_zero_or_abort(); } @@ -1852,21 +1849,21 @@ void geom_epsilon::add_susceptibilities(meep::field_type ft, meep::structure *s) ss->drude); } else if (gyrotropic) { - meep::gyrotropy_model model = - ss->saturated_gyrotropy - ? meep::GYROTROPIC_SATURATED - : ss->drude ? meep::GYROTROPIC_DRUDE : meep::GYROTROPIC_LORENTZIAN; + meep::gyrotropy_model model = ss->saturated_gyrotropy ? meep::GYROTROPIC_SATURATED + : ss->drude ? meep::GYROTROPIC_DRUDE + : meep::GYROTROPIC_LORENTZIAN; sus = new meep::gyrotropic_susceptibility(meep::vec(ss->bias.x, ss->bias.y, ss->bias.z), ss->frequency, ss->gamma, ss->alpha, model); } - else { - sus = new meep::lorentzian_susceptibility(ss->frequency, ss->gamma, ss->drude); - } + else { sus = new meep::lorentzian_susceptibility(ss->frequency, ss->gamma, ss->drude); } if (meep::verbosity > 0) { master_printf("%s%s susceptibility: frequency=%g, gamma=%g", - noisy ? "noisy " : gyrotropic ? "gyrotropic " : "", + noisy ? "noisy " + : gyrotropic ? "gyrotropic " + : "", ss->saturated_gyrotropy ? "Landau-Lifshitz-Gilbert-type" - : ss->drude ? "drude" : "lorentzian", + : ss->drude ? "drude" + : "lorentzian", ss->frequency, ss->gamma); if (noisy) master_printf(", amp=%g ", ss->noise_amp); if (gyrotropic) { @@ -1937,7 +1934,7 @@ void add_absorbing_layer(absorber_list alist, double thickness, int direction, i /* create a geom_epsilon object that can persist if needed */ -geom_epsilon* make_geom_epsilon(meep::structure *s, geometric_object_list *g, vector3 center, +geom_epsilon *make_geom_epsilon(meep::structure *s, geometric_object_list *g, vector3 center, bool _ensure_periodicity, material_type _default_material, material_type_list extra_materials) { // set global variables in libctlgeom based on data fields in s @@ -2001,16 +1998,15 @@ void set_materials_from_geometry(meep::structure *s, geometric_object_list g, ve absorber_list alist, material_type_list extra_materials) { meep_geom::geom_epsilon *geps = meep_geom::make_geom_epsilon(s, &g, center, _ensure_periodicity, _default_material, extra_materials); - set_materials_from_geom_epsilon(s, geps, use_anisotropic_averaging, tol, - maxeval, alist); + set_materials_from_geom_epsilon(s, geps, use_anisotropic_averaging, tol, maxeval, alist); delete geps; } /* from a previously created geom_epsilon object, set the materials as specified */ void set_materials_from_geom_epsilon(meep::structure *s, geom_epsilon *geps, - bool use_anisotropic_averaging, - double tol, int maxeval, absorber_list alist) { + bool use_anisotropic_averaging, double tol, int maxeval, + absorber_list alist) { // store for later use in gradient calculations geps->tol = tol; @@ -2220,8 +2216,7 @@ fragment_stats::fragment_stats(geom_box &bx) } void init_libctl(material_type default_mat, bool ensure_per, meep::grid_volume *gv, - vector3 cell_size, vector3 cell_center, - geometric_object_list *geom_) { + vector3 cell_size, vector3 cell_center, geometric_object_list *geom_) { geom_initialize(); set_default_material(default_mat); ensure_periodicity = ensure_per; @@ -2289,8 +2284,9 @@ void fragment_stats::compute_stats() { for (int i = 0; i < geom.num_items; ++i) { geometric_object *go = &geom.items[i]; - // tolerance and max number of function evaluations of numerical quadrature are increased and decreased - // from default values of 0.0001 and 100000, respectively, to obtain fast, approximate result + // tolerance and max number of function evaluations of numerical quadrature are increased and + // decreased from default values of 0.0001 and 100000, respectively, to obtain fast, approximate + // result double overlap = box_overlap_with_object(box, *go, 0.05 /* tol */, 1000 /* maxeval */); bool anisotropic_pixels_already_added = false; @@ -2486,35 +2482,16 @@ geom_box_tree calculate_tree(const meep::volume &v, geometric_object_list g) { static std::complex cvec_to_value(vector3 diag, cvector3 offdiag, int idx) { std::complex val = std::complex(0, 0); switch (idx) { - case 0: - val = std::complex(diag.x, 0); - break; - case 1: - val = std::complex(offdiag.x.re, offdiag.x.im); - break; - case 2: - val = std::complex(offdiag.y.re, offdiag.y.im); - break; - case 3: - val = std::complex(offdiag.x.re, -offdiag.x.im); - break; - case 4: - val = std::complex(diag.y, 0); - break; - case 5: - val = std::complex(offdiag.z.re, offdiag.z.im); - break; - case 6: - val = std::complex(offdiag.y.re, -offdiag.y.im); - break; - case 7: - val = std::complex(offdiag.z.re, -offdiag.z.im); - break; - case 8: - val = std::complex(diag.z, 0); - break; - default: - meep::abort("Invalid value in switch statement."); + case 0: val = std::complex(diag.x, 0); break; + case 1: val = std::complex(offdiag.x.re, offdiag.x.im); break; + case 2: val = std::complex(offdiag.y.re, offdiag.y.im); break; + case 3: val = std::complex(offdiag.x.re, -offdiag.x.im); break; + case 4: val = std::complex(diag.y, 0); break; + case 5: val = std::complex(offdiag.z.re, offdiag.z.im); break; + case 6: val = std::complex(offdiag.y.re, -offdiag.y.im); break; + case 7: val = std::complex(offdiag.z.re, -offdiag.z.im); break; + case 8: val = std::complex(diag.z, 0); break; + default: meep::abort("Invalid value in switch statement."); } return val; } @@ -2523,46 +2500,27 @@ static std::complex cvec_to_value(vector3 diag, cvector3 offdiag, int id double vec_to_value(vector3 diag, vector3 offdiag, int idx) { double val = 0.0; switch (idx) { - case 0: - val = diag.x; - break; - case 1: - val = offdiag.x; - break; - case 2: - val = offdiag.y; - break; - case 3: - val = offdiag.x; - break; - case 4: - val = diag.y; - break; - case 5: - val = offdiag.z; - break; - case 6: - val = offdiag.y; - break; - case 7: - val = offdiag.z; - break; - case 8: - val = diag.z; - break; - default: - meep::abort("Invalid value in switch statement."); + case 0: val = diag.x; break; + case 1: val = offdiag.x; break; + case 2: val = offdiag.y; break; + case 3: val = offdiag.x; break; + case 4: val = diag.y; break; + case 5: val = offdiag.z; break; + case 6: val = offdiag.y; break; + case 7: val = offdiag.z; break; + case 8: val = diag.z; break; + default: meep::abort("Invalid value in switch statement."); } return val; } void invert_tensor(std::complex t_inv[9], std::complex t[9]) { -#define m(x,y) t[x*3+y] -#define minv(x,y) t_inv[x*3+y] +#define m(x, y) t[x * 3 + y] +#define minv(x, y) t_inv[x * 3 + y] std::complex det = m(0, 0) * (m(1, 1) * m(2, 2) - m(2, 1) * m(1, 2)) - - m(0, 1) * (m(1, 0) * m(2, 2) - m(1, 2) * m(2, 0)) + - m(0, 2) * (m(1, 0) * m(2, 1) - m(1, 1) * m(2, 0)); + m(0, 1) * (m(1, 0) * m(2, 2) - m(1, 2) * m(2, 0)) + + m(0, 2) * (m(1, 0) * m(2, 1) - m(1, 1) * m(2, 0)); std::complex invdet = 1.0 / det; minv(0, 0) = (m(1, 1) * m(2, 2) - m(2, 1) * m(1, 2)) * invdet; minv(0, 1) = (m(0, 2) * m(2, 1) - m(0, 1) * m(2, 2)) * invdet; @@ -2577,7 +2535,8 @@ void invert_tensor(std::complex t_inv[9], std::complex t[9]) { #undef minv } -void get_chi1_tensor_disp(std::complex tensor[9], const meep::vec &r, double freq, geom_epsilon *geps) { +void get_chi1_tensor_disp(std::complex tensor[9], const meep::vec &r, double freq, + geom_epsilon *geps) { // locate the proper material material_type md; geps->get_material_pt(md, r); @@ -2585,18 +2544,18 @@ void get_chi1_tensor_disp(std::complex tensor[9], const meep::vec &r, do // loop over all the tensor components for (int i = 0; i < 9; i++) { - std::complex a,b; + std::complex a, b; // compute first part containing conductivity vector3 dummy; dummy.x = dummy.y = dummy.z = 0.0; double conductivityCur = vec_to_value(mm->D_conductivity_diag, dummy, i); - a = std::complex(1.0, conductivityCur / (2*meep::pi*freq)); + a = std::complex(1.0, conductivityCur / (2 * meep::pi * freq)); // compute lorentzian component including the instantaneous ε b = cvec_to_value(mm->epsilon_diag, mm->epsilon_offdiag, i); - for (const auto &mm_susc: mm->E_susceptibilities) { - meep::lorentzian_susceptibility sus = meep::lorentzian_susceptibility( - mm_susc.frequency, mm_susc.gamma, mm_susc.drude); + for (const auto &mm_susc : mm->E_susceptibilities) { + meep::lorentzian_susceptibility sus = + meep::lorentzian_susceptibility(mm_susc.frequency, mm_susc.gamma, mm_susc.drude); double sigma = vec_to_value(mm_susc.sigma_diag, mm_susc.sigma_offdiag, i); b += sus.chi1(freq, sigma); } @@ -2607,63 +2566,67 @@ void get_chi1_tensor_disp(std::complex tensor[9], const meep::vec &r, do } void eff_chi1inv_row_disp(meep::component c, std::complex chi1inv_row[3], - const meep::vec &r, double freq, geom_epsilon *geps) { + const meep::vec &r, double freq, geom_epsilon *geps) { std::complex tensor[9], tensor_inv[9]; get_chi1_tensor_disp(tensor, r, freq, geps); // invert the matrix invert_tensor(tensor_inv, tensor); // get the row we care about - switch (component_direction(c)) { - case meep::X: - case meep::R: - chi1inv_row[0] = tensor_inv[0]; - chi1inv_row[1] = tensor_inv[1]; - chi1inv_row[2] = tensor_inv[2]; - break; - case meep::Y: - case meep::P: - chi1inv_row[0] = tensor_inv[3]; - chi1inv_row[1] = tensor_inv[4]; - chi1inv_row[2] = tensor_inv[5]; - break; - case meep::Z: - chi1inv_row[0] = tensor_inv[6]; - chi1inv_row[1] = tensor_inv[7]; - chi1inv_row[2] = tensor_inv[8]; - break; - case meep::NO_DIRECTION: chi1inv_row[0] = chi1inv_row[1] = chi1inv_row[2] = 0; break; - } + switch (component_direction(c)) { + case meep::X: + case meep::R: + chi1inv_row[0] = tensor_inv[0]; + chi1inv_row[1] = tensor_inv[1]; + chi1inv_row[2] = tensor_inv[2]; + break; + case meep::Y: + case meep::P: + chi1inv_row[0] = tensor_inv[3]; + chi1inv_row[1] = tensor_inv[4]; + chi1inv_row[2] = tensor_inv[5]; + break; + case meep::Z: + chi1inv_row[0] = tensor_inv[6]; + chi1inv_row[1] = tensor_inv[7]; + chi1inv_row[2] = tensor_inv[8]; + break; + case meep::NO_DIRECTION: chi1inv_row[0] = chi1inv_row[1] = chi1inv_row[2] = 0; break; + } } -std::complex cond_cmp(meep::component c, const meep::vec &r, double freq, geom_epsilon *geps) { +std::complex cond_cmp(meep::component c, const meep::vec &r, double freq, + geom_epsilon *geps) { // locate the proper material material_type md; geps->get_material_pt(md, r); const medium_struct *mm = &(md->medium); // get the row we care about - switch (component_direction(c)) { - case meep::X: - case meep::R: return std::complex(1.0, mm->D_conductivity_diag.x / (2*meep::pi*freq)); - case meep::Y: - case meep::P: return std::complex(1.0, mm->D_conductivity_diag.y / (2*meep::pi*freq)); - case meep::Z: return std::complex(1.0, mm->D_conductivity_diag.z / (2*meep::pi*freq)); - case meep::NO_DIRECTION: meep::abort("Invalid adjoint field component"); - } -} - -std::complex get_material_gradient( - const meep::vec &r, // current point - const meep::component adjoint_c, // adjoint field component - const meep::component forward_c, // forward field component - std::complex fields_f, // forward field at current point - double freq, // frequency - geom_epsilon *geps, // material - meep::grid_volume &gv, // simulation grid volume - double du, // step size - double *u, // matgrid - int idx // matgrid index + switch (component_direction(c)) { + case meep::X: + case meep::R: + return std::complex(1.0, mm->D_conductivity_diag.x / (2 * meep::pi * freq)); + case meep::Y: + case meep::P: + return std::complex(1.0, mm->D_conductivity_diag.y / (2 * meep::pi * freq)); + case meep::Z: + return std::complex(1.0, mm->D_conductivity_diag.z / (2 * meep::pi * freq)); + case meep::NO_DIRECTION: meep::abort("Invalid adjoint field component"); + } +} + +std::complex +get_material_gradient(const meep::vec &r, // current point + const meep::component adjoint_c, // adjoint field component + const meep::component forward_c, // forward field component + std::complex fields_f, // forward field at current point + double freq, // frequency + geom_epsilon *geps, // material + meep::grid_volume &gv, // simulation grid volume + double du, // step size + double *u, // matgrid + int idx // matgrid index ) { /*Compute the Aᵤx product from the -λᵀAᵤx calculation. The current adjoint (λ) field component (adjoint_c) @@ -2702,30 +2665,33 @@ std::complex get_material_gradient( const double sd = 1.0; // FIXME: make user-changable? meep::volume v(r); LOOP_OVER_DIRECTIONS(dim, d) { - v.set_direction_min(d, r.in_direction(d) - 0.5*gv.inva*sd); - v.set_direction_max(d, r.in_direction(d) + 0.5*gv.inva*sd); + v.set_direction_min(d, r.in_direction(d) - 0.5 * gv.inva * sd); + v.set_direction_max(d, r.in_direction(d) + 0.5 * gv.inva * sd); } double row_1[3], row_2[3], dA_du[3]; double orig = u[idx]; u[idx] -= du; geps->eff_chi1inv_row(adjoint_c, row_1, v, geps->tol, geps->maxeval); - u[idx] += 2*du; + u[idx] += 2 * du; geps->eff_chi1inv_row(adjoint_c, row_2, v, geps->tol, geps->maxeval); u[idx] = orig; - for (int i=0;i<3;i++) dA_du[i] = (row_1[i] - row_2[i])/(2*du); + for (int i = 0; i < 3; i++) + dA_du[i] = (row_1[i] - row_2[i]) / (2 * du); return dA_du[dir_idx] * fields_f; - } else { + } + else { double orig = u[idx]; std::complex row_1[3], row_2[3], dA_du[3]; u[idx] -= du; - eff_chi1inv_row_disp(adjoint_c,row_1,r,freq,geps); - u[idx] += 2*du; - eff_chi1inv_row_disp(adjoint_c,row_2,r,freq,geps); + eff_chi1inv_row_disp(adjoint_c, row_1, r, freq, geps); + u[idx] += 2 * du; + eff_chi1inv_row_disp(adjoint_c, row_2, r, freq, geps); u[idx] = orig; - for (int i=0;i<3;i++) dA_du[i] = (row_1[i] - row_2[i])/(2*du); - return dA_du[dir_idx] * fields_f * cond_cmp(forward_c,r,freq,geps); + for (int i = 0; i < 3; i++) + dA_du[i] = (row_1[i] - row_2[i]) / (2 * du); + return dA_du[dir_idx] * fields_f * cond_cmp(forward_c, r, freq, geps); } } @@ -2736,27 +2702,24 @@ With the addition of subpixel smoothing, however, the vJp became much more complicated and it is easier to calculate the entire gradient using finite differences (at the cost of slightly less accurate gradients due to floating-point roundoff errors). */ -void add_interpolate_weights(double rx, double ry, double rz, - double *data, int nx, int ny, int nz, int stride, - double scaleby, double *udata, int ukind, double uval, - meep::vec r, geom_epsilon *geps, - meep::component adjoint_c, meep::component forward_c, - std::complex fwd, std::complex adj, - double freq, meep::grid_volume &gv, double du) { +void add_interpolate_weights(double rx, double ry, double rz, double *data, int nx, int ny, int nz, + int stride, double scaleby, double *udata, int ukind, double uval, + meep::vec r, geom_epsilon *geps, meep::component adjoint_c, + meep::component forward_c, std::complex fwd, + std::complex adj, double freq, meep::grid_volume &gv, + double du) { int x1, y1, z1, x2, y2, z2; double dx, dy, dz, u; - meep::map_coordinates(rx, ry, rz, nx, ny, nz, - x1, y1, z1, x2, y2, z2, - dx, dy, dz); - int x_list[2] = {x1,x2}, y_list[2] = {y1,y2}, z_list[2] = {z1,z2}; + meep::map_coordinates(rx, ry, rz, nx, ny, nz, x1, y1, z1, x2, y2, z2, dx, dy, dz); + int x_list[2] = {x1, x2}, y_list[2] = {y1, y2}, z_list[2] = {z1, z2}; int lx = (x1 == x2) ? 1 : 2; int ly = (y1 == y2) ? 1 : 2; int lz = (z1 == z2) ? 1 : 2; /* define a macro to give us data(x,y,z) on the grid, in row-major order (the order used by HDF5): */ -#define IDX(x,y,z) (((x)*ny + (y)) * nz + (z)) * stride +#define IDX(x, y, z) (((x)*ny + (y)) * nz + (z)) * stride #define D(x, y, z) (data[(((x)*ny + (y)) * nz + (z)) * stride]) #define U(x, y, z) (udata[(((x)*ny + (y)) * nz + (z)) * stride]) @@ -2770,13 +2733,13 @@ in row-major order (the order used by HDF5): */ if (ukind == material_data::U_MIN && u != uval) return; // TODO look into this if (ukind == material_data::U_PROD) scaleby *= uval / u; - for (int xi=0;xi prod = adj*get_material_gradient( - r,adjoint_c,forward_c,fwd,freq,geps,gv,du,udata,u_idx); + for (int xi = 0; xi < lx; xi++) { + for (int yi = 0; yi < ly; yi++) { + for (int zi = 0; zi < lz; zi++) { + int x = x_list[xi], y = y_list[yi], z = z_list[zi]; + int u_idx = IDX(x, y, z); + std::complex prod = adj * get_material_gradient(r, adjoint_c, forward_c, fwd, freq, + geps, gv, du, udata, u_idx); D(x, y, z) += prod.real() * scaleby; } } @@ -2821,7 +2784,9 @@ void material_grids_addgradient_point(double *v, vector3 p, double scalegrad, ge } else if ((tp) && ((mg->material_grid_kinds == material_data::U_MIN) || (mg->material_grid_kinds == material_data::U_PROD))) { - meep::abort("%s:%i:material_grids_addgradient_point does not support overlapping MATERIAL_GRIDs with U_MIN or U_PROD.\n",__FILE__,__LINE__); + meep::abort("%s:%i:material_grids_addgradient_point does not support overlapping " + "MATERIAL_GRIDs with U_MIN or U_PROD.\n", + __FILE__, __LINE__); } // Iterate through grids and add weights as needed @@ -2843,62 +2808,66 @@ void material_grids_addgradient_point(double *v, vector3 p, double scalegrad, ge uval = tanh_projection(matgrid_val(p, tp, oi, mg), mg->beta, mg->eta); do { vector3 pb = to_geom_box_coords(p, &tp->objects[oi]); - add_interpolate_weights(pb.x, pb.y, pb.z, vcur, sz.x, sz.y, sz.z, 1, - scalegrad, ucur, kind, uval, - vector3_to_vec(p), geps, adjoint_c, forward_c, - fwd, adj, freq, gv, tol); + add_interpolate_weights(pb.x, pb.y, pb.z, vcur, sz.x, sz.y, sz.z, 1, scalegrad, ucur, kind, + uval, vector3_to_vec(p), geps, adjoint_c, forward_c, fwd, adj, freq, + gv, tol); if (kind == material_data::U_DEFAULT) break; tp = geom_tree_search_next(p, tp, &oi); } while (tp && is_material_grid((material_data *)tp->objects[oi].o->material)); } // no object tree -- the whole domain is the material grid if (!tp && is_material_grid(default_material)) { - map_lattice_coordinates(p.x,p.y,p.z); + map_lattice_coordinates(p.x, p.y, p.z); vector3 sz = mg->grid_size; double *vcur = v; double *ucur = mg->weights; uval = tanh_projection(material_grid_val(p, mg), mg->beta, mg->eta); - add_interpolate_weights(p.x, p.y, p.z, vcur, sz.x, sz.y, sz.z, 1, - scalegrad, ucur, kind, uval, - vector3_to_vec(p), geps, adjoint_c, forward_c, - fwd, adj, freq, gv, tol); + add_interpolate_weights(p.x, p.y, p.z, vcur, sz.x, sz.y, sz.z, 1, scalegrad, ucur, kind, uval, + vector3_to_vec(p), geps, adjoint_c, forward_c, fwd, adj, freq, gv, tol); } } -void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector fields_a, std::vector fields_f, - double *frequencies, double scalegrad, meep::grid_volume &gv, - meep::volume &where, geom_epsilon *geps, double du) { +void material_grids_addgradient(double *v, size_t ng, size_t nf, + std::vector fields_a, + std::vector fields_f, double *frequencies, + double scalegrad, meep::grid_volume &gv, meep::volume &where, + geom_epsilon *geps, double du) { /* ------------------------------------------------------------ */ // initialize local gradient array /* ------------------------------------------------------------ */ - double *v_local = new double[ng*nf]; - for (int i = 0; i < ng*nf; i++) { + double *v_local = new double[ng * nf]; + for (int i = 0; i < ng * nf; i++) { v_local[i] = 0; } /* ------------------------------------------------------------ */ // store chunk info in vectors for simplicity /* ------------------------------------------------------------ */ - std::vector> adjoint_dft_chunks; - std::vector> forward_dft_chunks; - for (int i=0;i<3;i++){ - std::vector c_adjoint_dft_chunks; - std::vector c_forward_dft_chunks; + std::vector > adjoint_dft_chunks; + std::vector > forward_dft_chunks; + for (int i = 0; i < 3; i++) { + std::vector c_adjoint_dft_chunks; + std::vector c_forward_dft_chunks; meep::dft_chunk *current_adjoint_chunk = fields_a[i]->chunks; meep::dft_chunk *current_forward_chunk = fields_f[i]->chunks; - while(current_adjoint_chunk) { - if (current_adjoint_chunk->omega.size() != nf) meep::abort("Supplied frequencies %d don't match dft frequencies %d\n",nf,current_adjoint_chunk->omega.size()); + while (current_adjoint_chunk) { + if (current_adjoint_chunk->omega.size() != nf) + meep::abort("Supplied frequencies %d don't match dft frequencies %d\n", nf, + current_adjoint_chunk->omega.size()); c_adjoint_dft_chunks.push_back(current_adjoint_chunk); current_adjoint_chunk = current_adjoint_chunk->next_in_dft; } - while(current_forward_chunk) { - if (current_forward_chunk->omega.size() != nf) meep::abort("Supplied frequencies %d don't match dft frequencies %d\n",nf,current_forward_chunk->omega.size()); + while (current_forward_chunk) { + if (current_forward_chunk->omega.size() != nf) + meep::abort("Supplied frequencies %d don't match dft frequencies %d\n", nf, + current_forward_chunk->omega.size()); c_forward_dft_chunks.push_back(current_forward_chunk); current_forward_chunk = current_forward_chunk->next_in_dft; } if (c_adjoint_dft_chunks.size() != c_forward_dft_chunks.size()) - meep::abort("The number of adjoint chunks (%ld) is not equal to the number of forward chunks (%ld).\n", - c_adjoint_dft_chunks.size(),c_forward_dft_chunks.size()); + meep::abort("The number of adjoint chunks (%ld) is not equal to the number of forward chunks " + "(%ld).\n", + c_adjoint_dft_chunks.size(), c_forward_dft_chunks.size()); adjoint_dft_chunks.push_back(c_adjoint_dft_chunks); forward_dft_chunks.push_back(c_forward_dft_chunks); } @@ -2911,36 +2880,36 @@ void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vectorc; - meep::grid_volume gv_adj = gv.subvolume(adj_chunk->is,adj_chunk->ie,adjoint_c); + meep::grid_volume gv_adj = gv.subvolume(adj_chunk->is, adj_chunk->ie, adjoint_c); // loop over forward components - for (int ci_forward=0; ci_forward<3; ci_forward++){ + for (int ci_forward = 0; ci_forward < 3; ci_forward++) { size_t num_f_chunks = forward_dft_chunks[ci_forward].size(); - if ((num_f_chunks == 0) || (cur_chunk>=num_f_chunks)) continue; - meep::dft_chunk* fwd_chunk = forward_dft_chunks[ci_forward][cur_chunk]; + if ((num_f_chunks == 0) || (cur_chunk >= num_f_chunks)) continue; + meep::dft_chunk *fwd_chunk = forward_dft_chunks[ci_forward][cur_chunk]; meep::component forward_c = fwd_chunk->c; - meep::grid_volume gv_fwd = gv.subvolume(fwd_chunk->is,fwd_chunk->ie,forward_c); + meep::grid_volume gv_fwd = gv.subvolume(fwd_chunk->is, fwd_chunk->ie, forward_c); // loop over each point of interest - LOOP_OVER_IVECS(gv_adj,adj_chunk->is_old,adj_chunk->ie_old,idx_adj){ + LOOP_OVER_IVECS(gv_adj, adj_chunk->is_old, adj_chunk->ie_old, idx_adj) { double cyl_scale; IVEC_LOOP_ILOC(gv_adj, ip); IVEC_LOOP_LOC(gv_adj, p); - std::complex adj = adj_chunk->dft[nf*idx_adj+f_i]; + std::complex adj = adj_chunk->dft[nf * idx_adj + f_i]; material_type md; geps->get_material_pt(md, p); /* if we have conductivities (e.g. for damping) then we need to make sure we correctly account for that here */ - if (!md->trivial) adj *= cond_cmp(adjoint_c,p,frequencies[f_i], geps); + if (!md->trivial) adj *= cond_cmp(adjoint_c, p, frequencies[f_i], geps); /**************************************/ /* Main Routine */ @@ -2950,15 +2919,17 @@ void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector fwd = fwd_chunk->dft[nf*idx_adj+f_i]; - cyl_scale = (gv.dim == meep::Dcyl) ? 2*p.r() : 1; // the pi is already factored in near2far.cpp - material_grids_addgradient_point( - v_local+ng*f_i, vec_to_vector3(p), scalegrad*cyl_scale, geps, - adjoint_c, forward_c, fwd, adj, frequencies[f_i], gv, du); - /* more complicated case requires interpolation/restriction */ - } else if ((md->do_averaging) || /* account for subpixel smoothing */ - (!is_material_grid(md)) || /* account for edge effects of mg */ - (has_offdiag(&(md->medium_1))) || /* account for offdiagonal components */ + std::complex fwd = fwd_chunk->dft[nf * idx_adj + f_i]; + cyl_scale = (gv.dim == meep::Dcyl) ? 2 * p.r() + : 1; // the pi is already factored in near2far.cpp + material_grids_addgradient_point(v_local + ng * f_i, vec_to_vector3(p), + scalegrad * cyl_scale, geps, adjoint_c, forward_c, + fwd, adj, frequencies[f_i], gv, du); + /* more complicated case requires interpolation/restriction */ + } + else if ((md->do_averaging) || /* account for subpixel smoothing */ + (!is_material_grid(md)) || /* account for edge effects of mg */ + (has_offdiag(&(md->medium_1))) || /* account for offdiagonal components */ (has_offdiag(&(md->medium_2)))) { /* we need to restrict the adjoint fields to the two nodes of interest (which requires a factor @@ -2969,35 +2940,40 @@ void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector fwd_avg, fwd1, fwd2; ptrdiff_t fwd1_idx, fwd2_idx; - //identify the first corner of the forward fields + // identify the first corner of the forward fields meep::ivec fwd_p = ip + gv.iyee_shift(forward_c) - gv.iyee_shift(adjoint_c); - //identify the other three corners - meep::ivec unit_a = unit_ivec(gv.dim,component_direction(adjoint_c)); - meep::ivec unit_f = unit_ivec(gv.dim,component_direction(forward_c)); - meep::ivec fwd_pa = (fwd_p + unit_a*2); - meep::ivec fwd_pf = (fwd_p - unit_f*2); - meep::ivec fwd_paf = (fwd_p + unit_a*2 - unit_f*2); + // identify the other three corners + meep::ivec unit_a = unit_ivec(gv.dim, component_direction(adjoint_c)); + meep::ivec unit_f = unit_ivec(gv.dim, component_direction(forward_c)); + meep::ivec fwd_pa = (fwd_p + unit_a * 2); + meep::ivec fwd_pf = (fwd_p - unit_f * 2); + meep::ivec fwd_paf = (fwd_p + unit_a * 2 - unit_f * 2); // store in vector for convenience std::vector fwd_pl = {fwd_p, fwd_pa}; std::vector fwd_pr = {fwd_pf, fwd_paf}; - //identify the two eps points + // identify the two eps points std::vector ieps = {(fwd_p + fwd_pf) / 2, (fwd_pa + fwd_paf) / 2}; - //operate on the each eps node - #pragma unroll - for (int node=0;node<2;node++) { // two nodes - fwd1_idx = gv_fwd.index(forward_c,fwd_pl[node]); - fwd1 = ((fwd1_idx >= fwd_chunk->N) || (fwd1_idx<0)) ? 0 : fwd_chunk->dft[nf*fwd1_idx+f_i]; - fwd2_idx = gv_fwd.index(forward_c,fwd_pr[node]); - fwd2 = ((fwd2_idx >= fwd_chunk->N) || (fwd2_idx<0)) ? 0 : fwd_chunk->dft[nf*fwd2_idx+f_i]; - fwd_avg = std::complex(0.5,0) * (fwd1 + fwd2); +// operate on the each eps node +#pragma unroll + for (int node = 0; node < 2; node++) { // two nodes + fwd1_idx = gv_fwd.index(forward_c, fwd_pl[node]); + fwd1 = ((fwd1_idx >= fwd_chunk->N) || (fwd1_idx < 0)) + ? 0 + : fwd_chunk->dft[nf * fwd1_idx + f_i]; + fwd2_idx = gv_fwd.index(forward_c, fwd_pr[node]); + fwd2 = ((fwd2_idx >= fwd_chunk->N) || (fwd2_idx < 0)) + ? 0 + : fwd_chunk->dft[nf * fwd2_idx + f_i]; + fwd_avg = std::complex(0.5, 0) * (fwd1 + fwd2); meep::vec eps1 = gv[ieps[node]]; cyl_scale = (gv.dim == meep::Dcyl) ? eps1.r() : 1; - material_grids_addgradient_point( - v_local+ng*f_i, vec_to_vector3(eps1), scalegrad*cyl_scale, geps, - adjoint_c, forward_c, fwd_avg, std::complex(0.5,0)*adj, frequencies[f_i], gv, du); + material_grids_addgradient_point(v_local + ng * f_i, vec_to_vector3(eps1), + scalegrad * cyl_scale, geps, adjoint_c, forward_c, + fwd_avg, std::complex(0.5, 0) * adj, + frequencies[f_i], gv, du); } } /********* compute λᵀbᵤ ***************/ @@ -3012,7 +2988,7 @@ void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector max_val) - max_val = data[i]; + if (data[i] < min_val) min_val = data[i]; + if (data[i] > max_val) max_val = data[i]; } return; } -void get_epsilon_grid(geometric_object_list gobj_list, - material_type_list mlist, - material_type _default_material, - bool _ensure_periodicity, - meep::grid_volume gv, - vector3 cell_size, - vector3 cell_center, - int nx, const double *x, - int ny, const double *y, - int nz, const double *z, - std::complex *grid_vals, - double frequency) { +void get_epsilon_grid(geometric_object_list gobj_list, material_type_list mlist, + material_type _default_material, bool _ensure_periodicity, + meep::grid_volume gv, vector3 cell_size, vector3 cell_center, int nx, + const double *x, int ny, const double *y, int nz, const double *z, + std::complex *grid_vals, double frequency) { double min_val[3], max_val[3]; for (int n = 0; n < 3; ++n) { int ndir = (n == 0) ? nx : ((n == 1) ? ny : nz); @@ -3062,10 +3028,9 @@ void get_epsilon_grid(geometric_object_list gobj_list, const double *adir = (n == 0) ? x : ((n == 1) ? y : z); find_array_min_max(ndir, adir, min_val[n], max_val[n]); } - const meep::volume vol(meep::vec(min_val[0],min_val[1],min_val[2]), - meep::vec(max_val[0],max_val[1],max_val[2])); - init_libctl(_default_material, _ensure_periodicity, &gv, - cell_size, cell_center, &gobj_list); + const meep::volume vol(meep::vec(min_val[0], min_val[1], min_val[2]), + meep::vec(max_val[0], max_val[1], max_val[2])); + init_libctl(_default_material, _ensure_periodicity, &gv, cell_size, cell_center, &gobj_list); dim = gv.dim; geom_epsilon geps(gobj_list, mlist, vol); for (int i = 0; i < nx; ++i) @@ -3074,11 +3039,12 @@ void get_epsilon_grid(geometric_object_list gobj_list, /* obtain the trace of the ε tensor (dispersive or non) for each grid point in row-major order (the order used by NumPy) */ if (frequency == 0) - grid_vals[k + nz*(j + ny*i)] = geps.chi1p1(meep::E_stuff, meep::vec(x[i],y[j],z[k])); + grid_vals[k + nz * (j + ny * i)] = + geps.chi1p1(meep::E_stuff, meep::vec(x[i], y[j], z[k])); else { std::complex tensor[9]; - get_chi1_tensor_disp(tensor, meep::vec(x[i],y[j],z[k]), frequency, &geps); - grid_vals[k + nz*(j + ny*i)] = (tensor[0] + tensor[4] + tensor[8]) / 3.0; + get_chi1_tensor_disp(tensor, meep::vec(x[i], y[j], z[k]), frequency, &geps); + grid_vals[k + nz * (j + ny * i)] = (tensor[0] + tensor[4] + tensor[8]) / 3.0; } } } diff --git a/src/meepgeom.hpp b/src/meepgeom.hpp index ccbb1296e..ef77c74ac 100644 --- a/src/meepgeom.hpp +++ b/src/meepgeom.hpp @@ -177,8 +177,8 @@ class geom_epsilon : public meep::material_function { geom_box_tree restricted_tree; geometric_object_list geometry; cond_profile cond[5][2]; // [direction][side] - double tol=DEFAULT_SUBPIXEL_TOL; - int maxeval=DEFAULT_SUBPIXEL_MAXEVAL; + double tol = DEFAULT_SUBPIXEL_TOL; + int maxeval = DEFAULT_SUBPIXEL_MAXEVAL; geom_epsilon(geometric_object_list g, material_type_list mlist, const meep::volume &v); geom_epsilon(const geom_epsilon &geps1); // copy constructor @@ -207,8 +207,8 @@ class geom_epsilon : public meep::material_function { virtual void eff_chi1inv_row(meep::component c, double chi1inv_row[3], const meep::volume &v, double tol, int maxeval); - void eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_matrix, - const meep::volume &v, double tol, int maxeval, bool &fallback); + void eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_matrix, const meep::volume &v, + double tol, int maxeval, bool &fallback); void fallback_chi1inv_row(meep::component c, double chi1inv_row[3], const meep::volume &v, double tol, int maxeval); @@ -218,17 +218,17 @@ class geom_epsilon : public meep::material_function { void add_susceptibilities(meep::field_type ft, meep::structure *s); void get_material_pt(material_type &material, const meep::vec &r); + private: material_type_list extra_materials; pol *current_pol; }; void set_dimensions(int dims); -geom_epsilon* make_geom_epsilon(meep::structure *s, geometric_object_list *g, - vector3 center = make_vector3(), - bool ensure_periodicity = false, - material_type _default_material = vacuum, - material_type_list extra_materials = material_type_list()); +geom_epsilon *make_geom_epsilon(meep::structure *s, geometric_object_list *g, + vector3 center = make_vector3(), bool ensure_periodicity = false, + material_type _default_material = vacuum, + material_type_list extra_materials = material_type_list()); //, geometric_object_list g, material_type_list extra_materials void set_materials_from_geometry(meep::structure *s, geometric_object_list g, vector3 center = make_vector3(), @@ -239,10 +239,10 @@ void set_materials_from_geometry(meep::structure *s, geometric_object_list g, material_type _default_material = vacuum, absorber_list alist = 0, material_type_list extra_materials = material_type_list()); void set_materials_from_geom_epsilon(meep::structure *s, geom_epsilon *geps, - bool use_anisotropic_averaging = true, - double tol = DEFAULT_SUBPIXEL_TOL, - int maxeval = DEFAULT_SUBPIXEL_MAXEVAL, - absorber_list alist = 0); + bool use_anisotropic_averaging = true, + double tol = DEFAULT_SUBPIXEL_TOL, + int maxeval = DEFAULT_SUBPIXEL_MAXEVAL, + absorber_list alist = 0); material_type make_dielectric(double epsilon); material_type make_user_material(user_material_func user_func, void *user_data, bool do_averaging); @@ -272,21 +272,13 @@ bool is_medium(material_type md, medium_struct **m); bool is_medium(void *md, medium_struct **m); bool is_metal(meep::field_type ft, const material_type *material); geom_box gv2box(const meep::volume &v); -void get_epsilon_grid(geometric_object_list gobj_list, - material_type_list mlist, - material_type _default_material, - bool _ensure_periodicity, - meep::grid_volume gv, - vector3 cell_size, - vector3 cell_center, - int nx, const double *x, - int ny, const double *y, - int nz, const double *z, - std::complex *grid_vals, - double frequency = 0); -void init_libctl(material_type default_mat, bool ensure_per, - meep::grid_volume *gv, vector3 cell_size, vector3 cell_center, - geometric_object_list *geom_list); +void get_epsilon_grid(geometric_object_list gobj_list, material_type_list mlist, + material_type _default_material, bool _ensure_periodicity, + meep::grid_volume gv, vector3 cell_size, vector3 cell_center, int nx, + const double *x, int ny, const double *y, int nz, const double *z, + std::complex *grid_vals, double frequency = 0); +void init_libctl(material_type default_mat, bool ensure_per, meep::grid_volume *gv, + vector3 cell_size, vector3 cell_center, geometric_object_list *geom_list); /***************************************************************/ // material grid functions @@ -297,10 +289,11 @@ meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_objec double matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md); double material_grid_val(vector3 p, material_data *md); geom_box_tree calculate_tree(const meep::volume &v, geometric_object_list g); -void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector fields_a, - std::vectorfields_f, - double *frequencies, double scalegrad, meep::grid_volume &gv, - meep::volume &where, geom_epsilon *geps, double du = 1e-6); +void material_grids_addgradient(double *v, size_t ng, size_t nf, + std::vector fields_a, + std::vector fields_f, double *frequencies, + double scalegrad, meep::grid_volume &gv, meep::volume &where, + geom_epsilon *geps, double du = 1e-6); /***************************************************************/ /* routines in GDSIIgeom.cc ************************************/ diff --git a/src/monitor.cpp b/src/monitor.cpp index 8b355defe..84439f65a 100644 --- a/src/monitor.cpp +++ b/src/monitor.cpp @@ -178,7 +178,9 @@ complex fields::get_chi1inv(component c, direction d, const ivec &origlo 0); return parallel ? sum_to_all(val) : val; } - return d == component_direction(c) && (parallel || am_master()) ? 1.0 : 0; // default to vacuum outside computational cell + return d == component_direction(c) && (parallel || am_master()) + ? 1.0 + : 0; // default to vacuum outside computational cell } complex fields_chunk::get_chi1inv(component c, direction d, const ivec &iloc, diff --git a/src/mpb.cpp b/src/mpb.cpp index db63ccf9a..b337c667d 100644 --- a/src/mpb.cpp +++ b/src/mpb.cpp @@ -298,9 +298,9 @@ void special_kz_phasefix(eigenmode_data *edata, bool phase_flip) { complex *H = (complex *)edata->fft_data_H; complex im(0, phase_flip ? -1 : 1); for (size_t i = 0; i < n; ++i) { - E[3*i + 2] *= im; // Ez - H[3*i + 0] *= im; // Hx - H[3*i + 1] *= im; // Hy + E[3 * i + 2] *= im; // Ez + H[3 * i + 0] *= im; // Hx + H[3 * i + 1] *= im; // Hy } } @@ -352,8 +352,7 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c // special case: 2d cell in x and y with non-zero kz if ((v.dim == D3) && (float(v.in_direction(Z)) == float(1 / a)) && - (boundaries[High][Z] == Periodic && boundaries[Low][Z] == Periodic) && - (real(k[Z]) != 0)) + (boundaries[High][Z] == Periodic && boundaries[Low][Z] == Periodic) && (real(k[Z]) != 0)) empty_dim[2] = true; if (resolution <= 0.0) resolution = 2 * gv.a; // default to twice resolution @@ -533,7 +532,7 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c if (am_master()) { set_random_seed(314159); for (int i = 0; i < H.n * H.p; ++i) { - ASSIGN_SCALAR(H.data[i], uniform_random(-1,1), uniform_random(-1,1)); + ASSIGN_SCALAR(H.data[i], uniform_random(-1, 1), uniform_random(-1, 1)); } restore_random_seed(); } @@ -604,17 +603,15 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c k[i] = dot_product(R[i], kdir) * kmatch; // kdir*kmatch in reciprocal basis if (gv.dim == D2) k[2] = beta; } - else { - k[d - X] = kmatch * R[d - X][d - X]; - } + else { k[d - X] = kmatch * R[d - X][d - X]; } update_maxwell_data_k(mdata, k, G[0], G[1], G[2]); } } while (match_frequency && fabs(sqrt(eigvals[band_num - 1]) - frequency) > frequency * match_tol); if (dp) { - scalar_complex s = {real(dp->get_s()),imag(dp->get_s())}; - scalar_complex p = {real(dp->get_p()),imag(dp->get_p())}; + scalar_complex s = {real(dp->get_s()), imag(dp->get_s())}; + scalar_complex p = {real(dp->get_p()), imag(dp->get_p())}; // compute sum of (kparallel+G)^2 in all the periodic directions double k2sum = 0, ktmp = 0; @@ -622,14 +619,15 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c LOOP_OVER_DIRECTIONS(v.dim, dd) { m = dp->get_g()[dd - X]; if (eig_vol.in_direction(dd) != 0) { - ktmp = kpoint.in_direction(dd) + m/eig_vol.in_direction(dd); - k2sum += ktmp*ktmp; + ktmp = kpoint.in_direction(dd) + m / eig_vol.in_direction(dd); + k2sum += ktmp * ktmp; } } if (((v.dim == D3) && (float(v.in_direction(Z)) == float(1 / a)) && - (boundaries[High][Z] == Periodic && boundaries[Low][Z] == Periodic) && (real(k[Z]) != 0)) || + (boundaries[High][Z] == Periodic && boundaries[Low][Z] == Periodic) && + (real(k[Z]) != 0)) || ((v.dim == D2) && (real(k[Z]) != 0))) { - k2sum += k[Z]*k[Z]; + k2sum += k[Z] * k[Z]; } // compute kperp (if it is non evanescent) OR @@ -647,23 +645,23 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c if (match_frequency) { vec cen = eig_vol.center(); double nn = sqrt(real(get_eps(cen, frequency)) * real(get_mu(cen, frequency))); - double k2 = frequency*frequency*nn*nn - k2sum; + double k2 = frequency * frequency * nn * nn - k2sum; if (k2 < 0) { master_printf("WARNING: diffraction order for g=(%d,%d,%d) is evanescent!\n", - dp->get_g()[0],dp->get_g()[1],dp->get_g()[2]); + dp->get_g()[0], dp->get_g()[1], dp->get_g()[2]); return NULL; } else if (k2 > 0) k[dd - X] = sqrt(k2); } else - frequency = sqrt(kpoint.in_direction(dd)*kpoint.in_direction(dd) + k2sum); + frequency = sqrt(kpoint.in_direction(dd) * kpoint.in_direction(dd) + k2sum); } if (am_master()) { update_maxwell_data_k(mdata, k, G[0], G[1], G[2]); maxwell_set_planewave(mdata, H, band_num, dp->get_g(), s, p, dp->get_axis()); - eigvals[band_num - 1] = frequency*frequency; + eigvals[band_num - 1] = frequency * frequency; evectmatrix_resize(&W[0], 1, 0); evectmatrix_resize(&W[1], 1, 0); for (int i = 0; i < H.n; ++i) @@ -751,7 +749,8 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c // so we need to divide the E-field amplitudes by -frequency; we also take this // opportunity to rescale the overall E and H amplitudes to yield unit power flux. double scale = -1.0 / frequency, factor = 2.0 / sqrt(fabs(vgrp)); - complex *efield = (complex *)fft_data_E, *hfield = (complex *)(mdata->fft_data); + complex *efield = (complex *)fft_data_E, + *hfield = (complex *)(mdata->fft_data); for (int n = 0; n < NFFT; ++n) { efield[n] *= factor * scale; hfield[n] *= factor; @@ -824,8 +823,7 @@ void fields::add_eigenmode_source(component c0, const src_time &src, direction d const volume &where, const volume &eig_vol, int band_num, const vec &kpoint, bool match_frequency, int parity, double resolution, double eigensolver_tol, complex amp, - complex A(const vec &), - diffractedplanewave *dp) { + complex A(const vec &), diffractedplanewave *dp) { /*--------------------------------------------------------------*/ /* step 1: call MPB to compute the eigenmode */ /*--------------------------------------------------------------*/ @@ -833,10 +831,9 @@ void fields::add_eigenmode_source(component c0, const src_time &src, direction d double frequency = real(src.frequency()); am_now_working_on(MPBTime); - global_eigenmode_data = - (eigenmode_data *)get_eigenmode(frequency, d, where, eig_vol, band_num, kpoint, - match_frequency, parity, resolution, eigensolver_tol, - NULL, NULL, dp); + global_eigenmode_data = (eigenmode_data *)get_eigenmode( + frequency, d, where, eig_vol, band_num, kpoint, match_frequency, parity, resolution, + eigensolver_tol, NULL, NULL, dp); finished_working(); if (is_real && beta != 0) special_kz_phasefix(global_eigenmode_data, true /* phase_flip */); @@ -849,8 +846,9 @@ void fields::add_eigenmode_source(component c0, const src_time &src, direction d global_eigenmode_data->center -= where.center(); if (!global_eigenmode_data) - meep::abort("eigenmode solver failed to find the requested mode; you may need to supply a better " - "guess for k"); + meep::abort( + "eigenmode solver failed to find the requested mode; you may need to supply a better " + "guess for k"); global_eigenmode_data->amp_func = A ? A : default_amp_func; @@ -868,9 +866,10 @@ void fields::add_eigenmode_source(component c0, const src_time &src, direction d component cE[3] = {Ex, Ey, Ez}, cH[3] = {Hx, Hy, Hz}; int n = (d == X ? 0 : (d == Y ? 1 : 2)); if (d == NO_DIRECTION) { - n = where.in_direction(X) == 0 - ? 0 - : where.in_direction(Y) == 0 ? 1 : where.in_direction(Z) == 0 ? 2 : -1; + n = where.in_direction(X) == 0 ? 0 + : where.in_direction(Y) == 0 ? 1 + : where.in_direction(Z) == 0 ? 2 + : -1; if (n == -1) meep::abort( "can't determine source direction for non-empty source volume with NO_DIRECTION source"); @@ -926,7 +925,7 @@ void fields::get_eigenmode_coefficients(dft_flux flux, const volume &eig_vol, in bool match_frequency = true; if (flux.use_symmetry && S.multiplicity() > 1 && parity == 0) meep::abort("flux regions for eigenmode projection with symmetry should be created by " - "add_mode_monitor()"); + "add_mode_monitor()"); vec kpoint(0.0, 0.0, 0.0); // default guess @@ -955,7 +954,8 @@ void fields::get_eigenmode_coefficients(dft_flux flux, const volume &eig_vol, in continue; } - if (is_real && beta != 0) special_kz_phasefix((eigenmode_data *) mode_data, false /* phase_flip */); + if (is_real && beta != 0) + special_kz_phasefix((eigenmode_data *)mode_data, false /* phase_flip */); double vg = get_group_velocity(mode_data); vec kfound = get_k(mode_data); diff --git a/src/multilevel-atom.cpp b/src/multilevel-atom.cpp index df9c2213f..a838d36e8 100644 --- a/src/multilevel-atom.cpp +++ b/src/multilevel-atom.cpp @@ -159,7 +159,8 @@ void multilevel_susceptibility::init_internal_data(realnum *W[NUM_FIELD_COMPONEN for (int i = 0; i < L; ++i) for (int j = 0; j < L; ++j) d->GammaInv[i * L + j] = (i == j) + Gamma[i * L + j] * dt / 2; - if (!invert(d->GammaInv, L)) meep::abort("multilevel_susceptibility: I + Gamma*dt/2 matrix singular"); + if (!invert(d->GammaInv, L)) + meep::abort("multilevel_susceptibility: I + Gamma*dt/2 matrix singular"); realnum *P = d->data + L * L; realnum *P_prev = P + ntot; diff --git a/src/mympi.cpp b/src/mympi.cpp index 501e987bf..57da0ad73 100644 --- a/src/mympi.cpp +++ b/src/mympi.cpp @@ -161,25 +161,24 @@ int verbosity = 1; // defined in meep.h code based on github.com/JuliaLang/julia/blob/master/src/processor_x86.cpp#L1087-L1104, which is free software under the GPL-compatible "MIT license" */ -static void _set_zero_subnormals(bool iszero) -{ +static void _set_zero_subnormals(bool iszero) { #if HAVE_IMMINTRIN_H - unsigned int flags = 0x00008040; // assume a non-ancient processor with SSE2, supporting both FTZ and DAZ flags - unsigned int state = _mm_getcsr(); - if (iszero) - state |= flags; - else - state &= ~flags; - _mm_setcsr(state); + unsigned int flags = + 0x00008040; // assume a non-ancient processor with SSE2, supporting both FTZ and DAZ flags + unsigned int state = _mm_getcsr(); + if (iszero) + state |= flags; + else + state &= ~flags; + _mm_setcsr(state); #else - (void) iszero; // unused + (void)iszero; // unused #endif } -void set_zero_subnormals(bool iszero) -{ +void set_zero_subnormals(bool iszero) { int n = omp_get_num_threads(); #ifdef _OPENMP -#pragma omp parallel for schedule(static,1) +#pragma omp parallel for schedule(static, 1) #endif for (int i = 0; i < n; ++i) _set_zero_subnormals(iszero); // This has to be done in every thread for OpenMP. @@ -188,8 +187,7 @@ void set_zero_subnormals(bool iszero) void setup() { set_zero_subnormals(true); #ifdef _OPENMP - if (getenv("OMP_NUM_THREADS") == NULL) - omp_set_num_threads(1); + if (getenv("OMP_NUM_THREADS") == NULL) omp_set_num_threads(1); #endif } @@ -199,7 +197,7 @@ initialize::initialize(int &argc, char **&argv) { int provided; MPI_Init_thread(&argc, &argv, MPI_THREAD_FUNNELED, &provided); if (provided < MPI_THREAD_FUNNELED && omp_get_num_threads() > 1) - abort("MPI does not support multi-threaded execution"); + abort("MPI does not support multi-threaded execution"); #else MPI_Init(&argc, &argv); #endif @@ -257,7 +255,7 @@ double wall_time(void) { fprintf(stderr, "meep: %s", error_msg.c_str()); if (fmt[strlen(fmt) - 1] != '\n') fputc('\n', stderr); // force newline MPI_Abort(MPI_COMM_WORLD, 1); - std::abort(); // Unreachable but MPI_Abort does not have the noreturn attribute. + std::abort(); // Unreachable but MPI_Abort does not have the noreturn attribute. #else throw runtime_error("meep: " + error_msg); #endif @@ -686,8 +684,8 @@ meep_printf_callback_func set_meep_printf_stderr_callback(meep_printf_callback_f return old_func; } -static void _do_master_printf(FILE* output, meep_printf_callback_func callback, - const char *fmt, va_list ap) { +static void _do_master_printf(FILE *output, meep_printf_callback_func callback, const char *fmt, + va_list ap) { if (am_really_master()) { if (callback) { char *s; diff --git a/src/near2far.cpp b/src/near2far.cpp index 92dacdf0f..ff8f5daf5 100644 --- a/src/near2far.cpp +++ b/src/near2far.cpp @@ -43,7 +43,7 @@ dft_near2far::dft_near2far(dft_chunk *F_, double fmin, double fmax, int Nf, doub } } -dft_near2far::dft_near2far(dft_chunk *F_, const std::vector& freq_, double eps_, double mu_, +dft_near2far::dft_near2far(dft_chunk *F_, const std::vector &freq_, double eps_, double mu_, const volume &where_, const direction periodic_d_[2], const int periodic_n_[2], const double periodic_k_[2], const double period_[2]) @@ -151,8 +151,7 @@ void green3d(std::complex *EH, const vec &x, double freq, double eps, do vec p = zero_vec(rhat.dim); p.set_direction(component_direction(c0), 1); double pdotrhat = p & rhat; - vec rhatcrossp = vec(rhat.y() * p.z() - rhat.z() * p.y(), - rhat.z() * p.x() - rhat.x() * p.z(), + vec rhatcrossp = vec(rhat.y() * p.z() - rhat.z() * p.y(), rhat.z() * p.x() - rhat.x() * p.z(), rhat.x() * p.y() - rhat.y() * p.x()); /* compute the various scalar quantities in the point source formulae */ @@ -632,7 +631,8 @@ dft_near2far fields::add_dft_near2far(const volume_list *where, const double *fr double s = j == 0 ? 1 : -1; /* sign of n x c */ if (is_electric(c)) s = -s; - F = add_dft(c, w->v, freq, Nfreq, true, s * w->weight, F, false, 1.0, false, c0, decimation_factor); + F = add_dft(c, w->v, freq, Nfreq, true, s * w->weight, F, false, 1.0, false, c0, + decimation_factor); } } } @@ -641,8 +641,10 @@ dft_near2far fields::add_dft_near2far(const volume_list *where, const double *fr period); } -//Modified from farfield_lowlevel -std::vector dft_near2far::near_sourcedata(const vec &x_0, double* farpt_list, size_t nfar_pts, const std::complex* dJ) { +// Modified from farfield_lowlevel +std::vector dft_near2far::near_sourcedata(const vec &x_0, double *farpt_list, + size_t nfar_pts, + const std::complex *dJ) { if (x_0.dim != D3 && x_0.dim != D2 && x_0.dim != Dcyl) meep::abort("only 2d or 3d or cylindrical far-field computation is supported"); greenfunc green = x_0.dim == D2 ? green2d : green3d; @@ -657,49 +659,49 @@ std::vector dft_near2far::near_sourcedata(const vec &x_0, dou component c0 = component(f->vc); /* equivalent source component */ vec rshift(f->shift * (0.5 * f->fc->gv.inva)); - std::complex EH6[6]; - size_t idx_dft = 0; - sourcedata temp_struct = {component(f->c), idx_arr, f->fc->chunk_idx, amp_arr}; - - LOOP_OVER_IVECS(f->fc->gv, f->is, f->ie, idx) { - IVEC_LOOP_ILOC(f->fc->gv, ix0); - IVEC_LOOP_LOC(f->fc->gv, x0); - x0 = f->S.transform(x0, f->sn) + rshift; - vec xs(x0); - double w; - w = IVEC_LOOP_WEIGHT(f->s0, f->s1, f->e0, f->e1, f->dV0 + f->dV1 * loop_i2); - - temp_struct.idx_arr.push_back(idx); - for (size_t i = 0; i < Nfreq; ++i) { - std::complex EH0 = std::complex(0,0); - for (int i0 = -periodic_n[0]; i0 <= periodic_n[0]; ++i0) { - if (periodic_d[0] != NO_DIRECTION) - xs.set_direction(periodic_d[0], x0.in_direction(periodic_d[0]) + i0 * period[0]); - double phase0 = i0 * periodic_k[0]; - for (int i1 = -periodic_n[1]; i1 <= periodic_n[1]; ++i1) { - if (periodic_d[1] != NO_DIRECTION) - xs.set_direction(periodic_d[1], x0.in_direction(periodic_d[1]) + i1 * period[1]); - double phase = phase0 + i1 * periodic_k[1]; - std::complex cphase = std::polar(1.0, phase); - for (size_t ipt = 0; ipt < nfar_pts; ++ipt){ - vec x = vec(farpt_list[3*ipt], farpt_list[3*ipt+1], farpt_list[3*ipt+2]); - if (x_0.dim == Dcyl) - greencyl(EH6, x, freq[i], eps, mu, xs, c0, w, f->fc->m, 1e-3); - else - green(EH6, x, freq[i], eps, mu, xs, c0, w); - for (int j = 0; j < 6; ++j) - EH0 += EH6[j] * cphase * (f->stored_weight) * dJ[6*Nfreq*ipt + 6*i + j]; - } + std::complex EH6[6]; + size_t idx_dft = 0; + sourcedata temp_struct = {component(f->c), idx_arr, f->fc->chunk_idx, amp_arr}; + + LOOP_OVER_IVECS(f->fc->gv, f->is, f->ie, idx) { + IVEC_LOOP_ILOC(f->fc->gv, ix0); + IVEC_LOOP_LOC(f->fc->gv, x0); + x0 = f->S.transform(x0, f->sn) + rshift; + vec xs(x0); + double w; + w = IVEC_LOOP_WEIGHT(f->s0, f->s1, f->e0, f->e1, f->dV0 + f->dV1 * loop_i2); + + temp_struct.idx_arr.push_back(idx); + for (size_t i = 0; i < Nfreq; ++i) { + std::complex EH0 = std::complex(0, 0); + for (int i0 = -periodic_n[0]; i0 <= periodic_n[0]; ++i0) { + if (periodic_d[0] != NO_DIRECTION) + xs.set_direction(periodic_d[0], x0.in_direction(periodic_d[0]) + i0 * period[0]); + double phase0 = i0 * periodic_k[0]; + for (int i1 = -periodic_n[1]; i1 <= periodic_n[1]; ++i1) { + if (periodic_d[1] != NO_DIRECTION) + xs.set_direction(periodic_d[1], x0.in_direction(periodic_d[1]) + i1 * period[1]); + double phase = phase0 + i1 * periodic_k[1]; + std::complex cphase = std::polar(1.0, phase); + for (size_t ipt = 0; ipt < nfar_pts; ++ipt) { + vec x = vec(farpt_list[3 * ipt], farpt_list[3 * ipt + 1], farpt_list[3 * ipt + 2]); + if (x_0.dim == Dcyl) + greencyl(EH6, x, freq[i], eps, mu, xs, c0, w, f->fc->m, 1e-3); + else + green(EH6, x, freq[i], eps, mu, xs, c0, w); + for (int j = 0; j < 6; ++j) + EH0 += EH6[j] * cphase * (f->stored_weight) * dJ[6 * Nfreq * ipt + 6 * i + j]; + } } } idx_dft++; - if (is_electric(temp_struct.near_fd_comp)) - EH0 *= -1; + if (is_electric(temp_struct.near_fd_comp)) EH0 *= -1; EH0 /= f->S.multiplicity(ix0); - if (x_0.dim == Dcyl){ + if (x_0.dim == Dcyl) { if (xs.r() != 0) EH0 /= xs.r(); - //Somehow, a factor of (2pi)r for r of the near2far region was double counted. - //2pi is canceled out by the integral over design region, where an extra factor of 2pi*r' (r' of the design region) is needed. See meepgeom.cpp + // Somehow, a factor of (2pi)r for r of the near2far region was double counted. + // 2pi is canceled out by the integral over design region, where an extra factor of 2pi*r' + // (r' of the design region) is needed. See meepgeom.cpp } temp_struct.amp_arr.push_back(EH0); } diff --git a/src/output_directory.cpp b/src/output_directory.cpp index caec99187..0b207cced 100644 --- a/src/output_directory.cpp +++ b/src/output_directory.cpp @@ -109,9 +109,7 @@ const char *make_output_directory(const char *exename, const char *jobname) { snprintf(sourcename, buflen, "%s.cpp", stripped_name); if (jobname != NULL) { snprintf(basename, buflen, "%s", jobname); } - else { - snprintf(basename, buflen, "%s", stripped_name); - } + else { snprintf(basename, buflen, "%s", stripped_name); } static char outdirname[buflen]; snprintf(outdirname, buflen, "%s-out", basename); diff --git a/src/random.cpp b/src/random.cpp index 928ca6738..74a4d7236 100644 --- a/src/random.cpp +++ b/src/random.cpp @@ -65,9 +65,7 @@ double gaussian_random(double mean, double stddev) { s = v1 * v1 + v2 * v2; } while (s >= 1.0); if (s == 0) { return mean; } - else { - return mean + v1 * sqrt(-2 * log(s) / s) * stddev; - } + else { return mean + v1 * sqrt(-2 * log(s) / s) * stddev; } } } // namespace meep diff --git a/src/sources.cpp b/src/sources.cpp index cc1dc91d3..61a55cde8 100644 --- a/src/sources.cpp +++ b/src/sources.cpp @@ -254,7 +254,7 @@ static void src_vol_chunkloop(fields_chunk *fc, int ichunk, component c, ivec is size_t npts = 1; LOOP_OVER_DIRECTIONS(is.dim, d) { npts *= (ie.in_direction(d) - is.in_direction(d)) / 2 + 1; } std::vector index_array(npts); - std::vector> amps_array(npts); + std::vector > amps_array(npts); complex amp = data->amp * conj(shift_phase); @@ -283,29 +283,33 @@ static void src_vol_chunkloop(fields_chunk *fc, int ichunk, component c, ivec is index_array[idx_vol++] = idx; } - if (idx_vol > npts) meep::abort("add_volume_source: computed wrong npts (%zd vs. %zd)", npts, idx_vol); + if (idx_vol > npts) + meep::abort("add_volume_source: computed wrong npts (%zd vs. %zd)", npts, idx_vol); field_type ft = is_magnetic(c) ? B_stuff : D_stuff; fc->add_source(ft, src_vol(c, data->src, std::move(index_array), std::move(amps_array))); } -void fields::add_srcdata(struct sourcedata cur_data, src_time *src, size_t n, std::complex* amp_arr, bool needs_boundary_fix){ +void fields::add_srcdata(struct sourcedata cur_data, src_time *src, size_t n, + std::complex *amp_arr, bool needs_boundary_fix) { if (n == 0) { - n = cur_data.idx_arr.size(); - assert(amp_arr == NULL); - amp_arr = cur_data.amp_arr.data(); + n = cur_data.idx_arr.size(); + assert(amp_arr == NULL); + amp_arr = cur_data.amp_arr.data(); } assert(n == cur_data.idx_arr.size()); sources = src->add_to(sources, &src); std::vector index_arr(cur_data.idx_arr); - std::vector> amplitudes(amp_arr, amp_arr+n); + std::vector > amplitudes(amp_arr, amp_arr + n); component c = cur_data.near_fd_comp; field_type ft = is_magnetic(c) ? B_stuff : D_stuff; - if (0 > cur_data.fc_idx or cur_data.fc_idx >= num_chunks) meep::abort("fields chunk index out of range"); + if (0 > cur_data.fc_idx or cur_data.fc_idx >= num_chunks) + meep::abort("fields chunk index out of range"); fields_chunk *fc = chunks[cur_data.fc_idx]; if (!fc->is_mine()) meep::abort("wrong fields chunk"); - fc->add_source(ft, src_vol(c, src, std::move(index_arr), std::move(amplitudes), needs_boundary_fix)); + fc->add_source(ft, + src_vol(c, src, std::move(index_arr), std::move(amplitudes), needs_boundary_fix)); // We can't do require_component(c) since that only works if all processes are adding // srcdata for the same components in the same order, which may not be true. // ... instead, the caller should call fields::require_source_components() @@ -402,13 +406,15 @@ void fields::add_volume_source(component c, const src_time &src, const volume &w std::string dataset_im = std::string(dataset) + ".im"; size_t re_dims[] = {1, 1, 1}; - realnum *real_data = (realnum *)eps_file.read(dataset_re.c_str(), &rank, re_dims, 3, sizeof(realnum) == sizeof(float)); + realnum *real_data = (realnum *)eps_file.read(dataset_re.c_str(), &rank, re_dims, 3, + sizeof(realnum) == sizeof(float)); if (verbosity > 0) master_printf("read in %zdx%zdx%zd amplitude function file \"%s:%s\"\n", re_dims[0], re_dims[1], re_dims[2], filename, dataset_re.c_str()); size_t im_dims[] = {1, 1, 1}; - realnum *imag_data = (realnum *)eps_file.read(dataset_im.c_str(), &rank, im_dims, 3, sizeof(realnum) == sizeof(float)); + realnum *imag_data = (realnum *)eps_file.read(dataset_im.c_str(), &rank, im_dims, 3, + sizeof(realnum) == sizeof(float)); if (verbosity > 0) master_printf("read in %zdx%zdx%zd amplitude function file \"%s:%s\"\n", im_dims[0], im_dims[1], im_dims[2], filename, dataset_im.c_str()); @@ -466,8 +472,8 @@ void fields::add_volume_source(component c, const src_time &src, const volume &w /* add_volume_source only if certain conditions are met */ /***************************************************************/ void fields::add_volume_source_check(component c, const src_time &src, const volume &where, - complex A(const vec &), complex amp, component c0, - direction d, int has_tm, int has_te) { + complex A(const vec &), complex amp, + component c0, direction d, int has_tm, int has_te) { if (!gv.has_field(c)) return; if (c0 != Centered && c0 != c) return; if (component_direction(c) == d) return; @@ -486,13 +492,13 @@ static std::complex gaussianbeam_ampfunc(const vec &p) { std::complex EH[6]; global_gaussianbeam_object->get_fields(EH, p); switch (global_gaussianbeam_component) { - case Ex: return EH[0]; - case Ey: return EH[1]; - case Ez: return EH[2]; - case Hx: return EH[3]; - case Hy: return EH[4]; - case Hz: return EH[5]; - default: meep::abort("invalid component in gaussianbeam_ampfunc"); + case Ex: return EH[0]; + case Ey: return EH[1]; + case Ez: return EH[2]; + case Hx: return EH[3]; + case Hy: return EH[4]; + case Hz: return EH[5]; + default: meep::abort("invalid component in gaussianbeam_ampfunc"); } } @@ -502,9 +508,10 @@ void fields::add_volume_source(const src_time &src, const volume &where, gaussia component cE[3] = {Ex, Ey, Ez}, cH[3] = {Hx, Hy, Hz}; int n = (d == X ? 0 : (d == Y ? 1 : 2)); if (d == NO_DIRECTION) { - n = where.in_direction(X) == 0 - ? 0 - : where.in_direction(Y) == 0 ? 1 : where.in_direction(Z) == 0 ? 2 : -1; + n = where.in_direction(X) == 0 ? 0 + : where.in_direction(Y) == 0 ? 1 + : where.in_direction(Z) == 0 ? 2 + : -1; if (n == -1) meep::abort( "can't determine source direction for non-empty source volume with NO_DIRECTION source"); @@ -515,22 +522,22 @@ void fields::add_volume_source(const src_time &src, const volume &where, gaussia int np2 = (n + 2) % 3; // Kx = -Hy, Ky = Hx (for d==Z) global_gaussianbeam_component = cH[np1]; - add_volume_source_check(cE[np2], src, where, gaussianbeam_ampfunc, +1.0, cE[np2], d, - has_tm, has_te); + add_volume_source_check(cE[np2], src, where, gaussianbeam_ampfunc, +1.0, cE[np2], d, has_tm, + has_te); global_gaussianbeam_component = cH[np2]; - add_volume_source_check(cE[np1], src, where, gaussianbeam_ampfunc, -1.0, cE[np1], d, - has_tm, has_te); + add_volume_source_check(cE[np1], src, where, gaussianbeam_ampfunc, -1.0, cE[np1], d, has_tm, + has_te); // Nx = +Ey, Ny = -Ex (for d==Z) global_gaussianbeam_component = cE[np1]; - add_volume_source_check(cH[np2], src, where, gaussianbeam_ampfunc, -1.0, cH[np2], d, - has_tm, has_te); + add_volume_source_check(cH[np2], src, where, gaussianbeam_ampfunc, -1.0, cH[np2], d, has_tm, + has_te); global_gaussianbeam_component = cE[np2]; - add_volume_source_check(cH[np1], src, where, gaussianbeam_ampfunc, +1.0, cH[np1], d, - has_tm, has_te); + add_volume_source_check(cH[np1], src, where, gaussianbeam_ampfunc, +1.0, cH[np1], d, has_tm, + has_te); } -gaussianbeam::gaussianbeam(const vec &x0_, const vec &kdir_, double w0_, double freq_, - double eps_, double mu_, std::complex E0_[3]) { +gaussianbeam::gaussianbeam(const vec &x0_, const vec &kdir_, double w0_, double freq_, double eps_, + double mu_, std::complex E0_[3]) { if (x0_.dim == Dcyl) meep::abort("wrong dimensionality in gaussianbeam"); x0 = x0_; @@ -596,61 +603,64 @@ gaussianbeam::gaussianbeam(const vec &x0_, const vec &kdir_, double w0_, double void gaussianbeam::get_fields(std::complex *EH, const vec &x) const { double n = sqrt(eps * mu); double k = 2 * pi * freq * n; - double ZR = sqrt(mu/eps); + double ZR = sqrt(mu / eps); double z0 = k * w0 * w0 / 2; - double kz0 = k*z0; + double kz0 = k * z0; vec xrel = x - x0; vec zhat = kdir / abs(kdir); - double rho = abs(vec(zhat.y()*xrel.z()-zhat.z()*xrel.y(), - zhat.z()*xrel.x()-zhat.x()*xrel.z(), - zhat.x()*xrel.y()-zhat.y()*xrel.x())); + double rho = + abs(vec(zhat.y() * xrel.z() - zhat.z() * xrel.y(), zhat.z() * xrel.x() - zhat.x() * xrel.z(), + zhat.x() * xrel.y() - zhat.y() * xrel.x())); double zhatdotxrel = zhat & xrel; bool UseRescaledFG = false; - complex zc = complex(zhatdotxrel,-z0); - complex Rsq = rho*rho + zc*zc; + complex zc = complex(zhatdotxrel, -z0); + complex Rsq = rho * rho + zc * zc; complex R = sqrt(Rsq); - complex kR = k*R, kR2 = kR*kR, kR3 = kR2*kR; - complex f,g,fmgbRsq; + complex kR = k * R, kR2 = kR * kR, kR3 = kR2 * kR; + complex f, g, fmgbRsq; if (abs(kR) > 1e-4) { complex coskR, sinkR; if (fabs(imag(kR)) > 30.0) { UseRescaledFG = true; - complex ExpI = exp(complex(0,1.0)*real(kR)); - complex ExpPlus = exp(imag(kR)-kz0); - complex ExpMinus = exp(-(imag(kR)+kz0)); - coskR = 0.5*(ExpI*ExpMinus + conj(ExpI)*ExpPlus); - sinkR = -0.5*complex(0,1.0)*(ExpI*ExpMinus - conj(ExpI)*ExpPlus); - } else { + complex ExpI = exp(complex(0, 1.0) * real(kR)); + complex ExpPlus = exp(imag(kR) - kz0); + complex ExpMinus = exp(-(imag(kR) + kz0)); + coskR = 0.5 * (ExpI * ExpMinus + conj(ExpI) * ExpPlus); + sinkR = -0.5 * complex(0, 1.0) * (ExpI * ExpMinus - conj(ExpI) * ExpPlus); + } + else { coskR = cos(kR); sinkR = sin(kR); } - f = -3.0 * (coskR/kR2 - sinkR/kR3); - g = 1.5 * (sinkR/kR + coskR/kR2 - sinkR/kR3); - fmgbRsq = (f-g)/Rsq; - } else { - complex kR4 = kR2*kR2; + f = -3.0 * (coskR / kR2 - sinkR / kR3); + g = 1.5 * (sinkR / kR + coskR / kR2 - sinkR / kR3); + fmgbRsq = (f - g) / Rsq; + } + else { + complex kR4 = kR2 * kR2; // Taylor series expansion for small R - f = kR4/280.0 - kR2/10.0 + 1.0; - g = 3.0*kR4/280.0 - kR2/5.0 + 1.0; - fmgbRsq = (kR4/5040.0 - kR2/140.0 + 0.1)*(k*k); + f = kR4 / 280.0 - kR2 / 10.0 + 1.0; + g = 3.0 * kR4 / 280.0 - kR2 / 5.0 + 1.0; + fmgbRsq = (kR4 / 5040.0 - kR2 / 140.0 + 0.1) * (k * k); } - complex i2fk = 0.5*complex(0,1)*f*k; + complex i2fk = 0.5 * complex(0, 1) * f * k; complex E[3], H[3]; - for (int j=0; j<3; ++j) { - E[j] = complex(0,0); - H[j] = complex(0,0); + for (int j = 0; j < 3; ++j) { + E[j] = complex(0, 0); + H[j] = complex(0, 0); } - double rnorm = sqrt(real(E0[0])*real(E0[0])+real(E0[1])*real(E0[1])+real(E0[2])*real(E0[2])); + double rnorm = + sqrt(real(E0[0]) * real(E0[0]) + real(E0[1]) * real(E0[1]) + real(E0[2]) * real(E0[2])); if (rnorm > 1e-13) { - vec xhat = vec(real(E0[0]),real(E0[1]),real(E0[2])) / rnorm; - vec yhat = vec(zhat.y()*xhat.z()-zhat.z()*xhat.y(), - zhat.z()*xhat.x()-zhat.x()*xhat.z(), - zhat.x()*xhat.y()-zhat.y()*xhat.x()); + vec xhat = vec(real(E0[0]), real(E0[1]), real(E0[2])) / rnorm; + vec yhat = + vec(zhat.y() * xhat.z() - zhat.z() * xhat.y(), zhat.z() * xhat.x() - zhat.x() * xhat.z(), + zhat.x() * xhat.y() - zhat.y() * xhat.x()); double xhatdotxrel = xhat & xrel; double yhatdotxrel = yhat & xrel; @@ -669,12 +679,13 @@ void gaussianbeam::get_fields(std::complex *EH, const vec &x) const { H[2] += rnorm * (gb_Hx * xhat.z() + gb_Hy * yhat.z() + gb_Hz * zhat.z()); } - double inorm = sqrt(imag(E0[0])*imag(E0[0])+imag(E0[1])*imag(E0[1])+imag(E0[2])*imag(E0[2])); + double inorm = + sqrt(imag(E0[0]) * imag(E0[0]) + imag(E0[1]) * imag(E0[1]) + imag(E0[2]) * imag(E0[2])); if (inorm > 1e-13) { - vec xhat = vec(imag(E0[0]),imag(E0[1]),imag(E0[2])) / inorm; - vec yhat = vec(zhat.y()*xhat.z()-zhat.z()*xhat.y(), - zhat.z()*xhat.x()-zhat.x()*xhat.z(), - zhat.x()*xhat.y()-zhat.y()*xhat.x()); + vec xhat = vec(imag(E0[0]), imag(E0[1]), imag(E0[2])) / inorm; + vec yhat = + vec(zhat.y() * xhat.z() - zhat.z() * xhat.y(), zhat.z() * xhat.x() - zhat.x() * xhat.z(), + zhat.x() * xhat.y() - zhat.y() * xhat.x()); double xhatdotxrel = xhat & xrel; double yhatdotxrel = yhat & xrel; @@ -685,29 +696,36 @@ void gaussianbeam::get_fields(std::complex *EH, const vec &x) const { complex gb_Hy = g + fmgbRsq * yhatdotxrel * yhatdotxrel + i2fk * zc; complex gb_Hz = fmgbRsq * yhatdotxrel * zc - i2fk * yhatdotxrel; - E[0] += complex(0,1.0) * inorm * (gb_Ex * xhat.x() + gb_Ey * yhat.x() + gb_Ez * zhat.x()); - E[1] += complex(0,1.0) * inorm * (gb_Ex * xhat.y() + gb_Ey * yhat.y() + gb_Ez * zhat.y()); - E[2] += complex(0,1.0) * inorm * (gb_Ex * xhat.z() + gb_Ey * yhat.z() + gb_Ez * zhat.z()); - H[0] += complex(0,1.0) * inorm * (gb_Hx * xhat.x() + gb_Hy * yhat.x() + gb_Hz * zhat.x()); - H[1] += complex(0,1.0) * inorm * (gb_Hx * xhat.y() + gb_Hy * yhat.y() + gb_Hz * zhat.y()); - H[2] += complex(0,1.0) * inorm * (gb_Hx * xhat.z() + gb_Hy * yhat.z() + gb_Hz * zhat.z()); + E[0] += + complex(0, 1.0) * inorm * (gb_Ex * xhat.x() + gb_Ey * yhat.x() + gb_Ez * zhat.x()); + E[1] += + complex(0, 1.0) * inorm * (gb_Ex * xhat.y() + gb_Ey * yhat.y() + gb_Ez * zhat.y()); + E[2] += + complex(0, 1.0) * inorm * (gb_Ex * xhat.z() + gb_Ey * yhat.z() + gb_Ez * zhat.z()); + H[0] += + complex(0, 1.0) * inorm * (gb_Hx * xhat.x() + gb_Hy * yhat.x() + gb_Hz * zhat.x()); + H[1] += + complex(0, 1.0) * inorm * (gb_Hx * xhat.y() + gb_Hy * yhat.y() + gb_Hz * zhat.y()); + H[2] += + complex(0, 1.0) * inorm * (gb_Hx * xhat.z() + gb_Hy * yhat.z() + gb_Hz * zhat.z()); } double Eorig; if (UseRescaledFG) - Eorig = 3.0/(2*kz0*kz0*kz0) * (kz0*(kz0-1) + 0.5*(1.0-exp(-2.0*kz0))); + Eorig = 3.0 / (2 * kz0 * kz0 * kz0) * (kz0 * (kz0 - 1) + 0.5 * (1.0 - exp(-2.0 * kz0))); else - Eorig = 3.0/(2*kz0*kz0*kz0) * (exp(kz0)*kz0*(kz0-1) + sinh(kz0)); + Eorig = 3.0 / (2 * kz0 * kz0 * kz0) * (exp(kz0) * kz0 * (kz0 - 1) + sinh(kz0)); EH[0] = E[0] / Eorig; EH[1] = E[1] / Eorig; EH[2] = E[2] / Eorig; - EH[3] = H[0] / (Eorig*ZR); - EH[4] = H[1] / (Eorig*ZR); - EH[5] = H[2] / (Eorig*ZR); + EH[3] = H[0] / (Eorig * ZR); + EH[4] = H[1] / (Eorig * ZR); + EH[5] = H[2] / (Eorig * ZR); } -diffractedplanewave::diffractedplanewave(int g_[3], double axis_[3], std::complex s_, std::complex p_) { +diffractedplanewave::diffractedplanewave(int g_[3], double axis_[3], std::complex s_, + std::complex p_) { for (int j = 0; j < 3; ++j) { g[j] = g_[j]; axis[j] = axis_[j]; diff --git a/src/step_db.cpp b/src/step_db.cpp index 1f08c7802..9841297a7 100644 --- a/src/step_db.cpp +++ b/src/step_db.cpp @@ -44,7 +44,7 @@ void fields::step_db(field_type ft) { bool fields_chunk::step_db(field_type ft) { bool allocated_u = false; - for (const auto& sub_gv : gvs_tiled) { + for (const auto &sub_gv : gvs_tiled) { DOCMP FOR_FT_COMPONENTS(ft, cc) { if (f[cc][cmp]) { const component c_p = plus_component[cc], c_m = minus_component[cc]; @@ -116,12 +116,10 @@ bool fields_chunk::step_db(field_type ft) { default: meep::abort("bug - non-cylindrical field component in Dcyl"); } - STEP_CURL(the_f, cc, f_p, f_m, stride_p, stride_m, - gv, sub_gv.little_owned_corner0(cc), sub_gv.big_corner(), - Courant, dsig, s->sig[dsig], - s->kap[dsig], s->siginv[dsig], f_u[cc][cmp], dsigu, s->sig[dsigu], s->kap[dsigu], - s->siginv[dsigu], dt, s->conductivity[cc][d_c], s->condinv[cc][d_c], - f_cond[cc][cmp]); + STEP_CURL(the_f, cc, f_p, f_m, stride_p, stride_m, gv, sub_gv.little_owned_corner0(cc), + sub_gv.big_corner(), Courant, dsig, s->sig[dsig], s->kap[dsig], s->siginv[dsig], + f_u[cc][cmp], dsigu, s->sig[dsigu], s->kap[dsigu], s->siginv[dsigu], dt, + s->conductivity[cc][d_c], s->condinv[cc][d_c], f_cond[cc][cmp]); } } } @@ -149,10 +147,10 @@ bool fields_chunk::step_db(field_type ft) { const direction dsigu0 = cycle_direction(gv.dim, d_c, 2); const direction dsigu = s->sigsize[dsigu0] > 1 ? dsigu0 : NO_DIRECTION; const realnum betadt = 2 * pi * beta * dt * (d_c == X ? +1 : -1) * - (f[c_g][1 - cmp] ? (ft == D_stuff ? -1 : +1) * (2 * cmp - 1) : 1); - STEP_BETA(the_f, cc, g, gv, gv.little_owned_corner0(cc), gv.big_corner(), - betadt, dsig, s->siginv[dsig], f_u[cc][cmp], dsigu, - s->siginv[dsigu], s->condinv[cc][d_c], f_cond[cc][cmp]); + (f[c_g][1 - cmp] ? (ft == D_stuff ? -1 : +1) * (2 * cmp - 1) : 1); + STEP_BETA(the_f, cc, g, gv, gv.little_owned_corner0(cc), gv.big_corner(), betadt, dsig, + s->siginv[dsig], f_u[cc][cmp], dsigu, s->siginv[dsigu], s->condinv[cc][d_c], + f_cond[cc][cmp]); } // in cylindrical coordinates, we now have to add the i*m/r terms... */ @@ -336,7 +334,7 @@ bool fields_chunk::step_db(field_type ft) { for (int iz = (ft == D_stuff); iz < nz + (ft == D_stuff); ++iz) { realnum df; realnum dfcnd = (sd * Courant) * (f_p[iz] - f_p[iz - sd] - f_m_mult * f_m[iz]) * - (cndinv ? cndinv[iz] : 1); + (cndinv ? cndinv[iz] : 1); if (fcnd) fcnd[iz] += dfcnd; the_f[iz] += (df = dfcnd * (siginv ? siginv[dk + 2 * (dsig == Z) * iz] : 1)); if (fu) fu[iz] += siginvu[dku + 2 * (dsigu == Z) * iz] * df; diff --git a/src/step_generic.cpp b/src/step_generic.cpp index 454d55a7a..aa49bdf92 100644 --- a/src/step_generic.cpp +++ b/src/step_generic.cpp @@ -12,7 +12,7 @@ KSTRIDE_DEF defines the relevant strides etc. and goes outside the LOOP, wheras KDEF defines the k index and goes inside the LOOP. */ #define KSTRIDE_DEF(dsig, k, is, gv) \ - const int k##0 = is.in_direction(dsig) - gv.little_corner().in_direction(dsig); \ + const int k##0 = is.in_direction(dsig) - gv.little_corner().in_direction(dsig); \ const int s##k##1 = gv.yucky_direction(0) == dsig ? 2 : 0; \ const int s##k##2 = gv.yucky_direction(1) == dsig ? 2 : 0; \ const int s##k##3 = gv.yucky_direction(2) == dsig ? 2 : 0 @@ -63,13 +63,13 @@ namespace meep { df/dt = dfu/dt - sigma_u * f and fu replaces f in the equations above (fu += dt curl g etcetera). */ -void step_curl(RPR f, component c, const RPR g1, const RPR g2, - ptrdiff_t s1, ptrdiff_t s2, // strides for g1/g2 shift - const grid_volume &gv, const ivec is, const ivec ie, - realnum dtdx, direction dsig, const RPR sig, const RPR kap, - const RPR siginv, RPR fu, direction dsigu, const RPR sigu, const RPR kapu, - const RPR siginvu, realnum dt, const RPR cnd, const RPR cndinv, RPR fcnd) { - (void) c; // currently unused +void step_curl(RPR f, component c, const RPR g1, const RPR g2, ptrdiff_t s1, + ptrdiff_t s2, // strides for g1/g2 shift + const grid_volume &gv, const ivec is, const ivec ie, realnum dtdx, direction dsig, + const RPR sig, const RPR kap, const RPR siginv, RPR fu, direction dsigu, + const RPR sigu, const RPR kapu, const RPR siginvu, realnum dt, const RPR cnd, + const RPR cndinv, RPR fcnd) { + (void)c; // currently unused if (!g1) { // swap g1 and g2 SWAP(const RPR, g1, g2); SWAP(ptrdiff_t, s1, s2); @@ -253,10 +253,10 @@ void step_curl(RPR f, component c, const RPR g1, const RPR g2, and/or PML). This is used in 2d calculations to add an exp(i beta z) time dependence, which gives an additional i \beta \hat{z} \times cross-product in the curl equations. */ -void step_beta(RPR f, component c, const RPR g, const grid_volume &gv, const ivec is, const ivec ie, realnum betadt, - direction dsig, const RPR siginv, RPR fu, direction dsigu, const RPR siginvu, - const RPR cndinv, RPR fcnd) { - (void) c; // currently unused +void step_beta(RPR f, component c, const RPR g, const grid_volume &gv, const ivec is, const ivec ie, + realnum betadt, direction dsig, const RPR siginv, RPR fu, direction dsigu, + const RPR siginvu, const RPR cndinv, RPR fcnd) { + (void)c; // currently unused if (!g) return; if (dsig != NO_DIRECTION) { // PML in f update KSTRIDE_DEF(dsig, k, is, gv); @@ -364,11 +364,11 @@ inline realnum calc_nonlinear_u(const realnum Dsqr, const realnum Di, const real */ -void step_update_EDHB(RPR f, component fc, const grid_volume &gv, const ivec is, const ivec ie, const RPR g, const RPR g1, - const RPR g2, const RPR u, const RPR u1, const RPR u2, ptrdiff_t s, - ptrdiff_t s1, ptrdiff_t s2, const RPR chi2, const RPR chi3, RPR fw, - direction dsigw, const RPR sigw, const RPR kapw) { - (void) fc; // currently unused +void step_update_EDHB(RPR f, component fc, const grid_volume &gv, const ivec is, const ivec ie, + const RPR g, const RPR g1, const RPR g2, const RPR u, const RPR u1, + const RPR u2, ptrdiff_t s, ptrdiff_t s1, ptrdiff_t s2, const RPR chi2, + const RPR chi3, RPR fw, direction dsigw, const RPR sigw, const RPR kapw) { + (void)fc; // currently unused if (!f) return; if ((!g1 && g2) || (g1 && g2 && !u1 && u2)) { /* swap g1 and g2 */ @@ -468,9 +468,7 @@ void step_update_EDHB(RPR f, component fc, const grid_volume &gv, const ivec is, f[i] += (kapwkw + sigwkw) * fw[i] - (kapwkw - sigwkw) * fwprev; } } - else if (g2) { - meep::abort("bug - didn't swap off-diagonal terms!?"); - } + else if (g2) { meep::abort("bug - didn't swap off-diagonal terms!?"); } else { PLOOP_OVER_IVECS(gv, is, ie, i) { realnum gs = g[i]; @@ -565,9 +563,7 @@ void step_update_EDHB(RPR f, component fc, const grid_volume &gv, const ivec is, calc_nonlinear_u(gs * gs + 0.0625 * (g1s * g1s), gs, us, chi2[i], chi3[i]); } } - else if (g2) { - meep::abort("bug - didn't swap off-diagonal terms!?"); - } + else if (g2) { meep::abort("bug - didn't swap off-diagonal terms!?"); } else { PLOOP_OVER_IVECS(gv, is, ie, i) { realnum gs = g[i]; diff --git a/src/stress.cpp b/src/stress.cpp index 8cc1a6e2d..bed26cd0b 100644 --- a/src/stress.cpp +++ b/src/stress.cpp @@ -93,8 +93,7 @@ static void stress_sum(size_t Nfreq, double *F, const dft_chunk *F1, const dft_c complex extra_weight(real(curF1->extra_weight), imag(curF1->extra_weight)); for (size_t k = 0; k < curF1->N; ++k) for (size_t i = 0; i < Nfreq; ++i) - F[i] += real(extra_weight * - complex(curF1->dft[k * Nfreq + i]) * + F[i] += real(extra_weight * complex(curF1->dft[k * Nfreq + i]) * conj(complex(curF2->dft[k * Nfreq + i]))); } } diff --git a/src/structure.cpp b/src/structure.cpp index b3fece4e4..e05c830e0 100644 --- a/src/structure.cpp +++ b/src/structure.cpp @@ -30,7 +30,6 @@ using namespace std; namespace meep { - typedef structure_chunk *structure_chunk_ptr; structure::structure(const grid_volume &thegv, material_function &eps, const boundary_region &br, @@ -64,10 +63,10 @@ structure::structure(const grid_volume &thegv, double eps(const vec &), const bo } } -static std::unique_ptr split_by_cost(int n, grid_volume gvol, - bool fragment_cost, int &proc_id) { - if (n == 1) return std::unique_ptr(new binary_partition(proc_id++ - % count_processors())); +static std::unique_ptr split_by_cost(int n, grid_volume gvol, bool fragment_cost, + int &proc_id) { + if (n == 1) + return std::unique_ptr(new binary_partition(proc_id++ % count_processors())); int best_split_point; direction best_split_direction; @@ -88,17 +87,18 @@ static std::unique_ptr split_by_cost(int n, grid_volume gvol, split_plane optimal_plane{best_split_direction, best_split_position}; grid_volume left_gvol = gvol.split_at_fraction(false, best_split_point, best_split_direction); grid_volume right_gvol = gvol.split_at_fraction(true, best_split_point, best_split_direction); - return std::unique_ptr( - new binary_partition(optimal_plane, - /*left=*/split_by_cost(num_left, left_gvol, fragment_cost, proc_id), - /*right=*/split_by_cost(n - num_left, right_gvol, fragment_cost, proc_id))); + return std::unique_ptr(new binary_partition( + optimal_plane, + /*left=*/split_by_cost(num_left, left_gvol, fragment_cost, proc_id), + /*right=*/split_by_cost(n - num_left, right_gvol, fragment_cost, proc_id))); } void structure::choose_chunkdivision(const grid_volume &thegv, int desired_num_chunks, const boundary_region &br, const symmetry &s, const binary_partition *_bp) { - if (thegv.dim == Dcyl && thegv.get_origin().r() < 0) meep::abort("r < 0 origins are not supported"); + if (thegv.dim == Dcyl && thegv.get_origin().r() < 0) + meep::abort("r < 0 origins are not supported"); user_volume = thegv; gv = thegv; @@ -107,11 +107,8 @@ void structure::choose_chunkdivision(const grid_volume &thegv, int desired_num_c a = gv.a; dt = Courant / a; - if (_bp) { - bp.reset(new binary_partition(*_bp)); - } else { - bp = meep::choose_chunkdivision(gv, v, desired_num_chunks, s); - } + if (_bp) { bp.reset(new binary_partition(*_bp)); } + else { bp = meep::choose_chunkdivision(gv, v, desired_num_chunks, s); } // create the chunks: std::vector chunk_volumes; @@ -149,7 +146,6 @@ void structure::choose_chunkdivision(const grid_volume &thegv, int desired_num_c chunks[i]->cost = chunks[i]->gv.get_cost(); } } - } std::unique_ptr choose_chunkdivision(grid_volume &gv, volume &v, @@ -165,8 +161,7 @@ std::unique_ptr choose_chunkdivision(grid_volume &gv, volume & const direction d = (direction)dd; break_this[dd] = false; for (int n = 0; n < S.multiplicity(); n++) - if (has_direction(gv.dim, d) && - (S.transform(d, n).d != d || S.transform(d, n).flipped)) { + if (has_direction(gv.dim, d) && (S.transform(d, n).d != d || S.transform(d, n).flipped)) { if (gv.num_direction(d) & 1 && !break_this[d] && verbosity > 0) master_printf("Padding %s to even number of grid points.\n", direction_name(d)); break_this[dd] = true; @@ -291,7 +286,8 @@ void structure::check_chunks() { } size_t v_grid_points = 1; LOOP_OVER_DIRECTIONS(gv.dim, d) { v_grid_points *= gv.num_direction(d); } - if (sum != v_grid_points) meep::abort("v_grid_points = %zd, sum(chunks) = %zd\n", v_grid_points, sum); + if (sum != v_grid_points) + meep::abort("v_grid_points = %zd, sum(chunks) = %zd\n", v_grid_points, sum); } void structure::add_to_effort_volumes(const grid_volume &new_effort_volume, double extra_effort) { @@ -339,8 +335,7 @@ void structure::add_to_effort_volumes(const grid_volume &new_effort_volume, doub structure::structure(const structure &s) : num_chunks{s.num_chunks}, shared_chunks{false}, gv(s.gv), user_volume(s.user_volume), a{s.a}, Courant{s.Courant}, dt{s.dt}, v(s.v), S(s.S), - outdir(s.outdir), num_effort_volumes{s.num_effort_volumes}, - bp(new binary_partition(*s.bp)) { + outdir(s.outdir), num_effort_volumes{s.num_effort_volumes}, bp(new binary_partition(*s.bp)) { chunks = new structure_chunk_ptr[num_chunks]; for (int i = 0; i < num_chunks; i++) { chunks[i] = new structure_chunk(s.chunks[i]); @@ -517,11 +512,9 @@ void structure::use_pml(direction d, boundary_side b, double dx) { pml_volume.set_num_direction(d, int(dx * user_volume.a + 1 + 0.5)); // FIXME: exact value? const int v_to_user_shift = (gv.big_corner().in_direction(d) - user_volume.big_corner().in_direction(d)) / 2; - if (b == Low){ - pml_volume.set_origin(d, user_volume.little_corner().in_direction(d)); - } + if (b == Low) { pml_volume.set_origin(d, user_volume.little_corner().in_direction(d)); } - if (b == High){ + if (b == High) { pml_volume.set_origin(d, user_volume.big_corner().in_direction(d) - pml_volume.num_direction(d) * 2); pml_volume.set_num_direction(d, pml_volume.num_direction(d) + v_to_user_shift); @@ -715,9 +708,7 @@ structure_chunk::structure_chunk(const structure_chunk *o) : v(o->v) { cur->next = ocur->clone(); cur = cur->next; } - else { - chiP[ft] = cur = ocur->clone(); - } + else { chiP[ft] = cur = ocur->clone(); } cur->next = NULL; } } @@ -736,18 +727,14 @@ structure_chunk::structure_chunk(const structure_chunk *o) : v(o->v) { for (size_t i = 0; i < gv.ntot(); i++) chi3[c][i] = o->chi3[c][i]; } - else { - chi3[c] = NULL; - } + else { chi3[c] = NULL; } if (is_mine() && o->chi2[c]) { chi2[c] = new realnum[gv.ntot()]; if (chi2[c] == NULL) meep::abort("Out of memory!\n"); for (size_t i = 0; i < gv.ntot(); i++) chi2[c][i] = o->chi2[c][i]; } - else { - chi2[c] = NULL; - } + else { chi2[c] = NULL; } } FOR_COMPONENTS(c) FOR_DIRECTIONS(d) { trivial_chi1inv[c][d] = true; } FOR_COMPONENTS(c) FOR_DIRECTIONS(d) { diff --git a/src/structure_dump.cpp b/src/structure_dump.cpp index e0db507d7..fadda6fbb 100644 --- a/src/structure_dump.cpp +++ b/src/structure_dump.cpp @@ -33,8 +33,7 @@ namespace meep { // Write the parameters required to reconstruct the susceptibility (id, noise_amp (for noisy), // omega_0, gamma, no_omega_0_denominator) -void structure::write_susceptibility_params(h5file *file, - bool single_parallel_file, +void structure::write_susceptibility_params(h5file *file, bool single_parallel_file, const char *dname, int EorH) { // Get number of susceptibility params from first chunk, since all chunks will have // the same susceptibility list. @@ -88,7 +87,8 @@ void structure::dump_chunk_layout(const char *filename) { } void structure::dump(const char *filename, bool single_parallel_file) { - if (verbosity > 0) printf("creating epsilon from file \"%s\" (%d)...\n", filename, single_parallel_file); + if (verbosity > 0) + printf("creating epsilon from file \"%s\" (%d)...\n", filename, single_parallel_file); /* * make/save a num_chunks x NUM_FIELD_COMPONENTS x 5 array counting @@ -122,9 +122,8 @@ void structure::dump(const char *filename, bool single_parallel_file) { if (single_parallel_file) { num_chi1inv.resize(num_chi1inv_size); sum_to_master(num_chi1inv_.data(), num_chi1inv.data(), num_chi1inv_size); - } else { - num_chi1inv = std::move(num_chi1inv_); } + else { num_chi1inv = std::move(num_chi1inv_); } // determine total dataset size and offset of this process's data size_t my_start = 0; @@ -407,9 +406,7 @@ susceptibility *make_sus_list_from_params(h5file *file, int rank, size_t dims[3] } if (sus->next) sus = sus->next; } - else { - meep::abort("Invalid number of susceptibility parameters in structure::load"); - } + else { meep::abort("Invalid number of susceptibility parameters in structure::load"); } } return res; } @@ -437,7 +434,8 @@ binary_partition::binary_partition(const split_plane &_split_plane, if (!left || !right) { meep::abort("Binary partition tree is required to be full"); } } -binary_partition::binary_partition(const binary_partition& other) : proc_id(other.proc_id), plane(other.plane) { +binary_partition::binary_partition(const binary_partition &other) + : proc_id(other.proc_id), plane(other.plane) { if (!other.is_leaf()) { left.reset(new binary_partition(*other.left)); right.reset(new binary_partition(*other.right)); @@ -476,10 +474,10 @@ void split_by_binarytree(grid_volume gvol, std::vector &result_gvs, } const auto &plane = bp->get_plane(); - int split_point = static_cast( - (plane.pos - gvol.surroundings().in_direction_min(plane.dir)) / - gvol.surroundings().in_direction(plane.dir) * gvol.num_direction(plane.dir) + - 0.5); + int split_point = static_cast((plane.pos - gvol.surroundings().in_direction_min(plane.dir)) / + gvol.surroundings().in_direction(plane.dir) * + gvol.num_direction(plane.dir) + + 0.5); // traverse left branch grid_volume left_gvol = gvol.split_at_fraction(false, split_point, plane.dir); split_by_binarytree(left_gvol, result_gvs, result_ids, bp->left_tree()); @@ -542,8 +540,7 @@ void structure::load_chunk_layout(const char *filename, boundary_region &br) { delete[] nums; } -void structure::load_chunk_layout(const std::vector &gvs, - const std::vector &ids, +void structure::load_chunk_layout(const std::vector &gvs, const std::vector &ids, boundary_region &br) { if (gvs.size() != size_t(num_chunks)) meep::abort("load_chunk_layout: wrong number of chunks."); // Recreate the chunks with the new grid_volumes @@ -558,7 +555,8 @@ void structure::load_chunk_layout(const std::vector &gvs, void structure::load(const char *filename, bool single_parallel_file) { h5file file(filename, h5file::READONLY, single_parallel_file, !single_parallel_file); - if (verbosity > 0) printf("reading epsilon from file \"%s\" (%d)...\n", filename, single_parallel_file); + if (verbosity > 0) + printf("reading epsilon from file \"%s\" (%d)...\n", filename, single_parallel_file); /* * make/save a num_chunks x NUM_FIELD_COMPONENTS x 5 array counting @@ -623,10 +621,9 @@ void structure::load(const char *filename, bool single_parallel_file) { // read the data file.read_size("chi1inv", &rank, dims, 1); if (rank != 1 || dims[0] != ntotal) { - meep::abort( - "inconsistent data size for chi1inv in structure::load (rank, dims[0]): " - "(%d, %zu) != (1, %zu)", - rank, dims[0], ntotal); + meep::abort("inconsistent data size for chi1inv in structure::load (rank, dims[0]): " + "(%d, %zu) != (1, %zu)", + rank, dims[0], ntotal); } for (int i = 0; i < num_chunks; i++) if (chunks[i]->is_mine()) { @@ -671,7 +668,9 @@ void structure::load(const char *filename, bool single_parallel_file) { int rank = 0; size_t dims[] = {0, 0, 0}; file.read_size("num_sigmas", &rank, dims, 1); - if (dims[0] != (size_t)num_chunks * 2) { meep::abort("inconsistent data size for num_sigmas in structure::load"); } + if (dims[0] != (size_t)num_chunks * 2) { + meep::abort("inconsistent data size for num_sigmas in structure::load"); + } if (am_master() || !single_parallel_file) { size_t start = 0; size_t count = num_chunks; diff --git a/src/support/mt19937ar.c b/src/support/mt19937ar.c index f5b5f286d..a532aa887 100644 --- a/src/support/mt19937ar.c +++ b/src/support/mt19937ar.c @@ -54,133 +54,131 @@ #define UPPER_MASK 0x80000000UL /* most significant w-r bits */ #define LOWER_MASK 0x7fffffffUL /* least significant r bits */ -static unsigned long mt[N]; /* the array for the state vector */ -static int mti=N+1; /* mti==N+1 means mt[N] is not initialized */ +static unsigned long mt[N]; /* the array for the state vector */ +static int mti = N + 1; /* mti==N+1 means mt[N] is not initialized */ static unsigned long mt_save[N]; /* save state from before latest genrand */ /* initializes mt[N] with a seed */ -void meep_mt_init_genrand(unsigned long s) -{ - int i; - for (i=0; i> 30)) + mti); - /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */ - /* In the previous versions, MSBs of the seed affect */ - /* only MSBs of the array mt[]. */ - /* 2002/01/09 modified by Makoto Matsumoto */ - mt[mti] &= 0xffffffffUL; - /* for >32 bit machines */ - } +void meep_mt_init_genrand(unsigned long s) { + int i; + for (i = 0; i < N; ++i) + mt_save[i] = mt[i]; + mt[0] = s & 0xffffffffUL; + for (mti = 1; mti < N; mti++) { + mt[mti] = (1812433253UL * (mt[mti - 1] ^ (mt[mti - 1] >> 30)) + mti); + /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */ + /* In the previous versions, MSBs of the seed affect */ + /* only MSBs of the array mt[]. */ + /* 2002/01/09 modified by Makoto Matsumoto */ + mt[mti] &= 0xffffffffUL; + /* for >32 bit machines */ + } } /* restores state to what it was before most recent call to genrand */ void meep_mt_restore_genrand() { - int i; - for (i=0; ikey_length ? N : key_length); - for (; k; k--) { - mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1664525UL)) - + init_key[j] + j; /* non linear */ - mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ - i++; j++; - if (i>=N) { mt[0] = mt[N-1]; i=1; } - if (j>=key_length) j=0; +void meep_mt_init_by_array(unsigned long init_key[], int key_length) { + int i, j, k; + meep_mt_init_genrand(19650218UL); + i = 1; + j = 0; + k = (N > key_length ? N : key_length); + for (; k; k--) { + mt[i] = + (mt[i] ^ ((mt[i - 1] ^ (mt[i - 1] >> 30)) * 1664525UL)) + init_key[j] + j; /* non linear */ + mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ + i++; + j++; + if (i >= N) { + mt[0] = mt[N - 1]; + i = 1; } - for (k=N-1; k; k--) { - mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1566083941UL)) - - i; /* non linear */ - mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ - i++; - if (i>=N) { mt[0] = mt[N-1]; i=1; } + if (j >= key_length) j = 0; + } + for (k = N - 1; k; k--) { + mt[i] = (mt[i] ^ ((mt[i - 1] ^ (mt[i - 1] >> 30)) * 1566083941UL)) - i; /* non linear */ + mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ + i++; + if (i >= N) { + mt[0] = mt[N - 1]; + i = 1; } + } - mt[0] = 0x80000000UL; /* MSB is 1; assuring non-zero initial array */ + mt[0] = 0x80000000UL; /* MSB is 1; assuring non-zero initial array */ } /* generates a random number on [0,0xffffffff]-interval */ -unsigned long meep_mt_genrand_int32(void) -{ - unsigned long y; - static unsigned long mag01[2]={0x0UL, MATRIX_A}; - /* mag01[x] = x * MATRIX_A for x=0,1 */ - - if (mti >= N) { /* generate N words at one time */ - int kk; - - if (mti == N+1) /* if init_genrand() has not been called, */ - meep_mt_init_genrand(5489UL); /* a default initial seed is used */ - - for (kk=0;kk> 1) ^ mag01[y & 0x1UL]; - } - for (;kk> 1) ^ mag01[y & 0x1UL]; - } - y = (mt[N-1]&UPPER_MASK)|(mt[0]&LOWER_MASK); - mt[N-1] = mt[M-1] ^ (y >> 1) ^ mag01[y & 0x1UL]; - - mti = 0; +unsigned long meep_mt_genrand_int32(void) { + unsigned long y; + static unsigned long mag01[2] = {0x0UL, MATRIX_A}; + /* mag01[x] = x * MATRIX_A for x=0,1 */ + + if (mti >= N) { /* generate N words at one time */ + int kk; + + if (mti == N + 1) /* if init_genrand() has not been called, */ + meep_mt_init_genrand(5489UL); /* a default initial seed is used */ + + for (kk = 0; kk < N - M; kk++) { + y = (mt[kk] & UPPER_MASK) | (mt[kk + 1] & LOWER_MASK); + mt[kk] = mt[kk + M] ^ (y >> 1) ^ mag01[y & 0x1UL]; } + for (; kk < N - 1; kk++) { + y = (mt[kk] & UPPER_MASK) | (mt[kk + 1] & LOWER_MASK); + mt[kk] = mt[kk + (M - N)] ^ (y >> 1) ^ mag01[y & 0x1UL]; + } + y = (mt[N - 1] & UPPER_MASK) | (mt[0] & LOWER_MASK); + mt[N - 1] = mt[M - 1] ^ (y >> 1) ^ mag01[y & 0x1UL]; + + mti = 0; + } - y = mt[mti++]; + y = mt[mti++]; - /* Tempering */ - y ^= (y >> 11); - y ^= (y << 7) & 0x9d2c5680UL; - y ^= (y << 15) & 0xefc60000UL; - y ^= (y >> 18); + /* Tempering */ + y ^= (y >> 11); + y ^= (y << 7) & 0x9d2c5680UL; + y ^= (y << 15) & 0xefc60000UL; + y ^= (y >> 18); - return y; + return y; } /* generates a random number on [0,0x7fffffff]-interval */ -long meep_mt_genrand_int31(void) -{ - return (long)(meep_mt_genrand_int32()>>1); -} +long meep_mt_genrand_int31(void) { return (long)(meep_mt_genrand_int32() >> 1); } /* generates a random number on [0,1]-real-interval */ -double meep_mt_genrand_real1(void) -{ - return meep_mt_genrand_int32()*(1.0/4294967295.0); - /* divided by 2^32-1 */ +double meep_mt_genrand_real1(void) { + return meep_mt_genrand_int32() * (1.0 / 4294967295.0); + /* divided by 2^32-1 */ } /* generates a random number on [0,1)-real-interval */ -double meep_mt_genrand_real2(void) -{ - return meep_mt_genrand_int32()*(1.0/4294967296.0); - /* divided by 2^32 */ +double meep_mt_genrand_real2(void) { + return meep_mt_genrand_int32() * (1.0 / 4294967296.0); + /* divided by 2^32 */ } /* generates a random number on (0,1)-real-interval */ -double meep_mt_genrand_real3(void) -{ - return (((double)meep_mt_genrand_int32()) + 0.5)*(1.0/4294967296.0); - /* divided by 2^32 */ +double meep_mt_genrand_real3(void) { + return (((double)meep_mt_genrand_int32()) + 0.5) * (1.0 / 4294967296.0); + /* divided by 2^32 */ } /* generates a random number on [0,1) with 53-bit resolution*/ -double meep_mt_genrand_res53(void) -{ - unsigned long a=meep_mt_genrand_int32()>>5, b=meep_mt_genrand_int32()>>6; - return(a*67108864.0+b)*(1.0/9007199254740992.0); +double meep_mt_genrand_res53(void) { + unsigned long a = meep_mt_genrand_int32() >> 5, b = meep_mt_genrand_int32() >> 6; + return (a * 67108864.0 + b) * (1.0 / 9007199254740992.0); } /* These real versions are due to Isaku Wada, 2002/01/09 added */ diff --git a/src/susceptibility.cpp b/src/susceptibility.cpp index d160dcdb2..e0abe05ad 100644 --- a/src/susceptibility.cpp +++ b/src/susceptibility.cpp @@ -347,8 +347,9 @@ void noisy_lorentzian_susceptibility::dump_params(h5file *h5f, size_t *start) { *start += num_params; } -gyrotropic_susceptibility::gyrotropic_susceptibility(const vec &bias, realnum omega_0, realnum gamma, - realnum alpha, gyrotropy_model model) +gyrotropic_susceptibility::gyrotropic_susceptibility(const vec &bias, realnum omega_0, + realnum gamma, realnum alpha, + gyrotropy_model model) : omega_0(omega_0), gamma(gamma), alpha(alpha), model(model) { // Precalculate g_{ij} = sum_k epsilon_{ijk} b_k, used in update_P. // Ignore |b| for Landau-Lifshitz-Gilbert gyrotropy model. @@ -619,8 +620,8 @@ void gyrotropic_susceptibility::dump_params(h5file *h5f, size_t *start) { size_t num_params = 9; size_t params_dims[1] = {num_params}; realnum bias[] = {gyro_tensor[Y][Z], gyro_tensor[Z][X], gyro_tensor[X][Y]}; - realnum params_data[] = {8, (realnum)get_id(), bias[X], bias[Y], bias[Z], omega_0, gamma, - alpha, (realnum)model}; + realnum params_data[] = {8, (realnum)get_id(), bias[X], bias[Y], bias[Z], omega_0, gamma, + alpha, (realnum)model}; h5f->write_chunk(1, start, params_dims, params_data); *start += num_params; } diff --git a/src/update_eh.cpp b/src/update_eh.cpp index 0a28c21af..76cc43d51 100644 --- a/src/update_eh.cpp +++ b/src/update_eh.cpp @@ -45,9 +45,8 @@ void fields::update_eh(field_type ft, bool skip_w_components) { if (loop_tile_base_eh > 0 && is_aniso) { split_into_tiles(chunks[i]->gv, &chunks[i]->gvs_eh[ft], loop_tile_base_eh); check_tiles(chunks[i]->gv, chunks[i]->gvs_eh[ft]); - } else { - chunks[i]->gvs_eh[ft].push_back(chunks[i]->gv); } + else { chunks[i]->gvs_eh[ft].push_back(chunks[i]->gv); } } for (int i = 0; i < num_chunks; i++) @@ -189,8 +188,8 @@ bool fields_chunk::update_eh(field_type ft, bool skip_w_components) { } if (f[ec][cmp] != f[dc][cmp]) - STEP_UPDATE_EDHB(f[ec][cmp], ec, gv, gvs_eh[ft][i].little_owned_corner0(ec), gvs_eh[ft][i].big_corner(), - dmp[dc][cmp], dmp[dc_1][cmp], dmp[dc_2][cmp], + STEP_UPDATE_EDHB(f[ec][cmp], ec, gv, gvs_eh[ft][i].little_owned_corner0(ec), + gvs_eh[ft][i].big_corner(), dmp[dc][cmp], dmp[dc_1][cmp], dmp[dc_2][cmp], s->chi1inv[ec][d_ec], dmp[dc_1][cmp] ? s->chi1inv[ec][d_1] : NULL, dmp[dc_2][cmp] ? s->chi1inv[ec][d_2] : NULL, s_ec, s_1, s_2, s->chi2[ec], s->chi3[ec], f_w[ec][cmp], dsigw, s->sig[dsigw], s->kap[dsigw]); diff --git a/src/vec.cpp b/src/vec.cpp index c64a4a74f..ff7024ace 100644 --- a/src/vec.cpp +++ b/src/vec.cpp @@ -454,12 +454,8 @@ bool grid_volume::owns(const ivec &p) const { return o.x() > 0 && o.x() <= nx() * 2 && o.y() > 0 && o.y() <= ny() * 2 && o.z() > 0 && o.z() <= nz() * 2; } - else if (dim == D2) { - return o.x() > 0 && o.x() <= nx() * 2 && o.y() > 0 && o.y() <= ny() * 2; - } - else if (dim == D1) { - return o.z() > 0 && o.z() <= nz() * 2; - } + else if (dim == D2) { return o.x() > 0 && o.x() <= nx() * 2 && o.y() > 0 && o.y() <= ny() * 2; } + else if (dim == D1) { return o.z() > 0 && o.z() <= nz() * 2; } else { meep::abort("Unsupported dimension in owns.\n"); return false; @@ -683,9 +679,7 @@ double grid_volume::rmin() const { const double qinva = 0.25 * inva; if (dim == Dcyl) { if (origin.r() == 0.0) { return 0.0; } - else { - return origin.r() + qinva; - } + else { return origin.r() + qinva; } } meep::abort("No rmin in these dimensions.\n"); return 0.0; // This is never reached. @@ -814,7 +808,7 @@ bool grid_volume::intersect_with(const grid_volume &vol_in, grid_volume *interse } if (initial_points != final_points) meep::abort("intersect_with: initial_points != final_points, %zd, %zd\n", initial_points, - final_points); + final_points); } return true; } @@ -858,11 +852,11 @@ ivec grid_volume::iloc(component c, ptrdiff_t ind) const { } size_t grid_volume::surface_area() const { - switch(dim) { - case Dcyl: return 2*(nr()+nz()); + switch (dim) { + case Dcyl: return 2 * (nr() + nz()); case D1: return 2; - case D2: return 2*(nx()+ny()); - case D3: return 2*(nx()*ny()+nx()*nz()+ny()*nz()); + case D2: return 2 * (nx() + ny()); + case D3: return 2 * (nx() * ny() + nx() * nz() + ny() * nz()); } return 0; // This is never reached. } @@ -909,15 +903,15 @@ vec grid_volume::dz() const { grid_volume volone(double zsize, double a) { if (!isinteger(zsize * a)) - master_printf_stderr( - "Warning: grid volume is not an integer number of pixels; cell size will be rounded to nearest pixel.\n"); + master_printf_stderr("Warning: grid volume is not an integer number of pixels; cell size will " + "be rounded to nearest pixel.\n"); return grid_volume(D1, a, 0, 0, (int)(zsize * a + 0.5)); } grid_volume voltwo(double xsize, double ysize, double a) { if (!isinteger(xsize * a) || !isinteger(ysize * a)) - master_printf_stderr( - "Warning: grid volume is not an integer number of pixels; cell size will be rounded to nearest pixel.\n"); + master_printf_stderr("Warning: grid volume is not an integer number of pixels; cell size will " + "be rounded to nearest pixel.\n"); return grid_volume(D2, a, (xsize == 0) ? 1 : (int)(xsize * a + 0.5), (ysize == 0) ? 1 : (int)(ysize * a + 0.5), 0); } @@ -928,8 +922,8 @@ grid_volume vol2d(double xsize, double ysize, double a) { return voltwo(xsize, y grid_volume vol3d(double xsize, double ysize, double zsize, double a) { if (!isinteger(xsize * a) || !isinteger(ysize * a) || !isinteger(zsize * a)) - master_printf_stderr( - "Warning: grid volume is not an integer number of pixels; cell size will be rounded to nearest pixel.\n"); + master_printf_stderr("Warning: grid volume is not an integer number of pixels; cell size will " + "be rounded to nearest pixel.\n"); return grid_volume(D3, a, (xsize == 0) ? 1 : (int)(xsize * a + 0.5), (ysize == 0) ? 1 : (int)(ysize * a + 0.5), (zsize == 0) ? 1 : (int)(zsize * a + 0.5)); @@ -937,8 +931,8 @@ grid_volume vol3d(double xsize, double ysize, double zsize, double a) { grid_volume volcyl(double rsize, double zsize, double a) { if (!isinteger(rsize) || !isinteger(zsize)) - master_printf_stderr( - "Warning: grid volume is not an integer number of pixels; cell size will be rounded to nearest pixel.\n"); + master_printf_stderr("Warning: grid volume is not an integer number of pixels; cell size will " + "be rounded to nearest pixel.\n"); if (zsize == 0.0) return grid_volume(Dcyl, a, (int)(rsize * a + 0.5), 0, 1); @@ -978,26 +972,27 @@ static double cost_diff(int desired_chunks, std::complex costs) { return right_cost - left_cost; } -void grid_volume::tile_split(int &best_split_point, - direction &best_split_direction) const { +void grid_volume::tile_split(int &best_split_point, direction &best_split_direction) const { const size_t ntot_thresh = 10; if (ntot() < ntot_thresh) { best_split_point = 0; best_split_direction = NO_DIRECTION; - } else if (nx() > 1) { + } + else if (nx() > 1) { best_split_point = nx() / 2; best_split_direction = X; - } else if (ny() > 1) { + } + else if (ny() > 1) { best_split_point = ny() / 2; best_split_direction = Y; - } else { + } + else { best_split_point = nz() / 2; best_split_direction = Z; } } -void grid_volume::find_best_split(int desired_chunks, bool fragment_cost, - int &best_split_point, +void grid_volume::find_best_split(int desired_chunks, bool fragment_cost, int &best_split_point, direction &best_split_direction, double &left_effort_fraction) const { if (size_t(desired_chunks) > nowned_min()) { @@ -1037,9 +1032,11 @@ void grid_volume::find_best_split(int desired_chunks, bool fragment_cost, std::complex costs = get_split_costs(d, split_point, fragment_cost); double left_cost = real(costs), right_cost = imag(costs); double total_cost = left_cost + right_cost; - double split_measure = std::max(left_cost / (desired_chunks / 2), right_cost / (desired_chunks - (desired_chunks / 2))); + double split_measure = std::max(left_cost / (desired_chunks / 2), + right_cost / (desired_chunks - (desired_chunks / 2))); // Give a 30% preference to the longest axis, as a heuristic to prefer lower communication costs - // when the split_measure is somewhat close. TODO: use a data-driven communication cost function. + // when the split_measure is somewhat close. TODO: use a data-driven communication cost + // function. if (d == longest_axis) split_measure *= 0.7; if (split_measure < best_split_measure) { best_split_measure = split_measure; @@ -1072,8 +1069,8 @@ grid_volume grid_volume::split_at_fraction(bool side_high, int split_pt, int spl // Halve the grid_volume for symmetry exploitation...must contain icenter! grid_volume grid_volume::halve(direction d) const { grid_volume retval(*this); - retval.set_num_direction(d, 1+(big_corner().in_direction(d)-icenter().in_direction(d)) / 2); - retval.set_origin(d, icenter().in_direction(d)-2); + retval.set_num_direction(d, 1 + (big_corner().in_direction(d) - icenter().in_direction(d)) / 2); + retval.set_origin(d, icenter().in_direction(d) - 2); return retval; } @@ -1089,7 +1086,6 @@ void grid_volume::pad_self(direction d) { shift_origin(d, -2); } - ivec grid_volume::icenter() const { /* Find the center of the user's cell. This will be used as the symmetry point, and therefore icenter-io must be *even* @@ -1218,9 +1214,8 @@ int symmetry::multiplicity() const { int symmetry::multiplicity(ivec &x) const { int m = multiplicity(); - for (int n=1; ntransform(sd.d, nrest) * ph; } - else { - return sd * ph; - } + else { return sd * ph; } } } @@ -1600,8 +1593,7 @@ const char *volume::str(char *buffer, size_t buflen) { buffer = sbuf; buflen = sizeof(sbuf); } - snprintf(buffer, buflen, "min_corner:%s, max_corner:%s", - min_corner.str(), max_corner.str()); + snprintf(buffer, buflen, "min_corner:%s, max_corner:%s", min_corner.str(), max_corner.str()); return buffer; } @@ -1613,23 +1605,21 @@ const char *grid_volume::str(char *buffer, size_t buflen) { buflen = sizeof(sbuf); } - written += snprintf(buffer+written, buflen-written, + written += snprintf(buffer + written, buflen - written, "grid_volume {\n dim:%s, a:%f, inva:%f, num:{%d, %d, %d}\n", dimension_name(dim), a, inva, num[0], num[1], num[2]); // Adapted from the print() method LOOP_OVER_DIRECTIONS(dim, d) { - written += snprintf(buffer+written, buflen-written, - " %s =%5g - %5g (%5g) \t", direction_name(d), origin.in_direction(d), - origin.in_direction(d) + num_direction(d) / a, num_direction(d) / a); - if (buflen-written <=0) - break; + written += snprintf(buffer + written, buflen - written, " %s =%5g - %5g (%5g) \t", + direction_name(d), origin.in_direction(d), + origin.in_direction(d) + num_direction(d) / a, num_direction(d) / a); + if (buflen - written <= 0) break; } - snprintf(buffer+written, buflen-written, "\n}"); + snprintf(buffer + written, buflen - written, "\n}"); return buffer; } - /********************************************************************/ /********************************************************************/ /********************************************************************/ @@ -1645,13 +1635,14 @@ grid_volume grid_volume::subvolume(ivec is, ivec ie, component c) { void grid_volume::init_subvolume(ivec is, ivec ie, component c) { ivec origin(dim, 0); - for (int i=0;i<3;i++) num[i] = 0; + for (int i = 0; i < 3; i++) + num[i] = 0; LOOP_OVER_DIRECTIONS(dim, d) { - num[(int)d] = (ie - is).in_direction(d)/2; - origin.set_direction(d, is.in_direction(d)-iyee_shift(c).in_direction(d)); + num[(int)d] = (ie - is).in_direction(d) / 2; + origin.set_direction(d, is.in_direction(d) - iyee_shift(c).in_direction(d)); } num_changed(); - //center_origin(); + // center_origin(); set_origin(origin); } diff --git a/tests/aniso_disp.cpp b/tests/aniso_disp.cpp index 4ae352c09..f17d2a5e3 100644 --- a/tests/aniso_disp.cpp +++ b/tests/aniso_disp.cpp @@ -117,7 +117,7 @@ int main(int argc, char **argv) { } double T2 = 200; int iT2 = T2 / f.dt; - std::vector> vals(iT2); + std::vector > vals(iT2); while (f.t - iT < iT2) { if ((f.t - iT) % (iT2 / 10) == 0) master_printf("%g%% done with harminv\n", (f.t - iT) * 100.0 / iT2); diff --git a/tests/array-slice-ll.cpp b/tests/array-slice-ll.cpp index c98f4a741..5cea412ef 100644 --- a/tests/array-slice-ll.cpp +++ b/tests/array-slice-ll.cpp @@ -30,7 +30,8 @@ vector3 v3(double x, double y = 0.0, double z = 0.0) { } // passthrough field function -std::complex default_field_function(const std::complex *fields, const vec &loc, void *data_) { +std::complex default_field_function(const std::complex *fields, const vec &loc, + void *data_) { (void)loc; // unused (void)data_; // unused return fields[0]; @@ -39,7 +40,7 @@ std::complex default_field_function(const std::complex *fields, /***************************************************************/ /***************************************************************/ /***************************************************************/ -const double RELTOL = sizeof(realnum) == sizeof(float) ? 1.0e-4: 1.0e-6; +const double RELTOL = sizeof(realnum) == sizeof(float) ? 1.0e-4 : 1.0e-6; double Compare(realnum *d1, realnum *d2, int N, const char *Name) { double Norm1 = 0.0, Norm2 = 0.0, NormDelta = 0.0; @@ -137,9 +138,7 @@ int main(int argc, char *argv[]) { symmetry sym = use_symmetry ? -mirror(Y, gv) : identity(); structure the_structure(gv, dummy_eps, pml(dpml), sym); meep_geom::material_type vacuum = meep_geom::vacuum; - auto material_deleter = [](meep_geom::material_data *m) { - meep_geom::material_free(m); - }; + auto material_deleter = [](meep_geom::material_data *m) { meep_geom::material_free(m); }; std::unique_ptr dielectric( meep_geom::make_dielectric(eps), material_deleter); geometric_object objects[7]; @@ -204,17 +203,17 @@ int main(int argc, char *argv[]) { // read 1D and 2D array-slice data from HDF5 file // h5file *file = f.open_h5file(H5FILENAME, h5file::READONLY); - std::unique_ptr rdata(static_cast( + std::unique_ptr rdata(static_cast( file->read("hz.r", &rank, dims1D, 1, sizeof(realnum) == sizeof(float)))); if (rank != 1 || dims1D[0] != NX) meep::abort("failed to read 1D data(hz.r) from file %s.h5", H5FILENAME); - std::unique_ptr idata(static_cast( + std::unique_ptr idata(static_cast( file->read("hz.i", &rank, dims1D, 1, sizeof(realnum) == sizeof(float)))); if (rank != 1 || dims1D[0] != NX) meep::abort("failed to read 1D data(hz.i) from file %s.h5", H5FILENAME); - std::vector> file_slice1d; + std::vector > file_slice1d; for (size_t n = 0; n < dims1D[0]; n++) file_slice1d.emplace_back(rdata[n], idata[n]); @@ -230,16 +229,18 @@ int main(int argc, char *argv[]) { // rank = f.get_array_slice_dimensions(v1d, dims1D, dirs1D, true, false); if (rank != 1 || dims1D[0] != NX) meep::abort("incorrect dimensions for 1D slice"); - std::unique_ptr []> slice1d(f.get_complex_array_slice(v1d, Hz, 0, 0, true)); - std::vector> slice1d_realnum; + std::unique_ptr[]> slice1d( + f.get_complex_array_slice(v1d, Hz, 0, 0, true)); + std::vector > slice1d_realnum; for (int i = 0; i < NX; ++i) slice1d_realnum.emplace_back(slice1d[i]); double RelErr1D = Compare(slice1d_realnum.data(), file_slice1d.data(), NX, "Hz_1d"); master_printf("1D: rel error %e\n", RelErr1D); rank = f.get_array_slice_dimensions(v2d, dims2D, dirs2D, true, false); - if (rank != 2 || dims2D[0] != NX || dims2D[1] != NY) meep::abort("incorrect dimensions for 2D slice"); - std::unique_ptr slice2d(f.get_array_slice(v2d, Sy, 0, 0, true)); + if (rank != 2 || dims2D[0] != NX || dims2D[1] != NY) + meep::abort("incorrect dimensions for 2D slice"); + std::unique_ptr slice2d(f.get_array_slice(v2d, Sy, 0, 0, true)); std::unique_ptr slice2d_realnum(new realnum[NX * NY]); for (int i = 0; i < NX * NY; ++i) slice2d_realnum[i] = static_cast(slice2d[i]); diff --git a/tests/bragg_transmission.cpp b/tests/bragg_transmission.cpp index 7830b2031..3cf2c42ab 100644 --- a/tests/bragg_transmission.cpp +++ b/tests/bragg_transmission.cpp @@ -224,11 +224,11 @@ void doit(bool use_hdf5) { if (errT > maxerrT) maxerrT = errT; if (errR > maxerrR) maxerrR = errR; if (errT * sqr(freq_min / (freq_min + i * dfreq)) > 0.01) - meep::abort("large error %g at freq = %g: T = %g instead of %g\n", errT, freq_min + i * dfreq, T[i], - T0[i]); + meep::abort("large error %g at freq = %g: T = %g instead of %g\n", errT, freq_min + i * dfreq, + T[i], T0[i]); if (errR * sqr(freq_min / (freq_min + i * dfreq)) > 0.01) - meep::abort("large error %g at freq = %g: R = %g instead of %g\n", errR, freq_min + i * dfreq, R[i], - R0[i]); + meep::abort("large error %g at freq = %g: R = %g instead of %g\n", errR, freq_min + i * dfreq, + R[i], R0[i]); } master_printf("Done (max. err in T = %e, in R = %e)\n", maxerrT, maxerrR); diff --git a/tests/convergence_cyl_waveguide.cpp b/tests/convergence_cyl_waveguide.cpp index 992631d5a..3c94de0ce 100644 --- a/tests/convergence_cyl_waveguide.cpp +++ b/tests/convergence_cyl_waveguide.cpp @@ -48,7 +48,7 @@ void test_convergence_without_averaging() { while (f.time() < f.last_source_time()) f.step(); int t_harminv_max = 2500; // try increasing this in case of failure - std::vector> mon_data(t_harminv_max); + std::vector > mon_data(t_harminv_max); int t = 0; monitor_point mp; while (t < t_harminv_max) { @@ -58,10 +58,10 @@ void test_convergence_without_averaging() { t++; } const int maxbands = 10; - std::vector> amps(maxbands); + std::vector > amps(maxbands); std::vector freq_re(maxbands), freq_im(maxbands), errors(maxbands); - int nfreq = do_harminv(mon_data.data(), t_harminv_max - 1, f.dt, 0.10, 0.50, maxbands, amps.data(), - freq_re.data(), freq_im.data(), errors.data()); + int nfreq = do_harminv(mon_data.data(), t_harminv_max - 1, f.dt, 0.10, 0.50, maxbands, + amps.data(), freq_re.data(), freq_im.data(), errors.data()); double w = 0.0; for (int jf = 0; jf < nfreq; jf++) if (abs(freq_re[jf] - w0) < abs(w - w0)) w = freq_re[jf]; @@ -117,7 +117,7 @@ void test_convergence_with_averaging() { while (f.time() < f.last_source_time()) f.step(); int t_harminv_max = 2500; // try increasing this in case of failure - std::vector> mon_data(t_harminv_max); + std::vector > mon_data(t_harminv_max); int t = 0; monitor_point mp; while (t < t_harminv_max) { @@ -127,10 +127,10 @@ void test_convergence_with_averaging() { t++; } const int maxbands = 10; - std::vector> amps(maxbands); + std::vector > amps(maxbands); std::vector freq_re(maxbands), freq_im(maxbands), errors(maxbands); - int nfreq = do_harminv(mon_data.data(), t_harminv_max - 1, f.dt, 0.10, 0.50, maxbands, amps.data(), - freq_re.data(), freq_im.data(), errors.data()); + int nfreq = do_harminv(mon_data.data(), t_harminv_max - 1, f.dt, 0.10, 0.50, maxbands, + amps.data(), freq_re.data(), freq_im.data(), errors.data()); double w = 0.0; for (int jf = 0; jf < nfreq; jf++) if (abs(freq_re[jf] - w0) < abs(w - w0)) w = freq_re[jf]; diff --git a/tests/cyl-ellipsoid-ll.cpp b/tests/cyl-ellipsoid-ll.cpp index a6fe6f471..5741aa5ef 100644 --- a/tests/cyl-ellipsoid-ll.cpp +++ b/tests/cyl-ellipsoid-ll.cpp @@ -33,8 +33,8 @@ bool compare_hdf5_datasets(const char *file1, const char *name1, const char *fil int rank1; std::vector dims1(expected_rank); - std::unique_ptr data1; - std::unique_ptr data2; + std::unique_ptr data1; + std::unique_ptr data2; data1.reset(static_cast( f1.read(name1, &rank1, dims1.data(), expected_rank, sizeof(realnum) == sizeof(float)))); if (!data1) return false; @@ -129,9 +129,7 @@ int main(int argc, char *argv[]) { // (make ellipsoid (center 0 0 0) (size 1 2 infinity) // (material air)))) double n = 3.5; // index of refraction - auto material_deleter = [](meep_geom::material_data *m) { - meep_geom::material_free(m); - }; + auto material_deleter = [](meep_geom::material_data *m) { meep_geom::material_free(m); }; std::unique_ptr dielectric( meep_geom::make_dielectric(n * n), material_deleter); geometric_object objects[2]; diff --git a/tests/cylindrical.cpp b/tests/cylindrical.cpp index 831fcd74e..150120fb6 100644 --- a/tests/cylindrical.cpp +++ b/tests/cylindrical.cpp @@ -32,9 +32,7 @@ int compare(double a, double b, const char *n, double eps = 4e-15) { master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; } - else { - return 1; - } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p, double eps = 4e-8) { diff --git a/tests/dft-fields.cpp b/tests/dft-fields.cpp index 6e1e44746..074528ae9 100644 --- a/tests/dft-fields.cpp +++ b/tests/dft-fields.cpp @@ -33,8 +33,8 @@ double dummy_eps(const vec &) { return 1.0; } /***************************************************************/ /***************************************************************/ /***************************************************************/ -void Run(bool Pulse, double resolution, std::complex **field_array = 0, int *array_rank = 0, - size_t *array_dims = 0) { +void Run(bool Pulse, double resolution, std::complex **field_array = 0, + int *array_rank = 0, size_t *array_dims = 0) { /***************************************************************/ /* initialize geometry */ /***************************************************************/ @@ -102,16 +102,18 @@ void Run(bool Pulse, double resolution, std::complex **field_arra /***************************************************************/ /* return L2 norm of error normalized by average of L2 norms */ /***************************************************************/ -double compare_array_to_dataset(std::complex *field_array, int array_rank, size_t *array_dims, - const char *file, const char *name) { +double compare_array_to_dataset(std::complex *field_array, int array_rank, + size_t *array_dims, const char *file, const char *name) { int file_rank; size_t file_dims[3]; h5file f(file, h5file::READONLY, false); char dataname[100]; snprintf(dataname, 100, "%s.r", name); - double *rdata = (double *)f.read(dataname, &file_rank, file_dims, 2, false /* single_precision */); + double *rdata = + (double *)f.read(dataname, &file_rank, file_dims, 2, false /* single_precision */); snprintf(dataname, 100, "%s.i", name); - double *idata = (double *)f.read(dataname, &file_rank, file_dims, 2, false /* single_precision */); + double *idata = + (double *)f.read(dataname, &file_rank, file_dims, 2, false /* single_precision */); if (!rdata || !idata) return -1.0; if (file_rank != array_rank) return -1.0; for (int n = 0; n < file_rank; n++) @@ -148,9 +150,11 @@ double compare_complex_hdf5_datasets(const char *file1, const char *name1, const int rank1; size_t *dims1 = new size_t[expected_rank]; snprintf(dataname, 100, "%s.r", name1); - double *rdata1 = (double *)f1.read(dataname, &rank1, dims1, expected_rank, false /* single_precision */); + double *rdata1 = + (double *)f1.read(dataname, &rank1, dims1, expected_rank, false /* single_precision */); snprintf(dataname, 100, "%s.i", name1); - double *idata1 = (double *)f1.read(dataname, &rank1, dims1, expected_rank, false /* single_precision */); + double *idata1 = + (double *)f1.read(dataname, &rank1, dims1, expected_rank, false /* single_precision */); if (!rdata1 || !idata1) return -1.0; // read dataset 2 @@ -158,9 +162,11 @@ double compare_complex_hdf5_datasets(const char *file1, const char *name1, const int rank2; size_t *dims2 = new size_t[expected_rank]; snprintf(dataname, 100, "%s.r", name2); - double *rdata2 = (double *)f2.read(dataname, &rank2, dims2, expected_rank, false /* single_precision */); + double *rdata2 = + (double *)f2.read(dataname, &rank2, dims2, expected_rank, false /* single_precision */); snprintf(dataname, 100, "%s.i", name2); - double *idata2 = (double *)f2.read(dataname, &rank2, dims2, expected_rank, false /* single_precision */); + double *idata2 = + (double *)f2.read(dataname, &rank2, dims2, expected_rank, false /* single_precision */); if (!rdata2 || !idata2) return -1.0; // check same size diff --git a/tests/dump_load.cpp b/tests/dump_load.cpp index befa65443..b6edbdd08 100644 --- a/tests/dump_load.cpp +++ b/tests/dump_load.cpp @@ -27,7 +27,8 @@ double one(const vec &) { return 1.0; } double targets(const vec &pt) { const double r = sqrt(pt.x() * pt.x() + pt.y() * pt.y()); double dr = r; - while (dr > 1) dr -= 1; + while (dr > 1) + dr -= 1; if (dr > 0.7001) return 12.0; return 1.0; } @@ -41,12 +42,10 @@ static const double tol = 1e-9, thresh = 1e-15; int compare(double a, double b, const char *n) { if (fabs(a - b) > fabs(b) * tol && fabs(b) > thresh) { master_printf("%s differs by\t%g out of\t%g\n", n, a - b, b); - master_printf("This gives a fractional error of %g\n", - fabs(a - b) / fabs(b)); + master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; - } else { - return 1; } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p) { @@ -58,12 +57,11 @@ int compare_point(fields &f1, fields &f2, const vec &p) { if (f1.gv.has_field(c)) { complex v1 = m_test.get_component(c), v2 = m1.get_component(c); if (abs(v1 - v2) > tol * abs(v2) && abs(v2) > thresh) { - master_printf("%s differs: %g %g out of %g %g\n", component_name(c), - real(v2 - v1), imag(v2 - v1), real(v2), imag(v2)); - master_printf("This comes out to a fractional error of %g\n", - abs(v1 - v2) / abs(v2)); - master_printf("Right now I'm looking at %g %g %g, time %g\n", p.x(), - p.y(), p.z(), f1.time()); + master_printf("%s differs: %g %g out of %g %g\n", component_name(c), real(v2 - v1), + imag(v2 - v1), real(v2), imag(v2)); + master_printf("This comes out to a fractional error of %g\n", abs(v1 - v2) / abs(v2)); + master_printf("Right now I'm looking at %g %g %g, time %g\n", p.x(), p.y(), p.z(), + f1.time()); return 0; } } @@ -80,12 +78,11 @@ int approx_point(fields &f1, fields &f2, const vec &p) { if (f1.gv.has_field(c)) { complex v1 = m_test.get_component(c), v2 = m1.get_component(c); if (abs(v1 - v2) > tol * abs(v2) && abs(v2) > thresh) { - master_printf("%s differs: %g %g out of %g %g\n", component_name(c), - real(v2 - v1), imag(v2 - v1), real(v2), imag(v2)); - master_printf("This comes out to a fractional error of %g\n", - abs(v1 - v2) / abs(v2)); - master_printf("Right now I'm looking at %g %g %g, time %g\n", p.x(), - p.y(), p.z(), f1.time()); + master_printf("%s differs: %g %g out of %g %g\n", component_name(c), real(v2 - v1), + imag(v2 - v1), real(v2), imag(v2)); + master_printf("This comes out to a fractional error of %g\n", abs(v1 - v2) / abs(v2)); + master_printf("Right now I'm looking at %g %g %g, time %g\n", p.x(), p.y(), p.z(), + f1.time()); return 0; } } @@ -126,40 +123,34 @@ int test_metal(double eps(const vec &), int splitting, const char *tmpdir) { grid_volume gv = vol3d(1.5, 0.5, 1.0, a); structure s(gv, eps, no_pml(), identity(), splitting); - std::string filename_prefix = - std::string(tmpdir) + "/test_metal_" + std::to_string(splitting); - std::string structure_filename = - structure_dump(&s, filename_prefix, "original"); + std::string filename_prefix = std::string(tmpdir) + "/test_metal_" + std::to_string(splitting); + std::string structure_filename = structure_dump(&s, filename_prefix, "original"); master_printf("Metal test using %d chunks...\n", splitting); fields f(&s); f.add_point_source(Ez, 0.8, 0.6, 0.0, 4.0, vec(1.299, 0.299, 0.401), 1.0); - while (f.time() < ttot) f.step(); + while (f.time() < ttot) + f.step(); - std::string fields_filename = - fields_dump(&f, filename_prefix, "original"); + std::string fields_filename = fields_dump(&f, filename_prefix, "original"); structure s_load(gv, eps, no_pml(), identity(), splitting); structure_load(&s_load, structure_filename); fields f_load(&s_load); - f_load.add_point_source(Ez, 0.8, 0.6, 0.0, 4.0, vec(1.299, 0.299, 0.401), - 1.0); + f_load.add_point_source(Ez, 0.8, 0.6, 0.0, 4.0, vec(1.299, 0.299, 0.401), 1.0); fields_load(&f_load, fields_filename); if (!compare_point(f, f_load, vec(0.5, 0.5, 0.01))) return 0; if (!compare_point(f, f_load, vec(0.46, 0.33, 0.33))) return 0; if (!compare_point(f, f_load, vec(1.301, 0.301, 0.399))) return 0; - if (!compare(f.field_energy(), f_load.field_energy(), " total energy")) - return 0; + if (!compare(f.field_energy(), f_load.field_energy(), " total energy")) return 0; if (!compare(f.electric_energy_in_box(gv.surroundings()), - f_load.electric_energy_in_box(gv.surroundings()), - "electric energy")) + f_load.electric_energy_in_box(gv.surroundings()), "electric energy")) return 0; if (!compare(f.magnetic_energy_in_box(gv.surroundings()), - f_load.magnetic_energy_in_box(gv.surroundings()), - "magnetic energy")) + f_load.magnetic_energy_in_box(gv.surroundings()), "magnetic energy")) return 0; return 1; } @@ -171,20 +162,18 @@ int test_periodic(double eps(const vec &), int splitting, const char *tmpdir) { grid_volume gv = vol3d(1.5, 0.5, 1.0, a); structure s(gv, eps, no_pml(), identity(), splitting); - std::string filename_prefix = - std::string(tmpdir) + "/test_periodic_" + std::to_string(splitting); - std::string structure_filename = - structure_dump(&s, filename_prefix, "original"); + std::string filename_prefix = std::string(tmpdir) + "/test_periodic_" + std::to_string(splitting); + std::string structure_filename = structure_dump(&s, filename_prefix, "original"); master_printf("Periodic test using %d chunks...\n", splitting); fields f(&s); f.use_bloch(vec(0.1, 0.7, 0.3)); f.add_point_source(Ez, 0.7, 2.5, 0.0, 4.0, vec(0.3, 0.25, 0.5), 1.0); - while (f.time() < ttot) f.step(); + while (f.time() < ttot) + f.step(); - std::string fields_filename = - fields_dump(&f, filename_prefix, "original"); + std::string fields_filename = fields_dump(&f, filename_prefix, "original"); structure s_load(gv, eps, no_pml(), identity(), splitting); structure_load(&s_load, structure_filename); @@ -197,15 +186,12 @@ int test_periodic(double eps(const vec &), int splitting, const char *tmpdir) { if (!compare_point(f, f_load, vec(0.5, 0.01, 0.5))) return 0; if (!compare_point(f, f_load, vec(0.46, 0.33, 0.2))) return 0; if (!compare_point(f, f_load, vec(1.0, 0.25, 0.301))) return 0; - if (!compare(f.field_energy(), f_load.field_energy(), " total energy")) - return 0; + if (!compare(f.field_energy(), f_load.field_energy(), " total energy")) return 0; if (!compare(f.electric_energy_in_box(gv.surroundings()), - f_load.electric_energy_in_box(gv.surroundings()), - "electric energy")) + f_load.electric_energy_in_box(gv.surroundings()), "electric energy")) return 0; if (!compare(f.magnetic_energy_in_box(gv.surroundings()), - f_load.magnetic_energy_in_box(gv.surroundings()), - "magnetic energy")) + f_load.magnetic_energy_in_box(gv.surroundings()), "magnetic energy")) return 0; return 1; @@ -219,12 +205,10 @@ int main(int argc, char **argv) { master_printf("Testing 3D dump/load: temp_dir = %s...\n", temp_dir.get()); for (int s = 2; s < 7; s++) - if (!test_periodic(targets, s, temp_dir.get())) - abort("error in test_periodic targets\n"); + if (!test_periodic(targets, s, temp_dir.get())) abort("error in test_periodic targets\n"); for (int s = 2; s < 8; s++) - if (!test_metal(one, s, temp_dir.get())) - abort("error in test_metal vacuum\n"); + if (!test_metal(one, s, temp_dir.get())) abort("error in test_metal vacuum\n"); delete_directory(temp_dir.get()); return 0; diff --git a/tests/h5test.cpp b/tests/h5test.cpp index 17e9ff2be..84640fd63 100644 --- a/tests/h5test.cpp +++ b/tests/h5test.cpp @@ -121,11 +121,13 @@ bool check_2d(double eps(const vec &), double a, int splitting, symfunc Sf, doub snprintf(dataname, 256, "%s%s", component_name(file_c), reim ? ".i" : (real_fields ? "" : ".r")); - realnum *h5data = (realnum *)file->read(dataname, &rank, dims, 2, sizeof(realnum) == sizeof(float)); + realnum *h5data = + (realnum *)file->read(dataname, &rank, dims, 2, sizeof(realnum) == sizeof(float)); file->prevent_deadlock(); // hackery if (!h5data) meep::abort("failed to read dataset %s:%s\n", name, dataname); if (rank != expected_rank) - meep::abort("incorrect rank (%d instead of %d) in %s:%s\n", rank, expected_rank, name, dataname); + meep::abort("incorrect rank (%d instead of %d) in %s:%s\n", rank, expected_rank, name, + dataname); if (expected_rank == 1 && file_gv.in_direction_min(X) == file_gv.in_direction_max(X)) { dims[1] = dims[0]; dims[0] = 1; @@ -234,11 +236,13 @@ bool check_3d(double eps(const vec &), double a, int splitting, symfunc Sf, comp snprintf(dataname, 256, "%s%s", component_name(file_c), reim ? ".i" : (real_fields ? "" : ".r")); - realnum *h5data = (realnum *)file->read(dataname, &rank, dims, 3, sizeof(realnum) == sizeof(float)); + realnum *h5data = + (realnum *)file->read(dataname, &rank, dims, 3, sizeof(realnum) == sizeof(float)); file->prevent_deadlock(); // hackery if (!h5data) meep::abort("failed to read dataset %s:%s\n", name, dataname); if (rank != expected_rank) - meep::abort("incorrect rank (%d instead of %d) in %s:%s\n", rank, expected_rank, name, dataname); + meep::abort("incorrect rank (%d instead of %d) in %s:%s\n", rank, expected_rank, name, + dataname); vec loc(loc0.dim); for (size_t i0 = 0; i0 < dims[0]; ++i0) { for (size_t i1 = 0; i1 < dims[1]; ++i1) { @@ -341,7 +345,8 @@ bool check_2d_monitor(double eps(const vec &), double a, int splitting, symfunc snprintf(dataname, 256, "%s%s", component_name(file_c), reim ? ".i" : (real_fields ? "" : ".r")); - realnum *h5data = (realnum *)file->read(dataname, &rank, dims, 2, sizeof(realnum) == sizeof(float)); + realnum *h5data = + (realnum *)file->read(dataname, &rank, dims, 2, sizeof(realnum) == sizeof(float)); file->prevent_deadlock(); // hackery if (!h5data) meep::abort("failed to read dataset %s:%s\n", file->file_name(), dataname); if (rank != 1) meep::abort("monitor-point data is not one-dimensional"); diff --git a/tests/harmonics.cpp b/tests/harmonics.cpp index 9deb62035..ac176d26a 100644 --- a/tests/harmonics.cpp +++ b/tests/harmonics.cpp @@ -45,12 +45,12 @@ void harmonics(double freq, double chi2, double chi3, double J, double &A2, doub f.add_point_source(Ex, src, vec(-0.5 * sz + dpml), J); vec fpt(0.5 * sz - dpml - 0.5); - dft_flux d1 = f.add_dft_flux(Z, volume(fpt), freq, freq, 1, true, true, - 1 /* decimation_factor */); - dft_flux d2 = f.add_dft_flux(Z, volume(fpt), 2 * freq, 2 * freq, 1, true, true, - 1 /* decimation_factor */); - dft_flux d3 = f.add_dft_flux(Z, volume(fpt), 3 * freq, 3 * freq, 1, true, true, - 1 /* decimation_factor */); + dft_flux d1 = + f.add_dft_flux(Z, volume(fpt), freq, freq, 1, true, true, 1 /* decimation_factor */); + dft_flux d2 = + f.add_dft_flux(Z, volume(fpt), 2 * freq, 2 * freq, 1, true, true, 1 /* decimation_factor */); + dft_flux d3 = + f.add_dft_flux(Z, volume(fpt), 3 * freq, 3 * freq, 1, true, true, 1 /* decimation_factor */); double emax = 0; @@ -106,7 +106,8 @@ int main(int argc, char **argv) { if (different(a2, 9.80330e-07, thresh, "2nd harmonic mismatches known val")) return 1; if (sizeof(realnum) == sizeof(float)) { if (different(a3, 9.99349e-07, thresh, "3rd harmonic mismatches known val")) return 1; - } else { + } + else { if (different(a3, 9.97747e-07, thresh, "3rd harmonic mismatches known val")) return 1; } @@ -130,7 +131,8 @@ int main(int argc, char **argv) { harmonics(freq, 0.0, 1e-4, 1.0, a2_2, a3_2); if (sizeof(realnum) == sizeof(float)) { if (different(a3, a3_2, 0.0017, "chi2 has too big effect on 3rd harmonic")) return 1; - } else { + } + else { if (different(a3, a3_2, 0.001, "chi2 has too big effect on 3rd harmonic")) return 1; } diff --git a/tests/integrate.cpp b/tests/integrate.cpp index def80d451..cbf92cc18 100644 --- a/tests/integrate.cpp +++ b/tests/integrate.cpp @@ -271,7 +271,7 @@ void check_loop_vol(const grid_volume &gv, component c) { meep::abort("FAILED: LOOP_OVER_VOL has %zd iterations instead of ntot=%zd\n", count, gv.ntot()); if (count_owned != gv.nowned(c)) meep::abort("FAILED: LOOP_OVER_VOL has %zd owned points instead of nowned=%zd\n", count_owned, - gv.nowned(c)); + gv.nowned(c)); if (min_i != 0) meep::abort("FAILED: LOOP_OVER_VOL has minimum index %td instead of 0\n", min_i); if (size_t(max_i) != gv.ntot() - 1) meep::abort("FAILED: LOOP_OVER_VOL has max index %td instead of ntot-1\n", max_i); @@ -284,12 +284,15 @@ void check_loop_vol(const grid_volume &gv, component c) { vec here0(gv[ihere0]); if (ihere0 != ihere) meep::abort("FAILED: wrong LOOP_OVER_VOL_OWNED iloc at i=%td\n", i); if (abs(here0 - here) > 1e-13) - meep::abort("FAILED: wrong LOOP_OVER_VOL_OWNED loc (err = %g) at i=%td\n", abs(here0 - here), i); - if (!gv.owns(ihere)) meep::abort("FAILED: LOOP_OVER_VOL_OWNED includes non-owned at i=%td\n", i); + meep::abort("FAILED: wrong LOOP_OVER_VOL_OWNED loc (err = %g) at i=%td\n", abs(here0 - here), + i); + if (!gv.owns(ihere)) + meep::abort("FAILED: LOOP_OVER_VOL_OWNED includes non-owned at i=%td\n", i); ++count; } if (count != count_owned) - meep::abort("FAILED: LOOP_OVER_VOL_OWNED has %zd iterations instead of %zd\n", count, count_owned); + meep::abort("FAILED: LOOP_OVER_VOL_OWNED has %zd iterations instead of %zd\n", count, + count_owned); count = 0; LOOP_OVER_VOL_NOTOWNED(gv, c, i) { @@ -299,7 +302,8 @@ void check_loop_vol(const grid_volume &gv, component c) { vec here0(gv[ihere0]); if (ihere0 != ihere) meep::abort("FAILED: wrong LOOP_OVER_VOL_NOTOWNED iloc at i=%td\n", i); if (abs(here0 - here) > 1e-13) - meep::abort("FAILED: wrong LOOP_OVER_VOL_NOTOWNED loc (err = %g) at i=%td\n", abs(here0 - here), i); + meep::abort("FAILED: wrong LOOP_OVER_VOL_NOTOWNED loc (err = %g) at i=%td\n", + abs(here0 - here), i); if (gv.owns(ihere)) meep::abort("FAILED: LOOP_OVER_VOL_NOTOWNED includes owned at i=%td\n", i); if (ihere < vmin || ihere > vmax) meep::abort("FAILED: LOOP_OVER_VOL_NOTOWNED outside V at i=%td\n", i); @@ -307,7 +311,7 @@ void check_loop_vol(const grid_volume &gv, component c) { } if (count != gv.ntot() - count_owned) meep::abort("FAILED: LOOP_OVER_VOL_NOTOWNED has %zd iterations instead of %zd\n", count, - gv.ntot() - count_owned); + gv.ntot() - count_owned); master_printf("...PASSED.\n"); } @@ -323,12 +327,11 @@ int main(int argc, char **argv) { const grid_volume v1d = vol1d(sz[0], a); const grid_volume vcyl = volcyl(sz[0], sz[1], a); - srand(0); // use fixed random sequence - check_splitsym(v3d, 0, identity(), "identity"); - check_splitsym(v3d, 0, mirror(X, v3d), "mirrorx"); - return 0; + check_splitsym(v3d, 0, identity(), "identity"); + check_splitsym(v3d, 0, mirror(X, v3d), "mirrorx"); + return 0; for (int splitting = 0; splitting < 5; ++splitting) { check_splitsym(v3d, splitting, identity(), "identity"); diff --git a/tests/known_results.cpp b/tests/known_results.cpp index 773e0346a..c8a3b77ba 100644 --- a/tests/known_results.cpp +++ b/tests/known_results.cpp @@ -45,9 +45,7 @@ void compare(double b, double a, const char *n) { if (fabs(a - b) > fabs(b) * thresh || b != b) { meep::abort("Failed %s (%g instead of %g, relerr %0.2g)\n", n, a, b, fabs(a - b) / fabs(b)); } - else { - master_printf("Passed %s\n", n); - } + else { master_printf("Passed %s\n", n); } } static double dpml = 1.0; diff --git a/tests/near2far.cpp b/tests/near2far.cpp index 164a8e15e..b1a2367af 100644 --- a/tests/near2far.cpp +++ b/tests/near2far.cpp @@ -252,7 +252,7 @@ int main(int argc, char **argv) { const double a2d = argc > 1 ? atof(argv[1]) : 20, a3d = argc > 1 ? a2d : 10; - if ((sizeof(realnum) == sizeof(double)) && !check_cyl(5.0, 10.0, 20.0)) return 1; + if ((sizeof(realnum) == sizeof(double)) && !check_cyl(5.0, 10.0, 20.0)) return 1; // NOTE: see hack above -- we require sources to be odd in Z and even in X or vice-versa if (!check_2d_3d(D3, 4, a3d, Ez, Hx, false)) return 1; diff --git a/tests/one_dimensional.cpp b/tests/one_dimensional.cpp index 14558e63d..844b39c53 100644 --- a/tests/one_dimensional.cpp +++ b/tests/one_dimensional.cpp @@ -37,9 +37,7 @@ int compare(double a, double b, const char *n) { master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; } - else { - return 1; - } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p) { diff --git a/tests/physical.cpp b/tests/physical.cpp index 938c088a3..5a2c9835b 100644 --- a/tests/physical.cpp +++ b/tests/physical.cpp @@ -54,8 +54,9 @@ int radiating_2D(const double xmax) { master_printf("Ratio is %g from (%g %g) and (%g %g)\n", ratio, real(amp1), imag(amp1), real(amp2), imag(amp2)); if (ratio > 2.12 || ratio < 1.88) - meep::abort("Failed: amp1 = (%g, %g), amp2 = (%g, %g)\n abs(amp1/amp2)^2 = %g, too far from 2.0\n", - real(amp1), imag(amp1), real(amp2), imag(amp2), ratio); + meep::abort( + "Failed: amp1 = (%g, %g), amp2 = (%g, %g)\n abs(amp1/amp2)^2 = %g, too far from 2.0\n", + real(amp1), imag(amp1), real(amp2), imag(amp2), ratio); return 1; } @@ -90,8 +91,9 @@ int radiating_3D(const double xmax) { master_printf("Ratio is %g from (%g %g) and (%g %g)\n", ratio, real(amp1), imag(amp1), real(amp2), imag(amp2)); if (ratio > 2.12 || ratio < 1.88) - meep::abort("Failed: amp1 = (%g, %g), amp2 = (%g, %g)\n abs(amp1/amp2) = %g, too far from 2.0\n", - real(amp1), imag(amp1), real(amp2), imag(amp2), ratio); + meep::abort( + "Failed: amp1 = (%g, %g), amp2 = (%g, %g)\n abs(amp1/amp2) = %g, too far from 2.0\n", + real(amp1), imag(amp1), real(amp2), imag(amp2), ratio); return 1; } diff --git a/tests/pml.cpp b/tests/pml.cpp index 6c3e1a8b4..7906d7d35 100644 --- a/tests/pml.cpp +++ b/tests/pml.cpp @@ -335,9 +335,12 @@ int main(int argc, char **argv) { if (sizeof(realnum) == sizeof(double)) { if (pml1d_scaling(one)) meep::abort("pml doesn't scale properly with length."); } - if (pmlcyl_scaling(one, 0)) meep::abort("m=0 cylindrical pml doesn't scale properly with length."); - if (pmlcyl_scaling(one, 1)) meep::abort("m=1 cylindrical pml doesn't scale properly with length."); - if (pmlcyl_scaling(one, 2)) meep::abort("m=2 cylindrical pml doesn't scale properly with length."); + if (pmlcyl_scaling(one, 0)) + meep::abort("m=0 cylindrical pml doesn't scale properly with length."); + if (pmlcyl_scaling(one, 1)) + meep::abort("m=1 cylindrical pml doesn't scale properly with length."); + if (pmlcyl_scaling(one, 2)) + meep::abort("m=2 cylindrical pml doesn't scale properly with length."); return 0; } diff --git a/tests/ring-ll.cpp b/tests/ring-ll.cpp index a478d6728..06f70b1ec 100644 --- a/tests/ring-ll.cpp +++ b/tests/ring-ll.cpp @@ -73,11 +73,9 @@ int main(int argc, char *argv[]) { // (radius (+ r w)) (material (make dielectric (index n)))) // (make cylinder (center 0 0) (height infinity) // (radius r) (material air)))) - auto material_deleter = [](meep_geom::material_data *m) { - meep_geom::material_free(m); - }; + auto material_deleter = [](meep_geom::material_data *m) { meep_geom::material_free(m); }; std::unique_ptr dielectric( - meep_geom::make_dielectric(n*n), material_deleter); + meep_geom::make_dielectric(n * n), material_deleter); geometric_object objects[2]; vector3 v3zero = {0.0, 0.0, 0.0}; vector3 zaxis = {0.0, 0.0, 1.0}; @@ -107,7 +105,7 @@ int main(int argc, char *argv[]) { double T = 300.0; double stop_time = f.round_time() + T; - std::vector> fieldData; + std::vector > fieldData; vec eval_pt(r + 0.1, 0.0); while (f.round_time() < stop_time) { f.step(); @@ -136,15 +134,15 @@ int main(int argc, char *argv[]) { int ref_bands = 4; double ref_freq_re[4] = {1.1807e-01, 1.4470e-01, 1.4715e-01, 1.7525e-01}; double ref_freq_im[4] = {-7.5657e-04, -8.9843e-04, -2.2172e-04, -5.0267e-05}; - std::complex ref_amp[4] = {std::complex(-6.40e-03,-2.81e-03), - std::complex(-1.42e-04,+6.78e-04), - std::complex(+3.99e-02,+4.09e-02), - std::complex(-1.98e-03,-1.43e-02)}; + std::complex ref_amp[4] = { + std::complex(-6.40e-03, -2.81e-03), std::complex(-1.42e-04, +6.78e-04), + std::complex(+3.99e-02, +4.09e-02), std::complex(-1.98e-03, -1.43e-02)}; if (bands != ref_bands) meep::abort("harminv found only %i/%i bands\n", bands, ref_bands); for (int nb = 0; nb < bands; nb++) if ((fabs(freq_re[nb] - ref_freq_re[nb]) > 1.0e-2 * fabs(ref_freq_re[nb]) || fabs(freq_im[nb] - ref_freq_im[nb]) > 1.0e-2 * fabs(ref_freq_im[nb]) || - abs(amp[nb] - ref_amp[nb]) > 1.0e-2 * abs(ref_amp[nb])) && (err[nb] < err_tol)) + abs(amp[nb] - ref_amp[nb]) > 1.0e-2 * abs(ref_amp[nb])) && + (err[nb] < err_tol)) meep::abort("harminv band %i disagrees with ref: {re f, im f, re A, im A}={%e,%e,%e,%e}!= " "{%e,%e,%e,%e}\n", nb, freq_re[nb], freq_im[nb], real(amp[nb]), imag(amp[nb]), ref_freq_re[nb], diff --git a/tests/symmetry.cpp b/tests/symmetry.cpp index 4997c38ba..4e8f6a6dd 100644 --- a/tests/symmetry.cpp +++ b/tests/symmetry.cpp @@ -56,9 +56,7 @@ int compare(double a, double b, const char *n) { master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; } - else { - return 1; - } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p) { @@ -978,7 +976,8 @@ int main(int argc, char **argv) { if (!nonlinear_ex(vol1d(1.0, 30.0), one)) meep::abort("error in 1D nonlinear vacuum\n"); if (sizeof(realnum) == sizeof(double)) { - if (!nonlinear_ex(vol3d(1.0, 1.2, 0.8, 10.0), one)) meep::abort("error in 3D nonlinear vacuum\n"); + if (!nonlinear_ex(vol3d(1.0, 1.2, 0.8, 10.0), one)) + meep::abort("error in 3D nonlinear vacuum\n"); } // disable for parallel runs due to a bug in splitting cylindrical cell @@ -986,7 +985,8 @@ int main(int argc, char **argv) { if ((count_processors() == 1) && (!test_cyl_metal_mirror_nonlinear(one))) meep::abort("error in test_cyl_metal_mirror nonlinear vacuum\n"); - if (!exact_metal_rot4z_nonlinear(one)) meep::abort("error in exact_metal_rot4z nonlinear vacuum\n"); + if (!exact_metal_rot4z_nonlinear(one)) + meep::abort("error in exact_metal_rot4z nonlinear vacuum\n"); if (!exact_metal_rot4z_nonlinear(rods_2d)) meep::abort("error in exact_metal_rot4z nonlinear rods_2d\n"); diff --git a/tests/three_d.cpp b/tests/three_d.cpp index 428635ab6..3155ac028 100644 --- a/tests/three_d.cpp +++ b/tests/three_d.cpp @@ -45,9 +45,7 @@ int compare(double a, double b, const char *n) { master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; } - else { - return 1; - } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p) { diff --git a/tests/two_dimensional.cpp b/tests/two_dimensional.cpp index f6f62c0aa..cab79303a 100644 --- a/tests/two_dimensional.cpp +++ b/tests/two_dimensional.cpp @@ -45,9 +45,7 @@ int compare(double a, double b, const char *n) { master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; } - else { - return 1; - } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p) { @@ -221,9 +219,7 @@ int test_pml(double eps(const vec &), int splitting) { new_energy / last_energy); return 0; } - else { - master_printf("Got newE/oldE of %g\n", new_energy / last_energy); - } + else { master_printf("Got newE/oldE of %g\n", new_energy / last_energy); } field_energy_check_time += deltaT; } } @@ -266,9 +262,7 @@ int test_pml_tm(double eps(const vec &), int splitting) { new_energy / last_energy); return 0; } - else { - master_printf("Got newE/oldE of %g\n", new_energy / last_energy); - } + else { master_printf("Got newE/oldE of %g\n", new_energy / last_energy); } field_energy_check_time += deltaT; } } @@ -313,9 +307,7 @@ int test_pml_te(double eps(const vec &), int splitting) { new_energy / last_energy); return 0; } - else { - master_printf("Got newE/oldE of %g\n", new_energy / last_energy); - } + else { master_printf("Got newE/oldE of %g\n", new_energy / last_energy); } field_energy_check_time += deltaT; } } diff --git a/tests/user-defined-material.cpp b/tests/user-defined-material.cpp index 681357e43..0080ad2da 100644 --- a/tests/user-defined-material.cpp +++ b/tests/user-defined-material.cpp @@ -54,13 +54,15 @@ bool compare_hdf5_datasets(const char *file1, const char *name1, const char *fil h5file f1(file1, h5file::READONLY, false); int rank1; size_t *dims1 = new size_t[expected_rank]; - realnum *data1 = (realnum *)f1.read(name1, &rank1, dims1, expected_rank, sizeof(realnum) == sizeof(float)); + realnum *data1 = + (realnum *)f1.read(name1, &rank1, dims1, expected_rank, sizeof(realnum) == sizeof(float)); if (!data1) return false; h5file f2(file2, h5file::READONLY, false); int rank2; size_t *dims2 = new size_t[expected_rank]; - realnum *data2 = (realnum *)f2.read(name2, &rank2, dims2, expected_rank, sizeof(realnum) == sizeof(float)); + realnum *data2 = + (realnum *)f2.read(name2, &rank2, dims2, expected_rank, sizeof(realnum) == sizeof(float)); if (!data2) return false; if (rank1 != expected_rank || rank2 != expected_rank) return false; From 00bc81af43d0b8ac6b753bdc2a11e9df32fa5215 Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 10:29:40 -0700 Subject: [PATCH 09/12] fix import --- python/tests/test_get_epsilon_grid.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/python/tests/test_get_epsilon_grid.py b/python/tests/test_get_epsilon_grid.py index d678d11a3..b6c5f71ce 100644 --- a/python/tests/test_get_epsilon_grid.py +++ b/python/tests/test_get_epsilon_grid.py @@ -1,17 +1,12 @@ import unittest +import meep.adjoint as mpa import numpy as np import parameterized +from meep.materials import Co, SiN import meep as mp -try: - import meep.adjoint as mpa -except: - import adjoint as mpa - -from meep.materials import Co, SiN - class TestGetEpsilonGrid(unittest.TestCase): def setUp(self): From 2cfd06094eef402799f9bf5e9874b96a998775eb Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 10:50:35 -0700 Subject: [PATCH 10/12] run ci/cd every day --- .github/workflows/build-ci.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build-ci.yml b/.github/workflows/build-ci.yml index 9a1c19b5b..72ff0c8a0 100644 --- a/.github/workflows/build-ci.yml +++ b/.github/workflows/build-ci.yml @@ -1,10 +1,10 @@ -name: CI +name: run tests on: - push: - branches: [ master ] pull_request: - branches: [ master ] + push: + schedule: + - cron: 0 2 * * * # run at 2 AM UTC workflow_dispatch: jobs: From 48194cf714938de5f97c3edbfd1c54cdde281146 Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 11:48:19 -0700 Subject: [PATCH 11/12] revert reorder imports from the adjoint module --- .gitignore | 1 + python/adjoint/__init__.py | 1 + 2 files changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index b8aac4c1c..32b98c60e 100644 --- a/.gitignore +++ b/.gitignore @@ -19,6 +19,7 @@ python/examples/.ipynb_checkpoints/ h5utils/ hdf5/ extra/ +configure~ # autotools stuff Makefile diff --git a/python/adjoint/__init__.py b/python/adjoint/__init__.py index 16de93442..72ba9f44c 100644 --- a/python/adjoint/__init__.py +++ b/python/adjoint/__init__.py @@ -2,6 +2,7 @@ Adjoint-based sensitivity-analysis module for pymeep. Authors: Homer Reid , Alec Hammond , Ian Williamson """ + from . import utils from .basis import BilinearInterpolationBasis from .connectivity import * From 83dc114c4047d3bc9b5ceec2d4d31a8973a60997 Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 11:51:09 -0700 Subject: [PATCH 12/12] remove isort from pre-commit --- .pre-commit-config.yaml | 12 ++++++------ python/adjoint/__init__.py | 14 ++++++++++---- 2 files changed, 16 insertions(+), 10 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index dc0a0d360..3c7f780f9 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,12 +11,12 @@ repos: # hooks: # - id: unimport # args: [--remove, --include-star-import] - - repo: https://github.com/pycqa/isort - rev: "12cc5fbd67eebf92eb2213b03c07b138ae1fb448" - hooks: - - id: isort - files: "python/.*" - args: ["--profile", "black", "--filter-files"] + # - repo: https://github.com/pycqa/isort + # rev: "12cc5fbd67eebf92eb2213b03c07b138ae1fb448" + # hooks: + # - id: isort + # files: "python/.*" + # args: ["--profile", "black", "--filter-files"] - repo: https://github.com/psf/black rev: "4f1772e2aed8356e57b923eacf45f813ec3324a0" diff --git a/python/adjoint/__init__.py b/python/adjoint/__init__.py index 72ba9f44c..a787e31e7 100644 --- a/python/adjoint/__init__.py +++ b/python/adjoint/__init__.py @@ -3,14 +3,20 @@ Authors: Homer Reid , Alec Hammond , Ian Williamson """ +from .objective import * + from . import utils +from .utils import DesignRegion + from .basis import BilinearInterpolationBasis -from .connectivity import * + +from .optimization_problem import OptimizationProblem + from .filter_source import FilteredSource + from .filters import * -from .objective import * -from .optimization_problem import OptimizationProblem -from .utils import DesignRegion + +from .connectivity import * try: from .wrapper import MeepJaxWrapper